Urban futures are contested and fraught with seemingly insurmountable challenges. It’s not surprising that much of the discourse around urban and global futures tends to be dystopian with visions of environmental and societal collapse, and business as usual forecasts that challenge planning and policymaking for more optimistic urban futures. More recently, research and practice demonstrate the role of positives visions that allow exploration of alternative and desirable futures in developing positive plans and delivering desirable outcomes for cities. I will present the visualized outcomes of co-designed stakeholder driven urban future scenarios developed by working closely with diverse multi-scale stakeholders to develop positive, transformative, and plausible visions of the future to guide decision-making for resilience to climate driven extreme events in 9 US and Latin American cities. This paper synthesizes work from the NSF UREx SRN with multiple collaborators that have contributed to the methods, analysis, modeling, and results and who will be acknowledged.
Core methods involve incorporating stakeholder scenarios into a spatially explicit modeling environment for advancing resilience in cities to climate driven extreme events. The social-ecological-technical systems (SETS) framework guides the scenarios development, modeling, and data visualization. I will present a custom, flexible spatial modeling environment to examine the interacting social, ecological, and technical drivers impacting the future possibilities for adapting and transforming urban communities for climate change adaptation and resilience. The modeling builds a series of spatially explicit, yet interacting sub-models (e.g. population demographics, infrastructure, land use, land cover, downscaled climate projections) for each major component of the urban SET system that interact dynamically with coded rules developed through participatory scenario development workshops. This allows for comparison and contrasting of alternative visions and strategies of the future. Modeling methods include a modified Cellular Automata (CA) approach to combine empirical, data-driven approaches (machine learning and projections) with theoretical and expert models.
Results/Conclusions:
This simple, flexible framework provides exceptional range of future possibilities, while remaining simple to implement, explain, and develop with stakeholders. Results include spatial projections at high resolution for each scenario, contrasted to examine trade-offs, synergies and uncover new opportunities for improving urban resilience strategies to meet long-term goals. Spatially explicit outcomes will be presented through a novel 3D data visualization environment. Results will focus on New York City and San Juan, Puerto Rico futures where coastal flooding from recent extreme events are driving transformative thinking, and need for transformative, transdisciplinary research to advance decision-making for resilience.