New quality productive forces driving green development under climate resilience regulation: an analysis of artificial intelligence enabled low-carbon energy transition - Shen, Bo; Wang, Zihao; Liu, Gang (Frontiers in Environmental Science, 2026)
Low-carbon energy transition is an inevitable requirement of the concept of green development, reflecting the imperative to rationalize the metabolic exchange between humanity and nature. As a driver of new quality productive forces, artificial intelligence (AI) propels the green transformation of energy systems yet also raises concerns over “technological alienation”. This reveals its dual potential within specific production relations. However, systematic evidence remains lacking on whether climate resilience can moderate this tension. Using panel data from 269 prefecture-level cities in China (2008–2022), this study constructs a panel fixed-effects model that incorporates a quadratic term for AI development level and its interaction term with climate resilience. The moderating effect model is employed to examine how climate resilience influences the relationship between AI development and the low-carbon energy transition. Findings reveal that AI development initially inhibits but later facilitates energy decarbonization, while climate resilience significantly shifts the inflection point leftward, enabling highly resilient regions to bypass the initial suppression phase earlier. This illustrates how adaptive production relations can steer productive forces toward ecologically rational outcomes. Heterogeneity analysis further indicates this moderating effect is more pronounced in non-eastern regions, areas outside the Yangtze River Economic Belt, old industrial base regions and the Yellow River basin, aligning with the law of uneven development. By integrating asset lifecycle theory with digital technology, this study underscores climate resilience’s vital function in mitigating negative technological externalities and facilitating the rationalization of human-nature material metabolism. The findings provide theoretical and policy guidance for leveraging technology to empower green transformation and formulating regionally differentiated strategies to advance AI-driven decarbonization.