Research On The Regulation Effect And Policy Optimization Of Chinese Environmental Protection Tax

As a necessities of life,peanuts occupy an important position in the national economy.However,in recent years,the price of peanuts in my country’s peanut market has fluctuated abnormally,which has led to damage to the market economy and brought great trouble to people’s lives and agricultural production and operation.Therefore,combining the market experience to analyze the influencing factors of peanut price and effectively predicting the price of peanut can provide a scientific basis for the government to formulate economic policies and regulate peanut price,and has practical significance for promoting the stable development of agricultural production and market.This paper first studies the factor analysis and forecasting methods of data mining at home and abroad,and collects the data of peanut prices and 17 kinds of peanut price factors from 1999 to 2019 from the public data on the official website of the National Bureau of Statistics and the public data of "National Agricultural Product Cost and Benefit Data Compilation".,based on the grey relational analysis theory,quantitatively analyzed the correlation degree of 17 factors to peanut price,and obtained the correlation degree of 17 factors and peanut price according to the grey relational analysis rules,which confirmed that the selected data is true and effective for the prediction of peanut price.Secondly,the prediction model of peanut price is constructed and empirical analysis is carried out.The ARIMA model was constructed for the peanut price time series data from 1999 to 2019 for prediction.In order to improve the prediction accuracy,the prediction residual of the ARIMA model and 17 kinds of peanut price influencing factors were used as the training set to construct the SVM model for residual prediction,and then the ARIMA model peanut The price prediction value and the SVM residual prediction value are added together,which is the prediction of the peanut price by the ARIMA-SVM combination model.According to the prediction result,the ARIMA-SVM combination model has a higher prediction accuracy for the peanut price.Finally,the We Chat applet for peanut price prediction is designed and implemented.Through the analysis of users’ needs,functions and performance,a low-cost and practical overall framework was designed,and technologies such as applet,background management,and API interface were used to realize the We Chat applet with peanut price prediction as the main function.The applet also has functions such as policy release and decision reference.The prediction algorithm based on the ARIMA-SVM combination model provides a scientific research theory for the prediction of peanut prices.The design and development of small programs meet the needs of customers.healthy development.