Analysis on the theory and practice of industrial symbiosis based on bibliometrics and social network analysis

Accepted Manuscript Analysis on the Theory and Practice of Industrial Symbiosis Based on Bibliometrics and Social Network Analysis

Maoxing Huang, Zhen zhen Wang, Ting Chen PII:

S0959-6526(18)33843-5

DOI:

10.1016/j.jclepro.2018.12.131

Reference:

JCLP 15189

To appear in:

Journal of Cleaner Production

Received Date:

19 November 2017

Accepted Date:

13 December 2018

Please cite this article as: Maoxing Huang, Zhen zhen Wang, Ting Chen, Analysis on the Theory and Practice of Industrial Symbiosis Based on Bibliometrics and Social Network Analysis, Journal of Cleaner Production (2018), doi: 10.1016/j.jclepro.2018.12.131

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ACCEPTED MANUSCRIPT

Analysis on the Theory and Practice of Industrial Symbiosis Based on Bibliometrics and Social Network Analysis Maoxing Huang, Zhenzhen Wang, Ting Chen School of Economics, Fujian Normal University, Fuzhou, 350108, China

Abstract: Industrial symbiosis (IS) can generate economic, social, and environmental benefits, and support sustainable development; thus, many governments and scholars are devoted to exploring it. To analyze and evaluate the current status and development trends of industrial symbiosis, this study selects “industrial symbiosis” as a research title to search the Web of Science (core collection) database, and uses the bibliometrics and social network analysis (SNA) methods. The results turn out that an increasing amount of research has been devoted to industrial symbiosis in recent years. They also show that the study of industrial symbiosis has obvious crossdisciplinary characteristics. Current research on industrial symbiosis mainly focuses on four issues: evolution and development, operation carriers, driving mechanisms, and efficiency evaluation of industrial systems. The research of the industrial symbiosis network will greatly promote the industrial green development for the industrial transformation and upgrading of various countries. Many methods are used to examine these four questions, including case studies, life cycle assessment (LCA), material flow analysis (MFA), data envelopment analysis (DEA), multi-criteria decision making (MCDM), emergy analysis, and SNA. This article provides the trends of industrial symbiosis research for further study. The data mining can be used in the industrial symbiosis network and a dynamic simulation industry symbiotic evolutionary process model can be discussed in the future. Keywords: Industrial Symbiosis; Co-word Analysis; Bibliometrics; Social Network Analysis (SNA)

1 Introduction Industrial symbiosis is a new concept proposed by imitation of natural ecosystems. As a subfield of industrial ecology (IE) and a key concept of the circular 

Corresponding author: [email protected]; +8615980287527.

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ACCEPTED MANUSCRIPT economy, industrial symbiosis (IS) was first proposed to describe organic relationships between dissimilar industries. It encourages traditionally separate industries to connect in a collective approach to gain competitiveness through physical exchanges of materials, energy, water, and by-products (Chertow, 2000). The most widely accepted definition of industrial symbiosis comes from book “Industrial Symbiosis” published by the Karenborg Company in Denmark: “Industrial symbiosis refers to the joint improving survive and profit ability through cooperation between different companies. While at the same time it can achieve the conservation of resources and environmental protection through this consensus." Here the core of industrial symbiosis is the use of mutually beneficial by-product industrial cooperation. Evidence strongly suggests that industrial symbiosis can generate financial, social, and environmental benefits, and support sustainable development (Ehrenfeld and Gertler, 1997; Roberts, 2004; Tudor et al., 2007; Yuan and Shi, 2009; Yap and Devlin, 2017). Industrial symbiosis is a complete industrial ecosystem with economy characteristic and ecological characteristic. Enterprises of various industries form symbiosis due to the sharing of similar resources or the complementary of heterogeneous resources. Thus, many countries and scholars are devoted to exploring it. Schiller et al. (2014) proposed that network analysis can be seen as the most suitable method for studying industrial symbiosis. Zhang et al. (2015) traced the development history of industrial symbiosis and suggested that network analysis methods were increasingly being used to analyze both the structural and functional characteristics of systems. To comprehensively analyze the systems, prevalent issues, and trends of industrial symbiosis research, this thesis combines bibliometric analysis and social network analysis (SNA) to analyze and judge the current status and development trends of industrial symbiosis. Although the study by Yu (2013) analyzed the citations, co-citations, and networks of industrial symbiosis, it did not focus on the different social network methods relevant to the study of industrial symbiosis. The thesis differs from previous studies in the following aspects: (1) SNA is one integration powerful model for social structure, which has a number of important formal aspects of analyzing networks and their participants. Therefore, the network density, network centrality, network sub-group and small world characteristic were used to analyze the characteristics of industrial symbiosis; (2) A more detailed analysis of the theory and practice of industrial symbiosis was provided on the basis of previous literature. 2

ACCEPTED MANUSCRIPT With the growing number of academic papers on the industrial symbiosis, it is more difficult to keep a clear track of its development. How to know more about the relationship of the research published, and obtain some useful insights into how this field evolves? Hence, this study combines bibliometric and social network analysis to explore the development of the industrial symbiosis, which can mine the influential journals, key researchers, major issues and so on in the industrial symbiosis and visually presented by using the network analysis. The paper is structured as follows. The first section introduces the research. The second section presents the methodology for bibliometrics and social network analysis, and explains how the database and document types were selected. In the third section, the authors present a bibliometric analysis and social network analysis of industrial symbiosis. In the fourth section, the authors present a theory and practice study of industrial symbiosis based on the third and fourth sections. Finally, an inclusive evaluation of the results and suggestions for future research are presented.

2 Methodology and data processing 2.1 Bibliometric analysis Bibliometric analysis is a combination of methods for conducting quantitative analyses of science research, to evaluate the contributions of researchers or of different research fields (Železink et al., 2017). In such analysis, mathematical and statistical methods are applied to study document use and publication patterns, to obtain insights into the distributed architecture and collaborative relationships of scientific activities and findings from a macro perspective (Bjurstrȍm and Polk, 2011). Bibliometrics has been widely used in the fields of library and information science, science and technology management, scientific evaluation, and prediction and quantitative management(Garg and Sharma, 2017; Huang et al.,2014; de Lima et al.,2012). Citation analysis, co-citation analysis, and co-authorship analysis were used extensively in previous studies (Harwood, 2009; Muhuri et al,2018). To differentiate our research from previous research, the co-word analysis method which originates from citation coupling in bibliometrics and the co-citation concept was used. Its connotation can be summarized as follows: there exist some essential relationships in which two or more keywords can be expressed as a research topic or research direction in the same literature at the same time. The more co-occurrences between two keywords, the closer their relationship is. The co-word analysis was useful in 3

ACCEPTED MANUSCRIPT analyzing the structure and development of the literature. 2.2 Social network analysis Social network analysis (SNA) plays a key role in economics, management, and sociology. Mitchell（1969）demonstrated the concepts and fundamental of social network. It has become a powerful strategy for searching social structures and identifying the knowledge domains of disciplines. It can help researchers analyze social phenomena and social structure on the basis of “relationships”, such as the relationship between different people and organizations, as well as dynamics, sentiment analysis and activities that other circles of networks being involved (Akuma et al.,2016; Karyotis et al.,2017). The most important aspect of SNA is finding the most influential node in the network and measuring centrality in the network (Yustiawan et al., 2015). It typically analyzes the structure of a network comprised of ties between nodes. So the social network analysis focuses on functional entities and the way which entities are connected (Stanton et al.,2012). Compared with the single approach, social network analysis can contribute to optimization of the entire work system, rather than parts in isolation (Stanton,2014). Increasingly, SNA is applied as a methodological tool and convenient heuristic to map relationships and quantify engagement between interdependent actors, resulting in an array of research endorsing the theoretical and mathematical components within management specific literature (Borgatti and Foster, 2003). The network can be analyzed in terms of the network density, centrality, subgroup cohesion, and core periphery. 2.2.1 Network density Network density is the degree of interconnectedness in the network, which is calculated as the number of connections compared to the total possible number of connections. A high density value implies strong coordination between groups. The computational formula for density is Density  L /  N  N  1 

(1)

Here, L denotes the number of arrows in the network. Values range from 0 to 1, with 0 indicating no connections and 1 indicating that all organizations are connected (Bodin et al, 2006). 2.2.2 Network centrality “Centrality” is one of the key points in social network analysis, and indicates the power and position of an individual or organization in a social network. A network 4

ACCEPTED MANUSCRIPT centrality analysis includes degree, closeness, and betweenness. Degree centralization assesses the degree to which the network is influenced by one or a few organizations, according to the number of direct connections with other organizations. A higher score indicates that the network is influenced by only a few organizations, and a lower score indicates that organizations within the network have a similar number of ties. The equation to calculate the closeness centrality of a node can be defined as closeness  n    v

1 d vu

(2)

Here, u is the focal node, v is another node in the network, and d vu is the shortest distance between these two nodes (Wayne and Oellermann, 2011). Betweenness centralization expresses the degree to which a few organizations control the relationships of other organizations in the network. It can be defined as the proportionality of the total shortest paths passing through that node to all possible shortest paths in the network (Brandes, 2001). The betweenness centrality of node n can be expressed as follows, CB  n  

 st  n  s  n  t  st



(3)

Here,  st denotes the total number of shortest paths and  st  n  defines the number of shortest paths passing through n . A higher betweenness centralization score indicates greater centralization, and that a relatively small number of gatekeepers dominate the network. 2.2.3 Network sub-group Generally speaking, groups have a certain cohesion, indicating the sense of identity and belonging that the members have toward the group. A high degree of cohesion can enhance the achievement of group goals. 2.2.4 Small world characteristic The σ was used to describe the small world coefficient, which can be defined as follows,  =  C Actual / CRandom  /  LActual / LRandom 

(4)

Here, C Actual and CRandom denote the clustering coefficients of the players’ network and random network, respectively. LActual and LRandom respectively signify the average path lengths of the players network and random network. The value should be greater 5

ACCEPTED MANUSCRIPT than 1 for a small world characteristic (Watts and Strogatz, 1998). 2.3 Data acquisition and processing procedure Our research followed two steps in the creation of the sample: data selection and document type selection. Firstly, our analysis is based on the Web of Science (WoS) database (Core Collection). The WoS was selected because it is a worldwide database that may be used for citation analysis and to obtain academic information, and has been widely used in previous publication activity studies (Sun et al., 2017). The searching strategy was as follows: Web of Science category (WC) = industrial symbiosis. Time frame: from 1984/1/1 to 2017/7/28; Language: all. Original papers and reviews were included; while letters, editorials, and corrections were excluded. After the search was completed, we examined the search page and 185 articles were retrieved. To reduce the possibility of selection bias, some short papers, workshop briefs, panel sessions, and posters were excluded. Finally, 170 papers were selected. Furthermore, retrieved data was downloaded using the “save for other file formats” export function; targeted information was extracted from the original data, including article title, year of publication, journal name, authors’ affiliations, abstracts, and keywords. The data was downloaded into a folder according to the required format for SCI data; the data was imported into BICOMB 2.0, which is useful software for literature analysis.

3 Results and analysis 3.1 Bibliometric analysis of industrial symbiosis research After the retrieved data was imported into the BICOMB 2.0 system, it was extracted and calculated according to the publishing years, authors, keywords, and so on. It was then imported into Excel to analyze the evolution of industrial symbiosis as a research domain. 3.1.1 Evolution of the numbers of articles concerning industrial symbiosis Figure 1 shows the number of articles in the Web of Science database with "industrial symbiosis" as the topic for each year from 2003 through 2016. It shows that researchers have paid increasing attention to the study of industrial symbiosis. A total of 170 articles have been published during the past 14 years. The graph shows a significant increase in publishing numbers from 2003 (two articles) to 2016 (30 articles). It also shows that the study of industrial symbiosis, which represents a 6

ACCEPTED MANUSCRIPT specific application of ecological symbiosis theory in the social economy, is still relatively new. Moreover, the research on this area had obvious cross-disciplinary characteristics. ------------------------------------------------------------------------------------------------------Insert Fig. 1 about here ------------------------------------------------------------------------------------------------------3.1.2 Analysis of the authors Tab. 1 lists the authors that have published the most papers on industrial symbiosis. From Tab. 1, it shows that the author who published the most industrial symbiosis articles is Fujita, who appears 13 times; the second is Geng (11 times), and the third is Dong (10 times). In more detail, many of the authors are Japanese from Tab. 1; this appears to show that Japanese researchers placed significant emphasis on studying industrial symbiosis. Furthermore, we obtained the relationships between authors by using the co-occurrence matrix from BICOMB 2.0; those results are shown in Tab. 2 and Fig. 1. The high connection can be seen from authors like Fujii, Dong, Geng, Fujita, and Ohnishi. They pay high attention on the china’s industrial ecological system by making case study of Dalian, Shenyang and so on. ------------------------------------------------------------------------------------------------------Insert Tab. 1, 2 and Fig. 2 about here ------------------------------------------------------------------------------------------------------3.1.3 Analysis of journal-published literature The five journals that published the most papers concerning industrial symbiosis were “Journal of Cleaner Production” (61 articles), “Journal of Industrial Ecology” (29 articles), “Resources Conservation and Recycling” (8 articles), “Business Strategy and the Environment” (5 articles), and “Waste and Biomass Valorization,” “Environmental Science and Pollution Research,” and “Energy Policy” (4 articles each). These results are listed in Tab. 3. From the table, the conclusion is quite obvious, all these journals mentioned above are highly concerned about the industrial symbiosis. All of them are the leading journals in the domain of industrial symbiosis. ------------------------------------------------------------------------------------------------------Insert Tab. 3 about here ------------------------------------------------------------------------------------------------------3.1.4 Keyword analysis While calculating keywords, we found that some of the keywords had the same 7

ACCEPTED MANUSCRIPT meaning; for example, “eco-industrial park,” “Eco-industrial park,” and “eco industrial park” had the same meaning, as did “life cycle assessment” and “life cycle analysis.” To address this issue, we put keywords with the same meaning together in the BICOMB system to reflect the current dynamic research of this field. The greater the number of times a keyword appeared, the higher the attention paid to the subject. We obtained keywords with five or more appearances to indicate the “hot spots” of current research. The current study on industrial analysis is mainly focused on the following areas which can be seen from Tab. 4: industrial ecology (56 times), which is a very important theory for supporting industrial symbiosis, EIP (22 times), which relates to the operational carriers of industrial symbiosis, sustainable development (12 times), which is another important theory supporting industrial symbiosis, China (12 times), which shows an increasing focus on China’s industrial symbiosis, life cycle assessment (11 times), which is the one of the most important methods for studying industrial symbiosis, and recycling (11 times), which is a specific detail of industrial symbiosis. ------------------------------------------------------------------------------------------------------Insert Tab. 4 about here ------------------------------------------------------------------------------------------------------Furthermore, a 36×36 keywords co-word matrix was built based on Tab.4. If the 36×36 keywords co-word matrix was given, the Table will too big. Therefore, Tab. 5 shows part of the co-word matrix between keywords, which the first 9×9 keywords was chosen as they have a high frequency of the keyword and the different correlations of the keywords in the articles can be seen. Using this matrix, the SNA will also be used to determine which issues are currently receiving the most interest from researchers. ------------------------------------------------------------------------------------------------------Insert Tab. 5 about here ------------------------------------------------------------------------------------------------------3.2 Social network analysis of industrial symbiosis 3.2.1 Whole network analysis Because data values for degree, density, and betweenness are normalized in Ucinet, the co-word matrix was imported into Ucinet and then chose the “Transform>Dichotomize” function in Netdraw. Based on this, we dichotomized the matrix and used it as an input matrix. Furthermore, the “Network>Cohesion>Density” 8

ACCEPTED MANUSCRIPT function was used, which showed that the density of the co-word network was 0.2508 while the standard deviation was 0.4335. These results show that industrial symbiosis is still a relatively new research area, and it needs further study. 3.2.2 Small world characteristic The “Network>Cohesion>Distance” function was executed, and it showed that the average distance was 1.749. According to the conclusion from Watts and Strogatz (1998), when the average distance was not more than 6, a “small world” characteristic exists; this means the study of industrial symbiosis is relatively active. The function also showed that the distance-based cohesion is 0.625. The closer this value is to 1, the more compact the whole network. At present, the distance-based cohesion is above the medium level, and the topic of industrial symbiosis can be further explored in the future. 3.2.3 Analysis of network centrality Centrality analysis was carried out using degree centrality, closeness centrality, and betweenness centrality. Degree centrality suggests that individuals who have more connections in a network may have a larger influence in that network. We executed the “Network>Centrality>Degree” function, and it showed that industrial ecology, EIP, industrial ecosystems, sustainable development, recycling, and China are currently prevalent issues. The “Network>Centrality>Closeness” function was then executed, and it showed that certain topics had a high degree of nCloseness to each other; examples include sustainable development, recycling, and China; resource efficiency, by-product and waste; circular economy, eco-industrial development, geographic proximity, and the National Industrial Symbiosis Programme; material flow analysis, by-product exchange, and by-product synergy; synergy, innovation, industrial cluster, industrial symbiosis networks, and Finland; and eco-efficiency, environmental performance, resilience, and agglomeration economics. Then, the “Network>Centrality>Freeman Betweenness>Node Betweenness” function was executed, and it showed that industrial ecology, EIP, sustainable development, China, industrial ecosystems, life cycle assessment, and recycling had higher values, meaning that they played an important role in the study of industrial symbiosis and had the most control over industrial symbiosis throughout the network. ------------------------------------------------------------------------------------------------------Insert Tab. 6 about here 9

ACCEPTED MANUSCRIPT ------------------------------------------------------------------------------------------------------3.2.4 Core/periphery analysis The social network visualization tool Netdraw was used to develop sociograms from the original social network, to group composition network matrices, and to develop clique network diagrams from the Ucinet clique analysis results (Abell, 2013). Then the “Network>Core/Periphery>Continuous” function was executed; the results showed that the correlation coefficient between the data and the ideal model was 0.601. This indicates a higher intensity relationship, and shows that a core periphery structure exists for high frequency words in industrial symbiosis research; this is illustrated in Fig. 3. ------------------------------------------------------------------------------------------------------Insert Fig. 3 about here ------------------------------------------------------------------------------------------------------3.2.5 Cohesive subgroup analysis The “Network>Roles&Positions>Structural>Concor” function was executed to analyze the internal structure of industrial symbiosis research; the results are shown in Fig. 4. It can be seen that the current industrial symbiosis areas of concern mainly include four categories: (1) connotations of industrial symbiosis, (2) carriers of industrial symbiosis, (3) driving force mechanisms of industrial symbiosis, and (4) efficiency evaluation of industrial symbiosis. In addition, the methods that were used in the studies include: case studies, life cycle assessment (LCA), material flow analysis (MFA), data envelopment analysis (DEA), multi-criteria decision making (MCDM), emergy analysis, and so on. A detailed description of the four categories will be provided in the following section. ------------------------------------------------------------------------------------------------------Insert Fig. 4 about here -------------------------------------------------------------------------------------------------------

4 Discussion--Theory and practice of industrial symbiosis 4.1 Evolution and development of industrial symbiosis The research efforts described in the literature first focused on understanding the evolution and development of industrial symbiosis. For example, Frosch and Gallopoulos (1989) first introduced “industrial ecosystems” as an important solution for achieving productive use of waste and by-products and minimizing environmental 10

ACCEPTED MANUSCRIPT degradation. Paquin (2012) demonstrated empirically how a new, facilitated industrial symbiosis initiative developed and evolved. Mattila et al. (2012) classified the research on industrial symbiosis into five basic groups: analysis (accounting), improvement, expansion, design, and circular. Meanwhile, Yu et al. (2014) divided the evolution of industrial symbiosis research into several stages (1997-2005, 20062012) according to the core literature and journals published between 1997 and 2012, using citation analysis, co-citation analysis, and network analysis. Boons et al. (2011) suggested that industrial symbiosis should be conceptualized as a process, so that every course that forms the process can be discussed. Saraceni et al. (2017) and Chertow (2007) used different techniques to diagnose existing symbiotic cases. The former created a pilot model based on the case of Brazilian industrial clusters, by using fuzzy logic as a multi-criteria decision analysis (MCDA) method to uncover industrial symbiosis and promote its development. The latter provided a historical view of the motivations and means for uncovering existing symbiosis. To identify potential symbiosis, Gonela and Zhang (2014) drew a decision framework for designing an optimal industrial symbiosis system to improve bioethanol production. Meanwhile, we found that the majority of researchers were focused on the following four parts: (1) the carriers of industrial symbiosis, such as EIPs, ISNs, and EICs, which provide a means of advancing sustainable production and consumption; (2) analyzing the factors that influence the development of industrial symbiosis in existing industrial ecosystems or the carriers’ implementations; (3) the efficiency or benefits that are created; (4) determining which methods can be used to measure IS. These are all the main issues to which scholars pay attention. ------------------------------------------------------------------------------------------------------Insert Tab. 7 about here ------------------------------------------------------------------------------------------------------4.2 Carriers of industrial symbiosis Numerous countries are devoted to promoting industrial symbiosis for achieving sustainable development, either through identifying and stimulating ecosystems or planning and developing eco-industrial parks (EIPs) or industrial symbiosis networks (ISNs). Eco-industrial parks (EIPs), which aim to imitate a natural system through conserving and recycling resources, cultivate symbiotic relationships to attain industrial symbiosis. They coordinate with green technology companies that make 11

ACCEPTED MANUSCRIPT eco-friendly products. As a strategy to help mitigate environmental concerns, EIPs had been adopted by many countries to implement a circular economy (Yong et al., 2002); for example, USA (Chertow and Lombardi, 2005), Australia (Beers et al., 2007), Canada (Nisbet et al., 1998), Korea (Behera et al., 2012), and China (Zhu et al., 2007; Li and Xiao, 2017; Zhang et al., 2010). In terms of planning and developing EIPs, the focus is often on the criteria for building EIPs and exploring the structure and functions of EIPs on the basis of topological structure. Leong et al. (2017) presented a multi-objective optimization approach that used numerical representation to rank each participating plant’s preferences regarding the predefined criteria for building an EIP. Industrial symbiosis networks (ISNs) represent a relationship among at least three different firms that exchange at least two different types of waste. They present new implications regarding the design and improvement of EIPs, which have received attention from many scholars. And the ecological network analysis is an important method, which is applying to identify the ecological relationships. Fan et al.(2017) showed that the method can examine the internal workings of the industrial metabolic. Then network stability analysis is introduced into the Ecological network analysis providing insights into the metabolic system (Fan et al., 2017). Moreover, the stability and reliability of ISNs are the key components in industrial symbiosis. Researchers found that perturbation was one of the factors that caused disruption in ISNs. Hence, resilience became a necessary research field in exploring the evolution of ISN vulnerability and the mechanisms of ISNs. Fraccascia et al. (2017) explored the resilience of ISNs and concluded that increases in stock redundancy could improve their stability to some extent by quantifying the effects of various types of symbioses. Domenech and Davies (2011) proposed a modeling framework to analyze the main mechanisms in the building of trust and embeddedness, and to identify different phases in cooperation that lead to effective industrial symbiosis exchanges. Wang et al. (2017) constructed an initial indicator system for vulnerability assessment of coal mining industrial ecosystems. Wu et al. (2017) used a hazardous waste symbiosis scenario to analyze the stability and reliability of ISNs. ------------------------------------------------------------------------------------------------------Insert Tab. 8 about here ------------------------------------------------------------------------------------------------------4.3 Driving force mechanisms of industrial symbiosis 12

ACCEPTED MANUSCRIPT There has been growing insight into the factors that promote the development of industrial symbiosis. A common insight involves the technical and economic capacity that creates the conditions for industrial symbiosis to occur (Roberts, 2004; Taddeo et al., 2012). Garner et al. (1995) stated that industrial symbiosis relationships were influenced by technical, economic, and legal points of view. Jacobsen (2006) further found that economic motivation was relevant to upstream or downstream operational performance. Networking and innovation may potentially affect the implementation of industrial symbiosis (Posch et al., 2011; Mirata et al., 2005). Boons and Spekkink (2012) added the depth insight and suggested that institutional capacity influences industrial symbiosis by altering the opportunity set of actors. Another strategy to stimulate industrial symbiosis in practice involves the use of policy actions (Costa et al., 2010). As Chen et al. (2016) mentioned earlier, government policy varied the coal carbon dioxide emissions in different regions. Thus, it was necessary to take account of government policy. Some studies have also explored the influence of policy measures on the emergence of ISNs, which include regulatory instruments, economic instruments, and voluntary instruments. Many of them concluded that public policies promoted the development of self-organized industrial symbiosis (Chertow, 2007; Deutz and Ioppolo, 2015; Lehtoranta et al., 2011). Fraccascia et al. (2017) show that landfill taxes and economic subsidies have a positive effect on the emergence of ISNs after IS relationships occur. On the basis of these findings, researchers often create frameworks for exploring the driving forces of the development of industrial symbiosis. Yap and Devlin (2017) offered an analytic framework for examining contextual factors such as markets forces, the state, civil society, and so on. Taddeo et al. (2017) developed an interpretative framework for analyzing limiting factors, including technical factors, non-technical factors, location factors, dynamics of changes, and factors inherent in people and organizations. Boons et al. (2011) outlined a theoretical framework for understanding the dynamics of industrial symbiosis. In addition, Boon et al. (2016) drew a conceptual framework for researching the influence of institutional capacity on implementing industrial symbiosis. ------------------------------------------------------------------------------------------------------Insert Tab. 9 about here ------------------------------------------------------------------------------------------------------4.4 Efficiency evaluation of industrial symbiosis 13

ACCEPTED MANUSCRIPT After exploring the formation and development of industrial symbiosis, it was necessary to quantify its performance. Some studies on industrial symbiosis have focused on its total impact, particularly quantifying the environmental benefits. Daddi et al. (2017) utilized data to show that industrial symbiosis initiatives led to improvements in all environmental impact categories, particularly in the area of climate change. Song et al.(2017) provided a set of scientific and axiomatized methods to evaluate environmental performance on the basis of big data. Sokka et al. (2015) pointed out that impacts occurring upstream should also be studied through a comparison with stand-alone production. Others have evaluated EIP’s eco-efficiency and ecological industry chain efficiency, analyzing not only the total environmental impact of industrial symbiosis, but also the carriers’ eco-efficiency. Xiong et al. (2017) introduced the closest targets concept to measure eco-efficiency, and concluded that the average eco-efficiencies of Chinese provincial industries gradually increased between 2006 and 2013. Sokka et al. (2011) concluded that EIPs and ISs could reduce GHG emissions by comparing the GHG emissions of the Kymi EIP with two hypothetical stand-alone systems operating on their own. Yong et al. (2010) showed that evaluating EIP’s overall eco-efficiency was important, but traditional evaluation methods, which were based on neoclassical economics, focused more on the maximization of economic and technical objectives than

the contribution of

ecological products and services, for example, life cycle analysis and material flow analysis generally assess economic and environmental performance of a humandominated ecosystem separately. While emergy analysis and synthesis attempted to do the overall eco-efficiency of the EIP (Hau and Bakshi, 2004; Ulgiati and Brown, 1998). Thus, the article of Yong et al.(2010) presented the new methodology of emergy analysis to evaluate an industrial park. Conversely, Chen et al. (2017) once considered the productive efficiency of China’s forestry industry with a stochastic frontier model. In eco-industrial parks and industrial ecosystems, the changes in a set of individual systems were expected to contribute to increased eco-efficiency. Salmi (2007) tested the link between industrial symbiosis and eco-efficiency improvement, and concluded that eco-efficiency indicators offered a practical tool for measuring the environmental and economic benefits of loop-closing in industrial symbiosis. Wu et al. (2015) showed some useful evaluation systems, including ones that measured environment efficiency and overall system effectiveness, by analyzing the relevant 14

ACCEPTED MANUSCRIPT literature; in addition, they pointed out the deep disparity between environment efficiency and performance in China. Park et al. (2014) proposed an indicator that integrated the economic and environmental benefits of industrial symbiosis networks; one economic indicator and three commonly used environmental indicators were included. Wang et al. (2017) developed a novel methodology to evaluate ecological industry chain (EIC) efficiency with a comprehensive consideration of energy, economic, and environmental constraints. They found that it provided useful references for EIP development, because it used links to rearrange the upstream and downstream enterprises. ------------------------------------------------------------------------------------------------------Insert Tab. 10 about here ------------------------------------------------------------------------------------------------------4.5 Methods used in industrial symbiosis research Sections 4.1 through 4.4 describe various methodologies and approaches that have been used in the study of industrial symbiosis. Firstly, case studies represented the most popular method used in the research of industrial symbiosis. Most scholars proposed some models to achieve their research goals and used the appropriate cases to verify their model. Some industrial symbiosis cases have been identified as certain canonical cases by scholars; examples include cases from the United States, Finland, Sweden, Denmark, Australia, South Korea, and the United Kingdom (Chertow, 2000; Mirata and Emtairah, 2005; Jacobsen, 2006; Park et al., 2008; Sokka et al., 2011), and increasingly cases from China (Fraccascia et al., 2017). Boons and Spekkink (2012) used a dataset of 233 projects to test the assumption that certain social conditions must be fulfilled for firms to develop symbiotic linkages. Jiao and Boons (2014) employed a static conceptualization of policy; they studied a case with policy translations for the circular economy concept and an eco-industrial park in China, to illustrate that policy was at its core a dynamic process. Fraccascia et al. (2017) used a real case study to simulate the agent-based model. A case study was conducted by Gonela and Zhang (2014) to compare the efficiency and effectiveness of the proposed model. Zhu et al. (2007) took the Guitang Group as an example to research industrial symbiosis in China. Chertow and Lombardi (2005) assessed the economic and environmental costs and advantages for symbiosis partners in Guayama. Simboli et al. (2014) explored one large firm and 18 small and midsize enterprises (SMEs) working in the motorcycle industry to obtain 15

ACCEPTED MANUSCRIPT factors that may affect the development of industrial symbiosis. Jacobsen (2006) analyzed central industrial symbiotic exchanges by using detailed economic and environmental data to understand relevant economic and environmental performance. Yang et al. (2008) studied the Nanning Sugar Co., Ltd. in China and obtained four factors that were essential to making this symbiosis achievable. Lehtoranta et al. (2011) explored the evolution and environmental performance of a pulp and paper mill in Finland. Thus, no matter what factors influence industrial symbiosis, or the level of efficiency achieved, we can examine many cases: the Xinfa EIP, the Songmudao chemical industrial park in China, an Italian tannery cluster located in Tuscany, three eco-industrial coal parks in China, the Ningdong coal chemical ecoindustrial park, and so on. Secondly, various methods have been developed to measure the environmental benefits of industrial symbiosis initiatives (Geng et al., 2009; Liu et al., 2009; Park et al., 2010; Yuan et al., 2006; Zhu et al., 2010). Life cycle assessment (LCA) was considered one of the most important tools to measure the environmental benefits of industrial symbiosis initiatives (Eckelman and Chertow, 2013; Mattila et al., 2012; Sokka et al., 2011; Yu et al., 2014; Scheepens et al., 2016). First, it was used to evaluate each material substitution to determine its effectiveness, particularly in chemical industries. Sokka et al. (2010) used it to assess the environmental impacts of a pulp and paper mill, while Adiansyah et al. (2017) compared coal mine tailings management strategies by using LCA and land-use area metrics methods. Moreover, to improve the theory of environmental technology measurement, Song et al. (2016) proposed an ML-LCA method based on LCA, which was often applied to measure environmental effects but not to assess technology progress. In addition, some methods were developed for the synthesis and optimization of symbiotic resource networks within EIPs. Material flow analysis (MFA) was an evaluation method to quantitatively analyze material flows and estimate the environmental load to the system caused by the flows; by analyzing the input and output of materials, the analysis revealed how economic activity affected the environmental load (Ohnishi, et al., 2017). Sendra et al. (2007) applied MFA methodology to evaluate how an area could be transformed into an eco-industrial park. Data envelopment analysis (DEA) can evaluate the relative effectiveness or benefit (and test the appropriateness) of decision making units. Some papers turn out 16

ACCEPTED MANUSCRIPT that DEA plays on the optimization design of EIC(Avadí et al., 2014; Balezentis et al.,2016; Ren et al., 2014). As DEA has been applied in the evaluation of industrial chain efficiency(Wang et al., 2014).It also can be used to evaluate ecological industry chain (EIC) efficiency, which is the core industry chain of EIPs (Wang et al., 2017). Meanwhile, Song et al. (2013) proposed an improved DEA-SBM model named ISBM-DEA to measure the efficiency evaluation of some undesirable outputs like environment pollution. Concurrently, Song et al. (2013) determined a model to measure environmental efficiency and used hierarchical cluster analysis to calculate the undesirable output. Zhao et al. (2017) assessed the comprehensive benefit of ecoindustrial parks by using the grey-Delphi method to quantify the critical role they play in the economy, and then used a hybrid multi-criteria decision making (MCDM) approach. Emergy analysis based on the thermodynamic theory was proven to be an effective and efficient method for evaluating overall eco-efficiency (Hau and Bakshi, 2004). Yong et al. (2010) proposed a new method based on emergy analysis and synthesis to analyze environmental performance and sustainability in Dalian, China. Thirdly, social network analysis (SNA) was used to identify the prevalence of industrial symbiosis linkage, which explores the organization of industrial ecosystems. Ashton (2008) found that in Barceloneta, industrial symbiosis practices were less prevalent than other types of business relations but were still the core of the manufacturing industry network. Doménech et al., (2011) used SNA to understand the social mechanisms, and the role of trust in building and realizing industrial symbiosis exchange. Generally speaking, there were usually many methods used in one article at the same time, and more detailed information can be seen from Table 7 to Table 10; these tables summarize the core of the research, methods, and case studies of each paper in detail.

5 Conclusions and implications By using the methods of bibliometrics and social network analysis, this study produced a quantitative and qualitative analysis of the most popular issues in industrial symbiosis in recent years. The results showed that an increasing amount of research has been devoted to industrial symbiosis in recent years. Results showed that the current industrial symbiosis field had cross-disciplinary characteristics that focused on evolution and development of industrial symbiosis, 17

ACCEPTED MANUSCRIPT operational carriers of industrial symbiosis, driving force mechanisms of industrial symbiosis, and efficiency evaluation of industrial symbiosis. The research of the industrial symbiosis network will greatly promote the industrial green development for the industrial transformation and upgrading of various countries and solve the contradiction between economic development and environmental protection. It can provide important theoretical and methodological support for the realization of human sustainable development. Nowadays, the discussion of the modes of industrial symbiosis is mainly based on two symbiotic units, while we can discuss three or more symbiotic units in the industrial symbiosis network in the future. Though industrial symbiosis has widely spread all areas of social life, most of the studies were limited to the EIP, so the research field of industrial symbiosis should be broadened in the future. The methods used in the studies included life cycle assessment (LCA), material flow analysis (MFA), data envelopment analysis (DEA), multi-criteria decision making (MCDM) and emergy analysis. During the analysis of the evaluation of the industrial symbiosis network, it is often difficult to obtain data so the case study can be used during the using of LCA/MFA/DEA/MCDM to state a clear and full research process. Therefore, it needs some relevant data support to safeguard the development of the industrial symbiosis networks. Therefore, the data mining can be used to analyze the industrial symbiosis network. During the analysis of the evolution of the industrial symbiosis network, a dynamic simulation industry symbiotic evolutionary process model can also be built up.

Acknowledgements This article is supported by the National Social Science Fund Projects (16AGJ004), the Major Bidding Project of Ministry of Education (16JZD028) ,and General Project of Soft Science in Fujian Province(2018R0033).

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38

35 30

30

25 20

19

15 10 5 0

9 2

2

2

12 7

14

12

14

7

2

articles

Figure 1. Number of WoS articles with "industrial symbiosis" in the title Note: 2016* includes articles from 2016 and 2017; there were 22 articles from 2016, and 16 from 2017.

27

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Figure 2. Author co-occurrences

28

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Figure 3. Core edge structure analysis of high-frequency keywords

29

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Figure 4. Cohesive subgroups of high-frequency keywords in industrial symbiosis

30

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Table 1. Authors of industrial symbiosis articles No.

Authors

Appeared times

1

Fujita

13

2

Geng

11

3

Dong

10

4

Fujii

7

5

Ohnishi

6

6

Sokka

5

7

Trokanas

5

8

Boons

5

9

Melanen

5

10

Raafat

5

11

Spekkink

5

12

Cecelja

5

31

ACCEPTED MANUSCRIPT

Table 2. Author co-occurrence matrix Fujita

Geng

Dong

Fujii

Ohnishi

Sokka

Trokanas

Boons

Melanen

Raafat

Spekkink

Cecelja

Fujita

13

8

6

6

6

0

0

0

0

0

0

0

Geng

8

11

5

5

4

0

0

0

0

0

0

0

Dong

6

5

10

5

5

0

0

0

0

0

0

0

Fujii

6

5

5

7

5

0

0

0

0

0

0

0

Ohnishi

6

4

5

5

6

0

0

0

0

0

0

0

Sokka

0

0

0

0

0

5

0

0

4

0

0

0

Trokanas

0

0

0

0

0

0

5

0

0

5

0

5

Boons

0

0

0

0

0

0

0

5

0

0

4

0

Melanen

0

0

0

0

0

4

0

0

5

0

0

0

Raafat

0

0

0

0

0

0

5

0

0

5

0

5

Spekkink

0

0

0

0

0

0

0

4

0

0

5

0

Cecelja

0

0

0

0

0

0

5

0

0

5

0

5

32

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Table 3. Analysis of journal-published literature No. 1 2 3 4 5 6 7 8 9 10 11 12

Journals

Published Papers

Journal of Cleaner Production

61

Journal of Industrial Ecology

29

Resources Conservation and Recycling

8

Business Strategy and the Environment

5

Waste and Biomass Valorization

4

Environmental Research

Science

and

Pollution

4

Energy Policy

4

Measurement & Control

3

Fresenius Environmental Bulletin

3

Environmental Science & Technology

3

Journal of Environmental Management

3

Computers & Chemical Engineering

3

33

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Table 4. Number of occurrences of IS keywords No.

Appearance times

Keywords

1

Industrial symbiosis

2

Industrial ecology

3

Eco-industrial park

4

Sustainable development

5

China

6

Life cycle assessment

7

Recycling

8

Industrial network

9

Industrial ecosystem

10 11 12 13 14 15 16 17 18

No.

102 56 22 12 12 11 11

symbiosis 10 10

Keywords

Appearance times

19

National Industrial Symbiosis Programme

4

20

Ontology

4

21

By-product synergy

4

22

Eco-efficiency

3

23

Eco-innovation

3

24

Resilience

3

25

Emergy analysis

3

26

Environmental performance

3

27

Resource synergies

3

29 30

Agglomeration economies Geographic proximity Institutional capacity

3 3

7

31

Innovation

3

7 6 5 5 5

32 33 34 35 36

Industrial cluster Industrial park Finland Resource efficiency Eco-industrial network

3 3 3 3 3

By-product

8

28

Circular economy Waste Eco-industrial development Network analysis Synergy Material flow analysis By-product exchange Energy

8 8

34

3

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Table 5. Co-word matrix formed by keywords (part)

Industrial symbiosis Industrial ecology Eco-industrial park Sustainable development China Life cycle assessment Recycling Industrial symbiosis network Industrial ecosystem

Industrial symbiosis

Industrial ecology

Eco-industrial park

Sustainable development

China

Life cycle assessment

Recycling

102 30 15

30 56 12

15 12 22

8 5 3

10 2 2

8 4 0

4 6 1

Industrial symbiosis network 4 1 1

8

5

3

12

0

0

0

2

0

10 8 4

2 4 6

2 0 1

0 0 0

12 1 0

1 11 0

0 0 11

0 0 0

1 1 1

4

1

1

2

0

0

0

10

0

4

5

3

0

1

1

1

0

10

35

Industrial ecosystem 4 5 3

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Table 6. NrmDegree, nCloseness, nBetweenness of keywords Keywords Industrial symbiosis Industrial ecology Eco-industrial park Industrial ecosystem Sustainable development

NrmDegree

nCloseness

nBetweenness

Keywords Network analysis Institutional capacity

NrmDegree

nCloseness

nBetweenness

100

100

38.293

20

55.556

0.266

88.571

89.744

21.640

20

55.556

0.126

51.429

67.308

4.585

Synergy

17.143

54.688

0.118

37.143

61.404

1.413

Innovation

17.143

54.688

0.168

34.286

60.345

2.090

17.143

54.688

0.196

Recycling

34.286

60.345

1.006

17.143

54.688

0.182

China Life cycle assessment Resource efficiency By-product

34.286

60.345

2.186

17.143

54.688

0.105

31.429

59.322

1.160

14.286

53.846

0

28.571

58.333

0.675

14.286

53.846

0.182

28.571

58.333

0.969

11.429

53.030

0

Waste

28.571

58.333

0.563

11.429

53.030

0

25.714

57.377

0.587

Resilience

11.429

53.030

0.034

25.714

57.377

0.324

Agglomeration economies

11.429

53.030

0.056

25.714

57.377

0.594

Industrial park

8.571

52.239

0

25.714

57.377

0.482

Resource synergies

8.571

52.239

0

22.857

56.452

0.762

Energy

8.571

52.239

0.056

22.857

56.452

0.242

Eco-innovation

5.714

51.471

0

22.857

56.452

0.269

Ontology

2.857

50.725

0

Circular economy Eco-industrial development Geographic proximity National Industrial Symbiosis Programme Material flow analysis By-product exchange By-product synergy

Industrial cluster Industrial symbiosis network Finland Eco-industrial network Emergy analysis Eco-efficiency Environmental performance

36

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Table 7. Papers concerned with evolution and development of industrial symbiosis Author

The core of the research

Methods or Models

Case Studies

Saraceni et al., 2017

How to diagnose industrial symbiosis?

Brazilian industrial clusters

Yu et al., 2014

the evolution of the IS research field

Paquin 2012

How different conditions and approaches influence IS evolution?

pilot testing model; multi-criteria decision analysis (MCDA) Bibliometric Methods (citation analysis, Co-citation analysis, coauthorship analysis, network analysis) data acquisition and clean up grounded theory

et

al.,

Chertow, 2007

How to uncover Industrial Symbiosis

A historical view of the motivations and means for pursuing symbiosis.

Gonela & Zhang, 2014

design of the optimal industrial symbiosis system to improve bioethanol production

Mattila 2012

methodological issues encountered in application of LCA to the various research questions arising from IS studies. identity the prevalence of industrial symbiosis linkages in Barceloneta A proposal for a conceptual framework based upon a comprehensive literature review introduced “industrial ecosystems” as an important solution for achieving productive use of waste and by-products

A decision framework that combines the Linear Programming models and large scale Mixed Integer Linear Programming model; Sensitivity analysis life cycle assessment(LCA); environmentally extended input-output analysis

et

al.,

Ashton, 2008 Boon, 2011

Frosch Gallopoulos, 1989

&

37

social (SNA)

network

Comprehensive review

analysis literature

The National Industrial Symbiosis Programme (NISP) 15 proposed projects that the U.S. President’s Council on Sustainable Development begin in the early 1990s; 12 projects about selforganization A system of five candidate plants

Reference case

Barceloneta

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Table 8. Papers concerned with carriers of industrial symbiosis Author Yong 2002

Operational Carriers et

al.,

Roberts, 2004 Chertow et al., 2005 Beers 2007

et

al.,

Nisbet et al., 1998

Behera et al., 2012 Zhu et al., 2007

Leong et al., 2017 Li and 2017

Xiao,

Ashton and Chopra, 2017

Zhang et al., 2010 Doménech and Davies, 2011

Wang 2017

et

al.,

Wu et al., 2017 Fraccascia al., 2017

et

EIP: the functions and roles of scavengers and decomposers in the natural ecosystem EIP: the planning and development of ecoindustrial park EIP: quantifies economic and environmental costs and benefits for the symbiosis participants EIP: comparative and assessment of Australia’s major heavy industrial region EIP: analyze the extent to which partnerships and networks had formed and identify the factors which prevented their formation EIP: retro-fitting the conventional industrial complexes EIP: analyze the GG which has been developing and implementing the IS strategy for more than four decades. EIP: predefined criteria for building an EIP EIP: the structure and function based on topological structure and eco-logical features in EIP SOIEs: the dynamics driving growth (life) and demise (death) of self-organized industrial ecosystems the practice of pilot EIPs in China ISN: mechanisms of industrial symbiosis network

Methods or Models

Case Studies Burnside Park

Industrial

Synergy Park from environmental, economic, and regulatory perspectives of the individual participants

The nascent industrial symbiosis network in Guayama, Puerto Rico Kwinana (Western Australia) and Gladstone (Queensland) The Sarnia-Lambton area of southern Ontario

the R&DB framework

Thirteen symbiotic networks in Ulsan The Guitang (GG),

a multi-objective optimization approach; AHP complex network theory

hypothetical models

A chilled and cooling water network case study Ningdong Coal Chemical EcoIndustrial Park

network

qualitative methods; a modeling framework; social network analysis; the concept of embeddedness; grounded theory;

ISN: Economic fluctuations are the most important driving factor in the evolution of CISN vulnerability ISN: the stability and reliability of ISN

a vulnerability analytical framework; an improved cascading failure model

ISN: the resilience of ISNs

design a new resilience index

38

Group

The NPEIPP and the NPCEZP Kalundborg (Denmark); The National Industrial Symbiosis Programme (UK); Sagunto Industrial Area(Spain); Three coal ecoindustrial parks in China A hazardous waste symbiosis network Jinan City in China; Kalundborg in Denmark

ACCEPTED MANUSCRIPT Table 9. Papers concerned with driving force mechanisms of industrial symbiosis. Authors

The driving mechanism

Roberts, 2004

technical and economic capacity

Yap & Devlin ,2017 Taddeo et al., 2012; Simboli et al., 2014; Morgante et al., 2017

Market Forces; the state; civil society; structural factors and other contingent factors, non-technical factors, location factors, dynamics of changes, factors embedded in people and organizations technical, economic and legal point of view economic motivation (upstream or downstream operational performance) the dynamics of industrial symbiosis Network and innovation

Garner et al, 1995 Jacobsen, 2006 Boons et al., 2011 Posch et al., 2011 Mirata et al., 2005 Costa et al., 2010

force

Methods or Models

An Australian study

an framework

interpretative

Kalundborg a theoretical framework A real trans-disciplinary discourse between IE researchers

Network and innovation

Jiao & Boons, 2014

The influence of policy

comparative statics research methods; causal model; a database that was developed for the Dutch policy program Duurzame Bedrijventerreinen (DBT)

Lehtoranta al., 2011

et

Impact of SCP instruments

Fraccascia al., 2017

et

Efficacy of landfill tax and subsidy policies influence the emergence of selforganized ISNs. Uncovering existing symbiosis

Chertow M R, 2007

In the Italian Region of Abruzzo

A framework

the middle-out approach

Boons & Spekkink ,201 2

case

Analytic framework

Policies and policy instruments introduced by governmental agencies provide objectives and incentives to businesses The principles of industrial ecology rational production structures; raw materials advantages; technical supports and correct diversification) Institutional capacity

Deutz & Ioppolo, 2015 Yang & Feng, 2008

Case Study

The first IS programme in Sweden IS in Portugal

Life-cycle assessment Nanning Sugar Co., Ltd

the conceptual framework;

policy

An agent-based model (ABM); the agents’ dynamics; Simulation Analysis Contrast analysis

39

233 projects that were initiated under the Dutch stimulation program for ecoindustrial parks (EIPs). The development of the concepts of Circular Economy (CE) and Eco-Industrial Park (EIP) in China An industrial symbiosis system centered on a Finnish pulp and paper A real case study concerning a threeindustry ISN. Case study from 15 proposed projects in the USA

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Table10. Papers concerned with efficiency of industrial symbiosis Author Yong 2010

et

al.,

Efficiency of the Industrial Symbiosis

Methods or Models

Case Study

EIP’s eco-efficiency

emergy analysis and synthesis based on Thermodynamics and general system theory emergy analysis

DEDZ (Dalian Economic Development Zone)

Fan et al., 2017

the performance industrial symbiosis

Qiang et 2011 Daddi et 2017

the total impacts of industrial symbiosis the environmental benefits of industrial symbiosis the total environmental impacts of an industrial symbiosis the environmental impacts of an industrial ecosystem the assessment of the exchange of byproducts,

life cycle assessment(LCA)

Ecological industry chain efficiency Evaluating the comprehensive benefit of eco-industrial parks

data envelopment analysis (DEA) a hybrid multi-criteria decision making (MCDM); superiority linguistic ratings and entropy weight; fuzzy-VIKOR for ranking alternatives a counterfactual method

al., al.,

Mattila et al., 2010 Sokka 2015

et

al.,

Zhang et al., 2017

Wang et 2017 Zhao et 2017

al., al.,

of

Salmi, 2007

test the link between IS and eco-efficiency improvement

Park & Behera, 2014

The performance of IS networks in an EIP.

Geng 2014

et

al.,

Sokka 2011

et

al.,

The interrelations between economic development and environmental protection Industrial Symbiosis Contributing to More Sustainable Energy use

life cycle assessment(LCA) input-output life assessment(LCA)

cycle

life cycle assessment(LCA) life cycle assessment(LCA)

a framework for application of the eco-efficiency concept as an evaluation tool for IS networks in order to translate the ecoefficiency ideas into reality. an emergy analysis-based method; emergy analysis

Hefei economic and technological development area Jinqiao EIP An Italian tannery cluster located in Tuscany. A forest industrial symbiosis, situated in Kymenlaakso, Finland a pulp and paper The synthetic gas chemical industry chain of Songmudao chemical industrial park, Dalian, China Xinfa EIP Six representative eco-industrial parks in China.

Five mining operations in the Kola Peninsula in Northwest Russia

Shenyang Economic and Technological Development Zone An example from the Forest Industry in Kymenlaakso Finland

40