Abstract. Industrial park development has become a critical strategy to promote economic ..... Eco-industrial development (EID) at DEDA started in early 2000.
Accepted Manuscript An emergy-based hybrid method for assessing industrial symbiosis of an industrial park Liu Zhe, Geng Yong, Park Hung-Suck, Dong Huijuan, Dong Liang, Fujita Tsuyoshi PII:
S0959-6526(15)00525-9
DOI:
10.1016/j.jclepro.2015.04.132
Reference:
JCLP 5501
To appear in:
Journal of Cleaner Production
Received Date: 16 January 2015 Revised Date:
10 March 2015
Accepted Date: 29 April 2015
Please cite this article as: Zhe L, Yong G, Hung-Suck P, Huijuan D, Liang D, Tsuyoshi F, An emergybased hybrid method for assessing industrial symbiosis of an industrial park, Journal of Cleaner Production (2015), doi: 10.1016/j.jclepro.2015.04.132. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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An emergy-based hybrid method for assessing industrial symbiosis of an industrial park
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Liu Zhea, Geng Yong b, a,*, Park Hung-Suck c, Dong Huijuan a,d, Dong Liangd, Fujita Tsuyoshid a Key Laboratory of Pollution Ecology and Environment Engineering, Institute of Applied Ecology, Chinese Academy of Science, Shenyang, Liaoning Province (110016), PR China b School of Environmental Science and Technology, Shanghai Jiao Tong University, Shanghai (200240), PR China c Center for Clean Technology and Resource Recycling, University of Ulsan d National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
Abstract
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Industrial park development has become a critical strategy to promote economic development in many countries since 1980s. Particularly, China is one key country to adopt such a method on enhancing its industrialization. However, rapid industrial park development has resulted in many issues, including resource depletion, environmental emissions and pressure on responding climate change. Industrial symbiosis has been proved that it has positive impacts on promoting resource utilization efficiency. Therefore, effective performance evaluation can help recognize the key barriers on industrial symbiosis of industrial parks so that more appropriate policies can be raised by considering the local realities. Emergy synthesis is one feasible method on addressing the contribution of local ecosystem to industrial park development due to its innovative perspectives and mature methodology, while impact, population, affluence, technology (IPAT) formula and index decomposition analysis (IDA) are suitable for identifying and quantitatively calculating the key impacts of various factors. This paper integrated emergy synthesis, IPAT formula and IDA methods together to investigate the impact factors of industrial symbiosis in an industrial park. A case study approach at Dalian Economic Development Area (DEDA) was carried out so that such an effort can be tested. The results show that waste reutilization by industrial symbiosis in DEDA increased by 3.0E+20 seJ from 2006 to 2010. Technological pressure for waste utilization by industrial symbiosis and energy structure of an industrial park had made negative effects on the waste reutilization from 2006 to 2010 in DEDA, while the efficiency of non-renewable energy consumption and emergy scale of an industrial park played positive effects on the waste reutilization. Among the four impact factors, technological pressure for waste utilization and the efficiency of non-renewable energy consumption took more significant impacts on the industrial symbiosis of DEDA than the other two factors, which contributed -1.92E+21 seJ and 2.18E+21 seJ for the waste reutilization by industrial symbiosis respectively. Key words: Emergy analysis; IPAT formula; Index decomposition analysis; Industrial park; Industrial symbiosis
1. Introduction
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Over the past few decades, a large amount of resource has been globally consumed due to rapid economic growth and poor resource management. In order to respond to this urgent issue, industrial ecology was raised as an innovative strategy to decouple economic growth and resource consumption by encouraging industrial systems to mimic natural ecosystems (Geng and Cote 2002). One key element of industrial ecology, namely industrial symbiosis (IS), has gained increasing attention, which can be categorized conceptually as collective resource optimization based on by-product exchanges and utility sharing among disparate yet typically co-located facilities. IS was defined as a physical exchange of materials, energy, water, and/or by-products to promote resource utilization efficiency between traditionally separate industries with the intent of promoting collective competitive advantage (Chertow, 2000). Through nearly twenty years’ development, industrial symbiosis has grown up from a curiosity to a meaningful business strategy and has been practiced at various scales, including eco-industrial parks (Cote & Cohen Rosenthal, 1998; Boix et al., 2012), industrial ecosystems (Cote & Hall, 1995; Wallner & Narodoslawsky, 1996; Geng et al., 2007a; Bain et al., 2010), industrial recycling networks (Schwarz & Steininger, 1997; Zhang et al., 2011), and by-product synergies (Forward &Mangan, 1999; Zhang et al., 2010). In China, the central government planned and developed economic and technological zones as one way of developing industrial parks to stimulate economic development across the whole country in the early 1980s (Geng and Zhao 2009). Particularly, due to its comprehensive advantages on attracting foreign investment, improving technological abilities, and concentrating industrial activities through appropriate zoning, industrial park has become one key economic development strategy by the Chinese governments at different levels (Geng and Cote 2003, 2004). Through nearly thirty years’ development, industrial parks have greatly contributed to national economic development, leading to national economic transformation with higher economic efficiency. For instance, in year 2011, industrial parks at national level completed a gross domestic production (GDP) with a value of 47 million US dollars per square kilometer, 59.2 times higher than the national average level and 7.9 times higher than the average level of 36 major cities. However, rapid development of industrial parks also created some problems, such as resource depletion and environmental pollution, and very recently increasing pressure on responding climate change. In order to deal with these emerging issues, Ministry of Environmental Protection of People’s Republic of China (MEP) initiated eco-industrial park (EIP) project in 2002 (Geng et al., 2009). To date, there are 85 EIPs approved by MEP (see fig 1). However, due to a lack of consideration of IS, these indicators have some common problems, such as a lack of prevention-oriented indicators, weak connection and interaction, a lack of interacted information (Geng et al., 2009, 2013a; Su et al., 2013). Therefore, it is critical to establish convincible indicators so that the overall effects of IS in these industrial parks can be scientifically evaluated. Academically, various studies on IS have been carried out. Regarding the impact factors of IS, previous studies focused on the description of the barriers of IS. For
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instance, Sakr et al., (2011) studied upon the Egyptian context for the identified EIP success and limiting factors, including the creation of symbiotic relationship, information sharing and awareness, financial benefits, organizational structure, and legal and regulatory framework. Taddeo et al., (2012) pointed out that the history of the long-standing industrial cluster influenced the presence and the role played by some key drivers, especially local community, which proved to be crucial in preventing the full implementation of the EIP. In general, the impact factors of IS were concluded such as the distances between various enterprises, scales and diversity of industrial clusters, economic conditions, etc (Lowe et al. 1995; Chertow 2000; Chertow 2003; Ehrenfeld and Gertler 1997). With regard to the evaluation of IS, most of previous studies focused on the efficiency evaluation. For instance, Zhang (2010) evaluated the eco-efficiency of an industrial park by applying material flow analysis (MFA). Wesley (2011) analyzed the waste emission scenarios in a mining industrial area in Peru by employing life cycle assessment (LCA). Dong et al., (2013) evaluated the carbon footprints generated by the IS within an industrial park in Shenyang, China. However, these studies mainly evaluated the efficiency of waste utilization improved by IS, but did not address those impacts factors driving the success of IS, such as the contribution of local ecosystem, and the appropriate identification of various inputs. Also, any single evaluation method cannot provide a clear guidance on how to improve IS since none of them was designed to the systemic, closed-loop, feedback feature of IS. Plus, these methods highlight the individual parameters of resource use and system metabolism, but disregard other parameters or driving forces, leading to incomplete assessment of the overall performance of IS. Under such a circumstance, it is critical to seek an innovative method by combining the advantages of different methods so that more feasible IS policies can be raised to address waste and emission management, reuse and recycle strategies. This paper tries to fill such a research gap by raising an emergy-based hybrid model, in which emergy analysis, IPAT formula and IDA (Index Decomposition Analysis) methods are merged. A case study approach is employed in order to test its applicability. Dalian Economic Development Area (DEDA), a larger industrial park in Dalian, Northeast China, was selected as one case industrial park since this park has made a great effort to promote the application of EIP. The whole paper is organized as below: after this introduction section, we first present our research methodology, including how to account emergy flows and how to apply IPAT formula and conduct IDA, how to collect and treat data. We then describe our case study park, especially introducing its IS stories. Our main focus is to present research results and have a detailed discussion on these results. Finally we draw our conclusions.
2. Methodology 2.1 Emergy accounting Emergy was defined as “the available solar energy used up directly and indirectly to make a service or product” (Odum, 1996). Rooted in ecology, thermodynamics, and general systems theory, emergy is the sum of all available energy inputs directly or indirectly required by a process to generate a product (Geng et al., 2013a). Emergy
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synthesis provides an integrated evaluation method for ecological-economic systems and has been successfully applied to systems of different scales (Brown and Ulgiati, 2004). In recent years, emergy analysis has been applied in industrial parks especially in the field of IS. For instance, Wang et al. (2005) conducted their emergy analysis study at Shuozhou industrial park, in which they particularly studied how a local power plant played a key role on promoting the efficiency of IS within one eco-industrial park. Geng et al., (2010) proposed their methodology on how to account one industrial park by employing emergy synthesis, in which they raised how to calculate the transformities of various wastes based upon their physical and chemical properties. Zhang et al., (2011) applied emergy analysis to evaluate the impact of waste exchanges on the sustainability of industrial systems. Geng et al., (2014a) further studied how to quantify IS by emergy synthesis at Tiexi industrial park in Shenyang, China. Liu et al. (2014) applied the emergy method to the Shenyang Economic Technological Development Area to uncover the efficiencies among different industrial clusters etc. In this study, we follow the emergy accounting procedure proposed by Geng and his colleagues (2010, 2014). Particularly, we focus on waste reutilization induced by IS to evaluate the waste efficiency of an industrial park. The transformity of the reutilized waste is equal to the raw materials, no matter it is one by-product or co-product. For instance, the calculation method for waste reutilization is as follows:
Transformity wood = Transformity reused
(1)
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2.2 IPAT equation Ehrlich and Holdren (1971) proposed the famous IPAT equation, in order to uncover the relationships of environmental impact (I), population (P), affluence (A) and technology (T). This equation has a normal formula, namely, I=P×A×T (1). In order to further quantify the relationship related with waste reutilization, this original equation is transformed into equation (2). Wr E GDP . . .U GDP U E
(2)
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Wr =
Where Wr represents the waste reutilization of an industrial park induced by IS. E represents non-renewable primary energy inputs, such as those from coal, petrol, natural gas, and purchased electricity etc. U represents the total emergy of an industrial park. GDP represents the total gross domestic production of an industrial park. In this equation, we deleted the population presented in the traditional equation because most wastes were generated by industrial activities in an industrial park, due to the scale of industrial agglomeration, but not due to the population. In this regard, we choose U to represent P in the equation (1) since U represents the scale of an industrial park and has a direct relation with waste generation and reutilization. Equation 2 is indicative only, but leads to the quantitative impact explanation of
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Wr (3) GDP
Yeffect = U (6)
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Here Teffect represents waste reutilization per unit GDP. It is a balance indicator, which could be used to measure the technological development for waste reutilization induced by IS. A bigger Teffect, means a bigger distance of waste reutilization induced by IS. This indicator can reflect the progress of IS due to technological advancement. Similarly, we define the equation 4, namely: E (4) Eeffect = U Here Eeffect represents the ratio of non-renewable primary energy input to the total emergy of an industrial park and can be used to measure energy structure of an industrial park. It demonstrates how much energy consumption contributes to waste reutilization or to the development of IS. As such, we define the economic-energy intensity indicator in equation 5: GDP (5) S effect = E Here Seffect represents the ratio of GDP generation per energy unit. This indicator is used to measure the efficiency of energy consumption. Especially, it can measure non-renewable energy consumption in the field of energy sector since a higher value indicates a higher energy efficiency or the increase of non-renewable energy utilization. Finally, we define the equation 6, namely:
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Here Yeffect represents the total emergy of an industrial park, which is used to reflect the production scale of an industrial park.
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2.3 Index Decomposition Analysis (IDA) Decomposition analysis has been widely used to analyze different drivers of industry-related emissions. There are two main decomposition analysis methods, namely, structural decomposition analysis (SDA) and index decomposition analysis (IDA). SDA is employed based on the available Input-Output table, capable of quantifying fundamental “sources” of changes in a wide range of variables including economic growth, energy use, material intensity of use, and pollution emissions (Caster & Rose, 1998). One of the first SDA studies (Leontiel & Ford, 1972) examined primary air pollutants generated by the US economy, in which a set of pollution coefficients appended to I-O tables. Since then, it has been widely used in energy and environmental topics in recent years (Zhang & Qi, 2011). IDA is another decomposition analysis method raised in the late 1970s in order to study the impact of changes in product mix on industrial energy demand (Ang &
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Zhang, 2000). To date, over 200 publications have been published in this field and five main application areas have been identified, including (a) energy demand and supply, (b) energy-related gas emissions, (c) material flows and dematerialization, (d) national energy efficiency trend monitoring, and (e) cross-country comparisons (Ang 2004). Several IDA methods have been proposed previously. Two most often used IDA methods are the Laspeyres index method and the logarithmic mean divisa index (LMDI) method using an arithmetic mean weight function (Ang et al., 1998). Both have been widely used to track economy-wide and sectoral energy efficiency trends. An integral part of this application is to identify the drivers of energy use for the energy consuming sector (Ang & Xu, 2013).
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Compared to SDA, IDA has some advantages. For instance, IDA doesn’t need an input-output table, while SDA relies on such a table. In China, an input-output table is usually not available at industrial park level. Also, no statistical data are available for recording product and consumption activities. Consequently, we decide to choose the Laspeyres index method for our analysis in this study.
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According to equation (1), the change of waste reutilization for one industrial park between a base year 2006 and a target year 2010, denoted by Wr, can be decomposed into the following determinant factors: (a) technology improvement effect (denoted as Teffect), (b) industrial structure effect (denoted as Eeffect), (c) energy intensity effect (denoted as Seffect), and (d) emergy scale effect (denoted as Yeffect). Thus, the difference is decomposed into its components in additive forms, as illustrated in equation 7, namely: Wr=Wr2010-Wr2006 (7) 1 1 1 Teffect = ∆TSEY + ∆T ( SY∆E + SE∆Y + YE∆S ) + ∆T ( S∆E∆Y + Y∆S∆E + E∆S∆Y ) + ∆T∆S∆E∆Y 2 3 4
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1 1 1 S effect = ∆STEY + ∆S (TY∆E + TE∆Y + YE∆T ) + ∆S (T∆E∆Y + E∆T∆Y + Y∆T∆E ) + ∆S∆T∆E∆Y 2 3 4 Eeffect = ∆ETSY +
1 1 1 ∆E (TS∆Y + YS∆T + YT∆S ) + ∆E (T∆S∆Y + S∆T∆Y + Y∆S∆T ) + ∆E∆T∆S∆Y 2 3 4
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1 1 1 Yeffect = ∆YEST + ∆Y ( ES∆T + ET∆S + ST∆E ) + ∆Y ( E∆S∆T + S∆E∆T + T∆E∆S ) + ∆Y∆E∆S∆T 2 3 4
2.4 Boundary definition and data collection In order to study an industrial park, boundary confirmation of an industrial park is the first step. Usually, we take the administrative boundary of an industrial park, which means that there is always a planned area for the development of an industrial park. Therefore, geological boundary of this planned area should be considered the boundary of an industrial park. Inside the boundary of an industrial park, the data of natural condition, industrial structure and production, material flow and societal development should be investigated. Data sources for such a study are diversified. Therefore, different data collection methods should be adopted, including face to face interviews, informal meetings, questionnaire surveys and literature investigations, etc. In this study, we did the field
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3. Case study park
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survey to the administration office of DEDA, where we held the informal meeting with the stakeholders including local officers, investors and even local citizens to acquire the direct information of DEDA. Also, we investigated the main tenant companies, asking them to provide information regarding to their raw material consumption, product yield etc. In addition, we collected useful data from local annual statistical documents and other related governmental documents. After receiving all the data, we hosted another workshop to further verify the accuracy of these data and collect more information that cannot be obtained from official documents, with the great help of local administrative officials. Then all the information and data are categorized into different groups, including renewable resource data (R) such as sunlight, rainfall etc, non-renewable data (N) like ore consumption, input data (F) like steel input, food input and labor and service (L&S) condition of an industrial park. These indicators are the composition indicators for emergy analysis at the industrial park level and can be used for calculating the total emergy U, namely U= R+ N + F + L&S (Vassallo et al. 2007). Another key issue is to determine transformities, the coefficients used to transform raw units, e.g., joules, grams, etc. into solar emergy. In this regard, the relevant references on transformities were reviewed and suitable transformities were directly adopted. For those transformities are not available, we re-calculated their values based upon their physical and chemical properties. The units of the transformities are most often solar emjoules per joule (seJ/J) or solar emjoules per gram (seJ/g). Through transformities, material and energy flows were transformed into emergy flows. In order to help identify input and output flows, system components, interactions among components etc, an emergy system diagram is needed. In this diagram, various flows are described as clockwise from left to right based upon their increasing transformities. This diagram would help simplify all the emergy flows so that the existing hierarchy of components and flows in this diagram can be easily understood.
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3.1 A brief introduction of DEDA Dalian Economic Development Area (DEDA) is the first approved national industrial park by the State Council of China and was established in 1984. DEDA is located in the southeast part of Liaoning province in Northeast China (See fig 2). The average annual rainfall in DEDA is 550-950 millimeters and the total annual length of sunlight on this site is 2500-2800 hours. DEDA provides essentially the same preferential policies, incentives, and flexible measures as other special economic zones in China. It has a planned area of nearly 72 km2, including separate sections for industrial development, mixed residential, financial and commercial uses (Geng et al., 2010). DEDA has many transportation advantages since it is close to the Dalian port, the biggest port in the northeast region of China. The comprehensive assessment shows that DEDA ranks No. 7 among all the national industrial parks. The GDP of DEDA accounts for over one fourth of the entire Dalian city, with a figure of 24.013 billion USD in 2012. There are six main industrial clusters in DEDA, such as
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petro-chemical industrial cluster, manufacturing industrial cluster, metallurgy industrial cluster etc. 3.2 Eco-industrial development in DEDA Eco-industrial development (EID) at DEDA started in early 2000. At that time UNEP-IETC initiated its demonstration project in four Chinese leading economic development areas (Dalian, Tianjin, Yantai and Suzhou), named as "Environmental Management in China's Industrial Park" (Chiu and Geng 2004). Its location on a peninsula with significant plant and sea life, make it even more sensitive to environmental burdens. Due to its aesthetic qualities and temperate climate, it is also a destination for tourists. Maintaining a pleasant and clean environment is thus beneficial to its economic well being. Given these regional characteristics the Dalian municipal administration has had a great desire to implement an “eco-city” program so as to improve the region's overall eco-efficiency. For example, almost 100 industrial plants have been closed because of economic inefficiency and environmental degradation (Geng et al., 2009a, b).
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DEDA has established six pillars of petro-chemicals, equipment manufacturing, IT industry, aviation metallurgy, ocean shipping industry, and bio-medicine. Examples of famous multinational companies include West Pacific Petrochemical, Intel, FAW, Toshiba, Canon, Volkswagen, etc. At present, industrial clusters of petro-chemicals, equipment manufacturing and IT industry are moving to the target of 100 billion US$ industrial product value per year while industrial clusters of aviation metallurgy, ocean shipping industry, and bio-medicine are forward to 50-100 billion US$ industrial product value per year. In order to develop eco-industries, local government carried out the strategies as the following eight aspects: integrating the investment and developing circular economies together, optimizing industrial structure to promote the levels of sharing resource and energy, improving the basic manufactures, carrying out national laws and regulations regarding circular economy development, innovating the study of mechanism of circular economy, implementing international cooperation, carrying out the activities of saving energy in each area and increasing the ratio of renewable resource, conducting environmental assessment. In doing so, the connection of industrial symbiosis has been formed. Figure 3 shows the current industrial symbiosis progress in DEDA.
4. Results and Discussions
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Fig4 The diagram of emergy flow of DEDA
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The emergy diagram in DEDA is shown in fig 4. By applying emergy analysis method, we present our emergy results for the years of 2006 and 2010 in table 1. In order to keep our results more appropriate, we refer to the new biosphere baseline (Odum, 2000), which means that all the emergy values calculated prior to that year were multiplied by 1.68 (the ratio of 15.83 / 9.44).
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Table 2 lists a comparison of key data in DEDA for the years of 2006 and 2010. The main reason to choose these two years is that the year of 2006 is the first year of China’s 12th five-year plan, while the year of 2010 is the last year of its 12th five-year plan. Such a comparison can help verify if all the planned targets can be realized and therefore may provide valuable policy insights for future targets-making. In the reality, DEDA experienced a rapid growth. Its GDP figures increased from 9.07 billion USD in 2006 to 19.4 billion USD in 2010, with an average annual growth rate of 20.9%. However, such a rapid growth was based upon a large amount of resource consumption, especially non-renewable resources, leading to the total emergy increased from 9.73E+22 in 2006 to 1.04E+23 in 2010. DEDA was selected as one of the earlier national EIP projects. During 2006-2010, significant progress in promoting circular economy had been made. For instance, the successful application of 3R (reduction, reuse and recycle) had reduced the total wastes and increased waste reutilization, leading to the amount of waste reutilization increased by nearly 3E+20 seJ (see table 2). However, few efforts were made in terms of optimizing energy structure and reducing energy consumption, leading to non-renewable primary energy consumption increased by 8E+20 seJ due to increasing energy demand.
In order to further identify the key impact factors inducing DEDA’s rapid
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development, a Laspeyes index decomposition analysis was conducted. Table 3 lists the results, while figure 5 demonstrates a comparison of four impacts factors for DEDA’s industrial symbiosis during the period of 2006-2010. It can be found that the Teffect and Eeffect had made negative effects on the waste reutilization , while Seffect and Yeffect played positive effects on the waste reutilization. Among the four impact factors, Teffect and Seffect had higher impacts than the other two effects.
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Teffect, representing waste reutilization per GDP, is a balance indicator between waste reutilization and economic development of DEDA. A negative Teffect indicates that waste reutilization does not catch up with the whole economic development. In the reality, although DEDA made a great effort on promoting industrial symbiosis through 3R and the total waste reutilization emergy increased from 2.56E+21 in 2006 to 2.86E+21 in 2010, due to the rapid economic development in DEDA (with an average annual GDP growth rate of 20.9%), the total emergy from domestic and foreign investment increased faster, which means that the whole industrial park consumed more virgin materials. Therefore, more innovative efforts should be initiated so that more synergy opportunities can recognized. Eeffect is the ratio of non-renewable primary energy (NPE) input to the total emergy of an industrial park and can be used to measure energy structure of an industrial park. A negative value of Eeffect indicates that energy structure of one industrial park does not move toward renewable direction and fossil fuels are still the dominating energy sources. With regard to DEDA, their EIP efforts mainly focused on dematerialization, but with a few attentions on optimizing energy structure. The main reason is that more tenant companies were attracted and need more energy supply due to rapid development of this industrial park. Also, more workers moved to this park, leading to more residential buildings. However, it is difficult for the park management to find alternative energy sources. Therefore, coal consumption continuously increased since it’s the cheapest and most available energy source in this region. Nevertheless, the recruitment of new tenant companies is coming to the end since the available land is almost sold out. This means that total energy demand will become stable. Consequently, the park management should consider how to optimize their energy structure by encouraging the use of renewable energy sources. In this regard, solar power, wind power, geothermal power, should be facilitated by considering the local endowments. Particularly, the use of geothermal power through the application of ground source heat pump (GSHP) should be supported. GSHP has been widely promoted in the Northeast China and has generated significant economic and environmental benefits. For instance, a case study in Shenyang helped reduce 3.4 million tons CO2 emissions, 3781.5 tons of SO2 emissions, and 2775 NOx emissions during 2006-2010, leading to significant co-benefits (Geng et al., 2013b). Since Shenyang is the capital of Liaoning and is close to Dalian (less than 400 km), it is necessary for the DEDA management to send their technique delegation to Shenyang to learn their experiences on promoting GSHP.
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Seffect is an economic-energy intensity indicator since it represents GDP generation per energy unit. A positive Seffect indicates that the increase of energy efficiency has a positive impact on the whole industrial park. In this regard, DEDA made a lot of efforts on implementing energy saving policies during the study period. For instance, a lot of low efficient coal-burning boilers were dismantled and replaced by more efficient district heating system. Also, DEDA conducted several workshops on promoting energy efficient technologies and green technologies, resulting in that many companies initiated their own energy saving programs. Moreover, several companies worked together to seek potential energy cascading opportunities so that the surplus heat from one company can be used for the operation of another neighboring company. Even though, in order to further improve the overall energy efficiency, more energy saving activities should also be initiated, such as energy audit, energy cascading, energy performance contract and energy saving technologies. In addition, more rigid local standards should be raised, such as green building criteria, corporate energy consumption indicators. Yeffect represents the emergy utilization scale of an industrial park. A positive Yeffect indicates that the total emergy utilization has a positive impact on the whole park. In the case of DEDA, this means that the increased emergy scale had created more chances on improving industrial symbiosis, resulting in more waste reutilization. With their recruitment efforts, more tenant companies operated in DEDA and created more diversified industrial sectors, enhancing the sustainability of symbiotic relationships and maintaining the stability of the whole system (Geng and Cote 2007). In this regard, if one company drops out of the industrial symbiosis network, there is usually a backup company dealing in the same material that can fill the respective niche and allow the web to remain intact. Therefore, park management should continue to recruit more companies with different industrial types so that more byproducts exchanges among different firms can occur. Such efforts can also reduce business risks, mitigate pollution, improve public images, and foster an eco-systemic approach at the whole park (Geng & Cote, 2007). However, necessary infrastructure should be further built up in order to facilitate such byproducts exchanges, such as pipelines, roads, and information platform (database on material, energy and water uses).
5. Conclusion
Rapid development of industrial parks created more business and employment opportunities, but also brought many challenges, such as resource depletion, environmental emissions and increasing pressures on responding climate change. In order to prepare sustainable industrial park management policies, it is critical to propose feasible evaluation methods so that decision-makers can identify the key problems and prepare appropriate development strategies. This paper aims to fill such a research gap by presenting an integrated evaluation approach, combining emergy analysis, IPAT formula and IDA methods. A case study approach was employed so as to test its applicability. The research results reveal that DEDA experienced a rapid development and consumed a lot of natural resources. Although
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industrial symbiosis activities help reduced the total wastes, more efforts should be made in order to further reduce the total material and energy consumption, such as energy structure optimization, energy saving, diversified industrial structure, appropriate infrastructure, etc. In general, eco-industrial park (EIP) initiatives can help guide the industrial parks evolve toward greater sustainability since the natural system metaphor that EIP applies is most useful for understanding the functional attributes that industrial systems should replicate. Appropriate evaluation methods can facilitate park managers to self-check their problems and prepare feasible development targets by considering their own realities. Consequently, it is critical for more industrial parks to apply such an evaluation approach so that more industrial parks can move toward sustainable development.
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Acknowledgment
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This work is supported by the Natural Science Foundation of China (71325006,71461137008, 71311140172). Especially, we want to thank those anonymous reviewers for their valuable comments and contributions to the revised version of this paper.
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Table 1 Emergy calculation in DEDA for years of 2006 and 2010 Solar
Item
Unit 2006
RI PT
Amount
Solar
Transformity
Reference for
emergy
emergy
(seJ/g or seJ/J)
transformity
(seJ/yr)
(seJ/yr)
2006
2010
2010
SC
1、 、 Renewable resource 5.44E+16
5.44E+16
J/yr
1.00E+00
By definition
5.44E+16
5.44E+16
Wind (kinetic energy)
3.30E+14
3.30E+14
J/yr
2.51E+03
Odum et al., 2000
8.28E+17
8.28E+17
3.80E+13
3.80E+13
J/yr
3.50E+04
Odum et al., 2000
Rain (geopotential energy)
6.25E+13
6.25E+13
J/yr
1.76E+04
Odum et al., 2000
1.10E+18
1.10E+18
Waves
5.56E+14
5.56E+14
J/yr
5.12E+04
Odum et al., 2000
2.85E+19
2.85E+19
Tidal
1.74E+15
2.82E+04
Odum et al., 2000
4.91E+19
4.91E+19
Geothermal Heat
3.01E+13
3.53E+13
J/yr
5.80E+04
Odum et al., 2000
1.75E+18
2.05E+18
Granite
8.73E+11
9.73E+11
g/yr
8.40E+08
Odum et al., 2000
7.33E+20
8.17E+20
Shale
1.27E+11
1.64E+11
g/yr
1.68E+09
Odum et al., 2000
2.13E+20
2.76E+20
Clay
1.79E+10
2.01E+10
g/yr
3.36E+09
Odum et al., 2000
6.01E+19
6.75E+19
Quartz
8.43E+11
9.45E+11
g/yr
1.68E+09
Odum et al., 2000
1.42E+21
1.59E+21
(chemical
potential
energy)
1.74E+15
J/yr
AC C
3.Imported source (F)
EP
2.Nonrenewable Resource (N)
TE D
Rain
M AN U
Sunlight
1.33E+18
1.33E+18
0.00E+00
Water
2.46E+13
3.45E+13
g/yr
2.27E+04
Coal
4.11E+17
4.14E+17
J/yr
6.71E+04
Coke
2.50E+14
3.60E+14
J/yr
6.71E+04
Diesel Fuel
5.39E+16
5.82E+16
J/yr
1.11E+05
average Odum and
5.58E+17
7.83E+17
Odum, 1996
2.76E+22
2.78E+22
Odum, 1996
1.68E+19
2.42E+19
5.98E+21
6.46E+21
Ortega, Comar, 2000;
Brown and Arding, 1991
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1.49E+09
3.20E+09
J/yr
1.11E+05
Odum, 1996
1.65E+14
3.55E+14
Crude Oil
3.61E+17
3.63E+17
J/yr
9.07E+04
Odum, 1996
3.27E+22
3.29E+22
Maize
1.69E+14
1.87E+14
J/yr
6.62E+05
Brandt-Williams, 2002
1.12E+20
1.24E+20
Bean
2.63E+14
3.67E+14
J/yr
3.66E+05
Brandt-Williams, 2002
9.63E+19
1.34E+20
1.25E+20
1.74E+20
1.76E+15
2.98E+15
2.55E+16
5.28E+16
Geng
RI PT
Gasoline
and
Zhang,
Seafood
1.47E+15
2.05E+15
J/yr
8.51E+04
Vegetable
3.20E+09
5.40E+09
g/yr
5.51E+05
Fruit
3.00E+11
6.20E+11
J/yr
8.51E+04
Wood
1.27E+14
3.48E+14
J/yr
4.53E+04
Tilley, 1999
5.75E+18
1.58E+19
Cotton
2.96E+13
3.45E+13
J/yr
1.06E+06
Brandt-Williams, 2002
3.14E+19
3.66E+19
Wool
1.79E+12
2.89E+12
J/yr
7.39E+06
Odum et al., 2000
1.32E+19
2.14E+19
Leather
2.07E+12
3.80E+12
J/yr
1.44E+07
Odum et al., 2000
2.98E+19
5.47E+19
Steel
4.48E+08
6.25E+08
g/yr
3.16E+09
1.42E+18
1.98E+18
Copper
1.62E+07
3.42E+07
g/yr
5.44E+16
1.15E+17
Aluminum
2.51E+06
4.20E+06
g/yr
Plastic
1.54E+07
4.32E+07
Ester
1.12E+10
2.42E+10
Glass
1.16E+07
3.46E+07
g/yr
2.77E+07
Paper
4.23E+10
6.53E+10
g/yr
6.55E+09
Electricity
6.12E+15
8.12E+15
J/yr
2.67E+05
4. Labor and Service
SC
2010
Comar, 2000 Geng
and
Zhang,
TE D
M AN U
2010
3.36E+09
Bargigli and Ulgiati, 2003 Brown
and
Ulgiati,
2004a, b Odum, 1996
3.61E+15
6.05E+15
g/yr
9.68E+09
Buranakarn, 1998
1.49E+17
4.18E+17
g/yr
5.51E+09
Buranakarn, 1998
6.17E+19
1.33E+20
3.21E+14
9.58E+14
2.77E+20
4.28E+20
1.63E+21
2.17E+21
AC C
EP
1.44E+09
Brown
and
Ulgiati,
2001 Brown and Arding, 1991 Odum, 1996
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3.57E+08
4.67E+08
$/yr
4.90E+12
Jiang et al., 2008.
1.75E+21
2.29E+21
Service
5.30E+09
5.90E+09
$/yr
4.90E+12
Jiang et al., 2008.
2.59E+22
2.89E+22
AC C
EP
TE D
M AN U
SC
RI PT
Labor
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GDP(USD） E (seJ)
U (seJ)
6.60 E+21 7.38 E+21
2.56E+21 2.86E+21
9.07E+10 1.94E+11
9.73E+22 1.04E+23
6.64E+22 6.72E+22
AC C
EP
TE D
M AN U
SC
RI PT
Year 2006 Year 2010
W(seJ)
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Seffect ($/Sej)
Yeffect (Sej)
2.82E+10 1.47E+10
6.82E-01 6.46E-01
1.37E-12 2.89E-12
9.73E+22 1.04E+23
AC C
EP
TE D
M AN U
SC
RI PT
Year 2006 Year 2010
Teffect (Sej/$)
M AN U
SC
RI PT
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AC C
EP
TE D
Fig 1 Distribution of eco-industrial park projects in China
M AN U
SC
RI PT
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AC C
EP
TE D
Fig 2 The geographical location of DEDA in China
M AN U
SC
RI PT
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AC C
EP
TE D
Fig 3 Diagram of industrial symbiosis in DEDA
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2.50E+21 2.00E+21 1.50E+21
5.00E+20 0.00E+00
RI PT
1.00E+21
Impact factors
T
E
S
-5.00E+20 -1.00E+21
SC
-1.50E+21
Y
-2.00E+21
M AN U
-2.50E+21
AC C
EP
TE D
Fig 5 A comparison of four impact factors on industrial symbiosis in DEDA during 2006-2010
