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Is China’s carbon reduction target allocation reasonable? An analysis based on carbon intensity convergence

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  • Hao, Yu
  • Liao, Hua
  • Wei, Yi-Ming

Abstract

To curb CO2 emissions, the Chinese government has announced ambitious goals to reduce the CO2 intensity of GDP, and the total target has been allocated to all Chinese provinces during the twelfth “Five-year Plan” period (2011–2015). Although setting the target allocation plan is an efficient way to achieve this goal, some key questions, including how the plan is designed, remained unanswered. From an economic perspective, this requires us to test for the existence of convergence in the CO2 intensity of GDP because the convergence is one of the most important intrinsic economic characteristics that policy makers should take into account: if the convergence exists, the provinces with a higher CO2 intensity of GDP tend to experience a more rapid reduction in the intensity and therefore could share a heavier burden of the intensity reduction. The existence of stochastic convergence and β-convergence is verified by employing different estimation methods and using various estimation specifications. As a result, the direct policy implication is that provinces with high CO2 intensity should be assigned tougher reduction targets to cut CO2 intensity at higher speeds, while the provinces with low carbon intensity should be allowed to reduce the CO2 intensity at a relatively lower speed. Because some social and economic indicators such as GDP per capita, industrial structure and population density may influence CO2 intensity, the policy makers should take all these factors into consideration to design reasonable reduction target allocation plan.

Suggested Citation

  • Hao, Yu & Liao, Hua & Wei, Yi-Ming, 2015. "Is China’s carbon reduction target allocation reasonable? An analysis based on carbon intensity convergence," Applied Energy, Elsevier, vol. 142(C), pages 229-239.
  • Handle: RePEc:eee:appene:v:142:y:2015:i:c:p:229-239
    DOI: 10.1016/j.apenergy.2014.12.056
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    Cited by:

    1. Jian-Xin Wu & Ling-Yun He, 2017. "The Distribution Dynamics of Carbon Dioxide Emissions Intensity across Chinese Provinces: A Weighted Approach," Sustainability, MDPI, Open Access Journal, vol. 9(1), pages 1-19, January.
    2. Jie Zhang & Lu Zhang, 2016. "Impacts on CO 2 Emission Allowance Prices in China: A Quantile Regression Analysis of the Shanghai Emission Trading Scheme," Sustainability, MDPI, Open Access Journal, vol. 8(11), pages 1-12, November.
    3. Francesch-Huidobro, Maria, 2016. "Climate change and energy policies in Shanghai: A multilevel governance perspective," Applied Energy, Elsevier, vol. 164(C), pages 45-56.
    4. repec:eee:eneeco:v:68:y:2017:i:c:p:31-43 is not listed on IDEAS
    5. Chang, Kai & Chang, Hao, 2016. "Cutting CO2 intensity targets of interprovincial emissions trading in China," Applied Energy, Elsevier, vol. 163(C), pages 211-221.
    6. repec:eee:rensus:v:81:y:2018:i:p2:p:2935-2946 is not listed on IDEAS
    7. Wu, Jianxin & Wu, Yanrui & Guo, Xiumei & Cheong, Tsun Se, 2016. "Convergence of carbon dioxide emissions in Chinese cities: A continuous dynamic distribution approach," Energy Policy, Elsevier, vol. 91(C), pages 207-219.
    8. Ping Wang & Bangzhu Zhu, 2016. "Estimating the Contribution of Industry Structure Adjustment to the Carbon Intensity Target: A Case of Guangdong," Sustainability, MDPI, Open Access Journal, vol. 8(4), pages 1-11, April.
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    13. Zheng, Bo & Zhang, Qiang & Borken-Kleefeld, Jens & Huo, Hong & Guan, Dabo & Klimont, Zbigniew & Peters, Glen P. & He, Kebin, 2015. "How will greenhouse gas emissions from motor vehicles be constrained in China around 2030?," Applied Energy, Elsevier, vol. 156(C), pages 230-240.
    14. Wu, Ya & Zhang, Wanying, 2016. "The driving factors behind coal demand in China from 1997 to 2012: An empirical study of input-output structural decomposition analysis," Energy Policy, Elsevier, vol. 95(C), pages 126-134.
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    16. Thomakos, Dimitrios D. & Alexopoulos, Thomas A., 2016. "Carbon intensity as a proxy for environmental performance and the informational content of the EPI," Energy Policy, Elsevier, vol. 94(C), pages 179-190.

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    Keywords

    CO2 intensity of GDP; Convergence; China; Panel data;

    JEL classification:

    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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