期刊
SUSTAINABLE CITIES AND SOCIETY
卷 74, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.scs.2021.103120
关键词
Risk assessment; PPP WTE Incineration plant projects; BWM; Improved DEMATEL; Weighted multigranulation fuzzy rough sets
资金
- Natural Science Foundation of China [71873015]
- Program for New Century Excellent Talents in University of China [NCE-13-0037]
- Humanities and Social Sciences Foundation of Ministry of Education of China [14YJA630019]
This methodology involves the construction of a risk evaluation index system, the utilization of both subjective and objective expert weights, and the application of weighted multigranulation fuzzy rough sets to evaluate PPP waste-to-energy incineration plant projects, ultimately selecting a promising project.
Implementing effective risk assessment for public-private partnership (PPP) waste-to-energy (WTE) incineration plant projects promotes the sustainable development of WTE incineration plants. However, some studies fail to consider multi-individuals participation and handle mutual compensation among risk factors, which affects the reliability of evaluation results and increases decision risks. Thus, we propose a methodology based on weighted multigranulation fuzzy rough sets (MGFRSs) over two universes to perform risk evaluation for PPP WTE incineration plant projects. Firstly, alternative risk factors are detected from different dimensions to construct a risk evaluation index system. Secondly, the BWM (best-worst method) is utilized to determined subjective expert weights and an improved DEMATEL (Decision Making Trial and Evaluation Laboratory) method is developed to derive objective expert weights. Combining subjective and objective expert weights, an optimization model is built to form the collective expert weights. Thirdly, to avoid mutual compensation among risk factors, multisource heterogeneous information system-based weighted MGFRSs over two universes are proposed to evaluate PPP WTE plant projects and select a promising one. Finally, a case study is performed to illustrate the feasibility of the proposed methodology. Sensitivity analysis and comparative analysis are implemented to demonstrate the robustness, effectiveness, and superiority of the proposed methodology.
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