Carbon emission prediction of construction industry in Sichuan Province based on the GA-BP model.

Abstract

The reduction of the carbon emissions of construction industry is urgent. Therefore, it is essential to accurately predict the carbon emissions of the provincial construction industry, which can support differentiation emission reduction policies in China. This paper proposes a carbon emission prediction model that optimizes the backpropagation (BP) neural network by genetic algorithm (GA) to predict carbon emission of construction industry, or "GA-BP". To begin with, the carbon emissions of construction industry in Sichuan Province from 2000 to 2020 are calculated by the emission factor method. Further, the electricity correction factor is introduced to eliminate the regional difference in electricity carbon emission coefficient. Finally, four factors are selected by the grey correlation analysis method to predict the carbon emission of construction industry in Sichuan Province from 2021 to 2025. The results show that the carbon emissions of construction industry in Sichuan Province have been trending up in the past two decades, with an average increase rate of 10.51%. The GA-BP model is a high-precision prediction model to predict carbon emissions of construction industry. The mean absolute percentage error (MAPE) of the model is only 6.303%, and its coefficient of determination is 0.853. Moreover, the carbon emissions of construction industry in Sichuan Province will reach 8891.97 million tons of CO2 in 2025. The GA-BP model can effectively predict the future carbon emissions of construction industry in Sichuan Province, which provides a new idea for the green and sustainable development of construction industry in Sichuan Province.