An Integrated Approach to Green Mines Based on Hesitant Fuzzy TOPSIS: Green Degree Analysis and Policy Implications

Dejiang Luo (), Su He, Hao Wu, Long Cheng and Junbo Li
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Dejiang Luo: College of Mathematics and Physics, Chengdu University of Technology, Chengdu 610059, China
Su He: Department of Artificial Intelligence and Big Data, Yibin University, Yibin 644000, China
Hao Wu: College of Management Science, Chengdu University of Technology, Chengdu 610059, China
Long Cheng: Institute of Multipurpose Utilization of Mineral Resources, Chinese Academy of Geological Sciences, Chengdu 610041, China
Junbo Li: College of Mathematics and Physics, Chengdu University of Technology, Chengdu 610059, China

Sustainability, 2023, vol. 15, issue 13, 1-14

Abstract: China proposes to construct green mines as one of the significant means to elevate the green degree of mining industry and protect the mining environment. This paper constructs six evaluation indicators, namely mine environment, Resource development mode, Comprehensive utilization of resources, Energy conservation and emission reduction, Technological innovation and digital mining, Enterprise management and corporate image, to reflect green degree of mining firms. An improved multiattribute evaluation method (MAEM) is proposed based on hesitant fuzzy TOPSIS and kernel method. A case study of green mining firms in Panxi, southwestern China is provided to demonstrate the application of the proposed model. According to the research in this paper, it is more appropriate to expand the data in accordance with the optimistic criteria. The results also show that the improved hesitant fuzzy TOPSIS is a better method in the evaluation of green degree. Through the results of this paper, a reasonable and scientific method is introduced for the evaluation of the green degree of mining firms, and it can be promoted to the evaluation of the mineral resource development efficiency.

Keywords: green degree; TOPSIS; hesitant fuzzy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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