Artificial Intelligence, Structural Transformation, and Carbon Emissions in the United States: A Stirpat–ARDL Approach

The accelerating diffusion of artificial intelligence is reshaping production systems, energy efficiency, and environmental outcomes in advanced economies. However, the environmental consequences of AI-driven technological progress remain theoretically ambiguous, particularly within high-income, energy-intensive contexts. This study re-examines the dynamic relationship between artificial intelligence innovation and carbon dioxide emissions in the United States within an extended STIRPAT framework incorporating economic growth, energy consumption, foreign direct investment, and urbanization over the period 1990 to 2022. Employing the autoregressive distributed lag approach to capture both long-run equilibrium relationships and short-run adjustments, the results confirm the existence of cointegration among the variables. The long-run estimates reveal that artificial intelligence innovation significantly reduces carbon emissions, suggesting that efficiency gains and technological optimization effects outweigh scale…