A multi-criteria decision support framework for municipal solid waste landfill siting: a case study of New South Wales (Australia)

被引:0
作者
Hossein Asefi
Yang Zhang
Samsung Lim
Mojtaba Maghrebi
Shahrooz Shahparvari
机构
[1] RMIT University,School of Accounting, Information systems, & Supply Chain
[2] Shanghai Maritime University,College of Transport and Communications
[3] Shanghai,School of Civil and Environmental Engineering
[4] The University of New South Wales,Department of Civil Engineering
[5] Ferdowsi University of Mashhad,undefined
来源
Environmental Monitoring and Assessment | 2020年 / 192卷
关键词
Municipal solid waste management; Landfill site selection; Multi-criteria decision-making; Analytical hierarchy process (AHP); Decision Making Trial Evaluation Laboratory (DEMATEL);
D O I
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学科分类号
摘要
Sanitary waste disposal and site selection for establishing landfills are challenging problems for environmental planners. This paper aims to take environmental, socio-economic, geological, geomorphological, hydrological and ecological factors into consideration to provide a decision support framework for landfill siting. Analytical hierarchy process (AHP) and Decision Making Trial and Evaluation Laboratory (DEMATEL) are coupled to develop an efficient multi-criteria decision-making method to be utilized in a Geographic Information System (GIS) environment for evaluating the suitability for landfill siting. As the first attempt to employ DEMATEL effectively in a landfill site selection problem, the proposed method is tested with landfill siting scenarios in New South Wales (NSW), Australia. Regional analysis is also performed to identify the potentially most suitable statistical divisions for landfill siting in NSW. The top two ranked zones covering 0.7% and 22% of the study area, respectively, are considered as the optimal areas for establishing landfills, while the bottom two ranked zones are not recommended for further consideration. Further detailed analysis is also conducted on the existing landfills, which shows that 1.0% and 37.0% of them are ranks 1 and 2, respectively. The scenario-based analysis implies that, among the contributing factors; geological and economic factors are highly important.
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