Article

Quantifying the Total Environmental Impacts of an Industrial Symbiosis - a Comparison of Process-, Hybrid and Input-Output Life Cycle Assessment

Finnish Environment Institute SYKE, Helsinki, Finland.
Environmental Science and Technology (Impact Factor: 5.33). 06/2010; 44(11):4309-14. DOI: 10.1021/es902673m
Source: PubMed

ABSTRACT

Industrial symbiosis, representing resource sharing and byproduct use among colocated firms, is a key concept of industrial ecology. Local co-operation in industrial symbioses can reduce raw material use and waste disposal, but material and energy flows extending outside symbiosis boundaries can cause considerable environmental impacts. These external impacts are often ignored in industrial symbiosis studies. In this study, we compared process, hybrid and input-output life cycle assessment (LCA) approaches in quantifying the overall environmental impacts of a forest industrial symbiosis, situated in Kymenlaakso, Finland. Conclusions from an earlier process-LCA were strengthened by the use of hybrid-LCA as local emissions were found to cause less than half of the global impacts. In some impact categories, the whole impact was caused by supply chain emissions (land use, metal depletion and ozone depletion). The cutoff in process-LCA was found to be less than 25%, except in metal depletion and terrestrial ecotoxicity. Input-output LCA approximated hybrid-LCA results well in most impact categories, but seriously underestimated land use and overestimated terrestrial ecotoxicity. Based on the results we conclude, that input-output based LCA can be used to analyze the global impacts of an industrial symbiosis, but a careful interpretation of the results is necessary in order to understand the influence of aggregation and allocation.

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    • "The bottom-up method presents detailed product-or technology-specific analyses, and facilitates the identification of green strategies and engineered solutions. However, due to time and cost constraints, the method fails to consider products' entire supply chains[100,101], and such inter-sectoral cutoff might introduce one-sidedness to policy making. In addition, subjective system boundaries and diverse indicator selections among different bottom-up studies make model results difficult to compare with one another, and harmonization is needed to adjust the study estimates to a consistent set of methods and assumptions[102]. "
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    • "Industrial Symbiosis (IS) is a growingly accepted paradigm for processing waste into material, energy and water with benefits to participants measured by economic, environmental and social gains. Although the practice of IS has demonstrated the need for evaluating these benefits either in the process of screening of impending options or monitoring the operation of symbiotic networks , and despite of some attempts to quantify them (Van Berkel, 2010; Mattila et al., 2010; Berkel et al., 2009), no unified metrics or methods for calculating concomitant indicators has been proposed (Eckelman and Chertow, 2009; Jacobsen, 2006a). Consequently, evaluation of IS networks performance has been identified as deficient (Martin et al., 2012). "
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    ABSTRACT: Industrial Symbiosis (IS) is a growingly accepted paradigm for processing waste into material, energy and water with benefits to participants measured by economic, environmental and social gains. Despite of some attempts to quantify them no unified metrics or methods for calculating concomitant indicators have been proposed. This paper presents a systemisation of IS relevant environmental metrics and a semantic approach based on knowledge modelling using ontologies to facilitate “a priori” calculation of respective indicators. The approach and metrics are presented and verified with a case study.
    Full-text · Article · Jun 2015 · Journal of Cleaner Production
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    • "Industrial Symbiosis (IS) is a growingly accepted paradigm for processing waste into material, energy and water with benefits to participants measured by economic, environmental and social gains. Although the practice of IS has demonstrated the need for evaluating these benefits either in the process of screening of impending options or monitoring the operation of symbiotic networks , and despite of some attempts to quantify them (Van Berkel, 2010; Mattila et al., 2010; Berkel et al., 2009), no unified metrics or methods for calculating concomitant indicators has been proposed (Eckelman and Chertow, 2009; Jacobsen, 2006a). Consequently, evaluation of IS networks performance has been identified as deficient (Martin et al., 2012). "

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