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Measuring plant level energy efficiency in China's energy sector in the presence of allocative inefficiency

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  • Hu, Baiding

Abstract

Most studies on measuring China's energy efficiency were conducted in the framework of the input-oriented Data Envelopment Analysis. This approach generally calculates the technical efficiency by shrinking all the input factors equally proportionally subject to the observed output still being producible. Thus, all the input factor efficiencies, including the energy efficiency, are measured as the technical efficiency. One drawback of this approach is the presumption of an identical input factor frontier for all input factors and of unrestricted factor substitutability. The present study employs a stochastic frontier analysis approach to measuring energy efficiency that not only allows for non-identical input factor frontiers, but also controls for the effects on the measure of energy efficiency of substitution away from energy or substitution of energy for non-energy factors. This approach is applied to evaluating the efficiency performances of three types of energy amongst a sample of coal mines, petroleum refineries and power plants in China's energy sector which is specifically targeted by the Chinese government to improve energy efficiency.

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  • Hu, Baiding, 2014. "Measuring plant level energy efficiency in China's energy sector in the presence of allocative inefficiency," China Economic Review, Elsevier, vol. 31(C), pages 130-144.
    • Handle: RePEc:eee:chieco:v:31:y:2014:i:c:p:130-144
    DOI: 10.1016/j.chieco.2014.08.011
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    3. Quan Guo & Jun Hong & Jing Rong & Haiyan Ma & Mengnan Lv & Mengyang Wu, 2022. "Impact of Environmental Regulations on High-Quality Development of Energy: From the Perspective of Provincial Differences," Sustainability, MDPI, vol. 14(18), pages 1-16, September.
    4. Chang, Chun-Ping & Wen, Jun & Zheng, Mingbo & Dong, Minyi & Hao, Yu, 2018. "Is higher government efficiency conducive to improving energy use efficiency? Evidence from OECD countries," Economic Modelling, Elsevier, vol. 72(C), pages 65-77.
    5. Yao, Xin & Zhou, Hongchen & Zhang, Aizhen & Li, Aijun, 2015. "Regional energy efficiency, carbon emission performance and technology gaps in China: A meta-frontier non-radial directional distance function analysis," Energy Policy, Elsevier, vol. 84(C), pages 142-154.
    6. Ouyang, Xiaoling & Wei, Xiaoyun & Sun, Chuanwang & Du, Gang, 2018. "Impact of factor price distortions on energy efficiency: Evidence from provincial-level panel data in China," Energy Policy, Elsevier, vol. 118(C), pages 573-583.
    7. Yue Liu & Siming Liu & Xueying Xu & Pierre Failler, 2020. "Does Energy Price Induce China’s Green Energy Innovation?," Energies, MDPI, vol. 13(15), pages 1-18, August.
    8. Jiang, Xuemei & Zhu, Kunfu & Green, Christopher, 2015. "The energy efficiency advantage of foreign-invested enterprises in China and the role of structural differences," China Economic Review, Elsevier, vol. 34(C), pages 225-235.
    9. Cui, Yadong & Jiang, Yaohui & Zhang, Zhaowen & Xu, Su, 2023. "Tax reduction, technological progress, and energy efficiency improvement: A quasi-natural experiment from China," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 618-633.
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    More about this item

    Keywords

    Energy efficiency; Stochastic frontier analysis; Substitutions; Technical efficiency; Allocative efficiency;
    All these keywords.

    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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