Abstract：Taking the 30 provinces in Mainland China as spatial analysis unit, the exploratory spatial data analysis (ESDA) method was employed to explore the spatio-temporal pattern of the transportation carbon emissions. Moreover, considering the spatial non-stationary, the geographically weighted regression (GWR) model was applied to analyze the spatio-temporal heterogeneity in the influencing factors of the transportation carbon emissions. The results indicated a significant spatial agglomeration in the transportation carbon emissions, and showing a gradual upward trend across time during the studied period 2000~2015. The Moran's I indices of the Bivariate spatial autocorrelation were 0.165~0.274 and the statistical significance levels were 0.016~0.045, indicating that there was a significant spatial positive correlation between the transportation carbon emissions and the variables, such as motor vehicle population, GDP, freight turnover and passenger turnover. The R2 of the GWR models were between 0.783 and 0.865, while the R2 of the OLS models were between 0.675 and 0.844; moreover, the AICc values of the GWR model were lower than those of the OLS models', demonstrating the goodness of the GWR models compared to the OLS models. This indicates that we can use the outcomes of the GWR models to better explain the impact mechanism of the transportation carbon emissions. The analysis of the GWR revealed that the influencing factors of the transportation carbon emissions had obvious spatio-temporal heterogeneity. GDP was among the major driving factors, with regression coefficient as high as 0.91 in some areas. The impact of GDP decreased from east to west in 2000, while decreasing from north to south in 2005, 2010, and 2015. The passenger turnover played a key inhibitory role, with its influence decreasing from northeast to southwest in all of the study years. In this context, the spatio-temporal heterogeneity of carbon emission influencing factors should be fully understood to formulate differentiated carbon emission reduction policies.
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