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Correcting the uneven burden sharing of emission reduction across provinces in China

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  • Zhang, Lin

Abstract

Although China committed to reduce its energy and emission intensity, the allocation of such national targets in the provincial level is still a political negotiation process and lack of a systematic principle from the perspective of efficiency. This paper proposes an allocation principle based on the efficiency levels. The efficiency levels are estimated by employing a stochastic frontier analysis approach and the links between energy intensity and efficiency are constructed. The results show that energy efficiency change is not the major contributor to energy intensity reduction. Furthermore, this analysis indicates that (i) the efficiency-based allocation can distribute reduction burdens among regions smoothly compared to the intensity-based allocation; and (ii) the national target of emission intensity reduction can be achieved solely through efficiency measures.

Suggested Citation

  • Zhang, Lin, 2017. "Correcting the uneven burden sharing of emission reduction across provinces in China," Energy Economics, Elsevier, vol. 64(C), pages 335-345.
    • Handle: RePEc:eee:eneeco:v:64:y:2017:i:c:p:335-345
    DOI: 10.1016/j.eneco.2017.04.005
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    1. Hunt, Lester C. & Judge, Guy & Ninomiya, Yasushi, 2003. "Underlying trends and seasonality in UK energy demand: a sectoral analysis," Energy Economics, Elsevier, vol. 25(1), pages 93-118, January.
    2. Dan Wei & Adam Rose, 2009. "Interregional Sharing of Energy Conservation Targets in China: Efficiency and Equity," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 81-112.
    3. Filippini, Massimo & Hunt, Lester C., 2012. "US residential energy demand and energy efficiency: A stochastic demand frontier approach," Energy Economics, Elsevier, vol. 34(5), pages 1484-1491.
    4. Massimo Filippini & Lester C. Hunt, 2011. "Energy Demand and Energy Efficiency in the OECD Countries: A Stochastic Demand Frontier Approach," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 59-80.
    5. Mehdi Farsi & Massimo Filippini & William Greene, 2005. "Efficiency Measurement in Network Industries: Application to the Swiss Railway Companies," Journal of Regulatory Economics, Springer, vol. 28(1), pages 69-90, July.
    6. Subal Kumbhakar & Gudbrand Lien & J. Hardaker, 2014. "Technical efficiency in competing panel data models: a study of Norwegian grain farming," Journal of Productivity Analysis, Springer, vol. 41(2), pages 321-337, April.
    7. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
    8. Zhou, Nan & Levine, Mark D. & Price, Lynn, 2010. "Overview of current energy-efficiency policies in China," Energy Policy, Elsevier, vol. 38(11), pages 6439-6452, November.
    9. Vishal Chandr Jaunky and Lin Zhang, 2016. "Convergence of Operational Efficiency in Chinas Provincial Power Sectors," The Energy Journal, International Association for Energy Economics, vol. 0(China Spe).
    10. Willam Greene, 2005. "Fixed and Random Effects in Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 23(1), pages 7-32, January.
    11. Subal C. Kumbhakar & Almas Heshmati, 1995. "Efficiency Measurement in Swedish Dairy Farms: An Application of Rotating Panel Data, 1976–88," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(3), pages 660-674.
    12. Auffhammer, Maximilian & Carson, Richard T., 2008. "Forecasting the path of China's CO2 emissions using province-level information," Journal of Environmental Economics and Management, Elsevier, vol. 55(3), pages 229-247, May.
    13. Roger Fouquet (ed.), 2013. "Handbook on Energy and Climate Change," Books, Edward Elgar Publishing, number 14429.
    14. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    15. Zhang, Da & Rausch, Sebastian & Karplus, Valerie J. & Zhang, Xiliang, 2013. "Quantifying regional economic impacts of CO2 intensity targets in China," Energy Economics, Elsevier, vol. 40(C), pages 687-701.
    16. Pitt, Mark M. & Lee, Lung-Fei, 1981. "The measurement and sources of technical inefficiency in the Indonesian weaving industry," Journal of Development Economics, Elsevier, vol. 9(1), pages 43-64, August.
    17. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    18. Mehdi Farsi & Massimo Filippini & Michael Kuenzle, 2005. "Unobserved heterogeneity in stochastic cost frontier models: an application to Swiss nursing homes," Applied Economics, Taylor & Francis Journals, vol. 37(18), pages 2127-2141.
    19. Joanne Evans & Massimo Filippini & Lester C. Hunt, 2013. "The contribution of energy efficiency towards meeting CO2 targets," Chapters, in: Roger Fouquet (ed.), Handbook on Energy and Climate Change, chapter 8, pages 175-223, Edward Elgar Publishing.
    20. Wei, Chu & Ni, Jinlan & Du, Limin, 2012. "Regional allocation of carbon dioxide abatement in China," China Economic Review, Elsevier, vol. 23(3), pages 552-565.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

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    5. Yong Wang & Han Zhao & Fumei Duan & Ying Wang, 2018. "Initial Provincial Allocation and Equity Evaluation of China’s Carbon Emission Rights—Based on the Improved TOPSIS Method," Sustainability, MDPI, vol. 10(4), pages 1-27, March.
    6. Lin Zhang and Philip Kofi Adom, 2018. "Energy Efficiency Transitions in China: How Persistent are the Movements to/from the Frontier?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 6).
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    More about this item

    Keywords

    Energy efficiency; Emission reduction allocation; Future outlook; China;
    All these keywords.

    JEL classification:

    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E61 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Policy Objectives; Policy Designs and Consistency; Policy Coordination
    • H70 - Public Economics - - State and Local Government; Intergovernmental Relations - - - General
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

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