Artificial Intelligence-Driven Financial Strategies for Renewable Energy Transition: A Cross-Country Analysis of Efficiency, Investment, and Policy Implications

Abstract

An integrated empirical framework was put in place in order to study the combined impacts of artificial intelligence (AI), green finance (GF), and tools of environmental policies on renewable energy investments and their efficiency on a global scale. The hybrid approach of combining econometric estimators i.e. two-way fixed effects, System-GMM, Mean/Pooled Mean Group and Dynamic Common Correlated Effects was employed with Explainable machine learning (XGBoost with SHAP) to assess the model. For the period of the 2005-2024 study, a balanced macro panel composed of 30 OECD and emerging economies was utilized, and it was assessed that with regard to AI readiness, most of the time it acted as a complementary catalyst, augmenting the effectiveness of finance and policies, rather than an autonomous driving force. In the framework of AI intensity and carbon pricing, AI appears with a negative sign in the short run, which unfolds as transitional adjustment costs. These results are reversed when the interaction terms GF×AI and Policy×AI are included; these terms are positive and statistically significant, meaning that an increase in digital capacity strengthens policy transmission. System-GMM results confirmed persistent investment dynamics through significant lag dependence, and the sign pattern for core variables was preserved in robustness checks (DCCE/MG/PMG). On the efficiency margin, green bonds exhibit a positive association, whereas short run AI and carbon price frictions remain. The SHAP diagnostics confirm the econometric findings and consider investment inertia and the pressures of transitions (emissions and fuel prices) as key contributors, while AI works with context-dependent complementarities. To conclude, the digital governance with the type of green-finance and the reliable policies in place will promote investment in renewables and improve energy efficiency.