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Impact of the national economy and air pollutant emissions on Chinese energy mix: Evidence from an SBMDEA-TOPSIS model

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Listed:
  • Han, Yongming
  • Ji, Wenjie
  • Liu, Lin
  • Cao, Lian
  • Ping, Weiying
  • Zeyu chu,
  • Fan, Jinzhen

Abstract

With Chinese rapid economic growth, fossil energy consumption, greenhouse gas emissions, and air pollution have intensified, posing significant threats to public health and economic sustainability. And improving energy efficiency and optimizing energy structures are essential for sustainable development. Therefore, the paper integrates the Slacks-Based Measure of Efficiency-Data Envelopment Analysis (SBM-DEA) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to comprehensively assess regional energy efficiency. The SBM-DEA accounts for both desirable and undesirable outputs, effectively measuring inefficiencies related to emissions, while the TOPSIS ranks decision-making units (DMUs) by their proximity to the ideal efficiency frontier, enabling precise benchmarking. Then, the proposed SBM-DEA-TOPSIS framework is applied to analyze Chinese energy efficiency in the context of economic growth, greenhouse gas emissions, and air pollution. The results reveal significant regional disparities, with economically developed provinces achieving higher efficiency than resource-dependent areas. Moreover, findings suggest that targeted policy interventions are crucial, including technological innovation, industrial restructuring, and an accelerated transition to clean energy. Finally, provinces with lower efficiency should focus on upgrading energy-intensive industries, optimizing resource allocation, and expanding renewable energy adoption. Additionally, incorporating incentives for green technology and stricter environmental regulations can drive efficiency improvements. Furthermore, the proposed SBM-DEA-TOPSIS model serves as a valuable tool for guiding evidence-based policymaking, supporting Chinese long-term energy transition and environmental objectives.

Suggested Citation

  • Han, Yongming & Ji, Wenjie & Liu, Lin & Cao, Lian & Ping, Weiying & Zeyu chu, & Fan, Jinzhen, 2025. "Impact of the national economy and air pollutant emissions on Chinese energy mix: Evidence from an SBMDEA-TOPSIS model," Energy, Elsevier, vol. 325(C).
    • Handle: RePEc:eee:energy:v:325:y:2025:i:c:s0360544225017402
    DOI: 10.1016/j.energy.2025.136098
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    References listed on IDEAS

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    1. Han, Yongming & Cao, Lian & Guo, Qing & Geng, Zhiqiang & Yang, Weiyang & Fan, Jinzhen & Liu, Min, 2024. "Economy and carbon dioxide emissions effects of energy structures in China: Evidence based on a novel AHP-SBMDEA model," Energy, Elsevier, vol. 290(C).
    2. Zhang, Ruchuan & Gao, Weiyan & Chen, Shanshan & Zhou, Li & Li, Aijun, 2024. "Dose digital transformation contribute to improving financing efficiency? Evidence and implications for energy enterprises in China," Energy, Elsevier, vol. 300(C).
    3. Han, Yongming & Li, Jingze & Lou, Xiaoyi & Fan, Chenyu & Geng, Zhiqiang, 2022. "Energy saving of buildings for reducing carbon dioxide emissions using novel dendrite net integrated adaptive mean square gradient," Applied Energy, Elsevier, vol. 309(C).
    4. Zhou, Lijuan & Zhang, Ruchuan & Zhang, Aizhen & Li, Miao & Li, Aijun, 2024. "Understanding the role of energy sector in the outbreak of epidemic in China: An analysis based on SEIDR epidemic model, dynamic input-output model and resource allocation DEA model," Energy, Elsevier, vol. 305(C).
    5. Xuwei Tang & Qi Zhang & Chunxin Li & Haitao Zhang & Haiyun Xu, 2023. "The Effects and Driving Factors of Low-Carbon Transition of International Oil Companies: Evidence from a Super-SBM Model," Energies, MDPI, vol. 17(1), pages 1-19, December.
    6. Camanho, Ana Santos & Silva, Maria Conceicao & Piran, Fabio Sartori & Lacerda, Daniel Pacheco, 2024. "A literature review of economic efficiency assessments using Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 315(1), pages 1-18.
    7. Wu, Jiaxi & Wang, Shali & Zhang, Rui & Zhao, Meilin & Sun, Xialing & Qie, Xiaotong & Wang, Yue, 2025. "Measurement of green innovation efficiency in Chinese listed energy-intensive enterprises based on the three stage Super-SBM model," International Review of Economics & Finance, Elsevier, vol. 97(C).
    8. Li, Ke & Zou, Danyu & Li, Hailing, 2023. "Environmental regulation and green technical efficiency: A process-level data envelopment analysis from Chinese iron and steel enterprises," Energy, Elsevier, vol. 277(C).
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