According to BP2014 report statistics,5 the non fossil fuel used by power generation sector in 2013 achieved a more robust growth, whose overall share in the primary energy has increased from 13.1% to 13.3%. Even so, the growth rate of global carbon dioxide emissions is almost the same as that of the primary energy which is 2.1% and 2.3%, respectively, as the proportion of coal is increasing. This is a very important trend in the past years that the growth rate of carbon dioxide emissions is below that of gross domestic product (GDP) but stays synchronized with that of energy consumption driven by an increase in energy efficiency. In other words, the carbon intensity of the global fuel structure has not changed in the past ten years. Carbon dioxide emissions per unit of energy of Organization for Economic Co-operation and Development (OECD) in 2013 has a decline due to the increased proportion of nonfossil fuels while the increase of nonfossil fuels is offset by the increase of coal and the decrease of natural gas in nonOECD countries which leads to the result that the growth rate of carbon dioxide emissions keeps pace with that of the primary energy and the rate is …show more content…
proposed a Log Mean Divisia Index (LMDI) decomposition model, aiming to reflect the decomposition of carbon productivity9 and Song et al.10 combined three models including logarithmic mean weight Divisia index (LMDI), mean-rate-of-change index(MRCI), and Shapley value decomposition model to decompose carbon emissions in Shandong province, then Kendall coordination coefficient method was employed for testing their compatibility and an optimal weighted combination decomposition model was built for improving the objectivity of decomposition. The IPAT model was applied to analyze the scenarios of China 's future primary energy demand and CO2 emissions based on the policies of economic, energy, and environment.11 Wang et al. employed ridge regression to fit the extended STIRPAT model.12 It can be indicated from the empirical results that factors such as population, urbanization level, GDP per capita, industrialization level, and service level can bring about an increase in CO2 emissions. While in the latter research field, Mahony used an extended Kaya identity as the scheme and applied the log mean Divisia index (LMDI) as the decomposition technique13 and the IPAT model combined