Artificial Intelligence and the Next-Gen Supply Chain: Energy-Economy Linkages in the United States (Innovations in Environmental Economics, 2025)
This study delves into the complex connections between AI advancements, energy usage, industrialization, population increase, GDP growth, and the cumulative effect on the ecological footprint (EF) in the United States from 1996 to 2022. The advanced econometric methods like unit root tests (ADF, P-P, and DF-GLS) were applied in the study to examine the non-stationary variables. Furthermore, the Autoregressive Distributed Lag (ARDL) was utilized for both short and long-term effects, and the paper delivers a comprehensive analysis of the dynamics of environmental sustainability.  Additional validation of the ARDL findings comes from robustness checks done on FMOLS, DOLS, and CCR estimation. The findings indicate that there is a positive correlation between the growth of GDP, energy consumption, industrialization, and increase in population and EF, which implies that more economic activities, increased industrial expansion, and a rise in the population cause increased levels of pollution and depletion of resources. 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 results demonstrate how AI innovation and renewable energy sources can improve environmental well-being while tackling the problems caused by industrialization and GDP growth. In order to achieve equilibrium between growth in the economy and environmental conservation, the investigation highlights the necessity of tailored regulations that encourage the use of alternative energy sources, environmentally friendly industrial processes, and AI-driven long-term viability systems. Policymakers can leverage these insights to foster sustainable innovation while reducing the environmental impact of population and industrial growth.