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Recent research, professional, and funding agendas have re-surfaced the importance of knowledge co-production and ethical participation to address urban tensions worldwide: urbanization and rapid climate change, disproportionately impacting socially vulnerable populations. Despite the rise of Digital Twins (DT), buoyed by the growth of computational and data technologies in the past 10 to 15 years, DT have fallen short of their promise to address these tensions. We present a participatory modeling (PM) platform, Fora.ai, to build on existing strengths of DT and overcome the most prevalent limitations of data-driven technologies. This platform (i.e., a set of visualization and simulation tools and facilitation and sense-making approaches) is organized around the iterative steps in PM: problem definition and goal setting, preference elicitation, collaborative scenario-building, simulation, tradeoff deliberation, and solution-building. We demonstrate the platform’s effectiveness when set within a stakeholder-led process that integrates diverse knowledge, data sources, and values in pursuit of equitable green infrastructure (GI) planning to address flooding. The immediate visualization of simulated impacts, followed by reflection on causal and spatial relationships and tradeoffs across diverse priorities, enhanced participants’ collective understanding of how GI interacts with the built environment and physical conditions to inform their intervention scenarios. The facilitated use of Fora.ai enabled a collaborative socio-technical sense-making process, whereby participants transitioned from untested beliefs to designs that were specifically tailored to the problem in the study area and the diversity of values represented, attending to both localized flooding and neighborhood-level impacts. They also derived generalizable design principles that could be applied elsewhere. We show how the combination of specific facilitation practices and platform features leverage the power of data, computational modeling, and social complexity to contribute to collaborative learning and creative and equitable solution-building for urban sustainability and climate resilience. 

Climate change impacts are not evenly distributed across the globe. Inequities also emerge at a local scale where buildings have the most perceivable impact, affecting anything from access and continuity of the public realm to microclimates.Design decisions can exacerbate or mitigate microspatial inequities—i.e. significant local variation in environmentalhazard exposures, like heat, air pollution, and flooding. Green Infrastructure (GI) is a range of nature-based solutionswith the potential to mitigate environmental hazards. Decentralizing GI is critical to health and resilience, buildingredundancy and capacity through a distributed network of smaller system nodes that are less prone to cascading failures.Architecture projects can support decentralization, targeted mitigation, and incremental implementation; however theircontribution to urban resilience, health, and environmental justice needs to be better characterized to support rationalizedexpansion of such approaches. This requires ways to explore complex and dynamic interactions of buildings within and beyond site boundaries, including: (1) methods for measuring local variation in hazards at relevant spatial scales and (2) tools for modeling the impacts of interventions in inclusive conversations with local stakeholders. This research examines an equity-focused approach to co-designing GI in architecture projects, using data and tools to inform and measure the impact of individual building projects and, eventually, networks of projects. In collaboration with the city of Chelsea, MA, our transdisciplinary team is studying sensor networks and a participatory modeling process to demonstrate how architecture projects can generate and leverage local knowledge about microspatial inequities and mitigation by GI to advance broader community health goals. Co-design activities around one pilot site reveal how decentralization becomes a significant paradigm shift—even among practitioners—eliciting ideas about maximizing capacity, connectivity, co-benefits, and shared responsibility. This paper examines the term decentralization in a multidisciplinary discourse, shares lessons from a specific context, and discusses implications to architectural practice. 

Environmental hazards vary locally and even street to street resulting in microspatial inequities, necessitating climate resilience solutions that respond to specific hyperlocal conditions. This study uses remote sensing data to estimate two environmental hazards that are particularly relevant to community health: land surface temperature (LST; from LandSat) and air pollution (AP; from motor vehicle volume via cell phone records). These data are analyzed in conjunction with land use records in Boston, MA to test (1) the extent to which each hazard concentrates on specific streets within neighborhoods, (2) the infrastructural elements that drive variation in the hazards, and (3) how strongly hazards overlap in space. Though these data rely on proxies, they provide preliminary evidence. Substantial variations in LST and AP existed between streets in the same neighborhood (40% and 70–80% of variance, respectively). The former were driven by canopy, impervious surfaces, and albedo. The latter were associated with main streets and zoning with tall buildings. The correlation between LST and AP was moderate across census tracts (r = 0.4) but modest across streets within census tracts (r = 0.16). The combination of results confirms not only the presence of microspatial inequities for both hazards but also their limited coincidence, indicating that some streets suffer from both hazards, some from neither, and others from only one. There is a need for more precise, temporally-dynamic data tracking environmental hazards (e.g., from environmental sensor networks) and strategies for translating them into community-based solutions.