Toward a circular U.S. economy: Green and artificial intelligence innovation, renewable energy, and domestic material consumption

Owing to the material-intensive industrial dependency of the United States (U.S.), reducing domestic material consumption (DMC) is vital for improving resource and material efficiency, addressing environmental challenges, achieving Sustainable Development Goals (SDGs), and advancing dematerialization. To address this important gap in the literature, this study aims to evaluate the long-term associations of green technology innovation (GTI), AI innovation (AIN), renewable energy consumption (REC), trade openness (TOP), and GDP growth (GDPG) with DMC. This study employs the ARDL time series method, which relies on U.S. aggregate national-level data from 1990 to 2023, to explore the long-term cointegrated relationships among them. On the basis of the ARDL long-run estimations, GTI has a significant negative association with DMC, indicating the significance of eco-friendly innovations in dematerialization. Although green technologies reduce material pressure, AIN’s significant positive associations reflect AI innovations’ concern with extensive resource and material consumption in data centers. REC, with its significant negative association, demonstrates the importance of the renewable energy transition for dematerialization. In addition, TOP with a significant negative association indicates the country’s control over trade integration to reduce pressure on territorial material consumption. Moreover, GDPG has a significant positive effect on DMC, indicating that economic growth is associated with scale effects within industries. All these findings remain robust in FMOLS, DOLS, and CCR. Granger causality reveals two unidirectional and two reverse Granger causes, indicating predictive patterns of these relationships. The investigation emphasized implementing action-based policies within the country to succeed with dematerialization.