Environmental challenges critically affect manufacturing firms which face numerous concerns regarding their sustainable operations. These operations aim to operationalize the dimensions of circular economy capabilities (CEC) and green technology innovation (GTI) to strengthen competitiveness in fragile environments. This research validates a holistic understanding of green performance by integrating theories and dimensions to identify effects that predict sustainable green performance. Drawing from the green dynamic capability view (GDCV), which is a contextual extension of the DCV and flexible systems management (FSM) paradigm, this study investigates how CEC and GTI predict green performance (GP). Survey data of 301 senior professionals from manufacturing firms acquired from a developing country, such as Bangladesh, were used. To assess the survey data, the study used a multimethodological approach using Necessary Condition Analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to investigate the suggested tie in the midst of the CEC and GTI on the GP. The findings reveal that all the antecedents of the circular economy are necessary conditions except absorptive capacity to predict green performance, as reported in the NCA. The fsQCA results show that combinations of CEC and GTI are sufficient conditions to predict high green performance. This research uses a unique combination of CEC and GTI to predict high GP via the supplementary method of fsQCA. Therefore, the findings should also motivate professionals of manufacturing firms to focus even more on the necessity effects of a single condition to predict GP and the asymmetric effects of combinations of CEC and GTI to produce multiple configurations to predict high green performance.

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.