Forecasting CO2 emissions in Chinas commercial department, through BP neural network based on random forest and PSO.

In recent years, with the worsening of the global climate problem, the issue of CO2 emissions has gradually attracted people's attention. Accurately predicting CO2 emissions and analyzing its change trends are important elements in addressing climate issues at this stage. Although the predecessors have done a lot of research on CO2 emissions and also established some prediction models, few people have adopted quantifiable methods to select prediction indicators and studied the CO2 emissions of commercial department. So this paper establishes a novel BP neural network prediction model based on the index quantization ability of random forest and the performance optimization ability of PSO. For further strengthening the prediction accuracy, several improvements have been made to PSO. Finally, the validity of the model is tested using panel data from 1997 to 2017 of the Chinese commercial sector. The results as follows: (1) Compared with other parallel models, the newly established hybrid forecasting model can more accurately predict the CO2 emissions of China's commercial department. (2) The prediction indexes selected after quantification based on the random forest can improve the prediction accuracy. (3) These improvements of PSO in this paper can greatly enhance the prediction effect of the hybrid prediction model.