Bank Stability Under ESG Uncertainty: Evidence from Threshold Regression, Causal Forest and SHAP Explanations
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This paper investigates the nonlinear effects of ESG-related macroeconomic uncertainty on bank stability in Vietnam, an emerging market undergoing rapid financial transformation. Using an integrated empirical framework that combines panel threshold regression, Causal Forest estimation, and SHAP explanations, the analysis explores how ESG-related uncertainty interacts with income diversification and FinTech development to influence bank resilience. The results indicate an inverted U-shaped relationship between ESG uncertainty and bank stability, suggesting that moderate uncertainty enhances governance discipline, whereas excessive uncertainty erodes resilience. Income diversification (IDI) and FinTech growth (G_FINTECH) also display threshold-dependent and nonlinear impacts, where moderate diversification strengthens stability, and FinTech becomes stabilizing only beyond a maturity threshold. Robustness tests using alternative measures of bank stability (non-performing loans - NPL) and ownership heterogeneity confirm that private banks are more sensitive to ESG shocks than state-owned counterparts. The study contributes by introducing a novel hybrid framework integrating threshold models with causal machine learning to capture nonlinear and heterogeneous effects, providing new evidence from Vietnam’s nascent ESG and FinTech landscape, and offering policy implications for regulators and banks to manage ESG uncertainty, optimize diversification, and promote sustainable FinTech-driven stability.
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