Do Artificial Intelligence Investments, Financial Development, and Energy Security Risks Promote Renewable Energy Transition? Evidence from the United States

Despite intensified global efforts to accelerate the renewable energy (RE) transition, the influence of artificial intelligence (AI) and energy security risk (ESR) on RE adoption remains underexplored in the United States. This study examines the nonlinear and time-varying effects of AI, ESR, financial development (FD), and economic growth (GDP) on RE consumption from 1990Q1 to 2020Q4. Annual data were converted to quarterly frequency using the quadratic match sum method, and the Wavelet Cross Quantile Regression (WCQR) technique was employed to capture dynamic relationships across quantiles and time scales. The results show that AI and FD consistently stimulate RE adoption, while ESR shifts from a negative short-term influence to a positive long-term effect. Similarly, GDP initially reduces RE consumption but becomes supportive over longer horizons. This study offers new contributions by providing the first empirical evidence on the role of AI in shaping the U.S. renewable energy transition and by jointly examining technological, financial development, and energy security determinants within a unified framework. Policy implications suggest prioritizing investment in AI-based grid and storage systems, expanding green financing tools to lower capital barriers, and adopting long-term energy security strategies to sustain progress toward a low-carbon energy system.