Investigating the differences in CO2 emissions in the transport sector across Chinese provinces: Evidence from a quantile regression model.

Carbon dioxide (CO 2 ) emission in China is currently the largest in the world. Considering that the transport sector, it is one of the major contributors, its CO2 emissions and their main driving factors have become the focus of many scholars. However, majority of the existing studies usually investigates CO2 emissions using the averaging method (i.e., ordinary least squares method). In fact, the data distribution of socio-economic variables is often non–normal, with the tail having hidden important information. In order to overcome the shortcomings of existing research, this paper uses a quantile regression approach to explore the main driving forces of the difference in CO 2 emissions under high, medium and low level of development. The results show that the effect of economic growth on CO 2 emissions in the 25th–50th quantile provinces is higher than those in the other quantile provinces due to the differences in fixed–asset investment and motor vehicles. Energy intensity has a similar story owing to different R&D funding and R&D personnel investments. The influences of urbanization in the upper 90th and 75th−90th quantile provinces are higher than those in the other quantile provinces because of the differences in human capital accumulation and patented technology. Freight turnover produces the same effect, which is due to the differences in freight transportation and transport mode. However, the impacts of passenger turnover in the lower 10th and 10th−25th quantile provinces are the highest in all the quantile provinces. Therefore, in the process of emission reduction, the relevant departments should be concerned about the heterogeneous effects of these driving forces on the different quantile provinces, rather than adopting the “one–size–fits–all approach”. [ABSTRACT FROM AUTHOR]