Search

Housing plays a critical role in health and has real consequences on people's wellbeing. Numerous studies have provided extensive insights into the housing–health–wellbeing association. However, in the Chinese context, the unique housing allocation system with institutional force may present a distinct picture from that based on international knowledge. Using sample data from housing surveys, this study employs statistical analysis and the Tobit model to explore the links between the physical environment, socio-psychological dispositions, and residential satisfaction at the neighborhood level across different housing types in the unique Chinese context. The analyses reveal that (1) residents' socio-psychological dispositions and residential satisfaction vary remarkably across different housing types, with residents in replacement housing experiencing particularly low levels of residential satisfaction, presenting a regionalization trend; (2) housing types appear to have a significant effect on socio-psychological dispositions; and (3) physical environment characteristics in conjunction with socio-psychological dispositions significantly affect residential satisfaction. These findings suggest that social housing development should integrate socio-psychological initiatives with physical environment improvement, particularly for disadvantaged groups in public and replacement housing, to achieve more livable communities with better residential satisfaction and higher social resiliency and sustainability. [ABSTRACT FROM AUTHOR]

Reducing car dependence is the key to achieving the goal of green and sustainable development. Compared with the existing studies, which mainly focus on administrative areas, this study takes residential areas as the research unit. Four spatial regression models were used to investigate the effect on car dependence of six factors of the built environment (land use mix, population density, jobs–housing balance, bus stop density, metro station density, and road network density). Various test results show that the geography-weighted regression (GWR) model has more substantial explanatory power and that the estimated coefficients of built environment characteristics vary positively or negatively in diverse residential communities. The findings demonstrate that the impact of built environment characteristics on car dependence is significantly spatially heterogeneous. These results are conducive to better comprehending how built environment factors affect car dependence and help establish policies and strategies to promote sustainable transportation. [ABSTRACT FROM AUTHOR]