Associated organisationsClemson University · Harvard University · The University of Tennessee, Knoxville · Three Cubed · University of Nebraska Medical CenterEurope PMC affiliations are not treated as award recipients or mapped locations.
Funding£1.4M
PeriodFeb 2024 — Feb 2027
In plain English
AI plain-English summary
A community in a low-to-moderate income area is co-designing its own solar microgrid and home electrification plan to cut energy bills and improve health. This project addresses a gap in how climate solutions are typically deployed: top-down, ignoring the specific social and technical barriers faced by vulnerable households. Energy burdens—the share of income spent on heating, cooling, and power—are highest in these communities, and poor indoor air quality from gas stoves or inefficient heating directly harms physical and mental health. The researchers will combine geographic data, machine learning, surveys, and computer simulations to build a framework that policymakers can use to assess where weatherization and microgrids will deliver the greatest health and equity gains. If successful, the project will produce an open-access data repository and a replicable planning tool. This could change how local authorities and utilities prioritise infrastructure upgrades—shifting from cost-only metrics to ones that account for asthma rates, hospital visits, and psychological stress. The community co-design process itself is a test of whether bottom-up energy planning can actually work at scale.
View original technical description
This project aims to provide climate mitigation solutions, i.e., community microgrids and weatherization with electrification to reduce energy burdens and greenhouse gas emissions and improve physical and mental health for low- to-moderate-income communities. At micro and macro levels, this project will build a social-technological, equitable framework and tool for policymakers and researchers to analyze the multidimensionality of concentrated social vulnerability, energy vulnerabilities, climate health, and psychological outcomes of low-to-moderate income areas. Our interdisciplinary methods include: (1) integration of GIS analyses of interdisciplinary datasets, including weather and climate, outdoor air quality, community vulnerability index, public housing location, energy burdens, health care system, and mental and physical health outcomes, etc.; (2) machine learning modeling on the energy-health-climate nexus; (3) mixed social science methods including surveys and focus groups on understanding the technical, social-psychological, and policy barriers toward weatherization with electrification and solar and community microgrids; (4) statistical analyses and cost-benefit assessment on the impacts of climate on households’ physical and mental health; and (5) computer simulation of microgrid test bed. We will create the open access and cross-domain data repository. This project is community-driven and uses community co-design strategies, and these engagement facets are essential to its success.
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