The role of morphology and threshold of urban green spaces in cooling efficiency: evidence of five cities, China

In March 2024, the World Meteorological Organization (WMO) said that the increasing frequency of extreme high -temperature events that were attributed to climate change has become a “new normal” (WMO, 2024). By 2050, the global urban population is expected to increase to 68% (UN Habitat, 2022). The rapid urbanization, which was driven by population growth, has changed the urban landscapes and exacerbated the EHI effect (UHI) urban heat, which is mainly caused by land surface temperature (LSt) (PENG et al., 2020; Liu et al., 2024b). Uhis represent direct threats to public health and increase the mortality risks (Huang et al., 2024) and at the same time increase other environmental crises such as air pollution (Cao et al., 2024) and accelerated energy consumption (du et al., 2024). In 2022 alone, the deaths in connection with heat -related people in China reached 50,900, which affects disproportionately in need of protection such as children and older people (The Lancet Public Health, 2023).

Changes in urban land use and cover (Lulc) are closely linked to urban environmental crises, including Uhis (Patel et al., 2024; Yang et al., 2022; Liu et al., 2024c; Marmay et al., 2021). Urban Green Spaces (UGSS) were widely examined as critical strategies to reduce these crises (Wong et al., 2024; Cui et al., 2024; Song et al., 2024a). As essential cooling sources, UGS LST effectively alleviate through mechanisms such as shading, evapo transpiration and improved ventilation (Zhou et al., 2023b; XU & ZHAO, 2023; Liu et al., 2024a; XU et al. Investigations have examined UGS-LST relationships on different scales, including macroscopic regional scales (Huang et al., 2024), Meso-based scales (XU et al., 2022) and microscopic block scales (Chen et al., 2024).

Many studies have quantified UGSS as an explanatory variable and LST as a dependent variable to investigate their relationships (KE et al., 2021; Stumpe et al., 2024). Common indicators for UGS are the ratio of green areas and surfaces (Cheng et al., 2023), landscape indices such as patch density (PD) (Zhou et al., 2023b) and morphological indicators such as UGS connectivity (Terfa et al., 2020). Landscape indices have shown significant characteristics in the quantification of UGSS and are often examined for reducing LST (Zhou et al., 2023b; Li et al., 2023b). For example, Li et al. (2023b) that aggregations, stains, larger and complex forms of UGS contribute to better cooling. However, landscape indices mainly quantify the spatial properties of UGSS from the perspective of mathematical statistics such as PD, which is usually used to measure the degree of landscape fragmentation, and is relatively weak in the reflection of the functional attributes of UGS (Chen et al., 2019). In addition, this comes from the traditional two-stage “UGS-U-UGS quantitative indicators”, which treats UGSS as a homogeneous category in the assessment of UGS-LST relationships (Zhou et al., 2023b; XU et al., 2022; Shah et al., 2021). However, UGSS are complex and include not only quantitative mathematical statistics, but also various morphological features and functional attributes. This is particularly important in urban areas with high density, in which only limited land resources are simply triggered by UGSS's area, which cannot be triggered often for combating environmental crises such as LSt (Lin et al., 2023). The morphology of Ugss (Urban Green Space Morphology, UGSM) reflects the comprehensive relationships between points, lines and surfaces within the examination area, which may be more critical than their area in the achievement of general ecological advantages (Zhou et al., 2004). However, research on UGSM types and identification methods remains relatively tight (Zhou et al., 2023b; Liu et al., 2024a).

The morphological spatial pattern analysis (MSPA) provides a promising approach for the UGSM identification by measuring spatial patterns. It classifies UGSS in seven non -overlapping morphological indicators (MSPS) – core, island, perforation, edge, loop, bridge and branch – and reflects different geometric properties, topological relationships and functional roles from UGSS (Chen et al., 2024a). For example, the bridge represents linear corridors that connect ecological sources (kernels) (Wang et al., 2024), and reflects the connectivity of UGSS within the investigation area through area shares and spatial distributions (Zhong et al., 2025), whereby potential ecological ventilation effects and cooling potential (GUO et al., 2023a). This approach has an advantage over the cohesion as a landscape indices to reflect on the functions of UGSS (e.g. connectivity) and geometric visual properties (e.g. linearity). Earlier studies have examined the effects of UGSMS on LST (Wang et al., 2024; Lin et al., 2023). However, the results of these studies were not always consistent. For example, Wang et al. (2024) found that six of seven MSPs, with the exception of the island, significantly contribute to the LST reduction, while Lin et al. (2023) Significant negative correlations observed only for core, perforation and loop indicators. These studies form an important basis for our research.

In addition, the influence of urban space on LST is complex and the cooling efficiency (LST) efficiently mitigate, has significant practical effects. The spatial zone and threshold analysis are decisive approaches to improve cooling efficiency (Han et al., 2022; Tang et al., 2023; Shen et al., 2022) to determine where and to what extent UGs should be optimized. For example, zoning methods that are used in the classification of land use (e.g. local climate zones (Han et al., 2022), urban functional zoning (Tang et al., 2023) or temperature gradient (e.g. high, medium -sized and low temperature zones (yang et al., 2022b) (e.g. the curvature analysis (CFA) was often used to To evaluate the threshold effects of UGS indicators such as landscape indices on LSt (Zhou et al., 2023b; Tang et al., 2023; Gao et al. Et al., 2022; therefore the identification of UGSM types and their highly efficient cooling regions as well as the uncovering of the threshold of UGSM is the key to improving the cooling effect.