| 绿色信贷政策如何激发重污染企业“脱虚向实”——基于双重机器学习的因果推断 |
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| 引用本文：赵娜1，2 ，袁梁3，龙汉4，朱维维1.绿色信贷政策如何激发重污染企业“脱虚向实”——基于双重机器学习的因果推断[J].湖南大学学报社会科学版,2025,(3):52-62 |
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| 中文摘要:基于中国绿色信贷政策的实践，采用双重机器学习模型实证检验了其对重污染企业“脱虚向实”的影响。研究发现，绿色信贷政策对重污染企业金融资产投资具有显著抑制作用，尤其是对东部地区企业、营商环境好的地区企业、金融发展水平较高的地区企业、非国有企业、小规模企业以及成熟企业。机制分析表明：绿色信贷政策加剧了企业的融资约束，但减弱了企业政策不确定性感知，提高了实体-金融投资的相对报酬。此外，企业减少金融资产配置后，资本配置趋于向实，绿色信贷政策促进了企业实体投资、环保投资和技术创新投资增加。 |
| 中文关键词:绿色信贷政策 重污染企业 脱虚向实 双重机器学习 |
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| How can Green Credit Policies Stimulate Heavily Polluted Enterprises to “Shift from Fictitious to Real”:Causal Inference Based on Dual Machine Learning |
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| Abstract:Based on the practice of China’s green credit policy, this paper employs a dual machine learning model to examine the impact of green credit policy on the “shift from fictitious to real”. The results reveal that green credit policies have a significant inhibitory effect on the financial asset investment of heavily polluted enterprises, particularly in eastern regions enterprises, enterprises in areas with high business environments, enterprises in regions with high levels of financial development, non-state-owned enterprises, smal-scale enterprises, and mature enterprises. The mechanism analysis shows that green credit policies have intensified financing constraints for enterprises; however, they also reduce the perception of uncertainty in corporate policies and increase the relative return on real financial investments. Furthermore, the reduction in corporate allocations to financial assets has led to a shift of capital toward the real economy. Green credit policies have effectively facilitated increased corporate investment in physical assets, environmental protection initiatives, and technological innovation. |
| keywords:green credit policy heavily polluted enterprises shift from fictitious to real dual machine learning |
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