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Application of data envelopment analysis in environmental impact assessment of a coal washing plant: A new sustainable approach

机译:数据包络分析在煤洗工区环境影响评价中的应用:一种新的可持续方法

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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.
机译:传统的环境影响评估方法(EIA)的主要问题是,在大多数现有的算法和方法中,如Leopold,Folchi和Riam,主要关注是拟议计划的破坏性影响,以及工业项目不太明显。这导致了环境组织与工业利益相关者之间的永久挑战。数据包络分析(DEA)是评估工业单位的新方法。此外,它考虑了该项目的积极经济和社会影响,并为工业单位进行了全面评估。通过这种方法,工业单位的环境影响被认为是“投入”,其正面的经济和社会影响被视为DEA模型的“产出”。因此,影响评估的问题变为DEA模型。在本研究中,伊朗北部的阿尔博尔兹希尔煤洗涤厂被认为是实施DEA-EA-EIA方法的案例研究,而19种植物活动和11种环境组成部分已被用于评估植物的环境影响。为了解决EIA问题,已经使用了两个常用的DEA方法,称为CRS(常量返回到缩放)和VRS(变量返回到比例)。 DEA结果确定了应该认真考虑的工厂的关键环境组成部分。此外,在DEA方法中绘制“潜在改进”图是一种有效的工具,用于确定工厂的高风险活动并将其应用于开发计划中。此外,使用具有最大化输出方法的VRS模型显示,一些植物活动具有最佳模式的差异,应在未来的发展计划中考虑这些组件。最后,可以得出结论,评估矿产与VRS最大化输出方法的环境影响,更接近可持续发展和成本效益分析的概念。

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