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Energy abatement in Chinese industry: Cost evaluation of regulation strategies and allocation alternatives

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  • Xia, X.H.
  • Chen, G.Q.

Abstract

For Chinese industry, the costs of different energy consumption abatement scenarios are evaluated by the method of directional distance function. These scenarios are based on the combinations of regulation strategies and allocation alternatives—the former are sectors and provinces, and the latter include the five principles of average, intensity share, absolute share, discriminatory absolute and discriminatory intensity. For all the scenarios, the quantitative impacts in terms of output potential loss are calculated and compared. Due to less output potential loss for all the allocation alternatives, the sector regulation strategy is shown to be more effective than the province regulation strategy. It is also demonstrated that, among all the scenarios considered, the sector regulation based on the intensity share principle and the province regulation based on the absolute share principle are the two optimal. The performances of energy abatement allocation of the 11th and 12th Five Year Plans of China are assessed against the simulated scenarios.

Suggested Citation

  • Xia, X.H. & Chen, G.Q., 2012. "Energy abatement in Chinese industry: Cost evaluation of regulation strategies and allocation alternatives," Energy Policy, Elsevier, vol. 45(C), pages 449-458.
    • Handle: RePEc:eee:enepol:v:45:y:2012:i:c:p:449-458
    DOI: 10.1016/j.enpol.2012.02.055
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    4. Baochen Yang & Chuanze Liu & Yunpeng Su & Xin Jing, 2017. "The Allocation of Carbon Intensity Reduction Target by 2020 among Industrial Sectors in China," Sustainability, MDPI, vol. 9(1), pages 1-19, January.
    5. Kejia Yang & Yalin Lei & Weiming Chen & Lingna Liu, 2018. "Carbon dioxide emission reduction quota allocation study on Chinese provinces based on two-stage Shapley information entropy model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(1), pages 321-335, March.
    6. Lijun Wang & Haizhong An & Xiaohua Xia & Xiaojia Liu & Xiaoqi Sun & Xuan Huang, 2014. "Generating Moving Average Trading Rules on the Oil Futures Market with Genetic Algorithms," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, May.
    7. Xia, X.H. & Chen, Y.B. & Li, J.S. & Tasawar, H. & Alsaedi, A. & Chen, G.Q., 2014. "Energy regulation in China: Objective selection, potential assessment and responsibility sharing by partial frontier analysis," Energy Policy, Elsevier, vol. 66(C), pages 292-302.
    8. Xia, X.H. & Hu, Y. & Chen, G.Q. & Alsaedi, A. & Hayat, T. & Wu, X.D., 2015. "Vertical specialization, global trade and energy consumption for an urban economy: A value added export perspective for Beijing," Ecological Modelling, Elsevier, vol. 318(C), pages 49-58.
    9. Zhang, Ning & Zhou, P. & Choi, Yongrok, 2013. "Energy efficiency, CO2 emission performance and technology gaps in fossil fuel electricity generation in Korea: A meta-frontier non-radial directional distance functionanalysis," Energy Policy, Elsevier, vol. 56(C), pages 653-662.
    10. Han, Mengyao & Xiong, Jiao & Wang, Siyuan & Yang, Yu, 2020. "Chinese photovoltaic poverty alleviation: Geographic distribution, economic benefits and emission mitigation," Energy Policy, Elsevier, vol. 144(C).

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