Machine Learning-Based Credit Risk Assessment for Green Bonds: Climate Factor Integration and Default Prediction Analysis
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Abstract
This study presents a comprehensive machine learning framework for credit risk assessment of green bonds that integrates climate factors with traditional financial metrics. The research develops an enhanced predictive model using ensemble methods including XGBoost, Random Forest, and neural networks to evaluate default probability in green bond markets. Climate transition risks, physical climate risks, and ESG factors are systematically incorporated into the credit assessment framework alongside conventional financial indicators. The methodology employs advanced feature engineering techniques and SHAP interpretability analysis to identify key risk drivers. Empirical analysis of 3,247 green bonds from 2014-2023 demonstrates significant improvement in prediction accuracy, with the climate-enhanced model achieving 92.4% AUC compared to 85.2% for traditional models. Climate policy uncertainty and carbon intensity emerge as critical predictors, particularly during market stress periods. The findings provide valuable insights for financial institutions, regulators, and investors in sustainable finance decision-making processes.
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1. J. Alvi, I. Arif, and K. Nizam, "Advancing financial resilience: A systematic review of default prediction models and future directions in credit risk management," Heliyon, vol. 10, no. 21, 2024, Art. no. e39770, doi: 10.1016/j.heliyon.2024.e39770.
2. S. Liu, "Statistical Assessment of Default Risks of Green Financial Bonds: A Joint Analysis Model of Environmental Benefits and Financial Indicators," SHS Web of Conferences, vol. 218, EDP Sciences, 2025, Art. no. 21802007, doi: 10.1051/shsconf/202521802007.
3. S. Romagnoli and A. Santini, "Climate Risk and Sovereign Debt: Country-Level Exposures and Scarcity Effects in Green Bonds," SSRN, 2024, doi: 10.2139/ssrn.5004937.
4. M. Singhania, G. Chadha, and R. Prasad, "Sustainable finance research: Review and agenda," Int. J. Finance Econ., vol. 29, no. 4, 2024, pp. 4010–4045, doi: 10.1002/ijfe.2854.
5. H. Awijen, S. Ben Jabeur, and J. Pillot, "Interpretable machine learning models for ESG stock prices under transition and physical climate risk," Ann. Oper. Res., vol. 2024, pp. 1–31, doi: 10.1007/s10479-024-06231-x.
6. S. Pashang and O. Weber, "AI for sustainable finance: Governance mechanisms for institutional and societal approaches," in The Ethics of Artificial Intelligence for the Sustainable Development Goals, Springer, Cham, 2023, pp. 203–229, doi: 10.1007/978-3-031-21147-8_12.
7. C. Gao, D. Huang, and Y. Xing, "Heterogeneous Environmental Preference and Green Municipal Bonds," SSRN, 2025, doi: 10.2139/ssrn.5181215.
8. S. Wiklund, "Evaluating physical climate risk for equity funds with quantitative modelling–how exposed are sustainable funds?," J. Sustain. Finance Invest., vol. 13, no. 2, 2023, pp. 893–918, doi: 10.1080/20430795.2021.1894901.
9. Y. Wang et al., "Do green finance and green innovation affect corporate credit rating performance? Evidence from machine learning approach," J. Environ. Manage., vol. 360, 2024, Art. no. 121212, doi: 10.1016/j.jenvman.2024.121212.
10. V. Capizzi, S. Dell'Atti, and P. di Biase, "EDITORIAL NOTE—CLIMATE RISK AND SUSTAINABILITY: EMERGING IM-PACTS AND FUTURE PERSPECTIVES FOR FINANCIAL INTERMEDIARIES," J. Financ. Manag. Markets Instit., vol. 11, no. 2, 2023, Art. no. 2301002, doi: 10.1142/S2282717X23010028.
11. X. Gao et al., "Environmental, social, and governance tools and investment fund portfolio characteristics: a practi-cal-question-oriented review," Front. Sustain., vol. 4, 2024, Art. no. 1323304, doi: 10.3389/frsus.2023.1323304.
12. M. Ferrara et al., "Climate change and asset pricing: a focused review of literature," DEMB Working Paper Series, vol. 236, 2024.
13. A. Mitra and A. Maity, "Balancing Green and Fair: Ethical AI in Sustainable Finance," Int. J. Bus. Manag. Leg. Aff., vol. 1, no. 1, 2025, pp. 55–76.
14. S.-S. M. Ajibade et al., "Uncovering the dynamics in the application of machine learning in computational finance: a bibli-ometric and social network analysis," 2024, doi: 10.32479/ijefi.16399.
15. H. Wang and M. Liu, "Credit risk assessment of green supply chain finance for SMEs based on multi-source information fusion," Sustainability, vol. 17, no. 4, 2025, Art. no. 1590, doi: 10.3390/su17041590.
16. W. Liu, K. Qian, and S. Zhou, "Algorithmic Bias Identification and Mitigation Strategies in Machine Learning-Based Credit Risk Assessment for Small and Medium Enterprises," Ann. Appl. Sci., vol. 5, no. 1, 2024.
17. J. M. Puaschunder, "Environmental Financialization," in The Future of Resilient Finance: Finance Politics in the Age of Sustainable Development, Springer, Cham, 2023, pp. 153–183, doi: 10.1007/978-3-031-30138-4_5.
18. M. Wang and L. Zhu, "Linguistic Analysis of Verb Tense Usage Patterns in Computer Science Paper Abstracts," Academia Nexus J., vol. 3, no. 3, 2024.
19. T. Mo, P. Li, and Z. Jiang, "Comparative Analysis of Large Language Models' Performance in Identifying Different Types of Code Defects During Automated Code Review," Ann. Appl. Sci., vol. 5, no. 1, 2024.
20. S. Xu, "Intelligent Optimization Algorithm for Chain Restaurant Spatial Layout Based on Generative Adversarial Networks," J. Ind. Eng. Appl. Sci., vol. 3, no. 3, 2025, pp. 32–41, doi: 10.70393/6a69656173.333031.
21. H. Guan and L. Zhu, "Dynamic Risk Assessment and Intelligent Decision Support System for Cross-border Payments Based on Deep Reinforcement Learning," J. Adv. Comput. Syst., vol. 3, no. 9, 2023, pp. 80–92.
22. L. Zhu and C. Zhang, "User Behavior Feature Extraction and Optimization Methods for Mobile Advertisement Recom-mendation," Artif. Intell. Mach. Learn. Rev., vol. 4, no. 3, 2023, pp. 16–29.
23. H. Kuang et al., "The Impact of Individual Factors on Careless Responding Across Different Mental Disorder Screenings: Cross-Sectional Study," J. Med. Internet Res., vol. 27, 2025, Art. no. e70451, doi: 10.2196/70451.
24. M. Sun, "AI-Driven Precision Recruitment Framework: Integrating NLP Screening, Advertisement Targeting, and Person-alized Engagement for Ethical Technical Talent Acquisition," Artif. Intell. Mach. Learn. Rev., vol. 4, no. 4, 2023, pp. 15–28.
25. Y. Wang and X. Wang, "FedPrivRec: A Privacy-Preserving Federated Learning Framework for Real-Time E-Commerce Recommendation Systems," J. Adv. Comput. Syst., vol. 3, no. 5, 2023, pp. 63–77, doi: 10.69987/JACS.2023.30506.
26. Z. Feng, C. Ni, and S. Zhou, "Option-Implied Information for Forward-Looking Market Risk Assessment: Evidence from Commodity Derivatives Markets," Spectrum of Research, vol. 5, no. 1, 2025.
27. Z. Feng, D. Yuan, and D. Zhang, "Textual Analysis of Earnings Calls for Predictive Risk Assessment: Evidence from Banking Sector," J. Adv. Comput. Syst., vol. 3, no. 5, 2023, pp. 90–104.
28. Z. Feng, D. Zhang, and Y. Wang, "Intraday Liquidity Patterns and Their Implications for Market Risk Assessment: Evidence from Global Equity Markets," Artif. Intell. Mach. Learn. Rev., vol. 5, no. 4, 2024, pp. 83–98.
29. X. Luo, "Cross-Cultural Adaptation Framework for Enhancing Large Language Model Outputs in Multilingual Contexts," J. Adv. Comput. Syst., vol. 3, no. 5, 2023, pp. 48–62, doi: 10.69987/JACS.2023.30505.
30. C. Cheng, L. Zhu, and X. Wang, "Knowledge-Enhanced Attentive Recommendation: A Graph Neural Network Approach for Context-Aware User Preference Modeling," Ann. Appl. Sci., vol. 5, no. 1, 2024.
31. H. Lian, T. Mo, and C. Zhang, "Intelligent Data Lifecycle Management in Cloud Storage: An AI-driven Approach to Optimize Cost and Performance," Academia Nexus J., vol. 3, no. 3, 2024.
32. A. Kang, Z. Li, and S. Meng, "AI-Enhanced Risk Identification and Intelligence Sharing Framework for Anti-Money Laun-dering in Cross-Border Income Swap Transactions," J. Adv. Comput. Syst., vol. 3, no. 5, 2023, pp. 34–47, doi: 10.69987/JACS.2023.30504.
33. Z. Wang and Z. Chu, "Research on Intelligent Keyframe In-betweening Technology for Character Animation Based on Generative Adversarial Networks," J. Adv. Comput. Syst., vol. 3, no. 5, 2023, pp. 78–89, doi: 10.69987/JACS.2023.30507.
34. W. Liu, G. Rao, and H. Lian, "Anomaly Pattern Recognition and Risk Control in High-Frequency Trading Using Rein-forcement Learning," J. Comput. Innov. Appl., vol. 1, no. 2, 2023, pp. 47–58.
35. H. Lian, P. Li, and G. Wang, "Dynamic Resource Orchestration for Cloud Applications through AI-driven Workload Pre-diction and Analysis," Artif. Intell. Mach. Learn. Rev., vol. 4, no. 4, 2023, pp. 1–14.
36. M. R. Eatherton et al., "Considering ductility in the design of bare deck and concrete on metal deck diaphragms," The 17th World Conf. Earthquake Eng., Sendai, Japan.
37. G. Wei et al., "Investigating partial tension field action in gable frame panel zones," J. Constr. Steel Res., vol. 162, 2019, Art. no. 105746, doi: 10.1016/j.jcsr.2019.105746.
38. G. Wei et al., "Computational study of tension field action in gable frame panel zones," 2018.
39. H. Foroughi et al., "Seismic Demands on Steel Diaphragms for 3D Archetype Buildings with Concentric Braced Frames." in Proc. 12th Canadian Conf. Earthq. Eng., Quebec City, QC, Canada, Jun. 17–20, 2019.
40. G. Wei et al., "Lateral bracing of beams provided by standing seam roof system: concepts and case study," 2020.
41. H. Foroughi et al., "Seismic response predictions from 3D steel braced frame building simulations."
42. G. Wei et al., "Seismic design of diaphragms for steel buildings considering diaphragm inelasticity," J. Struct. Eng., vol. 149, no. 7, 2023, Art. no. 04023077, doi: 10.1061/JSENDH.STENG-11832.
43. S. Wu et al., "More is better: Enhancing open-domain dialogue generation via multi-source heterogeneous knowledge," Proc. 2021 Conf. Empirical Methods Nat. Lang. Process., 2021, doi: 10.18653/v1/2021.emnlp-main.175.
44. S. Wu et al., "Improving the applicability of knowledge-enhanced dialogue generation systems by using heterogeneous knowledge from multiple sources," Proc. 15th ACM Int. Conf. WEB Search Data Mining, 2022, doi: 10.1145/3488560.3498393.
45. S. Wu et al., "Knowledge-Aware Dialogue Generation via Hierarchical Infobox Accessing and Infobox-Dialogue Interaction Graph Network," IJCAI, 2021.
46. G. Rao et al., "Jump prediction in systemically important financial institutions' CDS prices," Spectrum of Research, vol. 4, no. 2, 2024.
47. G. Rao et al., "A Hybrid LSTM-KNN Framework for Detecting Market Microstructure Anomalies: Evidence from High-Frequency Jump Behaviors in Credit Default Swap Markets," J. Knowl. Learn. Sci. Technol., vol. 3, no. 4, 2024, pp. 361–371, doi: 10.60087/jklst.v3.n4.p361.
48. G. Rao, Z. Wang, and J. Liang, "Reinforcement learning for pattern recognition in cross-border financial transaction anoma-lies: A behavioral economics approach to AML," Appl. Comput. Eng., vol. 142, pp. 116–127, 2025. ISBN: 978-1-83558-999-1.
49. G. Rao, C. Ju, and Z. Feng, "AI-driven identification of critical dependencies in US-China technology supply chains: Impli-cations for economic security policy," J. Adv. Comput. Syst., vol. 4, no. 12, 2024, pp. 43–57, doi: 10.69987/JACS.2024.41204.
50. G. Rao, S. Zheng, and L. Guo, "Dynamic Reinforcement Learning for Suspicious Fund Flow Detection: A Multi-layer Transaction Network Approach with Adaptive Strategy Optimization," 2025, doi: 10.20944/preprints202504.1440.v1.
51. C. Ju and G. Rao, "Analyzing foreign investment patterns in the US semiconductor value chain using AI-enabled analytics: A framework for economic security," Pinnacle Acad. Press Proc. Ser., vol. 2, 2025, pp. 60–74.
52. L. Ge and G. Rao, "MultiStream-FinBERT: A Hybrid Deep Learning Framework for Corporate Financial Distress Prediction Integrating Accounting Metrics, Market Signals, and Textual Disclosures," Pinnacle Acad. Press Proc. Ser., vol. 3, 2025, pp. 107–122.
53. Z. Wang et al., "Temporal evolution of sentiment in earnings calls and its relationship with financial performance," Appl. Comput. Eng., vol. 141, 2025, pp. 195–206.
54. M. Li, W. Liu, and C. Chen, "Adaptive financial literacy enhancement through cloud-based AI content delivery: Effectiveness and engagement metrics," Ann. Appl. Sci., vol. 5, no. 1, 2024.
55. X. Jiang, W. Liu, and B. Dong, "FedRisk A Federated Learning Framework for Multi-institutional Financial Risk Assessment on Cloud Platforms," J. Adv. Comput. Syst., vol. 4, no. 11, 2024, pp. 56–72, doi: 10.69987/JACS.2024.41105.
56. J. Fan, H. Lian, and W. Liu, "Privacy-preserving AI analytics in cloud computing: A federated learning approach for cross-organizational data collaboration," Spectrum of Research, vol. 4, no. 2, 2024.
57. W. Liu, K. Qian, and S. Zhou, "Algorithmic Bias Identification and Mitigation Strategies in Machine Learning-Based Credit Risk Assessment for Small and Medium Enterprises," Ann. Appl. Sci., vol. 5, no. 1, 2024.
58. W. Liu and S. Meng, "Data Lineage Tracking and Regulatory Compliance Framework for Enterprise Financial Cloud Data Services," Academia Nexus J., vol. 3, no. 3, 2024.
59. Z. Wu et al., "Adaptive traffic signal timing optimization using deep reinforcement learning in urban networks," Artif. Intell. Mach. Learn. Rev., vol. 5, no. 4, 2024, pp. 55–68, doi: 10.69987/AIMLR.2024.50405.
60. Z. Wu, E. Feng, and Z. Zhang, "Temporal-Contextual Behavioral Analytics for Proactive Cloud Security Threat Detection," Academia Nexus J., vol. 3, no. 2, 2024.
61. K. Xiong, Z. Wu, and X. Jia, "Deepcontainer: a deep learning-based framework for real-time anomaly detection in cloud-native container environments," J. Adv. Comput. Syst., vol. 5, no. 1, 2025, pp. 1–17, doi: 10.69987/JACS.2025.50101.
62. Z. Zhang and L. Zhu, "Context-aware feature selection for user behavior analytics in zero-trust environments," J. Adv. Comput. Syst., vol. 3, no. 5, 2023, pp. 21–33, doi: 10.69987/JACS.2023.30503.
63. Z. Wu, Z. Feng, and B. Dong, "Optimal feature selection for market risk assessment: A dimensional reduction approach in quantitative finance," J. Comput. Innov. Appl., vol. 2, no. 1, 2024, pp. 20–31, doi: 10.63575/CIA.2024.20103.
64. Y. Lei and Z. Wu, "A Real-Time Detection Framework for High-Risk Content on Short Video Platforms Based on Hetero-geneous Feature Fusion," Pinnacle Acad. Press Proc. Ser., vol. 3, 2025, pp. 93–106.
65. Z. Wu, C. Cheng, and C. Zhang, "Cloud-Enabled AI Analytics for Urban Green Space Optimization: Enhancing Microclimate Benefits in High-Density Urban Areas," Pinnacle Acad. Press Proc. Ser., vol. 3, 2025, pp. 123–133.
66. L. Yun, "Analyzing Credit Risk Management in the Digital Age: Challenges and Solutions," Econ. Manag. Innov., vol. 2, no. 2, pp. 81–92, Apr. 2025.
67. L. Zhu, H. Yang, and Z. Yan, "Extracting temporal information from online health communities," Proc. 2nd Int. Conf. Crowd Sci. Eng., 2017, doi: 10.1145/3126973.3126975.
68. L. Zhu, H. Yang, and Z. Yan, "Mining medical related temporal information from patients' self-description," Int. J. Crowd Sci., vol. 1, no. 2, 2017, pp. 110–120, doi: 10.1108/IJCS-08-2017-0018.
69. Z. Zhang and L. Zhu, "Intelligent detection and defense against adversarial content evasion: A multi-dimensional feature fusion approach for security compliance," Spectrum of Research, vol. 4, no. 1, 2024.
70. C. Cheng, L. Zhu, and X. Wang, "Knowledge-Enhanced Attentive Recommendation: A Graph Neural Network Approach for Context-Aware User Preference Modeling," Ann. Appl. Sci., vol. 5, no. 1, 2024.
71. X. Wang, Z. Chu, and L. Zhu, "Research on Data Augmentation Algorithms for Few-shot Image Classification Based on Generative Adversarial Networks," Academia Nexus J., vol. 3, no. 3, 2024.