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Multi-Scale Analysis of Green Space Patterns in Thermal Regulation Using Boosted Regression Tree Model: A Case Study in Central Urban Area of Shijiazhuang, China

Author

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  • Haotian Liu

    (Department of Urban and Rural Planning, School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

  • Yun Qian

    (Department of Urban and Rural Planning, School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China)

Abstract

Multi-scale thermal regulation of urban green spaces is critical for climate-adaptive planning. Addressing the limited research on key indicators and cross-scale synergies in high-density areas, this study developed an integrated framework combining multi-granularity grids and boosted regression tree (BRT) modeling to investigate nonlinear scale-dependent relationships between landscape parameters and land surface temperature (LST) in the central urban area of Shijiazhuang. Key findings: (1) Spatial heterogeneity and scale divergence: Vegetation coverage (FVC) and green space area (AREA) showed decreasing contributions at larger scales, while configuration metrics (e.g., aggregation index (AI), edge density (ED)) exhibited positive scale responses, confirming a dual mechanism with micro-scale quality dominance and macro-scale pattern regulation. (2) Threshold effects quantification: The BRT model revealed peak marginal cooling efficiency (0.8–1.2 °C per 10% FVC increment) within 30–70% FVC ranges, with minimum effective green patch area thresholds increasing from 0.6 ha (micro-scale) to 3.5 ha (macro-scale). (3) Based on multi-scale cooling mechanism analysis, a three-tier matrix optimization framework for green space strategies is established, integrating “micro-level regulation, meso-level connectivity, and macro-level anchoring”. This study develops a green space optimization paradigm integrating machine learning-driven analysis, multi-scale coupling, and threshold-based management, providing methodological tools for mitigating urban heat islands and enhancing climate resilience in high-density cities.

Suggested Citation

  • Haotian Liu & Yun Qian, 2025. "Multi-Scale Analysis of Green Space Patterns in Thermal Regulation Using Boosted Regression Tree Model: A Case Study in Central Urban Area of Shijiazhuang, China," Sustainability, MDPI, vol. 17(11), pages 1-22, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:4874-:d:1664714
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    References listed on IDEAS

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    1. Sebastian Sippel & Nicolai Meinshausen & Erich M. Fischer & Enikő Székely & Reto Knutti, 2020. "Climate change now detectable from any single day of weather at global scale," Nature Climate Change, Nature, vol. 10(1), pages 35-41, January.
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