Do Artificial Intelligence and Green Finance Accelerate Sustainable Energy Outcomes? Global Evidence

Abstract

This study examines the impact of artificial intelligence, green finance, and environmental policy instruments on renewable energy investment and energy efficiency across 35 economies over the period 2000 to 2024. Grounded in endogenous growth theory, environmental economics, and innovation systems theory, the study explores how technological advancement interacts with financial and policy frameworks to influence sustainable energy outcomes. A comprehensive panel data approach is employed, utilizing two-way fixed effects, Driscoll–Kraay standard errors, and system generalized method of moments estimation to address heterogeneity, cross-sectional dependence, and endogeneity. The model incorporates key variables including renewable energy investment, energy efficiency, green finance, artificial intelligence adoption, policy instruments, and macroeconomic controls. The empirical findings reveal that renewable energy investment has a positive and statistically significant effect when dynamic adjustments and endogeneity are accounted for, confirming its critical role in advancing sustainable energy transitions. Green finance demonstrates a strong and significant impact in static models, although its direct effect weakens in dynamic specifications, suggesting indirect transmission through technological and investment channels. Artificial intelligence exhibits a negative and statistically significant effect across robust models, reflecting short-term adjustment costs such as increased energy demand and transitional inefficiencies. However, interaction effects indicate that artificial intelligence enhances renewable energy investment and efficiency when supported by strong financial systems and effective policy frameworks. Policy instruments show mixed and often insignificant effects, highlighting limitations in implementation and enforcement.