Zhou Xuechun, Zou Xiaofei, Xiong Wenzuixiong
School of Social and Political Sciences, University of Glasgow, Glasgow, United Kingdom.
School of Art, Hubei University, Wuhan, China.
Front Public Health. 2025 Mar 6;13:1490857. doi: 10.3389/fpubh.2025.1490857. eCollection 2025.
Urban green spaces play a critical role in addressing health issues, ecological challenges, and uneven resource distribution in cities. This study focuses on Wuhan, where low green coverage rates and imbalanced green space allocation pose significant challenges. Adopting a healthy city development perspective, the research aims to assess the impact of green space optimization on urban health, economic performance, and social structure.
A multivariable model was constructed using random forest and Support Vector Machine (SVM) algorithms to evaluate the influence of key indicators on urban green space. Core indicators were integrated from three dimensions: residents' health, environmental quality, and community interaction. Multiple linear regression analysis was employed to quantify the potential benefits of green space optimization on economic and social outcomes.
The findings reveal that optimizing health and environmental quality indices significantly enhances green space development. Green space improvements drive a 73% increase in economic efficiency by improving residents' health and extending life expectancy. Additionally, enhancements in social structure are achieved at rates of 61% and 52% through strengthened community cohesion and improved environmental quality, respectively. The model demonstrates high stability and adaptability after multiple iterations, providing a robust quantitative foundation for green space optimization.
This study highlights the multidimensional value of green space optimization in promoting urban health, economic growth, and social stability. The results offer a solid theoretical basis and practical guidance for green space planning and management in healthy cities, contributing to scientific decision-making and sustainable urban development.
城市绿地在应对城市健康问题、生态挑战和资源分配不均方面发挥着关键作用。本研究聚焦于武汉，该市绿地覆盖率低且绿地空间分配不均衡，带来了重大挑战。本研究从健康城市发展的角度出发，旨在评估绿地优化对城市健康、经济表现和社会结构的影响。
使用随机森林和支持向量机（SVM）算法构建多变量模型，以评估关键指标对城市绿地的影响。核心指标从居民健康、环境质量和社区互动三个维度进行整合。采用多元线性回归分析来量化绿地优化对经济和社会成果的潜在益处。
研究结果表明，优化健康和环境质量指标可显著促进绿地发展。通过改善居民健康和延长预期寿命，绿地改善推动经济效率提高73%。此外，通过加强社区凝聚力和改善环境质量，社会结构分别以61%和52%的速度得到改善。该模型经过多次迭代后显示出高稳定性和适应性，为绿地优化提供了坚实的定量基础。
本研究强调了绿地优化在促进城市健康、经济增长和社会稳定方面的多维价值。研究结果为健康城市的绿地规划和管理提供了坚实的理论基础和实践指导，有助于科学决策和城市可持续发展。
