Carbon transition risk, green debt pricing, and environmental governance: Evidence from Chinese high-energy-consuming firms
炭素移行リスク、グリーン債務価格、および環境ガバナンス：中国の高エネルギー消費企業からの証拠 (AI 翻訳)
Lin Sun, Jun Zeng
🤖 gxceed AI 要約
日本語
本研究は、中国の高エネルギー消費企業を対象に、炭素移行リスクがグリーン債務の利ざやに与える影響を分析。パネル固定効果モデルやイベントスタディに加え、ランダムフォレスト、XGBoost、ニューラルネットワークを用いて予測と解釈を行う。結果、炭素強度が高いほど利ざやが拡大し、炭素価格へのエクスポージャーやグリーンパテントが多いほど縮小することを示した。また、XGBoostが最良の予測性能を示し、炭素変数を除去すると精度が低下することから、炭素情報のモニタリング価値を確認した。
English
This study examines how carbon transition risk affects green debt financing spreads for Chinese high-energy-consuming firms. Using panel fixed-effects models, event studies, and machine learning (random forest, XGBoost, neural networks), it finds that higher carbon intensity widens spreads, while stronger carbon-pricing exposure and green patent output narrow them. XGBoost performs best, and removing carbon variables reduces predictive accuracy, confirming the incremental monitoring value of carbon information.
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
中国の事例だが、日本の高エネルギー消費産業やグリーンファイナンス市場にとっても示唆に富む。特にSSBJ開示やトランジション・ファイナンスにおいて、炭素情報の債務価格への影響を機械学習で補足する手法は参考になる。
In the global GX context
This paper provides evidence from China linking carbon transition risk to green debt pricing, relevant for global disclosure frameworks like ISSB/TCFD. The use of machine learning to validate carbon information's monitoring value offers a novel approach that can be applied in other markets and supports the integration of carbon metrics into credit analysis.
👥 読者別の含意
🔬研究者:Demonstrates a dual-track design combining econometrics and ML to study carbon risk pricing, offering methodological insights for empirical climate finance research.
🏢実務担当者:Shows that carbon intensity and green innovation are priced in debt markets; corporate treasury and ESG teams should monitor these metrics for financing cost optimization.
🏛政策担当者:Provides evidence that green debt pricing can reinforce carbon regulation; useful for designing carbon markets and mandatory disclosure rules.
📄 Abstract（原文）
Carbon transition risk increasingly affects the financing conditions of firms in high-energy-consuming industries, yet debt-side evidence remains fragmented and often mixes policy inference with predictive monitoring. This study examines how carbon-related transition signals are associated with green debt financing spreads for Chinese A-share listed firms in high-energy-consuming industries during 2010–2024. The dependent variable is the green debt financing spread measured in basis points (Spread_bps). To preserve evidentiary boundaries, the study adopts a dual-track design: panel fixed-effects models, an event-study analysis around the 2015 Paris Agreement, and a segmented difference-in-differences design around China’s national emissions trading system in 2021 are used for policy-timing evidence, while random forest, extreme gradient boosting (XGBoost), and neural network models are used for out-of-sample prediction, ablation tests, Shapley additive explanations interpretation, and conditional scenario simulation. The econometric results show that higher carbon intensity is associated with wider green debt spreads, whereas stronger carbon-pricing exposure and greater green patent output are associated with narrower spreads. Pricing sensitivity to carbon-related factors increases after major policy milestones, and green innovation partly mitigates post-emission-trading-system financing pressure on high-carbon issuers. The predictive results show that XGBoost performs best and that removing carbon variables reduces forecasting accuracy, confirming the incremental monitoring value of carbon information. These findings suggest that green debt pricing can complement environmental regulation by translating carbon intensity, carbon-market exposure, and transition capacity into financing signals useful for credit analysts, issuers, investors, and policymakers.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.36922/ajwep026170117first seen 2026-06-22 05:35:44
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