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The Development of Circular Economy in China’s Coal Industry: Facing Challenges of Inefficiency in the Waste Recycling Process

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Listed:
  • Yunbing Hou

    (School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Shiyu Xi

    (School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Huaqing Li

    (Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China)

  • Yudong Fan

    (School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Fuchun Li

    (School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Qiang Wen

    (School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Junwei Hao

    (School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)

Abstract

This paper innovatively constructs a comprehensive material cycle network framework for the circular economy system of the coal industry and evaluates the circular economy efficiency of China’s provincial coal industry from 2011 to 2021 using a comprehensive evaluation model that integrates emergy analysis and dynamic network data envelopment analysis (DEA). The research delves into the evolutionary characteristics of the coal industry’s circular economy and identifies the underlying causes of inefficiency. The results reveal that the circular economy in China’s coal industry has gone through three stages: the transformation period, the reinforcement period, and the growth period, with the inefficiency of waste reutilization being the key factor restricting the overall improvement in efficiency. The circular economy model in the production phase is gradually shifting from an extensive linear model to a clean, closed-loop model, while a significant gap remains between the high-emission linear model and the low-pollution closed-loop model in the utilization phase. Furthermore, regional heterogeneity mainly arises from imbalances in the operational efficiency of the circular economy system. This study not only reveals the deep-seated reasons for the low efficiency of circular economy in China’s coal industry but also provides strategies and directions for achieving a more efficient circular economy and carbon mitigation goals.

Suggested Citation

  • Yunbing Hou & Shiyu Xi & Huaqing Li & Yudong Fan & Fuchun Li & Qiang Wen & Junwei Hao, 2025. "The Development of Circular Economy in China’s Coal Industry: Facing Challenges of Inefficiency in the Waste Recycling Process," Sustainability, MDPI, vol. 17(18), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:18:p:8147-:d:1746534
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    References listed on IDEAS

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    1. Joanna Kulczycka & Ewa Dziobek & Anita Szmiłyk, 2020. "Challenges in the management of data on extractive waste—the Polish case," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 33(3), pages 341-347, October.
    2. John S. Liu & Louis Y. Y. Lu & Wen-Min Lu, 2016. "Research Fronts and Prevailing Applications in Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, chapter 0, pages 543-574, Springer.
    3. Oksana Marinina & Natalia Kirsanova & Marina Nevskaya, 2022. "Circular Economy Models in Industry: Developing a Conceptual Framework," Energies, MDPI, vol. 15(24), pages 1-21, December.
    4. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    5. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min, 2016. "Research fronts in data envelopment analysis," Omega, Elsevier, vol. 58(C), pages 33-45.
    6. V.V. Gedam & R.D. Raut & Ana Beatriz Lopes de Sousa Jabbour & N. Agrawal, 2021. "Moving the Circular Economy Forward in the Mining Industry: Challenges to Closed-Loop in an Emerging Economy," Post-Print hal-04275964, HAL.
    7. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    8. Hessampour, Reza & Bastani, Aboubakr & Hassani, Mehrdad & Failla, Sabina & Vaverková, Magdalena Daria & Halog, Anthony, 2023. "Joint life cycle assessment and data envelopment analysis for the benchmarking of energy, exergy, environmental effects, and water footprint in the canned apple supply chain," Energy, Elsevier, vol. 278(C).
    9. Chambers, Robert G. & Chung, Yangho & Fare, Rolf, 1996. "Benefit and Distance Functions," Journal of Economic Theory, Elsevier, vol. 70(2), pages 407-419, August.
    10. Oksana Marinina & Marina Nevskaya & Izabela Jonek-Kowalska & Radosław Wolniak & Mikhail Marinin, 2021. "Recycling of Coal Fly Ash as an Example of an Efficient Circular Economy: A Stakeholder Approach," Energies, MDPI, vol. 14(12), pages 1-21, June.
    11. Gedam, Vidyadhar V. & Raut, Rakesh D. & Lopes de Sousa Jabbour, Ana Beatriz & Agrawal, Nishant, 2021. "Moving the circular economy forward in the mining industry: Challenges to closed-loop in an emerging economy," Resources Policy, Elsevier, vol. 74(C).
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