Prediction of Financial Distress Risk for Green Enterprises from the Perspective of Climate Resilience
気候レジリエンスの観点から見たグリーン企業の財務危機リスクの予測 (AI 翻訳)
Haoying Niu, Qinzi Xiao, Mingyun Gao
🤖 gxceed AI 要約
日本語
本論文は、2015年から2024年までの中国A株上場グリーン企業を対象に、年次報告書のテキストデータから気候レジリエンス指標を構築し、財務危機予測における追加的な情報価値を検証した。XGBoostが最適なモデルであり、気候レジリエンス指標は財務指標と相乗効果を持ち、信用リスクの早期警告として有効であることを示した。
English
This study uses text from annual reports of Chinese A-share listed green companies (2015-2024) to construct a climate resilience indicator via word frequency and sentiment analysis. It compares four ensemble learning models, finding that XGBoost optimally integrates financial and climate textual features, and that the climate resilience indicator provides incremental predictive power for financial distress, supporting its use as an early-warning signal for credit risk.
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
本論文は、日本の有報における気候関連記述の活用可能性を示唆。SSBJ基準や統合報告書での非財務情報が信用リスク評価に活用できる可能性があり、金融機関の与信判断や企業の開示戦略に示唆を与える。
In the global GX context
This paper provides global evidence that climate narratives in annual reports can serve as early-warning signals for credit risk, complementing traditional financial indicators. It supports the integration of climate disclosure (TCFD/ISSB) into credit risk assessment frameworks and highlights the value of NLP techniques in extracting forward-looking risk information.
👥 読者別の含意
🔬研究者:Demonstrates that climate narratives in annual reports have predictive power for financial distress, supporting further integration of NLP in credit risk models.
🏢実務担当者:Firms can use climate resilience indicators from annual reports to monitor credit risk exposure from climate transition.
🏛政策担当者:Provides evidence for regulators to consider mandating climate disclosure as part of financial early-warning systems.
📄 Abstract（原文）
Traditional financial distress early-warning models mostly rely on lagged structured financial indicators, which fail to capture the potential credit risks associated with the climate transition of green enterprises. Taking A-share listed green companies from 2015 to 2024 as research samples, this paper centers on the core research question of whether mandatory climate narratives in annual reports can deliver incremental risk warning information beyond accounting indicators. Based on textual data from annual reports, this study constructs a corporate climate resilience indicator by integrating word frequency statistics and sentiment analysis. Two data-partitioning schemes (random sampling and time-series extrapolation) are adopted to compare the predictive performance of four ensemble learning models. Extended tests are further conducted via SHAP values, partial dependence plots, polynomial Logit regression, interaction effect regression and grouped regression. The results indicate that the climate resilience indicator carries incremental information supplementary to financial indicators and possesses predictive power for financial distress. XGBoost demonstrates optimal adaptability to the hybrid feature framework, combining financial data and climate textual features. The climate resilience indicator exerts synergistic effects with financial variables and presents a non-linear statistical correlation with default probability. This study verifies that climate narratives disclosed in annual reports can serve as valid early-warning signals for credit risks. The conclusions provide empirical evidence for financial risk control, corporate disclosure management and the formulation of climate regulatory policies.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://www.mdpi.com/2079-8954/14/7/863/pdf?version=1784533817first seen 2026-07-23 06:09:21
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