This research investigates the different strategies and the dynamic connectedness among AI, ESG, and brown assets from 19 March 2017 to 19 March 2025. Differentiating between contemporaneous and lagged spillover influences provides a detailed view of how the technology-driven and traditional energy sectors interact under shifting geopolitical and environmental conditions. The results indicate that the AI and ESG markets serve as the primary transmitters of information to traditional energy, particularly under extreme market conditions, including the second Trump administration and the Red Sea tensions. Despite rising geopolitical tensions, the findings document that such developments are catalyzing significant shifts toward AI and ESG markets. The findings demonstrate that integrating AI and sustainability principles enhances energy market stability, reduces systemic risk, and accelerates the transition toward low-carbon, climate-resilient energy futures.

The BRICS economies, facing the threat of climate change, face a policy challenge in transitioning from fossil-fuel-based energy systems and improving environmental quality. This necessitates urgent policy changes in the outdated energy infrastructure of BRICS countries. However, there still remains a policy gap regarding how countries in the BRICS, a group of rapidly developing economies, can grow their economies in line with the Sustainable Development Goals. The aim of our study is to investigate the impact of green energy, green technology, and forest cover on environmental quality in BRICS countries. The BRICS group of countries offers an ideal field of study for both examining the impacts of green energy, green technology, and forest areas on environmental quality in developing economies, as well as for evaluating national and global energy policies. Although numerous studies have empirically examined the relationship between environmental variables and green initiatives, the impact of green initiatives on the load capacity factor has been overlooked. These studies have generally used various econometric methods and have not included machine learning in the process. This study addresses this research gap by focusing on green energy and green technology, which are preferred for their various advantages and make significant contributions to the load capacity factor. To analyze this relationship in BRICS economies between 2000 and 2022, the Augmented Mean Group (AMG) estimator and Machine Learning algorithms were used. According to the results, strong evidence emerges of a positive relationship between green energy, the digital economy, forested area, and the load capacity factor, while a negative relationship exists between green technology, growth, and the load capacity factor. Based on robust empirical findings, renewable energy sources are a key driver of clean energy adoption and can ultimately increase the load capacity factor in BRICS economies. The results also imply that, since developments in green technological innovation in BRICS countries are still in their infancy, investments in green technologies for a sustainable environment need to be qualitatively increased.

This study examines the dynamic and distributional effects of financial technology (FinTech) and renewable energy (RE) on financial stability (FST) in BRICS economies from 2012 to 2022. Using a combination of Panel Autoregressive Distributed Lag (Panel ARDL) and Panel Quantile Regression (PQR) models, the analysis captures both short-run versus long-run adjustment mechanisms and heterogeneous effects across different levels of financial stability. The ARDL results reveal a dual effect of FinTech: while FinTech expansion initially increases short-run financial volatility, it enhances long-run stability as regulatory and institutional frameworks mature. Renewable energy consistently strengthens financial stability, with its impact intensifying in higher quantiles of the stability distribution. The quantile results further show that the stabilizing effects of FinTech, RE, institutional quality, and industrial development become stronger in more resilient financial systems. These findings highlight the need for BRICS policymakers to coordinate digital financial innovation with clean energy strategies under robust governance frameworks to promote a more stable, inclusive, and sustainable macro-financial environment.

Energy demand has become a significant challenge worldwide, and leading economies are seeking the most effective alternatives. Also, a well-developed financial system is needed to meet this energy demand. The association between financial development and renewable energy share (RES) has sparked a newly attractive research direction. The current study estimates this direction in the BICS (Brazil, India, China, and South Africa) nations from 2000 to 2023 using Ridge Regression (RR) and the Baltagi-Wu Locally Best Invariant (LBI) tests. Undoubtedly, a single financial measure may not express the response of the overall financial sector; therefore, this study considers six financial proxies, i.e., loans by depositors (FDB), broad money (BM), loans by financial corporations (FC), financial depth (FDP), financial efficiency (FEF), and financial stability (FST). The results indicate that financial proxies, such as FDB, BM, and FC, negatively affect RES. Similarly, financial depth and efficiency play an inverse but insignificant role in promoting RES. Finally, financial stability plays a positive but insignificant role in the explained variable. In addition, socio-institutional factors describe mixed responses across the different models. Finally, the core concept regarding the RKC hypothesis remains invalid for BICS economies due to its insignificant role. However, core implications are being proposed to increase the renewable energy share in overall energy consumption.

This paper studies the impact of crisis management mechanisms (bailouts and bail-ins) on moral hazard. Through an econometric study of financial crises in emerging countries since 1993, the results indicate that recurrent bailouts exacerbate moral hazard, while bail-ins have only a minor impact due to their perceived lack of credibility. The results highlight the impact of crisis management measures on financial stability and the scale of the mobilised resources. Analysed in the context of efforts to support sustainable development, the study recommends reassessing the fundamentals of the bailout approach, developing a robust and sustainable bailout framework that aligns immediate interventions with sustainability objectives towards sustainable recovery strategies.

This study examines crude oil return dynamics in the context of the global energy transition, where decarbonization policies, technological innovation, and shifting energy demand increasingly influence market behavior. We propose a heavy-tailed distributional LSTM framework to jointly model the conditional mean, volatility, and tail risk of West Texas Intermediate (WTI) returns, incorporating key transition-related drivers: carbon allowance returns (ETS), artificial intelligence (AI) activity, electric vehicle (EV) market returns (SPKS), and geopolitical risk (GPR). Granger causality results show that ETS significantly predicts mean returns, reflecting the growing impact of climate policy signals, while AI and EV markets primarily affect volatility, indicating transmission through uncertainty channels. The model adopts a Student-t specification to capture heavy-tailed behavior and extreme price movements. Out-of-sample results reveal limited mean predictability but improved forecasting of return magnitude and tail risk. These findings highlight that, under energy transition dynamics, oil market predictability is increasingly concentrated in the risk dimension rather than in average returns.