Towards low carbon cities: A machine learning method for predicting urban blocks carbon emissions (UBCE) based on built environment factors (BEF) in Changxing City, China

Abstract

Predicting urban blocks carbon emissions (UBCE) accurately based on built environment factors (BEF) is an effective way to reduce UBCE and alleviate urban heat islands (UHI) from the perspective of urban planning. At an urban level, this study collected various sources of data and proposed a machine learning method (Back Propagation Neural Network - BPNN) to predict different functions of UBCE by BEF in Changxing, a representative small city in China. The study found that UBCE can be significantly affected by BEF such as density, function, and morphology. The BPNN has a good prediction performance on different functions of UBCE, and the mean absolute percentage error (MAPE) is stable within 10 %–20 %. The prediction results showed that the average value of different functions of UBCE presented an obvious variation. The carbon emission map showed that the high UBCE are mainly clustered in the middle-east and south of the central city of Changxing. By comparison with other studies, the accuracy of this method was proved. This method could be applied to predict carbon emissions of the urban planning scheme. Optimizing the BEF of urban blocks can play key roles in the reduction of carbon emissions and then alleviation of the UHI.