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    Rights statement: This is the author’s version of a work that was accepted for publication in Journal of Cleaner Production. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Cleaner Production, 142, 4, 2017 DOI: 10.1016/j.jclepro.2016.10.049

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Assessing industrial ecosystem vulnerability in the coal mining area under economic fluctuations

Research output: Contribution to journalJournal articlepeer-review

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  • Delu Wang
  • Jianping Zheng
  • Xuefeng Song
  • Gang Ma
  • Yun Liu
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<mark>Journal publication date</mark>20/01/2017
<mark>Journal</mark>Journal of Cleaner Production
Issue number4
Volume142
Number of pages13
Pages (from-to)4019-4031
Publication StatusPublished
Early online date13/10/16
<mark>Original language</mark>English

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.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Journal of Cleaner Production. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Cleaner Production, 142, 4, 2017 DOI: 10.1016/j.jclepro.2016.10.049