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Coupling and Coordination Analysis of Thermal Power Carbon Emission Efficiency under the Background of Clean Energy Substitution

Author

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  • Yujing Liu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Dongxiao Niu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

With the proposed goals of reaching its “carbon peak” by 2030 and becoming “carbon neutral” by 2060, China will comprehensively build a diversified, efficient and clean energy system. The differences in China’s resource endowments have made the development of carbon emission reduction in the thermal power industry uncoordinated in various regions. Therefore, it is necessary to optimize the method for measuring thermal power carbon emission efficiency and determine the impact of regional development imbalances on the carbon emission efficiency of thermal power. For this article, we used the stochastic frontier analysis method and selected a variety of influencing factors as technical inefficiency items. After that, we measured the thermal power carbon emission efficiency in 30 provinces and municipalities (autonomous regions) in China in the past 10 years, and it was found that the efficiency was increasing yearly and showed obvious spatial differences. The impact of the clean energy substitution effect on the thermal power carbon emission efficiency cannot be ignored. After performing a coupled and coordinated analysis on the efficiency of thermal carbon emission in various regions and its influencing factors, the three indicators of power consumption intensity, urbanization level and clean energy substitution effect were selected. The weight of the indicator subsystem was determined in view of the estimation of the technical inefficiency. The results of the coupling and coordination analysis show that the degree of coupling and coordination of thermal power carbon emission efficiency is increasing yearly and presents a distribution of “high in the eastern region and low in the western region”. Therefore, all provinces need to vigorously carry out clean replacement work to enhance the coordinated development of carbon emission reduction in the thermal power industry and the level of regional economic development.

Suggested Citation

  • Yujing Liu & Dongxiao Niu, 2021. "Coupling and Coordination Analysis of Thermal Power Carbon Emission Efficiency under the Background of Clean Energy Substitution," Sustainability, MDPI, vol. 13(23), pages 1-17, November.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:23:p:13221-:d:690889
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    References listed on IDEAS

    as
    1. Duan, Na & Guo, Jun-Peng & Xie, Bai-Chen, 2016. "Is there a difference between the energy and CO2 emission performance for China’s thermal power industry? A bootstrapped directional distance function approach," Applied Energy, Elsevier, vol. 162(C), pages 1552-1563.
    2. Yamaji, Kenji & Matsuhashi, Ryuji & Nagata, Yutaka & Kaya, Yoichi, 1993. "A study on economic measures for CO2 reduction in Japan," Energy Policy, Elsevier, vol. 21(2), pages 123-132, February.
    3. Dariush Khezrimotlagh & Yao Chen, 2018. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 217-234, Springer.
    4. Jingdong Zhong, 2019. "Biased Technical Change, Factor Substitution, and Carbon Emissions Efficiency in China," Sustainability, MDPI, vol. 11(4), pages 1-17, February.
    5. Yuhong Wang & Xin Yao & Pengfei Yuan, 2015. "Strategic Adjustment of China’s Power Generation Capacity Structure Under the Constraint of Carbon Emission," Computational Economics, Springer;Society for Computational Economics, vol. 46(3), pages 421-435, October.
    6. Pai Wang & Mengna Qi & Yajia Liang & Xuebing Ling & Yan Song, 2019. "Examining the Relationship between Environmentally Friendly Land Use and Rural Revitalization Using a Coupling Analysis: A Case Study of Hainan Province, China," Sustainability, MDPI, vol. 11(22), pages 1-19, November.
    7. Sun, J. W., 2005. "The decrease of CO2 emission intensity is decarbonization at national and global levels," Energy Policy, Elsevier, vol. 33(8), pages 975-978, May.
    8. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    9. Daeho Lee & Kyunam Kim & Chung Choe, 2017. "An analysis of the impact of unionization on efficiency: evidence from a meta-frontier analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 24(8), pages 575-578, May.
    10. Chunhua Chen & Jianwei Ren & Lijun Tang & Haohua Liu, 2020. "Additive integer-valued data envelopment analysis with missing data: A multi-criteria evaluation approach," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-20, June.
    11. Mark Andor & Frederik Hesse, 2014. "The StoNED age: the departure into a new era of efficiency analysis? A monte carlo comparison of StoNED and the “oldies” (SFA and DEA)," Journal of Productivity Analysis, Springer, vol. 41(1), pages 85-109, February.
    12. Liu, Liwei & Zong, Haijing & Zhao, Erdong & Chen, Chuxiang & Wang, Jianzhou, 2014. "Can China realize its carbon emission reduction goal in 2020: From the perspective of thermal power development," Applied Energy, Elsevier, vol. 124(C), pages 199-212.
    13. Wang, Keying & Wu, Meng & Sun, Yongping & Shi, Xunpeng & Sun, Ao & Zhang, Ping, 2019. "Resource abundance, industrial structure, and regional carbon emissions efficiency in China," Resources Policy, Elsevier, vol. 60(C), pages 203-214.
    14. Ling Wang & Zhongchang Chen & Dalai Ma & Pei Zhao, 2013. "Measuring Carbon Emissions Performance in 123 Countries: Application of Minimum Distance to the Strong Efficiency Frontier Analysis," Sustainability, MDPI, vol. 5(12), pages 1-14, December.
    15. Wanke, Peter & Tsionas, Mike G. & Chen, Zhongfei & Moreira Antunes, Jorge Junio, 2020. "Dynamic network DEA and SFA models for accounting and financial indicators with an analysis of super-efficiency in stochastic frontiers: An efficiency comparison in OECD banking," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 456-468.
    16. Cheng, Zhonghua & Li, Lianshui & Liu, Jun & Zhang, Huiming, 2018. "Total-factor carbon emission efficiency of China's provincial industrial sector and its dynamic evolution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 330-339.
    17. Xu, Shi-Chun & He, Zheng-Xia & Long, Ru-Yin, 2014. "Factors that influence carbon emissions due to energy consumption in China: Decomposition analysis using LMDI," Applied Energy, Elsevier, vol. 127(C), pages 182-193.
    18. Shi Wang & Hua Wang & Li Zhang & Jun Dang, 2019. "Provincial Carbon Emissions Efficiency and Its Influencing Factors in China," Sustainability, MDPI, vol. 11(8), pages 1-21, April.
    19. Jin Zhu & Huaping Sun & Dequn Zhou & Lin Peng & Chuanwang Sun, 2020. "Carbon emission efficiency of thermal power in different regions of China and spatial correlations," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(7), pages 1221-1242, October.
    20. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    21. Jianxue Chai & Lihui Zhang & Meng Yang & Qingyun Nie & Lei Nie, 2020. "Investigation on the Coupling Coordination Relationship between Electric Power Green Development and Ecological Civilization Construction in China: A Case Study of Beijing," Sustainability, MDPI, vol. 12(21), pages 1-29, October.
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    1. Minyoung Yang & Jinsoo Kim, 2022. "A Critical Review of the Definition and Estimation of Carbon Efficiency," Sustainability, MDPI, vol. 14(16), pages 1-18, August.

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