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Data-Driven Robust Data Envelopment Analysis for Evaluating the Carbon Emissions Efficiency of Provinces in China

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  • Shaojian Qu

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Yuting Xu

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Ying Ji

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Can Feng

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Jinpeng Wei

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Shan Jiang

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

Abstract

To combat global warming, China proposed the “dual carbon” policy in 2020. In this context, it becomes crucial to improve carbon emissions efficiency. Currently, some scholars have utilized data envelopment analysis (DEA) to study carbon emissions efficiency. However, uncertainty about climate and government economic policy is ignored. This paper establishes a robust DEA model to reduce uncertainty and improve robustness. First, robust optimization theory is combined with DEA to establish the robust DEA model. Second, considering three uncertainty sets (box set, ellipsoid set, and polyhedron set), a robust DEA model for different situations is considered. Finally, to address the problem of over-conservatism in robust optimization, this paper applies the data-driven robust DEA model to further analyze the carbon emissions efficiency of China. The results of the data-driven robust DEA model suggest that the government should focus on coordinated regional development, promote the transformation and upgrading of the energy structure, innovate in green technology, and advocate for people to live a green and low-carbon lifestyle.

Suggested Citation

  • Shaojian Qu & Yuting Xu & Ying Ji & Can Feng & Jinpeng Wei & Shan Jiang, 2022. "Data-Driven Robust Data Envelopment Analysis for Evaluating the Carbon Emissions Efficiency of Provinces in China," Sustainability, MDPI, vol. 14(20), pages 1-26, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13318-:d:944230
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    References listed on IDEAS

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    Cited by:

    1. Shengqing Chang & Jingjing Ding & Chenpeng Feng & Ruifeng Wang, 2024. "A Hybrid Parallel Processing Strategy for Large-Scale DEA Computation," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2325-2349, June.
    2. Shilei Wang & Ying Ji & M. I. M. Wahab & Dan Xu & Changbao Zhou, 2022. "A New Decision Framework of Online Multi-Attribute Reverse Auctions for Green Supplier Selection under Mixed Uncertainty," Sustainability, MDPI, vol. 14(24), pages 1-23, December.
    3. Bai, Bing, 2023. "Fiscal stimulus and natural resource efficiency: A comprehensive approach to a green economic recovery," Resources Policy, Elsevier, vol. 86(PB).
    4. Kuang, Hewu & Liang, Yiyan & Zhao, Wenjia & Cai, Jiahong, 2023. "Impact of natural resources and technology on economic development and sustainable environment – Analysis of resources-energy-growth-environment linkages in BRICS," Resources Policy, Elsevier, vol. 85(PB).
    5. Shutian Cui & Renlong Wang, 2024. "A Novel {\delta}-SBM-OPA Approach for Policy-Driven Analysis of Carbon Emission Efficiency under Uncertainty in the Chinese Industrial Sector," Papers 2408.11600, arXiv.org, revised Dec 2024.
    6. Shaojian Qu & Ying Ji, 2023. "Sustainable Supply Chain Management and Optimization," Sustainability, MDPI, vol. 15(4), pages 1-2, February.

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