Assessing industrial ecosystem vulnerability in the coal mining area under economic fluctuations. (20th January 2017)

• Record Type: Journal Article
• Authors: Wang, Delu; Zheng, Jianping; Song, Xuefeng; Ma, Gang; Liu, Yun
• Is Part Of: Journal of cleaner production. Volume 142:Part 4(2017)
• Page Start: 4019
• Page End: 4031
• Publication Date: 2017-01-20
• Subjects: Industrial ecosystem -- Vulnerability -- Composite index -- Integrated assessment -- Coal mining area; CMIES Coal Mining Industrial Ecosystem -- CVI CMIES Vulnerability Index -- RS Rough Set -- TOPSIS Technique for Order Preference by Similarity to Ideal Solution -- RSR Rank-sum Ratio; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2016.10.049
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: In the context of the depth adjustment of the global economy and wild fluctuations in energy prices, the vulnerability issue of the coal mining industrial ecosystem (CMIES) has seriously affected the sustainable development of the regional economy. Comparisons of CMIES health status at a regional level are worthy of being conducted. This not only contributes to understanding a particular coal mining area's situation in regards to CMIES vulnerability, but also helps to discover a meaningful benchmark to learn the experiences in terms of action programmes formulation. In this study, based on the analysis of the vulnerability response mechanism of CMIES to economic fluctuations, an initial indicator system for vulnerability assessment of CMIES was constructed. Ultimately, 14 vulnerability-evaluating indicators and their weights were obtained using rough set attribute reduction. Based on a composite CMIES Vulnerability Index (CVI), the Rough Set-Technique for Order Preference by Similarity to Ideal Solution-Rank-sum Ratio (RS-TOPSIS-RSR) methodology is proposed to conduct the CMIES vulnerability assessment process from an overall perspective. Using this methodology, 33 coal mining areas in China are ranked as well as grouped into three specific groups based on the CVI score. The results demonstrate the feasibility of the proposed method as a valuable tool for decision making and performance evaluation with multiple alternatives and criteria. Highlights: Introducing a hierarchical structure of Coal Mining Industrial Ecosystem Vulnerability Index. Obtaining fourteen evaluating indexes without losing information using rough sets. Proposing a new method to compare the vulnerability of coal mining industrial ecosystem. Providing with a promising decision support system for industrial ecosystem management.