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

Abstract

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 objective indicators were established based on 3D point clouds: the overall green exposure index(GEI), floor-level green exposure index(FGEI), and activity-site green exposure index(AGEI). The individual factors (i.e. perception, emotion, and behaviour towards RGS) are then weighted to these objective indicators to obtain modified GCIs through the Brunswikian lens model. We also used this novel framework to examine the effects of the RGS indicators on environmental satisfaction based on a case study including 1594 participants in Nanjing, China. The Random Forest Model was used to examine 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 demonstrated that incorporating subjective indicators to optimise objective RGS indicators could offer advantages in predicting environmental satisfaction. This framework is also applicable for predicting the potential effects of RGS exposure on other health-related outcomes.