On the Critical Role of Human Feces and Public Toilets in the Transmission of COVID-19: Evidence from China

The surprising spread speed of the COVID-19 pandemic creates an urgent need for investigating the transmission chain or transmission pattern of COVID-19 beyond the traditional respiratory channels. This study therefore examines whether human feces and public toilets play a critical role in the transmission of COVID-19. First, it develops a theoretical model that simulates the transmission chain of COVID-19 through public restrooms. Second, it uses stabilized epidemic data from China to empirically examine this theory, conducting an empirical estimation using a two-stage least squares (2SLS) model with appropriate instrumental variables (IVs). This study confirms that the wastewater directly promotes the transmission of COVID-19 within a city. However, the role of garbage in this transmission chain is more indirect in the sense that garbage has a complex relationship with public toilets, and it promotes the transmission of COVID-19 within a city through interaction with public toilets and, hence, human feces. These findings have very strong policy implications in the sense that if we can somehow use the ratio of public toilets as a policy instrument, then we can find a way to minimize the total number of infections in a region. As shown in this study, pushing the ratio of public toilets (against open defecation) to the local population in a city to its optimal level would help to reduce the total infection in a region.

Introduction
The outbreak of COVID-19 diseases spreads very quickly all over the world, infecting millions of people in a very short period. The number of confirmed cases of COVID-19 continues to rise; according to real-time statistics released by the World Health Organization (WHO), the cumulative number of cases as of September 8, 2021, was around 223 million globally. 1 The virus that causes the infection and hence the diseases is formally called Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The spread of COVID-19 disease is a multiscenario and multi-factor concept. Although such concept is very different from the virus of SARS-CoV-2, the two terms are used mixedly to express the similar meaning in many situations. However, this study mainly describes the spread of SARS-CoV-2 in a specific scenario. Therefore, even though there are several terms of COVID-19 throughout this paper, when we talk about the transmission, we really mean the SARS-CoV-2 virus.
The surprising speed of the virus's spread created an urgent need to investigate the chain and pattern of transmission of COVID-19. As it is considered a respiratory syndrome, the major route of its transmission is commonly considered to be through respiratory channels. Indeed, most current studies imply that the major routes of transmission of the virus are through respiratory droplets and fomites (Chan, Yuan, & Kok, 2020;Hellewell, Abbott, & Gimma, 2020;Lu, Zhao, & Li, 2020;WHO, 2020). Therefore, people are encouraged to wear face masks to prevent the active virus from entering and lodging in the nasal cavity and upper respiratory tract.
However, it has been more than twenty months since the outbreak of COVID-19, and mankind is yet not certain about its exact transmission route. Now, the exacerbation of the global pandemic indicates that other potential methods of transmission might also exist. More recent studies are emerging, not only in the social vulnerability (Kashem, Baker, & González, 2021), but also in the practical challenges (Poon & Tee, 2021). However, knowledge about other potential vehicles for the spread of SARS-CoV-2 remains to be determined. 2 As the COVID-19 pandemic evolves to a global disaster, even to be out of control in many countries and regions, we need to seek for new answers by exploring new routes of transmission of the virus of SARS-CoV-2. Up till now, we cannot rule out other routes besides the traditional respiratory route (Dhama, Khan, & Tiwari, 2020), especially the fecal-associated routes (Sun & Han, 2021 and many others).
The transmission chain of COVID-19 is still a puzzle. The SARS-CoV-2 virus can survive for a long time, remaining viable on the surface of objects. In fact, China Center for Disease Control and Prevention (CDC, China) released a report on October 17, 2020, saying that it detected a COVID-positive live virus in the samples of imported frozen cod in Qingdao. 3 Under certain environmental conditions, the virus on the surface of an object might lead to infection. In addition, it has been reported that transmission could have occurred from a COVID-positive family whose children smeared feces around their room in a quarantine hotel in Australia. It is possible that the nursing staff was infected by the high viral concentration in feces when they entered the room. 4 Because "the living environment matters" (Das, Ghosh, & Das, 2021), concern about the possible involvement of human feces as well as wastewater and garbage in the transmission of COVID-19 has been raised since the outbreak of the pandemic (Liu, 2020;Quilliam, Weidmann, & Moresco, 2020). In fact, evidences showing the potential for SARS-CoV-2 to be spread by fecal-oral, fecal-fomite, or fecal-aerosol routes have been accumulated (de Graaf et al., 2017;Arslan, Xu, & El-Din, 2020; de Graaf & Beck, 2017Ling, Xu, & Lin, 2020Tian, Rong, & Nian, 2020;Xu, Li, & Zhu, 2020a), which raise concern about public toilets.
Indeed, several incidents led to a suspicion of potential fecal transmission. In June 2020, a couple in Beijing was infected with COVID-19 after a visit to a public restroom, where samples of the environment were confirmed to be positive for  In August 2020, an evidence indicates that a woman was infected with coronavirus in the restroom while on a flight from Italy to South Korea. 6 In December 2020, Chengdu experienced a small wave of COVID-19, and according to an official bulletin, people who had gone to a particular public restroom were notified that they should take the nucleic acid test for possible exposure to the virus. Thereafter, the official guide advised people to use the public toilets less or not at all. 7 During the same period, in Wenjiang district in Chengdu, the 29 public toilets there were sanitized 118 times every day. 8 The most typical case was the one in Guangzhou in June 2021, which was confirmed in an official investigation. A person could be infected after only 14 s in a public restroom. 9 In recent decades, "sustainability" has become a popular topic of discussion. Because people need to use the toilet every day, it can be considered as a fundamental object involved in sustainability in daily life that involves meeting basic needs. However, if everyday activities are associated with the transmission of an infectious disease such as COVID-19, attention needs to be paid to avoid seriously negative outcomes.
After the outbreak of COVID-19, researchers inspected the sewers of university residence halls and found SARS-CoV-2 RNA in wastewater samples. 10 Moreover, a study conducted by the Istituto Superiore di Sanità suggests that SARS-CoV-2 existed in wastewater collected from the entrance of treatment plants in northern Italy. 11 SARS-CoV-2 was also identified in sewage in Brazil 12 and Spain 13 respectively. Xiao and Torok (2020) suggest that governments should take measures to control the potential threat of reinfection from sewage. In February 1, 2020, China's Ministry of Ecology and the Environment (MEE, China) released a notice about the handling of medical sewage and urban sewage, to standardize the emergency treatment and disinfection requirements of medical wastewater, and to prevent the spread of SARS-CoV-2 through feces and sewage. 14 In August 2020, the US Centers for Disease Control and Prevention (CDC, U.S.A) announced the establishment of a national wastewater surveillance system (NWSS) to help local public health officials to better understand the spread of the COVID-19 in their communities. 15 In addition, the US CDC recommended that workers at wastewater treatment plants take standard precautions to prevent exposure to aerosolized sewage.
In this paper, we conjecture that one possible route of transmission is via public toilets, and our results show strong associations between wastewater and infection. One plausible reason for this result is that people infected with COVID-19 emitted airborne aerosols that were propelled upward and outward when they flushed the toilet. Because the toilets are in a public space, those who enter that area after them could become infected. However, the use of physical barriers (Ren, Xi, & Wang, 2021) and better ventilation (Kong, Guo, & Lin, 2021) may decrease the spread of infection.
As stated above, the transmission chain of COVID-19 still remains unclear. Because the virus can be found in public toilets, wastewater, and sewage, we now suspect the virus can be transmitted in ways other than the traditional respiratory channels. In addition, the concentration of the virus (i.e., virus load) matters a great deal in the transmission of COVID-19. So, this study examines whether human feces and public toilets play a critical role in that transmission. First, we develop a theoretical model that simulates the transmission chain of COVID-19 through public restrooms. Second, we use stabilized epidemic data in China to empirically examine this theory. Finally, we outline policy implications based on our findings.

A critical review of related studies
There has been a burst of related studies emerging in recent months of 2021. Hence, we present perhaps the most up-to-date, comprehensive literature review before we formally develop our analysis. Next, we briefly review current support for the potential for fecal transmission and discuss the possible impacts from a public health perspective. By examining the relevant case reports, we provide a valuable reference for the prevention and control of infection with SARS-CoV-2.
One alternative route of infection might be the digestive system, however, previous studies have ignored or underestimated the number of asymptomatic people with early mild GI symptoms (Gu, Han, & Wang, 2020). Studies have shown that COVID-19 depends on the ACE2 host cell factor (Hoffmann, Weber, & Schroeder, 2020;Lu et al., 2020;Zhou, Yang, & Wang, 2020) and can be transmitted through feces by entering host cells through the ACE2 cellular receptor (Zhang, Kang, & Gong, 2020), so the digestive system is a potential route for SARS-CoV-2 transmission. Recent reports also point out that some patients who were infected with COVID-19 had GI symptoms, including diarrhea (Cheung, Hung, & Chan, 2020;Liang, Feng, & Rao, 2020;Yeo, Kaushal, & Yeo, 2020). In addition, some researchers find that some patients infected with COVID-19 have active and prolonged viral infection in their gut, even in the absence of GI symptoms. 16
Other studies have shown that COVID-19 can be found in the feces of people who feel sick as well as those without any symptoms (Bai, Yao, & Wei, 2020;Kam, Yung, & Cui, 2020;Mesoraca, Margiotti, & Viola, 2020;Tang, Tong, & Wang, 2020;Wu, Guo, & Tang, 2020). Wang, Li, and Jin (2004)) find that SARS-CoV-1 can survive for more than 17 days in feces and urine. Holshue, Debolt, and Lindquist (2020) report the epidemiological and clinical characteristics of the first confirmed case of COVID-19 infection in the United States. After the patient's hospitalization, the results showed that the stool and respiratory tract samples were COVID-19 positive, but the serum was still negative. Obviously, the virus remains in the feces longer than in the respiratory tract. Studying a stool screening test, Zuo et al. (2021) note that some COVID-19 patients had positive stool tests but negative tests with respiratory samples.
Moreover, Wu et al. (2020) find that viral RNA shed in fecal samples for approximately five weeks after the patients' respiratory samples tested negative for SARS-CoV-2 RNA. Xu et al. (2020a) examine SARS-CoV-2 infection in children and find that eight of ten children tested positive on rectal swabs even after nasopharyngeal testing was negative. Studies in children's specimens of feces show positive RNA up to 30 or even 54 days (Xie, Long, & Ren, 2020). Xiao, Tang, and Zheng (2020) show that SARS-CoV-2 can be found in feces, and even though the virus is not detected in the respiratory tract, stool samples can remain positive. Zhu, Liu, and Xu (2015) state that viral RNA could be detected in fecal samples from 15 patients with a real-time reverse transcription polymerase chain reaction. Moreover, Mesoraca et al. (2020) write that six fecal specimens became positive for SARS-CoV-2 RNA, while 13 respiratory tests returned negative results 15 days after the first positive respiratory specimens test.

The potential fecal-oral transmission
A natural implication of the fecal viral shedding of SARS-CoV-2 leads to the concern of the potential fecal-oral transmission. In fact, A few studies raise the possibility of fecal-oral transmission based on the extended duration of viral shedding in stool samples. Such gastrointestinal illness induced by COVID-19 indicates high likelihood of transmission route of fecal material (Chen et al., 2021a;Olusola-Makinde et al., 2020). Thus, the possibility of fecal-oral route has become a popular hypothesis which is suggested by the facts shown above (Bonato, Dioscoridi, & Mutignani, 2020;Chen et al., 2021a;Troisi, Venutolo, & Tanyà, 2021). Some scholars even consider it to be the cause of community transmission with a major environmental concern (Jones, Baluja, & Graham, 2020; Mohan, Hemalatha, & Kopperi, 2021). Moreover, it has been prompting from clinical research (Pola, Karnam, & Santhekadur, 2021) to a major public health concern (Panchal, Prakash, Bobde, & Pal, 2021a; Usman, Farooq, & Anastopoulos, 2021).

Aerosols: the more critical matter?
However, the fecal-oral transmission is not the only concern we have. In fact, it may not even be the primary route (Albert, Ruiz, & Peman, 2021). Perhaps, we may need to worry more about the fecal associated aerosols (Cao, Shao, & Jones, 2021b). Now, the respiratory aerosols with much smaller size than the droplets are considered to contribute a lot to the infection of SARS-CoV-2 (Wang, Prather, & Sznitman, 2021b).
Although proper physical isolation measures, such as social distancing and wearing masks (Choi & Shim, 2021;Liao, Liu, & Wang, 2021;Su et al., 2021;Sun, & Zhai, 2020), are effective for reducing the transmission of infection, indoor environments can still be risky, which leaves open the possibility of transmission via feces until this is disproven.
Some scholars have paid attention to the ventilation system which may contribute either positively or negatively to the virus spread (Kong et al., 2021;Senatore, Zarra, & Buonerba, 2021;Sha, Zhang, & Qi, 2021). There is no doubt that optimized ventilation system can decrease the transmission of virus (Dumont-Leblond, Veillette, & Mubareka, 2020). Apart from the ventilation, heating and air conditioning systems also add new concern in their roles with the transmission of SARS-CoV-2 RNA (Horve, Dietz, & Fretz, 2021).

The public toilets: why do they matter?
A typical example of the closed indoor environment with poor hygiene condition is the public restrooms. The risk of exposure in public toilets theoretically comes from aerosol transmission, which has been suggested to be an additional pathway. 17 Flushing produces aerosol sprays, and if the viruses are present in feces, the concern is that inhalation of aerosols is produced by people infected with SARS-CoV-2 when they cough or even speak, which can cause the virus to spread. Even the toilet paper is concerned for its hoarding (Labad, Gonzales-Rodriguez, & Cobo, 2021).
In fact, the aerosol generation with unhealthy outcomes has been studied even before the COVID-19 pandemic (Aithinne, Cooper, & Lynch, 2019). As early as discussed by the classical research of microbiological hazards (Gerba, Wallis, & Melnick, 1975), the aerosol can be generated by toilet plume or flushing which may cause aerosolization (Barker & Jones, 2005;Best, Sandoe, & Wilcox, 2012;Hamilton, Hamilton, & Johnson, 2018;Johnson, Lynch, & Marshall, 2013a, 2013b or bioaerosol concentrations (Knowlton, Boles, & Perencevich, 2018). In the process, the emission strength can be strong (Lai, Tan, & Li, 2018), which may promote the transmission of virus if there is any according to the fluid dynamics (Li, Wang, & Chen, 2020). An earlier study of hospital toilets also found that the bioaerosol concentrations were significantly greater after fecal waste was flushed down the toilet and might remain in the air for more than 30 min Knowlton et al., (2018).
After the outbreak of COVID-19 pandemic, the risk of fecal transmission in public toilets via bioaerosols has soon obtained attention by researchers both in hospital conditions (Ding et al., 2020) and other scenarios (Schreck, Lashaki, & Hashemi, 2021;Usman et al., 2021). This adds alternative explanations to the "traditional" respiratory droplets. A recent study measuring viral RNA in aerosols at two hospitals in China during the outbreak of COVID-19 found that aerosols were higher in the area near the toilets in a patient's room. This suggests that SARS-CoV-2 might be transmitted through aerosols (Casanova, Rutala, & Weber, 2009;Liu, Ning, & Chen, 2020). Van Doremalen, Bushmaker, and Morris (2020) report that SARS-CoV-2 could stay in the aerosols up to three hours. Li et al. (2020) also find that flushing the toilet can disperse virus-laden aerosols about 3 feet high, which computer simulations show can be inhaled. They indicate that after the toilet is flushed, 40-60% of the particles rose above the toilet seat and hovered in the air.
As  point out, massive number of people in many low-income and developing countries and regions have no access to private sanitary facilities with clean environment, which may become a leak of containment in the virus spread. Even in the developed world, the public restrooms are difficult to maintain high sanitation standards. Besides the fecal related issues, the risk of indoor aerosols oriented viral transmission itself in the enclosed spaces for public use can be high (Chen, Jia, & Han, 2021b;Sun, Li, & Han, 2021).

Focusing on the wastewater-based epidemiology (WBE)
Moreover, concerns have been raised about whether solid human waste washed into the sewers can cause reinfection. Researchers around the world have traced the spread of SARS-CoV-2 through wastewater and sewage. Some hold an optimistic attitude, believing that this reinfection is unlikely happened because the chemicals added to the water destined for urban sewer pipes are disinfectants which prevent the spread of the virus. Gundy, Gerba, and Pepper (2009) demonstrate that the coronavirus dies off very quickly in wastewater, and the time required for the virus titer to decrease 99.9% (T99.9) is between two and four days.
However, Casanova et al. (2009) state that the coronavirus can remain infectious for long periods in water and pasteurized settled sewage. Previous studies have said that SARS-CoV-1 was detected in sewage from hospitals in China during the SARS outbreak (Lee, 2003;Wang, Li, & Guo, 2005). Bibby and Peccia (2013) detected that different types of human viruses can be discharged into the sewage collection system and concentrated in sewage sludge.
In fact, there are many studies show the prolonged survival of SARS-CoV-2 in the environment of wastewater and sewage, which can be up to several days or even longer (Giacobbo et al., 2021). Therefore, wastewater as well as the corresponding treatments become very vulnerable to the transmission of virus (Foladori et al., 2020). As a result, the pipeline and the plumbing systems are turning into hazardous zones (Collivignarelli, Collivignarelli, & Miino, 2020;Dight & Gormley, 2021).
Then, where would the water in the sewage system finally go to? The freshwater environments and water safety are substantially jeopardized by SARS-CoV-2 RNA (Mahlknecht, Reyes, & Ramos, 2021). In fact, the natural water system such as rivers has already been detected with SARS-CoV-2 (Coronado et al., 2021;de Oliveira et al., 2021;Kolarevic et al., 2021).

Establishing the early surveillance (warning) system
According to the fecal transmission route, the treatment facilities for municipal wastewater have become the critical node in the fight against COVID-19 (Panchal et al., 2021a;Saththasivam et al., 2021;Sherchan et al., 2021;Wolfem, Archana, & Catoe, 2021). It has been noted that even the treated water samples are tested positive (Randazzo et al., 2020). Therefore, the sanitation process in the wastewater treatment plants (WWTPs) must be reinforced, and it also provides important measure to monitor the epidemiological trend, which is essentially an early surveillance (warning) system (Foladori et al., 2020;Mao, Zhang, & Yang, 2020;Panchal et al., 2021b;Shao, Ge, & Jones, 2021).
A promising surveillance tool as it appears to be, some scholars believe that the rapid monitoring is at least one of the keys to end the current massive transmission (Panchal et al., 2021b;Zhu et al., 2021). Some even consider it to be a breakthrough in the clinical practice with limited testing capacity in laboratories (Alygizakis et al., 2021), which is superior to the traditional clinical methods in population-level testing (Bar-Or et al., 2021). It has been called the "Sewage Tracking" that aims to detect the presence of SARS-CoV-2 RNA in the sewage samples (Martin et al., 2020). While some focus on the hospital wastewater (Acosta, Bautisca, & Hollman, 2021), some others focus on the wastewater of residential building (Wong et al., 2021) as well as university campus (Gibas et al., 2021).
This may be particularly useful in the developing world (Saini & Deepak, 2021). Indeed, there are already some successful examples, such as the sewage detection of SARS-CoV-2 RNA two weeks before the outbreak in Hungary (Roka et al., 2021), one to two weeks ahead of the official announcement in India , three days before the first case in England (Martin et al., 2020), the incidence in Switzerland (Fernandez-Cassi et al., 2021), and others. Now, hundreds of studies have related COVID-19 with water science (Ji, Zhao, & Wei, 2021). Researchers in top academic journals have called for the establishment of the global database for wastewater surveillance as part of the international cooperation (Bivins, North, & Ahmad, 2020) to support the decision-making in the public health sectors (Lundy, Fatta-Kassinos, & Slobodnik, 2021). Of course, there are uncertainties in every step of the above estimation process (Li, Zhang, & Shi, 2021d), which needs future studies to upgrade it.

What can we do to add to the literature?
To the best of our knowledge, most of these studies are review or descriptive type of articles. First, few city-wide level evidences are found to support the interaction of public toilets in the SARS-CoV-2 transmission. Second, few proper analytical frameworks are set up to conduct the corresponding research. And these are the gaps that this study tries to fill during this hard time.

The model
The initial number of infections in a region is I 0 . P RR is the probability of visit to a public restroom by someone who is infected. The infection multiplier of the public restroom is Inf RR . Therefore, the number of new infections equals the initial number of infections times the visits to a public restroom, which can be expressed as follows.
Thus the ratio of reproduction can be calculated as follows. Thus, If this ratio is the same in every round, then: where T I is the total number of infections in a region, and N is the number of rounds in the transmission of the virus. After we take the logarithm on both sides of equation, we obtain: This mathematical model is not the focus of this study. The purpose of introducing the mathematical model (i.e., theory) is to provide a general mechanism of viral transmission with a particular focus that is discussed in depth in the statistical models later. Many purely empirical studies do not have a logically sound mechanism, but ours does have one.
Therefore, beyond the mathematical model (theory), the central interest of this study is the design of a novel statistical method to examine the complex role of public restrooms as well as wastewater and garbage on the transmission of COVID-19 to their interaction.
Eq. (5) has similar empirical implications. With appropriate data, the left-hand side of Eq. (5) becomes the dependent variable, and the righthand side becomes the independent variables (along with a stochastic error term). However, the complex nature of COVID-19 transmission requires more sophisticated handling with special care.
Although I 0 can be considered as a constant in the regression, P RR is difficult to quantify in reality. We therefore use the ratio of the number of public toilets to the local population in a city or simply use the number of public toilets to represent the likelihood of a visit to a public toilet by someone who is infected; although we do not use them at the same time, we compare the results of different model specifications. In addition, to proxy for Inf RR , we use the local urban population. These two proxies are not perfect because acquiring accurate data on the two key variables might be technically impossible, but they are necessary. Of course, more control variables must be introduced to delve into the transmission of COVID-19 further.
The statistical methods we use in this study are simple but meaningful. In addition to the standard ordinary least squares (OLS) method, we use two-stage least squares (2SLS) with appropriate instrumental variables (IVs) to conduct the empirical estimation. However, the choice of IV is a real challenge for researchers. In this study, the key explanatory variable-the number of public toilets or its ratio to population-can be correlated with the stochastic error term in the regression, which is identified as an endogeneity issue. To correct the inconsistent estimation results, we need to use an appropriate IV, hence, we employ the 2SLS estimation method. In this case, urban residential wastewater or residential garbage is used as the IV, which might show important clues as to the possible intermediate role played by human feces on the transmission of COVID-19.
In fact, their complex relationship might be difficult or even impossible to observe. We can observe the viral concentration in human feces in the laboratory, but doing so in an urban context for a large-scale outbreak, i.e., at a city-wide scale, seems to be difficult or even impossible. When we consider wastewater as well as garbage at the city level, the task becomes even harder.
Our design here is novel (and somewhat "tricky"). The novelty of our design is that we use statistical tests to check whether wastewater or garbage at the city level is explicitly influential in viral transmission or implicit but functional as an intermediary for feces in transmission. Because the exact influence of these important factors at the city level (rather than in the lab) cannot be quantified and measured, if our statistical design passes appropriate statistical tests, then in a statistical sense, we can confirm the impact and interaction of these factors (i.e., human feces, wastewater, and garbage) at the city level.
The specific regression method employed is that of two-stage least squares (2SLS) with appropriate instrumental variables (IVs). The 2SLS model is not new. But the use of a 2SLS model in examining the role and interaction of public restrooms as well as wastewater and garbage is novel, constituting this study's contribution to the literature. The key here is the application of IV in the 2SLS model, so we conduct tests for endogeneity and weak instruments to confirm that our choice of IV is appropriate statistically.
In the endogeneity test with robust F-statistics, the precise value of F does not matter, but we check its corresponding p value for statistical significance (i.e., p<0.01, p<0.05, p<0.1). In performing the Montiel-Pflueger test of weak instruments with effective F-statistics, we check whether the value of F is close to 10, or the higher the better.
To our knowledge, the design of this analytical framework is novel, as discussed further below.

Data
This study uses data combined with several sources from China. We select data from China for two reasons. First, the Chinese government began to implement quarantine measures around the country in late January 2020, and these measures have largely controlled the epidemic situation in China for a long time. Therefore, the data in China, especially at the early stage of transmission, is valuable for the purpose of this study. Second, public health conditions are much better in China than some other countries, such as India. For example, refugees and people in countries or regions at a low socioeconomic stratum often lack clean water and adequate sanitation, which could exacerbate fecal-oral transmission. Consequently, if we find significant results using Chinese data, we can infer that these factors are more critical in other countries, such as India, which could help to explain why the pandemic is spreading so rapidly there.
We obtained the data on confirmed cases of COVID-19 for this study from China's National Health Commission (NHC, China), 18 covering 284 major cities with reported infections with COVID-19. However, the number of infections is constantly changing, so choosing the appropriate study period is challenging. In China, the epidemic of COVID-19 can be divided into roughly two stages. In the first stage, from late January to late March 2020, the infection in mainland China spread from Wuhan, the epicenter, to other provinces, first to those nearby and then to those farther away. Then, in the second stage, new confirmed cases were primarily imported from outside the country. Therefore, for the purpose of studying the possible chain of transmission associated with human feces, the data from the first stage are more valuable. For most of the cities in our dataset, we use numbers of infections confirmed as of the night of March 31, 2020. However, because of the lag in confirmation and reports for the epicenter, for Wuhan as well as surrounding cities in Hubei Province, we use the confirmed numbers at the end of June 2020. As shown in Fig. 1, there appears to be strong correlation between confirmed COVID-19 cases and the number of public toilets in most of the sample cities.
As mentioned earlier, we need to introduce several control variables into our regression. First, we include the distance from a city to the epicenter in mainland China, i.e., Wuhan, as measured with the digital map on Baidu.com. 19 S , we add data on the local urban characteristics for the sample cities, primarily obtained from the China City Statistical Yearbook 2018. 20 We pay particular attention to urban wastewater and residential garbage, which might be directly or indirectly linked with human feces. In addition, we include some other variables, such as urban district area, green coverage area, and urban built district, though they are not the primary focus of this paper. All the summary statistics are shown in Table 1.

Results
Here, we try to keep the description of the empirical results as clear as possible, since all the results are in Tables 2, 3, and 4. Therefore, in the results section, we try to highlight the comparisons of the results in different models.

Empirical estimation results using ln(Wastewater) as an instrumental variable
In Tables 2 and 3, we show several groups of empirical estimation results using ln(Wastewater) as the IV. In Table 3, the explanatory variable is "ln(Garbage)," unlike in Table 2. In addition, we differentiate between "ln(Number_of_Latrines)" and "ln(Number_of Latrines_Ratio)" as the explanatory variable. The corresponding OLS version of the models are presented here as well.
In Table 2, which does not consider the impact of garbage, the key focus of this study, i.e., the number of public toilets, tends to be positive and significant in its estimation parameter regardless of its absolute value (e.g., Models (1)-(2) and Models (5)-(6)) or its relative or ratio form (e.g., Models (3)-(4) and Models (7)-(8)), which gives us confidence that the results are in consistent with our expectations. For example, in Model (2), we use logarithmic form of all the variables, so the explanation of the coefficients is in percentage. When the number of public toilets increases by 1%, the confirmed cases of COVID-19 infection increase by 0.807%. In Model (4), the ratio of the number of public toilets has a marginal impact on the increase in COVID-19 cases of 1% to 4.038%. However, Model (4) fails the test of weak instruments as the Fstatistic is only 2.694. However, when the variable for garbage is added to the model (Table 3), the number of public toilets is no longer statistically significant in general, which is not the desired outcome. In addition, all the F-statistics in the tests of weak instruments in the 2SLS models in Table 3 are less than 10, which means they fail the tests.
Notably, ln(Dist_to_Wuhan) is generally negative and significant in all models, and its estimation parameters appear to be robust among different model specifications. For example, in Model (2), the result shows that an increase of 1.372% in the number of infections occurs in locations that are 1% closer to Wuhan (i.e., a smaller distance). In Model (4), this marginal impact is 1.265%, which is very similar to that in Model (3). This result shows that the distance from Wuhan is a very powerful control variable that can help us to isolate the local characteristic of the cities from the COVID-19 transmission. Additionally, ln (Urban_District_Population) is typically found to be positive and significant in the OLS estimations. Only in Model (4) in Table 2 showing its coefficient is positive and significant under 2SLS estimation. As shown in the results, the marginal impact is 1% to 1.428%.
Moreover, the inclusion of ln(Urban_District_Area) or ln(Green_-Coverage_Area) has no persuasive results. In Model (6), the result shows that a 1% increase in the green coverage area would decrease infections by 0.615%. In Table 3, although the coefficients of ln(Garbage) are generally positive and significant, it distorts other aspects of the models, in particular the primary research target of this study, i.e., the number of public toilets.
So, how can our empirical results be improved?

Empirical estimation results using ln(Garbage) as an instrumental variable
In Table 4, we use ln(Wastewater) as the explanatory variable and ln Note: Please see the Data section in the main text for more explanations of the variables.
Model (1)   (Garbage) as the IV. The overall performance of this group of models appears to be better: the number of public toilets is positive and significant in its estimation coefficients, either in its original form or the ratio form. In Model (22), when the number of public toilets increases by 1%, the number of COVID-19 infections rises by 0.819%. Then, in Model (24), the marginal impact is 1% to 1.147% using the ratio version of the number of public toilets. With regard to the control variables, ln(Dist_to_Wuhan) and ln (Urban_District_Population) are as good as expected in the estimation parameters. In Model (22), the estimated marginal impact of the distance from Wuhan is 1% to − 1.318%, and the coefficient in Model (24) is − 1.298, which is still very close in percentage terms. In addition, ln (Green_Coverage_Area) and ln(Built_District) tend to have negative and significant estimation coefficients. This result means that larger areas tend to have a lower number of infections. In Model (20), the marginal impact of the green coverage area on local infections is 1% to − 0.343%, and in Model (22) the impact of built area on the number of infections is 1% to − 0.704%. Although the reasons for these results might be complex, when cities have more such areas, viral transmission is more difficult, hence those cities have fewer confirmed COVID-19 infections.
Notably, we combine the use of the IV ln(Garbage) and the explanatory variable ln(Wastewater) in the group of models shown in Table 4. The coefficients of ln(Wastewater) are positive and statistically very significant in all these models, which confirms the role of wastewater in the urban transmission of COVID-19. The marginal impact of wastewater on infections is estimated as 1% to 0.416% in Model (22), compared with 1% to 0.609% in Model (24). In early August 2021, Zhengzhou experienced widespread flooding, and shortly afterwards it had a small wave of COVID-19 infection. This might be a typical example of the role of wastewater in COVID-19 transmission.
Although the endogeneity test fails in Model (18), it passes in Model (20), (22), and (24). The results of the weak instrument test are generally good, either close to the empirically critical value of 10 or much larger than 10. Among all the models in this group, we prefer Models (22) and (24). Although the statistical results appear to be slightly better in Model (22), in terms of more significant estimation coefficients (larger R 2 value and F-statistics in the test of weak instruments), the two models essentially differ very little.

Discussion
Many scholars have placed their concerns on the low-income countries or developing world where there is inadequate water for sanitation use (Castro, Bernegossi, & Sousa, 2021;Eichelberger, Dev, & Howe, 2021;Troisi et al., 2021) or the usage of virus-contaminated water (Adelodun, Ajibade, & Ighalo, 2021). In such scenarios, the fecal-oral transmission of SARS-CoV-2 has much higher risk (Chacin-Bonilla et al., 2021) and the community spread would be a more common consequence due to poor sanitation infrastructure (Eichelberger et al., 2021).
In the more vulnerable case, the squat toilets (especially with lidless designs) become new risk factors  and things may get harder in the scenarios of open defecation. In fact, open defecation is the common practice with a high proportion of population in some countries Table 4 Empirical estimation results using instrumental variable (IV) of ln(Garbage) (Dependent variable: ln(Confirmed_COVID), N = 284).
Model (17)   with large number of COVID-19 cases . Some scholars have mentioned India, where the dense population along with the poor sanitation condition make things worse (Panchal et al., 2021b).
In the previous section, we showed that human feces and public toilets might play a critical role in the transmission of COVID-19. In regions with relatively poor public sanitation, where human waste might be openly exposed, viral transmission could be even greater. Therefore, using the infection multipliers Inf OPEN and Inf RR , respectively, we consider the effect of open exposure of human feces in the environment at open and public restrooms. Now we revise Eq. (4) as follows.
Because we are particularly interested in how the ratio of P RR affects the total number of infections T I , we take the first derivative of T I with respect to P RR .

∂T I ∂P RR
Simplifying this equation yields: By solving for P RR in Eq. (8), we obtain the "optimal" value as follows: Eq. (9) shows that the value of P RR * must be between 0 and 1. Therefore, it satisfies the requirement of probability. However, we do not know whether this "optimal" value is the maximum or the minimum. Thus, we need to examine the second-order condition. Therefore, we differentiate T I with respect to P RR twice as follows.
It is not difficult for us to conclude that ∂ 2 TI ∂PRR 2 > 0. We therefore confirm that the "optimal" value of P RR * is the minimum.
These findings have very strong policy implications in the sense that if we can use the ratio of public toilets (against open defecation) to the local population in a city or simply the number of public toilets (i.e., P RR *) as a policy instrument, then we can minimize the number of total infections in a region due to exposure at public toilets as well as open defecation.

Conclusions
This study presents novel insights into potential COVID-19 infection after fecal exposure at public toilets. It briefly reviews the current supports for the possibility of fecal transmission and discusses the possible impacts from a public health perspective. First, a theoretical model is employed. Second, the statistical connection is established between infection and public sanitation. Then, it statistically simulates the transmission chain of COVID-19 at public restrooms and concludes that wastewater, along with garbage, promote the transmission of COVID-19.
The results can provide a valuable reference for the prevention and control of the ongoing pandemic.
So, we can draw a conclusion with very strong statistical significance that wastewater directly promotes the transmission of COVID-19 within a city. However, the role of garbage in this transmission chain is more indirect in the sense that garbage has a complex relationship with public toilets, and it promotes the transmission of COVID-19 within a city through the interaction of public toilets and human feces. The complicated potential transmission chain of COVID-19 is illustrated in Fig. 2.
These findings in this study may be of particular importance for India, as it suffers from relatively poor public sanitation conditions, which might contribute to its rapid increase in COVID-19 infection. As shown above, increasing the ratio of public toilets (against open defecation) to the optimal level would help to reduce the total infection in a region. In addition, as shown in the empirical results, effective control of urban wastewater and residential garbage would help to cut the transmission chain of COVID-19. The findings in this study can be of general interest and applied elsewhere. We therefore hope that this study can provide new clues which can contribute to the control of COVID-19 during such a time with hardship.
Because the information and data currently available are rather limited, our discussion lacks many important factors. For example, wearing face masks and washing hands can effectively reduce fecal exposure. Critical information on wearing face masks and washing hands is missing under the circumstances of human feces and public toilets. In addition, some other temporal factors, such as how long the virus remains transmissible, are also important. Furthermore, we should also consider the impact of some auxiliary equipment such as the ventilation system (e.g., exhaust fans), which might already be installed. These factors could also play a significant role in COVID-19 transmission and should be examined in future studies.


The Analysis of Factors Affecting Preparation Level for the Industry 4.0 Era in the COVID-19 Pandemic on Employees in DKI Jakarta

License: AttributionNonCommercial-ShareAlike (CC BY-NC-SA) This study analyses significant factors affecting the level of preparation for the industry 4.0 era related to anxiety about the COVID-19 pandemic on employees in DKI Jakarta. Apart from being influenced by the anxiety of the COVID-19 pandemic, the level of preparation for the industry 4.0 is also differentiated by gender, age, education, marital status, decreasing income, and loyalty. Therefore, companies can arrange the planning related to preparing the industry 4.0 during the COVID-19 pandemic if those factors have been figured out. This study used a quantitative method, and the population of this study is full-time employees in DKI Jakarta. The sample was selected by using the snowball sampling technique distributed to 12 companies in DKI Jakarta. The distribution was conducted through the WhatsApp application for a month, and it was gained 228 respondents. Snowball sampling technique used in correlation with the implementation of large-scale social restrictions caused by COVID-19 pandemic. The data analysis used a multiple linear regression model. The results show that factors affecting the level of employees’ preparation in DKI Jakarta for industry 4.0 related to the anxiety of the COVID-19 pandemic are loyalty and a decrease in income. Society, 9 (1), 115-123, 2021

mosques, and also work from home for employees of the company, which certainly affect companies' performance (Turambi & Wuryaningrat, 2020). An employee who usually uses work equipment in the office must adjust to working from home. This is proven that the COVID-19 pandemic has significantly reduced the preparation to face Industry 4.0 era.
The number of people and the number of deaths infected by COVID-19 were reported massively through various print media, electronic, and social media. This raises anxiety for the community, especially for employees, the anxiety of infected risk and the anxiety of occupation, which disrupts their economy, because the certainty of COVID-19 pandemic disappearance is unpredictable. It cannot be denied that uncertainty does not always stand side by side with preparation. This happened when people feel comfortable and accustomed to the conditions around them. Health, close relationship, and sufficient economy resulting in the neglect that circumstances such as the COVID-19 pandemic can happen at any time, so the comfort turns into prolonged anxiety and stress (Putri, 2020). In addition, anxiety can make employees more focused on personal needs than the preparation for Industry 4.0.
Men and women generally have different levels of preparation in facing the Industry 4.0 era, especially after the COVID-19 pandemic. In addition, age also becomes a factor influencing the preparation for Industry 4.0 era. The youth generation generally is more prepared in facing the Industry 4.0 era, rather than older people who are not familiar with the digital age, and they certainly need more time to adjust to using digital devices. With the COVID-9 pandemic, marital status, the decreasing of income, and loyalty are possible in affecting the level of preparation for Industry 4.0. Suppose the factors affecting the level of preparation for the Industry 4.0 era related to anxiety about the COVID-19 pandemic are figured out. In that case, companies can make an effort to keep the preparation of employees in facing the Industry 4.0 era. This becomes the purpose of this study to find out what factors which significantly affect the level of employees' preparation in DKI Jakarta for Industry 4.0 era during the COVID-19 pandemic, and then analyzing the factors affecting the level of employees' preparation in DKI Jakarta for Industry 4.0 era during COVID-19 pandemic.
The variables involved in this study were the level of preparation for the Industry 4.0 era during the COVID-19 pandemic according to gender, age, level of education, marital status, decreasing income, and loyalty.

1) Level of preparation in facing Industry 4.0 era
The preparation for Industry 4.0 era is a measurement to see what extent company's employee in DKI Jakarta has made the preparation. This variable is a latent variable measured by using the Likert scale, which indicators as follow: a. I am ready to work on the new system of the Industry 4.0 era. b. I am ready to work by using digital facilities. c. The use of computer in working is bothering me (-) d. 4.0 industrial era makes me stressed out (-) e. I am capable of working by using digital facilities f. My salary is quite enough to live in Industry 4.0 era. g. I can do good teamwork with other employees.
2) The anxiety on COVID-19 COVID-19 pandemic gives multiple stress on community's lives, such as the anxiety of infected risk, the anxiety of death, losing families and relation, and getting stress caused by the termination of employment and decreasing income. On the other hand, anxiety can lead to preventive measures such as washing hands frequently, wearing a mask, avoiding talking to a person, insomnia, and changing food habits. The anxiety variables can be measured by using the Likert scale, which indicators as follows: a. I am worried about getting infected by COVID-19 b. I wash my hands in every 30 minutes c. I buy a lot of masks d. I use hand sanitizer often e. I avoid talking to people who are coughing even though I wear a mask f. I change my food habits during the COVID-19 pandemic g. I suffer from insomnia during the COVID-19 pandemic h. I feel unhealthy during the COVID-19 pandemic i. I do not dare to leave the house during the COVID-19 pandemic 3) Gender 4) Age 5) Education, the recent education finished by respondents which the category is Bachelor degree and below Bachelor degree 6) Marital status, which the category is single, married, and divorce 7) The decreasing of income during the pandemic 8) According to the Great Dictionary of Bahasa, loyalty is obedience or fidelity' Loyalty in a job is the tendency of employees not to move to other companies (Siagian, 2010). This variable is measure by using the Likert scale, which indicators as follow: a. I stay working in this company even though my salary is reduced b. I do not have a plan to work in another company c. I work with all my heart for the company I work for now d. I already fit into this company e. I can say that I found no weakness in my workplace now

Research Methodology
This study used a quantitative method in which the population is full-time employees in companies in DKI Jakarta. The sample was taken by using snowball sampling, distributed questionnaires to employees in 12 companies. The distribution was conducted through the WhatsApp application for a month and gained 228 employees. WhatsApp application was chosen as a media for questionnaire distribution because of large-scale social restrictions implementation due to the COVID-19 pandemic.
Data analysis was conducted by using multiple linear regression models. The examination of reliability and validity of data from latent variables resulted that for the preparation variable for Industry 4.0 era, the Cronbach Alpha coefficient was 0,788 and the items used are valid. For the preparation variable, the Cronbach Alpha coefficient is 0.658, and the items used are valid. For the loyalty variable, the Cronbach Alpha coefficient is 0,667, and the items used are valid. Multiple linear regression models analysis: The level of preparation = b0 + b1 gender + b2 age + b3 education + b4 marital status + b5 the decreasing of income + b6 anxiety + b7 loyalty. From the formula, the result of estimation and testing of the regression coefficient parameters as follows:  Table 3 indicates that the decreasing income and loyalty during the COVID-19 pandemic affect preparation levels for the Industry 4.0 era.

Table 4. R square and Durbin Watson
From the VIF value in Table 3, which is less than 10, Durbin Watson's value on Table 4 is close to 2, while from Figure 1 above, the assumption from the regression model is completed even though the R square value is not strong enough. Regression equation from Table 3 as follows: The level of preparation =22.180-1.624 decreased income + 0.342 loyalty.

Results and Discussion
From the equation above, it can be concluded that loyalty has a significant positive effect on the preparation level for the Industry 4.0 era. The higher employees' loyalty to their company, the higher the preparation level in facing the Industry 4.0 era. In addition, it can be concluded that the decreasing income has a negative effect on the level of preparation for Industry 4.0, which means employees who have low income during the COVID-19 pandemic will also have a low level of preparation for the Industry 4.0 era. However, the value from the regression coefficient is difficult to interpret because the level of preparation variable in facing the 4.0 industrial era is latent.
The loyalty of workers to their expertise in a long period is an investment in developing their competence and skill in facing every change. Therefore, the investment of time work will impact readiness to face the changes for individuals or even organizations. Sima et al. (2020) emphasized several key aspects of human resource development in the Industry 4.0 revolution decade: technological skill, training, education, and innovation. On the other side, there are three factors which give a contribution to human resource development in industry 4.0 revolution, 1) reducing the number of workers allocating for labor, 2) allocating workers to another region which produces higher value, and 3) increasing the demand of workers who have skill and competence related to new technology. However, the article did not mention specifically the correlation between workers' loyalty and the readiness to face the development of skill and competence in the Industry 4.0 era.
However, the article implied that experience in a long time is necessary to face and anticipate work activity changes in the industrial 4.0 era. One of the key factors in the effort of human resource development is a technological skill. As we know, the development of technology will impact the changes in any industrial line. Thus, workers' loyalty significantly influences the readiness to face the Industry 4.0 revolution era.
The second conclusion is the decreased income factor, which negatively impacts the level of preparation in facing the industrial 4.0 era. The phenomenon indicates that workers experiencing the decreased income level will have a low level of preparation in facing the Industry 4.0 era compared with the workers who do not experience the decreased income during the COVID-19 pandemic.
It cannot be denied that the COVID-19 pandemic has devastated various sectors of human life, such as economic, business, politics, art, and education. Moreover, the COVID-19 pandemic has changed various business platforms significantly. Therefore, every business person must adopt the business process of their company with all those changes of the challenges faced by business persons in COVID-19 pandemic is remuneration.
COVID-19 pandemic causes the decrease in purchasing public interest, which also impacts the decreasing company sales level. This condition placed the company in a dilemma, among others 1) keeping the workers but providing them full remuneration, 2) keeping the workers, but providing them half of the remuneration, 3) giving layoff on some workers, and 4) closing the business. This condition will prove the resilience of a business company facing force majeure, which was never expected to impact the economic sector. Therefore, the expectation is that companies will stay afloat in any disadvantaged situation and try to keep their workers. However, with the decreasing of income, it can be ascertained that companies will give their workers full remuneration.
This study mentioned that the decreasing of income variable would have a negative impact on the level of preparation in facing the Industry 4.0 era. This is because workers will decrease work motivation due to the less remuneration they receive. This condition impacts every worker's performance behavior, which will become an obstacle in facing the preparation of the Industry 4.0 era.

Conclusion
This study concludes that the factors affecting the level of preparation for the Industry 4.0 era on employees in DKI Jakarta during the COVID-19 pandemic are loyalty and decreasing income. Therefore, the problem of loyalty and decreasing income should be given more attention by companies and increasing employees' welfare during the pandemic, which also increases the company's performance. The high loyalty of workers will have a better impact on preparing to face the Industry 4.0 era and can be actualized by providing better remuneration.
One of the strategies that the company can do is to reduce the percentage of profit gained to provide full remuneration. This strategy is certainly not applied in the long term. When vaccination programs conducted by the government reduces the COVID-19 pandemic, economic conditions will be stable, and companies can increase their profit. Companies that are


The Bridge of Corporate Real Estate Sustainable Management

The emergence of corporations dabbling in sustainable real estate investment hascaused a shift in corporate real estate management field fromthe conventional way of managing a property to a more systematic approach involving high technology. This is due to the need for sustainable buildings to comply with certain requirements in order to maintain their green certification while at the same time improving their business performance. Many discussions mentioned that sustainable buildings arecapableof improving business performance. However, most of these discussions did not specify which element of sustainable buildings significantly contributed to business performance and business goals. This research attempts to discover the relationship between sustainable corporate real estate and corporate sustainability in order to determine the most significant element of sustainable real estate influencing overall business performance. A sustainable triple bottom line theory was used as a guide. A questionnaire survey was carried out involving 100 combinations of corporate real estate managers, property managers, facility managers, operation managers, building managers, and financial managers that are directly involved in managing green corporate real estate. Data was then analysed using Smart PLS. Results indicate six elements of sustainable corporate real estate that significantly affect business performance. They are energy management, workspace management, internal green management, innovation management, human satisfaction management and workplace management.

Introduction
Recently, most corporations and business entitiesembed sustainable concepts in their overall business operations including to their corporate visions and missions, organizational management styles, production processes, delivery systems, and many more.Corporations are also involved in sustainable real estate investments and some added that as one of their investment portfolios. This is due to the speeded spread of knowledge on benefit of sustainable real estate to the business performance. When involving sustainable real estate investment, a corporation directly has been linking to the sustainable corporate real estate management operation in order to well manage and maintain the properties. Questions arise, which element that should be look on in order to ensure business success? And which elements are significantly related to the corporation goals? In conjunction with that, this research aimed to discover the relationship between sustainable corporate real estate and corporate sustainability in order to determine the most significant element of sustainable corporate real estate that will influence overall business performance. This will help management to more focus on those particular important elements and assist them to make any adjustment for improvement in the future.

Corporate Real Estate Sustainable Management (CRESM)
Corporate real estate sustainable management (CRESM) is a term that was developed to bridge sustainable corporate real estate management and corporate sustainability. CRESM is apractice of corporate real estate management that embedssustainable triple bottom line principle of environmental, social and economic (Fauzi, Zainuddin, Noraini, Mohd Ali, & Nawawi, 2016). Some use sustainable CREM term (Ziemba, Ramian, & Kania 2015).This is due to more sustainable practices linked to John Elkington's (1994) principle of sustainable triple bottom line that encompasses economic, environmental and social sustainable objectives (Collins, Junghans, & Haugen, 2018). Fauzi et al. (2016) argued that economic sustainability is the common element,which normally relates to the financial results of the corporation. Meanwhile the environmental element, also known as the ecological dimension thatmaintains or improves the integrity of the life supporting systems on earth, maximizes future options for the current as well as future generations. The third principle ofsocial elements ismore concerned on the impact of the organization towards labor, human rights and the society.

Corporate Sustainability (CS)
Corporate sustainability (CS) represents the organization's willingness to involve in environmental programmed toengage with both internal and external sustainable factors (Janda, Bright, Patrick, Sara, & Dixon, 2016). It includes factor or regulation, economic and social for external factors while for internal factors include management style, organizational style, organizational culture and organizational structure (Janda et al., 2016).This is due to the pressing task of all sectors towards decarbonisation (Cass, 2018). Fauzi et al (2019a)augmented that sustainability is the current trend adopted in management, where more businesses embed sustainable concerns in their management practices. Not only that, the emergence of knowledge by the corporations on the benefits that they will receive by going sustainable has also encouraged them to be involved (Fauzi, et al., 2017). Hopkins, Read, & Goss (2017)supported that in order to encourage businesses to be involved in sustainable practices they need to see evidence of capital improvement especially on the benefits and advantages that they will receive. Chang & Devine(2019) concurred as they found that such business investments must be economically sound to encourage a commitment to environmental sensitivity. Epstein & Roy, (2001) advised that in managing CR, the examination of the impact towards social and environmental initiatives are required especially on business profitability. Much benefit received from CS has been discussed such as improving environmental and social performances including the costs and revenues of the business (Hopkins et al., 2017). As postulated by Osgood (2010), sustainable practice is a strategy that can develop organizational outcome of revenue growth and cost saving. Epstein & Roy(2001) added that, CS would enhance the company's public image and increase their market share. Meanwhile, Cass (2018) found that CS is well in pursuit of a stable ROI whereasOlaleye, Ayodele, & Komolafe (2015) found that CS encourage more energy conservation.This angle was earlier reported by Laposa & Villupuram (2010) that CS not only promotes energy saving but also reduces maintenance cost, encourages high occupant satisfaction, and reduces CO2 emission. Looking at the economic perspective Hopkins et al (2017) found that CS is able to improve their environmental social performance,increase revenue and reduce cost. Mansfield (2009) and Amprinidi & Ringland (2006) also found that competent sustainable practices reduce cost. While Cass (2018) stated that sustainable practices provide financial advantages, improve productivity, improve internal environment, reduce energy costs, provide value for money, and control indoor climate. On a similar note,Christensen, Baldwin, & Ellis (2012) iterate that sustainability can increase productivity thus leading to the revenue results for the corporation. Chang & Devine (2019) added that sustainable practices are associated with the economic impact onthree categories namely real estate, consumers and business. However when looking from a purely economic view,economics is defined as a target concept related to performance targets (procurement, inventory, production, sales), financial targets (liquidity, investment, financing), and success targets (turnover, earnings, profitability) (Glatte, 2012).Recently, Fauzi et. al (2019b) found that several benefits of sustainable practices associated with economics such as maximizing value (value of real estate, image value of the corporation, increased productivity, increased rental rate, increased profitability of the business, increased service provided by the business, marketing strategies benefits, and increased innovation). Additional economic benefit is minimizing costs, which include reduced management, operational, renovation and replacement costs. Not only that, Chang & Devine (2019) found that CS enables mitigating impact to the environment. This is somewhat similar to what was found by Fauzi, Zainuddin, Chuweni, et al. (2019b) where sustainability contributes to reduced emission of hazardous gas, improves sustainable concern, encourages use of natural resources, and increases innovation to the business. While Hopkins et al. (2017) found that the benefits of CS towards societyis interpreted through employee satisfaction and customer satisfaction that directly impacts corporate profitability. In addition, sustainability also benefits occupants' health, and improve employees' life quality thus boosting their satisfaction level. Besides that, it is on record that sustainable practices are able to increase staff skills and reduce turnover problems (Fauzi et al., 2019b).

Sustainable Corporate Real Estate Management
According to Sinke (2015), sustainable real estate management combines theories concerning corporate real estate performance management and sustainability. Almost similar to that, Ziemba et al.(2015); Fauzi et al. (2016)reiterated that it is a combination of corporate real estate management practices that embed sustainable triple bottom line principles of environmental, social and economic principles. Ziemba et al (2015) recommended that the CRE need to holistically involve several CRE-related concepts such as FM (facility management), REAM (real estate asset management), REPM (real estate portfolio management), PM (process management), SD (sustainable development), CSR, RPI (responsible property investment), and ESG (environmental, social, governance). In general, Fauzi, Zainuddin, Chuweni, Johari, & Nawawi (2018) found six imperative elements of green office management including energy efficiency, water efficiency, indoor environmental quality, sustainable site planning, and management and innovation. Apartfrom that, two significant elements were found namely energy saving and water saving elements. Sustainable corporate real estate from the Islamic viewpoint also recorded almost similar results such as sustainable practice, sustainable utilization of water, organizational management and good governance, human satisfaction (employees, occupants, customer), environmental protection, waste generation, energy management innovation, and workspace management (Fauzi et al., 2019c).

Research Method
This research adopts the questionnaire survey as the best method to collect data in order to generalize the results. The purposive sample method involved more than 100 respondents who are directly involved in managing sustainable corporate real estate in Malaysia including corporate real estate managers, property managers, facility managers, operation managers, building managers, and financial managers. The number of samples chosen refers to the Gpower software. The sustainable buildings selected were buildings owned or leased by the corporation as per the definition of CRE certified with the Malaysian Green Building Index (GBI). Data collected was then analysed using structural equation modelling (SEM-PLS). The resulting relationship and the significant elementsare as highlighted in Table 1.