Short-term prediction of carbon emissions based on the EEMD-PSOBP model.

The recovery of carbon emissions in the past 2 years has alerted us that carbon emissions are a long-term process, and setting short-term emission reduction targets can more effectively curb the rising trend of carbon emissions. Therefore, the research on short-term prediction of carbon emissions is particularly important. In this paper, the idea of "decomposition-prediction" is put forward in the short-term prediction of carbon emissions, and the combined model of "decomposition-prediction" is constructed. The model is composed of ensemble empirical mode decomposition (EEMD) and the backpropagation neural network based on particle swarm optimization (PSOBP). It is also the first time that EEMD has been applied to the field of carbon emission prediction. Firstly, EEMD is used to decompose the daily carbon emission monitoring data into 6 modal functions and one residual sequence, and the partial autocorrelation function (PACF) is used to determine the input of each modal function. Then, PSOBP was used to predict. Finally, adding the prediction results of each sequence to get the final prediction results. To verify the effectiveness and superiority of the EEMD-PSOBP model, 14 comparative models were constructed, and the prediction effect of the models was evaluated by R