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Forecasting China’s Carbon Intensity: Is China on Track to Comply with Its Copenhagen Commitment?

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  • Yuan Yang
  • Junjie Zhang
  • Can Wang

Abstract

In the 2009 Copenhagen Accord, China agreed to slash its carbon intensity (carbon dioxide emissions/GDP) by 40% to 45% from the 2005 level by 2020. We assess whether China can achieve the target under the business-as-usual scenario by forecasting its emissions from energy consumption. Our preferred model shows that China’s carbon intensity is projected to decline by only 33%. The results imply that China needs additional mitigation effort to comply with the Copenhagen commitment. The emission growth is more than triple the emission reductions that the European Union and the United States have committed to in the same period.

Suggested Citation

  • Yuan Yang & Junjie Zhang & Can Wang, 2018. "Forecasting China’s Carbon Intensity: Is China on Track to Comply with Its Copenhagen Commitment?," The Energy Journal, , vol. 39(2), pages 63-86, March.
    • Handle: RePEc:sae:enejou:v:39:y:2018:i:2:p:63-86
    DOI: 10.5547/01956574.39.2.yyan
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    References listed on IDEAS

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    1. Badi Baltagi & Dong Li, 2006. "Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(2), pages 175-185.
    2. Eric Girardin & Konstantin A. Kholodilin, 2011. "How helpful are spatial effects in forecasting the growth of Chinese provinces?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(7), pages 622-643, November.
    3. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
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