Study on the Influencing Factors of Carbon Intensity Using Skew-normal Mixed Model

Ye Ren-dao^1, , Xu Li-jun¹, Luo Kun² and Zhang Yong¹

¹College of Economics, Hangzhou Dianzi University, Hangzhou, China

²Alibaba Business College, Hangzhou Normal University, Hangzhou, China

Cite this paper:
Ye Ren-dao, Xu Li-jun, Luo Kun and Zhang Yong. Study on the Influencing Factors of Carbon Intensity Using Skew-normal Mixed Model. Journal of Finance and Economics. 2018; 6(1):14-18. doi: 10.12691/jfe-6-1-2

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.

Keywords:
skew-normal distribution carbon intensity EM algorithm mixed model

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