Making residential green space exposure evaluation more accurate: A composite assessment framework that integrates objective and subjective indicators

Traditional satellite or land use-derived indicators for assessing residential green space (RGS) exposure have limitations in predicting health benefits, owing to the individual differences in the absorption of ‘real’ green exposure. This study developed a novel framework, Greenspace Exposure Composite Indices (GCIs), that modifies objective RGS metrics by including residents’ subjective factors. First, three RGS indicators were established based on 3D point clouds: the green exposure index, floor green exposure index, and active green exposure index. The individual factors (i.e. perception, emotion, and behaviour towards RGS) are weighted with respect to the objective indicators to obtain modified GCIs based on the Brunswikian lens model. We used this novel framework to examine the effects of the RGS indicators on environmental satisfaction. The case study included 1594 participants from 40 residential communities in Nanjing, China. The Random Forest Model was used to explore the associations between GCIs and environmental satisfaction, and the results showed that GCIs had a higher explanatory degree of environmental satisfaction than traditional objective ones. Our findings demonstrate that incorporating subjective indicators to optimise objective RGS indicators offers advantages in predicting environmental satisfaction. This framework is also applicable to predicting environmental exposure and other health-related outcomes.