Comparative Study on the Cooling Effects of Green Space Patterns in Waterfront Build-Up Blocks: An Experience from Shanghai

Abstract

Different structural patterns of waterfront green space networks in built-up areas have different synergistic cooling characteristics in cities. This study’s aim is to determine what kinds of spatial structures and morphologies of waterfront green spaces offer a good cooling effect, combined with three different typical patterns in Shanghai. A multidimensional spatial influence variable system based on the cooling effect was constructed to describe the spatial structural and morphological factors of the green space network. The ENVI-met 4.3 software, developed by Michael Bruse at Bochum, German, was used to simulate the microclimate distribution data, combined with the boosted regression tree (BRT) model and the correlation analysis method. The results showed that at the network level, the distance from the water body and the connectivity of green space had a stronger cooling correlation. The orientation of green corridors consistent with a summer monsoon had larger cooling effect ranges. In terms of spatial morphology, the vegetation sky view factor (SVF) and Vegetation Surface Albedo (VSAlbedo) had an important correlation with air temperature (T), and the green corridor with a 20–25 m width had the largest marginal effect on cooling. These results will provide useful guidance for urban climate adaptive planning and design.

The Challenge in the Management of Historic Trees in Urban Environments during Climate Change: The Case of Corso Trieste (Rome, Italy)

“…Analysing the results, it is possible to observe that, in line with the scientific literature, due to their shading effect, energy balance and transpiration cooling, vegetation results to be the most effective strategy to mitigate daytime urban human heat stress and so it is an important climate change mitigation strategy [37][38][39][40][41]. Specifically, here, the presence of Pinus pinea L. has a positive effect on the mitigation of air temperature and the improvement of thermal comfort along Corso Trieste.…”

Interaction of Urban Rivers and Green Space Morphology to Mitigate the Urban Heat Island Effect: Case-Based Comparative Analysis

“…The BRT model has strong adaptability to datasets and can handle both continuous and categorical data, and can reflect the comprehensive interaction of variables. The cooling effect of the influencing factors of blue–green space depends on the morphological composition and spatial pattern elements, which has been verified by a large number of preliminary studies [ 49 , 50 ]. The correlation methods primarily analyzed the unilateral effect of indicators in an independent manner and did not comprehensively examine the combined effect of multiple indices [ 13 , 78 ].…”

“…The decision tree complexity, learning, rate, and split ratio were 5, 0.01, and 0.5, respectively, and Gaussian data were adopted. This model extracted 50% of the data points for analysis each time, with 50% of the data used for training, and 10-fold cross-validation was performed to estimate the number of optimal trees [ 50 , 75 , 76 ]. The contribution ratios of each factor by BRT regression reflected the importance of independent variables to LST distribution; The marginal effect (ME) changes presented the UCI threshold values and correlation characteristics of each factor.…”

“…A river with a width of 35 m can lead to a decrease of approximately 1–1.5 °C in the ambient temperature and the decrease can be increased if green space is present on both sides of the river [ 49 ].The case study of an urban region in Fujian indicated that green spaces adjacent to rivers can exert obvious synergistic cooling effects, which can intensify the cooling effect by approximately 2.7 °C [ 22 ]. The green space network pattern in waterfront areas, especially with a high connectivity degree, considerably influences the transportation of cold air in urban rivers [ 50 ]. The comprehensive cooling effect of the optimized blue–green space network is greater than that of any single green space element in Shanghai city [ 42 ].…”

“…Among them, ENVI-met is widely used to simulate the microclimate of green spaces or waterbodies. The maximum simulation grid scale of ENVI-met software is limited to 2 × 2 km, which is more suitable for the micro-scale [ 13 , 50 ]. The technology of RS and GIS can provide continuous LST data at the macro-scale and can also provide overall data for urban meso-scale research.…”