Leveraging AI for a Greener Future: Exploring the Economic and Financial Impacts on Sustainable Environment in the United States

Abstract

In response to increasing environmental challenges, the United States has deliberately adopted technical advancements to promote sustainable development. This includes efforts to decrease pollution, improve energy efficiency, and encourage the use of environmentally friendly technology in different industries. This study investigates the role of Artificial Intelligence (AI) technology in promoting environmental sustainability in the United States from 1990 to 2019. It also examines the impacts of financial development, ICT use, and economic growth on the Load Capacity Factor (LCF). Various unit root tests revealed no unit root issues and mixed integration orders among variables. The Autoregressive Distributive Lag (ARDL) model explored cointegration, indicating long-run relationships among the variables. The ARDL findings confirm the Load Capacity Curve hypothesis for the United States, with AI technology and ICT use positively correlating with LCF in both the short and long run. Conversely, financial development and population growth significantly reduce LCF. Robustness checks using FMOLS, DOLS, and CCR estimation approaches align with the ARDL results. Granger causality tests reveal unidirectional causality from economic growth, AI, financial development, and ICT use to LCF and bidirectional causality between population and LCF. Diagnostic tests confirm the results are free from heterogeneity, serial correlation, and specification errors. This study underscores the importance of AI and ICT in enhancing environmental sustainability while highlighting the adverse impacts of financial development and population growth on LCF.