绿色金融对碳减排的因果森林处理效应及影响因素识别

Causal Forest Treatment Effect of Green Finance on Carbon Emission Reduction and Influencing Factors Identification

运用因果森林思想方法,以中国30个省份为准实验对象,选取碳排放量作为经济绿色低碳发展的目标表征变量,以识别绿色金融政策体系的处理效应存在性及异质性。实证发现,绿色金融平均处理效应具有显著可信性特征。各省份的绿色金融平均处理效应呈现区域之间、区域之内的空间差异性格局;在进行中位数分组、三分位数分组情况下,绿色金融典型表征变量、经济发展背景变量与绿色金融个体处理效应之间的变动关系,均呈现异质性规律,以及差异化的显著可信性。因此,建议强化绿色金融政策体系的区域协同,优化绿色金融产品工具组合,创新完善绿色金融政策体系的顶层设计,渐进推动绿色低碳转型。

Average treatment effects about Chinese Provinces can been identified by applying causalforest algorithm.Carbon footprint is target dependent variable representing low carbon economy development;and Provincial municipalities,autonomous regions are Quasi-Experimental research object in the process of identifying influence and Heterogeneity of Green finance policy system.The average treatment effect of green finance has the characteristic of significant credibility.Spatial difference of average treatment effect is obvious among provinces and cities.Under the perspective of median grouping and three-sections grouping,the supporting constraints of typical representational variables of green finance and background variables of economic development on the individual treatment effect of green finance show heterogeneity and significant credibility of differentiation.Coordination between green financial policy and regional low carbon development should be strengthened.Systemic combination of green financial policy tools should be optimized.Top design of green finance policy should be continued to explored perfection to gradually advance low carbon transformation.