A random forest-based model for the prediction of construction-stage carbon emissions at the early design stage

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Carbon dioxide (CO2) emissions is a major greenhouse gas that causes global warming. Many researchers in the fields of architecture, engineering, and construction try to measured CO2 emissions during a building's lifecycle. However, research on the CO2 emissions during construction stage are less studied than those during other stages because they are considered to be lower than the emissions from the building's materials' production or operational stage. In addition, research has been hindered by a complicated calculation process and a lack of data, and thus few methods are available for forecasting construction-stage carbon emissions, especially at the early design stage. In order to estimate the environmental effects of the emissions from the vast number of construction activities, this study applies a random forest (RF) based predictive method to predict construction-stage carbon emissions. The RF-based model uses data from 38 buildings in the Pearl River Delta region of China for the initial training set to find the relation between construction-stage carbon emissions and design parameters. Compared with the multilinear regression method, the RF-based model has a higher coefficient of determination and lower mean square error. The model developed in this study facilitates the prediction of construction-stage carbon emissions at the early design stage of a building. This opens up novel opportunities to reduce carbon emissions from buildings, which had previously been possible only at the latter stages of a building's life cycle. It will also help policymakers account for the probable distribution and amount of CO2 emissions in a city when multiple construction projects are proceeding simultaneously, so that measures can be implemented to avoid excessive emissions.