Forecast of Energy Consumption and Carbon Emissions in China's Building Sector to 2060

The goal of reaching the peak of carbon in the construction industry is urgent. However, the research on the feasibility of realizing this goal and the implementation of relevant policies in China is relatively superficial. In view of the historical data of energy consumption and building CO2 emission from 1995 to 2019, this paper establishes a BP neural network model for predicting building CO2 emissions. Moreover, the influencing factors, such as population, GDP, and total construction output, are introduced as the parameters in the model. Through the scenario analysis method explores the practical path to accomplish the peak of building CO2 emissions. When using traditional prediction methods to predict building carbon emissions, the long prediction cycle will increase the possibility of significant errors. Therefore, this paper constructs the calculation model of building carbon emission and forecasts the future carbon emission value through the BP neural network to avoid the error caused by the nonlinear relationship between influencing factors and predicted value. It will effectively predict the feasibility of the carbon peak and the carbon-neutral target set by government, and provide a useful predictive tool for adjusting the new energy structure and formulating related emission reduction policies.

Automated summary

The goal of reaching the peak of carbon in the construction industry is urgent, but research and implementation of relevant policies in China are relatively superficial. This paper establishes a BP neural network model for predicting building CO2 emissions and explores the practical path to accomplish the peak of building CO2 emissions through scenario analysis. The model effectively predicts the feasibility of the carbon peak and carbon-neutral target set by the government and provides a useful predictive tool for adjusting the energy structure and formulating emission reduction policies.