The current study examines the application of advanced machine learning (ML) techniques for forecasting credit risk in Islamic (participation) and traditional banks in the United Kingdom in 2010–2023. Leveraging an equally weighted panel dataset and guided by robust empirical literature, we integrate structural econometric modeling—i.e., the stochastic frontier approach (SFA) to measuring the Lerner index of market power—with current best-practice tree-based ML algorithms (CatBoost, XGBoost, LightGBM, and Random Forest) to predict non-performing loans (NPLs). The results show that bank-level financial performance measures, particularly loan ratio, profitability, and market power, outperform macroeconomic factors in forecasting credit risk. Among the models tested, CatBoost was more accurate and explainable, as confirmed by SHAP-based explainability analysis. The implications of the research have practical applications for risk managers, regulators, and policymakers in terms of valuing the explanatory power of explainable AI tools to enhance financial oversight and decision-making in post-crisis UK banking.

The primary aim of this research endeavor is to elucidate the intricate dynamics among renewable energy utilization, CO2 emissions, and the trajectory of economic growth within the framework of G7 Countries over the extensive timeframe from 1997 to 2021. Through the meticulous application of cointegration analysis and the Panel Vector Error Correction Model, our empirical investigation unveils a substantiated positive impact of the utilization of renewable energy sources and CO2 emissions on economic growth trends in the long term. Nevertheless, our comprehensive analysis suggests a notable absence of any observable relationship between renewable energy adoption, CO2 emissions, and short-term economic growth dynamics. In light of our findings, it is recommended that policymakers focus on fostering sustainable long-term strategies for renewable energy adoption and CO2 emissions reduction, while also acknowledging the limited short-term impact on economic growth dynamics.

In the wake of rapid digital transformation, emerging technologies like FinTech, AI, and Blockchain are reimagining how countries pursue sustainable development. This study examines how FinTech adoption, Artificial Intelligence (AI) readiness, and Blockchain activity influence sustainable development performance across G20 economies over the period 2015–2023. Drawing on Innovation-Driven Growth Theory, the Technology–Organization–Environment framework, and Institutional Theory, the analysis evaluates both the direct and complementary effects of these digital technologies on Sustainable Development Goal (SDG) outcomes using cross-country panel data and key macroeconomic controls. The results show that FinTech, AI, and Blockchain each exert a positive and statistically significant impact on national sustainability performance, with AI exhibiting the strongest effect. Moreover, the findings reveal meaningful digital complementarities, indicating that coordinated adoption of these technologies amplifies sustainable development gains. Overall, the study provides robust macro-level evidence that digital transformation functions as a strategic driver of sustainability and offers policy-relevant insights for G20 governments seeking to accelerate inclusive, transparent, and environmentally responsible development.

This paper considers the impact of Central Bank Digital Currencies (CBDCs) on the world’s financial systems with a special emphasis on G20 economies. Using quarterly macro-financial data for the period of 2000 to 2024, collected from the IMF, BIS, World Bank, and Atlantic Council, a Global Vector Autoregression (GVAR) model is applied to 20 G20 countries. The results reveal significant heterogeneity across economies: CBDC shocks intensify emerging market financial instability (e.g., India, Brazil), while more digitally advanced countries (e.g., UK, Japan) experience stabilization. Retail CBDCs increase disintermediation risks in more fragile banking systems, while wholesale CBDCs improve cross-border liquidity. This article contributes to the literature by providing the first GVAR-based estimation of CBDC spillovers globally.

This study looks at the role of foreign finance in promoting the shift to renewable energy in the Developing-8 (D8) countries—Bangladesh, Egypt, Indonesia, Iran, Malaysia, Nigeria, Pakistan, and Turkey—between 2000 and 2023, with particular focus given to the moderating role of globalization. Utilizing an unbalanced panel dataset covering eight D8 countries over 2000–2023 and applying advanced econometric techniques, including System-GMM, Common Correlated Effects, nd Driscoll–Kraay estimators, the analysis accounts for slope heterogeneity, cross-sectional dependence, and possible endogeneity. The results indicate that foreign finance, and particularly foreign direct investment (FDI), is highly significant in enhancing the supply and demand of renewable energy. Globalization also has an amplification effect as it spurs technology transfer, policy convergence, and market access. The combined impact of foreign finance and globalization is significant and positive in all specifications, indicating that the optimal benefits of foreign capital inflows are realized in highly integrated economies. Alternative globalization measures and tests of renewable energy robustness confirm the stability of the findings. It argues that institutionally reinforcing the foundations, strengthening global integration, and channeling foreign finance into green sectors are central policies for fostering renewable energy transitions in developing economies. This paper provides three contributions to the existing literature. First, it is the pioneering paper that examines systematically the moderating function of globalization on the foreign finance–renewable energy transition nexus in the D8 economies. Second, it applies the latest econometric techniques—System-GMM, CCE, and Driscoll–Kraay—that control for slope heterogeneity, cross-sectional dependence, and endogeneity. Third, it offers policy recommendations for emerging economies on how best to mobilize foreign finance in a globalization context. Unlike prior works that examine these dimensions separately, this study highlights their joint influence, thereby contributing a dual perspective that has been largely absent from the literature.

This study investigates the effects of agricultural CO2 emissions, agricultural exports, and financial development on economic growth in the Asia-Pacific region from 1990 to 2022, using data from 37 countries. The findings reveal that increased capital investment and agricultural exports significantly boost economic growth, while higher final consumption expenditure also plays a crucial role. Conversely, agricultural methane emissions negatively impact economic growth, underscoring the need for sustainable agricultural practices. The analysis highlights the importance of capital accumulation, agricultural sector expansion, and financial development for economic advancement. It also suggests that policies promoting technological innovation, environmental sustainability, and robust domestic demand are essential for sustained economic growth. Future research should address potential biases and explore additional variables to refine these insights.