绿色金融发展与影响因素的探究

Exploration on the Development and Influencing Factors of Green Finance

随着我国经济的快速增长,环境问题日益尖锐。究其根源在于我国经济增长是以高能耗为代价,致使环境问题日益严重,制约了我国经济的可持续发展。因此,本文决定开展有关绿色金融发展与相关各影响因素的分析。文章第一个模型选取了2016年至2022年5月发行的1842支绿色债券作为研究样本,获取相关债券的信用评级、发行时的票面利率,发行总额,发行年限、以及上市日期数据,通过使用Matlab以及Eviews软件建立多元线性回归模型。随后我们进行了VIF检验、异方差检验以及残差检验。经验证,得出以下结论:专项指引政策、债券评级等越高或越好,绿色债券发行利率越低。文章第二个模型针对时间序列模型,选择了1978~2021年共44年的全国每万元GDP的综合能耗作为研究样本来探究各因素对绿色金融的影响程度。首先对原始数据进行了平稳性检验,随后根据结果进行了二阶差分。然后开展了单位根以及白噪声检验,发现此模型较好通过了上述两个检验。接下来进行模型的构建,经过检验,认为此模型能较好拟合序列。于是我们对接下来3年中国的每万元GDP的综合能耗进行了预测,并给出了相应建议及结论。

With the rapid growth of China’s economy, environmental problems have become increasingly acute. The root cause is that China’s economic growth is at the cost of high energy consumption, re-sulting in increasingly serious environmental problems, restricting the sustainable development of China’s economy. Therefore, this paper decided to carry out the analysis of the development of green finance and related influencing factors. The first model of this article selected 1,842 green bonds issued from 2016 to May 2022 as research samples to obtain the credit rating, coupon rate, total issuance amount, issuance date, and launch date data, by using Matlab and Eviews software a multiple linear regression model was developed. Subsequently, we performed the VIF test, the het-eroscedasticity test, and the residual test. It has been proved that the following conclusions are drawn: the higher or better the special guidance policy and bond rating, the lower the interest rate of green bond issuance. The second model in the paper aims at the time-series model. The national comprehensive energy consumption per 10,000 Yuan of GDP for 44 years from 1978 to 2021 was selected as the research sample to explore the influence of various factors on green finance. The original data were first tested for stationarity, followed by a second-order difference from the re-sults. Then the unit root and white noise test, found that the model is better after the above two tests. Next, the model is constructed, and after testing, the model can fit the sequence well. There-fore, we predicted the comprehensive energy consumption per 10,000 Yuan of GDP in China in the next three years, and gave the corresponding suggestions and conclusions.