Feng Langang, Shang Shu, An Sufang, Yang Wenli
Guizhou Key Laboratory of Big Data Statistics and Analysis, Guizhou University of Finance and Economics, Guiyang 550025, China.
School of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang 550025, China.
Entropy (Basel). 2022 Jul 29;24(8):1042. doi: 10.3390/e24081042.
This paper uses the entropy method to estimate China's green financial development from four aspects, namely, green credit, green securities, green insurance, and green investment, based on the provincial-level panel data from 2008 to 2019. The spatial Durbin model (SDM) is adopted to estimate the spatial effect of green finance on carbon emissions. We then compare the heterogeneous effect in the South and North of China. The results show that China's green financial development can significantly reduce carbon emissions, and regional heterogeneities are obvious. In the South of China, this effect from local and adjacent regions is not significant, while on the whole, green finance can significantly reduce carbon emissions; but for Northern China, this effect is not significant; nationally, the development of green finance and carbon emissions in adjacent areas showed an inverted U-shaped relationship. China's green financial development and carbon emissions also showed an inverted U-shaped relationship. These results suggest that the effect of green finance development on carbon emissions exhibits substantial regional heterogeneity in China. Our paper provides some concrete empirical evidence for policymakers to formulate green financial policies to achieve the double carbon goal in China.
本文基于2008年至2019年的省级面板数据，从绿色信贷、绿色证券、绿色保险和绿色投资四个方面运用熵值法来估算中国绿色金融发展水平。采用空间杜宾模型（SDM）来估计绿色金融对碳排放的空间效应。然后我们比较了中国南方和北方的异质性效应。结果表明，中国绿色金融发展能够显著降低碳排放，且区域异质性明显。在中国南方，本地及相邻地区的这种效应不显著，但总体而言，绿色金融能够显著降低碳排放；而对于中国北方，这种效应不显著；在全国范围内，绿色金融发展与相邻地区碳排放呈倒U形关系。中国绿色金融发展与碳排放也呈倒U形关系。这些结果表明，中国绿色金融发展对碳排放的影响存在显著的区域异质性。本文为政策制定者制定绿色金融政策以实现中国碳达峰碳中和目标提供了一些具体的实证依据。
