Do Banks in the Middle East and North Africa Region Price Carbon Exposure?
中東・北アフリカ地域の銀行は炭素エクスポージャーを価格設定しているか？ (AI 翻訳)
Yaacob Ibrahim, Khaled Hussainey, Taghred Mokhtar Sayed Moawad
🤖 gxceed AI 要約
日本語
本研究は、MENA地域の上場非金融企業771社・年次のパネルデータを用いて、炭素強度が借入コストに与える影響を検証した。固定効果、操作変数、GMM、分位点回帰、機械学習を組み合わせた結果、炭素強度は借入コストに統計的に有意な影響を与えず、移行リスクがまだ価格設定されていないことを示唆する。ガバナンスの質が一部緩和効果を持ち、機械学習では非線形関係が検出された。
English
This study examines whether carbon exposure affects corporate borrowing costs in the MENA region using a panel of 771 firm-year observations from 2016 to 2023. Combining fixed effects, IV, GMM, quantile regression, and machine learning, it finds that carbon intensity has no significant effect on cost of debt, suggesting transition risk is not yet priced. Governance quality moderates the relationship, and ML reveals nonlinear patterns.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本ではSSBJ開示が始まり、金融機関の投融資判断に気候リスクの織り込みが求められる中、本研究成果は、新興市場における炭素リスクの価格形成の現状を示し、日本の金融機関が海外投融資先を評価する際の参考となる。また、AI・MLを用いたESG評価手法の実証例としても有用。
In the global GX context
Globally, this paper contributes to the literature on climate transition risk pricing in emerging markets, complementing studies from developed economies. It demonstrates that despite TCFD/ISSB momentum, carbon risk is not yet priced in MENA debt markets, highlighting the role of governance and institutional quality. The use of machine learning for predictive analysis offers methodological insights for climate risk assessment.
👥 読者別の含意
🔬研究者:Provides empirical evidence on carbon risk pricing in an understudied region and showcases ML methods for ESG analysis.
🏢実務担当者:Highlights that carbon exposure may not affect borrowing costs in MENA, but governance quality matters; useful for risk assessment in emerging markets.
🏛政策担当者:Suggests that regulatory frameworks and governance improvements are needed to integrate climate risk into financial pricing.
📄 Abstract（原文）
ABSTRACT 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, whereas 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.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1002/bse.71303first seen 2026-08-08 04:52:00
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