Research On Carbon Trading Price And Green Finance Development Status

China’s carbon emission trading market is booming.As the world’s largest carbon emitter,China is actively taking measures.In order to cope with global climate change,China has proposed a long-term goal of reaching carbon peak by 2030 and carbon neutral by 2060,shouldering important international social responsibilities.The construction and development of green finance and carbon trading market is a crucial part of achieving the dual carbon goal.Research on the influencing factors of the transaction price of China’s regional carbon market and the development of regional green finance can provide decision support for realizing China’s dual carbon goal.First of all,this article from the domestic and international economic trends,the dome-stic and international energy price,the international dimension of the carbon price and exchange rate four choose 12 independent variables,using adaptive \ Lasso \method,this paper analyzes the factors which influence on carbon trading price in Shenzhen,also using the stepwise regression model to analyze the same variable,the analysis results of two models are compared.The analysis shows that Shenzhen carbon trading price is most affected by exchange rate,and energy price is also a key factor.Domestic economy,certified emission reduction price,domestic coal price and European economy have positive influence on domestic regional carbon price.However,the relationship between foreign energy prices and domestic regional carbon prices is weak.Finally,the expert prediction model of \ SPSS\ is used to predict the trend of carbon trading price.The second,this paper constructs an evaluation system from four different dimensions of green credit,green insurance,green investment and government input to quantitatively analyze the development level of green finance in central and western China.Considering the correlation of investment and financing projects in the green financial market,the commonly used entropy method cannot properly reflect the conflict between indicators,so in this paper,\ critic-entropy weight method is used to reflect the weight of indicators more objectively.The calculated weights show that green credit and green insurance are very important to the development of green finance.Finally,the green finance development level index in central and western China is obtained by calculating the weight,which shows that the green finance development in most parts of central and western China does not reach the average level.The end,this paper adopts panel data regression model to quantify the impact of green finance on carbon neutrality,and selects urbanization rate,government input,openness and other indicators as control variables to explore the influencing factors in the development process of carbon neutrality.Firstly,the grey correlation analysis model is used to study the correlation between green finance and carbon neutrality.The study shows that green finance is highly correlated with carbon neutrality.Then,by \Hausman\ test,the fixed effect model was adopted.The panel data regression results show that green finance has a positive impact on carbon neutrality in China,and the input of science and technology and openness also have a positive impact on carbon neutrality development,while the impact of urban development and government input on carbon neutrality is negative.Finally,the central and western regions are divided into the central and western regions to study whether there is regional difference.The regression results show that green finance has an effective restraining effect on the growth of industrial pollution emissions in both regions,and also makes the industrial structure of the two regions more optimized.However,in terms of energy consumption structure,green finance plays a positive role in promoting the central region,while the estimated coefficient of green finance in the western region is negative.Therefore,there are regional differences in the impact of green finance on carbon neutrality,which may be related to the location advantage of the central region.