Assessment and regression of carbon emissions from the building and construction sector in China: A provincial study using machine learning.

The building and construction sector is a major contributor to carbon emissions in China. Hence, it is crucial to explore the characteristics and trends of building carbon emissions to achieve the carbon peak and neutrality. While previous studies have made efforts to analyze the influencing factors through different approaches, developing an effective and intelligent regression model based on machine learning algorithms remains challenging in predicting the carbon emission trend. This study analyzed carbon emissions and per capita indicators of the building and construction sector in 30 provincial regions in China from 2005 to 2021. While embodied and operational carbon emissions contribute equally to the total emissions, the results showed a significant spatial-temporal correlation. Considering the emissions as target features, nine alternative machine learning regression models were developed using eight identified explanatory features incorporating scale, economic, technological, and classification factors. Based on performance metrics encompassing root mean squared error, coefficient of determination, and mean absolute percentage error, the stacking ensemble regression model was identified to have superior performance. This model was further employed to conduct a sensitivity analysis of explanatory features on carbon emissions. The results indicated that urbanization rate and population were the most sensitive factors, with varying effects on different target features. These findings can be used to predict carbon emission trends and promote carbon reduction policies in the building industry. • Characteristics and trends analyzed for building carbon emissions during 2005–2021. • Embodied and operational carbon emissions contribute equally to the building sector. • Nine regression models developed for embodied and operational carbon emissions. • Stacking ensemble model shows superior performance with R2 values exceeding 0.910 • Sensitive features exert different influences on varying carbon emission targets. [ABSTRACT FROM AUTHOR]