Is Carbon Risk Always Bad News? The Impact of Carbon Risk on Financial Distress Based on China

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Researchers can learn about the counterintuitive negative relationship between carbon risk and financial distress in China, and the use of ML methods for prediction.

🏢実務担当者:Practitioners may use the findings to reassess carbon risk as a factor in financial health, but must consider China-specific institutional contexts.

🏛政策担当者:Policymakers can note the role of green innovation and ESG in mitigating distress, informing low-carbon policy design.

全球气候挑战和监管压力加强了碳风险与企业财务困境之间的联系。我们以中国加入《巴黎协定》作为外生冲击，结合双重差分法和双机器学习方法，考察碳风险对企业财务困境的影响及其内在机制。我们发现，在中国，高碳企业相较于低碳企业发生财务困境的可能性显著更低。机制分析表明，碳风险与企业财务困境之间的关系受到绿色创新效应、ESG表现和媒体关注度的正向调节。异质性分析表明，碳风险在缓解财务困境方面在高科技行业、高污染行业、竞争性市场以及具有较强环境治理实践的企业中表现得更为显著。此外，我们考察了碳风险是否是财务困境的有效预测指标。基于XGBoost（eXtreme Gradient Boosting，版本Python 3.13）和SHAP（Shapley Additive Explanations）值分析，我们发现碳风险显著提升了财务困境预测模型的准确性和解释力。研究结果为政策制定者制定低碳战略、企业改善财务管理以及投资者评估碳风险提供了重要指导。

AI 翻訳（deepseek-v4-flash）。 正確を期す場合は下の原文を参照してください。

Global climate challenges and regulatory pressures have strengthened the link between carbon risk and corporate financial distress. We examine the impact of carbon risk on corporate financial distress and its underlying mechanisms using China’s accession to the Paris Agreement as an exogenous shock, employing a combination of difference-in-differences and double machine learning approaches. We find that high-carbon firms are significantly less likely to experience financial distress compared to low-carbon firms in China. Mechanism analysis indicates that the relationship between carbon risk and corporate financial distress is positively moderated by the green innovation effect, ESG performance, and media attention. The heterogeneity analysis indicates that carbon risk mitigates financial distress more pronouncedly in high-tech industries, high-pollution industries, competitive markets, and firms with strong environmental governance practices. Furthermore, we investigate whether carbon risk is an effective predictor of financial distress. Based on XGBoost (eXtreme Gradient Boosting, version Python 3.13) and SHAP (Shapley Additive Explanations) value analysis, we find that carbon risk significantly enhances the accuracy and explanatory power of financial distress prediction models. The results provide important guidance for policymakers in creating low-carbon strategies, businesses in improving financial management, and investors in assessing carbon risk.

• openalex https://doi.org/10.3390/systems14070836first seen 2026-07-15 05:08:50