Quantifying the Social Costs of Power Outages and Restoration Disparities Across Four U.S. Hurricanes

Quantifying the Social Costs of Power Outages and Restoration Disparities Across Four U.S. Hurricanes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Quantifying the Social Costs of Power Outages and Restoration Disparities Across Four U.S. Hurricanes Xiangpeng Li, Junwei Ma, Bo Li, Ali Mostafavi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7686156/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The multifaceted nature of disaster impact reveals that areas with concentrated populations may contribute more to overall economic burden, while sparsely populated but highly impacted regions suffer disproportionately at the individual level. This study presents a framework that quantifies the societal costs of power outages by converting customer-weighted outage exposure into monetized social losses. This framework integrates welfare metrics with three recovery measures—average outage-days per customer, restore duration, and relative restoration rate—computed from sequential EAGLE-I observations and linked to Zip Code Tabulation Area-level demographics. Through a standardized pipeline applied to four U.S. hurricanes—Beryl (2024, Texas), Helene (2024, Florida), Milton (2024, Florida), Ida (2021, Louisiana), we produce the first cross-event, fine-scale accounting of societal outage costs and their underlying drivers. The analysis reveals substantial heterogeneity in impacts: Ida generated $ 1.50 billion in total deprivation costs ( $ 1,757 per capital) Milton, $ 1.26 billion ( $ 387 per capita); Beryl, $ 629 million, ( $ 674 per capita); and Helene, $ 411 million ( $ 285 per capita). Across all events, the results document consistently regressive burden distributions, with deprivation-cost shares declining systematically with income. Mechanistic analysis demonstrates that deprivation costs increase with restoration duration and decrease with relative restoration rates. Explainable modeling identifies restore duration as the dominant predictor of costs, while unsupervised clustering reveals distinct recovery typologies invisible to conventional reliability metrics. By shifting from simple outage counts and subjective assessments to welfare-based, exposure-weighted measurement, this study delivers three key contributions: (I) a transferable framework for quantifying outage impacts and equity, (ii) comparable cross-event evidence linking restoration dynamics to social losses, and (iii) actionable spatial analyses and community typologies that enable equity-informed restoration prioritization and resilience investment decisions. Earth and environmental sciences/Environmental social sciences Scientific community and society/Geography Social science/Geography Physical sciences/Mathematics and computing Earth and environmental sciences/Natural hazards Deprivation cost Power outages Hurricane resilience Energy equity Welfare economics Infrastructure restoration Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7686156","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":519035185,"identity":"87fafb28-aa99-41c5-b1ea-c314e1ce5343","order_by":0,"name":"Xiangpeng 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Hurricanes","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Deprivation cost, Power outages, Hurricane resilience, Energy equity, Welfare economics, Infrastructure restoration","lastPublishedDoi":"10.21203/rs.3.rs-7686156/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7686156/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe multifaceted nature of disaster impact reveals that areas with concentrated populations may contribute more to overall economic burden, while sparsely populated but highly impacted regions suffer disproportionately at the individual level. This study presents a framework that quantifies the societal costs of power outages by converting customer-weighted outage exposure into monetized social losses. This framework integrates welfare metrics with three recovery measures\u0026mdash;average outage-days per customer, restore duration, and relative restoration rate\u0026mdash;computed from sequential EAGLE-I observations and linked to Zip Code Tabulation Area-level demographics. Through a standardized pipeline applied to four U.S. hurricanes\u0026mdash;Beryl (2024, Texas), Helene (2024, Florida), Milton (2024, Florida), Ida (2021, Louisiana), we produce the first cross-event, fine-scale accounting of societal outage costs and their underlying drivers. The analysis reveals substantial heterogeneity in impacts: Ida generated \u003cspan\u003e$\u003c/span\u003e1.50\u0026nbsp;billion in total deprivation costs (\u003cspan\u003e$\u003c/span\u003e1,757 per capital) Milton, \u003cspan\u003e$\u003c/span\u003e1.26\u0026nbsp;billion (\u003cspan\u003e$\u003c/span\u003e387 per capita); Beryl, \u003cspan\u003e$\u003c/span\u003e629\u0026nbsp;million, (\u003cspan\u003e$\u003c/span\u003e674 per capita); and Helene, \u003cspan\u003e$\u003c/span\u003e411\u0026nbsp;million (\u003cspan\u003e$\u003c/span\u003e285 per capita). Across all events, the results document consistently regressive burden distributions, with deprivation-cost shares declining systematically with income. Mechanistic analysis demonstrates that deprivation costs increase with restoration duration and decrease with relative restoration rates. Explainable modeling identifies restore duration as the dominant predictor of costs, while unsupervised clustering reveals distinct recovery typologies invisible to conventional reliability metrics. By shifting from simple outage counts and subjective assessments to welfare-based, exposure-weighted measurement, this study delivers three key contributions: (I) a transferable framework for quantifying outage impacts and equity, (ii) comparable cross-event evidence linking restoration dynamics to social losses, and (iii) actionable spatial analyses and community typologies that enable equity-informed restoration prioritization and resilience investment decisions.\u003c/p\u003e","manuscriptTitle":"Quantifying the Social Costs of Power Outages and Restoration Disparities Across Four U.S. Hurricanes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-24 06:10:24","doi":"10.21203/rs.3.rs-7686156/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5a726dac-dcbd-4ccb-9524-6923b3591a5d","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55155131,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":55155132,"name":"Scientific community and society/Geography"},{"id":55155133,"name":"Social science/Geography"},{"id":55155134,"name":"Physical sciences/Mathematics and computing"},{"id":55155135,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2026-01-11T12:53:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-24 06:10:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7686156","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7686156","identity":"rs-7686156","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}