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 influenced 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 influence 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 influencing 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 find that median income is a significant 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 findings 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.

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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.

In the age of big data, case studies build the foundation for the large-scale models that are increasingly being used for decision and policymaking. In this systematic literature review, we investigated the geographic, methodological, and conceptual characteristics of case studies in climate change science to evaluate the extent they provide policy recommendations to answer the questions: how can researchers best gather and report policy-relevant information for climate change adaptation, resilience, and/or recovery? What are the current themes within the literature, and how can these areas best advance as policy-relevant fields within climate change science? Findings highlight that policy recommendations were more robust, and significantly more likely, in case studies that employ participatory research methods; and geographic characteristics and use of theoretical frameworks are associated with providing policy recommendations. On the other hand, studies that focus on biophysical parameters of climate change offered weak or no policy recommendations. Thus, we conclude that local-level case study research can serve as validation and calibration data for large-scale models as long as they accurately represent the local values and perceptions of the people in the study area. We elaborate on the opportunities that exist in non-human, biophysical, research for communicating findings to policy-friendly audiences.

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