Research on a Carbon Emission Prediction Model for the Construction Phase of Underground Space Engineering Based on Typical Resource Carbon Consumption and Its Application

The trend of global warming remains severe. As one of the major sources of carbon emissions, the construction industry still requires large-scale and effective transformations. This study, grounded in Life Cycle Assessment (LCA) theory, carbon emission factor calculation methods, the Monte Carlo method, and feedforward neural network algorithms, develops a carbon emission prediction model based on the carbon emissions generated by typical resource consumption. The model is established in the context of typical carbon emission patterns observed during the construction phase of subway stations in China. Furthermore, the feasibility of the proposed model is validated through its application to specific engineering projects. The results demonstrate that (1) the newly developed carbon emission model can accurately predict the carbon emissions associated with the construction phase of subway stations in China; (2) actual carbon emission calculations in construction projects require the integration of data from multiple sources to ensure comprehensive coverage and avoid omissions; and (3) during the construction phase of subway stations, the use of concrete and steel constitutes significant sources of carbon emissions.