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.

The United States is trapped in a fossil fuel infrastructure lock-in, where decades of investment in coal, oil, and gas systems create economic and political resistance to renewable energy transitions, resulting in a renewable energy share (RES) of only 6.8%, compared with 24.5% in the European Union (EU). In addition, the fragmented state–level approach, lacking federal coordination, has created extreme disparities, with a 30-fold gap between leading states, such as Maine (27.55%), and laggards, such as the District of Columbia (0.89%). Therefore, this study examines the long-term determinants and short-term dynamics of renewable energy adoption across 51 US jurisdictions from 1970 to 2023, addressing the income-renewable energy paradox while accounting for cross-sectional dependence (CSD), parameter heterogeneity, and mixed integration orders. Employing advanced econometric techniques, including the cross-sectionally augmented ARDL (CS-ARDL), pooled mean group (PMG), augmented mean group (AMG), and the common correlated effects mean group (CCE-MG) estimators, on a comprehensive dataset of 2754 observations, we investigate the long-run and short-run factors associated with renewable energy transitions in an interconnected federal system. The analysis shows strong CSD and substantial persistence in RES, reflecting integrated energy markets and shared federal policies. Across the main specifications, higher energy prices are positively associated with RES, whereas carbon intensity (CIN) is negatively associated with it. The Mundlak decomposition further suggests that the income-renewable relationship differs across dimensions: Structurally wealthier states tend to exhibit lower average renewable shares, but within-state income changes are weaker and less stable across specifications. Alternative lag structures and heterogeneous factor-augmented estimators broadly support the robustness of the energy price and CIN results, although some coefficients remain specification-sensitive. The results point to the importance of federal-state coordination and state-specific transition strategies in an interconnected energy system. They also suggest that relative energy prices, infrastructure conditions, and structural differences across states are relevant for understanding renewable energy adoption.