In recent years, global emphasis on reducing carbon emissions has intensified. As a pivotal sector in national economic development, the logistics industry represents a critical area for carbon mitigation efforts. This study aims to harness the potential of the digital economy to enhance carbon emission efficiency (CEE) within the logistics sector by developing a comprehensive evaluative framework and analyzing provincial panel data from China spanning 2012 to 2022. The entropy-weighted TOPSIS method and the super-efficiency Slack-Based Measure (SBM) model incorporating undesirable outputs are employed to quantify the development level of the digital economy (DIG) and the logistics industry’s CEE, respectively. Subsequently, baseline regression, mediation effect analysis, and Spatial Durbin Models (SDM) are utilized to explore impact mechanisms and spatial spillover effects. Findings reveal sustained growth in both DIG and logistics CEE across China, albeit with pronounced regional disparities favoring eastern provinces. The relationship between DIG and logistics CEE exhibits a U-shaped nonlinear pattern, with a threshold at 0.5972, indicating an initial inhibitory effect followed by a promotive influence. This dynamic is primarily mediated through reductions in energy intensity. Furthermore, DIG exerts significant spatial externalities, producing positive spillover effects on logistics CEE in neighboring regions. The study’s principal contributions include empirically elucidating the U-shaped nonlinear mechanism, identifying the critical threshold and mediating role of energy intensity, and confirming spatial spillover effects of the digital economy on logistics carbon efficiency. Additionally, it proposes an integrated analytical framework that incorporates nonlinear mediation and spatial dependencies, thereby offering theoretical and empirical foundations for the formulation of phased and regionally coordinated digital and low-carbon logistics policies.
Chen et al. (Sun,) studied this question.