Abstract: This paper develops a dynamic stochastic general equilibrium (DSGE) model incorporating green and polluting firms with financing constraints. The model parameters are calibrated and estimated by Bayesian techniques based on China’s data. The simulation results verify the incentive effect of the green credit policy on green firms and the punishment effect on polluting firms. In other words, the green credit policy increases the cost of labor and capital by stimulating the green firms’ demand for employment and investment, thereby inhibiting the polluting firms’ employment, investment, and financing behaviors, resulting in the reduction of polluting firms’ output and pollution emissions. Therefore, the green credit policy helps to promote the industrial structure and realize the sustainable development of the green economy. Additionally, a comparative analysis reveals that the integration of green technology innovation with permanent green credit policies outperforms the combination of pollution fees and permanent green credit policies in curbing inflation, reducing emissions, and enhancing output.

Key words: dynamic stochastic general equilibrium (DSGE) model, financing constraints, green credit, pollution reduction, green technology innovation

摘要： 本文构建了融资约束下包含绿色企业与污染企业的两部门动态随机一般均衡（DSGE）模型，并基于中国数据对模型参数进行了校准与贝叶斯估计。模拟结果验证并解释了绿色信贷政策对绿色企业的激励效应与对污染企业的惩罚效应，即绿色信贷政策通过刺激绿色企业的就业和投资，推高要素市场中劳动与资金的成本，从而抑制污染企业的劳动雇佣与投融资行为，迫使其缩减产出并减少污染排放。因此，绿色信贷政策有助于推动产业结构优化，实现绿色经济可持续发展。此外，对比分析表明，在抑制通胀、降低污染排放、促进产出等方面，绿色技术创新与永久性绿色信贷政策的结合效果优于排污费与永久性绿色信贷政策的组合。

关键词: 动态随机一般均衡模型, 融资约束, 绿色信贷, 污染减排, 绿色技术创新