Do Banks in the Middle East and North Africa Region Price Carbon Exposure?

This study examines whether carbon exposure is incorporated into corporate borrowing costs within the Middle East and North Africa (MENA) region. Using a panel of 771 firm-year observations from publicly listed non-financial firms between 2016 and 2023, the analysis investigates the relationship between carbon intensity and firms’ cost of debt using a comprehensive empirical framework that combines two-way fixed effects estimation, instrumental-variable regression, Generalized Method of Moments (GMM), quantile regression, and machine learning techniques. The baseline fixed effects results show that carbon intensity does not exert a statistically significant effect on the cost of debt. This finding remains robust across alternative carbon measures and industry-year specifications. Governance quality partially moderates the relationship between emissions exposure and borrowing costs, particularly for Scope 3 emissions. System GMM estimation confirms the persistence of borrowing costs through a significant lagged dependent variable coefficient, while carbon intensity remains insignificant. Machine learning results reveal substantial nonlinear predictive relationships. Random Forest achieves the strongest out-of-sample performance, followed by XGBoost, whereas Artificial Neural Network (ANN) performs considerably weaker. Shapley Additive Explanations (SHAP) analysis further identifies leverage and carbon intensity as among the most influential predictors of borrowing costs. Overall, the findings suggest that climate transition risk is not yet systematically priced into MENA debt markets, with institutional structures and governance quality shaping how environmental exposure influences financing conditions.