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  1. Emergency managers have the important responsibility of planning and implementing mitigation policies and programs to reduce losses to life and property. To accomplish these goals, they must use limited time and resources to ensure the communities they serve have adequately mitigated against potential disasters. As a result, it is common to collaborate and coordinate with a wide variety of partner agencies and community organizations. While it is well established that strengthening relationships and increasing familiarity improve coordination, this article advances that narrative by providing direct insights on the ways a select group of local, state, and federal emergency managers view relationships with other mitigation stakeholders. Using insights from a 1-day workshop hosted at the University of Delaware to gather information from mitigation stakeholders, this article provides a discussion of commonalities and challenges workshop participants identified with other stakeholder groups. These insights can inform other emergency managers about potential collaborators and coordination opportunities with similar stakeholders in their own communities. 
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    Free, publicly-accessible full text available May 16, 2024
  2. During evacuations, households make a number of important, related choices including accommodation type, destination, and departure time. They may make trade-offs among these choices where one decision affects the others. The analysis models the linkages among these three aforementioned choices using data from a household behavioral intention survey conducted in 2017 in the Hampton Roads, VA area. Statistical tests and a theoretical basis show that the approach that best fits the dataset was to estimate the three choices in a sequence, where the first decision serves as an independent variable in the next choice process. To model the sequence, we began by modeling accommodation choice using a multinomial logit (MNL) model. Next, the accommodation choice decisions were used with other control variables to estimate destination choice in a second MNL model. Last, evacuation distance (related to destination decisions) was used in a Cox proportional-hazards model to estimate departure time choices. The models that provide the best estimates included the following control variables that help explain the sequence of decisions residents in the Hampton Roads area expect to make: (1) a variable expressing residential stability helps explain accommodation choice; (2) prior evacuation experience, the geographic location of a household, and the duration of living in the area help predict the destination choice; and (3) distance to the chosen destination helps predict departure time. Findings from this study provide evidence that the decisions associated with these three choices influence each other and help emergency managers identify additional actions that potentially can improve the evacuation experiences of local residents.

     
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  3. Abstract. Regional hurricane risk is often assessed assuming a static housing inventory, yet a region's housing inventory changes continually. Failing to include changes in the built environment in hurricane risk modeling can substantially underestimate expected losses. This study uses publicly available data and a long short-term memory (LSTM) neural network model to forecast the annual number of housing units for each of 1000 individual counties in the southeastern United States over the next 20 years. When evaluated using testing data, the estimated number of housing units was almost always (97.3 % of the time), no more than 1 percentage point different than the observed number, predictive errors that are acceptable for most practical purposes. Comparisons suggest the LSTM outperforms the autoregressive integrated moving average (ARIMA) and simpler linear trend models. The housing unit projections can help facilitate a quantification of changes in future expected losses and other impacts caused by hurricanes. For example, this study finds that if a hurricane with characteristics similar to Hurricane Harvey were to impact southeastern Texas in 20 years, the residential property and flood losses would be nearly USD 4 billion (38 %) greater due to the expected increase of 1.3 million new housing units (41 %) in the region. 
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    Abstract We hypothesize that for disaster risk mitigation, many households, despite being aware of their risk and possible mitigation actions, never seriously consider doing anything about them. In mitigation-focused decisions, since there is no equivalent to warning messages, the decision process is likely to evolve over an extended time. We explore what activates hurricane mitigation protective action decisions through three research questions: (1) to what extent are homeowners unengaged in protective action decision making? (2) What homeowner characteristics are associated with lack of engagement? And (3) to what extent do different life events trigger engagement in the decision-making process? We use the Precaution Adoption Process Model to conceptualize engagement as distinct from decision making; the broader protective action decision-making literature to explore drivers of engagement; and Life Course Theory to examine potential transitions from unengaged to engaged. We use survey data of homeowners in North Carolina to examine these questions empirically. Findings suggest that one-third of respondents had never engaged in protective action decisions, that life experiences differ in their occurrence frequency and effect on households’ mitigation decisions, and that some events, such as renovating, reroofing, or purchasing a home may offer critical moments that could be leveraged to encourage greater engagement in mitigation decision making. 
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  7. Abstract

    We develop a computational framework for the stochastic and dynamic modeling of regional natural catastrophe losses with an insurance industry to support government decision‐making for hurricane risk management. The analysis captures the temporal changes in the building inventory due to the acquisition (buyouts) of high‐risk properties and the vulnerability of the building stock due to retrofit mitigation decisions. The system is comprised of a set of interacting models to (1) simulate hazard events; (2) estimate regional hurricane‐induced losses from each hazard event based on an evolving building inventory; (3) capture acquisition offer acceptance, retrofit implementation, and insurance purchase behaviors of homeowners; and (4) represent an insurance market sensitive to demand with strategically interrelated primary insurers. This framework is linked to a simulation‐optimization model to optimize decision‐making by a government entity whose objective is to minimize region‐wide hurricane losses. We examine the effect of different policies on homeowner mitigation, insurance take‐up rate, insurer profit, and solvency in a case study using data for eastern North Carolina. Our findings indicate that an approach that coordinates insurance, retrofits, and acquisition of high‐risk properties effectively reduces total (uninsured and insured) losses.

     
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  8. Any entity offering flood insurance, whether it is private or government‐administered such as the National Flood Insurance Program (NFIP), faces the challenge of solvency. This is especially true for the NFIP, where homeowner affordability criteria limit the opportunity to charge fully risk‐based premiums. One solution is to remove the highest flood risk properties from the insurer's book of business. Acquisition (buyout) of flood‐prone structures is a potentially permanent solution that eliminates the highest risk properties while providing homeowners with financial assistance to relocate in a less risky location. To encourage participation, homeowners are offered a preflood fair market value of their damaged (or at risk of damage) structures. Although many factors have been shown to affect a homeowner's decision to accept an acquisition offer, very little research has been devoted to the influence of price or monetary incentive offered on homeowners' willingness to participate in acquisition programs. We estimate a pooled probit model and employ a bootstrap methodology to determine the effects of hypothetical home price offers on homeowners' acquisition decisions. We do so while controlling for environmental factors, property characteristics, and homeowner sociodemographic characteristics. Results show that price indeed has a positive effect on likelihood of accepting an acquisition contract. Furthermore, estimated homeowner supply curves differ significantly based on the damage status of the acquisition offer, as well as homeowner and property characteristics.

     
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  9. Abstract Objective Policy-makers and practitioners have a need to assess community resilience in disasters. Prior efforts conflated resilience with community functioning, combined resistance and recovery (the components of resilience), and relied on a static model for what is inherently a dynamic process. We sought to develop linked conceptual and computational models of community functioning and resilience after a disaster. Methods We developed a system dynamics computational model that predicts community functioning after a disaster. The computational model outputted the time course of community functioning before, during, and after a disaster, which was used to calculate resistance, recovery, and resilience for all US counties. Results The conceptual model explicitly separated resilience from community functioning and identified all key components for each, which were translated into a system dynamics computational model with connections and feedbacks. The components were represented by publicly available measures at the county level. Baseline community functioning, resistance, recovery, and resilience evidenced a range of values and geographic clustering, consistent with hypotheses based on the disaster literature. Conclusions The work is transparent, motivates ongoing refinements, and identifies areas for improved measurements. After validation, such a model can be used to identify effective investments to enhance community resilience. ( Disaster Med Public Health Preparedness . 2018;12:127–137) 
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