Scenario Discovery for Urban Planning: The Case of Green Urbanism and the Impact on Stress
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Lahoz, Lorena Torres Azevedo, Carlos Lima Ancora, Leonardo Morgado, Paulo Kotval, Zenia Miranda, Bruno Pereira, Francisco Camara |
| author_facet | Lahoz, Lorena Torres Azevedo, Carlos Lima Ancora, Leonardo Morgado, Paulo Kotval, Zenia Miranda, Bruno Pereira, Francisco Camara |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02905 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scenario Discovery for Urban Planning: The Case of Green Urbanism and the Impact on Stress Lahoz, Lorena Torres Azevedo, Carlos Lima Ancora, Leonardo Morgado, Paulo Kotval, Zenia Miranda, Bruno Pereira, Francisco Camara Computers and Society Machine Learning 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. |
| title | Scenario Discovery for Urban Planning: The Case of Green Urbanism and the Impact on Stress |
| topic | Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2504.02905 |