Identifying latent workforce capacities for extreme heat resilience: An artificial intelligence assisted approach
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. T...
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Elsevier
2025-09-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546825001120 |
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| author | Jieshu Wang Patricia Solís |
| author_facet | Jieshu Wang Patricia Solís |
| author_sort | Jieshu Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ea8a8753b32d4aa0a8b83ff7021cf090 |
| institution | Kabale University |
| issn | 2666-5468 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-ea8a8753b32d4aa0a8b83ff7021cf0902025-08-24T05:14:49ZengElsevierEnergy and AI2666-54682025-09-012110058010.1016/j.egyai.2025.100580Identifying latent workforce capacities for extreme heat resilience: An artificial intelligence assisted approachJieshu Wang0Patricia Solís1Department of Technology and Society, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA; Corresponding author.Knowledge Exchange for Resilience, Arizona State University, 777 E. University Dr., Tempe, AZ 85281, USAExtreme 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.http://www.sciencedirect.com/science/article/pii/S2666546825001120Extreme heatWorkforceHeat resilienceOccupationsHeat-solution occupationsClimate adaptation |
| spellingShingle | Jieshu Wang Patricia Solís Identifying latent workforce capacities for extreme heat resilience: An artificial intelligence assisted approach Energy and AI Extreme heat Workforce Heat resilience Occupations Heat-solution occupations Climate adaptation |
| title | Identifying latent workforce capacities for extreme heat resilience: An artificial intelligence assisted approach |
| title_full | Identifying latent workforce capacities for extreme heat resilience: An artificial intelligence assisted approach |
| title_fullStr | Identifying latent workforce capacities for extreme heat resilience: An artificial intelligence assisted approach |
| title_full_unstemmed | Identifying latent workforce capacities for extreme heat resilience: An artificial intelligence assisted approach |
| title_short | Identifying latent workforce capacities for extreme heat resilience: An artificial intelligence assisted approach |
| title_sort | identifying latent workforce capacities for extreme heat resilience an artificial intelligence assisted approach |
| topic | Extreme heat Workforce Heat resilience Occupations Heat-solution occupations Climate adaptation |
| url | http://www.sciencedirect.com/science/article/pii/S2666546825001120 |
| work_keys_str_mv | AT jieshuwang identifyinglatentworkforcecapacitiesforextremeheatresilienceanartificialintelligenceassistedapproach AT patriciasolis identifyinglatentworkforcecapacitiesforextremeheatresilienceanartificialintelligenceassistedapproach |