From innovation to sustainability: Unravelling AI-driven solutions in the U.S.'s ecologically sustainable practices

Since the United States (U.S.) is falling behind the net-zero carbon emissions target by 2050, understanding effective long-term policies to reduce carbon intensity/emissions is essential for the country. To address key literature gaps, this study aims to provide novel evidence of the comparative assessment of the long-term effectiveness of artificial intelligence (AI) patterns, environmental policy stringency (EPS), renewable energy consumption (REC), natural resource rents (NRR), and trade openness (TO) to facilitate the outcome of ecological sustainability (ECOI), which can either reduce or increase carbon emission intensity. This study is based on ecological modernization theory (EMT) and assumes these variables. The analysis employs the autoregressive distributed lag (ARDL) and the vector error correction model (VECM) based Granger causality test, which uses U.S. national-level data from 1990–2022 (33 years). The long-run ARDL findings suggest that AI and REC significantly enhance ECOI, lowering carbon emissions. Moreover, the NRR and EPS are insignificantly related to the result, but these two factors can also reduce emissions. Surprisingly, TO significantly reduces ECOI, increasing emissions in the long run, driven by carbon-intensive product imports and global trade-integrated industrial activities. These results are essential for federal policymakers to emphasize AI innovations and renewable transitions and control trade openness with eco-friendly policies on imports, enabling the country to reduce significant levels of carbon intensity/emissions in the long run and remain ahead of the net-zero-carbon target.