Spatial Effects of Artificial Intelligence Innovation on Regional Carbon Intensity

This study investigates the spatial effects of artificial intelligence (AI) innovation on carbon intensity using provincial panel data from 30 Chinese provinces over 2010–2023. Employing the Spatial Durbin Model (SDM), we find that a 1% increase in AI patent count reduces local carbon intensity by 0.034% (direct effect, p < 0.01) but increases carbon intensity in neighboring regions by 0.069% (indirect effect, p < 0.05). Heterogeneity analysis shows that AI innovation reduces local carbon intensity by 0.069% in non-western regions (p < 0.01) but has no significant effect in the western region. In regions with above-median R&D intensity, both direct and indirect effects become negative (−0.094% and −0.069%, respectively), indicating that AI innovation reduces carbon intensity locally and in neighboring areas. Mechanism tests confirm that industrial structure upgrading mediates this relationship, with AI innovation increasing the industrial structure hierarchy coefficient by 0.004 (p < 0.05). These findings provide quantitative evidence that AI innovation has opposing local and spillover effects on carbon intensity, and that high R&D intensity can reverse negative spillovers into positive ones. The results offer empirically grounded policy recommendations for China’s dual-carbon targets and sustainable development.