Do Investments in Artificial Intelligence, Economic Policy Uncertainty and Economic Growth Hurt or Help Biodiversity in the United States?

Biodiversity loss remains a critical environmental concern, particularly in developed economies like the United States where rapid technological advancement and economic growth pose sustainability challenges. This study investigates the effects of investment in Artificial Intelligence (AI), Economic Policy Uncertainty (EPU), Renewable Energy Consumption (REC) and Economic Growth (GDP) on Biodiversity (BIO) in the United States from 2013Q1 to 2024Q4. Employing a wavelet‐based quantile framework, specifically Wavelet Quantile Correlation (WQC) and Wavelet Quantile‐on‐Quantile Granger Causality (WQQGC), the analysis captures dynamic, nonlinear and scale‐dependent interactions. The results reveal that AI and GDP exhibit predominantly negative long‐term effects on biodiversity, while REC demonstrates conditional positive impacts. EPU shows mixed influences depending on quantile levels and time horizons. These findings highlight the complex and asymmetric nature of the relationship between economic and technological drivers and biodiversity. Policy implications call for AI innovations aligned with ecological goals, stable energy policies and sustainable growth strategies that integrate biodiversity considerations. The study contributes to the literature by applying a novel time‐frequency‐quantile approach to uncover hidden dependencies, offering a robust basis for biodiversity‐conscious policymaking in the face of economic and technological transformation.