Impact of urban built environment on ride-hailing carbon emissions based on XGBoost model

YIN Chaoying; GE Yaoxia; CHEN Wendong; WANG Xiaoquan; SHAO Chunfu

doi:10.11860/j.issn.1673-0291.20240107

Abstract：To investigate the interaction between the built environment and ride-hailing carbon emissions,this study uses ride-hailing order operation data from Nanjing.The built environment indicators are characterized based on factors such as population size,land use,distance to the city center,and housing prices.An Extreme Gradient Boosting(XGBoost)model is established,incor-porating built environment factors at both the origin and destination of trips.The model aims to identify key factors affecting ride-hailing carbon emissions and reveal the nonlinear relationships and variable interactions between them.Additionally,the regression results of the XGBoost model are compared with those of the traditional Gradient Boosting Decision Tree(GBDT)model to verify the former's advantage in regression fitting.The results indicate that the XGBoost model outperforms the traditional GBDT model,with R-squared,mean absolute error,and root mean square error values of 0.541,0.364,and 0.275,respectively.The distance between ride-hailing trip origins and destinations and the city center contributes significantly,with contribution rates of 20.544%and 29.127%,respectively.Furthermore,the distance from the metro station to the ori-gin and destination exhibits opposing feedback mechanisms on carbon emissions,indicating an asymmetric impact of metro station proximity on emissions.The nonlinear relationship between the distance from the origin to the city center and ride-hailing carbon emissions follows a U-shaped distribution,with significant threshold effects at 7 km and 20 km.Additionally,there are notable interaction effects between the distance from the city center and road density at the trip origin on ride-hailing carbon emissions.