Author
Listed:
- Lu, Huaixin
- Pan, Yong
- Fan, Rujie
- Guan, Wei
Abstract
This study empirically examines how green credit policy affects the green transition of heavily polluting enterprises. We use the implementation of the Green Credit Guidelines as a quasi-natural experiment to analyze the effects from production and emissions perspectives. The results reveal that green credit policy significantly promotes a prevention-led green transition while inhibiting a governance-led green transition in heavily polluting enterprises. The policy effects analysis confirms that green credit policy promotes heavily polluting enterprises' green transition by restricting credit scale, tightening credit channel constraints, improving environmental information disclosure quality, and lowering credit costs. The findings suggest that policy intensity should increase with enterprises' pollution level. Heterogeneity analysis of policy effects on different types of enterprises reveals that green credit policy significantly aids heavily polluting enterprises' green transition with low tax contributions, high-quality internal controls, and favorable financial geographic positioning. Extended analysis reveals that state ownership and political connections can hinder effective green credit policy implementation, while executives with financial backgrounds can enhance its effects. This study provides empirical evidence on how green credit policy promotes heavily polluting enterprises' green transition and highlights the need for policy to focus on identifying the characteristics of enterprises' internal and external resources. Strengthening green credit policy's environmental information disclosure and regulatory mechanisms and optimizing the identification and feedback mechanisms related to enterprise characteristics will ensure the policy's fairness and effectiveness.
Suggested Citation
Lu, Huaixin & Pan, Yong & Fan, Rujie & Guan, Wei, 2025.
"Green credit policy and heavily polluting enterprises' green transition,"
International Review of Financial Analysis, Elsevier, vol. 103(C).
Handle:
RePEc:eee:finana:v:103:y:2025:i:c:s1057521925002492
DOI: 10.1016/j.irfa.2025.104162
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