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.

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.