Building Material Carbon Emission Prediction Models for Reinforced-Concrete Shear-Wall Urban Residential Buildings in Northern China

Reinforced-concrete shear walls stand as the primary construction method for urban residential structures in northern China. In alignment with national carbon neutrality goals for residential construction, this study developed a set of prediction models with which to estimate the building material carbon emissions of reinforced-concrete shear-wall urban residential buildings. Specifically, this study clarified the boundaries, content, and calculation method for carbon emissions in the stage of material production. Using consumption data for building materials from 20 reinforced-concrete shear-wall urban residential buildings in northern China, the study evaluated the composition and distribution of building material carbon emissions. Linear and ridge regression was performed to fit the coupling relationship between spatial design parameters and building material carbon emissions. Adopting two technical approaches of direct and indirect prediction, 10 carbon emission prediction models based on residential design parameters were established and validated. The results indicate that, although the indirect prediction models, based on concrete, steel, cement mortar, and the transparent envelope, had relatively low accuracy in estimating carbon emissions from cement mortar and the transparent envelope, they performed well overall. Additionally, the prediction performance of the four models was similar. In contrast, except for M1 and M3, the other direct prediction models, based on the number of building stories, number of basement levels, number of primary rooms on the standard floor or in the unit, and building width and depth, also had good fitting and prediction performance. These models effectively predicted the total building material carbon emissions in the phases of conceptual design, schematic design, preliminary design, and working drawing. Three prediction models could produce fast and effective data support for the low-carbon design of urban residential buildings.

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