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Lookup NU author(s): Professor Richard DawsonORCiD, Dr Alistair FordORCiD, Professor Stuart Barr
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© 2025 Elsevier LtdIn recent decades, China's multiple cities have expanded rapidly, intensifying urban heat island (UHI) effects. The spatiotemporal patterns of UHI intensity and their driving factors have become a research focus. Some studies focus on machine learning or statistical methods regarding the spatiotemporal patterns of the UHI intensity in multiple cities and their driving factors, with few exploring UHI intensity variations using interpretable neural networks. We calculated the UHI of 31 provincial capital cities across seven physical geographic regions over two decades, analyzed cluster characteristics with Fourier fitting and K-means algorithms, and disclosed their driving factor using the explainable deep learning model (TabNET). Results show that (1) Between 2000 and 2020, the spatial expansion rate of first-tier cities was 176.92 %, while that of second-tier cities reached 197.12 %. (2) 94.62 % of the cities experienced increases in UHI during the summer months, with Urumqi showing the smallest change in UHI across the seasons and Shenyang showing the greatest change. (3) The four distinct UHI patterns observed across 31 Chinese cities can be categorized into six clusters that closely correspond to China's six natural geographical regions, indicating its dominant influence on UHI spatial variation. (4) The geographical location, population, GDP, elevation, month, and vegetation cover are significant drivers of UHI, with weights of 20.3 %, 14 %, 13.3 %, 13.2 %, 12.2 % and 10 %, respectively. Our results visually summarize the regional UHI patterns and disclose driving factors, providing intuitive guidance for mitigating UHI and urban thermal environment optimization.
Author(s): Yang H, Wu Z, Qiu S, Wu F, Dawson RJ, Ford A, Barr S
Publication type: Article
Publication status: Published
Journal: Sustainable Cities and Society
Year: 2025
Volume: 130
Print publication date: 15/07/2025
Online publication date: 14/07/2025
Acceptance date: 13/07/2025
ISSN (print): 2210-6707
ISSN (electronic): 2210-6715
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.scs.2025.106640
DOI: 10.1016/j.scs.2025.106640
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