Abstract
This study examines Nanchang, a typical subtropical water-network city, to quantify the nonlinear impacts of blue‑green space (BGS) patterns on the urban heat island (UHI) effect and identify critical thresholds. Using multi‑source geographic data and a random forest model, it reveals: BGS proportion (BGP) exhibits cooling thresholds at 27.05%, 39.49%, 60.74%, and 71.46%, with optimal cooling at 73.32% and benefits saturating beyond 74.90%; the largest patch index of BGS (LPI_BG) must exceed 13.18 to be effective; building density (PBPA) above 19.34% sharply intensifies UHI; while increasing building height variation (AUCHR > 2.25) and BGS aggregation (AI_BG > 81.44) enhance cooling. Recommendations include setting neighborhood BGP targets within 60%–74% and strictly controlling building density below 20%. Planning should shift from “area expansion” to “structural optimization,” focusing on continuous, aggregated BGS patches and coordinating three‑dimensional building morphology to improve ventilation. The framework is climate‑adaptive: in high‑humidity environments, higher BGS aggregation and connectivity compensate for constrained transpiration; in regions with pronounced prevailing winds, blue‑green corridors aligned with wind direction provide stronger dynamic cooling, allowing threshold adjustments. The study uncovers “ineffective green space” in subtropical cities, emphasizes multi‑factor coupled regulation beyond single area metrics, and provides empirical evidence for climate‑adaptive planning in hot‑humid regions.
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Data availability
The administrative boundary data of China (GS (2024) 0650) can be downloaded from https://cloudcenter.tianditu.gov.cn/administrativeDivision. The boundary of the research area was obtained by vectorization based on Google satellite imagery (https://earth.google.com/web/). Landsat 8 OLS Imagery: downloaded from the NASA Earth Data Search Platform (https://search.earthdata.nasa.gov/search), with a spatial resolution of 30 m and a temporal range of 2022. Through land surface temperature (LST) inversion algorithms (such as the single-window algorithm or split-window algorithm), land surface temperature data for the study area were obtained. Land Cover Data: sourced from the Zenodo data repository (https://zenodo.org/records/441781033)29, with a spatial resolution of 30 m and a temporal range of 2022. BGS were extracted through supervised/unsupervised classification, and landscape pattern indices such as patch density and aggregation were calculated using landscape index tools (e.g., Fragstats). Road Vector Data: obtained from the Tianditu Vector Service (https://map.tianditu.gov.cn/)30, in Shapefile (shp) format, with a temporal range of 2020. These data are used for buffer analysis or network analysis to assist in delineating the spatial boundaries of study units. Five-Year Average Land Surface Temperature (LST): processed based on the GEE platform (https://code.earthengine.google.com/), with a spatial resolution of 30 m and a temporal range of 2018–2022. Five-year average LST data were generated using time-series aggregation algorithms (e.g., weighted average, median filtering) to reduce cloud interference and temporal heterogeneity. Building Height Data: sourced from the Zenodo data repository (https://zenodo.org/records/1545902534)31, in Shapefile (shp) format, with a temporal range of 2020. Urban canopy height was calculated through spatial interpolation (e.g., Kriging interpolation) or machine learning algorithms (e.g., RF), and urban canopy height roughness was further analyzed. Other data and the data that support the findings of this study are available from the corresponding author, C.H., upon reasonable request.
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Acknowledgements
This study was supported by Jiangxi Provincial Social Science Foundation Project (Project No. 23JL09, 22YS08); The National Natural Science Foundation of China (Project No. 32260418); Jiangxi University Humanities and Social Sciences Program (Project No. YS22127); Research Project of Jiangxi Forestry Bureau (No. 201901).
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Conceptualization: Cheng Huang, Jinkui Ning, Shebao Yu; data curation: Cheng Huang, Shebao Yu, Xinyi Qiu, Ziwei Liu; formal analysis: Jinkui Ning, Shebao Yu, Ziwei Liu, Jie Xiong, Shengke Fu; funding: Cheng Huang, Jinkui Ning, Lvshui Zhang; investigation: Jinkui Ning, Jie Xiong, Yuxing Zhan; methodology: Shebao Yu, Yuxing Zhan, Cheng Huang; project administration: Cheng Huang, Lvshui Zhang; software: Xinyi Qiu, Jie Xiong, Ziwei Liu; writing—original draft: Cheng Huang, Yuxing Zhan, Jie Xiong, Shengke Fu; writing—review and editing: Jinkui Ning, Cheng Huang.
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Huang, C., Ning, J., Qiu, X. et al. Quantifying blue-green space cooling thresholds in a subtropical water-network city. npj Urban Sustain (2026). https://doi.org/10.1038/s42949-026-00379-0
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DOI: https://doi.org/10.1038/s42949-026-00379-0
