Study on Ecosystem Service Values of Urban Green Space Systems in Suzhou City Based on the Extreme Gradient Boosting Geographically Weighted Regression Method: Spatiotemporal Changes, Driving Factors, and Influencing Mechanisms

Tailong Shi and Hao Xu ()
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Tailong Shi: Department of Landscape Architecture, School of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
Hao Xu: Department of Landscape Architecture, School of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China

Land, 2025, vol. 14, issue 3, 1-26

Abstract: Urban green space systems (UGSS) play a crucial role in enhancing citizens’ well-being and promoting sustainable urban development through their ecosystem service values (ESV). However, understanding the spatiotemporal changes, driving factors, and influencing mechanisms of ESV remains a critical challenge for advancing urban green theories and formulating effective policies. This study focuses on Suzhou, China’s third-largest prefecture-level city by economic volume and ecological core city of the Taihu watershed, to evaluate the ESV of its UGSS from 2010 to 2020, identify key driving factors, and analyze their influencing mechanisms. Using the InVEST model combined with the entropy weight method (EWM), we assessed the ESV changes over the study period. To examine the influencing mechanisms, we employed an innovative XGBoost-GWR approach, where XGBoost was used to screen globally significant factors from 37 potential drivers, and geographically weighted regression (GWR) was applied to model local spatial heterogeneity, providing a research perspective that balances global nonlinear relationships with local spatial heterogeneity. The results revealed three key findings: First, while Suzhou’s UGSS ESV increased by 9.92% from 2010 to 2020, the Global Moran’s I index rose from 0.325 to 0.489, indicating that its spatial distribution became more uneven, highlighting the increased ecological risks. Second, climate, topography, landscape pattern, and vegetation emerged as the most significant driving factors, with topographic factors showing the greatest variation (the negatively impacted area increased by 455.60 km 2 ) and climate having the largest overall impact but least variation. Third, the influencing mechanisms were primarily driven by land use changes resulting from urbanization and industrialization, leading to increased ecological risks such as soil erosion, pollution, landscape fragmentation, and habitat degradation, particularly in the Kunshan, Wujiang, and Zhangjiagang Districts, where agricultural land has been extensively converted to constructed land. This study not only elucidates the mechanisms influencing UGSS’s ESV driving factors but also expands the theoretical understanding of urbanization’s ecological impacts, providing valuable insights for optimizing UGSS layout and informing sustainable urban planning policies.

Keywords: urban green space system; ecosystem services value; inVEST model; XGBoost-GWR (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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