Scenario Discovery for urban planning: The case of green urbanism and the impact on stress

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 exist- ing 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) techniques within an Robust Decision Making (RDM) context in urban planning, by focusing on static robustness of urban vegetation interventions in stress reduction across dif- ferent 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 inter- vention 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 intervencions in urban planning, opening the door for its exploration in other urban decision-making contexts where uncertainty and design resiliency are critical. • Systematic scenario-based analysis to guide urban planning decisions. • Novel Scenario Discovery framework application in a Neurorubanism case study. • Case study in reducing stress levels in Copenhagen. • High-density urban environments, crowding, and individual psychological traits (e.g., extraversion) can reduce the effectiveness of a proposed vegetation policy. • The use of active Learning reduces the number of sample points needed to arrive at a similar conclusion in Scenario Discovery.