Organizational resilience is a key aspect for sustaining comparative benefit and performance amidst uncertainties such as pandemics, political volatility, and financial crises. Despite its significance, limited studies have explored the potential sufficient solutions to resilience-enabling constructs, especially in emerging economies. This research combines the Resource-Based View (RBV) and Transaction Cost Economics (TCE) to propose a theoretical framework for understanding and predicting organizational resilience. Using survey data from 348 respondents serving corporate industries in Bangladesh, we employ Necessary Condition Analysis (NCA) and fuzzy set Qualitative Comparative Analysis (fsQCA) to identify causal configurations to predict organizational resilience. The findings reveal five configurations that are sufficient for achieving high resilience and four configurations associated with low resilience, highlighting the nuanced interplay between resources, costs, and adaptability. Specifically, flexibility, response, recovery, benevolence, and commitment must need conditions for achieving organizational resilience in NCA analysis. In fsQCA analysis, flexibility and commitment are core conditions, whereas response and information sharing are peripheral conditions for achieving high organizational resilience. This study strengthens resilient strategies by demonstrating the supplementary contributions of RBV and TCE. This combination offers policymakers actionable insights to develop resilient strategies that enhance organizational adaptability and performance in turbulent times.

Green Growth Strategies (GGS) are a win–win opportunity for not only the nation’s economy but also the environment. However, many countries are not concerned about reaping these benefits, and they continue to harm the environment for short-term gain, neglecting long-term sustainability. Southern European countries can utilize green growth policies to enhance their competitiveness, bypass older, more polluting technologies by directly adopting cleaner alternatives, and achieve economic and environmental progress. To address this concern, we analyzed 12 Southern European countries from 2010 to 2019, investigating how they can enhance their green growth performance by incorporating critical predictors. We employed panel data estimators, mean group (MG) to accommodate slope heterogeneity, and Common Correlated Effects Mean Group (CCEMG), which opens the opportunity to observe the influence of unobserved common factors and allows us to capture cross-sectional dependence and heterogeneous behavior better. We applied the Method of Moment Quantile Regression (MMQR) technique further as a robustness check to capture heterogeneous effects across the green growth distribution. The same methodology with ecological footprint data and implementation of the Generalized Method of Moments (GMM) has eliminated the endogeneity concern. The findings highlighted that educational attainment, globalization, and renewable energy consumption have positive influences on green growth. In comparison, trade openness and natural resource rent exhibit negative effects when we shift the methodology from MG to CCEMG, giving us a total of four significant factors to be concerned with. The study emphasizes the crucial importance of tailored policy approaches and regional collaboration in addressing environmental challenges effectively, and offers actionable insights for achieving green growth in the region by providing empirically grounded and practical recommendations that account for diverse socioeconomic contexts and ecological vulnerabilities.

In an era of intensifying global competition where nations aggressively pursue economic advancement, the imperative to balance progress with ecological preservation has become paramount. However, the race for advancement should not harm nature or future generations. Our study investigates the drivers that can lead to green growth, aligning with sustainable development principles that integrate economic growth, environmental stewardship, and social equity as per the UN's Sustainable Development Goals (SDGs), across nine Western European countries from 2010 to 2019. By utilizing panel data from reputable sources, this research investigates the influence of globalization, natural resource rents, renewable energy consumption, trade openness, and total energy consumption on green growth. Employing contemporary panel diagnostic tests, cointegration analyses, and fixed- and random-effects models, the study also validates its findings through quantile regression, fully modified ordinary least squares, and dynamic ordinary least squares. Our study has fulfilled its destiny by finding the right drivers. According to various analyses, globalization and trade openness consistently and significantly promote green growth, confirming their potential as reliable mechanisms for achieving green growth. The complex impact of renewable energy consumption and natural resource rents opens a new door for exploration by revealing the transitional barriers, such as initial costs and policy lags, in contrast to maintaining the resource rent tendency. However, the beneficial impact of total energy consumption of carbon and fossil fuel underscores the urgency of effective resource utilization and a shift toward renewable sources to decouple growth from unsustainable consumption before running out.

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.

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.

Since the United States (U.S.) is falling behind the net-zero carbon emissions target by 2050, understanding effective long-term policies to reduce carbon intensity/emissions is essential for the country. To address key literature gaps, this study aims to provide novel evidence of the comparative assessment of the long-term effectiveness of artificial intelligence (AI) patterns, environmental policy stringency (EPS), renewable energy consumption (REC), natural resource rents (NRR), and trade openness (TO) to facilitate the outcome of ecological sustainability (ECOI), which can either reduce or increase carbon emission intensity. This study is based on ecological modernization theory (EMT) and assumes these variables. The analysis employs the autoregressive distributed lag (ARDL) and the vector error correction model (VECM) based Granger causality test, which uses U.S. national-level data from 1990–2022 (33 years). The long-run ARDL findings suggest that AI and REC significantly enhance ECOI, lowering carbon emissions. Moreover, the NRR and EPS are insignificantly related to the result, but these two factors can also reduce emissions. Surprisingly, TO significantly reduces ECOI, increasing emissions in the long run, driven by carbon-intensive product imports and global trade-integrated industrial activities. These results are essential for federal policymakers to emphasize AI innovations and renewable transitions and control trade openness with eco-friendly policies on imports, enabling the country to reduce significant levels of carbon intensity/emissions in the long run and remain ahead of the net-zero-carbon target.