Artificial intelligence and the evolving behaviour of clean energy and green technology market

As artificial intelligence increasingly reshapes investment decisions and technological transformation, its influence is extending beyond conventional technology markets into the global clean-energy transition. Against this backdrop, this study examines the heterogeneous association of artificial-intelligence market performance (AI) with the clean-energy market (CEM) and the global-technology market (GTM) from 06/03/2020 to 05/06/2026. Oil-price returns (OP) and changes in the CBOE Volatility Index (VIX) are incorporated as controls to distinguish the AI–market association from broad energy-price and financial-risk conditions. The empirical design wavelet quantile-based approaches to capture the association at various quantiles and periods. The results show that the AI–CEM relationship is weak and occasionally negative at the lowest quantiles, particularly over medium and long horizons, but becomes positive across most central and upper quantiles. The positive long-horizon association is especially persistent and remains evident after controlling for OP and VIX, varying lag length, and altering the control-variable set. For GTM, AI exhibits a consistently strong and positive association across nearly all quantiles and horizons, with the largest coefficients generally concentrated in the medium horizon. These findings indicate that AI-market innovations are more tightly integrated with the wider technology market than with clean-energy equities, while the AI–clean-energy linkage is more state dependent and sensitive to extreme downside conditions. Based on these findings, relevant policy recommendations were proposed.