企業炭素パフォーマンス指標の財務危機予測における増分予測価値：中国炭素集約型A株企業のエビデンス
The Incremental Predictive Value of Corporate Carbon Performance Indicators for Financial Distress: Evidence from Carbon-Intensive Chinese A-Share Firms (原題)
Xuân Hưng Nguyễn, Maojie You
🤖 gxceed AI 要約
日本語
中国の炭素集約型A株企業8産業・2012〜2025年のパネルデータを用い、財務指標に加えて炭素パフォーマンス指標が財務危機（2年後のST指定）予測を改善するかを検証。LASSOで財務指標を選別し、排出・ガバナンス・開示を統合した炭素指標を構築、入れ子Logitモデルを時系列分割で比較した。炭素指標の追加は識別力と確率予測精度を有意に高め、効果は資金制約の強い企業に集中し、3年先では消える。多次元の炭素パフォーマンスは短期の補完的リスクシグナルとなる。
English
Using panel data from Chinese A-share firms in eight carbon-intensive industries (2012–2025), this study tests whether carbon performance indicators improve out-of-sample prediction of financial distress (ST designation two years ahead) beyond financial indicators. LASSO selects financial variables, and a composite carbon performance index spans emissions, governance, transition, and disclosure. Adding the index significantly improves discrimination and probability accuracy, with gains concentrated among financially constrained firms and fading at a three-year horizon. Multidimensional carbon performance offers a complementary short-term risk signal.
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
SSBJ基準の開示拡大や有報でのサステナビリティ情報記載が進む日本では、炭素パフォーマンスが財務リスク・信用リスク評価にどう織り込まれるかが投資家・金融機関の関心。本論文は炭素指標の短期予測力と資金制約との関係を示し、日本企業の開示高度化や銀行の与信判断への示唆を提供する。
In the global GX context
As ISSB/SSBJ-aligned disclosure expands globally, linking carbon performance to credit and financial-distress risk is central to transition finance and risk management. This paper provides empirical evidence that multidimensional carbon metrics carry incremental short-term predictive value, especially for financially constrained firms, informing how investors and lenders might integrate carbon data into credit models.
👥 読者別の含意
🔬研究者:炭素パフォーマンス指標の増分予測力と資金制約による異質性を示す実証枠組みを提供。
🏢実務担当者:炭素開示・排出管理の強化が資金調達や信用評価に影響しうる点を財務・IR部門が認識する材料。
🏛政策担当者:炭素情報開示の義務化が金融安定・信用リスク評価に資する可能性を示唆。
📄 Abstract（原文）
The low-carbon transition has strengthened the link between corporate carbon performance and financial risk. This study examines whether carbon performance improves the out-of-sample prediction of financial distress beyond conventional financial indicators and whether its incremental value varies with financing constraints. Using panel data from Chinese A-share firms in eight carbon-intensive industries over 2012–2025, we compile 40 candidate financial indicators and 34 candidate carbon performance indicators covering emissions performance, carbon governance and low-carbon transition, and carbon disclosure. Financial indicators are selected using LASSO, while a corporate carbon performance index is constructed following data-quality screening and multicollinearity diagnostics. Defining financial distress as an ST/*ST designation two years ahead, we estimate two nested Logit models using a chronological training–test split and compare their out-of-sample performance through company-clustered paired bootstrap tests. Adding the carbon performance index significantly improves model discrimination and the accuracy of probability predictions. The results remain robust to Firth Logit estimation and changes in industry coverage. The improvement is concentrated among firms with stronger financing constraints and becomes insignificant at a three-year prediction horizon. These findings suggest that multidimensional carbon performance provides a complementary short-term risk signal for predicting financial distress among carbon-intensive firms.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18199754first seen 2026-09-25 04:45:30
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