Inequality in Fossil Fuel Power Plants in China: A Perspective of Efficiency and Abatement Cost

Yongrok Choi, Yunning Ma (), Yu Zhao and Hyoungsuk Lee ()
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Yongrok Choi: Program of Industrial Security Governance, Inha University, Incheon 22212, Republic of Korea
Yunning Ma: Program of Industrial Security Governance, Inha University, Incheon 22212, Republic of Korea
Yu Zhao: Institute of Blue and Green Development, Shandong University, Jinan 264209, China
Hyoungsuk Lee: Energy Environment Policy and Technology, Graduate School of Energy and Environment (KU-KIST Green School), Korea University, Seoul 02841, Republic of Korea

Sustainability, 2023, vol. 15, issue 5, 1-15

Abstract: Quantifying the shadow price (SP) of CO 2 emissions is the key to achieving China’s “double carbon” targets. Considering technology heterogeneity, this study applies stochastic frontier analysis combined with meta-frontier technology to estimate the environmental technical efficiency (ETE) and SP of CO 2 emissions for China’s fossil fuel power plants from 2005 to 2015. This approach overcomes the lack of statistical inference and consistency of traditional methods and improves the reliability of results. The main results are as follows: (a) the average ETE of China’s power plants is 0.9444, indicating that inefficient production accounts for 5.66%. The difference in efficiency between the central and local groups is significant. (b) The national average SP of CO 2 is 266.8 US dollars per ton, which is much higher than the carbon price in the emission trading system. This result implies the need to design a carbon trading price mechanism. (c) The distribution of SP shows obvious corporation and geographical characteristics that are closely related to the level of regional economic development. Finally, the findings provide policy implications for the improvement of the efficiency and abatement of costs of power plants and the determination of carbon prices.

Keywords: CO 2 emissions; meta-frontier stochastic frontier analysis; shadow price; China’s fossil fuel power plants (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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