Scenario Discovery for Urban Planning: The Case of Green Urbanism and the Impact on Stress

2025-04-03

AI Summary

Urban environments significantly impact mental health, yet decision-support frameworks for resilience planning under deep uncertainty (DMDU) remain lacking. This study pioneers the integration of Scenario Discovery (SD)—a robust decision-analytic method—into green urbanism. Combining neuroscientific field experiments (Lisbon), predictive models of affective responses, multi-source urban geographic data (Copenhagen), and SD algorithms, we identify critical failure thresholds beyond which vegetation-based interventions no longer alleviate psychological stress. Results demonstrate that while urban vegetation generally reduces stress, its efficacy is significantly moderated by built-environment density, crowd intensity, and individual personality traits (e.g., extraversion). The study delivers actionable, robust policy pathways, explicitly defining key regulatory thresholds for intervention effectiveness. By bridging neuroscience, urban analytics, and DMDU-aware decision science, it provides both methodological innovation and empirical evidence to guide precision green interventions and resilience-oriented urban design under uncertainty.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Reasoning under Uncertainty: Decision/Utility Theory
• Planning, Routing, and Scheduling: Planning under Uncertainty

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Sustainability of Web economics
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• Evaluating urban vegetation's impact on stress reduction
• Lack of systematic scenario-based urban planning analysis
• Identifying thresholds for effective vegetation interventions

Innovation

• Scenario Discovery for urban vegetation interventions
• Predictive model based on neuroscience experiments
• Identifying thresholds for stress reduction effectiveness