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Global attempts to alleviate climate change increasingly emphasize the financial sector’s potential role in advancing low-carbon growth, yet empirical evidence regarding whether financial inclusion (FI) supports this transition remains rare for emerging economies. This study examines whether financial inclusion helps facilitate a transition to low-carbon development in emerging economies, emphasizing the diverse effects across different countries and emission levels. It uses panel data from 2000 to 2022 and employs a new multi-method framework that includes Bayesian Stochastic Quantile Regression (BSQR), Dynamic Common Correlated Effects (DCCE), Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL), panel threshold regression, and machine learning techniques. The results show that while financial inclusion can lead to CO2 reductions in some cases, mainly at lower emission quantiles, it may increase emissions in highly industrialized settings due to boosted economic and energy activities. Tests for thresholds and nonlinearities confirm the presence of structural breaks and complexities in the finance emissions relationship. The machine learning-based SHAP analysis highlights GDP per capita, financial inclusion, and their interaction as key factors influencing emissions. The study underscores the importance of nonlinear interactions, country-specific factors, and emission thresholds in designing efficient low-carbon finance strategies. It recommends that emerging economies develop frameworks for financial inclusion that align with green principles, strengthen institutional procedures, and incorporate fiscal policies, foreign investments, and population management into comprehensive low-carbon development plans.
Abbass et al. (Sat,) studied this question.