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.