Credit Risk Evaluation of New Energy Companies

DOI

10.2991/978-94-6463-742-7_9How to use a DOI?

Keywords

New Energy Companies; Credit Risk; Principal Component Analysis; Logistic Regression

Abstract

This study examines credit risk in China’s new energy sector, which is critical for the country’s sustainable energy transition. While government policies have supported sector growth, they have also increased financial risks, particularly credit risk, due to untested business models and capital-intensive projects. This research proposes a model combining Principal Component Analysis (PCA) and logistic regression to improve credit risk assessment. PCA simplifies complex data and identifies key factors, while logistic regression predicts credit risk reliably. The model achieves 87% accuracy and correctly identifies low-risk companies 99.4% of the time. An application example using China Bao’an Group Co., Ltd. Shows the model’s strength compared to the Altman Z-score model. This study provides a useful tool for financial institutions and policymakers in assessing credit risk.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jiaxin Chang
AU  - Zhenying Wu
AU  - Sijin Chen
PY  - 2025
DA  - 2025/05/31
TI  - Credit Risk Evaluation of New Energy Companies
BT  - Proceedings of the 2025 4th International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2025)
PB  - Atlantis Press
SP  - 73
EP  - 79
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-742-7_9
DO  - 10.2991/978-94-6463-742-7_9
ID  - Chang2025
ER  -