Digital Economy, Innovation Factor Mobility, and Urban Green Energy Efficiency: Evidence from Double Machine Learning

Amidst booming digital economy and tightening climate governance, enhancing green total-factor energy efficiency has become pivotal for socioeconomic transformation. Whether digital economy drives urban green energy transition remains unresolved, particularly regarding factor mobility mechanisms and spatial spillovers. Using panel data of 281 Chinese cities from 2011 to 2022, this study applies Double Machine Learning and Spatial Durbin Models to examine digital economy’s impact on urban green energy efficiency. Findings indicate that (i) digital economy significantly enhances local green energy efficiency through green technological innovation, green finance development, and industrial upgrading; (ii) it facilitates talent and capital agglomeration toward digitally advanced regions, with innovation factor mobility serving as a crucial mediator; and (iii) significant spatial positive correlation exists, where digital economy generates pronounced spillovers to neighboring cities that exceed direct effects, fostering regional synergies. This research overcomes conventional methodological limitations and pioneers integrating innovation factor mobility into digital economy-green transition analysis, revealing factor reconfiguration as the core mechanism. Findings provide policy implications for cross-regional digital-energy coordination, factor marketization reforms, and differentiated green strategies.