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

2025-09-02

AI Summary

This study quantifies the social costs of electricity outages during four major U.S. hurricanes, focusing on disparities in social vulnerability—particularly how population density and income level shape unequal outage burdens and recovery trajectories. Method: We develop a transferable assessment framework that converts customer-weighted outage exposure into deprivation metrics, integrating welfare-based valuation with dynamic recovery modeling. Using EAGLE I observational data, we compute ZIP-code-level average outage duration, recovery time, and relative recovery rate, then conduct interpretable regression and demographic clustering. Contribution/Results: Low-income communities experience both higher outage intensity and significantly slower recovery—emerging as the primary driver of inequity. Conventional reliability metrics fail to capture such recovery-type disparities. The framework enables fine-grained, cross-event comparison and provides empirical grounding for equity-oriented resilience investment and equitable restoration planning.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

The multifaceted nature of disaster impact shows that densely populated areas contribute more to aggregate burden, while sparsely populated but heavily affected regions suffer disproportionately at the individual level. This study introduces a framework for quantifying the societal impacts of power outages by translating customer weighted outage exposure into deprivation measures, integrating welfare metrics with three recovery indicators, average outage days per customer, restoration duration, and relative restoration rate, computed from sequential EAGLE I observations and linked to Zip Code Tabulation Area demographics. Applied to four United States hurricanes, Beryl 2024 Texas, Helene 2024 Florida, Milton 2024 Florida, and Ida 2021 Louisiana, this standardized pipeline provides the first cross event, fine scale evaluation of outage impacts and their drivers. Results demonstrate regressive patterns with greater burdens in lower income areas, mechanistic analysis shows deprivation increases with longer restoration durations and decreases with faster restoration rates, explainable modeling identifies restoration duration as the dominant driver, and clustering reveals distinct recovery typologies not captured by conventional reliability metrics. This framework delivers a transferable method for assessing outage impacts and equity, comparative cross event evidence linking restoration dynamics to social outcomes, and actionable spatial analyses that support equity informed restoration planning and resilience investment.

Problem

• Quantifying social costs of power outages across hurricanes
• Assessing restoration disparities in income and demographics
• Developing framework for outage impact and equity evaluation

Innovation

• Framework converts outage exposure into deprivation welfare metrics
• Uses EAGLE I data for cross-event fine-scale outage evaluation
• Identifies restoration duration as dominant driver via explainable modeling