Temporal-Spatial Pattern and Influencing Factorsof China’s Province-Level Transport SectorCarbon Emissions Efficiency

The transport sector, as an industry with high energy consumption and high carbon emissions, plays an increasing role in achieving the goal of carbon emissions reduction in China. Understanding the situation of the transport sector's carbon emissions efficiency and the relevant dominating driving forces is an important prerequisite for formulating carbon emissions reduction polices. This study evaluated the transport sector carbon emissions efficiency of 30 provinces in China from 2004 to 2016 using the Super slacks-based measure (Super-SBM) model,which employs Moran's I indexand spatial econometric approaches to examine its spatial dependence and the dominating driving factors. The results are shown as follows. Firstly, the transport carbon emissions efficiency had a noticeable disparity across the provinces and regions, and the spatial distribution characteristic of transport sector carbon emissions efficiency could be described as "high in the east and low in the west". Secondly, transport sector carbon emissions efficiency presented significant spatial dependence and clustering characteristics, and the pattern evolutions of spatial distribution presented a path-dependence effect to some extent. Thirdly, the regression results of the spatial Durbin model (SDM) indicated that the per-capita GDP and transportation energy consumption structure had significantly positive effects on transport sector carbon emissions efficiency, whereas the urbanization, transportation intensity, transportation energy intensity, and transportation service structure hada negative effect on transport sector carbon emissions efficiency.