Research On Prediction Of Chinese Carbon Emissions Based On EMD-GA-LSTM

With the continuous development of science and technology,the demand for energy in human society has been increasing.The use of a large number of fossil fuels has led to the increasing concentration of greenhouse gases in the atmosphere,making the problem of climate change more prominent,and bringing serious challenges to Chinese ecosystem security and economic and social development.In order to actively address the issue of climate change,China has put forward the goals of "carbon peak" and "carbon neutral".To achieve this goal,we need reasonable and feasible policy guidance and effective emission reduction measures.The accurate prediction of carbon dioxide emissions can provide reliable data support for the formulation of carbon emission-related policies and the implementation of emission reduction measures.However,the carbon dioxide emissions are affected by many factors and have great randomness.The sequence data are nonlinear,non-stationary and volatile,which leads to the poor performance of the traditional time series prediction methods in carbon emissions prediction.Therefore,in order to improve the prediction accuracy,this paper proposes a carbon emission prediction model based on empirical mode decomposition and LSTM model to achieve accurate prediction of Chinese carbon dioxide emissions.The main research contents of this paper include:First of all,it summarizes the current domestic and international research status related to carbon emissions,and introduces the theories and methods used in this paper.Second,BP,LSTM and GRU neural network models are constructed to predict carbon emissions,and genetic algorithm is used to optimize the parameters of each model.The prediction results show that the single neural network model has poor adaptability to carbon emissions and low prediction accuracy.Third,in order to improve the prediction accuracy,the wavelet transform theory and empirical mode decomposition method are fused with three neural network models respectively,and the prediction results of each composite model are compared and analyzed using various evaluation indicators to explore the model with the highest accuracy.The results show that the model based on empirical mode decomposition and LSTM(EMD-GA-LSTM model)has the best prediction effect.Fourth,the model is used to predict the carbon emissions data of Chinese provinces and cities in January 2022,and the reliability of the prediction results is analyzed.Finally,the characteristics of carbon emissions of provinces and cities are analyzed.The research results of this article indicate that it is feasible to apply EMD-GALSTM model to carbon emissions prediction,and this model performs well in predicting carbon emissions in various provinces and cities,with high stability and high reliability of prediction results.This provides a new idea for the prediction of carbon emissions.Finally,when analyzing the characteristics of carbon emissions in various provinces and cities,it was found that the regions with higher levels of carbon emissions in China were mainly concentrated in North China and Guangdong Province.After the outbreak of the epidemic,the coastal and southwestern regions were the most affected areas,with the transportation industry being the most affected and the industry being the least affected.