Hot and powerless

Identifying vulnerable population clusters under hypothetical concurrent heatwave and loss-of-electricity conditions in Australia

Bibliographic Data

ID23312432
AuthorsAlexandra Bratanova (0000-0002-2000-125X, Commonwealth Scientific and Industrial Research Organisation, corresponding author), Haohui Chen (0000-0001-8976-3634, Commonwealth Scientific and Industrial Research Organisation), Hien Pham (0000-0003-1710-4597, Commonwealth Scientific and Industrial Research Organisation), Ainura Tursunalieva (0000-0003-2784-9031, Commonwealth Scientific and Industrial Research Organisation), Simon Dunstall (0000-0002-9518-7474, Commonwealth Scientific and Industrial Research Organisation), Rob Dunne (Commonwealth Scientific and Industrial Research Organisation), Guy Barnett (0000-0002-4991-9427, Commonwealth Scientific and Industrial Research Organisation)
Year2026
Volume12
Pages102033
Publication date2026-12-01
Open AccessYes
TypeARTICLE
VenueSustainable Futures (JOURNAL)
Journal identifiersISSN: 2666-1888
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.sftr.2026.102033
OpenAlexW7169767329
LanguageEN
References cited41

Extreme heat is Australia’s deadliest natural hazard, accounting for 80% of extreme weather-related hospitalisations between 2012 and 13 and 2021–22 and contributing to 293 deaths. These risks are expected to intensify as climate change makes heatwaves longer and more severe. When heat coincides with loss of electricity supply (LOS), health impacts can escalate because cooling, communications and some health-related supports depend on electricity. This study assesses population vulnerability under hypothetical concurrent heat and LOS conditions across Australia. It does not estimate outage probability; rather, it examines which population groups and regions may be most vulnerable should widespread LOS occur during extreme heat. The assessment uses the Exposure–Sensitivity–Adaptive Capacity framework at Statistical Area Level 1. Eighteen indicators capture heat exposure, socio-demographic sensitivity and adaptive capacity when electricity-dependent mitigation is constrained. The study identifies three clusters among the 10% most vulnerable regions: (1) remote regions with high heat exposure and limited service accessibility; (2) communities with lower education and constrained adaptive resources; and (3) suburban populations with high proportions of older residents and limited English proficiency. These distinct profiles highlight the limitations of uniform interventions. The resulting cluster typology provides an evidence base for targeted adaptation and preparedness, including strengthened system resilience, prioritised restoration planning, wellness checks and tailored risk communication. The analysis also identifies data gaps that would improve future assessments. Overall, the study advances understanding of compound climate-health vulnerability and provides a scalable framework for identifying populations most vulnerable to heat stress when electricity-dependent adaptive capacity is disrupted.

Adaptation (eye) · Adaptive capacity · Climate change · Cluster (spacecraft) · Population · Psychological resilience · Typology · Vulnerability (computing) · Vulnerability assessment · Climate Change and Health Impacts · Climate Change Communication and Perception · Sustainability and Climate Change Governance

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