How Does Green and AI ‐Related Innovation Activity Influence Ecological Decoupling in the U.S.? Evidence From Aggregate and Industry‐Adjusted Measures

Managing sustainability transitions demands not only adopting the newest technology and innovations but also policy alignment that reduces ecological pressure. Linking to ecological modernization theory, this study examines the U.S. time series data spanning 1990–2023 to understand how the combined innovation activities related to green technologies and AI (artificial intelligence) are associated with ecological footprint per capita (ECF) and industrial ecological footprint intensity (IEFI) using the autoregressive distributed lag (ARDL). The findings indicate that the influence of AI‐related and environmental technology innovation (AIETI) differs between aggregate ECF and IEFI. Specifically, AIETI has a negative association with ECF, which helps achieve a lower ecological footprint in the U.S. economy in the long run. However, green and AI‐related innovation activity show a substantial positive association with industrial ecological footprint intensity in the short run, but the association weakens in the long run. Industrial renewable energy consumption has shown a consistent contribution to lowering both ECF and IEFI. In addition, an efficient supply chain is also associated with ECF but failed to show any association with IEFI. Moreover, it is necessary to ensure that clean‐manufacturing policies are accompanied by monitoring of renewable‐energy use, inventory management, and ecological pressure.