From Dependency to Resilience: How Artificial Intelligence Can Rescue the Renewable Energy Transition from Critical Mineral Vulnerabilities?

This paper examines how artificial intelligence (AI), supply-chain pressure (SCP), and energy insecurity (EIS) shape the renewable energy transition (RET) in the United States, China, Japan, South Korea, and the United Kingdom from 2010 to 2022. Using monthly data, we estimate Method-of-Moments Quantile Regressions (MMQR) and validate results with random-effects and IV-GMM models. SCP is associated with weaker RET at lower quantiles, indicating that early-stage or less advanced transition states are more vulnerable to supply disruptions. AI and EIS are positively related to RET, with stronger effects in the lower quantiles. The interaction term (SCP×AI) is positive and significant at the lower quantiles, suggesting AI adoption mitigates supply-chain frictions that stem from critical mineral constraints. Results are robust across estimators and diagnostics. Policy should pair targeted AI deployment in logistics, forecasting, and grid operations with measures that harden mineral supply chains. Doing so accelerates early-stage transition progress and cushions shocks without raising inflationary pressure.