Pioneering sustainable energy: a dynamic analysis of AI and green patents in renewable and nuclear power

Artificial intelligence (AI) is emerging as a transformative force in advancing technological progress, with profound implications for energy efficiency and sustainable development. This pioneering study explores the joint impact of AI and green patents (GPT) on RENE across 34 countries from 2000 to 2019. It is a novel dual-sector focus that fills a critical gap in the literature. We employ a multi-method framework combining Method of Moments Quantile Regression (MMQR), Quantile Regression (QR), and causality techniques to capture the complex dynamics between these variables. Our results reveal that both AI and GPT significantly enhance RENE, highlighting the value of technological innovation in sustainable energy transitions. Economic growth is also found to positively influence energy efficiency, while foreign direct investment (FDI) and research and development (R&D) show mixed and sometimes negative effects. Population dynamics and energy prices, especially oil prices, exhibit varying influences across the efficiency distribution. These findings offer important policy insights, particularly for middle-income countries promoting green innovation and improving energy efficiency. The results suggest that integrating AI into nuclear and renewable energy systems can increase efficiency.