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A comparison of carbon dioxide (CO2) emission trends among provinces in China

期刊

RENEWABLE & SUSTAINABLE ENERGY REVIEWS
卷 73, 期 -, 页码 19-25

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.rser.2017.01.102

关键词

Decomposition; Shepherd distance function; Production-theory decomposition analysis; Data envelopment analysis

资金

  1. National Natural Science Foundation of China [71603084, 71603148]
  2. China Postdoctoral Science Foundation [2016M590627]
  3. Ministry of Education of China [16YJC790078]
  4. Shandong Provincial Natural Science Foundation, China [ZR2016GB10]
  5. Shanghai Planning Office of Philosophy and Social Science [2016EJB003]
  6. East China Normal University [2016zk017]

向作者/读者索取更多资源

As the world leader in CO2 emissions, China is a key focus for climate change mitigation. In this paper, we conducted a cross-province comparison of CO2 emission trends in China from 2006 to 2012. We determined effects of CO2 emission factor (EMF), energy mix change (EIVIX), potential energy intensity change (PEI), industrial structure (STR), economic activity (EAT), technological change (BPC) and energy efficiency change (EC) as underlying forces of CO2 emission changes with production-based decomposition. Compared to other production-theory decomposition analyses (PDA), the method used in this paper can overcome the weakness of PDA on the measurement of structural changes and energy mix effect. The results provided strong evidence that EAT is the main driver behind rising emissions, while changes in PEI, EMX and EC have led to CO2 emission reductions in most provinces/municipalities in China. In particular, we introduced the global benchmark technology to establish the relationship between CO2 emissions and energy use technology. The potential CO2 reductions in China were further measured under the scenarios of contemporaneous technology and global technology. The principal empirical implication is that the promotion of energy conservation technology and reductions in inter-regional technological disparity would be effective in reducing CO2 emissions in technically inefficient regions.

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