Digital-Driven Green Transition: The Impact of Artificial Intelligence on Green Technology Innovation—Perspectives on Nonlinearity and Spatial Heterogeneity

Artificial intelligence (AI), as a representative general-purpose technology, has attracted increasing attention regarding its potential with green technological innovation (GTI). Understanding this relationship is important for promoting sustainable development and supporting China’s ‘Dual Carbon’ objectives. Using a balanced panel dataset of 108 prefecture-level cities in the Yangtze River Economic Belt (YREB) from 2012 to 2023, this study examines the relationship between AI development and urban GTI. To investigate this relationship, we employ a two-way fixed effects (TWFE) model as the primary empirical framework and supplement the analysis with spatial econometric models and exploratory machine learning techniques. Specifically, we examine whether AI development is associated with urban GTI, whether such associations exhibit spatial dependence, and whether potential non-linear patterns can be observed in the data. The results indicate that: (1) AI development is positively associated with urban GTI, with stronger associations observed for substantive green innovation than for strategic green innovation. (2) Spatial econometric estimates suggest that AI development is correlated with higher GTI levels in neighboring cities, although these findings should be interpreted as spatial associations rather than causal spillover effects. (3) Exploratory machine learning analyses reveal potential non-linear relationships between AI development and GTI. However, these patterns should not be interpreted as evidence of causal threshold effects. (4) The positive association between AI and GTI appears stronger in the middle reaches of the YREB and in regions characterized by relatively stringent environmental regulation. Overall, the findings provide evidence that AI development is associated with urban green technological innovation within the YREB. The results also offer preliminary insights into the spatial and non-linear characteristics of this relationship and may inform future research on digital technologies and sustainable development.