Journal
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
Volume 77, Issue -, Pages 386-394Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.rser.2017.04.042
Keywords
China's energy intensive industries; Kaya identity; LMDI method; Cointegration method; Carbon dioxide emissions reduction
Funding
- Grant for Collaborative Innovation Center for Energy Economics and Energy Policy [1260-Z0210011]
- Xiamen University Flourish Plan Special Funding [1260-Y07200]
- China National Social Science Fund [15ZD058]
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According to China's Economic and Social Development Statistical Bulletin of 2010, the energy intensive industries include six highest energy intensive sub-industries. Because China is still in the process of urbanization and industrialization, it requires the products of energy intensive industries. In this study, we review the main method of doing decomposition analysis on CO2 emissions, investigate the main factors affecting CO2 emissions in China's energy intensive industries using Kaya identity and Logarithinic Mean Divisia Index (LMDI) method and then adopt cointegration theory to construct the long-term relationship among CO2 emissions and the main factors. Finally, we estimate the reduction potential of CO2 emissions in China's energy intensive industries in the future. The results show that industrial scale and labor productivity are the main factors increasing CO2 emissions while energy intensity is negative to emissions. If moderate measures are taken, the emission reduction potential will be 1.98 billion tons and 9.34 billion tons in 2020 and 2030 respectively. If aggressive measures are taken, the emission reduction potential will be 3.33 billion tons and 14.12 billion tons in 2020 and 2030 respectively. The results indicate that emission reduction potentials are substantial. Some related policy suggestions are proposed to support emissions reduction.
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