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.

Currently, the United States (U.S.) has issues with declining industrial values and supply chain digitalization, which threaten the country's industrialization and Industry 4.0 transformation. The country needs to reverse this trend through effective policy implementation. However, the literature's understanding of effective policies' long-run associations with the industrial value added (INV) of the country remains limited. Thus, this research investigates the long-term cointegrated relationships of several variables, such as artificial intelligence innovation (AIP), trade openness (TOP), supply chain digitalization (SCD), foreign direct investment (FDI), and GDP growth (GDPG), with INV based on the national level data from 1990 to 2023. The autoregressive distributed lag findings suggest that TOP, SCD, and GDPG have significant positive associations with INV in the long run, indicating that the significance of trade openness, supply chain digitalization, and economic growth is associated with the positive performance of industrial values within the country. However, both AIP and FDI exhibit significant negative associations with INV, indicating transitional and adjustment cost pressure of AI innovations and crowding out effects of domestic investments associated with FDI. That particular research suggests that all the variables would play individual effects and interact with one another as an Industry 4.0 transformation system to facilitate the improvement of industrial values in the country over long-run dynamics. Together, these findings can help federal policymakers empirically tailor long-term strategies to turn decreasing industrial value-adding performance into an upward trend and transform into Industry 4.0 by ensuring positive combined associations from technology-driven strategies.