Urban and individual correlates of subjective well-being in China: An application of gradient boosting decision trees - Huang, Xiaoyan; Kang, Chenchen; Yin, Chun; Li, Yu (Frontiers in Public Health, 2023)
Subjective well-being (SWB) is attributable to both individual and environmental attributes. However, extant studies have paid little attention to the contribution of urban environmental attributes to SWB or to their nonlinear associations with SWB. This study applies a machine learning approach called gradient boosting decision trees (GBDTs) to the 2013 China Household Income Survey data to investigate the relative importance and nonlinear association of urban and individual attributes on SWB. The results show that urban and individual attributes make similar relative contributions to SWB. Income and age are the most important predictors. Urban facilities make a larger contribution than urban development factors. Moreover, urban attributes exert nonlinear and threshold effects on SWB. Cultural facilities and green space have inverted U-shaped correlations with SWB. Educational facilities, medical facilities, and population size are monotonically associated with SWB and have specific thresholds. We conclude that improving urban attributes is important to enhancing residents' SWB.