SCC-IRG Track 1: Enabling Smart Cities in Coastal Regions of Environmental and Industrial Change: Building Adaptive Capacity through Sociotechnical Networks on the Texas Gulf Coast

The Coastal Bend Region (CBR) of Texas is vulnerable to acute and chronic environmental stressors stemming from natural and industrial sources, including flooding and erosion from high tides, storm surge events, and ship traffic, as well as higher levels of air and water pollution due to expansion of nearby industrial operations. Despite the multitude of environmental hazards facing the region, formal monitoring systems are limited and provide an incomplete view of local-level conditions. In addition, networks for communication and decision-making are often localized and/or fragmented. As a result, CBR communities lack the comprehensive data and decision-making structures needed to plan for, respond to, and mitigate the impacts of potential hazards. This project will advance the understanding of how smart and connected technologies can be integrated into and support regional communication networks to build adaptive capacity in the face of cumulative impacts from climate change and industrial expansion, using the CBR as an exemplar. Research activities will be co-developed and coordinated with residents, community-based organizations, elected officials, and city/county staff to strengthen multidisciplinary, cross-sector partnerships, enhance public engagement with science and technology, and broaden participation by underrepresented groups and frontline communities in the scientific process.

This project will apply a mixed-methods approach to assess how sociotechnical networks can be leveraged to increase knowledge and awareness of environmental and industrial hazards and to build community adaptive capacity equitably among diverse residents of the CBR. This project's main objectives are to (1) evaluate the structure and evolution of regional communication, information-sharing, and policy-making networks focused on environmental change and industrial expansion using grounded theory, (2) develop and leverage real-time sensing technologies, machine learning models, and data dissemination tools to monitor, predict, and communicate local-level environmental conditions, and (3) integrate the social and technical components through usability testing, tabletop exercises, and longitudinal questionnaires to assess how the generated data can be effectively interpreted and presented to various stakeholders to increase knowledge of environmental hazards, strengthen regional decision-making processes, and build adaptive capacity. Community workshops and symposia will provide opportunities to refine the study needs and objectives, obtain feedback on the sensor network and data products, share project results, co-develop a vision for long-term sustainability of the project, and discuss opportunities for integration with other regional efforts.