Analyzing How AI impact Environmental Sustainability: Case Study for USA

This study investigates the role of private investment in Artificial Intelligence (AI) in promoting environmental sustainability in the United States from 1990 to 2019. It also analyzes the impact of financial globalization, technological innovation, and urbanization by testing the Load Capacity Curve (LCC) hypothesis. The study employs stationarity tests, which indicate that the variables are free from unit root problems and exhibit mixed orders of integration. Using the Autoregressive Distributive Lag (ARDL) Model bound test, the study finds that the variables are cointegrated in the long run. The short-run and long-run estimations of the ARDL model confirm the existence of the LCC hypothesis in the United States, revealing a U-shaped relationship between income and load capacity factor. The results show that private investment in AI has a significant positive correlation with the load capacity factor, thus promoting environmental sustainability. Conversely, technological innovation and financial globalization exhibit a negative correlation with the load capacity factor in both the short and long run. To validate the ARDL estimation approach, the study employs Fully Modified OLS, Dynamic OLS, and Canonical Correlation Regression estimation methods, all of which support the ARDL results. Additionally, the Granger Causality test reveals a unidirectional causal relationship from private investment in AI, financial globalization, economic growth, technological innovation, and urbanization to the load capacity factor.