text
stringlengths 645
543k
| pmid
stringlengths 1
8
| accession_id
stringlengths 9
11
| license
stringclasses 8
values | last_updated
stringdate 2021-01-04 15:57:02
2024-12-12 23:16:03
| retracted
stringclasses 1
value | citation
stringlengths 16
77
|
|---|---|---|---|---|---|---|
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
35917071
22221
10.1007/s11356-022-22221-7
Research Article
The impact of COVID-19 on small- and medium-sized enterprises (SMEs): empirical evidence for green economic implications
Du Lijie [email protected]
12
Razzaq Asif [email protected]
3
Waqas Muhammad [email protected]
4
1 grid.495269.5 Sichuan Tourism University, Chengdu, China
2 grid.445020.7 0000 0004 0385 9160 Faculty of Business, City University of Macau, Macau, China
3 grid.30055.33 0000 0000 9247 7930 School of Economics and Management, Dalian University of Technology, Dalian, 116000 People’s Republic of China
4 grid.411501.0 0000 0001 0228 333X Schools of Economics, Bahauddin Zakariya University, Multan, Pakistan
Responsible Editor: Philippe Garrigues
2 8 2022
122
4 6 2022
21 7 2022
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Small- and medium-sized enterprises (SMEs) in China have been hit hard by the coronavirus (COVID-19) outbreak, which has jeopardized their going out of business altogether. As a result, this research will shed light on the long-term impacts of COVID-19 lockdown on small businesses worldwide. The information was gathered through a survey questionnaire that 313 people completed. Analyzing the model was accomplished through the use of SEM in this investigation. Management and staff at SMEs worldwide provided the study’s data sources. Research shows that COVID-19 has a significantly bad influence on profitability, operational, economic, and access to finance. In the study’s findings, outside funding aids have played an important role in SMEs’ skill to persist and succeed through technological novelty than in their real output. SME businesses, administrations, and policymakers need to understand the implications of this study’s results.
Keywords
COVID-19
Sustainable business practices
SME
Technology innovation
Access to finance
==== Body
pmcIntroduction
This pandemic caused by COVID-19 is unprecedented in many respects (Jabeen et al. 2021; Irfan et al. 2022a). In the first place, it endangers the lives of millions upon millions of people worldwide. By the end of June 2021, it had already caused the deaths of nearly four million people worldwide as a direct consequence (Ahmad et al. 2020; Müller et al. 2021). The service industry, which depends on many small businesses than the manufacturing sector, was also negatively impacted by the social distancing guidelines implemented to contain the virus (Ipsmiller et al. 2021).
Compared to MNEs, small- and medium-sized enterprises (SMEs) have fewer considerations of standards, fewer financial resources, fewer R&D sources, less organizational culture, and fewer uses of advanced manufacturing technologies. Regardless of this, one cannot deny the importance of SMEs, which are widely regarded as indispensable for producing new jobs, the equitable distribution of existing ones, and the expansion of Asian exports. The MNEs have a lot of resources, but they are not very close to the outside world. On the other hand, SMEs are a lot more flexible when putting their decisions into action, and they are very in touch with the outside world. According to Kukanja et al. (2022a, b), ecological sustainability is not well understood among SMEs. They believe this is because most researchers focus their attention on the sustainable performance of large organizations in developed economies. According to the published research, sustainable performance practices are successful in large organizations, but they do not necessarily work in SMEs.
The efficiency of small- and medium-sized firms in terms of technology can be significantly improved by adopting a sustainable performance orientation (Ahmad et al. 2019; Irfan et al. 2019, 2022b). In addition, the degree of commitment to sustainability is heavily influenced by entrepreneurial orientation and market (Ahmad et al. 2021; Lau et al. 2021; Liu et al. 2021; Rao et al. 2022). It is believed that the attitude of owner-managers toward the natural environment can be used to predict the level of sustainable performance achieved by small businesses (Irfan et al. 2021; Işık et al. 2021; Jan et al. 2021). Integration of top organization teams’ behaviors has an optimistic impact on the organization’s orientation toward sustainability (Khokhar et al. 2020; Fu et al. 2021; Yumei et al. 2021; HUANG et al. 2022). Small- and medium-sized enterprises in developing economies typically have limited access to resources (Tang et al. 2021a, b; Rao et al. 2022; Tang et al. 2022b). Many believe that investing in sustainable performance constitutes an unnecessary additional expense. However, sustainable performance is an essential resource for organizations to consider when determining their performance. This resource cannot be ignored because some of the researchers used the sustainable performance of SMEs to determine the performance of firms, new ventures, and financial presentation. A finite number of studies examine the connection between sustainable performance and environmental performance; consequently, this study aims to develop this connection. In addition, the researchers suggested that further research into sustainable performance is required in both developing countries and developed countries. This is because sustainable performance is considered an essential component for organizations.
Xia et al. (2021) developed the technology-organization-environment (TOE) model, used in the present study. The model is helpful when adapting to changing circumstances because it allows one to study the interactions between processes, inputs, and outputs. The technology-organization-environment structure is adaptable and provides the optimal balance of external and internal drivers, essential for implementing innovations within organizations (Yung et al. 2021; Iqbal et al. 2021; Xiang et al. 2022b; Jin et al. 2022). There have only been a few studies that have used this framework to examine sustainability. These studies cover sustainable manufacturing practices (Baah et al. 2021), green IT initiatives, and a green supply chain. This model helps develop a holistic structure of sustainable functioning because it combines numerous elements that can each play their part in attaining sustainable business practices. Consequently, this model is useful for establishing a comprehensive structure of sustainable business practices. Sustainable business practices (such as green supply chain, green recycling and remanufacturing, green HRM and information technology) are conceptualized as a process in this study, and sustainable performance is conceptualized as an output. Technological innovation is conceptualized as an input. The panel’s theoretical foundation is the resource-based view (RBV) theory.
Because of the current COVID-19 condition, a more in-depth investigation into the relationship between illogical behavior and attitude of SMEs and economic morality is required. Standard economic theory cannot be relied upon to gain a deeper comprehension of this issue; rather, behavioral economics theory is what is required. We are developing a model of the practical connection between irrationality, morality, and commercial ethics for SMEs. SMEs can benefit from this research. It provides a foundation for developing programs to permit SMEs to improve their economic mindset and behavior.
The COVID-19 crisis has impacted international commerce and tourism, which impacts the global economy (Rintanalert and Sirisunhirun 2021)(Chen et al. 2021a). The adverse effects are widespread and permeate every aspect of human life, mainly related to health. The majority of financial activities continue to be restricted as a result of public isolation, travel limitations, lockdowns, and other similar measures, which is a significant blow to a great number of businesses (Saputra and Herlina 2021). Industries and businesses of all sizes have been severely wedged as a result of COVID-19, and many are struggling to maintain their viability as a result of these effects. Despite this, some companies have seized the opportunity to carve out a new market, and many SMEs are attempting to adapt to the new environment. Small businesses are more susceptible to global crises because of their limited resources, and COVID-19 is having an especially negative impact on them. There is still a requirement to adopt strategies that will ensure long-term survival, even though environmental sustainability has become one of the most important factors in the present situation (Lu et al. 2021). Consequently, small- and medium-sized businesses are under increasing pressure to modernize their operations through various technologies (Zaverzhenets and Łobacz 2021). Sustainability in the industry is achieved by utilizing technologies that either develop or adopt environmentally friendly practices. The current study will focus on the process for achieving sustainable performance that SMEs can use during the COVID-19 pandemic.
The term “governance mechanism” refers to a broad concept fraught with a number of debates on account of the numerous theoretical approaches and points of view (Chen et al. 2021b)(Wu and Zhu 2021). Previous research has consistently found that a company’s governance mechanism has an impact on both the company’s financial results and its communal outcomes (Costa et al. 2020)(Zhang et al. 2022)(Li et al. 2022). With fierce competition, governance mechanisms tend to focus on ensuring that the interests of society, the environment, and the economy are all taken into account. To accomplish this goal, it is no longer a recommendation but rather a prerequisite for companies that participate in social and environmental responsibility initiatives (Wellalage et al. 2021).
There have only been a limited amount of experiential research conducted on the result that technological innovation has on sustainable performance in the SME sector through sustainable green practices. Further investigation is necessary to fill in this gap. Generally speaking, only 2% of businesses use various forms of technology (Ko et al. 2021). The ongoing COVID-19 pandemic, on the other hand, has made it unavoidable for businesses to abandon their traditional methods in favor of more environmentally friendly ones to operate sustainably. To achieve this goal, SMEs will require technologies that can be customized. SME research focuses on the factors that lead to environmental and social practices in small- and medium-sized enterprises (SMEs). They are linked to environmental productivity and performance, social performance, governance mechanisms, and green innovation. On the other hand, the authors do not discuss how new technology affects environmental stewardship and SMEs’ ability to sustain themselves. The goal of the current research is to develop a model that will offer a comprehensive theoretical structure for assessing how environmental, organizational, and technological factors can lead to long-term functioning levels in SME through the adoption of environmentally friendly green practices. As a result, it will be a developed methodology for assessing how technology advancement can contribute to long-term performance levels in SMEs.
Literature review and hypothesis development
COVID-19 factors and sustainable business practices
Equated to SARS, a lethal endemic in China in 2003, COVID-19 is more contagious and hardy. Within 2 months, there were more confirmed cases than SARS had in the preceding 8 months, and within 3 months, it had spread around the world and was considered a pandemic. That many cases of SARS were reported after 8 months. When it comes to its effect on society and the economy, the COVID-19 outbreak is unlike any other crises or epidemic in China or elsewhere (Al-Hakimi et al. 2021; Lau et al. 2021; Liu et al. 2021; Rao et al. 2022).
Small- and medium-sized enterprises in China are finding it difficult to survive due to COVID-19’s rapid spread. On February 15, 2020, the China Association of SMEs published a “Research Report on Countermeasures and Suggestions for the Impact of COVID-19 Pandemic” that found that nearly 67.69% of SMEs have reduced their operating income; 21.61% of SMEs cannot timely repay debts; 86.22% of SMEs cannot survive with funds on the account for more than three months, and 33.73% of SMEs do not have enough funds to last for more than six months (Wendt et al. 2021)(Iqbal et al. 2021; Tang et al. 2022a, b). China’s economic growth was negatively impacted by the precarious financial situation of SMEs. As a result, China’s GDP growth rate fell 6.8% year over year in the first quarter to its minimum level in the past couple of decades (Jafari-Sadeghi et al. 2022). Meanwhile, the labor market was sluggish, with the unemployment rate rising from 5.3% in January to 6.0% in April. Because of this, China’s SMEs continue to feel the effects of this year’s pandemic outbreak. As soon as the COVID-19 pandemic comes to an end, the negative impact on the economy will not go away. SMEs will continue to face the challenge of long-term recovery and the pressures of survival and growth as the economy continues to recover (García-Pérez-de-Lema et al. 2021).
The COVID-19 virus has been a major concern for the Chinese government. Wuhan’s urban bus, subway, ferry, and long-distance passenger transportation services were shut down on January 23, 2020. At the airport and train stations, the exit channels were also closed. More than 10 million people were completely cut off from a large metropolitan area. China’s first-level public health emergency reaction was initiated sharply after the lockdown of Wuhan in an attempt at virus containment and prevention (Zainal et al. 2022a). Chinese authorities mobilized the entire country to implement the most rigorous and comprehensive control methods possible to save lives. Gatherings, meetings, and conferences were banned; towns, municipalities, and communities were put on lockdown; factories, businesses, and stores were shut down. As a precautionary measure, travelers from Wuhan and other epidemic zones had to report their travel proceedings and self-quarantine for 2 weeks. To keep population migration to a minimum, the Spring Festival holiday was extended for 2 days. In addition, businesses in several provinces, including Sichuan Province, were not permitted to return to normal operations until February 10. In order to keep the epidemic under control, many businesses and factories across the country were forced to shut down for the majority of February.
These measures did help to slow the spread of the coronavirus. Still, at the same time, they posed a threat to the continued existence of businesses in all fields and sectors, which could have had potentially disastrous economic repercussions, societal and individual, such as a significant reduction in the number of available jobs and increased social vulnerability (Sun et al. 2021b). Rahman et al. (2022) discovered that they could not return to the cities in which they worked because the cities had been locked down, highways had been hindered off, and suburban populations had been sealed off. Although these stringent measures successfully prevented the spread of the epidemic, they did so at the expense of preventing workers from returning to work and disrupting the movement of raw materials and finished goods. If there are further limitations on traveling and, as a result, trade, and business within China, the economic impact of the widespread disease may increase significantly (Ganlin et al. 2021a). Only time will tell whether or not this is the case.
H1: Small- and medium-sized businesses (SMEs) are more likely to implement sustainable business practices (SBP) when put into lockdown.
Technological innovation–sustainable business practices nexus
In technology in the business world, “innovation” refers to a strategy that gives a company an advantage over its rivals by facilitating the diversification of existing markets and creating new avenues for financial gain. Adopting a novel strategy or way of doing things within an organization is one definition of innovation. According to Miocevic (2022), innovativeness can be defined as the grade to which an organization uses technology or new ideas compared to their competitors to gain competitive returns in terms of cost, time, and the value of service. The ability to innovate is not only necessary for businesses to continue existing in today’s economy, but it is also a significant factor in growth, productivity, and competitiveness (Miocevic 2022).
Compliance with sustainable practices is just as important in the highly competitive business environment as the innovation of new technologies; as a result, policies on corporate social responsibility (CSR) are becoming more prevalent in SMEs. The achievement of excellence in the management of organizations is the primary focus of environmental sustainability. “A commitment to improving social well-being through discretionary business practices and the contribution of corporate resources” is how corporate social responsibility can be defined in a general sense.
The literature (Sun et al. 2021b; Ganlin et al. 2021a; Rahman et al. 2022; Miocevic 2022) contains significant research on the relationship between innovative practices and environmentally sound practices. Therefore, the addition of new products, processes, and managerial activities involved in the delivery of a product or service can be understood to constitute innovation. Pedauga et al. (2022) found a relationship between environmental sustainability practices and innovation and suggested that businesses should act following the ethics of corporate social responsibility (CSR) in their manufacturing practices by making use of new technology that is kind to the environment (Eldeeb et al. 2021). This was based on Bansal’s finding a relationship between innovation and environmental sustainability practices. The purpose of corporate social responsibility (CSR) is to simultaneously advance fiscal growth, community justice, and environmental protection (Surya et al. 2022). Many initiatives have recently been launched to encourage businesses to frame their disclosure activities to demonstrate their commitment to sustainable development (Le and Nguyen 2022a).
It is becoming increasingly accepted that small- and medium-sized businesses are the most important source of developing new products and technologies (Le and Nguyen 2022b). Small- and medium-sized enterprises (SMEs) continue to pose a significant challenge for business policymakers and investors in light of the current dynamic business climate. While innovation is important and even essential, performance improvement is the key to motivating SMEs to implement CSR practices (Belyaeva and Levis 2022). SMEs typically have a greater aptitude to increase performance through their CSR practices as a result of their increased emphasis on innovation (Adam and Alarifi 2021). Companies with strategic CSR objectives to improve performance, such as achieving growth (Belyaeva and Levis 2022; Le and Nguyen 2022a, b), may need to make product and process innovations.
Different researchers use different definitions of SMEs due to the varied characteristics of these businesses (Abed 2020). These definitions are frequently based on size or turnover, and they are meant to reflect the economic, cultural, and social norms of each country. It is generally agreed that the number of employees, the level of investment, and the volume of sales are the defining characteristics of SMEs. According to the European Commission, small- and medium-sized businesses have between 10 and 49 employees, while large businesses have between 50 and 250 employees (Adam and Alarifi 2021). The Small and Medium Enterprises Act of 2012 in Kenya classifies SMEs according to their industry, the number of employees, and the amount of investment they have (Yu and Schweisfurth 2020).
H2: Green innovation (GIN) positively affects the implementation of sustainable business practices (SBP) by SMEs.
Organizational factors and sustainable business practices
Albats et al. (2020) corporate social performance model was employed in the study to further describe how sustainable practises are implemented in firms. There are three stages of solicitation for groups committed to sustainable development. As a first step, enterprises need to understand the fundamentals of sustainable development (SD). These principles, as established by society, are institutional forces (Sun et al. 2021a). In response, organizations establish policies and programmes tailored to their specific qualities. Stakeholders have a right to expect the entrepreneur to respond appropriately to their demands.
According to Jesemann et al. (2021), one of the steps that must be taken to comprehend the phenomenon of corporate ownership of SD is to investigate the company’s attributes. Corporations are required to address the potentially bad impacts their organizational circumstances may have in order to prevent a similar situation from occurring in the future (Zainal et al. 2022b). As a result, organizational circumstances influence how businesses respond to SD concerns. According to this strategy, the behaviors anticipated in SD are those dictated by the very nature of the business that is being analyzed. The business plan, financial performance, and innovation are examples of these contingent factors (Rakshit et al. 2021). Dai et al.’s (2021) interpersonal behavior theory is applied to the characteristics of entrepreneurs in this study. According to this theory of interpersonal behavior, individuals make decisions about how they will act based on their intentions, habits, and other supportive factors (Hossain et al. 2021). The theory’s goal is to shed light on human behavior. Affect, self-image, and the perception of consequences are all factors that are considered in addition to the norms and roles that are discussed above. In light of the above, we will assume that:
H3: Green marketing (GMK) has a positive impact on SMEs’ application of sustainable practices.
H4: The acceptance of a green supply chain (GSCM) has a beneficial affect on SMEs’ use of corporate sustainability.
H5: Green HRM (GHRM) has a positive impact on SMEs’ application of sustainable practices.
Environmental awareness–sustainable business practices nexus
The external environment that the organization operates in is represented in the TOE model by the environmental factors component. The willingness of small- and medium-sized businesses to adopt environmentally responsible practices heavily depends on the government’s support (Bai et al. 2021). There is a strong correlation between technological advancement and the ability of organizations to implement sustainable green practices in uncertain environments.
There is a strong correlation between environmental factors and the adoption of green practices. Without additional support from the outside, it is impossible to implement green HRM practices. In the absence of support from the government, the organization that is committed to implementing environmentally friendly practices might fail. As a result, the support provided by ecological factors makes the adoption of environmentally responsible HR practices easier. An important part of supply chain management is outward activities. Therefore, the kinds of elements surrounding the organization can affect the practices that are used in the supply chain. Regulations imposed by environmental bodies have become an important factor in spreading environmentally responsible behaviors (Hrovatin et al. 2021). For an organization to implement environmentally friendly supply chain practices, relevant environmental aspects must be present. Environmental factors are also a factor in how marketing strategies are implemented. The adoption of environmentally friendly marketing practices is made easier by the relevant environmental factors, and environmentally friendly marketing practices can only be effective in an encouraging external environment. The implementation of environmentally responsible marketing strategies needs backing from environmental factors. Factors in the environment drive organizations to pursue and implement environmentally friendly innovations (Zutshi et al. 2021). The policies that the government and the legal system have in place to protect the environment have an effect on the innovative practices of an organization. Incorporating environmentally friendly innovations into daily life relies heavily on preexisting environmental factors such as policy orientation, certainty, support, and market alignment. These factors have the potential to trigger different kinds of innovations. Consequently, environmental factors are also a factor in whether or not green innovations are adopted. Consequently, the subsequent premises are put forward for consideration:
H6: Social awareness has a positive impact on SMEs’ application of sustainable practices.
H7: Environmental awareness has a positive impact on SMEs’ application of sustainable practices.
Green practices–sustainable business practices nexus
It is generally accepted that protecting the environment leads to the development of socially desirable outcomes (Denicolai et al. 2021). The regulatory setting defines the business context and identifies the types of private and public institutions that adhere to CSR practices and services (Iborra et al. 2020). Enhancing collaboration among stakeholders, providing economic support for environmental conservation, and providing sufficient substructure to support environmental sustainability are all part of the Regulatory Setting’s mission to control environmental conservation (Yeon et al. 2022)(Chen et al. 2020) and protect international environmental legislation. These goals will be accomplished by ensuring environmental sustainability (Yang et al. 2020)(Hu et al. 2021).
There are three points of view on the current state of the business environment, according to Hussain et al. (2022). The first point of view concentrates on parties external to the company that interferes with its operations. These parties include customers, other businesses in the industry, sellers, and government regulations. The second point of view emphasizes the qualities exhibited by external forces, such as intricacy, lethargy, and generosity (Aidoo et al. 2021). The marketplace orientations can be moderated by the external environmental aspects, which influence firm performance (Tolstoy et al. 2021). According to the findings of a study carried out by Raymond (Le and Nguyen 2022c), the third perspective expresses concern regarding the sensitivities associated with decision-making regarding environmental aspects. It was discovered that the alignment of an organization’s interior and exterior environments had a moderating effect on the performance of the business (Dyduch et al. 2021). Top management ought to comprehend their results, grab chances to safeguard the company from extortions, and measure both internal and external factors that affect the company’s performance (Zhang et al. 2021). After going through all of this previous research, the researcher concluded the following hypothesis.
H8: Green manufacturing (GRNM) has a positive impact on SMEs’ application of sustainable practices.
H9: Green design (GRND) positively impacts the application of sustainable practices by SMEs.
H10: Recycling and remanufacturing (R&R) positively impact the application of sustainable practices by SMEs.
Information technology–sustainable business practices nexus
As was previously mentioned, the establishment of close collaborations and the exchange of information both within and between companies, made possible by information systems, are necessary for companies’ growth of sustainability abilities. It has been demonstrated that the combination of information technology resources with their corresponding human and management resources can support the capabilities that provide companies with a competitive advantage (e.g., Górska et al. (2021)). The three factors found in information technology systems are technical elements, human technical and management abilities, and undefinable IT-enabled assets like efficiency and customer awareness. It has been found that combining human and informational technology (IT) resources (such as infrastructural applications, for example) confers companies with advanced abilities that help them increase overall effectiveness, revenue growth, innovation, and gain a long-term advantage in the market. However, most of this type of research concentrates on the effects of information technology on the economy. These types of arguments bring us to our first proposal, which will serve as the basis for our investigation into the role that various kinds of information technology resources play in contributing to the strategy of businesses in developing their capacity for sustainability (Fig. 1).Fig. 1 Flow diagram
H11: Information technology adoption has a positive effect on SMEs’ application of sustainable practices.
Methodology
Sample and data collection
A research method known as a survey was used in this particular study. Previous research served as the foundation for the development of the questionnaire. The validity of the initial questions was established through a pilot study, in which academics and managers were allowed to review the questions. A preliminary test was carried out to validate both the content and the appearance of these instruments. Five experts thoroughly reviewed the scales’ content validity to determine their reliability. The appropriateness of the wording, ease of understanding, relevance, and item sequence of each measurement scale was brought to the attention of specialists who were invited to conduct the review. Additionally, the experts were allowed to provide qualitative comments regarding the measures. The content validity ratio (CVR) was determined by following the guidelines provided by Turkyilmaz et al. (2021). After the initial review by the experts, the CVR for the scale was set at 1, and no experts recommended removing any of the items from the scale. However, all of the experts did recommend making some minor adjustments to the item sequence and original wording. Ten industry and academic professionals with relevant expert knowledge were required to consider the scales in order to deliver proof of the scales’ alleged face validity. They had to rate for every item’s specificity and significance on a Likert scale and suggest any items for removal. All fourteen items were kept after receiving comments from the ten people who participated.
There are several factors that go into calculating the environmental impact of a company, and these factors are not limited to just one company. However, there is a lack of relevant research on supply chain performance metrics (Miocevic 2022). Sustainable business practices (SBP) and its relation with other variables (like green innovation, green design, green HRM, COVID cases, green, information technology, recycling and remanufacturing, and green marketing) assessed in the study, which focuses mostly on perception data (Turkyilmaz et al. 2021; Zhang et al. 2021; Górska et al. 2021). With important suppliers and customers, SBP are measured in terms of four activities: strategic planning, employing various information or communication technologies, attaining operational-level integration, and forming strong partnerships (Younis and Elbanna 2021; Miocevic 2022). Results are categorized under the following outcomes: green innovation, green HRM, green marketing, green supplychain, COVID-19, and green information rechnology (Yao and Yang 2022). When evaluating the value of SBP to small- and medium-sized businesses, we look at few key performance indicators: green marketing, green innovation, green HRM, green design and green recycling and green remanufacturing. We also look at social and environmental indicators, such as charity support and relationships with the local community (Le and Ikram 2021).
SMEs in this study are defined as companies with 100 employees, and we use the terms “small” and “medium” to describe these two categories. Business owners or managers of SMEs were the primary focus of the study, and a representative sample of these companies was drawn at random from the yellow pages, trade publications, and other industry-specific resources. The survey method was used in this investigation (both mail and online). In order to safeguard the privacy of respondents and the confidentiality of information that was exposed, the entire data gathering process was anonymous. We sent out a thousand surveys, but only 313 people completed them. Despite the low response rate (10.7%), we used Armstrong and Overton’s (1977) to measure non-response bias. We used a t-test to compare the first 25% of responses with the last 25% of responses based on demographic characteristics (degree of professional job experience, annual sales, industry type). There were no statistically significant differences in the means of the questions between the two groups; therefore, there is no reason to worry about non-response bias. We followed the advice of Ganlin et al. (2021b) and used Xiang et al. (2022a, b) single factor to check for common method bias.
When using the maximum likelihood (ML) method in structural equation modeling (SEM) analysis, the observed variable and sample size ratio of 1:10 is the smallest sample size requirement (Wendt et al. 2021). It was possible to collect 313 reliable samples as part of this investigation (Table 1).Table 1 Enterprise profile
Characterization Frequency Percent
SME size 5–10 employees 29 35.5
11–20 employees 24.5 24.5
21–50 employees 36.5 29
51–99 employees 10 11
SME age Less than 10 47 54
11 to less than 20 18 15
21 to less than 30 22 8.7
31 to less than 50 21 13.3
51 and more 12 9
The demographic information of the SME managers surveyed is presented in Table 1. All these SMEs are organizations with 1 to 100 employees, where 81.9% of the respondents indicated that they had had 2–15 years of professional work experience. The sample is split between firms with annual sales less than or equal to NZ$16.5 m (43.7%) and those with more than NZ$16.5 m (56.3%), which suggests that there is good coverage of SMEs. Responses were collected from various types of organization: third-party logistics providers (25.7%); manufacturing (18.9%); retail (17.3%); and automotive and transportation (11.8%). Overall, 80.2% of the respondent firms are involved in distribution, retail, and manufacturing.
AMOS version 23 and SPSS version 23 are both utilized throughout carrying out the analysis for SEM. When using this software, the primary objective is to determine whether or not the preliminary model is reinforced and decoded by the data gathered. As can be seen in Fig. 2, the preliminary study model has been redrawn as a path diagram within AMOS to facilitate analysis. All of the observed variables in this study are self-rating items, which were done so that researchers could gauge the respondents’ opinions. These observed variables are also taken as gages of various underlying variables, which are depicted by great abbreviations in the graph. Indicator variables of a latent practice variable include things like partnerships, strategic planning, process integration, and the use of information and communications technology (ICT). In other words, we are modeling the various latent variables by utilizing variables that act as indicators. Even though we do not directly measure the latent variables, it is still very important that we have a good understanding of them. To determine whether or not a certain practice positively influences the outcome, we must have a solid understanding of the fundamental connections between the practice and the result. For the initial analysis, we have all of the weights set to 1, which is the default setting. After completing the numbering, the model is now in a usable state. It is now possible to estimate AMOS linkages, such as practice leading to outcome and outcome leading to performance.Fig. 2 Path diagram
Several solutions were implemented to decrease the amount of missing data. As a direct consequence of this, the rate of missing data has been successfully maintained at or below 5 percent. Because this study only has a small number of missing data points, the researchers decided to use list-wise deletion as their data imputation method.
The maximum likelihood estimation (MLE) method was selected as the optimal choice for use in combination with confirmatory factor examination to assess the viability of the primary model. The most critical assumption is that each indicator variable follows a normal distribution when running MLE. Byrne (2001) suggests that the value of any skewness coefficients or kurtosis for indicator variables must not exceed the value of 1.96 in order to guarantee that the variables are normally distributed. Despite this, the total value of the multivariate kurtosis statistic (45.035) is significantly greater than the threshold value of 10. Based on this result, it is clear that the initial model suffers from a severe breach of multivariate normality. The MLE solution will produce more biased estimates than they would be otherwise, even though Tevapitak and Bert Helmsing (2019) claims that an SEM result may still be precise and valid even after a severe violation of multivariate normality. The application of a bootstrap estimate is considered a potential solution to reduce the amount of bias present in this study. The model does not fit the raw data well, as demonstrated by the bootstrap estimate. According to the results of the MLE analysis, the chi-square for the first model is 170.330, and the associated probability level is 0.000. The conclusion suggests that the model does not fit the raw data well, which contradicts the null hypothesis. TLI, CFI, RMSEA, and HOELTER are the descriptive model-fit tests that were chosen to be referenced after being chosen from a larger pool of candidates. According to Hu and Bentler (1999), a good model fit would have TLI values larger than 0.9, CFI values larger than 0.9, RMSEA values less than 0.08, and HOELTER values larger than 66. On the other hand, the descriptive statistics tests do not support the assertion that the model is a good fit. That is to say, by signifying to the bootstrapping, chi-square, and descriptive model-fit tests, we are now able to realize that the original model has a poor entire fit to the raw data (chi-square = 170.330, p < 0.000, TLI = 0.721, CFI = 0.770, RMSEA = 0.140 and HOELTER = 37).
In particular, to respond to the questions posed by the research, it is essential to understand the one-to-one relationships that exist between practice, outcome, and performance. The consequences of the study are presented in Table 2, and when the p values are considered, it appears that all of the regression weights and the loadings are significant. Two deductions can be strained from this: The first is that each indicator variable has a significant loading on the latent variables. According to regression weights, practice positively impacts performance, and the outcome has a positive impact on performance. This is consistent with the research question. One example of this is strategic planning, which significantly influences practice. Other examples include partnership, process integration, and information and communication technology (ICT).Table 2 Respondent profile
Item Characterization Frequency Percent
Gender Female 218.4 32.655
Male 484.05 72.345
Employee type (domestic and global) Local 542.85 81.165
Global 159.6 23.835
Employees work in local or global companies Domestic industry 672 100.485
Global companies 30.45 4.515
Specialization HRM department 178.5 26.67
Marketing department 166.95 24.99
Bookkeeping/funding division/section 137.55 20.58
Information technology department 115.5 17.22
Cusomer care center 78.75 11.76
Operations department 25.2 3.78
Position level Top level managers 300.3 44.94
Middle level managers 139.65 20.895
Lower middle level management 177.45 26.565
Lower level managers 85.05 12.705
We referred to the modification indices in AMOS to locate a model that was a better fit for the data. These modification indices included strategic planning, greenness, resiliency, and innovation. After taking out these four observed variables—strategic planning (in practice), environmental friendliness (in outcome), resiliency (in outcome), and innovativeness—a model that fits the data well has been developed (in outcome).
All of the Cronbach’s alpha values are more than 0.7, depicting that the structure is consistent. The practise value is 0.718, the outcome value is 0.840, and the performance value is 0.709. The validity of the model is demonstrated by the fit indices, which are as follows: chi-square = 45.807, probability level = 0.068; Bollen-Stine bootstrap p = 0.433; CFI = 0.955; TLI = 0.938; RMSEA = 0.07; and HOELTER = 83.
It was determined whether the model assumption held for the modified model. The conclusion drawn from the findings is that the revised model exhibits serious deviations from normality distribution. In light of this, the chi-square test will be carried out in conjunction with the bootstrap estimate. TLI = 0.938, CFI = 0.955, RMSEA = 0.07, and HOELTER = 83 all show a consistent result, which further approves that the final model has a good fit to the raw data. This can be seen by further examining TLI, CFI, and HOELTER tests. TLI = 0.938, CFI = 0.955, RMSEA = 0.07, and HOELTER = 83. As shown in Table 3, each weight and loading is of significant importance.Table 3 Measurement assessment results
Variable Item Alpha CR AVE Loadings
COVID-19 COVID1 0.843 0.889 0.615 0.778
COVID2 0.755
COVID3 0.812
COVID4 0.787
Green innovation GIN1 0.877 0.907 0.620 0.787
GIN2 0.769
GIN3 0.773
GIN4 0.821
Green marketing GMK1 0.868 0.901 0.603 0.816
GMK2 0.797
GMK3 0.744
GMK4 0.762
Green supply chain GSCM1 0.879 0.906 0.581 0.750
GSCM2 0.752
GSCM3 0.825
GSCM4 0.778
Green HRM GHRM1 0.879 0.908 0.642 0.790
GHRM2 0.747
GHRM3 0.757
GHRM4 0.804
GHRM5 0.796
Social awareness SOCA1 0.866 0.903 0.651 0.785
SOCA2 0.725
SOCA3 0.717
Environmental awareness ENVA1 0.861 0.900 0.642 0.749
ENVA2 0.822
ENVA3 0.799
ENVA4 0.818
Green manufacturing GRNM1 0.828 0.879 0.593 0.806
GRNM2 0.740
GRNM3 0.796
GRNM4 0.787
Green design GRND1 0.860 0.898 0.618 0.842
GRND2 0.827
GRND3 0.782
GRND4 0.780
GRND5 0.818
Recycling and remanufacturing R&R1 0.868 0.901 0.603 0.810
R&R2 0.811
R&R3 0.787
R&R4 0.792
Information technology INFT1 0.879 0.906 0.581 0.769
INFT2 0.809
INFT3 0.771
INFT4 0.707
Sustainable business practices SBP1 0.879 0.908 0.642 0.769
SBP2 0.809
SBP3 0.771
SBP4 0.707
We refer to this modified model as the final model.
The proposed structural model was analyzed to test the hypothesis and determine whether the initial model had a poor fit. We modified it and obtained a good-fit model after removing four observed variables (chi-square = 45.807, probability level = 0.068; Bollen-Stine bootstrap p = 0.433; CFI = 0.955; TLI = 0.938; RMSEA = 0.07; and HOELTER = 83). We found that these variables, which had a high contribution to the modification indices, could be an accurate representation of the current situation. Strategic planning and SBP are two practices lacking in China’s small- and medium-sized enterprises (SMEs) (Troise et al. 2022). Based on the data above, we can conclude that strategic planning, partnership, process integration, and ICT use significantly influence practice. More importantly, to some degree, practice is positively related to outcome, while the outcome is positively related to performance.
This study also shows that SBP is not an artifact exclusively for large enterprises. Although the collected data did not fully support the initial model, the final model proposes that SMEs can work on three characteristics of sustainable business practices to obtain competitive advantages. The SMEs’ outcomes mainly reflect efficiency, agility, and security performance.
Common method bias
Since the data for this study were gathered through the use of the key informant method, common method bias is a potential issue that can lead to measurement errors. These errors, in turn, have the potential to call into question the validity of the inferences regarding the relationships between the hypotheses. In the first step of the process, a single-factor test developed by Harman was used to examine the possibility of common method bias. According to the findings, no single variable could be credited with adequately explaining all of the variances. In addition to the Harman test, the marker-variable technique was used. This was done because the Harman test tends to be quite conservative when detecting bias (Fleming et al. 2016). Although it had no theoretical association with any of the other variables in this study, the questionnaire that asked respondents, “Would you prefer to visit Ha Long Bay during the national holiday this year?” was used as a marker variable. When the effects of the relationships between the marker variable and the other constructs were taken into account, the mean change in the correlations between the primary constructs was only 0.03. As a result, it appears from the results of all of the tests that have been outlined above that this study does not exhibit common method bias. In addition to that, the study investigated whether or not this study could be subject to multicollinearity. The maximum inner variance inflation value was 2.48, which is a significant amount lower than the value of 10, which is the “rule of thumb.” As a result, the degree of multicollinearity in this study is practically nonexistent.
Measures
An investigation of the relationship between technological innovation (inputs) represented by technological, organizational, and environmental factors, as well as green practices (processes) like green human resources management, green innovation, green supply chain, and sustainable performance was the primary goal of this study (as outputs). In addition to this, the study investigated the causal impact that technological innovation has on environmentally friendly practices and the impact that environmentally friendly practices have on sustainable performance. A survey instrument composed of questions culled from previously developed surveys was created to test this conceptual model. This survey was translated into Arabic in order to confirm that all participants understood the questions. In this case, the measurement was made using perception, and the individual served as the unit of analysis. Each scale consisted of multiple dimensions and had multiple levels. The level of technological innovation was evaluated based on responses to 18 questions, which were then broken down into three categories: environmental factors, organizational factors, and technological factors, each of which was evaluated based on responses to seven, six, and five questions, respectively. Based on previous research, these factors have been incorporated into the new study (Krasniqi et al. 2021). Twenty-two criteria were used to evaluate green practices during this period, which were then grouped into four categories: green technology, green marketing, green human resources, and green supply chain. Criteria for evaluating the effectiveness of green HRM were drawn from six sources (Barrett et al. 2021). In addition, five items taken from (Crovini et al. 2021) were used to evaluate green marketing, and five items were taken from (El Chaarani et al. 2021), were used to evaluate the green supply chain. If you want to know more about the effects of cannabis on the human body, you need to look no further than these studies. When it came to measuring sustainable performance, five items were selected.
Statistical analysis procedure
There was an online survey that could be completed and submitted. Each of the responses received was given an initial scan using the SPSS 23 program. The procedure resulted in the submission of 756 questionnaires. Eighty-seven of the responses received were deemed ineligible for further analysis based on the initial scanning of the responses and the fact that many of the questions had the same response (e.g., all of the questionnaires were strongly agreed or strongly disagreed). As a result, 88.5% of the total completed questionnaires were valid, resulting in 669 questionnaires for analysis. Using partial least squares (PLS), this sample size was appropriate for the study (Miocevic 2021). Using the PLS-SEM technique, the structural model used in this study was evaluated on a computer running the SmartPLS program.
Two primary stages of analysis were performed on the final valid data, which were the questionnaires. The first phases of the project focused on testing the adopted measurement to ensure its validity. The tests for skewness and kurtosis ensured that the items being used had a normal distribution. The Cronbach’s alpha and composite reliability (CR) tests ensured that the study mode had reliable internal consistency. Following that, the research model was validated by using convergent and discriminant validity tests. The multicollinearity test, which included both an inner and an outer test, was performed with the variance inflation factor (VIF) method before the structural equation modeling test. This was done to know whether or not any strong errors could occur as a result of the strong connections between the latent variables.
Analysis and results
Measurement assessment
In mode A, the variables used for the research were measured using reflective one-dimensional variables of first and second-order reflective types. The SEM-PLS methodology was selected for this investigation because of the subsequent considerations: (1) the landscape of the objects being reflective; (2) the design of the quantitative–predictive type of research; and (3) the size of the sample in addition to the robustness of the model with first- and second-order constructs. According to Le and Nguyen (2022) the constructs and indicators of this research are classified as a part of the social sciences because they examine the actions and business activities of SMEs’ front-runners. Reflective models are best suited for measuring this type of construction because of their versatility. This model works on the premise that the variance of a group of effects can be completely accounted for by an unnoticed variable known as the common factor and the random errors associated with that variable. This model assumes that measurement errors do not correlate with any of the other variables, constructs, or other types of errors. The latent variable cannot be directly observed in any given situation. The only evidence that indirectly supports the existence of the indicators is the pattern of correlations between them. The indicators all have something in common and the fact that the chain of causality goes from the construct to the measurements.
The two-stage approach was utilized when analyzing the multidimensional constructs (of the second order). In order to accomplish this, the scores of the latent variable were analyzed. These aggregate scores are used to model the second-order construct after the first-order dimensions have been estimated. This process is broken down into two stages. Using the SEM-PLS, the method based on variance was applied to perform an analysis of the statistical data of the anticipated theoretical model. The software SmartPLS Professional version 3.3.5 was utilized to analyze both the structural and measurement models. All of the first- and second-order constructs had factorial loads that were either very close to or significantly higher than the value of 0.707.
Model validity
In this study, a quantitative approach was taken, and the technique known as CB-SEM (covariance-based structural equation model) was utilized. CB-SEM was utilized in this investigation as a result of the following considerations. In terms of sample size, this study used the “10-times rule” and an additional precaution against non-response bias to compute the sample size. This was done to ensure that the results were not skewed by non-response bias. Consequently, an initial sample size of up to 510 was calculated, which is considered a large sample size. A total of 475 valid samples were collected after the survey was completed. Because this was a large sample size, CB-SEM was chosen as the approach most appropriate in this case. CB-SEM estimates are more accurate than PLS-SEM estimates for sample sizes of fifty or more, according to Loader (2015). The findings of their investigation lead them to this conclusion.
In order to test the discriminant validity of the scales, the procedure developed by Fornell and Larcker (1981) was utilized. This procedure calls for a comparison of the variance extracted for each pair of constructs (AVE coefficient) with the squared correlation estimate between these two constructs. The results of this comparison determine whether or not the scales are discriminant (Table 4). The fact that the extracted variances for each construct were higher than the squared correlation between them in every instance provides evidence in favour of the measurement scales’ capacity to discriminate between different types of phenomena.Table 4 Square roots of AVE and factor correlation coefficients
COVID-19 GIN GRM GSCM GHRM SOCA ENVA GRNM GRND R&R INFT
COVID-19 0.76
GIN 0.45 0.74
GRM 0.36 0.45 0.81
GSCM 0.38 0.39 0.48 0.71
GHRM 0.25 0.31 0.36 0.39 0.81
SOCA 0.22 0.36 0.35 0.36 0.32 0.73
ENVA 0.44 0.52 0.46 0.44 0.40 0.45 0.69
GRNM 0.39 0.42 0.47 0.46 0.51 0.39 0.63 0.74
GRND 0.43 0.41 0.38 0.40 0.39 0.35 0.58 0.54 0.70
R&R 0.46 0.38 0.37 0.40 0.41 0.45 0.55 0.56 0.54 0.78
INFT 0.39 0.42 0.40 0.41 0.41 0.47 0.61 0.61 0.62 0.78 0.89
The bold values in the diagonal line represent the square root of the average variance extracted
Second, compared to PLS-SEM, CB-SEM has some advantages. In particular, CB-SEM can distinguish between the measurement error and the manifest variable, enabling it to produce the most accurate estimate of the factor loadings possible. One more benefit of using CB-SEM is that it can accurately identify the multicollinearity problem. This is made possible by the modification index tool associated with this application (Caballero-Morales 2021). In contrast to PLS-SEM, which does not offer this advantage, the CB-SEM methodology makes it possible for the researcher to determine which components of the construct have the potential to have a negative impact on the meaning of the construct. In addition, the construct reliability of CB-SEM may be more reliable than that of PLS-SEM due to the possibility of bias in the estimates of factor loading value produced by PLS-SEM. PLS-SEM has the potential for bias, so this is not recommended. It is also clear that CB-effect on construct reliability is more pronounced than PLS-when SEM’s it comes to average variance extract estimates in various samples and models. Furthermore, Kijkasiwat (2021) claims that CB-SEM is preferred to PLS-SEM in parameter precision. As a result of using a consistent estimator, the CB-SEM estimation method can produce consistent attributes across different contexts. A questionnaire-based online survey was used as the primary source of information.
Analysis of structural model
Before the structural modeling test was carried out, the variance inflation factor (VIF) method attempted to rule out the possibility of any errors being caused by strong correlations between the latent variables utilized in the study. Using PLS-SEM, a collinearity problem was indicated when the VIF was less than 3.3. The VIF values (both inner and outer) that fall below this threshold are shown in Table 5. Therefore, in addition to the results of Harman’s single factor test deliberated earlier, these two results guarantee that there will not be any issues with multicollinearity.Table 5 Hypothesis test results
Hypothesis Path β coefficients T statistics P values Result
H1 COVID-19 → SBP − 0.371*** 6.57 0 Support
H2 Green innovation (GIN) → SBP 0.125** 2.15 0.032 Support
H3 Green marketing (GMK) → SBP 0.267*** 5.29 0.001 Support
H4 Green supply chain (GSCM) → SBP 0.378*** 4.72 0.001 Support
H5 Green HRM (GHRM) → SBP 0.33** 5.86 0.021 Support
H6 Social awareness → SBP 0.506*** 9.07 0.002 Support
H7 Environmental awareness → SBP 0.175** 2.98 0.001 Support
H8 Green manufacturing (GRNM) → SBP 0.227 2.91 0.401 Support
H9 Green desing (GRND) → SBP 0.117** 1.85 0.035 Reject
H10 Recycling and remanufacturing (R&R) → SBP 0.22* 4.22 0.051 Support
H11 Information technology (INFT) → SBP 0.373*** 8.67 0.001 Support
In Fig. 2, all of the model’s path coefficients (β) values are displayed. To find out whether or not something is significant, a bootstrapping algorithm with 5000 samples was used in PLS (Table 5). The t value was greater than 1.96, and the p value needed to be less than 0.05 to validate the hypothesis. The error probability for the β values was estimated to be 5% based on these two values.
The findings shows that organizational factors have a positive impact on green HRM, green innovation, green marketing, and a green supply chain. In addition, technological factors contribute to green HRM, green innovation, green marketing, and green supply chain (= 0.33, 0.50, 0.17, and 0.20, respectively). In addition, environmental factors (= 0.22, 0.37, 0.25, and 0.41, respectively) increase green innovation, green marketing, and green supply chain. Sustainable performance can also be improved by implementing green HRM, green innovation, and green marketing strategies (= 0.41, 0.24, and 0.19, respectively).
According to a p value of 0.109, environmental factors did not affect green human resources management. H9 is also rejected because the green design has no significant impact on sustainable performance (= 0.01; p 0.05). All remaining constructs had significant path coefficients () and p values. Thus, all remaining hypotheses were found to be correct. Furthermore, the coefficient of determination, R2, was used to evaluate the relationship between the latent dependent variable (sustainable performance) and the total variance. GHRM, GIN, GR&R, and GSC each accounted for about 51.5% of the total variance, explained by technological, organizational, and environmental factors, as shown in Fig. 2. About 52.9% of the variation in long-term performance could be explained by the four factors mentioned above.
Discussion
Small- and medium-sized enterprises (SMEs) faced many obstacles and setbacks due to the COVID-19 restrictions, making it difficult for them to maintain their viability in this unstable environment. Technology innovation had an impact on small- and medium-sized businesses’ sustainable performance through the use of sustainable practices such as green human resource management (GHRM), green supply chain management (GSCM), and green marketing (GMK).
A few pioneering studies were conducted during the COVID-19 pandemic, and this one is one of them. Using the theory of resource-based view (RBV) in conjunction with the TOE framework created a comprehensive framework for sustainable performance. Because it is one of the few pioneering studies, this one is significant. By applying environmentally friendly practices, this integration aimed to gauge the long-term sustainability of Middle Eastern SMEs. The findings show the importance of environmental factors in green marketing because it has the highest impact level compared to other environmentally friendly practices.
Ref (2020) conducted a similar study that found that green marketing plays a critical role in making companies more competitive by focusing on environmental variables as drivers of the firm’s competitive values as the primary strategic goals.
Although environmental variables have had an impact on green supply chains and green innovation, it does not appear that they have had a significant impact on green human resource management. This circumstance suggests that thru the COVID-19 disease, directors of SMEs may have been forced to choose between continuing to operate sustainably or going out of business as many other companies did. Because this crisis has caused people to be forced to work remotely, they did not focus on environmental variables related to HRM practices. As a result, they minimize the number of resources (i.e., energy, water, and raw materials) that they consume and lessen the number of emissions they release into the atmosphere. In light of COVID-19’s disruption, SMEs may not be influenced by environmental context pressures to engage employees in environmental issues or choose workers based on environmental criteria. Similarly important, reducing the number of resources used by the company and the emissions it releases into the air or water can have an influence on the adoption of green marketing strategies and practices. Some examples include promoting electronic commerce, increasing the number of digital communication methods used to promote their goods and facilities, and implementing a paperless procurement policy in their operations.
One of the main drivers of new, positive GSC practices is the pressure from a specific stakeholder group. Though Ardito et al. (2021) support the current belief that GSCM practices can powerfully enhance the environmental performance of a firm, it is worth noting that this pressure represents one of the key drivers of GSCM implementation. Accordingly, the different contexts in which GSCM practices are implemented can be linked to various performance outcomes. The Omani economy is expanding rapidly in areas where a variety of international companies are present. On the other hand, most Omani SMEs are relatively new and small; for example, they have very few employees and are still in the early stages of operation and investment; for example, they have fewer than 5. Omani environmental laws and regulations are still in their infancy, despite the Omani economy’s rapid growth and the presence of many international corporations. SMEs in Oman might need more time to evaluate the long-term effects of GSC before deciding to implement it. An argument can be made that GSCM does not improve performance because it takes time, experienced leadership support, and commitment to implement. Zainal et al. (2022c) found the same thing, concluding that GSCM practices had no impact on environmental performance and development in the United Arab Emirates, a similar context (Le 2022). Because supply chains in crisis are more likely to wisely use their unique and uncommon internal resources when formulating strategies to achieve their competitive advantages, the GSC has little impact on sustainable performance. This is particularly critical in the current competitive environment, which favors supply chains over individual companies. To encourage others to take greater ethical responsibility in protecting our planet, companies could instead share their success stories about GSC practices’ advantages and positive effects (Rahman et al. 2022).
As a result of the current crisis, the vast majority of traditional and physical marketing activities have begun to shift to digital ones. Green HRM and the green supply chain were influenced by organizational factors, whereas green innovation and the green supply chain had the greatest impact on technological factors. Both of these factors are considered to be environmentally friendly. The results are in line with the hypothesis (Ahokangas et al. 2021). According to these findings, SMEs in Omani society are more likely to provide relevant training and advise their contractors and suppliers to follow environmental criteria if they care about their societal values and invest more time and effort in adopting sustainable development practices. As a bonus, this circumstance is in line with Wu et al. (2021).
According to the findings of this study, Omani SMEs can benefit from research and development in order to produce high-quality goods or by using new technology in the manufacturing process. The findings were also reported by Wu et al. (2022). These findings also indicate that Omani SMEs are using new technology to produce and deliver products during the COVID-19 pandemic. Additionally, they work with subcontractors and suppliers who adhere to environmental standards and participate in eco-friendly design and development. Researchers (reference) have previously found a link between technological factors and green management practices, and this new finding now corroborates their findings.
Recent years have seen a rise in the number of business professionals and academics who are interested in the concept of sustainability. In addition to concentrating on the financial profits that can be made from their activities, businesses need to take into account the effects that those activities will have on both society and the environment (Elkington 1994; Elkington et al. 2004). It is possible that by doing so, businesses will be able to cut costs, increase effectiveness, maintain their business marketplace, and ultimately become practical contributors to both society and the business market over a long period of time.
We summarise an integrated sustainability framework and research model based on the resource-based view of the firm and literature in SCM, MIS, HRM, and sustainability. In this model, it is suggested that the integration of HR, SCM, and IT resources is vital for businesses to advance sustainability capabilities that enable them to deliver sustainability value to stakeholders while all at the same are adding wealth and gaining sustainable competitive advantages for the firms themselves. Specifically, our proposals argue that different types of IT resources, such as automation, information, transformation, and infrastructure, can make distinct contributions to businesses in developing sustainability capabilities for various sustainability goals spread across the four quadrants of the integrated sustainability framework. Our study adds several important new insights to the body of knowledge we already possess regarding environmental responsibility.
In this section, we will discuss the most important findings from the research study that was conducted based on the theory of competitive behavior and the theory based on resources and capabilities. The following describes the findings obtained from the testing of the theories contained within the suggested theoretical model to address the aims and concerns raised by the investigation.
The first section will examine hypotheses H1, H2, and H3. According to the findings, SME competitiveness has not been boosted by financial or market-oriented business strategies. According to Ulvenblad and Barth (2021), Business strategy has no significant impact on the management of innovation or SMEs’ economic indicators, according to these findings. To survive in a highly competitive market during a pandemic, businesses often shift their focus to new kinds of activities that permit them to achieve outcomes quickly. This suggests that small- and medium-sized businesses are presently implementing novel policies, such as using skill for their market tactics. In totaling, they are experiencing severe economic problems and inferring this from the fact that corporate tactic has a minor but significant negative impact on business functioning. According to the theory of competitive behavior, these findings support the use of business strategies in the face of external impacts from the COVID-19 pandemic. The theory of business resources and capabilities can now be used to explain these findings. SMEs’ lack of managerial and financial capabilities are strong barriers to gaining organizational and financial advantages. It has been hypothesized in studies of competitive behavior that risen net profit for businesses is a result of successful competition. RBV displays that businesses that successfully implement market and economic tactics are primarily capable to act so because of the quantity of physical and impalpable resources they have, which provides them with a modest benefit and eventually leads to more long-term financial achievement. Financial constraints can have an effect on how quickly a company adopts new products and market strategies as well as on the adoption of innovative practices and economic indicators. Because of this, the market cannot expand as quickly as it could Ahokangas et al. (2021). SMEs may have financial constraints or capacities and organizational uncertainty, which may explain why a substantial relationship between market and economic techniques and novelty has not been located. Ulvenblad and Barth’s (2021) findings align with our findings, confirming the importance of learning from consumers, opponents, and suppliers in identifying chances and improving innovation controlling in SMEs. However, these challenges can inspire managers of small- and medium-sized enterprises (SMEs) to be more creative and, of course, to put the focus on promoting innovation (Belas et al. 2021). According to our findings, SMEs have become more vulnerable to the COVID-19 pandemic, according to Barabaschi et al.’s (2022) findings. SMEs are struggling to keep their financial health in check and grow innovative marketing strategies, which have resulted in a significant and critical company’s competitive wear, to the point where they are only capable of surviving and covering their operational expenses, which has a severe impact on their financial returns.
The findings of hypotheses 4 and 5 are discussed in the second block, where they are shown to have significant and favorable effects. Based on these findings, we can conclude that small- and medium-sized enterprises in this area are engaged in a fiercely competitive tussle. The organizations known as SMEs are providing support for this attack of the COVID-19 pandemic. Despite Mexico’s lack of economic incentives to combat the global economic crisis, this is still the case. In order to maintain a competitive edge in the market, it is essential to keep an eye on and manage innovation. As a result of their ability to maintain a reasonable amount of sales and financial advantages, they can meet their commitments (Saguy 2022). Considering that those in charge of running SMEs are putting their innovative and creative skills to adapt to the market’s shifting dynamics — from physical markets to virtual ones — these results are in relevent with the theory of resources and competencies. The findings support the theory of resources and competencies. Pattinson et al. (2022) found that management models and processes need to be adopted and implemented to foster organizational innovation. As a result, the companies’ employees and managers must put in a lot of effort, which helps the companies become more competitive and deliver better results over time. Researchers Molina-Ibáñez et al. (2021) found that intangible assets and corporate strategy play an important role in determining a company’s innovation and financial performance. Our research results are very similar to theirs. SMEs during the COVID-19 epidemic were more inventive and were able to establish new kinds of promoting tactics and acclimate their goods to the requirements of customers, as Canhoto et al. (2021) reveal. These factors permitted them to stay in the market and achieve satisfactory financial outcomes, which allowed them to steady their processes and maintain their profitability. As a result of the current market’s shift to the digital age, studies like those done by Kukanja et al. (2022b) show that ambidextrous innovation (both disruptive and incremental) is a daily occurrence in SMEs.
The outcome of the H6 experiment is discussed in the third and final blocks. According to the findings, the economic impact had a moderately positive and statistically significant effect on the operational performance of SMEs in this region. Because of these findings, we can conclude that the business abilities and competitive behavior of these companies’ managers have enabled these businesses to keep their economic fitness in a balanced state (in terms of the level of debt, liquidity, and economic solvency), while also being able to cover their operating expenses and costs. Innovative actions (improvements in products, processes, and management) and fresh sales networks like the use of social linkages and digital networks have permitted SMEs to invest in these strategies to enhance their marketing practices within new sales networks. Due to the actions taken by these companies, they have successfully maintained and gained new clientele to boost sales and profits (Ulvenblad and Barth 2021). Thanks to these actions, they have been able to improve their marketing practices through the use of new sales networks, such as digital programs and social linkages. Resource-based theory and competitive behavior theory both support these findings. As Belas et al. (2021) found, small- and medium-sized enterprises (SMEs) in most regions face high levels of debt and cannot generate a profit from their operations, which is in line with our findings. Research conducted by Barabaschi et al. (2022) is similar to our findings. SMEs in underdeveloped regions, on the other hand, have unreliable economic strategies and a deprived ethos of economic health, which results in economic indicators that are neither profitable nor sustainable; Pattinson et al. (2022) demonstrated this.
In totaling to the basic hypotheses on which the model was based, two additional control variables were examined. According to the results, small- and medium-sized businesses in the zone can improve their innovation practices and overall business performance by conducting business online. SMEs adopted and applied technological innovation during COVID-19 by enhancing their sales and marketing practices via social linkages and digital platforms. On the other hand, small- and medium-sized businesses adapt to new ways of working by implementing home offices as part of their innovative strategies. In order to meet current global challenges, these actions and strategies are commanding firms to optimize their innovation management methods.
Work–life balance is a hot topic right now, and the COVID-19 crisis may be the catalyst for a paradigm shift in corporate culture. This is true not only for established businesses but also for those who have started their businesses themselves. Associations and people that work jointly for a common goal can achieve great things. Many of the world’s most challenging issues could benefit from the expertise of people in other countries. Fresh inspiration for creative endeavors can be found when different ideas are brought together in new ways. As a result of companies realizing that they cannot do everything on their own, they have formed partnerships, collaborations, and alliances with like-minded organizations. The illustration of people getting together to face a shared opponent is previously improbable partnerships. One example of society coming together to face a common adversary is previously impossible partnerships. Remote innovators rely on different information, such as information based on technology or science, that is fewer responsive to time than market relevent information.
Thus, implementation, open innovation, and dynamic practices constitute a challenge for businesses on both organizational and financial levels. Telecommuting and other forms of remote work can serve as a catalyst for open innovation and the development of latest business techniques by facilitating the gathering of new information regarding society and markets. Businesses can precisely syndicate client feedback while also permitting customers to be more deeply engaged in the design development and management of product cycles.
In conclusion, we have included this section because we believe it is very significant to examine the findings of the multigroup investigation incorporated into the study. In order to achieve this objective, the categorical variable, which consists of family businesses and non-family businesses, was incorporated into the proposed theoretical model to test its effects. In order to lessen the impact of the global economic crisis, managers of non-family businesses were found to have paid particular attention to implementing innovative strategies. This was revealed by the fact that they were the ones who express the most responsiveness to the research and development of these strategies. The multigroup study’s findings, which revealed that family-owned and non-family-owned businesses had been hit hard by the COVID-19 pandemic, should be cited because they are critical to understanding the situation. Within the scope of this investigation, we took a look at two important strategic moves that can be made as part of open innovation. The research on open innovation strategies in family and non-family businesses for small- and medium-sized enterprises is completely incompatible. In some circles, non-family businesses are thought to be better able to foster innovation and make larger capital investments than family businesses. As a result, open innovation is more common in non-family businesses, giving them the appearance of being more open to collaborating with the outside world and capturing new knowledge. A company’s financial performance can be affected by its ability to improve its innovative practices (Molina-Ibáñez et al. 2021). The findings of our multigroup analysis are reflected in these postulates.
Conclusion and implications
This study contributes to the expanding body of research on sustainable business practices for SMEs. It utilizes the technological, organizational,, and environmental (TOE) factors and resource-based theory view to investigate the impact that SMEs’ internal green resources have on their ability to maintain a high level of sustainable performance. The original TOE framework has been expanded as part of this study by adding green exercises to find the SMEs’ level of sustainable business practices. The TOE factors went through the conceptualization stage as the input. The process stage consisted of sustainable green practices such as green remanufacturing and recycling, green supply chain, green marketing, and green HRM, and the output stage was sustainability. The purpose of incorporating this model was to investigate how adopting environmentally responsible business practices in SMEs can process the TOE factors that influence sustainable performance. The current research on SMEs, on the other hand, covers the factors that determine the implementation of environmentally friendly and socially responsible practices. These factors include environmental productivity and performance, environmental and social practices, social performance, and green innovation.
The findings of this study provide conclusive proof that technical and organizational factors, as opposed to environmental factors, are more important inputs for green innovation, green human resource management, and green marketing when small- and medium-sized businesses are looking to achieve sustainable performance. Since environmental responsibility and commitment are key components of sustainable development, small- and medium-sized enterprises (SMEs) must devote more time and resources toward this goal during a pandemic by adopting recruitment strategies based on environmental standards, such as being environmentally responsible and committed. It also necessitates higher investment in research and development to improve ecological efficiency and better-quality goods or services to encounter new environmental standards. This necessitates the use of updated technology in the production process and the delivery of service. Businesses of all sizes, especially small- and medium-sized ones, can benefit from the pandemic by using environmentally friendly products and promoting their services and goods via digital communication methods that promote an internal culture of reducing emissions, resource consumption, and the environmental impact of their products. SMEs are better equipped to weather economic downturns and continue to reduce hazardous waste, increase the use of environmentally friendly materials, improve environmental compliance, and emphasize green human resource management and green innovation due to these efforts.
Implications
Theoretical implications
Several factors were combined in this study to create a comprehensive model of sustainable performance in small- and medium-sized businesses. These factors showed a strong indication of achieving sustainable performance (SMEs). Measuring the performance of sustainability has been given a new perspective as a result of the integration of the theory of RBV and the model of the (TOE framework), particularly through the use of environmentally friendly practices as processes. In other words, the research has broadened the application of the TOE framework by incorporating a recently developed approach to environmentally friendly business practices. The TOE framework’s validity has been confirmed by this new approach, as all of its factors show a positive coefficient when measuring the level of sustainability achieved by SMEs. Internal and external factors can be syndicated using TOE, while green practices aligned with RBV have strengthened the theoretical view of sustainability performance in this framework. Green human resource management, green marketing, and green innovation all play an important role in keeping Omani SMEs on track during the COVID-19 crisis, according to this study’s findings The theoretical evidence that environmentally friendly business practices as input play an important role in the sustainable performance of small- and medium-sized enterprises is strongly supported by this combination (SMEs). Sustainable human resource management should be top of mind for Omani SMEs. For Omani SMEs to succeed in green marketing strategies, they need to focus more on environmental considerations, according to the findings of this study. When developing a strategy for green innovation, technological factors are the most important to consider, as expected.
Practical and academic implications
This study contributes to the existing body of academic research by providing a conceptual framework that simultaneously integrates the constructs of technical, human/organizational, green product development practices, and company performance. According to the findings, the importance of considering technical aspects of GPD and environmental management, in general, is reinforced by previously published research (Canhoto et al. 2021). These findings support the hypothesis that environmental management can be competitive. This is because GPD may have an impact on performance. Despite this, the findings of this study do not support the hypothesis that human and organizational factors significantly influence GPD. Research on GHRM and green organizational culture will benefit from these findings. Organizations must prioritize environmental management’s human component and provide more direct support for the field’s most cutting-edge environmental management practices. These new perspectives on improving these performance indicators have not been highlighted in previous research. Still, the findings of this study show a connection between GPD and the three different types of performance (e.g., and reliability and GPD or success and GPD in beginning new products).
Environmental management and the adoption of GPD practices are suggested to organizational managers as potential influences on a company’s performance, including market, functional, and environmental outcomes. Performance in all three areas is included here. As a result, when working to improve one’s performance, GPD is crucial. Study shows that businesses must invest in HOA, which is not always the case, based on the technical aspects. GPD implementation is not limited to large companies, businesses with an EMS, or companies under environmental pressure from green regulations, according to the findings of this study. Is it something that could be implemented and has a wide-ranging impact on performance is GPD?
There must be more research into the link between human factors and GDP and the development of strategies to make this link more important for business owners and executives to understand. This study also improved our understanding of the control variables we examined. As a result of these restrictions, the study’s findings and conclusions may have been tainted. These restrictions include the study’s focus on Brazilian respondents and the sample’s overall size.
Limitations and future research
Despite the significant contributions it makes, the current study has some shortcomings that should be considered. Even though there were two waves of data collection, it was impossible to deduce any causal correlations between the different variables. As a result, the validity of such associations can be strengthened through the conduct of a longitudinal study. Second, during the current COVID-19 pandemic, this study investigated Vietnamese manufacturing SMEs’ industrial hygiene (IO) and environmental practices. This study relied on self-reported data from managers because, third, it was difficult to obtain objective data on environmental performance from manufacturing SMEs in Vietnam (because of privacy issues), so the study relied on data reported by the managers themselves. In the future, research should collect objective data so that environmental performance can be evaluated. A further potential limitation of the study is that it was conducted in a geographically specific context. Because this study was carried out with Vietnamese small- and medium-sized manufacturing companies, it may have some restrictions in its applicability in other countries. Accordingly, from the perspective of institutional theory, there is a need for additional research to be carried out in nations that have a variety of political systems, approaches to international relations and international trade, cultural values, and environmental regulations.
Author contribution
Lijie Du, conceptualization, data curation, methodology; Asif Razzaq, writing — original draft, data curation, visualization, supervision; Muhammad waqas, editing, writing — review and editing, and software.
Data availability
The data can be available on request.
Declarations
Ethics approval and consent to participate
We declare that we have no human participants, human data, or human tissues.
Consent for publication
N/A
Competing interests
The authors declare no competing interests.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Abed SS Social commerce adoption using TOE framework: an empirical investigation of Saudi Arabian SMEs Int J Inf Manage 2020 53 102118 10.1016/j.ijinfomgt.2020.102118
Adam NA Alarifi G Innovation practices for survival of small and medium enterprises (SMEs) in the COVID-19 times: the role of external support J Innov Entrep 2021 10 1 22 10.1186/S13731-021-00156-6/TABLES/6
Ahmad M Zhao ZY Irfan M Mukeshimana MC Empirics on influencing mechanisms among energy, finance, trade, environment, and economic growth: a heterogeneous dynamic panel data analysis of China Environ Sci Pollut Res 2019 10.1007/s11356-019-04673-6
Ahmad M Iram K Jabeen G Perception-based influence factors of intention to adopt COVID-19 epidemic prevention in China Environ Res 2020 190 109995 10.1016/j.envres.2020.109995 32739626
Ahmad M Jan I Jabeen G Alvarado R Does energy-industry investment drive economic performance in regional China: implications for sustainable development Sustain Prod Consum 2021 27 176 192 10.1016/j.spc.2020.10.033
Ahokangas P, Haapanen L, Golgeci I, et al (2021) Knowledge sharing dynamics in international subcontracting arrangements: the case of Finnish high-tech SMEs. J Int Manag 100888. 10.1016/j.intman.2021.100888
Aidoo SO, Agyapong A, Acquaah M, Akomea SY (2021) The performance implications of strategic responses of SMEs to the Covid-19 pandemic: evidence from an African economy. 101080/2332237320211878810 7:74–103. 10.1080/23322373.2021.1878810
Albats E Alexander A Mahdad M Stakeholder management in SME open innovation: interdependences and strategic actions J Bus Res 2020 119 291 301 10.1016/j.jbusres.2019.07.038
Al-Hakimi MA Saleh MH Borade DB Entrepreneurial orientation and supply chain resilience of manufacturing SMEs in Yemen: the mediating effects of absorptive capacity and innovation Heliyon 2021 7 e08145 10.1016/j.heliyon.2021.e08145 34660936
Ardito L Raby S Albino V Bertoldi B The duality of digital and environmental orientations in the context of SMEs: implications for innovation performance J Bus Res 2021 123 44 56 10.1016/j.jbusres.2020.09.022
Armstrong JS Overton TS Estimating nonresponse bias in mail surveys J Mark Res 1977 14 396 402 10.1177/002224377701400320
Baah C Opoku-Agyeman D Acquah ISK Examining the correlations between stakeholder pressures, green production practices, firm reputation, environmental and financial performance: evidence from manufacturing SMEs Sustain Prod Consum 2021 27 100 114 10.1016/j.spc.2020.10.015
Bai W Johanson M Oliveira L Ratajczak-Mrozek M The role of business and social networks in the effectual internationalization: insights from emerging market SMEs J Bus Res 2021 129 96 109 10.1016/j.jbusres.2021.02.042
Barabaschi B Barbieri L Cantoni F Remote working in Italian SMEs during COVID-19. Learning Challenges of a New Work Organization J Work Learn Ahead Print 2022 10.1108/JWL-10-2021-0132/FULL/PDF
Barrett G Dooley L Bogue J Open innovation within high-tech SMEs: a study of the entrepreneurial founder’s influence on open innovation practices Technovation 2021 103 102232 10.1016/j.technovation.2021.102232
Belas J Gavurova B Dvorsky J The impact of the COVID-19 pandemic on selected areas of a management system in SMEs Econ Res Istraz 2021 10.1080/1331677X.2021.2004187
Belyaeva ZS, Levis PN (2022) Post-COVID business transformation: organizational constraints and managerial implications for SMEs in Cameroon. 245–266. 10.1007/978-3-030-76575-0_12
Caballero-Morales SO (2021) Innovation as recovery strategy for SMEs in emerging economies during the COVID-19 pandemic. Res Int Bus Financ 57:. 10.1016/j.ribaf.2021.101396
Canhoto AI Quinton S Pera R Digital strategy aligning in SMEs: a dynamic capabilities perspective J Strateg Inf Syst 2021 30 101682 10.1016/j.jsis.2021.101682
Chen Y kumara EK Sivakumar V Invesitigation of finance industry on risk awareness model and digital economic growth Ann Oper Res 2021 10.1007/s10479-021-04287-7
Chen D, Gao H, Ma Y (2020) Human capital-driven acquisition: evidence from the inevitable disclosure doctrine. 101287/mnsc20203707 67:4643–4664. 10.1287/MNSC.2020.3707
Chen J, Wang Q, Huang J (2021a) Motorcycle ban and traffic safety: evidence from a quasi-experiment at Zhejiang, China. J Adv Transp 2021b:. 10.1155/2021/7552180
Costa E Soares AL de Sousa JP Industrial business associations improving the internationalisation of SMEs with digital platforms: a design science research approach Int J Inf Manage 2020 53 102070 10.1016/j.ijinfomgt.2020.102070
Crovini C Ossola G Britzelmaier B How to reconsider risk management in SMEs? An advanced, reasoned and organised literature review Eur Manag J 2021 39 118 134 10.1016/j.emj.2020.11.002
Dai R Feng H Hu J The impact of COVID-19 on small and medium-sized enterprises (SMEs): evidence from two-wave phone surveys in China China Econ Rev 2021 67 101607 10.1016/j.chieco.2021.101607
Denicolai S Zucchella A Magnani G Internationalization, digitalization, and sustainability: are SMEs ready? A survey on synergies and substituting effects among growth paths Technol Forecast Soc Change 2021 166 120650 10.1016/j.techfore.2021.120650
Dyduch W Chudziński P Cyfert S Zastempowski M Dynamic capabilities, value creation and value capture: evidence from SMEs under Covid-19 lockdown in Poland PLoS One 2021 16 e0252423 10.1371/JOURNAL.PONE.0252423 34129597
El Chaarani H Vrontis PD El Nemar S El Abiad Z The impact of strategic competitive innovation on the financial performance of SMEs during COVID-19 pandemic period Compet Rev 2021 10.1108/CR-02-2021-0024/FULL/PDF
Eldeeb MS Halim YT Kamel EM The pillars determining financial inclusion among SMEs in Egypt : service awareness, access and usage metrics and macroeconomic policies Futur Bus J 2021 10.1186/s43093-021-00073-w
Elkington J (1994) Towards the sustainable corporation: Win-win-win business strategies for sustainable development. California Manag Rev 36(2)90–100
Elkington H, White P, Addington-Hall J, Higgs R, Pettinari C (2004) The last year of life of COPD: a qualitative study of symptoms and services. Respir Med 98(5)439–445
Fleming D Lynch P Kelliher F The process of evaluating business to business relationships facing dissolution: an SME owner manager perspective Ind Mark Manag 2016 58 83 93 10.1016/j.indmarman.2016.05.017
Fu FY, Alharthi M, Bhatti Z et al (2021) The dynamic role of energy security, energy equity and environmental sustainability in the dilemma of emission reduction and economic growth. J Environ Manage 280:. 10.1016/j.jenvman.2020.111828
Ganlin P, Qamruzzaman MD, Mehta AM et al (2021a) Innovative finance, technological adaptation and SMEs sustainability: the mediating role of government support during COVID-19 pandemic. Sustain 13:9218 13:9218. 10.3390/SU13169218
Ganlin P, Qamruzzaman MD, Mehta AM et al (2021b) Innovative finance, technological adaptation and SMEs sustainability: the mediating role of government support during COVID-19 pandemic. Sustain 13:9218 13:9218. 10.3390/SU13169218
García-Pérez-de-Lema D Ruiz-Palomo D Diéguez-Soto J Analysing the roles of CEO’s financial literacy and financial constraints on Spanish SMEs technological innovation Technol Soc 2021 64 101519 10.1016/j.techsoc.2020.101519
Górska K, Horzela A, Pogány TK (2021) Non-Debye relaxations: smeared time evolution, memory effects, and the Laplace exponents. Commun Nonlinear Sci Numer Simul 99:. 10.1016/j.cnsns.2021.105837
Hossain M Yoshino N Taghizadeh-Hesary F Optimal branching strategy, local financial development, and SMEs’ performance Econ Model 2021 96 421 432 10.1016/j.econmod.2020.03.027
Hrovatin N Cagno E Dolšak J Zorić J How important are perceived barriers and drivers versus other contextual factors for the adoption of energy efficiency measures: an empirical investigation in manufacturing SMEs J Clean Prod 2021 323 129123 10.1016/j.jclepro.2021.129123
Hu L, Bentler PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Model Multidiscip J 6(1):1–55
Hu T Wang S She B Human mobility data in the COVID-19 pandemic: characteristics, applications, and challenges Int J Digit Earth 2021 14 1126 1147 10.1080/17538947.2021.1952324
Huang W Saydaliev HB Iqbal W Irfan M Measuring the impact of economic policies on Co2 emissions: ways to achieve green economic recovery in the post-Covid-19 era Clim Chang Econ 2022 10.1142/s2010007822400103
Hussain A, Akbar M, Shahzad A et al (2022) E-commerce and SME performance: the moderating influence of entrepreneurial competencies. Adm Sci 12:13 12:13. 10.3390/ADMSCI12010013
Iborra M Safón V Dolz C What explains the resilience of SMEs? Ambidexterity capability and strategic consistency Long Range Plann 2020 53 101947 10.1016/j.lrp.2019.101947
Ipsmiller E Brouthers KD Dikova D Which export channels provide real options to SMEs? J World Bus 2021 56 101245 10.1016/j.jwb.2021.101245
Iqbal W Tang YM Chau KY Nexus between air pollution and NCOV-2019 in China: application of negative binomial regression analysis Process Saf Environ Prot 2021 150 557 565 10.1016/j.psep.2021.04.039
Irfan M Zhao ZY Ahmad M Competitive assessment of Indian wind power industry: a five forces model J Renew Sustain Energy 2019 11 063301 10.1063/1.5116237
Irfan M Elavarasan RM Ahmad M Prioritizing and overcoming biomass energy barriers: application of AHP and G-TOPSIS approaches Technol Forecast Soc Change 2022 177 121524 10.1016/j.techfore.2022.121524
Irfan M Salem S Ahmad M Interventions for the current COVID-19 pandemic: frontline workers’ intention to use personal protective equipment Front Public Heal 2022 9 793642 10.3389/fpubh.2021.793642
Irfan M, Akhtar N, Ahmad M et al (2021) Assessing public willingness to wear face masks during the COVID-19 pandemic: fresh insights from the theory of planned behavior. Int J Environ Res Public Health 18:. 10.3390/ijerph18094577
Işık C Ahmad M Ongan S Convergence analysis of the ecological footprint: theory and empirical evidence from the USMCA countries Environ Sci Pollut Res 2021 28 32648 32659 10.1007/s11356-021-12993-9
Jabeen G Ahmad M Zhang Q Perceived critical factors affecting consumers’ intention to purchase renewable generation technologies: rural-urban heterogeneity Energy 2021 218 119494 10.1016/J.ENERGY.2020.119494
Jafari-Sadeghi V AmoozadMahdiraji H Busso D Yahiaoui D Towards agility in international high-tech SMEs: exploring key drivers and main outcomes of dynamic capabilities Technol Forecast Soc Change 2022 174 121272 10.1016/j.techfore.2021.121272
Jan A Xin-gang Z Ahmad M Do economic openness and electricity consumption matter for environmental deterioration: silver bullet or a stake? Environ Sci Pollut Res 2021 28 54069 54084 10.1007/s11356-021-14562-6
Jesemann I Beichter T Constantinescu C Investigation of the “lean startup” approach in large manufacturing companies towards customer driven product innovation in SMEs Procedia CIRP 2021 99 711 716 10.1016/j.procir.2021.03.095
Jin Y Tang YM Chau KY Abbas M How government expenditure mitigates emissions: a step towards sustainable green economy in belt and road initiatives project J Environ Manag 2022 303 113967 10.1016/j.jenvman.2021.113967
Khokhar M Iqbal W Hou Y Assessing supply chain performance from the perspective of Pakistan’s manufacturing industry through social sustainability Processes 2020 8 1064 10.3390/pr8091064
Kijkasiwat P The influence of behavioral factors on SMES’ owners intention to adopt private finance J Behav Exp Financ 2021 30 100476 10.1016/j.jbef.2021.100476
Ko DWW Chen PY Chen DC-HS Proactive environmental strategy, foreign institutional pressures, and internationalization of Chinese SMEs J World Bus 2021 56 101247 10.1016/j.jwb.2021.101247
Krasniqi BA, Kryeziu L, BaǦiS M et al (2021) COVID-19 and SMEs in Kosovo: assessing effect and policy preferences. 101142/S1084946721500059 26:. 10.1142/S1084946721500059
Kukanja M Planinc T Sikošek M Crisis management practices in tourism SMEs during COVID-19 - an integrated model based on SMEs and managers’ characteristics Tour An Int Interdiscip J 2022 70 113 126 10.37741/T.70.1.8
Kukanja M Planinc T Sikošek M Crisis management practices in tourism SMEs during COVID-19 - an integrated model based on SMEs and managers’ characteristics Tour An Int Interdiscip J 2022 70 113 126 10.37741/T.70.1.8
Lau YY Tang YM Chau KY COVID-19 crisis: exploring community of inquiry in online learning for sub-degree students Front Psychol 2021 12 1 14 10.3389/fpsyg.2021.679197
Le TT Corporate social responsibility and SMEs’ performance: mediating role of corporate image, corporate reputation and customer loyalty Int J Emerg Mark Ahead-of-Print 2022 10.1108/IJOEM-07-2021-1164/FULL/PDF
Le TT Ikram M Do sustainability innovation and firm competitiveness help improve firm performance? Evidence from the SME sector in Vietnam Sustain Prod Consum 2021 10.1016/j.spc.2021.11.008
Le TT Nguyen VK Effects of quick response to COVID-19 with change in corporate governance principles on SMEs’ business continuity: evidence in Vietnam Corp Gov 2022 10.1108/CG-09-2021-0334
Le TT Nguyen VK Effects of quick response to COVID-19 with change in corporate governance principles on SMEs’ business continuity: evidence in Vietnam Corp Gov Int J Bus Soc Ahead-of-Print 2022 10.1108/CG-09-2021-0334
Li Z Teng M Yang R Sb-doped WO3 based QCM humidity sensor with self-recovery ability for real-time monitoring of respiration and wound Sensors Actuators B Chem 2022 361 131691 10.1016/J.SNB.2022.131691
Liu Z Tang YM Chau KY Incorporating strategic petroleum reserve and welfare losses: a way forward for the policy development of crude oil resources in South Asia Resour Policy 2021 74 102309 10.1016/j.resourpol.2021.102309
Loader K SME suppliers and the challenge of public procurement: evidence revealed by a UK government online feedback facility J Purch Supply Manag 2015 21 103 112 10.1016/j.pursup.2014.12.003
Lu L Peng J Wu J Lu Y Perceived impact of the Covid-19 crisis on SMEs in different industry sectors: evidence from Sichuan China Int J Disaster Risk Reduct 2021 55 102085 10.1016/j.ijdrr.2021.102085 35719701
Miocevic D (2021) Investigating strategic responses of SMEs during COVID-19 pandemic: a cognitive appraisal perspective: 101177/23409444211005779. 10.1177/23409444211005779
Miocevic D Don’t get too emotional: how regulatory focus can condition the influence of top managers’ negative emotions on SME responses to economic crisis Int Small Bus J Res Entrep 2022 40 130 149 10.1177/02662426211020654
Molina-Ibáñez E-L Rosales-Asensio E Pérez-Molina C Analysis on the electric vehicle with a hybrid storage system and the use of superconducting magnetic energy storage (SMES) Energy Rep 2021 7 854 873 10.1016/j.egyr.2021.07.055
Müller JM Buliga O Voigt K-I The role of absorptive capacity and innovation strategy in the design of industry 4.0 business models - a comparison between SMEs and large enterprises Eur Manag J 2021 39 333 343 10.1016/j.emj.2020.01.002
Pattinson S Cunningham J Preece D Davies MAP Trust building in science-based SMEs in the North East of England: an ecosystem perspective J Small Bus Enterp Dev 2022 10.1108/JSBED-11-2019-0360/FULL/PDF
Pedauga L Sáez F Delgado-Márquez BL Macroeconomic lockdown and SMEs: the impact of the COVID-19 pandemic in Spain Small Bus Econ 2022 58 665 688 10.1007/S11187-021-00476-7/TABLES/8
Rahman MS AbdelFattah FAM Bag S Gani MO Survival strategies of SMEs amidst the COVID-19 pandemic: application of SEM and fsQCA J Bus Ind Mark 2022 10.1108/JBIM-12-2020-0564/FULL/PDF
Rakshit S Islam N Mondal S Paul T Mobile apps for SME business sustainability during COVID-19 and onwards J Bus Res 2021 135 28 39 10.1016/J.JBUSRES.2021.06.005 34751197
Rao F Tang YM Chau KY Assessment of energy poverty and key influencing factors in N11 countries Sustain Prod Consum 2022 30 1 15 10.1016/j.spc.2021.11.002
Rintanalert S Sirisunhirun S A model of business ethics promotion for small and medium enterprises (SMEs) development in Thailand Psychol 2021 58 9480 9492
Saguy IS (2022) Chapter 3 - Food SMEs’ open innovation: opportunities and challenges. In: Galanakis CMBT-IS in the FI (Second E (ed). Academic Press, pp 39–52
Saputra N Herlina MG Double-sided perspective of business resilience: leading SME rationally and irrationally during COVID-19 GATR J Manag Mark Rev 2021 6 125 136 10.35609/JMMR.2021.6.2(4)
Sun J Maksimov V Wang SL Luo Y Developing compositional capability in emerging-market SMEs J World Bus 2021 56 101148 10.1016/j.jwb.2020.101148
Sun T, Zhang WW, Dinca MS, Raza M (2021b) Determining the impact of Covid-19 on the business norms and performance of SMEs in China. http://www.tandfonline.com/action/authorSubmission?journalCode=rero20&page=instructions. 10.1080/1331677X.2021b.1937261
Surya B Hernita H Salim A Travel-business stagnation and SME business turbulence in the tourism sector in the era of the COVID-19 pandemic Sustain 2022 14 2380 10.3390/su14042380
Tang YM Chau KY Hong L Financial innovation in digital payment with wechat towards electronic business success J Theor Appl Electron Commer Res 2021 16 1844 1861 10.3390/jtaer16050103
Tang YM Chau KY Xu D Liu X Consumer perceptions to support IoT based smart parcel locker logistics in China J Retail Consum Serv 2021 62 102659 10.1016/j.jretconser.2021.102659
Tang YM Chau KY Fatima A Waqas M Industry 4.0 technology and circular economy practices: business management strategies for environmental sustainability Environ Sci Pollut Res 2022 10.1007/s11356-022-19081-6
Tang YM Chau KY Kwok APK A systematic review of immersive technology applications for medical practice and education - trends, application areas, recipients, teaching contents, evaluation methods, and performance Educ Res Rev 2022 35 100429 10.1016/j.edurev.2021.100429
Tevapitak K Bert Helmsing AHJ The interaction between local governments and stakeholders in environmental management: the case of water pollution by SMEs in Thailand J Environ Manage 2019 247 840 848 10.1016/j.jenvman.2019.06.097 31336349
Tolstoy D, Nordman ER, Vu U (2021) The indirect effect of online marketing capabilities on the international performance of e-commerce SMEs. Int Bus Rev 101946. 10.1016/j.ibusrev.2021.101946
Troise C Corvello V Ghobadian A O’Regan N How can SMEs successfully navigate VUCA environment: the role of agility in the digital transformation era Technol Forecast Soc Change 2022 174 121227 10.1016/j.techfore.2021.121227
Turkyilmaz A Dikhanbayeva D Suleiman Z Industry 4.0: challenges and opportunities for Kazakhstan SMEs Procedia CIRP 2021 96 213 218 10.1016/j.procir.2021.01.077
Ulvenblad P Barth H Liability of smallness in SMEs – using co-creation as a method for the ‘fuzzy front end’ of HRM practices in the forest industry Scand J Manag 2021 37 101159 10.1016/j.scaman.2021.101159
Wellalage NH, Kumar V, Hunjra AI, Al-Faryan MAS (2021) Environmental performance and firm financing during COVID-19 outbreaks: evidence from SMEs. Financ Res Lett 102568. 10.1016/J.FRL.2021.102568
Wendt C Adam M Benlian A Kraus S Let’s connect to keep the distance: how SMEs leverage information and communication technologies to address the COVID-19 crisis Inf Syst Front 2021 1 1 19 10.1007/S10796-021-10210-Z/TABLES/4
Wu Y Zhu W The role of CSR engagement in customer-company identification and behavioral intention during the COVID-19 pandemic Front Psychol 2021 12 3171 10.3389/fpsyg.2021.721410
Wu B Wang Q Fang CH Capital flight for family? Exploring the moderating effects of social connections on capital outflow of family business J Int Financ Mark Institutions Money 2022 77 101491 10.1016/J.INTFIN.2021.101491
Wu B, Liang H, Chan S (2021) Political connections, industry entry choice and performance volatility: evidence from China. 101080/1540496X20211904878 58:290–299. 10.1080/1540496X.2021.1904878
Xia H, Milevoj E, Goncalves M (2021) Local response to global crisis – the effect of COVID-19 pandemic on SMEs and government export assistance programs in Central California. 101080/1547577820211989566 26:204–232. 10.1080/15475778.2021.1989566
Xiang D Zhao T Zhang N How can government environmental policy affect the performance of SMEs: Chinese evidence J Clean Prod 2022 336 130308 10.1016/j.jclepro.2021.130308
Xiang H, Chau KY, Tang YM, Iqbal W (2022b) Business ethics and Irrationality in SMEs: w e i v re In w e i v re
Yang Y Gong Y Land LPW Chesney T Understanding the effects of physical experience and information integration on consumer use of online to offline commerce Int J Inf Manage 2020 51 102046 10.1016/J.IJINFOMGT.2019.102046
Yao L Yang X Can digital finance boost SME innovation by easing financing constraints?: evidence from Chinese GEM-listed companies PLoS One 2022 17 e0264647 10.1371/journal.pone.0264647 35239717
Yeon G Hong PC Elangovan N Divakar GM Implementing strategic responses in the COVID-19 market crisis: a study of small and medium enterprises (SMEs) in India J Indian Bus Res 2022 10.1108/JIBR-04-2021-0137/FULL/PDF
Younis H, Elbanna S (2021) How do SMEs decide on international market entry? An empirical examination in the Middle East. J Int Manag 100902. 10.1016/j.intman.2021.100902
Yu F Schweisfurth T Industry 4.0 technology implementation in SMEs – a survey in the Danish-German border region Int J Innov Stud 2020 4 76 84 10.1016/j.ijis.2020.05.001
Yumei H Iqbal W Irfan M Fatima A The dynamics of public spending on sustainable green economy: role of technological innovation and industrial structure effects Environ Sci Pollut Res 2021 1 1 19 10.1007/s11356-021-17407-4
Yung KL Ho GTS Tang YM Ip WH Inventory classification system in space mission component replenishment using multi-attribute fuzzy ABC classification Ind Manag Data Syst 2021 121 637 656 10.1108/IMDS-09-2020-0518
Zainal M, Bani-Mustafa A, Alameen M et al (2022a) Economic anxiety and the performance of SMEs during COVID-19: a cross-national study in Kuwait. Sustain 14:1112 14:1112. 10.3390/SU14031112
Zainal M, Bani-Mustafa A, Alameen M et al (2022b) Economic anxiety and the performance of SMEs during COVID-19: a cross-national study in Kuwait. Sustain 14:1112 14:1112. 10.3390/SU14031112
Zainal M, Bani-Mustafa A, Alameen M et al (2022c) Economic anxiety and the performance of SMEs during COVID-19: a cross-national study in Kuwait. Sustain 14:1112 14:1112. 10.3390/SU14031112
Zaverzhenets M Łobacz K Digitalising and visualising innovation process: comparative analysis of digital tools supporting innovation process in SMEs Procedia Comput Sci 2021 192 3805 3814 10.1016/j.procs.2021.09.155
Zhang Z Ma P Ahmed R Advanced point-of-care testing technologies for human acute respiratory virus detection Adv Mater 2022 34 2103646 10.1002/ADMA.202103646
Zhang L, Zhang H, Yu X, Feng Y (2021) Will the supporting policies help the recovery of SMEs during the pandemic of COVID-19? — evidence from Chinese listed companies. 101080/1540496X20211878021 57:1640–1651. 10.1080/1540496X.2021.1878021
Zutshi A, Mendy J, Sharma GD et al (2021) From challenges to creativity: enhancing SMEs’ resilience in the context of COVID-19. Sustain 13:6542 13:6542. 10.3390/SU13126542 | 35917071 | PMC9344445 | NO-CC CODE | 2023-01-05 23:15:18 | yes | Environ Sci Pollut Res Int. 2023 Aug 2; 30(1):1540-1561 |
==== Front
Sci Total Environ
Sci Total Environ
The Science of the Total Environment
0048-9697
1879-1026
Elsevier B.V.
S0048-9697(22)04912-9
10.1016/j.scitotenv.2022.157813
157813
Article
Short-term exposure of the mayfly larvae (Cloeon dipterum, Ephemeroptera: Baetidae) to SARS-CoV-2-derived peptides and other emerging pollutants: A new threat for the aquatic environments
Freitas Ítalo Nascimento ab
Dourado Amanda Vieira a
da Silva Matos Stênio Gonçalves a
de Souza Sindoval Silva c
da Luz Thiarlen Marinho ab
Rodrigues Aline Sueli de Lima b
Guimarães Abraão Tiago Batista a
Mubarak Nabisab Mujawar d
Rahman Md. Mostafizur e
Arias Andrés Hugo f
Malafaia Guilherme abdg⁎
a Laboratory of Toxicology Applied to the Environment, Goiano Federal Institute, Urutaí, GO, Brazil
b Post-Graduation Program in Conservation of Cerrado Natural Resources, Goiano Federal Institute, Urutaí, GO, Brazil
c Post-Graduation Program in Biotechnology and Biodiversity, Federal University of Goiás, Goiânia, GO, Brazil
d Petroleum and Chemical Engineering, Faculty of Engineering, Universiti Teknologi Brunei, Bandar Seri Begawan BE1410, Brunei Darussalam
e Laboratory of Environmental Health and Ecotoxicology, Department of Environmental Sciences, Jahangirnagar University, Dhaka 1342, Bangladesh
f Instituto Argentino de Oceanografía (IADO), Universidad Nacional del Sur (UNS)-CONICET, Florida 8000, Complejo CCT CONICET Bahía Blanca, Bahía Blanca, Argentina
g Post-Graduation Program in Ecology, Conservation, and Biodiversity, Federal University of Uberlândia, Uberlândia, MG, Brazil
⁎ Corresponding author at: Laboratory of Toxicology Applied to the Environment, Goiano Federal Institution – Urutaí Campus, Rodovia Geraldo Silva Nascimento, 2,5 km, Zona Rural, Urutaí, GO CEP: 75790-000, Brazil.
3 8 2022
25 11 2022
3 8 2022
849 157813157813
13 6 2022
26 7 2022
31 7 2022
© 2022 Elsevier B.V. All rights reserved.
2022
Elsevier B.V.
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
The input of SARS-CoV-2 or its fragments into freshwater ecosystems (via domestic or hospital sewage) has raised concerns about its possible impacts on aquatic organisms. Thus, using mayfly larvae [Cloeon dipterum (L.), Ephemeroptera: Baetidae] as a model system, we aimed to evaluate the possible effects of the combined short exposure of SARS-CoV-2-derived peptides (named PSPD-2001, PSPD-2002, and PSPD-2003 – at 266.2 ng/L) with multiple emerging pollutants at ambient concentrations. After six days of exposure, we observed higher mortality of larvae exposed to SARS-CoV-2-derived peptides (alone or in combination with the pollutant mix) and a lower-body condition index than those unexposed larvae. In the “PSPD” and “Mix+PSPD” groups, the activity of superoxide dismutase, catalase, DPPH radical scavenging activity, and the total thiol levels were also lower than in the “control” group. In addition, we evidenced the induction of nitrosative stress (inferred by increased nitrite production) and reduced acetylcholinesterase activity by SARS-CoV-2-derived peptides. On the other hand, malondialdehyde levels in larvae exposed to treatments were significantly lower than in unexposed larvae. The values of the integrated biomarker response index and the principal component analysis (PCA) results confirmed the similarity between the responses of animals exposed to SARS-CoV-2-derived peptides (alone and in combination with the pollutant mix). Although viral peptides did not intensify the effects of the pollutant mix, our study sheds light on the potential ecotoxicological risk associated with the spread of the new coronavirus in aquatic environments. Therefore, we recommend exploring this topic in other organisms and experimental contexts.
Graphical abstract
Unlabelled Image
Keywords
Novel coronavirus
Non-target organisms
Freshwater ecosystems
Insects
Biomarkers
Editor: Henner Hollert
==== Body
pmc1 Introduction
COVID-19 (Coronavirus Disease-2019), caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has been considered an unprecedented pandemic in the modern era (Yan, 2020; Nkengasong, 2021). As of 07 June 2022, the WHO Coronavirus (COVID-19) Dashboard recorded 530,266,292 confirmed cases of COVID-19, including 6,299,364 deaths (WHO, 2022). Economically, as McKibbin and Fernando (2020) highlighted, the short- and long-term fiscal and budgetary effects associated with COVID-19 point to the most significant recession in contemporary history. In addition, socially, the COVID-19 pandemic has influenced the daily lives of millions of people, from the obligation to follow social isolation rules to the planning and adoption of health measures (Saladino et al., 2020; Fraser et al., 2022). However, recent studies have shown that the impacts of the COVID-19 pandemic can be even broader, especially considering the potential effect of SARS-CoV-2 on non-target organisms (Charlie-Silva and Malafaia, 2022). The identification of SARS-CoV-2 or its fragments in hospital and domestic sewage (Markt et al., 2021; Ahmed et al., 2021; Dharmadhikari et al., 2022; Pellegrinelli et al., 2022) and in aquatic environments (e.g., rivers – De Oliveira et al., 2021; Fongaro et al., 2021; Fonseca et al., 2022; Rocha et al., 2022), have raised concerns not only about the possible secondary transmission of SARS-CoV-2 (Liu et al., 2020; Thakur et al., 2021; Ahmed et al., 2022), as well as its ecotoxicological impacts (Charlie-Silva and Malafaia, 2022).
Since the pandemic's beginning, only recently have some studies been dedicated to evaluating the ecotoxicological effects associated with SARS-CoV-2 on non-target organisms. In the pioneering study by Charlie-Silva et al. (2021), the authors exposed Physalaemus cuvieri tadpoles to peptide fragments of the Spike protein in SARS-CoV-2 (named PSPD-2001, PSPD-2002, and PSPD-2003), for a period of only 24 h, and reported an increase of the oxidative processes, as well as alterations in the activity of the enzyme acetylcholinesterase (AChE) of the animals. Subsequently, Mendonça-Gomes et al. (2021) and Malafaia et al. (2021) observed that the effects of exposure to SARS-CoV-2 peptide fragments are not restricted to the tadpoles evaluated in Charlie-Silva et al. (2021). On that occasion, alterations in locomotor activity and the olfactory behavior of Culex quinquefascitus larvae, as well as a significant increase in production of reactive oxygen species (ROS) and AChE activity, were correlated with larval exposure to PSPD-2002 and PSPD-2003 peptides (to 40 μg/L) (Mendonça-Gomes et al., 2021). In addition, Poecilia reticulata juveniles exposed to peptide fragments also show behavioral changes, redox imbalance, and impaired growth/development (Malafaia et al., 2022).
More recently, it has been demonstrated the potential of SARS-CoV-2 to induce genomic instability and DNA damage in P. reticulata juveniles (Gonçalves et al., 2022), histopathological inflammatory reaction, and damage in different organs (Fernandes et al., 2022), as well as olfactory dysfunction in Danio rerio adults (Kraus et al., 2022). On the other hand, Luz et al. (2022) reported that mice exposed to SARS-CoV-2-derived peptides show behavioral changes predictive of memory deficit, which demonstrates that the effects of the novel coronavirus fragments are not restricted to aquatic organisms. Although these studies represent preliminary and incipient findings about the potential impact of SARs-CoV-2 on non-target organisms, they certainly “shed light” on the (eco)toxicological potential of peptide fragments of SARS-CoV-2 in biota.
Thus, this scenario raises the concern that the presence and dispersion of SARS-CoV-2 may intensify the impacts on the biota caused by the pollution already known in several river systems. Pollution by heavy metals (Muhammad and Usman, 2022), surfactants (Al-Ani et al., 2020), phenolic compounds (Ramos et al., 2021), petroleum (Edori and Edori, 2021), pharmaceutical waste (He et al., 2022; Quincey et al., 2022), pesticides (Kalantary et al., 2022; Jorfi et al., 2022), personal care products (Liu et al., 2021), microplastics (Talbot and Chang, 2022), among others, has been well documented in recent years. Thus, it is questioned whether the co-existence of multiple pollutants with SARS-CoV-2 in the aquatic system constitutes an additional concern for aquatic species. Assessing “if” and “how” the COVID-19 pandemic can affect wildlife, even intensifying the effects of current water pollution, is an opportunity to anticipate actions to reduce its impact on non-target organisms.
Therefore, we used mayfly larvae [Cloeon dipterum (L.), Ephemeroptera: Baetidae] to evaluate the possible effects of combined exposure to SARS-CoV-2-derived peptides with multiple pollutants of diverse chemical nature. Using biometric, antioxidant, nitrosative, and cholinesterasic biomarkers, we tested the hypothesis that co-exposure to viral peptides and the pollutant mix induces more intense changes in the growth/development of animals, more significant redox imbalance, increased nitrosative stress, and reduced activity of the acetylcholinesterase (AChE) when compared to isolated exposure to peptides and the mix of pollutants. According to Subramanian and Sivaramakrishnan (2007), insects are critical ecological components of freshwater ecosystems and are widely used as environmental indicators.
In particular, C. dipterum is part of the order Ephemeroptera, which includes hemimetabola insects that live in freshwater ecosystems and have over 3000 species distributed in 40 different families approximately (Barber-James et al., 2007; Almudi et al., 2019). Salles et al. (2004) highlight that the mayflies are found in almost all freshwater habitats worldwide and display an amphibiotic life cycle; the larval stage is aquatic, and the alate stage is terrestrial. According to Jacobus et al. (2019), mayflies constitute a significant part of the macroinvertebrate biomass and production in freshwater habitats and are widely endorsed as bioindicators of water quality and ecological integrity (Barbour et al., 1999; Menetrey et al., 2007; Vilenica et al., 2022), and therefore included in many of the biological water quality assessment methods for streams (e.g., Hilsenhoff, 1988; and Kietzka et al., 2019]. Therefore, we believe that our study contributes to advancing knowledge about the global impacts of the COVID-19 pandemic under an ecological/environmental optimum, going beyond what we already know about transmissibility, pathogenesis, and therapeutics of the disease.
2 Material and methods
2.1 SARS-CoV-2-derived peptides
The synthesis, cleavage, purification, and characterization of the peptides of the Spike protein of SARS-CoV-2 used in our study (PSPD-2001, PSPD-2002, and PSPD-2003) were performed according to methods described in detail by Charlie-Silva et al. (2021). Briefly, the synthesis was conducted using the solid phase peptide synthesis method (SPPS) following the Fmoc strategy, based on Behrendt et al. (2016). The resins used for synthesis were Fmoc-Cys (Trt)-Wang, Fmoc-Thr (TBu)-Wang, and Fmoc-Asn (Trt)-Wang for peptides Arg-Val-Tyr-Ser-Ser-Ala-Asn-Asn-Cys-COOH (PSPD-2001); Gln-Cys-Val-Asn-Leu-Thr-Thr-Arg-Thr-COOH (PSPD-2002) and Asn-Asn-Ala-Thr-Asn-COOH (PSPD-2003) (Fig. 1 ). This resin made it possible to obtain peptides with a carboxylated C-terminal end at the end of the synthesis. After coupling all the amino acid residues of the peptide sequence, the chains were removed from the solid support utilizing acid cleavage via trifluoroacetic acid, similarly to Guy and Fields (1997). The crude compounds were purified by high-performance liquid chromatography [based on Klaassen et al., 2019], using different purification methods according to the retention time obtained in a gradient program of 5 to 95 % in 30 min (exploration gradient) in analytical HPLC. Only compounds with purity equal to or >95 % were considered for in vivo evaluation, following the National Health Surveillance Agency (ANVISA/Brazil) rules and Food and Drug Administration (FDA/USA).Fig. 1 The present study synthesized and used structural models of peptides (PSPD-2001, PSPD-2002, and PSPD-2003).
Fig. 1
2.2 Mix of emerging pollutants
The pollutant mix used to simulate the aquatic contamination by different xenobiotics was composed of 14 pollutants (in addition to those that make up the tannery effluent) in environmentally relevant concentrations (i.e., that were previously identified in water surfaces), based on da Costa Araújo et al. (2023) and Araújo et al. (2022). The physicochemical and inorganic characterization and the profile of organic compounds identified in this mix can be observed in Souza et al. (2018). Briefly, such pollutants more realistically represent the diversity of chemical compounds/substances that can enter freshwater ecosystems, including pesticides (glyphosate and abamectin), agro-industrial effluent (tannery effluent), pharmaceutics (amoxicillin, acetylsalicylic acid, sodium diclofenac, ibuprofen, fluoxetine, clonazepam, dipyrone monohydrate, and ranitidine), hormones (estradiol cypionate), agricultural fertilizers (nitrogen), surfactants (domestic detergent), and constituent substances of petroleum (benzene). General information about the mixed emerging pollutants used in our study is presented in Table S1.
2.3 Model systems and experimental design
In this study, we used larvae of C. dipterum collected in a semi-natural breeding site installed outdoors on the premises of the Goiano Federal Institute– Campus Urutaí (GO, Brazil). Animals were captured by sweeping a net through the water column. After that, the animals were immediately taken to the laboratory and kept in an aquarium with dechlorinated water for seven days (for acclimatization), under temperature (21.9 °C ± 0.54 °C, mean ± standard deviation) and luminosity (12:12-h light: dark photoperiod) controlled. Before and during the experiment, larvae were fed once a day with commercial fish food (composition: 45 % crude protein, 14 % ether extract, 5 % natural fiber, 14 % mineral matter, and 87 % dry matter) (10 mg ration/L). After that, 320 healthy larvae [stages L4–5, according to Cianciara, 1979, Cianciara, 1979, total length: 5.30 mm ± 0.66 mm; body biomass: 1.85 mg ± 0.48 mg – mean ± standard deviation] presenting normal swimming behavior and no morphological deformities, or apparent lesions, were distributed into four experimental groups (n = 80 larvae/each - 8 replicates composed of 10 larvae/each). While the group “PSPD” was formed of animals kept in water containing an equitable mixture of the viral peptides PSPD-2001, PSPD-2002, and PSPD-2003, totaling 266.2 ng PSPD/L); C. dipterum larvae exposed to the mix of emerging pollutants described above (see Table S1) (without the presence of SARS-CoV-2-derived peptides), composed the “Mix” group. On the other hand, the group “Mix+PSPD” was formed of larvae submitted to the combined exposure of SARS-CoV-2-derived peptides with the mix of pollutants at the same concentrations defined in the previous groups. In the “control” group, C. dipterum larvae were kept in dechlorinated water free of viral peptides and any component of the pollutant mix. All experimental groups were kept in polyethylene containers filled with 200 mL of dechlorinated water added with viral peptides and/or pollutants in the respective experimental groups. The exposure period was six days (to simulate brief exposure to pollutants), and the complete water renewal occurred every two days, characterizing a semi-static exposure system.
The concentration of SARS-CoV-2-derived peptides tested in our study simulates the presence of viral particles in a predicted environmental concentration, lower than the concentrations tested in Mendonça-Gomes et al. (2021), Malafaia et al. (2021) and Gonçalves et al., 2022. In addition, we are based on the study by Tampe et al. (2021), in which urinary levels of SARS-CoV-2 nucleocapsid protein (SARS-CoV-2-N) from patients with confirmed SARS-CoV-2 infection (admitted to the Department of Anesthesiology, Emergency and Intensive Care Medicine, University Medical Center Göttingen, Germany) ranged from 475 to 1484 pg/mL = 475 to 1484 ng/L). The concentration tested in our study (222.6 ng/L) corresponds to 15 % of the highest concentration detected in the urine of these patients, which constitutes realistic concentrations, possibly resulting from dilution in areas close to the discharge point of hospital sewage (untreated) in a small watercourse.
2.4 Toxicity biomarkers
2.4.1 Larvicidal effect
Daily, the experimental units were monitored, and, in case of deaths, the individuals were counted and removed from the experimental container. The total percentage mortality was corrected (TMRc) considering the natural mortality observed in the “control” group, applying the formula proposed by Abbott (1925) (see Eq. (1)).(1) TMRc%=%Mortality observed in treatments−%Mortality observed in the control group100−%Mortality observed in the control group×100
2.4.2 Biometry
Assuming that the treatments could induce adverse effects on the growth/development of the animals, at the end of the experiment, the developmental phase was determined, as Cianciara, 1979, Cianciara, 1979 proposed. In addition, the total length and body biomass were measured to determine the body condition index, based on Hayes and Shonkwiler (2001).
2.4.3 Biochemical biomarkers
2.4.3.1 Sample preparation
For biochemical evaluation, samples were prepared based on Mendonça-Gomes et al. (2021). Briefly, pools of five larvae/sample (8 samples/group - total of 40 animals per group) formed the analyzed samples. These animals were weighed, individually euthanized by immersion in either an ice-water and, subsequently, macerated in 1 mL of phosphate-buffered saline (PBS) solution (pH 7,2) and centrifuged at 13,000 rpm for 5 min (at 4 °C). The supernatant was separated into aliquots to evaluate the biomarkers described below.
2.4.3.2 Lipid peroxidation
The levels of malondialdehyde (MDA) were helpful in inference and the possible consequence of increased production of reactive species induced by viral peptides (alone or in combination with the pollutant mix), considering that the MDA is an indicator of lipid peroxidation (LPO) level (Grotto et al., 2009). We adopted the procedures from Esterbauer and Cheeseman (1990) and Lushchak et al. (2005). Briefly, 100 μL of the supernatant was mixed with 200 μL of trichloroacetic acid solution (to 30 % w/v) and centrifuged for 10 min (10,000 rpm, 4 °C). Then, 200 μL of the supernatant formed in the centrifugation was mixed with 200 μL of thiobarbituric acid (0.67 %, w/v) and hydrochloric acid (0.1 M) solution and incubated at 95 °C for 15 min. Subsequently, the samples were cooled (in a freezer -80 °C, for 10 min), plated on a 96-well sterile microplate (in duplicate, 180 μL/each), and read at 492 nm in an ELISA reader.
2.4.3.3 Antioxidant capacity
To estimate the possible effects of treatments on the antioxidant capacity of C. dipterum larvae, we evaluated the superoxide dismutase (SOD) and catalase (CAT) activities, which are considered enzymes that make up the organisms' first line of antioxidant defense (Ighodaro and Akinloye, 2018). Furthermore, the total antioxidant capacity was estimated using the DPPH (2,2-diphenyl-1-picryl-hydrazyl-hydrate) free radical method and by thiol groups assay. SOD activity was measured by the indirect spectrophotometric method of riboflavin photoreduction, which was previously described (Deawati et al., 2017; Deawati et al., 2018). We used the method described by Hadwan and Abed (2016) to determine CAT activity, which is based on the reaction of undecomposed hydrogen peroxide with ammonium molybdate to produce a yellowish color. DPPH free radical method was performed according to Brand-Williams et al. (1995). This method is used worldwide to predict antioxidant activities by a mechanism in which antioxidants inhibit lipid oxidation, scavenging DPPH radicals and, therefore, determining free radical scavenging capacity. Total thiol concentration or sulfhydryl groups were measured by the methods described initially by Ellman (1959) and modified by Hu (1994). Here, thiols interact with 5, 5′-dithiobis-(2-nitrobenzoic acid) (DTNB), forming a highly colored anion.
2.4.3.4 Nitric oxide production
For the measurement of NO, we used the Griess colorimetric reaction (Grisham et al., 1996), which consisted of the detection of nitrite (NO2 −), resulting from the oxidation of NO, like Nascimento et al. (2021).
2.4.3.5 Acetylcholinesterase activity
To assess the potential of treatments to induce changes in the cholinergic system, we also evaluated the activity of acetylcholinesterase (AChE), which is responsible for the termination of impulse transmission at cholinergic synapses by hydrolysis of the acetylcholine (ACh) (Silman and Sussman, 2008). For this, we have adopted the procedures proposed by Ellman et al. (1961), modifications that are detailed in Malafaia et al. (2020).
2.4.3.6 Total protein, carbohydrate, and lipid levels
Assuming that treatments could interfere with the content of molecules important in energy metabolism, we also evaluated the total protein, carbohydrate, and triglycerides levels in the C. dipterum larvae. Total tissue protein levels were determined based on Lowry et al. (1951) method. On the other hand, triglycerides and total carbohydrate levels were determined using the Folch method (Folch et al., 1957) and the methodology suggested by Dubois et al. (1956), respectively. A detailed description of these methods is presented in the previous study by Guimarães et al. (2021). It is noteworthy that the results referring to MDA levels, antioxidant capacity biomarkers, NO production, and AChE activity were expressed by the “g of proteins” of the samples.
2.5 Measurement of water physicochemical parameters
To assess the possible influence of SARS-CoV-2-derived peptides and pollutant mix (alone or in combination) on water quality conditions, different physicochemical water parameters of each experimental replica were measured daily. Water temperature (°C), electronic conductivity (μS/cm), total dissolved solids (mg/L), resistivity (MΩ.cm), oxidation-reduction potential (mV), salinity (%), and pH were measured on-site with a portable multi-parameter (Instrutemp, ITPH-3000). The dissolved oxygen levels (mg/L) were measured using a dissolved oxygen meter (CommerceAll, AT-155).
2.6 Integrated biomarker response index (IBRv2)
To evidence the toxicity of the treatments, the results of all biomarkers evaluated were applied to the “Integrated Biomarker Response Index” (IBRv2), which is based on the reference deviation between a disturbed and undisturbed state. For this, we adopted the methodology proposed by Sanchez et al. (2013) and described in Malafaia et al. (2022). In our study, the deviation between biomarkers measured in larvae exposed to SARS-CoV-2-derived peptides and a mix of pollutants were compared to those in C. dipterum larvae unexposed (“control” group). The biomarker response scores were plotted as radar graphs. The area above 0 reflects biomarker induction, and the area below 0 indicates biomarker.
2.7 Statistical analysis
2.7.1 Standard curves (biochemical evaluations)
Standard curves were created by correlation analysis and linear regression obtained by absorbances versus known concentrations of MDA, NO, and total thiol levels. For MDA we used different concentrations of tetraethoxypropane (66; 33; 16.5; 8.3; 4.1; 2.1 and 1 μM) (used as the standard MDA), similarly to Mendes et al. (2009) (correlation analysis: Spearman r = 1.0; p-value = 0.0004; linear regression: equation: y = 20.9x − 0.007682; R2 = 0.9996; F-value = 13,579; p-value <0.0001). For NO, different concentrations of sodium nitrite (33.3; 16.7; 8.3; 4.2; 2.1; 1.0 and 0.5 and μM) were used to create the standard curve (analysis of correlation: Spearman r = 1.0; p-value = 0.0001; linear regression: equation: y = 0.01407x + 0.01808; R2 = 0.9966; F-value = 1447; p-value <0.0001) and in the thiol groups assay we used different concentrations of reduced glutathione [used as sulfhydryl group standard; 6.9; 5.7; 4.8; 4.2; 3.7; 3.3 and 3.0 mmol/L; like Costa et al., 2006] (correlation analysis: Spearman r = 1.0; p-value = 0.0001; linear regression: equation: y = 0.05255x + 0.08234; R2 = 0.9841; F-value = 308.5; p-value <0.0001). Concentrations of quality controls and unknown samples were estimated by applying the linear regression equation of the standard curve to the unknown sample.
2.7.2 Mean comparison and correlations analysis
Initially, all data obtained were evaluated regarding the assumptions for using parametric models. For this, we used the Shapiro-Wilk test to assess the distribution of residual data, and the Bartlett test was used to assess the homogeneity of variances. The data that met the assumptions for parametric models were analyzed via the one-way ANOVA test (with Tukey post-test). The non-parametric data were compared via the Kruskal-Wallis test (with Dunn's post-test). Significance levels were set at Type I error (p) values lower than 0.05. Additionally, correlations were performed using Pearson's (for parametric data) or Spearman's (for non-parametric data) correlation coefficients. GraphPad Prism Software Version 9.0 software was used to perform the statistical analyzes.
2.7.3 Principal component analysis
The principal component analysis (PCA) was also applied to the dataset obtained in this study, a widely used statistical method and well-documented in textbooks. Briefly, the technique uses a linear mathematical algorithm to derive a new set of variables, called principal components (PCs), from the original variables so that the new set of variables are no longer correlated. PCA was applied to process the structural and compositional descriptors of the samples to remove the correlations between the descriptors and reduce dimensionality. The obtained PCs were then used for clustering and link analysis. The hypothesis was that the experimental groups with high toxicity would be distinguished as a cluster based on the structural and compositional descriptors. The descriptors underlying clustering can then be identified as those responsible for the observed toxicity. In all PCA analyses in this work, the outliers' values (identified via the Grubbs test) were excluded from the original data, sequentially logarithmized before PCA analysis. The variables considered in the PCA were total protein (TP), total carbohydrates (TC), triglycerides (TRI), superoxide dismutase (SOD), catalase (CAT), DPPH radical scavenging activity (DPPH), acetylcholinesterase activity (AChE), body biomass (BB), malondialdehyde (MDA), development stages (DS), nitrite (NO), total thiols (TT), and body condition index (BCI). After PCA, the rotated loading (coefficient) matrix, loading plot, PCA score plot, the proportion of variance plot, scree plot, and PCA biplot of the first two PCs were generated in GraphPad Prism Software Version 9.0.
3 Results
Initially, our data showed that larval mortality in the “PSPD” and “Mix+PSPD” groups was, on average, 22.5 % higher than that observed in the “control” and “Mix” groups (Table 1 ). Furthermore, we noted that body biomass and larval developmental stage did not differ between experimental groups at the end of the experiment (Fig. 2A-B). However, larvae exposed to SARS-CoV-2-derived peptides (alone or in combination with the pollutant mix) had a lower body condition index when compared to unexposed larvae (Fig. 2C).Table 1 The mortality rate of Cloeon dipterum larvae recorded in the different experimental groups.
Table 1 Experimental groups
C Mix PSPD Mix + PSPD
Number of replicas/group 8 8 8 8
Number of larvae/group 80 80 80 80
Mortality (%)a 25.00 ± 17.73 25.00 ± 15.12 31.25 ± 6.40 30.0 ± 15.12
Summary statistical analysis One-way ANOVA test - F-value = 0.4256; p-value = 0.7361
Corrected mortality (%)b – 0 8.33 6.66
“C” refers to the control group, “Mix” refers to those exposed to the mix of pollutants (see concentrations in Table S1), and “PSPD” refers to the group composed of animals exposed only to SARS-CoV-2-derived peptides (PSPD-2001 + PSPD-2002 + PSPD-2003; at 266.2 ng/L), and “Mix+PSPD” include animals exposed to SARS-CoV-2-derived peptides in association with the mix of pollutants.
a Values represent mean ± standard deviation.
b The values indicate the average of corrected mortality.
Fig. 2 (A) Body biomass, (B) development stages, and (C) body condition index of Cloeon dipterum larvae (mayfly) exposed or unexposed to different treatments. Parametric data are presented by the mean + standard deviation, whereas non-parametric are presented by the median and interquartile range. Summaries of statistical analyzes are displayed at the top of the graphs. Distinct lowercase letters indicate significant differences between the experimental groups. “C” refers to the control group, “Mix” refers to those exposed to the mix of pollutants (see concentrations in Table S1), and “PSPD” refers to the group composed of animals exposed only to SARS-CoV-2-derived peptides (PSPD-2001 + PSPD-2002 + PSPD-2003; at 266,2 ng/L), and “Mix+PSPD” include animals exposed to SARS-CoV-2-derived peptides in association with the mix of pollutants. n = 20 (“C” group); n = 20 (“Mix” group); n = 15 (“PSPD” group), and n = 18 (“Mix+PSPD” group).
Fig. 2
We also evidenced suppression of antioxidant activity in exposed larvae, especially to SARS-CoV-2-derived peptides (alone or in combination with mixed pollutants) (“PSPD” and “Mix+PSPD” groups). SOD and CAT activity, DPPH radical scavenging activity in these groups, and total thiol levels were statistically significantly lower than in the “control” group (Fig. 3A-D, respectively). On the other hand, surprisingly, MDA levels in larvae exposed to treatments were significantly lower than in unexposed larvae (Fig. 4A). On average, the MDA levels in the “Mix”, “PSPD” and “Mix+PSPD” groups were 58.5 % lower compared to the “control” group.Fig. 3 (A) Superoxide dismutase activity, (B) catalase activity, (C) DPPH radical scavenging activity, and (D) total thiol levels in Cloeon dipterum larvae (mayfly) exposed or unexposed to different treatments. Parametric data are presented by the mean + standard deviation, whereas non-parametric are presented by the median and interquartile range. Summaries of statistical analyzes are displayed at the top of the graphs. Distinct lowercase letters indicate significant differences between the experimental groups. “C” refers to the control group, “Mix” refers to those exposed to the mix of pollutants (see concentrations in Table S1), and “PSPD” refers to the group composed of animals exposed only to SARS-CoV-2-derived peptides (PSPD-2001 + PSPD-2002 + PSPD-2003; at 266.2 ng/L), and “Mix+PSPD” include animals exposed to SARS-CoV-2-derived peptides in association with the mix of pollutants. To evaluate biochemical biomarkers, pools of five larvae/sample (8 samples/group) formed the analyzed samples, totaling 40 animals/group. All statistical comparisons were based on the averages of each replicate (i.e., n = 8 replicates).
Fig. 3
Fig. 4 (A) Malondialdehyde, (B) nitrite levels, and (C) acetylcholinesterase activity in Cloeon dipterum larvae (mayfly) exposed or unexposed to different treatments. Parametric data are presented by the mean + standard deviation, whereas non-parametric are presented by the median and interquartile range. Summaries of statistical analyzes are displayed at the top of the graphs. Distinct lowercase letters indicate significant differences between the experimental groups. “C” refers to the control group, “Mix” refers to those exposed to the mix of pollutants (see concentrations in Table S1), and “PSPD” refers to the group composed of animals exposed only to SARS-CoV-2-derived peptides (PSPD-2001 + PSPD-2002 + PSPD-2003; at 266,2 ng/L), and “Mix+PSPD” include animals exposed to SARS-CoV-2-derived peptides in association with the mix of pollutants. To evaluate biochemical biomarkers, pools of five larvae/sample (8 samples/group) formed the analyzed samples, totaling 40 animals/group. All statistical comparisons were based on the averages of each replicate (i.e., n = 8 replicates).
Fig. 4
Fig. 4B demonstrates a significant increase in nitrite levels in larvae exposed to SARS-CoV-2-derived peptides (alone and in combination with the pollutant mix). On average, the increase in NO in the “PSPD” and “Mix+PSPD” groups was 480 % higher than in the “control” group. However, AChE activity in these groups was significantly reduced compared to unexposed larvae (Fig. 4C). In addition, our data showed that exposure to treatments induced changes in the content of molecules important in energy metabolism in animals. C. dipterum larvae exposed to the pollutant mix and SARS-CoV-2-derived peptides (alone or in combination) showed a significant reduction in total carbohydrate and triglyceride levels (Fig. 5A-B, respectively) compared to unexposed larvae. On the other hand, total protein levels were increased only in the “Mix+PSPD” group (Fig. 5C).Fig. 5 (A) Total carbohydrates, (B) triglycerides, and (C) total proteins in Cloeon dipterum larvae (mayfly) exposed or unexposed to different treatments. Parametric data are presented by the mean + standard deviation, whereas non-parametric are presented by the median and interquartile range. Summaries of statistical analyzes are displayed at the top of the graphs. Distinct lowercase letters indicate significant differences between the experimental groups. “C” refers to the control group, “Mix” refers to those exposed to the mix of pollutants (see concentrations in Table S1), and “PSPD” refers to the group composed of animals exposed only to SARS-CoV-2-derived peptides (PSPD-2001 + PSPD-2002 + PSPD-2003; at 266.2 ng/L), and “Mix+PSPD” include animals exposed to SARS-CoV-2-derived peptides in association with the mix of pollutants. To evaluate biochemical biomarkers, pools of five larvae/sample (8 samples/group) formed the analyzed samples, totaling 40 animals/group. All statistical comparisons were based on the averages of each replicate (i.e., n = 8 replicates).
Fig. 5
We also showed that the SARS-CoV-2-derived peptides or the pollutant mix did not change the oxidation-reduction potential (68.49 mV ± 6.00 mV), pH (8.13 ± 0.1), temperature (21, 92 °C ± 0.54 °C), and dissolved oxygen (8.53 mg/L ± 0.24 mg/L) of the exposure waters of the respective experimental groups (Fig. S1). On the other hand, we observed that the pollutant mix significantly increased the electrical conductivity, total dissolved solids, and salinity (Fig. 6A-C, respectively), as well as reduced the water resistivity of the “Mix” and “Mix+PSPD” groups' exposure waters. (Fig. 6D). However, our statistical analyzes showed no significant correlation (or weak correlations) between most of the evaluated biomarkers and the altered physicochemical parameters in the exposure waters of these groups (Fig. S2).Fig. 6 (A) Electric conductivity, (B) total dissolved solids, (C) salinity, and (D) resistivity of the exposure waters of the different experimental groups. Parametric data are presented by the mean + standard deviation, whereas non-parametric are presented by the median and interquartile range. Summaries of statistical analyzes are displayed at the top of the graphs. Distinct lowercase letters indicate significant differences between the experimental groups. “C” refers to the control group, “Mix” refers to those exposed to the mix of pollutants (see concentrations in Table S1), and “PSPD” refers to the group composed of animals exposed only to SARS-CoV-2-derived peptides (PSPD-2001 + PSPD-2002 + PSPD-2003; at 266.2 ng/L), and “Mix+PSPD” include animals exposed to SARS-CoV-2-derived peptides in association with the mix of pollutants.
Fig. 6
Considering the set of responses of the C. dipterum larvae when exposed to SARS-CoV-2-derived peptides and a mix of pollutants (alone or in combination), the results obtained were applied to the IBRv2. In Fig. 7 , it is possible to notice a high similarity between the IBRv2 values and star graph (polygon) obtained for the “PSPD” and “Mix+PSPD” groups. Regarding PCA, we observed that the first two principal components (PC1 and PC2) cumulatively explained 92.09 % of the total variation (Fig. 8A), whose eigenvalues for PC1 and PC2 were superior à 2.5 (Fig. 8B). The loadings plot (Fig. 8C) and Table 2 demonstrate that most biomarkers were negatively associated with PC1 and PC2. Furthermore, we observed that the experimental groups were clearly separated by PC1, with “PSPD” and “Mix+PSPD” groups positioned in positive quadrants (PC score: 1.977 and 2.382, respectively) and opposite to the “control” group (PS score: −4.332). The “Mix” group showed intermediate positioning in PC1 (PC score: −0.026) (Fig. 10C-D). Therefore, these data confirm the similarity between the responses of the “PSPD” and “Mix+PSPD” groups.Fig. 7 (A) Integrated biomarker responses index (IBRv2) values and (B-D) star graph (polygon) obtained with the IBRv2 method for the (B) “Mix”, (C) “PSPD”, and (D) “Mix+PSPD” groups. Total protein (TP), total carbohydrates (TC), triglycerides (TRI), superoxide dismutase (SOD), catalase (CAT), DPPH radical scavenging activity (DPPH), acetylcholinesterase activity (AChE), body biomass (BB), malondialdehyde (MDA), development stages (DS), nitrite (NO), total thiols (TT), and body condition index (BCI). “C” refers to the control group, “Mix” refers to those exposed to the mix of pollutants (see concentrations in Table S1), and “PSPD” refers to the group composed of animals exposed only to SARS-CoV-2-derived peptides (PSPD-2001 + PSPD-2002 + PSPD-2003; at 266,2 ng/L), and “Mix+PSPD” include animals exposed to SARS-CoV-2-derived peptides in association with the mix of pollutants.
Fig. 7
Fig. 8 (A) proportion of variance plot (PC1, PC2, and PC3), (B) scree plot (eigenvalue), (C) loadings plot of the investigated variables, (D) PC score plot, and (E) PCA biplot of the first two principal components that simultaneously shows scores of experimental groups (gray points) and loadings of explanatory variables (vectors – arrows). Total protein (TP), total carbohydrates (TC), triglycerides (TRI), superoxide dismutase (SOD), catalase (CAT), DPPH radical scavenging activity (DPPH), acetylcholinesterase activity (AChE), body biomass (BB), malondialdehyde (MDA), development stages (DS), nitrite (NO), total thiols (TT), and body condition index (BCI). “C” refers to the control group, “Mix” refers to those exposed to the mix of pollutants (see concentrations in Table S1), and “PSPD” refers to the group composed of animals exposed only to SARS-CoV-2-derived peptides (PSPD-2001 + PSPD-2002 + PSPD-2003; at 266.2 ng/L), and “Mix+PSPD” include animals exposed to SARS-CoV-2-derived peptides in association with the mix of pollutants.
Fig. 8
Table 2 Rotated loading (coefficient) matrix provided by the multivariate analysis to define factors or principal components PC1 and PC2.
Table 2Biomarkers Abbreviation Principal components
PC1 PC2
Total protein TP 0,879 −0,415
Total carbohydrates TC −0,865 −0,145
Triglycerides TRI −0,997 −0,059
Superoxide dismutase SOD −0,833 0,535
Catalase CAT −0,730 −0,681
DPPH radical scavenging activity DPPH −0,973 0,163
Acetylcholinesterase AChE −0,742 0,669
Body biomass BB 0,840 0,124
Malondialdehyde MDA −0,932 −0,264
Development stages DS 0,757 0,623
Nitrite NO 0,929 −0,330
Total thiols TT −0,981 0,082
Body condition index BCI −0,488 −0,715
Variables with a high loading coefficient are highlighted in bold.
4 Discussion
It is a consensus that the early identification of impacts caused by compounds, molecules, or substances dispersed in aquatic environments on organisms can favor the proposition of mitigation/remediation measures, in addition to contributing to the conservation of species. Thus, using C. dipterum larvae as an experimental model, we demonstrated that the dispersion of SARS-CoV-2-derived peptides in freshwater ecosystems constitutes an imminent threat to aquatic organisms. Although we did not observe a synergistic or additive effect of the exposure of the larvae to the viral fragments in combination with the pollutant mix, the IBRv2 value observed in the “PSPD” group demonstrates that the ecotoxicological effects induced by the SARS-CoV-2-derived peptides were superior to those caused by a combination of emerging pollutants (“Mix” group). Therefore, our data reinforce recent studies that show the impacts of the COVID-19 pandemic on non-target organisms of SARS-CoV-2 infection (Charlie-Silva and Malafaia, 2022) and, mainly, demonstrate that the effects of exposure to viral fragments are not restricted to fish (Malafaia et al., 2021; Gonçalves et al., 2022; Kraus et al., 2022; Fernandes et al., 2022), amphibians (Charlie-Silva et al., 2021) or mammals (Luz et al., 2022).
Particularly in our study, we noticed that the suppression of the antioxidant system, the induction of nitrosative stress, the reduction of the content of molecules important in energy metabolism, and AChE activity constituted the main mechanisms of action of the SARS-CoV-2-derived peptides (alone or in combination with the pollutant mix) on C. dipterum larvae. In Fig. 3, we can see that the equitable suppression of SOD, CAT, and DPPH radical scavenging activities and the content of total thiols in the “PSPD” and “Mix+PSPD” groups suggest that the SARS-CoV-2-derived peptides negatively interfered on the enzymatic and non-enzymatic antioxidant system of the experimental model. Such results are intriguing, as they show adverse effects in an animal model (C. dipterum) tolerant to different adverse conditions in its habitat (e.g., Cianciara, 1979, Cianciara, 1979; Lee et al., 2013), but also because they differ from previous reports in which other organisms were exposed to the viral peptides tested in our study. Contrary to what we observed, Charlie-Silva et al. (2021), Mendonça-Gomes et al. (2021), and Malafaia et al. (2022) showed increased antioxidant activity (inferred by SOD and CAT activity) when Physalaemus cuvieri tadpole, Culex quinquefasciatus larvae, and juvenile guppy (Poecilia reticulata), respectively, were exposed to PSPD-2002 and PSPD-2003 peptides. In these studies, in particular, such increases were associated with an adaptive response against increased production of reactive species (ROS), as well as a possible interaction between the SARS-CoV-2-derived peptides and antioxidant enzymes evaluated (confirmed by molecular docking).
On the one hand, we acknowledge that the physiological differences of the animals evaluated, associated with the different concentrations of viral peptides and exposure periods in these studies, are factors that may explain that divergence with our research; however, the induction of a redox imbalance appears to be a common mechanism of SARS-CoV-2-derived peptides on these models, either because of the inefficiency of increased SOD and CAT activity in controlling ROS production (observed in the studies mentioned above) or by the suppression of antioxidant activity, as demonstrated in our research. In this case, it is tempting to speculate that this suppression is also related to the interactions of viral peptides with the enzymatic and non-enzymatic components of C. dipterum larvae, as suggested by Luz et al. (2022) and Gonçalves et al. (2022) when evaluating the effect of exposure of Swiss mice and juvenile guppy to PSPD-2002, respectively.
On the other hand, the nitrosative stress observed in the “PSPD” and “Mix+PSPD” groups - similar to that observed in P. cuvieri tadpoles exposed to PSPD-2002 and PSPD-2003 (Charlie-Silva et al., 2021) - suggests participation influence of the increase in NO production (Fig. 4B) on the animals' response. It is known that when produced at low/moderate levels, NO participates in signaling events that regulate a series of physiological processes, such as the maintenance of vascular tone, the control of ventilation and erythropoietin production, and signal transduction from membrane receptors in different processes that regulate the response of cells to pro-inflammatory stimuli (Patel et al., 2000; Bruckdorfer, 2005; Khazan and Hedayati, 2015; Ghimire et al., 2017). However, at high concentrations, NO reacts with ROS producing reactive nitrogen species (RNS) that are known to have harmful implications for biological systems. As discussed by Paakkari and Lindsberg (1995) and Lee et al. (2016), there is strong evidence indicating that NO itself serves as a cytotoxic mediator by reacting with superoxide anions or hydrogen peroxide to produce peroxynitrite, which is much more reactive and toxic than NO or superoxide anions alone. Therefore, it is possible that the generation of peroxynitrite in C. dipterum larvae affected essential macromolecules and, consequently, contributed to the impairment of antioxidant defenses, which would explain the low activity of SOD and CAT, as well as the reduction of DPPH radical scavenging activity. and the total thiols levels observed in the larvae of the “PSPD” and “Mix+PSPD” groups (Fig. 3). This hypothesis is supported especially by the studies by Asahi et al. (1995) and Dobashi et al. (1997), involving the inactivation of glutathione peroxidase by nitric oxide and the modulation of endogenous antioxidant enzymes by nitric oxide in Rat C6 Glial cells, respectively. Furthermore, the increase in energy demand to reestablish redox homeostasis may be the cause of the reduced levels of triglycerides and total carbohydrates (Fig. 5) and the lower indices of body condition observed in C. dipterum larvae of “PSPD” and “Mix+PSPD” groups (Fig. 2C). As discussed by Arrese and Soulages (2010), the pre-metamorphic phase of insects consists of a phase of high energy demand. Therefore, the reallocation of energy to maintain physiological homeostasis can compromise the growth and development of animals.
On the other hand, it is possible that the nitrosative stress observed in larvae exposed to SARS-CoV-2-derived peptides (alone or in combination with the mix of pollutants) plays a role in the reduction of MDA levels (Fig. 4A), as corroborated by Rubbo et al. (2000). At the time, these authors demonstrated that NO also serves as a more potent inhibitor of lipid peroxidation propagation reactions than α-tocopherol (α-TH) and protects α-TH from oxidation. Thus, the induction of high NO production by SARS-CoV-2-derived peptides might have played a paradoxical role in C. dipterum larvae, which requires further research focused on viral peptides and their role in regulating membrane and lipoprotein lipid oxidation reactions.
Intriguing data also refers to the reduction of AChE activity in the larvae of the “PSPD” and “Mix-PSPD” groups, suggesting a divergent anticholinesterase effect from that cholinesterase stimulation observed in P. cuvieri tadpole (Charlie-Silva et al., 2021), C. quinquefasciatus larvae (Mendonça-Gomes et al., 2021), and P. reticulata juveniles (Malafaia et al., 2022) exposed to PSPD-2002 and PSPD-2003 peptides. In those studies, the authors suggest that the increase in AChE activity is related to a compensatory mechanism in response to the catalytic deficit induced by the peptides or to a more efficient response of the AChE to the increase in the release of ACh in the synaptic clefts via activation of the cholinergic anti-inflammatory pathway. However, in C. dipterum larvae exposed to SARS-CoV-2-derived peptides (alone or in combination with the pollutant mix), such hypotheses do not seem to be valid, and this could be due to not only the physiological differences between the evaluated models but also the locations (organs/tissues) where AChE activity was measured, as well as concentrations and exposure periods to viral peptides. In this sense, future investigations will be helpful to understand better the mechanisms that led to the anticholinesterase effect observed in our study. It is questioned whether the reduction in AChE activity in the larvae would be related to alterations in the association and catalysis mechanisms or the reduction of substrate affinity for the enzyme's active site induced by SARS-CoV-2-derived peptides. In parallel, studies on the influence of viral peptides on the cholinergic anti-inflammatory pathway will be essential to clarify whether the reduction in AChE activity is related to the decrease in acetylcholine in the synaptic clefts or the downregulation of the AChE gene by SARS-CoV-2-derived peptides.
Finally, it should be borne in mind that our study is the first to assess the possible toxicological effects of combining SARS-CoV-2-derived peptides with a mix of emerging pollutants. Therefore, several issues still need to be explored. The fact that the effects observed in our study were not more intense in C. dipterum larvae exposed to viral fragments in association with the pollutant mix (see IBRv2 and PCA – Fig. 7, Fig. 8, respectively) may reflect the tolerance of these organisms to the concentrations of pollutants in the mixture and to the exposure time evaluated (six days). Therefore, it is possible that longer exposures to treatments induce differentiated effects or that other organisms are more susceptible and/or sensitive to the impact of SARS-CoV-2-derived peptides (alone or in combination with the pollutant mix). On the other hand, evaluating other toxicity biomarkers (e.g., histopathological, molecular, genetic, etc.) in C. dipterum larvae may indicate alterations that have not been observed in our study.
5 Conclusions
From the results obtained in our study, we conclude that the exposure of C. dipterum larvae to SARS-CoV-2-derived peptides (alone or in combination with a mix of pollutants) induces changes in body condition, changes suggestive of redox imbalance and negative effect on the cholinesterase system of these animals. However, different from what we assumed, we did not evidence a synergistic or additive action between the viral peptides and the pollutants that composed the mix, considering the set of biomarkers evaluated in this study. In any case, it is crucial to consider that our research is not exhaustive and that the continuity of investigations in this area is essential for understanding the real impact of SARS-CoV-2-derived peptides on aquatic organisms, both when exposed to these peptides alone and in contexts where the novel coronavirus fragments coexist with various environmental pollutants.
Ethical aspects
All experimental procedures were performed according to the ethical standards for animal experimentation, and meticulous efforts were made to ensure that the animals suffered as little as possible and reduce external sources of stress, pain, and discomfort. The current study has not exceeded the number of animals needed to produce reliable scientific data. This article does not refer to any study with human participants performed by any authors.
CRediT authorship contribution statement
Ítalo Nascimento Freitas: designed and performed experiments, analyzed data, and co-wrote the paper. Amanda Vieira Dourado: performed experiments. Stênio Gonçalves da Silva Matos: performed experiments. Sindoval Silva de Souza: performed experiments. Thiarlen Marinho da Luz: performed experiments. Abraão Tiago Batista Guimarães: performed experiments. Aline Sueli de Lima Rodrigues: revised the article critically for important intellectual content. Nabisab Mujawar Mubarak: revised the article critically for important intellectual content. Md. Mostafizur Rahman: revised the article critically for important intellectual content. Andrés Hugo Arias: revised the article critically for important intellectual content. Guilherme Malafaia: designed and performed experiments, analyzed data, co-wrote the paper, supervised the research, and provided funding acquisition, project administration, and resources.
Declaration of competing interest
We confirm no known conflicts of interest associated with this work, and there has been no significant financial support for this work that could have influenced its outcome. Furthermore, we ensure that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that all have approved the order of authors listed in our manuscript. Due care has been taken to ensure the integrity of the work.
Appendix A Supplementary data
Supplementary material
Image 1
Data availability
Data will be made available on request.
Acknowledgments
The authors are grateful to the Goiano Federal Institute (IF Goiano/GO/Brazil) and the 10.13039/501100003593 National Council for Scientific and Technological Development (CNPq/Brazil) for the financial support needed to conduct this research (Proc. No. 23219.000854.2022-50 and 403065/2021-6, respectively). We thank the Ph.D. student Helyson Lucas Bezerra Braz (Federal University of Ceará; UFC/CE/Brazil) for his kind collaboration in the elaboration of Fig. 1 presented in this article. Malafaia G. holds a productivity scholarship from CNPq (Proc. No. 308854/2021-7).
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2022.157813.
==== Refs
References
Abbott W.S. A method of computing the effectiveness of an insecticide J. Econ. Entomol. 18 2 1925 265 267
Ahmed W. Bibby K. D'Aoust P.M. Delatolla R. Gerba C.P. Haas C.N. Bivins A. … Differentiating between the possibility and probability of SARS-CoV-2 transmission associated with wastewater: empirical evidence is needed to substantiate risk FEMS Microbes 2 2022
Ahmed W. Tscharke B. Bertsch P.M. Bibby K. Bivins A. Choi P. Mueller J.F. … SARS-CoV-2 RNA monitoring in wastewater as a potential early warning system for COVID-19 transmission in the community: a temporal case study Sci. Total Environ. 761 2021 144216
Al-Ani R.R. Hassan F.M. Al-Obaidy A.H.M.J. Environmental evaluation of surfactant: case study in sediment of Tigris River, Iraq River Deltas-Recent Advances 2020 IntechOpen
Almudi I. Martín-Blanco C.A. García-Fernandez I.M. López-Catalina A. Davie K. Aerts S. Casares F. Establishment of the mayfly Cloeon dipterum as a new model system to investigate insect evolution EvoDevo 10 1 2019 1 10 30637095
Araújo A.P.C. da Luz T.M. Rocha T.L. Ahmed M.A.I. Rahman M.M. Malafaia G. e Silva D.D.M. Toxicity evaluation of the combination of emerging pollutants with polyethylene microplastics in zebrafish: perspective study of genotoxicity, mutagenicity, and redox unbalance J. Hazard. Mater. 432 2022 128691
Arrese E.L. Soulages J.L. Insect fat body: energy, metabolism, and regulation Annu. Rev. Entomol. 55 2010 207 19725772
Asahi M. Fujii J. Suzuki K. Seo H.G. Kuzuya T. Hori M. Taniguchi N. … Inactivation of glutathione peroxidase by nitric oxide: implication for cytotoxicity (∗) J. Biol. Chem. 270 36 1995 21035 21039 7673130
Barber-James H.M. Gattolliat J.L. Sartori M. Hubbard M.D. Global diversity of mayflies (Ephemeroptera, Insecta) in freshwater Freshwater Animal Diversity Assessment 2007 Springer Dordrecht 339 350
Barbour M. Gerritsen J. Snyder B. Stribling J. Rapid Bioassessment Protocols 1999
Behrendt R. White P. Offer J. Advances in fmoc solid-phase peptide synthesis J. Pept. Sci. 22 1 2016 4 27 26785684
Brand-Williams W. Cuvelier M.E. Berset C.L.W.T. Use of a free radical method to evaluate antioxidant activity LWT- Food Sci. Technol. 28 1 1995 25 30
Bruckdorfer R. The basics about nitric oxide Mol. Asp. Med. 26 1–2 2005 3 31
Charlie-Silva I. Malafaia G. Fragments Sars-Cov-2 in aquatic organism represent an additional environmental risk concern: urgent need for research Sci. Total Environ. 817 2022 153064
Charlie-Silva I. Araújo A.P. Guimarães A.T. Veras F.P. Braz H.L. de Pontes L.G. Malafaia G. … Toxicological insights of spike fragments SARS-CoV-2 by exposure environment: a threat to aquatic health? J. Hazard. Mater. 419 2021 126463
Cianciara S. Some study on the biology and bioenergetics of Cloeon dipterum (L.), Ephemeroptera (preliminary data) Proc. 2nd Int. Conf. on Ephemeroptera, Krakow 1975 1979 175 192
Cianciara S. Life cycles of Cloeon dipterum (L.) in natural environment Pol. Arch. Hydrobiol. 26 1979 501Ð513
Costa C.M.D. dos Santos R.C. Lima E.S. A simple automated procedure for thiol measurement in human serum samples J. Bras. Patol. Med. Lab. 42 2006 345 350
da Costa Araújo A.P. da Luz T.M. Ahmed M.A.I. Ali M.M. Rahman M.M. Nataraj B. …Malafaia G. Toxicity assessment of polyethylene microplastics in combination with a mix of emerging pollutants on Physalaemus cuvieri tadpoles J. Environ. Sci. 127 2023 465 482
De Oliveira L.C. Torres-Franco A.F. Lopes B.C. da Silva Santos B.S.Á. Costa E.A. Costa M.S. Mota C.R. … Viability of SARS-CoV-2 in river water and wastewater at different temperatures and solids content Water Res. 195 2021 117002
Deawati Y. Onggo D. Mulyani I. Hastiawan I. Kurnia D. Activity of superoxide dismutase mimic of [Mn (salen) OAc] complex compound non-enzymatically in vitro through riboflavin photoreduction Molekul 12 1 2017 61 69
Deawati Y. Onggo D. Mulyani I. Hastiawan I. Kurnia D. Lönnecke P. Hey-Hawkins E. … Synthesis, crystal structures, and superoxide dismutase activity of two new multinuclear manganese (III)-salen-4, 4′-bipyridine complexes Inorg. Chim. Acta 482 2018 353 357
Dharmadhikari T. Rajput V. Yadav R. Boargaonkar R. Patil D. Kale S. Dharne M.S. … High throughput sequencing based direct detection of SARS-CoV-2 fragments in wastewater of Pune, West India Sci. Total Environ. 807 2022 151038
Dobashi K. Pahan K. Chahal A. Singh I. Modulation of endogenous antioxidant enzymes by nitric oxide in rat C6 glial cells J. Neurochem. 68 5 1997 1896 1903 9109515
Dubois M. Gilles K.A. Hamilton J.K. Rebers P.T. Smith F. Colorimetric method for determination of sugars and related substances Anal. Chem. 28 3 1956 350 356
Edori O. Edori E. Profile of total petroleum hydrocarbons in the water and sediment columns of the Orashi river, Engenni, rivers state, Niger delta, Nigeria J. Glob. Ecol. Environ. 11 2 2021 12 21
Ellman G.L. Tissue sulfhydryl groups Arch. Biochem. Biophys. 82 1 1959 70 77 13650640
Ellman G.L. Courtney K.D. Andres V. Jr. Featherstone R.M. A new and rapid colorimetric determination of acetylcholinesterase activity Biochem. Pharmacol. 7 2 1961 88 95 13726518
Esterbauer H. Cheeseman K.H. [42] Determination of aldehydic lipid peroxidation products: malonaldehyde and 4-hydroxynonenal Methods in Enzymology 186 1990 Academic Press 407 421 2233308
Fernandes B.H.V. Feitosa N.M. Barbosa A.P. Bomfim C.G. Garnique A.M. Rosa I.F. Charlie-Silva I. … Toxicity of spike fragments SARS-CoV-2 S protein for zebrafish: a tool to study its hazardous for human health? Sci. Total Environ. 813 2022 152345
Folch J. Lees M. Sloane Stanley G.H. A simple method for the isolation and purification of total lipids from animal tissues J. Biol. Chem. 226 1 1957 497 509 13428781
Fongaro G. Rogovski P. Savi B.P. Cadamuro R.D. Pereira J.V.F. Anna I.H.S. da Silva Lanna M.C. SARS-CoV-2 in human sewage and river water from a remote and vulnerable area as a surveillance tool in Brazil Food Environ. Virol. 2021 1 4
Fonseca M.S. Machado B.A.S. de Araújo Rolo C. Hodel K.V.S. dos Santos Almeida E. de Andrade J.B. Evaluation of SARS-CoV-2 concentrations in wastewater and river water samples Case Studies in Chemical and Environmental Engineering 339 2022 Environmental Protection Agency, Office of Water for use in streams and wadeable rivers: periphyton, benthic macroinvertebrates and fish
Fraser T. Page-Tan C. Aldrich D.P. Social capital's impact on COVID-19 outcomes at local levels Sci. Rep. 12 1 2022 1 15 34992227
Ghimire K. Altmann H.M. Straub A.C. Isenberg J.S. Nitric oxide: what’s new to NO? Am. J. Phys. Cell Phys. 312 3 2017 C254 C262
Gonçalves S.O. da Luz T.M. Silva A.M. de Souza S.S. Montalvão M.F. Guimarães A.T.B. Malafaia G. … Can spike fragments of SARS-CoV-2 induce genomic instability and DNA damage in the guppy, poecilia reticulate? An unexpected effect of the COVID-19 pandemic Sci. Total Environ. 825 2022 153988
Grisham M.B. Johnson G.G. Lancaster J.R. Jr. Quantitation of nitrate and nitrite in extracellular fluids Methods in Enzymology Vol. 268 1996 Academic Press 237 246
Grotto D. Maria L.S. Valentini J. Paniz C. Schmitt G. Garcia S.C. Farina M. … Importance of the lipid peroxidation biomarkers and methodological aspects for malondialdehyde quantification Quim. Nova 32 1 2009 169 174
Guimarães A.T.B. Estrela F.N. de Lima Rodrigues A.S. Chagas T.Q. Pereira P.S. Silva F.G. Malafaia G. Nanopolystyrene particles at environmentally relevant concentrations causes behavioral and biochemical changes in juvenile grass carp (Ctenopharyngodon idella) J. Hazard. Mater. 403 2021 123864
Guy C.A. Fields G.B. Trifluoroacetic acid cleavage and deprotection of resin-bound peptides following synthesis by Fmoc chemistry Methods Enzymol. 289 1997 67 83 9353718
Hadwan M.H. Abed H.N. Data supporting the spectrophotometric method for the estimation of catalase activity Data Brief 6 2016 194 199 26862558
Hayes J.P. Shonkwiler J.S. Morphometric indicators of body condition: worthwhile or wishful thinking? – Spearman J.R. Body Composition Analysis of Animals: A Handbook of Non-destructive Methods 2001 Cambridge Univ. Press 8 38
He P. Wu J. Peng J. Wei L. Zhang L. Zhou Q. Wu Z. Pharmaceuticals in drinking water sources and tap water in a city in the middle reaches of the Yangtze River: occurrence, spatiotemporal distribution, and risk assessment Environ. Sci. Pollut. Res. 29 2 2022 2365 2374
Hilsenhoff W.L. Rapid field assessment of organic pollution with a family-level biotic index J. N. Am. Benthol. Soc. 7 1 1988 65 68
Hu M.L. [41] measurement of protein thiol groups and glutathione in plasma Methods in Enzymology Vol. 233 1994 Academic Press 380 385
Ighodaro O.M. Akinloye O.A. First line defence antioxidants-superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPX): their fundamental role in the entire antioxidant defence grid Alexandria J. Med. 54 4 2018 287 293
Jacobus L.M. Macadam C.R. Sartori M. Mayflies (Ephemeroptera) and their contributions to ecosystem services Insects 10 6 2019 170 31207933
Jorfi S. Poormohammadi A. Maraghi E. Almasi H. Monitoring and health risk assessment of organochlorine pesticides in Karun River and drinking water Ahvaz city, South West of Iran Toxin Rev. 41 2 2022 361 369
Kalantary R.R. Barzegar G. Jorfi S. Monitoring of pesticides in surface water, pesticides removal efficiency in drinking water treatment plant and potential health risk to consumers using Monte Carlo simulation in Behbahan City, Iran Chemosphere 286 2022 131667
Khazan M. Hedayati M. The role of nitric oxide in health and diseases Scimetr 3 1 2015
Kietzka G.J. Pryke J.S. Gaigher R. Samways M.J. Applying the umbrella index across aquatic insect taxon sets for freshwater assessment Ecol. Indic. 107 2019 105655
Klaassen N. Spicer V. Krokhin O.V. Universal retention standard for peptide separations using various modes of high-performance liquid chromatography J. Chromatogr. A 1588 2019 163 168 30626502
Kraus A. Huertas M. Ellis L. Boudinot P. Levraud J.P. Salinas I. Intranasal delivery of SARS-CoV-2 spike protein is sufficient to cause olfactory damage, inflammation and olfactory dysfunction in zebrafish Brain Behav. Immun. 102 2022 341 359 35307504
Lee C.T. Yu L.E. Wang J.Y. Nitroxide antioxidant as a potential strategy to attenuate the oxidative/nitrosative stress induced by hydrogen peroxide plus nitric oxide in cultured neurons Nitric Oxide 54 2016 38 50 26891889
Lee C.Y. Kim D.G. Baek M.J. Choe L.J. Bae Y.J. Life history and emergence pattern of Cloeon dipterum (Ephemeroptera: Baetidae) in Korea Environ. Entomol. 42 6 2013 1149 1156 24280314
Liu D. Thompson J.R. Carducci A. Bi X. Potential secondary transmission of SARS-CoV-2 via wastewater Sci. Total Environ. 749 2020 142358
Liu S. Wang C. Wang P. Chen J. Wang X. Yuan Q. Anthropogenic disturbances on distribution and sources of pharmaceuticals and personal care products throughout the Jinsha River basin, China Environ. Res. 198 2021 110449
Lowry O. Rosebrough N. Farr A.L. Randall R. Protein measurement with the folin phenol reagent J. Biol. Chem. 193 1 1951 265 275 14907713
Lushchak V.I. Bagnyukova T.V. Lushchak V. Storey J.M. Storey K.B. Hypoxia and recovery perturb free radical processes and antioxidant potential in common carp (Cyprinus carpio) tissues Int. J. Biochem. Cell Biol. 37 6 2005 1319 1330 15778094
Luz T.M. da Costa Araújo A.P. Rezende F.N.E. Silva A.M. Charlie-Silva I. Braz H.L.B. Malafaia G. … Shedding light on the toxicity of SARS-CoV-2-derived peptide in non-target COVID-19 organisms: a study involving inbred and outbred mice Neurotoxicology 90 2022 184 196 35395329
Malafaia G. Ahmed M.A.I. Araújo A.P.C. Souza S.S. Resende F.N.E. Freitas I.N. Mendonça-Gomes J.M. … Can spike fragments SARS-CoV-2 affect the health of neotropical freshwater fish? A study involving Poecilia reticulata juveniles Aquat. Toxicol. 245 2021 106104
Malafaia G. da Luz T.M. Ahmed M.A.I. Karthi S. da Costa Araújo A.P. When toxicity of plastic particles comes from their fluorescent dye: a preliminary study involving neotropical Physalaemus cuvieri tadpoles and polyethylene microplastics J. Hazard. Mater. Adv. 6 2022 100054
Malafaia G. da Luz T.M. Guimarães A.T.B. da Costa Araújo A.P. Polyethylene microplastics are ingested and induce biochemical changes in Culex quinquefasciatus (Diptera: Culicidae) freshwater insect larvae Ecotoxicol. Environ. Contam. 15 1 2020 79 89
Markt R. Mayr M. Peer E. Wagner A.O. Lackner N. Insam H. Detection and stability of SARS-CoV-2 fragments in wastewater: impact of storage temperature Pathogens 10 9 2021 1215 34578246
McKibbin W. Fernando R. 3 The economic impact of COVID-19 Economics in the Time of COVID-19 45 2020
Mendes R. Cardoso C. Pestana C. Measurement of malondialdehyde in fish: a comparison study between HPLC methods and the traditional spectrophotometric test Food Chem. 112 4 2009 1038 1045
Mendonça-Gomes J.M. Charlie-Silva I. Guimarães A.T.B. Estrela F.N. Calmon M.F. Miceli R.N. Malafaia G. … Shedding light on toxicity of SARS-CoV-2 peptides in aquatic biota: a study involving neotropical mosquito larvae (Diptera: Culicidae) Environ. Pollut. 289 2021 117818
Menetrey N. Oertli B. Sartori M. Wagner A. Lachavanne J.B. Eutrophication: are mayflies (Ephemeroptera) good bioindicators for ponds? Pond Conservation in Europe 2007 Springer Dordrecht 125 135
Muhammad S. Usman Q.A. Heavy metal contamination in water of Indus River and its tributaries, northern Pakistan: evaluation for potential risk and source apportionment Toxin Rev. 41 2 2022 380 388
Nascimento Í.F. Guimarães A.T.B. Ribeiro F. de Lima Rodrigues A.S. Estrela F.N. da Luz T.M. Malafaia G. Polyethylene glycol acute and sub-lethal toxicity in neotropical Physalaemus cuvieri tadpoles (Anura, Leptodactylidae) Environ. Pollut. 283 2021 117054
Nkengasong J.N. COVID-19: unprecedented but expected Nat. Med. 27 3 2021 364-364
Paakkari I. Lindsberg P. Nitric oxide in the central nervous system Ann. Med. 27 3 1995 369 377 7546627
Patel R.P. Levonen A.L. Crawford J.H. Darley-Usmar V.M. Mechanisms of the pro-and anti-oxidant actions of nitric oxide in atherosclerosis Cardiovasc. Res. 47 3 2000 465 474 10963720
Pellegrinelli L. Castiglioni S. Cocuzza C.E. Bertasi B. Primache V. Schiarea S. WBE Study Group Evaluation of pre-analytical and analytical methods for detecting SARS-CoV-2 in municipal wastewater samples in Northern Italy Water 14 5 2022 833
Quincey D.J. Kay P. Wilkinson J. Carter L.J. Brown L.E. High concentrations of pharmaceuticals emerging as a threat to himalayan water sustainability Environ. Sci. Pollut. Res. 2022 1 9
Ramos R.L. Moreira V.R. Lebron Y.A. Santos A.V. Santos L.V. Amaral M.C. Phenolic compounds seasonal occurrence and risk assessment in surface and treated waters in Minas Gerais—Brazil Environ. Pollut. 268 2021 115782
Rocha A.Y. Verbyla M.E. Sant K.E. Mladenov N. Detection, quantification, and simplified wastewater surveillance model of SARS-CoV-2 RNA in the Tijuana River ACS ES&T Water 2022 10.1021/acsestwater.2c00062
Rubbo H. Radi R. Anselmi D. Kirk M. Barnes S. Butler J. Freeman B.A. … Nitric oxide reaction with lipid peroxyl radicals spares α-tocopherol during lipid peroxidation: greater oxidant protection from the pair nitric oxide/α-tocopherol than α-tocopherol/ascorbate J. Biol. Chem. 275 15 2000 10812 10818 10753874
Saladino V. Algeri D. Auriemma V. The psychological and social impact of Covid-19: new perspectives of well-being Front. Psychol. 2550 2020
Salles F.F. Da-Silva E.R. Hubbard M.D. Serrão J.E. As espécies de ephemeroptera (Insecta) registradas Para o brasil Biota Neotropica 4 2 2004 1 34
Sanchez W. Burgeot T. Porcher J.M. A novel “Integrated biomarker response” calculation based on reference deviation concept Environ. Sci. Pollut. Res. 20 5 2013 2721 2725
Silman I. Sussman J.L. Acetylcholinesterase: how is structure related to function? Chem. Biol. Interact. 175 1–3 2008 3 10 18586019
Souza J.M. Rabelo L.M. de Faria D.B.G. Guimarães A.T.B. da Silva W.A.M. Rocha T.L. Malafaia G. … The intake of water containing a mix of pollutants at environmentally relevant concentrations leads to defensive response deficit in male C57Bl/6J mice Sci. Total Environ. 628 2018 186 197 29432930
Subramanian K.A. Sivaramakrishnan K.G. Aquatic insects for biomonitoring freshwater ecosystems-a methodology manual Ashoka Trust for Ecology and Environment (ATREE), Bangalore, India 2007 31pp
Talbot R. Chang H. Microplastics in freshwater: a global review of factors affecting spatial and temporal variations Environ. Pollut. 292 2022 118393
Tampe D. Hakroush S. Bösherz M.S. Franz J. Hofmann-Winkler H. Pöhlmann S. …Tampe B. Urinary levels of SARS-CoV-2 nucleocapsid protein associate with risk of AKI and COVID-19 severity: a single-center observational study Front. Med. 2021 650
Thakur A.K. Sathyamurthy R. Velraj R. Lynch I. Saidur R. Pandey A.K. GaneshKumar P. … Secondary transmission of SARS-CoV-2 through wastewater: concerns and tactics for treatment to effectively control the pandemic J. Environ. Manag. 290 2021 112668
Vilenica M. Petrović A. Rimcheska B. Stojanović K. Tubić B. Vidinova Y. How important are small lotic habitats of the Western Balkans for local mayflies? Small Water Bodies of the Western Balkans 2022 Springer Cham 313 336
World Health Organization (WHO) WHO Coronavirus (COVID-19) Dashboard 2022 Access: 27 May Available in: https://covid19.who.int
Yan Z. Unprecedented pandemic, unprecedented shift, and unprecedented opportunity Hum. Behav. Emerg. Technol. 2020 10.1002/hbe2.192 | 35931160 | PMC9345649 | NO-CC CODE | 2022-12-15 23:16:06 | yes | Sci Total Environ. 2022 Nov 25; 849:157813 |
==== Front
Pathol Res Pract
Pathol Res Pract
Pathology, Research and Practice
0344-0338
1618-0631
Elsevier GmbH.
S0344-0338(22)00372-7
10.1016/j.prp.2022.154128
154128
Review
The pathogenicity of COVID-19 and the role of pentraxin-3: An updated review study
Margiana Ria abcd⁎
Sharma Satish Kumar e⁎⁎
Khan Bilal Irshad f
Alameri Ameer A. g
Opulencia Maria Jade Catalan h
Hammid Ali Thaeer i
Hamza Thulfeqar Ahmed j
Babakulov Sharaf Khamrakulovich kl
Abdelbasset Walid Kamal mn
Jawhar Zanko Hassan o
a Department of Anatomy, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
b Master's Programme Biomedical Sciences, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
c Andrology Program, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia
d Dr. Soetomo General Academic Hospital, Surabaya, Indonesia
e Department of Pharmacology, Glocal School of Pharmacy, The Glocal University, Saharanpur, India
f Mbbs, Rawalpindi Medical University, Rawalpindi, Pakistan
g Department of Chemistry, University of Babylon, Iraq
h College of Business Administration, Ajman University, Ajman, United Arab Emirates
i Computer Engineering Techniques Department, Faculty of Information Technology, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq
j Medical laboratory techniques department, Al-Mustaqbal University College, Babylon, Iraq
k Tashkent State Dental Institute, Makhtumkuli Street 103, Tashkent 100047, Uzbekistan
l Research scholar, Department of Scientific affairs, Samarkand State Medical Institute, Amir Temur Street 18, Samarkand, Uzbekistan
m Department of Health and Rehabilitation Sciences, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Al Kharj, Saudi Arabia
n Department of Physical Therapy, Kasr Al-Aini Hospital, Cairo University, Giza, Egypt
o Department of Medical Laboratory Science, College of Health Science, Lebanese French University, Kurdistan Region, Iraq
⁎ Corresponding author at: Department of Anatomy, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia.
⁎⁎ Corresponding author.
15 9 2022
10 2022
15 9 2022
238 154128154128
22 5 2022
3 9 2022
13 9 2022
© 2022 Elsevier GmbH. All rights reserved.
2022
Elsevier GmbH
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
In recent years, the COVID-19 pandemic has become one of the most crucial scientific issues in the world, and efforts to eradicate the disease are still ongoing. The acute inflammatory reaction associated with this disease is associated with several complications such as cytokine storm, multiple organ damage, lung fibrosis, and blood clots. PTX3, as part of the humoral innate immune systems, is one of the acute-phase proteins that perform various functions, such as modulating inflammation, repairing tissue, and recruiting immune cells. PTX3 is increased in people with SARS-CoV-2, and its level decreases with proper treatment. Therefore, it can be regarded as a suitable marker for the prognosis of the COVID-19 and evaluating the effectiveness of the treatment method applied. However, some studies have shown that PTX3 can be a double-edged sword and develop tumors by providing an immunosuppressive environment.
Keywords
COVID-19
SARS-CoV-2
Pentraxin-3
Acute-phase proteins
==== Body
pmc1 Introduction
Coronaviruses as the single-strand RNA viruses are at the size of 60–140 nm and hold mainly spike-shaped protein owing to the main ligand for target cell entry [1]. Among the population, four main strains of the virus are more common, including HKU1, OC43, NL63, and 229E causing gentle respiratory disorder. In the past several years, it was demonstrated that animal β-coronaviruses transmission to humans happened and could give rise to severe disease. Three major types of diseases associated with coronaviruses have been detected so far, in the order of emergence including SARS (Severe acute respiratory syndrome; in Foshan, China, 2002, 8000 cases and 800 deaths), MERS (Middle East respiratory syndrome,), and COVID-19 (Coronavirus Disease 2019) [2]. In 2002, the first case of beta-coronavirus transmission from bats to humans was reported. The palm cats acted as a host-mediated for the virus and provided an easy route for its transmission. In 2012, in Jeddah, Saudi Arabia, a mysterious illness appeared as the “Middle East Respiratory Syndrome Coronavirus (MERS-COV)” that had spread through bats. In this case, the medial host was a dromedary camel, which infected 2494 people and caused 900 cases of death [3], [4]. In December 2019, a pneumonia sickness of an unknown source was declared in Wuhan. A new form of coronavirus was reported in January 2020, known as SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) [5], [6]. In early 2020, the pandemic infection was denominated as COVID-19 (Coronavirus disease 2019) by WHO. Since then, SARS-CoV-2 has been spreading over 200 countries while leading to social constraints and recession in society, and it has imposed several difficulties on health organizations and 3.9 million deaths. In some patients, the latency period of the disease can vary between 2 and 14 days [7], [8]. Very mild symptoms are identified in some infected patients without any symptoms of the disease. However, more severe symptoms are developed in the individuals with chronic diseases such as lung disease and diabetes and the elderly leading to “ARDS” (Acute Respiratory Distress Syndrome), as well as multiple organs failure with an abundant mortality rate. Presently, no effective drug exists for treating COVID-19 cases. This causes challenges in its management and control [9], [10], [11], [12], [13]. Given the fact that COVID-19 and its severe side effects are most dependent on acute inflammatory reactions, the study of inflammatory factors and their function is inevitable. One of these inflammatory factors is the PTX3 (Pentraxin 3), a superfamily of acute-phase proteins, which has significant functions in innate humoral immunity, such as regulating inflammatory responses, controlling complement pathways, affecting immune system cells, and tissue repair [14], [15]. In this review study, we are going to take a look at and discuss the role of PTX3 in COVID-19.
2 Pathophysiology of COVID-19
Generally, the disease clinical steps can be found and traced in 3 different phases each with specific features.
2.1 Step 1
During the first infection phase, the lung parenchyma is infiltrated by SARS-CoV-2 leading to proliferation. TMPRSS2 and ACE2 have vital roles in the SARS-CoV-2 cellular entry, the same as SARS-CoV. The spike protein S1 subunit connects to the ACE2 ligand located on the membrane of the target cells. The virus-specific spike protein is broken down by a protease enzyme, TMPRSS2, into two subunits, the S1, and the S2. The S2 subunit plays a primary role in the virus entering the host cell [16]. At cytoplasmic membranes, coronavirus genome transcription and replication happen. The discontinuous and continuous synthesis of RNA is mediated by the replicase complex. Then, about 16 virus-related subunits and some cellular proteins are made. Unlike the family of RNA viruses, the replicase complex just exists in SARS-CoV-2 for employment of some enzymes associated with RNA processing such as putative sequence-specific endoribonuclease, 2’-O-ribose methyltransferase, 3’ -to- 5’ exoribonuclease, and ADP ribose 1’- phosphatase. By incorporation of the replicated RNA into virions, budding from the host cell occurs. The response of the innate immunity and the presence of mild constitutional symptoms are the characteristics of this phase [17]. Followed by the viral invasion, the production of type I IFNs is activated, which promotes the downstream JAK-STAT signals to increase the expression of ISGs (IFN-stimulated genes). The macrophage presenting activity and NK cells leads to confine the spreading of the virus. It is proved that blocking the production of IFN by the SARS-CoV’s N protein is important directly for viral survival [18], [19]. Theoretically, the virus is effectively helped by the extended incubation period of viral infection to abscond from the innate immunity at an early infection stage. Nowadays, there is in dire need of more studies on innate immunity, especially related to monocyte-macrophages and type I IFN in COVID-19 patients; because any functional defect in this immune system part can lead to fatal pneumonia. The Function and response of Th1 cells are vital for virus clearance in adaptive immunity. T-dependent B cells are activated by helper T cells for the development of high-affinity antibodies against the virus. Virus-infected cells are directly killed by the cytotoxic T cells. Mainly, the signaling pathway of NF-kB is promoted by the helper T cells for the synthesis of pro-inflammatory cytokines [20].
2.2 Step 2
The presence of an inflammatory response, respiratory failure, and tissue damage are the characteristics of the pulmonary phase. In most cases, mild upper respiratory tract dysfunction is induced by the viral entry in human lung tissues [20]. According to scientists, type 2 alveolar cells are triggered by the viral replication and budding to undergo epithelial regeneration of cells and apoptosis, the same as SARS-CoV. COVID-19 obliged respiratory failure revealed various properties from that of typical ARDS, though it matches the serving state Berlin definitions. Non-cardiogenic pulmonary edema causes ARDS as a clinical complex syndrome of acute respiratory failure. The most prevalent clinical disorders are viral and bacterial pneumonia related to ARDS development. Briefly, injuring the lung by inflammatory conditions or infection causes inflammatory pathways. There are excessive cytokines levels. Alveolar cell damage and necrosis are resultant from developing oxidative stress, and the existence of excessive and dysregulated inflammation, cause [21], [22]. The protein-rich alveolar edema fluid is accumulated by the incremented epithelial and endothelial permeability. Following the defect in the alveolar barrier, the process of clearing and removing the alveolar fluid is challenged. More pronounced immune responses and inflammatory are triggered by the accumulation of cell necrosis and edema fluid. Impaired gas exchange is caused by the repletion of pulmonary fluid in the airspace and interstitium of the lung gives rise to impaired carbon dioxide excretion, hypoxemia, and acute respiratory failure [23]. Ground-glass infiltrates, hypoxia, and ARDS were developed by about 20 % of the infected patients resulting in organs failures. Severe fibrosis and scarring of the respiratory system cells were found [18]. Some multinucleated giant cells were presented along with an extended alveolar lesion with a fibrin-enriched hyaline membrane. The decreased capability of mucociliary clearance and epithelium repairing in the elderly worsens the condition quickly and leads to death eventually [21].
2.3 Step 3
The damage to distant organs and the existence of systemic inflammation are the characteristics of the hyper-inflammation phase. This is caused by the hypercoagulable state and incremented host inflammatory response leading to multi-organ failure. Higher white blood cells quantity alongside lymphopenia and incremented plasma pro-inflammatory cytokines levels were found, particularly the higher levels of C-reactive protein (CRP), IL-10 IL-7 IL-6, IL-2, interferon-gamma inducible protein (IP) 10, G-CSF, macrophage inflammatory protein (MIP) 1α, MCP1 (monocyte chemoattractant protein 1), as well as TNF-α in patients who suffer from a severe type of COVID-19 [24], [25]. By such a “Cytokine storm,” intense-inflammatory-induced respiratory system injury is triggered with serious abnormalities like ARDS, MOF, acute heart/kidney/liver injury, septic shock, hemorrhage/coagulopathy, and bacterial infections. This is the same as the conditions for MERS-CoV and SARS-CoV infections [26]. According to the studies, the compounds with the progesterone basis could influence the susceptibility to infections and immune responses at various mucosal sites like the genital area, respiratory and gastrointestinal tract by changing the cellular activity and signaling, which in turn influence the infection results. By inhibition of producing the pro-inflammatory cytokines, incrementing the formation of anti-inflammatory cytokines within the course of the virus infection, and promoting repairing the damaged lung epithelium, these compounds can decrease the inflammation. Various outcomes and courses of COVID-19 may be affected by the difference in the sex steroids levels in females and males [27].
2.4 Blood clot formation in the COVID-19
Vascular damage is induced by SARS-CoV-2. Then, the free platelets are subjected not only to endothelium but also collagen too and turn into the effective platelet. The essential factors are released by the activated platelets such as thromboxane A2, serotonin, prothrombin, and adenosine diphosphate further activating the platelets. Alternatively, to initiate the clotting process in the arteries, 12 coagulation factors are required. In brief, activation of factor XII results in the conversion of prothrombin to thrombin. Ultimately, fibrinogen converts to fibrin, making a fibrin network at the harmed site to clot blood. There are several significant and life-threatening complications associated with blood clots that are induced by the virus; for instance, venous thromboembolism pulmonary, disseminated intravascular coagulation, and embolism [28].
3 Immune response in SARS-CoV-2
Only recently effective vaccines have been developed in December 2020 for immunizing the COVID-19 patients. Though, the immune system can be operative in the natural response of the body to infections and pathogens [29]. The ACE2 is used by SARS-CoV-2 as a receptor for connecting to the host cells such as respiratory system epithelial cells. The TMPRSS2 has a vital role in the breakage of the ‘spike protein’ into S2 and S1 subunits. Thus, binding the virus to the target cell membrane is facilitated by S2 [30], [31], [32]. ACE2 regulates the Renin-Angiotensin System (RAS). Hence, RAS dysfunction may be caused by a reduction in ACE2 activity followed by SARS-CoV-2 infection impressing the electrolyte/fluid and blood pressure levels as well as the vascular permeability and boosting inflammation in the respiratory system [33], [34], [35]. In the case of contamination of coronavirus in cells expressing the TMPRSS2 and ACE2, diffusion multiplication of the virus can induce cell pyroptosis. Therefore, several factors are released associated with the cell injury such as nucleic acids, ASC and ATP, oligomers. Epithelial cells, lung alveolar macrophages, and adjacent endothelial cells can identify these molecular factors leading to the formation of pro-inflammatory compounds and chemokines like IL-6, CXCL10, MCP1 (monocyte chemoattractant protein 1), MIP1α (macrophage inflammatory protein 1α), and MIP1β. Macrophages, monocytes, and T lymphocytes are brought by these protein factors to the infection place. Thus, subsequent inflammation is increased with higher IFN-γ levels released by T lymphocyte cells. Hence, a novel pro-inflammatory response is initiated. This may lead to the chemotaxis of immune system cells to the lungs within an incomplete immune reaction. Let's put it this way the mass formation of pro-inflammatory cytokines is caused, thus damaging the lung tissue; moreover, different organs are damaged by spreading the resultant cytokine storm to other tissues [36], [37]. According to the new findings, PTX3 is one of the acute phase proteins that can play a significant role in the balance of inflammation. PTX3 can be synthesized by some tissues and cells of the immune system and constitutes part of innate humoral immunity. The function of this protein during the inflammatory process can be due to the complement system activation. For instance, it can involve in complement activation by interacting with factor H as a C3b parser, C1q synthesis through the classical pathway, and complex formation with MBL (mannose‐binding leptin) in the lectin pathway. Interestingly, PTX3 can also regulate inflammation by inhibiting neutrophil migration to the damaged sites [38], [39].
3.1 B cell immunity
The response of B cells can be assessed through follicular helper T cells for almost 7 days followed by the arrival of the first symptoms in COVID-19 patients. In these patients, the B lymphocyte response is usually first versus the N protein within 4–8 days after the beginning of the symptoms; however, in the next step, a specific antibody against the S protein is synthesized and released [40], [41]. During the second week of illness, protective antibodies are made, normally against protein S. Although, within the third week, neutralizing antibodies are found in most people. Furthermore, there is a specific region in the S1 protein subunit that is the main target of the antibodies synthesized in COVID-19 patients. This area is known as the "receptor-binding-domain (RBD)" comprising 193 amino acids. The infection process is initiated by attaching the RBD to the ACE2 represented on the host target cell [42], [43], [44].
By binding the complexes of coronavirus-antibody to the Fc receptors on immune system cells such as alveolar macrophages, the formation of pro-inflammatory mediators can be obliged like IL-8 and MCP1 enhancing the immune activity circumstances. The complement system is agitated by these complexes causing another unfavorable inflammation. Therefore, the design of high-throughput antibodies with no pro-inflammatory impacts while being able to neutralize the virus has been considered. For instance, by changing the Fc area of the antibodies or their glycosylation, it will have the modified affinity to bind to the Fc receptor [45], [46], [47].
A current study reveals that a specific produced antibody versus the coronavirus particles is possibly preserved only for two months and for a short time; it can provide immunity to infected people. Similarly, in mild cases, a fast reduction in the antibodies titer is found. This is justified by the fact that the half-life of the IgG is about 21 days in COVID-19 cases. According to the experimental studies on the IgG and IgM antibodies level in COVID-19 subjects, these antibodies also exist in asymptomatic individuals. However, it should be noted that such patients have much lower antibody titers [48], [49], [50].
According to Sokal et al., memory B cells have a pivotal function in host immunity against viruses. Although their role in COVID-19 is relatively well known; but, complex issues remain unresolved. In this work, they reported a repertoire profiling of the B lymphocyte cells response within six months in patients with severe and mild COVID-19. An activated specific clone of B cells differentiates against SARS-CoV-2 antigens and produces antibodies. While neutralizing of coronavirus' RBD specific clones accumulated with time contributed highly to the considerably stable, recent memory B cell pool, highly mutated memory B cells were recruited in the initial response, such as pre-existing cross-reactive seasonal Beta-coronavirus-specific clones. Highlighting germinal center maturation, a clear accumulation of somatic mutations was displayed by these cells in the genes of their variable region over time. Generally, it was revealed that an antigen-based activation persisted and matured for more than 6 months followed by SARS-CoV-2 infection providing long-term immunity [51].
3.2 T cell immunity
Almost seven days followed by starting the disease symptoms, the responses of T cells against the cause of COVID-19 disease are traceable in the body. T CD8 + lymphocytes are significant for removing the virus-infected host cells. Albeit, throughout this process, T CD4 + lymphocytes effectively enhance the efficiency of both T CD8 + cells and B lymphocytes by secretion of various mediators [52]. In spite of the reports about lymphopenia and reduced circulating T lymphocytes count in patients, by these explorations, it was proposed that T lymphocytes are recruited from the peripheral blood into the infected site for restricting the illness. In patients possessing severe COVID-19, an intense form of the disease [53], [54], [55] is caused by the augmented T cell inability and reduced functional activity. The IL-2, TNF-α, and IFN-γ are expressed by SARS-CoV-2 specific T CD4 + cells. Thus, it is revealed that a Th1 response is presented by COVID-19 patients mostly through cellular immunity for eliminating the infection [56].
Alternatively, human T lymphocytes can be influenced by SARS-CoV-2 via CD147 on the T cell. Moreover, the expression of CD147 occurs in several tissues and cells, and it has a key role in cell migration, apoptosis, proliferation, metastasis, and differentiation of tumor cells, especially under hypoxic conditions [57].
MHC I (Type I major histocompatibility complex) proteins present viral-associated peptides to the CD8 + T lymphocytes when SARS-CoV-2 enters the host cells. Then, activation and triggering of the CD8 + T cells occur in order to cell proliferation, which causes clonal expansion and expands virus-based effector and memory T lymphocytes. Virus-infected host cells are lysed through CD8 + T cells; in the following step, the virus or viral components are processed by particular "antigen-presenting cells" such as macrophages and dendritic cells and provide peptides to T CD4 + lymphocytes through MHC-II molecules. Pathogens can be identified by B cells that are activated via a direct pathway or by interacting with CD4 + T cells [58], [59].
Despite the restricted immune response, primary investigations represented that patients who got rid of COVID-19 established specific memory T lymphocytes against the coronavirus, which can be traced within two years followed by recovery [60], [61].
3.3 NK cells immunity
Natural killer (NK) cells as a part of the innate immune system can target the virus-infected cells [62]. Through cytotoxic mechanisms, NK cells can potentially lyse abnormal cells and overlook normal cells expressing MHC. According to the studies, the inhibitory natural killer receptor can control the NK cells’ cytotoxicity function [63]. Based on the experimental works on the immune cell profiles, during COVID-19 infection, NK cell counts are reduced owing to the infiltration into the COVID-19- affected sites such as the lung [64], [65], [66]. T cells and NK cells are functionally affected by NKG2A (NK group 2A) receptor as a suppressing signaling transmitter; so that it can reduce cytotoxicity and cytokine production. According to the studies, there is an upregulation of NKG2A in people infected via SARS-CoV-2, while lower expression of markers, such as TNF-ɑ, IFN-γ, IL-2, and CD107 is noticeable as activator factors [66], [67]. In addition, it was indicated that a massive deal of damage is caused by the recruited NK cells' hyperactivation in the respiratory organ causing lung injury [68], [69].
3.4 Monocytes response
Monocytes are one of the mononuclear cells of white blood cells that are derived from myeloid precursors and circulate in the bloodstream. They possess a plasticity feature as well as the aptitude for differentiating to other cells like macrophages and dendritic cells [70]. There are two main subgroups of monocytes with different features including monocytes CD16-/CD14 + + known as classical and monocytes CD16 + classifying into CD16 + +/CD14 + and CD16 +CD14 + + cells (non-Classical) [71]. Researchers have shown that some of the important functions of monocytes, such as cytokine secretion and chemotaxis, are impaired during coronavirus infection. Presently, a pattern of remodeled cytokine profiles and chemokine was proved in COVID-19 patients’ monocytes. This change in the cell has a role in the incompetent responses chain, thus boosting the SARS-CoV-2 damaging and causing an increment in mortality [72], [73]. Generally, a reduction in monocyte count was reported in infected patients and indicating that the monocytes’ phenotype in intense cases includes CD14 + monocytes frequently, as well as CD16 + inflammatory monocytes applying inflammatory activity via secretion of IL-6 in COVID-19 [74].
3.5 Neutrophil response
Eliminating the pathogens through the phagocytosis process is the main responsibility of neutrophils. They are also responsible for releasing the “Neutrophil Extracellular Traps”, a type of innate response, to restrict virus and cytokine to inhibit replication [75], [76], [77]. The chromatin fibers in these NETs interact with certain enzymes and neutrophil-specific factors, such as myeloperoxidase, neutrophil elastase, and cathepsin G [78]. NETs have a dual opposite role; in this way, the main contribution of these traps have been illustrated in the anti-inflammatory process. On the other hand, they can accelerate tissue damage too [79], [80]. The results of a study revealed that activation of these cells and degranulation are sorely activated procedures in COVID-19 disease [81]. An autopsy obtained from people who have died from the SARS-CoV-2 demonstrated neutrophil localization and penetration in capillaries of the lungs alongside colonization to alveolar space; hence, inflammation in the respiratory system’s lower overall part is justifiable [82], [83]. Moreover, immature neutrophils and ineffective mature neutrophils are reported in COVID-19 sickness [84]. Obviously, a high raised level of CXCL-2, chemokines, and CXCL-8 is documented in the bronchoalveolar fluid of patients that can develop recruiting of neutrophils to the infection site [85], [86]. This phenomenon can be one of the major causes of ARDS due to long-term inflammation caused by hyper-activated neutrophils [87]. What is more, some toxic factors are secreted by neutrophils possibly contributing to ARDS as well [88]. Some ROSs such as superoxide radicals and H2O2 can be made after a respiratory burst via neutrophil cells. Ultimately, oxidative stress is developed by this mechanism which is related to the cytokine storms and blood clots in patients with COVID-19 [89], [90].
4 Different variants of the SARS-CoV-2
There have been diverse mutations of SARS-CoV-2 creating different variants since the diagnosis and spread of COVID-19. Regardless of the global program of the vaccination to defeat the virus, deep concern has been caused by the arrival of new variants. The variants and their properties are presented in Table 1.Table 1 Most important variants of the SARS-CoV-2 and features [138], [139].
Table 1Variant Origin Country of the virus Mutation Characteristics
Alpha
B.1.1.7 United Kingdom • N501Y
• P681H
• NTD deletions
• Susceptible to neutralization by mAb
• Increases virus replication
Beta
B.1.351 South Africa • K417N, E484K and N501Y
• NTD mutation
• Reinfections
• Reduced susceptibility to mAbs
• Associated with reduced vaccine efficacy
Gamma
P.1 Brazil • N501Y, E484K and K417T
• NTD mutations
• High infection rate
• cause disease in patients formerly infected with previous variants
• Reduction in neutralizing activity
Delta
B.1.617.2 India • T478K, L452R, P681R.
• orf3, orf7a
• Increased transmissibility
• associated with high-level reduced bamlanivimab susceptibility
Omicron
B.1.1.529 South Africa • G339D, S371L, S373P, K417 N, S375F, N440K, G446S, S477 N, T478K, Q493R, E484A, G496S, N501Y, Q498R, and Y505H
• More infectious
• High transmission rate
• More reinfections
5 Pentraxin family of proteins
The pentraxin superfamily consists of similar domains and is seen as a pentameric structure. According to the length of the protein sequence, the pentraxin family can be classified into two main groups, long pentraxin, and short pentraxin. C-reactive protein and serum amyloid P are kinds of short pentraxins and on the other hand, PTX4, PTX3, neuronal pentraxin receptor, neuronal pentraxin 2, and neuronal pentraxin 1 are long pentraxins. Several studies have shown that the members of this protein superfamily have different functions. For example, CRP and serum amyloid P play a role in regulating the immune system as short pentraxins. Their roles in immune adjustment include removing mutant cells, triggering inflammation, and acting against pathogen invasion. In addition, neuronal pentraxin is involved in the development of the central nervous system, and pentraxin-3 plays a central role in activating the immune system, tissue repair, and tumor progression. Researchers have shown that the increases in pentraxin-3 concentration in the blood can be considered a marker to detect the onset of inflammation [91].
5.1 Pentraxin 3
PTX-3 as the primary long pentraxin was detected 3 decades ago. In some cells like fibroblasts, endothelial, and epithelial cells, it is induced by TNF-alpha and interleukin-1 beta (IL-1beta). In addition, PTX3 can synthesize in human antigen processor cells like macrophages, dendritic cells, and monocytes that induced by non-viral and viral infectious agents [92]. PTX3 is a critical component of the innate immunity humoral arm and a vital inflammatory mediator. The PTX3 expression levels are much lower in serum and tissues of normal subjects and elevate quickly upon inflammatory stimuli [93], [94], [95]. The human PTX3 gene is placed on chromosome 3q band 25 and includes three exons, the first two of which encode for the N-terminal domain (amino acids 18–179) and the signal peptide, respectively. The third exon is encoded for the C-terminal domain presenting the pentraxin signature (amino acids 179–381) [96]. Enhancer-binding elements are contained in the PTX3 promoters. During proteosynthesis, the final effect of PTX3 is fine-tuned on its target structures. According to literature, Selective promoter factor 1 (SP1), nuclear factor-kappa B (NF-κB), and activator protein-1 (AP-1) are the most dominant ones. Briefly, the basal transcription of PTX3 is enhanced by AP-1 whereas, the binding region of NF-κB works when inflammatory cytokines such as IL-1β and TNF-α are present in the environment. These transcription factors are complemented in their activities of proteosynthesis-modulating through enzymatic biochemical mechanisms. Scientists have shown that if the lung epithelial cells are exposed to inflammatory conditions, the TNF-α can increase the expression of the PTX3 mRNA; nonetheless, the PTX3 protein synthesis is not dependent on the transcription of the NF-κB. Alternatively, PTX3 is made through the “c-Jun N-terminal kinase” path. In endothelial cells, TNF-α and IL-1β induce the PTX3 expression in a good way. Then, within an acute cellular alteration, converting the endothelial cell occurs from an anti-inflammatory phenotype to a pro-inflammatory and pro-coagulant cellular surface [97], [98], [99]. The structure, sources, and functions of PTX3 are illustrated in Fig. 1.Fig. 1 Pro-inflammatory cytokines are the primary trigger for PTX3 production from various cells. Released PTX3 is involved in the processes of regulating inflammation, tissue regeneration, and clearing pathogens through different mechanisms.
Fig. 1
6 Pentraxin 3 and COVID-19
There is a lower circulating PTX3 level in healthy human circumstances (<2 ng/ml); however, its value will increase sharply at the onset of inflammation. Acute lung injury and ARDS are the characteristics of tissue damage related to hyper-activation of the innate immunity in the lung [95]). PTX3 is a crucial component of humoral-innate-immunity that contributes to resisting pathogens and controlling inflammation. PTX3 and CRP are very similar together, and both belong to the acute phase family of proteins. Furthermore, some studies have suggested PTX3 as an ideal marker for inflammation and infection screening in humans. In these conditions, the local production by various types of cells at inflammatory sites and releasing the preformed protein through neutrophils against primary pro-inflammatory cytokines or microbial particles account for the increase of PTX3 level [100], [101], [102]. Preliminary results of a study showed that the SARS-CoV-2 induces and enhances the expression of PTX3 transcript in two respiratory tract epithelial cell lines, A549 and Calu-3 [103]. According to the analysis RNA and sequencing of purified monocytes from the peripheral blood mononuclear cells, single-cell level attained from COVID-19 patients, PTX3 is expressed selectively by COVID-19 monocytes and neutrophils [104]. PTX3 can play a role in modulating inflammation through two major pathways, the former by interfering with the recruiting of selectin-based neutrophils and the latter by controlling the complement cascade pathway [105]. It is found that uncontrolled complement activation has a significant role in COVID-19 disease pathogenesis, representing a proper therapeutic target. Some scientists believe that high PTX3 protein titers in people with the SARS-CoV-2 indicate a failed negative adjustment of uncontrolled inflammation [106]. The recent studies revealed the deep profiling of immune responses in COVID-19 and relevant signatures as prognostic indicators or disease classifiers. For example, studies showed PTX3 as a hard endpoint, a confident prognostic indicator of short-term death, better than other markers like CRP and IL-6 [107].
The vascular endothelium plays an intricate function in inflammation and immune modulation, an axis for the disturbance of coronavirus infection. According to reports from medical centers, there is a direct correlation between the severity of the disease and uncontrolled activation of the immune system, which leads to macrophage activation syndrome, cytokine storm, and immune exhaustion. Such a hyper-inflammatory mode deteriorates the vascular system, with the resultant EC dysfunction. ECs undergo a transition to an activated state participating in host defenses by the circulation of inflammatory mediators like IL-6, IL-1, DAMPs, and PAMPs. Localized inflammation is promoted by activated ECs by induction of pro-inflammatory gene expression, the attraction of immune cells, promotion of attracting of inflammatory cells to the infected or injured tissues, vascular leak by incrementing the endothelial permeability [64], [108]. As mentioned earlier, PTX3 can increase following COVID-19 and subsequently increase the endothelial damage caused by SARS-CoV-2. PTX3 has a role in endothelial dysfunction and vascular inflammation through different mechanisms. A noticeable relationship has been confirmed between endothelial dysfunction and PTX3 along with various pathogenetic pathways. Inflammatory cells are modulated by PTX3, thus inducing vascular inflammation. It reduces NO synthesis, prevents cell duplication, and changes the functions of endothelial cells. The effect of “fibroblast growth factor 2 (FGF2)” is hindered by PTX3 via creating a molecular complex with such molecules and inactivating them. Though, some factors block the PTX3-FGF2 interaction such as the “tumor necrosis factor-inducible gene 6 protein (TSG-6)”. Endothelial dysfunction and vascular inflammatory response are promoted by interaction with P-selectin. Furthermore, the matrix metalloproteinases synthesis is directly increased by PTX3 or through inhibiting the NO production. Clinically, PTX3 has a positive correlation with flow-mediated dilation, arterial hypertension, and intima-media thickness. Hence, PTX3 is clearly included in the pathogenesis and assessment of endothelial dysfunction [109].
Extensive studies today have well established the role of macrophage-M1 and macrophage-M2 in the COVID-19 [110]. Briefly, macrophages-M1 can be induced by IFN-γ and produce inflammatory cytokines, such as IL-1, TNF-α, iNOS, and IL-6. These factors provide the conditions for killing viruses, cancer cells, and other pathogens, and dead cells are removed through phagocytosis in the next step. So, macrophages-M1 are contributed to the maintenance of body homeostasis via anticancer effects and infection defense. Inordinate immune responses give rise to inflammatory diseases and chronic inflammation; Therefore, it seems that the function of these cells should be adjusted. Conversely, macrophages-M2 have a role in immune tolerance and tissue repair. Macrophages-M2 are differentiated by cytokines including IL-13 and IL-4. They also can suppress the inflammatory response through IL-10, TGF, and arginase [111]. In a study conducted by Hao Zhang et al., they demonstrated that PTX3 is involved in the migration, infiltration, M2-polarization of macrophages and regulates an immunosuppressive microenvironment [112].
7 Pentraxin 3 and tissue repair
Beyond its contribution as the first resistance barrier against pathogens, Innate immunity is a vital component in initiating tissue repair. Specific DAMPs are sensed by the innate immune system cellular arm regulating the inflammatory responses at the damaged areas. The innate immunity humoral arm has complex and different roles including the regulation of immune cell migration and activation to regulate the remodeling cell activity as well as clearance of apoptotic cells [113], [114], [115]. For example, fibrosis is regulated by SAP by inhibition of the macrophages’ alternative activation through FcγRs or by modulation of immune cell activities through DC-SIGN [116], [117], [118]. Complement system components and pentraxins have also interacted with elements existing within the extracellular matrix (ECM). Therefore, further regulatory roles of the innate immune system are indicated within the tissue response to injury. Moreover, there are various ECM components that improve opsonic activity like fibronectin, osteopontin, mindin, and vitronectin interact with microbes [119], [120]. After the tissue damage, induction of PTX3 occurs in the blood and locally against IL-1β amplification and TLR activation. Fascinatingly, it was reported that PTX3 is among the genes compelled by thrombin in monocytes. At the wound sites, PTX3 is released by neutrophils, within the clot and in the macrophages pericellular matrix and mesenchymal PDGFRα+FAP+ cells indicating that the wound site is invaded collectively [121], [122], [123].
Besides, PTX3 can be involved in tissue remodeling indirectly by regulating inflammation. The inflammatory response is regulated by PTX3 by acting on inflammatory cell attracting. Such an influence is mediated by the PTX3 capacity interacting with the P-selectin including the PTX3 N-linked glycosidic moiety. In other words, leukocyte rolling on the endothelium is inhibited by the interaction between P-selectin and PTX3 resulted. Hence, leukocyte recruitment is reduced by PTX3 prescription in vivo in models of pleurisy, ischemia/reperfusion-induced kidney damage, and acute lung injury [105], [124]. Interestingly, the main anomalies in liver, lung, and skin injury have been found in models of PTX3-deficient mice that referred to redundant fibrin accumulation and incremented collagen deposition accompanied by the dominant function of the fibrin as a provisional matrix protein guiding consequent repair. Such phenotypes are the attributes of the PTX3 interaction via the NH2-terminal domain with plasminogen and fibrinogen /fibrin at acidic pH promoting the degradation of fibrin [125].
8 Pentraxin 3 and pulmonary fibrosis
Pulmonary fibrosis is one of the life-threatening and most critical complications of COVID-19, which increases mortality [126]. Pathologically, there are more than 200 different conditions in pulmonary fibrosis that are distinguished in terms of inflammation and scar tissue of the lung. The first symptoms of pulmonary fibrosis are manifested by the spread of scar tissue, which includes fatigue, shortness of breath, dry cough, and dysfunction of the respiratory system [127]. As a progressing scarring disease, pulmonary fibrosis occurs in the lungs, with the characteristics of the injury and alveolar epithelial cells hyperplasia of and fibroblasts, consistent deposition of ECM, accumulation of inflammatory cells, and scars formation. The beginning of an inflammatory response and existing immune infiltrate are attracted a huge deal of attention, which develops and endures a fibrotic and damaging context in the lungs [128], [129]. Attaching to various ligands, such as growth factors, microbial moieties, ECM proteins, and complement components, PTX3 exerts its function. It is upregulated to perform a protective impress in different lung disorders [130], [131], [132]. The protective function of PTX3 is reported in lung infections in various pathological settings such as SARS and pneumonia. Researchers by using PTX3 null mice have shown that PTX3 deficiency can lead to an abnormal innate immune response in the lungs and cause acute lung damage [133], [134]. According to Federica et al., PTX3 is made during fibrosis in wild-type mice. They revealed that the induction of fibrotic tissue in the lungs is limited by the accumulation of PTX3 in the Tie2-PTX3 mice’s stroma compartment, with decreased collagen deposition and fibroblast activation, while reducing the immune infiltrate recruitment. On the other hand, an exacerbated fibrotic response was represented by PTX3-null mice along with the reduced survival against BLM treatment. They revealed the protective contribution of the endogenous PTX3 during lung fibrosis. Hence, it is facilitated to investigate the novel PTX3-driven therapeutic methods for the disease [135].
9 Conclusion
With the outbreak of the SARS-CoV-2, the international community affected by the COVID-19 has incurred high costs; despite the widespread vaccination program against this disease, we are still witnessing people becoming infected with the virus. COVID-19 is an inflammatory disease that presents symptoms in three different clinical phases and gradually becomes a severe and fatal disease. Life-threatening complications include blood clots, vascular damage, cytokine storm, multiple organ failure, and pulmonary fibrosis. Different acute-phase molecules and proteins can be detected during infection; PTX3 is one of them. PTX3 can be induced by TNF-α and IL-1β in cells, such as endothelial, epithelial, and fibroblasts, and can also be synthesized in macrophages, monocytes, and dendritic cells. Different functions of PTX3 have been identified today. It is involved in regulating inflammation, repairing tissue, controlling the complement pathway, recruitment of immune cells, and endothelial dysfunction. Sometimes PTX3 acts as a double-edged sword in humans. For instance, Wesley et al. indicated that overexpression of PTX3 promotes tumor growth, invasion, and metastasis by providing an immuno-suppressive condition [136]. On the other hand, Stebbing et al. showed that the PTX3 level in COVID-19 increases significantly, and if patients are treated, the PTX3 titer will decrease [137]. All in all, these findings suggest that PTX3 can be a reliable marker for the prognosis of the disease; and by examining its level during treatment, we can ensure the effectiveness of the treatment used.
Compliance with ethical standards
NA.
Ethical approval
This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.
Funding
There is no financial support for this study.
CRediT authorship contribution statement
Conceptualization, writing original draft: Ria Margiana. Supervision, validation, and review: Satish Kumar Sharma.
Data availability
Not applicable.
Consent to participate
Not applicable.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
==== Refs
References
1 Singhal T. A review of coronavirus disease-2019 (COVID-19) Indian J. Pediatr. 2020 1 6
2 Barth R.F. Buja L. Barth A.L. Carpenter D.E. Parwani A.V. A Comparison of the clinical, viral, pathologic, and immunologic features of severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and coronavirus 2019 (COVID-19) diseases Arch. Pathol. Lab. Med. 145 10 2021 1194 1211 34232978
3 da Costa V.G. Moreli M.L. Saivish M.V. The emergence of SARS, MERS and novel SARS-2 coronaviruses in the 21st century Arch. Virol. 165 7 2020 1517 1526 32322993
4 Zaki A.M. Van Boheemen S. Bestebroer T.M. Osterhaus A.D. Fouchier R.A. Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia N. Engl. J. Med. 367 19 2012 1814 1820 23075143
5 Huang C. Wang Y. Li X. Ren L. Zhao J. Hu Y. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China lancet 395 10223 2020 497 506 31986264
6 Valizadeh H. Abdolmohammadi-Vahid S. Danshina S. Gencer M.Z. Ammari A. Sadeghi A. Nano-curcumin therapy, a promising method in modulating inflammatory cytokines in COVID-19 patients Int. Immunopharmacol. 89 2020 107088
7 Ahn D.-G., Shin H.-J., Kim M.-H., Lee S., Kim H.-S., Myoung J., et al. Current status of epidemiology, diagnosis, therapeutics, and vaccines for novel coronavirus disease 2019 (COVID-19). 2020.
8 Lauer S.A. Grantz K.H. Bi Q. Jones F.K. Zheng Q. Meredith H.R. The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application Ann. Intern. Med. 172 9 2020 577 582 32150748
9 Chen N. Zhou M. Dong X. Qu J. Gong F. Han Y. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study Lancet 395 10223 2020 507 513 32007143
10 Guan W-j Ni Z-y Hu.Y. W-h Liang C-q Ou He J.-x Clinical characteristics of coronavirus disease 2019 in China N. Engl. J. Med. 382 18 2020 1708 1720 32109013
11 Tsuchiya A. Takeuchi S. Iwasawa T. Kumagai M. Sato T. Motegi S. Therapeutic potential of mesenchymal stem cells and their exosomes in severe novel coronavirus disease 2019 (COVID-19) cases Inflamm. Regen. 40 1 2020 1 6 31938077
12 Taghizadieh A. Mikaeili H. Ahmadi M. Valizadeh H. Acute kidney injury in pregnant women following SARS-CoV-2 infection: a case report from Iran Respir. Med. case Rep. 2020 101090
13 Ghaebi M. Osali A. Valizadeh H. Roshangar L. Ahmadi M. Vaccine development and therapeutic design for 2019–nCoV/SARS‐CoV‐2: Challenges and chances J. Cell. Physiol. 235 12 2020 9098 9109 32557648
14 Mairuhu A.T. Peri G. Setiati T.E. Hack C.E. Koraka P. Soemantri A. Elevated plasma levels of the long pentraxin, pentraxin 3, in severe dengue virus infections J. Med. Virol. 76 4 2005 547 552 15977234
15 Suksatan W. Chupradit S. Yumashev A.V. Ravali S. Shalaby M.N. Mustafa Y.F. Immunotherapy of multisystem inflammatory syndrome in children (MIS-C) following COVID-19 through mesenchymal stem cells Int. Immunopharmacol. 101 2021 108217
16 Hoffmann M. Kleine-Weber H. Schroeder S. Krüger N. Herrler T. Erichsen S. SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor Cell 181 2 2020 271 280 e8 32142651
17 Leila M. Sorayya G. Genotype and phenotype of COVID-19: their roles in pathogenesis J. Microbiol. Immunol. Infect. 2020 10
18 Mason R.J. Pathogenesis of COVID-19 from a cell biology perspective Eur. Respir. Soc. 2020
19 Wilson L. Mckinlay C. Gage P. Ewart G. SARS coronavirus E protein forms cation-selective ion channels Virology 330 1 2004 322 331 15527857
20 Prompetchara E. Ketloy C. Palaga T. Respuestas inmunitarias en COVID-19 y posibles vacunas: lecciones aprendidas de la epidemia de SARS y MERS Asian Pac. J. Allergy Immunol. 38 1 2020 1 9 32105090
21 Batah S.S. Fabro A.T. Pulmonary pathology of ARDS in COVID-19: a pathological review for clinicians Respir. Med. 176 2021 106239
22 Imai Y. Kuba K. Neely G.G. Yaghubian-Malhami R. Perkmann T. van Loo G. Identification of oxidative stress and Toll-like receptor 4 signaling as a key pathway of acute lung injury Cell 133 2 2008 235 249 18423196
23 Huppert L.A. Matthay M.A. Ware L.B. Pathogenesis of Acute Respiratory Distress Syndrome. Seminars In Respiratory and Critical Care Medicine 2019 Thieme Medical Publishers
24 Gustine J.N. Jones D. Immunopathology of Hyperinflammation in COVID-19 Am. J. Pathol. 191 1 2021 4 17 32919977
25 Jørgensen M.J. Holter J.C. Christensen E.E. Schjalm C. Tonby K. Pischke S.E. Increased interleukin-6 and macrophage chemoattractant protein-1 are associated with respiratory failure in COVID-19 Sci. Rep. 10 1 2020 1 11 31913322
26 Gao Y.M. Xu G. Wang B. Liu B.C. Cytokine storm syndrome in coronavirus disease 2019: a narrative review J. Intern. Med. 289 2 2021 147 161 32696489
27 Hall O.J. Klein S.L. Progesterone-based compounds affect immune responses and susceptibility to infections at diverse mucosal sites Mucosal Immunol. 10 5 2017 1097 1107 28401937
28 Wang J. Hajizadeh N. Moore E.E. McIntyre R.C. Moore P.K. Veress L.A. Tissue plasminogen activator (tPA) treatment for COVID‐19 associated acute respiratory distress syndrome (ARDS): a case series J. Thromb. Haemost. 18 7 2020 1752 1755 32267998
29 Chowdhury M.A. Hossain N. Kashem M.A. Shahid M.A. Alam A. Immune response in COVID-19: a review J. Infect. Public Health 2020
30 Hoffmann M. Kleine-Weber H. Schroeder S. Krüger N. Herrler T. Erichsen S. SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor Cell 2020
31 Zhou P. Yang X.-L. Wang X.-G. Hu B. Zhang L. Zhang W. A pneumonia outbreak associated with a new coronavirus of probable bat origin Nature 579 7798 2020 270 273 32015507
32 Viveiros A. Gheblawi M. Aujla P.K. Sosnowski D.K. Seubert J.M. Kassiri Z. Sex-and age-specific regulation of ACE2: insights into severe COVID-19 susceptibility J. Mol. Cell. Cardiol. 164 2022 13 16 34774871
33 Kuba K. Imai Y. Penninger J.M. Angiotensin-converting enzyme 2 in lung diseases Curr. Opin. Pharmacol. 6 3 2006 271 276 16581295
34 Novel CPERE The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China Zhonghua liu xing Bing. xue za zhi= Zhonghua liuxingbingxue zazhi 41 2 2020 145 32064853
35 Augustine R. Abhilash S. Nayeem A. Salam S.A. Augustine P. Dan P. Increased complications of COVID-19 in people with cardiovascular disease: Role of the renin–angiotensin-aldosterone system (RAAS) dysregulation Chem. -Biol. Interact. 351 2022 109738
36 Tay M.Z. Poh C.M. Rénia L. MacAry P.A. Ng L.F. The trinity of COVID-19: immunity, inflammation and intervention Nat. Rev. Immunol. 2020 1 12 31792373
37 Khan S. Wang X. The COVID-19 infection in children and its association with the immune system, prenatal stress, and neurological complications Int. J. Biol. Sci. 18 2 2022 707 716 35002519
38 Haapasalo K. Meri S. Regulation of the complement system by pentraxins Front Immunol. 10 1750 2019 2019 31552020
39 Ma Y.J. Garred P. Pentraxins in complement activation and regulation Front. Immunol. 9 2018 3046 30619374
40 Thevarajan I. Nguyen T.H. Koutsakos M. Druce J. Caly L. van de Sandt C.E. Breadth of concomitant immune responses prior to patient recovery: a case report of non-severe COVID-19 Nat. Med. 26 4 2020 453 455 32284614
41 Wu H.-S. Hsieh Y.-C. Su I.-J. Lin T.-H. Chiu S.-C. Hsu Y.-F. Early detection of antibodies against various structural proteins of the SARS-associated coronavirus in SARS patients J. Biomed. Sci. 11 1 2004 117 126 14730215
42 Temperton N.J. Chan P.K. Simmons G. Zambon M.C. Tedder R.S. Takeuchi Y. Longitudinally profiling neutralizing antibody response to SARS coronavirus with pseudotypes Emerg. Infect. Dis. 11 3 2005 411 15757556
43 Wong S.K. Li W. Moore M.J. Choe H. Farzan M. A 193-amino acid fragment of the SARS coronavirus S protein efficiently binds angiotensin-converting enzyme 2 J. Biol. Chem. 279 5 2004 3197 3201 14670965
44 Zhu Z. Chakraborti S. He Y. Roberts A. Sheahan T. Xiao X. Potent cross-reactive neutralization of SARS coronavirus isolates by human monoclonal antibodies Proc. Natl. Acad. Sci. 104 29 2007 12123 12128 17620608
45 Bournazos S. DiLillo D.J. Ravetch J.V. The role of Fc–FcγR interactions in IgG-mediated microbial neutralization J. Exp. Med. 212 9 2015 1361 1369 26282878
46 Kaneko Y. Nimmerjahn F. Ravetch J.V. Anti-inflammatory activity of immunoglobulin G resulting from Fc sialylation Science 313 5787 2006 670 673 16888140
47 Nimmerjahn F. Ravetch J.V. Fcγ receptors as regulators of immune responses Nat. Rev. Immunol. 8 1 2008 34 47 18064051
48 Ibarrondo F.J. Fulcher J.A. Goodman-Meza D. Elliott J. Hofmann C. Hausner M.A. Rapid decay of anti–SARS-CoV-2 antibodies in persons with mild Covid-19 N. Engl. J. Med. 383 11 2020 1085 1087 32706954
49 Long Q.-X. Tang X.-J. Shi Q.-L. Li Q. Deng H.-J. Yuan J. Clinical and immunological assessment of asymptomatic SARS-CoV-2 infections Nat. Med. 26 8 2020 1200 1204 32555424
50 Seow J. Graham C. Merrick B. Acors S. Steel K.J. Hemmings O. Longitudinal evaluation and decline of antibody responses in SARS-CoV-2 infection MedRxiv 2020
51 Sokal A. Chappert P. Barba-Spaeth G. Roeser A. Fourati S. Azzaoui I. Maturation and persistence of the anti-SARS-CoV-2 memory B cell response Cell 184 5 2021 1201 1213 e14 33571429
52 Xu Z. Shi L. Wang Y. Zhang J. Huang L. Zhang C. Pathological findings of COVID-19 associated with acute respiratory distress syndrome Lancet Respir. Med. 8 4 2020 420 422 32085846
53 Li T. Qiu Z. Zhang L. Han Y. He W. Liu Z. Significant changes of peripheral T lymphocyte subsets in patients with severe acute respiratory syndrome J. Infect. Dis. 189 4 2004 648 651 14767818
54 Zheng H.-Y. Zhang M. Yang C.-X. Zhang N. Wang X.-C. Yang X.-P. Elevated exhaustion levels and reduced functional diversity of T cells in peripheral blood may predict severe progression in COVID-19 patients Cell. Mol. Immunol. 17 5 2020 541 543 32203186
55 Sadeghi A. Tahmasebi S. Mahmood A. Kuznetsova M. Valizadeh H. Taghizadieh A. Th17 and Treg cells function in SARS‐CoV2 patients compared with healthy controls J. Cell. Physiol. 2020
56 Janice Oh H.-L. Ken-En Gan S. Bertoletti A. Tan Y.-J. Understanding the T cell immune response in SARS coronavirus infection Emerg. Microbes Infect. 1 1 2012 1 6
57 Ulrich H. Pillat M.M. CD147 as a target for COVID-19 treatment: suggested effects of azithromycin and stem cell engagement Stem Cell Rev. Rep. 2020 1 7 31907765
58 Jansen J.M. Gerlach T. Elbahesh H. Rimmelzwaan G.F. Saletti G. Influenza virus-specific CD4+ and CD8+ T cell-mediated immunity induced by infection and vaccination J. Clin. Virol. 119 2019 44 52 31491709
59 Thomas S. Towards determining the epitopes of the structural proteins of SARS-CoV-2 Vaccine Design 2022 Springer 265 272
60 Yang L.-T. Peng H. Zhu Z.-L. Li G. Huang Z.-T. Zhao Z.-X. Long-lived effector/central memory T-cell responses to severe acute respiratory syndrome coronavirus (SARS-CoV) S antigen in recovered SARS patients Clin. Immunol. 120 2 2006 171 178 16781892
61 Glynne P. Tahmasebi N. Gant V. Gupta R. Long COVID following mild SARS-CoV-2 infection: characteristic T cell alterations and response to antihistamines J. Invest. Med. 70 1 2022 61 67
62 Biron C.A. Byron K.S. Sullivan J.L. Severe herpesvirus infections in an adolescent without natural killer cells N. Engl. J. Med. 320 26 1989 1731 1735 2543925
63 Orr M.T. Lanier L.L. Natural killer cell education and tolerance Cell 142 6 2010 847 856 20850008
64 Giamarellos-Bourboulis E.J. Netea M.G. Rovina N. Akinosoglou K. Antoniadou A. Antonakos N. Complex immune dysregulation in COVID-19 patients with severe respiratory failure Cell Host Microbe 27 6 2020 992 1000 e3 32320677
65 Wen W. Su W. Tang H. Le W. Zhang X. Zheng Y. Immune cell profiling of COVID-19 patients in the recovery stage by single-cell sequencing Cell Discov. 6 1 2020 1 18 31934347
66 Zheng M. Gao Y. Wang G. Song G. Liu S. Sun D. Functional exhaustion of antiviral lymphocytes in COVID-19 patients Cell. Mol. Immunol. 17 5 2020 533 535 32203188
67 Guo A.-L. Jiao Y.-M. Zhao Q.-W. Huang H.-H. Deng J.-N. Zhang C. Implications of the accumulation of CXCR5+ NK cells in lymph nodes of HIV-1 infected patients EBioMedicine 75 2022 103794
68 Cong J. Wei H. Natural killer cells in the lungs Front. Immunol. 10 2019 1416 31293580
69 Marquardt N. Kekäläinen E. Chen P. Kvedaraite E. Wilson J.N. Ivarsson M.A. Human lung natural killer cells are predominantly comprised of highly differentiated hypofunctional CD69− CD56dim cells J. Allergy Clin. Immunol. 139 4 2017 1321 1330 e4 27670241
70 Auffray C. Sieweke M.H. Geissmann F. Blood monocytes: development, heterogeneity, and relationship with dendritic cells Annu. Rev. Immunol. 2009 27
71 Boyette L.B. Macedo C. Hadi K. Elinoff B.D. Walters J.T. Ramaswami B. Phenotype, function, and differentiation potential of human monocyte subsets PloS One 12 4 2017 e0176460
72 Pence B.D. Severe COVID-19 and aging: are monocytes the key? GeroScience 42 4 2020 1051 1061 32556942
73 Schulte-Schrepping J. Reusch N. Paclik D. Baßler K. Schlickeiser S. Zhang B. Severe COVID-19 is marked by a dysregulated myeloid cell compartment Cell 182 6 2020 1419 1440 e23 32810438
74 Zhou Y. Fu B. Zheng X. Wang D. Zhao C. Qi Y. Aberrant pathogenic GM-CSF+ T cells and inflammatory CD14+ CD16+ monocytes in severe pulmonary syndrome patients of a new coronavirus BioRxiv 2020
75 Barr F.D. Ochsenbauer C. Wira C.R. Rodriguez-Garcia M. Neutrophil extracellular traps prevent HIV infection in the female genital tract Mucosal Immunol. 11 5 2018 1420 1428 29875403
76 Lamichhane P.P. Samarasinghe A.E. The role of innate leukocytes during influenza virus infection J. Immunol. Res. 2019 2019
77 Rosales C. Neutrophils at the crossroads of innate and adaptive immunity J. Leukoc. Biol. 108 1 2020 377 396 32202340
78 Delgado-Rizo V. Martínez-Guzmán M.A. Iñiguez-Gutierrez L. García-Orozco A. Alvarado-Navarro A. Fafutis-Morris M. Neutrophil extracellular traps and its implications in inflammation: an overview Front. Immunol. 8 2017 81 28220120
79 Clark S.R. Ma A.C. Tavener S.A. McDonald B. Goodarzi Z. Kelly M.M. Platelet TLR4 activates neutrophil extracellular traps to ensnare bacteria in septic blood Nat. Med. 13 4 2007 463 469 17384648
80 Hahn J. Knopf J. Maueroder C. Kienhofer D. Leppkes M. Herrmann M. Neutrophils and neutrophil extracellular traps orchestrate initiation and resolution of inflammation Clin. Exp. Rheumatol. 34 4 Suppl 98 2016 6 8 27586795
81 Hemmat N. Derakhshani A. Bannazadeh Baghi H. Silvestris N. Baradaran B. De Summa S. Neutrophils, crucial, or harmful immune cells involved in coronavirus infection: a bioinformatics study Front. Genet. 11 2020 641 32582303
82 Barnes B.J. Adrover J.M. Baxter-Stoltzfus A. Borczuk A. Cools-Lartigue J. Crawford J.M. Targeting potential drivers of COVID-19: neutrophil extracellular traps J. Exp. Med. 217 2020 6
83 Yao X. Li T. He Z. Ping Y. Liu H. Yu S. A pathological report of three COVID-19 cases by minimally invasive autopsies Zhonghua Bing. li xue za zhi= Chin. J. Pathol. 49 2020 E009-E
84 Parackova Z. Zentsova I. Bloomfield M. Vrabcova P. Smetanova J. Klocperk A. Disharmonic inflammatory signatures in COVID-19: augmented neutrophils’ but impaired monocytes’ and dendritic cells’ responsiveness Cells 9 10 2020 2206
85 Li X. Liu Y. Li J. Sun L. Yang J. Xu F. Immune characteristics distinguish patients with severe disease associated with SARS-CoV-2 Immunol. Res. 68 6 2020 398 404 32989677
86 Xiong Y. Liu Y. Cao L. Wang D. Guo M. Jiang A. Transcriptomic characteristics of bronchoalveolar lavage fluid and peripheral blood mononuclear cells in COVID-19 patients Emerg. Microbes Infect. 9 1 2020 761 770 32228226
87 Miyazawa M. Immunopathogenesis of SARS-CoV-2-induced pneumonia: lessons from influenza virus infection Inflamm. Regen. 40 1 2020 1 13 31938077
88 Yang S.-C. Tsai Y.-F. Pan Y.-L. Hwang T.-L. Understanding the role of neutrophils in acute respiratory distress syndrome Biomed. J. 2020
89 Laforge M. Elbim C. Frère C. Hémadi M. Massaad C. Nuss P. Tissue damage from neutrophil-induced oxidative stress in COVID-19 Nat. Rev. Immunol. 20 9 2020 515 516 32728221
90 Czerwińska K. Poręba R. Gać P. Renalase—a new understanding of its enzymatic and non‐enzymatic activity and its implications for future research Clin. Exp. Pharmacol. Physiol. 49 1 2022 3 9 34545616
91 Wang Z. Wang X. Zou H. Dai Z. Feng S. Zhang M. The basic characteristics of the pentraxin family and their functions in tumor progression Front. Immunol. 11 2020 1757 33013829
92 GenÇ A.B. Yaylaci S. DheIr H. Genç A.C. İşsever K. ÇekIÇ D. The predictive and diagnostic accuracy of long pentraxin-3 in COVID-19 pneumonia Turk. J. Med. Sci. 51 2 2021 448 453 33315349
93 Bottazzi B. Doni A. Garlanda C. Mantovani A. An integrated view of humoral innate immunity: pentraxins as a paradigm Annu. Rev. Immunol. 28 2009 157 183
94 Deban L. Bottazzi B. Garlanda C. De La Torre Y.M. Mantovani A. Pentraxins: multifunctional proteins at the interface of innate immunity and inflammation Biofactors 35 2 2009 138 145 19449441
95 He X. Han B. Liu M. Long pentraxin 3 in pulmonary infection and acute lung injury Am. J. Physiol. -Lung Cell. Mol. Physiol. 292 5 2007 L1039 L1049 17277044
96 Bottazzi B. Vouret-Craviari V. Bastone A. De Gioia L. Matteucci C. Peri G. Multimer formation and ligand recognition by the long pentraxin PTX3: similarities and differences with the short pentraxins C-reactive protein and serum amyloid P component J. Biol. Chem. 272 52 1997 32817 32823 9407058
97 Basile A. Sica A. d'Aniello E. Breviario F. Garrido G. Castellano M. Characterization of the promoter for the human long pentraxin PTX3: role of NF-κB in tumor necrosis factor-α and interleukin-1β regulation J. Biol. Chem. 272 13 1997 8172 8178 9079634
98 Han B. Mura M. Andrade C.F. Okutani D. Lodyga M. dos Santos C.C. TNFα-induced long pentraxin PTX3 expression in human lung epithelial cells via JNK J. Immunol. 175 12 2005 8303 8311 16339571
99 Norata G.D. Marchesi P. Pirillo A. Uboldi P. Chiesa G. Maina V. Long pentraxin 3, a key component of innate immunity, is modulated by high-density lipoproteins in endothelial cells Arterioscler., Thromb. Vasc. Biol. 28 5 2008 925 931 18218986
100 Cunha C. Aversa F. Lacerda J.F. Busca A. Kurzai O. Grube M. Genetic PTX3 deficiency and aspergillosis in stem-cell transplantation N. Engl. J. Med. 370 5 2014 421 432 24476432
101 Garlanda C. Bottazzi B. Magrini E. Inforzato A. Mantovani A. PTX3, a humoral pattern recognition molecule, in innate immunity, tissue repair, and cancer Physiol. Rev. 98 2 2018 623 639 29412047
102 Sprong T. Peri G. Neeleman C. Mantovani A. Signorini S. Van Der Meer J.W. Pentraxin 3 and C-reactive protein in severe meningococcal disease Shock 31 1 2009 28 32 18650775
103 Blanco-Melo D. Nilsson-Payant B.E. Liu W.-C. Uhl S. Hoagland D. Møller R. Imbalanced host response to SARS-CoV-2 drives development of COVID-19 Cell 181 5 2020 1036 1045 e9 32416070
104 Wilk A.J. Rustagi A. Zhao N.Q. Roque J. Martínez-Colón G.J. McKechnie J.L. A single-cell atlas of the peripheral immune response in patients with severe COVID-19 Nat. Med. 26 7 2020 1070 1076 32514174
105 Deban L. Russo R.C. Sironi M. Moalli F. Scanziani M. Zambelli V. Regulation of leukocyte recruitment by the long pentraxin PTX3 Nat. Immunol. 11 4 2010 328 334 20208538
106 Risitano A.M. Mastellos D.C. Huber-Lang M. Yancopoulou D. Garlanda C. Ciceri F. Complement as a target in COVID-19? Nat. Rev. Immunol. 20 6 2020 343 344 32327719
107 Laing A.G. Lorenc A. Del Barrio I.D.M. Das A. Fish M. Monin L. A dynamic COVID-19 immune signature includes associations with poor prognosis Nat. Med. 26 10 2020 1623 1635 32807934
108 Pons S. Arnaud M. Loiselle M. Arrii E. Azoulay E. Zafrani L. Immune consequences of endothelial cells’ activation and dysfunction during sepsis Crit. care Clin. 36 2 2020 401 413 32172821
109 Zlibut A. Bocsan I.C. Agoston-Coldea L. Pentraxin-3 and endothelial dysfunction Adv. Clin. Chem. 91 2019 163 179 31331488
110 Djordjevic B. Milenkovic J. Stojanovic D. Velickov A. Djindjic B. Stoimenov T.J. Vitamins, microelements and the immune system: current standpoint in the fight against COVID-19 Br. J. Nutr. 2022 1 43
111 Kadomoto S. Izumi K. Mizokami A. Macrophage polarity and disease control Int. J. Mol. Sci. 23 1 2022 144
112 Zhang H., Wang Y., Zhao Y., Liu T., Wang Z., Zhang N., et al. PTX3 Mediates the Infiltration, Migration, and M2-Polarization of Macrophages in Glioblastoma by Large-Scale Single Cell Sequencing Analysis and in vitro Experiments. Migration, and M2-Polarization of Macrophages in Glioblastoma by Large-Scale Single Cell Sequencing Analysis and in vitro Experiments.
113 Holmskov U. Thiel S. Jensenius J.C. Collectins and ficolins: humoral lectins of the innate immune defense Annu. Rev. Immunol. 21 1 2003 547 578 12524383
114 Interactions between coagulation and complement—their role in inflammation Oikonomopoulou K. Ricklin D. Ward P.A. Lambris J.D. Seminars in immunopathology 2012 Springer
115 Takeda K. Kaisho T. Akira S. Toll-like receptors Annu. Rev. Immunol. 21 1 2003 335 376 12524386
116 Bottazzi B. Inforzato A. Messa M. Barbagallo M. Magrini E. Garlanda C. The pentraxins PTX3 and SAP in innate immunity, regulation of inflammation and tissue remodelling J. Hepatol. 64 6 2016 1416 1427 26921689
117 Cox N. Pilling D. Gomer R.H. DC-SIGN activation mediates the differential effects of SAP and CRP on the innate immune system and inhibits fibrosis in mice Proc. Natl. Acad. Sci. 112 27 2015 8385 8390 26106150
118 Pilling D. Gomer R.H. Persistent lung inflammation and fibrosis in serum amyloid P component (APCs-/-) knockout mice PloS One 9 4 2014 e93730
119 Humoral innate immunity at the crossroad between microbe and matrix recognition: the role of PTX3 in tissue damage Doni A. D'Amico G. Morone D. Mantovani A. Garlanda C. Seminars in cell & developmental biology 2017 Elsevier
120 Doni A. Garlanda C. Mantovani A. Innate Immunity, Hemostasis and Matrix Remodeling: PTX3 as a Link. Seminars in Immunology 2016 Elsevier
121 Cappuzzello C. Doni A. Dander E. Pasqualini F. Nebuloni M. Bottazzi B. Mesenchymal stromal cell-derived PTX3 promotes wound healing via fibrin remodeling J. Invest. Dermatol. 136 1 2016 293 300 26763449
122 Doni A. Musso T. Morone D. Bastone A. Zambelli V. Sironi M. An acidic microenvironment sets the humoral pattern recognition molecule PTX3 in a tissue repair mode J. Exp. Med. 212 6 2015 905 925 25964372
123 Lopéz M.L. Bruges G. Crespo G. Salazar V. Deglesne P.-A. Schneider H. Thrombin selectively induces transcription of genes in human monocytes involved in inflammation and wound healing Thromb. Haemost. 112 11 2014 992 1001 25057055
124 Lech M. Römmele C. Gröbmayr R. Susanti H.E. Kulkarni O.P. Wang S. Endogenous and exogenous pentraxin-3 limits postischemic acute and chronic kidney injury Kidney Int. 83 4 2013 647 661 23325083
125 Bugge T.H. Kombrinck K.W. Flick M.J. Daugherty C.C. Danton M.J.S. Degen J.L. Loss of fibrinogen rescues mice from the pleiotropic effects of plasminogen deficiency Cell 87 4 1996 709 719 8929539
126 Amit Jain M., Doyle D.J. Apoptosis and pericyte loss in alveolar capillaries in COVID-19 infection: choice of markers matters.
127 Diridollou T. Sohier L. Rousseau C. Angibaud A. Chauvin P. Gaignon T. Idiopathic pulmonary fibrosis: significance of the usual interstitial pneumonia (UIP) CT-scan patterns defined in new international guidelines Respir. Med. Res. 77 2020 72 78 32416587
128 Kuhn C. Mason R.J. Immunolocalization of SPARC, tenascin, and thrombospondin in pulmonary fibrosis Am. J. Pathol. 147 6 1995 1759 7495300
129 Laskin D.L. Malaviya R. Laskin J.D. Role of macrophages in acute lung injury and chronic fibrosis induced by pulmonary toxicants Toxicol. Sci. 168 2 2019 287 301 30590802
130 Balhara J. Koussih L. Zhang J. Gounni A.S. Pentraxin 3: an immuno-regulator in the lungs Front. Immunol. 4 2013 127 23755050
131 Giacomini A. Ghedini G.C. Presta M. Ronca R. Long pentraxin 3: a novel multifaceted player in cancer Biochim. Et. Biophys. Acta (BBA)-Rev. Cancer 1869 1 2018 53 63
132 Presta M. Foglio E. Churruca Schuind A. Ronca R. Long Pentraxin-3 modulates the angiogenic activity of fibroblast growth factor-2 Front. Immunol. 9 2018 2327 30349543
133 Han B. Ma X. Zhang J. Zhang Y. Bai X. Hwang D.M. Protective effects of long pentraxin PTX3 on lung injury in a severe acute respiratory syndrome model in mice Lab. Investig. 92 9 2012 1285 1296 22732935
134 Okutani D. Han B., Mura M., Waddell T.K., Keshavjee S., Liu M. High volume ventilation induces pentraxin.3.
135 Maccarinelli F. Bugatti M. Churruca Schuind A. Garlenza S. Vermi W. Presta M. Endogenous Long Pentraxin 3 exerts a protective role in a murine model of pulmonary fibrosis Front. Immunol. 12 2021 109
136 Wesley U.V. Sutton I. Clark P.A. Cunningham K. Larrain C. Kuo J.S. Enhanced expression of pentraxin-3 in glioblastoma cells correlates with increased invasion and IL8-VEGF signaling axis Brain Res. 1776 2022 147752
137 Sims J.T. Krishnan V. Chang C.-Y. Engle S.M. Casalini G. Rodgers G.H. Characterization of the cytokine storm reflects hyperinflammatory endothelial dysfunction in COVID-19 J. Allergy Clin. Immunol. 147 1 2021 107 111 32920092
138 Poudel S. Ishak A. Perez-Fernandez J. Garcia E. León-Figueroa D.A. Romaní L. Highly mutated SARS-CoV-2 Omicron variant sparks significant concern among global experts–What is known so far? Travel Med. Infect. Dis. 45 2022 102234
139 Tao K. Tzou P.L. Nouhin J. Gupta R.K. de Oliveira T. Kosakovsky Pond S.L. The biological and clinical significance of emerging SARS-CoV-2 variants Nat. Rev. Genet. 22 12 2021 757 773 34535792 | 36137396 | PMC9476367 | NO-CC CODE | 2022-12-03 23:16:15 | yes | Pathol Res Pract. 2022 Oct 15; 238:154128 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
36125680
23026
10.1007/s11356-022-23026-4
Research Article
RETRACTED ARTICLE: The role of green energy deployment and economic growth in carbon dioxide emissions: evidence from the Chinese economy
Bhuiyan Miraj Ahmed [email protected]
[email protected]
1
Kahouli Bassem [email protected]
23
Hamaguchi Yoshihiro [email protected]
4
Zhang Qiannan [email protected]
1
1 grid.443372.5 0000 0001 1922 9516 School of Economics, Guangdong University of Finance & Economics, Guangzhou, 510320 People’s Republic of China
2 grid.443320.2 0000 0004 0608 0056 Management Information Systems Department, University of Ha’il, Community College, PO Box 2440, Hail City, Saudi Arabia
3 grid.7900.e 0000 0001 2114 4570 University of Sousse, Higher Institute of Finance and Taxation, Sousse, Tunisia
4 grid.505750.2 0000 0004 0441 9482 Department of Management Information, Kyoto College of Economics, 3–1, Oehigashinagacho, Nishikyo-ku, Kyoto, 610-1195 Japan
Responsible Editor: Ilhan Ozturk
20 9 2022
2023
30 5 1316213173
10 5 2022
10 9 2022
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
After reform and opening-up, rapid industrialization and urbanization led to environmental degradation in China, including excessive energy consumption, soil contamination, and water pollution. Toward sustainable development, the Chinese government has promoted the introduction of clean energy sources such as geothermal and hydroelectric power generation, which have reduced the environmental burden. However, the impact of this energy shift on environmental improvement and economic growth is unclear. This study empirically analyzes the impact of green energy deployment and economic growth on CO2 emissions in China. The analysis of time series data from 1980 to 2020 shows that in the long run, a 1% increase in renewable energy significantly reduces CO2 emissions by 0.87%, and a 1% increase in GDP significantly increases CO2 emissions by 0.26%. In contrast, in the short run, the negative effect of renewable energy on CO2 emissions and the positive effect of GDP on it are not significant. This result was confirmed after the robustness checks. Based on the results obtained, several policy recommendations are made.
Keywords
Sustainable development
Green energy deployment
Economic growth
China
JEL Classification
Q01
Q51
Q55
issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023
==== Body
pmcIntroduction
After opening up in the mid-1980s, China has witnessed tremendous transformation in improving economic indicators, industrialization, and rapid urbanization. To do so, China had to use many natural resources; consume much energy; pollute the soil, air, and water; and do other things to degrade the environment. Nonetheless, since the late 1990s, the Chinese government has taken necessary steps in the interest of the environment and sustainable development, launching several proactive environmental policies and legislative projects that demonstrate a growing awareness of the ecological and energy crisis. In this regard, the Chinese government is trying hard to shift toward green energy sources. Moreover, policymakers are trying to develop innovative concepts or introduce new technology to reduce environmental loss while sustainable ecological measures. The intensive use of green energy sources such as solar and wind will help increase the involvement of renewable energy, lessen pollution, and improve the air quality in urban areas, thus making a substantial contribution to building sustainable anticipations of energy systems in China. In this process, the lifespan of oil and gas resources should be longer while retaining China’s status as a major consumer of petroleum and renewable energy in the future. However, does this shift toward renewable energy impact protecting the environment? In this long journey, does trading off with economic growth be significant?
Environmental innovation is also a proposed way of maintaining economic growth, conserving the environment, and encouraging sustainable development. Studies found a consistent long-term correlation exists between CO2 emissions, commerce, income, environmental innovation, and the use of renewable energy (Khan et al. 2020). Thus, the question addressed here has the recent implementation of environmental innovation measures and exploitation of several green energy sources contributed to reduced greenhouse gasses in China? In this context, this investigation aims to discuss and explore the role of green energy source deployment in China’s carbon dioxide (CO2) emissions while maintaining its economic growth. There has been extensive research in energy policy regarding the causal relationship between CO2 emissions and energy use or non-renewable energies. Recently, researchers have been looking at the causal relationship between the use of green energy and CO2 emissions. The studies have taken into consideration various geographical areas, complex econometric instruments, and various describing variables. For example, Khan et al. (2020) have tried to identify the role of environmental innovation and renewable energy for developed countries regarding consumption-based carbon emissions and international trade. Another study by Ma et al. (2021) examined the effects of emission levies, energy sector investments, R&D spending, technical advancement, and tertiary sector development on the Chinese province’s carbon dioxide emission data between 1995 and 2019. Huang and Li (2018) have sorted how resource alignment moderates the relationship between green innovation performance and environmental innovation strategy. Li et al. (2020) have tested new determinants of renewable energy consumption concerning eco-innovation and energy productivity. Belaïd et al. (2021) conducted empirical research on key drivers of renewable energy in the MENA region. Usman et al. (2021) looked into the environmental quality generated by renewable energy and recorded innovative research investment in renewables under the environmental Kuznets curve (EKC) model for G-7 nations. Their results supported the validity of the EKC hypothesis for the G-7 nations. However, analyzing related articles, we have noticed that the environmental effects of environmental innovation should be studied. The environmental effects of environmental innovation have gained very little research attention. Some individual studies on China’s economic growth are focused on environmental protection. But as far as we are concerned, there are few existing investigations that have examined the connection between green energy use and CO2 emissions in the Chinese economy. Maintaining economic growth and protecting the environment are crucial and challenging for China at this current stage. Thus, one hypothesis testing in our proposal is a comparatively new research and will create a new scope for future research in a similar arena. These two indicators should give us a clear idea of maintaining the trend.
Additionally, after analyzing the dataset and testing the hypothesis, we will suggest realistic and suitable solutions based on the obtained findings to the concerned authorities. This paper will examine this causal association for China based on research objectives and key scientific problems as follows: first, display the debate between green energy use and the CO2 emissions nexus in China, mainly after economic reform; second, explore the role of green energy deployment in improving and protecting the environment in China; third, suggest realistic and suitable solutions based on the obtained findings for the concerned authorities. The paper theme is demonstrated in Fig. 1.Fig. 1 Demonstration of the theme
This paper’s rest is organized as follows: “Literature review” presents the literature review. “Econometric methodology” displays the econometric methodology. “Result and interpretation” provides results and interpretations. “Sensitivity analysis” discusses the sensitivity analysis. In the end, “Conclusion and future research” concluded and suggested some future research.
Literature review
One of the earliest studies was made by Sadorsky (2009), citing the literature on causality between green energy use and CO2 emission in developing economies. The authors found proof of a long-term conservation hypothesis and a short-term neutrality hypothesis in emerging economies. Subsequent studies are still extending his primary findings. For example, Zeb et al. (2014) find bidirectional Granger causality between energy production and poverty in Pakistan, as well as Granger causality from energy production to poverty in Bangladesh and India, and from poverty to energy production in Sri Lanka, when looking at the relationship between energy production and poverty in selected SAARC countries.
Moreover, GDP and poverty positively influence energy production. Sadorsky’s (2009) results, which cover the period 1994–2003, are valid for more extended periods. Paramati et al. (2017) intend to investigate renewable energy consumption’s role in output and CO2 emissions in the next fastest developing economies of the world from the period 1990 to 2012. Empirical results indicate that the variables have a significant long-term association. The findings also show that green energy use contributes positively to economic growth and harms CO2 emissions. Studies so far have shown that poverty increases energy consumption and production. Renewable energy, a form of green energy, reduces CO2 emissions and promotes sustainable development (Usman et al. 2022) and energy intensity, while it enhances aggregate national savings (Salahuddin et al. 2020). The authors have found that economic growth still seems expensive for the region as it stimulates CO2 emissions for 34 sub-Saharan African nations.
Generally, poorer households are more dependent on fossil fuels as a source of energy. In other words, deprivation leads to global warming through energy. However, education, which is important for economic development, is also effective in reducing poverty. Consequently, education may reduce greenhouse gas emissions by reducing poverty and reducing energy consumption with greenhouse gas emissions. Zafar et al. (2020) examined the effects of renewable energy on CO2 emissions and other relevant factors in 28 OECD nations from 1990 to 2015. The empirical findings suggest that green energy positively impacts environmental quality. This is due to the fact that renewable energy use boosts economic growth, while education reduces CO2 emissions. They also discovered a bidirectional causal association between CO2 emissions, education, and renewable energy consumption. This means that these factors have a feedback effect. As a result, countries should encourage investment in green energy and education to create a more sustainable society.
The empirical findings obtained separately for emerging and industrialized countries imply a structural shift from energy- and carbon-intensive to service-oriented economies. According to Sarkodie and Adams (2018), (i) a 1% increase in fossil fuel consumption increases CO2 emissions, (ii) a 1% increase in renewable energy consumption reduces CO2 emissions, (iii) pollution is exacerbated by energy consumption and economic growth, and (iv) improvements in political and institutional quality minimize pollution. Significantly, the research from 1971 to 2017 suggests that the environmental Kuznets curve (EKC) theory may be valid in South Africa. They reveal the importance of the quality of political institutions in adapting to climate change. Responding to climate change requires governments to implement environmental policies. However, their policies impose a financial burden on pollutive firms in the energy- and carbon-intensive economy. When political institutions are immature, firms may try to escape the responsibility of environmental policies by paying bribes (Hamaguchi 2020). The study reveals that fossil fuel-rich countries need energy portfolio diversification, which promotes environmental sustainability and, at the same time, reduces price volatility vulnerabilities of their economies. The government must encourage R&D in renewable energy for a sustainable society to realize this aim. Ike et al. (2020) examined renewables use, electricity costs, and trade carbon effects. They also confirm the EKC hypothesis at the panel and country-specific levels in G7 countries. The findings suggest that while the scale of exchange exerts a strong positive impact on CO2, clean energies and oil costs exert negative pressure on emissions. Previous studies have clarified the relationship between renewable energy, the environment, and growth while expanding the period and region of interest. As a result, the EKC hypothesis has also been discovered. Under this hypothesis, the relationship above will change depending on where the turning point is located. Environmental innovations should significantly impact the relationship between renewable energy, the environment, and growth through the touring point because they affect energy efficiency and economic growth. Besides, it will also play a decisive role in oil prices and other factors, which Ike et al. (2020) point out. However, there has not been sufficient analysis of the relevance and effect of this important variable on the relationship between renewable energy and the environment in China. This study will help fill this gap.
As an empirical investigation examining the relationship between environmental protection and innovation, Carrión-Flores and Innes (2010) assessed the nexus between toxic air pollution rate and environmental innovation using the simultaneous panel data model from 1989 to 2004. They found that environmental innovation is a significant catalyst for reducing harmful emissions in the USA and that tighter emission standards cause environmental improvements leading to greater reductions in emissions. Besides, Chiou et al. (2011) displayed that innovation in the environmental field considerably increases the environmental efficiency of “greening” suppliers in Taiwan. They found that environmental process and goods innovation can be more successful than environmental managerial innovation in enhancing environmental efficiency. Recently, Wang et al. (2020) found that technological innovation has a damaging effect on carbon emissions for N-11 economies. However, the impact of environmental innovation on economic growth and pollution emissions is unclear. Zhang et al. (2017) show that environmental innovation in China’s case is essential and impactful and considers energy efficiency and R&D as the critical elements in reducing CO2 emissions from 2000 to 2013. The conclusions of the latter study are validated by the consistent outcomes of Long et al. (2017), while further additional findings show that environmental control and attitudes are positive for environmental innovation. This finding is important because theoretical analysis using an endogenous growth model reveals how environmental policy leads to sustainable development. For example, increasing environmental taxes reduce pollution emissions, expand economic growth, and improve welfare via innovation (Nakada 2004; Grimaud and Tournemaine 2007; Chu and Lai 2014). Moreover, decreasing permits leads to sustainable development (Hamaguchi 2019, Hamaguchi 2021a). By contrast, Grimaud (1999) and Hamaguchi (2020) indicate that stricter environmental policy expands pollution emissions and reduces economic growth. Hence, the theoretical analysis results continue to debate the impact of environmental policy on pollution emissions and economic development through innovation. Our analysis will make specific contributions to these contexts.
Dogan and Ozturk (2017) used the environmental Kuznets curve (EKC) model to examine the effects of real income (GDP), renewable energy use, and non-renewable energy use on carbon dioxide (CO2) emissions for the United States of America (USA) during the years 1980 through 2014. They found that escalating non-renewable energy use increases CO2 emissions, whereas growing renewable energy consumption mitigates environmental damage. Chien et al. (2022) looked at how urbanization, economic development, and clean energy from renewable sources affected the amount of greenhouse gas emissions in ten Asian countries from 1995 to 2018. The study’s findings demonstrated that GDP and renewable energy contributed positively to lowering GHG emissions in the environment or specific economies. Thus, a new form of green energy invention, environmental innovation, may foster economic growth by lessening environmental protection expenditure. However, the summary of the covered literature is listed in Table 1.Table 1 Summary of covered literature (selected)
Authors Method/Model Content Result
Carrión-Flores and Innes (2010) Bi-directional linkage Env. innovation and air pollution Env. innovations reduce toxic emission; tighten pollution targets induce Env. innovation
Chiou et al. (2011) Structural equation modeling Green supply chain, Green Inno., Env. Performance, competitive advantage Green supply + green inn benefits Env. performance and competitive advantage
Zhang et al. (2017) SGMM Env. Inno. → CO2 emission, CO2 emission effect on CET Env. Inno. reduce CO2 emission
Wang et al. (2020) Pesaran unit root test (2007) Fin. Dev., human capital, RE, GDP → CO2 emission CO2 emisison & Fin Dev. and GDP has ( +) relation
Long et al. (2017) Structural equation model Env. innovation and TPB Env. norm Env. behavior ( +) affect Env. innovation
Dogan and Ozturk (2017) EKC GDP, RE, N-RE, and CO2 nexus RE reduce Env. pollution, N-RE increase CO2
Chien et al. (2022) CS-ARDL Urbanization, ED, RE, and CO2 nexus GDP growth and RE lower CO2
Sadorsky P. 2009 Panel cointegration Per capita income and RE nexus Increases in per capita promotes RE/per capita use
Zeb et al. (2014) FMOLS RE, CO2, NRD, GDP, and Poverty GDP and poverty has pos.; CO2 has negative impact. Energy production
Zafar et al. (2020) Second generation methodologies RE and CO2 nexus with Edu., → FDI, ED RE promotes Env. quality
Edu. reduce CO2, FDI detoriate Env. quality
Sarkodie and Adams (2018) CUSUM, OLS Energy, ED, Urban., Poli. Ins Polit. Ins. plays a huge role in climate change
Hamaguchi (2020) R&D based growth model Env. Pol → pollution, corruption, welfare, Growth rate Env. tax decrease growth rate
Ike et al. (2020) EKC RE → energy, Trd in emission RE and energy price exert neg. pressure on CO2
From the above discussion, we formulate the following hypothesis for our proposed research project: CO2 emissions are adversely related to green energy use. Regarding the hypothesis, we can get an idea of whether there is a correlation between green energy use and CO2 emission level. It will also allow us to show some evidence of whether the economic growth has suffered due to environmentally friendly actions taken recently.
Econometric methodology
In this study, we used monthly time series data of China for the period from 1980 to 2020. Data for the dependent variable CO2 emission and all independent variables for model one are as follows: green energy (proxied by renewable energy use in the percentage of total energy), GDP per capita US$ (2010), and labor force (proxied as a number of persons engaged in millions). The data for all of these time series variables have been taken from World Development Indicators (WDI). The designations of the variables are listed in Table 2 below:Table 2 Used variables and interpretation
Variables Designation
Labor force (L) Proxied as the number of persons engaged in millions
Green energy (GE) Proxied by renewable energy use in the percentage of total energy use
Economic growth (GDP) Proxied per capita US$ (2010)
According to the time series data, if cointegration exists among the time series variables, there is a long-run relationship among and between the time series variables. An essential requirement for analyzing the cointegration between the time series variables is that the variables must be cointegrated in the same order. Furthermore, researchers generally used Dicky-Fuller and augmented Dicky-Fuller tests to see the integration order. Therefore, we must perform the unit root test and decide which technique is applicable. Because of the time series variables are integrated at the order I(0) or stationary at level, we will use the ordinary least square (OLS) methodology. But if variables are integrated at the order I(1) or stationary at the first difference, we will use Johansen’s cointegration methodology to estimate the long-run relationship among the time series variables (Dickey and Fuller 1979). Furthermore, according to Pesaran et al. (2001), if time series variables are showing mixed order of integration I(0) and I(1) but unintegrated at the order I(2), then we will use (ARDL) autoregressive distributed lag approach methodology. The whole study process is pictured in Fig. 2.Fig. 2 Flowchart of the study plan
Unit root test
In this study, we use an augmented Dickey-Fuller (ADF) unit root test to test which time series variable is integrated at order I(0) or I(1), or I(2). Therefore, as an example, we consider the AR (1) model for the unit root test via the following:1 Yt=θYt-1+ϵt,
where if the error term ϵt is distributed normally, three possible potential circumstances will happen: If θ<1, then we conclude that the time series variable is stationary. If θ>1, then we conclude that the time series variable is non-stationary. If θ=1; then, we conclude that the time series variable has a unit root problem and this series may show trend and non-stationary.
To answer the issue of unit root when θ=1, then we subtract from both aspects of an equation (A):Yt-Yt-1=θYt-1-Yt-1+ϵt,ΔYt=ϵt,
Here, we can say that series is normally distributed and integrated at order I(1), which means that it is stationary at the first difference. Conversely, if the series is stationary without any difference, we may say it is integrated at order I(0) or stationary at level.
Augmented Dickey-Fuller unit root test
Augmented Dickey-Fuller (ADF) unit root test is added to the Dickey-Fuller (DF) test. This is because the ADF test includes an extra lagged period of the dependent variable to resolve the issue of autocorrelation between the disturbance terms. We use AIC, Akaike information criteria, and SBC means Schwartz Bayesian criteria (Dickey and Fuller 1979).
Based on succeeding equations, the ADF test is Dickey and Fuller (1979):ΔYt=θYt-1+∑i=1ρβiΔYt-i+ϵt,
where Δt, ρ, and ϵ indicate the first difference, time subscript, lag length, and the disturbance term, respectively. Generally, there are three possible types of ADF tests (Dickey and Fuller 1979):With intercept but no trend
ΔYt=αo+θYt-1+∑i=1ρβiΔY+ϵt, (A)
With trend and intercept
ΔYt=αo+θYt-1+βt+∑i=1ρβiΔYt-i+ϵt, (B)
No intercept and no trend
ΔYt=θYt-1+∑i=1ρβiΔY+ϵt. (C)
Here, the ADF test null hypothesis is non-stationary, and alternatively, the series is stationary (Dickey and Fuller 1979). Therefore, if the t-statistics value is greater than the critical value of ADF, we accept the null hypothesis. It reports that the series has a unit root problem. However, if the t-statistics value is less than the critical value of ADF, we reject the null hypothesis and conclude that the series has no unit root problem (Dickey and Fuller 1979). Therefore, based on the ADF test, we used the autoregressive distributed lagged (ARDL) approach model to estimate the impact of green energy, GDP, and labor force on the CO2 emissions in China.2 Δyt=∑κ=1ιακyt-κ+∑κ=0τβ′iκXi,t-k+εt,
where t shows the time periods from 1, 2, 3, 4… to T. The dependent variable yt is the CO2 emission, Xi,t is a vector of independent variables, and εt is the error term. We will also repeat the analysis for this model for the error correction model (Frank 2009). However, we obtain the following equation by adding yt-1 on both sides of Eq. (2) (Frank 2009):3 yt=∑κ=1ιγκyt-κ+∑κ=0τβ′iκXi,t-κ+εt,
where γκ=ακ,κ≠1,andγκ=α1+1 hold. We can rewrite Eq. (3) in the form of an error correction model (Frank 2009):4 Δyt=φiyt-1-ωi′Xi,t+∑κ=1ι-1γκΔyt-j+∑κ=0τ-1β′iκΔXi,t-κ+εt,
where the following hold:φi=-1-∑κ=1ιδi,
ωi=∑κ=0τβiκ1-∑qγiq,
γκ=-∑n=κ+1ιγn,
βiκ=-∑n=κ+1qβin.
Result and interpretation
Augmented Dickey-Fuller unit root test
In this study, we used the unit root test to estimate the stationarity level of the time series variables. All of the time series variables are converted into a log form. However, observing Table 3 for the variables, carbon dioxide emissions, renewable energy use, and GDP are stationary at the first difference, or we can say they are integrated at the order I(1). In contrast, a variable of the labor force is stationary at the level. However, according to the unit root test, if variables show mixed order of integration, we will use the ARDL methodology to see the short-run and the short-run relationship between the dependent and independent time series variables (Cheema and Atta 2014).Table 3 Results in unit root test
Variables Level First difference Integrated order
Ln_CO2 − 0.036 − 3.466*** I(1)
Ln_Renewenergy − 0.672 − 2.791* I(1)
Ln_GDP − 0.793 − 3.245** I(1)
Ln_LaborForce − 6.83*** - I(0)
* is p < 0.05, ** is p < 0.01, and *** is p < 0.001
VAR lag order selection criteria
Before applying the ARDL methodology, we must first see the lag order selection criteria in Table 4. Therefore, after using the var test, the SBIC criteria indicate that the optimal lag order should be one and the HQIC, AIC, and FPE criteria suggest that the optimal lag order should be four.Table 4 VAR results
Lag LL LR df p FPE AIC HQIC SBIC
0 134.604 - - - 4.3e − 09 − 7.91537 − 7.85433 − 7.73397
1 379.994 490.78 16 0.000 4.0e − 15 − 21.8178 − 21.5126 − 20.9108*
2 402.308 44.629 16 0.000 2.8e − 15 − 21.2005 − 21.6512 − 20.5679
3 416.918 29.22 16 0.000 3.5e − 15 − 21.1162 − 21.3228 − 19.7581
4 449.212 64.588* 16 0.000 1.7e − 15* − 23.1038* − 22.0662* − 20.02
Endogenous: lnc2, lnrenewenergyn, lngdp, lnlaborforcenn. Exogenous: _cons
Selection order criteria
Sample 1984–2016; number of obs = 33
Variables of the model are integrated at different orders so far according to the properties of time series data, and thus, we are applying the autoregressive distributed lags (ARDL) model approach. Table 5 indicates that a coefficient of Ln_CO2 at first lagged indicates a positive and significant effect at the 5% level on the current period of Ln_CO2. The coefficient of Ln_Renewenergy indicates a negative and significant effect at a 1% level on Ln_CO2. It shows that a 1% increase in the use of renewable energy will decrease 0.74% carbon dioxide emissions. Coefficient Ln_LaborForce at the current period shows a statistically significant positive impact on Ln_CO2 and negatively affects at first lagged. It was significant at the 1% level. This implies that a 1% increase in Ln_LaborForce will increase 7.5% Ln_CO2. In contrast, a 1% increase in Ln_LaborForce at lagged will decrease 7.9% Ln_CO2. However, the variable Ln_GDP shows an insignificant impact on the dependent variable Ln_CO2. The R square shows the goodness or fitness of the model, and it also explains how many explanatory variables are explained about the dependent variable. So, according to the results, the independent variables explain 99% variation in carbon dioxide emissions. Although many researchers say that a high R square shows a good model, in the opposite, many researchers say that our main concern is the relationship between independent and dependent variables. F-statistics (1737.40) show that the overall model is significant at a 1% level.Table 5 Resultsin ARDL regression
ARDL long-run and short-run model results
Table 6 indicates that the long-run coefficient of Ln_Renewenergy indicates a negative and significant effect at 1% level on Ln_CO2. It shows that a 1% increase in the use of renewable energy will decrease 0.87% carbon dioxide emissions. The number of studies also highlights the importance of renewable energy. They found that renewable energy reduces carbon dioxide emissions (Bilgili et al. 2016; Qi et al. 2014; Silva et al. 2012). Notably, Qi et al. (2014) stated that if the cost of renewable energy reduces because of policy efficiency, GDP will also consistently increase yearly. Therefore, the cost-efficient policy can reduce carbon dioxide emissions and achieve China’s economic growth target. Coefficient Ln_GDP shows a statistically significant positive impact on Ln_CO2, significant at 5% level. A 1% increase in Ln_GDP will increase by 0.26% Ln_CO2. However, in the long run, variable Ln_laborforce shows an insignificant negative impact on dependent variable Ln_CO2.Table 6 Long- and short-run results (ARDL)
D.lnc2 Coef Std.Err t P > t [95%Conf Interval]
ECT (− 1) − 0.644 0.146 − 4.420 0.000 − 0.942 − 0.345
Long-run
lnrenewenergyn − 0.876 0.115 − 7.630 0.000 − 1.111 − 0.641
lngdp 0.267 0.120 2.220 0.034 0.021 0.514
lnlaboreforcenn − 0.478 0.595 − 0.800 0.428 − 1.696 0.740
Short-run
lnrenewenergyn
D1 − 0.185 0.204 − 0.910 0.372 − 0.603 0.233
Lngdp
D1 0.180 0.231 0.780 0.443 − 0.294 0.653
Lnlaboreforcenn
D1 7.907 2.592 3.050 0.005 2.598 13.216
_cons 7.474 7.109 1.050 0.302 − 7.089 22.036
Sensitivity analysis
Error correction model
We estimate the error correction results to see whether our long-run relationship among time series variables is true or not. The error correction methodology is used to see the short-run results. The first pioneer who used the error correction term (ECT) was Sargan (1964) to identify short-run cointegration (Cheema and Atta 2014). This technique explains how adjusting time series variables from the short-run to long-run equilibrium position occurs (Engle and Granger 1987). Lagged value of the coefficient of ECT (− 1) indicates the speed of adjustment from the short-run to long-run equilibrium only if it has a negative sign. If it has a positive sign, then it means that variables will move away from the long-run equilibrium position (Lal et al. 2010). Using these empirical techniques, our results in Table 4 demonstrate that the error correction term (− 1) shows a significant negative sign at the 1% level. So, the value of the error correction term (− 0.644) explains that the time series variables will move toward a long-run equilibrium position at a speed of 64% after a short-run shock. The short-run coefficient Ln_LaborForce shows a statistically significant positive impact on Ln_CO2 in the current period. It was significant at 1% level. This implies that a 1% increase in Ln_LaborForce will increase 7.90% Ln_CO2 in the short run. However, variable Ln_Renewenergy showed insignificant negative and inconsistent Ln_GDP showing an insignificant positive impact on dependent variable Ln_CO2 in the short run.
ARDL bound test results
The null hypothesis is that no cointegration exists. Table 7 shows that the F-statistics value is 5.40 greater than the upper bound value of − 3.78 at 5% level. Therefore, we reject the null hypothesis and can say that there is cointegration that exists among the time series variables.Table 7 Results in ARDL bound test
The diagnostic and stability tests are used to determine the ARDL model’s goodness of fit. The model’s serial correlation, functional form, normalcy, and heteroscedasticity are all examined in the diagnostic test. CUSUM (cumulative sum of recursive residuals) and CUSUM (cumulative sum of squares of recursive residuals) are used in the stability test (CUSUMsq). Another method of determining the ARDL model’s reliability is to look at the model’s prediction error. The model can be regarded as the best fitting if the error or discrepancy between the actual observation and the forecast is minuscule. The recursive CUSUM test is shown in (Fig. 3).Fig. 3 CUSUM test
In this study, we used Durbin Watson (DW) (Durbin and Watson 1950) test to see whether the problem of autocorrelation exists in the model or not. The DW test value of 2.11 explains that there is no autocorrelation problem in the ARDL model via the following:DurbinWatsond-statistic(8,36)=2.115082
Furthermore, we also use the Breusch-Godfrey LM test for the autocorrelation test to see whether the problem of serial correlation exists in the model or not. In Table 8, the p-value of 0.121 of the Breusch-Godfrey LM test for autocorrelation explains that we will accept the null hypotheses and say that no serial correlation problem exists in the ARDL model.Table 8 Results in LM test
Breusch-Godfrey LM test for autocorrelation
chi2 df Prob > chi2
5.822 3 0.121
Note that H0 indicates no serial correlation
In the same context, we use the heteroskedasticity test to see whether the problem of hetero exists in the model or not. In Table 9, according to the p-value 0.33, we will accept the null hypotheses and say that no heater problem exists in the ARDL model.Table 9 Results in heterosecadistity test
White’s test for Ho: homoskedasticity against Ha: unrestricted heteroskedasticity
chi2(33) = 35.89
Prob > chi2 = 0.3344
Cameron and Trivedi’s decomposition of IM-test
chi2 df p
35.890 33 0.334
12.510 7 0.085
0.000 1 0.957
48.400 41 0.199
CUSUM square test is used to see the stability of parameters in the model. Figure 4 states that the predicted line is within the region. This means that our parameters are stable and reliable.Fig. 4 Results in CUSUM square
Conclusion and future research
This study empirically analyzes how China’s green energy deployment and economic growth affect CO2 emissions. Aiming for economic development, the Chinese government implemented reform and an open-door policy in the mid-1980s, which led to rapid industrialization and urbanization. However, in addition to excessive energy consumption, environmental degradation such as soil and water pollution becomes a social problem. In the 1990s, the Chinese government actively introduced environmental policies to achieve a sustainable society. As a result, the introduction of clean energy sources such as geothermal and hydroelectric power generation has progressed, and the environmental burden has been reduced by reducing greenhouse gases. However, it is unclear whether a series of initiatives by the Chinese government has brought about these results. This study investigates this relationship by developing the following hypotheses. Under hypothesis, carbon dioxide emissions are inversely related to green energy use. In the long run, a 1% increase in renewable energy significantly reduces carbon dioxide emissions by 0.87%, while a 1% increase in GDP significantly increases carbon dioxide emissions by 0.26%. In contrast, in the short run, the negative effect of renewable energy on carbon dioxide emissions and the positive effect of GDP on it is not significant. This result was confirmed after the robustness checks. Given the analysis results, we can say that the diffusion of green energy in China is effective in eventually reducing carbon dioxide emissions. Hence, the diffusion of renewable energy through environmental innovation has enhanced China’s sustainability. However, certain reservations must be placed on this result.
Theoretical analysis reveals that environmental policies reduce pollution and economic growth through innovation (e.g. Nakada 2004; Grimaud and Tournemaine 2007; Chu and Lai 2014; Hamaguchi 2019, Hamaguchi 2021b). Empirical studies also captured this relationship (e.g., Carrión-Flores and Innes 2010; Chiou et al. 2011; Long et al. 2017; Zhang et al. 2017; Wang et al. 2020). These previous studies commonly regarded environmental innovation as an essential variable. In particular, theoretical analyzes believe that environmental policies stimulate innovation. Our analysis argues that the Chinese government’s environmental policy played a significant role in the energy shift through environmental innovation. However, this environmental innovation is not considered an explanatory variable. For our argument to be more convincing, we should show that our results are not overturned in an empirical analysis that considers this variable. Additionally, in our analysis, the error term of the labor force is high in both the short and long run. This suggests that we may be missing some important explanatory variables. It is worth noting here that education and awareness may significantly negatively impacts the carbon emission. Zafar et al. (2020) found that poverty alleviation through education contributes to reducing fossil energy consumption as a share of household expenditures another theoretical analysis shows that households substitute leisure time for education through environmental policies to achieve economic growth and pollution reduction through human capital accumulation (e.g., Hettich 1998; Pautrel 2012; Hamaguchi 2021a). Given these various results, the effect of improving labor productivity through education on reducing carbon emissions may have been overlooked. This effect may be newly discovered if education is considered an explanatory variable.
Thus, we would like to suggest that even though China is facing some economic difficulties (due to COVID-19, lockdowns, inbound tourism, unemployment, and domestic force), the country should continue to promote using of green energy, green growth, environmental innovation subsidies, ETS (energy trading system), electronic vehicles, MREs (marine renewable energy), public awareness, and so on. Few policies like enhancing public perception, increasing government support, feed-in tariffs (FiT), and green certificate systems (GCS) could be implemented to promote green growth at a larger scale.
Some other variables (like rapid urbanization, fossil fuel consumption, unplanned industrialization) can influence CO2 emission. In addition, due to data limitations, we have used the 1980–2020 data for this study. These limitations of our analysis remain a topic for future research.
Author contribution
MAB: original draft, BK: proofreading, YH: draft, conceptualizing, QZ: supervising.
Funding
This work was supported by the Japan Society for the Promotion of Science (JSPS KAKENHI Grant Number JP19K13706, JP22K13409).
Data availability
Will be provided if needed.
Declarations
Ethical approval
All co-authors give consent that there is no unethical experiment conducted in this research.
Consent to participate
All co-authors give consent to participate in this manuscript.
Consent for publication
All co-authors consent to publish this article upon acceptance.
Competing interests
The authors declare no competing interests.
This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s11356-023-26648-4
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Change history
3/29/2023
This article has been retracted. Please see the Retraction Notice for more detail: 10.1007/s11356-023-26648-4
==== Refs
References
Belaïd F Elsayed AH Omri A Key drivers of renewable energy deployment in the MENA region: empirical evidence using panel quantile regression Struct Chang Econ Dyn 2021 57 225 238 10.1016/j.struec2021.03.011
Bilgili F Koçak E Bulut Ü The dynamic impact of renewable energy consumption on CO2 emissions: a revisited Environmental Kuznets Curve approach Renew Sustain Energy Rev 2016 54 838 845 10.1016/j.rser.2015.10.080
Carrión-Flores CE Innes R Environmental innovation and environmental performance J Environ Econ Manag 2010 59 1 27 42 10.1016/j.jeem.2009.05.003
Cheema AR Atta A Economic determinants of unemployment in Pakistan: Co-integration analysis Int J Bus Soc Sci 2014 5 3 209 221
Chien F Hsu CC Ozturk I Sharif A Sadiq M The role of renewable energy and urbanization towards greenhouse gas emission in top Asian countries: evidence from advance panel estimations Renew Energy 2022 186 207 216 10.1016/j.renene.2021.12.118
Chiou TY Chan HK Lettice F Chung SH The influence of greening the suppliers and green innovation on environmental performance and competitive advantage in Taiwan Transp Res Part e: Logist Transp Rev 2011 47 6 822 836 10.1016/j.tre.2011.05.016
Chu H Lai CC Abatement R&D, market imperfections, and environmental policy in an endogenous growth model J Econ Dyn Control 2014 41 20 37 10.1016/j.jedc.2014.02.011
Dickey DA Fuller WA Distribution of the estimators for autoregressive time series with a unit root J Am Stat Assoc 1979 74 366a 427 431 10.1080/01621459.1979.10482531
Dogan E Ozturk I The influence of renewable and non-renewable energy consumption and real income on CO2 emissions in the USA: evidence from structural break tests Environ Sci Pollut Res 2017 24 11 10846 10854 10.1007/s11356-017-8786-y
Durbin J Watson GS Testing for serial correlation in least-squares regression I Biometrika 1950 37 3 409 428 10.2307/2332391 14801065
Engle RF Granger CW Co-integration and error correction: representation, estimation, and testing Econometrica 1987 55 2 251 276 10.2307/1913236
Frank MW Inequality and growth in the United States: evidence from a new state-level panel of income inequality measures Econ Inq 2009 47 1 55 68 10.1007/s11293-009-9172-z
Grimaud A Pollution permits and sustainable growth in a Schumpeterian model J Environ Econ Manag 1999 38 3 249 266 10.1006/jeem.1999.1088
Grimaud A Tournemaine F Why can an environmental policy tax promote growth through the channel of education? Ecol Econ 2007 62 1 27 36 10.1016/j.ecolecon.2006.11.006
Hamaguchi Y Positive effect of pollution permits in a variety expansion model with social status preference Manch Sch 2019 87 4 591 606 10.1111/manc.12270
Hamaguchi Y Dynamic analysis of bribery firms’ environmental tax evasion in an emissions trading market J Macroecon 2020 63 103169 10.1016/j.jmacro.2019.103169
Hamaguchi Y Environmental policy and social status preference for education in an Uzawa-Lucas model Bull Econ Res 2021 73 3 456 468 10.1111/boer.12259
Hamaguchi Y Environmental policy effects: an R&D-based economic growth model with endogenous labour supply J Econ Policy Reform 2021 24 2 236 252 10.1080/17487870.2019.1631598
Hettich F Growth effects of a revenue-neutral environmental tax reform J Econ 1998 67 3 287 316 10.1007/BF01234647
Huang JW Li YH How resource alignment moderates the relationship between environmental innovation strategy and green innovation performance J Bus Ind Mark 2018 33 3 316 324 10.1108/JBIM-10-2016-0253
Ike GN Usman O Alola AA Sarkodie SA Environmental quality effects of income, energy prices and trade: the role of renewable energy consumption in G7 countries Sci Total Environ 2020 721 137813 10.1016/j.scitotenv.2020.137813 32197283
Khan Z Ali S Umar M Kirikkaleli D Jiao Z Consumption-based carbon emissions and international trade in G7 countries: the role of environmental innovation and renewable energy Sci Total Environ 2020 730 138945 10.1016/j.scitotenv.2020.138945 32416502
Lal I Muhammad SD Jalil MA Hussain A Test of Okun’s law in some Asian countries cointegration approach Eur J Sci Res 2010 40 1 73 80 10.2139/ssrn.1562602
Li J Zhang X Ali S Khan Z Eco-innovation and energy productivity: new determinants of renewable energy consumption J Environ Manage 2020 271 111028 10.1016/j.jenvman.2020.111028 32778308
Long X Chen Y Du J Oh K Han I Yan J The effect of environmental innovation behavior on economic and environmental performance of 182 Chinese firms J Clean Prod 2017 166 1274 1282 10.1016/j.jclepro.2017.08.070
Ma Q Murshed M Khan Z The nexuses between energy investments, technological innovations, emission taxes, and carbon emissions in China Energy Policy 2021 155 112345 10.1016/j.enpol.2021.112345
Nakada M Does environmental policy necessarily discourage growth? J Econ 2004 81 3 249 275 10.1007/s00712-002-0609-y
Paramati SR Sinha A Dogan E The significance of renewable energy use for economic output and environmental protection: evidence from the Next 11 developing economies Environ Sci Pollut Res 2017 24 15 13546 13560 10.1007/s11356-017-8985-6
Pautrel X Environmental policy, education and growth: a reappraisal when lifetime is finite Macroecon Dyn 2012 16 5 661 685 10.1017/S1365100510000830
Pesaran MH Shin Y Smith RJ Bounds testing approaches to the analysis of level relationships J Appl Economet 2001 16 3 289 326 10.1002/jae.616
Qi T Zhang X Karplus VJ The energy and CO2 emissions impact of renewable energy development in China Energy Policy 2014 68 60 69 10.1016/j.enpol.2013.12.035
Sadorsky P Renewable energy consumption and income in emerging economies Energy Policy 2009 37 10 4021 4028 10.1016/j.enpol.2009.05.003
Salahuddin M Habib MA Al-Mulali U Ozturk I Marshall M Ali MI Renewable energy and environmental quality: a second-generation panel evidence from the Sub Saharan Africa (SSA) countries Environ Res 2020 191 110094 10.1016/j.envres.2020.110094 32846170
Sargan JD Three-stage least-squares and full maximum likelihood estimates Econometrica 1964 32 77 81 10.2307/1913735
Sarkodie SA Adams S Renewable energy, nuclear energy, and environmental pollution: accounting for political institutional quality in South Africa Sci Total Environ 2018 643 1590 1601 10.1016/j.scitotenv.2018.06.320 30189575
Silva S Soares I Pinho C The impact of renewable energy sources on economic growth and CO2 emissions: a SVAR approach Eur Res Stud J 2012 15 4 133 144 10.35808/ersj/374
Usman O Iorember PT Jelilov G Isik A Ike GN Sarkodie SA Towards mitigating ecological degradation in G-7 countries: accounting for economic effect dynamics, renewable energy consumption, and innovation Heliyon 2021 7 12 e08592 10.1016/j.heliyon.2021.e08592 34977411
Usman O Alola AA Saint Akadiri S Effects of domestic material consumption, renewable energy, and financial development on environmental sustainability in the EU-28: Evidence from a GMM panel-VAR Renew Energy 2022 184 239 251 10.1016/j.renene.2021.11.086
Wang R Mirza N Vasbieva DG Abbas Q Xiong D The nexus of carbon emissions, financial development, renewable energy consumption, and technological innovation: what should be the priorities in light of COP 21 Agreements? J Environ Manage 2020 271 111027 10.1016/j.jenvman.2020.111027 32778307
Zafar MW Shahbaz M Sinha A Sengupta T Qin Q How renewable energy consumption contribute to environmental quality? The role of education in OECD countries J Clean Prod 2020 268 122149 10.1016/j.jclepro.2020.122149
Zeb R Salar L Awan U Zaman K Shahbaz M Causal links between renewable energy, environmental degradation and economic growth in selected SAARC countries: progress toward a green economy Renew Energy 2014 71 123 132 10.1016/j.renene.2014.05.012
Zhang YJ Peng YL Ma CQ Shen B Can environmental innovation facilitate carbon emissions reduction? Evidence from China Policy 2017 100 18 28 10.1016/j.enpol.2016.10.005 | 36125680 | PMC9485794 | NO-CC CODE | 2023-04-15 23:15:20 | yes | Environ Sci Pollut Res Int. 2023 Sep 20; 30(5):13162-13173 |
==== Front
J Affect Disord
J Affect Disord
Journal of Affective Disorders
0165-0327
1573-2517
Elsevier B.V.
S0165-0327(22)01172-7
10.1016/j.jad.2022.09.158
Article
Anxiety and depressive symptoms of German university students 20 months after the COVID-19 outbreak – A cross-sectional study
Heumann Eileen a⁎
Helmer Stefanie M. b
Busse Heide c
Negash Sarah d
Horn Johannes d
Pischke Claudia R. e
Niephaus Yasemin f
Stock Christiane ag
a Institute of Health and Nursing Science, Charité – Universitätsmedizin Berlin, Berlin, Germany
b Human and Health Sciences, University of Bremen, Bremen, Germany
c Department Prevention and Evaluation, Leibniz-Institute for Prevention Research and Epidemiology—BIPS, Bremen, Germany
d Institute for Medical Epidemiology, Biometrics and Informatics, Interdisciplinary Center for Health Sciences, Medical School of the Martin-Luther University Halle-Wittenberg, Halle (Saale), Germany
e Institute of Medical, Sociology, Centre for Health and Society, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
f Department of Social Sciences, University of Siegen, Siegen, Germany
g Unit for Health Promotion Research, University of Southern Denmark, Esbjerg, Denmark
⁎ Corresponding author at: Campus Virchow-Klinikum – Augustenburger Platz 1, D-13353 Berlin, Germany.
8 10 2022
1 1 2023
8 10 2022
320 568575
12 7 2022
24 9 2022
30 9 2022
© 2022 Elsevier B.V. All rights reserved.
2022
Elsevier B.V.
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Background
Given the long duration of the COVID-19 pandemic, monitoring mental health remains important. This study aimed to determine (1) the prevalence of anxiety and depressive symptoms among university students 20 months after the first COVID-19 restrictions and (2) which factors were associated with these outcomes.
Methods
The cross-sectional COVID-19 German Student Well-being Study (C19 GSWS) collected data of 7025 students at five German universities. Associations between anxiety and depressive symptoms with sociodemographic and other factors were analysed using multivariable logistic regression models.
Results
The mean age of the participants was 23.9 years (SD = 4.9), 67 % were female and 31 % male. The prevalence for depressive symptoms was 29 % (PHQ-2) and 12 % (CES-D 8) and 32 % for anxiety. A complicated relationship status, the lack of a trusted person, and financial difficulties were associated with anxiety and depressive symptoms. University students who were worried about (re-) infection with COVID-19 had a 1.37-times higher chance for reporting anxiety (GAD-2: OR, 95 % CI: 1.09–1.71). Those with pre-existing cardiovascular health conditions had an up to 3.21-times higher chance for reporting depressive symptoms (OR, CESD-D 8, 95 % CI: 1.44–7.14).
Limitations
The study design is cross-sectional and uses self-reported outcomes.
Conclusions
Concepts for prevention and counselling to tackle mental health problems in students are needed and programmes should take specific stressors related to the pandemic into account.
Keywords
University students
Students' well-being
Depression
Anxiety
Mental health
==== Body
pmc1 Background
Mental health problems, such as depressive symptoms and anxiety, are widespread among university students worldwide (Akhtar et al., 2019; Auerbach et al., 2016) and also in Germany (Grützmacher et al., 2018). In the World Mental Health Survey, Auerbach et al. (2016) found that one-fifth (20.3 %) of university students aged 18–22 years in 21 countries indicated the presence of a mental health disorder (including anxiety, depressive mood, behavioural, and substance use disorders). A recent systematic review by Sheldon et al. (2021), which included data from North America, Europe, Asia, and Australia, confirmed high prevalence rates among undergraduate students. Depressive symptoms and anxiety rates among university students were higher than rates of their not studying counterparts in the general population (Ibrahim et al., 2013; Lim et al., 2018).
During the COVID-19 pandemic, the current available evidence showed exacerbated trends in depressive symptoms and anxiety among university students (Li et al., 2021). Cross-national studies conducted during the early phase of the COVID-19 pandemic found the prevalence of depressive symptoms in university students to differ between European countries (Van de Velde et al., 2021b), with German students presenting higher rates of depressive symptoms compared to Northern European university students (Iceland, Sweden, Denmark, Norway, Finland) and lower rates compared to Southern European university students (e. g. Hungary, Italy, Portugal, Spain) (Van de Velde et al., 2021a).
Mental health problems in university students are associated with poor academic achievements (Hysenbegasi et al., 2005), increased risk for early university dropout (Arria et al., 2013), substantial role impairment (Alonso et al., 2018), as well as increased tobacco, alcohol and drug consumption (Serras et al., 2010). Further associations are described with unsafe sexual behavior, decreased physical activity and poorer physical health status (Cranford et al., 2009). Moreover, studies found associations between depressive symptoms in university students and lower quality of life, self-injurious behavior (Serras et al., 2010), and increased risk of suicide (Eisenberg et al., 2013).
Reasons for poor mental health among university students may include the presence of stressors at individual, interpersonal, or systemic level (Kannangara et al., 2018; Sheldon et al., 2021) of which some are modifiable and some are not (Sheldon et al., 2021). Among the non-modifiable risk factors, females (Hunt and Eisenberg, 2010), sexual and gender minorities (Borgogna et al., 2019), and people with lower socio-economic status (Ibrahim et al., 2013) are at higher risk. Among the modifiable factors, fewer social support resources (Hefner and Eisenberg, 2009), the presence of interpersonal stressors (e. g., romantic and peer relationships) (Hunt and Eisenberg, 2010; Li et al., 2021) or pre-existing health problems (Sheldon et al., 2021) are considered to increase the risk for depressive disorders.
The COVID-19 pandemic might have produced additional stressors in the daily lives of students. As in many other countries, restrictions to limit the spread of the virus were in place in Germany, in general, and also at university level. Empirical data is revealing an impact of the pandemic and associated life changes on university students' mental health due to COVID-19 protection measures at several levels, such as the switch from face-to-face to online teaching and the cancellation of internships and other practice-based teaching (Barada et al., 2020). Consequences for mental health during the COVID-19 pandemic include, among others, anxiety, fear, stress, worry, and depressive symptoms (Cao et al., 2020; Kannangara et al., 2021; Li et al., 2021; Van de Velde et al., 2021a). According to Cao et al. (2020), 25 % of university students were experiencing anxiety associated with academic concerns, financial worries, and the impact of COVID-19 on their daily life. A high proportion of university students indicated that they were not able to adequately cope with the stress related to the current pandemic situation (Wang et al., 2020). A study based on data from German university students conducted during the first COVID-19-related lockdown in spring 2020 shows that higher academic stress and dissatisfaction at university were associated with higher levels of depressive symptoms (Matos Fialho et al., 2021).
Most data based on university students' well-being during the COVID-19 pandemic has been collected towards the beginning of the pandemic in early 2020. Due to the long duration of the pandemic and the persistence of stressors that can affect mental health, it is important to continue monitoring mental health in this target group. Some longitudinal studies already reveal a causal relationship and an impact of the pandemic on the mental health of university students, but most of them were conducted in the US (Shanahan et al., 2020; Zimmermann et al., 2020). Thus, the relevant evidence in Europe and Germany on whether the pandemic affected the mental health of university students in the longer term is still lacking.
This study aimed to examine the well-being of university students in Germany in a later phase of the ongoing COVID-19 pandemic. The objectives were to examine (1) the levels of depressive symptoms and anxiety among higher-education students, and (2) which factors were associated with these outcomes.
2 Methods
2.1 Study design
The German COVID-19 Student Well-being Study (C19 GSWS) is a cross-sectional study in which an online questionnaire was implemented at five universities in Germany (Charité – Universitätsmedizin Berlin, University of Bremen, University of Siegen, Martin-Luther-University Halle/Wittenberg, and Heinrich-Heine-University Düsseldorf) in a later phase of the pandemic. The study is based on the COVID-19 International Student Well-being Study (C19 ISWS) that was conducted during the early phase of the COVID-19 pandemic in the Spring 2020 in 27 European countries (Van de Velde et al., 2021b).
2.2 Data collection and context
The data collection was carried out using LimeSurvey between October 27th and November 14th, 2021, at each of the participating universities. During the survey period, several COVID-19 regulations, such as the obligation of wearing masks indoors or hygiene measures were still in place. At this time, the incidence of infection increased a fourth time, driven by the spread of the SARS-CoV-2 type delta. Therefore, some German universities decided not to return to face-to-face teaching at all and to teach remotely throughout the whole winter semester, whereas others offered only a few face-to-face courses in smaller learning groups. Accordingly, the learning and teaching situation at German universities varied substantially at the time of the data collection but was different from the normal situation at all universities.
The questionnaire used was based on the original questionnaire of the C19 ISWS and was slightly modified considering changed circumstances such as the availability of vaccines. The modified questionnaire included questions on socio-demographic factors, health behavior, health condition, mental well-being, financial resources, perceived study conditions during the pandemic, critical health literacy, vaccination status against coronavirus, and attitudes towards COVID-19 vaccinations. The core questionnaire used can be found in the web appendix.
2.3 Recruitment and participation
University students aged 18 and above who were currently enrolled as students in undergraduate, graduate, or doctoral programmes were invited to participate in the study. University students were invited via email, as well as through e-learning platforms (Martin-Luther-University Halle/Wittenberg and University of Bremen). At Heinrich-Heine-University Düsseldorf invitations were also distributed via Instagram. University students were given the option to complete the survey in German or English. All participants provided their informed consent before completing the survey. Ethical approval was obtained from the ethics committees of each of the five participating universities.
2.4 Measures
2.4.1 Subjective depressive symptoms and anxiety
Subjective depressive symptoms were assessed using the Centre for Epidemiological Studies Depression Scale (CES-D 8) (Radloff, 1977) and a short-form version of the Patient Health Questionnaire (PHQ-2 scale) (Kroenke et al., 2003; Löwe et al., 2005). Also, an abbreviated form of the Generalized Anxiety Disorder scale (GAD-2) was used to assess anxiety (Kroenke et al., 2007).
The CES-D 8 was used to assess the frequency and severity of depressive symptoms (Radloff, 1977). University students were asked how often during the last week (1) they felt depressed, (2) everything was an effort, (3) they slept restlessly, (4) could not get going, (5) felt lonely or sad, or (6) they enjoyed life and felt happy (last two items were reverse coded items). Responses ranged from (0) ‘none or almost none of the time’; (1) ‘some of the time’; (2) ‘most of the time’ to (3) ‘all or almost all of the time’ on a four-point Likert scale. Summarising all items resulted in a non-weighted CES-D 8 rating, with a higher score indicating a higher level of depressive symptoms. The CES-D 8 was used with a cut-off point of 16 to indicate the presence of a depressive disorder as it is typically recommended (Vilagut et al., 2016).
The PHQ-2 consists of the first two items of the PHQ-9 (Löwe et al., 2005). The master question is: ‘Over the last two weeks, how often have you been bothered by the following problems?’ The two items are ‘feeling down, depressed or hopeless’ and ‘little interest or pleasure in things’. For each item, the response options are (0) ‘not at all’, (1) ‘several days’, (2) ‘more than half the days’, and (3) ‘nearly every day’. The PHQ-2 score can range from 0 to 6 (Löwe et al., 2005). For the analysis, we used a cut-off point of 3 as suggested based on the available literature (Kroenke et al., 2003) to indicate whether the participants showed depressive symptoms or not (0 to 3 ‘no depressive symptoms’; 4 to 6 ‘depressive symptoms’). Using the same master question with the same scaling and cut-off, the GAD-2 was conducted with the following items: ‘feeling nervous, anxious, or on edge’ and ‘not being able to stop or control worrying’ (Byrd-Bredbenner et al., 2021; Kroenke et al., 2007). The instruments are reliable and validated for the university context (Ghazisaeedi et al., 2021; Jiang et al., 2019; Khubchandani et al., 2016). The Cronbach's alpha in our sample was 0.862 for CES-D 8, 0.787 for PHQ-2, and 0.784 for GAD-2.
2.5 Covariates
2.5.1 Socio-demographic factors
The following information on the socio-demographic characteristics was assessed for this investigation: age (‘between 18 and 25 years old’ (ref.) vs. ‘aged 26 and older’), gender (‘male’/‘female’ (ref.)/‘diverse’), relationship status (‘single’/‘in a relationship’ (ref.)/‘it is complicated’), migrant background (‘no migrant background’ (ref.)/‘one parent born outside Germany’/‘both parents born outside Germany’), as well as a place of birth (‘Germany’ (ref.) vs. ‘other’), residence status in Germany (‘permanent residency’ (ref.) vs. ‘temporary residency’) and housing situation (‘living alone’ vs. ‘living with other persons in the household’ (ref.)). Age was dichotomised as suggested by Van de Velde et al. (2021a).
2.5.2 Socio-economic and social support factors
As university students have not completed their educational training, yet, and their actual income or employment status is not adequate for assessing their socio-economic status, following Van de Velde et al. (2021a), the highest level of education of each parent (‘less than secondary’/‘secondary’/‘higher education’ (ref.)), was used as a proxy of their socio-economic status (Marmot, 2005). For university students' current subjective financial status, they were asked to indicate to what extent they agreed with the statement “I have sufficient financial resources to cover my monthly costs'. Those who (strongly) agreed with this statement were grouped (ref.) and distinguished from those who (strongly) disagreed. Again following Van de Velde et al. (2021a) participants were asked from how many people within their network (partner, parents, siblings, grandparents, friends, colleagues, and/or acquaintances) they could easily borrow 500 euros within two days (‘zero’/‘one to two’/‘three to four’/‘five or more persons’ (ref.)) to assess their social and economic capital. Lastly, the extent of social support was measured by assessing the availability of a trusted person with whom to discuss intimate matters (‘yes’ (ref.) vs. ‘no’).
2.5.3 Study-related factors
University students were asked which degree programme they were enrolled in (‘Bachelor's degree programme’ (ref.)/‘Master's degree programme’/‘State examination (medicine, law)’). Students with unspecific or without information on their degree programme as well as doctoral students were excluded (total 242 persons). Doctoral students typically hold a paid employee status in Germany, thereby making them less comparable to other university students. Moreover, it was assessed whether university students were in the first semester or in a higher semester (ref.). The field of study was dichotomised for the analysis (‘health-related degree programme’ (ref.) vs. ‘other’).
2.5.4 Health-related factors
Further health-related variables were included for more in-depth analyses including pre-existing diseases. University students could select several pre-existing conditions in the questionnaire. For the analysis, we kept a nominal variable with seven values (‘no pre-existing health condition’ (ref.)/‘metabolic disease/‘cardiovascular disease’/‘lung disease’/‘obesity’/immunosuppressed disease/‘two or more pre-existing health conditions’). COVID-19-related factors were also included, such as whether or not participants currently have or had COVID-19 disease. If so, they were asked to indicate on an 11-point numeric rating scale how worried they were that (1) they would get infected with the coronavirus one more time and that (2) they would get seriously ill from a new infection (0= ‘not worried at all’, 10 = ‘very worried’). Participants who had previously stated that they did not have COVID-19 disease were asked how worried they were about (1) becoming infected with the virus and (2) becoming severely ill from infection (0= ‘not at all worried’, 10 = ‘very worried’). University students were also asked how worried they were that someone from their personal network would (1) become infected with COVID-19 or (2) become seriously ill from an infection. Finally, concerns were assessed about medical staff and hospitals not being adequately equipped to deal with the pandemic, as well as confidence about whether the necessary medical support can be obtained in case of COVID-19 disease. All COVID-19-related variables were dichotomised for analysis (‘not at all’ to ‘little concerned’ (ref.) vs. ‘fairly to very concerned’ or ‘not at all to little optimistic’ vs. ‘fairly to very optimistic’ (ref.)).
2.6 Data analysis
Descriptive statistics were calculated to summarise the sample in terms of socio-demographic data and study-related information. Prevalence rates were calculated for depressive symptoms and anxiety during the COVID-19 pandemic.
Two multivariable logistic regressions for each well-being outcome were carried out to determine the associations with selected independent variables. In the first model, socio-demographic, socio-economic, and social support factors, as well as study-related factors, were considered. In the second model, health-related factors and COVID-19-related stressors were also included. The analyses were based upon a prior analysis conducted on the C19-ISWS dataset during the first phase of the pandemic (Van de Velde et al., 2021a). Before entering the independent variables into the model, multicollinearity between independent variables was assessed based on tolerance and VIF coefficients. Indices for the second regression model indicated that a multicollinearity problem occurred, which was solved by removing one COVID-19 related variable (‘How worried are you that you will get severely ill from a COVID-19 infection?’). The data analysis was conducted using IBM SPSS version 26.
3 Results
3.1 Sample
After data cleaning 7025 cases remained for data analysis. Somewhat less than a third of the participants came from the Martin-Luther-University Halle-Wittenberg (29.7 %), about a quarter from the University of Bremen (25.2 %), a fifth from the University of Siegen (21.9 %), 15.5 % from Charité – Universitätsmedizin Berlin and 7.3 % from Heinrich-Heine-University Düsseldorf.
The characteristics of the sample are shown in Table 1 . Most participants were between 20 and 23 years old (48.2 %), the mean age was 23.9 years (SD = 4.8; Min = 18; Max = 68). More than two-thirds of the participants were female (67.3 %). Almost half of the respondents were studying for a bachelor's degree (47.2 %). The study sample is described in further detail in Table 1.Table 1 Socio-demographic characteristics of the sample (n = 7025).
Table 1 Socio-demographic characteristics of the participants
n %
Age
<20 668 9.5
20–23 3378 48.2
24–27 1856 26.5
28–30 517 7.4
≥31 584 8.3
Gendera
Male 2127 30.3
Female 4722 67.3
Diverse 74 1.1
Degree program
Bachelor program 3319 47.2
Master program 1396 19.9
State examination (medicine, law) 2310 32.9
Relationship statusa
In a steady relationship 3682 52.5
Single 2910 41.5
It is complicated 292 4.2
Living situation
Alone 1424 20.9
With others 5391 76.1
Person to discuss intimate matters with
Yes 5548 90.4
No 592 9.6
Financial resources
Sufficient to cover monthly costs 5440 77.7
Neither nor 578 8.3
Not sufficient to cover monthly costs 979 14.0
Residency status in Germanya
Permanent residency 6789 96.9
Temporary residency 198 2.8
a Missing percentages are due to answer options ‘no information’ or ‘I don't know’.
3.2 Prevalence of depressive symptoms and anxiety
According to the PHQ-2, 28.9 % of participants reported depressive symptoms. Female university students were more affected by depressive symptoms than male university students (29.6 % compared to 26.3 %). Among people of diverse gender, the prevalence rate was 43.1 %.
An analysis of the CES-D 8 showed that 12.1 % indicated depressive symptoms. Considering the continuous distribution of the CES-D 8, only 3.5 % indicated a value of ≥20, i.e., described severe depressive symptoms, and 23.3 % indicated a value of ≤5, and, thus, have no to very mild depressive symptoms. The average was 9.38 points. Using the cut-off threshold of 16, male university students showed a lower prevalence (9.2 %) compared to female university students (13.1 %) and persons of diverse gender (22.2 %).
The GAD-2 revealed that 31.5 % of the sample reported anxiety symptoms, whereas the gender-specific prevalence again differed: 34.0 % of female university students reported anxiety symptoms, while among male university students it was only 24.2 %. Persons with diverse gender identity stated that they were distinctly more often affected by anxiety (63.9 %).
3.3 Model 1
Considering associations between socio-demographic, socio-economic, social support factors, as well as study-related factors and well-being outcomes, the results of model 1 (Table 2 ) showed that male compared to female university students, master's compared to bachelor's students, as well as university students of health-related subjects compared to university students of other subjects, experienced fewer depressive symptoms for both depression scales. On the other side, having a complicated rather than a steady relationship, not having a trusted person and living alone compared instead of living with others were factors that were significantly associated with more frequent depressive symptoms, again for both depression scales. Moreover, financial issues were found to be associated with depressive symptoms: E.g., university students who were not able to borrow money from anyone as well as those who did not have enough money to cover their monthly expenses reported more depressive symptoms. Only according to the CES-D 8 outcome, single university students compared to those in a steady relationship had a higher chance of suffering from depressive symptoms (OR 1.37, 95 % CI: 1.11–1.68), whereas those, who were striving for a state examination experienced fewer depressive symptoms (OR = 0.75, 95 % CI: 0.57–0.98). The PHQ-2, on the other hand, indicated that university students in the first year of study reported to be less likely to be affected (OR = 0.76, 95 % CI: 0.64–0.91).Table 2 Associations between socio-demographic, socio-economic, social support factors, study-related factors and depressive symptoms as well as anxiety: results of three multivariable logistic regressions (n = 4980).
Table 2 Depressive symptoms (PHQ-2) Depressive symptoms (CES-D 8) Anxiety (GAD-2)
Variables
OR 95 %-CI OR 95 %-CI OR 95 %-CI
Age 18–25 1.0 1.0 1.0
≥26 0.91 (0.77–1.08) 1.02 (0.80–1.30) 1.02 (0.87–1.20)
Gender Female 1.0 1.0 1.0
Male 0.71 (0.61–0.83) 0.57 (0.46–0.72) 0.54 (0.47–0.63)
Diverse 0.91 (0.77–1.08) 1.07 (0.47–2.41) 3.32 (1.76–6.25)
Migration background No migrant background 1.0 1.0 1.0
One parent born outside Germany 1.24 (0.98–1.58) 1.26 (0.91–1.73) 1.35 (1.08–1.70)
Both parents born outside Germany 1.27 (0.97–1.65) 1.31 (0.92–1.85) 1.37 (1.06–1.77)
Place of birth Germany 1.0 1.0 1.0
Outside Germany 1.05 (0.73–1.52) 1.31 (0.81–2.12) 0.86 (0.60–1.23)
Relationship status Relationship 1.0 1.0 1.0
Single 1.01 (0.87–1.16) 1.37 (1.11–1.68) 1.05 (0.92–1.21)
Complicated 1.59 (1.17–2.15) 2.49 (1.70–3.65) 1.46 (1.08–1.97)
Residency status Permanent residency 1.0 1.0 1.0
Temporary residency 1.05 (0.73–1.52) 0.70 (0.36–1.38) 1.03 (0.62–1.72)
Housing situation Living with others 1.0 1.0 1.0
Living alone 1.19 (1.01–1.40) 1.40 (1.12–1.75) 1.07 (0.91–1.25)
Level of education (Mother) Higher education 1.0 1.0 1.0
Secondary 0.92 (0.76–1.11) 0.98 (0.75–1.29) 0.99 (0.83–1.19)
Less than secondary 0.98 (0.82–1.17) 1.04 (0.81–1.34) 0.97 (0.82–1.15)
Level of education (Father) Higher education 1.0 1.0 1.0
Secondary 1.05 (0.86–1.27) 1.05 (0.80–1.39) 0.95 (0.79–1.15)
Less than secondary 1.09 (0.92–1.29) 0.97 (0.76–1.24) 1.02 (0.86–1.20)
Financial situation Sufficient financial resources 1.0 1.0 1.0
Insufficient financial resources 2.19 (1.83–2.61) 2.44 (1.95–3.05) 1.92 (1.61–2.29)
Possibility of borrowing money ≥5 persons 1.0 1.0 1.0
3–4 persons 1.37 (1.17–1.61) 1.33 (1.05–1.69) 1.57 (1.35–1.83)
1–2 persons 1.57 (1.30–1.88) 2.07 (1.62–2.65) 1.85 (1.55–2.21)
0 persons 2.15 (1.58–2.93) 2.69 (1.86–3.88) 2.25 (1.66–3.05)
Person to trust Yes 1.0 1.0 1.0
No 3.42 (2.75–4.26) 3.15 (2.44–4.07) 2.66 (2.14–3.31)
Degree program Bachelor 1.0 1.0 1.0
Master 0.82 (0.68–0.99) 0.70 (0.53–0.92) 0.88 (0.74–1.05)
State examination 0.89 (0.74–1.07) 0.75 (0.57–0.98) 0.80 (0.67–0.96)
Year of study ≥2 semesters 1.0 1.0 1.0
First year 0.76 (0.64–0.91) 0.86 (0.67–1.10) 0.98 (0.83–1.15)
Field of study Other 1.0 1.0 1.0
Health-related 0.63 (0.52–0.77) 0.64 (0.48–0.87) 0.76 (0.63–0.92)
Concerning anxiety, male gender, and a health-related degree programme were protective factors compared to women and university students in other degree programmes respectively. People of diverse gender (as compared to female university students), those with a complicated relationship status, without a trusted person or in a difficult financial situation (i.e., without the possibility of borrowing money from someone) and difficulty covering monthly expenses had a higher chance of experiencing anxiety. The migration background was also negatively associated with anxiety (i.e., both parents born outside Germany compared to both parents born in Germany).
3.4 Model 2
For model 2, health-related variables were added to model 1 (Table 3 ). This second model showed that university students with pre-existing conditions reported more depressive symptoms compared to those without underlying health conditions: Suffering from obesity or cardiovascular disease was associated with an increased likelihood for reporting depressive symptoms on the CES-D 8 (OR = 2.13, 95 % CI: 1.20–3.77; OR = 3.21, 95 % CI: 1.44–7.14) whereas in regard to the PHQ-2, having a cardiovascular disease or more than one previous disease was associated with depressive symptoms (OR = 1.97, 95 % CI: 1.00–3.87; OR = 1.87, 95 % CI: 1.06–3.29). A pre-existing cardiovascular disease was also associated with anxiety (OR = 2.25, 95 % CI: 1.14–4.42). Metabolic health conditions seemed to be a protective factor for anxiety.Table 3 Associations between socio-demographic, socio-economic, and social support factors, study-, health- and COVID-related factors and depressive symptoms and anxiety: Results of three multivariable logistic regressions (n = 2311).
Table 3 Depressive symptoms (PHQ-2) Depressive Symptoms (CES-D 8) Anxiety (GAD-2)
Variables
OR 95 %-CI OR 95 %-CI OR 95 %-CI
Age 18–25 1.0 1.0 1.0
≥26 0.78 (0.59–1.01) 0.78 (0.54–1.14) 0.90 (0.70–1.16)
Gender Female 1.0 1.0 1.0
Male 0.79 (0.63–1.00) 0.73 (0.52–1.03) 0.63 (0.50–0.79)
Diverse 1.41 (0.59–3.37) 1.37 (0.45–4.17) 3.90 (1.58–9.54)
Migration background No migrant background 1.0 1.0 1.0
One parent born outside Germany 1.13 (0.80–1.60) 0.99 (0.60–1.63) 1.27 (0.90–1.78)
Both parents born outside Germany 1.21 (0.80–1.83) 1.09 (0.62–1.90) 1.87 (1.26–2.79)
Place of birth Germany 1.0 1.0 1.0
Outside Germany 1.00 (0.55–1.83) 1.28 (0.56–2.90) 0.81 (0.45–1.43)
Relationship status Relationship 1.0 1.0 1.0
Single 1.05 (0.85–1.30) 1.27 (0.93–1.73) 1.22 (0.99–1.50)
Complicated 1.78 (1.11–2.86) 2.60 (1.44–4.70) 1.56 (0.97–2.51)
Residency status Permanent residency 1.0 1.0 1.0
Temporary residency 0.66 (0.28–1.57) 0.73 (0.24–2.23) 0.66 (0.30–1.47)
Housing situation Living with others 1.0 1.0 1.0
Living alone 1.08 (0.84–1.38) 1.58 (1.13–2.22) 0.92 (0.72–1.17)
Level of education (Mother) Higher education 1.0 1.0 1.0
Secondary 0.99 (0.75–1.30) 1.04 (0.70–1.55) 0.92 (0.72–1.19)
Less than secondary 1.11 (0.86–1.45) 0.85 (0.58–1.24) 0.99 (0.75–1.29)
Level of education (Father) Higher education 1.0 1.0 1.0
Secondary 1.07 (0.80–1.43) 1.07 (0.74–1.56) 0.97 (0.75–1.24)
Less than secondary 0.99 (0.76–1.27) 1.32 (0.88–1.98) 0.97 (0.73–1.29)
Financial situation Sufficient financial resources 1.0 1.0 1.0
Insufficient financial resources 1.90 (1.44–2.51) 2.47 (1.75–3.49) 1.71 (1.29–2.25)
Possibility of borrowing money ≥5 persons 1.0 1.0 1.0
3–4 persons 1.47 (1.17–1.86) 1.74 (1.23–2.47) 1.73 (1.38–2.16)
1–2 persons 1.38 (1.05–1.82) 2.05 (1.40–3.00) 1.93 (1.49–2.50)
0 persons 2.06 (1.25–3.41) 2.99 (1.65–5.42) 2.09 (1.27–3.44)
Person to trust Yes 1.0 1.0 1.0
No 3.89 (2.75–5.48) 3.17 (2.13–4.73) 2.62 (1.86–3.69)
Degree program Bachelor 1.0 1.0 1.0
Master 0.85 (0.65–1.11) 0.70 (0.47–1.05) 1.00 (0.77–1.30)
State examination 0.90 (0.68–1.18) 0.63 (0.42–0.94) 0.87 (0.67–1.14)
Year of study ≥2 semesters 1.0 1.0 1.0
First year 0.73 (0.56–0.95) 0.64 (0.43–0.95) 1.01 (0.79–1.29)
Field of study Other 1.0 1.0 1.0
Health-related 0.62 (0.47–0.83) 0.65 (0.41–1.02) 0.68 (0.52–0.89)
Pre-existing disease None 1.0 1.0 1.0
Metabolic disease 0.38 (0.10–1.41) 1.41 (0.36–5.54) 0.20 (0.04–0.93)
Cardiovascular disease 1.97 (1.00a–3.87) 3.21 (1.44-7.14) 2.25 (1.14–4.42)
Lung disease 0.86 (0.52–1.43) 0.92 (0.44–1.94) 0.94 (0.58–1.52)
Obesity 1.60 (0.99–2.57) 2.13 (1.20–3.77) 1.59 (1.00–2.55)
Immunosuppressed conditions 0.91 (0.39–2.11) 1.28 (0.45–3.63) 1.02 (0.46–2.27)
≥one 1.87 (1.06–3.29) 1.88 (0.94–3.76) 1.33 (0.75–2.34)
COVID infection No 1.0 1.0 1.0
Yes 1.14 (0.78–1.67) 1.06 (0.61–1.86) 1.14 (0.79–1.64)
Concern to get infected No 1.0 1.0 1.0
Yes 1.30 (1.03–1.65) 1.24 (0.88–1.73) 1.37 (1.09–1.71)
Concern relatives get infected Yes 1.0 1.0 1.0
No 0.93 (0.65–1.32) 1.06 (0.97–2.72) 1.25 (0.89–1.75)
Concern relatives get severely ill No 1.0 1.0 1.0
Yes 0.77 (0.55–1.08) 2.64 (1.54–4.53) 1.56 (1.12–2.17)
Confidence in receiving medical care in case of infection Yes 1.0 1.0 1.0
No 1.41 (0.98–2.04) 1.40 (0.89–2.20) 1.39 (0.97–2.00)
Concern doctors/hospitals don't have adequate supplies Yes 1.0 1.0 1.0
No 0.90 (0.72–1.12) 0.88 (0.63–1.23) 0.85 (0.69–1.06)
a Due to rounding of the results.
Regarding the COVID-19-related factors, university students who were (very) worried that someone from their personal network got severely ill with COVID-19 reported more depressive symptoms measured with CES-D 8, but the association was not found for PHQ-2. In addition, this worry was associated with anxiety. Moreover, concern about (re-)infection with COVID was associated with anxiety.
Migration background of both parents (compared to no migration background) was associated with anxiety in the second model. Living alone was associated with depressive symptoms when measured by CES-D 8 (OR = 1.9, 95 % CI: 1.13–2.22), but not when measured with PHQ-2. Being a first-year university student seemed to be a protective factor for depressive symptoms for both scales (Table 3).
4 Discussion
Our study aimed at assessing the prevalence of anxiety and depressive symptoms in the later stage of the pandemic among German university students. Regarding the prevalence of depression within this sample, 12.1 % and 28.9 %, respectively, appear to be lower compared to other studies conducted during the COVID-19 pandemic (Deng et al., 2021). In their systematic reviews, Deng et al. (2021) and Chang et al. (2021) stated considerably higher prevalences for depressive symptoms among university students during the pandemic (34 % and 44 %, respectively), which could be explained, for instance, by the focus of the included studies on China. Only few studies from Europe were included, none from Germany.
On the other hand, the calculated average of the CES-D 8 in this sample is with 9.38 points slightly higher than in the German sample of the ISWS study that was conducted in the beginning of the pandemic (9.25 points, Matos Fialho et al., 2021). This is an important finding showing that the mean level of depressive symptoms remains overall at the same level, because the samples are from the same universities and therefore to some extent comparable. We can thus conclude, that during the long duration of the pandemic the level of depressive symptoms that was measured during the first lockdown did not diminish towards the later phase of the pandemic.
In terms of anxiety, we found a prevalence of 31.5 %, which is comparable to the pooled prevalence rates of two meta-analysis considering data among university students worldwide (32 % Deng et al. (2021), 31 %, Chang et al. (2021)). A study conducted in April 2020 involving college students from the US found that anxiety increased due to the COVID-19 pandemic. It showed that university students reported an increased worry about their own health and the health of their loved ones during the COVID-19 pandemic (Son et al., 2020). These results are in accordance with our findings that university students who were concerned to get (re-)infected or that someone in their personal network gets severely ill from COVID-19 were at a higher risk for experiencing anxiety.
A second aim of our study was to examine the factors associated with depressive symptoms and anxiety. Our study showed that a complicated relationship status, the lack of a trusted person and financial difficulties (referring both to the possibility of borrowing money and to difficulties in covering monthly expenses) were associated with depressive symptoms and with anxiety. Male university students and those of health-related subjects showed fewer depressive symptoms and less anxiety. Likewise, university students with pre-existing cardiovascular health conditions had a higher chance of experiencing mental health problems.
Our findings are in line with other studies in the field conducted during the COVID-19 pandemic (Cao et al., 2020). In line with Van de Velde et al.'s analysis (Van de Velde et al., 2021a), we found similar associations with gender, a complicated relationship, financial problems, not having a trusted person and depressive symptoms. However, some findings are not in line with Van de Velde et al. (2021a), and e.g., was neither age associated in any of our models, nor was migration background associated with depressive symptoms as reported by Van de Velde et al. (2021a). This disparity may be explained by the fact that our sample contained only university students from Germany, while the prior analysis was based on an international sample.
Considering the factors associated with depression, some previous evidence can be confirmed by our results. Similar to our findings, other studies showed that females (Hunt and Eisenberg, 2010), people of diverse gender (Borgogna et al., 2019), those with a lower socio-economic status (Ibrahim et al., 2013) and fewer social support resources (Hefner and Eisenberg, 2009) and those with pre-existing other health problems (Sheldon et al., 2021) were more likely to experience depressive symptoms. The associations between cardiovascular diseases and mental health problems have also been confirmed in the general population (Chaddha et al., 2016). According to our results, enrolment in a health-related study subject was a protective factor for depressive symptoms. As medical students have often been the research focus so far (Seweryn et al., 2015; Zeng et al., 2019), more research should also consider other student groups to verify our results.
Our finding that being a fist-year university student was found to be a protective factor for depressive symptoms is not in line with other findings in the literature (applicable for both instruments within the second model): The analysis by Puthran et al. (2016) e. g., showed that the prevalence of depression was highest in first-year medical students and decreased in the following years. According to them, medical school itself could be a stressor for university students, especially in the first year (Puthran et al., 2016). This trend could not be found in our study.
A few variables did not consistently show significant associations in both models, which may be explained by confounding or by the different number of cases included in the logistic models. In our analyses, also single university students and those who lived alone were more likely to report depressive symptoms compared to those in a steady relationship or those who were living with others, but these findings were not consistent across scales and models. Similarly, an association between this relationship status and depressive symptoms was found by Shah et al. (2021) in their survey conducted during the pandemic. People who were living alone or were single may feel lonely which may explain the higher rates of depression (Matthews et al., 2016).
4.1 Strengths and limitations
This multi-centre study provides evidence on the prevalence of depressive symptoms and anxiety among university students in Germany and the factors associated with both indicators for mental well-being of university students. However, some limitations of the study must be considered. First, it was conducted with a convenience sample, meaning that the results are not representative of the German university student population in general. Likewise, more than a quarter of the participants were university students of medicine or health-related subjects, which again limits the generalisability of the results to the entire university student population. The sample was gender imbalanced and a selection bias cannot be ruled out. Moreover, as this is a cross-sectional study that is largely based upon the C19 ISWS survey and involves a similar, but not the same study population, it is not possible to draw final conclusions about causality or the change in depressive symptomatology or anxiety over the duration of the pandemic. Therefore, we cannot estimate, for example, the onset of mental health problems or after what degree of financial difficulties a significant association between anxiety and depressive symptoms can be found.
In the C19 GSWS survey, self-assessed measures were offered, this may have resulted in response bias. To mitigate this potential distortion, our data was collected via a confidential online survey. Furthermore, our study showed that mental well-being of university students could not accurately be determined by dichotomised measurements. The CES-D 8 in place for depressive symptoms is not validated for university students, what could explain the diverging prevalence values compared to the PHQ-2. Further studies will need to explore the validity of cut-offs for this specific target group.
The study did not include measurements on psychological stress, which may, especially for university students, also be an important factor in the COVID-19 pandemic.
Furthermore, one variable could not be considered in our models as a covariate due to multicollinearity, namely the concern on getting severely ill by (re-)infection with COVID-19.
5 Conclusions
This study showed that mental health problems such as anxiety and depressive disorders were widespread among university students and associated with a variety of factors. The analysis showed that social support factors, financial difficulties and pre-existing health conditions were associated with mental health problems. Participants who were worried about (re-)infection with COVID-19 and those who were (very) worried about someone in their personal network becoming seriously ill with COVID-19 reported more anxiety. The findings can help to develop specific concepts for prevention and counselling, that also consider the burdens, e.g., financial issues, caused by the COVID pandemic and beyond.
Our study and other research in this area shows that university students are a vulnerable target group when it comes to mental well-being. Long-term studies that continuously report on the mental health of university students are scarce but should be implemented in the future. For this purpose, it is also important to consider a sample of university students that is representative to the student body in terms of gender distribution and field of study.
In addition, further research should broaden the focus beyond the assessment of associated factors and its associations with depressive disorders and anxiety. For example, intervention studies are needed that show how university students' mental well-being can be promoted or maintained. The COVID-19 pandemic shows the importance for online interventions of counselling services tailored to the needs of university students, their mental well-being and further health conditions.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
CRediT authorship contribution statement
All authors participated in the research process. Conceptualization of survey: all. Data assessment: EH, SMH. Data processing and analyses: EH, SMH. Interpretation of results: all. Drafting: EH. Revising the work critically for important intellectual content: all. Final approval of the version to be published and agreement to be accountable for all aspects of the work: all.
Conflict of Interest
None.
Appendix A Supplementary data
Supplementary material
Image 1
Acknowledgements
We would like to thank all participants who took part in this survey.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.jad.2022.09.158.
==== Refs
References
Akhtar M. Herwig B.K. Faize F.A. Depression and anxiety among international medical students in Germany: the predictive role of coping styles J. Pak. Med. Assoc. 69 2 2019 230 234 30804589
Alonso J. Mortier P. Auerbach R.P. Bruffaerts R. Vilagut G. Cuijpers P. Demyttenaere K. Ebert D.D. Ennis E. Gutiérrez-García R.A. Severe role impairment associated with mental disorders: results of the WHO world mental health surveys international college student project Depress. Anxiety 35 9 2018 802 814 10.1002/da.22778 29847006
Arria A.M. Caldeira K.M. Vincent K.B. Winick E.R. Baron R.A. O'Grady K.E. Discontinuous college enrollment: associations with substance use and mental health Psychiatr. Serv. 64 2 2013 165 172 10.1176/appi.ps.201200106 23474608
Auerbach R.P. Alonso J. Axinn W.G. Cuijpers P. Ebert D.D. Green J.G. Hwang I. Kessler R.C. Liu H. Mortier P. Nock M.K. Pinder-Amaker S. Sampson N.A. Aguilar-Gaxiola S. Al-Hamzawi A. Andrade L.H. Benjet C. Caldas-de-Almeida J.M. Demyttenaere K. Bruffaerts R. Mental disorders among college students in the World Health Organization world mental health surveys Psychol. Med. 46 14 2016 2955 2970 10.1017/s0033291716001665 27484622
Barada V. Doolan K. Burić I. Krolo K. Tonković Ž. Student life during the COVID-19 pandemic lockdown: Europe-wide insights 2020 University of Zadar
Borgogna N.C. McDermott R.C. Aita S.L. Kridel M.M. Anxiety and depression across gender and sexual minorities: implications for transgender, gender nonconforming, pansexual, demisexual, asexual, queer, and questioning individuals Psychol. Sex. Orientat. Gend. Divers. 6 1 2019 54
Byrd-Bredbenner C. Eck K. Quick V. GAD-7, GAD-2, and GAD-mini: psychometric properties and norms of university students in the United States Gen. Hosp. Psychiatry 69 2021 61 66 10.1016/j.genhosppsych.2021.01.002 33571925
Cao W. Fang Z. Hou G. Han M. Xu X. Dong J. Zheng J. The psychological impact of the COVID-19 epidemic on college students in China Psychiatry Res. 287 2020 112934 10.1016/j.psychres.2020.112934
Chaddha A. Robinson E.A. Kline-Rogers E. Alexandris-Souphis T. Rubenfire M. Mental health and cardiovascular disease Am. J. Med. 129 11 2016 1145 1148 27288855
Chang J.-J. Ji Y. Li Y.-H. Pan H.-F. Su P.-Y. Prevalence of anxiety symptom and depressive symptom among college students during COVID-19 pandemic: a meta-analysis J. Affect. Disord. 292 2021 242 254 10.1016/j.jad.2021.05.109 34134022
Cranford J.A. Eisenberg D. Serras A.M. Substance use behaviors, mental health problems, and use of mental health services in a probability sample of college students Addict. Behav. 34 2 2009 134 145 10.1016/j.addbeh.2008.09.004 18851897
Deng J. Zhou F. Hou W. Silver Z. Wong C.Y. Chang O. Drakos A. Zuo Q.K. Huang E. The prevalence of depressive symptoms, anxiety symptoms and sleep disturbance in higher education students during the COVID-19 pandemic: a systematic review and meta-analysis Psychiatry Res. 301 2021 113863 10.1016/j.psychres.2021.113863
Eisenberg D. Hunt J. Speer N. Mental health in american colleges and universities: variation across student subgroups and across campuses J. Nerv. Ment. Dis. 201 1 2013 60 67 10.1097/NMD.0b013e31827ab077 23274298
Ghazisaeedi M. Mahmoodi H. Arpaci I. Mehrdar S. Barzegari S. Validity, reliability, and optimal cut-off scores of the WHO-5, PHQ-9, and PHQ-2 to screen depression among university students in Iran Int. J. Ment. Heal. Addict. 2021 10.1007/s11469-021-00483-5
Grützmacher J. Gusy B. Lesener T. Sudheimer S. Willige J. Gesundheit Studierender in Deutschland 2017 Ein Kooperationsprojekt zwischen dem Deutschen Zentrum für Hochschul- und Wissenschaftsforschung, der Freien Universität Berlin und der Techniker Krankenkasse 2018
Hefner J. Eisenberg D. Social support and mental health among college students Am. J. Orthopsychiatry 79 4 2009 491 499 10.1037/a0016918 20099940
Hunt J. Eisenberg D. Mental health problems and help-seeking behavior among college students J. Adolesc. Health 46 1 2010 3 10 10.1016/j.jadohealth.2009.08.008 20123251
Hysenbegasi A. Hass S.L. Rowland C.R. The impact of depression on the academic productivity of university students J. Ment. Health Policy Econ. 8 3 2005 145 151 16278502
Ibrahim A.K. Kelly S.J. Adams C.E. Glazebrook C. A systematic review of studies of depression prevalence in university students J. Psychiatr. Res. 47 3 2013 391 400 10.1016/j.jpsychires.2012.11.015 23260171
Jiang L. Wang Y. Zhang Y. Li R. Wu H. Li C. Wu Y. Tao Q. The reliability and validity of the Center for Epidemiologic Studies Depression Scale (CES-D) for Chinese university students [Original Research] Frontiers in Psychiatry 10 2019 10.3389/fpsyt.2019.00315
Kannangara C.S. Allen R.E. Waugh G. Nahar N. Khan S.Z.N. Rogerson S. Carson J. All that glitters is not grit: three studies of grit in university students Front. Psychol. 9 2018 1539 10.3389/fpsyg.2018.01539
Kannangara C. Allen R. Vyas M. Carson J. Every cloud has a SILVER lining: short-term psychological effects of COVID-19 on British university students Br. J. Educ. Stud. 1–22 2021 10.1080/00071005.2021.2009763
Khubchandani J. Brey R. Kotecki J. Kleinfelder J. Anderson J. The psychometric properties of PHQ-4 depression and anxiety screening scale among college students Arch. Psychiatr. Nurs. 30 4 2016 457 462 10.1016/j.apnu.2016.01.014 27455918
Kroenke K. Spitzer R.L. Janet B.W.W. The patient health Questionnaire-2: validity of a two-item depression screener Med. Care 41 11 2003 1284 1292 http://www.jstor.org/stable/3768417 14583691
Kroenke K. Spitzer R.L. Williams J.B.W. Monahan P.O. Löwe B. Anxiety disorders in primary care: prevalence, impairment, comorbidity, and detection Ann. Intern. Med. 146 5 2007 317 325 10.7326/0003-4819-146-5-200703060-00004 17339617
Li Y. Zhao J. Ma Z. McReynolds L.S. Lin D. Chen Z. Wang T. Wang D. Zhang Y. Zhang J. Fan F. Liu X. Mental health among college students during the COVID-19 pandemic in China: a 2-wave longitudinal survey J. Affect. Disord. 281 2021 597 604 10.1016/j.jad.2020.11.109 33257043
Lim G.Y. Tam W.W. Lu Y. Ho C.S. Zhang M.W. Ho R.C. Prevalence of depression in the community from 30 countries between 1994 and 2014 Sci. Rep. 8 1 2018 2861 10.1038/s41598-018-21243-x 29434331
Löwe B. Kroenke K. Gräfe K. Detecting and monitoring depression with a two-item questionnaire (PHQ-2) J. Psychosom. Res. 58 2 2005 163 171 10.1016/j.jpsychores.2004.09.006 15820844
Marmot M. Social determinants of health inequalities Lancet 365 9464 2005 1099 1104 10.1016/S0140-6736(05)71146-6 15781105
Matos Fialho P.M. Spatafora F. Kühne L. Busse H. Helmer S.M. Zeeb H. Stock C. Wendt C. Pischke C.R. Perceptions of study conditions and depressive symptoms during the COVID-19 pandemic among university students in Germany: results of the international COVID-19 student well-being study [Original Research]. Frontiers Public Health 9 669 2021 10.3389/fpubh.2021.674665
Matthews T. Danese A. Wertz J. Odgers C.L. Ambler A. Moffitt T.E. Arseneault L. Social isolation, loneliness and depression in young adulthood: a behavioural genetic analysis Soc. Psychiatry Psychiatr. Epidemiol. 51 3 2016 339 348 26843197
Puthran R. Zhang M.W. Tam W.W. Ho R.C. Prevalence of depression amongst medical students: a meta-analysis Med. Educ. 50 4 2016 456 468 10.1111/medu.12962 26995484
Radloff L.S. The CES-D scale: a self-report depression scale for research in the general population Appl. Psychol. Meas. 1 3 1977 385 401 10.1177/014662167700100306
Serras A. Saules K.K. Cranford J.A. Eisenberg D. Self-injury, substance use, and associated risk factors in a multi-campus probability sample of college students Psychol. Addict. Behav. 24 1 2010 119 10.1037/a0017210 20307119
Seweryn M. Tyrała K. Kolarczyk-Haczyk A. Bonk M. Bulska W. Krysta K. Evaluation of the level of depression among medical students from Poland, Portugal and Germany Psychiatr. Danub. 27 Suppl. 1 2015 S216 S222 26417766
Shah S.M.A. Mohammad D. Qureshi M.F.H. Abbas M.Z. Aleem S. Prevalence, psychological responses and associated correlates of depression, anxiety and stress in a global population, during the coronavirus disease (COVID-19) pandemic Community Ment. Health J. 57 1 2021 101 110 10.1007/s10597-020-00728-y 33108569
Shanahan L. Steinhoff A. Bechtiger L. Murray A.L. Nivette A. Hepp U. Ribeaud D. Eisner M. Emotional distress in young adults during the COVID-19 pandemic: evidence of risk and resilience from a longitudinal cohort study Psychol. Med. 1–10 2020 10.1017/S003329172000241X
Sheldon E. Simmonds-Buckley M. Bone C. Mascarenhas T. Chan N. Wincott M. Gleeson H. Sow K. Hind D. Barkham M. Prevalence and risk factors for mental health problems in university undergraduate students: a systematic review with meta-analysis J. Affect. Disord. 287 2021 282 292 10.1016/j.jad.2021.03.054 33812241
Son C. Hegde S. Smith A. Wang X. Sasangohar F. Effects of COVID-19 on college Students' mental health in the United States: interview survey study J. Med. Internet Res. 22 9 2020 e21279 10.2196/21279 32805704
Van de Velde S. Buffel V. van der Heijde C. Çoksan S. Bracke P. Abel T. Busse H. Zeeb H. Rabiee-khan F. Stathopoulou T. Van Hal G. Ladner J. Tavolacci M. Tholen R. Wouters E. Depressive symptoms in higher education students during the first wave of the COVID-19 pandemic. An examination of the association with various social risk factors across multiple high- and middle-income countries SSM - populationHealth 16 2021 100936 10.1016/j.ssmph.2021.100936
Van de Velde S. Buffel V. Bracke P. Van Hal G. Somogyi N. Willems B. Wouters E. The COVID-19 international student well-being study Scand. J. Public Health 49 1 2021 114 122 33406995
Vilagut G. Forero C.G. Barbaglia G. Alonso J. Screening for depression in the general population with the Center for Epidemiologic Studies Depression (CES-D): a systematic review with meta-analysis PloS one 11 5 2016 e0155431 10.1371/journal.pone.0155431
Wang X. Hegde S. Son C. Keller B. Smith A. Sasangohar F. Investigating mental health of US College students during the COVID-19 pandemic: cross-sectional survey study J. Med. Internet Res. 22 9 2020 e22817 10.2196/22817
Zeng W. Chen R. Wang X. Zhang Q. Deng W. Prevalence of mental health problems among medical students in China: a meta-analysis Medicine (Baltimore) 98 18 2019 e15337 10.1097/md.0000000000015337
Zimmermann M. Bledsoe C. Papa A. The impact of the COVID-19 pandemic on college student mental health: a longitudinal examination of risk and protective factors 2020 PsyArXiv Web | 36220498 | PMC9547656 | NO-CC CODE | 2022-10-14 23:25:05 | yes | J Affect Disord. 2023 Jan 1; 320:568-575 |
==== Front
J Gen Intern Med
J Gen Intern Med
Journal of General Internal Medicine
0884-8734
1525-1497
Springer International Publishing Cham
36253633
7784
10.1007/s11606-022-07784-y
Original Research
Doctors’ and Patients’ Perceptions of Impacts of Doctors’ Communication and Empathy Skills on Doctor–Patient Relationships During COVID-19
https://orcid.org/0000-0003-0588-9120
Wang Yanjiao 12
https://orcid.org/0000-0003-4454-7502
Wang Peijuan 3
https://orcid.org/0000-0002-9110-8361
Wu Qing 4
https://orcid.org/0000-0002-5265-9224
Wang Yao 1
http://orcid.org/0000-0003-3141-2174
Lin BingJun 5
http://orcid.org/0000-0001-9214-4930
Long Jia 1
https://orcid.org/0000-0002-7784-1778
Qing Xiong 6
http://orcid.org/0000-0002-6309-2366
Wang Pei PhD [email protected]
21
1 https://ror.org/02n96ep67 grid.22069.3f 0000 0004 0369 6365 Faculty of Education, East China Normal University, Shanghai, China
2 https://ror.org/036prgm77 grid.443487.8 0000 0004 1799 4208 School of Teacher Education, Honghe University, Mengzi, China
3 https://ror.org/03rc6as71 grid.24516.34 0000 0001 2370 4535 School of Foreign Languages, Tongji University, Shanghai, China
4 https://ror.org/03cd4ja39 grid.464358.8 0000 0004 6479 2641 College of Education, Lanzhou City University, Lanzhou, China
5 Psychological Counseling Center, Fujian Vocational College of Art, Fuzhou, China
6 The Fourth People’s Hospital of Huaihua, Huaihua, China
17 10 2022
2 2023
38 2 428433
14 4 2022
6 9 2022
© The Author(s), under exclusive licence to Society of General Internal Medicine 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Background
During the COVID-19 pandemic, the performance of Chinese doctors may have led to improved doctor–patient relationships (DPRs). However, it is unclear how doctors and patients perceived the impact of doctors’ communication and empathy skills on DPRs during the COVID-19 pandemic.
Objective
To examine the perceptions of doctors and patients on how doctors’ communication skills and empathy skills influence DPRs during COVID-19.
Main Measures
Doctors’ and patients’ perceptions of doctors’ communication skills were measured using the Chinese version of the SEGUE Framework. To measure empathy skills and DPRs, the Jefferson Scale of Empathy and Difficult Doctor-Patient Relationship Questionnaire were administered to doctors, and the Consultation and Relational Empathy Measure and Patient-Doctor Relationship Questionnaire were administered to patients.
Results
A total of 902 doctors and 1432 patients in China were recruited during the pandemic via online or offline surveys (overall response rate of 69.8%). Both doctors and patients rated doctors’ empathy skills as more impactful on DPRs than communication skills. Doctors believed that only their empathy skills influenced DPRs. But patients believed that there was a significant bi-directional relationship between doctors’ communication and empathy skills and these two skills interacted to directly and indirectly influence DPRs, and doctors’ empathy had a greater mediating effect than their communication.
Conclusions
During COVID-19, there were both similarities and differences between Chinese doctors’ and patients’ views on how doctors’ communication and empathy skills influenced DPRs. The greater effect of doctors’ empathy skills suggests that both doctors and patients attach more importance to doctors’ empathy in doctor–patient interactions. The bi-directional effect on patient outcomes suggests that both doctors’ communication and empathy skills are important to patients’ perceptions of DPRs.
KEY WORDS
Doctor–patient relationship
Doctors’ communication skills
Doctors’ empathy skill
COVID-19
China
Major bidding projects for the National Social Sciences Fund of China(17ZDA327 issue-copyright-statement© Society of General Internal Medicine 2023
==== Body
pmcINTRODUCTION
Doctors’ interpersonal skills, such as communication ability1 and empathy,2,3 are key factors in the doctor–patient relationship (DPR). According to the communication accommodation theory (CAT), 4,5 and the Russian doll model of empathy,6 the quality of this relationship is enhanced when a doctor’s communication skills7,8 and empathic ability9,10 improve. However, patients’ and doctors’ perspectives on doctors’ communication11,12 and empathy skills13,14 differ. While doctors may believe that they express empathy, patients often disagree.15 In addition, the reciprocal relationship between doctors’ communication and empathy skills is supported by theoretical models, including empathic neural responses,16 and empathic communication.17 There is also empirical evidence for this relationship. Norfolk et al.18 conducted a study using semi-structured interviews and found a significant relationship between doctors’ communication ability and empathy skills: patient-centered communication is based on an understanding of the patients’ viewpoint. Appropriate summaries and responses to patients’ feelings can improve doctors’ empathic understanding. Schrooten and de Jong19 also found that doctor’s communication skills and empathic abilities complemented each other. Doctors’ empathic responses to patients can promote increased patient communication and positive communication behavior can enhance doctors’ empathy. Intervention studies have found that improving doctors’ empathy improves communication,6 and training in communication skills enhances empathy.20,21 It follows then that the complementary effects that doctors’ communication abilities and empathy skills have on each other are a mechanism for improving patient satisfaction and the DPR.18,19 Thus, this study aimed to investigate the impact of doctors’ communication skills and empathic abilities on the DPR during the coronavirus disease of 2019 (COVID-19) pandemic.
METHODS
The study is primarily based on research by Wang et al.,22 and Norfolk et al.’s model and viewpoint18: In doctor–patient interactions, the relationship between doctors’ communication and empathy skills is mutual. We propose a bi-directional model (Fig. 1), testing the hypothesis that there is a bi-directional relationship between doctors’ communication and empathy skills. Figure 1 Bidirectional relationship.
Study Design and Participants
This study utilized a cross-sectional design with a convenience sample taken from the Chinese population. Doctor and patient data were collected through online and offline surveys. Participating doctors filled out questionnaires online and patient participants completed written surveys before their appointments with their doctors. We included patients (1) that were older than 18 years, (2) that could consent to participation, and (3) who could read Chinese. None of the participants received any compensation. The surveys were conducted between January and April 2020. The study was approved by the Ethics Committee of the Shanghai Normal University.
Measures
Doctors’ Communication Skills
Doctors’ evaluation of their own communication abilities was measured using the 25-item Chinese version of SEGUE Framework. This scale was developed by Makoul23 and was revised in China in 2017.24 It has five dimensions: preparation, requesting information, providing information, understanding the patient, and ending the consultation. Each item is scored on a 5-point scale where 1 = never and 5 = all the time. The patients’ evaluation of the doctors’ communication skills was consistent with doctors’ communication scale in terms of content and scoring. Higher scores represented higher ratings of doctors’ communication skills by patient participants. In our study, Cronbach’s alpha coefficients for the two scales were .95 and .96, respectively.
Doctors’ Empathy Skills
Doctors’ evaluation of their own empathic abilities was measured using the 20-item Chinese version of the Jefferson empathy scale.25 Patients’ evaluation of doctors’ empathic abilities was measured using the 10-item Chinese version of the Consultation and Relational Empathy Scale.26 For both scales, higher scores represented better ratings of doctors’ empathy skills. In this study, Cronbach’s alpha coefficients for the two scales were .82 and .92, respectively.
Doctor–Patient Relationship
The doctors’ evaluation of the doctor–patient relationship was measured using the 10-item Chinese version of the Difficult Doctor Patient Relationship Questionnaire.27 It comprised three dimensions, namely doctors’ subjective perceptions, objective manifestations of patient behavior, and combining patient behavior and doctors’ subjective responses to patients’ symptoms. The patients’ evaluation of the doctor–patient relationship was measured using the 15-item Patient-Doctor Relationship Questionnaire.27 Its three dimensions are patients’ satisfaction, doctors’ approachability, and doctors’ attitude. Both scales indicated that higher scores represented better quality doctor–patient relationships. In our study, Cronbach’s alpha coefficients for the two scales were .77 and .94, respectively.
Statistical Analysis
Data analysis progressed with SPSS Version 25.0 in three stages. First, we examined demographic characteristics of participants, and descriptive and correlational analyses of main study variables. Second, to yield standardized coefficients, the original data of all variables were normalized as z-scores. After controlling for demographic variables, we conducted two mediation analysis to test whether doctors’ communication skills mediated the relationship between doctors’ empathy skills and DPRs (model 1), and doctors’ empathy skills mediated the relationship between doctors’ communication skills and DPRs (model 2). Thus, the two mediation models were fitted with DPRs as the dependent variable. Third, we further verified the mediated effect of the two models. PROCESS macro for the Statistical Package for the Social Sciences (SPSS)28 was used to calculate a bias-corrected and accelerate bootstrapped confidence interval (CI) (5000 resamples) for the size of each models’ direct effect of independent variables on the outcome (label c), the effect of the independent variable on mediator (label a), and the effect of the mediator on outcome (label b), total effect (label a*b+c) and the indirect effect (label a*b). Significant mediation was indicated by CI of indirect effect that does not contain zero.
RESULTS
Participant Characteristics.
We enrolled 903 doctors, with a mean age of 33.51 years old (SD = 6.22, range =20–79 years), and 1432 patients, with a mean age of 36.09 years old (SD = 7.03, range =18–99 years, Table 1). Table 1 Demographic Characteristics of Doctors (N = 902) and Patients (N = 1432)
Doctors N (%) Patients N (%) χ2
Gender 13***
Male 482 (53%) 656 (46%)
Female 420 (47%) 776 (56%)
Age 200***
18–30 347 (38%) 604 (42%)
31–40 470 (52%) 414 (29%)
41–50 75 (8%) 212 (15%)
51–60 8 (1%) 166 (12%)
>60 3 (0.3%) 36 (3%)
Education level 710***
High school/technical secondary school graduation 17 (2%) 443 (31%)
Junior college 70 (8%) 324 (23%)
Undergraduate 407 (45%) 577 (40%)
Graduate 409 (45%) 89 (6%)
Medical institution grade 50***
Tertiary 711 (79%) 1031 (72%)
Secondary 129 (14%) 160 (11%)
Primary 63 (7%) 242 (17%)
Region 202**
East 144 (16%) 622 (43%)
Central 754 (83%) 787 (55%)
West 5 (1%) 24 (2%)
Note: **p < .01, ***p < .001
Descriptive Statistics and Correlations Among Variables
Correlations showed that doctors’ communication skills and doctor–patient relationship were not significantly related to each other in the doctors’ evaluation, while patients’ evaluations showed that all variables are significantly correlated (Table 2). Table 2 Descriptive and Correlational Analyses of Main Study Variables
Variable Range Min Max M (SD) 1 2
Doctors
Doctors’ communication skills 0~125 68 125 102.53(11.08)
Doctors’ empathy skills 0~100 24 94 67.76(8.54) −.08*
Doctor–patient relationship 0~50 17 46 32.57(5.34) −0.06 .37**
Patients
Doctors’ communication skills 0~125 31 125 94.46 (16.13) -
Doctors’ empathy skills 0~50 13 50 39.06 (5.60) 0.71** -
Doctor–patient relationship 0~75 28 75 58.42 (7.47) 0.72** 0.79**
Note. *p < .05, **p < .01, ***p < .001
Mediation Analyses
Figure 2 presents doctors’ and patients’ views on the effects of doctors’ empathic abilities and communication skills on DPR, respectively, and the effect of doctors’ empathy and communication ability on each other. Figure 2 Standardized regression coefficients among the three variables from doctors’ (a) and patients’ (b) perspectives. The black solid lines represent significant, and the gray dotted lines represent not significant. ***p < .001.
While models based on the doctors’ perspective indicated that only the effect of empathy on DPR was significant (β = 0.37, 95% CI: 0.19–0.47), patients’ evaluations suggested that both empathic ability (β = 0.56, 95% CI: 0.69–0.80) and communication skills (β = 0.33, 95% CI: 0.13–0.17) had an effect on DRP, as well as there being evidence of a relationship between empathy and communication (β = 0.71, 95% CI: 1.9–2.2) and between communication and empathy (β = 0.32, 95% CI: 0.18–0.45).
Table 3 demonstrates the standardized total effects, direct and indirect, associated with each of the three variables. From the doctors’ perspective, we found that empathy had a direct effect on DPR (β =0.37, 95% CI: 0.19–0.47), but there were no indirect effects. In contrast patients reported both direct and indirect effects, including that a doctor’s empathic abilities had a direct effect on DPR (β =0.56, 95% CI: 0.69–0.80) and that a doctor’s communication skills had a direct effect on DPR (β =0.33, 95% CI: 0.18–0.45). We further found that a doctor’s empathic abilities indirectly affected DPR by way of their communication skills and that this effect was significant (a*b =0.23, 95% CI: 0.18–0.28). Doctor’s communication skills were also found to have an indirect effect (a*b = 0.18, 95% CI: 0.16–0.21) on DPR. Table 3 Total, Direct, and Indirect Effects
Effect BCBCI
Label b Lower Upper
Model 1: Doctors’ empathy skills (X) - Doctors’ communication skills (M) - DPR (Y)
Doctors
Total effect a*b+c .37 .19 .47
Direct effect c .37 .19 .47
Indirect effect a*b .002 −.004 .001
Patients
Total effect a*b+c .79 .63 .95
Direct effect c .56 .69 .80
Indirect effect a*b .23 .18 .28
Model 2: Doctors’ communication skills (X) - Doctors’ empathy skills (M) -DPR (Y)
Doctors
Total effect a*b+c −.03 −.06 .10
Direct effect c −.03 −.07 .01
Indirect effect a*b −.003 −.008 .001
Patients
Total effect a*b+c .51 .36 .66
Direct effect c .33 .27 .39
Indirect effect a*b .18 .16 .21
BCBCI bias-corrected bootstrap confidence interval
DISCUSSION
Our findings suggest that both doctors and patients acknowledge that doctors’ empathic abilities are crucial to the DPR. We noted that while doctors presumed that only their empathy skills were important, patients believed that doctors’ empathy skills influenced their communication skills, and vice versa. In addition, we found that patients’ views regarding the DPR were that doctors’ communication skills mediated empathy and vice versa. For patients the mediating effect of doctors’ empathy was more significant than that of the effect of their communication skills, thus highlighting the importance of good empathic abilities for patients.
Our findings are consistent with studies conducted prior to the COVID-19 pandemic22 and supports both the CAT model as well as the Russian Doll model.4,5,29 For patients, doctors’ empathic abilities were found to have influenced their medical care experience.30 Doctors with well-developed empathy skills were able to perceive patients’ emotions accurately31 and were more likely to generate appropriate emotional responses and to express them in a suitable manner to obtain patients’ feedback. This process served to enhance the DPR.32 Thus, this study suggests that the patients perceived a better DPR and regarded the doctors’ empathy skills as better when their communication skills were excellent.
We found that Chinese doctors did not believe that there was a bi-directional relationship between communication skills and empathic ability, but that Chinese patients were of the opinion that these two skills influenced each other, which is consistent with previous research.33,34 Our findings are inconsistent with the results reported by Norfolk et al.18 who found that doctors also believed in a bi-directional relationship. Norfolk et al.’s study participants were British practitioners and they made use of qualitative methods. Our sample included Chinese practitioners and we utilized surveys to determine the extent and strength of the relationships. It is unclear whether our results differ from those of Norfolk et al. due to a difference in the viewpoints and culture of the doctors or if quantitative exploration would corroborate British doctor’s belief in a bi-directional relationship.
Our study has a number of limitations. First is cross-sectional data and relies on surveys. Longitudinal data would be useful to corroborate this relationship. Second, we did not include variables that have previously been demonstrated to impact perspectives on interactions, including visit duration We also did not directly observe the interactions to corroborate either the patient or provider perception of interaction quality and have no information on specific behaviors that may influence perception.
Despite these limitations, our results supported doctor and patient perceptions that doctors’ empathy skills are important to the DPR and both doctors’ communication and empathy skills influence patients’ perceptions of DPRs. Given that training can improve doctor–patient interactions,35,36 Chinese medical schools should incorporate training in interpersonal communication skills.
Author Contribution
Yanjiao Wang and Peijuan Wang were responsible for study conception and methodology. Yanjiao Wang, Qing Wu, and Yao Wang were responsible for data collection and cleaning. Yao Wang, Bingjun Lin, and Jia Long were responsible for analysis and interpretation. Xiong Qing is responsible for collecting and organizing the raw data. PW performed validation, investigation, resources, writing, reviewing, and editing of the manuscript, supervision, project administration, funding acquisition, and final approval of the version to be published. All authors were responsible for manuscript writing and editing.
Funding
This research was supported by Major bidding projects for the National Social Sciences Fund of China (17ZDA327).
Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and Consent to participate
This study was approved by the local ethics committee of Shanghai Normal University (The IRB number is 18015SJD008) and was conducted in accordance with the Declaration of Helsinki (2013). All participants were informed before the investigation began.
Conflict of interest
The authors declare that they do not have a conflict of interest.
Yanjiao Wang and Peijuan Wang are co-first authors.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Change history
11/9/2023
This article has been retracted. Please see the Retraction Notice for more detail: 10.1007/s11606-023-08499-4
==== Refs
References
1. Makoul G Curry RH The value of assessing and addressing communication skills JAMA. 2007 289 9 1057 1059 10.1001/jama.298.9.1057
2. Wu Q, Jin ZY, Wang P. The relationship between the physician-patient relationship, physician empathy, and patient trust. J Gen Intern Med. 2021;8(26). 10.1007/s11606-021-07008-9
3. Jensen K Gollub RL Kong J Reward and empathy in the treating clinician: the neural correlates of successful doctor-patient interactions Transl Psychiatry. 2020 10 1 17 10.1038/s41398-020-0712-2 32066692
4. Gallois C Giles H Palmer MT Barnett GA Accommodating mutual influence in intergroup encounters Progress in communication sciences 1998 Stamford, CT Ablex Corporation 135 162
5. Watson BM Gallois C Weatherall A Watson BM Gallois C Language, Discourse, and Communication About Health and Illness: Intergroup Relations, Role, and Emotional Support Language, Discourse and Social Psychology 2007 Basingstoke, UK Palgrave Macmillan 108 130
6. de Waal FBM Putting the altruism back into altruism: the evolution of empathy Annu Rev Psychol. 2008 59 279 300 10.1146/annurev.psych.59.103006.093625 17550343
7. Drossman DA 2012 David Sun Lecture: helping your patient by helping yourself-how to improve the patient-physician relationship by optimizing communication skills Am J Gastroenterol. 2013 108 521 8 10.1038/ajg.2013.56 23511457
8. Drossman DA Chang L Deutsch JK A review of the evidence and recommendations on communication skills and the patient-provider relationship: a Rome Foundation Working Team report Gastroenterology 2021 161 5 1670 1688 10.1053/j.gastro.2021.07.037 34331912
9. Anderson PF Wescom E Carlos RC Difficult doctors, difficult patients: building empathy J Am Coll Radiol. 2016 13 1590 8 10.1016/j.jacr.2016.09.015 27888946
10. Garcia D Bautista O Venereo L Training in empathic skills improves the patient-physician relationship during the first consultation in a fertility clinic Fertil Steril. 2013 99 1413 1418 10.1016/j.fertnstert.2012.12.012 23294674
11. Guo A Wang P The current state of doctors’ communication skills in Mainland China from the perspective of doctors’ self-evaluation and patients’ evaluation: a cross-sectional study Patient Educ Couns. 2021 104 1674 80 10.1016/j.pec.2020.12.013 33384190
12. Kenny DA Veldhuijzen W Weijden T Interpersonal perception in the context of doctor-patient relationships: a dyadic analysis of doctor-patient communication Soc Sci Med. 2010 70 763 768 10.1016/j.socscimed.2009.10.065 20005618
13. Hermans L Olde Hartman TC Dielissen PW Differences between GP perception of delivered empathy and patient-perceived empathy: a cross-sectional study in primary care Br J Gen Pract. 2018 68 e621 e6 10.3399/bjgp18X698381 30012809
14. Katsari V Tyritidou A Domeyer PR Physicians’ self-assessed empathy and patients’ perceptions of physicians’ empathy: validation of the Greek Jefferson Scale of Patient Perception of Physician Empathy Biomed Res Int. 2020 12 9379756 10.1155/2020/9379756
15. Schwartz R Dubey M Blanch-Hartigan D Physician empathy according to physicians: a multi-specialty qualitative analysis Patient Educ Couns. 2021 104 10 2425 31 10.1016/j.pec.2021.07.024 34330597
16. Singer T Seymour B O'Doherty JP Empathic neural responses are modulated by the perceived fairness of others Nature. 2006 439 7075 466 9 10.1038/nature04271 16421576
17. Platt FW Keller VF Empathic communication: a teachable and learnable skill J Gen Intern Med. 1994 9 4 222 6 10.1007/bf02600129 8014729
18. Norfolk T Birdi K Walsh D The role of empathy in establishing rapport in the consultation: a new model Med Educ. 2007 41 7 690 7 10.1111/j.1365-2923.2007.02789.x 17614890
19. Schrooten I de Jong MD If you could read my mind: the role of healthcare providers’ empathic and communicative competencies in clients’ satisfaction with consultations Health Commun. 2017 32 1 111 118 10.1080/10410236.2015.1110002 27177385
20. Bonvicini KA Perlin MJ Bylund CL Impact of communication training on physician expression of empathy in patient encounters Patient Educ Couns. 2009 75 1 3 10 10.1016/j.pec.2008.09.007 19081704
21. Yamada Y Fujimori M Shirai Y Changes in physicians’ intrapersonal empathy after a communication skills training in Japan Acad Med. 2018 93 12 1821 1826 10.1097/acm.0000000000002426 30134272
22. Wang Y Wu Q Wang Y The effects of physicians’ communication and empathy ability on physician-patient relationship from physicians’ and patients’ perspectives J Clin Psychol Med Settings. 2022 28 1 1 12 10.1007/s10880-022-09844-1
23. Makoul G The SEGUE Framework for teaching and assessing communication skills Patient Educ Couns. 2001 45 1 23 34 10.1016/s0738-3991(01)00136-7 11602365
24. Lijun S Gang S Study on doctor-patient communication Skill evaluation based on SEGUE scale Chinese General Pract. 2017 20 16 1998 2002
25. Hojat M Gonnella JS Nasca TJ Physician empathy: definition, components, measurement, and relationship to gender and specialty Am J Psychiatry. 2002 159 9 1563 1569 10.1176/appi.ajp.159.9.1563 12202278
26. Mercer SW Fung CSC Chan FWK The Chinese-version of the CARE Measure reliably differentiates between doctors in primary care: a cross-sectional study in Hong Kong Bmc Family Practice. 2011 12 43 10.1186/1471-2296-12-43 21631927
27. Hui Y Development and evaluation of Chinese version PDRQ_DDPRQ Scale -- quantitative study of doctor-patient relationship 2011 Shanxi Medical University
28. Hayes AF Introduction to mediation, moderation, and conditional process analysis 2013 New York Guilford
29. Batson CD Ahmad NY Using empathy to improve intergroup attitudes and relations Soc. Issues Policy Rev. 2009 3 1 141 77 10.1111/j.1751-2409.2009.01013.x
30. Chaitoff A Sun B Windover A Associations between physician empathy, physician characteristics, and standardized measures of patient experience Acad Med. 2017 92 10 1464 1471 10.1097/acm.0000000000001671 28379929
31. Jordan KD Foster PS Medical student empathy: interpersonal distinctions and correlates Adv Health Sci Educ Theory Pract. 2016 21 5 1009 1022 10.1007/s10459-016-9675-8 26971115
32. Cano A de C Williams AC. Social interaction in pain: reinforcing pain behaviors or building intimacy? Pain. 2010 149 1 9 11 10.1016/j.pain.2009.10.010 19892466
33. Riess H Kraft-Todd G EMPATHY: a tool to enhance nonverbal communication between clinicians and their patients Acad Med. 2014 89 8 1108 1112 10.1097/acm.0000000000000287 24826853
34. Derksen F Bensing J Lagro-Janssen A Effectiveness of empathy in general practice: a systematic review Br J Gen Pract. 2013 63 606 76 84 10.3399/bjgpbjgp13X660814
35. Ickes W Stinson L Bissonnette V Naturalistic social cognition - empathic accuracy in mixed-sex dyads J Pers Soc Psychol. 1990 59 4 730 742 10.1037/0022-3514.59.4.730
36. Suchman AL Markakis K Beckman HB A model of empathic communication in the medical interview JAMA. 1997 277 8 678 82 10.1001/jama.277.8.678 9039890 | 36253633 | PMC9575631 | NO-CC CODE | 2023-12-07 00:22:48 | yes | J Gen Intern Med. 2023 Feb 17; 38(2):428-433 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
23723
10.1007/s11356-022-23723-0
Research Article
Do renewable energy consumption and green innovation help to curb CO2 emissions? Evidence from E7 countries
http://orcid.org/0000-0003-4584-2036
Hao Yuanyuan [email protected]
1
http://orcid.org/0000-0002-4990-4380
Chen Pengyu [email protected]
2
1 grid.440785.a 0000 0001 0743 511X School of Economics, Jiangsu University of Technology, Changzhou, 213001 China
2 grid.411982.7 0000 0001 0705 4288 Department of Economics, Dankook University, Yongin-si 16890, Korea
Responsible Editor: Ilhan Ozturk
20 10 2022
117
13 7 2022
15 10 2022
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Global climate change is profoundly affecting human survival and development and is a major challenge facing the international community today. Therefore, this study aims to examine the effect of renewable energy consumption and green innovation on CO2 emission reduction in E7 countries within the framework of macroeconomic indicators, and whether they can contribute to achieving carbon neutrality targets. To achieve the purpose of the study, firstly, the fully modified OLS, dynamic OLS, classical cointegration regression, Bayer–Hanck cointegration, and ARDL bounds test are employed in this study. The existence of a long-term cointegration or long-term linkage is confirmed by empirical evidence. Secondly, the empirical outcomes of FMOLS, DOLS, and CCR reveal that a 1% increase in renewable energy consumption and financial innovation reduces the CO2 emissions by 0.357% (0.301%), 0.428% (0.336%), and 0.348% (0.306%), while a 1% rise in economic growth and inflation raises the CO2 emissions by 0.881% (0.015%), 0.946% (0.043%), and 0.875 (0.022%), respectively. Similarly, the results of ARDL demonstrate that renewable energy consumption and financial innovation contribute to the improvement of environmental quality, while economic growth and inflation exacerbate the deterioration of environmental quality. However, green innovation has no apparent impact on environmental sustainability. Finally, in the short term, the paths of renewable energy consumption and economic growth on environmental sustainability under macroeconomic conditions are almost identical to those in the long term, while green innovation significantly improves the environmental quality of economic development in E7 countries. To sum up, to achieve sustainable economic and environmental development in the context of carbon neutrality, policy makers in developing countries should fully consider the role of renewable energy and green innovation, and actively strive to promote green and low-carbon energy development, to make new contributions to global environmental governance.
Keywords
Renewable energy consumption
Green innovation
Economic growth
Environmental sustainability
E7 countries
==== Body
pmcIntroduction
In recent years, researchers in energy, environment, and science have increasingly discussed the challenging implications of climate change for future human well-being, economic growth, and environmental sustainability in the context of global warming (Kirikkaleli and Adebayo 2021a). What we all know is that greenhouse gases (e.g., CO2, CH4 and N2O) are the main cause of global warming, which not only causes various environmental problems, but also reduces the carrying capacity of the earth and hinders sustainable development (Salem et al. 2021). In addition to the greenhouse effect, it is widely believed that this climate change risk is directly and closely related to the large-scale burning of fossil fuel energy, so it has attracted widespread attention from the perspective of economic and environmental sustainability (Martins et al. 2021). According to the 2020 Emissions Gap Report released by the United Nations Environment Programme, despite the reduction in CO2 emissions in 2020 due to the COVID-19 outbreak, the concentration of the main greenhouse gas (CO2) produced in the atmosphere continues to rise in both 2019 and 2020, leading to a global temperature rise of more than 3 °C (Hao, 2022a). For the global temperature continues to rise, it is likely to lead to catastrophic weather events, ozone depletion, and ecosystem degradation, which will pose a serious threat to human production and life. In response, Inger Andersen, Executive Director of the United Nations Environment Programme, also said that as economic globalization continues, the impact of CO2 emissions on the environment will continue to intensify and deal the heaviest blow to sustainable development in developing and less developed countries. Therefore, researchers and decision-makers from many nations have underlined the significance of lowering greenhouse gas (GHG) emissions in order to prevent the calamity that global warming brings to human society (Amran et al. 2014; Anwar et al. 2022a), but how to reduce greenhouse gas emissions has become a pressing issue for countries around the world.
Carbon dioxide emissions have had a key impact on ecological environment and sustainable economic development throughout the history of human development, yet there is still a great deal of debate regarding global climate change (Hotak et al., 2020). According to the United Nations Environment Programme’s 2020 Emissions Gap Report, although the Covid-19 pandemic has quickly reduced CO2 emissions, overall global carbon emissions are still at high levels and the concentration of carbon dioxide emissions continues to increase (Kang 2021). If left unattended, future catastrophic weather events, ozone depletion, and ecological degradation are all anticipated to result from this, posing grave risks to human life and production (Hasnisah et al., 2019). In this context, the international community is engaged in a heated debate on carbon neutrality in response to the climate crisis. Carbon neutrality ensures that net carbon emissions from economic activities are zero, so that the concentration of CO2 emissions no longer increases, also known as “net zero emissions.” In real life, carbon neutrality can be achieved through afforestation, energy conservation and emission reduction, and the use of negative emission technologies (e.g., biochar and bioenergy with CO2 capture and storage) to offset the carbon dioxide or greenhouse gas emissions generated by oneself, achieving positive and negative offsetting, and further achieving the goal of relative “zero emissions” (see Fig. 1).Fig. 1 Carbon neutrality in today’s climate (Sustainability Report 2015)
Nevertheless, in the context of globalization, greenhouse gas emissions (e.g., carbon dioxide) are an inevitable consequence of a country’s economic activities, while energy consumption is the core of economic growth (Saidi and Hammami 2015; Tong et al. 2020; Kang 2021). According to the “2020 BP World Energy Statistics” and “Global Energy and Carbon Dioxide Status Report,” global carbon emissions mainly come from CO2 emitted from the combustion of fossil fuels in various economies around the world. Global energy consumption grew by 1.3% in 2019, almost double the average growth rate since 2010 (IEA 2020), but less than half the 2.8% increase in 2018, with China and India accounting for more than 80% of this growth. On the other hand, the global economy grew by 2.6% in 2019, compared with 3.7% in the previous year and an average growth of 3.2% in the previous 10 years, driving the increase in global energy demand, especially for fossil fuel energy. Fossil fuel energy consumption accounted for 78.9% of total global energy consumption in 2019, an increase of 0.7% compared to 2018. Among them, 7 emerging countries (Brazil, China, India, Indonesia, Mexico, Russia, and Turkey) account for more than 42% of fossil fuel consumption, far exceeding the G7 countries (Dong et al. 2020). In addition, according to the “Global CO2 Emissions Report 2019,” the economic growth rate of developed economies averaged 1.7% in 2019, but total energy-related CO2 emissions fell by 3.2%. However, the electric power sector has led the decline resulting in currently 36% of energy-related emissions across developed economies, but more than 47% of CO2 emissions in E7 countries (IEA 2020). As a result, the E7 countries are playing an increasing role in the world energy market and climate change, both in terms of CO2 emissions and energy consumption (see Figs. 2 and 3). Based on the above, the current challenge for E7 countries is to find reliable and affordable energy sources to replace fossil fuel energy sources while reducing greenhouse gas emissions (Jaforullah and King 2015).Fig. 2 Portion of E7 countries in total CO2 emissions (BP-Statistics 2021)
Fig. 3 Portion of E7 countries in total energy consumption (BP-Statistics 2021)
There are several reasons why we focused our study on E7 countries. Firstly, since 2010, the economic development of emerging market economies (e.g., E7 countries) has promoted the rapid growth of energy consumption and CO2 emissions, becoming the main driving force for the future growth of global CO2 emissions (Anwar et al. 2022b; Ojekemi et al. 2022). However, E7 countries generally face the problems of different accounting calibers, imperfect scales, and inconsistent methodologies for CO2 emissions inventories. The lack of basic data has also become a major obstacle for studying the characteristics of CO2 emissions in E7 countries, which limits the research and policy discussion on the low-carbon development path of E7 countries to a certain extent. Secondly, the E7 countries are the seven developing countries in the G20 with relatively high economic growth rates, high economic openness, broad representation, and comparable economic and population size. In the past three decades, the E7 countries have been particularly prominent in terms of their share of global output, becoming the new engines of global economic growth and green innovation, but at the same time the fastest growing group in terms of global energy consumption and CO2 emissions. Thirdly, compared with developed and developing economies, these countries have developed faster in industrialization, and the excessive consumption of energy in the process of producing a large number of goods and services has led to increased environmental pollution. To reduce CO2 emissions, E7 countries have intensified their investments in green innovation and renewable energy in the last decades. In particular, green innovation and renewable energy investments in China and India have grown by more than 38% and 42%, while investments in all emerging countries have also grown by more than 16% and 30%. Finally, according to PwC’s forecast (The World in 2050, 2017), the average annual economic growth rate of the E7 countries by 2050 is more than twice that of the G7 countries. For example, the seven largest emerging market countries (E7), namely, China, Brazil, India, Indonesia, Mexico, Russia, and Turkey, will have an average annual economic growth rate of 3.5%, but the seven largest industrial countries (G7), namely, the USA, the UK, Canada, France, Germany, Italy, and Japan, will have an average annual economic growth rate of only 1.6%. In addition, the report also states that by 2050, E7 countries will account for half of the global economy in terms of GDP levels, while the share of G7 countries will shrink to 20%. Therefore, the study of the correlation between renewable energy consumption, green innovation, economic growth, and environmental pollution has significant theoretical and practical significance for promoting sustainable development of 7 emerging market economics, which will provide a certain reference value for helping other emerging market economics formulate emission reduction policies.
The rest of this paper is structured as follows. After this introductory section (“Introduction”), “Literature review” provides a critical review of previous studies on the renewable energy, green innovation, economic growth, and CO2 emissions. “Methodology and data” presents the data and discusses the methodology. The empirical results and discusses are set out in “Empirical results and discussions.” Finally, “Conclusions and policy implications” concludes by setting out the key findings and wider policy implications.
Literature review
In recent years, the problems caused by the continuous increase in global warming have seriously affected socioeconomics, human health, population migration, food security, and terrestrial and marine ecosystems. Therefore, this chapter is devoted to summarizing (reviewing) literature on the impact of renewable energy consumption, green innovation, and economic growth on CO2 emissions (see Fig. 4).Fig. 4 The conceptual framework
Relationship between renewable energy consumption and CO2 emissions
Since the 1990s, the massive emissions of greenhouse gases, mainly carbon dioxide, have led to a frequent occurrence of extreme weather around the world. As a result, the debate among researchers around the world on the accelerated development of renewable energy and its impact on environmental quality has been intense and has not reached a consensus so far (Kahia et al. 2017; Bai et al. 2021; Salem et al. 2021; Djellouli et al. 2022). From the perspective of global climate change, the use of renewable energy is considered to be the most effective and direct means of reducing atmospheric CO2 concentrations and has a significant impact on environmental sustainability (Kirikkaleli and Adebayo 2021a, b; Sun et al. 2022). However, the empirical results are mixed due to the characteristics of adopted technology, key countries or regions, research cycle, and economic variables. For example, the consumption of renewable energy significantly reduces CO2 emissions, according to Omri and Nguyen (2014), who looked at the factors influencing this consumption in 64 nations between 1990 and 2011. To this end, Anwar et al. (2022a) and Yunzhao (2022) found a positive contribution to carbon emission reduction by examining the role of renewable energy in carbon reduction in E7 countries. This is consistent with the findings of Lei et al. (2022) for China and Ojekemi et al. (2022) for the BRICS countries, but they found that negative shocks to renewable energy consumption lead to increased pollution emissions in the long term. On the contrary, Padhan et al. (2020) used the Machado and Silva Panel quantile regression method to study the relationship between renewable energy consumption and CO2 emissions in OECD countries from 1970 to 2015. They have found a long-term association between renewable energy consumption and per capita carbon emissions, and that per capita carbon emissions had a positive effect on renewable energy consumption in these countries. Moreover, Djellouli et al. (2022) concluded that there is cross-independence between carbon emissions and renewable energy and that renewable energy has a significant negative impact on CO2 emissions. In addition, some researchers have also found a feedback causal relationship between renewable energy consumption and CO2 emissions. For example, Musah et al. (2020) explored the relationship between carbon emissions and renewable energy consumption in West African countries during 1990–2018 using the CCEMG and DCCEMG estimation methods. They have found a feedback causal relationship between renewable energy and CO2 emissions in West African countries. Çıtak et al. (2021) used a NARDL estimation method to explore the asymmetric impact of renewable energy on carbon dioxide emissions in selected ten most populous states in the USA over the period 1997–2017. They have found a long-term relationship between renewable energy and CO2 emissions in the eight states used in the research and support the feedback hypothesis. On the other hand, from a non-economic standpoint, the development of green and clean energy offers a number of economic and environmental benefits. These economic advantages include, but are not limited to, addressing a variety of issues, including energy security and portfolio diversification, as well as job creation, as renewable energy is more labor- and technology-intensive than the non-renewable energy sector (Blazejczak et al., 2014). Therefore, we propose the following research hypothesis:Hypothesis 1. Renewable energy consumption significantly reduces CO2 emissions in E7 countries.
Relationship between green innovation and CO2 emissions
The sustainable global economy is severely constrained by the increasing environmental pollution and greenhouse effect (Li et al. 2020). Therefore, under the background of “carbon neutrality,” governments around the world have formulated corresponding environmental regulations to reduce pollutant emissions to promote environmental technology innovation, industrial structure upgrade, and sustainable development of green economy (Hsu et al. 2021). However, Brunel (2019) suggested that in addition to measuring environmental policy, innovation needs to be measured. For example, Chen et al. (2006) explored the impact of green innovation on competitive advantage, which they consider to be equivalent to hardware or software innovation related to green products or green processes. Regardless of the way in which green innovation is defined, the general end purpose is related to technological progress with environmental effects, in the form of new products or processes that contribute to environmental protection and sustainable development (Liu et al. 2022a, b).
The available literature has extensively explored green product innovation from an environmental sustainability perspective (Luttropp and Lagerstedt 2006; Dangelico 2016). Mensah et al. (2019) used the ARDL method to examine the impact of innovation on CO2 emissions in OECD countries, and found that green innovation reduced CO2 emissions to a certain extent and supported the feedback hypothesis. However, Liu et al. (2022a, b) argue that the emission reduction effect of green innovation (environmental innovation) is only significant at higher levels of carbon emissions. Based on the above research, Li et al. (2022) used the NARDL method to examine the impact of green innovation on China’s CO2 emissions from 1991 to 2019, and found that green innovation plays an important role in creating jobs, improving green economic activities and enhancing environmental sustainability. Moreover, in the long term, the improvement of green innovation will reduce China’s CO2 emissions, whereas the decline in green innovation will increase China’s carbon dioxide emissions. Similarly, Doğan et al. (2022) examined the impact of green innovation on environmental quality in E7 and G7 countries using an augmented model, which found that green innovation reduced carbon emissions in G7 countries, but exacerbated environmental problems in E7 countries. This view is supported by the findings of Yuan et al. (2021), Habiba et al. (2022), Khattak et al. (2020), and You et al. (2022) for China, Top 12 Carbon Emitters, OECD countries, and the USA, respectively, which vary widely among countries. Furthermore, Ganda (2019) used the GMM estimation method to examine the impact of green innovation and technology investment on CO2 emissions in selected OECD economics from 2000 to 2014 and found that green innovation and technology investments in these countries have different impacts on CO2 emissions and still have the potential to reduce environmental quality. Khattak et al. (2020) investigated the complex interplay between green innovation and CO2 emissions in BRICS economies from 1980 to 2016 within the framework of the environmental Kuznets curve. They have found that green innovation activities in China, India, Russia, and South Africa had a suppressive effect on CO2 emissions, but had a positive impact on CO2 emissions in Brazil. Therefore, we propose the following research hypothesis:Hypothesis 2. Green innovation significantly reduces CO2 emissions in E7 countries.
Relationship between economic growthand CO2 emissions
Reviewing some past research efforts, researchers in various countries have mainly focused on studying the relationship between CO2 emissions and economic growth in a single context, especially CO2 emissions due to energy use, but the empirical results are contradictory. For example, Xiong and Xu (2021) used the ARDL bounds test method to examine the relationship between energy use, economic growth, and environmental pollution in China from 1995 to 2015. The research found that industrial growth has a positive impact on CO2 emissions and environmental degradation is caused by economic growth, which are consistent with the findings of Wang et al. (2016) for China, but they argued that the impact of CO2 emissions shocks on economic growth or energy consumption is marginal. Not only that, some researchers have also found the “EKC” hypothesis between environmental pollution and economic growth, especially the inverted “U-shaped” relationship between CO2 emissions and income levels. For example, Danish et al. (2019) and El Menyari (2021) used the environmental Kuznets curve (EKC) model to examine the relationship between economic growth and CO2 emissions in the BRICS and four North African countries (namely, Morocco, Algeria, Tunisia), respectively. The study found an inverted “U-shaped” relationship between environmental quality and real income (income per capita), but the relationship was initially negative. However, the findings of Laverde-Rojas et al. (2021) for Colombia reject this view, who argue that for developing countries like Colombia, the “EKC” hypothesis does not exist and does not benefit from increased economic complexity. Moreover, some researchers also found that the balance between environmental protection and economic growth has been a great challenge for policy makers (Zheng et al. 2015). Liu et al. (2020) examined the relationship among economic growth, governance, and carbon dioxide emissions within the framework of the “EKC” in five high carbon dioxide–emitting countries from 1996 to 2017, and found that political, economic, and institutional governance can improve the quality of the environment. Espoir et al. (2022) used panel and time-series techniques to examine the heterogeneous effects of CO2 emissions and temperature on incomes in Africa from 1995 to 2016. It was found that environmental policies specifically designed to reduce CO2 emissions across Africa could have a significant impact on production in the long term. As such, they emphasize country-specific policies aimed at sustained reductions in CO2 emissions in Africa, rather than global climate policies. Therefore, we propose the following research hypothesis:Hypothesis 3. Economic growth positively moderates green innovation’s reduction of CO2 emissions in E7 countries.
In summary, researchers in energy, environment, and science have done a lot of research on the relationship among renewable energy consumption, green innovation, economic growth, and environmental pollution, but there are still some deficiencies: (1) Existing studies typically use a single variable to examine the effects of renewable energy consumption, green innovation, and economic growth on CO2 emissions, but ignore the impact of macroeconomic variables on CO2 emissions. (2) Most studies only focus on the direct effects of renewable energy consumption, green innovation, and economic growth on CO2 emissions, and do not reveal the long-term and short-term dynamic effects of renewable energy consumption, green innovation, and economic growth on CO2 emissions under the framework of the EKC hypothesis. Based on this, an in-depth analysis of the relationship between green innovation and environmental sustainability in the economies of E7 countries in a two-carbon context is necessary, an issue that has previously been neglected in existing studies. Importantly, the current study captures the joint effects of green innovation, renewable energy, and economic growth on CO2 emissions. Therefore, this study aims to fill the literature gap on the impact of economic growth, green innovation, and renewable energy on CO2 emissions in a global context. From a macroeconomic perspective, the long-term interconnection between economic growth, green innovation, and renewable energy with CO2 emissions in E7 countries from 1990 to 2020 is examined respectively with OLS, FMOLS, DOLS, and CCR estimators. Likewise, it employs the ARDL bounds test to examine the effects of differentiating short-term and long-term at different frequencies, which will help policy makers formulate sound environment-related policies for the sustainable development of “energy-green economy innovation-environment” in individual countries. The conceptual framework in this study is shown in Fig. 4.
Methodology and data
Econometrics methodology
Unit-root test
To avoid the “pseudo-regression” problem, it is necessary to test the data for stationarity, and different methods of testing for stationarity may lead to different results (Hao, 2022a). For this purpose, four unit root tests (the null hypothesis of the KPSS test is that the sequence has a unit root, and the null hypothesis of the other three tests is that the sequence does not have a unit root) were used in this study, namely, Dickey and Fuller (1979, ADF), Phillips and Perron (1988, PP), Kwiatkowski et al. (1992, KPSS), and Elliott et al. (1992, DF-GLS), which can make the test results more convincing. Since the ADF test and PP test may be ineffective for small-sample data, the KPSS stationarity test is more effective for small samples when choosing lower lag truncation parameters (Sabuhoro and Larue 1997). Therefore, this study will take advantage of the KPSS statistic to judge the stationarity of the data, and the test equation is as follows.1 yt=α+βt+d∑i=1tui+εt,t=1,2,⋯,T
Bayer and Hanck combined cointegration
Intending to get rid of the defects of traditional cointegration test methods, Bayer and Hanck (2013) proposed a new method of combined cointegration test, namely, the Bayer and Hanck (2013) combined cointegration test method (Rjoub et al., 2021), which is an extended combination of the cointegration test methods proposed by Engle and Granger (1987), Johansen (1991), Boswijk (1994), and Banerjee et al. (1998). As stated by Kirikkaleli and Adebayo (2021a), the Bayer–Hanck combined cointegration test method is mainly to eliminate unnecessary multiple testing techniques, thereby giving effective estimates for the typical problems arising from other combined cointegration tests. Moreover, Bayer and Hanck (2013) combined the statistical significance levels of individual combined cointegration tests to enhance test reliability and accuracy by using Fisher’s formula when building a combined cointegration test model (Olanipekun and Usman 2019). Individual combined cointegration tests are written in the following form.2 EG-JOH=-2lnpEG+ln(pJOH)
3 EG-JOH-BO-BDM=-2lnpEG+lnpJOH+lnpBO+ln(pBDM)
where pEG, pJOH, pBO, and pBDM are the p-values of the combined cointegration tests of Engle and Granger (1987), Johansen (1991), Boswijk (1994), and Banerjee et al. (1998), respectively. Bayer and Hanck (2013) believed that when the calculated Fisher statistic is greater than the B–H critical value, the null hypothesis is rejected, that is, there is no cointegration relationship.
Long-term relationship: combined cointegration vector FMOLS, DOLS, and CCR estimation
Aiming at testing whether there is a long-term combined cointegration relationship among variables, it is necessary to estimate the combined cointegration equation. The most commonly used combined cointegration estimation method is OLS, but if the explanatory variables are endogenous or the regression error term is serial correlation, the parameters estimated by OLS are biased (second-order bias consisting of endogenous bias and non-centrality bias). To solve the problem caused by the OLS parameter estimation, Phillips and Hansen (1990) modified the OLS estimator with a nonparametric method, which is called FMOLS (fully modified OLS, fully modified least squares). Phillips (1995) and Kitamura and Phillips (1997) further extended FMOLS, and Park (1992) proposed canonical cointegrating regression (CCR), which is like FMOLS in that it uses a nonparametric method to revise the OLS estimator and uses a different method to eliminate non-centrality bias. Phillips and Loretan (1991), Stock and Watson (1993) modified the parameters of the OLS estimator by using the lead and lag periods of the first-order differences of the I(1) variables as explanatory variables. On this basis, Saikkonen (1991) proved that FMOLS, DOLS, and CCR estimators are asymptotically efficient estimators.
ARDL bounds test
Besides the Bayer and Hanck (2013) combined cointegration test, the ARDL bounds test developed by Pesaran et al. (2001) can be adopted to detect whether there is a long-term combined cointegration relationship among variables (Rjoub et al., 2021), which is similar to the traditional combined cointegration test. Compared with the traditional combined cointegration test, it has the following advantages. Firstly, when variables are integrated with different orders, it can be used. Secondly, it is more robust for small samples. Finally, it can be used for impartial assessment of long- and short-term frameworks. In addition, as stated by Hao (2021), if the F-statistics is greater than the upper limit of the critical value of the asymptotic distribution, the combined cointegration relationship or the long-term relationship exists; otherwise, there is no long-term combined cointegration relationship. The general form of the ARDL bounds test model is as follows:4 ΔCEt=α0+∑i=1pβ1iΔCEt-i+∑i=0qβ2iΔREt-i+∑i=0qβ3iΔGDPt-i+∑i=0qβ4iΔGINt-i+∑i=0qβ5iΔXt-i+γ1CEt-1+γ2REt-1+γ3GDPt-1+γ4GINt-1+γ5Xt-1+εt
where X represents macroeconomic variables (for example, foreign direct investment (FDI), trade openness (TRO), financial development (FIN), inflation rate (INF), and government expenditure (GOE));βi represents the short-term coefficient; γi represents the long-term coefficient; p and q represent the lag term; and εt represents a white noise sequence that obeys a normal distribution. If there is a long-term relationship between renewable energy consumption, green innovation, and economic growth with CO2 emissions in E7 countries under macroeconomic conditions, the corresponding conditional error correction model is as follows:5 ΔCEt=α0+∑i=1pβ1iΔCEt-i+∑i=0qβ2iΔREt-i+∑i=0qβ3iΔGDPt-i+∑i=0qβ4iΔGINt-i+∑i=0qβ5iΔXt-i+δECMt-1+εt
where δ represents the speed of model adjustment or the error mechanism of returning to long-term equilibrium; ECM represents the error correction term, the coefficients (βi) of the lag difference term reflects the short-term dynamic coefficient of the model converging to equilibrium. Under the ECM technology, if the value of the ECM coefficient δ of the error correction term is negative and significant, it indicates that there is a long-term causality. On the other hand, if the coefficients of each variable in the ARDL-VECM model are significant, it indicates that there is a short-term causality.
Data
This study examines the impact of renewable energy consumption, green innovation, and economic growth on environmental sustainability in seven emerging market economics (Brazil, China, India, Indonesia, Mexico, Russia, and Turkey) using annual panel data for the period 1990–2020. The choice of time span is based on the basic principles of availability and comprehensiveness of data from different sources. These data include the ratio of total renewable energy consumption (RE) in million tons of oil equivalent (Mtoe) to total primary energy consumption using the substitution method, carbon dioxide emissions per capita (tons) (CE), GDP per capita (GDP) in constant dollars in 2010, green innovation (GIN), and other macroeconomic variables that affect environmental sustainability. Among them, data come from Global Carbon Project, BP Statistical Review of World Energy, World Bank, and OECD green growth indicators (2022) (see Table 1). Moreover, we logarithmically transform all variables so that the interconversion can help to obtain a better normal distribution of all data and improve the heteroskedasticity criticality problem, making the final result meaningful and easy to interpret. Table 2 shows that the interquartile range (IQR) of all variables has no outliers.Table 1 Data source and description
Variables Description Data source Measure
CE Carbon emissions per capita Global Carbon Project Tons
RE Renewable energy consumption BP Statistical Review of World Energy % of primary energy
GDP GDP per capita World Bank Current US$
GIN Green innovation: total number of patents on green technologies OECD green growth indicators (2022) % of total number of patents
Macroeconomic variable
FDI Foreign direct investment World Bank % of GDP
TRO Trade openness World Bank % of GDP
FIN Financial innovation World Bank Broad money (% of GDP)
INF Inflation rate: measured in percentage change by using GDP deflator World Bank Annual %
GOE Government final consumption expenditure World Bank % of GDP
Table 2 Descriptive statistics
Economic variables CE RE GDP GIN FDI TRO FIN INF GOE
Mean 1.406 2.476 8.658 2.229 0.621 3.786 4.090 2.838 2.612
Median 1.414 2.464 8.637 2.269 0.705 3.828 4.091 2.297 2.598
Max 1.582 2.751 9.033 2.426 1.142 3.936 4.514 6.071 2.734
Min 1.225 2.367 8.355 1.885 − 0.568 3.386 3.578 1.394 2.539
Std. dev 0.134 0.081 0.244 0.146 0.381 0.138 0.250 1.449 0.050
Skewness − 0.036 1.737 0.180 − 0.671 − 1.474 − 1.522 − 0.281 1.243 0.560
Kurtosis 1.406 6.422 1.455 2.326 5.381 4.752 2.051 3.358 2.372
IOR 0.288 0.061 0.481 0.248 0.289 0.123 0.396 1.477 0.082
Empirical results and discussions
Stationarity test of panel data
The four methods mentioned above perform unit root tests on renewable energy consumption, green innovation, economic growth, CO2 emissions, and other macro variables, for example, the logarithm of foreign direct investment (FDI), trade openness (TRO), financial innovation (FIN), inflation rate (INF), and government final consumption expenditure (GOE), and their original series or first-order difference series, that affect environmental sustainability in E7 countries. The test regression formula contains constant terms, and the regression formula includes both constant terms and trend terms. The test results are shown in Table 3. The results of the unit root tests for CE, RE, GDP, FIN, and GOE using ADF, DF-GLS, PP, and KPSS tests, respectively, at the 10% significance level indicate that the first-order differences of the series are smooth. However, the unit root test results for GIN, FDI, TRO, and INF show that the series is stationary at the primitive order level. Therefore, CE, RE, GDP, GIN, FDI, TRO, FIN, INF, and GOE are all single integral I(1) or I(0) processes over the period 1990–2020.Table 3 Test result of unit root tests
Variables ADF DF-GLS PP KPSS
(C, 0) (C, T) (C, 0) (C, T) (C, 0) (C, T) (C, 0) (C, T)
GIN − 2.720c − 2.815 − 2.653a − 2.770 − 2.681c − 2.747 0.191 0.124c
FDI − 3.926a − 2.774 − 1.491 − 1.967 − 4.229a − 2.795 0.503b 0.179b
TRO − 3.184b − 3.329c − 1.748c − 2.970c − 3.342b − 3.222c 0.548b 0.194b
INF − 4.639a − 2.573 − 0.895 − 2.450 − 2.725c − 2.169 0.624b 0.168b
ΔCE − 3.062b − 2.893 − 2.947a − 3.129c − 3.062b − 2.893 0.260 0.211b
ΔRE − 4.213a − 5.094a − 4.303a − 5.257a − 4.294a − 5.091a 0.490b 0.147b
ΔGDP − 3.587b − 3.414c − 3.159a − 3.609b − 3.587b − 3.414c 0.267 0.212b
ΔFIN − 4.824a − 4.663a − 4.184a − 4.791a − 6.529a − 6.191a 0.378c 0.335a
ΔGOE − 5.711a − 5.730a − 5.541a − 6.076a − 9.737a − 16.45a 0.500b 0.500a
(1) determines the optimal lag term of ADF and DF-GLS according to the modified Akaike criterion; (2) determines the PP and KPSS window widths according to the Andrews bandwidth; (3) △ represents the first-order difference of the original sequence; (4) lowercase a, b, and c indicate significance at the 1%, 5%, and 10% confidence levels, respectively
Long-term cointegration relationship
To further verify the long-term cointegration characteristics among the used variables, we used the Bayer–Hanck combined cointegration test. Table 4 presents the results of the Bayer and Hanck (2013) combined cointegration test; the Fisher statistics of EG-JOH and EG-JOH-BAN-BOS are much larger than the 5% critical values of 3.814 and 9.165, respectively. Therefore, there is a long-term combined cointegration relationship among CE, RE, GDP, GIN, FDI, TRO, FIN, INF, and GOE at the 5% significance level. Table 5 shows the ARDL Wald test results which also further confirms the long-term combined cointegration relationship among these variables, since the F-statistic is 8.414, which is greater than 3.77 at 1%.Table 4 Bayer–Hanck cointegration test
Model specifications Fisher statistics F-statistic CV at 5%
CE=f(RE,GDP,GIN,X) EG-JOH 4.960b 3.814
EG-JOH-BAN-BOS 19.275a 9.165
Lowercase a and b, are for 1% and 5% levels of significance. X denotes other macroeconomic variables (FDI, TRO, FIN, INF, GOE). EG-JOH denote Engle–Granger–Johansen; EG–JOH–BAN–BOS denotes Engle–Granger–Johansen–Banerjee–Boswijk. CV is for the critical value
Table 5 ARDL Wald test results (F-value) for long-term cointegration
Estimated model Lower bound Upper bound Significance levels
FCE(RE,GDP,GIN,FDI,TRO,FIN,INF,GOE) 1.85 2.85 10%
F = 8.414 2.11 3.15 5%
K = 8 2.33 3.42 2.5%
2.62 3.77 1%
Source: authors’ computation
The long-term effects of renewable energy, green innovation, and economic growth on CO2 emissions in the E7 countries are further examined in this study after determining the existence of cointegration or long-run relationships among the variables under macroeconomic (e.g., trade openness, FDI, financial innovation, inflation, and government spending) conditions. Firstly, as clearly pointed out in the overview section of this study, GIN is an important factor in reducing CO2 emissions (Mensah et al. 2019; Khattak et al. 2020; Li et al. 2022). However, surprisingly, within the framework of macroeconomic variables (FDI, TRO, FIN, INF, and GOE), there is no significant negative effect of GIN on CE at the 10%, 5%, and 1% significance levels.
Secondly, RE has a negative impact on CE, as expected. The long-term estimates of FMOLS, DOLS, and CCR show that for every 1% increase in RE, CE will decrease by 0.357%, 0.428%, and 0.348%, respectively, which indicates that RE has a favorable impact on the improvement of environmental quality in E7 countries. The reason for this may be that in the context of carbon neutrality, E7 countries are trying to promote high-quality green transformation of energy-intensive industries, build a green, low-carbon, circular development economic system, and vigorously develop safe, clean energy, and renewable energy to meet the requirements of the present and the future. Meanwhile, replacing non-renewable energy sources, such as fossil energy, with renewable energy, which is considered a green and clean form of energy, in real life and production processes can reduce the environmental consequences of energy use, such as CO2 emissions. This finding is supported by the studies of Omri and Nguyen (2014), Kirikkaleli and Adebayo (2021a, b), Djellouli et al. (2022), and Hao (2022b). However, this result is contrary to the findings of Musah et al. (2020) for West Africa, Padhan et al. (2020) for OECD countries and Çıtak et al. (2021) for USA, who concluded that RE has a significant positive effect on CO2 emissions, which is detrimental to these countries’ environmental quality improvement. This is mainly because these countries have not yet reached the threshold point for reducing CO2 emissions by renewable energy usage, which is consistent with the findings of Amer’s (2020) research of global economies, which concluded that environmental improvement is facilitated when renewable energy consumption accounts for about 8.39% of the country’s total energy consumption. Furthermore, as we expected, GDP has a positive impact on CO2 emissions in developing countries, especially in E7 countries. When other macroeconomic conditions (influencing factors) are constant (e.g., FDI, TRO, FIN, INF, and GOE), the results according to FMOLS, DOLS, and CCR show that a 1% increase in GDP per capita will increase CO2 emissions by 0.881%, 0.946%, and 0.875%, respectively, which indicates that the current economic growth has not reached the inflection point (inverted U-shaped relationship) for energy saving and emission reduction, i.e., the EKC assumption is valid for E7 countries (Doğan et al., 2022). Meanwhile, this also means that the rapid economic development of the E7 countries in the context of economic globalization has led to an increase in energy demand and thus to environmental degradation. The findings of Xiong and Xu (2021), Kirikkaleli and Adebayo (2021a), and Espoir et al. (2022), who found a long-term positive association between GDP and CO2 emissions, are supported by this one.
Finally, in terms of macro variables, FIN has a significant negative impact on CE. According to FMOLS, DOLS, and CCR, a 1% increase in FIN would result in a reduction in CO2 emissions of 0.301%, 0.336%, and 0.306%, respectively. This is similar to the findings of Kirikkaleli and Adebayo (2021a), Hung et al. (2022). and Emenekwe et al. (2022), who found that FIN improves environmental quality. However, this result is contrary to the case of Rjoub et al. (2021) for Turkey, Yao and Zhang (2021) and Khan et al. (2022) for the global case, and Ling et al. (2022) for China, who argue that the development of financial innovation has an adverse impact on the improvement of the environment, which further indicates that the development of financial innovation in these countries is mainly a model of economic development at the expense of the environment. Therefore, to achieve carbon emission reduction targets, financial innovation should be considered as a tool for a country’s financial development or economic development, which can be implemented to keep the environment clean by implementing financial regulations to promote sustainable financial and environmental development. Table 6 also proves that CO2 emissions will increase by 0.015%, 0.043%, and 0.022% for every 1% increase in INF according to FMOLS, DOLS, and CCR estimates, respectively. It indicates that INF has adverse effects on the environment; the main reason for this may be that the uncertainty of INF under macroeconomic conditions has greatly weakened the regulations of E7 countries related to activities that emit more carbon pollution, and indirectly led to the reduction of environmental-related economic investment, such as clean energy, renewable energy investment, and pollution control investment (Musarat et al. 2021; Pardo 2021). However, this finding is contrary to the studies of Ullah et al. (2020) for Pakistan and Ahmad et al. (2021) for Asian economies, who concluded that INF to a certain extent strongly encourages businesses and governments to invest in environmentally friendly type technology, so INF contributes to improving the environment (reduction of CO2 emissions). Furthermore, it is also unexpected to find that GOE has a positive effect on CE only under the DOLS estimates for GOE, but the significant effect at 10% is relatively weak and negligible. In the context of economic globalization, the governments of developing countries are facing huge fiscal revenue pressure, especially E7 countries, which leads local government officials to pay too much attention to economic growth indicators and weakens the country or regional environmental quality standards. It also indirectly condones the emission of CO2 by enterprises. This finding is consistent with the findings of He et al. (2017), Yang et al. (2018), and Wang et al. (2021), but contradicts the findings of Wang and Li (2019) and Lingyan et al. (2021), who argue that government expenditure is conducive to reducing CO2 emissions.Table 6 Long-term cointegration estimators for FMOLS, DOLS, and CCR
Dependent variable: CE
Independent variables FMOLS DOLS CCR
RE − 0.357a − 0.428a − 0.348a
GDP 0.881a 0.946a 0.875a
GIN − 0.007 − 0.017 − 0.025
FDI − 0.036 − 0.008 − 0.027
TRO − 0.003 0.064 0.059
FIN − 0.301c − 0.336b − 0.306b
INF 0.015c 0.043b 0.022c
GOE 0.365 0.071c 0.509
C − 5.196 − 6.565 − 5.756
R-squared 0.979 0.954 0.974
S.E. of regression 0.024 0.023 0.026
Lowercase a, b, and c are for 1%, 5%, and 10% levels of significance
ARDL bounds test
As demonstrated by the ARDL bounds test and Bayer–Hanck combined cointegration, there are long-term cointegration correlations between CE, RE, GDP, GIN, FDI, TRO, FIN, INF, and GOE. Therefore, we need to use the ARDL (1, 1, 1, 1, 0, 1, 1, 1) model to estimate the long-term and short-term coefficients of each variable proxy, and further analysis of the impact of RE, GDP, and GIN on CE under macroeconomic conditions (see Table 7). Firstly, the long-term impact paths of RE, GDP, and GIN on CE are basically consistent with the previous estimates of FMOLS, DOLS, and CCR. As previously expected, per capita GDP and inflation are the main factors affecting the increase in CO2 emissions in E7 countries (Musarat et al., 2021; Xiong and Xu 2021), while renewable energy consumption is beneficial to the environment in these countries’ quality improvement (Anwar et al. 2022a; Hao 2022a; Yunzhao 2022). However, unlike the previous long-term estimates of FMOLS, DOLS, and CCR, there is no significant negative effect of FIN on CE. While it is supported by the studies of Hung et al. (2022) and Jiang et al. (2022), they concluded that innovation has strong uncertainties in reducing CO2 emissions. Secondly, the short-term impact paths of RE, GDP, GIN, FDI, TRO, and INF on CE are almost consistent with the long-term estimates (symbols) of FMOLS, DOLS, CCR, and ARDL. However, in stark contrast to long-term estimates, GIN has a negative effect on CE at the 5% significance level, suggesting that GIN is beneficial for improving environmental quality. GIN aims to reduce the adverse impact on the environment, that is, by introducing new ideas, behaviors, products, and processes to reduce the environmental burden of enterprises or to achieve specific ecological sustainability goals (Brunel 2019; Hsu et al. 2021; Li et al. 2022; Liu et al. 2022a, b). Finally, FIN positively affects CE at the 1% level of significance, which indicates that FIN in these countries exacerbates environmental pollution and is not conducive to the improvement of environmental quality (Shen et al. 2021). The main reason is that under the background of economic globalization, developing countries pay too much attention to economic development and financial innovation and development, while neglecting environmental protection, especially E7 countries. The development of the financial industry in E7 countries indirectly optimizes resource allocation and reduces financing costs while promoting economic development, which expands the scale of production to a certain extent and leads to an increase in energy consumption and pollutant emissions (Xia et al. 2022). Finally, it is also found that GOE has a significant negative effect on CE, but the effect is relatively weak and negligible at the 10% significance level, which suggests that GOE in the short term can help reduce CO2 emissions in these countries. Moreover, this result is supported by the research of Wang and Li (2019) and Lingyan et al. (2021), but it is diametrically opposite to the previous long-term estimates of FMOLS, DOLS and CCR.Table 7 ARDL estimation results for long and short terms
Dependent variable: CE
Independent variables Coefficients Std. errors t-statistics
Long-term results
RE − 0.490a 0.108 − 4.527
GDP 0.892a 0.141 6.345
GIN − 0.081 0.092 − 0.879
FDI 0.037 0.046 0.808
TRO − 0.022 0.125 − 0.172
FIN − 0.181 0.216 − 0.838
INF 0.059b 0.021 2.747
GOE 0.686 0.518 1.324
C − 6.137a 1.407 − 4.361
Short-term results
Δ RE − 0.092b 0.043 − 2.140
Δ GDP 1.274a 0.073 17.620
Δ GIN − 0.067b 0.032 − 2.352
Δ FDI − 0.040a 0.010 − 3.831
Δ TRO − 0.010 0.056 − 0.175
Δ FIN 0.264a 0.052 5.119
Δ INF 0.001 0.003 0.491
Δ GOE − 0.155c 0.075 − 2.052
ECM(-1) − 0.450a 0.049 − 9.151
R2 0.948
F stat 10.530a
Diagnostic tests
Test Prob
Normality (Jarque–Bera) 0.392
Serial correlation (LM test) 0.702
Heteroskedasticity (Breusch–Pagan–Godfrey) 0.644
Functional form (Ramsey RESET) 0.080
Lowercase a, b, and c stand for 1%, 5%, and 10% significance levels, respectively. Source: authors’ computation
In addition, the short-term estimates of the ARDL in Table 7 also show that the error correction term (ECM(-1)) for the specified (CE) model is negative and statistically significant at the 1% significance level, indicating a good rate of adjustment in the post-shock relationship process, as any periodic shocks in the model adjust to long-term equilibrium at a rate of 45%. Ahmed et al. (2013) confirmed this result by arguing that a highly significant error correction term provides further evidence for the existence of a stable long-term relationship. Moreover, once a shock has occurred, this also means that the long term would be shortly corrected back by 2.2 years for the CE (CO2 emissions) models.
To avoid model unreliability due to parameter instability, this thesis uses the cumulative sum of recursive residuals (CUSUM) and the cumulative sum of squares of recursive residuals (CUSUMSQ) to test the stability of the long- and short-term parameters estimated by the model. The test results are shown in Fig. 5. According to Fig. 5, the fluctuations of both CUSUM and CUSUMSQ do not exceed the boundary region, so all the fitted models are stable and accurate because the black line is within the red bandwidth, which is favorable for the policy impact. According to Fig. 3, the fluctuations of both CUSUM and CUSUMSQ do not exceed the boundary region, so all the fitted models are stable and accurate because the black line is within the red bandwidth, which is favorable for the policy impact.Fig. 5 Plots of recursive cumulative sum of residuals (CUSUM) and recursive cumulative sum of squared residuals (CUSUMQ)
Conclusions and policy implications
This study explores the long-term and short-term impacts of renewable energy consumption, green innovation, and economic growth on environmental sustainability in E7 countries over the period 1990–2020 period within a macroeconomic framework. This study attempts to fill this gap in the environmental literature using the FMOLS, DOLS, and CCR, Bayer–Hanck cointegration, and ARDL bounds test. Firstly, the results of the Bayer–Hanck cointegration test and ARDL bounds test showed a long-term association between the environment and its possible causes (e.g., renewable energy, economic growth, green innovation, FDI, trade openness, financial innovation, inflation, and government spending).
Secondly, long-term estimates of FMOLS, DOLS, and CCR indicate that financial innovations in renewable energy consumption have a negative impact on CO2 emissions. As expected with the development of financial innovation, the use of renewable energy has improved the environmental quality of the E7 countries. Furthermore, economic growth in the E7 countries exacerbates the deterioration of environmental quality, which may be the result of these countries’ attempts to expand their economies without considering the environmental sustainability impacts of their actions. In the context of economic globalization and consumption reduction and emission reduction, E7 countries are striving to promote high-quality green transformation of energy-intensive industries, build a green, low-carbon, and circular development economic system, and vigorously develop safe, clean energy, and renewable energy, so as to meet the requirements of current and future economic and environmental sustainability. Furthermore, inflation has a negative impact on the environment (CO2 emissions). Uncertainty about inflation in macroeconomic conditions has weakened environmental regulations in these countries related to activities that emit more carbon pollution. Moreover, as previously analyzed, the increase in government expenditure in the E7 countries exacerbates environmental pollution and is not conducive to environmental improvement. Under the background of economic globalization for a long time, the governments of these countries are likely to face huge fiscal revenue pressures, leading local government officials to pay too much attention to economic growth indicators, thus weakening the environmental quality standards of the country or region. This also indirectly indulges companies to emit CO2.
Finally, the ARDL long-term estimates are basically consistent with the FMOLS, DOLS, and CCR long-term estimates, further indicating that economic growth and inflation are the main reasons for the increase in CO2 emissions in E7 countries. Economic growth increases the burning of carbon emissions, and the use of renewable energy has improved the environmental quality of these countries and regions. Moreover, the short-term impact paths of renewable energy consumption, economic growth, green innovation, FDI, trade openness, and inflation on CO2 emissions are almost consistent with the long-term estimates of FMOLS, DOLS, CCR, and ARDL. However, the impact of green innovation on CO2 emissions is diametrically opposed to long-term estimates. It is shown that green innovation development leads to environmental sustainability in E7 countries, that is, by introducing new ideas, behaviors, products, and processes to reduce the burden of enterprises on the environment or to achieve specific ecological sustainable development goals. Besides, ARDL’s short-term estimation results confirm that financial innovation and government expenditure has exacerbated environmental pollution in E7 countries, which is not conducive to sustainable economic and environmental development. Under the background of economic globalization, government officials are overly concerned with economic development and financial innovation development at the expense of environmental protection in developing countries (e.g., E7 countries). These results therefore make the following recommendations for policy makers in countries and regions around the world, especially developing countries.Governments and enterprises around the world should actively promote and implement green innovation strategies. On the one hand, it is necessary to further improve the industry green standard system, continue to carry out the identification of green processes, green factories, green products, green parks, and green supply chains, and build a green manufacturing system for the entire life cycle of the industry. On the other hand, it is necessary to strictly implement the intellectual property protection system, focus on the major needs of energy conservation and carbon emission reduction and environmental pollution control and governance, and provide targeted financial and tax assistance to promote green innovation.
Governments should establish a list of smart carbon reduction technology proposals through top-level planning, and at the same time establish technical value evaluation criteria. It is also necessary to help enterprises make technology choices and strategic decisions based on the characteristics and applicable scenarios of specific technologies, as well as the emission reduction potential and potential profit margins of smart carbon reduction technologies.
Government agencies, energy enterprises, and industry associations cooperate to build industry-level carbon emission monitoring platforms. The platform can monitor the carbon emissions of high-energy-consuming enterprises in real time, and conduct data analysis on the application effect of smart carbon reduction technology, providing data support for the popularization of technology and the formulation of relevant national policies.
A green supply chain with key enterprises as the core should be actively built. Key enterprises should be encouraged and supported to implement green procurement in the whole life cycle of product production and services, implement ecological design, develop green products, promote green production, guide green consumption, promote the green manufacturing industry’s leading enterprises to actively build a green supply chain, take the lead, and drive the industry chain upstream and downstream enterprises to carry out energy-saving and environmental protection transformation as well as greening into the industrial development process, to achieve green and low-carbon development of the entire industrial system.
Enterprises should be encouraged to develop long-term low-carbon development strategies and carbon neutral roadmaps. According to the carbon peak and carbon neutral targets of each country, we will develop a carbon peak roadmap and carbon neutral action plan for enterprises and set reasonable medium- and long-term and phased targets. Through planning design and roadmap advancement, we ensure that enterprises complete their carbon reduction tasks within the established timeline, thereby supporting the achievement of industry and national carbon neutrality targets. Governments should also strengthen support and guidance, promote within the key enterprise industry, and strengthen the depth of cooperation between enterprises to give full play to the leading role of key enterprises.
In addition, there are certain limitations to this study which give directions for future research. Firstly, based on the availability of data, the institutional indicator data used in this study is only for 7 emerging market economics, not for countries or specific regions in the world, so it may produce errors of regional heterogeneity. Secondly, in examining the factors affecting environmental sustainability, this study only adopts five macroeconomic indicators and ignores other different determinants that affect environmental sustainability, such as resources, population, industrialization, urbanization, globalization, institutions, resource rent, and tax on environment or governance. Finally, this study used CO2 emissions as a predictor of environmental degradation, so future investigations should use other proxies of environmental degradation.
Author contribution
Conceptualization, Y. Hao and P. Chen; methodology, Y. Hao; software, Y. Hao; validation, Y. Hao and P. Chen; formal analysis, Y. Hao; investigation, Y. Hao and P. Chen; data curation, Y. Hao; writing-original draft preparation, Y. Hao; writing—review and editing, Y. Hao; visualization, Y. Hao; supervision, P. Chen. All authors have read and agreed to the published version of the manuscript.
Data availability
The datasets used in this study are available from the corresponding author upon request.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
The article is original, has not already been published in a journal, and is not currently under consideration by another journal.
Competing interests
The authors declare no competing interests.
Abbreviations
CO2 Carbon dioxide
ADF Augmented Dickey–Fuller
PP Phillips–Perron
KPSS Kwiatkowski–Phillips–Schmidt–Shin
DF-GLS Dickey–Fuller GLS
FMOLS Fully modified OLS
DOLS Dynamic OLS
CCR Canonical cointegrating regression
ARDL Auto-regressive distributed lag
ECM Error correction term
IQR Interquartile range
CUSUM Cumulative sum of recursive residuals
CUSUMQ Cumulative sum of squares of recursive residuals
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Ahmad W Ullah S Ozturk I Majeed MT Does inflation instability affect environmental pollution? Fresh evidence from Asian economies Energy & Environment 2021 32 7 1275 1291 10.1177/0958305X20971804
Ahmed MU Muzib M Roy A Price-wage spiral in Bangladesh: evidence from ARDL bound testing approach Int J Appl Econ 2013 10 2 77 103
Amer, H. (2020). The impact of renewable energy consumption on the human development index in selected countries: panel analysis (1990–2015). International Journal of Economy, Energy and Environment, 5(4), 47. 10.11648/j.ijeee.20200504.12
Amran A Periasamy V Zulkafli AH Determinants of climate change disclosure by developed and emerging countries in Asia Pacific Sustain Dev 2014 22 3 188 204 10.1002/sd.539
Anwar A, Chaudhary AR, Malik S (2022a) Modeling the macroeconomic determinants of environmental degradation in E‐7 countries: the role of technological innovation and institutional quality. Journal of Public Affairs e2834. 10.1002/pa.2834
Anwar A, Malik S, Ahmad P (2022b) Cogitating the role of technological innovation and institutional quality in formulating the sustainable development goal policies for E7 countries: evidence from quantile regression. Glob Bus Rev 09721509211072657.10.1177/09721509211072657
Bai J, Li S, Kang Q, Wang N, Guo K, Wang J, Cheng J (2021) Spatial spillover effects of renewable energy on carbon emissions in less-developed areas of China. Environ Sci Pollut Res 1-14.10.1007/s11356-021-17053-w
Banerjee A Dolado J Mestre R Error-correction mechanism tests for cointegration in a single-equation framework J Time Ser Anal 1998 19 267 283 10.1111/1467-9892.00091
Bayer C Hanck C Combining non-cointegration tests J Time Ser Anal 2013 34 1 83 95 10.1111/j.1467-9892.2012.00814.x
Blazejczak J Braun FG Edler D Schill W Economic effects of renewable energy expansion: a model-based analysis for Germany Renew Sustain Energy Rev 2014 40 1070 1080 10.1016/j.rser.2014.07.134
Boswijk HP Testing for an unstable root in conditional and structural error correction models Journal of Econometrics 1994 63 37 60 10.1016/0304-4076(93)01560-9
British Petrolium (BP) Statistical review (2021) Statistical Review of World Energy. British Petrolium.
Brunel C Green innovation and green imports: links between environmental policies, innovation, and production J Environ Manage 2019 248 109290 10.1016/j.jenvman.2019.109290 31357152
Chen YS, Lai SB, Wen CT (2006) The influence of green innovation performance on corporate advantage in Taiwan. J Bus Ethics 81(3):531–543
Çıtak F Uslu H Batmaz O Hoş S Do renewable energy and natural gas consumption mitigate CO2 emissions in the USA? New insights from NARDL approach Environ Sci Pollut Res Int 2021 28 45 63739 63750 10.1007/s11356-020-11094-3 33051846
Dangelico RM Green product innovation: where we are and where we are going Bus Strateg Environ 2016 25 8 560 576 10.1002/bse.1886
Danish, & Wang, Z. Dynamic relationship between tourism, economic growth, and environmental quality J Sustain Tour 2019 26 1 16 10.1080/09669582.2018.1526293
Dickey DA Fuller WA Distribution of the estimators for autoregressive time series with a unit root J Am Stat Assoc 1979 74 366a 427 431 10.1080/01621459.1979.10482531
Djellouli N Abdelli L Elheddad M Ahmed R Mahmood H The effects of non-renewable energy, renewable energy, economic growth, and foreign direct investment on the sustainability of African countries Renewable Energy 2022 183 676 686 10.1016/j.renene.2021.10.066
Doğan B Ghosh S Hoang DP Chu LK Are economic complexity and eco-innovation mutually exclusive to control energy demand and environmental quality in E7 and G7 countries? Technol Soc 2022 68 101867 10.1016/j.techsoc.2022.101867
Dong K Dong X Jiang Q How renewable energy consumption lower global CO2 emissions? Evidence from countries with different income levels The World Economy 2020 43 6 1665 1698 10.1111/twec.12898
El Menyari Y The effects of international tourism, electricity consumption, and economic growth on CO2 emissions in North Africa Environ Sci Pollut Res Int 2021 28 32 44028 44038 10.1007/s11356-021-13818-5 33844140
Elliott G, Rothenberg TJ, Stock JH (1992) Efficient tests for an autoregressive unit root
Emenekwe CC Onyeneke RU Nwajiuba CU Financial development and carbon emissions in Sub-Saharan Africa Environ Sci Pollut Res Int 2022 29 13 19624 19641 10.1007/s11356-021-17161-7 34719760
Engle RF, Granger CW (1987) Co-integration and error correction: representation, estimation, and testing. Econometrica: Journal of the Econometric Society 55:251–276. 10.2307/1913236
Espoir DK Mudiangombe BM Bannor F Sunge R Tshitaka JLM CO2 emissions and economic growth: assessing the heterogeneous effects across climate regimes in Africa Sci Total Environ 2022 804 150089 10.1016/j.scitotenv.2021.150089 34798723
Ganda F The impact of innovation and technology investments on carbon emissions in selected organisation for economic co-operation and development countries J Clean Prod 2019 217 469 483 10.1016/j.jclepro.2019.01.235
Habiba UMME Xinbang C Anwar A Do green technology innovations, financial development, and renewable energy use help to curb carbon emissions? Renewable Energy 2022 193 1082 1093 10.1016/j.renene.2022.05.084
Hao Y Effect of economic indicators, renewable energy consumption and human development on climate change: an empirical analysis based on panel data of selected countries Frontiers in Energy Research 2022 10 243 261 10.3389/fenrg.2022.841497
Hao Y (2021) The relationship between LNG price, LNG revenue, non-LNG revenue and government spending in China: an empirical analysis based on the ARDL and SVAR model. Energy & Environment, 0958305X211053621. 10.1177/0958305X211053621
Hao Y (2022b) The relationship between renewable energy consumption, carbon emissions, output, and export in industrial and agricultural sectors: evidence from China. Environ Sci Pollut Res 1-18.10.1007/s11356-022-20141-0
Hasnisah A, Azlina AA, Che CMI (2019) The impact of renewable energy consumption on carbon dioxide emissions: empirical evidence from developing countries in Asia. International Journal of Energy Economics and Policy 9(3):135–143. 10.32479/ijeep.7535
He L Yin F Zhong Z Ding Z The impact of local government investment on the carbon emissions reduction effect: an empirical analysis of panel data from 30 provinces and municipalities in China PLoS ONE 2017 12 7 e0180946 10.1371/journal.pone.0180946 28727783
Hotak S Islam M Kakinaka M Kotani K Carbon emissions and carbon trade balances: international evidence from panel ARDL analysis Environ Sci Pollut Res 2020 27 19 24115 24128 10.1007/s11356-020-08478-w
Hsu CC Quang-Thanh N Chien F Li L Mohsin M Evaluating green innovation and performance of financial development: mediating concerns of environmental regulation Environ Sci Pollut Res Int 2021 28 40 57386 57397 10.1007/s11356-021-14499-w 34089450
Hung NT, Trang NT, Thang NT (2022) Quantile relationship between globalization, financial development, economic growth, and carbon emissions: evidence from Vietnam. Environ Sci Pollut Res Int 1-19.10.1007/s11356-022-20126-z
IEA. Global Energy Review (2020). Global Energy Review 2020 – Analysis - IEA
Jaforullah M King A Does the use of renewable energy sources mitigate CO2 emissions? A reassessment of the US evidence Energy Economics 2015 49 711 717 10.1016/j.eneco.2015.04.006
Jiang W Cole M Sun J Wang S Innovation, carbon emissions and the pollution haven hypothesis: climate capitalism and global re-interpretations J Environ Manage 2022 307 114465 10.1016/j.jenvman.2022.114465 35091246
Johansen S (1991) Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica: Journal of the Econometric Society, 59, 1551–1580. 10.2307/2938278
Kahia M Aïssa MSB Lanouar C Renewable and non-renewable energy use-economic growth nexus: the case of MENA net oil importing countries Renew Sustain Energy Rev 2017 71 127 140 10.1016/j.rser.2017.01.010
Kang H CO2 emissions embodied in international trade and economic growth: empirical evidence for OECD and non-OECD countries Sustainability 2021 13 21 12114 10.3390/su132112114
Khan H Weili L Khan I Institutional quality, financial development and the influence of environmental factors on carbon emissions: evidence from a global perspective Environ Sci Pollut Res Int 2022 29 9 13356 13368 10.1007/s11356-021-16626-z 34585358
Khattak SI Ahmad M Khan ZU Khan A Exploring the impact of innovation, renewable energy consumption, and income on CO2 emissions: new evidence from the BRICS economies Environ Sci Pollut Res Int 2020 27 12 13866 13881 10.1007/s11356-020-07876-4 32036520
Kirikkaleli D Adebayo TS Do renewable energy consumption and financial development matter for environmental sustainability? New Global Evidence Sustainable Development 2021 29 4 583 594 10.1002/sd.2159
Kirikkaleli D Adebayo TS Do public-private partnerships in energy and renewable energy consumption matter for consumption-based carbon dioxide emissions in India? Environ Sci Pollut Res 2021 28 23 30139 30152 10.1007/s11356-021-12692-5
Kitamura Y Phillips PC Fully modified IV, GIVE and GMM estimation with possibly non-stationary regressors and instruments Journal of Econometrics 1997 80 1 85 123 10.1016/S0304-4076(97)00004-3
Kwiatkowski D Phillips PC Schmidt P Shin Y Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root? Journal of Econometrics 1992 54 1–3 159 178 10.1016/0304-4076(92)90104-Y
Laverde-Rojas H Guevara-Fletcher DA Camacho-Murillo A Economic growth, economic complexity, and carbon dioxide emissions: the case of Colombia Heliyon 2021 7 6 e07188 10.1016/j.heliyon.2021.e07188 34124406
Lei W Xie Y Hafeez M Assessing the dynamic linkage between energy efficiency, renewable energy consumption, and CO2 emissions in China Environ Sci Pollut Res 2022 29 19540 19552 10.1007/s11356-021-17145-7
Li L Msaad H Sun H Tan MX Lu Y Lau AKW Green innovation and business sustainability: new evidence from energy intensive industry in China Int J Environ Res Public Health 2020 17 21 7826 10.3390/ijerph17217826
Li Y, Zhang C, Li S, Usman A (2022) Energy efficiency and green innovation and its asymmetric impact on CO2 emission in China: a new perspective. Environ Sci Pollut Res Int 1-8.10.1007/s11356-022-19161-7
Ling G Razzaq A Guo Y Fatima T Shahzad F Asymmetric and time-varying linkages between carbon emissions, globalization, natural resources and financial development in China Environ Dev Sustain 2022 24 5 6702 6730 10.1007/s10668-021-01724-2 34421336
Lingyan M Zhao Z Malik HA Razzaq A An H Hassan M Asymmetric impact of fiscal decentralization and environmental innovation on carbon emissions: Evidence from highly decentralized countries Energy Environ 2021 33 4 752 728 10.1177/0958305x211018453
Liu L, Anwar A, Irmak E, Pelit I (2022a) Asymmetric linkages between public-private partnership, environmental innovation, and transport emissions. Economic Research-Ekonomska Istraživanja 1-22.10.1080/1331677X.2022a.2049979
Liu J, Jiang Y, Gan S, He L, Zhang Q (2022b) Can digital finance promote corporate green innovation?. Environ Sci Pollut Res 1-13.10.1007/s11356-022-18667-4
Luttropp C Lagerstedt J EcoDesign and The Ten Golden Rules: generic advice for merging environmental aspects into product development J Clean Prod 2006 14 15–16 1396 1408 10.1016/j.jclepro.2005.11.022
Martins T Barreto AC Souza FM Souza AM Fossil fuels consumption and carbon dioxide emissions in G7 countries: empirical evidence from ARDL bounds testing approach Environ Pollut 2021 291 118093 10.1016/j.envpol.2021.118093 34543957
Mensah CN Long X Dauda L Boamah KB Salman M Innovation and CO 2 emissions: the complimentary role of eco-patent and trademark in the OECD economies Environ Sci Pollut Res Int 2019 26 22 22878 22891 10.1007/s11356-019-05558-4 31177415
Musah M Kong Y Mensah IA Antwi SK Donkor M The link between carbon emissions, renewable energy consumption, and economic growth: a heterogeneous panel evidence from West Africa Environ Sci Pollut Res Int 2020 27 23 28867 28889 10.1007/s11356-020-08488-8 32418102
Musarat MA Alaloul WS Liew MS Maqsoom A Qureshi AH The effect of inflation rate on CO2 emission: a framework for Malaysian construction industry Sustainability 2021 13 3 1562 10.3390/su13031562
Ojekemi OS, Rjoub H, Awosusi AA, Agyekum EB (2022) Toward a sustainable environment and economic growth in BRICS economies: do innovation and globalization matter?. Environ Sci Pollut Res 1-18.10.1007/s11356-022-19742-6
Olanipekun IO, Usman O (2019) Modelling food and nonfood production in India: the effects of oil price using Bayer-Hanck combined cointegration approach. MPRA Paper
Omri A Nguyen DK On the determinants of renewable energy consumption: International evidence Energy 2014 72 554 560 10.1016/j.energy.2014.05.081
Padhan H Padhang PC Tiwari AK Ahmed R Hammoudeh S Renewable energy consumption and robust globalization (s) in OECD countries: do oil, carbon emissions and economic activity matter? Energ Strat Rev 2020 32 100535 10.1016/j.esr.2020.100535
Pardo Á Carbon and inflation Financ Res Lett 2021 38 101519 10.1016/j.frl.2020.101519
Park JY (1992) Canonical cointegrating regressions. Econometrica: Journal of the Econometric Society 119–143. 10.2307/2951679
Pesaran MH Shin Y Smith RJ Bounds testing approaches to the analysis of level relationships J Appl Economet 2001 16 3 289 326 10.1002/jae.616
Phillips PC Hansen BE Statistical inference in instrumental variables regression with I (1) processes Rev Econ Stud 1990 57 1 99 125 10.2307/2297545
Phillips PC Loretan M Estimating long-term economic equilibria Rev Econ Stud 1991 58 3 407 436 10.2307/2298004
Phillips PC Perron P Testing for a unit root in time series regression Biometrika 1988 75 2 335 346 10.1093/biomet/75.2.335
Phillips PC (1995) Fully modified least squares and vector autoregression. Econometrica: Journal of the Econometric Society, 1023–1078. 10.2307/2171721
Rjoub H Odugbesan JA Adebayo TS Wong WK Sustainability of the moderating role of financial development in the determinants of environmental degradation: evidence from Turkey Sustainability 2021 13 4 1844 10.3390/su13041844
Sabuhoro JB Larue B The market efficiency hypothesis: the case of coffee and cocoa futures Agric Econ 1997 16 3 171 184 10.1111/j.1574-0862.1997.tb00452.x
Saidi K Hammami S The impact of CO2 emissions and economic growth on energy consumption in 58 countries Energy Rep 2015 1 62 70 10.1016/j.egyr.2015.01.003
Saikkonen P Asymptotically efficient estimation of cointegration regressions Economet Theor 1991 7 1 1 21 10.1017/S0266466600004217
Salem S Arshed N Anwar A Iqbal M Sattar N Renewable energy consumption and carbon emissions—testing nonlinearity for highly carbon emitting countries Sustainability 2021 13 21 11930 10.3390/su132111930
Shen Y Su ZW Malik MY Umar M Khan Z Khan M Does green investment, financial development and natural resources rent limit carbon emissions? A provincial panel analysis of China Sci Total Environ 2021 755 142538 10.1016/j.scitotenv.2020.142538 33045608
Stock JH, Watson MW (1993) A simple estimator of cointegrating vectors in higher order integrated systems. Econometrica: journal of the Econometric Society 783–820. 10.2307/2951763
Sun Y Anwar A Razzaq A Liang X Siddique M Asymmetric role of renewable energy, green innovation, and globalization in deriving environmental sustainability: evidence from top-10 polluted countries Renewable Energy 2022 185 280 290 10.1016/j.renene.2021.12.038
Sustainability Report. (2015). Global Sustainable Development Report. https://www.3blmedia.com/news/campaign/sustainability-report-2015
The World in 2050. (2017). The long view: how will the global economic order change by 2050? http://globaltrends.thedialogue.org/publication/the-long-view-how-will-the-global-economic-order-change-by-2050/
Tong T Ortiz J Xu C Li F Economic growth, energy consumption, and carbon dioxide emissions in the E7 countries: a bootstrap ARDL bound test Energy, Sustainability and Society 2020 10 1 1 17 10.1186/s13705-020-00253-6
Ullah S Apergis N Usman A Chishti MZ Asymmetric effects of inflation instability and GDP growth volatility on environmental quality in Pakistan Environ Sci Pollut Res 2020 27 25 31892 31904 10.1007/s11356-020-09258-2
Wang J Li H The mystery of local fiscal expenditure and carbon emission growth in China Environ Sci Pollut Res Int 2019 26 12 12335 12345 10.1007/s11356-019-04591-7 30847812
Wang S Li Q Fang C Zhou C The relationship between economic growth, energy consumption, and CO2 emissions: Empirical evidence from China The Science of the Total Environment 2016 542 Pt A 360 371 10.1016/j.scitotenv.2015.10.027 26520261
Wang W, Wang Y, Zhang X, Zhang D (2021) Effects of government subsidies on production and emissions reduction decisions under carbon tax regulation and consumer low-carbon awareness. International journal of environmental research and public health, 18(20). 10.3390/ijerph182010959
Xia W Apergis N Bashir MF Ghosh S Doğan B Shahzad U Investigating the role of globalization, and energy consumption for environmental externalities: empirical evidence from developed and developing economies Renewable Energy 2022 183 219 228 10.1016/j.renene.2021.10.084
Xiong J Xu D Relationship between energy consumption, economic growth and environmental pollution in China Environ Res 2021 194 110718 10.1016/j.envres.2021.110718 33421428
Yang S Cao D Lo K Analyzing and optimizing the impact of economic restructuring on Shanghai’s carbon emissions using STIRPAT and NSGA-II Sustain Cities Soc 2018 40 44 53 10.1016/j.scs.2018.03.030
Yao S Zhang S Energy mix, financial development, and carbon emissions in China: a directed technical change perspective Environ Sci Pollut Res Int 2021 28 44 62959 62974 10.1007/s11356-021-15186-6 34218385
You C, Khattak SI, Ahmad M (2022) Do international collaborations in environmental-related technology development in the U.S. pay off in combating carbon dioxide emissions? Role of domestic environmental innovation, renewable energy consumption, and trade openness. Environ Sci Pollut Res Int 29(13):19693–19713. 10.1007/s11356-021-17146-6
Yuan B, Li C, Yin H, Zeng M (2021) Green innovation and China’s CO2 emissions–the moderating effect of institutional quality. J Environ Planning Manage 1-30.10.1080/09640568.2021.1915260
Yunzhao L Modelling the role of eco innovation, renewable energy, and environmental taxes in carbon emissions reduction in E−7 economies: evidence from advance panel estimations Renewable Energy 2022 190 309 318 10.1016/j.renene.2022.03.119
Zheng H Huai W Huang L Relationship between pollution and economic growth in China: empirical evidence from 111 cities J Urban Environ Eng 2015 9 1 22 31 10.4090/juee.2015.v9n1.22-31 | 36264463 | PMC9582398 | NO-CC CODE | 2023-02-18 23:15:08 | yes | Environ Sci Pollut Res Int. 2023 Oct 20; 30(8):21115-21131 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
36282386
23650
10.1007/s11356-022-23650-0
Research Article
Assessing the nexus between COVID-19 pandemic–driven economic crisis and economic policy: lesson learned and challenges
Chang Lei [email protected]
1
Mohsin Muhammad [email protected]
2
http://orcid.org/0000-0003-3751-9634
Iqbal Wasim [email protected]
3
1 grid.11135.37 0000 0001 2256 9319 School of Economics, PEKING University, Beijing, 100871 China
2 grid.440785.a 0000 0001 0743 511X School of Finance and Economics, Jiangsu University, Zhenjiang, 212013 China
3 grid.444859.0 0000 0004 6354 2835 Department of Business Administration, ILMA University, Karachi, 75190 Pakistan
Responsible Editor: Philippe Garrigues
25 10 2022
114
31 3 2022
5 8 2022
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
This study examines China’s budgetary policy during the COVID-19 pandemic as a result of China’s insufficient ability to deal with a new crisis when the epidemic struck in March 2020 and as a result of the economic crisis that began in China in March 2020. In order to better comprehend China’s economic status during COVID-19, the study relies on secondary data. The fiscal response of emerging market economies like India is less than in advanced economies. However, it is generally considered to be in line with the average for emerging market economies. As a result of the Disaster Management authority imposing a rigorous lockdown, unemployment rose, the trade cycle was interrupted, and manufacturing and service activities were affected. According to the study’s findings, China’s economic policies, namely its fiscal policy, responded in the years leading up to 2019 by increasing health expenditure, income transfer, welfare payments, subsidies, and reducing short-term unemployment. As a result of the COVID-19 pandemic, China’s government has adopted a number of measures to minimize the damage to the economy. This article also focuses on China’s numerous budgetary actions with COVID-19.
Keywords
COVID-19
Public healthcare expenses
Asymmetric information
Fiscal space
==== Body
pmcIntroduction
When governments use spending and taxing to gauge economic health, this is referred to as fiscal policy (Yuan et al. 2022). To build a strong and stable economy and minimize the frequency of poverty, fiscal policy is frequently utilized by governments (Burger and Calitz 2021; Wu and Zhu 2021). Since the beginning of the current economic downturn, governments have used fiscal policy to revitalize development with stimulus measures to ease the adverse consequences of the economic slump on the most vulnerable members of society (Tang et al. 2022a). The G20 leaders announced this unusual and coordinated fiscal expansionary move following a meeting in London in April 2008. That fiscal policy is viewed as a cure for getting out of economic binds was the overarching theme of their communication (Iqbal et al. 2021). It is their belief that the use of fiscal policy instruments can help jump-start the global economy. As a result, governments have the power to either increase or decrease taxation in order to respond to the economic crisis (Irfan et al. 2021c). Economic predicaments triggered by the epidemic might necessitate government intervention, leading to inflationary pressures, a decrease in foreign exchange reserves, and an overburdening of the non-public sector (Chen et al. 2020; Tran 2021). Financial institutions are wary of the government’s ability to help them stay within their budgets, reverse stimulus measures that have already been put in place, and deal with structural imbalances caused by the government’s weakened financial position in the long term (Li et al. 2022). Reduced taxation due to bad tax composition, tax avoidance, evasion, insufficient public finance supervision power, increasing public healthcare expenditures, or population growth are all possible explanations for this (Lau et al. 2021; Jin et al. 2022). As a result of falling income, some economies decided to cut back on spending. Fiscal policy may not function or be beneficial in areas with strong inflation or foreign current account imbalances (Mohsin et al. 2021).
COVID-19 virus crises allow us to quickly alter and reconstruct our economic structures to recover following COVID. Consequently, in addition to COVID-19, countries must prioritize liability sustainability (Liu et al. 2022a). Many countries’ public debt and debt-to-GDP ratios have risen dramatically as a result of the economic crisis’s impact on GDP and tax income, as well as the charge of fiscal responses to monetary crises, particularly after coronavirus (COVID-19). Governments’ financial situations have deteriorated as a result of financial aid and assurances provided to the financial and manufacturing sectors of the economy during COVID-19 (Yuan et al. 2022; He et al. 2022). A number of countries can maintain long-term budget imbalances by excluding local and worldwide financial markets following COVID-19. Furthermore, mutual partners can be certain that they will fulfill their existing and future commitments (Charoensukmongkol 2021; Lu et al. 2021; Solarin and Gil-Alana 2021). Managers’ faith in the economy has been shaken by growing fiscal deficits that can no longer be controlled. When governments began to realize how bad things were getting economically, the International Monetary Fund (2020) recommended that countries implement a four-tiered strategy to fiscal policy to ensure they were creditworthy. In order to boost economic development, motivation packages should not have a long-term influence on deficits, and medium-term techniques should include the need to repair fiscal imbalances when conditions change. A robust public healthcare system and pension changes should be implemented in countries that face long-term demographic issues in order to meet the needs of a mounting workforce (Abbasi et al. 2021; Yao et al. 2022). Even in prosperous economies in South Asia, the pandemic’s economic impact is diminishing, highlighting the need for this strategy (Hu et al. 2021; Feng et al. 2022).
Suppose the benefits of external borrowing outweigh the costs of borrowing. In that case, even if it occurs throughout a business cycle and is not utilized effectively and carefully, it is not necessarily harmful to an economy (Ma et al. 2022; Onubi et al. 2022). Debt financed by sources other than domestic sources can increase capacity while simultaneously expanding productivity (Streimikiene and Kaftan 2021; Xu et al. 2022). Alternatively, the debt could lead to a fiscal imbalance and an increase in foreign lending, putting the country at risk of facing various economic difficulties. Fiscal policies are less effective because of debt, which limits the capability of monetary controllers to raise interest rates for monetary causes (Yang et al. 2021). Even while policymakers may find it challenging to consider the impact of enormous public debt on productivity growth from the public’s perspective, scientific analysis of the debt-growth connection in Bangladesh is grossly lacking. Our study assesses the difficulties posed by the dynamic relationship between growth and the level of foreign debt (Song et al. 2022; Ma and Zhu 2022). This is because Bangladesh has a low debt-to-GDP ratio and a lower external debt per capita than other countries in South Asia. It would be interesting to investigate the debt-GDP nexus and see how MEP effects this assembly in the context of Asia between 1980 and 2017 (Shabir et al. 2021; Huang et al. 2022).
Economic growth is influenced by a number of factors, including employment levels, the openness of the government, and the public spending mix. Corruption, a market economy, and democracy have all been considered in empirical studies on fiscal policy’s ability to influence causative factors’ fiscal policy potency. In the meantime, the impact of external loans on fiscal policy sustainability is also a concern. For example, Canelli et al. (2021) found that debt maturity and the fraction of foreign-based debt play a significant role in the stability of the Italian currency rate. The Indian economy is also prejudiced by the central government’s debt, total industrial production growth, and debt services in the short run, according to Aktar et al. (2021).
Here are some of the article’s contributions: (1) First and foremost, the findings contain a number of cautions, and conceptualizations gleaned from many perspectives. Many important aspects of fiscal policy analysis are represented here, including the possible supplemental and substitutability of programs. As a result of this research, we may better understand the medium-term effects of policies such as tax cuts and jobless benefits by incorporating endogenous distribution decisions in the workforce supply. To reduce the duration and intensity of the headwind caused by the pandemic, fiscal policy is likely to be used to reduce its possibility. There are also a number of issues that were not considered before. Operational deficiencies could become catastrophic if the policy aim is pursued more aggressively. Extra costs associated with red tape may be part of a well-targeted policy. Implementing a less stringent regulation requiring less information and time may benefit from this. That is the transfer of suitable individuals rather than the extension of jobless benefits. Due to the pandemic outbreak, we have to alter the normal econometric estimate method in conjunction with a dual measurement. Furthermore, econometric approximations are confined since the pandemic fallout directly impacts the forecasting of parameters that have not been affected as much by the pandemic. The equations are approximated using data from that pandemic. Those are the most recent data points, and 2020 data points are not included in the approximation. I believe this research will be a pioneering attempt to explain and prove countries’ budgetary responses that prior epidemics have infected. When a disease spreads, one of the most pressing concerns for policymakers is how to fund the health care system. The nonlinearity of healthcare spending as a function of income levels was also used to determine the different responses by information lead beyond the information gaps. A comparison of fiscal policy variations and economies’ fiscal latitude is eventually made.
It’s a well-executed essay. Next, we’ll go into the specifics of the theory and evidence that were the basis for the second unit’s findings. Sample projection analysis is explained in unit three, while unit four provides the results of the projection analysis. The course comes to an end after the completion of unit five.
Literature review
External debt borrowing has a negative impact on economic growth in dual settings of zero and one, according to new research by (Del Lo et al. 2022; Zhang et al. 2022). Nevertheless, public debt has stronger effects on the expansion and development of the economic system. As a result, they accomplish that there is a curvilinear association between borrowing and economic progress. Despite this, there isn’t enough study on the efficiency of fiscal policy, institutions, and foreign debts (Chen and Bashir 2022). This is a significant problem. As a result, this study relies significantly on Cueto et al. (2022) to analyze the impact of fiscal policy on financial system activity development in conjunction with variances in institutions and issues with foreign debt in developing nations.
Fiscal policy is also considered a vast literature, but the success of the fiscal policy is observed in long-run sustainable growth and economic development influence. There’s a natural tendency to look to Keynesian theory regarding academic research on fiscal policy effectiveness. Keynesian theory assumes that wages and prices adjust steadily to changes in demand and supply, leading to deficiencies and surpluses due to both the sticky price and excess capacity (Yang et al. 2020; Lyu et al. 2021). As a result, fiscal policy appears to be more effective than classical economists predicted, despite the theory that demand and public spending are affected by a multiplier effect (Vo et al. 2022). As a result of a lack of private spending and consumption during an economic slump, the government should spend more money to put money in the hands of the public (Li et al. 2021). There is still an opportunity for fiscal policy to have a crowding-out effect, which means that increases in public spending crowd out the public sector demand. In open economies, this negatively influences productivity because of the fluctuation in interest rates (Xu et al. 2022). In addition to the assumptions that a rise in interest rates has a negative impact on private investment, fiscal policy that encourages borrowing has resulted in lower private investment as a result of a rise in interest rates (Halkos and Gkampoura 2021). The neo-classical school of thought also focuses on the determination of productivity, goods, and income supplies in markets via the demand and supply fronts by linking the conjecture of utility maximization of earnings-muted individuals and enterprises within the broader factors of generation and information convenience, where neo-classical economics increases the reasonable expectations in Keynesian economics (Fang and Chang 2022). As the fiscal policy is no longer significant in the long or near term, this highlights the changes in economic variables that have taken place. As a result, the long-term budgetary variances could crowd out the private sector’s expectations of stable changes in interest rates and currency rates (Sarkodie and Owusu 2021; Cueto et al. 2022).
Studies demonstrate that an epidemic with large public health implications may slow economic development in the short run and later extend to the economic markets, producing a substantial shock to the economy. The COVID-19 epidemic, according to De Blasio et al. (2022), could disrupt China’s economy’s fragile enterprise stability as of the end of 2019, degrade operating efficiency and prospects of businesses, decrease the development rate of inhabitants’ incomes, enhance employment burden, and raise debt and financial risks. Asikha et al. (2021) predict short-term effects on the domestic economy. All types of assets were sold off in response to the new COVID-19 outbreak, and van der Wielen and Barrios (2021) found that financial market liquidity accelerated contraction due to this epidemic’s global spread. According to Kusa et al. (2022), the “de-Chineseization” of the financial industry had begun because of the spread of the COVID-19 outbreak. According to international academics, investor and consumer confidence will plummet as this epidemic spreads swiftly across the globe, significantly influencing global financial markets. The global economy would be badly affected by limits on foreign travel and other monetary policy measures. According to Burhan et al. (2021) and Juergensen et al. (2020), home countries or regions. Sendroiu (2022) claimed that the COVID-19 infection could create worldwide production shutdowns and supply chain disruptions that could have an unprecedented impact on all economic sectors. The global economic crisis was compounded through financial channels by the COVID-19 epidemic’s impact on world health (Hoang et al. 2021; Onubi et al. 2022). While fundamental economic weaknesses created the previous recession, Ye et al. (2022) pointed out that this crisis was entirely exogenous and exceedingly unknown, with global ramifications.
Macroeconomic tools like financial and economic policy can influence the equities and bond markets, and they’ve also been used to regulate the financial market in the wake of shocks like COVID-19. We found that government expenditure could significantly reduce the economic impact of uncommon disasters, as Shirish et al. (2021) documented. Following terrorist attacks and seismic disasters, Yuan et al. (2021) stated that the central banks of the USA and Japan have also used huge cross-delivery initiatives to keep the economy stable. Ficetola and Rubolini (2021) and Sun and Wang (2021) found that combined economic and financial measures might effectively assist economic rescue and stabilize the financial system in the wake of unforeseen occurrences like the SARS pandemic in 2003 and the financial crisis of 2008 (Umar et al. 2021). In massive government purchases, the surplus in the production economic structure is solidified and formed (Barrero et al. 2021), which may have a negative impact on economic development later on. According to Ridzuan and Abd Rahman (2021), DSGE’s model fiscal policy intervention could relieve the negative effects of the COVID-19 outbreak.
A review of past studies reveals that most of the literature focuses on the impact of crises like epidemics on macroeconomic fluctuations (Chau et al. 2021; Rao et al. 2022; Tang et al. 2022b). It is usually assumed that crises lead to a short-term economic decline. There has been a lot of study on the effects of fiscal and monetary policies on economic activity during an emergency (Yasir et al. 2020; Ahmad et al. 2020; Irfan et al. 2021a, b, c; Ali Shah et al. 2021; Ali et al. 2021). Still, there has been little research on the implications of these policies on the stock and bond markets following the catastrophe. When the stock and bond markets are experiencing an epidemic, little is known about the impact of economic policies, especially fiscal and monetary policy.
The COVID-19 outbreak has resulted in higher social management expenses, higher unemployment rates, and decreased economic vitality, unlike any other crisis in recent memory. Therefore, it is of theoretical and practical importance to measure and examine the effect of economic policy in alleviating financial market shocks induced by the COVID-19 outbreak. This work addresses the following research queries: Stock and bond markets are affected by the COVID-19 outbreak. Considering financial market volatility caused by the COVID-19 epidemic, what role do fiscal and monetary policies play? Is there any way to make the COVID-19 pandemic’s impact even more manageable in the future?
Methodology and data
We calculated the link between public health spending and pandemic-related signals using the theoretical model presented above. To see if “information-lead” nations respond differently from “information-laggard” nations, researchers used a quasi-dynamic econometric analysis. In studies such as Wren-Lewis (2020) and Una et al. (2020), health sector factors are not continuous or thoroughly examined, as (Bilger and Manning 2015) have demonstrated. Because of the censored data, non-linear estimation of health sector variables has gotten more attention.
Dynamic threshold estimation was used in this investigation. The Hansen (1999) panel data estimation method with threshold variables is widely utilized in the literature. Many macroeconomic applications, such as the one examined here, cannot use the Hansen (1999) technique because it was designed for a static panel model, and its fixed effect estimator requires strongly exogenous covariates. The dynamic panel threshold model, generally known as the extended Hansen (1999) model that includes the dynamic relationship and endogenous covariates presented by (Seo and Shin 2016), was employed in the current investigation. Roodman (2009) type instruments have been utilized to cope with potential endogeneity issues utilizing FD-GMM estimate techniques.
This study applies a threshold approach by examining the relationship between per-capita income, governmental debt, and the impact of pandemic signals on health spending. A dynamic framework is needed because of a lack of immediate responsiveness to pandemic warnings. The following are the hypothesized equations:
1 GHEit=αi+β1GHEi,t-1+β2lnGDPPCit+β3PUIit+β4PUIi,t-1+(δ0+δ1GHEi,t-1+δ2lnGDPPCit+δ3PUIit+δ4PUIi,t-1)I·(lnGNIPCit>γ)+ηit
And
2 GHEit=αi+β1GHEi,t-1+β2lnGDPPCit+β3PUIit+β4PUIi,t-1+(δ0+δ1GHEi,t-1+δ2lnGDPPCit+δ3PUIit+δ4PUIi,t-1)I·(lnFDYit>γ)+ηit
Public health expenditures (GHE), real GDPPC (PUI), GNIPC (GNI per capita), and financial debt (FDY) are all measured as percentages of GDP. If the situation in the parenthesis is actual, the I(.) parameter will take on the value one; otherwise, it will take on the value zero. As you can see, _0 denotes the contrast between the different regimes, in aspects of the constant terms, in terms of these terms. This means that when the threshold variable is greater than and equal to the coefficient of one variable, it is called β_k + δ_ k (k = 1,2,3,4). Otherwise, it is β_k + δ_k.
In order to remove the individual effects, the first difference transformation has been considered as follows:3 ΔGHEit=Δxitβ′+δ′Xit′Iit(γ)+Δηiti=1,2,⋯,n;t=1,2,⋯,T;
For example, in the following example, we will use a four-variable vector called x it, and the difference operator β = 〖(β_1,β_2,β_3,β_4)〗^', and γ = 〖(γ_1,γ_2,γ_3,γ_4)〗^'. In Hall (2015)’s GMM estimation technique, the unknown parameters θ = (β^',δ^',γ^') were estimated via a grid search method minimizing the objective function. A bootstrap test of linearity using the null hypothesis H_0:δ_0 = 0 against the alternative hypothesis H_1:δ_0 ≠ 0for any γ ∈ Γ was also used to check for a threshold effect, following Hall’s (2015) work. The various parameter estimates are then acquired for a given threshold value, gamma before the operation is repeated for gamma corresponding to another subset of the threshold variable. These independent variables all have a threshold value repeated in this subgroup. According to the GMM function, the ideal estimated parameters 1 are those that fall below this threshold.
According to our research, we have utilized the ratio of public health expenditure to GDP as an explained variable; and real per capita GDP and the pandemic uncertainty index (PUI) as explanatory factors, respectively, to analyze the fiscal procyclicality of pandemic uncertainties. Fiscal procyclicality and fiscal countercyclicality have been defined as positive and negative responses to pandemic uncertainty. As a healthcare cycle, pandemics have been viewed as a boom or bust depending on the level of worry about pandemics. As a result, the relative responsiveness of the public and private health expenditures has been evaluated. These two effects were captured using per capita GDP and the public debt to GDP ratio as threshold variables. These results are seen on a contemporaneous and lagged basis to capture the dynamic effects of health sector spending. Using Table 1, you can see which variables are dependent and which are independent.Table 1 Variables and their description
Tax cut TC
Economic recovery model ERM
Worker support WS
Utility benefits UB
Covid relief fund CRF
Government spending GS
Coronavirus aid CA
Unemployment insurance EI
Inflation INF
Data from 2000 to 2017 have been used to conduct an empirical analysis. The World Development Indicators database contains public and private health expenditure data as a percentage of GDP and real per capita GDP. The PUI developed by Ramli and Jamri (2021) has been widely used as a proxy for a pandemic in the country. In addition to discussing pandemics within the country, this index also takes into account global conversations. Economist Intelligence Unit (EIU) nation reports are analyzed using text mining algorithms to determine the frequency with which a word connected to pandemics is referenced. To compute the index, the percentage of words in EIU nation reports relating to pandemics is multiplied by 1000. The greater the number, the more people are talking about pandemics. SARS, H5N1, avian flu, H1N1, swine flu, H1N1, Middle East respiratory syndrome, MERS, bird flu, Ebola, coronavirus, COVID-19, influenza, H1V1, World Health Organization, and WHO are among the terms searched in the Economist Intelligence Unit nation reports. WPUI is a global measure of pandemic uncertainty, not just for the USA but for the rest of the world. As far as pandemics go, SARS and Ebola are the most talked about.
Economic impact of initial public health responses
In the early stages of the ebola outbreak, states were shut down to safeguard their residents from a rapidly spreading and potentially lethal illness. These difficult decisions were based on a practical cost/benefit analysis with obstacles. The Economist noted, “a government trying to privilege the health of its economy over the health of its citizenry would likely end up with neither. This is one reason why, in the acute phase of the epidemic, a comparison of costs and benefits comes down clearly on the side of action along the lines being taken in many countries” to minimize the spread of the virus. As expected, this necessitated shutting down a wide swath of the economy (Pop 2022). Unemployment soared from 3.5% in 2019 to 14.7% in April 2020 before falling to nearly 10% in July and 7% in October, with more than 11 million people still out of work at the end of the month (Lau et al. 2021).
The government’s first response to the economic crisis was rapid, comprehensive, and comprised of fiscal, regulatory, and monetary policy solutions. There are links to all federal government activities, except financial regulation and reporting adjustments, on the official website https://www.usa.gov/coronavirus. Achim et al. (2021) summarize regulatory relief measures.
Initial fiscal responses
The government took comprehensive and swiftly executed fiscal measures to reduce the impact on citizens’ wallets. Public Law 116–136 (2020), called the CARES Act, was passed by Congress on March 27, 2020, and several of its expiring provisions were extended in December. LaBrecque provides a brief explanation of the Act (2020). Until the virus infection rate dropped and reopening became more realistic, the main idea was to keep as many organizations and individuals financially solvent as possible (Liu et al. 2022b). With an initial $454 billion authorization, the CARES Act also offered additional funds to help states with COVID-19 spending, which the closure had hit. Despite its hefty price tag, the CARES Act was designed with the belief that early financial aid would lessen the economic hit. The Paycheck Protection Program (PPP) was established for businesses and nonprofit organizations with less than 500 employees as a result of the Act, which increased unemployment benefits by $600 per week for up to four months. Employee retention was one of the primary goals of the PPP for these businesses, but some of the monies were also available for other uses. The loans could be forgiven if the money was used for one of these permitted purposes. The CARES Act provided further support for those particularly heavily impacted industries. Taxes imposed on aviation enterprises, for example, have been deferred for the balance of 2020.
Results and discussion
Descriptive statistics
The Statistic Descriptive is shown in Table 2. DPS is overdispersed because it contains corporate samples that distribute dividends consistently and infrequently during the study period. Negative growth is indicated by a GDP value of − 0.0207 or − 2.07%. A company is said to be in the red if its EPS is even slightly negative. Samples that consistently and inconsistently distribute dividends during the research period are included in the minimum PYD, which is the same as DPS. The behavior of the sample as a whole is also influenced by the presence of anomalous situations.Table 2 Statistic descriptive
Variables Mean Maximum Minimum Std. dev
TC 41.20614 750 0 87.86223
ERM 0.040157 0.0517 − 0.020700 0.024866
WS 0.142857 1 0 0.350045
UB 122.6161 2915.996 − 1616.927 261.3724
CRF 1.214568 35.4656 0.00032 1.646576
GS 2.055611 246.4597 0.049962 6.794951
CA 15.33299 19.67902 11.08373 1.512957
EI 35.95755 162 5 16.91732
INF 262,492.90 16,608,751 0 1,124,672
Panel unit root test
Next, we conduct a unit root test using LLC tests for the variables of interest. Because the p value for the LLC test is less than 5%, the null hypothesis (Ho)—all panels possessing a unit root—is rejected, as shown in Table 3.Table 3 Levin, Lin, and Chu panel unit root test results
Variables T statistics p value
TC − 41.57223 0
ERM − 41.198325 0
WS − 2.25477 0.0159
UB − 6.365415 0
CRF − 7.03542 0
GS − 2.254875 0
CA − 6.4405425 0
EI − 6.507543 0.015
INF − 2.25498 0.01
LLC Levin-Lin-Cu, TC tex cut, ERM economic recovery model, WS worker support, CRF corona relief funds, CA corona aid, GS government
To find the optimum panel data regression econometric model, a likelihood test was conducted in Table 4. FEM was selected as a model after the Chow test findings for GDP and COV models, with a p value cross-section of 2 = 0.000 (5%), leading to the Hausman test.Table 4 Chow test and Hausman test
Proxy of crisis variable Chow test Hausman test
Cross-section χ2 (statistics) df p value Cross-section random (χ2 statistic) χ2 p value
GDP 760.4814 221.55 0 75.63885 7.35 0
COV 760.4814 221.55 0 75.63885 7.35 0 0
The p value of a random cross-section = 0.000 (5%) for the Hausman test results for the GDP and COV models led to the selection of the FEM model. As a result, it was determined that the least square dummy variable method works best with the fixed effect model. For the Lagrange multiplier test, Chow and Hausman consistently used FEM.
Both FEM models were subjected to a goodness of fit test, and the results are shown in Table 5. Adjusted R2 was 62% for GDP-FEM and COV-FEM in the variability or coefficient of determination analysis. F-tests were also performed simultaneously, and F = 0.0005%, which proves that at least one exogenous variable had a significant impact on the others. The T-test was used for a portion of the analysis.Table 5 Static panel data regression
Proxy of predictor GDP-CEM GDP-FEM GDP-REM COV-CEM COV-FEM COV-REM
Constant − 0.569
(19.63)
**
− 232.980
(101.35)
21.554
(28.26)
− 3.813
(19.31)
**
− 233.360
(101.30)
− 21.554
(28.16)
TC − 73.588
(66.97)
28.150
(77.14)
− 47.681
(61.10)
––––– ––––– –––––
ERM ––––– ––––– ––––– *6.692
(5.03)
-2.591
(5.42)
-47.681
(61.10)
WS ***0.226
(0.0073)
***0.168
(0.0084)
***0.199
(0.0073)
***0.226
(0.0073)
***0.172
(0.0084)
***0.199
(0.0073)
UB 0.918
(1.15)
*1.874
(1.38)
*1.634
(1.20)
0.905
(1.15)
*1.880
(1.38)
*1.634
(1.20)
CRF 1.171
(0.273)
0.141
(0.278)
0.513
(0.259)
1.173
(0.273)
0.142
(0.278)
0.513
(0.259)
GS 0.969
(1.28)
**15.671
(7.92)
*2.426
(1.84)
0.954
(1.28)
**15.566
(7.91)
*2.426
(0.014)
CA 0.0651
(1.05)
0.2499
(1.155)
0.0231
(1.155)
0.070
(0.105)
0.344
(0.184)
0.023
(0.155)
EI ***0.000
0.000
***0.000
0.000
***0.000
0.00
*** 0.000
0.000
***0.000
0.000
*** 0.000
0.000
R2 0.494 0.708 0.373 0.494 0.709 0.373
Adj-R2 0.492 0.651 0.370 0.492 0.651 0.370
F-statistics ***197.247 ***12.682 ***122.245 ***197.405 ***12.684 ***122.245
Number of panel observations 1484 1484 1484 1484 1484 1484
Description: the numbers in parenthesis show standard errors and (*) shows significance at 10%, (**) shows significance at 5%, and (***) depicts significance at 1%
The model specification test was performed to guarantee that the estimation did not contradict the classical assumptions (Kumar and Ayedee 2021; Wang et al. 2021; Paul et al. 2021) in order to ensure that it was free of habitual traits. Table 6 shows the results of the method’s testing of the fixed-effect model’s classical assumptions.Table 6 Classical assumption test on fixed effect model
Proxy of crisis variable Normality test: p-value of Jarque–Bera Multicollinearity test: bivariate Pearson correlation Autocorrelation test: Durbin–Watson test Heteroscedasticity test: Glejser test
GDP 0.000 No multicollinearity 2.159 Heteroscedasticity
COV 0.000 No multicollinearity 2.160 Heteroscedasticity
The Jarque–Bera technique was used to test for normalcy in GDP-FEM and COV-FEM. Because the results gathered for GDP-FEM and COV-FEM have a p value of 0.0005%, it was determined that the error does not follow a normal distribution. Therefore, the normality assumption was broken. This was reached to reach this conclusion. The multicollinearity test was not broken in either the GDP-FEM or the COV-FEM, according to the results of the Pearson two variables among independent factors. Which was based on the Pearson bivariate correlation between exogenous variables. For GDP-FEM and COV-FEM, the autocorrelation test with the DW analysis yielded findings of 2.159 and 2.160, respectively. With the values of n = 1484 and k = 7, dL = 1.5922 and dU = 1.7582 were calculated. Between 1.7582 and 2.2418, the autocorrelation-free area is located. Neither the GDP-FEM nor the COV-FEM suffers from autocorrelation issues. GDP-FEM and COV-FEM violated the heteroscedasticity assumption.
FEM models that violate the classical assumptions of the two selected FEM models are examined by estimating a resilient parameter by including Yit1 as an instrumental variable in the dynamic panel data regression. Outliers or data transformations were not used to correct the violation. But rather, the company sample was not included in all the estimations.
A dynamic panel data regression with the first difference generalized method of moments (FD-GMM) and the generalized system method of moments (GSMM) is shown in Table 7 (SYS-GMM). As a result of the COVID-19 pandemic and GDP growth, the crisis variable’s measurement dimension was established. A model specification test was the kickoff for the following dynamic panel data regression analysis.Table 7 Dynamic panel data regression and unbiased test
Proxy of predictor GDP GDP GDP GDP OLS-RSE COV COV COV COV
LSDV-RSE FD-GMM SYS-GMM LSDV -RSE FD-GMM SYS-GMM OLS-RSE
Constant *** − 295.437 * 208.794 − 76.007 2.42 *** − 292.810 * 207.980 − 80.625 − 1.444
− 118.375 − 158.821 − 83.786 − 16.98 -s117.716 − 159.421 − 82.704 − 16.536
TC − 0.446 * 0.027 *** 0.049 *** 0.282 − 0.446 * 0.027 *** 0.049 *** 0.282
− 0.063 − 0.021 − 0.017 − 0.058 − 0.063 − 0.021 − 0.017 − 0.058
ERM * 46.056 − 24.308 ** − 76.599 * − 78.078 ––––– ––––– ––––– –––––
-75.16 -43.989 -40.249 -52.182
GS ––––– ––––– ––––– ––––– − 2.718 1.926 ** 5.569 * 6.031
− 5.369 − 3.155 − 2.898 − 3.584
UB *** 0.181 *** 0.227 *** 0.233 *** 0.185 *** 0.181 *** 0.227 *** 0.233 *** 0.185
− 0.032 − 0.021 − 0.016 − 0.023 − 0.032 − 0.021 − 0.016 − 0.023
CRF 3.078 *** 9.812 *** 9.472 2.248 3.073 *** 9.802 *** 9.450 2.246
− 2.936 − 2.556 − 2.069 − 2.349 − 2.942 − 2.57 − 2.078 − 2.351
WS ** 3.652 − 0.333 − 0.659 *** 4.055 ** 3.647 − 0.346 − 0.662 *** 4.059
− 1.977 − 0.819 − 0.853 − 0.996 − 1.977 − 0.819 − 0.853 − 0.996
EI ** 18.502 ** − 21.485 5.673 0.027 ** 18.664 ** − 21.395 5.847 0.018
− 8.739 − 12.856 − 5.745 − 1.137 − 8.747 − 12.873 − 5.713 − 1.136
CA 0.578 ** 3.289 − 0.274 − 0.061 0.498 ** 3.239 − 0.326 − 0.061
− 1.429 − 1.474 − 0.593 − 0.067 − 1.444 − 1.468 − 0.593 − 0.067
Number of groups 212 212 212 ––––– 212 212 212 –––––
Number of instruments ––––– 22 27 ––––– ––––– 22 27 –––––
Wald χ2
––––– *** 135.940 *** 269.660 ––––– ––––– *** 135.940 *** 269.250 –––––
R2
0.359 ––––– ––––– 0.555 0.361 ––––– ––––– 0.555
F-statistics *** 9.440 ––––– ––––– *** 60.190 *** 9.400 ––––– ––––– *** 58.842
A robust standard error is larger than the standard error. If we use one-tailed statistics, then (*) has a significance level of = 10%, (**) of = 5%, and (***) of = 1%
Robustness test
We could confirm our findings’ validity by modeling the regressions with other COVID-19 variables, such as COVID shocks (Sarkodie and Owusu 2021). In order to calculate COVID shocks, we use the gross export gap (also known as the export gap) (Pu et al. 2022). The export gap was selected since it directly affects trade fluctuations. To top it all off, pinpointing this weakness can lead to better export results (Rahman et al. 2022). When potential value surpasses actual value, a recession is triggered. The estimated value of potential exports is often used as a proxy because they are difficult to observe in the real world. In order to estimate the export value, we applied the Hodrick-Prescott (HP) filter (Belitski et al. 2022). Exported goods have changed over time, as seen by COVID shock data. This approach can determine if other components of COVID-19 have an impact on a country’s fiscal policies.
Backward (0.082) and forward (0.059) links show that the COVID shock has a negative impact on economic policy in any country that is affected by it. The rising fragmentation of production increases a country’s vulnerability to global shocks. Table 8 reveals that GDP per capita is in line with expectations. The two index measures exhibit positive and significant coefficients when controlling for institutional quality. Export performance is strongly influenced by institutional characteristics in the exporting country, such as government efficiency and the rule of law. Specifically, the exporting country’s market-oriented institutional systems positively impact export performance. All parameters representing institutional quality are substantial and favorable in both the backward and forward GVC participation models. That is why institutions’ quality plays an important role in trade performance, as our data show (Iancu et al. 2022). In addition, these alternative measures’ estimation results are consistent with the preliminary findings.Table 8 Empirical results for robustness tests
Variables GVCF GVCB
(1) (2)
TC 0.355 *** 0.057 ***
(0.012) (0.008)
ERM − 0.059 *** − 0.082 ***
(0.006) (0.005)
GS 0.223 *** 0.216 ***
(0.046) (0.038)
UB 0.084 *** 0.070 ***
(0.003) (0.008)
CRF 0.030 *** 0.028 ***
(0.006) (0.005)
Constant 0.367 *** 0.276 ***
(0.099) (0.060)
No. of observations 328 410
No. of countries 41 41
Hansen test, p value 35.10; 0.167 37.15; 0.244
AB–AR(1); p value − 1.27; 0.203 − 2.43; 0.015
AB–AR(2); p value 0.61; 0.545 − 1.69; 0.092
It should be noted that the significance level is *** p 0.01. In parenthesis are the standard deviations. Stata 17 was used by the authors to do the calculations
Finally, the validity of our instrument was confirmed by our specification tests. All regressions were found to be free of bad instrument selection or model specification based on the over-identification and AR (2) test statistics at the 95% confidence level. The COVID-19 epidemic has a major impact on GVC involvement is supported by the findings of the dynamic panel. With GDP per capita as a control variable, GVC involvement was considerably higher.
Discussion
The worldwide health crisis brought on by coronavirus-2019 has had a negative impact on the global economy, as has been debated and proven in the literature. This detrimental effect has already been acknowledged on both the local and global levels. COVID-19 may cost the world economy an estimated USD 2.7 trillion, according to a number of early estimates from several studies (Dudek and Śpiewak 2022). Scholars say COVID-19 has negatively impacted the economy, resulting in lower GDPs and higher import and export expenses. In addition, the negative impact varies widely across sectors of the economy, with tourist and domestic services suffering the most and natural resources and agriculture suffering the least. Tourism and domestic services suffer the most significant losses. Reports also show significant regional disparities (Vătămănescu et al. 2021). The initial pieces of empirical data suggesting a connection between the COVID-19 epidemic and the financial crisis were presented by Zheng et al. (2021). Applying the fundamental method of ordinary least squares along with panel fixed effects regressions, they analyzed data from the first three quarters of 2020 for 42 nations. In order to explain the connection between the pandemic and GDP, they emphasized two key elements. The first is the result of restrictions imposed by various governments. Their findings show that government restrictions reduced GDP growth in the same quarter but that GDP dynamics improved noticeably in the next quarter after limits were lifted. Secondly, health risks associated with the epidemic lead to social withdrawal, reducing GDP. They used fatality rates as a measure of health risk and found that high fatality rates significantly contribute to the negative growth rates. Unlike König and Winkler’s work, which was based in Germany, our research was conducted in China over a more extended time period (a full year instead of three quarters) and with a different econometric approach and database. According to our findings, which are in line with those of other researchers, the COVID-19 pandemic has had a negative impact on world economic policy. Using Kendall tau-B coefficients, we found a negative association between COVID-19 fatality rates and GDP relative increases (Zhao et al. 2022). Because the p-value is so little (below 0.05), the findings are considered reliable. We also found a link between the decline in taxes and the rate of COVID-19 infections. This connection was marginally significant for CCR levels below 7, i.e., up to 7 infections per 1000 people in 2020. It has now been proven that the COVID-19 pandemic has a negative connection with the economic recovery model (ERM), as previously hypothesized.
Efforts to restrict the spread of the COVID-19 pandemic in China were tackled in different ways in different regions of the country. Lockdown, isolation, and quarantine were regularly used to combat the pandemic. Policymakers imposed more stringent limitations as the pandemic spread faster in China (Albulescu 2021). COVID-19 tests have also been routinely deployed to stop the pandemic’s spread. Increasing the number of people who get tested could, according to some, slow the spread of the pandemic (Surya et al. 2022). Despite this, the countries employed diverse testing methodologies due to the high expenses and financial constraints. Third, government corona relief funds were used to assist businesses and residents during periods of isolation and lockdowns in order to lessen the economic burden of the COVID-19 epidemic [29,30]. According to a number of researchers, the epidemic also led to an increase in cross-border communication between countries. We hypothesized that the association between the epidemic and tax cut fluctuations is not linear. With slopes of 12.9928 (or up to 200 per 1 million) and intercepts of 1.3475 (or above the same), we calculated that the affiliation between the COVID-19 mortality rate change and changes in the relative growth of GDP may be well represented by the intersection of two straight lines. This illustrates that for every 100,000 individuals that die, GDP drops by 0.013 percentage points. Only 0.0013 percentage points of GDP are lost for every new death in a million people when the COVID-19 mortality rate exceeds 0.2 deaths per 1000 persons. The correlation between GDP and SARS-CoV-19 mortality is identical at a threshold of 7 infections per 1000 individuals. To put it differently, the GDP was reduced by 0.4581 for every additional sick individual for every thousand persons. As soon as the number of newly discovered illnesses reaches seven, the statistical significance of the association is lost (very high p-value Kendall tau-B). As a result, new COVID-19 deaths have a much greater impact on GDP than infections. Two unique ranges of GDP and COVID-19 morbidity and mortality rates demonstrate that countries can adapt to the pandemic above a certain threshold. In light of this finding, we must adopt hypothesis number two. COVID-19 pandemic–induced economic crisis is a multidimensional issue that differs from other recent economic downturns. This is partly a result of the pandemic’s direct impact on employee mortality, large-scale absences, productivity declines, negative stock returns, production activity decrease, international supply chain disruptions, and demise of the tourism industry (Kumari and Bhateja 2022). Only a few economists adopted a high-level, comprehensive approach to their research because of the complexity of the crisis. At the same time, the majority of them concentrated on specific economic issues. According to the authors, economic policy uncertainty significantly determines the current crisis’s trajectory. As a result, consumers, organizations, and governments all put off making important financial decisions because of the uncertainty surrounding the economic policy. As a result, there will be less spending, fewer loans, and less investment. The authors note that political and regulatory instability affects commodity and crypto-currency markets (Gregurec et al. 2021). According to Song and Zhou, uncertainty is a major factor in the current economic crisis. In light of China’s economic woes and poor recent growth, synchronized global economic slowdown, de-globalization, and negative macroeconomic settings, they highlight peak periods previous to COVID-19 (Zamfir and Iordache 2022). The impact of the COVID-19 pandemic on crude oil prices and some stock indices, such as the DJI, S&P 500, and NASDAQ, was examined by (Irfan et al. 2021e). The independent variable was the number of new COVID-19 infections. Uncertainty and supply shocks in global crude oil inventories have led to a decline in crude oil prices. There was also evidence of a link between stock market prices and the pandemic, but it was based on expectations and monetary and fiscal incentives rather than on the real state of the economy (Onubi et al. 2022). There were also investigations into how COVID-19 affected several industries, including tourism, hospitality and sporting events; education; and financial services. Each industry they looked at had negative approximations (Alyahya et al. 2021). SARS-CoV-19 pandemic’s impact on China’s economy was the primary subject of our research. Instead of focusing on a certain industry or economy percentage, we focused on the overall economy. To understand how the economy was faring, we looked at the relative rise in the gross domestic product over the pandemic in the countries investigated. Compared to other research, ours included a broader range of countries and spanned a longer period. Given the preceding and based on the study’s strong statistical findings, our findings demonstrate how the COVID-19 pandemic has impacted various countries’ economic systems globally.
There are a number of drawbacks to the research reported here. To begin, only data about China were included in this analysis. As a result, not all countries were eligible for inclusion in our investigation. Second, we computed each country’s GDP change based on the International Monetary Fund’s initial estimates of 2020 GDP as of March 7, 2021. Is there a way to tweak these variables? Although the two constraints above may alter the coefficient values produced, our total results will not be affected. Since our findings had low p-values, we may say that they were robust (below 0.05). This study’s final restriction stems from the time period in which it was conducted. We cannot conclude the long-term influence of the COVID-19 pandemic on the economies of the analyzed countries because we researched only one year, 2020.
As part of this study, we analyzed each country’s economy concerning the COVID-19 epidemic. We did not investigate the fundamental causes, which we believe to be quite complex. As a result, additional studies of adaptive mechanisms in the context of the pandemic could be of scientific interest. Global dynamics associated with the pandemic and GDP fluctuations were examined in this article. This is why we didn’t pay attention to the timing or severity of the epidemic in different regions. Although this analysis was not required for our study, we believe it is a helpful starting point for future research. As a last check, we’ll look into the long-term effects of coronavirus-2019 on worldwide economic activity.
Conclusion and policy implication
Currently, China’s economy is being tested by the coronavirus epidemic that has been sweeping the country since December 2019. This year’s GDP in China expanded by 5.94% to $142.29.94 billion. During the outbreak of COVID-19, China’s economy fell 6.8% in the first quarter of 2020. Beijing began publishing GDP data quarterly in 1992, the first time the figure fell. After the pandemic of COVID-19 was finished, China’s economy rebounded. As of late 2019, China’s gross domestic product had grown at an annual rate of 6.5% before the Coronavirus took hold, according to the National Statistics Bureau. When all other major economies were devastated by the global financial crisis of 2019, China was the only one to grow by 2.3%. Other vital countries and geopolitical rivals are battling a winter wave, just as the USA, Europe, India, and Japan. China’s GDP is expected to surpass 100 trillion Yuan by 2020. (15 trillion USD). After the pandemic, China’s economic growth was re-ignited by innovation and digitization. New York City’s economy rose by 18.3% in the first quarter of 2019. Since China began keeping track of quarterly GDP in 1992, this has been the most substantial GDP rise. In the second and third quarters of this year, China’s GDP increased by 7.9% and 2.3%, respectively. It is projected that China’s GDP will grow by 5.5% in the fourth quarter and 8.5% this year. Next year’s growth will likely fall to 5.4% as low base effects disappear and the economy returns to its pre-COVID-19 trend rate.
Some rich and developing countries’ currencies had fallen sharply by mid-June. Systematic action is required to address the issues produced by currency devaluation. The economic impact of the epidemic necessitates an international response. According to Guo and Shi (2021), tighter coordination between the fiscal and monetary policy is needed to improve policy response to COVID-19. There is a need for global cooperation in health and medical infrastructure, trade, finance, and macroeconomic policy. National coordination of monetary, macroprudential, and fiscal policies could lessen the effects of COVID-19. The macroprudential policy aims to maintain financial stability and avoid systemic risk, whereas monetary policy aims to stabilize prices and control liquidity. The budgetary policy is to boost the economy and build a fiscal cushion. There is a range of tools at the disposal of macroprudential policy, monetary policy, and tax and discretionary countercyclical measures (fiscal policy). Under a central bank’s purpose of fostering price and financial stability, macroprudential and monetary policies encounter time inconsistencies (Faria-e-Castro 2021). We conclude that more effective policymaking in the face of COVID-19 would be enhanced by improved coordination of policies.
Since real interest rates have fallen due to an expansionary monetary policy, it is logical to predict that policymakers will increase inflation to lower the national debt. Depleting a country’s reserve currency would be the most effective way to stabilize the global currency market in this situation. Instead, the “dual deficit hypothesis,” which holds that an increase in the fiscal deficit is accompanied by an increase in the current account deficit, is supported by a more expansive fiscal policy. Shortfalls can lead to debt and inflation if money is borrowed or printed. When inflation rises, the real interest rate falls, causing capital outflows, while an increase in foreign debt may lead to debt sustainability. The employment of macroprudential policy measures can promote and diminish price stability and debt sustainability at the same time. The coordination of macroeconomic, monetary, and fiscal policies is necessary to lower the costs of COVID-19 and provide price stability, financial security, and a sustainable level of debt. Liquidity circulation and a fiscal buffer could be used to mitigate the effects of a pandemic under a strong financial system.
Policy recommendations
According to the findings of this study, early and coordinated implementation of fiscal policies can save lives, prevent people from losing their jobs and incomes, even prevent enterprises from going bankrupt, and facilitate long-term recovery. Among fiscal policy options are public sector loans or equity infusions, loan guarantees, and greater spending. Income and consumption are stabilized by automatic stabilizers like progressive taxes and unemployment benefits, which also provide fiscal assistance. As a result of the pandemic’s economic and societal impact, all of these instruments are now in use. People and businesses in developed economies can take advantage of various spending mechanisms and use social conversation to find solutions. In the wake of recent global crises, it has become clear that governments are incapable of dealing with the issues that arise from major shocks. COVID-19 pandemic’s unprecedented nature has made social interaction between governments and employers and the representation of employees more critical than ever. In order to address the immediate health issue and reduce the effects of some of these measures on employment and incomes, policies and programs can be established and implemented through dialogue and coordinated action by governments, companies, and worker’s organizations.
During and after a pandemic, fiscal policy can successfully protect individuals, stabilize demand, and allow economic recovery across economies. The fiscal policies should be adjusted to healthcare services to give emergency lifelines to protect individuals considering the continuity of lockdowns throughout economies (Moretto and Caniato 2021). Fiscal measures should support households and businesses to alleviate the economy’s informality during lockdowns. To assist the economy recover quickly after the epidemic, job support measures should encourage a safe return to work and facilitate structural shifts. Public investment in healthcare, healthcare systems, and physical and digital infrastructure will be critical after the pandemic slows. Economies must raise revenue, increase spending, and encourage productive investment while fiscal space is minimal. To minimize fiscal risks, all policy initiatives must be arranged in a medium-term fiscal framework with open management (International Monetary Fund 2020).
Author contribution
Lei Chang: conceptualization, data curation, methodology, writing—original draft, visualization, and supervision. Muhammad Mohsin: visualization and editing. Wasim Iqbal: review and editing and software.
Data availability
The data can be available upon request.
Declarations
Ethics approval and consent to participate
The authors declare that they have no known competing financial interests or personal relationships that seem to affect the work reported in this article. We declare that we have no human participants, human data, or human tissues.
Consent for publication
N/A.
Competing interests
The authors declare no competing interests.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Abbasi KR, Shahbaz M, Jiao Z, Tufail M (2021) How energy consumption, industrial growth, urbanization, and CO2 emissions affect economic growth in Pakistan? A novel dynamic ARDL simulations approach. Energy 221:119793. 10.1016/j.energy.2021.119793
Achim MV, Safta IL, Văidean VL, et al (2021) The impact of covid-19 on financial management: evidence from Romania. http://www.tandfonline.com/action/authorSubmission?journalCode=rero20&page=instructions. 10.1080/1331677X.2021.1922090
Ahmad M Iram K Jabeen G Perception-based influence factors of intention to adopt COVID-19 epidemic prevention in China Environ Res 2020 190 109995 10.1016/J.ENVRES.2020.109995 32739626
Aktar MA Alam MM Al-Amin AQ Global economic crisis, energy use, CO2 emissions, and policy roadmap amid COVID-19 Sustain Prod Consum 2021 26 770 781 10.1016/j.spc.2020.12.029 33786357
Albulescu CT (2021) COVID-19 and the United States financial markets’ volatility. Financ Res Lett 38. 10.1016/j.frl.2020.101699
Ali H Yilmaz G Fareed Z Impact of novel coronavirus (COVID-19) on daily routines and air environment: evidence from Turkey Air Qual Atmos Heal 2021 14 381 387 10.1007/S11869-020-00943-2/FIGURES/5
Ali Shah SA, Longsheng C, Solangi YA, et al (2021) Energy trilemma based prioritization of waste-to-energy technologies: implications for post-COVID-19 green economic recovery in Pakistan. J Clean Prod 284:124729. 10.1016/J.JCLEPRO.2020.124729
Alyahya MA, Elshaer IA, Sobaih AEE, et al. (2021) The impact of job insecurity and distributive injustice post COVID-19 on social loafing behavior among hotel workers: mediating role of turnover intention. Int J Environ Res Public Heal 2022 19:411. 10.3390/IJERPH19010411
Asikha M Alam M Al-amin AQ Global economic crisis, energy use, CO 2 emissions, and policy roadmap amid COVID-19 Sustain Prod Consum 2021 26 770 781 10.1016/j.spc.2020.12.029 33786357
Barrero JM Bloom N Davis SJ Meyer BH COVID-19 is a persistent reallocation shock AEA Pap Proc 2021 111 287 291 10.1257/PANDP.20211110
Belitski M Guenther C Kritikos AS Thurik R Economic effects of the COVID-19 pandemic on entrepreneurship and small businesses Small Bus Econ 2022 58 593 609 10.1007/S11187-021-00544-Y/FIGURES/3
Bilger M Manning WG Measuring overfitting in nonlinear models: a new method and an application to health expenditures Heal Econ (United Kingdom) 2015 24 75 85 10.1002/HEC.3003
Burger P Calitz E Covid-19, economic growth and South African fiscal policy S Afr J Econ 2021 89 3 24 10.1111/saje.12270
Burhan M, Salam MT, Hamdan OA, Tariq H (2021) Crisis management in the hospitality sector SMEs in Pakistan during COVID-19. Int J Hosp Manag 98:103037. 10.1016/j.ijhm.2021.103037
Canelli R Fontana G Realfonzo R Passarella MV Are EU policies effective to tackle the Covid-19 crisis? The Case of Italy Rev Polit Econ 2021 33 432 461 10.1080/09538259.2021.1876477
Charoensukmongkol P Does entrepreneurs’ improvisational behavior improve firm performance in time of crisis? Manag Res Rev 2021 10.1108/MRR-12-2020-0738/FULL/PDF
Chau KY Law KMY Tang YM Impact of self-directed learning and educational technology readiness on synchronous E-learning J Organ End User Comput 2021 33 1 20 10.4018/joeuc.20211101.oa26
Chen D, Gao H, Ma Y (2020) Human capital-driven acquisition: evidence from the inevitable disclosure doctrine. 67:4643–4664. 10.1287/MNSC.2020.3707
Chen Mo Bashir Rabia Role of e-commerce and resource utilization for sustainable business development: goal of economic recovery after Covid-19 Economic Change and Restructuring 2022 55 4 2663 2685 10.1007/s10644-022-09404-5
Cueto LJ Frisnedi AFD Collera RB Digital innovations in MSMEs during economic disruptions: experiences and challenges of young entrepreneurs Adm Sci 2022 12 8 10.3390/admsci12010008
De Blasio V Pavone P Migliaccio G Cosmetics companies: income developments in time of crisis J Small Bus Enterp Dev 2022 10.1108/JSBED-11-2019-0369/FULL/XML
Del Lo G Basséne T Séne B COVID-19 And the african financial markets: Less infection, less economic impact ? Financ Res Lett 2022 45 102148 10.1016/j.frl.2021.102148 35221813
Dudek M, Śpiewak R (2022) Effects of the COVID-19 pandemic on sustainable food systems: lessons learned for public policies? The Case of Poland. Agriculture 12. 10.3390/agriculture12010061
Fang M, Chang C-L (2022) Nexus between fiscal imbalances, green fiscal spending, and green economic growth: empirical findings from E-7 economies. Econ Change Restruct 55:2423–2443. 10.1007/s10644-022-09392-6
Faria-e-Castro M (2021) Fiscal policy during a pandemic. J Econ Dyn Control 125. 10.1016/j.jedc.2021.104088
Feng H Liu Z Wu J Nexus between Government spending’s and Green Economic performance: role of green finance and structure effect Environ Technol Innov 2022 27 102461 10.1016/j.eti.2022.102461
Ficetola GF, Rubolini D (2021) Containment measures limit environmental effects on COVID-19 early outbreak dynamics. Sci Total Environ 761. 10.1016/j.scitotenv.2020.144432
Gregurec I, Furjan MT, Tomičić-pupek K (2021) The impact of COVID-19 on sustainable business models in SMEs. Sustain 13:1098. 10.3390/SU13031098
Guo YM Shi YR Impact of the VAT reduction policy on local fiscal pressure in China in light of the COVID-19 pandemic: a measurement based on a computable general equilibrium model Econ Anal Policy 2021 69 253 264 10.1016/j.eap.2020.12.010 35702722
Halkos GE Gkampoura EC Evaluating the effect of economic crisis on energy poverty in Europe Renew Sustain Energy Rev 2021 144 110981 10.1016/j.rser.2021.110981
Hall AR Econometricians have their moments: GMM at 32 Econ Rec 2015 91 1 24 10.1111/1475-4932.12188
Hansen BE Threshold effects in non-dynamic panels: estimation, testing, and inference J Econ 1999 93 345 368 10.1016/S0304-4076(99)00025-1
He L Mu L Jean JA Contributions and challenges of public health social work practice during the initial 2020 COVID-19 outbreak in China Br J Soc Work 2022 10.1093/bjsw/bcac077
Hoang TDL Nguyen HK Nguyen HT Towards an economic recovery after the COVID-19 pandemic: empirical study on electronic commerce adoption of small and medium enterprises in Vietnam Manag Mark 2021 16 47 68 10.2478/mmcks-2021-0004
Hu T Wang S She B Human mobility data in the COVID-19 pandemic: characteristics, applications, and challenges Int J Digit Earth 2021 14 1126 1147 10.1080/17538947.2021.1952324
Huang J, Dong X, Chen J, Zhong M (2022) Do oil prices and economic policy uncertainty matter for precious metal returns? New insights from a TVP-VAR framework. Int Rev Econ Financ 78:433–445. 10.1016/j.iref.2021.12.010
Iancu A, Popescu L, Varzaru AA, Avram CD (2022) Impact of Covid-19 crisis and resilience of small and medium enterprises. Evidence from Romania. East Europ Econ. 10.1080/00128775.2022.2032177
International Monetary Fund (2020) World economic outlook, April 2020: the great lockdown
Iqbal W Tang YM Lijun M Energy policy paradox on environmental performance: the moderating role of renewable energy patents J Environ Manage 2021 297 113230 10.1016/j.jenvman.2021.113230 34303199
Irfan M, Ahmad M, Fareed Z, et al. (2021a) On the indirect environmental outcomes of COVID-19: short-term revival with futuristic long-term implications. Int J Environ Health Res 1–11. 10.1080/09603123.2021a.1874888
Irfan M Akhtar N Ahmad M Assessing public willingness to wear face masks during the COVID-19 pandemic: fresh insights from the theory of planned behavior Int J Environ Res Public Heal 2021 18 4577 10.3390/IJERPH18094577
Irfan M Ikram M Ahmad M Does temperature matter for COVID-19 transmissibility? Evidence across Pakistani provinces Environ Sci Pollut Res 2021 28 59705 59719 10.1007/s11356-021-14875-6
Irfan M, Elavarasan RM, Ahmad M, Mohsin M, Dagar V, Hao Y (2022) Prioritizing and overcoming biomass energy barriers: application of AHP and G-TOPSIS approaches. Technol Forecast Soc Change 177:121524. 10.1016/j.techfore.2022.121524
Jin Y Tang YM Chau KY Abbas M How government expenditure mitigates emissions: a step towards sustainable green economy in belt and road initiatives project J Environ Manage 2022 303 113967 10.1016/j.jenvman.2021.113967 34810022
Juergensen J Guimón J Narula R European SMEs amidst the COVID-19 crisis: assessing impact and policy responses J Ind Bus Econ 2020 47 499 510 10.1007/S40812-020-00169-4/TABLES/2
Kumar A, Ayedee N (2021) An interconnection between COVID-19 and climate change problem. J Stat Manag Syst 1–20. 10.1080/09720510.2021.1875568
Kumari P Bhateja B How COVID-19 impacts consumer purchase intention towards health and hygiene products in India? South Asian J Bus Stud 2022 10.1108/SAJBS-05-2021-0185/FULL/XML
Kusa R Duda J Suder M How to sustain company growth in times of crisis: the mitigating role of entrepreneurial management J Bus Res 2022 142 377 386 10.1016/J.JBUSRES.2021.12.081
Lau YY Tang YM Chau KY COVID-19 crisis: exploring community of inquiry in online learning for sub-degree students Front Psychol 2021 12 1 14 10.3389/fpsyg.2021.679197
Li F, Liang T, Zhang H (2021) Does economic policy uncertainty affect cross-border M&As? —— a data analysis based on Chinese multinational enterprises. Int Rev Financ Anal 73:101631. 10.1016/j.irfa.2020.101631
Li W Tang YM Yu KM To S SLC-GAN: an automated myocardial infarction detection model based on generative adversarial networks and convolutional neural networks with single-lead electrocardiogram synthesis Inf Sci (ny) 2022 589 738 750 10.1016/j.ins.2021.12.083
Liu S, Zhang J, Niu B et al (2022a) A novel hybrid multi-criteria group decision-making approach with intuitionistic fuzzy sets to design reverse supply chains for COVID-19 medical waste recycling channels. Comput Ind Eng 169:108228. 10.1016/j.cie.2022.108228
Liu Z, Vu TL, Phan TTH, Ngo TQ, Anh NHV, Putra ARS (2022b) Financial inclusion and green economic performance for energy efficiency finance. Econ Change Restruct 55:2359–2389. 10.1007/s10644-022-09393-5
Lu L, Peng J, Wu J, Lu Y (2021) Perceived impact of the Covid-19 crisis on SMEs in different industry sectors: evidence from Sichuan, China. Int J Disaster Risk Reduct 55:102085. 10.1016/j.ijdrr.2021.102085
Lyu Y Tuo S Wei Y Yang M Time-varying effects of global economic policy uncertainty shocks on crude oil price volatility: new evidence Resour Policy 2021 70 101943 10.1016/J.RESOURPOL.2020.101943
Ma D, Zhang C, Hui Y, Xu B (2022) Economic uncertainty spillover and social networks. In: J. Bus. Res. https://www.sciencedirect.com/science/article/pii/S0148296322002430?casa_token=vjF1yWPeox8AAAAA:m9akyPY558zldNdaE-lzEOHtWGA9uO_8SJLnsFj8UmTIZt3JvKGlo-LndgyzZbN_j9wlrYYHTbw. Accessed 28 Jun 2022
Ma D Zhu Q Innovation in emerging economies: research on the digital economy driving high-quality green development J Bus Res 2022 145 801 813 10.1016/J.JBUSRES.2022.03.041
Mohsin M, Ullah H, Iqbal N, Iqbal W, Taghizadeh-Hesary F (2021) How external debt led to economic growth in South Asia: a policy perspective analysis from quantile regression. Econ Anal Policy 72:423–437
Moretto A Caniato F Can Supply Chain Finance help mitigate the financial disruption brought by Covid-19? J Purch Supply Manag 2021 27 100713 10.1016/J.PURSUP.2021.100713
Onubi HO Hassan AS Yusof N Bahdad AAS Moderating effect of project size on the relationship between COVID-19 safety protocols and economic performance of construction projects Eng Constr Archit Manag 2022 10.1108/ECAM-11-2021-1035
Paul SK Chowdhury P Moktadir MA Lau KH Supply chain recovery challenges in the wake of COVID-19 pandemic J Bus Res 2021 136 316 329 10.1016/j.jbusres.2021.07.056 34538979
Pop ID (2022) COVID-19 crisis, voters’ drivers, and financial markets consequences on US presidential election and global economy. Financ Res Lett 44. 10.1016/j.frl.2021.102113
Pu S Ali Turi J Bo W (2022) Sustainable impact of COVID-19 on education projects: aspects of naturalism Environ Sci Pollut Res 2022 1 1 18 10.1007/S11356-022-20387-8
Rahman MS AbdelFattah FAM Bag S Gani MO Survival strategies of SMEs amidst the COVID-19 pandemic: application of SEM and fsQCA J Bus Ind Mark 2022 10.1108/JBIM-12-2020-0564/FULL/PDF
Ramli MW Jamri MH The impact of COVID-19 pandemic: a closer look at the night market traders’ experience in Penang, Malaysia Int J Acad Res Bus Soc Sci 2021 11 741 760
Rao F Tang YM Chau KY Assessment of energy poverty and key influencing factors in N11 countries Sustain Prod Consum 2022 30 1 15 10.1016/j.spc.2021.11.002
Ridzuan MR, Abd Rahman NAS (2021) The deployment of fiscal policy in several ASEAN countries in dampening the impact of COVID-19. J Emerg Econ Islam Res 9:16. 10.24191/jeeir.v9i1.9156
Roodman D How to do xtabond2: an introduction to difference and system GMM in Stata Stata J 2009 9 86 136 10.1177/1536867x0900900106
Sarkodie SA Owusu PA Global assessment of environment, health and economic impact of the novel coronavirus (COVID-19) Environ Dev Sustain 2021 23 5005 5015 10.1007/s10668-020-00801-2 32837273
Sendroiu I (2022) From reductive to generative crisis: businesspeople using polysemous justifications to make sense of COVID-19. Am J Cult Sociol 1–27. 10.1057/S41290-021-00147-W/TABLES/1
Seo MH Shin Y Dynamic panels with threshold effect and endogeneity J Econom 2016 195 169 186 10.1016/J.JECONOM.2016.03.005
Shabir M, Jiang P, Bakhsh S, Zhao Z (2021) Economic policy uncertainty and bank stability: threshold effect of institutional quality and competition. Pac-Basin Financ J 68:101610. 10.1016/j.pacfin.2021.101610
Shirish A, Chandra S, Srivastava SC (2021) Switching to online learning during COVID-19: theorizing the role of IT mindfulness and techno eustress for facilitating productivity and creativity in student learning. Int J Inf Manage 61:102394. 10.1016/j.ijinfomgt.2021.102394
Solarin SA, Gil-Alana LA (2021) The persistence of economic policy uncertainty: evidence of long range dependence. Phys A Stat Mech its Appl 568:125698. 10.1016/j.physa.2020.125698
Song L, Tian G, Jiang Y (2022) Connectedness of commodity, exchange rate and categorical economic policy uncertainties — evidence from China. N Am J Econ Financ 101656. 10.1016/j.najef.2022.101656
Streimikiene D, Kaftan V (2021) Green finance and the economic threats during COVID-19 pandemic. Terra Econ 19:105–113. 10.18522/2073-6606-2021-19-2-105-113
Sun L, Wang Y (2021) Global economic performance and natural resources commodity prices volatility: evidence from pre and post COVID-19 era. Resour Policy 74:102393. 10.1016/j.resourpol.2021.102393
Surya B Hernita H Salim A Travel-business stagnation and SME business turbulence in the tourism sector in the era of the COVID-19 pandemic Sustain 2022 14 2380 10.3390/su14042380
Tang YM, Chau KY, Fatima A, Waqas M (2022a) Industry 4.0 technology and circular economy practices: business management strategies for environmental sustainability. Environ Sci Pollut Res. 10.1007/s11356-022-19081-6
Tang YM Chau KY Kwok APK A systematic review of immersive technology applications for medical practice and education - trends, application areas, recipients, teaching contents, evaluation methods, and performance Educ Res Rev 2022 35 100429 10.1016/j.edurev.2021.100429
Tran QT (2021) Economic policy uncertainty and cost of debt financing: international evidence. N Am J Econ Financ 57:101419. 10.1016/j.najef.2021.101419
Umar Z, Gubareva M, Teplova T (2021) The impact of Covid-19 on commodity markets volatility: analyzing time-frequency relations between commodity prices and coronavirus panic levels. Resour Policy 73:102164. 10.1016/j.resourpol.2021.102164
Una G, Allen R, Pattanayak S, Suc G (2020) Special series on fiscal policies to respond to COVID-19 digital solutions for direct cash transfers in. Int Monet Fund 1–9
van der Wielen W, Barrios S (2021) Economic sentiment during the COVID pandemic: evidence from search behaviour in the EU. J Econ Bus 115. 10.1016/j.jeconbus.2020.105970
Vătămănescu EM Dabija DC Gazzola P Before and after the outbreak of Covid-19: Linking fashion companies’ corporate social responsibility approach to consumers’ demand for sustainable products J Clean Prod 2021 321 128945 10.1016/J.JCLEPRO.2021.128945
Vo H, Phan A, Trinh Q-D, Vu LN (2022) Does economic policy uncertainty affect trade credit and firm value in Korea? A comparison of chaebol vs. non-chaebol firms. Econ Anal Policy 73:474–491. 10.1016/j.eap.2021.12.011
Wang Q, Li S, Jiang F (2021) Uncovering the impact of the COVID-19 pandemic on energy consumption: new insight from difference between pandemic-free scenario and actual electricity consumption in China. J Clean Prod 313. 10.1016/j.jclepro.2021.127897
Wren-Lewis S (2020) The economic effects of a pandemic
Wu Y Zhu W The role of CSR engagement in customer-company identification and behavioral intention during the COVID-19 pandemic Front Psychol 2021 12 3171 10.3389/fpsyg.2021.721410
Xu L, Chen W, Wang S, et al (2022a) Analysis on risk awareness model and economic growth of finance industry. Ann Oper Res 1–22. 10.1007/s10479-021-04516-z
Yang T, Zhou F, Du M, et al (2021) Fluctuation in the global oil market, stock market volatility, and economic policy uncertainty: a study of the US and China. Q Rev Econ Financ. 10.1016/j.qref.2021.08.006
Yang Y Gong Y Land LPW Chesney T Understanding the effects of physical experience and information integration on consumer use of online to offline commerce Int J Inf Manage 2020 51 102046 10.1016/J.IJINFOMGT.2019.102046
Yao L Li X Zheng R Zhang Y The impact of air pollution perception on urban settlement intentions of young talent in China Int J Environ Res Public Heal 2022 19 1080 10.3390/IJERPH19031080
Yasir A Hu X Ahmad M Modeling impact of word of mouth and E-government on online social presence during COVID-19 outbreak: a multi-mediation approach Int J Environ Res Public Heal 2020 17 2954 10.3390/IJERPH17082954
Ye J, Al-Fadly A, Huy PQ, et al (2022) The nexus among green financial development and renewable energy: investment in the wake of the Covid-19 pandemic. http://www.tandfonline.com/action/authorSubmission?journalCode=rero20&page=instructions. 10.1080/1331677X.2022.2035241
Yuan B, Leiling W, Saydaliev HB, Dagar V, Acevedo-Duque Á (2022) Testing the impact of fiscal policies for economic recovery: does monetary policy act as catalytic tool for economic Survival. Econ Change Restruct 55(4):2215–2235. 10.1007/s10644-022-09383-7
Yuan J, Wu Y, Jing W, et al (2021) Non-linear correlation between daily new cases of COVID-19 and meteorological factors in 127 countries. Environ Res 193. 10.1016/j.envres.2020.110521
Zamfir IC Iordache AMM The influences of covid-19 pandemic on macroeconomic indexes for European countries Appl Econ 2022 10.1080/00036846.2022.2031858
Zhang H, Jiang Z, Gao W, Yang C (2022a) Time-varying impact of economic policy uncertainty and geopolitical risk on tourist arrivals: evidence from a developing country. Tour Manag Perspect 41:100928. 10.1016/j.tmp.2021.100928
Zhao J, Patwary AK, Qayyum A, Alharthi M, Bashir F, Mohsin M, Hanif I, Abbas Q (2022b) The determinants of renewable energy sources for the fueling of green and sustainable economy. Energy. 10.1016/j.energy.2021.122029
Zheng W, Ma YY, Lin HL (2021) Research on blended learning in physical eduscation during the COVID-19 pandemic: a Case Study of Chinese students. 10.1177/21582440211058196 | 36282386 | PMC9593987 | NO-CC CODE | 2023-02-19 23:15:06 | yes | Environ Sci Pollut Res Int. 2023 Oct 25; 30(9):22145-22158 |
==== Front
Sci Total Environ
Sci Total Environ
The Science of the Total Environment
0048-9697
1879-1026
Elsevier B.V.
S0048-9697(22)06938-8
10.1016/j.scitotenv.2022.159838
159838
Article
Toxicity assessment of SARS-CoV-2-derived peptides in combination with a mix of pollutants on zebrafish adults: A perspective study of behavioral, biometric, mutagenic, and biochemical toxicity
Freitas Ítalo Nascimento ab
Dourado Amanda Vieira a
Araújo Amanda Pereira da Costa c
Souza Sindoval Silva de d
Luz Thiarlen Marinho da ae
Guimarães Abraão Tiago Batista a
Gomes Alex Rodrigues ab
Islam Abu Reza Md. Towfiqul f
Rahman Md. Mostafizur g
Arias Andrés Hugo h
Mubarak Ali Davoodbasha i
Ragavendran Chinnasamy j
Kamaraj Chinnaperumal k
Malafaia Guilherme abde⁎
a Laboratory of Toxicology Applied to the Environment, Goiano Federal Institute, Urutaí, GO, Brazil
b Post-Graduation Program in Ecology, Conservation, and Biodiversity, Federal University of Uberlândia, Uberlândia, MG, Brazil
c Post-Graduation Program in Environmental Sciences, Federal University of Goiás, Goiânia, GO, Brazil
d Post-Graduation Program in Biotechnology and Biodiversity, Federal University of Goiás, Goiânia, GO, Brazil
e Post-Graduation Program in Conservation of Cerrado Natural Resources, Goiano Federal Institute, Urutaí, GO, Brazil
f Begum Rokeya University, Department of Disaster Management, Rangpur 5404, Bangladesh
g Laboratory of Environmental Health and Ecotoxicology, Department of Environmental Sciences, Jahangirnagar University, Dhaka 1342, Bangladesh
h Instituto Argentino de Oceanografía (IADO), Universidad Nacional del Sur (UNS)-CONICET, Florida 8000, Complejo CCT CONICET Bahía Blanca, Edificio E1, B8000BFW Bahía Blanca, Argentina
i Petroleum and Chemical Engineering, Faculty of Engineering, Universiti Teknologi Brunei, Bandar Seri Begawan BE1410, Brunei Darussalam
j Department of Conservative Dentistry and Endodontics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, India
k Interdisciplinary Institute of Indian System of Medicine (IIISM), Directorate of Research and Virtual Education, SRM Institute of Science and Technology (SRMIST), Kattankulathur 603203, Tamil Nadu, India
⁎ Corresponding author at: Laboratory of Toxicology Applied to the Environment, Goiano Federal Institution, Urutaí Campus, Rodovia Geraldo Silva Nascimento, 2,5 km, Zona Rural, Urutaí, GO, Brazil.
4 11 2022
1 2 2023
4 11 2022
858 159838159838
2 10 2022
26 10 2022
26 10 2022
© 2022 Elsevier B.V. All rights reserved.
2022
Elsevier B.V.
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
The dispersion of SARS-CoV-2 in aquatic environments via the discharge of domestic and hospital sewage has been confirmed in different locations. Thus, we aimed to evaluate the possible impacts of zebrafish (Danio rerio) exposure to SARS-CoV-2 peptide fragments (PSPD-2001, 2002, and 2003) alone and combined with a mix of emerging pollutants. Our data did not reveal the induction of behavioral, biometric, or mutagenic changes. But we noticed an organ-dependent biochemical response. While nitric oxide and malondialdehyde production in the brain, gills, and muscle did not differ between groups, superoxide dismutase activity was reduced in the “PSPD”, “Mix”, and “Mix+PSPD” groups. An increase in catalase activity and a reduction in DPPH radical scavenging activity were observed in the brains of animals exposed to the treatments. However, the “Mix+PSPD” group had a higher IBRv2 value, with NO levels (brain), the reduction of acetylcholinesterase activity (muscles), and the DPPH radical scavenging activity (brain and muscles), the most discriminant factors for this group. The principal component analysis (PCA) and hierarchical clustering analysis indicated a clear separation of the “Mix+PSPD” group from the others. Thus, we conclude that exposure to viral fragments, associated with the mix of pollutants, induced more significant toxicity in zebrafish adults than in others.
Graphical abstract
Unlabelled Image
Keywords
Novel coronavirus
Non-target organisms
Freshwater ecosystems
Danio rerio
COVID-19
Biomarkers
Editor: Damià Barceló
==== Body
pmc1 Introduction
COVID-19 (Coronavirus Disease-2019), caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has caused worldwide impacts unprecedented in recent human history. Such consequences encompass the world economy (Ozili and Arun, 2023), education (Meinck et al., 2022; Reimers, 2022; Panakaje et al., 2022), social aspects (Kiran, 2020), travel behavior, and community living (Park et al., 2022), petroleum consumption (Wang et al., 2022), sports governance and management (Byers et al., 2022), as well as the health of populations, including aspects of mental health (Samji et al., 2022; Banna et al., 2022), eating disorders (Linardon et al., 2022), and life expectancy (Aburto et al., 2022), among others. Regarding mortality, as of September 5, 2022, the WHO Coronavirus (COVID-19) Dashboard has 6,460,493 deaths (WHO, 2022).
However, recent studies have also pointed to multiple impacts (direct and indirect) of the COVID-19 pandemic on the environment, which have been associated with increased use of chemicals (particularly detergents, soap, and sanitizers (Chirani et al., 2021; Dhama et al., 2021)), increase in solid waste generation (e.g., hospital, biomedical (Ye et al., 2022; Parida et al., 2022) and domestic (Jebaranjitham et al., 2022; Sharma et al., 2022), and climate change (Marazziti et al., 2021). In addition, the consumption of plastic-based products has increased considerably during the COVID-19 pandemic (Silva et al., 2021; Benson et al., 2021), especially those related to petrochemical-based synthetic fibers, which are typically used for single-use protective clothing (Uddin et al., 2022), as well as personal protective equipment kits, face masks, and gloves (Kumar et al., 2021). Thus, it has been a consensus that the COVID-19 pandemic has been intensifying water pollution, already noted for some time, in terms of heavy metals (Muhammad and Usman, 2022), surfactants (Al-Ani et al., 2020), phenolic compounds (Ramos et al., 2021), petroleum (Edori and Edori, 2021), pharmaceutical residues (Quincey et al., 2022), pesticides (Kalantary et al., 2022), personal care products (Liu et al., 2021) and microplastics (Talbot and Chang, 2022), among others.
Associated with this, the identification of SARS-CoV-2 or its fragments in hospital and domestic sewage (Dharmadhikari et al., 2022; Pellegrinelli et al., 2022; Yang et al., 2022; Zhao et al., 2022) and in natural aquatic environments (e.g., rivers – De-Oliveira et al., 2021; Fongaro et al., 2021; Rocha et al., 2022; Fonseca et al., 2022) have raised concerns about possible secondary transmission of SARS-CoV-2 (Ahmed et al., 2022) and its impacts on non-target organisms (Charlie-Silva and Malafaia, 2022). In this regard, studies conducted in the laboratory have already shown that exposure to peptide fragments of SARS-CoV-2 induces changes in the health of different animal species, such as Physalaemus cuvieri tadpoles (Charlie-Silva et al., 2021), Culex quinquefasciatus larvae (Mendonça-Gomes et al., 2021), Poecilia reticulata juveniles (Malafaia et al., 2021; Gonçalves et al., 2022), Danio rerio (Fernandes et al., 2022; Kraus et al., 2022), and mice (Luz et al., 2022). Although these studies represent preliminary and incipient findings on the potential effects of SARs-CoV-2, considering not only the great diversity of viral fragments dispersed in aquatic environments but also the effects of exposure to SARS-CoV-2 itself in situ have not yet been evaluated, they certainly “shed light” on the (eco)toxicological potential of the spread of the new coronavirus on non-target organisms.
However, only one study (so far) has aimed to assess whether the co-existence of multiple pollutants with SARS-CoV-2 in the aquatic system constitutes an additional concern for aquatic species. On that occasion, Freitas et al. (2022) demonstrated that the combined exposure of mayfly larvae (Cloeon dipterum) to SARS-CoV-2-derived peptides (PSPD-2001, PSPD-2002, and PSPD-2003) with multiple emerging pollutants at ambient concentrations induces changes in the health of these animals. After six days of exposure, higher mortality of larvae exposed to SARS-CoV-2-derived peptides (alone or in combination with the pollutant mix) and a lower body condition index than those unexposed larvae were reported. Furthermore, in the “PSPD” and “Mix+PSPD” groups, reduced antioxidant activity, nitrosative stress, and anticholinesterase effects were reported. However, there was no evidence of synergistic or additive action between the viral peptides and the pollutants that composed the mix. Therefore, our knowledge of the global impacts of the COVID-19 pandemic, associated with current water pollution levels, under an ecological/environmental optimum is still minimal, which justifies further studies.
Therefore, in this study, we used Danio rerio adults (zebrafish) as a model system to assess the possible effects of combined exposure of SARS-CoV-2-derived peptides with multiple pollutants commonly identified in aquatic environments. D. rerio is a freshwater tropical teleost fish belonging to the Cyprinidae family, originally from South Asia (Grunwald and Eisen, 2002; Spence et al., 2006; Engeszer et al., 2007); having been used worldwide as an animal model in different ecotoxicological studies (Verma et al., 2021; Ribeiro et al., 2022; Da-Silva-Brito et al., 2022). Using biometric, antioxidant, nitrosative, cholinesterase, and behavioral biomarkers, we tested the hypothesis that co-exposure to viral peptides and the pollutant mix induces more intense impacts on animals than those exposed to peptides and the pollutant mix, alone. Conducting studies like ours is essential to understanding the magnitude of the effects of the COVID-19 pandemic on wild animals. It supports the planning and proposition of actions to reduce its impact on non-target organisms. Although the COVID-19 pandemic has been controlled in recent months (due to advances in peptide discovery (MubarakAli et al., 2021), vaccine strategies (Jafari et al., 2022; Kumar et al., 2022), therapeutics (Yin et al., 2022; Menéndez, 2022), and clinical outcomes (Al-Musa et al., 2022)), the persistence of SARS-CoV-2 in aquatic environments even after the end of the pandemic (Yang et al., 2022) may pose a risk to wild biota, which may be even greater if associated with the different pollutants present in aquatic ecosystems.
2 Material and methods
2.1 SARS-CoV-2-derived peptides
In this study, we used the peptides of the Spike protein of SARS-CoV-2 synthesized, purified, and characterized (called PSPD-2001, PSPD-2002, and PSPD-2003) in a previous study of our research group (Charlie-Silva et al., 2021) (Fig. 1 ). Such peptides were synthesized via the solid phase peptide synthesis method following the Fmoc strategy, according to Behrendt et al. (2016). The resins used for synthesis were Fmoc-Cys (Trt)-Wang, Fmoc-Thr (TBu)-Wang, and Fmoc-Asn (Trt)-Wang for peptides Arg-Val-Tyr-Ser-Ser-Wing-Asn-Asn-Cys- COOH (PSPD-2001); Gln-Cys-Val-Asn-Leu-Thr-Thr-Arg-Thr-COOH (PSPD-2002) and Asn-Asn-Ala-Thr-Asn-COOH (PSPD-2003). The purification of peptides was performed via high-performance liquid chromatography [based on Klaassen et al., 2019]. Only compounds with purity equal to or >95 % were used in the present study, as determined by the National Health Surveillance Agency (ANVISA/Brazil) and Food and Drug Administration (FDA/USA).Fig. 1 Structural models of peptides (PSPD-2001, PSPD-2002, and PSPD-2003) synthesized and used in the present study.
Fig. 1
In our study, the concentration of SARS-CoV-2-derived peptides used in animal exposure (see details in section “2.3”) aimed to simulate the presence of viral particles at a predicted environmental concentration (222.6 ng/L), considering the complete absence, to date, of studies aimed at identifying and quantifying viral protein fragments in freshwater ecosystems. In addition, the tested concentration corresponds to approximately 15 % of the highest urinary level of SARS-CoV-2 nucleocapsid protein (SARS-CoV-2-N) in patients with confirmed SARS-CoV-2 infection admitted to the Emergency and Intensive Care Medicine at the University Medical Center Göttingen (Germany) (Tampe et al., 2021). Therefore, this percentage constitutes a plausible dilution of SARS-CoV-2 in areas near hospital sewage disposal sites (untreated) in a small watercourse.
2.2 Mix of emerging pollutants
Similar to the diversity of emerging pollutants already identified in freshwater ecosystems, which can coexist with SARS-CoV-2 or its fragments, we add in the exposure waters distinct compounds/substances, including pesticides, agro-industrial effluent, pharmaceutics/hormone, agricultural fertilizers, surfactant, and constituent substances of petroleum. A total of 14 pollutants (in addition to those that make up the tannery effluent) in environmentally relevant concentrations (Table 1 ) were chosen based on the studies by Souza et al. (2018), Araújo et al. (2022), and Araújo et al. (2023), as well as their previous identifications in freshwater ecosystems (see references cited in Table 1).Table 1 General information about the mixed emerging pollutants used in our study.
Table 1Components Molecular formula Concentrations References
Amoxicilina C16H19N3O5S 0.0045 μg/L Sodré et al. (2010)
Acetylsalicylic acidb C9H8O4 0.34 μg/L Ternes (1998)
Diclofenac Sodiumc C14H10Cl2NNaO2 1.8 μg/L Hoeger et al. (2005)
Ibuprofend C13H18The2 2.7 μg/L Flippin et al. (2007)
Fluoxetinee C17H18F3NO 0.030 μg/L Perreault et al. (2003)
Clonazepamf C15H10ClN3O3 0.053 μg/L Ternes et al. (2001)
Dipyrone monohydrateg C13H18N3NaO5S 5 μg/L Pamplona et al. (2011)
Ranitidineh C13H22N4O3S 0.01 μg/L Boxall (2004)
Benzenei C6H6 5000 μg/L Brazil (2004)
Tannery effluentj – 1 % Rabelo et al. (2016)
Estradiol cypionatek C26H36The3 2.6 μg/L Jardim et al. (2012)
Nitrogenl N2 2400 μg/L Xu et al. (2014)
Glyphosatem C3H8NO5P 700 μg/L Peruzzo et al. (2008)
Abamectinn C95H142O28 4 μg/L Vasconcelos et al. (2016)
Detergento – 740 μg/L Mortatti et al. (2012)
a TEUTO, Laboratório Teuto Brasileiro S/A, Anápolis, GO, Brazil.
b Pharmaceutical Brasterápica, Atibaia, SP, Brazil.
c BrainFarma Indústria Química e Farmacêutica Ltda, Anápolis, GO, Brazil.
d GeoLab Pharmaceutical Industry S/A, Anápolis, GO, Brazil.
e Hipolabor Farmacêutica ltda, Belo Horizonte, MG, Brazil.
f GERMED Pharmaceuticals, Hortolândia, SP, Brazil
g Farmace ind. Chemical-Pharmaceutical Vearense, Barbalha, CE, Brazil.
h Medquímica Pharmaceutical Industry, Juiz de Fora, MG, Brazil.
i Proquímicos, Rio de Janeiro, RJ, Brazil.
j Tannery effluent from the industry Cencil – Centro Couros (Inhumas, GO, Brazil)
k ZOETIS PFIZER, Itapevi, SP, Brazil.
l USI FERTIL, Maruim, SE, Brazil.
m UPL, Ituvebara, SP, Brazil.
n Bayer CropScience Ltda, Belford Roxo, RJ, Brazil.
o Química Amparo (Ypê), Figueira, SP, Brazil.
2.3 Model systems and experimental design
In our study were used 48 adults of Danio rerio (zebrafish) (wild-type strain) of both sexes (ratio 1:1), aged between 5 and 6 months [body biomass of 0.238 g ± 0.042 g (mean ± standard deviation)]. These animals were obtained and maintained in the animal facility for aquatic organisms of the Laboratory of Toxicology Applied to the Environment of the Goiano Federal Institute – Campus Urutaí (GO, Brazil). Seven days before the experiment, the animals were acclimatized in aquariums containing dechlorinated water under temperature (28.1 °C ± 0.24 °C, mean ± standard deviation) and controlled luminosity (12:12-h light: dark photoperiod). Before and during the experiment, the animals were fed once a day with commercial fish food. After the acclimatization period, the animals were distributed into four experimental groups (containing three replicates of 4 animals/each, for 12 animals/group). While the animals of the “control” group were not exposed to any component of the mix of pollutants or peptides of SARS-CoV-2; those of the “PSPD” group were exposed to an equitable mixture of peptides PSPD-2001, PSPD-2002, and PSPD-2003, whose total concentration was 266.2 ng PSPD/L). The “Mix” group was composed of animals exposed to the mix of emerging pollutants (at concentrations presented in Table 1) without the presence of SARS-CoV-2-derived peptides. On the other hand, in the group “Mix+PSPD” zebrafish were exposed to SARS-CoV-2-derived peptides with a mix of pollutants at the same concentrations defined in the previous groups. During the experimental period (30 days), the groups were kept in cylindrical glass aquariums containing 1.3 L of dechlorinated water added with the respective treatments (PSPD, Mix, or Mix+PSPD).
2.4 Toxicity biomarkers
2.4.1 Behavioral assessment
2.4.1.1 Locomotor activity and possible anxiety-like behavior
To evaluate the possible induction of locomotor alterations and anxiety-like behavior by treatments, on the 29th experimental day, the animals were submitted to the open field test, which has been widely used in behavioral tests involving zebrafish (Stewart et al., 2012; Godwin et al., 2012; Hamilton et al., 2021; Borba et al., 2022). Briefly, the test consisted of introducing the animals (individually) into a rectangular polypropylene box (40 cm length x 30 cm width x 16.5 cm height; opaque white walls) containing 5 L of dechlorinated water (free of treatments, temperature: 28 °C) and evaluating their overall activity for 5 min, like Thompson et al. (2022). The test was performed in a room containing acoustic isolation, light, and temperature control. Between one session and another, the boxes were sanitized (with 30 % alcohol) and dried. The waters were replaced entirely to avoid an animal's possible interference with the following animal's behavior. The cameras (coupled to an external computer) were positioned 1.2 m high in the boxes. The number of times the animals crossed the quadrants plotted virtually in the boxes (Fig. 2A) was used to evaluate the general locomotor activity of zebrafish. In addition, the swimming speed of the animals was calculated. To assess the possible anxiogenic effect of the treatments, the box was divided into two zones (peripheral and central) (Fig. 2B). The time that the animal remained in the peripheral zone was used to calculate the anxiety index, according to Eq. 1. In addition, the frequency of exploration was recorded in the state's central area. Higher locomotion rates in the peripheric quadrants are associated with increased behavior of thigmotaxis, which is typically considered an indicator of anxiety (Maximino et al., 2010; Schnörr et al., 2012; De-Oliveira et al., 2021). PlusMZ software was used for behavior recording.(1) Anxiety index=Time spent in the peripheral zone of the apparatusTotal test time300sx100
Fig. 2 Schematic images of the behavioral tests to which Danio rerio adults were submitted. (A-B) Open field test, with emphasis on (A) demarcations of the movement of animals in the apparatus and (B) the central and peripheral areas of the apparatus. (C—D) Social aggregation test in the presence of a potential predator, highlighting the “habituation” (i.e., without predator) and (D) “test” sessions (i.e., with predator – Hoplias malabaricus). On the computer screen, dashed lines were drawn to evaluate the behavioral biomarkers evaluated in the present study.
Fig. 2
2.4.1.2 Social aggregation in response to a potential aquatic predator
On the 30th experimental day, the animals were submitted to the social aggregation test in the presence of a potential predator whose adopted procedures were like those described in Chagas et al. (2021). In this test, we aimed to evaluate the shoals' responses when confronted with a potential aquatic predator, represented by a juvenile male Hoplias malabaricus (known locally as the traíra) (total length: 16.26 ± 1.02 cm). H. malabaricus is a carnivorous freshwater fish of the Erythrinidae family, for which the main food items are other fish (Carvalho et al., 2002). Briefly, the test consisted of introducing the animals of each replica into a polypropylene box (40 cm length x 30 cm width x 16.5 cm height) containing 5 L of dechlorinated water (free of treatments, temperature: 28 °C) and filming them for 5 min (habituation session) (Fig. 2C). Then, the potential predator was introduced into the box, and the animals were filmed for another 5 min (test session) (Fig. 2D). Six specimens of H. malabaricus were used alternately between the replicas of the same group and between the experimental groups, avoiding reducing the possible influence of behavior predator on the behavior of their potential prey (zebrafish).
After testing, animals' social aggregation, which is also a defensive response, was evaluated based on the calculation of cluster scores. Such scores were determined from the demarcation of 30 quadrants (40 cm2/each) in virtual images displayed on the researcher's computer screen, like in Collins et al. (2011) and Parker et al. (2014). Cluster scores were generated by dividing the maximum number of zebrafish positioned in a given quadrant by the number of quadrants occupied by all the animals (total). Scores were provided at 3 s intervals every 3 s in a 5-min (300 s) test.
2.4.2 Biometrics
To evaluate the possible effects of treatments on zebrafish biometrics, at the end of the experiment, the following parameters were assessed: total length (cm), body width (cm), caudal peduncle width (mm), head length (mm), and head height (mm), as described by Chagas et al. (2021). The results were expressed in indexes, considering each animal's total length.
2.4.3 Biochemical biomarkers
2.4.3.1 Sample preparation
To evaluate the possible biochemical effects of the treatments, fragments of the brain, gills, and muscles were extracted. Then, the fragments were macerated in 1 mL of phosphate-buffered saline (PBS) solution (pH 7.2) and centrifuged at 13,000 rpm for 5 min (at 4 °C), like Araújo et al. (2022). Then, the supernatant was filtered through 0.45-μm syringe filters and subsequently stored at -80 °C until its use in evaluating the biomarkers described below.
2.4.3.2 Lipid peroxidation, antioxidant capacity, and nitric oxide production
The malondialdehyde (MDA) levels were evaluated according to the method described in Esterbauer and Cheeseman (1990), with detailed modifications in Freitas et al. (2022). The evaluation of this biomarker was proper to infer lipid peroxidation (LPO) levels (Grotto et al., 2009) in the different organs of the animals. The total antioxidant capacity was estimated from the evaluation of the free radical scavenging capacity (via DPPH (2,2-diphenyl-1-picryl-hydrazyl-hydrate) method, described in Brand-Williams et al. (1995)), total thiols levels (nonenzymatic antioxidant), determined based on Ellman (1959) (with modifications described in Hu (1994)); as well as superoxide dismutase (SOD) (according to the procedures described in Deawati et al. (2017) and Deawati et al. (2018)) and catalase activities, like Hadwan and Abed (2016). On the other hand, no levels were evaluated using the Griess colorimetric reaction (Grisham et al., 1996), according to the steps adopted by Araújo et al. (2022). The results of all biochemical biomarkers were relativized with the level of total proteins of the analyzed samples, which was determined based on the Bradford (1976) method.
2.4.3.3 Acetylcholinesterase activity
Considering that acetylcholinesterase (AChE) is responsible for the termination of impulse transmission at cholinergic synapses (Silman and Sussman, 2008), we evaluated the possible anticholinesteratic effect of treatments on the brain and muscles of animals. For this, the Ellman et al. (1961) method was used, as described by Guimarães et al. (2021).
2.4.4 Mutagenicity
To evaluate the possible mutagenicity induced by the treatments, we performed the micronucleus test and other erythrocytic abnormalities according to the methodology described in Anifowoshe et al. (2022). This test consists of evaluating the ability of compounds/substances to induce structural or numerical chromosomal damage (Hayashi et al., 2007). Immediately after being removed from the aquariums, the fish were anesthetized on ice, and a cut in the caudal region removed the blood. Subsequently, blood smears were prepared to make a blade per animal. After drying for 24 h at room temperature, the slides were flushed using the Rapid Panoptic Kit (Laborclin, Pinhais, PR, Brazil) to color the nuclei and cytoplasm of erythrocytes, like Castro et al. (2022). Cell analysis was performed blindly by a single researcher using a binocular optical microscope. The frequency of micronuclei and other nuclear abnormalities in erythrocytes was assessed using 2000 cells per sample, considering only red blood cells with intact nuclear and cytoplasmic membranes. The classification of micronuclei was based on Hooftman and De Raat (1982) and the nuclear abnormalities (e.g., nuclear bud, apoptotic fragments, bilobed cells, and binucleated cells) according to Baršienė et al. (2006).
2.5 Physicochemical quality of the display waters
The potential interference of treatments (SARS-CoV-2-derived peptides and/or a mix of pollutants) on water quality conditions was evaluated using the following attributes: water temperature (°C), electronic conductivity (μS/cm2), total dissolved solids (mg/L), resistivity (MΩ.cm), potential oxidation-reduction (mV), salinity (%), and pH. Such attributes were measured with a portable multi-parameter (Instrutemp, ITPH-3000), like Freitas et al. (2022). The dissolved oxygen levels (mg/L) were recorded through a dissolved oxygen sensor (CommerceAll, AT-155).
2.6 Integrated Biomarker Response Index (IBRv2)
To evidence the toxicity of the treatments, the results of all biomarkers evaluated were applied to the “Integrated Biomarker Response Index” (IBRv2). Such an index is an effective way of combining multiple biomarkers into a single index. The second-generation IBR index (IBRv2) was calculated following the methods described in Malafaia et al. (2022). The deviation between biomarkers measured in zebrafish exposed to SARS-CoV-2-derived peptides and a mix of pollutants compared to those in unexposed animals (“control” group). The biomarker results are shown as a star plot, where the area above zero reflects biomarker induction, and the area below zero indicates biomarker inhibition.
2.7 Data analysis
2.7.1 Mean comparison
Initially, all data obtained were evaluated regarding the assumptions for using parametric models. For this, we used the Shapiro-Wilk test to assess the distribution of residual data, and the Bartlett test was used to assess the homogeneity of variances. The datasets that met the assumptions for parametric models were analyzed via the one-way ANOVA test (with Tukey post-test). The non-parametric data were compared via Kruskal-Wallis test (with Dunn's post-test). Data on the social aggregation test in response to a potential aquatic predator were analyzed as a paired test because the zebrafish in the first (habituation) and second (test) sessions were the same. The student's t-test was used in these cases to compare the parametric data. The differences between the experimental groups in the first and second sessions were compared through one-way ANOVA (parametric data). Significance levels were set at Type I error (p) values lower than 0.05. GraphPad Prism Software Version 9.0 software was used to perform the statistical analyzes.
2.7.2 Principal component analysis
To comprehensively evaluate the data set obtained in our study, the principal component analysis (PCA) was also applied. By then focusing on these “principal components,” the data can be re-analyzed to assess whether certain combinations of key variables account for differences between, in our case, experimental groups. In all PCA analyses in this work, the outliers' values (identified via the Grubbs test) were excluded from the original data, and sequentially logarithmized before the PCA analysis. The variables considered in the PCA were CAT brain (CATB), CAT gills (CATG), CAT muscle (CATM), SOD brain (SODB), SOD gills (SODG), SOD muscle (SODM), DPPH brain (DPPHB), DPPH gills (DPP HG), DPPH muscle (DPPHM), thiol brain (TB), thiol gills (TG), thiol muscle (TM), MDA brain (MDAB), MDA gills (MDAG), MDA muscle (MDAM), body biomass (BB), body biomass/total length (BB/TL), swimming speed (SS), AChE brain (AChEB), AChE muscle (AChEM), nitrite brain (NB), nitrite gills (NG), nitrite muscle (NM), anxiety index (AI), total crossings (open field test(TC), frequency in the central zone (open field test) (FCZ), total length (TL), body width/total length (BW/TL), peduncle depth/total length (PD/TL), head length/total length (HL/TL), head width/total length (HW/TL), total erythrocytic nuclear abnormalities (TENA), and cluster score (test session) (CS). After PCA, we use the rotated loading (coefficient) matrix, loading plot, and PCA score plot of the first two PCs generated in GraphPad Prism Software Version 9.0. Ward's hierarchical clustering method was also used to identify the group distributions according to the variables on the PCA results via PAST (PALaeontology STatistic) software.
3 Results
Our study did not register any deaths during the experimental period in the different groups. The analysis of the animals in the open field test did not reveal locomotor alterations, as inferred by the total crossings of the quadrants of the apparatus (Fig. 3A) and the swimming speed (Fig. 3B). We also did not report an anxiolytic or anxiogenic effect in the animals exposed to the treatments, as suggested by the anxiety index (Fig. 3) and the frequency of visits to the central zone of the open field (Fig. 3D). Also, animals in all groups reacted to predator potential, reducing the cluster score in the social aggregation test (Fig. 3E).Fig. 3 (A) Total quadrants crossings, (B) swimming speed, (C) anxiety index, (D) frequency in the central zone, and (E) cluster scores of Danio rerio (zebrafish) adults exposed or unexposed to different treatments. Parametric data are presented by the mean + standard deviation, whereas non-parametric are presented by the median and interquartile range. Summaries of statistics are displayed at the top of the graphs. “Mix” refers to zebrafish exposed to the mix of pollutants (see concentrations in Table 1), and “PSPD” refers to the group composed of animals exposed only to SARS-CoV-2-derived peptides (PSPD-2001 + PSPD-2002 + PSPD-2003; at 266.2 ng/L), and “Mix+PSPD” include zebrafish exposed to SARS-CoV-2-derived peptides in association with the mix of pollutants.
Fig. 3
In biometric terms, we also did not show differences between the groups regarding the biomarkers evaluated (total length, body biomass, and the indices “body biomass/total length”, “body width/total length”, “peduncle depth/total length”, “head length/total length”, and “head width/total length”) (Fig. S1). The total number of erythrocyte nuclear abnormalities recorded in the “Mix”, “PSPD” and “Mix+PSPD” groups did not differ from the “control” group (Fig. S2), suggesting that the treatments did not induce a mutagenic effect from the test of the micronucleus and other abnormalities.
On the other hand, the biochemical response of the animals to the treatments was organ-dependent. While in the gills, we observed a significant increase in MDA levels in the “PSPD” and “PSPD+Mix” groups (compared to the “control” group); in the brain and muscles, the production of this metabolite in animals exposed to treatments did not differ from unexposed zebrafish (Fig. 4A). The increase in nitrite production was observed only in the brains of the animals in the “PSPD+Mix” group (Fig. 4B). A 65.7 % increase was observed in this group compared to that reported in unexposed animals, which suggests that co-exposure to PSPD and the pollutant mix induced nitrosative brain stress. In addition, we noticed that SOD activity was drastically affected by exposure to treatments in all organs evaluated (Fig. 5A). In contrast, catalase activity was superior to the “control” group only in the brains of the animals in the “Mix”, “PSPD” and “Mix+PSPD” groups (Fig. 5B).Fig. 4 (A) Malondialdehyde levels and (B) nitrite production in the brain, gills, and muscle of Danio rerio (zebrafish) adults exposed or unexposed to different treatments. Parametric data are presented by the mean + standard deviation, whereas non-parametric are presented by the median and interquartile range. Summaries of statistics are displayed at the top of the graphs. “Mix” refers to zebrafish exposed to the mix of pollutants (see concentrations in Table 1), and “PSPD” refers to the group composed of animals exposed only to SARS-CoV-2-derived peptides (PSPD-2001 + PSPD-2002 + PSPD-2003; at 266.2 ng/L), and “Mix+PSPD” include zebrafish exposed to SARS-CoV-2-derived peptides in association with the mix of pollutants.
Fig. 4
Fig. 5 (A) Superoxide dismutase and (B) catalase activity in the brain, gills, and muscles of Danio rerio (zebrafish) adults exposed or unexposed to different treatments. Parametric data are presented by the mean + standard deviation, whereas non-parametric are presented by the median and interquartile range. Summaries of statistics are displayed at the top of the graphs. “Mix” refers to zebrafish exposed to the mix of pollutants (see concentrations in Table 1), and “PSPD” refers to the group composed of animals exposed only to SARS-CoV-2-derived peptides (PSPD-2001 + PSPD-2002 + PSPD-2003; at 266.2 ng/L), and “Mix+PSPD” include zebrafish exposed to SARS-CoV-2-derived peptides in association with the mix of pollutants.
Fig. 5
On the other hand, the suppression of DPPH radical scavenging activity was observed only in the brain (with an average reduction of 58.7 %, about the “control” group) of these same animals (Fig. 6A), similarly to the total thiol levels reported in the gills (mean reduction of 19.5 %) (Fig. 6B). In addition, we observed an anticholinesterase effect in the muscles of the animals in the “PSPD-Mix” group, when compared to the “control” group, marked by a significant reduction in AChE activity (Fig. 7 ).Fig. 6 (A) DPPH radical scavenging activity and (B) total thiol levels in the brain, gills, and muscles of Danio rerio (zebrafish) adults exposed or unexposed to different treatments. Parametric data are presented by the mean + standard deviation, whereas non-parametric are presented by the median and interquartile range. Summaries of statistics are displayed at the top of the graphs. “Mix” refers to zebrafish exposed to the mix of pollutants (see concentrations in Table 1), and “PSPD” refers to the group composed of animals exposed only to SARS-CoV-2-derived peptides (PSPD-2001 + PSPD-2002 + PSPD-2003; at 266.2 ng/L), and “Mix+PSPD” include zebrafish exposed to SARS-CoV-2-derived peptides in association with the mix of pollutants.
Fig. 6
Fig. 7 Acetylcholinesterase (AChE) activity in the brain and muscles of Danio rerio (zebrafish) adults exposed or unexposed to different treatments. Parametric data are presented by the mean + standard deviation. The summary of statistical measures is displayed at the top of the graph. “Mix” refers to zebrafish exposed to the mix of pollutants (see concentrations in Table 1), and “PSPD” refers to the group composed of animals exposed only to SARS-CoV-2-derived peptides (PSPD-2001 + PSPD-2002 + PSPD-2003; at 266.2 ng/L), and “Mix+PSPD” include zebrafish exposed to SARS-CoV-2-derived peptides in association with the mix of pollutants.
Fig. 7
Regarding the physicochemical attributes of the exposure waters throughout the experimental period, we observed that the pollutant mix (alone (“Mix” group) or in combination with the SARS-CoV-2-derived peptides (“Mix+PSPD” group) increased the electrical conductivity, the total dissolved solids (mg/L) and salinity, in addition to reducing resistivity (p-value < 0,0001). Table 2 shows that the treatments did not change the other attributes (oxidation-reduction potential, pH, temperature, and dissolved oxygen).Table 2 Physicochemical quality of the exposure waters of the different experimental groups.
Table 2Attributes Experimental groups Summary of statistical analyzes
Control Mix PSPD Mix + PSPD
Electric conductivity (mS/cm) 88.260 ± 0.954b 133.200 ± 0.954a 88.580 ± 0.759b 131.400 ± 5.591a F-value = 191,3; p-value <0,0001
Resistivity (MW.cm) 11.710 ± 0.105a 7.827 ± 0.036b 11.620 ± 0.085a 7.736 ± 0.107b F-value = 1932.00; p-value <0,0001
Total dissolved solids (mg/L) 80.590 ± 4.084 b 126.200 ± 2.869 a 83.180 ± 0.497 b 127.600 ± 2.113 a F-value = 273.90; p-value <0,0001
Salinity (%) 0.100 ± 0.00 b 0.200 ± 0.00 a 0.100 ± 0.00 b 0.200 ± 0.00 a H-value = 11.000; p-value = 0.006
Oxidation-reduction potential (mV) 82.830 ± 4.827 85.230 ± 2.033 89.380 ± 0.764 88.460 ± 2.003 F-value = 3.404; p-value = 0,0737
pH 8.427 ± 0.071 8.468 ± 0.022 8.500 ± 0.029 8.490 ± 0.026 H-value = 3.205; p-value = 0.4090
Temperature (°C) 28.02.91 ± 0.070 28.65 ± 0.054 28.63 ± 0.027 28.58 ± 0.064 F-value = 2.962; p-value = 0.097
Dissolved oxygen (mg/L) 8.629 ± 0.153 8.257 ± 0.107 8.323 ± 0.271 8.217 ± 0.547 H-value = 2.547; p-value = 0.510
Parametric data are presented by the mean + standard deviation, whereas non-parametric are presented by the median and interquartile range. Distinct lowercase letters in the line indicate significant differences between the experimental groups. “Mix” refers to zebrafish exposed to the mix of pollutants (see concentrations in Table 1), and “PSPD” refers to the group composed of animals exposed only to SARS-CoV-2-derived peptides (PSPD-2001 + PSPD-2002 + PSPD-2003; at 266,2 ng/L), and “Mix+PSPD” include zebrafish exposed to SARS-CoV-2-derived peptides in association with the mix of pollutants.
The results obtained were applied to PCA to reduce the dimensionality of the data and determine the similarity between the experimental groups considering the zebrafish’ responses to SARS-CoV-2-derived peptides and a mix of pollutants (alone or in combination). We observed that the first two principal components (PC1 and PC2) cumulatively explained 80.94 % of the total variation, whose eigenvalues for PC1 and PC2 were 16.03 and 10.68, respectively. The loadings plot (Fig. 8A) and Table 3 demonstrated that most biomarkers were negatively associated with PC1 and PC2. Furthermore, we observed that the experimental groups were clearly separated by PC1, with the “Mix”, “PSPD” and “Mix+PSPD” groups positioned in positive quadrants (PC1 scores: 1.931, 1.381, and 2.643, respectively) and opposite to the “control” group (PC1 score: −5.955). However, the groups exposed to the treatments were separated by PC2, and the “Mix” and “PSPD” groups had positive PC2 scores (0.356 and 4.105, respectively), while the “Mix+PSPD” group had negative PC2 scores (−3.830)) (Fig. 8B). Therefore, based on PCA, we observed the separation of groups into three subgroups, which was also confirmed by the hierarchical clustering analysis (Fig. 8C). Furthermore, in Fig. 9A, it is possible to notice a similarity between the IBRv2 values and star graph (polygon) obtained for the “Mix” and “PSPD” groups. Despite this, on average, the “Mix+PSPD” group presented an IBRv2 value 41.68 % higher than the other groups. Increased brain nitrite levels, reduced muscle AChE activity, and radical DPPH scavenging activity in the brain and muscle were the most discriminant factors for this group (Fig. 9B).Fig. 8 (A) Loadings plot of the investigated variables, (B) PC score plot of the first two principal components, and (C) cluster analysis dendrogram. PC: principal component. “Mix” refers to zebrafish exposed to the mix of pollutants (see concentrations in Table 1), and “PSPD” refers to the group composed of animals exposed only to SARS-CoV-2-derived peptides (PSPD-2001 + PSPD-2002 + PSPD-2003; at 266,2 ng/L), and “Mix+PSPD” include zebrafish exposed to SARS-CoV-2-derived peptides in association with the mix of pollutants. See the meanings of the abbreviations in “A” in Section 2.7.2 (“Materials and Methods”) or Table 3.
Fig. 8
Table 3 Loading matrix provided by the multivariate analysis to define factors or principal components PC1 and PC2.
Table 3Biomarkers Abbreviation Principal components
PC1 PC2
Catalase activity in the brain CATB 0.971 −0.172
Catalase activity in gills CATG −0.846 0.247
Catalase activity in muscle CATM 0.222 −0.965
Superoxide dismutase activity in the brain SODB −0.801 −0.096
Superoxide dismutase activity in gills SODG −0.982 −0.179
Superoxide dismutase activity in muscle SODM −0.997 −0.059
DPPH radical scavenging activity in the brain DPPHB −0.904 0.253
DPPH radical scavenging activity in gills DPPHG 0.278 −0.228
DPPH radical scavenging activity in muscle DPPHM −0.437 0.344
Total thiol in the brain TB −0.900 −0.426
Total thiol in gills TG −0.963 −0.189
Total thiol in muscle TM −0.533 −0.805
Malondialdehyde levels in the brain MDAB 0.179 0.879
Malondialdehyde levels in gills MDAG 0.889 0.017
Malondialdehyde levels in muscle MDAM −0.009 0.894
Body biomass BB 0.697 0.610
body biomass/total length BB/TL −0.085 0.851
swimming speed SS 0.917 −0.328
Acetylcholinesterase activity in the brain AChEB 0.095 −0.925
Acetylcholinesterase activity in muscle AChEM −0.429 0.903
Nitrite levels in the brain NB 0.437 −0.707
Nitrite levels in gills NG −0.699 −0.466
Nitrite levels in muscle NM −0.823 −0.543
Anxiety index AI −0.734 0.173
Total crossings (open field test) TC 0.917 −0.329
Frequency in the central zone (open field test) FZC −0.595 −0.522
Total length TL 0.591 −0.671
Body width/total length BW/TL −0.490 0.763
Peduncle depth/total length PD/TL 0.924 −0.153
Head length/total length HL/TL 0.895 0.410
Head width/total length HW/TL −0.351 −0.720
Total erythrocytic nuclear abnormalities TENA 0.659 0.587
Cluster score CS −0.607 0.751
Large loadings are highlighted in boldface to emphasize the variables contributing to each principal component.
Fig. 9 (A) Integrated biomarker responses index (IBRv2) values and (B) star graph (polygon) obtained with the IBRv2 method for the “Mix”, “PSPD”, and “Mix+PSPD” groups. “Mix” refers to zebrafish exposed to the mix of pollutants (see concentrations in Table 1), and “PSPD” refers to the group composed of animals exposed only to SARS-CoV-2-derived peptides (PSPD-2001 + PSPD-2002 + PSPD-2003; at 266.2 ng/L), and “Mix+PSPD” include zebrafish exposed to SARS-CoV-2-derived peptides in association with the mix of pollutants. See the meanings of the abbreviations in “B” in Section 2.7.2 (“Materials and Methods”) or Table 3.
Fig. 9
4 Discussion
Undoubtedly, the COVID-19 pandemic has been a milestone in the recent history of humanity, given its impacts on the health of populations, its indirect and direct effects on the economy, social issues, and the environment (Panakaje et al., 2022; Kiran, 2020; Park et al., 2022; Wang et al., 2022; Banna et al., 2022; Aburto et al., 2022; Ozili and Arun, 2023). In our study, using a design that simulates the exposure of freshwater fish to a mix of emerging pollutants in combination with SARS-CoV-2-derived peptides, we demonstrate that the impacts of the COVID-19 pandemic can be even more significant and comprehensive. Even though the treatments did not cause any changes in behavior (Fig. 3), we noticed a biochemical response in different organs when we evaluated biometric biomarkers (Fig. S1) or mutagenic biomarkers (Fig. S2), which can affect the fitness of individuals and pose a risk to their health.
Regarding the behavior of animals exposed to SARS-CoV-2 (alone) peptides, our data differ from those evidenced in recent studies by Mendonça-Gomes et al. (2021) and Malafaia et al. (2022). While in Malafaia et al. (2022), exposure of P. reticulata juveniles to PSPD-2002 and PSPD-2003 peptides induced anxiety-like behavior in the open field test and increased AChE activity, Mendonça-Gomes et al. (2021) reported changes in the locomotor and olfactory-driven behavior of the C. quinquefascitus larvae (exposed to these same peptides), which were associated with increased production of ROS and cholinesterasic effect. Furthermore, we did not report alterations suggestive of mutagenicity by the micronucleus test and other erythrocyte abnormalities performed in zebrafish subjected to the peptides (Fig. S2), unlike what was evidenced in P. reticulata juveniles exposed to PSPD-2022 peptides (Gonçalves et al., 2022). In particular in this study, a ten days exposure to peptides was enough to significantly increase the frequency of erythrocytic nuclear alteration and all parameters assessed in the comet assay (length tail, %DNA in tail, and Olive tail moment), suggesting that PSPD-2002 peptides were able to cause genomic instability and erythrocyte DNA damage. In general, it can be assumed that the biological differences between the evaluated models (i.e., different species), exposure times, age (or phase development), and peptide concentrations are reasons which might explain the discrepancy between the results.
Another plausible reason for these differences refers to how the animals were exposed. While in our study the peptides were introduced in the aquariums together (PSPD-2002 + PSPD-2003 + PSPD-2003), in the other studies the animals were exposed to the peptides alone and free from any combination with other pollutants. In this case, although there are no studies involving the combined toxicity of SARS-CoV-2 peptides (to each other), previous investigations into the combined toxicity of emerging pollutants have suggested that their interactions may induce effects different from those reported when isolated. This is the case, for example, of the work by Estrela et al. (2021), in which the authors did not observe an increase in lipid peroxidation processes in the brain of Swiss mice exposed to the combination of zinc oxide nanoparticles and polystyrene nanoplastics, whereas the increase in the production of TBARs was observed when the animals were exposed to pollutants alone. In Araújo et al. (2022), the exposure of zebrafish to polyethylene microplastics induced a significant reduction in the locomotor activity of the animals; but when combined with a pollutant mix, a hyperactive-like behavior was observed in the animals.
On the other hand, the combined exposure of newly emerged bees (Apis cerana cerana) to the pesticides acetamiprid and propiconazole significantly reduced glutathione-S-transferase activity in the midguts, which was not observed in isolated exposures to pesticides (Han et al., 2019). Furthermore, Caceres et al. (2007) first reported that the combined exposure of Daphnia carinata to chlorpyrifos and 3,5,6-trichloropyridinol did not induce toxicity in animals when present together in concentrations up to 0.12 μg/L. In humans, however, Iyyadurai et al. (2014) found that patients with mixed poisoning (chlorpyrifos + cypermethrin) appear to have fewer ventilator-free days than patients poisoned by either of the pesticides alone. Although these studies are not directly related to our investigation, they support the hypothesis that chemical interactions between compounds, substances, or molecules can interfere with their mechanisms of action, environmental availability, and, therefore, their toxicity. Consequently, it is plausible to suppose that the interaction between the peptides (whether in the aquatic environment or animals) has interfered with the mechanisms of action that culminated in the behavioral, biometric, and mutagenic changes reported in other animal models exposed to the peptides in a non-combined way (Mendonça-Gomes et al., 2021; Gonçalves et al., 2022; Malafaia et al., 2022). This would also explain why the behavioral, biometric, and mutagenic biomarkers evaluated in zebrafish exposed in our study (“PSPD” group) did not show similar effects.
We also observed divergent results on the toxicity of the pollutant mix (assessed in isolation) and reported in previous investigations in which the same mixture of pollutants was tested. While the increase in MDA production and the induction of nitrosative stress by the mix was not verified in our study; in Araújo et al. (2022), zebrafish adults exposed for 15 days showed increased production of nitrite in the brain and muscle, as well as a reduction of this metabolite in the gills. In Araújo et al. (2023), tadpoles exposed to the mix for 30 days significantly increased the production of nitrite and MDA compared to the control group. Furthermore, the suppression of brain, gill, and muscle SOD activity observed in zebrafish in our study (Fig. 5A) was different from that observed in P. cuvieri tadpole, which showed a 21.7 % increase in the activity of this enzyme after exposure to the mix of pollutants (Araújo et al., 2023). On the other hand, the increase in catalase activity in the brains of the animals in our study (Fig. 5B) was like that observed in the brain and muscle of zebrafish (Araújo et al., 2022), as well as in P. cuvieri tadpole (Araújo et al., 2023). The effects of the pollutant mix in these studies have been attributed to the toxicity of several of its chemical components. As demonstrated in a previous study by our group (Souza et al., 2018), >350 organic compounds and high concentrations of heavy metals (e.g., Pb, Ni, Zn, Cr, and Co) were reported in the composition of the pollutant mix used. In any case, such studies suggest that the toxicity of the pollutant mix depends not only on the period of exposure but also on the organ being evaluated, the stage of development (adults vs. juveniles), and the species assessed.
On the one hand, we observed a significant impact of the animals' exposure to the SARS-CoV-2 peptides in combination with the pollutant mix, particularly in the production of nitrite in the brain (Fig. 4B), muscle AChE activity (Fig. 7), and in the DPPH radical scavenging activity in the brain and muscle of the zebrafish evaluated (Fig. 6A), which were the most discriminant factors for the “Mix-PSPD” group (Fig. 9B). Regarding nitrite levels in the brains of the animals in this group, we observed an increase of >60 % compared to the “control” group (Fig. 4B), suggesting a synergistic effect of the combination of pollutants/PSPD on NO production. As already demonstrated in the literature, at high concentrations, NO reacts with ROS producing reactive nitrogen species (RNS) that are known to have harmful implications for biological systems. Evidence reported by Paakkari and Lindsberg (1995) and Lee et al. (2016), for example, suggest that NO itself serves as a cytotoxic mediator by reacting with superoxide anions or hydrogen peroxide to produce peroxynitrite, which is much more reactive and toxic than NO or superoxide anions alone. Thus, it is reasonable to assume that the generation of peroxynitrite in zebrafish of the “Mix+PSPD” group – induced by the combined action of PSPDs and the different pollutants in the mix – affected essential macromolecules, implying the impairment of SOD and DPPH radical scavenging activities in the brain of the animals in this group (Fig. 5A and 6A, respectively).
On the other hand, this effect does not seem to have affected the behavior of the animals in the tests performed, which can be explained by the increased activity of other antioxidant components not evaluated in our study (e.g., glutathione peroxidase, DT-diaphorase, vitamin E, vitamin C, carotenes, ferritin, ceruloplasmin, selenium, reduced glutathione, manganese, ubiquinone, zinc, flavonoids, coenzyme Q, melatonin, bilirubin, taurine, cysteine, etc.) individuals. Alternatively, it is possible that such alterations were not sufficient to impact the neural circuits that regulate the locomotor activity or aspects related to fear/anxiety in the animals or that such circuits were not affected. In this case, digging deeper into these questions could lead to interesting ideas that could be studied in the future.
Interestingly, we also observed that the anticholinesterase effect observed in the muscle of the animals of the “Mix+PSPD” group (Fig. 7) was not associated with the locomotor ability of the zebrafish (Figs. 3A-B). These data are intriguing not only because reduced AChE activity has already been related to locomotor disorders in different fish species (Tierney, 2011; Sarasamma et al., 2018; De-Farias et al., 2019; Pullaguri et al., 2020; Mishra et al., 2022; Chen et al., 2022; Wan et al., 2022); but also because they diverge from the cholinesterase stimulation observed in P. cuvieri tadpole (Charlie-Silva et al., 2021), C. quinquefasciatus larvae (Mendonça-Gomes et al., 2021), and P. reticulata juveniles (Malafaia et al., 2022) exposed to PSPD-2002 and PSPD-2003 peptides. These studies have suggested that this increase reflects a compensatory mechanism in response to the catalytic deficit induced by the peptides or a more efficient response of the AChE to the increase in the release of ACh in the synaptic clefts. However, in the zebrafish evaluated in our study, such assumptions are not plausible, which can be explained by the physiological and biochemical characteristics of the animal models investigated, the organs/tissues where AChE activity was measured, as well as by the concentrations and periods of exposure to the viral peptides. In this sense, further investigations are necessary to understand the mechanisms responsible for the anticholinesterase effect observed in our study. In this case, it might be a good idea to look into how viral peptides (alone or with the pollutant mix) affect association and catalysis mechanisms, the affinity of substrates for the AChE active site, and/or the cholinergic anti-inflammatory pathway.
5 Conclusion
We concluded in our study that despite more intense effects for some biomarkers in some organs/tissues were not observed in the “Mix+PSPD” group, PCA and IBRv2 values indicate that viral fragments are associated with the pollutant mix-induced increased toxicity in zebrafish adults. Therefore, our study reinforces the hypothesis that the spread of the new coronavirus in aquatic environments, especially in those already polluted, represents an imminent risk to the health of aquatic organisms. However, it should be borne in mind that our study is the first to assess the possible toxicological effects of combining SARS-CoV-2-derived peptides with a mix of emerging pollutants in an aquatic vertebrate model. Thus, several questions can and should be continuously explored, including conditions that allow us to evaluate the effects of the factors “exposure time”, “age”, “sex”, “species”, and “biomarkers of toxicity” on the responses of animals. In addition, studies that seek to elucidate whether the adverse effects observed in our study are reversible in an aquatic depollution scenario, or even if they are transgenerational, will be essential to support strategies and actions for mitigation and remediation of aquatic pollution.
Declaration of competing interest
We confirm that there are no known conflicts of interest associated with this work, and there has been no significant financial support for this work that could have influenced its outcome. Furthermore, we ensure that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that all have approved the order of authors listed in the manuscript of us. Due care has been taken to ensure the integrity of the work.
Appendix A Supplementary data
Supplementary figures
Image 1
Data availability
Data will be made available on request.
Acknowledgments
The authors are grateful to the Goiano Federal Institute (IF Goiano/GO/Brazil) and the 10.13039/501100003593 National Council for Scientific and Technological Development (CNPq/Brazil) for the financial support needed to conduct this research (Proc. No. 23219.000854.2022-50 and 403065/2021-6, respectively). We thank the Ph.D. student Helyson Lucas Bezerra Braz (Federal University of Ceará; UFC/CE/Brazil) for his kind collaboration in the elaboration of Fig. 1 presented in this article. Malafaia G. holds a productivity scholarship from CNPq (Proc. No. 308854/2021-7).
Ethical aspects
All experimental procedures were performed in accordance with the ethical standards for animal experimentation, and meticulous efforts were made to ensure that the animals suffered as little as possible and to reduce external sources of stress, pain, and discomfort. The current study has not exceeded the number of animals needed to produce reliable scientific data. This article does not refer to any study with human participants performed by any authors.
CRediT authorship contribution statement
Ítalo Nascimento Freitas: designed and performed experiments, analyzed data, and co-wrote the paper. Amanda Vieira Dourado: performed experiments. Amanda Pereira da Costa Araújo: performed experiments. Sindoval Silva de Souza: performed experiments. Thiarlen Marinho da Luz: performed experiments. Abraão Tiago Batista Guimarães: performed experiments. Alex Rodrigues Gomes: performed experiments. Abu Reza Md. Towfiqul Islam: revised the paper critically for important intellectual content. Md. Mostafizur Rahman: revised the paper critically for important intellectual content. Andrés Hugo Arias: revised the paper critically for important intellectual content. Nabisab Mujawar Mubarak: revised the paper critically for important intellectual content. Chinnaperumal Kamaraj: revised the paper critically for important intellectual content. Guilherme Malafaia: designed and performed experiments, analyzed data, co-wrote the paper, supervised the research, provided funding acquisition, project administration, and resources.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2022.159838.
==== Refs
References
Aburto J.M. Schöley J. Kashnitsky I. Zhang L. Rahal C. Missov T.I. Kashyap R. … Quantifying impacts of the COVID-19 pandemic through life-expectancy losses: a population-level study of 29 countries Int. J. Epidemiol. 51 1 2022 63 74 34564730
Ahmed W. Bibby K. D'Aoust P.M. Delatolla R. Gerba C.P. Haas C.N. Bivins A. … Differentiating between the possibility and probability of SARS-CoV-2 transmission associated with wastewater: empirical evidence is needed to substantiate risk FEMS Microbes 2 2022
Al-Ani R.R. Hassan F.M. Al-Obaidy A.H.M.J. Environmental evaluation of surfactant: case study in sediment of Tigris River, Iraq River Deltas-Recent Advances 2020 IntechOpen
Al-Musa A. LaBere B. Habiballah S. Nguyen A.A. Chou J. Advances in clinical outcomes: what we have learned during the COVID-19 pandemic J. Allergy Clin. Immunol. 149 2 2022 569 578 34958811
Anifowoshe A.T. Roy D. Dutta S. Nongthomba U. Evaluation of cytogenotoxic potential and embryotoxicity of KRS-Cauvery River water in zebrafish (Danio rerio) Ecotoxicol. Environ. Saf. 233 2022 113320
Araújo A.P.C. da Luz T.M. Rocha T.L. Ahmed M.A.I. e Silva D.D.M. Rahman M.M. Malafaia G. Toxicity evaluation of the combination of emerging pollutants with polyethylene microplastics in zebrafish: a perspective study of genotoxicity, mutagenicity, and redox unbalance J. Hazard. Mater. 432 2022 128691
Araújo A.P.C. da Luz T.M. Ahmed M.A.I. Ali M.M. Rahman M.M. Nataraj B. …Malafaia G. Toxicity assessment of polyethylene microplastics in combination with a mix of emerging pollutants on Physalaemus cuvieri tadpoles J. Environ. Sci. 127 2023 465 482
Banna M.H.A. Sayeed A. Kundu S. Christopher E. Hasan M.T. Begum M.R. Khan M.S.I. … The impact of the COVID-19 pandemic on the mental health of the adult population in Bangladesh: a nationwide cross-sectional study Int. J. Environ. Health Res. 32 4 2022 850 861 32741205
Baršienė J. Dedonytė V. Rybakovas A. Andreikėnaitė L. Andersen O.K. Investigation of micronuclei and other nuclear abnormalities in peripheral blood and kidney of marine fish treated with crude oil Aquat. Toxicol. 78 2006 S99 S104 16603255
Behrendt R. White P. Offer J. Advances in fmoc solid-phase peptide synthesis J. Pept. Sci. 22 1 2016 4 27 26785684
Benson N.U. Bassey D.E. Palanisami T. COVID pollution: impact of COVID-19 pandemic on global plastic waste footprint Heliyon 7 2 2021 e06343
Borba J.V. Biasuz E. Sabadin G.R. Savicki A.C. Canzian J. Luchiari A.C. …Rosemberg D.B. Influence of acute and unpredictable chronic stress on spatio-temporal dynamics of exploratory activity in zebrafish with emphasis on homebase-related behaviors Behav. Brain Res. 435 2022 114034
Boxall A.B. The environmental side effects of medication: how are human and veterinary medicines in soils and water bodies affecting human and environmental health? EMBO Rep. 5 12 2004 1110 1116 15577922
Bradford M.M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding Anal. Biochem. 72 1–2 1976 248 254 942051
Brand-Williams W. Cuvelier M.E. Berset C.L.W.T. Use of a free radical method to evaluate antioxidant activity LWT Food Sci. Technol. 28 1 1995 25 30
Byers T. Gormley K.L. Winand M. Anagnostopoulos C. Richard R. Digennaro S. COVID-19 impacts on sport governance and management: a global, critical realist perspective Manag. Sport Leisure 27 1–2 2022 99 107
Caceres T. He W. Naidu R. Megharaj M. Toxicity of chlorpyrifos and TCP alone and in combination to Daphnia carinata: the influence of microbial degradation in natural water Water Res. 41 19 2007 4497 4503 17624397
Carvalho L.N. Fernandes C.H.V. Moreira V.S.S. Alimentação de Hoplias malabaricus (Bloch, 1794)(Osteichthyes, Erythrinidae) no Rio Vermelho, pantanal sul Mato-grossense Rev. Bras. Zootec. 4 2 2002
Castro M.S. Penha L.C.C. Torres T.A. Jorge M.B. Carvalho-Costa L.F. Fillmann G. Luvizotto-Santos R. Genotoxic and mutagenic effects of chlorothalonil on the estuarine fish Micropogonias furnieri (Desmarest, 1823) Environ. Sci. Pollut. Res. 29 16 2022 23504 23511
Chagas T.Q. Freitas I.N. Montalvão M.F. Nobrega R.H. Machado M.R.F. Charlie-Silva I. Malafaia G. … Multiple endpoints of polylactic acid biomicroplastic toxicity in adult zebrafish (Danio rerio) Chemosphere 277 2021 130279
Charlie-Silva I. Malafaia G. Fragments Sars-Cov-2 in aquatic organism represent an additional environmental risk concern: urgent need for research Sci. Total Environ. 817 2022 153064
Charlie-Silva I. Araújo A.P. Guimarães A.T. Veras F.P. Braz H.L. de Pontes L.G. Malafaia G. … Toxicological insights of spike fragments SARS-CoV-2 by exposure environment: a threat to aquatic health? J. Hazard. Mater. 419 2021 126463
Chen X. Zheng J. Teng M. Zhang J. Qian L. Duan M. Wang C. … Tralopyril affects locomotor activity of zebrafish (Danio rerio) by impairing tail muscle tissue, the nervous system, and energy metabolism Chemosphere 286 2022 131866
Chirani M.R. Kowsari E. Teymourian T. Ramakrishna S. Environmental impact of increased soap consumption during COVID-19 pandemic: biodegradable soap production and sustainable packaging Sci. Total Environ. 796 2021 149013
Collins L.M. Asher L. Pfeiffer D.U. Browne W.J. Nicol C.J. Clustering and synchrony in laying hens: the effect of environmental resources on social dynamics Appl. Anim. Behav. Sci. 129 1 2011 43 53
Da-Silva-Brito R. Pereira A.C. Farias D. Rocha T.L. Transgenic zebrafish (Danio rerio) as an emerging model system in ecotoxicology and toxicology: historical review, recent advances, and trends Sci. Total Environ. 848 2022 157665
Deawati Y. Onggo D. Mulyani I. Hastiawan I. Kurnia D. Activity of superoxide dismutase mimic of [Mn (salen) OAc] complex compound non-enzymatically in vitro through riboflavin photoreduction Molekul 12 1 2017 61 69
Deawati Y. Onggo D. Mulyani I. Hastiawan I. Kurnia D. Lönnecke P. Hey-Hawkins E. … Synthesis, crystal structures, and superoxide dismutase activity of two new multinuclear manganese (III)-salen-4, 4′-bipyridine complexes Inorg. Chim. Acta 482 2018 353 357
De-Farias N.O. Oliveira R. Sousa-Moura D. de Oliveira R.C.S. Rodrigues M.A.C. Andrade T.S. Grisolia C.K. … Exposure to low concentration of fluoxetine affects development, behaviour and acetylcholinesterase activity of zebrafish embryos Comp. Biochem. Physiol. C: Toxicol. Pharmacol. 215 2019 1 8 30195060
De-Oliveira J.P.J. Estrela F.N. de Lima Rodrigues A.S. Guimarães A.T.B. Rocha T.L. Malafaia G. Behavioral and biochemical consequences of Danio rerio larvae exposure to polylactic acid bioplastic J. Hazard. Mater. 404 2021 124152
Dhama K. Patel S.K. Kumar R. Masand R. Rana J. Yatoo M. Harapan H. … The role of disinfectants and sanitizers during COVID-19 pandemic: advantages and deleterious effects on humans and the environment Environ. Sci. Pollut. Res. 28 26 2021 34211 34228
Dharmadhikari T. Rajput V. Yadav R. Boargaonkar R. Patil D. Kale S. Dharne M.S. … High throughput sequencing based direct detection of SARS-CoV-2 fragments in wastewater of Pune, West India Sci. Total Environ. 807 2022 151038
Edori O. Edori E. Profile of total petroleum hydrocarbons in the water and sediment columns of the Orashi river, Engenni, rivers state, Niger delta, Nigeria J. Glob. Ecol. Environ. 11 2 2021 12 21
Ellman G.L. Tissue sulfhydryl groups Arch. Biochem. Biophys. 82 1 1959 70 77 13650640
Ellman G.L. Courtney K.D. Andres V. Jr. Featherstone R.M. A new and rapid colorimetric determination of acetylcholinesterase activity Biochem. Pharmacol. 7 2 1961 88 95 13726518
Engeszer R.E. Patterson L.B. Rao A.A. Parichy D.M. Zebrafish in the wild: a review of natural history and new notes from the field Zebrafish 4 1 2007 21 40 18041940
Esterbauer H. Cheeseman K.H. [42] determination of aldehydic lipid peroxidation products: malonaldehyde and 4-hydroxynonenal Methods in Enzymology Vol. 186 1990 Academic Press 407 421
Estrela F.N. Guimarães A.T.B. da Costa Araújo A.P. Silva F.G. da Luz T.M. Silva A.M. Malafaia G. … Toxicity of polystyrene nanoplastics and zinc oxide to mice Chemosphere 271 2021 129476
Fernandes B.H.V. Feitosa N.M. Barbosa A.P. Bomfim C.G. Garnique A.M. Rosa I.F. Charlie-Silva I. … Toxicity of spike fragments SARS-CoV-2 S protein for zebrafish: a tool to study its hazardous for human health? Sci. Total Environ. 813 2022 152345
Flippin J.L. Huggett D. Foran C.M. Changes in the timing of reproduction following chronic exposure to ibuprofen in Japanese medaka, Oryzias latipes Aquat. Toxicol. 81 1 2007 73 78 17166604
Fongaro G. Rogovski P. Savi B.P. Cadamuro R.D. Pereira J.V.F. Anna I.H.S. da Silva Lanna M.C. … SARS-CoV-2 in human sewage and river water from a remote and vulnerable area as a surveillance tool in Brazil Food Environ. Virol. 2021 1 4
Fonseca M.S. Machado B.A.S. de Araújo Rolo C. Hodel K.V.S. dos Santos Almeida E. de Andrade J.B. Evaluation of SARS-CoV-2 concentrations in wastewater and river water samples Case Stud. Chem. Environ. Eng. 6 2022 100214
Freitas Í.N. Dourado A.V. da Silva Matos S.G. de Souza S.S. da Luz T.M. de Lima Rodrigues A.S. …Malafaia G. Short-term exposure of the mayfly larvae (Cloeon dipterum, ephemeroptera: Baetidae) to SARS-COV-2-derived peptides and other emerging pollutants: a new threat for the aquatic environments Sci. Total Environ. 849 2022 157813
Godwin J. Sawyer S. Perrin F. Oxendine S.E. Kezios Z.D. Adapting the open field test to assess anxiety-related behavior in zebrafish Zebrafish Protocols for Neurobehavioral Research 2012 Humana Press Totowa, NJ 181 189
Gonçalves S.O. da Luz T.M. Silva A.M. de Souza S.S. Montalvão M.F. Guimarães A.T.B. Malafaia G. … Can spike fragments of SARS-CoV-2 induce genomic instability and DNA damage in the guppy, poecilia reticulate? An unexpected effect of the COVID-19 pandemic Sci. Total Environ. 825 2022 153988
Grisham M.B. Johnson G.G. Lancaster J.R. Jr. Quantitation of nitrate and nitrite in extracellular fluids Methods in Enzymology Vol. 268 1996 Academic Press 237 246
Grotto D. Maria L.S. Valentini J. Paniz C. Schmitt G. Garcia S.C. Farina M. … Importance of the lipid peroxidation biomarkers and methodological aspects for malondialdehyde quantification Quim. Nova 32 1 2009 169 174
Grunwald D.J. Eisen J.S. Headwaters of the zebrafish—emergence of a new model vertebrate Nat. Rev. Genet. 3 9 2002 717 724 12209146
Guimarães A.T.B. Charlie-Silva I. Malafaia G. Toxic effects of naturally-aged microplastics on zebrafish juveniles: a more realistic approach to plastic pollution in freshwater ecosystems J. Hazard. Mater. 407 2021 124833
Hadwan M.H. Abed H.N. Data supporting the spectrophotometric method for the estimation of catalase activity Data Brief 6 2016 194 199 26862558
Hamilton T.J. Krook J. Szaszkiewicz J. Burggren W. Shoaling, boldness, anxiety-like behavior and locomotion in zebrafish (Danio rerio) are altered by acute benzo [a] pyrene exposure Sci. Total Environ. 774 2021 145702
Han W. Yang Y. Gao J. Zhao D. Ren C. Wang S. Zhong Y. … Chronic toxicity and biochemical response of Apis cerana cerana (Hymenoptera: Apidae) exposed to acetamiprid and propiconazole alone or combined Ecotoxicology 28 4 2019 399 411 30874992
Hayashi M. MacGregor J.T. Gatehouse D.G. Blakey D.H. Dertinger S.D. Abramsson-Zetterberg L. Gibson D. … In vivo erythrocyte micronucleus assay: III. Validation and regulatory acceptance of automated scoring and the use of rat peripheral blood reticulocytes, with discussion of non-hematopoietic target cells and a single dose-level limit test Mutat. Res. Genet. Toxicol. Environ. Mutagen. 627 1 2007 10 30
Hoeger B. Köllner B. Dietrich D.R. Hitzfeld B. Water-borne diclofenac affects kidney and gill integrity and selected immune parameters in brown trout (Salmo trutta f. fario) Aquat. Toxicol. 75 1 2005 53 64 16139376
Hooftman R.N. De Raat W.K. Induction of nuclear anomalies (micronuclei) in the peripheral blood erythrocytes of the eastern mudminnow Umbra pygmaea by ethyl methanesulphonate Mutat. Res. Lett. 104 1–3 1982 147 152
Hu M.L. [41] Measurement of protein thiol groups and glutathione in plasma Methods in Enzymology Vol. 233 1994 Academic press 380 385
Iyyadurai R. Peter J.V. Immanuel S. Begum A. Zachariah A. Jasmine S. Abhilash K.P.P. Organophosphate-pyrethroid combination pesticides may be associated with increased toxicity in human poisoning compared to either pesticide alone Clin. Toxicol. 52 5 2014 538 541
Jafari A. Danesh Pouya F. Niknam Z. Abdollahpour-Alitappeh M. Rezaei-Tavirani M. Rasmi Y. Current advances and challenges in COVID-19 vaccine development: from conventional vaccines to next-generation vaccine platforms Mol. Biol. Rep. 2022 1 15 34762224
Jardim W.F. Montagner C.C. Pescara I.C. Umbuzeiro G.A. Bergamasco A.M.D.D. Eldridge M.L. Sodré F.F. An integrated approach to evaluate emerging contaminants in drinking water Sep. Purif. Technol. 84 2012 3 8
Jebaranjitham J.N. Christyraj J.D.S. Prasannan A. Rajagopalan K. Chelladurai K.S. Gnanaraja J.K.J.S. Current scenario of solid waste management techniques and challenges in Covid-19-a review Heliyon 8 7 2022 e09855
Kalantary R.R. Barzegar G. Jorfi S. Monitoring of pesticides in surface water, pesticides removal efficiency in drinking water treatment plant and potential health risk to consumers using Monte Carlo simulation in Behbahan City, Iran Chemosphere 286 2022 131667
Kiran E. Prominent Issues About the Social Impacts of Covid 19 Gaziantep Univ. J. Soc. Sci. 19 COVID-19 Special Issue 2020 752 766
Klaassen N. Spicer V. Krokhin O.V. Universal retention standard for peptide separations using various modes of high-performance liquid chromatography J. Chromatogr. A 1588 2019 163 168 30626502
Kraus A. Huertas M. Ellis L. Boudinot P. Levraud J.P. Salinas I. Intranasal delivery of SARS-CoV-2 spike protein is sufficient to cause olfactory damage, inflammation and olfactory dysfunction in zebrafish Brain Behav. Immun. 102 2022 341 359 35307504
Kumar A. Jain V. Deovanshi A. Lepcha A. Das C. Bauddh K. Srivastava S. Environmental impact of COVID-19 pandemic: more negatives than positives Environ. Sustain. 4 3 2021 447 454
Kumar V. Kumar S. Sharma P.C. Recent advances in the vaccine development for the prophylaxis of SARS Covid-19 Int. Immunopharmacol. 111 2022 109175
Lee C.T. Yu L.E. Wang J.Y. Nitroxide antioxidant as a potential strategy to attenuate the oxidative/nitrosative stress induced by hydrogen peroxide plus nitric oxide in cultured neurons Nitric Oxide 54 2016 38 50 26891889
Linardon J. Messer M. Rodgers R.F. Fuller-Tyszkiewicz M. A systematic scoping review of research on COVID-19 impacts on eating disorders: a critical appraisal of the evidence and recommendations for the field Int. J. Eat. Disord. 55 1 2022 3 38 34773665
Liu S. Wang C. Wang P. Chen J. Wang X. Yuan Q. Anthropogenic disturbances on distribution and sources of pharmaceuticals and personal care products throughout the Jinsha River Basin, China Environ. Res. 198 2021 110449
Luz T.M. da Costa Araújo A.P. Rezende F.N.E. Silva A.M. Charlie-Silva I. Braz H.L.B. Malafaia G. … Shedding light on the toxicity of SARS-CoV-2-derived peptide in non-target COVID-19 organisms: a study involving inbred and outbred mice Neurotoxicology 90 2022 184 196 35395329
Malafaia G. Ahmed M.A.I. Araújo A.P.C. Souza S.S. Resende F.N.E. Freitas I.N. Mendonça-Gomes J.M. … Can spike fragments SARS-CoV-2 affect the health of neotropical freshwater fish? A study involving Poecilia reticulata juveniles Aquat. Toxicol. 245 2021 106104
Malafaia G. da Luz T.M. Ahmed M.A.I. Karthi S. da Costa Araújo A.P. When toxicity of plastic particles comes from their fluorescent dye: a preliminary study involving neotropical Physalaemus cuvieri tadpoles and polyethylene microplastics J. Hazard. Mater. Adv. 6 2022 100054
Marazziti D. Cianconi P. Mucci F. Foresi L. Chiarantini I. Della Vecchia A. Climate change, environment pollution, COVID-19 pandemic and mental health Sci. Total Environ. 773 2021 145182
Maximino C. Marques de Brito T. Dias C.A.G.D.M. Gouveia A. Morato S. Scototaxis as anxiety-like behavior in fish Nat. Protoc. 5 2 2010 209 216 20134420
Meinck S. Fraillon J. Strietholt R. The Impact of the COVID-19 Pandemic on Education: International Evidence from the Responses to Educational Disruption Survey (REDS) 2022 International Association for the Evaluation of Educational Achievement
Mendonça-Gomes J.M. Charlie-Silva I. Guimarães A.T.B. Estrela F.N. Calmon M.F. Miceli R.N. Malafaia G. … Shedding light on toxicity of SARS-CoV-2 peptides in aquatic biota: a study involving neotropical mosquito larvae (Diptera: Culicidae) Environ. Pollut. 289 2021 117818
Menéndez J.C. Approaches to the potential therapy of COVID-19: a general overview from the medicinal chemistry perspective Molecules 27 3 2022 658 35163923
Mishra A.K. Gopesh A. Singh K.P. Acute toxic effects of chlorpyrifos on pseudobranchial neurosecretory system, brain regions and locomotory behavior of an air-breathing catfish, Heteropneustes fossilis (Bloch 1794) Drug Chem. Toxicol. 45 2 2022 670 679 32408778
Mortatti J. Vendramini D. Oliveira H.D. Avaliação da poluição doméstica fluvial na zona urbana do município de Piracicaba, SP, Brasil Rev. Ambiente Agua 7 2012 110 119
MubarakAli D. MohamedSaalis J. Sathya R. Irfan N. Kim J.W. An evidence of microalgal peptides to target spike protein of COVID-19: in silico approach Microb. Pathog. 160 2021 105189
Muhammad S. Usman Q.A. Heavy metal contamination in water of Indus River and its tributaries, northern Pakistan: evaluation for potential risk and source apportionment Toxin Rev. 41 2 2022 380 388
Ozili P.K. Arun T. Spillover of COVID-19: impact on the global economy Managing Inflation and Supply Chain Disruptions in the Global Economy 2023 IGI Global 41 61
Paakkari I. Lindsberg P. Nitric oxide in the central nervous system Ann. Med. 27 3 1995 369 377 7546627
Pamplona J.H. Oba E.T. Da Silva T.A. Ramos L.P. Ramsdorf W.A. Cestari M.M. Subchronic effects of dipyrone on the fish species Rhamdia quelen Ecotoxicol. Environ. Saf. 74 3 2011 342 349 21040974
Panakaje N. Rahiman H.U. Rabbani M.R. Kulal A. Pandavarakallu M.T. Irfana S. COVID-19 and its impact on educational environment in India Environ. Sci. Pollut. Res. 29 19 2022 27788 27804
Parida V.K. Sikarwar D. Majumder A. Gupta A.K. An assessment of hospital wastewater and biomedical waste generation, existing legislations, risk assessment, treatment processes, and scenario during COVID-19 J. Environ. Manag. 308 2022 114609
Park K. Chamberlain B. Song Z. Esfahani H.N. Sheen J. Larsen T. Christensen K. … A double jeopardy: COVID-19 impacts on the travel behavior and community living of people with disabilities Transp. Res. A Policy Pract. 156 2022 24 35
Parker M.O. Annan L.V. Kanellopoulos A.H. Brock A.J. Combe F.J. Baiamonte M. Brennan C.H. … The utility of zebrafish to study the mechanisms by which ethanol affects social behavior and anxiety during early brain development Prog. Neuro-Psychopharmacol. Biol. Psychiatry 55 2014 94 100
Pellegrinelli L. Castiglioni S. Cocuzza C.E. Bertasi B. Primache V. Schiarea S. WBE Study Group Evaluation of pre-analytical and analytical methods for detecting SARS-CoV-2 in municipal wastewater samples in Northern Italy Water 14 5 2022 833
Perreault H.A. Semsar K. Godwin J. Fluoxetine treatment decreases territorial aggression in a coral reef fish Physiol. Behav. 79 4–5 2003 719 724 12954414
Peruzzo P.J. Porta A.A. Ronco A.E. Levels of glyphosate in surface waters, sediments and soils associated with direct sowing soybean cultivation in north pampasic region of Argentina Environ. Pollut. 156 1 2008 61 66 18308436
Pullaguri N. Nema S. Bhargava Y. Bhargava A. Triclosan alters adult zebrafish behavior and targets acetylcholinesterase activity and expression Environ. Toxicol. Pharmacol. 75 2020 103311
Quincey D.J. Kay P. Wilkinson J. Carter L.J. Brown L.E. High concentrations of pharmaceuticals emerging as a threat to himalayan water sustainability Environ. Sci. Pollut. Res. 2022 1 9
Rabelo L.M. Silva B.C. de Almeida S.F. da Silva W.A.M. de Oliveira Mendes B. Guimarães A.T.B. Memory deficit in swiss mice exposed to tannery effluent Neurot. Teratol. 55 2016 45 49
Ramos R.L. Moreira V.R. Lebron Y.A. Santos A.V. Santos L.V. Amaral M.C. Phenolic compounds seasonal occurrence and risk assessment in surface and treated waters in Minas Gerais—Brazil Environ. Pollut. 268 2021 115782
Reimers F.M. Primary and Secondary Education During COVID-19: Disruptions to Educational Opportunity During a Pandemic 2022 Springer Nature 475
Ribeiro O.M. Pinto M.Q. Félix L. Monteiro S.M. Fontaínhas-Fernandes A. Carrola J.S. O Peixe-zebra (Danio rerio) Como Modelo emergente na ecotoxicologia Rev. Ciênc. Elementar 10 2 2022
Rocha A.Y. Verbyla M.E. Sant K.E. Mladenov N. Detection, quantification, and simplified wastewater surveillance model of SARS-CoV-2 RNA in the Tijuana River ACS ES&T Water 2022 10.1021/acsestwater.2c00062
Samji H. Wu J. Ladak A. Vossen C. Stewart E. Dove N. Snell G. … Mental health impacts of the COVID-19 pandemic on children and youth–a systematic review Child Adolesc. Mental Health 27 2 2022 173 189
Sarasamma S. Audira G. Juniardi S. Sampurna B.P. Liang S.T. Hao E. Hsiao C.D. … Zinc chloride exposure inhibits brain acetylcholine levels, produces neurotoxic signatures, and diminishes memory and motor activities in adult zebrafish Int. J. Mol. Sci. 19 10 2018 3195
Schnörr S.J. Steenbergen P.J. Richardson M.K. Champagne D. Measuring thigmotaxis in larval zebrafish Behav. Brain Res. 228 2 2012 367 374 22197677
Sharma D. Patil G. Shah P. Impact of Covid-19 on domestic wastes after lockdown Indian J. Ecol. 49 2 2022 353 357
Silman I. Sussman J.L. Acetylcholinesterase: how is structure related to function? Chem. Biol. Interact. 175 1–3 2008 3 10 18586019
Silva A.L.P. Prata J.C. Walker T.R. Duarte A.C. Ouyang W. Barcelò D. Rocha-Santos T. Increased plastic pollution due to COVID-19 pandemic: challenges and recommendations Chem. Eng. J. 405 2021 126683
Sodré F.F. Locatelli M.A.F. Jardim W.F. Occurrence of emerging contaminants in Brazilian drinking waters: a sewage-to-tap issue Water Air Soil Pollut. 206 1 2010 57 67
Souza J.M. Rabelo L.M. de Faria D.B.G. Guimarães A.T.B. da Silva W.A.M. Rocha T.L. The intake of water containing a mix of pollutants at environmentally relevant concentrations leads to defensive response deficit in male C57Bl/6J mice Sci. Total Environ. 628 2018 186 197 29432930
Spence R. Fatema M.K. Reichard M. Huq K.A. Wahab M.A. Ahmed Z.F. Smith C. The distribution and habitat preferences of the zebrafish in Bangladesh J. Fish Biol. 69 5 2006 1435 1448
Stewart A.M. Gaikwad S. Kyzar E. Kalueff A.V. Understanding spatio-temporal strategies of adult zebrafish exploration in the open field test Brain Res. 1451 2012 44 52 22459042
Talbot R. Chang H. Microplastics in freshwater: a global review of factors affecting spatial and temporal variations Environ. Pollut. 292 2022 118393
Tampe D. Hakroush S. Bösherz M.S. Franz J. Hofmann-Winkler H. Pöhlmann S. Tampe B. … Urinary levels of SARS-CoV-2 nucleocapsid protein associate with risk of AKI and COVID-19 severity: a single-center observational study Front. Med. 650 2021
Ternes T.A. Occurrence of drugs in german sewage treatment plants and rivers Water Res. 32 11 1998 3245 3260
Ternes T. Bonerz M. Schmidt T. Determination of neutral pharmaceuticals in wastewater and rivers by liquid chromatography–electrospray tandem mass spectrometry J. Chromatogr. A 938 1-2 2001 175 185 11771837
Thompson W.A. Shvartsburd Z. Vijayan M.M. Sex-specific and long-term impacts of early-life venlafaxine exposure in zebrafish Biology 11 2 2022 250 35205116
Tierney K.B. Behavioural assessments of neurotoxic effects and neurodegeneration in zebrafish Biochim. Biophys. Acta (BBA) - Mol. Basis Dis. 1812 3 2011 381 389
Uddin M.A. Afroj S. Hasan T. Carr C. Novoselov K.S. Karim N. Environmental impacts of personal protective clothing used to combat COVID-19 Adv. Sustain. Syst. 6 1 2022 2100176
Vasconcelos A.M. Daam M.A. dos Santos L.R. Sanches A.L. Araújo C.V. Espíndola E.L. Acute and chronic sensitivity, avoidance behavior and sensitive life stages of bullfrog tadpoles exposed to the biopesticide abamectin Ecotoxicology 25 3 2016 500 509 26758616
Verma S.K. Nandi A. Sinha A. Patel P. Jha E. Mohanty S. Suar M. … Zebrafish (Danio rerio) as an ecotoxicological model for nanomaterial induced toxicity profiling Precis. Nanomed. 4 1 2021 750 781
Wan T. Au D.W.T. Mo J. Chen L. Cheung K.M. Kong R.Y.C. Seemann F. Assessment of parental benzo [a] pyrene exposure-induced cross-generational neurotoxicity and changes in offspring sperm DNA methylome in Medaka Fish Environ. Epigenetics 8 1 2022
Wang Q. Li S. Zhang M. Li R. Impact of COVID-19 pandemic on oil consumption in the United States: a new estimation approach Energy 239 2022 122280
World Health Organization (WHO) WHO Coronavirus (COVID-19) dashboard Available in: https://covid19.who.int/ 2022 Access: 5 September
Xu Z. Zhang X. Xie J. Yuan G. Tang X. Sun X. Yu G. Total nitrogen concentrations in surface water of typical agro-and forest ecosystems in China, 2004-2009 PLoS One 9 3 2014 e92850
Yang S. Dong Q. Li S. Cheng Z. Kang X. Ren D. Qu J. … Persistence of SARS-CoV-2 RNA in wastewater after the end of the COVID-19 epidemics J. Hazard. Mater. 429 2022 128358
Ye J. Song Y. Liu Y. Zhong Y. Assessment of medical waste generation, associated environmental impact, and management issues after the outbreak of COVID-19: a case study of the Hubei Province in China PloS one 17 1 2022 e0259207
Yin J. Li C. Ye C. Ruan Z. Liang Y. Li Y. …Luo Z. Advances in the development of therapeutic strategies against COVID-19 and perspectives in the drug design for emerging SARS-CoV-2 variants Comput. Struct. Biotechnol. J. 20 2022 824 837 35126885
Zhao L. Atoni E. Nyaruaba R. Du Y. Zhang H. Donde O. Xia H. … Environmental surveillance of SARS-CoV-2 RNA in wastewater systems and related environments in Wuhan: April to May of 2020 J. Environ. Sci. 112 2022 115 120 | 36343805 | PMC9635251 | NO-CC CODE | 2022-11-28 23:47:43 | yes | Sci Total Environ. 2023 Feb 1; 858:159838 |
==== Front
Life Sci
Life Sci
Life Sciences
0024-3205
1879-0631
Elsevier Inc.
S0024-3205(18)30483-1
10.1016/j.lfs.2018.08.037
Article
RETRACTED: MicroRNA-494 inhibition alleviates acute lung injury through Nrf2 signaling pathway via NQO1 in sepsis-associated acute respiratory distress syndrome
Ling Yun 1
Li Zheng-Zhao 1
Zhang Jian-Feng ⁎
Zheng Xiao-Wen
Lei Zhuo-Qing
Chen Ru-Yan
Feng Ji-Hua
Department of Emergency, the Second Affiliated Hospital of Guangxi Medical University, Nanning 530007, PR China
⁎ Corresponding author at: Department of Emergency, the Second Affiliated Hospital of Guangxi Medical University, No. 166, Daxue East Road, Xixiangtang District, Nanning 530007, Guangxi Zhuang Autonomous Region, PR China.
1 These authors contributed equally to this work.
17 8 2018
1 10 2018
17 8 2018
210 18
© 2018 Elsevier Inc. All rights reserved.
2018
Elsevier Inc.
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
==== Body
pmcThis article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy).
This article has been retracted at the request of the Editor-in-Chief.
Concern was raised about the reliability of the Western blot results in Figures 5G,H+I, which appear to have a similar phenotype as many other publications, as detailed here: https://pubpeer.com/publications/7C9483B2551952AD53CCFCE206C4EB; and here: https://docs.google.com/spreadsheets/d/1r0MyIYpagBc58BRF9c3luWNlCX8VUvUuPyYYXzxWvgY/edit#gid=262337249. The journal requested that the corresponding author comment on these concerns and provide the raw data. The authors did not respond to this request and therefore the Editor-in-Chief decided to retract the article. | 30121199 | PMC9673760 | NO-CC CODE | 2022-11-22 23:42:22 | yes | Life Sci. 2018 Oct 1; 210:1-8 |
==== Front
Operations Research Perspectives
2214-7160
2214-7160
The Authors. Published by Elsevier Ltd.
S2214-7160(22)00036-7
10.1016/j.orp.2022.100265
100265
Article
SIR model for the spread of COVID-19: A case study
Salimipour Ayoob a⁎
Mehraban Toktam a
Ghafour Hevi Seerwan b
Arshad Noreen Izza c
Ebadi M.J. d⁎
a Department of Mathematics, Quchan University of Technology, Quchan, Iran
b Department of Biomedical Sciences, Cihan University, Erbil, Kurdistan Region, Iraq
c Positive Computing Research Group, Institute of Autonomous Systems, Department of Computer & Information Sciences, Universiti Teknologi Petronas, 32610, Bandar Seri Iskandar, Perak, Malaysia
d DICEAM Department, Mediterranea University of Reggio Calabria, Via Graziella, Feo di Vito, Reggio Calabria, 89122, Italy
⁎ Corresponding Author
28 12 2022
28 12 2022
10026516 8 2022
1 12 2022
27 12 2022
© 2022 The Authors. Published by Elsevier Ltd.
2022
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
In this article, we study the spread pattern of the epidemic of COVID-19 disease from the point of view of mathematical modeling. Considering that this virus follows the basic rules of epidemic disease transmission, we use the SIR model to show the spread process of this disease in Iran. Then we estimate the primary reproduction number (R0) of COVID-19 in Iran by matching an epidemic model with the data of reported cases.
Keywords
Epidemic
COVID-19
Effective reproduction number
SIR model
Monte Carlo Simulation
==== Body
pmc1 Introduction
The World Health Organization, the Centers for Disease Control and Prevention, and all governments are trying to prevent the widespread of COVID-19 and reduce its spread as much as possible. The essential part of this effort is the modeling of infectious diseases.
Many efforts have been made to modeling this pandemic all over the world [1], [2], [3], [4]. For example, Gang Xie published an article that determined the progress of COVID-19 with a Monte Carlo simulation model, similar to a random point process model [5]. There was information in the model outputs that included the answers to the most basic decision-making questions; Such as when the number of active cases of COVID-19 reaches its peak, the time it takes for the number of active cases to reach a certain level, choosing the total number of confirmed cases until the end of the outbreak, determining the most critical parameter of the simulation model, which is the expected number of individuals that an infectious person would be infected during his illness (which is known as the infection rate parameter), etc. This simulation of the model can help calculate the effect of population size and safety level; there were considered three hypothetical infection rate patterns for the simulation study for this purpose. This study shows the conditions in which the infection active cases curve could be flattened. In the outputs of the simulation model, three sections of information can be seen: the daily estimated number of active cases, the estimated daily number of new topics, and a total number of all confirmed cases. Hence, the total estimated number of daily new confirmed cases should equal the total number of confirmed cases.
Stavros Maltezos and Angelika Georgakopoulou, examine, in the standard method of analyzing or fitting epidemic data with appropriate mathematical models to face the problem of the spread of this virus [6]. For this purpose, a Monte Carlo simulation has been created with the aim of investigating the dynamics of the spread of the new virus. The new approach in this method is based on the basic mechanisms during the epidemic outbreak. An important issue was the selection of statistical distributions so that the results of the simulation are close to reality and correspond to what has been recorded in different countries during the first and second "waves" of the epidemic so far. It is necessary to study appropriate statistical distributions and related parameters. They were considering that the effective reproduction number changes during the Monte Carlo simulation and described a mathematical process to determine this number. This quantity plays a significant role, the "figure of merit," in investigating and understanding the dynamics of epidemic spread. Also, the effect of implementing different levels of quarantine was studied on the shape of the epidemic curve and its parameters. In addition, some real epidemic results analyzed using a specific parameterization model are presented.
In an article, Ismail Farshi [7] investigated the behavior of the spread of COVID-19 with a Monte Carlo simulation based on the random walk method. In this work, he studied the effect of herd immunity in achieving high levels of immunity against COVID-19. In epidemiology, there is a concept called herd immunity, which describes how infections disappear despite the percentage of safe individuals. For this purpose, a software program was developed that simulates the spreading behavior of this disease in New York City. The primary goal is to find the percentage of individuals who are immune to the disease and prevent the spread of the disease. Ozge Kilicoglu et al. [8] used Monte Carlo simulation and MIRD nuclear medicine methods to assess the impact of radiopharmaceuticals on COVID-19. D. P. Mahapatra and S. Triambak [9] applied random 2D Monte Carlo computations to sufficiently understand the spread of COVID-19 through contact interactions. Abdelouahed Alla Hamou et al. [10] built a fractional version of the Adams-Bashforth four-step method and the estimation of the error of that method. Ç Oğuz, M Yağanoğlu [11] designed a model to reduce the duration and number of disease transmissions by shortening the diagnostic time for COVID-19 patients using Computed Tomography (CT). F Al-Areqi and MZ Konyar [12] suggested using machine learning methods to rapidly diagnose COVID-19 and focused on the impact of several characteristics on classification accuracy. Sheinson et al. [13] demonstrated how sequential Monte Carlo (SMC) methods can be used to track the COVID-19 pandemic in the US and estimate the impact of interventions on health outcomes and resource utilization. M Liu et al. [14] offer an effective privacy preservation solution in automated tracing scenarios. BA Ojokoh et al. [15] used a synthesis approach to provide an examine of current research efforts to predict the spread of COVID-19 on continents.
Kamal Shah et al. [16] considered a fractional fractal order SIR-type model for the COVID-19 survey. First, they investigated the stability of the corresponding deterministic model using the next-generation matrix and baseline replication numbers. They then looked at qualitative analysis using the "fixed point theory" approach. Next, they used the fractional Adams-Bash forth system for the discourse of two disuse approximate results to the considered model. Nikita Jain et al. [17] substantially focus on the vaccination of SARS-CoV and SARS-CoV-2 using the B-cells dataset. The paper also proposes different ensemble literacy strategies that came out to be salutary while making prognostications. Oyoon Abdul Razzaq et al. [18] designed an optimal dynamic control model to examine the performance of each strategy to reduce the virulence of COVID-19. Ali Raza et al. [19] investigated a non-linear delayed coronavirus pandemic model studied in human populations. For this study, they found an equilibrium of the vulnerability-exposure-infection-quarantine-recovery lag time model. Model stability was examined using well-being, the Routh-Hurwitz criterion, the Volterra-Lyapunov function, and the Lasalle invariant principle. Muhammad Zamir et al. [20] focus on eliminating and controlling transmission caused by COVID-19. A mathematical model of the disease was formulated. With the help of sensitivity analysis, the most sensitive parameters were determined to the transmission of infection. Massimiliano Ferrara and Luca Guerrini [21] extended the Solow-Swan model to include logistic population growth. Within this framework, they determined the solution of the model and proved that the economy converges in the long run. S. Ahmad et al. developed a novel coronavirus infection system with a fuzzy fractional differential equation defined in Caputo's sense. Using the fuzzy Laplace method coupled with the Adomian decomposition transform obtained numerical results for a better understanding of the dynamic structures of the physical behavior of COVID-19 [22]. Soheil Malekshah et al. [23] proposed a new approach based on dynamic reconfiguration to improve the reliability of distribution networks in the presence of distributed generation by reinforcement learning algorithms. Rohit Kundu et al. [24] proposed an automatic detection system for COVID-19 using lung CT scan images and classified them into COVID and non-COVID cases. Muhammad Arfan et al. [25] addressed the dynamics of fractal-fractional modified SEIR models under the fractal ordering of data available in Pakistan and the Atangana-Baleanu Caputo (ABC) derivative of the fractal dimension. Pritam Saha et al. [26] proposed GraphCovidNet, a Graph Isomorphic Network (GIN)-based model used to detect COVID-19 from CT scans and CXRs of affected patients. Fathi Vajargah et al. [27] presented an analytical view of variance reduction by the control variable method. They introduced a correlation effect between two random variables. S. Triambak and D.P. Mahapatra [28] conducted a random walk Monte Carlo simulation study of proximity-based infection outbreaks. In this work, a two-dimensional random walk model is used, in which the entire population of susceptible individuals is independent random walkers. They are initially represented by points that are uniformly distributed in a given area. Assuming that all topics are "normal" and yet 100% susceptible, the simulations start with the initial condition of an infected walker, for three different step sizes as follows:a) For the step size Equal to the average distance between individuals (l=〈r〉), the epidemic growth follows intermediate power-law growth exponents.
b) For the smaller step sizes of〈r〉/2, (l≤〈r〉/2) we obtain a quadratic growth.
And finally, c) for step sizes greater than〈r〉, we see the exponential growth of infections.
Then, in two specific simulations, the effect of 1- population density and fixed step length, different population sizes, and 2- fixed population size and density, different step lengths, on the progress of the disease are investigated.
In the research of the working group for the analysis of the epidemiology of COVID-19 of the Ministry of Health and Medical Education in February 2020, based on the dynamic models produced to simulate the epidemic of COVID-19, the prediction of possible events in the whole country has been presented quantitatively1 .
At first, were modeled the effects of changing an individual's behavior and also the seasonal changes in weather on the spread of the disease. Considering the highest amount of primary reproductive number in winter in the country which is around 2.7, and in summer at 1.6, therefore, to stop the epidemic, in the hot season of the year, approximately 37% and in the cold season, 63% of the population should have immunity. Therefore, the only effect and application of all existing interventions are to make the period of reaching this level of safety in society longer and the peak of to the epidemic wave is lower.
If the epidemic is compressed into a short time, it will cause very heavy social and especially health and medical complications, and it can create the ground for very heavy social chaos. Then, the natural process of this infection in pessimistic conditions was designed as a basic model. In this model, the number of individuals infected with this infection will reach more than two million eight hundred thousand individuals by the end of May. It should be noted that this model is the most pessimistic view of the epidemic and has not considered the effects of many limiting factors and even some limiting findings. Finally, three scenarios have been added to the model to analyze the impact of three levels of intervention in isolating suspicious and sick Individuals. In the first scenario, with minimal intervention until the end of May, the number of infected individuals in the whole country will decrease very little. In the second scenario, with moderate intervention, the epidemic in the entire country will have a very slight decreasing trend, which is the result of the minimum intervention of the system, changing Individuals' behavior. The third scenario with the most system intervention is a drastic change in an individual's behavior and better care.
If a model is well designed, it can predict the possible course of disease progression and provide us with the best and most realistic solutions to control it. In addition, in terms of health care, modeling is essential to reduce social and economic damage caused by disease and save human lives.
2 An introduction to mathematical epidemiology and compartmental modeling
2.1 Mathematical modeling
A model can be considered an abstract representation of a system or an object similar to it in some ways. In working with the model, does not exist the complexities seen in the original plan, so it is simpler to work with it. Models have many uses for the following reasons:1- Users know the system with more insight and understanding.
2- The behavior of the system in the face of different parameters of an actual situation is checked and predicted and, simulated by the model.
3- System control: The most critical goal in modeling is to control a system, in which one faces various issues, as follows:• There is not only one unique model for a system and, different models can be used for it.
• The model is valid only in the scope in which it is defined because a model is only a part of reality.
• Considering how much we want to simplify the model and pay attention to its specific aspects, modeling can be done at different levels of abstraction.
Among the types of models that are used for biological systems, mathematical models are more popular because they enable the prediction and control of biological systems [29, 30].
The first report of mathematical modeling for the spread of disease was in 1760 by Daniel Bernoulli. He developed a model to analyze smallpox deaths in England and showed that vaccination against the virus increased life expectancy at birth by about three years [31].
Mathematical models help to make quantify mental equations by writing a set of equations which are then solved for specific values of the parameters within the equations. With mathematical modeling, facts are simplified and, questions are answered using subsets of data [32], [33], [34], [35], [36], [37], [38].
By using the accepted principle of model parsimony, it is possible to do better in choosing between different models. Parsimony in the model means that a model should be as simple as possible and complex if necessary; it is also an essential factor in estimating the unknown parameters of the model using actual data. Of course, it is clear that more attention is paid to a model that is both more accurate and the number of parameters used in it is less.
To choose between available models suitable for a given topic with a variable number of parameters and different levels of accuracy, the balance between the number of observations and the unknown parameters of the model must vary by criteria such as Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Minimum Description Length (MDL) was established. And finally, what makes modeling meaningful is being able to interpret the model parameters physically and also estimate the parameters in such a way that the model matches the real-world data [29].
2.2 Types of epidemic models
In the studies of epidemic diseases, one of the primary and essential goals is to correctly predict the time evolution process of a certain disease in a population. The prediction method, which may involve numerical or analytical formulas, faces obstacles, often due to heterogeneous populations and limited knowledge of the dynamics of disease spread. Two approaches are used to simulate the dynamics of disease spread in populations with fixed size N as follows:1- Compartment,
2- stochastic.
In the compartmental approach, relevant properties derived from either infected or susceptible agents, which are direct results of the random-mixing hypothesis, can be well described by averages, which enables one to derive non-linear differential equations from matching the evolution of the disease across the population. In Stochastic approaches, their classification may be based on their descriptive variable [39, 40].
2.3 Stochastic infection propagation models and ordinary differential equation modeling
The spread of an infectious disease in a large population is a stochastic event. Because when an infected person enters a population, the infection is accidental or a result of proximity, direct contact, or environmental effects (objects infected with pathogenic agents that are in the environment) others are transferred. Newly infected individuals also transmit the infection to them when meeting or dealing with healthy individuals.
The spread of an infectious disease in a large population is a stochastic event because when an infected person enters a population, the infection is the stochastic manner or a result of proximity, direct contact, or environmental effects (objects infected with pathogenic agents that are in the environment) to Others are transferred. Newly infected individuals also transmit the infection to them when meeting or dealing with healthy individuals. At the beginning of an epidemic, the encounters between healthy and infected individuals are statistically independent. Therefore, the probability that several infected individuals will meet a healthy person is low. We assume that each infected individual infectsR0new individual on average (known as the reproduction number). Therefore, the disease progresses exponentially in society if it isR0>1.
However, in a finite population, depending on population size and contact patterns, the probability of infected individuals encountering independent healthy individuals’ decreases, and infected individuals tend to encounter each other and healthy individuals previously infected by another infected individual. Therefore, the random model of disease spread is somehow saturated and the exponential growth ends. To model such an epidemic spread, the probabilistic models used are usually based on the branching process, and the Poisson distribution for the probability of contact between infected and healthy individuals.
Let x(t) represent infected individuals in a population at time t. Then, assuming that the probability of infection has a direct relationship with the number of infected individuals, between times t and t+Δ (in relatively small intervalsΔ), we show the infected population variations as follows:(1) dx(t)dt≈x(t+Δ)−x(t)Δ=φ(t)x(t)
φ(t) is the reproduction function, which models how the infected population evolves over time. This function can account for the expectation of various probabilistic factors, such as infection transmission rate, population density, and contact patterns. On the right side of equation (1), x(t) can be unified toφ(t), but the above figure has the advantage that φ(t) can be interpreted as an exponential rate, with inverse time units.
If we denote the Kth generation of infection spread asxkΔ=x(kΔ), equation (1) can be discretized as follows:(2) xk+1=[1+Δφ(kΔ)]xk
We define the reproduction number as follows:(3) rk=Δ[1+Δφ(kΔ)]
According to equations (2) and (3), the population can be found in the discrete-time index K, as a return from the initial condition x0:(4) xk=(rkrk−1...r0)x0
Apparently, if for everyK,rk<1, or equivalently φ(t)<0, the infection goes to zero, in which case the infection spreads. In the simplest case, where the reproduction function is constant φ(t)=λ, with a constant reproduction number, we have the form φ(t)=λ, which leads to exponential growth/decay:(5) xk=x0R0k
And in continuous cases:(6) x(t)=x(0)eλt
The equivalence of continuous and discrete solutions up to the first-order derivative is evident (as assumed in equation (1)).
In general, the reproduction function φ(t) in the case of continuous or rk in the case of discrete is a time-varying function that includes some factors; such as the total susceptible population, the population of exposed individuals (carriers of the disease but without symptoms), contact patterns, and countermeasures such as social distancing including social and quarantine.
2.3.1 Proposed SIR-Poisson model
Let Y(t) be the number of infected people at time t (t = 0,1,2,...). Suppose the conditional distribution of Y(t+1) given Y(t) is a Poisson distribution with parameters (pI(t)+(1−p)Y(t)).λ(t) increase where I(t) is the number of infected people given by the SIR model and λ(t) is estimated from observed data (Yo(t), t = 0…,T). In this case,λ(t)=Yo(t+1)Yo(t)
Therefore, we combine the SIR process with the Poisson-Markov process to get a hybrid model. Similarly, we can estimate the number of deaths at time t.
2.4 Compartment modeling
In many modeling problems, differential equations arise. They are used when the rate of change of one variable is related to other variables, as is the case in most physical and biological systems. To analyze and numerical solve models based on differential equations, powerful mathematical tools can be used. Considering that their application is vast, it is difficult to imagine and interpret differential equations without visualization. For this purpose, compartmental models can be used to visually display the differential equations of dynamic systems. The compartment is an abstract entity that, depending on the degree of abstraction, each of the variables in the model, which is equivalent to the system states in dynamic systems, is represented by a compartment as a box.
We consider each compartment homogeneous so that all entities inside the compartment are indistinguishable. The compartments are connected through a set of rate equations visually indicated by arrows between the compartments. A compartmental model is technically a weighted directed graph that represents a dynamic system. So that each compartment corresponds to a node of the graph and the connecting arrows are the edges of the graph.
The basic steps in compartmental modeling are as follows:1- Separate compartments are marked with the quantities of interest and a time function variable is selected for each quantity.
2- Showing the rate of quantity flow from each compartment to another compartment, with arrows connecting the compartments.
3- For each of the model state variables, a first-order differential equation is written, so that the edge weights are multiplied by their start node state variable and added (subtracted) to the end node rate change equation. External inputs can be considered as originating from an external node with a value of 1.
4- Set the initial conditions and solve the system of equations, either analytical or numerical, in the form of the first-order state-space model.
A compartmental model can be considered linear (non-linear) when its rate flow factors are independent (dependent) of state variables, and it can be considered time-constant (time-variable) if its rate flow factors are independent (dependent). For time chamber models, two open or closed modes are considered. When quantities are only transferred between compartments, the system is closed and the sum of all differential equations for each t is zero. But if values can flow into or out of the system, the system is open.
In Figure 1 , a three-chamber model corresponding to the following set of equations is shown:(7) dx(t)dt=u−γx(t)2−αx(t)dy(t)dt=αx(t)−βy(t)dz(t)dt=γx(t)2+βy(t)−ρz(t)
Figure 1 A sample compartmental model related to the set of dynamic equations (7)
Figure 1:
According to the flow rate dependence between xand z, the model is non-linear, and because the sum of the rate changes is non-zero, there is also a net flow in and out of the whole system, so the plan is open.
2.5 Mathematical epidemiology
To be able to model the spread of epidemic disease in a population, we need certain disease- and population-specific assumptions, some of which are as follows:- Diseases are contagious and their transfer takes place through contact.
- The disease may or may not be fatal.
- During the study period of the disease, there may be births, and at birth, the disease is congenitally transmitted from mother to child (not transmitted).
- There can be a period of exposure to the disease, during which infected Individuals carry and spread the disease even though they have no visible symptoms.
- Individuals who get this disease may not have short-term or long-term immunity. Some recovered patients can be susceptible again.
- The distribution pattern can change due to the effect of interventions such as medicine, vaccination, quarantine, vacation, and social distancing.
2.5.1 SIR model
The SIR model [41, 42] is one of the simplest compartmental models, and many models are derived from this basic form. This model consists of three compartments:
S: number of vulnerable people. "Infectious contact" between a susceptible person and an infected person causes the susceptible person to become infected with the disease and enter the infection compartment. I: number of infected people. These are people who have been infected and can infect susceptible people. R: number of people removed (and vaccinated) or died. These individuals have become infected, recovered from the disease and entered remote compartments, or died. The number of deaths is assumed to be negligible for the total population. This compartment is also called "recovery" or "resistance." This model is highly potent against infections such as measles, mumps, and rubella, transmitted from person to person and where recovery leads to durable resistance. Spatial SIR model simulation. Each cell can infect up to 8 immediate neighbors. These variables (S, I, and R) represent the number of people in each compartment at any given time. To demonstrate that the number of susceptible, infectious, and cleared individuals can change over time (even if the overall population size remains constant), exact numbers are Make it a function of t (time). S(t), I(t), R(t). For specific diseases in specific populations, these features can be manipulated to predict and control the likelihood of occurrence. As can be seen from the variable function of t, the model is dynamic, and the numbers in each compartment can change over time. The importance of this dynamic aspect is most evident in endemic diseases with short infectious durations, such as measles in the United Kingdom, before the introduction of a vaccine in 1968. t)) over time. During an epidemic, the number of susceptible individuals declines rapidly and more people become infected and move to compartments distant from the infectious compartment.
Each member of the population usually progresses from susceptibility to recovery. This can be represented as a flow chart. Here, boxes represent different subjects, and arrows represent transitions between topics.
Modeling of disease progression in the SIR model, according to population changes in each compartment and using the system of ordinary differential equations (ODEs), is done as follows:(8) ds(t)dt=−αs(t)i(t)+γr(t)di(t)dt=αs(t)i(t)−βi(t)dr(t)dt=βi(t)−γr(t)
Figure 2 shows a compartmental representation of the SIR model.Figure 2 The basic susceptible-infected-recovered (SIR) model
Figure 2:
According to Figure 2, susceptible individuals are transferred to the infected group at speed proportional to the population of the infected and susceptible groups, with the parameter α. Infected individuals also transfer to the recovered group with a constant rate of β. Given that the disease does not confer lifelong immunity to recovered individuals, they, therefore, return to the susceptible group at a constant rate of γ.
Due to the movement of individuals among the compartments, the number of individuals in the compartments changes over time. Because the system has no input and output and is closed, we have:(9) s(t)+i(t)+r(t)=1
From equation (8), is evident that:(10) ds(t)dt+di(t)dt+dr(t)dt=0
which is in accordance with the closeness of the system and equation (9) [29].
The following set of equations for modeling in a particular case, that is, when the disease is in the early stages of the epidemic (during the epidemic growth) so that the susceptible population is much more than the number of infected individuals, or when the epidemic is in the last stage (reduction or suppression) when the total number of infected individuals is an unbiased sample is from the population, it can be used [43]:(11) dSdt=−aNSI
(12) dIdt=aNSI−βI
(13) dRdt=βI
The numerical solution of a basic (non-fatal) SIR model is shown in Figure 3 , with and without lifetime safety.Figure 3 Simulation of a basic non-lethal (safe) SIR model with α=0.5,β=0.05andγ=0.0
Figure 3:
2.5.2 The fatal SIR model:
In a lethal version of the SIR model, it can be seen that its birth rate is μ*and its death rates according to different groups, in the form of susceptible (μs), infected (μi), and recovered (μr). Considering that we have inputs and outputs in the system, the system is no longer closed, and its state equations can be written as follows [29]:(14) ds(t)dt=γr(t)−αs(t)i(t)−μss(t)+μ*di(t)dt=αs(t)i(t)−βi(t)−μii(t)dr(t)dt=βi(t)−γr(t)−μrr(t)
2.6 Basic reproduction number
One of the most important concerns of epidemiologists is the ability of a disease to attack the population. When the population remains in the absence of disease, it can be said that the epidemiological model has a disease-free equilibrium (DFE). These models usually have a threshold parameter called the base reproduction number (R0), which is considered one of the most important values in modeling. As mentioned in Section 2.3, 1 is the average number of susceptible individuals in a population that an infected person will contract during his or her disease course. WithR0, the contagiousness of a disease can be measured. If it isR0<1, each infected person infects, on average less than one new person during their disease period, and the disease does not develop. On the contrary, if it is R0>1, it means that each infected person will make more than one new person sick on average, and the disease will grow exponentially [44].
3 The method of applying the SIR model to Iranian data
According to the report of the Iranian government, the first cases of coronavirus were confirmed in Qom on February 29, 2020 (February 30, 2020). The number of detected cases increased rapidly after that and this new virus spread to all the provinces of the country. This study was conducted with the aim of matching an epidemic model with the data of reported cases to estimate the basic reproduction number (R0) of COVID-19 in Iran.
3.1 Materials and methods
We use the SIR epidemic model to show the trend of the spread of COVID-19 in Iran and estimate the parameters of the model to obtain the best fit with the data reported in Iran. As mentioned in the previous section, when R0>1 virus spreads among the population and according to equation (12) when the number of infected individuals increases, we have:(15) dI(t)dt>0→αS(t)I(t)N−βI(t)>0⇒I(t)(αS(t)N−β)>0
In a population where the ratio of the number of infected individuals to the total population is very small, can be consideredS(t)≈N, and equation (15) can be written as follows:(16) I(t)(αS(t)N−β)>0→S(t)≈NI(t)(α−β)>0→α−β>0→α>β⇒αβ>1
According to equation (16) and definitionR0, we have:(17) R0=αβ
We use Monte Carlo simulation based on the random walk method for SIR models.
3.2 Simulation results
The data of infected people from February 19, 2020, to December 19, 2020 (308 days) are shown in Figure 5. Because it is not possible to use the same parameter values for this whole interval, therefore, according to the diagram and the local maximum points, 308 days have been divided into five periods. The first 73 days (February 19, 2020, to April 30, 2020) are considered the first period, and the rate of infection (transmission) and removal rate (recovery) has been obtained by trial and error. Similarly, the parameters of the second period (April 30, 2020, to June 10, 2020), the third period (June 10, 2020, to August 31, 2020), the fourth period (August 31, 2020, to November 28, 2020) and the fifth period (November 28, 2020, to December 21, 2020) is obtained. The values of α,β parameters related to the above periods are given in Table 1 .Table 1 Parameters infection rate and elimination rate for different intervals
Table 1:time period a (infection rate) β (removal rate)
0.085 0.208 2020/02/19-2020/04/30
0.082 0.17 2020/04/30-2020/06/10
0.036 0.063 2020/06/10-2020/08/31
0.0001 0.07 2020/08/31-2020/11/28
0.033 0.073 2020/11/28-2020/12/21
Considering that the total number of individuals is assumed to be constant in time, then at any time t we have:(18) S(t)+I(t)+R(t)=N
The data used to compare the model estimation of this disease with the real data of Iran during the mentioned period and, according to equation (18), have been normalized in the range between zero and one.
Figures 6 to 10 show the comparison of the number of infected individuals and mortality with I(t) and R(t) predicted by the model, respectively, for the five intervals mentioned.
In the graphs related to R(t), there is a big difference between the value of R(t) estimated by the model and the number of reported deaths, this difference is due to the fact that R(t) is equal to the sum of mortality and recovered individuals. Suppose the available data is only available for the number of deaths. The greater this difference is, means that the proportion of individuals who have recovered is more than the number of individuals who have died.
According to Figure 6, it can be seen that the model was able to make a good estimate of infected and recovered individuals. The small difference in affected individuals at the beginning of the first period with the initial conditions of the model can be considered due to the lack of familiarity with the disease at the beginning of entering the country and the lack of accurate statistics. Also, the very small difference between the model's estimate of the removed individuals and the dead individuals at the beginning of the simulation is completely logical and the deceased individuals at the beginning of the simulation are completely logical and are due to the fact that at least 14 days are needed for complete recovery. Therefore, in the first 14 days, the number of removed communities equals the number of individuals who died.
In Figure 6A, the comparison chart of infected individuals in the first period and the estimation of the model of infected individuals according toα=0.208,β=0.085. In Figure 6B, we can see the red points of the number of individuals who died due to corona and the green line of the model's estimate of the individuals who were excluded in the first interval.
The maximum point of this period coincides with the end of the Nowrouz holidays in Iran and coincides with March 29, 2020. After that, due to the quarantine measures and the government's warnings to observe the health protocols, and on the other hand, individuals’ awareness of taking the disease seriously, we saw a decrease in infected individuals until the end of this period and in the middle of May. In the second and third periods, which are shown in Figure 7 and Figure 8, respectively, it can be seen that the model was able to make a good estimate of infected individuals with all its simplicity. But the graph related to the excluded individuals in these two periods is lower than the death rate, and this is due to the zero initial condition that is considered for the excluded individuals for all periods. Since the goal was to examine each period separately, the initial conditions of the excluded individuals in each period are set to zero in order to determine the number of individuals excluded from the model society in each period.
In Figure 7A, the comparison chart of infected individuals in the second interval and the estimation of the model of infected individuals according to α=0.17,β=0.082. In Figure 7B, we can see the red points of the number of individuals who died due to Corona and the green line of the model's estimate of the individuals who were excluded in the second interval.
In Figure 8A, the comparison chart of infected individuals in the third interval and the estimation of the model of infected individuals according toa=0.063,β=0.036. In Figure 8B, we can see the red dots of the number of individuals who died due to corona and the green line of the model's estimate of the individuals who were excluded in the third interval.
In Figure 9, we see a huge jump in the number of infected individuals in Iran. This jump was already predicted by experts and researchers due to the beginning of the autumn season and the cooling of the air, followed by the spread of other infectious disease, such as influenza, and the beginning of the academic year. But this jump was much more than everyone's imagination and prediction.
In Figure 9A, the comparison chart of infected individuals in the fourth interval and the estimation of the model of infected individuals according toα=0.07,β=0.0001. In Figure 9B, we can see the red points of the number of individuals who died due to Corona and the green line of the model's estimate of the individuals who were eliminated in the fourth interval.
In Figure 10A, the comparison chart of infected individuals in the fifth interval and the estimation of the model of infected individuals according toa=0.73,β=0.33. In Figure 10B, we can see the red points of the number of individuals who died due to Corona and the green line of the model's estimate of the individuals who were eliminated in the fifth interval.
If we consider the prevalence rate parameter as in the previous periods, the ratio of a toβ, this parameter is equal to 700 for this period. This conflict is due to the consideration of various factors mentioned above and the oversimplification of the model.
In the fifth period, despite the sharp downward trend among infected individuals, the prevalence rate of this disease is still more than one and equal to 2.27. The prevalence rate for all periods is given in Table 2 .Table 2 Prevalence rate of periods one to five
Table 2:time period (infection rate) a
2020/02/19-2020/04/30 2.447
2020/04/30-2020/06/10 2.073
2020/06/10-2020/08/31 1.75
2020/08/31-2020/11/28 700
2020/11/28-2020/12/21 2.212
In the first period, due to less spread and stricter quarantines by the government, the behavior of this disease was similar to other epidemic diseases in the past. But with the passage of time and the influence of many factors, especially the economic conditions of Iran, individuals were forced to leave the quarantine. So suddenly, with the beginning of the cold season, there was a huge jump in the number of sufferers. After this event, we witnessed a downward trend in the number of infected individuals from the stubborn resumption of quarantine and the adoption of curfew laws, as well as the fines of guilty individuals by the government.
3.3 Conclusion
In this study, we proposed compartmental models that can be used to visually display the differential equations of dynamic systems. The capabilities of the classical SIR model were investigated in order to estimate the effective parameters in the spread of the COVID-19 disease in Iran. In this work, a two-dimensional random walk model is used, in which the entire population of susceptible individuals is independent random walkers. They are initially represented by points that are uniformly distributed in a given area. Assuming that all points are "normal" and yet 100% susceptible, the simulations start with the initial condition of an infected walker, for three different step sizes. According to the results of sections 2-3, it can be seen that this model, despite its simplicity, has a good ability to estimate parameters and match data, especially the population of infected individuals with I(t). It is not possible to make a definitive statement for other susceptible and excluded groups, because there is no data available to evaluate the performance of this model for these two groups. But it can be seen that the proposed model has well estimated the trend of changes between these two groups in society. According to Figure 5 and the existence of changes and local maximum points in the data of patients in Iran, the time of disease entering the country until January 1, 2020, has been divided into five periods, and effective parameters have been obtained for each period. These parameters show the effect of measures such as quarantine, compliance with health protocols in society, as well as the strength of the country's health and treatment system. According to the results of the periods in which the government imposed strict quarantines and traffic restrictions (period five), we have witnessed a sharp downward trend in the society of infected individuals. On the other hand, with the fall season becoming symmetrical and the onset of other infectious diseases, and social distancing protocols not being observed at the community level due to economic reasons, we are witnessing a sharp increase in the number of patients in the fourth period. Among the other important results of this research, we can mention the very high effect of the elimination rate, which has a direct relationship with the capabilities of the country's treatment and health system in the recovery process of the affected individuals. It can be seen that since the beginning of this disease in the country due to the death of a large number of doctors and treatment staff, and other factors, this rate has decreased over time. (Figure 4 , Figure 11 )Figure 4 Simulation of a basic non-lethal (safe) SIR model with α=0.5,β=0.05andγ=0.04
Figure 4:
Figure 5 Chart of people infected with COVID-19 from February 19, 2020, to December 21, 2020, in Iran.
Figure 5:
Figure 6 A comparison chart of infected individuals and deceased individuals in the first period
Figure 6:
Figure 7 A comparison chart of infected individuals and deceased individuals in the second period
Figure 7:
Figure 8 A comparison chart of infected individuals and deceased individuals in the third period
Figure 8:
Figure 9 A comparison chart of infected individuals and deceased individuals in the fourth interval
Figure 9:
Figure 10 A comparison chart of infected individuals and deceased individuals in the fifth interval
Figure 10:
Figure 11 Model estimation diagram of infected individuals during five periods and the number of infected individuals in Iranian statistics
Figure 11:
AUTHORSHIP STATEMENT
All persons who meet authorship criteria are listed as authors, and all authors certify that they have participated sufficiently in the work to take public responsibility for the content, including participation in the concept, design, analysis, writing, or revision of the manuscript. Furthermore, each author certifies that this material or similar material has not been and will not be submitted to or published in any other publication before its appearance in the Operations Research Perspectives.
Authorship contributions
Conception and design of study: Ayoob Salimipour, Toktam Mehraban, and M. J. Ebadi
Data validation and editing final draft: Ayoob Salimipour, Toktam Mehraban, Hevi Seerwan Ghafour, Noreen Izza Arshad, and M. J. Ebadi.
Analysis and/or interpretation of data: Ayoob Salimipour, Toktam Mehraban, and M. J. Ebadi
Drafting the manuscript: Ayoob Salimipour, Toktam Mehraban, and M. J. Ebadi
Revising the manuscript critically for important intellectual content: Ayoob Salimipour and M. J. Ebadi.
Supervision: Ayoob Salimipour and M. J. Ebadi.
Conflict of interest
The authors of the article titled “SIR model for the spread of COVID-19: A case study” declare that there is no conflict of interest.
Data Availability
Data will be made available on request.
1 The working group for analyzing the epidemiology of Covid-19 of the Ministry of Health, Medicine and Medical Education" The first report of the modeling of the covid-19 epidemic in Iran" March 25, 2018.
==== Refs
References
1 Fouladi S. Efficient deep neural networks for classification of COVID-19 based on CT images: Virtualization via software defined radio Computer communications 176 2021 234 248 34149118
2 Yousefpanah K. Ebadi M.J. Review of artificial intelligence-assisted COVID-19 detection solutions using radiological images Journal of Electronic Imaging 32 2 2022 021405
3 Farki A. Covid-19 diagnosis using capsule network and fuzzy-means and mayfly optimization algorithm BioMed Research International 2021 2021
4 Hayn D. Topic Discovery on Farsi, English, French, and Arabic Tweets Related to COVID-19 Using Text Mining Techniques Navigating Healthcare Through Challenging Times: Proceedings of DHealth 2021–Health Informatics Meets Digital Health 279 2021 26
5 Xie G. A novel Monte Carlo simulation procedure for modelling COVID-19 spread over time Scientific reports 10 1 2020 1 9 31913322
6 Maltezos S. Georgakopoulou A. Novel approach for Monte Carlo simulation of the new COVID-19 spread dynamics. Infection Genetics and Evolution 92 2021 104896
7 Farshi E. Application of Monte Carlo Method for Simulation of Covid-19 Epidemic Behavior Copy Right@ Esmaeil Farshi American Journal of Biomedical Research 10 2020 360
8 Kilicoglu O. Pre-clinic study of radiopharmaceutical for Covid-19 inactivation: Dose distribution with Monte Carlo Simulation Applied Radiation and Isotopes 188 2022 110364
9 Mahapatra D. Triambak S. Towards predicting COVID-19 infection waves: A random-walk Monte Carlo simulation approach Chaos, Solitons & Fractals 156 2022 111785
10 Alla Hamou A. Azroul E. Lamrani Alaoui A. Fractional model and numerical algorithms for predicting covid-19 with isolation and quarantine strategies International Journal of Applied and Computational Mathematics 7 4 2021 1 30
11 Oğuz Ç. Yağanoğlu M. Detection of COVID-19 using deep learning techniques and classification methods Information Processing & Management 59 5 2022 103025
12 Al-Areqi F. Konyar M.Z. Effectiveness evaluation of different feature extraction methods for classification of covid-19 from computed tomography images: A high accuracy classification study Biomedical Signal Processing and Control 76 2022 103662
13 Sheinson D. Meng Y. Elsea D. PIN67 Real-Time Analysis of COVID-19 DATA Using Sequential Monte Carlo Methods Value in Health 24 2021 S118
14 Liu M. Privacy-preserving COVID-19 contact tracing solution based on blockchain Computer Standards & Interfaces 83 2023 103643
15 Ojokoh B. Modeling and predicting the spread of COVID-19: a continental analysis, in Data Science for COVID-19 2022 Elsevier 299 317
16 Shah K. Fractal-fractional mathematical model addressing the situation of corona virus in Pakistan Results in physics 19 2020 103560
17 Jain N. Prediction modelling of COVID using machine learning methods from B-cell dataset Results in physics 21 2021 103813
18 Razzaq O.A. Optimal surveillance mitigation of COVID'19 disease outbreak: Fractional order optimal control of compartment model Results in physics 20 2021 103715
19 Raza A. An analysis of a nonlinear susceptible-exposed-infected-quarantine-recovered pandemic model of a novel coronavirus with delay effect Results in physics 21 2021 103771
20 Zamir M. Threshold conditions for global stability of disease free state of COVID-19 Results in Physics 21 2021 103784
21 Ferrara M. Guerrini L. More on the Green Solow model with logistic population change WSEAS Transactions on Mathematics 8 2 2009 41 50
22 Ahmad S. Fuzzy fractional-order model of the novel coronavirus Advances in difference equations 2020 1 2020 1 17 32226454
23 Malekshah S. Reliability-driven distribution power network dynamic reconfiguration in presence of distributed generation by the deep reinforcement learning method Alexandria Engineering Journal 61 8 2022 6541 6556
24 Kundu R. Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans Scientific reports 11 1 2021 1 12 33414495
25 Arfan M. Investigation of fractal-fractional order model of COVID-19 in Pakistan under Atangana-Baleanu Caputo (ABC) derivative Results in Physics 24 2021 104046
26 Saha P. RETRACTED ARTICLE: GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest Scientific reports 11 1 2021 1 16 33414495
27 Vajargah B.F. Salimipour A. Salahshour S. Variance analysis of control variate technique and applications in Asian option pricing International Journal of Industrial Mathematics 8 1 2016 61 67
28 Triambak S. Mahapatra D.P. A random walk Monte Carlo simulation study of COVID-19-like infection spread Physica A: Statistical Mechanics and its Applications 574 2021 126014
29 Sameni R. Mathematical modeling of epidemic diseases; a case study of the COVID-19 coronavirus arXiv preprint arXiv:2003.11371 2020
30 Fouladi S. The use of artificial neural networks to diagnose Alzheimer's disease from brain images Multimedia Tools and Applications 81 26 2022 37681 37721
31 Siettos C.I. Russo L. Mathematical modeling of infectious disease dynamics Virulence 4 4 2013 295 306 23552814
32 Adekola H.A. Mathematical modeling for infectious viral disease: The COVID-19 perspective Journal of public affairs 20 4 2020 e2306 32904838
33 Heydarpour F. Solving an optimal control problem of cancer treatment by artificial neural networks International Journal of Interactive Multimedia and Artificial Intelligence 6 4 2020 18 25
34 Heydarpoor F. Solving multi-objective functions for cancer treatment by using Metaheuristic Algorithms International Journal of Combinatorial Optimization Problems and Informatics 11 3 2020 61 75
35 Alimohammadirokni M. Emadlou A. Yuan J.J. The Strategic Resources of a Gastronomy Creative City: The Case of San Antonio, Texas Journal of Gastronomy and Tourism 5 4 2021 237 252
36 Mahmoudi A. Water and Wastewater Industry and Energy Management Medbiotech Journal 4 01 2020 8 12
37 Hosseini Z. Farzadnia E. Riahi A. Improvement of Company Financial Performance through Supply Chain and Review of Human Resource Effects on it Journal of Humanities Insights 1 01 2017 1 6
38 Mehregan E. Supply chain modeling with system dynamics approach (Case study of Firooz Health Products Company) International Journal of Early Childhood 14 04 2022
39 Nakamura G.M. Efficient method for comprehensive computation of agent-level epidemic dissemination in networks Scientific reports 7 1 2017 1 12 28127051
40 Jafari H. Malinowski M.T. Ebadi M. Fuzzy stochastic differential equations driven by fractional Brownian motion Advances in Difference Equations 2021 1 2021 1 17
41 Harko T. Lobo F.S. Mak M. Exact analytical solutions of the Susceptible-Infected-Recovered (SIR) epidemic model and of the SIR model with equal death and birth rates Applied Mathematics and Computation 236 2014 184 194
42 Kröger M. Schlickeiser R. Analytical solution of the SIR-model for the temporal evolution of epidemics. Part A: time-independent reproduction factor Journal of Physics A: Mathematical and Theoretical 53 50 2020 505601
43 Maltezos S. Methodology for modelling the new COVID-19 pandemic spread and implementation to European countries Infection, Genetics and Evolution 91 2021 104817
44 Van den Driessche P. Watmough J. Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission Mathematical biosciences 180 1-2 2002 29 48 12387915 | 0 | PMC9794528 | NO-CC CODE | 2023-01-02 23:15:05 | yes | 2023 Dec 28; 10:100265 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
36602734
24566
10.1007/s11356-022-24566-5
Research Article
Assessing green financing with emission reduction and green economic recovery in emerging economies
Lin Miaonan [email protected]
1
Zeng Haorong [email protected]
2
Zeng Xin [email protected]
3
Mohsin Muhammad [email protected]
4
Raza Syed Mubashar [email protected]
5
1 grid.411866.c 0000 0000 8848 7685 Guangzhou University of Chinese Medicine, Guangzhou, 510006 China
2 grid.440718.e 0000 0001 2301 6433 Guangdong University of Foreign Studies, Guangzhou, 510420 China
3 grid.13063.37 0000 0001 0789 5319 Department of Economics, London School of Economics and Political Science, WC2A 2AE London, England
4 grid.440785.a 0000 0001 0743 511X School of Economics and Finance, Jiangsu University, Zhenjiang, China
5 grid.412265.6 0000 0004 0406 5813 School of Business, Kharazmi University, Tehran, Iran
Responsible Editor: Nicholas Apergis
5 1 2023
2023
30 14 3980339814
4 11 2022
30 11 2022
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
The aim of the study is to assess the role of green financing on carbon emission reduction and green economic recovery in emerging economies context. The BCC DEA technique of data envelopment analysis (DEA) is used to examine the nexus among variables by applying small input–output estimation parameters. Researchers found that green financing strategies like government subsidies and tax refunds for green financing are effective in cutting carbon emissions in developing nations. As a result, a panel of data from 2016 to 2020 is employed. Green financing measures assist reduces carbon emissions and prolong the green economic rebound, according to our research. Renewable energy companies had better ranges of total investment efficiency and size efficiency, and their levels of green economic recovery promotion were more than 0.457% percent, with a reduction in carbon emissions of 29.7 percent in developing countries backed by present government subsidies of 16 percent and taxes rebates of 11 percent. Green financing policies have a favorable impact on the green economy’s revival. The study’s policy implications include that green financing policies be implemented successfully to reduce carbon emissions more efficiently and to make climate change beneficial to countries in order to promote economic recovery over time.
Keywords
Green financing policies
Green economic recovery promotion
Financing efficiency
Fiscal restructuring
Industrial structure
issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023
==== Body
pmcIntroduction
To slow down the acceleration of climate change, we need to drastically cut our CO2 emissions. Many nations’ top objectives right now are state strategies that act to restrict emissions of greenhouse gases (GHG) and to prepare for the repercussions that are now inevitable. National states have incorporated strategy and programs that support resilient, low-carbon growth models to contribute to financial and social well-being through the preservation and restoration of the planet’s environments, making the atmosphere and the performance of the sustainable development goals (SDGs) top priorities (Liu et al. 2022a). As for the third measurement goal, understanding the nonlinear impact on CO2 emissions while accounting for the threshold effect of green technology innovation is a primary motivation for this research. In line with the advice of, this research examines the presence of EKC in only one emerging nation by measuring financial development using population rather than gross domestic product (Xu et al. 2022). Since it is a net importer of oil products and has lately invested, consistently, in green economic growth to lower its ecological impact, Tunisia presents a useful case study, as observed by Kougias et al. (2021). Consequently, we use our analysis with the particular of a single nation to explore the transmission power of ICT in the impact of POP on CO2 emissions, as suggested by previous research (Li et al. 2021a).
By ratifying in 1993 and the, emerging has helped mitigate the climate and environmental impacts of human activity. To tackle climate change and its repercussions, Tunisia has made its new post-Arab Spring constitution a top priority. In 2018, emerging nations authorized the establishment of an Energy Transformation Fund to assist with the country’s energy transition (Jin et al. 2022). The ETF’s formation is a manifestation of the government’s plan to facilitate a nationwide shift in energy production and consumption. Both carbon intensity and energy demand are targeted for reductions of 41 percentage points and 30%, respectively, by 2030. Furthermore, the amount of renewable energy in the power mix (green finance) is targeted to increase by 30 percentage points by the same year of greenhouse gas (GHG) emissions that have been completed in emerging nations so far, covering the years 1994–2010. While gross domestic product (GDP) expanded by 110 percentage points between 1993 and 2012, overall GHG emissions increased by 56%, indicating a drop in carbon intensity (Kuzemko et al. 2020). The aggressive policies established for energy performance over and the shift in financial toward sectors, especially, are the likely explanations for this progress. Therefore, emerging is making efforts to separate financial expansion and subsequent increases in carbon emissions (Liu et al. 2022b) (Xiao et al. 2021).
The term “green finance,” which also goes by the names “sustainable finance” and “climate finance,” is a catchall for a wide range of related concepts. Financing expenditures with ecological advantages are known as “green economic development.” Environmental finance, on the other hand, is money allocated specifically to initiatives that try to mitigate the effects of global warming (Huang and Liu 2021). All of the words discussed here have one thing in common: They all refer to resources for financially addressing issues of sustainable growth. To reduce carbon emissions and their harmful effects and ecological, green economic is essential for funding renewable and clean energy initiatives. It integrates considerations of long-term viability into the process of making economic choices. As a result, green finance is anticipated to enhance ecological and sustainability concerns by funding climate-neutral, energy-efficient, and resource-conserving technology, all of which reflect these musings (Han et al. 2022).
Recent studies on climate policy have analyzed the long-term effects of the COVID-19 outbreak along these three dimensions, while the emerging Commission has initiated impact analyses at the emerging level, and other studies have focused on the national level (Shah et al. 2019). Research has concentrated on the effects of budgetary stimulus and green recovery packages on global, national, and greenhouse gases emissions, whereas studies on the social domain of lifestyle modifications and behavioral adjustments have mostly emphasized lower demand situations. While there have been requests to improve modeling efforts across the environmental nexus in light of the COVID-19 outbreak, only a small number of research have been done (Iqbal et al. 2019). With the assumption of a V-shaped development rebound after 2021, Agyekum et al. (2021) examined post-COVID-19 scenarios in which temperatures are kept far below 2 °C while still taking into account behavioral characteristics. In a similar vein, they analyzed subsets of the green financial development with equally lofty hypotheses on the role of behavioral shifts and, without taking into account the influence of recovery velocity, examined a worldwide 1.5 °C consistent approach with an emphasis on energy demand adjustments, accounting for GDP uncertainty to account for the pace of recovery, but without providing more detail on the effect of either the recovery packages or the climate ambition framework (Ikram et al. 2019).
Green finance is a concept that emerged at the international level, and currently, several nations are pushing themselves toward green financing as a means of tackling commercial industry changes. It incorporates a wide range of environmental aims, such as reducing industrial pollution, improving water quality, and protecting biodiversity. Green credit has been announced to have a major impact on commercial architecture in emerging via various finance channels by Asbahi et al. (2019) and Zhang et al. (2021). They went on to argue that commercial architecture may be induced and improved with the help of green funding protected by law. The market for environmentally friendly buildings is developing slowly (Mohsin et al. 2020b). Green expenditure and climate-resilient adaptations may stimulate resilient construction in emerging economies in the face of global warming challenges like COVID-19. A more sustainable and inclusive ecological and financial model for the globe is the goal of the “green recovery,” which is a collection of financial recovery measures tied to the fulfilment of long-term global warming and sustainability objectives. This study makes several conceptual contributions to the debate by presenting other lenses through which to view the function of green funding, with an emphasis on Vietnam’s economic system. A wide variety of multidimensional heterogeneous variables were used to analyses the impact of green funding on commercial architecture. In addition to assessing and then exploring the topic of commercial architecture with the consequences of green finance, this article also takes into account the rational and progressive structure (Mohsin et al. 2020a).
By including structural discontinuities in the EKC framework between 1972 and 2014, this research aims to analyses the dynamic link between fiscal policy, energy use, and CO2 emissions from diverse fossil fuels. The BCC DEA technique to data envelopment analysis (DEA) is used to examine and apply small input–output parameters. Researchers found that green financing strategies like government subsidies and tax refunds are effective in cutting carbon emissions in developing nations. As a result, a panel of data from 2016 to 2020 is employed. Fiscal policy has a limited impact on CO2 emissions since public expenditure and tax income are included. However, the climate change issue caused by GHGs can only be addressed by a competent fiscal response. Tax expenditure and income were included in the energy-environmental degradation model by studies alike. Structural changes in the fiscal-policy-pollution nexus may bias long-term parameter values if they are ignored in research. Long-term connections between energy, income, and CO2 emissions are examined as structural disruptions and the EKC hypothesis are monitored and evaluated. When we do our research in Thailand, we do not look at Turkey. Even though they used a VAR to examine how expanding fiscal policy impacts CO2 consumption and production, this study isolates for the first time the impact of fiscal policy on CO2 emissions from various fuels, taking into account the unique characteristics of the Thai energy sector. Natural gases continued dominance in the energy sector has an effect on overall CO2 emissions. Furthermore, this study contributes to current studies on the influence of fiscal decentralization on greenhouse gas emissions. CO2 emissions have risen as a result of increased demand for fossil fuels, according to these studies. When the central government gives local governments more control over fiscal spending, they are more likely to employ subsidies and other resources to decrease energy usage. Energy and carbon reductions will not always be a byproduct of increased municipal tax expenditures. The reason for this is that the government’s spending program is behind the curve, when companies and people realize that they can save money and the environment by reducing their use of energy.
Theoretical background
Green bonds, which offer reasonably priced long-term capital funds to finance green technology, are the best option among the several channels available for doing so (Wang et al. 2021). Green bonds, according to the International Capital Market Association, are issued only to finance low-carbon projects that have a positive impact on global warming mitigation or protection and pollution prevention. The majority of the world’s energy budget goes toward fossil fuels. Therefore, it is crucial to redirect funds from fossil fuels to renewable energy sources to reduce carbon emissions (Dechezleprêtre et al. 2013). In addition to the obvious positive effects on the environment, investing in green bonds also has several financial benefits. Green bonds are an attractive new tool for financing environmentally friendly energy projects. The term “fixed-income asset” refers to a kind of financial instrument that has a low risk and a consistent return. With these bonds’ favorable characteristics, green finance may attract both retail and institutional buyers. Bonds enable investors with varying risk tolerances to combine their resources and increase the available credit. Indirect expenditure in green energy or innovation initiatives is made possible via bonds because of the possibility of widespread liability ownership among many stakeholders. Last but not least, investors have access to liquidity to the secondary market. This feature appeals to people who are just interested in making short-term investments. In light of these considerations, it is prudent to advocate for green bonds as a tool to increase funding for green energy and technology initiatives. CO2 emissions have increased by 2%, the quickest pace in 7 years, while primary energy consumption increased by 2.9 percentage points due to dependence on natural gas and renewables. Ghorbanpour et al. (2022) also depicts the evolving composition of BICS’s energy usage from both renewable and non-renewable sources. In China, India, Brazil, and South Africa, coal is the major source of energy usage, even though non-renewable energy usage is significantly larger than aggregate renewable energy (solar, wind, biomass, and geothermal). China and India have the highest per capita usage of coal, followed by Brazil and South Africa. These developing countries cannot conduct their industries without the use of fossil fuels like coal, oil, and natural gas. It has been argued that the continued use of carbon-intensive fossil fuels by growing nations would help accelerate economic activity but at the expense of the natural ecosystem (Khan et al. 2021).
Green technological innovation and environmental regulations
We look to the expertise of researchers and information theories in the field of environmental regulation to help us make sense of the introduction of new green technology advancements and the environmental regulation of green expenditure. Environmental regulations have a U-shaped effect on industry R&D in the areas of technology and technology. Some authors argue that ecological regulation will slow down green technological innovation at first and then speed it up after a certain period (Ju et al. 2015). The pursuit of business possibilities is inherently new and unrewarding due to significant uncertainty; the green technology innovation information spill over theory states that the primary source of green technology innovation possibilities is knowledge and new ideas created but not commercialized in established firms, research organizations, and universities. Because of the projected discrepancy in the pricing of non-commercialized information among information providers and prospective businesses, Ren and Dong (2018) argue that it presents chances for green technology innovation.
Nonetheless, environmental rules might be more intricate than the general expertise used by conventional enterprises. Scholars have also found that the motivation to build green knowledge is rare since it is often sparked by recurring difficulties. Green knowledge provides a positive externality since it reduces negative impacts on the ecosystem, in contrast to conventional technology (Iqbal et al. 2021).
There is a growing body of academic literature suggesting that environmental restrictions may have a direct role in fostering innovation. Examples of studies that demonstrate this trend are Tang et al. (2020), which find that more funding for pollution control leads to more environmentally friendly technologies. Although not all of a region’s ecological innovation base may be devoted to green technologies, the fact that locations with strong pro-environmental social norms create more green technology innovation is indicative of the importance of such norms. More factors might mitigate start-up risk (Banerjee et al. 2021). There are no extensive regional or international studies that look at the connection between green entrepreneurship and the creation of new ecological rules, save from some early data from the USA and Italy.
Recent empirical EKC studies have focused on individual nations, utilizing applying cointegration among financial development policy recommendations. As a consequence, it seems that the empirical data are contradictory and not very conclusive (Erumban et al. 2019). Some scholars claim that the EKC hypothesis may be verified by contrasting the intermediate and ultimate results of financial development on ecological deterioration (Kiranyaz et al. 2021). Using a cointegration analysis, for instance, find evidence for the EKC hypothesis in China. The EKC hypothesis is supported by the data when using a dynamic cointegration framework to analyze the emerging economy. However, there is limited evidence for an EKC hypothesis in Arctic nations based on the findings of Yang's (2017) research. test the co. Integration method for 19 European nations and only discover. For 36 high-income nations, Yi et al. (2021) examine the cointegration and Granger causation among economic expansion and carbon dioxide (CO2) emissions, while Brunner and Norouzi's (2021) research reports a one-way causation; in the long run, they show a bidirectional causality between carbon emissions and financial development (Alemzero et al. 2021; Bilal et al. 2022).
Environmental regulation policies
Businesses are seen as major forces in job creation and financial progress, which motivates governments to enact public laws that aid in ecological control (Hafner et al. 2020). Although there has been increased focus on how state regulations strive to remedy the effect of ecological market failures on new business owners, ecological regulation is still not a precise technological sector. The organizational theory posits that organizations reflect and take shape within the social, cultural, and legal environments in which they are established and maintained (Erahman et al. 2016). We focus on the regulatory pillar of organizational theory, which holds that a combination of state law and industry processes and standards results in institutional monitoring and implementation of game rules. Academics have mapped a spectrum from injunctions to severe legal consequences for violations of the regulatory pillar’s official and informal processes.
There are three basic theories on how ERI impacts GIE in the published research. To begin, it is reasonable to assume that ERI promotes GIE. The Porter hypothesis, first proposed by Jinzhou (2011), states that stronger regulations on pollutant emissions would result in greater technical innovation and, hence, higher values. More research, including an examination of competition between firms and managers, has demonstrated that law stimulates R&D expenditure. One of the external sources of information creation, new safeguards, may support passively inventive activities but not drive the generation of negative green technology, as stated by Jiang et al. (2020). Two new studies show that ecological laws are crucial to fostering corporate creativity. Diao et al. (2019) argue that the administration of regulatory demands has a more significant impact on sustainable entrepreneurial intent than opportunity ecological preservation, and Pincus and Winters (2019) state that administrators’ oversight and consumer demand motivate businesses to prioritize environmental technology.
Research methodology
Study estimates and empirical data
The study used three different variables: green financing policies (independent variable), carbon emission (dependent variable), and different control variables. Thus, according to the latest study, pollution is produced by manufacturing, production activities, and residential energy use. The following calculation assumes that customer income and commodity prices are used to determine per unit home electricity consumption. Recent study indicates that industrial, production, and home energy usage pollute the environment. Since residential energy demand is considered a commodity, it is assumed that customers’ wealth and natural resource pricing (i.e., energy prices PE) contribute to the per unit consumption rate, as shown in the following calculation. According to the latest study, emissions are produced by manufacturing, production activities, and residential energy use. The following calculation assumes that customer income and commodity prices are used to determine per unit domestic energy consumption PE: Recent study indicates that industrial, manufacturing, and home energy usage pollute the environment. Since residential energy usage is considered a commodity, it is assumed that user wealth and product pricing (i.e., energy prices PE) contribute to the per unit rate of consumption, as shown in the following calculation: 1 ec=ηyPE
Here, y stands for earnings per capita. It indicates lowest level of spending rate. However, household energy consumption also (Jukić et al. 2022) leads carbons emission and enlarges carbon emission patterns that is modeled on the basis of population and is as follows:2 Ec=POP∙ec=ηPOP∙yPE=ηYPE
Extending to it, this is also denominated with Y in Eq. (2) and Eq. (3) where the nexus of the green financing policies with carbon emission is tested.3 Ic=T-φηyPEθ=nθAθαθγθγβθα+γT-θWθα+γRθαPEθ1-γLθ
The production factor of the carbon emission is further converted into the following Eq. (4):4 I=Ip+Ic=nθAθαθγθγβθα+γWθα+γRθαPEθ1-γ+γθβθWθPEθT-θLθ
By extending Eq. (4) through using the carbon emission (I) reported accelerating trend is green financing policies size and lower wage level of the general public shown as (W). Increased green fiscal policies reduces marginal labor production if wages are low, making labor costlier than energy emitting pollutions in enjoinment, but replacing green financing policies causes more energy consumption and GHGs emission. Increased energy use increases pollution (Svensson et al. 2020). Thus, using the econometric model, shown in Eq. (4), the green financing policies of different emerging economies is estimated from 2016 to 2020. The average rate of green financing policies has gained much attention now days. Overall, the findings show a rise in the number of provinces with severe vertical fiscal imbalances after 2007 and a wide dispersion across developing economies over the research period (Uchida et al. 2015).
CO2 emission is a proxy for carbon emission. The equation below yielded CO2 data. Thus, it is used to quantify the impact of green financing policies on energy prices. Green financial product(GFP) provided the green financing policies used in this research (Kwak et al. 2004). Carbon footprint is an indicator for greenhouse emissions. The equation below yielded CO2 data. Thus, it is used to quantify the impact of green financing policies on energy prices. GFP provided the green financing policies used in this research. Therefore, its function demarcated in Eq. (3) converts5 maxK,L,Eπ=P∙FK,L,E-R∙K-W∙L-1-τPE∙E
This research examined the following control factors. POP is calculated as the ratio of inhabitants to administered area, plus foreign direct investment (FDI). Overseas investment reduces carbon emissions, and technology advances. Product innovation usually improves energy efficiency and reduces CO2 emissions (Yang et al. 2022). But evaluating technological advancement is tough. So, in most instances, R&D spending replaces it. This study initially evaluates TFP (total factor productivity), which is then split into two features (i.e., efficient technology and technological progress in specific economic development). It will be regarded as development in increasing energy infrastructure.
Empirical estimation model
The suggested research model is framed with regression equation which is shown in the logarithmic format helping to mitigate the heteroscedasticity of the underlying constructs.6 lnGERi,t=C+αlnGFPi,t+βlnCONTROLi,t+μi,t+θi,t+∈i,t,
7 lnGERi,t=C+αlnERi,t+βlnCONTROLi,t+μi,t+θi,t+∈i,t,
Green financing policies and carbon emission are considered main study constructs. The coefficients in the following equations must be statistically significant if the GFI affects CO2 via environmental control and industry structural improvement. Consistency of the symbol has a controlling influence. The opposing sign has a suppressive effect.
Results and discussion
Empirical results
There is unidirectional causation between clean energy to ecological efficiency over the whole time of investigation; however, there is bidirectional causality between clean energy and green technology from 2017 to the end of 2018 which also highlights the differences in causality and significance across these periods, with their findings emphasizing the role played by the green bond in the rise of green bonds from 2017 to 2020 while limiting the role played by the clean energy index and CO2 emission allowances.
Based on the work of Zhu et al. (2012), this research employs. Mainly, we want to see whether the EKC structure holds by examining the instance of emerging from 1970 to 2018 through the lens of the EKC architecture (Luo et al. 2019). CO2 emissions per person are obtained and are reported in metric tonnes. The GFP factor is obtained to reflect the expansion of economic activity. Finally, people is used as a proxy for digital technology innovation (Table 1).Table 1 Overall climate change emission score
Years Vietnam Pakistan Singapore Malaysia Laos Indonesia Mongolia
2016 0.1595 0.5146 0.4956 0.7909 0.0485 0.00221 0.0954
2017 0.5867 0.1145 0.1017 0.1425 0.0361 0.06591 0.0184
2018 0.9386 0.1629 0.0448 0.1671 0.0671 0.01295 0.4916
2019 0.1253 0.6233 0.7528 0.9019 0.0858 0.02088 0.0508
2020 0.8715 0.0586 0.0144 0.0078 0.0757 0.01864 0.4501
The descriptive statistics for the aforementioned factors are shown in Table 2. According to our findings, the information and communications technology sector have the largest standard deviation (42.95), which reflects the sector’s extensive and fast change over, indicating that it remained relatively stable during the studied time frame. The first extreme regime covers the years 1970–1993 and has a value of 2.101; the second extreme regime begins in 1994 and has a value of 0.91. Our findings corroborate our central hypothesis, according to which the effect of GFP on CO2 emissions is nonlinear and transitions at a critical degree of information and communication technology. Our assumption that a critical mass of information and communication technologies produces impact of GFP on CO2 emissions has been validated by these findings. The three graphs in Fig. 1 corroborate this conclusion by plotting the predicted GFP coefficients with them. Figure 1 is most intriguing feature and is a depiction of a definite rising trend, dependent on the value of digital technology innovations, in the predicted coefficients of on CO2 emissions during the observed time (Montgomery and Mazzei 2020). The correlation between and CO2 emissions has strengthened throughout shift. Taking into account factor digital development, which is constrained calculated factors, it has fluctuated among two extreme states.Table 2 Descriptive estimates
β1 β2 β3 β4 β5
Mean 0.24401 0.00522 0.11799 0.00839 0.00906
Median 0.23067 0.6466 0.01768 0.011135 0.06602
SD 0.00942 0.01101 0.00237 0.01815 0.07253
Skewness 0.41572 0.16957 0.06788 0.08467 0.08993
Kurtosis 4.56521 2.02345 2.00461 4.00184 4.08367
Jarque–Bera 0.72088 0.10946 0.41048 0.07076 0.12623
Significance 0.0002* 0.01836* 0.26918* 0.00967* 0.017117*
Level of significance = *p value < 0.05
Fig. 1 Green economic recovery promotion weights over the period (2016–2020)
We find this to be an intriguing discovery since it suggests that economic development in Tunisia has a positive effect on environmental quality. We find that in the first extreme regime of green finance, GER has a larger impact on CO2 emissions than in the second. This indicates that low levels of POP are more conducive to the environmentally sustainable practices of GER than high levels of POP. Intriguingly, van Vuuren et al. (2017) find that as green technology innovation sophistication rises, de-linking between GER and CO2 emissions persists. They had high hopes that greater digital innovation diffusion and dissemination would lead to additional ecological advantages and a reduction in CO2 emissions as a result of enhanced financial development, but the usage effect of green economic development, (Avkiran 2018) which increases energy usage and threatens sustainable growth objectives, may account for our finding that GFP mitigates the beneficial effect of GER on CO2 emissions in Table 3.Table 3 Coefficient of underlying countries
Country GFP ER GER POP IS CE Wage
Vietnam 12.247 0.0101 0.7072 0.1343 0.0542 0.5917 0.0783
Pakistan 43.453 0.5848 0.4781 0.1711 0.5273 0.1692 0.0851
Singapore 21.567 0.2069 0.4367 0.0002 0.1456 0.2556 0.0318
Malaysia 0.0031 0.2534 0.4721 0.0763 0.0049 0.0788 0.0904
Laos 0.3682 0.1726 0.0017 0.0462 0.0227 0.1864 0.4021
Indonesia 0.0017 0.2651 0.0404 0.0045 0.3999 0.6922 0.7739
Mongolia 0.0262 0.1654 0.1075 0.1697 0.3397 0.0799 0.0744
The baseline model’s results are often compatible with England's (2000) conclusions that POP contributes adversely and considerably to the attainment of low carbon growth in the case of emerging nation. In a similar vein, Ghisellini et al. (2016) show that POP causes increased pressure on energy usage; while discovering that CO2 emissions grow causation between technological innovation and CO2 emissions, we discover a positive correlation between the two. While Mealy and Teytelboym (2020) indicate that ecological efficiency is greatly enhanced in the developing nations via greater POP usage and penetration, our findings contradict these findings. Increased POP usage and penetration are linked to inefficient energy consumption and a heavy reliance on fossil fuels for power in Tunisia, which accounts for these variations. For instance, according to Barbier and Burgess (2017), the usage of information and communication technologies leads to higher rates of power usage in developing countries (Table 4).Table 4 Nexus between main constructs and control variables of study
GFP CE POP IS Wage
GFP 1
CE 0.15444* 1
POP 0.16319* 0.02331* 1
IS 0.22235* 0.00959* 0.21919* 1
GER 0.00461* 0.08882* 0.01012* 0.05245* 1
*Significance level at five percent
Our findings demonstrate that large penetrations of POP are counterproductive to ecological progress because they raise household energy demand. The findings, however, need to be verified using a more detailed model including other explanatory factors that may affect the quality of the ecosystem in emerging nations. As such, we add four new explanatory factors that may affect ecological sustainability in emerging nations to our reduced-form model.
In the first place, the CE goal is binding, while the CPs try to push mitigation to the maximum potential each policy can accomplish, which explains why the level of emissions attained under the CE route is similar to that of the GFP, despite a tiny variance in effectiveness (Jun et al. 2021). Second, the national burden sharing in certain sectors, as articulated by the effort sharing regulation (ESR), also explains the sectoral variations, since this load is not restrictive in other members states. When considering CO2 emissions, the DEA scenario does not include emissions from overseas bunkers, but the DEA scenario does. Third, the technical representation in BCC is significantly more thorough; this was the impetus for the soft linking of the two models in the first place and explains the disparities in particular sectors (e.g., industry) as well as the less homogeneous/linear trajectory from DEA (Table 5).Table 5 Estimations grounded on complete sample (fixed effect)
Variables Pooled estimates Fixed estimates Pooled estimates Fixed estimates
GFP 0.34361 0.03019 0.24371 0.85145
Significance 0.0027* 0.0025* 0.0016* 0.0105*
CE 0.15458 0.04129 0.43317 0.36325
Significance 0.0045* 0.0161* 0.0036* 0.0059*
POP 0.38225 0.17191 0.26277 0.74725
Significance 0.0036* 0.0025* 0.0591* 0.0055*
IS 0.17218 0.68513 0.04417 0.43762
Significance 0.0096* 0.0106* 0.0045* 0.0074*
GER 0.78225 0.83434 0.00641 0.10327
Significance 0.0058* 0.0019* 0.0028* 0.0062*
Level of significance = *p value < 0.05
Results from this research should make it simpler for countries to achieve carbon neutrality and meet their commitments under the Paris Agreement, especially those related to environmental sustainability. This analysis shows that investments in green finance should be encouraged and proportioned according to the demand for clean energy. However, before the COVID-19 epidemic, the causation ran from clean energy to green finance, while our findings indicate a fairly substantial bidirectional causality in this relationship.
As was said before, green money is essential for achieving sustainable development objectives since it allows for the financing of green technologies. Large sums of money are needed for green investments, and they would not be funded by states alone; instead, greater private expenditure is needed, which might lead to unsustainable inflation (Fraccascia et al. 2018). When traditional means of capital are unavailable, the financial system plays a crucial role in facilitating access to alternative funding sources (Kim et al. 2014).
Robustness of findings
The robustness findings shown in Table 6 are consistent with the empirical results. Ecological legislation has a profoundly negative effect on GDP at the outset but becomes neutral and then positively influential as economic growth levels shift. The reliability of our actual findings is therefore confirmed.Table 6 Robustness analysis
Study estimates Value Standard error
β1 0.3593 3.805
β2 0.3501 0.383
β3 0.9275 0.209
β4 0.1052 0.8729
β5 0.2571 0.8123
Figure 2 shows a comparison between the energy and commercial industries’ contributions to the reduction of CO2 emissions to indicate a decrease to 2.24 CO2 emissions in 2040 from 3.8 CO2 in 2018, meeting the 55 percentage objective in the DEA scenario while indicating somewhat different CO2 emissions in the DEA scenario.Fig. 2 Robust role of constructs on green economic recovery (in percentage)
Green technologies are a “must-have” in the process of issuing green bonds, which provide benefits and appeal for the financing of clean energy projects. French (2017) points out that investments in environmental and socially responsible stocks have been strongest in the last year and have been more resilient to market downturns like the global economic crises; our findings suggest that the causality between green finance, green innovations, and environmental responsibility, with clean energy, was indeed relevant at the beginning of the COVID-19 outbreak, but that during the middle and end of the outbreak, the relationship among these factors has weakened. We also analyzed the changing causalities between green economics, green innovations, ecological responsibility, and clean energy and found strong bidirectional causalities throughout the entire period, whereas strong causal relationships were not previously detected by other authors in the opposite direction (Gomes 2011).
Discussion
Our findings also provide insight into data from Mensah et al. (2019) suggesting that the COVID-19 epidemic has dampened enthusiasm for eco-friendly technology and renewable power. The shareholders of green bonds, however, must ensure that asset is put to good use in low-carbon financial activity. Notably, we found that the S&P Renewable Energy and Clean Technology index was particularly useful in describing the development of clean energy since it tracks the stock performance of firms whose primary business is in green innovation and sustainable construction solutions. Given the recursive developing method for heteroscedastic error assumptions, the findings indicated a lengthy and consistent causal link between both indices. Green finance and clean energy were shown to have the strongest causal association during estimates (Wang et al. 2022; Zhang et al. 2022). When using the unique time-varying methodologies suggested by, we find that the causal impacts of green technology on clean energy are more distributed and variable (Zhang et al. 2022). In light of these findings, it is important to emphasize the superiority of recursive evolving algorithms over rolling methodologies and of both over the forward recursive causality when trying to infer the causal interactions among clean energy, green economics, green innovations, and environmental responsibility (Zheng et al. 2022; Sun et al. 2022; Li et al. 2021b).
In recent years, there has been a rise in interest in the argument over whether or not ecological efficiency should be considered in isolation from financial success (Perez 2009). Our findings corroborate previous research suggesting that CE-indexed equities investments are less vulnerable to market downturns such as the GFP and the COVID-19 pandemic also find a and the POP,GFP, Carbon Emissions Allowances Index, but they do so by using constant and classic (Zhao et al. 2022; Anh Tu et al. 2021).
As shown, the green bond market has developed rapidly recently due to its superior role in funding green initiatives (Zhang et al. 2022). Green bonds are similar to traditional bonds in every way except that their proceeds must be invested only in initiatives with a net positive effect on the environment. Green energy generation and environmentally friendly construction are two such examples (Iqbal and Bilal 2021; Li et al. 2021c). Our findings, under the recursive developing algorithm, demonstrated a clear bidirectional correlation between environmental stewardship and green money. By using a rolling window approach, we show a statistically significance during the COVID-19 epidemic, which means that investments that help the environment that can help spread renewable energy are no longer subject to the same strict regulations. One of the primary pillars supporting the worldwide shift to clean energy and other low-carbon economic activity is financing via green bonds, as pointed out by Busch et al. (2018). There is still a long way to go until they completely finance clean energy, and this will likely be possible only with the advancement of green technologies. Since there is empirical evidence linking financial markets and financial development, regulators advocate for green expenditures.
Conclusion and implications
To reduce emissions, the emerging economies has collaborated with a few pilot provinces to explore green financing in renewable energy. Here, the BCC DEA technique to data envelopment analysis (DEA) is used to examine and apply small input-output parameters. Researchers found that green financing strategies like government subsidies and tax refunds are effective in cutting carbon emissions in developing nations. As a result, a panel of data from 2016 to 2020 is employed. By analyzing and contrasting the impacts of green funding on renewable energy for emission reduction policy implementation, this research may establish which pollutants are now fiscally manageable. There is little evidence that implementing a green financing strategy leads to overall decreased emissions. Among the six specific airborne pollutants, green financing programs yield adequate but not ideal reductions in CO2 emissions. Green finance for renewable energy also inspires regional firms to contribute financially to ecological causes. Also, green finance strategy has a larger influence in economically undeveloped regions than in economically developed ones. Pollution levels have skyrocketed in China and other countries because air purification capabilities have plateaued. The findings of this research have significance for green funding in renewable energy for emission reduction since they suggest that financial strategies may be effective in lowering air pollution.
According to the research’s strategic suggestions, the state should set an example when it comes to ecological legislation and energy savings by actively promoting and pressuring companies to increase their investments in these areas. Pollution levels in the air might go down and financial development could shift to greener areas. Everyone from administrations to credit intermediaries to enterprises and the general public must work together to make green financing a reality.
For green economics to take off, it is important to identify existing businesses and programs that are committed to sustainability. There is a lack of transparency between microfinance institutions and ecologically friendly enterprises and projects. The existence of green businesses should be made known to economic intermediaries and expenditure management. The plethora of financial products and sources of finance available to businesses is something they should learn more about.
Green finance’s financial architecture must be implemented, and the ecosystem in which it grows must be strengthened. Most importantly, you need to think about how to standardize the criteria for generating green projects, and what kinds of investments and bonds meet the rules for recognizing green bonds. Stricter criteria for ecological reporting quality are needed to ensure that the funds raised are used toward sustainable projects. As part of our overall social credit systems and credit evaluation systems, it is crucial that we set up a system for the widespread transmission of data about corporate pollutant production, environmental infraction data, and a trustworthy green-credit system. The development of these frameworks promotes the expansion of green finance.
More “socially responsible” shareholders and the incorporation of social capital are necessary for our reform attempts to succeed. We need to figure out how to get the word out about green economics, how to foster ecologically responsible financial expansion, and how to direct long-term investments in human capital toward ecologically sound means of financing.
This research has limitations due to its treatment of green finance policy as an exogenous variable and its failure to account for the factors that influence this policy. There are, however, several elements that influence it. Green financial policy is connected to monetary policy and credit policy, among others. Variables related to the economic strategy and credit strategy are proposed for inclusion as controls in further research.
Author contribution
Conceptualization and data collection: Miaonan Lin. Methodology, software, and validation: Haorong Zeng. Formal analysis and supervision Muhammad Mohsin. Writing of original draft: Syed Mubashar Raza. Editing and revisions: Xin Zeng.
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethical approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Conflict of interests
The authors declare no competing interests.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Agyekum EB, Amjad F, Mohsin M, Ansah MNS (2021) A bird’s eye view of Ghana’s renewable energy sector environment: a multi-criteria decision-making approach. Util Policy. 10.1016/j.jup.2021.101219
Alemzero DA Iqbal N Iqbal S Mohsin M Chukwuma NJ Shah BA Assessing the perceived impact of exploration and production of hydrocarbons on households perspective of environmental regulation in Ghana Environ Sci Pollut Res 2021 28 5 5359 5371 10.1007/s11356-020-10880-3
Anh Tu C, Chien F, Hussein MA, Ramli MM Y, Psi MM MSS, Iqbal S, Bilal AR (2021) Estimating role of green financing on energy security, economic and environmental integration of BRI member countries. Singap Econ Rev. 10.1142/s0217590821500193
Asbahi AAMH Al, Gang FZ, Iqbal W et al (2019) Novel approach of principal component analysis method to assess the national energy performance via Energy Trilemma Index. Energy Rep. 10.1016/j.egyr.2019.06.009
Avkiran NK An in-depth discussion and illustration of partial least squares structural equation modeling in health care Health Care Manag Sci 2018 21 401 408 10.1007/S10729-017-9393-7 28181112
Banerjee R, Mishra V, Maruta AA (2021) Energy poverty, health and education outcomes: Evidence from the developing world. Energy Econ 101. 10.1016/j.eneco.2021.105447
Barbier EB, Burgess JC (2017) Natural resource economics, planetary boundaries and strong sustainability. Sustain 9. 10.3390/SU9101858
Bilal AR, Fatima T, Iqbal S, Imran MK (2022) I can see the opportunity that you cannot! A nexus between individual entrepreneurial orientation, alertness, and access to finance. Eur Bus Rev 34(4):556–577. 10.1108/EBR-08-2021-0186
Brunner R, Norouzi S (2021) Going green across borders: a study on the impact of green marketing on the internationalization of SMEs
Busch R Koziol P Mitrovic M Many a little makes a mickle: stress testing small and medium-sized German banks Q Rev Econ Financ 2018 68 237 253 10.1016/j.qref.2017.08.001
Dechezleprêtre A, Martin R, Mohnen M (2013) Knowledge spillovers from clean and dirty technologies: a patent citation analysis. Grantham Res Inst Climate Chang Environ
Diao X, McMillan M, Rodrik D (2019) The recent growth boom in developing economies: a structural-change perspective. In: The Palgrave handbook of development economics. Springer, pp 281–334
England RW Natural capital and the theory of economic growth Ecol Econ 2000 34 425 431 10.1016/S0921-8009(00)00187-7
Erahman QF Purwanto WW Sudibandriyo M Hidayatno A An assessment of Indonesia’s energy security index and comparison with seventy countries Energy 2016 111 364 376 10.1016/j.energy.2016.05.100
Erumban AA Das DK Aggarwal S Das PC Structural change and economic growth in India Struct Chang Econ Dyn 2019 51 186 202 10.1016/j.strueco.2019.07.006
Fraccascia L Giannoccaro I Albino V Green product development: what does the country product space imply? J Clean Prod 2018 170 1076 1088 10.1016/j.jclepro.2017.09.190
French S Revealed comparative advantage: what is it good for? J Int Econ 2017 106 83 103 10.1016/j.jinteco.2017.02.002
Ghisellini P Cialani C Ulgiati S A review on circular economy: the expected transition to a balanced interplay of environmental and economic systems J Clean Prod 2016 114 11 32 10.1016/j.jclepro.2015.09.007
Ghorbanpour A Pooya A NajiAzimi Z Application of green supply chain management in the oil industries: modeling and performance analysis Mater Today Proc 2022 49 542 553 10.1016/j.matpr.2021.03.672
Gomes CP (2011) Computational sustainability. 8–8. 10.1007/978-3-642-24800-9_2
Hafner S Jones A Anger-Kraavi A Pohl J Closing the green finance gap–a systems perspective Environ Innov Soc Transitions 2020 34 26 60 10.1016/j.eist.2019.11.007
Han Y, Tan S, Zhu C, Liu Y (2022) Research on the emission reduction effects of carbon trading mechanism on power industry: plant-level evidence from China. Int J Clim Chang StrategManag ahead-of-p. 10.1108/IJCCSM-06-2022-0074
Huang S, Liu H (2021) Impact of COVID-19 on stock price crash risk: evidence from Chinese energy firms. Energy Econ. 10.1016/j.eneco.2021.105431
Ikram M, Mahmoudi A, Shah SZA, Mohsin M (2019) Forecasting number of ISO 14001 certifications of selected countries: application of even GM (1,1), DGM, and NDGM models. Environ Sci Pollut Res. 10.1007/s11356-019-04534-2
Iqbal S, Bilal AR (2021) Energy financing in COVID-19: how public supports can benefit? China Finance Review International
Iqbal W, Yumei H, Abbas Q et al (2019) Assessment of wind energy potential for the production of renewable hydrogen in Sindh Province of Pakistan. Processes. 10.3390/pr7040196
Iqbal S Bilal AR Nurunnabi M Iqbal W Alfakhri Y Iqbal N It is time to control the worst: testing COVID-19 outbreak, energy consumption and CO2 emission Environ Sci Pollut Res 2021 28 15 19008 19020 10.1007/s11356-020-11462-z
Jiang L, Wang H, Tong A et al (2020) The measurement of green finance development index and its poverty reduction effect: dynamic panel analysis based on improved Entropy method. Discret Dyn Nat Soc 2020
Jin C, Tsai FS, Gu Q, Wu B (2022) Does the porter hypothesis work well in the emission trading schema pilot? Exploring moderating effects of institutional settings. Res Int Bus Financ 62. 10.1016/j.ribaf.2022.101732
Jinzhou W Discussion on the relationship between green technological innovation and system innovation Energy Procedia 2011 5 2352 2357 10.1016/j.egypro.2011.03.404
Ju K Su B Zhou D Oil price crisis response: capability assessment and key indicator identification Energy 2015 93 1353 1360 10.1016/j.energy.2015.09.124
Jukić T Pluchinotta I Hržica R Vrbek S Organizational maturity for co-creation: towards a multi-attribute decision support model for public organizations Gov Inf Q 2022 39 101623 10.1016/J.GIQ.2021.101623
Jun W Ali W Bhutto MY Examining the determinants of green innovation adoption in SMEs: a PLS-SEM approach Eur J Innov Manag 2021 24 67 87 10.1108/EJIM-05-2019-0113
Khan SAR, Godil DI, Jabbour CJC et al (2021) Green data analytics, blockchain technology for sustainable development, and sustainable supply chain practices: evidence from small and medium enterprises. Ann Oper Res 1–25
Kim SE Kim H Chae Y A new approach to measuring green growth: application to the OECD and Korea Futures 2014 63 37 48 10.1016/j.futures.2014.08.002
Kiranyaz S Avci O Abdeljaber O 1D convolutional neural networks and applications: a survey Mech Syst Signal Process 2021 151 107398 10.1016/j.ymssp.2020.107398
Kougias I Taylor N Kakoulaki G Jäger-Waldau A The role of photovoltaics for the European Green Deal and the recovery plan Renew Sustain Energy Rev 2021 144 111017 10.1016/j.rser.2021.111017
Kuzemko C Bradshaw M Bridge G Covid-19 and the politics of sustainable energy transitions Energy Res Soc Sci 2020 68 101685 10.1016/j.erss.2020.101685 32839704
Kwak W, Shi Y, Cheh JJ, Lee H (2004) Multiple criteria linear programming data mining approach: an application for bankruptcy prediction. In: Chinese Academy of Sciences Symposium on Data Mining and Knowledge Management. Springer, pp 164–173
Li J Zhao Y Zhang A Effect of grazing exclusion on nitrous oxide emissions during freeze-thaw cycles in a typical steppe of Inner Mongolia Agric Ecosyst Environ 2021 307 107217 10.1016/J.AGEE.2020.107217
Li W, Chien F, Ngo QT, Nguyen TD, Iqbal S, Bilal AR (2021b) Vertical financial disparity, energy prices and emission reduction: empirical insights from Pakistan. J Environ Manage 294:112946
Li W, Chien F, Hsu CC, Zhang Y, Nawaz MA, Iqbal S, Mohsin M (2021c) Nexus between energy poverty and energy efficiency: estimating the long-run dynamics. Res Policy 72:102063
Liu L, Li Z, Fu X et al (2022a) Impact of power on uneven development: evaluating built-up area changes in Chengdu based on NPP-VIIRS images (2015–2019). Land 11
Liu X Tong D Huang J What matters in the e-commerce era? Modelling and mapping shop rents in Guangzhou, China Land Use Policy 2022 123 106430 10.1016/j.landusepol.2022.106430
Luo Q Miao C Sun L Efficiency evaluation of green technology innovation of China’s strategic emerging industries: an empirical analysis based on Malmquist-data envelopment analysis index J Clean Prod 2019 238 117782 10.1016/j.jclepro.2019.117782
Mealy P, Teytelboym A (2020) Economic complexity and the green economy. Res Policy 103948
Mensah CN Long X Dauda L Technological innovation and green growth in the Organization for Economic Cooperation and Development economies J Clean Prod 2019 240 118204 10.1016/j.jclepro.2019.118204
Mohsin M, Nurunnabi M, Zhang J et al (2020a) The evaluation of efficiency and value addition of IFRS endorsement towards earnings timeliness disclosure. Int J Financ Econ. 10.1002/ijfe.1878
Mohsin M, Zaidi U, Abbas Q et al (2020b) Relationship between multi-factor pricing and equity price fragility: evidence from Pakistan. Int J Sci Technol Res 8
Montgomery T Mazzei M Social innovation: how societies find the power to change Int Rev Appl Econ 2020 34 691 696 10.1080/02692171.2020.1790341
Perez C Technological revolutions and techno-economic paradigms Camb J Econ 2009 34 185 202 10.1093/CJE/BEP051
Pincus JR Winters JA Reinventing the World Bank 2019 Cornell University Press
Ren J Dong L Evaluation of electricity supply sustainability and security: multi-criteria decision analysis approach J Clean Prod 2018 172 438 453 10.1016/j.jclepro.2017.10.167
Shah SAA Zhou P Walasai GD Mohsin M Energy security and environmental sustainability index of South Asian countries: a composite index approach Ecol Indic 2019 106 105507 10.1016/j.ecolind.2019.105507
Sun L Fang S Iqbal S Bilal AR Financial stability role on climate risks, and climate change mitigation: implications for green economic recovery Environ Sci Pollut Res 2022 29 22 33063 33074 10.1007/s11356-021-17439-w
Svensson PG Andersson FO Mahoney TQ Ha JP Antecedents and outcomes of social innovation: a global study of sport for development and peace organizations Sport Manag Rev 2020 23 657 670 10.1016/J.SMR.2019.08.001
Tang DYY Yew GY Koyande AK Green technology for the industrial production of biofuels and bioproducts from microalgae: a review Environ Chem Lett 2020 18 1967 1985 10.1007/s10311-020-01052-3
Uchida H Miyakawa D Hosono K Financial shocks, bankruptcy, and natural selection Japan World Econ 2015 36 123 135 10.1016/j.japwor.2015.11.002
van Vuuren DP Stehfest E Gernaat DEHJ Energy, land-use and greenhouse gas emissions trajectories under a green growth paradigm Glob Environ Chang 2017 42 237 250 10.1016/j.gloenvcha.2016.05.008
Wang H, Cui H, Zhao Q (2021) Effect of green technology innovation on green total factor productivity in China: evidence from spatial Durbin model analysis. J Clean Prod 288. 10.1016/J.JCLEPRO.2020.125624
Wang S Sun L Iqbal S Green financing role on renewable energy dependence and energy transition in E7 economies Renew Energy 2022 200 1561 1572 10.1016/j.renene.2022.10.067
Xiao H Huang S Shui A Government spending and intergenerational income mobility: evidence from China J Econ Behav Organ 2021 191 387 414 10.1016/j.jebo.2021.09.005
Xu X Lin Z Li X Multi-objective robust optimisation model for MDVRPLS in refined oil distribution Int J Prod Res 2022 60 6772 6792 10.1080/00207543.2021.1887534
Yang JS The governance environment and innovative SMEs Small Bus Econ 2017 48 525 541 10.1007/s11187-016-9802-1
Yang Y, Liu Z, Saydaliev HB, Iqbal S (2022) Economic impact of crude oil supply disruption on social welfare losses and strategic petroleum reserves. Resour Policy 77:102689
Yi M, Lu Y, Wen L et al (2021) Whether green technology innovation is conducive to haze emission reduction: empirical evidence from China. Environ Sci Pollut Res 1–13
Zhang D, Mohsin M, Rasheed AK et al (2021) Public spending and green economic growth in BRI region: mediating role of green finance. Energy Policy. 10.1016/j.enpol.2021.112256
Zhang L, Huang F, Lu L, Ni X, Iqbal S (2022) Energy financing for energy retrofit in COVID-19: recommendations for green bond financing. Environ Sci Pollut Res 29(16):23105–23116
Zhao L, Saydaliev HB, Iqbal S (2022) Energy financing, COVID-19 repercussions and climate change: implications for emerging economies. Climate Change Economics 13(03):2240003. 10.1142/S2010007822400036
Zheng X, Zhou Y, Iqbal S (2022) Working capital management of SMEs in COVID-19: role of managerial personality traits and overconfidence behavior. Econ Anal Policy 76:439–451
Zhu Q Sarkis J Lai K Green supply chain management innovation diffusion and its relationship to organizational improvement: an ecological modernization perspective J Eng Technol Manag 2012 29 168 185 10.1016/j.jengtecman.2011.09.012 | 36602734 | PMC9815058 | NO-CC CODE | 2023-04-09 23:17:24 | yes | Environ Sci Pollut Res Int. 2023 Jan 5; 30(14):39803-39814 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
36602740
24969
10.1007/s11356-022-24969-4
Research Article
Does environmental psychology matter: role of green finance and government spending for sustainable development
Feng Haiyan [email protected]
1
Yang Fen [email protected]
2
1 grid.440656.5 0000 0000 9491 9632 College of Economics and Management, Taiyuan University of Technology, Taiyuan, 030002 Shanxi China
2 grid.418265.c 0000 0004 0403 1840 Beijing Academy of Science and Technology, Beijing, 100089 China
Responsible Editor: Nicholas Apergis
5 1 2023
2023
30 14 3994639960
14 11 2022
19 12 2022
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Over 30% of the global GDP and 60% of the worldwide population are involved in the Belt and Road Initiative (BRI), making it one of the greatest development projects in the world. If infrastructure developments in BRI countries are successful, economic growth in those nations will increase dramatically. Using data from 2005 to 2020, this research examines the relationships between environmental psychology, green finance, and sustainable development and variables such as GDP per capita and its square, green financing, government expenditure, and human capital in 57 strategically chosen BRI economies. Economists used cutting-edge techniques that take into account multiple variables at once in their analysis, such as cross-sectional dependence, unit root testing, co-integration analysis, IFE estimation, dynamic panel data (DCCE), and generalized method of moments (system GMM). The findings indicate that green financing, government spending, and GDP per capita squared reduce emissions of carbon dioxide. In this analysis, the level of human capital is similar to GDP per capita in its beneficial effect on carbon emissions. Carbon emissions are negatively impacted by government spending, which has a minor effect on GDP per capita, green financing, and human capital. Using the results of this study, the authors offer recommendations for how a country can reduce its carbon output.
Keywords
Environmental psychology
CO2
Green finance
GDPC
GDPC2
Human capital
issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023
==== Body
pmcIntroduction
Initiated in 2013 by Chinese President Xi Jinping, the Belt and Road Initiative (BRI) was formerly known as the One Belt One Road initiative (Huang et al. 2022b; Rao et al. 2022). Connecting the “Silk Road Economic Belt” and the “21st Century Maritime Silk Road,” the BRI is one of the largest infrastructure projects in the world. In a nutshell, it will link China to the rest of Africa, the Middle East, Europe, and South and Southeast Asia. Therefore, more than 60% of the world’s population and 30% of the world’s GDP are involved in this enormous undertaking (Khokhar et al. 2020; Zhang et al. 2021c). One hundred forty-one nations and 32 intergovernmental organizations have committed to working together on BRI projects as of October 2021 (International Cooperation Advisory Council of the Belt and Road Forum in 2019 and 2020). According to projections, annual investment in the BRI will be between $2.90 and $6.30 trillion (Hou et al. 2019; Huang et al. 2022a).
There has never been a time in human history when climate change has posed such a grave threat to human survival. The increased use of fossil fuel energies and economic growth experienced over the last century has led to historically high levels of GHG emissions, which are widely acknowledged as a primary cause of global climate change. In order to lessen the impact of CO2 emissions on the environment, there is a large body of research devoted to understanding what factors contribute to this pollutant’s release into the atmosphere. Since fossil fuels account for about 80% of global energy consumption, it stands to reason that they are also a major source of CO2 emissions (among many others). The environmental and health impacts of GHG emissions may be mitigated, however, if the world’s total renewable-based power capacity grows by 50% between 2019 and 2024 (Anser et al. 2020a; Latif et al. 2021). Some research has shown that green finance can help reduce global CO2 emissions, for example (Iqbal et al. 2020; Iqbal et al. 2021a; Liu et al. 2022a; Zhang et al. 2022).
According to Zheng et al. (2021a), BRI countries like China are aware that pollution and other environmental damage are inevitable consequences of economic development. To lessen the impact of economic growth, the governments of these nations are prioritizing the green economy and greening their infrastructure (Iqbal et al. 2021b). China has proposed a comprehensive BRI rollout to support and cover numerous areas of the 2030 SDGs. By strictly enforcing standards for low-carbon and environmentally friendly infrastructure development, China is committed to boosting green and low-carbon operations (Hou et al. 2022). One of the new ideas for the BRI is for its members to collaborate on environmental regulation and the development of green technologies, energy technology, and green funding (Advisory Council of the Belt and Road Forum for International Cooperation in 2019 and 2020, 2020). Furthermore, China has introduced an eco-friendly feature of its management system for supply chains to bring green products to consumers under the green BRI umbrella (Ikram et al. 2019; Sarfraz et al. 2021; Yang et al. 2021). In addition to the aforementioned, the current strong supply chain network of BRI would preserve the quality of perishable foods to stop environmental degradation caused by the food going bad. To sum up, Green BRI is designed to encourage new BRI investment projects that prioritize environmental friendliness, such as through the use of green financial instruments (Mohsin et al. 2020a, 2020b; Naseem et al. 2021).
Green finance is a relatively new form of finance that attempts to reconcile the conflicting goals of economic development and environmental protection by integrating these two crucial but traditionally separate spheres of activity: resource allocation and environmental governance (Anser et al. 2020b; Sun et al. 2019; Zhang et al. 2021a). Green financial policies can successfully internalize environmental externalities, resolving the conflict between non-equilibrium flows of financial resources and environmental aims. Green finance policies can steer the more desirable growth of the economic and social environment by providing precedence to green industries and low-carbon technology innovation initiatives in the distribution of social resources. China’s overall green finance reached $1.93 trillion by the year 2020, with green credit balances accounting for $1.78 trillion (or more than 90%) of the total, and the financing size of green bonds and green equity both reaching $0.15 trillion (Hsu et al. 2021; Mohsin et al. 2022; Sun et al. 2020). Green finance is a new trend in China’s future financial development and is experiencing significant growth in both rate and increase, which has far-reaching implications for the attainment of reductions in carbon emissions goals (Chandio et al. 2022; Mohsin et al. 2020c). While researchers have looked at how green financial regulations affect corporate investment and finance, green technological innovation, and carbon emissions in the past, they can not seem to agree on how this financial development affects carbon reductions (Liu et al. 2022d). Given the recent rapid growth of green financing and the importance of decreasing CO2 emissions in China, elucidating the connection between the two is vital (Liu et al. 2022e). In addition, China’s rapid industrial regionalization pattern has spurred the development of cross-regional industrial chain division networks and innovation networks (Si et al. 2021), which have strengthened industrial ties across regions and had some impact on regional emissions. In fact, carbon emission reduction patterns and low-carbon technology application in the region may result from the existence of inter-regional industrial associations and imitation behaviors, setting off a strong demonstration effect in the surrounding regions through technology spillover and knowledge dissemination (Sarma and Roy 2021). At the same time, environmental pollutants like carbon dioxide are easily diffused, and differences in local environmental policies and the absence of coordinated emission reduction policies among regions may induce the near transfer of pollution (Wang et al. 2021). This suggests that reducing carbon emissions goes beyond the local environmental governance issues in specific regions. However, the existing literature has only examined the impact of green finance policies on local CO2 emissions, paying little attention to the domino effect that these policies have on nearby regions.
Recent empirical research on transitional economies are supported by the estimated results, which reveal a positive and large impact of green financing on growth whereas government spending has a negative and significant impact on growth (Xiong et al. 2022; Xu et al. 2022, 2021). Furthermore, the results demonstrate that there is a negative and statistically significant interaction between green financing and government spending, indicating that the connection is not complementary. At very high levels of government spending, the negative and material impact of financial development on growth becomes more pronounced. But when budgets are tight, the impact is beneficial and noticeable. According to the projected results, excessive government spending can dampen the growth-promoting effects of financial development, whereas insufficient spending has the opposite effect. The data also show that financial efficiency and access have a favorable and large impact on economic growth, but financial depth has a negligible impact. More so than financial efficiency, it is argued that access to financial resources is what really drives economic expansion. In a similar vein, when government spending is very large, the marginal impact of financial depth, access, and efficiency is negative. The crowding out and crowding in effects of government expenditure are examined, and the study concludes with new data on the nonlinear influence of finance on growth (Ren et al. 2020).
To that purpose, we use annual data for 57 BRI provinces from 2005 to 2020 to assess the significance of variables such as environmental psychology, green financing, government expenditure, and human capital in attaining sustainable growth. This research fulfills an important function by contributing useful new knowledge to the literary canon. The findings of this research can lend credence to the importance of public funding for green finance. There has not been a definitive study showing that government expenditure does not affect the market mechanism. This research suggests reducing carbon emissions by increasing public spending, and provides empirical evidence for the existence of a composition and technique effect. This research finds that increases in public spending are correlated with green financing. In addition, this study’s overarching goal is to explore the moderating impact of GDP per capita, green financing, and human capital on carbon emission. However, the authors of this work use a novel set of econometric techniques—including cross-sectional dependence, CADF and CIPS unit root tests, panel cointegration testing, IFE & DCCE estimator, and system GMM—to achieve their goals. The rest of the study is organized as follows: A thorough literature review is provided in the “Literature Review” section, followed by an explanation of the research’s data and methods in the “Data and Methodology” section, a discussion of the study’s empirical findings in the “Results and discussion” section, and a summary and policy recommendations in the “Conclusion and policy suggestions” section.
Literature review
Only the effects on people’s ability to stay alive and grow are meant by “environment” in this strict meaning. Environmental problems, as defined by Liu et al. (2022b), include loss of environmental quality and ecological damage caused by both natural causes and human actions. The environmental issues identified by Tu et al. (2021) were further categorized into two groups: primary and secondary. Primary environmental problems, or those generated by natural processes, are distinguished from secondary environmental problems, or those caused by humans, by the terms “type one” and “type two,” respectively. Carbon emissions are created in numerous ways during the manufacturing process (Sarangi 2019). The term “trade embodied carbon,” coined by Guang-Wen and Siddik (2022a), describes the amount of carbon dioxide equivalents used in both the direct and indirect production of a country’s traded goods and services. The influence of external influences on carbon emissions is referred to as the carbon emission effect (Du et al. 2022; United Nations Environment Programme 2017).
Environmental protection and long-term economic growth both benefit from green finance. Policies taken straight from the textbook, such as carbon levies and emissions reduction targets, have been found wanting. One issue is that there is a lot of uncertainty around green investments due to the high volatility of emissions trading systems, particularly certificates purchased on the financial market (Shipalana 2020). While carbon taxes are straightforward to enact and maintain, they do not provide enough of a spur for enterprises to make the transition to green technology since they are too sluggish to initiate replacement procedures and can be handed on to customers (Arif et al. 2021). Furthermore, the policy requires an extremely high tax in order to be effective. Saeed Meo and Karim (2021) examine this phenomenon from a global viewpoint and find that pollution burdens are being transferred among Belt and Road economies as a result of FDI and preferential trade agreements. They also discover that middle-income nations are the ones hit the most by the pollution burden shifting caused by technical advancements, and that environmental measures in trade agreements have little binding influence on this. This highlights the importance of taking a fresh approach to environmental protection by actively encouraging funding for green technology. One way to accomplish this is by official government action. Chinese Green Climate Fund financing and allocation strategies are documented by Steckel et al. (2017) to aid local governments in climate change adaptation and mitigation efforts. They conclude that the financing burden should fall mostly on large historical polluters, whereas the greatest gains would accrue to less developed regions. The use of green finance to incentivize green investment is a free-market strategy.
The impact of green finance, which prioritizes environmental safety and long-term economic growth, is twofold: First, the demand and supply for eco-friendly, long-term growth are both influenced by green finance. It does double duty, steering firms toward a greener, more sustainable production while piquing consumers’ interest in being green. In order to demonstrate how a company’s sustainability performance can significantly impact its financial situation, Mngumi et al. (2022) give an example of how non-compliance with environmental requirements can negatively impact cash flow and lower market value. Second, green finance encourages the financial sector as a whole to prioritize long-term sustainability and reduce reliance on short-term gains by emphasizing the importance of sustainable development (Zheng et al. 2021b). Ecological screening and risk evaluation are used to determine which investments and loans best adhere to sustainability requirements, as explained in more detail by Ünüvar (2019). In addition, insurance against environmental and climatic risks is a part of green finance. Emission right-based lending is an intriguing form of green finance that Zerbib (2019) investigate. When businesses invest in pollution prevention and environmentally friendly technologies, they are rewarded with a reduction in their emission quotas under this system. Then, the loan applications are secured by the emission rights as collateral. Based on their theoretical model, they conclude that the emission right-based funding model benefits both firms and banks and encourages investment in green technology at reasonable levels of market demand. For this reason, Zheng et al. (2021c) demonstrate that green financing can generate acceptable returns.
One of the biggest obstacles to investing in environmentally friendly technology is its price tag. Investing in green technologies requires substantial capital upfront but pays dividends only in the very long run. Also, credit scores will be worse because there is more risk involved with investing in cutting-edge tech. Green investments are more susceptible to market swings due to their dependence on rising or falling carbon prices (Pham and Luu Duc Huynh 2020). Investments in environmentally friendly technologies face an increasingly challenging economic environment due to these factors. For this reason, (Nassiry 2018) advocate for government-backed bonds as a useful instrument for risk mitigation. The government can improve the return and cut the cost of capital for environmentally friendly initiatives by guaranteeing all or part of the service and repayment. Because of this, a broader economic revolution can take place, not only one confined to giant corporations that have few financial constraints, and it also has the effect of allowing smaller companies to join in the move towards green technology. Also, because of their long maturities, bonds are ideally suited for the massive infrastructure projects required by green technology; and because of stricter disclosure requirements than regular bonds, green bonds carry less of a chance of default, bringing returns and a sense of social responsibility to investors Liu et al. (2019). Instead, Srivastava et al. (2021) focus on the function of “green central banking,” in which central banks facilitate the growth of green financing models and mandate the price of environmental and carbon risk.
But it is an empirical question whether or not green money is successful in protecting the environment. Data for the study from the Global Footprint Network and the Asian Development Bank are used by Dmuchowski et al. (2021) to analyze the effect of climate protection banking (as a proxy for sustainable banking) on the ecological footprint in 26 countries, controlling for factors such as GDP per capita, inhabitants, trade openness, and energy consumption. Green financing is found to be effective, in that it greatly reduces the environmental impact of financing, even after taking into account the fixed effects of individual countries. It is worth noting that this study has limitations due to its limited sample size and the possibility of self-selection; for example, countries with higher costs associated with lowering their ecological footprint may spend less on green financing. Another similar analysis is conducted by Hafner et al. (2020), albeit with different data sets. In particular, the authors warn against relying on either public or private sector-led green finance alone, as doing so would be to neglect the joint influences.
Similar research is conducted by Wang and Zhi (2016) who look into the connection between environmentally friendly financing and greenhouse gas production. Using a quantile-on-quality regression, the authors discover a negative correlation between green financing and carbon dioxide emissions in the USA, Sweden, Hong Kong, the UK, and Switzerland, with weaker correlations in, Norway, New Zealand, Denmark, Japan, and Canada. Yet there are differences even within nations. In the USA, for instance, a positive correlation holds between low levels of green finance and high levels of carbon dioxide emissions. When carbon dioxide emissions are high, however, demand for green investments rises, as shown by data for New Zealand. Overall, green finance has a negative association with emissions, but the effect is asymmetric and varies with both the level of emissions and the quantity of green finance, which may point to a more nuanced relationship between the two. Clark (2019) examines whether China’s green finance-related regulations have considerably decreased industrial gas emissions by analyzing data from 290 cities between 2011 and 2018. Using a difference-in-differences method, they discover green finance-related policies caused substantial positive environmental outcomes, including a 38% decrease in sculpture dioxide emissions, a 28% decrease in industrial gas and smoke emissions, and a 20% decrease in overall sculpture dioxide production. They also find that the growth of financial technology helps China cut down on emissions by easing the country’s adaptation to a green financial system. Endogeneity and simultaneity are not addressed, however, which is a problem that Jin and Han (2018) also had.
A greater degree of fiscal decentralization in China means more money for provincial and municipal governments. But this will not always lead to more spending on CO2 mitigation by municipal governments (Tolliver et al. 2019). This is because there is little pressure placed on municipal governments to cut carbon output. However, CO2 is a worldwide public good (Sachs et al. 2019). Local CO2 emissions have a smaller impact on people’s quality of life than do most other types of pollution. This means they will not put enough pressure on local governments to get them to cut back on carbon emissions. On the other hand, higher-ups in government are the ones who ultimately decide which officials at the lower levels to promote. High-level governments care more about economic indicators than they do about reducing CO2 emissions (Lv et al. 2021). So, it is more likely that municipalities will invest in economic growth. The current literature also suggests that as local fiscal decentralization and the fiscal authority of local governments rise, local CO2 emissions may not decrease, but increase (Khan et al. 2021). This means that federal authorities need to intervene in local efforts to reduce CO2 emissions. From a survey of the scholarly literature, we learn that most research on CO2 emissions in China have been conducted at the province level (Dong et al. 2022; Guild 2020; Liu et al. 2022b). To reduce carbon dioxide emissions, however, urban areas must be taken into account.
Data and Methodology
The 57 BRI economies have their statistics reviewed from 2005 to 2020. Carbon emission, GDP per capita, GDP per capita square, green finance, government spending, and human capital are all represented by the variables CO2, GDPC, GDPC2, GF, GE, and HC. This article defines green finance from the viewpoint of financial institutions, and uses green credit, green securities, green insurance, green investment, and carbon finance as indicators of the degree to which green financial growth has occurred in BRI economies. The index system is built with reference to the Chinese green financial development measuring system established by (Akomea-Frimpong et al. 2021), taking into account the validity and availability of data. This study provides a numerical estimate of the annual amounts of over- and under-investment in businesses, using the investment efficiency model proposed by Zhou et al. (2022). To better predict businesses’ optimal investment levels, Lee and Lee (2022) upgraded this model to include panel data fixed effect modelling. Other factors include government spending, CO2 emissions, gross enrolment in higher education, and GDPC all assessed in current US dollars (Table 1). Figure 1, however, displays box plots for a few well-chosen variables.Table 1 Variables description
Variables Unit Sources
CO2 Carbon emission (kt) WDI
GDPC Per capita income (current US dollar) WDI
GF Green finance (the index system is constructed from the four dimensions of green credit, green insurance, green investment, and green securities) Wind database
CSMAR database
Flush Ifind database
GE Government expenditure (% of total expenditure) WDI
HC Human capital (gross enrolment in higher education) WDI
Fig. 1 Box plots of the selected variables
Theoretical background and model specification
The benefits of investing in carbon abatement are non-excludable; hence, environmental protection is a public goods game, as argued by Criscuolo and Menon (2015). This results in a Prisoner’s dilemma where the dominant option is for enterprises to free ride on the investments of others and not abate, leading to a Nash equilibrium that is socially undesirable. Consequently, Ning et al. (2021) advocated a climate club to address the free-riding issue, in which members would be obligated to invest in abatement technology while trade fines would be levied on non-members. Therefore, countries with low emissions and cheap abatement costs will be the ones to join the climate club. However, countries with high emission levels and expensive abatement costs are unlikely to join the club since they would rather face the consequences than reduce their emissions. It is reasonable to treat corporations as if they were countries, provided that there was a price to pay for polluting the environment. Companies with higher emissions and higher abatement costs will opt to pay the penalty rather than reduce their emissions, whereas companies with lower emissions and cheaper abatement costs will join the club. Therefore, the expense of abatement and the need to invest in abatement technologies will determine whether a company chooses to abate or pay the penalty. Bringing down the price of investing in abatement technology is one way that green BRI and other member countries may encourage more businesses to join the club and abate. One strategy for doing this is to use green finance to reduce the cost of funding green construction projects. Therefore, in agreement with Liu et al. (2022c), green money aids in mitigating costs in a roundabout way. Green financing will likely improve environmental quality while simultaneously reducing environmental degradation, it follows.
The function is as follows:1 CO2=fGDPCβ1,GDPCβ2,GFβ3,GEβ4,HCβ5
In Eq. (1), CO2 represents carbon emission, GDPC (GDP per capita), GDPC2 (GDP per capita square), GF (green finance), GE (govt. expenditure), and HC (human capital).
The fundamental econometric model for this analysis can be established as follows:2 LCO2i,t=β0+β1LGDPCi,t+β2LGDPCi,t2+β3LGFi,t+β4LGEi,t+β5LHCi,t+εi,t
In Eq. (3), other variables remain constant LGE * GDPC has an impact on carbon emission in this model.3 LCO2i,t=β0+β1LGDPCi,t+β2LGDPCi,t2+β3LGFi,t+β4LGEi,t+β5LHCi,t+β6LGE∗GDPC+εi,t
In Eqs. (4) and (5), other variables remain constant LGE * GF and LGE * HC has an impact on carbon emission in this model.4 LCO2i,t=β0+β1LGDPCi,t+β2LGDPCi,t2+β3LGFi,t+β4LGEi,t+β5LHCi,t+β6LGE∗GF+εi,t
5 LCO2i,t=β0+β1LGDPCi,t+β2LGDPCi,t2+β3LGFi,t+β4LGEi,t+β5LHCi,t+β6LGE∗HC+εi,t
Estimation strategy
Cross-sectional dependence test
The cross-sectional dependence (CD) of Yu et al. (2021) is crucial for panel data since it might cause inaccurate and contradictory results. Economic, social, political, and other channels such as bilateral trade and sharing boards are examples of real-world links. The development of CD might be a consequence of such forms of the international association. For this purpose, we use the cyclic dilation (CD) test developed by Zheng et al. (2021d) and the Lagrange Multiplier (LM) test developed by Li et al. (2022). The following equation is used in CD testing to look for the existence of CD in data.6 CD=2TNN-1(∑J=1+1NPji)
Cross-sections are denoted by the number N and the time T. In order to understand the heterogeneous association between random fluctuations, we can consider the following: This equation is used in the following LM test example to analyze panel data for CD.7 Yit=αi+βixit+εit
where T is the time range and I is the in-betweens. Both of these approaches to estimation use the null hypothesis that CD does not exist in the panel data, whereas alternative hypotheses take CD into account.
Cross-sectional unit root test
For this reason, first-generation unit root tests fail miserably when dealing with cross-sectional reliance. Cross-sectional variation is also supposed to have no effect on the outcomes, which is not the case. That is why Xiong and Sun (2022) came up with the CIPS and CADF models, which take the best features of both the Pesaran-Shin and Dickey-Fuller models and merge them into a single, flexible framework. Both of these assessments account for cross-sectional and panel heterogeneity. Second-generation unit tests were used to check the variables’ consistency.8 ΔXi,t=αi,t+βiXi,t-1+ρiT+∑j=1nθi,t∙ΔXi,t-j+εi,t
where xit represents the variable of interest, i stands for cross-sections, t represents the time interval, and r stands for the residuals of the model. Non-stationarity is accounted for in the null hypothesis, while stationarity is assumed in the alternative hypothesis.
Cointegration test
Like the first-generation panel unit root approaches, the first-generation panel cointegration estimators ignore CD issues. The Iqbal et al. (2021a) second-generation panel cointegration estimate was introduced to ascertain the cointegrating properties between the parameters in the presence of CD. By evaluating the standard errors of the Gt, Ga, Pt, and Pa statistical tests using a bootstrapped method, this method eliminates the cross-sectional reliance. Group-mean statistics Gt and Ga are estimated when testing the non-cointegration null hypothesis against the cointegration alternative hypothesis across at least one cross-section. On the other hand, the two panel-mean statistics Pt and Pa are to be anticipated if the alternate theory of cointegration among the lines in all cross-sections is correct.9 Pt=β/SEβ
10 Pβ=Tβ
11 Gt=1/M∑i=1mβi/SEβi
12 Ga=1/M∙∑i=1mTβi/βi∙1
Pt and Pβ in Eqs. (9) and (10) respectively reflect panel statistics, while Ga and Gt in Eqs. (12) and (11) respectively stand for group means. In this case, we do not test the null hypothesis of cointegration.
Interactive fixed effect
Prior works on the measurement of regression models with a factor error structure fall into two categories. In the first, we will talk about the D-CCE method, which is an improvement of Raberto et al. (2019) work by Streimikiene and Kaftan (2021) and is frequently used to quantify the error structure’s common factor. Linear static panel models with a homogeneous and heterogeneous coefficient inspired Pesaran to create the CCE method in 2006. The cross-sectional average of the described and explanatory variables is used as a proxy of common factors, which can be highly problematic in econometric contexts because the estimator does not quantify the factor component. The D-CCE technique developed by Guang-Wen and Siddik (2022b) was used in this investigation for several reasons. It solves the CSD issue in the data by incorporating both the cross-sectional and lagged cross-sectional means of explained variables; it is robust against omitted variable bias and bidirectional feedback for various environmental proxies and their determinants; and it allows for heterogeneity within the ED Ning et al. (2022). Secondly, Bai’s IFE proposal is the second method. This method uses an iterative process to calculate both the factor element and the regression coefficient simultaneously. The following Eq. (13) is tested to determine if this methodology is appropriate for this investigation.13 Yi,t=X,tα+φa+st+ei,t
where Yi,t is the ED indicator in an economy i for period t.14 ei,t=Γ′ift+μi,t
In Eq. (14), Γ′i,t is an error term, Ft is an r-multiply-by-one vector of unobserved time-specific common shocks, and µi,t is a vector of factor loadings that captures the unit-specified reactions to the standard shocks. The factoring procedure and the regression coefficients were both proposed by van Veelen (2021), who used the method of minimizing the sum of residual squares (SSR) in Eq. (15).15 SSR=∑i=1N∑t=1TYi,t-Xi,tα-φi-St-Γ′ift
To get a feel for the structural model and the regression coefficient, the following equation can be given in two different ways: first, if the factor structure Γ′iFt is recognized, the regression coefficient can be determined by minimizing the SSR.16 SSR1=∑i=1N∑t=1TYi,t1-Xi,tα-φi-St2
17 SSR2=∑i=1N∑t=1TYi,t1-Γ′ift2
18 Y2I,t=Yi,t-Xi,tβ-φi-St
Based on SSR1 and SSR2 given in Eqs. (16), (17), and (18), Bai (2009) suggested starting iterations between measuring one given the other until the difference in the SSR became less than a pre-specified benchmark set.
Generalized moment estimation
Some crucial variables in our model may be the endogenous result of backward causation. For moderately large cross-sections and moderately small spans (T), the generalized moment method (GMM) is an appropriate choice (N). Because it accounts for the heteroscedasticity and endogenous difficulties of panel data, it is useful for studying the dynamics of long-term associations between related variables (Zheng et al. 2021c). Difference GMM and system GMM are two branches of the generalized moment technique. Both difference and system GMM approaches benefit from the use of instruments that are robust under the premise that the disturbance terms are really independent and are serially uncorrelated. With GMM, the system is more resistant to heteroscedasticity and autocorrelation. However, the system GMM is superior to the differential GMM when the dependent variable is significantly more persistent (Lee 2020a). Our research makes use of a panel data model that is constantly adapting to new information. In this research, we apply the systematic generalized method of moments (GMM) approach to study the long-term relationship between linked factors in order to better assess the substantial impact of associated explanatory variables on environmental quality.
Results and discussion
Descriptive statistics test
The descriptive statistics (mean, median, maximum, minimum, and standard deviation) for the variables are shown in Table 2. When comparing the means of the different categories, we find that HC has the lowest mean value and green GDPC has the greatest. This indicates that the GDPC value of the chosen economies continues to be high. Additionally, there is no statistically significant difference between the mean and the median, indicating the absence of an outlier. The primary values for CO2 were 12.764, GDPC was 21.378, GF was 13.548, GE was 19.937, and HC was 7.043.Table 2 Descriptive statistics
Variables Mean Median Maximum Minimum Std.D
CO2 12.764 12.395 37.437 5.657 0.797
GDPC 21.378 21.175 48.298 7.575 2.685
GF 13.548 13.127 21.284 3.439 1.739
GE 19.937 19.474 28.574 5.766 2.588
HC 7.484 7.043 18.946 2.499 0.828
Pairwise correlation matrix test
Prior to using multivariate regression models, it may be necessary to examine multicollinearity in a subset of panel data. Using a pairwise correlation matrix, we were able to confirm that our model was multicollinearity free. Exciting results can be obtained through pairwise correlation, as demonstrated in (Table 3). Findings indicate a positive association between GDPC and CO2 at the 1% level of significance. At the same 1% level of significance, green financing and government expenditure also correlate adversely with the explanatory variables. Additionally, human capital has a positive association (at the 5% level of significance) with carbon dioxide emissions. This implies that rising levels of human capital contribute to rising CO2 levels.Table 3 Correlation matrix
Variables CO2 GDPC GF GE HC
CO2 1
GDPC 0.323** 1
GF − 0.547* 0.425** 1
GE − 0.375** 0.645* 0.275* 1
HC 0.638* 0.077** 0.488* 0.267** 1
*1% and 5% level of significance
Cross-sectional dependency test
In particular, it presupposed that the cross-sections are independent of one another. But because of international trade and travel, cross-sectional dependencies may emerge in the panel data. Inefficient and biased estimators could result if CSD is ignored during estimation. Therefore, CSD analyses were conducted for this investigation, and the results are shown in Table 4. Indeed, cross-sectional dependence was discovered to exist. First-generation unit root test results from Zeng et al. (2022a) could be skewed and deceptive in the presence of CSD. This discovery compels us to tackle the problem of CSD using a different method of unit root testing, specifically the second-generation method created by Zhang et al. (2020), which is known as the CIPS and CADF.Table 4 CSD
Variables Pesaran’s test Frees’test Friedman’s test
CO2 13.738 (0.021) 5.967 (0.000) 128.865 (0.000)
GDPC 18.655 (0.000) 3.657 (0.000) 132.978 (0.001)
GF 9.646 (0.004) 3.987 (0.076) 76.977 (0.003)
GE 4.854 (0.000) 2.876 (0.012) 65.785 (0.025)
HC 7.657 (0.023) 3.965 (0.045) 76.964 (0.000)
Results of CADF and CIPS unit root test
Unit root tests of the second generation should be used to investigate the integration qualities of the variables under examination when the cross-sectional dependency is present. Table 5 displays the findings of this investigation, which use a CADF and CIPS unit root. After the initial difference between the two models, the model variables become stationary despite having a unit root at the model level. When a level and stationary first difference unit root exists, the CADF indicator can be used to identify it. The CIPS and ADF panel unit root test, developed by Guang-Wen and Siddik (2022b), is the most effective tool for dealing with cross-country dependencies in a cross-sectional dependence problem. The analysis variables are proven to be stationary in first difference (Table 5), allowing us to delve deeper into the inter-variable cointegration.Table 5 CADF and CIPS
Variables CADF unit root test CIPS unit root test
Level 1st difference Level 1st difference
CO2 − 2.743* − 3.386 − 3.646 − 5.779**
GDPC − 2.326** − 4.658 − 1.775* − 3.876
REI − 1.875 − 3.695** − 1.986 − 4.965*
GFD − 2.287* − 4.268 − 2.497* − 4.956*
HE − 2.856 − 3.986* − 2.678 − 3.657*
TR − 1.875* − .1569 − 1.478* − 2.573
*1% and 5% level of significance
Panel cointegration approach
The findings show that all of the variables in the panel are stationary; we use the cointegration test developed by Saha et al. (2022) to see if any of them are actually connected. Table 6 displays the findings of the tests. It is clear from the data that the assumption of no cointegration can be rejected for all regions. For the years 2005–2020, each region and group of regions exhibits a unique long-run link between GDP per capita, GDP per capita square, green finance, government expenditure, and human capital. The data in the table argue against the notion that the variables are not cointegrated. Therefore, long-term estimators IFE, DCCE, and the system GMM are required due to co-integration.Table 6 Westerlund cointegration
Statistics Value Z-value P-value Robust P-value
Gt − 8.878 4.776 0.000 0.021
Ga − 3.658 1.667 0.021 0.747
Pt − 7.765 3.976 0.018 0.867
Pa − 5.665 2.736 0.001 0.099
Interactive fixed effect estimator
Carbon emissions are positively correlated with the GDP per capita (GDPC) coefficient that has been provided. IFE estimate predicts increases in environmental degradation of 0.376%, 0.563%, 0.276%, and 0.398% for 1% increases in GDP per capita. The findings point to a negative correlation between GDP per capita square and carbon output. This means a 1% increase in this component would result in a − 0.625%, − 0.726%, − 0.087%, and − 0.386% reduction in carbon emissions, respectively. The results imply that the GDP per capita and ecological degradation may have a tenuous link in the selected economies due to slow growth and insufficient environmental standards. According to Ye et al. (2022), conventional technologies have a negative effect on GDP per capita by lowering energy efficiency, which is the root source of carbon emissions. Even more so, such results imply that economies of worry expand, which may lead to an increase in environmental degradation. Ecological consciousness is yet in its formative stages; hence, citizens of several developing economies do not insist on using green energy. Additionally, numerous plants and factories that are the major cause of ED have not shut down despite the aforementioned logic over the period. So, our result is similar to what was found in Lee (2020b).
According to Table 7, there is a negative effect of green funding on carbon emissions of − 0.462%, − 0.486%, − 0.573%, and − 0.186%, with a statistically significant coefficient at the 1% level. As such, it agrees with international research by scholars such as Adeleke and Josue (2019); Feng et al. (2022); Stojanović (2020); Zhang et al. (2019); Zhang et al. (2021b). This is a significant finding because it proves that green financial instruments can help a region become environmentally friendlier (Geddes et al. 2018). This is one of the finest ways to slow down or stop environmental deterioration (Li et al. 2021). In turn, sustainable growth in the region will be bolstered by the increased production of green energy thanks to the region’s cleaner surroundings. This finding also demonstrates the success of the Green Finance Task Force and the governments of BRI nations in their efforts to promote green finance. Green loans enable businesses to reduce environmental impact and preserve environmental quality by funding the purchase and implementation of environmentally friendly raw materials and green technologies in ecologically beneficial initiatives. Results show that green finance plays a crucial part in lowering environmental deterioration in the BRI region, despite being novel and not yet well embraced by standard financial institutions. However, the global economic downturn was exacerbated by a lack of funding for green initiatives because of the COVID-19 pandemic, as stated by Rasoulinezhad and Taghizadeh-Hesary (2022). Green bonds are an important instrument of green finance that can be used to support a wide variety of environmentally conscious initiatives, and the governments of BRI member countries would be wise to issue more of them in light of empirical evidence linking green finance and reduced environmental degradation to greater sustainable growth. This will, in turn, aid in reviving the economy in the BRI region.Table 7 IFE results
Variables Model 1 Model 2 Model 3 Model 4
LGDPC 0.376 (0.006) 0.563 (0.000) 0.276 (0.34) 0.398 (0.000)
LGDPC2 − 0.625 (0.065) − 0.726 (0.032) − 0.087 (0.76) − 0.386 (0.029)
LGF − 0.462 (0.084) − 0.486 (0.023) − 0.573 (0.000) − 0.186(0.006)
LGE − 0.238 (0.015) − 0.275 (0.076) − 0.176 (0.004) − 0.876 (0.043)
LHC 0.647 (0.007) 0.245 (0.016) 0.063 (0.005) 0.721 (0.000)
LGE * GDPC - -0.635 (0.056) - -
LGE * GF - - − 0.617 (0.012) -
LGE * HC - - - − 0.532 (0.000)
The results of this study dispel the worry that rising government spending will lead to higher levels of carbon dioxide emissions. With a 1% increase in government spending, carbon emissions are reduced by − 0.238%, − 0.275%, − 0.176%, and − 0.876%, respectively, improving environmental quality in BRI at the 5% level of significance. Many BRI provinces are unhappy with the country’s growing size of government because of the strain it has placed on public spending. Government spending on public services and amenities has also grown. According to the results of this investigation, the BRI administration appears to have spent money with environmental considerations in mind. There have been checks and balances put in place to make sure the environment is not severely harmed by increased government expenditures, which may increase energy consumption by government ministries, departments, and agencies. In the vein of Zeng et al. (2022b), this study also hints that the country’s recent spending on social and public goods has contributed to a decrease in carbon emission. Spending on things like school buildings, health education, and enforcing environmental laws are all worthy of mention. Money spent trying to stop illicit logging could have played a role in this result. Furthermore, one may argue that the growing government expenditure has placed money in people’s pockets, which has allowed them to acquire energy-efficient technologies, helping to cut carbon pollution and verifying the hypothesis. Similar results have been observed in the empirical work of Pal and Russel (2015).
As a contrast, human capital is found to have a positive and statistically significant relationship with carbon emissions at the 0.647%, 0.245%, 0.063%, and 0.721% levels, respectively. This is not shocking, though, because human capital is crucial to economic expansion, and because, according to the EKC hypothesis, more economic activity has the potential to increase carbon emissions. Inferences similar to this have been drawn in previous studies, which imply that environmental pollution is directly tied to human economic activity due to increased energy demand and consumption (Nassani et al. 2017). The findings imply that the production of human capital reduces the effect of carbon emissions in the manufacturing and other sectors, while increasing emissions in the residential and transportation sectors.
It is important to look into the secondary effects, such as those of government spending on GDP per capita, green financing, and human capital on carbon emission levels, in addition to the primary effect. Carbon emissions were negatively impacted by − 0.635%, − 0.617%, and − 0.532% due to the government’s limited participation in spending.
Robust check for DDCE test
The robust check of DCCE shows that all variables have the same impact on carbon emission (Table 8). The results indicate that GDP per capita has a positive impact on carbon emission. This implies that a 1% increase in this factor would cause to increase the level of carbon emission by 0.758%, 0.967%, 0.065%, and 0.367%. The outcomes show that GDP per capita square has a negative impact on carbon emission by − 0.987%, − 0.254%, − 0.665%, and − 0.233%. The green finance has also had negatively related to carbon emission by − 0.658%, − 0.765%, − 0.547%, and − 0.446%. The 1% increase in government expenditure would cause to decrease the level of carbon emission by − 0.363%, − 0.636%, − 0.754%, and − 0.854%. The human capital has a positive impact on carbon emission by 0.436%, 0.366%, 0.557%, and 0.768%. The moderate effect has negatively impact on carbon emission. This shows that a 1% increase in this factor would cause to decrease the level of carbon emission by − 0.956%, − 0.643%, and − 0.746%.Table 8 DCCE results
Variables Model 1 Model 2 Model 3 Model 4
LGDPC 0.758 (0.257) 0.967 (0.053) 0.065 (0.000) 0.367 (0.006)
LGDPC2 − 0.987 (0.076) − 0.254 (0.004) − 0.665 (0.065) − 0.233 (0.000)
LGF − 0.658 (0.278) − 0.765 (0.048) − 0.547 (0.028) − 0.446 (0.000)
LGE − 0.363 (0.053) − 0.636 (0.000) − 0.754 (0.075) − 0.856 (0.000)
LHC 0.436 (0.000) 0.366 (0.010) 0.557 (0.000) 0.768 (0.000)
LGE * GDPC − 0.956 (0.054)
LGE * GF − 0.643(0.007)
LGE * HC − 0.746 (0.000)
Robust check of system GMM estimation for variables
The results show that GDP per capita and GDP per capita square have positive and negative impact on carbon emission by 0.757%, 0.645%, 0.356%, 0.687%, and − 0.236%, − 0.256%, − 0.658%, and − 0.045% (Table 9). The green finance has also a negative impact on carbon emission. This shows that a 1% increase in this factor would cause to decrease the level of carbon emission by − 0.564%, − 0.087%, − 0.054%, and − 0.574%. The government expenditure has a negative impact on carbon emission by − 0.798%, − 0.398%, − 0.578%, and − 0.876%. The results indicate that a 1% increase in human capital would cause to increase the level of carbon emission by 0.356%, 0.267%, 0.879%, and 0.148%. The moderate effect has also a negative impact on carbon emission by − 0.647%, − 0.835%, and − 0.306%.Table 9 System GMM results
Variables Model 1 Model 2 Model 3 Model 4
LGDPC 0.757 (0.003) 0.645 (0.056) 0.356 (0.086) 0.687 (0.006)
LGDPC2 − 0.436 (0.001) − 0.256 (0.000) − 0.658 (0.007) − 0.045 (0.000)
LGF − 0.564 (0.000) − 0.087 (0.006) − 0.054 (0.012) − 0.574 (0.054)
LGE − 0.798 (0.000) − 0.398 (0.015) − 0.578 (0.000) − 0.876 (0.005)
LHC 0.356 (0.009) 0.267 (0.000) 0.879 (0.007) 0.148 (0.006)
LGE * GDPC - − 0.647 (0.016) − -
LGE * GF - - − 0.835 (0.013) -
LGE * HC - - - − 0.306 (0.032)
Conclusion and policy suggestions
There is a popular concern that environmental quality may not be sustained for future generations because of government spending on green finance, as evidenced by both historical literature and data. Human capital, the square root of GDP per capita, and the GDP per capita are also important factors in determining carbon emissions. The BRI’s massive construction projects exacerbate the environmental degradation that is plaguing the world at large. This work uses cross-sectional dependence, CADF and CIPS unit root tests, Westerlund co-integration, and the IFE and DCCE tests, as well as a system generalized method of moments (GMM) to examine the long-run behavior of the explained variable. This research found that higher levels of CO2 are the result of higher per capita GDP and higher levels of human capital. Green financing and government spending as well as GDP per capita squared have all been linked to lower emissions. These unintended consequences lead to lower carbon emissions and greater sustainable development.
Our study was conducted with the intention of informing effective policy actions on CO2 in BRI economies. The report finds that when income is included, CO2 emissions are reduced. According to these results, a greener approach to economic growth is something to think about for the future. To be more specific, the plan will cause greater pollution if it depends on oil, coal, and metal production. Promoting expansion in the service and technology economies is preferred in this context. Cutting back on fossil fuel use and increasing the use of renewables in power generation is another policy option to think about. As a third strategy, the governments of the BRI economies can enact legislation like a high carbon tax, carbon capture, and emission trading programs.
The empirical findings suggest green financing considerably mitigates environmental degradation in the BRI region. To this end, it is imperative that governments of BRI nations maintain their efforts to advance environmentally responsible forms of financing. Interest rate subsidies on green loans are only one example of how governments could use green finance to help slow environmental degradation. Green finance should be promoted not just by the government, but also by private and traditional financial institutions. Governments can encourage private sector participation by offering large incentives, including large corporation tax savings, to private financial institutions that promote and deliver green finance. In addition, there is a higher chance of losing money in the green industry because its revenues are less assured than those of more established traditional industries. This means private lenders will be more selective when it comes to providing green loans. As a result, governments may explore creating green credit guarantee schemes to expand the availability of green loans. In addition to green finance, BRI member countries are advised to consider environmental concerns in urban development and increase domestic production for export owing to trade openness in order to prevent environmental damage.
Carbon emissions are reduced as a result of government expenditures, suggesting that the government is careful with its money. It will be useful in achieving a low-carbon economy if the government can ensure a steady flow of tax income to fund its operations. It is crucial for the government to make good use of income earned in this era when economic, political, social, and health crises might cause revenue shortfall for government expenditure. The current revenue and expenditure system can be improved to accomplish this. There should be no easing of civic society’s checks and balances. Future research on the topic for other emerging countries should, when possible, aggregate government spending and examine its implications on carbon emissions.
Our results imply that national strategies to foster advanced human capital not only contribute to economic growth, in line with endogenous growth theory, but can also be an effective method to mitigate climate change. Therefore, rather than enacting rules and regulations to deal with climate change, investing in highly skilled human capital may be a more efficient and less distorting option. Our findings imply that protecting the environment is a significant social externality that comes from monetary investment in human capital.
Acknowledgements
Supported by Shanxi Federation of Social Science “The Research on the Upgrading Path of Coal Industry in Shanxi Province” (Grant No. SSKLZDKT2016049); Beijing Innovation Project “Research on the mechanism of digital economy promoting regional green development” (Grant No. 11000022T000000445886)
Author contribution
Haiyan Feng; conceptualization, data curation, methodology, writing—original draft, data curation, visualization, Fen Yang; supervision, editing, writing—review and editing, and software.
Data availability
The data can be available on request.
Declarations
Ethical approval and consent to participate
We declare that we have no human participants, human data, or human tissues.
Consent for publication
N/A
Competing interests
The authors declare no competing interests.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Adeleke O Josue M Poverty and green economy in South Africa: what is the nexus? Cogent Econ Financ 2019 7 1646847 10.1080/23322039.2019.1646847
Akomea-Frimpong I Adeabah D Ofosu D Tenakwah EJ A review of studies on green finance of banks, research gaps and future directions J Sustain Financ Invest 2021 10.1080/20430795.2020.1870202
Anser MK Iqbal W Ahmad US Fatima A Chaudhry IS Environmental efficiency and the role of energy innovation in emissions reduction Environ Sci Pollut Res 2020 27 29451 29463 10.1007/s11356-020-09129-w
Anser MK Mohsin M Abbas Q Chaudhry IS Assessing the integration of solar power projects: SWOT-based AHP–F-TOPSIS case study of Turkey Environ Sci Pollut Res 2020 27 31737 31749 10.1007/s11356-020-09092-6
Arif M Hasan M Alawi SM Naeem MA COVID-19 and time-frequency connectedness between green and conventional financial markets Glob Financ J 2021 49 100650 10.1016/j.gfj.2021.100650
Bai J (2009) Panel data models with interactive fixed effects. Econom 77(4):1229–1279
Chandio AA, Jiang Y, Abbas Q, Amin A, Mohsin M (2022) Does financial development enhance agricultural production in the long-run? Evidence from China. J Public Aff 2210.1002/pa.2342
Clark WW (2019) Chapter 9 - smart green healthy communities: cases of science parks and microcities, in: Climate Preservation in Urban Communities Case Studies. pp. 357–414
Criscuolo C Menon C Environmental policies and risk finance in the green sector: cross-country evidence Energy Policy 2015 10.1016/j.enpol.2015.03.023
Dmuchowski P Dmuchowski W Baczewska-Dąbrowska AH Gworek B Green economy – growth and maintenance of the conditions of green growth at the level of polish local authorities J Clean Prod 2021 301 126975 10.1016/j.jclepro.2021.126975
Dong F Zhu J Li Y Chen Y Gao Y Hu M Qin C Sun J How green technology innovation affects carbon emission efficiency: evidence from developed countries proposing carbon neutrality targets Environ Sci Pollut Res 2022 29 35780 35799 10.1007/s11356-022-18581-9
Du L Razzaq A Waqas M The impact of COVID - 19 on small - and medium - sized enterprises ( SMEs ): empirical evidence for green economic implications Environ Sci Pollut Res 2022 10.1007/s11356-022-22221-7
Feng H Liu Z Wu J Iqbal W Ahmad W Marie M Nexus between government spending’s and green economic performance: role of green finance and structure effect Environ Technol Innov 2022 27 102461 10.1016/j.eti.2022.102461
Geddes A Schmidt TS Steffen B The multiple roles of state investment banks in low-carbon energy finance: an analysis of Australia, the UK and Germany Energy Policy 2018 115 158 170 10.1016/j.enpol.2018.01.009
Guang-Wen Z Siddik AB Do corporate social responsibility practices and green finance dimensions determine environmental performance? An empirical study on Bangladeshi banking institutions Front Environ Sci 2022 10 858 10.3389/FENVS.2022.890096/XML/NLM
Guild J The political and institutional constraints on green finance in Indonesia J Sustain Financ Invest 2020 10 157 170 10.1080/20430795.2019.1706312
Hafner S Jones A Anger-Kraavi A Pohl J Closing the green finance gap – a systems perspective Environ Innov Soc Transitions 2020 34 26 60 10.1016/j.eist.2019.11.007
Hou D Chan KC Dong M Yao Q The impact of economic policy uncertainty on a firm’s green behavior: evidence from China Res Int Bus Financ 2022 59 101544 10.1016/j.ribaf.2021.101544
Hou Y Iqbal W Shaikh GM Iqbal N Solangi YA Fatima A Measuring energy efficiency and environmental performance: a case of South Asia Processes 2019 7 325 10.3390/pr7060325
Hsu CC Quang-Thanh N Chien FS Li L Mohsin M Evaluating green innovation and performance of financial development: mediating concerns of environmental regulation Environ Sci Pollut Res 2021 28 57386 57397 10.1007/s11356-021-14499-w
Huang W, Saydaliev HB, Iqbal W, Irfan M (2022a) Measuring the impact of economic policies on co2emissions: ways to achieve green economic recovery in the post-covid-19 era. Clim Chang Econ 1–2910.1142/S2010007822400103
Huang X Chau KY Tang YM Iqbal W Business ethics and irrationality in SME during COVID-19: does it impact on sustainable business resilience? Front Environ Sci 2022 10 275 10.3389/fenvs.2022.870476
Ikram M Sroufe R Mohsin M Solangi YA Shah SZA Shahzad F Does CSR influence firm performance? A longitudinal study of SME sectors of Pakistan J Glob Responsib 2019 11 27 53 10.1108/jgr-12-2018-0088
Iqbal S, Taghizadeh-Hesary F, Mohsin M, Iqbal W (2021a) Assessing the role of the green finance index in environmental pollution reduction. Estud Econ Apl 39 10.25115/eea.v39i3.4140
Iqbal W, Fatima A, Yumei H, Abbas Q, Iram R (2020) Oil supply risk and affecting parameters associated with oil supplementation and disruption. J Clean Prod 25510.1016/j.jclepro.2020.120187
Iqbal W Tang YM Chau KY Irfan M Mohsin M Nexus between air pollution and NCOV-2019 in China: application of negative binomial regression analysis Process Saf Environ Prot 2021 150 557 565 10.1016/j.psep.2021.04.039
Jin J Han L Assessment of Chinese green funds: performance and industry allocation J Clean Prod 2018 171 1084 1093 10.1016/j.jclepro.2017.09.211
Khan MA Riaz H Ahmed M Saeed A Does green finance really deliver what is expected? An empirical perspective Borsa Istanbul Rev 2021 10.1016/j.bir.2021.07.006
Khokhar M Hou Y Rafique MA Iqbal W Evaluating the social sustainability criteria of supply chain management in manufacturing industries: a role of BWM in MCDM Probl Ekorozwoju 2020 10.35784/pe.2020.2.18
Latif Y Shunqi G Bashir S Iqbal W Ali S Ramzan M COVID-19 and stock exchange return variation: empirical evidences from econometric estimation Environ Sci Pollut Res 2021 28 60019 60031 10.1007/s11356-021-14792-8
Lee CC Lee CC How does green finance affect green total factor productivity? Evidence from China Energy Econ 2022 107 105863 10.1016/j.eneco.2022.105863
Lee JW Green finance and sustainable development goals: the case of China J Asian Financ Econ Bus 2020 10.13106/jafeb.2020.vol7.no7.577
Lee JW Green finance and sustainable development goals: the case of China J Asian Financ Econ Bus 2020 7 577 586 10.13106/jafeb.2020.vol7.no7.577
Li M Hamawandy NM Wahid F Rjoub H Bao Z Renewable energy resources investment and green finance: evidence from China Resour Policy 2021 74 102402 10.1016/j.resourpol.2021.102402
Li Z Kuo TH Siao-Yun W The Vinh L Role of green finance, volatility and risk in promoting the investments in renewable energy resources in the post-covid-19 Resour Policy 2022 76 102563 10.1016/J.RESOURPOL.2022.102563
Liu H Tang YM Iqbal W Raza H Assessing the role of energy finance, green policies, and investment towards green economic recovery Environ Sci Pollut Res 2022 29 21275 21288 10.1007/s11356-021-17160-8
Liu H Yao P Latif S Aslam S Iqbal N Impact of Green financing, FinTech, and financial inclusion on energy efficiency Environ Sci Pollut Res 2022 29 18955 18966 10.1007/s11356-021-16949-x
Liu L Li Z Fu X Liu X Li Z Zheng W Impact of power on uneven development: evaluating built-up area changes in chengdu based on NPP-VIIRS images (2015–2019) Land 2022 11 489 10.3390/LAND11040489
Liu X Tong D Huang J Zheng W Kong M Zhou G What matters in the e-commerce era? Modelling and mapping shop rents in Guangzhou, China Land Use Policy 2022 123 106430 10.1016/J.LANDUSEPOL.2022.106430
Liu X Wang E Cai D Green credit policy, property rights and debt financing: quasi-natural experimental evidence from China Financ Res Lett 2019 29 129 135 10.1016/j.frl.2019.03.014
Liu Z, Vu TL, Phan TTH, Ngo TQ, Anh NHV, Putra ARS (2022e) Financial inclusion and green economic performance for energy efficiency finance. Econ Chang Restruct 1–3110.1007/s10644-022-09393-5
Lv C Bian B Lee CC He Z Regional gap and the trend of green finance development in China Energy Econ 2021 102 105476 10.1016/j.eneco.2021.105476
Mngumi F Shaorong S Shair F Waqas M Does green finance mitigate the effects of climate variability: role of renewable energy investment and infrastructure Environ Sci Pollut Res 2022 1 1 13 10.1007/s11356-022-19839-y
Mohsin M Naiwen L Zia-UR-Rehman M Naseem S Baig SA The volatility of bank stock prices and macroeconomic fundamentals in the Pakistani context: an application of GARCH and EGARCH models Oeconomia Copernicana 2020 11 609 636 10.24136/OC.2020.025
Mohsin M Naseem S Zia-ur-Rehman M Baig SA Salamat S The crypto-trade volume, GDP, energy use, and environmental degradation sustainability: an analysis of the top 20 crypto-trader countries Int J Financ Econ 2020 10.1002/ijfe.2442
Mohsin M Taghizadeh-Hesary F Iqbal N Saydaliev HB The role of technological progress and renewable energy deployment in green economic growth Renew Energy 2022 190 777 787 10.1016/j.renene.2022.03.076
Mohsin M Taghizadeh-Hesary F Panthamit N Anwar S Abbas Q Vo XV Developing low carbon finance index: evidence from developed and developing economies Financ Res Lett 2020 10.1016/j.frl.2020.101520
Naseem S Mohsin M Zia-UR-Rehman M Baig SA Sarfraz M The influence of energy consumption and economic growth on environmental degradation in BRICS countries: an application of the ARDL model and decoupling index Environ Sci Pollut Res 2021 299 29 13042 13055 10.1007/S11356-021-16533-3
Nassani AA Aldakhil AM Qazi Abro MM Zaman K Environmental Kuznets curve among BRICS countries: spot lightening finance, transport, energy and growth factors J Clean Prod 2017 154 474 487 10.1016/j.jclepro.2017.04.025
Nassiry D (2018) The role of fintech in unlocking green finance: policy insights for developing countries
Ning QQ, Guo SL, Chang XC (2021) Nexus between green financing, economic risk, political risk and environment: evidence from China. Econ Res Istraz 1–2510.1080/1331677X.2021.2012710
Ning Y Cherian J Sial MS Álvarez-Otero S Comite U Zia-Ud-Din M Green bond as a new determinant of sustainable green financing, energy efficiency investment, and economic growth: a global perspective Environ Sci Pollut Res 2022 1 1 16 10.1007/S11356-021-18454-7/TABLES/10
Pal S, Russel AH (2015) Advancement of green banking layout and trend in Bangladesh. Int J Econ Commer Manag III 1160–1182
Pham L, LuuDuc Huynh T (2020) How does investor attention influence the green bond market? Financ Res Lett 3510.1016/j.frl.2020.101533
Raberto M Ozel B Ponta L Teglio A Cincotti S From financial instability to green finance: the role of banking and credit market regulation in the Eurace model J Evol Econ 2019 29 429 465 10.1007/s00191-018-0568-2
Rao F Tang YM Chau KY Iqbal W Abbas M Assessment of energy poverty and key influencing factors in N11 countries Sustain Prod Consum 2022 30 1 15 10.1016/j.spc.2021.11.002
Rasoulinezhad E Taghizadeh-Hesary F Role of green finance in improving energy efficiency and renewable energy development Energy Effic 2022 15 1 12 10.1007/S12053-022-10021-4/TABLES/11 34961811
Ren X Shao Q Zhong R Nexus between green finance, non-fossil energy use, and carbon intensity: empirical evidence from China based on a vector error correction model J Clean Prod 2020 277 122844 10.1016/j.jclepro.2020.122844
Sachs JD Woo WT Yoshino N Taghizadeh-Hesary F Importance of green finance for achieving sustainable development goals and energy security Handb Green Financ 2019 10 3 12 10.1007/978-981-13-0227-5_13
Saeed Meo M Karim MZA The role of green finance in reducing CO2 emissions: an empirical analysis Borsa Istanbul Rev 2021 10.1016/j.bir.2021.03.002
Saha T Sinha A Abbas S Green financing of eco-innovations: is the gender inclusivity taken care of? Econ Res Istraz 2022 10.1080/1331677X.2022.2029715
Sarangi U (2019) Green economy, environment and international trade for global sustainable development. J Int Econ
Sarfraz M Muhammad M Naseem S Kumar A Modeling the relationship between carbon emissions and environmental sustainability during COVID-19: a new evidence from asymmetric ARDL cointegration approach Environ Dev Sustain 2021 2311 23 16208 16226 10.1007/S10668-021-01324-0
Sarma P Roy A A scientometric analysis of literature on green banking (1995-March 2019) J Sustain Financ Invest 2021 11 143 162 10.1080/20430795.2020.1711500
Shipalana P Green finance mechanisms in developing countries : emerging practice. Covid-19 Macroecon Policy Responses Africa 2020 2 1 19
Si DK Li XL Huang S Financial deregulation and operational risks of energy enterprise: the shock of liberalization of bank lending rate in China Energy Econ 2021 93 105047 10.1016/J.ENECO.2020.105047
Srivastava AK Dharwal M Sharma A Green financial initiatives for sustainable economic growth: a literature review Mater Today Proc 2021 10.1016/j.matpr.2021.08.158
Steckel JC Jakob M Flachsland C Kornek U Lessmann K Edenhofer O From climate finance toward sustainable development finance Wiley Interdiscip Rev Clim Chang 2017 8 e437 10.1002/WCC.437
Stojanović D Sustainable economic development through green innovative banking and financing Econ Sustain Dev 2020 4 35 44 10.5937/esd2001035s
Streimikiene D Kaftan V Green finance and the economic threats during COVID-19 pandemic Terra Econ 2021 19 105 113 10.18522/2073-6606-2021-19-2-105-113
Sun H Ikram M Mohsin M Zhang J Abbas Q Energy security and Environmental Efficiency: evidence from OECD countries Singapore Econ Rev 2019 10.1142/s0217590819430033
Sun H Pofoura AK Adjei Mensah I Li L Mohsin M The role of environmental entrepreneurship for sustainable development: evidence from 35 countries in Sub-Saharan Africa Sci Total Environ 2020 741 140132 10.1016/J.SCITOTENV.2020.140132 32886991
Tolliver C Keeley AR Managi S Green bonds for the Paris agreement and sustainable development goals Environ Res Lett 2019 10.1088/1748-9326/ab1118
Tu Q Mo J Liu Z Gong C Fan Y Using green finance to counteract the adverse effects of COVID-19 pandemic on renewable energy investment-the case of offshore wind power in China Energy Policy 2021 158 112542 10.1016/j.enpol.2021.112542 34539036
United Nations Environment Programme, 2017. On the role of Central Banks in enhancing green finance. United Nations Environ Program 1–27
Ünüvar B (2019) Financing the green economy, in: Acar, S., Yeldan, E. (Eds.), Handbook of Green Economics. Academic Press, pp. 163–181. 10.1016/b978-0-12-816635-2.00010-9
van Veelen B Cash cows? Assembling low-carbon agriculture through green finance Geoforum 2021 118 130 139 10.1016/j.geoforum.2020.12.008
Wang M Li X Wang S Discovering research trends and opportunities of green finance and energy policy: a data-driven scientometric analysis Energy Policy 2021 154 112295 10.1016/j.enpol.2021.112295
Wang Y Zhi Q The role of green finance in environmental protection: two aspects of market mechanism and policies Energy Procedia 2016 104 311 316 10.1016/j.egypro.2016.12.053
Xiong Q, Sun D (2022) Influence analysis of green finance development impact on carbon emissions: an exploratory study based on fsQCA. Environ Sci Pollut Res 1–1210.1007/S11356-021-18351-Z/TABLES/9
Xiong Z Weng X Wei Y SandplayAR: evaluation of psychometric game for people with generalized anxiety disorder Arts Psychother 2022 80 101934 10.1016/J.AIP.2022.101934
Xu X Lin Z Li X Shang C Shen Q Multi-objective robust optimisation model for MDVRPLS in refined oil distribution Int J Prod Res 2022 60 6772 6792 10.1080/00207543.2021.1887534
Xu X Wang C Zhou P GVRP considered oil-gas recovery in refined oil distribution: From an environmental perspective Int J Prod Econ 2021 235 108078 10.1016/J.IJPE.2021.108078
Yang Z Abbas Q Hanif I Alharthi M Taghizadeh-Hesary F Aziz B Mohsin M Short- and long-run influence of energy utilization and economic growth on carbon discharge in emerging SREB economies Renew Energy 2021 10.1016/j.renene.2020.10.141
Ye J, Al-Fadly A, Huy PQ, Ngo TQ, Hung DDP, Tien NH (2022) The nexus among green financial development and renewable energy: investment in the wake of the Covid-19 pandemic. http://www.tandfonline.com/action/authorSubmission?journalCode=rero20&page=instructions. 10.1080/1331677X.2022.2035241
Yu CH, Wu X, Zhang D, Chen S, Zhao J (2021) Demand for green finance: resolving financing constraints on green innovation in China. Energy Policy 15310.1016/j.enpol.2021.112255
Zeng Y Wang F Wu J Zeng Y Wang F Wu J The impact of green finance on urban haze pollution in china: a technological innovation perspective Energies 2022 15 801 10.3390/EN15030801
Zerbib OD The effect of pro-environmental preferences on bond prices: evidence from green bonds J Bank Financ 2019 98 39 60 10.1016/j.jbankfin.2018.10.012
Zhang D, Mohsin M, Rasheed AK, Chang Y, Taghizadeh-Hesary F (2021a) Public spending and green economic growth in BRI region: mediating role of green finance. Energy Policy 15310.1016/j.enpol.2021.112256
Zhang D Zhang Z Managi S A bibliometric analysis on green finance: current status, development, and future directions Financ Res Lett 2019 10.1016/j.frl.2019.02.003
Zhang M Lian Y Zhao H Xia-Bauer C Unlocking green financing for building energy retrofit: a survey in the western China Energy Strateg Rev 2020 30 100520 10.1016/j.esr.2020.100520
Zhang S Wu Z Wang Y Hao Y Fostering green development with green finance: an empirical study on the environmental effect of green credit policy in China J Environ Manage 2021 296 113159 10.1016/j.jenvman.2021.113159 34237675
Zhang Y Abbas M Iqbal W Perceptions of GHG emissions and renewable energy sources in Europe, Australia and the USA Environ Sci Pollut Res 2022 29 5971 5987 10.1007/s11356-021-15935-7
Zhang Y Abbas M Koura YH Su Y Iqbal W The impact trilemma of energy prices, taxation, and population on industrial and residential greenhouse gas emissions in Europe Environ Sci Pollut Res 2021 28 6913 6928 10.1007/s11356-020-10618-1
Zheng GW, Siddik AB, Masukujjaman M, Fatema N (2021a) Factors affecting the sustainability performance of financial institutions in Bangladesh: the role of green finance. Sustain 1310.3390/su131810165
Zheng GW Siddik AB Masukujjaman M Fatema N Factors affecting the sustainability performance of financial institutions in bangladesh: the role of green finance Sustain 2021 13 10165 10.3390/SU131810165
Zheng GW Siddik AB Masukujjaman M Fatema N Factors affecting the sustainability performance of financial institutions in Bangladesh: the role of green finance Sustain 2021 13 10165 10.3390/su131810165
Zheng GW Siddik AB Masukujjaman M Fatema N Alam SS Green finance development in Bangladesh: the role of Private Commercial Banks (PCBs) Sustain 2021 13 795 10.3390/SU13020795
Zhou G Zhu J Luo S The impact of fintech innovation on green growth in China: mediating effect of green finance Ecol Econ 2022 193 107308 10.1016/J.ECOLECON.2021.107308 | 36602740 | PMC9815070 | NO-CC CODE | 2023-04-09 23:17:24 | yes | Environ Sci Pollut Res Int. 2023 Jan 5; 30(14):39946-39960 |
==== Front
Mol Biol Rep
Mol Biol Rep
Molecular Biology Reports
0301-4851
1573-4978
Springer Netherlands Dordrecht
36662451
8246
10.1007/s11033-023-08246-2
Review
Review novel insights into the diagnostic and prognostic function of copeptin in daily clinical practice
Wu Penglong 1
Wang Lilan 2
Su Xin 1
http://orcid.org/0000-0003-2299-415X
Wang Bin 1
Cheng Ye [email protected]
1
1 grid.12955.3a 0000 0001 2264 7233 Department of Cardiology, The Xiamen Cardiovascular Hospital of Xiamen University, No. 2999 Jinshan Road, Fujian 363001 Xiamen, China
2 grid.12955.3a 0000 0001 2264 7233 School of Medicine, Xiamen University, Xiamen, Fujian China
20 1 2023
2023
50 4 37553765
10 10 2022
4 1 2023
© The Author(s), under exclusive licence to Springer Nature B.V. 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
As is shown in previous reports, arginine vasopressin (AVP), as one of the most important hormones within circulation in human beings, is of great clinically significance given that it could maintain the body fluid balance and vascular tone. However, the laboratory measurements AVP in daily clinical practice are shown to be difficult and with low accuracy. Concerning on this notion, it is unpractical to use the serum levels of AVP in diagnosing multiple diseases. On the other hand, another key serum biomarker, copeptin, is confirmed as the C-terminal of the AVP precursor which could be released in equal amounts with AVP, resultantly making it as a sensitive marker of arginine vasopressin release. Notably, emerging recent evidence has demonstrated the critical function of copeptin as a clinical indicator, especially in the diagnosis and prognosis of several diseases in diverse organs, such as cardiovascular disease, kidney disease, and pulmonary disease. In addition, copeptin was recently verified to play an important role in diagnosing multiple acute diseases when combined it with other gold standard serum biomarkers, indicating that copeptin could be recognized as a vital disease marker. Herein, in the current review, the functions of copeptin as a new predictive diagnostic and prognostic biomarker of various diseases, according to the most recent studies, are well summarized. Furthermore, the importance of using copeptin as a serum biomarker in diverse medical departments and the impact of this on improving healthcare service is also summarized in the current review.
Keywords
Copeptin
Arginine vasopressin
All-cause mortality
Biomarker
Prognostic markers
http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 81900365 Wu Penglong issue-copyright-statement© Springer Nature B.V. 2023
==== Body
pmcIntroduction
Hormones are identified as complex chemical substances which could be secreted into circulation by the endocrine system. The most important function of hormones is regulating biological metabolism [1]. Diverse hormones act different functions as the chemical messengers which maintains normal physical catabolism and homeostasis Furthermore, diverse hormones affect almost all the essential life processes, including cell growth, metabolism, and adaptation to the conditions of existence [2]. Notably, the mechanisms of hormones depend on the different locations of receptors. As shown, diverse receptors interact with different hormones which affect the metabolic processes of different target cells and present multiple physiological effects.
Among several hormones, AVP and copeptin are identified as two vital hormones released in equal amounts from the posterior pituitary gland into the systemic circulation, as a response to osmotic and hemodynamic stimulants. Notably, in healthy individuals, the AVP modulates the vascular tone and the fluid homeostasis which resultantly maintains the equilibrium of the cardiovascular system. Moreover, the AVP is also confirmed to embrace an essential function in affecting multiple systems, including endocrine, hemostatic, and central nervous systems, besides the direct renal and vasoconstrictor effects [3]. It is well-established that the serum concentrations of AVP are highly important in diagnosis of multiple diseases, however, the measurements of serum AVP concentrations in daily clinical practice are limited since several reasons. For instance, the short half-life time of AVP, the low molecular stability of AVP even if stored at − 20 °C, also almost all of the serum AVP is bound to platelets, which leads to under-estimation of the actual AVP concentrations [4].
Given that AVP and copeptin are released together in equimolar amounts, copeptin was approved to be used for mirroring the existence of AVP. Importantly, this is due to its high molecular stability compared to AVP and its high serum concentrations which could be easily determined by diverse easy assays. Currently, the main function of copeptin is as a stable surrogate biomarker of AVP concentration in human beings, where it was approved to be a biomarker for prognosis of several diseases, such pulmonary disease, cardiovascular disease, and kidney disease [3]. Herein, in the current review, the functions of copeptin as a new predictive diagnostic and prognostic biomarker of various diseases were well summarized. Furthermore, the importance of using copeptin as a biomarker in diverse medical departments and the impacts of this on improving healthcare service are also summarized.
Basic characteristics of AVP and copeptin
Basic characteristics of AVP
AVP, also known as anti-diuretic hormone or argipressin, is the most important form of vasopressin in most mammals. It is a synthetic non-peptide composed of nine amino acids with arginine at residue eight and two cysteines at residues 1 and 6 linked together by a disulfide bond [5]. The structure of AVP is shown in Fig. 1. Furthermore, the AVP hormone is produced as a part of 164 amino acid precursor protein, as pro-AVP, in the hypothalamus. This precursor is composed of signal peptide, AVP moiety, neurophysin-2 protein, and copeptin [3].
Fig. 1 The structure of arginine vasopressin, which is shown as C46H65N15O12S2
Pro-AVP precursor is produced in the hypothalamus (as shown in Fig. 2) and released through two mechanisms, as one for the posterior pituitary, the other for the anterior pituitary. As for the first mechanism, pro-AVP is produced in the magnocellular neurons of the supraoptic (SON) and paraventricular (PVN) hypothalamic nuclei, after that it is treated in the endoplasmic reticulum where the signal peptide is removed and carbohydrate chain is added [6]. This process is followed by axonal transport to the posterior pituitary gland where pro-AVP is subjected to enzymatic cleavages producing three diverse peptides, copeptin, AVP, and Neurophysin-2, as shown in Fig. 3.
Fig. 2 Pro-arginine-vasopressin is produced in the hypothalamus followed by two different release mechanisms towards the anterior and posterior pituitary glands
Fig. 3 The precursor of arginine vasopressin is subjected to several enzymatic cleavages resulting in production of arginine vasopressin, Neurophysin-2, and copeptin. AVP arginine vasopressin
The three diverse peptides are stored in the neurohypophysis and secreted in case of hemodynamic or osmotic stimuli. Concerning on the second mechanism, pro-AVP is synthesized and processed in the parvocellular neurons of hypothalamus, where corticotropin-releasing hormone is also synthesis and release [7]. The AVP is transported to the pituitary portal system where it acts co-operatively with corticotropin-releasing hormone stimulating the adrenocorticotropic hormone release from the anterior pituitary gland, resulting in cortisol secretion from the adrenal gland.
Importantly, this backup system of AVP and corticotropin-releasing hormone, which further facilitates the release of adrenocorticotropic hormone, reflects how the endocrine stress response is physiologically important. Additionally, it gives indication about the ability of using AVP and copeptin, as two potential biomarkers in determining stress levels [7]. AVP is considered to have three main physiological functions, including maintaining the fluid balance homeostasis, modulating the endocrine stress response, and influencing the vascular tone regulation. Clinically, AVP presents an essential function in affecting the osmotic balance, blood pressure, and kidney function, which explains the high clinical importance of AVP. On the other hand, the functions of AVP are mediated by three G-protein coupled receptors, including the vascular receptor (V1aR), the anterior pituitary receptor (V1bR), and the anti-diuresis-mediating receptor (V2R) [8]. Nevertheless, the measurement of AVP as a clinical biomarker showed a lot of difficulties and inaccuracy. For instance, the measurement of AVP started since 19th century using radio-immuno-assay (RIA), after that measurement of AVP exhibited several limitations. Notably, the key limitations is the long-timed assays due to complicated pre-analytical requirements which is not suitable in clinical diagnosis. Also, the instability of AVP in isolated serum, even if stored at − 20 °C, made it more difficult for AVP to be used as a biomarker. This was in addition to the fact that a huge amount of AVP is bound to platelets within the circulation which affects the actual determination of AVP concentration [9]. Finally, the amount of serum required in AVP measurement is actually large as greater than one mini lite. All those limitations led to much more researches about the probability of using copeptin as an alternative biomarker for AVP as they are both released in equimolar amounts in response to stress [3].
Basic characteristics of copeptin
Copeptin was first described in 1972 by Holwerda and colleagues, where it was first detected in the pig posterior pituitary [10]. It is a 39-amino acid glycosylated peptide containing a leucine rich core region, found in the C-terminal part of pro-arginine-vasopressin. The molecular mass of copeptin is approximately 5 kDa.
According to the reports, copeptin is released into the blood stream in equivalent amounts to arginine vasopressin, thus reflecting its presence and activity. However, unlike AVP, the physiological function of copeptin within circulation is not yet clarified. After description of copeptin in 1972, several early basic experiments demonstrated that copeptin might act as a prolactin releasing factor, but this function could not be confirmed by other experiments [11]. Recently, copeptin was presumed to act as a chaperone protein in the folding and proteolytic maturation of AVP [12]. As copeptin is assumed to be strictly regulated within circulation, it might have a specific peripheral function. Accordingly, much more experiments are needed to find out copeptin’s specific role in circulation. At present, as copeptin was proved to show same respond as AVP to stress, osmotic and hemodynamic stimuli, and as it is produced in equimolar quantities together with AVP, the main and most important role of copeptin is giving indication about the amount of AVP which reversely reflects its importance in prognosis and diagnosis of AVP disorder-related diseases. As for copeptin elimination, no specific mechanism was approved, whereas emerging evidence shows that it could eliminated via kidney relying on the fact that it could be measured in kidney [13]. It has been discussed before the difficulties in using AVP as a clinical biomarker, the following table might show the advantages of using copeptin, as a clinical biomarker, over AVP, as shown in Table 1.
Table 1 Advantages of copeptin over arginine vasopressin
Copeptin Arginine vasopressin
Assay Sandwich immunoassay Radio immunoassay
Duration of assay 30 min to 2 h Usually more than 48 h
Amount of serum or plasma 50 µL serum or plasma ≥ 1 mL plasma
Sensitivity of the assay Sandwich immunoassay is highly sensitive (1.7 pmol/L) Arginine vasopressin must be measured using low sensitive immunoassays because of its small size
Pre-analytical steps None -Extraction step is needed
-Addition of protease inhibitor
Half-life time Long half life time (≈ 86 min) Short half life time (≈ 44 min)
Stability In both serum and plasma, it is stable for 7 days at room temperature, and for 14 days at 4 °C Unstable in isolated plasma even if stored at −20 °C
According to the mechanism of AVP production discussed before, it has been well-reported that after cleavage of the pro-AVP precursor, AVP, neurophysin-2, and copeptin are produced [14]. Concerning on this notion, the current question is why copeptin is preferred to be the perfect fragment to reflect the release of AVP and not neurophysin-2. The complicated structure of neurophysin-2 (approximately seven intramolecular disulfide bonds), and its ability to bind to AVP denies the possibility of being an ideal AVP alternative target. Nonetheless, in case of copeptin such limitations are absent. Concerning on this notion, it is suitable as an AVP alternative target in clinical practice [15].
The circulating concentrations of copeptin respond, similar to AVP, to blood pressure, stress, and osmolality changes. It was observed that copeptin is more associated with serum osmolality compared with the AVP in healthy individuals. Where in healthy controls concentrations of copeptin are shown to be closely correlated to osmolality alterations with a rapid rise in copeptin concentrations during thirsting and a rapid drop after fluid intake. Copeptin might be also better than cortisol in physiological stress levels determination, as it is difficult to measure cortisol as a free hormone, given that it has a strong circadian rhythm, and it is downstream during stress response [16]. Normal serum copeptin concentrations in healthy controls have been found to be approximately 1.70–11.25 pmol/L. In normal situations, the serum concentrations of copeptin were observed to be higher in men compared to those in women. The reason of higher serum copeptin concentrations in males is still unknown, but it is proposed to be higher osmolar intake in men [17]. Nonetheless, gender differences in serum concentrations of AVP were not correlated with the differences in serum sodium concentration, blood pressure, or plasma volume. It was reported that copeptin had no regular variability in circadian rhythm, whereas the serum concentrations of AVP presented a diurnal rhythm where its levels increased at night-time in both males and females. In addition, the age differences could not affect copeptin concentrations. On the other hand, it was observed that the serum concentrations of copeptin might increase by physical exercise. For instance, in a large general-population cohort study which enrolled approximately 6,801 participants, it was found that life-style and diet-related factors, such as smoking, alcohol use, and sodium intake were associated with the serum copeptin concentration. Recently, it is also confirmed that the serum concentrations of copeptin were associated with higher systolic blood pressure, lower 24-hour urine volume, and higher renal sodium in healthy individuals, indicating that copeptin could be identified as an essential biomarker in cardio-metabolic disorders [18].
Introducing copeptin in daily clinical practice gave the evidence which copeptin could act as a promising serum biomarker for diagnosing AVP-dependent fluid homeostasis disorders, such as hyponatremia and diabetes insipidus. Despite the fact that copeptin serum concentrations are quite variable and more related to osmolality, copeptin demonstrated significantly higher concentrations in different cases including myocardial infarction (MI), diabetes mellitus, kidney diseases, and critical conditions, which in turn increases the probability of depending on copeptin as an effective prognostic biomarker of diverse diseases [19]. The functions of copeptin as a biomarker in daily clinical practice is being highlighted through the following pages according to results obtained from recent studies.
Functions of copeptin in cardiovascular diseases
It is well-established that the cardiovascular diseases term refers to all types of diseases affecting the heart or blood vessels, such as atherosclerosis, hypertension, and valvar heart disease. According to the world health organization (WHO), the cardiovascular diseases are the leading cause of death worldwide, posing serious to the human health all over the world. In 2019, approximately 17.9 million deaths were due to the cardiovascular diseases, which represents about 32% of the whole global deaths [20]. Concerning on this notion, early diagnosis of cardiovascular diseases might significantly facilitate disease control and early treatment. Indeed, the diagnosis of cardiovascular diseases depends on both laboratory assessments and symptoms. As a biomarker, copeptin has been studied in many clinical cases especially diagnosis of MI and heart failure, and prognosis of chronic stable heart failure, in which the results were highly satisfying [21].
Evidence of copeptin in modulating the risk of myocardial infarction
Several studies demonstrated the relationship between copeptin and the risk of acute myocardial infarction (AMI). Several cardiac troponins, including troponin T (TnT), troponin C (TnC), and troponin I (TnI), have been firmly demonstrated as the gold standard serum biomarkers for diagnosing AMI together with electrocardiography [22]. Despite being the most important methods in diagnosis of AMI, perfect diagnosis is still under challenging, this is due to the fact that levels of troponins took time to increase in the circulation (approximately 6–9 h) in after chest pain onset, which in turn delays the accurate diagnosis of AMI, even after using highly sensitive troponin assays (hs-cTn), which increased the diagnostic sensitivity of AMI compared to normal tests, the troponin gap is still present [23]. In addition, electrocardiography is of low accuracy, as approximately 25–30% of patients with AMI did not show serious electrocardiography alternations during acute cardiac ischemia [17]. Accordingly, researches were directed towards finding novel biomarkers of AMI which must fulfill two important criteria accuracy and speed. The fast and early activation of vasopressin system in response to stress draw scientists’ attention towards copeptin. The first study to report that copeptin could help in early diagnosis of AMI was in 2009 [24]. Where Reichlin et al. found that when the serum concentrations of TnT were undetectable in the circulation, serum concentrations of copeptin were significantly high in patients with AMI, this is owing to the rapid and early release of copeptin in blood, directly after the onset of chest pain, compared to troponins [25]. The study also reported that combination between copeptin with concentration lower than 14 pmol/L and TnT concentration lower than or equal 0.01 µg/L excludes AMI at presentation with sensitivity of 98.8% [24]. A recent study reported that using both copeptin and TnI improve diagnosis of AMI in patients with onset chest pain in emergency department. The study targeted 271 patients complaining chest pain. Serum copeptin, creatine kinase-MB (CK-MB), and TnI concentrations were measured within six hours of onset. For AMI, the diagnostic performance of the biomarkers was assessed separately and in combination using ROC curve analysis [26]. It was found that copeptin was better than TnI in diagnosing chest pain patients within two hours of onset.
Additionally, combination of copeptin and TnI showed better diagnostic performance compared to CK-MB and TnI combination in patients with AMI and ST elevation myocardial infarction (STEMI) [26]. Another study also reported the ability of copeptin to improve prognosis and diagnosis of AMI especially when combined with troponins [27, 28].
Evidence of copeptin in modulating the risk of heart failure
The brain-type natriuretic peptide (BNP) and its precursor N-terminal brain-type natriuretic peptide (NT-pro-BNP) is confirmed as the gold standard biomarkers of heart failure. Concentrations of those two parameters within circulation in response to cardiac stretching and they provide diagnostic and prognostic information especially in chronic heart failure, whereas it was found that heart failure is not significantly detected in approximately 55% of the patients [29]. For this reason, emerging research was conducted to find out novel serum biomarker to help in better diagnosis and prognosis of heart failure. AVP and copeptin levels show higher elevation in case of heart failure patients, even in cases with low plasma osmolality [30]. This might be due to the fact that release of AVP is due to both osmotic and non-osmotic factors like intra-arterial pressures, intra-cardiac pressures, pain, angiotensin-2, and adrenergic (α-2) central nervous system stimuli. In case of edematous, states the non-osmotic stimuli are predominant over the osmotic stimuli, which explains the high concentrations of AVP and copeptin levels in case of patients with chronic heart failure despite of lower osmolality [30].
Regarding the disease prognosis, several studies reported that copeptin was superior to BNP and NT-proBNP in predicting mortality and monitoring the disease risk for long- and short-term clinical outcomes [31]. In a study targeting 155 patients of heart failure acute symptoms who were monitored for 90 days concerning the composite end point of cardiovascular death or acute heart failure-related re-hospitalization, BNP, NT-pro-BNP, and copeptin concentrations were measured at admission. Copeptin was superior to BNP and NT-pro-BNP in predicting the disease development within 90 days period of follow up [32]. The superiority of copeptin over BNP and NT-pro-BNP in prognosis might be due to that BNP and NT-pro-BNP are closely correlated with age and renal function, whereas copeptin is not. Likewise, the fact that serum BNP concentrations are extremely variable over time in chronic heart failure might be another reason.
According to the published reports, it is better to combine copeptin together with symptoms and natriuretic peptides for more prognostic and diagnostic accuracy in heart diseases [17]. Another study where serum levels of copeptin and NT-pro-BNP were determined in 314 cases of acute dyspnea. The diagnostic value and prognostic significance of both were then analyzed in acute exacerbation of chronic obstructive pulmonary disease and acute heart failure patients. Other results revealed that copeptin was superior to NT-pro-BNP in predicting mortality in both acute heart failure and acute exacerbation of chronic obstructive pulmonary disease, whereas, serum NT-pro-BNP concentrations predicted mortality in acute heart failure cases only. Nevertheless, NT-pro-BNP showed higher diagnostic values compared to copeptin in diagnosis of acute heart failure in acute dyspnea patients [33]. Another study suggested that copeptin has high prognostic values in long term clinical outcomes in patients with heart failure. The primary outcome of the study was a combination between all-cause death and re-admission of heart failure. During approximately five-year of follow up higher serum copeptin concentrations were associated with both all-cause death and re-admission heart failure cases. After confounders’ adjustment copeptin remained an independent predictor for all-cause death and heart failure [34]. Several other studies are mentioned also assures the ability of copeptin could be identified as a useful marker in improving the prognosis and diagnosis of heart failure together with the natriuretic peptides [28].
Functions of copeptin in other diseases
Evidence of copeptin in modulating the risk of pulmonary diseases
Respiratory diseases are among the main causes of death and disability worldwide. Indeed, five of the thirty most common globally death causes are strongly correlated to respiratory diseases, which are chronic obstructive pulmonary disease, third reason of death all over the world, tracheal, lower respiratory tract infection, lung and bronchial cancer, asthma and tuberculosis [35].
Given that it has been firmly established that copeptin is associated with several diseases, current focus is shifting towards composing and implementing the underlying relationship between copeptin with the risk of pulmonary diseases. As reported, the serum concentrations of copeptin were observed to be elevated in several respiratory disturbances, which also reflects the increase of AVP concentrations [36]. Patients with lower respiratory tract infection demonstrated the higher serum copeptin concentrations, especially in patients with the community acquired pneumonia, where copeptin showed a predictive role for deterioration, all-cause mortality, and clinical instability [37]. In addition, in chronic obstructive pulmonary disease, higher serum copeptin concentrations were useful in prognosis of exacerbation and all-cause mortality [38]. Notably, the increased serum copeptin concentrations were observed to have better predictive value for short-term mortality over NT-pro-BNP in case of acute pulmonary injury, cardiopulmonary edema, and acute respiratory distress syndrome [39]. As for diagnosis in daily clinical practice, copeptin was also demonstrated to embrace an important function in diagnosis and risk stratification in case of pulmonary hypertension and pulmonary embolism patients [40].
Multiple studies found that the circulating copeptin concentrations were higher in patients with the severe coronavirus disease-19 (COVID-19) compared to those in the moderate and mild patients, at admission and during the follow up period [41]. In addition, several recent studies indicated the probability to use copeptin to distinguish between the patients with community-acquired pneumonia and COVID-19 [42]. On the other hand, in case of obstructive sleep apnea, which is considered the most common sleep disorder, copeptin demonstrated an important function as a diagnosis biomarker of obstructive sleep apnea, since hypoxemia is a strong stimulant to facilitate the secretion of AVP [43]. As for prognostic role in case of obstructive sleep apnea, it is a debatable matter as some studies revealed correlations between copeptin concentrations and severity of obstructive sleep apnea [44]. More studies also give an indication about the function of copeptin in both diagnosis and prognosis in several respiratory diseases [45, 46].
Evidence of copeptin in modulating the risk of renal diseases
As reported, the recent reports from the WHO state that the renal diseases are the top 10th leading cause of death all over the world. The mortality due to the renal diseases have risen from 813,000 deaths in 2000 to 1.3 million deaths in 2019, posing serious to the human health [47].
As mentioned in the previous studies, the main function of AVP in the body is regulation of the extracellular fluid volume through regulating renal handling of water. Where AVP performs its function by acting on renal collecting ducts through V2 receptors to increase water permeability, as the cyclic adenosine monophosphate (cAMP)-dependent mechanism, which results in decreasing urine formation [48]. In renal diseases, it was found that V2 receptors are responsible for both renal damage progression in diabetes and stimulating intracellular pathways related to polycystic renal development [49]. One of the most common renal diseases in the world is chronic kidney disease, according to several studies copeptin presented an important roles in both diagnosis and prognosis of chronic kidney disease. In a recent study, where copeptin was investigated in 149 patients with clinically stable chronic kidney disease, the increased serum copeptin concentrations were confirmed to be associated with the biopsy verified media calcification. Furthermore, after several analyses it was found that copeptin is correlated to the extent of vascular calcification with no association with age, gender, or diabetes mellitus [50]. Regarding predictive functions of copeptin in renal diseases, several studies showed that copeptin presented an important function in either predicting the development of the disease [51], or prognosis of disease severity [52]. On the other hand, it was observed that copeptin could predict the results and efficacy of Tolvaptan treatment in auto-somal dominant polycystic kidney disease, which is considered the most common hereditary kidney disease [53]. In a study took place on 100 patients of hyponatremia, which is the most common body fluid and electrolyte balance disorder [54], whereas the copeptin showed limited diagnostic ability for hyponatremia, its role in predicting the efficacy and the safety of treating hyponatremia patients with hypertonic saline [55].
Evidence of copeptin in modulating the risk of neurological diseases
According to the results of epidemiological investigation by WHO, the neurological diseases are considered as the second reason of mortality in humans. In details, the activation of hypothalamus-pituitary axis was observed in some neurological diseases, such as intracellular hemorrhage and ischemic stroke [56]. The hypothalamus-pituitary axis activation results in release of both corticotropin releasing hormone and AVP leading to stimulation of adrenocorticotropic hormone release as a response to stress. This reversely supports the idea of using AVP and copeptin as two biomarkers in neurological diseases [57]. Several studies have examined the patients with different neurological diseases, such as Traumatic brain injury, ischemic stroke, and subarachnoid hemorrhage. In a study of 36 patients with Generalized convulsive seizures, it was observed that copeptin could help diagnosing Generalized convulsive seizures, where serum copeptin concentrations were increased after most Generalized convulsive seizures. Nevertheless, the authors also suggested that copeptin specificity was needed to be tested [58].
Another study investigating serum neuro-filament light chain, prolactin, and copeptin concentrations in children with febrile seizures and epileptic seizures. Although the serum copeptin and prolactin concentrations were increased in both febrile seizures and epileptic seizures in contrast with serum neuro-filament light chain, the authors could not confirm them as prognostic biomarkers as neither of them was correlated with recurrent seizures [59]. In a group of 125 mixed trauma patients, copeptin was found as a precise prognostic and diagnostic biomarker over lactate, where it showed more accuracy in hospital admission prediction, blood transfusion, and identification of patients with injury severity score (ISS) > 15 than lactate [60]. More recently, in a meta-analysis including 17 studies and 2654 participants with Traumatic brain injury, the authors suggested that copeptin could be a useful prognostic and diagnostic biomarker for Traumatic brain injury [61]. In addition, copeptin was observed to exhibit a prognostic function in subarachnoid hemorrhage [61, 62]. In the further research, it is still necessary to conduct more studies to explore the role of copeptin in influencing the risk of neurological diseases.
Evidence of copeptin in modulating the risk of psychological disorders
Importantly, the number of deaths and diseases caused by stress is so worrying. According to the Centers for Disease Control and Prevention (CDC), in the United States more than 50% might suffer from mental illness in a certain stage in their life. Even one in five adults might be diagnosed by mental illness in a given year. In addition, the emotional stress was found to be the main factor to six death causing diseases in the United States which are: respiratory disorders, cancer, accidental injuries, cirrhosis of the liver, coronary heart disease, and suicide [63]. Psychological disorders may be due to psychological stress or psychological diseases like major depressive disorder in other words depression, anorexia nervosa, and bipolar disorder.
In the CoEXAM study, where the effect of psychological stress on levels of copeptin was investigated in 25 healthy students prior and following a written examination. The serum copeptin concentrations before the exam showed higher values than after the exam. The same results were observed for cortisol, where serum cortisol levels were elevated before the exam in comparison to their levels after the exam [64]. In another study, a group of 100 healthy women and men were subjected to the trier social stress test (TSST) as a psychological stress, a positive correlation between copeptin percent changes and salivary and serum cortisol percent change was observed in males only, however in females there was no any significant association observed. In addition, the authors suggested the ability of copeptin to act as a biomarker for both arginine vasopressin and the activation of hypothalamus-pituitary axis [57]. Results obtained from a study made to compare serum copeptin concentrations in 25 normo-hydrated stable women with anorexia nervosa and 25 age-matched women as control group were unexpected. Where there was no noticeable change in serum copeptin concentrations between the two groups, which gives indication that anti-diuretic hormone may not be critical for the pathophysiological implication of psychological stress in cases of anorexia nervosa [65]. The probability of using copeptin as a biomarker of response to anti-depressant treatment in major depressive disorder was indicated in a pilot study [66]. Where serum copeptin, adrenocorticotropic hormone, and cortisol were measured in patients with major depressive disorder prior and after hypothalamus-pituitary axis manipulation. Copeptin levels showed significant differences before and after treatment, however cortisol and adrenocorticotropic hormone did not show any significant differences before and after treatment [66]. Bipolar disorder patients were found to be characterized by lower serum copeptin concentrations when compared to those in the healthy individuals [67]. Also, it was observed that abnormal serum copeptin concentrations are associated with abnormal metabolic parameters only in bipolar disorder cases.
Evidence of copeptin in modulating the risk of metabolic diseases
Metabolic diseases occur due to metabolism interruption. In another word, the metabolic diseases are mainly characterized by abnormal glucose and lipids values. These diseases might be acquired or congenital. Under chronic psychological stress, the hypothalamus-pituitary axis is activated by release of AVP, resulting in cortisol secretion through V1a-R activation, leading to glycogeno-lysis as a result of interrupting insulin activity and glucagon stimulation. In addition, epinephrine is released as a result of V1b-R which leads to glycogeno-lysis and hyperglycemia [68]. Diabetes, with its two main types as Type 1 diabetes mellitus (T1DM) and Type 2 diabetes mellitus (T2DM), is considered to be the main common metabolic disorder. A study comparing copeptin values between prediabetes, diabetes mellitus without nephropathy, diabetic nephropathy and healthy individuals, the highest circulating copeptin concentrations were observed in the prediabetic individuals, followed by diabetic nephropathy individuals, and diabetic individuals without nephropathy, and the control individuals. In addition, participants with positive family history of diabetes mellitus showed higher serum copeptin concentrations compared to those of negative diabetes mellitus family history [69].
Evidence of copeptin in modulating the risk of hepatic diseases
Hepatic diseases account for approximately two million deaths per year globally, of which one million stands for cirrhosis complications [70]. Non-alcoholic fatty liver disease and steatohepatitis (NAFLD/NASH) is considered as the most common hepatic diseases all over the world. Barchetta et al. explored the relationship between plasma copeptin with the severity of NAFLD/NASH and found that the obese patients with NAFLD had higher copeptin levels than both obese individuals without NAFLD and non-obese subjects. In addition, serum copeptin concentrations were correlated with hepatic macro- and micro-vesicular steatosis, lobular inflammation, and significantly increased throughout degrees of NASH severity. These findings indicated that increased copeptin is independently associated with the presence and severity of NAFLD and NASH, pointing to a novel mechanism behind human fatty liver disease potentially modifiable by pharmacological treatment and lifestyle intervention [71].
Cirrhosis is mostly characterized by disturbance in body water hemostasis, which results in some complications [72]. In a cohort of 321 patients with both compensated and decompensated cirrhosis, the circulating copeptin concentrations were shown to be increased progressively with the severity of cirrhosis. Thereby, the serum copeptin concentrations were higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis. In additiona, the serum copeptin concentrations were higher in patients who developed disease complications. Regarding the mortality, copeptin showed the ability of being an independent predictive biomarker for three months mortality [73]. Those results were in accordance with another study in which 40 cirrhosis individuals, divided into 4 groups, were investigated for the serum copeptin concentrations together with control group.
The serum copeptin concentrations were also shown to be higher in patients with cirrhosis accompanied with gastrointestinal hemorrhage, hepatorenal syndrome, and hepatic failure compared to compensated cirrhosis cases, resultantly giving evidence that copeptin could be a novel marker for liver cirrhosis prognosis. Also, it was observed that there is an association between serum copeptin and cirrhosis complications, thereby copeptin could be used in disease progression [74]. In a large cohort investigation, where 779 patients were admitted for acute decompensation, among them 139 patients were diagnosed for acute-on-chronic liver failure, at admission copeptin concentrations were higher in patients with both acute-on-chronic hepatic failure and acute decompensation compared to patients with acute decompensation only. Additionally, after 28 and 90 days of follow up copeptin demonstrated higher concentrations in patients who died or transplanted in comparison with those who survived or were in no need for transplantation, revealing that copeptin could be considered as an independent predictor biomarker in cirrhosis mortality [75].
A recent study investigating serum copeptin concentrations in chronic liver disease’s complications, such as hepatic encephalopathy, portosystemic shunts, ascites, and all mortality causes, showed that copeptin concentrations were elevated in patients with hepatic cirrhosis accompanied by ascites and portosystemic shunts formation resulted from portal hypertension. In addition, patients with hepatocellular carcinoma and hepatic encephalopathy patients presented higher copeptin concentrations [75]. Also, the study results found that copeptin was strongly correlated with the parameter of hepatic function, such as albumin-bilirubin score, rather than renal function estimated glomerular filtration rate (eGFR) score, resultantly giving probability of using copeptin as a surrogate biomarker for complications of chronic liver disease’s complications. Furthermore, it was observed that copeptin exhibited an important function in predicting both short-term mortality (approximately one year), and long-term mortality (approximately 4 years) in chronic liver disease’s complications [76]. A most recent study stated that serum copeptin could predict the response of patients with hepatic cirrhosis associated with ascites to Tolvaptan treatment. The study cohort was 113 hepatic cirrhosis patients with ascites, where serum copeptin together with several treatment related factors were investigated for their ability to predict response of patients to Tolvaptan treatment. Circulating copeptin concentrations together with zinc-α2-glycoprotein, and basal blood urea nitrogen were higher in non-responders compared to responders to Tolvaptan [77].
Conclusions and future perspectives
The facts that copeptin and AVP are released in equimolar amounts, and due to the advantages of copeptin over AVP, copeptin was approved as a surrogate serum biomarker of AVP and vasopressinergic activation. Recent studies showed the role of copeptin as a prognostic and diagnostic marker for several diseases. In some cases, due to its low specificity, a combination of copeptin with another biomarker provided a more accurate diagnosis and prognosis for several diseases. Copeptin is able to diagnose a variety of diseases earlier than other biomarkers, especially in cardiovascular diseases which could help in the early management of certain diseases, accordingly decreasing death rates, and improving health care services. It is worthy to have more studies with large cohorts providing more clear information about the predictive values of copeptin in daily clinical practice.
Abbreviations
SON Supraoptic
AVP Arginine vasopressin
PVN Paraventricular
RIA Adio-immuno-assay
CK-MB Creatine kinase-MB
WHO World Health Organization
TnT Troponin T
TnC Troponin C
TnI Troponin I
STEMI ST elevation myocardial infarction
BNP Brain-type natriuretic peptide
NT-pro-BNP Precursor N-terminal brain-type natriuretic peptide
COVID-19 Coronavirus disease-19
eGFR Estimated glomerular filtration rate
Acknowledgements
Not applicable.
Author contributions
PLW, YC, and XS contributed to the study design; LLW and BW wrote the manuscript. All authors reviewed drafts and approved the final version of the manuscript.
Funding
This work was supported by grants from the National Natural Science Foundation of China (No. 81900365) and the Fujian Clinical Medical Research Center for Cardiovascular Interventional Diagnosis and Treatment (No. 2020Y2016).
Declarations
Conflict of interest
The authors have no other competing interests or conflicts of interest to declare.
Ethical approval
This article does not contain any studies with human participants performed by any of the authors.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Penglong Wu and Lilan Wang contributed equally to this work.
==== Refs
References
1. Ashwell E The endocrine system and associated disorders Br J Nurs 2022 31 6 316 320 10.12968/bjon.2022.31.6.316 35333557
2. Caglayan MO Sahin S Ustundag Z An overview of aptamer-based sensor platforms for the detection of bisphenol-A Crit Rev Anal Chem 2022 10.1080/10408347.2022.2113359 36001397
3. Christ-Crain M Vasopressin and copeptin in health and disease Rev Endocr Metab Disord 2019 20 3 283 294 10.1007/s11154-019-09509-9 31656992
4. Lukaszyk E Malyszko J Copeptin: pathophysiology and potential clinical impact Adv Med Sci 2015 60 2 335 341 10.1016/j.advms.2015.07.002 26233637
5. Abramova O Zorkina Y Ushakova V Zubkov E Morozova A Chekhonin V The role of oxytocin and vasopressin dysfunction in cognitive impairment and mental disorders Neuropeptides 2020 83 102079 10.1016/j.npep.2020.102079 32839007
6. Kothari V Cardona Z Eisenberg Y Adipsic diabetes insipidus Handb Clin Neurol 2021 181 261 273 10.1016/B978-0-12-820683-6.00019-1 34238462
7. Hagiwara D Tochiya M Azuma Y Tsumura T Hodai Y Kawaguchi Y Miyata T Kobayashi T Sugiyama M Onoue T Takagi H Ito Y Iwama S Suga H Banno R Arima H Arginine vasopressin-Venus reporter mice as a tool for studying magnocellular arginine vasopressin neurons Peptides 2021 139 170517 10.1016/j.peptides.2021.170517 33647312
8. Lagunas N Marraudino M de Amorim M Pinos H Collado P Panzica G Garcia-Segura LM Grassi D Estrogen receptor beta and G protein-coupled estrogen receptor 1 are involved in the acute estrogenic regulation of arginine-vasopressin immunoreactive levels in the supraoptic and paraventricular hypothalamic nuclei of female rats Brain Res 2019 1712 93 100 10.1016/j.brainres.2019.02.002 30731078
9. Evers KS Wellmann S Arginine vasopressin and copeptin in Perinatology Front Pediatr 2016 4 75 10.3389/fped.2016.00075 27532032
10. Fairhall KM Robinson IC The carboxy-terminal glycopeptide of the vasopressin precursor in the Guinea-pig: release studies using a specific and sensitive homologous radioimmunoassay J Neuroendocrinol 1989 1 2 95 102 10.1111/j.1365-2826.1989.tb00086.x 19210465
11. Nagy G Mulchahey JJ Smyth DG Neill JD The glycopeptide moiety of vasopressin-neurophysin precursor is neurohypophysial prolactin releasing factor Biochem Biophys Res Commun 1988 151 1 524 529 10.1016/0006-291X(88)90625-0 3126738
12. Barat C Simpson L Breslow E Properties of human vasopressin precursor constructs: inefficient monomer folding in the absence of copeptin as a potential contributor to diabetes insipidus Biochemistry 2004 43 25 8191 8203 10.1021/bi0400094 15209516
13. Roussel R Fezeu L Marre M Velho G Fumeron F Jungers P Lantieri O Balkau B Bouby N Bankir L Bichet DG Comparison between copeptin and vasopressin in a population from the community and in people with chronic kidney disease J Clin Endocrinol Metab 2014 99 12 4656 4663 10.1210/jc.2014-2295 25202818
14. Beglinger S Drewe J Christ-Crain M The circadian rhythm of Copeptin, the C-Terminal portion of Arginine Vasopressin J Biomark 2017 2017 4737082 10.1155/2017/4737082 28656120
15. Morgenthaler NG Struck J Jochberger S Dunser MW Copeptin: clinical use of a new biomarker Trends Endocrinol Metab 2008 19 2 43 49 10.1016/j.tem.2007.11.001 18291667
16. Katan M Morgenthaler N Widmer I Puder JJ Konig C Muller B Christ-Crain M Copeptin, a stable peptide derived from the vasopressin precursor, correlates with the individual stress level Neuro Endocrinol Lett 2008 29 3 341 346 18580851
17. Dobsa L Edozien KC Copeptin and its potential role in diagnosis and prognosis of various diseases Biochem Med (Zagreb) 2013 23 2 172 190 10.11613/BM.2013.021 23894863
18. Tenderenda-Banasiuk E Wasilewska A Filonowicz R Jakubowska U Waszkiewicz-Stojda M Serum copeptin levels in adolescents with primary hypertension Pediatr Nephrol 2014 29 3 423 429 10.1007/s00467-013-2683-5 24375010
19. Nattero-Chavez L Martinez-Garcia MA Fernandez-Duran E Redondo Lopez S Dorado Avendano B Escobar-Morreale HF Luque-Ramirez, Fasting serum copeptin and asymptomatic peripheral artery disease: no association in patients with type 1 diabetes mellitus Diabetes Metab 2021 47 3 101207 10.1016/j.diabet.2020.10.005 33160031
20. Kishore SP Blank E Heller DJ Patel A Peters A Price M Vidula M Fuster V Onuma O Huffman MD Vedanthan R Modernizing the World Health Organization list of essential Medicines for Preventing and Controlling Cardiovascular Diseases J Am Coll Cardiol 2018 71 5 564 574 10.1016/j.jacc.2017.11.056 29406862
21. Giannopoulos G Deftereos S Panagopoulou V Kossyvakis C Kaoukis A Bouras G Pyrgakis V Cleman MW Copeptin as a biomarker in cardiac disease Curr Top Med Chem 2013 13 2 231 240 10.2174/15680266113139990088 23470080
22. Hasic S Kiseljakovic E Jadric R Radovanovic J Winterhalter-Jadric M Cardiac troponin I: the gold standard in acute myocardial infarction diagnosis Bosn J Basic Med Sci 2003 3 3 41 44 10.17305/bjbms.2003.3527 16232149
23. Tilea I Varga A Serban RC Past, present, and future of blood biomarkers for the diagnosis of acute myocardial infarction-promises and challenges Diagnostics (Basel) 2021 11 5 881 10.3390/diagnostics11050881 34063483
24. Reichlin T Hochholzer W Stelzig C Laule K Freidank H Morgenthaler NG Bergmann A Potocki M Noveanu M Breidthardt T Christ A Boldanova T Merki R Schaub N Bingisser R Christ M Mueller C Incremental value of copeptin for rapid rule out of acute myocardial infarction J Am Coll Cardiol 2009 54 1 60 68 10.1016/j.jacc.2009.01.076 19555842
25. Mockel M Searle J Copeptin-marker of acute myocardial infarction Curr Atheroscler Rep 2014 16 7 421 10.1007/s11883-014-0421-5 24844208
26. Jeong JH Seo YH Ahn JY Kim KH Seo JY Chun KY Lim YS Park PW Performance of Copeptin for early diagnosis of acute myocardial infarction in an emergency department setting Ann Lab Med 2020 40 1 7 14 10.3343/alm.2020.40.1.7 31432633
27. Zhong Y Wang R Yan L Lin M Liu X You T Copeptin in heart failure: review and meta-analysis Clin Chim Acta 2017 475 36 43 10.1016/j.cca.2017.10.001 28982590
28. Schill F Timpka S Nilsson PM Melander O Enhorning S Copeptin as a predictive marker of incident heart failure ESC Heart Fail 2021 8 4 3180 3188 10.1002/ehf2.13439 34056865
29. Wang Q An Y Wang H Zhang N Deng S The clinical significance of changes in cTnT, CRP and NT-proBNP levels in patients with heart failure Am J Transl Res 2021 13 4 2947 2954 34017460
30. Bolignano D Cabassi A Fiaccadori E Ghigo E Pasquali R Peracino A Peri A Plebani M Santoro A Settanni F Zoccali C Copeptin (CTproAVP), a new tool for understanding the role of vasopressin in pathophysiology Clin Chem Lab Med 2014 52 10 1447 1456 10.1515/cclm-2014-0379 24940718
31. Jia J Chang GL Qin S Chen J He WY Lu K Li Y Zhang DY Comparative evaluation of copeptin and NT-proBNP in patients with severe acute decompensated heart failure, and prediction of adverse events in a 90-day follow-up period: a prospective clinical observation trial Exp Ther Med 2017 13 4 1554 1560 10.3892/etm.2017.4111 28413508
32. Ozmen C Deveci OS Tepe O Yesildas C Unal I Yildiz I Eker Akilli R Deniz A Demir M Kanadasi M Usal A Prognostic performance of copeptin among patients with acute decompensated heart failure Acta Cardiol 2021 76 8 842 851 10.1080/00015385.2020.1786624 32666903
33. Winther JA Brynildsen J Hoiseth AD Strand H Folling I Christensen G Nygard S Rosjo H Omland T Prognostic and diagnostic significance of copeptin in acute exacerbation of chronic obstructive pulmonary disease and acute heart failure: data from the ACE 2 study Respir Res 2017 18 1 184 10.1186/s12931-017-0665-z 29100503
34. Yoshikawa Y Shiomi H Kuwahara K Sowa N Yaku H Yamashita Y Tazaki J Imai M Kato T Saito N Shizuta S Ono K Kimura T Utility of copeptin for predicting long-term clinical outcomes in patients with heart failure J Cardiol 2019 73 5 379 385 10.1016/j.jjcc.2018.11.008 30591319
35. Cote P Hartvigsen J Axen I Leboeuf-Yde C Corso M Shearer H Wong J Marchand AA Cassidy JD French S Kawchuk GN Mior S Poulsen E Srbely J Ammendolia C Blanchette MA Busse JW Bussieres A Cancelliere C Christensen HW De Carvalho D De Luca K Du Rose A Eklund A Engel R Goncalves G Hebert J Hincapie CA Hondras M Kimpton A Lauridsen HH Innes S Meyer AL Newell D O’Neill S Page I Passmore S Perle SM Quon J Rezai M Stupar M Swain M Vitiello A Weber K Young KJ Yu H The global summit on the efficacy and effectiveness of spinal manipulative therapy for the prevention and treatment of non-musculoskeletal disorders: a systematic review of the literature Chiropr Man Therap 2021 29 1 1 23
36. Szczepanska-Sadowska E Zera T Sosnowski P Cudnoch-Jedrzejewska A Puszko A Misicka A Vasopressin and related peptides; potential value in diagnosis, prognosis and treatment of Clinical Disorders Curr Drug Metab 2017 18 4 306 345 10.2174/1389200218666170119145900 28117000
37. Kolditz M Halank M Schulte-Hubbert B Bergmann S Albrecht S Hoffken G Copeptin predicts clinical deterioration and persistent instability in community-acquired pneumonia Respir Med 2012 106 9 1320 1328 10.1016/j.rmed.2012.06.008 22732597
38. Zhao YF Jiang YP Zhou LF Wu XL The value of assessment tests in patients with acute exacerbation of chronic obstructive pulmonary disease Am J Med Sci 2014 347 5 393 399 10.1097/MAJ.0b013e31829a63b1 24270077
39. Lin Q Fu F Chen H Zhu B Copeptin in the assessment of acute lung injury and cardiogenic pulmonary edema Respir Med 2012 106 9 1268 1277 10.1016/j.rmed.2012.05.010 22728017
40. Hellenkamp K Pruszczyk P Jimenez D Wyzgal A Barrios D Ciurzynski M Morillo R Hobohm L Keller K Kurnicka K Kostrubiec M Wachter R Hasenfuss G Konstantinides S Lankeit M Prognostic impact of copeptin in pulmonary embolism: a multicentre validation study Eur Respir J 2018 10.1183/13993003.02037-2017 29599188
41. Hammad R Elshafei A Khidr EG El-Husseiny AA Gomaa MH Kotb HG Eltrawy HH Farhoud H Copeptin: a neuroendocrine biomarker of COVID-19 severity Biomark Med 2022 16 8 589 597 10.2217/bmm-2021-1100 35350852
42. Kuluozturk M In E Telo S Karabulut E Geckil AA Efficacy of copeptin in distinguishing COVID-19 pneumonia from community-acquired pneumonia J Med Virol 2021 93 5 3113 3121 10.1002/jmv.26870 33570194
43. Ozben S Guvenc TS Huseyinoglu N Sanivar HS Hanikoglu F Cort A Ozben T Low serum copeptin levels in patients with obstructive sleep apnea Sleep Breath 2013 17 4 1187 1192 10.1007/s11325-013-0822-7 23407918
44. Cinarka H Kayhan S Karatas M Yavuz A Gumus A Ozyurt S Cure MC Sahin U Copeptin: a new predictor for severe obstructive sleep apnea Ther Clin Risk Manag 2015 11 589 594 25914540
45. Gregoriano C Molitor A Haag E Kutz A Koch D Haubitz S Conen A Bernasconi L Hammerer-Lercher A Fux CA Mueller B Schuetz P Activation of Vasopressin System during COVID-19 is Associated with adverse clinical outcomes: an observational study J Endocr Soc 2021 5 6 bvab045 10.1210/jendso/bvab045 34056499
46. Indirli R Bandera A Valenti L Ceriotti F Di Modugno A Tettamanti M Gualtierotti R Peyvandi F Montano N Blasi F Costantino G Resi V Orsi E Arosio M Mantovani G Ferrante E C.-N.W. Group, Prognostic value of copeptin and mid-regional proadrenomedullin in COVID-19-hospitalized patients Eur J Clin Invest 2022 52 5 e13753 10.1111/eci.13753 35128648
47. Sazgar M Kidney Disease and Epilepsy J Stroke Cerebrovasc Dis 2021 30 9 105651 10.1016/j.jstrokecerebrovasdis.2021.105651 33581988
48. Boone M Deen PM Physiology and pathophysiology of the vasopressin-regulated renal water reabsorption Pflugers Arch 2008 456 6 1005 1024 10.1007/s00424-008-0498-1 18431594
49. Gonzalez AA Salinas-Parra N Cifuentes-Araneda F Reyes-Martinez C Vasopressin actions in the kidney renin angiotensin system and its role in hypertension and renal disease Vitam Horm 2020 113 217 238 10.1016/bs.vh.2019.09.003 32138949
50. Golembiewska E Qureshi AR Dai L Lindholm B Heimburger O Soderberg M Brismar TB Ripsweden J Barany P Johnson RJ Stenvinkel P Copeptin is independently associated with vascular calcification in chronic kidney disease stage 5 BMC Nephrol 2020 21 1 43 10.1186/s12882-020-1710-6 32033584
51. Tasevska I Enhorning S Christensson A Persson M Nilsson PM Melander O Increased levels of Copeptin, a surrogate marker of Arginine Vasopressin, are Associated with an increased risk of chronic kidney disease in a General Population Am J Nephrol 2016 44 1 22 28 10.1159/000447522 27347674
52. Enhorning S Christensson A Melander O Plasma copeptin as a predictor of kidney disease Nephrol Dial Transplant 2019 34 1 74 82 10.1093/ndt/gfy017 29471407
53. Gansevoort RT van Gastel MDA Chapman AB Blais JD Czerwiec FS Higashihara E Lee J Ouyang J Perrone RD Stade K Torres VE Devuyst O Investigators T Plasma copeptin levels predict disease progression and tolvaptan efficacy in autosomal dominant polycystic kidney disease Kidney Int 2019 96 1 159 169 10.1016/j.kint.2018.11.044 30898339
54. Spasovski G Vanholder R Allolio B Annane D Ball S Bichet D Decaux G Fenske W Hoorn EJ Ichai C Joannidis M Soupart A Zietse R Haller M van der Veer S Van Biesen W Nagler E Clinical practice guideline on diagnosis and treatment of hyponatraemia Intensive Care Med 2014 40 3 320 31 10.1007/s00134-014-3210-2 24562549
55. Go S Kim S Son HE Ryu JY Yang H Choi SR Seo JW Jo YH Koo JR Baek SH Association between copeptin levels and treatment responses to hypertonic saline infusion in patients with symptomatic hyponatremia: a prospective cohort study Kidney Res Clin Pract 2021 40 3 371 382 10.23876/j.krcp.20.233 34233437
56. Baranowska B Kochanowski J Copeptin - a new diagnostic and prognostic biomarker in neurological and cardiovascular diseases Neuro Endocrinol Lett 2019 40 5 207 214 32112544
57. Spanakis EK Wand GS Ji N Golden SH Association of HPA axis hormones with copeptin after psychological stress differs by sex Psychoneuroendocrinology 2016 63 254 261 10.1016/j.psyneuen.2015.10.009 26520685
58. Nass RD Motloch LJ Paar V Lichtenauer M Baumann J Zur B Hoppe UC Holdenrieder S Elger CE Surges R Blood markers of cardiac stress after generalized convulsive seizures Epilepsia 2019 60 2 201 210 10.1111/epi.14637 30645779
59. Evers KS Hugli M Fouzas S Kasser S Pohl C Stoecklin B Bernasconi L Kuhle J Wellmann S Serum neurofilament levels in children with febrile seizures and in Controls Front Neurosci 2020 14 579958 10.3389/fnins.2020.579958 33132834
60. Salvo F Luppi F Lucchesi DM Canovi S Franchini S Polese A Santi F Trabucco L Fasano T Ferrari AM Serum copeptin levels in the emergency department predict major clinical outcomes in adult trauma patients BMC Emerg Med 2020 20 1 14 10.1186/s12873-020-00310-5 32093639
61. Zhang J Wang H Li Y Zhang H Liu X Zhu L Dong L The diagnosis and prognostic value of plasma copeptin in traumatic brain injury: a systematic review and meta-analysis Neurol Sci 2021 42 2 539 551 10.1007/s10072-020-05019-8 33389249
62. Zuo Z Ji X Prognostic value of copeptin in patients with aneurysmal subarachnoid hemorrhage J Neuroimmunol 2019 330 116 122 10.1016/j.jneuroim.2019.03.007 30875611
63. Goldberg N Rodriguez-Prado Y Tillery R Chua C Sudd Infant Death Syndrome: Rev Pediatr Ann 2018 47 3 e118 e123
64. Urwyler SA Schuetz P Sailer C Christ-Crain M Copeptin as a stress marker prior and after a written examination–the CoEXAM study Stress 2015 18 1 134 137 10.3109/10253890.2014.993966 25472823
65. Goetze JP Stoving RK Copeptin in anorexia nervosa Brain Behav 2020 10 4 e01551 10.1002/brb3.1551 32073757
66. Agorastos A Sommer A Heinig A Wiedemann K Demiralay C Vasopressin surrogate marker copeptin as a potential novel endocrine biomarker for antidepressant treatment response in Major Depression: a pilot study Front Psychiatry 2020 11 453 10.3389/fpsyt.2020.00453 32508691
67. Mansur RB Rizzo LB Santos CM Asevedo E Cunha GR Noto MN Pedrini M Zeni-Graiff M Cordeiro Q McIntyre RS Brietzke E Plasma copeptin and metabolic dysfunction in individuals with bipolar disorder Psychiatry Clin Neurosci 2017 71 9 624 636 10.1111/pcn.12535 28457001
68. Saleem U Khaleghi M Morgenthaler NG Bergmann A Struck J Mosley TH Jr Kullo IJ Plasma carboxy-terminal provasopressin (copeptin): a novel marker of insulin resistance and metabolic syndrome J Clin Endocrinol Metab 2009 94 7 2558 2564 10.1210/jc.2008-2278 19366852
69. Noor T Hanif F Kiran Z Rehman R Khan MT Haque Z Nankani K Relation of Copeptin with Diabetic and renal function markers among patients with diabetes Mellitus Progressing towards Diabetic Nephropathy Arch Med Res 2020 51 6 548 555 10.1016/j.arcmed.2020.05.018 32505416
70. Asrani SK Devarbhavi H Eaton J Kamath PS Burden of liver diseases in the world J Hepatol 2019 70 1 151 171 10.1016/j.jhep.2018.09.014 30266282
71. Barchetta I Enhorning S Cimini FA Capoccia D Chiappetta C Di Cristofano C Silecchia G Leonetti F Melander O Cavallo MG Elevated plasma copeptin levels identify the presence and severity of non-alcoholic fatty liver disease in obesity BMC Med 2019 17 1 85 10.1186/s12916-019-1319-4 31035998
72. Gaglio P Marfo K Chiodo J Hyponatremia in cirrhosis and end-stage liver disease: treatment with the vasopressin V(2)-receptor antagonist tolvaptan Dig Dis Sci 2012 57 11 2774 2785 10.1007/s10620-012-2276-3 22732834
73. Sola E Kerbert AJ Verspaget HW Moreira R Pose E Ruiz P Cela R Morales-Ruiz M Lopez E Graupera I Sole C Huelin P Navarro AA Ariza X Jalan R Fabrellas N Benten D de Prada G Durand F Jimenez W van der Reijden JJ Fernandez J van Hoek B Coenraad MJ Gines P Plasma copeptin as biomarker of disease progression and prognosis in cirrhosis J Hepatol 2016 65 5 914 920 10.1016/j.jhep.2016.07.003 27422752
74. Tawfik AK Helmy A Yousef M Abou-Saif S Kobtan A Asaad E Abd-Elsalam, Copeptin as a novel marker predicting prognosis of liver cirrhosis and its major complications Hepat Med 2018 10 87 93 10.2147/HMER.S174267 30214326
75. Kerbert AJC Verspaget HW Navarro AA Jalan R Sola E Benten D Durand F Gines P van der Reijden JJ van Hoek B Coenraad MJ C S I o t E C Consortium, Copeptin in acute decompensation of liver cirrhosis: relationship with acute-on-chronic liver failure and short-term survival Crit Care 2017 21 1 321 10.1186/s13054-017-1894-8 29268760
76. Shigefuku R Iwasa M Eguchi A Tamai Y Yoshikawa K Sugimoto R Takei Y Serum copeptin level is a biomarker associated with ascites retention and the formation of a portosystemic shunt in chronic liver disease J Gastroenterol Hepatol 2021 36 4 1006 1014 10.1111/jgh.15215 32790956
77. Shigefuku R Iwasa M Eguchi A Tempaku M Tamai Y Suzuki T Takei Y Serum copeptin and Zinc-alpha2-glycoprotein levels are novel biomarkers of Tolvaptan Treatment in Decompensated Cirrhotic Patients with Ascites Intern Med 2021 60 21 3359 3368 10.2169/internalmedicine.7291-21 34719623 | 36662451 | PMC9853489 | NO-CC CODE | 2023-04-04 23:15:12 | yes | Mol Biol Rep. 2023 Jan 20; 50(4):3755-3765 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
36757594
25407
10.1007/s11356-023-25407-9
Research Article
Impact of government governance and environmental taxes on sustainable energy transition in China: fresh evidence using a novel QARDL approach
Chien FengSheng [email protected]
12
Zhang YunQian [email protected]
13
Li Li [email protected]
13
Huang Xiang-Chu [email protected]
4
1 grid.411604.6 0000 0001 0130 6528 School of Finance and Accounting, Fuzhou University of International Studies and Trade, Fuzhou, China
2 grid.445020.7 0000 0004 0385 9160 Faculty of Business, City University of Macau, Macau, China
3 grid.445020.7 0000 0004 0385 9160 Faculty of International Tourism and Management, City University of Macau, Macau, China
4 grid.440686.8 0000 0001 0543 8253 School of Maritime Economics and Management, Dalian Maritime University, Dalian, China
Responsible Editor: Arshian Sharif
9 2 2023
113
18 11 2022
15 1 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Although economies have experienced immense growth in recent times, however, it also comes with environmental and social consequences which question the current practices and threaten the well-being of current as well as the future generation. This realization, thus, pushes institutions to bring change in existing energy-related policies in order to incorporate social and environmental concerns. Clean energy transition, in this regard, is gaining attraction all over the world as it shifts away economies from non-renewable resources. The study, thereby, intends to explore the role of governance and environmental taxes in the energy transition in China economy over the period 1999–2019. The roles of industrialization and economic growth in the transition of energy are taken into consideration. The recently introduced legit quantile autoregressive distributed lag (QARDL) model and Granger causality in quantiles are applied to quarterly data spanning 1999Q1 to 2019Q4 for empirical quantile analysis. Results echoed that governance has a positive impact and environmental resources have a negative impact on energy transition across all quantiles. However, economic growth influences clean energy transition only at extremely higher quantiles (0.60–0.95), and industrialization does not have any effect on energy transition over the entire quantile range. The findings of the Granger causality analysis reveal the presence of a bidirectional causal association between clean energy transition and all the variables. Worthy policies are recommended on the basis of the findings.
Keywords
Government governance
Environmental taxes; Energy transition
QARDL
industrialization
Economic growth
==== Body
pmcIntroduction
Energy, being a crucial element of the present economy and human existence, directly affects all human activity and is fundamental to socioeconomic progress (Sadiq et al. 2022a; Halder et al. 2012; Hosseini et al. 2013; Nakata et al. 2011; Tzanakis et al. 2012). Likewise, energy is profoundly rooted in every aspect of social, environmental, and economic progress. The increasing worldwide requirement for energy raises concerns regarding energy security, fossil fuel source availability and dependence, anthropogenic greenhouse gas emissions, and ecological pollution, all of which spark arguments about the future usefulness of fossil fuels (Abdul Hamid et al. 2020; Yazdanpanah et al. 2015). Thus, given the unstable nature of contemporary energy supply systems and consumption, shifting from fossil fuels towards clean resources while reducing demand for energy is a significant worldwide topic which has received a great deal of legislative and academic attention (Sadiq et al. 2022b; Ainou et al. 2022; Dowling et al. 2018). The rapid transition to zero or low-carbon clean energy is critical for the management of climate change. Transitioning to alternative energy sources is critical for solving a variety of environmental, conservation, justice, health, and distributive problems arising due to the extraction and use of conventional energy. The severe and unevenly distributed health and environmental repercussions of fossil fuel-based extraction, transportation, and energy generation lead to land deterioration, ecological disruption, and community relocation (Sadiq et al. 2022c; Ali et al. 2022; Tzankova, 2020). Increased diversification of renewable resources could result in averting climate change, lowering energy inadequacy, expanding energy supply, and decreasing reliance on fossil fuel industries (Bai et al. 2022; Belaïd & Zrelli, 2019). Clean or renewable energies reached their maximum generation capacity in 2019, exceeding 200 gigawatts (REN21, 2020), while fossil fuels remain dominant in the global energy mix, providing 81% of the world’s primary energy (Chien, 2022a; Saadaoui, 2021; Sadiq et al. 2022d).
This energy transition phase is mostly within the control of official social units, such as institutions, policymakers, and energy providers, with concentrated influence over energy choice preferences. Governments, public authorities, and regional and city regulatory authorities are examples of formal social entities (Chien et al. 2022b; Tiberio et al. 2020). Discussions about energy transition increasingly focus on governance issues (Bridge et al. 2013; Chien et al. 2022c Haarstad, 2016; Kuzemko et al. 2016). Over the last two decades, programs aimed at easing such transitions at various scales have proliferated, focusing on renewable energy, energy storage, and management methods of challenging current energy policies, business models, and market models (Chien, 2022d; Dowling et al. 2018).
China recognizes the significance of the shift to clean energy for environmental and economic sustainability and is therefore stepping up its efforts in the energy industry to expedite what appears to be an urgent change. China is among the leading emitters of carbon dioxide from energy, responsible for 28% of worldwide emissions in 2018 (Dale, 2019; Liu et al. 2022a). China is hailed as being among the most significant examples of the renewable energy transition, owing to the country’s strong government and what is often thought to be good top-down policy implementation (Cai & Aoyama, 2018; Haroon et al. 2021). The government has made rigorous attempts to limit the increase of pollutants from the early 2000s. A persistent decline in energy intensity in the country, a substantial increase in the implementation of solar, nuclear, and wind power facilities, and a major reduction in the proportion of fossil fuel in the total energy mix have all been achieved as a result of these efforts. Between 2013 and 2017, annual emissions of CO2 from the energy sector saw a temporary peak of over 9200 million tons, an increase from 3300 million tons in 1999 (Dale, 2019; Khattak et al. 2021). The government’s careful application of executive policy instruments, backed by considerable public investment, has resulted in this success (Chen, 2015; Kamarudin et al. 2021; Toke, 2017). Despite the fact that emissions began to climb again in 2018, the leadership of the country remains committed to passing the peak of greenhouse gas emissions by 2030 (Lan et al. 2022).
As discussed, the transition of energy primarily happens in order to reduce the environmental harm which is caused by the excessive use of fossil fuels. Resultantly, efforts are being made to limit harmful emissions. Scholars argue that improvising energy efficiency and maximizing the share of renewable energy in the energy mix can help economies in carbon reduction in considerable ways. Scholars also argue that technological progress also restricts carbon emissions, hence, defined governance rules that go beyond legislations. They also introduced new lifestyles that are not much energy-intensive and ensure successful energy transition (Edomah, 2021; Zhao et al. 2021).
It has become a necessity to make transitions away from fossil fuel energy systems. The current practices of energy resources are simply unsustainable. Moreover, the current techno-institutional policies are complex and favor fossil fuels. Because of this, the future of energy has now become a great concern for economies (Chien et al. 2021a; Dung et al. 2022; Sriyakul et al. 2022). Thereby, it is crucial to confront government and industry players in order to address the challenges that are associated with energy transition. In this lieu, the study is novel in five prominent ways: firstly, unlike previous studies that make qualitative assessments or evaluations of the role of governance in energy transition, the present study estimates this relationship through quantitative analysis. Secondly, in previous studies, a number of social, economic, and environmental factors are linked to the energy transition; however, to the best of our knowledge, no studies attempt to estimate the role of environmental policies on energy transition. Therefore, this study serves as the first investigation of the effect of environmental taxes on energy transition, particularly in the context of China. Thirdly, we take the QARDL approach, established by Cho et al. (2015) and Jermsittiparsert (2021), and show quantile asymmetries in the long- and short-run adjustments between dependent and independent variables. As far as we know, this strategy is a novel addition to the literature on governance and energy transition and effectively addresses asymmetry problems. Fourthly, we examine the consistency of long-run associations in quantiles, proposing a versatile econometric approach to observing the relationships in question. The QARDL model outperforms the linear ARDL technique by allowing for possible asymmetries in the response of energy transition to changes in governance and other factors across a wide quantile range. Fifthly, the present study investigates Granger causality in quantile ranges Troster (2018) and Wirsbinna and Grega (2021), using a causality in quantile method to demonstrate the causal association in every conditional quantile. This approach is consistent across quantiles and stresses the nonlinearity criteria within each quantile. Furthermore, it is possible to differentiate between causality impacting the middle and causality affecting the distribution tails. If all quantiles are centered, it provides proper conditions for Granger causality.
Energy situation in China
Despite its rich energy resources, China has low energy per capita. The energy resources are characterized by large coal deposits and limited natural gas and oil reserves. In 2018, China’s energy generation came from 2.6 billion tons of oil (Chien et al. 2021b; Caineng et al. 2020; Liu et al. 2022b), contributing 7.2%, while coal accounted for 68.5% of energy production, gas contributed 5.5%, and clean energy contributed 18.8%. China was dependent on foreign energy for about 21%, with gas and oil dependency of 43% and 71%, respectively. The primary energy consumption of China climbed by 4.3% in 2018.
Energy consumption in China is still rising as the country’s urbanization and industrialization processes continue. China’s primary energy use is dominated by coal (see Fig. 1). Over the period 2014 to 2018, the proportion of fossil-based energy consumption gradually decreased (see Fig. 2). Clean energy, as a percentage of total energy consumption, has consistently climbed because of the speeding up of the energy transition and ongoing reform of the power sector. Nevertheless, fossil fuel continues to dominate China’s energy use (Kurniawan et al. 2022; Quynh et al. 2022).Fig. 1 Energy Consumption in China (2018). Source: BP, Statistical Review (2018)
Fig. 2 Fossil fuel energy consumption (%) in China. Source: BP Statistical Review (2018)
We organize the study as follows. A brief review of earlier studies is provided in the “Literature review” section. The “Data and methodology” section presents the data and methodology. The results and discussion are given in the “Discussion” section. The “Conclusion” section provides the conclusion of the study along with its policy implications.
Literature review
The recent literature on economic energy concentrates on the factors that influence renewable energy use and generation. There is, however, no agreement on which elements help or impede the move to renewable energy. As part of the energy revolution, the shift to the renewable energy sector is collaborative, long term, and complex, involving various players and including broad sociological, technological, administrative, economic, political, and socio-cultural variations. Therefore, a number of past studies make qualitative assessments of the role of governance in the energy transition in various countries or areas and make varied conclusions (Hartani et al. 2021; Shibli et al. 2021). For instance, Laes et al. (2014) review and analyze the difficulties of energy transition management towards a low-carbon future as a political achievement in Germany, Netherlands and the UK. The authors recommend that governance practices require innovation, long-term vision, short- and mid-term action, societal engagement, and reflexivity of learning. Sung et al. (2018) analyze the actors that influence the transition to clean energy in OECD economies using a vector autoregressive model and bias-corrected least squares dummy variable. The authors test complex dynamic associations between the public, governments, traditional energy sector and market, and the proportion of renewables to total energy supply. According to the findings, markets and governments directly promote renewable energy transition, while the traditional energy sector has a direct negative impact on transition. Kotzebue and Weissenbacher (2020) examine spatial governance in the energy transition of the island of Malta, where the hierarchical and bureaucratic spatial structure generates an environment in which to argue for decentralized generation and oppose renewable technologies. Lazaro et al. (2022) study the role of governance and policies in the energy transition in Sao Paulo, Brazil. The institutional systems that facilitate energy transition are investigated. Despite growing renewable energy production (especially ethanol), the authors show that fossil fuel use increases over the study period, indicating a trend of addition instead of a complete energy transformation. Energy governance in Brazil is found to be still largely based on a centralized system. Wagemans et al. (2019) analyze the governance role of local renewable energy cooperatives in the energy transition in the Netherlands. Cooperatives promote energy transition through five governance roles: public mobilization, brokering between citizens and governments, provision of specific expertise and knowledge, accepted change initialization, and proffering sustainability integration (Chien et al. 2021b; Godil et al. 2021a; Tan et al. 2021).
Nochta and Skelcher (2020) analyze the limitations and opportunities of network governance in three European cities, Frankfurt, Birmingham, and Budapest, which provide support for the energy transition. The authors employ network structure statistical measures and network visualization combined with qualitative case study data for a comparative investigation of the energy transition. They conclude that present networks differ in integration, authority distribution, and the extent to which they necessitate the significant consideration for transition management, aiming at stable transition through governance. Dowling et al. (2018) analyze emerging energy transitions in Australia to understand the dynamics of energy demand and energy infrastructure. The authors find that the strategic advancement of urban political and economic interests, in collaboration with non-state and state factors, opens up prospects for energy transitions which were previously hampered by material and institutional obduracy. Baye et al. (2021) explore the important factors that shift energy consumption towards renewable energy sources, including governance, technological advancement, economic progress, and biomass energy consumption in sub-Saharan African countries. The authors conclude that governance makes a positive contribution to renewable energy consumption.
Many studies making quantitative assessments of the role of governance in energy transition merely focus on institutional quality and ignore other components of good governance. In the MENA region, Bellakhal et al. (2019) and Lin et al. (2022) examine the association between trade liberalization, governance, and investment in renewable energy. Their findings suggest that renewable energy investment is linked to strong institutional quality. Furthermore, trade governance appears to be a factor in this association. In highly open economies, weak governance is less harmful to investment in renewable energy. Likewise, trade between nations with bad institutions has a greater positive impact on investment in renewable energy than trade between nations with good institutions. Similarly, Belaïd et al. (2021) study the renewable energy production factors of MENA economies using a panel quantile regression model. The findings suggest that the impact of political stability varies and that it promotes renewable energy investment. Saadaoui (2022) examine the effect of institutional quality and political factors on the clean energy transition in MENA economies. According to their AMG and ARDL results, institutional quality affects energy transition positively. Saadaoui and Chtourou (2022) analyze the association between institutional quality, economic growth, financial development, and renewable energy consumption by applying an ARDL approach. The authors conclude that institutional quality is a leading factor that enhances renewable energy consumption, whereas financial development reduces it. Gailing and Moss (2016) and Moslehpour et al. (2022c) also find that institutions play a major role in the energy transition. Wu and Broadstock (2015) scrutinize dynamic panel data for 22 developing economies and conclude that institutional quality affects clean energy consumption positively. The authors stress the relevance of institutional quality in promoting clean energy use. Similarly, the need to develop an institutional structure for the expansion of marine clean energy is emphasized by Chang and Wang (2017). The Chinese government, according to the authors, should reform its administrative framework to support marine energy development. According to Moslehpour et al. (2022b) and Uzar (2020), institutional quality has a beneficial effect on renewable energy use, while economic expansion has a negative and significant effect on the spread of clean energy. Akintande et al. (2020) and Moslehpour et al. (2022a) study the effect of institutional variables on clean energy growth and conclude that political stability, government effectiveness, rule of law, and corruption control are the main drivers of the energy transition.
Summing up, the literature to date tries to evaluate and assess governance and its role in sustainable energy transition qualitatively in various cities and countries and comes to various conclusions about the importance of government governance in the energy transition. However, to the best of our knowledge, only a few studies make quantitative assessments of the institutional quality (i.e., a single component of governance) and energy transition nexus, and China particularly remains understudied in this context. Moreover, the important role of environmental policies and economic factors remains somewhat neglected in earlier studies. Identifying these research gaps, our study contributes to the existing literature by making a quantitative assessment of the role of governance in the energy transition in China by applying a QARDL approach. In contrast to previous research, our study estimates the role of environmental taxes in helping energy transition in China. The findings of the analysis have various policy implications in the country concerned.
Data and Methodology
To study the role of governance, environmental taxes, and economic growth in energy transition modelling, we specify the regression model as:1 ETt=α0+β1GOVt+β2ERTt+β3INDt+β4GDPt+μt
where t denotes the time period; βs are variable coefficients; μ represents the error term; energy transition is represented by ET; and GOV, ERT, IND, and GDP represent governance, environmental taxes, industrialization, and economic growth, respectively. Following Edomah (2021), Nochta and Skelcher (2020), Lazaro et al. (2022), and Sung and Park (2018), governance is taken as the main determinant of energy transition and we expect a positive effect of improved governance on energy transition, i.e., β1 > 0. Governance is measured by generating a composite index of governance comprising all six components of governance, corruption control, government effectiveness, rule of law, political stability, regulatory quality, and voice and accountability, using principal component analysis. Environmental taxes help a country transition to renewable energy resources (Bashir et al. 2022; Ojogiwa, 2021). As a result, environmental taxes are expected to have a positive effect on the transition towards clean energy in China, i.e., β2>0. Furthermore, industrialization is a determinant of the energy transition as greater industrialization makes it easier to adopt new technologies that aid in the transition to clean energy (Hussain et al. 2021; Zhao et al. 2022). Hence, a positive sign of industrialization in energy transition is expected in the analysis, i.e., β3>0. Following Li et al. (2020), Lin and Omoju (2017), and Yao et al. (2019), GDP is taken to measure income. A positive influence of GDP on ET is expected, i.e., β4>0. The time period of the study spans 1999 to 2019 on the basis of the data availability. The detailed measurement and the data sources of all of the study variables are given in Table 1.Table 1 Variable measurement and data sources
Variable Acronym Measurement Data source
Energy transition ET Contribution of renewables to total power generation (%) IEA
Governance GOV Governance Index comprising corruption control, government effectiveness, rule of law, political stability, regulatory quality and voice and accountability WGI
Environmental related taxes ERT Environmental tax such as on emissions to energy sources, air, water, autos, garbage, and so on OECD
Economic growth GDP Gross Domestic Product (constant $=2015) WDI
Industrialization IND Industry value added (% of GDP) WDI
IEA stands for International Energy Agency, WGI for World Governance Indicators, OECD for Organization for Economic Cooperation and Development, and WDI for World Development Indicators
QARDL methodology
The current study applies the Cho et al. (2015) QARDL approach to investigate the cointegration relationship between governance, environmental taxation, industrialization, economic growth, and energy transition in China over various quantiles. The QARDL model permits the long-term quantile impact of governance, environmental taxes, industrialization, economic growth, and energy transition to be tested. The Wald test is applied to examine the consistency of integrated parameters around the matrix of quantiles, as well as the time-varying integration associations. On at least three grounds, the QARDL technique outperforms linear methods from a methodological standpoint. Firstly, since the parameters might be dependent on the explained variable location inside the distribution, this method allows for location-based asymmetry. Secondly, the QARDL technique considers both the long-run association between governance, environmental taxes, economic growth, industrialization, and energy transition as well as the short-run dynamics of these relationships across a set of quantiles from the conditional energy transition distribution. Thirdly, in contrast to the current study, many investigations using classic linear econometric approaches, such as the ARDL model and Johansen causality analysis, find no cointegration between particular time series. These negative findings can be explained by the existence of varying coefficients across quantiles in the short run. Because of shocks, the cointegrating coefficient can differ between quantiles using the QARDL approach. Furthermore, the QARDL technique outperforms several nonlinear techniques, such as the nonlinear ARDL technique, that exogenously defines non linearity, because the threshold cannot be determined by a data-driven approach, setting it to zero instead. The QARDL strategy, which integrates both nonlinear and asymmetric links, is thought to be the most suited technique based on these considerations. The ARDL model’s derivation and extension are given below:1 ETt=α+∑ipβ1ETt-i+∑iqβ2GOVt-i+∑irβ3ERTt-i+∑isβ4INDt-i+∑iuβ5GDPt-i+ϵt
where εt is an error term; p, q, r, s, and u represent the Schwarz information lag order criterion (SIC); ET, GOV, ERT, IND, and GDP are energy transition, governance, environmental taxes, industrialization, and economic growth, respectively. According to Cho et al. (2015), the extension of Eq. (1) to produce quantile estimations pertains to the QARDL model:2 QETt=ατ+∑ipβ1τETt-i+∑iqβ2τGOVt-i+∑irβ3τERTt-i+∑isβ4τINDt-i+∑iuβ5τGDPt-i+εtτ
where ε(τ) = ETt – QETt(τεt-1) and 0 < τ < 1 shows quantile (Kim & White, 2003). Equation 2 is rewritten as follows, because of the expected frequency of serial correlation:3 QΔETt=ατ+ρETt-i+φ1GOVt-i+φ2ERTt-i+φ3INDt-i+φ4GDPt-i+∑ipβ1τETt-i+∑iqβ2τGOVt-i+∑irβ3τERTt-i+∑isβ4τINDt-i+∑iuβ5τGDPt-i+εtτ
The following is the dynamic quantile ECM of QARDL:4 QΔETt=ατ+ρτETt-i-ω1τGOVt-i-ω2τERTt-i-ω3τINDt-i-ω4τGDPt-i-+∑i=1p-1β1τΔETt-i+∑i=1q-1β2τΔGOVt-i+∑i=1r-1β3τΔERTt-i∑i=1s-1β4τΔINDt-i+∑i=1u-1β5τΔGDPt-i+εtτ
We use the delta method to compute the short-run impact of prior ET on current ET through ∑i=1p-1β1 whereas the collective short-run impact of current and prior levels of GOV, ERT, IND, and GDP are estimated by ∑i=1q-1β2,, ∑i=1r-1β3,∑i=1s-1β4, and ∑i=1u-1β5respectively. In addition, β, representing the long-run integrating coefficients of all series, is estimated as follows:
βET* = -βETρ,βGOV*= -βGOVρ,βERT* = -βERTρ,βIND* = βINDρ,βGDP* -βGDPρ
It is necessary that ECM should be significant, negative, and less than one.
Wald test
We apply the Wald test to find asymmetric short-run and long-run impacts of GOV, ERT, IND, and GDP on ET. For ρ (speed of adjustment parameters), H0 states that ρ*(0.05) = ρ*(0.10) ρ*(0.95). The same hypothesis is tested on the long-run parameters and the short-run coefficient (Godil et al. 2021a).
Quantile causality
Granger causality analysis is used to determine whether a variable is a precursor to another variable. In general, the Granger causality test assumes that the explained variable’s current value is influenced by its own previous value and lagged values of explanatory variables (Granger, 1969). A slew of new causality tests has been developed, utilizing a variety of approaches. Quantile Granger causality estimation, introduced by Troster (2018), is used to analyze the quantile causality of the energy transition with governance, economic growth, environmental taxes, and industrialization in this study. Any variable (Xi) does not contribute to another variable (Yi) if the previous Xi does not assist in the assessment of Yi, which results in the earlier Yi. We suppose a vector (Ni= Niy, Nix)’ ∈ Re, P= s+r, and Nix represents earlier indicator group Xi Nix := (Xi-1, ….., Xi-q)’ ∈ Rq.. In addition, H0 of granger causality from one variable to another variable is described as:5 H0X-YFyyNiY,Nix=FyyNiY,fory∈R
where the purpose of conditional distribution Yi is Fy (.| NiY, NiX) provided that (NiY, NiX ). The QAR method of classification employs the DT test. m (∙) for π ∈ ⊂ Γ [0,1], based on the null hypotheses of no Granger causal connection, shown as:6 QAR(1):m1NiY,∂π=λ1π+λ2πXi-1+μ2ΩY-1π
The coefficients, e.g., [∂(π )= (λ1(π) λ2 (π)] and μt are calculated using maximum likelihood in quantile points of identical size, and − Ω-1Y (.) denotes the inverse of a usual primary probability function. The QAR technique in Eq. (6) is evaluated to confirm the presence of causality between both the components with a lagged to alternate factor. Finally, using Eq. (6), the QAR (1) equation is:7 QπYYiNiY,NiX=λ1π+λ2πYi-1+nπXi-1+μ2ΩY-1π
Discussion
The primary goal of the study is to investigate the nexus between governance, environmental taxes, industrialization, economic growth, and energy transition in China. Table 2 gives the descriptive statistics of all of the series considered in this study. The average values are all positive. The mean value of ET is 0.334, with a range of values from 11.9 to 22.3. The average value of GOV is 0.04, with maximum and minimum of −1.39 and 2.34. ERT has a mean value of 0.53, with maximum and minimum values of 1.68 and 0.15. IND has a mean of 44.30, a maximum value of 47.55 and a minimum value of 38.58. Finally, the mean GDP is 7.398, with maximum and minimum values of 1.433 and -2.551. Furthermore, at the 1% level of significance, the Jarque-Bera test shows that GOV, ET, ERT, IND, and GDP are not normally distributed, meaning that a nonlinear model can be used for further investigation.Table 2 Summary statistics analysis
Variable/series Average value Min value Max value Standard deviation Jarque-Bera Stats
ET 0.334 11.9 22.3 0.171 13.644***
GOV 0.047 −1.39 2.345 0.834 21.329***
ERT 0.53 0.15 1.68 0.033 17.232***
IND 44.30 38.58 47.55 2.84 33.507***
GDP 7.398 -2.551 1.433 3.798 41.635***
*P>0.05, **P=0.05, and **P<0.05
It is important to establish the integration order of the time series before estimating the QARDL model. As a result, we apply the Zivot-Andrews (ZA) and augmented Dickey-Fuller (ADF) stationarity tests, and the findings are given in Table 3. The ZA test is preferable because it takes into account structural breaks in the data. The ADF and ZA results show that all series are integrated of order 1, and at a 1% level of significance, both tests fail to accept the null hypothesis. Furthermore, the ZA unit root reveals that our time series data have structural breaks. As a result, the QARDL method, which allows for nonlinearity, structural breaks, and dynamic trend, is most appropriate (Godil et al. 2020; Godil et al. 2021b; Sharif et al. 2020; Zhan et al. 2021) Table 4.Table 3 Findings of stationarity/unit root test
Variable/series ADF (level) ADF (Δ) ZA (level) Break year ZA (Δ) Break year
EFP −1.345 −2.344*** −5.011 13/07/2016 −3.433*** 11/03/2010
EC −0.138 −4.446*** −2.631 07/06/2019 −4.843*** 23/04/2015
ENER −0.364 −2.343*** −4.753 05/06/2021 −3.743*** 22/04/2010
EI −0.155 −5.011*** −2.545 21/02/20218 −4.444*** 11/11/2013
GDP −1.335 −2.235*** −3.450 120/05/2020 −3.776*** 14/08/2015
ADF and ZA test statistics are specified by the values in the table
*P<0.05, **P=0.05, and ***P> 0.05
Table 4 QARDL results
Quantile Constant ECM Long run Short run
(τ) α∗(τ) ρ∗(τ) ΒGOV(τ) ΒERT(τ) ΒIND(τ) ΒGDP(τ) φ1(τ) ω0(τ) λ0(τ) θ0(τ) ∞(τ)
0.05 0.021 −0.581*** 0.310** −0.344** 0.30 0.540 0.562*** 0.311** −0.348*** 0.814 0.848
(0.104) (-3.034) (3.243) (−2.524) (0.379) (1.644) (3.337) (3.915) (−4.101) (0.881) (0.081)
0.10 0.131 −0.139*** 0.532*** −0.224*** 0.472 0.652 0.346*** 0.446** −1.419*** 0.774 0.234
(0.018) (-2.133) (4.247) (−2.844) (0.207) (1.753) (3.432) (3.502) (−2.342) (0.090) (0.440)
0.20 0.025 −0.474*** 0.315*** −0.665*** 0.876 0.463 0.450*** 0.353 −0.039** 0.037 0.239
(0.005) (−3.740) (3.414) (−3.143) (0.060) (1.563) (4.303) (1.016) (−2.994) (0.097) (0.897)
0.30 0.033 −0.247*** 0.313*** −0.855*** 0.473 0.654 0.567*** 0.917 −0.553*** 0.424 0.054
(0.005) (−4.650) (3.128) (−2.656) (0.188) (1.283) (5.645) (1.118) (−2.341) (0.003) (0.003)
0.40 0.043 −0.248*** 0.634*** −0.954*** 0.064 0.546 0.667*** 0.018 −0.020 0.088 0.056
(0.031) (−4.646) (2.321) (−2.303) (0.445) (1.446) (3.368) (0.674) (−0.303) (0.005) (0.309)
0.50 0.055 −0.405*** 0.344** −0.224** 0.543 0.349 0.619*** 0.017 −0.400 0.061 0.055
(0.003) (−3.036) (3.134) (−2.445) (0.876) (1.235) (3.506) (0.078) (−0.408) (0.091) (0.091)
0.60 0.042 −0.348*** 0.331*** −1.425*** 0.030 0.554** 0.954*** 0.044 −0.092 0.032 0.134
(0.003) (−4.045) (3.040) (−3.714) (1.049) (2.428) (3.445) (0.391) (−0.950) (0.498) (0.884)
0.70 0.041 -0.340*** 0.234** −2.333** 0.235 0.224*** 0.555*** 0.718 −0.059 0.154 0.456**
(0.005) (−5.670) (2.634) (−2.231) (0.537) (2.424) (2.454) (1.523) (−0.889) (0.832) (2.561)
0.80 0.045 −0.661*** 0.238*** −1.225*** 0.054 0.725*** 1.235*** 0.350 −0.021 0.864 0.144**
(0.005) (−3.839) (2.838) (−2.624) (0.040) (2.440) (4.330) (0.695) (−0.032) (0.881) (2.357)
0.90 0.026 −0.352*** 0.338*** −0.436*** 0.733 0.424*** 0.455*** 0.916 −0.035 0.048 0.335**
(0.002) (−5.355) (2.536) (−2.168) (0.040) (2.544) (3.155) (0.151) (−0.464) (0.009) (2.849)
0.95 0.032 −0.757*** 2.624*** −1.924*** 0.116 0.445*** 0.564*** 0.425 −0.814 0.098 0.488**
(0.004) (−5.353) (2.443) (−2.628) (0.011) (3.435) (4.411) (0.696) (−0.931) (0.087) (2.069)
*** significant at 1% level, ** significant at 5% level, * significant at 10% level
According to the long-run QARDL estimation, the estimated parameter ƿ* is negatively significant in all quantiles (0.05–0.95), indicating a reversion to long-term equilibrium between energy transition and the explanatory variables. Firstly, as expected, the coefficient of governance is positive and significant at all quantiles (0.05–0.95). Our findings corroborate the claim that improvements in institutional aspects can help smooth the clean/renewable energy transition, for example, the advancement of democracy, corruption control, improvement in the quality of bureaucracy, and political stability. As a result of the passage of sound legislation, public demand for environmental protection is taken into account, and projects with negative environmental impacts are rejected. These issues encourage the adoption of ecologically responsible energy consumption, which increases the use of renewable energy. The findings imply that the government has a progressive long-term strategy for developing ecological innovation, such as the downscaling and transformation of conventional energy sources, systems of energy deployment, and public infrastructure. This conclusion is supported by previous research, such as Sung and Park (2018), who find that government has a positive contribution to the energy transition. Similar findings are reported by Edomah (2021), who concludes that government intervention has a powerful influence on the transition of energy infrastructure. The coefficient of ERT is negative, but significant at all quantiles (0.05–0.95), suggesting that the imposition of environmental taxes has a negative impact on China’s shift to renewable energy use. These findings also suggest that, in order to control total energy intensity and consumption, the Chinese government should enact stringent laws and implement additional institutional changes to encourage the use of clean energy sources in the energy mix. These findings match a number of earlier studies; according to Hájek et al. (2019) [26], environmental taxes do not boost renewable energy in the short run, and their influence is only visible in the long run. In a study of European countries, Hájek et al. (2019) find that environmental taxes do not assist in the increase of renewable energy use.
Only at higher quantiles (0.60–0.95) does the GDP coefficient show a positive relationship with the energy transition, implying that clean energy consumption is encouraged in the context of economic growth. The demand for clean energy grows as economic activity improves. Economic growth, according to feedback theory, increases the use of renewable energy. Indeed, increased economic growth provides funds for renewable energy investment, and a comparatively high level of clean energy penetration encourages economic growth. From previous research, Al-Mulali et al. (2013) finds that GDP and renewable energy have a feedback association in Zambia and Uzbekistan. Similar findings are presented by Apergis et al. (2010) and Tugcu et al. (2012). Lastly, we find that the association between industrialization and energy transition is positive but insignificant at all quantiles (0.05–0.95), showing that industrialization does not have any impact on the transition of energy towards clean energy sources. This finding is consistent with [38], who examine the effect of industrialization in China and South Korea and conclude that rapid industrialization necessitates the use of fossil fuels which harm the environment and slow the advance of clean energy use. The positive impact of industrialization is consistent with Bulut et al. (2018) and Hussain et al. (2021), as they conclude that, in the medium and long term, industrialization has a positive relationship with renewable energy use.
For the short-run dynamics, the findings show that at the lower and medium quantiles (0.05–0.60), current ET fluctuation is significantly and positively influenced by its own previous values. In the extremely lower quantiles (0.05–0.10), earlier and present fluctuations in GOV have a significant impact on current ET variation, suggesting that, in the short run, GOV has a favorable effect on ET only at lower levels of ET. ERT has a significant effect on ET, especially in the lower quantile range (0.05–0.30). In comparison to the long term, where ERT has a negative and significant coefficient over all quantiles, there is an asymmetric negative influence of ERT on ET only at the lowest quantiles. Just like the long-run analysis, fluctuations in IND in the past and present do not have any significant effect on contemporary ET variations in the short run. Finally, GDP shows a significant positive impact on ET changes, mostly in the higher quantiles (0.70–0.95), implying that GDP only boosts ET at the greatest level of ET in the short run. Despite the fact that the effect at lower and mid quantiles is insignificant, it is still positive. Finally, we establish a long-run quantile association between GOV, ERT, GDP, and ET, but, in either the short or long run, IND does not influence ET in any significant way.
The Wald test results are presented in Table 5 and indicate that parameter consistency and the null speed of adjustment parameter linearity are not supported. The findings from the Wald test aid in accepting H1, which states that a long-term parameter research variable such as GP, OP, or SP is highly dynamic across various quantiles. Finally, the Wald test estimation results for H0 of linearity in the cumulative short-run impact of prior ET are denied.Table 5 Wald test results
Variable Wald Stat [Prob-Value]
Ρ 17.471***
[0.000]
ΒGOV 25.454***
[0.000]
ΒERT 5.752***
[0.000]
ΒIND 14.043***
[0.000]
ΒGDP 11.109
[0.000]
φ1 2.086***
[0.000]
ω0 0.647***
[0.000]
λ0 3.141
[0.006]
θ0 4.505***
[0.000]
∞0 2.778
[0.000]
P-values are in square brackets. *P<0.05, **P=0.05, and ***P>0.05
Source: Author’s own estimations
Table 6 gives the findings of quantile Granger causality. We observe that all of the series possess bidirectional causal relations over the entire range of quantiles. Thus, earlier and current realizations of ERT, GOV, IND, and GDP are evidenced to be better predictors of ET and vice versaTable 6 Results of quantile granger causality test
Quantile ΔEPt
↓
ΔGOVt ΔGOVt
↓
ΔEPt ΔERTt
↓
ΔEPt ΔEPt
↓
ΔERTt ΔINDt
↓
ΔEPt ΔEPt
↓
ΔINDt ΔGDPt
↓
ΔEPt ΔEPt
↓
ΔGDPt
[0.05–0.95] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.05 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.10 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.20 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.30 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.40 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.50 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.60 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.70 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.80 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.90 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.95 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Source: Authors’ estimation
Conclusion
This paper is a first attempt to understand the quantile behavior of the relationship between governance, environmental taxation, industrialization, economic growth, and energy transition. A new combination of these variables is taken for quantitative assessment, which has never been done before. The study uses the QARDL technique proposed by Cho et al. (2015) and quantiles causality to analyze quarterly data from 1999Q1 to 2019Q4. The findings demonstrate that the association is quantile-dependent, which may reveal erroneous results from past research utilizing standard approaches, such as ARDL or OLS, dealing with averages. Unlike conventional approaches, QARDL allows the cointegrating component to fluctuate between quantiles due to a variety of system shocks. According to the QARDL results, ECM is statistically significant across all quantiles, showing the presence of a considerable reversion to the long-term equilibrium association between the series under study and energy transition. Fundamentally, the results indicate that governance has a positive impact on energy transition at all quantile ranges (0.05–0.95), whereas environmental taxes have a negative impact on energy transition at all quantile ranges (0.05–0.95). However, economic growth exerts a positive influence on energy transition only at higher quantiles (0.60–0.95), and industrialization does not have any significant impact on energy transition over the entire quantile range (0.05–0.95). Furthermore, the Granger causality results show that ERT, GOV, IND, GDP, and ET have asymmetrical bidirectional causality.
From a policy point of view, the findings help us answer the challenging question of what role governance can play in the future. We suggest that the Chinese government implement anti-corruption strategies, eliminate lobbyists’ power, improve political stability, eliminate bureaucracy, improve democratic quality, and protect property rights. These policies are critical for stimulating clean energy investment and simplifying the transition from polluting to clean energy. Significant institutional reforms are required to achieve substantial and continuous change, and the transition towards clean energy. Furthermore, in order to realize China’s full clean energy potential, it must execute a number of other successful initiatives to support the transition away from fossil fuels. These initiatives are predicated on expanding research and development spending on green technologies and improving energy efficiency measures. Because of the unfavorable relationship between environmental taxes and clean energy, China should create a green finance system to support renewable energy. Because renewable energy projects require a large amount of capital, financial changes are required to encourage green financing, and loan availability must be prioritized.
To give closure to the study, three utmost implications can be drawn from the study. Firstly, government institutions must tackle energy access problems that enlighten progressive ways to make the transition process smooth and effective. Secondly, by looking into a variety of factors under good governance, the exploration of technological options seems to be more sustainable and, hence, is advised to be inculcated in procedures. Thirdly, energy consumption patterns are needed to be addressed, especially those which are more energy-intensive. There is no doubt that plenty of available resources, technological advancement in energy supply systems, and energy geographies are essential factors that highly impacts energy-related dynamics. Thereby, there needs a full consideration of these factors in energy decisions and political choices.
Indeed, available energy resources, technological changes in electricity supply systems, and the “geographies of energy” are major factors that influence energy production and consumption dynamics. All of them need to be considered as energy decisions are primarily political choices.
One major limitation of the present study is the sole dependence on secondary records for data collection and analysis. Indeed, collecting primary information from the sample who are involved in the transition process provides better insights and certain transition outcomes which are necessary for energy systems. Secondly, the study analyzed the constructs only in Chinese economy. The findings might vary as per geography. Moreover, the present study illustrates a single case, to analyze the effectiveness of governance and environmental taxes; hence, the results can be generalized at a broader level. Moreover, governance should not be viewed as an end but rather as an effective tool to achieve a successful energy transition. This implies that other powerful factors along with governance should be considered by economies to advance transition methods which lightens the abuse towards the environment and natural resources. Moreover, in order to transform governments, digital transformation is far from being achieved; hence, it is suggested to inculcate it in public institutions in order to achieve sustainable development. Moreover, in future research, it will be quite important to evaluate the effects of governance in COVID-19 context as the specific era faced an economic crisis; hence, it is crucial to study the factors to make swift policy decisions to accelerate good governance for socio-economic effects.
Data availavility
The data that support the findings of this study are attached.
Author contributions
FengSheng Chien: conceptualization, writing—original draft. YunQian Zhang: writing—literature review: Li Li software. Xiang-Chu Huang: visualization, methodology, supervision, data curation, editing.
Declarations
Ethics approval and consent to participate
It can be declared that there are no human participants, human data, or human tissues.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Abdul Hamid B Azmi W Ali M Bank risk and financial development: evidence from dual banking countries Emerging Markets Finance and Trade 2020 56 2 286 304 10.1080/1540496X.2019.1669445
Ali M Ibrahim MH Shah ME Impact of non-intermediation activities of banks on economic growth and volatility: an evidence from OIC Singapore Econ. Rev 2022 67 1 333 348 10.1142/S0217590820420023
Apergis N Payne JE Menyah K Wolde-Rufael Y On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth Ecol Econ 2010 69 11 2255 2260 10.1016/j.ecolecon.2010.06.014
Ainou FZ, Ali M, Sadiq M (2022) Green energy security assessment in Morocco: green finance as a step toward sustainable energy transition. Environ. Sci. Pollut. Res. 10.1007/s11356-022-19153-7
Akintande, O. J., Olubusoye, O. E., Adenikinju, A. F., & Olanrewaju, B. T. (2020). Modeling the determinants of renewable energy consumption: evidence from the five most populous nations in Africa. Energy, 206, 117992. https://doi.org/10.1016/j.energy.2020.117992
Al-Mulali, U., Fereidouni, H. G., Lee, J. Y., & Sab, C. N. B. C. (2013). Examining the bi-directional long run relationship between renewable energy consumption and GDP growth. Renewable and sustainable energy reviews, 22, 209-222. https://doi.org/10.1016/j.rser.2013.02.005
Bashir MF Ma B Bashir MA Radulescu M Shahzad U Investigating the role of environmental taxes and regulations for renewable energy consumption: evidence from developed economies Econ. Res.-Ekon. Istraz 2022 35 1 1262 1284
Bai X, Wang KT, Tran TK, Sadiq M, Trung LM, Khudoykulov K (2022) Measuring China’s green economic recovery and energy environment sustainability: econometric analysis of sustainable development goals. Econ Anal Policy. 10.1016/j.eap.2022.07.005
Baye RS Olper A Ahenkan A Musah-Surugu IJ Anuga SW Darkwah S Renewable energy consumption in Africa: evidence from a bias corrected dynamic panel Sci. Total Environ 2021 766 142583 10.1016/j.scitotenv.2020.142583 33143916
Belaïd F Zrelli MH Renewable and non-renewable electricity consumption, environmental degradation and economic development: evidence from Mediterranean countries Energy Policy 2019 133 110929 10.1016/j.enpol.2019.110929
Belaïd F Elsayed AH Omri A Key drivers of renewable energy deployment in the MENA Region: empirical evidence using panel quantile regression Struct. Chang. Econ. Dyn 2021 57 225 238 10.1016/j.strueco.2021.03.011
Bellakhal R Kheder SB Haffoudhi H Governance and renewable energy investment in MENA countries: how does trade matter? Energy Econ 2019 84 104541 10.1016/j.eneco.2019.104541
Bridge G Bouzarovski S Bradshaw M Eyre N Geographies of energy transition: Space, place and the low-carbon economy Energy policy 2013 53 331 340 10.1016/j.enpol.2012.10.066
Bulut U Muratoglu G Renewable energy in Turkey: great potential, low but increasing utilization, and an empirical analysis on renewable energy-growth nexus Energy policy 2018 123 240 250 10.1016/j.enpol.2018.08.057
Cai Y Aoyama Y Fragmented authorities, institutional misalignments, and challenges to renewable energy transition: a case study of wind power curtailment in China Energy Res. Soc. Sci 2018 41 71 79 10.1016/j.erss.2018.04.021
Caineng ZOU Songqi PAN Qun HAO On the connotation, challenge and significance of China’s “energy independence” strategy Pet. Explor. Dev. 2020 47 2 449 462 10.1016/S1876-3804(20)60062-3
Chang YC Wang N Legal system for the development of marine renewable energy in China Renew. Sust. Energ. Rev. 2017 75 192 196 10.1016/j.rser.2016.10.063
Chen, C. F. (2015). The politics of renewable energy in China: towards a new model of environmental governance? (Doctoral dissertation, University of Bath). https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.665423
Chien F (2022a) How renewable energy and non-renewable energy affect environmental excellence in N-11 economies? Renew Energy. 10.1016/j.renene.2022.07.013
Chien F (2022d) The mediating role of energy efficiency on the relationship between sharing economy benefits and sustainable development goals (Case Of China). Int. J. Innov. 10.1016/j.jik.2022.100270
Chien F, Chau KY, Sadiq M, Hsu CC (2022c) The impact of economic and non-economic determinants on the natural resources commodity prices volatility in China. Resour Policy. 10.1016/j.resourpol.2022.102863
Chien F, Hsu CC, Sibghatullah A, Hieu VM, Phan TTH, Hoang Tien N (2021a) The role of technological innovation and cleaner energy towards the environment in ASEAN countries: proposing a policy for sustainable development goals. Econ. Res.-Ekon. Istraz. 10.1080/1331677X.2021.2016463
Chien, F., Zhang, Y., Sharif, A., Sadiq, M., & Hieu, M. V. (2022b). Does air pollution affect the tourism industry in the USA? Evidence from the quantile autoregressive distributed lagged approach. Tour. Econ., 10.1177/13548166221097021
Chien F, Hsu CC, Sibghatullah A, Hieu VM, Phan TTH, Hoang Tien N (2021b) The role of technological innovation and cleaner energy towards the environment in ASEAN countries: proposing a policy for sustainable development goals. Econ. Res.-Ekon. Istraz. 10.1080/1331677X.2021.2016463
Cho JS Kim TH Shin Y Quantile cointegration in the autoregressive distributed-lag modeling framework Journal of econometrics 2015 188 1 281 300 10.1016/j.jeconom.2015.05.003
Dale S. (2019). BP statistical review of world energy. BP Plc, London, United Kingdom,14-6. https://www.imemo.ru/files/File/ru/events/2021/BP-2021.pdf
Dowling R McGuirk P Maalsen S Multiscalar governance of urban energy transitions in Australia: the cases of Sydney and Melbourne Energy Res. Soc. Sci. 2018 44 260 267 10.1016/j.erss.2018.05.027
Dung NTT Yen VK Luan VM Toan NQ Phuong NTT Socioeconomic analysis of investment projects to build urban drainage works with ODA of the World Bank in Vietnam Int. J. Finance Econ. 2022 14 03 22 41
Edomah N The governance of energy transition: lessons from the Nigerian electricity sector Energy Sustain. Soc. 2021 11 1 1 12
Gailing L Moss T Conclusions and outlook for future energy transitions research Conceptualizing Germany’s Energy Transition 2016 London Palgrave Pivot 109 119
Godil DI Sharif A Agha H Jermsittiparsert K The dynamic nonlinear influence of ICT, financial development, and institutional quality on CO2 emission in Pakistan: new insights from QARDL approach Environ. Sci. Pollut. Res 2020 27 19 24190 24200 10.1007/s11356-020-08619-1
Godil DI Ahmad P Ashraf MS Sarwat S Sharif A Shabib-ul-Hasan S Jermsittiparsert K The step towards environmental mitigation in Pakistan: do transportation services, urbanization, and financial development matter? Environ. Sci. Pollut. Res 2021 28 17 21486 21498 10.1007/s11356-020-11839-0
Godil DI Sharif A Ali MI Ozturk I Usman R The role of financial development, R&D expenditure, globalization and institutional quality in energy consumption in India: new evidence from the QARDL approach J. Environ. Manage. 2021 285 112208 10.1016/j.jenvman.2021.112208 33618139
Granger CW Investigating causal relations by econometric models and cross-spectral methods Econometrica: J. Econom Society 1969 37 3 424 438 10.2307/1912791
Haarstad H Where are urban energy transitions governed? Conceptualizing the complex governance arrangements for low-carbon mobility in Europe Cities 2016 54 4 10 10.1016/j.cities.2015.10.013
Sadiq M, Ou JP, Duong KD, Van L, Ngo TQ, Bui TX (2022c) The influence of economic factors on the sustainable energy consumption: evidence from China. Econ. Res.-Ekon. Istraz. 10.1080/1331677X.2022.2093244
Hájek M Zimmermannová J Helman K Rozenský L Analysis of carbon tax efficiency in energy industries of selected EU countries Energy Policy 2019 134 110955 10.1016/j.enpol.2019.110955
Halder P Prokop P Chang CY Usak M Pietarinen J Havu-Nuutinen S Cakir M International survey on bioenergy knowledge, perceptions, and attitudes among young citizens Bioenergy Res 2012 5 1 247 261 10.1007/s12155-011-9121-y
Haroon O Ali M Khan A Khattak MA Rizvi SAR Financial market risks during the COVID-19 pandemic Emerg. Mark. Finance Trade 2021 57 8 2407 2414 10.1080/1540496X.2021.1873765
Hartani NH Haron N Tajuddin NII The impact of strategic alignment on the sustainable competitive advantages: mediating role of it implementation success and it managerial resource Int. J. eBusiness eGovernment Stud. 2021 13 1 78 96
Hosseini SE Andwari AM Wahid MA Bagheri G A review on green energy potentials in Iran Renew. Sust. Energ. Rev. 2013 27 533 545 10.1016/j.rser.2013.07.015
Hussain M Bashir MF Shahzad U Do foreign direct investments help to bolster economic growth? New insights from Asian and Middle East economies World Rev. Entrepreneurship, Manag. Sustain. Dev. 2021 17 1 62 84 10.1108/WJEMSD-10-2019-0085
Jermsittiparsert K Linkage between energy consumption, natural environment pollution, and public health dynamics in ASEAN Int. J. Finance Econ. 2021 13 2 1 21
Kamarudin F Anwar NAM Chien F Sadiq M Efficiency of microfinance institutions and economic freedom nexus: empirical evidence from four selected ASIAN countries Transform. Bus. Econ. 2021 20 2b 845 868
Kim, T. H., & White, H. (2003). Estimation, inference, and specification testing for possibly misspecified quantile regression. In Maximum likelihood estimation of misspecified models: twenty years later. Emerald Group Publishing Limited. 10.1016/S0731-9053(03)17005-3
Khattak MA Ali M Rizvi SAR Predicting the European stock market during COVID-19: a machine learning approach MethodsX 2021 8 101198 10.1016/j.mex.2020.101198 33425689
Kotzebue JR Weissenbacher M The EU’s Clean Energy strategy for islands: a policy perspective on Malta’s spatial governance in energy transition Energy Policy 2020 139 111361 10.1016/j.enpol.2020.111361
Kurniawan K Supriatna J Sapoheluwakan J Soesilo TEB Mariati S Gunarso G The analysis of forest and land fire and carbon and greenhouse gas emissions on the climate change in Indonesia AgBioForum 2022 24 2 1 11
Kuzemko C Lockwood M Mitchell C Hoggett R Governing for sustainable energy system change: politics, contexts and contingency. Energy Research & Social Science 2016 12 96 105 10.1016/j.erss.2015.12.022
Laes E Gorissen L Nevens F A comparison of energy transition governance in Germany, the Netherlands and the United Kingdom Sustainability 2014 6 3 1129 1152 10.3390/su6031129
Lan J, Khan SU, Sadiq M, Chien F, Baloch ZA (2022) Evaluating energy poverty and its effects using multi-dimensional based DEA-like mathematical composite indicator approach: findings from Asia. Energy Policy. 10.1016/j.enpol.2022.112933
Lazaro LLB Soares RS Bermann C Collaço FMA Giatti LL Abram S Energy transition in Brazil: Is there a role for multilevel governance in a centralized energy regime? Energy Res. Soc. Sci. 2022 85 102404 10.1016/j.erss.2021.102404
Li J Zhang X Ali S Khan Z Eco-innovation and energy productivity: new determinants of renewable energy consumption J. Environ. Manage. 2020 271 111028 10.1016/j.jenvman.2020.111028 32778308
Lin B Omoju OE Does private investment in the transport sector mitigate the environmental impact of urbanisation? Evidence from Asia J. Clean. Prod. 2017 153 331 341 10.1016/j.jclepro.2017.01.064
Lin CY Chau KY Tran TK Sadiq M Van L Phan TTH Development of renewable energy resources by green finance, volatility and risk: Empirical evidence from China Renew Energy 2022 201 821 831 10.1016/j.renene.2022.10.086
Liu Z, Lan J, Chien F, Sadiq M, Nawaz MA (2022b) Role of tourism development in environmental degradation: a step towards emission reduction. J. Environ. Manage. 10.1016/j.jenvman.2021.114078
Liu Z, Yin T, Surya Putra AR, Sadiq M (2022a) Public spending as a new determinate of sustainable development goal and green economic recovery: policy perspective analysis in the post-covid ERA. Clim Chang Econ. 10.1142/S2010007822400073
Moslehpour M Chau KY Tu YT Nguyen KL Barry M Reddy KD Impact of corporate sustainable practices, government initiative, technology usage, and organizational culture on automobile industry sustainable performance Environ. Sci. Pollut. Res. 2022 29 55 83907 83920 10.1007/s11356-022-21591-2
Moslehpour M Shalehah A Wong WK Ismail T Altantsetseg P Tsevegjav M Economic and tourism growth impact on the renewable energy production in Vietnam Environ. Sci. Pollut. Res. 2022 29 53 81006 81020 10.1007/s11356-022-21334-3
Moslehpour M, Chau KY, Du L, Qiu R, Lin CY, Batbayar B (2022a) Predictors of green purchase intention toward eco-innovation and green products: evidence from Taiwan. Econ. Res.-Ekon. Istraz.:1–22
Nakata T Silva D Rodionov M Application of energy system models for designing a low-carbon society Prog. Energy Combust. Sci. 2011 37 4 462 502 10.1016/j.pecs.2010.08.001
Nochta T Skelcher C Network governance in low-carbon energy transitions in European cities: a comparative analysis Energy Policy 2020 138 111298 10.1016/j.enpol.2020.111298
Ojogiwa OT The crux of strategic leadership for a transformed public sector management in Nigeria Int. J. Bus. Stud. 2021 13 1 83 96
Quynh MP Van MH Le-Dinh T Nguyen TTH The role of climate finance in achieving Cop26 goals: evidence from N-11 countries Cuadernos de Economía 2022 45 128 1 12
Sadiq M Lin CY Wang KT Trung LM Duong KD Ngo TQ Commodity dynamism in the COVID-19 crisis: are gold, oil, and stock commodity prices, symmetrical? Resour Policy 2022 79 103033 10.1016/j.resourpol.2022.103033 36187223
Saadaoui, H. (2021). The impact of financial development on the clean energy transition in MENA region: the role of institutional and political factors.
Saadaoui H, Chtourou N (2022) Do institutional quality, financial development, and economic growth improve renewable energy transition? Some Evidence from Tunisia. J. Knowl. Econ. 1-32. 10.1007/s13132-022-00999-8
Sadiq M, Amayri MA, Paramaiah C et al (2022b) How green finance and financial development promote green economic growth: deployment of clean energy sources in South Asia. Environ. Sci. Pollut. Res. 10.1007/s11356-022-19947-9
Sadiq M, Ngo TQ, Pantamee AA, Khudoykulov K, Ngan TT, Tan LL (2022a) The role of environmental social and governance in achieving sustainable development goals: evidence from ASEAN countries. Econ. Res.-Ekon. Istraz. 10.1080/1331677X.2022.2072357
Sharif A Baris-Tuzemen O Uzuner G Ozturk I Sinha A Revisiting the role of renewable and non-renewable energy consumption on Turkey’s ecological footprint: evidence from quantile ARDL approach Sustainable Cities and Society 2020 57 102138.https://doi.org/10.1016/j.scs.2020.102138 10.1016/j.scs.2020.102138
Shibli R Saifan S Ab Yajid MS Khatibi A Mediating role of entrepreneurial marketing between green marketing and green management in predicting sustainable performance in Malaysia’s organic agriculture sector AgBioForum 2021 23 2 37 49
Sriyakul T Chankoson T Sukpasjaroen K Economic growth, e-government, and environmental degradation during COVID-19: a panel analysis of selected Asian economies Int. J. eBusiness eGovernment Stud 2022 14 3 46 60
Sung B Park SD Who drives the transition to a renewable-energy economy? Multi-actor perspective on social innovation Sustainability 2018 10 2 448 10.3390/su10020448
Tan LP, Sadiq M, Aldeehani TM, Ehsanullah S, Mutira P, Vu HM (2021) How COVID-19 induced panic on stock price and green finance markets: global economic recovery nexus from volatility dynamics. Environ. Sci. Pollut. Res. 10.1007/s11356-021-17774-y
Tiberio L De Gregorio E Biresselioglu ME Demir MH Panno A Carrus G Psychological processes and institutional actors in the sustainable energy transition: a case-study analysis of a local community in Italy Front. Psychol. 2020 11 980 10.3389/fpsyg.2020.0098 32508724
Toke D (2017) China’s role in reducing carbon emissions: the stabilisation of energy consumption and the deployment of renewable energy. Routledge. 10.4324/9781315276946
Troster V Testing for Granger-causality in quantiles Econom Rev 2018 37 8 850 866 10.1080/07474938.2016.1172400
Tugcu CT Ozturk I Aslan A Renewable and non-renewable energy consumption and economic growth relationship revisited: evidence from G7 countries Energy Econ 2012 34 6 1942 1950 10.1016/j.eneco.2012.08.021
Tzanakis I Hadfield M Thomas B Noya SM Henshaw I Austen S Future perspectives on sustainable tribology Renew. Sust. Energ. Rev. 2012 16 6 4126 4140 10.1016/j.rser.2012.02.064
Tzankova Z Public policy spillovers from private energy governance: new opportunities for the political acceleration of renewable energy transitions Energy Res. Soc. Sci. 2020 67 101504 10.1016/j.erss.2020.101504
Uzar U Political economy of renewable energy: does institutional quality make a difference in renewable energy consumption? Renew Energy 2020 155 591 603 10.1016/j.renene.2020.03.172
Wagemans D Scholl C Vasseur V Facilitating the energy transition—the governance role of local renewable energy cooperatives Energies 2019 12 21 4171 10.3390/en12214171
Wirsbinna A Grega L Assessment of economic benefits of smart city initiatives Cuadernos de Economía 2021 44 126 45 56
Wu L Broadstock DC Does economic, financial and institutional development matter for renewable energy consumption? Evidence from emerging economies Int. J. Econ. Policy Emerg. 2015 8 1 20 39
Yao Y Ivanovski K Inekwe J Smyth R Human capital and energy consumption: evidence from OECD countries Energy Economics 2019 84 104534 10.1016/j.eneco.2019.104534
Yazdanpanah M Komendantova N Ardestani RS Governance of energy transition in Iran: investigating public acceptance and willingness to use renewable energy sources through socio-psychological model Renew. Sust. Energ. Rev. 2015 45 565 573 10.1016/j.rser.2015.02.002
Zhan, Z., Ali, L., Sarwat, S., Godil, D. I., Dinca, G., & Anser, M. K. (2021). A step towards environmental mitigation: do tourism, renewable energy and institutions really matter? A QARDL approach. Science of the Total Environment, 778, 146209.10.1016/j.scitotenv.2021.146209Zhang, S., & Andrews-Speed, P. (2020). State versus market in China’s low-carbon energy transition: an institutional perspective. Energy Research & Social Science, 66, 101503. 10.1016/j.erss.2020.101503
Zhao L, Chau KY, Tran TK, Sadiq M, Xuyen NTM, Phan TTH (2022) Enhancing green economic recovery through green bonds financing and energy efficiency investments. Econ Anal Policy. 10.1016/j.eap.2022.08.019
Zhao L, Zhang Y, Sadiq M, Hieu VM, Ngo TQ (2021) Testing green fiscal policies for green investment, innovation and green productivity amid the COVID-19 era. Econ. Change Restruct. 10.1007/s10644-021-09367-z | 36757594 | PMC9909652 | NO-CC CODE | 2023-04-16 23:15:20 | yes | Environ Sci Pollut Res Int. 2023 Feb 9; 30(16):48436-48448 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
36773269
25656
10.1007/s11356-023-25656-8
Research Article
Role of financial decentralization on carbon taxation and carbon emission: Way forwards for economic recovery
Zeng Chunying 1
Zhao Jiaojiao [email protected]
2
1 grid.459584.1 0000 0001 2196 0260 School of Economic and Management, Guangxi Normal University, 541004 Guilin, China
2 grid.218292.2 0000 0000 8571 108X School of Management and Economics, Kunming University of Science and Technology, Kunming, 650031 Yunnan China
Responsible Editor: Nicholas Apergis
11 2 2023
2023
30 17 4935449367
19 12 2022
27 1 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
The study intends to assess the role of financial decentralization on carbon taxation and carbon emission to recommend the way forwards for economic recovery. To estimate the nexus, study applied the cointegration analysis technique, CGE estimation model, long-run analysis using t-CGE model, and robustness analysis technique on Chinese data. Research findings declare that financial decentralization has significant role on extending the carbon taxation in China and financial decentralization supported 14.92% to expand carbon taxation throughout the Chinese industries. In such industries, pollution emission industries are the top of the list including transportation industry and other manufacturing companies. Overall, manufacturing industries size is about 78% and 11% size of transportation industry is included. Correspondingly, the findings also revealed that financial decentralization supports climate change mitigation with 29% and carbon taxation limits carbon emission with 44% in Chinese industries. Study directs to the stakeholders to enhance carbon taxation schemes in all sectors of the all the industries of China and come up with the viable policy action so that the desired sustainable development goals may achieve effectively. Hence, stakeholders need to consider recommendations of preceding research to enhance green economic recovery.
Keywords
Financial decentralization
Financial systems
Carbon taxation
Carbon emission mitigation
Green economic recovery
http://dx.doi.org/10.13039/100017414 Beijing Municipal Social Science Foundation 22CJL007 Zhao Jiaojiao issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023
==== Body
pmcIntroduction
Greenhouse gas (GHG) emissions, brought on by electricity and rapid nitrogen industrialization for productivity expansion, are a multidimensional problem (Sims et al. 2003). Despite ongoing international initiatives, such as the last several COP26, the Sustainable Development 2030 Initiative, and the 2015 (Cao et al. 2020) Paris Climate Change Agreement, the targets of decarbonization and net harmful carbon dioxide emissions remain a pipe dream that will likely never be realized given the current humanitarian challenges, notably the COVID-19 virus outbreak that is still very much alive (Zhang et al. 2015). Ecological deterioration has primarily been assessed using metrics related to greenhouse gasses, which are the primary cause of meteorological challenges. Nevertheless, only the environmental part of durability was included in these measures; the economic output component was left out (Acheampong et al. 2019). Instead, fuel efficiency, which measures the price of trying to convert each step of energy into a certain number of additional segments of total gross domestic product (GDP), has been used as a measuring stick to measure how energy-efficient an economic growth is. This is because a reduced energy density is associated with a higher level of energy efficiency, and vice versa, catching the idea of ecological conservation as a whole (Yun et al. 2018). Regarding carbon efficiency being the economy-inclusive metric, greenhouse gas, or the quantity of carbon generated per unit of GDP, may also assist in establishing ecological responsibility. According to contemporary research, socioeconomic elements, including political setup, financial institutions, and natural resources, are crucial for a global green economy (Gao et al. 2020). Hence, study motivation is to estimate the nexus between financial decentralization, carbon emission, and carbon taxation.
Economic growth has long been recognized as the primary goal of financial decentralization, distributing authority to lower governmental levels through shifting responsibility for spending and income (Oates 1993). According to one point of view, financial decentralization may be a helpful strategy for enhancing sectorial economic growth and public expenditure effectiveness (Arumugam and Shaik 2021). Additionally, domestic energy statutory success criteria might emerge as essential instruments for promoting the use of renewable energy by enterprises for environmentally friendly practices (Epple and Nechyba 2004). The opposing claim is that financially decentralized societies could have the so-called horizontal budget deficit, which occurs when municipal authorities experience regional disparities in revenue collection provided in the following section and harm economic growth (Bahl 1999). Two types help to explain how financial decentralization influences the environment. First, countries that adopt the “race to the top” form of financial decentralization are more likely to improve the sustainability of the building by effectively delivering social infrastructure like a good environment at the sub-national level (Shao and Razzaq 2022). Those countries can prioritize mitigating climate change strategies in their public policies, ushering in systemic reform in the energy industry via creation or creativity. Notably, it has been shown that in 148 emerging and transitional countries, technology in renewable energy enhances the effectiveness of both carbon pollution and fine particles (Khan et al. 2021).
While optimal environmental taxation has been the subject of academic inquiry for some time, rising public awareness of climate change has brought the topic into the spotlight. The Paris Climate Accord reflects the widespread enthusiasm for global cooperation and the goal of reducing carbon dioxide emissions. Still, it is deliberately vague so that individual nations may follow their strategies to achieve this goal. It is terrible that both Kuai et al. (2019) and a study by the United Nations Panel on Climate Change raise severe doubts about meeting the 2 °C objective of the Paris Agreement. While the Paris Agreement’s generality and lack of detail may help countries to keep pursuing their own expense (or politically viable) techniques, lawmakers and economists have little agreement on the best way to tax pollution and reuse the collected money. Hao et al. (2020), who later won a Nobel Prize for his DICE model, said of the need to quantify and include a system to recycle carbon tax money. The relevance of revenue recycling is startling and remarkable. These results highlight the significance of cautious equipment selection and monetary application. It seems that its tail, income recycling, is leading climate change legislation. This research revisits the issue of optimum carbon pricing and redistribution within the framework of a general dynamic stochastic equilibrium (DSGE) model to contribute to economic discussions taking place at the global and regional scales. DSGE modeling of climate change globally has been highlighted as a component of the “Third Wave in the Economics of Climatic Changes” by many writers, who note that agent-based models that address a few of the “inadequacies” of previous stuff are up-and-coming (Ahmad and Satrovic 2023). There is a natural impulse toward integrating ecological and global economics. Management of carbon dioxide (CO2) emissions seems to be an economic issue because CO2 is a transnational contaminant (Li et al. 2021a, b).
In addition, it is concluded that, rather than being used to lessen the tax burden on workers, the proceeds should be given to consumers as a lump sum payment (Sun et al. 2022a, b). The fundamental contribution of this study is to evaluate and translate the optimum dynamic tax into a policy rule that is derived from conventional, easily accessible macroeconomic data, thereby bridging the divide between both theory and policy (Elkins and Baker, 2001; Hanif et al. 2020; Jiang et al. 2019; Kassouri, 2022). The study take this step, but since both individuals and companies alike may be resistant to a tax rate that fluctuates over time, seeing it instead as arbitrary and subject to the preferences of legislators (Huang et al. 2021). To this purpose, it is to devise a rules-based tax that is optimum in terms of the tax burden while being easy to compute and grounded on publicly accessible macroeconomic aggregates, such as the consumer price index for energy items and the GDP “output gap.” It may be compared to a predictable and formulaic environmental “Taylor rule,” which is what the tax advocated in this study amounts (Zhao et al. 2022a, b). This work makes a significant addition to the field of ecological economics by comparing the benefits and costs of a vibrant tax policy with those of a static one in a model that accounts for market failures and energy price shocks (Yang et al. 2022a, b).
Further, the full potential of a dynamic carbon tax may be better understood by discussing and analyzing the double dividend concept (Cheng et al. 2020; Criqui et al. 2019; Du and Sun 2021; Zheng et al. 2022). The study compares the welfare impacts of a rules-based emotional tax to those of a static tax and a toggled form of compensation in a series of four policy simulations. The study finds that a dynamic carbon tax is preferable to no tax at all for households using a measure of compensatory variability (Savin et al. 2020). This finding is consistent with either negative utility externalities or a production positive externalities model for the positive externalities impact of emission and with either a circular or non-circular use of the income (Lee et al, 2008; Lin and Zhou, 2021; Lingyan et al, 2022; Liu et al, 2022, Shan et al, 2021). This result is also independent of the specific internal conflicts considered (Ding et al. 2019). Research shows that financial decentralization’s fundamental objective is to boost economic development and decrease carbon emissions, improving environmental quality and energy consumption. To motivate local governments to take action toward solving ecological consequences, the federal government should clearly define what roles and duties each level of government is expected to play (Song et al, 2018; Wang et al, 2016; Xia et al, 2021). Ultimately, the government may impose legislative limits on polluting businesses to encourage the development of clean enterprise technologies. It is argued that decentralizing authority has the potential to boost energy efficiency and foster the country’s move toward a more environmentally friendly energy structure. The federal government has established more stringent standards over the last several years addressing the evaluation of ecological performance. Local councils’ interest in performance assessment indicators is a step toward more ecological environment governance. In response, citizens prioritize environmental responsibility, giving authorities an extra layer of oversight. The domino effect of rivalry in regional politics also must be overlooked. The “neighbor avoidance effect” increases local government spending and positively impacts air quality. Modest decentralization is a position shared by other perspectives. Total green factor production may benefit from some degree of financial decentralization, but too much of it might be counterproductive, as well used data from Pakistan to confirm this conclusion. The effectiveness of the Chinese municipal administration’s plans to reduce CO2 emissions will be judged by the criteria used to evaluate their previous efforts. The government often uses the economic growth rate as a key performance indicator. A damaging “scale competition” has emerged between municipal governments, which is simple to induce “free-riding” activity. The municipality has lowered environmental laws and allowed pollution release from businesses with significant future growth to boost tax income. As a result, the quality of the surroundings under their control suffers. It has been discovered that financial decentralization under the Chinese model encourages urban growth via top-down increasing power, which significantly negatively impacts the environment.
Literature review
Financial decentralization and carbon emission
There are both direct and indirect effects of fiscal decentralization on poverty. There may be several paths connecting these two factors (Yuan et al. 2019). Possible inverse causality exists between the relevant factors. According to many scholars, decentralization’s direct impact on impoverishment has mixed results. Some studies have shown that local governments are crucial in helping shape and execute policies to reduce poverty. Local governments are often closer to their constituents, which aids in locating the impoverished at lower switching costs (You et al. 2019). The provincial government has an edge since they usually have more accurate information on what the residents want. By having this knowledge at their disposal, they can better serve the public interest of their citizens than the national government (Yang et al. 2020). This data may be used in a manner analogous to the currency’s advantage over the government in locating financing options for essential services. The local government’s involvement in policymaking may affect society’s welfare (Zhao et al. 2022, b). By allowing citizens to have a say in which low-income residents receive benefits from technologies that help legislation, local governments can help raise the standard of living for everyone in the area by increasing access to essential public services and leveling the playing field when it comes to the resource base. According to the research of Bardhan (2002), the local president’s participation in neighborhood decision-making tends to offer greater responsibility, accountability, and motivation to the local contacts, and local knowledge may discover less expensive and more suitable methods of delivering public goods. Democratically elected local governments may be better able to respond to citizen concerns and include economically disadvantaged people in political decision-making (Qiao et al. 2019). The research suggests that financial decentralization may affect poverty by changing how government spending is organized (Cheng and Zhu 2021).
Redistribution policies that provide money from the municipal budget to the poor may have a significant impact on their take-home pay (Zhang et al. 2022). Allocation programs for the poor are often linked to other redistributions of income, most notably those concerning public health and education. With financial decentralization, more money may be allocated to social programs like health care and public schools. Total employment as part of a regional president’s redistribution strategy is another case. Hence, decentralization strategies and their cross-national applicability differ significantly (Sanogo 2019). First, how much control do local authorities have over industries crucial to the public good? Furthermore, there is a wide range of variation in the relationship between locally available resources and customer service results from country to country (Hao et al. 2021).
In addition, families would be encouraged to buy expensive things, such as electronics, which account for a significant portion of the annual energy consumption and contribute to the worsening environmental destruction due to their use of the available credit. Nevertheless, individuals might also use those credits to invest in solar photovoltaic and biogas facilities, contributing to environmental stewardship in roundabout ways (Hou et al. 2021). In addition to aiding in achieving SDG 7, “ensuring that all citizens have access to cheap, clean, and renewable sources,” green sign of improvement for people in low-income communities, particularly women and those who lack energy access (Shaik et al. 2022). The final effect of financial inclusivity on economic and environmental performance is still debatable, notwithstanding these processes. Financial decentralization, microcredit, and energy wealth have individual impacts on environmental sustainability, which have been the subject of many studies but have received very little attention. Prior empirical research concluded that countries rich in natural resources grew more slowly than those with fewer materials (Jin and Kim 2018). Theoretically, it was unclear to these researchers why NRA would cause slower development. Thus, they coined the phrase “resources curse dilemma” to describe this conundrum (RCP). Experts in politics and economics pointed fingers at a wide range of issues, including macroeconomic stability, economic deepening, and government structure, for the failure of mineral wealth to spur economic progress (Mossler et al. 2017). The conventional wisdom is that resource-constrained development (RCD) results in insufficient financial strength, hindering the provision of public goods like environmental regulations. When this is taken into account, we see that an increase in pollutant emissions is possible due to the availability of fossil fuels encouraging dioxide oil and gas extraction power production (Zhang et al. 2020).
On the other hand, many who are against the RCP argue that NRA may help countries reap the positive consequences of sustainable mineral wealth. Natural capital dependency (NRD) may also fuel impressive economic expansion, which may help keep global climate prevention initiatives in the spotlight (Ulucak et al. 2020). Overusing fossil fuels, such as coal and oil, is another potential source of pollution that might hasten environmental decline and species extinction. This research looked at the influence of mineral wealth on power consumption in socioeconomic growth and concluded that it was favorable immediately and after a lag of one period (Zhang et al. 2019).
Financial decentralization and carbon taxation
Research on a carbon tax may be traced back to the late 1980s when the revenue from such a carbon tax could be used to reduce the impact of other taxes on businesses, leading to more hiring and investing and a more prosperous economy (Cheng et al. 2021). It has been suggested that the ideal forestry rotational and, by extension, the amount of carbon deposited in woods will be impacted by environmental taxes and subsidies (Shaik et al. 2021). Examined how the price of carbon policies impacts supply chain operations for corporations. A general equilibrium computational model was used to investigate the effects of a range of carbon tax rates in Ukraine on the country’s economy and ecosystem (Li et al. 2021a, b). Examined how cutting subsidies and implementing carbon prices will affect Mexican households in terms of distribution. Employing quality management systems and the Suits index, we calculated how a carbon tax would affect families of certain wage levels. Designed a strategy to maximize profits while considering a fossil fuel-based commodity’s period and rate of growth. A study was done to see what effect a carbon price would have and whether it could be sufficient to get people to put in grid-connected solar or wind power. He et al. (2016) attempted to predict how a carbon price would affect decarbonization and financial losses across 30 provinces in China. The fact that carbon taxes, up to a point, increase social safety nets during manufacturing but decrease it during consumption and redistribution is taken into account. Utilizing a CGE model, we investigated how a carbon tax and various tax revenue recycling and reuse might affect the Chinese economy (Ryu et al. 2014). While many studies have been conducted on carbon tax policies, very few have examined how varying carbon tax rates and industry coverage affect China’s economic growth, energy consumption, and carbon dioxide emissions (Hornsey and Fielding 2016). This research aims to investigate potential carbon pricing rate alternatives and the insurance sector to provide viable choices for China (Ma et al. 2020).
The literature has stated that financial decentralization might be counterproductive to anti-poverty efforts since city councils often lack the information and resources necessary to combat poverty effectively (Arumugam et al. 2022). It is possible that the government does not have enough money to deliver adequate services to the population. The government may mobilize resources from other federal agencies to mitigate significant regional discrepancies, but local and state governments have little to no say. It is not always safe to assume that a local level of government would deliver better public services than a national one (Wang et al. 2021a, b). To achieve the necessary economies of scale, several essential facilities must be vast and exceedingly technological (Li et al. 2022). If a municipality lacks the resources to create critical infrastructure, the responsibility falls on businesses or the federal government. The complexity of the decentralized system’s spending control compared to the single network seems another possible issue. Failure to properly regulate spending at the municipal level may lead to the acquisition of public money by the ruling families, which in turn can lead to excluding the intended beneficiaries and corruption (Smith et al. 2000). While decentralization is necessary to reduce poverty, specific research suggests it may not be adequate. The degree to which decentralization reduces unemployment will depend on other variables, such as the dedication of centralized administration, the efficiency of centralized administration in delivering the common good, and the transparency of local markets. According to Xu et al. (2017), better margins are ineffective in a decentralized economy because they reward high-tax areas. Growth, price stability, federal spending, the effectiveness of government institutions, and geographical convergence are all possible intermediate variables connecting financial decentralization and impoverishment.
Despite some inconsistency, there does seem to be a link between decentralization and industrial progress. As a result, we still do not know how decentralization’s indirect effects on poverty via economic development will play out (Wang et al. 2022a, b). Because of their heightened susceptibility to the impact of financial instability, such as a decline in consumer spending caused by high inflationary, the poor may be negatively impacted by economic decentralization. Larger governments are better able to adopt comprehensive progressive taxation that has a noticeable effect on lowering poverty rates. Income disparity in per capita terms is also a significant factor in establishing poverty levels. As of yet, there are no well-established investigations on financial decentralization’s impact on regional inequality. Finally, the assistance agency’s ability to fight unemployment may be diminished by the institutional transformation necessary for financial decentralization to take effect (Thanh and Canh 2020). Wealth disparity and financial decentralization may have reciprocal effects. Various works on economic reform point to specific avenues via which the income gap might affect decentralization levels (Zhang et al. 2021).
Theoretical gaps
Numerous regions worldwide are implementing emissions trading mechanisms for themselves to achieve respective long- and short-term pollution goals in the context of a worldwide consensus on carbon pollution regulation. Redirecting manufacturing to already-built, pretty low facilities is one way to reduce carbon emissions in the short term (Siburian 2020). During the shale oil and gas explosion in the USA, fuel swapping resulted from reduced energy prices, making geothermal power producers more comparable with the more expensive and carbon-polluting coal power (Li et al, 2021). Although it may be expected that emissions will decrease due to relatively inexpensive cleaning technology (for instance, due to a pollution tax), this is not necessarily the case, as observed initially (Yang et al. 2021). Environmental taxes in monopolistic marketplaces may affect company market behavior under certain circumstances (Bilal et al, 2022). To examine how a carbon tax might affect the economy, ecosystem, and energy sector, the research develops a dynamic recursive CGE model (Zhou et al. 2020). CGE modelers may use this publication as a reference. That used a CGE model, and this research examines the effects of a carbon price on the economy, the climate, and electricity. In particular, we analyze various carbon tax rates and scopes of application to identify the best strategy for implementing a carbon tax in China.
Methodology
Theoretical support
The geographical spillover impact of energy efficiency has been neglected in previous research, which instead emphasizes the linear link between numerous influential factors and clean energy. To begin, Moran’s I index and the SAR model are not the only tools of analysis used in this research. We apply the gravity model to create a two-dimensional energy consumption system throughout China’s provinces. Using a fresh viewpoint, we will take a closer look at how different elements of energy efficiency throughout China’s areas relate to one another. This improves the robustness and interoperability of China’s energy infrastructure and indicates a multiple supply overlap phenomenon of geographical spillovers. Furthermore, we stress the importance of financial decentralization as a regulation. None of those mentioned earlier studies have reached a conclusive statement on the effects of FD or IS on energy consumption. Each region in China has a unique level of independence. We split the nation into the East, the Midwest, and the West to see if there was a regional difference in FD’s effect on EE. Lastly, under the financial decentralization structure, numerous things might impact policy and decisions. To stimulate GDP development, they will almost certainly alter the metropolitan economic processes, and the rise of the intermediate goods will have consequences for resource use and environmental quality. Electricity convenience’s significance from this angle has been the subject of very few academic investigations. This work uses the public equation as a starting point to develop a theoretical framework for investigating the connection between FD and CEM. Assuming a central and regional authority, each with its production and usage agencies. Currently, state councils in China are incentivized politically by the prospect of development, so they spend money strategically on common goods organizations or economic growth with high external costs. This context is used to build the utility function:1 Uil=μ,yi,pi=(1-μ)yi+μpi
The link between ecological health and government interference is given by Eq. (1). The government financial expenditure scale, denoted by Gi, is the inverse of the financial expenditure structure characterized by i.2 Gi=Ai2eiϑkγiϑk21-φiGi1+ϑ1-γi1+ϑc1Aiγi+ξi
local tax and transfer payments from the higher level of government.3 ∂G1∂ei=∂pi∂ei=kiγi+ϑk(1-μ)1+ϑc+eiγi+ϑkμ1+ϑc2
The budgetary outlay of the municipality is shown in Eq. (3).
Study data and variables
The study estimates the role of financial decentralization on carbon taxation and emission in a Chinese context. The study obtained data from different sources (Table 1). The data range comprises 2005 to 2019.Table 1 Variables and data sources
Variables Abbreviations Measure Data source
Carbon emission CE CE per capita GFN
Financial decentralization FD Self-developed measure OECD
Carbon taxation CT Tax percentage Input–output table of China
Economic growth GDP USD constant (2010) WDI
A self-developed measure of financial decentralization includes proxies titled revenue decentralization and expanses decentralization. Both representatives are used to calculate and represent the measurement of economic decentralization. The formulas are as follows:RevenueDecentralization=ProvincialRevenueTotalGovetRevenue
ExpanseDecentraliztion=ProvincialExpensesTotalGovetExpanses
CompositeDecedentalizationIndex=RevenueDecentralization1-ExpanseDecen
Estimation technique
CGE model
Numerous policy analyses use the CGE model. All CGE models are built using the same foundational principles, which describe the concept as a network of algebraic expressions derived from maximizing all agents’ behavior. The CGE model simulates the interactions between various groups, including locals, businesses, the state, and visitors. This paper’s model of the CGE foundation is taken from ref. [38]. Some new components, including a sectoral categorization, level of output, energy component, energy-policy block, dynamic recursion, and two people, are included in this architecture. The five sections are production, income and spending trade, energy policy, macro-closure, and marketplace clearance. The CGE model used here is a stationary, one-region model and is referred to as such throughout the paper. To maximize the emissions taxing plan, we adjusted the energy aggregation of the model by distinguishing between power generated from fossil fuels and generated power from other sources. In this approach, the holistic framework and sectoral produce branches were built using the consistent stiffness of replacement (CES). The model expands the ecological component to compute energy-related CO2 emissions and compare the consequences of various carbon taxing regimes. A carbon tax, or ad valorem tax, is a kind of environmentalist levy that raises the price of producing and using electricity that produces carbon dioxide. Since the administration would both be the receiver and the tax collectors, we decided not to consider it in our study. The basic framework of the CGE technique is given as follows:4 CCMi=αinoeδinoeFDiρinoe+1-δinoeCTiρinoe1ρinoe
5 PFDiPCTi=δinoe1-δinoeFDiCTi1-ρinoe
6 CCMi=αieneδieneFDiρiene+1-δieneCTiρiene1ρiene
7 EENCCMi=CO2iCO2i+GHGiNHGi
8 CTi,l=βi,lxpPQi∑iγllabLABi·PLABi+γlcapCAPi·PCAPi-SPl-TDl
9 CT=τld∑iγllabLABi·PLABi+γlcapCAPi·PCAPi
10 FDi=αivaeδivaeVAipipue+1-δivaFNEipipoe1/ρiva
Although the CGE model is an excellent resource for analyzing policy, it does have certain constraints. The CGE model predicts that the amount of structural unemployment and the property of the labor pool, the type of rivalry among enterprises, and the pace of technological advancement is unaffected by new policies. Assumptions of adaptability and productivity growth of labor or capital mean that the CGE model is not a valuable event planned. Thirdly, the CGE model requires data that is further complicated and hard to collect than that needed by input–output evaluation, as it examines not just business but also people and political choices.
Scheme of analysis
More recently conducted quantitative research has also focused on the CSD, which 1st generational estimation techniques ignore, including an apparent absolute deviation and conventional linear regression estimates.11 αiLΔyit=γ1i+γ2it+βiyit-1-a´ixit-1)+γi(L)′vit+ηi
Following Eq. (11), the study tested the cointegration analysis results and found the Y means of climate change mitigation as the initial dependent variable and the X mean of independent variables, carbon taxation, and financial decentralization.12 Yit=αi+Xit′β+δi+Zit′γUit
As such, the research begins with estimate strategies such as dynamic ordinary least squares and fixed effect regular least squares before moving on to others.13 Qyτ∣Xit=αi+δi(τ)+Xit′β+Zit′γq(τ)
In particular, the research standard errors are used when using the CGM technique.
Results and discussion
Identifying stationary level of variables
The empirical finding of the study revealed that around 1,500,000 new companies have emerged in China till 2019 with the potential contributor to carbon emission that needs to be enlisted in the carbon tax list. This figure hampers the fiscal decentralization of China as a whole. It is anticipated that the number of companies doing business in China is increased from 7.5% in 2018 to 11.4% in 2019 only. Similarly, better environmental quality is estimated to save 40,000 lives annually. Between 2005 and 2015, the Netherlands wanted to produce 100% more energy (Iqbal et al. 2021). Over the last 6 years, the Chinese economy has increased financial decentralization at an average of 6.6%. By 2030, it is expected that around 2.35 billion amount is expected to be invested in carbon emission taxation through carbon taxation using financial decentralization support mechanisms. Over the study’s sample period, study findings revealed that around a 7% rise in economic decentralization is noted for carbon emission and 11% to promote carbon taxation. The percentages of clean environment for 2020 and 2050 are 5 and 11%, accordingly. In the nation, 228 MW of installed wind power is also used on the other side, and by the end of 2020, the Chinese government wants to enhance the carbon tax net. To extend the reported findings, Table 2 represents the unit root test of the study using augmented Dicky-Fuller (ADF) technique and Phillip-Parron (PP) technique.Table 2 Analyzing the stationary level of variables
Level 1st difference
Intercept Intercept and trend Intercept Intercept and trend
ADF estimates
C 10.91 (0.7621) 10.2 (0.1421) 34.90 (0.1113) 0.55 (0.1330)
CO2 13.10 (0.2290) 8.89 (0.2576)* 12.17 (0.2179)* 3.94 (0.1019)*
GHG 11.01 (0.4441) 4.44 (0.2210)* 77.65 (0.4456) 2.55 (0.8967)*
NHG 16.43 (0.4435) 12.61 (0.2750) 20.33 (0.1265) 7.87 (0.2341)*
CT 4.5527 (0.1010) 2.75 (0.1144)* 74.44 (0.1739)* 4.45 (0.8501)
ED 8.683 (0.3012) 5.19 (0.6754)* 11.19 (0.2474) 4.01 (0.7004)*
RD 5.227 (0.2960) 0.19 (0.2475) 67.50 (0.2089)* 6.11 (0.2012)
CD 4.312 (0.7580) 4.29 (0.0334)* 11.79 (0.0567)* 7.71 (0.7013)*
PP estimates
C 29.29 (0.7031) 11.39 (0.1289) 11.37 (0.3030) 4.008 (0.1271)
CO2 40.23 (0.0440) 88.14 (0.3050)* 16.88 (0.7646)* 12.91 (0.1496)
GHG 78.14 (0.8045) 7.69 (0.7146) 14.91 (0.2147)* 24.17 (0.6714)*
NHG 5.545 (0.3204) 0.79 (0.0022)* 18.91 (0.2192)* 19.89 (0.3708)
CT 6.098 (0.6040) 1.71 (0.1930)* 74.75 (0.2778)* 23.45 (0.2113)*
ED 12.07 (0.5556) 9.78 (0.1414)* 50.21 (0.1635)* 14.58 (0.0466)*
RD 13.41 (0.3318) 7.92 (0.0333) 18.23 (0.0789)* 4.181 (0.044)*
CD 92.31 (0.7652) 5.05 (0.1591)* 34.09 (0.9472) 9.676 (0.2117)*
The results of the ADF and PP unit roots are comparable among measurements, according to Table 3. Consequently, the null hypothesis was verified, and it was discovered that when variables initially became stable, they co-integrated in a particular order. By enlarging it, the founding test improves the socioeconomic accuracy of study results. The elements in Table 4 seem to be strongly correlated with one another. Study hypothesis is therefore authorized. According to research findings, the growth in fiscal decentralization is increasing energy use and promoting carbon taxation for carbon emission mitigation. Following the GDP rise of the Chinese economy, environmental assets and social resources regarding carbon taxation among industrial individuals have risen as the economy grows (Tu et al. 2021). The demand for clean energy would increase by 22%, boosting world economic development by 1%. A country’s carbon pollution increases by 4.55% for every point margin increase in GDP. An increasing corpus of evidence indicates that as GDP and populations expand, so does carbon dioxide despite having GDPs of more than $1 trillion budget of fiscal decentralization of prominent Chinese people. The carbon emission efficiency drops are minimal in the Chinese setting and about − 4% when the number of companies utilizing the carbon taxation schemes (Li et al. 2021a, b). It is due to fiscal decentralization having a sizable average effect on the group’s total direct investment, even though only 3 to 9% of Chinese high-pollution emission economies contributed significantly to the country’s economic growth. Hence, the role of fiscal decentralization in promoting carbon taxation, climate change emission, and economic growth development is significant (Table 4).Table 3 Assessing cointegration estimates
FD P value CT P value CCM P value GHG P value
Within region
V-estimate 4.19 (0.005)* 7.51 (0.000)* 11.86 (0.002)* 3.14 (0.004)*
Rho-estimate 6.85 (0.007)* 9.82 (0.000)* 28.28 (0.004)* 3.41 (0.006)*
PP-estimate 6.19 (0.001)* 9.61 (0.000)* 13.49 (0.007)* 45.9 (0.009)*
ADF-score 7.21 (0.009)* 8.91 (0.000)* 21.72 (0.001)* 24.18 (0.023)*
Outside region
V-score 17.6 (0.006)* 5.45 (0.000)* 13.71 (0.011)* 17.19 (0.017)*
Rho-estimate 8.45 (0.022)* 16.40 (0.000)* 18.9 (0.015)* 18.91 (0.021)*
PP-estimate 13.8 (0.041)* 18.91 (0.000)* 21.92 (0.020)* 12.17 (0.015)*
ADF-score 9.24 (0.3041) 12.71 (0.000)* 30.45 (0.001)* 5.16 (0.004)*
Between region
Group rho-statistic 1.02 (0.8874) 4.44 (0.7932) 1.89 (0.5521) 6.21 (0.7247)
Group PP-statistic 3.19 (0.8839)* 2.58 (0.7932) 3.99 (0.0012)* 3.19 (0.2311)*
Group ADF-statistic 3.31 (0.3192)* 5.72 (0.6819)* 3.15 (0.0003)* 5.17 (0.2891)*
Table 4 Split analysis estimates
RD ED CT CEM
Carbon taxation 0.081* 0.088* 0.073* 0.059*
(0.044) 0.056 0.021 0.42
CO2 emission 0.071*** 0.039
(0.041) 0.028
GHG emission 0.317* 0.058*
(0.045) 0.047
NHG emission − 0.298*** 0.239* 0.441* − 0.495*
(0.99) (0.81) (0.073) (0.065)
CID 0.013 0.034 0.080 0.031
(0.073) (0.083) (0.539) (0.041)
ED − 0.022 0.027 0.781** 0.067***
(0.029) (0.025) (0.283) (0.028)
RD − 0.038* − 0.124 0.039 0.039*
(0.032) (0.024) (0.068) (0.035)
Constant 4.723*** 4.815*** 3.888*** 3.50***
(0.685) (0.6.61) 0.667 0.848
Observations 111 111 111 111
Arellano-Bond AR (1) − 4.026 − 4.141 − 4.583 − 4.876
[0.001] [0.003] [0.032] [0.041]
Arellano-Bond AR (2) 0.828 0.778 − 0.084 − 0.074
[0.619] [0.567] [0.754] [0.783]
Sargan test 246.847 275.789 234.782 247.237
[0.880] [0.606] [0.801] [0.838]
Split analysis
In this section, the study results extended that Chinese financial decentralization has a detrimental role in carbon emission mitigation via carbon taxation scheme utilization. Indicators of necessary carbon taxation and emission mitigation functions are significantly connected in split analysis findings. A perfect score of 100 would indicate that all 21 had been met. The gap between current and unsustainable situations is 40 points, even in China, the top-scoring country. There is a considerable variation in environmental efficiency across different ecological jobs, devastatingly affecting environmental integrity. Stabilized financial development based on economic decentralization is one of the core reasons carbon taxation influences China’s GDP and carbon emissions, as reported in results. Moreover, carbon taxation significantly affects Chinese companies working in different industries.
The estimates for this are reported in Tables 4 and 5 sequentially. Specifically, the parameters for CO2 emission per capita are 0.057 and 0.126. Compared to that, the financial decentralization scores were much lower (0.022) and much higher (0.073) per capita. Moreover, with a high GDP per capita like those shown here, China can accurately predict the effects of structural and technical changes. Public expenditure is correlated with low GDP per capita, with a value of 0.215. It is worth noting that this data is intriguing even at the 1% level of significance. Correspondingly, Iqbal and Bilal (2021) supported these findings. For China, with a high income per capita, however, the coefficient is just 0.79. This percentage, even at only 5%, is relatively large. Hence, the split analysis confirmed the detrimental role of financial decentralization with carbon emission and taxation along with economic growth.Table 5 LM test estimates
Fixed effect Random effect
Coefficient Sig Coefficient Sig
LM-lag 20.57 0.0092 16.23 0.0006
RLM-lag 6.64 0.0001 5.57 0.0001
LM-error 41.77 0.0041 34.62 0.0004
RLM-error 0.258 0.0000 0.162 0.0008
As seen in Table 5, climate change may have far-reaching consequences for the conventional market function of the Chinese setting. A large portion of the dramatic growth in energy consumption may be attributed to the aging of the population. Table 5 shows how China responded to a second input variable in the simulation, revealing a spectrum of economic data estimates related to carbon taxation. Since CO2 emission statistics within a country show remarkable consistency over time, it is reasonable to assume that international differences in emissions are responsible for no more than 1% of the total. However, Zhang et al. (2022) supported these estimates of study.
Long-run and robustness estimation using the CGM model
The study reported the long-run estimated trend in Table 6. The results show that cutting CO2 emissions is good for the Chinese industrial economy and is derived based on carbon taxation and financial decentralization. Changes of this magnitude are primarily attributable to economic decentralization and carbon taxation sources. However, fiscal decentralization is crucial in spreading awareness of and supporting renewable energy. The Chinese government has agreed that the variables had a significant relationship. Thus, budgetary decentralization strategies for cleaning and greening ecosystems rely heavily on wind and solar electricity, as shown below. These findings corroborated the financial growth and development and demonstrated the direct causal relationship between carbon taxation and carbon emission mitigation in the Chinese context (Yang et al. 2022a, b). Thus, renewable energy solutions are directly and indirectly related to local economic growth and reducing the effects of climate change.Table 6 Long-run estimates of the CGM model
Economies CGM model function Durbin-Watson
Ln(FD) Ln(CT) Ln(CCM)
CO2 0.29 (0.011)* 0.28 (0.001)* 0.34 (0.001)* 0.211 (0.000)*
GHG 0.47 (0.017)* 0.34 (0.000)* 0.45 (0.002)* 0.199 (0.000)*
NHG 0.33 (0.021)* 0.56 (0.003)* 0.78 (0.002)* 0.213 (0.000)
CT 0.57 (0.024)* 0.39 (0.007)* 0.31 (0.006)* 0.334 (0.000)*
ED 0.41 (0.005)* 0.62 (0.009)* 0.89 (0.000)* 0.451 (0.000)*
RD 0.23 (0.040)* 0.31 (0.011)* 0.67 (0.003)* 0.339 (0.000)*
CD 0.44 (0.018)* 0.81 (0.023)* 0.33 (0.007)* 0.465 (0.000)*
The study’s findings are interpreted with care, and the fiscal decentralization index evaluates how prosperous countries are doing regarding a wide range of ecological and resource-related sustainability indicators that have been established following scientific consensus. Theoretically, in two nations where one-quarter of the population is exposed to air pollution just beyond environmental limitations, a normalized score of 75 is achievable. Meanwhile, the other 25% is subjected to hundreds of times greater concentrations. These findings are endorsed by Sun et al. (2022a, b) and with this way, the index’s measurements are based on geography rather than individual purchases.
Results from the panel are still essential both theoretically and regionally. Residual error probabilities range from 1% at the lowest percentile to 99.995% at the greatest. More than half of all foreign residents and 48% of all international property are at risk from floods. The vast majority of people in the world are crammed into countries with poor infrastructure. In 2018, it was estimated that the world’s 1.5 billion people would produce a nominal GDP of around $6.5 trillion. Despite having a larger demographic, they have a GDP that is on par with China. The current rate of carbon taxation expansion was amplified by a factor of one, resulting in a boost of 0.11%. Our results are in line with other studies on regional efforts in China under various scenarios; therefore, we highlight the importance of climate funding on regional scales like China to promote a cleaner environment, boost economic development, and enlarge financial decentralization, as shown in Table 7. It is evident from this data that carbon emission levels may increase or decrease in tandem with the development of China. The empirical research community should include environmental protection in its emphasis on houses for the elderly (Wang et al. 2022a, b). As a first point, the concept of environment gerontology proposes that the interaction between the home and an individual’s competence significantly impacts the individual’s well-being. Homeowners over 65 may enjoy a higher quality of life by incorporating sustainable design components into their dwellings. It is impossible to understate the value of seniors’ contributions to sustainable development.Table 7 Robustness analysis through CGM function estimates
DV F-statistics
CO2 GHG NHG CT ED RD CD ECT (− 1)
CO2 – 0.25 (0.001)*
GHG 0.36* – 0.29 (0.005)*
NHG 0.28* 0.31* – 0.36 (0.023)*
CT 0.38* 0.44* 0.88* – 0.48 (0.010)*
ED 0.37* 0.52* 1.541* 3.27* – 0.32 (0.019)*
RD 0.49* 0.44* 2.591* 2.51* 2.67* – 0.25 (0.0021)*
CD 0.24* 0.67* 3.24* 1.43* 2.69* 2.88* – 0.48 (0.018)*
This empirical section highlights some variations in the numbers across the Chinese economy. Overall, it is noted that China is better equipped to deal with climate change using both carbon taxation schemes and financial decentralization. These scores range from 46 to 54%, with carbon taxation scoring the lowest. In terms of environmental performance, Jiangsu is over 75%. With a score of over 93%, China has established a new benchmark for environmental performance as best meeting standard practices for climate change mitigation (Fig. 1). With just 60% of the population concerned about the environment, it is clear that immediate action is needed to safeguard the future of China and its survival.Fig. 1 Comparison of CGM-based long-run estimates (source: authors’ calculation)
Discussion
The results showed and explained that a 10% carbon tax rate is more economically damaging than a 5% tax rate since it reduces consumer spending, housing well-being, and income growth. Moreover, because of the interconnected nature of the three sectors, raising the carbon tax rate in one might harm production in the remaining two. Moreover, a 3% drop in production is experienced by all three sectors because when the stream carbon tax is raised from 5 to 10%, a 5% rise in the intermediate carbon tax primarily affects the upstream sector sectors. The carbon tax rate paid downstream equally has a negligible effect on output farther down the pipeline and in the middle. Our finding indicates that perhaps the downstream industry is more sensitive to the carbon tax rate than the main business when calculating the stable state of industrial output. Production declines in the headwaters and the upstream sectors are possible due to increased carbon tax rates. This decrease is harmful because it decreases downstream production’s supply and demand. However, the carbon tax rate in the downstream sector has a more significant influence on output in the intermediate sector because of the larger size of the upstream segment.
The effects of climate change mitigation through carbon taxation were examined. Still, other factors, such as carbon legislation, human resource management, carbon capture rates, and the need for environmentally responsible technological progress, were also considered. We selected a cluster of Chinese provinces to analyze because of many factors. These seven economies produce over half of the world’s GDP, so their actions are crucial if we are going to keep CO2 levels down. In 2010, China had the highest emissions in the world, but today it. It is all because of economic stability, having much room for extended financial decentralization and carbon taxation schemes. Given China’s continuous support for fossil fuels, the country’s approach to combating climate change will be rated as only adequate. And although China ranks low in efficiency, the UK, Indonesia, and China all achieve remarkable results in terms of greenhouse gas emissions and carbon taxation, respectively. The study’s examination of the ASEAN member nations’ quasi-traits is fascinating. The world’s leading countries might use the study’s results to craft policies that promote global stability. The research assumes that the Chinese areas will grow in terms of building over the long run. These findings support the study’s central hypothesis that efforts to improve environmental quality (such as those taken to combat climate change) are correlated with higher income growth. Adapting climate finance strategies will enhance China’s ecological, economic, and social conditions. In light of this, the following assumptions have been accepted, and the findings of our study are likely to hold up over the long term (Fig. 1). Assume a 5% threshold of significance. According to the study results of the long-term unidirectional causation, Tables 3 and 4, which are based on split analysis and CGM method, show quantitative findings of the study with excellent precision. Our results are consistent with those found in previous studies. Its findings are consistent with those of prior research. The current research fills a theoretical, empirical, and practical void, offering crucial suggestions to lawmakers.
Conclusion and policy recommendation
The goal of this research is to provide guidance for reviving the economy by evaluating the impact of decentralized finance on carbon pricing and carbon emissions. This research used a cointegration analysis method, a CGE estimation model, a long-run analysis using a t-CGE model, and a robustness analysis method to estimate the nexus using data from China. According to the study’s results, financial decentralization plays a crucial part in furthering carbon taxation in China, and it also provided 14.92% of the funding for furthering carbon taxation across all of China’s businesses. Businesses in the transportation sector and other manufacturing firms rank high among the sectors that contribute to pollution. The main findings are as follows:(i) The inefficiencies of centralization are eliminated via decentralization reform, ensuring China will continue its development wonderfully. The lowest part of competitive rivalry within and between local authorities will provide a fertile ground for weakening pollution rules, causing global consideration around its environmental effects because more managerial acceptance and financial individuality are devolved to local councils partnering with local productivity performance advertising subsidies for representatives. Several researchers have linked decentralization to environmental deterioration, and they have discovered quantitative evidence to support their claims.
(ii) These perspectives and facts call into question the certainty with which we can assume that municipal authorities would use their expanded control to strike a sustainable economic balance between economic development and environmental protection due to localization. To mitigate tensions between administrative and fiscal decentralization in environmental regulation, policymakers should integrate the different reform pilot policies and promote the organized growth of pilot counties and reform projects. Promoting economic development and improving ecological circumstances require the federal government to prioritize environmental protection by developing the national diversity performance assessment system. Please pay close attention to the fiscal power and administrative authority structure of provincial, municipal, county, and other levels of government as they pertain to environmental resource allocation and endeavor to strengthen the fiscal decentralization system.
(iii) Local governments should prioritize encouragement programs, pollution prevention, and control technology to save the environment. The federal government has to reorganize the local fiscal decentralization system to clarify what roles each county is supposed to play in addressing pollution and environmental issues. Local governments with more significant financial clout should better distinguish between their administrative power and market processes and integrate government control with an effective market. Future studies should employ big data and machine learning to examine the micro emission behavior of firms affected by decentralization to make up for the knowledge deficit.
Author contribution
Conceptualization, methodology, and writing—original draft: Chunying Zeng; data curation, visualization, editing: Jiaojiao Zhao.
Funding
This work was sponsored in part by (1) Guangdong Natural Science Foundation (2022A1515110614), and (2) Guangxi Philosophy and Social Science Planning Project (22CJL007).
Data availability
The data that support the findings of this study are openly available on request.
Declarations
Ethics approval and consent to participate
The authors declared that they have no known competing financial interests or personal relationships, which seem to affect the work reported in this article. We declare that we have no human participants, human data, or human issues.
Consent for publication
We do not have any individual person’s data in any form.
Competing interests
The authors declare no competing interests.
Preprint service.
Our manuscript is posted at a preprint server prior to submission.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Acheampong AO Adams S Boateng E Do globalization and renewable energy contribute to carbon emissions mitigation in Sub-Saharan Africa? Sci Total Environ 2019 677 436 446 31059886
Ahmad M Satrovic E Relating fiscal decentralization and financial inclusion to environmental sustainability: criticality of natural resources J Environ Manage 2023 325 116633 36419297
Arends H The dangers of fiscal decentralization and public service delivery: a review of arguments Politische Vierteljahresschrift 2020 61 3 599 622
Arumugam C Shaik S Transforming waste disposals into building materials to investigate energy savings and carbon emission mitigation potential Environ Sci Pollut Res 2021 28 12 15259 15273
Arumugam C Shaik S Shaik AH Kontoleon KJ Mazzeo D Pirouz B Polymer and non-polymer admixtures for concrete roofs: thermal and mechanical properties, energy saving and carbon emission mitigation prospective J Build Eng 2022 45 103495
Bahl R (1999) Implementation rules for fiscal decentralization. International Studies Program Working Paper, 30
Bilal AR, Fatima T, Iqbal S, Imran MK (2022) I can see the opportunity that you cannot! A nexus between individual entrepreneurial orientation, alertness, and access to finance. Eur Bus Rev 34(4):556–577
Cao Z, Myers RJ, Lupton RC, Duan H, Sacchi R, Zhou N, ... Liu G (2020) The sponge effect and carbon emission mitigation potentials of the global cement cycle. Nature Commun 11(1):1–9
Chen X Chang CP Fiscal decentralization, environmental regulation, and pollution: a spatial investigation Environ Sci Pollut Res 2020 27 25 31946 31968
Cheng S, Fan W, Meng F, Chen J, Liang S, Song M, ... Casazza M (2020) Potential role of fiscal decentralization on interprovincial differences in CO2 emissions in China. Environ Sci Technol 55(2):813–822
Cheng S, Meng L, Xing L (2021) Energy technological innovation and carbon emissions mitigation: evidence from China. Kybernetes
Cheng Z Zhu Y The spatial effect of fiscal decentralization on haze pollution in China Environ Sci Pollut Res 2021 28 36 49774 49787
Criqui P Jaccard M Sterner T Carbon taxation: a tale of three countries Sustain 2019 11 22 6280
Ding Y McQuoid A Karayalcin C Fiscal decentralization, fiscal reform, and economic growth in China China Econ Rev 2019 53 152 167
Du J Sun Y The nonlinear impact of fiscal decentralization on carbon emissions: from the perspective of biased technological progress Environ Sci Pollut Res 2021 28 23 29890 29899
Elkins P Baker T Carbon taxes and carbon emissions trading J Econ Surv 2001 15 3 325 376
Epple D Nechyba T Fiscal decentralization Handb Reg Urban Econ 2004 4 2423 2480
Gao Y Li M Xue J Liu Y Evaluation of effectiveness of China’s carbon emissions trading scheme in carbon mitigation Energy Econ 2020 90 104872
Hanif I Wallace S Gago-de-Santos P Economic growth by means of fiscal decentralization: an empirical study for federal developing countries SAGE Open 2020 10 4 2158244020968088
Hao Y Chen YF Liao H Wei YM China’s fiscal decentralization and environmental quality: theory and an empirical study Environ Dev Econ 2020 25 2 159 181
Hao Y Liu J Lu ZN Shi R Wu H Impact of income inequality and fiscal decentralization on public health: evidence from China Econ Model 2021 94 934 944
He L Hu C Zhao D Lu H Fu X Li Y Carbon emission mitigation through regulatory policies and operations adaptation in supply chains: theoretic developments and extensions Nat Hazards 2016 84 1 179 207
Hornsey MJ Fielding KS A cautionary note about messages of hope: focusing on progress in reducing carbon emissions weakens mitigation motivation Glob Environ Chang 2016 39 26 34
Hou H Feng X Zhang Y Bai H Ji Y Xu H Energy-related carbon emissions mitigation potential for the construction sector in China Environ Impact Assess Rev 2021 89 106599
Huang J Wang X Liu H Iqbal S Financial consideration of energy and environmental nexus with energy poverty: promoting financial development in G7 economies Front Energy Res 2021 9 777796
Iqbal S Bilal AR Nurunnabi M Iqbal W Alfakhri Y Iqbal N It is time to control the worst: testing COVID-19 outbreak, energy consumption and CO2 emission Environ Sci Pollut Res 2021 28 15 19008 19020
Iqbal S, Bilal AR (2021) Energy financing in COVID-19: how public supports can benefit? China Finance Review International
Jiang K You D Merrill R Li Z Implementation of a multi-agent environmental regulation strategy under Chinese fiscal decentralization: an evolutionary game theoretical approach J Clean Prod 2019 214 902 915
Jin T Kim J What is better for mitigating carbon emissions–renewable energy or nuclear energy? A panel data analysis Renew Sustain Energy Rev 2018 91 464 471
Kassouri Y Fiscal decentralization and public budgets for energy RD&D: a race to the bottom? Energy Policy 2022 161 112761
Khan Z Ali S Dong K Li RYM How does fiscal decentralization affect CO2 emissions? The roles of institutions and human capital Energy Econ 2021 94 105060
Kuai P Yang S Tao A Khan ZD Environmental effects of Chinese-style fiscal decentralization and the sustainability implications J Clean Prod 2019 239 118089
Lee CF Lin SJ Lewis C Analysis of the impacts of combining carbon taxation and emission trading on different industry sectors Energy Policy 2008 36 2 722 729
Li W, Chien F, Hsu CC, Zhang Y, Nawaz MA, Iqbal S, Mohsin M (2021) Nexus between energy poverty and energy efficiency: estimating the long-run dynamics. Res Policy 72: 102063
Li M Gao Y Meng B Yang Z Managing the mitigation: analysis of the effectiveness of target-based policies on China’s provincial carbon emission and transfer Energy Policy 2021 151 112189
Li W Chien F Ngo QT Nguyen TD Iqbal S Bilal AR Vertical financial disparity, energy prices and emission reduction: empirical insights from Pakistan J Environ Manage 2021 294 112946 34153632
Li K, Wang X, Musah M, Ning Y, Murshed M, Alfred M, ... Wang L (2022) Have international remittance inflows degraded environmental quality? A carbon emission mitigation analysis for Ghana. Environ Sci Pollut Res 1–17
Lin B Zhou Y Does fiscal decentralization improve energy and environmental performance? New perspective on vertical fiscal imbalance Appl Energy 2021 302 117495
Lingyan M Zhao Z Malik HA Razzaq A An H Hassan M Asymmetric impact of fiscal decentralization and environmental innovation on carbon emissions: evidence from highly decentralized countries Energy Environ 2022 33 4 752 782
Liu R Zhang X Wang P A study on the impact of fiscal decentralization on green development from the perspective of government environmental preferences Int J Environ Res Public Health 2022 19 16 9964 36011608
Ma M Ma X Cai W Cai W Low carbon roadmap of residential building sector in China: historical mitigation and prospective peak Appl Energy 2020 273 115247
Mossler MV Bostrom A Kelly RP Crosman KM Moy P How does framing affect policy support for emissions mitigation? Testing the effects of ocean acidification and other carbon emissions frames Glob Environ Chang 2017 45 63 78
Oates WE Fiscal decentralization and economic development Natl Tax J 1993 46 2 237 243
Qiao M Ding S Liu Y Fiscal decentralization and government size: the role of democracy Eur J Polit Econ 2019 59 316 330
Ryu H Dorjragchaa S Kim Y Kim K Electricity-generation mix considering energy security and carbon emission mitigation: case of Korea and Mongolia Energy 2014 64 1071 1079
Sanogo T Does fiscal decentralization enhance citizens’ access to public services and reduce poverty? Evidence from Côte d’Ivoire municipalities in a conflict setting World Dev 2019 113 204 221
Savin I Drews S Maestre-Andrés S van den Bergh J Public views on carbon taxation and its fairness: a computational-linguistics analysis Clim Change 2020 162 4 2107 2138
Shaik S Gorantla K Ghosh A Arumugam C Maduru VR Energy savings and carbon emission mitigation prospective of building’s glazing variety, window-to-wall ratio and wall thickness Energies 2021 14 23 8020
Shaik S Maduru VR Kirankumar G Arıcı M Ghosh A Kontoleon KJ Afzal A Space-age energy saving, carbon emission mitigation and color rendering perspective of architectural antique stained glass windows Energy 2022 259 124898
Shan S Ahmad M Tan Z Adebayo TS Li RYM Kirikkaleli D The role of energy prices and non-linear fiscal decentralization in limiting carbon emissions: tracking environmental sustainability Energy 2021 234 121243
Shao S Razzaq A Does composite fiscal decentralization reduce trade-adjusted resource consumption through institutional governance, human capital, and infrastructure development? Resour Policy 2022 79 103034
Siburian ME Fiscal decentralization and regional income inequality: evidence from Indonesia Appl Econ Lett 2020 27 17 1383 1386
Sims RE Rogner HH Gregory K Carbon emission and mitigation cost comparisons between fossil fuel, nuclear and renewable energy resources for electricity generation Energy Policy 2003 31 13 1315 1326
Smith P Milne R Powlson DS Smith JU Falloon P Coleman K Revised estimates of the carbon mitigation potential of UK agricultural land Soil Use Manag 2000 16 4 293 295
Song M Du J Tan KH Impact of fiscal decentralization on green total factor productivity Int J Prod Econ 2018 205 359 367
Sun L Fang S Iqbal S Bilal AR Financial stability role on climate risks, and climate change mitigation: implications for green economic recovery Environ Sci Pollut Res 2022 29 22 33063 33074
Sun Y Guan W Razzaq A Shahzad M An NB Transition towards ecological sustainability through fiscal decentralization, renewable energy and green investment in OECD countries Renew Energy 2022 190 385 395
Thanh SD Canh NP Fiscal decentralization and economic growth of Vietnamese provinces: the role of local public governance Ann Public Coop Econ 2020 91 1 119 149
Tu CA, Chien F, Hussein MA, RAMLI MM YA, S. PSI MS, Iqbal S, Bilal AR (2021) Estimating role of green financing on energy security, economic and environmental integration of BRI member countries. The Singapore Economic Review
Tufail M Song L Adebayo TS Kirikkaleli D Khan S Do fiscal decentralization and natural resources rent curb carbon emissions? Evidence from developed countries Environ Sci Pollut Res 2021 28 35 49179 49190
Ulucak R Khan SUD Baloch MA Li N Mitigation pathways toward sustainable development: is there any trade-off between environmental regulation and carbon emissions reduction? Sustain Dev 2020 28 4 813 822
Wang Q Hubacek K Feng K Wei YM Liang QM Distributional effects of carbon taxation Appl Energy 2016 184 1123 1131
Wang J Huang Y Teng Y Yu B Wang J Zhang H Duan H Can buildings sector achieve the carbon mitigation ambitious goal: case study for a low-carbon demonstration city in China? Environ Impact Assess Rev 2021 90 106633
Wang KH Liu L Adebayo TS Lobonț OR Claudia MN Fiscal decentralization, political stability and resources curse hypothesis: a case of fiscal decentralized economies Resour Policy 2021 72 102071
Wang D Zhang Z Shi R Fiscal decentralization, green technology innovation, and regional air pollution in China: an investigation from the perspective of intergovernmental competition Int J Environ Res Public Health 2022 19 14 8456 35886302
Wang S Sun L Iqbal S Green financing role on renewable energy dependence and energy transition in E7 economies Renew Energy 2022 200 1561 1572
Xia S You D Tang Z Yang B Analysis of the spatial effect of fiscal decentralization and environmental decentralization on carbon emissions under the pressure of officials’ promotion Energies 2021 14 7 1878
Xu X Yang G Tan Y Zhuang Q Tang X Zhao K Wang S Factors influencing industrial carbon emissions and strategies for carbon mitigation in the Yangtze River Delta of China J Clean Prod 2017 142 3607 3616
Yang S Li Z Li J Fiscal decentralization, preference for government innovation and city innovation: evidence from China Chin Manag Stud 2020 14 2 391 409
Yang Y Yang X Tang D Environmental regulations, Chinese-style fiscal decentralization, and carbon emissions: from the perspective of moderating effect Stoch Env Res Risk Assess 2021 35 10 1985 1998
Yang X Wang J Cao J Ren S Ran Q Wu H The spatial spillover effect of urban sprawl and fiscal decentralization on air pollution: evidence from 269 cities in China Empir Econ 2022 63 2 847 875
Yang Y Liu Z Saydaliev HB Iqbal S Economic impact of crude oil supply disruption on social welfare losses and strategic petroleum reserves Resour Policy 2022 77 102689
You D Zhang Y Yuan B Environmental regulation and firm eco-innovation: evidence of moderating effects of fiscal decentralization and political competition from listed Chinese industrial companies J Clean Prod 2019 207 1072 1083
Yuan F Wei YD Xiao W Land marketization, fiscal decentralization, and the dynamics of urban land prices in transitional China Land Use Policy 2019 89 104208
Yun PENG Xiangda LI Wenyuan WANG Ke LIU Chuan LI A simulation-based research on carbon emission mitigation strategies for green container terminals Ocean Eng 2018 163 288 298
Zhang X Luo L Skitmore M Household carbon emission research: an analytical review of measurement, influencing factors and mitigation prospects J Clean Prod 2015 103 873 883
Zhang W Zhang N Yu Y Carbon mitigation effects and potential cost savings from carbon emissions trading in China’s regional industry Technol Forecast Soc Chang 2019 141 1 11
Zhang L Li Z Jia X Tan RR Wang F Targeting carbon emissions mitigation in the transport sector–a case study in Urumqi China J Clean Prod 2020 259 120811
Zhang S Wu X Zheng X Wen Y Wu Y Mitigation potential of black carbon emissions from on-road vehicles in China Environ Pollut 2021 278 116746 33676196
Zhang L Huang F Lu L Ni X Iqbal S Energy financing for energy retrofit in COVID-19: recommendations for green bond financing Environ Sci Pollut Res 2022 29 16 23105 23116
Zhao L Shao K Ye J The impact of fiscal decentralization on environmental pollution and the transmission mechanism based on promotion incentive perspective Environ Sci Pollut Res 2022 29 57 86634 86650
Zhao L, Saydaliev HB, Iqbal S (2022a) Energy financing, COVID-19 repercussions and climate change: implications for emerging economies. Clim Chang Econ 2240003
Zheng X Zhou Y Iqbal S Working capital management of SMEs in COVID-19: role of managerial personality traits and overconfidence behavior Econ Anal Policy 2022 76 439 451 35990757
Zhou K Zhou B Yu M The impacts of fiscal decentralization on environmental innovation in China Growth Chang 2020 51 4 1690 1710 | 36773269 | PMC9922042 | NO-CC CODE | 2023-04-18 23:15:18 | yes | Environ Sci Pollut Res Int. 2023 Feb 11; 30(17):49354-49367 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
36787070
25406
10.1007/s11356-023-25406-w
Research Article
Impact of the COVID-19 outbreak on China’s tourism economy and green finance efficiency
Hu Zhaolin [email protected]
Zhu Suting [email protected]
Henan Polytechnic, Zhengzhou, 450000 China
Responsible Editor: Arshian Sharif
14 2 2023
2023
30 17 4996349979
17 11 2022
15 1 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
As a result of the COVID-19 pandemic, production costs have grown, while human and economic resources have been reduced. COVID-19 epidemic costs can be reduced by implementing green financial policies, including carbon pricing, transferable green certificates, and green credit. In addition, China’s tourist industry is a significant source of revenue for the government. Coronavirus has been found in 30 Chinese regions, and a study is being conducted to determine its influence on the tourism business and green financial efficiency. Econometric strategies that are capable of dealing with the most complex issues are employed in this study. According to the GMM system, the breakout of Covid-19 had a negative effect on the tourism business and the efficiency of green financing. Aside from that, the effects of gross capital creation, infrastructural expansion, and renewable energy consumption are all good. The influence of per capita income on the tourism industry is beneficial but detrimental to the efficiency of green finance. Due to the current pandemic condition, this report presents a number of critical recommendations for boosting tourism and green financial efficiency.
Graphical abstract
Keywords
Covid-19
Tourism
Green Finance Efficiency
Renewable energy use
China
issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023
==== Body
pmcIntroduction
One of the twenty-first century’s most significant public health disasters has been the COVID-19 pandemic (Benamraoui 2021). Global economic and financial markets have been adversely affected by the development of this disease. China, the first country to be hit by the outbreak, has made no compromises when implementing virus prevention and control measures (Sharif et al. 2020e). This section summarizes China’s response to the COVID-19 outbreak over a limited period of time. Pneumonia cases with an unknown origin were reported in Wuhan, Hubei Province, at the end of December 2019 (Zhang et al. 2019; Sharif et al. 2020c). WHMHC issued an emergency notification to all medical institutions in Wuhan on December 30, 2019, and ordered them to properly treat patients with this type of pneumonia as the number of cases continued to rise. After that, the National Health Commission (NHC) organized and sent a working group and an expert team to the city to better lead the response to the epidemic and perform on-site investigations (Sharif et al. 2019, 2020d; Deng et al. 2022). There have been 27 confirmed cases of pneumonia in this outbreak, and the World Health Organization has issued a statement on its official website urging people to use face masks outside (Sharif et al. 2017, 2020a; Suki et al. 2020). A special leadership group was established by NHC on January 1st, 2020, in order to develop emergency measures for the outbreak. It was on January 3, 2020, that the World Health Organization (WHO) and other relevant countries began receiving real-time updates from China on the epidemic outbreak’s current status. NHC experts said on January 9, 2020, that a novel coronavirus had been preliminarily found to be the source of pneumonia in Wuhan after a few dedicated days of investigation. Zhong Nanshan, an academician at the Chinese Academy of Engineering, warned that the virus might quickly spread among citizens and urged people not to visit Wuhan unless they had an urgent need to do so (Khan et al. 2019; Jian and Afshan 2022; Sharif et al. 2022; Wan et al. 2022).
China’s tourism industry contributes significantly to the country’s economy. In terms of outbound and inbound tourism, China is regarded as one of the world’s most popular destinations. The most lucrative source of capital in China is generated through domestic visits (Iqbal et al. 2020; Abbas et al. 2020, 2021). About CN 5128 billion is generated annually by China’s tourism industry. Even though Coronavirus had spread throughout the country, the tourism industry had been severely affected (Chang et al. 2022; Pu et al. 2022). The people have been ordered to stay home to protect themselves from the virus. In both domestic and foreign markets, the tourism industry has suffered as a result of this (Ip et al. 2022). An extensive examination of the impact of Coronavirus on China’s tourism business is the subject of this research study. Silva and Henriques (2021), new data on this topic has been gathered and analyzed. Almost half of China’s population has been killed by the deadly Coronavirus. There will be no tour groups departing China until further notice, thanks to a decision by Beijing (Liu et al. 2022c; Feng et al. 2022). Foreign nationals who have previously visited China face additional entry restrictions in Singapore, the USA, and Australia. Anxiety over the Corona Virus outbreak has forced many Chinese domestic and international flights to be canceled (Huang et al. 2022). Because of China’s Coronavirus, airlines have had to cancel flights to and from China. It has had a substantial influence on the sector because of flight cancellations, which have affected sales and revenue for the Airlines Company. Numerous cruise lines, including Royal Caribbean and Norwegian Cruise Lines, have ceased operating from and to China (Ahmad et al. 2021). There has been a decrease in passenger numbers on cruise ships since the outbreak began (Sharif et al. 2020b). In addition, if the suspension of travel continues for an extended period of time, the company is likely to become financially weak. It will have a negative impact on the company's finances, making it impossible for it to continue. Even though China is experiencing one of its busiest seasons, the Coronavirus has struck at a time when many people are on the move. Chinese tourists account for more than 10% of all visitors worldwide. As a result of these efforts, multinational travel companies can better serve the Chinese market.
Because of the fear of the virus spreading, some nations, including the USA and the UK, have cut off commerce and travel with China (Pjanić 2019). Covid-19, a lethal virus, has already had an impact on the Asian continent. According to China’s tourist agency, the country generated $127.3 billion in revenue in 2019 alone. Travel and tourist agreements with China and other Asian nations are being canceled at a higher rate because of an outbreak of a virus similar to pneumonia (Abbas et al. 2022). According to the travel firm, many were simply fed up and would either declare they were not interested in any tours or return in the next year if questioned. 72% of passengers booked via luxury travel have canceled their trips to Southeast Asian countries scheduled to depart during February and March (Zhang et al. 2022; Hailiang et al. 2022). The worldwide visitors had scheduled a number of Southeast Asia destinations, including Beijing, Shanghai, Xi’an, Chengdu, and numerous places in Malaysia and Singapore, which were then canceled and rebooked for other destinations, such as the Maldives, Southern Africa, and Australia (Pham et al. 2021). Even countless investors who invested in Chinese companies like the cosmetics and electronics industries expected the impact of the tiresome virus to continue for roughly 6–12 months. This proposes that COVID-19 has a harmful impact on China’s tourism industry. CEO Chris Nassetta of Hilton has indicated that the Coronavirus is costing his company a significant amount of money. According to him, the possible loss ranges from $25 million to $50 million (Zhang and Zhang 2021).
Since China is the world’s second-largest energy consumer, it is responsible for a quarter of the global total energy consumption (Mach and Ponting 2021). Restructuring the energy industry is, therefore, critical to fostering long-term economic growth that is both healthy and sustainable (Hafsa 2020). The key goals of transitioning the energy sector are to increase the use of non-fossil fuels and reduce carbon emissions, which cannot only rely on tight internal control. The environmental protection industry must help the sector. Environmental protection firms can provide environmentally friendly technology and innovative equipment for energy extraction, refinement, power generation, and distribution (Gössling et al. 2020). When the Chinese government put up its 13th 5-year plan in 2016, it aimed to make China’s energy conservation and environmental protection (ECEP) business a key part of its economy by 2020. As a result, green financing (such as green bonds and loans) can help the ECEP industry grow in this setting. As an emerging industry, the ECEP industry has a number of distinguishing qualities that set it apart from other industries. Because of its high investment risk, long-term investment time, and extensive capital requirements, this business is difficult to obtain green financing. To some extent, financial resources appear to interfere with the expansion of the ECEP business. Because of this, the energy sector’s transformation is being hampered by the need to increase financial efficiency while developing an efficient financial market mechanism to assist the ECEP industry (Apicella et al. 2022). The ECEP industry can be financially supported through green finance regulations and financial market mechanisms that mobilize resources for businesses and projects. There have been a number of measures put forth by the Chinese government to help the ECEP industry grow rapidly and healthily by promoting and guiding green financing. The government is still the driving force behind ECEP business expansion, and particular monies are made available to give subsidies for this purpose. Bank lending (i.e., green credits) offers indirect funding resources for the ECEP industry in China’s financial market, which is a major player in the banking sector. Direct financing (i.e., the equity and bond markets) has lately developed greatly; nonetheless, the higher entry threshold of the motherboard in the A-share market and unsound bond issuances are the two impediments to financing ECEP enterprises, which are developing and ad hoc (Khan et al. 2021). As a result, policymakers and financial intermediaries must work together to find a viable link between green funding strategies and the financial market.
This work adds to the body of knowledge. First, we present quantitative evidence for policymakers about the success of green finance policies by statistically characterizing the spatially varied financing effectiveness that supports the ECEP industry and exhibiting dynamic variations in financing efficiency. We examine the effectiveness of a specific study focusing on the effects of the Covid-19 outbreak on the tourism business and financial efficiency. It is the third time we have devised a solution for increasing efficiency in the financial sector by reinforcing the market mechanism and doing more studies of elements influencing financial efficiency. In addition to a stock market catastrophe, the tourism industry appears to be in the middle of an all-out crisis, according to many observers. Tourists who cannot enter China because of the virus are a burden on the country’s tourism economy. It was decided to stop the activities of the hotels, airlines, and cruise ships. This is leading to a negative influence on China’s GDP because the virus was not prevented. Because of what we have seen, the travel and tourism sector faces unprecedented challenges. Worldwide health is being threatened by COVID-19, a global health alert that has established healthcare instability and a negative influence on the economic breakdown of the activities (Wu et al. 2021). Cross-sectional dependence tests, CADF and CIPS unit root tests, Westerlund co-integration tests, as well as the system GMM test are used in this study to address the specified objectives. Moreover, study results suggest new policy options for increasing tourism and maximizing its financial returns.
The remainder of this document is structured as follows. China’s ECEP sector is discussed at length in the “Literature review” section of this report. The “Data and methods” section is a review of the literature. Figures and techniques are shown in the “Results and discussion” section. Afterward, the “Conclusion and policy recommendations” section offers a summary of the paper’s findings.
Literature review
This section has been divided into two subsections, (1) studies related to Covid-19 and tourism, and (2) studies related to Covid-19 and green finance efficiency.
Studies related to Covid-19 and green finance efficiency
ASEAN economies’ positive depiction of the positive association between green finance and economic development has been validated. COVID-19 shows the dominance of financial development and the frequently observed correlation between these elements’ financial indicators. As a result, economic growth is prominently included, along with the importance of foreign policies of credits and investments (Croes et al. 2021). The maturity of financial movements allows for the greater exercise of green finance aspects, even though financial markets encounter many indications with both internal and external influences. During the COVID-19 epidemic, many academics discussed the role of green finance and its effects on economic development. Some researchers have looked at financial variables other than green finance when evaluating economic growth in a country exposed to pollution or infectious disease (Mitręga and Choi 2021). Validation has been given to ASEAN economies’ description of the favorable link between economic growth and green finance. COVID-19 demonstrates the importance of financial development and the frequent association between these aspects’ financial indicators. As a result, economic growth and foreign policy of loans and investments are heavily included (Akhtaruzzaman et al. 2021; Xu et al. 2021). Green financing can now be used to a greater extent due to the maturity of financial movements, although financial markets face numerous indications from inside and without. There was a lot of academic discussion on the role of green financing during the COVID-19 outbreak. Aside from green finance, other researchers have looked at capital creation and government educational expenditures to gauge economic growth in countries affected by environmental pollution or infectious disease (Liu et al. 2022a; Liu et al. 2022b). On the other hand, the rising prominence of aspects of green finance offers safeguards for economic development through the inception of numerous financial projects (Vahdat 2022). This way, countries can maintain economic growth through product innovation and launching new items. COVID-19 and other financial restraints have been studied extensively recently, and their impact on the global economy has been documented. Different financial techniques have been presented in previous literature to guarantee the foundation for economic growth, which could lead to a more prosperous economy and greater opportunities (Bhat et al. 2021). Developing countries generally require financial stability before producing substantial items with strong methods to boost overall prosperity and economic progress. Since the intended activities help countries improve residents’ quality of life and well-being, green financing is essential, consequently (Yarovaya et al. 2021).
Current literature has already established that green finance’s transmission of financial aspects positively impacts a country’s economic progress. When a worldwide pandemic occurs, these economies can make rapid progress even if they focus on green finance (i.e., green credit, green investment, and green security) (Nilashi et al. 2022). Economic growth can nonetheless be made more sustainable through eco-friendly financial instruments like green financing and investment as well as green security, even when local conditions and environmental quality impact economic performance. In addition, regulatory policies have been extensively discussed in the literature, while the impact of COVID-19 has also been eminently underlined in the context of ASEAN economies. Even while the unexpected components of the pandemic affect both developed and emerging economies, certain scenarios are supported by green finance’s beneficial aspects. Emerging economies with more advanced institutions are adopting these economic principles (Hu et al. 2021). Green finance and financial innovation have also been highlighted in related research as ways to strengthen economies. Economic growth is strongly influenced by a number of green finance control variables, such as capital formation and government educational expenditures. Despite the COVID-19 pandemic’s frightening emergence, green finance has offered good measures that allow the safety precautions to sustain economic growth. For green finance, it has been suggested that GDP per capita might be used as a controlling factor for economic development (Hasselwander et al. 2021). It may be said that, in this regard, per capita GDP reflects the signals of proper economic development that have been seen in the last decades. It is not only ASEAN economies that concentrate on the value of per capita GDP, but global economies as a whole also promote positive elements of per capita GDP. Evidence suggests that increasing per capita GDP directly impacts the creation of long-term economic strategies. In the aftermath of COVID-19, the world’s most powerful economies are working to create a sustainable environment (He et al. 2022).
Typically, the industry is seen as the primary driver of economic growth. Effective approaches to keep GDP per capita stable include various reforms that can improve the economy and keep it growing. According to this theory, the economy can be supported by changes to policies relating to the capital formation (Bilal et al. 2021). Furthermore, it has been shown in the literature that GDP per capita variation has an important bearing on the likelihood of green finance, which in turn has the potential to inspire economic development. The pandemic’s aftershocks have considerably impacted the economic conditions during COVID-19, notwithstanding the favorable influence of green finance (Tadano et al. 2021). The prevalence of green finance aspects, characterized by a wide dispersion of per capita structure in economic development, is also evident in some leveled circumstances. Green financing has substantially impacted economic development, as evidenced by the per capita GDP charts (Karayianni et al. 2022). GDP per capita is often used to measure the impact of economic performance and the dominance of green finance, including the importance of a specific population (Wen et al. 2022). As a result of the importance of financial contributions and economic performance, economic well-being and living standards are intertwined (Wang et al. 2021).
On the other hand, financial growth contributes to economic development and green financing. Green finance, including green investment, credit, and security, is a potent tool in the fight against COVID-19 s spread over the globe. Even during a pandemic outbreak, economic growth can be maintained thanks to ecological security and a healthy workforce (Tadano et al. 2021). It is possible that the use of green financing features could help ASEAN economies recover after COVID-19 (Ansari et al. 2022). Although there may be short-term connections between green financing and advertising, their overall impact on economic development is well-documented (Raj et al. 2022). To generate capital, import and export elements tend to be prominent, and workers are rewarded with various conquering outputs as part of their compensation package (Agboola et al. 2021). When the defects in employees’ capital formation prevail, capital stocks that express the significance of green finance will positively affect economic growth (Apicella et al. 2022b). In this way, the stock of capital goods often necessitates certain set-asides, which help to strengthen financial ties and contribute to capital formation and economic growth (Ansari et al. 2022). Usually, capital raised from various sources denotes the establishment of green finance that achieves numerous steps to support the economic grounds in this agreement. Safeguards against unstable economies are seen as beneficial to economic growth with a rise in investment and demand for goods and services (Hoang et al. 2021). This year’s COVID-19 economic growth can be attributed to using a methodical approach to the education sector’s expenditures. When employee capital formation defects prevail, capital stocks expressing green finance’s significance will positively affect economic development (Su and Urban 2021). In this way, the stock of capital goods often necessitates specific set-asides, which help to strengthen financial ties and contribute to capital formation and economic growth (Zanke et al. 2021). Usually, capital raised from various sources denotes the establishment of green finance that achieves numerous steps to support the economic grounds in this agreement. Safeguards against unstable economies are seen as beneficial to economic growth with a rise in investment and demand for goods and services (Azomahou et al. 2021). This year’s COVID-19 economic growth can be attributed to using a methodical approach to the education sector’s expenditures.
Studies related to Covid-19 and tourism
The tourism industry began to feel the effects of COVID-19 almost immediately. Australia’s Tourism and Transport Forum anticipated that the epidemic would result in a 90–100% drop in tourism earnings from China in February of this year. There have been a number of scholarly research and official assessments on this topic since March 2020. The vast majority of studies on COVID-19 have focused on global effects or regional comparisons based on available data. According to a 2020 working paper by Chen et al. (2021), the first academic study on COVID-19, its main goal is to model COVID-19’s effect. According to Kawasaki et al. (2022), the DSGE/CGE model was created and extended by Lu et al. (2021). The GTAP database is the primary data source for the model’s six industry segments and 24 countries/regions (Aguiar et al. 2019). There were up to seven possible outcomes to their study because of uncertainty in disease progression at that time. Workers’ supply, equity risk premium, sector-specific costs, household demand for goods, and government spending are just a few of the shocks that will be applied. Based on China’s assumed infection and fatality rates, the magnitude of these shocks was calculated using the SARS outbreak of 2003 as a reference point. It is possible to estimate the shocks for different countries and regions using a country risk index or an index of vulnerability. Governance risk (Li et al. 2020), the risk to finance (Wellalage et al. 2021), and health policy risk are the three components that make up this index (Baser 2021). According to their simulations, China might have 279,000 to 12,573,000 COVID deaths, and the world could see 279,000 to 68,347,000 deaths as a result of the disease. China’s death toll was greatly exaggerated, partly because the SARS pandemic was used as a standard for comparison. The mortality toll from COVID-19 in China is substantially lower than that of SARS, as we now know. However, according to their findings, China’s GDP decreased by 0.4–6.0%, the USA by 0.1–8.4%, Japan by 0.3–9.9%, and the Eurozone by 0.2–8.4%. International organizations’ current official estimates are generally in line with these findings.
Using a worldwide adaptive multiregional input–output model, Wei and Han (2021) explored COVID-19 and scenarios of lockdown and fiscal stimulus packages. They determined that COVID-19 will reduce world emissions from the economic sector by 3.9% and 5.6% over the next 5 years (2020 to 2024). In 2020, the reduction in emissions from electricity production can be attributed to the supply chain to 90.1%. Depending on the strength and structure of incentives, fiscal stimulus in 41 major nations raises global emissions during the next 5 years by 6.6 to 23.2 Gt (4.7 to 16.4%). Using Irfan et al. (2021a, b, c, d)’s earlier global pandemic research and the GTAP modeling framework, the researchers constructed a quarterly dynamic CGE analysis. This analysis divided the world economy into 27 areas, each with 30 sectors. In the case of a pandemic, the model accounted for people’s tendency to reduce their risk by taking preventive measures. According to these findings, a 2.97% drop in global GDP is expected in the second quarter of 2020. In the third quarter of 2021, the world GDP is expected to expand by 0.98%, a slow but steady recovery. A 2.97% drop in GDP is expected in the second quarter of 2020, followed by a gradual recovery through 2021 for Australia. A multi-sectoral disequilibrium model with 56 industries and 44 countries was used by Park et al. (2022) to perform computational experiments replicating the time sequential lockdown of various countries. Global output has already dropped by 7% as a result of the pandemic’s early stages. As a result, they concluded that supply-chain spillover effects might have a significant impact on the global economy. Modifying the GTAP model, Ficetola and Rubolini (2021) calculated how mandatory business closures affect macroeconomics across the USA and a number of other countries. Their three-month company closure scenario predicted a yearly drop in US GDP of 20.3% (or $4.3 trillion). In the USA, a 22.4% decrease in employment is expected.
Irfan et al. (2021a, b, c, d) simulate the adverse economic effects of border restrictions on the Australian economy. Approximately 3.2 to 3.45% points of unemployment are expected to be added to the economy in 2020 due to a 2 to 2.2% drop in GDP. Research shows that qualified workers, such as managers, professionals, and technicians, are losing their jobs and unskilled workers. Global Trade and Development (UNCTAD) employed the GTAP model to estimate COVID-19’s economic impact and applied various assumptions about incoming tourism expenditure. Version 10 of the GTAP database for 2014 has been updated to 2018. “Accommodation, food and services” and “recreation and other activities” were cited as a stand-in for “tourist attractions”. One-third of yearly inbound tourism expenditure was reduced in the moderate (optimistic) scenario, while two-thirds of the spending was reduced in the intermediate scenario. The most challenging situation was the total damage to all inbound tourism. Even under the most optimistic scenario, tourism-oriented countries would suffer a 10% GDP reduction and a 15% decrease in unskilled employment. In the middle condition, employment might fall by as much as 29%, while in the severe case, it could fall by as much as 44%. Governments are urged in the study to safeguard citizens while preserving a thriving tourism business. Using panel structural vector auto-regression modeling, Dudek and Śpiewak (2022) calculated COVID-19’s global tourism impact. One hundred eighty-five countries from 1995 to 2019 were used to create their panel data for the models they used. It was estimated that the pandemic would reduce tourism’s GDP contribution by $4–12.8 trillion and reduce employment by 164.506–514.080 million jobs. Both tourist receipts and capital investment fell by between US$362.9 billion and US$1.1 trillion as a result of the economic downturn. They recommended a combination of private and public policy support to ensure that the tourism industry has the resources it needs to grow and be viable in the long term.
Covid-19 has been compared to earlier epidemics, pandemics, and other global catastrophes by Kawasaki et al. (2022). Visitors and other tourism-related segments were subjected to analyses of the effects of anti-pandemic measures (travel restrictions and social seclusion). According to the researchers, counter-pandemic strategies are particularly vulnerable to tourism. When they looked into tourism’s direct and indirect effects on epidemics, they concluded that tourism had a substantial role. Travel, for example, has dispersed the virus, although industrialized food manufacturing patterns for tourists have been accountable for periodic outbreaks of COVID-19. For instance, Climate change has a comparable influence on pandemics. According to the study’s authors, deforestation has harmed species and contributed to climate change, according to the argument put forth by this group’s members. This has resulted in human movement and displacement, contributing to the spread of pandemic diseases. As a result, they concluded that the current model of tourist expansion is unsustainable, and the COVID-19 pandemic may serve as a catalyst for change in the tourism sector. Academic studies on COVID-19 that will be published in the future include Irfan et al. (2021a, 2021b) and Wang et al. (2021). Iqbal et al. (2021) used artificial neural networks to predict that India’s foreign exchange revenues and tourist arrivals would decline significantly. COVID-19’s effect on tourism workers in the unreported economy was estimated using a Eurobarometer survey and a probit model. He discovered that 0.6% of all Europeans had worked illegally in the tourism industry. He said that COVID-19 had a significant impact on them and that they were not covered by government funding. This pandemic should be seen as an opportunity to revolutionize the tourism business and tourism research, as advocated by Ge et al. (2021), Irfan et al. (2021a, b, c, d), and van der Wielen and Barrios (2021) in the context of pandemics, according to Dai et al. (2021). For many, e-tourism and virtual services were considered viable alternatives. Articles such as this also raise concerns about the efficacy of government initiatives to stimulate the economy (e.g., stimulus packages, subsidies, and tax aids). On the other hand, Sun et al. (2021) argued that Malaysia’s tourism industry may be saved by government intervention. Khalid et al. (2021) used text-mining techniques to examine the influence of COVID-19 on the tourism industry. They observed that global catastrophes like pandemics substantially impact the tourist sector and indicated that tourism insurance packages may revitalize the business. The study demonstrates the suitability of the CGE modeling technique for examining the effects of Chinese government response policies in dealing with COVID-19. COVID-19, on the other hand, has a more significant impact on the tourism industry than on any other part of the economy. With the exclusion of Pham et al., existing CGE models do not explicitly include tourism (2021). Accordingly, the study directs our approach to adopt a single-nation model with an explicit tourism module for more direct research into policy issues and solutions.
Data and methods
Food, hospitality, travel, shopping, and entertainment are just a few of the various components of the tourist industry (Hussain et al. 2021; Vătămănescu et al. 2021). For our comprehensive analysis of the COVID-19 epidemic outbreak on the price movements of Chinese-listed tourism stocks, we followed the Wind Industry Classification Standards by selecting tourism-related stocks (airlines, marine, road, and rail, and hotels, restaurants, and leisure) in its three-tier industries as samples. Stocks that had been suspended or had been listed for less than 3 years were removed from consideration due to their low reliability. In this way, we gathered the information starting in January 2020, when China regularly reported to the WHO about the spread of an epidemic, continuing through January 2022. Both green funding efficiency and tourist arrivals can be quantified using the number of visitors. The income per capita is a standard metric for gauging the health of an economy. Infrastructure development as a share of economic growth, the Covid-19 instances, and capital formation as a measure of green financial efficiency (financial efficiency), the number of patients, gross capital formation in percentage of economic growth, and renewable energy use in percentage of total energy consumption are utilized. The Chinese Year Book is used to gather data on the variables that are being discussed.
Model construction
The impact of the tourism sector and the financial efficiency of the previous period is also taken into account in the static panel model. This research employs the generalized method of moments (GMM) (Hall 2015) to estimate a dynamic panel model to investigate better the impact of financial efficiency on the tourism industry. The selection of variables, data sources, and the stationarity test of variables have been excluded from this part because empirical data agrees with the preceding static panel model data. The dynamic panel regression model developed in this paper has the following expressions:FE=(Covid,CF,ID,REU,GDPC,ε)(Model1)
TRI=(Covid,CF,ID,REU,GDPC,ε)(Model2)
Models 1 and 2 can be written as follows,1 FE=β0+β1Covidi,t+β2CFi,t+β3IDi,t+β4REUi,t+β5GDPCi,t+ε
And,2 TRI=β0+β1Covidi,t+β2CFi,t+β3IDi,t+β4REUi,t+β5GDPCi,t+ε
Among them, β0, α0 is a constant term, (i = 1 … 5) is the coefficient of each variable, and FE and TRI are the explained variables, representing the financial efficiency (Fig. 1) and tourism industry (Fig. 2) of the ith province in month ith. Likewise, Covid, CF, ID, REU, and GDPC present the Covid-19 cases, capital formation, infrastructure development, renewable energy use, and economic development.Fig. 1 Model 1: Dependent variable: financial efficiency
Fig. 2 Model 2: Dependent variable: tourism industry
Method
Cross-sectional dependence and slope homogeneity
It is assumed that there is no correlation between cross-section units and slope coefficients in standard panel data methodologies. However, ignoring cross-sectional dependence can lead to incorrect conclusions (Chudik and Pesaran 2013). Cross-sectional units may have different estimates of the coefficients. This is why it is first necessary to determine if cross-sectional dependence and slope homogeneity exist. The cross-sectional dependence of the error term produced from the model examined by Pesaran (2004) CDLM and Pesaran et al. (2008) bias-adjusted LM test. It is possible to use these strategies if N > T and if T > N. This is how CDLM and bias-adjusted LMM tests deemed to be appropriate can be calculated;3 CDLM=(1/N(N-1))1/2∑N-1i=1∑Nj=i+1(Tρij2-1)
A bias-adjusted LM test statistic, as calculated by Pesaran et al. (2008), can be found in Eqs. (3) and (6). Mean, variance, and correlation between cross-section units are all represented by VTij, μTij, and ρ^ij, the alternative and the non-null hypothesis for both statistical tests.
Panel unit root test
Using cross-sectional averages as a surrogate for unobserved common factors to avoid cross-sectional dependence, Pesaran et al. (2004) proposed a factor modeling approach. Pesaran (2007) presented a unit root test resulting from this methodology. This method uses lagged cross-sectional mean and its first difference to enhance the augmented Dickey-Fuller (ADF) model to deal with cross-sectional dependence. Cross-sectional dependence is considered in this method, which can be employed when N > T and T > N. The regression of the CADF is;4 Δyit=αi+ρ*iyi,t-1+d0y-t-1+d1Δy-t+ϵit
This y—ty–t is the mean of all N observations, with y ty t being the median. yit and y—ty–t should have their lagged initial differences added to the regression in the following way to avoid serial correlation:5 Δyit=αi+ρ*iyi,t-1+d0y-t-1+∑pj=0dj+1Δy-t-j+∑pk=1ckΔyi,t-k+ϵit
Next, Pesaran (2007) averages each cross-sectional unit (CADFi)’s statistics and performs the following calculations to arrive at the CIPS statistic:6 CIPS=1N∑i=1NCADFi
This test’s null hypothesis is that the panel in question has a unit root. The null of the unit root will be rejected if the CIPS statistic exceeds the crucial value.
In China’s east, central, and west regions, economic progress, and the development of industrial arrangement are at somewhat diverse historical growth phases. The social environments such as ethnic traditions, customs, and cultures vary by location. Time series or cross-sectional analyses employing aggregate indicators may hide their true link. This sample design, on the other hand, is not standardized. Additionally, time series analysis requires checking for co-integration across variables to prevent the problem of “false regression”, and while cross-sectional data analysis is simple, there are issues of missing variables and heteroscedasticity. Econometric models are typically affected by changes in the economic system in China, which is currently in a transitional stage. As a result of these “breakpoints”, the model will become more complicated, and the parameter estimation theory will be influenced. In addition, the size of the sample must be limited in order to avoid bias. In contrast, panel data has the apparent advantage of handling unobserved individual effects and time effects of distinct cross-sections, as well as dynamic adjustment procedures and error components. Using panel data minimizes the risk of collinearity across variables while increasing estimation freedom and validity because panel data have greater information than time series, and the cross-sectional study is broader.
The dependent variables in our econometric model are often serially correlated across time in normal circumstances. The estimation findings may be incorrect at this time if we only look at the standard panel model. Therefore, the lag term of the reliant on variable must be added to the regression equation in order to get an accurate approximation of the relationship. This is what the dynamic panel model looks like in practice:7 Yi,t=α+PYi,t-1+βj∑jXj,i,t+ηi+εi,t
There are a number of explanatory variables in the econometric model that do not contain the dependent variable's lag term and numerous period lag terms in X. Normal circumstances presuppose that Yi,0 and Xi,0 are already known or are the result of a certain process of data generation. Unobservable individual effects are represented by I, whereas error terms are represented by εi,t. Endogenous problems plague general estimation methods in dynamic panel models (fixed-effects models and random-effects models) under normal circumstances, as the variance–covariance matrix between the error term and its lag term, is not zero, violating the severe assumptions about the effects of individual effects and lag terms on the explained variable and explanatory variables. For this article’s econometric model, the lagging explanatory variable appears as an explanatory variable, whereas the dependent variable may negatively affect the explanatory variable. Thus, the model’s endogeneity will be simultaneous.
Arellano and Bond (1991) presented two highly classic moment estimation approaches, namely generalized differential moment estimation (differential GMM) and horizontal generalized moment estimation, in order to successfully cope with the challenges outlined above (horizontal GMM). However, when the sample size is small, the results of these two estimating approaches are less accurate. Bond (1991) produced a moment condition with better qualities by putting the assumption of stationarity on the beginning value after thoroughly examining the merits of these two estimation approaches. An overview of the system GMM estimate approach is provided in this article, which is based on Blundell and Bond (1998) research on the topic of system GMM.
Do the first-order difference operation on both sides of the model first to get the following formula:8 Δyi,t=ρΔyi,t-1+ΔXI,tβ′+Δϵi,t
And yi,t-1 is an endogenous variable because Δ yi,t-1 is related to ϵ i,t. Autocorrelation implies that yi,t-2 is uncorrelated with y i,t,t-1 and can be used as an instrumental variable for yi, estimation—t-2’s. Similarly, higher-order lag variables {yi,t-3, yi,t-4, ….} are also useful instrumental variables. The difference GMM estimate can be produced using lagging variables as instrumental variables for GMM estimation. The individual effect coefficient estimate cannot be estimated using differential GMM estimation, which is the same as a within-group estimator.
The correlation is relatively weak when close to a random walk, resulting in poor instrumental variables for differential estimation. Vertical GMM employs an instrumental variable in the level equation before difference. This is the solution. It is possible to acquire a consistent estimate of the model’s amount when and does not have any serial autocorrelation. The following are some of the benefits of estimating the effect of green finance on energy consumption structure using the systematic GMM method. To begin, provincial heterogeneity that could influence green funding and energy consumption patterns can be removed. The lag term of the dependent variable in the regression equation is also taken into account. When assessing the influence of green finance on energy consumption, however, most work fails to address the issue of the lagging term of the dependent variable. Finally, in our system GMM paradigm, this potential reverse causality from energy consumption structure to green finance is completely removed. A GMM estimation shows that the system has just one causal relationship.
Results and discussion
Sample data must be understated before econometric model results can be calculated. Using mean, median, maximum, and minimum values as well as standard deviation and the Jarque–Bera statistic, Table 1 describes the data. Financing efficiency is at its lowest point in the table, while infrastructure development is at its highest. This indicates that industrial infrastructure is still worth a lot. Our data does not have any outliers, as the difference between the mean and median values is small (Table 2).Table 1 Descriptive statistics
Variable Mean Std. Dev Minimum Maximum
FE 0.1489 0.0523 0.369 1.965
TI 0.3385 0.9412 0.125 3.665
GDPC 9.4568 0.7825 0.517 12.852
CF 6.2228 0.6438 3.696 10.338
ID 24.962 5.9641 11.784 55.152
EU 14.632 3.7412 2.782 20.632
Table 2 Homogeneity test
Test Value P value
Pearson (CD) 9.356 0.000
Frees (Q) 4.289 0.005
Friedman (CD) 95.472 0.000
In this section, the results of the CD test are discussed (Table 3). Because the results of the various tests can violently deny H0 of cross-sectional independence for the specified panel, they support our first judgment. Second-generation unit root and co-integration tests should be used because the selected panel displayed CD. Results of homogeneity testing are also included in Table 2’s lower panel. The results of the homogeneity test follow the established requirements.Table 3 CADF test results
Variable CADF
Level 1st difference
FE − 3.9652* − 5.4523
TRI − 1.2589 − 4.8823*
COVID − 1.2631 − 5.8879*
CF − 3.2275* − 7.4419
ID − 1.2358 − 4.2101*
REU − 1.6431 − 3.1298*
GDPC − 1.5549 − 6.6923*
Table 3 shows the results of the unit root tests in more detail. TRI, Covid cases, infrastructure development, renewable energy use, and economic development are stationary at the first difference in the CADF test results for the tested economies.
Since it is compatible with data sets, the co-integration association between model variables results are shown below in Table 4, utilizing Westerlund and Edgerton (2007)’s error correction model for co-integration (Table 5). Constant and trend data are included in Table 6 to show the co-integration results. No co-integration is rejected with bootstrapped P values of 1 and 5% for Gt and Pt test statistics in the model and nations. As an example, the co-integration vectors in both models are consistent. According to the study model, all relevant variables are linked in a co-integration relationship.Table 4 Co-integration test
Statistics Value Z value P value Robust P value
Gt − 11.234 4.258 0.006 0.000
Ga 2.987 3.745 1.000 0.552
Pt − 1.582 6.458 1.000 0.234
Pa − 7.562 5.985 1.000 0.001
Table 5 Regression results
Variable Model 1 Model 2
Coefficient Std. Error Variable Coefficient Std. error
TIi,t-1 − 0.6589* 0.5523 FEi,t−1 − 0.0783** 0.0123
Covid − 0.3612** 0.1132 COVID − 0.0631* 0.0041
CF 0.4523* 0.2460 CF 0.6123** 0.2035
IS 0.0114* 0.0032 IS 0.2145* 0.1096
REU 0.8932** 0.2578 REU 0.4269 0.1994
GDPC 0.6528* 0.1322 GDPC − 0.4236** 0.1420
Sargan test 0.8557 – – 0.985 –
AR(1) 0.0423 – – 0.0320 –
AR(2) 0.3022 – – 0.2892
Long run results of system GMM
The results of the co-integration test show that variables have long-term co-integration. As a result, the outcomes of this investigation were further examined using the GMM system. According to the findings, the Covid-19 epidemic has had a negative impact on China’s tourism business in some provinces. Under the GMM specification, a 1% increase in this component would result in a 0.361% decrease in tourism. As a result of the COVID-19 issue, less cash will be available and easier to obtain, which could lead to a reallocation of financial resources to the most critical aspects of a company’s operations. Corporate sustainability initiatives, high-profile, expensive marketing campaigns promoting ocean and river cruises, re-engaging with previous customers, and investment in ensuring that returning consumers receive high-quality experiences may take priority over social and environmental agendas. Operational issues like hygiene and cleanliness, health screenings for visitors and patrons, and food supply chain provenance have been highlighted by the COVID-19 disaster. At the same time, employees should be able to receive regular health exams, and enterprises in the tourism industry may be recommended to have a wider welfare focus on their staff. The costs of such interventions may be high, but they may be necessary to retain a healthy workforce and reestablish consumer confidence and provide a source of competitive advantage in an increasingly competitive economy.
Capital formation also has a favorable correlation with the tourism industry in the selected location, as evidenced by the given coefficient value. Tourists would see an increase of 0.452% if this component increased by 1%. According to research, tourist arrivals rise by 0.72% for every 1% growth in capital formation, showing a long-term positive correlation between the two variables. In 2016, China’s tourism-related investments accounted for a larger share of the country’s total capital investments. A rise in the amount of money governments spend on public infrastructure like roads and FDI in the tourism industry means more people coming to the country to visit, which means more hotels, restaurants, and recreational facilities. There is a strong correlation between our findings and the necessity for the Chinese tourism industry to fight for favorable policies for the country’s economy. In order to make the transition to a tourism industry that is more environmentally friendly, China’s tourism policy must be aligned with the country’s energy policy. Reorienting the tourism industry in line with the sustainable development goals will require an integrated energy, environment, and tourism policy framework. We need a new policy framework incorporating environmental, energy, and tourism policies.
It also illustrates that China’s tourism business is benefiting from improving its infrastructure. Tourism would climb by 0.011% if this component increased by 1%. According to a recent study, community satisfaction is a key indicator of local support for tourism (Samadi et al. 2021). According to Fagbemi (2021), community satisfaction is a key predictor of locals' views on tourist growth in general. Several techniques can be used to explain our ID effect theme. If, for example, the building of the development projects is nearing completion, the favorable impact of infrastructure development on the tourism business may be supportive enough to increase tourism. This project’s reputation could be enhanced even further if it receives widespread media coverage locally and internationally. According to the media, for example, road and transportation infrastructure development has had a favorable impact on the local economy, businesses, and jobs, as well as on the availability of education and health care facilities for the local population. The media and the government have highlighted environment-friendly developments like modern national parks and wildlife conservation efforts, in addition to their negative impacts on human health and quality of life (Dou et al. 2022a). People’s good ID impressions probably significantly affect the positive ID impact. The tourist business is also generally clean compared to other manufacturing industries, and this clean development can therefore have a good effect on the local population and increase tourism.
Renewable energy utilization and the tourism business have a beneficial link. This implies that an increase of 1% in this component would increase to 0.893% in tourists. Additionally, it was discovered that undergoing RE was an excellent way to boost tourism in the studied provinces. If renewable energy shares climb by a percentage point, the number of foreign tourist arrivals rises by 0.893%, on average. Because renewable energy resources cannot only supplement nonrenewable ones in terms of meeting the region’s total energy needs but also help to electrify tourist sites without grid access, this conclusion makes sense in the context of China’s national energy balance. Su and Urban (2021) also advised using renewable energy to electrify rural tourist locations in order to ensure that the Chinese tourism business is sustainable. Statistical significance and positive signals of the estimated elasticity parameter associated with the interaction term show that regional integration and REU have a combined positive impact on the long-term development of China’s inbound tourism business. China’s provinces need to work together more, especially by reducing barriers preventing cross-border energy commerce.
In the second model, financial efficiency is used as a variable to explain the results. First, we look at the long-term effects of the Covid-19 epidemic on economic efficiency in our analysis of financial efficiency. One percent more of these factors would reduce financial efficiency by 0.063%. External funding sources for FE have been discovered in the literature on the financial supply side. To begin, FE relied on donations, grants from philanthropic and governmental organizations, and other forms of free money (Tu et al. 2021). Domestic institutions are now permitted to accept deposits from the general public and to do business in the manner of a “conventional bank”, thanks to the rapid advancement of commercialization (Riza and Wiriyanata 2021). Commercial investors like commercial banks, pension funds, insurance companies, and other private equity firms have recently invested in the institution as socially responsible investment, in addition to donations and deposits (Siddique et al. 2021). Financial and social efficiency are expected to be affected by the pandemic's financing rates in a variety of ways for the reasons outlined below. This means that the cost of capital will rise due to the higher funding rates; nevertheless, long-term investors are more likely to demand higher rates of return to compensate for the opportunity costs of their capital. The long-term benefits of a steady source of financing may include increased financial efficiency. In the second place, greater funding rates offered by financial institutions to depositors and other lenders may encourage more people to save and invest their money with local financial institutions. As a result, more low-income households and underrepresented microbusinesses will be able to access banking services. Consequently, the institution’s social performance (in terms of its scope and depth of reach) will be improved. For this reason, depositors may withdraw their funds. Risk-averse investors may become exceedingly hesitant about making new investments due to increased uncertainty and the resulting loss of trust in banks. This means that even with more financing, it will be unable to reach the lowest people because of limited funding.
Gross capital formation demonstrates a favorable correlation between financial efficiency and gross capital formation. This means that a 1% increase in this factor would result in a 0.612% gain in financial efficiency. Investment in China’s financial sector has a favorable impact on the economy, promoting growth. In order for China to establish a capital basis that supports economic growth, the government must therefore coordinate its efforts to improve the efficiency of the China money market. Suppose the Federal Government of China does not take urgent remedial actions to improve liquidity in the financial markets and the real economy by injecting more liquidity into the China financial markets. In that case, the Government for Emerging Markets’ bonds may extend to corporate bonds. The directors of many Chinese listed businesses, who allow the market system to decide the values of securities on the capital market, are the reason for the favorable influence of gross capital formation on financial efficiency in China. Gross capital formation has a positive impact on financial efficiency in China, which means that I the confidence of both local and foreign investors in the China capital market will be strong; (ii) the China capital markets might be market-driven or well regulated; (iii) the China capital markets are efficient, perhaps in the strong form; (iv) the China capital markets might not crash in the future, if immediate measures are taken.
Similarly, infrastructure development is employed as a factor of financial efficiency and causes a 0.214% improvement in financial efficiency due to a 1% rise in infrastructure development. The panel-ordered system GMM model is used for regression on a specific panel. In other words, China’s inequities can be reduced through infrastructure development. Infrastructural improvements can lower the cost of financial activities and boost the growth of financial institutions. Interregional trade has historically benefited from infrastructure investment, which has helped link markets, ease the movement of goods and people, lower transaction and transportation costs, and boost China’s economic development and energy consumption. By completing the infrastructure, China has narrowed the gap between regional manufacturing by increasing capital accumulation, distributing sophisticated energy technologies, and facilitating labor transfer.
The entire utilization of renewable energy has a considerable positive impact on financial effectiveness. As a result, an increase of 1% in this factor would result in a marginal rise in financial efficiency of 0.426% (see Table 5). In line with (Dou et al. 2022a, b), who found positive correlations for India, this study also supports (Irfan et al. (2021a, b, c, d)). In contrast to earlier findings, this study focuses on the financial efficiency of the linkages between overall financial development and renewable energy usage. According to the results, the selected panel’s financial efficiency is not considerably impacted by the use of renewable energy. There is evidence that these member states’ renewable energy demand is influenced by exchange rates, interest rates, and investments.
The given economic and financial data results show the inverse relationship between economic development and financial efficiency. An increase in the average household’s income does not affect how well a company is run. The region’s high level of financial repression and the presence of state-owned banks, which lack governance and are unable to select growth-enhancing initiatives, may be to blame for this disparity. A lack of legislation and monitoring hinders credit allocation. China’s policymakers should put more effort into enhancing the quality and allocation of credit in the banking industry rather than just increasing the amount of credit available. Gaining more money does not help with financial independence. In the end, what matters most is how these funds are used and how efficiently they are allocated to various initiatives that fund them. A country’s financial efficiency can easily be improved if it can attract investors, stimulate domestic investment, control inflation, increase the quality of its institutions, and open up trade.
Conclusion and policy recommendations
Data from 30 Chinese provinces from January 2020 to January 2022 was culled from the body of previous research and combined in this publication. Using panel regression models and dynamic GMM models, we could empirically verify the national. The Covid-19 outbreak, gross capital formation, infrastructure development, renewable energy use and economic development in the tourism sector, and financial efficiency were explored in this study under the ear of the pandemic of COVID-19. Findings demonstrate that there is a negative correlation between the Covid-19 instances as well as the other variables that were examined. Similarly, the growth in gross capital formation, infrastructure development, and the use of renewable energy show that tourism and financial efficiency benefit from these factors. The negative impact on financial efficiency offsets the favorable impact of economic growth on the tourism industry.
Policy recommendation
In light of the study’s findings, there are a number of policy implications. A single policy instrument may not be enough for China’s green finance policy system to promote offshore green investment today or in the short-term future, resulting in lower financial efficiency. For this reason, a policy mix that includes two or three of the above policies may be necessary for the near future to ensure their effectiveness and feasibility. To maintain offshore green investment profitable after the COVID-19 epidemic, however, the government has the option of slowing down the FE’s decrease. The COVID-19 pandemic’s success will be determined by a number of parameters, including the cost of operation, the utilization rate of green projects offshore, and the capacity factor. Specifically, differentiated policies may be necessary to promote the development of offshore green projects in different regions of China.
There is currently a lot of support for this strategy because of the worth of human life. Any financial advantages cannot compensate for this damage to life if disease control measures are not implemented. This argument has not stopped some countries from adopting a lenient policy to stimulate economic activity despite the threat of economic loss. The simulation results, however, do not justify a no-control policy, not even based on pure economic reason. More stringent policies that control the infection in a smaller period of time also limit the negative economic repercussions. This can be seen in China, Korea, Singapore, Australia, and New Zealand. A broad economic policy that softens the economy, such as increasing government investment, encouraging spending, and decreasing taxes, can be extremely beneficial to the tourism industry during a pandemic or epidemic. On the other hand, the tourism industry will likely take longer than other sectors to recover from the outbreak/pandemic. Tourism-oriented recovery policies are essential to regaining the trust of the tourism industry and promoting a quick recovery of the broader economy.
Energy sources that do not use fossil fuels can be employed to boost economic growth while also reducing pollution. There are active processes to cope with global warming at the same time. Alternative and renewable energy production is of critical importance to the USA. For this reason, tourism and financial institutions are doing very well because renewable energy use promotes the regulation of the industrial arrangement, enables high-energy-consuming industries to improve their technology and use uncontaminated power, and has a significant impact on the progress of energy consumption structure towards zero-carbon ecological protection. The influence of financial organizations on energy consumption and energy reserve policies should be thoroughly recognized when developing policies to make them more realistic and achievable.
Government funding for green infrastructure development (green walls, tree health mapping) is critical since these investments benefit the tourism industry and address the negative effects of urbanization. In order to accommodate the increased demand for tourism-related industries, effective tourism policies must be implemented. The correlation between tourist arrivals and energy use is not as simple as policymakers believe and the facts show. China’s energy consumption is rising and should not be used as a factor in tour itinerary planning.
However, this study has several limitations regarding selecting the globalization indicator and its more specific implications. With these limitations in mind, one of the authors’ contributions is to emphasize the importance of curbing the Covid-negative outbreak’s effects on tourism and financial efficiency while also highlighting the close relationship between developed societies and fossil fuels as a means of increasing their income levels. Policy recommendations are also included in our report in order to help these countries’ economies avoid the worst effects of the pandemic on tourism and financial efficiency. In order to reduce negative externalities, energy dependency, and poverty levels, policymakers should focus on developing the tourism industry and renewable energy sources. First, governments need to promote renewable energy and more efficient and innovative energy use in order to minimize the percentage of fossil fuels in the energy mix.
Author contribution
Zhaolin Hu: conceptualization, data curation, methodology, writing — original draft, and data curation. Suting Zhu: visualization, supervision, editing, writing — review and editing, and software.
Data availability
The data can be available on request.
Declarations
Ethical approval and consent to participate
The authors declare that they have no known competing financial interests or personal relationships that seem to affect the work reported in this article. We declare that we have no human participants, human data or human tissues.
Consent for publication
N/A
Competing interests
The authors declare no competing interests.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Abbas M Zhang Y Koura YH The dynamics of renewable energy diffusion considering adoption delay Sustain Prod Consum 2022 30 387 395 10.1016/j.spc.2021.12.012
Abbas Q, Hanif I, Taghizadeh-Hesary F, et al (2021) Improving the energy and environmental efficiency for energy poverty reduction. Econ Law, Institutions Asia Pacific 231–248. 10.1007/978-981-16-1107-0_11
Abbas Q Nurunnabi M Alfakhri Y The role of fixed capital formation, renewable and non-renewable energy in economic growth and carbon emission: a case study of Belt and Road Initiative project Environ Sci Pollut Res 2020 27 45476 45486 10.1007/s11356-020-10413-y
Agboola MO Bekun FV Balsalobre-Lorente D Implications of social isolation in combating reduction Sustain 2021 13 94 10.3390/SU13169476
Ahmad Z Chao L Chao W (2021) Assessing the performance of sustainable entrepreneurship and environmental corporate social responsibility: revisited environmental nexus from business firms Environ Sci Pollut Res 2021 2915 29 21426 21439 10.1007/S11356-021-17163-5
Akhtaruzzaman M Boubaker S Sensoy A Financial contagion during COVID–19 crisis Financ Res Lett 2021 10.1016/j.frl.2020.101604
Ansari ZA Bashir M Pradhan S Impact of corona virus outbreak on travellers’ behaviour: scale development and validation Int J Tour Cities 2022 10.1108/IJTC-06-2021-0123
Apicella F Gallo R Guazzarotti G Insurers’ investments before and after the Covid-19 outbreak SSRN Electron J 2022 10.2139/ssrn.4032813
Azomahou TT, Ndung’u N, Ouédraogo M (2021) Coping with a dual shock: the economic effects of COVID-19 and oil price crises on African economies. Resour Policy 72:. 10.1016/j.resourpol.2021.102093
Baser O Population density index and its use for distribution of Covid-19: a case study using Turkish data Health Policy (New York) 2021 125 148 154 10.1016/j.healthpol.2020.10.003
Benamraoui A The world economy and islamic economics in the time of COVID-19 J King Abdulaziz Univ Islam Econ 2021 10.4197/Islec.34-1.4
Bhat SA Bashir O Bilal M Impact of COVID-related lockdowns on environmental and climate change scenarios Environ Res 2021 195 110839 10.1016/j.envres.2021.110839 33549623
Bilal MFB Komal B Nexus between the COVID-19 dynamics and environmental pollution indicators in South America Risk Manag Healthc Policy 2021 14 67 74 10.2147/rmhp.s290153 33447110
Blundell R Bond S Initial conditions and moment restrictions in dynamic panel data models J Econom 1998 87 115 143 10.1016/S0304-4076(98)00009-8
Bond S Some tests of specification for panel data:monte carlo evidence and an application to employment equations Rev Econ Stud 1991 58 277 297 10.2307/2297968
Chang L Mohsin M Iqbal W Assessing the nexus between COVID-19 pandemic–driven economic crisis and economic policy: lesson learned and challenges Environ Sci Pollut Res 2022 10.1007/S11356-022-23650-0
Chen X, Huang C, Wang H, et al (2021) Negative emotion arousal and altruism promoting of online public stigmatization on COVID-19 pandemic. Front Psychol 12:. 10.3389/fpsyg.2021.652140
Chudik A Pesaran MH Econometric analysis of high dimensional VARs featuring a dominant unit Econom Rev 2013 32 592 649 10.1080/07474938.2012.740374
Croes R Ridderstaat J Bąk M Zientara P Tourism specialization, economic growth, human development and transition economies: the case of Poland Tour Manag 2021 82 104181 10.1016/j.tourman.2020.104181
Dai R Feng H Hu J The impact of COVID-19 on small and medium-sized enterprises (SMEs) evidence from two-wave phone surveys in China China Econ Rev 2021 67 101607 10.1016/j.chieco.2021.101607 36568286
Deng Z Liu J Sohail S Green economy design in BRICS: dynamic relationship between financial inflow, renewable energy consumption, and environmental quality Environ Sci Pollut Res 2022 29 22505 22514 10.1007/S11356-021-17376-8/METRICS
Dou Y Li Y Dong K Ren X Dynamic linkages between economic policy uncertainty and the carbon futures market does Covid-19 pandemic matter Resour Policy 2022 75 102455 10.1016/j.resourpol.2021.102455
Dou Y Li Y Dong K Ren X Dynamic linkages between economic policy uncertainty and the carbon futures market: does Covid-19 pandemic matter? Resour Policy 2022 75 102455 10.1016/j.resourpol.2021.102455
Dudek M, Śpiewak R (2022) Effects of the COVID-19 pandemic on sustainable food systems: lessons learned for public policies? The Case of Poland. Agric 12:. 10.3390/agriculture12010061
Fagbemi F COVID-19 and sustainable development goals (SDGs): an appraisal of the emanating effects in Nigeria Res Glob 2021 3 100047 10.1016/j.resglo.2021.100047
Feng H Liu Z Wu J Nexus between government spending’s and green economic performance: role of green finance and structure effect Environ Technol Innov 2022 27 102461 10.1016/j.eti.2022.102461
Ficetola GF, Rubolini D (2021) Containment measures limit environmental effects on COVID-19 early outbreak dynamics. Sci Total Environ 761:. 10.1016/j.scitotenv.2020.144432
Ge Y Bin ZW Wang J Effect of different resumption strategies to flatten the potential COVID-19 outbreaks amid society reopens: a modeling study in China BMC Public Health 2021 21 1 10 10.1186/s12889-021-10624-z 33388037
Gössling S, Scott D, Hall CM (2020) Pandemics, tourism and global change: a rapid assessment of COVID-19. J Sustain Tour 1–20. 10.1080/09669582.2020.1758708
Hafsa S (2020) Economic contribution of tourism industry in Bangladesh: at a glance. Glob J Manag Bus Res. 10.34257/gjmbrfvol20is1pg29
Hailiang Z, Iqbal W, Chau KY, et al. (2022) Green finance, renewable energy investment, and environmental protection: empirical evidence from B.R.I.C.S. countries. Econ Res Istraz . 10.1080/1331677X.2022.2125032
Hall AR Econometricians have their moments: GMM at 32 Econ Rec 2015 91 1 24 10.1111/1475-4932.12188
Hasselwander M Tamagusko T Bigotte JF Building back better: the COVID-19 pandemic and transport policy implications for a developing megacity Sustain Cities Soc 2021 69 102864 10.1016/J.SCS.2021.102864 36568855
He L Mu L Jean JA Contributions and challenges of public health social work practice during the initial 2020 COVID-19 outbreak in China Br J Soc Work 2022 10.1093/bjsw/bcac077
Hoang TDL Nguyen HK Nguyen HT Towards an economic recovery after the COVID-19 pandemic: empirical study on electronic commerce adoption of small and medium enterprises in Vietnam Manag Mark 2021 16 47 68 10.2478/mmcks-2021-0004
Hu T Wang S She B Human mobility data in the COVID-19 pandemic: characteristics, applications, and challenges Int J Digit Earth 2021 14 1126 1147 10.1080/17538947.2021.1952324
Huang X Chau KY Tang YM Iqbal W Business ethics and irrationality in SME during COVID-19: does it impact on sustainable business resilience? Front Environ Sci 2022 10 275 10.3389/fenvs.2022.870476
Hussain S, Xuetong W, Hussain T, et al (2021) Assessing the impact of COVID-19 and safety parameters on energy project performance with an analytical hierarchy process. Util Policy 70:. 10.1016/j.jup.2021.101210
Ip Y Iqbal W Du L Akhtar N Assessing the impact of green finance and urbanization on the tourism industry—an empirical study in China Environ Sci Pollut Res 2022 2022 1 17 10.1007/S11356-022-22207-5
Iqbal S Bilal AR Nurunnabi M It is time to control the worst: testing COVID-19 outbreak, energy consumption and CO2 emission Environ Sci Pollut Res 2021 28 19008 19020 10.1007/s11356-020-11462-z
Iqbal W, Fatima A, Yumei H, et al. (2020) Oil supply risk and affecting parameters associated with oil supplementation and disruption. J Clean Prod 255:. 10.1016/j.jclepro.2020.120187
Irfan M Akhtar N Ahmad M Assessing public willingness to wear face masks during the COVID-19 pandemic: fresh insights from the theory of planned behavior Int J Environ Res Public Health 2021 18 4577 10.3390/IJERPH18094577 33925929
Irfan M, Akhtar N, Ahmad M, et al. (2021b) Assessing public willingness to wear face masks during the covid-19 pandemic: fresh insights from the theory of planned behavior. Int J Environ Res Public Health 18:. 10.3390/ijerph18094577
Irfan M Akhtar N Ahmad M Assessing public willingness to wear face masks during the covid-19 pandemic: fresh insights from the theory of planned behavior Int J Environ Res Public Health 2021 18 4577 10.3390/ijerph18094577 33925929
Irfan M Ikram M Ahmad M Does temperature matter for COVID-19 transmissibility? Evidence across Pakistani provinces Environ Sci Pollut Res 2021 28 59705 59719 10.1007/s11356-021-14875-6
Jian X, Afshan S (2022) Dynamic effect of green financing and green technology innovation on carbon neutrality in G10 countries: fresh insights from CS-ARDL approach. http://www.tandfonline.com/action/authorSubmission?journalCode=rero20&page=instructions. 10.1080/1331677X.2022.2130389
Karayianni E, Van Daele T, Despot-Lučanin J, et al (2022) Psychological science into practice during the COVID-19 pandemic. 10.1027/1016-9040/A000458
Kawasaki T Wakashima H Shibasaki R The use of e-commerce and the COVID-19 outbreak: a panel data analysis in Japan Transp Policy 2022 115 88 100 10.1016/j.tranpol.2021.10.023
Khalid U Okafor LE Burzynska K Does the size of the tourism sector influence the economic policy response to the COVID-19 pandemic? Curr Issues Tour 2021 24 2801 2820 10.1080/13683500.2021.1874311
Khan SAR Ponce P Thomas G Digital technologies, circular economy practices and environmental policies in the era of covid-19 Sustain 2021 13 12790 10.3390/su132212790
Khan SAR Sharif A Golpîra H Kumar A A green ideology in Asian emerging economies: from environmental policy and sustainable development Sustain Dev 2019 27 1063 1075 10.1002/SD.1958
Liu L Li Z Fu X Impact of power on uneven development: evaluating built-up area changes in chengdu based on NPP-VIIRS images (2015–2019) Land 2022 11 489 10.3390/LAND11040489
Li Q, Miao Y, Zeng X, et al (2020) Prevalence and factors for anxiety during the coronavirus disease 2019 (COVID-19) epidemic among the teachers in China. J Affect Disord 277:153–158. 10.1016/J.JAD.2020.08.017
Liu X Tong D Huang J What matters in the e-commerce era? Modelling and mapping shop rents in Guangzhou, China Land Use Policy 2022 123 106430 10.1016/J.LANDUSEPOL.2022b.106430
Liu Z Hasan MM Xuan LI Trilemma association of education, income and poverty alleviation: managerial implications for inclusive economic growth Singapore Econ Rev 2022 10.1142/S0217590822440052
Lu L Peng J Wu J Lu Y Perceived impact of the Covid-19 crisis on SMEs in different industry sectors: evidence from Sichuan, China Int J Disaster Risk Reduct 2021 55 102085 10.1016/j.ijdrr.2021.102085 35719701
Mach L Ponting J Establishing a pre-COVID-19 baseline for surf tourism: trip expenditure and attitudes, behaviors and willingness to pay for sustainability Ann Tour Res Empir Insights 2021 2 100011 10.1016/j.annale.2021.100011
Mitręga M Choi TM How small-and-medium transportation companies handle asymmetric customer relationships under COVID-19 pandemic: a multi-method study Transp Res Part E Logist Transp Rev 2021 148 102249 10.1016/J.TRE.2021.102249
Nilashi M Ali Abumalloh R Alrizq M What is the impact of eWOM in social network sites on travel decision-making during the COVID-19 outbreak? A two-stage methodology Telemat Informatics 2022 69 101795 10.1016/j.tele.2022.101795
Park I Lee J Lee D Changes in consumption patterns during the COVID-19 pandemic: analyzing the revenge spending motivations of different emotional groups J Retail Consum Serv 2022 65 102874 10.1016/J.JRETCONSER.2021.102874
Pesaran MH General diagnostic tests for cross-sectional dependence in panels Empir Econ 2004 60 13 50 10.1007/s00181-020-01875-7
Pesaran MH A simple panel unit root test in the presence of cross-section dependence J Appl Econom 2007 22 265 312 10.1002/jae.951
Pesaran MH Schuermann T Weiner SM Modeling regional interdependences using a global error-correcting macroeconometric model J Bus Econ Stat 2004 22 129 162 10.1198/073500104000000019
Pesaran MH Ullah A Yamagata T A bias-adjusted LM test of error cross-section independence Econom J 2008 11 105 127 10.1111/j.1368-423X.2007.00227.x
Pham TD Dwyer L Su JJ Ngo T COVID-19 impacts of inbound tourism on Australian economy Ann Tour Res 2021 88 103179 10.1016/j.annals.2021.103179 36540369
Pjanić M (2019) Economic effects of tourism on the world economy. SSRN Electron Journal, ISSN 1556–5068, Elsevier BV, 291–305. 10.31410/tmt.2019.291
Pu S Ali Turi J Bo W Sustainable impact of COVID-19 on education projects: aspects of naturalism Environ Sci Pollut Res 2022 1 1 18 10.1007/s11356-022-20387-8
Raj A Mukherjee AA de Sousa Jabbour ABL Srivastava SK Supply chain management during and post-COVID-19 pandemic: mitigation strategies and practical lessons learned J Bus Res 2022 142 1125 1139 10.1016/J.JBUSRES.2022.01.037 35079190
Riza F, Wiriyanata W (2021) Analysis of the viability of fiscal and monetary policies on the recovery of household consumption expenditures because of the Covid-19 pandemic. Jambura Equilib J 3:. 10.37479/jej.v3i1.10166
Samadi AH Owjimehr S Nezhad Halafi Z The cross-impact between financial markets, Covid-19 pandemic, and economic sanctions: the case of Iran J Policy Model 2021 43 34 55 10.1016/j.jpolmod.2020.08.001 32994651
Sharif A Afshan S Chrea S The role of tourism, transportation and globalization in testing environmental Kuznets curve in Malaysia: new insights from quantile ARDL approach Environ Sci Pollut Res 2020 27 25494 25509 10.1007/S11356-020-08782-5/METRICS
Sharif A, Aloui C, Yarovaya L (2020c) Since January 2020c Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID- 19. The COVID-19 resource centre is hosted on Elsevier Connect, the company ’ s public news and information
Sharif A Baris-Tuzemen O Uzuner G Revisiting the role of renewable and non-renewable energy consumption on Turkey’s ecological footprint: evidence from quantile ARDL approach Sustain Cities Soc 2020 57 102138 10.1016/J.SCS.2020d.102138
Sharif A Godil DI Xu B Revisiting the role of tourism and globalization in environmental degradation in China: fresh insights from the quantile ARDL approach J Clean Prod 2020 272 122906 10.1016/j.jclepro.2020e.122906
Sharif A Raza SA Ozturk I Afshan S The dynamic relationship of renewable and nonrenewable energy consumption with carbon emission: a global study with the application of heterogeneous panel estimations Renew Energy 2019 133 685 691 10.1016/J.RENENE.2018.10.052
Sharif A Saha S Loganathan N Does tourism sustain economic growth? Wavelet-based evidence from the United States Tour Anal 2017 22 467 482 10.3727/108354217X15023805452022
Sharif A Saqib N Dong K Khan SAR Nexus between green technology innovation, green financing, and CO2 emissions in the G7 countries: the moderating role of social globalisation Sustain Dev 2022 30 1934 1946 10.1002/SD.2360
Siddique A Shahzad A Lawler J Unprecedented environmental and energy impacts and challenges of COVID-19 pandemic Environ Res 2021 193 110443 10.1016/j.envres.2020.110443 33171120
Silva HE Henriques FMA The impact of tourism on the conservation and IAQ of cultural heritage: the case of the Monastery of Jerónimos (Portugal) Build Environ 2021 190 107536 10.1016/j.buildenv.2020.107536
Su C Urban F Circular economy for clean energy transitions: a new opportunity under the COVID-19 pandemic Appl Energy 2021 289 116666 10.1016/j.apenergy.2021.116666 36567826
Suki NM Sharif A Afshan S Suki NM Revisiting the environmental kuznets curve in Malaysia: the role of globalization in sustainable environment J Clean Prod 2020 264 121669 10.1016/J.JCLEPRO.2020.121669
Sun Y Bao Q Lu Z Coronavirus (Covid-19) outbreak, investor sentiment, and medical portfolio: evidence from China, Hong Kong, Korea, Japan, and U.S Pacific-Basin Financ J 2021 65 101463 10.1016/J.PACFIN.2020.101463
Tadano YS, Potgieter-Vermaak S, Kachba YR, et al (2021) Dynamic model to predict the association between air quality, COVID-19 cases, and level of lockdown. Environ Pollut 268:. 10.1016/j.envpol.2020.115920
Tu Q Mo J Liu Z Using green finance to counteract the adverse effects of COVID-19 pandemic on renewable energy investment-the case of offshore wind power in China Energy Policy 2021 158 112542 10.1016/j.enpol.2021.112542 34539036
Vahdat S The role of IT-based technologies on the management of human resources in the COVID-19 era Kybernetes 2022 51 2065 2088 10.1108/K-04-2021-0333
van der Wielen W, Barrios S (2021) Economic sentiment during the COVID pandemic: evidence from search behaviour in the EU. J Econ Bus 115:. 10.1016/j.jeconbus.2020.105970
Vătămănescu EM Dabija DC Gazzola P Before and after the outbreak of Covid-19: Linking fashion companies’ corporate social responsibility approach to consumers’ demand for sustainable products J Clean Prod 2021 321 128945 10.1016/J.JCLEPRO.2021.128945
Wan Q Miao X Afshan S Dynamic effects of natural resource abundance, green financing, and government environmental concerns toward the sustainable environment in China Resour Policy 2022 79 102954 10.1016/J.RESOURPOL.2022.102954
Wang Q, Li S, Jiang F (2021) Uncovering the impact of the COVID-19 pandemic on energy consumption: new insight from difference between pandemic-free scenario and actual electricity consumption in China. J Clean Prod 313:. 10.1016/j.jclepro.2021.127897
Wei X, Han L (2021) The impact of COVID-19 pandemic on transmission of monetary policy to financial markets. Int Rev Financ Anal 74:. 10.1016/j.irfa.2021.101705
Wellalage NH, Kumar V, Hunjra AI, Al-Faryan MAS (2021) Environmental performance and firm financing during COVID-19 outbreaks: evidence from SMEs. Financ Res Lett 102568. 10.1016/J.FRL.2021.102568
Wen C Akram R Irfan M The asymmetric nexus between air pollution and COVID-19: evidence from a non-linear panel autoregressive distributed lag model Environ Res 2022 209 112848 10.1016/j.envres.2022.112848 35101402
Westerlund J Edgerton DL A panel bootstrap cointegration test Econ Lett 2007 97 185 190 10.1016/j.econlet.2007.03.003
Wu S Zhang K Parks-Stamm EJ Increases in anxiety and depression during COVID-19: a large longitudinal study from China Front Psychol 2021 12 1 12 10.3389/fpsyg.2021.706601
Xu X Wang C Zhou P GVRP considered oil-gas recovery in refined oil distribution: From an environmental perspective Int J Prod Econ 2021 235 108078 10.1016/J.IJPE.2021.108078
Yarovaya L Elsayed AH Hammoudeh S Determinants of spillovers between Islamic and conventional financial markets: exploring the safe haven assets during the COVID-19 pandemic Financ Res Lett 2021 43 101979 10.1016/j.frl.2021.101979
Zanke AA Thenge RR Adhao VS A pandemic declare by World Health Organization: COVID-19 Technol Innov Pharm Res 2021 11 47 61 10.9734/bpi/tipr/v11/3766f
Zhang J Zhang Y A qualitative comparative analysis of tourism and gender equality in emerging economies J Hosp Tour Manag 2021 46 284 292 10.1016/j.jhtm.2021.01.009
Zhang Y Abbas M Iqbal W Perceptions of GHG emissions and renewable energy sources in Europe, Australia and the USA Environ Sci Pollut Res 2022 29 5971 5987 10.1007/s11356-021-15935-7
Zhang Y Khan SAR Kumar A Is tourism really affected by logistical operations and environmental degradation? An empirical study from the perspective of Thailand J Clean Prod 2019 227 158 166 10.1016/j.jclepro.2019.04.164 | 36787070 | PMC9926458 | NO-CC CODE | 2023-04-18 23:15:18 | yes | Environ Sci Pollut Res Int. 2023 Feb 14; 30(17):49963-49979 |
==== Front
International Journal of Multiphase Flow
0301-9322
0301-9322
Elsevier Ltd.
S0301-9322(23)00045-9
10.1016/j.ijmultiphaseflow.2023.104422
104422
Article
Influence of indoor relative humidity on the number concentration, size distribution, and trajectory of sneeze droplets: Effects on social distancing guidelines
Bahramian Alireza a⁎
Ahmadi Goodarz b
a Department of Chemical Engineering, Hamedan University of Technology, P.O. Box 65155, Hamedan, Iran
b Department of Mechanical and Aerospace Engineering, Clarkson University, Potsdam, NY 13699, USA
⁎ Corresponding author.
16 2 2023
6 2023
16 2 2023
163 104422104422
29 10 2022
12 2 2023
14 2 2023
© 2023 Elsevier Ltd. All rights reserved.
2023
Elsevier Ltd
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
The spread of the COVID-19 pandemic is mainly due to the direct transmission routes of SARS-CoV-2 virus-carrying aerosols in indoor environments. In this study, the effect of indoor relative humidity (RH∞) on the number concentration, size distribution, and trajectory of sneeze droplets was studied in a confined space experimentally and numerically. Computational fluid dynamics (CFD) simulations using the renormalization group k-ε turbulence model by considering the one-way and two-way (humidity) coupling models were performed to assess the effects of humidity fields on the propagation of droplets. Number concentration profiles indicated that the RH∞ affected the dispersion modes of droplets differently for the puff, droplet cloud, fully-dispersed, and dilute-dispersed droplets phases identified by the shadowgraph imaging technique. The two-way (humidity) coupling model led to a close agreement with the experimental data in all phases. In particular, the two-way coupling provided better agreement with the data in the puff phase compared to the one-way coupling model. However, the one-way coupling model was sufficient for studying the motion of airborne droplets in the other phases. The velocity fields in the droplet cloud were more sensitive to RH∞ than the puff and fully-dispersed droplets phases. Also, the effect of RH∞ on the maximum spreading distance of droplets, dmax,sp, in the puff was insignificant, while its effect became dominant in the dilute-dispersed droplets phase. A dynamic change in the velocity profile of the sneeze jet was seen at a critical relative humidity RH∞,crit of about 48%. At RH∞< RH∞,crit, the number concentration of aerosolized droplets increases, significantly affecting the size distribution and the velocity of droplets. At RH∞≥ RH∞,crit, the effect of evaporation time on the number concentration, and diameter of droplets was negligible. At RH∞ of 24 and 64%, dmax,sp was 2.14 m (7 feet) and 3.05 m (10 feet), respectively. However, a dry indoor environment led to an increase in evaporation rate and more than four times number concentration of aerosolized droplets compared to a humid environment. Thus, the risk of direct transmission of Covid-19 in a humid indoor environment was higher than the dry conditions, which suggested the requirements for incorporating the RH∞ effect in the social distancing guideline.
Graphical abstract
Image, graphical abstract
Keywords
Indoor relative humidity
Direct transmission
Sneeze droplets
CFD simulation
Social distancing guideline
==== Body
pmcNomenclature
Acronyms
CFD Computational fluid dynamic
LED Light-emitting diode
LES Large eddy simulation
RANS Reynolds Average Navier-Stokes
RH Relative humidity
WHO World Health Organization
Symbols
Cc Cunningham slip factor (-)
Di Aerodynamic mean diameter (μm)
FD Drag force (N)
Fg Gravitational force (N)
g Gravitational acceleration (m/s2)
Kn Knudsen number (-)
m Mass (g)
Ni Total number of droplets (-)
P Pressure (Pa)
Pn,i Number fraction (-)
PV,i Ratio of the total volume of droplets (m3)
R Droplet radius (μm)
Sh Heat source (W/m2)
T Temperature (K)
T′ Integral time scale (s)
TL Lagrangian integral time scale (s)
t Time (s)
u→,v→ Velocity (m/s)
V Total volume of droplets (m3)
z Longitudinal distance (m)
Greek letters
ζ Random variable subject to a normal distribution (-)
λeff Effective thermal conductivity (W/mK)
µ Dynamic viscosity (Pa.s)
ρ Density (kg/m3)
Δt Time interval (s)
τ Droplet kinematic time scale (s)
τeff Effective stress tensor (N/m2)
Subscripts
a Air
d Droplet
k Turbulent kinetic energy
i Inlet
w Water
1 Introduction
The world faced a global outbreak of the COVID-19 pandemic, with the airborne transmission of the SARS-CoV-2 virus first reported by the World Health Organization (WHO) in China (WHO, 2020; Liu et al., 2020). The SARS-CoV-2 virus, with a diameter of ∼130 nm, is carried by respiratory droplets in the size range of 0.1–1000 μm exhaled during talking, coughing, sneezing, and even breathing of an infected person (Morawska and Cao 2020; Stadnytskyi et al., 2020). At the start of the pandemic, recommendations related to infection control suggested by the WHO (WHO 2020) acknowledged respiratory droplets (≥5 μm) as the primary mode of transmission, while possible transmission by aerosols (<5 μm) was disputed, considering the limited evidence available (Anand and Mayya, 2020; Aganovic et al., 2021). It is well known that large droplets (>100 μm) would fall due to gravitational settling. At the same time, the size of medium droplets (5–100 μm) decreases due to the evaporation process to form aerosols that remain airborne for a long time (Prather et al., 2020; Bazant and Bush, 2021; Yin et al., 2022). Respiratory aerosols have been recognized as the primary pathway for people's exposure at distances beyond 2 m from an infected person (Morawska et al., 2009). The lifetime of ejected droplets from an infected person's mouth with a height of 1.6 m during normal speaking is expected to be 33 min and 12.2 h, respectively, for aerosols with diameters of 5 and 1 μm (Eiche and Kuster 2020). Therefore, preventing the SARS-CoV-2 laden droplets from reaching occupants’ mouths, noses, and eyes through “social distancing” and wearing masks is important.
Host-to-host transmission routes of virus-laden droplets have been categorized by direct and indirect transmission (Balachandar et al., 2020). The direct transmission route involves large droplets that can ballistically reach the recipient's mucosa or settle on surfaces, while the indirect route involves formations of respiratory droplet nuclei, which remain airborne over long distances (Morawska and Cao, 2020). The ambient temperature (T ∞) and relative humidity (RH ∞) play an important role in the aerodynamic size, dispersion mode, and lifetime of respiratory droplets in the indoor environment (Chen et al., 2020; Chong et al., 2021; Manik et al., 2022; Bahramian, 2023; Bahramian et al., 2023). However, the social distancing policy neglects the environmental effects (Drossinos and Stilianakis 2020). At low RH ∞ (less than 20%), the large and medium droplets rapidly evaporate to form aerosols (Chen et al., 2020). The dependence of the equilibrium size of droplets on their non-volatile compounds, e.g., salts, proteins, and viruses, was expressed by the fundamental interpretations of equilibrium thermodynamics (Lieber et al., 2021). At RH ∞ below 80%, the respiratory droplets reach an ultimate diameter of approximately 20–40% of their initial size (Marr et al., 2019; Lieber et al., 2021).
As expected, the diameter of droplets is reduced over time so that the droplets with diameters smaller than 50 μm evaporate in less than 3.0 s at RH ∞ <50% (Vuorinen et al., 2020). A droplet with a 2.5 μm size remains suspended in the air for ∼41 min, while a droplet with a 100 μm size falls after 1.5 s (Das et al., 2020). Using a computational modeling approach, Feng et al. (2020) found that the shrinkage of cough droplets led to a prolonged suspension of resulting aerosols accelerated at RH ∞< 40%. In contrast, an increase in the droplet size caused by the hygroscopic growth of droplets takes place at RH ∞ ≥ 90% (Ng et al., 2021). The building design regulations based on the regulation of indoor RH ∞ in the U.S. (RH ∞< 65% as per ASHRAE 2013b (ANSI/ASHRAE 2013) and Europe (20 < RH ∞ <70% as per EN 16,798–1 (CEN, EN 16798, 2019) suggest the requirements for incorporating the RH ∞ values in the epidemiological models for estimating SARS-CoV-2 airborne transmission in confined spaces (Aganovic et al., 2021).
A pandemic influenza A virus (H1N1) study showed that the viruses remained highly stable over RH ∞ ranges between 20–100% (Kormuth et al., 2018). Recent findings showed that the RH ∞ could affect the survival and deactivation of the SARS-CoV-2 virus in indoor environments (Biktasheva, 2020; Pani et al., 2020). van Doremalen et al. (2020) reported that the SARS-CoV-2 virus remained viable in aerosols for a median of about 2.7 h with a reduction in infectious titer from 103.5 to 102.7 TCID50/L (defined as median tissue culture infectious dose per liter of air). At room temperature and 20% RH ∞, the mean log reduction in titer was reported to be in the range of 0.5–3.7 for 19 types of viruses (Buckland and Tyrrell 1962). Lin and Marr (2020) reported that viruses survive well at RH ∞ below 33% and degrade considerably at RH ∞ of ∼55%. Therefore, RH ∞ controls the evaporation rate of droplets and is an important parameter in virus survival degradation. Animal studies have shown that RH ∞ values that are less than 40% increase the transmission rate of the influenza virus (Gustin et al., 2015). In contrast, other studies showed the sensitivity of airborne viruses to RH ∞; some viruses survived better at RH ∞ > 50% (Kormuth et al., 2018). The chance for virus survival is enhanced when a droplet evaporates at RH ∞ of 20–30% (Bhardwaj and Agrawal, 2020), while the survival of the SARS-CoV-2 virus is reduced in the RH ∞ range of 40–60% (Dabisch et al., 2021). Parhizkar et al. (2022) showed that an increase of ∼11.85% in RH ∞ led to an approximately 50% reduction in the viral load of aerosols.
According to the Stokes law, the terminal velocity of a single SARS-CoV-2 virus is estimated to be 4 × 10−7 m/s, which is negligible (Aydin et al., 2020). The motion of respiratory aerosols with a low Stokes number is influenced by their physical properties (Morawska et al., 2009; Lieber et al., 2021) and the velocity of initial droplets (Balachandar et al., 2020). The presence of a ventilation system could transport the aerosols to distances far more than 2 m (Morawska et al., 2009). Bourouiba et al. (2014) and Wang et al. (2022) reported that a strong sneeze with a sustained velocity of 20–30 m/s could spread the droplets to distances of ∼7–8 m. Other findings have implied that sneeze droplets less than 5 μm could be transported over much longer distances than the conventional social distancing rules of 2.0 m (Bourouiba 2020).
The composition of the saliva/mucus through the protein concentrations affects the evaporation of the droplets (Nicas et al., 2005). de Oliveira et al. (2021) reported that the ultimate size of a respiratory droplet after evaporation is approximately 20–40% of the original size, depending on the protein concentration in the initial droplets. Lin and Marr (2020) reported that the evaporation rate of droplets is high at RH ≤ 33%, and the droplets' salt and protein concentrations gradually increase as the droplets dry out. Under these conditions, the viruses are viable. At RH ∞ ≥ 55%, the droplet size reduction slows and does not significantly affect the virus viability (Lin and Marr 2020). In addition, the high humidity reduces evaporation, increasing droplets' gravitational settling and removal. At intermediate RH ∞ levels, however, the virus viability decreases. These observations indicate that the accurate assessment of the RH ∞ level in the indoor environment could help with the control strategies of the transmission of the SARS-CoV-2 viruses through their viability and inactivation.
Understanding the dynamic behavior of respiratory droplets under different indoor conditions can suggest prevention guidelines to reduce COVID-19 spread (Wang et al., 2021). Previous studies introduced various techniques for measuring the respiratory droplet velocity profiles, including particle image velocimetry (PIV) and laser Doppler anemometry (Bahl et al., 2020; Stiehl et al., 2022), and airflow visualization methods such as shadowgraph imaging (Tang et al., 2013). However, due to the limitations of experiments in determining short-range/direct transmission of respiratory droplets, computational fluid dynamics (CFD) modeling was also used in the literature (Vuorinen et al., 2020).
Typically, the Direct Numerical Simulation (DNS), Large Eddy Simulation (LES), and Reynolds-Averaged Navier-Stokes equation (RANS) with turbulence models (i.e., standard, realizable, and renormalization group (RNG) k-ε and k-ω models) were used for generating the mean airflow velocity conditions and including cough and sneezing airflows (Vuorinen et al., 2020; Feng et al., 2020; Zeng et al., 2021; Rosti et al., 2021; Jaiswal et al., 2022; Olivieri et al., 2022). RANS approach evaluates the mean turbulent flow motion. LES resolves the mean motion and large-scale turbulent fluctuations. Thus, evaluating the interactions of the small-scale turbulent fluctuations with the droplets requires proper modeling. In contrast, the DNS approach resolves all fluctuation scales of turbulence and does not require subgrid-scale modeling. Ng et al. (2021) applied the spherical point-particle model to numerically predict the growth of cough droplets in cold, humid air by considering the DNS approach in the airflow phase. The results indicate that the droplets grow as the turbulent vapor mass becomes supersaturated. The DNS and LES approach, however, are computationally intensive. Many CFD-based studies were focused on the determination of appropriate “social distancing” through the modeling of averaged airflow field and respiratory droplet transport (Feng et al., 2020; Li et al., 2020; Balachandar et al., 2020). The obtained numerical results were compared with the guidelines represented by WHO. For example, Hosseinpour Shafaghi et al. (2020) reported that coronavirus-containing aerosols could be transmitted up to 6.5 m, while the minimum “social distancing” recommended by the WHO guidelines is 1.83 m (6 feet).
The effects of temperature and humidity field on the evaporation of airborne droplets over time were studied in the literature (Zeng et al., 2021; Lieber et al., 2021; Bahramian, 2023; Bahramian et al., 2023). Meanwhile, the effect of RH ∞ on the time evolution of respiratory droplet sizes during their transport is not fully understood. This study focuses on the influence of indoor RH ∞ on the time evolution of direct transmission and trajectories of droplets in confined spaces over a short duration after sneezing. The shadowgraph imaging experiments were conducted to study the dispersion modes of sneeze droplets for RH ∞ ranges of 24–64%. The CFD simulations using one-way and two-way (humidity) coupling approaches with the renormalization group k-ε turbulence model were performed to determine the dynamic characteristics of the airflow and droplets. The simulation results are validated by the experimental data. Finally, the effect of RH ∞ and its local variation on the size distribution and number concentration of aerosolized droplets at different longitudinal distances from a sneezer was examined to determine the maximum spreading distance of droplets immediately after a sneeze. The findings of this study could be used for determining the safe social distancing in different indoor relative humidity.
2 Experimental procedure
2.1 Volunteered subjects
Ten healthy male 22–23 years-old students with a mean height of 1.70±0.1 m participated in the experiments to determine the dynamic characteristics of the airflow and droplets during sneezing. Ethics approval for this study using human volunteers was granted by protocols of the Ministry of Health and Medical Education of Iran (IR.IUMS.REC, reference no. 1396/367). The height and weight of each subject were recorded individually in the form of a characteristic. The physiological characteristics of the students who participated in the sneeze imaging experiments are shown in the Supporting Information (Table S1). The means (standard deviations) of the body mass index (BMI) and forced vital capacity (FVC, ml) for all participants were 24.55 kg/m2 (1.45 kg/m2) and 4630 (390 ml), respectively. The mean area and standard deviation of the mouth opening of participants were 391±28 mm2. At the beginning of each experiment, all subjects participated in the thermographic test, and their negative PCR tests were verified. The use of pepper aided the sneezing. All experiments were carried out at least one hour after breakfast to diminish the impact of the drinks and foods on the physic-chemical properties of the sneezing droplets. At the end of the tests, the air-ventilation system was turned on to remove any residual airborne droplets.
2.2 Shadowgraph imaging system
Fig. 1 shows the schematic diagram of the shadowgraph imaging system to study the airborne transmission of sneezing droplets. A spherical concave mirror with a radius of 40 cm and a focal length of 20 cm was adjusted based on the volunteers’ height. A white-colored light-emitting diode (LED) was provided by the halolux system (halolux LED-15, STREPPEL). A high-speed digital camera (BA 150 CCD, Ophir-Spiricon Inc. 250–500 fps, 640 pixels × 480 pixels) was used to record the images. The volunteered subject was oriented at 90° to the reflected LED light by a concave mirror, while the CCD camera was placed at 60° to the direction of the LED light.Fig. 1 The schematic diagram of the shadowgraph imaging system.
Fig 1
2.3 Particle image velocimetry measurements
The velocity distribution of sneeze droplets was determined by the Particle Image Velocimetry (PIV) method (LaVision FlowMaster). A schematic diagram of the PIV setup is shown in Fig. 2 . The laser beam leaves from a double resonator Nd:YAG laser with a power of 120 mJ per pulse, a beam voltage of 12 V, and a frequency of 532 nm. The optimized time separation between pulses was 1 ms. Two cylindrical and spherical lenses with focal lengths of 15 and 500 mm, respectively, were used to reduce the laser-sheet thickness. A laser sheet with a width of less than 2 mm was generated to ensure the measured flow field was in a plane. For safety reasons and due to physical constraints of the laser optics, the laser sheet was aligned, pointing away from the volunteer, with the origin of fanning underneath and 0.2 m in front of the subject's mouth. The laser beam was then passed through cylindrical and spherical lenses to focus the beam at the center of the field of view (the region of interest). Then, the laser beam was expanded to one dimension using a cylindrical lens. The CCD camera was equipped with 55 mm lenses (Nikkor) and an interference filter (532 nm). The obtained images were analyzed by commercial software (LaVision Inc's DaVis 10). The PIV image pairs were taken at a frequency of 5 Hz. A panel spanning the measurement domain with dots of 20 mm diameter regularly spaced at 60 mm was used to calibrate the CCD camera.Fig. 2 The schematic diagram of the PIV measurement set-up.
Fig 2
A standard set of procedures for PIV data processing, including image background subtraction followed by multi-pass PIV calculations, was used to study the motion of sneeze droplets within the measurement domain. The mean droplet velocity was obtained by repeated sneezing at least 4 times against the fixed position of the PIV setup from the participant's mouth. The cross-correlation analysis was used for interrogation windows of 32 × 32 pixels, which corresponds to ∼15 × 15 mm, yielding a 74 × 99 array of velocity vectors (Recursive Nyquist Grids) with a 50% overlap. Erroneous vectors were eliminated by a standard deviation filter followed by a local median filter. This filtering process resulted in the elimination of less than 5% of the vectors. The moving average filtering was normalized, and the mean droplet velocity, instead of instantaneous velocity, was recorded for better analysis and generalized data comparison. The background and sensor noise in the images were eliminated by the average background subtraction. The sneezing jet airflow velocity was determined by an anemometer (Multiprobe anemometer, MC 4.0). The averaging was performed over repeated sneezing (a minimum of four times). The sensor (Hygro Air 20) linked to the anemometer was sensitive to the RH ∞ (%) in the range of 10–90% (with an accuracy of ±1.8%).
2.4 Droplets size distribution
A laser particle sizer (Malvern Instruments Ltd, UK) was used to analyze the size distribution of sneeze droplets. The schematic image of the laser system is shown in the Supporting Information (Fig. S1). The laser light source with a beam diameter of 1.2 mm was supplied by a helium-neon laser, which passed through the imaging area. Forty-two optical sensors were installed in the isolation room to examine the light diffraction pattern. The droplet size distribution caused by sneezing was recorded at different times to examine the effect of the residence time of droplets on their size distribution at indoor RH ∞ levels of 24, 32, 40, 48, 56, and 64%. In these experiments, the room temperature was fixed at 298 K. More details about the procedure for determining the number fraction of droplets and the estimated droplet number concentration during the simulated weak sneeze are found in the Supporting Information (Table S2). The total number of sneeze droplets is about 5535, corresponding to 5.535 #/cm3, which was in agreement with the data reported by Duguid (1946) in the case of a single simulated weak sneeze. This value is evaluated by averaging the data obtained from the weak sneeze experiments through repeated sneezing of the 10 participants (a total of 25 times sneezing).
2.5 Performance of imaging experiments
The imaging experiments were carried out in an isolation room with dimensions of 4 m (length) × 4 m (width) × 3 m (height) and a volume of 48 m3. For the health and safety of occupants in the isolating room, an airflow rate of 160 L/s (ACH = 15), recommended by WHO, entered from the ventilation inlet on the ceiling. The room temperature was controlled by an air-conditioning system, while the RH ∞ was adjusted by heating a water container to reach the desired indoor relative humidity. The imaging experiments were carried out when the ventilation system was off. The room temperature was fixed at T ∞=298 K, while the individual exhalation temperature was at Texh=309.5 K. The indoor RH ∞ was kept at 24, 32, 40, 48, 56, and 64% during the experiments. The LED light shines on the surface of a concave mirror, while the reflected light illuminates the sneeze droplets exhaled from participants’ mouths. The LED light passed through the sharp edge of a metal piece in the path of reflected light to eliminate marginal images. Each volunteer subject sneezed several times at a distance of 15 cm from the mirror. High-speed imaging of the droplet cloud from a single sneeze was recorded at 1000–4000 frames per second with a Phantom high-speed video. An image processor (ImageJ software, version 1.49) was used to analyze the obtained images.
3 Numerical methodology
3.1 Geometry and computational mesh
Fig. 3 shows the schematic of the computational domain with two virtual humans (a) and mesh configuration details (b). A three-dimensional model of the isolation room with the dimensions of 4 × 4 × 3 m3 (length, width, and height, respectively) was used as the computational domain (Fig. 3a). The ANSYS-Fluent 2020R1 commercial CFD code was used to simulate the airflow in the room with two virtual human models with a height of 1.70 m that were placed in the computational domain at a distance of 1.83 m (6 feet) from each other. It is generally expected that the effects of the susceptible individual presence on the air and droplet flow fields were insignificant without inhalation breathing. Therefore, in most previous numerical studies of personal exposure, the presence of the susceptible person was neglected for simplicity. However, the presence of a susceptible person leads to blockage of the airflow and the formation of a localized secondary flow field and, consequently, the local contaminant field, which affects actual human exposure (Gao and Niu, 2006; Feng et al., 2020).Fig. 3 (a) The schematic of the computational domain with two human models. (b) Mesh configuration.
Fig 3
The mouth opening of the virtual human was a circle with an area of 3.14 cm2. An unstructured tetrahedral mesh containing 4.72 × 108 elements in the confined space was generated by ANSYS-Fluent Meshing 19.2 software (ANSYS Inc., Canonsburg, PA) for the room configuration (Fig. 3b). A near-wall mesh refinement strategy was used to generate the mesh near the face of virtual human models. This strategy was used due to the sensitivity of velocity gradient results near the models’ faces.
3.2 Governing equations
The Eulerian-Lagrangian-based multiphase flow model was used in the simulations to predict the airborne transmission and propagation mode of droplets generated from a sneeze. The governing equations of the RANS-based simulations are described in the following section.
3.2.1 Continuous (airflow) phase
The RANS equations are used for evaluating the mean airflow (continuous phase) conditions in the room. The corresponding equations of mass and momentum equations are given as,(1) ∂ρ∂t+∂(ρu¯i)∂xi=0
(2) ρ(∂u¯i∂t+u¯j∂u¯i∂xj)=∂∂xj[μeff(∂u¯i∂xj+∂u¯j∂xi)−23μeff∂u¯k∂xkδij]−∂P∂xi+ρgi
(i,j,k=1,2,3)
where P is the mean pressure, μeff=μ+μT is the effective dynamic viscosity, μ is the airflow viscosity, μT is the turbulent viscosity, and g i is the acceleration of gravity. The turbulent diffusion of droplets is simulated by the Discrete Random Walk (DRW) model, in which the instantaneous velocity of the airflow ui is expressed as:(3) ui=u¯i+ui′
where u¯iand ui′are the mean airflow velocity and fluctuating velocity components, respectively. The mean airflow velocity, u¯i, is evaluated using the RANS approach and the RNG k-ε turbulence model. The transport equations for the RNG k-ε turbulence model are given as,(4) ρ(∂k∂t+u¯i∂k∂xi)=∂∂xi(σkμeff∂k∂xj)+Gk+Gb−ρε−YM+Sk
(5) ρ(∂ε∂t+u¯i∂ε∂xi)=∂∂xi(σεμeff∂ε∂xj)+C1εεk(Gk+C3εGb)−C2ερε2k−Rε+Sε
where k is the turbulence kinetic energy, ε is the turbulence dissipation rate, and G k, and G b, represent the generation of turbulence kinetic energy due to the mean velocity gradients and buoyancy, respectively. Here, the buoyancy effect is neglected for the isothermal condition. Y M represents the effect of compressibility on turbulence, and S k and S ε are source terms for k and ε equations, respectively. σ k and σ ε are the turbulent Prandtl numbers for k and ε, respectively.
The air temperature is evaluated using the energy equation,(6) ∂T∂t+u¯i∂T∂xi=∂∂xi[λeff∂T∂xi]+Se,evap
where λ eff = λ +λ T is the effective thermal conductivity, λ is the thermal conductivity, λ T is the turbulent thermal conductivity, and Se,evap is the energy source term caused by evaporation of droplets. The mass transfer equation for the vapor phase is governed by a convection-diffusion equation given as,(7) ∂Yv∂t+∂(u¯iYv)∂xi=Da∇2Yv+SY,evap
where Yv is the vapor concentration in the indoor air, and Da is the vapor diffusivity in air, and S Y,evap is the mass fraction source term due to the evaporation of droplets.
3.2.2 Discrete (droplet) phase
The CFD modeling was used to assess the impact of RH ∞ on the velocity field and the spreading of the droplets expelled during sneezing at a fixed temperature of T ∞ =298 K. The main assumptions used are:(a) The virtual humans were assumed to be static without any motion during sneezing. Also, only one sneezing process was modeled for simplicity.
(b) The droplet shapes at the source were spherical. The velocity of small and large droplets was assumed to be the same as the sneezing airflow velocity at the mouth.
(c) As small droplets were considered, their surface tension was sufficiently strong, so they behaved like small rigid spheres (Marr et al., 2019).
(d) The exhalation airflow temperature, T exh, was fixed (309.5 K) in the simulations.
(e) The injection angle of the droplets cloud is set to 26° relative to the horizontal direction, based on the shadowgraph imaging experiments.
(f) The effect of breathing on the velocity fields before sneezing was ignored. This is because the momentum of inhaled air by breathing before sneezing is relatively small.
(g) The effect of the non-volatile compounds, such as salt and proteins, on droplets' size change during the sneeze due to evaporation was ignored. The droplets evaporate like water droplets but reach a final droplet size (i.e., formation of droplet nuclei). Therefore, the assumption of pure water in sneeze droplets might lead to under-predicting the mean droplet velocity compared to including the nonvolatile content of respiratory droplets (Li et al., 2020).
The Lagrangian force balance equation was solved to track the sneeze droplets' trajectory. The corresponding equation of motions in Cartesian coordinates is given as,(8) mdduddt=(ρd−ρ)Vdg−34CDρρdmd2Rd|u−ud|(u−ud)
where u and ud, respectively, are the instantaneous airflow and droplet velocity vectors. Here, ρ and ρd are the densities of the air, and droplets, m d, V d, and R d, are the sneeze droplets' mass, volume, and radius, respectively. C D is the drag coefficient, which is a function of the Reynolds number as:(9) CD={24Red(Red<1)Stokesregime20Red0.7(1<Red<1000)transitionregime10Red(Red>1000)turbulentregime
where Red=(ρ|u−ud|dd/μ).
In the absence of airflow, Eq. (8) leads to the gravitational settling of small droplets in the Stokesian flow regime (Red<<1), with a terminal velocity of(10) ud,iT=giτp
where τp is the relaxation time of droplets defined as,(11) τp=Ccρgdd218μg
Here C c is the Cunningham slip factor (Allen and Raabe 1985), which is given by:(12) Cc=1+Kn(2.514+0.8exp(−0.55/Kn))
where Kn (Knudsen number) is the ratio of the mean free path of the air molecules (λ) to the radius of the particles (dp/2). At higher particle Reynolds number, the variation of drag coefficient CD=f(Red)as given by Eq. (9) need to be included. Clift et al. (2005) suggested to adjusting Eq. (9) to the dynamic relaxation time of a non-Stokesian droplet. That is,(13) τp=4Ccρgdd23μgCDRed
The evaporation of sneezing droplets between water content and ambient water vapor was considered by solving the mass and energy balance force for each droplet as follows (Chen et al., 2017; Feng et al., 2020):(14) dmddt=−∫0sndA≈−(n¯·A)
where n¯w is the average mass flux of the water, as an evaporable component, on the surface, which can be defined as (Allen and Raabe 1985):(15) n¯w=ρgShD˜wCmddlnYw,s−Yw,∞1−Yw,s
where D˜w is the mass diffusivity of the water molecules, and C m is the Fuchs-Knudsen number correction, which is given by the following:(16) Cm=1+Kn1+Kn(43am+0.377)+Kn2(43am)
Sh is the Sherwood number, which is defined as a function of the Schmidt number Sc and Re d (Clift et al., 2005):(17) Sh=2+0.552Red1/2Sc1/3
whereRed=(ρ(u−ud)/μ), and Sc is the Schmidt number(Sc=μ/ρdd). For the size of droplets under consideration, the Stokes number (St=(ρd/ρ)dd2ud/9ν) was less than one. The modified vapor pressure at the droplet surface can be expressed by Raoult's law, Pvap(Ts,Xw,s)=Xw,sPsat(Ts). Thus, the mass fractions of water molecules on the droplet surface Yw,sand in the gas phaseYw,∞ can be calculated as:(18) Yw,s=Pvap(Ts,Xw,s)MwPvap(Ts,Xw,s)Mw+(1−Pvap(Ts,Xw,s))Mda
(19) Yw,∞=(RH∞)Psat(T∞)Mw(RH∞)Psat(T∞)Mw+(1−(RH∞)Psat(T∞))Mda
Here subscripts da, vap, and sat, respectively, denote the dry air, vapor, and saturation. The energy balance equation for the droplets is expressed by:(20) ∑i=1m(mdcd)dT=πddλgNu(T∞−Td)−∑n=1k∫∫dnwLwdA
where Nu is the Nusselt number, which is a function of Re d, and the Prandtl number (Pr=μgcp,g/kg), and is given as:(21) Nu=2+0.552Red0.5Pr0.33
3.2.3 Coupling between continuous and discrete phases
The continuous and discrete phases interact and require being coupled in the RANS-based simulations. A one-way coupling approach has been proposed for turbulent multiphase flow when the volume fraction of droplets is less than 10−6. In this case, the fluid flow fields carry the droplets, but droplets have a negligible effect on fluid flow fields. In contrast, a two-way coupling approach, which considers the interactions between droplets and the surrounding environment, can model higher droplet concentration. In the full two-way coupling model, a series of source terms accounting for momentum, energy, and mass interactions between two phases are added to the corresponding conservative equations of each phase (Elghobashi, 1994; Balachandar, 2009; Gualtieri et al., 2013; Rosti et al., 2021). In this study, the dilute one-way coupling and two-way (humidity) coupling models that account for the vapor mass generation and the heat of evaporation were used.
The RANS-based governing equations described in Sections 3.2.1 and 3.2.2 were written for the two-way (humidity) coupling model based on the Eulerian multicomponent gas phase. The energy conservation Eq. (6) and vapor transport Eq. (7) included the energy source term Se,evap, and vapor concentration source term SY,evap from droplet evaporation (Fontes et al., 2020; Stiehl et al., 2022). Therefore, they included the influences of droplets' evaporation on changing the local humidity and temperature. However, the momentum interaction terms were ignored. Therefore, the model is suitable for dilute concentrations of evaporating droplets.
The simulations were first performed using the one-way coupling model that ignored the interactions between phases. Then the results of two-way (humidity) coupling simulations were presented. These included the variation of water vapor concentration due to the higher sneeze jet humidity and temperature and also the effect of evaporation of droplets as source terms in the humidity transport and energy equations.
3.2.4 Numerical procedure
Transient CFD simulations were performed in the numerical modeling of sneeze jet flow. A SIMPLE algorithm was used for pressure and velocity coupling in the continuous phase. The Lagrangian particle trajectory analysis approach was used to simulate the droplet motions. The energy equation was activated to account for the evaporation of droplets in the discrete phase. In particle-laden turbulent flows, the RANS-based CFD simulations using one-way and two-way (humidity) coupling approaches were carried out to model airflow behavior and motions of discrete evaporating droplet phases. In the one-way coupling approach, the influence of droplets on airflow is neglected. The simulations were performed using the commercial CFD code, Star-CCM+, with an Eulerian-Lagrangian multiphase model. A transient Eulerian approach was used for evaluating the water vapor concentration by solving Eq. (7) to model the relative humidity RH ∞. The relative humidity in the computational domain is varied by changing the value of the water vapor content. The discretized governing equations were solved by a finite-volume method using the second-order upwind scheme to reach residuals of less than 10−6. There is considerable variability in sneeze duration among subjects. Bourouiba et al. (2014) recently reported a value of 0.5 s for sneezing. According to the performed experimental data, the sneeze duration time of 0.6 s was used for the sneezing airflow and droplet injection duration.
The total number concentration of sneeze droplets is estimated to be 5535, corresponding to 5.535 #/cm3. A time step sensitivity analysis in the range of 1 × 10−5 to 1 × 10−4 s was used for obtaining accurate RH ∞ values. Based on these results, a time step of 1 × 10−4 s for the injection of droplets was used in all simulations. The simulations were performed on four parallel personal computers (Intel Xeon CPU E5–2630 v4, Core 10, 20, Ram: 32 G, Hard STAT: 4 TB). The computational time for simulating the 0.6 s of real-time using the optimized mesh for each run was approximately 90 h.
3.2.5 Boundary conditions (BCs)
The inlet boundary conditions in the computational model are set with identical dimensions and conditions to the isolation room in the experimental section. Different RH ∞ levels of 24, 32, 40, 48, 56, and 64% for the air in the isolation room are considered. The room temperature is fixed at T ∞ = 298 K, while the body temperature is 309 K. The initial diameter classes of 5, 22, 45, 112, 260, and 680 μm were selected in the simulations based on the mean experimental values of the size distribution curve of sneeze droplets (Supporting Information, Table S2). The total mass of 19.6 ± 2.1 mg was considered for one order of simulated weak sneeze droplets. An exhaled sneeze velocity of 25.5 m/s with a mass airflow rate of 1.1 kg/s is injected from the mouth of the virtual human model with an opening area of 3.14 cm2. According to experimental data, the injection time of droplets was fixed at 0.025 s, where all droplets with a constant velocity were injected in the simulation domain. The pressure outlet BC was applied at the ventilation outlet and adiabatic wall BC was used on the isolation room wall. In addition, no-slip velocity BC and no-temperature jump conditions were used on the human model. The “trap” BC was used in the contact of expiratory aerosols with solid surfaces, which indicates that re-suspension of droplets, was not considered in the simulations.
3.3 Grid sensitivity study
Table 1 shows the results of grid independency analysis of the predicted droplet velocity at the longitudinal distance of 0.2 m and 0.08 s after ejecting droplets from the emitter for room RH ∞ of 32%. The RH of sneeze jet flow at the mouth was set to 100%. The results were obtained from simulations with one-way and two-way (humidity) coupling approaches. The grid independency analysis was refined until an acceptable relative error (RE,%) between the simulation results obtained from two consecutive grid cell numbers was found in predicting the droplet velocity. An analysis of variance (ANOVA) was performed to determine whether the predictions were independent of the cell number. It is seen that the relative error in the predicted droplet velocity by increasing the mesh number to 1311,280 is below 1.0%. The results of computational times indicated that the CPU time for the two-way (humidity) coupling model by including the vapor transport equations was approximately 5% higher than that for the one-way coupling model. Considering the accuracy and computational time, the mesh with 1311,280 cells was selected for the subsequent simulation cases.Table 1 Grid independency analysis of the predicted droplet velocity at the longitudinal distance of 0.2 m and 0.08 s after ejecting droplets from the emitter for the room RH∞ of 32% and mouth RH = 100% for one-way and two-way coupling models.
Table 1Grid cell No. RE (%) between results of two sequential grid cells Simulation time (h)
One-way coupled Two-way coupled One-way coupled Two-way coupled
598,772 – – ∼30.7 ∼32.0
633,965 14.15 13.99 ∼37.8 ∼39.5
746,331 11.43 11.34 ∼44.1 ∼46.1
894,235 9.87 9.75 ∼49.9 ∼52.2
989,602 6.95 6.88 ∼57.7 ∼60.4
1,045,768 2.65 2.58 ∼66.8 ∼70.1
1,311,280 0.95 0.90 ∼75.7 ∼79.4
1,465,298 0.82 0.79 ∼85.3 ∼89.4
4 Results and discussion
4.1 Experimental data
Fig. 4 shows the typical experimentally measured time-evolution of the airflow velocities generated by 10 participants (cases 1–10) at RH ∞ = 48% and T ∞ = 298 K. These results were obtained at the longitudinal distance of 0.2 m from the participant's mouth. Error bars denote the standard deviation corresponding to each volunteer's sneezes within the duration of 0.6 s. The airflow velocity increases sharply over time in the short period of 0.02–0.03 s after sneezing (peak velocity-time), reaching its maximum (peak velocity) value. Then the airflow velocity resembles an exponential decay function in the time duration of ∼0.03–0.45 s and reaches near zero at times more than ∼0.60 s, referred to as sneeze duration time. The typical sneeze airflow velocity is shown in the Supporting Information (Fig. S2). The airflow velocity characteristics, including peak velocity, peak velocity-time, and sneeze duration time, were previously introduced in the literature (Oh et al., 2022). By averaging the experimental data, the airflow velocity peaks at 0.025±0.005 s, depending on the physiological characteristics of participants, and then decreases as the sneezing jet pulse passes the measurement point. This result is in good agreement with the data of Busco et al. (2020) for the sneeze duration time (0.0275 s). Earlier, Duguid (1946) and Dbouk and Drikakis (2020) reported that the peak velocity for the sneeze airflow lies in the range of 0.01 to 0.025 s.Fig. 4 The experimental time-evolution of airflow velocities generated by 10 participants (cases 1–10) at RH∞ = 48% and T∞ = 298 K. [These results were obtained at the longitudinal distance of 0.2 m from the participant's mouth].
Fig 4
The relationships between the sneeze airflow peak velocity and the physiological characteristics of the participants are shown in the Supporting Information (Figs. S3 and S4). The linear regression analysis to assess the effects of BMI and FVC parameters on the peak velocity is reported in the Supporting Information (Table S3). The data analysis showed that the P-value of FVC was lower than 0.05, indicating that the FVC parameter's change plays a significant role in the peak velocity of the airflow sneeze jet. In contrast, the P-value of BMI was higher than 0.1, indicating that the BMI parameter variation had no significant effect on the peak velocity of the airflow sneeze jet. In addition, the adjusted R 2 value of the BMI (0.721) is lower than the FVC (0.951), which indicates that the correlation between the FVC value and peak velocity is considerably higher than the BMI-peak velocity curve. Considering the closeness of the experimental data to the linear fitting curve, the data obtained from participant 3 showed a minimum mean square error (less than 5% statistical significance) in both peak velocity-BMI (Fig. S3) and peak velocity-FCV curves (Fig. S4). Therefore, the data obtained from participant 3 was used in the following sections to evaluate the CFD results. This approach helped minimize the effect of the physiological characteristics of people on experimental results.
Fig. 5 shows the measured time evolution of airborne droplet concentration after sneezing by participant 3 at times of 0.02, 0.15, 0.30, and 0.50 s. These images were obtained for a room relative humidity of RH ∞ = 56% and room temperature of T ∞ = 298 K. At 0.02 s, the dense mass of droplets with a roughly spherical shape, known as a “puff” is pushed forward in the longitudinal direction by the sneeze jet pulse. The bulk part of the puff phase, which contains small and medium droplets that are detectable at t < 0.09 s, moves together, while a low fraction of large droplets with high initial momentum separate from the bulk of the puff and travel faster. As a result of the moving of large droplets, vortex flows are created around these droplets, which quickly form and then disappear (Fig. 5, inset). Finally, the droplets of more than 100 microns lose their initial momentum and fall to the ground by gravity (Arumuru et al., 2020; Liu et al., 2021; Yin et al., 2022). Based on repeated sneeze experiments, the puff volume varies from 0.0002 to 0.0024 m3, with the maximum spreading distance d max,sp of 0.29 to 0.47 m and a mean d max,sp of 0.41 m. These values were obtained based on the maximum spreading distance of sneeze droplets that the CCD camera detected.Fig. 5 The measured time-evolution of droplet concentration after sneezing of participant 3 at different times [RH∞ = 56%, T∞= 298 K].
Fig 5
At 0.15 s, the puff expands and evolves to form a “droplet cloud” structure. As the initial sneeze pulse dissipates, the influence of gravitational force in expanding the droplet cloud increases (Balachandar et al., 2020; Li et al., 2020; Feng et al., 2020). Most droplets move downward at an angle of 25±3° relative to the horizontal direction, which agrees with the earlier reported trends in the literature (Han et al., 2013). The image analysis shows that the droplet cloud phase, which can be detectable in the sneeze duration time of 0.10–0.27 s, loses about 35% of its initial mass, while d max,sp of the droplet cloud reaches 1.34±0.07 m. At 0.30 s, a noticeable expansion in the droplet propagation is seen as the “fully-dispersed droplets” phase forms. In this phase, which can be visible in the period of 0.28–0.43 s, the sneeze droplets lose over 55–65% of their initial mass due to evaporation and gravitational sedimentation. In addition, the d max,sp of the fully-dispersed droplets reach 1.81±0.02 m.
At 0.50 s, the sneeze droplets lose over 75–85% of their original liquid mass due to evaporation to form the “dilute-dispersed droplets.” In this phase, which is in the sneeze duration time of 0.44–0.60 s, the droplets are mainly in the form of individual small aerosols that remain suspended in the air, while the inertial and gravitational sedimentation significantly affects the low number of large droplets’ trajectories. In the dilute-dispersed droplets phase, d max,sp reaches 2.66±0.03 m, which indicates a decrease in the propagation of droplets compared to the fully-dispersed droplets phase because of the droplets’ evaporation.
Fig. 6 shows the experimental size distribution of droplets after sneezing by participant 3 (who was selected based on the minimum standard deviation of results) at different RH ∞ levels and T ∞ of 298 K at (a) 0.30 s and (b) 0.50 s. The mentioned times are selected to examine the effect of RH ∞ on droplet size evolution due to the evaporation of the dilute-dispersed droplets. The results clearly show that the evaporation time significantly impacts the size distribution of airborne droplets. The curves of the size distribution of droplets roughly follow a Gaussian distribution with the formation of bi-modal peaks. At t = 0.30 s (Fig. 6a), the height of the first primary peak in the range of ∼3 to 15 μm is nearly 1.5 to 2 times the secondary peak, in the range of ∼100 to 300 μm, depending on different RH ∞ levels. This finding was nearly consistent with the earlier data of Han et al. (2013), which reported that the sneeze droplets exhaled immediately at the mouth to reduce the evaporation effect generate a bimodal distribution curve with a primary peak in the range of 50–200 µm and a secondary peak in the range of ∼250–900 µm. Han et al. (2013) reported that the geometric mean diameter of the bimodal distribution for primary and secondary peaks was 386.2 and 72.0 µm, respectively.Fig. 6 The experimental size distribution of droplets after sneezing by participant 3 at different RH∞ levels and T∞ of 298 K at 0.30 s (a) and 0.50 s (b) after sneezing.
Fig 6
The data analysis indicates that the average diameter of droplets at t = 0.30 s increases from ∼5.9 to 14.2 μm by increasing the RH ∞ from 24 to 40%. In comparison, the average droplet diameter is increased from 21.2 to 38.5 μm by increasing the RH ∞ from 48 to 64%, respectively. These results indicate that the evaporation of droplets sharply decreases at RH ∞ ≥48%, which is known as critical indoor relative humidity RH ∞,crit. At the same time, the average diameter of large droplets increases from ∼190 to 260 μm by increasing RH ∞ from 24 to 64%. Thus, the position of the primary peak significantly shifts to a higher diameter by increasing the RH ∞, while the position of the secondary peak slightly shifts to a lower droplet diameter, which indicates, in a short sneeze duration time, the large droplets with more than 100 microns are less affected by the evaporation than the droplets less than 100 microns. In addition, the magnitude of the primary peak decreases, and secondary peaks increase by increasing the RH ∞ (Fig. 6a).
At 0.50 s after sneezing (Fig. 6b), roughly mono-modal strong peaks in the range of ∼2 to 15 μm, depending on the RH ∞, are seen. The data analysis indicated that the position of peak height changed from 1.9 to 8.7 μm by increasing the RH ∞ from 24 to 64%. Also, the average diameter of droplets increases from ∼3.1 to 18.9 μm by increasing the RH ∞ from 24 to 64% at 0.50 s after sneezing. This result indicated that the size distribution of droplets shifts to higher diameters by increasing indoor RH ∞. In addition, an increase in the average diameter of droplets at 0.30 s was almost twice that of 0.50 s, indicating the effect of RH ∞ on the time evolution of the average diameter of droplets. At RH ∞ ≥ RH ∞,crit, the effect of evaporation time on the number concentration of aerosols was significantly lower than the RH ∞ < RH ∞,crit. That is, the impact of evaporation time on the reduction in the number of large droplets was significant at RH ∞ < RH ∞,crit.
4.2 Predictions of one-way and two-way (humidity) coupling models
4.2.1 Droplet velocity profile
Fig. 7 shows the comparison between the experimental and numerical velocity of droplets as a function of time in the (a) puff, (b) droplet cloud, (c) fully-dispersed droplets, and (d) dilute-dispersed droplets phases. The initial diameter classes of 5, 22, 45, 112, 260, and 680 μm were selected in the simulations based on the mean experimental data. The simulation results were obtained using the one-way and two-way (humidity) models. Here T ∞ and RH ∞ in the simulation runs were fixed at 298 K and 48%, respectively, and relative humidity at the emitter mouth was 100%. Here the error bars denote the standard deviation of the experimental data obtained by repeated sneezing (at least 4 times) of volunteer 3. A good agreement between the experimental data and the two-way (humidity) coupled simulation results were found in the puff and dilute-dispersed droplets phases (Figs. 7a and 7d). The simulation results of turbulent puff and droplet cloud phase obtained by the one-way coupling model slightly over-predicted the two-way (humidity) coupling model (Figs. 7a and 7b). These observations can be explained by the fact that the relative humidity of injected flow from the mouth was 100%, which was far more than the surrounding environment. In reality, the droplet velocity is mainly influenced by the inlet vapor concentration inside the puff, which is generally higher than the environmental humidity. Therefore, in a short time, forming the puff phase, the droplets are exposed to higher humidity than the surrounding environment and would not evaporate as fast. As a result, the evaporation rate of droplets is slow until the air entrainment and turbulent mixing reduce the relative humidity of the sneezing airflow to values close to the room environment.Fig. 7 The comparison between the experimental and numerical velocity of droplets as a function of time in the (a) puff, (b) droplet cloud, (c) fully-dispersed droplets, and (d) dilute-dispersed droplets phases [RH∞ = 48% and T∞ = 298 K, mouth RH = 100%].
Fig 7
The MRE% between the experimental and numerical results of droplet velocities at different phases is shown in the Supporting Information (Table S4). The lowest and highest deviations between the results are found in the puff and fully-dispersed droplets phases, respectively. By comparing the simulation results and experimental data in Figs. 7b and 7c, it is concluded that the predicted droplet velocity in the first stage of the puff and droplet cloud phase was more sensitive to the choice of one-way or two-way (humidity) coupling models than the fully-dispersed droplets phase. On the other hand, Fig. 7d shows no difference between the predicted droplet velocities by the one-way and two-way (humidity) coupling models in the dilute-dispersed droplets phase. That is, it is sufficient to use the one-way coupling RANS-based simulation for the fully-dispersed droplets, and dilute-dispersed droplets phases. In these regimes, the low concentration of droplets allows neglecting the forces exerted by the droplets on the airflow.
4.2.2 Number density of droplets
Fig. 8 shows the comparison between the experimental and numerical number density of droplets suspended in an isolation room as a function of time in the (a) puff, (b) droplet cloud, (c) fully-dispersed droplets, and (d) dilute-dispersed droplets phases. The initial diameter classes of 5, 22, 45, 112, 260, and 680 μm were selected in the simulations. Here the error bars denote the standard deviation of the experimental data obtained by repeated sneezing (at least 4 times) of volunteer 3. The total number density of droplets at different phases was obtained from related droplet size distribution curves, which can be found in the Supporting Information (Fig. S5). The simulation results were obtained using one-way and two-way (humidity) coupling models. Here T ∞ and RH ∞ in the simulation runs were set to 298 K and 48%, respectively. The RH of the sneeze jet at the mouth was set as 100%. The one-way coupling model's results showed a more rapid droplet evaporation rate than the numerical results obtained by the two-way (humidity) coupling model. The larger relative humidity in the puff and initial stage of droplet cloud phases due to the high relative humidity of the sneeze jet leaving the mouth and the additional water vapor generated by the evaporation of droplets, which the two-way (humidity) coupling model captures, led to a decrease in the evaporation rate of droplets, and an increase in the number density of droplets.Fig. 8 Comparison between the experimental and numerical number density of droplets suspended in an isolation room as a function of time in the (a) puff, (b) droplet cloud, (c) fully-dispersed droplets, and (d) dilute-dispersed droplets phases.
Fig 8
Inside the puff, the water vapor concentration of the injected airflow from the mouth is near saturation. Therefore, droplet evaporation is negligible. However, the droplet evaporation increases as the sneeze jet mix with the background air and the humidity reduces. That is, the relative humidity in the puff phase is mainly influenced by the inlet vapor concentration of airflow and inlet temperature, which leads to an increase in the number of larger droplets due to a low evaporation rate. Ng et al. (2021) reported that the supersaturated vapor field could drive the growth of respiratory droplets in cold and humid environments. The evaporation time of droplets is controlled by their initial size and local RH ∞ and the combined effect of turbulence and droplet inertia (Rosti et al., 2021).
The comparison between the experimental and numerical number density of suspended droplets as a function of time for all phases is shown in the Supporting Information (Fig. S6). The maximum and minimum loss in the number density of droplets occurs in the fully-dispersed droplets and puff phases, respectively. Our results showed that more than 85% of large droplets are typically separated from the sneeze airflow in the puff and fall to the ground because of gravitational force. The significant drop in the number of droplets in the fully-dispersed droplet phase is attributed to the high evaporation rate of suspended droplets in the indoor air. According to the d 2 law, small droplets evaporate almost immediately due to the larger specific surface area, while a much longer time is required for the evaporation of large droplets; thus, they are settled relatively quickly on surfaces ( Wang et al., 2021).
The MRE% between the experimental and numerical number density of suspended droplets at different phases is shown in the Supporting Information (Table S5). The lowest and highest deviations between the results are found in the puff and fully-dispersed droplets phases, respectively. Fig. 8 shows that the two-way (humidity) coupling model leads to better agreement with the experimental data than the one-way coupling model in the puff. In contrast, the one-way or two-way (humidity) coupling models lead to roughly the same number density of droplets in the fully-dispersed droplets and dilute-dispersed droplets phases. Therefore, the two-way (humidity) coupling model is recommended for closer agreement with the experimental data in the puff in the numerical modeling of droplets. In contrast, using a one-way coupling model is sufficient for predicting the dispersion of sneeze droplets in the other phases.
4.3 Numerical model validation by experimental data
4.3.1 Time evolution of droplets concentration
Fig. 9 shows the predicted droplet number concentration contour plots obtained from one-way coupled simulations at 0.02, 0.15, 0.30, and 0.50 s. Here T ∞ and RH ∞ were set to 298 K and 56%, respectively. The relative humidity is set based on the value of water vapor content introduced in Eq. (7). At 0.02 s after sneezing, the formation of a coherent puff structure can be observed, which agrees with the imaging experiments. The number concentration of droplets in the puff evolves because of the change in the size of droplets due to evaporation (Morawska et al., 2009). The droplet diameter and trajectories are evaluated by solving the corresponding governing equations given by (7) and (8). Fig. 9 shows a high number concentration of droplets in the core of the puff. In contrast, a low concentration of droplets is found in the surrounding area of the puff. A mean d max,sp of 0.42±0.03 m, is determined by the puff structure obtained by simulation results. Since the large droplets are not deposited on the person who sneezes, the possibility of backward movement of droplets is assumed to be negligible.Fig. 9 The contour plots of the number concentration of droplets as predicted by the one-way coupled simulations at different times [RH∞ = 56% and T∞ = 298 K].
Fig 9
At t = 0.15 s, the droplet number concentration contour plot indicates that the dispersion of suspended droplets in the air increases to form the droplet cloud phase. The image analysis based on the number concentration of droplets showed that the droplet cloud phase lost ∼36% of their initial mass, while the d max,sp of droplets was 1.37±0.02 m. The mean relative error (MRE,%) between the experimental data and simulation results in the estimation of d max,sp is ∼2.23%. At 0.15 s, the sneezing jet was more influenced by inertial force than the gravity force. Balachandar et al. (2020) reported that larger droplets could travel longer distances from the source than the smaller droplets due to their higher initial force. At 0.30 s, the suspended droplets further disperse in the air to form the fully-dispersed droplets. In this phase, the droplet cloud significantly expanded in the indoor air, while the high concentration of droplets remained in the central core, as found in the experiments. The image analysis showed that the droplet cloud loses ∼55% of its initial volume, while d max,sp of droplets is 1.87±0.03 m. The MRE% between the experimental data and simulation results in the value of d max,sp was ∼3.31%.
At 0.50 s, the suspended droplets' concentration evolves into the dilute-dispersed droplets phase. It is seen that there is no significant change in the volume of suspended droplets in the dilute-dispersed droplets compared to the fully-dispersed droplets. However, the number concentration of droplets considerably decreases, and the coherent nature of droplets in the core becomes weaker in the dilute-dispersed droplets phase compared to the fully-dispersed droplets. The result shows that the d max,sp of droplets reaches 2.21±0.03 m. The MRE% between the experimental data and simulation results for the value of d max,sp was ∼2.31%. In this phase, the suspended droplets lose more than 85% of their initial mass, indicating that the evaporation process significantly affects the volume reduction of the dilute-dispersed droplets.
4.3.2 Size distribution of droplets
Fig. 10 compares the simulation results obtained by a one-way coupling model for the size distribution of droplets at 0.30 s (a) and 0.50 s (b) after sneezing. T ∞ and RH ∞ were set to 298 K and 48%, respectively. This figure shows that droplet size distribution shifts to lower mean diameters over time. Based on data analysis, ∼42 and ∼10% of the total droplets remain suspended in the simulation domain at 0.30 s and 0.50 s, respectively, which is in good agreement with our experimental data. After 0.50 s, only ∼2% of droplets settle to the ground and exit from the simulation domain because of gravity. The large difference between the number concentrations of suspended droplets in the fully-dispersed droplets (∼85%) represents the high evaporation rate of droplets, while the medium droplets consist of ∼80 and 94% of total suspended droplets at 0.30 and 0.50 s, respectively.Fig. 10 Comparison of the simulation results using the one-way coupling model for the droplet number concentration versus droplet diameter at 0.30 s and 0.50 s after sneezing. [T∞ and RH∞ were set to 298 K and 48%, respectively.].
Fig 10
At t = 0.24 s, 90% of droplets are in the diameter range of ∼4–62 μm, while the main portion of droplets at t = 0.50 s lies in the diameter range of ∼3–28 μm. Thus, the mean diameter of droplets decreases over time because of evaporation. The time evolution of the droplet size distributions occurs due to the evaporation of droplets during airborne transmission. The data analysis showed that the number of the large droplets (d p>100 μm) at 0.50 s was ∼70% lower than the number of large droplets at 0.30 s, which indicates the droplets quickly settled on the ground because of the effect of gravitational force. This result was in agreement with the observation of Zeng et al. (2021) that the sedimentation patterns become chaotic by reducing the size distribution of droplets. Furthermore, by comparing the location and magnitude of the secondary peak at 0.30 s and 0.50 s (Fig. 10), it can be found that the secondary peak disappears, similar to the experiment. Moreover, the diameter of the first peak decreases over time due to evaporation. Comparing Fig. 10 with the experimental data in Fig. 6 indicates that the one-way coupling simulation results show a more rapid droplet evaporation rate than the experimental data. It is seen that the one-way coupling model led to a reduced secondary peak at around 100 μm at 0.3 s and smaller average droplet sizes at 0.5 s.
4.3.3 Airflow velocity profile
Fig. 11 shows the time-evolution of the airflow velocity obtained from the experimental data (participant 3) and simulation results at different RH ∞ levels of (a) 24%, (b) 32%, (c) 40%, (d) 48%, (e) 56%, and (f) 64% and T ∞ = 298 K. It should be noted that the simulation results reported in the puff (t < 0.09 s) were obtained from the two-way (humidity) coupling model, while the one-way coupling model was used to study the airflow velocity profiles in the other phases. Symbols represent the experimental data obtained from participant 3, while the lines denote the simulation results. These results were obtained at a 0.2 m longitudinal distance from the participant's mouth. There is a good agreement between the mean experimental data and simulation results. The airflow velocity increases over time in the short period of 0.022–0.026 s, corresponding to the turbulent puff phase, to reach its maximum value. The velocity profile shows an exponential decay in the period of ∼0.03–0.44 s and reaches near zero at 0.6 s, which indicates the sneeze jet lost most of its initial momentum. In the turbulent puff phase, the airflow velocity ranges from 23.8 to 25.5 m/s, depending on RH ∞ levels. In the period of 0.03–0.09 s, the volume of the puff expands to form an expanded puff, where a high concentration of droplets is wrapped into the exhaled turbulent airflow (Balachandar et al., 2020).Fig. 11 The time-evolution of the airflow velocity profiles obtained from the experimental data (participant 3) and simulation results at a longitudinal distance of 0.2 m from the mouth for RH∞ levels of (a) 24%, (b) 32%, (c) 40%, (d) 48%, (e) 56%, and (f) 64% and T∞ = 298 K. [Symbols are experimental data, while the lines represent the simulation results].
Fig 11
A significant difference in the airflow velocity is seen in the period of 0.10 < t < 0.27 s, which corresponds to the transition state from droplet cloud to fully-dispersed droplets phase. In the period of 0.28 < t < 0.43 s, representing the transition phase from fully-dispersed phase to dilute-dispersed droplets, the airflow velocity gradually decreases, indicating that the inertial force is reduced (Balachandar et al., 2020). A significant loss in the airflow velocity is seen in the period of 0.44 < t < 0.60 s, representing the dilute-dispersed regime (Lieber et al., 2021). The aerosolized droplets are less influenced by the sneeze airflow velocity over time, while the characteristics of the vapor phase are more affected by the droplet evaporation. The evaporation time of droplets increases by increasing RH ∞, since the droplet evaporation rate is reduces (Liu et al., 2021).
The effect of the indoor RH ∞ on the airflow velocity is considerable in the transition phase from the fully-dispersed to dilute-dispersed droplets. It can be seen that the airflow velocity also decreases by increasing indoor RH ∞. At RH ∞ ≥ RH ∞,crit, a significant drop in the peak velocity is seen because of an increase in the drag force between the sneeze jet and the surrounding airflow caused by the air resistance. At RH ∞ of 24% (Fig. 11a), the environmental humidity has a lower effect on the peak velocity than RH ∞ = 64% (Fig. 11f). At RH ∞ = 24%, the experimental airflow velocity reaches approximately zero at t = 0.33 s, while the airflow velocity approaches zero at twice the mentioned value (∼0.60 s) for the RH ∞ = 64%. The airflow velocity approaches zero for RH ∞ of 48% (Fig. 11e) and 56% (Fig. 11f) at about 0.35 and 0.44 s, respectively. These findings indicate that the impact of indoor RH ∞ on the reduction of sneeze duration-time is more important at smaller RH ∞, while the impact of indoor RH ∞ on the reduction of peak velocity is more important at larger RH ∞.
After the sneezing jet is exhaled from the mouth, the airflow velocity gradually decreases, and the influence of drag and gravity forces increases. The magnitude of drag force is large at the start of the sneezing process, where the airflow velocity is large. However, the drag force decreases by decreasing the airflow velocity, and the deceleration in airflow velocity also decreases, which leads to a negligible drop in the airflow velocity. The evaporation rate of the droplets within the sneeze jet and the exhaled airflow velocity also affects the motion of the suspended droplets in the short time durations. It is known that the droplet dispersion pattern is described by airflow velocity, demonstrating the importance of accurately predicting the airflow in predicting virus transmission under different environmental conditions (Liu et al., 2021).
MRE% results between the mean experimental data and simulation results of airflow velocity at different RH ∞ levels in the periods of t < 0.09 s, 0.10–0.27 s, 0.28–0.43 s, and 0.44–0.60 s is shown in the Supporting Information (Table S6). At t < 0.09 s, which corresponds to the puff, the lowest deviations between the mean experimental data and simulation results are found. In contrast, the highest deviations were observed in the period of 0.28–0.43 s, which corresponds to the fully-dispersed droplets, where the effect of evaporation rate on the airflow jet is dominant. In the period of 0.10 < t < 0.27 s, which corresponds to the droplet cloud, the MRE% values are increased compared to obtained values for the puff phase. At t ≤ 0.43 s, the values of MRE% are increased with increasing RH ∞ from 24 to 64%. This result is explained by the fact that the magnitude of the air resistance between sneeze jet flow and surrounding air increases with an increase in indoor humidity. This is due to the increased air density with RH ∞ that was also included in the simulations. Uncertainty in the experimental data caused by increasing indoor RH ∞ and its effect on the analysis of images obtained by shadowgraph imaging experiments can be attributed to the deviation in results. During the measurement, because the exhaled sneeze jet airflow disperses quickly, it is challenging to ensure that the entire sneeze jet passes through the measurement zone, which leads to uncertainties in the results. In addition, the variation of physiological characteristics of participants and the airflow during sneeze experiments could affect the uncertainty of results. In the dilute-dispersed droplets phase, where the initial momentum of the sneezing jet pulse significantly decreases, the low deviations between the results were attributed to the generally low airflow velocity approaching zero.
4.3.4 Droplet velocity profile
Fig. 12 shows the time-evolution of the mean droplet velocity at room RH ∞ levels of (a) 24%, (b) 32%, (c) 40%, (d) 48%, (e) 56%, and (f) 64% at a distance of 0.2 m from the mouth of the emitter. The points indicate the experimental data obtained from participant 3, while the lines represent the simulation results. Here the error bars denote the standard deviation of the experimental data obtained by repeated sneezing (at least 4 times) of volunteer 3. The droplet velocity data were obtained from the data averaging participant 3 at different RH ∞ levels. The droplet velocity at the distance of 0.2 m increases as the sneeze pulse reaches the measurement point. The simulation results reported in the puff were obtained from the two-way (humidity) coupling model, while the one-way coupling model was used to study the velocity of droplets in the other phases. The results showed that the trend of the droplet velocity profile over time is similar to that of airflow velocity. Inside the puff, the droplets are moved forward by their inertia in the longitudinal direction. By comparing the results, it can be concluded that the droplets' velocity in the puff was close to the initial airflow velocity (Figs. 11 and 12). Similar to what was seen in airflow velocity, the mean droplet velocity decreases in the 0.10 < t < 0.27 s interval, which corresponds to the droplet cloud, while the droplet velocity decreases gradually in the period of 0.28–0.43 s, which is attributed to the fully-dispersed droplets.Fig. 12 The time-evolution of the mean droplet velocity at a longitudinal distance of 0.2 m from the mouth for RH∞ levels of (a) 24%, (b) 32%, (c) 40%, (d) 48%, (e) 56%, and (f) 64%. [Symbols are experimental data, while the lines represent the simulation results.].
Fig 12
The results in Fig. 12 also show that the peak velocity of droplets decreases by increasing RH ∞, while the sneeze duration time increases. In contrast, decreasing RH ∞ decreases the mean droplet velocity in the fully-dispersed and dilute-dispersed droplets. The effect of RH ∞ on the mean droplet velocity in the dilute-dispersed droplets phase was more dominant than in the fully-dispersed droplets, which indicates that the evaporation time plays a significant role in the size and velocity of droplets. The results showed that both sneezing jet velocity and dispersion trajectories of droplets depend on their evaporation rate (Zeng et al., 2021). A loss in the peak velocity over time at RH ∞ ≤ RH ∞,crit is significantly higher than that at RH ∞ > RH ∞,crit, which can be explained by the increased effect of indoor RH ∞ on the sneeze jet flow. As noted before, the simulation included the effect of air density with humidity, which reduces the peak velocity.
A significant decrease in the peak velocity (∼5.05 m/s) is found by increasing RH ∞ from 24 to 64%. This result was justified by the fact that the droplets' evaporation rate reduces at high RH ∞, and the effect of drag force increases at relatively high RH ∞. For all values of RH ∞, the droplet velocity reaches approximately zero at t∼0.57 s, which follows the same time variation trend observed in the airflow velocity (Fig. 11). At RH ∞ = 24% (Fig. 12a), a sharp reduction in the diameter of droplets caused by the evaporation process led to a loss in the droplet velocity. At RH ∞ of 32% (Fig. 12b) and 40% (Fig. 12c), it is seen that the droplet velocity reaches approximately zero at 0.40 and 0.49 s, respectively.
Based on the literature (Balachandar et al., 2020; Cheng et al., 2020), the evaporation process reduces the size and velocity of droplets. On the other hand, an increase in room RH ∞ level leads to an increase in air resistance against the movement of droplets and a decrease in their velocity. At RH ∞ > RH ∞,crit, the evaporation rate of droplets decreases; thus, the change in the droplets' size slows, and the droplets keep their initial momentum for a longer time. The large droplets maintain their size in humid indoor air and thus sediment to the ground faster while evaporating faster in a dry environment. At low RH ∞, the evaporation rate is high, and droplets rapidly change to aerosols that remain suspended in the air for a long time (Cheng et al., 2020; Zeng et al., 2021). In addition, when a droplet vaporizes and its size decreases, the drag force per unit mass increases (Balachandar et al., 2020). This, in turn, is the reason for the decreasing trend of the droplet velocity at RH ∞ < RH ∞,crit. In the dilute-dispersed droplets phase, due to the increase in the evaporation rate, the velocity of the droplets is more dependent on their size than the RH ∞. Therefore, the droplet velocity gradually decreases in a low RH ∞ environment faster than in a high RH ∞ environment.
The MRE% between the experimental data and simulation results of droplet velocity at RH ∞ levels of 24, 32, 40, 48, 56, and 64% at different time periods, representing the different dispersion modes of droplets is shown in the Supporting Information (Table S7). The simulation results reported in the puff phase were obtained from the two-way (humidity) coupling model, while the one-way coupling model was used to calculate the MRE% of results in the other phases. At t < 0.09 s, which corresponds to the puff phase, the lowest deviations between the experimental data and simulation results by considering the two-way (humidity) coupling model are found, while the highest deviations occur in the period of 0.28–0.43 s, representing the fully-dispersed droplets phase. This finding agrees with the airflow velocity profiles, where the magnitude of drag force is more dominant than the gravitational force by increasing the RH ∞. In the period of 0.10 < t < 0.27 s, indicating the droplet cloud phase, high deviations between the results are found, which are attributed to the non-volatile soluble components of salt and protein in the sneezing droplets. The effect of evaporation time is dominant by decreasing the diameter of the sneeze droplets. The lowest deviations between the results are found in the period of 0.44–0.60 s, which is ascribed to the significant loss in the inertial force of the dilute-dispersed droplets phase. At RH ∞≥ RH ∞,crit, where a lower shrinkage rate of medium droplets due to lower evaporation of droplets is seen compared to the RH ∞< RH ∞,crit, which leads to a continuous change in the size distribution of droplets and the velocity fluctuations of sneezing jet. The smallest change in the droplet velocity is seen in the puff structure, where a low turbulent fluctuation occurs in the airflow velocity (Balachandar et al., 2020, Ling et al., 2016).
4.4 Application of the validated numerical model
4.4.1 Effect of humidity fields on the number of droplets
Fig. 13 presents the time evolution of droplets deposited in the computational domain (in the percentage) obtained from the simulation results at different RH ∞ levels and T ∞ = 298 K. The simulation results reported in the puff were obtained from the two-way (humidity) coupling model, while the results of the one-way coupling model were used in the other phases. As shown, the percentage of large droplet deposition is ∼17.4% of total droplets in the initial stage of the sneezing model (for t < 0.09 s), where the deposition rate of droplets is higher than their evaporation. After 0.32 s, the remaining roughly 0.5% of large droplets settles on the ground and exit from the simulation domain because of the gravity force. A low difference between the remaining droplets (∼0.1%) in the period of 0.44–0.60 s represents the low percentage of large droplets, which consisted of previous studies (Bourouiba et al., 2014; Liu et al., 2021). Also, the impact of RH ∞ on large droplet deposition is significant in the period of ∼0.06–0.28 s, while its effect can be ignored after ∼0.32 s.Fig. 13 The time evolution of droplets deposited in the computational domain (percentage) as predicted by simulations at different RH∞ levels and T∞ = 298 K.
Fig 13
At RH ∞ ≥ RH ∞,crit, the number of large droplets deposition is higher than that for RH ∞ < RH ∞,crit. As noted before, the higher RH ∞ (64%) decreases evaporation, thus increasing large droplets' settling on the ground due to gravitational force. In contrast, the lower RH ∞ (24%) leads to shrinking droplets into smaller sizes due to evaporation. Zeng et al. (2021) reported that the disappearance of the vortex ring happens with the disappearance of the humidity field, which indicates the large droplets deposited on the ground faster at low RH ∞. However, the higher deposition rate of respiratory droplets does not necessarily mean a higher infection risk. Previous results indicated that an RH ∞ of more than 48% reduces the number of suspended respiratory droplets that carry viruses, including different types of coronavirus (Casanova et al., 2010).
Fig. 14 presents the time evolution of suspended droplets in the computational domain (percentage) obtained from the simulation results at different RH ∞ levels and T ∞ = 298 K. The simulation results reported in the puff phase were obtained from the two-way (humidity) coupling model, while the results of the one-way coupling model were used in the other phases. This figure shows that the number of suspended droplets decreases sharply in the period of ∼0.22–0.43 s because of evaporation, while it gradually decreases to less than 0.5% in the period of ∼0.50–0.60 s. The effect of RH ∞ on the number of suspended droplets can be ignored in the initial time (t < 0.09 s) and final stages (t > 0.50 s). An increase of RH ∞ from 24 to 64% leads to an increase in the percentage of suspended droplets in the period of ∼0.20–0.43 s. That is, a rise in the humidity field leads to a considerable decrease in the evaporation rate of suspended droplets. On the other hand, an increase of RH ∞ from 24 to 64% leads to a four times increase in the number of suspended droplets due to reduced droplet fall-out through evaporation. This observation agrees with the previous results of Arumuru et al. (2020). The aerosols form the bulk of suspended droplets that remain in the air for a long time (Balachandar et al., 2020; Aganovic et al., 2021; Liu et al., 2021). Thus, the risk of direct transmission of Covid-19 in a humid indoor environment is higher than the dry conditions. This observation is in good agreement with the Wang et al. (2021) findings, which suggested that the safe distance between two individuals should be extended by an increase in the ambient relative humidity. Thus, the obtained results could help improve the social distancing guidelines.Fig. 14 The time evolution of suspended droplets in the computational domain (percentage) as predicted by simulations at different RH∞ levels and T∞ = 298 K.
Fig 14
4.4.2 Effect of humidity fields on the evolution of droplets size
Fig. 15 shows the effect of different relative humidity fields on the average diameter of (a) deposited droplets and (b) suspended droplets in the computational domain at different RH ∞ levels. The simulation results reported in the puff phase were obtained from the two-way (humidity) coupling model, while the results of the one-way coupling model were used in the other phases. The exhaled sneeze velocity was 25.5 m/s, while T ∞ was fixed at 298 K. It can be seen that the average diameters of deposited droplets are approximately constant at t < 0.09 s, which indicates the RH ∞ does not significantly affect the size of large droplets in the short time after their release. In contrast, the average droplets’ diameters decrease in the period of 0.12–32 s. The results also show that the average diameters of large droplets are decreased by decreasing the RH ∞ levels. At RH ∞ ≥ RH ∞,crit, the decreasing trend of droplet diameter over time decelerates, while at RH ∞ < RH ∞,crit, which causes droplet evaporation, the decreasing trend of droplet diameter over time accelerates (Fig. 15a). At RH ∞ = 24%, the diameter changes of deposited droplets is limited to the first 0.42 s, while it increases to 0.60 s at RH ∞ = 64%. These results indicate that the droplet size distribution and deposition time increase by increasing RH ∞. Thus, the deposition of droplets with the same initial diameters becomes more prominent at high RH ∞ than at low RH ∞. This result is in good agreement with the data reported in the literature (Feng et al., 2020). It is worth mentioning that the evaporation time has a negligible effect on the deposited diameter of large droplets because they quickly deposited on the ground.Fig. 15 The temporal variation in the effect of different humidity fields on the average diameter of (a) deposited droplets and (b) suspended droplets in the computational domain at different RH∞ values [the results were obtained for average droplet velocity.].
Fig 15
The effect of humidity fields on the mean diameter changes of the suspended droplet at different times is shown in Fig. 15b. It can be found that suspension time plays an important role in the diameter changes of suspended droplets. Also, the diameter of the suspended droplet increases by increasing the RH ∞ levels. At RH ∞ ≥ RH ∞,crit, the diameter of the suspended droplets remains roughly constant due to the lower evaporation rate of droplets in the period of 0.50–0.60 s. Then the diameter decreases gradually by evaporation to reach sizes less than 15 μm. At RH ∞ = RH ∞,crit, which is the critical indoor environmental humidity, the diameter changes of the suspended droplets over time becomes negligible in the period of 0.50–0.60 s. Then the diameter of droplets decreased to reach sizes less than 10 μm by evaporation. Morawska et al. (2009) reported that the equilibrium size distribution of evaporating respiratory droplets with diameters between 0.5 and 22 μm occurred within 0.7 s. At RH ∞ < RH ∞,crit, the diameter changes of the suspended droplets over time decrease to reach sizes less than 10 μm by evaporation at 0.60 s. These results clearly show that the aerosols could remain indoors for a long time and make a susceptible person sick if they carry an infectious agent.
4.4.3 Effect of humidity fields on droplets trajectory and social distancing
Fig. 16 shows the effect of different humidity levels on the longitudinal transmission of initially 260 μm droplets in the computational domain. In the experiment (Fig. 6), the mean diameter of droplets was 260 μm when T ∞ was fixed at 298 K, and the sneeze velocity was 25.5 m/s. As clearly seen in Fig. 16, the humid environment influences the motion of droplets. At RH ∞ of 24% and 64%, the values of maximum spreading distance d max,sp of droplets, as computed numerically by considering the one-way coupling model through the distance traveled by droplets before total evaporation, are 0.77 and 0.71 m, respectively. However, the value of d max,sp in the case of 260 μm droplets is ∼20–30% higher than the d max,sp of the main puff. As noted before, the large droplets travel faster than the main puff because of their high initial momentum; as a result, they settle on the surface before the completion of their evaporation (Arumuru et al., 2020; Liu et al., 2021; Avni and Dagan 2022). This behavior is consistent with the experimental observations in Fig. 5. The net drag acting on a droplet decreases with decreasing RH ∞, which leads to a slight increase in the d max,sp. The impact of RH ∞ on the longitudinal airborne transmission of droplets is noticeable at distances between 0.16 to 0.77 m (Fig. 16), which also affects the droplet's trajectory in the vortex ring zone. It is seen that the droplet's trajectory tends to move upward slightly in the vortex zone due to the interaction of the droplet with the airflow shear in this region, which was also seen in the experiment (Fig. 5). Under a lower RH ∞, a smaller number of larger droplets remain because of evaporation that generates smaller sizes and gravitational sedimentation.Fig. 16 The effect of different humidity levels on the longitudinal transmission of initially 260 μm droplets in the computational domain at T∞ = 298 K and sneeze velocity of 25.5 m/s.
Fig 16
The relationship between the RH ∞ and the lifetime of droplets containing viruses was studied in the literature (Kormuth et al., 2018; Biktasheva, 2020; Chong et al., 2021). Accordingly, at RH ∞ < RH ∞,crit the droplets evaporate and form aerosols and aerosols nuclei suspended in the air for a long time (Drossinos and Stilianakis, 2020; de Oliveira et al., 2021; Lieber et al., 2021). After evaporation, all initially present viruses stay in the droplet nuclei (Morawska et al., 2009; Bourouiba et al., 2014; Balachandar et al., 2020). In addition, enveloped viruses (such as influenza viruses, coronaviruses, and RSV) tend to survive longer at RH ∞ values of less than 30% (Bozic and Kanduc 2021). Moriyama et al. (2020) and Ahlawat et al. (2020) reported that the relative humidity in the range of 40–60% was appropriate for reducing respiratory virus transmission in indoor places.
The virus survival time depends on the size and lifetime of droplets (Lieber et al., 2021; Chong et al., 2021). Aerosols have a relatively high evaporation time but longer persistence in the air, while large droplets (>100 μm) have a slower evaporation time instead are settled quickly on the ground (Coldrick et al., 2022). The lifetime of saliva droplets smaller than 50 μm was mainly determined by the equilibrium nuclei size of droplets, while the lifetime of droplets with diameters between 50–150 μm was affected by environmental conditions (Lieber et al., 2021). Therefore, a decrease in indoor air humidity increases the number of aerosolized respiratory droplets carrying viruses. From the infection viewpoint, this, together with lower droplet deposition, makes the drier air more effective for the spread of infections. In summary, it can be concluded that a low (RH ∞ = 24%) directly or indirectly influences the lifetime of droplets and increases the chance for transmission of respiratory diseases.
Fig. 17 shows the effect of different humidity levels on the trajectories of initially 10 μm droplets at T ∞ = 298 K and the sneeze velocity of 25.5 m/s. The 10 μm-sized droplets were considered in the computational domain because of a high number concentration of medium droplets in Fig. 6. The initial trajectories of the droplet show that the aerosol and medium droplets travel at the same velocity as the puff, which was found by the experimental observations (Fig. 5, inset). Fig. 17 shows that the trajectory of suspended droplets, which can be attributed mainly to the airborne motion of small droplets, was more influenced by the humidity field than the large droplets. It is also seen that d max,sp increases by increasing the RH ∞. At RH ∞ of 24 and 64%, d max,sp of suspended droplets are 2.14 m (7 feet) and 3.05 m (10 feet), respectively, due to the effect of the room humidity field. This result shows that the distance of 1.83 m (6 feet), suggested for social distancing, is insufficient for protecting healthy subjects from exposure to airborne virus-laden aerosols in the absence of ventilation airflow.Fig. 17 The effect of different humidity fields on the dynamic trajectory of initially 10 μm droplets in the computational domain at T∞ = 298 K and a sneeze velocity of 25.5 m/s.
Fig 17
At the longitudinal distance range of ∼0.5–2.2 m, in the absence of large droplets, the effect of inertial force decreases as the initial momentum dissipates, while the drag force is also small as the droplets mostly move with the airflow. Medium droplets evaporate, become smaller, and are aerosolized, which leads to a change in the drag force that is proportional to the difference between the droplet velocity and the surrounding airflow. As the droplet diameter decrease, the drag force on the suspended droplets increases, reducing the longitudinal distance traveled by the droplets. A gradual decrease in the longitudinal movement of droplets in the distance range of ∼2.2–4.0 m indicates that the drag force significantly influences the traveling distance of small droplets. At RH ∞ = 24%, many droplets evaporate rapidly and become aerosols, which remain suspended in the air for several hours (Bourouiba et al., 2014). The large droplets move faster and stay more concentrated than the small droplets because the large droplets have a relatively higher Stokes number (Fen et al., 2020). The large droplets keep their initial momentum longer, while the small droplets lose their initial momentum more quickly because of the drag force (Balachandar et al., 2020). Thus, the pathogens within the nuclei and aerosolized droplets are expected to present a more significant long-range transmission threat than the large droplets in high-humidity rooms.
Fig. 18 shows the effect of RH ∞ on the maximum spreading distance d max,sp of aerosolized droplets with an initial sneeze velocity of 25.5 m/s at different simulation times of 0.02, 0.15, 0.24, 0.43, 0.50, and 0.60 s. The results show that the d max,sp increases significantly during the period of 0.02 to 0.43 s, while the d max,sp increases gradually at times more than 0.43 s. The effect of evaporation time on the d max,sp is significant in a short time duration of 0.02–0.15 s, while its effect on d max,sp is small at 0.50 s and 0.60 s after sneezing. Also, Fig. 18 shows that the d max,sp increases as RH ∞ increases, which is in agreement with the reported data in the literature (Chong et al., 2021; Ng et al., 2021; Wang et al., 2022). Wang et al. (2022) reported that the evaporation dynamics become faster, and the lifetime decreases for 60–100 μm droplets by reducing the RH ∞.Fig. 18 The effect of RH∞ on the maximum spreading distance of aerosolized droplets for identifying the appropriate social distancing at different simulation times.
Fig 18
The effect of on d max,sp becomes significant at times of ≥ 0.43 s after a sneeze, while the RH ∞ effect is negligible at 0.02 s after sneezing. These observations indicate that the RH ∞ effect on d max,sp is insignificant in the puff phase, while its effect becomes more important in the dilute-dispersed droplets phase. This result is attributed to the low differences in droplet size change due to the low impact of the RH ∞ in the puff phase, where the droplets reach their peak velocities. Results presented in Fig. 18 suggest that the social distancing of more than 3.0 m reduces the risk of direct transmission of SARS-CoV-2 viruses carried by sneeze droplets in the absence of a ventilation system. After 0.43 s, all predicted values of d max,sp are more than 2.2 m, which is significantly higher than the presently recommended social distancing policy of 6 feet. Therefore, the current policy does not protect the healthy subject from exposure to droplets emitted by sneezing in the absence of a ventilation system.
5 Conclusions
The effect of indoor relative humidity (RH ∞) on the trajectory and dispersion of sneezing droplets in short time durations after sneezing was studied experimentally and numerically. Transient computational fluid dynamics simulations were performed by applying the RNG k-ε turbulent model using the one-way and two-way (humidity) coupling model to evaluate the effects of humidity levels on the number concentrations, diameters, and velocities of the droplets. The findings of the study may be summarized as follows:· The RANS-based two-way (humidity) coupling model led to a close agreement with the experimental data in the puff phase. In contrast, a one-way coupling model was sufficient for predicting the motion of airborne droplets in the other phases.
· A critical relative humidity RH ∞,crit of 48%, is found, where a dynamic change of droplets in the time-evolution of the airflow and droplet velocity profiles occurs. At RH ∞ < RH ∞,crit, the decreasing trend of droplet diameter over time continues until the droplet reaches sizes less than 10 μm. For which the droplet velocity also reduces. At RH ∞ ≥ RH ∞,crit, the effect of evaporation time on the number of aerosols is negligible, while the decreasing trend of droplets diameter over time decelerates. At RH ∞ ≥ RH ∞,crit, the number of large droplets deposition is higher than those for RH ∞ < RH ∞,crit.
· The sneeze droplet behavior in humid air is sensitive to their diameters. Larger droplets have greater settling velocities at higher RH ∞. The effect of indoor RH ∞ on droplet diameter and mean droplet velocity is significant in the dilute-dispersed droplets phase, while in the puff phase, the effect of RH ∞ on the peak velocity is important, where a maximum change in the mean droplet velocity is seen. In the fully-dispersed droplets, the evaporation of droplets plays a significant role in the time evolution of the sneezing jet velocity profiles, while a minor change is seen in the puff velocity because of the high initial momentum of sneezing airflow.
· The lowest and highest deviations between the experimental data and simulation results are found in the puff and fully-dispersed droplets phases, respectively. The main reason for the discrepancy in the fully-dispersed droplets phase is attributed to ignoring the non-volatile compounds of droplets during the droplets’ evaporation.
· The effect of RH ∞ on the maximum spreading distance of droplets in the puff phase is negligible, while its effect becomes more significant in the dilute-dispersed droplets phase. The maximum spreading distances of droplets are 2.14 m (7 feet) and 3.05 m (10 feet) at RH ∞ of 24 and 64%, respectively. These results match the reported data by Aydin et al. (2020). However, they do not match the reported data of Bourouiba (2020), which indicates sneezing droplet cloud transport up to 8 m (27 feet). Thus, the results suggest that people's sneeze velocity and duration vary based on their body mass index in different parts of the world.
· An increase of RH ∞ from 24 to 64% leads to an increase in the number concentration of medium droplets (5–100 μm). That is, a rise in the humidity field leads to a considerable decrease in the evaporation rate of droplets, especially in the fully-dispersed droplets phase. In contrast, a decrease in the RH ∞ leads to a decrease in the equilibrium size of droplets, which provides a suitable condition for the survival of the aerosols containing the respiratory viruses in the air for longer times. Although, dry indoor conditions increase by more than four times the concentration of aerosolized droplets (less than 5 μm) compared to a humid environment. Thus, the risk of direct transmission of Covid-19 in humid indoor environments is higher than the dry conditions. Therefore, the inclusion of the influence of air humidity is needed to improve the social distancing guidelines.
CRediT authorship contribution statement
Alireza Bahramian: Conceptualization, Methodology, Software, Validation, Writing – original draft. Goodarz Ahmadi: Supervision, Data curation, Writing – review & editing.
Declaration of Competing Interest
The authors have no conflicts to disclose.
Appendix Supplementary materials
Image, application 1
Data availability
No data was used for the research described in the article.
The data that support the findings of this study are available on request from the corresponding author upon reasonable request.
Acknowledgments
The author would like to thank the Hamedan University of Technology (Grant No. 99/2227) and the Iran National Science Foundation (INSF) organization (Grant No. 99022422) for their support. In addition, the work of GA was supported by Clarkson University Ignite fellowship.
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ijmultiphaseflow.2023.104422.
==== Refs
References
Ahlawat A. Wiedensohler A. Mishra S.K. An overview on the role of relative humidity in airborne transmission of SARS-CoV-2 in indoor environments Aerosol Air Qual. Res. 20 2020 1856 1861 10.4209/aaqr.2020.06.0302
Aganovic A. Bib Y. Caob G. Drangsholta F. Kurnitskic J. Wargocki P. Estimating the impact of indoor relative humidity on SARS-CoV-2 airborne transmission risk using a new modification of the Wells-Riley model Build. Environ. 205 2021 108278 10.1016/j.buildenv.2021.108278
Allen M.D. Raabe O.G. Slip correction measurements of spherical solid aerosol particles in an improved Millikan apparatus Aerosol Sci. Technol. 4 1985 269 286 10.1080/02786828508959055
Anand S. Mayya Y.S. Size distribution of virus laden droplets from expiratory ejecta of infected subjects Sci. Rep. 10 2020 1 9 10.1038/s41598-020-78110-x 31913322
ANSI/ASHRAE ANSI/ASHRAE standard 169-2013 Climatic Data for Building Design Standards 8400 2013 104 10.1038/s41598-020-78110-x
Arumuru V. Pasa J. Samantaray S.S. Experimental visualization of sneezing and efficacy of face masks and shields Phys. Fluids 32 2020 1 11 10.1063/5.0030101
Avni O. Dagan Y. Dynamics of evaporating respiratory droplets in the vicinity of vortex dipoles Int. J. Multiphase Flow 148 2022 103901 10.48550/arXiv.2108.07068
Aydin M. Evrendilek F. Aydin Savas S. Erkan Aydin I. Eren Evrendilek D. Falling dynamics of SARS-CoV2 as a function of respiratory droplet size and human height J. Med. Biol. Eng. 1 2020 1 7 10.1007/s40846-020-00575-y
Bahl ·.P. de Silva Ch.M. Chughtai A.A. MacIntyre C.R. Doolan C. An experimental framework to capture the flow dynamics of droplets expelled by a sneeze Exp. Fluids 61 2020 176 10.1007/s00348-020-03008-3 32834458
Bahramian A. Infuence of indoor environmental conditions on airborne transmission and lifetime of sneeze droplets in a confned space: a way to reduce COVID‑19 spread Environ. Sci. Pollut. Res. 2023 10.1007/s11356-023-25421-x
Bahramian A. Mohammadi M. Ahmadi G. Effect of indoor temperature on the velocity fields and airborne transmission of sneeze droplets: an experimental study and transient CFD modeling Sci. Total Environ. 159444 2023 10.1016/j.scitotenv.2022.159444
Balachandar S. A scaling analysis for point–particle approaches to turbulent multiphase flows Int. J. Multiph. Flow 35 2009 801 810 10.1016/j.ijmultiphaseflow.2009.02.013
Balachandar S. Zaleski S. Soldati A. Ahmadi G. Bourouiba L. Host-to-host airborne transmission as a multiphase flow problem for science-based social distance guidelines Int. J. Multiph. Flow. 132 2020 103439 10.1016/j.ijmultiphaseflow.2020.103439
Bazant M.Z. Bush J.W.M. A guideline to limit indoor airborne transmission of COVID-19 PNAS 118 2021 e2018995118 10.1073/pnas.201899511
Bhardwaj R. Agrawal A. Likelihood of survival of coronavirus in a respiratory droplet deposited on a solid surface Phys. Fluids 32 061704 2020 0012009 10.1063/5.0012009
Biktasheva I.V. Role of habitat's air humidity in COVID-19 mortality Sci. Total Environ. 736 2020 138763 10.1016/j.scitotenv.2020.138763
Bozic A. Kanduc M. Relative humidity in droplet and airborne transmission of disease J. Biol. Phys. Mar. 47 2021 1 29 10.1007/s10867-020-09562-5
Bourouiba L. Dehandschoewercker E. Bush J.W.M. Violent expiratory events: on coughing and sneezing Fluid Mech. 745 2014 537 563 10.1017/jfm.2014.88
Bourouiba L. Turbulent gas clouds and respiratory pathogen emissions: potential implications for reducing transmission of COVID-19 Clinic. Rev. Educ. 18 2020 1837 1838 10.1001/jama.2020.4756
Buckland F.E. Tyrrell D.A.J. Loss of infectivity on drying various viruses Nature 195 1962 1063 1064 10.1038/1951063a0 13874315
Busco G. Se Ro Yang S.R. Seo J. Hassan Y.A. Sneezing and asymptomatic virus transmission Phys. Fluids 32 2020 073309 10.1063/5.0019090
Casanova L.M. Jeon S. Rutala W.A. Weber D.J. Sobsey M.D. Effects of air temperature and relative humidity on coronavirus survival on surfaces Appl. Environ. Microbiol. 76 2010 2712 2717 10.1128/AEM.02291-09 20228108
CEN E.N. 16798 Energy performance of buildings- part 1: indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality Therm. Environ. Light. Aco Ustics 2019
Chen L.D. Effect of ambient temperature and humidity on droplet lifetime-A perspective of exhalation sneeze droplets with COVID-19 virus transmission Int. J. Hygiene Environ. Health 229 2020 113568 10.1016/j.ijheh.2020.113568
Chen X. Feng Y. Zhong W. Kleinstreuer C. Numerical investigation of the interaction, transport and deposition of multicomponent droplets in a simple-mouth-throat model J. Aerosol Sci. 105 2017 108 127 10.1016/j.jaerosci.2016.12.001
Cheng C.H. Chow C.L. Chow W.K. Trajectories of large respiratory droplets in indoor environment: a simplified approach Build. Environ. 183 2020 107196 10.1016/j.buildenv.2020.107196
Chong K.L. Ng Ch.Sh. Hori N. Yang R. Verzicco R. Lohse D. Extended lifetime of respiratory droplets in a turbulent vapour puff and its implications on airborne disease transmission Phys. Rev. Lett. 126 2021 034502 arXiv preprint arXiv:2008.01841
Clift R. Grace J.R. Weber M.E. Bubbles, drops, and Particles 2005 Courier Corporation
Coldrick S. Kelsey A. Ivings M.J. Foat T.G. Parker S.T. Noakes C.J. Bennett A. Rickard H. Moore G. Modeling and experimental study of dispersion and deposition of respiratory emissions with implications for disease transmission Indoor Air 32 2022 e13000 10.1111/ina.13000 35225395
Dabisch P. Schuit M. Herzog A. The influence of temperature, humidity, and simulated sunlight on the infectivity of SARS-CoV-2 in aerosols Aerosol Sci. Technol. 55 2021 142 153 10.1080/02786826.2020.1829536
Das S.K. Alam J. Plumari S. Greco V. Transmission of airborne virus through sneezed and coughed droplets Phys. Fluids 32 9 2020 097102 10.1063/5.0022859
Dbouk T. Drikakis D. On coughing and airborne droplet transmission to humans Phys. Fluids 32 2020 053310 10.1063/5.0011960
de Oliveira P.M. Mesquita L.C.C. Gkantonas S. Giusti A. Mastorakos E. Evolution of spray and aerosol from respiratory releases: theoretical estimates for insight on viral transmission Proc. R. Soc. A 477 2021 20200584 10.1098/rspa.2020.0584
Drossinos Y. Stilianakis N.I. What aerosol physics tells us about airborne pathogen transmission Aerosol Sci. Technol. 2020 1 5 10.1080/02786826.2020.1751055 32308568
Duguid J.P. The size and the duration of air-carriage of respiratory droplets and droplet-nuclei J. Hyg. 44 1946 471 480 10.1017/s0022172400019288 20475760
Eiche Th. Kuster M. Aerosol release by healthy people during speaking: possible contribution to the transmission of SARS-CoV-2 Int. J. Environ. Res. Public Health 17 2020 9088 10.3390/ijerph17239088 33291404
Elghobashi S. On predicting particle-laden turbulent flows Appl. Sci. Res. 52 1994 309 329 10.1007/BF00936835
Feng Y. Marchal T. Sperry T. Yi H. Influence of wind and relative humidity on the social distancing effectiveness to prevent COVID-19 airborne transmission: a numerical study J. Aerosol Sci. 147 2020 105585 10.1016/j.jaerosci.2020.105585
Fontes D. Reyes J. Ahmed K. Kinzel M. A study of fluid dynamics and human physiology factors driving droplet dispersion from a human sneeze Phys. Fluids 32 11 2020 111904 10.1063/5.0032006
Jaiswal A. Bui M.D. Rutschmann P. Evaluation of RANS-DEM and LES-DEM methods in OpenFOAM for simulation of particle-laden turbulent flows Fluids 7 2022 337 10.3390/fluids7100337
Gao N. Niu J. Transient CFD simulation of the respiration process and inter-person exposure assessment Build. Environ. 41 2006 1214 1222 10.1016/j.buildenv.2005.05.014 32287998
Gualtieri P. Picano F. Sardina G. Casciola C.M. Clustering and turbulence modulation in particle-laden shear flow J. Fluid Mech. 715 2013 134 162 10.1017/jfm.2012.503
Gustin K.M. Belser J.A. Veguilla V. Zeng H. Katz J.M. Tumpey T.M. Maines T.R. Environmental conditions affect exhalation of H3N2 seasonal and variant influenza viruses and respiratory droplet transmission in ferrets PLoS ONE 10 2015 e0125874 10.1371/journal.pone.0125874 pmid: 25969995
Han Z.Y. Weng W.G. Huang Q.Y. Characterizations of particle size distribution of the droplets exhaled by sneeze J. R. Soc. Interface 10 2013 20130560 10.1098/rsif.2013.0560
Hosseinpour Shafaghi A. Talabazar F.R. Kosar A. Ghorbani M. On the effect of the respiratory droplet generation condition on COVID-19 transmission Fluid 5 2020 1 13 10.3390/fluids5030113
Kormuth K.A. Lin K. Prussin A.J. Vejerano E.P. Tiwari A.J. Cox S.S. Myerburg M.M. Lakdawala S.S. Marr L.C. Influenza virus infectivity is retained in aerosols and droplets independent of relative humidity J. Infect. Dis. 218 2018 739 747 10.1093/infdis/jiy221 pmid: 29878137 29878137
Li Zh. Wang H. Zhang X. Wu T. Yang X. Effects of space sizes on the dispersion of cough-generated droplets from a walking person Phys. Fluids 32 2020 121705 10.1063/5.0034874
Lin K. Marr L.C. Humidity-dependent decay of viruses, but not bacteria, in aerosols and droplets follows disinfection kinetics Environ. Sci. Technol. 54 2020 1024 1032 10.1021/acs.est.9b04959 31886650
Ling Y. Balachandar S. Parmar M. Inter-phase heat transfer and energy coupling in turbulent dispersed multiphase flows Phys. Fluids 28 2016 033304 10.1063/1.4942184
Lieber Ch. Melekidis S. Koch R. Bauer H.-.J. Insights into the evaporation characteristics of saliva droplets and aerosols: levitation experiments and numerical modeling Aerosol Sci. Technol. 154 2021 105760 10.1016/j.jaerosci.2021.105760
Liu J. Liao X. Qian S. Yuan J. Wang F. Liu Y. Wang Zh. Wang F. Liu L. Zhang Zh. Community transmission of severe acute respiratory syndrome Coronavirus 2, Shenzhen, China Emerg. Infect. Dis. 26 2020 1320 1323 10.3201/eid2606.200239 32125269
Liu K. Allahyari M. Salinas J.S. Zgheib N. Balachandar S. Peering inside a cough or sneeze to explain enhanced airborne transmission under dry weather Sci. Rep. 11 2021 9826 10.1038/s41598-021-89078-7 33972590
Manik S. Mandal M. Pal S. Patra S. Acharya S. Impact of climate on COVID-19 transmission: a study over Indian states Environ. Res. 211 2022 113110 10.1016/j.envres.2022.113110
Marr L.C. Tang J.W. Van Mullekom J. Lakdawala S.S. Mechanistic insights into the effect of humidity on airborne influenza virus survival, transmission and incidence J. R. Soc. Interface 16 2019 20180298 10.1098/rsif.2018.0298
Moriyama M. Hugentobler W.J. Iwasaki A. Seasonality of respiratory viral infections Annu. Rev. Virol. 7 2020 83 101 10.1146/annurev-virology-012420-022445 32196426
Morawska L. Cao J. Airborne transmission of SARS-CoV-2: the world should face the reality Environ. Int. 139 2020 105730 10.1016/j.envint.2020.105730
Morawska L. Johnson G.R. Ristovski Z.D. Hargreaves M. Mengersen K. Corbett S. Chao C.Y.H. Li Y. Katoshevski D. Size distribution and sites of origin of droplets expelled from the human respiratory tract during expiratory activities J. Aerosol Sci. 40 2009 256 269 10.1016/j.jaerosci.2008.11.002
Ng Ch.Sh. Chong K.L. Yang R. Li M. Verzicco R. Lohse D. Growth of respiratory droplets in cold and humid air PNAS 118 2021 1 7 10.1101/2020.10.30.20222604
Nicas M. Nazaroff W.W. Hubbard A. Toward understanding the risk of secondary airborne infection: emission of respirable pathogens J. Occup. Environ. Hyg. 2 2005 143 154 10.1080/15459620590918466 15764538
Oh W. Ooka R. Kikumoto H. Han M. Numerical modeling of cough airflow: establishment of spatial-temporal experimental dataset and CFD simulation method Build. Environ. 207 2022 108531 10.1016/j.buildenv.2021.108531
Olivieri S. Cavaiola M. Mazzino A. Rosti M.E. Transport and evaporation of virus-containing droplets exhaled by men and women in typical cough events Meccanica 57 2022 567 575 10.1007/s11012-021-01469-2 35039689
Pani S.K. Lin N.H. Ravindra Babu S. Association of COVID-19 pandemic with meteorological parameters over Singapore Sci. Total Environ. 740 2020 140112 10.1016/j.scitotenv.2020.140112
Parhizkar H. Dietz L. Olsen-Martinez A. Horve P.F. Barnatan L. Northcutt D. van Den Wymelenberg K.G. Quantifying environmental mitigation of aerosol viral load in a controlled chamber with participants diagnosed with coronaviru disease 2019 Clin. Infect. Dis. 75 2022 e174 e184 10.1093/cid/ciac006 34996097
Prather K.A. Wang C.C. Schooley R.T. Reducing transmission of SARS-CoV-2 Science 6498 2020 1422 1424 10.1126/science.abc6197
Rosti M.E. Cavaiola M. Olivieri S. Seminara A. Mazzino A. Turbulence role in the fate of virus-containing droplets in violent expiratory events Phys. Rev. Res. 3 2021 013091 10.1103/PhysRevResearch.3.013091
Stadnytskyi V. Bax Ch.E. Bax A. Anfinrud Ph. The airborne lifetime of small speech droplets and their potential importance in SARS-CoV-2 transmission PNAS 117 2020 11875 11877 10.1111/joim.13326 32404416
Stiehl B. Shrestha R. Schroeder S. Delgado J. Bazzi A. Reyes J. Kinzel M. Ahmed K. The effect of relative air humidity on the evaporation timescales of a human sneeze AIP Adv. 12 2022 075210 10.1063/5.0102078
Tang J.W. Nicolle A.D. Klettner Ch.A. Pantelic J. Wang L. Bin Suhaimi A. Tan A.Y.L. Airflow dynamics of human jets: sneezing and breathing-potential sources of infectious aerosols PLoS One 8 4 2013 e59970 10.1371/journal.pone.0034818
van Doremalen N. Bushmaker T. Morris D.H. Holbrook M.G. Gamble A. Williamson B.N. Aerosol and surface stability of SARS-CoV-2 as compared with SARS-Cov-1 N. Engl. J. Med. 382 2020 1564 1567 10.1056/NEJMc2004973 32182409
Vuorinen V. Aarnio M. Alava M. Alopaeus V. Atanasova N. Auvinen M. Österberg M. Modeling aerosol transport and virus exposure with numerical simulations in relation to SARS-CoV-2 transmission by inhalation indoors Safety Sci. 130 2020 104866 10.48550/arXiv.2005.12612
Yin J. Norvihoho L.K. Zhou Zh.-F. Chen B. Wu W.-.T. Investigation on the evaporation and dispersion of human respiratory droplets with COVID-19 virus Int. J. Multiphase Flow 147 2022 103904 10.1016/j.ijmultiphaseflow.2021.103904
Zeng G. Chen L. Yuan H. Yamamoto A. Maruyama Sh. Evaporation flow characteristics of airborne sputum droplets with solid fraction: effects of humidity field evolutions Phys. Fluids 33 2021 123308 10.1063/5.0076572
Wang J. Alipour M. Soligo G. Roccon A. De Paoli M. Picano F. Soldati A. Short-range exposure to airborne virus transmission and current guidelines PNAS 118 37 2021 e2105279118 10.1073/pnas.2105279118
Wang J. Dalla Barba F. Roccon A. Sardina G. Soldati A. Picano F. Modelling the direct virus exposure risk associated with respiratory events J. R. Soc. Interface 19 2022 20210819 10.1098/rsif.2021.0819
WHO Modes of Transmission of Virus Causing COVID-19: Implications For IPC Precaution recommendations, Scientific brief, 29 March 2020 c 2020 World Health Organization Geneva | 0 | PMC9931425 | NO-CC CODE | 2023-03-09 23:16:03 | yes | 2023 Jun 16; 163:104422 |
==== Front
Eng Anal Bound Elem
Eng Anal Bound Elem
Engineering Analysis with Boundary Elements
0955-7997
0955-7997
Elsevier Ltd.
S0955-7997(23)00100-5
10.1016/j.enganabound.2023.02.043
Article
Analysis of Conocurvone, Ganoderic acid A and Oleuropein molecules against the main protease molecule of COVID-19 by in silico approaches: Molecular dynamics docking studies
Le Quynh Hoang 12
Far Bahareh Farasati 3⁎
Sajadi S. Mohammad 4
Jahromi Bahar Saadaie 5
Kaspour Sogand 6
Cakir Bilal 78
Abdelmalek Zahra 12
Inc Mustafa 910⁎
1 Institute of Research and Development, Duy Tan University, Da Nang, Vietnam
2 School of Medicine and Pharmacy, Duy Tan University, Da Nang, Vietnam
3 Department of Chemistry, Iran University of Science and Technology, Tehran, Iran
4 Department of Nutrition, Cihan University-Erbil, Kurdistan Region, Iraq
5 Biological Science department, Western Michigan University, 1903 W Michigan Avenue, Kalamazoo, MI 49008-5410, USA
6 Department of Paramed, Tehran University of Medical Science, Tehran, Iran
7 İstanbul S. Zaim University (İZÜ), Halal Food R&D Center, Halkalı, Küçükçekmece, İstanbul, Turkey
8 İZÜ Food and Agricultural Research Center (GTUAM), Halkalı Campus, 34303, Küçükçekmece, İstanbul, Turkey
9 Firat University, Science Faculty, Department of Mathematics, 23119 Elazig, Turkiye
10 Department of Medical Research, China Medical University, 40402 Taichung, Taiwan
⁎ Corresponding authors.
27 2 2023
27 2 2023
25 12 2022
21 2 2023
23 2 2023
© 2023 Elsevier Ltd. All rights reserved.
2023
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
Traditional medicines against COVID-19 have taken important outbreaks evidenced by multiple cases, controlled clinical research, and randomized clinical trials. Furthermore, the design and chemical synthesis of protease inhibitors, one of the latest therapeutic approaches for virus infection, is to search for enzyme inhibitors in herbal compounds to achieve a minimal amount of side-effect medications. Hence, the present study aimed to screen some naturally derived biomolecules with anti-microbial properties (anti-HIV, antimalarial, and anti-SARS) against COVID-19 by targeting coronavirus main protease via molecular docking and simulations. Docking was performed using SwissDock and Autodock4, while molecular dynamics simulations were performed by the GROMACS-2019 version. The results showed that oleuropein, ganoderic acid A, and conocurvone exhibit inhibitory actions against the new COVID-19 proteases. These molecules may disrupt the infection process since they were demonstrated to bind at the coronavirus major protease's active site, affording them potential leads for further research against COVID-19.
Keywords
Molecular Dynamics Simulation
In Silico Study
Antiviral COVID-19
Oleuropein
Ganoderic acid A
Conocurvone
==== Body
pmc1 Introduction
Presently, COVID-19 has spread throughout the world leading to high-mortality disease which is being dealt with no approved pharmaceutical drugs; has arisen as an international public health emergency concern and pandemic disease by the World Health Organization (WHO) [1,2] in terms of public safety and global economic loss [3]. Further, WHO stated the prevalence of COVID-19 is more than 2 million in population including billions of deaths [4], [5], [6] suggesting the novel anti-viral agent against COVID-19.
Coronaviruses are bat-sourced RNA viruses that primarily invade the human alveolar’ cells via the utilization of its spike protein by interacting aside angiotensin-converting enzyme 2 (ACE2) of human cells [7], leading to typical respiratory symptoms (cough and fever) followed by fatigue, myalgia, and diarrhea [8]. The current method for treating the COVID-19 disease is supportive medication, accompanied by broad-spectrum antibiotics, antivirals, corticosteroids, and regeneration plasma. Although the vaccine is developed and the population is vaccinated, no specific anti-corona virus molecule has been produced yet. The subjects are being treated with HIV protease inhibitors (ritonavir and lopinavir) in combination with effective antibiotics, or IFNAα-2b inhibitors [9,10] and are limited with multiple side effects, such as anemia, and uncertainty with adequate SARS-CoV-2 antiviral activity [11,12] which suggests identifying the new drug molecule against COVID-19.
Natural-sourced bioactive has drawn widespread interest in traditional Chinese medicine and other complementary medicines because of their broad-spectrum biological processes with minimum side effects [13]. Also, the concept of utilization of traditional medicines against COVID-19 has taken significant outbreaks evidenced by multiple cases, controlled clinical research, and randomized clinical trials [14]. Further, other studies focused on the prediction and classification of COVID-19 infection by CT-scan images via neural network modeling, and the role of nanomaterials in the diagnosis, prevention, and therapy of COVID-19 [15], [16], [17], [18], [19]. Oleuropein, a Bioactive Compound from Olea europaea L. and has diverse pharmacological action [20]. Similarly, ganoderic acid is a natural product found in Ganoderma sinense, Ganoderma lucidum, and Wolfiporia cocos [21] and is used in managing multiple pathogenic states. Further, the design and chemical synthesis of protease inhibitors, one of the latest therapeutic approaches for virus infection, is to search for enzyme inhibitors in herbal compounds to achieve a minimal amount of side-effect medications [6].
This research aimed to investigate some naturally derived bioactive with previously reported anti-HIV, anti-malarial, and anti-SARS molecules against COVID-19 by computational approach mainly targeting main coronavirus protease via molecular docking and simulations and compared with the drug candidates which are being considered to treat COVID-19 [22], [23], [24].
2 Materials and Methods
2.1 Ligand preparation
The studied compounds include alkaloids, coumarins, phenolics, quinones, and terpenes/steroids compounds. All the 3D structures (.sdf format) of the ligands (Quinine, Cryptolepine, Dictamnine, Ajoene, Ellagic acid, Gedunin, Simalikalactone, Samaderine, Conocurvone, Chlorogenic acid; Figure 1 ) were retrieved from PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and converted into .pdb using Discovery Studio (DS-2020). The energy of each ligand was minimized using mmff94 forcefield and saved in.pdbqt format.Figure 1 a) 2D structures of the naturally occurring bioactive considered to screen against COVID-19, b) Similar chemical structure of Conocurvone (blue), Calceolarioside B (Purple), and Ganoderic acid A (Green).
Figure 1
2.2 Macromolecule preparation
The 3D crystallographic protein of coronavirus main protease (3CLpro; PDB ID: 6LU7) was retrieved from RCSB protein databank (https://www.rcsb.org/) and was made free from hetero molecules using DS-2020. In addition, protein was visualized for phi (φ) and psi (ψ) degree distribution and 3D/1D profile in the Ramachandran plot and VERIFY3D (https://www.doe-mbi.ucla.edu/verify3d/), respectively using SAVES v 6.0 (https://saves.mbi.ucla.edu/).
2.3 Molecular docking
Docking was performed using Swiss Dock and Autodock4 after optimizing the three-dimensional geometry with energy minimization of every compound via density functional theory at B3LYP/631+G (d, p) level by implementing Gaussian 09 program package. Since docking 10 varied validations of ligand are gained. After docking, pose with min. binding energy is preferred to visualize ligand-protein interaction employing Ligplot.
2.4 Molecular docking simulation
Molecular dynamic simulations were done by the GROMACS-2019 version employing the OPLS force field during ten ns via appointing periodic boundary conditions and TIP3P water pattern to solve complexes, thenceforth the extension of ions to neutralize. Energy minimization Tolerance for energy minimization is 1000 kJ/mol.nm.
2.5 Molecular docking governing equations
To compute molecular docking, first, governing equations of radius of gyration (Rg), Root-mean-square fluctuation (RMSF), and Root-mean-square deviation (RMSD) amounts should be solved:
The Rg is solved by equation 1 [25], [26], [27], [28]:(1) I=m1r12+m2r22+...+mnrn2
Where “I” is moment of inertia, “m” is mass, and “r” is perpendicular distances from rotation axis.
The RMSF is solved by equation 2 [29], [30], [31], [32]:(2) RMSFi=1T∑t=1T{[ri′(t)−ri(tref)]2}
Where “ri” is position of residue i, “ri’” is position of atoms that consisted of residue i in frame x after superimposing with a reference frame, and “tref” is reference frame time.
The RMSD is solved by equation 3 [33,24,32,27]:(3) RMSD=∑i=1N(xi−x^i)2N
Where “i” is variable i, “N” is number of non-missing data points, “xi” is actual observations time series, and “^ xi” is estimated time series.
3 Results and Discussion
3.1 Preliminary evaluation of the protein for docking
Ramachandran plot analysis revealed that 90.6 % of the amino acids of 3CLpro were in preferred zone, 8.6 % in additional allowable zone, 0.4 % in generally allowable zone, and 0.4 % in a disallowable zone (Figure 2 ). Likewise, 94.44% of residues had moderated 3D-1D score >= 0.2 at the cutoff of 80 % amino acids with averaged 3D-1D score >= 0.2; Figure 4. The result of the protein-ligand interaction was shown in Figure 3 . The results show that except for the quinine, the estimated ∆G of other compounds is within the range of drugs recommended for treatment. Among these compounds, Conocurvone (23), Calceolarioside B (3), and Ganoderic acid A (10) showed better binding energy. Previous studies have shown that these compounds have good anti-protease activity against HIV, confirming effective binding to protein protease.Figure 2 (a) 3D crystallographic structure of the ligand-free 3CLpro (Visualized in DS-2020). The protein is presented in Line ribbon style. The “+++++” represents the binding site and (b) Ramachandran plot of 3CLpro (PDB: 6LU7). Residues in most favored, additional allowed, generously allowed, and disallowed regions are presented in red, yellow, light yellow, and white.
Figure 2
Figure 3 Interaction of (a) Concurvone, Ganoderic Acid A, and (c) Oleoropin
Figure 3
Finally, to understand the action of these compounds against protein protease, other similar structures were selected and interacted with the protein protease (Figure 4, Figure 5, Figure 6 ). [[34], [35], [36],28].Figure 4 3D/1D profile of 3CLpro (PDB: 6LU7)
Figure 4
Figure 5 Comparison of estimated ∆G of the natural compound with common drugs for COVID-19 treatment.
Figure 5
Figure 6 Comparison of estimated ∆G of the similar chemical structure of Conocurvone (blue), Calceolarioside B (Purple), and Ganoderic acid A (Green)
Figure 6
3.2 Molecular docking
Concurvon is foretoken to have the most binding attachment aside 3CLpro with 1 hydrogen bond interaction aside Gly109 and 9 hydrophobic interactions i.e., 9 with Val1104, Ile06, Pro108, Pro132, Cys160, Ile200, Val202, Leu242, Ile249 (Table 1 ); interaction is presented in Figure 3.Table 1 Binding energy, number of hydrogen bonds and hydrogen bond residues of Concurvon, Ganoderic acid, and Oleuropin with 3CLpro
Table 1Ligand Binding energy (kcal/mol) Number of hydrogen bonds Hydrogen bond residue
Concurvon -9.76 1 Gly109
Ganoderic acid -9.49 2 Thr26, Cys44
Oleuropin -8.92 6 Thr26, Tyr54, Leu141, Asn142, Gly143, Cys145,
The first section showed better binding affinity, which is comparable to the kernel density estimator. Among these compounds, Calceolarioside B similarly has shown better affinity than others among the studied compounds in 2 sections, nine compounds (Ganoderic acid A, Calceolarioside B, Conocurvone, Conocurvone isomer, Plantainoside E, Martinoside, Oleuropein, Echinacoside, and Isoacteoside) were selected, and other studies were continued using these compounds. Angiotensin-converting enzyme 2 (ACE2) s also involved in the occurrence of the disease. The estimated ΔG of protein and ACE2 receptor results showed that the compounds have a greater tendency than protein protease because most compounds' energy ratio is over one (Figure 7 ). To ensure the selectivity of the compounds, the interaction with the proposed estimated target was studied. The results show Ganoderic acid A, Conocurvone, and oleuropein are more susceptible to the protein protease (Figure 8 ).Figure 7 Kernel density estimator ∆G of the similar chemical structure of Conocurvone (Red), Calceolarioside B (Purple), and Ganoderic acid A (Green)
Figure 7
Figure 8 The ratio of estimated ∆Gcovid/ACE2.
Figure 8
The results indicate that Ganoderic acid, Conocurvone, and oleuropein are more susceptible to the protein protease. Additionally, the 2D Oleuropein-Protein interaction diagram indicated three hydrogen bonds by Thr25, Thr26, and Cys44. Ganoderic acid gets to hydrogen bonds by Thr26, and Asn 142 in this protein (Figure 9 ). Also, by examining active sites of protein, it was found hydrogen bonds generated by these two compounds inhibited the protein (Figure 10 ) [29,30,37,38].Figure 9 Investigation the hydrogen bonding of the studied compounds with the protein active site
Figure 9
Figure 10 The ratio of estimated ∆Gcovid/estimated target.
Figure 10
The results of molecular dynamic studies as Rg, RMSF, and RMSD amounts as a function of time is displayed in Figure 11 .Figure 11 (a) RMSD, (b) RMSF amounts and (c) Rg outcomes of protein-Ganoderic acid A (blue) and Oleuropein complex during 10 ns.
Figure 11
As seen from the RSMD outcomes, after two ns, structure stabilized where mean amounts for Ganoderic acid A and Oleuropein were 0.40 nm and 0.45 nm, respectively. The RSMF calculated for the 306 amino acids of these compounds represents a fewer shift aside ave. amounts of 0.31 nm and 0.4 nm for Ganoderic acid A and Oleuropein, respectively. The Rg with an average of 2.31ns for Ganoderic acid A and 2.33 nm for oleuropein showed stability after two ns, followed by a stable binding pose. It should be noted that in these studies, Ganoderic acid A showed better stability than oleuropein. For the anti-protease activity of these compounds for HIV, the Pharmacokinetics of these compounds were calculated and compared with other anti-HIV drugs used for treatment. Results represent that these compounds could only be used concomitantly with Favipiravir. The order of their solubility is Oleuropein, Ganoderic acid A, and Conocurvone, respectively, and Ganoderic acid A is the only compound that can inhibit Cytochrome P450 3A4 (CYP3A4).
3.3 Molecular dynamics simulation
The MD was conducted for 10 ns in which Rg, RMSF, RMSD and amounts are assessed; Figure 11. As observed from RSMD outcomes, after 2 ns, structure stabilized where ave. amounts for ganoderic acid A and oleuropein were 0.40 nm and 0.45 nm, respectively. The RSMF calculated for the 306 amino acids of these compounds represents a fewer shift aside ave. amounts of 0.31 nm and 0.4 nm for ganoderic acid A and oleuropein, respectively. The Rg with an average of 2.31ns for ganoderic acid A and 2.33 nm for oleuropein showed stability after 2 ns, followed by a stable binding pose. Further, it was noted that in these studies, ganoderic acid A showed better stability than oleuropein. Table 2 also shows the pharmacokinetics study of compounds.Table 2 Pharmacokinetics study of compounds
Table 2 Drug
Oleuropein Ganoderic acid A Conocurvone Ritonavir Remdesivir Favipiravir Lopinavir
GI absorption A few A few A few A few A few High High
BBB permeant ○ ○ ○ ○ ○ ○ ○
P-GP substrate ● ● ● ● ● ○ ●
CYP1A2 inhibitor ○ ○ ○ ○ ○ ○ ○
CYP2C19 inhibitor ○ ○ ○ ○ ○ ○ ●
CYP2C9 inhibitor ○ ○ ○ ○ ○ ○ ○
CYP2D6 inhibitor ○ ○ ○ ○ ○ ○ ○
CYP3A4 inhibitor ○ ● ○ ● ● ○ ●
Log Kp (skin permeation) -9.92 cm/s -7.90 cm/s -3.50 cm/s -6.40 cm/s -8.62 cm/s -7.66 cm/s -5.93 cm/s
Solubility Soluble Moderately soluble Insoluble Insoluble Poorly soluble Very soluble Poorly soluble
*GI= gastrointestinal, (BBB)= blood-brain barrier, CYP3A4= Cytochrome P450 3A4, CYP2D6= Cytochrome P450 2D6, CYP2C9= Cytochrome P450 2C9, CYP2C19= Cytochrome P450 2C19, CYP1A2= Cytochrome P450 1A2, P-gp= P-glycoprotein
**Symbol of ”○” stands for “no”, Symbol of ” ●” stands for “yes”
4 Conclusions
The result of the present study shows that three natural compounds (Oleuropein, Ganoderic acid A, and Conocurvone) exhibit inhibitory actions against novel COVID-19 proteases which were predicted using molecular docking as well as molecular dynamics. These results can be of interest for laboratory research as a natural compound drug.
In the simulations study, these compounds bind to COVID-19 leading to protease active sites and thus interfering with the cycle of infection. The inhibitory actions, low-risk products, and low side effects will train the immune system to combat the latest coronavirus infection. The compounds found in these natural products can also be studied on their own or in combination with other natural sources or synthetically produced substances.
This outcome provides a symbol of a small stage in international cooperation to assist human society to resolve this worldwide issue.
Availability of data and material
The data and material are available and can be presented in the case of needed.
Code availability
N/A
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A
Tables A1, A2 and A3 Table A1 Biological processes of chlorogenic acid-regulated proteins
Table A1term ID term description detected gene count background gene count strength false discovery rate matching proteins
GO:0009605 response to external stimulus 10 2152 0.88 2.03E-05 HMOX1, RARA, MDM2, FLT1, CD14, RAC1, CASP8, PLAU, NPPB, CHEK1
GO:0009628 response to abiotic stimulus 8 1052 1.09 2.10E-05 HMOX1, PLAT, MDM2, CD14, RAC1, CASP8, PLAU, CHEK1
GO:0048583 regulation of response to stimulus 11 3882 0.66 8.38E-05 HMOX1, PLAT, RARA, MDM2, FLT1, CD14, RAC1, CASP8, PLAU, NPPB, CHEK1
GO:0051239 regulation of multicellular organismal process 10 2788 0.77 8.38E-05 HMOX1, PLAT, RARA, MDM2, FLT1, CD14, RAC1, CASP8, PLAU, NPPB
GO:0010646 regulation of cell communication 10 3327 0.69 0.0002 HMOX1, RARA, MDM2, FLT1, CD14, RAC1, CASP8, PLAU, NPPB, CHEK1
GO:0023051 regulation of signaling 10 3360 0.69 0.0002 HMOX1, RARA, MDM2, FLT1, CD14, RAC1, CASP8, PLAU, NPPB, CHEK1
GO:0031638 zymogen activation 3 34 2.16 0.0002 PLAT, CASP8, PLAU
GO:0046677 response to antibiotic 5 305 1.43 0.0002 HMOX1, RARA, MDM2, CD14, CASP8
GO:0051241 negative regulation of multicellular organismal process 7 1098 1.02 0.0002 HMOX1, PLAT, RARA, MDM2, RAC1, PLAU, NPPB
GO:0071496 cellular response to external stimulus 5 305 1.43 0.0002 HMOX1, MDM2, RAC1, CASP8, CHEK1
GO:0007165 signal transduction 11 4738 0.58 0.00021 HMOX1, PLAT, RARA, MDM2, FLT1, CD14, RAC1, CASP8, PLAU, NPPB, CHEK1
GO:0048519 negative regulation of biological process 11 4953 0.56 0.0003 HMOX1, PLAT, RARA, MDM2, FLT1, CD14, RAC1, CASP8, PLAU, NPPB, CHEK1
GO:0030335 positive regulation of cell migration 5 452 1.26 0.00046 HMOX1, MDM2, FLT1, RAC1, PLAU
GO:0009636 response to toxic substance 5 468 1.24 0.0005 HMOX1, RARA, MDM2, CD14, CASP8
GO:0009966 regulation of signal transduction 9 3033 0.68 0.00055 HMOX1, MDM2, FLT1, CD14, RAC1, CASP8, PLAU, NPPB, CHEK1
GO:0043627 response to estrogen 3 74 1.82 0.00086 HMOX1, RARA, MDM2
GO:0071260 cellular response to mechanical stimulus 3 78 1.8 0.00095 RAC1, CASP8, CHEK1
GO:0048523 negative regulation of cellular process 10 4454 0.56 0.00098 HMOX1, PLAT, RARA, MDM2, FLT1, CD14, RAC1, CASP8, NPPB, CHEK1
GO:0001666 response to hypoxia 4 288 1.35 0.0012 HMOX1, PLAT, MDM2, PLAU
GO:0014909 smooth muscle cell migration 2 10 2.51 0.0012 PLAT, PLAU
GO:0032879 regulation of localization 8 2524 0.71 0.0012 HMOX1, MDM2, FLT1, CD14, RAC1, CASP8, PLAU, NPPB
GO:0071214 cellular response to abiotic stimulus 4 282 1.36 0.0012 MDM2, RAC1, CASP8, CHEK1
GO:0031639 plasminogen activation 2 11 2.47 0.0013 PLAT, PLAU
GO:0051246 regulation of protein metabolic process 8 2668 0.69 0.0016 HMOX1, PLAT, RARA, MDM2, FLT1, RAC1, CASP8, CHEK1
GO:2000026 regulation of multicellular organismal development 7 1876 0.78 0.0016 HMOX1, RARA, MDM2, FLT1, RAC1, CASP8, NPPB
GO:0010038 response to metal ion 4 339 1.28 0.0017 HMOX1, MDM2, CD14, CASP8
GO:0071391 cellular response to estrogen stimulus 2 14 2.37 0.0017 RARA, MDM2
GO:0080134 regulation of response to stress 6 1299 0.88 0.0021 PLAT, MDM2, CD14, CASP8, PLAU, CHEK1
GO:0032026 response to magnesium ion 2 18 2.26 0.0024 MDM2, CD14
GO:0045471 response to ethanol 3 134 1.56 0.0025 RARA, CD14, CASP8
GO:0051179 localization 10 5233 0.49 0.0025 PLAT, RARA, MDM2, FLT1, CD14, RAC1, CASP8, PLAU, NPPB, SMN2
GO:1901564 organonitrogen compound metabolic process 10 5281 0.49 0.0026 HMOX1, PLAT, RARA, MDM2, FLT1, RAC1, CASP8, PLAU, NPPB, CHEK1
GO:0042730 fibrinolysis 2 21 2.19 0.0028 PLAT, PLAU
GO:0002684 positive regulation of immune system process 5 882 0.97 0.0032 HMOX1, RARA, CD14, RAC1, CASP8
GO:0007166 cell surface receptor signaling pathway 7 2198 0.72 0.0032 HMOX1, PLAT, FLT1, CD14, RAC1, CASP8, NPPB
GO:0009266 response to temperature stimulus 3 155 1.5 0.0032 HMOX1, CD14, CASP8
GO:0035666 TRIF-dependent toll-like receptor signaling pathway 2 24 2.13 0.0032 CD14, CASP8
GO:0042493 response to drug 5 900 0.96 0.0032 HMOX1, RARA, MDM2, CD14, CASP8
GO:0048518 positive regulation of biological process 10 5459 0.48 0.0032 HMOX1, RARA, MDM2, FLT1, CD14, RAC1, CASP8, PLAU, NPPB, CHEK1
GO:0048585 negative regulation of response to stimulus 6 1483 0.82 0.0032 HMOX1, PLAT, MDM2, CD14, CASP8, PLAU
GO:0050776 regulation of immune response 5 873 0.97 0.0032 HMOX1, RARA, CD14, RAC1, CASP8
GO:0042060 wound healing 4 461 1.15 0.0033 HMOX1, PLAT, RAC1, PLAU
GO:0035556 intracellular signal transduction 6 1528 0.81 0.0034 HMOX1, MDM2, CD14, RAC1, CASP8, CHEK1
GO:0051240 positive regulation of multicellular organismal process 6 1551 0.8 0.0036 HMOX1, RARA, FLT1, CD14, CASP8, NPPB
GO:0006950 response to stress 8 3267 0.6 0.0037 HMOX1, PLAT, MDM2, CD14, RAC1, CASP8, PLAU, CHEK1
GO:0050878 regulation of body fluid levels 4 483 1.13 0.0037 PLAT, RAC1, PLAU, NPPB
GO:0032101 regulation of response to external stimulus 5 955 0.93 0.0038 PLAT, CD14, RAC1, CASP8, PLAU
GO:0002376 immune system process 7 2370 0.68 0.0039 HMOX1, FLT1, CD14, RAC1, CASP8, PLAU, NPPB
GO:0006909 phagocytosis 3 185 1.42 0.0039 RARA, CD14, RAC1
GO:0010039 response to iron ion 2 32 2.01 0.0041 HMOX1, MDM2
GO:0032268 regulation of cellular protein metabolic process 7 2486 0.66 0.0049 PLAT, RARA, MDM2, FLT1, RAC1, CASP8, CHEK1
GO:1902042 negative regulation of extrinsic apoptotic signaling pathway via death domain receptors 2 36 1.96 0.0049 HMOX1, CASP8
GO:0007584 response to nutrient 3 208 1.37 0.005 HMOX1, RARA, MDM2
GO:0051049 regulation of transport 6 1732 0.75 0.0054 HMOX1, MDM2, CD14, RAC1, CASP8, NPPB
GO:0065008 regulation of biological quality 8 3559 0.56 0.0055 HMOX1, PLAT, RARA, MDM2, RAC1, CASP8, PLAU, NPPB
GO:2001234 negative regulation of apoptotic signaling pathway 3 218 1.35 0.0055 HMOX1, MDM2, CASP8
GO:1902531 regulation of intracellular signal transduction 6 1764 0.74 0.0057 MDM2, FLT1, CD14, RAC1, CASP8, CHEK1
GO:0050778 positive regulation of immune response 4 589 1.04 0.0061 RARA, CD14, RAC1, CASP8
GO:0070887 cellular response to chemical stimulus 7 2672 0.63 0.0066 HMOX1, RARA, MDM2, FLT1, CD14, RAC1, CASP8
GO:0001817 regulation of cytokine production 4 615 1.03 0.0069 HMOX1, RARA, CD14, RAC1
GO:0001818 negative regulation of cytokine production 3 245 1.3 0.0069 HMOX1, RARA, RAC1
GO:0048522 positive regulation of cellular process 9 4898 0.48 0.0069 HMOX1, RARA, MDM2, FLT1, CD14, RAC1, CASP8, PLAU, CHEK1
GO:1904705 regulation of vascular smooth muscle cell proliferation 2 49 1.82 0.007 HMOX1, MDM2
GO:0032501 multicellular organismal process 10 6507 0.4 0.0085 HMOX1, PLAT, RARA, MDM2, FLT1, RAC1, CASP8, PLAU, NPPB, SMN2
GO:0045765 regulation of angiogenesis 3 277 1.25 0.0091 HMOX1, FLT1, NPPB
GO:0006807 nitrogen compound metabolic process 11 8349 0.33 0.0095 HMOX1, PLAT, RARA, MDM2, FLT1, RAC1, CASP8, PLAU, NPPB, SMN2, CHEK1
GO:0031670 cellular response to nutrient 2 59 1.74 0.0095 HMOX1, MDM2
GO:0007167 enzyme linked receptor protein signaling pathway 4 698 0.97 0.0097 PLAT, FLT1, RAC1, NPPB
GO:0007596 blood coagulation 3 288 1.23 0.0097 PLAT, RAC1, PLAU
GO:0014910 regulation of smooth muscle cell migration 2 61 1.73 0.0097 MDM2, PLAU
GO:0051094 positive regulation of developmental process 5 1286 0.8 0.0097 HMOX1, RARA, FLT1, RAC1, CASP8
GO:0051173 positive regulation of nitrogen compound metabolic process 7 2946 0.59 0.0098 HMOX1, RARA, MDM2, FLT1, RAC1, CASP8, CHEK1
GO:0097190 apoptotic signaling pathway 3 295 1.22 0.0098 HMOX1, CD14, CASP8
GO:0032496 response to lipopolysaccharide 3 298 1.22 0.0099 RARA, CD14, CASP8
GO:0044419 interspecies interaction between organisms 4 724 0.95 0.0102 MDM2, RAC1, CASP8, NPPB
GO:0043618 regulation of transcription from RNA polymerase II promoter in response to stress 2 67 1.69 0.0106 HMOX1, CHEK1
GO:0048010 vascular endothelial growth factor receptor signaling pathway 2 67 1.69 0.0106 FLT1, RAC1
GO:0031325 positive regulation of cellular metabolic process 7 3060 0.57 0.0114 HMOX1, RARA, MDM2, FLT1, RAC1, CASP8, CHEK1
GO:0042221 response to chemical 8 4153 0.5 0.0115 HMOX1, RARA, MDM2, FLT1, CD14, RAC1, CASP8, PLAU
GO:0010604 positive regulation of macromolecule metabolic process 7 3081 0.57 0.0117 HMOX1, RARA, MDM2, FLT1, RAC1, CASP8, CHEK1
GO:0002757 immune response-activating signal transduction 3 332 1.17 0.0118 CD14, RAC1, CASP8
GO:0019538 protein metabolic process 8 4194 0.49 0.0118 PLAT, RARA, MDM2, FLT1, RAC1, CASP8, PLAU, CHEK1
GO:0060411 cardiac septum morphogenesis 2 74 1.64 0.0118 RARA, MDM2
GO:0072422 signal transduction involved in DNA damage checkpoint 2 73 1.65 0.0118 MDM2, CHEK1
GO:0034644 cellular response to UV 2 78 1.62 0.0122 MDM2, CHEK1
GO:0051234 establishment of localization 8 4248 0.49 0.0122 RARA, MDM2, CD14, RAC1, CASP8, PLAU, NPPB, SMN2
GO:0071310 cellular response to organic substance 6 2219 0.64 0.0122 HMOX1, RARA, MDM2, FLT1, CD14, CASP8
GO:1901700 response to oxygen-containing compound 5 1427 0.76 0.0122 HMOX1, RARA, MDM2, CD14, CASP8
GO:0048661 positive regulation of smooth muscle cell proliferation 2 80 1.61 0.0124 HMOX1, MDM2
GO:0072359 circulatory system development 4 807 0.91 0.0124 HMOX1, RARA, MDM2, FLT1
GO:0016477 cell migration 4 812 0.9 0.0125 PLAT, FLT1, RAC1, PLAU
GO:0033993 response to lipid 4 825 0.9 0.0131 RARA, MDM2, CD14, CASP8
GO:0033273 response to vitamin 2 87 1.57 0.014 RARA, MDM2
GO:0051128 regulation of cellular component organization 6 2306 0.63 0.014 MDM2, CD14, RAC1, CASP8, NPPB, CHEK1
GO:0009408 response to heat 2 89 1.56 0.0142 HMOX1, CD14
GO:0043066 negative regulation of apoptotic process 4 859 0.88 0.0143 HMOX1, RARA, MDM2, CASP8
GO:0045787 positive regulation of cell cycle 3 376 1.11 0.0143 RARA, MDM2, CHEK1
GO:0065003 protein-containing complex assembly 5 1514 0.73 0.0143 HMOX1, MDM2, RAC1, CASP8, SMN2
GO:0065009 regulation of molecular function 7 3322 0.54 0.0147 HMOX1, RARA, MDM2, FLT1, CASP8, PLAU, NPPB
GO:0001819 positive regulation of cytokine production 3 390 1.1 0.0151 HMOX1, RARA, CD14
GO:0008284 positive regulation of cell population proliferation 4 878 0.87 0.0151 HMOX1, RARA, MDM2, FLT1
GO:0048771 tissue remodeling 2 94 1.54 0.0151 MDM2, RAC1
GO:0032649 regulation of interferon-gamma production 2 97 1.53 0.0153 RARA, CD14
GO:0045321 leukocyte activation 4 894 0.86 0.0153 CD14, RAC1, CASP8, PLAU
GO:0051050 positive regulation of transport 4 892 0.86 0.0153 MDM2, CD14, CASP8, NPPB
GO:0071704 organic substance metabolic process 11 9135 0.29 0.0153 HMOX1, PLAT, RARA, MDM2, FLT1, RAC1, CASP8, PLAU, NPPB, SMN2, CHEK1
GO:1904951 positive regulation of establishment of protein localization 3 397 1.09 0.0153 MDM2, CD14, CASP8
GO:0051247 positive regulation of protein metabolic process 5 1587 0.71 0.0159 HMOX1, MDM2, FLT1, RAC1, CASP8
GO:0006915 apoptotic process 4 915 0.85 0.0161 HMOX1, CD14, CASP8, CHEK1
GO:0042127 regulation of cell population proliferation 5 1594 0.71 0.0161 HMOX1, RARA, MDM2, FLT1, PLAU
GO:0006468 protein phosphorylation 4 923 0.85 0.0163 RARA, FLT1, RAC1, CHEK1
GO:0097529 myeloid leukocyte migration 2 103 1.5 0.0163 FLT1, RAC1
GO:0001952 regulation of cell-matrix adhesion 2 105 1.49 0.0165 RAC1, PLAU
GO:0002683 negative regulation of immune system process 3 425 1.06 0.017 HMOX1, RARA, CD14
GO:0022603 regulation of anatomical structure morphogenesis 4 961 0.83 0.0178 HMOX1, FLT1, RAC1, NPPB
GO:0051704 multi-organism process 6 2514 0.59 0.0178 RARA, MDM2, CD14, RAC1, CASP8, NPPB
GO:1902533 positive regulation of intracellular signal transduction 4 959 0.83 0.0178 FLT1, CD14, RAC1, CASP8
GO:0042542 response to hydrogen peroxide 2 112 1.46 0.0179 HMOX1, MDM2
GO:0032680 regulation of tumor necrosis factor production 2 115 1.45 0.0186 RARA, CD14
GO:0002761 regulation of myeloid leukocyte differentiation 2 116 1.45 0.0188 RARA, CASP8
GO:0044267 cellular protein metabolic process 7 3603 0.5 0.0196 PLAT, RARA, MDM2, FLT1, RAC1, CASP8, CHEK1
GO:0001568 blood vessel development 3 464 1.02 0.0203 HMOX1, MDM2, FLT1
GO:0001889 liver development 2 123 1.42 0.0203 HMOX1, RARA
GO:0001936 regulation of endothelial cell proliferation 2 122 1.43 0.0203 HMOX1, FLT1
GO:0090066 regulation of anatomical structure size 3 464 1.02 0.0203 HMOX1, RAC1, NPPB
GO:0002694 regulation of leukocyte activation 3 470 1.02 0.0204 HMOX1, RARA, RAC1
GO:0032355 response to estradiol 2 126 1.41 0.0207 RARA, CASP8
GO:0006935 chemotaxis 3 491 1 0.021 FLT1, RAC1, PLAU
GO:0006954 inflammatory response 3 482 1.01 0.021 HMOX1, CD14, RAC1
GO:0030595 leukocyte chemotaxis 2 130 1.4 0.021 FLT1, RAC1
GO:0034097 response to cytokine 4 1035 0.8 0.021 HMOX1, RARA, CD14, CASP8
GO:0035296 regulation of tube diameter 2 129 1.4 0.021 HMOX1, NPPB
GO:0043312 neutrophil degranulation 3 485 1 0.021 CD14, RAC1, PLAU
GO:0060627 regulation of vesicle-mediated transport 3 480 1.01 0.021 HMOX1, CD14, RAC1
GO:1901796 regulation of signal transduction by p53 class mediator 2 129 1.4 0.021 MDM2, CHEK1
GO:0007169 transmembrane receptor protein tyrosine kinase signaling pathway 3 499 0.99 0.0215 PLAT, FLT1, RAC1
GO:0032103 positive regulation of response to external stimulus 3 499 0.99 0.0215 CD14, RAC1, CASP8
GO:0046903 secretion 4 1070 0.78 0.0215 CD14, RAC1, PLAU, NPPB
GO:0097746 regulation of blood vessel diameter 2 137 1.38 0.0215 HMOX1, NPPB
GO:0006897 endocytosis 3 510 0.98 0.0216 RARA, CD14, RAC1
GO:0071407 cellular response to organic cyclic compound 3 505 0.99 0.0216 RARA, MDM2, CASP8
GO:0071456 cellular response to hypoxia 2 139 1.37 0.0216 HMOX1, MDM2
GO:1902107 positive regulation of leukocyte differentiation 2 139 1.37 0.0216 RARA, CASP8
GO:0071222 cellular response to lipopolysaccharide 2 146 1.35 0.0228 RARA, CD14
GO:0009968 negative regulation of signal transduction 4 1160 0.75 0.0257 HMOX1, MDM2, CD14, CASP8
GO:0045766 positive regulation of angiogenesis 2 162 1.3 0.0266 HMOX1, FLT1
GO:0051707 response to other organism 4 1173 0.75 0.0266 RARA, CD14, CASP8, NPPB
GO:0016032 viral process 3 571 0.93 0.027 MDM2, RAC1, CASP8
GO:0002758 innate immune response-activating signal transduction 2 168 1.29 0.0276 CD14, CASP8
GO:0009653 anatomical structure morphogenesis 5 1992 0.61 0.0276 HMOX1, RARA, MDM2, FLT1, RAC1
GO:0006508 proteolysis 4 1203 0.73 0.0279 PLAT, MDM2, CASP8, PLAU
GO:0030522 intracellular receptor signaling pathway 2 173 1.28 0.0287 RARA, CASP8
GO:0002685 regulation of leukocyte migration 2 175 1.27 0.0292 HMOX1, RAC1
GO:0006464 cellular protein modification process 6 2999 0.51 0.0296 PLAT, RARA, MDM2, FLT1, RAC1, CHEK1
GO:0008217 regulation of blood pressure 2 177 1.27 0.0296 HMOX1, NPPB
GO:0070507 regulation of microtubule cytoskeleton organization 2 177 1.27 0.0296 RAC1, CHEK1
GO:1901988 negative regulation of cell cycle phase transition 2 177 1.27 0.0296 MDM2, CHEK1
GO:0006810 transport 7 4130 0.44 0.0299 RARA, CD14, RAC1, CASP8, PLAU, NPPB, SMN2
GO:0032502 developmental process 8 5401 0.38 0.0299 HMOX1, RARA, MDM2, FLT1, RAC1, CASP8, SMN2, CHEK1
GO:0035821 modification of morphology or physiology of other organism 2 182 1.25 0.0299 CASP8, NPPB
GO:0048584 positive regulation of response to stimulus 5 2054 0.6 0.0299 RARA, FLT1, CD14, RAC1, CASP8
GO:0098657 import into cell 3 609 0.9 0.0299 RARA, CD14, RAC1
GO:0006796 phosphate-containing compound metabolic process 5 2065 0.6 0.03 RARA, FLT1, RAC1, NPPB, CHEK1
GO:0035239 tube morphogenesis 3 615 0.9 0.03 HMOX1, RARA, FLT1
GO:0048731 system development 7 4144 0.44 0.03 HMOX1, RARA, MDM2, FLT1, RAC1, CASP8, SMN2
GO:0002699 positive regulation of immune effector process 2 186 1.24 0.0302 HMOX1, RARA
GO:0030155 regulation of cell adhesion 3 623 0.89 0.0302 RARA, RAC1, PLAU
GO:2001020 regulation of response to DNA damage stimulus 2 188 1.24 0.0302 MDM2, CHEK1
GO:0051129 negative regulation of cellular component organization 3 632 0.89 0.0309 MDM2, RAC1, CHEK1
GO:0050870 positive regulation of T cell activation 2 193 1.23 0.0312 RARA, RAC1
GO:0097237 cellular response to toxic substance 2 195 1.22 0.0316 HMOX1, MDM2
GO:0008285 negative regulation of cell population proliferation 3 669 0.86 0.0348 HMOX1, RARA, FLT1
GO:0043281 regulation of cysteine-type endopeptidase activity involved in apoptotic process 2 209 1.19 0.0351 MDM2, CASP8
GO:0034612 response to tumor necrosis factor 2 217 1.18 0.0373 CD14, CASP8
GO:0044237 cellular metabolic process 10 8797 0.27 0.0381 HMOX1, PLAT, RARA, MDM2, FLT1, RAC1, CASP8, NPPB, SMN2, CHEK1
GO:0044238 primary metabolic process 10 8808 0.27 0.0384 PLAT, RARA, MDM2, FLT1, RAC1, CASP8, PLAU, NPPB, SMN2, CHEK1
GO:0034599 cellular response to oxidative stress 2 222 1.17 0.0385 HMOX1, MDM2
GO:0030100 regulation of endocytosis 2 229 1.15 0.0407 CD14, RAC1
GO:0051046 regulation of secretion 3 728 0.83 0.0425 HMOX1, CD14, NPPB
GO:0007264 small GTPase mediated signal transduction 2 239 1.13 0.0433 HMOX1, RAC1
GO:0030162 regulation of proteolysis 3 742 0.82 0.0439 PLAT, MDM2, CASP8
GO:0045930 negative regulation of mitotic cell cycle 2 243 1.13 0.0443 MDM2, CHEK1
GO:0006974 cellular response to DNA damage stimulus 3 749 0.81 0.0444 HMOX1, MDM2, CHEK1
GO:0060341 regulation of cellular localization 3 766 0.81 0.0469 HMOX1, MDM2, CASP8
GO:0032270 positive regulation of cellular protein metabolic process 4 1496 0.64 0.0472 MDM2, FLT1, RAC1, CASP8
GO:0043170 macromolecule metabolic process 9 7453 0.29 0.0483 PLAT, RARA, MDM2, FLT1, RAC1, CASP8, PLAU, SMN2, CHEK1
Table A2 Chlorogenic acid-regulated cellular components
Table A2term ID term description DGC BGC strength FDR matching proteins
GO:0098805 whole membrane 6 1554 0.8 0.0215 HMOX1, MDM2, CD14, RAC1, CASP8, PLAU
GO:0005615 extracellular space 5 1134 0.86 0.026 HMOX1, PLAT, FLT1, PLAU, NPPB
GO:0005576 extracellular region 6 2505 0.59 0.0288 HMOX1, PLAT, FLT1, CD14, PLAU, NPPB
GO:0009986 cell surface 4 690 0.98 0.0288 PLAT, RARA, CD14, PLAU
GO:0030141 secretory granule 4 828 0.9 0.0288 PLAT, CD14, RAC1, PLAU
GO:0030659 cytoplasmic vesicle membrane 4 724 0.95 0.0288 MDM2, CD14, RAC1, PLAU
GO:0030667 secretory granule membrane 3 298 1.22 0.0288 CD14, RAC1, PLAU
GO:0031410 cytoplasmic vesicle 6 2226 0.64 0.0288 PLAT, MDM2, FLT1, CD14, RAC1, PLAU
GO:0032991 protein-containing complex 8 4792 0.43 0.0288 MDM2, FLT1, CD14, RAC1, CASP8, NPPB, SMN2, CHEK1
GO:0043005 neuron projection 4 1142 0.76 0.0288 RARA, RAC1, CASP8, SMN2
GO:0043232 intracellular non-membrane-bounded organelle 8 4005 0.51 0.0288 HMOX1, RARA, MDM2, FLT1, RAC1, CASP8, SMN2, CHEK1
GO:0044297 cell body 3 526 0.97 0.0288 RARA, CASP8, SMN2
GO:0045121 membrane raft 3 300 1.21 0.0288 HMOX1, CD14, CASP8
GO:0070820 tertiary granule 2 164 1.3 0.0288 RAC1, PLAU
GO:0098588 bounding membrane of organelle 5 1950 0.62 0.0288 MDM2, CD14, RAC1, CASP8, PLAU
GO:0036464 cytoplasmic ribonucleoprotein granule 2 191 1.23 0.0364 RAC1, SMN2
GO:0031090 organelle membrane 6 3337 0.47 0.05 HMOX1, MDM2, CD14, RAC1, CASP8, PLAU
GO:0036477 somatodendritic compartment 3 731 0.83 0.05 RARA, RAC1, SMN2
Table A3 Chlorogenic acid-regulated KEGG pathways
Table A3#term ID term description DGC BGC strength FDR matching proteins in network (labels)
hsa05202 Transcriptional misregulation in cancer 6 169 1.76 3.93E-08 PLAT, RARA, MDM2, FLT1, CD14, PLAU
hsa05203 Viral carcinogenesis 4 183 1.55 0.00018 MDM2, RAC1, CASP8, CHEK1
hsa04115 p53 signaling pathway 3 68 1.86 0.00028 MDM2, CASP8, CHEK1
hsa05200 Pathways in cancer 5 515 1.2 0.00028 HMOX1, RARA, MDM2, RAC1, CASP8
hsa04620 Toll-like receptor signaling pathway 3 102 1.68 0.00052 CD14, RAC1, CASP8
hsa05215 Prostate cancer 3 97 1.7 0.00052 PLAT, MDM2, PLAU
hsa05418 Fluid shear stress and atherosclerosis 3 133 1.57 0.00094 HMOX1, PLAT, RAC1
hsa05206 MicroRNAs in cancer 3 149 1.52 0.0011 HMOX1, MDM2, PLAU
hsa05205 Proteoglycans in cancer 3 195 1.4 0.0022 MDM2, RAC1, PLAU
hsa05134 Legionellosis 2 54 1.78 0.0049 CD14, CASP8
hsa05416 Viral myocarditis 2 56 1.77 0.0049 RAC1, CASP8
hsa04010 MAPK signaling pathway 3 293 1.22 0.0054 FLT1, CD14, RAC1
hsa05221 Acute myeloid leukemia 2 66 1.69 0.0056 RARA, CD14
hsa01524 Platinum drug resistance 2 70 1.67 0.0058 MDM2, CASP8
hsa04151 PI3K-Akt signaling pathway 3 348 1.15 0.0067 MDM2, FLT1, RAC1
hsa04610 Complement and coagulation cascades 2 78 1.62 0.0067 PLAT, PLAU
hsa05132 Salmonella infection 2 84 1.59 0.0068 CD14, RAC1
hsa04064 NF-kappa B signaling pathway 2 93 1.54 0.0079 CD14, PLAU
hsa04066 HIF-1 signaling pathway 2 98 1.52 0.0082 HMOX1, FLT1
hsa04110 Cell cycle 2 123 1.42 0.0122 MDM2, CHEK1
hsa04145 Phagosome 2 145 1.35 0.0159 CD14, RAC1
hsa04932 Non-alcoholic fatty liver disease (NAFLD) 2 149 1.34 0.016 RAC1, CASP8
hsa04218 Cellular senescence 2 156 1.32 0.0167 MDM2, CHEK1
hsa05152 Tuberculosis 2 172 1.28 0.0194 CD14, CASP8
hsa05167 Kaposi's sarcoma-associated herpesvirus infection 2 183 1.25 0.0209 RAC1, CASP8
hsa04510 Focal adhesion 2 197 1.22 0.0232 FLT1, RAC1
hsa04015 Rap1 signaling pathway 2 203 1.21 0.0236 FLT1, RAC1
hsa04810 Regulation of actin cytoskeleton 2 205 1.2 0.0236 CD14, RAC1
hsa04014 Ras signaling pathway 2 228 1.16 0.0275 FLT1, RAC1
hsa05165 Human papillomavirus infection 2 317 1.01 0.0497 MDM2, CASP8
Data Availability
Data will be made available on request.
Acknowledgments
N/A
==== Refs
References
1 Zhou P. Yang X.L. Wang X.G. Hu B. Zhang L. Zhang W. Shi Z.L. A pneumonia outbreak associated with a new coronavirus of probable bat origin nature 579 7798 2020 270 273 32015507
2 Cooper J.A. vanDellen M. Bhutani S. Self-weighing practices and associated health behaviors during COVID-19 American Journal of Health Behavior 45 1 2021 17 30 33402235
3 Weaver R.H. Jackson A. Lanigan J. Power T.G. Anderson A. Cox A.E. Weybright E. Health behaviors at the onset of the COVID-19 pandemic American journal of health behavior 45 1 2021 44 61 33402237
4 Wu F. Zhao S. Yu B. Chen Y.M. Wang W. Song Z.G. Zhang Y.Z. A new coronavirus associated with human respiratory disease in China Nature 579 7798 2020 265 269 32015508
5 Raheem R. Alsayed R. Yousif E. Hairunisa N. Coronavirus new variants: the mutations cause and the effect on the treatment and vaccination Baghdad J Biochem Appl Biol Sci 2 2021 71 79
6 Grinter S.Z. Zou X. Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design Molecules 19 7 2014 10150 10176 25019558
7 Yang Y. Islam M.S. Wang J. Li Y. Chen X. Traditional Chinese medicine in the treatment of patients infected with 2019-new coronavirus (SARS-CoV-2): a review and perspective International journal of biological sciences 16 10 2020 1708 32226288
8 Peng S. Yang X.Y. Yang T. Zhang W. Cottrell R.R. Uncertainty stress, and its impact on disease fear and prevention behavior during the COVID-19 epidemic in China: a panel study American Journal of Health Behavior 45 2 2021 334 341 33888193
9 Suleiman A. Rafaa T. Alrawi A. Dawood M. The impact of ACE2 genetic polymorphisms (rs2106809 and rs2074192) on gender susceptibility to COVID-19 infection and recovery: A systematic review Baghdad Journal of Biochemistry and Applied Biological Sciences 2 03 2021 167 180
10 Chen N. Zhou M. Dong X. Qu J. Gong F. Han Y. Zhang L. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study The lancet 395 10223 2020 507 513
11 Wang M. Cao R. Zhang L. Yang X. Liu J. Xu M. Xiao G. Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro Cell research 30 3 2020 269 271 32020029
12 Organization, W.H. Clinical management of severe acute respiratory infection when novel coronavirus (2019-nCoV) infection is suspected: interim guidance, 28 January 2020 2020 World Health Organization
13 Al-Doori A. Ahmed D. Kadhom M. Yousif E. Herbal medicine as an alternative method to treat and prevent COVID-19 Baghdad J Biochem Appl Biol Sci 2 01 2021 1 20
14 Li G. De Clercq E. Therapeutic options for the 2019 novel coronavirus (2019-nCoV) Nature reviews Drug discovery 19 3 2020 149 150
15 Ghaemi F. Amiri A. Bajuri M.Y. Yuhana N.Y. Ferrara M. Role of different types of nanomaterials against diagnosis, prevention and therapy of COVID-19 Sustainable Cities and Society 72 2021 103046
16 Kundu R. Singh P.K. Ferrara M. Ahmadian A. Sarkar R. ET-NET: an ensemble of transfer learning models for prediction of COVID-19 infection through chest CT-scan images Multimedia Tools and Applications 81 1 2022 31 50 34483709
17 Shariq M. Singh K. Bajuri M.Y. Pantelous A.A. Ahmadian A. Salimi M. A secure and reliable RFID authentication protocol using digital schnorr cryptosystem for IoT-enabled healthcare in COVID-19 scenario Sustainable Cities and Society 75 2021 103354
18 Arfan M. Alrabaiah H. Rahman M.U. Sun Y.L. Hashim A.S. Pansera B.A. Salahshour S. Investigation of fractal-fractional order model of COVID-19 in Pakistan under Atangana-Baleanu Caputo (ABC) derivative Results in Physics 24 2021 104046
19 Fouladi S. Ebadi M.J. Safaei A.A. Bajuri M.Y. Ahmadian A. Efficient deep neural networks for classification of COVID-19 based on CT images: Virtualization via software defined radio Computer communications 176 2021 234 248 34149118
20 Nediani C. Ruzzolini J. Romani A. Calorini L. Oleuropein, a bioactive compound from Olea europaea L., as a potential preventive and therapeutic agent in non-communicable diseases Antioxidants 8 12 2019 578 31766676
21 PubChem [Internet]. Bethesda (MD): National Library of Medicine (US), National Center for Biotechnology Information; 2004-. PubChem Compound Summary for CID 471002, Ganoderic acid A; [cited 2023 Jan. 11]. Available from: https://pubchem.ncbi.nlm.nih.gov/compound/Ganoderic-acid-A
22 Suksatan W. Chupradit S. Yumashev A.V. Ravali S. Shalaby M.N. Mustafa Y.F. Siahmansouri H. Immunotherapy of multisystem inflammatory syndrome in children (MIS-C) following COVID-19 through mesenchymal stem cells International Immunopharmacology 101 2021 108217
23 Al-Obaidi Z.M.J. Hussain Y.A. Ali A.A. Al-Rekabi M.D. The influence of vitamin-C intake on blood glucose measurements in COVID-19 pandemic The Journal of Infection in Developing Countries 15 02 2021 209 213 33690202
24 Farasati Far B. Bokov D. Widjaja G. Setia Budi H. Kamal Abdelbasset W. Javanshir S. Dey S.K. Metronidazole, acyclovir and tetrahydrobiopterin may be promising to treat COVID-19 patients, through interaction with interleukin-12 Journal of Biomolecular Structure and Dynamics 2022 1 19
25 Malekahmadi O. Zarei A. Botlani Esfahani M.B. Hekmatifar M. Sabetvand R. Marjani A. Bach Q.V. Thermal and hydrodynamic properties of coronavirus at various temperature and pressure via molecular dynamics approach Journal of Thermal Analysis and Calorimetry 143 2021 2841 2850 33250660
26 Yuanlei S. Jokar Z. Khedri E. Khanaman P.M. Mohammadgholian M. Ghotbi M. Inc M. In-silico tuning of the nano-bio interface by molecular dynamics method: Amyloid beta targeting with two-dimensional metal-organic frameworks Engineering Analysis with Boundary Elements 149 2023 166 176
27 Khanal P. Zargari F. Far B.F. Kumar D. Mahdi Y.K. Jubair N.K. Dey Y.N. Integration of system biology tools to investigate huperzine A as an anti-Alzheimer agent Frontiers in Pharmacology 2021 3362
28 Farasati Far B. Naimi-Jamal M.R. Jahanbakhshi M. Mohammed H.T. Altimari U.S. Ansari J. Poly (3-thienylboronic acid) coated magnetic nanoparticles as a magnetic solid-phase adsorbent for extraction of methamphetamine from urine samples Journal of Dispersion Science and Technology 2022 1 11
29 Asadi S. Mortezagholi B. Hadizadeh A. Borisov V. Ansari M.J. Shaker Majdi H. Chaiyasut C. Ciprofloxacin-loaded titanium nanotubes coated with chitosan: A promising formulation with sustained release and enhanced antibacterial properties Pharmaceutics 14 7 2022 1359 35890255
30 Foroutan Z. Afshari A.R. Sabouri Z. Mostafapour A. Far B.F. Jalili-Nik M. Darroudi M. Plant-based synthesis of cerium oxide nanoparticles as a drug delivery system in improving the anticancer effects of free temozolomide in glioblastoma (U87) cells Ceramics International 48 20 2022 30441 30450
31 Eshrati Yeganeh F. Eshrati Yeganeh A. Fatemizadeh M. Farasati Far B. Quazi S. Safdar M. In vitro cytotoxicity and anti-cancer drug release behavior of methionine-coated magnetite nanoparticles as carriers Medical Oncology 39 12 2022 252 36224407
32 Akbarzadeh I. Poor A.S. Khodarahmi M. Abdihaji M. Moammeri A. Jafari S. Far B.F. Gingerol/letrozole-loaded mesoporous silica nanoparticles for breast cancer therapy: In-silico and in-vitro studies Microporous and Mesoporous Materials 337 2022 111919
33 Farasati Far B. Asadi S. Naimi-Jamal M.R. Abdelbasset W.K. Aghajani Shahrivar A. Insights into the interaction of azinphos-methyl with bovine serum albumin: Experimental and molecular docking studies Journal of Biomolecular Structure and Dynamics 40 22 2022 11863 11873 34427168
34 Decosterd L.A. Parsons I.C. Gustafson K.R. Cardellina J.H. McMahon J.B. Cragg G.M. Steiner J.R. HIV inhibitory natural products. 11. Structure, absolute stereochemistry, and synthesis of conocurvone, a potent, novel HIV-inhibitory naphthoquinone trimer from a Conospermum sp Journal of the American Chemical Society 115 15 1993 6673 6679
35 El-Mekkawy S. Meselhy M.R. Nakamura N. Tezuka Y. Hattori M. Kakiuchi N. Otake T. Anti-HIV-1 and anti-HIV-1-protease substances from Ganoderma lucidum Phytochemistry 49 6 1998 1651 1657 9862140
36 Keefover-Ring K. Holeski L.M. Bowers M.D. Clauss A.D. Lindroth R.L. Phenylpropanoid glycosides of Mimulus guttatus (yellow monkeyflower) Phytochemistry Letters 10 2014 132 139
37 Farasati Far B. Asadi S. Naimi-Jamal M.R. Abdelbasset W.K. Aghajani Shahrivar A. Insights into the interaction of azinphos-methyl with bovine serum albumin: Experimental and molecular docking studies Journal of Biomolecular Structure and Dynamics 2021 1 11
38 Eshrati Yeganeh F. Eshrati Yeganeh A. Fatemizadeh M. Farasati Far B. Quazi S. Safdar M. In vitro cytotoxicity and anti-cancer drug release behavior of methionine-coated magnetite nanoparticles as carriers Medical Oncology 39 12 2022 1 11 | 36875283 | PMC9968613 | NO-CC CODE | 2023-03-01 23:16:12 | yes | Eng Anal Bound Elem. 2023 May 27; 150:583-598 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
25871
10.1007/s11356-023-25871-3
Research Article
Assessing the impact of COVID-19 on economic recovery: role of potential regulatory responses and corporate liquidity
Lin Renzao [email protected]
1
Liu Xianchang [email protected]
2
Liang Ying [email protected]
3
1 grid.411604.6 0000 0001 0130 6528 School of Finance and Accounting, Fuzhou University of International Studies and Trade, Fuzhou, 350202 China
2 grid.411503.2 0000 0000 9271 2478 School of Economics, Fujian Normal University, Fuzhou, 350117 China
3 grid.256111.0 0000 0004 1760 2876 College of Management and Economics, Fujian Agriculture and Forestry University, Fuzhou, 350002 China
Responsible Editor: Arshian Sharif
4 3 2023
120
13 12 2022
7 2 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
We use a variety of organization-level datasets to examine the effectiveness and efficiency of the nations for the coronavirus epidemic. COVID-19 subsidies appear to have saved a significant number of jobs and maintained economic activity during the first wave of the epidemic, according to conclusions drawn from the experiences of EU member countries. General allocation rules may yield near-optimal outcomes in favor of allocation, as firms with high ecological footprints or zombie firms have lower access to government financing than more favorable, commercially owned, and export-inclination firms. Our assumptions show that the pandemic has a considerable negative impact on firm earnings and the percentage of illiquid and non-profitable businesses. Although they are statistically significant, government wage subsidies have a modest impact on corporate losses compared to the magnitude of the economic shock. Larger enterprises, which receive a lesser proportion of the aid, have more room to increase their trade liabilities or liabilities to linked entities. In contrast, according to our estimations, SMEs stand a greater danger of insolvency.
Keywords
Novel coronavirus
Probability of default
Cash flows
EU
Solvency
==== Body
pmcIntroduction
The Covid-19 pandemic has caused the worst worldwide disaster in recent decades, hurting health systems, economics, and society worldwide. Recovery plans are needed to mitigate the damage caused by the Covid-19 crisis and the looming threat of climate change (Liu et al. 2021) since the impacts are long-lasting (X. Huang et al. 2022a, b, c; Iqbal et al. 2021). The COVID-19 epidemic has profoundly and unexpectedly impacted every aspect of human life. Because of the measures taken to stop the virus from spreading, such as the imprisonment of citizens and the shutdown of non-essential economic operations, GDP and employment have dropped dramatically. The EU's GDP shrank by 6.1% in 2020, and the unemployment rate rose to 7.0%, while the public deficit grew by 6.9% (Li et al. 2020, 2023).
To combat the economic repercussions of the epidemic, the Governments of the Member States (MS) and the European Commission (EC) have proclaimed and prepared several retrieval strategies. From the long-term viewpoint, the MS and the EC collaborate on devising financing schemes to aid economic recovery. The Next-Generation EU (NGEU) supply is the basis of the retrieval agenda of the EU. In order to support recovery from the instant social and economic harm caused by the COVID-19 epidemic, this short-term recovery tool includes greater than €800 billion. This strategy aims to develop a greener, hardier, more digital Europe and a well suited for the present and incoming problems (Chau et al. 2021; Huang and Liu 2021). As part of the NGEU, the member states have presented their resilience plans and national recovery to the European Commission, describing how the capitals would be invested and what means they will participate in achieving the goal of equitable, justifiable, digital, and green transformation (EC 2021). The restructurings and funds proposed in the policies should be applied by 2026. The NGEU investment will be between 2021 and 2023 and will be related to the EU’s steady, lasting financial plan, extending to 2027 from 2021. There will be a total of €2 trillion invested in Europe’s long-term budget and the National Growth and Employment Program (NGEU).
Based on factors like well-positioned lobbies and huge corporations, the requirement to take benefit of planned initiatives, and required or built infrastructure, political economics can help us predict how this will play out in the long run (Jin et al. 2022). In light of these developments, academic, Internet, and political debates continue to modify and adapt the aforementioned concepts. As a result, investors are becoming warier about investing in the economy (Salvo and Laborde 2021; Si et al. 2021) because the Covid-19 pandemic may have unintended and unintended repercussions.
According to Asikha et al. (2021), we can detect three economic shocks: coronavirus health, economic repercussions of restraint efforts, and expectancy shocks. According to Rajput et al. (2021), the economies faced an economic slump that would be more severe if they did not have the necessary macroeconomic support, leading to higher losses. For this reason, governments took the necessary steps to ensure that businesses could withstand the epidemic without laying off workers or going bankrupt and that the economy would not suffer further (Tang et al. 2022b, 2021). Governments and financial and monetary agencies implemented various fiscal, monetary, and financial policy actions worldwide. Tax deferrals, public guarantees, and direct handouts are among the most common policy responses.
The action had to be taken immediately because of the speed and magnitude of the economic impact of the novel coronavirus. According to preliminary studies, firms, especially those working in highly disturbed industries with limited or no income, could be immediately hurt by inadequate liquidness (He et al. 2022; Wei and Han 2021). Innovative economic initiatives were pushed through without thorough ex-ante impact evaluations. There are a lot of important questions. Were the enterprises in need of assistance able to obtain it? Where have the funds been used to help these companies? Is the support that was provided efficient and effective enough for you? Is there any evidence that the support will affect the macroeconomics?
We look at how enterprises are selected for subsidies based on several factors, such as their size and location. More beneficial enterprises with a bigger percentage of labor costs and prior knowledge in tackling the situation obtained more help. During the pandemic in Slovakia, economically less well-ordered, troubled, and zombie enterprises had a lesser probability of being sustained (Xu and Jia 2022; Liu et al. 2022c). Furthermore, enterprises with negative ecological influences were less likely to receive financial assistance. For the most part, our data show that the laws established directed support to enterprises in need, reduced their liquidity or insolvency, and spared significant economic employment during the first phase of the epidemic (from March to June 2020).
When COVID-19 first came to light, an unparalleled number of papers were issued on it. This includes papers in drugs, biochemistry, and the social sciences (such as sociology, psychology, and anthropology) (Astawa et al. 2021; Hai Ming et al. 2022). A new literature review by Strielkowski et al. (2021) includes not only containment strategies but also governmental responses. A number of key studies on the influence of COVID-19 at the firm level have been linked to our research. Biswas et al. (2021) use a cost-minimizing theoretical framework to quantify the impact of the crisis on company failures across European SMEs. Sector-specific sales-cost elasticity is used by Bashir et al. (2022) to quantify the impact of crises on business revenue and the impact of investments-debt trade-offs on that revenue by companies. Many studies have examined the possibility of corporate insolvency due to the decrease in equity buffers and an increase in their debt ratios (Latif et al. 2021; Tang et al. 2022a). We use a similar approach to gauge the impact of a sales shock on the company’s revenue and insolvency risk. To our knowledge, this is the first study to employ government-sponsored COVID-19 datasets at the corporate level. This allows us to recognize and analyze the properties of the enterprises that gain the assistance and equate the amount of tremor with the government assistance for every company. We use a very extensive sample of non-economic enterprises from EU countries. Several researchers have looked at the distribution and consequences of non-financial private-sector subsidies (Jiang et al. 2021; Nasir et al. 2022), and these works influence our methodology (Chau et al. 2021; Tang et al. 2022b, 2022a). When it comes to the COVID-19 epidemic, there may be differences in the motivations and features of the businesses seeking government awards designed to foster innovation (Ismarau Tajuddin et al. 2017).
According to OECD (2020), salary subsidies are the most commonly used group of policies. It is possible to preserve employment and productivity by implementing this set of policies rather than relying on typical budgetary ones. Pay subsidies are not new, and they can come in various shapes and sizes. For example, Nicola et al. (2020) investigate the impact of pay subsidies on labour demand in Germany, where they have had a long history of various employment subsidies in place. A number of other countries’ experiences are discussed in papers like those by Su and Urban (2021). In all three trials, there is no or just a short-term rise in employment for the treatment group as a result of the policy. There is no evidence that subsidies have an influence on employment at the firm level. In order to conduct this kind of review, there is a delay. COVID-19 support is still active, and post-pandemic firm employment or performance metrics are unavailable at the time of this study’s production. The COVID-19 epidemic and its immediate microeconomic and macroeconomic implications are better understood. Industrial organization literature is enriched by our findings on the distribution of public subsidies and their effect on business behavior. We introduce some empirical insights to corporate finance by focusing on the liquidity and solvency of firms. Last but not least, we contribute to the field of public economics with our findings on the function of subsidies in alleviating the negative economic impact.
The following section contains more information on the data from various firm-level datasets (the “Theoretical underpinnings” section). The “Methodology and data description” section explains the process. Using logistic and Tobit regressions, we compare enterprises that got government support with those that did not get government support. Slovak authorities have taken various measures to mitigate the economic impact of the COVID-19 pandemic and debate the short-term macroeconomic effects of this support. The “Results and discussion” section shows our findings on the support distribution logically and organized, without ignoring the important implications for the green economy or the prevalence of zombie enterprises. Second-wave pandemic support and its impact on company profitability, liquidity, and solvency are examined in the “Result in liquidity asset or cash flow sufficiency” section. The last component closes our study.
Theoretical underpinnings
In light of the ongoing COVID-19 pandemic, recovery strategies and plans support operational stabilization, revenue generation, financial re-emergence, coping mechanisms for the affected labor force’s employment structure, and marketing/re-branding policies and efforts lodging establishments that are now more important than ever (Irfan et al. 2021; Razzaq et al. 2020; Sharif et al. 2020). A return to normal operation and, eventually, a resumption of growth (i.e., resuming the flow of guests and tourists) are the primary goals of economic crisis structures and resilience plans. These strategies and policies are intended to help businesses recover. These tactics and policies attain recovery through the best potential return to normal processes (Edomah and Ndulue 2020; Irfan et al. 2021). The chaos theory has evolved as a valuable structure for learning managerial crisis and interaction strategy. From a theoretical standpoint, the hardiness and recovery efforts of the firms can be described by the chaos theory.
Moreover, the term has referred to more than merely a state of disarray or confusion. Rather, it focuses on how anything evolves through time, whether that item is the manufacturing average, the cost of food, or the number of bug populations (Mngumi et al. 2022; Noureddine and Tan 2021). The theory offers a helpful structure for comprehending the organizational crises in their entirety. More explicitly, chaos theory connects organizational crises and their respective communicative elements to wider conceptions of system steadiness, unsteadiness, and consequent decay and rebirth (Sinha et al. 2021; Xiangyu et al. 2021). The theory also seeks to realize systems that do not function in a conventional, linear, and foreseeable fashion based on cause and effect relationships (Juergensen et al. 2020). The chaos theory is predicated on the concept of subtle dependency on initial conditions, sometimes referred to as the butterfly effect. According to this concept, relatively minor adjustments made at the start of an emerging event can quickly snowball into significant deviations by its conclusion (Liu et al. 2022a; Gourinchas et al. 2022). The butterfly effect was first proposed by Society (1991), who posed whether or not the simple flapping of a butterfly’s wing in Brazil might be a contributing factor in the development of a tornado in Texas. In the case that this hypothesis is correct and its validity can be demonstrated to a high degree, the instability that it creates would create difficulties for the ability to forecast future events accurately. An ideal theoretical framework for examining how corporations shift their objectives and develop new business strategies during and after a crisis, such as the ongoing COVID-19 pandemic, can be found in the central postulation of chaos theory. This is because the chaos theory is based on the idea that chaotic systems are inherently unstable (Dörr et al. 2022; Li et al. 2022).
It is possible that the chaos theory, with its prism and principles encapsulated in it, can provide signs as to what to suppose at the expiration of chaotic times when applied to managing and resisting the COVID-19 pandemic and when taken into account across the entire rooming, lodging, and hotel business (Cowling et al. 2020; Wu et al. 2023). According to this hypothesis, both a return to business as usual and a paradigm shift in the way hotels conduct their operations are possible outcomes. Both of these outcomes are possible in this context. Financial measures like revenue enhancement, cost reduction, and cash management, for example, may be the first foundational focus in pandemic revamping efforts for hotel enterprises, as the initial circumstances and tiny bundles produced by COVID-19; the example of this is managing a perceived business and operational risk (Cirera et al. 2021; W. Huang et al. 2022a). The following sections will discuss how lodging businesses can thrive in a normal operating environment by focusing on financial recovery techniques such as inventory management, cost reduction, capital restructuring, revenue enhancement, and cash management. Chaos theory gives a theoretical framework to develop new operational priorities and strategies. These sections also recognize that a new set of operational strategies and priorities can be derived from chaos theory.
Methodology and data description
We investigate whether previous funding limitations enhance the financial impacts of pandemic crises on firms. Therefore, we first generate financial limitation signs from the WBES data before the COVID-19 epidemic, hence paying the finance restraints measure employed (Barrero et al. 2021; Wang and Zhang 2021a). For robustness, we additionally utilize a credit self-rationing technique by deliberating depressed enterprises, i.e., companies that did not seek a bank loan and the requirement for exterior capital as they predicted denial (Hu et al. 2022). We intend to study how previous-epidemic factor production regulations, firm risk, financing limitations, and trade credit (late commitments and outflows) are disturbed by actual and perceived financing restrictions encountered by enterprises before the emergence of the COVID-19 epidemic (Caballero-Morales 2021; Guerini et al. 2020).
Our major study target is to investigate how finance constraints imitate firms’ reactions to the epidemic. In specific, our analysis includes three key research purposes. First, we look into whether financial fragility at the business level was exacerbated due to the pandemic due to prior financing difficulties. To this goal, we propose three subresearch topics. Prior financial constraints may have increased the chance of encountering pandemic-driven cash flow and liquidity difficulties. To measure a company’s economic health, we create a variable LIQDTY by exercising the reaction to a query in pandemic influence continuation analyses that asks, in the outburst of COVID-19, has this establishment’s liquidity or cash flow improved, continued the same, or reduced? If a company reports increased cash flows and liquidity, the variable LIQDTY is set to 1. Still, if a company reports a decrease in liquidity or cash flows, the variable LIQDTY is set to 0.
Our second sub-study topic is how organizations administer pandemic-driven cash flow and temporary liquidity difficulties. Prior financial constraints are examined to see if they increase the possibility of obtaining bank credit in order to deal with current liquidity issues. To analyze a firm availability to different causes of exterior finance to handle cash flow challenges, we employ the following CIFS question since the onset of COVID-19. Where has this business typically turned to when it needed to deal with a lack of cash? (Cove2). Based on the reaction, we create four dummy variables to determine the firms’ principal source of external finance to address pandemic-stimulated liquidity concerns. BNK FIN stands for bank credit; DELAY PAYMENT stands for late payments to workers or sellers; MKT FIN stands for market (equity) financing; and GOV GRANT stands for grants from the federal government (Du et al. 2022; Rajagopal and Reyes 2022).
Furthermore, we examine if prior financing limits compound a company’s credit risk by determining whether those restraints raised the likelihood that the firm will be overdue on payments due to financial organizations through the pandemic crises. Since the eruption of pandemic crises, has this formation been unpaid on its payments to any economic institution? If so, how long has it been since? Whether or not (Cove4) we can use this data to create a fake variable, OVDU, which will take on the value 1 if a company answers positively and 0 otherwise. The firm’s capability to deal with COVID-19 by changing or converting its operations or production processes in order to continue offering goods and services during the COVID-19 epidemic is also investigated. The next CIFS query prompts us to create a dummy variable. The outbreak of COVID-19 has forced this company to change its production or service offerings in some way: yes/no. Similarly, we analyze whether the firm’s capability to alter its organizational or manufacturing processes is affected by earlier funding constraints. We want to know if the COVID-19 epidemic affects the likelihood of online activity or the delivery of products and services, especially if credit is tight. Our ONLINE STRTD dummy variable examines this sub-area of study using the COVc4 and COVc4b CIFS questions: In reaction to the COVID-19 epidemic, did this establishment begin or boost its online business activity (COVe4a) or increase distribution or carry-out facilities (COVe4b)?Country Observations Percent of total sample Credit constrained (FINCON3) Decreased liquidity Overdue in financial obligations
Albania 377 4.14% 6.71% 70.80% 20.06%
Azerbaijan 225 2.47% 23.12% 77.20% 14.85%
Belarus 600 6.59% 18.22% 58.49% 6.80%
Bulgaria 772 8.48% 20.68% 71.35% 5.95%
Chad 153 1.68% 31.50% 92.0% 8.91%
El Salvador 719 7.9% 17.83% 94.80% 27.88%
Greece 600 6.59% 15.95% 61.89% 8.68%
Guatemala 345 3.79% 13.10% 80.90% 18.01%
Guinea 150 1.65% 36.52% 96.12% 18.45%
Honduras 332 3.65% 21.38% 87.12% 36.20%
Moldova 360 3.96% 34.35% 80.92% 13.70%
Mongolia 360 3.96% 47.41% 83.80% 23.60%
Morocco 1096 12.04% 42.80% 72.10% 20.61%
Nicaragua 333 3.66% 13.06% 79.35% 19.60%
Niger 151 1.66% 29.31% 88.06% 37.30%
Poland 1369 15.04% 13.85% 44.10% 15.40%
Slovenia 409 4.49% 3.40% 56.22% 9.64%
Togo 150 1.65% 36.40% 90.20% 35.30%
Zimbabwe 600 6.59% 63.80% 86.57% 10.82%
Total 9101
Empirical strategy for solvency assessment and policy response
Numerous contributions have been made as a result of our research. This study is the first to investigate the impact that the COVID-19 pandemic has had on the liquidity of European businesses. In the same vein as Hai Ming et al. (2022) and Liu et al. (2021), we take an up-close and personal look at a particular threat. On the other hand, the research model is comprehensive, considering both accounting-based and market-based nonpayment default predictions. In addition, the prior stress evaluations that made use of stress testing were conducted within the settings of the banks (Liu et al. 2022d; Huang et al. 2022b; Liu et al. 2021; Yang et al. 2021; Young 2020). For the purpose of conducting a stress test on a company’s solvency, we focused on three primary aspects: the danger of default, enough cash flow, and liquidity. First, each company’s real state was determined by employing these components, and then, it was compared to the forecasts of a distress scenario. In the end, analysis was done to evaluate the many treatments that may be implemented and how each one might influence the assessments of the concerned areas. To confirm that economic reports are produced, the year 2019 has been designated as the base case scenario. The particulars of the framework for assessing solvency metrics are presented in the following paragraphs.
Market-based model
In order to reduce the chance of future bankruptcies, market-based or structured bankruptcy frameworks can be implemented. These structures are based on Merton (1974) theoretical foundations, which attach corporate bankruptcy with a pricing structure for choices. Standard deviation and market value of support are hard to observe in a structured default model since not all assets are marked to market. However, Khan et al. (2019) developed a recursive method of using share prices to determine volatility and asset market values at the enterprise stage. This strategy has been used in a plethora of investigations (Naqvi et al. 2018; Grundke and Kühn 2020; Attaoui and Poncet 2013). Although several additions to Merton (1974) have previously been proposed, recent relative research such as Gharghori et al. (2009) and Afzal and Mirza (2012) shows that the initial framework beats all versions.
In a contingent claim scenario, the likelihood of default for company i is demonstrated by,1 PDi=1-Nln(VAiXi)+μi+0.5σAi2TσAiT
where VA denotes the sample’s individual business market values and µ is the projected growth of assets (with a standard deviation of σA). X represents a density function N of monetary obligations that must be paid off by time T. In order to probe a complex situation, we define X as the sum of the shorter and longer economic liability periods for each firm in the sample (without subordinate). Suppose we assign the value T as the average term to maturity of all these obligations. Value of equity (VE), risk-free rate (r), and two unknowns in the options pricing framework’s system of simultaneous equations are proposed by Tong et al. (2022).1.1 dVA=μVAdt+σAVAdW
1.2 VE=VANd1-Xe-rTN(d2)
when.1.3 d1=ln(VAX)+(μ+0.5σA2)TσAT,andd2=d1-σAT
Starting with return data for 12 months, representative companies evaluate the daily standard equity variance. This is used as a proxy for a standard deviation of asset values in order to calculate their intraday market value. After calculating the pseudo market worth of property, the standard asset variation was evaluated. In the absence of convergence (within 0.0001) between the first-pass normal equity variation and the second-pass normal asset variation, this is finished. Finally, the market price and default probability can be calculated using the convergent value. In this way, distressed historical situation PDs are measured to analyze the effects of the corona pandemic.
Accounting-based discriminant models of default
Using a company’s financial data, Mirza et al. (2016) created the first discriminant default models. These models work so well because they are based on the core strengths and weaknesses of the problem. In addition, the models are immune to external influences, such as those found in the market. Predictive scores that can be used to categorize stressed businesses from those that are not are supported by discriminant prototypes. The probability of default can be estimated using these scores. Two of the most important metrics for gauging discriminant analysis performance were utilized here. This is the Altman Z score that Altman proposed (Merton 1974), which is a modified version of Afik et al. (2016) and Vassalou and Xing (2005). For example, many studies back up the Z and O scores’ effectiveness in predicting non-financial firm distress (Kiseleva et al. 2020). For every firm, the Altman Z score was estimated in the same way as Kwak et al. (2004).2 Z′′=3.25+6.56X1+3.26X2+6.72X3+1.05X4
Both X1 and X2 above represent networking capitals, which may be thought of as the difference between net worth overall assets and profits surplus to net worth overall assets. Similar to how X2 represents operational profit as a percentage of total assets, X3 represents market value of equity as a percentage of carrying value of liabilities, and X4 represents market value of equity as a percentage of total assets. Using an approximation of Z, we may calculate the probability of insolvency or default (Pz):3 Pz′′⟨Y=1|X⟩=11+e-Z′′
Note that studies like Lin et al. (2016) and, more recently, Merkevicius et al. (2006) have advocated for using the coefficients from the first version of the Altman model. According to their results, true coefficient values are more dependable than re-estimated factor loadings for forecasting insolvency. In light of this, the study employed Altman’s argument’s initial coefficients.
For each sample firm, the estimation of the Xu and Zhang (2009) using the 0 scores was estimated applying the following equation.4 O=-1.32-0.407logTAtGNPt+6.03TLtTAt-1.43WCtTAt+0.0757CLtCAt-1.72D1-2.37NItTAt-1.83FFOtTLt+0.285D2-0.521NIt-NIt-1NIt+NIt-1
where FFO = funds from operation, WC = working capitals, TL = total liabilities, CL = current liabilities, NI = net income, CA = current asset, GNP = gross national product price index level, TA = total assets, D1 is a dummy that equals 1 if TL > TA, and D2 is a dummy that equals 1 if the prior 2 years have resulted in a net loss. Time is indicated by the t-suffix. A default probability (po) can be determined with the use of the following formula:5 po=eOScore1+eOScore
For the past time frame, pz and po are determined, and troubled circumstances are derived.
Cash flow sufficiency
Research also found that standard liquidity asset use ratios were used to examine all enterprises’ current and stressed solvency positions in our sample. Financial risk can be measured by comparing a company’s cash flow surplus to its cash commitments. Two categories are used to categorize the numbers mentioned above: debt payback and insurance. Operational funds and free-functioning liquidity assets are included in the reimbursement ratios (FOCFs). We use EBITDA as interest for the coverage ratios and FFO as cash interest. Free cash flow modifies operating cash flows, including capital costs such as physical and intellectual property funding. Decreases in the necessities of life can strain cash flow, increasing debt repayment, and contributing to inadequate coverage in a weak economy. Due to the importance of cash flows in optimizing the capital composition and financial flexibility (Tinoco and Wilson 2013), this is possible (Hillegeist et al. 2004).
Policy interventions
Three policy interventions are taken into account to measure the influence of potential business support. Deferral of tax payments, inclusion via a secondary bridging credit, and equity insertion are a few options available. Backers can also offer secondary credits and equity upgrades in addition to tax deferral. It has been discovered that governmental and sponsor initiatives can minimize the possibility of default after natural tragedies (Can et al. 2021; D’Adamo et al. 2020; Lim et al. 2021). A company’s internal cash flow can be boosted by tax deferrals, while external cash flow can be reduced by subordinated debt and stock. We will investigate the influence of these three options in a troubling situation to see which interference can best maximize the solvency view and maintain it at the degree of the base time.
The COVID-19 insolvency gap
Two statistics are needed to calculate the insolvency gap. For each sector-size stratum s, we determine the actual insolvency rates, IR actuals, observed following the COVID-19 outbreak. For our computation, we only consider corporations whose credit ratings have been updated as of April 1, 2020.6 IRsactual=NsinsolventNs
The matched pre-crisis sample comprises at least k nearest neighbors for each firm observed during the disaster stage, allowing us to estimate counterfactuals or hypothetical insolvency rates, IRscounterfactual as follows.7 IRscounterfactual=∑j=1N¯swj,s1fj,t+4=1∑j=1N¯swj,s
With N~s=∑j=1N~swj,s as the amount of harmonized explanations from the period before crises for band s. wj,s is the weight allocated to before crises observation j imitating how frequently j is preferred as control observation in the alike procedure and 1fj,t+4=1 equals 1 if control observation j filed for insolvency at greatest 4 months later its last grade update and 0 else.
A sector-size specific estimate of the insolvency gap, IGs, can be obtained by comparing actual and counter factual insolvency rates for every sector-size stratum.8 IGs=IRscounterfactual-IRsactual.
It is a way of assessing how far reported insolvencies through the COVID-19 differ from the hypothetical insolvencies that would have occurred without policy intervention before the crisis. The insolvency gap measures comparing real insolvency rates with counterfactual ones. Figure 1 shows them side by side, while Table 1 shows the estimated insolvency gaps by sector size and the statistical significance. There is a slew of takeaways to be had from that.Fig. 1 Actual insolvency rates against counterfactual insolvency rates
Table 1 Outcome analysis: insolvency gap estimation results
Sector affiliation Size of company
Micro Small Medium Large
IGs IGs IGs IGs
Lodging and cookery + 0.0028 + 0.0115*** 0 +0.0005
Carriage and logistics + 0.0030 + 0.007*** 0 +0.0002
Retail and wholesale business + 0.0001 + 0.0107*** − 0.0006 + 0.0004
Developed − 0.0004 + 0.0103*** − 0.0035 + 0.0002
Services relevant to business − 0.0005 + 0.0070 0 +0.0001
Innovative and entertaining industry 0 + 0.0012 0 + 0.0017
Production of food − 0.0019 + 0.0027*** − 0.0105 + 0.0024
Well-being and societal services − 0.0011 + 0.0037*** − 0.0004 +0.0005
Banking and insurance 0 + 0.0037*** 0 0
Please keep in mind that the significance thresholds are as follows: *p = 0.10, **p = 0.05, ***p = 0.01. Based on Rao-Scott’s corrections to the 2 statistics, statistical significance is determined using the 2-test for equal insolvency parts in the real and counterfactual samples.
First and foremost, it is clear that insolvency rates are higher among micro-enterprises in practically every sector. According to our survey results, insolvency rates are highest among micro-enterprises in the industry’s most hard hit by the economic crisis. Of the lodging and catering sector, 1.11% is insolvent. In comparison, 0.94% of the logistics and transportation sector is insolvent, and 0.76% of the creative industries and entertainment sector is insolvent, according to the latest data. These findings are in line with what we learned through the survey. All sectors of micro-enterprises are found to have higher than expected insolvency rates, and this disparity is statistically significant for the majority of these businesses. All sectors of micro-enterprises have an average insolvency gap of 0.80%, which is significant when matched to the pre-crisis total bankruptcy rate of 1.05%.
When it comes to actual and counterfactual insolvency rates, we detect comparable patterns among small businesses, although at a lower scale. In reality, Table 1 shows that expected rates in most sectors are higher than actual rates for small businesses, but this discrepancy is not statistically significant in any of the sectors examined therein. Small enterprises have an insolvency gap of about 0.03 percentage points on average.
The patterns identified in the lesser-size classes begin to fade away as we get into medium-sized businesses. Adjustment and cooking, as well as logistics and transportation, are two of the worst-hit sectors in terms of predicted insolvency rates, but the insolvency gap (i.e., the difference in insolvency rates) is statistically insignificant. For the rest of the economy, things are a little more muddled. Some insolvencies occurred in two industries (mechanical production and food production), although the counterfactual scenario predicted nearly none of these events. Actual and counterfactual rates are nearly identical in every other industry. There are no statistically significant changes between the sectors (save for mechanical engineering).
In the end, the patterns entirely break down for the group of large companies. There are almost no insolvency filings in either the crisis or the counterfactual context. According to our findings, insolvencies among large firms are exceedingly infrequent. Food production and the trade of data processing kit are the two industries with the highest real insolvency rates. Because there is only one insolvency for which there is no before crises control observation with similar financial features, both situations are unique. As a result, one must be careful when decoding the outcomes of the huge class size.
There is a huge backlog of insolvencies in the micro-enterprise sector based on the fact that counterfactual insolvency rates consistently and, in many areas, greatly surpass reality rates. We hypothesize that Germany’s fiscal policy reaction to the pandemic crisis excessively preferred the existence of smaller enterprises, which is consistent with our findings. Particularly, micro-enterprises continued in business due to a transitory shift in Germany’s insolvency regime and a large level of liquidity assistance. We suggest that using the temporary suspension of the duty to file for insolvencies as a loophole to avoid insolvency procedures has been particularly easy for smaller enterprises. Smaller companies have fewer disclosure obligations, making it more difficult for policymakers to compel them to file for bankruptcy protection. If the non-filing firm does not meet the eligibility criteria for the deferment, it allows these firms to collect state subsidies further. This is particularly troublesome. It has also been possible for smaller enterprises to bridge falling revenues by the early establishment of direct and indirect liquidity, which would otherwise have been forced to leave the market due to illiquidity.
There are several ways in which the insolvency gap estimations can be aggregated and converted into a single number that can be used to comprehend the magnitude of the insolvency gap better. The bankruptcy gap amounts to around 25,000 enterprises based on the number of inexpensively active German businesses. In addition, the figure exposes two other aspects. As can be seen from the time series, during the most recent economic shock, the Great Recession of 2008–2009, the number of bankruptcies surged significantly, consistent with the Schumpeterian cleansing device. Second, the real number of business insolvencies has decreased compared to the Great Recession throughout the current crisis. Large-scale government support programs have distorted company dynamics, as seen by the fewer bankruptcy filings during economic crises than during non-crisis times. Indeed, German policy measures have saved a considerable number of enterprises from going bankrupt. Which companies were spared bankruptcy is of critical importance? This subject is further narrowed down in the following part by taking into account the pre-crisis financial position of the firms.
Results and discussion
As a result of the ongoing COVID-19 situation, the policy now plays an important role in mitigating the negative economic effects that many businesses are experiencing. Liquidity subsidies and loan guarantees have proven essential in the near term in order to keep enterprises facing severe liquidity constraints from going bankrupt. An insolvency suspension was coupled with liquidity subsidies in Germany, a country where fiscal policy was vital in lessening the crisis impact. Despite their differing designs, both policies aim to prevent an unprecedented number of corporate insolvencies. Analyzing German policy, it is clear that a number of aids programs were either specifically intended to save slighter businesses or at the very least indirectly encouraged the existence of exceptionally tiny innovative businesses. According to our hypothesis, the COVID-19 policy reaction has led to a large backlog of insolvencies among small- and medium-sized businesses. If, on the other hand, support systems delay or even prohibit the departure of economically fragile SMEs, there is a risk of long-term negative repercussions on the economy. It is expected that early liquidity concerns will lead to an erosion of enterprises' equity in the current crisis. Over-indebted companies should not have their bankruptcy proceedings postponed for an extended period, as doing so not only denies reality but also impedes the efficient reallocation of resources. Crises in the economy act as clearing mechanisms to free up resources that would otherwise be squandered on unproductive and uninspiring businesses. Smaller businesses have been disproportionately targeted by the German government’s early policy response, which was implemented without screening processes.
Data on factors unique to a company can be found in Table 2. This chart shows each industry’s yearly weighted average (a percentage of an asset's value) from 2001 to 2019. When it comes to wholesale and retail, the WC/TA is at its greatest because of stocking needs (25% of total assets). Following manufacturing enterprises are mining and construction firms. Of a company’s total assets, 18% and 15% are invested in working capital. A smaller proportional investment in working capital is required in the industrial sector since it is more likely to hold inventories than the retail sector. While manufacturing has a RE/TA of just 12.4%, wholesale and retail sectors have a 30.2% risk absorption capability. At 21.5%, service industries’ operational return on assets is the highest; retail has the lowest ROA at 4.1%. The manufacturing sector’s mining, construction, and chemical companies had TL/TA ratios of 61% on average. When it comes to debt payback, wholesale and retail have the lowest FFO/Debt ratio of 48%. An FFO/debt ratio of 20% is the highest in agriculture, forestry, and fishing. The FFO/cash interest ratio and the EBITDA/interest ratio are five and seven times, respectively, for wholesale and retail enterprises. Table 3 displays the relationship between these variables.Table 2 Descriptive statistics
Production Utilities Construction, mining, and chemicals Retail and wholesale Agriculture, forestry, and fishing Services
WC/TA Mean 167 116 199 275 155 108
SD 28 15 26 44 21 18
Skewness 410 499 499 410 499 410
Kurtosis 47 219 100 88 42 47
JB Stats 52,900 81,887 78,921 53,333 78,266 52,899
RE/TA Mean 136 169 231 332 166 139
SD 16.5 39 53 40 22 16
Skewness 560 289 289 560 499 560
Kurtosis 108 94 26 25 88 13
JB Stats 98,996 26,872 26,247 98,177 78,737 98,143
EBIT/TA Mean 103 65 71 45 82 236
SD 28 46 19 5 58 27
Skewness 251 94 2501 560 93 560
Kurtosis 65 4 63 3 10 104.5
JB Stats 19,990 2735 19,970 98,130 2740 98,981
MVE/BD Mean 2374 4622 5632 6702 3415 5586
SD 161 336 409 453 899 572
Skewness 487 454 454 487 125 322
Kurtosis 8 0 2 2 7 0
JB Stats 74,446 64,657 64,657 74,442 4918 32,532
NI/TA Mean 56 34 39 24 34 176
SD 11 6 7 6 2 29
Skewness 350 404 403 289 910 404
Kurtosis 1 9 11 0 0 11
JB Stats 38,206 51,013 51,016 26,193 258,625 51,017
TL/TA Mean 594 462 671 570 595 442
SD 20 30 45 42 20 32
Skewness 979 513 487 454 979 454
Kurtosis 3 39 59 66 3 2
JB Stats 299,879 82,544 74,720 64,993 299,878 64,660
FFO/debt Mean 349 443 352 532 221 352
SD 39 89 38 121 42 80.3
Skewness 606 328 606 289 350 289
Kurtosis 9 10 2 7 0 6
JB Stats 115,044 33,685 115,038 26,196 38,206 26,195
FOCF to debt Mean 277 331 264 451 165 308
SD 43 12 10 69 27 51
Skewness 429 1756 1756 429 404 404
Kurtosis 0 1 2 11 11 8
JB Stats 57,653 963,587 9,635,867 57,662 51,017 51,012
FFO/cash interest Mean 4349 4512 2971 5556 2311 4951
SD 1421 2332 971 1959 527 2560
Skewness 202 128 202 187 289 128
Kurtosis 2.2 2 34 4 7 2
JB Stats 12,762 5100 12,853 10,965 26,198 5100
EBITDA/interest Mean 5529 7485 3433 7718 4291 6603
SD 1498 2264 1038 1552 979 1998
Skewness 243 218 218 327.8 289 218
Kurtosis 3 7 3 3 1 2
JB Stats 18,554 14,897 14,894 33,680 26,193 14,894
Author calculation. Standard deviation is denoted by Std Dev, excess Kurtosis is denoted by Kurtosis, and JB denotes Jarque–Bera Stats
Table 3 Selected financial ratios’ correlation matrix
WC/TA RE/TA EBIT/TA MVE/BD NI/TA TL/TA FFO/debt FOCF to debt FFO/cash interest
RE/TA 0.1042
EBIT/TA 0.0604 0.0755
MVE/BD 0.2739 0.0784 0.0678
NI/TA 0.1745 0.0658 0.0775 0.0692
TL/TA 0.0275 0.0181 0.0254 0.0955 0.2557
FFO/debt 0.1571 0.0781 0.0351 0.2157 0.1741 0.1305
FOCF to debt 0.0569 0.0909 0.0497 0.1030 0.2483 0.1589 0.0423
FFO/cash interest 0.2726 0.0811 0.0722 0.2030 0.1569 0.1603 0.1061 0.0463
EBITDA/interest 0.0178 0.0339 0.0232 0.0384 0.0592 0.0536 0.1357 0.319 0.1569
Author calculation
Panel A of Table 4 uses Eq. 6 to determine the sensitivity of sales to expenditures, current liabilities, current assets, and capital assets. According to the fixed effects regression results, all sectors’ sensitivities range from 95 to 999%. In terms of total revenues, wholesale and retail expenses fluctuate by 85.1%, followed by manufacturing (75.1%) and services (56.1%). Manufacturing and services are the least sensitive to changes in sales. We estimate a maximum sensitivity of 91.5% and 85.1% for current asset estimates with wholesale, retail, and manufacturing. This is to be expected, given the company’s heavy reliance on inventory, the value of which varies with manufacturing and wholesale sales.Table 4 Sales and forecast accuracy are linked to varying levels of sensitivity
Manufacturing Utilities Mining, construction and chemicals Wholesale and retail Agriculture, forestry and fishing Services
Panel A
Eopex 7995** 74,798** 68,381*** 89,401** 7260*** 58,954***
ECA 89,379** 748,175** 71,878*** 96,025*** 5380.2** 43,317**
ECL 88,330** 79,186** 7535** 858*** 6109** 4733***
EFA 225*** 1496** 259** 207*** 130** 25**
Within sample forecast accuracy
Panel B — RMSE
Eopex 0.00742% 0.00549% 0.00406% 0.00300% 0.00222% 0.00165%
ECA 0.00121% 0.00406% 0.00300% 0.00222% 0.00165% 0.00121%
ECL 0.00090% 0.00067% 0.00049% 0.00036% 0.00027% 0.00019%
EFA 0.00062% 0.00046% 0.00033% 0.00025% 0.00018% 0.00013%
Panel C — MSE
Eopex 0.00352% 0.00260% 0.00192% 0.00143% 0.00105% 0.00078%
ECA 0.00057% 0.00192% 0.00142% 0.00105% 0.00077% 0.00057%
ECL 0.00043% 0.00032% 0.00023% 0.00016% 0.00013% 0.00009%
EFA 0.00029% 0.00076% 0.00055% 0.00011% 0.00042% 0.00006%
Panel D — MAE
Eopex 0.00285% 0.00211% 0.00156% 0.00115% 0.00086% 0.00063%
ECA 0.00047% 0.00156% 0.00115% 0.00086% 0.00063% 0.00047%
ECL 0.00034% 0.00025% 0.00018% 0.00014% 0.00011% 0.00007%
EFA 0.00024% 0.00062% 0.00045% 0.00009% 0.00034% 0.00005%
Panel E — MAPE
Eopex 0.00502% 0.00372% 0.00275% 0.00203% 0.00151% 0.00111%
ECA 0.00082% 0.00275% 0.00203% 0.00151% 0.00111% 0.00082%
ECL 0.00061% 0.00045% 0.00033% 0.00025% 0.00018% 0.00014%
EFA 0.00042% 0.00031% 0.00023% 0.00017% 0.00013% 0.00009%
Panel F — RMSE
Eopex 0.00236% 0.00205% 0.00201% 0.00191% 0.00100% 0.00077%
ECA 0.00039% 0.00152% 0.00149% 0.00141% 0.00074% 0.00057%
ECL 0.00029% 0.00025% 0.00024% 0.00023% 0.00012% 0.00009%
EFA 0.00020% 0.00017% 0.00017% 0.00016% 0.00008% 0.00006%
Panel G — MSE
Eopex 0.00112% 0.00097% 0.00095% 0.00090% 0.00047% 0.00037%
ECA 0.00018% 0.00072% 0.00071% 0.00067% 0.00035% 0.00027%
ECL 0.00014% 0.00012% 0.00012% 0.00011% 0.00006% 0.00004%
EFA 0.00009% 0.00029% 0.00028% 0.00008% 0.00019% 0.00003%
Panel H — MAE
Eopex 0.00091% 0.00079% 0.00077% 0.00073% 0.00038% 0.00030%
ECA 0.00015% 0.00059% 0.00057% 0.00054% 0.00028% 0.00022%
ECL 0.00011% 0.00010% 0.00009% 0.00009% 0.00005% 0.00004%
EFA 0.00008% 0.00023% 0.00023% 0.00006% 0.00015% 0.00002%
Panel I — MAPE
Eopex 0.00167% 0.00146% 0.00143% 0.00135% 0.00071% 0.00055%
ECA 0.00027% 0.00108% 0.00106% 0.00100% 0.00052% 0.00041%
ECL 0.00020% 0.00018% 0.00017% 0.00016% 0.00009% 0.00007%
EFA 0.00014% 0.00012% 0.00012% 0.00011% 0.00006%
Author calculation
There are also many receivable accounts in the industrial sector. Due to their greater trade payables, manufacturing companies have the highest sensitivity to current liabilities. The smallest coefficient is found in the service industry, where the change is only 41.2%. Manufacturing companies have the highest fixed asset elasticity, which is understandable given their capacity constraints and need for additional investments to maintain sales—manufacturing companies. On average, the fixed assets of manufacturing companies will increase by 2% in response to higher revenues. Projection accuracy in a sample is shown in panels B through E. Our sensitivities were tested with the use of this method. There is no difference between mean squared error (MSE), root means square error (RMSE), mean fundamental percentage error (MAPE), and mean absolute error (MAE). The projected values of all four measures imply that the forecasts for the four elasticities will be extremely accurate. Panels F through I display the out-of-sample forecast figures. The validity of our findings is further supported by out-of-sample statistics, such as within-sample forecasting. It is possible that COVID-19’s changing business dynamics could cause a structural break in our data. Model estimate during changing market cycles relies heavily on structural stability, as demonstrated in works like these (Khalid et al. 2021; Lahr et al. 2022; Mundle and Sahu 2021; Riza and Wiriyanata 2021). We cannot do a complete structural break analysis using the two-quarters post-COVID-19 firm-level available data (Wang and Zhang 2021b). Liu et al. (2022b) and our team applied this method to ensure that our rapid evaluation had the same solidity as theirs; Stiglitz (2021) and Zhang et al. (2021) are the primary sources for this method. Panel data with non-linearity, opacity, and bridge dependence is included because it is considered robust compared to other hypotheses (Sadiq et al. 2021). Table 5 provides the SPSM estimates from Su and Urban (2021), which show that our elasticity coefficients are consistent with the SPSM results. Firm data after COVID-19 may suggest structural break has had no significant influence, but we must stress the limitations of post-COVID data (Table 5).Table 5 SPSM results
I(0) series FAE tAEas tNL
Eopex 71,315** 29,125** 41,325**
ECA 101,855*** 42,056*** 32,940**
ECL 51,256** 33,660*** 31,015**
EFA 80,125*** 31,255*** 27,050***
Author calculation
COVID-19 outbreak’s influence on firm liquidity is highly sectorial. Firms in the accommodation and food service activities and transportation sectors are predicted to face liquidity shortages without policy intervention. The information and communication and the professional services sectors share illiquid firms consistently lower than 20% in our sample (Fig. 2; Table 6). Firms with a higher concentration of intangible assets or less reliance on external financing, as indicated in Fig. 2’s right panel, are better prepared to weather the crisis than those with more tangible assets or greater dependence on external financing. With their unique financial structure, defined by bigger cash buffers in normal time, and their diverse ability to rely on modern technology and teleworking arrangements, intangible-intensive enterprises are less vulnerable to sales shocks.Fig. 2 The effect of COVID-19
Table 6 Scenario-based market-based predictive analytics
Manufacturing Utilities Mining, construction, and chemicals Wholesale and retail Agriculture, forestry, and fishing Services
Base case end of 2019
Max 0.3181 0.2103 0.2365 0.2598 0.2233 0.1845
Average 0.1208 0.0601 0.1252 0.0515 0.0315 0.0558
Falling-off in market cap by 15%
Max 0.5003 0.4172 0.3122 0.3384 0.3131 0.2310
Mean 0.1572 0.0790 0.2473 0.1252 0.0570 0.1082
Falling-off in market cap by 30%
Max 0.8042 0.7854 0.4353 0.5865 0.4952 0.2421
Mean 0.2792 0.1367 0.3872 0.1981 0.1058 0.1524
Falling-off in market cap by 45%
Max 0.9264 0.8641 0.6599 0.6281 0.7786 0.3656
Mean 0.3773 0.1969 0.5681 0.2875 0.1608 0.1862
Author calculation
COVID-19 utility effects
All sectors saw an increase in the likelihood of default as a result of COVID-19 in the stress scenarios. However, the market value of the average mining business has dropped by 15% to $24.7 billion, while the PD of the retail sector has increased to 12.5%. Manufacturing firms’ productivity has risen by 13% since last year. A further reduction in market capitalization to 30% has a greater impact on solvency. There are PDs of 38.7%, 27.9%, 19.8%, and 15.2% in the mining, manufacturing, retail, and service industries, respectively. Mining has a PD of 56.8%, manufacturing a PD of 37.7%, retail a PD of 28.7%, and utilities have a PD of 19.7% with a 45% reduction in market capitalization. Retail, mining, and manufacturing companies are all at risk. Solvency is an issue as market capitalization drops, according to these data. In the primary and stress scenarios, the utilities and services companies indicate moderate problems with 15% and 30% market value declines, respectively. It is possible these corporations could default in the worst-case situation.
According to Eqs. 2 to 5, Table 7 offers PD estimations. The findings of the Altman Zand Ohlson O scores are identical. Ohlson OO’s PDs are larger than ZZ’s because of the estimation method. Base case scenarios show that the PD for mining and construction companies is 13% (15% from Ohlson O), while the PD for manufacturing companies is 11.5%. The retail price is being sold at 5.7%, which is in line with market-based default forecasts. Stress model COVID-19 and susceptibility listed in Table 7 suggested a high probability of failure in all industries. The average product differentiation (PD) is around 17% in the manufacturing industry. Sales will be down by 25% as a result of this. A 50% or a 75% drop in sales will result in a PD of 22.4%, while a PD of 43.44% is required. Average PDs rise to 17%, 28%, and 40% in the mining industry in three sales situations. Services, agriculture, forestry, utility, retail, and wholesale round out the list of industries. When doing our PD-based research, we drew on market and accounting data. Measurements create an intriguing contrast. In market-based PD volatile, the wholesale and retail sections are more efficient than their accounting counterparts. This discrepancy can be attributed to an underlying solid position that the equity market may not have adequately valued. There is, however, evidence of a worsening solvency profile across the board.Table 7 Probabilities of bankrupt or default results of Z and O indicators
Manufacturing Utilities Mining, construction, and chemicals Wholesale and retail Agriculture, forestry, and fishing Services
Base cases as of 2019
PD (Z) Max 0.3544 0.2216 0.2575 0.2360 0.2237 0.1569
Mean 0.1211 0.0545 0.1369 0.0599 0.0706 0.0439
PD(O) Max 0.4692 0.2519 0.3087 0.2524 0.2954 0.2040
Mean 0.1381 0.0805 0.1590 0.0914 0.1031 0.0692
Sales falling-off 25%
PD (Z) Max 0.5794 0.2501 0.2635 0.2680 0.2656 0.1972
Mean 0.1726 0.0955 0.1703 0.0961 0.0943 0.0864
PD(O) Max 0.6517 0.3159 0.3558 0.3155 0.3144 0.2345
Mean 0.1912 0.1032 0.1995 0.1160 0.1231 0.1089
Sales falling-off 50%
PD (Z) Max 0.6489 0.3640 0.4226 0.2951 0.3092 0.2431
Mean 0.2244 0.1284 0.2872 0.1369 0.1621 0.1372
PD(O) Max 0.7829 0.4123 0.5276 0.3975 0.3746 0.3017
Mean 0.2570 0.1661 0.2449 0.2078 0.1518 0.1489
Sales falling-off 75%
PD (Z) Max 0.9017 0.4884 0.6725 0.3374 0.5009 0.3573
Mean 0.4559 0.2629 0.4227 0.1888 0.2316 0.2260
PD(O) Max 0.9662 0.5458 0.7519 0.4656 0.5348 0.4066
Mean 0.5829 0.2804 0.6080 0.2536 0.2197
Author calculation
Result in liquidity asset or cash flow sufficiency
A cash flow validation study is depicted in Table 8 and Fig. 3. There are a lot of cash flows because of the shorter cash cycles. As a result, the wholesale and retail industries frequently dominate financial leverage recovery and coverage. Utilities, manufacturing, and service industries all have creditworthiness in normal circumstances. Since the COVID-19 plan was adopted, the working capital ratio has dropped dramatically. Even though revenues fell by 75%, the FOCF’s manufacturing debt ratio dropped to 9.5% in response (35.1%). The utility sector’s FOCF debt-to-GDP ratio is expected to shrink from 42.1 to 9.7%, a significant reduction. According to mining businesses, sales will fall by 29 to 6% in the most basic case. In the worst-case scenario, wholesalers’ and retailers’ FOCF debt ratios fall from 42.8 to 9%. There is a significant falling-off in EBITDA obligations and FFO cash interest at all stress levels.Table 8 Cash flow suitability
Production Utilities Construction, mining, and compounds Retail and wholesale Farming, fisheries, and forestry Services
FFO/debt 3689 4429 3123 5269 2113 3223
FOCF to debt 3025 3742 2205 4500 1623 2523
FFO/cash interest 30,179 40,919 26,106 52,185 20,089 42,315
EBITDA/interest 32,841 54,209 22,506 81,248 38,201 51,193
Sales falling-off by 25%
FFO/debt 0.2560 0.3365 0.2545 0.3585 0.897 0.2250
FOCF to debt 0.1960 0.2007 0.1372 0.1853 0.1324 0.1883
FFO/cash interest 11,947 31,397 18,965 22,303 15,812 35,226
EBITDA/interest 12,090 38,575 17,820 51,193 15,496 42,315
Sales falling-off by 50%
FFO/debt 0.1715 0.2282 0.1682 0.2149 0.1252 0.1435
FOCF to debt 0.1274 0.1304 0.0892 0.1204 0.0861 0.1224
FFO/cash interest 0.7168 18,838 11,379 13,382 0.9487 21,136
EBITDA/interest 10,881 34,718 16,038 46,074 13,946 38,084
Sales falling-off by 75%
FFO/debt 0.1029 0.1369 0.1009 0.1289 0.0751 0.0861
FOCF to debt 0.0956 0.0978 0.0669 0.0903 0.0645 0.0857
FFO/cash interest 0.5735 15,070 0.9103 10,705 0.7590 16,908
EBITDA/interest 0.9793 31,246 14,434 41,466 12,552 3.42
Author calculation
Fig. 3 Cash flow sufficiency with all models’ results
According to our results, major EU industries have found significant cash flow suitability and solvency problems. There must be an immediate legislative response to COVID-19 to prevent a global collapse. For each of the three proposed techniques, the country-by-country results are summarized in Table 9. Programmers’ effectiveness in reducing the chance of bankruptcy (money-based), debt compensation, and coverage at levels similar to those seen before COVID-19 in 2019 was evaluated in this study. If the income loss is limited to 25%, the optimal solution is to stop taxes within the EU. Of Irish enterprises, 74% will be able to sustain PD levels previous to COVID-19 as a result of this change. Because of the drop-in taxes, Serbian enterprises may maintain their current default rate of 52%. (minimum). Sales are reduced by less than 25%, and no significant capital expenditures are required to make up for the loss in revenue.Table 9 Policy interventions’ effects on a country’s population
Deferred tax Subordinated loan Equity support
25% 50% 75% 25% 50% 75% 25% 50% 75%
EU countries Pd (Z) and Pd (O)
Spain 6600 3300 1100 3300 5500 2200 1100 2200 7700
Sweden 5830 2483 1039 3850 5488 5599 1320 3029 4362
Austria 7370 3138 1315 3080 4390 4479 550 3472 5206
Germany 7700 3278 1373 2200 3136 3199 1100 4586 6427
Belgium 6050 2576 1079 3410 4861 4959 1540 3563 4962
Poland 7370 3138 1314 3300 4704 4799 330 3158 4887
Romania 6930 2950 1235 970 4234 4319 1100 3816 50,445
Portugal 6380 2717 1137 3300 4704 4799 1320 3579 5063
Italy 5830 2483 1039 3080 4390 4479 2090 4127 5481
Ireland 8140 3466 1452 2200 3136 3199 660 4398 6349
Netherlands 7150 3045 1275 2200 3136 3199 1650 4819 6526
Switzerland 6710 2857 1197 2750 3920 3100 1540 4223 5805
France 7040 2998 1255 1650 2352 2399 2310 5651 7345
Serbia 6600 2811 1177 2860 4077 4159 1540 4113 5664
Denmark 5720 2435 1019 3740 5332 5438 1540 3233 4541
EU countries Debt payback and coverage
Spain 6938 1834 1014 3307 8611 1787 755 557 8198
Sweden 6129 1379 958 3859 8592 4550 1013 1030 5492
Austria 7747 1744 1211 3087 6874 3640 166 2384 6149
Germany 8095 1822 1265 2204 4909 2599 701 4269 7135
Belgium 6360 1431 994 3418 7610 4029 1222 1959 5976
Poland 7747 1744 1211 3307 7364 3899 0055 1892 5889
Romania 7285 1639 1139 2977 6627 3509 738 2732 6351
Portugal 6707 1509 048 3307 7364 3899 986 2126 6052
Italy 6129 1379 958 3087 6874 3640 1784 2748 6403
Ireland 8557 1925 1338 2205 4909 2599 238 4165 7063
Netherlands 7516 1692 1175 2205 4909 2599 1279 4399 7226
Switzerland 7054 1587 1102 2756 6137 3249 1190 3276 6648
France 7401 1665 1157 1653 3682 1949 1946 5653 7894
Serbia 6938 1561 1085 2867 6382 3379 1196 3056 6536
Denmark 6013 1353 941 3748 8347 4420 1239 1300 5641
As a result, subordinated loans and equity infusions are preferable when revenue declines by 50% or 75%. Firms in Spain (maximum) and Austria (minimum) will be able to recoup up to 50% and 21% of their income, respectively, using subordinated loans. 51.3% and 67% of Austrian businesses will need equity help if revenues fall by 50 to 75%. Spanish companies seeking equity participation may reach 70% in the worst-case scenario. If sales are likely to decline by 50% to 75% in other EU nations, a combination of subordinated debt and equity will be required. Tax deferral can help offset a 25% drop in sales by deferring debt repayment and covering payments. In addition, any further reduction will necessitate a hybrid reaction.
EU temporary capital relief in response to COVID-19 pandemic
As we shift our attention to policy measures, we find the Stringency Index and the Containment Index, two indices that rate pandemic response efforts in areas other than economics. They are highly connected (always above 0.87) across all three time periods. As a result of these variances, the two indices are ranked differently among nations in terms of their relative positions with each other. In comparison to constraints, a greater Stringency Index implies a dependence on testing and tracing rather than prohibitions (e.g., school and work closures, lockdowns). In contrast, countries that have a higher Containment Index than their Stringency score are less stringent than those that do not. Most countries in the periphery are clustered in the upper right quadrant, which indicates that both Indices are at higher levels. At the same time, Southern Member States have the most overshooting in terms of stringency, with Spain showing the widest 9-point disparity, with the lone exception of Malta, which has a lower Containment level. According to this conclusion, stringency and other measures (e.g., healthcare-based) may have a synergistic effect on reducing the use of limitations.
The Stringency Index, which includes expensive measures like lockdowns and closures, is the most useful metric for tracking outbreaks and reactions to uncover likely perpetrators of varied economic consequences that cannot be related to the strength of the pandemic. Restriction measures in the periphery are far higher than in the core. In other words, there are over seven measures at one level with greater restriction (e.g., suggested closures vs. required closures) or even larger gaps (e.g., no limitations vs. required closures) in a smaller number of indicators, resulting in a considerable discrepancy of 7.5 points. There is a widening chasm for all three time periods, with the first wave having a marginally greater gap, which only narrows to 6.6 points in the second wave. Increased restrictions are not just for the initial wave or even for countries at a disadvantage because of the outbreak’s severity, so they are not just for responding to the emergency. Each country’s overall Stringency Index is broken down into core and peripheral. The periphery is over-represented among nations with high closures and mild outbreaks when comparing the three dimensions of cases, deaths, and the Stringency Index. Only in core countries does the reverse dynamic arise. However, no country in the periphery falls below the mean for limitations, regardless of pandemic levels.
Conclusion
First responses to the pandemic in health care, job protection, and pensions by European countries (and the EU) have been discussed in this article. COVID-19 socio-economic repercussions have yet to be studied in-depth, but we have looked for any signs of neoliberal change in Europe’s recent decades. A slew of new developments has surfaced. Thought leaders, as well as policymakers, have pushed new ideas and agendas. Economic experts from the mainstream have emphasized the importance of greater public involvement in social and employment policy and increased healthcare and workplace safety funding. At the same time, political leaders have articulated the lessons to be gained from the crisis in favor of initiatives to combat inequality and cut back on spending. As a result, European policymakers have expanded public spending by deploying short-term measures and longer-term reforms in the policy. Public spending on health care has increased as a result of additional investments in infrastructure and staff. European countries’ statutory healthcare systems now cover treatment and vaccination for COVID-19. Most countries have enhanced current programs by reducing eligibility, duration, and payment conditions, and others have implemented new programs. Benefits have been enhanced while new worker groups have been covered. Major reforms prompted by cost containment in the pension scheme have been put on hold. In contrast, governments have made ad hoc improvements (such as minimum old-age protection) and — at least in certain nations — greater chances for early retirement. More money is being spent, the state is taking on a larger role, and efforts are being made to provide a wider range of security. Neoliberalism’s core ideals are at odds with all of this.
As discussed in the preceding sections, the EU has made significant advancements. As part of the policies under review, the European Union has established new programs, including EU4Health in health care and SURE in job protection. Some policy changes in the EU member states have resulted from the suspension of the Stability and Growth Pact, reductions in the European Semester’s recommendations, and the Recovery Plan’s altered goals.
There is, however, no proof of a paradigm shifts from the data and information offered here. There is a need for more comprehensive and structured information. There is a chance that the modifications described in the preceding sections are only temporary. There are a number of new programs, particularly at the EU level, that are explicitly designed. Policymakers could return to more liberal economic policies after the crisis is resolved. After the Great Recession, this was already the reality. In addition, the economic framework in which conceptual and policy changes have occurred may also vanish. Rising inflation and interest rates may again bring public debt into focus.
Because of these and other reasons, we need more time to thoroughly evaluate the new legislation that has been passed thus far. In order to avoid a return to neoliberal policies, policymakers must be able to maintain or even strengthen the new trend. As a final thought, referencing ideas and policies in this piece simultaneously reminds us of the need to look at correlations across large-scale crises and subsequent change. It appears that intellectuals and political leaders have been open to new ideas and new circumstances, as evidenced by the data presented in this article. To address what they regard as failures, they have shown that they are a little more willing to alter their discourses. Research into the link between policymakers’ views of the crisis and their ability to generate fresh suggestions is an area that has great potential.
Author contribution
Renzao Lin and Xianchang Liu: conceptualization, data curation, methodology, writing — original draft, data curation, visualization. Ying Liang: supervision, visualization, editing, and software.
Data availability
The data can be available on request.
Declarations
Ethics approval and consent to participate
We declare that we have no human participants, human data or human tissues.
Consent for publication
N/A.
Competing interest
The authors declare no competing interests.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Afik Z Arad O Galil K Using Merton model for default prediction: an empirical assessment of selected alternatives J Empir Financ 2016 35 43 67 10.1016/j.jempfin.2015.09.004
Afzal A Mirza N The determinants of interest rate spreads in Pakistan’s commercial banking sector SSRN Electron J 2012 25 987 1004 10.2139/ssrn.1653875
Asikha M Alam M Al-amin AQ Global economic crisis, energy use, CO2 emissions, and policy roadmap amid COVID-19 Sustain Prod Consum 2021 26 770 781 10.1016/j.spc.2020.12.029 33786357
Astawa IP, Astara IWW, Mudana IG, Dwiatmadja C (2021) Managing sustainable microfinance institutions in the Covid-19 situation through revitalizing balinese cultural identity. Qual-Access to Success 22:131–137. 10.47750/qas/22.184.17
Attaoui S Poncet P Capital structure and debt priority Financ Manag 2013 42 737 775 10.1111/fima.12011
Barrero JM Bloom N Davis SJ Meyer BH COVID-19 is a persistent reallocation shock AEA Pap Proc 2021 111 287 291 10.1257/pandp.20211110
Bashir MF Ma B Bashir MA Radulescu M Shahzad U Investigating the role of environmental taxes and regulations for renewable energy consumption: evidence from developed economies Econ Res Istraz 2022 35 1262 1284 10.1080/1331677X.2021.1962383
Biswas D Chatterjee S Sultana P Stigma and fear during COVID-19: essentializing religion in an Indian context Humanit Soc Sci Commun 2021 8 130 10.1057/s41599-021-00808-8
Caballero-Morales SO Innovation as recovery strategy for SMEs in emerging economies during the COVID-19 pandemic Res Int Bus Financ 2021 57 101396 10.1016/j.ribaf.2021.101396
Can U Can ZG Bocuoglu ME Dogru ME The effectiveness of the post-Covid-19 recovery policies: evidence from a simulated DSGE model for Turkey Econ Anal Policy 2021 71 694 708 10.1016/j.eap.2021.07.006
Chau KY Law KMY Tang YM Impact of self-directed learning and educational technology readiness on synchronous e-learning J Organ End User Comput 2021 33 1 20 10.4018/joeuc.20211101.oa26
Cirera X Cruz M Davies E Grover A Iacovone L Cordova JEL Medvedev D Maduko FO Nayyar G Reyes Ortega S Torres J Policies to support businesses through the COVID-19 shock: a firm level perspective World Bank Res Obs 2021 36 41 66 10.1093/wbro/lkab001
Cowling M Brown R Rocha A Did you save some cash for a rainy COVID-19 day? The crisis and SMEs Int Small Bus J Res Entrep 2020 38 593 604 10.1177/0266242620945102
D’Adamo I Gastaldi M Morone P The post COVID-19 green recovery in practice: assessing the profitability of a policy proposal on residential photovoltaic plants Energy Policy 2020 147 111910 10.1016/j.enpol.2020.111910 32989340
Dörr JO Licht G Murmann S Small firms and the COVID-19 insolvency gap Small Bus Econ 2022 58 887 917 10.1007/s11187-021-00514-4
Du L Razzaq A Waqas M The impact of COVID-19 on small- and medium-sized enterprises (SMEs): empirical evidence for green economic implications Environ Sci Pollut Res 2022 10.1007/s11356-022-22221-7
EC (2021) European Commission: facts and figures | Policy Commons [WWW Document]. Policy Common. https://policycommons.net/artifacts/1426528/european-commission/2040971/. Accessed 5.15.22
Edomah N Ndulue G Energy transition in a lockdown: an analysis of the impact of COVID-19 on changes in electricity demand in Lagos Nigeria Glob Transitions 2020 2 127 137 10.1016/j.glt.2020.07.002
Gharghori P Chan H Faff R Default risk and equity returns: Australian evidence Pacific Basin Financ J 2009 17 580 593 10.1016/j.pacfin.2009.03.001
Gourinchas P-O, Kalemli-Ozcan S, Penchiakova V, Sander N (2022) Estimating SME failures in real time: an application to the Covid-19 crisis. NBER Work Pap Ser 42. 10.3386/W27877
Grundke P Kühn A The impact of the Basel III liquidity ratios on banks: evidence from a simulation study Q Rev Econ Financ 2020 75 167 190 10.1016/j.qref.2019.02.005
Guerini M Nesta L Ragot X Schiavo S Firm liquidity and solvency under the Covid-19 lockdown in France Sci OFCE Policy Br 2020 76 1 20
Hai Ming L Gang L Hua H Waqas M Modeling the influencing factors of electronic word-of-mouth about CSR on social networking sites Environ Sci Pollut Res 2022 29 44 66204 66221 10.1007/s11356-022-20476-8
He Q, Xia P, Hu C, Li B (2022) Public information, actual intervention and inflation expectations. Transform Bus Econ 21(3C):644–666
Hillegeist SA, Keating EK, Cram DP, Lundstedt KG (2004) Assessing the probability of bankruptcy. Rev Acc Stud 9:5–34
Hu F, Qiu L, Xi X, Zhou H, Hu T, Su N, Zhou H, Li X, Yang S, Duan Z, Dong Z, Wu Z, Zhou H, Zeng M, Wan T, Wei S (2022) Has COVID-19 changed China’s digital trade?—Implications for health economics. Front Public Health 10:831549. 10.3389/fpubh.2022.831549
Huang S Liu H Impact of COVID-19 on stock price crash risk: evidence from Chinese energy firms Energy Econ 2021 101 105431 10.1016/J.ENECO.2021.105431 34876761
Huang W Saydaliev HB Iqbal W Irfan M Measuring the impact of economic policies on CO2 emissions: ways to achieve green economic recovery in the post-Covid-19 era Clim Chang Econ 2022 13 2240010 10.1142/S2010007822400103
Huang W Saydaliev HB Iqbal W Irfan M Measuring the impact of economic policies on CO2 emissions: ways to achieve green economic recovery in the post-covid-19 era Clim Chang Econ 2022 13 2240010 10.1142/S2010007822400103
Huang X Chau KY Tang YM Iqbal W Business ethics and irrationality in SME during COVID-19: does it impact on sustainable business resilience? Front Environ Sci 2022 10 275 10.3389/fenvs.2022.870476
Iqbal W Tang YM Chau KY Irfan M Mohsin M Nexus between air pollution and NCOV-2019 in China: application of negative binomial regression analysis Process Saf Environ Prot 2021 150 557 565 10.1016/j.psep.2021.04.039
Irfan M Ahmad M Fareed Z Iqbal N Sharif A Wu H On the indirect environmental outcomes of COVID-19: short-term revival with futuristic long-term implications Int J Environ Health Res 2021 10.1080/09603123.2021.1874888
Ismarau Tajuddin NI, Abdullah R, Jabar M, Yah Jusoh Y, Arbaiy N (2017) Rasch model application in validating instrument for knowledge integration in small medium enterprises. Acta Inform Malaysia 1:15–16. 10.26480/aim.02.2017.15.16
Jiang P Fan Y. Van Klemeš JJ Impacts of COVID-19 on energy demand and consumption: challenges, lessons and emerging opportunities Appl Energy 2021 285 116441 10.1016/j.apenergy.2021.116441 33519038
Jin Y Tang YM Chau KY Abbas M How government expenditure mitigates emissions: a step towards sustainable green economy in belt and road initiatives project J Environ Manage 2022 303 113967 10.1016/j.jenvman.2021.113967 34810022
Juergensen J Guimón J Narula R European SMEs amidst the COVID-19 crisis: assessing impact and policy responses J Ind Bus Econ 2020 47 499 510 10.1007/S40812-020-00169-4/TABLES/2
Khalid U Okafor LE Burzynska K Does the size of the tourism sector influence the economic policy response to the COVID-19 pandemic? Curr Issues Tour 2021 24 2801 2820 10.1080/13683500.2021.1874311
Khan TM Rizvi SKA Sadiq R Disintermediation of banks in a developing economy: profitability and depositor protection in adverse economic conditions Manag Financ 2019 45 222 243 10.1108/MF-11-2017-0493
Kiseleva IA, Kuznetsov VI, Sadovnikova NA, Pikalov AV, Dolgaya AA (2020) Models for assessing the probability bankruptcy of enterprises. J Crit Rev 7:1037–1042. 10.31838/jcr.07.09.191
Kwak W, Shi Y, Cheh JJ, Lee H (2004) Multiple criteria linear programming data mining approach: an application for bankruptcy prediction, in: Lecture notes in artificial intelligence (subseries of lecture notes in computer science). Springer, pp. 164–173. 10.1007/978-3-540-30537-8_18
Lahr D Adams A Edges A Bletz J Where do we go from here? The survival and recovery of black-owned businesses post-COVID-19 Humanity & Society 2022 46 3 460 477 10.1177/01605976211049243
Latif Y Shunqi G Bashir S Iqbal W Ali S Ramzan M COVID-19 and stock exchange return variation: empirical evidences from econometric estimation Environ Sci Pollut Res 2021 28 60019 60031 10.1007/s11356-021-14792-8
Le Quéré C Jackson RB Jones MW Smith AJP Abernethy S Andrew RM De-Gol AJ Willis DR Shan Y Canadell JG Friedlingstein P Creutzig F Peters GP Temporary reduction in daily global CO2 emissions during the COVID-19 forced confinement Nat Clim Chang 2020 10 647 653 10.1038/s41558-020-0797-x
Li Q, Miao Y, Zeng X, Tarimo CS, Wu C, Wu J (2020) Prevalence and factors for anxiety during the coronavirus disease 2019 (COVID-19) epidemic among the teachers in China. J Affect Disord 277:153–158. 10.1016/j.jad.2020.08.017
Li M Yao-Ping Peng M Nazar R Ngozi Adeleye B Shang M Waqas M How does energy efficiency mitigate carbon emissions without reducing economic growth in post COVID-19 era Front Energy Res 2022 10 1 14 10.3389/fenrg.2022.832189
Li X, Wang J, Yang C (2023) Risk prediction in financial management of listed companies based on optimized BP neural network under digital economy. Neural Comput Applic 35(3):2045–2058. 10.1007/s00521-022-07377-0
Lim G Nguyen V Robinson T Tsiaplias S Wang J The Australian economy in 2020–21: the COVID-19 pandemic and prospects for economic recovery Aust Econ Rev 2021 54 5 18 10.1111/1467-8462.12405 33821015
Lin HWW Lo HC Wu RS Modeling default prediction with earnings management Pacific Basin Financ J 2016 40 306 322 10.1016/j.pacfin.2016.01.005
Liu LJ Yao YF Liang QM Qian XY Xu CL Wei SY Creutzig F Wei YM Combining economic recovery with climate change mitigation: a global evaluation of financial instruments Econ Anal Policy 2021 72 438 453 10.1016/j.eap.2021.09.009
Liu L, Li Z, Fu X, Liu X, Li Z, Zheng W (2022a) Impact of power on uneven development: evaluating built-up area changes in chengdu based on NPP-VIIRS images (2015–2019). Land 11(4):1–21. 10.3390/land11040489
Liu F, Kong D, Xiao Z, Zhang X, Zhou A, Qi J (2022b) Effect of economic policies on the stock and bond market under the impact of COVID-19. J Saf Sci Resil 3:24–38. 10.1016/j.jnlssr.2021.10.006
Liu X, Kong M, Tong D, Zeng X, Lai Y (2022c) Property rights and adjustment for sustainable development during post-productivist transitions in China. Land Use Policy 122:106379. 10.1016/j.landusepol.2022.106379
Liu X, Tong D, Huang J, Zheng W, Kong M, Zhou G (2022d) What matters in the e-commerce era? Modelling and mapping shop rents in Guangzhou, China. Land Use Policy 123:106430. 10.1016/j.landusepol.2022.106430
Merkevicius E, Garšva G, Girdzijauskas S (2006) A hybrid SOM-Altman model for bankruptcy prediction, in: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). Springer, pp. 364–371. 10.1007/11758549_53
Merton RC On the pricing of corporate debt: the risk structure of interest rates J Finance 1974 29 449 10.2307/2978814
Mirza N, Rahat B, Reddy K (2016) Financial leverage and stock returns: evidence from an emerging economy. Economic Research-Ekonomska Istraživanja 29(1):85–100. 10.1080/1331677X.2016.1160792
Mngumi F Shaorong S Shair F Waqas M Does green finance mitigate the effects of climate variability: role of renewable energy investment and infrastructure Environ Sci Pollut Res 2022 1 1 13 10.1007/s11356-022-19839-y
Mundle S, Sahu A (2021) Fiscal compression, jeopardised recovery, the humanitarian crisis and reforms. Econ Polit Wkly
Naqvi B Rizvi SKA Uqaili HA Chaudhry SM What enables Islamic banks to contribute in global financial reintermediation? Pacific Basin Financ J 2018 52 5 25 10.1016/j.pacfin.2017.12.001
Nasir MH Wen J Nassani AA Haffar M Igharo AE Musibau HO Waqas M Energy security and energy poverty in emerging economies: a step towards sustainable energy efficiency Front Energy Res 2022 10 1 12 10.3389/fenrg.2022.834614
Nicola M Alsafi Z Sohrabi C Kerwan A Al-Jabir A Iosifidis C Agha M Agha R The socio-economic implications of the coronavirus pandemic (COVID-19): a review Int J Surg 2020 78 185 193 10.1016/j.ijsu.2020.04.018 32305533
Noureddine B, Tan Ö (2021) Does renewable energy index respond to the pandemic uncertainty Related papers
OECD The impact of the coronavirus (COVID-19) crisis on development finance Tackling coronavirus Contrib to a Glob effort 2020 100 468 470
Rajagopal A, Reyes JAP (2022) Innovation in knowledge-intensive businesses: a collaborative approach for post-pandemic recovery 157–180. 10.1007/978-3-030-91532-2_9
Rajput H Changotra R Rajput P Gautam S A shock like no other: coronavirus rattles commodity markets Environ Dev Sustain 2021 23 6564 6575 10.1007/s10668-020-00934-4 32837284
Razzaq A Sharif A Aziz N Irfan M Jermsittiparsert K Asymmetric link between environmental pollution and COVID-19 in the top ten affected states of US: a novel estimations from quantile-on-quantile approach Environ Res 2020 191 110189 10.1016/j.envres.2020.110189 32919963
Riza F, Wiriyanata W (2021) Analysis of the viability of fiscal and monetary policies on the recovery of household consumption expenditures because of the Covid-19 pandemic. Jambura Equilib J 3. 10.37479/jej.v3i1.10166
Sadiq M, Hsu C, Zhang Y, Chien F (2021) COVID-19 fear and volatility index movements : empirical insights from ASEAN stock markets 67167–67184
Salvo CP De, Laborde D (2021) Haiti: the impact of COVID-19 and preliminary policy implications. Interim report. Int. foof policey Res Inst
Sharif A, Aloui C, Yarovaya L (2020) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company’s public news and information
Si DK Li XL Huang S Financial deregulation and operational risks of energy enterprise: the shock of liberalization of bank lending rate in China Energy Econ 2021 93 105047 10.1016/J.ENECO.2020.105047
Sinha A Mishra S Sharif A Yarovaya L Does green financing help to improve environmental & social responsibility? Designing SDG framework through advanced quantile modelling J Environ Manage 2021 292 112751 10.1016/j.jenvman.2021.112751 33991831
Society TA (1991) Fairness author(s): John Broome Source: Proceedings of the Aristotelian Society, 1990–1991, New Series, Vol. 91 (1990 - Published by: Oxford University Press on behalf of The Aristotelian Society Stable. https://www.jstor.org/stable/45451. 91:87–101
Stiglitz JE The proper role of government in the market economy: the case of the post-COVID recovery J Gov Econ 2021 1 100004 10.1016/j.jge.2021.100004
Strielkowski W Firsova I Lukashenko I Raudeliūniene J Tvaronavičiene M Effective management of energy consumption during the COVID-19 pandemic: the role of ICT solutions Energies 2021 14 893 10.3390/en14040893
Su C Urban F Circular economy for clean energy transitions: a new opportunity under the COVID-19 pandemic Appl Energy 2021 289 116666 10.1016/j.apenergy.2021.116666 36567826
Tang YM Chau KY Fatima A Waqas M Industry 4.0 technology and circular economy practices: business management strategies for environmental sustainability Environ Sci Pollut Res 2022 29 49752 49769 10.1007/s11356-022-19081-6
Tang YM Chau KY Kwok APK Zhu T Ma X A systematic review of immersive technology applications for medical practice and education - trends, application areas, recipients, teaching contents, evaluation methods, and performance Educ Res Rev 2022 35 100429 10.1016/j.edurev.2021.100429
Tang YM Chau KY Xu D Liu X Consumer perceptions to support IoT based smart parcel locker logistics in China J Retail Consum Serv 2021 62 102659 10.1016/j.jretconser.2021.102659
Tinoco MH, Wilson N (2013) Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. Int Rev Financ Anal 30:394–419
Tong D, Chu J, Han Q, Liu X (2022) How land finance drives urban expansion under fiscal pressure: evidence from Chinese cities. Land 11(2). 10.3390/land11020253
Vassalou M, Xing Y (2005) Equity returns following changes in default risk: new insights into the informational content of credit ratings. SSRN Electron J. 10.2139/ssrn.413905
Wang Q Zhang F What does the China’s economic recovery after COVID-19 pandemic mean for the economic growth and energy consumption of other countries? J Clean Prod 2021 295 126265 10.1016/j.jclepro.2021.126265 33589853
Wang Q Zhang F What does the China’s economic recovery after COVID-19 pandemic mean for the economic growth and energy consumption of other countries? J Clean Prod 2021 295 126265 10.1016/j.jclepro.2021.126265 33589853
Wei X, Han L (2021) International review of financial analysis the impact of COVID-19 pandemic on transmission of monetary policy to financial markets. Int Rev Financ Anal 74:101705. 10.1016/j.irfa.2021.101705
Wu B, Liu Z, Gu Q, Tsai F (2023) Underdog mentality, identity discrimination and access to peer-to-peer lending market: Exploring effects of digital authentication. J Int Financ Mark Inst Money 83:101714. 10.1016/j.intfin.2022.101714
Xiangyu S Jammazi R Aloui C Ahmad P Sharif A On the nonlinear effects of energy consumption, economic growth, and tourism on carbon footprints in the USA Environ Sci Pollut Res 2021 28 20128 20139 10.1007/s11356-020-12242-5
Xu M Zhang C Bankruptcy prediction: the case of Japanese listed companies Rev Account Stud 2009 14 534 558 10.1007/s11142-008-9080-5
Xu Z Jia H The influence of COVID-19 on entrepreneur’s psychological well-being Front Psychol 2022 12 1 11 10.3389/fpsyg.2021.823542
Yang Z Choe Y Martell M COVID-19 economic policy effects on consumer spending and foot traffic in the U.S J Saf Sci Resil 2021 2 230 237 10.1016/j.jnlssr.2021.09.003
Young KG The idea of a human rights-based economic recovery after COVID-19 Int J Public Law Policy 2020 6 1 10.1504/ijplap.2020.10037231
Zhang H Song H Wen L Liu C Forecasting tourism recovery amid COVID-19 Ann Tour Res 2021 87 103149 10.1016/j.annals.2021.103149 36540616 | 36869958 | PMC9985437 | NO-CC CODE | 2023-04-21 23:15:10 | yes | Environ Sci Pollut Res Int. 2023 Mar 4; 30(18):53977-53996 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
26231
10.1007/s11356-023-26231-x
Research Article
The moderating impact of government support on the relationship between tourism development and growth, natural resources depletion, sociocultural degradation, economic environment, and pollution reduction: case of Indonesian economy
http://orcid.org/0000-0001-8808-2407
Moslehpour Massoud [email protected]
12
http://orcid.org/0000-0001-8338-932X
Firman Afrizal [email protected]
3
http://orcid.org/0000-0002-1768-503X
Lin Chen-Hsien [email protected]
4
http://orcid.org/0000-0003-1697-032X
Bilgiçli İsmail [email protected]
5
http://orcid.org/0000-0002-1205-3746
Tran Trung Kien [email protected]
6
http://orcid.org/0000-0003-2598-3720
Nguyen Tran Thai Ha [email protected]
7
1 grid.252470.6 0000 0000 9263 9645 Department of Business Administration, Asia Management College, Asia University, 500, Lioufeng Rd., Wufeng, Taichung, 41354 Taiwan
2 grid.253565.2 0000 0001 2169 7773 Department of Management, California State University, San Bernardino, 5500 University Parkway, San Bernardino, CA 92407 USA
3 grid.252470.6 0000 0000 9263 9645 Department of Business Administration, College of Management, Asia University, 500, Lioufeng Rd., Wufeng, Taichung, 41354 Taiwan
4 grid.459835.6 0000 0004 0639 0273 Department of Hotel & MICE Management, Overseas Chinese University, Chiao Kwang Rd, 100, Taichung, 40721 Taiwan
5 Tourism and Hotel Management, Karasu Vocational School, Sakarya Applied Sciences University, Karasu, Sakarya Turkey
6 grid.444827.9 0000 0000 9009 5680 School of Public Finance, College of Economics, Law and Government, University of Economics Ho Chi Minh City, 59C Nguyen Dinh Chieu Street, District 3, Ward Vo Thi Sau, Ho Chi Minh City, Vietnam
7 grid.444823.d 0000 0004 9337 4676 Faculty of Finance and Banking, Van Lang University, 69/68 Dang Thuy Tram Street, Ward 13, Binh Thanh District, Ho Chi Minh City, Vietnam
Responsible Editor: Arshian Sharif
16 3 2023
116
4 12 2022
27 2 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Tourism development is being treated as an essential element of national establishment as it has the potential of promoting cultural diversity and increase economic growth of country. However, it is also viewed as a culprit due to depletion of natural resources. In this respect, it is quite thoughtful to probe the government support and its moderating impact on association of tourism development with sociocultural degradation, national resource depletion, economic environment, and pollution reduction in Indonesian context, as Indonesia is known to be rich in terms of natural resources and recognized as the multicultural country. By using PLS methodology, the association among outlined construct and model significance has been probed in the sample of tourism management authorities. Findings disclose that government support and policy intervention significantly moderates tourism development and growth and depletion of natural resources in Indonesia. Insights from the findings finally help in proposing some unique implications that are beneficial for policymakers and practitioners.
Keywords
Tourism development and growth
Environmental sustainability
Depletion of natural resources
Sociocultural degradation
Government support and policy intervention
==== Body
pmcIntroduction
Over the past few decades, tourism has become essential in developing any country’s economy. The tourism sector development further opens the horizons for economic development in terms of revenue generation, employment opportunities, and entrepreneurship. Many times the development of the tourism sector depends upon the resources provided by nature to the country (Deng et al. 2021; Firman et al. 2022). On the other hand, in this modern era, the countries also generate sources considered vital for international tourists, like Burj Khalifa. Indonesia hosted 4 million tourists in 2020, ranking 44 in the world. Indonesia prefers halal tourism being an Islamic country. With a contribution to the GDP of over 5%, tourism is one of the significant sectors of the Indonesian economy (Jian and Afshan 2022; Rindrasih et al. 2019). Although the significance of foreign tourism in different sections of the country varied substantially, the island of Bali is Indonesia’s most popular tourist destination. Before 2020, Indonesia’s tourism industry was developing rapidly in terms of a notable increase in the number of international tourists. However, the COVID pandemic demonstrated how risky it was for an economy to rely highly on tourism (Khan et al. 2019; Sharif et al. 2017). Indonesia is a large archipelago that offers visitors a wide range of sights and activities, including some of the top jumping locations in the world, like Raja Ampat, romantic beach vacations, wellness travel, culture, and adventure.
There are five “super-priority” travel sites that were chosen by Indonesia’s Tourism Ministry in 2020. To develop these locations, 351.6 billion Indonesian rupiahs will be invested by the end of 2022 (Darsana and Sudjana 2022; Nugroho and Numata 2022; Sharif et al. 2020a). The Indonesian government planned to exploit this sector to boost economic growth by creating and promoting additional tourist spots to foreign visitors. The Indonesian economy is supported by significant inflows of foreign currency from tourism. Typically the tourism sector improves the local economy by generating jobs. The number of people employed in Indonesia’s tourism industry has been rising rapidly over the past 10 years (Sharif et al. 2020b; Widjaja et al. 2020). However, COVID slowed down the development of global tourism and travel. In early 2020, several nations introduced travel restrictions (Sharif et al. 2019, 2022c). The Indonesian government reopened Bali to foreign tourists in October 2021 to boost the island’s economy. International visitors to Bali, Batam, and Bintan Islands will not be subject to quarantine beginning in 2022. However, foreign visitors must also be immunized entirely and tested negative, like other arrivals from abroad. Many believe this will considerably improve the situation (Achmad & Yulianah 2022; Suki et al. 2020).
The Indonesian government has been actively pushing domestic travel to grow the sector. The increase in domestic tourism could not offset the losses fully brought by declining foreign visitors. International travel must be safe for the sector to recover to pre-pandemic levels. By the end of 2022, it is envisaged that the tourism industry will develop and recover due to the increased number of individuals who had COVID-19 vaccines around the globe, including in Indonesia (Sumarmi) (Wan et al. 2022). Indonesian tourism industry value is given in Fig. 1.Fig. 1 Value of Indonesian tourism industry.
Source: Invest islands
Due to its immense natural resources, Indonesia is recognized as a prosperous nation. Moreover, the Indonesian government’s policy on environmental law in regional autonomy is considered part of public policy because it affects the lives of individuals. If the management of environment and natural resources do not carry out according to carrying capacity, the country might face food, water, or energy crisis (Ainou et al. 2022). Also, with the passage of time, the country’s natural resources as well as environmental components are depleting. Moreover, their sustainability is also ignored due to which environmental quality is also compromised. Besides, the country is also known as a multicultural country due to variety of cultural resources, thereby, the official motto of country is “Unity in diversity.” These interesting facts aligned with tourism development make the argument interesting to probe natural resource depletion, sociocultural degradation, economic environment, and pollution reduction in the presence of government support, hence becoming the motivation of study (Jermsittiparsert 2021; Wirsbinna and Grega 2021).
The past literature gaps that the present study will address are as follows: (1) the countries are rich in natural resources like mountains, greenery, hills, deserts, and ancients; tourism is considered a blessing to nature. Tourism development is extensively researched, but the investigation from multiple perspectives, such as from the natural resources, economy, socio-culture, environment, and government perspective, has not been explored. The current investigation will fill this gap by investigating tourism development from multiple perspectives. (2) Streimikiene et al. (2021) worked on tourism development and competitiveness; however, the present investigation will also work on tourism development by replacing competitiveness with the depletion of natural resources, sociocultural degradation, economic environment, pollution reduction, and also with the addition of government support and policy intervention moderation effect particularly in Indonesia with fresh data set; (3) although there are multiple models pertaining to tourism development and growth that have been explored, but the model consists of tourism development and growth, depletion of natural resources, sociocultural degradation, economic environment, pollution reduction, and government support and policy intervention which are not tested before, particularly in Indonesia in recent time; (4) Wang and Yotsumoto (2019) worked on tourism development from a conflict point of view; however, the present investigation will also work on tourism development but from an environment, socio-culture, economy, and natural resources perspective with a fresh data set; (5) Hartani et al. (2021) and Kayumovich (2020) worked on the digital tourism development; however, the present investigation will also work on it with the addition of moderation effect, i.e., government support and policy intervention, particularly in Indonesia; (6) Ojogiwa (2021) and Seow et al. (2021) checked the moderation effect of government support from SMEs performance in tourism industry; however, the present study will also check its moderation effect with tourism development particularly in Indonesia in recent times with fresh data sample. The significance of the present investigation is as follows: (1) tourism is the source of revenue generation for any country by investing less. Tourism-based locations are the gift of nature for any country. This industry can lead to prosperity in the country in the form of job creation, infrastructure development, and culture exchange. Accordingly, the present investigation will highlight the importance of tourism development for any country in terms of natural resources, economic development, environment, and economy; (2) the present investigation will also provide the support to economy, environment, and natural resources related to professional to understand the tourism industry relation with these sectors and upgrade their policies with the view to uplift the tourism sector, and (3) the present investigation will also provide the support to future researchers to explore more aspects about tourism development.
Literature review
Tourism development and natural resources depletion
Natural resources are the gift of nature for any country. There are numerous ways to explore these resources. These resources play a vital role in the country’s prosperity, like revenue generation and social welfare in terms of employment. Many times the countries remain in the process of generating revenue from these resources but an oversight that these resources are depleting with revenue generation activities (Zhang et al. 2023; Zhao et al. 2021). Bai et al. (2022), Bhuiyan et al. (2018), and Tan et al. (2021) investigated the relationship between energy, tourism, and resource depletion. The investigation was carried out on a panel of counties. The study used 21 years of data covering the range between 1195 and 2016 as a sample. The gathered sample was analyzed with the help of a GMM estimator. The results of the investigation revealed that while international tourism raises carbon emissions and depletes energy resources globally, bank-specific variables significantly limit resource depletion. It is crucial to develop robust rules for sustainable production across nations since nuclear energy use slows down resource depletion while industrial value added accelerates it and raises carbon emissions.
Similarly, Ali et al. (2021) investigated whether there is any association between tourism, renewable energy, economic growth, and natural resources. The investigation was carried out on the panel of countries. The investigation used that data from 25 years covering the range from 1995 to 2019 as a sample. The gathered sample was analyzed with the help of the EKC hypothesis. The investigation results revealed that increased usage of renewable energy, urbanization (in both the upper-medium and low-income economies), and cultural globalization are all contributing to a decrease in the ecological footprint (in both lower-middle as well as low-income economies).
Furthermore, rising GNP, commerce (in all high- and low-income economies), and urbanization are to blame for the growth in ecological footprints (in all the high-income economies). Tourism growth (in all the high-, higher-medium, and lower-middle-income economies), renewable energy, urbanization (in all the upper-middle and low-income economies), and cultural globalization are responsible for the decrease in resource depletion (Sadiq et al. 2023a, b). Moreover, studies show a nexus between tourism, natural resources, and the environment (Chau et al. 2022; Sadiq et al. 2022a, b; Sun et al. (2021). The investigation was carried out on 88 countries that fall into BRI. The data of 20 years covering the tenure from 1995 to 2015 was collected as a sample. The gathered data was analyzed with the help of the D&K estimator. The results of the investigation revealed that there is a significant association between this triangle consisting of tourism, energy, and natural resources.
Further, Ibrahim et al. (2022) worked on the nexus between resources and tourism. The investigation was carried out in African countries. The investigation used the data from 30 years covering the tenure from 1990 to 2019 as a sample. The gathered sample was analyzed with the help of the CS-ARDL approach. The results of the investigation revealed that there is a significant association between tourism development as well as natural resources. Further, the policymakers of African economies pay special attention to safeguarding natural resources. Thus, the article established the hypothesis given below:
H1: Tourism development and growth significantly influences the depletion of natural resources.
Tourism development and sociocultural degradation
The “human repercussions” focus on how the quality of everyday life for locals in tourist locations varies and on how tourism has a cultural influence on changes in traditional values, conventions, and identities. The culture of any country plays a vital role in developing that country’s tourism. Countries that are rich in their culture tend to promote culture-based tourism. On the other side, there are many other forms of tourism, like heritage and destination-based tourism. Indonesia is one of the countries which is rich in culture as well as heritage (Phuoc et al. 2022; Quynh et al. 2022). Usually, Indonesia promotes cultural tourism, especially halal tourism. In this context, Zhuang et al. (2019) investigated whether tourism is associated with culture. The investigation was carried out in China. The results of the investigation revealed that there is a significant association between tourism and culture in the form of heritage culture sites. Human behaviors, culture, and customs are shaped by religions and influenced by them. For instance, it is clear that different lifestyles and traditions, including eating and drinking, are frequently founded on religion (Richards 2018). There are two ways that religious beliefs affect conduct.
On the one hand, it may affect actions based on certain taboos and duties; for example, Muslims are not permitted to consume alcohol or pork. However, religions shape societal norms, practices, and culture, which unquestionably affects behavior (Hjorthén, 2021; Nguyen et al. 2021). In this context, Heydari Chianeh et al. (2018) checked whether culture and tourism are associated. The investigation was carried out in Iran. The results of the investigation revealed that there is a significant association between culture and tourism in the form of religious places.
Similarly, Holder et al. (2022) investigated whether there is any association between indigenous tourism and the tourist’s interest in sociocultural tourism. The investigation revealed that while xenophobia and racism might predict how domestic and foreign visitors would feel about indigenous tourism, this does not always translate into a lack of willingness to partake. Regarding self-congruity bias, people are less likely to engage the less they relate to or identify with indigenous tourism. This has ramifications for the marketing and attraction of tourist-related goods, particularly those supported by sociocultural elements like indigenous tourism. Cultural customs, locations, and values are the focus of heritage tourism. It involves both manufactured and natural tourist attractions’ riches (Firman et al. 2022). Heritage should frequently be incorporated as a key tourist attraction in modern tourism activities. Authentic local culture, history, and natural attractions are all included in heritage tourism offerings. Therefore, the growth of legacy tourism will be essential to advancing the sociocultural development of tourist sites. In this context, Kebete (2022) checked whether there is any association between culture and tourism. The results revealed that the culture is one of the key players of tourism development. Thus, the article established the hypothesis given below:
H2: Tourism development and growth significantly influences sociocultural degradation.
Tourism development and economic environment
Every country in the world always continues its efforts to bring stability and uplift its people’s standard of living. Thus, the country ensures the maximum efforts to bring betterment to its economy. The economy of any country is the combination of multiple sectors. Tourism is one of the vital sectors of the country’s economy (Moslehpour et al. 2022a; Nguyen et al. 2021). In this context, Adedoyin et al. (2021) investigated whether there is any nexus between tourism and the economic environment in the form of economic growth. The investigation was carried out on the top ten countries that are the highest tourism earners. The investigation used the data from 20 years covering the tenure from 1995 to 2015 as a sample. The gathered sample was analyzed with the help of the D&H test. The investigation results revealed that environmental deterioration is caused by economic policy uncertainty, tourism, and energy use. However, economic policy uncertainty significantly decreases the contribution of energy consumption to ecological footprint, so a 1% rise in the latter lessens environmental harm by 0.71%.
Similarly, Khan et al. (2021) investigated whether there is any nexus between the environment, tourism, and the economic environment in the form of economic growth. The investigation was carried out in six countries of the Commonwealth. The investigation used the data from 23 years covering the tenure from 1995 to 2018 as a sample. The gathered sample was analyzed with the help of RLS estimator. The results of the investigation revealed that a 1% rise in CO2 will slow economic development by 0.14%, whereas a 1% increase in tourism activities can accelerate growth by 0.04%. Tourism, population growth, and commerce considerably contribute to economic growth, according to the fixed-effect and RLS estimates. However, CO2 harms growth. According to the Granger causality test, economic growth has a two-way causal relationship with CO2 and commerce.
Additionally, empirical findings point to a one-way causal relationship between population growth and tourism, population and CO2, and growth and FDI (Moslehpour et al. 2022b, c). Moreover, Akadiri et al. (2020) worked on globalization, tourism, carbon emission, and the economic environment in the form of economic growth. The investigation was carried out in OECD countries. The investigation used the data from 20 years covering the tenure from 1995 to 2014 as a sample. The gathered sample was analyzed with the help of SU regression. The investigation results revealed that variables causing environmental pollution are mainly internal, especially inside the tourism island areas, and support the demand-flowing and supply-leading theories.
Correspondingly, Chong and Balasingam (2019) worked on the association between tourism and the economic environment in the form of economic benefits. The investigation was carried out in South Asian countries. The investigation used the data from 20 years covering the tenure from 1995 to 2014 as a sample. The gathered sample was analyzed with the help of SU regression. The results of the investigation revealed that high visitor numbers and revenue, industry ripple effects, and the development of job prospects for the local population are all advantages of historic tourism. The suggested tactics include stakeholder empowerment, cooperation, and engagement, as well as the adaptive reuse strategy (Chien 2022a, b; Chien et al. 2022a, b. Thus, the article established the hypothesis given below:
H3: Tourism development and growth significantly influences the economic environment.
Tourism development and pollution reduction
One of the horrible issues the world has been facing over the past few decades is global warming. Environmental degradation is one of the prime issues that is the core reason for this global warming (Lan et al. 2022; Lin et al. 2022). However, the world is expressing much interest in resolving this issue to save the upcoming generation. This environmental degradation issue is not limited to global warming but affects almost every aspect of life. Among many factors, tourism is also one cause of environmental degradation. Ahmad and Ma (2022) explored how tourism changes environmental pollution. The investigation results revealed that a 1% increase in tourist growth might result in a 0.38622% decrease in carbon emissions.
Furthermore, the tourism industry may reduce carbon emissions by increasing renewable energy sources and displacing high-emission businesses; the former strategy has a 4% larger impact than the latter (Dinh et al. 2022; Kamarudin et al. 2021). Similarly, Liu et al. (2022a) investigated the role of tourism in environmental degradation. The investigation was carried out on a panel of 70 countries. The investigation used the data from 17 years covering the tenure from 2000 to 2017 as a sample. The gathered sample was analyzed with the help of the GNS model. The investigation results revealed that there are both positive direct and adverse indirect effects of tourism, both of which are significant at the 1% level. Tourism has a considerably negative overall impact because its negative indirect effect is more prominent than its good direct effect. Furthermore, the direct and indirect effects of financial development and carbon emissions are fashioned in an inverted U and a U pattern, respectively.
Similarly, Tian et al. (2021) checked whether tourism development plays any role in improving environmental quality. The investigation was carried out in G20 economies. The investigation used the data from 20 years covering the tenure from 1995 to 2015 as a sample. The gathered sample was analyzed with the help of FMOLS. The research results revealed that a 1% increase in tourism growth reduces pollutant emissions over time by 0.05%. Additionally, consuming more renewable energy results in lower environmental emissions. Long-term, a 1% increase in renewable energy lowers pollution emissions by 0.15%. Long-term data showed an inverted U-shaped relationship between pollution and real GDP, supporting the environmental Kuznets curve's validity. Finally, the growth of the tourism industry can help to reduce CO2 emissions. Thus, the article established the hypothesis given below:
H4: Tourism development and growth significantly influences pollution reduction.
Moderating role of government presence
Although tourism, on the one hand, contributes to the country’s economy through revenue generation and employment creation, on the other side, the bout also results in the depletion of natural resources in the form of environmental degradation. When tourists visit any country, they usually care less about maintaining natural resources. Thus, the countries adopt different ways to ensure the safety of natural resources. Those factors, like government policies, play a vital role in protecting natural resources (Kim et al. 2016; Huang et al. 2022). Accordingly, the present study employed government support and policy investment as a moderator in the relationship between tourism development and natural resource depletion. In this context, Hoque (2018) investigated the moderating role of government support in the relationship between entrepreneurial orientation and SME performance. The investigation was carried out in Bangladesh. The investigation used the data of 150 SMEs owner as a sample. The gathered sample was analyzed with the help of AMOS. The study results revealed that government support significantly moderates the relationship between entrepreneurial orientation and SME performance, particularly in Bangladesh.
Likewise, Jan et al. (2019) investigated the moderating effect of the government’s role in the relationship between values and purchase behavior. The investigation was carried out in China. The investigation used the data of 238 green product consumers as the sample. The gathered sample was analyzed with the help of SEM. The results of the investigation revealed that attitudes toward purchasing green products have not been considerably impacted by ecological or economic value. In contrast, the health and safety benefits of green products have had a favorable impact on consumers’ attitudes about green product purchases. Additionally, consumers’ purchasing attitudes significantly impact their propensity to make green goods purchases. Moreover, the government’s participation significantly reduced the link between safety value and purchasing behavior for green products. Thus, government support is an active moderator (Anwar et al. 2020; Duong et al. 2022). Thus, the article established the hypothesis given below:
H5: Government support and policy investment significantly moderate the association between tourism development and growth and depletion of natural resources.
Research methods
The article examines the role of tourism development and growth in the depletion of natural resources, sociocultural degradation, economic environment, and pollution reduction. Also, it investigates the moderating role of government support and policy intervention in tourism development and growth and depletion of natural resources in Indonesia. The study used primary data sources such as questionnaires to collect the primary data from the selected respondents. The article used the items for the measurement of the variables. Tourism development and growth has five items taken from Gannon et al. (2021), depletion of natural resources is measured with four items extracted from Barchielli et al. (2022), sociocultural degradation is measured with eight items taken from Aman et al. (2019), the economic environment has four while pollution reduction has three items taken from Rasoolimanesh et al. (2019), and government support and policy intervention has four items taken from Fotiadis et al. (2019). These measurements are given in Table 1.Table 1 Measurements of the constructs
Items Statements Sources
Tourism development and growth
TDG1 The residents participated in tourism development conservation programs Gannon et al. (2021)
TDG2 I believe that tourism should be actively encouraged in my community
TDG3 I support tourism and want it to become an important part of my community
TDG4 The local authorities and state government should support the promotion of tourism
TDG5 Developing plans to manage the conservation of historical sites and tourism growth is important
Depletion of natural resources
DNR1 Tourism development increases the depletion of natural resources Barchielli et al. (2022)
DNR2 My country is more focused on tourism than natural resources appreciation
DNR3 My country earns low income from natural resources but high income from tourism
DNR4 Tourism has played more contribution in the GDP than natural resources development
Sociocultural degradation
SCD1 Tourism increases the drugs addictions ratio in the country Aman et al. (2019)
SCD2 Tourism increases social crime in the country
SCD3 Tourism reduces the community’s image
SCD4 Tourism has played a role in infrastructure development
SCD5 Tourism development reduces cultural activities
SCD6 Tourism development reduces recreational opportunities
SCD7 Tourism development increases business opportunities
SCD8 Tourism development reduces environmental damage or destruction
Economic environment
EE1 Tourism development creates more jobs for my community Rasoolimanesh et al. (2019)
EE2 Tourism development attracts more investment to my community
EE3 Our standard of living has increased considerably because of tourism
EE4 Tourism development provides more infrastructures and public facilities
Pollution reduction
PR1 Tourism development helps to preserve the natural environment Rasoolimanesh et al. (2019)
PR2 Tourism development helps to preserve the historical buildings
PR3 Tourism development improves the area’s appearance
Government support and policy intervention
GSPI1 Independent investors are strengthened and supported by financing contracts with the government Fotiadis et al. (2019)
GSPI2 The local government is coordinated in their efforts to support natural resources and promote tourism
GSPI3 Strengthening of the subsidies for work relevant to tourism development and protection of natural resources
GSPI4 The local government tax regulations support our natural resources from tourism activities
The study used the tourism management authorities of Indonesia as the respondents. These respondents are selected based on simple random sampling. The researchers distributed the surveys using mail and distributed around 451 surveys. After 1 month, only 290 valid surveys were received, which represents around a 64.30% response rate. PLS-SEM methodology was used to assess the model reliability and validity. This tool effectively deals with complex models and provides the best estimation, even in the case of small and large data sets (Hair et al. 2020). This tool assesses the measurement model that shows the correlation of the items, known as convergent validity with the help of Alpha, average variance extracted (AVE), composite reliability (CR), and factor loadings (Hair et al. 2014). In addition, the assessment of the measurement model also includes the variables’ correlation, known as discriminant validity, with the help of cross-loadings, heterotrait-monotrait (HTMT) ratio, and Fornell-Larcker (Hair et al. 2020). Finally, the tool assesses the structural model that shows the nexus among the understudy variables (Ringle et al. 2015). The study used tourism development and growth (TDG) as an independent construct. In addition, the study used the four dependent variables such as depletion of natural resources (DNR), sociocultural degradation (SCD), economic environment (EE), and pollution reduction (PR). Finally, the study used the moderating variable named government support and policy intervention (GSPI). These variables are presented in Fig. 2.Fig. 2 Theoretical framework
Research findings
The study assesses the measurement model that shows the correlated items known as convergent validity with the help of Alpha, AVE, CR, and factor loadings. The outcomes indicated that the Alpha values are not lower than 0.70, factor loadings are not less than 0.50, CR values are not lower than 0.70, and AVE values are not lower than 0.50. These figures exposed valid convergent validity and high correlation among items. Table 2 shows the outcomes of the study.Table 2 Convergent validity
Variables Items Loadings Alpha CR AVE
Depletion of natural resources DNR1 0.847 0.826 0.884 0.656
DNR2 0.859
DNR3 0.823
DNR4 0.702
Economic environment EE1 0.801 0.812 0.876 0.638
EE2 0.819
EE3 0.751
EE4 0.823
Government support and policy intervention GSPI1 0.902 0.923 0.946 0.813
GSPI2 0.899
GSPI3 0.906
GSPI4 0.899
Pollution reduction PR1 0.889 0.873 0.922 0.797
PR2 0.891
PR3 0.899
Sociocultural degradation SCD1 0.656 0.922 0.938 0.687
SCD2 0.876
SCD4 0.875
SCD5 0.860
SCD6 0.870
SCD7 0.793
SCD8 0.847
Tourism development and growth TDG1 0.965 0.970 0.977 0.894
TDG2 0.956
TDG3 0.926
TDG4 0.963
TDG5 0.916
In addition, the measurement model study also includes the variables’ correlation, known as discriminant validity with the help of cross-loadings, HTMT ratio, and Fornell-Larcker. Firstly, the outcomes indicated the results of Fornell-Larcker, and the figures indicated an association with the variable itself that was than the figures that indicated the association with other variables. These figures exposed valid discriminant validity and low correlation among variables. Table 3 shows the outcomes of the study.Table 3 Fornell-Larcker
Secondly, the outcomes indicated the results of cross-loadings, and outcomes indicated that the figures indicating an association with the variable itself are larger than the figures indicating the association with other variables. These figures exposed valid discriminant validity and low correlation among variables. Table 4 shows the outcomes of the study.Table 4 Cross-loadings
DNR EE GSPI PR SCD TDG
DNR1 0.847 0.453 0.485 0.299 0.148 0.388
DNR2 0.859 0.460 0.448 0.325 0.120 0.419
DNR3 0.823 0.444 0.395 0.308 0.169 0.389
DNR4 0.702 0.258 0.246 0.227 0.158 0.322
EE1 0.411 0.801 0.658 0.314 0.174 0.388
EE2 0.442 0.819 0.729 0.330 0.125 0.445
EE3 0.387 0.751 0.546 0.313 0.113 0.324
EE4 0.387 0.823 0.678 0.334 0.126 0.395
GSPI1 0.463 0.759 0.902 0.300 0.163 0.447
GSPI2 0.446 0.722 0.899 0.357 0.141 0.438
GSPI3 0.451 0.767 0.906 0.302 0.165 0.452
GSPI4 0.441 0.721 0.899 0.360 0.134 0.440
PR1 0.322 0.309 0.305 0.889 0.163 0.354
PR2 0.332 0.393 0.354 0.891 0.144 0.363
PR3 0.313 0.378 0.318 0.899 0.167 0.357
SCD1 0.250 0.168 0.146 0.263 0.656 0.281
SCD2 0.204 0.209 0.193 0.159 0.876 0.325
SCD4 0.119 0.158 0.157 0.142 0.875 0.293
SCD5 0.097 0.064 0.107 0.103 0.860 0.299
SCD6 0.078 0.118 0.095 0.111 0.870 0.276
SCD7 0.078 0.121 0.118 0.097 0.793 0.204
SCD8 0.189 0.128 0.139 0.137 0.847 0.306
TDG1 0.452 0.472 0.462 0.386 0.323 0.965
TDG2 0.434 0.476 0.456 0.395 0.310 0.956
TDG3 0.442 0.455 0.475 0.368 0.350 0.926
TDG4 0.454 0.466 0.467 0.379 0.325 0.963
TDG5 0.443 0.449 0.471 0.367 0.336 0.916
Values in bold indicate the maximum factor loading for each item
Thirdly, the outcomes indicated the results of the HTMT ratio, and outcomes indicated that the figures are not larger than 0.85. These figures exposed valid discriminant validity and low correlation among variables (Figs. 3, 4, and 5). Table 5 shows the outcomes of the study.Fig. 3 Measurement model assessment
Fig. 4 Structural model assessment
Fig. 5 Moderation analysis
Table 5 Heterotrait-monotrait ratio
Finally, the tool assesses the structural model that shows the nexus among the understudy variables. The outcomes indicated that tourism development and growth has a positive nexus with the depletion of natural resources, sociocultural degradation, economic environment, and pollution reduction in Indonesia and accepts H1, H2, H3, and H4. The findings also exposed that government support and policy intervention significantly moderates among tourism development and growth and depletion of natural resources in Indonesia and accepts H5. Table 5 shows the outcomes of the study.
Discussions
The results showed that natural resource depletion has a positive association with tourism development and growth (Table 6). These results are supported by the previous research (Anwar Khan et al. (2020a, b), which highlights the country where the inhabitants mostly rely on natural resources to meet most of their food and other needs and make appropriate use of these resources, and they also give attention to the abundance and quality of natural resources. They not only maintain the quality of the natural resources but also try to bring improvement. The supply of an abundance of quality resources assures a healthy, pleasant, and comfortable environment for tourism. So, natural resource depletion ultimately raises tourism development and growth. These findings are consistent with the findings of Kongbuamai et al. (2020), who discovered that when tourist enterprises use natural resources to prepare the tourism destination and rely on natural resources for housing and dining facilities, they help to protect the natural environment. The preservation of the natural environment and sustainable growth of natural resources sustain tourism development and growth. These results agree with Nathaniel et al. (2021) proclaiming that when tourism service providers have positive behavior while interacting with nature and utilizing natural resources to create minimum waste, they save costs and ensure an appropriate tourism environment. Hence, tourism development and growth can be sustainable.Table 6 A path analysis
Relationships Beta S.D T-statistics P values
GSPI—> DNR 0.309 0.059 5.210 0.000
TDG—> DNR 0.244 0.058 4.206 0.000
TDG—> EE 0.490 0.047 10.532 0.000
TDG—> PR 0.401 0.065 6.141 0.000
TDG—> SCD 0.348 0.055 6.277 0.000
TDG*GSPI—> DNR − 0.229 0.057 4.007 0.000
The results showed that sociocultural degradation has a positive association with tourism development and growth. These results are supported by Rustanto and Syah (2018), which state that if individuals consider themselves free from the limits of social culture, they begin to take an interest in the lifestyles of others living in other regions across the world, and this opens the way for foreigners to their homes. Thus, the tourism industry grows within the country. These results are also in line with Awang and Mustapha (2021). This previous study explains that when people of a country are not blind slaves of the culture developed and followed by their forefathers and have flexible thinking regarding cultural traditions, they accept the presence of people of diverse cultures wholeheartedly. This flexible and positive attitude of country people is beneficial to the tourism firms to expand their marketing. Hence, sociocultural degradation helps improve tourism development and growth. These results agree, highlighting that the increase in sociocultural degradation, according to Richards (2018), enhances tourist arrivals and foreign exchange. So, it enables tourism firms to develop the industry.
The results showed that the economic environment has a positive association with tourism development and growth. These results are supported by Brida et al. (2020), which implies that the economic environment, comprised of working, productivity, and the growth of all sorts of economic units, somehow affects tourism resources and processes. The favorable economic environment encourages tourism functions and improves tourism development and growth. These results are also in line with Gao et al. (2021), which examines the role of the economic environment in tourism development and growth. This study posits that if the other business firms do their functions having the consciousness of social responsibilities and do not create problems for other firms or the public, they do not cause damage to the environment and resources around them. This enables tourism firms to serve their clients in a pleasant atmosphere and give them health security. So, these firms can grow well. These results agree with Manzoor et al. (2019), which state that economic development provides a supply of different resources and healthy human resources for tourism.
The results showed that pollution reduction has a positive association with tourism development and growth. These findings are backed by Ahmad and Ma (2022), who argue that the environment and natural resources of tourism are essential factors. When pollution is reduced through diverse initiatives, it cleans the environment and produces high-quality natural resources. In this situation, the tourism destinations gain better advertisement, and the tourism industry grows well. These results are also in line with Liu et al. (2022b), who claim that when a country’s government takes initiatives and firms are also forced to preserve the environment against pollution, foreigners who have the chance to visit the country find life security. This motivates them to visit the country again and again. Hence, tourism grows. These results agree with Asif Khan et al. (2020a, b), who also state that in a country where the environment is preserved against pollution emissions, the number of tourist arrivals increases and indicates tourism development and growth.
The results showed that GSPI is a significant moderator between natural resources depletion and tourism development and growth. These results are supported by Li et al. (2018), which highlight that when government prepares its policies in such a way as to intervene in the practices of tourism, like a restriction on tourist arrivals from a particular country, it restricts the marketing of tourism and restricts its development and growth. These results are also in line with Scheyvens and Hughes (2019). According to this previous study, if the government is not flexible and primarily defines its policies from a political point of view, it may restrict the transformation of people and resources from a particular to another. In this situation, natural resources are not likely to be used properly. And there may be a gap in the performance of tourism practices. So, government support can only help improve the relationship between natural resource depletion and tourism development and growth. These results agree with Xu et al. (2020), who posit that if it manages its policies in such a way as it intervenes in tourism practices less frequently, it can encourage tourism and let it grow.
Implication
It guides many authors on how they may deal with the subject matter of tourism and its progress. The study checks the relationship between natural resource depletion, sociocultural degradation, economic environment, and pollution reduction with tourism development and growth. It is a literary contribution because it examines the GSPI as a moderator between natural resource depletion and tourism development and growth. The current study contributes to the literature because it explores the impacts of natural resource depletion, sociocultural degradation, economic environment, and pollution reduction with tourism development and growth for the Indonesian economy.
The current article has many empirical guidelines for tourism firms, economists, and the government on how they attain high tourism development and growth. This study guides that economic and tourism policies should be designed to encourage natural resource depletion to improve tourism development and growth. The study also suggests that there must be a struggle to bring flexibility in socio-culture to broaden the premises for tourism development and growth. It is suggested that there must be an improvement in economic conditions to provide an economic environment for tourism where it can develop and grow fast. The article conveys that policies must effectively reduce environmental pollution so that the tourism industry develops and grows at a higher rate. The study helps policymakers make policies related to environmental sustainability using government support, policy intervention, and tourism development and growth. It also implies that there must be control over GSPI to develop and increase the growth of the tourism industry.
Conclusion
The research objective was to examine the role of natural resource depletion, sociocultural degradation, economic environment, and pollution reduction in tourism development and growth. It was also to highlight GSPI’s role in the association between natural resource depletion and tourism development and growth. A quantitative approach was applied, and data were collected from Indonesia. The results concerning collected data showed that natural resource depletion, sociocultural degradation, economic environment, and pollution reduction have a positive linkage with tourism development and growth. The results showed that when people of a country, including the tourism service providers, make full use of natural resources without wasting them or damaging them, there is a sure supply of natural resources for tourism. It allows tourism to develop well and gain growth. Likewise, the results revealed that socio-culture is getting degraded, and people are opening their hearts to accept those from different cultures and societies; tourists’ arrivals and departures increase, leading the industry to develop and grow. The results indicated that the growing industries around the tourism companies and destinations supply the resources and form an environment for tourism firms. The developing economic environment assures development and growth in tourism. In addition, the environment and natural resources are significant elements of the tourism industry. Pollution reduction gives a better environment with quality resources and improves tourism development and growth. The study concluded that GSPI moderates natural resource depletion and tourism development and growth. GSPI control improves the role of natural resource depletion in tourism development and growth.
Limitations
There are several limitations still linked to the current study. With some improvement in research, these limitations can be overcome. The factors like natural resource depletion, sociocultural degradation, economic environment, and pollution reduction, which have been considered predictors of tourism development and growth, are few and are unable to meet the requirement of a good piece of literature. Good literature must be comprehensive; for this, future authors must check more factors that could influence tourism development and growth. In this study, only one moderator has been used, and it is between natural resource depletion and tourism development and growth. Future researchers must take a moderator suitable to discuss all variables.
Author contribution
Massoud Moslehpour: supervision, writing—original draft. Afrizal Firman: writing—literature review. Chen-Hsien Lin: software. İsmail Bilgiçli, Trung Kien Tran: visualization, methodology, conceptualization. Tran Thai Ha Nguyen: data curation, editing.
Funding
This research is partly funded by the University of Economics Ho Chi Minh City, Vietnam. This research is partly funded by the Van Lang University, Ho Chi Minh City, Vietnam.
Data availability
The data that support the findings of this study are attached.
Declarations
Consent to participate
It can be declared that there are no human participants, human data, or human tissues.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Achmad W Yulianah Y Corporate social responsibility of the hospitality industry in realizing sustainable tourism development Enrichment: J Manag 2022 12 2 1610 1616
Adedoyin FF Nathaniel S Adeleye N An investigation into the anthropogenic nexus among consumption of energy, tourism, and economic growth: do economic policy uncertainties matter? Environ Sci Pollut Res 2021 28 3 2835 2847 10.1007/s11356-020-10638-x
Ahmad N Ma X How does tourism development affect environmental pollution? Tour Econ 2022 28 6 1453 1479 10.1177/13548166211000480
Ainou FZ Ali M Sadiq M Green energy security assessment in Morocco: green finance as a step toward sustainable energy transition Environ Sci Pollut Res 2022 10.1007/s11356-022-19153-7
Akadiri SS Lasisi TT Uzuner G Akadiri AC Examining the causal impacts of tourism, globalization, economic growth and carbon emissions in tourism island territories: bootstrap panel Granger causality analysis Curr Issue Tour 2020 23 4 470 484 10.1080/13683500.2018.1539067
Ali Q Yaseen MR Anwar S Makhdum MSA Khan MTI The impact of tourism, renewable energy, and economic growth on ecological footprint and natural resources: a panel data analysis Resour Policy 2021 74 10 26 10.1016/j.resourpol.2021.102365
Aman J Abbas J Mahmood S Nurunnabi M Bano S The influence of Islamic religiosity on the perceived socio-cultural impact of sustainable tourism development in Pakistan: a structural equation modeling approach Sustainability 2019 11 11 1 27 10.3390/su11113039
Anwar M Tajeddini K Ullah R Entrepreneurial finance and new venture success-the moderating role of government support Bus Strateg Dev 2020 3 4 408 421 10.1002/bsd2.105
Awang KW Mustapha M Development growth of beach resorts: practitioners’ perspective Int J Bus Glob 2021 28 1–2 161 171 10.1504/IJBG.2021.115298
Bai X Wang KT Tran TK Sadiq M Trung LM Khudoykulov K Measuring China’s green economic recovery and energy environment sustainability: econometric analysis of sustainable development goals Econ Anal Policy 2022 10.1016/j.eap.2022.07.005
Barchielli B Cricenti C Gallè F Sabella EA Liguori F Da Molin G Liguori G Orsi GB Giannini AM Ferracuti S Climate changes, natural resources depletion, COVID-19 pandemic, and Russian-Ukrainian war: what is the impact on habits change and mental health? Int J Environ Res Public Health 2022 19 19 1 18 10.3390/ijerph191911929
Bhuiyan MA Zaman K Shoukry AM Gani S Sharkawy MA Sasmoko Khan A Ahmad A Hishan SS Energy, tourism, finance, and resource depletion: panel data analysis Energy Sources Part B 2018 13 11–12 463 474 10.1080/15567249.2019.1572837
Brida JG Gomez DM Segarra V On the empirical relationship between tourism and economic growth Tour Manag 2020 81 104 116 10.1016/j.tourman.2020.104131
Chau KY, Moslehpour M, Tu YT, Tai NT, Tien NH, Huy PQ (2022) Exploring the impact of green energy and consumption on the sustainability of natural resources: empirical evidence from G7 countries. Renew Energy 196:1241–1249
Chien F How renewable energy and non-renewable energy affect environmental excellence in N-11 economies? Renew Energy 2022 10.1016/j.renene.2022.07.013
Chien F The mediating role of energy efficiency on the relationship between sharing economy benefits and sustainable development goals (Case Of China) J Innov Knowl 2022 10.1016/j.jik.2022.100270
Chien F, Zhang Y, Sharif A, Sadiq M, Hieu MV (2022a) Does air pollution affect the tourism industry in the USA? Evidence from the quantile autoregressive distributed lagged approach. Tour Econ. 10.1177/13548166221097021
Chien F, Chau KY, Sadiq M, Hsu CC (2022b) The impact of economic and non-economic determinants on the natural resources commodity prices volatility in China. Resour Policy. 10.1016/j.resourpol.2022.102863
Chong KY Balasingam AS Tourism sustainability: economic benefits and strategies for preservation and conservation of heritage sites in Southeast Asia Tourism Review 2019 74 2 268 279 10.1108/TR-11-2017-0182
Darsana IM Sudjana IM A literature study of Indonesian tourism human resources development in the era of society 5.0 AL-ISHLAH: J Pendidikan 2022 14 3 2691 2700
Deng Z, Liu J, Sohail S (2021) Green economy design in BRICS: dynamic relationship between financial inflow, renewable energy consumption, and environmental quality. Environ Sci Pollut Res 1–10.
Dinh HP Tran KN Van Cao T Vo LT Ngo TQ Role of eco-financing in COP26 goals: empirical evidence from ASEAN Countries Cuadernos De Economía 2022 45 128 24 33
Duong KD Hai Thi Thanh T Association between post-Covid socio-economic development and energy-growth-environment nexus from developing economy Int J Econ Finance Stud 2022 14 2 247 270
Firman A, Moslehpour M, Qiu R, Lin PK, Ismail T, Rahman FF (2022) The impact of eco-innovation, ecotourism policy and social media on sustainable tourism development: evidence from the tourism sector of Indonesia. Economic Research-Ekonomska Istraživanja 1–21
Fotiadis A Nuryyev G Achyldurdyyeva J Spyridou A The impact of EU sponsorship, size, and geographic characteristics on rural tourism development Sustainability 2019 11 8 1 15 10.3390/su11082375
Gannon M Rasoolimanesh SM Taheri B Assessing the mediating role of residents’ perceptions toward tourism development J Travel Res 2021 60 1 149 171 10.1177/0047287519890926
Gao J Xu W Zhang L Tourism, economic growth, and tourism-induced EKC hypothesis: evidence from the Mediterranean region Empir Econ 2021 60 3 1507 1529 10.1007/s00181-019-01787-1
Hair JF Gabriel M Patel V AMOS covariance-based structural equation modeling (CB-SEM): guidelines on its application as a marketing research tool Braz J Market 2014 13 2 1 12
Hair JF Jr Howard MC Nitzl C Assessing measurement model quality in PLS-SEM using confirmatory composite analysis J Bus Res 2020 109 101 110 10.1016/j.jbusres.2019.11.069
Hartani NH Haron N Tajuddin NII The impact of strategic alignment on the sustainable competitive advantages: mediating role of it implementation success and it managerial resource Int J eBus eGov Stud 2021 13 1 78 96
HeydariChianeh R Del Chiappa G Ghasemi V Cultural and religious tourism development in Iran: prospects and challenges Anatolia 2018 29 2 204 214 10.1080/13032917.2017.1414439
Hjorthén A Old world homecomings: campaigns of ancestral tourism and cultural diplomacy, 1945–66 J Contemp Hist 2021 56 4 1147 1170 10.1177/0022009420986841
Holder A Walters G Ruhanen L Mkono M Exploring tourist’s socio-cultural aversions, self-congruity bias, attitudes and willingness to participate in indigenous tourism J Vacat Mark 2022 2 1 21
Hoque A Does government support policy moderate the relationship between entrepreneurial orientation and Bangladeshi SME performance? A SEM approach Int J Bus Econ Manag Stud 2018 6 3 37 59
Huang SZ Chien F Sadiq M A gateway towards a sustainable environment in emerging countries: the nexus between green energy and human capital Econ Res-Ekonomska Istraživanja 2022 35 1 4159 4176 10.1080/1331677X.2021.2012218
Ibrahim RL Ajide KB Usman M Kousar R Heterogeneous effects of renewable energy and structural change on environmental pollution in Africa: do natural resources and environmental technologies reduce pressure on the environment? Renew Energy 2022 200 244 256 10.1016/j.renene.2022.09.134
Jan IU Ji S Yeo C Values and green product purchase behavior: the moderating effects of the role of government and media exposure Sustainability 2019 11 23 42 66 10.3390/su11236642
Jermsittiparsert K Linkage between energy consumption, natural environment pollution, and public health dynamics in ASEAN Int J Econ Finance Stud 2021 13 2 1 21
Jian, X., Afshan S (2022) Dynamic effect of green financing and green technology innovation on carbon neutrality in G10 countries: fresh insights from CS-ARDL approach. Econ Res-Ekonomska Istraživanja 1–18.
Kamarudin F Anwar NAM Chien F Sadiq M Efficiency of microfinance institutions and economic freedom nexus: empirical evidence from four selected ASIAN countries Transform Bus Econ 2021 20 2b 845 868
Kayumovich KO Prospects of digital tourism development Economics 2020 1 44 23 24
Kebete Y Heritage tourism as a driver of socio-economic development and implications for sustainable tourism: dropped from previous research works Acad Lett 2022 20 47 61
Khan SAR Sharif A Golpîra H Kumar A A green ideology in Asian emerging economies: from environmental policy and sustainable development Sustain Dev 2019 27 6 1063 1075 10.1002/sd.1958
Khan A Bibi S Ardito L Lyu J Hayat H Arif AM Revisiting the dynamics of tourism, economic growth, and environmental pollutants in the emerging economies—sustainable tourism policy implications Sustainability 2020 12 6 253 269 10.3390/su12062533
Khan A Chenggang Y Hussain J Bano S Nawaz A Natural resources, tourism development, and energy-growth-CO2 emission nexus: a simultaneity modeling analysis of BRI countries Resour Policy 2020 68 1017 1028 10.1016/j.resourpol.2020.101751
Khan S Azam M Ozturk I Saleem SF Analysing association in environmental pollution, tourism and economic growth: empirical evidence from the commonwealth of independent states J Asian Afr Stud 2021 57 8 1544 1561 10.1177/00219096211058881
Kim S-J Kim E-M Suh Y Zheng Z The effect of service innovation on R&D activities and government support systems: the moderating role of government support systems in Korea J Open Innov Technol Market Complex 2016 2 1 51 69 10.1186/s40852-016-0032-1
Kongbuamai N, Bui Q, Yousaf HMAU, Liu Y (2020) The impact of tourism and natural resources on the ecological footprint: a case study of ASEAN countries. Environ Sci Pollut Res 27:19251–19264
Lan J Khan SU Sadiq M Chien F Baloch ZA Evaluating energy poverty and its effects using multi-dimensional based DEA-like mathematical composite indicator approach: findings from Asia Energy Policy 2022 10.1016/j.enpol.2022.112933
Li KX Jin M Shi W Tourism as an important impetus to promoting economic growth: a critical review Tour Manag Perspect 2018 26 135 142 10.1016/j.tmp.2017.10.002
Lin CY Chau KY Tran TK Sadiq M Van L Phan TTH Development of renewable energy resources by green finance, volatility and risk: empirical evidence from China Renew Energy 2022 10.1016/j.renene.2022.10.086
Liu Z Lan J Chien F Sadiq M Nawaz MA Role of tourism development in environmental degradation: a step towards emission reduction J Environ Manag 2022 303 114 128 10.1016/j.jenvman.2021.114078
Liu Z Lan J Chien F Sadiq M Nawaz MA Role of tourism development in environmental degradation: a step towards emission reduction J Environ Manag 2022 303 1140 1159 10.1016/j.jenvman.2021.114078
Manzoor F Wei L Asif M Haq MZU Rehman HU The contribution of sustainable tourism to economic growth and employment in Pakistan Int J Environ Res Public Health 2019 16 19 378 398 10.3390/ijerph16193785 30699988
Moslehpour M, Shalehah A, Wong WK, Ismail T, Altantsetseg P, Tsevegjav M (2022a) Economic and tourism growth impact on the renewable energy production in Vietnam. Environ Sci Pollut Res 29(53):81006–81020
Moslehpour M, Chau KY, Tu YT, Nguyen KL, Barry M, Reddy KD (2022b) Impact of corporate sustainable practices, government initiative, technology usage, and organizational culture on automobile industry sustainable performance. Environ Sci Pollut Res 29(55):83907–83920
Moslehpour M, Chau KY, Du L, Qiu R, Lin CY, Batbayar B (2022c) Predictors of green purchase intention toward eco-innovation and green products: evidence from Taiwan. Econ Res-Ekonomska Istraživanja 1–22.
Nathaniel SP Yalçiner K Bekun FV Assessing the environmental sustainability corridor: linking natural resources, renewable energy, human capital, and ecological footprint in BRICS Resour Policy 2021 70 101 118 10.1016/j.resourpol.2020.101924
Nguyen CH Ngo QT Pham MD Nguyen AT Huynh NC Economic linkages, technology transfers, and firm heterogeneity: the case of manufacturing firms in the Southern Key Economic Zone of Vietnam Cuadernos De Economía 2021 44 124 1 25
Nugroho P Numata S Resident support of community-based tourism development: evidence from Gunung Ciremai National Park, Indonesia J Sustain Tour 2022 30 11 2510 2525 10.1080/09669582.2020.1755675
Ojogiwa OT The crux of strategic leadership for a transformed public sector management in Nigeria Int J Bus Manag Stud 2021 13 1 83 96
Phuoc VH Thuan ND Vu NPH Tuyen LT The impact of corporate social and environmental responsibilities and management characteristics on SMES’ performance in Vietnam Int J Econ Finance Stud 2022 14 2 36 52
Quynh MP Van MH Le-Dinh T Nguyen TTH The role of climate finance in achieving Cop26 goals: evidence from N-11 countries Cuadernos De Economía 2022 45 128 1 12
Rasoolimanesh SM Taheri B Gannon M Vafaei-Zadeh A Hanifah H Does living in the vicinity of heritage tourism sites influence residents’ perceptions and attitudes? J Sustain Tour 2019 10 1 31
Richards G Cultural tourism: a review of recent research and trends J Hosp Tour Manag 2018 36 12 21 10.1016/j.jhtm.2018.03.005
Rindrasih E Witte PA Spit TJM Zoomers EB Tourism and disasters: Impact of disaster events on tourism development in Indonesia 1998–2016 and structural approach policy responses J Serv Sci Manag 2019 12 93 115
Ringle C Da Silva D Bido D Structural equation modeling with the SmartPLS Struct Equ Model Smartpls Braz J Market 2015 13 2 29 36
Rustanto AE Syah DO Tourism to Socio Culture and Economy of Community in Panusupan Purbalingga Trikonomika 2018 17 1 14 19 10.23969/trikonomika.v17i1.438
Sadiq M Ou JP Duong KD Van L Ngo TQ Bui TX The influence of economic factors on the sustainable energy consumption: evidence from China Econ Res-Ekonomska Istraživanja 2022 10.1080/1331677X.2022.2093244
Sadiq M Lin CY Wang KT Trung LM Duong KD Ngo TQ Commodity dynamism in the COVID-19 crisis: are gold, oil, and stock commodity prices, symmetrical? Resour Policy 2022 10.1016/j.resourpol.2022.103033
Sadiq M Ngo TQ Pantamee AA Khudoykulov K Thi Ngan T Tan LP The role of environmental social and governance in achieving sustainable development goals: evidence from ASEAN countries Econ Res-Ekonomska Istraživanja 2023 36 1 170 190 10.1080/1331677X.2022.2072357
Sadiq M Moslehpour M Qiu R Hieu VM Duong KD Ngo TQ Sharing economy benefits and sustainable development goals: empirical evidence from the transportation industry of Vietnam J Innov Knowl 2023 10.1016/j.jik.2022.100290
Scheyvens R Hughes E Can tourism help to “end poverty in all its forms everywhere”? The challenge of tourism addressing SDG1 J Sustain Tour 2019 7 1061 1079 10.1080/09669582.2018.1551404
Seow AN Choong YO Ramayah T Small and medium-size enterprises’ business performance in tourism industry: the mediating role of innovative practice and moderating role of government support Asian J Technol Innov 2021 29 2 283 303 10.1080/19761597.2020.1798796
Sharif A Saha S Loganathan N Does tourism sustain economic growth? Wavelet-based evidence from the United States Tour Anal 2017 22 4 467 482 10.3727/108354217X15023805452022
Sharif A Raza SA Ozturk I Afshan S The dynamic relationship of renewable and nonrenewable energy consumption with carbon emission: a global study with the application of heterogeneous panel estimations Renew Energy 2019 133 685 691 10.1016/j.renene.2018.10.052
Sharif A Afshan S Chrea S Amel A Khan SAR The role of tourism, transportation and globalization in testing environmental Kuznets curve in Malaysia: new insights from quantile ARDL approach Environ Sci Pollut Res 2020 27 25494 25509 10.1007/s11356-020-08782-5
Sharif A Baris-Tuzemen O Uzuner G Ozturk I Sinha A Revisiting the role of renewable and non-renewable energy consumption on Turkey’s ecological footprint: evidence from Quantile ARDL approach Sustain Cities Soc 2020 57 102138 10.1016/j.scs.2020.102138
Sharif A, Saqib N, Dong K, Khan SAR (2022c) Nexus between green technology innovation, green financing, and CO2 emissions in the G7 countries: the moderating role of social globalisation. Sustain Dev.
Shibli R Saifan S Ab Yajid MS Khatibi A Mediating role of entrepreneurial marketing between green marketing and green management in predicting sustainable performance in Malaysia’s organic agriculture sector AgBioforum 2021 23 2 37 49
Streimikiene D Svagzdiene B Jasinskas E Simanavicius A Sustainable tourism development and competitiveness: the systematic literature review Sustain Dev 2021 29 1 259 271 10.1002/sd.2133
Suki NM Sharif A Afshan S Suki NM Revisiting the environmental Kuznets curve in Malaysia: the role of globalization in sustainable environment J Clean Prod 2020 264 121669 10.1016/j.jclepro.2020.121669
Sumarmi KE Aliman M Community based tourism (CBT) to establish blue economy and improve public welfare for fishing tourism development in Klatak beach, Tulungagung Indonesia. GeoJ Tour Geosites 2020 31 3 979 986 10.30892/gtg.31307-530
Sun X Chenggang Y Khan A Hussain J Bano S The role of tourism, and natural resources in the energy-pollution-growth nexus: an analysis of belt and road initiative countries J Environ Plan Manage 2021 64 6 999 1020 10.1080/09640568.2020.1796607
Tan LP Sadiq M Aldeehani TM Ehsanullah S Mutira P Vu HM How COVID-19 induced panic on stock price and green finance markets: global economic recovery nexus from volatility dynamics Environ Sci Pollut Res 2021 10.1007/s11356-021-17774-y
Tian X-L Bélaïd F Ahmad N Exploring the nexus between tourism development and environmental quality: role of Renewable energy consumption and Income Struct Chang Econ Dyn 2021 56 53 63 10.1016/j.strueco.2020.10.003 35317019
Wan Q Miao X Afshan S Dynamic effects of natural resource abundance, green financing, and government environmental concerns toward the sustainable environment in China Resour Policy 2022 79 102954 10.1016/j.resourpol.2022.102954
Wang L Yotsumoto Y Conflict in tourism development in rural China Tour Manage 2019 70 188 200 10.1016/j.tourman.2018.08.012
Widjaja DC Jokom R Kristanti M Wijaya S Tourist behavioural intentions towards gastronomy destination: evidence from international tourists in Indonesia Anatolia 2020 31 3 376 392 10.1080/13032917.2020.1732433
Wirsbinna A Grega L Assessment of Economic Benefits of Smart City Initiatives Cuadernos De Economía 2021 44 126 45 56
Xu Z Chau SN Chen X Zhang J Li Y Dietz T Wang J Winkler JA Fan F Huang B Assessing progress towards sustainable development over space and time Nature 2020 577 7788 74 78 10.1038/s41586-019-1846-3 31894145
Zhang Y Li L Sadiq M Chien F The impact of non-renewable energy production and energy usage on carbon emissions: evidence from China Energy Environ 2023 10.1177/0958305X221150432
Zhao L Zhang Y Sadiq M Hieu VM Ngo TQ Testing green fiscal policies for green investment, innovation and green productivity amid the COVID-19 era Econ Chang Restruct 2021 10.1007/s10644-021-09367-z
Zhuang X Yao Y Li J Sociocultural impacts of tourism on residents of world cultural heritage sites in China Sustainability 2019 11 3 840 861 10.3390/su11030840 | 36929254 | PMC10018598 | NO-CC CODE | 2023-04-22 23:15:15 | yes | Environ Sci Pollut Res Int. 2023 Mar 16; 30(19):56863-56878 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
26229
10.1007/s11356-023-26229-5
Research Article
Green financing role on climate change-supportive architectural design development: directions for green architectural designs
Li Qiang [email protected]
grid.454879.3 0000 0004 1757 2013 School of Architectural Engineering, Binzhou University, Binzhou, 256600 China
Responsible Editor: Nicholas Apergis
17 3 2023
114
17 12 2022
27 2 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
The purpose of the study is to study the role of green financing in developing climate change supportive architectural design development to shift the modern world towards the idea of green architectural designs. Thus, the research estimated the nexus among green financing, green architectural development, and climate change mitigation by using the unit root analysis technique, co-integration analysis technique, bound-test estimates, auto-regressive distributive lag-error correction modeling (ARDL-ECM) technique to predict different short-run and long-run relationships, and robustness analysis technique. Following the previous study, modeling green financing index and green architectural design index are used to measure the variables. The findings of the study confirmed that green financing has significant role in supporting the climate change induction in architectural design development both in short run and long run. Moreover, green financing supports in promoting green architectural designs. By this, the viability of green financing in climate change that induces architecturally designed building is confirmed. Correspondingly, empirical results have shown that green financing contributes in climate change with 0.66, green infrastructure development with 0.72, and economic development with 0.31. While in long-run, green financing role in changing inside of climate of the architectural design is 0.74, supports in green infrastructure development with 0.67, and holds the 0.29 percent potential of contributing in economic development. These findings are robust with the 0.74 value of F-statistics, 1.89 value of t-statistics, and 110 value of Narayan standard estimate. In last, the study suggested way forward for stakeholders to promote green architectural designs to achieve SDG 8, SDG 11, and SDG 13.
Keywords
Green finance
Financial access
Architectural design development
Climate change mitigation
Green architectural designs
SDGs
==== Body
pmcIntroduction
The United Nations estimates that a worldwide expenditure of $5–7 trillion USD is necessary to achieve the 2030 Agenda for Sustainable Agenda through architectural design developers. Unfortunately, due to the shock of architectural design developers caused by the coronavirus outbreak, research shows a funding shortage of 1.2 trillion USD annually to achieve this aim. China, the world’s most excellent emission, will need special legislative assistance from the USA to become carbon neutral. China Development Cooperation Expansion Scientific Report 2019, published by the Chinese People’s Bank, shows that in 2018, the overall demand for green finance financing was 2.1 trillion RMB. Still, the current quantity was just 1.3 trillion, creating a funding gap of 0.8 billion US dollars RMB. To satisfy China’s carbon reduction promises, the country has to advance the technology required to do so, which necessitates a well-developed green finance system.
According to Ragheb et al. (2016), architectural design requires a careful balancing of both social and scientific considerations to achieve a reasonable reply that may meet the demands of its intended stakeholders of the architectural design developers. When it comes to concreting an architectural response to a given issue, the sociological materialist cognitive perspective is crucial for the architectural design developers, as it guarantees the final disposal of the services and usually helps to meet the demands for supportive architectural design development (Kembel et al. 2012). Green finance is integral to international carbon reduction because it can eliminate harmful emissions without severely hurting architectural design development (Huseynov 2011). Green finance involves financial flows (Brophy and Lewis 2012) from the government and corporations instead of domains to sustainability programs meant to phase out pollution emitters and other stimulate alternatives. Notwithstanding the critical drivers of world trade and economic growth, green financing is vital to expanding renewable energy sources and the transition to reduced civilization and architectural design development (Yuan et al. 2017). The growth of the architectural design development sector is essential to a large amount of up-front cash. It depends on financial mechanisms to attract currency traders to contribute to architectural development via climate change mitigation strategies. Given the lengthy expenditure phases and poor short-term viability of green businesses of architectural design developers, the funding challenge is unlikely to be solved only by economic measures, requiring the backing of public policy (Well and Ludwig 2020). This is one of the core problems of architectural design developers that need a fix. Economic growth often encourages environmentalism in nations with a thriving money system, such as China (Shi and Yang 2013).
To seek green financing for green architectural design development, the role of banking institution is much integral (Chang et al. 2023). They have a responsibility to help prevent climate change by integrating “green” forms of finance into national policies to reduce exposure to psychological and economic risks arising from climate change in architectural designs (Irfan et al. 2022). Green financing is a valuable policy tool for recovering from the thread downturn (Lee and Lee 2022). Wind energy, for instance, is essential to emission reduction and cutting down on carbon emissions. Still, its expenditure and generating costs have been rising only until the recent breakout of the COVID-19 virus. Carbon tax, transferable green credentials, and ecological credit are all examples of green finance policies that aid in mitigating the pandemic’s negative impacts (Debrah et al. 2022). That will further strengthen the economy in the post-pandemic period, and green finance may successfully mobilize a broader pathway involving (especially from the corporate companies) and demonstrating the importance of financialization in the growth of green finance by using the example of reduced agriculture to argue for the prioritization of a “financing reservoir” in which current assets are integrated to optimize reducing emission activities in architectural designs (McDonough 2002).
Since architectural system theory can assemble numerous iteration stances of its different stages, elucidating the dilemma, drawing up the specifications, developing a new artifact, displaying the artifact, and reviewing the artifact by background information are going to result from studies to comprehend the world. This result recommends that the scientific architecture method be executed within the architectural design process rationale to develop a robust template for making a good result (Meo and Karim 2022). As far as the writers are aware, this research is the first effort to integrate building science principles into creating a built structure. To demonstrate the method’s viability, a singular case study method is chosen for developing the architectural designs (Khan et al. 2022). Investigations and historical analyses of the plight of refugees in shelters suggest that the present iteration of the shelters is inadequate. Researchers use a design technique to fill in the design’s holes by creating a structure that considers consumers’ requirements while providing a comprehensive appeal solution. An anthropological method is used to learn on how people feel about shelters (Zhou et al. 2022). The proposed approach for architectural design development is into action first to generate a set of requirements that may be used to direct the workflow. Then, it is tested via the actual design of shelters by a group of designers and architects (Yin and Xu 2022).
Prior research confirms that green finance helps advance climate mitigation efforts and develop the green architectural design in the architectural design domain (Zhang et al. 2022a, b). China’s green financing regulations bolstered the case for more sustainable energy growth, which resulted in a substantial decrease in industrial gas emissions between 2011 and 2018 and a decrease in coal use between 2004 and 2017 (Li et al. 2021b). At the same time, green finance has been more common in China over the last two decades in different domains, including architectural design development. Its popularity has expanded rapidly in the country’s eastern provinces and is still relatively low in its middle and western regions (Sharma et al. 2022). A decrease in total emissions would protect the beneficial properties of credit derivatives. At the same time, an increase in carbon intensity would impair China’s market-based financial sector and general adoption of green finance (Jinru et al. 2022). For an appropriate threshold changeover, the researchers also detail the continuous green funding strategies required over the long term. Nevertheless, global emissions of CO2 decreased by 6.4% in 2020, but they began rising again by the middle of the year and have since recovered to there, which was before proportions (Lu et al. 2022). This reduction has accelerated the 14.5% capacity of green architectural design developments in China that has been witnessed before (Lu et al. 2022). It is a sobering indication of the magnitude of the continuing issue that existing measures have not reduced carbon output in line with the objectives set out by the 2015 Paris climate accord (Su et al. 2022).
Thus, the research objective is (i) to investigate the short-run and long-run role of green financing in developing green architectural design developments by promoting climate change mitigation in green buildings and infrastructures in China. (ii) Furthermore, this is the first theoretical contribution of the research. Secondly, the study contributes by presenting holistic empirical details verifying such nexus among the variables using the ARDL-ECM test and ARDL-bound text, short-run and long-run analysis, co-integration technique, and sensitivity analysis. (iii) Thirdly, the study suggests recommendations for four main stakeholders: financial institutions dealing with green finance, climate change managers, green infrastructure developers, and China’s regulatory authorities. Furthermore, the environmental protection hypotheses are tested using the autoregressive-distributed lag-error estimation technique to look at any links between trade openness and carbon dioxide emissions per capita (ARDL–ECM). (iv) In addition, this research will examine whether or not the conservation/feedback hypothesis holds for the variables in question, given its prevalence in ARDL analysis. The data supports the sustainability theory but disproves the actual being considered. The conserved impact of trade openness and economic growth may be seen in green banking and carbon dioxide emissions; the response assumption is only valid in this context.
The structure of the study includes five main sections. “Introduction” section explains the introduction; “Architectural Design Development” section elaborates on the previous efforts on green architectural designs and process development and the role of green finance in climate change mitigation for architectural design development. “Data and Methodology” section presented the methodology, “Results and Discussion” section interpreted the results and discussion of empirical findings, and “Conclusion and Implications” section concluded the subject matter of preceding research and directed the implications to the stakeholders.
Architectural design development
Green architectural designs and process
The field of green architectural design development research is the latest in modern times. This field aims to innovate by producing novel green architectural designs for the modern future. According to Su et al. (2022), architectural design development was initiated in the 1960s. In 1920, projects like Le Corbusier’s “computer for life” began receiving backing from engineers and architects who saw architecture as an objective definition to demonstrate creative expertise (Ong 2003). To illustrate this point, Stang and Hawthorne (2005) suggested “a conceptual framework for architectural design development. This developed and shifted the goals of architectural design development towards a new direction, and they also changed the intent to move for urban planning” (p. 14). By this, urban planning planned to create an artifact based on accurate information to solve problems in a particular circumstance (Edwards and Naboni 2013). With time, the architectural design development concept emerges, and the conceptual framework approach is intended to enable a product creation that addresses a challenge in the real universe by developing the latest innovative buildings. In urban planning, there are several stages, each of which necessitates its study procedure. Design evidence goes beyond the goals of many experimental methodological approaches by actively pursuing development, progress, and the development of new worlds in the shape of methodologies, concepts, ideas, and specifications (Wasley 2000).
According to Megahed and Ghoneim (2021), diverse people and places may raise awareness about a problem and provide novel insights into its nature and potential solutions. This is followed by a proposal, which yields a preliminary idea. Other methodological approaches may propose a design, but these features are but one piece of the puzzle. A design project has links and integrations to design science (Rijal et al. 2021). If the customer expects that the recommended design does not solve the study question, the designers will go back to the proposal phase and reexamine the problem criteria. The artifact is the result of the development process when a new product is built and tested to meet the requirements established by the study (Sussman and Hollander 2021). Researchers involved in this research create a set of requirements for an artifact that considers the demands and routines of refugees in terms of things like security, weather, consistency, and social difficulties. In another analytical framework, the evaluation phase may propose more studies be done based on the results. Still, in urban planning, the evaluation phase provides additional information and data on the artifact by professionals, allowing a further round of the recommender process to develop and implement a new proposed design. In this experiment, architects and engineers work together to create housing concepts using predetermined criteria (Zhang et al. 2021).
Green architecture designs draw from 2 different academic realms, the first being the study of human behavior and the second being the study of designing (Maturana et al. 2021). Whether in the natural or the social sciences, the issue at hand drives the formulation of a scientific assumption, gathering relevant evidence, and testing that supposition (Chen et al. 2021). Several empirically show that green financing, green architecture designs, and climate change mitigation have been inferred. More specifically, the architecture designs and engineering phases include analyzing real-world issues, developing solutions, and demonstrating and evaluating (Layton 2021). Therefore, finishing the tasks associated with design science and achieving an awareness of the right necessitate the development of a novel artifact. The design science research cycle is more applicable to theory and practice than other research techniques. Investigators can review and adjust their study findings and approach to the fully described issue during the first four stages of urban planning because of the iterative process involved (Kuhn et al. 2021). Therefore, the many phases of design and architecture development may be reinterpreted as iterative evaluative processes according to the usability evaluation logic (Wang et al. 2021).
Green financing role in green architectural development
The fundamental challenge that scholars’ address is how to create a green finance indicator that is both thorough and technically solid for green architecture designs. A recent study is also one attempt (Debrah et al. 2022). For this, these approaches have sparked a slew of quantitative research on green financing’s role in green architecture designs. The variables they use only provide a limited picture of the whole spectrum of green finance implementation and hence are not entirely indicative of the recent trend (Chua and Oh 2011). The issue was addressed by developing green finance indexes for the Chinese market (Bai et al. 2022). We followed the Guideline on Building a Green Financial System, a study published by China People’s Bank and its partners. We built a green finance freedom index using an enhanced entropy technique rarely used in studying green architecture designs (Butler 2008), building a green finance index on eco-friendly credit, equities, medical coverage, and financing using the exact enhanced intermittent nature (i.e., by implementing green credit, green bonds, green medical coverage, and eco-friendly investment). At the same time, they combine information obtained from eco-friendly credit, eco-friendly equities, and green financing (Zhang et al. 2022a, b). The index is built using ecological lending, green bonds, green investments, and environmental finance, including green finance legislation and is then subjected to a worldwide principal component evaluation (Li et al. 2020).
Create a greener foundation that considers China’s local indexes’ distinctive chronological and geographical features and evaluation assessments for developing green architecture designs. The study concludes that between 2010 and 2019, their sustainable financing index showed a general upward trend, with regional differences narrowing in the field of green architecture designs (Meirel et al., 2019). Moreover, the indicator level generally increased from northwestern to eastern regions, except for northeast China, which had the lowest (Singh et al. 2020). It is important to note that a new approach quantifies green finance by focusing on the additional financing arrangements of firms for climate change-induced development of green architecture designs, with both short- and long-term loan financing (Matsuo et al. 2022). Chinese academics constructing green finance indexes rely heavily on official material from both national and provincial governments (Hockett 2020). Although appealing, solutions developed with inputs from the Chinese context only apply inside that country (Chegut et al. 2019).
Research shows that green financing increases GDP (GDP). Nonetheless, green finance has trailed beyond economic development in China, and synchronization between the two is still inadequate due to insufficient integration of green financing with pollution prevention and modernization (Firmansyah 2016). It would seem that state R&D expenditure on renewable electricity and human and technological assets boosts the greener future, though the amount of this boost may vary by region. Additionally, quantitative income activity is becoming a reality due to the rise of green investment and eco-friendly technology. Qualitative improvement may be defined as creating “a sustainable ecological and a joyful community for humans during economic growth.” Using this idea as a guide, we discover that China’s green finance creation positively affects all three dimensions of productivity expansion: the state of the environment, the economy’s productivity, and the economy’s nature. Investment policy may help the industry structure in certain situations by channeling funds toward energy efficiency and pollution prevention projects (Khan 2019).
Furthermore, polluting emissions (such as industrial sulphur dioxide, sewerage, and pollen), innovation, green value-added, and resource adaptation in China are all primarily driven by green financing. In particular, a temporal data analysis demonstrates that the benefits of green financing may spread to nearby areas and that green finance levels often rise when geographical gaps are closed. It has been shown that green financing in China favors green architectural designs by taking climate change mitigation steps (Hwang and Tan 2012).
Developing climate change-supportive architectural design
In current history, scientists have begun focusing more on the actual effect of green financing on carbon dioxide emissions. (Bilal 2021). It shows that green financial programs have a massive effect on output in 10 developed economies and validates the importance of green finance-related policies in advancing supportive architectural design. Investment policy for developing climate change seems to be a successful instrument in addressing climate change and preserving the environment in both developing and industrialized countries. According to the research by Dong et al. (2020), a climate change emission system often offers an ideal backdrop for carbon moderation in support of architectural design. Correspondingly, the decarburization effect performs best in eastern China for developing supportive architectural designs as such architectural designs have the essential feature of remaining climate-induced via climate change mitigation (Kibert 2016). However, research suggests developing climate change-supportive architectural designs for which a recent study is intended to investigate. Studies suggested that green financing is a set of source of funds to achieve supportive architectural designs in developing climate change mitigation (Tanzer and Longoria 2007). On this, the government and legislators play a critical role in developing green principles, but businesses must put those policies to use via investments, innovation, and output.
An extensive distinction analysis shows a favorable impact of green financing reforms in Russia’s pilot regions on corporate innovation. A successful green financing strategy helps impoverished provinces, particularly companies in China’s western and central regions preoccupied with saving energy and environmental protection (Drobnick 2021). Despite their tremendous innovation potential, closely held businesses face more funding limitations than their publicly held counterparts (Meloni et al. 2021). Climate change regulations have also helped decrease pollution in developing green architectural designs, including dioxide emissions and sewerage (Huang et al. 2021a, b). In most cases, China’s strict green credit regulations make it more challenging to get a mortgage (Filiaturault et al. 2021). Large companies often use environmentally friendly equipment and technology to reduce emissions independent of this legislation, while small companies prefer to restrict output to achieve environmental laws (Norouzi et al. 2021). On this, the green financing positive impact on lowering emissions is moderated, however, by the use of renewable energy. In other words, the advancement of new energy like wind, solar, hydroelectric, and nuclear would further cut carbon dioxide emissions in the value of current to these industries. This study attempts to affirm the connection of green financing’s role in climate change-mitigated development of green architectural designs. The results show that China’s green architectural design initiatives are more effective than other countries and have encouraged the advancement of green financial-related policy initiatives in China (Sun et al. 2021).
Data and methodology
Theoretical support
Green financing (GF) is a collection of payment systems created to support initiatives that boost the environment and develop green architectural designs (Fawzy et al. 2020). The research intends to enhance the long-term stability of green financing in developing green architectural designs (Schwanen et al. 2011). Theoretically, previous research has described that developing green architectural designs are needed to remain supportive of climate change, for which green financing is essential. Studies also established that green financing has the potential to support green architectural designs through climate change mitigation, and this is one of the core funding sources of the business (Wright and Fulton 2005). Using such theoretical backgrounds, this research offered an empirical discussion about the study variables and discussed the potential of green architectural design development using green financing (Lamb and Steinberger 2017).
It is because green finance has access to a wide variety of financial tools, such as public money, angel investors, venture capitalist, equities, borrowing, retirement funds, and infrastructure improvement financing, and it calls attention to the value of GF and the necessity for new forms of green growth finance. The theoretical background of the study highlighted the requirement for a theoretical and purposeful approach to developing and implementing a green financing framework for climate change mitigation to construct green architectural designs of the buildings (Paltsev and Capros 2013). The literature has highlighted that green financing prospects in a nation have been the subject of academic evidence by several academics. Studies further examined and theoretically supported the link among green financing, green architectural designs, and climate change mitigation. The literature supported the nexus and was demonstrated, and the findings demonstrated that green financing has the potential to substantially increase green architectural design development (Peeters and Dubois 2010). Despite this, some investment risks in GF may be mitigated by creating green bond principles (GBP) and expanding the green bond marketplace. Previous studies also highlighted that green financing, climate change mitigation, and green architectural designs depend highly on geographic location (Nielsen et al. 2020). The study’s theoretical background supported the research findings and suggested that China may be affected by coordinating its GF with its sustainable future of green architectural design development. This output is developed by industrial specialists and designers (Newell 2010).
Research data
The study analyzes the nexus among the parameters using the long-run data range from 2014 to 2019, respectively. Green finance is a composite variable comprising several sub-indicators, house repair, house repair/permanent/core housing, transitional shelter, core shelter, climate impact, and climate control. The details of the sources are mentioned in Table 1.Table 1 Description of variables
Variable Function Proxy/sources
Green financing Green finance index See the appendix
CO2 emission per capita CO2 emission per capita metric tons Global Carbon Atlas
Green infrastructure design House repair/permanent/core housing Design function implementation
Transitional shelter Material implementation and urban upgrade
Core shelter Planning implementation
Climate impact CO2 emission
Climate control Environmental Stability
Estimation technique
It is a usual practice in finance-environment-green architectural designs to look for static long term between variables; nevertheless, it is also feasible for variables to have an interactive interconnection over the short and long term. The auto-regressive distributive lag-error correction modeling (ARDL-ECM) technique and the ARDL bound testing model are often used to examine this vector error correction process since it not only evaluates the error correction model to capture the connection among study variables. Thus, the study applied the ARDL-ECM technique, ARDL bound test, co-integration analysis, correlation analysis, fixed and random effects for model specification, and the sensitivity analysis technique. The ARDL technique corrects the main flaw of specific popular approaches, including the maximal likelihood estimation (MLE) approach (Huang et al 2021a, b; Sun et al 2022). The ARDL bound testing model requires the following verification procedures. This research begins with a comparison of the outcomes of the unit root test. With the ARDL co-integration method, time-series variables may be merged at either the first or second difference level, depending on the nature of something like the series. Next, we use a bound test to see whether there is only one stable connection between the parameters. To verify that the connection is not stable over time. Two benchmarks based on 30–80 samples are used when determining the critical value. Third, because Gaussian error terms are needed while building the ARDL-ECM process, choosing the suitable lag duration is a crucial problem. In this research, we perform and compare the most popular model order selection procedures. Finally, the study allows for the accompanying variant of the generic-estimated model to be created, which can be used to investigate bidirectional causality across all variables. Thus, using such a technique, the econometric notion form of the research variables is given below:1 CO2t=fFINt,ECOt,TRDt
The econometric form of the study model is as follows:2 lnCO2t=C0+α1ln(FIN)t+α2ln(ECO)t+α3ln(TRD)t+εt
These results are based on the simplification assumption that all variables have the same lag order and that yt is the error term. At last, the static ARDL model gets metamorphosed into the functional ECM framework.3 yt=C+αT+∑i=1pθiyt-i+∑j=1k∑i=0qβjixjt-i+μt
While investigating long-term associations, the limitations of the traditional ARDL regression analysis in providing insight into short-term component behavior could also provide obstacles. ECM enables the simultaneous integration of empirical fluctuations among the study variables and the long-term stabilization while causing mistakes such as false estimations from non-stationary parameters. For this reason, the ARDL model is re-parameterized. The error-correcting ARDL measurement model may be written like this by linearly transforming problem (Eq. (1)):4 Δyt=C1+α′T-φECMt-1+∑i=1p-1θi′Δyt-i+∑i=1k∑i=0q-1βji′Δxjt-i+μt
The ARDL-ECM methodology improves upon previous approaches in many ways: its projection parameters have used various lag frameworks, resulting in more accurate regression results; it differentiates the variance from the predictors if the long-run homeostasis connection occurs; it permits testing co-integration relationships; it diminishes anomalies and serial correlation, and it is suitable. From Eq. (4), we may deduce the ARDL-ECM, which is defined as follows:5 ΔlnGIDt=C1+∑i=1pβiΔlnCO2t-i+∑j=0qβjΔln(GF)t-j+∑m=0rβmΔln(ECO)t-m+∑n=0sβnΔln(TRD)t-n+εt
where Δ represents a divergence of parameters within the first order, C1 is a coefficient of determination, and t is a standard deviation at the level of white noise. Long-run vector error correction linkages exist between the three indicators. A similar idea may be made because of the t-statistic.6 ECMt-1=lnCO2t-1+ln(FIN)t-1+ln(ECO)t-1-ln(TRD)t-1
Nevertheless, in the long term, this correlation breaks off. The regression relationship shows a surprisingly delayed return to balance, which suggests around a 7% deviation from the long-run equilibrium.
Results and discussion
Developing green architectural structure
The study findings reveal a significant lack of funds to the architecture design sector for producing green architectural buildings for climate change-friendly environments inside the building. According to empirical results in 2014, residing individuals around 8% who have chosen to reside outside the green architecture building have access to reliable and safe housing (Table 2). Without consulting survivors, humanitarian groups and local officials hid the transitory condition. After two years, in 2016, the residing individuals in green architecture buildings were found to have no other option than to take permanent countermeasures. Still, the focus on max velocity and minimal housing prices seems to concern the residing individuals. To evaluate where and how these nations are generating less in climate as a credible funding source using climate change mitigation, the research specifically deduced the redeployment of green financing system with the climate change mitigation metrics in the Chinese context. As a basis for analyzing improvements in carbon risks—another source to quantify global climate issues—familiarity with green energy financial indices may enhance countries’, enterprises’, and pension funds’ ability to make decisions to reduce global warming.Table 2 Green finance index
Eigenvalues Variations Percentage Cumulative KMO-Bartlet test
Composition 1 0.898 0.803 0.332 0.719 0.159
Composition 2 0.273 0.751 0.129 0.396 0.949
Composition 3 0.004 0.384 0.352 0.161 0.322
Eigen vector parameters
Green financing index 0.935 0.813 0.564 0.144 0.915
Climate change mitigation 0.805 0.805 0.183 0.503 0.856
Green infrastructure development 0.607 0.014 0.521 0.313 0.306
0.368 0.946 0.754 0.694 0.478
Correlation
GFI CCM GID TOP ED
Green financing index 1
Climate change mitigation 0.636* 1
Green infrastructure development 0.409* 0.312* 1
Trade openness 0.781* 0.787* 0.473* 1
Economic development 0.321* 0.078* 0.935* − 0.111* 1
Moreover, Table 3 presents the unit root test, where all variables are significant at level one. Furthermore, it is essential to note that China has measured to address climate change and promoted sustainable power that differs greatly depending on the country’s condition regarding green architecture building. With such a high score of green architecture buildings, it is clear that the environment has changed for the better green architecture building treatment using climate change mitigation through green financing. Trade openness (TOP) and economic development (ED) are also found stationary at level but significant at level one.Table 3 Unit root test
CIPS CADF
Level 1st diff Level 1st diff
GFI 1.949 1.492* 0.458 1.621*
CCM 1.322 0.274* 1.821 1.514*
GID 0.915 0.299* 0.243 1.159*
TOP 1.856 1.013* 0.607 0.854*
ED 1.478 0.135* 0.009 1.828*
Stationary and co-integration analysis
Table 4 indicates that green financing is recurring, meaning its usual power is relatively higher than others. Correspondingly, the co-efficient value of the parameters spanning the various facets of climate change adaptation is more excellent in the treatment countries.Table 4 Co-integration analysis estimates
Zero shift Average shift Structural shift
β P-value β P-value β P-value
LMσ 0.517 0.009 0.212 0.000 0.271 0.001
LMρ 0.312 0.002 0.348 0.000 0.778 0.000
Table 4 shows that China’s quickest development in climate change mitigation is found to decrease. A growing number of household individuals residing in green architecture buildings are concerned that the rise in buildings pollutes more per capita than traditional buildings. Climate change emission mitigation is complicated but a bit easy through green financing in the context of green architecture building development. According to research findings, the Chinese setting somewhat has a “quasi-trailing effect” that confirms the findings in Table 4. Consequently, many inconsistencies are uncovered, many of which may be traced back to contextual movements and external conditions. To keep the temperature rise to “less than two degrees Celsius” in green architecture building designs, it may be claimed that significant private investments in sustainable power from various companies are required. The reduced carbon index empirical findings in China are 0.78, while the fewest in China is 0.43. The findings suggest that China has a solid foundation to build sustainable and green funding and decarburization initiatives towards green architecture building.
The more significant the amount of the green finance index for green architecture building via climate change mitigation, the more it may contradict signals and advantages of the cost type. If the index value is over 100, the country or organization has made considerable strides in improving its renewable infrastructure. Countries with a higher index score are more likely to have reduced pollution and a more diverse portfolio of renewable energy sources. The power sector is a growing industry that can and ought to promote global growth by helping to narrow the wealth gap between green architecture building designs and climate change mitigation due to green financing. It is due to green financing (Table 5) extending to it. Climate change effects on green architectural building designs are found to be limited in terms of coefficient of determination, and the findings revealed that it might well be able to slow the progression of changing climate change by increasing their use of renewable energy, investing in R&D, and increasing their renewable production (Table 6).Table 5 Specifying model: green financing (IV) and green infrastructure development (DV)
Study parameters Fixed effect Random effect
GFI 0.935 − 0.00094
(0.003) (0.000)
CCM 0.159 0.177
(0.009) (0.001)
GID 0.496 0.753
(0.004) (0.000)
TOP 0.516 0.435
(0.001) (0.003)
ED 0.148 0.586
(0.001) (0.001)
Constant 0.647 0.432
(1.693) (1.123)
R-square 0.796*
Hausman test 0.1092 0.3234
Table 6 Specifying model: climate change (IV) and green infrastructure development (DV)
Study parameters Fixed effect Random effect
CCM 0.448 0.572
(0.000) (0.000)
GID 0.255 0.304
(0.001) (0.000)
GF 0.505 0.801
(0.007) (0.000)
TOP 0.233 0.862
(0.005) (0.001)
ED 0.389 0.756
(0.003) (0.001)
Constant 0.760 0.555
(2.994) (1.2648)
R-square 0.809*
Hausman test 0.1134 0.1718
Specifying model through fixed effect and random effect technique
Energy from renewable sources, such as wind, sunlight, and hydroelectric, would make these nations more eco-friendly and productive. The research indicates that between 2 and 3% of GDP would be required between 2011 and 2030 to switch to a low-carbon economy. On the other hand, as shown in Table 1, low-income nations struggle because of insufficient incentives, weak carbon development support mechanisms provided by financial institutions, and a lack of expertise in implementing low-carbon programs.
The results of Table 5 and Table 6 show a potential confounder adjustment. The study results illustrate that a 36% increase in energy availability might affect the changing climate in the Chinese provinces for green infrastructural design development and promotion. While green financing, on the other hand, it has an influence on the Chinese provinces under research, with a meaningful impact of 25.17%. Meanwhile, as of Table 6, climate change mitigation in biofuels has a negligible impact at 17.11% and 21.28%. For green infrastructural design development, green financing with means points to a significant influence on climate change. There is a 7% chance of environmental effects from energy-related pollution because of climate change. Using the marginal effect findings, we may infer that there is a possibility of percentage growth in one unit due to the variation in the other variable unit. Our analysis reveals a robust relationship between GDP per capita and the purchase power parity of the US dollar in 2016, 2017, 2018, and 2019. New information may be uncovered with the help of this parameter, which in turn encourages environmental issues by attracting local and global expenditure in the energy economy (see Table 2). The findings also highlighted that the climate change mitigation sectors depend in large part on the quality of their efforts for research and development for a total of $237 billion, and the six MDBs have supported renewable energy initiatives during the last 3 months.
In our findings reported above, a one-unit shift in the fraction having a transitional shelter to manage climate impact and climate control in green infrastructural design development has a rate of 0.184%. However, these are valuable indicators determined significantly by green financing and climate change mitigation.
Optimum lag-length test
The empirical results of Table 7 suggest that the number of delays (H) for green infrastructural design development due to climate change mitigation is crucial. In this study, we focus on choosing the estimates and determining and utilizing the best number of lags in the Ljung-Box test, ensuring that the test size does not go over the test level, and the strength does not fall below a certain quantity. The impact of picking the wrong amounts of H on the actual size and power of the Ljung-Box test is explored via simulated exercise. Findings corroborate that the superior value is context and data-dependent.
Table 7 Optimum lag size determination of green architectural development
Lag GF GID CCM DF Significance AIC SC HQC
0 7.52 - 9.75 6 0.0008 − 1.34 − 3.54 − 1.29
1 6.19 2.13 8.33 6 0.0002 − 1.99 − 5.78 − 0.65
2 9.78 2.92 7.82 6 0.0009 − 1.04 − 4.14 − 0.42
3 6.72 8.29 3.46 6 0.0005 − 1.62 − 3.06 − 1.59
4 5.75 6.58 7.25 6 0.0000 − 1.74 − 3.45 − 1.76
5 - 0.62 1.72 6 0.0000 − 1.67 − 2.89 − 1.93
ARDL-bounded test may be inferred using the natural log green financing index (ln(GFI)), natural log of green infrastructure development (ln(GID)), and the natural log of climate change mitigation (ln(CCM)). The results of the study are found positive. All the results are inferred at the 95% upper limits and the 95% of lower limits. The f-statistics value is 7.14, the t-statistic value is 1.89, Narayan standard value is about 110, KMO-test is inferred with 0.773, and the values of the Chi-square revealed green financing at an upper limit of 0.467, the lower limit with 0.339, green infrastructural design development upper limit with 0.535 and lower limit with 0.962, and climate change mitigation with upper limit as 0.169 and the lower limit with 0.148 respectively. The findings are significant with previous studies (Zheng et al 2022; Bilal et al 2022; Wang et al 2022).
Table 8 Bounded test estimations
ln(GFI) ln(GID) ln(CCM)
95% upper limit 95% lower limit 95% upper
limit 95% lower limit 95% upper
limit 95% lower limit
F-statistics (value = 7.14) 0.551 0.479 0.105 0.798 0.761 0.146
t-statistics (value = 1.89) 0.211 0.281 0.586 0.135 0.566 0.884
Narayan standard (value = 110) 0.267 0.202 0.605 0.495 0.238 0.745
KMO–test value (0.773) 0.325 0.247 0.549 0.657 0.134 0.851
Chi-square estimates 0.467 0.339 0.535 0.962 0.169 0.148
This article gives a holistic empirical presentation of the empirical nexus among green financing, climate change mitigation, and green infrastructural design development, along with the critical values for the testing on the delayed levels of the distinct variables based on theories that determine the restricting probabilities of this statistical test. By making the mean value tables available, the assay may be used by a wider variety of scientists with less effort. This is the significant contribution of the study (Table 8). Incorporating all three experiments into the commonly used ARDL process provides a definitive answer to the question of serial correlation, and this addition will be helpful for those who decide to do so.
ARDL-ECM estimates
ARDL-ECM estimates are only a few examples of authors that have employed the ARDL limit test to test for short-run estimates (Table 9). The study results further re-evaluated the connection here between the capacity of climate control, house translation, green infrastructural design development, the total cost of green infrastructural design development, and wealth creation in China, confirming the nexus in the short-run link between the green financing index, green infrastructural development, and climate change management. Several of these research results have significant consequences for monetary strategy in green infrastructural development. The data must be accurate and robust so that policymakers and organizations may make informed judgments and conduct thorough analyses of government policy.
Table 9 ARLD-ECM short-run estimates (DV: Green Infrastructure Development)
Parameters Coefficient SE t-value
ln(GFI) 0.9812* 0.2743 1.55
ln(GID) 0.6959* 0.0332 2.33
ln(CCM) 0.2633* 0.2277 1.02
Constant 0.1354 0.0261 2.56
R-square 0.81
A massive quantity of sustainable green money is required for rapid development and sustained health. This means that all economies must ensure that their customers and producers have access to cheap electricity at reasonable prices. Real-world data-driven analysis has placed China atop the energy and environmental performance league tables. This is probably connected to China's ongoing environmental and energy programs. China has implemented rigorous tariffs and energy efficiency, renewables, and green energy consumption programs despite increasing imported energy. Another case in point is the work being done by the Chinese government to cut pollution by 80% by the year 2050. Several researchers came to this conclusion. The clean air strategy has set a target of lowering carbon dioxide emissions from mobility by 40% by 2020 and 46% (Table 10) since this sector accounts for a disproportionate share of the country’s total energy consumption. With the adoption of new global change laws in 2008, China set a goal of reducing its carbon emissions by 80% by 2050.
Table 10 ARLD-ECM long-run estimates (DV: Green Infrastructure Development)
Coefficient SE t-value
ln(GFI) 0.4116 0.0433 1.84
ln(GID) 0.2797 0.1781 2.34
ln(CCM) 0.1351 0.5747 1.06
Constant 0.0181 0.4692 1.28
R-square 0.85
Robustness test
The robustness of the study’s findings is illustrated in Table 11, and their sensitivity analysis is displayed in Table 11. This ground-breaking research indicates that climate science will disrupt routine business. Fifty-four percent of Asian countries face the threat of flooding due to tides and storms, whereas just 29% of China countries are at risk. Energy saving financing in the example of China is also mapped out in the research. The bulk of the world’s people live in countries like China. There has been an increase in the need for alternative power sources as a direct consequence of the rising population. It controls 37% of the global market. You can see the global reach of China’s trade policy by considering where it has been implemented. When Chinese firms and other native firms invest in host countries, the playing field expands to include more players.Table 11 Sensitivity analysis
χ2 statistics P-value
Breusch-Godfrey LM test 0.187 0.008
White heteroscedasticity test 0.571 0.001
Ramsey RESET test 0.565 0.001
Skewness: 0.079 0.842 0.000
Durbin-Watson 1.717 0.000
To build carbon credit markets, it is necessary to use policy instruments with binding powers. In today’s setting of fast change and national boundaries for green infrastructural development, it is conceivable for these activities to reduce carbon dioxide emissions using green finance successfully. Money flow restrictions and other fiscal tightness have real diplomatic ramifications on the national and international levels of green infrastructural design. In addition, the leaders of China are short of time to carry out analyses and document other planned efforts, such as the China green energy fund Covid-19, to mitigate the effects of climate change on future pandemics. China has established this fund, although supply chain interruptions and poor asset flow have momentarily impeded the acquisition of inevitable green infrastructural design development.
Discussion
With this real-world problem-solving orientation, designing intelligence bridges the distinction between the theoretical realm of scientific work and the applied one. The study findings claimed that the inability of physical scientific research to alter existing or developing occurrences impedes the delivery of novel solutions. This research is the first to use a design research methodology to provide a comprehensive architectural process grounded in facts, prioritize its end users’ needs, and answer more conventional “scientific” inquiries. This research endeavor takes on the design challenge of reforming refugee shelters to accomplish this goal. The diversity of customers’ demands is not fully considered in the existing design of refugee shelters. Hence, this is the design problem chosen. Accordingly, a novel answer (artifact) is proposed using the design research process, and it is intended for use in refugee shelters in hot-dry regions. The catalog of requirements serves as the artifact in this analysis. There is a lack of clarity in the evidence provided in the literature, according to the vast majority of contemporary university careers on refugees. Recognizing the difficulties associated with refugee housing in settlements may be achieved through the use of design research to the creation of a creative approach. Scholarly work on the minimum requirements for a shelter is few. Green infrastructural design development and the shelter project provide rules and criteria for alternative evacuation centers. In contrast to behavioral science and research, system theory focuses on potential resolutions. The outcome is the product of rational research into theoretical and applied issues in the subject. As a result, experts may use the solutions to create a user-centered style based on an iterative manner of gathering feedback and refining the plan.
System theory and other research techniques, including case studies and research methods, have certain things in common for green infrastructural design development (Yang Et al 2022; Zhao et al. 2022). There is a distinction in epistemology perspectives, nevertheless, since interpretive viewpoints lead to an interpretative circle of creation that places more emphasis on the researcher’s interpretation than does positivist or critical-realist thought. In contrast to case studies and action research, which may or may not seek to generalize their findings, urban planning aims to create an artifact that many people can use. Past research has shown that shelters often prioritize design performance above the needs of those using them (Iqbal and Bilal 2021; Zhang et al 2022a, b). According to research findings, the design science approach builds linkages among many aspects by continuously creating an artifact for green infrastructural design development (Li et al 2021a) This scenario is relevant to the present study because several stakeholder groups are involved in defining and solving the issue. Some of these participants are also acting as customers from various viewpoints. With the design science approach, builders and architects have a solid footing to build a procedure that backs up the original solution by combining the results of surveys and research for green infrastructural design development. The research will follow the five steps of design research necessary to get the conclusions drawn from the investigation. There is a pause between each step, and the following begins after all the previous ones are finished. (Iqbal et al 2021; Tu et al 2021).
The new study is hindered by the fact that there are not enough shelters to meet the demands of migrants. A novel approach is created by considering the many causes of the issue. The demands of refugees serve as a benchmark against whom requirements are developed. As illustrated in Table 9 and Table 10, we may trace the origins of the issue back to the discordance between the factory-made houses’ designs, the environments they are built in, and the cultural norms of the refugees who live in them. Through studying the published evidence, both the nature of the issue and possible solutions may be better understood at this stage of development. According to Table 7, a survey of the relevant literature may help provide a conceptual base for a complete understanding of the problem. The artifact was developed through academic research and investigation into the existing and desired conditions. This study examines the problems of providing adequate shelter in a hot, dry region with limited resources and a short period. Green finance, in the form of joint public–private collaboration or private enterprises’ investments, is an efficient policy instrument to reduce pollution and attain emission reduction aims by developing renewable energy sources. Financial goals in ecologically responsible technology and sustainable initiatives assist in directing the flow of resources from industries with high energy efficiency to those with a moderate frequency. The government is involved in a crucial role in this process via subsidies and other legislative measures. China is amid a decarbonization that will see renewables replace traditional sources as the country’s primary energy source; therefore, maintaining stable pricing for these sources is crucial to the country’s long-term energy viability and job prosperity. The complementing integrated power sector must be developed to mitigate the severe price swings associated with sustainable power and raise investor confidence in its long-term sustainability. First, in this comment environment, it is crucial to emphasize the importance of environmentally friendly financing for lowering carbon emissions. Decarbonization attempts in China have been hindered by the COVID-19 pandemic, which has reduced investment in renewable energy and delayed the growth of the green finance industry. Financial and legislative assistance was given to polluting companies like power stations that use fossil fuels to boost the economy and lower the unemployment rate. Finding demonstrates a jump in productivity in China’s contaminating sectors to rise mainly in underdeveloped and some industrialized countries. As a result of the epidemic, the Chinese implemented some expansionary fiscal initiatives, many of which were unrelated to green financing and consequently ignored global warming.
Conclusion and implications
This research examines how green financing index supports in developing green infrastructure building by mitigating the climate change in China. Using the study data, we used fixed-effect and random-effect regression including correlation test, co-integration test, KMO test, ARDL-bound test, ARDL-ECM test, and sensitivity analysis. To accurately assess the function and impact of green finance, we created an indicator based on three subunits: green financing, climate change emission mitigation, and green infrastructure development indicators (Table 1). Overall, the results indicate that carbon emissions in the China are adversely impacted by green financing and supported positively to develop green infrastructure building. The correlation between green financing and climate change mitigation varies in different stages, despite the fact that the green finance coefficient hardly changes. Although this reduction in climate change emission was transient, it was a notable result of non-fossil energy usage in China. Last but not least, the climate change mitigation in the China revealed a variety of outcomes. The majority of traditional topology optimization techniques over the last two decades have often prioritized structural performance above other design considerations. Globally optimal structural designs, however, often defy different architectural needs or conceptions because they are original or seem comparable. Additionally, only a small number of layouts can be created in a particular building context using standard topology optimization-based architectural design work, leaving architects with little choice but to choose one shape or none at all. Following such findings, the study recommends the following implications:The complex needs of architectural design and the new conceptions of designers cannot be met by these strict working patterns. In this study, an open working framework is proposed that combines a well-liked parametric modelling platform with structural analysis methods and restores design flexibility to architects when they do form discovery based on topological optimization. Associated stakeholders are advised to develop the viable way forwards on this to promote green infrastructure design development and need supportive legislation framework.
With the help of this integrated framework, many design needs and objectives may be reconciled with structural performance. Using the aforementioned framework as a foundation, three BESO-based methodologies are created to assist engineers and architects in conceptual design with topology optimization and the quick predisposition of the structural detail constraint. Each of them offers a particular area where users may include specific intents into the topology optimization procedure to covertly affect the final designs with a little loss in structural performance. On this, stakeholders also need to put focus and fix the matter.
The financing stakeholders are expected to pay attention on easing up on how green financing is applied to the green infrastructural development using carbon market initiatives. The Chinese carbon market is not yet completely functional. Businesses misunderstand the financial benefits and rights associated with carbon emissions. By encouraging investment in the carbon financial market, efficient financial system would facilitate the sound development of the carbon market and enhance emission reduction initiatives.
Future studies should look at how to build green financial indicators better. A small number of research have created this index; however, one of the most difficult and creative contributions of the current research is the creation of this index. The accuracy of the study findings may be improved by improving this indicator. For a deeper study and to take into account the various viewpoints on the correlations between the variables being researched, many methodologies may be used.
Author contribution
Conceptualization, methodology, writing—original draft, data curation, visualization, and editing: Qiang Li.
Funding
This work is supported by the Binzhou soft science research plan project “research and application of environmental protection and energy-saving technology in traditional dwellings in the Yellow River Delta,” project no.: 2018brk08.
Availability of data and materials
The data that support the findings of this study are openly available on request.
Declarations
Consent for publication
We do not have any individual person’s data in any form.
Ethical approval and consent to participate
We declare that we have no human participants, human data, or human issues.
Competing interests
The author declares no competing interests.
Preprint service
Our manuscript is posted at a preprint server prior to submission.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Bai X Wang KT Tran TK Sadiq M Trung LM Khudoykulov K Measuring China’s green economic recovery and energy environment sustainability: econometric analysis of sustainable development goals Econ Anal Policy 2022 75 768 779 10.1016/j.eap.2022.07.005
Bilal AR Fatima T Iqbal S Imran MK I can see the opportunity that you cannot! A nexus between individual entrepreneurial orientation, alertness, and access to finance Eur Bus Rev 2022 34 4 556 577 10.1108/EBR-08-2021-0186
Bilal S (2021) The rise of public development banks in the European financial architecture for development. Elcano Royal Institute Working Paper 12/2021. Madrid: ETTG/Elcano Royal Institute
Brophy V Lewis JO A green vitruvius: principles and practice of sustainable architectural design 2012 Routledge
Butler J The compelling “hard case” for “green” hotel development Cornell Hospitality Quarterly 2008 49 3 234 244 10.1177/1938965508322174
Chang L, Iqbal S, Chen H (2023) Does financial inclusion index and energy performance index co-move? Energy Policy 174:113422
Chegut A Eichholtz P Kok N The price of innovation: An analysis of the marginal cost of green buildings J Environ Econ Manag 2019 98 102248 10.1016/j.jeem.2019.07.003
Chen LK, Yuan RP, Ji XJ, Lu XY, Xiao J, Tao JB, ... Jiang LZ (2021) Modular composite building in urgent emergency engineering projects: A case study of accelerated design and construction of Wuhan Thunder God Mountain/Leishenshan hospital to COVID-19 pandemic. Automation in Construction, 124, 103555
Chua SC Oh TH Green progress and prospect in Malaysia Renew Sustain Energy Rev 2011 15 6 2850 2861 10.1016/j.rser.2011.03.008
Debrah C Chan APC Darko A Green finance gap in green buildings: A scoping review and future research needs Build Environ 2022 207 108443 10.1016/j.buildenv.2021.108443
Demirel P Li QC Rentocchini F Tamvada JP Born to be green: new insights into the economics and management of green entrepreneurship Small Bus Econ 2019 52 4 759 771 10.1007/s11187-017-9933-z
Dong J Zuo J Luo J Development of a management framework for applying green roof policy in urban China: A preliminary study Sustainability 2020 12 24 10364 10.3390/su122410364
Drobnick J (2021) Volatile effects: olfactory dimensions of art and architecture. In Empire of the Senses (pp. 265–280). Routledge
Edwards B Naboni E Green buildings pay: Design, productivity and ecology 2013 Routledge
Fawzy S Osman AI Doran J Rooney DW Strategies for mitigation of climate change: a review Environ Chem Lett 2020 18 6 2069 2094 10.1007/s10311-020-01059-w
Filiatrault A Perrone D Merino RJ Calvi GM Performance-based seismic design of nonstructural building elements J Earthquake Eng 2021 25 2 237 269 10.1080/13632469.2018.1512910
Firmansyah AY Architecture metabolism approach which integrates the concept Magersari in supporting balanced development with green agricultural land in suburbs Procedia Soc Behav Sci 2016 227 609 616 10.1016/j.sbspro.2016.06.122
Hockett RC (2020) A Green New Deal Financial Architecture. In Financing the Green New Deal (pp. 37–81). Palgrave Macmillan, Cham
Huang J Wang X Liu H Iqbal S Financial consideration of energy and environmental nexus with energy poverty: Promoting financial development in G7 economies Front Energy Res 2021 9 777796 10.3389/fenrg.2021.777796
Huang L, An Q, Geng L, Wang S, Jiang S, Cui X, ... Wang C (2021b) Multiscale Architecture and Superior High‐Temperature Performance of Discontinuously Reinforced Titanium Matrix Composites. Adv Mater, 33(6), 2000688
Hwang BG Tan JS Green building project management: obstacles and solutions for sustainable development Sustain Dev 2012 20 5 335 349 10.1002/sd.492
Iqbal S Bilal AR Energy financing in COVID-19: how public supports can benefit? China Fin Rev Intl 2021 12 2 219 240 10.1108/CFRI-02-2021-0046
Iqbal S Bilal AR Nurunnabi M Iqbal W Alfakhri Y Iqbal N It is time to control the worst: testing COVID-19 outbreak, energy consumption and CO 2 emission Environ Sci Pollut Res 2021 28 19008 19020 10.1007/s11356-020-11462-z
Irfan M Razzaq A Sharif A Yang X Influence mechanism between green finance and green innovation: exploring regional policy intervention effects in China Technol Forecast Soc Chang 2022 182 121882 10.1016/j.techfore.2022.121882
Jinru L Changbiao Z Ahmad B Irfan M Nazir R How do green financing and green logistics affect the circular economy in the pandemic situation: key mediating role of sustainable production Econ Res-Ekonomska Istraživanja 2022 35 1 3836 3856 10.1080/1331677X.2021.2004437
Kembel SW, Jones E, Kline J, Northcutt D, Stenson J, Womack AM, ... Green JL (2012) Architectural design influences the diversity and structure of the built environment microbiome. The ISME J, 6(8), 1469–1479
Khan MA Riaz H Ahmed M Saeed A Does green finance really deliver what is expected? An imperical perspective Borsa Istanbul Review 2022 22 3 586 593 10.1016/j.bir.2021.07.006
Khan T (2019) Reforming Islamic finance for achieving sustainable development goals. J King Abdulaziz University: Islamic Economics, 32(1)
Kibert CJ Sustainable construction: green building design and delivery 2016 John Wiley & Sons
Kuhn TE Erban C Heinrich M Eisenlohr J Ensslen F Neuhaus DH Review of technological design options for building integrated photovoltaics (BIPV) Energy and Buildings 2021 231 110381 10.1016/j.enbuild.2020.110381
Lamb WF Steinberger JK Human well-being and climate change mitigation Wiley Interdisciplinary Reviews: Climate Change 2017 8 6 e485
Layton E (2021) Building by Local Authorities: The Report of an inquiry by the Royal Institute of Public Administration into the organization of building construction and maintenance by Local Authorities in England and Wales. Routledge
Lee CC Lee CC How does green finance affect green total factor productivity? Evidence from China Energy Economics 2022 107 105863 10.1016/j.eneco.2022.105863
Li Q Long R Chen H Chen F Wang J Visualized analysis of global green buildings: Development, barriers and future directions J Clean Prod 2020 245 118775 10.1016/j.jclepro.2019.118775
Li W, Chien F, Ngo QT, Nguyen TD, Iqbal S, Bilal AR (2021a) Vertical financial disparity, energy prices and emission reduction: empirical insights from Pakistan. J Environ Manage 294:112946
Li W, Chien F, Hsu CC, Zhang Y, Nawaz MA, Iqbal S, Mohsin M (2021b) Nexus between energy poverty and energy efficiency: estimating the long-run dynamics. Resources Policy 72:102063
Lu N Wu J Liu Z How Does Green Finance Reform Affect Enterprise Green Technology Innovation? Evidence from China Sustainability 2022 14 16 9865 10.3390/su14169865
Matsuo T, Malhotra A, Schmidt TS (2022) Catching-up in green industries: the role of product architecture. Innovation and Development, 1–30
Maturana B, Salama AM, McInneny A (2021) Architecture, urbanism and health in a post-pandemic virtual world. Archnet-IJAR: International Journal of Architectural Research
McDonough W (2002) Big and green: toward sustainable architecture in the 21st century. Princeton Architectural Press
Megahed NA, Ghoneim EM (2021) Indoor Air Quality: Rethinking rules of building design strategies in post-pandemic architecture. E
Meloni M, Cai J, Zhang Q, Sang‐Hoon Lee D, Li M, Ma R, ... Feng J (2021) Engineering Origami: A comprehensive review of recent applications, design methods, and tools. Adv Sci, 8(13), 2000636
Meo MS Abd Karim MZ The role of green finance in reducing CO2 emissions: An empirical analysis Borsa Istanbul Rev 2022 22 1 169 178 10.1016/j.bir.2021.03.002
Newell RG The role of markets and policies in delivering innovation for climate change mitigation Oxf Rev Econ Policy 2010 26 2 253 269 10.1093/oxrep/grq009
Nielsen KS, Stern PC, Dietz T, Gilligan JM, van Vuuren DP, Figueroa MJ, ... Wood R (2020) Improving climate change mitigation analysis: a framework for examining feasibility. One Earth, 3(3), 325–336
Norouzi M Chàfer M Cabeza LF Jiménez L Boer D Circular economy in the building and construction sector: A scientific evolution analysis J Build Eng 2021 44 102704 10.1016/j.jobe.2021.102704
oglu Huseynov EF (2011) Planning of sustainable cities in view of green architecture. Procedia Engineering, 21, 534-542
Ong BL Green plot ratio: an ecological measure for architecture and urban planning Landsc Urban Plan 2003 63 4 197 211 10.1016/S0169-2046(02)00191-3
Paltsev S Capros P Cost concepts for climate change mitigation Climate Change Economics 2013 4 supp01 1340003 10.1142/S2010007813400034
Peeters P Dubois G Tourism travel under climate change mitigation constraints J Transp Geogr 2010 18 3 447 457 10.1016/j.jtrangeo.2009.09.003
Ragheb A El-Shimy H Ragheb G Green architecture: A concept of sustainability Procedia Soc Behav Sci 2016 216 778 787 10.1016/j.sbspro.2015.12.075
Rijal HB Yoshida K Humphreys MA Nicol JF Development of an adaptive thermal comfort model for energy-saving building design in Japan Archit Sci Rev 2021 64 1–2 109 122 10.1080/00038628.2020.1747045
Schwanen T Banister D Anable J Scientific research about climate change mitigation in transport: A critical review Transport Res Part a: Policy Practice 2011 45 10 993 1006
Sharma GD Sarker T Rao A Talan G Jain M Revisiting conventional and green finance spillover in post-COVID world: Evidence from robust econometric models Glob Financ J 2022 51 100691 10.1016/j.gfj.2021.100691
Shi X Yang W Performance-driven architectural design and optimization technique from a perspective of architects Autom Constr 2013 32 125 135 10.1016/j.autcon.2013.01.015
Singh SK Del Giudice M Chierici R Graziano D Green innovation and environmental performance: The role of green transformational leadership and green human resource management Technol Forecast Soc Chang 2020 150 119762 10.1016/j.techfore.2019.119762
Stang A, Hawthorne C (2005) The green house: New directions in sustainable architecture. Princeton Architectural Press
Su Y Zhao M Wei G Wei C Chen X Probabilistic uncertain linguistic EDAS method based on prospect theory for multiple attribute group decision-making and its application to green finance Int J Fuzzy Syst 2022 24 3 1318 1331 10.1007/s40815-021-01184-w
Sun H Burton HV Huang H Machine learning applications for building structural design and performance assessment: State-of-the-art review J Build Eng 2021 33 101816 10.1016/j.jobe.2020.101816
Sun L Fang S Iqbal S Bilal AR Financial stability role on climate risks, and climate change mitigation: implications for green economic recovery Environ Sci Pollut Res 2022 29 22 33063 33074 10.1007/s11356-021-17439-w
Sussman A, Hollander JB (2021) Cognitive architecture: Designing for how we respond to the built environment. Routledge
Tanzer K Longoria R The Green Braid 2007 New York Routledge
Tu CA, Chien F, Hussein MA, Ramli MM, Y. A. N. T. O., S. PSI, MS, Iqbal S, Bilal AR (2021) Estimating role of green financing on energy security, economic and environmental integration of BRI member countries. The Singapore Economic Review, 1–19
Vale B, Vale R (2014) Principles of Green Architecture: from Green Architecture (1991). In The Sustainable Urban Development Reader (pp. 318–322). Routledge.
Wang S Sun L Iqbal S Green financing role on renewable energy dependence and energy transition in E7 economies Renewable Energy 2022 200 1561 1572 10.1016/j.renene.2022.10.067
Wang T, Zhang Q, Zhong J, Chen M, Deng H, Cao J, ... Lu B (2021) 3D holey graphene/polyacrylonitrile sulfur composite architecture for high loading lithium sulfur batteries. Adv Energy Mater, 11(16), 2100448.
Wasley J Safe houses and green architecture: reflections on the lessons of the chemically sensitive J Architect Educ 2000 53 4 207 215 10.1162/104648800564617
Well F Ludwig F Blue–green architecture: A case study analysis considering the synergetic effects of water and vegetation Front Architect Res 2020 9 1 191 202 10.1016/j.foar.2019.11.001
Wright L Fulton L Climate change mitigation and transport in developing nations Transp Rev 2005 25 6 691 717 10.1080/01441640500360951
Yang Y Liu Z Saydaliev HB Iqbal S Economic impact of crude oil supply disruption on social welfare losses and strategic petroleum reserves Resour Policy 2022 77 102689 10.1016/j.resourpol.2022.102689
Yin X Xu Z An empirical analysis of the coupling and coordinative development of China's green finance and economic growth Resour Policy 2022 75 102476 10.1016/j.resourpol.2021.102476
Yuan Y Yu X Yang X Xiao Y Xiang B Wang Y Bionic building energy efficiency and bionic green architecture: A review Renew Sustain Energy Rev 2017 74 771 787 10.1016/j.rser.2017.03.004
Zhang H Geng C Wei J Coordinated development between green finance and environmental performance in China: The spatial-temporal difference and driving factors J Clean Prod 2022 346 131150 10.1016/j.jclepro.2022.131150
Zhang L Huang F Lu L Ni X Iqbal S Energy financing for energy retrofit in COVID-19: Recommendations for green bond financing Environ Sci Pollut Res 2022 29 16 23105 23116 10.1007/s11356-021-17440-3
Zhang X Ju Z Zhu Y Takeuchi KJ Takeuchi ES Marschilok AC Yu G Multiscale understanding and architecture design of high energy/power lithium-ion battery electrodes Adv Energy Mater 2021 11 2 2000808 10.1002/aenm.202000808
Zhao L Saydaliev HB Iqbal S Energy financing, COVID-19 repercussions and climate change: implications for emerging economies Climate Change Economics 2022 13 03 2240003 10.1142/S2010007822400036
Zheng X Zhou Y Iqbal S Working capital management of SMEs in COVID-19: role of managerial personality traits and overconfidence behavior Econ Anal Policy 2022 76 439 451 10.1016/j.eap.2022.08.006 35990757
Zhou G Zhu J Luo S The impact of fintech innovation on green growth in China: Mediating effect of green finance Ecol Econ 2022 193 107308 10.1016/j.ecolecon.2021.107308 | 36930310 | PMC10020768 | NO-CC CODE | 2023-04-22 23:15:12 | yes | Environ Sci Pollut Res Int. 2023 Mar 17; 30(19):56984-56997 |
==== Front
Funct Integr Genomics
Funct Integr Genomics
Functional & Integrative Genomics
1438-793X
1438-7948
Springer Berlin Heidelberg Berlin/Heidelberg
1027
10.1007/s10142-023-01027-x
Original Article
Prognostic subtypes of thyroid cancer was constructed based on single cell and bulk-RNA sequencing data and verified its authenticity
Yang Fan
Yu Yan
Zhou Hongzhong
Zhou Yili [email protected]
grid.414906.e 0000 0004 1808 0918 Department of Thyroid Surgery, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang People’s Republic of China
18 3 2023
2023
23 2 8912 2 2023
9 3 2023
10 3 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
There has been an increase in the mortality rate of thyroid cancer (THCA), which is the most common endocrine malignancy. We identified six distinct cell types in the THAC microenvironment by analyzing single-cell RNA sequencing (Sc-RNAseq) data from 23 THCA tumor samples, indicating high intratumoral heterogeneity. Through re-dimensional clustering of immune subset cells, myeloid cells, cancer-associated fibroblasts, and thyroid cell subsets, we deeply reveal differences in the tumor microenvironment of thyroid cancer. Through an in-depth analysis of thyroid cell subsets, we identified the process of thyroid cell deterioration (normal, intermediate, malignant cells). Through cell-to-cell communication analysis, we found a strong link between thyroid cells and fibroblasts and B cells in the MIF signaling pathway. In addition, we found a strong correlation between thyroid cells and B cells, TampNK cells, and bone marrow cells. Finally, we developed a prognostic model based on differentially expressed genes in thyroid cells from single-cell analysis. Both in the training set and the testing set, it can effectively predict the survival of thyroid patients. In addition, we identified significant differences in the composition of immune cell subsets between high-risk and low-risk patients, which may be responsible for their different prognosis. Through in vitro experiments, we identify that knockdown of NPC2 can significantly promote thyroid cancer cell apoptosis, and NPC2 may be a potential therapeutic target for thyroid cancer. In this study, we developed a well-performing prognostic model based on Sc-RNAseq data, revealing the cellular microenvironment and tumor heterogeneity of thyroid cancer. This will help to provide more accurate personalized treatment for patients in clinical diagnosis.
Supplementary Information
The online version contains supplementary material available at 10.1007/s10142-023-01027-x.
Keywords
Thyroid cancer
Single cell sequencing
Prognostic model
Tumor microenvironment
issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023
==== Body
pmcIntroduction
There has been an increase in the mortality rate of thyroid cancer (THCA), which is the most common endocrine malignancy (Cao et al. 2021). The most prevalent histological subtype of thyroid cancer, papillary thyroid carcinoma (PTC), accounts for more than 90% of all thyroid cancer cases (Wen et al. 2021). Compared with other THCA subtypes, most PTC cases have a relatively good prognosis after surgery and treatment, but there are still patients with recurrence and metastasis (Huang et al. 2021). Medullary thyroid carcinoma (MTC) refers to malignant tumors of thyroid C-cell origin, often slowly progressive disease, but most patients often miss the best time for treatment, with a large local growth of the neck mass and compression of the nearby trachea and esophagus (Romei and Elisei 2021). Anaplastic thyroid cancer (ATC) is the most malignant form of thyroid cancer, which has a rapid onset, invasion and systemic metastasis can occur in the early stage, and the prognosis is very poor (Molinaro et al. 2017). Because thyroid cancer is highly tumor heterogeneous and molecular mechanisms are complex, the treatment and diagnosis of THAC are challenging due to the ineffectiveness of many molecular targeted drugs in some patients.
Individual cells in the tumor mass tend to have the same origin. However, tumor cells tend to exhibit heterogeneity during growth and differentiation (Navin et al. 2010). Mutations and clonal selection dynamics during tumor growth produce intratumoral heterogeneity, in which different mutations accumulate in specific tumor cells (Navin et al. 2010; Bashashati et al. 2013; Gerlinger et al. 2012). There is a significant association between genetic heterogeneity and tumor progression and treatment outcome in cancer (Mroz et al. 2013; Jamal-Hanjani et al. 2014). In addition, as a result of this wide intratumoral heterogeneity, bulk mRNA sequencing is difficult to identify genetic variants. Single-cell RNA sequencing (Sc-RNAseq) technology is a powerful tool to unravel tumor heterogeneity and has been widely used to investigate intra- and inter-tumor transcriptome heterogeneity (Zhao et al. 2018; Kim et al. 2020; Wang et al. 2014). The Sc-RNAseq data provide insight into the diversity and complexity of tumor cell types (cancer cells, immune cells, and stromal cells) (Lei et al. 2021; Ziegenhain et al. 2017). Cancer cells were clustered or novel cell types were identified based on expression profiles to obtain dynamic information, such as the origin, evolution, and development of tumor subclones, the presence of cancer stem cells, or quantification of tumor stemness (Zhang et al. 2020; Baslan and Hicks 2017). Studies using sc-RNAseq data have made additional contributions by comparing the subtype composition of tumors with different pathological types, clinical features, and response to treatment, and identifying differentially expressed genes between different tumor groups (Zhang et al. 2021; Chen et al. 2021; Dai et al. 2019). Single-cell sequencing technology has made remarkable progress in studying tumor heterogeneity and shed new light on predicting tumor prognosis and survival.
In this study, we identified six distinct cell types in the THCA microenvironment by analyzing single-cell RNA sequencing data from 23 THCA tumor samples, indicating high intratumoral heterogeneity. Through re-dimensional clustering of immune subset cells, myeloid cells, cancer-associated fibroblasts, and thyroid cell subsets, we deeply reveal differences in the tumor microenvironment of thyroid cancer. Through an in-depth analysis of thyroid cell subsets, we identified the process of thyroid cell deterioration (normal, intermediate, malignant cells). Through cell-to-cell communication analysis, we found a strong link between thyroid cells and fibroblasts and B cells in the MIF signaling pathway. In addition, we found a strong correlation between thyroid cells and B cells, T & NK cells, and bone marrow cells. Finally, we developed a prognostic model based on differentially expressed genes in thyroid cells from single-cell analysis. Both in the training set and the testing set, it can effectively predict the survival of thyroid patients.
We developed a well-performing prognostic model based on single-cell sequencing data from GSE184362 and bulk transcriptome and clinical data from TCGA, revealing the cellular microenvironment and tumor heterogeneity in thyroid cancer. This will help provide more accurate personalized treatment to patients in clinical diagnosis.
Materials and methods
Data collection
Single-cell RNA sequencing (scRNA-seq) data for thyroid cancer were obtained from GSE184362 in the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) database, which contained 23 samples from 11 patients. Data for the bulk transcriptome were obtained from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) database using intersection samples of transcribed data and survival time, and filtered out samples with survival time less than 30 days, for a total of 507 samples used for analysis.
Single cell data processing
Data filtering and correction of scRNA-seq data was performed using “Seurat” and “SingleR” software packages. We filtered cells with unique feature counts > 5000 or < 500 and cells with mitochondrial counts > 10%. Normalizing feature expression measurements by total expression was achieved through Seurat’s “NormalizeData” function. All cell data were transferred to a combined Seurat object using the Harmony software package. The “FindClusters” function (resolution = 0.5) and significant principal components were selected for umap analysis and cluster analysis. The subsequent dimension reduction method UMAP and the clustering algorithm Louvian were used, both from Seurat.
Cell annotation
To identify cell types, we performed a total of two annotation patterns. Automated annotation (this annotation is used for the first clustering): SingleR is an automated annotation method for scRNAseq data. By comparing the test dataset to a sample reference dataset (single cell or batch size) with known labels, it marks new units in the test dataset that are similar to the reference dataset. As a result, the burden of manually interpreting clusters and defining marker genes only needs to be done once for reference datasets, and this biological knowledge can also be applied to new datasets in an automated manner.
Manual annotation (this annotation was used for secondary cluster analysis of cell subsets): we checked whether well-studied marker genes were among the top differentially expressed genes (DEGs) for each cell cluster after annotating the most likely identity of the cluster, by manually searching the cell labeling database (http://biocc.hrbmu.edu.cn/CellMarker/) for identification.
Secondary analysis of each cell group
Immune cells, thyroid cells, fibroblasts, and endothelial cells were isolated separately to further distinguish their subsets, similarly using Seurat’s standard process, and subsets also used specific markers as the basis for grouping, and UMAP dimensionality reduction clustering maps were drawn.
Thyroid cell cluster TDS score was calculated for 13 mRNA genes (TG, TPO, SLC26A4, DIO2, TSHR, PAX8, DUOX1, DUOX2, NKX2-1, GLIS3, FOXE1, TFF3, FHL1) using Seurat function AddModuleScore. InferCNV software is used for CNV analysis of thyroid cell subsets, mainly to identify malignant cells among them.
Transcription factor analysis
We used SCENIC software, for transcriptional factor analysis of each cell subset, to construct co-expression networks using the grnboost algorithm and regulatory networks using RcisTarget.
An analysis of cell developmental trajectory in a quasi-chronological fashion
Pseudochronological analysis of cell differentiation was performed using the Monocle2 package. First, the expression matrix was extracted from the corresponding Seurat object with Get Assay Data in the Seurat package and then imported into Monocle2 for use as the cell dataset object. Data normalization and preprocessing were performed using the preprocessing function. Differentiation trajectory inference was performed on the data using a learngraph function. Cell development trajectories were displayed using a plotcell trajectory function.
Cell interaction analysis
CellChat is a database containing information on ligands, receptors, and their interactions. This databased can be used for comparative inference analysis and quantitative descriptions of communication networks between cells (Jin et al. 2021). Cell–cell communications analysis uses the R “Cellchat” package, and the pathway selects the secreated signaling pathway. The reference human ligand receptor database was CellChatDB. Human intercellular communication (R package CellChat 0.0.2) is determined by assessing the expression of ligands and receptors in CellChatDB. We examined interactions between different cell types, filtering pathways with cell numbers less than 10.
Build prognostic model
Using FPKM data, we calculated differentially upregulated genes in tumor cells compared with normal thyroid cells in single cells as markers and used LASSO cox regression analysis to construct prognostic models. Data were randomly divided into training and test sets in a 1:1 ratio. Our survival analysis was calculated using the R package “survival,” and Kaplan–Meier survival curves were plotted. To test the accuracy of the prediction model, ROC curves were plotted using the R package “survivalROC.”
Immune cell infiltration analysis
Our approach to cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) is a general approach to measuring cellular components based on gene expression profiling (Newman et al. 2015), which can accurately estimate the immune components of tumor biopsies.
Cell culture
Human thyroid follicular epithelial normal cells Nthy-ori3-1 and thyroid carcinoma cells FTC133 were gifts from Dr. Ding. Cells were maintained in RPMI-1640 medium containing 10% FBS at 37 °C and 5% CO2.
Quantitative real-time PCR (qRT-PCR)
We reverse-transcribed RNA into cDNA after treating cells with TRIzol reagent (Takara, Japan). NPC2 mRNA levels were quantified by RT-qPCR using TB Green (Takara, Japan) and normalized to GAPDH. The primers involved in this study are listed in Table S1.
Apoptosis analysis
We analyzed cell apoptosis using flow cytometry after pre-cooling PBS washing and digestion with trypsin digestion solution containing no EDTA (Solarbio, China). After centrifugation at 1000 rpm for 5 min, cells were harvested, stained with 7-AAD, and stained with annexin-APC for 15 min.
Statistical analysis
For normally distributed continuous variables, the Student’s T test was used. In the case of continuous variables that were not normally distributed, the Mann-U test was used. Correlations between continuous variables were evaluated using Pearson’s correlation analysis. All statistical methods set P < 0.05 as statistically significant. For data analysis and figure generation, R software version 4.1.3 was used.
Results
The flow chart is shown in Fig. 1.Fig. 1 The overall experimental process of this study
Clustering of THCA cells
THCA single cell data were processed and screened, and the data from 23 samples were divided into 29 clusters annotated as six cell types, including B cells, endothelial cells, fibroblasts, myeloid cells, NK & T cells, and thyroid cells (Fig. 2A, Fig. S1A). Marker genes of each cell type were highly expressed in their cell types, demonstrating that our cell clustering was correct (Fig. 2B, C). By histogram, we can observe that there are significant differences in the content of each type of cells in the sample, which indicates that there are significant differences in their intratumoral cellular environment (Fig. S1B). Subsequently, we analyzed the expression of individual genes in THCA cells to further ensure the reliability of our experiments (Fig. 2D).Fig. 2 A dimensional cluster analysis of single cell sequencing data from thyroid cancer. A Clustering of thyroid cancer single-cell sequencing data with dimensionality reduction, cell annotation, and UMAP map of sample composition. B Heat map showing standard gene expression in each cell group. C Bubble plots show standard gene expression across cell groups. D Using UMAP plots, we were able to visualize the expression of each standard gene in each cell type
Cluster analysis of immune cell subsets
Subsequently, we performed differential expression gene analysis on six cell subsets, obtained genes differentially expressed in each cell subset, and visualized them (Fig. 3A, B). By re-clustering immune-related cell subsets (T NK cells, B cells, and myeloid cells), we clustered them into ten immune subtype cells, including CD8 + NKT-like cells, ISG expressing immune cells, macrophages, memory CD4 + T cells, naive B cells, naive CD4 + T cells, natural killer cells, non-classical monocytes, plasma B cells, and plasmacytoid dendritic cells (Fig. 3C). We found that macrophages, non-classical monocytes, and plasma B cells were mainly derived from tumor samples. In addition, we performed KEGG enrichment pathway analysis and found that differentially expressed genes in TampampNK cells were significantly enriched in coronavirus disease — COVID-19, ribosome, and cell adhesion molecules related pathways (Fig. 3D). Whereas genes differentially expressed in myeloid cells were significantly enriched in Salmonella infection, tuberculosis, and phagosome-related pathways (Fig. 3E). Interestingly, genes differentially expressed in B cells were similarly significantly enriched in coronavirus disease — COVID-19 and ribosome-related pathways (Fig. 3F). This suggests that there may be a common mechanism of action for TNK cells and B cells.Fig. 3 Analysis of immune cell subsets based on dimensional clustering. A Differentially expressed genes in each cell type are represented by a heat map. B Gene expression bubble plots showing differential expression in each cell type. C Clusters of immune cell-related subsets, cell annotations, and sample composition shown in UMAP plots. D Bubble plots showing KEGG enriched pathways for T & NK cell subsets. E Bubble plots showing KEGG enriched pathways for myeloid cell subsets. F Bubble plots showing KEGG enriched pathways for B-cell subsets
Dimensional cluster analysis of fibroblasts and endothelial cells
By re-dimensionality reduction analysis of cancer-associated fibroblast (CAF) subsets, we divided fibroblasts into two cell types, giving iCAF cells and myoCAF cells, respectively (Fig. 4A). Subsequently, we analyzed the levels of transcription factors enriched in the two cell subtypes and could find a more significant difference between the two cells at the level of individual transcription factor viability, with PPARG and MEF2C highly expressed in a subset of mCAF cells (Fig. 4B). Hierarchical clustering revealed unique mean transcription factor viability expression profiles for each of the two cell subsets, with significant differences in mean transcription factor levels between the two cell subsets, with MEF2C appearing to have the highest specific expression (187 genes) (Fig. 4C).Fig. 4 Analysis of fibroblast and endothelial cell subsets by dimensional clustering. A Fibroblast subsets analyzed using dimensional clustering. B Transcriptional factor viability analysis of fibroblast-related cells. C A heatmap showing the mean viability of transcription factors in fibroblasts. D Dimensionality reduction cluster analysis of endothelial cell subsets. E Heatmap for transcription factor activity analysis of endothelial cell subsets. F Transcriptional activity of endothelial cell subsets as a heatmap
Through a dimensionality reduction cluster analysis of endothelial cells, we further revealed the microenvironment composition of endothelial cells in thyroid cancer patients, which co-clustered into four types of cells: arterial cells, immature tip cells, lymphatic cells, and venous cells (Fig. 4D). By analyzing the levels of transcription factors enriched in the four cell subtypes, it can be found that there are significant differences in the levels of transcription factor viability among the four cells. In addition, we found that congenic cells, there were also significant differences between different samples, for example, the transcription factor JUN had both high and low expression in arterial cells and immature tip cells (Fig. 4E). Hierarchical clustering revealed unique mean transcription factor viability expression profiles for each of the four cell subsets, with significant differences in mean transcription factor levels between the four cell subsets, and JUND, SOX4, CREB5, CEBPD, and ELK3 being significantly highly expressed in lymphatic cell subsets (Fig. 4F).
Cluster analysis of thyroid cell subsets
Through dimensionality reduction cluster analysis of thyroid cells, we further revealed the cellular microenvironment composition of thyroid cancer patients, which co-clustered into three cell groups: malignant, normal, and premalignant, of which malignant cells accounted for the vast majority (Fig. 5A). Through a quasi-chronological analysis of cell developmental trajectories, we identify the process of thyroid cell carcinogenesis, that is, normal cells to premalignant cells to malignant cells (Fig. 5B). Additionally, we evaluated the stemness score of thyroid cells using TDS score analysis, and it can be seen that cluster 5 is larger than cluster 4 than cluster 0, 1, 2, and 3, so we can deduce that 5 are normal cells, 4 are normal to malignant intermediate cells, and the rest are malignant cells, which is consistent with our quasi-chronological analysis (Fig. 5C). Subsequently, we performed copy number variation analysis of thyroid cells, using fibroblasts and endothelial cells as a reference for normal cells, to identify malignant cells in thyroid cells, and it can be seen that essentially all cells underwent copy number variation, which represents that the vast majority of thyroid cells do belong to malignant or intermediate cells, which is consistent with our previous results (Fig. 5D).Fig. 5 Dimensionality reduction cluster analysis of thyroid-associated cell subsets. A Thyroid-related cell subsets clustered with UMAP plot for dimensionality reduction. B Semi-chronological analysis of thyroid-associated cell subsets with UMAP lot. C Box plots showing stemness scores for each cluster of thyroid-associated cell subsets. D Heat map showing copy number variations of each gene on chromosomes in thyroid cells. E Heat map showing transcription factor activity in thyroid cell subsets. F Heat map showing mean transcription factor activity in thyroid cell subsets. G Bubbles show GO enrichment analysis of thyroid cell subsets. H Bubbles show KEGG enrichment analysis of thyroid cell subsets
Through transcriptional factor analysis of malignant cells, normal cells, and precancerous cells, we found that FOS was significantly highly expressed in malignant and precancerous cells, which may represent its role in carcinogenesis (Fig. 5E). At the mean transcription factor level, we found that XBP1 was significantly highly expressed in normal cells, but lowly expressed in malignant and premalignant cells (Fig. 5F). In addition, we found that CREB3L2 is significantly highly expressed in precancerous cells, but lowly expressed in malignant cells, an interesting phenomenon that means that there are significant differences in cellular transcript levels during carcinogenesis.
For genes differentially expressed in thyroid cancer cells, we analyzed GO and KEGG enriched pathways. We found that differentially expressed genes were significantly enriched in the generation of precursor metabolites and energy related pathways in biological process (BP), cadherin binding related pathways in molecular function (MF), and mitochondrial inner membrane related pathways in cellular component (CC) (Fig. 5G). In KEGG enriched pathway analysis, differentially expressed genes were significantly enriched in Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease related pathways (Fig. 5H).
Cell communication analysis
Subsequently, we performed cell communication analysis to further investigate cell–cell interactions. We found a higher intensity of interaction between cell subsets (Fig. 6A). Interestingly, we found a strong association between thyroid cells and immune-related cell subsets (T NK cells, myeloid cells, B cells) (Fig. 6B). In addition, we found a strong link between thyroid cells and fibroblasts and B cells in the MIF signaling pathway (Fig. 6C). Through ligand receptor pair analysis of interactions between various cell subsets, we found that ligand receptors between thyroid cells and fibroblasts and B cells were significantly activated on MIF − (CD74 + CXCR4), which is consistent with our previous study (Fig. 6D). These results help to further elucidate the cellular microenvironment of thyroid cancer and provide help for cancer heterogeneity studies.Fig. 6 Cell communication analysis. A Left panel: number of ligand-receptor pairs, right panel: intensity of combined ligand-receptor pairs. B Diagram showing how thyroid cells communicate with other cells. C A network diagram showing how the MIF signaling pathway communicates with other cells. D Bubble plots show ligand receptor pairs involved in communication between various cell types, with the size and color of the bubbles reflecting the P-value and the strength of communication
Construction of thyroid cancer related gene prognostic model
To establish a prognostic model highly relevant to THCA, we extracted differentially expressed genes from thyroid cell subsets from single-cell data and constructed a prognostic model by LASSO cox regression analysis (RPS4Y1, NPC2, IGSF1, C8orf4, APOE, S100A1, HSPA1B, CTSC, HSPA1A, ECM1, DPP4, CCL5, NAPSA, SPOCK2, CXCL8, AGR2, MGST1, ACTB) (Fig. 7A). According to median risk scores, patients were assigned to high- and low-risk groups, and we divided the TCGA cohort into training and testing sets for validation by a 1:1 ratio. Both the training set and the in-house validation set showed that THCA patients in the low-risk group fared better than those in the high-risk group (Fig. 7B, C, D). In the training set, the area under the curve (AUC) of OS at 1, 3, and 5 years was 1.00, 0.90, and 0.93, whereas in the internal validation set, it was 0.83, 0.66, and 0.77, respectively. It is evident that our model is useful for predicting THCA patients’ prognoses (Fig. 7E, F, G).Fig. 7 Based on differentially expressed genes related to thyroid cells, LASSO Cox regression analysis is performed. A Partial likelihood deviations and coefficients of change for the log (λ) changes have been plotted using LASSO Cox regression with tenfold cross-validation. B In the TCGA dataset, Kaplan–Meier survival curves are shown. C Survival curves of training sets based on Kaplan–Meier analysis. D Survival curves in the test set according to Kaplan–Meier. E The time-dependent ROC curve of the risk score model for predicting 1, 3, and 5 years in the TCGA data. F Time ROC curve of the risk score model to predict 1, 3, and 5 years in the training set. G Time ROC curve of risk score model predicting 1, 3, and 5 years in test set. H Difference analysis of immune cell infiltration between high-risk group and low-risk group
Lastly, we analyzed immune infiltration in high- and low-risk THCA patients to determine differences in immune composition. Significant differences were found between high- and low-risk groups in levels of CD8 T-cells, CD8 T-cells, CD8 T-cells, CD8 T-cells, CD8 T-cells, B-cell memory, resting dendritic cells, and activated dendritic cells and mast cells (Fig. 7H). We also found that the levels of CD8 T-cells, gamma-delta T-cells, and resting dendritic cells were significantly higher in THCA patients in the high-risk group than in those in the low-risk group. CD4 naive B-cell, B-cell memory, and T-cell levels were significantly higher in low-risk THCA patients than in high-risk patients. CD4 naive B-cell, B-cell memory, and T-cell levels were significantly higher in low-risk THCA patients than in high-risk patients.
In vitro experimental verification
To validate the validity of our model, and to identify a potential biomarker, we selected NPC2 from model genes for in vitro experimental validation. It can be found by boxplot that NPC2 has a very high expression level in thyroid cancer patients (Fig. 8A). In thyroid carcinoma cells FTC133, the expression level of NPC2 gene was significantly higher than that in normal thyroid cells Nthy-ori3-1, demonstrating our experiment’s accuracy (Fig. 8B). In addition, we knocked down the expression level of the NPC2 gene in FTC133 cells and quantified it again to verify our knockdown efficiency (Fig. 8C). Flow cytometry was used to analyze the function of NPC2 in thyroid cancer. Knocking down NPC2 significantly increased thyroid cancer cell apoptosis, according to the results (Fig. 8D). In order to treat thyroid cancer, NPC2 may be a potential therapeutic target.Fig. 8 Physiological role of NPC2 in thyroid cancer. A Expression of NPC2 in tumor and paracancer tissues based on the GEPIA2.0 database (http://gepia2.cancer-pku.cn/#index). B qPCR results showed the expression level of NPC2 gene in both cell lines. C qPCR results demonstrated the effect of NPC2 knockdown assay. D Flow cytometry showed the apoptosis level of cell lines. *** means P < 0.001
Discussion
Globally, thyroid cancer (THCA) is the most common endocrine malignancy, and the number of patients is growing (Cao et al. 2021). Tumor heterogeneity is increasingly recognized in clinical importance, and different tumor subsets, which tend to harbor different genetic mutations, may have different sensitivities to targeted therapies (Parker et al. 2015; McGranahan and Swanton 2017). Because a single tumor biopsy may not provide complete information about the molecular characteristics of primary and metastatic tumors, intratumoural heterogeneity is important for the diagnosis and treatment of solid tumors (Yadav Stockert Hackert Yadav Tewari 2018; Almendro et al. 2013). Therefore, the analysis of the clonal composition of a tumor at the genetic level is essential for the understanding of the biological nature and developmental status of cancer, and subsequently for the assessment of prognosis and the design of effective therapeutic strategies (Swanton 2012; Esposito et al. 2016). Due to the high heterogeneity of thyroid cancer tumors and the complexity of the molecular mechanisms involved, many molecularly targeted drugs are ineffective in some patients, which poses a major challenge for the treatment and diagnosis of THAC.
In this study, we identify six distinct cell types in the THCA microenvironment by analyzing single-cell RNA sequencing data from 23 THCA tumor samples, indicating high intratumoral heterogeneity. Through re-dimensional clustering of immune subset cells, myeloid cells, cancer-associated fibroblasts, and thyroid cell subsets, we deeply reveal differences in the tumor microenvironment of thyroid cancer. Through an in-depth analysis of thyroid cell subsets, we identified the process of thyroid cell deterioration (normal, intermediate, malignant cells). In an analysis of transcription factor activity in cells of the three thyroid subtypes, we found that XBP1 was highly expressed in normal cells, but lowly expressed in malignant and premalignant cells. XBP1 is a unique basic region leucine zipper transcription factor involved in the immunosuppressive unfolded protein response (UPR) in cancer, potentially useful as an anti-tumor treatment, and essential for endoplasmic reticulum stress (ERS) (Chen et al. 2020). Researchers have found that IRE1α-XBP1 regulates mitochondrial activity in ovarian cancer (Song et al. 2018). CREB3L2 encodes a protein that is a transcriptional activator, and recent studies have found that androgen receptor with CREB3L2 regulates ER-to-Golgi trafficking pathways to promote prostate cancer progression by single cell analysis (Hu et al. 2021). Furthermore, previous studies have demonstrated that CREB3L2 is an oncogenic pathway (Lui et al. 2008). It is interesting to note that intramembrane proteolysis regulates this pathway, which is disrupted in cancer, which is consistent with the results from our transcription factor viability experiments.
We found a strong connection between thyroid cells, fibroblasts, and B cells through cell-to-cell communication analysis. Macrophage migration inhibitory factor (MIF) is one of the key cytokines involved in cancer and inflammation, and its main mechanism is to trigger the mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K) signaling pathways by binding to CD74 and other receptors, which are essential for cancer to develop (Rafiei et al. 2019). To develop a prognostic model highly relevant to THCA, we extracted differentially expressed genes from thyroid cell subsets from single-cell data and constructed a prognostic model by LASSO cox regression analysis. TCGA patients were divided into high-risk and low-risk groups based on their median risk scores, and training and test sets were divided 1:1 for validation. In both the training and in-house validation sets, low-risk THCA patients had a better prognosis than high-risk patients. Our model was also helpful in predicting the prognosis of THCA patients based on ROC analysis. Knocking down NPC2 in thyroid cancer cells revealed that it is highly expressed in the cells. We found that knocking down NPC2 could significantly increase apoptosis in thyroid cancer cells.
However, this study has several limitations. First, most of the findings of this study were obtained through retrospective analysis. Furthermore, this study was not validated using an external dataset of THCA patients. In the future, we will further verify our research results through prospective, multi-center studies.
Conclusion
In conclusion, we combined Sc-RNAseq and bulk transcriptome data to develop a prognostic model that accurately predicts the prognosis of THCA patients and reveals the cellular microenvironment and tumor heterogeneity of thyroid cancer. Furthermore, we identified NPC2 as a potential therapeutic target in thyroid cancer through in vitro experiments. This will help provide more accurate personalized treatment to patients in clinical diagnosis.
Supplementary Information
Below is the link to the electronic supplementary material. Figure S1 (A) Histograms show the proportion of cells in each cluster, already in each tissue type, for each sample. (B) Histogram showing the proportion of each cell type in each sample. (PNG 608 kb)
High resolution image (TIF 5316 kb)
Table S1 (XLSX 9 kb)
Acknowledgements
This study would not have been possible without the efforts of all the staff involved.
Author contribution
Document writing, data collection, and chart making: Fan Yang and Yan Yu. Paper review and verification: Yili Zhou. Hongzhong Zhou contributed to this report.
Funding
This study was funded by the Foundation of Wenzhou Municipal Science and Technology Bureau, China (No. Y20190209 and Y2020739) and the Hospital Research Incubation Program (No. FHY2019075).
Availability of data and materials
The above analysis was based on R tools, excel, and graphpad software. Data sources include GSE184362 (https://www.ncbi.nlm.nih.gov/geo/) and TCGA-THCA (https://portal.gdc.cancer.gov/).
Declarations
Competing interests
The authors declare no competing interests.
Consent for publication
All authors consent to the publication of this study.
Conflict of interest
The authors declare no competing interests.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Fan Yang and Yan Yu contributed equally to this work.
==== Refs
References
Almendro V Marusyk A Polyak K Cellular heterogeneity and molecular evolution in cancer Annu Rev Pathol 2013 8 1 277 302 10.1146/annurev-pathol-020712-163923 23092187
Bashashati A Ha G Tone A Ding J Prentice LM Roth A Distinct evolutionary trajectories of primary high-grade serous ovarian cancers revealed through spatial mutational profiling J Pathol 2013 231 1 21 34 10.1002/path.4230 23780408
Baslan T Hicks J Unravelling biology and shifting paradigms in cancer with single-cell sequencing Nat Rev Cancer 2017 17 9 557 569 10.1038/nrc.2017.58 28835719
Cao YM Zhang TT Li BY Qu N Zhu YX Prognostic evaluation model for papillary thyroid cancer: a retrospective study of 660 cases Gland Surg 2021 10 7 2170 2179 10.21037/gs-21-100 34422588
Chen S Chen J Hua X Sun Y Cui R Sha J The emerging role of XBP1 in cancer Biomed Pharmacother 2020 127 110069 10.1016/j.biopha.2020.110069 32294597
Chen B Zhu L Yang S Su W Unraveling the heterogeneity and ontogeny of dendritic cells using single-cell RNA sequencing Front Immunol 2021 12 711329 10.3389/fimmu.2021.711329 34566965
Dai H Li L Zeng T Chen L Cell-specific network constructed by single-cell RNA sequencing data Nucleic Acids Res 2019 47 11 e62 10.1093/nar/gkz172 30864667
Esposito A Criscitiello C Locatelli M Milano M Curigliano G Liquid biopsies for solid tumors: understanding tumor heterogeneity and real time monitoring of early resistance to targeted therapies Pharmacol Ther 2016 157 120 124 10.1016/j.pharmthera.2015.11.007 26615782
Gerlinger M Rowan AJ Horswell S Larkin J Endesfelder D Gronroos E Intratumor heterogeneity and branched evolution revealed by multiregion sequencing N Engl j Med 2012 366 883 892 10.1056/NEJMoa1113205 22397650
Hu L Chen X Narwade N Lim MGL Chen Z Tennakoon C Single-cell analysis reveals androgen receptor regulates the ER-to-Golgi trafficking pathway with CREB3L2 to drive prostate cancer progression Oncogene 2021 40 47 6479 6493 10.1038/s41388-021-02026-7 34611310
Huang Y Xie Z Li X Chen W He Y Wu S Development and validation of a ferroptosis-related prognostic model for the prediction of progression-free survival and immune microenvironment in patients with papillary thyroid carcinoma Int Immunopharmacol 2021 101 Pt A 108156 10.1016/j.intimp.2021.108156 34624650
Jamal-Hanjani M Hackshaw A Ngai Y Shaw J Dive C Quezada S Tracking genomic cancer evolution for precision medicine: the lung TRACERx study PLoS Biol 2014 12 7 e1001906 10.1371/journal.pbio.1001906 25003521
Jin S Guerrero-Juarez CF Zhang L Chang I Ramos R Kuan C-H Inference and analysis of cell-cell communication using Cell Chat Nat Commun 2021 12 1 1 20 10.1038/s41467-021-21246-9 33397941
Kim K Park S Park SY Kim G Park SM Cho JW Single-cell transcriptome analysis reveals TOX as a promoting factor for T cell exhaustion and a predictor for anti-Pd-1 responses in human cancer Genome Med 2020 12 1 22 10.1186/s13073-020-00722-9 32111241
Lei Y Tang R Xu J Wang W Zhang B Liu J Applications of single-cell sequencing in cancer research: progress and perspectives J Hematol Oncol 2021 14 1 91 10.1186/s13045-021-01105-2 34108022
Lui WO Zeng L Rehrmann V Deshpande S Tretiakova M Kaplan EL CREB3L2-PPARgamma fusion mutation identifies a thyroid signaling pathway regulated by intramembrane proteolysis Cancer Res 2008 68 17 7156 7164 10.1158/0008-5472.Can-08-1085 18757431
McGranahan N Swanton C Clonal heterogeneity and tumor evolution: past, present, and the future Cell 2017 168 4 613 628 10.1016/j.cell.2017.01.018 28187284
Molinaro E Romei C Biagini A Sabini E Agate L Mazzeo S Anaplastic thyroid carcinoma: from clinicopathology to genetics and advanced therapies Nat Rev Endocrinol 2017 13 11 644 660 10.1038/nrendo.2017.76 28707679
Mroz EA Tward AD Pickering CR Myers JN Ferris RL Rocco JW High intratumor genetic heterogeneity is related to worse outcome in patients with head and neck squamous cell carcinoma Cancer 2013 119 16 3034 3042 10.1002/cncr.28150 23696076
Navin N Krasnitz A Rodgers L Cook K Meth J Kendall J Inferring tumor progression from genomic heterogeneity Genome Res 2010 20 1 68 80 10.1101/gr.099622.109 19903760
Newman AM Liu CL Green MR Gentles AJ Feng W Xu Y Robust enumeration of cell subsets from tissue expression profiles Nat Methods 2015 12 5 453 457 10.1038/nmeth.3337 25822800
Parker NR Khong P Parkinson JF Howell VM Wheeler HR Molecular heterogeneity in glioblastoma: potential clinical implications Front Oncol 2015 5 55 10.3389/fonc.2015.00055 25785247
Rafiei S Gui B Wu J Liu XS Kibel AS Jia L Targeting the MIF/CXCR7/AKT signaling pathway in castration-resistant prostate cancer Mol Cancer Res 2019 17 1 263 276 10.1158/1541-7786.Mcr-18-0412 30224544
Romei C, Elisei R (2021) A narrative review of genetic alterations in primary thyroid epithelial cancer. Int J Mol Sci 22(4). 10.3390/ijms22041726
Song M Sandoval TA Chae CS Chopra S Tan C Rutkowski MR IRE1α-XBP1 controls T cell function in ovarian cancer by regulating mitochondrial activity Nature 2018 562 7727 423 428 10.1038/s41586-018-0597-x 30305738
Swanton C Intratumor heterogeneity: evolution through space and time Can Res 2012 72 19 4875 4882 10.1158/0008-5472.CAN-12-2217
Wang Y Waters J Leung ML Unruh A Roh W Shi X Clonal evolution in breast cancer revealed by single nucleus genome sequencing Nature 2014 512 7513 155 160 10.1038/nature13600 25079324
Wen S Luo Y Wu W Zhang T Yang Y Ji Q Identification of lipid metabolism-related genes as prognostic indicators in papillary thyroid cancer Acta Biochim Biophys Sin (shanghai) 2021 53 12 1579 1589 10.1093/abbs/gmab145 34693452
Yadav SS, Stockert JA, Hackert V, Yadav KK, Tewari AK, (eds) (2018) Intratumor heterogeneity in prostate cancer. Urologic Oncology: Seminars and Original Investigations. Elsevier
Zhang L Li Z Skrzypczynska KM Fang Q Zhang W O’Brien SA Single-cell analyses inform mechanisms of myeloid-targeted therapies in colon cancer Cell 2020 181 2 442 59.e29 10.1016/j.cell.2020.03.048 32302573
Zhang J Song C Tian Y Yang X Single-cell RNA sequencing in lung cancer: revealing phenotype shaping of stromal cells in the microenvironment Front Immunol 2021 12 802080 10.3389/fimmu.2021.802080 35126365
Zhao Q Eichten A Parveen A Adler C Huang Y Wang W Single-cell transcriptome analyses reveal endothelial cell heterogeneity in tumors and changes following antiangiogenic treatment Cancer Res 2018 78 9 2370 2382 10.1158/0008-5472.Can-17-2728 29449267
Ziegenhain C Vieth B Parekh S Reinius B Guillaumet-Adkins A Smets M Comparative analysis of single-cell RNA sequencing methods Mol Cell 2017 65 4 631 43.e4 10.1016/j.molcel.2017.01.023 28212749 | 36933059 | PMC10024289 | NO-CC CODE | 2023-03-22 23:19:39 | yes | Funct Integr Genomics. 2023 Mar 18; 23(2):89 |
==== Front
Lancet
Lancet
Lancet (London, England)
0140-6736
1474-547X
Elsevier Ltd.
S0140-6736(23)00394-X
10.1016/S0140-6736(23)00394-X
Correspondence
Chinese medical personnel after the COVID-19 pandemic
Sun Liyang a
Jia Hangdong a
Yang Tian ab
a Department of General Surgery, Cancer Center, Division of Hepatobiliary and Pancreatic Surgery, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou 310014, China
b Eastern Hepatobiliary Surgery Hospital, Second Military Medical University (Navy Medical University), Shanghai, China
23 3 2023
25-31 March 2023
23 3 2023
401 10381 10001000
© 2023 Elsevier Ltd. All rights reserved.
2023
Elsevier Ltd
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
==== Body
pmcAs the COVID-19 pandemic comes to an end in China,1 medical personnel who have worked tirelessly to fight the omicron (B.1.1.529) variant are now facing a new challenge. Despite their heroic efforts, many of them are now struggling to receive the financial compensation they deserve.2
The requirements for receiving the compensation are very strict, with medical personnel needing to provide proof of their involvement in the fight against the virus, including the number of days they have been in contact with patients who tested positive for COVID-19, attendance records, nucleic acid or antigen positive certificates, medical records, and other details. In some hospitals, even one day of contact with patients who have tested positive for COVID-19 is not considered as a full day of work; only half a day of work is counted.2 Furthermore, some medical staff have bought their own medical protective supplies. However, when requesting reimbursement, some hospitals required evidence of the purchasing of these materials.
This situation has left many medical personnel feeling frustrated and helpless. After all, it was not long ago that we, the 1·4 billion Chinese people, witnessed their dedication and hard work.
The distribution of special subsidies to those who have made special contributions is a matter of justice and should be welcomed by all. However, the current situation is far from satisfactory. Some have argued that the management is also helpless because the problem is so difficult to solve. But is dividing people into groups really necessary when it comes to rewarding those who have worked hard?
We sincerely hope that the Chinese Ministry of Health can address this issue and ensure that medical personnel receive the compensation they deserve. The compensation is not only a matter of money, but also a recognition of their hard work and dedication. We should not let them down.
We declare no competing interests.
==== Refs
References
1 Pan Y Wang L Feng Z Characterisation of SARS-CoV-2 variants in Beijing during 2022: an epidemiological and phylogenetic analysis Lancet 401 2023 664 672 36773619
2 Bao A If you don't give the doctor money, you can give something else (in Chinese). https://mp.weixin.qq.com/s/okprK5R0CENaQVnZyQTG5Q February 20, 2023 | 36965963 | PMC10036125 | NO-CC CODE | 2023-03-25 23:20:13 | yes | Lancet. 2023 Mar 23 25-31 March; 401(10381):1000 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
26334
10.1007/s11356-023-26334-5
Research Article
A hybrid model analysis of digitalization energy system: evidence from China’s green energy analysis
Xiao Jie [email protected]
grid.412017.1 0000 0001 0266 8918 University of South China, Hunan, 421001 China
Responsible Editor: Philippe Garrigues
31 3 2023
112
6 1 2023
3 3 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
The integration of renewable energy sources can be supported by the digitalization of energy systems, which increase dependability and lower costs of energy production and consumption. However, the energy digitalization support energy infrastructures and technologies currently in place are insufficient. This research presented the study results by using the generalized least square estimates (GLS) model and the international sample of China regions from 2003 to 2017. Main results of the dynamic fixed effect (DFE) estimator for the autoregressive distributed lag (ARDL) method, establishing ES goals for lowering energy consumption and pollution emission fosters a country’s renewable energy business sector’s digital transformation in the short term, while encouraging the use of renewable energy sources fosters a country’s long-term digitalization efforts. Based on this, the direct effects and dynamic effects of digitalization and financial development on environmental are explored, respectively, using the panel data regression model and panel vector autoregression (PVAR) model. The threshold regression model is then used to examine the two parameters’ threshold effects on eco-efficiency. An accurate estimate of the resource consumption in smart factories is made possible by the digital twin that is created using the product’s and its attributes as well as manufacturing data. The results suggests the future directions for the associated stakeholders.
Keywords
Emission reduction
Digitalization energy system
China
Green energy
Hybrid analysis
==== Body
pmc Introduction
The necessity for efficient design approaches of energy systems that are sustainable in terms of economy, the environment, and society is shown by climate change mitigation, one of the most important concerns civilization is currently facing (Chang et al. 2023). In order to reduce congestion on urban local networks, a sizeable fraction of the energy demand for cities should be met by on-site renewable energy (Li and Umair 2023). In this context, rooftop photovoltaics (RPVs) have demonstrated tremendous promise because rooftops provide a significant amount of underutilized space for utilizing solar electricity in urban areas where land is scarce and expensive (Liu et al. 2023). Additionally, they ensure that economic growth is attained using sustainable development tactics that can lessen the effects of big occurrences like local or worldwide diseases as well as environmental degradation. It is unaffected by any loss coefficients and takes into account roof superstructures to the extent that the resolution permits. The features of usable rooftop areas are represented by three components, namely geometric, technical, and solar (Fang et al. 2022).
This increase in focus illustrated how the market and technology are evolving in reaction to the digital revolution, forcing established enterprises to adapt at a rapid pace. Typically, these external contingencies cause established enterprises to reconsider the type of innovation they have selected (Pan et al. 2023). It is still unclear, however, which innovation categories will help manufacturing companies undergo a digital transformation and how the process of digital transformation will affect the innovation portfolio. In light of this, the literature now in circulation has produced some fascinating findings. For instance, technical advancement, increased energy investment, and R&D spending reduce carbon dioxide (CO2) emissions and support China’s carbon-abatement program (Wu et al. 2022). In the long run, technological innovation is necessary for green growth, and benefits from technological innovation (Umair and Dilanchiev 2022). However, there is little data on how innovation and the information economy affect green growth globally through renewable energy industries that contributes in environmental development and in financial success. Investigating whether knowledge-intensive expansion is environmentally benign is therefore imperative.
In order to become carbon neutral by 2060 and attain a peak in carbon emissions by 2040, the Chinese government unveiled a plan in March 2022 to establish a “new power system” dominated by “green power.” China currently continues to rely significantly on fossil fuels, particularly coal (Zhao et al. 2022). Despite the fact that the share of coal-fired power has decreased from 90.2 to 27.2% due to the growth of renewable energy and the coal consumption rate has improved from 333 to 310 gce/kWh, the amount of coal used for power generation increased from 1.13 billion tones (in 2010) to 1.65 billion metric tons (in 2016). This is a result of the ongoing increase in power demand (Tu et al. 2021; Li et al. 2021). Power generated primarily by coal without decarburization. The availability of current information on energy production, distribution, and consumption is crucial for Singapore's effective operation (Xiuzhen et al. 2022). The process of data gathering, sharing, and processing based on contemporary digital technologies, particularly the expanding roll-out of smart meters, therefore play a crucial role in the energy transition. Energy suppliers and consumers may benefit greatly from smart grids, but there are also certain drawbacks, including costs, privacy concerns, and data security difficulties (Ullah et al. 2020). Most smart grid implementations to date have been pilot programs in which a few small-scale generators (such as PV or wind) are deployed in homes or communities (Zhang et al. 2022; Iqbal and Bilal 2021). Smart grids utilize renewable, storage systems, and smart home products (e.g., smart plugs).
The current study the feasible generalized least square estimates (FGLS) model and the international sample of China regions from 2003 to 2017 are used in this study to yield remarkable results. The main findings of the autoregressive distributed lag (ARDL) method’s dynamic fixed effect (DFE) estimator. Furthermore, we offer a thorough analysis of the advantages as well as potential disadvantages and dangers associated with the use of current smart contract technologies, including security flaws, operational and digital communications hazards, as well as potential expenses, both monetary and environmental. Therefore, this paper only takes into account future photovoltaic deployment scenarios for China with a short- and long-term horizon for the green and digital energy model estimates (2030 and 2050, respectively). This decision was made because, within the Chinese policy framework, only these model possibilities have had political support up to this point. The rest of the paper is as follows: The “Theoretical context” section theoretical contribution initiatives to investigate the green economy using economic complexity methodologies. Data and methods in the “Model specifications” section. The “Empirical findings and analysis” section represent the empirical results, while the “Conclusion and policy implication” section highlights the conclusion and policy implications.
Theoretical understanding
Numerous important contributions to the digitalization of the energy business could come from digital platforms (Abbas et al. 2022). Platforms could offer improved matching services as energy generation increasingly uses a heterogeneous, decentralized network with lesser capacity but the ability to regulate supply and demand in real time. Grids with a rising proportion of renewable energy could be efficiently managed by digital platforms. According to Ikram et al. (2019), platforms are advantageous from the social media and sharing economy perspectives since they offer new connections at more affordable prices. In order to address coordination issues in numerous economic sectors, including the energy sector, digital platforms are organizational innovations reliant on technical solutions (Wang et al. 2022). For primarily three reasons, it is anticipated that the built environment, particularly the residential sector, would get major attention in order to meet the new target. First, meeting energy efficiency goals has proven to be particularly difficult as a result of growing demand, in contrast to other 2020 EU commitments (Iram et al. 2020). When looking at 2030, the 32.5% energy efficiency goal (anticipated to be further upgraded to meet the new, more ambitious climate targets of the EU) seems highly unrealistic, especially with all present policies in place and despite forecast over performance against the obsolete 40% GHG target (Mohsin et al. 2022). Off-grid renewable energy-based electrification programs face significant challenges due to a lack of managerial and technical expertise required to operate and maintain the systems, low energy demand density, unpredictability of household energy demand, dispersed homesteads, inadequate community engagement, inappropriate financial models, inconsistent policy, and lack of political will (Mohsin et al. 2021). The socio-economic effects of the rural off-grid renewable energy transition projects are constrained by these difficulties. Access to electricity serves as a portal to other types of development aid, but electrification alone cannot address all development issues. According to a recent study by Iqbal et al. (2019) in urban India, giving women with low incomes better access to welfare goods like refrigerators and washing machines increases household performance.
The creation of energy-optimized control systems, advanced route planning, and the choice of the best operation techniques for hybrid vessels are a few of the important marine uses of digital twins recently described a digital twin for the simultaneous simulation of a ship’s AC power and propulsion systems. An easy transition function provided the diesel engine’s dynamics. Zhang et al. (2021) recently went one step farther and suggested a more physics-based strategy for a ship’s digital twin (Zheng et al. 2022). The model’s use was restricted to a straightforward two-stroke diesel engine with direct drive. The Wiebe continuous time-domain mean-value engine model and the Wiebe crank-angle resolved phenomenological combustion model were merged in the engine modeling (Sun et al. 2022). This departs from the idea of a digital twin being based on physics but satisfies the real-time demand in system-level simulation (Yang et al. 2022). This aberration, while intended to get around the combustion element’s enormous phenomenological complexity, instead makes the entire digital-twin non-transferable and non-predictive in terms of modifications to combustion engine architecture or controls. Co-optimization efforts and coordinated vessel/grid-level control techniques are hampered by the lack of comprehensive hybrid power plant digital twins with a physics-based engine component. Modern combustion engines have extremely nonlinear dynamics and a tendency to eventually adopt sophisticated model-based control techniques, creating a self-learning architecture that can be adjusted to applications and restrictions. As a result, this flaw is becoming more and more significant (Chang et al. 2022a). Additionally, for enhanced hybrid engine and battery storage power plant performance, coordinated.
The total amount of energy used by a civilization is influenced by a number of factors. The amount of energy consumed can be influenced by a variety of factors, including societal developments like opportunity disparity, technical advancements, and economic growth. Numerous studies have demonstrated that the growth of energy consumption is influenced by economic development (Chang et al. 2022b). Because their standards of life are higher, people tend to consume more in more developed civilizations. Lower income consumers and households use less energy, but they also have fewer opportunities to convert to more energy-efficient sources. The decision of consumers to purchase household goods that need a lot of energy is influenced by rising purchasing power. Additionally, this rise in the number of products has. The significance of the influencing factors on energy usage is highlighted by Huang et al. (2022). Smart meters and other digital devices that can assess energy consumption may cause consumers to adjust their behavior and get rid of out-of-date habits (Chabi Simin Najib et al. 2022). Applications for mobile phones and energy efficiency measurement using the Internet of things are examples of more recent technologies. A reduction in the number of people needed to read household meters would be made possible by the installation of smart meters. Additionally, adjustment bills that are currently received by all households would be eliminated (adjustment bills are estimates of consumption, and a credit or debit note are issued to the consumer at specific times throughout the year based on these estimates). Emphasize the importance of the influencing factors on energy consumption (Iqbal et al. 2021). Consumers may modify their behavior and give up outdated behaviors as a result of smart meters and other digital energy consumption monitoring devices. More contemporary technologies include mobile phone application and Internet of things-based energy efficiency measurement (Vivoda 2019). The installation of smart meters would enable a reduction in the number of people required to read household meters. Additionally, all households would no longer receive the adjustment bills that they currently do (adjustment bills are estimates of consumption, and a credit or debit note is issued to the consumer at specific times throughout the year based on these estimate).
Model specifications
Environmental taxes and eco-innovations are essential for sustainable growth. Environmental tariffs, for instance, prohibit pollutant emissions with financial incentives, whereas eco-innovation promotes the use of environmentally benign technology. The following hypothesized linkage can be calculated for the empirical estimations:1 CE=fY,ER,EI,RE,HC,DE
2 EFT=fY,ER,EI,RE,HC,DE
3 ΔCEit=α+β1Yit+β2ERit+β3EIit+β4REit+β5HCit+μ
Equations (3) and (4), where CE and EFT are abbreviations for carbon emissions and ecological footprints, respectively, indicate the regression form of Eqs. (1) and (2). GDP growth is represented by Y, environmental regulation is represented by ER (measured by eco-taxation), eco-innovation is represented by EI (in the form of patent applications), renewable energy consumption is represented by RE, and human capital is represented by HC.4 ln(ESpatentit)=βDigitalit+λXit+ωt+ηj+γk+εit
where ln(ESpatentit)=βDigitalit is the number of energy storage patents held by firm I in year t serves as the dependent variable. The number of statements in a company’s annual reports that use keywords related to digitalization as λXit a stand-in for their digital strategy constitutes the independent variable. Determines how a γk+εit company’s patenting actions will affect its digital strategy. We concentrated on how digital strategy affected business operations in year t-1 since firm strategy may have a delayed effect on business Research and Development (R&D is a vector matrix of the firm covariates and is a vector of the control variable coefficients). The year fixed effects, industry fixed effects, and provincial fixed effects, respectively, are used to control for unobserved factors. is the name for the error.5 Th=Xh,Zh,Gh,Bh:Xh≥∑i=1NGHGλihXih,Zh≥∑i=1NDRλihZih,Gh≤∑i=1NDFλihGih,Bh≥∑i=1NGDPλihBih,λih≥0i=1,2,⋯,N
The primary distinction between Eqs. (3) and (4) is that Eq. (5) incorporates the two sectors, k and h, into a single digital framework and takes into account the connecting constraints of intermediate goods. The movement of intermediate products from sector k to sector h is indicated by the superscript (k,h). There are numerous approaches to handle the link of intermediate goods, according to (Yu et al. 2022). In this study, the free link value situation constrains the production technology. The term “free link case” refers to a scenario in which linking activities are independently chosen, and their ideal values may differ from observed values in a variety of ways. In the actual world, the government has the power to choose how to allocate its financial budget for the delivery of public goods and services. As a result, the connecting constraints in this study are assumed to be free links6 Tk=Xk,Xh,Gk,Gh,Bk,Bh,Zk,h:Xk≥∑i=1NEFλikXik,Xh≥∑i=1NEDFλihXih,Gk≤∑i=1NREλikGik,Gh≤∑i=1NPEλihGih,Bk≥∑i=1NPIλikBik,Bh≥∑i=1NHCIλikBih,∑i=1NRE2λikZik,h=∑i=1NPOPλihZik,h,λik,λih≥0i=1,2,⋯,N
The patent analysis in the preceding part summarizes the general digital development trend in energy storage, ∑i=1NRE2λikZik,h=∑i=1NPOPλihZik,h shows renewable and population it was unable to quantitatively investigate the internal relationship between digitalization and energy storage patenting activities. This section’s goal is to experimentally analyze how the digital revolution has affected the advancement of energy storage technology. Using a sample of Chinese A-share listed companies, we determine the association between digital transformation and ES patenting activity in this section. The negative binomial regression model and the Poisson regression model are two potential solutions to the count problem given the count nature of patent application numbers. Both are frequently employed in analyses in which the dependent variable is a count number with numerous zero values.
Data and statistical estimation approach
This research produces fascinating results by using the feasible generalized least square estimates (FGLS) model and the international sample of China regions from 2003 to 2017, the gross domestic product (GDP) increased rapidly from $40.3 billion to $300.4 billion, correlating with an increase in greenhouse gas emissions and energy consumption that is also consistent with a decrease in the demand for renewable energy. We divide sample digital energy between subgroups based on the mean income of digital, as initially specified, to examine if digitalization may alleviate the negative effects of where carbon emissions and ecological footprints. Many academics have studied the factors influencing the globalization of stock exchange. Additionally, existing research supports the long-held belief that most businesses gradually internationalize once they have attained local competence. This is important when we assess the effects of CO2 emission and digital energy on the China economy. The data availability alone determines the time you select. One external variable and four internal macroeconomic variables make up the dataset. Additionally, domestic variables include the actual effective exchange rate, trade balance, consumer price index inflation, and GDP. The deviation of the natural logarithms. When measured in crises between 2000 and 2018, the rise in GHG is unlikely to have much of an impact during times of economic control (decreased TRO). The rise in GDP could lead to an increase in GHG because it raised the need for energy for industry and transportation. According to Yumei et al. (2022), high GHG emissions occurred globally between 1998 and 2013 including in Russia due to high energy demand or a high GDP (2017). In the OECD between 1971 and 2000 (Rao et al. 2022), or in Norway and Sweden between 1995 and 2010. The aforementioned data also demonstrate that China economic growth is overly dependent on fossil fuels, and that there is a lack of alternative energy sources, which results in high gas output and difficult gas reduction. Table 1 shows the descriptive statistics outcomes of the original digital energy data.Table 1 Descriptive statistic of variables
Variables Obs Mean Std. dev Min Max Skew Kurt
GHG 50 − 0.479 − 0.656 − 0.397 − 0.496 − 0.467 − 0.455
GDP 39 0.761 0.825 − 0.017 0.849 − 0.353 − 0.390
RE 39 0.705 0.629 0.475 0.604 − 0.725 − 0.790
CE 50 0.520 0.704 0.259 0.574 − 0.725 0.532
EFT 50 0.996 0.917 0.516 0.931 − 0.489 0.738
ER 50 0.801 0.881 0.156 0.849 − 0.570 0.937
EI 50 0.926 0.824 0.729 0.798 − 0.476 0.546
DF 50 0.703 0.619 0.398 0.656 − 0.384 0.374
DE 50 − 0.479 − 0.656 − 0.397 − 0.496 0.785 0.76
HC 50 0.761 0.825 − 0.017 0.849 − 0.429 0.856
Source: STATA 13 analyses
According to Table 1, descriptive statistic findings, resource efficiency was significantly and negatively impacted by green trade and growth. The two-step least squares method is used to solve a set of Eqs. (3) and (4), using all independent variables as tools and the carbon emissions and ecological footprints for autocorrelation. According to the calculations in Table 1, the stock price decreases by more than 7% for every 1% of GDP in the deficit. Because of this, the stock price is significantly impacted by even a small increase in CO2 emission. Additionally, changes in digital framework were related to variations in the population share of the main income categories. A 1% shift in this age group results in a 1.74% shift in the GDP. This demonstrates that if digital energy estimation positive rise and the number of people with the highest earnings falls.
Results and discussion
ARDL findings
The model under study was predicated on the idea that a focus on green economic development and trade would impact resource utilization, i.e., that resource utilization would become more effective and reserves would be depleted through efficient use. It is therefore likely that these variables will cointegrate, which can result in the estimation of a stationary variable. The results of the preceding experiment showed that the variables were stationary at the initial difference. Additionally, the digital energy of a typical manufacturer is around 17%. Sales will drop by 25% as a result. If sales decline by 50% or 75%, the GDP will increase to 22.4% and 43.4%, respectively. The mining sector follows a similar trajectory for the three sales scenarios, with average digital energy increasing to 17%, 28%, and 40%. Then come services, forestry, agriculture, utility, retail, and wholesale. This section digital energy-based study, we used marketing and accounting-based research. Here, there is an intriguing contrast. The wholesale and retail sectors are thought to be more effective than the accounting counterpart in market-based RE volatile. The equity market may not have properly valued an underlying strong position, which is reflected in some of this difference. Table 2 despite this, statistics indicate. This demonstrates the co-integration of digital energy, gross domestic product, carbon emissions and ecological footprints.Table 2 Impact of DE and EFT on renewable resource coefficient
Variable Coefficient Std. error t-Statistic Prob.*
GHG 0.7614** 0.71236** 0.65125*** 0.0090
GDP 0.85123** 0.712548** 0.68455*** 0.0083
RE 0.84124** 0.75415** 0.7176** 0.0073
CE 0.0214*** 0.01425** 0.0247** 0.9087
EFT 0.85144** 0.6914*** 0.9460*** 0.0094
ER 0.91452*** 0.5124** − 0.004 0.0088
EI 0.817*** 0.5819** 5.42% 0.0077
DF 3565.972 0.333 0.0018*** 0.4356
DE 0.578 − 0.186 − 0.0004 0.0078
HC 2.338 1.775 − 0.0052*** 0.0081
GHG 14.334 13.245 − 0.0009 0.0085
***, **, * denotes significance level at 10%, 25%, and 100%. Parenthesis denotes t-statics. Source: Author calculation
Table 3 findings show that the coefficients of the study variables are significantly positive for the main explanatory variable, indicating that ER can have a considerable impact on the HC with a beta value of 8.083, p 0.1, in POLS. Its value in the panel is 15.688, with a p value of 0.001. This suggests that ER greatly raises China’s EFT performance. As a result, hypothesis 1, according to which environmental regulation is a highly significant positive predictor of green total factor productivity in China, was accepted. For both figures, the first subplot displays a contribution analysis that takes investment, operation, replacement, and operation and maintenance costs into account. The horizontal red line indicates the costs of the reference system. It is important to note that replacement costs and costs for grid injection may be negative, for instance because of the PV system’s prolonged lifespan and the compensation for grid electricity injection. For both figures, the second subplot shows a contribution analysis for annual GHG emissions that takes into account GHG emissions produced during the manufacture of system components and during system operation. The horizontal red line shows the GHG emissions of the reference system. Additionally, the total percentage of system component production from total annual GHG emissions has decreased.Table 3 Results of Philip-Perron estimation
Variables Augmented Dicky-Fuller test (ADF) Philip-Perron test
Level(1) Level(2)
lnGHG − 10.449*** − 10.367***
lnGDP − 13.630*** − 17.695***
lnRE − 11.928*** − 14.889***
lnCE − 09.448*** − 15.367***
lnEFT − 16.875*** − 15.784***
lnER − 13.716*** − 14.715***
lnEI − 15.785*** − 14.865***
lnDF − 18.375*** − 15.439***
lnDE − 19.095*** − 16.142***
lnHC − 08.083*** − 15.688***
R2 − 6.550*** − 5.928***
***, **, * denotes significance level at 10%, 25%, and 100%. Parenthesis denotes t-statics. Source: Author calculation
According to the findings, China has severe environmental protection laws in place and uses strong ER to compel businesses to embrace green practices. To address this issue, nations should adopt comparable strategies. The results of the current study suggest that GF growth can raise levels of green output and growth. With a beta value of 1.7616, p 0.1, GF considerably positively affects DE in the case of POLS. The beta value between GF and ER in the instance of RE is 1.2627, p 0.05. The FE model exhibits a 1.6626 beta and 0.05 p value positive correlation with DE and DF. With a beta value of 1.7161, the GMM model exhibits the most significant correlation between DE and HC. As a result, hypothesis 2 was accepted, which states that in China, green financing is a highly significant positive predictor of green total factor productivity. As a result, there is a pressing need to promote green economic growth and recovery. This conclusion is consistent with the study (Irfan et al. 2022), which shows that green expenditure and growth may nurture a win–win culture for the environment and the economy. According to the findings, China’s central government has a priority on minimizing financial obstacles that businesses face while implementing environmentally friendly projects, which calls for a well-designed GF system. As a result, companies are more inclined to participate in economic and ecological projects that prioritize green growth.
With a beta value of 1.8267, p 0.05, the augmented Dicky-Fuller test (ADF) examination of the GDP link with EFT produced favorable and significant results. With 1.6886 beta and 0.05 p values, the RE model similarly demonstrates a favorable and significant association between CE and EFT. With a beta value of 1.6671, p 0.05, and 1.8621, p 0.05, respectively, the two models, FE and ADF, also demonstrated the considerable beneficial influence of CE on ER. This result points to acceptance of the third hypothesis, which states that “foreign direct investment strongly predicts China’s green total factor productivity.” This study deviates from Wen et al. (2022) finding that FDI has no appreciable impact on green productivity. On the other hand, the current results are in line with the findings of Sun et al. (2020), who found that CO2 largely affects GHG through capital allocation and technical advancement. The development of technology is necessary for long-term economic progress. Due to the significant positive externalities connected to financial help, GDP has evolved into a driving force for EFT. Efficiency improvements and technical advancements can greatly lower economic system friction, which will increase EFT. Profitable capital allocation does not, however, inevitably follow financial success. This shows that copper producers in 2020 may be able to save money by converting to recycled feedstocks from virgin ores, while steel and aluminum makers may not be able to do the same. Due to its widespread availability and use, steel is often also far less expensive than other metals. When market prices are highly volatile and established industry patterns, contracts, or regional restrictions are present, producers may find it difficult to quickly alter their purchasing habits in order to benefit from price breaks. However, this analysis informs that the benefits of converting to secondhand manufacturing for particular metals may go beyond greenhouse gas reductions to include financial savings as well. In their research, Ellabban et al. (2014), Raza et al. (2020), and Sun et al. (2020) also noted a comparable outcome. RE is essential for producing top-notch economic growth. By investing in innovation and information dissemination, lowering transaction and resource matching costs, and enhancing national risk detection and risk management capabilities, it can provide enough impetus for EFT. In order to keep extra money for DE in reserve, it also builds a new structure that is quality-focused. The development of various green businesses, the cultivation of new economic growth poles, the promotion of quality, and the accomplishment of the SDGs in China will all be made possible by empowering financial science and technology.
Heterogeneity analysis findings
After that, the authors ran a heterogeneity analysis to see how ER and GF levels affected GTFP at low and high levels. It assisted the authors in confirming that increased ER and GF activity cause GTFP. According to Table 4, the study sample is split into two groups, ER and GF, with low and high viewpoints. In contrast to DE and RE, which showed non-significant results, EFT, GDP, and EFT showed significant and favorable relationships. It proves that higher levels of RE and DE result in more pronounced renewable energy and digital energy in Table 4. On the other hand, with a beta value of 2.7572, the low RE has a negligible effect on DE. It suggests that rules should be rigid and unforgiving to ensure proper application.Table 4 Heterogeneity model results
Variables DE RE EFT HC
lnDE
lnGDP
0.4551 1.4525*** 0.2277 2.0207***
(0.522) (2.5275) (0.5725) (2.5052)
lnRE
lnCE
0.7577 2.7525*** 0.2277 5.0207***
(2.7572) (5.5275) (0.5725) (5.5052)
lnEFT 0.7505 0.7702** 0.7570 0.7725**
(5.7577) (2.7527) (7.0577) (2.0775)
lnHC 0.0077 0.0557* 0.0052 0.0227**
(2.7055) (0.5557) (5.702) (0.2705)
Constant 5.5572* 5.0707** 5.7777* 5.2727*
(2.2757) (2.5507) (5.777) (2.725)
R2 0.7725 0.72 0.7722 0.7757
Obs 500 500 500 500
***, **, * denotes significance level at 10%, 25%, and 100%. Parenthesis denotes t-statics. Source: Author calculation
Macroeconomic effects assessment
Particularly in the situations of ambitious reduction scenarios, the GHG emission reduction targets result in a considerable increase in government spending and a sharp fall in family consumption. Under the RE, the government’s consumption will dramatically rise from US$53 billion in 2010 to US$251 billion in 2050. Under the different scenarios (Gasparatos et al. 2017), it is predicted that household consumption will rise from US$172 billion in 2010 to US$662 billion in 2050, expanding at a EFT R of 3.4%. According to the ERT20 to ERT25-90 scenarios, the cumulative government consumption from 2010 to 2050 will raise by 24.9 to 99.0% in comparison to the DE due to the growing emission reduction targets. The dynamic ADF model block can be used to assess the policy’s macroeconomic effects by using these output quantities and price changes as the inputs for the policy scenarios. The scenarios’ parameters for the independent variable parameters are provided in Table 5 in accordance with the findings from the SD model block.Table 5 The selected independent variable parameters
Scenarios Renewable and digital energy GDP and GHG EFT and HC
r1 4.598% − 3.370% 0.876%
r2 4.595% − 3.372% 1.456%
r3 5.591% − 4.388% 2.088%
r4 8.177% − 5.730% 0.987%
r5 9.190% − 4.735% 1.634%
r7 5.189% − 6.738% 2.077%
r8 7.777% − 09.099% 0.984%
r9 10.779% − 11.095% 1.654%
r10 11.766% − 13.096% 2.056%
Source: Author calculation
Heterogeneous results of regulatory pressure
For more details on the examination of renewable and digital energy as well as human resources and production expenses, see Table 6. This table offers helpful information for developing and running a cost-effective system when standard energy and energy analysis, as well as economic analysis, are unable to produce the desired outcome. For a study of economic energy, the terms “fuel” and “product” must be defined. The corresponding outcomes are shown in Table 6. Columns 1 to 4 show the regression results for the impact of environmental monitoring stations situated in important cities on business green innovation. We can conclude that the level of green innovation in the businesses is greatly encouraged, regardless of how monitoring stations are set up or how many monitoring stations are distributed in the counties where the businesses are located. We discover that population density pop has a detrimental effect on the growth of the green economy by examining the regression findings of the controllable variables. This demonstrates how, despite providing labor for economic growth, increased population densities will also highlight the issues of resource depletion and environmental pollution. Population density as a whole is not a favorable factor for the expansion of a green economy. The value of consuming renewable energy is shown in Table 6. More consideration should be given to how to link the use of renewable energy with the growth of the green economy.Table 6 Heterogeneous effects of regulatory pressure
Variables (1) (2) (3) (4) (5) (6) (7) (8)
Digital cities for environmental protection Non-key cities for environmental protection
lnDE lnRE lnHC lnGDP lnGHG lnCO2 lnEFT lnDF
DE 0.020* (0.012) 0.026*
(0.014)
0.023 (0.021) 0.008 (0.025)
EFT 0.019*** (0.005) 0.023*** (0.005) 0.011** (0.005) 0.010 (0.009)
_cons − 1.014*** (0.149) − 0.974*** (0.168) − 1.029*** (0.149) − 0.992*** (0.167) − 0.414 (0.318) − 0.047 (0.393) − 0.417 (0.318) − 0.052 (0.393)
Control variables 100% agreed 100% agreed 100% agreed 100% agreed 100% agreed 100% agreed 100% agreed 100% agreed
City-year effect Not agreed 100% agreed Not agreed 100% agreed Not agreed 100% agreed Not agreed 100% agreed
Firm effect 100% agreed 100% agreed 100% agreed 100% agreed 100% agreed 100% agreed 100% agreed 100% agreed
Year effect 100% agreed 100% agreed 100% agreed 100% agreed 100% agreed 100% agreed 100% agreed 100% agreed
Observations 22,677 22,264 22,677 22,264 5297 4163 5297 4163
R2 0.594 0.626 0.594 0.626 0.565 0.709 0.565 0.710
***, **, * denotes significance level at 10%, 25%, and 100%. Parenthesis denotes t-statics. Source: Author calculation
Performance evaluation of study model
Tables 7, 8, and 9 shows the assessment of long-term and short-term elasticity coefficients; measured in crises between 2000 and 2018, the rise in GHG is unlikely to have much of an impact during times of economic control (decreased GHG). The rise in GDP could lead to an increase in GHG because it raised the need for energy for industry and transportation. The estimated results of the ARDL regression are shown in columns 1 and 2, and they suggest that the transition to renewable energy is positively impacted by the digital energy. The estimated effects of the relationship between the digital energy and RE are shown in column 3; the estimates show that the digital energy has a favorable impact on government governance. In other words, a 1% growth in the digital economy will result in a 0.026% increase in the level of government governance. The digital economy is the effective use of technology in managing government.Table 7 Results of ARDL tests
Variable DE coefficient Standard error T-ratio
lnGHG 0.2132* 0.0418 3.3382
lnGDP 0.2610* 0.0378 − 1.4606
lnRE 0.1973 0.1723 − 4.6396
lnCE 0.2987 0.0345 4.4364
lnEFT 0.33 0.0528 1.8435
lnER 0.0666* 0.2354 4.4525
lnEI 0.323132* 0.0339 1.7386
lnDF R2 = 0.3434; F-Stat. F(3,50) ¼ 8.5438* [0.000]; DW-statistic = 1.6703
***, **, * denote significance level at 10%, 25%, and 100%. Parenthesis denotes t-statics. Source: Author calculation
Table 8 Heterogeneity test: grouped by digital industry
Variable OLS-DID FE-DID
Non-GHG emission 3.31% 5.15%
(N = 11,791) 0.0007*** 0.0008***
Polluting − 0.0002 − 0.0003
(N = 5099) − 0.0042*** − 0.0031
Control Var − 0.001 − 0.003
Time FE − 0.0523*** − 0.0195***
Regional FE − 0.0055 − 0.0048
Industrial FE 0.1787** 0.1918***
Individual FE − 0.0761 − 0.0648
***, **, * denote significance level at 10%, 25%, and 100%. Parenthesis denotes t-statics. Source: Author calculation
Table 9 Speed at which the target leverage is adjusted
Model a COVID-19 (Pre-COVID-19 COVID-19
lnGHG 0.9517*** 0.9264***
− 0.0006 − 0.0016
lnRE 0.9460*** 0.8870***
− 0.004 − 0.0039
lnEFT 4.81% 7.38% 5.42% 11.30%
0.0018*** 0.0026*** 0.0018*** 0.0060***
lnEI − 0.0002 − 0.0002 − 0.0004 − 0.0005
− 0.0012*** − 0.0033*** − 0.0052*** − 0.0216***
lnDE − 0.0003 − 0.0012 − 0.0009 − 0.0032
− 0.0755*** 0.0131*** − 0.0708*** − 0.0362***
R2 − 0.0006 − 0.0027 − 0.0053 − 0.0091
lnGHG 0.5125*** 0.1594** 0.3454*** 0.8277***
lnGDP − 0.0068 − 0.0588 − 0.015 − 0.1259
lnRE − 0.0304*** 0.0292*** − 0.0572*** − 0.0965***
lnCE − 0.0018 − 0.0044 − 0.0055 − 0.0056
lnEFT − 0.0002*** 0.0004 − 0.0011*** − 0.0010***
lnER − 0.00002 − 0.0003 − 0.00004 − 0.0005
lnEI 0.0261*** 0.0304***
lnDF − 0.0025 − 0.0069
lnDE 0.0394*** 0.0314***
lnHC − 0.0032 − 0.0053
− 0.00003 0.0355*** 0.0009* − 0.224***
lnEFT − 0.0002 − 0.0025 − 0.0006 − 0.0026
lnGDP − 0.0003 0.0162*** − 0.0012 − 0.0929***
− 0.0007 − 0.0016 − 0.0021 − 0.0025
lnCE − 0.00003 − 0.0002*** 0.0002 0.0006***
− 0.00002 − 0.00002 − 0.0002 − 0.00002
0.00004 − 0.0003*** − 0.00004 − 0.0012***
− 0.00002 − 0.00002 − 0.0003 − 0.00002
Constants − 0.0258*** − 0.1161*** − 0.0498*** 0.8891***
− 0.0029 − 0.0075 − 0.0114 − 0.0116
Fixed time impacts 100% agreed 100% agreed 100% agreed 100% agreed
Fixed country impacts 100% agreed 100% agreed 100% agreed 100% agreed
Observations 64,228 5,884 63,724 5,809
Adjustment R-squared 0.8818 0.8822 0.897 0.8538
***, **, * denote significance level at 10%, 25%, and 100%. Parenthesis denotes t-statics. Source: Author calculation
The long- and short-term effects of these variables on digital energy growth were investigated. At the 1% significance level, the estimated values of the F statistics in the last row of Table 8 are statistically significant. This indicates that the model’s overall flexibility is adequate. The Durbin-Watson statistic for this model is less than 2, meaning that there is no autocorrelation problem. Furthermore, the EI value is 0.323132%, indicating that these self-sufficient persons can expand 0.323132% of RE and DE change information in a short period, while other elements can be interpreted as 66%. In addition, due to structural changes in the country.
In Table 9, the empirical findings are displayed. The negligible regression findings of the establishments and the number of monitoring stations’ impact on the companies’ front-end green innovation are shown in columns 1 to 4. Columns 5 to 8 show the regression results of the two circumstances’ effects on enterprise-wide green innovation. We discover that the discount for higher EFT values is higher if a neighborhood is more environmentally friendly or has greater purchasing power when comparing the results of the various specifications (Models 9–12). On the basis of the numerous control variables and the adjusted R2, we conclude that model 9 is the best. Comparing the various base effects and interaction variables included in a 3-way interaction regression at the same time is more difficult. So, using log (EFT), green, and PP as the three variables, we compute the slopes for various combinations and display them in a margin plot. The dynamic of the autoregressive (AR) coefficients for all panel members and is based on the augmented ADF method. The LLC technique (Foley and Olabi 2017), in particular, assumes that each unit in the panel data has the same AR (1) coefficient, but allows for separate effects on digital energy, period wise effects, and, finally, a time trend. The panel-based ADF test that allow for instantaneous stationary and non-stationary series wise assessment. The IPS proposes a novel, more bendable and computationally very simple unit root testing approach. The (Danish et al. 2019), ADF test, a generalized of panel stationary test of (Yang and Lee 2020) and based on the assumption that the Long-Run Test homogenous variance or heterogonous variance. Under the (Khandker et al. 2013), Fisher-ADF and Fisher-ADF panel test the null hypotheses is an unit root.
Robustness analysis findings
The authors conducted a secondary study using other environmental variables, such as environmental governance and product quality, because the impact of EFT varies depending on how green productivity metrics are evaluated (Ghafoor et al. 2016; Raheem et al. 2016; Asmelash et al. 2020) (Table 10). According to the research, ER has a significant impact on environmentally friendly production, especially ecological authority and environmental excellence. The outcomes of the variance decomposition technique are shown in Tables 11 and 12. It is demonstrated that the country’s revolutionary shocks are responsible for 0.712548% of its energy consumption, while RE, DE, and GHG emissions are responsible for 0.71236, 0.65125, and 0.68455% of that total. One standard deviation’s worth of renewable energy can explain 0.84124% of the variance. While CO2 emissions account for 13.649% of global economic development, energy consumption accounts for 7.605% of it. A one standard deviation energy price shock can account for 26.778% of economic growth. The results also show that 63.656% of CO2 emissions are attributed to robust results. DF, DE, and HC account for 0.5124%, 0.9460%, and 3565.97% of CO2 emissions, respectively; 9.203 is explained by an energy consumption of one coefficient of related variables.Table 10 Robustness test (DE and EFT coefficient)
Variable Coefficient Std. error t-Statistic Prob.*
GHG 0.7614** 0.71236** 0.65125*** 0.0090
GDP 0.85123** 0.712548** 0.68455*** 0.0083
RE 0.84124** 0.75415** 0.7176** 0.0073
CE 0.0214*** 0.01425** 0.0247** 0.9087
EFT 0.85144** 0.6914*** 0.9460*** 0.0094
ER 0.91452*** 0.5124** − 0.004 0.0088
EI 0.817*** 0.5819** 5.42% 0.0077
DF 3565.97 0.333 0.0018*** 0.4356
DE 0.578 − 0.186 − 0.0004 0.0078
HC 2.338 1.775 − 0.0052*** 0.0081
GHG 14.334 13.245 − 0.0009 0.0085
***, **, * denote significance level at 10%, 25%, and 100%. Parenthesis denotes t-statics. Source: Author calculation
Table 11 Robustness test (Philip-Perron estimation)
Variables Augmented Dicky-Fuller test (ADF) Philip-Perron test
Level(1) Level(2)
lnGHG − 10.449*** − 10.367***
lnGDP − 13.630*** − 17.695***
lnRE − 11.928*** − 14.889***
lnCE − 09.448*** − 15.367***
lnEFT − 16.875*** − 15.784***
lnER − 13.716*** − 14.715***
lnEI − 15.785*** − 14.865***
lnDF − 18.375*** − 15.439***
lnDE − 19.095*** − 16.142***
lnHC − 08.083*** − 15.688***
R2 − 6.550*** − 5.928***
***, **, * denote significance level at 10%, 25%, and 100%. Parenthesis denotes t-statics. Source: Author calculation
Table 12 Robustness test
Variables (1) (2)
EFT 0.2944** 4.2444**
(4.4441) (4.6644)
DE 0.1444** 4.4444**
(4.5541) (4.231)
RE 0.3404** 0.3404**
(4.1141) (4.7714)
GHG 0.4124* 0.1444*
(1.4044) (0.1774)
Constant 4.4441* 4.1404**
(4.4764) (1.2404)
R2 0.5644 0.434
Obs 500 500
***, **, * denote significance level at 10%, 25%, and 100%. Parenthesis denotes t-statics. Source: Author calculation
Renewable energy development has a useful correlation with carbon emissions, which is important statistics at the 1% level. It means that monetary development is not environmentally benign, implying that financial activity results in higher emissions, posing a threat to the environment. The main focus of this article, which analyzes the possible benefits of a change to a greener economy, is the ecological aspects of sustainability. A green digital has several benefits.
Conclusion and implications
The integration of renewable energy sources can be supported by the digitalization of energy systems, which can also increase dependability and lower costs. However, the digitalization support infrastructures and technologies currently in place are insufficient to bring about the necessary drastic change. This research produces fascinating results by using the feasible generalized least square estimates (FGLS) model and the international sample of China regions from 2003 to 2017. Main results of the dynamic fixed effect (DFE) estimator for the autoregressive distributed lag (ARDL) method, establishing ES goals for lowering energy consumption and pollution emission fosters a country’s business sector’s digital transformation in the short term, while encouraging the use of renewable energy sources fosters a country’s long-term digitalization efforts. Based on this, the direct effects and dynamic effects of digitalization and financial development on environmental are explored, respectively, using the panel data regression model and panel vector autoregression (PVAR) model. The threshold regression model is then used to examine the two parameters’ threshold effects on eco-efficiency. An accurate estimate of the resource consumption in smart factories is made possible by the digital twin that is created using the product’s and its attributes as well as manufacturing data. These results can be explained by the fact that China’s consumption of oil and gas is less harmful to the environment and may rise as a result of strong green growth. The green trade of China and oil consumption is positively and directly correlated. Similar to this, a direct and favorable correlation between China’s green trade and natural gas use was discovered. These results can be explained by the fact that the usage of oil and gas along trade routes can lower environmental costs and carbon footprints, increasing the sustainability of China’s green trade and economic growth. In order to precisely influence locals’ purchase habits, use digital energy systems. Digital money stimulates immediate home spending, which raises carbon emissions, by removing their liquidity limits. Therefore, digital finance platforms should actively take on environmental commitments in order to realize the conversion of the stimulating influence of digital finance on residential consumption at the current stage into accurate recommendations for low-carbon consumption.
Practical implications
In addition to the normal theoretical, practical, and policy-making repercussions, this challenging research will undoubtedly have clear social ramifications in the future. The research will benefit academics, researchers, and readers in general while adding to the body of information on the green growth hypothesis, which has gained significant interest in the last 10 years. As China is viewed as a major contributor to pollution and the depletion of natural resources, the current study can also add useful future implications to support the reduction of coal consumption as a major source of energy and can also promote the uptake of environmentally friendly sources of energy. Additionally, this study provides workable options for reducing China’s carbon footprint. This study has ramifications for the green trade sector in addition to resource consumption and green economic growth. It suggests that employing green tactics and sustainable practices may result in a decrease in the amount of coal used by the Chinese industrial sector. Chinese trade policymakers and those from other nations who prioritize growing their green trade have made direct contributions to this study. The results of this study have societal implications and benefits because they encourage better use of natural resources, a smaller carbon footprint, the use of sustainable trade routes, and an increase in green practices. Not to mention, one of the main objectives should be to promote the concentration of cutting-edge resources in the high value-added and greener industries, followed by the increase of their production scales. Given that the HC and DE look to be a leader in the green-innovation-driven paradigm, the Chinese government should seek to construct and strengthen a long-term development pattern of inter-sectoral collaboration between HC sectors and other sectors. By following this pattern, the HC sectors may successfully pull the modernization of other low-value-added and pollutant-intensive industrial sectors while also dispersing and transferring sophisticated green technology.
Author contribution
Jie Xiao: conceptualization, methodology, validation, data collection, writing—original draft, writing—review and editing, visualization.
Data availability
The data that support the findings of this study are openly available on request.
Declarations
Ethics approval and consent to participate
We declare that we have no human participants, human data, or human issues.
Consent for publication
We do not have any individual person’s data in any form.
Conflict of interest
The authors declare no competing interests.
Preprint service
Our manuscript is not posted at a preprint server prior to submission.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Abbas Q Mohsin M Iqbal S Iram R Does ownership change and traders behavior enhancing price fragility in green funds market Pakistan J Soc Sci 2022 39 1245 1256
Asmelash E, Prakash G, Gorini R, Gielen D (2020) Role of IRENA for global transition to 100% renewable energy. In: Accelerating the Transition to a 100% Renewable Energy Era. Springer, pp 51–71
Chabi Simin Najib D, Fei C, Dilanchiev A, Romaric S (2022) Modeling the impact of cotton production on economic development in Benin: a technological innovation perspective. Front Environ Sci 10. 10.3389/fenvs.2022.926350
Chang L, Baloch ZA, Saydaliev HB et al (2022a) Testing oil price volatility during Covid-19: Global economic impact. Resour Policy 102891. 10.1016/j.resourpol.2022.102891
Chang L Qian C Dilanchiev A Nexus between financial development and renewable energy: empirical evidence from nonlinear autoregression distributed lag Renew Energy 2022 193 475 483 10.1016/j.renene.2022.04.160
Chang L, Iqbal S, Chen H (2023) Does financial inclusion index and energy performance index co-move?. Energy Policy 174:113422
Danish BMA Mahmood N Zhang JW Effect of natural resources, renewable energy and economic development on CO2 emissions in BRICS countries Sci Total Environ 2019 678 632 638 10.1016/j.scitotenv.2019.05.028 31078854
Ellabban O Abu-Rub H Blaabjerg F Renewable energy resources: Current status, future prospects and their enabling technology Renew Sustain Energy Rev 2014 39 748 764 10.1016/j.rser.2014.07.113
Fang W Liu Z Surya Putra AR Role of research and development in green economic growth through renewable energy development: Empirical evidence from South Asia Renew Energy 2022 194 1142 1152 10.1016/j.renene.2022.04.125
Foley A Olabi AG Renewable energy technology developments, trends and policy implications that can underpin the drive for global climate change Renew Sustain Energy Rev 2017 68 1112 1114 10.1016/j.rser.2016.12.065
Gasparatos A Doll CNH Esteban M Renewable energy and biodiversity: implications for transitioning to a green economy Renew Sustain Energy Rev 2017 70 161 184 10.1016/j.rser.2016.08.030
Ghafoor A, Rehman TU, Munir A et al (2016) Current status and overview of renewable energy potential in Pakistan for continuous energy sustainability. Renew. Sustain. Energy Rev
Huang W Chau KY Kit IY Relating sustainable business development practices and information management in promoting digital green innovation: evidence from China Front Psychol 2022 13 930138 10.3389/fpsyg.2022.930138 35800951
Ikram M Sroufe R Mohsin M Does CSR influence firm performance? A longitudinal study of SME sectors of Pakistan J Glob Responsib 2019 10.1108/jgr-12-2018-0088
Iqbal S, Bilal AR (2021) Energy financing in COVID-19: how public supports can benefit?. China Finance Rev 12(2):219–240
Iqbal W Yumei H Abbas Q Assessment of wind energy potential for the production of renewable hydrogen in Sindh Province of Pakistan Processes 2019 10.3390/pr7040196
Iqbal S, Bilal AR, Nurunnabi M, Iqbal W, Alfakhri Y, Iqbal N (2021) It is time to control the worst: testing COVID-19 outbreak, energy consumption and CO2 emission. Environmental Science and Pollution Research 28:19008–19020
Iram R Zhang J Erdogan S Economics of energy and environmental efficiency: evidence from OECD countries Environ Sci Pollut Res 2020 10.1007/s11356-019-07020-x
Irfan M Shahid AL Ahmad M Assessment of public intention to get vaccination against COVID-19: evidence from a developing country J Eval Clin Pract 2022 28 63 73 10.1111/jep.13611 34427007
Khandker SR Barnes DF Samad HA Welfare impacts of rural electrification: a panel data analysis from Vietnam Econ Dev Cult Change 2013 61 659 692 10.1086/669262
Li C, Umair M (2023) Does green finance development goals affects renewable energy in China. Renew Energy 203:898–905. 10.1016/j.renene.2022.12.066
Li W, Chien F, Ngo QT, Nguyen TD, Iqbal S, Bilal AR (2021) Vertical financial disparity, energy prices and emission reduction: empirical insights from Pakistan. Journal of Environmental Management 294
Liu F Umair M Gao J Assessing oil price volatility co-movement with stock market volatility through quantile regression approach Resour Policy 2023 81 103375 10.1016/j.resourpol.2023.103375
Mohsin M Kamran HW Atif Nawaz M Assessing the impact of transition from nonrenewable to renewable energy consumption on economic growth-environmental nexus from developing Asian economies J Environ Manage 2021 10.1016/j.jenvman.2021.111999
Mohsin M Taghizadeh-Hesary F Shahbaz M Nexus between financial development and energy poverty in Latin America Energy Policy 2022 165 112925 10.1016/j.enpol.2022.112925
Pan W Cao H Liu Y “Green” innovation, privacy regulation and environmental policy Renew Energy 2023 203 245 254 10.1016/j.renene.2022.12.025
Raheem A Abbasi SA Memon A Renewable energy deployment to combat energy crisis in Pakistan Energy Sustain Soc 2016 6 1 13 10.1186/s13705-016-0082-z
Rao F Tang YM Chau KY Assessment of energy poverty and key influencing factors in N11 countries Sustain Prod Consum 2022 30 1 15 10.1016/j.spc.2021.11.002
Raza MY Wasim M Sarwar MS Development of renewable energy technologies in rural areas of Pakistan Energy Sources, Part A Recover Util Environ Eff 2020 42 740 760 10.1080/15567036.2019.1588428
Sun H Khan AR Bashir A Energy insecurity, pollution mitigation, and renewable energy integration: prospective of wind energy in Ghana Environ Sci Pollut Res 2020 27 38259 38275 10.1007/s11356-020-09709-w
Sun L Fang S Iqbal S Bilal AR Financial stability role on climate risks, and climate change mitigation: implications for green economic recovery Environmental Science and Pollution Research 2022 29 22 33063 33074 10.1007/s11356-021-17439-w 35025040
Tu CA, Chien F, Hussein MA, RAMLI MM YA, S. PSI MS, Iqbal S, Bilal AR (2021) Estimating role of green financing on energy security, economic and environmental integration of BRI member countries. Singap Econ Rev 8:1–9
Ullah K Rashid I Afzal H SS7 Vulnerabilities—a survey and implementation of machine learning vs rule based filtering for detection of SS7 network attacks IEEE Commun Surv Tutorials 2020 22 1337 1371 10.1109/COMST.2020.2971757
Umair M, Dilanchiev A (2022) Economic recovery by developing business starategies: mediating role of financing and organizational culture in small and medium businesses. Proc B 683
Vivoda V LNG import diversification and energy security in Asia Energy Policy 2019 129 967 974 10.1016/j.enpol.2019.01.073
Wen C Akram R Irfan M The asymmetric nexus between air pollution and COVID-19: evidence from a non-linear panel autoregressive distributed lag model Environ Res 2022 209 112848 10.1016/j.envres.2022.112848 35101402
Wang S Sun L Iqbal S Green financing role on renewable energy dependence and energy transition in E7 economies Renewable Energy 2022 200 1561 1572 10.1016/j.renene.2022.10.067
Wu Q Yan D Umair M Assessing the role of competitive intelligence and practices of dynamic capabilities in business accommodation of SMEs Econ Anal Policy 2022 10.1016/j.eap.2022.11.024
Xiuzhen X Zheng W Umair M Testing the fluctuations of oil resource price volatility: a hurdle for economic recovery Resour Policy 2022 79 102982 10.1016/j.resourpol.2022.102982
Yang K, Lee L (2020) Estimation of dynamic panel spatial vector autoregression: Stability and spatial multivariate cointegration. J Econom
Yang Y, Liu Z, Saydaliev HB, Iqbal S (2022) Economic impact of crude oil supply disruption on social welfare losses and strategic petroleum reserves. Resources Policy 77
Yu J Tang YM Chau KY Role of solar-based renewable energy in mitigating CO2 emissions: Evidence from quantile-on-quantile estimation Renew Energy 2022 182 216 226 10.1016/j.renene.2021.10.002
Yumei H Iqbal W Irfan M Fatima A The dynamics of public spending on sustainable green economy: role of technological innovation and industrial structure effects Environ Sci Pollut Res 2022 29 22970 22988 10.1007/s11356-021-17407-4
Zhao L Saydaliev HB Iqbal S Energy financing, COVID-19 repercussions and climate change: implications for emerging economies Climate Change Economics 2022 13 03 2240003 10.1142/S2010007822400036
Zhang D Mohsin M Rasheed AK Public spending and green economic growth in BRI region: mediating role of green finance Energy Policy 2021 10.1016/j.enpol.2021.112256
Zhang L Huang F Lu L Ni X Iqbal S Energy financing for energy retrofit in COVID-19: recommendations for green bond financing Environmental Science and Pollution Research 2022 29 16 23105 23116 10.1007/s11356-021-17440-3 34800272
Zheng X Zhou Y Iqbal S Working capital management of SMEs in COVID-19: role of managerial personality traits and overconfidence behavior Economic Analysis and Policy 2022 76 439 451 10.1016/j.eap.2022.08.006 35990757 | 37000394 | PMC10063944 | NO-CC CODE | 2023-05-08 23:15:27 | yes | Environ Sci Pollut Res Int. 2023 Mar 31; 30(20):58986-58997 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
26089
10.1007/s11356-023-26089-z
Research Article
The role of green financing to enhance tourism growth by mitigating carbon emission in China
Xu Shiqin [email protected]
Wang Hengyi [email protected]
grid.411578.e 0000 0000 9802 6540 School of Finance, Chongqing Technology and Business University, Chongqing, 400067 China
Responsible Editor: Arshian Sharif
3 4 2023
111
19 11 2022
19 2 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
The tourism industry has undergone rapid inquiry in modern times. Based on climatic importance, current research intends to inquire about the role of green financing in enhancing tourism growth by mitigating carbon emissions in China. The study used Data Envelopment Analysis to infer the efficiency of the study model in the study context based on research topicality. Our findings highlighted that China’s local tourism destination, renowned for its health and wellness tourism, indicated tourist inspiration to visit climate-supporting visit stations. Study results extended that using green financing for climate change mitigation in a Chinese tourist destination is essential. Empirical results confirmed that green funding directly mitigated climate change and enhanced tourism growth in Chinese settings by solving related issues. On such findings, the study yielded the practical implications for green financing institutions, climate change policymakers and Chinese officials for tourism development.
Keywords
Green financing
Carbon emission
Tourism growth
Tourism wellness
China
==== Body
pmcIntroduction
China’s tourism sector is expanding rapidly. With almost $40 billion in income, 57.6 million international tourists visited the country in 2011 (Sun, Gossling and Zhou, 2022b). This is according to the United Nations World Tourism Organization (UNWTO) (He et al., 2022). China has surpassed France and the USA regarding tourist arrivals. Conversely, in contrast to many Western nations, China views tourism as somewhat of an emerging industry. The tourist industry is a significant driver of economic growth as the government modernizes (Razzaq, Fatima and Murshed, 2021). The latest estimates by the UNWTO see China overtaking the USA as the most frequented nation by 2020 (Mishra et al., 2021). Considering this importance, researchers intend to research enhancing tourism growth through carbon emission mitigation with the role of green finance (Ip et al., 2022). This is the core motivation for studying.
Compared to when Chairman Mao was in charge, tourism in China has grown exponentially (Gao et al., 2022). The nation is often featured in front of travel guides (Lee and Brahmasrene, 2013). Many publications describing visitors’ experiences in the Middle Kingdom line bookstore shelves, and tourists from all over the globe may upload photos of their travels to Asia to share with friends and family online. China’s tourist sector is booming, but this is no surprise (Ma et al., 2021). Innumerable natural and manufactured marvels may be found around the nation. There’s something for everyone in this region, from the Great Wall to the Terracotta Army, from the vast mountainous areas to the pulsating cities (Tong, Zhang and He, 2022). No one could have imagined how prosperous our nation would be 40 years ago. And the irony that followed his death was not anything he could have predicted (Ahmed and Laijun, 2014). Ironically, a guy who hated tourists will become one, and his corpse will be maintained and displayed for commercial advantage (Pan et al., 2021).
To enhance tourism growth, previous researchers have discovered and given directions to mitigate carbon emissions through green finance (Sun, 2016). Green financing has historically been followed in good times and severe economic downturns, which indicates a causal connection significance in every time with different industries, including climate change and tourism development (Zaman, Khan and Ahmad, 2011). This is the primary evidence for the theory. Currently, tourism development also needs green financing to support tourism industry for growth motives through climate change mitigation, given that green financing is relatively inelastic with the tourism sector (Meng et al., 2017), because the volatility of the global green funding is anticipated to assist in stabilizing local investing costs in the tourism industry and keep the economy from stagnating (Bi and Zeng, 2019). Many countries that implemented green financing policies had imperfect pass-through of green financing costs to domestic inflation.
Nevertheless, whether the climate change subsidy policy has a positive or negative impact on tourism development is up for debate (Chen, Mao and Morrison, 2021). Previous literature shows that green financing reduces environmental flexibility and impacts domestic resource allocation choices, income distribution, tourism growth, and tourism sectoral adjustment (Tang, Zhong and Ng, 2017). Furthermore, from the Chinese perspective, green financing is seen as a distortionary fiscal policy instrument of financing for the society as a whole, specifically for energy management, environmental management, and now for tourism development (Sekrafi and Sghaier, 2018).
Few more studied roles of green financing in different nations came up with additional findings; for example, Taiwan’s significant reliance on green private funding (almost 58% of its green financing) is invested in climate effects’ mitigation and tourism purposes which makes it an excellent economy (Zha et al., 2021a). China’s green financing policies come from both the environmental and tourism industries. They do this in two ways: by reducing the cost of fuel in specific sectors and subsidizing the price of electricity. According to a 2017 study by the Asia-Pacific Economic Cooperation, China receives five green financing policies worth more than US$400 million annually for the tourism industry (Zha et al., 2021b). On offshore islands, there were some different types of fiscal policies. When it came to green private budgetary policies, the Legislative Yuan established rates for electricity use. Because of the political stalemate, the electricity price stayed stable when fossil fuel costs were high. More than 80% of China’s tourism industry comes from different tourism and hospitality industries, and government-owned thousands of tourist visit stations (Akadiri et al., 2020). Climate change mitigation in modern times of the rapidly increasing cost of green financing led to eight straight years of losses incurred. Results summarize the significant role of green funding in China’s tourism growth via climate change mitigation (Sun et al., 2019). Thus, climate change mitigation is anticipated to provide a clean and green environment for tourists to ensure their leisure and the quality of their visits or the future because large-scale tourism growth ensures tourism sustainability (Tang and GE, 2018). Another study revealed that 80% of China’s green financing is used in the renewable energy industry, with the remaining 20% used by other industries including climate change that need to be revisited (Liu, Feng and Yang, 2011).
The rapid pace of environmental change is increasing the need for environmentally friendly and long-term green solutions. The most effective strategy for achieving this goal is to create a green economy. Active public support is required to maintain the green private infrastructure and increase personal efficiency by using green technologies (Zha et al., 2021a). For even more positive outcomes under environmental openness, this encourages the movement of economic resources and economic development (Didier et al., 2021). Green private efficiency development via public assistance is thus a cutting-edge accelerator for accelerating green growth. During the COVID-19 epidemic, it is critical to focus on public support for green private efficiency (Zhang and Shang, 2022). As a result, this research investigated the substantial nexus among tourism growth, climate change mitigation, and green financing. Too far, a limited comprehensive analysis has been done on green financing’s role in tourism growth. This study’s complete sample papers from China’s tourism industry are a starting point for creating a connection among the study constructs. This is the objective of research and the first contribution of research; some carbon emission mitigation-related studies, including Can and Hanging (2011) and Ehigiamusoe et al. (2022), provided theoretical support for the development of a theoretical framework for examining the relationship among tourism growth, green financing, and carbon emission mitigation. Thus, testing this theoretical rationale using an advanced approach is the second contribution of the primary research. More specifically, approximately $2 trillion in the tourism industry is anticipated to be spent via green financing policies for environmental management using pollution reduction efforts and sustainable economic development initiatives. It raises another question for the study: whether this financing ensures the strategic goal. However, to achieve this goal, the study also provides practical implications for the stakeholders of the tourism industry and climate change mitigation–related officials. This is the third contribution. Hence, this study intends to use this significance.
The study covers five sections. The first section introduces the research, and the second section presents the review of the literature, the third section discusses the research methodology, and the fourth section interprets the study results and discussion on findings. At the same time, the last quarter concluded the study matter along with some practical recommendations and upcoming investigations suggestions.
Literature review
Sustainable tourism is not only ingrained in tourism curriculums from basic (high school) throughout graduate education, but it is also one of the greatest, if not the most, sought-after topics of inquiry in the field (Zhong et al., 2015). An abundance of previous studies cover the subject, whether from a broad theoretical vantage point, within specific settings like a country, archipelago, or regional tourist creation, or under the garb of “ecotourism” as a more precise and, perhaps, rigidly defined comment thread of tourism development (Liu et al., 2022). However, the Journal of Sustainable (which has been there since 1992) and the Journal of Ecotourism (which has been around since 2002) offer a platform for study on the topic. Scientific publications are devoted to studying the tourist industry, urban studies, earth sciences, and geology, all of which routinely issue papers on subjects connected to responsible tourism planning. While the notion of sustainable development in tourism has been well-researched in the intellectual world, there is still a massive chasm between this objective and the realities of the tourist industry, specifically in Chinese settings (Shakouri, Yazdi and Ghorchebigi, 2017). To put it another way, it has been challenging, if not unattainable, to transform the theoretical principles of sustainable tourism activities into a practical, actionable set of practices applicable to the actual tourism sector (Ahmad and Ma, 2022).
Many organizations and businesses do what they are doing well, which is typically acknowledged with certification or prizes. However, this is a tiny fraction of the tourist supply. That is why overall progress toward eco-friendly tourism has not materialized. Nonetheless, international tourist arrivals continue to be a growing industry and are being used more often as a stimulus for social and economic development (Hu, 2022). Similarly, the current elevation of climate change to the forefront of the global political and economic agenda has heightened awareness of the environmental impact of international travel, even as concerns about the localized effects of tourist expansion persist (Chishti et al., 2020). Consequently, beyond the basic concepts of ecotourism, it is necessary to re-examine the connection between tourism, its position as an engine of progress, and its significant environmental implications (Wang et al., 2022). Therefore, the stalemate in the academic research of tourism’s long-term viability indicates that it is time to shift beyond its stringent, closely aligned principles and instead investigate attractions and innovation within the context of the emerging international economic and political foundation (Fethi and Senyucel, 2021).
Ever since the early 1990s, the topic of sustainable tourism development has been the focal point of discussion among academics, policymakers, and politicians (Aslan et al., 2021). To be sure, two generalizations may be drawn. First, there seems to be a stalemate in the research on how to make the tour more sustainable. Even though it has received a significant amount of research over the past 20 years, as evidenced by a plethora of books, journal articles, conference papers, and other publications, there is still a lack of consensus over not only the concept’s definition and theoretical foundations, but also the extent to which it can be translated into a set of practical policies and measures for the efficient management and planning of tourist industry in the real world (Jin et al., 2018). For instance, it has long been argued that tourism development approaches amount to a short answer to a global issue and that the responsible tourism argument is disconnected, philosophically incorrect, and founded upon weak or misleading notions (Zhang and Zhang, 2018). Indeed, the typical “blueprint” method for sustainable tourism activities, which combines western-centric ecologic nodes assigned with doctrines derived from the various improvement, is only suitable for specific circumstances or clearly defined developments and has constrained significance to the tourism industry as a whole (Ma, Liu and Xi, 2022).
Since 1978, China has flourished thanks to the country’s enormous economic expansion and commitment to environmental protection goals. Unfortunately, China’s agricultural, service, industrial, and tourist sectors’ massive and rapid aggregate levels of energy consumption may (unpleasantly) impair the natural landscape via CO2 emissions. Prior research has shown that tourism’s positive effects on a country’s economy—including increased revenue, new jobs, and a more exceptionally focused profile—make it an essential factor in the growth of that country’s economy. Increasing tourism’s effect on countries’ greenhouse gas emission trends is a significant policy issue. If increasing tourism can reduce national carbon emissions while also boosting GDP, then doing so would be a sensible policy choice. After the COVID-19 epidemic, it makes sense to restart the tourist industry to improve sustainability while enhancing employment, revenue, and prosperity through access to finance (Bilal et al., 2022). Financial catastrophe, poverty, and social discontent are all symptoms of the worldwide epidemic, but reviving the tourist industry might be a solution if it is increased while other, more carbon-intensive industries are reduced (Deng, Zhou and Xu, 2022). If, on the other hand, tourist expansion results in an increase in national carbon emissions, the pro-growth tactics now adopted by several sites will conflict with carbon-neutral targets and will need to be rethought. If this is the case, the tourist strategy used before the COVID-19 outbreak is not ideal since it would increase emissions. Instead, it is better to undertake a healing process that will work toward all of the SDGs, decarbonization following the Climate Accord, and the broader principles of a supply chain all at once (Cheng and Jiang, 2018). This calls for a comprehensive recovery strategy that takes into account the demand and the supply sides of international tourism, including the number and composition of guests a location should aim to attract, the introduction of new technology, and the promotion of exciting new tourist pursuits (Li et al., 2022; Zheng, Zhou and Iqbal, 2022).
The term green finance is used to describe the public, private, and alternative funding streams that are used at different scales (local, nationwide, and international) to assist with global warming mitigation and adaptation efforts in promoting industrial businesses including tourism industry (Huang et al., 2021). Chinese Framework Convention on Climate Change mandates that wealthier countries provide financial aid to poor nations that are less well-off and more susceptible to the repercussions of climate change. Developed nations were supposed to contribute US$100 billion by 2020, but a research by the OECD showed that they would only be able to handle $80 billion by that year (Yang et al., 2022; Sun et al., 2022a). New research from the Intergovernmental Panel on Climate Catastrophe highlights the importance of green financing in keeping climate change below 1.5 °C and preventing catastrophic climate change for extensive tourism development. Eliminating coal use is crucial if we are to keep global warming below 1.5 °C. Curtailing Chinese funding for tourism industry is a step in the right direction, but nature-based solutions like restoring degraded areas and replanting trees may also help slow biodiversity loss and climate change (Zhang et al., 2022; Iqbal and Bilal, 2021). A large increase in green finance is anticipated since upcoming generations including tourists have environmentalist perspectives and values, and therefore want financial businesses to obtain those services. Hence, green financing has significant role on carbon emission reduction for the development of tourism.
Methodology
Measuring variables and study data
Following Xiao et al. (2022), tourism development is measured in this study including three indicators, i.e., tourism scale, tourism benefit, and tourism service. Tourism scale includes different proxy measures number of domestic tourists, number of international tourists, number of employees of tourism industry, and total retail sales of hotels and catering services. Tourism benefit includes earnings from domestic tourists, and earnings from international tourists. Tourism service includes the proxy of length of highways, mobile phone coverage, and public toilets per 10 thousand people, number of star hotels, and number of travel agencies. The green financing index includes different measures, such as Standard & Poor’s green bond measures, NASDAQ green financing, and geothermal pollution measure. Hence, using these sub-proxies of tourism development, green financing, and carbon emission mitigation, this research measured the variables. The study has used the data covering the period of 2016 to 2020. It includes green finance index, CO2, and tourism development index-based proxies.
Study model
To simplify the process for the experts, the model does not allow for any exporting. Green financing is the only source that is utilized for climate change mitigation and tourism development in China, and it is utilized by the government and subsidized. Thus, the study model is computed as follows:1 uCA,t,1-Ht=φlogCA,t+1-φlog1-Ht,0<φ<1
2 CA,t=NtγθDt-1ρ+1-θEh,tρ1-yp,0<γ<1,0<θ<1,ρ<0
Both D and E are constant deterioration rates that are applied across order to develop an overall financing options for carbon emission mitigation. The adaptation expenses are thought to be quadratic format. To calculate the stock market value at time t, we use the formula,3 PEh,tEh,t+Nt+ID,t+IK,t=1-τtWtHt+RtKt-1+Γt
where,4 Kt=IK,t+1-δKKt-1-ΦK2Kt-Kt-1Kt-1Kt-1
where 𝜙𝐾 > 0 is how much financing it will take to make changes in carbon emissions and tourism development. The stock of long-lasting products during time t as5 Dt=ID,t+1-δDDt-1-ϕD2Dt-Dt-1Dt-12Dt-1
where 𝜙𝐷 > 0 is the cost parameter of adjusting the durable goods stock. Denote the time discount factor by 𝛽. The household’s problem is6 maxE0∑t=0∞βtφlogNtγθDt-1ρ+1-θEh,1ρ1-jρ+1-φlog1-Ht
Maximum utility from consumption of nondurable items may be calculated using the logistic regression t, which is equal to when evaluating the unit price (or relative possibility of loss) of boosting current cycle substantive expenditure; the inter-temporal first condition (7) weighs the postponed expected substantive worth of future period consumption.7 Kt:1=βEtNtNt+11-δK+1-τf+1Rt+1+EtΦACK,t+1
8 Ht:Nt1-Ht=γφ1-φ1-τtWt
where9 ΦACK,t+1=βϕKNtNt+1Kt+1-KtKt+βϕK2NtNt+1Kt+1-KtKt2-ϕKKt-Kt-1Kt-1,
10 ΦACD,t+1=βϕDNtNt+1Dt+1-DtDt+βϕD2NtNt+1Dt+1-DtDt2-ϕDDt-Dt-1Dt-1
and11 Rt+1D=1-γθγNt+1Dtρ-1θDtρ+1-θEh,t+1ρ-1
In tourism sector, green financing choices are made by a representation business, which is highly competitive and measured using above said equations.
Theoretical background
Sustainable social progress depends on the two fields of tourism and environmental protection developing in tandem (Anh Tu et al., 2021). Ever since turn of the century, growing tourism has helped boost national revenue and the demand for foreign travel, rendering tourism-related industries a significant contributor to GDP. The rise of the social sector has been aided by the expansion of tourist facilities and the tourism sector, which has raised the standard of living of locals and created more jobs in the area. When it comes to tourism, the environmental, economic, and social (RECC) dimensions are all interconnected (Li et al., 2021). Rapid tourist growth is damaging the planet because it leads to wasteful use of environmental assets (Liu et al., 2022; Sadiq et al., 2022). For descriptive statistics, Table 1 is developed. Pollution, soil erosion, forest decline, and algal blooms of freshwater resources are only a few examples of the rising profile of unsustainable challenges. Numerous studies have been conducted on the topic of tourism’s effect on the natural world, with most focusing on the ecological growth of towns and regions as a result of this trend (Iqbal et al., 2021). China’s tourist industry has exploded in recent years because of improvements in both accessibility and affordability (Kumar et al., 2022). China’s GDP has seen a continually rising contribution from taxation on tourists. Economic growth is strongly radiated and profoundly influenced by tourism (Chien et al., 2022; Rahman et al., 2020). It is undeniable that the hospitality industry in China has played a crucial role in the country’s impressive economic rise (Wang and Wang, 2020). The dichotomy between tourist growth and ecological sustainability is, nonetheless, becoming more obvious as the industry continues to flourish. While tourism may play a beneficial role in encouraging the conservation of natural environments, the influx of visitors and vehicle traffic can have a negative impact on those same ecosystems (Nguyen et al., 2021). Furthermore, steel production as the main growth model will directly bring a series of ecological pollution of the environment like soil erosion and air quality, resulting in a poor ecologic founding for tourism development in these territories due to their small geographic potential of commodity ecologic load capacity (Sikarwar et al., 2021). Since TD relies on the use of supplies and the preservation of the environment, its unchecked growth would entail these negative outcomes, as well as the introduction of major ecological issues and the impediment to the establishment of an environmental protection (Usman et al., 2021). Defending the environmental quality and implementing protection development are two of China’s top goals as part of its national plan to advance the cause of environmental civilization (Wang, Sun and Iqbal, 2022).Table 1 Descriptive results of study constructs
Proxies Mean SD Min Max Control Treat
Number of domestic tourists 0.124 0.562 0.764 0.025 0.964 0.988
Number of international tourists 0.073 0.642 0.441 0.311 0.963 0.965
Number of employees of tourism industry 0.075 0.264 0.774 0.353 0.655 0.827
Total retail sales of hotels and catering services 0.381 0.262 0.689 0.099 0.203 0.816
Earnings from domestic tourists 0.742 0.614 0.642 0.507 0.349 0.834
Earnings from international tourists 0.585 0.152 0.775 0.018 0.441 0.156
Length of highways 0.174 0.434 0.615 0.716 0.986 0.278
Mobile phone coverage 0.3117 0.903 0.327 0.038 0.275 0.249
Public toilets per 10 thousand people 0.119 0.763 0.657 0.121 0.984 0.337
Number of star hotels 0.266 0.564 0.294 0.394 0.301 0.561
Number of travel agencies 0.171 0.189 0.309 0.482 0.453 0.004
CO2e (million tons) 0.241 0.102 0.721 0.181 0.343 0.457
Standard & Poor Green Financing Index 0.296 0.152 0.944 0.234 0.333 0.233
NASDAQ Index 0.444 0.175 0.450 0.292 0.934 0.286
Geothermal pollution financing 0.188 0.191 0.404 0.423 0.771 0.700
Results and discussion
Empirical results of study
With the adoption of specific power and green private laws in recent years, it is anticipated that increasing green private efficiency and dealing with the green private transition study sample countries would occur. Electricity production, reliance, efficiency, and transition are all considered to be important sources of economic development. Thermal power plants account for about 75% of the total installed generating capacity in developing markets.
This data table begins with a descriptive analysis of the coefficients of the basic indicator. Indicator values such as minimum and maximum values, skewness and standard deviation, are mean and variance are shown. What we learn from this investigation is that a composite indication is the sum of many individual indications. The tourism development index is meant to record abstract concepts that defy easy measurement. If you want to build a reliable composite index, for which, a subset of indications that are most applicable to your situation before attempting to develop a composite indicator, issues of tourism development through carbon emissions, green financing, and environmental sustainability may be easier to grasp if we use indicators. Academics and policymakers in the Chinese settings may use these indicators to assess the state of the region and the impact of domestic tourism reforms. It has been shown that the tourism development is jointly studied with green financing and carbon emission which is found significant in the current study (see Table 2). Most of the Chinese destinations has cut down on its carbon emissions from electricity generation since the 2014, making it more reliant for tourists. This is because more people are linking up to this, and more tourists are turning to clean, green, intermittent renewable energy sources. As long as there is natural light or cool breeze or green environment, tourists will be able to take in more and send it back out as needed. The International Green Private Agency claims Chinese tourism likeability and this has been shifted positively since a decade. Therefore, up to 40% of China’s existing power capacity might be used through interconnections. Buyers in Europe rely greatly on the private green initiatives of their tourists.Table 2 Descriptive statistics
Unit Tourism development Carbon emission Green financing Population
Minimum 23.11 27.06 34.88 43.12
Maximum 7009.01 2021.4 0.5108 7675.0
Kurtosis 10.41 8.77 4.10 1.9183
SD 505.27 44.35 35.79 60.56
Mean 3338.31 5398.1 8071.16 300.9
Variance 4.09 2.02 6.31 0.159
Thus, these economies are often held up as models of eco-friendliness, kept a low profile, and performed well during the meeting. The highest returns may be expected from them because of the greater need for environmentally friendly privacy in such areas. Given the relative paucity of technical resources in coastal regions, these places have emerged as critical sites for private, environmentally conscious conservation and pollution mitigation efforts. As can be shown in Table 4, Asian and Pacific Rim countries have consistently been the most dishonest about their track records in green private consumption and environmental preservation from 2010 to 2014. Green private markets that aim to reduce carbon dioxide emissions in the future are being set up with better policies in place now. It has led researchers and professionals to investigate the effectiveness of green private finance.
Table 3 shows that Chinese tourists like the Chinese destinations the most. On the other hand, a number of countries seem to be in the middle of the main data set’s green financing efficiency spectrum. Findings like these are interesting, but they’re also very useful for nations like South Asia, who are working toward more environmentally friendly types of green financing efficiency in the future. Some countries have been asked to implement these changes and be rigorous in their follow-up to ensure compliance with the previously stated green financing efficiency standards (included in the power sector green private reforms section). As a result of this hypothetical situation, these countries have made significant steps to reform the electricity industry and are continuously implementing the reforms’ implications to achieve high levels of green private efficiency, as shown by China and Ireland. The entire green private sector has been deregulated in several other countries. When it comes to implementing power sector reforms, these countries have struggled. They have not put equal weight on end results (such as access to power) and, as a result, are dubbed. China’s total performance fluctuates when they follow the stages but fail to keep their efficiency ratings. This study’s primary results are in line with prior findings related to electrical reform implementation and long-term stability (Lee et al, 2021). In Table 4, it can be seen that, with the exception of China, green financing is improving throughout the area’s carbon emission and developing the positive impression for tourism development. Green financing efficiency is fairly similar across the world as a result of the use of broadly comparable green private investing technologies. All of the selected countries’ total efficiency is represented by the statistics in this table. It is essential to note that, although this shows that there is a significant distinction between the UK, Ireland, and other countries, it is not indicative of the whole European Union. The DEA window analysis in Table 3 was performed first, followed by the use of model (2) to determine the score (see Table 4) efficiency of different regions in each of these countries. They show how successful this method is in reducing emissions while simultaneously increasing efficiency (Ying et al, 2000). As a result, this study will continue to utilize this paradigm in its future research. By overlaying seven overlapping periods that were all finished between 2010 and 2014, we may establish which European countries’ environmental and green private financing performance can be evaluated (see Table 3).Table 3 Green financing impact on carbon emission
2016 2017 2018 2019 2020
CO2e (million tons) 0.70 0.23 0.20 0.12 0.11
S&P Green Financing Index 0.44 0.31 0.61 0.67 0.78
NASDAQ Index 0.29 0.40 0.57 0.45 0.97
Geothermal pollution financing 0.91 0.88 0.30 0.56 0.91
Table 4 Tourism development score (2016–2020)
Measures Indicators score
Number of domestic tourists 0.61
Number of international tourists 0.56
Number of employees of tourism industry 0.34
Total retail sales of hotels and catering services 0.91
Earnings from domestic tourists 0.83
Earnings from international tourists 0.21
Length of highways 0.80
Mobile phone coverage 0.76
Public toilets per 10 thousand people 0.44
Number of star hotels 0.87
Number of travel agencies 0.62
As a consequence of the research, Table 4 displays China’s tourism development. According to this table, the green financing systems of China are heavily reliant on the measures mentioned in Table 4. The tourism-based economic system and improved green financing efficiency are linked in previous studies which is therefore confirmed in the current study that the nexus between tourism development, green financing, and carbon emission are significant. Extending to it, Faraz et al. (2019) found this for the world’s major oil importers, including the USA, Japan, and China. Importing oil has two important economic consequences: To begin with, it helps pay for green financing and carbon emission–based projects for tourism development in order to meet the nation’s growing industrial demand for the tourism industry of China. As a second measure, boosting the use of renewable green financing sources helps to bring down the price of existing green private. Table 2 shows that China has the highest efficiency rating in the category, with a value of 1.596. In each case, there are two possibilities. Officially, scale expansion is preferred above quality improvement since it increases the amount of investments and other measures that help accelerate short-term economic development (see Table 5).Table 5 Standard regression estimates
Tourism development estimates Carbon emission Green financing Population
Number of domestic tourists 0.5864* 0.0811 0.0147*
Number of international tourists 0.1371* 0.0044* 0.0141*
Number of employees of tourism industry 0.9322* 0.0318* 0.1348*
Total retail sales of hotels and catering services 0.2612* 0.1343 0.0188*
Earnings from domestic tourists 0.0155 0.0381* 0.7099*
Earnings from international tourists 0.6159* 0.2096 0.8455
Length of highways 0.63118 0.6217 0.0198*
Mobile phone coverage 0.6932* 0.1612* 0.0171
Public toilets per 10 thousand people 0.0418* 0.0467* 0.0732*
Number of star hotels 0.2781 0.8952* 0.2369*
Number of travel agencies 0.2181* 0.4452* 0.9862*
R-Square 0.066 0.059 0.043
Sargan estimates 0.231* 0.394* 0.255*
(0.001) (0.000) (0.004)
Note "*" show significance at 1%
Sensitivity analysis
Green financing efficiency has been tested with carbon emission mitigation and tourism development using fresh data with 15% fluctuation and 15% variation using sensitivity analysis to determine how stable it is. Improperly structured inlet and outlet data may result in policy messages being delivered due to poor structure or incorrect interpretation. Input factors have a significant impact on overall efficiency, as seen in Table 4. Table 4 shows results that are extremely similar to the original efficiency score derived from the original dataset, indicating that our conclusions are rock-solid and robust.
According to the DEA, a stock’s relative performance score may be used to gauge its performance. The output analysis does not need any prior definition of limitations since it may do both input and output analysis concurrently (see Table 6). Frameworks are provided by the DEA to aid users in the selection of indicators and DEA model properties. The “policy management unit” (DMU) of an evaluation may be the actual process of producing products or services using convex (or even linear) technology, in which case the DEA is an excellent performance assessment and benchmarking tool for evaluating a DMU in real time.Table 6 Sensitivity analysis
DMU Score Rank
Tourism development 0.34 11
Carbon emission 0.71 7
Green financing 0.27 10
Tourist population 0.11 34
Discussion
Sustainable social progress depends on the two fields of tourism and environmental protection developing in tandem. Ever since turn of the century, growing tourism has helped boost national revenue and the demand for foreign travel, rendering tourism-related industries a significant contributor to GDP. The rise of the social sector has been aided by the expansion of tourist facilities and the tourism sector, which has raised the standard of living of locals and created more jobs in the area. When it comes to tourism, the environmental, economic, and social (RECC) dimensions are all interconnected. Rapid tourist growth is damaging the planet because it leads to wasteful use of environmental assets. Pollution, soil erosion, forest decline, and algal blooms of freshwater resources are only a few examples of the rising profile of unsustainable challenges (Liang and Hui, 2016). Numerous studies have been conducted on the topic of tourism’s effect on the natural world, with most focusing on the ecological growth of towns and regions as a result of this trend (Pratt, 2015).
China’s tourist industry has exploded in recent years to improve in both accessibility and affordability (Gao, Huang and Huang, 2009). China’s GDP has seen a continually rising contribution from taxation on tourists (Luo et al., 2016). Economic growth is strongly radiated and profoundly influenced by tourism. It is undeniable that the hospitality industry in China has played a crucial role in the country’s impressive economic rise (Yang, 2012). The dichotomy between tourist growth and ecological sustainability is, nonetheless, becoming more obvious as the industry continues to flourish. While tourism may play a beneficial role in encouraging the conservation of natural environments, the influx of visitors and vehicle traffic can have a negative impact on those same ecosystems (Zeng and Ryan, 2012). Furthermore, steel production as the main growth model will directly bring a series of ecological pollution of the environment like soil erosion and air quality, resulting in a poor ecologic founding for tourism development in these territories due to their small geographic potential of commodity ecologic load capacity (Chen, Huang and Bao, 2016). Since TD relies on the use of supplies and the preservation of the environment and its unchecked growth would entail these negative outcomes, as well as the introduction of major ecological issues and the impediment to the establishment of an environmental protection. Defending the environmental quality and implementing protection development are two of China’s top goals as part of its national plan to advance the cause of environmental civilization (Liu et al., 2017).
Conclusion and recommendations
The tourism business is a contemporary field that has seen intensive study. The purpose of this study was to examine the potential of green finance to stimulate economic expansion in China’s tourist sector while decreasing the country’s carbon footprint, a matter of great climatic significance. Given the importance of the research subject, we employed Data Envelopment Analysis (DEA) to infer the efficacy of the model assessment in the research framework. Our research showed that climate-supportive visit stations were an inspiration for tourists at a well-known health and wellness destination in China. The research concluded that the use of green funding to lessen the effects of climate change in a popular Chinese tourist spot is crucial. By addressing environmental policy problems, green funding has been shown to have a direct impact on climate change mitigation and to boost tourist development in Chinese contexts. An essential part of achieving region objectives for sustainable development is fostering a positive feedback loop between tourism’s positive impact on the economy and efforts to preserve the region’s surrounding ecosystems. Through the use of the dynamic, we examine the spatial-temporal evolution features of CCD by constructing an assessment indicator of the coupling coordination between tourist development and resource environment payload capacity in the Yangtze River Economic Belt from 2006 to 2018. The following are the findings from the empirical analysis: (1) the historical evolution features revealed an upward tendency from 2006 to 2018 for both the two systems and the whole system. (2) The geographical features of China are greater in the eastern area and lower in the center region of China, indicating a substantial difference between tourism development and carbon emissions in this regard. (3) The primary causes of the disparity in the degree of linkage synchronization among study variables. Evidence is analyzed and discussed, and recommendations are made for advancing TD and RECC in tandem throughout the Yangtze River Economic Belt.
Author contributions
Conceptualization, methodology, writing—original draft: Shiqin Xu; data curation, visualization, editing: Hengyi Wang
Data Availability
The data that support the findings of this study are openly available on request.
Declarations
Ethical approval and consent to participate
The authors declared that they have no known competing financial interests or personal relationships, which affect the work reported in this article. We declare that we have no human participants, human data, or human issues.
Consent for publication
We do not have any person’s data.
Competing interests
The authors declare no competing interests.
Preprint service
Our manuscript is posted at a preprint server prior to submission.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Ahmad N Ma X How does tourism development affect environmental pollution? Tour Econ 2022 28 6 1453 1479 10.1177/13548166211000480
Ahmed A Laijun Z A study of the relationship between carbon emission and tourism development in Maldives Afr J Bus Manag 2014 8 20 962 971 10.5897/AJBM2014.7440
Akadiri SS Lasisi TT Uzuner G Akadiri AC Examining the causal impacts of tourism, globalization, economic growth and carbon emissions in tourism island territories: bootstrap panel Granger causality analysis Curr Issues Tour 2020 23 4 470 484 10.1080/13683500.2018.1539067
Anh HQ, Le TPQ, Da Le N, Lu XX, Duong TT, Garnier J, Nguyen TAH (2021) Antibiotics in surface water of East and Southeast Asian countries: A focused review on contamination status, pollution sources, potential risks, and future perspectives. Sci Total Environ 764:142865
Anser MK Yousaf Z Awan U Nassani AA Qazi Abro MM Zaman K Identifying the carbon emissions damage to international tourism: turn a blind eye Sustainability 2020 12 5 1937 10.3390/su12051937
Aslan A Altinoz B Özsolak B The nexus between economic growth, tourism development, energy consumption, and CO2 emissions in Mediterranean countries Environ Sci Pollut Res 2021 28 3 3243 3252 10.1007/s11356-020-10667-6
Bi C Zeng J Nonlinear and spatial effects of tourism on carbon emissions in China: a spatial econometric approach Int J Environ Res 2019 16 18 3353
Bilal AR Fatima T Iqbal S Imran MK I can see the opportunity that you cannot! A nexus between individual entrepreneurial orientation, alertness, and access to finance Eur Bus Rev 2022 34 4 556 577 10.1108/EBR-08-2021-0186
Can H Hongbing D The model of developing low-carbon tourism in the context of leisure economy Energy Procedia 2011 5 1974 1978 10.1016/j.egypro.2011.03.339
Chen G Huang S Bao J The multiple logics of tourism development in China J Sustain Tour 2016 24 12 1655 1673 10.1080/09669582.2016.1178754
Chen Q Mao Y Morrison AM Impacts of environmental regulations on tourism carbon emissions Int J Environ Res Public Health 2021 18 23 12850 10.3390/ijerph182312850 34886575
Cheng X Jiang K Study on tourism carbon emissions and distribution efficiency of tourism economics Asian J Bus Environ 2018 8 2 15 22
Chien F, Hsu CC, Ozturk I, Sharif A, Sadiq M (2022) The role of renewable energy and urbanization towards greenhouse gas emission in top Asian countries: Evidence from advance panel estimations. Renew Energy 186:207–216
Chishti MZ Ullah S Ozturk I Usman A Examining the asymmetric effects of globalization and tourism on pollution emissions in South Asia Environ Sci Pollut Res 2020 27 22 27721 27737 10.1007/s11356-020-09057-9
Deng Z Zhou M Xu Q How to decouple tourism growth from carbon emissions? A spatial correlation network analysis in China Sustainability 2022 14 19 11961 10.3390/su141911961
Ehigiamusoe KU, Majeed MT, Dogan E (2022) The nexus between poverty, inequality and environmental pollution: Evidence across different income groups of countries. J Clean Prod 341:130863
Faraz F, Imran SM, Ali B, Haider S (2019) Thermo-diffusion and multi-slip effect on an axisymmetric Casson flow over a unsteady radially stretching sheet in thepresence of chemical reaction. Processes 7(11):851
Fethi S Senyucel E The role of tourism development on CO2 emission reduction in an extended version of the environmental Kuznets curve: evidence from top 50 tourist destination countries Environ, Dev Sustain 2021 23 2 1499 1524 10.1007/s10668-020-00633-0
Gao S Huang S Huang Y Rural tourism development in China Int J Tour Res 2009 11 5 439 450 10.1002/jtr.712
Gao L, Guo Y, Zhan J, Yu G, Wang Y (2022) Assessment of the validity of the quenching method for evaluating the role of reactive species in pollutant abatement during the persulfate-based process. Water Res 221:118730
He X Shi J Xu H Cai C Hu Q Tourism development, carbon emission intensity and urban green economic efficiency from the perspective of spatial effects Energies 2022 15 20 7729 10.3390/en15207729
Hu Y Where have carbon emissions gone? Evidence of inbound tourism in China Sustainability 2022 14 18 11654 10.3390/su141811654
Huang C, Wang JW, Wang CM, Cheng JH, Dai J (2021) Does tourism industry agglomeration reduce carbon emissions? Environ Sci Pollut Res 28(23):30278–30293
Ip Y Iqbal W Du L Akhtar N Assessing the impact of green finance and urbanization on the tourism industry—an empirical study in China Environ Sci Pollut Res 2022 30 1 17
Iqbal S Bilal AR Energy financing in COVID-19: how public supports can benefit? China Finance Rev Int 2021 12 2 219 240 10.1108/CFRI-02-2021-0046
Iqbal S Bilal AR Nurunnabi M Iqbal W Alfakhri Y Iqbal N It is time to control the worst: testing COVID-19 outbreak, energy consumption and CO2 emission Environl Sci Pollut Res 2021 28 15 19008 19020 10.1007/s11356-020-11462-z
Jin C Cheng J Xu J Huang Z Self-driving tourism induced carbon emission flows and its determinants in well-developed regions: a case study of Jiangsu Province, China J Clean Prod 2018 186 191 202 10.1016/j.jclepro.2018.03.128
Kumar A Singh P Raizada P Hussain CM Impact of COVID-19 on greenhouse gases emissions: a critical review Sci Total Environ 2022 806 150349 10.1016/j.scitotenv.2021.150349 34555610
Lee JW Brahmasrene T Investigating the influence of tourism on economic growth and carbon emissions: evidence from panel analysis of the European Union Tour Manag 2013 38 69 76 10.1016/j.tourman.2013.02.016
Li W Chien F Ngo QT Nguyen TD Iqbal S Bilal AR Vertical financial disparity, energy prices and emission reduction: empirical insights from Pakistan J Environ Manag 2021 294 112946 10.1016/j.jenvman.2021.112946
Li G Li W Dou Y Wei Y Antarctic shipborne tourism: carbon emission and mitigation path Energies 2022 15 21 7837 10.3390/en15217837
Liang ZX Hui TK Residents' quality of life and attitudes toward tourism development in China Tour Manag 2016 57 56 67 10.1016/j.tourman.2016.05.001
Liu J Feng T Yang X The energy requirements and carbon dioxide emissions of tourism industry of Western China: a case of Chengdu city Renew Sustain Energy Rev 2011 15 6 2887 2894 10.1016/j.rser.2011.02.029
Liu J Nijkamp P Huang X Lin D Urban livability and tourism development in China: analysis of sustainable development by means of spatial panel data Habitat Int 2017 68 99 107 10.1016/j.habitatint.2017.02.005
Liu Z Lan J Chien F Sadiq M Nawaz MA Role of tourism development in environmental degradation: a step towards emission reduction J Environ Manag 2022 303 114078 10.1016/j.jenvman.2021.114078
Luo JM Qiu H Goh C Wang D An analysis of tourism development in China from urbanization perspective J Qual Assur Hosp 2016 17 1 24 44
Ma X Han M Luo J Song Y Chen R Sun X The empirical decomposition and peak path of China's tourism carbon emissions Environ Sci Pollut Res 2021 28 46 66448 66463 10.1007/s11356-021-14956-6
Ma H Liu J Xi J Decoupling and decomposition analysis of carbon emissions in Beijing's tourism traffic Environ, Dev Sustain 2022 24 4 5258 5274 10.1007/s10668-021-01657-w
Meng W Xu L Hu B Zhou J Wang Z Reprint of: quantifying direct and indirect carbon dioxide emissions of the Chinese tourism industry J Clean Prod 2017 163 S401 S409 10.1016/j.jclepro.2016.03.177
Mishra HG Pandita S Bhat AA Mishra RK Sharma S Tourism and carbon emissions: a bibliometric review of the last three decades: 1990–2021 Tour Rev 2021 77 2 636 658 10.1108/TR-07-2021-0310
Nguyen NTT, Nguyen NP, Hoai TT (2021) Ethical leadership, corporate social responsibility, firm reputation, and firm performance: A serial mediation model. Heliyon 7(4):e06809
Pan Y Weng G Li C Li J Coupling coordination and influencing factors among tourism carbon emission, tourism economic and tourism innovation Int J Environ Res Public Health 2021 18 4 1601 10.3390/ijerph18041601 33567625
Pratt S Potential economic contribution of regional tourism development in China: a comparative analysis Int J Tour Res 2015 17 3 303 312 10.1002/jtr.1990
Rahman HU Ghazali A Bhatti GA Khan SU Role of economic growth, financial development, trade, energy and FDI in environmental Kuznets curve for Lithuania: evidence from ARDL bounds testing approach Eng Econ 2020 31 1 39 49 10.5755/j01.ee.31.1.22087
Razzaq A, Ajaz T, Li JC, Irfan M, Suksatan W (2021) Investigating the asymmetric linkages between infrastructure development, green innovation, and consumption-based material footprint: Novel empirical estimations from highly resource-consuming economies. Resour Policy 74:102302
Sadiq M Amayri MA Paramaiah C Mai NH Ngo TQ Phan TTH How green finance and financial development promote green economic growth: deployment of clean energy sources in South Asia Environ Sci Pollut Res 2022 29 43 65521 65534 10.1007/s11356-022-19947-9
Sekrafi H Sghaier A Exploring the relationship between tourism development, energy consumption and carbon emissions: a case study of Tunisia Int J Soc Ecol Sustain Dev 2018 9 1 26 39 10.4018/IJSESD.2018010103
Shakouri B Khoshnevis Yazdi S Ghorchebigi E Does tourism development promote CO2 emissions? Anatolia 2017 28 3 444 452 10.1080/13032917.2017.1335648
Sikarwar VS Reichert A Jeremias M Manovic V COVID-19 pandemic and global carbon dioxide emissions: a first assessment Sci Total Environ 2021 794 148770 10.1016/j.scitotenv.2021.148770 34225159
Sun YY Decomposition of tourism greenhouse gas emissions: revealing the dynamics between tourism economic growth, technological efficiency, and carbon emissions Tour Manag 2016 55 326 336 10.1016/j.tourman.2016.02.014
Sun YY Lenzen M Liu BJ The national tourism carbon emission inventory: its importance, applications and allocation frameworks J Sustain Tour 2019 27 3 360 379 10.1080/09669582.2019.1578364
Sun L Fang S Iqbal S Bilal AR Financial stability role on climate risks, and climate change mitigation: implications for green economic recovery Environ Sci Pollut Res 2022 29 22 33063 33074 10.1007/s11356-021-17439-w
Sun YY Gossling S Zhou W Does tourism increase or decrease carbon emissions? A systematic review Ann Tour Res 2022 97 103502 10.1016/j.annals.2022.103502
Tang M Ge S Accounting for carbon emissions associated with tourism-related consumption Tour Econ 2018 24 5 510 525 10.1177/1354816618754691
Tang C Zhong L Ng P Factors that influence the tourism industry's carbon emissions: a tourism area life cycle model perspective Energy Policy 2017 109 704 718 10.1016/j.enpol.2017.07.050
Tong Y Zhang R He B The carbon emission reduction effect of tourism economy and its formation mechanism: an empirical study of China's 92 tourism-dependent cities Int J Environ Res Public Health 2022 19 3 1824 10.3390/ijerph19031824 35162847
Usman M Husnain M Riaz A Riaz A Ali Y Climate change during the COVID-19 outbreak: scoping future perspectives Environ Sci Pollut Res 2021 28 35 49302 49313 10.1007/s11356-021-14088-x
Wang Q Wang S Preventing carbon emission retaliatory rebound post-COVID-19 requires expanding free trade and improving energy efficiency Sci Total Environ 2020 746 141158 10.1016/j.scitotenv.2020.141158 32745860
Wang Y Wang L Liu H Wang Y The robust causal relationships among domestic tourism demand, carbon emissions, and economic growth in China SAGE Open 2021 11 4 21582440211054478 10.1177/21582440211054478
Wang S Sun L Iqbal S Analyzing green financing role on renewable energy dependence and energy transition in E7 economies Renew Energy 2022 200 1561 1572 10.1016/j.renene.2022.10.067
Xiao Y Tang X Wang J Huang H Liu L Assessment of coordinated development between tourism development and resource environment carrying capacity: a case study of Yangtze River economic Belt in China Ecol Ind 2022 141 109125 10.1016/j.ecolind.2022.109125
Yang Y Agglomeration density and tourism development in China: an empirical research based on dynamic panel data model Tour Manag 2012 33 6 1347 1359 10.1016/j.tourman.2011.12.018
Yang Y Liu Z Saydaliev HB Iqbal S Economic impact of crude oil supply disruption on social welfare losses and strategic petroleum reserves Resour Policy 2022 77 102689 10.1016/j.resourpol.2022.102689
Ying Y, Liu XM, Marble A, Lawson KA, Zhao GQ (2000) Requirement of Bmp8b for the generation of primordial germ cells in the mouse. Mol Endocrinol 14(7):1053–1063
Zaman K Khan MM Ahmad M Exploring the relationship between tourism development indicators and carbon emissions: a case study of Pakistan World Appl Sci J 2011 15 5 690 701
Zeng B Ryan C Assisting the poor in China through tourism development: a review of research Tour Manag 2012 33 2 239 248 10.1016/j.tourman.2011.08.014
Zha J Dai J Ma S Chen Y Wang X How to decouple tourism growth from carbon emissions? A case study of Chengdu, China Tour Manag Perspect 2021 39 100849
Zha J Fan R Yao Y He L Meng Y Framework for accounting for tourism carbon emissions in China: an industrial linkage perspective Tour Econ 2021 27 7 1430 1460 10.1177/1354816620924891
Zhang J Shang Y The influence and mechanism of digital economy on the development of the tourism service trade—analysis of the mediating effect of carbon emissions under the background of COP26 Sustainability 2022 14 20 13414 10.3390/su142013414
Zhang J Zhang Y Carbon tax, tourism CO2 emissions and economic welfare Ann Tour Res 2018 69 18 30 10.1016/j.annals.2017.12.009
Zhang L Huang F Lu L Ni X Iqbal S Energy financing for energy retrofit in COVID-19: recommendations for green bond financing Environ Sci Pollut Res 2022 29 16 23105 23116 10.1007/s11356-021-17440-3
Zhao L Saydaliev HB Iqbal S Energy financing, Covid-19 repercussions and climate change: implications for emerging economies Clim Change Econ 2022 13 3 2240003 10.1142/S2010007822400036
Zheng X Zhou Y Iqbal S Working capital management of SMEs in COVID-19: role of managerial personality traits and overconfidence behavior Econ Anal Policy 2022 76 439 451 10.1016/j.eap.2022.08.006 35990757
Zhong Y Shi S Li S Luo F Luo W Xiao Q Empirical research on construction of a measurement framework for tourism carbon emission in China Chinese J Popul Resour Environ 2015 13 3 240 249 10.1080/10042857.2015.1033806 | 37012561 | PMC10069730 | NO-CC CODE | 2023-05-08 23:15:27 | yes | Environ Sci Pollut Res Int. 2023 Apr 3; 30(21):59470-59480 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
37022552
26613
10.1007/s11356-023-26613-1
Research Article
Obtaining green environmental revival through natural resources price variations: Estimations through GARCH technique
Ma Lei [email protected]
Aumo Mechanotronics Technology Co., Ltd (Weihai), Weihai, 264200 China
Responsible Editor: Philippe Garrigues
6 4 2023
111
8 1 2023
19 3 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
The study aims to determine how price fluctuations in metallic resource supplies impact China’s environmental performance. This research evaluates the impact of the price volatility of nickel, aluminum, gold, and aluminum on environmental performance in China from 2001 to 2019 to provide an answer to this topic. By examining the robustness of outcomes, the conventional DCC-GARCH approach clarifies the study findings and offers wide policy implications for the most recent topicality CS-ARDL. According to the study, the fluctuation in metal prices significantly influences the nation’s GDP. The research’s findings show that over the sample period, the price volatility of metallic resources was 23%, and this shift implied a 17.24% change in environmental performance. The findings of the study so ensure that every effort will be made to prevent environmental instability by supporting financial resource volatility recovery via governmental agencies, environmental ministries, and departments. The research has several policy implications, including the necessity for different government aid programs and financial agreements that guarantee environmental growth and resilience. The research’s policy recommendations are intended to lessen the impact of structural events and increase environmental effectiveness. Research on financial resource recovery is dispersed and understudied despite the issue having a growing corpus of literature.
Keywords
Financial re-structuring
Green environmental performance
Price volatility
Ecological protection
Revival plan
China
==== Body
pmcIntroduction
Asset allocation in planning and leadership often involves using precious metals like platinum, silver, and gold (Renner and Wellmer 2020). The unusual qualities of metals like these have also caught the public’s interest throughout history (Behmiri and Manera 2015). Whether or whether the risk of loss associated with increased levels of price volatility can be forecast is still a matter of contention in the monetary system (Husain et al. 2019). Potentially useful for gauging where future spot prices are headed (Singhal and Ghosh 2016). Additionally, the strong form of market efficiency (EMH) states that security customers will pay all available and relevant information about the securities that are measured at fair value; thus, under a lesser version of EMH, no one can anticipate the price increases and returns of investments, and these events are instead characterized by a choke collar procedure and the martingale difference sequence (Chen 2010). There is a growing corpus of literature on precious metals; these books and articles use a wide range of empirical investigations to investigate the volatility of gold, silver, and platinum. While the efficient market hypothesis (EMH) is often used to analyze the predictability of commodity markets, the adaptive market hypothesis (AMH) is only included in a small number of studies (Chang, Iqbal and Chen, 2023). (Shao et al. 2021). Adapted from the original EMH, AMH was developed by Batten et al (2010). For instance, according to AMH, market conditions like bubbles, crashes, crises, and cycles may significantly impact the predictability of production and can occur at any time (Todorova et al. 2014). In light of this, the current study investigates whether or not AMH can reliably predict the prices of gold, silver, and other metals (Van der Ploeg and Poelhekke 2010).
The stability of the price of precious metals is an issue of paramount importance to investors and a hot area of research for scholars (Hu et al. 2020). As explained by Ewees et al. (2020), base metals may serve two different environmental purposes. Firstly, they may “enlarge,” implying variety. For another, they may be used to protect against inflation (Dehghani and Bogdanovic 2018). Risk transfer is the first component of “risk management”. Although there is less study on the predictability of returns, these studies explore the characteristics and properties of metal’s return distribution that are important to investors. The AMH postulates that competitive pressures, the willingness of investors to change their strategies, and the appearance of new opportunities for financial gain all play a role in initiating and maintaining investment cycles. It is important to anticipate market changes, formulate strategies for survival, and stay abreast of developments in financial technology.
Market efficiency is likely to show a clocking mechanism due to the quick changes in the macroenvironmental institutions, information technology, market norms, and laws. The present market efficiency cycle differs significantly from the classic EMH. The latter is hard to forecast since it is grounded on imprecise knowledge, as shown principally by asset values (Alameer et al. 2019). Assuming that one day it will be possible to foresee patterns, AMH will have two important practical effects. The contribution of the study is manifold.
Nonetheless, the present study is attempting to broaden the scope of AMH research and literature by using autocorrelation, runs, and variance ratio (VR) tests to identify linear dependencies in both the full-sample and sub-samples to embrace the concept of AMH. For investors, Fama (1965) suggests that statistical analysis of reliance may not be necessary since the degree of dependency may be too low to permit successful trading owing to transaction costs. Investigating this rationale to extend the body of knowledge is the first contribution of the study. Due to severe difficulties in estimating accurate historical management fees, the degree to which levels of reliance enable revenue for investment is left out of the focus of this research. Studying the missing link is the second contribution of the study. This is the practical contribution of research. Due to severe difficulties in estimating actual historical management fees, the degree to which levels of reliance enable revenue for investment is left out of the focus of this research. Studying the missing link is the second contribution of the study. According to our knowledge, the commodities under investigation have never been examined from the perspective of the AMH model. These commodities indexes are being investigated using linear empirical studies based on daily returns. According to the research of Urquhart and Hudson, the study has split the sample into five annual equal sub-samples for each product studied. The 5-yearly sub-samples give adequate and credible data to evaluate the evening behavior of precious metals returns across time. On this, the study intends to recommend the policy implications. This is the practical contribution of research.
Literature review
Review of studies
Mining stocks have favorable properties, such as being a source of income during a financial crisis when all other assets are questionable in the economy. There are no two pieces of golden the same, which is why it has been used as a “means of trade, value unit and repository of wealth” for millennia (Iqbal and Bilal 2021). Additionally, gold plays a significant function in investment opportunities during environmental/political downturns and stock market collapses (Huang et al. 2021). Aside from that, silver has long been prized as both a precious metal and an essential financial tool for investors. As a result of its well-recognized properties, it has been employed in a wide range of sectors. When it comes to technology, silver has risen to the top since it is used in a wide range of products, including batteries, solar energy equipment, and electronics. Silver will continue to play an essential part in international markets since the number of methods to purchase it and the size of the worldwide market have grown over time (Iqbal and Bilal 2021). In addition, bullion is one of the most durable and rigid precious metals. At its peak in 2006, it produced 514 tonnes of the commodity. After that, it began to fall and eventually dipped below 500 tonnes (Zhang et al. 2021). This commodity, like precious metals, is widely accepted as a medium of trade due to its fineness and uniform form. Because of its unique physical properties, “platinum” has become a highly sought-after metal for various industrial applications, including the automobile and jewelry industries (Zhang and Tu 2016). This metal is used in around 20% of all consumer devices, making it even more vital. Despite precious metals’ distinct and diverse features, these traits strongly rely on current socioenvironmental circumstances (Ahmadi et al. 2016). According to Abd Elaziz et al. (2020), gold and silver’s short-term returns cannot be predicted using historical data, but long-term projections are attainable.
Early investigations on the predictability of gold. According to Labys et al. (1999), the currency market forecast has been overlooked by scholars compared to other asset classes, such as equities and bonds. Studying gold market forecasts is essential even though gold has critical features of balancing, diversification, and being a haven (Bhatia et al. 2018). The price of valuable metals in various forms has been studied experimentally, and the structure of these markets extensively by academics. According to empirical research, gold prices are affected by multiple variables, including rising prices and the Canadian dollar. This suggests that the best investment choices may be made by analyzing variations in the exchange rate and periods of inflation pressures (Trolle and Schwartz 2009). Traditionally, gold has been utilized as a conventional investment asset as a buffer and haven versus inflation and other banking collapses (Sarwar et al. 2020). Investors have also used gold to secure their money when the US dollar swings (Mensi et al. 2013). As a result, participating in valuable metals is vital since it reduces investors’ risk due to market volatility (Adam and Goyal 2008). Through the lens of asset allocation, Watkins and McAleer (2004) investigated “whether gold is a strong (weak) hedge or a haven” concerning stocks in major developing countries between 1979 and 2009. According to the data, gold is an effective hedge for China and European countries but a poor hedge and a hazardous shelter for Japan, Canada, Australia, and other global economies.
Cochran et al, (2012), Antonakakis, and Kizys (2015), following in the footsteps of Li et al, (2021), analyze daily statistics from 2001 to 2011 to determine whether gold functions as a safeguard or haven in the Thai market. Evidence suggests that gold is not a haven or hedging tool for Thai needs. Additionally, Iqbal et al. (2021) use the same approach to analyze the role of gold and zinc as a hedge of haven in China and China (Alemzero et al. 2021). Mo et al. (2018) augment the existing research on the resurgence of four precious metals: silver, palladium, platinum, and gold. When gold is not “serving as a haven,” China turns to other precious metals. An analysis of the average monthly price of gold from 1980 to 2010 by Li et al. (2021) reveals that the autumn effect significantly affected gold prices, leading to increased demand from consumers, especially around wedding time. Long-term or short-term predictions of commodity prices are unattainable. Thus, investors must instead build investment strategies based on information from the past. The 5-min data collection for silver, palladium, platinum, and gold from 2000 to 2015 is analyzed by Yıldırım et al. (2020). The link between risk and reward is a hot topic of discussion.
The number of trades for each precious metal has increased steadily over time, and the spread between the asking and the highest bid has shrunk due to increased liquidity and pricing efficiency, as shown by their findings (Shao et al. 2021). The “Capital Asset Pricing Model (CAPM)” and the “Markov-Switching CAPM” are used to the S&P 500 and FTSE 100 to investigate whether or not gold is a “safe haven” (special relationship with other assets). According to the studies, gold is not an effective portfolio diversifier in China (Ezeaku et al. 2021). Li et al. investigate the prediction power of overnight technical analysis MA (moving average) rules in precious metals indices (2021). After considering a wide variety of data from the universe, they concluded that the silver market lacked significant predictive potential. Tu et al. (2021) use the AMH to investigate the predictability of the cryptocurrency market. Their research, which uses linear and nonlinear methods, purports to demonstrate that the bitcoin market is inefficient and does not permit the deployment of artificial mental health. We find that certain environmental fundamentals are more beneficial to return predictive ability during times of significant independence and dependence on environmental models.
Theoretical framework
Geman and Smith (2013) examine whether monthly excess returns from precious metals can be predicted, given the little data available to the general public. The study found that the precious metals market is efficient from a quantitative perspective, but that real-time forecasting techniques can only set up simple trading rules that lead to worse performance. The data shows that silver and gold are doing better in weak form as their volatility decreases. Virtual reality testing was recently employed to analyze the daily spot prices of 28 mature and emerging precious metals markets from January 1968 to August 2014. Using the MDS assumption and the random walk hypothesis, they analyze the efficiency of the gold market as a whole (in its most basic form) (RWH). Some of the 28 markets examined do not work well under RWH or the martingale difference hypothesis, while others reject the inefficiency hypothesis. The probability of rejecting an inefficient form lowers in more developed marketplaces but rises in less developed ones. In addition, Sayim and Rahman (2015) Iqbal and Bilal, (2021) analyze the performance of investments and the mood of investors on the Istanbul Stock Exchange using a vector autoregressive model and an impulse response function (ISE). The researchers hypothesize that investor sentiment may significantly affect ISE performance. Using Markov Switching Model to examine time-varying volatility, recent research find that investors’ optimistic expectations dampen returns’ turbulence and uncertainty. It is discovered that the framework in which BRICS environmental assets are exchanged affects the volatility of their returns. Thus, the adaptive market hypothesis may be used to trade on predicted signals to predict market movements in reaction to future and 1-day-ahead fresh signs. The findings of the Lo-MacKinlay VR test support the AMH theory, which shows that the most significant financial crisis reduces market efficiency. In their analysis of the Japanese stock index, Sensoy (2013) argue that the level of predictability of returns fluctuates over time and is compatible with the efficient market hypothesis (AMH). Inferences drawn from this study that efficiency and predictability of returns change cyclically and that predicting is common at periods are supported (Mensi et al. 2019).
Methodology
Data and measurement
The copper, tin, copper, nickel, zinc, aluminum, and gold price volatility indexes are made. The study data was taken from refinit.com, westmetall.com, WDI, and fastmarkets.com. In this study, data from China Was obtained only. A five-commodity portfolio was built using the prices of copper, tin, zinc, aluminum, and gold. The most actively traded commodities are those that can be exchanged. Zinc and gold are the most popular commodities for trading. That commodity price volatility whose maturities were the shortest were chosen for further consideration, e.g., from 2001 to July 2019, respectively. The bid-ask spread builds the commodity liquidity measure (Lt).1 Vt=Vt-1-μt
where μt introduces new information assumed to be self-correcting based on the context provided by an arcade-style argument that has already shown its worth. From this, the extrapolates St as the prior experience zinc commodity price on day t and then picks up that St to follow the subsequent process:2 St=Vt+1EQt
In (2), E is the true disparity, and Qt is a buy/sell dimension for the most recent zinc commodity price and gold commodity price, each of which equals 1. Moreover, the most significant probability may be sequentially uncorrelated. Combining the yields indicated by Eqs. (1) and (2) for other commodities (such as gold, zinc, and stock prices) yields the first-order difference as well.3 ΔSt,ΔSt-11E2orequlltoE22√ΔSt,ΔSt-1
The formula will be undefined if Eq. (4) oscillation is fair and beneficial. To account for this, nevertheless, we modify Eq. (4) as follows:4 lt=spread={2√-ΔSt,ΔSt-1,CovΔSt,ΔSt-1≤0,-CovΔSt,ΔSt-1>0}
Empirical estimation technique
An interest in Engle’s DCC-GARCH model has emerged due to its computational benefits. An asymmetric DCC-GARCH variant of the ADCC-GARCH model was discovered. To analyze how climate bonds influence the economy and its markets, the VAR-ADCC-GARCH model is used. In the multivariate regression analysis, a modified DCC-GARCH model is used. Due to its computational efficiency, the DCC-GARCH model has been widely used in the research community. It is presented the asymmetry DCC-GARCH (ADCC-GARCH) model (2006). It is possible extreme volatility in the markets and the climate bond index if they react similarly. Therefore, we use a method called VAR-ADCC-GARCH. We use many alternative multivariate models for a robustness study, including the corrected DCC-GARCH. Nonetheless, the updated model is described in detail below:5 SPt=l+GCPt-1+OCPt-1+εt
Thus, using Eqs. (6) and (7) of DCC-GARCH equation models, the symmetrical connection among gold commodity prices, zinc commodity prices, and stock prices is computed through the following equations.6 Ht=SPt,GPt,OPt
7 SPt=√GPt,OPt
8 GPt=√SPt,OPt
9 OPt=√SPt,GPt
The technique for examining recent study topicality robustness estimation is also performed. Therefore, the authors have applied the Dicky-Fuller test to estimate the robustness of each series of commodity prices and stock prices.
Results and analysis
Unit root test
We combined data from various sources and intervals to create a relatively complete collection of commodities prices and relevant correlates that may be used to develop business theories and predictions. We offer a data library with monthly, quarterly, and yearly data. The magnesium data is obtained from the International Financial Statistics (IFS) of the IMF every quarter and includes inflationary pressures for several metals (or related products)—aluminum, copper, lead, nickel, tin, and zinc. From 1957 to 2013, metal price data at this frequency are available. The nominal price data were deflated using the US inflation rate (CPI), which was collected from the St. Louis Federal Reserve’s FRED database. Additionally, we have a regular estimate of factory output developed by J.P. Morgan that is employed in constructing quasi-structural models. It comprises factory output in China and India. As a result, this series is only accessible from 1992:01 to 2012:09. We forecasted using covariates (predictors) that are possibly connected with the prices of these metals or their derivatives (Table 1).Table 1 Descriptive estimates
Copper price volatility Tin price volatility Nickel price volatility Zinc price volatility Aluminum price volatility Gold price volatility Environmental performance
Mean 0.114 0.347 0.777 0.190 0.296 0.450 0.983
Median 0.291 0218 0.237 0.222 0.100 0.290 0.451
Max 3.561 6.19 4.67 5.01 12.11 14.56 4.34
Min 2.191 5.67 5.59 4.99 3.39 4.57 1.16
These almost always consist of monetary statistics retrieved from the libraries, spanning 1965 to 2008. This category contains global, US, and Chinese industrial output, the China Geological Service’s (USGS) primary metals coincident and leading indices, and a few budgetary comorbidities, such as the VIX volatility index, the US accurate exchange rates, the need to return and excess returns on various maturities of US sovereign debt, and the return on the S&P 500 index. We gather quarterly pricing data for almost the same metals (or derived products) we do monthly. They covered the years 1957:1 to 2012:1 and were retrieved from the IFS database. The wholesale price statistics were deflated using the US Consumer Price Index. Additionally, we utilized the (very lengthy) list of covariates.
Our examination of shared characteristics will concentrate on the GMM tests presented in “Methodology”, which are good testing techniques when the variability and dependency of the moment limitations under consideration are unknown. Cointegration analysis is used to determine if long-run linkages exist between environmental statistics. As is generally known, this necessitates data usage over a considerable period. Increased frequency is not a replacement for it. As a result, we place a higher premium on cointegration tests utilizing yearly data, as they cover the most extended period—110 years. We continue to test for cointegration at other frequencies, but we downplay the significance of the findings.
Estimating ARCH model
We then examine whether metal commodity prices cointegrate with expected industrial output. The study used pairwise analysis, focusing on single commodity prices. Table 2 summarises the findings.Table 2 Unit root estimates
Constructs Intercept and trend
I(0) I(1)
Copper price volatility − 1.13 − 2.41
Tin price volatility − 1.99 − 3.26
Nickel price volatility − 1.45 -
Zinc price volatility − 2.60 -
Gold price volatility − 1.21 -
Aluminum price volatility − 1.83 -
Environmental performance − 1.01 -
Significance level: p value < 0.05
Regarding cointegration, we find a little strong link between metal prices and global industrial output during the previous two decades, except perhaps aluminum. In any event, the only statistically massive growth rate permutations are those combining aluminum and tin. As a result, the evidence for the US economy is weaker than the evidence for the international economy based on distinctive entreprenurial ventures' access to finance (Bilal et al, 2022). It is instructive to compare the information for standard cycles between metal-commodity prices and global industrial output to the input for similar processes between the former and US manufacturing output. As is generally known, energy production has recently shifted away from industrialized nations and toward developing ones, particularly China (Zhao, Saydaliev and Iqbal, 2022).
Testing t-GCC model
International industries’ output is strongly influenced by these two nations’ manufacturing output, which may explain why we did not discover substantial evidence of synchrony between US industrial output and metal commodity prices.
Data for (log) metals (or derived products) prices—aluminum, copper, lead, nickel, tin, and zinc—are available every quarter from 1957:01 to 2012:01. The findings of unilateral cointegration between metal prices are shown in Table 3, demonstrating overwhelming evidence of cointegration between the costs of several metal consumables. This is similar to our earlier monthly finding; however, our quarterly results are far better—14 out of 15 couples vs. 10 out of 15—in terms of pairing shared oscillations across input costs. There is a familiar cycle for zinc at a 5% significance level but not a 10% level. Third, we discover ample proof of similar processes in industrial output in China, but only for aluminum. There is a similar cycle at 5% for copper, tin, and zinc (Table 4).Table 3 ARCH effect test
Copper price volatility Tin price volatility Nickel price volatility Zinc price volatility Aluminum price volatility Gold price volatility Environmental performance
ADF (level) 23.19 − 19.00 − 24.41 − 7.01 − 23.45 241.45 516.01
Significance 0.000 0.000 0.000 0.003 0.006 0.000 0.000
ADF 1st difference 9.27 − 27.17 − 45.21 − 35.13 − 13.99 25.10 14.56
Significance 0.000 0.001 0.040 0.012 0.000 0.000 0.000
Q statistics 4.01 7.99 5.21 5.14 9.33 6.17 9.03
Significance 0.001 0.040 0.030 0.020 0.000 0.000 0.000
LM test 60.29 64.31 33.16 29.01 94.44 8.91 2.56
Significance 0.000 0.010 0.030 0.003 0.000 0.000 0.000
ARCH test 111.00 199.23 177.93 299.11 200.05 30.77 2.75
Significance 0.009 0.000 0.040 0.001 0.001 0.000 0.000
Table 4 Maximum likelihood estimates of the t-DCC model on China’s commodities prices
PVt INt Significance 1 – (PVt − INt)
Copper price volatility 0.44 0.45 0.010 0.0565
Tin price volatility 0.35 0.95 0.004 0.0121
Nickel price volatility 0.80 0.83 0.002 0.0229
Zinc price volatility 0.10 0.90 0.000 0.0631
Gold price volatility 0.10 0.38 0.001 0.0537
Aluminum price volatility 0.32 0.45 0.020 0.0250
Significance level: p value < 0.05
From 2001 through 2010, the China Geological Service (USGS) supplied yearly statistics on metal prices. The CPI in China depressed prices. The findings of pairwise cointegration between metal prices are shown in Table 5. We discovered compelling evidence of price stationarity between several metal prices. Given the yearly database’s longer duration—more than twice as long as the monthly and quarterly databases—cointegrating evidence should be given greater weight in this study than in earlier studies. We discovered cointegration between ten pairings of market volatility in all 15 feasible scenarios. We hypothesize that operations around the start of the twentieth century consumed much more aluminum, lead, and tin than those in the middle of the century—copper, nickel, and zinc exhibit an inverted pattern. As previously stated, cointegration analysis needs long-span data. Increased frequency does not compensate for the lack of span.Table 5 Maximum likelihood estimates of the Gaussian DCC model
PVt INt Significance 1 – (PVt − INt)
Copper price volatility 0.88 0.35 0.000 0.0125
Tin price volatility 0.91 0.44 0.000 0.0230
Nickel price volatility 0.55 0.83 0.000 0.0131
Zinc price volatility 0.38 0.19 0.000 0.0267
Gold price volatility 0.90 0.61 0.000 0.0444
Significance level: p value < 0.05
Thus, we prefer the Johansen cointegration results when yearly data is used since it has the most considerable duration. We next analyze the presence of similar cycles for yearly price data in paired studies based on the cointegration findings. Table 5 demonstrates unequivocally that commodity prices follow identical processes in testing. We discovered strong-form SCCF for 14 possible 15 pairwise combinations, except tin-zinc. Similar findings are achieved for SCCF in its weak form. Another potential reason for synchronization is that we analyze yearly data over a more extended study period. Finally, we examined whether metal markets and factory production (in China or globally) exhibit cointegration and standard cycles. Unfortunately, the instantaneous growth rates of US and worldwide industrial output did not exhibit the prolific property required to test for regular cycles. As a result, we have avoided proceeding further in that direction here.
CS-ARDL estimation technique
We build multivariate models for (log) metal prices (or derived products)—aluminum, copper, lead, nickel, tin, and zinc—and industrial production, one for metal prices and global industrial production and another for commodities prices and US industrialization, both with seven variables, and then test for predictable patterns using Johansen’s (1991) test and widely accepted prohibitions via the GMM approach described in “Methodology”.
We concentrate on average usage because they best reflect the short-run analysis that is the subject of “Literature Review’s” numerical simulations and the research shreds of the evidence above. We used caution in determining the lag order of the VAR to prevent models that are “interactively incomplete”. We used a VAR with two-level delays for monthly data and global industrial output. When all metal prices and worldwide industrial output are simulated together, there is no indication of cointegration. When we substituted US industrial output for the delays, we discovered that the system once again lacked cointegration. Following that, we give GMM tests for common-cyclical-feature limitations in the previously discussed techniques. Table 6 summarizes the findings. In both situations, we infer that six feature pathways exist. Thus, given the nature of the accompanying links in the metals markets, all metal prices cycle in lockstep with industrial output (whether in China or globally) (6). This is similar to our earlier bivariate findings but somewhat stronger, given the former were not unequivocal. To illustrate the parsimony required by applying common law in a multivariate situation, consider that the unconstrained VAR in differences with one lag, such as (6), contains a total of 56 components for six metal prices and worldwide industrial output.Table 6 CS-ARDL
Variables Coefficient Standard error z-statistics
Short-run
Copper price volatility 0.065* 0.19 6.89
Tin price volatility 0.043* 0.84 4.70
Nickel price volatility 0.022* 0.02 4.13
Zinc price volatility 0.085 0.15 6.73
Gold price volatility 0.036* 0.72 5.55
Aluminum price volatility 0.074 0.20 4.29
Environmental performance 0.055* 0.41 3.46
Long-run
ECM (I)
Copper price volatility 0.078* 0.70 6.34
Tin price volatility 0.095* 0.41 5.88
Nickel price volatility 0.027* 0.10 7.06
Zinc price volatility 0.091* 0.38 3.11
Gold price volatility 0.065* 0.90 4.12
Aluminum price volatility 0.044* 0.71 5.16
Environmental performance 0.039* 0.34 3.19
Significance level: p value < 0.05
Sensitivity analysis
We need many different forecasts to filter out the outliers to satisfy the asymptotic condition of our WLLN cross-section (high N). For this reason, we employed a wide range of econometric models, including AR (VAR), CS-ARDL (common-cycle restrictions), and restricted CS-ARDL (common-cycle restrictions), each of which relied on a different set of covariates (predictors), functional forms (levels, logs), stationarity assumptions (stationarity vs. difference-stationarity), and variable target characteristics. To keep things simple, we discarded several of these models, bringing the total number of models and forecasts for each time horizon down to somewhere between 115 and 125. It is easy to see that our models are nested. We will not have this issue if there are enough courses from various demographics (Table 7).Table 7 Sensitivity analysis
Variables Coefficient Standard error z-statistics
AMG
Copper price volatility 0.031* 0.23 3.14
Tin price volatility 0.044* 0.88 2.75
Nickel price volatility 0.019* 0.01 2.10
Zinc price volatility 0.077 0.10 3.14
Gold price volatility 0.017* 0.66 3.39
Aluminum price volatility 0.044 0.27 4.01
Green economic recovery 0.010* 0.44 2.56
Significance level: p value < 0.05
Our approach is distinct from the literature in several ways, including the starting position discussed above. Prior research mostly used macroenvironmental indicators to forecast commodity prices, but we employed cross-market information instead. In addition, since most macroenvironmental indicators are only accessible monthly or quarterly, earlier research can only utilize low-frequency data. LME metals prices greatly affect Chinese metals prices, whereas Chinese metals prices have a limited impact on LME metals prices, save for lead. Even while Chinese forces have become the primary influencers in the global commodity market, the price of Chinese commodities cannot influence the price of other commodities except in the lead market. The Chinese zinc price has little effect on the global zinc price. It is also possible that Chinese metal prices benefit LME metal prices since all the impacts are positive.
Our findings in Table 8 also show the Chinese and LME markets respond to market shocks for seven to 8 trading days using structural break analysis, indicating that metal commodity prices require an adjustment time of at least that long when there is an influence from external price shocks.Table 8 Structural Break test for robustness
Parameters β SE T Sig
Break 1: 2001/01–2009/05
Copper price volatility 13.78 12.5 2.12 0.000
Tin price volatility 2.71 0.77 1.23 0.020
Nickel price volatility 0.05 0.21 2.15 0.031
Zinc price volatility -0.11 0.82 0.21 0.005
Gold price volatility 0.13 2.12 2.01 0.001
Aluminum price volatility 0.67 0.34 1.34 0.045
Green economic recovery 4.56 129.0 3.63 0.003
Break 2: 2010/06–2019/12
Copper price volatility 1.44 3.14 0.49 0.004
Tin price volatility 0.34 0.45 1.33 0.001
Nickel price volatility 0.45 0.02 0.89 0.009
Zinc price volatility 0.29 0.04 0.36 0.000
Gold price volatility 2.18 2.44 1.67 0.005
Aluminum price volatility 0.23 1.78 0.86 0.004
Green economic recovery 2.56 2.09 0.77 0.000
R-squared 0.78 Mean dependent var 1.88
Adjusted R-squared 0.29 S.D. dependent var 4.29
S.E. of regression 5.55 Akaike info criterion 3.12
Sum squared resid 3610.78 Schwarz criterion 5.09
Log likelihood 2.19 Hannan-Quinn criter 3.18
F-statistic 2.01 Durbin-Watson stat 2.01
Prob (F-statistic) 0.002
Discussion
Additionally, it was demonstrated that civic participation helps the Nordic-Baltic Sea Region shift from fossil fuels to renewable energy. It emphasizes how crucial multinational management is, with several sub-national regulatory agencies interacting and designing thorough energy transitions for suitable environmental policy initiatives developed through green financing (Wang, Sun and Iqbal, 2022). Foremost, decentralized authorities engaged in a “race to the bottom” are much more likely to find themselves in the traps of poor ecological performance because they employ lax ecosystem protections to draw in national and international capital for local economies, allowing pollutants to take advantage of the local atmosphere (Sun et al, 2022). The creation of financial systems is necessary for the effectiveness and effectiveness of reasonable prices at any legislative level, even if the long-term effects of a financially decentralized structure on the ecology are still unclear in terms of oil supply disruptions (Yang et al, 2022).
The widespread mining of coal, oil, and oil and gas offers a cheap source of electricity for environmental expansion while industrialization is increasing (Zhang et al, 2022). The overuse of conventional energy also has substantial negative consequences, which are the leading causes of air pollution (Reboredo and Ugolini, 2016). Although sources of renewable energy have mainly replaced carbon emissions in the latest days by several nations, factory production still relies heavily on them. Therefore, it is generally agreed that increasing productivity is the best method for lowering energy use and emission levels. Efficiency is a broad indicator that measures the sophistication and environment of power consumption. It is defined as the ratio of efficiently used electricity to total energy usage by a system (country, region, firm, or electricity apparatus). Information on energy performance may enhance business operations and machinery, harness the opportunities for energy saving, and enhance the financial leads to energy use. To balance environmental preservation and growth, administrations have pushed energy saving and reduced emissions in the global corporate strategy. The tertiary sector of the economy is very dominating in China. Electricity-endangered animals are also concentrated in manufacturing, building, communication, and other sectors (Raza et al, 2016).
In contrast, nearly 60% of the energy used by the entire community is used in the manufacturing sector (Iqssanke, 2012). Following COVID-19, the electricity sector has been mainly stimulated by the sudden rise in foreign consumption orders. The lack of generation capacity has been made worse by activities required for product manufacture becoming more difficult under conditions of high consumption and a considerable rise in energy usage. The chemical, non-ferrous, and non-metallic mineral products industries will dominate by 2020, driving energy consumption. Government should develop and oversee heavy industry to boost productivity (Lima and Suslick, 2006). Remarkably, given the lack of development in ancillary sectors, policymakers may rely on substantial technological expansion and conversion to integrate wind, light, nuclear, gas, and other innovations into the industry (Fleming, 2011). Actions along these lines foster the continuous enhancement of industrialization from the perspective of environmental protection. One of China’s most distinguishing features is the government’s rigorous control over raw resources for manufacturing. This is why cities must invest in clean energy and pollution control (Christopher and Holweg, 2017; Dutta et al, 2019). During the 13th Five-Year Plan, China implemented a dual management system for energy consumption, established national goals for reducing carbon pollution or use, and made the “dual administration” targets a primary factor in judging the performance of local governments. Implementing measures to reduce emissions has been facilitated by this employee evaluation index, which has also aided in the shift toward an improvement and learning culture. Therefore, whether or not the management can carry out significant changes depends critically on the progress of the financial decentralization structure, which improves the autonomy of the local government’s judgments to enhance the region’s industrial base (Brunetti and Gilbert, 1995). If firms were to reallocate their resources quickly, how much impact would it have on increasing energy efficiency? Considering all these concerns, this research aims to expand and strengthen energy production theory, which might have substantial implications for the forthcoming shift in the financial austerity process (Ahmad et al, 2021; Arouri et al, 2012).
Conversely, CO2 emissions may be seen as a “macro-effect” that is the product of “micro-motives” since they are the outcome of the acts of individual firms and consumers (Tu et al. 2021; Blattman et al, 2007). The DSGE framework provides an engaging environment to study optimum taxes. This article utilizes a dynamic stochastic general equilibrium (DSGE) framework to analyze different carbon tax designs and rebatement strategies in a financial system with forward purchasers who maximize utility, firms with market power, tax-related social tensions in the employment market, marginal social uncertainties in price reduction, and business-cycle fluctuations (Iqbal and Bilal 2021). Through theoretical analysis and the computation of a segment and sub-DSGE model, it is demonstrated that a CO2 tax ought to be adaptable, fluctuating with the state of the economy and the value of energy and that buyers are stronger off (in welfare terms) under such an interactive tax than under either a stationary tax or no tax at all (Zheng et al. 2022; Zhang et al. 2021).
Conclusion and recommendations
We have proven conceptually that if metallic availability is maintained constant in the short term, and consumption is ideally set for the industrial sector, there must be a positive connection between metals price fluctuation and factory output fluctuation. The derived-demand model for cost-cutting enterprises, which is central to cost accounting, is to blame for this (“Literature Review”). This mathematical conclusion is supported by our actual data (monthly and quarterly). Indeed, we have provided evidence that metal prices follow factory output cycles. This data is more compelling in terms of international industry, but it still applies to the Chinese economy, although to a lesser extent. According to our knowledge, we were the first to study and uncover common cycles in this manner, considering theory and empirical evidence rather than just stating a stylized fact. You may see this as one of the paper’s most important contributions. The study findings are as follows:i. The article’s second objective was to provide long-term and short-term predictions for metal prices (annual data). A cross-sectional average of forecasts from several sources may be used to determine the best forecast in the MSE sense, which we treat as a shared property using the mean-bias term (latent variable). While there are several ways to combine predictions for optimal outcomes, these mixes often perform better than individual models. This is precisely what we found; in some instances, the combination outperformed the control variables and the random walk model. To bridge the gap between this work’s forecasting and comprehension portions, many prediction models were used to enforce the common-cycle restrictions established in the comprehension section strictly. Six metal commodities’ future returns and prices were predicted using various models (linear and non-linear, single equation and multivariate). When comparing RMSE performance, we found that the average forecast (AF), the bias-corrected average forecast (BCAF), and the weighted average forecast (WAF) all excelled. These forecast combination methods wipe out individual model prediction errors, which are all based on a weak law of large numbers. These empirical results for commodity prices and periodicity are generally robust over a wide range of time ranges and horizon lengths.
ii. At last, we zeroed down on the most accurate algorithms and forecasts for a wide range of metal prices. Multivariate methods, including limited vector error correction models (CS-ARDL), that account for all metal prices and industrial production give the highest predicting performance for future frequency payments. We rank the S&P 500 and implied volatility measures as the second and third-best indicators, respectively. The AR model beat the VAR and the CS-ARDL for predicting the price of metals over a year. Best predictors included measures of US manufacturing production and market volatility.
iii. There may be theoretical significance in our findings. Liquidity is crucial for gathering data missing from the price dynamics in a market that is either incomplete or inefficient. In an efficient market, as proposed by Fama in his efficient market hypothesis (Fama 1965), price changes accurately represent all relevant information. The metal and zinc markets are very efficient markets; the prices of all commodities traded in these markets accurately represent all available information. This may help to explain the observed cointegration with other cross-markets.
iv. For this reason, zinc and metal price forecasts may make use of global commodity prices. On the other hand, the agricultural and gold markets are fragmented and inefficient. This prevents them from factoring in all the necessary information when setting prices. Theoretically, liquidity is important because it might explain phenomena that are not captured by the dynamics of prices. Furthermore, in illiquid or inefficient markets like the agriculture and gold markets, liquidity is more essential than the price in determining daily price progression across markets globally. Only by factoring in bridge price rises and cash flow levels can costs be predicted for these markets.
Several potential implications of our research have been identified. Since metal and zinc markets are interrelated, traders may effectively hedge price risk by maintaining suitable holdings in cross-market commodities or the global commodity index. Regarding agriculture and gold, cross-market or global hedging is challenging to deploy due to market incompleteness caused by liquidity interference. Inflation can be contained if policymakers monitor commodities pricing and supply. To accurately forecast the inevitable swings in commodity prices, researchers have found that cointegration provides a reliable framework for coordinating information on liquidity. We can now predict how zinc pricing and liquidity changes will affect other commodity markets.
Author contribution
Conceptualization, methodology, and writing (original draft), data curation, visualization, editing: Lei Ma.
Data availability
The data supporting this study’s findings are available on request.
Declarations
Ethical approval and consent to participate
The author declares that there are no human participants, human data, or human issues.
Consent for publication
There is not any person’s data in any form.
Competing interests
The author declares no competing interests.
Preprint service
Our manuscript is posted at a preprint server prior to submission.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Abd Elaziz M Ewees AA Alameer Z Improving adaptive neuro-fuzzy inference system based on a modified salp swarm algorithm using genetic algorithm to forecast crude oil price Nat Resour Res 2020 29 4 2671 2686 10.1007/s11053-019-09587-1
Adam T Goyal VK The investment opportunity set and its proxy variables J Financ Res 2008 31 1 41 63 10.1111/j.1475-6803.2008.00231.x
Ahmad B, Iqbal S, Hai M, Latif S (2021) The interplay of personal values, relational mobile usage and organisational citizenship behavior. Interact Technol Smart Educa
Ahmadi M Behmiri NB Manera M How is volatility in commodity markets linked to oil price shocks? Energy Environ 2016 59 11 23
Alameer Z AbdElaziz M Ewees AA Ye H Jianhua Z Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimisation algorithm Resour Policy 2019 61 250 260 10.1016/j.resourpol.2019.02.014
Alemzero, D. A., Iqbal, N., Iqbal, S., Mohsin, M., Chukwuma, N. J., & Shah, B. A. (2021). Assessing the perceived impact of exploration and production of hydrocarbons on households perspective of environmental regulation in Ghana. Environ Sci Pollut Res Int, 28:5359–5371
Antonakakis N Kizys R Dynamic spillovers between commodity and currency markets Int Rev Financ Anal 2015 41 303 319 10.1016/j.irfa.2015.01.016
Arouri MEH Hammoudeh S Lahiani A Nguyen DK Long memory and structural breaks in modeling the return and volatility dynamics of precious metals Q Rev Environ Financ 2012 52 2 207 218 10.1016/j.qref.2012.04.004
Batten JA Ciner C Lucey BM The macroenvironmental determinants of volatility in precious metals markets Resour Policy 2010 35 2 65 71 10.1016/j.resourpol.2009.12.002
Behmiri NB Manera M The role of outliers and oil price shocks on volatility of metal prices Resour Policy 2015 46 139 150 10.1016/j.resourpol.2015.09.004
Bilal, A. R., Fatima, T., Iqbal, S., & Imran, M. K. (2022). I can see the opportunity that you cannot! A nexus between individual entrepreneurial orientation, alertness, and access to finance. Eur Bus Rev, 34(4):556–577
Bhatia V Das D Tiwari AK Shahbaz M Hasim HM Do precious metal spot prices influence each other? Evidence from a nonparametric causality-in-quantiles approach Resour Policy 2018 55 244 252 10.1016/j.resourpol.2017.12.008
Blattman C Hwang J Williamson JG Winners and losers in the commodity lottery: The impact of terms of trade growth and volatility in the Periphery 1870–1939 J Dev Environ 2007 82 1 156 179
Brunetti C Gilbert CL Metals price volatility, 1972–1995 Resour Policy 1995 21 4 237 254 10.1016/0301-4207(96)85057-4
Chen MH Understanding world metals prices—Returns, volatility and diversification Resourc Policy 2010 35 3 127 140 10.1016/j.resourpol.2010.01.001
Cochran SJ Mansur I Odusami B Volatility persistence in metal returns: A FIGARCH approach J Environ Bus 2012 64 4 287 305
Chang, L., Iqbal, S., & Chen, H. (2023). Does financial inclusion index and energy performance index co-move?. Energy Policy, 174:113422.
Christopher M, Holweg M (2017) Supply chain 2.0 revisited: a framework for managing volatility- induced risk in the supply chain. Int J Phys Distrib Logist Manag
Dehghani H Bogdanovic D Copper price estimation using bat algorithm Resour Policy 2018 55 55 61 10.1016/j.resourpol.2017.10.015
Dutta A Bouri E Roubaud D Nonlinear relationships amongst the implied volatilities of crude oil and precious metals Resourc Policy 2019 61 473 478 10.1016/j.resourpol.2018.04.009
Ezeaku HC Asongu SA Nnanna J Volatility of international commodity prices in times of COVID-19: Effects of oil supply and global demand shocks Extr Ind Soc 2021 8 1 257 270
Ewees AA AbdElaziz M Alameer Z Ye H Jianhua Z Improving multilayer perceptron neural network using chaotic grasshopper optimisation algorithm to forecast iron ore price volatility Resour Policy 2020 65 101555 10.1016/j.resourpol.2019.101555
Fama EF The behavior of stock-market prices J Bus 1965 38 1 34 105 10.1086/294743
Fleming NR (2011) Metal price volatility: a study of informative metrics and the volatility mitigating effects of recycling (Doctoral dissertation, Massachusetts Institute of Technology)
Huang, J., Wang, X., Liu, H., & Iqbal, S. (2021). Financial consideration of energy and environmental nexus with energy poverty: Promoting financial development in G7 economies. Frontiers in Energy Research, 9:777796
Hu M Zhang D Ji Q Wei L Macro factors and the realised volatility of commodities: a dynamic network analysis Resour Policy 2020 68 101813 10.1016/j.resourpol.2020.101813 34173417
Husain S Tiwari AK Sohag K Shahbaz M Connectedness among crude oil prices, stock index and metal prices: An application of network approach in the USA Resour Policy 2019 62 57 65 10.1016/j.resourpol.2019.03.011
Iqssanke M Commodity market linkages in the global financial crisis: Excess volatility and development impacts J Dev Stud 2012 48 6 732 750 10.1080/00220388.2011.649259
Iqbal, S., & Bilal, A. R. (2021). Energy financing in COVID-19: how public supports can benefit?. China Finance Review Int, 12(2):219–240
Iqbal, S., Bilal, A. R., Nurunnabi, M., Iqbal, W., Alfakhri, Y., & Iqbal, N. (2021). It is time to control the worst: testing COVID-19 outbreak, energy consumption and CO 2 emission. Environmental Science and Pollution Research, 28:19008–19020
Geman H Smith WO Theory of storage, inventory and volatility in the LME base metals Resour Policy 2013 38 1 18 28 10.1016/j.resourpol.2012.06.014
Labys WC Achouch A Terraza M Metal prices and the business cycle Resour Policy 1999 25 4 229 238 10.1016/S0301-4207(99)00030-6
Lima GAC Suslick SB Estimating the volatility of mining projects considering price and operating cost uncertainties Resour Policy 2006 31 2 86 94 10.1016/j.resourpol.2006.07.002
Li, W., Chien, F., Ngo, Q. T., Nguyen, T. D., Iqbal, S., & Bilal, A. R. (2021). Vertical financial disparity, energy prices and emission reduction: empirical insights from Pakistan. J Environ Manage, 294:112946.
Mensi W Beljid M Boubaker A Managi S Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold Environ Model 2013 32 15 22
Mo D Gupta R Li B Singh T The macroenvironmental determinants of commodity futures volatility: Evidence from Chinese and Indian markets Environ Model 2018 70 543 560
Mensi W Sensoy A Aslan A Kang SH High-frequency asymmetric volatility connectedness between Bitcoin and major precious metals markets N Am J Environ Financ 2019 50 101031 10.1016/j.najef.2019.101031
Reboredo JC Ugolini A The impact of downward/upward oil price movements on metal prices Resour Policy 2016 49 129 141 10.1016/j.resourpol.2016.05.006
Renner S Wellmer FW Volatility drivers on the metal market and exposure of producing countries Mineral Environ 2020 33 3 311 340
Raza N Shahzad SJH Tiwari AK Shahbaz M Asymmetric impact of gold, oil prices and their volatilities on stock prices of emerging markets Resour Policy 2016 49 290 301 10.1016/j.resourpol.2016.06.011
Sensoy A Dynamic relationship between precious metals Resour Policy 2013 38 4 504 511 10.1016/j.resourpol.2013.08.004
Singhal S Ghosh S Returns and volatility linkages between international crude oil price, metal and other stock indices in India: Evidence from VAR-DCC-GARCH models Resour Policy 2016 50 276 288 10.1016/j.resourpol.2016.10.001
Sayim, M., & Rahman, H. (2015). The relationship between individual investor sentiment, stock return and volatility: Evidence from the Turkish market. International Journal of Emerging Markets, 10(3):504–520
Sarwar S Tiwari AK Tingqiu C Analysing volatility spillovers between oil market and Asian stock markets Resour Policy 2020 66 101608 10.1016/j.resourpol.2020.101608
Shao L Zhang H Chen J Zhu X Effect of oil price uncertainty on clean energy metal stocks in China: Evidence from a nonparametric causality-in-quantiles approach Int Rev Environ Financ 2021 73 407 419 10.1016/j.iref.2021.01.009
Sun, L., Fang, S., Iqbal, S., & Bilal, A. R. (2022). Financial stability role on climate risks, and climate change mitigation: implications for green economic recovery. Environ Sci Pollut Res, 29(22):33063–33074
Trolle AB Schwartz ES Unspanned stochastic volatility and the pricing of commodity derivatives Rev Financ Stud 2009 22 11 4423 4461 10.1093/rfs/hhp036
Todorova N Worthington A Souček M Realised volatility spillovers in the non-ferrous metal futures market Resour Policy 2014 39 21 31 10.1016/j.resourpol.2013.10.008
Tu, C. A., Chien, F., Hussein, M. A., RAMLI MM, Y. A. N. T. O., S. PSI, M. S., Iqbal, S., & Bilal, A. R. (2021). Estimating role of green financing on energy security, economic and environmental integration of BRI member countries. The Singapore Economic Review, 1–19
Van der Ploeg F Poelhekke S The pungent smell of "red herrings": Subsoil assets, rents, volatility and the resource curse J Environ Environ Manag 2010 60 1 44 55 10.1016/j.jeem.2010.03.003
Wang, S., Sun, L., & Iqbal, S. (2022). Green financing role on renewable energy dependence and energy transition in E7 economies. Renew Energy, 200:1561–1572
Watkins C McAleer M Econometric modelling of non- ferrous metal prices J Environ Surv 2004 18 5 651 701 10.1111/j.1467-6419.2004.00233.x
Yıldırım DÇ Cevik EI Esen Ö Time-varying volatility spillovers between oil prices and precious metal prices Resour Policy 2020 68 101783 10.1016/j.resourpol.2020.101783
Yang, Y., Liu, Z., Saydaliev, H. B., & Iqbal, S. (2022). Economic impact of crude oil supply disruption on social welfare losses and strategic petroleum reserves. Resour Policy, 77:102689.
Zhang C Tu X The effect of global oil price shocks on China's metal markets Energy Policy 2016 90 131 139 10.1016/j.enpol.2015.12.012
Zhang L, Huang F, Lu L, Ni X, Iqbal S (2021) Energy financing for energy retrofit in COVID- 19: Recommendations for green bond financing. Environ Sci Pollut Res 1–12
Zhang, L., Huang, F., Lu, L., Ni, X., & Iqbal, S. (2022). Energy financing for energy retrofit in COVID-19: recommendations for green bond financing. Environ Sci Pollut Res, 29(16):23105–23116
Zhao, L., Saydaliev, H. B., & Iqbal, S. (2022). Energy financing, COVID-19 repercussions and climate change: implications for emerging economies. Clim Chang Econ, 13(03):2240003
Zheng, X., Zhou, Y., & Iqbal, S. (2022). Working capital management of SMEs in COVID-19: role of managerial personality traits and overconfidence behavior. Economic Econ Anal Policy, 76:439–451 | 37022552 | PMC10078022 | NO-CC CODE | 2023-05-08 23:15:27 | yes | Environ Sci Pollut Res Int. 2023 Apr 6; 30(21):60303-60313 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
37022543
26647
10.1007/s11356-023-26647-5
Research Article
Association of the corona virus (Covid-19) epidemic with environmental risk factors
Neisi Abdolkazem [email protected]
1
Goudarzi Gholamreza [email protected]
1
Mohammadi Mohammad Javad [email protected]
12
Tahmasebi Yasser 2
Rahim Fakher 3
Baboli Zeinab 4
Yazdani Mohsen 5
Sorooshian Armin 6
Attar Somayeh Alizade [email protected]
7
Angali Kambiz Ahmadi [email protected]
8
Alam Khan 9
Ahmadian Maryam 10
Farhadi Majid [email protected]
11
1 grid.411230.5 0000 0000 9296 6873 Department of Environmental Health, School of Public Health and Air Pollution and Respiratory Diseases Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
2 grid.411230.5 0000 0000 9296 6873 Department of Environmental Health, School of Public Health and Environmental Technologies Research Center (ETRC), Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
3 grid.411230.5 0000 0000 9296 6873 Thalassemia & Hemoglobinopathy Research Center, Health Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
4 Department of Environmental Health Engineering, Behbahan Faculty of Medical Sciences, Behbahan, Iran
5 Department of Environmental Health, School of Nursing, Torbat Jaam Faculty of Medical Sciences, Torbat Jaam, Iran
6 grid.134563.6 0000 0001 2168 186X Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ USA
7 grid.411230.5 0000 0000 9296 6873 Department of Environmental Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
8 grid.411230.5 0000 0000 9296 6873 Department of Biostatistics and Epidemiology, School of Health, Social Determinants of Health Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
9 grid.266976.a 0000 0001 1882 0101 Department of Physics, University of Peshawar, Peshawar, 25120 Pakistan
10 grid.411600.2 Department of Biostatistics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
11 grid.411230.5 0000 0000 9296 6873 Department of Environmental Health Engineering, School of Public Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
Responsible Editor: Lotfi Aleya
6 4 2023
112
18 12 2022
20 3 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
The current outbreak of the novel coronavirus SARS‐CoV‐2 (coronavirus disease 2019; previously 2019‐nCoV), epicenter in Hubei Province (Wuhan), People’s Republic of China, has spread too many other countries. The transmission of the corona virus occurs when people are in the incubation stage and do not have any symptoms. Therefore, the role of environmental factors such as temperature and wind speed becomes very important. The study of Acute Respiratory Syndrome (SARS) indicates that there is a significant relationship between temperature and virus transmission and three important factors, namely temperature, humidity and wind speed, cause SARS transmission. Daily data on the incidence and mortality of Covid-19 disease were collected from World Health Organization (WHO) website and World Meter website (WMW) for several major cities in Iran and the world. Data were collected from February 2020 to September 2021. Meteorological data including temperature, air pressure, wind speed, dew point and air quality index (AQI) index are extracted from the website of the World Meteorological Organization (WMO), The National Aeronautics and Space Administration (NASA) and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Statistical analysis carried out for significance relationships. The correlation coefficient between the number of infected people in one day and the environmental variables in the countries was different from each other. The relationship between AQI and number of infected was significant in all cities. In Canberra, Madrid and Paris, a significant inverse relationship was observed between the number of infected people in one day and wind speed. There is a significant positive relationship between the number of infected people in a day and the dew point in the cities of Canberra, Wellington and Washington. The relationship between the number of infected people in one day and Pressure was significantly reversed in Madrid and Washington, but positive in Canberra, Brasilia, Paris and Wuhan. There was significant relationship between Dew point and prevalence. Wind speed showed a significant relationship in USA, Madrid and Paris. AQI was strongly associated with the prevalence of covid19. The purpose of this study is to investigate some environmental factors in the transmission of the corona virus.
Keywords
SARS‐CoV‐2
Environmental factors
AQI
Dew point
Temperature
Humidity
==== Body
pmcIntroduction
Covid-19 is a virus (specifically called a coronavirus) that is known to be the cause of an outbreak of respiratory disease that was first found in Wuhan, China.The government and health researchers in China have taken swift action to control the epidemic and have begun etiological research. The World Health Organization (WHO) temporarily named the new virus the New Coronavirus 2019 On January 12, 2020. The new strain of Corona (Covid-19) spread in China at the end of 2019, and since then it has brought the people of the world into a serious challenge. The spread of this viral disease had no boundaries and threatened the whole world (Gorbalenya et al. 2020; Wu et al. 2020). The outbreak of this disease started in China and in a very short period of time, it has been reported in other countries around the world including South Korea, Italy, Iran, Japan, the United States and other countries (Control and Prevention 2020; Cooper et al. 2021). SARS-CoV-2 is transmitted mainly through droplets and close contact with symptomatically infected cases (Mario Coccia 2022c; Nunez-Delgado et al. 2021; Benati and Coccia 2022b; Bontempi and Coccia 2021). The study of Acute Respiratory Syndrome (SARS) shows that there is a significant relationship between temperature and virus transmission. Several factors cause SARS transmission, including temperature, humidity, and wind speed (K.-H. Chan et al. 2011; Weaver et al. 2021; Jingsong Yuan et al. 2006; Srivastava 2021). The prevalence of coronavirus is widespread with SARS. Corona virus transmission can be affected by a variety of factors, including weather conditions (such as temperature and humidity), population density, and health (Bogoch et al. 2020a, 2020b; C. Huang et al. 2020a, b; L. Wang et al. 2020a, b; R. Huang et al. 2020a, b; Apaliya et al. 2022; Bashir et al. 2020; Haque and Rahman 2020; Islam et al. 2021). Laboratory, epidemiological and modeling studies show that ambient temperature and humidity play a key role in virus transmission and survival (Barreca and Shimshack 2012; Lowen et al. 2007; Shaman et al. 2009, 2010; Żuk et al. 2009). This dangerous and deadly disease, in addition to the destructive aspects of medicine, has caused stagnation in economic, social and educational conditions(Nilashi et al. 2020). Preliminary data from the researchers showed that people without symptoms can infect others (Benati and Coccia 2022a; Rosario et al. 2020; Coccia 2020a; M. Coccia 2022a, b, c, d). To better understand the role of transmission from infected and asymptomatic individuals, it is important to distinguish between transmission from infected individuals who never develop symptoms and transmission from infected individuals who have not yet developed symptoms (Hong and Van 2022; Coccia 2020; Shen et al. 2021; Bontempi et al. 2021; M. Coccia 2022a, b, c, d). Schools and universities were closed due to quarantine conditions. Commercial exports and imports faced many restrictions. Because of this viral disease, some businesses were also closed(Dickens et al. 2020). But despite the health, economic and social problems caused by the spread of Corona, it also brought positive environmental changes. Among these changes, we can mention the reduction of air pollution (due to less traffic of vehicles and the adoption of quarantine laws)(Kanniah et al. 2020) and the reduction of surface water pollution(Lokhandwala and Gautam 2020). COVID-19 control measures affect air quality and the overall environment and must be designed taking into account various aspects, including economic and social factors (Akan and Coccia 2022; Mario Coccia 2022b; Song et al. 2022). There are various factors that affect the spread of Corona. Greater population density and household size and less social distancing will increase the probability of the spread of Corona. The severity of the body's response to the corona virus is different in people. Many factors can be involved, including having an underlying disease, age, genetics, smoking and lack of equal access to health services. (Saadat et al. 2020; Coccia 2023).There is a relationship between climate and COVID-19 transmission to predict the severity and timing of the onset of the epidemic. Prior to March 11, 2020, 90% of 2019-COV cases were recorded in locations with temperatures below 11 ° C. The spread of coronavirus is affected by ambient temperature, so that there is an inverse relationship between temperature and the prevalence of coronavirus Approximately 10,000 new cases have been reported between 16 and 18 ° C between 10 March 2020 and 21 March 2020, which refutes this hypothesis and states that rising ambient temperatures could reduce coronavirus transmission. (Bukhari and Jameel 2020; Mario Coccia 2022a; Jing Yuan et al. 2020) However, there is no strong evidence for the effect of temperature and humidity on COVID-19 transmission. For example, Michael Ryan, executive director of the World Health Organization's Emergency Medical Program, said that no one still knows about the impact of the weather on COVID-19.(J. Wang et al. 2020a, b) Study of Sajjadi et al. shows a correlation between COVID-19 and latitude (e.g. France, Italy, Iran, Wuhan, China, Korea, Japan, Seattle, and New York (USA) are located 30-50 degrees north.) (Shokouhi et al. n.d.). Findings from the agricultural study may lead to more information about the role of environmental factors (such as climate, altitude, air pressure, etc.) and COVID-19 transmission. (Keshavarzi 2020; Coccia 2021b). Researchers found that temperature and relative humidity have an inverse relationship with the spread of the corona virus (Coccia 2021a, 2021c; Sarkodie and Owusu 2020). They also showed that the lower the air temperature and the higher the air pressure and wind speed, the higher the possibility of virus transmission (Diao et al. 2021; Coocia 2021; Rahimi et al. 2021; Coccia 2021d). Zhu et al. found that whenever the temperature is low (temperature below 3 degrees Celsius), the number of corona cases increases linearly(Xie and Zhu 2020).
Therefore, the aim of this study was to determine the relationship between the prevalence and infection rate of Covid-19 disease with temperature, Dew Point, air pressure, AQI index on a global scale.
Method
Measures of variables
Daily data on the incidence of Covid-19 disease were collected from World Health Organization (WHO) website and World Meter website for several major cities the world. Data were collected from February 2020 to September 2021. Meteorological data including temperature, air pressure, Dew Point and AQI index are extracted from the website of the World Meteorological Organization, NASA and the MODIS sensor. MODIS It is a scientific instrument that was installed on the Terra satellite by NASA and launched into the earth's orbit. WMO is an international organization that succeeded the "International Meteorological Organization" that is active in the field of weather, hydrology (applied climatology) and geophysical sciences.
Study Area
In this study, meteorological data and the rate of infection of people with Covid-19 in several cities from different regions of the world have been used. These cities include two cities from the Asian continent (Wuhan and Canberra (the capital of Australia)), two cities from the American continent (Washington (the capital of the United States of America) and Brasilia (the capital of Brazil)), 3 cities from the European continent (Paris) (capital of France), Rome (capital of Italy) and Madrid (capital of Spain)) and a city from the continent of Oceania (Wellington (capital of New Zealand)). Due to the fact that the independent variables in this research were weather conditions and health parameters, the geographical location of the countries was of great importance, as several parameters (such as latitude and longitude) are mentioned in Table 1.Table 1 Geographic information of different cities of this study
City Population DMS Latitude DMS Long Longitude Latitude
Canberra 453,558 35° 16′ 55.2036'' S 149° 7′ 44.3928'' E 149.12 -35.28
Brasília 4,804,000 15° 47′ 38.0004'' S 47° 52′ 58.0008'' W -47.882778 -15.793889
Madrid 3,223,000 40° 25′ 0.3900'' N 3° 42′ 13.6440'' W -3.703790 40.416775
Wellington 212,700 41° 16′ 36.5700'' S 174° 46′ 40.6884'' E 174.777969 -41.276825
Paris 2,161,000 48° 51′ 52.9776'' N 2° 20′ 56.4504'' E 2.349014 48.864716
Rome 2,873,000 41° 54′ 10.0152'' N 12° 29′ 46.9176'' E 12.496366 41.902782
Washington 7,739,000 47° 45′ 3.8736'' N 120° 44′ 24.460'' W -120.740135 47.751076
Wuhan 8,592,000 30° 34′ 59.9952'' N 114° 16′ 59.9988'' E 114.283333 30.583332
Data analysis procedure
Spearman's correlation coefficient was used to analyze the statistical data of the article. Spearman's correlation coefficient or more precisely "Spearman's rank-order correlation coefficient" is a non-parametric measure or index to show the dependence between two ordinal variables. Of course, Spearman's correlation coefficient can also be used for quantitative (numerical) variables.
Result and Discussion
Several studies confirm the impact of environmental issues (climatic conditions) on the transmission of infectious diseases (Bedford et al. 2015; Lemaitre et al. 2019). Studies by James D. et al. Showed that humidity and temperature can be effective in predicting influenza epidemics in the tropics (Tamerius et al. 2013). Temperate regions of the Northern and Southern Hemispheres are characterized by highly synchronized annual influenza circulations during their winter months respectively (Tamerius et al. 2013; Lemaitre et al. 2019; Bedford et al. 2015).
Among the countries of Australia (Canberra), Brazil (Brasília), Spain (Madrid), New Zealand (Wellington), France (Paris), Italy (Rome), America (Washington, D.C.) and China (Wuhan), the highest and lowest average of confirmed patients In one day, until April 30, 2020, it belonged to America and New Zealand, respectively, with an average (standard deviation) of 15,512.6 (14,098.9) and 36.8 (36.0) people. The highest and lowest average daily temperature in this period belonged to Brazil and America with the mean (standard deviation) of 23.1(1.4) and 11.0(4.1) degrees Celsius. Table 2 shows the average (standard deviation) number of infected patients in one day, temperature, AQI, Wind. Speed, Dew. Point and Pressure.Table 2 Mean and standard deviation of the number of infected people in one day and environmental factors
Variable Number of infected people Temperature AQI Wind. Speed Dew. Point Pressure
City mean (standard deviation)
Canberra 100.6(134.0) 15.5(3.2) 22.8(9.8) 4.5(2.8) 8.7(3.6) 950.0(5.2)
Brasília 1447.1(1728.9) 23.1(1.4) 14.3(7.5) 2.7(0.8) 18.9(1.4) 896.9(2.2)
Madrid 3578.3(2734.4) 12.1(2.7) 48.7(12.5) 2.8(1.9) 6.0(3.6) 944.8(4.5)
Wellington 36.8(36.0) 15.0(1.5) 29.8(12.2) 33.9(4.0) 10.4(2.4) 1011.7(8.3)
Paris 2390.1(2192.0) 11.2(4.3) 55.4(17.9) 4.4(2.1) 4.2(4.5) 1004.0(8.3)
Rome 2776.5(1908.1) 13.1(2.8) 49.3(19.7) 3.1(1.2) 6.5(4.4) 1000.2(6.5)
Washington, D.C 15,512.6(14,098.9) 11.0(4.1) 19.1(6.6) 4.3(1.5) 3.1(6.7) 1016.3(8.3)
Wuhan 730.2(1172.2) 12.9(5.5) 121.0(28.7) 2.1(0.7) 7.0(4.4) 1015.8(5.9)
The Spearman correlation coefficient between the number of confirmed people in one day and the average temperature, AQI, Wind. Speed, Dew. Point and Pressure in the previous four days plus the same day of these countries is shown in Table 3.Table 3 Spearman's correlation coefficient between the number of confirmed people in one day and environmental factors in the previous four days and the same day
Variable Temperature AQI Wind. Speed Dew. Point Pressure
City The correlation coefficient (p-valve)
Canberra -0.105(0.399) -0.290*(0.017) -0.409*(0.001) 0.457*(< 0.001) 0.522*(< 0.001)
Brasília -0.383*(0.003) -0.562*(< 0.001) 0.224(0.088) -0.275*(0.035) 0.579*(< 0.001)
Madrid -0.162(0.191) -0.371*(0.009) -0.263*(0.031) 0.230(0.061) -0.518*(< 0.001)
Wellington -0.268(0.095) -0.517*(0.001) 0.392*(0.012) 0.376*(0.017) 0.270(0.092)
Paris 0.382*(0.002) 0.636*(< 0.001) -0.402*(0.001) -0.161(0.195) 0.704*(< 0.001)
Rome 0.175(0.135) -0.232*(0.047) -0.074(0.532) -0.179(0.127) 0.046(0.699)
Washington 0.711*(< 0.001) -0.406*(< 0.001) 0.116(0.336) 0.634*(< 0.001) -0.575*(< 0.001)
Wuhan -0.684*(< 0.001) 0.217*(0.031) -0.110(0.278) -0.681*(< 0.001) 0.656*(< 0.001)
The correlation coefficient between the number of infected people in one day and the environmental variables in the countries was different from each other. In Brasilia and Wuhan, there was a negative correlation between the temperature variable and the number of infected people, but in Paris and Washington, a significant positive correlation was observed.
Spearman's correlation coefficient of time and average temperature, AQI, Wind. Speed, Dew. Point and Pressure in the previous four days plus the same day are shown in Table 4. All correlations between time and temperature were significant. The relationship between time and temperature was inverse in Australia, Brazil, and New Zealand, but in Spain, Italy, America, and China, this relationship is direct. According to these two tables (Tables 3, 4), the temperature in Brasilia has decreased over time and due to the decrease in temperature, the number of infected people has increased every day.Table 4 Spearman's correlation coefficient of time and average environmental factors in the previous four days and the same day
Variable Temperature AQI Wind. Speed Dew. Point Pressure
City (p-valve) The correlation coefficient
Canberra -0.885*(< 0.001) 0.465*(< 0.001) -0.254*(0.032) 0.139(0.248) 0.066(0.587)
Brasília -0.343*(0.006) -0.607*(< 0.001) 0.326*(0.010) -0.366*(0.003) 0.546*(< 0.001)
Madrid 0.522*(< 0.001) -0.104(0.405) -0.259*(0.034) 0.740*(< 0.001) -0.638*(< 0.001)
Wellington -0.305*(0.035) 0.809*(< 0.001) 0.101(0.496) -0.053(0.721) -0.248(0.089)
Paris 0.812*(< 0.001) 0.535*(< 0.001) -0.611*(< 0.001) 0.344*(0.005) 0.237(0.055)
Rome 0.746*(< 0.001) -0.623*(< 0.001) 0.001(0.994) 0.433*(< 0.001) -0.166(0.157)
Washington 0.647*(< 0.001) -0.437*(< 0.001) 0.003(0.982) 0.645*(< 0.001) -0.541*(< 0.001)
Wuhan 0.845*(< 0.001) -0.449*(< 0.001) 0.010(0.918) 0.767*(< 0.001) -0.702*(< 0.001)
In Wuhan, there has been an increase in temperature over time, and the increase in temperature has caused a decrease in the number of infected people per day. In Paris and America, with the passage of time, the temperature has increased and the increase in temperature has caused an increase in the number of infected people every day (Fig. 1).Fig. 1 The trend of changes in the number of infected people due to temperature changes
In a study conducted by Tan et al., They found that temperature could be a suitable factor for SARS-CoV transmission, that temperature changes could affect the prevalence of the virus (Weaver et al. 2021). Peng Shi inferred that the number of new COVID-19 outbreaks in mainland China reached its highest level on February 1, 2020. Covid-19 was the least common at low temperatures and the most common at high temperatures(Shi et al. 2020). Peng Shi found that there was an inverse relationship between temperature and the rate of virus transmission. As the temperature rises, the prevalence of infection decreases. Therefore, temperature is a good indicator for optimal prediction of coronavirus transmission(Shi et al. 2020). Jianfeng Li's studies showed that there is a negative correlation between the mean temperature and the prevalence of COVID-19. The results of linear regression showed that increasing the temperature by one degree Celsius could reduce the prevalence of 72 people. Studies have reported that SARS-CoV has a lower transmission power at low temperatures.(J. Li et al. 2020) Recently studies demonstrated that relationship between temperature and COVID-19 mortality. Every increase of 1°C in the diurnal temperature range caused COVID-19 mortality 2.92% increase (Ma et al. 2020; Xie and Zhu 2020). Yueling Ma found that not associated with COVID-19 mortality for DTR (diurnal temperature range)but were strongly positive for temperature.(Ma et al. 2020) Couple of studies reported that respiratory diseases mortality increased with decreasing temperature (Ghalhari and Mayvaneh 2016), and was strongly associated with low temperature (Dadbakhsh et al. 2017; Gomez-Acebo et al. 2013). While another study found that both cold and heat effects might have adverse impacts on respiratory mortality (M. Li et al. 2019). Several studies have shown that temperature plays a key role in the survival and transmission of SARS-CoV and MERS-CoV. (Bi et al. 2007; Casanova et al. 2010; J. F.-W. Chan et al. 2020; Weaver et al. 2021; Van Doremalen et al. 2013). According to research conducted by Mr. Tan et al., The optimal ambient temperature for SARS cases is 16 to 28 degrees Celsius. These studies were conducted in China, Hong Kong and Taiwan (Weaver et al. 2021). Moreover, (Bi et al. 2007) Casanova reported that rising temperatures could reduce the spread of SARS. In fact, there is a negative relationship between SARS transmission and air temperature. In this laboratory study, it was shown that the SARS virus is inactivated at high temperatures (above 20 ° C). (Casanova et al. 2010). Another laboratory study showed that the coronavirus has little chance of surviving at high temperatures. The coronavirus can survive for 5 days at 22 to 25 ° C. (J. F.-W. Chan et al. 2020). Van Dormalen also concluded that viruses have very little resistance to temperatures above the optimum temperature (above 30). He also observed that MERS-CoV was less stable at high temperatures (Van Doremalen et al. 2013). Mr. Jingui Xie did some research on the effect of temperature on coronavirus infection, but since these studies were conducted in the cold season, he could not find a link between temperature and the prevalence of COVID-19(Xie and Zhu 2020).
The relationship between AQI and number of infected was significant in all cities. In the cities of Paris and Wuhan, this relationship was positive, but in other cities it was opposite. Except for Spain, there is a significant relationship between time and AQI in other countries under study. The relationship between time and AQI was direct in Australia, New Zealand and France, but in other places this relationship was inverse (Fig 2).Fig. 2 Average changes in the number of infected people and AQI changes in the four days before and on the same day
In the cities of Brasília, Rome and Washington, AQI decreased over time and as AQI decreased, the number of confirmed people per day increased. In Wuhan, the AQI decreased over time, and as the AQI decreased, the number of confirmed cases per day decreased. In Australia and New Zealand, there has been an increase in AQI over time, and with the increase in AQI, there has been a decrease in the number of infected people per day. In Paris, AQI has increased over time, and with the increase in AQI, the number of infected people per day has also increased.
Ground-level O3 (As an indicator of air quality) is created by the reaction of surface pollution and sunlight. This compound is extremely harmful to human health. Inhalation of this gas disrupts the respiratory system and lung function and can eventually lead to respiratory infections. (Niu et al. 2020). Exposure to ozone can lead to hospitalization of respiratory patients (Lauer et al. 2020; Burnett et al. 1997). Ozone can reduce lung function, so there is a risk of infectious and viral diseases in ambient ozone. Therefore, this gas can facilitate COVID-19 transmission by acting on the lungs. The air quality index is inversely related to the prevalence of Covid-19. When this index is high, the prevalence of coronavirus will be lower. Studies in India and China confirm this. (Lauer et al. 2020; Burnett et al. 1997)Some cities were quarantined during the corona outbreak. Studies in quarantined cities have shown that the concentration of air pollutants, especially NO2, has been reduced by about 56 percent. (Lauer et al. 2020; Burnett et al. 1997)
In Australia, Madrid and Paris, a significant inverse relationship was observed between the number of infected people in one day and Wind. Speed, while in Wellington, a significant positive relationship was observed between these two variables. The relationship between time and Wind. Speed became significant positive only in Brazil, but in Australia, Spain and France it became significant inversely. In the cities of Canberra, Madrid and Paris, over time, Wind. Speed has decreased, and with the decrease of Wind. Speed, the number of infected people has increased every day (Fig 3).Fig. 3 Changes in the average number of infected people in one day and changes in wind speed in the previous four days and the same day
Wind speed (wind speed above 6 meters per second) is inversely related to coronavirus infection. The higher the wind speed, the lower the risk of coronavirus infection. (Islam et al. 2020; Qiu et al. 2020). The negative associations were also observed in Iran (Akula and Singh 2021), Turkey (Şahin 2020)and China (Xie and Zhu 2020). In contrast, three multi-city studies (Sajadi et al. 2020; Pan et al. 2021; Chiyomaru and Takemoto 2020) did not observe significant linear association between wind speed and the cases/basic reproduction number of COVID-19.
The temperature at which water vapor cools and condenses and turns into liquid droplets due to condensation is called dew point(Althouse et al. 2018).A significant linear relationship between the number of infected people in one day and Dew. Point was observed in the cities of Canberra, Wellington and Washington, and a significant negative relationship was observed in the cities of Brasilia and Wuhan. There was an inverse significant relationship between time and Dew. Point in Brasilia, but in Madrid, Paris, Rome and America, the relationship between these two variables was significantly positive. In Brasilia, with the passage of time, Dew. Point has decreased and with the decrease of Dew. Point, the number of infected people has increased every day. In America and Wuhan, with the passage of time, Dew. Point has increased and with the increase of Dew. Point, the number of infected people has increased and decreased respectively. (Fig. 4)Fig. 4 Changes in the average number of infected people in one day and changes in Dew. Point in the four days before and on the same day
In a study conducted by Virginia in the United States in 2020 titled the effect of population density, temperature and dew point on the spread of Corona, it was concluded that temperature and dew point have a negative correlation with the spread of Corona(Hughes 2020). In a study conducted in 2020 titled the effect of weather variables on the spread of Corona in Turkey, Mehmet Şahin and colleagues concluded that the dew point within 3 days has a positive correlation with the number of cases of Corona (Şahin 2020).
The relationship between the number of infected people in one day and Pressure was significantly reversed in Madrid and Washington, but positive in Canberra, Brasilia, Paris and Wuhan. And finally, the relationship between time and pressure was positive only in Brasilia, but in Madrid, America and Wuhan, it became significant inversely. In Madrid and America, with the passage of time, pressure has decreased, and with the decrease of pressure, the number of infected people has increased every day. In Brazil, with the passage of time, pressure has increased, and with the increase of pressure, the number of infected people has increased every day. In Wuhan, with the passage of time, the pressure has decreased, and with the decrease in pressure, the number of infected people has decreased every day (Fig. 5).Fig. 5 Changes in the average number of infected people in one day and pressure changes in the previous four days and the same day
Atmospheric pressure is the force exerted on the surface at any point by the weight of a column of air above that point. It is claimed that the higher the atmospheric pressure, the higher the prevalence of coronavirus. Even in several studies, this has been confirmed. The average atmospheric pressure in Hubei province is 1021.7 (1006.0 ~ 1031.2) hPa and the correlation coefficient is r = 0.358 (P <0.05). (Soebiyanto et al. 2010). Atmospheric pressure may be the cause of influenza (Sundell et al. 2016). In Beijing (Jingsong Yuan et al. 2006)and Hong Kong(Bi et al. 2007), atmospheric pressure was positively correlated with the spread of SARS during the SARS epidemic.
Wei Yan's new finding shows that there is a direct relationship between AAP (average atmospheric pressure) and SARS-CoV-2 transmission. This means that with an increase in mean atmospheric pressure, the prevalence of SARS-CoV-2 infectious disease increases. Our results are consistent with a Gunther study in Hubei Province, China, which showed that the prevalence of SARS-CoV-2 is positively correlated with AAP. (Gunthe et al. 2020; Guasp et al. 2020). Other multi-country preprint studies have also found similar negative associations (To et al. 2021; Islam et al. 2020)
Figure 6 shows the trend of the number of infected people in the time interval of the present study. In this figure, the increase and decrease of infected cases can be seen in the cities of Paris, Rome, Wellington, Wuhan, Brasilia, Canberra, Madrid and Washington.Fig. 6 The trend of average logarithmic changes in the number of infected people in the cities under study
Conclusion
As the temperature increased in some cities (such as Brasilia and Wuhan), the number of infected people increased, while in some cities (such as Paris and Washington) this relationship was reversed. The relationship between the number of infected people in one day and AQI was significant in all cities. The relationship between infected people and AQI changes in two cities, Paris and Wuhan, was consistent, but in the rest of the cities under study, this relationship was inverse. In the city of Canberra, Madrid and Paris, Wind Speed decreased over time, and with the decrease of Wind Speed, the number of infected people increased every day. In Brasilia, the dew point has decreased over time, and as the dew point decreases, the number of infected cases increases every day. In Washington and Wuhan, the dew point has increased over time, and as the dew point has increased, the number of infected cases has increased and decreased, respectively. In Madrid and Washington, over time, pressure has decreased, and with the decrease of pressure, the number of confirmed people per day has increased. In Brasilia, pressure has increased over time, and with the increase of pressure, the number of confirmed people has increased every day. In Wuhan, pressure has decreased over time, and with the decrease in pressure, the number of confirmed people per day has decreased. The results of this study showed that various environmental factors can cause the transmission of the dangerous covid-19 virus. In this study, it was found that not only environmental factors affect the transmission of the virus, but also the corona virus can affect environmental factors. On the one hand, the transmission of the corona virus is affected by wind speed, air temperature and dew point, on the other hand, the Air Quality Index (AQI) is affected by the epidemic and spread of this disease. In the end, it is suggested that in order to prevent other viral epidemics, they should pay attention to vaccination, personal protective equipment (such as masks), social distancing and other control measures that are provided by doctors and environmental health professionals.
Acknowledgements
This work was part of a funded at Ahvaz Jundishapur University of Medical Sciences (AJUMS), and the financial support of this study (APRD-9903) was provided by AJUMS.
Author Contributions
A-BN, GG, M-JM, YT, FR, ZB, MY, AS, HM, SA-A, KA-A, KA, MA and MF were principal investigators of the study and drafted the manuscript. A-BN, GG, M-JM and MF were advisors of the study. A-BN, GG, M-JM, YT, FR, ZB, MY, AS, HM, SA-A, KA-A, KA, MA and MF performed the statistical analysis. All authors contributed to the design and data analysis and assisted in the preparation of the final version of the manuscript. All authors read and approved the final version of the manuscript.
Funding
This work was part of a funded at Ahvaz Jundishapur University of Medical Sciences (AJUMS), and the financial support of this study (IR.AJUMS.REC.1399.366) was provided by AJUMS.
Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval
The Ethics Committee of Ahvaz Jundishapur University of Medical Sciences approved the study protocol. This study was originally approved by the Ahvaz Jundishapur University of Medical Sciences with code IR.AJUMS.REC.1399.366.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Disclosure statement
The authors confirm that these roles and any other governmental positions or membership of relevant committees, did not influence the outcomes of the research.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Akan AP Coccia M Changes of Air Pollution between Countries Because of Lockdowns to Face COVID-19 Pandemic Appl Sci 2022 12 24 12806 10.3390/app122412806
Akula SC, Singh P (2021) Role of microfinance, women decision making and previous work experience in women entrepreneurship during Covid-19. Int J Econ Finance Stud 13(1):359–372
Althouse BM Flasche S Thiem VD Hashizume M Ariyoshi K Anh DD Seasonality of respiratory viruses causing hospitalizations for acute respiratory infections in children in Nha Trang Vietnam Intl J Infect Diseas 2018 75 18 25 10.1016/j.ijid.2018.08.001
Apaliya MT, Kwaw E, Osae R, Alolga RN, Aikins ASS, Otoo GS (2022) The impact of COVID-19 on food security: Ghana in review. J Food Technol Res 9(3):160–175
Barreca AI, Shimshack JP (2012) Absolute humidity, temperature, and influenza mortality: 30 years of county-level evidence from the United States. Am J Epidemiol, 176(suppl_7), S114-S122
Bashir MF Ma B Komal B Bashir MA Tan D Bashir M Correlation between climate indicators and COVID-19 pandemic in New York, USA Sci Total Environ 2020 728 138835 10.1016/j.scitotenv.2020.138835 32334162
Bedford T, Riley S, Barr I, Broor S, Chadha M, Cox N et al. (2015) 514 McCauley JW, Odagiri T, Potdar V, Rambaut A, Shu Y, Skepner E, Smith DJ, et al. Global circulation patterns of seasonal influenza 515 viruses vary with antigenic drift. Nature, 523, 217
Benati I, Coccia M (2022a) Effective contact tracing system minimizes COVID-19 related infections and deaths: policy lessons to reduce the impact of future pandemic diseases. J Public Administ Govern, 12(3)
Benati I, Coccia M (2022b) Global analysis of timely COVID-19 vaccinations: improving governance to reinforce response policies for pandemic crises. Intl J Health Governance(ahead-of-print)
Bi P Wang J Hiller J Weather: driving force behind the transmission of severe acute respiratory syndrome in China? Intern Med J 2007 37 8 550 554 10.1111/j.1445-5994.2007.01358.x 17445010
Bogoch II, Watts A, Thomas-Bachli A, Huber C, Kraemer MU, Khan K (2020a) Pneumonia of unknown aetiology in Wuhan, China: potential for international spread via commercial air travel. J Travel Med, 27(2), taaa008
Bogoch II, Watts A, Thomas-Bachli A, Huber C, Kraemer MU, Khan K (2020b) Potential for global spread of a novel coronavirus from China. J Travel Med, 27(2), taaa011
Bontempi E Coccia M International trade as critical parameter of COVID-19 spread that outclasses demographic, economic, environmental, and pollution factors Environ Res 2021 201 111514 10.1016/j.envres.2021.111514 34139222
Bontempi E Coccia M Vergalli S Zanoletti A Can commercial trade represent the main indicator of the COVID-19 diffusion due to human-to-human interactions? A comparative analysis between Italy, France, and Spain Environ Res 2021 201 111529 10.1016/j.envres.2021.111529 34147467
Bukhari Q, Jameel Y (2020) Will coronavirus pandemic diminish by summer? Available at SSRN 3556998
Burnett RT Brook JR Yung WT Dales RE Krewski D Association between ozone and hospitalization for respiratory diseases in 16 Canadian cities Environ Res 1997 72 1 24 31 10.1006/enrs.1996.3685 9012369
Casanova LM Jeon S Rutala WA Weber DJ Sobsey MD Effects of air temperature and relative humidity on coronavirus survival on surfaces Appl Environ Microbiol 2010 76 9 2712 2717 10.1128/AEM.02291-09 20228108
Chan JF-W Yuan S Kok K-H To KK-W Chu H Yang J A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster The Lancet 2020 395 10223 514 523 10.1016/S0140-6736(20)30154-9
Chan K-H, Peiris JM, Lam S, Poon L, Yuen K, Seto WH (2011) The effects of temperature and relative humidity on the viability of the SARS coronavirus. Advances in virology, 2011
Chiyomaru K, Takemoto K (2020) Global COVID-19 transmission rate is influenced by precipitation seasonality and the speed of climate temperature warming. MedRxiv
Coccia M (2020) Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID. Sci Total Environ 729:138474
Coccia M The effects of atmospheric stability with low wind speed and of air pollution on the accelerated transmission dynamics of COVID-19 Int J Environ Stud 2021 78 1 1 27 10.1080/00207233.2020.1802937
Coccia M (2021b) High health expenditures and low exposure of population to air pollution as critical factors that can reduce fatality rate in COVID-19 pandemic crisis: a global analysis. Environ Res, 199, 111339, doi:10.1016/j.envres.2021.111339.
Coccia M How do low wind speeds and high levels of air pollution support the spread of COVID-19? Atmos Pollut Res 2021 12 1 437 445 10.1016/j.apr.2020.10.002 33046960
Coccia M Pandemic prevention: lessons from COVID-19 Encyclopedia 2021 1 2 36 10.3390/encyclopedia1020036
Coccia M (2021e) Preparedness of countries to face covid-19 pandemic crisis: Strategic positioning and underlying structural factors to support strategies of prevention of pandemic threats. Environ Res, 111678–111678
Coccia M (2022a) COVID-19 pandemic over 2020 (with non-pharmaceutical measures) and 2021 (with vaccinations): Seasonality and environmental factors. Environ Res, 112711–112711
Coccia M (2022a) COVID-19 vaccination is not a sufficient public policy to face crisis management of next pandemic threats. Public Organization Review, 1–15
Coccia M Improving preparedness for next pandemics: Max level of COVID-19 vaccinations without social impositions to design effective health policy and avoid flawed democracies Environ Res 2022 213 113566 10.1016/j.envres.2022.113566 35660409
Coccia M Optimal levels of vaccination to reduce COVID-19 infected individuals and deaths: A global analysis Environ Res 2022 204 112314 10.1016/j.envres.2021.112314 34736923
Coccia M Effects of strict containment policies on COVID-19 pandemic crisis: lessons to cope with next pandemic impacts Environ Sci Pollut Res 2023 30 1 2020 2028 10.1007/s11356-022-22024-w
Cooper JA, VanDellen M, Bhutani S (2021) Self-weighing practices and associated health behaviors during COVID-19. Am J Health Behav 45(1):17–30
Control, C. f. D., & Prevention (2020) Interim infection prevention and control recommendations for patients with suspected or confirmed coronavirus disease 2019 (COVID-19) in healthcare settings
Coocia M How do low wind speeds and high levels of air pollution support the spread of COVID-19? Atm Pollut Res 2021 12 437 445 10.1016/j.apr.2020.10.002
Dadbakhsh M Khanjani N Bahrampour A Haghighi PS Death from respiratory diseases and temperature in Shiraz, Iran (2006–2011) Int J Biometeorol 2017 61 2 239 246 10.1007/s00484-016-1206-z 27418166
Diao Y Kodera S Anzai D Gomez-Tames J Rashed EA Hirata A Influence of population density, temperature, and absolute humidity on spread and decay durations of COVID-19: A comparative study of scenarios in China, England, Germany, and Japan One Health 2021 12 100203 10.1016/j.onehlt.2020.100203 33344745
Dickens BL Koo JR Wilder-Smith A Cook AR Institutional, not home-based, isolation could contain the COVID-19 outbreak The Lancet 2020 395 10236 1541 1542 10.1016/S0140-6736(20)31016-3
Ghalhari GF, Mayvaneh F (2016) Effect of air temperature and universal thermal climate index on respiratory diseases mortality in Mashhad, Iran. Arch Iranian Med, 19(9), 0–0
Gomez-Acebo I Llorca J Dierssen T Cold-related mortality due to cardiovascular diseases, respiratory diseases and cancer: a case-crossover study Public Health 2013 127 3 252 258 10.1016/j.puhe.2012.12.014 23433803
Gorbalenya AE, Baker SC, Baric RS, de Groot RJ, Drosten C, Gulyaeva AA et al. (2020) Severe acute respiratory syndrome-related coronavirus: The species and its viruses–a statement of the Coronavirus Study Group. BioRxiv
Guasp M Laredo C Urra X Higher solar irradiance is associated with a lower incidence of coronavirus disease 2019 Clin Infect Dis 2020 71 16 2269 2271 10.1093/cid/ciaa575 32426805
Gunthe SS, Swain B, Patra SS, Amte A (2020) On the global trends and spread of the COVID-19 outbreak: preliminary assessment of the potential relation between location-specific temperature and UV index. J Public Health, 1–10
Haque SE Rahman M Association between temperature, humidity, and COVID-19 outbreaks in Bangladesh Environ Sci Policy 2020 114 253 255 10.1016/j.envsci.2020.08.012 32863760
Hong MP, Van DD (2022) The role of socio-economic development after COVID-19 and energy-growth-environment in ASEAN economies. Cuad Econ 45(127):171–180
Huang C Wang Y Li X Ren L Zhao J Hu Y Clinical features of patients infected with 2019 novel coronavirus in Wuhan China the Lancet 2020 395 10223 497 506 10.1016/S0140-6736(20)30183-5
Huang R Zhu L Xue L Liu L Yan X Wang J Clinical findings of patients with coronavirus disease 2019 in Jiangsu province, China: A retrospective, multi-center study PLoS Negl Trop Dis 2020 14 5 e0008280 10.1371/journal.pntd.0008280 32384078
Hughes VC The Effect of Temperature, Dewpoint, and Population Density on COVID-19 Transmission in the United States: A Comparative Study Am J Public Health Res 2020 8 112 117
Islam N Bukhari Q Jameel Y Shabnam S Erzurumluoglu AM Siddique MA COVID-19 and climatic factors: A global analysis Environ Res 2021 193 110355 10.1016/j.envres.2020.110355 33127399
Islam N, Shabnam S, Erzurumluoglu AM (2020) Temperature, humidity, and wind speed are associated with lower Covid-19 incidence. MedRxiv
Kanniah KD Zaman NAFK Kaskaoutis DG Latif MT COVID-19's impact on the atmospheric environment in the Southeast Asia region Sci Total Environ 2020 736 139658 10.1016/j.scitotenv.2020.139658 32492613
Keshavarzi A (2020) Coronavirus infectious disease (covid-19) modeling: Evidence of geographical signals. Available at SSRN 3568425
Lauer SA Grantz KH Bi Q Jones FK Zheng Q Meredith HR The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application Ann Intern Med 2020 172 9 577 582 10.7326/M20-0504 32150748
Lemaitre J Pasetto D Perez-Saez J Sciarra C Wamala JF Rinaldo A Rainfall as a driver of epidemic cholera: comparative model assessments of the effect of intra-seasonal precipitation events Acta Trop 2019 190 235 243 10.1016/j.actatropica.2018.11.013 30465744
Li J, Zhang L, Ren Z, Xing C, Qiao P, Chang B (2020) Meteorological factors correlate with transmission of 2019-nCoV: Proof of incidence of novel coronavirus pneumonia in Hubei Province, China. MedRxiv
Li M Zhou M Yang J Yin P Wang B Liu Q Temperature, temperature extremes, and cause-specific respiratory mortality in China: a multi-city time series analysis Air Qual Atmos Health 2019 12 5 539 548 10.1007/s11869-019-00670-3
Lokhandwala S Gautam P Indirect impact of COVID-19 on environment: A brief study in Indian context Environ Res 2020 188 109807 10.1016/j.envres.2020.109807 32574854
Lowen AC Mubareka S Steel J Palese P Influenza virus transmission is dependent on relative humidity and temperature PLoS Pathog 2007 3 10 e151 10.1371/journal.ppat.0030151 17953482
Ma Y Zhao Y Liu J He X Wang B Fu S Effects of temperature variation and humidity on the death of COVID-19 in Wuhan China Sci Total Environ 2020 724 138226 10.1016/j.scitotenv.2020.138226 32408453
Nilashi M Asadi S Abumalloh RA Samad S Ibrahim O Intelligent recommender systems in the COVID-19 outbreak: the case of wearable healthcare devices J Soft Comput Decision Support Syst 2020 7 4 8 12
Niu Y, Chen R, Wang C, Wang W, Jiang J, Wu W et al. (2020) Ozone exposure leads to changes in airway permeability, microbiota and metabolome: a randomised, double-blind, crossover trial. European Respiratory J, 56(3)
Nunez-Delgado A, Bontempi E, Coccia M, Kumar M, Domingo JL (2021) SARS-CoV-2 and other pathogenic microorganisms in the environment. (Vol. 201, pp. 111606): Elsevier
Pan J Yao Y Liu Z Meng X Ji JS Qiu Y Warmer weather unlikely to reduce the COVID-19 transmission: An ecological study in 202 locations in 8 countries Sci Total Environ 2021 753 142272 10.1016/j.scitotenv.2020.142272 33207446
Qiu Y Chen X Shi W Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China J Popul Econ 2020 33 4 1127 1172 10.1007/s00148-020-00778-2 32395017
Rahimi NR Fouladi-Fard R Aali R Shahryari A Rezaali M Ghafouri Y Bidirectional association between COVID-19 and the environment: a systematic review Environ Res 2021 194 110692 10.1016/j.envres.2020.110692 33385384
Rosario DK Mutz YS Bernardes PC Conte-Junior CA Relationship between COVID-19 and weather: Case study in a tropical country Int J Hyg Environ Health 2020 229 113587 10.1016/j.ijheh.2020.113587 32917371
Saadat S Rawtani D Hussain CM Environmental perspective of COVID-19 Sci Total Environ 2020 728 138870 10.1016/j.scitotenv.2020.138870 32335408
Şahin M Impact of weather on COVID-19 pandemic in Turkey Sci Total Environ 2020 728 138810 10.1016/j.scitotenv.2020.138810 32334158
Sajadi MM Habibzadeh P Vintzileos A Shokouhi S Miralles-Wilhelm F Amoroso A Temperature, humidity, and latitude analysis to estimate potential spread and seasonality of coronavirus disease 2019 (COVID-19) JAMA Netw Open 2020 3 6 e2011834 e2011834 10.1001/jamanetworkopen.2020.11834 32525550
Sarkodie SA Owusu PA Impact of meteorological factors on COVID-19 pandemic: Evidence from top 20 countries with confirmed cases Environ Res 2020 191 110101 10.1016/j.envres.2020.110101 32835681
Shaman J, Pitzer V, Viboud C, Lipsitch M, Grenfell B (2009) Absolute humidity and the seasonal onset of influenza in the continental US. PLoS currents, 2
Shaman J Pitzer VE Viboud C Grenfell BT Lipsitch M Absolute humidity and the seasonal onset of influenza in the continental United States PLoS Biol 2010 8 2 e1000316 10.1371/journal.pbio.1000316 20186267
Shen L Zhao T Wang H Liu J Bai Y Kong S Importance of meteorology in air pollution events during the city lockdown for COVID-19 in Hubei Province, Central China Sci Total Environ 2021 754 142227 10.1016/j.scitotenv.2020.142227 32920418
Shi P, Dong Y, Yan H, Li X, Zhao C, Liu W et al. (2020) The impact of temperature and absolute humidity on the coronavirus disease 2019 (COVID-19) outbreak-evidence from China. MedRxiv
Shokouhi, M., Miralles-Wilhelm, F., & Anthony Amoroso, M. Temperature and latitude analysis to predict potential spread and seasonality for COVID-19
Soebiyanto RP Adimi F Kiang RK Modeling and predicting seasonal influenza transmission in warm regions using climatological parameters PLoS ONE 2010 5 3 e9450 10.1371/journal.pone.0009450 20209164
Song P Han H Feng H Hui Y Zhou T Meng W High altitude Relieves transmission risks of COVID-19 through meteorological and environmental factors: evidence from China Environ Res 2022 212 113214 10.1016/j.envres.2022.113214 35405128
Srivastava A COVID-19 and air pollution and meteorology-an intricate relationship: A review Chemosphere 2021 263 128297 10.1016/j.chemosphere.2020.128297 33297239
Sundell N Andersson L-M Brittain-Long R Lindh M Westin J A four year seasonal survey of the relationship between outdoor climate and epidemiology of viral respiratory tract infections in a temperate climate J Clin Virol 2016 84 59 63 10.1016/j.jcv.2016.10.005 27723525
Tamerius JD Shaman J Alonso WJ Bloom-Feshbach K Uejio CK Comrie A Environmental predictors of seasonal influenza epidemics across temperate and tropical climates PLoS Pathog 2013 9 3 e1003194 10.1371/journal.ppat.1003194 23505366
To T Zhang K Maguire B Terebessy E Fong I Parikh S UV, ozone, and COVID-19 transmission in Ontario, Canada using generalised linear models Environ Res 2021 194 110645 10.1016/j.envres.2020.110645 33359457
Van Doremalen N Bushmaker T Munster V Stability of Middle East respiratory syndrome coronavirus (MERS-CoV) under different environmental conditions Eurosurveillance 2013 18 38 20590 24084338
Wang J, Tang K, Feng K, Li X, Lv W, Chen K et al. (2020a) High temperature and high humidity reduce the transmission of COVID-19. arXiv preprint arXiv:2003.05003.
Wang L Li X Chen H Yan S Li D Li Y Coronavirus disease 19 infection does not result in acute kidney injury: an analysis of 116 hospitalized patients from Wuhan China Am J Nephrol 2020 51 5 343 348 10.1159/000507471 32229732
Weaver RH, Jackson A, Lanigan J, Power TG, Anderson A, Cox AE (2021) Health behaviors at the onset of the COVID-19 pandemic. Am J Health Behav 45(1):44–61
Wu JT Leung K Leung GM Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study The Lancet 2020 395 10225 689 697 10.1016/S0140-6736(20)30260-9
Xie J, Zhu Y (2020) Association between ambient temperature and COVID-19 infection in 122 cities from China. Sci Total Environ, 724, 138201, doi:10.1016/j.scitotenv.2020.138201
Yuan J Li M Lv G Lu ZK Monitoring transmissibility and mortality of COVID-19 in Europe Int J Infect Dis 2020 95 311 315 10.1016/j.ijid.2020.03.050 32234343
Yuan J Yun H Lan W Wang W Sullivan SG Jia S A climatologic investigation of the SARS-CoV outbreak in Beijing China Am J Infect Control 2006 34 4 234 236 10.1016/j.ajic.2005.12.006 16679182
Żuk T Rakowski F Radomski JP Probabilistic model of influenza virus transmissibility at various temperature and humidity conditions Comput Biol Chem 2009 33 4 339 343 10.1016/j.compbiolchem.2009.07.005 19656728 | 37022543 | PMC10078041 | NO-CC CODE | 2023-05-08 23:15:28 | yes | Environ Sci Pollut Res Int. 2023 Apr 6; 30(21):60314-60325 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
37059950
26804
10.1007/s11356-023-26804-w
Research Article
Dynamics of renewable energy index in G20 countries: influence of green financing
Fang Liyun [email protected]
School of Economics & Management, Yiwu Industrial & Commercial College, Yiwu, 322000 China
Responsible Editor: Nicholas Apergis
14 4 2023
2023
30 23 6381163824
3 3 2023
30 3 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
The research intends to investigate the green financing trends movement with renewable energy dependence of G-20 economies. The data envelopment analysis (DEA) technique explains research results and illustrates current topicality. The Wald econometric method is utilized for robustness analysis, and a comparative picture of public support is provided. The research demonstrated that green financing metrics are significantly affected by public support during the COVID-19 crisis. Due to the volatility of COVID-19, public assistance funding plays an uneven role in green finance. G-20 member nations financed 17% of total green financing using public funds, which contributed 4% to GDP and achieved 16% of annual energy dependence improvement due to COVID-19 and 24% additional production from renewable energy resources. The results of this research demand maximal support by using positions in the government, ministries in charge of energy efficiency, and departments for energy efficiency improvement. Several possible policy interventions are discussed in this paper that may increase renewable energy efficiency via several alternative approaches, including on-bill financing, direct efficiency grant, guaranteed energy efficiency contracts, and credit lines for energy efficiency. If recommended policies are implemented successfully, they are expected to reduce the crisis’ impact and elevate funding for energy efficiency.
Keywords
Green financing
Energy dependence
Energy efficiency
COVID-19 crises
G-20 economies
DEA
issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023
==== Body
pmcIntroduction
The rise in global temperatures and changes in energy supply provide significant problems for monetary and funds growth (Bücher et al. 2015). As a result of environmental and climatic peculiarities (mischief to affiliation’s assets, organization, and supply chains) and the advances made for the transition to a low-carbon sector, there are both physical and progressing threats (Liu et al. 2022). Petroleum products will create a dreadful situation for countries if there is a relationship between natural change and ozone-depleting chemical emissions. An unnatural rise of 2 °C or less by 2100 is a goal that can only be met by placing our economy on a reduced path. New challenges must be taken up to move toward a low-carbon future, including a liberal and feasible decrease in GHG emissions via more visible energy conservation, overall energy capacity, and the enhancement of financial power supplies. The final option is the focus of this research. As the driving force behind human progress, energy serves as the basis for monetary and social growth that is both reasonable and commendable. As the world’s second-largest industry and a vital collecting force, G-20 countries’ rapid and enormous growth has resulted in a significant need for energy, triggering a constantly rising number of challenges.
Energy transformation may be reduced by progressing to a point where the force is no longer harmful to the environment. A 10% increase in innocuous electricity usage to the ecology will reduce surges by 1.6%. All else being equal, the supportable force has been behind the levels needed to accomplish the Paris Treaty’s stated goals. Even though wind and solar energy are now regarded as legitimate sources of energy, they are characterized by high capital costs and confront an enormous theoretical opportunity that is predicted to surpass US$3 trillion continuously over the next decade. This effort opening is evident in the Global South, where 58% of all radiation originates (Ajayi 2009). Due to their poor institutional context, lack of matured investment business areas, political and monetary uncertainty, devaluation, and inadequate organization and norms, non-modern nations typically need local and fresh capital (Zhang et al. 2022; Hall et al. 2016). MDBs may play an essential role in bridging this hypothesis gap. MDBs are non-profit organizations that fund initiatives that cannot get complete finance from the commercial sector (Yemelyanov et al. 2020). They combine international organizations, such as the World Bank (WB), and local institutions, such as the African Development Bank (ADB), into a single entity. MDBs have a two-pronged role in advancing the transition to a more sustainable energy system (Wustenhagen and Teppo 2006). In any event, they can put resources into real-world force programs. It is also possible to preserve a point where the private industry may enter the fight (Li et al. 2020).
G-20 countries’ dependence on various energy sources was the subject of the following questions: What is the G-20 countries’ primary energy source? How has G-20 countries’ dependence on different energy sources varied throughout time? Are there specific sectors that are more susceptible to power than others? Which energy reforms in G-20 countries will be most important in the next phase? G-20 countries’ genuine dependence on different energy sources may be seen and handled by answering the following questions. However, it can also help explain which sectors are more susceptible to various energy sources so that the public authority can more effectively progress the transformation of the changing shape (Dubash and Florini 2011). It is found recommended the double-dealing of native peat assets to reduce the country’s dependence on imported coal, arguing that these assets were almost endless in comparison to the 4 billion tonnes of coal that the country imports.
Furthermore, he argued that the high transportation costs were a significant impediment to their wider usage, and he proposed that the State Railways reduce the freight prices for peat by half. Person’s initiative was successful in decreasing peat cargo rates, but this did not increase domestic peat production or reduce coal imports. Despite the tensions, Swedish coal imports continued to grow until the mid-twentieth century. From then on, vast oil imports immediately replaced them and then uranium. Swedish energy imports peaked in the 1970s, accounting for 75% of its total energy supply (Holdren 2006). Energy imports now account for more than 60% of our entire supply. The Damocles blade has loomed over Sweden for over a century, and the thread carrying the sword almost exploded during the two global crises.
The G-20 policymakers, business people, and government officials have adopted various methods to cope with energy import restrictions. For the most part, they have adopted two approaches. There are some ways in which they have tried to mitigate the vulnerability of energy imports, like increasing imports as far as countries and types of sources, creating trusting relationships with exporters or aiding out various shippers, etc. They have also worked to reduce their dependence on imported energy sources, including hydropower, bioenergy, and peat, by developing and growing domestic energy sources and increasing energy production. Sweden is an exciting country to study regarding energy reliance since it imports most of its energy, even though it has abundant resources like wood, peat, and uranium. As a result, Sweden has pursued an international policy based on nonpartisanship and has succeeded in both universal battles. What impact has this worldwide policy had when it comes to energy imports? Since it relied so heavily on energy throughout the wars, how did Sweden adjust to the situation? As a follow-up question, how has the long-term perception of energy dependency evolved? There are six phases in the article’s structure, each focusing on a different aspect of energy imports.
This paper’s layout is based on the following: There is considerable energy dependence, the establishment of an information source, and data management in the “Review of literature” section; the “Methodology” section provides data collection and handling; the “Results and discussion” section explains the results and discussion, while the “Results and discussion” section concludes the study results.
Review of literature
Literary nexus between constructs
We apply the same benchmark to the GDP’s energy content (Reboredo 2015). A critical step in determining GDP’s overall energy effect involves calculating the energy used in imports while subtracting the energy used in exports. Most of these economies’ manufacturing has been relocated to G-20 countries, making it an important location for expanding assembly globally (Wüstenhagen and Menichetti 2012). The energy element of the exchange balance is becoming more critical in international financial cooperation (Kim and Park 2016). Also applicable to the electrical industry, as with this approach. Although it may not be possible to decarbonize a country’s energy supply completely, its GDP and electrical mix might be lowered by importing a large amount of force. One can only estimate a country’s carbon footprint by looking at the carbon content of the force it creates. There are some things to consider while evaluating an energy plan. There seems to be a strong correlation between a country’s decline in fossil fuel byproducts from a bygone era and its current sustainable power level (Bointner et al. 2016). Thermal power may potentially improve the environment and save the world. If irregular REs are created, would there be a greater need for electrical interconnections? Is the rise in power imports linked to the increased use of renewable energy? That suggests that reducing carbon dioxide emissions in power plants to achieve energy independence is inherently irrational (Kong and Gallagher 2017).
Despite the EU’s shared goals, each region has its energy infrastructure (Taylor et al. 2008). Decarburization plans in certain nations do not include the use of atomic force. Europe is a battleground for both proponents of “dark” change and opponents. The first option acknowledges that we cannot meet our electrical needs without atomic or gas power. In contrast, the final option concedes that we can meet our needs with sustainable electricity (Ahmad et al. 2019). European countries have other priorities regarding reducing CO2 emissions (Haselip et al. 2014). The controller has two options for reducing GHG emissions: the cost of carbon credits and the availability of discharge incentives. An outflow exchange scheme or a carbon tax may be used to manage the cost of carbon (Schwerhoff and Sy 2017). Both of these cycles are present throughout Europe. Assess the arrangement’s soundness in light of the desired specialized and societal outcomes (Kissock and Eger 2008). According to the Paris Climate Agreement, global rising temperatures are expected to be limited to 2 °C or less by this century. World civilization must achieve carbon neutrality by 2050 to comply with the Paris Agreement On climate change. By 2050, it is hoped to have reduced GHG emissions by more than half. In the decade of the 1990s, we need a broad range of programmers at all levels and in all sectors. Towards 2030, the EU’s energy policy will call for a 40% reduction in CO2 emissions and an increase in renewable energy to 32% of total energy consumption. Recognizing the findings of Popescu, (2015), we must remember that the research did not just look at pollutants that occur within the nation; we must also include emissions that are present in imported goods (Li et al. 2021).
They applied the same concept to the amount of energy in GDP (Iqbal et al. 2021). This requires calculating the economic impact of energy consumption on the GDP, which can be accomplished by considering all the energy used in imports while subtracting all the energy consumed in exports. China has emerged as a critical site for manufacturing growth in the developed world. Significant economies have moved a substantial part of their industrial base to the country in recent decades (Tu et al. 2021). It is becoming more important to consider the calorific value of trade balances when considering global economic interconnectedness. This approach may also be used in the electrical sector, as shown by this example. It is possible that a country that imports a large quantity of electricity would have the impression that it is reducing both the amount of energy in its GDP and the amount of carbon in its electrical mix, even if the power is not decarbonized. We apply the same benchmark to the GDP’s energy content (Iqbal and Bilal 2021). A critical step in determining GDP’s overall energy effect involves calculating the energy used in imports while subtracting the energy used in exports. Most of these economies’ manufacturing has been relocated to G-20 countries, making it an important location for expanding assembly globally (Wirth et al. 2003). The energy element of the exchange balance is becoming more critical in international financial cooperation (Bhattacharya et al. 2016). Also applicable to the electrical industry, as with this approach.
Although it may not be possible to completely decarbonize a country’s energy supply, its GDP and electrical mix might be lowered by importing a large amount of force. One can only estimate a country’s carbon footprint by looking at the carbon content of the force it creates (Ottinger and Bowie 2016). There are several things to consider while evaluating an energy plan. There seems to be a strong correlation between a country’s decline in fossil fuel by-products from a bygone era and its current level of sustainable power (Corrocher and Cappa 2020). Thermal management may potentially improve the environment and save the world. If irregular REs are created, would there be a greater need for electrical interconnections? Is the rise in power imports linked to the increased use of renewable energy? That suggests that reducing carbon dioxide emissions in power plants to achieve energy independence is inherently irrational. Despite the EU’s shared goals, each region has its energy infrastructure (Fagiani et al. 2013). Decarburization plans in certain nations do not include the use of atomic force. Europe is a battleground for both proponents of “dark” change and opponents. The first option acknowledges that we cannot meet our electrical needs without atomic or gas power. In contrast, the final option admits we can meet our needs with sustainable electricity (Zhang et al. 2022). Different countries in Europe have other priorities regarding reducing CO2 emissions. The controller has two options for reducing GHG emissions: the cost of carbon credits and the availability of discharge incentives. An outflow exchanging scheme or a carbon tax may be used to manage the cost of carbon (ETS). Both of these cycles are present throughout Europe. Assess the arrangement’s soundness for the desired specialized and societal outcomes. Furthermore, even though the final destinations have been broadly conceded, there is a dispute about the path to achieving these objectives (Yang et al. 2022). We aim to reduce the carbon content of GDP, improve energy efficiency, better regulate force utilization, and advance environmentally friendly power sources, among other things (Wang et al. 2022). It is common for EU points to be traded off since EU conditions compete.
Theoretical framework
Measurement of energy security is most closely linked to energy dependency. There has been a clear shift in energy market interest, and energy security has become more urgent due to the financial crisis. With coal as its primary energy source, G-20 countries have a high degree of independence and minimal dependency on new sources that have not exceeded 10%. On the other hand, oil is very dependent on unproven business sectors, and its dependence on unproven sources reached 67.59% in 2016, posing a real threat to global energy supplies. A rise in imports of dangerous gas has been seen recently, and the degree of new dependency increased to 34.25% percent in 2016, making energy security concerns more pressing. However, the level of energy security depends not only on the natural market of energy but also on energy use efficiency and technological progress. The biological climate primarily reflects the ecological collapse’s need for energy. There has been a rapid rise in the economy and society, but the environmental difficulties brought on by large-scale development and energy have decreased. Since 2006, G-20 countries have had a carbon dioxide level of 9232.6 million tonnes, or 27.6% of the global total, according to the “2017 G-20 countries Greenhouse Gas Bulletin” issued by the G-20 countries Meteorological Administration. This figure is greater than the worldwide average of 1.3% for the same time. It is becoming increasingly difficult to ignore the dangers of polluted rain and murky water caused by the widespread use of fossil fuels like coal (Sun et al. 2022). Ecological insurance is becoming more and more critical due to this threat to public health. There is no denying that the natural climate and energy are intimately intertwined. We must first address the challenges of global warming, corrosive downpours, and dimness by modifying the energy structure. According to the belief that “clean lakes and beautiful mountains are rare resources,” the “green turn of events” street is being polished all over G-20 countries (Chang et al. 2023). Thus, climate change was projected to affect energy dependency in three ways: occupants’ attention to ecological assurance, contaminant outflows, and public administration.
Even though the EU has common objectives, each member state has its energy policy. Nuclear power is included in certain countries’ decarburization programs, while atomic energy is not included in others. Supporters of a “gray” transition and supporters of a “green” change are at odds throughout most of Europe (Zhao et al. 2022). The former thinks we will be unable to meet our electrical requirements unless we use nuclear or natural gas. At the same time, the latter believes that renewable energy will be sufficient to meet our needs. The various methods for reducing CO2 emissions are not given similar weight in every European nation, which is a problem (Sun et al. 2022).
Regarding lowering greenhouse gas emissions, the price of carbon and emission permits are two alternatives for the regulator. The carbon price may be regulated by a carbon tax or an emission trading system, for example, ETS. Both of these processes are taking place in Europe. Therefore, evaluating the policy coherence concerning technological and social objectives is critical (Ahmad et al. 2022). While there is widespread agreement on the goals that must be achieved, there is substantial disagreement on the route taken to achieve those goals (Liu et al. 2022). Instead, we have reached a consensus on basic concepts and measurable goals, such as lowering the carbon content of GDP, increasing energy efficiency, better controlling electricity use, and encouraging renewable energy sources (Zhang et al. 2022). Because member states of the European Union compete with one another, EU objectives are often a balance between presidential ambitions (Zheng et al. 2022).
On November 30, 2016, it was announced that the Clean Energy for All Europeans Plan, known as the January Bundle, would be unveiled. It is aimed at two goals: increasing the use of renewable energy sources and integrating Europe’s energy markets. There is a significant rise in electrical connections. Studies have shown that increasing the use of renewable energy in power production reduces CO2 emissions in Europe (Bilal et al. 2022), the United States (Zhang et al. 2022), and everywhere else (see Fig. 1). It has also helped decrease CO2 emissions in Europe via nuclear power. In Europe, there are two distinct groups of countries: those that believe that renewable energy can reduce emissions, increase security, and benefits business and those who say that renewable energy is not yet capable of replacing fossil fuels. Research has investigated how a hybrid nuclear-renewable mix could impact carbon emissions in the USA, the European Union, and a panel of G-20 nations, among other places. Studies have also been conducted in which renewable and nonrenewable energy sources have been compared across all G-20 countries. It is tough to do further research (Sarkar and Singh 2010). A study published in 2020 found that nuclear power has a negligible impact on CO2 emissions, whereas renewable energy significantly affects CO2 emissions (Anser et al. 2021).Fig. 1 Energy dependence matrix of G-20 economies
Challenges of the energy sector of G-20 in the COVID-19 crises
Due to the ongoing difficulty in boosting output, the power sector emphasizes improving its balance sheet and working capital. Helen Currie, the senior economist of ConocoPhillips, made this point at a recent AICPA Oil and Gas Conference session. Companies engaged in oil exploration in the USA face increasing capital expenses as investors ponder whether or not to participate in the market. According to Ms. O'Connell, acquiring capital “will continue to be greater than pre-COVID for high yield and investment-grade enterprises.” Since the coronavirus pandemic began earlier this year, oil and gas firms have faced difficulties. Energy firms’ stock prices plummeted in the spring as demand collapsed and several cities and states were locked down. In August, ExxonMobil, a longtime blue chip firm, was removed from the Dow Jones Industrial Average, leaving Chevron as the sole surviving energy company in the index. Modern spending habits have changed, affecting demand projections made 9 months ago. Many people are still working from home, plane traveling has already been cut significantly, e-commerce sales have increased, and worldwide freight transportation has decreased dramatically, Currie remarked during an online discussion on November 18. Other disease outbreak issues have somewhat reduced the overall fall in energy consumption. More individuals are now driving themselves rather than depending on public transit to go to and from work, school, and other activities. There has been a rise in the number of kilometers traveled by delivery vehicles as more and more consumers purchase online. Since March, using single-use plastic containers for food has increased, benefiting the petrochemical sector. Due to decreased global demand, US oil producers remain prominent at a reduced scale. According to the forecaster, demand is increasing, but it is still below what Currie had predicted. Specific oil projects may likely be postponed shortly.
In October, the oil giant said it had accepted the Paris-aligned climate risk framework and aimed to achieve net-zero operating emissions by 2050. Since the Paris Agreement aims to restrict global temperature rise to less than 2 °C, ConocoPhillips stated it will concentrate on “more aggressive greenhouse gas emissions intensity objectives and actions.” By 2030, the corporation has set a new aim of reducing emissions by 35 to 45% from the previous 5 to 15 percentage points. Since 2015, the corporation has reduced its methane intensity by 66%. Producers and customers worldwide will benefit from technological advancements, according to Currie. According to her, the best interests of investors must come first when switching to alternative energy sources. British Petroleum, the country’s largest oil firm, has devised a comprehensive energy strategy to reduce pollution and enhance reliance on renewable energy sources like wind and solar panels. As recently as August, the business aimed to cut oil and gas output by 40% by 2030, invest more in shorter wavelengths, and increase its sustainable production capacity. Oil giant BP paid Equinor $1.1 billion this year for shares in two wind energy facilities in the USA.
Methodology
Data collection and context
The articles include G-20 economic data about energy efficiency and energy dependence indicators. The data is acquired from the databases of almost every country’s world bank, OECD, and energy distribution offices for macroeconomic and ESF data. The research looks at 7 years’ annual data for every bank’s aspect to see how they compared between 2008 and 2019. By equivalence, we mean that every country had a Refinitiv-produced ESF report of last year in question, that, at minimum, one ESF element was assessed for a specified diameter, and that Refinitiv provided an overall ranking for each of those dimensions as part of that assessment. A foundation is the cumulative total of multiple closely related qualities, while different components constitute every ESF pillar.
Regarding comparison, we mean the capacity to compare measurement ratings across time and inside a single unit. The goal was to increase the inter-variance while maintaining high levels of data reliability. Therefore, both goals were pursued concurrently. Since the data period ended, the ABN-AMRO Bank Berhad and Asia Bank were left out.
Study measurement designs
Suppose j equals 1,., n; each country L is an energy input, whereas L is a non-energy input (per capita CO2, energy consumption, and work). The aim is to optimize output by utilizing as little input as possible in a production process. In the whole production process, renewable energy (coal, gas, and oil) as an energy input generates undesired production.E1=minθ
s.t
1 ∑j=1nλjXij+Six-=Xij0,i=1,⋯,m,
2 ∑j=1nλjelj+Sle-=θelj0,l=1,⋯,L,
3 ∑j=1nλjyrj+Sry+=yrj0,r=1,⋯,S,
4 ∑j=1nλjbkj=θbkj0,k=1,⋯,K,
5 λj,Six-,Sle-,Sry+≥0,forallj,I,l,
We should thus predict energy use or encourage conservation to enhance atmospheric efficiency and decrease leakage. However, traditional probability distributions do not allow reducing contaminants. There are many ways to address this issue, including using unpleasant incentives for results, unwanted outcomes as input (labor, energy consumption), and turning undesirable effects into good, categorized goods. This study often generates unwanted production via fossil fuels, which may be a significant obstacle to ecological sustainability. Thus, the quantity of energy burned is lowered if electricity consumption decreases or efficiency increases throughout production.
E2=min121L∑l=1Lθle+1K∑k=1Kθkb
s.t
6 ∑j=1nλjXij+Six-=Xij0,i=1,⋯,m
7 ∑j=1nλjelj+Sle-=θleelj0,l=1⋯L,
8 ∑j=1nλjyrj-Sry+=yrj0,r=1,⋯,S
9 ∑j=1nλjbkj=θkbbkj0,k=1⋯K,
10 λj,Six-,Sle-,Sry+≥0,forallj,I,l,
Model (1) represents the undesired generation of products with electrical energy and a specific quantity of non-energy input at the intended output with a “Co” amplitude between 0 and 1. A higher absolute value indicates a more significant reduction in energy usage or emissions of pollutants. If this is not the case, the nation is ecologically ineffective. Evaluation of energy efficiency is biased since the comparison utilizes various energy types. Extending energy efficiency and energy dependence needs multiple techniques. This study utilized a framework to evaluate highlighted countries’ total energy dependence efficiency and energy. This means many venture groups are flexible enough to develop cross-broader speculations and advance their monetary interests. Relationship banking may be compared to this training since non-monetary components are prominent in propagating G-20 countries’ loans. Distinguish this cycle from the regular lending cycles in OECD countries. OECD business and concessional moneylenders pay strict attention to risk and expect special rates in high-risk circumstances, while strategic banks in G-20 countries prioritize promoting G-20 countries’ initiatives. The relevance of national hazards is diminished as a result of this decision.
Study analysis design
This technique utilizes a simple moving idea to get an efficiency measure each week or every month for each DMU. The same study framework for Vietnam’s developing nation with a series of overlays and years, the research evaluates the efficiency changes in many countries during 2008–2019 of G-20 economies. The Charnel and Cooper software provides changing and cross-cutting data for evaluations of dynamic effects. The windshield had the remarks it saw. There is a limited chance of dealing with this big problem. For efficiency measurement, it was found that the width of a window is 3 or 4 cycles. To generate good electricity results (imagine a window of width three). Thus, these three years (2009-2019) were utilized for the first session. Our next window will last 1 year, and this procedure will continue until the window is over. A radius of impact may be calculated using and applying DEA based on window analysis for each country.
Results and discussion
DEA findings of renewable energy index
It is possible to fund RE initiatives using various methods and tools, from grants to concessional commitment and worth to simple corporate responsibility and worth, aiming to produce results that can be relied on. Cash may be divided into four distinct categories: notes, coins, and bills. Performing artists combine monetary assistance from both economic institutions (such as banks and other financial institutions) and non-monetary patrons (such as individuals, foundations, and other non-profits) to create their monetary patrons (energy firms, private utilities, various firms, and families). Business banks provide green money, but institutional financial backers need collateral like public and personal assets and securities to secure their loans. People who do not need money to support their projects may raise money via various means, such as crowdfunding, esteem ventures, or self-financing. The ability, as mentioned earlier, is obscured by adventure financing, which is a sponsorship provided by an assistance (either an association or an organization foundation), banks, and other financially valuable supporters (Tables 1, 2, 3, 4, 5 and 6).Table 1 Energy dependence index score of G-20 countries in the sample period
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
South Africa 0.450 0.415 −0.025 0.921 0.571 −0.168 0.622 0.641 −0.156 −0.128 −0.446
Singapore 0.763 −0.249 0.138 0.609 0.168 0.577 0.446 −0.325 0.645 −0.359 −0.778
Argentina 0.299 −0.762 0.311 −0.377 0.846 0.332 −0.673 0.872 −0.218 0.554 0.467
Saudi Arabia 0.525 0.301 0.966 0.744 0.513 0.424 −0.465 0.629 0.465 0.505 0.237
Turkey 0.338 0.075 0.644 0.865 0.835 0.741 0.531 −0.148 −0.654 −0.412 0.479
Switzerland 0.595 0.847 −0.864 0.259 0.103 0.855 −0.494 0.877 −0.648 0.124 0.199
Netherlands −0.002 -0.035 −0.756 0.426 0.693 −0.583 −0.179 −0.647 −0.104 −0.937 −0.978
Indonesia 0.118 0.657 0.308 0.194 0.488 0.849 0.672 0.472 0.523 0.546 0.791
United States 0.315 0.379 −0.999 0.111 0.727 0.266 0.969 −0.101 0.264 −0.231 0.197
Spain 0.094 0.835 0.132 0.571 −0.444 0.099 0.369 0.677 0.316 −0.253 −0.299
Australia 0.494 0.336 −0.765 0.592 0.465 −0.727 −0.063 −0.025 −0.716 −0.117 −0.205
Russia 0.263 0.805 0.018 −0.326 0.975 0.815 0.764 0.611 0.954 −0.155 0.444
Brazil 0.946 −0.333 0.343 0.259 0.175 −0.183 0.884 0.324 0.788 0.973 0.832
Canada −0.337 0.007 −0.042 0.289 0.522 0.972 −0.057 0.408 0.235 0.305 −0.606
South Korea 0.618 0.435 0.749 −0.635 −0.078 −0.175 0.029 −1.505 0.326 0.236 −0.776
India −0.331 0.147 0.553 0.764 0.118 0.796 −0.041 0.672 0.049 0.388 0.854
Japan −0.136 0.646 0.531 −0.201 0.962 −0.134 −0.275 0.492 0.405 0.183 0.996
United Kingdom −0.763 −0.571 −0.401 −0.549 0.398 −0.299 0.011 0.374 −0.249 0.553 0.589
G-20 countries 0.581 −0.875 −0.263 −0.407 0.595 −0.089 −0.037 −0.387 0.224 −0.445 0.581
Germany −0.103 0.268 0.622 −0.147 −0.344 −0.686 0.384 −0.602 0.303 −0.348 0.308
Table 2 DEA score of nexus between green financing nexus and energy depended on the index
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
South Africa 0.789 −0.634 −0.596 0.336 0.282 0.759 0.381 0.149 −0.183 0.884 0.921
Singapore −0.187 0.305 −0.493 0.853 0.969 −0.313 −0.402 0.211 0.633 0.663 −0.723
Argentina −0.262 −0.169 −0.108 0.509 −0.433 −0.261 0.992 −0.959 −0.727 −0.197 0.502
Saudi Arabia 0.809 0.865 0.877 0.268 0.901 0.304 −0.409 0.906 0.156 0.202 0.953
Turkey 0.348 0.491 0.271 0.388 0.916 −0.925 −0.338 −0.577 0.693 0.176 −0.279
Switzerland 0.715 −0.588 0.443 0.683 −0.112 0.532 0.435 −0.371 0.555 0.307 −0.054
Netherlands −0.259 0.266 −0.401 0.226 −0.489 −0.626 0.341 −0.034 0.439 −0.028 −0.743
Indonesia 0.344 −0.078 0.175 0.066 −0.428 −0.149 −0.203 −0.664 0.828 0.116 0.239
United States −0.046 0.243 −0.079 0.966 0.529 0.042 −0.813 −0.307 0.785 −0.119 0.644
Spain −0.136 −0.385 −0.105 −0.199 0.711 0.965 −0.042 0.498 −0.585 −0.225 −0.708
Australia −0.648 0.422 −0.266 0.498 −0.021 −0.625 −0.313 −0.506 0.439 0.488 0.348
Russia 0.179 0.735 0.763 0.694 −0.764 0.605 −0.154 0.518 0.608 0.006 −0.597
Brazil 0.579 0.563 0.172 −0.102 −0.309 −0.748 0.645 −0.168 −0.869 0.652 −0.487
Canada −0.062 0.234 0.302 −0.662 0.085 −0.113 −0.053 −0.318 −0.026 0.764 −0.291
South Korea 0.938 0.912 −0.286 0.069 −0.297 0.813 −0.364 −0.161 0.845 0.748 0.834
India 0.625 0.172 0.514 0.978 0.467 −0.319 0.576 0.855 0.972 0.405 0.249
Japan 0.464 0.325 −0.573 0.279 0.557 0.357 −0.059 −0.005 −0.188 −0.234 −0.124
United Kingdom −0.695 0.274 −0.529 −0.188 −0.253 0.143 −0.197 0.485 −0.803 0.519 −0.429
G-20 countries −0.157 0.872 0.493 −0.203 0.194 0.973 −0.573 −0.081 0.921 0.166 0.152
Germany 0.674 0.777 0.320 0.639 0.431 0.542 0.551 0.982 0.505 0.123 0.111
Table 3 Renewable energy dependence and green financing temporal connection
Energy Dependence index parameter 2014–2015 2015–2016 2016–2017 2017–2018 2018–2019
Energy consumption per GDP 0.857 0.111 0.336 0.041 −0.296
Household energy elasticity 0.179 0.101 −0.597 0.471 −0.052
Energy structure ratio −0.941 0.893 −0.353 −0.289 0.697
Energy processing conversion 0.647 −0.838 0.515 0.895 −0.365
Energy demand and supply ratio −0.406 −0.814 0.893 0.674 −0.349
Energy production elasticity −0.294 −0.828 −0.269 −0.246 −0.745
Energy consumption elasticity −0.183 −0.387 −0.048 −0.264 0.961
Table 4 Changes in renewable green financing and energy dependence
Green financing Energy dependence index R2 F2
xij pij ej dj wj xij pij ej dj wj
South Africa 0.981 −0.966 0.262 0.082 0.495 −0.191 −0.598 −0.091 0.661 0.129 0.375 0.134
Singapore 0.384 −0.327 −0.467 0.401 0.345 0.975 −0.059 −0.359 0.173 −0.394 0.151 0.167
Argentina −0.489 0.398 0.989 0.689 −0.088 −0.781 0.523 0.343 −0.847 −0.932 0.282 0.121
Saudi Arabia 0.049 −0.966 0.137 0.837 0.956 −0.978 0.139 0.321 −0.409 0.326 0.207 0.193
Turkey −0.039 −0.253 0.163 −0.455 0.513 −0.083 −0.314 −0.127 0.155 −0.155 0.511 0.127
Switzerland −0.405 0.189 0.253 0.128 0.679 0.518 −0.696 −0.212 0.297 −0.296 0.454 0.108
Netherlands −0.349 0.226 0.991 0.845 0.401 0.545 −0.925 −0.234 -0.077 0.567 0.917 0.117
Indonesia −0.054 −0.513 −0.248 0.548 −0.524 0.413 −0.307 −0.772 0.258 −0.318 0.876 0.297
United States 0.095 −0.319 0.352 −0.607 0.143 0.856 −0.407 −0.103 −0.065 −0.021 0.252 0.102
Spain −0.526 −0.409 0.927 0.443 −0.361 0.649 −0.376 −0.229 −0.294 0.234 0.747 0.163
Australia −0.653 0.819 −0.264 −0.002 0.762 0.802 −0.216 0.854 −0.392 0.903 0.839 0.179
Russia −0.001 −0.832 0.664 −0.001 0.101 0.341 −0.461 0.985 −0.265 0.116 0.847 0.173
Brazil 0.658 0.596 0.942 0.298 0.124 0.955 0.932 −0.262 −0.255 −0.261 0.408 0.189
Canada −0.039 −0.174 0.615 0.148 0.817 −0.405 −0.334 0.442 0.984 −0.156 0.725 0.199
South Korea −0.816 −0.467 0.735 −0.224 −0.378 −0.446 0.404 0.179 −0.224 −0.003 0.173 0.194
India −0.067 0.436 0.591 0.263 −0.183 0.636 0.175 0.793 −0.073 0.853 0.624 0.137
Japan 0.654 −0.223 0.049 0.662 0.895 −0.398 0.434 0.368 0.444 −0.959 0.253 0.193
United Kingdom 0.816 0.474 -0.217 0.417 0.793 0.839 −0.166 0.976 0.137 −0.645 0.225 0.195
G-20 countries 0.266 0.844 0.714 0.197 0.531 0.289 0.299 0.355 −0.744 −0.121 0.814 0.168
Germany −0.644 −0.278 −0.044 0.283 −0.685 −0.171 0.614 −0.466 −0.177 −0.077 0.168 0.166
Table 5 Green financing role and efficiency analysis of renewable energy efficiency, renewable energy transition, and energy activity
Time First-tier score Second tier score Third tier score
RED REA RET RED REA RET RED REA RET
2012–2013 −0.135 0.798 0.331 −0.231 0.855 −0.256 0.506 −0.278 −0.102
2013–2014 0.568 −0.594 −0.403 0.6279 −0.186 0.268 0.331 0.624 0.722
2014–2015 0.576 −0.841 -0.029 −0.541 0.495 −0.502 −0.423 0.129 −0.064
2015–2016 −0.883 0.227 −0.568 0.876 −0.524 0.029 −0.097 −0.679 0.216
2016–2017 −0.681 −0.386 0.169 −0.465 0.438 0.533 0.039 0.011 −0.589
2017–2018 0.532 0.527 0.556 −0.202 0.404 −0.004 0.227 0.916 0.261
2018–2019 0.869 0.271 0.833 0.639 −0.115 −0.005 0.994 −0.578 0.084
Mean 0.117 0.635 0.734 0.062 0.791 0.128 0.651 0.867 0.791
SD 0.025 0.908 0.171 0.888 0.186 0.145 0.193 0.223 0.323
RED renewable energy dependence; REA renewable energy transition; RET renewable energy transition
Table 6 Crescendos of green financing for energy dependence mitigation
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
South Africa −0.163 0.948 −0.707 −0.358 −0.168 −0.027 −0.559 −0.474 −0.271 −0.116 −0.294
Singapore 0.831 0.375 0.384 −0.217 0.961 −0.136 −0.067 0.013 0.682 0.088 0.328
Argentina −0.205 0.824 0.689 0.772 0.431 0.547 −0.161 0.114 −0.076 −0.682 0.543
Saudi Arabia 0.957 0.707 0.294 −0.185 −0.228 −0.439 0.072 0.625 −0.845 0.975 −0.145
Turkey −0.066 0.281 −0.148 −0.071 −0.955 0.362 −0.354 −0.066 −0.619 −0.369 −0.715
Switzerland 0.285 0.431 −0.205 0.111 0.416 0.443 0.605 0.717 0.341 −0.091 0.114
Netherlands 0.695 0.757 0.242 0.232 0.139 0.941 0.613 0.046 0.886 0.482 0.886
Indonesia 0.782 −0.414 0.883 −0.624 0.897 −0.213 −0.706 −0.812 −0.905 0.937 −0.093
United States 0.762 0.643 0.388 0.025 −0.319 0.592 0.037 0.803 0.847 0.002 0.333
Spain 0.129 −0.506 −0.415 −0.029 0.635 −0.125 0.768 −0.133 0.848 −0.248 −0.428
Australia −0.524 0.738 −0.022 −0.405 0.483 0.796 −0.412 −0.501 −0.152 0.746 −0.164
Russia −0.126 −0.878 −0.039 −0.145 −0.356 −0.445 0.456 0.201 −0.036 −0.325 −0.655
Brazil 0.848 −0.775 −0.102 −0.215 0.762 0.616 0.309 0.199 0.736 0.556 0.224
Canada −0.597 0.736 0.862 −0.601 0.858 −0.368 0.905 −0.784 −0.258 0.273 −0.238
South Korea −0.557 0.164 −0.343 −0.796 −0.818 0.232 0.659 −0.927 −0.183 0.061 −0.694
India −0.583 −0.319 0.114 −0.696 0.213 0.447 0.544 0.526 −0.935 0.598 −0.072
Japan −0.225 −0.153 0.839 −0.103 −0.066 0.647 −0.109 −0.973 0.676 0.288 0.182
United Kingdom −0.735 0.591 −0.599 0.278 0.347 −0.761 −0.116 −0.439 0.013 0.546 0.108
G-20 countries −0.204 0.947 −0.669 0.704 −0.324 0.959 0.474 −0.108 −0.231 −0.049 −0.254
Germany −0.323 0.797 0.523 0.228 0.592 0.115 −0.778 0.752 −0.899 0.719 −0.395
As things stand right now, a company’s commitment or value is repaid from its profits. These profits are secured against the company’s assets in the same way that an association’s profits are secured against the company’s assets. Public entertainers might serve as financial backers (through open money-related associations like public, corresponding and multilateral banks, state utilities, government workplaces, and the government hold). With these money-linked streams, public actors may also cover the risk and bring new company fields into development upstream. Every year, enormous public resources are devoted to implementing a broad range of processes intended to aid in transmitting RE, including regulatory instruments and money-related incentives. These. They can supply money-related streams there via the collection of public actions. Devices may be divided into the market and non-market categories following de Serres et al. (2010). We can tack on an exact cost to everyday externalities using market mechanisms. Some devices affect prices directly (such as GHG spreads) or indirectly (tax breaks, net metering, FIT2 on renewable energy generation) and incentive systems that may be traded. There was a low degree of dependency on oil, and dependence on vaporous petroleum and renewable energy increased. Since the “11th Five-Year Plan for Energy Development” projected a decrease in coal and oil consumption while increasing the use of vaporized petroleum and green power, this has happened. Dependency on combustible gas peaked in 2011, while dependence on renewable energy rose steadily from a low base until it peaked in 2016. There was the little dependency on either coal or oil, although the latter has been steadily decreasing.
According to the four energy dependence records, coal dependence is decreasing, oil dependence is fluctuating, gasoline dependence is increasing, and clean energy dependence is expanding. This visual depicts G-20 countries’ efforts to develop a low-carbon, energy-efficient infrastructure that reduces the country’s reliance on coal while increasing its reliance on renewable energy. The results of the study are aligned with Chang et al. (2023)’s research. The amount of oil used is relatively low, and only a few tasks are necessary and unaffected. The most crucial combustible gas reliance list was 0.6406, the least was 0.3646, the most critical clean energy dependence list was 0.6405, and the most lowered was 0.3008. It was challenging to keep track of energy dependency at a certain level since the rest of the globe had a significant impact.
Since various energies have many distinct qualities, the vacillation degree of energy dependence was also variable. Coal, petroleum gas, oil, and clean energy comprised most of the annual average value of the comprehensive energy dependency file in sliding demand. It was clear that G-20 countries’; asset enrichment of “rich coal, helpless oil, and less gas” was strongly linked to their dependence on coal (Yang et al. 2022; Wang et al. 2022). Coal has dominated G-20 countries’ energy use structure for a long time, and it will continue to be the primary energy source in the medium term. This means that coal dependency will continue to rise in the future. As a result of the public authority policy of converting coal into gas, G-20 countries have seen an increase in their gaseous petrol market and a rise in energy dependency beyond the overall level of energy dependency. As a perfect energy source, gaseous petrol has minimal environmental pollution and enormous improvement potential, and its dependence may continue to rise. The amount of dependence on oil was moderate (Fig. 2).Fig. 2 DEA-based variations in energy dependence index
DEA findings of nexus between green finance and renewable energy index
The global oil market significantly influenced G-20 countries’ dependence on unknown oil; therefore, the oil dependency ranking was constantly changing. Due to the lack of use and late development of clean energy, this year’s usual value of the ideal energy dependency list was the lowest. It has been a while since renewable energy has advanced dramatically, but that is all because of public agreements and endowments. With its new and endless features, G-20 countries’ dependence on clean energy has room to grow in the future. The relative change level of the list is used to make an unbiased assessment of the weight of a record using the entropy approach (Zhang et al. 2022). A smaller entropy value, more prominence given to the measure of data, and greater weight are all indicators of a record’s relative change level. However, this technique relies heavily on accurate information and requires careful consideration and research of real-world difficulties, which might put excellent judgment at risk.
As shown in Fig. 3, the inhabitants’ living loads were more remarkable for the four energy sources, whereas the natural climate measurement loads were lower. Due to increased occupants’ expectations for daily amenities, the four lists significantly increased the residents’ living measurement weight. The low weight of biological climate measurements may have two reasons. One was that the number of people with a higher level of education, the pace at which SO2 levels are decreasing, and the degree to which people care about environmental pollution management were all based on the exact data for the four energy sources (Tu et al. 2021). There was a noticeable difference in the relative change levels of the four files. Oil, petroleum gas, and clean energy loads were relatively near in the financial improvement measurement, while coal’s heaviness was typically low. Two major proposals were to increase non-fossil energy, oil, or gas use while lowering coal use in the “action plan on prevention and control of air pollution” and “Thirteenth Five-Year Plan for Energy Development.” The overall value of the drop in coal use under this strategy’s base was considerable, but as G-20 countries’ primary energy source, its general shift was slight. However, compared to coal’s massive use, the other three were little used; however, this seemingly insignificant increase has resulted in enormous shifts in the ratio.Fig. 3 Green financing trends to mitigate energy dependence over the sample period
Due to coal’s lower relative weight and the other three’s more substantial financial improvement, coal and gaseous petrol had the highest loads in the energy security measurement, while oil and renewable energy had the lowest loads. Coal’s greater mass might be attributed to G-20 countries’ transformation from net coal importers in the years after 2009 into net coal shippers, resulting in a significant shift in the data on energy security. Higher flammable gas loads resulted from the “coal to gas” project’s increased pace in 2013 and increased petroleum gas imports.
Sensitivity analysis
G-20 countries’ high oil and clean energy needs, which should have remained consistent, contributed to the low weight values for oil and clean energy. As a result, there were only minor changes in the benefits of the energy security assessment for these two energy sources. Coal stood out among the four energy sources used by the residents, with the other three being quite close together. Accelerating “coal-to-gas” and “coal-to-power” projects is one of the goals of the United Nations’ “Action Plan on Prevention and Control of Air Pollution” (Fig. 4).Fig. 4 Energy dependence threshold and variating role of green financing
Due to this method, residents could use more coal, primarily distributed coal, and the era of clean energy power was born. Even though coal was replaced by clean and flammable gas in the residents’ living area, it was not used to its full extent, which resulted in a significant relative improvement in the occupants’ life measurements. In contrast, that for clean energy and gaseous fuel was little. Clean e is used to gauge the natural climate.
Discussions
To fully understand G-20 countries’ speculative activity, it is necessary to consider an equally important debate: G-20 countries’ banks are often accused of favoring countries with an elevated risk of defaulting on their debt obligations (Mazzucato and Semieniuk 2018). G-20 country’s banks should be more indulgent when it comes to funding projects in countries with particular country hazard profiles, such as high credit risk (move and conversion hazard), high administration hazard (lower government adequacy and more defilement), and moderately stable global politics (Yoshino et al. 2019). As a result, according to this theory, unlike non-G-20 countries’ MDBs, G-20 countries’ banks are predicted to be mostly unconcerned about the political system in which a government operates (Chirambo 2016). This idea is based on the fact that the G-20 countries’ and non-G-20 country’s MDBs have different goals: The G-20 countries’ banks want to spread G-20 countries’ money over the world, while the non-G-20 countries’ MDBs wish to help the countries they are investing in the energy sector (De Jager et al. 2011). Even though G-20 countries do not want to interfere in international affairs, the decentralization of the venture interaction (Egli et al. 2018) and the monetary and political objectives of entertainers engaged with dynamic cycles influence task selection and lead to the grouping of interests in countries with a previously mentioned hazard profile.
The review also discusses these factors (Painuly and Wohlgemuth 2006). First, note that G-20 countries’ banks provide money to specific operations outside G-20 countries rather than to foreign countries. Unlike an unknown guide, their hypothesis offers a meaningful comparison between businesses and governments. The ownership of a task is not held by a company based in the country where the study is located (Delina 2011). G-20 countries’ entities claim a large portion of the responsibilities performed. G-20 countries Petrochemical Corporation received $800 million from CDB for Ghanaian energy projects in 2011. When dealing with domestic challenges, G-20 countries’ businesses adhere to the same norms as the rest of the world (Zhang et al. 2022). In contrast to non-G-20 countries’ MDBs, such as the World Bank, G-20 countries’ banks do not impose non-monetary requirements on the organizations, groups, and governments to whom they provide loans (Liming 2009).
This leads to the last argument: that the projects are decided based on the financial and political aims of the G-20 countries’ governments and corporations (Anser et al. 2021). G-20 countries’ strategy banks’ venture selections are decentralized, overseen by focal and local authorities, and influenced by domestic and foreign enterprises and organizations (Butu et al. 2021). Most theories are built from the ground up. It is hoped that administrators in both countries would give the initiative the go-ahead, bringing together task implementers, lenders, and other public and private benefit seekers (Ming et al. 2014). Administrators like to fund initiatives that help them achieve their personal financial and political goals or the group's goals with which they are affiliated. Often, undertakings are evaluated at the sub-public level, where a great deal of speculation is not subject to the approval of critical authorities (Narbel 2013). Most local government officials are vested in supporting initiatives that benefit them personally. Therefore, they are likely to form informal alliances with nearby SOEs and depend on them partly (Bell et al. 2011). Sub-public states rely heavily on the standard and civil SOEs for employment opportunities and revenue (Ng and Tao 2016).
Conclusion and policy recommendations
This study’s overarching goal is to learn more about the current green funding trends movement in relation to the G-20 countries’ reliance on renewable energy sources. Data envelopment analysis (DEA) is a method that provides context for studies and examples that are timely. A contrasting image of popular support is presented, and the Wald econometric approach is used for robustness analysis. According to the findings, public support during the COVID-19 crisis has a major impact on green funding measures. There is no consistent role for public assistance funds in green finance because of the unpredictability of COVID-19. With public money, G20 countries funded 17% of overall green funding, adding 4% to GDP; as a result, they reduced their yearly energy reliance by 16%, thanks to COVID-19 and increased their renewable energy generation by 24%. The findings of this study need heavy backing from government bureaus and agencies dedicated to boosting energy efficiency. This article discusses a variety of policy interventions, such as on-bill financing, direct efficiency grants, guaranteed energy efficiency contracts, credit lines for energy efficiency, and more, which may improve the efficiency of renewable energy sources. If the proposed regulations are put into action, the crisis’ effects should be mitigated, and energy efficiency spending should increase. On this, study directs following implications to the stakeholders;
The reliance on coal and oil need a fix. Coal dependency has deteriorated the most, while dependency on oil has remained relatively stable, with just a tiny decrease. This makes sense with coal dependence’s four components, notably economic progress estimate, in decline, and oil dependence’s four segments, particularly residents’ living estimation, in phenomenal growth. In other words, we have become more dependent on gas and renewable energy due to how the four estimates for the four forms of energy are expanding. Clean energy dependency has grown significantly, which would be a direct outcome of the more significant advancements in the score of the four aspects of pure energy dependency. In other words, growing scores in the inhabitants’ living estimations for oil, vapor oil, and renewable technology reveal a more considerable effect on overall energy reliance, which is predictable given the mountains of different energy tenants’ living estimates. The following are the most important takeaways from the close mentioned above per the supporting material. Reducing coal and oil dependency depends on an increase in ephemeral hydrocarbon and green power reliance, which shows that the G-20 countries’ government has achieved significant progress in their energy cleansing approach lately and that G-20 countries’ dependence on energy has also migrated to renewable power. The disparity between the four energy situations may be found in the inhabitants’ living fields. By cutting down on coal use in homes, the objective of faultless warmth of cooking and heating may be achieved, for example, via schemes like “coal to gas” and the “coal to control” projects, which can, in turn, lead to a rise in gas and clean energy use. Generally, reducing oil dependency in the tenants’ living fields is intended to speed up the development and advancement of new energy vehicles, which may increase the use of clean energy. As G-20 countries’ primary energy source, reducing coal dependency must also include energy security, and a steady and assured coal supply must be secured. Using clean coal, combustible gas, and oil coke to replace oil may be made possible by public authorities. This can be proved by various situations that might create dependency on gasoline, coal, and gas in the area of the financial new building. An increase in fuel gas dependency also demands consideration of energy security, which includes expanding local gas examinations, improving gas importing pathways, and ensuring that the gas supply is safe and secure.
Author contribution
Conceptualization, methodology, and writing (original draft), Data curation, visualization, editing: Liyun Fang
Data availability
The data that support the findings of this study are openly available on request.
Declarations
Ethical approval and consent to participate
The author declares that there are no human participants, human data, or human issues.
Consent for publication
We do not have any individual person’s data in any form.
Competing interests
The author declares no competing interests.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Ahmad B Iqbal S Hai M Latif S The interplay of personal values, relational mobile usage and organizational citizenship behavior Interactive Technology and Smart Education 2022 19 2 260 280 10.1108/ITSE-01-2021-0016
Ahmad M Zhao ZY Irfan M Mukeshimana MC Empirics on influencing mechanisms among energy, finance, trade, environment, and economic growth: a heterogeneous dynamic panel data analysis of China Environ Sci Pollut Res 2019 26 14 14148 14170 10.1007/s11356-019-04673-6
Ajayi OO Assessment of utilization of wind energy resources in Nigeria Energy Policy 2009 37 2 750 753 10.1016/j.enpol.2008.10.020
Anser MK, Usman M, Godil DI, Shabbir MS, Tabash MI, Ahmad M, Zamir A, Lopez LB (2021) Does air pollution affect clean production of sustainable environmental agenda through low carbon energy financing? evidence from ASEAN countries. Energy Environ 33:472–486
Bell CJ, Nadel S, Hayes S (2011) On-bill financing for energy efficiency improvements. A review of current program challenges, opportunities and bets practices
Bhattacharya M Paramati SR Ozturk I Bhattacharya S The effect of renewable energy consumption on economic growth: Evidence from top 38 countries Appl Energy 2016 162 733 741 10.1016/j.apenergy.2015.10.104
Bilal AR Fatima T Iqbal S Imran MK I can see the opportunity that you cannot! A nexus between individual entrepreneurial orientation, alertness, and access to finance Eur Bus Rev 2022 34 4 556 577 10.1108/EBR-08-2021-0186
Bointner R Pezzutto S Grilli G Sparber W Financing innovations for the renewable energy transition in Europe Energies 2016 9 12 990 10.3390/en9120990
Bücher A Jäschke S Wied D Nonparametric tests for constant tail dependence with an application to energy and finance J Econom 2015 187 1 154 168 10.1016/j.jeconom.2015.02.002
Butu HM Nsafon BEK Park SW Huh JS Leveraging community based organisations and fintech to improve small-scale renewable green financing in sub-Saharan Africa Energy Res Soc Sci 2021 73 101949 10.1016/j.erss.2021.101949
Chang L Iqbal S Chen H Does financial inclusion index and energy performance index co-move? Energy Policy 2023 174 113422 10.1016/j.enpol.2023.113422
Chirambo D Addressing the renewable green financing gap in Africa to promote universal energy access: Integrated renewable green financing in Malawi Renew Sustain Energy Rev 2016 62 793 803 10.1016/j.rser.2016.05.046
Corrocher N Cappa E The role of public interventions in inducing private climate finance: An empirical analysis of the solar energy sector Energy Policy 2020 147 111787 10.1016/j.enpol.2020.111787
De Jager D Klessmann C Stricker E Winkel T De Visser E Koper M Ragwitz M Held A Resch G Busch S Panzer C Gazzo A Roulleau T Gousseland P Henriet M Bouillé A Financing renewable energy in the European energy market 2011
Delina LL Clean green financing at Asian development bank Energy Sustain Dev 2011 15 2 195 199 10.1016/j.esd.2011.04.005
Dubash NK Florini A Mapping global energy governance. Global Policy 2011 2 6 18
Egli F Steffen B Schmidt TS A dynamic analysis of financing conditions for renewable energy technologies Nat Energy 2018 3 12 1084 1092 10.1038/s41560-018-0277-y
Fagiani R Barquín J Hakvoort R Risk-based assessment of the cost-efficiency and the effectivity of renewable energy support schemes: Certificate markets versus feed-in tariffs Energy Policy 2013 55 648 661 10.1016/j.enpol.2012.12.066
Hall S Foxon TJ Bolton R Financing the civic energy sector: How financial institutions affect ownership models in Germany and the United Kingdom Energy Res Soc Sci 2016 12 5 15 10.1016/j.erss.2015.11.004
Haselip J Desgain D Mackenzie G Financing energy SMEs in Ghana and Senegal: Outcomes, barriers and prospects Energy Policy 2014 65 369 376 10.1016/j.enpol.2013.10.013
Holdren JP The energy innovation imperative: Addressing oil dependence, climate change, and other 21st century energy challenges Innov Technol Gov Glob 2006 1 2 3 23
Iqbal S Bilal AR Energy financing in COVID-19: how public supports can benefit? China Finance Review International 2021 12 2 219 240 10.1108/CFRI-02-2021-0046
Iqbal S, Bilal AR, Nurunnabi M, Iqbal W, Alfakhri Y, Iqbal N (2021) It is time to control the worst: testing COVID-19 outbreak, energy consumption and CO 2 emission. Environ Sci Pollut Res 28:19008–19020.
Kim J Park K Financial development and deployment of renewable energy technologies Energy Econ 2016 59 238 250 10.1016/j.eneco.2016.08.012
Kissock JK Eger C Measuring industrial energy savings Appl Energy 2008 85 5 347 361 10.1016/j.apenergy.2007.06.020
Kong B Gallagher KP Globalising Chinese energy finance: the role of policy banks J Contemp China 2017 26 108 834 851 10.1080/10670564.2017.1337307
Li J Li J Zhu X Risk dependence between energy corporations: A text-based measurement approach International Review of Economics & Finance 2020 68 33 46 10.1016/j.iref.2020.02.009
Li W, Chien F, Ngo QT, Nguyen TD, Iqbal S, Bilal AR (2021) Vertical financial disparity, energy prices and emission reduction: empirical insights from Pakistan. J Environ Manag 294:112946
Liming H Financing rural renewable energy: a comparison between China and India Renew Sustain Energy Rev 2009 13 5 1096 1103 10.1016/j.rser.2008.03.002
Liu F Yu J Shen Y He L Does the resource-dependent motivation to disclose environmental information impact company financing? Evidence from renewable energy companies of China Renew Energy 2022 181 156 166 10.1016/j.renene.2021.09.032
Mazzucato M Semieniuk G Financing renewable energy: Who is financing what and why it matters Technological Forecasting and Social Change 2018 127 8 22 10.1016/j.techfore.2017.05.021
Ming Z Ximei L Yulong L Lilin P Review of renewable energy investment and financing in China: Status, mode, issues and countermeasures Renew Sustain Energy Rev 2014 31 23 37 10.1016/j.rser.2013.11.026
Narbel PA The likely impact of Basel III on a bank's appetite for renewable green financing 2013 NHH Dept. of Business and Management Science Discussion Paper
Ng TH Tao JY Bond financing for renewable energy in Asia Energy Policy 2016 95 509 517 10.1016/j.enpol.2016.03.015
Ottinger RL Bowie J Innovative financing for renewable energy Energy, Governance and Sustainability 2016 Edward Elgar Publishing
Painuly JP, Wohlgemuth N (2006) Renewable green financing-what can we learn from experience in developing countries? Energy Studies Review 14(2)
Popescu MF The economics and finance of energy security Procedia Economics and Finance 2015 27 467 473 10.1016/S2212-5671(15)01022-9
Reboredo JC Is there dependence and systemic risk between Oil and renewable energy stock prices? Energy Econ 2015 48 32 45 10.1016/j.eneco.2014.12.009
Sarkar A Singh J Financing energy efficiency in developing countries—lessons learned and remaining challenges Energy Policy 2010 38 10 5560 5571 10.1016/j.enpol.2010.05.001
Schwerhoff G Sy M Financing renewable energy in Africa–Key challenge of the sustainable development goals Renew Sustain Energy Rev 2017 75 393 401 10.1016/j.rser.2016.11.004
Sun L Fang S Iqbal S Bilal AR Financial stability role on climate risks, and climate change mitigation: implications for green economic recovery Environ Sci Pollut Res 2022 29 22 33063 33074 10.1007/s11356-021-17439-w
Taylor RP Govindarajalu C Levin J Meyer AS Ward WA Financing energy efficiency: lessons from Brazil, China, India, and beyond 2008 World Bank Publications
Tu CA, Chien F, Hussein MA, Yanto Ramli MM, Psi MSS, Iqbal S, Bilal AR (2021) Estimating role of green financing on energy security, economic and environmental integration of BRI member countries. The Singapore Econ Rev 1–19
Wang S Sun L Iqbal S Green financing role on renewable energy dependence and energy transition in E7 economies Renew Energ 2022 200 1561 1572 10.1016/j.renene.2022.10.067
Wirth TE, Gray CB, Podesta JD (2003) The future of energy policy. Foreign Aff:132–155
Wüstenhagen R Menichetti E Strategic choices for renewable energy investment: Conceptual framework and opportunities for further research Energy Policy 2012 40 1 10 10.1016/j.enpol.2011.06.050
Wustenhagen R Teppo T Do venture capitalists really invest in good industries? Risk-return perceptions and path dependence in the emerging European energy VC market Int J Technol Manag 2006 34 1-2 63 87 10.1504/IJTM.2006.009448
Yang Y Liu Z Saydaliev HB Iqbal S Economic impact of crude oil supply disruption on social welfare losses and strategic petroleum reserves Resources Policy 2022 77 102689 10.1016/j.resourpol.2022.102689
Yemelyanov O Symak A Lesyk L Petrushka T Kryvinska N Vovk O Modeling of Parameters of State Participation in Financing of Energy Saving Projects at Enterprises Conference on Computer Science and Information Technologies 2020 Cham Springer 498 511
Yoshino N Taghizadeh-Hesary F Nakahigashi M Modelling the social funding and spill-over tax for addressing the green green financing gap EconModel 2019 77 34 41
Zhang L Huang F Lu L Ni X Iqbal S Energy financing for energy retrofit in COVID-19: recommendations for green bond financing Environ Sci Pollut Res 2022 29 16 23105 23116 10.1007/s11356-021-17440-3
Zhao L Saydaliev HB Iqbal S Energy financing, COVID-19 repercussions and climate change: implications for emerging economies Clim Change Econ 2022 13 03 2240003 10.1142/S2010007822400036
Zheng X Zhou Y Iqbal S Working capital management of SMEs in COVID-19: role of managerial personality traits and overconfidence behavior Econ Anal Policy 2022 76 439 451 10.1016/j.eap.2022.08.006 35990757 | 37059950 | PMC10104432 | NO-CC CODE | 2023-05-14 23:15:53 | yes | Environ Sci Pollut Res Int. 2023 Apr 14; 30(23):63811-63824 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
37059954
26832
10.1007/s11356-023-26832-6
Research Article
Assessing and validating tourism business model in hospitality industry: role of blockchain platform
Cheng Ruifen [email protected]
grid.495491.4 School of Management, Zhengzhou University of Industrial Technology, Xinzheng, Zhengzhou, 451100 China
Responsible Editor: Arshian Sharif
14 4 2023
2023
30 23 6370463715
25 9 2022
3 4 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
The research aims to investigate the potential of blockchain technology to address the challenges facing traditional tourism businesses in the hospitality industry. By assessing and validating tourism business models, the research explores how blockchain can enhance transparency, efficiency, and cost reduction. This research utilizes the ARDL technique to examine the role of blockchain in the tourism in reducing environmental deterioration in China for the period of 2010–2020. The empirical analysis was used in this study. The study presents findings that support the effectiveness of blockchain in validating tourism business models. The authors conclude by discussing the implications of their research for the hospitality industry and suggest future research directions.
Keywords
Tourism
Blockchain
Environmental quality
China
Innovation
issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023
==== Body
pmcIntroduction
In 2018, one of the most significant businesses in the world was tourism, which generated 300 million jobs and 4.9% of the global GDP (Han et al. 2022a). One out of every four new jobs created during the 5-year period prior to the COVID-19 epidemic was in the tourism sector. For example, the sector grew at a rate of 3.5% in 2018, which was faster than the world economy for the ninth year in a row. Nevertheless, despite the firm’s benefits, there are still worries about its possible negative impacts on the environment and public health. Travelers use more energy, food, and water than they do at home, creating more trash as a result (Han et al. 2022b). This puts stress on some of the most fragile and/or underdeveloped parts of the world. Sustainability is being thought of as a professional service for the tourism sector to solve these issues (Xu et al. 2021). In order to guarantee the tourist industry’s long-term survival, a suitable equilibrium must be discovered between these factors. A change in economics in favor of preserving and restoring ecological processes as well as a significant restructuring of technical, economic, and social systems is needed to address the problem of sustainable tourism business (Lin 2022).
The convergence of information communication and technology (ICT) has become the primary tool for easing people’s workloads, fostering national economic growth, and enhancing energy conservation worldwide. In 2014, the EU outlined how ICT helps reduce energy use and boost efficiency (Liu et al. 2022b). Smart ICTs promise more sustainable use of both, leading to a brighter future. ICTs also play a revolutionary role in energy usage and production. Using equipment and online platforms based on computer clouds, ICT provides significant advances on the sustainable energy side. Possible applications include online sustainable charging techniques and marketing tactics (Liu et al. 2022c). No socioeconomic studies have comprehensively connected the ICTs with green sources (Yousaf et al. 2021). Economists and politicians may have a new tool to derive clean energy mechanisms, leading to new settings for sustainable power in those countries/regions if there is an objective link between the two metrics. As a result, this research contributes by conducting empirical tests of ICTs related to renewable power. There are several ways in which rising unpredictability might influence tourist investment. The “actual channel” is the first choice. If the investments are reversible, managers will take an “action” approach and put off making financial choices until a more definite time (Gadeikiene and Svarcaite 2021). The next is the “supply-side channel,” which describes how uncertainty may raise the price of capital, the dividend yield, and the probability of default (Antiniene et al. 2021). Organizations may be prompted to take preventative actions in response to these concerns, including postponing or abandoning investments. The “consumer demand-side” channel is the fifth one. A significant drop in travel demand is possible if uncertainty continues to rise. The index of EPU has been shown to positively affect tourist demand by Piligrimienė et al. (2021). Companies might put off making costly investments until tourist picks up again. Given that the tourist industry is not only a significant income stream but also a necessary means of income and entrepreneurial ventures, China has strongly emphasized it as an economic development engine for its member nations. Cultural tourism contributes around 5% to global GDP and over 30% to worldwide exports of services. There has been a lot of attention and study into the correlation involving tourist expansion and economic development, especially in China.
Previously, Chien et al. (2021) have investigated how unpredictability affects investment decisions using solid macroeconomic data. Firms in the USA are the focus of research by Sun et al. (2021), whereas Chinese businesses are the focus of research. These studies show that EPU (the journalism ambiguity assessment) has a detrimental effect on business spending. Investment portfolio levels are said to be highly impacted by the EPU, as stated by research by Baker, with almost 40% of all international visitors in 2011; Europe is often regarded as a top tourist attraction (Chien et al. 2022).
The contribution of this study is as follows: To begin with, to the best of our knowledge, this study represents the first attempt to isolate the driving and reliance powers of the obstacles to blockchain adoption in the service of improving sustainability in the tourism industry in China by utilizing of ARDL. Second, because there are not many studies devoted to the topic, this investigation connects the difficulties of blockchain adoption to the innovation-decision procedure of DOI to pinpoint the proper phases of blockchain adoption in the tourism sector. In this investigation, specialists were polled about their expectations for the future of intermediaries following the widespread adoption of blockchain technology. This research adds to the existing body of literature in the field of tourism by examining the potential of blockchain technology as a disruptive innovation to completely alter the nature of the tourism industry and the role of intermediaries in the new business model. As a result of the research conducted, companies will be better able to prepare for the effects of widespread blockchain usage. Finally, the study contributes to the literature by explaining why BCT is essential in tourism. In contrast, the suggested rough ISM-MICMAC expresses the interdependencies among variables as an intermission using approximate values. Therefore, in the duration form, the evaluation of the connection sways from absolute certainty to an increased likelihood of being true. Additionally, the analyses are more trustworthy because the evaluation periods are based on expert opinions. The ARDL findings also indicate that tourism may substantially decrease emissions of greenhouse gases. Policy implications are discussed in the last section, focusing on the need to encourage eco-friendly transport modes and adventure-based tourism (including hiking and water sports) to reach a zero-carbon economy.
Literature review
There have previously been several investigations into how to handle emergencies in the hotel sector. Above all, the importance of indiscriminate violence in tourist-oriented areas was a significant factor in this analysis (Sun et al. 2022). Other catastrophe circumstances included financial distress or difficulties brought about by natural disasters (Sohail et al. 2022). Li et al. (2022) proposed the first approaches to crisis management, which included intensifying commercial efforts to connect with local consumers, demolishing systems, and requesting government assistance. Additional research by Safdar et al. (2022) expanded on a list of 21 possible strategies that hotels may use to get through a crisis. Their findings demonstrated that the option of a particular fixed payment term was the most crucial element for surviving a catastrophe at that time (Iqbal et al. 2021). Furthermore, businesses in the hotel industry might take advantage of crises to increase client prices by providing extra value. Additionally, according to other research, cost savings are crucial for surviving a crisis (Wangzhou et al. 2022). Customers appreciate innovations made by hotels and restaurants, which is vital to keep in mind given the significance of devoted and local patrons in the rehabilitation of crises (Li et al. 2021); Andlib et al. 2012). Technologies in ecotourism can take the form of new products or services, processes, managerial decisions, marketing strategies, or structural arrangements. “Everything else that differs from normal business practices or that reflects an abrupt cessation of past practice in some context for the developing new technologies firm” (Iram et al. 2020) is defined as an invention. In order to compete in the market, hospitality businesses themselves are conscious that their clients want ongoing innovation (Abbas et al. 2022). As a result, they make an effort to innovate constantly (Mohsin et al. 2021). The majority of the time, however, and as a result of sometimes restricted financial possibilities and capabilities, these service and product improvements are generally incremental (as opposed to significant developments linked to more technological advances like the development of smartphones) (Tu et al. 2021). As tourist locations compete with one another and are sometimes seen as a complete product bundle, developments frequently include a large number of participants (Iqbal et al. 2019).
A thorough investigation of the sufficient pathways to permit the development and minimize hazardous discharges is necessary because of the increasing importance of tourism on the global stage, the need to adapt quickly to evolving customer expectations, and the complexity of the procedure of climate change adaptation (Iqbal et al. 2022). Although the tourism sector has considerable potential to change external air pollution, there is a worrying dearth of publications that examine the consequences of this trend in carbon dioxide emissions in the existing literature (Mohsin et al. 2020). Only a few researchers have examined how tourism affects state greenhouse gases, although it is a significant contributor to global emissions (Iqbal and Bilal. 2021). The world impact of tourism on carbon dioxide emissions is reducing at a quicker rate in industrialized nations than in developing ones. Observations with these characteristics are consistent with an EKC in the surroundings (Yang et al. 2022a). According to the EKC curve, the proportion of a country’s GDP spent on tourism declines as its standard of living improves (Zhang et al. 2022). However, there has been little time series research on the correlation between the two variables (Zhang et al. 2021).
Sustainable tourism is crucial to reducing greenhouse gas emissions and meeting Kyoto Protocol targets (French 2017). Despite the apparent environmental importance of tourism, research on the correlation between the two has been few and, more crucially, has shown contradictory empirical findings. While some researchers have pointed out that tourist revenue is a significant cause of increased carbon dioxide emission, others have pointed out that it helps to lower air pollution levels.
Using many panels recorded for the future, we discovered that tourists are a driver of more excellent carbon release for the top ten most toured states during 1976–2019. That is why policymakers must promote initiatives that encourage ecotourism and green technology (Wang et al. 2022). This research backs up the literature’s contention that tourism-related activities raise carbon emissions and contribute to environmental degradation (Sun et al. 2022). The effects of trade and investment on greenhouse gases on seven small islands from 1995 to 2008 have been examined (Shah et al. 2019). They have determined the long-term optimal using the EKC assumptions based on energy consumption and financial consideration development channels (Zhao et al. 2022b). The results of the dynamic panel analysis point to a long-run connection and the detrimental effect of international visitors on CO2 emissions (Zheng et al. 2022).
However, there is a lack of consensus among the econometric analysis reported in the literature (Ahmad et al. 2022). For example, using a Granger causality test based on the work of the Emirmahmutoglu-Kose panel, we analyzed information for the European Union and member governments from 1995 to 2001. The indicated unidirectional causation claims, supported by evidence, that tourism helps lower air pollution levels by cutting down on anything from travel to carbon dioxide emissions. Thailand has made great strides in recent years. Significant efforts build a robust tourist sector in respect to its nationwide economic plan (Bilal et al. 2022). The exploratory work investigated the link between international visitors and Thai carbon particulate emissions, 1986–2010 using the coefficient correlation, and found that hospitality generates higher transportation and energy utilization. Similarly, Ullah et al. (2020) observed similar outcomes for Malaysia. The author scanned the economic growth, population, and international tourism consequences on greenhouse gas emissions using an enhanced version of the EKC model (Chang et al. 2023). It turns out that tourism has a detrimental effect on the air quality around us and that this effect is caused by tourism in a causal chain that runs only one way.
Using annual data from 1976 to 2019 and an EKC specification identical to that used by Perez (2009) for the top 20 popular destination states, we conducted several tests to determine if the data showed evidence of unit root error correction model or panel correlation. They noted that expansion in tourism causes a significant temporal shift in CO2 output and that international travel alters the whole meaning of EKC. The findings also highlight the beneficial benefits of the latter on carbon pollution for several nations, including Thailand (Xiuzhen et al. 2022). demonstrated others, tourism exacerbates global worming by analyzing the connections between tourist numbers, industrialization, development, energy usage, and emissions of carbon dioxide in the Organization for Economic Co-operation and Development countries from 1994 to 2014. The results also showed connecting tourism and air pollution using an EKC curve levels. To rephrase, it does seem that industrialization lessens ecological harm caused by inbound visitors. According to these empirical studies, regulatory actions should be structured to alter the current energy mix in OECD countries by boosting the effectiveness of resources.
Using time series studies, researchers in Northern Cyprus (Wu et al. 2022) revealed an EKC curve that looks like an upside-down U in the presence and absence of tourist growth. This region has seen a big data in the leisure sector in recent decades. It has been discovered that tourism affects environmental damage by considerably increasing carbon emissions over time. There are two ways in which our study stands apart from similar ones conducted before, enabling us to fill a need in the current literature. To the best of our knowledge, this is the first research in Australia to employ the relatively new ARDL method to analyze the causes of carbon dioxide emissions. In addition, a new aspect of the study is that the subdomain unit root test method is employed to account for the break in the data set since such a possibility has been neglected in earlier research of negligible analysis. This research, 1976–2019 inspired by the bootstrapping ARDL causality model proposed, aims to examine the role of green innovation and tourism in reducing environmental harm in Thailand. These predicted findings suggest that tourism is a significant barrier to China’s efforts to become carbon neutral. Carbon emissions have a positive and statistically crucial long-term association with energy consumption, gross domestic products, and visiting tourists.
Methodology and data
In the framework of the Chinese economy, this research examines the changing connection between blockchain (BC), tourist, urbanization, demographic, income, and emissions of carbon dioxide. The ARDL method is then used to evaluate the conceptual framework laid forth.
Testing ARDL boundaries using bootstrap
We employ the models to conduct an empirical evaluation of the cointegration among the research variables, as proposed by Janet Ruiz-Mendoza and Sheinbaum-Pardo (2010). Energy qualities and small size are typical of the conventional ARDL approach presented by Zhao et al. (2022a). We address these restrictions by using the bootstrap ARDL architecture, which incorporates a new cointegration test with the standard bound testing strategy. An important advantage of using bootstrapping ARDL is that it enhances the significance of the t-test as well as the f-test. The mainly two criteria for serial correlation, though, are provided by Irawan and Okimoto (2022). To begin, the error-correcting indices must be statically important. Second, it is important that the values of the delayed explained variable be statically important. Correlations on error-correcting terms are statically important for the first scenario; however, there is no limit testing for the initial case. Because of this, the test may be utilized if the woman’s variables are combined at the first order. Traditional unit root test is cumbersome in their view since they have poor informative and explanatory qualities. However, the problem may be fixed by bootstrap ARDL bound test (Liu et al. 2022a).
There are two distinguishing characteristics of bootstrap Granger causality testing. Firstly, it has simple features of integrating towards to the order of variables. In addition, models that are fluid time series data in nature may benefit greatly from using this method. Additionally, the bootstrap ARDL constraint test solves the issue of ambiguous results, which is not addressed by the standard methods. Further, since essential numbers are produced, the likelihood of undecided instances (areas) is reduced. For model equations with several explanatory variables, limit testing is also seen as less appropriate. The conventional bootstrap ARDL limit testing procedure, as proposed by, may be stated with the use of three variables, as shown below.1 yt=∑i=1pαiyt-i+∑j=0qβjxt-j+∑k=0rγkzt-k+∑i=1sτtDt,l+μt
where I j, k, and l represent delays ranging from 1 to p, 0 to q, 0 to r, and 0 to s, respectively. Time is represented by the symbol t; a multiple regression, yt, is indicated by the letter x; and two predictors, x and z, are indicated by the letters z and d, respectively. According to Phung et al. (2022), the test of unit root should use a break year and denotes the values for regression models. The standard error that accounts for both zero means and limited variation is denoted by t.
The above equation has a set of related variables, denoted by I _j, _k, and _i. The following equation is derived from Eq. (1) by approximating its error-correcting version, an AR vectors in the level. To illustrate Eq. (2), a conditional model centered on the single equation is shown below.2 Δyt=φyt-1+γxt-1+ψzt-1+∑i=1p-1λiyt-1+∑j=1q-1δjxt-j+∑i=1r-1πkzt-k+∑i=1sωiDt,l+μt
All three alternative hypotheses must be true in order to provide an explanation for the serial correlation of the parameters in the solution. Some possible formulations of the assumptions are as follows:3 Δyt=c~+ϕ~yt-1+γ~xt-1+ψ~zt-1+∑i=1p-1λi~yt-i+∑j=1q-1δi~xt-j+∑i=1p-1πt~zt-k+∑i=1p-1ωi~Dt,l+μt~
The bootstrap method is compared to the standard version, as presented by Piao et al. (2022). Therefore, we have relied on the shown crucial values to achieve empirical results (Yang et al. 2022b).
Data
The Bureau of Statistics of China and Chinese blockchain system statistics databases were used statistics yearbooks and bulletins to compile data for the total tourism earnings, blockchain systems and visitor arrivals from 2010 to 2020. Furthermore, today’s Internet data are an important source of information for tourism research and accurately describe traveler behavior. The geographical locations of tourist destinations, blockchain systems and their network attention were generated in this research using data from the two most popular travel and tourism websites in China, Baidu Map, and Mafengwo. Its point of interest (POI) data has been extensively utilized in tourist research. Baidu Map has a large amount of information on distant places, such as the current study area, and has a sizable market share in China for these types of applications. By integrating the POI, high-resolution remote sensing imagery, and field research, the precise geographical coordinates of the tourist attractions in the Shengsi archipelago were determined. Mafengwo is distinguished by its positive user experiences and displays a lot of online visitor remarks, which mostly point out the places of interest. Mafengwo, one of China’s tourism websites, offers the most thorough visitor feedback on the islands and tourist destinations.
Result and discussion
There were 44 samples with each variable chosen, thanks to the yearly observation period of 2010–2020. The variables’ qualitative data (measured in natural logarithms) were normalized within an acceptable range. As a result, it is doubtful that the data would provide false conclusions. According to Jarque–Bera (Busch et al. 2018) statistics, every series has a mean of zero and a finite correlation. To estimate without non-normality and to compute demand elasticity, all data were changed to square root. The expansion of the Australian economy is being hampered by efforts to save energy and reduce pollution. In China, reducing carbon emissions is critical. Thus, the country needs a realistic plan (Yang et al. 2021). Tourism-related environmental effects are inescapable in the decarburization process since most tourism-related activities depend on fossil fuels for energy, resulting in significant CO2 emissions. Thus, the purpose of this research is to find out whether ecotourism helps achieve net-zero-carbon emissions. For this purpose, we apply long-run and short-run ARDL econometric approaches and the autoregressive distributed lag (ADF, PP, and ZA) unit root tests. The logarithm forms of the examined variables (CO2 emissions, tourist arrivals, energy demand, GDP, tourism, and urbanization estimated number) were stable at first divergence (Kwak et al. 2004), as shown by the ADF, PP, and ZA tests (Table 1).Table 1 Findings of descriptive statistics
Variables Mean Min Max Std. dev JB P-value
CO2 3.487 1.365 3.454 0.076 2.765 0.376
BC 4.054 3.887 2.198 0.098 2.043 0.298
TOR 3.178 4.143 1.343 0.054 3.898 0.376
URB 3.287 3.187 3.276 0.087 2.954 0.327
POP 2.276 2.232 2.234 0.054 3.487 0.232
PI 4.454 3.409 4.587 0.087 2.587 0.376
Source: author estimation
Long-term findings from the random effect test indicate that rising visitor numbers significantly impact Brazil’s CO2 output. This adds further evidence that foreign tourists are a significant cause of environmental damage in China. These results coincide with those found in prior research (Uchida et al. 2015). The tourism sector contributes significantly to global warming due to the high levels of carbon dioxide (CO2) pollution caused by the high energy levels used by the sector. One of the more essential facets of the climate warming argument is the link between rising greenhouse gases, rising energy use, and expanding economic growth. The principal drivers of environmental destruction are the economy’s expansion and energy use. Our long-term analyses show that EC, GDP, GFCF, and FD (Svensson et al. 2020) contribute to China’s CO2 emissions. Australia’s fast economic expansion in recent years may help explain these findings, but its high energy consumption also drives its position among the top 10 polluters. In China, fossil fuels are used almost exclusively to provide power. Similar results were found by Jukić et al. (2022).
Therefore, China must choose between expanding its economy and reducing its dioxide emissions. While our findings on TOR are consistent with those of Pustovrh et al. (2020), our findings on capital formation are consistent with those of Hwang and Choi (2017). Therefore, the growth of wealth and the expansion of the Chinese economy are also factors in the deterioration of the environment. In addition, the long-term value of the total density coefficient is negative. While this finding contradicts (Marzi et al. 2022), it is consistent with the findings of Leckel et al. (2020). They found that the top half of nations had a 0.182% and 0.147% decrease in carbon pollution for every 1% rise in total inhabitants. Furthermore, our finding is in line with that of do Adro and Leitão (2020) (Table 2).Table 2 Findings of correlation analysis
Correlation CO2 BC TOR URB POP PI
CO2 1
BC − 0.775*** 1
TOR − 0.588*** 0.746*** 1
URB 0.607*** 0.987*** 0.376** 1
POP 0.665*** 0.498*** 0.874*** 0.776*** 1
PI 0.875*** 0.6265*** 0.763*** 0.276* 0.665*** 1
***, **, and * represent level of significance at 1%, 5%, and 10%
Our projected findings suggest that, like TA and GDP, tourist numbers have a beneficial and considerable influence on greenhouse gases in TOR in the short term. Since the contribution of tourism, industry, and sustainable growth on carbon dioxide (CO2) releases and climate variability is also both long and short terms, this makes sense (Wodecka-Hyjek 2014). Therefore, this research finds that in China, travel, energy consumption, and gross domestic product each have a beneficial and statistically significant influence on CO2 emissions.
Therefore, client merchants are aware of the risk to their relations should their efforts to earn rewards on purchases be regarded as taking priority over their loved ones. These consumer-entrepreneurs may also opt to give the items out to sustain for free or cover the costs of the renewable energy. Essentially, they want their loved ones to have a chance to sample high-quality items (Fig. 1).Fig. 1 Impact of blockchain validation on tourism for different time periods
The annual increase rates are somewhat more significant than the compounding rates of annual growth across all of the parameters and sub-panels. In general, these rates of expansion point toward a faster rate of expansion in low-income countries (Dilanchiev and Taktakishvili 2021), to tourist investments and general economic development. EPU is more of a priority in China than in other provinces of China. There is an example of the opposite effect in Table 3 columns. Each series’ combined contribution, including its own, is also included in the table. The net annual growth spillovers are the disparity between payments TA others and the contributions (Huang et al. 2022). If the value is positive, then the series is a net transmitter of shocks; if it is negative, then the series is a net receiver of shocks. Increasing incidence spillover index is calculated by dividing the sum of all contributions (own and others) by the sum of all efforts (own and others). For this reason, the peak growth spillover index represents the typical impact on both series throughout the whole data set. Results of a built from scratch cointegration study are displayed in Table 4.Table 3 Findings of unit root test
Variables ADF (level) ADF (Δ) ZA (level) Break year ZA (Δ) Break year
CO2 − 0.787 − 4.988*** − 0.998 2010 Q1 − 7.186*** 2018 Q4
BC − 0.245 − 6.626*** − 0.265 2012 Q2 − 5.389*** 2010 Q1
TOR 0.687 − 5.177*** 0.198 2016 Q1 − 4.476*** 2016 Q1
URB 0.798 − 6.176*** − 0.879 2018 Q4 − 6.581*** 2012 Q2
POP − 1.761 − 2.209*** − 0.777 2012 Q1 − 2.598*** 2020 Q1
PI 0.276 − 6.743*** 0.376 2020 Q2 − 6.457*** 2016 Q2
Consistent with other research (Huang et al. 2022) that discovered both a short- and long-term impact. Take note that the table numbers showed the statistical validity of the ADF and ZA tests. There are three different significance levels indicated by asterisks: 5% (*), 10% (**), and 1% (***)
Table 4 Results of cointegration
Bootstrapped ARDL cointegration analysis Diagnostic tests
Estimated models Lag length Break year FPSS TDV TIV R−2 Q-stat LM (2) JB
Model 2, 1, 1, 3, 3, 1 2010 Q1 17.548*** − 8.006*** − 6.546*** 0.936 4.015 1.028 0.421
The null hypothesis may be rejected if the test significance level is larger than the critical value (1%, 5%, and 10%). All t-statistics after the initial variation are above the crucial levels TA, indicating the normality of the data. All series with a single structural break is first differential static (I (1)), as shown by the ZA findings. As a result of the global economic downturn that began in 2008, industrial output and total energy usage dropped in 2009, resulting in lower carbon emissions. ARDL bounds test results and lag order selection. Compared to the industrialized nations, the developing economies see a considerably lower and inconsequential negative impact from the increasing fuel prices. For developing countries compared to mature ones, the impacts on GDP and fossil fuel usage are substantially less pronounced (Chang et al. 2022). The findings also show that a higher CO2 pricing would have less of a negative effect on GDP in comparison to fuel use. Overall, the experiments demonstrate that a major rise in the price of CO2 may dramatically lower global usage of fossil fuels. In comparison to CO2 usage, the impacts on GDP are less significant. In developing countries compared to industrialized ones, the impact of rising CO2 prices on GDP is less severe.
The correlation among components is analyzed using the vector error correction limit test. We use the Akaike information criterion (AIC) to determine the lag length of the variables of interest so that we may analyze the long-term connection between the episodes and get the boundary tests (Umair and Dilanchiev 2022). Table 5 displays the study evidence of a limit test for regression analysis.Table 5 Findings bootstrapped ARDL cointegration analysis (long run)
Dependent variable = CO2t
Variable Coefficient T-statistics P-value
Constant 0.182*** 4.946 0.000
BCt − 0.839*** − 3.882 0.002
TORt − 0.611*** 2.344 0.000
URBt 0.364*** 4.189 0.000
POPt 0.357*** 6.028 0.000
PIt 0.185*** 2.087 0.000
D2009 0.285*** 2.017 0.047
R2 0.917
Adj-R2 0.907
Durbin Watson 2.088
Stability test
Test F-statistics P-value
χNORMAL2 0.209 0.158
χSERIAL2 0.358 0.307
χARCH2 0.459 0.298
χHETERO2 0.461 0.557
χRESET2 0.754 0.125
CUSUM Stable
CUSUMsq Stable
A second test for stationarity, the Johansen cointegration test, is performed after the ARDL limit test to see whether the results indicate that any variable subset is interrelated. Table 6 displays the findings. Since the trace numbers are below the 5% threshold, we accept the null hypothesis, indicating a single serial correlation between the series of variables, as shown by the most significant integer statistics. The (Scaliza et al. 2022) cointegration test contains a null hypothesis that if the trace and maximum value are more than the 5% critical value, we reject the false null hypothesis (Pan et al. 2023). At least one serial correlation between the parameters was found using the Johansen cointegration test in Fig. 2.Table 6 Findings bootstrapped ARDL cointegration analysis (short run)
Dependent variable = CO2t
Variable Coefficient T-statistics P-value
Constant 0.126 0.518 0.608
GTIt − 0.267*** − 3.086 0.000
TORt − 0.182*** − 2.161 0.002
URBt 0.056*** 2.997 0.046
POPt 0.184*** 8.206 0.000
PIt 0.239*** 6.148 0.000
D2009 0.086 1.088 0.287
ECMt-1 − 0.274*** − 3.474 0.000
R2 0.877
Adj-R2 0.878
Durbin Watson 1.906
Stability test
Test F-statistics P-value
χNORMAL2 0.417 0.248
χSERIAL2 0.226 0.597
χARCH2 0.338 0.207
χHETERO2 0.189 0.606
χRESET2 0.227 0.728
CUSUM Stable
CUSUMsq Stable
Fig. 2 Results of break year of blockchain on tourism
Initially, despite the significant and instantaneous negative effect on GDP, which is unanticipated considering current history, they show that the developed economies’ production recovers to its pre-pandemic stage in the 2-year period whereas the emerging markets’ production recovers even more quickly. Secondly, current cross-country research on energy requirements indicates that shifts in earnings may not have a significant impact on energy usage. According to Fig. 3, scenario 1 predicts a GDP loss of more than 5% for developing nations in the first quarter and more than 10% for major economies in the second quarter. Table 6 reveals, however, that the early quarterly declines in carbon dioxide emission in both nation categories are of a lesser size. These findings agree with estimations of changes in income from the research (Li and Umair 2023). Fossil fuel use eventually caught up when income levels rise in the latter quarters, leading to a quick recovery of carbon dioxide emission. To summarize, developed economies would struggle to return their energy utilization increase to the no COVID-19 rates till end of 2021 under scenario 1, in which the COVID-19 shocks severely impact the world’s trade in early 2020 but not from late 2020 to 2021. The rising countries, on the other hand, may recover from the decline in early 2020 more quickly and use more power than in the absence of COVID-19. As a result, the COVID-19 shock may not have a significant impact on global emissions in 2020–2021 (Liu et al. 2023 and Fang et al. 2022). Consequently, power consumption in developed and developing nations would decrease further if a second COVID-19 pandemic occurs. As a consequence, the amount of global carbon dioxide emission may be slightly lower than it would be in the absence of COVID-19.Fig. 3 Impact of different factors including blockchain on different levels
Conclusion and policy implications
Both tourists and locals can reap many advantages from the tourist industry. However, negative social and environmental effects are frequently coexisting with these positive outcomes. Sustainable tourism allows for increased ecological viability, commercial feasibility, and social comparison; it also helps maximize the potential advantages of tourism while minimizing or eliminating its negatives. We have employed the bootstrapping ARDL causality model proposed, which aims to examine the role of blockchain in the tourism in reducing environmental deterioration in China for the period of 2010–2020. Preliminary findings imply that blockchain, as a revolutionary technology, may contribute to the long-term viability of several different sectors. They also imply that blockchain is not perfect; issues with scalability, data security, speed, laws, interoperability, technical immaturity, and so on are all mentioned. So far, research has been done on the difficulties of using blockchain in different sectors. However, no previous research has been done on the barriers to blockchain implementation to enhance sustainable tourism performance. Thus, the first stage in our research was identifying the barriers to blockchain adoption using a comprehensive literature analysis and discussion with an expert panel. Then, a preliminary decision framework based on ISM and MICMAC was provided to learn the driving and reliance powers of the difficulties from the expert data. Finally, our research showed that the industry’s lack of technological maturity and interoperability poses serious threats to the widespread use of blockchain technology. In addition, they used the theory of DOI to suggest that blockchain deployments in the tourist sector are still in the learning and convincing phases. These findings will aid in developing more effective tourism industry-wide and local policies. Our research may also serve as a standard, so long as regional differences are considered.
The following are some of the study’s contributions: (1) Several industry- and country-specific researches on the difficulties of using blockchain technology have been published so far. However, no previous research has examined the barriers to blockchain adoption when it comes to improving the tourism industry’s sustainability performance. A literature review and interviews with industry experts were used to compile this investigation’s final list of obstacles to wider blockchain implementation. (2) Studies have shown connections between the barriers to blockchain adoption. Nonetheless, there is a lack of research that uses expert data to examine the driving and dependency powers of the challenges towards the sustainability of the tourism industry. Improving the tourism industry’s long-term viability was the primary motivation for this research, which proposed using rough ISM-MICMAC to illustrate the connections between existing barriers to blockchain adoption. Thirdly, difficulties with technology adoption are related to the innovation-decision process of DOI to pinpoint the proper phases of blockchain adoption. Fourthly, we also discussed the managerial and policy ramifications of our findings.
The goal of the current study was to provide a comprehensive overview of the causal factors (i.e., stakeholders and impediments) of blockchain technology adoption by the tourism and hospitality industries. To this end, a mixed-method research approach that combines qualitative analysis with MCDM and ISM methodologies (purely mathematical) was used. To identify, rank, and examine causal links among the components, which was one of the study’s several research objectives, a mixed approach was required. Our study design differs from the few previous studies that were conducted since they mostly used cross-sectional and subjective approaches. Our study design and methodology may be used by academics to get a thorough knowledge of the many drivers of other new technologies in the travel and hospitality industry, such as big data, financial technology, and digital twins. The AHP-ISM-DEMATEL model is exceptional and makes several contributions to the literature. For example, it may be used to rank driving factors based on both an AHP prioritization mixture and a DEMATEL cause-and-effect group matrix. This offers an understanding of both how elements work alone and how they interact. It is important to grasp the factor’s position in the hierarchy in order to comprehend the contextual and reciprocal interactions between factors. The study findings will provide managers with a path for improving blockchain technology adoption in the context of HTS, particularly for businesses operating in various sectors.
According to policymakers, enterprises in tourism and hospitality should be given instructions, rules, and laws to embrace blockchain technology. These regulations will increase companies’ confidence in blockchain technology. It is also advised that legislators establish awareness-raising initiatives for blockchain technology-related advantages that may inform and enlighten employees, businesses, and their clients, who can aid in expediting the implementation of such policies. The businesses that might assist with their main focus on the effects on performance may use the final rankings of obstacles and drivers gathered in this investigation. The research has a few drawbacks. First, while we were able to cover a number of notably different locations, future work might be done to make comparisons between our observations by empirically examining our model on supplemental tourist attractions, such as additional tourist spots showing a contemporary measurement heritage (for example, locations in China) or an Eastern validation culture. Second, this is a pilot study that could be expanded to other tourist spots and countries (e.g., destinations in Asia and more specifically countries like Sri Lanka and Thailand). Parallel to this, it should be emphasized that huge datasets, as known in the statistics literature, have the potential to result in type 1 errors. Second, other factors might be included as control variables in the conceptual framework, such as the submitting tool used to compose the review and factors relating to geography, facilities, staff skill levels, and firm management. Last but not least, it may be useful to compare the intensity to statistics to accessibility and polarity which differ among the cultural and linguistic groups.
Author contribution
Writing original draft, review, and editing: R.C. The author has read and approved the final manuscript.
Data availability
The data used to support the findings of this study are available from the corresponding author upon request.
Declarations
Ethics approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The author declares no competing interests.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Abbas Q Mohsin M Iqbal S Iram R Does ownership change and traders behavior enhancing price fragility in green funds market Pak J Soc Sci 2022 39 1245 1256
Ahmad B, Iqbal S, Hai M, Latif S (2022) The interplay of personal values, relational mobile usage and organizational citizenship behavior. Interact Technol Smart Educ 19(2):260–280
Andlib Z Khan A UlHaq I The coordination of fiscal and monetary policies in Pakistan: an empirical analysis 1980–2011 Pak Dev Rev 2012 51 695 704 10.30541/v51i4IIpp.695-704
Antiniene D Seinauskiene B Rutelione A Do demographics matter in consumer materialism? Eng Econ 2021 32 296 312 10.5755/j01.ee.32.4.28717
Bilal AR, Fatima T, Iqbal S, Imran MK (2022) I can see the opportunity that you cannot! A nexus between individual entrepreneurial orientation, alertness, and access to finance. Eur Bus Rev 34(4):556–577
Busch R Koziol P Mitrovic M Many a little makes a mickle: stress testing small and medium-sized German banks Q Rev Econ Financ 2018 68 237 253 10.1016/j.qref.2017.08.001
Chang L Qian C Dilanchiev A Nexus between financial development and renewable energy: empirical evidence from nonlinear autoregression distributed lag Renew Energy 2022 193 475 483 10.1016/j.renene.2022.04.160
Chang L, Iqbal S, Chen H (2023) Does financial inclusion index and energy performance index co-move? Energy Pol 174:113422
Chien F Ajaz T Andlib Z The role of technology innovation, renewable energy and globalization in reducing environmental degradation in Pakistan: a step towards sustainable environment Renew Energy 2021 177 308 317 10.1016/j.renene.2021.05.101
Chien F Hsu C-C Andlib Z The role of solar energy and eco-innovation in reducing environmental degradation in China: evidence from QARDL approach Integr Environ Assess Manag 2022 18 555 571 10.1002/ieam.4500 34314085
Dilanchiev A Taktakishvili T Currency depreciation nexus country’s export: evidence from Georgia Univers J Account Financ 2021 9 1116 1124 10.13189/ujaf.2021.090521
do Adro FJN Leitão JCC Leadership and organizational innovation in the third sector: a systematic literature review Int J Innov Stud 2020 4 51 67 10.1016/J.IJIS.2020.04.001
Fang W Liu Z Surya Putra AR Role of research and development in green economic growth through renewable energy development: empirical evidence from South Asia Renew Energy 2022 194 1142 1152 10.1016/j.renene.2022.04.125
French S Revealed comparative advantage: what is it good for? J Int Econ 2017 106 83 103 10.1016/j.jinteco.2017.02.002
Gadeikiene A Svarcaite A Impact of consumer environmental consciousness on consumer perceived value from sharing economy Eng Econ 2021 32 350 361 10.5755/j01.ee.32.4.28431
Han Y Xu X Zhao Y Impact of consumer preference on the decision-making of prefabricated building developers J Civ Eng Manag 2022 28 166 176 10.3846/JCEM.2022.15777
Han Y Yan X Piroozfar P An overall review of research on prefabricated construction supply chain management Eng Constr Archit Manag 2022 10.1108/ECAM-07-2021-0668
Huang W Chau KY Kit IY Relating sustainable business development practices and information management in promoting digital green innovation: evidence from China Front Psychol 2022 13 930138 10.3389/fpsyg.2022.930138 35800951
Hwang K Choi M Effects of innovation-supportive culture and organizational citizenship behavior on e-government information system security stemming from mimetic isomorphism Gov Inf Q 2017 34 183 198 10.1016/J.GIQ.2017.02.001
Iqbal W Yumei H Abbas Q Assessment of wind energy potential for the production of renewable hydrogen in Sindh Province of Pakistan Processes 2019 10.3390/pr7040196
Iqbal S, Bilal AR (2021) Energy financing in COVID-19: how public supports can benefit? China Finance Rev Int 12(2):219–240
Iqbal S, Bilal AR, Nurunnabi M, Iqbal W, Alfakhri Y, Iqbal N (2021) It is time to control the worst: testing COVID-19 outbreak, energy consumption and CO2 emission. Environ Sci Pollut Res 28:19008–19020
Iqbal N Tufail MS Mohsin M Sandhu MA Assessing social and financial efficiency: the evidence from microfinance institutions in Pakistan Pak J Soc Sci 2022 39 149 161
Iram R, Zhang J, Erdogan S, et al (2020) Economics of energy and environmental efficiency: evidence from OECD countries. Environ Sci Pollut Res.10.1007/s11356-019-07020-x
Irawan D, Okimoto T (2022) Conditional capital surplus and shortfall across renewable and non-renewable resource firms. Energy Econ 112:. 10.1016/j.eneco.2022.106092
Janet Ruiz-Mendoza B, Sheinbaum-Pardo C (2010) Electricity sector reforms in four Latin-American countries and their impact on carbon dioxide emissions and renewable energy. Energy Policy.10.1016/j.enpol.2010.06.046
Jukić T Pluchinotta I Hržica R Vrbek S Organizational maturity for co-creation: towards a multi-attribute decision support model for public organizations Gov Inf Q 2022 39 101623 10.1016/J.GIQ.2021.101623
Kwak W, Shi Y, Cheh JJ, Lee H (2004) Multiple criteria linear programming data mining approach: an application for bankruptcy prediction. In: Chinese Academy of Sciences Symposium on Data Mining and Knowledge Management. Springer, pp. 164–173
Leckel A Veilleux S Dana LP Local open innovation: a means for public policy to increase collaboration for innovation in SMEs Technol Forecast Soc Change 2020 153 119891 10.1016/J.TECHFORE.2019.119891
Li W, Chien F, Ngo QT, Nguyen TD, Iqbal S, Bilal AR (2021) Vertical financial disparity, energy prices and emission reduction: empirical insights from Pakistan. J Environ Manag 294:112946
Li C Umair M Does green finance development goals affects renewable energy in China Renew Energy 2023 203 898 905 10.1016/j.renene.2022.12.066
Li X Ozturk I Ullah S Can top-pollutant economies shift some burden through insurance sector development for sustainable development? Econ Anal Policy 2022 74 326 336 10.1016/j.eap.2022.02.006
Lin H-H After the epidemic, is the smart traffic management system a key factor in creating a green leisure and tourism environment in the move towards sustainable urban development? Sustainability 2022 14 1 22 10.3390/su14073762
Liu X Tong D Huang J What matters in the e-commerce era? Modelling and mapping shop rents in Guangzhou, China Land use policy 2022 123 106430 10.1016/j.landusepol.2022.106430
Liu F Umair M Gao J Assessing oil price volatility co-movement with stock market volatility through quantile regression approach Resour Policy 2023 81 103375 10.1016/j.resourpol.2023.103375
Liu H, Zhang J, Lei H (2022a) Do imported environmental goods reduce pollution intensity? The end use matters. Energy Econ 112:. 10.1016/j.eneco.2022.106130
Liu L, Li Z, Fu X, et al (2022b) Impact of power on uneven development: evaluating built-up area changes in Chengdu based on NPP-VIIRS images (2015-2019). Land 11:
Marzi G, Fakhar Manesh M, Caputo A, et al (2022) Do or do not. Cognitive configurations affecting open innovation adoption in SMEs. Technovation 102585. 10.1016/J.TECHNOVATION.2022.102585
Mohsin M, Nurunnabi M, Zhang J, et al (2020) The evaluation of efficiency and value addition of IFRS endorsement towards earnings timeliness disclosure. Int J Financ Econ.10.1002/ijfe.1878
Mohsin M, Hanif I, Taghizadeh-Hesary F, et al (2021) Nexus between energy efficiency and electricity reforms: a DEA-based way forward for clean power development. Energy Policy.10.1016/j.enpol.2020.112052
Pan W Cao H Liu Y “Green” innovation, privacy regulation and environmental policy Renew Energy 2023 203 245 254 10.1016/j.renene.2022.12.025
Perez C Technological revolutions and techno-economic paradigms Cambridge J Econ 2009 34 185 202 10.1093/CJE/BEP051
Phung TQ, Rasoulinezhad E, Luong Thi Thu H (2022) How are FDI and green recovery related in Southeast Asian economies? Econ Chang Restruct.10.1007/s10644-022-09398-0
Piao Z, Miao B, Zheng Z, Xu F (2022) Technological innovation efficiency and its impact factors: an investigation of China’s listed energy companies. Energy Econ 106140. 10.1016/j.eneco.2022.106140
Piligrimienė Ž Banyte J Dovaliene A Sustainable consumption patterns in different settings Eng Econ 2021 32 278 291 10.5755/j01.ee.32.3.28621
Pustovrh A Rangus K Drnovšek M The role of open innovation in developing an entrepreneurial support ecosystem Technol Forecast Soc Change 2020 152 119892 10.1016/J.TECHFORE.2019.119892
Safdar S Khan A Andlib Z Impact of good governance and natural resource rent on economic and environmental sustainability: an empirical analysis for South Asian economies Environ Sci Pollut Res 2022 29 82948 82965 10.1007/s11356-022-21401-9
Scaliza JAA Jugend D ChiappettaJabbour CJ Relationships among organizational culture, open innovation, innovative ecosystems, and performance of firms: evidence from an emerging economy context J Bus Res 2022 140 264 279 10.1016/J.JBUSRES.2021.10.065
Shah SAA Zhou P Walasai GD Mohsin M Energy security and environmental sustainability index of South Asian countries: a composite index approach Ecol Indic 2019 106 105507 10.1016/j.ecolind.2019.105507
Sohail MT Majeed MT Shaikh PA Andlib Z Environmental costs of political instability in Pakistan: policy options for clean energy consumption and environment Environ Sci Pollut Res 2022 29 25184 25193 10.1007/s11356-021-17646-5
Sun Y Yesilada F Andlib Z Ajaz T The role of eco-innovation and globalization towards carbon neutrality in the USA J Environ Manage 2021 299 113568 10.1016/j.jenvman.2021.113568 34479153
Sun L, Fang S, Iqbal S, Bilal AR (2022) Financial stability role on climate risks, and climate change mitigation: implications for green economic recovery. Environ Sci Pollut Res 29(22):33063–33074
Sun Y Li H Andlib Z Genie MG How do renewable energy and urbanization cause carbon emissions? Evidence from advanced panel estimation techniques Renew Energy 2022 185 996 1005 10.1016/j.renene.2021.12.112
Svensson PG Andersson FO Mahoney TQ Ha JP Antecedents and outcomes of social innovation: a global study of sport for development and peace organizations Sport Manag Rev 2020 23 657 670 10.1016/J.SMR.2019.08.001
Tu CA, Chien F, Hussein MA, Yanto Ramli MM, PSI S, Iqbal MS, Bilal AR (2021) Estimating role of green financing on energy security, economic and environmental integration of BRI member countries. Singapore Econ Rev:1–19
Uchida H Miyakawa D Hosono K Financial shocks, bankruptcy, and natural selection Japan World Econ 2015 36 123 135 10.1016/j.japwor.2015.11.002
Ullah K Rashid I Afzal H SS7 vulnerabilities—a survey and implementation of machine learning vs rule based filtering for detection of SS7 network attacks IEEE Commun Surv Tutorials 2020 22 1337 1371 10.1109/COMST.2020.2971757
Umair M, Dilanchiev A (2022) Economic recovery by developing business starategies: mediating role of financing and organizational culture in small and medium businesses. Proc B 683:
Wang S, Sun L, Iqbal S (2022) Green financing role on renewable energy dependence and energy transition in E7 economies. Renew Energy 200:1561–1572
Wangzhou K Wen JJ Wang Z Revealing the nexus between tourism development and CO2 emissions in Asia: does asymmetry matter? Environ Sci Pollut Res 2022 29 79016 79024 10.1007/s11356-022-21339-y
Wodecka-Hyjek A A learning public organization as the condition for innovations adaptation Procedia-Soc Behav Sci 2014 110 148 155 10.1016/J.SBSPRO.2013.12.857
Wu Q, Yan D, Umair M (2022) Assessing the role of competitive intelligence and practices of dynamic capabilities in business accommodation of SMEs. Econ Anal Policy.10.1016/j.eap.2022.11.024
Xiuzhen X Zheng W Umair M Testing the fluctuations of oil resource price volatility: a hurdle for economic recovery Resour Policy 2022 79 102982 10.1016/j.resourpol.2022.102982
Xu X Wang C Zhou P GVRP considered oil-gas recovery in refined oil distribution: from an environmental perspective Int J Prod Econ 2021 10.1016/j.ijpe.2021.108078
Yang Y, Liu Z, Saydaliev HB, Iqbal S (2022a) Economic impact of crude oil supply disruption on social welfare losses and strategic petroleum reserves. Resour Policy 77:102689
Yang Z Zhang M Liu L Zhou D Can renewable energy investment reduce carbon dioxide emissions? Evidence from scale and structure Energy Econ 2022 112 106181 10.1016/J.ENECO.2022.106181
Yang Z, Abbas Q, Hanif I, et al (2021) Short- and long-run influence of energy utilization and economic growth on carbon discharge in emerging SREB economies. Renew Energy.10.1016/j.renene.2020.10.141
Yousaf Z Radulescu M Nassani AA Environmental management system towards environmental performance of hotel industry: does corporate social responsibility authenticity really matter? Eng Econ 2021 32 484 498 10.5755/J01.EE.32.5.28619
Zheng X, Zhou Y, Iqbal S (2022) Working capital management of SMEs in COVID-19: role of managerial personality traits and overconfidence behavior. Econ Anal Polic 76:439–451
Zhang D, Mohsin M, Rasheed AK, et al (2021) Public spending and green economic growth in BRI region: mediating role of green finance. Energy Policy.10.1016/j.enpol.2021.112256
Zhang L, Huang F, Lu L, Ni X, Iqbal S (2022) Energy financing for energy retrofit in COVID-19: recommendations for green bond financing. Environ Sci Pollut Res 29(16):23105–23116
Zhao J Dong K Dong X Is green growth affected by financial risks? New global evidence from asymmetric and heterogeneous analysis Energy Econ 2022 113 106234 10.1016/J.ENECO.2022.106234
Zhao L, Saydaliev HB, Iqbal S (2022b) Energy financing, COVID-19 repercussions and climate change: implications for emerging economies. Clim Chang Econ 13(03):2240003 | 37059954 | PMC10104692 | NO-CC CODE | 2023-05-14 23:15:55 | yes | Environ Sci Pollut Res Int. 2023 Apr 14; 30(23):63704-63715 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
37061636
26808
10.1007/s11356-023-26808-6
Research Article
Investigating financialization perspective of oil prices, green bonds, and stock market movement in COVID-19: empirical study of E7 economies
Gao Yuanruida [email protected]
[email protected]
Zhang Jiaxi [email protected]
grid.137628.9 0000 0004 1936 8753 Leonard N. Stern School of Business, New York University, New York, NY 10012 USA
Responsible Editor: Nicholas Apergis
15 4 2023
2023
30 23 6411164122
6 3 2023
30 3 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
The drastic influence of the COVID-19 crisis halted almost every industry and economy and made the quality of doing business in the oil industry and stock markets large. Also, COVID-19 diminished financial and economic performance to a greater extent. This issue still warrants modern solutions. Thus, preceding research inquired about the financialization perspective of oil prices, green bonds, and stock market movement in the COVID-19 crisis. For this, E7 economies’ data is selected to analyze the empirical findings of the research. The findings revealed that the green bonds have a weak link to crude oil, a weak correlation to stocks in the E7 settings, and a strong correlation to gold prices. While stock market return is also little correlated in COVID-19, stock volatility is highly significant in both directions with oil prices and green bonds movement. The hedging ratio has also shown a significant connection with oil prices and green bonds movement in determining the financialization of E7 economies. Hence, the study directs the implications for important industrial planning and policymaking decisions.
Keywords
Financialization
Green bonds
Oil prices movement
Stock market movement
COVID-19
E7 economics
issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023
==== Body
pmcIntroduction
Growing concern over negative environmental effects during the past decade or so has given rise to several environmentally conscious investment alternatives, including green and green bonds (Azhgaliyeva et al. 2022). The group produced global green bonds as part of a worldwide fixed-income green strategy. Most of the L.O. fund’s holdings comprise a diverse mix of branded and unbranded green bonds (Lee et al. 2021). As a result, green fixed-income instruments are cutting-edge financial tools developed to support green initiatives, including investments in emission reduction and green change management. Green bond issuers include non-governmental organizations, governments at all levels, local governments, and businesses with shares traded on public exchanges (Su et al. 2023). Green bonds, as opposed to conventional bonds, have attracted the interest of a specific class of long-term official investors driven by values other than profit: those with a strong preference for assisting in developing a low-carbon economy (Li et al. 2022). As a result, these buyers of green bonds are more likely to stay on their investments until maturity and are less likely to sell them in response to market volatility (Iqbal et al. 2021). Green bonds may function as active diversifiers and a hedge contrary to the disadvantage risk of the stock and product markets during tense times since they are so strong to negative market sentimentality in times of high financial uncertainty and market volatility (Mensi et al. 2022). Green bonds are intriguing (Li et al. 2021). After all, they are simple to include in institutional investment portfolios because they have a comparable structure to conventional government (Abakah et al. 2023).
Considering green bonds as prospective hedging assets during the COVID-19 epidemic phase presented a unique problem in March and April 2020 (Dutta et al. 2021). The financial markets worldwide were experiencing extraordinary stress at the time, and due to extensive price volatility and pervasive uncertainty, they almost went into panic mode (Ferrer et al. 2021). There was a lack of information regarding the connections between green bonds and other asset classes, such as equities, crude oil, and gold when the COVID-19 epidemic struck the E7 economies. Some financial professionals consider green bonds a feasible alternative to more conventional investments like stocks and crude oil (Syed et al 2022). This research gap has not yet been filled, given the crucial relevance of precise information about such dynamic interactions for market players to assess whether green bonds might reduce the risk of investment portfolios (Tu et al. 2021). For ethical investors who want to increase their exposure to sustainable practices through green bonds, considering the return and volatility relations among green bonds and other economic markets and the effectiveness of their hedging is essential (Umar et al. 2023).
By illuminating the dynamic connections and volatility spillovers among green bonds and actively traded stock and commodities markets, our findings add to the scant body of literature on the subject. We also examine the effectiveness of hedging during the market turbulence that followed the release of COVID-19. These models are referred to as VAR-ADCC-GARCH. We will start by examining how protected green bond investors are from the volatility of other markets. This is essential knowledge since financial time series frequently display irregular behavior over long epochs, and crises, like the COVID-19 pandemic, may make an asset less effective as a hedge. For instance, green bonds may impact the financial markets positively, neutrally, or negatively. The first-ever decline in WTI oil prices coincided with the COVID-19 outbreak, which received a lot of media coverage. Data from the commodity markets, such as the price of gold and crude oil, are regularly used in our empirical investigations. Financial products from the E7 economies based on the stock market are good. The S&P 500’s standing as a gauge of the world economy can be credited to the method’s wide popularity; you can use gold and oil as a hedge against the potential that the value of the financial markets will decline if you are aware of their values. Please provide an example of how gold excels as a hedge for the reader’s benefit. The commodities market can be a helpful tool for moral investors who want to diversify their resources. Studies that explore the dynamic linkages, the volatility spillovers, and the hedging effectiveness between green bonds and the main financial and commodities markets would be extremely beneficial to committed ecological financiers who search to green their holdings in support of the change to a low-carbon reduced. “Green bonds,” a particular kind of bond traded on financial markets, can make it easier to finance initiatives to offset the negative effects of climate change. First, our findings can benefit those worried about their environmental effect by motivating them to diversify their income sources and factor in the price of green bonds when calculating their risk. Secondly, policymakers can use these findings to reduce the dangers of climate change and advance the transition to a low-carbon future.
Literature review
Along with the subsequent collapse of financial markets and steep drops in the cost of both fossil fuels and renewable alternatives, the COVID-19 problem and the worldwide economic slowdown have contributed to a decline in global energy usage, which has also contributed to the global economic slowdown (Naeem et al. 2021a, b). As a result, competition and concentration in projects related to the green economy are decreasing, which poses a risk to efforts to achieve neutrality and sustainable development (Kanamura 2020). The overarching objective of the study is to investigate the dynamic relationship between fluctuating oil prices, green economies, Bitcoin, and other cryptocurrencies, and the current condition of the financial markets. This analysis of the USA is carried out by utilizing daily data beginning in August 2016 and continuing through August 2021. Quantile-on-quantile regression (QQR), which has only recently come into existence, and quantile Granger causality are two methods we use for this purpose (Reboredo et al. 2020). According to our empirical research findings, a green economy or green institutional finance is particularly susceptible to economic shocks, oil price fluctuations, and general issues associated with sustainability (Iqbal and Bilal. 2021). In addition, empirical research has indicated a negative association between sustainability and oil prices and stock markets, illustrating these industries’ vulnerability to sustainability changes (Bhutta et al. 2022). According to our research, stock markets, and oil prices, green bonds are a reliable alternative to clean energy, and equities in renewable energy companies are a kind of equity that can be utilized to assist sustainable growth. Both of these options are available to investors (Wang et al. 2022a, b,c). The findings of this research provide some helpful recommendations for reaching sustainable development goals and putting green economic principles into action (Tiwari et al. 2023).
This study investigates the frequency and dynamic spillovers in return and volatility, as well as the ability of green bonds, gold, silver, oil, the US Dollar Index, and the volatility index to protect against a decline in stock prices in the USA before, during, and after the COVID-19 pandemic outbreak. We will use the TVP-VAR model that Diebold and Yilmaz produced in 2014 to meet the goals we have set for ourselves and the frequency spillover indicator. We present statistical evidence to show that short-term volatility spillovers are far more common than long-term ones. Green bonds are net transmitters of spillovers in the short term, but in the long term, they are net receivers for the system as a whole (Yang et al. 2022) . Nonetheless, green bonds are net transmitters of spillovers in the short term. In the short and long terms, the S&P 500 Index and silver operate as net transmitters and receivers of spillovers, whereas the US Dollar Index and oil act as net receivers. Gold and the VIX are both net recipients of short-term spillovers, which means that in addition to being net transmitters of long-term spillovers, both markets are net transmitters of spillovers (Zhang et al. 2022). The issue with COVID-19 will have highly significant short-term spillover, with the worst of it occurring in early 2020. Both the number of spillovers and the direction they go can be affected by COVID-19 and the temporal parameters. The examination of the quantile-based regression shows that there are substantial nonlinear correlations between the markets that were looked at. The fact that at COVID-19, we were able to prove that gold and green bonds are haven investments for owners of US stocks is exciting enough in and of itself. Yet, diversification is most beneficial for investment portfolios that contain a wide range of different asset classes. In conclusion, COVID-19 and the temporal horizon play a role in the effectiveness of the hedging strategy (Huynh et al. 2020).
Most of the current research on green finance has focused on two main areas: the significance of green finance in funding the transition to a low-carbon economy and the advantages of green finance in portfolio diversification (Liu). In the context of study into environmentally friendly financial practices, much attention has recently been dedicated to the green bond market (Mensi et al. 2023). This is because the green bond market is a substantial component of green finance and the fixed-income markets (Braga et al. 2021). This study of the dangers associated with the green financial industry included investigating how the green bond market reacts when confronted with highly negative shocks. After this, a comprehensive examination of the nature of market volatility and the elements that influence it was carried out. The findings of this study provide three new contributions to the existing body of scholarly knowledge (Rao et al. 2022). Initially, it examines a case study that investigated the impact that the COVID-19 pandemic had on the green bond market and offered some insights from that study. Also, the market for environmentally responsible bonds now has access to volatility estimates (Cagli et al. 2022). The third benefit is that it sheds light on the factors contributing to the volatile nature of the green financial market. Another benefit of this inquiry is that it sheds light on an event analysis that examined how the green bond market responded to the unexpected outbreak of the COVID-19 pandemic. According to the study’s findings, having a non-pecuniary property or a green property on a financial instrument does not assist in lowering the risk levels of a financial market when the condition is excessive (Wang et al. 2022a,b,c). According to the study’s findings, the most significant factor contributing to the instability of green bonds is the unpredictability of traditional fixed-income markets, followed by the volatility of currency and stock markets and green inversions. However, if a dynamic link suddenly becomes unstable, the predictions may not be accurate (Pham and Do 2022).
Most recent studies on green finance have concentrated on two aspects: first, how crucial green finance is for portfolio diversification, and second, how helpful green financing is for transitioning to a low-carbon economy (Le et al. 2021). Studies on ecologically friendly monetary techniques have recently shifted their focus to the green bond market, which has attracted a lot of attention. To begin, the fixed-income and green finance industries both substantially rely on the market for green bonds. The durability of the green bond market in the face of especially severe shocks was one of the aspects of the green finance industry under investigation during this study (Naeem et al. 2022). After then, experts dug deep into the causes behind market volatility and the sources of those changes. The investigation contributes three fresh pieces of information to the existing body of knowledge. This is followed by a discussion of a case study that looked at the effects of the COVID-19 pandemic on the green bond market and provided some implications based on those findings. This discussion kicks off the main body of the essay (Jin et al. 2020). In addition, it is essential to be aware that the market for green bonds currently provides volatility estimates (Jiang et al. 2022). A notable advantage is that it elucidates the factors contributing to the volatile market for environmentally friendly products (Pham and Nguyen 2021). This inquiry sheds further light on an event analysis that investigated how the green bond market reacted to the unexpected outbreak of the COVID-19 epidemic and provided additional insights as a result. According to the research findings conducted under extreme conditions, having a non-pecuniary property or a green property attached to a financial instrument does not assist in lowering the risk levels associated with a financial market (Sun et al. 2022). The analysis reveals that conventional fixed-income market volatility, green inversions, and currency and stock market volatility significantly impact green bonds’ instability. If a dynamic link suddenly begins to malfunction, the predictions may not be accurate (Wang et al. 2022a, b,c).
These investments can be made using green bonds. The criteria used to pick green bonds are significantly more severe than ordinary bonds. As a result, the market for so-called green bonds might or might not contain bonds with some connection to the natural world. Looking through the L.O. Funds—Global Green Bond, you should remember that the designated and unlabeled green-aligned bond markets are thriving. This is something that you should keep in mind (Pham and Cepni 2022). According to the L.O. Group, “Green Bonds have certain noticeable currency, country, regional, and industrial-sector biases.” We believe some of these issues can be remedied by expanding the pool of investors from “labeled” green bonds to the more common, non-labeled green bond marketplace. This will allow more people to participate in the investment process (Zhao, Saydaliev and Iqbal, 2022). To make ethical investment more accessible, we believe that the more extensive green-bond market necessitates the utilization of innovative data and research methods, in addition to specific criteria for both selection and monitoring. Indeed, a wise investor equipped with these materials will quickly find that not all “branded” green bonds meet their requirements for sustainability or impact. Although companies issued them with more than 95% of their income coming from green-aligned projects, not all bonds that claim to be “green” are green, at least according to the assessment made by the Green Bond Initiative. This is the case even though businesses issued these bonds (Su et al. 2022). In that concern, oil price movement goes above and beyond the “green” certification by focusing not just on eco-friendliness but also on the influence that its products have on the environment and the community (Kung et al. 2022). According to green bonds reporting, more than 15% of so-called green bonds are turned down since they do not satisfy the organization’s established requirements. When investors move some of their traditional investment-grade bond holdings into a green bond portfolio, they risk needing to control tracking errors compared to a broad fixed-income benchmark (Ahmad et al. 2022).
The green bond portfolio is more volatile than the traditional investment-grade bond portfolio (Zheng, Zhou and Iqbal, 2022). Therefore, it is essential to widen the focus beyond “green bonds” in the academic literature to “green-aligned bonds,” which can assist in mitigating the danger of benchmark tracking inaccuracy. This can be done by using the term “green-aligned bonds.” the potential for a sizable “green premium,” also known as a “greenie,” in green bonds above traditional bonds has been investigated for the first time. Research into the dynamics between green bonds and the monetary system can affect investment strategies and risk management. Investors concerned with global sustainability have learned to view green bonds as an investment instrument despite the growing but unfinished research on the connection between green bonds and economic markets. That is because there is a persistent need for study into the far-reaching effects of green bonds on the economy. Using copula functions and conditional diversification criteria, this research demonstrates the low correlations between green bonds, stock markets, and energy markets. On the other hand, green bonds have a significant degree of correlation with corporate and government bonds (Li et al. 2021). They also find that the stock and energy markets derive very little advantage from diversification, whereas the benefits of diversification derived from pairing green bonds with government bonds are significant. Green bonds, on the other hand, do not have significant interactions with equities or energy markets. Research the effects of green bonds on the US and European Union financial markets over the long term. This holds in both regions. In addition, they demonstrate that green bonds have only a weak connection to the elevation produced corporate bond market, the stock market, and the energy market across all periods (Tu et al. 2021).
Research is conducted to observe the connections among green bonds, the cost of carbon emission permits, the yield on 10-year E7 economies treasury bonds, and the clean energy stock index. This finding indicates that green bonds cannot predict changes in the values of the asset indexes being considered. Please provide quantitative proof showing the extent of the return spillover between green and non-green investments is determined by the conditions of the macroeconomy and that it is at its highest point during times of stress (Sun et al. 2022). According to the research, green bond indices were given more weight than green bonds. Prior research often only considered price spillovers, even though return volatility is connected with clustering, asymmetries, and leverage effects, which are stylized elements of financial markets (Alemzero et al. 2021) This holds even though such features of the financial market are associated with return volatility. Several statistical methods can be used to understand better or characterize these aesthetic characteristics, including quantiles, wavelet analysis, and variance analysis of covariance (VAR). Most recent studies focus on an aggregate index for the energy market, employ a time-varying hedging strategy, and give just passing attention to the gold market. Because of this, there is a significant information gap concerning the connection between green bonds and the economic markets. The sample data did not contain the COVID-19 outbreak, a major event that could affect the connection between green bonds and the economic markets. Politicians and investors both need this problem fixed as quickly as possible (Li et al. 2021). The recent discussion suggests that in 2021, countries will need to tackle major COVID-19 and environmental difficulties. The current analysis uses VAR-ADCC-GARCH models to analyze the interrelationships between green bonds, E7 economies stock markets, crude oil prices, and the gold commodities market to address these gaps and fill in previously unavailable information. In addition, we use univariate asymmetric GARCH processes to characterize the volatility of green bonds and the volatility of each financial market index (Chang, Iqbal and Chen, 2023). In the second and last parts, we will investigate how green bonds can shield investors from the turbulence in the E7 economy’s stock, oil, and gold markets. Despite the topic’s importance in the academic literature on green bonds, relatively little is known about the hedging capability of green bonds during either the ordinary stress period of the COVID-19 pandemic or the uncommon stress phase of the COVID-19 epidemic. As a result, we can determine the intermarket linkages between all of the indices that have been analyzed (Bilal et al. 2022).
Methodology
Study data
Researchers procure daily pricing of the S&P Green Bonds (SPGRBND), S&P Green Bond Select (SPGRSLL), S&P 500 Matrix (SP500), S&P 500 Energy (SP5GENE), S&P 500 Bond (SP500BD), and S&P Global Shariah (SPBMIGSI) from DataStream and Thompson Press release to examine the orientation consistency, overreliance, but instead macro-prudential spill-over effects weekly earnings typically expressed as logistic functions. Major world events that have already affected international financial markets occurred throughout our quarterly data. To start, our data covers the time of the global financial crisis (GFC), when the world’s financial and financial industries were under extraordinary strain. Thus, the study collected data from around 2010 to 2021. Furthermore, your data set represents the E7 markets’ patterns. My research also covers when the global COVID-19 epidemic began, and many countries took steps to contain the disease. Research data indicates that the execution of governmental measures against COVID-19 significantly affected world markets.
Green bonds
The L.O. Funds—Global Green Bond details were available to the general public in March 2016. This is why we are looking at 818 days’ worth of observations, beginning on March 1, 2016, and ending on June 25, 2020. DataStream offers its information for sale in massive quantities for a fixed price in US dollars. When evaluating the status of the E7 country’s stock market, we focus on the 500 indexes. The gold prices and current crude oil can be seen in the spot prices traded on the West Texas Intermediate (WTI) and London Bullion markets. There are four indicators, and their level series are being analyzed now. Like the price of gold, the green bond index has risen since the third quarter of 2018. The S&P 500 and oil prices dropped drastically in March 2020, when the expected peak of the COVID-19 epidemic approached, while the markets for bonds and gold fell much less. This research illustrates the dramatic shifts in stock values during the COVID-19 pandemic by plotting the natural logarithmic return series and highlighting the spikes and dips in various markets and the WTI oil index. Summarizes, using descriptive language, the usual logarithmic returns for green bonds and the other three indices. For this purpose, we may provide the median positive return across all indices. In line with expectations, the green bond index shows less instability than the major economic flea market, except the oil marketplace, which shows more instability in the stock and the golden ingots marketplaces Table 1Table 1 VAR-ADCC-GARCH analysis of green bond and stock markets
Variables ↓ C-B S&P 500
rt-1b 0.1346 (0.00) * − 1.0825 (0.00) **
rt-1s 0.0082 (0.00) * − 1.0812 (0.00) *
εb,t-12 0.1086 (0.00) ** − 1.0498 (0.00) **
εs,t-12 0.0011 (0.00) ** 0.2851 (0.00) *
ht-1b 0.6188 (0.00) * 0.0526 (0.00) *
ht-1s − 1.0005 (0.00) ** 0.7217 (0.00) **
θ1 0.1333 (0.00) *
θ2 0.7615 (0.00) *
θ3 0.0007 (0.00) *
Log likelihood − 398.62
ARCH-LM 0.63 (0.82)
Empirical estimation technique
The study used GARCH-based empirical models, VAR-DCC-GARCH estimation technique, including ARHC-LM estimation techniques for empirical analysis. The GARCH-based models are recommended for dealing with heteroscedasticity because all revisit series display up to 10 lags in the past. Finding the unit root of a recurrent series requires calculating the series with an intercept. The ARCH-LM test examines the null hypothesis that the return series do not display heteroscedasticity at lag 10. That is hardly shocking, specifying the wild fluctuations in WTI pricing when prices went downbeat. Using the Jarque–Bera test, we find that no returning series follows the normality law. Using the augmented Dickey and Fuller (ADF) test, we find that all arrival series are stationary at the 1% significance level. In recent years, the DCC-GARCH model has surpassed the BEKK, CCC, and VAR-GARCH models in academic popularity because of its computational efficiency and power. In particular, it accounts for the asymmetric special effects arising from economic series often taking on an asymmetric DCC-GARCH (ADCC-GARCH) structure. However, volatility and returns in the green bond catalog and the financial markets may be intertwined. We also examine toughness using alternative multivariate models, including a DCC-GARCH model variant. Our VAR-ADCC-GARCH approach uses the following formula to approximate the mean equation:1 Rt=L+τRt-1+εt
2 εt=Ht12ξt
Ht1/2 indicates the conditional volatility, where ξt represents the innovations matrix, L denotes the intercepts vector, and εt resembles error terms vector and symbolizes the returns for the green bond catalog and the other economic assets.3 Ht=DtRtDt
4 Dt=diaghtb,hto
5 Rt=diagQt-12QtdiagQt-12
6 Qt=1-θ1-θ2Q¯-θ3Z¯+θ1ξt-1ξt-1′+θ2Qt-1+θ3zt-1zt-1′
Qt symbolizes the provisional dependence matrix of homogenous returns, b represents the returns of green bond investments and the outlays of gold or oil.θ1andθ2 Non-negative scalars s.t.θ1+θ2<1 indicating a stable underlying framework; indicating the asymmetry’s direction; and asymmetric effect, defined as the hypothesis that good and bad shocks result in different connections among indices, denoted by ztzt′]. Furthermore, for green bonds and other economic markets, the following is the mapping of htb and hto Onto conditional volatilities:7 htb=db2+b112ht-1b+b212ht-1o+a112εb,t-12+a212εo,t-12
8 hto=do2+b122ht-1b+b222ht-1o+a122εb,t-12+a222εo,t-12
This formula will allow you to determine the distorted dynamic conditional connection between green bond table b and another index o.9 ρt=htbohtbhto
The interdependencies among green bonds, other bond markets, and other financial markets are depicted in Tables 2, 3 and 4. Most computed coefficients are statistically significant at the 95% confidence level, and considerable heteroscedasticity is no longer present, suggesting that the employed models are appropriateTable 2 As illustrated, the VAR-ADCC-GARCH model findings
Variables ↓ C.B Gold
rt-1b 1.0947 (0.00) *** 1.1053 (0.01) **
rt-1g − 1.0082 (0.11) 1.0074 (0.74)
εb,t-12 1.0402 (0.00) *** 2.4286 (0.01) **
εg,t-12 1.0015 (0.00) *** 1.0527 (0.01) *
ht-1b 1.9389 (0.00) *** − 3.0853 (0.01) **
ht-1s − 1.0024 (0.00) *** 1.9225 (0.01) *
θ1 1.0091 (0.00) ***
θ2 1.2464 (0.00) ***
θ3 1.0037 (0.00) ***
Log likelihood − 287.16
ARCH-LM 0.89 (0.60)
Table 3 VAR-ADCC-GARCH analysis of green bond and WTI markets
Variables CB WTI
V 1.0136 (0.03) * − 1.2282 (0.73)
rt-1b 1.0023 (0.12) − 1.0991 (0.01) **
rt-1o 1.1032 (0.01) ** 45.8427 (0.01) *
εb,t-12 1.00002 (0.17) 1.0909 (0.01) **
εo,t-12 1.8146 (0.01) * − 13.5618 (0.01) **
ht-1b − 1.00002 (0.03) * 1.8809 (0.01) *
ht-1s 1.2704 (0.01) **
θ1 1.0035 (0.05) *
θ2 1.0045 (0.01) **
Log likelihood − 1404.29
ARCH-LM 0.77 (0.68)
Table 4 Dynamic time-varying correlations and Summary statistics
M.N Std Max Mini
CB/S&P 500 − 1.2649 1.2277 1.6718 − 1.7371
CB/Gold 1.2944 1.1539 1.6654 − 1.1036
CB/WTI 1.0274 1.1727 1.6914 − 1.6268
Results and discussion
Empirical findings of VAR-ADCC-GARCH
In Table 2, we see that a model that includes both green bond and stock indexes results in the arrival of the spillover from the E7 economies stock marketplace to the green bond marketplace and vice versa (Table 1). As the coefficients for h one and h 12 in the variance equation have large values, it may be inferred that recent volatility shocks and trailing news have significantly affected the current volatility level in the S&P index. The markets’ reactions to news and volatility shocks are consistent. Green bonds are superficial to fresh instability shocks and old news.
The table below summarizes the findings of the study of the bond stock portfolio. This indicates that at time t-1, the S&P 500 returned rt-1b, whereas the market for green bonds returned rt-1s. The provisional variance of bond returns at time t-1 is represented by ht-1b for bonds. In contrast, the provisional variance of stock marketplace returns at time t-1 is represented by ht-1s for stocks. The stock and bond markets can be significantly impacted by unexpected news or shocks, and these impacts can be measured using the squared error terms and 12. ARCH-LM statistics compare the hypothesis to the arrival series at lag 10 for heteroscedasticity. Remember that the significance levels at which ***, **, and * are indicated as 1%, 5%, and 10%, respectively.
The table displays the results of a model analysis of the bond-gold combination rt-1g arrival on green bonds at time t-1. The table below summarizes the findings from a model of the bond-oil relationship. The oil index return at the time t-1 is represented by symbol rt-1o, while the performance of the green bond index at time t-1 is shown by symbol rt-1b. Bond price returns and oil marketplace proceeds at time t-1 are both represented by the constant ht-1b and ht-1s. Assess the shocks and surprises in the oil and bond markets by computing the mean squared error εo,t-12 and εb,t-12. ARCH-LM statistics compare the hypothesis to the arrival series at lag 10 for heteroscedasticity. Remember that the significance levels at which ***, **, and * are indicated as 1%, 5%, and 10%, respectively.
The unexpected outcomes of the green bond and gold index model are shown in Table 3. The return formulae show a small but noticeable impact of gold returns on green bond returns. The performance of green bonds can help forecast gold prices. Furthermore, the results demonstrate that, with a lag of one period, the gold market and the green bond market each suffer distinct news/shocks and historical volatility. This scenario is explosive on both sides. One market’s news shock could have an impact on another. Traders can extrapolate information from one market to forecast the behavior of another. Table 4 displays the results for the crude oil index and green bonds models. It is incredible how much the stock–bond model resembles it. This indicates that the consequences of initial shocks and the lag in the volatility of the bond and oil markets are in play. The crude oil and green bond markets exhibit substantial evidence of bidirectional volatility spillovers. Still, no such spillovers are in the opposite direction. Unexpected developments in the bond market impact oil price volatility, but not the other way around.
Time-varying conditional correlations analysis
Table 5 shows that there is a positive average relationship between bond prices and the prices of gold and oil. So, rising commodity prices like gold or oil can be expected to increase green bond prices. With the availability of green bonds on the market to support green efforts like clean energy projects, this finding is not shocking for the WTI oil market. Since the ultimate goal of renewable energy corporations is to supply an alternative to crude oil, a rise in the price of oil would drive economic players to shift to other energy sources. So, it is reasonable to assume that the rising cost of renewable energy sources will track the rising cost of crude oil due to demand-side dynamics. That is why it stands to reason that the oil and green bond markets will have a mutually beneficial interaction. Oil market participants may find some hedging alternatives in the nearly non-existent correlation between WTI and green bonds. In contrast, there appears to be a substantial correlation between the gold market and environmental bonds, which suggests that gold is not a good investment choice for ethically minded people. Because gold and green bonds can act as haven investments during economic downturns, this study likely has a clear correlation between asset classes. According to the bond markets in the USA and the UK, gold is not utilized as a hedge.Table 5 Average values of hedging effectiveness and hedge ratio
H-ratio H-effective
CB-stocks − 2.931 13.18%
CB-gold 2.6793 12.03%
CB-oil − 1.4282 4.05%
New statistics demonstrate a negative correlation between the S&P 500 index and green bonds, whereas older studies found a clear correlation between regular bond markets and stock and found that investors only chose safe fixed-income products like corporate investment yield and treasury bonds when the market is volatile along these lines. Our data, however, shows that green bonds and the S&P 500 index do not follow each other in a perfect correlation, suggesting that green bonds can be used to hedge against the market’s decline. Our research significantly impacts moral investors who want to reduce their exposure to stock market risk through diversification. Positive and negative correlations were found between every possible pair in this analysis. Although our sample period is more extended and includes the COVID-19 pandemic, our results for the bond-oil pair are consistent with those found in the results. In addition, since the COVID-19 epidemic began, the correlation between green bonds and the stock and oil markets has expanded dramatically, in line with previous research that indicated more significant correlations amid stressful times. However, the connection between the gold and green bond markets decreases throughout the outbreak, from about 1.41 to around 0. In conclusion, the results demonstrate a temporal connection between the aforementioned financial markets and green bonds. These temporal correlations affect forecasting, risk management, and policymaking. Consequently, when modeling the volatility of green bonds, it is crucial to account for these dynamic links.
Findings of oil prices efficiency, stock market portfolio with hedging ratio
We establish the optimal hedging ratio (βt) in a time-varying environment: The formula10 βt=htbohto
where htbo is the provisional variance of other indices used to calculate the provisional covariance among the green bond index and the stock, the gold, and the oil indices at the time t. The time-varying variance–covariance matrix hto Of GARCH model is where the variances and covariances are drawn from. The typical hedge ratio and typical hedging success rate are shown in Table 6. Suppose your portfolio consists primarily of securities from the E7 economies. In that case, you can protect against a decline in value by taking a long position in green bonds in place of that position (crude oil). You will still have to pay $1.9310 ($0.4281) even if the average hedging ratio for both parties is negative (Reference “Rough Oil”). The low $1.6792 needed to offset a long gold position with a short green bond position is a good development for both gold and green bonds. More so than buying gold or crude oil, utilizing green bonds as a hedge lowers the volatility of the S&P 500 index. The appropriate hedging ratio depends on the current market situation and the anticipated investment term. The ideal hedging ratio between green bonds and crude oil fluctuates more radically over time since the oil price is more subject to various economic and non-economic factors. The COVID-19 pandemic-related changes in the hedge ratio required frequent and expensive adjustments to the hedging position.Table 6 ECM technique outputs
Co-efficient Standard Error t-Statistic Probability
C − 2.986 8.423 0.1824 0.000
LnOPV − 1.062 0.543 0.0884 0.000
LnY − 4.264 0.038 0.0345 0.000
LnSMV 2.6273 0.371 0.0734 0.000
LnGB 3.209 0.769 0.1411 0.000
We estimate the time-varying hedge effectiveness, which is the percentage of volatility that the hedge removes:11 TVHEt=βt2htbhto
The TVHEt is the hedging condition which is applicable unless this number is 1, which is ideal. The greatest way to protect stock portfolios in the E7 economies is to invest in the Environment Bond Index, which decreases S&P 500 volatility the most. By the end of 2020, some estimates suggest that hedging can reduce bond and stock portfolio risk by as much as 50%. However, the efficiency of green bonds as a hedge for the three assets dropped drastically in March and April 2020, during the height of the COVID-19 outbreak, and then significantly increased in May and June 2020, when the equities and gold markets in E7 economies stabilized. Green bonds, as a kind of oil market hedging, can provide insight into reduced volatility in the oil market. Some experts in the field have confirmed our finding that hedge ratios and hedging effectiveness evolve. We provide the first hard data showing that green bonds can help stabilize the financial system during extreme events like the COVID-19 epidemic.
The hedging ratio is more stable than it would be in the bond-gold scenario, despite bond-stock indices being more volatile. It is hardly unexpected that gold has historically low return volatility, especially in difficult economic circumstances. One may reduce the risk of a long position in gold by selling short green bonds due to gold’s generally favorable hedging ratio. Bond stock and bond oil have negative hedging ratios due to the S&P 500 index’s subpar performance throughout the COVID-19 outbreak. A suitable short-term hedge against the volatility of equities in the E7 economies is long-term exposure to the green bond index. The findings show that to keep a cushion against volatility, investors in the oil and stock markets of the E7 economies should periodically rebalance their green bond holdings. We also consider the green bond index and its particular characteristics concerning financial market risk, providing novel insights into green bonds and the financial markets in addition to the time-varying hedging ratio and the effects of the COVID-19 outbreak.
The study’s findings are clear that an immediate adjustment of the oil price level can change the true worth of oil resources. The oil price level may move more diminish in actuality. In light of this, it should be taken as an instance of a very polar situation. To extend it, green bonds must be non-contingent in the other extreme polar situation in Table 4, in which financialization policy is restricted to avoid causing oil price shocks. Table 6 shows that the economy does not revert to its pre-crisis stable position. As the financial stability, the increased stock market movement is covered by the reduced level of financialization in the COVID-19 crisis. It is. Therefore, the COVID-19 crisis produced steady declines. Our research has demonstrated that green bonds significantly enhance oil prices movement for financialization when the E7 economy experiences an unfavorable financialization shock. Furthermore, findings highlighted that it minimizes a lengthy transition in green bonds and oil prices stock market movement and limits their risk exposure.
The research results contrast the policies with and without the profit tax on green bond financing. Other options include credit incentives for stock market price movement, non-contingent real borrowing, and current price control (Table 7). It is no longer optimum to completely stabilize spreads in response to financial shocks if dispersed earnings are not taxed. When the price of goods and services fluctuates, the government uses quasi-nominal debt to fund subsidies. Internal resources, however, are equally affected by price changes, affecting their true worth and, therefore, their earnings. The household’s profits are to blame for the lack of comprehensive flattening of the wedges. Whether or not the incentives are financed, is important because it interacts with the desire to stray from complete spread stability. If negative financial shock results in a greater spread, the loan subsidies should compensate for this (see Table 4). Shocks are long-lasting due to constraints on state debt contingencies.Table 7 Regression results
Estimators Values Estimators Values
R-square 54.56 Mean dependent variable 0.3384
Adjusted R-square 6.876 Standard deviation dependent variable 0.0118
Standard error of the regression 0.013 Akaike statistic 0.0061
Sum squared residual 0.798 Schwarz statistic 0.0571
Log-likelihood 0.534 Hannan-Quinn statistic 0.0971
F-statistics 0.449 Durbin Watson Statistic 0.3164
A probability value (F-statistics) 0.000
Oil price movement may be a helpful policy instrument, as shown in the “Methodology” section by the model, without outside money or monetary policy. Credit subsidies will have different effects depending on the exact source of monetary non-neutrality. This study assumes that the stock market movement is the most detrimental to green bonds. The two instruments would be comparable if not for the top and lower limits on climate risk awareness and interest rates.
Sensitivity analysis
In a study model where oil prices stability, green bond financing efficiency, and stock market movement are desired, the nominal market prices will likewise be constrained in their ability to mitigate the effects of financial shocks on the economy. The average distortion can be reduced, but not the distortion caused by volatile spreads. While policy interest rates may be favorable and erratic in a cashless environment, the same costs would be present in a model with money demand distortions. Our model predicts that a high and variable lending rate will lead to a high and variable cost of borrowing (see Table 5). Unless additional fiscal tools are deployed, monetary policy will confront a trade-off in a model with both sticky prices and monetary frictions.
It is clear from this analysis that climate risk awareness is an effective tool for addressing the distortions caused by large and variable spreads. Money policy may be an imperfect replacement or a complementing policy tool for other distortions, such as price dispersion, owing to sticky pricing or knowledge, depending on the source of non-neutrality in a monetary model.
Discussion
Studies given here help to the understanding of producers’ interest in financing global warming mitigation actions using different funding choices. Future crowdfunding initiatives with farmers in Norway will benefit from our findings. According to research, farmers are more inclined to use donation- or reward-based crowdfunding than lending crowdfunding. A contribution or incentive generates money at free or minimal costs to the farmers, while a lending strategy requires growers to return the type of loan. This is not a shock (Table 8). Additional expenses are associated with loan-based arrangements, which may need a lot of contact between funders and the farmer. Producers with a fiscally solid agricultural company who previously used crowdsourcing or have a strong feeling of duty to combat climate change should be the primary targets of oil price reduction crowdfunding campaigns. Higher-asset farmers are more likely to change, and farmers with stronger climate attitudes are more likely to undertake abatement or adaption techniques, consistent with previous research.Table 8 Robustness test
Indicators Estimated outputs
Oil prices movement t-1 0.672
Wald test 0.14*
P-value (0.00)
Green bonds financing t-1 3.46
Wald test 2.01
P-value (0.00)
Financialization Structure t-1 0.509
Wald test 0.19*
P-value (0.34)
Stock Market Movement 2.17
Wald test 2.33
P-value (0.00)
The p-value for significance is *p < 0.05
In Norway, farmers often collaborate, so it is unsurprising that responders like advertising that includes them. Many farmers collaborate in tiny joint ventures of two to five farmers, pooling the assets of mainly modest-scale businesses. They work together. Nevertheless, by swapping labor and exchanging information and experience, residents could handle climatic fluctuation in a mountainous farm town in Norway. Single fundraising projects have perks, but a collaborative effort offers many more. People’s marketing expenditures may be reduced, for example, by lowering the amount of time required to handle investors. Fundraising is also a good way to alleviate the fear of personal disgrace in case of a fundraising effort. As a last point to consider, studies suggest that recruiting an entrepreneurial team to help with fundraising has a beneficial impact on campaign success rates. Finally, crowdfunding is unlikely to appeal to those who are more reserved by nature since it is a social activity.
According to our findings, respondents prefer campaigns that pay the whole cost of mitigation. Farmers are already financially stressed and will likely pay the extra debt for carbon reduction initiatives. However, if the campaign were to pay the whole investment costs, certain mitigation methods need big initial contributions, increasing the overall amount of crowdsourcing necessary. Big crowdsourcing initiatives tend to be less effective since supporters may regard them as irrational, discouraging investment. More testing is needed to discover the correct “balance” between the fundraising amount asked for mitigation strategies and the cost of such projects. Despite their preference for a fully financed project, farmers expressed some readiness to spend their resources if a project did not produce enough funds to pay the entire mitigation costs. There is a need for comparisons better to understand farmers’ choices in diverse market conditions. Market price support and other direct subsidies from Norway farms may differ from those in freer markets like Australia and New Zealand. Varied methods of crowdsourcing have different appeals depending on ethnic inclinations. Farmers in Norway may be reluctant to utilize crowdfunding because of cultural traditions.
Jante Law is an essential component of Norwegian culture, communicating the necessity of not “sticking out” or assuming that a person is superior to the rest of society. Regarding rural Norway, the Law of Jante has a greater impact since farmers do not want to be publicly identified as crowdsourcing recipients. Some types of crowdsourcing may be more attractive in some social, economic environments and marketplaces than others. It is more aligned with the concept of “civic agriculture”—the idea that farming has social responsibilities and is not only commercial in nature—than traditional crowdsourcing. In a free market agricultural setting, projects that depend on loan or equity-based crowdfunding may be seen as more economically driven and so more acceptable. We discovered that agriculture advisers and farmers’ organizations were the most trusted organizations among respondents in terms of practical consequences. As middlemen, these organizations will be able to help launch and execute initiatives for you. Crowdsourcing is a viable alternative for small-scale farmers because of intermediate institutions like the Law of Jante and a misperception about how long it takes to use crowdfunding. As “aggregators,” intermediaries may help bring farmers together to form joint campaigns, which are more popular than solo efforts in the agricultural community. For crowdfunding platforms, there is a chance to provide training and solutions for collaborative groups instead of solo fundraisers. There should be training for farmers on how to use crowdfunding sites effectively in their own business setting. There is a last function for policymakers to play in encouraging businesses to employ various fundraising business strategies via supportive regulatory frameworks (be they individual farmers or intermediary organizations). With the right incentives in place like matching money for crowdsourced projects that focus on agriculture mitigating climate change, producers may utilize crowdsourcing as a source of money.
Conclusion and implications
The study investigated the financialization perspective of oil prices movement, green bonds financing efficiency, and stock market movement of E7 economies. Analysis of the potential impact of green bonds on the Standard & Poor’s 500, the price of crude oil, and the value of gold is provided. Results from the bivariate model show that the COVID-19 pandemic has had a profound impact on all of the major financial and commodity markets, and that the connections between green bonds and these markets have changed over time. During the great majority of the study period, the price of green bonds was favorably correlated with both of the investigated commodity markets and negatively correlated with the S&P 500. Despite the fact that green bonds were created to fund environmentally friendly initiatives, studies like this one reveal that they have some characteristics with traditional bonds when it comes to their interaction with financial markets. In contrast to the marginal return relationships, we find that green bonds and financial markets are correlated with high volatility. Green bonds were found to be an even more reliable hedge against market risk than gold or the stocks of the E7 economies. Recent shifts in hedge ratios, especially during the COVID-19 pandemic and in the oil and equities markets, underscore the importance of routine position monitoring and hedging. The apex of the COVID-19 pandemic unfortunately corresponds with a precipitous drop in the efficacy of green bonds in safeguarding US markets. Oil hedging is more expensive and, to a lesser extent, less effective than hedging US stocks due to the COVID-19 epidemic.
Stock market investors can increase their returns and lower their risk exposure by strategically adding green bonds to their investment portfolios. To do so, however, requires an in-depth familiarity with the relationships between green bonds and the world’s major financial markets. These numbers show how susceptible green bonds are to price and yield shocks in equities and commodities. There are a few business implications arising from our research. Given the strong inverse link between equities in the E7 economies, gold, and green bonds, investors may find that using green bonds as a hedging technique is quite advantageous. The green bond market is unrelated to oil prices; however, there may be a small hedging advantage. In conclusion, the increased market volatility brought on by the COVID-19 epidemic severely limits the hedging potential of green bonds. Investors that care about the environment put making a profit ahead of minimizing their impact on the planet. The transition to a low-carbon economy would be slowed if decarbonizing investor portfolios did not offer incentives for investors to convert to ethical investments. Therefore, our findings will pique the interest of donors who are interested in supporting socially conscious businesses. Consequences for businesses that issue green bonds are discussed. If you want to invest in something that will help the environment and promote social justice, consider purchasing green bonds. The dangers of the hedging technique, including as its impact on public health crises like the COVID-19 outbreak, should be made clear to investors. It increases the issuer’s potential to attract new investors and improve its ESG ratings.
Author contribution
Conceptualization, methodology, data curation, data analysis: Yuanruida Gao. Writing (original draft), visualization, editing: Jiaxi Zhang.
Data availability
The data that support the findings of this study are openly available on request.
Declarations
Ethics approval and consent to participate
The authors declare that there are no human participants, human data or human issues.
Consent for publication
We do not have any individual person’s data in any form.
Competing interests
The authors declare no competing interests.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Abakah EJA Tiwari AK Adekoya OB Oteng-Abayie EF An analysis of the time-varying causality and dynamic correlation between green bonds and US gas prices Technol Forecast Soc Chang 2023 186 122134 10.1016/j.techfore.2022.122134
Ahmad B, Iqbal S, Hai M, Latif S (2022) The interplay of personal values, relational mobile usage and organizational citizenship behavior. Interact Technol Smart Educ 19(2):260–280
Alemzero DA, Sun H, Mohsin M, Iqbal N, Nadeem M, Vinh Vo X (2021) Assessing energy security in Africa based on multi-dimensional approach of principal composite analysis. Environ Sci Pollut Res 28:2158–2171. 10.1007/s11356-020-10554-0
Azhgaliyeva D Kapsalyamova Z Mishra R Oil price shocks and green bonds: An empirical evidence Energy Econ 2022 112 106108 10.1016/j.eneco.2022.106108
Bhutta US Tariq A Farrukh M Raza A Iqbal MK Green bonds for sustainable development: Review of literature on development and impact of green bonds Technol Forecast Soc Chang 2022 175 121378 10.1016/j.techfore.2021.121378
Bilal AR, Fatima T, Iqbal S, Imran MK (2022) I can see the opportunity that you cannot! A nexus between individual entrepreneurial orientation, alertness, and access to finance. Eur Bus Rev 34(4):556–577
Braga JP Semmler W Grass D De-risking of green investments through a green bond market–Empirics and a dynamic model J Econ Dyn Control 2021 131 104201 10.1016/j.jedc.2021.104201
Cagli EC, Taşkin D & Evrim Mandaci P (2022). The role of uncertainties on sustainable stocks and green bonds. Qual Res Financ Markets
Chang L, Iqbal S, Chen H (2023) Does financial inclusion index and energy performance index co-move? Energy Pol 174:113422
Dai Z, Zhang X, & Yin Z (2023). Extreme time-varying spillovers between high carbon emission stocks, green bond and crude oil: Evidence from a quantile-based analysis. Energy Econ 106511
Dutta A Bouri E Noor MH Climate bond, stock, gold, and oil markets: Dynamic correlations and hedging analyses during the COVID-19 outbreak Resour Policy 2021 74 102265 10.1016/j.resourpol.2021.102265 34580555
Ferrer R Shahzad SJH Soriano P Are green bonds a different asset class? Evidence from time-frequency connectedness analysis J Clean Prod 2021 292 125988 10.1016/j.jclepro.2021.125988
Huynh TLD Hille E Nasir MA Diversification in the age of the 4th industrial revolution: The role of artificial intelligence, green bonds and cryptocurrencies Technol Forecast Soc Chang 2020 159 120188 10.1016/j.techfore.2020.120188
Iqbal S, Bilal AR (2021) Energy financing in COVID-19: how public supports can benefit? Chin Finance Rev Int 12(2):219–240
Iqbal S, Bilal AR, Nurunnabi M, Iqbal W, Alfakhri Y, Iqbal N (2021) It is time to control the worst: testing COVID-19 outbreak, energy consumption and CO2 emission. Environ Sci Pollut Res 28:19008–19020
Jiang Y Wang J Ao Z Wang Y The relationship between green bonds and conventional financial markets: Evidence from quantile-on-quantile and quantile coherence approaches Econ Model 2022 116 106038 10.1016/j.econmod.2022.106038
Jin J Han L Wu L Zeng H The hedging effect of green bonds on carbon market risk Int Rev Financ Anal 2020 71 101509 10.1016/j.irfa.2020.101509
Kanamura T Are green bonds environmentally friendly and good performing assets? Energy Econ 2020 88 104767 10.1016/j.eneco.2020.104767
Karim S Naeem MA Hu M Zhang D Taghizadeh-Hesary F Determining dependence, centrality, and dynamic networks between green bonds and financial markets J Environ Manage 2022 318 115618 10.1016/j.jenvman.2022.115618 35949085
Kung CC Lan X Yang Y Kung SS Chang MS Effects of green bonds on Taiwan's bioenergy development Energy 2022 238 121567 10.1016/j.energy.2021.121567
Le TL Abakah EJA Tiwari AK Time and frequency domain connectedness and spill-over among fintech, green bonds and cryptocurrencies in the age of the fourth industrial revolution Technol Forecast Soc Chang 2021 162 120382 10.1016/j.techfore.2020.120382
Lee CC Lee CC Li YY Oil price shocks, geopolitical risks, and green bond market dynamics N Am J Econ Finance 2021 55 101309 10.1016/j.najef.2020.101309
Li W, Chien F, Ngo QT, Nguyen TD, Iqbal S, Bilal AR (2021) Vertical financial disparity, energy prices and emission reduction: empirical insights from Pakistan. J Environ Manag 294:112946
Li H Zhou D Hu J Guo L Dynamic linkages among oil price, green bond, carbon market and low-carbon footprint company stock price: Evidence from the TVP-VAR model Energy Rep 2022 8 11249 11258 10.1016/j.egyr.2022.08.230
Liu N Liu C Da B Zhang T Guan F Dependence and risk spillovers between green bonds and clean energy markets J Clean Prod 2021 279 123595 10.1016/j.jclepro.2020.123595
Mensi W Naeem MA Vo XV Kang SH Dynamic and frequency spillovers between green bonds, oil and G7 stock markets: Implications for risk management Econ Anal Policy 2022 73 331 344 10.1016/j.eap.2021.11.015
Mensi W Vo XV Ko HU Kang SH Frequency spillovers between green bonds, global factors and stock market before and during COVID-19 crisis Econ Anal Policy 2023 77 558 580 10.1016/j.eap.2022.12.010 36570097
Naeem MA Adekoya OB Oliyide JA Asymmetric spillovers between green bonds and commodities J Clean Prod 2021 314 128100 10.1016/j.jclepro.2021.128100
Naeem MA Bouri E Costa MD Naifar N Shahzad SJH Energy markets and green bonds: A tail dependence analysis with time-varying optimal copulas and portfolio implications Resour Policy 2021 74 102418 10.1016/j.resourpol.2021.102418
Naeem MA Conlon T Cotter J Green bonds and other assets: Evidence from extreme risk transmission J Environ Manage 2022 305 114358 10.1016/j.jenvman.2021.114358 34974217
Pham L Cepni O Extreme directional spillovers between investor attention and green bond markets Int Rev Econ Financ 2022 80 186 210 10.1016/j.iref.2022.02.069
Pham L Do HX Green bonds and implied volatilities: Dynamic causality, spillovers, and implications for portfolio management Energy Econ 2022 112 106106 10.1016/j.eneco.2022.106106
Pham L Nguyen CP Asymmetric tail dependence between green bonds and other asset classes Glob Financ J 2021 50 100669 10.1016/j.gfj.2021.100669
Rao A, Gupta M, Sharma GD, Mahendru M & Agrawal A (2022). Revisiting the financial market interdependence during COVID-19 times: a study of green bonds, cryptocurrency, commodities and other financial markets. Int J Manag Finance (ahead-of-print)
Reboredo JC Ugolini A Aiube FAL Network connectedness of green bonds and asset classes Energy Econ 2020 86 104629 10.1016/j.eneco.2019.104629
Su T Zhang ZJ Lin B Green bonds and conventional financial markets in China: A tale of three transmission modes Energy Econ 2022 113 106200 10.1016/j.eneco.2022.106200
Su CW Chen Y Hu J Chang T Umar M Can the green bond market enter a new era under the fluctuation of oil price? Econ Res-Ekonomska Istraživanja 2023 36 1 536 561 10.1080/1331677X.2022.2077794
Sun L, Fang S, Iqbal S, Bilal AR (2022) Financial stability role on climate risks, and climate change mitigation: implications for green economic recovery. Environ Sci Pollut Res 29(22):33063–33074
Syed AA Ahmed F Kamal MA Ullah A Ramos-Requena JP Is there an asymmetric relationship between economic policy uncertainty, cryptocurrencies, and global green bonds? Evidence from the United States of America Mathematics 2022 10 5 720 10.3390/math10050720
Tiwari AK Abakah EJA Adekoya OB Hammoudeh S What do we know about the price spillover between green bonds and Islamic stocks and stock market indices? Glob Financ J 2023 55 100794 10.1016/j.gfj.2022.100794
Tu CA, Chien F, Hussein MA, Yanto Ramli MM, Psi S, Iqbal MS, Bilal AR (2021) Estimating role of green financing on energy security, economic and environmental integration of BRI member countries. Singap Econ Rev:1–19
Umar Z, Abrar A, Hadhri S & Sokolova T (2023). The connectedness of oil shocks, green bonds, sukuks and conventional bonds. Energy Econ 106562
Wang KH, Su CW, Umar M & Peculea AD (2022a). Oil prices and the green bond market: Evidence from time-varying and quantile-varying aspects. Borsa Istanbul Rev
Wang X Li J Ren X Asymmetric causality of economic policy uncertainty and oil volatility index on time-varying nexus of the clean energy, carbon and green bond Int Rev Financ Anal 2022 83 102306 10.1016/j.irfa.2022.102306
Wang S, Sun L, Iqbal S (2022c) Green financing role on renewable energy dependence and energy transition in E7 economies. Renew Energy 200:1561–1572
Yang Y, Liu Z, Saydaliev HB, Iqbal S (2022) Economic impact of crude oil supply disruption on social welfare losses and strategic petroleum reserves. Resour Pol 77:102689
Zhang L, Huang F, Lu L, Ni X, Iqbal S (2022) Energy financing for energy retrofit in COVID-19: recommendations for green bond financing. Environ Sci Pollut Res 29(16):23105–23116
Zhao L, Saydaliev HB, Iqbal S (2022) Energy financing, COVID-19 repercussions and climate change: implications for emerging economies. Clim Chang Econ 13(03):2240003
Zheng X, Zhou Y, Iqbal S (2022) Working capital management of SMEs in COVID-19: role of managerial personality traits and overconfidence behavior. Econ Anal Pol 76:439–451 | 37061636 | PMC10105613 | NO-CC CODE | 2023-05-14 23:15:54 | yes | Environ Sci Pollut Res Int. 2023 Apr 15; 30(23):64111-64122 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
37071360
26302
10.1007/s11356-023-26302-z
Research Article
Banking sectors and carbon neutrality goals: mediating concern of financial inclusion
Sun Chenghao [email protected]
1
Zhang Yuxin [email protected]
2
1 grid.495260.c 0000 0004 1791 7210 School of Economics and Trade, Shandong Management University, 250357 Jinan, China
2 grid.464402.0 0000 0000 9459 9325 School of Management, Shandong University of Traditional Chinese Medicine, 250357 Jinan, China
Responsible Editor: Nicholas Apergis
18 4 2023
2023
30 23 6463764650
21 1 2023
2 3 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Since industrialization, GHGs have steadily grown, and climate change threatens human civilization. The Chinese government actively engages in the administration of the global environment and has suggested that carbon neutrality be attained by 2060. Regional communities must understand their current carbon neutrality status and objectively design a course to attain carbon neutrality due to significant regional development disparities. This research uses a GMM model in order to investigate the effect of the banking sector and financial inclusion on carbon neutrality for 30 provinces in China for the period of 2000–2020. The following are the key conclusions: (1) clean and efficient energy use, primarily reflected by carbon emissions intensity, carbon dioxide emissions per capita, and coal expenditure per capita, had the most significant influence on attaining carbon neutrality. (2) In terms of energy, economics, and environmental considerations, water consumption per capita, the volume of technology distribution, and carbon pollution intensity were the elements that had the most significant impact on carbon neutrality. (3) The provinces might be categorized into three groups depending on their ability to become carbon neutrality, with developed economies having an easier time doing so than resource-dependent provinces. Financial inclusion should also be increased in order to achieve long-term sustainability of the environment. The findings stand up well to both immediate and long-term policy consequences. The sustainable development goals (SDGs) of the United Nations (UN) are supported by this research.
Keywords
Carbon neutrality
Carbon intensity
Financial inclusion
Carbon sequestration
Non-linear analysis
issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023
==== Body
pmcIntroduction
Recently, Chinese officials said that the country’s goal of carbon neutrality will be reached by 2060. Communities are crucial in reducing carbon emissions and adapting to global warming. Several cities worldwide are taking measures to mitigate the consequences of climate change (Liu et al. 2023). The emission goal comprises 91 large cities that together aim to invest US $48.8 billion in low-carbon buildings over the next 3 years to slow the rate of climate change and lower carbon emissions by the year 2050 (Li and Umair 2023). The metropolitan regions of China are responsible for 85 percentage points of the country’s total CO2 emissions, making China the world’s largest energy consumer and CO2 emitter. Currently, metropolitan regions in China use more energy than the country’s industrial segment. China’s big cities are at the forefront of policymaking and action in the fight against climate change. To attain peak carbon dioxide emissions by 2050 and achieve carbon neutrality by 2050, China has pledged to impose more strict regulatory measures beginning in September 2019. These “low-carbon pilot cities” are expected to provide a path forward for other municipalities in achieving these targets. Evaluating the policy technology of low-carbon pilot towns over time is essential for the federal state to choose appropriate strategy instruments and for other cities to benefit from the excellent experience (Wu et al. 2022). There is now more pressure than ever to make the financial sector’s shift to digital because of the limitations of the present global financial system. Overall, digital finance has contributed to expanding the financial services sector. Consequently, customers may forego expensive traditional.
Thus, digital finance may address financing issues plaguing the new energy industry. Increasing access to other financing options may help solar power companies overcome cash-flow problems. As shown, digital finance may be helpful in the development of clean energy (Guo et al. 2022). Our study examines how green digital finance helps the environment while dealing with limited resources. For instance, the development of digital finance has filled the voids left by traditional finance, as stated by Xiuzhen et al. (2022). Business financing problems might be solved quickly and effectively, freeing money for more incredible research and development in cutting-edge technologies. Due to fintech, banks have boosted their lending to SMEs. This study’s originality lies in that, to the best of our knowledge, it is the first to conduct a complete analysis of how green digital finance affects sustainability metrics from a resource-constrained perspective (Fang et al. 2022). Individuals, families, neighborhoods, and companies may all benefit significantly from greater access to financial resources are only a few of the academic works that examine the topic of financial inclusion measurement (Ullah et al. 2020). Expanding access to financial services has far-reaching implications for society. For instance, it helps level the economic playing field and spurs expansion. It may also increase domestic spending while decreasing income and wealth disparities. Earlier empirical research has looked at the effects of financial inclusion on GS in the setting of China countries or on green economic efficiency. While the impact of financial inclusion on GS in a developing country is still being determined, more work needs to be done to investigate this question. One possible line of reasoning is that expanding access to financial services might lead to a shift in the composition of energy use (Li et al. 2021). However, having more accessible access to loans from banks and other financial institutions may positively impact energy usage by encouraging investment and driving up demand for products that make efficient use of energy (Levin et al. 2002). Thus, it has additional consequences for environmental sustainability. Second, we use a mediation effect study to verify the link between financial inclusion and GS. According to previous research, market-related changes may considerably enhance environmental protection. It is unclear whether the increased mercerization made possible by financial inclusion would affect environmental growth (Umair and Dilanchiev 2022).
Green finance, in either an organizational form or a structural element of the financial sector, makes it feasible for the economy to expand sustainably. The limited scale of green finance depending on money to assure circumstances and the inherent lagging features of green finance need to be revised to attain the expected level of interpersonal environmental preservation and enhancement. Yet, there need to be more incentives for shareholders and financial institutions to become involved in the green area. In light of this, it is necessary to boost the production of renewable energy sources, including solar, wind, and biomass (Pan et al. 2023). Sustainable renewable energy (RE) has several additional advantages that help guarantee reliable access to electrical power, such as creating new energy infrastructure and reinvesting existing energy resources. Also, RE may help decrease climate change and the associated damage to people’s lives and the ecosystem, making it a viable choice for increasing energy supply and supporting social and financial growth. Much research has been motivated by the need for ecological tracking of emissions from highly polluting industries. Environmentally conscious companies have been the focus of studies on ecological business management (Mohsin et al. 2022).
This research uses the GMM model to investigate the impact of the banking industry and financial inclusion on China’s pursuit of carbon neutrality from 2000 to 2020. We corroborate the investment climate, economic expansion, and technological innovation as possible transmission routes of financial inclusion on carbon neutrality. The examination of the relationship between the banking industry and carbon emissions has also increased. Our critical empirical conclusion is that the banking industry has a favorable influence on local cities’ carbon neutrality but a negative impact on nearby cities. Additionally, when the mediation impact mechanism is examined, modernization and economic development pass the test, proving that financial inclusion influences CO2 emissions through these two factors. By creating a unified and consistent approach, we build on the growing research on the linkages between climate risks, financial policy, central banks, and regulators. According to heterogeneity research, the banking industry performs better in eastern regions with more economic variance and high-pollution industries with more significant carbon emissions. In contrast to traditional banking, the inclusive nature of digital finance may promote green innovation among private enterprises, particularly in central and western regions and non-high emission industries. The research’s conclusions serve as a road map for significant regions across the country that are attempting to achieve their carbon emission reduction goals and give factual evidence for the positive influence that China’s expansion in digital finance has played in carbon neutrality. The “Methodology” section presents early considerations of empirical models, variables, and information. In portion 4, the empirical results are provided together with a discussion. In conclusion, we will provide a concise review of our results and some recommendations for developing future policy initiatives.
Literature review
It is essential to employ carbon sinks and other technical solutions to remove GHG emissions from all human economic and social activities to become carbon neutral. Carbon neutrality rests on a foundation of zero net emissions. When carbon neutrality is achieved, it will send a message that sustainable development is possible. At this time, when the world economy is entering a new phase of recovery, further research on the reasons behind governments’ stated carbon neutrality targets is necessary (Chen et al. 2023).
The long-term effects of carbon mitigation strategies, including net-zero emissions in energy, politics, finances, and technology, have been the subject of much research. Energy and business are typically singled out as leading players in limiting climate change, including coal, oil, and gas. Consider the power required to generate electricity, heat homes, businesses, and run vehicles (Mohsin et al. 2021). Without strict limitations imposed by climate policy, achieving the 1.5 °C targets will be difficult. Legally binding targets may govern national energy consumption and its rate of rise. To reduce carbon emissions, they are switching to electricity as their primary energy source and creating new forms of renewable energy. Increasing energy consumption is inevitable as long as the economy continues to grow. As a result of uneven international efforts to reduce emissions, carbon will be transferred across borders in large quantities, undermining the effectiveness of mitigation goals. Cement, steel, petrochemical manufacturing, transportation, and construction must be prioritized if carbon neutrality at the industrial level is to be achieved (Al Asbahi et al. 2019). Energy conservation and emissions reduction will remain paramount until carbon capture, utilization, storage, and energy storage become economically feasible. There has been talk about the need to move quickly away from our existing fossil fuel-based financial model and towards a green, circular economy that will be more sustainable. Most countries’ claims to become carbon neutral are empty political promises without any underlying legislation or policy. To achieve zero emissions, fundamental, system-wide changes in all facets of society and the economy are required.
A comprehensive look at priorities and perspectives on carbon peaking and neutrality is offered by Mohsin et al. (2020b). The advantages and disadvantages of China’s transition to carbon neutrality are outlined, and solutions are suggested. Shah et al. (2019) critically analyze the methods through which national-level design influences carbon-neutral behavior. Employing bibliometrics, they looked at specific patterns in the world of solar energy. Research on carbon capture and storage for bioenergy was analyzed using ARDL. Xia et al. (2020) used DEA methods to assess current carbon accounting practices and provide recommendations for improvement. Using bibliometric analysis, assess the energy sector now and project its future course in light of COVID-19. Specialists and curious laypeople may benefit from researching carbon neutrality’s past and future developments. The present level of research needs to be more cohesive and context-dependent because of the unknowns of financial development phases, technology advancements, energy usage, governmental initiatives, and climate hazards.
Digital finance and financial performance
Investors in new energy projects are less appealing than those in classic energy efforts because of their dependence on energy usage, high up-front costs, long payback periods, and risk unpredictability (Iqbal et al. 2022). Due to a lack of accessible, structured funding, renewable energy enterprises in developing countries face significant obstacles to entry. Yet, there are accusations that renewable energy enterprises in China suffer particular financial challenges. Firms must create a market-oriented expenditure and finance framework when funding renewable energy projects (Shang et al. 2023).
Crowdfunding platforms’ popularity has skyrocketed in recent years. Crowdfunding refers to raising money for a good cause by soliciting contributions from many people at once. As each supporter in a crowdfunding campaign earns a relatively small amount, it is more feasible for startups to raise capital. The second benefit is that digital finance makes the formerly cumbersome process of completing financial transactions much more straightforward and time-saving. In facilitating remote transactions and real-time engagement, new businesses may get reliable financing with less hassle and red tape. Financing companies may save time and money by moving to this method, which shortens the funding process, ensures payments are made practically instantly, and removes the need for physical sites and time-consuming manual operations (Agyekum et al. 2021). Finally, digital finance levels the information playing field and improves risk management. The proliferation of digital data storage has made extensive data collection an inevitable byproduct of everyday activities (Zhang et al. 2021). When examined using well-thought-out methodologies, the internet has made it easier for company owners to connect, share resources, and get insight that may guide their decision-making. Combined, they make previously risky and costly transactions more accessible. Fourthly, traditional banks are compelled to up their game by the rise of digital banking by providing superior customer service and innovative products (Iqbal et al. 2019).
Economic growth and carbon neutrality
Global warming research and environmental preservation may reap the rewards of a thriving economy. Growth in the economy has a significant impact on CO2 emissions (as measured by GDP). The environmental Kuznets curve was developed to show the relationship between GDP and carbon dioxide emissions (Mohsin et al. 2020a). This model anticipates a reverse U-shaped relationship between GDP and CO2 emissions. The analysis found that during the modernization era, CO2 emissions rose as the global economy expanded.
Renewable energy utilization and the environment
The “carbon peak” is the most significant historical annual carbon dioxide emissions point for a particular area or industry. Carbon neutrality occurs when human activities produce as much carbon dioxide gas as they consume. Carbon neutrality is attainable for businesses, neighborhoods, and individuals by monitoring and offsetting carbon emissions through reforestation, energy conservation, and using renewable energy sources. On the other hand, reaching climate neutrality is different from reaching net-zero emissions. When both emissions and their removal are eliminated, we say there are “net-zero emissions.” On the other hand, “climate neutrality” alludes to the impact of human actions on the environment.
Methodology
Theoretical framework
A generalized method of moments (GMM) was used for the calculations in this investigation. The goal was to limit and allocate expenditure funds to provide private shareholders with the most significant benefit. The elements of a utility function that are both predictable and subject to random error characterize the factors that have a role in allocating resources. For the ith individual, the expenditure utility (U) may be expressed as:1 Ui=x′iβ+εi
where x′i is a data vector describing potential investments and specific societal and economic characteristics and is a vector reflecting random errors considered independent and homogeneous of variance with zero mean and constant variance, latent variable models are used to predict the variables. The intangible worth of a person’s utility, Ui, is represented in a total budget. This framework assumes that conventional attention goods and investments in sustainable energy may be divided into two categories. In this approach, the observable allocation maximizes utility for each respondent individually. As the allocation of resources might range from nothing to the whole budget, this model fits the characteristics of censored data well. Maximum loan amounts may reach 100% of the project cost, with a minimum expenditure requirement of $0. Hence, a GMM model became the macroeconomic definition for investigating different investment tactics. Via a linear model (Eq. (1)), the independent variable is associated with a latent dimension in this model.
Basic model
In this study, a simple polynomial model was used for the purpose of doing an initial analysis of the basic influence that DFI has on coal. Equation 1summaries this essential model in a way that’s easy to read and remember:2 Cit=a0+a1DIFit+a2(DIFit)2+a3(DIFit)3+∑k=48akcontrolit+ωi+γt+εit
The letter i stands for the city; the letter t for the year, Cit and Car_Iitci and cs respectively Car_Iit and DIFit is development degree of digital financial inclusion in a city controlit is group control variables, papulation density PDit. Higher education HEit, SST and educations SSTEit,govt participation GOVit, openness OPENit, ωi, Individual fixed effect γt, εit individual fixed effect time.
Threshold regression model
To confirm the nonlinear connection and gauge the potential for an outsidejerkstrength, the threshold model is used in this research. This standard perfect may be explained as follows:3 Cit=b0+b1DIFit∗I(Varit<Q1)+b2DIFit∗I(Varit≥Q1)+∑k=37bkcontrolit+ωi+γt+εit
The threshold variable is denoted by the letter Q1, and all other variables are interpreted in accordance with the formula (3). A single-threshold model, such as (2), may serve as a foundation upon which to build multi-threshold models.
Mediation model
This model is widely used in the social sciences to survey the influence mechanism and the cascade of an influence’s consequences. We use this technique to examine the effect of access to services on emission intensity and coal confiscation. To put together the model, do as follows:4 Cit=a0+a1DIFit+a2(DIFit)2+a3(DIFit)3+∑k=48akcontrolit+ωi+γt+εit
5 Medit=c0+c1DIFit+∑k=26ckcontrolit+ωi+γt+εit
6 Cit=d0+d1DIFit+d2(DIFit)2+d3(DIFit)3+Medit+∑k=48akcontrolit+ωi+γt+εit
The mediation model consists of formula (3), the base model, and formulas (4) and (5), which are equivalent to one another Medit show the mediation, and disposable income ICit, digitization GTit, green technology GTit, green space GSit.
SYS-GMM model
In the robustness test, the method of system GMM estimation is utilized to further analyze the dynamics of impact and to overcome indignity to some degree. The mechanics of an impact may be better understood in this way. To simply outline the building method of the model:7 Cit=e0+e1Ci(t-1)+e2DIFit+e3(DIFit)2+e4(DIFit)3+∑k=59ekcontrolit+ωi+γt+εit
In this case, Ci(t-1) represents the previous year of the dependent variable, which is shown by CarIit-1 and Car_Si(t-1).
The formula offers a structure for understanding how the remaining variables should be read one.
Data sources and variable instruction
This study used statistics yearbooks and reports to build a balanced panel data set for empirical research on 277 cities in China. The remaining information was collected from publicly available sources, such as academic databases and scholarly journals. The GDP database came from the China City Statistical Yearbook, and the CEADs figures came from the Carbon Emissions Database. PKU-DFIIC is responsible for providing data for the digital financial inclusion index. Independent variables collected at Peking University formed the basis for these results. The former categories of control variables and mediating factors are gathered by combing through the China Statistical Yearbook for each prefecture and municipality and the China Statistical Yearbook for the National Patent Office. Data for the variables under investigation in the actual study may be summarized with the help of descriptive statistics. Mean, median, range, standard deviation, skewness, and kurtosis were calculated in the current study by using the data’s generic numeric form. Extra precautions have been taken to guarantee the accuracy of the data. The results demonstrate a shift in the average and dispersion of these variables. Hence, GII, GDP, and the other variables all have time-dependent distributions of their transition probabilities (including REI, REP, and PSP). If the initial variance is accounted for, there is no change in the probability density distributions of the variables. The findings reveal the predicted outcomes of various statical methods. By contrast, the gap between the mean and median is narrower in Table 1. While the range of values does encompass substantial swings in value, the standard deviation of each data point is larger than the mean. Standard deviations for the numbers mentioned above are as follows in Table 1.Table 1 Descriptive statistics
Variables Mean S.D C.V Minimum Maximum obs. Element
carI 6.031 0.632 0.206 3.744 5.826 1943 MT/108Yuan
CarS 6.577 0.999 0.219 4.857 11.87 1943 MT
DIF 2.848 0.878 0.599 0.413 4.855 1943 -
Exposure 1.459 0.653 0.507 0.119 3.63 1943 -
Custom 1.627 0.71 0.527 0.825 4.857 1943 -
expense 1.549 0.829 0.603 -0.370 3.66 1943 -
cover 2.764 1.401 0.588 0.114 5.482 1943 -
praise 1.876 0.532 0.501 -0.529 1.853 1943 -
asset 0.817 0.872 0.723 0.134 2.988 1943 -
P.D 45.558 34.897 0.861 0.61 265.811 1943 10persons/km2
H.E 1.476 2.847 1.804 0.103 14.112 1943 %
SSTE 1.694 1.659 0.978 0.011 20.483 1943 %
GOVT 19.907 12.919 0.73 1.836 373.889 1943 %
OPEN 9.797 2.59 0.264 0 14.941 1943 $
I.U 37.362 8.753 0.248 11.15 82.98 1943 %
I.R 0.404 0.515 0.807 0.002 2.225 1943 -
I.C 11.14 0.368 0.126 8.366 10.044 1943 Yuan/person
DIGITI 9.391 2.042 0.224 6.642 14.56 1943 person
G.S 0.094 0.184 1.969 0.002 1.741 1943 hectare
G.T 5.979 2.622 0.526 0.01 11.27 1943 Pcs
The standard deviation (S.D.) is affected by the mean
Increased investments in renewable energy HE can be directly linked to growing GOVT. Depreciation costs for eco-friendly enterprises exhibit a normal distribution, as shown by the same graph. Businesses are absorbing the disadvantages in natural and human capital, which is helping the environmental sector recover. The COVID-19 pandemic, as reported by the survey’s respondents, had a profound effect on the development of the new continental bankruptcy concept and the management of PSP remedies’ associated finances (Chang et al. 2022c). In the former, participants rated the importance of producing renewable energy as higher. With this regulation in place, small and medium-sized firms are more likely to pay attention to political metrics as presented in Table 2.Table 2 Correlation matrix between variable
Fixed (DIF) (P.0) (H.E) (SSTE0 (GOVT) (OPEN) (I.U) (I.R) (I.C) (DIGI) G.S G.T
DIF 2
PD 1.111∗∗∗ 2
HE 0.229∗∗∗ 0.240∗∗∗ 2
SSTE 0.235∗∗∗ 0.371∗∗∗ 0.378∗∗∗
GOVT -0.011 -0.283∗∗ -0.305∗∗ -0.326∗∗ 2
OPEN 0.119∗∗∗ 0.533∗∗∗ 0.433∗∗∗ 0.513∗∗∗ -0.425∗∗∗ 2
I.U 0.516∗∗∗ 0.300∗∗∗ 0.646∗∗∗ 0.395∗∗∗ 0.006 0.240∗∗∗ 2
I.R -0.302∗∗∗ -0.050∗∗ -0.241∗∗ -0.015 0.056∗∗ -0.109∗∗∗ -0.336∗∗ 2
I.C 0.862∗∗∗ 0.407∗∗∗ 0.504∗∗∗ 0.602∗∗∗ -0.637∗∗∗ 0.649∗∗∗ 0.836∗∗∗ -0.208∗ 2
DIGI 0.274∗∗∗ 0.438∗∗∗ 0.589∗∗∗ 0.416∗∗∗ -0.215∗∗∗ 0.447∗∗∗ 0.839∗∗∗ -0.256∗ 0.574∗ 2
G.S 0.214∗∗∗ 0.459∗∗∗ 0.465∗∗∗ 0.431∗∗∗ -0.135∗∗∗ 0.355∗∗∗ 0.485∗∗∗ -0.107∗ 0.448∗ 0.669∗∗ 2
G.T 0.509∗∗∗ 0.519∗∗∗ 0.549∗∗∗ 0.589∗∗∗ -0.329∗∗∗ 0.559∗∗∗ 0.538∗∗∗ -0.149∗ 0.738∗ 0.738∗∗ 0.5∗ 2
Coefficient significance levels of 1%, 5%, and 10% are shown by ***, **, and *, respectively
Large enterprises and government organizations, both of which have assumed increasingly pivotal roles due to this transition, a significant firm that participated in the research stated, “The legislation is not changing anything in actuality but is focusing on the necessity of assessing how economic criteria are considered towards small and medium businesses.” If these companies are not appropriately managed, it might harm their extensive client base. There is an immediate need to make adjustments to current procedures in light of this impending change. One focus group member opined that this shift may pave the way for significant system improvements, such as developing novel solutions like “financial reform.” More choices may be made when several reaction elements are considered.
Empirical analysis
Table 3 indicates the popularity of novel GS solutions, including equity and comprehensive financing. In the business model, we devised creative ways to employ existing technology, such as variable discounting, to improve ecological behaviors. It has also been determined that in the event of a crisis, new economic development, like reorganization funding, may develop. To provide conclusive evidence, environmental sustainability must be broadened to cover all facets of working capital administration. Some alternatives have already entered the industry, while others are still in the research phase. Is it possible that they have become a reality and expanded green financing and expenditure in renewable energy?Table 3 Basic model results
Variable car-i car-s
1 2 3 4 5 6
DIF3 −1.055*** 1.014
−1.016 −1.009
DIF2 −1.075*** 1.202*** −1.036** −1.094**
−1.023 −1.074 −1.008 −1.039
DIF −1.284*** 1.011 −1.388** 1.025 1.160*** 1.239***
−1.075 −1.107 −1.158 −1.025 −1.048 −1.069
(P.D) −1.004** −1.003 −1.004 −1.001 1.025 1.025
−1.002 −1.002 −1.006 −1.006 −1.006 −1.002
(H.E) 1.006 1.012 1.025 1.025 0.009* 1.009**
1.016) 1.016) −1.025 −1.025 −1.16 1.016)
(SSTE) −1.019** −1.017** −1.017** 1.002 1.002 1.002
−1.008 −1.008 −1.008 −1.008 −1.008 −1.002
(GOVT) 1.003 1.011 −1.388** 1.025 1.011 −1.388**
−1.002 −1.002 −1.002 −1.001 −1.001 −1.001
(OPEN) −1.024*** −0.024*** −0.024*** −1.002 −1.002 −1.002
−1.006 −1.006 −1.006 −1.006 −1.006 −1.006
(Cons) 6.748*** 5.558*** 5.668*** 7.529*** 7.445*** 7.424***
−1.094 −1.107 −1.117 −1.117 −1.117 −1.117
Separate ff sure sure sure sure sure sure
Phase ff sure sure sure sure sure sure
r2 1.672 1.679 1.682 1.672 1.679 1.682
n 1943 1943 1943 1943 1943 1943
Coefficient significance levels of 1%, 5%, and 10% are shown by ***, **, and *, respectively
Basic regression analysis
These findings partially support the study that digital banking may boost energy and ecological efficiency. Specifically, the “carbon paradise” impact generated by heterogeneity in its growth tends to diminish as emerging digital finance converges across areas, releasing low-carbon, and green financial impacts (Li et al. 2023). Long-term emission reduction is also improved by ecological legislation, which the study reveals has an abatement impact locally and regionally. While diverging from research, this finding is generally in line with that of the study, which posits that the beneficial effect of ecological control on lowering carbon emissions grows more prominent as it is improved in Table 3.
The industrial sector is subject to stronger ecological regulations and higher ecological standards in areas with higher regional expenditure on ecological administration. Carbon emissions are reduced as a result of the increased research and use of energy-efficient and emission-cutting technology. However, the pollution refuge effect of carbon emissions is to blame for the superior efficiency of environmental regulation in neighboring regions compared to the local region (Hu et al. 2022). As local environmental regulations become more stringent, high-carbon industries are more likely to relocate to neighboring regions with fewer restrictions. As a corollary, our results corroborate the findings of those who found that carbon emissions do migrate among regions with differing levels of ecological control. Ultimately, this gives data to back up the need for stricter ecological regulations and their further improvement (Chang et al. 2022a). The anticipated increasing influence of economic institutions’ rivalry to mitigate the negative impact of digital finance and ecological legislation on the industrial sector argue that a more competitive banking and finance sector is beneficial to increase ecosystem quality and lower carbon emissions, which holds true. Our findings are highly significant because they reaffirm the significance of sector competition for carbon decrease. More crucially, they lend credence to the policy merits of combining effective institutions with a responsive government.
Threshold regression analysis
The results from the SYS-GMM model are displayed in Table 4. Consistent with the findings of the grey correlation model, the regression coefficient for the green finance development level (GS) is positive. All of this points to the importance of green financing in bolstering the transformation of industrial infrastructures into more advanced forms. China, like many other nations, is being held back in terms of financial growth and effectiveness by the country’s notoriously erratic market (Chang et al. 2022e). Recent studies have shown, however, that the instability in the pricing of natural resources caused by COVID-19 has a major depressing effect on financial expansion. The previous study has also validated the positive benefits of external variables on economic success. The effects of the directive are listed in Table 4. Both “configuration imbalances” and “orientation financial effects” can be seen to be examples of the “capability to contribute from others.” The forecast model error variances for the other markets are given in the sub-diagonal regions of each column. Each row, except the main diagonal, depicts the contribution of other products to the overall variation in the industry’s forecast inaccuracy.Table 4 Threshold selection based on the bootstrap method
Reliant on variable Autonomous variable Verge variable Perfect Verge worth B.S P-value f-test
car I (DIF) DIF SINGLE 1.044 400 0.001 41.58***
DOUBLE 2.315 400 0.049 18.47**
(DIF) (OPEN) SOLE 9.959 400 0.001 64.87***
DUAL 11.211 400 0.075 26.82*
(DIF) (SSTE) SOLE 2.269 400 0.001 180.59***
DUAL 2.639 400 0.001 73.72***
car s (DIF) (DIF) SOLE 1.159 400 0.039 17.75**
DUAL 1.889 400 0.029 18.89**
(DIF) H.E SOLE 1.209 400 0.039 17.39**
Coefficient significance levels of 1%, 5%, and 10% are shown by ***, **, and *, respectively
For this reason, there is a distinct variety of predicted errors that can occur across various markets. The word “contribution” is used to describe the impact that unexpected developments within a certain industry have on the overall market variance. The title of the input–output table is “Achievement from others.” Yet, causality statistics illuminate the processes of taking in, relaying, and assessing disruptions (Chang et al. 2022b). The environmental sustainability and the median threshold regression results are both important metrics for green finance. As can be seen in Table 5, there is a strong interaction between green financing and the overall economic growth index.Table 5 Threshold regression results
Flexible car i car s
l rar = DIF Var = SSTE var = OPEN var = DIF var = H.E
(DIF) * I (var < Q1) −1.315*** −1.069 −1.180*** 1.100*** 1.045
−1.058 −1.057 −1.058 −1.038 −1.039
(DIF) * I (Q1 ≤ var < Q2 or var ≥ Q1) −1.259*** −1.129** −1.129*** 1.075** 1.029
−1.058 −1.05 −1.059 −1.039 −1.035
(DIF) * I (var ≥ Q2) −11.280*** −1.189*** −1.253*** 1.059*
−1.055 −1.055 −1.056 −1.039
Controller flexible yes yes yes Yes yes
Separate FF yes yes yes yes yes
Period FF yes yes yes yes yes
R2 1.683 1.711 1.688 1.137 1.129
N 1943 1943 1943 1943 1943
Coefficient significance levels of 1%, 5%, and 10% are shown by ***, **, and *, respectively
The expansion of environmentally friendly financial systems is a growing trend. Between 2010 and 2017, green funding in the China increased, albeit growth rates varied.
Mediation regression analysis
The growth rate was higher between 2015 and 2018 than in prior years but has since stabilized. In 2017, the Chinese state encouraged a greater degree of green finance growth, which was most noticeable in the country’s central and western areas. Green finance in China has not developed at the same rate as the country’s economy. When compared on a global scale, the rate of expansion of green financing varies widely results of a mediation regression analysis in Table 6. While the green economy improved significantly in the other areas in 2011, the eastern region’s financial infrastructure was perfect that year.Table 6 Results of the transmission mechanism
Variable car i car s
Medi = I.C Medi = DIGIT Medi = G.S Medi = GT
I.C Car i DIGI car-i GS car-s G.T car s
DIF3 −1.059*** −0.061***
−0.019 −0.019
DIF2 0.189** 0.199*** −0.030*** −0.059***
−1.169 −1.169 −1.169 −1.169
DIF 1.099*** −1.349** 1.881*** −1.369** 1.049** 1.169*** 2.022*** 0.201***
−1.169 −1.169 −1.169 −1.169 −1.169 −1.169 −1.169 −1.169
IC −1.159*
-1.089
DIGI −1.029**
−1.019
GS 1.069*
−1.039
GT 1.009***
−1.003
Controller variable Yes Yes Yes Yes Yes Yes Yes Yes
Separate FE Yes Yes Yes Yes Yes Yes Yes Yes
Timey FE Yes Yes Yes Yes Yes Yes Yes Yes
R2 1.915 1.685 1.198 1.688 1.159 1.139 1.289 1.139
N 1943 1943 1943 1943 1943 1943 1943 1943
Coefficient significance levels of 1%, 5%, and 10% are shown by ***, **, and *, respectively
Table 6 displays the results of a mediation regression analysis.
The widespread use of agricultural finance made prolonged development in 2013. All counties in the southwest were in the medium to lower ranges in 2017 compared to 2012 and prior years. There has been an increase in interest in China’s green finance industry, and gaps in green economy effectiveness between regions have begun to close. It is possible that 2017s implementation of a pilot green finance program, which promotes China’s expansion of green finance in a complementary way, is to blame. According to a time series evolutionary viewpoint, the expansion of China’s southeastern shorelines is still more significant than that of the western and northern areas, even if China’s green finance industry has risen greatly from 2012 to 2019.
Heterogeneity analysis 1: different dimensions of digital financial inclusion
Investment opportunities in renewable energy production var in China were analyzed to see the extent to which mainstream financial institutions would be willing to support such initiatives. The study’s authors examined how the targeted countries have invested in green energy. Increased sustainability means increased financial incentives for var3 and reduced costs for financial intermediaries because of required disclosure (Chang et al. 2022d). Var2 have improved their reporting of environmental risks, which is excellent news for the government subsidies and interest-free green loans they can access. To get government subsidies and to be eligible for reduced interest rates on green loans, renewable energy providers are encouraged to report more environmental data. Applying the var3 model, Table 7 shows that the bigger the quantity of environmental disclosure, the worse the organizational sustainability.Table 7 Heterogeneity analysis
Variable car-I
var = exposure var = use var = pay var = cover var = glory var = asset
var3 −0.059***
−0.015
var2 0.208*** −0.126*** −0.017*** −0.288*** −0.111***
−0.028 −0.015 −0.008 −0.039 −0.018
var −0.529*** 0.289*** −0.249*** 0.059* 0.479*** 0.259***
−0.149 −0.069 −0.049 −0.039 −0.089 −0.069
Controller variables Yes Yes Yes Yes Yes Yes
Separate FE Yes Yes Yes Yes Yes Yes
Timey FE Yes Yes Yes Yes Yes Yes
R2 0.689 0.699 0.688 0.679 0.709 0.689
N 1943 1943 1943 1943 1943 1943
Coefficient significance levels of 1%, 5%, and 10% are shown by ***, **, and *, respectively
Approximation findings with regional variation are shown in Table 8 for the regression analysis. We found that financial inclusion had the expected favorable and substantial influence on GS. More specifically, a 1% increase in financial inclusion would boost GS by 5–6% percentage points across the board. In particular, financial inclusion had more significant effects on carbon neutrality in central/western. As a result, the following points should be addressed to policymakers to enhance sustainable development goals.Table 8 Heterogeneity analysis
Variable car-s
var = exposure var = practice var = expense var = cover var = glory var = asset
var3
var2 −0.031*** −0.029*** −0.019*** 0.001 −0.069*** −0.009
−0.008 −0.009 −0.007 −0.008 −0.019 −0.006
var 0.187*** 0.039 0.059 −0.039** 0.078*** −0.027
−0.045 −0.039 −0.039 −0.017 −0.029 −0.033
Controller variables Yes Yes Yes Yes Yes Yes
Separate FE Yes Yes Yes Yes Yes Yes
Timely FE Yes Yes Yes Yes Yes Yes
R2 0.139 0.133 0.131 0.134 0.128 0.129
N 1943 1943 1943 1943 1943 1943
Coefficient significance levels of 1%, 5%, and 10% are shown by ***, **, and *, respectively
On the one hand, redistributing wealth away from the east and toward the center and west is essential. Yet, financial inclusion has widely varying impacts on IC across urban areas. Hence, to effectively and completely promote var3, policymakers should stick to categorized guiding and character development based on the city's unique resource endowments and factor circumstances in Table 8.
Robustness test
To ensure the validity and reliability of our empirical findings, we utilize several different estimate methods, model specifications, financial inclusion metrics, and DIF3 measures in this section. For the two-stage efficiency estimation issue, first offer two bootstrap approaches. Table 9’s estimate findings, shown in column (1), reveal that financial inclusion was substantially and positively linked with DIF3 promotion, suggesting that it passes their robustness check. Second, we employ a one-period lag and lead of financial inclusion to represent the financial inclusion level of the current period and mitigate the reverse causality induced by endogeneity, allowing us to run a synchronism test. Evidence from our study demonstrates that monetary inclusion significantly influenced DIF3 advocacy efforts. Finally, to describe the amount of financial inclusion, we utilize three sub-indicators developed by, namely, coverage breadth and digital support service extent. Indeed, the estimated values agree with the facts (Wang et al. 2023).Table 9 Robustness test: SYS-GMM, multidimensional fixed, adding control variables of IU and IR, and winsorizing
SYST-GMM Multidimensional fix Add controller variables—IU, IR Winsorizing
Variables car-I car-S car-I car-S car-I car-S car-I car-S car-I car-S
car-I 1.94***
−1.044
car-S 1.047***
−1.044
DIF3 −1.139*** −1.063*** −1.034** −1.065*** −1.040***
−1.045 −1.045 −1.045 −1.045 −1.045
DIF2 1.708*** −1.488*** 1.202*** −1.045*** 1.101* −1.038*** 1.219*** −1.039*** 1.198*** −1.041***
−1.045 −1.045 −1.045 −1.045 −1.045 −1.042 −1.049 −1.047 −1.048 −1.045
DIF −1.228*** 1.610** −1.228*** −1.228*** −1.228*** −1.228*** −1.228*** −1.228*** −1.228*** −1.228***
−1.411 −1.411 −1.411 −1.411 −1.411 −1.411 −1.411 −1.411 −1.411 −1.411
AR (1) 1.94** 1.99**
AR (2) 1.94*** 1.94*
Hansen test 1.94*** 1.94***
Controller variables Sure Sure Sure Sure Sure Sure Sure Sure Sure Sure
IU Sure Sure
IR Sure Sure
Individual ff Sure Sure Sure Sure Sure Sure Sure Sure Sure Sure
Time ff Sure Sure Sure Sure Sure Sure Sure Sure Sure Sure
Province ff Sure Sure
r2 1.964 1.964 1.964 1.964 1.964 1.964 1.964 1.964
n 1663 1663 1943 1943 1943 1943 1943 1943 1943 1943
Coefficient significance levels of 1%, 5%, and 10% are shown by ***, **, and *, respectively
At last, the SYS-GMM model is used to re-estimate the DIF, which concurrently accounts for non-convex Meta frontier and super efficiency. As there was no appreciable variation in the outcomes in Table 9, the inferences are drawn before remaining sound. Hence, our empirical results are not sensitive to the specific estimate approach or the measures of DIF and financial inclusion used.
It can be demonstrated that financial inclusion encouraged GS in China by having a large, beneficial influence on GS. Increases in financial inclusion are associated with increases in GS of about 6.5% per unit. Contrary to the findings of, we find no evidence that financial inclusion has a detrimental effect on economic growth. One explanation might be that chosen economic indicator, the green growth to the primary energy consumption ratio, does not completely account for substituting energy input with other input variables. also utilized a provincial sample, which did not allow them to account for city-level variation (Chang et al. 2023). As predicted, carbon neutrality had a positive and substantial relationship with carbon neutrality and innovation but a negative and significant relationship with coefficients demonstrate that IC had a positive influence on green innovation, suggesting that the pollution haven theory might not apply to China. One of the many aspects that might influence a company’s performance is the pace at which new ideas are introduced and implemented. Earlier studies did not account for the mediating role that technology may play in digital banking, financial restrictions, and financial success. Digital finance appears to have less of a marginal effect on larger enterprises, according to the statistics.
Larger businesses have broader economic requirements, and their investments are more likely to focus on high-risk, high-reward technologies. While smaller businesses may be able to weather the storm of a shifting economic climate and economic constraints, more giant corporations often find themselves in a more precarious position. Nonetheless, the negative impacts of economic restraints are lessened since small enterprises cannot develop smoothly. Reach a similar result, arguing that the green credit policy’s punitive effect is mitigated in small businesses due to a lack of capital. As this is the case, H1 is confirmed by the data: there have been significant advantages to using digital banking. As prior studies have found that monetary limitations negatively affect financial performance, H2 is supported. This finding is in line with that of those who demonstrate that increases in green total factor production lead to fewer emissions. The cost of more economic resources and tighter financial constraints may reduce production (Choudhary et al. 2018).
Conclusion and policy implications
Ecological and environmental resources are being degraded, and resources are being consumed constantly, making the present global climate problem very obvious. This research uses a GMM modeling order to investigate the effect of the banking sector and financial inclusion on carbon neutrality for 30 provinces in China for the period of 2000–2020. In too many countries’ minds, achieving carbon neutrality as soon as possible is a top priority. For the next many decades, one key aim of economic and environmental literature will be how to actualize a low-carbon economy. At the same time, there has been an uptick in curiosity about a novel form of finance that emphasizes digitization and diversity; this type of finance appears to pave the way for establishing a low-carbon economy. Hence, this study employs panel data from 60 developing and non-emerging economies between 2010 and 2020 to analyze the effect of green digital finance on long-term viability. Five perspectives direct, mediating, premise, spatial, spillover, and policy shock are used to examine the results of the empirical studies. Key findings are presented in full. In addition to its direct benefits, streamlining industrial structure and encouraging green technology innovation made feasible by inclusive digital finance may have a substantial knock-on effect on carbon intensity reduction. Yet, conditions such as a suitable technological environment, adequate economic growth, and a receptive mentality are needed before the influence may manifest. The carbon intensity may be lowered within a specific range due to the regional spillover effect of inclusive digital funding. The introduction of digital finance has the potential to efficiently handle the “difficult and costly financing” problem facing crucial growing enterprises while simultaneously reducing the motivation for such organizations to “transition away from the truth to reality” and the related financial dangers. At the same time, it will help major developing enterprises achieve technical advancements, strengthen their innovation capacities, speed up their digital transformation, and raise their value. Compared to locations with underdeveloped traditional banking, industrialized countries have a more substantial impact from inclusive green digital finance when cutting down on carbon emissions. Across the industrialized globe, commonalities may be seen in the neighborhoods. Based on the results, the research suggests the following measures be taken.
States should prioritize optimizing the energy utilization structure and sector framework, encouraging the development of inclusive green finance, and promoting the building of an organization’s economic remarks, all while working to increase the close collaboration between municipal governments and financial firms. As they steer the development of inclusive finance, state organizations should maximize resource allocation, implement the growth strategy, and adjust to the local financial climate. This will enable business owners to get green growth funding despite their difficulties. Additionally, multidimensional growth is the most beneficial of the three digital inclusive financial index components in supporting urban economies and safeguarding the environment using digital instruments.
Acknowledgements
This work was sponsored in part by the Key Project of Philosophy and Social Science Research in Jinan (JNSK22B46) and Project of Philosophy and Social Science Research in Philosophy and Social Sciences Planning Project in Jinan(JNSK22C72) and Youth Innovation Team Plan of University in Shandong Province（2022RW052).
Author contribution
Conceptualization, methodology; writing—original draft, data curation, data analysis: Chenghao Sun; visualization, editing, proof reading, corrections: Yuxin Zhang.
Data availability
The data that support the findings of this study are openly available on request.
Declarations
Ethical approval and consent to participate
The authors declared that they have no known competing financial interests or personal relationships, which seem to affect the work reported in this article. We declare that we have no human participants, human data, or human issues.
Consent for publication
We do not have any individual person’s data in any form.
Competing interest
The authors declare no competing interests.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Preprint service
Our manuscript is not posted at a preprint server prior to submission.
==== Refs
References
Agyekum EB Amjad F Mohsin M Ansah MNS A bird’s eye view of Ghana’s renewable energy sector environment: a multi-criteria decision-making approach Utilities Policy 2021 10.1016/j.jup.2021.101219
Al Asbahi AAMH Gang FZ Iqbal W Abass Q Mohsin M Iram R Novel approach of principal component analysis method to assess the national energy performance via energy trilemma index Energy Rep 2019 10.1016/j.egyr.2019.06.009
Chang L, Baloch ZA, Saydaliev HB, Hyder M, Dilanchiev A (2022a) Testing oil price volatility during Covid-19: global economic impact. Resour Policy:102891. 10.1016/j.resourpol.2022.102891
Chang L Gan X Mohsin M Studying corporate liquidity and regulatory responses for economic recovery in COVID-19 crises Econ Anal Policy 2022 76 211 225 10.1016/j.eap.2022.07.004 36043124
Chang L Qian C Dilanchiev A Nexus between financial development and renewable energy: empirical evidence from nonlinear autoregression distributed lag Renew Energy 2022 193 475 483 10.1016/j.renene.2022.04.160
Chang L Saydaliev HB Meo MS Mohsin M How renewable energy matter for environmental sustainability: evidence from top-10 wind energy consumer countries of European Union Sustain Energy Grids Netw 2022 31 100716 10.1016/j.segan.2022.100716
Chang L Taghizadeh-Hesary F Saydaliev HB How do ICT and renewable energy impact sustainable development? Renew Energy 2022 199 123 131 10.1016/j.renene.2022.08.082
Chang L Shi F Taghizadeh-Hesary F Saydaliev HB Information and communication technologies development and the resource curse Resour Policy 2023 80 103123 10.1016/j.resourpol.2022.103123
Chen X, Chen W, Lu K (2023) Does an imbalance in the population gender ratio affect FinTech innovation? Technol Forecast Soc Chang 188:122164. 10.1016/j.techfore.2022.122164
Choudhary P Srivastava RK De S Integrating greenhouse gases (GHG) assessment for low carbon economy path: live case study of Indian national oil company J Clean Prod 2018 198 351 363 10.1016/j.jclepro.2018.07.032
Fang W Liu Z Surya Putra AR Role of research and development in green economic growth through renewable energy development: empirical evidence from South Asia Renew Energy 2022 194 1142 1152 10.1016/j.renene.2022.04.125
Guo B Wang Y Zhou H Hu F Can environmental tax reform promote carbon abatement of resource-based cities? Evidence from a quasi-natural experiment in China Environ Sci Pollut Res 2022 10.1007/s11356-022-23669-3
Hu F, Qiu L, Xi X, Zhou H, Hu T, Su N, Zhou H, Li X, Yang S, Duan Z, Dong Z, Wu Z, Zhou H, Zeng M, Wan T, Wei S (2022) Has COVID-19 changed China's digital trade?-Implications for health economics. Front Public Health 10:831549. 10.3389/fpubh.2022.831549
Iqbal W Yumei H Abbas Q Hafeez M Mohsin M Fatima A Jamali MA Jamali M Siyal A Sohail N Assessment of wind energy potential for the production of renewable hydrogen in Sindh Province of Pakistan Processes 2019 10.3390/pr7040196
Iqbal N, Tufail MS, Mohsin M, Sandhu MA (2022) Assessing social and financial efficiency: the evidence from microfinance institutions in Pakistan. Pak J Soc Sci 39(1):149–161. http://pjss.bzu.edu.pk/index.php/pjss/article/view/646
Levin A Lin C-F Chu C-SJ Unit root tests in panel data: asymptotic and finite-sample properties J Econ 2002 108 1 1 24 10.1016/S0304-4076(01)00098-7
Li X Sun Y Application of RBF neural network optimal segmentation algorithm in credit rating Neural Comput & Applic 2021 33 14 8227 8235 10.1007/s00521-020-04958-9
Li C Umair M Does green finance development goals affects renewable energy in China Renew Energy 2023 203 898 905 10.1016/j.renene.2022.12.066
Li X Wang J Yang C Risk prediction in financial management of listed companies based on optimized BP neural network under digital economy Neural Comput & Applic 2023 35 3 2045 2058 10.1007/s00521-022-07377-0
Liu F Umair M Gao J Assessing oil price volatility co-movement with stock market volatility through quantile regression approach Resour Policy 2023 81 103375 10.1016/j.resourpol.2023.103375
Mohsin M Nurunnabi M Zhang J Sun H Iqbal N Iram R Abbas Q The evaluation of efficiency and value addition of IFRS endorsement towards earnings timeliness disclosure Int J Financ Econ 2020 10.1002/ijfe.1878
Mohsin M, Zaidi U, Abbas Q, Rao H, Iqbal N, Chaudhry S (2020b) Relationship between multi-factor pricing and equity price fragility: evidence from Pakistan. Int J Sci Technol Res 8
Mohsin M Ullah H Iqbal N Iqbal W Taghizadeh-Hesary F How external debt led to economic growth in South Asia: a policy perspective analysis from quantile regression Econ Anal Policy 2021 72 423 437 10.1016/J.EAP.2021.09.012
Mohsin M Taghizadeh-Hesary F Shahbaz M Nexus between financial development and energy poverty in Latin America Energy Policy 2022 165 112925 10.1016/j.enpol.2022.112925
Pan W Cao H Liu Y “Green” innovation, privacy regulation and environmental policy Renew Energy 2023 203 245 254 10.1016/j.renene.2022.12.025
Shah SAA Zhou P Walasai GD Mohsin M Energy security and environmental sustainability index of South Asian countries: a composite index approach Ecol Indic 2019 106 66 105507 10.1016/j.ecolind.2019.105507
Shang Y Zhu L Qian F Xie Y Role of green finance in renewable energy development in the tourism sector Renew Energy 2023 206 890 896 10.1016/j.renene.2023.02.124
Ullah K Rashid I Afzal H Iqbal MMW Bangash YA Abbas H SS7 Vulnerabilities—a survey and implementation of machine learning vs rule based filtering for detection of SS7 network attacks IEEE Commun Surv Tutorials 2020 22 2 1337 1371 10.1109/COMST.2020.2971757
Umair M, Dilanchiev A (2022) Economic recovery by developing business starategies: mediating role of financing and organizational culture in small and medium businesses. Proceedings Book 683
Wang J Cui M Chang L Evaluating economic recovery by measuring the COVID-19 spillover impact on business practices: evidence from Asian markets intermediaries Econ Chang Restruct 2023 10.1007/s10644-023-09482-z
Wu Q Yan D Umair M Assessing the role of competitive intelligence and practices of dynamic capabilities in business accommodation of SMEs Econ Anal Policy 2022 10.1016/j.eap.2022.11.024
Xia Z Abbas Q Mohsin M Song G Trilemma among energy, economic and environmental efficiency: can dilemma of EEE address simultaneously in era of COP 21? J Environ Manage 2020 10.1016/j.jenvman.2020.111322
Xiuzhen X Zheng W Umair M Testing the fluctuations of oil resource price volatility: a hurdle for economic recovery Resour Policy 2022 79 102982 10.1016/j.resourpol.2022.102982
Zhang D Mohsin M Rasheed AK Chang Y Taghizadeh-Hesary F Public spending and green economic growth in BRI region: mediating role of green finance Energy Policy 2021 10.1016/j.enpol.2021.112256 | 37071360 | PMC10111334 | NO-CC CODE | 2023-05-14 23:15:54 | yes | Environ Sci Pollut Res Int. 2023 Apr 18; 30(23):64637-64650 |
==== Front
Lancet Public Health
Lancet Public Health
The Lancet. Public Health
2468-2667
Published by Elsevier Ltd.
S2468-2667(23)00083-X
10.1016/S2468-2667(23)00083-X
Editorial
Addressing hearing loss at all ages
The Lancet Public Health
27 4 2023
5 2023
27 4 2023
8 5 e318e318
© 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
2023
Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
==== Body
pmcMore than 1·5 billion people experience some degree of hearing loss, a number that could increase to 2·5 billion by 2050, according to WHO's World Report on Hearing. Hearing loss is often seen as an invisible disability. Not only because it is not associated with visible signs but also because it does not receive the attention it should. The consequences of hearing loss can be profound. Untreated hearing loss can be associated with unemployment, social isolation, and affect quality of life. In children, hearing loss can affect spoken language and development. In adults, untreated hearing loss is associated with cognitive decline and dementia. WHO estimates that unaddressed hearing loss poses an annual global cost of US $980 billion (including costs of educational support, loss of productivity, and societal costs). Importantly, hearing loss can be prevented but remains commonly undetected and is too often seen as a consequence of ageing. Limited awareness and stigma have contributed to this situation. Recognising hearing loss as an increasing public health issue that affects people of all ages is crucial.
In 2021, WHO outlined the H.E.A.R.I.N.G. package, recommending key public health interventions to address hearing loss across the life course, focusing on prevention and early intervention. Disabling hearing loss affects 34 million children. Yet, almost 60% of childhood hearing loss can be prevented through measures such as immunisation or improved prenatal and neonatal care. Among young people, 50% are at risk of avoidable hearing loss due to unsafe sound levels from personal audio devices and 40% at entertainment venues. During adulthood, minimising loud noise exposure in the occupational, recreational, and environmental settings can reduce hearing loss occurrence and delay the onset of age-related hearing loss. In addition, measures including screening for early detection of hearing loss are important. However, progress has been slow. Universal hearing screening for newborns, school screening for children, and regular screening for adults aged older than 50 years have been recommended by WHO, but implementation of such guidance varies around the globe.
Among older adults, timely intervention could help prevent cognitive decline and dementia, which is associated with hearing impairment. In this issue of The Lancet Public Health, Fan Jiang and colleagues report that people with hearing loss not using hearing aids could be at higher risk of dementia than those without hearing loss. Those using hearing aids did not appear to be at an increased risk of dementia. Although further research is needed to ascertain a causal relationship, for Gill Livingston and Sergi Costafreda writing in a linked Comment, the evidence on hearing aids is as good as it gets without randomised controlled trials, which might not be practically possible or ethical. However, hearing aids are underused. Hearing aids are provided for free by the UK National Health Service, France recently increased the reimbursement of hearing aids, and the USA has enabled the purchase of over-the-counter hearing aids for people with mild to moderate hearing loss. Nevertheless, to increase the use of hearing aids, beyond removing affordability and accessibility barriers, it is necessary to increase awareness and address the stigma associated with stereotypes and misconceptions.
There is a clear need to address hearing loss as a social challenge. Greater community engagement, educating people about hearing loss and how to protect their hearing health could help address the roots of stigma and ensure people seek help when needed. Health professionals have a central part to play to raise awareness around hearing health, as recently shown in Belgium with a campaign directed at General Practitioners providing information on hearing loss and its associated consequences on health and wellbeing, to recognise hearing loss, discuss it, and refer patients.
Hearing loss is a modifiable determinant of health and wellbeing. Policy makers should realise the huge benefits (including economic benefits) of taking action. Investing in hearing health through the integration of WHO‘s recommended H.E.A.R.I.N.G. interventions could result in a return of US $16 for every dollar invested. Prevention across the life course will be key. Governments, health professionals, and civil society can help reduce the burden of hearing loss, tackle stigma, and support people of all ages. | 37120254 | PMC10139013 | NO-CC CODE | 2023-05-22 23:15:03 | yes | Lancet Public Health. 2023 May 27; 8(5):e318 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
37119485
26947
10.1007/s11356-023-26947-w
Research Article
Financial stability influence on climate risk, GHG emission, and green economic recovery of China
Hua Long [email protected]
12
1 grid.464506.5 0000 0000 8789 406X School of Economics, Yunnan University of Finance and Economics, Kunming, 650221 China
2 grid.464483.9 0000 0004 1799 4419 School of Business, Yuxi Normal University, Yuxi, 653100 China
Responsible Editor: Nicholas Apergis
29 4 2023
2023
30 25 6783967853
6 3 2023
6 4 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
This study examines the nexus between financial stability, climate risks, GHG emission mitigation, and green economic recovery of China. Financing efforts to protect against and reduce the hazards associated with climate change need to consider these risks and resources. Study used the Kalman technique of analysis for empirical inference. This research focuses on the carbon risk in China by employing a Kalman estimation approach. Although environmental mitigation was found to be important at 39%, financial strength and carbon hazards were considerable at 34%. Moreover, the report demonstrates the relationship between climatic threats and environmental drift in China, at a rate of 17%, emphasizing the need to address climate change issues. A state’s fiscal health guarantees national economic security while pursuing green economic recovery initiatives. Researchers concluded that precise policy suggestions were needed to promote green economic development.
Keywords
Financial stability
Climate risks
GHG emission
Green economic recovery
China
issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023
==== Body
pmcIntroduction
Climate change is one of humanity’s biggest problems, and its effects are becoming clearer (Nieto 2019). Some effects of global warming caused by greenhouse gas (GHG) emissions are extreme weather, rising sea levels, and more natural disasters (Alessi et al. 2021). Because of this, reducing GHG emissions has become one of the most important goals for policymakers all over the world (Schoenmaker and Tilburg 2016). But the COVID-19 pandemic has caused the world economy to slow down, and many countries are still struggling to get back on their feet. As governments and central banks try to boost economic growth with stimulus measures and fiscal policies, reducing greenhouse gas emissions and dealing with climate risks is getting harder (Benthem et al 2022). Because of this, it is important to know how financial stability affects economic recovery and climate goals (Walenta 2020).
People worldwide have become more aware of how climate change has affected the economy, society, and environment in the past few years (Gambhir et al 2022). Governments and international organizations have passed climate policies because they are becoming more aware of this effect (Roncoroni et al 2021). But putting these policies into action requires a lot of money, which may not always be easy. So, the role of financial stability in addressing climate risks and reducing greenhouse gas (GHG) emissions has become increasingly important (Sun et al 2022). This literature review examines the link between financial stability, climate risks, and greenhouse gas (GHG) emissions. It also looks at what this means for economic recovery (Andersson et al. 2016). For the stability of the economy and climate risks, climate risks must be dealt with, in a big way, by the financial sector. The ability to find, measure, and deal with climate-related risks is closely linked to the stability of financial systems (Figueres et al 2017). Several studies have shown that climate risks are a major threat to financial stability, especially when it comes to extreme weather events that can cause damage to property and stop business (Aljughaiman et al. 2021). Climate risks can also lead to transitional risks, such as changes in the value of assets and liabilities because of changes in regulations, technology, and consumer tastes. Financial institutions need to include climate risks in their risk management frameworks to reduce these risks (Zhao et al 2012). Several programs have been made to help with this, such as the Task Force on Climate-related Financial Disclosures (TCFD), which advices on climate-related financial disclosures, and the Network for Greening the Financial System (NGFS), which works to include climate risks in financial regulation and supervision (Guth et al 2021).
To reduce climate change’s effects, GHG emissions must be cut down (Dietz and Stern 2015). To move to a low-carbon economy, much money must be spent on technologies like renewable energy, energy efficiency, and others. But money might not be easy to get for these investments, and the cost of capital might be higher than for traditional investments (Nathwani et al 2021). This can be hard for the financial sector, which has to balance the need for money and the risks of investing in low-carbon technologies. Several studies have shown that switching to a low-carbon economy can be good for the financial stability of a country (Raihan et al 2022; Chaudhry et al 2020). For example, a report from the National Greenhouse Gas Accounting System (NGFS) found that switching to a low-carbon economy can make financial systems more stable by lowering the risks related to climate change. Other studies have shown that investing in low-carbon technologies can have long-term economic benefits, like creating jobs, lowering energy costs, and making people healthier (Monasterolo et al. 2018).
The COVID-19 pandemic has shown how important it is for economic recovery plans to simultaneously deal with climate risks and support financial stability (Sun et al. 2022). GHG emissions have decreased significantly because of the crisis, but this is likely to be a short-term effect unless changes are made to how the economy works (Scott et al 2016). Recovery measures can be a chance to invest in low-carbon technologies and infrastructure, which can help the economy grow, create jobs, and reduce greenhouse gas (GHG) emissions. But these measures cannot be implemented until governments, financial institutions, and other stakeholders work together (Demaria and Rigot 2021). The financial industry needs to develop new ways to finance the transition to a low-carbon economy while keeping the economy stable. Governments must implement policies and rules to support these investments, and other groups must work together to create an environment good for low-carbon investments. Therefore, financial stability is key to reducing GHG emissions and dealing with climate risks (Chenet 2021). The financial sector is one of the most important parts of managing climate risks and helping the economy move to a low-carbon one. But for this to happen, governments, financial institutions, and other stakeholders must work together (Lasco et al. 2014).
This research paper aims to look into the connection between financial stability, climate risks, and reducing GHG emissions. It also looks into what this connection means for economic recovery. The paper will start by looking at the research on the relationship between financial stability and climate change. Its first research contribution. It will look at how climate risks can affect financial stability and how financial stability can affect climate risks. It is the second research contribution. The report will then examine how policies to reduce GHG emissions affect financial stability and economic growth. It is the third research contribution. The paper will also look at how climate risks and reducing GHG emissions affect economic recovery, including the risks and opportunities for financial markets and the role of public policy in promoting sustainable finance and investment. It is the practical contribution of the research. In the end, the paper will come to some conclusions and make suggestions for policymakers and people who work in the financial market about how to deal with the problem of climate change while helping the economy get back on its feet and keep finances stable. It is another practical contribution. Hence, this research paper aims to add to the ongoing discussion about the role of sustainable finance in creating a low-carbon economy and a more stable financial system. It does this by explaining the complex interactions between financial stability, climate risks, and reducing GHG emissions.
The study comprises five main parts: “Introduction,” “Literature review,” “Methodology,” “Results and discussion,” and “Conclusion and implications” for associated stakeholders of the research.
Literature review
Climate risks and financial stability
Climate change is a big threat to the stability of the economy’s finances because it exposes the economy to physical and climate risks (Palea and Santhia 2022). The physical risks come from the immediate effects of climate change, like more frequent and stronger natural disasters (Dawson et al. 2022). The climate risks come from the changes an economy might make as it moves towards a future with fewer carbon emissions. Climate change risks could make the financial sector unstable and hurt the economy as a whole (Manta et al 2020). It is important to understand the link between climate risks and financial stability to reduce the negative effects of climate change (Chien et al 2023a, b). The main ways that climate risks affect financial stability are directly through the balance sheets of financial institutions, indirectly through the spread of climate-related risks across markets and sectors, and directly through the growth of macroeconomic risks (Adebayo et al 2019). Climate risks can also affect the economy as a whole and the financial system because they can disrupt supply chains, change the prices of commodities, and cause sudden changes in the prices of assets. The risks that climate change poses to the balance sheets of financial institutions are real and physical (Chen 2018). The risks can come from the fact that financial institutions have assets and liabilities vulnerable to climate change, such as real estate and infrastructure in areas prone to natural disasters (Kedward et al 2022). Also, banks and insurance companies that back assets vulnerable to physical climate risks, like crops that do not grow, are vulnerable to direct physical risks (Bowman 2010).
A sudden and big drop in the value of these assets could cause financial institutions to lose a lot of money, which could lead to a shortage of cash and bankruptcy (Diluiso et al 2021). Also, climate change can worsen macroeconomic risks, increasing the risk to financial stability. Climate change can cause systemic risks by affecting financial institutions’ balance sheets, asset prices, and liability structures (Hamid et al 2023). For example, if climate change causes real estate value to drop a lot, it could create a chain reaction in which borrowers do not repay their loans, and banks lose a lot of money (Yang et al 2022). This could lead to broader financial instability, slowing down the economy in the long run (Chien et al 2023a, b). Climate risks also indirectly affect financial stability because risks can spread from one market or sector to another. Climate change can cause the value of some assets to go down, which can spread to other assets, sectors, and markets and lower their values Sadiq et al, (2022a, b). For example, a drop in the value of assets in the energy sector could cause a drop in the stock market’s value as a whole. This could start a chain reaction that makes the economy less stable (Ben-Amar et al. 2017).
Climate risks also affect the economy as a whole (Tao et al 2022). For example, if supply chains get messed up, production costs will increase, and the economy will slow down (Abhayawansa and Adams 2022). Climate change can also affect commodity prices, leading to inflationary pressures that can force central banks to raise interest rates and slow down the economy. So, the relationship between climate risks and financial stability is complicated and nonlinear. This shows the importance of understanding the risks to manage them well and fully (Jiang et al 2022). In the end, climate risks pose big problems for financial stability and the economy as a whole. To lessen the bad effects of climate change, it is important to understand the link between climate risks and financial stability (Orsato et al 2015). Financial institutions and policymakers should focus on building climate-related risks into their risk management frameworks and developing ways to manage and reduce them. Also, policymakers should make it easier for companies to share information. This will give investors more information about how much risk financial institutions face because of climate change, which will help them make better investment decisions (Azam et al 2022). For a smooth transition to a low-carbon future, it will take coordinated efforts from policymakers, financial institutions, market participants, and good international cooperation to handle climate risks well (Caselli and Figueira 2020).
Financial stability and GHG emission reduction
Climate change has become one of the most important problems in the world, affecting many different parts of the economy (Jobst and Pazarbasioglu 2019). Climate risks, such as physical, transition, and liability risks, can also affect the financial sector. Big investments in low-carbon technologies and infrastructure are needed to deal with these risks, which could affect the economy’s stability (Wahab et al 2022). This essay examines how financial stability and reducing greenhouse gas (GHG) emissions are related. Policymakers, investors, and businesses are increasingly aware of the link between financial stability and lowering GHG emissions (Sadiq et al 2022a, b). Financial stability means the financial system can do its job well and efficiently, even when bad things happen (Yunzhao 2022). On the other hand, GHG emission reduction means lowering the amount of carbon dioxide and other greenhouse gases that are put into the air. Some of the ways that financial stability and reducing greenhouse gas emissions are linked. Climate risks can threaten the stability of the economy (Kirikkaleli et al 2022). The financial system is at risk because of climate change. Floods, droughts, and hurricanes are all examples of physical risks that can damage physical assets and disrupt economic activities, costing businesses and individuals money (Xu et al 2022a, b). Changes in policy and technology, for example, can cause stranded assets and cause investments that use a lot of carbon to lose value. Legal actions against entities that are thought to be to blame for climate change can lead to liability risks (Habiba et al 2022). These risks can greatly affect financial stability because they can cause banks, insurers, and investors to lose money (Lee et al 2022).
For GHG emissions to go down, there needs to be financial stability (Ling et al 2022). To lower greenhouse gas emissions, much money must be spent on low-carbon technologies and infrastructure. The financial sector must be involved for this to happen because it provides the money for these investments. But financial stability is needed for long-term financing because it gives investors the confidence to put money into long-term projects (Hassan, et al 2022). So, financial stability is a must for reducing greenhouse gas emissions. Putting a price on carbon can make the economy more stable and reduce greenhouse gas emissions. Carbon pricing is a policy tool that uses a tax or a cap-and-trade system to put a price on carbon emissions (Li et al. 2021). Carbon pricing can improve financial stability by giving investors a clear price signal. This can help reduce the uncertainty of investments that use a lot of carbon. It can also bring in money that can be used to pay for investments with low carbon emissions, which can help reduce GHG emissions even more. So, pricing carbon can improve both financial stability and the reduction of greenhouse gas (GHG) emissions (Sun et al 2022).
Regulating the economy can help reduce greenhouse gas emissions and stabilize the economy. Regulators can greatly help reduce greenhouse gas emissions and keep the economy stable (Ling et al 2022). For example, regulators can require banks and insurance companies to say how much risk they face from climate change (Lee et al. 2021). This can increase transparency and help people make better investment decisions. Regulators can also make financial institutions do stress tests examining how climate change will affect their portfolios. These stress tests can help find and fix possible risks to financial stability caused by climate change (Habiba et al 2022). The link between financial stability and reducing greenhouse gas (GHG) emissions is complicated and has many parts. Climate risks can hurt the economy, but the economy needs to be stable for GHG emissions to decrease. Pricing carbon and regulating the economy can help reduce greenhouse gas emissions and stabilize the economy (Yunzhao 2022). Policymakers, investors, and businesses must work together to deal with the problems caused by climate change and make the financial system more stable and sustainable.
Green economic recovery
The COVID-19 pandemic has caused economic and social problems on a global scale that have never been seen before (Huang et al 2022). Governments worldwide have been putting in place fiscal and monetary policies to protect their economies from the effects of the pandemic (Liu et al 2022). But people are becoming more aware that the recovery from the pandemic must not only help everyone but also last. Many countries now see a green economic recovery as one of their most important policy goals (Dai et al 2023). This essay examines the idea of “green economic recovery,” including its possible benefits and difficulties. A “green” economic recovery is meant to speed the move towards a low-carbon, climate-resilient economy. It means putting money into infrastructure that will last, clean energy, and protect the environment (Sharma et al 2022). The goal is not only to create jobs and grow the economy but also to cut greenhouse gas emissions and make the environment safer. Green economic recovery is based on the idea that economic growth and protecting the environment do not have to be at odds with each other. Instead, they can help each other (Xiuzhen et al. 2022).
A green economic recovery could help in several ways. First, it can help create new jobs in renewable energy, energy efficiency, and sustainable transportation (Teng et al 2022). These industries have the potential to create good jobs that are also less likely to be hurt by changes in the economy. Second, a green economic recovery can help reduce greenhouse gas emissions and make climate change less bad (Arif et al 2022). This is very important because the climate crisis is worsening quickly, and we need to keep global warming below 2 °C. Third, a green economic recovery can improve the long-term health of the environment by investing in green infrastructure, promoting the principles of a circular economy, and protecting biodiversity (Debrah et al. 2022). Lastly, a green economic recovery can improve social inclusion by ensuring that more people enjoy the benefits of economic growth and that vulnerable groups are not left behind. But some problems must be solved to make a green economic recovery happen. First, there is not enough money for investments that are good for the environment, especially in developing countries (Chai et al 2022). The COVID-19 pandemic has led to a big rise in public debt, which makes it hard for governments to pay for investments that are good for the environment. Second, a green economic recovery needs political will and policies that work well together (Xu et al 2022a, b). Government agencies, private sector actors, and civil society groups need to work together. Third, many countries do not have the skills and knowledge to implement green policies and projects (Zachariadis et al 2023).
To deal with these problems, governments can take some steps to help the economy recover in a way that is good for the environment (Yu et al 2023). First, they can give green investments more importance in their fiscal stimulus plans. This can include investments in natural capital, renewable energy, energy efficiency, and sustainable transportation. Second, they can set up ways for the private sector to invest in green projects through green finance. This can include green bonds, green banks, and green investment funds (Zhang et al 2023). Third, they can set up policy frameworks that encourage green investments and punish practices that are not sustainable (Wang et al 2023a, b). This can be done by putting a price on carbon, giving money to renewable energy, or making rules that support the principles of a circular economy. After the COVID-19 pandemic, a green economic recovery has become a key policy goal for many countries (Chelwa et al. 2023). It could help create jobs, reduce greenhouse gas emissions, make the environment more sustainable, and unite people. But there are also some problems with putting it into action, such as money, political will, and capacity (Wang et al 2023a, b). To promote a green economic recovery, governments can take several policy steps, such as giving green investments top priority, setting up green finance mechanisms, and putting policy frameworks that encourage green investments in place (Dey 2023). A green economic recovery is not only needed to deal with the urgent problem of climate change, but it is also a chance to build a more sustainable and resilient future (Stergiou 2023).
Methodology
Study data and variables
The study obtained empirical data from World Bank Database and World Energy council reports to draw the inference among the study variables. The study selected China for the estimation and reporting of the findings. The data range comprises 2015–2021, respectively. The study includes financial stability, climate risks, GHG emission reduction, and green economic recovery as study parameters.
Concepts and measurement of study variables
The concept and measurement of study variables are given below:Financial stability is defined as a condition in which the financial system functions smoothly and can withstand economic shocks without significant disruption. It is attained when people believe in the financial system and its ability to distribute money effectively to foster expansion and mitigate risk and weather volatility. The task of empirically measuring financial stability is difficult because it requires the examination of numerous indicators, such as the health of the financial system, the value of bank holdings, the prevalence of credit and liquidity risks, and the degree to which the financial system is vulnerable to disruption. Macroeconomic measures like gross domestic product growth, inflation, unemployment rates, and financial indicators like interest rates, credit spreads, and stock market performance are all examples of alternative methods. A stable financial system may also be evaluated regarding financial inclusion and access to financial services.
Climate risk: Climate risk is the threat that climate change will have unfavorable consequences for human and non-human systems. Physical risks (such as those brought on by harsh weather occurrences), transition risks (such as those brought on by shifts in government policy or technology norms), and liability risks are only some of how these effects might appear (e.g., from legal action). Assumptions concerning climate change, such as the rate of policy responses and the extent of global warming, are the foundation of every scenario analysis. The impacts of climate change on industries, including agriculture, energy, and real estate, may be evaluated using these hypothetical scenarios. Researchers employ mathematical models to evaluate the possible economic and societal implications of climate change. These models may be used to determine the relative merits of various mitigation and adaptation strategies, which can influence policy choices. Climate-related indicators such as changes in temperature, precipitation, and sea levels may be analyzed, as can social and economic elements such as migration patterns and shifts in labor productivity, to provide more insight into the magnitude of climate threats.
Greenhouse gas (GHG) emission reduction reduces the gases that trap heat in the Earth’s atmosphere and contributes to global warming. Carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and fluorinated gases are the main GHGs. The empirical measurement for reducing GHG emissions includes low carbon, energy efficiency, waste reduction and management, and sustainable agricultural productivity.
Green economic recovery: The term “green economic recovery” describes a policy strategy to mitigate COVID-19’s repercussions on the economy and society while promoting environmental sustainability and climate action. Implementing recovery measures that prioritize investments in low-carbon and sustainable infrastructure, green employment, and technology that enable the transition to a more sustainable economy is central to this idea. Green economic recovery is measured by taking the different proxies, including (a) investment in green infrastructure, (b) encouragement of green innovation, (c) development of green-sector employment, and (d) social and economic justice, teamwork, and cooperation.
Empirical estimation technique
Economic researchers often use the Kalman filter. The latest models to utilize with both the Kalman filter are the ones that can use either moment factors to assess quantities or produce numbers for characteristics that have not yet been measured. Studies indicate that data from Kalman filter models can be applied regardless of serial correlation.1 IC=∑jCiverrLossj+Gi=∑jCminωj∗V2+Gj,j
2 HC=∑jChGj-EDj,j={p,o}
3 SC=∑jCxEDj-Gj,j={p,o}
4 Umat=hln∑jLPj-Gjmin+θ,j={p,o}
Variable estimates using time-varying parameters are possible even when the estimates are uncertain. As a result of their interconnectedness, it is hard to isolate any one aspect that has an impact on sustainable power. Prior research has shown that as the variety of factors increases, it becomes impossible to describe their complex interactions accurately.5 ∏Pj,Gj,L=TR-IC-HC-SC-IP=∑jPjy⋆EDj-∑jCim=xLLj+Gj-∑jCbGj-EDj+-∑jCxEDj-Gj++λUmit-IegL-r(1-λ)L
The initial research has to decide which factors are important before they can begin to zero.6 Gji∗=Root152γChMjmam4PjV2+⋯.+32ChV2+32CxV2-32PjV2#15,ij={p,o},i=[1,5]
The metrics’ reliability should always be checked using Kalman’s testing technique. These coefficients deviate from the 4th-order model used in various estimate methods. The OLS paradigm is shown in Eq. 1.7 Yt=α0+α1Xt+α2Zt+ut
If all members of matrix is equal 1 in Eq. (6), or if the of as in Eq. (6), then the nonstationary process is followed:8 ME:Yt=β+β1tXt+β2tZt+utTE:βit=∅iβit-1+vit
If the phase formulation includes stochastic wandering, βt is almost certainly extraordinary. On each occasion, when there is a shift in the green economic recovery or a transition in government, there is a small variation in financial stability at the time (t).9 ME:Yt=β+β1Xt+β2tZt+utTE:β2t=∅β2t-1+vt
Mathematical expression (7) demonstrates that the actual price of the elements equals respective initial parameters plus the total of the preceding n economic recovery surprises. In describing the state equation, the equations’ architecture is determined by the criteria of a good measurement model.10 WPEt=β+β1CPIt+β2EP+β3tEE+β4tEFFt+β5tHJIt+ut
The project, therefore, intends to learn how different types of renewables fare in the face of a significant interest price or money creation disruption. The study also concludes. The VAR method is so named because it uses variable delaying green finance frameworks and other industrial complexes.11 βit=βit-2+vit-1+vit
12 βit=βi0+∑h=0nvit-k
Two instruments of money supply, rate of interest and currency value, are examined herein to see how they have affected the Vietnamese economy. The VAR technique uses the Kalman filter to determine the time-varying variables. Like previous regressors, this now uses both time-varying coefficients and constant components. Variable research can be extended throughout periods and conditions that could alter the variables by considering the night shift before going to bed values. A new technique called Kaplan filtering is presented to deal with unstable systems. So, the Andersen test should be used to verify the consistency of the variables in the initial phase.
Results and discussion
Fractional order modeling results
This research employs a statistical method on a framework to determine the impact of climate risk on a green economic recovery across nations (Table 1). This study uses a two-way fixed effect model to adjust for time and nation control variables empirically. The threshold extrapolation results are shown in Table 2. The outcomes with and without process parameters are shown in columns (1) and (2). The adjusted R2 remains considerably unfavorable at the 1% level in these two rows.Table 1 Fractional order specification
Years Financial stability Climate risk GHG emission Green economic recovery Cx Gj Pj G*ji
2015 0.2359 0.2875 0.4935 0.3434 0.3748 0.8115 0.5838 0.1639
2016 0.1179 0.0277 0.1794 0.9578 0.0698 0.5659 0.2358 0.0706
2017 0.6866 0.0239 0.2982 0.1571 0.1133 0.0712 0.1113 0.2771
2018 0.6809 0.6668 0.3332 0.4384 0.9721 0.3524 0.7193 0.1673
2019 0.2137 0.8932 0.0078 0.1872 0.1814 0.0086 0.9235 0.1176
2020 0.0632 0.7794 0.0681 0.0372 0.1471 0.1782 0.1317 0.0393
2021 0.3902 0.0963 0.9835 0.0268 0.5814 0.0639 0.1629 0.7215
Table 2 Kalman fractional index point determination
Index point FS CR GHG GER Cx Gj Pj G*ji Significance
0.1 0.762 0.287 0.273 0.016 0.634 0.348 0.524 0.196 0.009
0.2 0.384 0.374 0.011 0.369 0.124 0.687 0.907 0.238 0.004
0.3 0.901 0.348 0.497 0.916 0.021 0.841 0.114 0.257 0.037
0.4 0.909 0.002 0.827 0.113 0.641 0.908 0.736 0.014 0.008
0.5 0.071 0.825 0.663 0.781 0.932 0.515 0.237 0.256 0.008
0.6 0.536 0.393 0.848 0.168 0.546 0.049 0.506 0.466 0.003
0.7 0.476 0.805 0.001 0.834 0.272 0.567 0.561 0.736 0.004
0.8 0.217 0.348 0.261 0.885 0.589 0.277 0.274 0.856 0.000
0.9 0.524 0.204 0.737 0.838 0.144 0.148 0.342 0.076 0.001
1.0 0.688 0.138 0.787 0.159 0.121 0.614 0.525 0.517 0.002
0 0.349 0.138 0.606 0.042 0.746 0.478 0.939 0.343 − 0.005
− 0.1 0.136 0.121 0.933 0.305 0.186 0.567 0.099 0.234 0.008
− 0.2 0.075 0.348 0.256 0.857 0.663 0.943 0.686 0.288 0.009
− 0.3 0.554 0.756 0.707 0.331 0.167 0.384 0.167 0.583 0.001
− 0.4 0.672 0.415 0.317 0.476 0.044 0.845 0.147 0.811 0.003
− 0.5 0.596 0.146 0.599 0.149 0.209 0.365 0.471 0.344 0.002
− 0.6 0.377 0.206 0.994 0.269 0.807 0.985 0.111 0.398 0.001
− 0.7 0.599 0.017 0.777 0.974 0.882 0.144 0.125 0.144 0.020
− 0.8 0.944 0.348 0.684 0.833 0.266 0.875 0.751 0.875 0.004
− 0.9 0.631 0.425 0.564 0.285 0.463 0.596 0.793 0.272 0.115
− 1.0 0.895 0.387 0.601 0.914 0.873 0.759 0.095 0.245 0.007
R2 value 0.877 0.558 0.721 0.735 0.658 0.649 0.885 0.903 0.002
In contrast, the coefficient of the yearly climate risk score is negative overall, indicating that increased climate risk is significantly connected with worse green economic recovery. The uninduced coefficient for climate change effects is 0.000399, which may be seen in column (2). Hence, expecting a 0.0399 percentage point drop in TFP per each 100-unit rise in the climate risk score is logical. Green economic recovery will be impeded as climate danger increases. Particularly calculating financial risk measurements for single banking firms, weather patterns transitioning danger have hitherto been ignored. A new technique was established by Battiston et al. (2017). Specifically, that approach may be used to calculate a Climate VaR and to stress test the whole regional banking system and particular banks. The methodology’s objective is to provide quantitative insight into the potential for a destabilizing price reduction in energy-related aspects of the economy.
The influence of climate change on financial market trading is the topic of three articles in this special issue. The bond market was the focus of this study’s investigation. In this paper, we provide a model for default ability bonds that account for transition risk utilizing a stochastic compound model. An abrupt change in climate policy could affect bond prices, reducing the value of a company’s shares and raising the probability of default. Barrett et al. (2021) perform an economic investigation to see whether greener bond rates are more affordable than traditional borrowing costs. Green bonds have lower interest rates for foreign companies and non-financial firms, while the returns on green and traditional bonds are the same for financial organizations. We find that perhaps the premiums on green bonds significantly reduced in the instance of reoccurring green government debt and after an independent organization has verified the certification process for green bonds (Table 3).Table 3 Kalman polynomial fitting estimates of study variables
Index point FS CR GHG GER Cx Gj Pj G*ji Polynomial estimate
0.1 0.189 0.958 0.419 0.595 0.139 0.321 0.403 0.867 0.311
0.2 0.213 0.085 0.259 0.125 0.529 0.743 0.075 0.631 0.531
0.3 0.603 0.467 0.794 0.348 0.063 0.133 0.627 0.187 0.426
0.4 0.461 0.356 0.738 0.263 0.106 0.348 0.507 0.224 0.882
0.5 0.197 0.989 0.179 0.243 0.905 0.205 0.576 0.279 0.679
0.6 0.081 0.796 0.646 0.794 0.344 0.525 0.694 0.593 0.241
0.7 0.795 0.446 0.912 0.209 0.742 0.211 0.625 0.223 0.458
0.8 0.356 0.485 0.386 0.878 0.947 0.449 0.148 0.754 0.239
0.9 0.668 0.838 0.909 0.146 0.476 0.232 0.069 0.114 0.251
1.0 0.641 0.255 0.213 0.722 0.007 0.649 0.435 0.553 0.526
0 0.223 0.483 0.349 0.394 0.808 0.162 0.245 0.241 0.223
− 0.1 0.964 0.217 0.869 0.381 0.906 0.615 0.709 0.217 0.017
− 0.2 0.187 0.402 0.534 0.985 0.601 0.604 0.748 0.449 0.454
− 0.3 0.003 0.998 0.301 0.939 0.469 0.664 0.322 0.495 0.429
− 0.4 0.987 0.054 0.704 0.439 0.331 0.816 0.158 0.271 0.622
− 0.5 0.323 0.795 0.811 0.837 0.521 0.089 0.847 0.591 0.686
− 0.6 0.067 0.466 0.706 0.529 0.187 0.667 0.036 0.473 0.006
− 0.7 0.962 0.336 0.565 0.937 0.822 0.564 0.614 0.832 0.634
− 0.8 0.342 0.316 0.241 0.648 0.336 0.769 0.073 0.167 0.421
− 0.9 0.678 0.194 0.957 0.477 0.646 0.256 0.607 0.174 0.522
− 1.0 0.336 0.266 0.268 0.097 0.557 0.635 0.078 0.356 0.513
R2 value 0.719 0.527 0.277 0.373 0.586 0.176 0.185 0.354 0.453
Accuracy estimates of climate risk, financial stability, and green recovery
Several articles in this special issue will start filling in some of the blanks in the field of climate policy, particularly in areas like climate warming studies that routinely use network finance models. There has to be a complete understanding of the socioeconomic and socio-feedback processes associated with transformations and environmental problems (Huang et al. 2022)—investigation on the role of finance in facilitating or impeding ongoing abridged change. To determine climate change adaptation strategies that will enable the goals of the Paris Agreement to be met, it is necessary to have a firm grasp of the nebulous function that money plays in adaptation plans. Examining how changes in fiscal, financial, and economic plans all impact the financial systems of individual countries is the focus of budgetary carbon pricing analysis and research. Evaluation of climate-related risks in light of the COVID-19 pandemic and the design of weather pattern recovery strategies for the aftermath of the pandemic.
An overall population may indeed affect how much climate risk affects TFP in a given nation. Governments with smaller and medium-sized demographics are now more vulnerable to climate change since the percentage of their inhabitants that will be impacted will be greater. According to World Bank information and criteria, nations having an overall number of above 25 million are expected for this role nations. The estimated accuracy evaluation results are shown in Table 4. Results reveal that the coefficient of determination for countries with small- and medium-sized populations is statistically significant at the 1% level, suggesting that rising climate hazards adversely affect green economic recovery in these nations. This low reactivity to climate hazards at the national level is reflected in the fact that big population nations have a non-significant correlation value, suggesting why most climatic catastrophes only impact a lesser portion of the entire population.Table 4 FOM parameter identification
Years R0/Ω R1/Ω R2/Ω C1/F C2/F α β
2015 0.736 0.832 0.649 0.859 0.372 0.374 0.781
2016 0.565 0.442 0.289 0.268 0.723 0.401 0.884
2017 0.446 0.595 0.469 0.699 0.652 0.385 0.909
2018 0.042 0.467 0.146 0.514 0.931 0.106 0.451
2019 0.555 0.675 0.115 0.123 0.435 0.987 0.327
2020 0.915 0.087 0.254 0.843 0.349 0.991 0.503
2021 0.571 0.874 0.364 0.087 0.352 0.712 0.222
According to a government’s size, the potential for green economic recovery to be hindered by climate risk may indeed fluctuate. Climate change impacts may be felt more keenly in smaller and middle-sized nations because they lack the resources of larger states. Crowards (2002) investigates the land area connections when dividing up tiny states. Utilizing United Nations definitions and a review of land areas, we consider governments with a land area of more than 500,000 km2 to be substantial. Of the major powers, Spain is the smallest and Russia the biggest. Table 4 displays the results of the heterogeneity test. The data shows that the beta value for small- and medium-sized nations is statistically meaningful at the 1% level, suggesting that rising environmental risk hurts green economic recovery in these economies (Table 5). Insignificant results for the regression line of big nations suggest that climatic catastrophes will not hamper green economic recovery at the national level in countries with huge geographies.Table 5 Accuracy estimates of study parameters
Parameters Bench Top-10 Top-25 Top-50 Top-75 Top-90 Top/bottom
Financial stability 0.5145 0.1028 0.0511 0.3517 0.9705 0.0339 0.8191
Climate risk 0.0066 0.6054 0.0093 0.0912 0.3319 0.2238 0.0299
Green economic recovery 0.1561 0.1681 0.3382 0.3826 0.1846 0.2745 0.3041
GHG emission 0.4524 0.2796 0.0182 0.6016 0.0041 0.0263 0.8986
Fractional order iterative extended Kalman filter results
It is recommended that the EKF technique be used to address the decline in estimating precision seen in nonlinear models. The typical EKF approach will truncate the linear expression at the point of the estimate, keep the expansion of the first-order series, and throw away the relatively high components. There will be a fixed linearization error using this method. Iteration is one way to cut down on this mistake. The IEKF technique is based on the premise that by continually linearizing the measurement equation and correcting the estimated worth based on the measurement point, the estimating error can be steadily reduced, and the filtration efficiency may be improved. The previous findings demonstrate system dynamics in the time domain. In this article, we expand upon three profitability contagion models to provide the first climate stress-test technique to include the ex ante worth of monetary assets, an ecological recovery rate, and a fire-sales response across a broad spectrum of banking organizations. The concept of “ex ante valuation” describes determining the value of interbank claims before the maturity of the corresponding contracts to consider the possibility of fluctuation in the market price of external assets between the time of asset value and the maturity of the contracts. Simply put, banks’ exterior assets include anything other than holdings in the liabilities of other financial organizations. Instead, banks and investment funds’ investments in other banks are known as interbank assets.
In the event of a default by one or more lenders, the spontaneous recovery rate suggests that the remaining banks’ claims are valued by recursively considering the amounts they may reclaim from their counterparty. Finding the endogenous recovery rate involves finding the solution to a fixed point issue. The preceding are methods in which governments might influence purchasing circumstances. The goal of central banks is financial stability, and one way they contribute is by ensuring that the unpredictability of asset prices stays inside acceptable bounds. They might use both traditional and innovative macroeconomic strategies to achieve their goals. First, we will briefly overview the most important ways climate change has affected the economic variables we’ve accounted for in our model. Consequences from climate change lower (i) demand for both goods and services, (ii) demand for traditional government debt by individuals (while simultaneously raising demand for depositors and sovereign bonds), (iii) future output influenced by labor (which is impacted by labor productivity and labor force), and (iv) production growth dictated by property (which is affected by capital stock and capital productivity). Gross damages impact (i) and (ii); we estimate 50% gross damage at T = 6 °C in our base case scenario. Yet, (iii) and (iv) are influenced by net damages, which, in our baseline scenario, account for a tiny fraction of the total losses incurred. Climate change damages not only affect a company’s bottom line (earnings are subject to economic growth and climate-induced deterioration of capital). Both factors influence companies’ preferred investments. Besides that, climate change affects labor supply because the growing production rate is frequently not the same as the labor-determined total output.
The findings concisely summarize the most important pathways via which climate change affects the financial system’s financial stability. The simulated findings are outlined in Table 6. In the “base case” scenario, CO2 emissions will rise dramatically in the next decades. Because of positive economic development, production has increased exponentially. In contrast, clean energy has improved slowly. The proportion of renewable energy to total energy has remained low. As a result, atmospheric CO2 concentrations rise, causing substantial greenhouse gases (Table 7). As shown in study results, average worldwide temperatures will rise by roughly 4 °C over their pre-industrial baseline by 2100. Damages from climate change are a direct result of a warmer atmosphere. As a result, production growth begins to slow. After 2060, when temperatures have risen by more than 2.5 °C, the economic decline will be more severe. Companies’ profitability and liquidity suffer as a result of slowing economic development and investment deterioration, leading to a higher default rate and lower capital adequacy ratio for banks. The overall effect is a tightening of financial access, which has repercussions for economic expansion and the liquidity and earnings of businesses. Development in environmental infrastructure is slowed. As a result, slowing the shift towards a limited, efficient alternative industry. Banks’ capital eventually becomes inadequate to meet regulatory standards, which is a major problem. In response, the authorities must jump in and bail out the financial institutions, which hurts the public’s debt-to-GDP ratio. It is important to remember that the falling tax revenues and rising interest costs contribute to the public debt-to-output ratio’s explosive growth as the economy slows. Additionally, families’ propensity to keep onto cash is impacted by climatic impacts. Due to corporate equipment deterioration and falling profits, investors shift their money from riskier bond funds to safer savings and government securities. As a consequence, the value of traditional bonds falls, boosting their money for expansion in the last centuries of our experiment. As a result of climate change, asset prices have fallen. Bond yields climb exponentially under the baseline period because damages are convex; they increase at a higher pace as climate warming worsens. After initially falling, the premium on green government debt rises in our base case scenario. Yet, the fundamental cause of this rise is no decrease in the customer market for green bonds. As the demand for green investment rises steadily throughout our experiment, a rise in the supply of green bonds is the most likely explanation for this trend.Table 6 Case-wise accuracy estimates
Accuracy in baseline scenario Case 1 Case 2 Case 3 Case 4 Case 5 Case 6 Case 7 Case 8
1.64 50% 57% 29% 37% 66% 96% 82% 66%
5.65 60% 89% 23% 57% 63% 18% 85% 63%
3.31 62% 19% 77% 45% 34% 89% 47% 76%
5.42 84% 92% 17% 25% 68% 19% 84% 45%
6.31 45% 64% 45% 97% 69% 74% 51% 94%
1.27 33% 39% 19% 64% 87% 58% 75% 95%
6.64 39% 32% 72% 53% 52% 28% 52% 82%
7.13 51% 85% 68% 39% 73% 45% 73% 56%
7.43 71% 42% 85% 56% 98% 59% 75% 71%
Table 7 Estimation results of the main parameters
Parameter Mean SD Geweke Inef ARM
Financial stability 0.5615 0.3621 0.2389 0.3018 0.2323
Climate risk 0.3194 0.0293 0.2982 0.3107 0.4327
Green economic recovery 0.0213 0.8262 0.4423 0.2823 0.2644
GHG emission 0.0329 0.7508 0.1746 0.1572 0.1156
Cx 0.0724 0.1562 0.2424 0.4983 0.2216
Gj 0.1045 0.4237 0.0031 0.965 0.9255
G*ji 0.1236 0.8861 0.2645 0.0432 0.0506
Robustness analysis
Financial security can only be attained and maintained in societal and political consensus. A robust financial sector is less susceptible to financial crises and more resilient when they occur, which is important since crises may significantly impact the real economy. The urgency with which reforms must be implemented varies greatly depending on the nature of the change and whether or not convenient timing is required. Supporting the establishment and deployment of a collection of positive financial capacity for scaling rules and processes is something the Security Council might do to assist. Financial stability worries have been lessened due to ongoing economic aid and a global economic rebound early this year. The chapter explains how the economic situation in industrialized nations has improved more on the net than in emerging markets, which have stayed constant. Inflationary pressures have increased due to rising uncertainties about the strength of the global economy and ongoing disruptions, which have dampened investor confidence. Although the business sector is on the upswing, several subsectors continue to exhibit indicators of overvalued assets, and the financial sector (in its literal and figurative senses) still has problems. We did a series of sensitivity analyses to ensure that the results, as mentioned above, hold up under scrutiny. Table 2 shows that the ranking of measures is rather stable across changes in the relative importance of different criteria. Furthermore, whether or not the stakeholders are concerned about China’s financial stability does not significantly alter the choices. On the other hand, the results indicate that the economists in our sample (four out of ten decision-makers) provided completely distinct input from the engineers and natural scientists who made up the remainder of the population. Theorists favored three sorts of actions: a free modification of existing regulations, grants/loans to enterprises, and energy construction projects for green economic reform. In contrast, they seem skeptical about climate dangers, perhaps because of the social cost, and believe that building improvements are the most practical technical solutions. Notwithstanding the mistrust of academics, it is notable that climate hazards are the favored metric of non-economists, placing it top of the overall list (Table 8).Table 8 Robustness analysis of study
Index point FS CR GHG GER
0.1 0.099 0.726 0.519 0.924
0.2 0.296 0.252 0.979 0.174
0.3 0.365 0.102 0.154 0.265
0.4 0.845 0.755 0.961 0.048
0.5 0.705 0.349 0.042 0.365
0.6 0.775 0.167 0.291 0.161
0.7 0.456 0.269 0.025 0.636
0.8 0.017 − 0.389 0.053 0.392
0.9 0.228 0.209 0.237 0.287
1.0 0.685 0.042 0.303 0.354
0 0.936 0.604 0.725 0.698
− 0.1 0.526 0.105 0.033 0.957
− 0.2 0.397 0.124 0.547 0.287
− 0.3 0.287 0.975 0.925 0.479
− 0.4 0.714 0.179 0.585 0.371
− 0.5 0.541 0.909 0.399 0.336
− 0.6 0.841 0.199 0.211 0.481
− 0.7 0.869 0.457 0.088 0.636
− 0.8 0.824 0.484 0.937 0.801
− 0.9 0.889 0.278 0.273 0.721
− 1.0 0.939 0.487 0.694 0.686
R2 value 0.863 0.104 0.116 0.753
Discussion
There is strong evidence from the present state of climate science that climate change may have negative societal and economic consequences in the decades to come (Iqbal et al. 2021). Nevertheless, this scientific understanding is not being reflected in market values (Li et al. 2021). One possible explanation is that financial markets cannot “internalize” externalities, particularly those with a long-term perspective. Moreover, conventional financial risk measurements look at past price data. Past occurrences are not accurate forecasts of the future. Thus, they cannot be used to evaluate climate risk. Thus, forward-looking climate risk has to be accounted for in financial risk measurements (Tu et al. 2021). Due to the interrelated nature of modern enterprises, network models of investment chains are required to provide more accurate assessments of risk and effect. The Paris Agreement has shifted the policy conversation to the link between climate risk and financial stability (Sun et al. 2022). Several financial regulators have included climate-related financial risk assessments in their initial risk evaluations of the sector (Yang et al. 2022). The Chinese Central Bank found that the average exposure of Chinese financial institutions to the Climate Policy Relevant Sectors was between 1 and 9% of their overall debt securities holdings (Zhang et al. 2022). It was stated by the Chinese Insurance and Occupational Pensions Administration that insurance firms’ aggregate exposures amounted to around 13% of their overall securities holdings.
A study of Chinese securities holdings indicated that investment funds had exposures of 36.8 and 47.7 percent to Climate Policy Relevant Sectors, while insurance companies had 36.4 and 43.1% exposures. Losses on Chinese portfolios of sovereign bonds might reach up to 1% in conservative scenarios, according to a partnership between financial regulators, academics in climate economics, and researchers in energy efficiency finance (Iqbal and Bilal. 2021). Yet, research into how financial stability is affected by the interaction of climate policy shocks and market circumstances is lacking. To address this deficiency, this study analyzes the relationship between climate policy shocks and market circumstances and an operational framework for climate stress testing (Wang et al. 2022). Public and private banks face climate change dangers, including physical and climate risks. Extreme weather events brought on by climate change pose a physical risk since they may destroy or endanger buildings, crops, and people. On the other hand, the shift to a low-carbon economy may bring about climate risk. The effect of climate risk on financial security is the primary topic of this research (Zhao et al. 2022).
The term “climate risk” describes the potential dangers some economic sectors may experience during shifting to a low-carbon economy. Carbon-intensive economic activity may experience substantial financial losses due to this shift (Zheng et al. 2022). Depending on investment choices and the rate at which capital is reallocated to low-carbon sectors, the financial system, particularly the banking system, is more or less vulnerable to climate risk. The market share of high-carbon industries would be more severely affected by a shift to a scenario with a stricter climate policy objective. Losses sustained by banks and investment funds at each step of a contagion increase in proportion to the magnitude of the climate policy shock that makes behaviors adverse (Ahmad et al. 2022). The contagion model predicts cumulative losses that scale linearly with the size of the first shock. If market circumstances are unfavorable, a chaotic transition may cause substantial losses for the financial sector, even in scenarios with minor climate policy shocks. A nation can achieve a stricter climate objective at the same cost regarding financial losses as a less stringent target achieved with a later transition if an earlier but chaotic energy transition happens with the help of supportive financial inclusion (Chang, Iqbal and Chen 2023). If market circumstances are poor enough, a chaotic shift towards a less strict climate objective might result in more losses than a chaotic shift towards a more severe target. So, the negative impacts, such as financial contagion, of a future greater climate policy shock may be mitigated if market conditions are reinforced.
Conclusion and implications
Conclusion
Our research focuses on China, where we want to understand better the connections between climate risk, natural resource extraction strategy, and efforts to reduce the effects of climate change there. The paper’s results provide suggestions for climate policy that might help China’s economy grow while mitigating climate change’s negative effects. Although China is one of the nations hit most by climate change, these suggestions may be used everywhere. Uncertainty in climate policy must be addressed first, followed by stabilizing financial markets and promoting economic growth. Based on empirical evidence, it is clear that not all banks have the same response to CPU, necessitating distinct economic approaches for each. To provide sufficient monitoring of market indicators, policymakers and financial regulators must work together to develop regulatory risk procedures. Second, there is a need for Chinese commercial banks to understand better climate policy uncertainty processes and lower asset allocation risks. As businesses grow, they should improve their internal risk management and oversight by including indications of climate policy uncertainty in their current risk management indicators. Lending and investing pose threats to commercial banks in China. In addition to being topical (it addresses the continuing COVID-19 epidemic), the study is relevant since it may serve as a reference for politicians, investors, and academics in China and beyond. The accuracy of a nation’s financial stability index depends on taking into account the specifics of that country. The impact of monetary policies on financial stability evolves over time and in response to changing circumstances, necessitating regular assessment and modification. Although each country and crisis is unique, no universally applicable policy exists. Monetary policies may have contrasting short- and long-term consequences, sometimes offsetting the latter. Consequently, following an economic recovery from a crisis, adjusting or reversing monetary policy is essential. Investor confidence in China has been bolstered by the country’s capacity to preserve financial stability and contribute to a swift economic recovery from crises thanks to the government’s monetary policies.
Practical implications
The report suggests various measures to guarantee economic development without jeopardizing ecological viability. A wide range of approaches will be necessary to accomplish this. Even while the carbon price change receives excellent marks, it will not be enough to bring about the anticipated results by itself. Carbon tax reform’s prominence highlights the need for awareness and consensus-building activities to spread the word. According to stakeholders, the potential for implementing steps is also crucial. Policymakers must combine basic procedures with complex models. Short-term vs. long-term versus climate neutrality goals are trade-offs in the research. Regarding stimulating GDP and employment, green measures outperform the “business as usual” demand stimulus. Carbon pricing and altering electricity laws for decentralized power production are two examples of institutional or regulatory reforms with minimal cost and long-term benefits. With input from many different groups, policymakers at the national level might feel more invested in the final ranking of acceptable policies. Ensuring that national recovery initiatives follow international policy objectives might be facilitated by aligning sustainability criteria with the UN SDGs. Although this study sheds light on potential avenues for recovery in the wake of a pandemic, it is not intended to stand alone as a thorough evaluation of recovery strategies; rather, it is meant to enhance such an evaluation. Public investments in areas like health and social care infrastructure, information and communication technology, and upgrading schools and hospitals should be made with green and climate concerns in mind to maximize social good and minimize environmental impact.
Author contribution
Conceptualization, methodology, data curation, data analysis, writing — original draft, visualization, editing: Long Hua.
Availability of data and materials
The data that support the findings of this study are openly available on request.
Ethics approval and consent to participate.
The authors declared that they have no known competing financial interests or personal relationships, which seem to affect the work reported in this article. We declare that we have no human participants, human data or human issues.
Declarations
Consent for publication
We do not have any individual person’s data in any form.
Competing interests
The authors declare no competing interests.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Abhayawansa S Adams C Towards a conceptual framework for non-financial reporting inclusive of pandemic and climate risk reporting Meditari Account Res 2022 30 3 710 738 10.1108/MEDAR-11-2020-1097
Adebayo TS, Akadiri SS, Riti JS, Tony Odu A (2019) Interaction among geopolitical risk, trade openness, economic growth, carbon emissions and Its implication on climate change in India. Energy Environ, 0958305X221083236
Ahmad B Iqbal S Hai M Latif S The interplay of personal values, relational mobile usage and organizational citizenship behavior Interactive Technol Smart Educ 2022 19 2 260 280 10.1108/ITSE-01-2021-0016
Alessi L Ossola E Panzica R What greenium matters in the stock market? The role of greenhouse gas emissions and environmental disclosures J Financ Stab 2021 54 100869 10.1016/j.jfs.2021.100869
Aljughaiman AA, Cao ND, Albarrak MS (2021) The impact of greenhouse gas emission on corporate’s tail risk. J Sustain Fin Invest, 1–18.
Andersson M Bolton P Samama F Hedging climate risk Financ Anal J 2016 72 3 13 32 10.2469/faj.v72.n3.4
Arif A Vu HM Cong M Wei LH Islam M Niedbała G Natural resources commodity prices volatility and economic performance: evaluating the role of green finance Resour Policy 2022 76 102557 10.1016/j.resourpol.2022.102557
Azam A Rafiq M Shafique M Yuan J Towards achieving environmental sustainability: the role of nuclear energy, renewable energy, and ICT in the top-five carbon emitting countries Front Energy Res 2022 9 971 10.3389/fenrg.2021.804706
Ben-Amar W Chang M McIlkenny P Board gender diversity and corporate response to sustainability initiatives: evidence from the carbon disclosure project J Bus Ethics 2017 142 2 369 383 10.1007/s10551-015-2759-1
Bowman M The role of the banking industry in facilitating climate change mitigation and the transition to a low-carbon global economy Environ Plann Law J 2010 27 448
Caselli G, Figueira C (2020) The impact of climate risks on the insurance and banking industries. Sustainability and financial risks: The impact of climate change, environmental degradation and social inequality on financial markets, 31–62
Chai S, Chu W, Zhang Z, Li Z, Abedin MZ (2022) Dynamic nonlinear connectedness between the green bonds, clean energy, and stock price: the impact of the COVID-19 pandemic. Annals of Operations Research, 1–28.
Chang L Iqbal S Chen H Does financial inclusion index and energy performance index co-move? Energy Policy 2023 174 113422 10.1016/j.enpol.2023.113422
Chaudhry SM Ahmed R Shafiullah M Huynh TLD The impact of carbon emissions on country risk: evidence from the G7 economies J Environ Manage 2020 265 110533 10.1016/j.jenvman.2020.110533 32421559
Chelwa G Hamilton D Green A Identity group stratification, political economy & inclusive economic rights Dædalus 2023 152 1 154 167
Chen DB (2018) Central banks and blockchains: the case for managing climate risk with a positive carbon price. In Transforming climate finance and green investment with blockchains (pp. 201–216). Academic Press
Chenet H (2021) Climate change and financial risk (pp. 393–419). Springer International Publishing
Chien F Chau KY Sadiq M Impact of climate mitigation technology and natural resource management on climate change in China Resour Policy 2023 81 103367 10.1016/j.resourpol.2023.103367
Chien F Hsu CC Zhang Y Sadiq M Sustainable assessment and analysis of energy consumption impact on carbon emission in G7 economies: mediating role of foreign direct investment Sustain Energy Technol Assess 2023 57 103111
Dai X Rao F Liu Z Mohsin M Taghizadeh-Hesary F Role of public and private investments for green economic recovery in the post-COVID-19 Econ Res-Ekonomska Istraživanja 2023 36 1 1146 1166 10.1080/1331677X.2022.2081865
Dawson C Dargusch P Hill G Assessing how big insurance firms report and manage carbon emissions: a case study of Allianz Sustainability 2022 14 4 2476 10.3390/su14042476
Debrah C Chan APC Darko A Green finance gap in green buildings: a scoping review and future research needs Build Environ 2022 207 108443 10.1016/j.buildenv.2021.108443
Demaria S Rigot S Corporate environmental reporting: are French firms compliant with the task force on climate financial disclosures’ recommendations? Bus Strateg Environ 2021 30 1 721 738 10.1002/bse.2651
Dey, S. (2023). Introduction: why green academia?. In Green Academia (pp. 1–22). Routledge India.
Dietz S Stern N Endogenous growth, convexity of damage and climate risk: how Nordhaus’ framework supports deep cuts in carbon emissions Econ J 2015 125 583 574 620 10.1111/ecoj.12188
Diluiso F Annicchiarico B Kalkuhl M Minx JC Climate actions and macro-financial stability: the role of central banks J Environ Econ Manag 2021 110 102548 10.1016/j.jeem.2021.102548
Figueres C Schellnhuber HJ Whiteman G Rockström J Hobley A Rahmstorf S Three years to safeguard our climate Nature 2017 546 7660 593 595 10.1038/546593a 28661507
Gambhir A, George M, McJeon H, Arnell NW, Bernie D, Mittal S, ... Monteith S (2022) Near-term transition and longer-term physical climate risks of greenhouse gas emissions pathways. Nature Climate Change, 12(1), 88–96
Guth M, Hesse J, Königswieser C, Krenn G, Lipp C, Neudorfer B, ... Weiss P (2021) OeNB climate risk stress test–modeling a carbon price shock for the Austrian banking sector. Financial Stability Report, 42, 27–45
Habiba UMME Xinbang C Anwar A Do green technology innovations, financial development, and renewable energy use help to curb carbon emissions? Renewable Energy 2022 193 1082 1093 10.1016/j.renene.2022.05.084
Hamid I Uddin MA Hawaldar IT Alam MS Joshi DP Jena PK Do better institutional arrangements lead to environmental sustainability: evidence from India Sustainability 2023 15 3 2237 10.3390/su15032237
Hassan T Song H Kirikkaleli D International trade and consumption-based carbon emissions: evaluating the role of composite risk for RCEP economies Environ Sci Pollut Res 2022 29 3417 3437 10.1007/s11356-021-15617-4
Huang W Saydaliev HB Iqbal W Irfan M Measuring the impact of economic policies on Co2 emissions: ways to achieve green economic recovery in the post-COVID-19 era Climate Chang Econ 2022 13 03 2240010 10.1142/S2010007822400103
Iqbal S Bilal AR Energy financing in COVID-19: how public supports can benefit? China Financ Rev Int 2021 12 2 219 240 10.1108/CFRI-02-2021-0046
Iqbal S Bilal AR Nurunnabi M Iqbal W Alfakhri Y Iqbal N It is time to control the worst: testing COVID-19 outbreak, energy consumption and CO2 emission Environ Sci Pollut Res 2021 28 19008 19020 10.1007/s11356-020-11462-z
Jiang L Hu X Zhang G Chen Y Zhong H Shi P Carbon emission risk and governance Intl J Disaster Risk Sci 2022 13 2 249 260 10.1007/s13753-022-00411-8
Jobst AA, Pazarbasioglu C (2019) Greater transparency and better policy for climate finance. Jobst, Andreas A. and C. Pazarbasioglu, 85–99
Kedward K, Ryan-Collins J, Chenet H (2022) Biodiversity loss and climate change interactions: financial stability implications for central banks and financial supervisors. Climate Policy, 1–19
Kirikkaleli D Güngör H Adebayo TS Consumption-based carbon emissions, renewable energy consumption, financial development and economic growth in Chile Bus Strateg Environ 2022 31 3 1123 1137 10.1002/bse.2945
Lasco RD Delfino RJP Espaldon MLO Agroforestry systems: helping smallholders adapt to climate risks while mitigating climate change Wiley Interdisc Rev Climate Chang 2014 5 6 825 833 10.1002/wcc.301
Lee CC Li X Yu CH Zhao J The contribution of climate finance toward environmental sustainability: new global evidence Energy Economics 2022 111 106072 10.1016/j.eneco.2022.106072
Li W Chien F Ngo QT Nguyen TD Iqbal S Bilal AR Vertical financial disparity, energy prices and emission reduction: empirical insights from Pakistan J Environ Manag 2021 294 112946 10.1016/j.jenvman.2021.112946
Ling G Razzaq A Guo Y Fatima T Shahzad F Asymmetric and time-varying linkages between carbon emissions, globalization, natural resources and financial development in China Environ Dev Sustain 2022 24 5 6702 6730 10.1007/s10668-021-01724-2 34421336
Liu H, Tang YM, Iqbal W, Raza H (2022) Assessing the role of energy finance, green policies, and investment towards green economic recovery. Environ Sci Pollut Res, 1–14
Manta AG Florea NM Bădîrcea RM Popescu J Cîrciumaru D Doran MD The nexus between carbon emissions, energy use, economic growth and financial development: evidence from central and eastern European countries Sustainability 2020 12 18 7747 10.3390/su12187747
Monasterolo I Zheng JI Battiston S Climate transition risk and development finance: a carbon risk assessment of China’s overseas energy portfolios Chin World Econ 2018 26 6 116 142 10.1111/cwe.12264
Nathwani J Lind N Renn O Schellnhuber HJ Balancing health, economy and climate risk in a multi-crisis Energies 2021 14 14 4067 10.3390/en14144067
Nieto MJ (2019) Banks, climate risk and financial stability. J Financial Regulation and Compliance
Orsato RJ de Campos JGF Barakat SR Nicolletti M Monzoni M Why join a carbon club? A study of the banks participating in the Brazilian “Business for Climate Platform” J Clean Prod 2015 96 387 396 10.1016/j.jclepro.2014.01.007
Palea V Santhia C The financial impact of carbon risk and mitigation strategies: insights from the automotive industry J Clean Prod 2022 344 131001 10.1016/j.jclepro.2022.131001
Raihan A Muhtasim DA Farhana S Pavel MI Faruk O Rahman M Mahmood A Nexus between carbon emissions, economic growth, renewable energy use, urbanization, industrialization, technological innovation, and forest area towards achieving environmental sustainability in Bangladesh Energy and Climate Change 2022 3 100080 10.1016/j.egycc.2022.100080
Roncoroni A Battiston S Escobar-Farfán LO Martinez-Jaramillo S Climate risk and financial stability in the network of banks and investment funds J Financ Stab 2021 54 100870 10.1016/j.jfs.2021.100870
Sadiq M Lin CY Wang KT Trung LM Duong KD Ngo TQ Commodity dynamism in the COVID-19 crisis: are gold, oil, and stock commodity prices, symmetrical? Resour Policy 2022 79 103033 10.1016/j.resourpol.2022.103033 36187223
Sadiq M Shinwari R Usman M Ozturk I Maghyereh AI Linking nuclear energy, human development and carbon emission in BRICS region: do external debt and financial globalization protect the environment? Nucl Eng Technol 2022 54 9 3299 3309 10.1016/j.net.2022.03.024
Schoenmaker D Van Tilburg R What role for financial supervisors in addressing environmental risks? Comp Econ Stud 2016 58 317 334 10.1057/ces.2016.11
Scott D Hall CM Gössling S A report on the Paris Climate Change Agreement and its implications for tourism: why we will always have Paris J Sustain Tour 2016 24 7 933 948 10.1080/09669582.2016.1187623
Sharma GD Verma M Shahbaz M Gupta M Chopra R Transitioning green finance from theory to practice for renewable energy development Renewable Energy 2022 195 554 565 10.1016/j.renene.2022.06.041
Stergiou A (2023) Eastern Mediterranean energy geopolitics revisited: green economy instead of conflict. J Balkan Near East Stud, 1–22
Sun L Fang S Iqbal S Bilal AR Financial stability role on climate risks, and climate change mitigation: implications for green economic recovery Environ Sci Pollut Res 2022 29 22 33063 33074 10.1007/s11356-021-17439-w
Tao H Zhuang S Xue R Cao W Tian J Shan Y Environmental finance: an interdisciplinary review Technol Forecast Soc Chang 2022 179 121639 10.1016/j.techfore.2022.121639
Teng B Wang S Shi Y Sun Y Wang W Hu W Shi C Economic recovery forecasts under impacts of COVID-19 Econ Model 2022 110 105821 10.1016/j.econmod.2022.105821 35261424
Tu C A, Chien F, Hussein MA, Mm YR, Mm MS, Iqbal S, Bilal AR (2021) Estimating role of green financing on energy security, economic and environmental integration of BRI member countries. The Singapore Econ Rev 1–19
van Benthem AA Crooks E Giglio S Schwob E Stroebel J The effect of climate risks on the interactions between financial markets and energy companies Nat Energy 2022 7 8 690 697 10.1038/s41560-022-01070-1
Wahab S Imran M Safi A Wahab Z Kirikkaleli D Role of financial stability, technological innovation, and renewable energy in achieving sustainable development goals in BRICS countries Environ Sci Pollut Res 2022 29 32 48827 48838 10.1007/s11356-022-18810-1
Walenta J Climate risk assessments and science-based targets: a review of emerging private sector climate action tools Wiley Interdisc Rev Climate Chang 2020 11 2 e628
Wang S Sun L Iqbal S Green financing role on renewable energy dependence and energy transition in E7 economies Renew Energy 2022 200 1561 1572 10.1016/j.renene.2022.10.067
Wang X, Yang Y, Tang M, Wang X (2023a). The effect of macroeconomic regimes, uncertainty, and COVID-19 outcomes on commodity price volatility: implications for green economic recovery. Economic Research-Ekonomska Istraživanja, 1–21
Wang Y Wang X Zhang Z Cui Z Zhang Y Role of fiscal and monetary policies for economic recovery in China Econ Anal Policy 2023 77 51 63 10.1016/j.eap.2022.10.011 36337175
Xiuzhen X Zheng W Umair M Testing the fluctuations of oil resource price volatility: a hurdle for economic recovery Resour Policy 2022 79 102982 10.1016/j.resourpol.2022.102982
Xu G Dong H Xu Z Bhattarai N China can reach carbon neutrality before 2050 by improving economic development quality Energy 2022 243 123087 10.1016/j.energy.2021.123087
Xu S Yang C Huang Z Failler P Interaction between digital economy and environmental pollution: new evidence from a spatial perspective Int J Environ Res Public Health 2022 19 9 5074 10.3390/ijerph19095074 35564469
Yang M Chen L Msigwa G Tang KHD Yap PS Implications of COVID-19 on global environmental pollution and carbon emissions with strategies for sustainability in the COVID-19 era Sci Total Environ 2022 809 151657 10.1016/j.scitotenv.2021.151657 34793787
Yang Y Liu Z Saydaliev HB Iqbal S Economic impact of crude oil supply disruption on social welfare losses and strategic petroleum reserves Resour Policy 2022 77 102689 10.1016/j.resourpol.2022.102689
Yu C Moslehpour M Tran TK Trung LM Ou JP Tien NH Impact of non-renewable energy and natural resources on economic recovery: empirical evidence from selected developing economies Resour Policy 2023 80 103221 10.1016/j.resourpol.2022.103221
Yunzhao L Modelling the role of eco innovation, renewable energy, and environmental taxes in carbon emissions reduction in E−7 economies: evidence from advance panel estimations Renewable Energy 2022 190 309 318 10.1016/j.renene.2022.03.119
Zachariadis T, Giannakis E, Taliotis C, Karmellos M, Fylaktos N, Howells M, ... Hallegatte S (2023) Science policy frameworks for a post-pandemic green economic recovery. Energy Strategy Reviews, 45, 101035
Zhang L Huang F Lu L Ni X Iqbal S Energy financing for energy retrofit in COVID-19: recommendations for green bond financing Environ Sci Pollut Research 2022 29 16 23105 23116 10.1007/s11356-021-17440-3
Zhang S Anser MK Peng MYP Chen C Visualizing the sustainable development goals and natural resource utilization for green economic recovery after COVID-19 pandemic Resour Policy 2023 80 103182 10.1016/j.resourpol.2022.103182 36530833
Zhao R Neighbour G Han J McGuire M Deutz P Using game theory to describe strategy selection for environmental risk and carbon emissions reduction in the green supply chain J Loss Prev Process Ind 2012 25 6 927 936 10.1016/j.jlp.2012.05.004
Zhao L Saydaliev HB Iqbal S Energy financing, COVID-19 repercussions and climate change: implications for emerging economies Clim Change Econ 2022 13 03 2240003 10.1142/S2010007822400036
Zheng X Zhou Y Iqbal S Working capital management of SMEs in COVID-19: role of managerial personality traits and overconfidence behavior Econ Anal Policy 2022 76 439 451 10.1016/j.eap.2022.08.006 35990757 | 37119485 | PMC10148011 | NO-CC CODE | 2023-05-31 23:15:24 | yes | Environ Sci Pollut Res Int. 2023 Apr 29; 30(25):67839-67853 |
==== Front
World J Microbiol Biotechnol
World J Microbiol Biotechnol
World Journal of Microbiology & Biotechnology
0959-3993
1573-0972
Springer Netherlands Dordrecht
37156882
3629
10.1007/s11274-023-03629-w
Review
The role of bismuth nanoparticles in the inhibition of bacterial infection
Salari Sedigh Somaye 1
Gholipour Arsalan 2
zandi Mahdiyeh 3
Qubais Saeed Balsam 4
Al-Naqeeb Bashar Zuhair Talib 5
Abdullah AL-Tameemi Noor M. 6
Nassar Maadh Fawzi 78
Amini Parya 9
Yasamineh Saman [email protected]
10
Gholizadeh Omid [email protected]
11
1 grid.412653.7 0000 0004 0405 6183 Department of Periodontology Dentistry, School of Dentistry, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
2 grid.411496.f 0000 0004 0382 4574 Nanotechnology Research Institute, School of Chemical Engineering, Babol Noshirvani University of Technology, Babol, Iran
3 grid.411746.1 0000 0004 4911 7066 Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
4 grid.412789.1 0000 0004 4686 5317 Clinical Sciences Department, College of Medicine, University of Sharjah, Sharjah, UAE
5 grid.460855.a Anesthesia technology department, Al-Turath University College, Al Mansour, Baghdad, Iraq
6 grid.517728.e 0000 0004 9360 4144 Nursing Department, Al-Mustaqbal University College, Hillah, Babylon, 51001 Iraq
7 grid.11142.37 0000 0001 2231 800X Integrated Chemical Biophysics Research, Faculty of Science, University Putra Malaysia, Serdang, 43400 UPM Selangor Malaysia
8 grid.11142.37 0000 0001 2231 800X Department of Chemistry, Faculty of Science, University Putra Malaysia, Serdang, 43400 UPM Selangor Malaysia
9 grid.413020.4 0000 0004 0384 8939 Department of Microbiology, School of Medicine, Yasuj University of Medical Sciences, Yasuj, Iran
10 grid.411705.6 0000 0001 0166 0922 Research Center for Clinical Virology, Tehran University of Medical Sciences, Tehran, Iran
11 grid.412888.f 0000 0001 2174 8913 Department of Bacteriology and Virology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
9 5 2023
2023
39 7 1907 3 2023
24 4 2023
© The Author(s), under exclusive licence to Springer Nature B.V. 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
Bismuth (Bi) combinations have been utilized for the treatment of bacterial infections. In addition, these metal compounds are most frequently utilized for treating gastrointestinal diseases. Usually, Bi is found as bismuthinite (Bi sulfide), bismite (Bi oxide), and bismuthite (Bi carbonate). Newly, Bi nanoparticles (BiNP) were produced for CT imaging or photothermal treatment and nanocarriers for medicine transfer. Further benefits, such as increased biocompatibility and specific surface area, are also seen in regular-size BiNPs. Low toxicity and ecologically favorable attributes have generated interest in BiNPs for biomedical approaches. Moreover, BiNPs offer an option for treating multidrug-resistant (MDR) bacteria because they communicate directly with the bacterial cell wall, induce adaptive and inherent immune reactions, generate reactive oxygen compounds, limit biofilm production, and stimulate intracellular impacts. In addition, BiNPs in amalgamation with X-ray therapy as well as have the capability to treat MDR bacteria. BiNPs as photothermal agents can realize the actual antibacterial through continuous efforts of investigators in the near future. In this article, we summarized the properties of BiNPs, and different preparation methods, also reviewed the latest advances in the BiNPs’ performance and their therapeutic effects on various bacterial infections, such as Helicobacter pylori, Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli.
Graphical abstract: BiNPs are antibacterial and ideal photothermal agents to inhibit various bacterial infections
Keywords
Bacterial infection
Bismuth nanoparticles
Antibacterial
Photothermal
Helicobacter pylori
issue-copyright-statement© Springer Nature B.V. 2023
==== Body
pmcIntroduction
The constituents of nanoparticles (NPs) allow for a simple categorization into organic and inorganic categories (Oveili et al. 2023; Yasamineh et al. 2023). Antigen conveyance as vaccination is ideal for inorganic NPs because of their tiny size, improved constancy, controlled adjustability, higher penetrance, superior drug loadings, and triggered release profile. These cutting-edge developments, known as hybrid inorganic NPs, often have an inorganic core surrounded by an organic shell (Gholizadeh et al. 2022; Yasamineh et al. 2022a, b). Among these methods, NPs, which generally range in size from 0.2 to 100 nm, performed well as new antimicrobial substances (Panáček et al. 2006). Nanotechnology, regarded as an interdisciplinary technology, has several applications; some of them include pharmacology, medical diagnostics, nutrition, chemistry, ecology, biotechnology, and even physical energy. Silver, magnesium, copper, titanium, zinc, gold, and bismuth (Bi) are the most common metals utilized for biomedical purposes (Dizaj et al. 2015; Rudramurthy et al. 2016). Metallic NPs, which may be manufactured in very minute sizes, have the capability of efficiently penetrating the peptidoglycan layer of bacterial cells (Siddiqi et al. 2018). Also, because of the anionic characteristics of lipopolysaccharides (LPS) and teichoic acids (TA), the negatively charged surfaces of bacterial cells have the potential to adsorb Cu2+, Ag+, and Zn2+ that are liberated by metallic NPs (Safarov et al. 2019). Bi compounds have been utilized in the cosmetics and pharmaceutical industries for more than 250 years (Udalova et al. 2008). Metal-containing medications have recently become prominent; one example is bismuth nanoparticles (BiNPs). Bi-based medicines have shown therapeutic efficacy in the treatment of wounds. The antibacterial properties of BiNPs have been verified in many lab tests (Neamati et al. 2023; Tiekink 2002). Bi is a metallic element of the 5 A group of the periodic table which are the pnictogens (atomic number (Z = 83)) with elements, including nitrogen (N), phosphorus (P), arsenic (As), and antimony (Sb). Further benefits, such as increased biocompatibility and specific surface area, are also seen in regular-size BiNPs. These characteristics make it an excellent medium for immobilizing proteins and enzymes (Mayorga-Martinez et al. 2013). The combinations of Bi attributes show an special improvement in exploiting singularly or concomitantly cytotoxicity and diagnostic efficacy (Bartoli et al., 2020). For example, the potential of influencing the release of donepezil hydrochloride (DO) through altering the current and voltage in the presence of bismuth ferrite (BiFeO3) results in an highly controllable and delicately tunable medicine release for Alzheimer’s disease treatment (Cesur et al. 2022). The bismuth tungstate (Bi2WO6) nanosheets have developed more opportunities for the rational preparation of novel electronic and biomedical nanosystems. The exceptional efficiency of Bi2WO6 makes it favorable as a multifunctional medicine delivery system for multimodal synergistic cancer treatment (Feng et al. 2018). In an investigation, the Bi2MoO6/HRP heterojunctions were prepared through hydrothermal treatment by Bi molybdate (Bi2MoO6) and hydrothermally treated red phosphorus (HRP). The remarkably effective and constant 5% Bi2MoO6/HRP composite was potentially successful in treating toxic heavy metals and pathogenic bacteria for water treatment (Tuerhong et al. 2022). In addition, Bi2MoO6/NH2-GO/PEG was offered as an effective and pH-sensitive anticancer drug delivery system (Sheykhisarem and Dehghani 2023). The production of additive-free bismuth vanadate (BiVO4) microspheres is used as an electrochemical sensor to determine the anti-tuberculosis medicine rifampicin (Li and Yan 2009). In addition, ultrafine photoetched BiVO4 nanorods improved with DSPE-PEG2000 (PEBVO@PEG NRs) were produced to attain in situ self-supply of oxygen (O2) and reactive oxygen species (ROS) for hypoxic cancer therapy (Yang et al. 2022).
Because of the limited toxicity, high atomic number, X-ray sensitivity, close-infrared driven semiconductor qualities, and low expense, highly efficient BiNPs with therapeutic activities show considerable potential for cancer diagnostics and treatments (Deng et al. 2018; Luo et al. 2013). BiNPs with a wide range of potential uses in the biomedical industry due to their low cost, low toxicity, and outstanding characteristics (Gomez et al. 2021). Dyspepsia, gastric ulcers, and Helicobacter Pylori infections are only a few examples of the many gastrointestinal diseases treated using Bi-based drugs. Its therapeutic use has recently been expanded to drug delivery, imaging, and biosensing, as well as possible therapies for cancer, multi-drug resistant (MDR) pathogens, and viral diseases (Griffith et al. 2021). It is assumed that BiNPs will not be hazardous to human cells since Bi subsalicylate is utilized to cure stomach disorders, and there have been no reports of any adverse consequences from exposure to BiNPs. No cytotoxic impact was seen when monkey kidney cells were exposed to BiNPs for 24 h at a final dosage of 2 mM (Claudio and Chellam 2014). Modern medical practice uses organic compounds of Bi (such as Bi subcitrate, Bi subsalicylate, and Bi subnitrate) as antibacterial agents (Chen et al. 2006; Mahony et al. 1999). Moreover, BiNPs offer an option for treating MDR bacteria because they communicate directly with the bacterial cell wall, induce adaptive and inherent immune reactions, generate reactive oxygen compounds, limit biofilm production, and stimulate intracellular impacts (AlMatar et al. 2018; Luo et al. 2013). The primary dangers related to communicable infections include the development of medication resistance, the scarcity and lack of variety in current therapies, and the advent of novel viruses, some of which can potentially cause global pandemics. Bi compounds have a long history of usage as antibacterial agents and recent research has shown that some Bi-based compounds and BiNPs display antibacterial action against bacterial diseases such as Staphylococcus aureus, H. pylori, Escherichia coli, and Pseudomonas aeruginosa (Hsu et al. 2018; Khameneh et al. 2016; Pop et al. 2022; Vazquez et al., 2020; Wu et al. 2023). In this article, we summarized the characteristics of BiNPs and the antibacterial properties of BiNPs, and their therapeutic effects on bacterial infections.
BiNPs properties and performance
As a heavy transition metal, Bi has a Pauling electronegativity of 2.02, a melting point of 271.5 °C, and a boiling temperature of 1564 °C. Poor thermal transmission (7.97 W/mK), rhombohedral structure density of 9.78 g/cm3, the electrical resistance of 1.29 µΩm at 20 °C, and thermal expansion of 13.4 μm/mK at 25 °C are some of its characteristics (Torrisi et al. 2018). Further to having a significant resistance to electricity for metal, the feature of Bi is that it expands when it freezes. It has a lower ability to conduct heat than any element except mercury (Briand and Burford 1999). Low toxicity and ecologically favorable attributes have generated interest in BiNPs for biomedical approaches. The low cost and abundance of Bi also make it an appealing material for various deployments. The semimetal bulk Bi has desirable properties for fabricating BiNPs of multiple shapes, sizes, and chemical compositions, including substantial magnetoresistance, large Fermi wavelengths, and robust diamagnetism. These Bi compounds, also known as Bi chalcogenides, are often found in the forms Bi2S3, Bi2O3, Bi2Se3, and Bi2Te3. Inherent electrical and optical characteristics of Bi chalcogenides nanostructures make them appropriate for various medical applications; nevertheless, these features are modified by their shape and crystal structure. The V-VI-VII ternary oxide semiconductor substances also include a subset of Bi compounds known as bi oxyhalides (BiOX, where X may be either Cl, Br, or I). These materials have gained considerable interest for photocatalytic performance under irradiation of visible light, in addition to electronics and energy storage, because of their layered formation and remarkable chemical resilience, as well as their electrical, optical, and mechanical characteristics. Moreover, Bi2WO6, BiFeO3, Bi2MoO6, BiPO4, BiVO4, Bi dimercaptopropanol (BisBAL), and (Bi2O)2CO3 nanostructures have been produced (Shahbazi et al. 2020). The majority of clinical experience using bi compounds has been in treating gastrointestinal diseases. Elemental Bi has antibacterial action, however, only at very high concentrations (on the millimolar scale) because of its poor solubility in water. Nevertheless, with chelation, solubility is improved, and Bi’s antibacterial characteristics are displayed at considerably lower concentrations (in the range of micromolar concentrations). For instance, BisBAL is particularly efficient against several different bacteria (Domenico et al. 1997; Velasco-Arias et al. 2012). Bismuth sulfide, Bismuth oxide, bismuth selenide, and bismuth telluride are just a few examples of non-metallic bismuth nanoparticles that have been synthesized and used for medicinal purposes. Bismuth is a very low-band-gap, diamagnetic semimetal. As a result of its unique combination of features, including strong magnetoresistance, thermal conductivity, and significant anisotropic electronic behavior, researchers have begun synthesizing BiNPs for electronic applications. BiNPs have also been investigated for their potential use as chemical catalysts. Newly developed BiNPs are effective in reducing 4-nitrophenol in the presence of NaBH4. Also, BiNPs’ photocatalytic action was described by Cui et al. (Cui et al. 2015; Gomez et al. 2021; Pothula et al. 2015; Thanh et al. 2014). Therefore, Bi2O3 NPs have potential medical, dental, and cosmetic applications because of their one-of-a-kind properties. These include, but are not limited to, their low cost and scalability, great stabilization, chemical inertness, nontoxicity, compatibility with living systems, and active properties (El-Batal et al. 2017) (Fig. 1).
Fig. 1 Illustration of a high-Z bismuth nanoparticle (BiNP) (for example, a 50 nm BiNP consists of about 1 million Bi atoms). The targeting vector is selected to have a high degree of specificity for a biological receptor, including a cell surface protein (Winter et al. 2018)
Different methods to prepare BiNPs
Although several publications explain the synthesis and biological uses of non-metallic BiNPs, notably Bi chalcogenides and Bi oxyhalides such as Bi sulfide, Bi oxide, Bi selenide, and Bi telluride, only around fifty studies have reported the fabrication of metallic BiNPs. In medicine, Bi(III) complexes play an essential role. In the case of diarrhea and stomach distress brought on by overeating or drinking, Bi subsalicylate is often used. This one-time dosage medication comprises milligram amounts of Bi(III) in combination with salicylate. In order to treat infections caused by Helicobacter pylori, another Bi(III) complex called Bi subcitrate potassium is frequently utilized with antibiotics and blockers of proton pump activity (Gomez et al. 2021). Because of the spherical size-confined reverse micelles, the water-in-oil (w/o) microemulsion approach has been extensively used in the NPs synthesis process. The w/o microemulsion technique has produced several types of NPs, including Bi, TiO2, CdS, Pd, Rh, and Pt. Bi subcarbonate (BiO)2CO3 NPs were generated from Bi citrate by a w/o microemulsion-assisted hydrothermal technique to boost the antibacterial activity of Bi subcarbonate and perhaps discover novel Bi medicines (Chen et al. 2010; Cushing et al. 2004; Fang et al. 2001; Holmberg 2004). Moreover, 25 nm BiNPs were effectively generated using laser ablation, and these nanoparticles have the potential to serve as a better contrast medium for high-resolution imaging in a variety of biological contexts. BiNPs with robust anti-wear characteristics have been found to have an average size in the region of 50–103 nm when produced using conventional solvent procedures. The reduction of nitro chemicals into azo compounds is another use for BiNPs as catalysts. Colloidal-chemically made 40-nm BiNPs in an aqueous medium were shown to have significant antimicrobial action against various microbial pathogens (Das et al. 2020; Pothula et al. 2015; Rieznichenko, Gruzina et al., 2015; Torrisi et al. 2018).
Preliminary data on the thermoelectric characteristics of Bi nanopowders are presented, as is an efficient technique for preparing these nanoparticles by thermal breakdown of Bi dodecyl-mercaptide Bi(SC12H25)3. BiNPs are produced in the thermolysis process because the by-product dodecyl-disulfide acts as an effective capping agent, tightly bonding the surface of the Bi clusters to prohibit them from aggregating and slowing their development. Thermoelectric analysis of the synthesized Bi nanopowders shows unusual behavior, including a semimetal-semiconductor transition and, at the smallest grain size, a significantly elevated Seebeck coefficient compared with bulk Bi (170 nm) (Carotenuto et al. 2009).
The Bi Ferrite NPs (BiFeO3) used in cancer therapy are manufactured through the sol-gel technique from Bi nitrate (Bi (NO3)3.H2O) and iron nitrate (Fe (NO3)3.9H2O) as a foundation material. To prevent Bi volatilization and meet the need for nanosized oxides, the development of low-temperature fabrication techniques is crucial. Manufacturing BiFeO3 NPs using conventional solid-state methods results in low reproducibility, particle size increase, and the production of an impurity phase composed of Bi2O3 and Bi2Fe4O9 (Rameshkumar et al. 2021).
Pulsed laser ablation (PLA) of a Bi subsalicylate (BSS) target in an aqueous condition was identified as the most appropriate method for producing BSS NPs. Physical vapor deposition, or immersed PLA, is a method for creating NPs while preserving their original chemical and elemental makeup. A colloidal form of BSS was obtained since it has limited solubility in water (Castañeda et al., 2015; Yang 2012). For instance, metal nanoparticles were produced immediately by the pulsed laser ablation (Nd: YAG, = 1064 nm) of Bi and tellurium plates submerged in clean water. The findings showed that as the energy of the pulses increased, the NP concentration elevated while the average NP diameter reduced. The antibacterial capabilities of NPs are thought to be attributable to their overall surface area since a greater surface-to-volume ratio of TeNPs offers more efficient ways for improved antibacterial action against harmful microorganisms (Jassim et al. 2015) (Fig. 2).
Bi2O3 NPs were produced through the sol-gel technique. A combination of bismuth nitrate and citric acid solution is taken in an equal molar ratio (1:1) and heated in a hot water bath. In the evaporation of water, a gel is formed, which generates nanocrystalline Bi2O3 particles by decomposition at a temperature of 400 °C (Jha et al. 2005; Mallahi et al. 2014). Bi2O3 NPs are a proper option of metal oxide for several uses in the production of nanostructures, photocatalyst, catalytic performance for reduction, and photovoltaic, biological sciences, medical, biological, and antibacterial efficacy. These NPs are used in medical science, including an astringent in medical and topical cream (Abudayyak et al. 2017; Kazemi & Yaqoubi, 2020). The preparation of Bi2S3 NPs through the hot injection technique was investigated in addition to their behavior, when covered with a biocompatible factor. The hot injection technique allowed us to produce Bi2S3 nanorods measuring in mean 4.2 ± 1.4 nm in width and 27.5 ± 16.3 nm in length (Galain et al. 2022). In an investigation, researchers prepared and utilized Bi2S3 as a booster of X-ray radiation therapy. Moreover, Bi2S3 was used as a carrier of curcumin (CUR), an anti-cancer substance, for the aim of multimodality treatment (Nosrati et al. 2019). Bi chalcogenides-based nanomedicines have attracted much attention as exceptionally effective radiosensitizers because of their high photoelectric efficacy and excellent biocompatibility. In addition, particularly synthesized nanocomposites can successfully reduce the radiation resistance of cancer tissues (Huang et al. 2022). Bi chalcogenides (Bi13S18I2 and BiSI) were produced through the Solvothermal technique. The solvothermal preparation method has proven to be a very effective and affordable for generating BiNPs of designed composition. It has a higher capability to realize large-scale generation for many practical uses. This method needs utilizing a solvent at an average to high pressure ( generally between 1 atm and 10,000 atm) and temperature (usually between 100 and 1000 °C) to allow precursors to interact during preparation (Li et al. 2020; Song et al. 2015).
Fig. 2 Laser ablation-made BiNPs. Polyol electrooxidation is facilitated by the generation of Bi(V) species. With the Bi(V) species, glucose undergoes more selective oxidation and C-C bond breakage, yielding arabinonic acid, erythronic acid, and ultimately glyceric acid instead of the more often observed gluconic acid as a result (Zheng et al. 2021)
The above-mentioned physical and chemical techniques require precision instruments, and the use of risky chemicals, and thus, green synthesis is ideal over other approaches. To produce metal NPs, bio-assisted methods, as well as recognized as biosynthesis or green synthesis, offer an eco-friendly, low-toxic, economical, and practical methodology that usages biological organizations, including bacteria, fungus, viruses, yeast, actinomycetes, plant extracts, and so on. Biosynthesized Bi2O3 NPs are inexpensive, more eco-friendly, easy to produce, and harmless to use than those made from microorganisms. Furthermore, compared to Bi2O3 NPs from microbes, biosynthesized Bi2O3 NPs are less dangerous since the solvents utilized to produce plant extracts are commonly distilled water and ethanol. Bi2O3 NPs from plant extracts are attained from different tree sections, such as the roots, barks, leaflets, flowers, fruit extracts, and peels (Prakash et al., 2022). In a study, BiNPs generating bacterial strain (designated as Delftia sp. SFG) was separated from salt marsh, and the biogenic BiNPs were purified, determined, and their cytotoxic and antioxidant actions were specified (Shakibaie et al. 2018). In another investigation, the Bi2O3 nanoflakes were prepared by a fruit peel extract of Nephelium lappaceum L.(Karnan and Samuel 2016). Presently, researchers utilize a one-step reduction manner to generate biomolecule-mediated BiNPs. BiNPs were prepared from various biomolecules, such as gelatin, bovine, and human serum albumin (Liu et al. 2020) (Table 1).
Table 1 Different preparation methods of BiNPs.
Production methods BiNPs Explain methods References
Chemical reduction process BAL-mediated PVP-BiNPs BiNPs were produced through a chemical reduction method, in less than 1 h, in a heated alkaline glycine solution; by the chelation and reduction of the Bi (III) ions using BAL and sodium borohydride, respectively, and next covered and fixed through PVP. This technique can be simply used to investigate BiNPs as non-antibiotics. (Vazquez et al., 2020)
Solvothermal method Bi2O3, Bi13S18I2, BiOCl-TiO2 and Bi2MoO6 This method needs utilizing a solvent at an average to high pressure (generally between 1 atm and 10,000 atm) and temperature (usually between 100 and 1000 °C) to allow precursors to interact during preparation. Bi subcarbonate was produced from Bi nitrate through an easy solvothermal technique and utilized an antibacterial agent versus Helicobacter pylori. (Cheng et al. 2010; Shahbazi et al. 2020; Sun et al. 2014; Xiao et al. 2020)
Sol-gel technique BiFeO3 An easy sol-gel low-temperature method has been produced to acquire bismuth titanate nanoplates with the crystal form of orthorhombic phase and lattice parameters approximately 30 nm in dimensions. Manufacturing BiFeO3 NPs using conventional solid-state methods results in low reproducibility, particle size increase, and the production of an impurity phase composed of Bi2O3 and Bi2Fe4O9. (Rameshkumar et al. 2021; Singh et al. 2023)
Pulsed laser ablation technique Bi subsalicylate Pulsed laser ablation of a Bi subsalicylate (BSS) target in an aqueous condition was identified as the most appropriate method for producing BSS NPs. Physical vapor deposition, or immersed this method, is a technique for creating NPs while preserving their original chemical and elemental makeup. A colloidal form of BSS was obtained since it has limited solubility in water. (Flores-Castañeda et al. 2015; Yang 2012)
Sonochemical technique Bi2O3, BiFeO3, and Bi2S3 A sonochemical reaction is a chemical reaction that utilizes powerful ultrasound diffusion, as well as the concept of sonochemistry (20 kHz-10 MHz). (Manavalan et al. 2019; Prakash et al. 2022; Shakibaie et al. 2018)
Biosynthesis Bi2O3 Biosynthesized Bi2O3 NPs are inexpensive, more eco-friendly, easy to produce, and harmless to use than those made from microorganisms. Furthermore, compared to Bi2O3 NPs from microbes, biosynthesized Bi2O3 NPs are less dangerous since the solvents utilized to produce plant extracts are commonly distilled water and ethanol. Bi2O3 NPs from plant extracts are attained from different tree sections, such as the roots, barks, leaflets, flowers, fruit extracts, and peels. (Prakash et al. 2022)
BiNPs inHelicobacter pylori.
To preserve the gastrointestinal mucosa and, more recently, to eliminate H. pylori, Bi-containing medications have been used on humans for almost 200 years (Himeno et al. 2022). One of the etiological causes of chronic gastritis, peptic ulcer disorder, and gastric cancer is H. pylori, the dominant member of the gastric microbiome of infected persons. Half of the world’s population may have H. pylori infection. H. pylori infection treatment is problematic due to the global rise in antibiotic resistance (Lee et al. 2022; Ren et al. 2022; Sousa et al. 2022). Nowadays, H. pylori is treated using Bi organic salts, which act as an antibacterial agent. For the first time, scientists have used a serial agar dilution technique to assess the antibacterial activity of elemental BiNPs against a variety of clinical isolates and a reference strain of H. pylori. All of the H. pylori strains put to the test were effectively countered by the antibacterial properties of these biogenic NPs. The obtained minimum inhibitory concentrations (MICs) for H. pylori (ATCC 26,695) and H. pylori clinical isolates ranged from 60 to 100 µg/ml. Formic acid, acetate, glutamate, glycine, valine, and uracil were among the metabolites secreted by H. pylori into their supernatants after exposure to an inhibitory dose of BiNPs (100 µg/ml). Inhibition of the nucleotide, Krebs cycle, and amino acid metabolism, as well as anti-H. pylori action, are all confirmed by these studies using NPs (Nazari et al. 2014). Another study found that the w/o microemulsion-assisted hydrothermal technique effectively synthesized well-crystallized Bi subcarbonate ((BiO)2CO3) NPs. Precursors employed in this synthesis are urea and Bi citrate, with the latter’s heat breakdown yielding the primary carbonate anion. Since the reactivity, nucleation, and growth processes are localized inside the water droplets, well-crystallized, monodisperse spherical NPs are produced. These NPs have anti-H. pylori effect comparable to those of the commercially utilized medication colloidal Bi subcitrate (CBS), suggesting that they may be helpful in building blocks for future nanomedicines (Chen et al. 2010). Due to the excellent efficacy of H. pylori eradication, Bi-comprising quadruple therapy (BQT), which contains proton pump inhibitor (PPI), Bi, and two antibiotics, is currently presented as first-line therapy. In an investigation, researchers showed that the patients eradicated through BQT had gut microbiota dysbiosis for more than one year. Moreover, the dysbiosis of the gut microbiome remarkably influenced human pathophysiology and was related to other diseases (Wu et al. 2022). Enhanced Bifidobacterium was detected in the gut microbiota after effective H. pylori eradication with 10-day BQT therapy (Guo et al. 2020). In another investigation, researchers demonstrated a remarkable decrease in the relative numbers of Bifidobacterium adolescentis, while Enterococcus faecium levels increased 0 or 2 days following the 14-day BQT therapy (Olekhnovich et al. 2019). Accordingly, it is essential to investigate the efficacy of H. pylori eradication treatment on the microbiota and the encouraging therapeutic methods to preserve gut microbiota homeostasis(Wu et al. 2022). In a study, researchers prepare a series of silica-covered Bi2S3 NPs (Bi2S3@SiO2) of several dimensions. 28 days following administration, Bi2S3@SiO2 NPs demonstrate low toxicity efficacy in vivo and nonsignificant effects on the construction and role of the gut microbiota in mice. This shows that no side effects on the gut homeostasis are stimulated through Bi2S3@SiO2 core-shell NPs and, therefore, they can act as very good and safe (Chen et al. 2022) (Table 2).
Table 2 Comparison of silver NPs (AgNPs) with BiNPs against H. pylori
Comparative cases BiNPs against H. pylori AgNPs against H. pylori
Type of NPs Bi subcarbonate NPs ((BiO)2 CO3), N-acylhomoserine lactonase stabilized AgNPs (AiiA-AgNPs)
Preparation method Biological synthesis by S. marcescens The reduction of aqueous Ag+ ion using the culture
MICs 60 to 100 µg/ml -
Performance against H. pylori Antibacterial action Protein-based NP
Explain inhibition method Formic acid, acetate, glutamate, glycine, valine, and uracil were among the metabolites secreted by H. pylori into their supernatants after exposure to an inhibitory dose of BiNPs (100 µg/ml). Inhibition of the nucleotide, Krebs cycle, and amino acid metabolism, as well as anti-H. pylori action, are all confirmed by these studies using NPs. AiiA-AgNPs suppressed quorum sensing (QS) through the destruction of QS molecules, thereby decreasing biofilm formation, urease generation, and changing cell surface hydrophobicity of H. pylori. AiiA‐AgNPs demonstrated no cytotoxic efficacy on RAW 264.7 macrophages at the efficient concentration (1–5 µM) of antibiofilm acting.
Advantages Good antibacterial effectiveness, possible targeted delivery of different anti-bacterial drugs, the long-term effect of AgNP on H. pylori, and long tissular persistence. Drugs containing Bi-based chemicals have found widespread application in treating H. pylori infections, multidrug-resistant microbial infections, and good antibacterial effectiveness.
Limitation Fewer and limited studies, need more effective analysis, lack of mass production methods. Fewer and limited studies, need more effective analysis, lack of mass production methods.
References (Nazari et al. 2014) (Gopalakrishnan et al. 2020)
BiNPs in other bacterial infections
Oral plaque is the most prevalent biofilm, and Streptococcus mutans is the most frequent bacterium responsible for dental caries. In addition to being found in instances of endocarditis, S. mutans has been found colonizing the endocardium and heart valves. This is likely owing to S. mutans’ capacity to cling to solid surfaces and create a biofilm (Banas 2004; Lemos et al. 2019). Early research on the antibacterial properties of zerovalent BiNPs has shown promising results. They were equally effective as chlorhexidine in preventing the spread of S. mutans. When considering zero-valent BiNPs to add in a mouthwash, it is essential to remember that their MIC for bacterial growth suppression was 0.5 mM. Chlorhexidine, the gold standard in oral antiseptics, has been shown to have comparable efficacy to these NPs in the studies conducted. The production of biofilm by S. mutans was entirely halted by the use of zerovalent BiNPs. Zero-valent BiNPs were predicted to have a suppressive impact on cell development but not a total block; therefore, this finding was unexpected. Because NPs inactivated 69% of cells, researchers speculated that the remaining cells weren’t enough to create a biofilm. Most of the experimental data suggests that these NPs may be a viable option for combating biofilm-based bacterial infection (Hernandez et al., 2012). In less than 30 min, it was possible to use a chemical reduction technique to create BiNPs with a stable PVP coating. Scientists have developed a crystalline structure for tiny, stable, spherical BiNPs covered with PVP. In planktonic and biofilm growth conditions, the PVP-BiNPs demonstrated antifungal efficacy against the opportunistic pathogenic yeast Candida albicans and a significant antibacterial effect on the pathogenic bacterium Staphylococcus aureus (Vazquez et al., 2020a).
Bi dimercaptopropanol (BisBAL) has been demonstrated to significantly reduce biofilm development by inhibiting the ability of Staphylococcus aureus, Klebsiella pneumoniae, and Pseudomonas spp. to secrete extracellular polymeric substances (EPS) (Domenico et al. 1999, 2001). Brevundimonas diminuta EPS expression was dramatically suppressed in suspension cultures at concentrations slightly below the MIC when Bi was combined with a lipophilic dithiol (3-dimercapto-1-propanol, BAL) at a molar ratio of 2:1. A slime-like EPS matrix generated by B. diminuta led to biofouling and poor hydrodynamic backwashing of microfiltration membranes in the absence of BisBAL treatment (Badireddy et al. 2008). BisBAL NPs were produced in another work by reducing sodium borohydride in water at ambient temperature. This research examined how BisBAL NPs influence Pseudomonas aeruginosa’s capacity for growth, adhesion, and biofilm formation. NP characterization revealed they were highly lipophilic, with a rhombohedral crystalline form and a crystallite size of about 18 nm. If administered at or above the MIC = 12.5 micromolar, bacterial growth is entirely stifled for at least 30 days. In the study, researchers demonstrate that lipophilic BisBAL NPs at the MIC prevented bacterial adhesion to track-etched polycarbonate membrane surfaces and lysed bacteria entrenched in biofilms within 1 h of contact (Badireddy et al. 2013).
Compared to other Bi salts, the antibacterial activity of Bi thiols is up to a thousand times higher, making them effective antibiofilm agents. According to the results of susceptibility tests, including agar diffusion and broth dilution, staphylococci are highly vulnerable. At concentrations ranging from 0.9 to 1.8 µM Bi3+, bi-ethanedithiol inhibited 10 strains of methicillin-resistant Staphylococcus epidermidis, Staphylococcus aureus ATCC 25,923 at 2.4 µM Bi3+, and S. epidermidis ATCC 12,228 at 0.1 µM Bi3+. S. aureus resistant to antiseptics, was susceptible to BisBAL at a concentration of ≤ 7 µM Bi3+. S. epidermidis was inhibited for 39 days by hydrogel-coated polyurethane rods that had been soaked in BisBAL (suppressive area diameter in agar, ≥ 30 mm for more than 25 days). At subinhibitory doses, the production of slime by 16 slime-producing S. epidermidis strains was strongly suppressed by Bi-3,4-dimercaptotoluene (BisTOL), whereas it was unaffected by AgNO3. To sum up, bi-thiols are not only bactericidal and bacteriostatic against staphylococci, even species that are resistant to them, but they are also inhibitors of slime at doses below those required for complete inhibition. BisTOL may be beneficial in avoiding the infection and colonization of indwelling intravascular lines if administered at doses below those required to suppress growth, given that staphylococci are significant pathogens in this environment (Domenico et al. 2001).
The most prevalent species responsible for tooth caries and biofilm production are Streptococcus salivarius and Enterococcus faecalis. The most effective method for eradicating these germs is a 7-day course of chlorhexidine 2% mouthwash. For Streptococcus salivarius and Enterococcus faecalis, the MICs of BiNP suspension were 2.5 and 5 µg/ml, respectively. BiNP suspension has a minimum bactericidal concentration (MBC) of 5 µg/ml against Streptococcus salivarius and 10 µg/ml against Enterococcus faecalis. BiNPs were compared to a 2% chlorhexidine solution for their antibacterial efficacy. When tested against Streptococcus salivarius and Enterococcus faecalis, MICs of BiNPs were 5% less than those of chlorhexidine. MBC of BiNPs was 10% less than that of chlorhexidine against both bacteria. It was shown that BiNPs outperformed chlorhexidine and had lower MICs and MBCs (Rostamifar et al. 2021). The co-precipitation approach was used to successfully create Bi oxychloride (BiOCl) NPs at ambient temperature. BiOCl NPs showed considerable suppressive action at both MIC and MBC levels against the infectious bacterial strains S.aureus and P.aeruginosa. Importantly, BiOCl NPs are non-toxic to human erythrocytes, and they inhibit the activity of the coagulation system in both platelet-rich plasma (PRP) and platelet-poor plasma (PPP) (Puttaraju et al. 2022). P. aeruginosa’s capacity for quorum sensing and generation of biofilm was the subject of another research, which assessed the impact that tobramycin loaded on niosomes and combined with Bi-ethanedithiol had on these processes. Niosomal tobramycin and niosomal tobramycin combined with Bi-ethanedithiol dramatically lowered the MIC of tobramycin, and together they were the most effective combination for preventing the development of several P. aeruginosa strains. Biofilm development was significantly decreased by these chemicals at sub-MIC concentrations, and AHL molecule synthesis was substantially suppressed compared to untreated bacteria. MIC of Tobramycin was decreased, and biofilm development was efficiently suppressed by encapsulation in niosomes along with Bi-ethanedithiol (Mahdiun et al. 2017).
Bi subsalicylate (BSS) NPs were tested for their antibacterial efficacy against four common opportunistic pathogens: Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Staphylococcus epidermidis. The production of BSS NPs was accomplished by performing pulse laser ablation on a solid target while it was suspended in distilled water and subjected to a variety of circumstances. Inhibition ratios for E. coli and S. epidermidis were found to be dose and size-dependent, whereas P. aeruginosa and S. aureus were shown to be more susceptible to the BSS NPs regardless of size or concentration. To achieve inhibition ratios > 80%, comparable to or greater than those achieved with the antibiotic employed as control, the BSS colloids with an average particle dimension of 20 nm were often the most effective. These findings prove that BSS colloids have promising medicinal uses as potent antibacterial agents (Castañeda et al., 2015). It was hypothesized that adding BisBAL NPs to mineral trioxide aggregate (MTA) would improve its already impressive antibacterial and antibiofilm capabilities; therefore, that was the focus of the study. After just 24 h of treatment, the biofilm of fluorescent E. faecalis was detached, and the growth of Enterococcus faecalis, Escherichia coli, and Candida albicans was suppressed by MTA-BisBAL NPs. The physical characteristics of MTA were not substantially altered by adding BisBAL NPs, and MTA-BisBAL NPs did not cause cytotoxicity in human gingival fibroblasts. Overall, these data imply that BisBAL NPs give antibacterial and antibiofilm capabilities to MTA while maintaining their biophysical features and without causing any adverse impacts on human gingival fibroblasts (Delgadillo et al., 2017).
Another study compared the MICs of three different colloidal dispersions of BiNPs to those of silver NPs to combat oral and nosocomial bacteria. Chemical reduction in DMSO was used to produce the NPs. Eight typical species of the subgingival biofilm and three species of medical interest (Pseudomonas aeruginosa, Staphylococcus aureus, and Escherichia coli) were examined to determine MICs for each colloidal dispersion. All of the Bi compounds exhibited antibacterial activity against the tested bacterial species, with MICs ranging from 37 to 329 µg/mL. Nevertheless, AgNPs revealed MICs between 16 and 32 µg/mL against bacteria in subgingival biofilm and between 32 and 65 µg/mL against medically essential species. The Bi2O3 NPs became the most effectual nanometric Bi compounds that were examined for this study, although having a lesser strength than AgNps (Campos et al. 2018).
BiNPs as photothermal agents in bacterial infection
Heavy element NPs (including gold and Bi) may be employed as radiosensitizers to increase the radiation dosage for bacterial death because of their broad cross-section for X-ray absorbance and photoelectron production (Kong et al. 2008; Wang et al. 2010; Werner et al. 2011). Nano-structured Bi has been the subject of theoretical investigation, with promising results suggesting it might be used in optical and electrooptic device applications, as well as having improved thermoelectric properties and functioning as a catalyst (Ma et al. 2013). Moreover, a technique based on enhancing X-ray irradiation by NPs can be employed to eradicate MDR microorganisms. In a proof-of-concept study, MDR P. aeruginosa was used as an example. In this experiment, polyclonal antibody-altered BiNPs were put into the microbial culture to target P. aeruginosa selectively. When MDR P. aeruginosa was exposed to X-rays at 40 kVp along with 200 ϻg ml− 1 BiNPs for 10 min, the results showed that up to 90% of the bacteria were killed. However, when BiNPs were not present, only around 6% of the bacteria were destroyed. A 35-fold increase in localized X-ray dosage is seen when 200 ϻg ml− 1 BiNPs are used, compared to a control without NPs. In addition, no significant detrimental impacts on human cells (MG-63 and HeLa cells) were detected with 200 ϻg ml− 1 BiNPs and 10 min of 40 kVp X-ray irradiation exposures, which provides the potential for future clinical usage. This antibacterial approach can be employed efficiently in destroying deeply embedded MDR bacteria in vivo due to the high penetrability of X-rays to human tissue (Luo et al. 2013) (Fig. 3). Synergistic antibacterial treatment is being studied, and one study involves the creation of silver-BiNPs (Ag-Bi@SiO2 NPs) based on mesoporous silica. BiNP-generated hyperthermia may impair cell integrity and speed up Ag ion release, according to in vitro investigations; this phenomenon has been shown to have potent antibacterial effects against methicillin-resistant Staphylococcus aureus (MRSA). Also, when exposed to laser pulses, 100 µg mL− 1 Ag-Bi@SiO2 NPs can eradicate mature MRSA biofilms and reduce biomass by 69.5%, demonstrating a more potent therapeutic impact than either the Bi@SiO2 NPs along with laser irradiation (26.8%) or Ag-Bi@SiO2 NPs (without laser treatment, 30.8%) groups. In vivo data further, demonstrate that the Ag-Bi@SiO2 NPs bactericidal platform effectively eliminates about 95.4% of abscess germs and expedites abscess ablation. The photothermal enhancement of the antimicrobial property of Ag-Bi@SiO2 NPs suggests that they may be helpful as a nano-antibacterial medication for treating skin infections (Cao et al. 2020). There have always been significant obstacles to wound healing. Bacterial infections are a major cause of delayed recovery and even death. In one research, nanoparticles of Bi sulfide (Bi2S3 NPs) with a significant photothermal impact were integrated with hydrogels of sodium alginate and acrylamide (PAAm/NaAlg hydrogels) to create nanocomposite adhesive hydrogels (Bi2S3 NPs hydrogels) that exhibited potent antibacterial activity and were compatible with living organisms. Bi2S3 NPs are capable of efficiently converting light energy into heat energy and producing a specific quantity of reactive oxygen species (ROS) to break up bacterial proteins and damage cell membranes. Hydrogels have been shown to have an adhesion property and to stimulate wound healing without the use of growth factors in in vivo investigations. Hydrogels containing photothermal Bi2S3 NPs were first created for wound treatment; these hydrogels generated heat energy for bactericidal purposes when exposed to near-infrared (NIR) light (Zhou et al. 2023). A NIR light catalyst (Bi2S3–S-nitrosothiol–acetylcholine (BSNA)) was developed in another work by converting •O2– into peroxynitrite in situ; this compound may increase bacteria’s sensitivity to ROS and heat, killing them at a relatively low temperature. The in situ-transformed peroxynitrite has enhanced membrane-penetrating and antioxidant properties. BSNA NPs hindered bacterial glucose metabolism by reducing xerC/xerD expression, and by nitrifying TYR179, they altered the secondary structure of HSP70 and HSP90. The antibacterial activity was further enhanced by the synergistic action of the developed BSNA and clinical antibiotics. In the case of antibiotics belonging to the tetracycline family, BSNA NPs caused alterations in the structure of the phenolic hydroxyl group. They hindered the interaction between tetracycline and the targeted t-RNA recombinant protein. Moreover, BSNA’s immunotherapy action was shown by its ability to increase CD8 + T cell production and decrease the incidence of typical sequelae associated with peritonitis (Li et al. 2022).
Researchers in another work describe synthesizing unique palladium NPs coated Bi oxybromide (Pd/BiOBr) nanostructures utilizing an energy-efficient solution-based technique; these nanostructures exhibit potent photocatalytic antibacterial activity. It was determined how effective the photocatalytic antibacterial activity of Pd/BiOBr was against several Gram-positive and Gram-negative bacterial strains that are often considered to be pathogenic (Pseudomonas aeruginosa, Pseudomonas fluorescens, Aeromonas salmonicida, Escherichia coli, Klebsiella pneumoniae, Salmonella typhimurium, Bacillus subtilis). Pd/BiOBr demonstrated remarkable photocatalytic disinfection efficiency, with bacterial inhibition rates of more than 99.9%. Even at a low dose of 0.5 µg/mL, Pd/BiOBr substantially reduced the growth of bacteria in addition to 2 h of visible light irradiation; at 1 µg/mL, Pd/BiOBr totally killed all the evaluated bacterial strains, demonstrating their remarkable bactericidal power (Bisht et al. 2022) (Table 3).
Fig. 3 BiNP improved the effectiveness of X-ray radiation in eliminating multidrug-resistant bacteria in deep tissue. The diagram illustrates the interaction between bacteria and BiNPs (Luo et al. 2013)
Table 3 The effects of BiNPs in the inhibition of bacterial infection
BiNPs Bacterial infection Physicochemical characteristic MIC Effects Ref
Carboxyl-Capped BiNPs H. pylori Irregular-shaped. BiNPs carried a carboxylic acid functional group on their surfaces. Varied between 60 and 100 µg/ml Several metabolites, including formic acid, acetate, valine, glutamate, uracil, and glycine, were secreted by H. pylori into their supernatant after exposure to an inhibitory dose of BiNPs (100 µg/ml). (Nazari et al. 2014)
Bi subcarbonate ((BiO)2 CO3) NPs H. pylori Spherical and nearly uniform NPs, Particle size varies from 5 to 15 nm, > 85% inhibition at 80 µg/mL of (BiO)2CO3 NPs; 65% at 20 µg/mL, and 50% at 15 µg/mL CBS had roughly 50% of the inhibitory action of (BiO)2CO3 NPs. The bulk form of (BiO)2CO3 had approximately 1/3 of the anti-H. pylori action of the NPs. It showed that as compared to the bulky (BiO)2CO3 and the antiulcer medication colloidal CBS, (BiO)2CO3 NPs display slightly improved and equivalent inhibitory characteristics, respectively. (Chen et al. 2010)
Polyvinylpyrrolidone (PVP)-coated BiNPs Staphylococcus aureus The mean diameter of the NPs is 8.4 nm ± 6.7 nm, mixed arrangement, conformed through cubic and hexagonal phases. 0.5 to 256 µg/mL BiNPs are effective against S. aureus and Candida albicans in both the planktonic and biofilm phases of their respective life cycles. Economical, Rapid, and Simple to Synthesize BiNPs may have widespread antimicrobial action, including against fungus and bacteria. (Vazquez et al., 2020a)
Bi dimercaptopropanol (BisBAL) Staphylococcus, Klebsiella, and Pseudomonas spp. Antiseptic-resistant S. aureus was sensitive to BisBAL) at < 7 mM Bi3+ 0.1 to 100 mM bismuth; 5 mM = 1 µg/ml BisBAL has been demonstrated to significantly inhibit EPS release by Klebsiella, Staphylococcus, and Pseudomonas spp. and hence limit biofilm development. (Domenico et al. 1999, 2001)
BisBAL Brevundimonas diminuta The NPs are formed of 18.7 nm crystallites on mean and have a rhombohedral construction, agglomerating into chains-like or clusters of small NPs. 12 µg/ml Brevundimonas diminuta EPS expression was significantly suppressed in suspension cultures at concentrations slightly below the MIC when Bi was combined with a lipophilic dithiol (3-dimercapto-1-propanol, BAL) at a molar ratio of 2:1. (Badireddy et al. 2008)
Bi-3,4-dimercaptotoluene (BisTOL) Staphylococcus epidermidis - 0.25 µg/ml Since staphylococci are common pathogens associated with indwelling intravascular lines, BisTOL at subinhibitory doses may be beneficial in avoiding colonization and infection of these lines. (Domenico et al. 2001)
Bi oxychloride (BiOCl) NP S.aureus and P.aeruginosa BiOCl NPs demonstrate tetragonal phase with mean crystalline dimensions were found to be 23 nm. The energy band gap of BiOCl NPs is 3.5 eV. The MIC of BiOCl versus S.aureus and P.aeruginosa was 32 and > 1024 µg/ml, respectively. BiOCl NPs showed considerable inhibitory action at both MIC and MBC levels against the infectious bacterial strains S.aureus and P.aeruginosa. The crucial non-toxic characteristics of BiOCl NPs on human erythrocytes have been shown. (Puttaraju et al. 2022)
Bi subsalicylate (BSS) NPs E. coli, P. aeruginosa, S. aureus, and S. epidermidis Mean particle size between 20 and 60 nm. 95 to 195 mg/L. Inhibition ratios > 80% were achieved by the BSS colloids with an average particle dimension of 20 nm, comparable to or higher than the ratios obtained using the control antibiotic. (Castañeda et al., 2015)
Bi2O3 NPs S. aureus, P. aeruginosa, and E. coli. - 37 to 329 µg/mL All of the examined Bi compounds had an antibacterial impact on the several bacterial species used in the study. While less powerful than AgNPs, Bi2O3 NPs were the most potent nanometric Bi compounds tested here. (Campos et al. 2018)
Bi(NO3)3 NP P. aeruginosa These NPs improve localized X-ray dose by 35 times higher than the control with no NPs. Bi(NO3)3 NP is a semiconductor photocatalyst with the advantages of low cost, low toxicity, high light stability, and photo corrosion. 200 µg/ml 90% of multidrug-resistant P. aeruginosa are killed by 40 kVp X-rays for 10 min when 200 ϻg/ml BiNPs are present, while only around 6% are destroyed without BiNPs. (Luo et al. 2013)
In vitro cytotoxicity of BiNPs
Various double-blind assessments have demonstrated that the blood Bi concentration of 50 µg/L (about 600 nM) is considered to be non-toxic during Bi compound injection, some adverse events, including Bi-stimulated encephalopathy, are still reported (Larsen et al. 2005). Meantime, 5.0 mg/L (about 10 µM) Bi2O3 can stimulate genotoxicity by enhancing the oxidative stress in the blood (Geyikoglu and Turkez 2005). 200 mM Bi citrate exposed J774 cells accumulate the metal in their lysosomes and lead to lysosomal rupture (Stoltenberg et al. 2002). In vivo toxicity investigations as well as show that 100 µg/L colloidal Bi subnitrate can stimulate liver damage and cerebellar involvement. The BiNPs are non-toxic at a concentration of 0.5 nM. NPs at a great concentration (50 nM) kill 45, 52, 41, and 34% HeLa cells for bare nanoparticles, amine-terminated BiNPs, silica-covered BiNPs, and polyethylene glycol (PEG) modified BiNPs, respectively; which shows cytotoxicity in terms of cell viability is in the decreasing order of amine-terminated BiNPs, naked BiNPs, silica covered BiNPs, and PEG-modified BiNPs (Luo et al. 2012). The 200 µg/ml BiNPs improved localized X-ray dose by 35 times greater than the control with no NPs. Moreover, no remarkable adverse events on human cells (HeLa and MG-63 cells) have been detected with 200 µg/ml BiNPs and 10 min 40 kVp X-ray irradiation exposures (Luo et al. 2013). In a study, BiNPs synthesizing bacterial strain (determined as Delftia sp. SFG) was separated from salt marsh, and the biogenic BiNPs were purified, defined, and their cytotoxic and antioxidant functions were characterized. The achieved outcomes of cytotoxic effects (defined through the MTT-based colorimetric method) of the bare BiNPs revealed IC50 of 10.9 ± 0.9 µg/mL, 35.4 ± 0.5 µg/mL, and 42.8 ± 1.7 µg/mL versus A549, MCF-7, and 3T3 cell lines. The definition of antioxidant function demonstrated IC50 amounts of 123.1 µg/mL and 307.2 µg/mL for butylated hydroxyanisole (BHA) and BiNPs, respectively (Shakibaie et al. 2018).
BiNPs limitations and advantages in bacterial infection
Gold, Silver, zinc, and titanium metal NPs have all been studied extensively because of their purported antibacterial potential. Bi, on the other hand, is considered to be a “green” element because it does not cause cancer and has little bioaccumulation and cytotoxicity (Badireddy and Chellam 2014; Khan et al. 2016; Norman 1997; Norouzi et al. 2019; Yasamineh et al. 2023). Drugs containing Bi-based chemicals have found widespread application in the treatment of gastrointestinal diseases such as gastric ulcers, dyspepsia, and H. pylori infections. Nowadays, their medical applications have been expanded to include imaging, medication delivery, biosensing, and the treatment of viral infections, MRD microbial infections, cancer, and more (Griffith et al. 2021; Betancourt et al., 2022). Antimicrobial activities of BiNPs have been established in several lab experiments, and they have been successfully utilized to treat H. pylori ulcers in humans. For instance, one research (NCT04209933) intends to examine the effectiveness and safety of several types of Bi (pectin Bi nanoparticles, Bi potassium citrate, and pectin Bi capsules) in H. pylori first-line eradication. Patients with an H. pylori infection were randomized into 4 groups (1:1:1:1) and treated with a 14-day bismuth-containing quadruple therapy. The 4 groups received either bismuth potassium citrate capsules (220 mg), colloidal Bi pectin capsules (200 mg), bismuth pectin granules (150 mg), or bismuth pectin granules (300 mg). This research had a total of 240 individuals, although only 211 of those patients followed up for the whole trial duration. According to an intent-to-treat analysis, the 4 groups had H. pylori eradication levels of 73.3%, 76.7%, 75.0%, and 71.7%. The per-protocol assessment revealed that the 4 groups had respective removal rates of 86.3%, 82.1%, 83.3%, and 86.0% for H. pylori. The rate of H. pylori elimination did not vary significantly (P > .05) across the 4 study groups. There were no substantial differences among the 4 groups regarding the pace at which patients’ symptoms improved, the rate at which they had overall adverse reactions or the rate at which they complied with the treatment. To eliminate H. pylori, Bi pectin may be used instead of Bi potassium citrate in Bi-based quadruple treatment (Cao et al. 2021).
Also, because of Bi modest absorption (about 1% absorbed), it was assumed to be relatively non-toxic to humans. Generally speaking, Bi compounds are unstable and tend to precipitate in the stomach’s acidic environment, making Bi ion absorption in the gastrointestinal system challenging. Overdosing on colloidal Bi subcitrate (CBS) or other Bi compounds for extended periods has been proven in recent publications to cause reversible nephrotoxicity in both adults and children. Glucosuria, proteinuria, and elevated creatinine and plasma urea levels were all signs of renal impairment brought on by Bi. When shotgun pellets were implanted in the muscle, there was a greater chance for Bi to be maintained in the tubular cells of the kidney for a more extended period. In addition, decades ago, when Bi salts were taken orally, over twenty instances of acute encephalopathy were observed. To sum up, nephrotoxicity and neurotoxicity caused by Bi compounds have been established (Liu et al. 2017, 2018). Intoxication with Bi, including instances that ended in death, has been reported in human beings as a result of the use of Bi medications in the previous one hundred years. Acute renal dysfunction is triggered by a toxic dose of Bi compounds. Patients who had consumed overdoses of Bi over long periods experienced outbreaks of a reversible neurological disorder termed Bi encephalopathy in the 1970s. However, the dose-response association between Bi consumption and these symptoms is unknown, since many other persons who had taken substantial doses of Bi did not experience these symptoms (Himeno et al. 2022). Bi compounds have shown potential effectiveness in combating SARS-CoV-2 and associated diseases, as well as potent antimicrobial activity on a wide range of microorganisms. With the ability to accurately regulate the release of Bi ions for targeted medication delivery, Bi-containing materials can successfully attack pathogenic bacteria and cure the resulting infections and inflammatory disorders. Rapid and large-scale production of Bi-based particles is now a significant technological challenge (Huang et al. 2023). BiNPs constitute a favorable method for inhibiting various infectious diseases, but further evaluation is essential to ensure their safe utilization in humans. It is imperative to as well as look at the dosage of BiNPs. Therefore, further research on the possible cytotoxicity of BiNPs is essential to detect any adverse effects in humans (Liman 2013). There are fewer studies in this area. We can also investigate the impacts of various BiNPs on a wide range of bacteria. The functions of BiNPs can be highly improved when conjugated or covered with other materials. In fact, amalgamating NPs with antibiotics can help decrease microbial resistance. In resistant strains, alteration in the mode of function of antibiotics and the BiNPs improve the sensitivity of the microbe. The BiNPs can as well as act as a delivery system of antibiotics, thus simplifying access to bacterial cell walls. For example, Bi2O3 NPs are a promising material for medicine delivery methods and for improving the attributes of other products in medical uses (Mba and Nweze 2021; Szostak et al. 2019). The primary mechanism behind the function of BiNPs is still not well understood. The non-access to an accurate method for in vitro analysis, also the complication of the bacterial membrane, makes it hard to acquire appropriate insight into the precise mechanism for the antimicrobial function of BiNPs. To successfully assess the precise therapeutic potentials of BiNPs and unmask the microbial reaction to these factors, in vivo investigations are essential. In vivo investigations are indispensable to explain their use in biological systems thoroughly. Thus, more studies on the BiNPs activity at structural, genetic, and proteomic levels are essential (Gomez et al. 2021; Luo et al. 2012; Mba and Nweze 2021).
Conclusion
Infectious diseases are a leading cause of mortality across the globe and a threat to public health and the economy. They also have far-reaching, detrimental effects on various societal and economic facets. In the fight against infectious diseases, nanomaterials represent a promising novel tool. Although many nanotechnology-based medicines (nanopharmaceuticals) are now undergoing preclinical and clinical research, several nanotechnology-based pharmaceuticals are already accessible for use in healthcare, including vaccines and nano antibiotics. The in vitro antibacterial activity of BiNPs has been evaluated against a diverse range of high-pathogen microorganisms that may contribute to the development of diseases in humans and other animals. Moreover, BiNPs have been used to improve the efficiency of killing bacteria by photothermal means. As a result of their advantageous properties for imaging and medication administration, BiNPs hold much potential for the future of disease detection and treatment. The in vitro antibacterial activity of BiNPs against H. pylori was shown in a number of investigations, suggesting that these NPs may be effective in the future for chemotherapy of H. pylori. Healthcare facilities might benefit from the use of BiNPs as sanitizers and possible therapeutics for a variety of bacterial diseases. It would be prudent to do further studies on the antibacterial properties of BiNPs.
Acknowledgements
Special thanks to all the families who are trying to raise their children.
Author contributions
S.Y. and O.G. writing–original draft. S.S., S.Y., O.G., A.G., M.Z., B.Q., B.Z., N.A., M.N., and P.A.: Conceptualization, Supervision, Writing – review & editing. All authors participated in the manuscript in the critical review process of the manuscript and approved the final version.
Funding
this study.
There is no Funding.
Data Availability
Not applicable.
Declarations
Competing interests
The authors declare no competing interests.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Abudayyak M Öztaş E Arici M Özhan G Investigation of the toxicity of bismuth oxide nanoparticles in various cell lines Chemosphere 2017 169 117 123 10.1016/j.chemosphere.2016.11.018 27870932
AlMatar M Makky EA Var I Koksal F The role of nanoparticles in the inhibition of multidrug-resistant bacteria and biofilms Curr Drug Deliv 2018 15 4 470 484 10.2174/1567201815666171207163504 29219055
Badireddy AR Chellam S Antibacterial and antifouling properties of lipophilic bismuth compounds Adv Chem Res Nova Sci Publ 2014 21 1 28
Badireddy AR Chellam S Yanina S Gassman P Rosso KM Bismuth dimercaptopropanol (BisBAL) inhibits the expression of extracellular polysaccharides and proteins by Brevundimonas diminuta: implications for membrane microfiltration Biotechnol Bioeng 2008 99 3 634 643 10.1002/bit.21615 17705249
Badireddy AR Marinakos SM Chellam S Wiesner MR Lipophilic nano-bismuth inhibits bacterial growth, attachment, and biofilm formation Surf Innovations 2013 1 3 181 189 10.1680/si.13.00009
Banas JA Virulence properties of Streptococcus mutans Front Bioscience-Landmark 2004 9 2 1267 1277 10.2741/1305
Bartoli M Jagdale P Tagliaferro A A short review on biomedical applications of nanostructured bismuth oxide and related nanomaterials Materials 2020 13 22 5234 10.3390/ma13225234 33228140
Bisht NS Tripathi AH Pant M Upadhyay SK Sahoo NG Mehta S Dandapat A A facile synthesis of palladium nanoparticles decorated bismuth oxybromide nanostructures with exceptional photo-antimicrobial activities Colloids Surf B 2022 217 112640 10.1016/j.colsurfb.2022.112640
Briand GG Burford N Bismuth compounds and preparations with biological or medicinal relevance Chem Rev 1999 99 9 2601 2658 10.1021/cr980425s 11749495
Campos V Almaguer-Flores A Velasco-Aria D Díaz D Rodil SE Bismuth and silver nanoparticles as antimicrobial agent over subgingival bacterial and nosocomial strains J Mater Sci Eng A 2018 8 7–8 142 146
Cao C Ge W Yin J Yang D Wang W Song X Dong X Mesoporous silica supported silver–bismuth nanoparticles as photothermal agents for skin infection synergistic antibacterial therapy Small 2020 16 24 2000436 10.1002/smll.202000436
Cao Y, Zhang J, Liu Y, Zhang L, Wang L, Wang J, Huo L (2021) The efficacy and safety of different bismuth agents in Helicobacter pylori first-line eradication: A multicenter, randomized, controlled clinical trial. Medicine, 100(50)
Carotenuto G Hison CL Capezzuto F Palomba M Perlo P Conte P Synthesis and thermoelectric characterisation of bismuth nanoparticles J Nanopart Res 2009 11 1729 1738 10.1007/s11051-008-9541-6
Cesur S Cam ME Sayin FS Gunduz O Electrically controlled drug release of donepezil and BiFeO3 magnetic nanoparticle-loaded PVA microbubbles/nanoparticles for the treatment of Alzheimer’s disease J Drug Deliv Sci Technol 2022 67 102977 10.1016/j.jddst.2021.102977
Chen R, So MH, Yang J, Deng F, Che C-M, Sun H (2006) Fabrication of bismuth subcarbonate nanotube arrays from bismuth citrate.Chemical communications(21),2265–2267
Chen R Cheng G So MH Wu J Lu Z Che C-M Sun H Bismuth subcarbonate nanoparticles fabricated by water-in-oil microemulsion-assisted hydrothermal process exhibit anti-Helicobacter pylori properties Mater Res Bull 2010 45 5 654 658 10.1016/j.materresbull.2009.12.035
Chen R Zhou R Qiao J Yang Y Zhou X Bai R Wu C Orally administered Bi2S3@ SiO2 core-shell nanomaterials as gastrointestinal contrast agents and their influence on gut microbiota Mater Today Bio 2022 13 100178 10.1016/j.mtbio.2021.100178 34938992
Cheng G Yang H Rong K Lu Z Yu X Chen R Shape-controlled solvothermal synthesis of bismuth subcarbonate nanomaterials J Solid State Chem 2010 183 8 1878 1883 10.1016/j.jssc.2010.06.004
Claudio C-R Chellam S Bismuth nanoparticles: antimicrobials of broad-spectrum, low cost and safety Professor Alexander Seifalian 2014 429 429
Cui Z Zhang Y Li S Ge S Preparation and photocatalytic performance of Bi nanoparticles by microwave-assisted method using ascorbic acid as reducing agent Catal Commun 2015 72 97 100 10.1016/j.catcom.2015.09.024
Cushing BL Kolesnichenko VL O’connor CJ Recent advances in the liquid-phase syntheses of inorganic nanoparticles Chem Rev 2004 104 9 3893 3946 10.1021/cr030027b 15352782
Das PE Majdalawieh AF Abu-Yousef IA Narasimhan S Poltronieri P Use of a hydroalcoholic extract of Moringa oleifera leaves for the green synthesis of bismuth nanoparticles and evaluation of their anti-microbial and antioxidant activities Materials 2020 13 4 876 10.3390/ma13040876 32075305
Deng J Xu S Hu W Xun X Zheng L Su M Tumor targeted, stealthy and degradable bismuth nanoparticles for enhanced X-ray radiation therapy of breast cancer Biomaterials 2018 154 24 33 10.1016/j.biomaterials.2017.10.048 29120816
Dizaj SM Mennati A Jafari S Khezri K Adibkia K Antimicrobial activity of carbon-based nanoparticles Adv Pharm Bull 2015 5 1 19 25789215
Domenico P Salo RJ Novick SG Schoch PE Van Horn K Cunha BA Enhancement of bismuth antibacterial activity with lipophilic thiol chelators Antimicrob Agents Chemother 1997 41 8 1697 1703 10.1128/AAC.41.8.1697 9257744
Domenico P Tomas J Merino S Rubires X Cunha BA Surface antigen exposure by bismuth dimercaprol suppression of Klebsiella pneumoniae capsular polysaccharide Infect Immun 1999 67 2 664 669 10.1128/IAI.67.2.664-669.1999 9916074
Domenico P, Baldassarri L, Schoch PE, Kaehler K, Sasatsu M, Cunha BA (2001) Activities of bismuth thiols against staphylococci and staphylococcal biofilms. Antimicrobial agents and chemotherapy, 45(5), 1417–1421
El-Batal AI El-Sayyad GS El-Ghamry A Agaypi KM Elsayed MA Gobara M Melanin-gamma rays assistants for bismuth oxide nanoparticles synthesis at room temperature for enhancing antimicrobial, and photocatalytic activity J Photochem Photobiol B 2017 173 120 139 10.1016/j.jphotobiol.2017.05.030 28570907
Fang J Stokes KL Wiemann JA Zhou WL Dai J Chen F O’Connor CJ Microemulsion-processed bismuth nanoparticles Mater Sci Engineering: B 2001 83 1–3 254 257 10.1016/S0921-5107(01)00528-1
Feng L Yang D Gai S He F Yang G Yang P Lin J Single bismuth tungstate nanosheets for simultaneous chemo-, photothermal, and photodynamic therapies mediated by near-infrared light Chem Eng J 2018 351 1147 1158 10.1016/j.cej.2018.06.170
Flores-Castañeda M Vega-Jiménez AL Almaguer-Flores A Camps E Pérez M Silva-Bermudez P Rodil SE Antibacterial effect of bismuth subsalicylate nanoparticles synthesized by laser ablation J Nanopart Res 2015 17 1 13 10.1007/s11051-015-3237-5
Galain I Cardoso M Tejería E Mourglia-Ettlin G Arbildi P Terán M Aguiar I Enhancement of radiation response of breast cancer cells through the incorporation of Bi2S3 nanorods J Nanopart Res 2022 24 3 68 10.1007/s11051-022-05455-x
Geyikoglu F Turkez H Genotoxicity and oxidative stress induced by some bismuth compounds in human blood cells in vitro Fresenius Environ Bull 2005 14 10 854 860
Gholizadeh O Yasamineh S Amini P Afkhami H Delarampour A Akbarzadeh S Hajiesmaeili M Therapeutic and diagnostic applications of nanoparticles in the management of COVID-19: a comprehensive overview Virol J 2022 19 1 1 22 10.1186/s12985-022-01935-7 34980196
Gomez C Hallot G Laurent S Port M Medical applications of metallic bismuth nanoparticles Pharmaceutics 2021 13 11 1793 10.3390/pharmaceutics13111793 34834207
Gopalakrishnan V Masanam E Ramkumar VS Baskaraligam V Selvaraj G Influence of N-acylhomoserine lactonase silver nanoparticles on the quorum sensing system of Helicobacter pylori: a potential strategy to combat biofilm formation J Basic Microbiol 2020 60 3 207 215 10.1002/jobm.201900537 31960983
Griffith DM Li H Werrett MV Andrews PC Sun H Medicinal chemistry and biomedical applications of bismuth-based compounds and nanoparticles Chem Soc Rev 2021 50 21 12037 12069 10.1039/D0CS00031K 34533144
Guo Y Zhang Y Gerhard M Gao J-J Mejias-Luque R Zhang L Suchanek S Effect of Helicobacter pylori on gastrointestinal microbiota: a population-based study in Linqu, a high-risk area of gastric cancer Gut 2020 69 9 1598 1607 10.1136/gutjnl-2019-319696 31857433
Hernandez-Delgadillo R, Velasco-Arias D, Diaz D, Arevalo-Niño K, Garza-Enriquez M, De la Garza-Ramos MA, Cabral-Romero C (2012) Zerovalent bismuth nanoparticles inhibit Streptococcus mutans growth and formation of biofilm.International journal of nanomedicine,2109–2113
Hernandez-Delgadillo R Angel-Mosqueda D Solís-Soto C Munguia-Moreno JM Pineda-Aguilar S Sánchez-Nájera N Cabral-Romero RI Antimicrobial and antibiofilm activities of MTA supplemented with bismuth lipophilic nanoparticles Dent Mater J 2017 36 4 503 510 10.4012/dmj.2016-259 28420830
Himeno S, Fujishiro H, Sumi D (2022) Bismuth Handbook on the Toxicology of Metals. Elsevier, pp 121–139
Holmberg K Surfactant-templated nanomaterials synthesis J Colloid Interface Sci 2004 274 2 355 364 10.1016/j.jcis.2004.04.006 15144806
Hsu C-L Li Y-J Jian H-J Harroun SG Wei S-C Ravindranath R Chang H-T Green synthesis of catalytic gold/bismuth oxyiodide nanocomposites with oxygen vacancies for treatment of bacterial infections Nanoscale 2018 10 25 11808 11819 10.1039/C8NR00800K 29911241
Huang J, Huang Q, Liu M, Chen Q, Ai K (2022) Emerging bismuth chalcogenides based nanodrugs for cancer radiotherapy.Frontiers in Pharmacology,13
Huang R Zhou Z Lan X Tang FK Cheng T Sun H Jin L Rapid synthesis of bismuth-organic frameworks as selective antimicrobial materials against microbial biofilms Mater Today Bio 2023 18 100507 10.1016/j.mtbio.2022.100507 36504541
Jassim AM Farhan SA Salman JA Khalaf KJ Marjani A Mohammed MT Study the antibacterial effect of bismuth oxide and tellurium nanoparticles Int j chem biol sci 2015 1 3 81 84
Jha R Pasricha R Ravi V Synthesis of bismuth oxide nanoparticles using bismuth nitrate and urea Ceram Int 2005 31 3 495 497 10.1016/j.ceramint.2004.06.013
Karnan T Samuel S A novel bio-mimetic approach for the fabrication of Bi2O3 nanoflakes from rambutan (Nephelium lappaceum L) peel extract and their photocatalytic activity Ceram Int 2016 42 4 4779 4787 10.1016/j.ceramint.2015.11.163
Khameneh B Diab R Ghazvini K Bazzaz BSF Breakthroughs in bacterial resistance mechanisms and the potential ways to combat them Microb Pathog 2016 95 32 42 10.1016/j.micpath.2016.02.009 26911646
Khan ST Musarrat J Al-Khedhairy AA Countering drug resistance, infectious diseases, and sepsis using metal and metal oxides nanoparticles: current status Colloids Surf B 2016 146 70 83 10.1016/j.colsurfb.2016.05.046
Kong T Zeng J Wang X Yang X Yang J McQuarrie S Xing JZ Enhancement of radiation cytotoxicity in breast-cancer cells by localized attachment of gold nanoparticles Small 2008 4 9 1537 1543 10.1002/smll.200700794 18712753
Larsen A Stoltenberg M West MJ Danscher G Influence of bismuth on the number of neurons in cerebellum and hippocampus of normal and hypoxia-exposed mouse brain: a stereological study J Appl Toxicology: Int J 2005 25 5 383 392 10.1002/jat.1061
Lee Y-C Dore MP Graham DY Diagnosis and treatment of Helicobacter pylori infection Annu Rev Med 2022 73 183 195 10.1146/annurev-med-042220-020814 35084993
Lemos J, Palmer S, Zeng L, Wen Z, Kajfasz J, Freires I, Brady L (2019) The biology of Streptococcus mutans. Microbiology spectrum, 7(1), 7.1. 03
Li L Yan B BiVO4/Bi2O3 submicrometer sphere composite: microstructure and photocatalytic activity under visible-light irradiation J Alloys Compd 2009 476 1–2 624 628 10.1016/j.jallcom.2008.09.083
Li S Xu L Kong X Kusunose T Tsurumachi N Feng Q Bismuth chalcogenide iodides Bi 13 S 18 I 2 and BiSI: Solvothermal synthesis, photoelectric behavior, and photovoltaic performance J Mater Chem C 2020 8 11 3821 3829 10.1039/C9TC05139B
Li Y Liu X Cui Z Zheng Y Jiang H Zhang Y Wu S Treating Multi-Drug-Resistant bacterial infections by Functionalized Nano-Bismuth Sulfide through the synergy of immunotherapy and Bacteria-sensitive phototherapy ACS Nano 2022 16 9 14860 14873 10.1021/acsnano.2c05756 36094899
Liman R Genotoxic effects of Bismuth (III) oxide nanoparticles by Allium and Comet assay Chemosphere 2013 93 2 269 273 10.1016/j.chemosphere.2013.04.076 23790828
Liu Y Zhuang J Zhang X Yue C Zhu N Yang L Zhang LW Autophagy associated cytotoxicity and cellular uptake mechanisms of bismuth nanoparticles in human kidney cells Toxicol Lett 2017 275 39 48 10.1016/j.toxlet.2017.04.014 28445739
Liu Y Yu H Zhang X Wang Y Song Z Zhao J Zhang LW The protective role of autophagy in nephrotoxicity induced by bismuth nanoparticles through AMPK/mTOR pathway Nanotoxicology 2018 12 6 586 601 10.1080/17435390.2018.1466932 29732938
Liu C Zhang L Chen X Li S Han Q Li L Wang C Biomolecules-assisted synthesis of degradable bismuth nanoparticles for dual-modal imaging-guided chemo-photothermal therapy Chem Eng J 2020 382 122720 10.1016/j.cej.2019.122720
Luo Y Wang C Qiao Y Hossain M Ma L Su M In vitro cytotoxicity of surface modified bismuth nanoparticles J Mater Science: Mater Med 2012 23 2563 2573
Luo Y Hossain M Wang C Qiao Y An J Ma L Su M Targeted nanoparticles for enhanced X-ray radiation killing of multidrug-resistant bacteria Nanoscale 2013 5 2 687 694 10.1039/C2NR33154C 23223782
Ma D Zhao J Chu R Yang S Zhao Y Hao X Yu C Novel synthesis and characterization of bismuth nano/microcrystals with sodium hypophosphite as reductant Adv Powder Technol 2013 24 1 79 85 10.1016/j.apt.2012.02.004
Mahdiun F Mansouri S Khazaeli P Mirzaei R The effect of tobramycin incorporated with bismuth-ethanedithiol loaded on niosomes on the quorum sensing and biofilm formation of Pseudomonas aeruginosa Microb Pathog 2017 107 129 135 10.1016/j.micpath.2017.03.014 28323149
Mahony D Lim-Morrison S Bryden L Faulkner G Hoffman P Agocs L Maguire H Antimicrobial activities of synthetic bismuth compounds against Clostridium difficile Antimicrob Agents Chemother 1999 43 3 582 588 10.1128/AAC.43.3.582 10049270
Mallahi M Shokuhfar A Vaezi M Esmaeilirad A Mazinani V Synthesis and characterization of bismuth oxide nanoparticles via sol-gel method AJER 2014 3 4 162 165
Manavalan S Rajaji U Chen S-M Govindasamy M Selvin SSP Chen T-W Elshikh M Sonochemical synthesis of bismuth (III) oxide decorated reduced graphene oxide nanocomposite for detection of hormone (epinephrine) in human and rat serum Ultrason Sonochem 2019 51 103 110 10.1016/j.ultsonch.2018.10.008 30514479
Mayorga-Martinez CC Cadevall M Guix M Ros J Merkoçi A Bismuth nanoparticles for phenolic compounds biosensing application Biosens Bioelectron 2013 40 1 57 62 10.1016/j.bios.2012.06.010 22809524
Mba IE Nweze EI Nanoparticles as therapeutic options for treating multidrug-resistant bacteria: Research progress, challenges, and prospects World J Microbiol Biotechnol 2021 37 1 30 10.1007/s11274-021-03070-x
Motakef-Kazemi N Yaqoubi M Green synthesis and characterization of bismuth oxide nanoparticle using mentha pulegium extract Iran J Pharm Research: IJPR 2020 19 2 70 33224212
Nazari P Dowlatabadi-Bazaz R Mofid M Pourmand M Daryani N Faramarzi M Shahverdi A The antimicrobial effects and metabolomic footprinting of carboxyl-capped bismuth nanoparticles against Helicobacter pylori Appl Biochem Biotechnol 2014 172 570 579 10.1007/s12010-013-0571-x 24104691
Neamati F, Kodori M, Feizabadi MM, Abavisani M, Barani M, Khaledi M, Fathizadeh H (2023) Bismuth nanoparticles against microbial infections. Nanomedicine(0).
Norman NC (1997) Chemistry of arsenic, antimony and bismuth. Springer Science & Business Media
Norouzi M Yasamineh S Montazeri M Dadashpour M Sheervalilou R Abasi M Pilehvar-Soltanahmadi Y Recent advances on nanomaterials-based fluorimetric approaches for microRNAs detection Mater Sci Engineering: C 2019 104 110007 10.1016/j.msec.2019.110007
Nosrati H Charmi J Salehiabar M Abhari F Danafar H Tumor targeted albumin coated bismuth sulfide nanoparticles (Bi2S3) as radiosensitizers and carriers of curcumin for enhanced chemoradiation therapy ACS Biomaterials Science & Engineering 2019 5 9 4416 4424 10.1021/acsbiomaterials.9b00489 33438407
Olekhnovich EI, Manolov AI, Samoilov AE, Prianichnikov NA, Malakhova MV, Tyakht AV, Kovarsky BA (2019) Shifts in the human gut microbiota structure caused by quadruple Helicobacter pylori eradication therapy. Frontiers in microbiology, 10, 1902
Oveili E Vafaei S Bazavar H Eslami Y Mamaghanizadeh E Yasamineh S Gholizadeh O The potential use of mesenchymal stem cells-derived exosomes as microRNAs delivery systems in different diseases Cell Communication and Signaling 2023 21 1 1 26 10.1186/s12964-022-01017-9 36597090
Panáček A Kvitek L Prucek R Kolář M Večeřová R Pizúrová N Zbořil R Silver colloid nanoparticles: synthesis, characterization, and their antibacterial activity J Phys Chem B 2006 110 33 16248 16253 10.1021/jp063826h 16913750
Pop R Tăbăran A-F Ungur AP Negoescu A Cătoi C Helicobacter Pylori-induced gastric infections: from pathogenesis to novel therapeutic approaches using silver nanoparticles Pharmaceutics 2022 14 7 1463 10.3390/pharmaceutics14071463 35890358
Pothula K Tang L Zha Z Wang Z Bismuth nanoparticles: an efficient catalyst for reductive coupling of nitroarenes to azo-compounds RSC Adv 2015 5 101 83144 83148 10.1039/C5RA17994G
Prakash M Kavitha HP Abinaya S Vennila JP Lohita D Green synthesis of bismuth based nanoparticles and its applications-A review Sustainable Chem Pharm 2022 25 100547 10.1016/j.scp.2021.100547
Puttaraju T Manjunatha M Nagaraju G Lingaraju K Naika HR Manjula M Devaraja S The evaluation of various biological properties for bismuth oxychloride nanoparticles (BiOCl NPs) Inorg Chem Commun 2022 144 109850 10.1016/j.inoche.2022.109850
Rameshkumar C, Gayathri R, Subalakshmi R (2021) Synthesis and characterization of undopped bismuth ferrite oxide nanoparticles for the application of cancer treatment. Materials Today: Proceedings, 43, 3662–3665
Ren S Cai P Liu Y Wang T Zhang Y Li Q Jin G Prevalence of Helicobacter pylori infection in China: a systematic review and meta-analysis J Gastroenterol Hepatol 2022 37 3 464 470 10.1111/jgh.15751 34862656
Rieznichenko L Gruzina T Dybkova S Ushkalov V Ulberg Z Investigation of bismuth nanoparticles antimicrobial activity against high pathogen microorganisms Am J Bioterror Biosecur Biodef 2015 2 1004
Rostamifar S, Azad A, Bazrafkan A, Modaresi F, Atashpour S, Jahromi ZK (2021) New Strategy of Reducing Biofilm Forming Bacteria in Oral Cavity by Bismuth Nanoparticles. BioMed Research International, 2021
Rudramurthy GR Swamy MK Sinniah UR Ghasemzadeh A Nanoparticles: alternatives against drug-resistant pathogenic microbes Molecules 2016 21 7 836 10.3390/molecules21070836 27355939
Safarov T Kiran B Bagirova M Allahverdiyev AM Abamor ES An overview of nanotechnology-based treatment approaches against Helicobacter Pylori Expert Rev anti-infective therapy 2019 17 10 829 840 10.1080/14787210.2019.1677464 31591930
Shahbazi M-A Faghfouri L Ferreira MP Figueiredo P Maleki H Sefat F Santos HA The versatile biomedical applications of bismuth-based nanoparticles and composites: therapeutic, diagnostic, biosensing, and regenerative properties Chem Soc Rev 2020 49 4 1253 1321 10.1039/C9CS00283A 31998912
Shakibaie M Amiri-Moghadam P Ghazanfari M Adeli-Sardou M Jafari M Forootanfar H Cytotoxic and antioxidant activity of the biogenic bismuth nanoparticles produced by Delftia sp SFG Mater Res Bull 2018 104 155 163 10.1016/j.materresbull.2018.04.001
Sheykhisarem R Dehghani H In vitro biocompatibility evaluations of pH-sensitive Bi2MoO6/NH2-GO conjugated polyethylene glycol for release of daunorubicin in cancer therapy Colloids Surf B 2023 221 113006 10.1016/j.colsurfb.2022.113006
Siddiqi KS Husen A Rao RA A review on biosynthesis of silver nanoparticles and their biocidal properties J Nanobiotechnol 2018 16 1 1 28 10.1186/s12951-018-0334-5
Singh S Yadawa Y Ranjan A Enhanced adsorption of methylene blue by mixed-phase bismuth ferrite prepared by non-aqueous sol-gel route J Environ Chem Eng 2023 11 1 109229 10.1016/j.jece.2022.109229
Song J Xia F Zhao M Zhong YL Li W Loh KP Bao Q Solvothermal growth of bismuth chalcogenide nanoplatelets by the oriented attachment mechanism: an in situ PXRD study Chem Mater 2015 27 9 3471 3482 10.1021/acs.chemmater.5b00903
Sousa C Ferreira R Azevedo NF Oleastro M Azeredo J Figueiredo C Melo LD Helicobacter pylori infection: from standard to alternative treatment strategies Crit Rev Microbiol 2022 48 3 376 396 10.1080/1040841X.2021.1975643 34569892
Stoltenberg M Larsen A Zhao M Danscher G Brunk U Bismuth-induced lysosomal rupture in J774 cells Apmis 2002 110 5 396 402 10.1034/j.1600-0463.2002.100505.x 12076257
Sun D Li J He L Zhao B Wang T Li R Sato T Facile solvothermal synthesis of BiOCl–TiO 2 heterostructures with enhanced photocatalytic activity CrystEngComm 2014 16 32 7564 7574 10.1039/C4CE00596A
Szostak K Ostaszewski P Pulit-Prociak J Banach M Bismuth oxide nanoparticles in drug delivery systems Pharm Chem J 2019 53 48 51 10.1007/s11094-019-01954-9
Thanh NT Maclean N Mahiddine S Mechanisms of nucleation and growth of nanoparticles in solution Chem Rev 2014 114 15 7610 7630 10.1021/cr400544s 25003956
Tiekink ER Antimony and bismuth compounds in oncology Crit Rev Oncol/Hematol 2002 42 3 217 224 10.1016/S1040-8428(01)00217-7 12050016
Torres-Betancourt JA Hernandez-Delgadillo R Flores-Treviño JJ Solís-Soto JM Pineda-Aguilar N Nakagoshi-Cepeda MAA Cabral-Romero C Antimicrobial potential of AH plus supplemented with bismuth lipophilic nanoparticles on E. faecalis isolated from clinical isolates J Appl Biomater Funct Mater 2022 20 22808000211069221 35114826
Torrisi L Silipigni L Restuccia N Cuzzocrea S Cutroneo M Barreca F Guglielmino S Laser-generated bismuth nanoparticles for applications in imaging and radiotherapy J Phys Chem Solids 2018 119 62 70 10.1016/j.jpcs.2018.03.034
Tuerhong M Chen P Ma Y Li Y Li J Yan C Zhu B Bi2MoO6/red phosphorus heterojunction for reducing Cr (VI) and mitigating Escherichia coli infection J Solid State Chem 2022 315 123468 10.1016/j.jssc.2022.123468
Udalova T, Logutenko O, Timakova E, Afonina L, Naydenko E, Yukhin YM (2008) Bismuth compounds in medicine. Paper presented at the 2008 Third International Forum on Strategic Technologies
Vazquez-Munoz R Arellano-Jimenez MJ Lopez-Ribot JL Bismuth nanoparticles obtained by a facile synthesis method exhibit antimicrobial activity against Staphylococcus aureus and Candida albicans BMC biomedical engineering 2020 2 1 1 12 10.1186/s42490-020-00044-2 32903350
Vazquez-Munoz R Arellano-Jimenez MJ Lopez-Ribot JL Fast, facile synthesis method for BAL-mediated PVP-bismuth nanoparticles MethodsX 2020 7 100894 10.1016/j.mex.2020.100894 32405464
Vazquez-Munoz R Lopez FD Lopez-Ribot JL Bismuth nanoantibiotics display anticandidal activity and disrupt the biofilm and cell morphology of the emergent pathogenic yeast Candida auris Antibiotics 2020 9 8 461 10.3390/antibiotics9080461 32751405
Velasco-Arias D Zumeta-Dube I Diaz D Santiago-Jacinto P Ruiz-Ruiz V-F Castillo-Blum S-E Rendon L Stabilization of strong quantum confined colloidal bismuth nanoparticles, one-pot synthesized at room conditions J Phys Chem C 2012 116 27 14717 14727 10.1021/jp304170k
Wang L Yang W Read P Larner J Sheng K Tumor cell apoptosis induced by nanoparticle conjugate in combination with radiation therapy Nanotechnology 2010 21 47 475103 10.1088/0957-4484/21/47/475103 21030759
Werner ME Copp JA Karve S Cummings ND Sukumar R Li C Wang AZ Folate-targeted polymeric nanoparticle formulation of docetaxel is an effective molecularly targeted radiosensitizer with efficacy dependent on the timing of radiotherapy ACS Nano 2011 5 11 8990 8998 10.1021/nn203165z 22011071
Winter H Brown AL Goforth AM Bismuth-based nano-and microparticles in X-ray contrast, radiation therapy, and radiation shielding applications Bismuth Adv Appl Defects Charact 2018 71 1121 1141
Wu D Li X Li T Xie W Liu Y Tan Q Jiang H The Effect of Quadruple Therapy with Polaprezinc or Bismuth on Gut Microbiota after Helicobacter pylori Eradication: a Randomized Controlled Trial J Clin Med 2022 11 23 7050 10.3390/jcm11237050 36498624
Wu L, Luo Y, Wang C, Wu S, Zheng Y, Li Z, Shen J (2023) Self-driven Electron transfer Biomimetic Enzymatic Catalysis of Bismuth-Doped PCN-222 MOF for Rapid Therapy of Bacteria-infected wounds. ACS nano
Xiao H Li X Zheng C Liu Q Sun C Huang J Yuan Y Intracellular pH-responsive polymeric micelle for simultaneous chemotherapy and MR imaging of hepatocellular carcinoma J Nanopart Res 2020 22 1 15 10.1007/s11051-020-04821-x 35517915
Yang G (2012) Laser ablation in liquids: principles and applications in the preparation of nanomaterials. CRC Press
Yang Z, Yuan M, Liu B, Zhang W, Maleki A, Guo B, Lin J (2022) Conferring BiVO4 nanorods with Oxygen Vacancies to realize enhanced Sonodynamic Cancer Therapy.Angewandte Chemie International Edition, 61(44), e202209484
Yasamineh S Kalajahi HG Yasamineh P Yazdani Y Gholizadeh O Tabatabaie R Dadashpour M An overview on nanoparticle-based strategies to fight viral infections with a focus on COVID-19 J Nanobiotechnol 2022 20 1 440 10.1186/s12951-022-01625-0
Yasamineh S, Yasamineh P, Kalajahi HG, Gholizadeh O, Yekanipour Z, Afkhami H, Yazdani Y (2022b) A state-of-the-art review on the recent advances of niosomes as a targeted drug delivery system.International journal of pharmaceutics,121878
Yasamineh S, Gholizadeh O, Kalajahi HG, Yasamineh P, Firouzi-Amandi A, Dadashpour M (2023) Future prospects of natural polymer-based drug Delivery Systems in combating Lung Diseases Natural Polymeric materials based Drug Delivery Systems in Lung Diseases. Springer, pp 465–482
Zheng W Li Y Tsang C-S So P-K Lee LYS Stabilizer-free bismuth nanoparticles for selective polyol electrooxidation Iscience 2021 24 4 102342 10.1016/j.isci.2021.102342 34027316
Zhou R Zhou Q Ling G Zhang P A cross-linked hydrogel of bismuth sulfide nanoparticles with excellent photothermal antibacterial and mechanical properties to combat bacterial infection and prompt wound healing Colloids Surf A 2023 660 130832 10.1016/j.colsurfa.2022.130832 | 37156882 | PMC10166694 | NO-CC CODE | 2023-05-31 23:15:03 | yes | World J Microbiol Biotechnol. 2023 May 9; 39(7):190 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
37162677
27159
10.1007/s11356-023-27159-y
Research Article
Evaluating the trilemma nexus of digital finance, renewable energy consumption, and CO2 emission: evidence from nonlinear ARDL model
Mo Tianyu [email protected]
1
Ke Hong [email protected]
2
1 grid.440634.1 0000 0004 0604 7926 Department of Finance Engineering, School of Finance, Shanghai Lixin University of Accounting and Finance, Shang Hai, 201209 China
2 Industrial Securities Co., Ltd, Shang Hai, 200135 China
Responsible Editor: Ilhan Ozturk
10 5 2023
2023
30 28 7213072145
3 2 2023
17 4 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
It has been established in 2030 sustainability objectives as per SDGs that highlight the critical importance of access to affordable, renewable energy, robust, long-term industrial progress, and digital financing in CO2 emission. The intent of study is to test the trilemma nexus between digital finance, renewable energy consumption, and carbon emission reduction with nonlinear ARDL tests. The study acquired the data and applied the nonlinear ARDL test, split analysis tests, and vector-error correction model (VECM) tests. The results of the study highlighted that the increase of digital finance positively enhances the renewable energy and negatively reduces the CO2 emissions which we calculate to be 11.4% of the digital finance funding on renewable energy goods. For this, a 39% increase in digital financing is noticed by the research findings during the COVID-19 crisis period. Such robust study findings present the latest insights that digital financing is an eminent and viable source of financing for the trilemma nexus with renewable energy consumption and the CO2 emissions. Following these, multiple research implications are also presented for the key stakeholders.
Keywords
Digital finance
Renewable energy consumption
CO2 emissions
ARDL
VECM
China
issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023
==== Body
pmcIntroduction
China is one of the South Asian countries expanding quickly, but it is also one of the most susceptible nations to changing climate (Lee et al. 2022). According to the World Climate Risk Index 2021, China is one of the seven countries most vulnerable to the impact of climate change, which poses an additional risk to the country’s economic stability (Chen 2022). Between 2000 and 2019, climate transition weaknesses cost China an estimated USD$3.72 billion. This number is only expected to increase if the authorities do not adopt sufficient viable strategies. These issues need a fix and some roadmaps by using digital financing and renewable energy sources. Consequently, the carbon dioxide emissions (CO2) in the environment are a significant contributor to global warming in the twentieth century (Qin et al. 2022). These gases are released mainly due to human activity, like the combustion of fossil fuels (Zhou et al. 2023). Consistently rising CO2 concentrations are predicted to have far-reaching effects on global climatic changes, with potentially catastrophic outcomes for humankind (Wang et al. 2022a, b, c). Consequently, many scientists now prioritize finding ways to reduce carbon dioxide emissions to create a sustainable and environmentally friendly future (Zakharov et al. 2022). These efforts consider various factors, including renewable power sources, technological advancements, and industrial progress (Tian 2022).
Correspondingly, it is noted that the nation’s dedication to its goal of lowering national carbon and adjusting to the repercussions of global warming (Razzaq et al. 2023). China plans on joining the world community to effectively determine the action to curb emissions shortly as part of an opportunity to use digital financing (Kingiri and Fu 2019). As part of the deal, China has agreed to cut its emissions of CO2 emission reductions by 15% below 2005 levels by the year 2030 by using a digital financing system as a critical source (Mukalayi and Inglesi-Lotz 2023). This includes a 10% unconditional premise and a 5% conditional basis dependent on industrialized nations contributing climate funding, technological transmission, and building capacity through digital financing (Zhang et al. 2022a, b, c, d). Assessing the possible consequences of digital financing, alternative energy consumption, and technical innovation on emissions of CO2 emission in Chinese could help address the vital issue of how China could attain ecological responsibility by lowering emissions by using renewable energy and digital financing as sources. Governments attempting to find a middle ground between climate change adaptation and sustained growth would benefit significantly from a deeper grasp of China’s reduce emissions possibilities. The question of whether or not perpetual digital financing contributes to the degradation of the environment and whether or not it is adequate to compensate for negative externalities underpins environment protection and expansion projects (Ozturk and Ullah 2022). Enhanced environmental quality is a byproduct of digital financing for renewable energy, which allows for the gradual replacement of harmful technology with more modern, less toxic alternatives. Decoupling digital financing from environmental destruction [9] may be achieved by shifts in product structure, adopting greener industrial methods, environmental regulations, and environmental protection (Qin et al. 2022). China’s economy is expanding at the fifth-fastest rate globally and the maximum rate in South Asia. China’s GDP is projected to reach US $271 billion by 2020, a dramatic growth from 1972’s US $27 billion. In light of this, it is reasonable to wonder how China’s rapid economic growth can coexist with environmental protection over the long run (Wang et al. 2022a, b, c).
Renewable energy sources are becoming more critical as worries about global warming and ecological stability grow (Zhong 2022). International societies are transitioning to renewable energy sources due to the rapid fossil fuel depletion and the disastrous effects on the ecosystem (Yan et al. 2023). The benefits of sustainable power include decreasing the need for traditional energy sources and safeguarding global GDP over the long run. The five most common kinds of renewable energy are photovoltaic, groundwater (hydropower), wind, thermal, and biofuel. Natural energy is more reliable than conventional energy sources, has less harmful byproducts, and is plentiful (Liu and Chen 2022). Fuel from energy power is seen as a viable solution to the world’s growing food security and environmental pollution concerns because of its zero-carbon footprint, which significantly impacts digital financing (Yang et al. 2022a, b). The worldwide goal of reducing emissions by half by 2050 relies heavily on the use of sources of renewable energy (Zheng and Li 2022). China has a wealth of renewable energy sources (Wang and Guo 2022). Numerous regulatory instruments have been developed and put into place in China to encourage the usage of renewable sources of energy. Considering this, not much investigation has been put into sustainable power and its impact on the planet’s long-term development (Yan et al. 2023; Zhao et al. 2022). Therefore, it is essential to look into the potential of employing renewable power to lessen China’s digital financing for renewable energy development (Ding et al. 2022).
It is undeniable that carbon emissions are hurting people’s lives and damaging the environment, and global temperatures and CO2 emission reductions are rising (Cao et al. 2021). Efficacious methods to reduce carbon pressure are now the attention of scholarly and social groups. The authorities and the general population are very conscious of environmental concerns, and the foundation of these problems is the inefficient electricity consumption system (Chao et al. 2022). Environmental accounts for 31.2%, coal for 27.2%, natural gas for 24.7%, hydropower for 6.9%, sustainable sources for 5.7%, and nuclear energy for 4.3% of global energy demand in 2020, as reported by the BP Statistical Review of World Energy (Zhang et al. 2022a, b, c, d). Based on the presented information and Fig. 1, it is clear that the share of renewables in the present framework of global power consumption is still small, despite a recent upward trend. On the other hand, renewable energies have emerged as a crucial means of satisfying energy demands while simultaneously lowering GHG emissions. As a result, researchers have moved their attention to finding ways to harness renewable power software and devices, emphasizing the role that technical advancement, especially in the energy sector, will play in facilitating the energy revolution. Additionally, there is a significant need for the government to create more stringent regulatory regulations to safeguard and advance ecological responsibility (Yang et al. 2022a, b; Xin et al. 2022). Therefore, advancements in renewable energy technology and strict environmental protections are critical to reducing emissions and easing the effects of carbon pressure (Feng et al. 2022). Based on this, the current study takes sustainable power digital finance and environmental laws as its research topic to get more people thinking about and learning about these topics, as well as to get a handle on the systemic issue of CO2 emission reduction pressure relief and to understand how all three of these factors communicate with one another (Lin et al. 2022).Fig. 1 Carbon emission trends over the study time
The research suggests a nuanced connection between advances in renewable energy, the stringency of environmental regulations, and the concentration of digital financing in the study context. This is the first contribution of the research. As carbon pressure increases, governments and citizens will become more aware of the need to reduce emissions and speed up the power generation transition toward cleaner, sustainable energy sources. This, in turn, will spur the development of new, cutting-edge digital financing. It is the second research contribution. In addition, advances in renewable energy technology and stricter enforcing of environmental regulations may significantly influence the atmospheric concentration of carbon dioxide. Additionally, there may be a connection between the rate of technological development in sustainable power and the strictness of environmental rules. The question then becomes, is there a time lag between the introduction of new digital financing, the tightening of regulations, and the resulting reduction of carbon pressure? This pressing issue has to be addressed right now, and its resolution is the cutting edge of the current field of research, which is the third contribution. Thus, the Chinese mainland was chosen as the study subject, and a research framework was constructed to investigate the interrelations between digital financing advancement, environment protection strength, and environmental constraint. This is the forth contribution of research. Hence, the research objective is to assess the trilemma connection between renewable energy, digital financing, and CO2 emission reduction.
The study covers five sections: introduction, literature review, methodology, results and discussion, and conclusion and recommendations.
Literature review
Nexus among renewable energy, carbon emission, and digital finance
Green technology is the term used to describe technological advancements that benefit the economy 9 Ma and Wu 2022). Climate development is an effective tool for lowering pollutants and has the potential to boost industrial prosperity. Sustainable energy is a significant energy resource because of its positive effects on the economy and the climate (Lv et al. 2022). Sustainable energy is the best option for meeting everyday energy requirements. These generators provide power without negatively impacting ecosystems (Liu 2022). New research has also broken out the effects of both nonrenewable and renewable power sources on environmental protection, with the former receiving more attention due to the advantages they provide (Ma et al. 2022; Iqbal and Bilal 2021a). For example, between 1984 and 2007, researchers in 19 developed and developing countries used a panel error-checking model to examine the connections between the use of sustainable power, nuclear energy, and industrial progress. The researchers discovered that using renewable energy sources tends to boost CE while using atomic force tends to lower carbon emissions (Shi et al. 2022). As a result, the authors hypothesize that electric firms were increasing pollutants to satisfy peak load needs because they lacked the means to deal with inconsistent supply without increasing their emissions.
Then, for 10 MENA nations, we used a panel full modified ordinary least square (FMOLS) model to evaluate the link between green power usage and carbon negative (Sun et al. 2022; Shahbaz et al. 2022). They concluded that using regenerative or nonrenewable energy sources results in pollutants. Used the same methods to get comparable results for Turkey between 1970 and 2013. In contrast, the FGLS model found the reverse for the G-7 nations. However, researchers in the top ten countries in sub-Saharan Africa in terms of power production looked into the link between energy use and CE. Renewable power was proven to reduce carbon dioxide emissions (Sadorsky 2020). Using the QARDL, we could determine that using sustainable energy has significantly reduced the quantity of CE. It is argued that ecologically sound power sources make it possible to realize sustainable development goals. Nonetheless, the reverse was found faithful for nations with a poor standard of living (Lin et al. 2022). Many current investigations aim to determine what elements encourage technical advancement in sustainable energy (Qadir et al. 2021). Unfortunately, there is a shortage of research on the connection between advances in digital financing and the enactment of stricter environmental protections. Wide-ranging regulations, such as marketable electricity credentials, are now more likely to drive the development of solutions that are near to becoming comparable with carbon fuels, according to the study’s authors (Ge et al. 2022). We discovered that the alternative power company (with a focus on emerging renewable technologies) has a sizable strong reception to the revelations of these restrictions, corroborating the results of other studies that environmental laws enhance the efficiency of ecologically responsible industries (Haldar and Sethi 2022). Earlier writers claimed that the rise in the use of renewable sources of energy had little to do with ecological mandates. It is argued that stricter federal pollution regulations are crucial to developing renewable energy sources. Regulatory laws meant to preserve the environment are frequently cited as the impetus for adopting environmentally friendly technologies (Cheng et al. 2023). The reciprocal causation between the use of renewable energies and green technology innovation cuts emissions via innovative activity (Fu et al. 2022).
The present study primarily focuses on the impact of renewable energy advancements on carbon emissions to examine the correlation between the two (Haldar and Sethi 2022). However, there is still no agreement on how the two are connected (Zhang et al. 2022a, b, c, d). It is noted that the influence of technological advances in renewable energy sources on carbon pollution exhibits an inversion U-shaped pattern at varying quantiles (Temesgen Hordofa et al. 2023). The study’s authors concluded that renewable energy has the potential to both lower and raise emissions of carbon dioxide over the long run. Disparities between low- and high-income nations in the correlation between alternative energy use and environmental emissions are analyzed (Wang et al. 2022a, b, c). There is a significant connection between sustainable energy use and decreased carbon emissions in developing nations and a negative correlation between regenerative electricity consumption and economic growth (Iqbal et al. 2021). Carbon dioxide emissions and production are favorably and adversely correlated with energy utilization, respectively, for high-income nations. At the 5% significance level, we find that initiatives reduce carbon emissions (Li et al. 2021). It is proposed that environmental technology may help mitigate carbon emissions when coupled with hydropower (Anh Tu et al. 2021). But rising GDP has a significant negative impact on the planet. Research shows that E7 countries need to spend more on environmental technology to cut CB carbon footprints in a sensible manner (Zhang et al. 2022a, b, c, d).
Theoretical background
As CO2 emissions have risen, the country’s temperature has grown faster than expected, and severe storm events have become more common (Qudrat-Ullah 2022). The time for immediate action to tackle climate change and cut CO2 emissions has arrived, as mandated by the United Nations Sustainable Development Goals (SDGs). China is a significant contributor to global warming. Thus, the country’s response to the need to reduce carbon dioxide emissions is crucial (Jiang et al. 2022). Towards such an end, the Chinese government has set forth the dual objectives of emissions capping and emission reduction and has made significant efforts to promote low-carbon consumer habits. Shenzhen, China, has been a leader in low-carbon growth by presenting a Carbon Inclusive System Construction Work Plan to encourage environmentally friendly, limited manufacturing and consumption (Cao et al. 2022). However, China still confronts substantial obstacles to accomplishing these objectives. As the Chinese move toward a liquidity double circulatory system, the residential segment, responsible for more than 40% of the country’s total CO2 emissions, is gaining prominence.
Sustainable affluence and sustained economic growth, another target of SDGs, are essential under the threat of COVID-19 and pro-government. A dual circulatory policy is crucial not only in China but around the globe. This implies that China must make difficult choices between two Sustainable Development Goals (Holland et al. 2022). Fortunately, the technological advance, exemplified by cutting-edge digital financing, has not only provided a shot in the arm for human society’s forward pace but has also been instrumental in helping achieve economic and social objectives (Daud et al. 2022). It enables us to meet the difficulties of today’s economy and society. In addition, research has shown that the digital economy, as exemplified by digital banking, may provide a unique chance to address the issue. Without compromising economic development or people’s level of life, they may advance emissions reductions. China is among the most developed countries regarding digital finance and has seen enormous growth in recent years. The total value of all third-party mobile payments processed in China rose from 39 billion yuan in 2009 to 190.5 trillion yuan in 2018. In light of this, studying the impact of digital banking on consumer domestic dioxide emissions is crucial (HCEs). The influence of the digital economy on carbon dioxide emissions is the subject of an expanding but unresolved collection of research. The detrimental effect of the digital economy on CO2 emission reduction emissions is a topic of much debate among academics (Runs and Höhle 2022). Most of them argue that if we alter our consumption and consumption habits and increase the effectiveness of our industrial processes, digitalization may significantly reduce our contribution to climate change. Certain studies claim that the digital economy will increase CO2 emissions since it will boost economic expansion and resource use (Chen et al. 2022).
Methodology
Empirical techniques for inquiry
This research deployed both the autoregressive distributed lag (ARDL) and vector error correction model (VECM) methodologies to assess the trilemma interplay of digital finance, renewable energy development, and CO2 emission reduction in a Chinese context. In the empirical portion, this study first examines the variables of the unit root. First, the Ng-Perron unit root test was used to analyze the non-stationarity properties of variables. If the order of the integration of the variable was found to be similar, this study then extrapolated their long run impact by using the VECM model association between variables. The VECM can produce accurate results when investigating more than two independent factors. Furthermore, this investigation employed the ARDL method for a robustness check. The ARDL was used because this model is more robust for a small sample like the one studied in this research. VECM and ARDL employed dynamic characterization, allowing the impact of lagged values of dependent and independent variables to be considered. These strategies also allow synchronized estimation of long- and short-term relationships via dynamic specification. Note that this study assesses only the long- and short-term landscapes of digital finance, renewable energy development, and CO2 emission reduction. Therefore, the calculation is shown per the following Eq. (1).1 CO2t=HDFt,RE,et
For a comprehensive list of abbreviations, see Eq. (3). This model will be transformed to Eq. (2) for a nonlinear correlation between digital finance, renewable energy development, and CO2 emission reduction.2 CO2t=φ0+φ1DFinvt+φ2REDt+μt
The study deployed specific necessary tests before using the basic model above per this study; as a consequence, our model was modified by using a twofold lag, as indicated in Eq. (3):3 lnCO2t=φ0+φ1lnDFinvt+φ2lnREDt+εt
where:
CO2 donates the measure of carbon emission reduction,
DF measures and indicates the digital financing.
RED measures and denominates the renewable energy development.
In is the natural logarithm,ε indicates the error term, andt obtains the time index.
The equation above specifies the independent and dependent variables.
Unit root test
This study uses unit root test. The Ng-Perron test is utilized at the level, and the first difference of the individual series also solves the issue of robustness and autocorrelation.4 MZα¯=T-1YTd-λ22T-2∑t=1Tγt-1d-1
5 MSB¯=T-2∑t-1Tyt-1dλ2-12and
6 MZt¯=MZα¯×MSB¯
The Ng-Perron test has good explanatory power and is a good unit root test for small data samples. Equations (4), (5), (6) which show the Ng-perron, are given above.
ARDL bound testing model
The ARDL method formed and developed by Labibah et al. (2021) was first used for Indonesian export analysis. The same model examined digital finance, renewable energy development, and carbon emission reduction in China. Li et al. (2021) used this model to observe the impact of Indonesia’s exports and imports on the country’s economic growth. All such studies produce lengthy- and short-term analyses per the ARDL method. The ARDL model’s key advantage is integrating variables with multiple lag orders and examining well-known models such as statistical regression. However, the ARDL approach does not demonstrate a clear relationship when the parameters have a unit root. If the dataset has a stochastic (random) tendency, an ARDL model’s dynamics will mimic that trend rather than demonstrate the actual dynamics. However, if the dataset does not show a hypothetical trend, then this analysis is invalid. The congregation associated between the variables can be measured at the upper bound l (1) and lower determined l (0) in the bond test of the ARDL analysis. The characteristics of horizontal and vertical samples were investigated using the ARDL model. Moreover, the ARDL approach allows the model to be performed even if the descriptive variables’ data are endogenous. Equation (7) is given for the method of the ARDL.7 ΔlnCO2=φ0∑i=1mφ1iΔlnDFt-i+∑i=1sφ2iΔlnREDt-i+εt
where:φ0 stipulates the constant intercept;∆ specifies the breakdown mechanism;m thru j shows the lag’s order;∂ denotes the long run coefficient; and,εt The sign indicates the error term. ARDL method starts with the bound test for empirical analyses.
The cointegration of the null hypothesis was used to determine the long-term relationship, H0=φ1=φ2=φ3=φ4=φ5=φ6=0 was tested in contrast to the alternative hypothesis, i.e., H0≠φ1≠φ2≠φ3≠φ4≠φ5≠φ6≠0. The F-test was used to determine whether there were any long-term relationships between the variables. The investigation was completed for the bound F-test based on the critical value. In the presence of two groups, integral values are intended for significance level within and without the time series. Among these critical values, we can check the outcomes of the upper and lower bound values (UBC and LBC). All variables are for order level l (0) and the first differential l (1). If the value determined for the F-statistics surpasses the upper bound value, the null hypothesis is rejected, and the alternative view is accepted. Conversely, if the F-state falls below the lower bound, the alternative hypothesis is rejected, and the null hypothesis is accepted. Such outcomes are exclusive if the F-state value remains constant under the UBC and the LBC. Correspondingly, the variable’s lag order was determined by considering the Schwarz–Bayesian criteria (SBC) and Akaike’s information criteria (AIC). The SBC was chosen based on the bottommost lag length, whereas the AIC was picked based on the most vigorous lag length. After examining the long-term relationship, the error correction term (ECT-1) was used to investigate the short-term relationship between variables. Concerning the correlation analysis in the short time, the same is shown per the equation below:8 ΔlnCO21=φ0+∑i=1nφ1iΔDFt-1+∑i=1nφ2iΔlnREDt-1+θECTt=1+Vt
The error correction term (ECT) demonstrated the pace of adaptation, showing how variables run into a long-term relationship over a short period. To establish the short-term association, ECT-1 must have a p-value of less than 0.5% and a negative coefficient value. The quality and suitability of the model were confirmed using diagnostic and stability tests. These methods established casual correlation, normality, and heteroscedasticity. The cumulative sum (CUSUM) and the sum of squares (CUSUMQ) were used to estimate the model’s stability and indicate the short-term equilibrium.
Vector error correction model (VECM)
The equations in the ARDL model are insufficient for analyzing long- and short-term relationships; they do not adequately establish the causal link between variables. Therefore, it is necessary to establish whether cointegration persists since the analysis’s primary goal is to estimate the VECM-based granger causality among the several variables. This method detects coincidences between variables by analyzing contingencies based on the substantial likelihood of the series’ realizable value. The study first minimizes the causal pathways and explores the short-term Granger causality (Engle and Granger 1987). The ambiguity variables in this technique are commonly mixed over the short- and long-term associations by analyzing the vector error correction model ECT-1.
Conversely, there is no method for estimating indecisive cointegration per the VECM approach. Prior research typically used a short-term cloud link per the VAR model. As a result, the VECM technique complicated the analytical causal relationship between the desired variables in this study.9 ΔCO2t=fDFt,RED
10 ΔCO2tΔDFtΔREtΔPOPtΔUrtΔTot=λ1λ2λ3λ4λ5λ6+β11,1β12,1β13,1β14,1β21,1β22,1β23,1β24,1β31,1β32,1β33,1β34,1β41,1β42,1β43,1β44,1β51,1β52,1β53,1β54,1β61,1β62,1β63,1β64,1
11 ΔCO2t-1ΔDFt-1ΔREt-1ΔPOPt-1ΔUrt-1ΔTot-1+⋯+β11,kβ12,kβ13,1β14,kβ21,kβ22,kβ23,kβ24,kβ31,kβ32,kβ33,kβ34,kβ41,kβ42,kβ43,kβ44,kβ51,kβ52,kβ53,kβ54,kβ61,kβ62,kβ63,kβ64,k
12 ΔCO2tΔDFtΔREtΔPOPtΔUrtΔTot+θ1θ2θ3θ4θ5θ6ECTt-1+γ1γ2γ3γ4γ5γ6
In the equation above, the sign of the error correction term, γ, is acquired from ECTt-1 per the sign of the long-term equilibrium. Whereas the coefficient of the error term, denoted by the letter “,” mathematically represents a negative impact with a probability significance value of less than 0.05, indicating the existence of a long-term link, the F-state per the Durbin Watson (DW) value demonstrates a short-term connection.
Data and study variables
The study uses the panel data of Chinese provinces from 2005 to 2019. The data is acquired from the China Energy Statistics Yearbook (various issues), Statistical Review of World Energy, and the Chinese emission accounts and datasets. Researchers reflect on the growth of digital finance in China using indices created at the province level by the Academy of Digital Finance at Peking University, the Shanghai Finance Institute, and the Ant Financial Services Group. With three stages of online banking in the nation, we analyze how each tier’s indices affect REC and the GDF. Researchers accomplish the former by employing some indexes at both the first and second levels of the online lending ecosystem, along with the coverage-breadth index of digital finance and the usage-depth indicator of digital finance (DDF), as well as the disbursement index of digital finance, the lending benchmark of digital finance, as well as the healthcare benchmark of digital financing. They also utilize carbon dioxide emissions as a parameter for REC because they could contribute to more robust decarburization strategies that promote REC. Asia’s Emission Accounts and Datasets are mined for information on the country’s CO2 output. Mediating factors include the amount of loan and economic status. More specifically, the credit scaling is represented by the per-population loans of inhabitants, whereas people’s per capita disposable income represents the income level. The Chinese Statistics Annual is the source for all demographic, discretionary cash, and credit information (various issues). Considering the gaps in knowledge about inhabitants’ expendable cash, they substitute data on urban residents’ expendable cash.
Results and discussion
A study analysis of the impact of digital financing, renewable energy development, and carbon emission reductions was conducted. The favorable influence of environment protection severity and environmental stress level in fostering innovativeness in sustainable power should not be overlooked. Module 2 demonstrates that the carbon burden lagging reported in Table 1 as per the period inhibits the current environmental policy density, the overall ecological laws severity is influenced by the previous sustainable power science and technology innovation and tolerable regulatory oversight concentration, and the current ecological regulatory density can raise the level of environmental legislation in the current cycle.Table 1 Mechanism of hypothesis testing in the empirical estimation
Cointegration test Null hypothesis Alternative hypothesis
F-test β1=β2=β3=β4=0 Any, β1,β2,β3,β4≠0
BDSM-test statistic β1=0 β1≠0
F-test on the lagged independent variable β2=β3=β4=0 Any, β2,β3,β4≠0
As the prevailing phase’s CO2 emission reduction water increases, federal agencies will devote more resources to policy and regulatory making, further clarifying the obligation subject areas, locking the significant manufacturing fields of carbon reduction, and the sustainability-related goals for Chinese entities.
Unit root analysis
Panel cointegration assessment should follow the processing of the sample size as the initial stage in the modeling procedure. If semi-data is effectively modeled, the pseudo-regression phenomena may emerge due to the large gap between the steady data modeling phases and the non-stationary data modeling processes. This is why a unit root test was performed on the statistics: to exclude the possibility of overfitting. Logarithms are used for environmental constraints and sustainable energy technical advancement to eliminate unobserved heterogeneity. Table 2 displays the results of the tests conducted on the information cointegration using LLC, IPS, and Hadri-LM. Table 2 shows that there is no unit root and firm constancy across all parameters, thereby rejecting the null hypothesis that all persons are quasi-processes (Table 2). Accordingly, the non-stationary test has been completed, and all parameters will be included in the PVAR regression model.Table 2 Unit root analysis using the Ng-Perron test
Parameters I (0) Parameters I (1)
CO2α DFt RED POP CO2α DFt RED POP
ln(CO2) 2.11 2.94 0.72 4.07 Δ ln(CO2) 2.09 1.67 0.89 1.20
ln(DF) 2.17 2.73 0.49 17.84 Δ ln(DF) 2.13 1.08 0.20 4.52
ln(RED) 2.41 2.30 0.10 9.72 Δ ln(RED) 3.97 1.27 0.31 2.63
ln(POP) 2.37 2.47 0.17 5.32 Δ ln(POP) 4.35 1.45 0.11 6.93
eit 7.76 1.29 0.31 4.45 eit 1.78 1.62 0.37 9.85
We have used the Ng-Perron test to estimate this study’s unit root data characteristics. The results show that the unit root null hypothesis (H0) is not rejected at any level. All effects have I(0) significance at the 10% level, but this is dismissed by the first-order differential I(1) for all variables. The linear constant covered these tests. Table 4 shows the outcomes of the Ng-Perron unit root test, demonstrating that all the parameters converge at order l (1). The variable lnDFt is a dependent variable; lnREDt is more extensive than I (1) at 5% and 10% critical value. Thus, this implies cointegration when lnCO2st has been occupied as an outcome variable. Following the establishment of a long-term relationship, two steps were taken (Table 3). The model’s optimum lag orders were determined based on the Akaike information criterion. The upper bound value (UBC) and lower bound value (LBC) of 4.65 and 2.36, respectively, were 10% of significant levels. The outcomes of this research are congruent with previous studies, such as Li et al. (2021), thus confirming the cointegration correlation between DF, RED, and CO2 emission reduction.Table 3 Results of Zivot and Andrews (2002) unit root test
At level 1st difference
T-statistic Time break T-statistic Time break
ln(CO2) 2.9765 2017 0.7427*** 2019
ln(DF) 1.2416 2009 0.4514*** 2017
ln(RED) 1.6280 2011 2.1409*** 2014
ln(POP) 1.8346 2013 1.4953*** 2012
Furthermore, no serial correlation appears in this model, which approves that the model is standard and correctly specified. Both ARDL and VECM models were used to verify this method. Hence, it can be determined that DF and RED impact China’s carbon emission reduction; therefore, we continue to examine the long-term elasticities and the ECM.
ARDL results
Table 3 displays the results of the paired Granger causality test, including the F-statistic and associated probabilities. The direct correlation between corruption, technical advancement, and dioxide emissions, as well as between CO2 emissions and GDP per capita squared, has been shown. In addition, we discovered a bidirectional causal link between economic growth and GDP, but we did not find any evidence of a similar relationship between sustainable power and CO2 emissions (Table 4). This helps to enhance policies by revealing the connections between CO2 emissions and things like graft, tech advancement, globalization, GDP, and GDP2 (all except for renewable energy). At the 1% significance, environmental laws complexity is the Granger cause of greenhouse pressure, whereas chemical pressure itself is not the Granger responsible for ecological regulation complexity. Overall, the CO2 emission reduction pressure level is affected by the brightness of environmental protections and alternative energy sources, which also influences the former. Carbon pressure, environmental policy concentration, and sustainable power digital finances positively affect the future, but this effect steadily decreases over time. The settings and make are particularly affected by these variables, but the later period is also affected, but not as strongly. It may be seen in Fig. 1 that it has a detrimental impact when influenced by CO2 emission reduction tension, reaching its lower limit in the first phase, clearly showing that environmental policy intensity limits the expansion of carbon pressure. The current ecological legislation severity determines the development of carbon tension to its optimum amount in the first period.Table 4 ARDL test estimates
Estimates ln(CO2), ln(DF), ln(RED), ln(POP)
OL (2,1,3,5,1,)
Scores F-stat, T-stat, DW-stat, serial correlation, normality, heteroscedasticity
Significance level Critical bonds for F-statistics Critical bonds for W-statistics
LCB UCB LCB UCB
5% 2.94 3.01 3.45 2.87
10% 2.19 3.13 3.76 3.60
F-value 28.1313
DW-statistics 1.8901
R2 0.8246
Normality 1.0046[0.5119] Heteroscedasticity 1.2341 [0.0146]
The government promotes environmental regulation, enhances ecological controls, and encourages the execution of emissions reduction objectives to reduce carbon emissions effectively. For example, the constraining effect of present carbon demand on environmental laws intensity reaches its minimal value (Table 5), suggesting that carbon demand hinders the degree of environmental protection.Table 5 Results of long run association: symmetric and asymmetric framework
Parameters China
Linear Nonlinear
ln(CO2), ln(DF), ln(RED), ln(POP) Overall 7.392*** 8.701***
tDV − 6.402*** − 7.348***
FIDV 8.892*** 8.741***
Table 3 displays the ARDL estimates for the study model. Although the explained variable’s indirect influence on the number of renewable energy developments is not statistically significant, its direct effect and total effect are positive and statistically significant at the 0.01% level. In reality, relieving regional energy pressure and environmental policies is facilitated by the development of renewable energy innovation. This is because fewer petroleum products are used, the regional resource utilization structure is enhanced, and the pressures on these two factors are reduced. In particular, advances in renewable energy technology may boost sustainability initiatives by increasing energy efficiency, decreasing energy usage per unit of consumer spending, and, to some degree, coordinating the connection between energy consumption and economic growth.
The impact of the exogenous variables rationalization of the industry sector on fostering digital finance is significant (Table 6), but its direct influence is small, and its unintended byproduct is nonexistent. With the help of rationalization of the economic organization, it is possible to prevent any one sector from having an outsized impact on the progress of society (Fig. 2). In other words, the adverse effects of primary and secondary industries on digital finance may be mitigated by optimizing the industry base. Rationalization’s fundamental function is to improve the level of importance among sectors by coordinating conflicts. Taking this approach reduces restrictions on the free movement of workers, which is a critical factor in achieving the desired outcome. Population development in the area and regions with solid connections, increased productivity across society, the creation of a “structured windfall,” and a state of great potential are all outcomes of an industrial structure that is both efficient and fair.Table 6 VECM Granger causality test
Short run Long run
Δ ln(CO2) Δ ln(DF) Δ ln(RED) Δ ln(POP) ECMt-1
Δ ln(CO2) 2.335* 0.113* 0.575* 0.617* 0.421*
Δ ln(DF) 5.327* 1.587* 0.003* 0.319* 4.918*
Δ ln(RED) 5.941* 0.964* 0.012* 0.837* 0.371*
Δ ln(POP) 0.034* 0.406* 0.228* 0.179* 0.262*
eit 0.837* 0.884* 0.437* 1.378* 7.888*
Fig. 2 Renewable energy development movement
Since the progress in renewable energy development is driven mainly by rationalizing the industrial structure, this is of utmost importance. Progress in industrialization, as a regressor, has a marginally insignificant direct influence, a substantial indirect effect, and a substantial overall effect on the slowdown in environmentally friendly growth. On the one hand, manufacturing sector development usually involves upgrading from a previous stage in the sector’s structure. From a scientific viewpoint, the pollution issues that arise when the primary industry is transformed into industrialization on the way to the advanced level are mitigated once the tremendously changed to the tertiary transition stage. Therefore, when the industrial structure evolves into a more sophisticated form, there should be a U-shaped shift in the degree of sustainable innovation (Table 7). However, the focus of China’s present growth is still on the country’s traditional sectors.Table 7 Results of Furious TY VECM
Δ ln(CO2) Δ ln(DF) Δ ln(RED) Δ ln(POP) Causalities
Δ ln(CO2) 4.934* 9.684* 4.989* 1.163* DF←→CO2
DF→RED
RED←→CO2
DF→POP
POP→RED
POP-→CO2
Δ ln(DF) 1.948* 1.371* 0.334* 0.367*
Δ ln(RED) 2.991* 5.164* 0.072* 0.404*
Δ ln(POP) 0.169* 6.045* 2.427* 2.983*
R-square 0.862 2.431* 0.683* 0.545*
Consequently, progress in the industrial structure harms the green economy, as measured by a higher level of industrial construction and a lower level of sustainable innovation. The findings of this study agree with China’s present development level. However, the “dirty wonderland” argument is supported further by the detrimental indirect impact of progress in the industry sector on sustainable innovation. Developed economies from locations with high levels of industrial structure have moved to surround regions due to locational disparities, stifling digital finance in those places.
VECM findings
After establishing the long-term association between variables, the Granger causality test for VECM was employed for verification. The causality result should be at least unidirectional if the variables are integrated. The Granger causality of VECM is the appropriate model for analyzing casualty between variables, as recommended. Table 6 shows the outcomes for the VECM. Table 6 demonstrates the results over the long-term, finding that CO2 emission reduction has a bidirectional relationship with GDP and Exp. The relationship between CO2 emission reductions is also bidirectional in all cases where RED and CO2 emission reduction are bidirectional.
These findings reveal a short-term feedback effect between CO2 emission reduction and GDP. It is demonstrated that GDP is significantly affected by CO2 emission reduction at 5% over the long-term, RED is thus affected at 1%, and Gdf at 10% over the same term. Thus, mixed results in the short-term translate to both negative and positive effects over a long time, as shown by Table 6. The stability tests are shown in Fig. 3, which corroborates. Figure 4 proves the consistency of the model, in which blue lines show that 5% is critical at the significance level. Thus, the line shows that the significance level of the present model is stable and can be utilized for further evaluations of policy suggestions.Fig. 3 Digital finance movement
Fig. 4 Model diagnostics
The current renewable energy system development has the most significant stimulating influence on the environmental demand in the initial phase. This effect is maximized when the innovation is introduced and subjected to the full force of carbon pressure. Since Beijing remains in moderate development of maximum atmospheric pressure, the consequence of adapting to the change of renewable energy development is low, and the outcome conversion cycle is lengthy, which may account for this impact. As can be seen in Fig. 2, the influence of sustainable renewable energy development on emissions starts to increase, with the most significant effect felt in the first phase. This suggests that higher levels of dioxide tension are generally favorable to REETI and that the beginning phase is where REETI is most likely to benefit from the most about digital finance (Fig. 3).
According to study findings, the emission pressure may serve as an impetus for creativity and lead to advances in renewable energy development. When considering the impact of environmental regulation frequency on sustainable energy technical advancement, it becomes clear that the former promotes the latter and that the latter’s advertising consequence on environmental legislation brightness in the first timeframe reaches its highest value at present. The government will use new laws and mechanisms with technical advances in the renewable energy sector to address the resulting shift in the electricity consumption structure. Figure 4 shows that the environmental laws frequency encourages sustainable energy technological innovation, and the promotion effect of prevailing environment protection intensity on sustainable power technical innovation reaches its most extraordinary worth in the initial period.
Furthermore, the anticipated coefficient of renewable energy consumption is negligible at the 1% level of significance, suggesting that an increase in renewable energy use by 1% is associated with a decrease in carbon dioxide emission by 1.42%. This shows that China can reduce pollution by expanding sustainable energy use. In addition, a 1% increase in urbanization is associated with a 2.04% increase in CO2 emissions, as shown by the significant and positive long run coefficients of LURB. This proves that increased energy usage, rainforest, and change in land use are all results of urbanization in China’s environmental degradation. In addition, at the 5% probability value, the projected long run coefficients of LIND are positive, showing that an increase of 1% in industrial value addition is associated with a rise of 0.17% in dioxide (CO2) emissions (Table 8) and corresponding in Table 9.Table 8 Results for mechanism analysis
IV IV-MA
(1) (2) (3) (1) (2) (3)
1st stage 2nd stage 1st stage 2nd stage 1st stage 2nd stage
DF 0.7284** 0.2121* 0.0778*
(2.13) (2.01) (1.89)
MZt 0.3717* 0.4285 0.5833
(3.97) (6.35) (2.31)
MZα 5.353* 0.1368* 0.7373* 0.8731* 0.1983* 0.0585*
(6.79) (0.023) (0.443) (0.014) (0.142) (0.997)
MSB¯ 1.557* 0.004* 0.0846* 0.9283* 0.1019* 0.9477*
(9.95) (5.37) (0.22) (0.337) (0.556) (0.013)
Δln(CO2) 0.1877* 0.2611* 0.0146* 0.0271* 0.1527* 0.1014*
(8.18) (2.89) (0.485) (0.553) (0.551) (0.1957)
Δln(DF) 3.5769* 0.9741* 0.2503* 0.0299* 0.3551* 0.0244*
(0.89) (0.88) (0.946) (0.112) (0.183) (0.727)
Δln(RED) 0.0135* 0.2324* 0.1403* 0.4396* 0.6006* 0.0702*
(0.803) (0.431) (0.746) (0.909) (0.265) (0.536)
Δln(POP) 0.0581* 0.419* 0.9123* 0.1518* 0.7463* 0.6783*
(0.013) (0.571) (0.432) (0.101) (0.252) (0.014)
R-square 0.9012 0.5361 0.1798 0.2136 0.0926 0.7909
Constant 0.5036 0.3893 0.0051 0.865 0.7841 0.9094
Table 9 Robustness test
Variables Coefficient STD T-value Coefficient STD T-value Coefficient STD T-value
Δ ln(CO2) 0.1024 0.1653 0.0473 0.1106 0.0296 0.1526 0.5774 0.8594 0.5836
Δ ln(DF) 0.2061 0.0217 0.0026 0.0449 0.6218 0.1022 0.2567 0.4113 0.0169
Δ ln(RED) 0.0791 0.1924 0.0585 0.1397 0.0326 0.2076 0.0637 0.1886 0.1124
Δ ln(POP) 0.2942 0.9326 0.0164 0.1639 0.0476 0.2144 0.7495 0.1968 0.4707
R-square 0.2581 0.5985 0.4274 0.0503 0.1789 0.2596 0.7227 0.1776 0.5464
Robustness analysis
This study strongly suggests that industrialization in China is a driving force behind the country’s rising pollution levels. In addition, at the 10% significance level, the expected long run coefficient of technical innovation is negative, suggesting that a 1% increase in technological innovation decreases carbon dioxide emissions by 0.04%. Finally, increasing forest area by 1% is associated with a 2.70% decrease in CO2 effects in the long run, as the projected long run coefficients of forestland are negatively and significantly different from zero at the 1% level.
However, research also shows that deforestation and degraded forests are responsible for an increase of 2.70% in China’s emissions of CO2 per unit of forest area lost. Because tropical rainforests absorb CO2 emissions and deposit them in forest vegetation, this finding implies that expanding forest acreage enhances ecological integrity (Fig. 4). This finding proves that China’s forest environments may be used as a tool to cut down on pollution by reducing the pace of deforestation and boosting forest preservation and preservation efforts.
The results of the DOLS display that while income growth, urbanization, and modernization contribute to a decline in soil stewardship in China by growing carbon footprints, the increase in renewable energy sources, technology development, and secondary forest assists in achieving ecological responsibility by carbon emissions.
Discussion
The devastating effects of global warming pose an existential danger to all life on Earth. The United Nations Development Program (UNDP) has set several Development Goals (SDGs) to address this issue (SDGs). To address the environmental problems, world leaders gathered in Glasgow, Scotland, for COP26, the 26th United Nations Climate Change Congress. The world’s academics and policymakers have concurred to reinforce the Paris Contract’s goal of limiting the average global temperature to 1.5 C, to seek income generated for the restitution of environmental damage to be offered by rich countries to poor ones, and to confidently tell to motivate the payment of sustainable power, abandoning fossil fuel-based power sources. The execution of strategies to reduce carbon emissions is hampered by corruption. In addition, to fight carbon dioxide emissions for safe and sustainable living, adequate attention should be given to sustainable power, technical innovation, internationalization, economic development, and bribery. Geographically, economically, and socially, Asian nations are seen as more susceptible to the severe effects of climate change and global warming than other areas. Natural catastrophes impacted more than 57 million individuals in 2021, which has been steadily rising. As such, this research considers the CO2 output of 47 Asian nations (specifics in section). In 2020, the total CO2 emissions from these nations were 16,406.055 million tonnes or about 50.763% of the global total.
Digital finance is now the most crucial factor in reducing global warming. Substantial environmental technology to decrease carbon dioxide emissions has gradually increased as environmental regulation has improved. To restructure and optimize the economy, technological progress is essential. Changing the focus of economic growth from producing to research helps reduce industrialization’s emissions of CO2 emission reduction. In addition, technical advancement is vital to improving a nation’s energy efficiency. Because of technological advances, the industry may now achieve a target production level while simultaneously reducing the energy required. In addition, technological developments have allowed businesses to switch from using finite power sources to using abundant ones. Because of this, energy usage and carbon dioxide emissions from burning fossil fuels have decreased due to technological developments. Chinese industrial structure might be reshaped and enhanced with the support of new technologies, and these advancements are also a primary driver of significant economic growth for the country. Therefore, it is essential to boost China’s economic development and reduce carbon emissions to investigate the theoretical and concrete consequences of technological innovation on sustainable development.
The relationship between digital finance and modernizing industrial structures. Different schools of thought exist among academics regarding the correlation between improving industrial systems and safeguarding the environment. In this opinion, updating the industry structure stifles environmentally friendly growth. The following are some concrete examples of this phenomenon. As a first point, the modernization of industrialization has sped up mechanized farming. Much of China’s population is rural, making it a farmland powerhouse. Damages in the market situation can be attributed solely to the wastewater, exhaust gas, and solid waste produced by mechanized manufacturing. Third, classical labor-intensive companies are still dominant in most regions of China, and then further advancement of the industrial structure can increase economic growth. Improving Chinese industrial configuration has come with rising commodity usage, and combustion density hampered the country’s efforts at eco-friendly progress.
Third, in geographical industrialization, there is an “industry gap” respectively zones due to differences in productivity expansion and geographic location space, as well as differences in the interest of activities in the organization within those spaces. This transfer of industry is known as “industrial spillover.” Distinct differences exist across China’s regional and industrial infrastructure. There are significant regional variations in manufacturing costs, integrated environmental guidelines, and public heating bills, and these discrepancies are necessary for manufacturing transmission to occur. Low-industrial-structure areas tend to be the source of energy, historically significant places where manufacturing plays a minor role. Access to cheap production resources, lower environmental laws, and looser government controls are commonplace in such areas to guarantee economic advantages and geographic and industrial growth. Thus, other sites are more likely to relocate companies to places with a low-level industry sector in the hopes of reaping provincial distinguishing dividend payments.
Contrastingly, regions with a more advanced industrial structure will shift their focus from traditional industries to those with less developed ones. Some local industries are very damaging, and the movement of such businesses indicates that locations with high levels of industrial infrastructure are establishing a “pollutants nirvana” by exporting their air degradation to other areas. Academics have shown that environmental damage has grown due to the movement of industries from one place to another.
Faced with the global financial crisis, resource limitations, and climate change, China can only transform by pursuing high-quality digital finance. Improved energy and environmental conditions are feasible due to recent developments in solar and wind power, which can aid in the reconstruction of the energy demand formation, the enhancement of the efficiency with which resources are utilized, the reduction of overhead expenses, and the promotion of productivity gains (Zheng et al. 2022). Innovations in green technologies are crucial to fostering eco-friendly growth on a regional scale. For neighboring regions, according to the “pollution paradise” hypothesis, parts with high industrial structure levels are more likely to transfer industry to areas they have a relationship with personal values in organizational employees mattering of industrial structural (Ahmad et al. 2022). Most of the shared industries are traditional industries, which causes pollution in the “associated” areas. At the same time, the advancement of the industrial structure is conducive to the absorption of talent from other regions, which will inevitably impact their development and transformation and thereby inhibit improvement in their level of green growth. Thirdly, the relationship between the development of new renewable energy sources and the improvement of structures was factored into the model. Before anything else, it was determined that advancements in solar and wind power and efforts to streamline the infrastructure have opposite effects on one another. In China’s frame of reference, renewable energy advancement is driven primarily by the desire to expand the country’s manufacturing sector (Chang et al. 2023). Manufacturing rationalization is an attempt at inter-sector cooperation. Asia is actively encouraging a shift from primary to secondary and tertiary manufacturing. Workers will be redirected to the service sector due to the rationalization of the industrial structure, driving up the service sector’s share of GDP and output value. Given China’s present time, it is challenging to coordinate the effect of renewable energy technology innovation and industrial structure rationalization on green development through access to finance in entreprenurial venture (Bilal et al. 2022) . Second, the generation of renewable technology has mitigated the adverse effects of industrialization on the rate of sustainable innovation.
In contrast, progress in industrial structure has boosted the advantageous effects of renewable energy technology on the rate of green development. There is currently a specific circumstance of industrialization structure on the level of sustainable innovation in China’s current approach to development, but this can be mitigated through the use of high-level renewable power innovation activities to improve the energy usage configuration of old industries while also lowering their energy costs and pollution levels. The class of functions between regions can be reduced if more parts with industrialization frameworks adopt renewable energy sources and increase their interregional industrial transmission via public supports for energy financing (Iqbal and Bilal 2021b). Consequently, the positive impact of digital financing technology on green building development can be furthered with the help of a formal industry sector that facilitates its provincial implementation.
Conclusion and implications
Conclusion
Digital finances in renewable energy sources and the strictness of regulations constitute a constant positive feedback loop. In the future, looking initial stage, the earlier has the most influence over the latter, while the last will have the most significant impact. Regarding climate change regulations, the beneficial effect of sustainable energy advancement is more prominent regarding the level of control and the deviation that addresses this problem. This indicates that the level of strict environmental concentration reflects the country’s approach and stance on vehicle emissions in the context of the current political climate of reducing carbon dioxide emissions. Increased new protections mean more stringent emission controls and a greater need to alter the energy system, both of which help spur the development of new sources of renewable electricity. Innovations in renewable energy technology will also stimulate the government to take mandatory bureaucratic steps to further the cause of decarbonization. To better understand where China is lacking in sustainable energy technological innovation, researchers need to compare China to other countries with more developed clean energy sources and appraise the correlation between clean energy creating a product and CO2 emission reduction operating pressure using a larger sample size. Technologies for renewable energy advancement and elevated carbon differential pressure mutually reinforce one another. In the end, in the nigh-first period, the atmospheric pressure level has a more beneficial impact on modernization in sustainable energy. In contrast, the encouragement impact of the latter on the earlier achieves its most incredible value. These study’s findings run counter, which may be due to the asynchronous nature of scientific advances. In addition, the PVAR model is employed to confirm the connection between the two variables in this investigation. Future studies will look into the long-term communication between sustainable power communications technology productivity and creativity and CO2 emission reduction anxiety levels within every region, pursue transnational economic collaborative ventures and integration of renewable energy technology solutions, and increase the speed of renewable power conversion because of the globalized disposition of CO2 emission reductions.
Practical implications
Study suggested multiple practical implications. Pollution legislation and dioxide pressure level intensity levels continuously block one another. The first stage is characterized by the most significant value of the original’s suppression of one or the other. In contrast, the second phase is characterized by the highest value of the latter’s suppression of the earlier. The variation contributing rate of ecological regulation concentration to total fossil pressure is more significant, and the natural carbon pressure more negatively impacts the environment protection strength. This demonstrates that the government will expend considerable cash to restrict air pollutants in response to stricter environmental legislation, influencing greenhouse emissions and reducing atmospheric pressures. Global carbon decrement will also be fine-tuned into military objectives for heavily loaded pollution of water businesses, and companies will be considered necessary to feel responsible for reducing carbon emissions as a result. Along with, and expanding upon, the work of Wang et al. [54], this result examines the effect of chemical occupational stress on atmospheric regulating instruments. Indications of environmental legislation will be measured in the additional investigation, and they will be categorized into instruction, the real economy, and public engagement models, with the blended influence on chemical pressure being taken into account.
The findings, as mentioned earlier, suggest that encouraging innovativeness in renewable energy sources, optimizing the strength of environmental regulation, and minimizing the buildup of environmental constraints are crucial means of reducing carbon emissions and setting the stage for ecological sustainability. The following policy suggestions are put forward to achieve this goal: there has to be a serious push to expand renewable energy sources and foster new forms of technical innovation. On the one extreme, city councils should advance rewards and regulations on sustainable power technology innovation and underline that decarbonization seeks to integrate with the provision of roads like smart charging, distribution systems, and microgrids to provide a clean environment for investors. However, to form a supplementary and authoritarian sequence of griddle advancement, businesses working in the renewables industry should focus on integrating digital finance with competitive sector design and economic models, insisting that the market decides the allocation of development considerations and assertively connecting with local resources and capabilities. It is also essential for the banking sector to actively establish property rights commitment funding to supply cash for advances in renewable energy.
Furthermore, air filtration and its severity need to be enhanced. Since specific areas are subjected to varying degrees of emission compression, the national government should devolve authority and allow state and municipal governments to make their own decisions regarding the nature and scope of environmental laws and restrictions. At the same time, members of the general public, communications, and interpersonal advocacy agencies are urged to get involved in efforts to cut down on carbon emissions, raise people’s consciousness about the importance of doing so in every field, and step up to the plate to rise to the challenge themselves. Third, we must maximize the nitrogen sink’s potential and cut emissions. The administration wants to invest in forest resources through tax breaks; prohibit extreme recycling, pasture, and trade skills; expand wilderness and pasture media attention; maximize geographical carbon storage great promise; proactively set targets for responsible use of forest products; and assign appropriate to all relevant parties. Green accreditations, feed-in tariffs, scientific and technological endorse, dioxide emissions buying and selling, and carbon fuels heat taxes are all methods used by the Northern countries to regulate CO2 emission reduction emissions and slow the rise in CO2 emission reduction stress.
Author contribution
Conceptualization, methodology, and writing — original draft: Tianyu Mo. Data curation, visualization, and editing: Hong Ke.
Data availability
The data that support the findings of this study are openly available on request.
Declarations
Ethical approval and consent to participate
The authors declared that they have no known competing financial interests or personal relationships, which affect the work reported in this article. We declare that we have no human participants, human data, or human issues.
Consent for publication
We do not have any person’s data.
Competing interests
The authors declare no competing interests.
Preprint service
Our manuscript is posted at a preprint server prior to submission.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Ahmad B Iqbal S Hai M Latif S The interplay of personal values, relational mobile usage and organizational citizenship behavior Interactive Technology and Smart Education 2022 19 2 260 280
Anh Tu C, Chien F, Hussein MA, Ramli MMY, Psi MM, M. S. S., Iqbal S and Bilal AR (2021) Estimating role of green financing on energy security, economic and environmental integration of BRI member countries. The Singapore Economic Review.
Bilal AR Fatima T Iqbal S Imran MK I can see the opportunity that you cannot! A nexus between individual entrepreneurial orientation, alertness, and access to finance Eur Bus Rev 2022 34 4 556 577
Cao S Nie L Sun H Sun W Taghizadeh-Hesary F Digital finance, green technological innovation and energy-environmental performance: evidence from China’s regional economies J Clean Prod 2021 327 129458
Cao Y Dhahad HA ABo-Khalil AG Sharma K Mohammed AH Anqi AE El-sShafay AS Hydrogen production using solar energy and injection into a solid oxide fuel cell for CO2 emission reduction; thermoeconomic assessment and tri-objective optimization Sustainable Energy Technologies and Assessments 2022 50 101767
Chang L Iqbal S Chen H Does financial inclusion index and energy performance index co-move? Energy Policy 2023 174 113422
Chao T, Yunbao X, Chengbo D, Bo L, Ullah S (2022) Financial integration and renewable energy consumption in China: do education and digital economy development matter?. Environmental Science and Pollution Research, 1–9
Chen P Is the digital economy driving clean energy development?-new evidence from 276 cities in China J Clean Prod 2022 372 133783
Chen Y Shao S Fan M Tian Z Yang L One man’s loss is another’s gain: does clean energy development reduce CO2 emissions in China? Evidence based on the spatial Durbin model Energy Economics 2022 107 105852
Cheng Y Zhang Y Wang J Jiang J The impact of the urban digital economy on China’s carbon intensity: spatial spillover and mediating effect Resour Conserv Recycl 2023 189 106762
Daud I, Nurjannah D, Mohyi A, Ambarwati T, Cahyono Y, Haryoko AE, ... and Jihadi M (2022) The effect of digital marketing, digital finance and digital payment on finance performance of Indonesian smes. Acad Scientific J, 6(1):37–44
Ding X Gao L Wang G Nie Y Can the development of digital financial inclusion curb carbon emissions? Empirical test from spatial perspective Front Environ Sci 2022 10 2093
Engle RF, Granger CWJ (1987) Co-integration and error correction: representation, estimation, and testing. Econometrica 55(2):251–276
Feng S Chong Y Yu H Ye X Li G Digital financial development and ecological footprint: evidence from green-biased technology innovation and environmental inclusion J Clean Prod 2022 380 135069
Fu H, Huang P, Xu Y, Zhang Z (2022) Digital trade and environmental sustainability: the role of financial development and ecological innovation for a greener revolution in China. Econ Res-Ekonomska Istraživanja, 1–19
Ge T Cai X Song X How does renewable energy technology innovation affect the upgrading of industrial structure? The moderating effect of green finance Renewable Energy 2022 197 1106 1114
Haldar A Sethi N Environmental effects of information and communication technology-exploring the roles of renewable energy, innovation, trade and financial development Renew Sustain Energy Rev 2022 153 111754
Holland SP Kotchen MJ Mansur ET Yates AJ Why marginal CO2 emissions are not decreasing for US electricity: estimates and implications for climate policy Proc Natl Acad Sci 2022 119 8 e2116632119 35165182
Iqbal S, Bilal AR (2021a) Energy financing in COVID-19: how public supports can benefit?. China Fin Rev Int
Iqbal S, Bilal AR (2021b) Investment performance: emotional beasts are dragging into the darkness of the castle. Glob Bus Rev. 10.1177/09721509211044309
Iqbal S Bilal AR Nurunnabi M Iqbal W Alfakhri Y Iqbal N It is time to control the worst: testing COVID-19 outbreak, energy consumption and CO2 emission Environ Sci Pollut Res 2021 28 15 19008 19020
Jiang T Yu Y Yang B Understanding the carbon emissions status and emissions reduction effect of China’s transportation industry: dual perspectives of the early and late stages of the economic “new normal” Environ Sci Pollut Res 2022 29 19 28661 28674
Kingiri AN, and Fu X (2019) Understanding the diffusion and adoption of digital finance innovation in emerging economies: M-Pesa money mobile transfer service in Kenya. Innov Dev
Labibah S, Jamal A, Dawood TC (2021) Indonesian export analysis: Autoregressive Distributed Lag (ARDL) model approach. Journal of Economics, Business, & Accountancy Ventura 23(3):320–328
Lee CC Wang F Lou R Digital financial inclusion and carbon neutrality: evidence from nonlinear analysis Resour Policy 2022 79 102974
Li W Chien F Ngo QT Nguyen TD Iqbal S Bilal AR Vertical financial disparity, energy prices and emission reduction: empirical insights from Pakistan J Environ Manage 2021 294 112946 34153632
Lin Y, Anser MK, Peng MYP, Irfan M (2022) Assessment of renewable energy, financial growth and in accomplishing targets of China’s cities carbon neutrality. Renewable Energy
Liu S Digital inclusive finance and carbon emissions: evidence from China World Sci Res J 2022 8 8 430 445
Liu Y Chen L The impact of digital finance on green innovation: resource effect and information effect Environ Sci Pollut Res 2022 29 57 86771 86795
Lv C Song J Lee CC Can digital finance narrow the regional disparities in the quality of economic growth? Evidence from China Econ Anal Policy 2022 76 502 521
Ma Z Wu F Smart city, digitalization and CO2 emissions: evidence from 353 cities in China Sustainability 2022 15 1 225
Ma Q Tariq M Mahmood H Khan Z The nexus between digital economy and carbon dioxide emissions in China: the moderating role of investments in research and development Technol Soc 2022 68 101910
Mukalayi NM Inglesi-Lotz R Digital financial inclusion and energy and environment: global positioning of sub-Saharan African countries Renew Sustain Energy Rev 2023 173 113069
Ozturk I Ullah S Does digital financial inclusion matter for economic growth and environmental sustainability in OBRI economies? An empirical analysis Resour Conserv Recycl 2022 185 106489
Qadir SA Al-Motairi H Tahir F Al-Fagih L Incentives and strategies for financing the renewable energy transition: a review Energy Rep 2021 7 3590 3606
Qin X Wu H Li R Digital finance and household carbon emissions in China China Econ Rev 2022 76 101872
Qudrat-Ullah H A review and analysis of renewable energy policies and CO2 emissions of Pakistan Energy 2022 238 121849
Razzaq A Sharif A Ozturk I Skare M Asymmetric influence of digital finance, and renewable energy technology innovation on digital finance in China Renewable Energy 2023 202 310 319
Runst P Höhle D The German eco tax and its impact on CO2 emissions Energy Policy 2022 160 112655
Sadorsky P Energy related CO2 emissions before and after the financial crisis Sustainability 2020 12 9 3867
Shahbaz M Li J Dong X Dong K How financial inclusion affects the collaborative reduction of pollutant and carbon emissions: the case of China Energy Economics 2022 107 105847
Shi F Ding R Li H Hao S Environmental regulation, digital financial inclusion, and environmental pollution: an empirical study based on the spatial spillover effect and panel threshold effect Sustainability 2022 14 11 6869
Sun L Fang S Iqbal S Bilal AR Financial stability role on climate risks, and climate change mitigation: implications for green economic recovery Environ Sci Pollut Res 2022 29 22 33063 33074
Temesgen Hordofa T, Minh Vu H, Maneengam A, Mughal N, The Cong P, Liying S (2023) Does eco-innovation and green investment limit the CO2 emissions in China? Econ Res-Ekonomska Istraživanja 36(1):1–16
Tian G Influence of digital finance on household leverage ratio from the perspective of consumption effect and income effect Sustainability 2022 14 23 16271
Wang H Guo J Impacts of digital inclusive finance on CO2 emissions from a spatial perspective: evidence from 272 cities in China J Clean Prod 2022 355 131618
Wang QJ Tang K Hu HQ The impact of digital finance on green innovation: evidence from provinces in China Innov Green Dev 2022 1 1 100007
Wang S Sun L Iqbal S Green financing role on renewable energy dependence and energy transition in E7 economies Renewable Energy 2022 200 1561 1572
Wang X Wang X Ren X Wen F Can digital financial inclusion affect CO2 emissions of China at the prefecture level? Evidence from a spatial econometric approach Energy Econ 2022 109 105966
Xin D Yi Y Du J Does digital finance promote corporate social responsibility of pollution-intensive industry? Evidence from Chinese listed companies Environ Sci Pollut Res 2022 29 56 85143 85159
Yan B Wang F Chen T Liu S Bai X Digital finance, environmental regulation and emission reduction in manufacturing industry: new evidence incorporating dynamic spatial-temporal correlation and competition Int Rev Econ Financ 2023 83 750 763
Yang G, Ding Z, Wu M, Gao M, Yue Z, Wang H (2022a) Can digital finance reduce carbon emission intensity? A perspective based on factor allocation distortions: evidence from Chinese cities. Environ Sci Pollut Res:1–21
Yang Y Liu Z Saydaliev HB Iqbal S Economic impact of crude oil supply disruption on social welfare losses and strategic petroleum reserves Resour Policy 2022 77 102689
Yu M Tsai FS Jin H Zhang H Digital finance and renewable energy consumption: evidence from China Financial Innovation 2022 8 1 1 19
Zakharov SV, Lushpey VP, Abbasova LR, and Zhongkai S (2022) Analysis of the impact of the energy industry on the environment. In IOP Conference Series: Earth and Environmental Science (Vol. 1070, No. 1, p. 012044). IOP Publishing
Zhang D Mohsin M Taghizadeh-Hesary F Does green finance counteract the climate change mitigation: asymmetric effect of renewable energy investment and R&D Energy Economics 2022 113 106183
Zhang L Huang F Lu L Ni X Iqbal S Energy financing for energy retrofit in COVID-19: recommendations for green bond financing Environ Sci Pollut Res 2022 29 16 23105 23116
Zhang W Liu X Wang D Zhou J Digital economy and carbon emission performance: evidence at China’s city level Energy Policy 2022 165 112927
Zhang X Song X Lu J Liu F How financial development and digital trade affect ecological sustainability: the role of renewable energy using an advanced panel in G-7 countries Renewable Energy 2022 199 1005 1015
Zhao L, Saydaliev HB, Iqbal S (2022) Energy financing, Covid-19 repercussions and climate change: implications for emerging economies. Climate Change Econ:2240003
Zheng H Li X The impact of digital financial inclusion on carbon dioxide emissions: empirical evidence from Chinese provinces data Energy Rep 2022 8 9431 9440
Zheng X Zhou Y Iqbal S Working capital management of SMEs in COVID-19: role of managerial personality traits and overconfidence behavior Econ Anal Policy 2022 76 439 451 35990757
Zhong K Does the digital finance revolution validate the environmental Kuznets curve? Empirical Findings from China Plos One 2022 17 1 e0257498 35025871
Zhou J Yin Z Yue P The impact of access to credit on energy efficiency Financ Res Lett 2023 51 103472 | 37162677 | PMC10170444 | NO-CC CODE | 2023-06-22 23:15:08 | yes | Environ Sci Pollut Res Int. 2023 May 10; 30(28):72130-72145 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
37183223
27254
10.1007/s11356-023-27254-0
Research Article
Achieving green tourism through environmental perspectives of green digital technologies, green innovation, and green HR practices
Hu Caishuang [email protected]
1
Liang Miya [email protected]
2
Wang Xiaoyi [email protected]
2
1 School of Management, Guangzhou Huashang College, Guangzhou, 511300 China
2 grid.495267.b 0000 0004 8343 6722 School of Accounting and Finance, Xi’an Peihua University, Xi’an, 710125 China
Responsible Editor: Philippe Garrigues
15 5 2023
114
23 3 2023
23 4 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
This study investigates tourism growth with the role of green digital technologies and green human resource management (GHRM) in China. We applied a fuzzy analysis technique using the 130 Chinese tourism SMEs that use digital technology. The study results declared that digitalization in tourism increases automation in both the process and the final product, raising demand and quality. Moreover, green digital technologies are significant in agile innovation and tourism growth. The study’s results extended that green HRM practices have a significant role in Chinese SMEs developing agile innovation, tourism growth, and green digital innovation. These findings were confirmed by using fuzzy robustness tools. The study proposes to achieve SDGs in China’s tourism industry using primitive measures to enhance tourism growth and agile innovation based on green HRM practices and green digital technologies. Such prudent measures suggested improving green digital technologies in the Chinese tourism industry for tourism growth and agile innovation maximization.
Keywords
Green tourism
Green environmental perspective
Green innovation
Green HR practices
Green technology
==== Body
pmcIntroduction
Small businesses typically have good internal communications and a dynamic and entrepreneurial management style (Wang et al. 2022b). Small and medium enterprises have a background in exploring cutting-edge technological fields. In conclusion, innovation can be (more) efficient and effective in small businesses (Tian et al., 2022). However, a lot of small and medium enterprises lack any innovation at all. In response to this question, researchers identified SMEs’ main obstacles to innovation (Proos & Hattingh, 2022). Given its significance, there are a lot of gaps in the literature, initially, despite the consensus among academics that small and medium enterprises play a growing role in innovation (Mergel, 2016). Hence, the motivation of the study is to examine China’s tourism growth with green digital technologies and human resources management.
Moreover, small and medium-sized enterprises (SMEs) are not included in the mainstream discussion on open innovation exclusions include. Additionally, digital technology is increasingly transitioning in the manufacturing industries from an operator of marginal efficiency improvements to a key enabler and catalyst of advancement, interruption, and flexibility (Adeleye et al., 2022). The Chinese digital SMEs identified four crucial components of the detective superintendent strategy: innovation management hubs, collaborations and platforms, abilities and job growth, and regulatory framework (Cardinali & De Giovanni, 2022). This research project adds three ways to the body of publications (Song & Wu, 2022). Initially, we thoroughly explain how innovation spreads across China provinces. Researchers investigate that the spatial innovation indirect impact is not always positive but could turn negative in a certain distance interval by exhaustively measuring spatial innovation spillovers with knowledge of digital technology for SMEs production function (Sharma et al., 2022). According to Pham et al. (2019), human resource management (HRM) positively relates to employees’ knowledge, skills, and behavior. Consequently, efficient human resource management can significantly enhance employee performance and productivity. Showed that talented and innovative HR contributes to long-term development and competitive advantage in manufacturing and service-based associations by generating new economic values and improving the organization’s capacity to recruit other top-notch workers (Islam et al., 2020). Organizations that are based on production and those that are based on services have different traits and workplace policies, particularly in terms of how people behave and how they interact with tourism (Ribeiro et al., 2022).
To achieve their goals and success in competitive environments, service-based organizations should focus more on employee behavior and the service-based aspect (Haldorai et al., 2022). Internal processes must incorporate sustainable firm performance and green innovation to remain competitive in today’s market (Tanova & Bayighomog, 2022). Furthermore, manufacturers must shift their focus to digital innovation to strengthen their relationships with suppliers. Thus, green practices have a very high significant impact on green innovation in the current SME scenario, allowing manufacturers to develop competitive advantages (Umar et al., 2022). The manufacturing sector in emerging economies remains in the initial stages of adopting sustainable green practices, even though HRM practices can aid any organization in developing and maintaining a favorable reputation (He et al., 2023). Findings demonstrate that current government innovation management methods have met with a muted response from the industries, even though the manufacturing sector contributes positively to economic development in emerging economies, particularly in China. However, there was broad consensus that the government should continue to play a role in industrial innovation (Wilson & Doz, 2011).
Thus, the research aims to assess the role of green digital technology in developing innovation in the tourism industry with the intervening connection of green HRM practices in Chinese settings. By this, current research signifies five research contributions. First, the study contributes theoretically by studying the aim of the research, which has never been studied before. Previous research shows scant evidence of GHRM’s role in the tourism industry with agile innovation using the local business environment of China. To contribute to this, 130 tourism-based SMEs in China were selected. Second, the study contributed that adopting digital technologies is essential. If yes, then to what extent the green HRM has the potential to contribute? Thirdly, the results will help clarify each element of green SMEs, give valuable recommendations for SME owners to participate in digital platform innovation activities, and provide pertinent research material on digital platforms. Fourth, the results are based on the dynamic capacity theory and also demonstrate how SMEs reorganize their strategic resources to attain successful outcomes in the digital environment. Firth, the results contribute to a more excellent knowledge of DBMI adoption in a developing country. This is crucial for practitioners, especially those SMEs just starting their digital transformation journey. It might act as a roadmap for SMEs to examine green human resources more thoroughly on agile innovation management in digital technologies.
The study consists of the five main sections including the “Introduction,” the “Literature review,” the “Methodology,” the “Results and discussion,” and the “Conclusion and policy implications” sections.
Literature review
Review of studies
As fundamental research in Small and medium-sized businesses (SMEs), this technique for utilizing its resources benefits SMEs’ growth. However, it is unclear how absorptive ability and other characteristics fit into the context of green innovation uptake in SMEs. According to Zhang et al. (2022b), the HRM green management literature provides information on HRM’s benefits for organizational performance and its gradual implementation in SMEs (Burbano et al., 2022). For instance, it discovered that the implementation process for performance management was gradual, informal, and very systematic in structure for SMEs in the tourism industry. Based on past studies, we demonstrate how different stabilizing green innovation and digital policies have different consequences. The operating conditions of SMEs do not appear to be improved by fiscal performance (Ozturk et al., 2022). Our study is one of the earliest studies on using policy tools to lessen the effects of digital technology and China’s manufacturing industry on SMEs. It adds to a rapidly expanding body of literature examining the pandemic’s economic impacts (Raihan et al., 2022).
Furthermore, it was suggested that little empirical study on the uptake of green innovations had been done in developing environments. The conditions necessary to set up small and medium enterprise manufacturing firms for the digital revolution are identified in work (Yang et al., 2022). Reviewing these situations demonstrates that implementing reasonable business practices must come first to increase manufacturing agility (Triantafyllidou & Zabaniotou, 2021). The industry’s ability to access production parameter data, ideally in real-time, is the second crucial factor because appropriate smart manufacturing technological solutions can only be implemented (Lichtenthaler, 2020).
Nevertheless, the above procedure needs a decent Internet connection. In this work, the requirement for staff training was also mentioned as a critical element (Vidmar et al., 2020). The variables mentioned in this study are necessary for the automation process, which also advanced and effective smart manufacturing solutions, significantly facilitating access and staff competency (Farooq et al., 2022). Khamdamov et al. (2023) proposed a comprehensive model for evaluating the digital readiness of Italian SMEs. The literature provides information on HRM’s benefits for organizational performance and its gradual implementation in SMEs (Du et al., 2023). For example, it was discovered that the implementation process for green HRM practices was gradual, informal, and systematic for SMEs in the manufacturing and transport industries (Yue et al., 2023).
Further research has revealed that small and medium-sized businesses frequently exhibit a lower degree of complexity, which is advantageous for implementing green innovation (Elshaer et al., 2023). They may also take a more collaborative approach to ensure that HR techniques involving employees are adopted and better fit the SME setting. Furthermore, long-term technique deployment has been demonstrated to enhance SME performance (Subramanian & Suresh, 2023).
Numerous small businesses also face significant difficulties with GHRM. For instance, the closure has had a significant negative impact on the textile and apparel industries. % Of China’s manufacturing exports are textiles, food, beverages, and tobacco (Faisal, 2023). Other SMEs studying Vietnam are disproportionately affected by the decrease in demand in these sectors. Agriculture-related businesses are not helpless (Goncalves & Bergquist, 2022). The department faces many difficulties due to a lack of transportation and human resources. Several local drivers, including train drivers, transport operators, and cab drivers, were also sent home (Kokol et al., 2022). Store closings and disruptions in the nationwide supply chain impact transportation, warehousing, communications, wholesale, and retail services. As a result, policy suggestions for the government’s efforts to lessen the effects of both big and small businesses ceasing operations can be made. Our results align with more disaggregate methodologies, which place more emphasis on intra-sector links than firm-specific shocks. The transmission of shock from individual enterprises to average variations is stronger and more intense the higher the green digital innovation indicator, which also tries to measure the accumulation of sales (Martínez-Velasco & Terán-Bustamante, 2022). Corporation disruptions, in particular, the low concentrated company is highly focused sectors—organization-to-organization covariance concepts, which could be interpreted as evidence of connections—contribute more to the overall volatility of the European economy than shocks (Trad & Kalpić, 2022).
Many solutions have been implemented for SMEs during the COVID-19 crisis, including an adaptation of marketing strategies, development and repositioning (coping) strategies, learning, and technology solutions (Kumar et al., 2022). The effectiveness of crisis management strategies is a topic that interests us particularly. According to Cooke et al. (2021), classifying crisis behaviors among startups, this paper proposed an excellent four-branch categorization based on the policy literature. They looked into retraction strategies, which involve cutting costs, maintaining operational activities to maintain the status quo, taking part in innovative strategic renewal, and taking advantage of opportunities presented by the crisis for businesses, primarily newly founded enterprises. Each of the complementary strategies has advantages and disadvantages of its own. Every technique has a specific or Broadway application.
Furthermore, relevant issues in SMEs have been largely ignored. The research on GHRM in SMEs is somewhat limited compared to many studies on large corporations. In light of this, the paper seeks to correct some of the disproportions in the literature by focusing on the role of green HRM in agile innovation management in SMEs (Basha & Kethan, 2022).
The conceptual model is a smart way to define analysis arguments and claims in the following discussion (Liu et al., 2022). We concentrate on why and how SMEs competing with one another are likely to collaborate for technological innovation because, as previously mentioned, the concept of co-opetition is comprehensive. It is crucial to note that technological innovation is the backdrop for this conceptual growth (Nitoslawski et al., 2019). The conceptual framework and recommendations are primarily related to the industry context, SMEs, and the requirement to build innovation capacity and successfully market innovations (Hao et al., 2023). The multilevel conceptual model created for this paper is shown in Fig. 1. A multilevel model is beneficial since it enables an integrated investigation into the subject and offers a broader and deeper portrayal of organizational phenomena (Schulz & Feist, 2021). A model like this enables us to examine co-complex, larger medium-sized firms and their contradictory nature more thoroughly. Figure 1 shows that the primary forces driving the founder in a given market are rising GHRM, shortening product life cycles, and technological convergence.Fig. 1 The fuzzy input structure of green technological innovation with tourism growth
Theoretical framework
Research on the factors contributing to tourist development has shown that green HRM is crucial. Li et al. (2021) argues that the tourist industry can't function without the Chinese business network. Agile innovation is further defined by the relationships between the many organizations operating there in competition and collaboration. The development of green digital technologies has altered the operation of tourism firms (Petrick 2004). Since most Chinese tourism companies now have an internet presence, tourists have difficulty choosing between the many green digital technologies. This system modifies the traveller’s outlook on unfamiliar destinations, potential dangers, character features, and mental state of mind. Therefore, the research must justify these connections to communicate agile innovation and tourism growth (Fig. 2). The current study intends to evaluate the interaction impacts of tourism growth, agile innovation, green HRM, and green digital technologies, all against the background of stimulus tourism reactions. In addition, steps have been taken to reduce the propensity of consumers to be confused, which might discourage them from travelling to unfamiliar places during times of high unpredictability. According to Gorji, Garcia, and Mercadé-Melé (2023), creating a unique and unforgettable experience is the most important strategic goal for the tourism industry’s future development. These parts complement one another and are necessary for a successful vacation. However, research on tourism and repeat visits. The model of study is elaborated in Fig. 1. Even though tourism literature often examines the connection between physical and intangible variables of equal significance, the aspects that matter to tourists—their happiness and their desire to return—are seldom examined (Zhang et al. 2022b). Moreover, some writers take a systems approach to the topic of tourism. “Agile tourism innovation” is fundamental to the way tourists evaluate attractions in the tourism literature, which defines them as “the qualities of personality features related to a location”.Fig. 2 Fuzzy input-put structure of green HRM with tourism growth
Methodology
Study data
China is the largest tourism industry and an economy that can withstand tourism development for tourism sustainability. Notwithstanding, despite consistent efforts, China needs to focus on more robust improvement of tourism, green HRM, agile innovation, and digital technologies. Thus, to investigate recent topicality, this research obtained the data through structured interviews of 10 tourism industry experts and nine top managers serving in different provinces of China, including Sichuan, Guangdong, Anohi, Beijing, Jilin, Tianjin, Chongqing, and Henan. The study obtained data from Chinese tourism and related databanks.
Measurement model
The empirical measurement of the study measures starts with green digital technologies as below:1 uC(t)uN(t)=RtPtWt
where t is the period, c indicates the green digital technologies, R shows the rate of revisit intentions, w is the weights, and P is the probability of occurrence for Chinese tourists. Extending to it Nt indicates the tourism industry labour units, and Yt = AtNt. This is the total output function of agile innovation of the tourists via destination visit awareness in study contexts. By this, Equation (1) is extended into a better way as2 WtNtRt≤St
In Equation (2), R indicates the gross rate of tourist return toward the tourism growth, S shows the green HRM, and W is the allocated weights with N time-period. Thus, Equation (3) about the study model is as follows, which indicates the tourist’s limit for green digital technologies in China based on study variables.3 Πtf=PtYt-WtNt-Rtl1-τtl-RtSt
4 PtAt=Rtl1-τtlRtWtandAtNt=Rtl1-τtlStPt
Thus, endorsing Equation (4), the rate of agile innovation after the enhancement of green HRM is computed with the following hypothesis, in which the Rt is the green digital technologies scored and constrained with the agile innovation of the tourism SME companies,5 H0:Rtl1-τtl≥Rt
6 H1:Rtl1-τtl
Fuzzy-based estimation technique
The study utilized the fuzzy envelope methodology to draw inferences about green digital technologies and green HRM practice’s role in agile innovation and tourism growth in China. However, using fuzzy envelop methodology includes two action approaches: fuzzy-based HFLTS model estimation and the HFL-AHP method for analysis.
Applying fuzzy-based HFLTS modeling
Torra (201) proposed the Hesitant fuzzy set (HFS) that holds two extensions, including HFLTS model estimation and the HFL-AHP method. Several values between zero and one may be assigned to the degree of membership of an element in HFSs. Scientists who must deal with a great degree measure the HFLTS model from Equation (8) onwards,8 E=<x,hE(x)∣x∈X
9 hM:M→0,1
Here the M = {μ1, μ2, …, μn} is a sort of associative function of n, whereby the HFS has been linked with E and U, respectively. However, hM is further formulated as10 hM(x)=⋃μ∈Mμ(x)
In Equation (11), S is distinct with the term S = {s0, …, sg}.11 EGH:SIl→Hs
12 EGHsi=silsi∈S
An HFLTS, denoted by Hs, is a sorted, finite subset of the sequential linguistic words in the set S.13 EGHatmostsi=sj∣sj∈Sandsj≤si
14 EGHlessthansi=sjsj∈Sandsj<si
15 EGHbetweensiandsj=sk∣sk∈Sandsi≤sk≤sj
16 envHS=Hs-,Hs+,Hs-≤Hs+
GH is a context-free grammar that makes use of the linguistic term set, and EGH is a function that converts word expressions into HFLTS and HS, which is further measured by Equations (13), (14), (15), and (16).
HFL AHP methods
Two primary benefits to the model of green digital technologies in tourism result from employing the clichés. Because this approach enables DMs to articulate their thoughts utilizing transfer functions, using fuzzy sets, including reluctance, facilitates the judgment call approach. Also, the model’s vital flexibility allows various language expressions to be chosen. This is advantageous for maintaining the original intent of the phrases throughout the spontaneous adaptation. Because of this capability, HFLTS is often preferred over other methods when many variables must be considered using Equations 17 and onwards.17 Fa1,a2,⋯,an=wbT=∑i=1nwibi
Using phrases in Table 2, DMs organize comparison matrix mixtures, and HFLTS provides tradeoff ratings. The OWA function is used to accumulate and construct the HFLTS fuzzy envelopes. This is the pairwise comparison matrices (C) you created where c̃ij = (cijl, cijm1, cijm2, ciju).18 c~ij=1ciju,1cijm2,1cijm1,1cijl
Every matrix used for evaluation between pairs gets checked for construct validity. Some vectors are then de-fuzzified to ensure their precision, and these are developed in Equations (19) and (20), (21), and (22).19 μd=l+m1+m2+u6
20 R∼=r~ijm×n
21 r~ij=yij-yi-yi+-yi-,j∈B
22 r~ij=yij-yi+yi+-yi-,j∈C;
To categorize the options, we also determine the coefficients of the criterion functions as averages of the distances out from the borders mathematical model. By summing together each column of cofactors, we can determine the score of the choices’ similarity measure.23 xij=Uxij-Lxij+Mxij-Lxij3+Lxij
This research combined the HFL MABAC technique with the fuzzy enveloping approach. HFL AHP is again utilized in this mixed approach. Inheriting the novel concept that HFS permits to employ of numerous parameters associated with representing the amount toward which components make up a particular set, HFLTS stands out among many other approaches. When paired with the fuzzy envelope method, many idioms accurately convey the ideas of the consulted expertise. This technique provides DMs with a lexical pool that is both expansive and easily compared. When evaluating people, employing language that considers their natural reserve is helpful. Thus, a recent study intends to test the relationship between green HRM, tourism growth, agile innovation mechanisms, and green digital technologies. Increased investment for alternatives and capacity development to engage in green HRM for tourism development facilities with long-term durability are needed with the emergence of agile innovation. Therefore, agile innovation also makes it easier to use cutting-edge technologies for green and clean production, which boosts the Chinese tourism industry. An increase in green HRM development has a favorable effect on the natural world. In this research, the GTI is the green technological innovation, TG is the tourism growth, AI is the agile innovation, and GHRM is the green human resource management practices.
Results and discussion
Reliability and model fit analysis
The study findings highlighted that two levels of competitiveness in Chinese tourism cause green digital technologies. From a macroeconomic standpoint, increased competitiveness is a global tourism industrial issue in China, including digital technologies, green HRM, and agile innovation. According to this, these advantages must be considered and integrated into the fabric of a vacation spot development strategy if it wishes to be competitive. The comparative and the main agile innovation are often distinguished in destination planning. Tourism growth refers to resource availability, while competitive advantage refers to a destination’s ability to use resources efficiently. When evaluating the competitive potential of agile innovation, Table 1 reveals that the need to consider new tourist destinations for visits gives the tourists the luxury experience advantage when calculating their capacity to leisure with other tourist sites using green digital technologies. The study results gained the concern of the researchers to explain the tourist’s visit intentions too. Such visit intention is the potential for tourists to plan a visit to a specific location in Chinese tourism. The results further described the intention to visit as the likelihood that tourists will travel to a particular area (Table 2). Hence, agile innovation and green HRM factors have a significant connection. Moreover, these results are valid and reliable (Table 1).Table 1 Reliability and validity of measures
α CR AVE HTMT VIF
Green digital technologies (GDT) 0.716 0.872 0.976 0.796 1.25
Tourism growth (TG) 0.759 0.902 0.828 0.737 1.61
Agile innovation (AI) 0.854 0.904 0.8515 0.869 1.09
Green HRM (GHRM) 0.731 0.743 0.718 0.856 1.16
Table 2 Model fit estimates
Parameters Measurement model Structural model Significance
X2 206.123 427.48 0.002
CFI 0.853 0.834 0.000
AGFI 0.865 0.994 0.006
NFI 0.745 0.763 0.001
TLI 0.703 0.791 0.003
GFI 0.814 0.776 0.002
RMSEA 0.048 0.007 0.007
PCLOSE 0.084 0.066 0.006
Tourist satisfaction at a destination is priceless to the tourism sector. The results further highlighted that this perceived risk must be reduced, and the green digital technologies of the tourist are found to be empirically significant with new tourism destinations (Table 3). Importantly, tourists are influential business stakeholders, and their view of CC affects how they feel about and rate places. Proactive social responsibility practices enhance tourists’ perceptions of the new destination.Table 3 Pairwise comparison values and normalized weights of the main factors
GDT TG GHRM AI Mean Relative score
GDT (1,9,4,5) (6,7,2,6) (3,4,1,6) (5,1,3,8) 0.397 0.825
TG (1,4,1,6) (2,8,7,6) (1,8,4) (2,6,8,5) 0.897 0.305
GHRM (2,1,2) (3,5,7,4) (1,5,9,1) (2,7,1,5) 0683 0.646
AI (1,1,7,6) (7,4,2) (3,8,7,1) (5,7,1,4) 0.099 0.676
C (3,5,0,4) (7,4,3) (1,8,1,4) (4,3,5,7) 0.487 0.224
The study shows that the buoyant rise in tourist visit intentions and the unique destination visits are the positive signs highlighted by the green digital technologies and tourism growth (Table 4), while the reduction in the level of persisting agile innovation. The tourist’s motivations are empirically verified, leading to a negative geographic location assessment (Table 9).Table 4 Normalized weighted estimates of fuzzy-based HFLTS
Sub-factors Relative score Predictive score Priority score Fuzzy value Normalized weights
w1 (1,3,7,6,3) (2,5,79,1) 0.6189 0.099 0.059
w2 (5,8,4,7,5) (2,8,4,7) 0.048 0.067
w3 (1,1,4,8,2) (7,2,4,1) 0.045 0.096
w4 (2,2,5,9,6) (3,4,6,2) 0.095 0.055
w5 (8,3,7,3) (4,2,1,4) 0.037 0.082
w6 (3,1,4,2,2) (3,3,7,6) -0.054 0.023
T1 (5,8,3,9,4) (8,7,6,1) 0.5371 0.068 0.022
T2 (1,5,3,3,2) (4,2,8,6) 0.063 0.057
T3 (7,3,3,6,2) (2,8,4,3) 0.089 0.081
T4 (9,2,6,8,4) (2,4,6,6) 0.038 0.047
T5 (2,,4,7,1,5) (6,8,3,8) 0.032 0.094
T6 (5,1,4,6,7) (2,2,5,1) 0.053 0.094
s1 (4,3,2,7) (8,6,1) 0.7079 0.068 0.058
s2 (1,5,5,3) (5,2,6) 0.015 0.023
s3 (9,7,9,1) (7,6,7,1) 0.062 0.028
s4 (9,3,2,4) (4,6,7,9) 0.035 0.065
s5 (2,9,2,2,1) (7,6,5,8) 0.011 0.046
s6 (1,6,4,5,1) (8,1,2,6) 0.048 0.063
Tourists who believe that a destination’s ultimate objective is tied to its interests (such as boosting profits) are said to have a selfish motive, which is consistent with the instrumental goal of a destination for tourism (Chang et al. 2023). Chinese travellers will be sceptical about their personality traits, especially neuroticism, and thus produce a negative response (i.e., negative word-of-mouth, unfavorable attitudes, and lower interest in visiting a destination) about new location visit intentions if they believe it has a selfish motive. By expanding China’s tourism industry and using its resources sustainably, green digital technologies can increase its prosperity (Table 5). The neighborhood and long-term economy will help both gains from this. Chinese tourism can give the periphery of society a sense of community and inclusion. Our results indicate that consumer confusion and green digital technologies toward tourism visit intentions significantly contributed. In fact, given that perceived risk dynamics also influenced considerably.Table 5 Fuzzy-based HFLTS estimates score for strategies development
SI Defuzzified score Ranking
Green digital technologies 0.1227 0.6415 0.3338 5
Tourism growth 0.2207 0.0062 0.0395 2
Agile innovation 0.5124 0.0787 0.1317 9
Green HRM 0.1897 0.2493 0.2846 1
Fuzzy-based HFLTS estimates
After waiting, people want to make the unpleasant experience justifiable by increasing the perceived value of the result. In this research, we hypothesize that potential tourists prefer to boost the perceived value of destination tourism products to justify the sunk cost of waiting time they have already paid. As research suggests (Bilal et al. 2022), their visit intention increases as a result since they increase their trust in the location.
Tourism researchers reached the same conclusions and found that people are more likely to consider information valuable and credible when they have waited a while to obtain it, which increases their propensity to base decisions on it, even to the point of making choices they deem. As a result, we suggested that trust acts as a mediator between material sunk costs and tourist visit intentions (Table 6). Understanding the massive potential of the combination of sports and tourism, especially sports tourism, is crucial to restoring local economies.Table 6 HFL AHP scale estimates
Scale si Abb TFN
High important s1 CC1 (2,3,4)
Extremely high important s2 CC4 (5,6,2,9)
Essentially high important s3 CC6 (1,4,1)
Weakly high important s4 PR2 (4,9,4,8)
Equally high important s5 PR3 (3,9,8,5)
Exactly low important s6 PR4 (6,6,4,2)
Equally low important s7 NTD1 (3,7,6)
Weakly low important s8 NTD4 (1,6,9,8)
Essentially low important s9 NTD5 (3,6,6,9)
Extremely low important s10 TRI1 (1,1,4,8)
Low important s11 TRI2 (2,1,8,6)
The idea has been accepted mainly as a vehicle for economic growth. This may have the advantage of fostering civic engagement, supporting local culture, and maintaining cultural traditions, all of which contribute to a strong sense of ethnic heritage and social identity and a sense of national identification (Table 4).
Tourism businesses in China usually exchange expertise and enhance their operations through cooperation (Zhao et al. 2022). According to study findings, such benefits result from lowering visit intentions and creating tourism development. Therefore, joining a destination of tourism network resulted in significant competitive benefits by organizing and merging tourism business links. Researchers have worked to develop destination-in management models that facilitate information sharing between businesses, fostering more cooperation (Table 7). For the destination competition, the knowledge transfer process is essential and crucial to assuring the field’s dissemination of innovation, especially in contexts characterized by tiny-sized businesses (Zheng et al. 2022). Constant communication between commercial and public institutions is critical for the tourism industry to provide diverse experiences, goods, and services at the destination level, which visitors view as a holistic and inclusive experience (Ahmad et al. 2022).Table 7 Estimated matrix risk factors based on HFL AHP modelling
Ri Cj М Wei Wej T-value P-value
GDT 2.1176 0.0767 0.6299 0.4891 0.0746 0.6863 -0.0073
TG 2.1053 1.1755 0.3598 0.0014 0.1291 0.2561 0.0055
AI 1.0752 0.0104 0.8033 0.8752 0.0688 0.2342 0.0906
GHRM 2.8775 0.0067 0.8999 0.0095 0.0531 0.0021 0.0161
As a result, the tourism industry is compelling companies that serve tourists to encourage extensive cooperation and cooperation. Increased coordination and tourism supply integration could lead to greater satisfaction of tourism demand, which is necessary to deliver unique experiences at the destination level and monitor related growth activities (Sun et al. 2022).
Tourists’ worries, anxieties, and other feelings related to perceived risks can alter how they perceive hazards. It should be emphasized that "perceived risk" is frequently one of the multidimensional tourist risk factors. The domain of tourism and hospitality acknowledges that psychological and cognitive factors influence visitors’ conduct after making the wrong purchasing decision. According to findings, current research investigation and evaluation of subjective and objective factors that affect tourists’ perceptions of danger is what a tourism risk perception assessment is all about. A study of the characteristics and elements that influence how tourists perceive risk explained the creation and weighting of an evaluation model for perceptions of tourism risk. The effect of statistical factors on tourists’ perceptions of risk is significant. The two primary methodologies for assessing tourism risk for the tourism system are risk expected evaluation and tourists’ perception of risk (Wang et al. 2022a). The former involves evaluating the risk associated with tourism based solely on reasonable expectations, disregarding the value of tourists. The latter considers tourists’ subjective experiences
HFL AHP estimates
Those in charge of managing and developing tourist attractions are continuously looking for ways to make them more appealing in an environment that is becoming more and more competitive. Different tourists in China have undertaken to evaluate the attraction of travel destinations. Main elements that may help in the development of successful plans while taking into account the available resources (Table 8). Goals, marketers, and travel agencies should prioritize both criteria equally while setting development objectives to improve visitors’ intentions to return.Table 8 Estimated matrix of HFL AHP modelling
Sub-factors Matrix score t-value Significance
w1 (1,5,8,4) 0.044 0.002
w2 (1,5,6,2) 0.033 0.001
w3 (1,1,7,4) 0.029 0.006
w4 (8,7,4,1) 0.089 0.003
w5 (1,3,6,6) 0.076 0.006
w6 (7,1,9,8) 0.071 0.011
T1 (2,1,3,5) 0.066 0.018
T2 (4,4,9,5) 0.038 0.045
T3 (2,6,5,7) 0.034 0.042
T4 (1,9,6,2) 0.069 0.001
T5 (8,9,7,3) 0.048 0.002
T6 (2,7,4,3) 0.055 0.010
s1 (9,3,3,2) 0.033 0.005
s2 (2,2,7,7) 0.072 0.004
s3 (4,2,6,1) 0.056 0.001
s4 (9,1,1.6) 0.095 0.014
s5 (1,1,6) 0.043 0.011
S6 (1,8,9,9) 0.058 0.0049
The factors above are dispersed over a range of product levels (such as macro or micro level) and throughout a variety of tourism (sub) sectors in terms of management or practical approach (e.g., transport, hospitality, and travel agencies). In addition, though some of these factors (such as local hospitality and security) fall outside the purview of managerial control, local governments and destination management organizations may be able to manage or (dis) stimulate these factors indirectly. A more comprehensive, integrated, and destination-level approach is recommended for overall service quality and complete tourist happiness.
Furthermore, significant external and internal consumer behavior factors should be considered due to the dissimilar and (in) direct ways they influence tourists’ happiness, propensity to act, and the likelihood of future visits. Nothing is fixed except for visitor satisfaction’s dynamic, variable, and complicated nature; it can be inferred from such a broad environment (Zhang et al. 2022a). This fact calls for a more in-depth, qualitative analysis of tourist behavior, which offers practitioners and academics a new chance to develop, use, and create models of tourist satisfaction understanding that are more effective and efficient (Yang et al. 2022). From a practical standpoint, this may be particularly significant and intriguing because it could develop the managerial capacity to find the ideal quality/price ratio to guarantee a specific degree of tourist pleasure. The effects of homelessness on destination representations have received minimal attention from tourism experts compared to the importance of healthy diets and cost living arrangements. Moreover, the convergence estimates of study estimates are also found to be robust.
Robustness analysis
Endorsing such findings, Ritchie and Crouch (2003) explained that tourists rely on their interpretation of whether a destination can meet their needs when visiting new green digital technologies in tourism growth. Tourism growth attractiveness is usually determined by multidimensional attributes that can attract visitors (Table 9). A destination’s magnificence and choice depend on its ability to meet visitor demand. Therefore, studying visitors’ destination selections requires a thorough grasp of the place’s attractiveness (Table 10). Specific destination characteristics impact visitors’ decisions, yet the significance of each attribute depends on agile innovation and green human resource management.Table 9 Robustness analysis evaluation factors
Indicators Matrix score T-value Weights
S11 0.0464 0.0707 0.2826
S12 0.0542 0.0929 0.4907
S13 0.1001 0.0195 0.3871
S21 0.0061 0.0589 0.0333
S22 0.2283 0.0131 0.5943
S23 0.6062 0.0287 0.9544
S31 0.0453 0.0438 0.1927
S32 0.4521 0.0647 0.3495
S33 0.1202 0.0013 0.7489
Table 10 Province-wise robust evaluation results
Parameters Provinces Matrix results T-value Ranking
β1 Sichuan 0.0227 0.0219 7
β2 Guangdong 0.2291 0.6266 4
β3 Anohi 0.7955 0.0468 9
β4 Beijing 0.6556 0.1214 14
β5 Jilin 0.1526 0.8858 10
β6 Tianjin 0.0928 0.0348 2
β7 Chongqing 0.0085 0.0565 8
β8 Henan 0.5146 0.0692 16
Risk score of FHL AHP 0.6368
Risk level
R 0.1794
M 0.1929
Earlier research has already identified several destination characteristics. The characteristics most frequently taken into account in these studies relate to both the nature and attraction of the culture, as well as tourist facilities and services (such as lodging and transportation). Various works of literature also consider the quality of the green digital technologies based on agile innovation, tourism growth, and green HRM. The robust results of the study are also reported.
According to the findings of this study that certain contextual and causative factors raise a destination’s likelihood of becoming a famous agile innovation and green human resource management of green digital technologies and tourism growth even though the number of unique destination-visiting individuals has been steadily increasing in many tourist hotspots destinations (Tu et al. 2021). The study finds that absorptive ability significantly impacts SMEs’ adoption of sustainable capabilities and green innovation. The findings in Table 5 broadly corroborate the idea that companies that care about the environment typically command higher market valuations. Table 6 presents the test results. The dummy variable is a constant at the company level that does not change over time. If a company is not in a highly polluting industry, the industry’s high pollution indicator does not appear in all regressions with firm fixed effects. It has the worth of small and medium-sized businesses instead (Iqbal and Bilal 2021). The SMEs *HRM interaction effect coefficient is 0.0343*** (5.290) at the 5% significance level.
This demonstrates how the industrial industries have an impact on green innovation. One of the main findings of this study is the significance of sustainable orientation, which has the most considerable influence on the adoption of green innovations, accounts for roughly 63.3% of the effect of knowledge transfer on this adoption, and influences other sustainable capabilities. Due to its significance in fostering tourists’ perceived risk, tourists’ satisfaction is the most important goal in tourism’s marketing theory and practice. Tourist pleasure and the desire to return are understudied, even though tourism literature often studies the relationship between tangible and intangible factors that have separate importance. Additionally, several authors look at tourism from a systems perspective. For tourist attractions in the context of tourism literature, “personality traits” belongs to the tourist’s primary thinking process and it is described as “the characteristics of personality attributes connected with a place.” Marketing organizations (DMOs) of destinations are engaged in a never-ending struggle to draw tourists as the rivalry among tourism locations becomes increasingly substitutable and equal (Iqbal et al. 2021). Destination personality is viewed as a workable metaphor for developing destination brands and creating a distinctive character for tourism sites as destinations for travel become increasingly interchangeable due to increased competition in global tourism markets. For tourism promotion, planning, and growth to go smoothly, it is crucial to identify consumption patterns, socioeconomic features, and desirable visitor activities. Hence, the research model is constructed based on the theoretical framework
Discussion
The managers of SMEs should be aware that using outside knowledge to develop environmentally friendly products or processes across several operating divisions is insufficient. Some authors have stated that SMEs are better positioned to rely extensively on external information to boost innovative success because of their adaptive management techniques and lack of bureaucracy. However, the company’s apparent sustainable attitude ought to assist this effort. The first step for SME managers should be acknowledging the significance of sustainability concerns affecting their organizations and identifying the crucial long-term green policy that optimizes their stakeholders’ environmental practices and human resource management. Absorption capacity enables SMEs to be more environmentally inventive if a green innovation plan anticipates and assesses future environmental changes. We aimed to provide ideas for the transport infrastructure to be digitalized and integrated into the digital economy. The article supports the need for the advancement of transportation infrastructure in the Arctic, examines global trends in the modernization of transportation systems, and offers illustrations of how innovative city technologies are used in extremely cold towns. As a consequence of the study, the idea of a digital platform for city transportation infrastructure is put out, helping to accelerate the area’s growth in the digital economy.
The findings also demonstrate that the analysis supports the notion that indirect organizational elements affect an SME’s impression of innovation and supports the influence of the industry in which the SME operates on this view. Technological competence ( = 0.099, t = 1.43, ns) and technology infrastructure ( = 0.021, t = 0.29, ns) had no discernible effects. H1A and H1B are thus not supported. We discovered that environmental drivers help maintain manufacturing in SMEs to some extent thanks to the considerable impact of environmental pressures (= 0.392, t = 6.89, p 0.001). This gives hypothesis H2a more strength. Unexpectedly, we discovered a weak and negligible impact of environmental laws on sustainable manufacturing in SMEs (= −0.099, t = 1.41, ns), which means that hypothesis H2b is unsupported. Organizational factors significantly influence sustainable manufacturing, which helps explain a lot of its variation.
The mean values of direct tourism, indirect tourism, new products, larger medium-sized firms, smaller medium-sized firms and green innovation are (7.457, 5.765, 5.456, 8.345, 8.345, and 83.211), respectively. The statistical mean results of sustainable collaboration, firm characteristics, total assets, and green capability are (−3.300, 0.055, 453.765, and −2.089), respectively. According to [56], managers may achieve high returns and wide-ranging market segments by effectively using organizational procedures and resources. This is because of the collaborative relationships between businesses and their partners in the environment. The impact of digital transformation on SMEs’ input and transportation expenses is significant. Economies with high GI or extensive digital adoption enable SMEs to lower operating expenses. It is reasonable to assume that most of these studies were done in a setting of a developed economy where specific green goods or processes are often targeted for developing green knowledge and skills backed by an appropriate organizational environment and confidence.
Additionally, the importance of green human capital may be more evident in international organizations where green environmental behaviors and activities are closely watched, and green leadership is needed. However, managers of SMEs in developing economies may actively encourage their staff to participate in customized green innovation training and instantaneous learning programs. This will equip them with the knowledge and abilities to spearhead enterprise-specific green innovative solutions and deal with the unavoidable sustainable changes in technologies, consumer preferences, and market conditions.
Third, the findings support earlier studies on the substantial influence of organizational variables on SMP. For SMEs, the successful implementation of sustainable initiatives depends on employee involvement and senior management support. Because local governments in developed nations adopt a dialog-based rather than a control-based approach regarding sustainable programs at work, as in the case of adopting environmental health and safety schemes in Denmark, employees of SMEs have significant influence over sustainable practices. However, this influence can be interpreted differently in the context of a developing nation. Most SMEs in Egypt are conducted as sole proprietorships, without legal separation between the individual and the company. As a result, managers are likely to impact the corporate culture and the adoption of sustainable practices.
Additionally, the small staff size, more superficial organizational structures, and casual working environment encourage information sharing among all team members and increase their participation to make choices about sustainability initiatives quickly. Although China SMEs lack the resources and technology needed to grow, their managers and workers frequently recognize and have a significant impact on SMP. Fourth, the results are consistent with our theories on how SMP affects competitive skills. The results support earlier studies on the effects of sustainable practices on price, quality, flexibility, and delivery. Although SMEs in developing nations face various obstacles, SMP frequently proves helpful in fostering competitive performance, which can help lessen these obstacles’ long-term impact. This follows, who found that businesses in developing nations are more impacted by environmental activities than those in industrialized countries regarding quality, cost, flexibility, and delivery. This is also consistent with the NRBV (Green et al. 2005; Wang et al. 2021), which links environmental initiatives strongly to competitive advantages, which SMEs’ adaptable nature and structure can facilitate. Environmental initiatives are related to product design, production processes, packaging, quality control, delivery, and other areas.
Conclusion and policy implications
The study tests the interplay between tourism growth, agile innovation, green digital technologies, and green HRM nexus in China. The study results showed a significant nexus among the variables, including agile innovation, green digital technologies, and green HRM. To solve this, our results confirmed that (a) green HRM is positively linked to agile innovation, (b) green digital technologies are positively linked with tourism growth, and (c) moreover, agile innovation is positively linked among green HRM, tourism growth, and green digital technologies. The sorts of memories and the qualities of memories have yet to be thoroughly examined in tourism literature, despite the extensive research on tourist experiences and memories. Most of this research has focused on happy experiences; meanwhile, the roots of terrible memories have received much less attention until lately. By concentrating on bad tourist memories and their contributing factors, this study contributes to the literature on recollection in tourists. Equally happy and sad thoughts of travel may make you feel diverse emotions, although the good ones have a little more staying power. Unfortunately, people’s horrible vacation experiences are still fresh in their minds. As a result, our study has the potential to expand our understanding of tourist remember, contribute statistical results to the existing research, and demonstrate the importance of exploring tourism remembrance from various perspectives. The bases of the study also suggested the practical implications.
Companies in the tourism business always work to improve their commercial standing by increasing the allure of tourism destinations at popular destinations. Tourist businesses often work to create one-of-a-kind adventures for their clients. In light of the results of this study, tourist businesses and marketers should capitalize on pleasant feelings associated with past trips that encourage repeat visits. Given the value of vacation recollections, we advise enterprises to enhance their evaluation studies to get more actionable information for better customer relationship management. In addition, marketers may improve their assessments by questioning visitors’ memories of tourist attractions (i.e., a verbal memory assignment; three pillars of recollection positivity).
Furthermore, utilizing information gathered from client satisfaction questionnaires, tourism destinations may create mental images of pleasant experiences. Respondents remembered both broad and detailed aspects of past travel experiences. Given this, tourist companies may use the comprehensive episodic and semantic parts of recollections to create more engaging hospitality goods and marketing campaigns.
Authors’ contribution
Conceptualization, methodology: Caishuang Hu; writing—original draft: Miya Liang; data curation, data analysis, interpretation: Xiaoyi Wang.
Funding
This work was supported by the 2022 Guangzhou Huashang College Tutorial System Research Project (2022HSDS30). This research was supported by a grant from the Guangzhou Huashang College (2020HSCXK05).
Data availability
The data that support the findings of this study are openly available on request.
Declarations
Ethical approval and consent to participate
The authors declared that they have no known competing financial interests or personal relationships, which seem to affect the work reported in this article. We declare that we have no human participants, human data, or human issues.
Consent for publication
We do not have any individual person’s data in any form.
Competing interests
The authors declare no competing interests.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Adeleye BN Ola-David O Jamal A Sankaran A Moderation analysis on tourism–growth–emissions nexus in South Asia J Policy Res Tour 2022 14 1 47 62
Ahmad B Iqbal S Hai M Latif S The interplay of personal values, relational mobile usage and organizational citizenship behavior Interact Technol Smart Educ 2022 19 2 260 280 10.1108/ITSE-01-2021-0016
Basha SM Kethan M Covid-19 pandemic and the digital revolution in academia and higher education: an empirical study Eduvest-Journal of Universal Studies 2022 2 8 1 648 10.59188/eduvest.v2i8.553
Bilal AR Fatima T Iqbal S Imran MK I can see the opportunity that you cannot! A nexus between individual entrepreneurial orientation, alertness, and access to finance Eur Bus Rev 2022 34 4 556 577 10.1108/EBR-08-2021-0186
Burbano DV Valdivieso JC Izurieta JC Meredith TC Ferri DQ “Rethink and reset” tourism in the Galapagos Islands: stakeholders’ views on the sustainability of tourism development Ann Tour Res 2022 3 2 100057
Cardinali PG De Giovanni P Responsible digitalization through digital technologies and green practices Corp Soc Responsib Environ Manag 2022 29 4 984 995 10.1002/csr.2249
Chang L Iqbal S Chen H Does financial inclusion index and energy performance index co-move? Energy Policy 2023 174 113422 10.1016/j.enpol.2023.113422
Cooke P Nunes S Lazzeretti L Oliva S The digital envelope: from ‘fashion city’ to digital ‘green influencer’ to ‘new greener cities’ after COVID 2021
Du L Razzaq A Waqas M The impact of COVID-19 on small-and medium-sized enterprises (SMEs): empirical evidence for green economic implications Environ Sci Pollut Res 2023 30 1 1540 1561 10.1007/s11356-022-22221-7
Elshaer IA Azazz A Fayyad S Green human resources and innovative performance in small-and medium-sized tourism enterprises: a mediation model using PLS-SEM data analysis Mathematics 2023 11 3 711 10.3390/math11030711
Faisal S Green human resource management—a synthesis Sustainability 2023 15 3 2259 10.3390/su15032259
Farooq R Zhang Z Talwar S Dhir A Do green human resource management and self-efficacy facilitate green creativity? A study of luxury hotels and resorts J Sustain Tour 2022 30 4 824 845 10.1080/09669582.2021.1891239
Goncalves D Bergquist M How startups utilize organizational adaptability in digital innovation Proceedings of the 55th Hawaii International Conference on System Sciences 2022
Gorji AS, Garcia FA, Mercadé-Melé P (2023) Tourists’ perceived destination image and behavioral intentions towards a sanctioned destination: comparing visitors and non-visitors. Tourism Management Perspectives 45:101062
Green H Facer K Rudd T Dillon P Humphreys P Personalization and digital technologies 2005 Bristol: Futurelab
Haldorai K Kim WG Garcia RF Top management green commitment and green intellectual capital as enablers of hotel environmental performance: the mediating role of green human resource management Tour Manag 2022 88 104431 10.1016/j.tourman.2021.104431
Hao X Li Y Ren S Wu H Hao Y The role of digitalization on green economic growth: does industrial structure optimization and green innovation matter? J Environ Manage 2023 325 116504 10.1016/j.jenvman.2022.116504 36272290
He Z Kuai L Wang J Driving mechanism model of enterprise green strategy evolution under digital technology empowerment: a case study based on Zhejiang Enterprises Bus Strategy Environ 2023 32 1 408 429 10.1002/bse.3138
Iqbal S Bilal AR Energy financing in COVID-19: how public supports can benefit? China Finance Rev Int 2021 12 2 219 240 10.1108/CFRI-02-2021-0046
Iqbal S Bilal AR Nurunnabi M Iqbal W Alfakhri Y Iqbal N It is time to control the worst: testing COVID-19 outbreak, energy consumption and CO 2 emission Environ Sci Pollut Res 2021 28 19008 19020 10.1007/s11356-020-11462-z
Islam MA, Jantan AH, Yusoff YM, Chong CW, Hossain MS (2020) Green Human Resource Management (GHRM) practices and millennial employees’ turnover intentions in tourism industry in malaysia: moderating role of work environment. Glob Bus Rev
Khamdamov A Tang Z Hussain MA Unpacking parallel mediation processes between green HRM practices and sustainable environmental performance: evidence from Uzbekistan Sustainability 2023 15 2 1434 10.3390/su15021434
Kokol P, Blažun Vošner H, Kokol M, Završnik J (2022) Role of agile in digital public health transformation. Front Public Health 1098
Kumar R Singh K Jain SK Agility enhancement through agile manufacturing implementation: a case study Total Qual Manag 2022 34 6 1527 1546
Li W Chien F Ngo QT Nguyen TD Iqbal S Bilal AR Vertical financial disparity, energy prices and emission reduction: empirical insights from Pakistan J Environ Manag 2021 294 112946 10.1016/j.jenvman.2021.112946
Lichtenthaler U A conceptual framework for combining agile and structured innovation processes Res Technol Manag 2020 63 5 42 48 10.1080/08956308.2020.1790240
Liu J, Gatzweiler F, Hodson S, Harrer-Puchner G, Sioen GB, Thinyane M et al (2022) Co-creating solutions to complex urban problems with collaborative systems modelling-insights from a workshop on health co-benefits of urban green spaces in Guangzhou. Cities Health: 1–10
Martínez-Velasco A Terán-Bustamante A Business model innovation and decision-making for the productive sector in times of crisis Business Recovery in Emerging Markets: Global Perspectives from Various Sectors 2022 Cham Springer International Publishing 129 156
Mergel I Agile innovation management in government: a research agenda Gov Inf Q 2016 33 3 516 523 10.1016/j.giq.2016.07.004
Nitoslawski SA Galle NJ Van Den Bosch CK Steenberg JW Smarter ecosystems for smarter cities? A review of trends, technologies, and turning points for smart urban forestry Sustain Cities Soc 2019 51 101770 10.1016/j.scs.2019.101770
Ozturk I Aslan A Altinoz B Investigating the nexus between CO2 emissions, economic growth, energy consumption and pilgrimage tourism in Saudi Arabia ECON RES-EKON ISTRAZ 2022 35 1 3083 3098
Petrick JF (2004) The roles of quality, value, and satisfaction in predicting cruise passengers’ behavioral intentions. J Travel Res 42:397–407. 10.1177/0047287504263037
Pham NT Tučková Z Jabbour CJC Greening the hospitality industry: how do green human resource management practices influence organizational citizenship behavior in hotels? A mixed-methods study Tour Manag 2019 72 386 399 10.1016/j.tourman.2018.12.008
Proos E Hattingh J Dark tourism: growth potential of niche tourism in the Free State Province, South Africa Dev South Afr 2022 39 3 303 320 10.1080/0376835X.2020.1847636
Raihan A Muhtasim DA Pavel MI Faruk O Rahman M Dynamic impacts of economic growth, renewable energy use, urbanization, and tourism on carbon dioxide emissions in Argentina Environ Process 2022 9 2 38 10.1007/s40710-022-00590-y
Ribeiro N Gomes DR Ortega E Gomes GP Semedo AS The impact of green HRM on employees’ eco-friendly behavior: the mediator role of organizational identification Sustainability 2022 14 5 2897 10.3390/su14052897
Schulz K Feist M Leveraging blockchain technology for innovative climate finance under the Green Climate Fund Earth System Governance 2021 7 100084 10.1016/j.esg.2020.100084
Sharma R de Sousa L Jabbour AB Jain V Shishodia A The role of digital technologies to unleash a green recovery: pathways and pitfalls to achieve the European Green Deal J Enterp Inf Manag 2022 35 1 266 294 10.1108/JEIM-07-2021-0293
Song H Wu DC A critique of tourism-led economic growth studies J Travel Res 2022 61 4 719 729 10.1177/00472875211018514
Subramanian N, Suresh M (2023) Green organizational culture in manufacturing SMEs: an analysis of causal relationships. Int J Manpow (ahead-of-print)
Sun L Fang S Iqbal S Bilal AR Financial stability role on climate risks, and climate change mitigation: implications for green economic recovery Environ Sci Pollut Res 2022 29 22 33063 33074 10.1007/s11356-021-17439-w
Tanova C Bayighomog SW Green human resource management in service industries: the construct, antecedents, consequences, and outlook Serv Ind J 2022 42 5-6 412 452 10.1080/02642069.2022.2045279
Tian F Yang Y Jiang L Spatial spillover of transport improvement on tourism growth Tour Econ. 2022 28 5 1416 1432 10.1177/1354816620982787
Trad A Kalpić D Business transformation project’s holistic agile management (BTPHAM) Bus Manag Rev 2022 13 1 103 120 10.24052/BMR/V13NU01/ART-12
Triantafyllidou E, Zabaniotou A (2021) Digital technology and social innovation promoting a green citizenship: development of the “go sustainable living” digital application. Circ Econ Sustain:1–24
Tu CA, Chien F, Hussein MA, Yanto Ramli MM, Psi MSS, Iqbal S, Bilal AR (2021) Estimating role of green financing on energy security, economic and environmental integration of BRI member countries. Singap Econ Rev:1–19
Umar M Khan SAR Zia-ul-haq HM Yusliza MY Farooq K The role of emerging technologies in implementing green practices to achieve sustainable operations TQM J 2022 34 2 232 249 10.1108/TQM-06-2021-0172
Vidmar M Rosiello A Vermeulen N Williams R Dines J New Space and Agile Innovation: understanding transition to open innovation by examining innovation networks and moments Acta astronaut 2020 167 122 134 10.1016/j.actaastro.2019.09.029
Wang L Chen Y Ramsey TS Hewings GJ Will researching digital technology really empower green development? Technol Soc 2021 66 101638 10.1016/j.techsoc.2021.101638
Wang S, Sun L, Iqbal S (2022a) Green financing role on renewable energy dependence and energy transition in E7 economies. Renew Energy 200:1561–1572
Wang Y, Wang L, Pan C (2022b) Tourism–growth nexus in the presence of instability. Sustainability 14(4):2170
Wilson K Doz YL Agile innovation: a footprint balancing distance and immersion Calif Manage Rev 2011 53 2 6 26 10.1525/cmr.2011.53.2.6
Yang Y, Gu R, Ma S, Chen W (2022) How does digital technology empower urban green development efficiency in the Beijing-Tianjin-Hebei region—mechanism analysis and spatial effects. Environ Sci Pollut Res :1–18
Yang Y Liu Z Saydaliev HB Iqbal S Economic impact of crude oil supply disruption on social welfare losses and strategic petroleum reserves Resour Policy 2022 77 102689 10.1016/j.resourpol.2022.102689
Yue G Wei H Khan NU Saufi RA Yaziz MFA Bazkiaei HA Does the environmental management system predict tbl performance of manufacturers? The role of green HRM practices and OCBE as serial mediators Sustainability 2023 15 3 2436 10.3390/su15032436
Zhang L, Huang F, Lu L, Ni X, Iqbal S (2022a) Energy financing for energy retrofit in COVID-19: recommendations for green bond financing. Environ Sci Pollut Res 29(16):23105–23116
Zhang X, Guo W, Bashir MB (2022b) Inclusive green growth and development of the high-quality tourism industry in China: the dependence on imports. Sustain Prod Consum 29:57–78
Zhao L Saydaliev HB Iqbal S Energy financing, COVID-19 repercussions and climate change: implications for emerging economies Climate Chang Econ 2022 13 03 2240003 10.1142/S2010007822400036
Zheng X Zhou Y Iqbal S Working capital management of SMEs in COVID-19: role of managerial personality traits and overconfidence behavior Econ Anal Policy 2022 76 439 451 10.1016/j.eap.2022.08.006 35990757 | 37183223 | PMC10183307 | NO-CC CODE | 2023-05-17 23:16:01 | yes | Environ Sci Pollut Res Int. 2023 May 15;:1-14 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
27523
10.1007/s11356-023-27523-y
Research Article
The impact of green intellectual capital on green innovation in Vietnamese textile and garment enterprises: mediate role of environmental knowledge and moderating impact of green social behavior and learning outcomes
Tran Tho Dat [email protected]
1
Huan Doan Minh [email protected]
2
Phan Thi Thu Hien [email protected]
3
Do Huong Lan [email protected]
4
1 grid.444954.c 0000 0004 0428 9139 National Economics University, Hanoi, Vietnam
2 Communist Review, Hanoi, Vietnam
3 grid.444961.a 0000 0004 0416 8548 Faculty of Accounting & Auditing Foreign Trade University, Hanoi, Vietnam
4 grid.444954.c 0000 0004 0428 9139 National Economics University, Hanoi, Vietnam
Responsible Editor: Arshian Sharif
20 5 2023
114
19 1 2023
5 5 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
The knowledge economy system shifts focus on the significance of intellectual capital. Moreover, the concept itself has gained generous amount of recognition at global level due to the increasing pressure from competitors, stakeholders, and environmental forces. Indeed, its antecedents and consequences have been assessed by scholars. However, the assessment appears to be inexhaustive with respect to meaningful frameworks. With the help of preceding literature, the present paper designed a model which involves green intellectual capital, green innovation, environmental knowledge, green social behavior, and learning outcomes. The model stipulates that green intellectual capital makes green innovation possible which further results in competitive advantage in the presence of environmental knowledge as a mediator as green social behavior and learning outcomes as a moderator. Interestingly the model acknowledges the proposed relationship through the empirical evidence collected from 382 Vietnamese textile and garment enterprises. The findings provide deeper insights regarding the issue that how firms could extract maximum benefits from their green assets and capabilities in the form of intellectual capital and green innovation.
Keywords
Green intellectual capital
Green innovation
Environmental knowledge
Green social behavior
==== Body
pmcIntroduction
Today, the environment has become one of the issues businesses are most concerned about because of pressures from stakeholders that have forced companies to be more responsible for the environment. This is reflected in stricter moves by authorities and a higher public awareness of environmental sustainability. On this basis, ecological issues are considered essential for the business as they relate to its operating processes and efficiency (Haldorai et al. 2022; Phuoc et al. 2022; Nguyen et al. 2023). To ensure that the activities carried out by the company are safe for the environment, green innovation should be encouraged. Green innovation is innovation in all its forms, addressing environmental issues and ensuring that natural resources are used as efficiently as possible. It is one of those procedures that raises a business’s performance in terms of its economy, environment, and competitiveness. For the execution of green innovation, businesses need sound governance strategies (Baima et al. 2020; Moslehpour et al. 2022a, b). One of these strategies is the improvement in green intellectual capital (GIC). GGIC is the outcome of mental processes, which includes the set of intangible objects that may be employed in economic activity and generate cash for their owner (organization) while maintaining environmental quality. A company’s competitive advantage is the culmination of all that everyone in the organization knows. There are three components of GIC: “green human capital, green structural capital, and green relational capital” (Ali et al. 2021a, b; Quynh, et al. 2022).
Green human capital is the presence of human resources who have environmental knowledge, good health, and abilities to handle business matters, run business processes, and utilize business resources in an appropriate manner to avoid environmental issues. The green human capital evaluates the environmental consequences of the business processes, has creative ideas, and helps apply the green innovative resources and processes (Dabić et al. 2020; Sadiq et al. 2023). Green structural capital includes infrastructure, procedures, processes, and databases of the company that is helpful to human capital for functioning with the least pollution emissions. The adoption of green structural capital adds value to business operations. Hence, it brings green innovation to business (Mahmood & Mubarik 2020; Tan et al. 2021). Green relational capital is the knowledge, processes, capabilities, and systems specific for establishing relations with external agents, the relations having eco-friendly value to the business. The struggles of the company to apply green relational capital result in green innovation in business operations and production (Mahmood and Mubarik 2020; Zhao et al. 2022).
Scholars argue that the manufacturing sector is counted in one of the energy intensive sectors which significantly increase carbon emissions. To restrict emissions, the sector must design the policies which could improve environmental quality. As per researchers, green innovation has the potential to improve firm’s performance and prevent environmental pollution (Zhao et al. 2021; Zhang et al. 2023a). The argument is worthy enough to motivate us to examine green innovation in the textile and garments industry of Vietnam. Vietnam is a developing country making progress at a low rate. The country’s GDP is estimated to be US $469.620 billion in 2023, which accounts for an 8.1% GDP growth rate for the year. The textile and garment industry is one of the major economic industries in Vietnam. Thu Tran has been the director of imago Vietnam since April 2017, with a location in the GVP neighborhood to the northwest of Ho Chi Minh City (Phan et al. 2020; Zhang et al. 2023b; Wirsbinna and Grega 2021). In recent years, Vietnam has risen to become one of the top exporters of textiles and garments worldwide. Vietnam is the 2nd largest textile exporter in the world, with a 6.4% market share in the global textile and apparel market; the US, Japan, South Korea, and Europe are the top importers of the nation’s goods. The USA is its largest export market, accounting for over US $16 billion (Trevlopoulos et al. 2021). Over the past 5 years, the textile industry has experienced rapid growth, averaging 20–26% yearly growth. Today, the textile and garment sector in the country employs close to 3 million people, making it to be one of the largest and most significant factors in its economic development. The sector contributes 16% of the nation’s total GDP and employs close to 5% of the workforce overall and about 12% of the industrial labor force (Hartani et al. 2021; Tuyen et al. 2019; Vu et al. 2023a, b).
The textile and garments firms in Vietnam, because of the manufacturing activities for clothes production and marketing practices, are creating pollution and damaging the environment. This negative side of the textile and garment industry causes hinderance to achieve sustainability in the said area. The industry requires some green innovations to ensure sustainable development (Jermsittiparsert 2021). The present study meets this need as it emphasizes green innovation and green intellectual capital. The study also aims to examine the mediating role of environmental knowledge between green intellectual capital and green innovation. It is also to analyze the moderating role of green social behavior and green learning outcome between green human capital and green innovation (Moslehpour et al. 2022c).
The study makes significant contributions to the literature. First, in the previous literature, authors debated on simply the relationship of GIC with green innovation. The current study examines the three dimensions of green intellectual capital: “green human capital, green structural capital, and green relational capital,” individually and their impacts on green innovation. Second, unlike the previous studies, the present study initiates to examine the moderating role of green social behavior and green learning outcome between green human capital and green innovation. Third, the previous authors have only checked the direct environmental knowledge of green innovation. The present article makes a distinction and talks about the mediating role of environmental knowledge. Fourth, the present study addresses the need for green innovation in Vietnam specifically.
The paper has five parts: the Literature review section throws light on previous literature and establishes hypotheses. The Research methodology section gives an account of all methods selected to collect and analyze data. In the Research results section, hypotheses are tested, and results are extracted. These results are aligned with the studies which have similar arguments. After discussions, the study is concluded with limitations and recommendations.
Literature review
Green intellectual capital is an essential part of sustainability as it covers the crucial factors such as knowledge, skills and expertise which equip firms to address environmental and social issues. Interestingly, even since the emergence of the concept, it has not received enough attention (Lin et al. 2022; Liu et al. 2022). There is a debate that firms can achieve superior performance and earn competitive edge with the help of rare intangible assets (Kamarudin et al. 2021; Lan et al. 2022). Scholars have this belief that green intellectual capital can be fitted in that category as it refers to the overall expertise of firm that not only benefits the environment but helps in achieving overall performance. Green intellectual capital also offers support to evaluate customers’ environmental awareness and knowledge along with the assurance of strict compliance with international regulators (Hsu et al. 2023; Duong and Hai Thi Thanh 2022).
Van et al. (2021), in the study, listed several subjects classified as knowledge capital, including science and technology, patents, intellectual property rights, learning capacity, organizational experience, group communication and information systems, customer relations, and corporate brand values. Building on previous studies, Irfan et al. (2022) study proposed a new concept—GICs to keep up with the trend of strict international environmental management regulations and the environmental consciousness of consumers around the world in the process of multi-regional economic cooperation. Based on previous studies on the classification of knowledge capital, GIC is classified into three categories: “green human capital (GHC), green structural capital (GSC), and green relational capital (GRC)”. GHC synthesizes workers’ knowledge, skills, competencies, experiences, attitudes, qualities, intellect, creative working ability, etc., on environmental management issues and environmental awareness Irfan et al. (2022). Rehman et al. (2021) have shown that GHC in an organization, whose role is to promote the application and practice of environmental governance, such as GSCM using green production lines and reverse logistics, will help achieve sustainability. Unlike GHC, GSC will not disappear when employees quit. In the context of environmental and cultural factors associated with global development, businesses must continually find ways to implement environmental strategies to seek opportunities and value or create competitive advantages, thereby aiming to build a sustainable organizational structure. Responding to this fact, Irfan et al. (2022) defined GSC as the source of organizational capacity, organizational commitment, knowledge management system, organizational culture, trademarks, patents, copying rights, and trademarks, etc., that are related to environmental protection or green innovation in an enterprise. Finally, Irfan et al. (2022) develops the concept of GRC, which synthesizes the relationships between stakeholders regarding corporate environmental management and green innovation. External organizations and business stakeholders are more concerned with environmental issues than businesses. Suppose businesses want to maintain close relationships and receive the resource support of external organizations and stakeholders to survive and thrive. In that case, they need to invest more resources to develop their relationships in a way that involves the benefits of the shared environment. For Vietnam’s a textile and garment industry today—one of the industries that have a significant impact on the living environment and has a great role in solving employment problems for workers, it becomes even more necessary to find solutions to promote GIC.
Green innovation (GI) is the improvement of products or processes regarding “energy saving, pollution prevention, waste recycling, green product design, and enterprise environmental management” in environmental management (Hsu and Chien 2023; Marco-Lajara et al. 2022). In this study, Chen et al. divided GI into “green product innovation and green process innovation.” In some other studies, such as that of Liu et al. (2021), green innovation is divided into four main categories: “green innovation management, green product innovation, green process innovation, and green technology innovation.” Based on the context of the study, the division in the study by Ullah et al. (2022) was inherited for this study. Specifically, green innovation will include three activities: green product innovation (GPD), green process innovation (GPC), and green innovation management (GMI). GPD can be understood as improving product design, quality, and reliability following environmental awareness, creating opportunities to distinguish their green products from competitors, consuming products at higher prices, and obtaining better profits. Businesses use GPD to promote environmental management activities to meet environmental protection regulations. On the other hand, GPC refers to using new or modified production equipment by applying new methods, science and technology, and production processes to minimize negative pressures on the environment. GPC is used to increase the efficiency of environmental management, meet environmental protection requirements, and partly reduce costs for businesses, helping them achieve a low-cost advantage (Abrudan et al. 2022; Dinh et al. 2022; Nguyen 2023). In the study of Jirakraisiri et al. (2021), GMI is mentioned in conducting a redesign or adjustment of existing operations, processes, products, and services to meet and address internal green management performance standards or criteria. Textile enterprises are involved in GMI processes as they redefine activities and strategies to ensure fuel efficiency while carrying out production projects.
In recent decades, especially when the COVID-19 epidemic appeared, the current governance and economic development model has revealed many limitations. To create sustainable, long-term developments, adapting to the ongoing industrial revolution, many experts said that we also need to convert green in addition to digital transformation. Because of this urgent issue, many studies have been conducted to explore the factors affecting green innovation. There are still some views that GIC is negatively associated with green innovation. According to Astuti and Datrini (2021), although many studies have been conducted to understand knowledge capital at the enterprise level, these studies do not delve into GIC research in particular. They do not clarify how GIC affects green innovation. Internal and external resources can influence green innovation through capacity development and capital investment. According to resource orchestration theory, an organization can only derive maximum benefit from its resources and capabilities when they are structured, combined, and managed effectively (Chen et al. 2023; Chien 2023; Chien et al. 2023a). Therefore, with abundant green knowledge capital, businesses can implement green innovation conveniently and effectively. According to Arie et al. (2019), several reports have identified that GIC is essential in allowing the green innovation process to take place. However, very few studies explore the specific mechanism of impact from GIC to green innovation in enterprises. The different impacts of each aspect of GIC on green innovation. Still, it did not specify the mechanism of these impacts.
In the era of increasing awareness of environmental management and sustainable energy development, GHC is an essential resource for organizational innovation because employees hold critical knowledge and can use green expertise for green innovation (Chien et al. 2022, 2023b). Asiaei et al. (2023) argue that GHC acts as a platform that connects employees’ environmental knowledge with green innovation. Businesses will leverage their GHC potential to conduct GPC and GPD, improving operational efficiency. According to the research results of Mansoor et al. (2021), differences in GHC investment needs can lead to significant gaps in the likelihood of success when implementing green innovation among businesses. On the other hand, in contrast to GHC, GSC is independent of employees. Malik et al. (2020) argue that managers must try to invest and establish a potent GSC that can help improve their ability to acquire environmental knowledge, thereby maintaining green innovation. In addition, Yusliza et al. (2020) also support the importance of GRC for green innovation by suggesting that managers can build green relationships with the organization’s strategic partners, facilitate the sharing of external information about environmental knowledge, and promote the development of green innovation. From the above rationales, the following hypotheses are offered:H1a: GHC has a positive impact on green innovation.
H1b: GSC has a positive impact on green innovation.
H1c: GRC has a positive impact on green innovation.
Mediate role of environmental knowledge
A study was conducted by Wang and Juo (2021) to investigate the relationship of GIC with GHC, GSC, and GRC impacts on environmental knowledge and green innovation. Via survey technique, information was gathered from 138 high-tech companies operating in Taiwan. Descriptive statistics, correlation, and structural equation modeling were applied to test the study hypotheses describing the relationship between these factors. Authors argue that if organizations establish GHC, GSC, and GRC with high potential, they raise attain sufficient quality environmental knowledge. With the knowledge regarding environmental problems and capacity for mitigating these problems, it helps develop a platform for green innovation. In a literary work by Ali et al. (2021a, b), which integrates the relationships among GIC including GHC, GSC, and GRC relation, with environmental knowledge and green innovation, samples of 235 SMEs in four manufacturing sectors, like textile, chemical, steel, and pharmaceutical, from the Pakistani economy were surveyed to collect data for the concerned variables. A multiple regression analysis was performed to assess the proposed relations. The findings revealed that the higher potential of GHC, GSC, and GRC improves the professionals’ knowledge of environmental innovation, like the ways to reduce energy consumption, minimize environmental pollution, and mitigate wastes from factory areas. The improved environmental knowledge adds to green innovation. Yong et al. (2019) investigate the relationship between GIC with its three components like GHC, GSC, and GRC, and environmental knowledge and green innovation. One hundred twelve significant Malaysian manufacturing companies were surveyed via mail using a quantitative research method to gain their insights. An analysis of partial least squares regression was used to look into the suggested association. The study implies that the green integration into GHC, GSC, and GRC develops a better understanding of environmental changes because of the particular business practices and resources, as well as processes that favor environmental preservation. The increased environmental knowledge reveals to the management how they must bring green innovation.H4: environmental knowledge mediates between GHC and green innovation.
H5: environmental knowledge mediates between GSC and green innovation.
H6: environmental knowledge mediates between GRC and green innovation.
Moderate role green learning outcomes and green social behavior
Previous studies have shown that learning orientation is generally a precursor to innovation, which influences the effectiveness of innovation activities in the enterprise, thereby enabling businesses to respond quickly to customer needs and market changes (Bai et al. 2022; Chau et al. 2022). Studies by Secundo et al. (2020) also suggest that the direction of learning will have positive effects on innovation. Therefore, GLO can also be a premise for green innovation in businesses. GLO impacts the learning orientation of human resources and employee attitudes to acquire new skills (Khan et al. 2021; Ojogiwa 2021), while promoting the initiative and enthusiasm of employees involved in green innovation. In addition, businesses with GLO tend to embrace environmental changes and can encourage proactive thinking of employees (Nirino et al. 2020). As a result, ideas and thoughts on the environment to meet green innovation goals can be accumulated and further facilitated for green innovation. In addition, businesses with GLO will demonstrate a strong corporate culture, which reinforces the company’s environmental vision (Minoja and Romano 2021), while encouraging employees to learn about environmental problems and solutions. As a result, the chances of success of green innovation will be further enhanced. Therefore, the hypothesis is given as follows:H7: GLO regulates the impact of GHC on green innovation.
The green social behavior of the organizational administrators, especially human resources managers, improves green human capital. The green development of human resources creates potential for human resources to perform efficiently while implementing different innovative eco-friendly processes. So, when organizations adopt green social behavior, the influences of green human capital on green innovation adoption get stronger (Obeidat et al. 2021; Shibli et al. 2021). Singh et al. (2020) investigate the influence of green social behavior on the association between green human capital and green innovation. We gathered triadic data from 309 small and medium-sized manufacturing sector businesses using a survey questionnaire. In this work, the covariance-based SEM technique was utilized to investigate the hypotheses. The study proclaims that when business organizations have green social behavior, they take care of the health and other needs of employees. The employees get attached to the organizations and show better struggles for green innovation. Thus, green social behavior improves the relationship between human capital and green innovation. Hao et al. (2021) examine the relation among green social behavior, green human capital, and green innovation. The research is based on the data from G7 countries for the period of 1991–2017, and CS-ARDL is applied for analysis. The research implies that green social behavior moderates the association between human capital and green innovation (Fig. 1).H8: green social behavior moderates human capital and green innovation.
Fig. 1 Theoretical model
Through the hypotheses outlined above, the empirical research model is formed as follows:
Research methodology
Currently, Vietnam’s textile and garment industry has paid attention to environmental issues, raising the awareness of textile and garment enterprises about environmental, social responsibility. The textile and garment industry 2030 can complete a green and environmentally friendly transition while ensuring the employment needs of workers and contributing well to the quality of life of society. Environmental awareness is one of the solutions that Vietnamese textile and garment enterprises are interested in. However, the proportion of Vietnamese textile and garment enterprises implementing this method is still low, so the awareness of the role of environmental knowledge is still incomplete. In addition, Vietnamese textile and garment enterprises lack environmental awareness. The law on environmental protection in Vietnam took effect in 2014. Still, until now, the specific environmental awareness has not been finalized. There are no documents to guide enterprises in general on the issue of monitoring and extracting ecological costs incurred in the production and business process.
This study conducted a survey of employees in Vietnamese textile and garment enterprises. The questionnaire was sent in two forms, directly and online (mainly online), to large, medium, and small textile enterprises in the North, Central, and South of Vietnam. The number of respondents was 405, and there were 382 valid observations, meeting the sample size required for the SEM model. Table 1 shows the details of the respondents.Table 1 Statistics describing research samples
Amount Proportion
Scale (number of employees)
1–100 50 13%
100–199 112 29%
200–399 88 23%
400–799 72 19%
Over 800 60 16%
Geographical location
North 103 27%
Middle 69 18%
South 210 55%
Percentage of exported products (%)
0–20 19 5%
20–40 42 11%
40–60 64 17%
60–80 72 19%
80–100 185 48%
The study has taken three independent variables, such as green human capital (GHC), green structural capital (GSC), and green relational capital (GRC), in which GHC consists of five questions, GSC consists of six questions, and GRC consists of five questions. The scales are all measured by Likert scales from 1 to 5, respectively, from (1) strongly disagree to (5) completely agree. In addition, environmental knowledge (ENK) is used as the mediating variable and consists of five questions measured using a Likert scale from 1 to 5, respectively, from (1) strongly disagree to (5) completely agree. Moreover, green innovation (GIN) is used as the dependent variable that was measured with seven questions. The scales are all measured by Likert scales from 1 to 5, respectively, from (1) strongly disagree to (5) completely agree. Finally, green learning outcomes (GLO) and green social behavior (GSB) are used as moderating variables and consist of 4 and 8 questions, respectively, and are also measured using a Likert scale from 1 to 5, respectively, from (1) strongly disagree to (5) completely agree. The study use the PLS-SEM using smart-PLS which is an effective tool for dealing with complex models (Hair et al. 2020).
Research results
The present study employed Cronbach’s alpha to evaluate reliability of model. It is important to make sure that items less than 0.7 were removed to maintain the reliability up to the standard (Hair et al. 2020). Table 2 shows that items GHC4, GRC2, GSB3, and GIN2 all have a total variable correlation coefficient of less than 0.05, so the study removed these items from the model. After removing these scales, Cronbach’s alpha coefficient of all factors has a value of > 0.7, thus also guaranteed, according to Hair et al. (2017). Therefore, the factors are all retained to take the next steps in the study. Moreover, the study also performed AVE to evaluate model validity (Hair et al. 2020). A loading value > 0.7 will ensure that the item used is fair and not discarded while AVE needs > 0.5 to show that the element used is fair in terms of convergence value. Table 2 shows that the items all have an outer loading value > 0.7 and that the elements all have an AVE value of > 0.5, thus ensuring the convergence value.Table 2 Convergent validity
Constructs Items Loadings Alpha CR AVE
Environmental knowledge ENK1 0.875 0.868 0.905 0.659
ENK2 0.871
ENK3 0.669
ENK4 0.813
ENK5 0.815
Green human capital GHC1 0.859 0.880 0.917 0.735
GHC2 0.864
GHC3 0.861
GHC5 0.844
Green innovation GIN1 0.697 0.840 0.882 0.555
GIN3 0.773
GIN4 0.743
GIN5 0.746
GIN6 0.749
GIN7 0.757
Green learning outcomes GLO1 0.802 0.869 0.910 0.717
GLO2 0.857
GLO3 0.830
GLO4 0.894
Green relational capital GRC1 0.808 0.860 0.900 0.643
GRC3 0.867
GRC4 0.740
GRC5 0.744
GRC6 0.842
Green social behavior GSB1 0.879 0.887 0.916 0.647
GSB2 0.711
GSB4 0.867
GSB5 0.893
GSB7 0.795
GSB8 0.652
Green structural capital GSC1 0.835 0.933 0.949 0.789
GSC2 0.940
GSC3 0.875
GSC4 0.875
GSC5 0.914
Recent studies have shown that the Heterotrait–Monotrait ratio (HTMT) is better suited for assessing the differentiation values of factors (the maximum value threshold of the HTMT system is 0.85 to make sure that the factors are distinct from each other (Hair et al. 2020)). Table 3 shows that the HTMT coefficients are all less than 0.85, thereby ensuring that the factors used are distinct from each other (Fig. 2).Table 3 Heterotrait–Monotrait ratio
ENK GHC GIN GLO GRC GSB GSC
ENK
GHC 0.503
GIN 0.732 0.706
GLO 0.353 0.679 0.638
GRC 0.546 0.471 0.692 0.443
GSB 0.583 0.574 0.835 0.456 0.759
GSC 0.475 0.670 0.650 0.566 0.544 0.516
Fig. 2 Measurement assessment model (Authors’ estimation)
Among the three aspects of GIC, the impact of GHC on green innovation is the strongest, with a coefficient of 0.145. This shows that human capacity is still the most important in the innovation of business. Developing green human resources will help businesses have many premises in green innovation, from innovative ideas to the implementation of innovation. Through improving green human resources, businesses will promote the innovation capacity of employees and management, which, in turn, will stimulate the ability to innovate green products and green processes and manage innovation capabilities. The lower GSC and GRC impacts still showed significant impact levels. Thus, in addition to improving GHC, improving GRC and GSC is also important in enhancing the green innovation capacity of Vietnamese textile and garment enterprises. With the challenges that Vietnam’s textile and garment industry faced during the COVID-19 pandemic, comprehensive improvement from people to organizations and relationships is an inevitable requirement for Vietnamese textile and garment enterprises. Through the improvement of GSC, the organization’s environmental management system issues will be improved, and through this, textile enterprises can implement green innovation ideas in the enterprise. In addition, more investment in environmental protection and green knowledge management systems also shows that businesses are more interested in green innovation and thereby promote green innovation. Improving GRC is similar, especially during the COVID-19 pandemic; good cooperation with both suppliers and customers will help businesses quickly innovate products or processes that better suit the requirements of stakeholders, meeting the needs of customers. Environmental issues pose the resurgence of the post-COVID-19 industry requires businesses to have innovation in dealing with environmental issues, so green knowledge capital will be a good driving force for green innovation of Vietnamese textile and garment enterprises.
The results of the empirical model reconstruction showed that the impact of aspects of GIC on ENK was positive and supported at a significant 5% level. Positive effects from GIC to ENK were found, thereby sharing. Thus, raising GIC can improve the ENK capacity of Vietnamese textile and garment enterprises. This shows that raising green structured capital plays the most important role in improving the ENK capacity of enterprises. The results are consistent with reality because thanks to the improvement of the structure of the business, especially in environmental issues, it will improve the capacity to handle environmental problems, so that improving the ENK will be even more necessary for the business. By placing emphasis on the environmental knowledge management system, the enterprise’s budget supporting the environmental governance accounting system will be greater and, therefore, more likely to improve the overall ENK. The impact from GRC to ENK is lower but also relatively insignificant. This shows that if textile businesses have better relations with suppliers and customers, it is also necessary to improve ENK. ENK is a picture for suppliers and customers to use to make decisions when considering environmental issues of the business, so having close relationships with stakeholders will motivate businesses to implement ENK. The impact of GHC on ENK indicates that the human capacity in the organization is also a positive influence on ENK. The contribution of employees and management on environmental issues will also be a good premise for businesses to be more proactive in improving ENK capacity.
The positive impact from ENK on green innovation was also found much greater than the direct impact from GHC and GSC on green innovation. This result has shown that in recent years, improving ENK can help improve the green innovation of businesses. For Vietnam’s textile and garment industry, the tool on environmental management accounting may not be widely applied, but the effects of aspects of ENK on green innovation can be found. For example, through the classification and creation of environmentally related costs, businesses can have a better understanding of these costs and thereby initiate green innovation ideas to help businesses cut costs. Similarly, inventory analysis and product improvement can also help businesses achieve green product innovation. Overall, the significant positive impact of ENK on green innovation suggests that the ENK variable used can be a very effective mediate variable. These outcomes are given in Table 4.Table 4 Direct path
Relationships Beta Standard deviation T statistics P values
ENK → GIN 0.252 0.052 4.877 0.000
GHC → ENK 0.230 0.057 4.048 0.000
GHC → GIN 0.145 0.052 2.804 0.005
GLO → GIN 0.151 0.046 3.272 0.001
GRC → ENK 0.309 0.064 4.860 0.000
GRC → GIN 0.041 0.062 0.666 0.501
GSB → GIN 0.392 0.045 8.766 0.000
GSC → ENK 0.143 0.061 2.347 0.019
GSC → GIN 0.116 0.041 2.837 0.005
Evaluation of the role of intermediaries was carried out. Evaluation of the mediate role based on the indirect impact from the independent variable to the dependent variable through the mediate variable is statistically significant. The results showed that ENK all acted as intermediaries explaining the impact of GHC, GSC, and GRC on green innovation, with the greatest level of explanation being for the relationship from GSC to green innovation. Although the coefficient may not seem too high, for the mediate impact, this is a very significant impact level, so the ENK mediate variable used is an effective mediate variable. In addition, through the statistical significance of the direct impact, it can be concluded that the ENK has a fully mediate role in the relationship between GSC and green innovation and has a fully mediate role in the impact of GHC and GRC on green innovation. Thus, through improving GHC, GRC, and GSC, Vietnamese textile and garment enterprises can improve ENK and thereby increase the green innovation ability of enterprises. However, considering that the above model does not include additional moderate factors such as enterprise size and export rate, the question arises as to whether there is any moderate impact on the impact from GHC, GSC, and GRC aspects of green innovation. This issue will be explained in the moderate role verification section later (Fig. 3).Fig. 3 Structural assessment model (Source: Authors’ estimation)
The moderate role is to assess whether factors such as GSB and GLO will make the impact of GIC on green innovation stronger or weaker. The study uses a two-stage approach to build the impact of moderate variables and uses bootstrap techniques to test the moderate role of these variables. When the product of the independent variable and the moderate variable has an impact on the dependent variable, the moderate role is confirmed. The results in Tables 5 and 6 and Fig. 4 show that GSB and GLO play a role in regulating the impact of GIC on green innovation due to the P-value. More specifically, GSB regulates the impact of GRC on green innovation, and GLO regulates the impact of GHC on green innovation. In addition, the direct impact of moderate variables shows that the size of the business, although it does not have a moderate role, has a positive effect on the ability of the business to innovate. This shows that larger businesses are more interested in green innovation. This result is consistent with reality because, for larger businesses, larger environmental problems require businesses to take green initiatives to solve these problems. In addition, GLO also has a positive impact on green innovation, which also shows that businesses orient employees and managers to be more excited to acquire and exchange green knowledge, which will help businesses have better green innovation capacity. Thanks to the widespread sharing of green knowledge in businesses, green innovation ideas will take shape, and the implementation of green innovation plans will be much more effective. Thus, the positive impact of GLO on green innovation also indicates that Vietnamese textile and garment enterprises should also consider GLO as an important factor in developing green innovation strategies for businesses.Table 5 Mediation analysis
Relationships Beta Standard deviation T statistics P values
GHC → ENK → GIN 0.058 0.019 3.024 0.003
GRC → ENK → GIN 0.078 0.025 3.059 0.002
GSC → ENK → GIN 0.036 0.017 2.158 0.031
Table 6 Moderation analysis
Relationships Beta Standard deviation T statistics P values
GHC*GLO → GIN 0.160 0.037 4.257 0.000
GHC*GSB → GIN 0.058 0.026 2.227 0.026
Fig. 4 Moderation analysis
The results showed that for enterprises with a higher GSB, the more GRC improved, the more businesses improved green innovation due to the steep slope of the high GSB line, while the low GSB line was almost horizontal. This result also shows that for enterprises, GSB is necessary to improve GRC because these enterprises mainly cooperate with foreign partners, so the capacity of green relations will play a very important role. Enhancing GRC helps these businesses acquire the green knowledge of partners, which can be applied in improving green innovation of businesses. On the contrary, for fewer exporters, it seems that improving GRC does not bring efficiency in green innovation in enterprises. Therefore, this business should choose to improve other factors, such as GHC and GSC, instead of focusing on improving GRC.
The results show that for businesses with a better green learning orientation, the more GHC is improved, the more businesses will improve their green innovation capacity (due to the steep slope of the high GLO road). On the contrary, businesses with a lower green learning orientation, even if they improve GHC, will not necessarily improve green innovation but also cause green innovation capacity to decrease (due to the low GLO road). This once again reaffirms the role of GLO in the organization. GLO is an effective solution and a premise for employees and managers to acquire green capacity and accumulate green knowledge capital. Then, improving GHC will be more effective and thereby increase the efficiency of green innovation in enterprises. Thus, businesses that do not have a good green learning-oriented strategy should focus on improving GLO, while businesses that have the premise that GLO is better should focus on improving GHC to be able to achieve green innovation goals in enterprises.
Discussions
GHC has a positive impact on green innovation which is consistent with Song et al. (2020). As per study, organizations where there is an investment in human resources to prepare them to undertake business operations with better environmental performance, new green concepts and technologies can be employed. Hence, GHC improves green innovation. These results are also in line with Munawar et al. (2022), according to which GHC creates an innovation-oriented environment with green business processes applied. So, GHC leads to green innovation adoption. Hsu et al. (2021) also state that the organizations where eco-friendly changes are made in the functioning of structural resources and processes are active in responding to business trends with green innovation. Aboelmaged and Hashem (2019) also highlighted that green improvement in structural capital is helpful in adopting green innovation.
Moreover, GRC also has a positive impact on green innovation. These results are supported by Ardito et al. (2019). For acquiring and implementing innovative resources, organizations need sound relations with the stakeholders. In case the company has GRC, green innovation adoption is possible. These results also agree with Abbas and Sağsan (2019), which reveals that GRC opens the ways for the organization to set business operations according to the innovative market requirements regarding environmental aspects. So, GRC is positively linked to green innovation. The results also showed that environmental knowledge is the significant mediator between GHC and green innovation. These results are supported by Aftab et al. (2022), who highlight that the development of GHC improves the environmental knowledge of organizational personnel and enables them to adopt the processes and technologies which reduce emissions of harmful emissions. These results also match with Awan et al. (2022), which show that GHC improves environmental knowledge with the business personnel and brings innovation to their business conduct.
The results revealed that environmental knowledge is the significant mediator between GSC and green innovation. These results are supported by Lv et al. (2021), who indicate that with the green changes in the structural capital, environmental knowledge, and information of the workers increase, and improvement in environmental knowledge enhances green innovation. Meng and Zhang (2022) also argued that environmental knowledge that is enhanced in case there is an increase in GSC improves green innovation. The results showed that environmental knowledge is the significant mediator between GRC and green innovation. These results are supported by Zhang et al. (2020), who posit that the development of GHC improves the environmental knowledge for organizations and helps adopt innovative eco-friendly resources. These results are also in line with Luo et al. (2022). This study states that the environmental knowledge of the organization is enhanced when there is an increase in GSC, and it improves green innovation adoption.
The results showed that GSB is a significant moderator between GHC and green innovation. These results are supported by Shahzad et al. (2020), who claim that GSB strengthens the relationship between GHC and green innovation. The results also showed that GLO is a significant moderator between GHC and green innovation. These results are supported by Zhang and Zhu (2019), according to which the better GLOs develop the ability of organizational managers to implement GHC and progressed toward green innovation. So, it improves the relationship between GHC and green innovation.
The present study, whose focus is on green innovation in an organization, is useful for developing companies to have a clean environment and attain sustainable development goals. The study provides guidelines to the business organizations for implementing green innovation. The study guides that effective policies should be designed to apply GHC as it would lead the organization to implement green innovation. It gives a guideline that business management must be responsible and form policies to implement GSC, and thereby, green innovation must be adopted. The study also has a suggestion that organizations must be attentive toward GRC while forming policies and, thus, ensure green innovation adoption. Moreover, the study conveys that business management must implement GIC’s three components like GHC, GSC, and GRC. It will enhance environmental knowledge, which further enhances organizations' progress in green innovation adoption. The study also suggests that GSB should be developed for implementing GHC and encouraging green innovation within the organizations. It is also suggested that the personnel in top organizational management must work for better GLOs in order to adopt GHC and attain green innovation.
Organizations should also recruit new employees who show passion for the environment and green principles. Managers should develop such kinds of recruiting policies that are linked to environmental sustainability. It is also to be noted according to findings that employees have the potential to shape their abilities and mold their behavior toward green principles if they care about environment and show deeper concern for it. Moreover, firms should also fulfill environmental expectations as it will lead them toward green innovation.
Conclusion
The objective of the research was to examine the influences of GIC’s three components, like GHC, GSC, and GRC, on green innovation and check the role of environmental knowledge between GHC, GSC, and GRC and green innovation in Vietnamese textile industry. The results showed a positive association between GHC, GSC, and GRC with green innovation. The results showed that the increase in GHC develops a creative environment and the adoption of novel strategies which brings green improvement in product quality. So, GHC leads to green innovation. The results indicated that the increase in GSC ensures an eco-friendly working environment and products which have better quality from the environmental point of view. Hence, the business organization shows higher green innovation. As a result, there is higher green innovation. The results indicated that environmental knowledge mediates between GHC, GSC, and GRC and green innovation. The improvement in GHC, GSC, and GRC helps organizations acquire environmental knowledge, which develops the ability for green innovation. The results also stated that in case there is a higher GSB and better GLOs, the organizations can improve GHC, GSC, and GRC and better attain green innovation.
The study, despite its limitations, has a scope to expand further in other areas. For example, the present study used data sample from textile industry of Vietnam. Hence, future scholars can use data samples from other sectors. Future scholars can also consider other countries and do comparative studies to evaluate the role of GIC with three components like, GHC, GSC, and GRC, in green innovation adoption. Also, the study uses cross-sectional data; hence, other studies can use longitudinal data as well as it might display different outcomes. Finally, this study is only concerned with checking the role of GIC with three components, like GHC, GSC, and GRC, in green innovation adoption. There are many other factors like green finance, firm size, and CSR which play a key role in green innovation adoption, but these are missing. Future researchers should also investigate these factors for evaluating innovation adoption.
Author contribution
Tho Dat Tran: conceptualization and writing—original draft. Doan Minh Huan: writing—literature review. Huong Lan Do: software and visualization. Thi Thu Hien Phan: methodology, supervision, data curation, and editing.
Funding
This research is funded by the National Economics University, Hanoi, Grant CBQT1.2021.03 (264/QĐĐHKTQD).
Data availability
The data that support the findings of this study are attached.
Declarations
Consent to participate
It can be declared that there are no human participants, human data, or human tissues.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Abbas J Sağsan M Impact of knowledge management practices on green innovation and corporate sustainable development: a structural analysis J Clean Prod 2019 229 611 620 10.1016/j.jclepro.2019.05.024
Aboelmaged M Hashem G Absorptive capacity and green innovation adoption in SMEs: the mediating effects of sustainable organisational capabilities J Clean Prod 2019 220 853 863 10.1016/j.jclepro.2019.02.150
Abrudan DB Rafi N Daianu DC Kalyar MN Linking green intellectual capital with green innovation: examining the roles of green dynamic capabilities and'motivation to achieve legitimacy' Agric Econ 2022 68 7 250 258
Aftab J Abid N Cucari N Savastano M Green human resource management and environmental performance: the role of green innovation and environmental strategy in a developing country Bus Strateg Environ 2022 7 76 94 10.1002/bse.3219
Ali MA Hussin N Haddad H Alkhodary D Marei A Dynamic capabilities and their impact on intellectual capital and innovation performance Sustainability 2021 13 18 128 147 10.3390/su131810028
Ali W Wen J Hussain H Khan NA Younas MW Jamil I Does green intellectual capital matter for green innovation adoption? Evidence from the manufacturing SMEs of Pakistan J Intellect Cap 2021 22 5 868 888 10.1108/JIC-06-2020-0204
Ardito L MesseniPetruzzelli A Pascucci F Peruffo E Inter-firm R&D collaborations and green innovation value: the role of family firms' involvement and the moderating effects of proximity dimensions Bus Strateg Environ 2019 28 1 185 197 10.1002/bse.2248
Arie AAPGB Kumalasari PD Manuari IAR The role of green intellectual capital on competitive advantage: evidence from Balinese financial institution Sriwijaya Int J Dynamic Econ Bus 2019 3 3 227 242 10.29259/sijdeb.v3i3.227-242
Asiaei K O'Connor NG Barani O Joshi M Green intellectual capital and ambidextrous green innovation: the impact on environmental performance Bus Strategy Environ 2023 32 1 369 386 10.1002/bse.3136
Astuti P Datrini L Green competitive advantage: Examining the role of environmental consciousness and green intellectual capital Manag Science Lett 2021 11 4 1141 1152 10.5267/j.msl.2020.11.025
Awan FH Dunnan L Jamil K Gul RF Stimulating environmental performance via green human resource management, green transformational leadership, and green innovation: a mediation-moderation model Environ Sci Pollut Res 2022 8 1 19 10.1007/s11356-022-22424-y
Bai X Wang KT Tran TK Sadiq M Trung LM Khudoykulov K Measuring China’s green economic recovery and energy environment sustainability: econometric analysis of sustainable development goals Econ Anal Policy 2022 10.1016/j.eap.2022.07.005
Baima G Forliano C Santoro G Vrontis D Intellectual capital and business model: a systematic literature review to explore their linkages J Intellect Cap 2020 22 3 653 679 10.1108/JIC-02-2020-0055
Chau KY Lin CH Tufail B Tran TK Van L Nguyen TTH Impact of eco-innovation and sustainable tourism growth on the environmental degradation: the case of China Econ Res-Ekonomska Istraživanja 2022 10.1080/1331677X.2022.2150258
Chen SL Su YS Tufail B Lam VT Phan TTH Ngo TQ The moderating role of leadership on the relationship between green supply chain management, technological advancement, and knowledge management in sustainable performance Environ Sci Pollut Res 2023 10.1007/s11356-023-26304-x
Chien F The impact of green investment, eco-innovation, and financial inclusion on sustainable development: evidence from China Eng Econ 2023 34 1 17 31 10.5755/j01.ee.34.1.32159
Chien F Chau KY Sadiq M Hsu CC The impact of economic and non-economic determinants on the natural resources commodity prices volatility in China Resour Policy 2022 10.1016/j.resourpol.2022.102863
Chien F Hsu CC Zhang Y Sadiq M Sustainable assessment and analysis of energy consumption impact on carbon emission in G7 economies: mediating role of foreign direct investment Sustain Energy Technol Assess 2023 10.1016/j.seta.2023.103111
Chien F Chau KY Sadiq M Impact of climate mitigation technology and natural resource management on climate change in China Resour Policy 2023 10.1016/j.resourpol.2023.103367
Dabić M Vlačić B Scuotto V Warkentin M Two decades of the Journal of Intellectual Capital: a bibliometric overview and an agenda for future research J Intellect Cap 2020 22 3 458 477 10.1108/JIC-02-2020-0052
Dinh HP Tran KN Van Cao T Vo LT Ngo TQ Role of eco-financing in COP26 goals: empirical evidence from ASEAN countries Cuadernos De Economía 2022 45 128 24 33
Duong KD Hai Thi Thanh T Association between post-COVID socio-economic development and energy-growth-environment nexus from developing economy Int J Econ Finance Stud 2022 14 2 247 270
Hair JF Hult GTM Ringle CM Sarstedt M Thiele KO Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods J Acad Mark Sci 2017 45 5 616 632 10.1007/s11747-017-0517-x
Hair JF Jr Howard MC Nitzl C Assessing measurement model quality in PLS-SEM using confirmatory composite analysis J Bus Res 2020 109 101 110 10.1016/j.jbusres.2019.11.069
Haldorai K Kim WG Garcia RF Top management green commitment and green intellectual capital as enablers of hotel environmental performance: the mediating role of green human resource management Tour Manage 2022 88 1044 1059 10.1016/j.tourman.2021.104431
Hao L-N Umar M Khan Z Ali W Green growth and low carbon emission in G7 countries: how critical the network of environmental taxes, renewable energy and human capital is? Sci Total Environ 2021 752 1418 1434 10.1016/j.scitotenv.2020.141853
Hartani NH Haron N Tajuddin NII The impact of strategic alignment on the sustainable competitive advantages: mediating role of it implementation success and it managerial resource Int J eBusiness and eGovernment Stud 2021 13 1 78 96
Hsu CC Chien F The impact of high economic growth and technology advancement on extensive energy production in China: evidence using NARDL model Environ Sci Pollut Res 2023 30 1 1656 1671 10.1007/s11356-022-22205-7
Hsu C-C Quang-Thanh N Chien F Li L Mohsin M Evaluating green innovation and performance of financial development: mediating concerns of environmental regulation Environ Sci Pollut Res 2021 28 40 57386 57397 10.1007/s11356-021-14499-w
Hsu CC Chau KY Chien F Natural resource volatility and financial development during COVID-19: implications for economic recovery Resour Policy 2023 10.1016/j.resourpol.2023.103343
Irfan M Razzaq A Sharif A Yang X Influence mechanism between green finance and green innovation: exploring regional policy intervention effects in China Technol Forecast Soc Chang 2022 182 121 148 10.1016/j.techfore.2022.121882
Jermsittiparsert K Linkage between energy consumption, natural environment pollution, and public health dynamics in ASEAN Int J Econ Finance Stud 2021 13 2 1 21
Jirakraisiri J Badir YF Frank B Translating green strategic intent into green process innovation performance: the role of green intellectual capital J Intellect Cap 2021 22 7 43 67 10.1108/JIC-08-2020-0277
Kamarudin F Anwar NAM Chien F Sadiq M Efficiency of microfinance institutions and economic freedom nexus: empirical evidence from four selected ASIAN countries Transform Bus Econ 2021 20 2b 845 868
Khan NU Anwar M Li S Khattak MS Intellectual capital, financial resources, and green supply chain management as predictors of financial and environmental performance Environ Sci Pollut Res 2021 28 16 19755 19767 10.1007/s11356-020-12243-4
Lan J Khan SU Sadiq M Chien F Baloch ZA Evaluating energy poverty and its effects using multi-dimensional based DEA-like mathematical composite indicator approach: findings from Asia Energy Policy 2022 10.1016/j.enpol.2022.112933
Lin CY Chau KY Tran TK Sadiq M Van L Phan TTH Development of renewable energy resources by green finance, volatility and risk: empirical evidence from China Renew Energy 2022 10.1016/j.renene.2022.10.086
Liu S Yu Q Zhang L Xu J Jin Z Does intellectual capital investment improve financial competitiveness and green innovation performance? Evidence from renewable energy companies in China Math Probl Eng 2021 2021 36 48 10.1155/2021/9929202
Liu Z Lan J Chien F Sadiq M Nawaz MA Role of tourism development in environmental degradation: a step towards emission reduction J Environ Manage 2022 10.1016/j.jenvman.2021.114078
Luo S Yimamu N Li Y Wu H Irfan M Hao Y Digitalization and sustainable development: how could digital economy development improve green innovation in China? Bus Strateg Environ 2022 6 54 61 10.1002/bse.3223
Lv C Shao C Lee C-C Green technology innovation and financial development: do environmental regulation and innovation output matter? Energy Econ 2021 98 105 117 10.1016/j.eneco.2021.105237
Mahmood T Mubarik MS Balancing innovation and exploitation in the fourth industrial revolution: role of intellectual capital and technology absorptive capacity Technol Forecast Soc Chang 2020 160 1202 1218 10.1016/j.techfore.2020.120248
Malik SY Cao Y Mughal YH Kundi GM Mughal MH Ramayah T Pathways towards sustainability in organizations: empirical evidence on the role of green human resource management practices and green intellectual capital Sustainability 2020 12 8 3228 3239 10.3390/su12083228
Mansoor A Jahan S Riaz M Does green intellectual capital spur corporate environmental performance through green workforce? J Intellect Cap 2021 22 5 823 839 10.1108/JIC-06-2020-0181
Marco-Lajara B Zaragoza-Sáez PC Martínez-Falcó J Sánchez-García E Does green intellectual capital affect green innovation performance? Evidence from the Spanish wine industry Br Food J 2022 7 57 79 10.1108/BFJ-03-2022-0298
Meng F Zhang W Digital finance and regional green innovation: evidence from Chinese cities Environ Sci Pollut Res 2022 29 59 89498 89521 10.1007/s11356-022-22072-2
Minoja M Romano G Managing intellectual capital for sustainability: evidence from a re-municipalized, publicly owned waste management firm J Clean Prod 2021 279 1232 1247 10.1016/j.jclepro.2020.123213
Moslehpour M Shalehah A Wong WK Ismail T Altantsetseg P Tsevegjav M Economic and tourism growth impact on the renewable energy production in Vietnam Environ Sci Pollut Res 2022 29 53 81006 81020 10.1007/s11356-022-21334-3
Moslehpour M Chau KY Tu YT Nguyen KL Barry M Reddy KD Impact of corporate sustainable practices, government initiative, technology usage, and organizational culture on automobile industry sustainable performance Environ Sci Pollut Res 2022 29 55 83907 83920 10.1007/s11356-022-21591-2
Moslehpour M Chau KY Du L Qiu R Lin CY Batbayar B Predictors of green purchase intention toward eco-innovation and green products: evidence from Taiwan Econ Res-Ekonomska Istraživanja 2022 36 2 1 22
Munawar S Yousaf HQ Ahmed M Rehman S Effects of green human resource management on green innovation through green human capital, environmental knowledge, and managerial environmental concern J Hosp Tour Manag 2022 52 141 150 10.1016/j.jhtm.2022.06.009
Nguyen NM The effect of FDI on domestic entrepreneurship: the case of greenfield investment and cross-border M&A activities J Econ Dev 2023 25 1 62 78 10.1108/JED-11-2022-0228
Nguyen N, Dao T, Nguyen H (2023) On the revision of detecting learning-by-exporting: empirical evidence from the small- and medium-sized enterprises in Vietnam. J Int Econ Manag 23(1). 10.38203/jiem.023.1.0057
Nirino N Ferraris A Miglietta N Invernizzi AC Intellectual capital: the missing link in the corporate social responsibility–financial performance relationship J Intellect Cap 2020 23 2 420 438 10.1108/JIC-02-2020-0038
Obeidat U Obeidat B Alrowwad A Alshurideh M Masadeh R Abuhashesh M The effect of intellectual capital on competitive advantage: the mediating role of innovation Manag Sci Lett 2021 11 4 1331 1344 10.5267/j.msl.2020.11.006
Ojogiwa OT The crux of strategic leadership for a transformed public sector management in Nigeria Int J Bus Manag Stud 2021 13 1 83 96
Phan TTH Tran HX Le TT Nguyen N Pervan S Tran MD The relationship between sustainable development practices and financial performance: a case study of textile firms in Vietnam Sustainability 2020 12 15 5930 5945 10.3390/su12155930
Phuoc VH Thuan ND Vu NPH Tuyen LT The impact of corporate social and environmental responsibilities and management characteristics on SMES' performance in Vietnam Int J Econ Finance Stud 2022 14 2 36 52
Quynh MP Van MH Le-Dinh T Nguyen TTH The role of climate finance in achieving Cop26 goals: evidence from N-11 countries Cuadernos De Economía 2022 45 128 1 12
Rehman SU Kraus S Shah SA Khanin D Mahto RV Analyzing the relationship between green innovation and environmental performance in large manufacturing firms Technol Forecast Soc Chang 2021 163 1204 1221 10.1016/j.techfore.2020.120481
Sadiq M Moslehpour M Qiu R Hieu VM Duong KD Ngo TQ Sharing economy benefits and sustainable development goals: empirical evidence from the transportation industry of Vietnam J Innov Knowl 2023 10.1016/j.jik.2022.100290
Secundo G Ndou V Del Vecchio P De Pascale G Sustainable development, intellectual capital and technology policies: a structured literature review and future research agenda Technol Forecast Soc Chang 2020 153 119 127 10.1016/j.techfore.2020.119917
Shahzad M Qu Y Zafar AU Rehman SU Islam T Exploring the influence of knowledge management process on corporate sustainable performance through green innovation J Knowl Manag 2020 24 9 2079 2106 10.1108/JKM-11-2019-0624
Shibli R Saifan S Ab Yajid MS Khatibi A Mediating role of entrepreneurial marketing between green marketing and green management in predicting sustainable performance in Malaysia's organic agriculture sector AgBioforum 2021 23 2 37 49
Singh SK Del Giudice M Chierici R Graziano D Green innovation and environmental performance: the role of green transformational leadership and green human resource management Technol Forecast Soc Chang 2020 150 119 135 10.1016/j.techfore.2019.119762
Song W Yu H Xu H Effects of green human resource management and managerial environmental concern on green innovation Eur J Innov Manag 2020 24 3 951 967 10.1108/EJIM-11-2019-0315
Tan LP Sadiq M Aldeehani TM Ehsanullah S Mutira P Vu HM How COVID-19 induced panic on stock price and green finance markets: global economic recovery nexus from volatility dynamics Environ Sci Pollut Res 2021 10.1007/s11356-021-17774-y
Trevlopoulos NS Tsalis TA Evangelinos KI Tsagarakis KP Vatalis KI Nikolaou IE The influence of environmental regulations on business innovation, intellectual capital, environmental and economic performance Environ Syst Decis 2021 41 1 163 178 10.1007/s10669-021-09802-6
Tuyen DQ Dung TV Dong HV Kien TT Huong TT Breast self-examination: knowledge and practice among female textile workers in Vietnam Cancer Control 2019 26 1 107 126 10.1177/1073274819862788
Ullah H Wang Z Mohsin M Jiang W Abbas H Multidimensional perspective of green financial innovation between green intellectual capital on sustainable business: the case of Pakistan Environ Sci Pollut Res 2022 29 4 5552 5568 10.1007/s11356-021-15919-7
Van LT-H Vo AT Nguyen NT Vo DH Financial inclusion and economic growth: an international evidence Emerg Mark Financ Trade 2021 57 1 239 263 10.1080/1540496X.2019.1697672
Vu TL Phan TTH Sadiq M Xuyen NTM Ngo TQ Nexus of natural resources, urbanization and economic recovery in Asia: the moderating role of innovation Resour Policy 2023 10.1016/j.resourpol.2023.103328
Vu TL Paramaiah C Tufail B Nawaz MA Xuyen NTM Huy PQ Effect of financial inclusion, eco-innovation, globalization, and sustainable economic growth on ecological footprint Eng Econ 2023 34 1 46 60 10.5755/j01.ee.34.1.32402
Wang CH Juo WJ An environmental policy of green intellectual capital: green innovation strategy for performance sustainability Bus Strateg Environ 2021 30 7 3241 3254 10.1002/bse.2800
Wirsbinna A Grega L Assessment of Economic Benefits of Smart City Initiatives Cuadernos De Economía 2021 44 126 45 56
Yong JY Yusliza M Ramayah T Fawehinmi O Nexus between green intellectual capital and green human resource management J Clean Prod 2019 215 364 374 10.1016/j.jclepro.2018.12.306
Yusliza MY Yong JY Tanveer MI Ramayah T Faezah JN Muhammad Z A structural model of the impact of green intellectual capital on sustainable performance J Clean Prod 2020 249 119 135 10.1016/j.jclepro.2019.119334
Zhang F Zhu L Enhancing corporate sustainable development: stakeholder pressures, organizational learning, and green innovation Bus Strateg Environ 2019 28 6 1012 1026 10.1002/bse.2298
Zhang Y Xing C Wang Y Does green innovation mitigate financing constraints? Evidence from China’s private enterprises J Clean Prod 2020 264 121 137 10.1016/j.jclepro.2020.121698
Zhang Y Li L Sadiq M Chien F The impact of non-renewable energy production and energy usage on carbon emissions: evidence from China Energy Environ 2023 10.1177/0958305X221150432
Zhang Y Li L Sadiq M Chien FS Impact of a sharing economy on sustainable development and energy efficiency: evidence from the top ten Asian economies J Innov Knowl 2023 10.1016/j.jik.2023.100320
Zhao L Zhang Y Sadiq M Hieu VM Ngo TQ Testing green fiscal policies for green investment, innovation and green productivity amid the COVID-19 era Econ Chang Restruct 2021 10.1007/s10644-021-09367-z
Zhao L Chau KY Tran TK Sadiq M Xuyen NTM Phan TTH Enhancing green economic recovery through green bonds financing and energy efficiency investments Econ Anal Policy 2022 10.1016/j.eap.2022.08.019 | 37209351 | PMC10199664 | NO-CC CODE | 2023-05-24 23:17:07 | yes | Environ Sci Pollut Res Int. 2023 May 20;:1-14 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
27965
10.1007/s11356-023-27965-4
Research Article
Economic development, social media awareness, and technological innovation in biogas sector under climate change in the post-COVID-19 pandemic conditions
Ali Shahid 1
Yan Qingyou 12
Dilanchiev Azer 1
http://orcid.org/0000-0003-1446-583X
Irfan Muhammad [email protected]
34
Balabeyova Narmina 5
1 grid.261049.8 0000 0004 0645 4572 School of Economics and Management, North China Electric Power University, Beijing, 102206 China
2 grid.261049.8 0000 0004 0645 4572 Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing, 102206 China
3 grid.411615.6 0000 0000 9938 1755 School of Economics and Management, Beijing Technology and Business University, Beijing, 100048 China
4 grid.444859.0 0000 0004 6354 2835 Department of Business Administration, ILMA University, Karachi, 75190 Pakistan
5 grid.11173.35 0000 0001 0670 519X Faculty of Economics, University of the Punjab, Lahore, Pakistan
Responsible Editor: Ilhan Ozturk
8 6 2023
120
28 2 2023
24 5 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
After COVID-19, financing for emerging nation reserves in renewable energy bases was deemed a crucial aspect of sustainable development. Investing in biogas energy plants can be highly beneficial for lowering the use of fossil fuels. Using a survey of shareholders, investors, biogas energy professionals, and active social media participants in Pakistan, this study evaluates the intentions of individual investors to invest in biogas energy plants. The primary purpose of this study is to increase investment intent for biogas energy projects following COVID-19. This study focuses on financing biogas energy plants in the post-COVID-19 era and evaluates the research’s assumptions using partial least squares structural equation modeling (PLS-SEM). The study employed the technique of purposive sampling to acquire data for this investigation. The results indicate that attitudes, perceived biogas energy benefits, perceived investment attitudes, and supervisory structure evaluations inspire one’s propensity to finance biogas vitality plant efforts. The study found a link between eco-friendly responsiveness, monetary benefits, and investors’ actions. The aspiration of investors to mark such reserves was set up to be unpretentious by their risk aversion. Conferring to the facts, evaluating the monitoring structure is the critical factor. The previous studies on investment behavior and other forms of pro-environmental intent and action yielded contradictory results. In addition, the regulatory environment was evaluated to see how the theory of planned behavior (TPB) affects financiers’ objectives to participate in biogas power plants. The consequences of the study indicate that feelings of pride and discernment of energy expansively affect people’s desire to invest in biogas plants. Biogas energy efficacy has little effect on investors’ decisions to invest in biogas energy plants. This study offers policymakers practical ideas on enhancing investments in biogas energy plants.
Keywords
Renewable energy investment
Clean energy
Energy shortage
Post-COVID-19 era
Pakistan
==== Body
pmcIntroduction
In recent decades, environmental sustainability and protection have risen to the forefront of research and policy concerns (Chandio et al. 2021; Rauf et al. 2018), given that climate change is the most prominent issue with negative impacts on the sustainable increase of global wealth (Dai et al. 2018; Irfan et al. 2020). Biogas can be produced from a wide variety of organic wastes (Adedoyin et al. 2021; Chandio et al. 2020). Managing garbage in emerging regions is quite tricky. The deterioration of the environment, the rising need for power, and the dearth of continuous energy plants have compelled governments worldwide to switch from conventional to renewable energy sources (Ahmad et al. 2021; Yasmin et al. 2022). Renewable energy options mitigate environmental issues and provide lasting solutions to such energy issues (Ahmar et al. 2022; Irfan et al. 2021). Due to the breakout of COVID-19 in 2020, international economic and trade arrangement has been significantly impacted. COVID-19 precipitated a vitality disaster that immediately affected the vitality area and posed formidable difficulties to energy creation, depletion, and source (Dyahrini et al. 2020). In April 2020, the weekly vitality demand in nations with a complete lockdown declined by 25%, while it decreased by 18% in countries with a partial lockdown (Karakosta et al. 2021). In the post-COVID-19 era, energy’s geopolitical instability has risen due to the expansion of global drive marketplace associations (Köhler et al. 2017). Indigenous and international stockholders are uncertain about the epidemic and deem it hazardous to finance alternative energy in Pakistan throughout COVID-19. During the post-COVID-19 era, however, 51 million people in Pakistan, or 27% of the country’s total residents, did not have admittance to electrical energy, and 50% lacked access to innocuous gastronomic conveniences (Energy-Outlook 2021). In 2006, the Ministry of Water and Power produced the primary alternative vitality policy for Pakistan, which envisages incorporating alternative energy into the nation’s development objectives.
The first energy policy was implemented sixteen years after its establishment in 2006; the country has only produced 3709 GWh of electrical energy from alternative bases. Consumers face exclusive voltage and disturbances caused by entire energy generation from trade in blast furnace oil, RLNG, gas, and other vitality causes, procedural problems at energy plants, and other causes (GoP. government of Pakistan RE.Policy 2006). International Energy Agency (IEA) reports that the monetary recapture commencing the COVID-19 epidemic and extreme meteorological conditions will increase energy consumption by above 6% in 2021 (Khan 2020). Nearly 96% of rural Pakistani families get their energy and culinary needs from conventional biofuels. Animal dung, woody vs. non-woody resources is used (wood, crop shells, straws, leaves). Biogas technology can produce 9.8 billion cubic meters of gas annually, and roughly $17.5 billion are produced annually from animal dung and other organic waste. In addition, bagasse, a byproduct of sugarcane and sorghum processing, may produce 5800 GWh of energy annually, or 6.6% of Pakistan’s current electrical output.
This study investigated green finance parameters that might entice sequestered part outlay in biogas power plants through the COVID-19 epidemic. Due to the unpredictability of the pandemic’s long-term position, local investors are worried about energy investments (Baloch et al. 2021). The relevant literature indicates that green expertise techniques affect the justifiable improvement of biogas energy plant installations. Conversely, stakeholders have evaluated the COVID-19 epidemic circumstances, and numerous additional issues discourage investment in biogas energy projects. Despite this, Pakistan has a great need for such investments. During the COVID-19 lockdown in Pakistan, excessive home and commercial energy use necessitated investment in new energy projects (Di Leo et al. 2020). Significant global difficulties and opportunities exist for an energy transition road map in the post-COVID-19 period, green funding mechanisms, improving green repossession strategies and bolstering worldwide collaboration (Tian et al. 2022). Conversely, in the era following COVID-19, alternative energy production and energy conversion are fraught with difficulty and uncertainty (Madurai et al. 2021). Globally, lawgiving and restrictions have been developed to boost old-fashioned energy usage (Sun et al. 2019). An imbalance between energy demand and supply hinders economic development, prosperity, and sustainable growth and seriously affects a country’s water resources, environment, human health, and agricultural output (Jha and Schmidt 2021). Even though various vitality plans foresee a bright forthcoming for renewable energy improvement, in the southern part of the globe, 250 million people still rely on traditional energy sources for heating, cooling, lighting, and other daily requirements (e Silva et al. 2022), the energy area was forced to endure a great surprise throughout the COVID-19 epidemic, impeding the energy alteration (Zhang et al. 2021).
Biogas could eliminate the energy imbalance and offer additional energy in the coming years. Biogas provides numerous advantages in energy production, gas production, biofertilizers production, socioeconomic development, and environmental protection. There are no emissions of carbon dioxide, sculpture, nitrogen, and other air pollutants (Situmorang et al. 2020). The energy transition post-epidemic is an increasing distress in emerging RE (Weko et al. 2020). The World Health Organization (WHO) declared the COVID-19 outbreak on March 11, 2020 as a global pandemic; many governments adopted precautionary steps to control this pandemic situation and imposed extraordinary restrictions on economic activity and travel (Forster et al. 2020). During the pandemic, the RE sector faced severe and notable consequences (Goodell 2020). Due to health and medical expenses, which drew cash from tax deductions and RE initiatives (Jiang et al. 2021), energy is necessary to improve a nation’s living standard and economic progress (Roy and Dalei 2020), the outlay arrangement of various nations was changed. Because of improper guidelines, Pakistan’s renewable energy area is currently inadequate. The domestic economy has not had the funds for fossil fuel requirements. To alleviate the energy issue, the management must build an original energy low-cost based on solar, wind, and biogas utilization. Pakistan’s biogas energy potential is enormous. It can address the nation’s energy difficulties by encouraging jurisdictive inducements, its vast market, steamy topography, and investigation conveniences (Jha and Schmidt 2021). During the COVID-19 epidemic, Pakistan’s biogas energy generation confronts policy challenges in luring private investment. A protracted rate of economic expansion is relative to other nations. Consequently, various forms of obstacles are complicated in the implementation of biogas energy plants. Financing in biogas schemes carries a short level of risk, but substantial project finance is crucial. At the beginning of the development phase, the primary obstacle affecting biogas energy plants is the strategy barrier, and then this preliminary blockade steadily deteriorations, as does the expertise obstacle. In the established phases of a scheme’s improvement, the overall energy obstacles might impact investments, and these barriers gradually become the primary source of unpredictability. According to prior research (Raina and Sinha 2019), such ventures can only be successful for a limited time if their profit-generating strategies are not sustainable (Wang et al. 2021a).
The research contends that the COVID-19 epidemic has detrimental effects on funds outlay volume in green vitality funding plans (Amir and Khan 2022). The outcome of COVID-19 on biogas energy plants investment decreased by 28% and 4.9 GW, respectively, in 2020 (Eroğlu 2020). Because of economic operations, including systematic enactment and collective obligations, a reliable energy supply is crucial for the current way of living (Popp et al. 2021), the reserved area is additional dynamic and dependable than the administration in financing conservational schemes. Because of COVID-19, it is necessary to restructure RE and climate policies (Hosseini 2020). The COVID-19 epidemic has damaged the energy zone and numerous firms’ complicated alternative energy, biogas energy, and the generation of ecological power (Deshwal et al. 2021). The investment structure employment perspective has been continuous; trades have experienced severe functioning disruptions, heightened pressures in positions of economic elasticity, and amplified investment costs (Situmorang et al. 2020). Transactions are continuously aware of and steadfast in their investment arrangement strategies, susceptible to macroeconomic astonishments (Hall and Klitgaard 2018). Pakistan confronts a severe electrical energy deficiency because of ineffective energy strategies, reliance on relic fuels, and negligent resource utilization.
Since the introduction of Pakistan’s leading alternative policy in 2006, merely 3709 GWh of power have been produced from alternative bases, of which only 1985 MW has come from wind power, 600 MW solar powers, and 364 MW from biogas energy (Ullah et al. 2018). Conversely, with the administration of Pakistan’s innovative power scheme, directives have been issued to achieve electrical energy exclusively from solar and biogas power. Pakistan has not yet generated electricity from biogas despite the necessity to reduce the nation’s power deficit. Previous biogas projects promoted biogas as an alternative to conventional wood fuel for home cooking and heating. This study gathers information about biogas power projects including poultry waste, household waste, and sugarcane bagasse (Hussain et al. 2019). Using the theory of planned behavior (TPB), this experiment examined people’s intentions for environmental protection with biogas energy (IEPBE) using the theory of planned behavior (TPB). The current study sought to determine whether outlay conduct and the COVID-19 financial dynamic moderated the propensity of IEPBE investors to invest in biogas energy plants via social media. The hypothetical fundamentals of TPB, which assert a significant influence on perceived actions and ecological mechanisms, are also congruent through the present research results. The current study indicates that expressive standards, conservational views, evidence persuasive, individual existences, and a predicted financing mindset significantly impact investor intentions to invest in biogas energy plants via social media.
The biogas energy industry has distinct obstacles. These obstacles must be addressed to create successful and efficient biogas megaprojects. For investors, the supreme substantial impediments to the preliminary overheads of biogas energy schemes can affect outcomes in long-standing financial gains. The present study has adjusted the TPB model to illuminate outlay behavior better using the perception of biogas energy efficacy (PBEE). This research model consists of perceived biogas energy benefits (PBEB), perceived investment attitude (PIA), perceived environment concern (PEC), intentions to environmental protection with biogas energy (IEPBE), local investor’s behavior (LIB), and perceived COVID-19 financing determinants (PC-19IF). The hypotheses were appraised with structural equation modeling through partial least squares (PLS-SEM). In Pakistan’s biogas energy development, however, obstacles are crucial difficulties. Regarding these challenges, the pragmatic study examined the perspectives of investors, investors, biogas energy professionals, and active social media participants in Punjab, accomplishing considerable outlay in alternative energy and entice overseas investment by eradicating energy obstructions; this study will answer the following questions: What procedure and dogmatic complications have the COVID-19 epidemic created for biogas energy technology developments in Pakistan? What obstacles have present and future investors deemed the most critical impediments to significant funds in biogas power plants? Are these impediments distinguished by domain and state or by the nature of post-pandemic investments?
Procedure and dogmatic impediments are cited as essential grounds for apprehension by investors and stockholders that escape investing in huge biogas energy plants in Pakistan. The critical independent of the current study is to evaluate the obstacles to developing biogas power plants. In addition, we address the essential theoretical background and research hypotheses for this investigation. We conducted a comprehensive literature review and theoretical prototypical expending TPB as the study’s theoretic foundation. This paper’s research technique section describes the investigation design and data examination, while the outcomes section describes the testing of suppositions. This paper’s discussion section describes the outcomes and their consequences. Lastly, the present study offers inferences grounded on the research outcomes.
Theoretical background
Theory of planned behavior, perceived biogas energy benefits, perceived investment attitude, and perceived environmental concern
Various scholars have utilized different theoretical frameworks to evaluate people’s conduct, which has been the subject of numerous theories and discussions. The TPB assists us in assessing the actions of others and ties theories to activities. As a behavioral model, TPB is a widely adopted psychological theory. Nevertheless, TPB has been commonly employed in healthcare and behavior estimation. This theory investigates human behavior more thoroughly than competing models (Andarge et al. 2020). TPB has been widely studied in contemporary studies explaining behavioral goals and the development of human behavior (Rana et al. 2022). TBP will have been utilized roughly 94,500 times by the end of 2022. TPB (Fornara et al. 2016) can evaluate certain activities’ performance, like perceived investment attitudes, and the global COVID-19 pandemic prompted investment in alternative energy, particularly biogas energy plants. The international travel limitations have a significant impact on the development of biogas energy’s sustainability. In response to the COVID-19 epidemic, the Pakistani government enacted a preventative policy. A long-run parameter calculation revealed that the COVID-19 outbreak harmed the stock prices of biogas energy providers (Wang et al. 2021b). The guidelines for renewable energy are at a crucial juncture. The financial recapture is the most significant post-epidemic component. The states own track and shape are sympathetic aspects (Kuzemko et al. 2020).
Stockholders are aware of and complexity of speculation fatalities and it is anticipated that the stock markets will reflect these concerns. Nonetheless, the level of investor panic during COVID-19 was so great that they were inclined to liquidate their assets irresponsibly. During COVID-19, alternative energy improvements were refunded to Asia, while the U.S. relic fuel power arcade considerably impacted eastern and western Europe (Chang et al. 2020). Due to the COVID-19 issue, several private investors altered their perspectives on sustainable investing, which exaggerated the economic success and ecological improvement of numerous supplementary industries (Mavlutova et al. 2022). TPB is a credible philosophy indicating that stakeholders are eager to participate in biogas power when participants believe their investment behavior has minimal risk. People’s intent to invest in biogas energy plants is anticipated to be influenced by sequestered clusters of financiers with expert assistance and monetary understanding regarding the upcoming predicting of biogas vitality funds. Perceived environmental concern (PEC) refers to public consciousness of conservational problems and their commitment to eliminating them (Jabeen et al. 2021). The PEC sustainable atmosphere idea defines alternative energy outlay deeds and concentrates energy sector outlay on environmental protection (He et al. 2018; Li et al. 2019). During the COVID-19 epidemic, the investment in biogas energy helped eliminate the energy crisis and prevent ecological collapse. The notion of PEC substantially impacted investors’ IEPBE as a vital factor (Schraufnagel et al. 2019).
Renewable energy can significantly lower CO2 emissions as its proportion in the overall energy mix increases (Farhani and Ozturk 2015; Hoang et al. 2021; Tanveer et al. 2021). Without a constant supply of energy on a global scale, the existing regular life style and economic progress are not possible (Lowe and Drummond 2022). Biogas energy is a significant, limitless energy source that emits no CO2 worldwide (Mikhail et al. 2020). Investing in biogas energy plants positively impacts the environment and reduces CO2 emissions. The actions and arrogances of investors to finance alternative energy, particularly biogas power plants, can alleviate the vitality disaster and safeguard the surroundings. The intentions of environmental protection with biogas energy (IEPBE) can result in economic and ecological paybacks. The conservational impact has a substantial positive effect on IEPBE. Biogas vitality arrangements have eco-friendly consequences; hence, suitable precautions and care are essential for their implementation (Bates et al. 2019). With other energy sources, biogas energy production poses health, environmental, and safety concerns (Wu et al. 2020). While LIB considers the impact of environmental regulation considerations on investor propensities to invest in biogas energy plants, the PEC-reinforced adaptable was comprised to investigate the present influence.
Biogas energy plants offer substantial financial and environmental protection benefits (Ilyas et al. 2021). Compared to conservative energy sources, investments in biogas energy systems are RE resources, including thermal solar structures and photovoltaic, offer substantial eco-friendly paybacks. Consequently, these arrangements subsidize humanoid economic events (Ingrao et al. 2019). In the present research, PBEB outlined the post-COVID financial dynamic of biogas vitality in the post-COVID-19 eras. The apparent welfare was related to real funds in biogas vitality plants, and a more significant consideration of equipment and paybacks was necessary (Endrejat et al. 2020). Investors’ investment activity has a favorable effect on IEPBE. Biogas energy delivers several societal benefits, such as monetary improvement, municipal fitness, and ecological safety, and might enhance network procedures (Hao et al. 2020). This research indicates that investments in biogas vitality substantially impact ecological safety and benefit LIB. The previous study demonstrated that biogas energy plants contribute to the region’s sustainable economic development (Abdul et al. 2022; Wang et al. 2020). Biogas systems are supportive and beneficial ways to decarbonize the vitality part and mitigate environmental variation, fitness significances, and eco-friendly degradation associated with vestige fuel-grounded vitality production (Mohsin et al. 2021). Finally, these instances enable us to communicate hypotheses for this investigation.
Hypothesis 1 (H1). PIA, PEC, PBEB, and PBEE are confidently associated with the financier’s IEPBE.
Intention to environmental protection with biogas vitality
Stakeholders mark decisive judgments to invest in diverse areas below particular conditions. Existing and prospective investors in this region have access to a comprehensive range of investment opportunities worldwide in the renewable energy sector. However, the conduct is deliberate and not ultimately observed randomly (Hoang et al. 2021). TPB anticipates prudent investor conduct as financier conduct is intended and strategic. The entity’s conduct impacts the interactive intent as a motivating influence and is regarded as a solid intention to perform the behavior. Several variables boost the choice for environmental protection with biogas energy. Investors examined the rates of return for potential renewable energy investment alternatives. Investors can receive higher profits if they are ready to tolerate more significant risk. Moreover, biogas energy plants allow financiers to engage in societal esteem. Specific stakeholders powerfully anticipate investing in biogas vitality plants, and this trend will continue. The current research uses TPB to evaluate the comprehensive spectrum of investor interactive intentions. In addition, TPB has been utilized to measure social intent in various ways. For illustration, Song et al. (2019) analyzed energy-saving behavior using this approach (Hong et al. 2019), adapted this approach to the evaluation of broad vaccination intent (Carpenter and Reimers 2005), used this idea to evaluate management actions. To assess behavioral intention, Mohan and Bhuvanam (2015) utilized TPB in stock trading. TPB has been applied by El Mosalamy and Metawie (2018) to investigate investor behavior utilizing stock market-affecting elements. In addition, TPB has been utilized to boost biogas energy adoption (Zulu et al. 2021). TPB has been used to influence customers’ adoption of biogas vitality (Rai and Beck 2017); TPB has been pragmatic to finance the effectiveness of thoughtful competitions using solar and biogas energy technology. These results enable the testing of the second assumption.
Hypothesis 2 (H2). IEPBE display mediating upshot among independent variables and dependent adaptability.
Local investor behavior
Strong evidence suggests that four psychological characteristics, including masses conduct, threat sensibility, extreme confidence, and bullishness, significantly influences the specific financier’s outlay assertiveness. The supportive research demonstrates that robust gender intervention is associated with investment arrogance and psychosomatic influences. These influences include interactive intent and intensity, interactive plan and individual standards, financiers’ social intention, and perceived interactive mechanism (Phan and Zhou 2014). Their level of confidence primarily determines investors’ intent to invest. The selection of investors has little effect on characteristics such as security, perception of luxury use, and perception of risk (Thas et al. 2019). Interactive and mechanical aspects and the association between assortment enactment and RE (biogas) outlay can influence investors' decisions (Chodkowska-Miszczuk 2021).
Hypothesis 3 (H3). The LIB-dependent adaptable positively affects the financier’s IEPBE.
The moderating character of the perceived COVID-19 financial dynamic (PC-19FD)
The worldwide epidemics produced via interfering through the COVID-19 infection and its effects on drive-outlay occupations have affected maximum nations. As a direct result of the substantial investment that local investors made in the energy sector, consumers now enjoy reduced vitality overheads and the eradication of the drive shortfall. Due to the COVID-19 outbreak, investments in biogas energy have been halted, contributing to a 17% decrease in Pakistan’s inclusive outlay in the nation’s power industry (He et al. 2019; Rockstuhl et al. 2021). During the COVID-19 epidemic, energy consumption and output have increased simultaneously. As a result of a shift in investment in biogas energy plants following the emergence of COVID-19, energy production has decreased significantly. Consequently, energy costs and losses have been substantial (Hina et al. 2020). As an instrumental variable in IEPBE, investors’ LIB can be used to reliably predict local investors’ behavior, according to the TPB. TPB has claimed for local investors’ behavior. Nonetheless, several studies indicate that because of the COVID-19 stoppages, there has been a rise in outlay in biogas power plants. Due to the inadequate IEPBE, they had to waste significantly more energy than was strictly required (Kiruba and Vasantha 2021). There is the possibility for more significant investment in biogas energy plants due to the disrupting environment of the COVID-19 epidemic, which motivates native financiers. We believe the following hypothesis should be considered in light of the preceding arguments. The conceptual framework utilized for this study is shown in Fig. 1.Fig. 1 Conceptual framework
Hypothesis 4 (H4). The COVID-19 financial dynamic is a progressive moderator of the encouragement of IEPBE on LIB.
Materials and methods
The present research employed purposive sampling (non-probability test group) to examine the enactment of the plan for environmental protection with biogas energy. Purposive sampling is applied for specific resident characteristics, qualitative, exploratory, and pilot research. Methods of non-probability selection include voluntary response, snowball, purposive, quota, and convenience sampling. Financiers, investors, biogas energy professionals, and active participants of social media, Pakistan’s industrialized municipalities (Multan, Faisalabad, and Sahiwal), the district of the motherland is a most suitable area for biogas survey after the COVID-19 epidemic, and survey questions were asked for this purpose. The sample was comprised of biogas energy plants from certain cities. To attain this purpose, researchers polled key Pakistani respondents between the beginning of August and the end of September 2022. In-person meetings were utilized for the distribution of surveys. When the entire population is available for study, systematic sampling is essential for generalizing theories (Hull et al. 2019). This study examines the impression of the theory of planned behavior on the intent to protect the environment with biogas energy, as well as the moderating role of perceived Covid-19 financial dynamic in the relationship between environmental protection with biogas energy benefits and local investor behavior in Pakistan. Respondents were chosen based on the following criteria: biogas energy plant investors, economists, environmental analysts, active social media specialists, and shareholders with a minimum of 1 to 5 years of expertise in the sector. Respondents must possess at least a master’s degree and professional credentials. According to the recruitment criteria, the cultural and behavioral backgrounds of the responders are diverse. In this context, the sample is sufficiently diversified and accurately reflects the characteristics of respondents. The demographic characteristics of respondents are scheduled in Appendix, Table 6. The interviews revealed fictitious and logical obstacles, for instance, mechanical obstacles being the best significant obstacle to outlay. Faisalabad and Sahiwal are the third and twenty-first largest cities of Pakistan, with 3.6 and 3.02 million inhabitants, respectively, whereas the population of Multan is 2.23 million.
These cities are considered the most suitable for biogas plants and other urban areas. To validate the hypotheses, survey data from 91 individuals were subjected to structural equation modeling (SEM). There were 31 biogas energy plant investors, 19 economists, 16 environmental analysts, 14 shareholders, and 11 active social media specialists. This investigation is significant and valuable due to the scarcity of research on the interplay between intellectual factors and intention to environmental protection with biogas energy (IEPBE) and local investor’s behavior (LIB) specialists. It contributes theoretically to the prevailing figure of collected works. The results indicate an enthusiastic and arithmetical substantial association between financiers’ perceived investment attitude (PIA) and IEPBE. Consistent with the results of earlier research, several researchers have demonstrated that contribution to perceived environment concern (PEC) proceedings increased financiers’ understanding of the significance of vitality competence. The outcomes indicate that perceived biogas energy benefits PBEB and perceived biogas energy efficacy PBEE are definitely and suggestively connected through the vitality preservation targets of biogas plant stakeholders. These consequences are grounded on the explanations made by researchers throughout this investigation (Akroush et al. 2019). In many studies, biographers discovered a link between conservational distress, economic recompenses, and financing conduct using SEM (Hair et al. 2006, 2019; Hoelz and Bataglia 2021). Participants were provided additional assurances that their responses would remain confidential. After completing the survey, a total of 82 replies that were filled out were received. The researchers took the opportunity of eliminating any unreachable or insufficient information from the surveys. After 73 questionnaires were completed, a reaction rate of 89.02% was reported crossways the panel. Table 1 contains demographic data on those who participated in the survey. Ninety-three percent of survey respondents had attained a master’s degree or above. A large proportion of the sample is associated with pleasant folks (61.3% of the total).Table 1 Convergent validity analysis
Constructs Items Loadings VIF C.B alpha C.R AVE
Intention to environmental protection with biogas energy IEPBE1 0.770 1.568 0.929 0.940 0.611
IEPBE2 0.808 1.617
IEPBE3 0.798 1.556
IEPBE4 0.819 1.487
IEPBE5 0.719 1.224
IEPBE6 0.727 1.478
IEPBE7 0.814 1.345
IEPBE8 0.708 1.456
Local investor’s behavior LIB1 0.775 1.790 0.924 0.938 0.654
LIB2 0.826 1.315
LIB3 0.783 1.664
LIB4 0.823 1.980
LIB5 0.811 1.409
LIB6 0.835 2.701
LIB7 0.826 1.291
LIB8 0.787 1.473
Perceived COVID-19 financial dynamics PC-19FD1 0.835 2.873 0.941 0.951 0.710
PC-19FD2 0.861 2.084
PC-19FD3 0.849 1.957
PC-19FD4 0.813 1.506
PC-19FD5 0.868 2.569
PC-19FD6 0.868 1.727
PC-19FD7 0.853 1.258
PC-19FD8 0.790 1.369
Perceived environmental concern PEC1 0.910 1.426 0.852 0.869 0.625
PEC2 0.739 1.889
PEC3 0.730 1.732
PEC4 0.769 1.858
Perceived biogas energy efficacy PBEE1 0.997 1.381 0.997 0.998 0.991
PBEE2 0.996 1.924
PBEE3 0.994 2.633
PBEE4 0.995 1.726
Perceived investment attitude PIA1 0.943 2.026 0.978 0.980 0.862
PIA2 0.920 1.346
PIA3 0.953 1.766
PIA4 0.928 2.618
PIA5 0.901 1.258
PIA6 0.949 1.265
PIA7 0.931 1.345
PIA8 0.900 1.234
Perceived biogas energy benefits PBEB1 0.783 2.192 0.908 0.926 0.678
PBEB2 0.721 1.147
PBEB3 0.839 1.920
PBEB4 0.871 1.575
PBEB5 0.837 2.210
PBEB6 0.878 1.702
N = 73; IEPBE intention to environmental protection with biogas energy, LIB local investor’s behavior, PC-19FD perceived COVID-19 financial dynamics, PEC perceived biogas energy efficacy, PIA perceived investment attitude, PBEB perceived biogas energy benefits
Measurement variables
In the current study, all significant predictors represent multiple-item scale-measured characteristics, which were tested with a five-plug Likert scale fluctuating starting with 1 for strongly agree and 5 for strongly disagree. This study utilized six-item scales from prior research to test perceived biogas energy benefits and eight-item rankings to evaluate perceived investment attitude (Aziz et al. 2021). For example, investing in a biogas energy plant could cut carbon emissions. The four-item scale for perceived environmental concern and the six-item scale for perceived monitoring benefits were developed from previous research (Mahmood et al. 2021). Five items were used to measure biogas energy efficacy, adapted and modified from an earlier study. Eight factors measured the local investor’s behavior as a dependent variable. These items were embraced and amended based on the previous study (Rana et al. 2022). As a moderating variable, the role of the Covid-19 financial dynamic was examined by eight items extracted and modified from the research (Bobrovska et al. 2021).
Analytical background and results
For data analysis, our research utilized structural equation modeling (SEM). This method was used to evaluate relationship dimensions since it is a component-centered method (Saleem et al. 2021; Schuberth et al. 2022). PLS-SEM has a high frequency of use and applicability, which is why the researcher embraced it for this investigation; later research (Chin et al. 2020; Rönkkö and Cho, 2022) provided evidence. Structural equation modeling (SEM) is superior to conventional statistical analytic techniques. It aids statistical analysis in terms of efficiency, ease, and precision (Hair Jr et al. 2016; Henseler et al. 2015). SEM addresses the issues associated with first-generation analysis; yet, it is a second-generation method. Because SEM is a multivariate analysis technique, it can aid in the investigation of numerous variables concurrently. Because it can simultaneously manage complicated and varied interactions, SEM continues to gain popularity in business research. We utilized smart-PLS 3 to perform an SEM analysis of the data. To validate our model, we analyzed the data using smart-PLS in a two-stage procedure comprising estimates for the measurement assessment and structural assessment prototypes (Adetola et al. 2021; Akroush et al. 2019). The elements’ dependability and interactions were evaluated with MM, whereas the correlations between the seven paradigms were analyzed via SEM. Tests of assumptions and interactions, or the estimation of the surface prototypical, served as the basis for a model of structural assessment for biogas plants. A component-focused approach investigates the relational characteristics of the research. PLS-SEM was chosen above for further covariance-grounded procedures because it permits investigators to evaluate computations and influence configuration accurately. In addition, this study can be undertaken through a minor tester magnitude due to the potential of PLS-capability SEMs to syndicate various dimension measures (Hair et al. 2006; Urbach 2010). Students can utilize this paradigm for equally philosophical and informative reasons. As in the present study, the PLS method is extensively acknowledged in the initial phases of prototypical development. Conferring to prior research, the minimum sample size for PLS-SEM is 68 (Pirouz 2006).
Measurement model
It was essential to investigate the measurement model (Fig. 2) to check whether the paradigms’ dependability might be established expanding the four utmost frequently practice procedures. The criteria stipulated that the factor loadings of substances required extra than 0.708; the entire staff was determined to match this condition. In addition, smart-PLS for variance-based structural equation modeling employs the partial least squares route modeling technique to investigate the interdependence of variables (Abdul et al. 2022). In research, smart-PLS aims to test hypotheses, and detailed model research has changed accordingly. The smart-PLS consists of two methods: measurement and a structural model for this research’s analysis. The item correlation by the Cronbach alpha, the composite reliability, and the item loading define the validity. Nonetheless, discriminant validity relates to the link between variables analyzed using Fornell–Larcker, cross-loading, and heterotrait–monotrait ratio. In addition, the measurement model includes examining hypotheses utilizing route analysis and the research analysis outlined in the section on outcomes. The item was consequently categorical to exclude from the prototypical all components through load factors of less than 0.70. Furthermore, Cronbach’s alpha was figured as the connotation inception. Additionally, the reliability of the combination was assessed. Loadings and extracted average variance (AVE) values are more significant than 0.50, or AVE exceeds the recommended threshold of 0.5 (Arbuckle 2011). However, alpha and C.R. values are more critical than 0.70. These results suggest that convergent validity is the excellent and robust relationship between the items.Fig. 2 Measurement assessment model
The research results also evaluate the association between items, known as convergent validity. The graphs demonstrated that factor loadings are more significant than 0.50, alpha values are greater than 0.70, AVE values are more critical than 0.50, and C.R. values are more important than 0.70. These numbers suggest a high degree of item correlation and valid convergent validity. Table 2 summarizes the results. Lastly, the normal of the modifications were determined (Hoelz and Bataglia 2021). These measurements move beyond and upstairs by 0.70 and 0.50, correspondingly (Table 1). Moreover, the Fornell and Larcker (1981) approach verified discriminant validity (Table 2). Consequently, the validity and reliability of the research have been confirmed. Heterotrait–monotrait ratio (HTMT) technique was also employed to investigate the paradigm’s discriminant validity. Table 3 presents the consequences of the HTMT research. For reaching discriminant validity, the HTMT technique needs HTMT standards to be lesser than HTMT 0.90 (Hair et al. 2019). Relationship scores under HTMT 0.90 suggested that the concept is discriminant, conferring to the HTMT research. Table 4 illustrates the significant results of mediating belongings with suppositions challenging. According to the outcomes of the biogas energy plants, all AVE values range are between 0.611 and 0.995 (intention to environmental protection with biogas energy) (perceived biogas energy efficacy), respectively. C.R. values range from 0.869 and 0.998 (perceived environmental concern) (perceived biogas energy efficacy). All additional loadings have values between 0.5 and 0.997. All validated validity and reliability values for this measurement model are shown in Tables 1, 2, and 3. All factor loading values are more than 0.50; hence, the convergent validity of all items in the measurement assessment model is valid.Table 2 Discriminant validity through Fornell–Larcker
S. no Inconstant Mean S.D 1 2 3 4 5 6 7
1 IEPBE 0.089 0.056 0.782
2 LIB 0.079 0.041 0.139 0.082 0.809
3 PBEB 0.179 0.116 0.097 0.001 0.448 0.823
4 PBEE 0.226 0.083 0.038 0.017 0.456 0.684 0.996
5 PC-19FD 0.429 0.042 0.099 0.037 0.426 0.415 0.350 0.842
6 PEC 0.031 0.092 0.088 0.124 0.223 0.185 0.152 0.259 0.791
7 PIA 0.169 0.056 0.139 0.085 0.437 0.423 0.461 0.435 0.357
N = 73; IEPBE intention to environmental protection with biogas energy, LIB local investor’s behavior, PC-19FD perceived COVID-19 financial dynamics, PEC perceived biogas energy efficacy, PIA perceived investment attitude, PBEB perceived biogas energy benefits
Table 3 Heterotrait–Monotrait ratio (HTMT)
S. no Inconstant 1 2 3 4 5 6 7
1 IEPBE -
2 LIB 0.142 -
3 PBEB 0.096 0.498 -
4 PBEE 0.047 0.475 0.743 -
5 PC-19FD 0.106 0.451 0.457 0.36 -
6 PEC 0.074 0.225 0.212 0.186 0.248 -
7 PIA 0.133 0.462 0.463 0.469 0.452 0.397 -
N = 73; IEPBE intention to environmental protection with biogas energy, LIB local investor’s behavior, PC-19FD perceived COVID-19 financial dynamics, PEC perceived biogas energy efficacy, PIA perceived investment attitude, PBEB perceived biogas energy benefits
Table 4 Mediating belongings outcome through hypotheses evolution
Inconstant B-value T-value P-value Judgments
PBEB -> IEPBE -> LIB 0.031 3.420 0.011 Accepted
PBEE -> IEPBE -> LIB 0.071 2.254 0.001 Accepted
PEC -> IEPBE -> LIB 0.033 1.645 0.004 Accepted
PIA -> IEPBE -> LIB 0.020 2.874 0.034 Accepted
N = 73; IEPBE intention to environmental protection with biogas energy, LIB local investor’s behavior; PC-19FD perceived COVID-19 financial dynamics, PEC perceived biogas energy efficacy, PIA perceived investment attitude, PBEB perceived biogas energy benefits
Structural model
As a result, exemplary structural assessment exploration (Fig. 3) was performed using the proper dimension prototypical evaluation. The PLS-SEM literature offers criteria for evaluating hypotheses and determining the importance of path coefficients. To evaluate hypotheses’ significance, 5000 subsamples were applied to a bootstrapping process with a 5% significance threshold (one-tailed) (Hair et al. 2011). There was a substantial and beneficial impact on perceived biogas energy benefits (β = 0.193, t = 1.656 > 1.64, p < 0.05) and perceived investment attitude (β = 0.160, t = 2.839 > 1.64, p < 0.05), according to the data. In addition, perceived environmental concern (β = 0.033, t = 0.356 < 1.64, p > 0.05) on the desire to intention to ecological protection with biogas energy support hypotheses H3, H4, H6, and H7. In addition, the influence of the desire to engage in biogas energy plants on local investors’ behavior was statistically significant and positive (β = 0.090, t = 1.619 < 1.64, p > 0.05), corroborating hypothesis H1. Moreover, hypotheses H1 and H2 demonstrated a substantial and favorable encouragement of the intent to finance biogas energy to perceive biogas energy efficiency (β = 0.249, t = 3.006 < 1.64, p > 0.05) and the intention to environmental protection with biogas energy (β = 0.083, t = 2.024 < 1.64, p > 0.05). Consequently, both are equally significant and recognized. In conclusion, the perceived COVID-19 financial dynamic moderates the influence of intent to environmental protection with biogas energy (β = 0.420, t = 10.013 < 1.64, p > 0.05), validating only Hypothesis 5. Additionally, perceived investment attitude, perceived environmental concern, and reported biogas vitality advantages to account for 76.3% of the variance in biogas energy investment over social media awareness. The model’s ability to explain 66.8% of the variance in biogas energy of local investors’ behavior indicates its validity, Table 5; findings provided. This study employed the theory of planned behavior, with its model illustrating the mediation and moderation impact. This study’s independent variables are PBEB, PIA, PEC, and PBEE; IEPBE denotes mediation, PC-19IF demonstrates moderation, and LIB is the dependent adaptable.Fig. 3 Structural assessment model
Table 5 Results of the structural model through indirect belongings and assumptions evaluation
Assumptions B-values T-values P-values Judgments
H1: IEPBE -> LIB −0.09 1.619 0.054 Accepted
H2: IEPBE*PC-FD -> LIB 0.083 2.024 0.023 Accepted
H3: PBEB -> IEPBE −0.193 1.656 0.05 Accepted
H4: PBEE -> IEPBE 0.249 3.006 0.002 Accepted
H5: PC-19FD -> LIB 0.420 10.013 0.000 Accepted
H6: PEC -> IEPBE −0.033 0.356 0.361 Rejected
H7: PIA -> IEPBE −0.16 2.839 0.003 Accepted
N = 73; IEPBE intention to environmental protection with biogas energy, LIB local investor’s behavior, PC-19FD perceived COVID-19 financial dynamics, PEC perceived biogas energy efficacy, PIA perceived investment attitude, PBEB perceived biogas energy benefits
Discussion and implications
This study explored people’s intention for environmental protection with biogas energy applying the theory of planned behavior as its base. The present research attempted to determine whether outlay behavior and the COVID-19 financial dynamic moderated the propensity of IEPBE investors to invest in biogas energy through social media. The hypothetical fundamentals of TPB, which is prerogative to exert a substantial influence on perceived behavior and ecological mechanism, are also congruent through the current research results (see Appendix). This study demonstrates that expressive standards, eco-friendly views, promotional evidence, subjective routines, and a predicted financing mindset significantly impact investor intentions to invest in biogas energy via social media (Wang et al. 2020). An undeviating influential inconstant of outlay behavior, for instance, stakeholders’ motivation to preserve vitality (Wen and Witteveen 2021), can be utilized to envisage an increase in biogas drive fabrication positively significantly. Moreover, this study revealed that the awareness of biogas energy efficacy ultimately and substantially affected both IEPBE and LIB. Operative experience philosophy proclaims that PIA in biogas vitality and PBEB are authoritative predictors of actual financier behavior because it creates a psychosomatic enthusiasm for performance. The consequences established that IEPBE and LIB have a robust association. Implementing extraordinary-proficiency biogas vitality and financing in biogas vitality development convertible apparatus are instances of biogas outlay behavior, conferring to Iqbal et al. (2018), who indicated that these activities establish biogas speculation behavior. Before the initiation of the COVID-19 epidemic, it may have been modest to identify the adjustable liable for the upsurge in ingesting. As stated by the study results, the perceived COVID-19 financial dynamic moderates the influences among financiers’ IEPBE and LIB. Subsequently, with the development of COVID-19, biogas vitality outlay has detonated because of unexpected demand, ensuing in stumpy vitality values. Following the findings of this study, biogas energy investment is advantageous for alleviating the energy disaster and avoiding conservational downfall throughout the COVID-19 epidemic. The notion of PEC substantially impacts financiers’ IEPBE as a vital influence (Trypolska et al. 2021). Biogas energy systems have debatable environmental repercussions. Thus, sufficient precautions and care are mandatory to implement them (Yasmin et al. 2022), and biogas energy production has fitness, eco-friendly, and protection problems similar to other power foundations.
These outcomes are consistent with prior research on PIA, including vitality preservation and ecologically accountable shopping (Adetola et al. 2021). These findings are consistent with earlier research on investment perceptions (Aziz et al. 2021), for instance, alternative energy implementation, energy competence labeling (Zhang et al. 2020), and COVID-19 speculation variables (Bobrovska et al. 2021). In line with the outcomes of previous research, additional research has demonstrated that contribution to PEC proceedings increased investors’ understanding of the significance of vitality proficiency. The results indicate that PBEB and PBEE confidently and suggestively accompany the vitality preservation intents of financiers. These outcomes are grounded on the explanations made by researchers throughout this investigation (Akroush et al. 2019). The biographers discovered a relationship between conservational consciousness, economic benefits, and financing behavior. The results reveal that PBEB and PEC significantly and positively accompany investors’ vitality preservation goals. The conclusions of the current research are founded on the investigators’ clarifications. Moreover, the consequences concur with the research’s conclusion that PIA and PEC favorably and suggestively accompany investors’ intents to conserve energy. This analysis agrees with the findings (Ali et al. 2022) that PBEB and LIB are significantly and favorably connected with investors’ vitality-safeguarding aspirations. These findings are grounded on the annotations of the researchers. According to the results of this research, PEC and PBEB are favorably and expressively connected with investors’ energy protection goals. In conclusion, the perceived COVID-19 outlay component moderates the influence of intent to intention to environmental protection with biogas energy (β = 0.420, p > 0.000), validating only Hypothesis 5.
The current section involves empirical research exploring a particular country using geographically remote locations, although the arguments are in-depth. Pakistan has been picked due to its strong potential for RE, such as biogas and solar energy. From the beginning of August and the end of September 2022, the research performed in-depth semi-structured and unstructured interviews with the key investors. For the judgment-building procedure, the study enlisted the participation of the leading typescripts and critical professionals with biogas vitality-connected work familiarity in this nation. We posed pertinent examination interrogations regarding their assessment of obstacles, and this study has significance and importance for South Asian countries. These nations have significant renewable energy potential, particularly biogas energy Appendix Table 7.
Conclusions
According to this survey, Pakistani private investors’ investment in renewable energy projects is a top priority. This study adopted TPB as its theoretical framework since it emphasizes the purpose of biogas vitality funds, which primary research has not adequately examined. Evaluation of the monitoring structure was additional to the prototypical alongside the four-extra adaptable. A person’s attitude toward biogas energy investments and perception of the benefits of biogas energy impact their decision to invest in biogas energy. It was discovered that their perceptions of the supervisory atmosphere most substantially prejudice investor intention for environmental protection with biogas energy. Innovative paradigms, for example, the observation of biogas vitality efficacy, and the evaluation of parameters, have been included in the study prototypical to comprehend distinct financiers’ motivations to invest in biogas drive. These unique structures enhanced the prediction supremacy of the TPB prototypical, predominantly regarding investments in alternative energy. Thus, it was shown that TPB might be utilized in this field. Consequently, knowledge of Pakistan’s biogas vitality speculation potential would be accumulated. According to the conclusions of the moderation investigation, examining supervisory backgrounds that have a moderating character in the present research prototype will also reveal new visions.
This study centers on the IEPBE and LIB of financiers as well as power effectiveness and speculation determinants. Regarding the COVID-19 financial dynamic, the relationship between IEPBE and LIB was also analyzed. Based on this study’s findings, the following significant conclusions can be drawn: Therefore, let us begin with the fourth component of perception. PBEB and PBEE closely correlate with investors’ aspirations to improve energy efficiency. The investment mindset has a substantial and positive effect on the energy-saving goals of investors.
Similarly, as a second point, PEC positively and considerably impacts IEP and how individuals view solar energy. IEPBE’s direct influence on the LIB is positively correlated. Lastly, the perceived financial dynamic of COVID-19 significantly alters the relationship between IEPBE and LIB. Our research indicates that the perception of biogas energy efficacy positively and substantially impacted both IEPBE and LIB. Operative experience philosophy (Yee et al. 2021) proclaims that PIA in biogas vitality and PBEB strongly predicts actual investor behavior because it generates psychosomatic inspiration for performance. The outcomes indicated that IEPBE and LIB have a solid association. Accepting incredible competence biogas drive and financing in biogas power plant charge-tradable tools are illustrations of biogas speculation behavior. Before the COVID-19 epidemic, it may have been easier to identify the adjustable answer for the surge in ingestion. The study outcomes indicate the perceived COVID-19 financial dynamic moderates the connections among financiers’ IEPBE and LIB. Subsequently the arrival of COVID-19, investment in biogas power has detonated due to unanticipated demand, resulting in low energy prices. All assumptions are acknowledged because they correlate with financiers’ energy preservation purposes. H1, H2, H3, H4, and H5 are recognized as direct associations, whereas H7 is accepted cautiously. While remaining H6 has supported a little bit to this study. Several factors of biogas energy, including reliability, decentralization, transmission, and distribution, must be considered to lower energy prices.
Regarding the relevance and effectiveness of biogas energy, clarification is required. To inspire and assist the enlargement of the biogas power sector, the Pakistani administration should produce a biogas vitality reserve with advantageous positions and circumstances, particularly for minor financiers. The action should be completed to promote and facilitate the expansion of the biogas vitality area. The National Electric Power Regulatory Authority (NEPRA) and the Alternative Energy Development Board (AEDB) should work together to eliminate obstacles to escalating the biogas vitality industry. This proposal would serve both groups’ best interests. It is crucial to design energy purchasing systems to commercialize wind and biogas projects’ energy output. The Ministry of the Environment provides various financial tools, for instance, the Global Environment Facility (GEF) and the Clean Development Mechanism (CDM), which could aid in the growth of the biogas energy business. For the nation to be able to optimize its indigenous and sequestered biogas drive funds, it is vital to create public awareness and spread knowledge. To assist the growth of biogas energy, the general public should be sophisticated on the guidelines, processes, inducements, and machinery related to biogas energy schemes. A session on biogas energy–associated expertise should be apprehended to enable Pakistan’s government to promote biogas energy use. Additionally, the approaches of diverse energy information, native staff preparation, the opportunity of advancement industrial performs, and the broadcast of biogas energy are related.
This investigation was primarily concerned with the participants’ goals to improve biogas energy plants. Thus, future research might analyze the participants’ increasing reliance on biogas energy and their divergent investment goals. The upcoming examination should focus on northern municipalities and a broader range of individuals interested in investing in biogas energy plants. In addition, cultural influences may have influenced the outcomes, as all variables in this study were collected via a questionnaire. Consequently, future research utilizing biogas vitality tempo bills will have extra precise figures on actual biogas energy speculation. Correspondingly, the research has reinforced the willingness to finance in biogas drive via social media and influence schemes developed in Pakistan, a developing budget. The study is, hence, invalid in both developing and developed nations. Therefore, future authors must research how social media affects the acceptance of biogas energy plants in industrialized economies.
Appendix
See Tables 6 and 7. Table 6 The smooth interrogations and how respondents (investors, stakeholders, biogas energy experts, and active social media participants) challenged the semi-structured interview. Part A: Demographic configurations of respondents
Part: A Demographic attendance of respondents (investors, stakeholders, biogas energy experts, and active social media participants).
Variables Characteristics Regularity %
Gender Male 64 87.67
Female 9 12.32
Age Less than 35 23 31.50
45–55 19 26.02
56–65 18 24.65
65 and above 13 17.80
Education (respondents) Master in Arts 21 28.76
Master in Economics 19 26.02
MBA 18 24.65
Master in Commerce 15 20.54
Experience (respondents) 1– 5 years 32 43.83
6–12 years 14 19.17
13–18 years 16 21.91
19–25 years 05 06.84
25 and above 06 08.21
Brand names (biogas energy plants) Balloon plants 29 39.72
Horizontal plants 21 28.76
Earth-pit plants 11 15.06
Ferro-cement plants 07 09.58
Batch plants 05 06.84
Table 7 Part B: Factors manipulating environmental improvement through biogas energy plants
Unpredictable Items Cross-examination %
Intention to environmental protection with biogas energy IEPBE1 The quantity of biogas production depends on the quality of biogas plants 17.4
IEPBE2 Biogas plants can play a vital in various types of biogas manufacturing 13.2
IEPBE3 There is a need to train the biogas plant team to increase the volume of biogas production 11.0
IEPBE4 Biogas plant operational staff should focus on resource material area 9.5
IEPBE5 Biogas production using new technology can minimize the household expense such as lighting, heating, and cooking 15.3
IEPBE6 Trained staff has more excellent biogas production performance as compared to untrained biogas plant operating staff 16.2
IEPBE7 There is an important to introduce new technology and familiarity, how to operate biogas plants in rural residents 11.1
IEPBE8 There is a need to realize the importance of the valuable output of biogas plants for rural areas 6.3
Local investor’s behavior LIB1 There is a need to use modern technology in the production of biogas through new biogas plant technology 11.8
LIB2 Implication of the latest technology to produce biogas can reduce household expenditure 15.8
LIB3 Modern technology should introduce in rural areas to produce biogas for environmental protection 19.7
LIB4 The use of modern technology in biogas production can solve the gas issue in rural areas 10.9
LIB5 There is a need to adopt new technology for biogas production that is used in developed countries 11.3
LIB6 There is a need for trained staff for excellent biogas production performance and a need for staff training conferences for biogas plant operating 9.7
LIB7 There is a need to realize the importance of the valuable output of biogas plants to residents of rural areas 11.9
LIB8 There is a need to realize the new technology of biogas plants’ importance for valuable output for residents of rural areas 8.9
Perceived COVID-19 financial dynamics PC-19FD1 There is a need for local public training to operate biogas plants to increase biogas output 21.3
PC-19FD2 Trained staff can produce more biogas as compared to untrained biogas plant operating staff 17.2
PC-19FD3 This is very important to introduce new technology and familiarity, how to operate biogas plants in rural residents 11.4
PC-19FD4 There is a need to realize rural people of the importance of biogas energy 10.1
PC-19FD5 Government should introduce a green energy policy and economic incentives to produce biogas energy with biogas plants 11.2
PC-19FD6 Economic incentives are highly needed to produce biogas for selected rural areas of the Punjab 13.4
PC-19FD7 Government and private sector should arrange biogas plant operating training for the young generation of rural and urban areas for biogas production 12.0
PC-19FD8 There is a need to introduce the latest technology for biogas production to rural residents 3.4
Perceived environmental concern PEC1 Green energy policy and economic incentives of the government to produce biogas can play a vital role in the development of rural areas 29.9
PEC2 Government should provide economic incentives to produce biogas energy for mentioned rural areas in this study 31.8
PEC3 The government needs to make a favorable green energy policy to enhance biogas production for cheap and low-cost biogas and energy to the residents 25.8
PEC4 Household women should be trained in biogas energy plants operating for biogas production 12.5
Perceived biogas energy efficacy PBEE1 There is a need to realize the importance of biogas production to rural people with a social media approach 27.8
PBEE2 Social media is an essential channel for training rural people on how to operate biogas plants 26.0
PBEE3 Government should organize biogas plant operating training for biogas production using the social media platform 22.8
PBEE4 Government should be introduced the latest technology for biogas production to rural residents using social media Apps 23.4
Perceived investment attitude PIA1 There is a need to realize the financial benefits of biogas production in rural areas 22.4
PIA2 Government should arrange training for the urban people not to waste inedible food and other locally resourced materials 17.6
PIA3 Women in local areas should be trained in biogas production through social media 13.6
PIA4 The financial benefits of biogas production can be measured through the quantity of local resourced material provided 16.4
PIA5 Government should make training centers for local area people to produce biogas energy to rural residents 8.0
PIA6 Government should make a satisfactory green energy policy to enhance biogas production on low-cost biogas energy to the residents 9.5
PIA7 Trained staff need to support untrained staff and produce more biogas for general people 5.0
PIA8 Trained staff can produce more biogas energy with the help of new technology for short period of time 7.5
PBEB1 Advance quality of biogas plants can produce the quantity of biogas energy 20.1
Perceived biogas energy benefits PBEB2 New biogas plants technology can play a vigorous for biogas energy manufacturing 19.5
PBEB3 Adoption of new technology for biogas energy production has played vital role that used in developed countries 15.6
PBEB4 Government should introduce economic incentives using biogas plants to produce biogas energy 14.7
PBEB5 Government should provide tax incentives in rural areas to produce biogas energy 12.8
PBEB6 The financial benefits of biogas production can be measured through social media role 17.3
Author contribution
Shahid Ali: conceptualization, Qingyou Yan: supervision, funding acquisition. Azer Dilanchiev: variable construction, Muhammad Irfan: writing—original draft, formal analysis. Narmina Balabeyova: data handling and methodology. Everyone from the authors has studied and approved the document’s final version.
Data availability
The statistics supporting the outcomes of this research are accessible upon reasonable request from the first author.
Declarations
Ethics approval
The present investigation obeyed the guidelines set in the Helsinki Declaration. The Authorized Valuation Board of North China electric power university has established China (protocol 1091–10 on 05 September 2022).
Consent to participate
All participants gave informed consent to participate in this investigation.
Consent to Publish
All participants agreed after being adequately informed about the study.
Competing interest
The authors have no competing interests to declare.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Abdul D Wenqi J Tanveer A Prioritization of renewable energy source for electricity generation through AHP-VIKOR integrated methodology Renew Energy 2022 184 1018 1032 10.1016/j.renene.2021.10.082
Adedoyin FF Ozturk I Agboola MO Agboola PO Bekun FV The implications of renewable and non-renewable energy generating in sub-Saharan Africa: the role of economic policy uncertainties Energy Policy 2021 150 112115 10.1016/j.enpol.2020.112115
Adetola OJ Aghazadeh S Abdullahi M Perceived environmental concern , knowledge , and intention to visit green hotels : do perceived consumption values matter ? Pakistan J Commer Soc Sci 2021 15 240 264
Ahmad F Draz MU Chandio AA Su L Ahmad M Irfan M Investigating the myth of smokeless industry: environmental sustainability in the ASEAN countries and the role of service sector and renewable energy Environ Sci Pollut Res 2021 28 55344 55361 10.1007/s11356-021-14641-8
Ahmar M Ali F Jiang Y Alwetaishi M Ghoneim SSM Households’ energy choices in rural Pakistan Energies 2022 15 3149 10.3390/en15093149
Akroush MN Zuriekat M Jabali HIA Asfour NA Determinants of purchasing intentions of energy-efficient products Int J Energy Sect Manag 2019 13 128 148 10.1108/IJESM-05-2018-0009
Ali S Yan Q Irfan M Chen Z Evaluating barriers on biogas technology adoption in China : the moderating role of awareness and technology understanding Front Environ Sci 2022 10 1 16 10.3389/fenvs.2022.887084
Amir M Khan SZ Assessment of renewable energy: Status, challenges, COVID-19 impacts, opportunities, and sustainable energy solutions in Africa Energy Built Environ 2022 3 348 362 10.1016/j.enbenv.2021.03.002
Andarge E Fikadu T Temesgen R Shegaze M Feleke T Haile F Endashaw G Boti N Bekele A Glagn M Intention and practice on personal preventive measures against the covid-19 pandemic among adults with chronic conditions in southern ethiopia: a survey using the theory of planned behavior J Multidiscip Healthc 2020 13 1863 1877 10.2147/JMDH.S284707 33299323
Arbuckle JL IBM SPSS Amos 20 user’s guide 2011 SPSS Inc Amos Development Corporation
Aziz S Alias Z Khan NU Do brand familiarity, perceived trust and attitude predict stock investment decision behavior? Acad Strateg Manag J 2021 20 1 12
Baloch MA Ozturk I Bekun FV Khan D Modeling the dynamic linkage between financial development, energy innovation, and environmental quality: does globalization matter? Bus Strategy Environ 2021 30 176 184 10.1002/bse.2615
Bates MN Pope K Ram T Pokhrel AK Pillarisetti A Lam NL Verma SC Household fuel use and pulmonary tuberculosis in western Nepal : a case- control study Environ Res 2019 168 193 205 10.1016/j.envres.2018.09.036 30317104
Bobrovska О Lysachok A Kravchenko T Akimova L Akimov О The current state of investment security in Ukraine in the context of Covid-19 and its impact on the financial and economic situation of the state Financ Credit Act Probl theory Pract 2021 1 233 242 10.18371/fcaptp.v1i36.227770
Carpenter TD Reimers JL Unethical and fraudulent financial reporting : applying the theory of planned behavior J Bus Ethics 2005 60 115 129 10.1007/s10551-004-7370-9
Chandio AA Jiang Y Rehman A Twumasi MA Pathan AG Mohsin M Determinants of demand for credit by smallholder farmers’: a farm level analysis based on survey in Sindh, Pakistan J Asian Bus Econ Stud 2020 28 3 225 240 10.1108/jabes-01-2020-0004
Chandio AA Jiang Y Akram W Adeel S Irfan M Jan I Addressing the effect of climate change in the framework of financial and technological development on cereal production in Pakistan J Clean Prod 2021 288 125637 10.1016/j.jclepro.2020.125637
Chang CL McAleer M Wang YA Herding behaviour in energy stock markets during the Global Financial Crisis, SARS, and ongoing COVID-19 Renew Sustain Energy Rev 2020 134 10349 10.1016/j.rser.2020.110349
Chodkowska-Miszczuk J A new narrative for sustainability: exploring biogas plants as ‘first movers’ in raising energy awareness Aust J Environ Educ 2021 38 152 167 10.1017/aee.2021.17
Dai J Yang X Wen L Development of wind power industry in China: a comprehensive assessment Renew Sustain Energy Rev 2018 97 156 164 10.1016/j.rser.2018.08.044
Deshwal D Sangwan P Dahiya N How will COVID-19 impact renewable energy in India? Exploring challenges, lessons and emerging opportunities Energy Res Soc Sci 2021 77 102097 10.1016/j.erss.2021.102097 36568134
Di Leo S Caramuta P Curci P Cosmi C Regression analysis for energy demand projection: an application to TIMES-Basilicata and TIMES-Italy energy models Energy 2020 196 117058 10.1016/j.energy.2020.117058
Dyahrini W Syahputra AR Nurlita N Management Analysis of Solar Power Plant Project 409 Kwp (Case Study at PT. Kideco Jaya Agung in Paser District, East Kalimantan Province) J Educ Psychol 2020 58 6335 6342 10.17762/pae.v58i1.3790
e Silva HLDC Córdova MEH Barros RM Tiago Filho GL Lora EES Santos AHM dos Santos IFS de Oliveira Botan MCC Pedreira JR Flauzino BK Lab-scale and economic analysis of biogas production from swine manure Renew Energy 2022 186 350 365 10.1016/j.renene.2021.12.114
El Mosalamy D Metawie M Predictors of investors’ participation in the egyptian stock market: application of theory of planned behavior J Bus Manag Sci 2018 6 118 125 10.12691/jbms-6-3-9
Endrejat PC Güntner AV Ehrenholz S Kauffeld S Tailored communication increases the perceived benefits of solar energy Energy Policy 2020 144 111714 10.1016/j.enpol.2020.111714
Energy-Outlook W International Energy Agency, Energy Access Database [WWW Document] 2021
Eroğlu H Effects of Covid - 19 outbreak on environment and renewable energy sector Environ Dev Sustain 2020 23 4782 4790 10.1007/s10668-020-00837-4 32837274
Farhani S Ozturk I Causal relationship between CO2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia Environ Sci Pollut Res 2015 22 15663 15676 10.1007/s11356-015-4767-1
Fornara F Pattitoni P Mura M Strazzera E Predicting intention to improve household energy ef fi ciency : the role of value-belief-norm theory , normative and informational in fl uence , and speci fic attitude J Environ Psychol 2016 45 1 10 10.1016/j.jenvp.2015.11.001
Fornell C, Larcker DF (1981) Evaluating structural equation models with unobservable variables and measurement error. J Market Res 18(1):39–50. 10.1177/002224378101800104
Forster PM Forster HI Evans MJ Gidden MJ Jones CD Keller CA Lamboll RD Le Quéré C Rogelj J Rosen D Schleussner C Richardson TB Smith CJ Turnock ST Current and future global climate impacts resulting from COVID-19 Nat Clim Chang 2020 10 913 919 10.1038/s41558-020-0883-0
Goodell JW COVID-19 and finance : Agendas for future research Financ Res Lett 2020 35 101512 10.1016/j.frl.2020.101512 32562472
GoP. government of Pakistan RE.Policy Policy for development of renewable energy for power generation 2006
Hair E Halle T Terry-Humen E Lavelle B Calkins J Children’s school readiness in the ECLS-K: Predictions to academic, health, and social outcomes in first grade Early Child Res Q 2006 21 431 454 10.1016/j.ecresq.2006.09.005
Hair JF Ringle CM Sarstedt M PLS-SEM: indeed a silver bullet J Mark Theory Pract 2011 19 139 152 10.2753/MTP1069-6679190202
Hair JF Jr Sarstedt M Matthews LM Ringle CM Identifying and treating unobserved heterogeneity with FIMIX-PLS: part I–method Eur Bus Rev 2016 28 63 76 10.1108/EBR-09-2015-0094
Hair JF Sarstedt M Ringle CM Rethinking some of the rethinking of partial least squares Eur J Mark 2019 53 566 584 10.1108/EJM-10-2018-0665
Hall CAS Klitgaard K Energy and the wealth of nations, energy and the wealth of nations: an introduction to biophysical economics 2018 Cham Springer International Publishing
Hao Y Guo Y Guo Y Wu H Ren S Does outward foreign direct investment (OFDI) affect the home country’s environmental quality? The case of China Struct Chang Econ 2020 52 109 119 10.1016/j.strueco.2019.08.012
He X Zhan W Hu Y Consumer purchase intention of electric vehicles in China: the roles of perception and personality J Clean Prod 2018 204 1060 1069 10.1016/j.jclepro.2018.08.260
He L Liu R Zhong Z Wang D Xia Y Can green financial development promote renewable energy investment efficiency? A consideration of bank credit Renew Energy 2019 143 974 984 10.1016/j.renene.2019.05.059
Henseler J Ringle CM Sarstedt M A new criterion for assessing discriminant validity in variance-based structural equation modeling J Acad Market Sci 2015 43 115 135 10.1007/s11747-014-0403-8
Hina A Nadeem S Zia R Impact assessment of COVID-19 on Energy and power sector of Pakistan 2020 Sustainable Development Policy Institute
Hoang AT Nižetić S Olcer AI Ong HC Chen W-H Chong CT Thomas S Bandh SA Nguyen XP Impacts of COVID-19 pandemic on the global energy system and the shift progress to renewable energy: opportunities, challenges, and policy implications Energy Policy 2021 154 112322 10.1016/j.enpol.2021.112322 34566236
Hoelz J, Bataglia W (2021) Corporate reputation and strategic alliance performance. Corp Reput Rev 25:161–175. 10.1057/s41299-021-00120-w
Hong J She Y Wang S Dora M Impact of psychological factors on energy-saving behavior: moderating role of government subsidy policy J Clean Prod 2019 232 154 162 10.1016/j.jclepro.2019.05.321
Hosseini SE An outlook on the global development of renewable and sustainable energy at the time of COVID-19 Energy Res Soc Sci 2020 68 101633 10.1016/j.erss.2020.101633 32839691
Hull L Goulding L Khadjesari Z Davis R Healey A Bakolis I Sevdalis N Designing high-quality implementation research: development, application, feasibility and preliminary evaluation of the implementation science research development (ImpRes) tool and guide Implement Sci 2019 14 1 20 10.1186/s13012-019-0897-z 30611302
Hussain M Tahir S Ishak D Sarwar G Haroon M Ahmad W Younas T Tahir H Hybrid energy sources status of Pakistan : an optimal technical proposal to solve the power crises issues Energ Strat Rev 2019 24 132 153 10.1016/j.esr.2019.02.001
Ilyas SZ Hassan A Mufti H Review of the renewable energy status and prospects in Pakistan Int J Smart grid 2021 5 167 173 10.20508/ijsmartgrid.v5i4.220.g174
Ingrao C Bacenetti J Adamczyk J Ferrante V Messineo A Huisingh D Investigating energy and environmental issues of agro-biogas derived energy systems: a comprehensive review of Life Cycle Assessments Renew Energy 2019 136 296 307 10.1016/j.renene.2019.01.023
Iqbal T Dong CQ Lu Q Ali Z Khan I Hussain Z Abbas A Sketching Pakistan’s energy dynamics: prospects of biomass energy JRSE 2018 10 023101 10.1063/1.5010393
Irfan M Hao Y Panjwani MK Khan D Chandio AA Li H Competitive assessment of South Asia’s wind power industry: SWOT analysis and value chain combined model Energ Strat Rev 2020 32 100540 10.1016/j.esr.2020.100540
Irfan M Zhao ZY Rehman A Ozturk I Li H Consumers’ intention-based influence factors of renewable energy adoption in Pakistan: a structural equation modeling approach Environ Sci Pollut Res 2021 28 432 445 10.1007/s11356-020-10504-w
Jabeen G Ahmad M Zhang Q Perceived critical factors affecting consumers ’ intention to purchase renewable generation technologies: rural-urban heterogeneity Energy 2021 218 119494 10.1016/j.energy.2020.119494
Jha P Schmidt S State of biofuel development in sub-Saharan Africa: how far sustainable? Renew Sustain Energy Rev 2021 150 111432 10.1016/j.rser.2021.111432
Jiang P Van Fan Y Klemeš JJ Impacts of COVID-19 on energy demand and consumption: challenges, lessons and emerging opportunities Appl Energy 2021 285 116441 10.1016/j.apenergy.2021.116441 33519038
Karakosta C Mylona Z Karásek J Papapostolou A Geiseler E Tackling covid-19 crisis through energy efficiency investments: decision support tools for economic recovery Energ Strat Rev 2021 38 100764 10.1016/j.esr.2021.100764
Khan MO Effect of foreign direct investment on CO2 emission with the role of globalization , institutional quality with pooled mean group panel ARDL Environ Sci Pollut Res 2020 28 5271 5282 10.1007/s11356-020-10823-y
Kiruba AS, Vasantha S (2021) Determinants in investment behaviour during the COVID-19 pandemic. Indones Cap Mark Rev 13:71–84. 10.21002/icmr.v13i2.13351
Köhler C Steiner A Saint-Drenan YM Ernst D Bergmann-Dick A Zirkelbach M Ben Bouallègue Z Metzinger I Ritter B Critical weather situations for renewable energies – Part B: Low stratus risk for solar power Renew Energy 2017 101 794 803 10.1016/j.renene.2016.09.002
Kuzemko C Bradshaw M Bridge G Goldthau A Jewell J Overland I Scholten D Van de Graaf T Westphal K Covid-19 and the politics of sustainable energy transitions Energy Res Soc Sci 2020 68 101685 10.1016/j.erss.2020.101685 32839704
Li G Li W Jin Z Wang Z Influence of environmental concern and knowledge on households’ willingness to purchase energy-efficient appliances: a case study in Shanxi, China Sustainability 2019 11 1 18 10.3390/su11041073
Lowe RJ Drummond P Solar, wind and logistic substitution in global energy supply to 2050 – barriers and implications Renew Sustain Energy Rev 2022 153 111720 10.1016/j.rser.2021.111720
Madurai R Pugazhendhi R Jamal T Dyduch J Arif T Manoj N Shafiullah GM Chopra SS Envisioning the UN Sustainable Development Goals ( SDGs ) through the lens of energy sustainability ( SDG 7 ) in the post-COVID-19 world Appl Energy 2021 292 116665 10.1016/j.apenergy.2021.116665
Mahmood A Wang X Shahzad AN Fiaz S Ali H Naqve M Javaid MM Mumtaz S Naseer M Dong R Perspectives on bioenergy feedstock development in pakistan: challenges and opportunities Sustainability 2021 13 8438 10.3390/su13158438
Mavlutova I, Fomins A, Spilbergs A, Atstaja D, Brizga J (2022) Opportunities to increase financial well-being by investing in environmental, social and governance with respect to improving financial literacy under covid-19: the case of Latvia. Sustain 14(1):339. 10.3390/su14010339
Mikhail J Gallego-schmid A Stamford L Azapagic A Science of the total environment environmental sustainability of cooking fuels in remote communities : life cycle and local impacts Sci Total Environ 2020 713 136445 10.1016/j.scitotenv.2019.136445 31955079
Mohan S Bhuvanam S A study of modelling investors behaviour towards online share trading, Coimbatore J Impact Factor 2015 6 44 54
Mohsin M, Waqas H, Atif M, Sajjad M, Samad A (2021) Assessing the impact of transition from nonrenewable to renewable energy consumption on economic growth-environmental nexus from developing Asian economies. J Environ Manage 284:111999. 10.1016/j.jenvman.2021.111999
Phan KC Zhou J Factors influencing individual investor behavior : an empirical study of the Vietnamese stock Market Am J Bus Manag 2014 3 77 94 10.11634/216796061403527
Pirouz DM (2006) An overview of partial least squares. SSRN Electron J 1631359. 10.2139/ssrn.1631359
Popp J Kovács S Oláh J Divéki Z Balázs E Bioeconomy: biomass and biomass-based energy supply and demand N Biotechnol 2021 60 76 84 10.1016/j.nbt.2020.10.004 33039697
Rai V Beck AL Energy research & social science play and learn : serious games in breaking informational barriers in residential solar energy adoption in the United States Chem Phys Lett 2017 27 70 77 10.1016/j.erss.2017.03.001
Raina G Sinha S Outlook on the Indian scenario of solar energy strategies: policies and challenges Energ Strat Rev 2019 24 331 341 10.1016/j.esr.2019.04.005
Rana MW, Zhang S, Ali S, Hamid I (2022) Investigating green financing factors to entice private sector investment in renewables via digital media: energy efficiency and sustainable development in the post-COVID-19 era. Sustain 14(20):13119. 10.3390/su142013119
Rauf A Liu X Amin W Ozturk I Rehman OU Sarwar S Energy and ecological sustainability: challenges and panoramas in belt and road initiative countries Sustainability 2018 10 1 21 10.3390/su10082743
Rockstuhl S Wenninger S Wiethe C Häckel B Understanding the risk perception of energy efficiency investments: investment perspective vs. energy bill perspective Energy Policy 2021 159 112616 10.1016/j.enpol.2021.112616
Roy H Dalei NN Empirical relation between energy use and human development: evidence from BRICS nations Energy, Environment and Globalization 2020 Singapore Springer
Saleem M Qadeer F Mahmood F Han H Giorgi G Ariza-Montes A Inculcation of green behavior in employees: a multilevel moderated mediation approach Int J Environ Res Public Health 2021 18 331 10.3390/ijerph18010331 33466298
Schraufnagel DE Balmes JR Cowl CT De Matteis S Jung SH Mortimer K Perez-Padilla R Rice MB Riojas-Rodriguez H Sood A Thurston GD To T Vanker A Wuebbles DJ Air pollution and noncommunicable diseases: a review by the forum of International Respiratory Societies’ Environmental Committee, Part 2: Air Pollution and Organ Systems Chest 2019 155 417 426 10.1016/j.chest.2018.10.041 30419237
Schuberth F, Rademaker ME, Henseler J (2022) Assessing the overall fit of composite models estimated by partial least squares path modeling. Eur J Mark 57:1678–702. 10.1108/EJM-08-2020-0586
Situmorang YA Zhao Z Yoshida A Abudula A Guan G Small-scale biomass gasification systems for power generation (<200 kW class): a review Renew Sustain Energy Rev 2020 117 109486 10.1016/j.rser.2019.109486
Song Y Zhao C Zhang M Does haze pollution promote the consumption of energy-saving appliances in China? An empirical study based on norm activation model Resour Conserv Recycl 2019 145 220 229 10.1016/j.resconrec.2019.02.041
Sun Y Xue J Shi X Wang K Qi S A dynamic and continuous allowances allocation methodology for the prevention of carbon leakage : emission control coefficients Appl Energy 2019 236 220 230 10.1016/j.apenergy.2018.11.095
Tanveer A Song H Faheem M Daud A Naseer S Unveiling the asymmetric impact of energy consumption on environmental mitigation in the manufacturing sector of Pakistan Environ Sci Pollut Res 2021 28 64586 64605 10.1007/s11356-021-14955-7
Thas TMABM, Thas THBM, Binti RMP, Bin AMF, Allah PA, Bin Oladokun ON (2019) Factors affecting investors’ intention to invest in peer-to-peer lending platform in Malaysia: An extended technology acceptance model Asian Dev. Bank Institute. http://hdl.handle.net/11540/11128
Tian J, Yu L, Xue R, Zhuang S, Shan Y (2022) Global low-carbon energy transition in the post-COVID-19 era. Appl Energy 307:118205. 10.1016/j.apenergy.2021.118205
Trypolska G Kyryziuk S Krupin V Wąs A Podolets R Economic feasibility of agricultural biogas production by farms in Ukraine Energies 2021 15 87 10.3390/en15010087
Ullah H Kamal I Ali A Arshad N Investor focused placement and sizing of photovoltaic grid-connected systems in Pakistan, Renewable Energy 2018 Elsevier Ltd.
Urbach N Structural equation modeling in information systems research using partial structural equation modeling in information systems research using partial least squares J Inf Technol Theory Appl 2010 11 5–40 228467554
Wang Z Ali S Akbar A Rasool F Determining the influencing factors of biogas technology adoption intention in Pakistan: the moderating role of social media Int J Environ Res Public Health 2020 17 2311 10.3390/ijerph17072311 32235442
Wang Q, Dong Z, Li R, Wang L (2021a) Renewable energy and economic growth: new insight from country risks. Energy 238:122018. 10.1016/j.energy.2021.122018
Wang QJ Chen D Chang CP The impact of COVID-19 on stock prices of solar enterprises: a comprehensive evidence based on the government response and confirmed cases Int J Green Energy 2021 18 443 456 10.1080/15435075.2020.1865367
Weko S, Eicke L, Quitzow R, Bersalli G, Lira F, Marian A, Süsser D, Thapar S, Xue B (2020) Covid-19 and carbon lock-in: impacts on the energy transition. Inst Adv Sustain Stud. 10.2312/iass.2020.027
Wen F, Witteveen D (2021) Does perceived social mobility shape attitudes toward government and family educational investment? Soc Sci Res 98:102579. 10.1016/j.ssresearch.2021.102579
Wu H Li Y Hao Y Ren S Zhang P Environmental decentralization, local government competition, and regional green development: evidence from China Sci Total Environ 2020 708 135085 10.1016/j.scitotenv.2019.135085 31812397
Yasmin I Akram W Adeel S Chandio AA Non-adoption decision of biogas in rural Pakistan: use of multinomial logit model Environ Sci Pollut Res 2022 29 53884 53905 10.1007/s11356-022-19539-7
Yee CH Al-mulali U Ling GM Intention towards renewable energy investments in Malaysia : extending theory of planned behaviour Environ Sci Pollut Res 2021 29 1021 1036 10.1007/s11356-021-15737-x
Zhang Y Xiao C Zhou G Willingness to pay a price premium for energy-saving appliances: role of perceived value and energy efficiency labeling J Clean Prod 2020 242 118555 10.1016/j.jclepro.2019.118555
Zhang H Yan J Yu Q Obersteiner M Li W Chen J Zhang Q Jiang M Wallin F Song X Wu J Wang X Shibasaki R 1.6 Million transactions replicate distributed PV market slowdown by COVID-19 lockdown Appl Energy 2021 283 116341 10.1016/j.apenergy.2020.116341 35996733
Zulu S, Zulu E, Chabala M (2021) Factors influencing households’ intention to adopt solar energy solutions in Zambia: insights from the theory of planned behaviour Smart Sustain 11:951–971. 10.1108/SASBE-01-2021-0008 | 37289390 | PMC10248343 | NO-CC CODE | 2023-06-13 23:15:10 | yes | Environ Sci Pollut Res Int. 2023 Jun 8;:1-20 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
37289392
28052
10.1007/s11356-023-28052-4
Research Article
Greening tourism with environmental wellness: importance of environmental engagement, green tourist intentions, and tourist’ environmental stimulus
Hou Menghan [email protected]
1
Zhang Mengyao [email protected]
2
Sun Yang [email protected]
1
1 grid.495262.e 0000 0004 1777 7369 School of Tourism, Shandong Women’s University, Shan Dong, Jinan, 250399 China
2 grid.256922.8 0000 0000 9139 560X Institute of Cultural Industry and Tourism Management, Henan University, He Nan, Kaifeng, 450046 China
Responsible Editor: Arshian Sharif
8 6 2023
115
12 3 2023
29 5 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
The appearance of COVID-19 has highlighted the critical nature of well-being and health in the modern world that affected the tourism industry at large. Thus, the research aims to estimate the nexus between green tourism inspiration and tourists’ environmental wellness, environmental engagement, and green revisit intentions in China. The study obtained data from Chinese tourists and applied the fuzzy estimation technique. The study estimated the findings using fuzzy HFLTS, fuzzy AHP, and fuzzy MABAC techniques. The study results show green tourism inspiration, environmental engagement, and green revisit intentions, while fuzzy AHP revealed that tourism engagement has the highest fuzzy-weighted score in developing the revisit intentions of Chinese tourists. Moreover, the fuzzy MABAC score indicated that green tourism inspiration and environmental wellness matter most in reshaping tourists’ revisit intentions. The study results are found to be robust in determining the relationship. Hence, research findings and recommendations for future study will help companies and society at large while elevating the Chinese tourism industry’s reputation, impact, and worth in the eyes of the public.
Keywords
Green tourism
Tourism development
Environmental wellness
Environmental engagement
Green tourist intentions
Fuzzy technique
==== Body
pmcIntroduction
Green tourism is often acknowledged as directly linked to their level of contentment, life satisfaction, and country-level conditions. Simultaneously, there has been an increased amount of empirical investigations focusing on enhancing tourists’ revisit intentions after the COVID-19 crisis. Correspondingly, current research considered this and motivated the study on tourists’ revisit intentions. The rise of “wellness tourism” may be traced partly to efforts to counteract the rising expense of the tourism industry in China (Gordon and Baker 2016). Environmental wellness describes vacations primarily focusing on the tourist’s mental, emotional, and spiritual needs. According to academic evidence, many aspects of tourist’s lives, from their mental and physical health to their sense of life purpose and happiness, are positively correlated when traveling (Li et al., 2022). However, academic research on motivation in the post-COVID-19 tourism future is scant (Harrigan et al 2017). Hence, the research motivation is to study the role of tourist’s inspiration, engagement, and environmental wellness influence on tourists’ revisit intentions during post COVID-19 period.
Previous studies highlighted that aspiring tourists are less likely to skip going to a dream tourist location. Such individuals understand tourism destination image and recognition and, therefore, visit the most (Haldrup and Larsen 2006). On the other side, vice-versa (Babb 2010). Researchers also explained that the ideas for your trip come during the preliminary planning stage when tourists are still just daydreaming. Companies in the tourism industry use a variety of subtle prompts to entice customers. Impulse is the emotional condition that propels one to put into practice an idea just acquired. Three primary theoretical foundations make up the following matrix of inspiration. Before everything else, evocation, transcendence, and motivation constitute the backbone of imagination. With its distinguishing characteristic of environmental wellness, tourists’ inspirations are sparked not by intentional self-awakening but by external stimulation (Rather 2020). When a tourist transcends, they open themselves up to options they were previously unaware of, which may be greater or more exciting (Teng 2021). The concept of “tourism inspiration” refers to the internal drive that pushes a person to put their thoughts into practice. Doing online research is an important part of deciding where to go on vacation. Several tourists now utilize social media for recommendations and reviews of potential vacation spots before deciding (Rasoolimanesh et al 2019). In reality, most tourists nowadays use social media well before leaving on a trip. Generally, friends and family weigh heavily on one’s mind when deciding where to go (Chen et al 2021). Since its significance was realized, there has been a rise in research into social networks about the hotel and tourist industry (Fletcher et al. 2016).
In most cases, the importance of the concept known as “tourism engagement” increases when social media networks serve as the primary focus of the research (Loureiro and Sarmento 2019). Because participation is the phenomenon that explains the nature of the particular interactions between businesses, consumers, and their cooperating emotions, it is important to confirm that it is a major subject in conversations about online communities for brands. Several scholars have deemed this examination of participation in the tourist sector interesting (So et al. 2014). A growing number of travel companies are investing in a stronger internet presence to attract more customers, claims (Thomas et al. 2018). On the other hand, tourists who are active on social media might be more inclined to make online purchases than those who are not. Tourists’ offline habits are mostly mirrored in their online ones. Because of this, the level of participation is the primary variable being examined (Smith et al 2019). There are some motivations for carrying out this investigation (Chen and Rahman 2018).
Environmental inspiration of tourists to visit a tourist destination is a complex matter to define and depends upon many aspects (Zheng et al 2022). In Theory, inspired tourists have had an emotionally pleasurable experience and found more satisfaction from the tourism industry. Motivated and helped inspire are the two steps proposed by the channel model of inspiration. The above theory proposes that exposure to new information triggers a chain reaction leading to a higher level of inspiration and activity in the tourism sector and the cognition of tourists for revisiting their liked tourism spot (Peng et al 2023). While on the other side, this research enhances the theoretical underpinnings of the field and puts an end to these criticisms of previous research using the recent topicality of the study (Torabi et al 2022). With this in consideration, this study expands on the work of those who have argued that the function of tourism inspirations, here understood as an emotional situation, is pivotal but under-studied in the field of tourist cognition (Yang et al 2022a, b). Therefore, previous studies theoretically recognized that tourists’ engagement and environmental wellness also matter in the nexus between tourism inspiration and tourist revisit intentions. To better recognize what individuals find intellectually satisfying, it is essential to delve further into the current body of research on imagination (Lyu et al 2023). This is because previous studies have shown that encouragement to investigate this topicality. To fill the research gap, the study aims to research how environmental inspiration of tourists, engagement, and environmental wellness matters for tourist revisit intentions.
Travel is a wonderful way for people to learn about themselves and the world. Implementations, communication, and social interaction are all essential components of society. Culture-curious tourists may use the materials they purchase on their trip back home (Tiwari et al 2022). It entails boosting the competitiveness of tourism for cultural heritage and developing new cultural hubs in underserved areas. It is no coincidence that the cultural and tourist industries are interconnected. Technological development makes industry boundaries less distinct, leading to industrial integration. Many studies have shown that cultural tourism and cultural exchange are interconnected from an economic perspective (Ding et al. 2022; Sun et al 2022; Juliana et al 2022). The cultural and monetary value of tourism is undeniable (Shi et al 2022). Culture, however, has aspects (both ideological and commodity) that might be useful to a business plan (Chen et al. 2022). The Chinese government has been actively promoting cultural tourism since 2009. The “Guidance on Promoting the Integration of Culture and Tourism” was released in August 2009 by the now-defunct Ministry of Culture and National Tourism Administration to foster cultural exchange, collaboration among businesses, and a new setting for tourism’s future. Until 2018, 25 provinces have established local integration programs. The blending of culture and tourism, symbolized by the formation of the department of culture and tourism in March 2018, has prompted major discussion in both the public and private sectors, with citizens becoming more alert to the repercussions of policies and wondering if it is resulted in increased tourist growth (Juliana et al 2022; Sun et al 2022).
This paper makes multiple contributions to the literature on tourism and practice. First, this research contributes by presenting the latest understanding of tourism inspiration, wellness, engagement, and tourists’ revisit intentions. This contribution is fulfilled by testing the empirical interplay among the variables using direct and indirect associations. Second, the study contributes by considering the Chinese tourism industry and tourists. Preceding research extends this contribution by presenting the latest insights from China on study topicality. Third, presenting insights about the nexus of tourists’ environmental engagement and environmental wellness with tourism inspiration and revisit intentions also provides practical insights using the research findings. Finally, the study presents the key practical implications for the stakeholders.
The study structure includes five sections sequentially: the “Introduction,” the “Literature review,” the “Methodology,” the “Results and discussion,” and the “Conclusion and implications” sections.
Literature review
Review of previous studies
The research intends to determine whether or not some aspects of tourism inspiration, environmental wellness, and tourists’ environmental engagement are particularly persuasive to potential tourists for revisiting (Ma et al. 2022). Unfortunately, their relationship has been the subject of very little research. Quite a few investigations have shown that medical tourism may bring in tourists who need medical care and are interested in visiting a particular place (Thipsingh et al 2022). Attractiveness can be increased through increased spending on advertisements, the provision of a high-quality warranty, cutting-edge medical care, and competitive well-being prices achieved through the implementation of an appropriate promotion mix, the establishment of great medical standards, and the provision of comprehensive medical tools and products (Wasaya et al. 2022). However, the researchers characterize medical services: “Patients decide to go to a foreign country for hospital attention depending on factors such as price and convenience, rather than on local availability or availability of treatment options (Shoukat and Ramkissoon 2022).” This shows that a terminal’s capacity to perform superior on the main factor of international care, such as therapeutic approaches or customer satisfaction, would increase the number of tourists visiting that location (Khoi et al. 2020).
However, other disciplines, like sociology, public administration, group dynamics, the education sciences, and the tourism industry, have also taken an interest in the topic of “engage,” which has its roots in psychology (Wei et al 2023). There is no universally accepted definition of “connection” in psychology and social sciences. Though some academics see involvement as having just one component, others see it as having several. As both a concept, participation is malleable and amenable to analysis in a variety of settings and fields. Since then, marketers have been discussing the concept of “connection.” According to previous research, the first-of-its-kind research (Whiting and Hannam 2014). Extending it, the tourism engagement Expressions of connection may be seen in how tourists interact with a visited destination through proper channels. Most focus is on revisiting that destination point. Prior research in psychology and advertising has hypothesized that source qualities and personal differences ultimately decide where we get our inspiration (Wang and Lyu 2019). A place itself may serve as the impetus for a tourist’s journey; in this case, the location’s related qualities, including sentimentality, serve as the cause. When a visitor develops a profound emotional connection to a certain destination, we say that they have developed a “destination loyalty.” It indicates a location’s importance in inspiring passionate, positive emotions in visitors (Squire 1996). Previous psychological research studies have shown that elevated arousal, attentiveness, and cheerfulness greatly aid the thinking processes and contribute to a profound sense of inspiration, strengthening the relationship between positive impact and emotional attachment (Brouder 2018). Hence, emotional investment in a destination may influence creative impulses while vacationing there (Postma et al. 2017). Although the primary goal of this research is to fill a gap in the tourist canon by uncovering a previously unknown causal connection, it also answers the demand to fully understand people’s emotional bonds with the places they visit regularly (Craik 2002).
Motivation is more of a transient motivating condition than a stable character characteristic so it may be invoked at any point in the consumer decision-making process (Stroebel 2015). Captivating consumers for the sake of gratifying, retaining, and developing customer satisfaction becomes more crucial due to an accumulation of advantages obtained during the lifespan of regular fans than motivating consumers just before a decision is made since it may help attract new customers. As a result, the creativity found in consuming, specifically traveling to a certain location, is the subject of this investigation. In line with the consumer environment, the goal of the tourism market is to provide visitors with amazing memories that revitalize, inspire, and widen their perspectives (Tsaur et al. 2022). As a result, the tourist experience, defined as “a continual exchange of ideas and sensations throughout periods of awareness that emerge via immensely complicated mental, social, and neurological dynamic interactions,” has received great attention in school studies related to tourism (Kline and Fischer 2021; Hunt and Harbor 2019).
This idea has been discussed to a certain extent in published works. A new study has even suggested conceptually exploring both negative and positive forms of participation (Black and Cobbinah 2017). While some research has focused on the negative aspects of participation, most studies have focused on the good. Learning more about them before getting them is important since it is so fresh to the marketing industry. Addressing this concept more thoroughly may be accomplished via research into the valence of participation. Previous research on healthcare tourists’ driving forces has shown a complex interplay of factors. Although extrinsic and internal variables might influence incentives, this investigation focuses particularly on the former (Xue et al 2022). For instance, the literature on tourism motivation led to the introduction of a general tourist incentive framework that incorporates the pursuit of grandeur and luxury, innovation and information, personal growth, and escape and relaxation (Duxbury et al 2020). There are three interrelated causes for this inward concentration (Sigala 2019). In the first place, the incentive framework has received a lot of academic backing and has been employed in the real world to study wellness-related travel reasons (Pencarelli 2020). Investigation on travel motivation has included emotional and cognitive drivers, giving us a better understanding of how healthcare objectives affect tourist motivation than previous models (Aftab and Khan 2019).
Most of the scholarship written on visitor participation in natural areas has focused on the mindsets and behavior of participants, with work engagement dominating the discussion (Libre et al. 2022). Whereas visitor participation is not novel in environmentalism, early films have relied mostly on survey studies or research methodology investigations to make sense of visitors’ actions. Thus, it is important to have a comprehensive framework to comprehend participation characteristics in ecotourism studies (Chan et al 2022). Scholarship on that well has spread across the tourism area as the concept of well-being has become more popular in the fields that serve as academic foundations. For many decades, scholars in the field of tourism literature particularly concentrated on the idea of “well-being” and its associated terms. Tourism academics typically employ the categories of pleasure, well-being, lifestyle quality, and satisfaction with life indiscriminately, contributing to recently changed, one of the key issues plaguing tourism studies on contentment, well-being, and living conditions (Li et al. 2023). Past developments have seen a shift in the focus of the tourist industry toward non-financial measures, including the happiness, health, the longevity of those involved in the industry. The United Nations approved a collection of targets in 2015 called the Sustainable Development Goals (SDGs) with the lofty aim of ensuring that everyone can have freedom and abundance by 2030. Goal 3 of the SDGs is specifically geared toward enhancing human health and well-being worldwide. The tourist industry must prioritize the SDGs (Miao et al 2022). Yet, typological difficulties, conceptual ambiguity, and scientific conflicts indicate that well-being research in tourism remains in its childhood. There have been calls for novel and imaginative measures to address worries like the world’s older workforce and protect some well of tourists (Larsen 2014).
Research gaps and theoretical framework
According to Zaitul et al. (2022), this study incorporates tourism attributes to investigate the research topic. Tourists’ desires and objectives drive them; therefore, it makes sense to consider how tourists’ motives connect to and align with their own. To further understand the connections underlying motives and evaluation characteristics, this research focuses on two fundamental impression management aspects: goal relevance and team cohesion (Richards 2020). Although team performance emphasizes how well an anticipated objective and the environment line up, objective significance defines how important a specific scenario is to achieving that goal (Larsen 2014). Their seminal work argues that the appraisal process generates control, including mutual influence between the evaluated event and the assessors doing the evaluation. Therefore, internal components like motives, expectancy, personality, imputation style, and external stimuli have a role in eliciting sensation. We thus hypothesize that personal factors—wellness tourists’ underlying goals—significantly impact their overall evaluations (Kwon and Boger 2021). When wellness tourists visit with more robust well-being objectives, they are more likely to find suitable and congruent options offered by wellness tourist areas (Hjalager 2010). Logically speaking, the new Hypothesis is that tourists’ wellness-related incentives are associated with more meaningful travel goals. Tourists’ objective relevance is significantly associated with the following motivations: status and elegance, learning and discovery, personal growth, and rest and recreation. The degree to which a traveler’s goals and the trip’s activities align is positively associated (Yang et al. 2022a).
Inspirational Factors in Cognitive Evaluation, Cognitive appraisal theory describes how the same experience may elicit a range of feelings in various individuals at differing times, depending on how every person interprets the significance of the situation in their own lives (Damanik and Yusuf 2022). Inspiring experiences sometimes go unrecognized as an emotion due to a disconnect between motivation and the cognitive component (Deng et al. 2023). According to research, previous research consistent with the value feelings (like enjoyment) are more likely to be generated in tourists when they feel their vacations have been tailored to their needs. Likewise, Tabaeeian et al. (2022) found that assessment of purpose applicability and goal congruence significantly correlated with the experience of joy (Mohammed et al. 2022). Therefore, we argue that inspiration is much more likely to happen whenever motives and intentions signals are viewed as important to a tourist’s objective, and a traveler’s judgment of the stimulation is congruent with their purpose. Hence, considering such elements, current research fulfills the research gaps (Laing and Crouch 2011).
As shown by the recent literature on imagination, three distinct methods may be used to develop a working definition of encouragement: categorization, (ii) deconstruction of procedure, and (iii) separation to purpose. Using the “comparing multiple” process, scientists may adopt the “inspiration” idea to a given situation. Several illustrations may be found in the conventional classification of these phenomena, including religious inspiration, artistic encouragement, and relational motivation. Influenced and inspired-to are two consecutive steps the “process of breaking down of method” recommends when analyzing the creative process (Thrash et al., 2007). The “segmentation of function” method pulls on the second method to zero in on emotionally diverse representations of the part of the responsibility, thereby resolving a recognized shortcoming between the first’s two methods. In this school of thought, the three transfer variants became classified as duplication, realization, and expressiveness. Imitation describes the actions tourists do after being exposed to motivational input.
Methodology
Research data and measures
Research data is collected using the questionnaire in China’s tourism and hospitality industry. Researchers traced 152 hotels where tourists stay in China during their visit and 24 key tourism destination all over China. Through this, researchers obtained data from 304 tourists on a questionnaire. Two components of empirical modeling are utilized by the researchers, (1) psycho-behavioral models, including tourists’ inspiration, revisit intentions, tourism well-being and tourists’ engagement, and (2) validation modeling to estimate the nexus among the study variables using the data as mentioned earlier collected from the Chinese tourists. The study variables are measured using the 5-point Likert scale ranging from 1 as strongly disagree to 5 as strongly agree.
Utilizing fuzzy-HFLTS technique
The complication of the tourism industry in China can is featured with the uncertainty linked with the tourist’s revisit intentions. Thus, the fuzzy hesitant (HFLTS) estimation technique is applied to choose the various criteria with insufficient information. When there is a shortage of data, a Hesitant Fuzzy MCDM technique is useful because it employs comparison HFLTS to expose information in hesitant scenarios. For elements in HFSs, the membership value might take on many possible values between 0 and 1. When there is much room for debate during an examination, academics often use HFS as a reliable tool. Employing HFLTS, DMs in an MCDM model communicated overall ratings via language. HFS may be portrayed as a function on the set [0, 1] or as a subdivision of the set [0, 1],1 E=⟨x,hEx⟩|x∈X
M = {μ1, μ2, …, μn} is restricted with the unit of empirical measurement shown with the n. The fuzzy member function is also reported. This HFS is linked with the M, and it is empirically explained below,2 hM::M→0,1
Using the S = {s0, …, sg} linguistic term, we explained the S, the discreet subset of the serial Fuzzy HFLTS linguistic terms of S. It also measures the GH denomination. Thus, this nexus is articulated as,3 hMx=⋃μϵMμx
Consequently, to Eq. (3), we transformed the Fuzzy HFLTS linguistic indicators with the approach mentioned below:4 EGHsi=si|si∈S
5 EGHatmostsi=sjssj∈Sandsj≤si
6 EGHatleastsi=sj/sj∈Sandsj≥si
7 EGHgreaterthansi=sjsj∈Sandsj>si
8 EGHbetweensiandsj=sk|sk∈Sandsi≤sk≤sj
Using the upper bound of upper bound Hs + and the lower bound Hs − , researchers extended the envelope of HFLTS, which is shown as env(HS):9 envHS=Hs-,Hs+,Hs-≤Hs+
Two major benefits to the virtual environment result from using idioms: Integrating linguistic term sets with reluctance facilitates the judgment call system, as it gives DMs a means of conveying what they think via the medium of language. Also, the photographer’s strong pliability makes available various grammatical manifestation options. This seems to help keep the original intent of the expression throughout the spontaneous adaptation. As it can consider a wide range of variables, HFLTS is a go-to technique in complex situations. Due to the many factors to be considered, health tourism presents a wonderful opportunity for strategic planning.
Fuzzy HFL AHP estimation technique
The relevance of each fuzzy empirical aspect is weighed using HFL AHP techniques in this research. The AHP represents the most popular model used for deciding. It is an effective and straightforward method of determining the most important aspects. Reluctance to make a choice is a typical occurrence. If there is much room for error in reaching a judgment, HFL AHP is the method of choice. A reluctant judgment represents a range of values rather than settling on one. There has been a substantial rise in the number of publications that use this strategy in the latest days. This study applied this strategy to draw the inference of environmental inspiration of tourists, well-being, engagement, and revisit intentions. The current research also introduced a cautious AHP approach to China’s tourism industry using a complete efficiency structure.
Next, we use the HFL AHP model to rank our options, as can be seen below:Step 1: Using the words in Table 1, DMs organize comparison matrix cubes, and HFLTS provides intermediate ratings.
Step 2: The OWA algorithm is used to capture information and construct the fuzzy environment of HFLTS.
Step 3: Moreover, the pairwise comparison matrix (C˜) is extended using the last step where c̃ij = (cijl, cijm1, cijm2, ciju).10 c~ij=1ciju,1cijm2,1cijm1,1cijl,
11 μd=l+m1+m2+u6
Table 1 Fuzzy HFLTS influence matrix score
C11 C12 C13 C21 C22 C23 C31 C32 C33 Fuzzified score Matrix cupula
C11 0.996 0.979 0.247 0.151 0.219 0.238 0.111 0.882 0.169 0.101 0.762
C12 0.107 0.829 0.257 0.251 0.123 0.539 0.456 0.056 0.226 0.565 0.107
C13 0.215 0.417 0.899 0.444 0.149 0.551 0.162 0.337 0.227 0.848 0.108
C21 0.732 0.378 0.271 0.913 0.242 0.898 0.273 0.133 0.829 0.334 0.776
C22 0.199 0.692 0.668 0.778 0.364 0.377 0.791 0.262 0.319 0.066 0.959
C23 0.967 0.813 0.999 0.315 0.894 0.765 0.448 0.131 0.508 0.179 0.049
C31 0.169 0.149 0.881 0.538 0.527 0.393 0.934 0.826 0.321 0.269 0.456
C32 0.589 0.133 0.763 0.257 0.067 0.264 0.119 0.084 0.258 0.671 0.283
C33 0.232 0.216 0.103 0.176 0.767 0.909 0.891 0.746 0.863 0.024 0.973
EGH (Si) 0.662 0.786 0.281 0.153 0.755 0.598 0.014 0.291 0.561 0.235 0.055
EGH (> Si) 0.889 0.261 0.537 0.752 0.104 0.833 0.752 0.688 0.342 0.127 0.906
Env (HS) 0.317 0.111 0.797 0.775 0.067 0.999 0.095 0.531 0.021 0.217 0.661
Hs – H4 + 0.312 0.154 0.675 0.176 0.122 0.171 0.248 0.129 0.341 0.218 0.056
Hs – ≤ H4 + 0.943 0.753 0.318 0.923 0.273 0.578 0.667 0.464 0.707 0.654 0.826
Moreover, the consistency ratio among estimates is also used to draw the consistency score of efficiency.12 CI=λmax-nn-1
Here CI measures the consistency index, λmax represents the highest matrix core of the eigenvector of fuzzy estimates, n is the numbering criteria, and RI is the random index.13 CR=CIRI
Step 4: Using Eq. (15) and Eq. (16), the fuzzy global weights are also measured.14 Fa1,a2,⋯,an=wbT=∑i=1nwibi
15 wijG=α+2β+2γ+δ6
16 wijN=wijG∑i∑jwijG
Fuzzy HFL MABAC estimation technique
The methods for Chinese tourism are evaluated using the HFL MABAC technique. The MABAC approach relies heavily on the gap between borders approximating region but every potential solution. For this, we use the HFLTS spacing standard. So even though MABAC is a recently established methodology, it has already been employed throughout numerous studies using a wide range of MCDM strategies and techniques. It was reluctantly using the MABAC technique. The hesitation was combined with further MABAC investigations. Here, the HFL MABAC technique is combined with the imprecise envelopes approach. The strategy provides DMs with a comprehensive vocabulary for expressing themselves in their own words. It includes the following steps:Step 1: These techniques assess the best possible empirical significance using the linguistic scale based on a 5-point Likert scale measurement of the study variables.
Step 2: The linguistic expressions are converted to fuzzy MABAC envelopes.
Step 3: A normalized fuzzy matrix method constructed using the following equations.17 R~=r~ijm×n
18 r~ij=yij-yi-yi+-yi-,j∈B
19 r~ij=yij-yi+yi+-yi-,j∈C;
20 U~=U~ijm×n
HFL AHP is then used in this mixed approach. In contrast with previous approaches, HFLTS uses the novel concept that HFS permits the use of multiple values to express the extent to which items are part of a specific collection. The professionals’ mental processes are reflected in these idioms integrated with the fuzzy envelope method.
Results and discussion
Fuzzy HFLTS findings
Given their unique financial and historical histories, the specially highlighted townships in China provide an ideal backdrop for major environmental inspiration of touristss and tourists’ revisit intention to study. The upgrading of the historic industry, tourism engagement, and environmental wellness are all goals of creating unique highlighted segments. Every tourism station that can span no more than 3 square kilometers serves a dual purpose as an economic and artistic center while maintaining its independence first from the central government. Among these are wellness, tourism design, heritage, entertainment, tourism inspiration, tourism engagement, and environmental wellness (Tables 1 and 2). The notable tourism industry in China is being constructed and planned per the 3A government touristic criteria (Table 6).Table 2 Scenario analysis of fuzzy HFLTS
Real scenario Scenario (1) Scenario (2) Scenario (3)
Evaluation output Evaluation output Evaluation output Evaluation output
Tourism inspiration (0.205, 0.322, 0.134) (0.019, 0.991, 0.868) (0.855, 0.147, 0.297) (0.888, 0.758, 0.463)
Tourists’ engagement (0.385, 0.285, 0.326) (0.713, 0.145, 0.223) (0.821, 0.051, 0.362) (0.878, 0.668, 0.152)
Tourists’ well-being (0.213, 0.505, 0.564) (0.565, 0.719, 0.647) (0.289, 0.025, 0.017) (0.399, 0.786, 0.124)
Tourists’ revisit intentions (0.592, 0.116, 0.034) (0.154, 0.646, 0.933) (0.511, 0.579, 0.367) (0.701, 0.416, 0.144)
C˜ (0.123, 0.676, 0.722) (0.193, 0.749, 0.109) (0.126, 0.562, 0.806) (0.475, 0.498, 0.046)
Uij (0.995, 0.334, 0.638) (0.543, 0.701, 0.176) (0.379, 0.786, 0.845) (0.111, 0.186, 0.567)
CI (0.574, 0.358, 0.567) (0.036, 0.786, 0.365) (0.754, 0.146, 0.551) (0.867, 0.596, 0.999)
CR (0.461, 0.884, 0.483) (0.491, 0.713, 0.509) (0.176, 0.721, 0.532) (0.586, 0.436, 0.888)
µ (0.146, 0.192, 0.469) (0.332, 0.537, 0.778) (0.532, 0.653, 0.375) (0.315, 0.666, 0.394)
Moreover, highlighted communities highlight the need to balance economic development with tourists’ revisit intentions (Table 7). Established in 2015 by Central China, the unique highlighted tourism station development plan spreads throughout China. Over 2,000 of China’s “special features townships” will be built by 2020. Thirty-seven municipalities in Zhejiang were selected for the study as the province’s first round of especially highlighted townships. Our findings show enthusiastic participation is significantly more common than dissatisfied participation. All of the quantitative hypotheses and interactions between the predictors were double-checked. Linear regression employing optimum scalability has been demonstrated to be a viable technique for examining the data. Convergent validity was improved using the simple regression technique as per prior studies. The Hypothesis studied was compared using a p-value cutoff of 0.05 and a 95% confidence range.
The study’s findings demonstrate that each of the three predictors we used (push motivations, pull reasons, and the characteristics of the tourist site) affects the good aspects of involvement but has no effect on the negative aspects. Five out of the six linear regressions have substantial supportive evidence. A description of the fuzzy results follows (see Tables 4 and 5). In general, nonlinearity in the connections among tourism inspiration, tourism revisit intentions, environmental wellness and tourists’ engagement effects was considered while interpreting the findings. The study’s empirical findings revealed that tourists’ inspiration among the individual propositions is accepted after evaluating the significance confirmation of environmental wellness and tourists’ engagement. Tourists were just more engaged with vacation spots when they were exposed to extrinsic incentives about the destination, such as the availability of contemporary metropolises to attend, exotic surroundings, fairs, happenings, and live operations, hospitable locals, this same opportunity to encounter diverse places, and historic old cities and areas. Findings in Table 8 show that exterior pull reasons cannot predict adverse interest, positive dedication, and negative help elevate. Thus, this interpretation has to be dismissed. Negative notoriety represented the sole metric that had statistically significant results. Pulling motivating factors (such as “contemporary environments & sports” and “nightlife & unique cuisine”) were also predictive of lower levels of positive reputation. As a result, we may consent to the null Hypothesis. Finally, the research suggests that interaction is greater in certain target categories. Research suggests that popular tourist spots can accurately gauge interest (this relationship is statistically highly significant). Therefore, tourists’ desire has little to do with the connection between locations and participation (Table 3). Contrary to expectations, there is no correlation between popularity and tourist participation in popular tourist spots.Table 3 Transformed HFLTS possibility distribution assessments of the study model
HFLTS parameters Weights Transformed HFLTS possibility distribution score
TI (0.561) TE (0.44) TW (0.69) TRI (0.76)
C11 0.586 a1/8b1/6c1/4 0.450 a1/191/8b1/211/8c1/101/8 0.119
C12 0.422 0.869 0.499 0.241 0.492
C13 0.234 0.418 0.178 0.167 0.652
C21 0.948 a1/1b1.7c1/8 0.933 a0.845b0.359c0.183 0.112
C22 0.267 0.109 0.808 a1/41/12b1/41/12c1/41/12 0.631
C23 0.427 0.123 0.402 0.232 a0.254b0.584c0.471
C31 0.967 a1/5b1/5c1/5 0.315 0.514 0.773
C32 0.095 0.123 0.214 0.739 0.445
C33 0.909 0.138 a0.277b0.376c0.437 0.228 a1/61/3b1/61/3c1/61/3
HFLTS Non-fuzzified Score 0.484 0.169 0.769 0.202
HFLTS Fuzzified Score 0.204 0.256 0.572 0.959
Fuzzy AHP findings
There is widespread agreement on the value of invention for the hospitality and tourist industry. While the research findings revealed that there is a shortage of data demonstrating a causal relationship between tourism well-being and tourists’ revisit intentions. The present study demonstrates that tourists’ inspiration and engagement completely signify the association with tourists’ revisit intentions in China. When applied to “inspiration-engagement-well-being,” this finding shows that a tourist’s inspiration may be understood as an innovative structure that relies on emotions and cognitions to build founding principles toward the desirable strategic edge in reshaping revisit intentions. The source of the tourist’s engagement perspective, which views inspiration and environmental wellness capabilities as sets of interconnected capabilities and the method used to harness those capabilities, is strengthened by the empirical significance between the input and output of the research framework (Fig. 1). This finding underscores the importance of distinction and “step-out” items in foretelling a tourist destination as distinctive and unique. It demonstrates the significance of technology development in boosting regarded innovation capability at a place. This is empirically found that the efforts to inspire Chinese tourists are often seen as behind-the-scenes efforts to increase the sense of belongingness in China.Fig. 1 Research framework
Furthermore, the findings from the empathetic method indicated that, although tourism well-being in the destination increased both expertise and warming judgments, primarily competent conceptions boosted revisit intentions. According to the positive competency phenomenon in cognitive science, who elaborates that tourism inspiration influences tourism revisit judgment greater positively than tourism destination image and even word of mouth often revealed by the tourists. These study findings are consistent with the study’s theoretical framework and qualify the study’s first research contribution.
When wellness tourists come with more powerful well-being motives, they are more likely to experience a better level of significance and coherence between their tour package and well-being aspirations. As a result, the accompanying conjectures are advanced. The importance of a traveler’s goals to a wellness vacation is strongly correlated with the significance of that vacation. Tourists are more likely to achieve their goals if they focus on grandeur and luxury, learning and discovery, personal growth, and escape. The second Hypothesis is that tourists’ degree of goal congruence is correlated with the extent to which they are motivated by Chinese tourists.
Particularly, tourists’ goal congruence is correlated with their pursuit of affluence and prominence, new experiences and learning, personal growth, and rest and recreation. Normative elements of cognitive evaluation and motivation Planned Behaviour Theory, presented by Arnold (1960) and Lazarus (1991), discusses why an equivalent experience may elicit a range of feelings in diverse tourists at various times; this is because each purely specific evaluation of the event can cause their extreme reaction to the incident to vary. Yet, there is no connection between the feeling of inspiration and the skill assessment characteristics.
Fuzzy HFL MABAC findings
Based on the findings, it is much more probable that consistent with the value responses (that enjoyment) would have been created whenever tourists feel that their requirements were fulfilled throughout a vacation encounter (Tables 4, 5, 6 and 7). Similarly, research shows that a feeling of joy significantly correlates with more favorable assessments of goal relevance and unity (Table 8). Hence, we argue that inspiration is more likely to occur when motives and intentions stimuli are viewed as pertinent to a traveler’s aim and when a tourist’s judgment of the stimuli is compatible with their interests. The research hopes to add to our knowledge of destinations creativity and its effects using knowledge gained from both the innovative typology and the SCM. This investigation makes diverse conceptual advancements possible (Table 9). To begin, this research contributes to the body of knowledge on how technological advancements in tourist spots influence the likelihood that tourists will return.Table 4 Fuzzy AHP consistency index (CI), consistency ratio (CR), and random index (RI) estimates
TI TE TW TRI W µd
CI 0.267 0.134 0.442 0.496 0.728 0.244
CR 0.743 0.239 0.367 0.237 0.969 0.506
(a) (b) (c) (d) (e) (f)
RI 0.075 0.146 0.024 0.999 0.093 0.169
Table 5 Normalized defuzzified criteria of fuzzy AHP
Defuzzified criteria Tourism inspiration Tourism well-being Tourism engagement
1 a0.201b0.433c0.534 a0.111b0.984c0.345 a0.109b0.249c0.673
2 a0.123b0.673c0.222 a0.321b0.210c0.667 a0.202b0.693c0.678
3 a0.210b0.003c0.333 a0.321b0.687c0.889 a0.233b0.219c0.242
4 a0.405b0.677c0.892 a0.209b0.450c0.409 a0.204b0.456c0.782
Table 6 Subjective weights of Fuzzy AHP evaluation index
Parameters Weights
C11 0.786
C12 0.245
C13 0.648
C21 0.872
C22 0.465
C23 0.124
C31 0.555
C32 0.799
C33 0.113
Table 7 Triangular fuzzy numbers of each criterion
Tourism inspiration Tourism engagement Tourism inspiration Tourism engagement Revisit intentions
C11 (0.766, 0.166, 0.356) (0.454, 0.599, 0.217) (0.385, 0.331, 0.042) (0.174, 0.559, 0.695) (0.124, 0.469, 0.575)
C12 (0.676, 0.056, 0.786) (0.102, 0.111, 0.592) (0.022, 0.971, 0.355) (0.117, 0.735, 0.876) (0.113, 0.733, 0.546)
C13 (0.909, 0.439, 0.125) (0.396, 0.721, 0.276) (0.697, 0.797, 0.579) (0.253, 0.677, 0.931) (0.441, 0.721, 0.476)
C21 (0.915, 0.111, 0.025) (0.492, 0.398, 0.706) (0.537, 0.327, 0.744) (0.861, 0.915, 0.123) (0.664, 0.301, 0.329)
C22 (0.128, 0.055, 0.869) (0.513, 0.129, 0.853) (0.003, 0.021, 0.954) (0.145, 0.875, 0.894) (0.678, 0.936, 0.671)
C23 (0.214, 0.017, 0.222) (0.487, 0.924, 0.118) (0.692, 0.388, 0.672) (0.574, 0.316, 0.774) (0.204, 0.232, 0.882)
C31 (0.231, 0.123, 0.452) (0.726, 0.374, 0.345) (0.137, 0.331, 0.894) (0.322, 0.032, 0.756) (0.022, 0.955, 0.849)
C32 (0.197, 0.172, 0.846) (0.458, 0.361, 0.119) (0.898, 0.936, 0.103) (0.486, 0.184, 0.522) (0.134, 0.837, 0.342)
C33 (0.418, 0.731, 0.252) (0.707, 0.888, 0.385) (0.892, 0.606, 0.873) (0.137, 0.171, 0.137) (0.443, 0.166, 0.673)
Table 8 Normalized fuzzy HFL MABAC decision-making matrix
β1 β2 β3 β4 β5 β6 β7
C1 0.285 0.025 0.138 0.503 0.212 0.178 0.785
C2 0.465 0.836 0.189 0.269 0.982 0.078 0.328
C3 0.232 0.847 0.707 0.116 0.525 0.967 0.549
C4 0.738 0.149 0.158 0.256 0.804 0.863 0.946
C5 0.306 0.589 0.279 0.653 0.143 0.382 0.058
C6 0.669 0.941 0.354 0.165 0.293 0.117 0.747
C7 0.333 0.628 0.775 0.464 0.282 0.395 0.913
C8 0.373 0.062 0.392 0.298 0.796 0.634 0.278
C9 0.364 0.247 0.448 0.422 0.302 0.198 0.907
C10 0.202 0.878 0.407 0.426 0.299 0.313 0.366
Table 9 Normalized fuzzy HFL MABAC decision-making matrix
Tourism inspiration Tourism engagement Tourism inspiration Tourism engagement
C11 (0.215, 0.739, 0.805) (0.648, 0.507, 0.696) (0.742, 0.328, 0.929) (0.775, 0.181, 0.652)
C12 (0.134, 0.497, 0.581 (0.421, 0.015, 0.985) (0.076, 0.704, 0.823) (0.158, 0.512, 0.752)
C13 (0.331, 0.628, 0.288) (0.482, 0.025, 0.033) (0.475, 0.182, 0.381) (0.414, 0.363, 0.023)
C21 (0.776, 0.346, 0.549) (0.327, 0.528, 0.157) (0.615, 0.396, 0.484) (0.375, 0.106, 0.532)
C22 (0.391, 0.994, 0.038) (0.563, 0.954, 0.988) (0.745, 0.052, 0.145) (0.189, 0.611, 0.681)
C23 (0.981, 0.005, 0.119) (0.498, 0.351, 0.238) (0.284, 0.232, 0.272) (0.602, 0.061, 0.192)
C31 (0.727, 0.515, 0.153) (0.918, 0.195, 0.777) (0.106, 0.823, 0.514) (0.782, 0.075, 0.712)
C32 (0.982, 0.592, 0.832) (0.696, 0.505, 0.356) (0.052, 0.616, 0.973) (0.927, 0.871, 0.348)
C33 (0.297, 0.897, 0.098) (0.144, 0.118, 0.362) (0.223, 0.273, 0.682) (0.277, 0.154, 0.574)
C˜ (0.555, 0.495, 0.558) (0.199, 0.211, 0.163) (0.642, 0.133, 0.397) (0.698, 0.228, 0.893)
Uij (0.286, 0.345, 0.227) (0.238, 0.667, 0.079) (0.997, 0.126, 0.893) (0.554, 0.156, 0.682)
CI (0.668, 0.845, 0.444) (0.054, 0.166, 0.896) (0.041, 0.374, 0.294) (0.793, 0.559, 0.024)
CR (0.276, 0.984, 0.199) (0.268, 0.358, 0.296) (0.845, 0.212, 0.676) (0.325, 0.823, 0.649)
µ (0.221, 0.876, 0.015) (0.393, 0.121, 0.679) (0.377, 0.003, 0.796) (0.997, 0.349, 0.957)
The connection between tourism engagement and tourists’ revisit intentions is posted by the current research findings as significant. The relationship explains how tourists feel about getting engaged in revisiting the tourist’s destination. We have constructed and tested an empirically grounded conceptual model of these interrelationships. Results from the investigation corroborated the Hypothesis. Tourists are tasked with mitigating potential hazards in tourist places as part of their matter in hospitality management. To achieve this goal, Chinese tourists must consider what factors may contribute to tourists’ engagement and tourism well-being at large and then evaluate whether or not they are taking enough steps to understand and control these factors. Together, these data corroborate the importance of tourists’ knowledge of potential crisis causes, depth of experience, and skill in addressing personnel during times of crisis. According to the results, the tourists’ inspirational engagement for revisit intentions is highly predictable, with the tourists’ prior experience, beliefs, and inspirations explaining around 50.9% of the variation (Table 5).
Fuzzy risk analysis findings
Moreover, tourists’ revisit intentions are also shown to be impacted by the tourists’ prior well-being at the tourist destination in China. Tourists’ experience of well-being in previous visits strongly correlates with their confidence in their abilities, their willingness to take preventative measures, and the prevalence of ad hoc or standardized approaches to reshaping their tourism inspirations (Table 10). Scholars agree on the same meaning and how one’s ideas shape their view. The results also showed that tourists’ risk inspirations are impacted by their ideas and knowledge, affecting their attitude. Other scholarly articles also documented the function of RP as a mediator. Hence, RP accounted for 38.3% of risk analysis. This explanation is mostly attributable to tourists’ varying degrees of knowledge and beliefs and, in turn, affects their ultimate actions and performance (Table 11).Table 10 Weighted risk factor matrix summary
Ri Ci Ri + Ci Ri- Ci Wij EGH (Si) EGH (> Si) Env (HS)
C11 0.437 0.113 0.576 0.487 0.361 0.657 0.101 0.936
C12 0.051 0.526 0.345 0.799 0.465 0.018 0.061 0.981
C13 0.957 0.001 0.835 0.233 0.284 0.082 0.283 0.446
C21 0.202 0.277 0.165 0.374 0.217 0.749 0.062 0.894
C22 0.195 0.102 0.868 0.659 0.907 0.263 0.453 0.935
C23 0.589 0.138 0.658 0.737 0.835 0.855 0.521 0.981
C31 0.131 0.647 0.871 0.496 0.981 0.187 0.544 0.726
C32 0.187 0.382 0.333 0.254 0.126 0.195 0.927 0.958
C33 0.438 0.316 0.943 0.027 0.115 0.314 0.495 0.605
Table 11 Robustness evaluation findings
Indicators Weight Assessment score
C11 0.861 (0.639, 0.003, 0.243, 0.299)
C12 0.151 (0.156, 0.024, 0.128, 0.541)
C13 0.442 (0.33,0.072, 0.128, 0.684)
C21 0.873 (0.416, 0.192, 0.134, 0.598)
C22 0.009 (0.524, 0.431, 0.456, 0.399)
C23 0.667 (0.243, 0.893, 0.939, 0.489)
C31 0.944 (0.168, 0.103, 0.855, 0.275)
C32 0.176 (0.907, 0.085, 0.234, 0.111)
C33 0.713 (0.893, 0.305, 0.446, 0.639)
Tourism inspiration 0.857 (0.536, 0.058, 0.147, 0.188)
Tourists’ engagement 0.128 (0.644, 0.314,, 0.193, 0.656)
Environmental wellness 0.387 (0.578, 0.431, 0.559, 0.909)
Revisit intentions 0.214 (0.622, 0.344, 0.925)
HM 0.588 (0.432, 0.702, 0.916)
Despite the widespread belief that one’s life experiences positively impact their risk perception (RP), this research discovered that the opposite was true for those working in the tourist and hospitality industries. Different types of victimization (violent or nonviolent experience; direct or indirect experience) and the specifics of one’s profession may each have a role in shaping one’s perspective on PE and RP, leading to a range of reasonable but conflicting explanations. Split shifts, night shifts, weekend shifts, and holiday employment are all examples of the unsociable working circumstances typical in the tourism and hospitality industries. Previous studies state that many scholars agree with this conclusion, which indicates that victimization is more prevalent among subordinate personnel like service ad production staff. We also found that tourists in these occupations are exposed to a wide range of hazards daily as a natural part of doing their professions. These tourists rated the risks listed in Table 4 as having the highest likelihood of occurring.
Robustness analysis
This result proves that key stakeholders and policymakers must seriously consider tourists’ personalities throughout the tourism visit and revisit processes. This is understood in light of recent results emphasizing the significance of knowing your audience and their perception of risk. It redresses their tourism inspirations, particularly in the tourism and hospitality sector, which relies on their cognitive and emotional senses. The ability to accurately manage risks in a given situation or make decisions regarding safety procedures depends on an individual’s risk perception, which can be improved through exposure to and education about the potential dangers faced in the workplace and through regular training and activities.
Discussion
Scientists have been fascinated by how to get individuals to change their behavior in ecologically positive ways for centuries since doing so might have a major impact on easing chronic stress and ensuring the long-term viability of our planet’s natural assets. Given the adverse effect of tourism products, ecology as an aim has also gained widespread recognition in the tourism and hospitality industries. But even though tourism is important to a country’s economy, it is also important to remember that it may harm the planet. Tourism is the fifth most disruptive business globally, producing 5 percent of the world’s carbon dioxide emissions (Scott et al., 2008). According to research by the United Nations Environment Program, solid waste accounts for 14% of the 4.8 million metric tonnes of rubbish created annually by tourists and hotel guests. Everyone benefits when people in the tourism and hospitality industries try to improve their environmental footprints in COVID-19 crises (Iqbal et al. 2021). As tourist and hotel guests’ actions may significantly impact the natural world, researchers have a vested interest in learning what motivates them to adopt more environmentally friendly practices. In the field of tourism and hospitality, there is a considerable body of literature that employs revisit intention as a stand-in for tourist satisfaction. This would be predicated on the idea, from the Theory of planned behavior, that purpose plays a significant role in determining actual action. According to this view, an individual’s performance expectancy is their propensity to act in a certain way (Li et al. 2021). Hence, visitors’ involvement may be seen as “the willingness to safeguard and develop the environment or society” and is, thus, a crucial antecedent of tourists’ intent to return. While many antecedents that impact visitors’ involvement with consumers in the tourism and hospitality sector have been found in earlier research, understanding these connections is restricted in three respects. To begin with, the current body of research on tourist motivation is fragmented and diversified, and each study only studied a few of the unique preceding relationships (Zhao et al. 2022). Thus, it fails to present a comprehensive perspective of environmental wellness in a tourism and hospitality setting.
This research adds important actual research to several ideas and the current literature about visitors’ motivations. It indicated that visitors’ happiness was the most often used Theory to predict tourists’ return intentions in tourism and hospitality studies, underlining the need to validate the influence of this Theory using meta-analysis. According to past postmodern work and survey data on visitors’ inclinations to return, there is a good connection between environmental attitude, social obligation, and perceived behavioral control (Chang et al. 2023). Given these correlations, it is reasonable to assume that visitors’ satisfaction may be used as a promotion model. The current research also found a beneficial connection among study constructs in robustness analysis about tourists’ revisit intentions 9). Consequently, this finding lends credence to self-congruity Theory and identity-based motivation theory, which postulate that individuals are more likely to do actions that align with their core selves. Theoretically, tourism and hospitality patrons are more inclined to participate if they consider themselves ecologically conscious.
In addition, this discovery points to the Way for further research. Affective elements were shown to positively impact both visitors and those partaking in the hospitality industry, as determined by the current investigation. Similarly, the organizational justice hypothesis proposes that individuals actively prioritize gratifying experiences or try to suppress unpleasant ones to succeed. Previous research and meta-analysis have emphasized the importance of anticipating positive and negative emotions and how these evaluations of the performance of specific behaviors to derive pleasure or avoid bad feelings substantially impact tourists’ willingness to participate in tourism activities. It is interesting to note that this review found a far better association between the positive feeling expected and actual behavior than between the negative emotion expected and actual behavior. It is possible that behavioral restrictions, such as a lack of access to public transportation or a recycling bin, account for the lack of a link between expected unpleasant feelings and pro-environmental activities (Iqbal and Bilal 2021). You could also mention that irritation, a possible side effect of intense negative emotions, may lessen guilt’s impact on practical actions. This is because other variables, such as mediation or moderation, may lessen or even cancel out the influence of the unpleasant feeling that is expected to be experienced.
Conclusion and implications
Conclusion
In light of recent unrest in tourism in Chinese provinces, this article seeks to understand the nexus among tourism inspiration, tourism engagement, revisit intentions and tourism well-being. The factors shape tourism’s awareness of and reaction to dangers inherent in the tourist and hospitality sector. However, the current literature that participated in research connected solely to tourists’ psycho-behavioral attributes with their intentions to revisit. This study is novel in its endeavor to comprehend the viewpoint and attitude of Chinese tourists and its concentration on those participating in regular occupational tourism visit-related tasks. To determine what makes tourism inspiration usually apprehensive or uncomfortable when such inspirational judgments are derived from tourists’ bias. The study findings reveal that tourists’ inspirations were the first and foremost driver in explaining the role of tourism well-being and engagement in tourists’ revisit intentions. There was a positive relationship between tourism inspiration, engagement, well-being and tourists’ revisit intentions. Moreover, their level of risk aversion, with tourists’ collected inspirational beliefs. According to the findings, adequate knowledge and beliefs motivate tourists to engage in safe practices, advocate for safety in the tourist place, and learn new skills that boost the safety record of their tourism-related business activity. Moreover, risk perception also influences significantly as a controlling indicator, a remarkable finding that has not been reported in other research. The study directed multiple practical implications for consideration and prudent decision-making of the associated stakeholders of the recent research.
Practical implications
Sustainability and efficiency are the two fundamental principles in contemporary tourist policy, which seek to balance economic progress and long-term viability. Research and policymakers may use this study’s findings to evaluate the efficacy of tourism development strategies before, throughout, and after emergencies. This study has certain flaws, even though it was conducted according to strict criteria. One major limitation is that the interviews occurred when the people had already been stressed out from trying to find solutions. While a face-to-face discussion would have been preferable, they gave up several of their schedules to participate in an electronic questionnaire instead. Nevertheless, the empirical study approach restricts the generalizability of the results, and the questions give insight into the thinking of the top management of tourist businesses. Given these results, it is suggested that a quantitative approach to sustainable tourism strategies be conducted. Furthermore, some issues plaguing the sector may be resolved by conducting a large-scale, cross-regional study that collects data on excellent behavior that could be used in other areas. The research shows that ecotourism should be the top priority for any tourist strategy. Even though competitiveness is emphasized as a crucial component of tourist policy, this study’s findings indicate the significance of sustainable tourism as a directing technique beyond the hemispheres due to environmental consciousness. Because of the COVID-19 pandemic, there is a greater emphasis on responsible tourism, which may explain this trend. In contrast to prior studies, they realize the importance of power-sharing in designing sustainable tourism policies, which they believe should be the topic of investigation. Specifically, synchronizing the tourist value chain among participants (individuals or organizations) requires effective governance structures. As a result, the tourist industry relies heavily on good communication and a robust stakeholder structure.
Author contribution
Conceptualization, methodology: Mengyao Zhang; writing—original draft: Menghan Hou; data curation, data analysis, interpretation: Yang Sun.
Data availability
The data that support the findings of this study are openly available on request.
Declarations
Ethical approval and consent to participate
The authors declared that they have no known competing financial interests or personal relationships, which seem to affect the work reported in this article. We declare that we have no human participants, human data, or human issues.
Consent for publication
We do not have any individual person’s data in any form.
Competing interest
The authors declare no competing interests.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Our manuscript is posted at a preprint server prior to submission.
==== Refs
References
Aftab S Khan MM Role of social media in promoting tourism in Pakistan J Soc Sci Hum 2019 58 1 101 113
Babb F (2010) The tourism encounter: fashioning Latin American nations and histories. Stanford University Press
Black R Cobbinah PB On the rim of inspiration: performance of AWF tourism enterprises in Botswana and Rwanda J Sustain Tour 2017 25 11 1600 1616 10.1080/09669582.2017.1296454
Brouder P The end of tourism? A Gibson-Graham inspired reflection on the tourism economy Tour Geogr 2018 20 5 916 918 10.1080/14616688.2018.1519721
Chan WC Wan Ibrahim WH Lo MC Mohamad AA Ramayah T Chin CH Controllable drivers that influence tourists’ satisfaction and revisit intention to Semenggoh Nature Reserve: the moderating impact of destination image J Ecotour 2022 21 2 147 165 10.1080/14724049.2021.1925288
Chang L, Iqbal S, Chen H (2023) Does financial inclusion index and energy performance index co-move?. Energy Policy 174:113422
Chen H Rahman I Cultural tourism: an analysis of engagement, cultural contact, memorable tourism experience and destination loyalty Tour Manag Perspect 2018 26 153 163
Chen S Han X Bilgihan A Okumus F Customer engagement research in hospitality and tourism: a systematic review J Hosp Market Manag 2021 30 7 871 904
Chen KH, Huang L, Ye Y (2022) Research on the relationship between wellness tourism experiencescape and revisit intention: a chain mediation model. Int J Contemp Hosp Manag, (ahead-of-print)
Craik J (2002) The culture of tourism. In Touring cultures (pp. 123–146). Routledge
Dai F Wang D Kirillova K Travel inspiration in tourist decision making Tour Manage 2022 90 104484 10.1016/j.tourman.2021.104484
Damanik J Yusuf M Effects of perceived value, expectation, visitor management, and visitor satisfaction on revisit intention to Borobudur Temple, Indonesia J Herit Tour 2022 17 2 174 189 10.1080/1743873X.2021.1950164
Deng CD Peng KL Shen JH Back to a Post-Pandemic city: the impact of media coverage on revisit intention of Macau J Qual Assur Hosp Tour 2023 24 1 1 23 10.1080/1528008X.2021.2002788
Ding L, Jiang C, Qu H (2022) Generation Z domestic food tourists’ experienced restaurant innovativeness toward destination cognitive food image and revisit intention. Int J Contemp Hosp Manag, (ahead-of-print)
Duxbury N Bakas FE Vinagre de Castro T Silva S Creative tourism development models towards sustainable and regenerative tourism Sustainability 2020 13 1 2 10.3390/su13010002
Fletcher C Pforr C Brueckner M Factors influencing Indigenous engagement in tourism development: an international perspective J Sustain Tour 2016 24 8–9 1100 1120 10.1080/09669582.2016.1173045
Gordon JE Baker M Appreciating geology and the physical landscape in Scotland: from tourism of awe to experiential re-engagement Geol Soc Lond Spec Publ 2016 417 1 25 40 10.1144/SP417.1
Haldrup M Larsen J Material cultures of tourism Leis Stud 2006 25 3 275 289 10.1080/02614360600661179
Harrigan P Evers U Miles M Daly T Customer engagement with tourism social media brands Tour Manage 2017 59 597 609 10.1016/j.tourman.2016.09.015
He M, Liu B, Li Y (2021) Environmental inspiration of tourists: how the wellness tourism experience inspires tourists environmental engagement. J Hosp Tour Res, 10963480211026376
Hjalager AM A review of innovation research in tourism Tour Manage 2010 31 1 1 12 10.1016/j.tourman.2009.08.012
Hunt CA, Harbor LC (2019) Pro-environmental tourism: lessons from adventure, wellness and eco-tourism (AWE) in Costa Rica. J Outdoor Recreat Tour 28
Iqbal S, Bilal AR (2021) Energy financing in COVID-19: how public supports can benefit?. China Finance Rev Int 12(2):219–240
Iqbal S, Bilal AR, Nurunnabi M, Iqbal W, Alfakhri Y, Iqbal N (2021) It is time to control the worst: testing COVID-19 outbreak, energy consumption and CO 2 emission. Environ Sci Pollut Res 28:19008–19020
Juliana J Putri FF Wulandari NS Saripudin U Marlina R Muslim tourist perceived value on revisit intention to Bandung city with customer satisfaction as intervening variables J Islam Mark 2022 13 1 161 176 10.1108/JIMA-08-2020-0245
Khoi NH Phong ND Le ANH Customer inspiration in a tourism context: an investigation of driving and moderating factors Curr Issue Tour 2020 23 21 2699 2715 10.1080/13683500.2019.1666092
Kline C, Fischer B (2021) Morality on holiday: inspiring ethical behaviour in animal-based tourism through nonmoral values. Tour Recreat Res:1–12
Kwon J Boger CA Influence of brand experience on customer inspiration and pro-environmental intention Curr Issue Tour 2021 24 8 1154 1168 10.1080/13683500.2020.1769571
Laing JH Crouch GI Frontier tourism: retracing mythic journeys Ann Tour Res 2011 38 4 1516 1534 10.1016/j.annals.2011.02.003
Larsen J (2014) The tourist gaze 1.0, 2.0, and 3.0. The Wiley Blackwell companion to tourism, 304–313
Li W, Chien F, Ngo QT, Nguyen TD, Iqbal S, Bilal AR (2021) Vertical financial disparity, energy prices and emission reduction: empirical insights from Pakistan. J Environ Manage 294:112946
Li C Lv X Scott M Understanding the dynamics of destination loyalty: a longitudinal investigation into the drivers of revisit intentions Curr Issue Tour 2023 26 2 323 340 10.1080/13683500.2021.2012433
Libre A Manalo A Laksito GS Factors influencing Philippines tourist’revisit intention: the role and effect of destination image, tourist experience, perceived value, and tourist satisfaction Int J Quant Res Model 2022 3 1 1 12 10.46336/ijqrm.v3i1.260
Liu B Li Y Kralj A Moyle B He M Inspiration and wellness tourism: the role of cognitive appraisal J Travel Tour Mark 2022 39 2 173 187 10.1080/10548408.2022.2061676
Loureiro SMC Sarmento EM Place attachment and tourists environmental engagement of major visitor attractions in Lisbon Tour Hosp Res 2019 19 3 368 381 10.1177/1467358418761211
Lyu J Li Y Mao Z Huang H The effect of innovation on tourists’ revisit intention toward tourism destinations Tour Rev 2023 78 1 142 158 10.1108/TR-05-2022-0258
Ma J Li F Shang Y Tourist scams, moral emotions and behaviors: impacts on moral emotions, dissatisfaction, revisit intention and negative word of mouth Tour Rev 2022 77 5 1299 1321 10.1108/TR-03-2022-0115
Miao L Baker M Hughes K Kim S Lu L Singal M Young C Launch of the JHTR featured section “insight & foresight”: inspire “homegrown” theorizing in hospitality and tourism research J Hosp Tour Res 2022 46 6 1087 1095 10.1177/10963480221104406
Mohammed I Mahmoud MA Hinson RE The effect of brand heritage in tourists’ intention to revisit J Hosp Tour Insights 2022 5 5 886 904 10.1108/JHTI-03-2021-0070
Pencarelli T The digital revolution in the travel and tourism industry Inform Technol Tour 2020 22 3 455 476 10.1007/s40558-019-00160-3
Peng J Yang X Fu S Huan TCT Exploring the influence of tourists’ happiness on revisit intention in the context of Traditional Chinese Medicine cultural tourism Tour Manage 2023 94 104647 10.1016/j.tourman.2022.104647
Postma A Cavagnaro E Spruyt E Sustainable tourism 2040 J Tour Futur 2017 3 1 13 22 10.1108/JTF-10-2015-0046
Rasoolimanesh SM Md Noor S Schuberth F Jaafar M Investigating the effects of tourists environmental engagement on satisfaction and loyalty Serv Ind J 2019 39 7–8 559 574 10.1080/02642069.2019.1570152
Rather RA Customer experience and engagement in tourism destinations: the experiential marketing perspective J Travel Tour Mark 2020 37 1 15 32 10.1080/10548408.2019.1686101
Richards G Designing creative places: the role of creative tourism Ann Tour Res 2020 85 102922 10.1016/j.annals.2020.102922
Shi H, Liu Y, Kumail T, Pan L (2022) Tourism destination brand equity, brand authenticity and revisit intention: the mediating role of tourist satisfaction and the moderating role of destination familiarity. Tour Rev
Shoukat MH Ramkissoon H Customer delight, engagement, experience, value co-creation, place identity, and revisit intention: a new conceptual framework J Hosp Market Manag 2022 31 6 757 775
Sigala M (2019) Scarecrows: an art exhibition at Domaine Sigalas inspiring transformational wine tourism experiences. Management and Marketing of Wine Tourism Business: Theory, Practice, and Cases, 313–343
Smith N, Suthitakon N, Gulthawatvichai T, Karnjanakit S (2019) The circumstances pertaining to the behaviors, demands and gratification in tourists environmental engagement in coffee tourism. PSAKU International Journal of Interdisciplinary Research 8(1)
So KKF King C Sparks B Customer engagement with tourism brands: scale development and validation J Hosp Tour Res 2014 38 3 304 329 10.1177/1096348012451456
Squire SJ Literary tourism and sustainable tourism: promoting ‘Anne of Green Gables’ in Prince Edward Island J Sustain Tour 1996 4 3 119 134 10.1080/09669589608667263
Stroebel M Tourism and the green economy: inspiring or averting change? Third World Q 2015 36 12 2225 2243 10.1080/01436597.2015.1071658
Sun T Zhang J Zhang B Ong Y Ito N How trust in a destination’s risk regulation navigates outbound travel constraints on revisit intention post-COVID-19: segmenting insights from experienced Chinese tourists to Japan J Destin Mark Manag 2022 25 100711
Tabaeeian RA, Yazdi A, Mokhtari N, Khoshfetrat A (2022) Host-tourist interaction, revisit intention and memorable tourism experience through relationship quality and perceived service quality in ecotourism. J Ecotourism:1–24
Teng HY Can film tourism experience enhance tourist behavioural intentions? The role of tourists environmental engagement Curr Issue Tour 2021 24 18 2588 2601 10.1080/13683500.2020.1852196
Thipsingh S Srisathan WA Wongsaichia S Ketkaew C Naruetharadhol P Hengboriboon L Social and sustainable determinants of the tourist satisfaction and temporal revisit intention: a case of Yogyakarta, Indonesia Cogent Soc Sci 2022 8 1 2068269
Thomas B Quintal VA Phau I Wine tourists environmental engagement with the winescape: scale development and validation J Hosp Tour Res 2018 42 5 793 828 10.1177/1096348016640583
Tiwari AV Bajpai N Singh D Vyas V Antecedents of hedonism affecting memorable tourism experience (MTE) leading to revisit intention in tourists Int J Tour Cities 2022 8 3 588 602 10.1108/IJTC-03-2021-0043
Torabi ZA Shalbafian AA Allam Z Ghaderi Z Murgante B Khavarian-Garmsir AR Enhancing memorable experiences, tourist satisfaction, and revisit intention through smart tourism technologies Sustainability 2022 14 5 2721 10.3390/su14052721
Tsaur SH Yen CH Lin YS Destination inspiration: scale development and validation J Travel Tour Mark 2022 39 5 484 500 10.1080/10548408.2022.2148040
Wang L Lyu J Inspiring awe through tourism and its consequence Ann Tour Res 2019 77 106 116 10.1016/j.annals.2019.05.005
Wasaya A Prentice C Hsiao A The influence of norms on tourist behavioural intentions J Hosp Tour Manag 2022 50 277 287 10.1016/j.jhtm.2022.02.023
Wei M Liu M Peng Y Zhou X Li S Effects of creative atmosphere on tourists’ post-experience behaviors in creative tourism: the mediation roles of environmental inspiration of tourists and place attachment Int J Tour Res 2023 25 1 79 96 10.1002/jtr.2553
Whiting J Hannam K Journeys of inspiration: working artists’ reflections on tourism Ann Tour Res 2014 49 65 75 10.1016/j.annals.2014.08.007
Xue J, Zhou Z, Majeed S, Chen R, Zhou N (2022) Stimulating environmental inspiration of tourists by tourist experience: the moderating role of destination familiarity. Front Psychol 3607
Yang S Isa SM Ramayah T Does uncertainty avoidance moderate the effect of self-congruity on revisit intention? A two-city (Auckland and Glasgow) investigation J Destin Mark Manag 2022 24 100703
Yang S Isa SM Ramayah T Wen J Goh E Developing an extended model of self-congruity to predict Chinese tourists’ revisit intentions to New Zealand: the moderating role of gender Asia Pac J Mark Logist 2022 34 7 1459 1481 10.1108/APJML-05-2021-0346
Zaitul Z Ilona D Novianti N Village-based tourism performance: tourist satisfaction and revisit intention Pol J Sport Tour 2022 29 2 36 43 10.2478/pjst-2022-0013
Zheng K, Kumar J, Kunasekaran P, Valeri M (2022) Role of smart technology use behaviour in enhancing tourist revisit intention: the theory of planned behaviour perspective. Eur J Innov Manag, (ahead-of-print)
Zhao L, Saydaliev HB, Iqbal S (2022) Energy financing, COVID-19 repercussions and climate change: implications for emerging economies. Clim Chang Econ 13(03):2240003 | 37289392 | PMC10248976 | NO-CC CODE | 2023-06-13 23:15:11 | yes | Environ Sci Pollut Res Int. 2023 Jun 8;:1-15 |
==== Front
Environ Sci Pollut Res Int
Environ Sci Pollut Res Int
Environmental Science and Pollution Research International
0944-1344
1614-7499
Springer Berlin Heidelberg Berlin/Heidelberg
37289394
27594
10.1007/s11356-023-27594-x
Research Article
Financial inclusion and low-carbon architectural design strategies: solutions for architectural climate conditions and architectural temperature on new buildings
Zhao Chi [email protected]
12
Zhou Jianliang [email protected]
1
Liu Yanan [email protected]
12
1 grid.411510.0 0000 0000 9030 231X School of Mechanics and Civil Engineering, China University of Mining and Technology, XuZhou, 221100 China
2 grid.495756.c 0000 0001 0550 9242 Jiangsu Vocational Institute of Architectural Technology, School of Architectyral Decoration, XuZhou, 221100 China
Responsible Editor: Nicholas Apergis
8 6 2023
115
5 4 2023
9 5 2023
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
The objective of this research is to explore the potential of financial inclusion and low-carbon architectural design strategies as solutions to improve the thermal comfort and energy efficiency of new buildings in different architectural climate conditions. The manufacture sector, which accounts for about 40% of all yearly greenhouse gas releases, has been stimulating with trying to reduce the amount of energy it consumes and the detrimental effects it has on the climate, in accordance with the standards outlined in the 2016 Paris Agreement. In this study, panel data analysis is used to examine the connection between green property financing and carbon dioxide emissions from the building sector in one hundred and five developed and developing countries. Although this analysis finds a negative correlation among the development of environmentally friendly real estate financing and firms' worldwide carbon dioxide emissions, it finds that this correlation is most robust in developing nations. A number of these countries are experiencing an unregulated and rapid population explosion, which has boosted their demand for oil, making this discovery essential for them. The difficulty in securing green funding during this crisis is slowing and even reversing gains made in past years, making it all the more important to keep this momentum going during the COVID-19 outbreak. It's critical to keep the momentum going by doing something.
Keywords
Financial inclusion
Architectural design
Low-carbon
Architectural climate conditions
Architectural temperature
==== Body
pmcIntroduction
Over the course of the last few decades, the necessity of taking action to counteract global warming and the climate change that it causes has only become more pressing. This activity is largely to blame for the current state of the environment (Jiang et al. 2020; Ofori et al. 2023). As a direct response to the human suffering and social unrest that climate change has caused, governments on every continent have enacted legislation designed to cut carbon dioxide emissions. Although a number of emerging markets are still in the beginning stages of their development, the level of urbanization in such countries is still relatively low (Li and Umair 2023). As a consequence of this, it is possible that both their economies and their carbon emissions may increase. The fact that China has the main economy that is expanding at the fastest rate in the world is directly responsible for the country's rapid urbanization. The urbanization rate in China is currently at 61.58% (as of 2019), and it is projected to sharply increase by the year 2060 (from the urbanization rate of 49.7% recorded in 2011). One of the environmental problems that urbanization brings is the production of gases that contribute to global warming. China and other nations require assistance in order to achieve a balance between accelerating urbanization and reducing emissions of greenhouse gases (Liu et al. 2023).
As a result of the widespread belief that carbon dioxide is the primary component responsible for the primary factor contributing to the planet's changing climate, it has been the primary topic of conversation concerning global warming. According to study carried out at the Lawrence Berkeley National Laboratory in the United States in the year 2006, China was the country that made the most contribution to the world emissions of carbon dioxide (Fang et al. 2022). China, which has the world's most developed economy, is often seen as a pivotal player in the effort to lessen the destructive impact that humans have on the globe. Despite this, China was held responsible in 2012 for producing 31% of the CO2 emissions that were produced worldwide (Pan et al. 2023).
Under the Climate Agreement signed in Stockholm in 2010, China committed to reducing its carbon dioxide emissions by between 35 and 40 percent by the year 2019. China has pledged to lowering its carbon dioxide emissions by 55–60% per year 2025, as stated in a study that was submitted to the UN Secretary General. The intensity of China's carbon emissions is expected to be lowered from their levels in 2004, which will allow China to accomplish these goals (Wu et al. 2022).
Emerging economies continue to have low rates of carbon dioxide emissions per person, despite the fact that the majority of greenhouse gases come from countries with very strong economies. This disparity can be partially explained by the fact that the rate of environmental deterioration in developed countries and rich countries differs substantially as a direct result of the substantial cultural, political, and economic disparities between them (SIDS). More in-depth and specific research is required in order to promote low-carbon growth in emerging economies. This is necessary in order to close the gap between legacy emission regulations and the requirements of modern development (Xiuzhen et al. 2022).
Countries are already cooperating with one another in an effort to slow the rate at which carbon dioxide is being released into the environment. The international carbon trading market has been adjusted in accordance with the provisions of the Climate Agreement and the Clean Development Mechanism Joint Implementation as a first step toward mitigating environmental damage. This adjustment was made in accordance with the Clean Development Mechanism Joint Implementation. The second category is referred to as "international low-carbon technologies," and it encompasses a variety of initiatives that aim to decrease the amount of carbon dioxide releases into the atmosphere. Some of these initiatives include support for solar, wind, and hydroelectric power plants (Ullah et al. 2020). These technologies have as their primary goal the reduction of dependency on fossil fuels and the increase in the use of renewable and environmentally benign forms of energy. The Nationally Determined Contributions, also known as NDCs, are a new compliance mechanism that was formed as a part of the Paris Agreement in 2016 to assist in the reduction of greenhouse gases globally. NDCs have now been issued by the states that are participating in the protocol (Agyekum et al. 2021).
Carbon taxes might be adopted in order to build a system for pricing carbon; however, this would result in an increase in corporation costs without resulting in significant investment in environmentally friendly technologies. It is estimated that between 2021 and 2070, China's green programs will fall short of reaching carbon neutrality by a total of 400 billion yuan. A number of countries, most notably China, are embracing the concept of green money as a policy instrument to increase their level of social capital (Zhang et al. 2021a, b). A great number of environmentally conscious projects have been finished in China since the beginning of the country's green finance scheme in the year 2008. In 2018, the areas of transportation (44%) and renewable clean energy (42%), respectively, had the greatest number of green initiatives being implemented. Quite a few quantitative studies have investigated the organizational dynamics and outcomes of green initiatives. For example, the utilization of payment funds as a means to encourage the expansion of environmentally conscious businesses is the subject of a number of academic research currently being conducted (Ikram et al. 2019).
In its current form, tax incentives for environmentally conscious financing options are more in line with an approach that zeroes in on specific projects as its primary concern. The top banks in China were only responsible for processing 10.4% of all loans made in China last year. Many studies on green financing come to the conclusion that in order to increase the proportion of green loans, banks will either offer incentives or set constraints. However, these studies don't go much further than the conventional bank-use models for determining wealth gaps and investment caps. Bets placed by large banks on carbon-intensive businesses determine market shares and establish lending bias in energy-hungry industries (Geyer-Klingeberg et al. 2019; Anu et al. 2023). It is possible that the underfunding problem may be significantly mitigated if the role of financial institutions in the development of low-carbon technology could be more clearly defined. As the study of carbon taxes advanced, it became obvious that a "green revolution" in financing was required for effective price distribution in order to achieve the desired results. Because of this, a new monetary system that is friendly to the environment needs to be developed. In order to be viewed as environmentally sensitive, financial institutions need to do rid of internal impediments to investment and actively guide firms as they transition to green economy practices (Xia et al. 2020).
This study contributes to the expanding body of knowledge on green finance and the Porter effect of green finance on environmental protection by estimating the relationship between green real estate financing and the emissions of carbon dioxide formed by the building developments sector in one hundred different countries between the years 2000 and 2020 using the panel method. It will be helpful for policymakers to gain an understanding of the impact green financial policies and practices have had in developing countries as well as developed countries on the total of air contamination formed by the construction sector over the course of the research period (Mohsin et al. 2020). If state officials are aware of which states have decreased their emissions of greenhouse gases the most, it may push them to continue the positive trend and reach their target of zero emissions by the year 2050. As a consequence of the increased scrutiny placed on economic and social policies, as well as the expenditures of the government and the financing of the manufacturing sector in the wake of COVID-19, it is anticipated that both access to financial resources and the quality of the environment will deteriorate. For instance, despite the fact that the epidemic has temporarily halted economic growth, the price of fossil fuels has undergone a significant shift. This is absolutely necessary in order for the government to fulfill its previous commitments to reduce carbon emissions and to keep its current and future green projects going.
The findings of this study present three novel approaches to green funding and reducing carbon emissions.Firstly, Studies that have been done in the past have focused on investigating the potential consequences that green money could have on ecological defense at the institutional level.
Secondly, there have been no studies that have looked at the long-term implications of green property finance, even though there is a lot of literature on the factors (such as economic activity, population, temperature, and resource use) that affect construction's contribution to greenhouse gas emissions. This is although the statistic that there is a lot of literature on the elements that affect construction's contribution to greenhouse gas emissions.
Thirdly, In conclusion, additional research is necessary to establish whether or not green property finance improves the environmental and social performance of the construction industry.
The rest of this essay is organized as follows: Section "Literature review" provides a literature review and theoretical context; Section "Methodology" details the paper's data and methodology; Section "Results and discussion" reports the study's empirical findings; and Section "Conclusion and policy implications" discusses the study's conclusions and potential policy implications.
Literature review
The goal of reaching carbon neutrality has finally been accomplished after a lot of hard work. Environmental justice needs for fast action. To accomplish these aims, there must be a significant transformation on the societal level. There are a great number of strategies for reducing carbon emissions; yet, only a few of these strategies appear to be truly effective. This transformation, in conjunction with the growing utilization of alternate forms of energy, might result in reduced carbon emissions. One method to make green money better is to raise awareness about it among the general public. One of the various strategies available for reducing emissions of greenhouse gases is to employ additional people and give them a wage to store carbon dioxide. A person's carbon footprint can be reduced if they make use of renewable energy sources or if they are required to pay for their emissions. We can achieve carbon neutrality more quickly and with less of a financial burden on society if we deregulate the banking industry. This was accomplished by removing the element of corporate competition and coordinating the spending of the firms (Iqbal et al. 2019).
In place of a system that deals with trading emissions permits, the researchers advocate for the implementation of a pricing mechanism for carbon. The benefits of placing a price on carbon have motivated scientists and governments in every region of the world to work toward the goal of achieving a carbon–neutral planet by cutting carbon emissions as quickly as is practically possible. A great number of studies have investigated what may happen to an economy if it stopped producing carbon. This investigation centered on determining the extent to which carbon neutrality is linked to environmental research and development (R&D) as well as alternative energy research and development (ERD). Recent studies have shown that there are a number of different ways in which research and development (R&D) might be able to assist in achieving carbon neutrality goals (these ways include the application of digitalization and economics). In the context of academic institutions, it has been demonstrated that research and development can occasionally have a negative overall impact on the planet's ability to remain carbon neutral. In 2015, the state of California initiated a program known as the Energy Revolution with the purpose of cutting down on the amount of carbon dioxide the state emits. A policy that went into effect in 2016 requires that by the year 2024, 35% of the nation's electricity come from non-carbon sources. It is projected that by the year 2060, the levels of pollution will have dropped by 55% compared to the year 2000 (Asbahi et al. 2019; Shah et al. 2019).
These reductions were to take place over the course of the next two decades, reaching their peak in 2026. It is anticipated that these decreases will reach their peak in 2026. The CGE energy-economic-environment (E3) model is the major tool that is used in the United Kingdom for the purpose of analyzing climate policy. This model was developed by the Center for Global Energy Economics. This is due to the fact that this framework includes the assumptions that all markets are transparent and that rational people consistently think and behave in the same way. Additionally, this framework assumes that all people consistently think and behave in the same way. This is especially relevant when considering the fact that this crisis began in 2007. It is feasible to utilize either the MDM-E3 or the more recent E3ME model that was produced by Cambridge Econometrics to inform policy ideas for the United Kingdom. Both of these models were created by Cambridge Econometrics. As a result of the economic crisis that occurred in 2007, new energy conversion models have been developed within the field of environmental economics. These models were developed to meet the limits of their predecessors and, in most cases, to surpass those constraints (Mohsin et al. 2022b).
When a country's population gets wealthier and more people want to buy things like refrigerators and vehicles, this leads to an increase in the country's indirect emissions of greenhouse gases. Previous research on the relationship between urbanization and carbon dioxide emissions has been supported financially by a number of different organizations. The industrial sector is the one that consumes the most energy and releases the most carbon dioxide into the atmosphere than any other economic sector. With the help of the random effects Ordinary Least Square (ols) model for population, wealth, and technology, we were able to determine that the influence of urbanization on CO2 releases was lower in the East from 1993 to 2013 than it was in the West during that same time period. According to the findings of this study, cutting down on carbon dioxide emissions at their sources might be all that's needed to cut down on industrial emission factors (Mohsin et al. 2021b).
It is vital for the government of China to conduct an examination of the country's transportation infrastructure in order to assist it in accommodating the country's fast expanding economy and population, both of which require expanded accessibility to transportation. According to the findings, urbanization is not the only factor that influences carbon emissions; greenhouse gases, gross domestic product, and energy consumption all play significant roles in the process (Mohsin et al. 2022a).
Methodology
In this study, we analyze the relationship between environmentally responsible real estate financing and carbon dioxide emissions in the building sector using data from an imbalanced panel. The panel considered information from 98 nations on a biannual basis between 2002 and 2018. (And every four years between 2008 and 2018). This metric served as a stand-in for "green" finance. The sample data includes information from every nation for which there are accounts of the carbon dioxide releases related to the building industry and statistics from JLL Sustainability (see Table 1).Table 1 Defining terms for variables and their sources of data
Description Transformation
BCO2 emissions from buildings Construction-related CO2 emissions Logarithm
JLL Sustainability Environmental sustainability transparency index for real estate
GDP per capita (ppp) GDP (gross domestic output) at parity with the dollar Logarithm
GDP Logarithm
Population Total population (000) Logarithm
Urbanization Urban population/total population
Energy consumption Consumption of energy by commercial and public services (e.g.offices, stores, clinics, warehouses) as a share of total consumption of energy
Temperature Average daily air temperature in degrees Celsius Logarithm
Dependent variables
Using the information found in the EDGAR database, we were able to calculate the sector's emission factors expressed in kilotons (2021). When compared to the average value, the data set's total CO2 emissions were 32.7 million metric tons lower. The Sustainability assessment of green property economics has been incorporated into the regression analysis that was conducted for this research as one of the additional important parameters that were employed. There are a great number of potential predictors of the amount of carbon dioxide releases formed by a building that have been found in the relevant literature (Mohsin et al. 2021a). A number of elements come into play, including the people who live in the area, the condition of the infrastructure, the state of the economy, the state of technology, and the state of one's own psyche. To what extent these elements truly have an effect on the levels of pollution caused by industrial operations is something that remains to be seen.
Independent variables
The influence of each component on emissions from buildings varies widely depending on the countries taken into consideration, the time period taken into consideration, and the estimation methodologies that are used. The control variables for the proposed model were chosen because (1) there was sufficient information on them in the nations that were chosen, and (2) there was sufficient consistency throughout the existing literature regarding their impact on construction pollution (Iram et al. 2020). This is despite the fact that many other variables in the research also operate at the micro-economic scale. This investigation makes use of a variety of control variables, such as total income, population density in large cities, average income, energy use by industries and the government, temperature, and so on.
Methods of estimation
A model with panel nations and fixed effects was utilized to obtain the estimates. In the equation, the unobserved heterogeneous components, also known as the fixed effects of the states, are shown. (1). The country-fixed effects can explain all of the cross-national differences that are shown to be stable over time. It is essential to specify that panel-fixed effects models are utilized frequently in investigating the causes of carbon emissions. The equation for Model (1) is as follows:1 BCO2it=β1GFi,t+β2Xit+vi+uit
The JLL Sustainability Index is a statistic for green property financing that integrates current values and a one-year lag for carbon dioxide emissions from buildings (BCO2). Estimates of parameters are designated by the letter u. In contrast, control variables are indicated by the letter X, the letter vi shows nation-fixed effects, and the country itself is indicated by the letter I = 1.
Results and discussion
Changes in occupational fields will be heavily influenced by the reorganization of the current corporate system. During the eleventh four-year plan, the focus of the labor force will shift from agriculture to non-agricultural service industries. At the end of the eleventh four-year plan, heavy industry profits are projected to drop from their current 29.6% to 14%. The rate of urbanization will increase if this forecast does not come true. It was at 55% in 2016, but by 2019 it had risen to 60% (Liu et al. 2021). This means that profit is not a prerequisite for economic expansion at the present time. Maintaining the current pace of reforms throughout the eleventh and forty-ninth plans will boost growth by 0.7% relative to the BAU rate. Both strategies will undergo this enhancement (Zhang et al. 2021a, b). The average annual growth rate from 2016–2019 was also higher than the baseline scenario, and the acceleration of reforms means that the forces driving GDP growth will soon look very different. The model predicts that by the year 2019, an increase in total factor productivity will have accounted for almost 40% of total economic growth. These changes are a direct result of the new policies (see Table 2).Table 2 Variables' descriptive statistics
Full sample Non-Asian Asian Non-G-20 G-20
Mean Std. dev Mean Std. dev Mean Std. dev Mean Std. dev Mean Std. dev
BCO2 emissions from buildings 3.32 09 7.6209 5.3809 2.1408 4.8907 4.0608 5.5309 8.0907 6.9207 2.0508
JLL Sustainability 1.9133255 1.8524433 1.6651446 1.8261257 1.456291 1.6313457 1.5530632 1.7074564 1.595433 1.6669124
GDP per capita (ppp) 41,431.82 41,551.22 42,471.89 41,171.38 49,348.48 28,288.38 28,091.82 41,769.71 61,439.42 31,258.92
GDP 911,751.4 3,431,132 951,188.4 3,860,698 799,251.3 819,588.3 530,444 2,531,739 2,541,281 3,471,888
Population 80,488.22 288,166 80,381.82 330,190.5 41,550.62 39,690.7 90,078.13 331,7631.8 48,129.05 70,199.89
Urbanization 1.692428 1.2101352 1.6752135 1.2375731 1.7313457 1.1210698 1.6461346 1.2329689 1.7823466 1.1116544
Energy consumption 1.0874434 1.0536114 1.0722548 1.0577692 1.1178866 1.0232132 1.0701742 1.0560569 1.1178846 1.0304866
Temperature 31.93157 8.5936 29.784524 5.393235 8.322568 4.88891 29.29632 7.376146 22.40691 6.203168
It is common practice to use the terms "green finance," "sustainable finance," "green bold “and” green investment" interchangeably; however, there are many people who maintain that these terms refer to distinct concepts, leading to a global discussion among academics and influential organizations about the precise meaning of "green finance." Green finance is a relatively new form of financing with a primary focus on environmental and resource conservation. Green financing has many advantages, including:Facilitated access to funding for environmentally responsible endeavors. New monetary strategies being developed.
Increased sales thanks to dissemination of data on sustainability's payoffs.
By investing in green bonds, investors gain access to long-term capital that may be put toward things like the refinancing of existing green structures. This is merely one of the many benefits available. In addition, developments in areas as diverse as big data, blockchain technology, are being factored into updated financial models. Green money, which supports things like renewable energy, sustainable projects, and carbon offsets, is a powerful weapon in the fight against climate change, according to a number of studies (Yu et al. 2021). Green financing is used in the construction sector to better plan for ecologically sustainable building designs, construction, and maintenance during the life of a project. Students require more instruction on ecologically sound financial practices. The financial sector may hasten the transition to a carbon-free economy by relieving environmentally conscious enterprises of the burdens of regulatory compliance.
All of the strategies that were suggested to lessen the likelihood of bias in the research methods were implemented (2016). The bias in this study was characterized as "systematic error variation shared across measurement items created by the functionality of the same technique or cause,". Using this approach, exploratory factor analysis (EFA) was carried out, and each and every data point was incorporated into the analysis. According to the available research, exploratory factor analysis results in the identification of a solitary, all-encompassing factor, and this primary, initially-derived factor is thought to be responsible for a sizeable amount of the overall variance. The newly combined requirement for the first portion was just 29.014%, which is significantly lower than the maximum percentage that can be accepted. As a consequence of this, the third condition was successfully completed.
Over the age ranges, the changes in CO2 emissions as a percentage of the base were as follows: 0.658%, 0.62%, 0.44%, 0.1%, and 0.085%. In the first five years of the forecast timeframe, there was only a negligible drop in CO2 emissions that could be attributed to the use of renewable energy sources. One possible explanation is that renewable energy sources release less carbon dioxide into the atmosphere. It is anticipated that the effect of renewable energy will increase from 0.094 percent in the next five years to 6.73% over the course of a 29-year projection, which is a significant increase from its initial expectation for the next five years. It is anticipated that the utilization of renewable energy sources will have an impact on future CO2 emission reductions that will be higher than that of any other component (Table 3).Table 3 Non-ASIAN samples: outcomes of fixed effects regressions
Dependent variable: Log. CO2 emissions from the building sector
Independent variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
JLL Sustainability −1.0379*** (1.0089) −1.0380*** (1.0089) −0.0238*** (1.0089) −0.0228*** (1.0092) −0.0390*** (1.0089) −0.0449*** (1.0122)
JLL Sustainability (lag) −1.0348*** (1.0072) −1.0351*** (1.0072) −1.0299*** (1.0071) −1.0289*** (1.0071) −1.0380*** (1.0082) −1.0388*** (1.0082)
Log. GDP per capita (ppp) 2.2758*** (1.0962) 1.2090*** (2.8994) 1.2516*** (1.0938) 0.2006** (1.0916)
Log. GDP 1.6189*** (1.1268) 0.3621*** (1.1218) 0.6088*** (1.1272) 0.3627*** (1.1222) 0.5390*** (1.1148) 0.4211*** (1.1162)
Log. Population −1.1856 (0.2488) 1.2007 (0.2788) −1.1462 (1.3113) 1.2479 (1.2789) −1.1751 (1.2372) 1.0872 (1.2652)
Urbanization 1.4323 (1.4491) 1.4823 (1.4662)
Energy consumption 1.8291*** (1.2022) 1.4531*** (1.1618) 1.9689*** (1.2181) 1.5691*** (1.1756)
Log. Temperature −1.3792 (1.2778) −1.3334 (1.2189) −1.3281 (1.2489) −0.3190* (1.2008) −0.2470 (1.2692) −0.2580 (1.2092)
R2 1.0772 1.1762 1.1172 1.2142 1.1862 1.2859 1.2128 1.2991 1.3272 1.4371 1.3052 1.3869
Number of observations 368 368 368 368 368 368 368 368 368 368 368 368
Number of countries 81 81 81 81 81 81 81 81 81 81 81 81
The study applied the auto-correlation function to a time series chart with ten-year intervals, allowing for the discovery of the dynamic changes within the seven variables. The findings of this research are presented in Table 5. Because there was a one-sigma rise in CO2 emissions, there was also a one-sigma rise in the confidence intervals calculated for the entire period. After reaching their respective peaks during the first phase of the CO2 emission rise, energy and CO2 intensity, both continued to fall during the second phase before reaching a plateau after this point. Following an early surge caused by a one-mean-difference shock, the annual growth in CO2 emissions reached its highest point during the second phase, and it has been steadily decreasing ever since. The primary reason for the increase in CO2 emissions is variation. During ten years, we could determine if this hypothesis was accurate. A positive sigma shock to the population was found to have a beneficial effect on CO2 emissions over the ten stages we investigated (Table 4).Table 4 ASIAN samples: Fixed effects regression findings
Independent variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
JLL Sustainability −1.0422 (1.0371) −1.0258 (1.0461) −1.0498 (1.0362) −1.0358 (1.0461) −1.0482 (1.0458) −0.0150 (1.0358)
JLL Sustainability (lag) 1.08522* (1.0352) 1.0290 (1.0451) 1.07222 (1.0339) 1.0728 (1.0438) 1.0782 (1.0461) 1.0117
(1.0471)
Log. GDP per capita (ppp) −0.0288 (0.1088) 0.3214*** (0.0813) 1.0367 (1.1322) 1.2531*** (1.1132)
Log. GDP 1.1232 (1.1771) 1.5451*** (1.1442) 1.1322 (1.1661) 1.5450*** (1.1351) 1.1562 (0.1791) 1.5655*** (1.1458)
Log. Population −1.4442 (1.7072) −3.0276** (1.7756) −1.2832 (1.7089) −3.0390*** (1.7862) −1.2568 (1.7149) −3.118*** (1.8217)
Urbanization −2.2958 (1.8562) 1.8121 (2.0168)
Energy consumption 1.2391 (2.5188) −1.2655 (2.6078) 1.2638 (2.5133) −1.0219 (1.6272)
Log. Temperature −1.1271 (1.0752) −1.0021 (1.0688) −1.1122 (1.762) −1.00562 (1.0823) −1.0891 (1.0749) 1.0152
(1.0828)
R2 0.022 0.0489 0.0179 0.1991 0.0242 0.2752 0.0391 0.2742 0.0422 0.2772 0.0589 0.2178
Number of observations 201 201 201 201 201 201 201 201 201 201 201 201
Number of countries 30 30 30 30 30 30 30 30 30 30 30 30
Reusing bio-energy and household garbage can promote resilience and a circular green economy. Let's assume that creating a sustainable economy is a priority. Despite the fact that many experts contend that a green economy already is circular, it is crucial to draw attention to the legislation's circularity in this situation. A circular economy's ability to decrease waste while simultaneously spread out the life of sources, constituents, and products are essential to its success. "Cyclic biofuels" are defined as biofuels that "maximize the value of cellulose over time via cascades" (e.g., bio refineries). All parties must cooperate in order to make the transition to a circular, low-carbon, environmentally friendly low-cost smoothly. More funding is also necessary. Fostering small, neighborhood companies that are representative of their neighborhoods and facilitating effective repayment chains are both vital (Table 5).Table 5 Non-G-20 samples are the results of fixed effects regression
Dependent variable: Log. CO2 emissions from the building sector
Independent variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
JLL Sustainability −1.0391*** (1.0089) −1.0391*** (1.0089) −1.0219*** (1.0092) −1.0232*** (1.0088) −1.0449*** (1.0089) −1.0482*** (1.0088)
JLL Sustainability (lag) −1.0249*** (1.0069) −1.0258*** (1.0071) −1.0301*** (1.0071) −1.0289*** (1.0071) −1.0388*** (1.0078) −1.0391*** (1.0091)
Log. GDP per capita (ppp) 1.3156*** (1.0822) 1.2089** (1.0891) 1.2242** (1.0868) 1.1728*** (1.0822)
Log. GDP 1.6141*** (1.1181) 1.4198*** (1.1291) 1.6132*** (1.1181) 1.4172*** (1.1323) 1.5248*** (1.1152) 1.4351*** (1.1162)
Log. Population −1.0218 (1.2328) 1.1422 (1.2739) −1.0291 (1.3138) 1.1389 (1.2762) −1.0232 (1.2248) 1.00223 (1.2832)
Urbanization 1.4289 (1.4088) 1.68565 (1.4491)
Energy consumption 1.7642*** (1.1655) 1.4355** (1.1767) 1.9349 (1.2068) 1.5138*** (1.1928)
Log. Temperature −1.0168 (1.1538) −1.0444 (1.1167) −1.0189 (1.1238) −1.0541 (1.11179) 1.0369 (1.1123)
R2 1.0881 1.1732 1.1168 1.2219 1.2382 1.2778 1.2381 1.2823 1.3868 1.4223 1.3189 1.3655
Number of observations 291 291 291 291 291 291 291 291 291 291 291 291
Number of countries 81 81 81 81 81 81 81 81 81 81 81 81
The majority of the 129 study studies concluded that nature-based alternatives should be protected and developed in order to support the creation of an economic system that is environmentally benign. Restoration, permaculture principles, and a low level of interference are a few examples of these solutions. The creation of towns and the logging of forests are two examples. GDP per capita (PPP) could be used to set a cap on house output, promoting more environmentally friendly construction practices. The current economic system may need to be revised in light of new ideas and evolving societal norms. Arctic sea ice has been melting as a result of global warming, which has had disastrous effects on the community's economy and way of life. Environmental pollutants have a disproportionately negative impact on minority populations. Indigenous people, particularly in less developed nations, are frequently forced to suffer the brunt of pollution's impacts, despite the fact that it is the greatest preventable cause of mortality globally. If vaccines are not provided equally throughout the world, this could occur.
The best strategy to move forward while dealing with major issues and being at the front of technological invention may be to assist American businesses and universities in enhancing their core competencies, such as by generating new procedures and expenditures. If the United States wishes to maintain its position as a technical leader, it must expand its R&D spending, particularly for meteorological products. Working with Chinese manufacturers, however, might hasten the deployment of the technology deriving from these initiatives in the interim. If so, the current economic downturn may end up helping these regions in the long run. The government may make investments in enhancing the capacities of domestic manufacturers as a strategy to support the growth of technical progress. Yet in order to advance, a fund to finance local production must be established, and a legislative environment that supports the operation of such marketplaces must be maintained. It's possible that one nation may house the headquarters of the whole value chain for cutting-edge energy technologies. Without cutting economic ties with China, Europe may better deploy its stimulus funds by supporting the growth of businesses that use green energy. Even though some of these breakthroughs were made in other countries, it is still worth the extra expense to invest in renewable energy businesses. Despite their higher initial cost, these investments will yield large financial returns, making them desirable. Funding is necessary for the development of all ecologically friendly transportation options, modifications to renewable energy systems, and renewable energy sources themselves. Regardless of the location or manufacturing of the components that make up renewable energy, there will always be a demand for personnel in the construction, implementation, operation, and related service sectors (Table 6). Spending on green recovery would quickly disperse funds throughout the sector, increasing the number of open positions.Table 6 G-20 samples are the outcome of fixed effects regression
Dependent variable: Log. CO2 emissions from the construction sector
Independent variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
JLL Sustainability −1.0123 (1.0289) 1.0062 (2.0358) 1.0172 (1.0342) 1.0258 (1.0332) 1.0171 (1.0332) 1.0128 (1.0249)
JLL Sustainability (lag) 1.0242 (1.0258) −1.0168 (1.0262) 1.0003 (1.0172) 1.00022 (1.0172) −1.0007 (1.0271) −1.0151 (1.0172)
Log. GDP per capita (ppp) −1.1189 (1.1132) 1.3468*** (2.0822) 1.0128 (1.1389) 1.3981*** (1.1061)
Log. GDP 1.1848 (1.2282) 1.5491*** (1.1661) 1.21988 (1.2178) 1.5498*** (1.1671) 1.2632 (1.2178) 1.5551*** (1.1758)
Log. Population −4.107*** (2.7141) −1.8748 (016182) −2.842*** (1.7051) −1.8762 (1.6298) −4.119*** (1.7298) −1.8842 (1.6212)
Urbanization −1.7766 (1.9122) −1.5178 (1.8267)
Energy consumption 1.5328 (1.5478) 1.6655 (1.5862) 1.2032 (1.5582) 1.7442 (1.5652)
Log. Temperature −1.3119** (1.1119) −1.0542 (1.1189) −1.2841** (1.1258) −1.0268 (1.1261) −1.3278*** (1.1422) −1.0428 (1.1198)
R2 .0019 0.0129 0.0112 0.2058 0.0882 0.1456 0.1433 0.1489 0.1645 0.1782 0.0863 0.2278
Number of observations 105 105 105 105 105 105 105 105 105 105 105 105
Number of countries 28 28 28 28 28 28 28 28 28 28 28 28
There may be a connection between trade liberalization and funding for environmental efforts, as suggested by the DOLS and FMOLS data sets. Many roadblocks on the path to carbon neutrality have emerged as a result of the growth of the banking sector. A number of macroeconomic parameters, such as the sum of consumer and business expenditure, corporate investment, and government spending, were taken into account while calculating the growth rate. When profits increase, a company needs increase spending to stay up with the cost of goods in terms of energy generation for modran buildings (Chang et al. 2023). As a benchmark, we looked at how differently enterprises in our sample distributed their available resources. Market distortion was observed to rise as the statistical significance of the total factor performance grew further from zero.
Management permission improvements were also found to have a positive effect on the energy industry's total factor output. This finding demonstrates that the modifications' facilitating benefits—like having assets that are easy to access outweigh the regulations' reducing effects on modernization. The conditions shifted over time as a result of project-related pathways, leading to a dramatic rise in the use of coal and oil. Hence, the carbon content of the fuels may have influenced the reported emissions even under similar conditions (Wang et al. 2022). GTS-1 reports that the demand for green gas has skyrocketed. However, while it has fallen slightly, overall consumption of fossil fuels has stayed relatively same. The World Resources Institute predicts that by 2060, 70% of global energy demand will be met by renewable sources, and by 2070, 65% of demand will be met by renewables. Data from DOLS and FMOLS were analyzed to determine if there is a correlation between GDP and CO2 emissions (Sun et al. 2022). People of color and those from poorer socioeconomic backgrounds and lesser levels of education bear a disproportionate share of the burden of environmental concerns in many countries (Table 7).Table 7 Entire sample: Fixed effects regression findings
Dependent variable: Log. CO2 emissions from the building sector
Independent variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
JLL Sustainability −1.0381 (1.0068) −2.0392** (2.0078) −1.0262** (1.0082) −1.0351** (1.0079) −1.0369*** (1.0091) −1.9787*** (1.042)
JLL Sustainability (lag) −2.0342** (2.0058) −1.0342** (1.0062) −1.0289** (1.0062) −1.0288*** (1.0062) −1.0391*** (1.0069) −1.0386*** (1.0072)
Log. GDP per capita (ppp) 1.1652** (1.0716) 1.2489** (1.0556) 1.0** (1.04323 1.2411*** (1.0684)
Log. GDP 1.4127** (1.1078) 1.4252*** (1.1122) 1.4755*** (1.1082) 1.4291*** (1.1019) 1.4398*** (1.0992) 1.4655*** (1.0981)
Log. Population −1.0478 (1.1751) 1.0632 (1.2323) −1.0581 (1.1819) 1.0544 (1.2322) −1.1009 (1.2312) −1.0592 (1.2433)
Urbanization 1.7686 (1.4551) 1.5598 (1.3778)
Energy consumption 1.7838*** (1.17622) 1.4644*** (1.1462) 1.7762*** (1.2232) 1.5519*** (1.1648)
Log. Temperature −1.2148* (1.1252) −1.0472 (1.0842) −1.1642* (1.0852) −1.0542 (1.0868) −1.0655 (1.0345) −1.0378 (1.0893)
R2 1.0581 1.1322 1.092 1.1932 1.1428 1.2533 1.1551 1.3118 1.3123 1.4122 1.3123 1.3519
Number of observations 268 268 268 268 268 268 268 268 268 268 268 261
Number of countries 105 105 105 105 105 105 105 105 105 105 105 92
The study's conclusions agreed with those of previous investigations. For various reasons, global economic growth needs to improve efforts to attain carbon neutrality. Since it considers a wide range of socioeconomic factors, such as savings rate and government spending, gross domestic product is a reliable indicator of economic health. Businesses have more excellent room to expand and make investments when consumers have more disposable cash. Fossil fuels, which are renewable in and of them, are considered the source of most of the world's non-renewable resources. Carbon dioxide emissions exacerbate global warming a result and make it more challenging to obtain carbon neutrality. Across all industries, access to capital is critical in driving innovation. Table 7 displays the outcomes of the brief study. GFi, as was previously stated, is essential for reducing carbon dioxide output. When all other variables are held constant, a rise in GFi causes carbon dioxide emissions to decline by 0.329 percent. There is a 1% positive correlation between the gross domestic product and carbon dioxide. As the economy grows, there is a correlation between urban growth and higher CO2 emissions. The ECM predicts that the rate of change will slow statistically significantly (0.365). A quarterly departure from the long-term trend of at least 33% is required by the ECM.
A more intelligent approach to green finance that more effectively utilizes both financial resources and environmental assets can have a significant positive impact on carbon reduction and company green transformation. A model of green finance that emphasizes giving direction is superior than one that does not when it comes to making good use of funds. An increase in green expenditures will significantly aid efforts to cut carbon emissions and spark a green revolution by making it easier to use environmental resources efficiently. By directing investment toward projects that promote environmental responsibility, green finance is a tool for policy that aids in the creation of a carbon peak. For guidance-focused green finance to be successful, green investment advice and service to green sectors must converge.
Conclusion and policy implications
Conclusion
Urbanization is currently occurring at a rate and scale that is unmatched in human history. By 2050, it is anticipated that metropolitan areas would house about 60% of the world's population. Legislators are creating new regulations to cut the industry's energy consumption and carbon imprint. Several governments think that it is essential to cut emissions from the construction sector, which make about 40% of yearly global greenhouse gas emissions, in order to achieve the challenging goals specified in their Nationally Determined Contributions and the Paris Agreement (An et al. 2021). Green property finance options have become increasingly popular over the past 10 years in both developed and developing countries with the aim of lowering greenhouse gas emissions from buildings (Li et al. 2021). The academic literature is replete with instances that show how green financing could reduce annual global emissions. Unfortunately, little research has been done to determine how carbon dioxide emissions might change as a result of green construction finance. In an effort to close that knowledge gap, this study employed the JLL Sustainability Transparency Index (STI) methodology to investigate the relationship between green finance and CO2 emissions (Tu et al. 2021). This score and the overall amount of green investments made in the nations we investigated have a strong link.
Our research demonstrates that funding for green buildings has a significant and detrimental effect on CO2 emissions from the global construction industry (100 countries were analyzed). Using different criteria can produce findings that are comparable. It was discovered that the GDP, income, and energy use were all associated with emissions from commercial and government support for energy financing (Iqbal and Bilal 2021). No statistically significant relationships between industrial emissions and other nations were found, with the exception of ASEAN and G-20 nations. Emerging and less developed nations have implemented laws and policies to limit carbon dioxide emissions and make their construction sectors more ecologically friendly. These nations have overcome significant challenges associated with the control of finite physical resources and quick urbanization (Shen et al. 2021; Liu et al. 2022). The UNEP states that even while building energy laws is increasingly prevalent in industrialized and high-income nations, significant global progress has been made toward standardization and control of the construction sector (2020). For instance, the use of UNEP (2020) MEP standards is expanding throughout sub-Saharan Africa. The development of green infrastructure and renewable energy sources is accelerating in South Asian nations because to non-financial incentives (Kuang et al. 2020). Compared to 2019, global construction slowed by 15–30% in 2020 (Zhang et al. 2022). Advancements in alternative energy, fuel efficiency, and other ecologically beneficial projects will be halted until further information about official policy decisions is available. The decline in green funding brought on by decreased fossil fuel prices has hindered the economic expansion of renewable energy sources. The need for pandemic recovery programs and regulatory frameworks focused on restoring industries to pre-pandemic levels in the upcoming years is indicated by a number of factors, including the chance for green restoration to be supported and improved performance criteria for new buildings. The UN made this point clear in its Global Status Report on Buildings in 2020. Public and private entities may choose to "reset" their commitment to green building practices at this time (Yang et al. 2022). The United Nations has emphasized the need for innovative finance or new banking institutions to support green efforts in the current financial environment. In addition to traditional banking, financial assets like green bonds could be vital in bridging the gap in green finance caused by the epidemic.
Policy recommendations
The following is a list of some of the most notable results and recommendations for changing policy that came out of the study:Rising levels of green property financing are harming the emission factors of the infrastructure sector. According to the Porter Hypothesis, which proposes that the expansion of environmentally friendly financial systems has a favorable effect on environmental safeguards, the numbers lend validity to the hypothesis by providing support. So, in countries where there is a high rate of CO2 production as a result of building projects, there may be skepticism directed against policies that encourage the creation of new opportunities and the use of sustainable funding. This point was emphasized in the document. This can only be accomplished efficiently with the money readily available in multi-billion-dollar organizations.
It has been demonstrated that countries with low incomes and developing economies are leading the way in implementing green funding schemes and legislation to minimize carbon dioxide emissions. It has been demonstrated that legal frameworks for green finance can significantly benefit the environment in underdeveloped countries. The results of this study are consistent with those of previous research conducted similarly. This suggests a significant possibility for the expansion and enhancement of environmentally friendly financing and credits, particularly in nations whose construction industries produce significant emissions, such as Japan, Brazil, and Russia.
Author contribution
Conceptualization, Methodology: Chi Zhao; Writing—original draft: Jianliang Zhou; Data curation, Data analysis, Interpretation: Yanan Liu.
Funding
1. This work was supported by the National Natural Science Foundation of China (Grant number 72171224, 71871116) 2. The Humanities and Social Sciences Foundation of China's Education Ministry (Grant number 19YJAZH122).
Data availability
The data that support the findings of this study are openly available on request.
Declarations
Ethical approval and consent to participate
The authors declared that they have no known competing financial interests or personal relationships, which seem to affect the work reported in this article. We declare that we have no human participants, human data or human issues.
Consent for publication
We do not have any individual person’s data in any form.
Competing interest statement
The authors declare no conflict of interest.
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References
Agyekum EB Amjad F Mohsin M Ansah MNS A bird’s eye view of Ghana’s renewable energy sector environment: a multi-criteria decision-making approach Util Policy 2021 10.1016/j.jup.2021.101219
Al Asbahi AAMH Gang FZ Iqbal W Abass Q Mohsin M Iram R Novel approach of principal component analysis method to assess the national energy performance via Energy Trilemma Index Energy Rep 2019 10.1016/j.egyr.2019.06.009
An S Li B Song D Chen X Green credit financing versus trade credit financing in a supply chain with carbon emission limits Eur J Oper Res 2021 292 1 125 142 10.1016/j.ejor.2020.10.025
Anu Singh AK Raza SA Nakonieczny J Shahzad U Role of financial inclusion, green innovation, and energy efficiency for environmental performance? Evidence from developed and emerging economies in the lens of sustainable development Struct Chang Econ Dyn 2023 64 October 2022 213 224 10.1016/j.strueco.2022.12.008
Chang L, Iqbal S, Chen H (2023) Does financial inclusion index and energy performance index co-move? Energy Policy 174:113422
Fang W Liu Z Surya Putra AR Role of research and development in green economic growth through renewable energy development: empirical evidence from South Asia Renew Energy 2022 194 1142 1152 10.1016/j.renene.2022.04.125
Geyer-Klingeberg J Hang M Rathgeber AW What drives financial hedging? A meta-regression analysis of corporate hedging determinants Int Rev Financ Anal 2019 61 203 221 10.1016/j.irfa.2018.11.006
Ikram M Mahmoudi A Shah SZA Mohsin M Forecasting number of ISO 14001 certifications of selected countries: application of even GM (1,1), DGM, and NDGM models Environ Sci Pollut Res 2019 10.1007/s11356-019-04534-2
Iqbal S Bilal AR Energy financing in COVID-19: how public supports can benefit? China Finance Review International 2021 12 2 219 240 10.1108/CFRI-02-2021-0046
Iqbal W Yumei H Abbas Q Hafeez M Mohsin M Fatima A Jamali MA Jamali M Siyal A Sohail N Assessment of wind energy potential for the production of renewable hydrogen in Sindh Province of Pakistan Processes 2019 10.3390/pr7040196
Iram R Zhang J Erdogan S Abbas Q Mohsin M Economics of energy and environmental efficiency: evidence from OECD countries Environ Sci Pollut Res 2020 10.1007/s11356-019-07020-x
Jiang L, Wang H, Tong A, Hu Z, Duan H, Zhang X, Wang Y (2020) The measurement of green finance development index and its poverty reduction effect: dynamic panel analysis based on improved entropy method. Discret Dyn Nat Soc 2020. 10.1155/2020/8851684
Kuang B, Lu X, Zhou M, Chen D (2020) Provincial cultivated land use efficiency in China: empirical analysis based on the SBM-DEA model with carbon emissions considered. Technol Forecast Soc Chang 151. 10.1016/J.TECHFORE.2019.119874
Li C Umair M Does green finance development goals affects renewable energy in China Renew Energy 2023 203 898 905 10.1016/j.renene.2022.12.066
Li W, Chien F, Ngo QT, Nguyen TD, Iqbal S, Bilal AR (2021) Vertical financial disparity, energy prices and emission reduction: empirical insights from Pakistan. J Environ Manag 294:112946
Liu H Huang F Huang J Measuring the coordination decision of renewable energy as a natural resource contracts based on rights structure and corporate social responsibility from economic recovery Resour Policy 2022 78 102915 10.1016/j.resourpol.2022.102915
Liu F Umair M Gao J Assessing oil price volatility co-movement with stock market volatility through quantile regression approach Resour Policy 2023 81 103375 10.1016/j.resourpol.2023.103375
Liu H, Yao P, Wang X, Huang J, Yu L (2021) Research on the peer behavior of local government green governance based on SECI expansion model. In Land (Vol. 10, Issue 5). 10.3390/land10050472
Mohsin M Nurunnabi M Zhang J Sun H Iqbal N Iram R Abbas Q The evaluation of efficiency and value addition of IFRS endorsement towards earnings timeliness disclosure Int J Financ Econ 2020 10.1002/ijfe.1878
Mohsin M Hanif I Taghizadeh-Hesary F Abbas Q Iqbal W Nexus between energy efficiency and electricity reforms: a DEA-Based way forward for clean power development Energy Policy 2021 10.1016/j.enpol.2020.112052
Mohsin M Taghizadeh-Hesary F Panthamit N Anwar S Abbas Q Vo XV Developing low carbon finance index: evidence from developed and developing economies Financ Res Lett 2021 43 101520 10.1016/j.frl.2020.101520
Mohsin M Taghizadeh-Hesary F Iqbal N Saydaliev HB The role of technological progress and renewable energy deployment in Green Economic Growth Renew Energy 2022 10.1016/j.renene.2022.03.076
Mohsin M Taghizadeh-Hesary F Shahbaz M Nexus between financial development and energy poverty in Latin America Energy Policy 2022 165 112925 10.1016/j.enpol.2022.112925
Ofori EK Onifade ST Ali EB Alola AA Zhang J Achieving carbon neutrality in post COP26 in BRICS, MINT, and G7 economies: the role of financial development and governance indicators J Clean Prod 2023 387 January 135853 10.1016/j.jclepro.2023.135853
Pan W Cao H Liu Y “Green” innovation, privacy regulation and environmental policy Renew Energy 2023 203 245 254 10.1016/j.renene.2022.12.025
Shah SAA Zhou P Walasai GD Mohsin M Energy security and environmental sustainability index of South Asian countries: a composite index approach Ecol Indic 2019 106 66 105507 10.1016/j.ecolind.2019.105507
Shen L, Zhang X, Liu H, Yao P (2021) Research on the economic development threshold effect of the employment density of the Shanghai consumer goods industry in the context of new manufacturing, based on the experience comparison with international Metropolis. In Mathematics (Vol. 9, Issue 9). 10.3390/math9090969
Sun L Fang S Iqbal S Bilal AR Financial stability role on climate risks, and climate change mitigation: implications for green economic recovery Environ Sci Pollut Res 2022 29 22 33063 33074 10.1007/s11356-021-17439-w
Tu CA, Chien F, Hussein MA, Ramli YMM, Soelton M SPSI, Iqbal S, Bilal AR (2021) Estimating role of green financing on energy security, economic and environmental integration of BRI member countries. Singap Econ Rev 1–19
Ullah K Rashid I Afzal H Iqbal MMW Bangash YA Abbas H SS7 vulnerabilities—a survey and implementation of machine learning vs rule based filtering for detection of SS7 network attacks IEEE Communications Surveys & Tutorials 2020 22 2 1337 1371 10.1109/COMST.2020.2971757
Wang S Sun L Iqbal S Green financing role on renewable energy dependence and energy transition in E7 economies Renew Energy 2022 200 1561 1572 10.1016/j.renene.2022.10.067
Wu Q Yan D Umair M Assessing the role of competitive intelligence and practices of dynamic capabilities in business accommodation of SMEs Econ Anal Policy 2022 10.1016/j.eap.2022.11.024
Xia Z Abbas Q Mohsin M Song G Trilemma among energy, economic and environmental efficiency: can dilemma of EEE address simultaneously in era of COP 21? J Environ Manage 2020 10.1016/j.jenvman.2020.111322
Xiuzhen X Zheng W Umair M Testing the fluctuations of oil resource price volatility: a hurdle for economic recovery Resour Policy 2022 79 102982 10.1016/j.resourpol.2022.102982
Yang Y, Liu Z, Saydaliev HB, Iqbal S (2022) Economic impact of crude oil supply disruption on social welfare losses and strategic petroleum reserves. Resources Policy 77:102689
Yu L, Chen Z, Yao P, Liu H (2021) A study on the factors influencing users’ online knowledge paying-behavior based on the UTAUT Model. In Journal of Theoretical and Applied Electronic Commerce Research (Vol. 16, Issue 5, pp. 1768–1790). 10.3390/jtaer16050099
Zhang D Mohsin M Rasheed AK Chang Y Taghizadeh-Hesary F Public spending and green economic growth in BRI region: mediating role of green finance Energy Policy 2021 10.1016/j.enpol.2021.112256
Zhang X, Liu H, Yao P (2021b) Research jungle on online consumer behaviour in the context of web 2.0: traceability, frontiers and perspectives in the post-pandemic era. In Journal of Theoretical and Applied Electronic Commerce Research (Vol. 16, Issue 5, pp. 1740–1767). 10.3390/jtaer16050098
Zhang L Huang F Lu L Ni X Iqbal S Energy financing for energy retrofit in COVID-19: recommendations for green bond financing Environ Sci Pollut Res 2022 29 16 23105 23116 10.1007/s11356-021-17440-3 | 37289394 | PMC10249549 | NO-CC CODE | 2023-06-13 23:15:11 | yes | Environ Sci Pollut Res Int. 2023 Jun 8;:1-15 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.