Abstract

The main problem of traditional methods of environmental impact assessment (EIA) is that in most of the existing algorithms and methods, such as Leopold, Folchi and RIAM, the main attention is to the destructive effects of the proposed plan, and the advantages of the industrial project are less noticeable. This has led to a permanent challenge between environmental organizations and industrial stakeholders. Data envelopment analysis (DEA) is a new approach of assessing the industrial units. Besides, it considers the positive economic and social impacts of the project and provides a comprehensive assessment of the industrial unit. With this approach, the environmental impacts of an industrial unit have been considered as “inputs” and its positive economic and social impacts considered as the “outputs” of the DEA models. Therefore, the problem of impact assessment changes into a DEA model. In the present study, the Alborz Sharghi Coal washing plant in northern Iran has been considered as a case study for implementing the DEA-EIA approach, and 19 plant activities and 11 environmental components have been used to evaluate the environmental effects of the plant. To solve the EIA problem, two commonly used DEA approaches, called CRS (constant returns to scale) and VRS (variable returns to scale), have been used. The DEA results identified the critical environmental components of the plant that should be considered seriously. Also, drawing the “potential improvement” diagram in the DEA method is an effective tool for determining the high risk activities of the factory and applying them in development plans. Besides, using the VRS model with maximize-output approach showed that some of the plant activities had the most differences with optimal mode and these components should be considered in future development plans. Finally, it can be concluded that, assessing the environmental impacts of the mineral industries with VRS maximize-output approach, is closer to the concept of sustainable development and cost-benefit analysis.

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