Research on the Spatio-Temporal Evolution and Driving Factors of Carbon Total Factor Productivity in China’s Provincial Transportation Industry

Against the backdrop of China’s Dual Carbon Initiative and national transportation empowerment strategy, accelerating the low-carbon green transition of the transport sector has emerged as an imperative developmental priority. Incorporating carbon emissions as undesirable outputs into the efficiency evaluation framework, this study adopts a multi-method analytical paradigm encompassing the super-efficiency SBM model, Malmquist–Luenberger index, kernel density estimation, Dagum Gini coefficient, geographical detector model, and Geographically and Temporally Weighted Regression (GTWR). Based on panel data covering 30 provincial administrative regions in China from 2004 to 2022, this paper systematically investigates the spatio-temporal evolutionary patterns and intrinsic driving mechanisms of carbon total factor productivity (CTFP) within the transportation industry. The main findings are as follows: (1) The static efficiency results reveal that the national mean CTFP is below unity, indicating overall inefficiency. Nevertheless, it exhibits a fluctuating upward trend after 2009. Regionally, CTFP follows this pattern: Eastern China > Central China > national mean > Northeastern China ≈ Western China. (2) Dynamic productivity analysis shows that the annual average ML index is close to 1, demonstrating an overall upward trend in CTFP, and productivity growth is primarily driven by technological progress. (3) In terms of spatio-temporal patterns, inter-regional disparities constitute the principal source of overall spatial gaps in CTFP, with considerable contribution from transvariation density. (4) Geographical detector analysis suggests that energy intensity (EI) and economic development level are the two factors most strongly correlated with the spatial differentiation of CTFP, and interaction effects exist between them. The results from geographically weighted regression (GWR) further confirm that the strength of the correlation between each driving factor and CTFP presents pronounced regional heterogeneity. Based on these findings, differentiated regional policies for emission reduction and efficiency improvement should be formulated in accordance with the law of diminishing marginal returns.