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THE APPROPRIATENESS OF MULTICRITERIA ANALYSIS IN ENVIRONMENTAL DECISION-MAKING PROBLEMS

机译:环境决策问题中多准则分析的适当性

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Environmental decision-making occurs in numerous environmental sectors and covers a diverse range of problems. Of the various decision tools available not all of them may be appropriate for any single decision problem, and any particular decision-aid may not be applicable in all decision problems. This article reviews applications of MCA, and considers the appropriateness of Multicriteria Analysis (MCA) in environmental decision-making problems. Due to natural and decision environments being multidimensional, environmental decision-making is characterized by complexity. Consequently MCA has commonly been used in this area. In Environmental Impact Assessment (EIA) the multiple dimensions of the impacts evaluated have been easily represented in MCA as multiple criteria. Waste management and water resource planning problems involving public participation have been facilitated by MCA, through the structuring and articulation of the public's values. MCA has also been applied in water quality problems, allowing the incorporation of incommensurable criteria into evaluations. The appropriateness of MCA for environmental decision problems can be viewed in the context of a typical decision-making process, making it easier to identify the contribution MCA can make at various decision-making stages. MCA can be particularly appropriate when the decision-making context is characterized by multiple objectives and multiple criteria, incommensurable criteria, mixed data and the need for ease of use, and the analysis context is characterized by multiple participants.
机译:环境决策发生在许多环境部门中,涉及各种各样的问题。在可用的各种决策工具中,并非所有工具都可能适合于任何单个决策问题,并且任何特定的决策辅助工具可能并不适用于所有决策问题。本文回顾了MCA的应用,并考虑了多准则分析(MCA)在环境决策问题中的适用性。由于自然和决策环境是多维的,因此环境决策的特征在于复杂性。因此,MCA通常用于此领域。在环境影响评估(EIA)中,已评估影响的多个维度已轻松地在MCA中表示为多个标准。 MCA通过构建和阐明公众价值,促进了涉及公众参与的废物管理和水资源规划问题。 MCA也已应用于水质问题,允许将不可估量的标准纳入评估。可以在典型的决策过程中查看MCA对于环境决策问题的适当性,从而更容易确定MCA在各个决策阶段可以做出的贡献。当决策环境具有多个目标和多个标准,无与伦比的标准,混合数据以及易于使用的需求,而分析环境具有多个参与者的特征时,MCA尤其合适。

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