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
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 ch...
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MDPI AG
2025-03-01
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| author | Tailong Shi Hao Xu |
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| description | 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<sup>2</sup>) 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. |
| format | Article |
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| language | English |
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| series | Land |
| spelling | doaj-art-d8d86ca8fd174d34bc0fc69a0eca9f4b2025-08-20T02:42:35ZengMDPI AGLand2073-445X2025-03-0114356410.3390/land14030564Study 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 MechanismsTailong Shi0Hao Xu1Department of Landscape Architecture, School of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, ChinaDepartment of Landscape Architecture, School of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, ChinaUrban 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<sup>2</sup>) 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.https://www.mdpi.com/2073-445X/14/3/564urban green space systemecosystem services valueinVEST modelXGBoost-GWR |
| spellingShingle | Tailong Shi Hao Xu 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 Land urban green space system ecosystem services value inVEST model XGBoost-GWR |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | urban green space system ecosystem services value inVEST model XGBoost-GWR |
| url | https://www.mdpi.com/2073-445X/14/3/564 |
| work_keys_str_mv | AT tailongshi studyonecosystemservicevaluesofurbangreenspacesystemsinsuzhoucitybasedontheextremegradientboostinggeographicallyweightedregressionmethodspatiotemporalchangesdrivingfactorsandinfluencingmechanisms AT haoxu studyonecosystemservicevaluesofurbangreenspacesystemsinsuzhoucitybasedontheextremegradientboostinggeographicallyweightedregressionmethodspatiotemporalchangesdrivingfactorsandinfluencingmechanisms |