用情景发现法分析城市绿化如何影响压力，找出有效干预的临界点。
Scenario Discovery for Urban Planning: The Case of Green Urbanism and the Impact on Stress
- 基于里斯本神经科学实验数据，构建情绪响应预测模型。
- 在哥本哈根数据中发现：高密度与人群会削弱绿化减压效果。
- 为城市规划提供抗不确定性的政策设计路径，适合公共健康研究者。
城市环境显著影响心理健康，但针对深不确定条件下决策支持框架（DMDU）以优化减压型城市政策的研究仍不足。现有研究虽证明了城市设计对心理健康的效应，却缺乏系统的情景分析来指导规划决策。本文通过应用情景发现（SD）方法，结合里斯本基于神经科学的户外实验情绪数据，评估不同城市环境中绿化干预对减压的效果。再融合哥本哈根的详细城市数据，识别出绿化方案成功或失败的关键干预阈值。结果表明，尽管增加植被通常降低压力水平，但在高密度城区、人流密集及个体心理特质（如外向性）影响下，其效果减弱。该研究展示了一种系统化的情景发现框架，可用于识别城市规划中具有韧性的政策路径，为未来在不确定性高、需设计韧性场景的决策领域提供新思路。
原文摘要 · Abstract (English)
Urban environments significantly influence mental health outcomes, yet the role of an effective framework for decision-making under deep uncertainty (DMDU) for optimizing urban policies for stress reduction remains underexplored. While existing research has demonstrated the effects of urban design on mental health, there is a lack of systematic scenario-based analysis to guide urban planning decisions. This study addresses this gap by applying Scenario Discovery (SD) in urban planning to evaluate the effectiveness of urban vegetation interventions in stress reduction across different urban environments using a predictive model based on emotional responses collected from a neuroscience-based outdoor experiment in Lisbon. Combining these insights with detailed urban data from Copenhagen, we identify key intervention thresholds where vegetation-based solutions succeed or fail in mitigating stress responses. Our findings reveal that while increased vegetation generally correlates with lower stress levels, high-density urban environments, crowding, and individual psychological traits (e.g., extraversion) can reduce its effectiveness. This work showcases our Scenario Discovery framework as a systematic approach for identifying robust policy pathways in urban planning, opening the door for its exploration in other urban decision-making contexts where uncertainty and design resiliency are critical.
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