Artificial Intelligence and Social–Ecological System Resilience: Effects and Regional Heterogeneity

In a global landscape characterized by interconnected risks and the deepening of digital transformation, understanding the impact and mechanisms of artificial intelligence on social–ecological resilience is crucial for advancing resilience governance and the green transition. This paper utilizes panel data from 30 provinces in China (2013–2023) to construct a comprehensive index of social–ecological resilience and artificial intelligence. It employs two-stage fixed-effects models, mediation effect models, and robustness and endogeneity tests to systematically examine the impact of AI and regional heterogeneity. The results demonstrate that: first, AI significantly enhances social–ecological resilience, and this conclusion remains robust after controlling for measurement errors, trimming, and instrumental variable estimation; second, employment structure and innovation levels play a partial mediating role in the influence of AI on social–ecological resilience; and third, the promoting effect of artificial intelligence on social–ecological system resilience exhibits a regional heterogeneity pattern: strongest in the central region, followed by the western region, and weakest in the eastern region. The study highlights that AI not only directly enhances resilience by improving resource allocation and governance responsiveness, but also indirectly optimizes the social–ecological coupling through employment restructuring and innovation diffusion. Therefore, efforts should be directed towards promoting AI-enabled resilience governance through digital capacity building, industrial-employment synergy, institutional innovation, and infrastructure improvements.