Systematic Literature Review of Walkability and the Built Environment

Walking is the most sustainable form of transportation. It is the socially equitable, economically viable and environmentally friendly mode of transportation. However, transportation technology has caused the desertion of the pedestrian space due to excessively motorized transport. Consequently, the pedestrian environment has degraded. In many cities, the abandonment of the pedestrian space has created a socially unfriendly environment. Walkability is a measure of how friendly an area is to walk. In measuring walkability, several criteria are considered, which include inter alia, the quality of pedestrian facilities, roadway conditions, land use patterns, community support, security and comfort for walking. Findings from studies are mixed; some stated that improving the built environment does not encourage people to walk more; however, there are other studies that indicated otherwise. The aim of this paper is to review the built environment characteristics that promote walking. A literature review of studies that focused on walking, walkability, the built environment, pedestrian and urban design was conducted. This study has searched the electronic databases that intertwined with the Web of Science database. The choice was made due to the comprehensiveness of quality academic studies indexed in the database, thus providing reliable sources of body of work. The database integrates numerous sub-databases such as Web of Science Core Collection, Derwent Innovations Index, KCI Korean Journal Database, Russian Science Citation Index and SciELO Citation Index. The data are then thematically coded. The fields of urban planning, urban design, geography, transportation, sociology, and other related areas were included in the research. The result of this review offers evidence to the criteria that promote walking. The review found that three criteria are somewhat constant in promoting walking, namely, population and building density, land use and land use mixes, and safety. In short, by making an area perceived as safe with the presence of land use mixes and density are the best combination to create a walkable environment.

INTRODUCTION
Walking is the most sustainable form of transportation. It is the socially equitable, economically viable and environmentally friendly mode of transportation most accessible to the masses. However, advancement in transportation technology has caused the desertion of the pedestrian spaces due to the availability of primarily motorized transport. Consequently, this phenomenon has caused a degradation of the pedestrian environment (Kim & Mateo-Babiano, 2018). A vibrant pedestrian environment has been found to contribute to a more livable urban area (Shamsuddin et al., 2012). Jacobs (1961) in her book, "The Death and Life of Great American Cities (1961)" advocated a vibrant urban life that promotes pedestrian activity. This seminal book emphasizes cities as social connector, a hub where people can enjoy each other and appreciate the vitality of the community. Alas, in many cities, the abandonment of the pedestrian space has created a socially unfriendly environment (Yassin, 2019). It is reiterated in many studies that sense of community would be stronger in individuals living in pedestrian-friendly neighborhoods (French, 2014;Tsai, 2014). As an example, the finding in a study by Lund (2002) showed that pedestrian environment factors significantly influence sense of community. The surveys for the study were conducted in one pedestrian-oriented neighborhood and one automobile-oriented neighborhood in Portland, Oregon. Another study undertaken in Seoul, Korea showed that residents of pedestrian-oriented neighborhood were more likely to feel a sense of community in their neighborhood than were residents of auto-oriented neighborhood (Jung et al., 2015). Furthermore, the results of the study also revealed that social interaction factors and walkability among pedestrian environment factors positively influence a sense of community. These findings are in line with the New Urbanists' belief that a high-quality pedestrian environment will enhance sense of community by increasing opportunities for interaction among neighbors.
The pollution caused by motorized vehicles and accidents involving pedestrians are issues faced by cities when the built environment that support walking is neglected (Zhang et al., 2014;Peng & Bongard, 1999). The World Health Organization (2018) estimated that almost 12 million pedestrian road traffic injuries occurred annually. While Khan et al. (2020) found in their study on pedestrian road traffic injuries that globally, although the mortality rate is decreasing, the incidences of pedestrian injuries increased by 3.31% in 2017 compared to 1990. Compared to other road injuries, pedestrian injuries carry the highest risk of a person being severely injured, leading to significant morbidity, disability, and death (Kim et al., 2008;Mayou & Bryant, 2003). Acknowledging the importance of road safety issue, the United Nations has included this as among their main concern in Goal 11 of the Sustainable Development Goals (United Nations, undated).
Walkability is the basis of a sustainable city and a fundamental concept in sustainable urban design (Rafiemanzela et al., 2017). It is a measure of how friendly an area is to walk. In measuring walkability, several criteria are considered, which include among others, the quality of pedestrian facilities (Blečić et al., 2014;Lo, 2009), roadway conditions (Lee et al., 2013;Wang et al., 2012), land use patterns (Cowen et al., 2018), community support, security and comfort for walking (Devarajan et al., 2019;Ranasinghe et al., 2015). Findings of several studies indicated that improving the built environment did not cause people to walk more (Forsyth et al., 2009). However, there are evidence from other studies that stated otherwise (Park et al., 2014;Pearce et al., 2011). Generally, studies on walkability have several basic attributes that are cited as antecedents to walking. Some listed proximity, comfort, and aesthetic, while others have included public security and pavement safety, among others. Amidst this background, this paper aims to review the built environment characteristics that promote walking.

Research Design
The systematic literature review (SLR) methodology was applied in this study to disclose the criteria of built environment that could promote walking. Preliminary step to understand this context is through conducting systematic literature review. This step is a fundamental aspect of an academic research. Initially, systematic literature review was done on healthcare research (Nightingale, 2009), and later developed and grew in other areas of research. Systematic literature review has become an established methodology to investigate the accumulated knowledge from various fields (Al-Tabbaa et al., 2019). Prior to starting any research, literature review must be conducted to understand the breadth and depth of the existing body of work and this process led to better understanding of the research field (Liu Ariffin, Rahman , & Zahari | Systematic Literature Review of Walkability and the Built Environment et al., 2020). The procedure of literature review must be valid, reliable and repeatable (Xiao & Watson, 2019). Besides that, systematic literature review helps avoid research bias and errors (Smith & Noble, 2014;Dada, 2018) and reduce implicit prejudice and discrimination towards a subject matter (Sirelkhatim et al., 2015). Systematic literature review aims to synthesize all available and accessible sources of topic or research (Tight, 2019). It provides powerful strategies to collect and combine the evidence from many studies (Mertens, 2018). This gives advantage of providing totality of evidence by incorporating and judging positive and negative studies in the literature analysis (Torgerson, 2003). Hence, it produces robust comprehension of the topic of research, which subsequently will be useful for designing robust research framework and identifying the research gap, the important pillars in any research.

Search Strategy
First step in operating systematic literature review is to decide and determine the search strategy (Carvalho et al., 2013) in order to obtain all relevant studies (Guo et al., 2016). With that, this study has searched the electronic databases that intertwined with Web of Science database. The choice was made due to the comprehensiveness of quality academic studies indexed in the database, thus providing reliable sources of body of work. The database integrates numerous sub-databases such as Web of Science Core Collection, Derwent Innovations Index, KCI Korean Journal Database, Russian Science Citation Index and SciELO Citation Index. This study adopts systematic search strategy proposed by Bramer et al. (2018), which consists of the following steps: Choose an appropriate database and interface to start with Important to consider all relevant synonyms to all key concepts. Some areas of study may have its own specific jargons to explain about something, phenomenon or activity, which most of the time will not be used in everyday language. Some studies will also use words that refer to the same thing interchangeably in the title, abstract and content. 6 Document the search process in a text document Record each step in search strategy taken during the process in a log document. Besides reporting this information in methodology part, it would also help other researchers to follow and reproduce the steps for other research areas or in the same research area in the future to see the development or trend of the area / discipline. 7 Identify appropriate index terms in the thesaurus of the first database Index all terms and arrange them from most appropriate to less appropriate to the context of study. The index can also be updated while searching for the studies.

Identify synonyms in the thesaurus
Most databases will usually provide structure of search with options and levels. This can be utilized if the words have a few equal important meanings. 9 Add variations in search terms Skill for searching is not lest important here. Variation in search terms should be identified such as spelling difference (e.g., UK English or US English), abbreviations (e.g., World Health Organization or WHO) and relevant opposite words of the terms (e.g., integrity and corruption). In this stage also, truncation would be helpful in searching relevant studies (e.g., organization, institution*) 10 Use database-appropriate syntax, with parentheses, Boolean operators, and field codes Utilizing parentheses and Boolean operators such as "AND" "OR" and "NOT" would help a lot in finding the right and relevant articles. For example, this can be done by combining the concepts and the context (e.g., Walkability AND built environment). Many databases provide selection of these functions. 11 Optimize the search Step 3 to 10 should be optimized at this stage. Search strategy should combine different terms, synonyms with parentheses/ Boolean operators. At this stage also, there are several functions that can be utilized for year of publication, open access articles, languages and types of sources. Selection of these functions will affect the results. 12 Evaluate the initial results After applying up to step 11, the initial results will be obtained. At this evaluation stage, examine all articles. Most of the time, the researcher will know which articles are considered as most important articles (for example by analyzing the most cited articles or the authors who are the expert in that specific areas). Several databases also have functions to sort it to researcher preferences such as the most relevant and oldest to newest or vice versa. 13 Check for errors Errors sometimes not easy to be detected. However, it can be done by doing initial screening to the titles. It is also important to pay attention to irrelevant articles that appear in the search result. 14 Translate to other databases Similar process should be done for each selected database to ensure the same procedure is conducted in maximizing the number of search results. Besides that, it is also part of the protocol of methodology to avoid bias and guarantee the research ethics. 15 Test and reiterate It is important to carefully take note what has been done for each step, so that it can be replicated in the next cycles for different databases. Usually, the first cycle is very vital to set the ground rule for the whole search strategy. Source: Adopted and adapted from Bramer et al. (2018)

Inclusion and Exclusion Criteria
It is important to establish inclusion and exclusion criteria based on the research objectives or questions (Kitchenham & Charters, 2007). There were few inclusion criteria set for searching the articles. The criteria are the articles that published in English language, published from January 2014 until January 2019, and the abstract is accessible. Whereas the exclusion criteria of searching articles were non-English articles and published outside the year range set for the study. These criteria were set based on the followings: (a) English language: Majority of the articles were written and published in English and most importantly all reviewers can read and understand English to do the identification and screening process.
(b) Published from January 2014 until January 2019: The period was chosen because this period is the second half of Decade of Action for the implementation of UN GA Resolution 64/255. The main aim of this resolution is to improve road safety at the global level through various aspects, including improving walkability. Thus, it is important to review what have been done to achieve this in the second half of its implementation through literature and empirical studies. This objective is also in line with the aim of the present study where the study purposes is to review the built environment characteristics that promote walking. (c) Abstract is accessible: Screening the abstract is part of the main element in PRISMA protocol.
Therefore, it is important to include articles that provide abstracts. In addition to this, the study follows suggestions by Cauwenberg et al. (2018) to exclude several types of articles published in the database such as conference proceeding and expert opinion. All these criteria were set to ensure the context of the study reflecting the current trend of the study area. To search for articles, the study has used several combinations of key terms, which were related to the objective of this study. The combinations of key terms are Walk* AND Built Environment. The study found 133 articles resulted in search based on the set key terms. However, only 20 articles were included in the study. Three reviewers have screened the articles' title and abstract in order to ensure the articles meet the inclusion and exclusion criteria that were set earlier. Summary of the selection criteria is presented in table 2 and pathway of articles included and excluded in review is shown in figure 2.

Data Extraction and Quality of Scoring
Data extraction from the reviewed studies should follow pre-established guidelines. For this purpose, this study adopts Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Protocol 2015 (PRISMA-P 2015) to guide the process. The PRISMA-P consists of a 17-item checklist in facilitating the preparation and reporting systematic review (Moher et al., 2015). This guideline helps to improve the reporting and screening of systematic review (prisma-statement.org, 2015). Three researchers conducted data extraction process using similar format to ensure relevant studies are included while minimizing bias and reducing errors (Munn et al., 2014). Screening process of each article was not only to determine the suitability of the articles with the context of this study, but also aims to extract information from each article. Data extraction flow diagram is presented in figure 1.
The extracted data consist of the details on the type of information such as title of the articles, first authors, year of publication, research objectives, study design, sample characteristics and study findings. Extraction of the data using a piloted form (table in excel document) is used to ensure that the information generated from each article is placed in the same self-created database, to create an easy screening process, especially, in identifying replicate articles, and to do contextual analysis by reviewing and analyzing all selected articles. During this screening process, the study also conducted assessment for quality of scoring for each included study. The quality indicators are adopted and adapted from Buckley et al. (2009), which are indicated in table 3.

RESULTS
The following results are based on analysis of the included studies, where the assessment investigated the characteristics of the articles, themes network and main theme.

Characteristics of Included Studies
There are 20 articles included in the study. The range of the year of publication is between 2014 to 2018. Out of 20 articles, 7 were using qualitative approach, 12 articles were quantitative works, and 1 article adopted mixed-method approach. The research settings are various namely meta-literature analysis, survey, video analysis, focus group discussion, street connectivity, interview, and streetscape photo evaluation. In the included study, the location of the studies involved 12 countries. Details for each article can be referred to in table 4.   Are the results supported by data from other studies? Source: Adopted and adapted from Buckley et al. (2009)

Themes Network Mapping Analysis
The study employed Gephi Software Tools to map the key findings to show the linkages between themes and selected studies. Fahimnia et al. (2015) proposed that Gephi Software Tools can be utilized to implement literature metric analysis for systematic literature review. Prior to conducting themes network mapping analysis, the keywords co-occurrence analysis was constructed using Excel file. Relevant information namely sources, year of publication and themes were keyed-in in the file. A generated .csv file format was then imported into Gephi Software Tool to map themes co-occurrence network. The contraction layout algorithm has been used to position the themes that are linked to each selected article. The results of themes network analysis mapping are presented in figure 2. Based on figure 2, it indicates that population and building density, safety as well as land use and land use mixes have the most linkages towards the selected studies compared to the other four identified themes. This analysis presents that these three themes received much attention from the literature, which also demonstrates that the importance of these three themes to be examined in walkability and built environment research.  Inequalities in perceived neighbourhood safety, pedestrian infrastructure and aesthetics in high-versus lowdeprivation areas. The attributes influence disparities in participants' outdoor walking levels.

Thematic Analysis of the Systematic Literature Review
The following sub-sections present the details explanation of the systematic literature review results. The results have been divided into seven sections, based on themes constructed through analysis of literature.

Population and Building Density
Several literatures have mentioned density as a very fundamental precursor for walking. Using a multi-variate model, Lamíquiz and López-Domínguez (2015) found density as one of the five indices that consistently influence the preference for walking. While the findings from a research undertaken by Mouada, Zemmouri and Meziani (2018) show that building density and tree density, which is known as urban morphology, are among the attributes that are significantly associated with the outdoor thermal conditions and potential pedestrian activity over short distances. This finding is supported by a study by Lai and Kontokosta (2018) who found building density to drive pedestrian activity regardless of the specific day or time of day.
The result of a study undertaken by Lee et al. (2017) in Seoul, Korea indicates that density has statistically significant association with pedestrian volume in Seoul. Meanwhile, a study involving adults in Calgary, Canada shows that a high walkable (HW) neighborhood has high population density compared to medium and low walkable neighborhood (Jack & McCormark, 2014). Although density is considered fundamental in promoting walking, some studies have shown that it has limitation. As an example, a research by Marquet and Miralles-Guasch (2015) suggests that beyond a certain level of density, income and sociological factors gained dominance in determining walkability at a local level.

3.3.2
Land Use and Land Use Mixes Lamíquiz and López-Domínguez (2015) in their study found land use as one of the indices that influence walking. Different land use attracts different characteristic of people, which is then very much associated with the usage of the type of transport mode. Many studies have shown the importance of land use mix in attracting walking. Land use mix is the level of integration among different land use types in an area, which means that it shows the spatial intensity of heterogeneous land use types (Tsiompras & Photis, 2017). In their study, Tsiompras and Photis (2017) found that other than proximity, land use mix is the next most popular motivation for walking. Another related study associated to land use mix was undertaken by Jack and McCormack (2014) who found that utilitarian and recreation destination mix is positively perceived as an inducer to walking in HW neighborhood. The findings from the two studies are consistent with a study by Hajrasouliha and Yin (2015) who suggest that land use mix has significant positive impacts on pedestrian volumes.
There is a growing body of literature on the elderly, ageing and their relationship with walkability. In this context, many a times, it is related to the importance of mobility as a precursor to a healthy lifestyle for the elderly. A study of the elderly in Hong Kong shows that the perceived degree of land use mix was predictive of higher levels of within neighbourhood walking (Cerin et al., 2014). A systematic review and meta-analysis study of relationships between neighbourhood physical environmental attributes and older adults' leisure-time physical activity observes that there are positive associations for walkability and landuse mix-access (Cauwenberg et al., 2018).

Safety
Many studies show that the lack of safety is a key barrier to walking. This is true of safety from crime as well as traffic (Forsyth, 2015). For instance, a study of a random cross-section of 1875 Canadian adults found that respondents in HW neighbourhood have a negative perception on motor vehicle traffic and crime related safety (Jack & McCormack, 2014). Meanwhile, the finding in another study undertaken in two cities, Granada and Valencia, shows that the main common barriers to walking were insecurity from crime (Ferrer & Ruiz, 2018). The insecurity was due to the absence of people, a poor street lighting or walking along a conflictive area. According to Wilson et al. (2014), perceptions of safety may also impact walking levels.
Local crime events have a constant negative impact on pedestrian activity (Lai & Kontokosta, 2018). On similar note, a study by Ruiz-Padillo et al. (2018) in Porto Alegre, Brazil found that the most important aspect in promoting walkability is the public security. They further stated that the result is not surprising, as urban violence, related mainly to thefts, robberies, and assaults, is one of the principal social problems in Brazil. The next important aspect after public security or safety from crime, according to the study, is traffic safety.

Aesthetics and Streetscape Feature
Aesthetic refers to a sense of beauty and visual appearance of a neighbourhood (Zandieh et al., 2016), while streetscape feature is the micro features of the street environment (Ewing et al., 2016). A study by Ewing et al. (2017) in New York found significant positive correlation between three out of twenty streetscape features with pedestrian counts. The significant streetscape features are the proportion of windows on the street, the proportion of active street frontage, and the number of pieces of street furniture. The street furniture is defined as all kinds of signs, benches, parking meters, trash cans, newspaper boxes, bollards, streetlights, and other things related to this category. The next feature is the percentage of active uses, which are defined as shops, restaurants, public parks, and other uses that generate significant pedestrian traffic. The final feature is 'windows as a percentage of ground floor façade'. According to the study, the last feature is a common operational definition of transparency. A high level of transparency at the ground level can facilitate interaction between adjacent buildings, which has been known to promote street vitality. Cauwenberg et al. (2018) in a systematic review observes that there is a positive association for walkability and aesthetically pleasing scenery with leisure time walking. Among the elements that can contribute to aesthetically pleasing scenery are greeneries and street furniture. A study by Jabbari et al. (2018) shows that green spaces are linked to higher walkable scores. They further stated that the green spaces create more attractive walking environments and have positive impact on the microclimatic conditions at the pedestrian level.

Street Network and Connectivity
Many studies have shown the importance of street network in motivating walking. Network design has been said to help in determining the ability of pedestrians to reach their destinations. Grid networks with short blocks allow for relatively direct routes, while long blocks and curvilinear streets lengthen pedestrian trips by requiring circuitous routes. A study by Singh (2016) in India concluded that block length and perceived walking distance are directly proportional to each other. He further stated that shorter block lengths make the streets more walkable as people perceive the distances to be shorter and prefer taking such routes, thus increasing street activity.
Finding in a study by Hajrasouliha and Yin (2015) suggests that physical connectivity has significant positive impacts on pedestrian volumes. Similarly, Jack and McCormack (2014) found in their study that respondents in HW neighborhoods positively perceived street connectivity. This is in line with the suggestion by Lamíquiz and López-Domínguez (2015) that the design of a street network configuration could insert multiple activities along the routes, and thus shorten distances if done appropriately. The findings introduce the idea that the configuration of the urban grid can influence the proportion of pedestrians who choose to walk on single-journey trips.

Visual Connectivity
Visual connectivity has significant positive impacts on pedestrian volumes (Hajrasouliha & Yin 2015). The results of a study by Oreskovic et al. (2014) show that perceived walkability varied according to the degree to which a particular design attribute was present. According to the study, the presence of ground-floor windows and a street focal point are two attributes that most consistently associated with a space's perceived walkability. The presence of a window at the eye-level of pedestrian, which indicate retail presence is an important attribute towards promoting walkability. This idea is similar to the study by Ewing (2016) that mentioned the importance of windows at the ground floor façade. A study by Singh (2016) also has a parallel concept with the above two studies where he found most of the recorded answers from the respondents stated that the presence of blank walls was the factor they thought made them feel unsafe or claustrophobic on the street. This factor discourages pedestrians to be on the roads and, as a result, decreased the walkability of that area.

Proximity
Basically, many definitions of walkable places are places that support short walking trips to essential facilities and services for running daily errands. Studies have shown that proximity to destinations supports active transport and mobility (Tsiompras & Photis, 2017). The results of a survey by Tsiompras and Photis (2017) indicate that proximity (up to 400 m) to urban destinations is the main motivation for people to walk. This finding is consistent with a study by Lamíquiz and López-Domínguez (2015) that found distance, either real or perceived, is one of the most principal barriers for travelling on foot. In general, smaller cities are more walkable because distance between different activity locations is shorter than in larger cities (Ferrer & Ruiz, 2018). Therefore, Ferrer and Ruiz (2018) suggested for large cities to pay more attention to the provision of mixed land uses as an important characteristic of the built environment in encouraging walking for transport. They suggested that proximity to destinations in conjunction with the land use mix seems to be the fundamental keys for walkability. Their result also show that participants perceive more facilitators to walking in Granada than in Valencia due to the smaller size of the former city.

DISCUSSIONS
Walking relies not only on the infrastructure but also on the state of the built environment (Cauwenberg, 2018). While pedestrian infrastructure is an imperative to fulfil certain pedestrian needs, others such as proximity, safety or aesthetics, depend not so much on the pavements, but on the surrounding environment. In this context, how pedestrian-friendly buildings are, or the type of land use found in the area, do influence walking. Some discourses on walkability focus on environmental features or means of making walkable environments. These environmental features include areas being passable, compact, physically enticing, and safe, while others are more concerned with the outcomes fostered by such environments (Forsyth, 2015). Among the potential outcomes are making the places lively, enhancing sustainable transportation options, and encouraging the physical exercise.
Many of the criteria found to be the antecedents to walking are not a detached or unconnected element. Many of them must be combined to create a walkable environment. For instance, a study by Mouada, Zemmouri and Meziani (2018) shows that to provide a comfortable condition and to increase pedestrian choice in the urban environment, the public space should include high building density, combined with deep streets with a high connection between them, and an abundance of vegetation along the lower parts of streets. This example shows that density is combined with street network, connectivity and aesthetic or pleasing scenery to encourage walking (Raja Noriza et al., 2019).
The review found that three criteria are somewhat constant in promoting walking, namely, population and building density, land use and land use mixes, and safety. Mixed or different uses in the same place tend to strengthen the identity of the place according to Jacobs (1961), while density or proximity of the mixed uses strengthen the economy of the place and enable people to travel less distance to access daily needs. Undoubtedly, safety is a very fundamental precursor to walking. Studies have indicated that people are very much reluctant to walk in the absence of safety. It means that although they are presented with pleasing scenery and good infrastructure, people do not want to jeopardize their safety by walking. Jacobs (1961), who triggers the concept of New Urbanism that advocated pedestrian-friendly design, mixed land use, and transit-oriented development has championed the 'eye on the street' concept, which emphasized the needs of having high pedestrian volume in an area. The presence of pedestrian, according to her, can deter crime and increase sense of community. There is an element of safety in promoting walking in this concept. In short, the literature review shows that by making an area perceived as safe and the presence of land use mixes and density are the best combination to create a walkable environment. Hence, cities need to consider these three fundamental criteria in formulating policy towards creating a friendly pedestrian environment. This review has some limitations. It does not differentiate between walking for leisure, walking for public transit or any other reasons for walking, such as commuting, going to work, running errands and others. Nor does it differentiate between the Eastern and Western countries. There may exist variance between the East and the West due to the environment, cultural and behavioral differences. Thus, we recommend that future systematic review to differentiate between these fundamental attributes.


s2fieldsofstudy:
Economics
Computer Science

text:
Universal Database for Economic Complexity

We present an integrated database suitable for the investigations of the Economic development of countries by using the Economic Fitness and Complexity framework. Firstly, we implement machine learning techniques to reconstruct the database of Trade of Services and we integrate it with the database of the Trade of the physical Goods, generating a complete view of the International Trade and denoted the Universal database. Using this data, we derive a statistically significant network of interaction of the Economic activities, where preferred paths of development and clusters of High-Tech industries naturally emerge. Finally, we compute the Economic Fitness, an algorithmic assessment of the competitiveness of countries, removing the unexpected misbehaviour of Economies under-represented by the sole consideration of the Trade of the physical Goods.

Introduction
Economic Fitness and Complexity [1,2,3,4,5,6] (EFC) is a novel conceptual and practical framework for the estimation of the competitiveness of nations and the relatedness between sectors, borrowing concepts and methods from Statistical Physics and the Complex Systems Science [7]. A key feature of the method resides in its bottom-up, data-driven approach, which relies on methods like complex networks and machine learning to reconstruct and investigate the ecosystem constituted by the economic actors and their activities; moreover, all scenarios are based on empirical observations and certifiable hypotheses. This approach departs from the canonical econometric narrative, where the economic performances are usually gauged by monetary metrics, such as the Gross Domestic Production (GDP). Instead, EFC aims to capture the competitiveness of a country, and not its wealth, by introducing a synthetic and non-monetary metric, the Fitness [1], bringing to light new and relevant economic patterns.
A key feature of Economic Fitness and Complexity is its reliance on homogeneous and high-quality data from which it effectively extracts information by aiming to achieve a maximal signal-to-noise ratio. In the Big-Data era, the novel and great size of available databases may generate a broad confidence on the new possibilities offered by large scale analyses, although issues related to the quality of the gathered data and the specific investigations are sometime underplayed [8]. It is rather intuitive that Big-Data may present Big-Noise [9], and so a careful selection of the sources must be accomplished at the starting point of the research in any data-driven field. The reference database in the classical EFC analysis is the International Trade database reconciled and regularized by Tacchella and collaborators [10] starting from the UN-COMTRADE data. This database covers the external flows of physical Goods between the countries in the World with a very fine level of detail of the classification collecting about 5000 product's codes. EFC is based on the export database for various reasons, both practical, due to its The analysis endowed by the previous works highlight the purposes of the Services in the correct estimation of the competitiveness of the nations. However, the reference database for the Services, the Balance of Payments and International Investment (BOP) data collected by the International Monetary Fund (IMF) [17], presents some weakness and important missing scores, especially in comparison with the quality of the Goods database. Strikingly, the actual IMF-BOP classification is unsuitable for the EFC analysis, presenting an overlapping and convoluted hierarchy of sector. The first core result of the present paper is the reconciliation of the quality of two databases, the Goods and the Services, obtained by reclassifying the IMF services sectors and reconstructing the missing elements of BOP by using and comparing different machine learning techniques. The best reconstruction method obtained is then used to create the so-called Universal database of EFC, aggregating Services and Goods for 160 countries and a total of 124 sectors, providing the largest set of common nations and sectors available from both the BOP and the Trade datasets. Finally, the Universal database is used in the Economic Fitness and Complexity analysis, obtaining two conceptual and practical advances: i) a statistically validated network of universal sectors, that will be of practical use to predict and recommend new sectors of development, and ii) the computation of the Universal fitness, the novel EFC indicator to assess the competitiveness of nations, now including also the services.

Methods
This section introduces the database used in the later analysis, aggregating Goods and Services; in particular, we will focus on the motivations and the methodologies we adopted for the re-classification of services and the reconstruction of the missing values.

Goods: the International Trade
Following the definition of the IMF [17], a Good is a physical item or commodity over which ownership can be passed via transaction. Consistently, it is recorded by the customs and available via UN-COMTRADE. Following the standard approach of EFC, the reference database collecting the scores of the Goods is the reconciliation of the International Trade database contained in UN-COMTRADE [18]. It collects the export flows of classes of physical products between nations. The classification of the product follows the Harmonized System revised in 1992 (HS92), consisting of 5040 sectors labelled by 6-digits codes at the sub-heading level. The temporal coverage of the database runs from 1996 to 2018, spanning slightly more than two decades, and it is made available for 169 countries corresponding to the principal economic players in the World.
The statistic of the codes at 6-digits does not present a level of details and a statistics comparable to the orders of magnitude of the data of the Services. It is also worthy to note that the HS classification varies in time and in the available years a relevant modification happens in 2007. Perhaps, such modification induced a re-balance in some sectors at 6-digits that create a non-uniform transition in few and low complexity codes [19]. Therefore, we aggregate the classification at the closest comparison to the Services database, being at the 2-digits classification. Note that this choice also mirrors the respective weight in the international trade [12]. Furthermore, the 2-digits classification has 97 codes, but we remove one sector (the code 99, the Commodities not specified according to kind ) because it does not represent a well defined sector and has a low impact on the overall exports.

Services: description and classification issues
According the IMF [17], a Service is the result of a production activity, or facilitate the exchange of Goods, or is a financial asset. Therefore, Services are usually non-separable items and cannot be separated from their production. The Service database is based on the Balance of Payments and International Investment (BOP) data collected by the International Monetary Fund (IMF) [17] 1 . The BOP database offers three measures of Services: the credit, the debit and the net values. In comparison with the measure available for the Goods, we select the credit indices, since they correspond to exported services. Two closely related classifications are present: the full BOP classification implemented by the IMF and an alternative alpha-numerical classification reported in their metadata. The full BOP classification is based on the composite nature of the codes of the Services but it presents a key drawback for the analytical purposes accounted below. In fact, it is possible that the sum of the sons of a code have a total value larger that the assigned value of the parent. This misleading situation happens because the classification may implement different methodologies in order to generate the hierarchy.
In order to explain this situation we discuss an example regarding the case of the sons of the 'Transport Services' ('STR' codes) where the dis-aggregation considers both the kind of transportation implemented or the nature of the transported Goods. For a visual inspection, figure 1 shows the network of the transport's codes, highlighting the two dis-aggregation paths in blue and red. Remarkably, only the blue codes sum up to the value of their parents. Instead, the red nodes do not sum to the available value of the parents, although it is possible that using the hierarchical tree obtained from the codes does not account properly for the red classification.
Contrarily, the alternative classification maintains only a single hierarchical structure removing the double methodologies, and the quality is corroborated by the fact that the sum of the values of the sons return the value of the parent codes. We want to stress that the scenario of the two methodologies derives from a misleading interpretation of the indices offered by the IMF and not an error in their implementation of the database, since it is possible to reconstruct the correct structure. We present the first three main layers of aggregations of the summable classification in figure 2. Among the layers, the first one accounts for the total sum each country produces in a year. The second and third layers are more representative and diverse of the heterogeneous aggregation of the services.

Missing values in Services data
In the construction of the Universal database we select the codes of table 1, covering with the lower level of aggregation. For the sake of clarity we highlight these codes in figure 2 with a green thick border, and we refer to the set as the complete set of codes, since with its knowledge it is possible to obtain the full database.
The BOP data covers about 197 countries although we select the subset of 160 countries intersecting the set of countries available for the Goods, and covering basically all the relevant economies in the world in terms of economic impact. Finally, in terms of temporal resolution, BOP goes back up to 1940 with very few sectors in the first years. However, we reconstruct the series starting from 1990 because the Universal database is constraint by the presence of also the Goods database, which starts in 1996.
Focusing on the temporal distribution of the missing elements, it is clear that the raw database is not uniform, as shown in figure 3. In the early years of the series there is a larger presence of missing values with a decreasing global trend approaching the recent times. In the developed economies these behaviours are primarily induced by the complete absence of the first segments of the series for all the indices. For example, in the case for Austria, no information is available before 2005; regarding Belgium before 2004, and Japan before 2000. Instead, less developed countries present subsets of the indices with missing scores on short to medium temporal windows, while single NaNs are rarer in the datasets. Hence, in between 2005 and 2015 the raw database offers its better quality, even if the raw version continues to have a high fraction of missing values.
Finally, also the geographical coverage of the raw database is not uniform, as shown in the top map of figure 5. Many countries have a large fraction of missing values, even if some of them are developed economies. Remarkably, Switzerland, China, Great Britain, and Spain are among the developed countries with a low quality representation in the raw data. This situation of heavy lack of data and  heterogeneity calls for a specific intervention to reconstruct the missing elements, which is the subject of the next section.

Reconstruction of the missing Services
We implement a reconstruction of the BOP database using different machine learning techniques. The use of the alternative classification discussed above allows the derivation of the full hierarchy reconstructing the complete subset of leafs by summing the indices (red-circled indices in figure 2). However, the knowledge of the values of the upper layers of the classification are used in the reconstruction because we do not want to change abruptly the relative importance of the nations or of the daughter indices. Moreover, in the following we assume that any not-missing value of the raw database is correct, thus also the entries with zero value are labelled as correct. A very basic and easily interpretable reconstruction technique is given by the linear interpolation of the temporal series in the forward direction, i.e. generating the elements for which there exists some previous information. Therefore, the linear interpolation is assumed as the reference method, giving relatively good results despite the simplicity of the method. Successively, we consider two families of methodologies: the Random Forest class and the Nearest-Neighbour imputations [20] class. The Random Forest consists in an ensemble of decision trees [21] trained to optimize the entropy of the reconstruction. Rather, a decision tree is a single and non-overlapping subdivision of the space of the features, i.e. the values of the series, into distinct regions following a hierarchical ordering of the feature-selection with a tree structure [22]. Since the splits of the tree are randomly chosen, it is possible to derive many different decision trees forming a statistical ensemble. Using the ensemble of trees it is possible to estimate each missing feature of the input database (here the Services) based on the behaviour of the other entries for which those features are specified. This method is applied on the complete set of series aggregating the entries in time and space (years and nations), the so-called Temporal Random Forest [23], such that the information from similar economies is taken into account. In reference [23], this method is found to be superior in terms of the quality of reconstruction with respect to the separate reconstruction of each country series.
The latter method we realize, the k-Nearest-Neighbour (kNN), consists in the generation of the missing features with a weighted network average from other features. In detail, the network of similitude between the economies, from the prior knowledge of the indices in the upper layers, is built under the assumptions that the reconstruction does not strongly modify the relative importance of the nations. Accordingly, the kNN imputer [24] links the first K nearest-neighbours elements from the available information on the upper layer. The resulting network is then applied for the selection of the nearest elements considered in the weighted reconstruction of the missing values. Finally, the weight is defined as the Euclidean feature-distance in the original space between the selected entries.
We test the quality of the reconstructions on a test-set of the database composed by countries and indices for which all the entries are specified. On this dataset we generate random samples setting some feature as missing, and we generate 100 samples of this process. For each replica we label as missing at random, and we compute the statistics on the reconstruction techniques described above. The quality of the reconstructions are finally evaluated by the computation of the Mean Absolute Error (MAE) between the reconstructed and the truth values. As shown by the left panel of figure 4, the interpolation method down-perform the MAE of the Temporal Random Forest, but the best achievement is obtained by using the kNN network built with only a few of neighbours.
Therefore, in order to reconstruct the database we will use in the following analyses we implement the kNN reconstruction technique with K = 5 2 neighbours on the complete set of indices described in the previous section. Figure 5 shows World maps drawing the colours of the countries in terms of the fraction of missing elements in the raw (upper) and reconstructed (lower) databases and allowing a visual representation of the quality of the reconstruction. Finally, the reconstructed set, truncated between 1996 and 2018, is then aggregated to the 2-digits UN-COMTRADE data in the so-called reconstructed Universal database.

The Economic Fitness and Complexity input matrices
The various Economic Complexity measures are computed starting from quantities derived from the export data. The raw export volumes are not directly used essentially for two reasons: i) they trivially depend on both the sizes of the sector and the country and ii) they do not provide an assessment of the competitiveness of the given country in exporting a product. In order to overcome these limitations, the Revealed Comparative Advantage (RCA), introduced by Balassa [25], is commonly used in the literature. In formula, the RCA of country c in activity a is computed as: where E c,a is the value of the Product or Service sector a (what we generically call an activity), in constant US dollars, exported by country c. Note that this formulation permits to identify a natural threshold of 1 to determine whether c exports a in a competitive way or not with respect to what the global set does. As a consequence, we can define the binary matrix M whose element M c,a is equal to 1 if RCA c,a ≥ 1, and it is equal to 0 otherwise. We also define the Marked Share (MS) matrix element as: that is an assessment of the importance of country c in the global trade of a. Note that in this case no natural threshold is available, and a residual correlation with the size of the country is usually present. Hence, we will call the Fitness based on MS the Extensive Fitness, recalling the concept of extensive and intensive quantities in statistical physics, and we may indicate the standard computations based on the binary RCA as intensive for comparison. Both the series of the Market Share and of the binary RCA present their sources of noise and different techniques can be implemented in order to reduce their negative effect. However, for the sake of simplicity and since the data at the aggregated level of 2-digits usually consider large volumes, in both the series of Market Share and RCA we apply a simple exponential smoothing with half-life of 3 years. The use of the exponential smoothing allows the reduction of the noise has the positive side effect of maintaining a persistence of a few years in the temporal series, without the use of more complex Machine Learning Models.

Results
The Economic Fitness and Complexity framework can be divided in two lines of research. The first one aims at assessing the Relatedness between sectors and sectors, and countries and sectors [26]. Usually, the output is a network of products, such as the Product Space [27] or the Taxonomy Network [28]. Actually, in this paper we will compute the Product Progression Network [14], based on the Assist Matrix validation framework introduced in [29]. With respect to other approaches, the one used here has two advantages: i) the time evolution is explicitly taken into account, permitting the construction of a directed network of sectors, and ii) each link is statistically validated by comparing its weight against a distribution arising from a suitable null model. The second line of research in Economic Fitness and Complexity consists in an assessment of the global competitiveness of a country, based on the idea that sophisticated capabilities are needed to export complex products. One example is the Economic Complexity Index [27]. Instead, in the present work we use the Fitness measure introduced in [1], which has a number of advantages from both a practical and a theoretical points of view. For a broad discussion on these arguments we direct the reader to the relevant literature [30,31,32,33]; herein we only emphasize the predictive power of the Fitness approach. As shown in [10], it outperforms the IMF in forecasting the GDP time evolution.

Goods and Services interaction
A measure of the interaction between two activities can be obtained considering their co-occurrences, that is counting how often such couple of activities is present in the baskets of different nations. In order to avoid the size effect induced by the nested nature of the system, where developed countries are competitive in many activities, and some activities are by far more widespread than other, one has to properly normalize the simple co-occurrences. Following [29], we compute the probability the information flows from one activity to the other along the shortest paths. The resulting weighted network, projecting the original bipartite structure into the activity layer, is the Assist Matrix Note that the time evolution of M is explicitly taken into account, so one has a different network of activities for each t and ∆. Moreover, this network is almost fully connected, and the presence of spurious links prevents also simple tasks such as a clear visualization of the connectivity. In order to filter the links maintaining only the relevant ones, we have to statistically validate each link against a suitable null model. The implementation of the statistical validation of the network of interaction is pursued by considering an ensemble of random networks preserving few suitable macroscopic constraints [34]. We generate the ensemble of randomized bipartite networks related to the empirical case, and we use them in the generation of the ensemble of the random Assist Matrix at fixed (t, ∆). Thus, we seize only those links in the empirical Assist Matrix that are above a percentile threshold, resulting in the statistically validated matrix at fixed (t, ∆). Successively, we validate only those links that are statistically significant on all the available years t at a fixed ∆, aiming to reduce the false inference problem. Therefore, we have the topological graph keeping only the links that are found relevant on all the temporal series analyzed. Finally, we consider the presence of auto-correlation on the temporal series of the raw signal, and therefore we collapse a range the graphs into a single network where each link has a weight equal to the number of times it has been validated at different ∆.
In detail, in the present work the random ensemble considered for the validation of the network of interaction is the Bipartite Configuration Model [35,36] (BiCM). The BiCM randomizes all the information available but the degrees of the nodes, a.k.a. the ubiquity and the diversification, which are kept constant (on average). Indeed, activities with a larger ubiquity have higher probability to be produced by a random country by chance and the BiCM contemplate this information. For the link selection we apply a small percentile threshold of 95%, compared to other cases in literature, because we require such level of statistical relevance along all the time series. The collected network spans 10 years of possible time delays (∆ ∈ [0, 10]) in order to keep track of the autocorrelation. Figure 6 shows the network following the HS classification. On the bottom right, a High-Tech cluster contains both high Complexity Goods and Services. Going counter-clockwise, immediately above we find all the Heavy Industries and then Textile related sectors. Few other clusters collects the mineral Raw Materials and the Vegetable and the living Materials. These sectors show a lower degree of connectivity, indicating that countries specialized in these sectors rarely jump to more complex industries. The Services are not strongly dispersed but are not forming a single block of nodes. Note that High-Tech related Services, those associated to royalties and R&D, are strongly connected with the high Complexity sectors. Regarding only the Service, solely the group of transport nodes create a cluster per se. Finally, the statistical nature of the construction of the network allows that few nodes are not connected to any elements because their links are not found to be statistically relevant. A lower percentile threshold may connect them at the price that the final network of interaction will be less statistically significant with more likely erroneous inferences.

Economic Fitness and Complexity
The Fitness and Complexity algorithm [1,3,37] aims at assessing the competitiveness, or Fitness, of a country, and the sophistication or Complexity of products using a set of coupled equations. The idea is the following. Each country is characterized by its endowments, or capabilities, which represent its social, cultural, and technological structure [38]. These capabilities are expressed in what a country produces and exports, so sectors (physical Goods and Services) and their Complexities are linked to the Fitness of each country; in particular, the Complexity of a sector increases with the number and the quality of the capabilities needed in order to be competitive in it, and the Fitness is a measure of the Complexity and the number of the competitively exported sectors. In order to make this line of reasoning more quantitative, we start by considering the global structure of the matrix M defined above. Once countries and products are suitably arranged, the matrix M is triangular, or nested [39], showing that developed countries have diversified exports, while less developed countries export fewer, lower Complexity products, and these products are actually the ones exported by all countries. In order to leverage on this structure to extract information about countries' competitiveness and sectors' Complexity, Tacchella et al. [1] proposed the following set of non-linear, coupled equations Here < · > x denotes the arithmetic mean with respect to the possible values assumed by the variable dependent on x, with initial condition: The iteration of the coupled equations leads to a single fixed point that does not depend on the initial conditions [1,3,37]. The fixed point defines the non-monetary metrics quantifying the Fitness F c and the Complexity Q a . The convergence properties of equations (4) are not trivial and have been extensively studied by Pugliese et al. [37]. The coupled equations in (4) relies only on one input, the matrix M stating which countries are competitive in which sectors. Usually, the matrix is represented by the binary RCA, although also the Market Shares can be entered in equation (4), return the Extensive Fitness [1,14]. The immediate consequence of this choice is a higher correlation with the size of the country, as expressed for instance by its GDP. In Figure 7 we compare the results of the Fitness algorithm when different input matrices are used. On the left we compare the Universal (i.e., considering Goods and Services) Extensive (i.e., the Market Shares matrix MS is used) Fitness, on the x-axis, with the Extensive Fitness computed at 6-digits, that is, considering only the Goods and the lower available level of aggregation, containing about 4500 different codes. On the right, instead, we show the intensive counterpart, that uses as input the binary RCA. By visually inspecting the figure one can notice various properties. First, a rather good correlation is present, highlighting that the overall economical results based on the Fitness metrics are robust when Services are integrated and the sectors are aggregated from the 6 to 2 digits levels. More importantly, the positive effect of specializing in high Complexity Services clearly emerges. Countries such as Great Britain (GBR) and Germany (DEU) provide two examples of Services or manufacturing driven countries, as correctly reflected by the Fitness indicator. As shown in references [10,40], the Fitness metrics can be used to forecast the GDP variations with a relatively long time interval and high accuracy. Hence, in this work we address a basic analysis in which we consider the time delayed cross-correlation between the Fitness measures and the respective GDP values with the aim to evaluate if the aggregation of the Services possibly introduce more signal or more noise in the forecasting. In Figure 8, on the left, we plot the correlation between the intensive Fitness and the GDP per capita (GDP pc). One can easily see that  lines are essentially flat, indicating that, albeit correlated, there is no clear time direction from Fitness to GDP pc or vice-versa for either measure. Interestingly, this is the case of also the 6-digits Fitness usually implemented in the GDP analysis. On the right, we show the extensive counterpart: the Fitness computed using the Market Shares and the total GDP, not normalized using the population. The Extensive Fitness, and in particular the Universal Extensive Fitness, that contains both physical Goods and Services, is clearly able to predict the GDP with a time delay of up to 20 years, that however is of the order of the maximal extension of our data. Note that we are using the term prediction in the Economic sense, i.e. this not an out of sample forecast but a time delayed correlation. In this sense, we can safety say that the Universal Extensive Fitness is able to statistically anticipate, or is a precursor of the total GDP. However, the converse is not completely true: although there is a signal of correlation, the GDP has a lower predictive power and the Fitness is somehow harder to be anticipated.
Finally, we show in figure 9 the time evolution of the Complexity rankings for both Goods (in red) and Services (in blue). Even if some noise is present, the rankings clearly follows the layman's intuition of which sector may be sophisticated and which is not. In the high Complexity rankings we find services such as R&D and royalties, and manufacturing sectors such as nuclear reactors, optical instruments, and aircraft. Instead, low Complexity products correctly correspond to lower capabilities requirements such as Agrifood sectors. Note that some top code such as 'Games, sport requisites' includes also accessories and sophisticated products necessary for any kind of sport and competition, while others codes like Umbrellas are boosted by the fact that more than 83% of their Trade is performed by China.

Discussion
All Economic Complexity measures are based on countries-activities bipartite networks, where the word activity usually indicates a physical product. Nonetheless, a fast growing fraction of both the International Trades and local economies is based on Services. Recent attempts tried to integrate the standard UN-COMTRADE database of physical Goods with the IMF BOP database collecting the Service data [13,14,15]. However, two problems prevented the necessary achievements to reach a level of data control and sanitation comparable to the Goods-only data. Firstly, the IMF classification is rather convoluted and, as it is, not suitable for a direct application into the EC framework, and the main issue is related to the structure of the classification tree. Secondly, the significant presence of missing values at different scales of aggregation prevents the realization of a satisfactory level of detail of the classification, forcing the use of various gimmicks to arrange the data. For instance, Stojkoski et al. [13] modified the original Fitness and Complexity algorithm to make it converge. In this work we address and propose a solution to both these problems by selecting, on one hand, a self-consistent and not overlapping Service classification and, on the other hand, filling the missing values with a suitable machine learning approach. In particular, we compare and test different sanitation strategies and find that a kNN approach gives the lowest reconstruction error.
Once the reconstruction is performed, the Universal dataset is available integrated, Goods and Services. Hence, we applied the new dataset in the Economic Fitness and Complexity framework. In particular, we build a statistically validated network of Universal sectors bringing to light the interaction among them. By construction the interaction is directed because the time evolution of the input matrices is explicitly taken into account.The resulting network is filtered by validating the interaction with the use of an ensemble of random matrices preserving the degree sequences of nations and activities. Thus, only the statistically significant connections are maintained. Meaningful patterns indicate that there exist a deep interrelation between High-Tech manufacturing and complex Services, and there is a partial isolation of the agrifood sector.
Successively, we computed the Fitness of countries and the Complexity of universal sectors, both in the extensive and the intensive fashions. The importance of Services once again emerges, as previous measures of Fitness clearly underestimate the competitiveness of countries such as the United Kingdom. Finally, we considered the so-called Extensive Fitness that, being computed starting from the Market Shares, is more connected with the cumulative importance of the country at the International level. We show that the Extensive Universal Fitness is a precursor of the GDP, in the statistical sense that it is highly correlated with the GDP when a time delay is accounted. In particular, this correlation increases with time, showing that the Universal Extensive Fitness anticipates the GDP, significantly more than the contrary. This work and most importantly the Universal database, opens up a number of possible lines of research. Firstly, even if the results of this paper are focused on the Economic Fitness and Complexity framework, the reconstruction of the Services database and its integration with the Goods data is of general interest and can be also used with other macroeconomic purposes. Secondly, the Universal Fitness can be used to build GDP and GDPpc forecasts using the Selective Predictability Scheme, as done in [40,10]. We expect that the inclusion of Services will improve the predictive performance of this approach. Finally, the Universal Progression Network, with or without the support of machine learning approaches, can be used to predict the future activation of sectors now absent in the export basket of countries.

Data Availability
The datasets generated and analysed during the current study are available in the Universal database repository, https://efcdata.cref.it/.

Usage Notes
The raw database of the Services is the Balance of Payments, collected by the International Monetary Fund (IMF), which is free of charge, as described in the terms of the Copyright and Usage (https://www.imf.org/external/terms.htm). All the database and the files containing the results of the Fitness and Complexity algorithm are available in the csv (comma-separated-values) format of Unicode (UTF-8) string format. The format of the file requires that the first column contains the row indexes while the first row contains the column indexes. The choice of the format of the database allows the implementation using various open-source codes and platform, as well as Office suites (and open or free versions).

Code availability
The repository contains the Jupyter notebooks written in Python 3 necessary to reproduce the reconstruction and the Fitness and Complexity algorithm.


Environmental protection of resource-based cities around the Bohai Sea from an ecological perspective

The huge demand promotes the continuous increase of the demand for resources by industrial enterprises, and the exploitation of resources will also cause ecological and environmental problems. This paper takes resource-based cities in the Bohai Rim region as the research object, analyzes the development status of the Bohai Rim resource-based cities from the perspective of ecological protection and explores the problems in them, which is beneficial to provide a reference for the green development of resource-based urban industrial enterprises, thereby reducing ecological Pollution. Analyze the status quo of industrial development by measuring the deviation and advancement of the industrial structure. The results show that the level of industrial advancement in resource-based cities around the Bohai Sea has a downward trend; the degree of industrial structure deviation has an upward trend. On the whole, the protective strategies for accelerating the green development of industries in resource-based cities around the Bohai Sea and promoting ecological governance are derived: industrial adjustments must be carried out in a scientific and orderly manner, and the proportion of development among the three industries must be balanced; technological innovation is used to promote the green development of industrial structure.

Introduction
In October 2015, the National Development and Reform Commission issued the "Outline for Cooperation and Development in the Bohai Rim Region", proposing to delimit the administrative regions of Beijing, Tianjin, Hebei, Liaoning, Shandong, Shanxi, and Inner Mongolia Autonomous Region as the ring Bohai area. This signifies that the concept of the Bohai Sea Rim has risen from spontaneous regional cooperation to a concept with a national strategic position. As the core area for economic development in northern my country, its mineral resources are generally relatively complete. The total amount is large but the per capita is small. The reserves of large-scale minerals are slightly insufficient compared with other regions, but the rare earth mineral resources are abundant, forming a rich and diverse resource structure. The sustainable development of resource-based cities plays a fundamental and crucial role in the sound operation of the regional economy. The Bohai Rim region is also a resource-based industry cluster, and the formation and characteristics of its industrial structure are affected by the resource structure. Therefore, a reasonable evaluation of the status quo of the industrial structure transformation and upgrading of the resource-based cities in the Bohai Rim region will help to explore and change the economic development model of the Bohai Rim region, thereby driving the region to achieve overall industrial optimization and upgrading and regional coordinated and sustainable development.

Literature review
The current research on the transformation and upgrading of the industrial structure of resource-based cities mainly focuses on the following two aspects: One is to study the relationship between the industrial transformation and upgrading of resource-based cities and economic growth. In the 1990s, western scholars put forward major research finding that resource enrichment has a negative correlation with economic growth [1], highlighting the urgency of the problems faced by industrial transformation. Some scholars in my country found the positive impact of industrial transformation on economic growth based on the industrial transformation index [2]. The second is to analyze the effects and influencing factors of the industrial transformation of resource-based cities. Some foreign scholars have evaluated the effects of industrial transformation in different resource-based communities [3], and the results show that the main driving factor for industrial transformation in resource-based cities is technological innovation [4]. Domestic scholars have also made relevant measurements for the industrial transformation of resource-based cities. The DEA method is currently the mainstream method for evaluating the industrial transformation and development of resource-based cities [5].
To sum up, the existing studies mostly focus on case studies of resource-based cities or start from a certain local perspective. Generally speaking, regional and macro perspectives are more general and have more reference significance. Therefore, this article takes the national strategic regional level-the Bohai Rim region as an example to measure the rationalization and advancement of the industrial structure of resource-based cities, and clarify the current problems facing industrial transformation and upgrading to propose relevant transformation strategies.

Deviation degree of industrial structure
The deviation degree of industrial structure reflects an asymmetry and unsuitable state between the employment structure and the industrial structure, as well as the coupling between the output structure and the employment structure. This article is used to measure and reflect the synergy of the industrial structure. The calculation formula is: Among them, P is the deviation degree of the industrial structure, Li is the number of employees in the i industry, L is the total employees, Yi is the output value of the i industry, and Y is the total output value. N is the number of industry categories, where N=3. The larger the value of P, the more unreasonable the industrial structure, which is a negative indicator.

Advanced industrial structure
The measurement of advanced industrial structure selects the hierarchical coefficient of industrial structure to express. The calculation formula is: In the formula, , , represents the ratio of the total output value of the n industry in city i to the regional GDP in year t, and ℎ ℎ , reflects the process of gradual evolution from the primary industry to the secondary and tertiary industries. The larger the value of ℎ ℎ , , the higher the level of industrial structure, which is a positive indicator.

Deviation degree of industrial structure
In the process of industrial structure change, maintaining the deviation degree of industrial structure at a relatively low level is very important for the development of economic benefits. Comparing the evolution trends of resource-based cities in the Bohai Rim and all cities in the Bohai Rim, it can be seen ( Fig. 1) that the deviation of the industrial structure of resource-based cities is on the rise, reflecting the increasing irrationality of employment structure and output value structure. This may be due to the failure to form substitute industries in time and the mismatch of human capital and physical capital when the mineral resources of the resource-based cities around the Bohai Sea are gradually approaching exhaustion, leading to the risk of decline in the benefits of the industrial structure. Looking at the interior of the resource-based cities around the Bohai Sea (Table 1), the top 5 cities in the ranking are Fushun, Anshan, Tangshan, Zibo, and Dongying. The bottom five are Handan, Fuxin, Jining, Chengde and Xingtai. It can also be seen from Table 1 that resource-based cities around the Bohai Sea have relatively large internal differentiation characteristics.

Advanced industrial structure
The measurement of the coefficient of advanced industrial structure can reflect the relative level of urban economic development and the overall direction of development in the region. Comparing the evolution trends of resource-based cities in the Bohai Rim and all cities in the Bohai Rim (Fig. 1), it can be seen that the industrial advancement coefficient of resource-based cities is significantly lower than the overall average level of the Bohai Rim. Combining the calculation results listed in Fig. 2 and Table 3, the changing trend of the coefficient of advanced industrial structure in many cities is relatively flat. From the perspective of the resource-based cities around the Bohai Sea, the top 5 cities with the industrial structure advancement coefficient are Tangshan, Jining, Zibo, Linyi, and Handan. The bottom five are Fushun, Benxi, Huludao, Laiwu, and Fuxin, reflecting the large differences in the industrial structure of resource-based cities around the Bohai Sea. 14 0.14 0.14 0.14 0.14 0.14 0.15 0.15 0.16 0.15 4 Handan 0.14 0.15 0.15 0.15 0.15 0.14 0.14 0.14 0.14 0.13 0.13 0.13 0.13 0.13 0.12 0.14 5 Dongying 0.11 0.11 0.12 0.12 0.12 0.12 0.11 0.11 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 6 Tai'an 0.11 0.10 0.11 0.11 0.11 0.12 0.12 0.11 0.11 0.12 0.12 0.12 0.12 0.12 0.12 0.11 7 Anshan 0.14 0.12 0.11 0.11 0.11 0.11 0.11 0.11 0.10 0.

Conclusion
The overall competitiveness of a region depends on whether the city where the industry is in trouble can successfully achieve industrial transformation and upgrading, which directly determines the potential for sustainable economic development in the region. Resource-based cities are now facing major development difficulties and have attracted the attention of the central government. The Bohai Rim region is the core area of China's economic development and the forefront of its opening strategy. The transformation of the Bohai Rim resource-based cities is of great significance to its overall development. Based on this, this article uses the corresponding indicators to compare and measure the industrial development situation in the Bohai Rim region from the aspects of industrial transformation and upgrading and rationalization. The main conclusions are: the growth trend of the index of advanced industrial structure is relatively slow, and the deviation degree of industrial structure also shows an unfavorable upward trend.
through the above conclusions, relevant suggestions are put forward for the industrial transformation and development of resource-based cities around the Bohai Sea: First, we must carry out industrial transformation in a scientific and orderly manner, and balance the development proportions of the three industries. As the resource-based cities around the Bohai Sea are located in the core area of economic development in northern my country, industrial transformation and upgrading are showing urgency, and the rash pursuit of advanced industrial structure will be counterproductive. The transformation and development of resource-based city industries need to identify their own positioning, focus on key areas, and accelerate the formation of new strategic emerging industrial clusters. To transform resource advantages into industrial advantages, it is necessary to explore new industrial development models, create distinctive urban industry labels, formulate relevant policies based on their own advantages, and eliminate blind industrial upgrading behavior.
Second, a number of high-tech clusters should be formed as soon as possible in resource-based cities. Resource-based cities should combine their own foundations to seek industries with strong transformational motivation when choosing continued industries, and appropriately develop tertiary industries. Transform and upgrade industries such as iron and steel, energy and chemical industry, equipment manufacturing, actively introduce and undertake the transfer of non-capital functional industries, vigorously develop advanced manufacturing industries such as new-generation information technology, energy-saving and environmental protection equipment, construction machinery, new materials, and new energy, and accelerate the promotion of low-end industries towards the mid-to-highend level. At the same time, in accordance with the main function zone planning, reasonable planning guides the industrial layout of resource-based cities, and promotes the development of characteristic industrial tourism industries that take resources as the advantage. Promote the innovative development of resource-based urban industrial parks. Build a batch of national and provincial industrial parks with important influence in the Bohai Rim.
Third, to enhance comprehensive competitiveness when optimizing the industrial structure and promoting industrial development. It is necessary to give full play to the comparative advantages of the industrial base, enhance the city's technological innovation capabilities, focus on the development of resource deep processing industries, promote the transformation of resource advantages into economic advantages, extend the resource industry chain, and develop a circular economy. At the same time, it links the optimization and upgrading of the industrial structure with the increase of jobs and the protection of people's lives, and combines the resources and industrial advantages of the resource-based cities in the Bohai Sea to develop regional logistics, characteristic tourism, cultural exhibitions and other service industries to enhance the lifting effect of industrial competitiveness of resource-based cities on economic growth. To optimize the industrial structure and improve the quality of industrial transformation as a breakthrough for resource-based cities to achieve sustainable development, transform and upgrade traditional resource-based industries, develop green extractive industries, cultivate superior alternative industries, accelerate the development of modern comprehensive service industries, and encourage experimental development strategic emerging industries. Finally, the government of each resource-based city should actively create a social atmosphere of "Mass Entrepreneurship and Mass Innovation", increase corporate innovation awareness, and change public entrepreneurship concepts, so as to promote traditional enterprises to move toward the high end of the value chain, enhance the economic and social vitality of resource-based cities, break through the cognitive lock brought by traditional business concepts and employment concepts, and promotes the cultivation and development of advantageous alternative industries in an open and inclusive social environment.


XXXV International Conference on Equations of State for Matter

This paper is a preface to the proceedings of the XXXV International Conference on Equations of State for Matter, which was held at the Cheget pension in the village of Terskol, in the settlement of Elbrus, in the Kabardino-Balkar Republic of the Russian Federation, from March 1 to 6, 2020.

Introduction
The XXXV International Conference on Equations of State for Matter (ELBRUS 2020) was held at the Cheget pension located on Polyana Cheget in the village of Terskol, in the settlement of Elbrus, in the Kabardino-Balkar Republic of the Russian Federation, from Sunday, March 1, to Friday, March 6, 2020 [1].

Conference location
The Cheget pension in Terskol is 5 km from Mount Elbrus (the highest peak of the Caucasus at 5642 m), about 120 km from Nalchik, which is the capital of the Kabardino-Balkar Republic, where the nearest international airport is located, and about 180 km from Mineralnye Vody, where there is another international airport.
The village of Terskol is located in the valley of the Baksan River, which begins on Mount Elbrus. It is a famous ski resort at altitude of about 2000 m. This valley is in a national park, which is a region of extreme natural beauty. Visit to Mount Elbrus is possible by cable car for seeing the Greater Caucasus range with permanent snow from the Old Krugozor (3000 m), Mir (3500 m) and Gara-Bashi (3780 m) stations.

Topics
• Equations of state and constitutive equations for matter under extreme conditions at high pressures and temperatures; • Shock waves, detonation and combustion physics; • Interaction of intense laser, x-ray and microwave radiation with matter; • Interaction of powerful particle beams with matter; • Experimental techniques of generation and diagnostics of extreme states of matter; • Methods of mathematical modeling in physics of extreme states of matter; • High-energy astrophysics; • Low-temperature plasma physics; • Issues of physics and power engineering, technology aspects.  Figure 1. Participants of the ELBRUS 2020 conference.
7. Participants 364 people were pre-registered as participants of the conference. They submitted 306 abstracts [2] with results of works of 751 co-authors from 121 institutions from 15 countries (Argentina, Australia, Belarus, the People's Republic of China, the Czech Republic, France, Germany, Italy, Japan, Poland, Russia, Turkey, Ukraine, the United Kingdom, the United States of America). 201 participants attended the sessions from different cities of Russia (figure 1).

Scientific program
The conference program consisted of 5 plenary, 66 regular oral and 235 poster presentations.

Poster session
All posters were presented during two poster sessions on March 2 and 3, 2020.

Best poster awards
A contest for the best poster was held among the participants. Prizes went to five: • Arseniy Burov (JIHT RAS, Moscow, Russia) "Ab initio study of MgO under pressure using quasiharmonic approximation"; • Ekaterina Godina (Institute for Electrophysics and Electrical Power RAS, Saint-Petersburg, Russia) "Arc pyrolysis of methane in the presence of oxygen"; • Alina Kirillova (Saint-Petersburg State Technological Institute [Technical University], Saint-Petersburg, Russia) "Investigation of wood ash melting by ac plasma torch"; • Anastasia Igorevna Krikunova (Moscow Institute of Physics and Technology, Dolgoprudny, Russia) "Flames under the reducing gravity"; • Alexandra Igorevna Khirianova (Lebedev Physical Institute RAS, Moscow, Russia) "The simultaneous use of interferometry and schlieren photography in laser diagnostics of plasma objects".

Prospects
The next meeting in the series is the XXXVI International Conference on Interaction of Intense Energy Fluxes with Matter, 1-6 March 2021, Elbrus, Kabardino-Balkaria, Russia [3].


Analytical Review and Mid-Term Forecast of the Development of the Russian Meat Market in the Context of a Pandemic of a New Coronavirus Infection

. Currently, the Russian Federation has a difficult economic situation on the meat market. The problems affecting the effective development of this market were especially aggravated in the context of the new coronavirus infection pandemic, and directly affected the food security of the Russian Federation. The authors of the article reviewed the meat products market at the turn of 2019 - 2020, and also presented two options for its development for the medium term - basic and crisis ones. In addition, barriers to the export of meat products to the world market have been identified and directions for their early overcoming have been proposed. The results obtained in the course of this study not only provide an answer to the question about the role of external interventions in the economy of the state, but may become the basis for thinking about the importance of careful planning of long-term studies to assess difficult-to-predict risks, such as a new coronavirus infection, in terms of concerning the food security of the state.

Introduction
Currently, a difficult situation has developed on the Russian meat market. Leading experts forecast an increase in meat production in Russia in the medium term, despite the tendencies for the devaluation of the Russian ruble and a decrease in the level of income among the population. It is noted that production growth will be achieved by increasing export volumes, as well as stabilizing and promoting domestic consumption of meat products. However, the profitability of this industry will gradually decline due to higher feed prices. It is necessary to consider the main options for the development of the meat market in Russia in order to overcome the barriers to its development in the medium term, taking into account the influence of negative production factors associated with the emergence of a new coronavirus infection.
For the basic variant of the development of the Russian market of meat products, the following directions will be characteristic. In connection with the commissioning of new production facilities, which had been launched earlier in the framework of various agricultural projects, a large increase in the volume of pork production in Russia will be achieved. Prices for chicken meat are also expected to rise. In addition, a slowdown in the growth of profits of domestic meat agricultural producers is possible (by an average of 10%). At the same time, the margins of the ten largest companies producing meat products in Russia will also decrease -approximately within 5-7%.
Previously, it was assumed that quarantine measures to prevent the development of a new coronavirus infection will last only until the end of the first half of 2020. At the moment, quarantine measures have been extended until the end of the first quarter of 2021. The second and third waves of new coronavirus infection are expected in Russia. These events will entail a decrease in the gross domestic product of Russia at the end of 2020 to 5-6%, and a drop in the total income of the population is predicted on average by 10% [1]. With such trends in the development of the events, the demand in the meat products market may recover closer to July-August 2021. In the event of the onset of the acute phase of a pandemic of a new coronavirus infection, agricultural producers will begin to fight for the main sales channel for meat products, that is, retail trade. Note that domestic consumption of all types of meat products in Russia will remain at the level corresponding to 2019, approximately 77 kilograms per person (Table 1). Naturally, consumer preferences will be redistributed towards cheaper types of meat products.
According to Table 1, we have that Russian agricultural producers of pork meat will increase the volume of production, since the existing investment projects for pig breeding in this period of time will reach the maximum amount of financing in Russia [4]. The growth of pork production volumes will be rational in conjunction with its export potential. During a pandemic of a new coronavirus infection in the world, the population is experiencing a deficiency of animal protein. Additionally, we note that at the end of 2020, an increase in profits attributable to the ten largest agricultural producers in Russia is possible. It can be about 10%. Compared to the same period last year, the decline in profit will be at least 9%. Product profitability in gross volume will decrease by at least 5%. At the same time, the cost of meat products will grow at a faster pace than the level of profit, which ultimately may lead to the bankruptcy of many agricultural producers in Russia.
The crisis scenario of the development of the Russian meat market will be characterized by a general decline in prices for all types of meat, a not very significant increase in the production of chicken and beef meat. It should also be noted that revenue growth is not expected even among large agricultural producers. Moreover, their marginality is likely to be negative. Gross profitability of the 10 largest agricultural producers 23% -18% -4% Revenue growth of the 10 largest agricultural producers 20% -10% 0% Volume of export of products, thousand tons 327 -500 400 Quarantine measures to prevent the spread of the new coronavirus infection in Russian regions may take longer than currently predicted. Expectations were confirmed, the second wave of a new coronavirus infection in Russia began in the 4th quarter of 2020 and may last until mid-2021 [2].
With such a development of events, the volume of the Russian gross domestic product will decline by 6-8%. The gross income of the population will also begin to recover no earlier than the first half of 2021. Experts note that in 2020 the overall decline in real incomes of the population in Russia will be at least 15%. With a slight drop in domestic consumption of meat products in 2020, the increase in export potential will continue.
Regardless of one or another variant of the development of the Russian market of meat products, it is necessary to envisage that there will be a gradual decrease in the number of small and medium-sized companies due to their bankruptcy or absorption by stronger companies.

Materials and Methods
The methodological background of the statistical study of the Russian meat market is based on the work of many scientists. Despite the high level of elaboration of the problem, in the modern scientific world not enough attention is paid to the issues of a comprehensive statistical analysis of the state and development trends of the Russian meat market [3]. In particular, this concerns taking into account possible transformations of the foreign economic market situation, as well as assessing the impact on the development of the market of the new coronavirus infection.
When conducting an analytical review of the Russian market of meat products, the methods of correlation and regression analysis, analysis of structural breaks, as well as tabular and graphical methods of visual presentation of data have been used as statistical tools. Also, data analysis tools from the MS Excel 2007 package have been used.

Results and Discussion
The pandemic of the new coronavirus infection has significantly affected the economic development of Russia in general and the meat market in particular. The unemployment rate has increased, the real income of the population has significantly decreased by the end of 2020. Similar trends may continue in the near future [5].
According to 2018 data available in the Federal State Statistics Service of Russia, the share of employed citizens in the total number of the working population in the sectors most affected by the pandemic was about 38% [6].
Note that the most affected industries include wholesale and retail trade, catering, agriculture, construction, hotel business, and logistics.
Nevertheless, in spite of the current situation in the meat market, agricultural producers intend to increase their meat production in 2020-2021. At the same time, the share of their costs in the amount of consumer spending will grow to at least 30-35%. When compared with 2019, this figure was 26%.
The above review of the meat market in Russia reflects the lack of interconnection between the level of consumption of meat and meat products and the changes that are currently taking place in Russia's gross domestic product and the level of real incomes of the population. Over the past five years, there has been a steady downward trend in the level of real incomes of the population in Russia. Despite this, the volume of consumption of meat products grew throughout 2013, as well as in the period from 2016 to 2019 inclusive ( Figure 1). A decrease in consumption was recorded only in 2014 and 2015.
Experts from the Agency for National Credit Ratings expect an increase in the cost of purchasing food products [1]. At the same time, non-food products will lose ground and become less significant in the consumer basket.  At the same time, it is necessary to take into account the downward trend in real incomes of the population, which may entail a redistribution of consumer preferences from expensive types of products to cheaper and more affordable ones. Therefore, the cost of production and sale of meat products can grow proportionally up to 30% (Figure 2). Most likely, consumers will give more preference to frozen products.
As for the export of meat products, it can grow due to the opening of a sales market to Vietnam (as well as to the PRC) up to 500,000 tons with the basic development option by the end of 2020. If favorable conditions are established, agricultural producers of meat in Russia can increase the volume of its supplies to the foreign market by at least 50%. In the event of unfavorable conditions for the export of meat to the external market, that is, in a crisis scenario, this figure by the end of 2020 will be about 400,000 tons [7].
Note that meat imports to Russia will also decrease by about 3.5-4.5%, which in monetary terms will amount to about 2 billion US dollars. At the same time, there is a tendency for a steady decrease in the cost of imports of meat and meat products in 2015 to 2020 inclusive by 67%, and from 2010 to 2020 by 73%, respectively. The main reasons for the reduction in the volume of imports of meat and meat products to Russia are the devaluation of the Russian ruble and an increase in the volume of its own production (the state policy of import substitution).

Conclusion
1. The novel coronavirus pandemic has had a major impact on employment levels; real incomes of the population in 2020 may decline more significantly than in previous crisis periods.
2. The volume of meat production in Russia in 2020 will grow against the background of relative stability in the domestic consumption market, despite the constant devaluation of the ruble and a decrease in real incomes of the population.
3. Exports of meat products can grow up to 500 thousand tons in 2020 under the basic scenario of economic development in Russia. 4. Self-sufficiency in meat products in Russia is close to 100%, so a shortage is unlikely. Imports of meat products are also declining.
5. The largest agricultural producers of meat products are increasing their production volumes and market share even if the price level falls.