Carbon emission reduction potential and its influencing factors in China’s coal-fired power industry: a cost optimization and decomposition analysis

Yunfei An, Dequn Zhou and Qunwei Wang ()
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Yunfei An: Nanjing University of Aeronautics and Astronautics
Dequn Zhou: Nanjing University of Aeronautics and Astronautics
Qunwei Wang: Nanjing University of Aeronautics and Astronautics

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2022, vol. 24, issue 3, No 26, 3619-3639

Abstract: Abstract China has become the world’s most carbon-emitting country, and the coal-fired power industry (CFPI) dominates China’s carbon emissions. Stimulating the carbon emission reduction potential of China’s CFPI is important for reducing global carbon emissions and mitigating global warming. To explore the potential for reducing carbon emissions in the CFPI, this study constructed a model based on the data envelopment analysis (DEA) method, considering profit motive and the cost of regulatory policy. To analyze the factors influencing carbon reduction potential (CRP), the Kaya-LMDI (Kaya Identity-Logarithmic Mean Divisia Index) method was also applied. Some policy implications for the regions in China came out. The results show that: (a) China’s coal power industry generation process has not yet reached its optimal profit. When China’s CFPI realizes the optimal profit, a CRP will also decrease industrial carbon emissions by 3.54%. (b) At the carbon costs ranging from 16.8 to 95.2 Yuan/ton caused by carbon regulation policy, the total CRP of China’s CFPI would be further enhanced to 4.32%. (c) The coal-fired power output rate and industry scale had a positive effect on CRP, while the labor productivity had a negative effect. Carbon costs caused by carbon regulation policies could promote the CFPI to realize a greater carbon emission reduction potential by adjusting labor productivity and the industry scale effect.

Keywords: Coal-fired power industry; Carbon emission; Data envelopment analysis; Decomposition analysis (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10668-021-01579-7

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