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Market-driven coal prices and state-administered electricity prices in China


  • Liu, Ming-Hua
  • Margaritis, Dimitris
  • Zhang, Yang


This paper analyzes how the Chinese government adjusts electricity prices for both industrial and residential users in response to changes in coal prices using an asymmetric error correction model. Our results show that there is a long-term relation between the coal price and electricity prices but the relationship is weak especially for residential pricing. In the short run, we find electricity prices are adjusting faster upwards than downwards. Price adjustment towards long-run equilibrium is faster for residential users upwards than downwards. On the other hand, we find no evidence of asymmetric equilibrium adjustment in the electricity price for industrial use. The weak long-term relationship reflects the rigidity in electricity pricing and government's reluctance to subject end users to fluctuations of raw materials cost shifts.

Suggested Citation

  • Liu, Ming-Hua & Margaritis, Dimitris & Zhang, Yang, 2013. "Market-driven coal prices and state-administered electricity prices in China," Energy Economics, Elsevier, vol. 40(C), pages 167-175.
  • Handle: RePEc:eee:eneeco:v:40:y:2013:i:c:p:167-175 DOI: 10.1016/j.eneco.2013.05.021

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    Cited by:

    1. Zeng, Ming & Yang, Yongqi & Wang, Lihua & Sun, Jinghui, 2016. "The power industry reform in China 2015: Policies, evaluations and solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 94-110.
    2. Nawrocki, Tomasz Leszek & Jonek-Kowalska, Izabela, 2016. "Assessing operational risk in coal mining enterprises – Internal, industrial and international perspectives," Resources Policy, Elsevier, vol. 48(C), pages 50-67.
    3. Biresselioglu, Mehmet Efe & Yelkenci, Tezer, 2016. "Scrutinizing the causality relationships between prices, production and consumption of fossil fuels: A panel data approach," Energy, Elsevier, vol. 102(C), pages 44-53.
    4. He, Xiaoping & Reiner, David, 2016. "Electricity demand and basic needs: Empirical evidence from China's households," Energy Policy, Elsevier, vol. 90(C), pages 212-221.
    5. Guo, Jin & Zheng, Xinye & Chen, Zhan-Ming, 2016. "How does coal price drive up inflation? Reexamining the relationship between coal price and general price level in China," Energy Economics, Elsevier, vol. 57(C), pages 265-276.
    6. Zeng, Ming & Yang, Yongqi & Fan, Qiannan & Liu, Yingxin & Zou, Zhuojun, 2015. "Coordination between clean energy generation and thermal power generation under the policy of “direct power-purchase for large users” in China," Utilities Policy, Elsevier, vol. 33(C), pages 10-22.
    7. Xu, Bin & Lin, Boqiang, 2016. "Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model," Applied Energy, Elsevier, vol. 161(C), pages 375-386.
    8. Cao, Jing & Karplus, Valerie J., 2014. "Firm-level determinants of energy and carbon intensity in China," Energy Policy, Elsevier, vol. 75(C), pages 167-178.
    9. Fan, Xinghua & Wang, Li & Li, Shasha, 2016. "Predicting chaotic coal prices using a multi-layer perceptron network model," Resources Policy, Elsevier, vol. 50(C), pages 86-92.
    10. Peng Ou & Ruting Huang & Xin Yao, 2016. "Economic Impacts of Power Shortage," Sustainability, MDPI, Open Access Journal, vol. 8(7), pages 1-21, July.
    11. repec:eee:enepol:v:106:y:2017:i:c:p:148-154 is not listed on IDEAS
    12. Zhang, Zibin & Cai, Wenxin & Feng, Xiangzhao, 2017. "How do urban households in China respond to increasing block pricing in electricity? Evidence from a fuzzy regression discontinuity approach," Energy Policy, Elsevier, vol. 105(C), pages 161-172.
    13. repec:eee:rensus:v:81:y:2018:i:p2:p:2870-2878 is not listed on IDEAS
    14. Zhao, Zhen-yu & Zhu, Jiang & Xia, Bo, 2016. "Multi-fractal fluctuation features of thermal power coal price in China," Energy, Elsevier, vol. 117(P1), pages 10-18.
    15. He, Yongxiu & Liu, Yangyang & Wang, Jianhui & Xia, Tian & Zhao, Yushan, 2014. "Low-carbon-oriented dynamic optimization of residential energy pricing in China," Energy, Elsevier, vol. 66(C), pages 610-623.

    More about this item


    Coal price; Electricity prices; Asymmetric adjustment; Error correction model; China;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices


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