ABSTRACT Despite growing policy efforts toward sustainable development, the mechanisms through which artificial intelligence, institutional quality, and macroeconomic policy variables jointly shape green growth remain insufficiently understood in advanced economies. This study investigates the key determinants of green growth across the G7 economies from 2000 to 2023, employing a panel ARDL framework combined with wavelet‐based time–frequency analysis. The model incorporates AI research activity, renewable energy use, natural resource rents, government effectiveness, environmental taxation, and education expenditure. The findings reveal that AI research and renewable energy adoption serve as major engines of sustainable growth, whereas natural resource rents and education spending exert adverse long‐run effects, suggesting inefficiencies and misalignment between fiscal and environmental objectives. Distinct national patterns emerge: innovation‐oriented economies such as Germany, Japan, and the United States benefit from the synergy between digitalization and green investment, while more resource‐dependent economies face institutional barriers that slow their green transition. The study further illustrates how the integration of AI and institutional effectiveness can reshape policy frameworks. The results underscore the importance of data‐informed policymaking, education reform toward environmental competencies, and smarter environmental taxation, providing actionable insights for governments aiming to align technological innovation with ecological resilience in advanced economies.
Aslan et al. (Tue,) studied this question.