Author
Listed:
- Wenxia Zeng
(Faculty of Geography, Yunnan Normal University, Kunming 650500, China
GIS Technology Research Center of Resource and Environment in Western China, Ministry of Education, Yunnan Normal University, Kunming 650500, China
Southwest United Graduate School, Yunnan Normal University, Kunming 650092, China)
- Kun Yang
(Faculty of Geography, Yunnan Normal University, Kunming 650500, China
GIS Technology Research Center of Resource and Environment in Western China, Ministry of Education, Yunnan Normal University, Kunming 650500, China
Southwest United Graduate School, Yunnan Normal University, Kunming 650092, China)
- Shaohua Zhang
(Faculty of Geography, Yunnan Normal University, Kunming 650500, China
GIS Technology Research Center of Resource and Environment in Western China, Ministry of Education, Yunnan Normal University, Kunming 650500, China)
- Changyou Bi
(Faculty of Geography, Yunnan Normal University, Kunming 650500, China
GIS Technology Research Center of Resource and Environment in Western China, Ministry of Education, Yunnan Normal University, Kunming 650500, China
Southwest United Graduate School, Yunnan Normal University, Kunming 650092, China)
- Jing Liu
(Faculty of Geography, Yunnan Normal University, Kunming 650500, China
GIS Technology Research Center of Resource and Environment in Western China, Ministry of Education, Yunnan Normal University, Kunming 650500, China
Southwest United Graduate School, Yunnan Normal University, Kunming 650092, China)
- Xiaofang Yang
(Faculty of Geography, Yunnan Normal University, Kunming 650500, China
GIS Technology Research Center of Resource and Environment in Western China, Ministry of Education, Yunnan Normal University, Kunming 650500, China)
- Yan Rao
(Faculty of Geography, Yunnan Normal University, Kunming 650500, China
GIS Technology Research Center of Resource and Environment in Western China, Ministry of Education, Yunnan Normal University, Kunming 650500, China)
- Yan Ma
(Faculty of Geography, Yunnan Normal University, Kunming 650500, China
GIS Technology Research Center of Resource and Environment in Western China, Ministry of Education, Yunnan Normal University, Kunming 650500, China)
Abstract
Blue and green spaces are well-known for their benefits in improving urban thermal environments. However, the optimal configuration of green, blue, and grey spaces (GBGSs) for the physical and mental health of urban residents remains unclear. Therefore, we employed land surface temperature (LST), near-surface air temperature (SAT), and Humidex to analyze the optimal configuration of GBGS. The results indicated the following: (1) The spatial distribution of Perceptual Urban Thermal Environments (PTEs) is consistent with that of Surface Urban Thermal Environments (STEs). However, the temperature of most perceptual indicators is lower than the daytime LST and higher than the SAT. (2) Blue spaces have higher cooling efficiency than green spaces. (3) The coverage of grey space is less than 40%, at least 35% for green space, and blue space covers between 15% and 25%, which is the optimal configuration to balance the thermal environment. Moreover, increasing blue space and simplifying green spaces is recommended where grey space coverage is below 30%. In areas with 30–40% grey space, enhancing the complexity and fragmentation of blue space boundaries is more effective. Maintaining at least 30% blue space and optimizing green space aggregation improves cooling efficiency where grey space coverage is over 40%. This study provides the scientific foundation for configuration of GBGSs in urban development and renovations.
Suggested Citation
Wenxia Zeng & Kun Yang & Shaohua Zhang & Changyou Bi & Jing Liu & Xiaofang Yang & Yan Rao & Yan Ma, 2025.
"Configuration of Green–Blue–Grey Spaces for Efficient Cooling of Urban Physical and Perceptual Thermal Environments,"
Land, MDPI, vol. 14(3), pages 1-25, March.
Handle:
RePEc:gam:jlands:v:14:y:2025:i:3:p:645-:d:1614991
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