Modelling the Impact of ESG-Bond Issuance on Corporate Credit Ratings and Cost of Capital Using Econometric and Machine-Learning Approaches
ESG債発行が企業信用格付けと資本コストに与える影響：計量経済学と機械学習アプローチによるモデル化 (AI 翻訳)
D. Anjaneyalu, M. V. Subramanyam, A. K. N. Rani, Shaik Inthiyaz, G. S. Prasad, P. S. Kumar
🤖 gxceed AI 要約
日本語
本論文は、ESG債発行が企業の信用格付けと資本コストに与える因果効果を、パネルデータ分析と機械学習（勾配ブースティング、説明可能AI）を組み合わせて推定した。ESG債発行企業は格付け改善と資本コスト低減を達成することを実証し、特に移行リスク低減と開示品質向上が寄与する。説明可能AIにより、サステナビリティスコア、レバレッジ、債券満期が重要予測因子であることを明らかにした。
English
This paper combines econometric panel-data modeling with machine learning (gradient boosting, explainable AI) to estimate the causal impact of ESG-bond issuance on corporate credit ratings and cost of capital. Findings show ESG-bond issuers achieve significant credit rating improvements and lower weighted average cost of capital, driven by reduced transition risk and enhanced disclosure quality. Explainable AI reveals sustainability scores, leverage dynamics, and bond maturity profiles as key predictors.
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開示基準の策定が進み、ESG債市場拡大が期待される。本論文の因果推論フレームワークは、日本企業がESG債発行の財務効果を投資家に説明する際のエビデンスとして有用であり、格付け機関や規制当局の評価モデルにも示唆を与える。
In the global GX context
As global markets align with ISSB standards and transition finance frameworks, this paper provides rigorous evidence on the financial benefits of ESG-bond issuance. The blended econometric-ML approach offers a replicable methodology for credit rating agencies and policymakers to assess sustainability-linked debt impacts, relevant to CSRD and SEC climate disclosure contexts.
👥 読者別の含意
🔬研究者:A methodological contribution combining causal inference with ML for ESG-finance research, offering a template for studying sustainability-linked debt impacts.
🏢実務担当者:Corporate finance teams can use the findings to quantify potential credit rating improvements and cost-of-capital reductions from ESG-bond issuance, supporting business cases for sustainable debt.
🏛政策担当者:The evidence on rating improvements and lower capital costs for ESG-bond issuers supports regulatory efforts to incentivize sustainability-linked financing and standardize disclosure.
📄 Abstract（原文）
Bond issuance associated with Environmental, Social and Governmental (ESG) has become a very important strategic tool, which involves companies pursuing greater financial security, indicating sustainability, and obtaining lower-cost capital. The paper represents a modeling of the effect of ESG-bond issues on corporate credit ratings and cost of capital, combining econometric panel-data modeling with state-of-the-art machine-learning architectures. The study separates the causal impacts of sustainability-related debt upon creditworthiness with the help of fixed effects estimators, ordered logit specifications, and counterfactual prediction designs. The data is captured by complementary machine-learn methods (such as gradient boosting and explainability methods) that capture non-linear interactions and effects of ESG attributes amongst firm financials and bond-market behavior. Findings suggest that the ESG-themed bond issuers of firms can also attain significant but statistically significant credit rating improvements, mainly due to decreased perceived transition risk and increased disclosure quality. At the same time, the weighted average cost of capital of ESG-active companies falls, caused by lower yield spreads and a positive investor screening. The incorporation of explainable AI solutions also reveal the important predictors of the sustainability risk scores, leverage dynamics, and bond maturity profiles. The results provide a contribution of policy value to the financial components of sustainable debt markets and the changing function of AI-enhanced modeling in credit evaluation frameworks.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/icmcsi67283.2026.11412767first seen 2026-07-18 07:54:27
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