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Abstract Hurricanes significantly harm homeowners through physical damage and long-term financial strain due to rising insurance costs, property value loss, and repair expenses. This paper focuses on the interrelated decisions of the government mitigation funding of residential acquisitions and retrofit subsidies and of price restrictions on the insurance market in eastern North Carolina to determine the financial effects on stakeholders. The introduction of these policy interventions have impacts that propagate through the system due to risk adjustments, homeowner take-up behaviour, and insurer profit-maximising behaviour. This study uses an integrated game theoretic model to demonstrate that there are cost-effective government spending levels that reduce residential loss from hurricane damage. When insurance prices are capped at preintervention levels, the number of households and their distribution of losses, which has been altered through mitigation, leads to increased insurer insolvency. When insurance prices are allowed to adjust after mitigation, some homeowners find insurance is no longer affordable. This highlights the tradeoff between ensuring insurer stability and expanding homeowner insurance accessibility. 

Abstract The eastern North Carolina Coastal Area Management Act region is one of the most hurricane-prone areas of the United States. Hurricanes incur substantial damage and economic losses because structures located near the coast tend to be high value as well as particularly exposed. To bolster disaster mitigation and community resilience, it is crucial to understand how hurricane hazards drive social and economic impacts. We integrate detailed hazard simulations, property data, and labor compensation estimates to comprehensively analyze hurricanes’ economic impacts. This study investigates the spatial distribution of probabilistic hurricane hazards, and concomitant property losses and labor impacts, pinpointing particularly hard hit areas. Relationships between capital and labor losses, social vulnerability, and asset values reveal the latter as the primary determinant of overall economic consequences. 

This paper develops a probabilistic earthquake risk assessment for the electric power transmis- sion system in the City of Los Angeles. Via a dc load flow analysis of a suite of damage scenarios that reflect the seismic risk in Los Angeles, we develop a probabilistic representation for load shed during the restoration process. This suite of damage scenarios and their associated annual probabilities of occurrence are developed from 351 risk-adjusted earthquake scenarios using ground motion that collectively represent the seismic risk in Los Angeles at the census tract level. For each of these 351 earthquake scenarios, 12 damage scenarios are developed that form a probabilistic representation of the consequences of the earthquake scenario on the components of the transmission system. This analysis reveals that substation damage is the key driver of load shed. Damage to generators has a substantial but still secondary impact, and damage to transmission lines has significantly less impact. We identify the census tracts that are substantially more vulnerable to power transmission outages during the restoration process. Further, we explore the impact of forecasted increases in penetration of residential storage paired with rooftop solar. The deployment of storage paired with rooftop solar is represented at the census tract level and is assumed to be able to generate and store power for residential demand during the restoration process. The deployment of storage paired with rooftop solar reduces the load shed during the restoration process, but the distribution of this benefit is correlated with household income and whether the dwelling is owned or rented. 

Abstract Evacuation destination choice modeling is an integral aspect of evacuation planning. Outputs from such models are required to estimate the clearance times on which evacuation orders are based. The number of evacuees arriving at each destination also informs allocation of resources and shelter planning. Despite its importance, evacuee destination modeling has not received as much attention as identifying who evacuates and when. In this study, we present a new approach to identify evacuees and determine where they go and when using privacy-enhanced smartphone location data. We demonstrate the method using data from four recent U.S. hurricanes affecting multiple geographies (Florence 2018, Michael 2018, Dorian 2019, and Ida 2021). We then build on those results to develop a new machine learning model that predicts the number of evacuees that move between pairs of metropolitan statistical areas. The machine learning model incorporates hurricane characteristics, which have not been thoroughly exploited by existing methods. The model’s predictive power is comprehensively evaluated through a tenfold cross validation, holdout validation using Hurricane Ida (2021), and comparison with the traditional gravity model. Results suggest that the new model substantially outperforms the traditional gravity model across all performance indicators. Analysis of feature importance in the machine learning model indicates that in addition to distance and population, hurricane characteristics are important in evacuee destination choices.