Spatial regression identifies socioeconomic inequality in multi-stage power outage recovery after Hurricane Isaac

Abstract

Power outages are a common outcome of hurricanes in the United States with potentially serious implications for community wellbeing. Understanding how power outage recovery is in uenced by factors such as the magnitude of the outage, storm characteristics, and community demographics is key to building community resilience. Outage data is a valuable tool that can help to better understand how hurricanes affect built infrastructure and in uence the management of short-term infrastructure recovery process. We conduct a spatial regression analysis on customers experiencing outages and the total power recovery time to investigate the factors in uencing power outage recovery in Louisiana after Hurricane Isaac. Our interest was in whether infrastructure damage and recovery times resulting from a hurricane disproportionately affect socio-economically vulnerable populations and racial minorities. We nd that median income is a signi cant predictor of 50%, 80%, and 95% recovery times, even after controlling for hurricane characteristics and total outages. Higher income geographies and higher income adjacent geographies experience faster recovery times. Our ndings point to possible inequities associated with income in power outage recovery prioritization, which cannot be explained by exposure to outages, storm characteristics, or the presence of critical services such as hospitals and emergency response stations. These results should inform more equitable responses to power outages in the future helping to improve overall community resilience.

Socioeconomic vulnerability and differential impact of severe weather-induced power outages

“…SVI, which is widely used in recent studies of the differential impact of strong storms [e.g. ( 3 , 5 , 7 , 14 , 40 )], reports the percentile ranking of counties according to four vulnerability themes: Socioeconomic Status, Household Characteristics, Racial & Ethnic Minority Status, and Housing Type & Transportation ( 25 ). In order to indicate that higher values for each theme represent more vulnerability, we refer to the first theme as Socioeconomic vulnerability , the second as Household characteristics vulnerability , the third as Racial & ethnic minority status vulnerability , the fourth as Housing type & transportation vulnerability .…”

“…But, appropriate policy interventions depend on the extent to which these outages are the result of these communities being more likely to be located in a storm’s path or, instead, are the result of prolonged recovery processes conditional on the storm’s impact. Quantitative evidence on the relationship between economic and social vulnerability and power outage duration conditional on storm impact is mixed, with some demographic factors correlated with vulnerability predicting longer duration outages (and only after removing other insignificant covariates from the statistical model) ( 3 ) or after aggregating the direct effect of the vulnerability on a focal county with estimated spillover effects from the focal county to its neighbors ( 7 ).…”