Although digital economic development is often viewed as a catalyst for green transformation, the causal implications of policy-driven AI deployment for low-carbon logistics development remain unclear. To address this gap, this study leverages China’s National New Generation Artificial Intelligence Innovation Development Pilot Zones (AIIDPZs) as a quasi-natural experiment. Using panel data from 30 provincial regions from 2012 to 2022, this research employs a double machine learning framework to rigorously quantify the AIIDPZ policy’s causal effects on the logistics industry’s green total factor productivity (GTFP). We further examine underlying transmission mechanisms and spatial spillover effects. Results show that the AIIDPZ policy significantly enhances logistics GTFP, a finding robust to parallel trend tests, sample adjustments, and algorithm substitutions. Mechanism analysis reveals that the AIIDPZ policy promotes logistics GTFP by alleviating manufacturing agglomeration and collaborative agglomeration. This occurs mainly through the mitigation of environmental externalities and the easing of inter-sectoral resource competition. Heterogeneity analysis highlights substantial regional variation: the policy impact is strongest in East China, Central China, and Southwest China; positive but weaker in Northeast and Northwest China; and statistically insignificant in North and South China. Spatial econometric results confirm significant positive spillovers to neighboring regions. Temporally, the logistics industry’s GTFP shows a sustained upward trajectory, while spatially it follows a spatial pattern of “Eastern leadership, Central rise, and Western catch-up.” Robust empirical evidence is presented to evaluate the environmental outcomes of AI policy implementation, alongside policy-relevant insights for advancing coordinated and spatially differentiated regional development.
Ma et al. (Sat,) studied this question.