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Carbon emission efficiency of thermal power generation in China: Empirical evidence from the micro-perspective of power plants

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  • Fang, Tao
  • Fang, Debin
  • Yu, Bolin

Abstract

The power industry is the key to achieving the carbon peak and carbon neutrality goals. It is of great theoretical and practical significance to investigate the carbon emission efficiency (CEE) of thermal power generation from the micro-perspective of power plants. Using the data of China's 42 thermal power plants in 2020, this paper firstly constructs a multi-dimensional index evaluation system for CEE from the aspects of energy, economy, and environment, considering the actual operational characteristics of power generation enterprises. Specifically, the super-efficiency slack-based measure (SBM) model with undesirable outputs is applied to identify and distinguish the multiple efficient decision-making units. Then, this paper explores the efficiency improvement path of inefficient thermal power plants. The results show that most power plants show the increasing scale payoffs, while the other power plants present constant returns to scale. The distribution of power plants' CEE is polarized into two clusters, i.e., low-CEE group and high-CEE group. Besides, Southern China shows the highest CEE, while the least is recorded in Central China. The main reasons for the low CEE are the high redundancies in inputs, carbon emission intensity of power supply and heat supply. It is necessary to adjust the resource allocation and input-output structure according to the redundancy and deficiency rates. This study can provide managers with targeted policy guidance to improve the sustainable development of power enterprises.

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  • Fang, Tao & Fang, Debin & Yu, Bolin, 2022. "Carbon emission efficiency of thermal power generation in China: Empirical evidence from the micro-perspective of power plants," Energy Policy, Elsevier, vol. 165(C).
  • Handle: RePEc:eee:enepol:v:165:y:2022:i:c:s030142152200180x
    DOI: 10.1016/j.enpol.2022.112955
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    as
    1. Li, Lan-Bing & Hu, Jin-Li, 2012. "Ecological total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 46(C), pages 216-224.
    2. Yu, Bolin & Fang, Debin & Yu, Hongwei & Zhao, Chaoyang, 2021. "Temporal-spatial determinants of renewable energy penetration in electricity production: Evidence from EU countries," Renewable Energy, Elsevier, vol. 180(C), pages 438-451.
    3. Azadeh, A. & Ghaderi, S.F. & Omrani, H. & Eivazy, H., 2009. "An integrated DEA-COLS-SFA algorithm for optimization and policy making of electricity distribution units," Energy Policy, Elsevier, vol. 37(7), pages 2605-2618, July.
    4. Golany, B & Roll, Y, 1989. "An application procedure for DEA," Omega, Elsevier, vol. 17(3), pages 237-250.
    5. Li, Xiao-Bai & Reeves, Gary R., 1999. "A multiple criteria approach to data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 115(3), pages 507-517, June.
    6. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
    7. Lee, Hsuan-Shih & Chu, Ching-Wu & Zhu, Joe, 2011. "Super-efficiency DEA in the presence of infeasibility," European Journal of Operational Research, Elsevier, vol. 212(1), pages 141-147, July.
    8. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    9. Costa, Marcelo Azevedo & Lopes, Ana Lúcia Miranda & de Pinho Matos, Giordano Bruno Braz, 2015. "Statistical evaluation of Data Envelopment Analysis versus COLS Cobb–Douglas benchmarking models for the 2011 Brazilian tariff revision," Socio-Economic Planning Sciences, Elsevier, vol. 49(C), pages 47-60.
    10. Teng, Xiangyu & Liu, Fan-peng & Chiu, Yung-ho, 2021. "The change in energy and carbon emissions efficiency after afforestation in China by applying a modified dynamic SBM model," Energy, Elsevier, vol. 216(C).
    11. Pan, Yuling & Dong, Feng, 2022. "Design of energy use rights trading policy from the perspective of energy vulnerability," Energy Policy, Elsevier, vol. 160(C).
    12. Chen, Yao, 2005. "Measuring super-efficiency in DEA in the presence of infeasibility," European Journal of Operational Research, Elsevier, vol. 161(2), pages 545-551, March.
    13. Yu, Yantuan & Zhang, Ning, 2021. "Low-carbon city pilot and carbon emission efficiency: Quasi-experimental evidence from China," Energy Economics, Elsevier, vol. 96(C).
    14. Fang, Debin & Yu, Bolin, 2021. "Driving mechanism and decoupling effect of PM2.5 emissions: Empirical evidence from China’s industrial sector," Energy Policy, Elsevier, vol. 149(C).
    15. Wang, Guofeng & Deng, Xiangzheng & Wang, Jingyu & Zhang, Fan & Liang, Shiqi, 2019. "Carbon emission efficiency in China: A spatial panel data analysis," China Economic Review, Elsevier, vol. 56(C), pages 1-1.
    16. Chen, Yao & Du, Juan & Huo, Jiazhen, 2013. "Super-efficiency based on a modified directional distance function," Omega, Elsevier, vol. 41(3), pages 621-625.
    17. Zhou, Guanghui & Chung, William & Zhang, Xiliang, 2013. "A study of carbon dioxide emissions performance of China's transport sector," Energy, Elsevier, vol. 50(C), pages 302-314.
    18. 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.
    19. Lin, Boqiang & Yang, Lisha, 2014. "Efficiency effect of changing investment structure on China׳s power industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 403-411.
    20. Adler, Nicole & Friedman, Lea & Sinuany-Stern, Zilla, 2002. "Review of ranking methods in the data envelopment analysis context," European Journal of Operational Research, Elsevier, vol. 140(2), pages 249-265, July.
    21. Y-W Chen & M Larbani & Y-P Chang, 2009. "Multiobjective data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(11), pages 1556-1566, November.
    22. Ghasemi, Mohammad Reza & Ignatius, Joshua & Rezaee, Babak, 2019. "Improving discriminating power in data envelopment models based on deviation variables framework," European Journal of Operational Research, Elsevier, vol. 278(2), pages 442-447.
    23. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    24. Wang, Ke & Wei, Yi-Ming & Zhang, Xian, 2012. "A comparative analysis of China’s regional energy and emission performance: Which is the better way to deal with undesirable outputs?," Energy Policy, Elsevier, vol. 46(C), pages 574-584.
    25. Hatami-Marbini, Adel & Emrouznejad, Ali & Tavana, Madjid, 2011. "A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making," European Journal of Operational Research, Elsevier, vol. 214(3), pages 457-472, November.
    26. Xie, Bai-Chen & Gao, Jie & Zhang, Shuang & Pang, Rui-Zhi & Zhang, ZhongXiang, 2018. "The environmental efficiency analysis of China’s power generation sector based on game cross-efficiency approach," Structural Change and Economic Dynamics, Elsevier, vol. 46(C), pages 126-135.
    27. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    28. Yu, Bolin & Fang, Debin & Meng, Jingxuan, 2021. "Analysis of the generation efficiency of disaggregated renewable energy and its spatial heterogeneity influencing factors: A case study of China," Energy, Elsevier, vol. 234(C).
    29. Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
    30. Wang, Jian & Lv, Kangjuan & Bian, Yiwen & Cheng, Yu, 2017. "Energy efficiency and marginal carbon dioxide emission abatement cost in urban China," Energy Policy, Elsevier, vol. 105(C), pages 246-255.
    31. Si, Tong & Wang, Chunbo & Liu, Ruiqi & Guo, Yusheng & Yue, Shuang & Ren, Yujie, 2020. "Multi-criteria comprehensive energy efficiency assessment based on fuzzy-AHP method: A case study of post-treatment technologies for coal-fired units," Energy, Elsevier, vol. 200(C).
    32. Ball, V. Eldon & Lovell, C.A. Knox & Luu, H. & Nehring, Richard F., 2004. "Incorporating Environmental Impacts in the Measurement of Agricultural Productivity Growth," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 29(3), pages 1-25, December.
    33. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    34. D K Despotis, 2002. "Improving the discriminating power of DEA: focus on globally efficient units," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(3), pages 314-323, March.
    35. Green, Rodney H. & Doyle, John R. & Cook, Wade D., 1996. "Preference voting and project ranking using DEA and cross-evaluation," European Journal of Operational Research, Elsevier, vol. 90(3), pages 461-472, May.
    36. Emrouznejad, Ali & Parker, Barnett R. & Tavares, Gabriel, 2008. "Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA," Socio-Economic Planning Sciences, Elsevier, vol. 42(3), pages 151-157, September.
    37. 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.
    38. Alwyn Young, 2003. "Gold into Base Metals: Productivity Growth in the People's Republic of China during the Reform Period," Journal of Political Economy, University of Chicago Press, vol. 111(6), pages 1220-1261, December.
    39. Yu, Bolin & Fang, Debin & Dong, Feng, 2020. "Study on the evolution of thermal power generation and its nexus with economic growth: Evidence from EU regions," Energy, Elsevier, vol. 205(C).
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