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.

Advanced economies are under growing pressure to downscale greenhouse gas (GHG) emissions without undermining growth, yet G7 (Group of Seven) nations, representing almost 10% of the world's population, still generate one quarter of global GHGs. We have investigated the G7's GHG emission problem from 2000 to 2020, by integrating macroeconomic and environmental panel data to determine how stricter environmental policies, higher green tax revenue, resource dependency, trade openness, and globalization can reduce the G7's emission problem. We applied second-generation panel estimators alongside a state-of-the-art quantile-based robust model, called the method of moment quantile regression (MMQR), and employed a two-step generalized method of moments (GMM) to address the endogeneity concern. In doing so, we found the following three findings. First, tougher regulations and higher environmental tax yields are consistently associated with reducing the GHG emissions, with the effect intensifying in all regimes. Second, resource dependence remains a stubborn emission amplifier across the entire distribution. Third, the role of trade and globalization is minimal, sometimes insignificant, referring to the fact that the policy and structural factors dominate trade and integration effects. Policy pathways for the G7 thus focus on (i) synchronizing environmental policy stringency targets to strict carbon-pricing floors, (ii) recycling environmental tax revenue and implementing green globalization with cross-border trade to accelerate clean-tech diffusion, and (iii) deploying resource diversification to neutralize resource rent-driven lock-ins. Our policy mix can help wealthy, integrated economies translate fiscal and regulatory leverage into a rapid and equitable solution to reduce GHG emissions.