eng Science and Education Publishing Journal of Finance and Economics 2328-7276 2018-01-08 6 1 14 18 10.12691/jfe-6-1-2 JFE2018612 article Ye Ren-dao yerendao2003@163.com 1 Xu Li-jun 1 Luo Kun 2 Zhang Yong 2 College of Economics, Hangzhou Dianzi University, Hangzhou, China Alibaba Business College, Hangzhou Normal University, Hangzhou, China 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. http://pubs.sciepub.com/jfe/6/1/2/jfe-6-1-2.pdf skew-normal distribution carbon intensity EM algorithm mixed model