School of Economics, Xinxiang University, Xinxiang, 453003, China.
Research Institute of Social Development, Southwestern University of Finance and Economics, Chengdu, 610074, China.
Environ Sci Pollut Res Int. 2022 Sep;29(44):66295-66314. doi: 10.1007/s11356-022-20501-w. Epub 2022 May 2.
The interaction between green finance and other factors, such as ecological environment, has been a research hotspot nowadays. Especially, the reasonable guiding of capital into energy conservation and environmental protection industries would greatly affect those factors, so as to the relation between them. This paper aimed to analyze the relationships between green finance, technological progress, and ecological performance quantitatively. The entropy method was used to respectively construct the system of index for green finance and technological progress, and index for ecological performance was measured by the super-SBM model. The panel vector autoregressive (PVAR) model was selected to empirically analyze dynamic relationships based on datasets from 30 provinces in China during 2008-2019 period. The results told that (1) from 2008 to 2019, China's overall level of green finance, technological progress and ecological performance increased to varying degrees. Spatially, the areas with high-developed green finance greatly coincided with those such as large cities or the eastern coast that had good financial development. The distribution of technological progress index were similar, except some underdeveloped areas with relatively advanced scientific research institutes. The ecological performance, however, was high in the South and low in the north. (2) In the lag for 3 years, the influence of green finance on ecological performance in different regions was all positive for that all the coefficient symbols that passed the significance test were above 0, while that on technological progress was negative first and then positive. And the effects of technological progress on ecological performance were positive in ecological regions and negative in low ecological regions (0.0893 and -0.1211 in the case of three-stage lag respectively). (3) The contribution of green finance to ecological performance was high according to the results of variance decomposition, maintained at about 30%, and that of technological progress increased year by year (from 0.000 to 0.039). Therefore, we proposed to strengthen the development of green finance in underdeveloped regions. The emphasis should be laid on the researches and applications of green technology, the formulation of financing policies in innovation compensation and the establishment of a dynamic monitoring system for the ecological environment.
绿色金融与生态环境等其他因素的相互作用一直是当今研究的热点。特别是，资本合理引导进入节能环保产业，将极大地影响这些因素及其相互关系。本文旨在定量分析绿色金融、技术进步和生态绩效之间的关系。采用熵值法分别构建绿色金融和技术进步的指标体系，采用超效率 SBM 模型测度生态绩效指标。基于 2008-2019 年中国 30 个省份的数据集，选择面板向量自回归（PVAR）模型进行实证分析。结果表明：（1）2008-2019 年，中国绿色金融、技术进步和生态绩效的整体水平都有不同程度的提高。从空间上看，发达地区的绿色金融与金融发展较好的大城市或沿海地区高度吻合。技术进步指数的分布相似，只是在一些科研机构相对发达的欠发达地区有所不同。生态绩效则呈现南高北低的特征。（2）在滞后 3 年的情况下，绿色金融对不同地区生态绩效的影响均为正，这是因为所有通过显著性检验的系数符号均大于 0，而对技术进步的影响则先负后正。技术进步对生态绩效的影响在生态地区为正，在低生态地区为负（分别为三阶段滞后时的 0.0893 和-0.1211）。（3）方差分解结果表明，绿色金融对生态绩效的贡献较高，保持在 30%左右，技术进步逐年增加（从 0.000 增加到 0.039）。因此，建议加强欠发达地区绿色金融的发展。应注重绿色技术的研究和应用，创新补偿融资政策的制定，以及生态环境的动态监测系统的建立。
