AI-Enabled Heatwave Resilience and Climate Risk Management in the United States

Suleman Asghar; Benjamin Damoah

doi:10.38142/ijesss.v7i5.2102

Authors

Suleman Asghar University of the Cumberlands, United States of Amerika
Benjamin Damoah University of Southern Mississippi, United Stated of America https://orcid.org/0000-0002-9204-3395

DOI:

https://doi.org/10.38142/ijesss.v7i5.2102

Keywords:

artificial intelligence, , climate resilience, , environmental justice, extreme heat, social vulnerability

Abstract

Extreme heat is an escalating climate-health and sustainability challenge in the United States, yet its effects remain uneven because exposure intersects with housing, energy insecurity, occupation, health, transportation, and historical disinvestment. This study examines how artificial intelligence (AI) can support equitable heatwave resilience without obscuring uncertainty or institutional responsibility. It combines an integrative evidence synthesis with an exploratory ecological analysis of publicly reported Centers for Disease Control and Prevention data for the 10 U.S. Department of Health and Human Services regions, comparing 2023 warm-season heat-related illness emergency-department visit rates, historical baseline rates, and days above regional historical thresholds. The regional measures showed strong geographic clustering and persistence, but robust estimation and influence diagnostics demonstrated that the adjusted elevated-day association was unstable in the small sample and should not be interpreted causally. The synthesis therefore develops a five-pillar climate-risk management framework linking hazard anticipation, social vulnerability mapping, targeted intervention, adaptive communication, and ethical governance. The framework positions AI as accountable decision support rather than an autonomous solution. Its sustainability contribution connects predictive capability with material protection, including functional cooling, energy assistance, worker safeguards, transportation, resilient housing, urban heat mitigation, and community participation. AI-enabled heat resilience should be evaluated by transparent validation, equitable service reach, institutional accountability, and measurable reductions in preventable harm.

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