Influencing Factors And Forecast Analysis Of China’s Carbon Emission Trading Price

The carbon emission trading system is an important way to effectively reduce carbon dioxide emissions and achieve the goal of carbon neutrality in China.However,due to the relatively late formation and development of China’s carbon emission trading market,it is not as perfect as other international carbon emission trading markets in many aspects.China has successively set up eight carbon emission trading markets.Therefore,this thesis takes the eight existing carbon trading pilots in China as the research object,and conducts research from the two aspects of carbon trading price influencing factors and prediction,hoping to have important significance in promoting the steady development of China’s carbon emission trading market.In terms of influencing factors of carbon emission trading price,this thesis selects a VAR model from four perspectives of energy price,economic factor,environmental factor and policy factor to analyze and study the influencing factors of carbon emission trading price.The research results show that there is a long-term stable relationship between 7 variables,including coking coal continuous price index,natural gas price index and carbon emission trading results,and natural gas price The positive industrial index and EU carbon quota have a positive correlation with carbon price,while coking coal price,CSI 300 index and Euro/RMB exchange rate have a negative impact on carbon price.In the long run,the exchange rate of Euro against RMB and coking coal price index have the greatest impact on carbon price,while the EU carbon quota has the lowest impact.Then this thesis establishes a VEC model to further explain the short-term dynamic relationship between variables.The empirical results show that the current fluctuations of the CSI 300 index,coking coal price index and EU carbon quota will negatively adjust the current fluctuations of carbon emission rights trading price,while the current fluctuations of the Euro RMB exchange rate will positively adjust the carbon price fluctuations,and the EU carbon trading price and the Euro RMB exchange rate have the largest fluctuations,followed by the daily closing price of coking coal futures and the CSI 300 index,and finally the daily average temperature LNG index and positive industrial index.In terms of the prediction of carbon emission trading price,this thesis selects BP neural network and LSTM neural network models to predict and analyze the carbon emission trading price,.By comparing the prediction accuracy of the two models,it is found that the LSTM neural network model has better prediction effect and higher fitting degree.Therefore,this thesis demonstrates that LSTM neural network model can be used as an effective model for carbon trading price prediction.