Abstract
This paper firstly applies the EM algorithm and gives the maximum likelihood estimates of unknown parameters in skew-normal mixed model. For empirical analysis, we verify the skew-normal distribution characteristics of provincial carbon intensity data in China from 2000 to 2014. A skew-normal mixed model is then constructed to study the main influencing factors of carbon intensity of China. It is found that energy intensity would have the most significant influence on carbon intensity, among a group of factors including GDP per capita, proportion of secondary industry, and dependence on foreign trade. Finally, the results are compared with those based on normal mixed model, so as to confirm the statistical excellent properties of skew-normal mixed model.
Highlights
Global warming caused by excessive carbon emissions has become a serious environmental problem, and every country around the world needs to be involved in emission control
China's carbon emissions would peak around 2030, and the Chinese government would endeavor to bring down carbon intensity by 60% to 65% compared to 2005, which is no doubt a real challenge for energy conservation, emissions reduction and economic development
Based on structural decomposition analysis (SDA) and index decomposition analysis (IDA), it is found that factors including economic growth, energy efficiency, population, and urbanization level would have great influences on carbon emissions [1,2,3]
Summary
Global warming caused by excessive carbon emissions has become a serious environmental problem, and every country around the world needs to be involved in emission control. China's carbon emissions would peak around 2030, and the Chinese government would endeavor to bring down carbon intensity by 60% to 65% compared to 2005, which is no doubt a real challenge for energy conservation, emissions reduction and economic development. Main methods applied in research on influencing factors of carbon intensity include decomposition anaylsis and econometric analysis. Econometric models are used to study the influencing factors of carbon intensity in different regions of China [4,5,6]. For empirical analysis, we verify the skew-normal distribution characteristics of provincial carbon intensity data in China from 2000 to 2014. A skew-normal mixed model is constructed to study the main influencing factors of carbon intensity of China. The estimation results are compared with those based on normal mixed model, so as to confirm the statistical excellent properties of skew-normal mixed model
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