Health evaluation of a regional logistics industrial ecosystem in China based on fuzzy matter-element analysis method

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

3 Scopus Citations
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  • Junjie Fan
  • Lingyun Zhou
  • Yushu Cao
  • Zhonghua Gu

Related Research Unit(s)


Original languageEnglish
Pages (from-to)2195-2202
Journal / PublicationJournal of Intelligent and Fuzzy Systems
Issue number4
Publication statusPublished - 9 Sept 2016


The construction of a regional logistics industrial ecosystem elicits increasing attention because of the growing awareness of environment protection, and its health evaluation by considering environmental impacts is one of the kernel problems that must be addressed to promote the development of regional logistics. In this study, a comprehensive methodology based on fuzzy mathematics and matter-element analysis theory was presented to accurately evaluate the health status of the regional logistics industrial ecosystem in China. First, the forming mechanism and influencing factors of regional logistics industrial ecosystem were analyzed. Second, a fuzzy matter-element model was constructed to evaluate the regional logistics industrial ecosystem, and the weights of characteristic values of evaluation indexes for fuzzy matter-element were obtained using entropy decision method. Finally, this proposed method was applied to evaluate the logistics industrial ecosystem health of eight provinces in China. In this case, these evaluation indexes include energy consumption, logistics cost, and CO2 emissions. The results show that this method is feasible and effective, and demonstrate the promising application of the proposed model in evaluating the health status of a regional logistics industrial ecosystem and in supplying reliable data for the environmental protection of regional logistics activities.

Research Area(s)

  • ecological health evaluation, entropy weight, Logistics industrial ecosystem, matter element analysis method