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Evaluation of Energy Utilization Efficiency in the Yangtze River Economic Belt

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  • Cuijie Lu

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

  • Gaopeng Jiang

    (School of Management Science and Engineering, Anhui University of Technology, Maanshan 243032, China)

  • Xintong Zhang

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

  • Pan Li

    (School of Management Science and Engineering, Anhui University of Technology, Maanshan 243032, China)

  • Jie Li

    (School of Management Science and Engineering, Anhui University of Technology, Maanshan 243032, China)

Abstract

The reasonable optimization of energy structures and improvement of energy utilization efficiency are the inevitable way to achieve new progress in ecological civilization construction. The Yangtze River Economic Belt, as the leading demonstration area of China’s ecological civilization construction, is of great significance to take the lead in clarifying its energy efficiency situation under the dual-carbon goal. For this purpose, this paper uses the super-efficiency SBM model, ML index and Tobit model considering undesired output to explore the energy efficiency and the main factors affecting it of nine provincial capitals and two municipalities in the Yangtze River Economic Belt from 2003 to 2019. The results show that: (1) During the investigation period, the energy efficiency values of 11 cities are above 1, with a recent trend of decline, and the energy efficiency difference between regions is still expanding. (2) From 2003 to 2019, the overall energy efficiency of the Yangtze River Economic Belt has gradually improved with an average annual growth rate of 4.7%. Technological progress is the main force behind efficiency improvement, technical efficiency plays a small role, and the growth rate of the scale efficiency change index is only 1.3%, which means that the existing scale system of the Yangtze River Economic Belt still needs to be further improved. (3) The impact of industrial structure and government dominance on energy efficiency is significantly positive, while the degree of opening to the outside world is significantly negative. The relationship between economic development level and energy efficiency is an inverted “U” shape.

Suggested Citation

  • Cuijie Lu & Gaopeng Jiang & Xintong Zhang & Pan Li & Jie Li, 2023. "Evaluation of Energy Utilization Efficiency in the Yangtze River Economic Belt," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1601-:d:1035263
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    as
    1. Utlu, Zafer & Hepbasli, Arif, 2007. "A review on analyzing and evaluating the energy utilization efficiency of countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(1), pages 1-29, January.
    2. Zhu, Junpeng & Lin, Boqiang, 2022. "Economic growth pressure and energy efficiency improvement: Empirical evidence from Chinese cities," Applied Energy, Elsevier, vol. 307(C).
    3. He, Yong & Liao, Nuo & Zhou, Ya, 2018. "Analysis on provincial industrial energy efficiency and its influencing factors in China based on DEA-RS-FANN," Energy, Elsevier, vol. 142(C), pages 79-89.
    4. Zhang, Yu & Jin, Warren & Xu, Miao, 2021. "Total factor efficiency and convergence analysis of renewable energy in Latin American countries," Renewable Energy, Elsevier, vol. 170(C), pages 785-795.
    5. Meng, Ming & Qu, Danlei, 2022. "Understanding the green energy efficiencies of provinces in China: A Super-SBM and GML analysis," Energy, Elsevier, vol. 239(PA).
    6. Wei, Wei & Hu, Haiqing & Chang, Chun-Ping, 2022. "Why the same degree of economic policy uncertainty can produce different outcomes in energy efficiency? New evidence from China," Structural Change and Economic Dynamics, Elsevier, vol. 60(C), pages 467-481.
    7. Cheng, Zhonghua & Liu, Jun & Li, Lianshui & Gu, Xinbei, 2020. "Research on meta-frontier total-factor energy efficiency and its spatial convergence in Chinese provinces," Energy Economics, Elsevier, vol. 86(C).
    8. Seiford, Lawrence M. & Zhu, Joe, 2005. "A response to comments on modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 161(2), pages 579-581, March.
    9. Zhang, Caiqing & Chen, Panyu, 2022. "Applying the three-stage SBM-DEA model to evaluate energy efficiency and impact factors in RCEP countries," Energy, Elsevier, vol. 241(C).
    10. Wang, Zhaohua & Feng, Chao & Zhang, Bin, 2014. "An empirical analysis of China's energy efficiency from both static and dynamic perspectives," Energy, Elsevier, vol. 74(C), pages 322-330.
    11. Zhao, Haoran & Guo, Sen & Zhao, Huiru, 2019. "Provincial energy efficiency of China quantified by three-stage data envelopment analysis," Energy, Elsevier, vol. 166(C), pages 96-107.
    12. Zhang, Xing-Ping & Cheng, Xiao-Mei & Yuan, Jia-Hai & Gao, Xiao-Jun, 2011. "Total-factor energy efficiency in developing countries," Energy Policy, Elsevier, vol. 39(2), pages 644-650, February.
    13. An, Qingxian & Wu, Qifan & Li, Jinlin & Xiong, Beibei & Chen, Xiaohong, 2019. "Environmental efficiency evaluation for Xiangjiang River basin cities based on an improved SBM model and Global Malmquist index," Energy Economics, Elsevier, vol. 81(C), pages 95-103.
    14. Xu, Mengmeng & Tan, Ruipeng & He, Xinju, 2022. "How does economic agglomeration affect energy efficiency in China?: Evidence from endogenous stochastic frontier approach," Energy Economics, Elsevier, vol. 108(C).
    15. Utlu, Zafer & Hepbasli, Arif, 2006. "Assessment of the energy utilization efficiency in the Turkish transportation sector between 2000 and 2020 using energy and exergy analysis method," Energy Policy, Elsevier, vol. 34(13), pages 1611-1618, September.
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