Carbon Emission Prediction Based on Variable Weight Combination Model
The prediction of carbon emissions has always been a hot spot of people's attention at home and abroad.In order to further improve the accuracy of the carbon emission prediction model,considering the impact of multiple factors on carbon emissions,this paper uses three traditional single-item carbon emission prediction models of Support Vector Regression,Ridge Regression and BP Neural Network and combines with the inverse of the error method to construct a variable weight combination model,and uses the new model to predict China's carbon emissions from 2022 to 2026.The empirical results show that the fitting and prediction accuracy of the combination model is 99.26%and 99.34%,respectively,and the combination model has higher accuracy than the three single models.The prediction results of the combination model show that by 2026,the growth rate of China's carbon emissions has slowed down compared with the present,and maintains the growth rate at 1.8%.
Support Vector RegressionRidge RegressionBP Neural Networkvariable weight combination model
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