What drives the spatial-temporal differentiation of transportation carbon emissions in China? Evidence based on the optimal parameter-based geographic detector model

Reducing carbon emissions is a crucial strategy in mitigating global climate change, with the transportation sector being a significant contributor to carbon emissions in China. Upon establishing the transportation carbon emissions (TCE) inventories in 30 provinces from 2006 to 2021 in China, this study employs the methods of mathematical statistics, standard deviation ellipse, as well as the Dagum Gini coefficient and its decomposition to systematically investigate the spatial-temporal differentiation of TCE. Additionally, this study innovatively applies the optimal parameter-based geographic detector model to elucidate the individual and interactive mechanisms of various driving factors, offering a novel perspective and providing a useful tool in this field. The results indicate that: (1) The TCE has shown an overall growth trend, increasing from 435.80 Mt in 2006 to 861.70 Mt in 2021. While TCE from conventional fossil fuels remains the predominant source, the proportion of electricity-related carbon emissions is gradually increasing. (2) The spatial distribution of TCE exhibits pronounced inequality, with a pattern of high emissions in the east and low emissions in the west. The center of gravity of spatial distribution is located in Henan Province, showing a trend of centripetal ag-glomeration in the northeast-southwest direction and spatial divergence in the north-west-southeast direction. (3) The spatial inequality of TCE is on a declining trend. The primary source of overall differences lies in the interregional differences, with an average contribution rate of 51.46%, significantly surpassing the contributions from intraregional differences (27.81%) and the intensity of transvariation (19.83%). (4) Private car ownership, population size, cargo turnover, and passenger turnover are the core driving factors in determining the spatial-temporal differentiation of TCE, with the dominant factors in the eastern, central, and western regions exhibiting certain heterogeneity. More importantly, the interactive effects across various driving factors are significantly higher than the explanatory power of individual factors. The findings provide a scientific reference for decision-makers to formulate targeted and effective policies for controlling TCE in China.