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Carbon emission reduction potential and driving factors of thermal power plants: Based on the combination of LMDI and DEA approach

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  • Zaixun Jia

    (Ocean University of China)

  • Xin Zhao

    (Ocean University of China)

Abstract

Amidst China's third wave of electricity shortages, maintaining power generation to meet societal demands has resulted in substantial carbon dioxide emissions. Against this backdrop, exploring the carbon emission reduction potential (CERP) and its driving factors of thermal power plants has become particularly important. Thus, we constructed an RBM-MinDS model to measure the CERP of 248 thermal power plants in China from 2014 to 2021. Then, the LMDI model is introduced into RBM-MinDS model to reveal the actual influence of internal factors on the carbon emission reduction behavior of thermal power plant. The results indicate (1) The CERP of Chinese thermal power plants is showing an M-shaped downward trend. It has obvious technological heterogeneity and remarkable spatial non-equilibrium. (2) Improving carbon emission efficiency and power generation is critical to decreasing CERP. Potential energy intensity and carbon intensity are the key factors to restrain the increase of CERP. (3) For driving factors, carbon emission efficiency and emission reduction capacity have greater impacts than regional inequality and ownership inequality. (4) The carbon intensity effect and power generation effect have greater impacts on central thermal power plants than the local thermal power plants. Graphical abstract

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  • Zaixun Jia & Xin Zhao, 2025. "Carbon emission reduction potential and driving factors of thermal power plants: Based on the combination of LMDI and DEA approach," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 30(1), pages 1-18, January.
    • Handle: RePEc:spr:masfgc:v:30:y:2025:i:1:d:10.1007_s11027-024-10192-8
    DOI: 10.1007/s11027-024-10192-8
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    1. Wang, Ke & Lu, Bin & Wei, Yi-Ming, 2013. "China’s regional energy and environmental efficiency: A Range-Adjusted Measure based analysis," Applied Energy, Elsevier, vol. 112(C), pages 1403-1415.
    2. Takayabu, Hirotaka, 2020. "CO2 mitigation potentials in manufacturing sectors of 26 countries," Energy Economics, Elsevier, vol. 86(C).
    3. Wang, Juan & Li, Ziming & Wu, Tong & Wu, Siyu & Yin, Tingwei, 2022. "The decoupling analysis of CO2 emissions from power generation in Chinese provincial power sector," Energy, Elsevier, vol. 255(C).
    4. Juan Aparicio & José Ruiz & Inmaculada Sirvent, 2007. "Closest targets and minimum distance to the Pareto-efficient frontier in DEA," Journal of Productivity Analysis, Springer, vol. 28(3), pages 209-218, December.
    5. Du, Limin & Hanley, Aoife & Zhang, Ning, 2016. "Environmental technical efficiency, technology gap and shadow price of coal-fuelled power plants in China: A parametric meta-frontier analysis," Resource and Energy Economics, Elsevier, vol. 43(C), pages 14-32.
    6. Du, Zhili & Wang, Yao, 2022. "Does energy-saving and emission reduction policy affects carbon reduction performance? A quasi-experimental evidence in China," Applied Energy, Elsevier, vol. 324(C).
    7. William Cooper & Kyung Park & Jesus Pastor, 1999. "RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models, and Relations to Other Models and Measures in DEA," Journal of Productivity Analysis, Springer, vol. 11(1), pages 5-42, February.
    8. Zhang, Xiaoyue & Huang, Guohe & Xie, Yulei & Liu, Lirong & Song, Tangnyu, 2022. "A coupled non-deterministic optimization and mixed-level factorial analysis model for power generation expansion planning – A case study of Jing-Jin-Ji metropolitan region, China," Applied Energy, Elsevier, vol. 311(C).
    9. Fare, Rolf, 1989. "Multilateral Productivity Comparisons When Some Outputs Are Undesirable: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 71(1), pages 90-98, February.
    10. Wang, H. & Ang, B.W. & Su, Bin, 2017. "Assessing drivers of economy-wide energy use and emissions: IDA versus SDA," Energy Policy, Elsevier, vol. 107(C), pages 585-599.
    11. Liu, Nan & Ma, Zujun & Kang, Jidong, 2017. "A regional analysis of carbon intensities of electricity generation in China," Energy Economics, Elsevier, vol. 67(C), pages 268-277.
    12. Song, Malin & Wang, Jianlin, 2018. "Environmental efficiency evaluation of thermal power generation in China based on a slack-based endogenous directional distance function model," Energy, Elsevier, vol. 161(C), pages 325-336.
    Full references (including those not matched with items on IDEAS)

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