Mid-term and long-term prediction of carbon emissions in Jiangsu Province based on PCA-STIRPAT improved GA-BP

Abstract

Carbon emission is a significant indicator to balance regional development and environmental pollution. Currently, to achieve the global goal of carbon neutrality, many countries had formulated relevant policies to control carbon emissions in the future. It is of great importance to solve the problem of mid-term and long-term prediction of regional carbon emissions. To improve the prediction accuracy and lower the calculation cost, this paper uses the Principal Component Analysis (PCA) method to reasonably screen the driving factors of carbon emission prediction. It combines the Back Propagation (BP) neural network and Genetic Algorithm (GA) to construct the Genetic Algorithm - Back Propagation neural network (GA-BP) model for mid-term and long-term prediction. Based on historical statistics and official policy indicators, the prediction of carbon emissions in Jiangsu Province of China from 2025 to 2050 is carried out. By comparing the prediction results of the Multiple Linear Regression (MLR) model and the classic BP model, the improved GA-BP model based on Principal Component Analysis Stochastic Impacts by Regression on Population, Affluence, and Technology (PCA-STIRPAT) is significantly better than other methods in the mid-term and long-term prediction of regional carbon emissions, with a maximum relative error of 0.76% and a high prediction accuracy. This result can provide a reasonable basis for relevant parties to make decisions on controlling carbon emissions.