Greener, Smarter, Stronger: How AI is reshaping the U.S. Energy Economy and Ecological Impact

This study investigates the complex relationships between economic growth, artificial intelligence (AI) innovation, energy consumption, industrialization, population expansion, and their collective impact on the ecological footprint (EF) in the USA from 1996 to 2022. Using advanced econometric techniques, including unit root tests (ADF, P-P, and DF-GLS) to assess the non-stationarity of variables, and the Autoregressive Distributed Lag (ARDL) approach to analyze both short- and long-term effects, the study provides a comprehensive understanding of environmental sustainability dynamics. Robustness checks with FMOLS, DOLS, and CCR further validate the ARDL findings. The results reveal a positive correlation between GDP growth, energy consumption, industrialization, and population growth with the EF, suggesting that increased economic activities, industrial expansion, and population growth contribute to higher pollution and resource depletion. In contrast, AI innovation exhibits a negative correlation with the EF, indicating that AI advancements can mitigate environmental degradation by optimizing resource usage and promoting sustainable practices. These findings highlight the potential of AI and sustainable energy solutions in improving ecological health while addressing the challenges posed by economic growth and industrialization. The study underscores the need for targeted policies that promote AI-driven sustainability, eco-friendly production methods, and renewable energy adoption, to balance economic development with ecological preservation. Policymakers can leverage these insights to foster sustainable innovation while reducing the environmental impact of population and industrial growth.