Arizona’s blistering summers are notoriously extreme: record-breaking temperatures, workers at risk, crowded emergency rooms. In a study led by Stony Brook University researcher Jieshu Wang, her team asks a pressing question: what if the people most exposed to extreme temperatures are also the ones working against climate change?
Wang, an assistant professor in the Department of Technology and Society in Stony Brook’s College of Engineering and Applied Sciences, used large language models (LLMs) to help map how Arizona’s workforce contributes to “heat resilience” — the ability of people, infrastructure and institutions to withstand and adapt to extreme heat. The study was published in the journal Energy and AI.
“Previous research often frames workers as victims of heat,” Wang said in an interview. “But our workers are a brilliant human resource that can help us build heat resilience. Heat-smart housing, green infrastructure, transportation — everything we rely on in extreme heat is built and maintained by people.”
The study grew out of Wang’s earlier work at Arizona State University’s Decision Theater and Knowledge Exchange for Resilience (KER), which was supporting the governor’s office on extreme heat preparedness. Arizona is one of the hottest places in the United States; in 2024, Phoenix recorded 70 days above 110°F and 113 consecutive days at or above 100°F. These conditions also made it an ideal test bed for understanding how labor, climate and infrastructure intersect.
Wang and her colleague Patricia Solís, KER’s executive director and a research professor in ASU’s School of Geographical Sciences and Urban Planning, turned to the U.S. Department of Labor’s database of occupations and the tasks these jobs involve. For Arizona alone, the database listed 663 occupations and 16,398 distinct work tasks, from installing air conditioners to drafting legislation.
Manually reading and rating each task for its relevance to heat resilience would have been impossible. Instead, the team instructed GPT-4o to score every task on a five-point scale based on how much it helps a community prepare for, withstand or recover from extreme heat. The model also assigned each task to one of several “heat-solution roles,” including engineering and technical work, health and safety, environmental research, or public service and emergency response.
“Using a large language model with carefully designed prompts turned out to be a pretty efficient way to automate this kind of human reasoning,” Wang said. “We can ask it how much each task contributes to heat resilience, and have it explain why!”
Read the full story by Ankita Nagpal on the AI Innovation Institute website.
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