Artificial Intelligence and Energy Efficiency for Ecological Sustainability: Can Regulatory Quality Overcome the Net Zero Challenges in <scp>G7</scp> Economies?

ABSTRACT It takes a concerted effort in the technological, financial, and policy spheres to reach the Sustainable Development Goals and the net‐zero objectives. This paper investigates the relationship between artificial intelligence (AI), energy efficiency (EE), financial development (FD), regulatory quality (RQ), economic growth (EG) and urbanization (URB), and ecological health (EH) in G7 countries between 1996 and 2023. Based on a balanced panel dataset and a dynamic Limited Information Maximum Likelihood (LIML) model, supplemented with Dynamic Common Correlated Effects Mean Group (DCCE‐MG), Panel‐Corrected Standard Errors (PCSE), and Granger causality tests, the analysis represents both the direct and conditional effects, including an interaction term between AI and RQ. The findings demonstrate that although AI and EG have strong yet contrasting effects on EH, EE is a crucial sustainability factor. The processing of AI and RQ lacks a statistically relevant mediating effect, which suggests that institutional quality without AI might not be adequate to increase the environmental benefits of AI. The research is relevant to the existing body of research on sustainability transitions in that the innovative methodologies combine with the latest advancements in research to explore the multifaceted relationship between technology, finance, and policy, which offers new perspectives on the role played by AI in supporting sustainable development. The study has indicated the need to embrace powerful, context‐driven policy frameworks that can capture dynamic interactions and structural diversity in the advanced economies in long‐term ecological ambitions.