Identifying latent workforce capacities for extreme heat resilience: An artificial intelligence assisted approach

Abstract

Extreme heat events, intensified by climate change, pose critical challenges to public health, infrastructure, and workforce resilience. Despite the urgency of these challenges, there is no systematic framework to identify workforce adaptive capacities that can help build regional heat resilience. This study introduces a novel large language model assisted approach, using task-level data from the O*NET dataset, to identify workforce capacities that enhance heat resilience. By defining heat-solution tasks as activities mitigating heat impacts, protecting public health, or improving infrastructure, we classify heat-solution occupations and dual-impact occupations, which are both vulnerable to heat and critical to heat resilience. A case study of the state of Arizona in the United States analyzed 16,398 tasks across 663 occupations, identifying 110 heat-solution occupations (about 14 % of Arizona’ workforce) and 31 dual-impact occupations. The study reveals how energy-relevant occupations, such as HVAC technicians, solar installers, and retrofit specialists, contribute to climate adaptation, linking occupational roles to the clean energy transition and resilient infrastructure. By leveraging large language models, our method provides a scalable, AI-powered tool to analyze workforce data and identify capacities necessary for energy efficiency and hazard resilience. The findings not only demonstrate the potential of large language models in workforce analysis but also contributed to shaping Arizona’s first Extreme Heat Preparedness Plan. This study offers a scalable method to uncover latent capacities and informs policies on workforce development, safety regulations, and climate-resilient infrastructure, serving as a model for other regions facing similar challenges.