Firm resilience is essential to manage response and rapid recovery from disruptive events for a firm. Moreover, there is limited literature that investigates the combined effects of dynamic capability and technological innovation that are interrelated with firm resilience. This study used the dimensions of firm resilience, which were investigated with both necessary condition analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA) methods using survey questionnaires from 308 respondents operating in Bangladeshi corporate industries that are currently facing uncertainties due to unforeseen crises. NCA results showed that visibility, market position, and digitalization achieved firm resilience as these antecedents reached the full percentile to achieve an optimal level of outcome. On the contrary, the influence of reserve capacity and big data analytics was not empirically significant for achieving firm resilience. Moreover, fsQCA results appreciated NCA results and showed four solutions that are sufficient for achieving a high level of firm resilience. The study reveals the configurational effects of dynamic capabilities and technological innovation to achieve firm resilience. The results show the necessary effects of configurational relationships that lead to outcomes. The configurational method is applied to identify the combined effects of antecedents that help managers predict high levels of firm resilience in a turbulent environment.

The United States (U.S.) faces challenges in achieving its ambitious net-zero carbon emissions target by 2050, with current emissions having fallen by less than 1% in 2024. Despite an investment of $500 billion in low-carbon resources while holding the second-largest green technology patent portfolio globally, it is further imperative to investigate ongoing innovations for suboptimal resource allocation and policy misalignment between investment strategies and environmental effectiveness. In this study, we examine the comparative impacts of artificial intelligence (AI) innovation, research and development (R&D) investment, government intervention, natural resource rents, and renewable energy consumption on U.S. environmental sustainability (ECOI) spanning 1990–2022. We bridge the gap in prior literature with respect to understanding which pathways of innovation lead to the highest carbon efficiency returns per dollar invested, moving beyond aggregate investment analysis toward identifying the optimal policy sequencing and resource allocation strategies. We implemented a comprehensive time series econometric framework, including autoregressive distributed lag bounds testing, the vector error correction model, and Granger causality analysis on 33 years of national-level data. Our findings suggest that R&D investment results in the greatest improvement in long-term carbon intensity, followed by AI patents and renewable energy usage. Government intervention has significant negative long-term effects despite positive short-term impacts, which may indicate potential crowding-out effects. Natural resource dependency has positive long-term benefits with negative short-term impacts, suggesting opportunities for strategic extraction. The error correction mechanism implies a moderate adjustment speed toward equilibrium, whereas impulse response functions (IRFs) reveal that AI innovations establish rapid environmental benefits peaking in the second period. These results provide crucial evidence for federal climate investment prioritization by suggesting that taking funds away from direct government spending and putting them into AI-integrated R&D initiatives could maximize carbon reduction outcomes and accelerate progress toward net-zero targets.