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We examined whether floods and cyclones, the shocks that are transient in nature, affect interregional migration differently compared to riverbank erosion that causes loss of lands and thus generates permanent shocks. We tracked Household Income and Expenditure Survey 2000 participants in nine coastal districts of Bangladesh and collected further information in 2015. Our analyses suggest that both transient and permanent shocks induce households to migrate, but the effect is higher for the latter category. Using a difference-in-differences setting, we find that migrants’ income and expenditure increase relative to their counterparts, indicating that facilitating migration may improve welfare in disaster-prone countries. 

Hurricane evacuation has become an increasingly complicated activity in the U.S. as it involves moving many people who live along the Atlantic coast and Gulf coast within a very limited time. A good deal of research has been conducted on hurricane evacuation, but only a limited number of studies have looked into the timing aspect of evacuation. This paper intends to contribute to the literature on evacuation timing decisions by investigating what factors influence the time preference at the household level. Two hurricane survey data sets were used to analyze household evacuation behaviors across the Gulf coast as well as the Northeast and Mid-Atlantic coast in a comparative perspective. Using the Heckman selection model, we examined various factors identified in the literature on the two possible outcomes (evacuation and early evacuation). We found that the most important determinants of evacuation were prior evacuation experience, evacuation orders, and risk perceptions, while the most important determinants of early evacuation were prior evacuation experiences, days spent at the evacuation destination, and the cost of evacuation. Socioeconomic factors also influenced the two decisions but differently. These results provide implications for future hurricane evacuation planning and for improving emergency management practices.

 

This article deals with household-level flood risk mitigation. We present an agent-based modeling framework to simulate the mechanism of natural hazard and human interactions, to allow evaluation of community flood risk, and to predict various adaptation outcomes. The framework considers each household as an autonomous, yet socially connected, agent. A Beta-Bernoulli Bayesian learning model is first applied to measure changes of agents' risk perceptions in response to stochastic storm surges. Then the risk appraisal behaviors of agents, as a function of willingness-to-pay for flood insurance, are measured. Using Miami-Dade County, Florida as a case study, we simulated four scenarios to evaluate the outcomes of alternative adaptation strategies. Results show that community damage decreases significantly after a few years when agents become cognizant of flood risks. Compared to insurance policies with pre-Flood Insurance Rate Maps subsidies, risk-based insurance policies are more effective in promoting community resilience, but it will decrease motivations to purchase flood insurance, especially for households outside of high-risk areas. We evaluated vital model parameters using a local sensitivity analysis. Simulation results demonstrate the importance of an integrated adaptation strategy in community flood risk management. 

We analyzed data from a survey administered to 1,212 respondents living in superstorm Hurricane Sandy-affected areas. We estimated the effect of having experienced hurricane-induced disruptions to utility services, such as electricity, water, gas, phone service, and public transportation, on having an evacuation plan. Around 39% of respondents reported having an evacuation plan in case a hurricane affects their neighborhood this year. Respondents who had experienced disruptions to electricity supply had an approximately 11 percentage-point higher likelihood of having an evacuation plan than those who had experienced no such disruptions. Respondents who had experienced monetary losses from Hurricane Sandy had around a five percentage-point higher likelihood of having an evacuation plan compared with those who had not. Among control variables, prior evacuation, distance to the coastline, residence in a flood zone, concern about the impacts of future natural disaster events, had window protection, and household members being disabled, each had an association with residents’ future evacuation planning and hurricane preparedness. In light of these findings, we discuss the policy implications of our findings for improving disaster management in hurricane-prone areas. 

Critical infrastructure and public utility systems are often severely damaged by natural disasters like hurricanes. Based on a framework of household disaster resilience, this paper focuses on the role of utility disruption on household-level recovery in the context of Hurricane Sandy. Using data collected through a two-stage household survey, it first confirms that the sample selection bias is not present, thus the responses can be estimated sequentially. Second, it quantitatively examines factors contributing to hurricane-induced property damages and household-level recovery. The finding suggests that respondents who suffered from a longer period of utility disruptions (e.g., electricity, water, gas, phone/cell phone, public transportation) are more likely to incur monetary losses and have more difficulty in recovering. Effective preparedness activities (e.g., installing window protections, having an electric generator) can have positive results in reducing adverse shocks. Respondents with past hurricane experiences and higher educational attainments are found to be more resilient compared to others. Finally, the paper discusses the implications of the findings on effective preparation and mitigation strategies for future disasters. 

Recent climatic disasters have shown the vulnerability of transportation infrastructures against natural hazards. To understand the risk of coastal hazards on urban travel activities, this study presents an activity-based modeling approach to evaluate the impacts of storm surge on the transportation network under sea-level rise in Miami-Dade County, FL. A Markov-Chain Monte Carlo (MCMC) based algorithm is applied to generate population attributes and travel diaries in the model simulation. Flooding scenarios in 2045 are developed based on different adaptation standards under the 100-year storm surge and population projections are from the land-use conflict identification strategy (LUCIS) model. Our analysis indicates that about 29.3% of the transportation infrastructure, including areas of the US No. 1 highway, roadways in the south and southwest of the county, and bridges connecting Miami Beach area, will be damaged under the storm surge when a low-level adaptation standard is chosen. However, the high-level adaptation standard will reduce the vulnerable infrastructures to 12.4%. Furthermore, the total increased travel time of the low-level adaptation standard could be as high as twice of that in the high-level adaptation standard during peak morning hours. Our model results also reveal that the average increased travel time due to future storm surge damage ranges between 14.2 and 62.8 min per trip.