Industrial value creation (INV) has become a vital priority in contemporary research. Understanding which long-run determinants are associated with it in the U.S. is vital. This research investigates the dynamic associations of several determinants of INV, such as Patent-based Technology Diffusion (PTD), renewable energy consumption (REC), supply chain digital capability (SCDC), GDP growth (GDPG), and natural resource rents (NRR), analyzing the national-level data of the United States (U.S.) from 1990 to 2023. In terms of methodology, it employs the autoregressive distributed lag (ARDL) method and integrates several robustness tests, including fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), canonical cointegrating regression (CCR), heteroskedasticity- and autocorrelation-consistent (HAC), and causality analysis. The bounds test confirms cointegration (F = 31.021, exceeding the 1% critical bound). In the long run, all determinants show significant positive associations with INV: PTD (0.322), REC (0.127), NRR (0.092), GDPG (0.052), and SCDC (0.015), all at the 1% level. The error-correction term (−0.389, p < 0.01) indicates that 38.9% of short-run deviations are corrected each year. Granger causality analysis also suggests mixed predictive directionality among variables. The robustness tests, including FMOLS, DOLS, and CCR, further confirm the main findings, although GDPG and SCDC show some sensitivity. The novelty of this research lies in the simultaneous examination of technology diffusion, renewable energy consumption, digitalization, natural resource revenues, and economic growth, associated with the outcome of INV within a unified time-series framework for the U.S. The findings offer analytical guidance for U.S. industrial, energy, and technology policy.