Optimization of Roof Greening Spatial Planning to Cool Down the Summer of the City

Abstract

Knowing the optimal spatial distribution of urban green infrastructure (UGI) is vital to launch effective greening projects and formulate the promotion strategy for sustainable urban transition. But few studies have explored spatial organization of UGI on an urban scale to maximize its benefits for urban renewal. Here, we developed a spatial optimization method that combines an urban cooling model and a genetic algorithm to study the optimized geographical arrangement of roof greening in Kowloon, Hong Kong. Two objectives are compared regarding the optimized spatial pattern and cooling effects: maximize the mean air temperature reduction and minimize population heat exposure. Optimization can reduce more air temperature than random distribution when the roof greening coverage target ranges between 30% and 70%. When the target increases, the geographical locations of prioritized buildings with roof greening proportion ranking the top quarter move from the near-forest area to the inner city, while the lower greening proportions are mostly found on dense and small-rooftop buildings. The difference of prioritized buildings between the two objectives becomes significant when the target increases, which shows the trade-offs between the cooling potential and population exposure among regions. Our analytic approach contributes to transforming strategies into planning solutions toward large-scale implementation.