Can artificial intelligence equitably mitigate climate change? Spatiotemporal evidence from Chinese cities - Lu Liu; Chenjing Yan; Ping Wang (Environmental Research Letters)
Artificial Intelligence (AI) is emerging as a critical enabler of climate mitigation. However, its spatial and temporal impacts remain insufficiently understood. Using balanced panel data from 279 Chinese cities (2006–2019), this study examines how AI (measured by robot adoption) affects carbon emission intensity. We apply the geographically and temporally weighted regression (GTWR) model to uncover heterogeneous impacts. The results indicate that AI significantly reduces carbon emission intensity, thus contributing to climate mitigation. Nonetheless, the magnitude of this effect varies substantially across cities. The mitigation benefits are more pronounced in cities with limited natural resources, those located within the five major economic zones, and non-traditional industrial centers. These patterns reflect the presence of a ‘natural resource curse’ in resource-rich cities and a ‘social resource blessing’ in socially advantaged regions. The GTWR results further reveal pronounced spatial disparities, with eastern cities experiencing greater reductions in carbon intensity than those in the west. Over time, this spatial imbalance has been narrowing, indicating a gradual convergence in AI’s climate mitigation effects. These findings underscore the importance for regionally differentiated AI development strategies and policy interventions to reduce spatial inequities in mitigation capacity. The study provides robust empirical evidence from China, offering new insights into AI’s potential to support equitable and effective climate action on a global scale.