Artificial Intelligence, Climate Policy Uncertainty, and the Changing Dynamics of Fossil‐Energy Resource Markets: A Wavelet Quantile Analysis

ABSTRACT Fossil‐energy markets are increasingly influenced by digital transformation and uncertainty surrounding climate policy. This study provides a comparative, fuel‐specific assessment of the nonlinear associations of artificial intelligence (AI) market developments and climate policy uncertainty (CPU) with coal, natural gas, and crude oil returns using daily observations from August 20, 2019, to June 9, 2025. Wavelet quantile‐on‐quantile regression is employed to identify how these relationships vary across market quantiles and short‐, medium‐, and long‐term horizons. The results reveal substantial heterogeneity across fossil fuels. AI is predominantly negatively associated with coal and natural gas returns over longer horizons, whereas its association with oil is positive and more persistent, particularly over the medium and long term. CPU exhibits mixed and localised short‐term relationships but becomes positively associated with coal and natural gas returns over longer horizons, while its relationship with oil remains weaker and more segmented across market states. By applying a consistent framework to two separate sources of market variation across three fossil fuels, the study shows that the observed relationships depend on fuel type, market conditions, and investment horizon. The findings are relevant to energy‐market risk management, investment decisions, and the design of credible climate‐transition policies.