Modeling and Evaluation of the Possibilities of Forming a Regional Industrial Symbiosis Networks ^†

Abstract

Industrial symbiosis (IS) is a term used to describe a network of diverse organizations that make use of different byproducts to improve their ability to achieve common goals, improve environmental conditions, and/or improve business and technical processes. In this paper, we propose a model for evaluation of the possibilities of the establishment of such IS on a regional level. This paper studied a benchmark of seven IS examples, which are used to build a qualitative multi-criteria decision model for evaluation of the development of IS network model. Through these examples, where two are the best known IS cases in the world, we demonstrate the importance of social actors’ involvement in IS in their industrial or non-industrial technological processes.

1. Introduction

Industrial Symbiosis (IS) emerged as a collective, cooperative, and multi-industrial approach towards economic and environmental sustainability. IS has been defined as bringing together “traditionally separate industries in a collective approach to competitive advantage involving physical exchange of materials, energy, water and byproducts” (Chertow 2000). Both economic considerations, such as lowering costs for waste disposal, as well as by environmental ones, such as accessing limited water supplies, motivate IS. Firms operating within an Industrial Ecology (IE) paradigm use IS as a collective approach to joint competitive advantage—usually within the framework of a geographically delimited cluster or industrial district—and simultaneously realize environmental benefits. Different authors (Chertow 2007; Howard-Grenville and Paquin 2008, p. 158; Gingrich 2012, p. 44) define IS as an approach to IE; as a synonym for IE (Phillips et al. 2005, p. 242); as a subset of IE (Chertow and Lombardi 2005, p. 6535); as activity within IE (Rui and Heijungs 2010); as eco-industrial symbiosis (EIS), which represents local or regional circular economy and environmental approach (Hartard 2008); and as one of the levels of studying IE (Hartard 2008). Deutz (2014, p. 4) sees IS as a stream of untapped resources (including materials, objects and/or energy), starting with social actors that would have discarded these resources, and ending with social actors that use these resources as new resources. Some are also hoping to be recognized as environmentally aware by their social environment (Costa and Ferrão 2010; Costa et al. 2010; Eilering and Vermeulen 2004). Through such collaboration, social relationships among participants get better (Zhu et al. 2007). Examination of IS networks (ISNs), from an economic and especially ecological point of view, is now the subject of numerous multidisciplinary research studies, which mainly focus on waste management (CTTÉI 2013), industrial ecology (Chertow 2000; Ehrenfeld and Gertler 1997), circular economy (Laybourn 2015), supply chain management (Leigh and Li 2015), and economics (Chertow and Lombardi 2005; Fric and Rončević, forthcoming).

Positive economic effects are demonstrated by lower waste/byproduct disposal costs, competitive advantages towards other economic actors and better reputation of the firm/economic actor. Firms hence participate in IS for exchange of resources, such as byproducts, materials, services, energy etc., in the framework of closed and sustainable cycles. This cooperation contributes to the reduction of required input of materials and energy, as well as a decrease of the output in the form of waste. Furthermore, the low price of waste as a material—if compared with no waste materials—increases economic effectiveness of individual organizations, which are recipients in the framework of ISNs. Open networks, on the other hand, allow entropy, which results in loss of energy, poor utilization of materials and consequently causes environmental damage (Chertow 2000; Chertow 2007). However, although participation in IS seems rational both from an organizational and societal point of view, and although we have sufficient technological and logistical knowledge to manage them successfully, IS could be further developed both on regional, national and international levels in terms of forming new IS relationships and networks.

Why is this the case? IS is not only a technological or logistical system; it transcends the boundaries of various systems, and it is also a social system. Hence ISNs may be considered to be social networks. This implies that nodes in IS networks—enterprises, households, policy actors—are social actors, operating in the context of bounded rationality, i.e., they are limited in their rational decision making not only by their own inherent organizational values but also constrained by the decisions of other organizations. Since exchange—the elementary activity in IS—is taking place between social actors and it requires cooperation, communication, and a certain level of trust, its morphogenesis can be study by sociological research. Our social research is based on Beckert’s social fields theory (Beckert 2009), according to which social fields—in our case IS and its networks—are shaped by the three social forces. These are institutions (laws, central concern for law, the formal mechanism for political rule-making and enforcement), social networks (social structure made up of a set of social actors such as individuals or organizations, a set of the dyadic ties between these actors) and cognitive frames (social interaction, meaning-making technologies, and strategically-selective opportunities for reflection and learning) (Beckert 2009). However, one should avoid the pitfalls of sociological determinism and study the IS as a complex phenomenon, taking other factors into account as well, to allow informed conclusions and policy-making process. In this paper, we introduce a model to support the decision makers in the process of IS Networking (ISN). We propose a model that includes environmental, technological, organizational, economic, and social aspects of IS (Boshkoska et al. 2017).

Forming an ISN is a complex process and its success is highly dependent on establishing a synergy among several spheres including physical, social and organizational ones. Neglecting even one of them may lead to a significant time delay or even failure of development of a useful ISN. In this paper, we propose a model of an ISN that includes all three spheres to support the decision makers in the process of the development of an ISN. The model allows evaluation of the current state of the ISN based on the three spheres and contributes to clarification and finding weak spots for improvement of ISNs. Hence, the results from the evaluation of ISNs suggest to the decision makers which particular spheres, and which attribute needs to be improved to develop or obtain a better ISN. In practice, the model could also help firms, local and state institutions and the public sector in understanding the weaknesses of the current system, and to define weak spots for which solutions are required. The model can also support the decision makers in the process of improving the ISNs and its temporal as well as long-term behavior (Criado Pacheco et al. 2017).

The data collection of the study is based on a mixed methods approach, conducted by method of triangulation (qualitative-quantitative method) to ensure the validity of the study. This means that data obtained from seven IS cases from sources such as literature reviews, empirical data, and empirical data from articles with interviews-practitioners who work with the facilitation of IS exchanges. A theoretical framework obtained from the literature on IS guides the data collection in all parts of the study. The main objective of the paper was the use of quantitative method of a multi-attribute decision-making program that is being able to “translate strategy into action”. Consequently, the model is based on the DEX methodology and its implementation into the DEXi computer program (Jereb et al. 2003). DEXi is a stand-alone computer program for multi-attribute decision-making. It facilitates interactive development of qualitative multi-attribute and hierarchical decision models and the evaluation of options. DEXi has been used in many real-life decision problems in areas such as selection and evaluation of computer hardware and software, evaluation of firms and business partners, individual management, project evaluation, land-use planning, risk assessment in medicine and health-care (Jereb et al. 2003; DEXi 2017).

The structure of this paper is as follows: in Section 2 we describe the materials and modelling approach for development of a decision support model for evaluation of an ISN. We outline the key benefits of ISN model and support our statements by seven of the most famous examples of IS best practice. The obtained model and evaluation of seven ISNs is given in Section 3. In Appendix A, we provide a detailed description of the attributes, while in Appendix B we provide the utility functions of the aggregated attributes. Finally, in Section 4 we give our conclusions.

2. Materials and Methods

To build the ISN model, we used the available literature on IS from the Web of Science database and searched for publications that listed “IS,” “Eco-Industrial Park,” or the combination of “Industrial Ecology”, and “IE” as a topic. From the available literature, we extracted seven relevant real-life cases of IS. Next, we used the model to evaluate ISNs located in Kalundborg and Aalborg in Denmark (Aalborg Kommune 2010), Rotterdam Europoort in Netherlands, Kwinana and Gladstone in Australia, Guayama and Barceloneta in Puerto Rico (detailed descriptions of the cases that we denote as options are provided in Section 3).

The modeling proceeded in five consecutive sub-stages (Beckert 2010):

• Definition of the relevant attributes for three spheres: environmental and technological, organizational and economy, and social. The result was an ordered list of all 22 (13 + 9) attributes.
• Developing attributes’ hierarchy based on their interrelations and anticipated influence on the final proposed outcomes of the model. The process involved structuring, comparing, and aggregating the list of attributes using a “bottom-up” approach.
• Implementation of the attribute hierarchy into a DEXi model. The result of this stage is the tree of attributes given in

  Table 1. DEX model tree, scale attributes and short description of attributes.

  Attribute	Scale	Description
  ISN Development	Partially developed; mostly developed; fully developed	Current ISN Development
  ├─ Environment and Technology	Weak; moderate; strong	Facets, location, diversity of industries, presence of river, sea, core businesses
  │ ├─ Accessibility of material resources	Low; moderate; satisfying	Scarcity of resources
  │ ├─Availability of resources	No; partially; yes	Industrial water, energy, production materials (primary and secondary)
  │ └─Origin of resources	External; mixed; internal	How the available resources are connected: internal, external, mixed
  │ └─ Proximity	Low; moderate; satisfying	Physical distance among industrial installations
  │  ├─Distances	Long; medium; short	Distances among firms
  │  └─Installation/viability of heavy infrastructures	Impossible; incomplete; possible	Vicinity of the main infrastructure suppliers to the IS area
  ├─ Organization and Economics	Low; moderate; satisfying	Economic benefits, profitability
  │ ├─ Economy and economics	Low; medium; high	Yielding the benefits of industrial symbiosis
  │ │ ├─Sustainable dynamics	Weak; partial; strong	Create economic growth, while advancing social and environmental objectives
  │ │ └─Reduction of raw material and cost saving	Weak; partial; strong	Reduction, reuse and recycling; Income generating synergies
  │ ├─ Learning organization	Low; moderate; satisfying	An approach to include innovative ideas of employees in the decision making process
  │ │ ├─Participative management	Weak; partial; strong	Involvement of employees into decision making processes
  │ │ └─Innovative ideas	Weak; partial; strong	Employees generate innovative ideas
  │ └─Common IS platform	No; partial; yes	Existence of a joint and comprehensive policy and communication platform
  └─ Social Forces	Low; moderate; satisfying	Social factors shaping the topography of IS networks
  ├─ Institutions	Weak; partial; strong	Rules, ritualized behaviours
  │├─Urban planning	Low; moderate; satisfying	Local and/or regional spatial planning
  │ └─Regulations for waste management	Weakly enforced; partially enforced; fully enforced	Implementation of the waste management regulations
  ├─Cognitive frames	Weak; partial; strong	Green values, public awareness and green procurement
  └─Networks	Weak; partial; strong	Social trust and ease of cooperation

  Note: The green coloured word means higher value in the evaluation of IS cases, red colour stands for lower, and the black colour is medium evaluate attribute.

  .
• Definition of attributes’ measurement scales and set of values that each attribute may obtain. The DEX method is a qualitative one and usually represents attributes whose values are represented by words, such as “impossible,” “incomplete,” or “possible “or other (see Table 1, column 2).

Definition of the utility function, in which we define an aggregation of several attributes into one attribute. The utility function of every aggregate attribute is presented in a table where rows can read as logical “if… then” expressions.

The model is based on the qualitative multi-criteria method DEX. In DEX, the qualitative attributes build a hierarchical structure, which represents a decomposition of the decision problem into smaller, less complex, and possibly easier-to-solve sub-problems. There are two types of attributes in DEX: basic attributes and aggregated ones. Basic attributes are the directly measurable attributes, also called input attributes, which used for describing the options. The latter are obtained by aggregating the basic and/or other aggregated attributes.

They represent the evaluations of the options. The hierarchical structure in DEX represents a tree. In the tree, attributes are structured so that there is only one path from each aggregate attribute to the root of the tree. The path contains the dependencies among attributes such that the higher-level attributes depend on their immediate descendants in the tree. This dependency is defined by a utility function represented in a tabular format such as the one presented in Figure 1. Utility function is the component of multi-attribute model that define the aggregation aspect of option evaluation. In Figure 1, the utility function gives the aggregation rules for the attribute “accessibility of material resources” based on the values of the input attributes “availability of resources” and “origin of resources”. For instance, one may read from the utility function in Figure 1 that if the origin of resources is external regardless of the available availability of resources (row number 3), then the accessibility of material resources that would be in line with the requirements for development of IS in the region is low. Also, if the availability of resources is none (regarded as “no” in the model),” then regardless of the value of the attribute “origin of resources,” the possibility of development of a suitable accessibility of material resource is low (DEXi 2017). To have at least moderate accessibility of material resource, the actual availability of resources should be evaluated at lease as partial and the origin of resources should be at least evaluated as mixed.

The rows in the utility function may be present as “if… then” decision rules. An example of such decision rule, given in the first row in Figure 1 is:

IF Availability of resource is “yes” AND Origin of resources is “mixed” THEN Accessibility of material resource = moderate.

The developed “ISN” model contains 13 basic and 9 aggregated attributes. Each decision alternative is firstly assessed with respect to each attribute, and these individual assessments are then aggregated into an overall evaluation of the alternative (DEXi 2017; Boshkoska et al. 2017). The green-higher values in the evaluation of IS cases are preferred. It follows that decision alternatives are hierarchical, and the best one is chosen in a transparent and justifiable way. Attributes in the decision model include a three of IS aspects: environment and technology, organization and economy, and social forces. The last one represents the main contribution to the DEX model in this paper given the fact that the social forces in IS have not been addressed before in the literature to the knowledge of the authors. The model tree, the scale values of all attributes and short description of attributes is given in Table 1, while the detailed descriptions of the attributes are given in Appendix A. The utility functions of all aggregated attributes are given in Appendix B. In Table 1 the scale values are color coded where the green color means most preferred value, and the red color means least preferred value. The proposed model is implemented using the DEXi software tool. The model evaluates the degree of compliance of the industrial areas with the IS prerequisites. They are intended to help decision makers in determining the facilitating mechanisms, the barriers that should be avoided, and provide recommendations regarding future operations to be undertaken for the improvement or development of a new IS network.

3. Modeling Results: Options Description End Evaluation

The DEX model tree and the results from the evaluation of the seven real cases of ISN are presented in

Table 2. DEX model and evaluation of seven IS cases shows that three of the seven IS cases are evaluated with highest qualitative measure as “fully developed”, while four IS cases are evaluated as “partially developed”. Such evaluation is in line with the information from the considered literature.

Attribute	Kalundborg	Aalborg	Guayama	Barceloneta	Kwinana	Gladstone	Rotterdam
ISN Development	fully developed	mostly developed	mostly developed	fully developed	mostly developed	fully developed	mostly developed
⊢─ Environment and Technology	strong	strong	weak	strong	weak	strong	weak
│ ⊢─ Accessibility of material resources	satisfying	satisfying	low	satisfying	low	satisfying	low
│ │ ⊢─Availability of resources	partially	partially	partially	yes	partially	partially	partially
│ │ └─Origin of resources	internal	internal	mixed	internal	mixed	internal	mixed
│ └─ Proximity	moderate	moderate	moderate	moderate	moderate	moderate	moderate
│  ⊢─Distances	short	short	medium	medium	medium	short	short
│   └─Installation/viability of heavy infrastructures	incomplete	incomplete	incomplete	incomplete	incomplete	incomplete	incomplete
⊢─ Organization and Economics	satisfying	moderate	satisfying	satisfying	satisfying	moderate	moderate
│ ⊢─ Economy and economics	medium	medium	high	high	medium	medium	medium
│ │ ⊢─Sustainable dynamics	partial	partial	strong	strong	partial	partial	partial
│ │ └─Reduction of raw material and cost saving	partial	partial	partial	partial	partial	partial	partial
│ ⊢─ Learning organization	satisfying	satisfying	satisfying	satisfying	satisfying	satisfying	satisfying
│ │ ⊢─Participative management	partial	strong	partial	partial	partial	partial	partial
│ │ └─Innovative ideas	partial	partial	partial	strong	partial	partial	partial
│ └─Common IS platform	yes	partial	partial	yes	yes	partial	partial
└─ Social Forces	satisfying	moderate	moderate	satisfying	moderate	satisfying	moderate
⊢─ Institutions	strong	partial	partial	strong	strong	strong	partial
│ ⊢─Urban planning	satisfying	moderate	satisfying	satisfying	satisfying	satisfying	moderate
│ └─Regulations for waste management	fully enforced	partially enforced	partially enforced	fully enforced	fully enforced	fully enforced	partially enforced
⊢─Cognitive frames	strong	strong	partial	partial	partial	strong	partial
└─Networks	strong	partial	strong	strong	partial	partial	partial

Note: The green coloured word means higher value in the evaluation of IS cases, red colour stands for lower, and the black colour is medium evaluate attribute.

and graphically, using polar maps of the evaluation of the alternatives given in next polar maps bellow from Figure 2 to Figure 8.

3.1. ISN in Kalundborg

The IS project in Kalunborg, Denmark, begun in 1972 and by 1994, 16 contracts had been negotiated. The extent of the material and energy exchanges in 1995 had been about 3 million tons a year. Estimated savings totaled US $10 million a year, giving an average payback time of six years (Kalundborg Symbiose 2017; Jacobsen 2005; Jacobsen and Anderberg 2005). The core participants were Asnaes, Denmark’s largest coal-fired power station, an oil refinery owned by Statoil, a pharmaceuticals plant owned by Novo Nordisk, a Gyproc, Scandinavia’s largest plasterboard manufacturer and the municipality of Kalundborg, which distributes water, electricity, and district heating to around 20,000 people (Kalundborg Symbiose 2017; Ehrenfeld and Gertler 1997). The symbiosis has grown over the years to include partners from other districts, including farmers who had put emphasis on reducing environmental costs. The environmental benefits of IS are quantified by measuring the changes in consumption of natural resources, and in emissions to air and water, through increased cycling of materials and energy. The economic benefits of IS are quantified by determining the extent to which firms’ cycling byproducts capture revenue streams or avoid disposal costs; those businesses receiving byproducts gain advantage by avoiding transport fees or obtaining inputs at a discount (Uzzi 1996; Kalundborg Symbiose 2017). IS involves economic benefits for social actors involved, e.g., savings in production cost, savings in purchase of new materials, savings in management of secondary resources, and additional revenue from re-processing secondary resources into byproducts and primary resources (Ehrenfeld and Gertler 1997; Doménech Aparisi 2010; Doménech Aparisi and Davies 2011; Ehrenfeld and Gertler 1997).

According to the DEX model, the ISN in Kalundborg is evaluated as fully developed. The polar map of selected attributes for Kalundborg is given in Figure 2

3.2. ISN in Aalborg

Even though most of the traditional activities in Aalborg, Denmark, have been off-shored abroad for (mainly) cost salary reasons, heavy manufacturing activities still characterize the city economy such as the cement industry, power production plant, and harbor. Synergies of the industries in Aalborg happen within a radius of 1 to 2 km distance between firms (Aalborg Industries 2017) Aalborg, in north Denmark, is a mid-sized town that gathers all the prerequisites present in most of the IS cases around the world: it has an opening on the sea, harbor activity, an important industrial area, manufacturing businesses, etc. However, very few symbiotic partnerships between firms are in place (Scrase et al. 2009).

According to the DEX model, Aalborg has a “partially developed” ISN due to the incompletion of the attribute installation/viability of heavy infrastructure, partial reduction of raw material and cost savings and partial common IS platforms. These results are in line with the literature, which states that because Aalborg East does not face too many problems with natural resource scarcity, firms are not significantly motivated to decrease their resource consumption. Furthermore, there was no data about input/output or massive material flows in this area, so firms tended to hold a skeptical or curious attitude regarding to what extent other firms would share information and knowledge within the area and protect their business confidentiality (Chertow and Lombardi 2005). Firms in this area had few extra-professional activities and relatively weak social links, which limited the space for developing deeper collaboration and mutual trust (Novokovic et al. 2012; Aalborg Industries 2017). As a result, very few symbiotic partnerships between firms were operating in this area, and firms’ willingness to have industrial cooperation is l. The polar map of selected attributes for Aalborg is given in Figure 3.

3.3. ISN in Guayama

An evolving network of interfirm exchanges in Guayama, Puerto Rico provides a tractable illustration to analyze the environmental and economic case for IS. Puerto Rico is a commonwealth of the United States (US), thus sharing most US laws and business practices. The municipality of Guayama, on the southeastern coast of Puerto Rico, measures 169 km², and has a population of approximately 42,000. Before 1940, Guayama was primarily an agricultural economy with some light manufacturing. After a period of light industrialization in the 1940s and 1950s, the current industrial profile began developing. Guayama hosts many of the same industries as Kalundborg: a fossil fuel power generation plant, pharmaceutical plants, an oil refinery, and various light manufacturers. Current exchanges in Guayama include the new AES coal-fired power plant using reclaimed water from a public wastewater treatment plant (WWTP) for cooling, and providing steam to the oil refinery. Additional steam and wastewater exchanges are under negotiation between neighboring pharmaceutical plants, the refinery, and the power plant. Beneficial reuse of the coal ash has begun as a means of stabilizing some liquid waste (Chertow 2000).

The case of Guayama was evaluated as “mostly developed” because of the partial availability of resources that are from mixed origin, partial reduction of raw material and cost savings and partial innovative ideas. In the cases of Guayama and Rotterdam it is known that there is a need for involvement of stakeholders in the IS communication platform (Baas 2008; Baas 2011), which is in line with the DEX model evaluations for the attribute “Common IS platform” as partial suggesting the possibilities for improvement. The polar map of selected attributes for Guayama is given in Figure 4.

3.4. ISN in Barceloneta

Barceloneta and its neighbouring municipalities, Arecibo and Manati, is often referred to as having one of the highest concentrations of pharmaceutical manufacturing facilities in the world, with 14 facilities located across the three municipalities. Despite food, chemical, packaging, electronic equipment, and metal fixture manufacturers and waste management firms, there are global leaders such as Abbott, Bristol-Myers Squibb, Merck, and Pfizer. Beginning in the 1950s, Puerto Rico, a US commonwealth territory, promoted generous tax benefits and low workforce costs relative to the continental US to attract manufacturing firms. Eight regional firms participate in the Barceloneta Wastewater Treatment Corporation Advisory Council, a joint agreement, started in 1978, to construct and oversee operation of the region’s secondary wastewater treatment plant, which handles 31,415 m³ per day (8.3 million gallons per day). The plant is owned and operated by the Puerto Rico Aqueduct and Sewerage Authority. Advisory Council members provided 70% of the plants’ operation and maintenance costs, as well as technical assistance to the authority (Ashton 2008). The pharmaceutical facilities were financed to manage the wastewater treatment plant initiative. Managers in the participating firms met frequently to discuss common problems; initially these were focused on the wastewater plant, but later evolved to include other shared resource constraints. The Advisory Council has served to institutionalize cooperative resource management practices among the firms and deepen the social embedding of inter-firm relations (Corder et al. 2014; Granovetter 1985). ISN in Barceloneta is evaluated as “fully developed”, however due to the medium distances the attribute proximity is evaluated as moderate because according to Velenturf and Jensen 2015, industrial ecologists commonly believe that trust and geographical proximity-vicinity are essential to the development of IS and consequently ISNs. Close geographical proximity and confidence among actors and firms are essential to the development of IS (Velenturf and Jensen 2015). According to Velenturf and Jensen (2015), to move IS research forward there is a need for additional research on the concept of proximity, with attention on institutional, geographical, cognitive, social, and organizational distances between actors (Velenturf and Jensen 2015). The polar map of selected attributes for Barceloneta is given in Figure 5.

3.5. ISN in Kwinana

The Kwinana Industrial Area of Western Australia is emerging as a world leading example of IS. The Kwinana Industrial Area (KIA) was established in the 1950s and is Western Australia’s most significant heavy industrial region. The area of 120 km² is located approximately 40 km south of Perth. About 3600 people work in the area’s core industries, and many more in related sectors and service jobs. The total economic output of the area exceeds a $4.3 billion annually (Harris 1987). The KIA is home to a diverse range of industries ranging from fabrication and construction facilities through to high-technology chemical and biotechnology plants and large resource processing industries, such as titanium dioxide pigment production and alumina, nickel, and oil refineries. The instance of IS has considerably increased since the late 1980s, providing economic, environmental, and social benefits to the firms involved, the neighboring communities and the state. Following the successful development and start-up of two co-generation plants in 1997 and 1999, the KIA decided to explore further opportunities for IS exchanges in the area.

Kwinana is “mostly developed” IS because of the low accessibility of material resources that are due to the partial availability of resources, which are from mixed origin, and incompleteness of installation/viability of heavy infrastructure. The case of Kwinana has moderate social forces and satisfying organization and economics. The limited competition between operating firms and the isolation of other major industrial centers in eastern Australia are believed to be a cause of such social forces and proximity between actors in Kwinana. There are rigorous regulations in Kwinana, as in Kalundborg, with regard to handling availability of resources to produce alternative fuels (Baas and Huisingh 2008). The polar map of selected attributes for Kwinana is given in Figure 6.

3.6. ISN in Gladstone

Gladstone is one of the main and largest mineral industrial clusters in Australia. It is located 540 km north of Brisbane, and 100 km southeast of Rockhampton and has a population of about 40,000 in the region that extends from Boyne Island in the south to Yarwun in the north. The Gladstone Area Industry Network (GAIN) was formed by the 9 members of the industries present in the region with a goal of improving the initiation of IS of large industrial area far away from the city and double the population compared to Kwinana. The district of enterprises is situated in Queensland, near the municipality of Gladstone and includes a coal power station, two aluminum refineries, an aluminum smelter, cement producer, and ammonia nitrate producer. There are 6 participant firms and the environmental benefits achieved by different symbiotic networks: approximately 10,000 tons per annum byproduct gypsum recovered for reuse, 170,000 tons per year CO₂ emissions reduction from one co-generation plant (Van Beers et al. 2006).

In Gladstone, the investment in water reclamation plant is a $29 million and 20% premium price on reclaimed water (Corder 2006; Corder et al. 2014). IS implementation activities are coordinated by the Gladstone Industrial Leadership Group and under the auspices and financial support of Centre for Sustainable Resource Processing. The district is mainly dominated by a single value chain, the alumina production (Gladstone Industry Leadership Group 2017).

ISN Gladstone is considered “fully developed”. Its strongest aggregate attributes are: environment and technology, learning organizations, social forces and accessibility of material resources. The polar map of selected attributes for Gladstone is given in Figure 7.

3.7. ISN in Rotterdam

The IS case Rotterdam Europoort is characterized by an IS organization located by the sea. The Eco-industrial park concept has been applied in the Rotterdam Harbor area by setting up an industrial ecosystem project. The Europoort/Botlek Interests (EBB) industry association has developed an intermediary role between government and industry. It defines goals and perspectives from the national environmental policy domain to its member firms. Even though the association does not have legal power, it has a position of “social control” to run the development in the area. Half the land area is looked after by Rotterdam Municipal Port Management (RMPM). Since 1998 the decisions and evaluation concerning different Industrial Ecosystem (INES) projects have been made by a decision-making platform that consists of members of national and regional industry associations, plant managers, national and regional governmental organizations, an environmental advocacy organization and academia (Baas 2011; Symbiosis 2010).

The attribute “environment and technology” is evaluated as “weak” due to “low” “accessibility of material resources”. The learning organization is evaluated as satisfying, due to the moderate values of economy and economics and partial common IS platform. The organization and economics are considered “moderate”. For Rotterdam, the evaluation for attributes “regulations for waste management” and “urban planning” are based on the comparison to the other six IS cases. We found that some of the “weak” impacts of environmental regulations in Rotterdam are caused by other dimensions of social embedding such as cultural embedding. This means that the effects of informal ISNs on innovative behavior and on innovation strategies of large firms have been found to be less common in comparison to the reported cases for other ISNs. Despite the dimensions of cognitive frames of embedded routines that are restricting new links in a holistic approach of Rotterdam Harbor region, there is a possibility for development of new concepts such as the design of compressed air systems for a group of firms (Baas 2011). Although the Rotterdam Harbor and industry complex is a heavily industrialized area, approximately 100 km² large, with about 1 million inhabitants in the urban surroundings, research projects such as the utilization of reduction waste heat, CO₂ and nutrients from the district heating firms in greenhouses, biofuel synergies, landfill and urban mining and planning only started in 2010 (Baas 2011). The polar map of selected attributes for Rotterdam is given in Figure 8.

4. Discussion and Conclusions

At this stage, a qualitative multi-criteria model for evaluation of the level of development of regional ISNs was developed. The model is based on the available literature for ISNs, from which we have extracted the most relevant and most general attributes. We have also defined the relations among attributes in terms of easily understandable “if… then” rules.

To construct the model, we used seven real ISNs. The evaluation of the seven IS cases based on the developed model as well as consulted literature resulted in the following conclusions. Forming an ISN is a complex process that requires an extensive knowledge from social, organizational, environmental spheres as well as a good understanding of economy and technology. The developed model for evaluation of an ISN is designed through a set of indicators from each of the spheres and has a comparative and/or descriptive role to understand the requirements of development of an ISN. Since evaluation of ISNs based on one qualitative indicator leads to a high uncertainty in the evaluations, we used an approach in which we developed several indicators for the input attributes in the model. Based on the number of indicators that the ISN fulfills for the given attribute, we have performed more realistic evaluation of ISNs. Still, the evaluations could be changed by adopting new literature sources. Our belief with the use of the new model is that the decision makers could surpass the imposed disciplinary boundaries by the involved interdisciplinary research areas and enable them to evaluate and analyze the requirements for development of improvement of an ISNs.

One of the biggest barriers for increased IS activities is the lack of awareness about the potentially strategic business and development of the value of IS. There is also limited management attention given to the ISN concept. These can be overcome by helping decision makers understand the importance of the development of ISNs and the proposed model could be used in providing support to the decision makers in this context. The next challenge is identifying opportunities for creating regional ISNs. Creation of local dynamics and platforms that intensify communication among actors is one effective way of addressing this challenge. Further support can be provided by systemic assessment of the needs and capacities of all included actors in the process of development of a local ISN. Other challenges include the rate of return of required investments, access to finance, regulatory requirements, and concerns about operational disruptions. Some of these barriers can be addressed by public sector interventions.