Prediction Of Carbon Emission Trading Price Based On BP Neural Network

With the continuous expansion of human production scale,greenhouse gas emissions are also increasing,which has triggered the greenhouse effect.When people realize the seriousness of climate warming,many countries around the world have successively launched carbon emission trading mechanisms to save energy and reduce emissions,in order to enter a sustainable low-carbon development path.In 2011,in accordance with the requirements of the national “Twelfth Five-Year Plan” for the gradual establishment of a carbon market,China successively piloted carbon trading work in 8 provinces and cities across the country,including power,cement,and chemical industries.After nearly ten years of operation,the eight regional carbon trading pilots have basically been able to achieve high compliance performance,abide by their own rights and responsibilities,maintain smooth operation,and maintain active transactions.However,the price of carbon emission rights in the market fluctuates greatly due to various factors such as economic,financial,and climate.For an enterprise that needs to conduct carbon trading activities,it is necessary to fully grasp the carbon trading price in the future.It is extremely necessary,so the research on the prediction method of carbon emission price is particularly necessary and urgent.As a special asset,carbon emission rights have attributes and characteristics that are not found in general commodities.Therefore,there are certain limitations and difficulties in estimating traditional methods.Under the above research background and purpose,this paper summarizes the important factors affecting the price of carbon emission rights by combing related literature and theoretical analysis,and applies artificial neural networks to the research of this paper.This paper finally selects four perspectives of energy price,climate environment,foreign carbon price and macroeconomic to theoretically analyze the influencing factors of the price of carbon emission rights,and then selects Hubei Province,the most frequently traded and most stable data from eight domestic exchanges.As the research object,the data of the trading days of the past two years are used as the research sample.BP neural network model was used for quantitative research.At the same time,a multiple linear regression model was introduced for comparison.Finally,the empirical results were tested in the market.According to the data,the error between the predicted value and the actual value of the BP neural network is much smaller than the predicted error value of the multiple regression model.It can be shown that the BP neural network model selected and established in this paper can reflect the value of such assets to a certain extent,and then the prediction of this model can provide some references for the formation of China’s carbon price.