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.

This study examines the long-run determinants of sustainable wealth accumulation (SWA) in the United States (U.S.) over the period from 1990 to 2023 by using the adjusted net savings as a direct measure of national wealth sustainability. Unlike prior literature that primarily relies on indirect environmental proxies, this study develops an integrated empirical framework through incorporating green technology innovation for environmental management (GTIEM), natural resource rents (NRR), industrial value creation (INV), information and communication technology investment growth (ICTIG), and GDP per capita (GDPC). It compares their long-run associations with adjusted net savings to reflect sustainable wealth accumulation of the country. In methodology, the study employs autoregressive distributed lag (ARDL) and estimates the dynamic relationships among them. It also integrates with the fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and canonical cointegrating regression (CCR) estimators, along with the Toda-Yamamoto causality test. The main ARDL long-run results suggest that GTIEM and INV significantly associate with positive contributions in SWA, while NRR significantly associates with negative contributions in SWA. Contrarily, ICTIG and GDPC remain statistically insignificant to the context. The robustness tests support these outcomes, and the causality test indicates several predictive directions of the relationships. Overall, these outcomes are vital for federal policymakers to emphasize targeting policies to facilitate the enhancement of adjusted net savings and ensure sustainable wealth accumulation in the country in the long run.

Owing to the challenges associated with achieving net-zero carbon dioxide (CO2) emissions and sustainable development goals (SDGs), reducing carbon emissions and their intensity is vital for the United States (U.S.). However, evidence suggests that the country still has ineffective policies towards sustainable industrialization and CO2 reduction in the long run. In addition, the literature has rarely investigated U.S. national-level data, considering the comparative effectiveness of several policies. This research addresses this gap and provides fresh evidence on the effectiveness of Industry 4.0 and the renewable energy transition to reduce carbon intensity (COI) in the long run. It employs autoregressive distributed lag (ARDL), which relies on U.S. national-level data from 1990 to 2023. It explores the associations among AI innovation (AIP), renewable energy consumption (REC), industrial value creation (INV), natural resource rents (NRR), and GDP growth (GDPG), considering the COI outcome. The long-run ARDL estimations suggest that AIP and REC have significant negative effects on COI, suggesting that the U.S. must prioritize adopting AI innovations and the renewable energy transition, and to reduce intensity, the government needs to emphasize implementing renewable industrialization, redirecting resource extraction toward the renewable transition, and decoupling economic growth for a longer period. Moreover, the Granger causality results suggest that AIP, NRR, and REC predict carbon intensity in a one-way direction, whereas carbon intensity predicts INV. Currently, the federal government should utilize these insights to reduce COI over the long run and succeed with sustainable industrialization, achieving SDGs, and net-zero carbon emissions by 2050.