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Abstract At present, there were countless documents and periodicals which have been paying more attention to the impact factors of carbon emissions at different levels. Nevertheless, in previous studies, regional classification methods are always based on geographical location or single factor which lack of comprehensive indicators for measurement. In this paper, 30 provinces are divided into three areas (advantageous area, potential area and backward area) from 1996 to 2015 by factor analysis and cluster analysis according to the different social development which is measured by urbanization, economy, energy utilization, industry and technology. An extented STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model containing with panel regression models are utilized to evaluate the influence of factors on CO 2 emissions in China at national and diverse regional levels. The results indicate that the R&D investment and energy cleanliness play important roles in reducing CO 2 emissions at advantageous area. Besides, the impact of demographic factor in potential area is higher than the other two areas. Meanwhile, improving the tertiary industry level is more essential in backward area than others. On the whole, the effect of economic growth has a greatest positive influence among three regions. Nevertheless, there exists EKC curve for three regions which shows a downward trend of CO 2 emissions with increasing GDP per capita in the future. Finally, the concrete solution depends, of course, on the circumstances in three differentiated regions. Graphical abstract Image 1 Highlights • The existence of EKC hypothesis for carbon emissions is tested for China in national and regional level. • The extend STIRPAT model to examine the impact factors of CO 2 emissions. • The SYS-GMM model and FE-DK model are employed to fit the extended STIRPAT model. • Economic growth is the most important impact factors of CO2 emissions. [ABSTRACT FROM AUTHOR]