Statistical Assessment of Default Risks of Green Financial Bonds: A Joint Analysis Model of Environmental Benefits and Financial Indicators
This study evaluates the default risk of green financial bonds by developing a joint analysis model that integrates environmental benefits and financial indicators. Using a dataset of 200 green bonds issued in China from 2018 to 2023, the research examines the impact of environmental performance met...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | SHS Web of Conferences |
| Online Access: | https://www.shs-conferences.org/articles/shsconf/pdf/2025/09/shsconf_icdde2025_02007.pdf |
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| Summary: | This study evaluates the default risk of green financial bonds by developing a joint analysis model that integrates environmental benefits and financial indicators. Using a dataset of 200 green bonds issued in China from 2018 to 2023, the research examines the impact of environmental performance metrics, such as carbon emission reduction (CER) and renewable energy ratio (RER), alongside traditional financial indicators like debt-to-asset ratio (DAR) and current ratio (CR). The results reveal that higher environmental performance significantly reduces default risk, with CER and RER showing negative correlations with default probability. The proposed model, validated by an AUC-ROC score of 0.83, provides a robust tool for investors and policymakers to assess and mitigate risks in the green bond market. The findings highlight the importance of integrating environmental and financial metrics for sustainable finance development and suggest future research directions, including the exploration of nonlinear effects and cross-country comparisons. This research highlights the significance of integrating environmental benefits with financial metrics in statistical models to comprehensively assess default risks in green bonds, fostering sustainable investing and strengthening climate-resilient governance. |
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| ISSN: | 2261-2424 |