Governing the Green Transition: Evaluating the Role of Artificial Intelligence, Green Technology, and Environmental Governance in <scp>G7</scp>

ABSTRACT Ecological restoration represents a central challenge for sustainable development, particularly in advanced economies facing persistent ecological deficits. This study empirically examines the effects of artificial intelligence, green transition, and environmental governance on ecological restoration in G7 countries, employing the Load Capacity Factor as an integrated indicator of ecological quality. Using panel data spanning 1990–2020 and advanced panel econometric techniques, the analysis reveals that artificial intelligence exerts a statistically significant negative effect on ecological restoration, reflecting its energy‐intensive deployment. In contrast, green transition variables, including renewable energy consumption and green technological innovation, exhibit robust positive impacts on the Load Capacity Factor, confirming its critical role in reducing environmental footprints and advancing sustainability goals. Environmental governance is found to be negatively associated with ecological restoration in the short run, suggesting transitional adjustment costs linked to policy stringency. Furthermore, the results confirm a U‐shaped relationship between economic growth and ecological balance, consistent with the Load Capacity Curve hypothesis that early phases of industrialization exacerbate degradation, but economic maturity enables sustainable development through green transitioning efforts. These findings offer nuanced insights into the ecological consequences of technological progress and policy interventions in advanced economies and underline the importance of aligning artificial intelligence development, green transitions, and environmental governance to support long‐term ecological sustainability in G7 nations.