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Incorporating the LCIA concept into fuzzy risk assessment as a tool for environmental impact assessment |
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| Authors: | Kevin Fong-Rey Liu Chih-Yuan Ko Chihhao Fan Cheng-Wu Chen |
| Institution: | 1. Department of Safety, Health and Environmental Engineering, Ming Chi University of Technology, New Taipei City, 24301, Taiwan, ROC 2. SG Development Environmental Consultants Ltd., 51591, Changhua, Taiwan, ROC 3. Institute of Maritime Information and Technology, National Kaohsiung Marine University, Kaohsiung, 80543, Taiwan, ROC |
| Abstract: | Environmental impact assessment (EIA) is a procedural tool for environmental management that identifies, predicts, evaluates and mitigates the environmental impact of development proposals. In the process of EIA, EIA reports, prepared by developers, are expected to delineate the environmental impact, but in practice they usually determine whether the amounts or concentrations of pollutants comply with the relevant standards. Actually, many analytical tools can improve the analysis of environmental impact in EIA reports, such as life cycle assessment (LCA) and environmental risk assessment (ERA). Life cycle impact assessment (LCIA) is one of steps in LCA that takes account of the causal relationships between environmental hazards and damage. Incorporating the concept of LCIA into an ERA as an integrated tool for the preparation of EIA reports extends the focus of the reports from the regulatory compliance of the environmental impact, to determine the significance of the environmental impact. Sometimes, when using integrated tools, it is necessary to consider fuzzy situations, because of a lack of sufficient information; therefore, so ERA should be generalized to a fuzzy risk assessment (FRA). Therefore, this paper proposes the integration of a LCIA and a FRA as an assessment tool for the preparation of EIA reports, whereby the LCIA clearly identifies the causal linkage for hazard–pathway–receptor–damage and then better explain the significance of the impact; furthermore, a FRA copes with fuzzy and probabilistic situations in the assessment of pollution severity and the estimation of exposure probability. Finally, the use of the proposed methodology is demonstrated in a case study of the expansion plan for the world’s largest plastics processing factory. |
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