Trends and applications of multi-criteria decision analysis in environmental sciences: literature review

被引：115
作者：
Cegan J.C. [1 ]
Filion A.M. [1 ]
Keisler J.M. [2 ]
Linkov I. [1 ]
机构：
[1] Environmental Laboratory, Engineer Research and Development Center, US Army Corps of Engineers, Washington, DC
[2] College of Management, University of Massachusetts Boston, Boston, MA
来源：
Environment Systems and Decisions | 2017年 / 37卷 / 2期
关键词：
Decision making; Environmental; Multi-criteria decision analysis; R; Review; Risk management; Text mining;
D O I：
10.1007/s10669-017-9642-9
中图分类号：
学科分类号：
摘要：
Approximately 3000 papers concerning multi-criteria decision analysis (MCDA) in the environmental field were identified through a series of queries in the Web of Science database and classified by MCDA method and environmental application using text mining in R. Stemming and stop word removal techniques were used to remove irrelevant text from the literature. Trends in MCDA methods (AHP/ANP, TOPSIS, outranking, MAUT/MAVT) associated with specific environmental applications (water, air, energy, natural resources, and waste management) or interventions/tools applications (stakeholders, strategies, sustainability, and GIS) were identified. The results show a linear growth in the share of MCDA papers in environmental science across all application areas. Furthermore, the results show that AHP/ANP and MAUT/MAVT are the most frequently mentioned MCDA methods in the literature. For environmental applications, the results showed that natural resource and waste management keywords were, respectively, the most and least commonly discussed applications within the MCDA papers. For intervention/tool applications, we found that keywords associated with ‘strategy’ and ‘GIS’ applications are, respectively, the most and least commonly discussed keywords within the MCDA papers. The authors found that MCDA method keywords were evenly distributed across the environmental and intervention/tool applications, indicating a lack of preference in the environmental field for use of specific MCDA methods. This paper demonstrates that text mining is an applicable tool to assess specific textual trends and patterns when analyzing larger bodies of MCDA literature. © 2017, Springer Science+Business Media New York (outside the USA).
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页码：123 / 133
页数：10
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