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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. 

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.