Carbon Emissions Scenario Prediction of the Thermal Power Industry in the Beijing-Tianjin-Hebei Region Based on a Back Propagation Neural Network Optimized by an Improved Particle Swarm Optimization Algorithm

Abstract

Rapid economic growth in the Beijing-Tianjin-Hebei region has been accompanied by a dramatic increase in carbon emissions. Therefore, a precise study of forecasting carbon emissions is important as regards curbing them. To identify the influence factors of carbon emissions and effectively predict carbon emissions under the three different GDP growth rate scenarios in the Beijing-Tianjin-Hebei thermal power industry, we employed a combination of the improved particle swarm optimization-back propagation algorithm (IPSO-BP) with scenario prediction. The results are as follows: