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Assessment of Key Socio-Economic and Environmental Challenges in the Mining Industry: Implications for Resource Policies in Emerging Economies

Author

Listed:
  • R. Marimuthu
  • B. Sankaranarayanan
  • S.M. Ali
  • Ana Beatriz Lopes de Sousa Jabbour

    (Métis Lab EM Normandie - EM Normandie - École de Management de Normandie)

  • K. Karuppiah

Abstract

This paper seeks to develop a framework to identify, analyse, and assess the mining industry's key challenges in terms of environmental, operational, and social issues. For each issue, 15 challenges have been identified from experts' opinions and from the relevant literature; each is examined in a real-world industrial setting. South India's mining industry is utilized to categorize and to determine crucial challenges based on an identification of their causal relationships. A fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) is used to assess and rank the challenges of each issue. Results reveal that climate change, lack of availability of capital, and unfair wages are the top challenges in the environmental, operational, and social issues, respectively, in India's mining industry. The proposed method is found effective in attaining the causal relationships and ranking amongst the identified challenges. The outcomes help decision-makers and industrial managers to take remedial actions such as adopting new technologies and innovations to protect the environment, improve the operating conditions, and facilitate social benefits to resolve the mining industry's challenges. \textcopyright 2021 Institution of Chemical Engineers

Suggested Citation

  • R. Marimuthu & B. Sankaranarayanan & S.M. Ali & Ana Beatriz Lopes de Sousa Jabbour & K. Karuppiah, 2021. "Assessment of Key Socio-Economic and Environmental Challenges in the Mining Industry: Implications for Resource Policies in Emerging Economies," Post-Print hal-04275970, HAL.
    • Handle: RePEc:hal:journl:hal-04275970
    DOI: 10.1016/j.spc.2021.02.005
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    Cited by:

    1. Ramos, Paulo Vitor B. & Villela, Saulo Moraes & Silva, Walquiria N. & Dias, Bruno H., 2023. "Residential energy consumption forecasting using deep learning models," Applied Energy, Elsevier, vol. 350(C).

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