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Due to its unique location, Bangladesh often faces devastating hydroclimatic shocks such as floods and cyclones. In the recent past, three major cyclones (Sidr in 2007, Aila in 2009, and Komen in 2015) claimed 3800 lives and damaged hundreds of thousands of houses with billions of dollars in property damages. In this paper, we focus on understanding people's evacuation behaviors in the face of approaching cyclones using survey data collected through face-to-face interviews with residents living in the coastal areas of Bangladesh. Through various statistical models, including probit, panel probit, bivariate probit, and multinomial logit models, we have explored the determinants of both past and future evacuation decisions, as well as the choice of evacuation destinations. Our findings reveal consistent patterns across different cyclone events, highlighting the significant roles played by warning time, proximity to the coast, property loss, shelter accessibility, housing structure, literacy, past evacuation experiences, and demographic factors such as age, gender, and employment status. Additionally, the analysis of evacuation destinations uncovers nuanced insights into the preferences and challenges faced by evacuees, including the need for improving shelter accessibility. With rising vulnerabilities in coastal areas in Bangladesh and worldwide, identifying what drives households' evacuation decisions and their destination choices can provide useful inputs for evacuation planning and effective disaster management.

 

Extreme weather events have significant economic and social impacts, disrupting essential public services like electricity, phone communication, and transportation. This study seeks to understand the performance and resilience of critical infrastructure systems in Houston, Texas, using Hurricane Harvey (2017) as a case study. We surveyed 500 Houston Metropolitan Statistical Area residents after Hurricane Harvey’s landfall about disruption experience in electricity, water, phone/cellphone, internet, public transportation, workplace, and grocery stores. Our household survey data revealed the proportion and duration of disruption in each system. Approximately 70% of respondents reported experiencing electricity outages, while half (51%) had no access to water for up to six days. Two-thirds of surveyed households lacked internet access, and 50% had their phone services disconnected. Additionally, around 71% of respondents were unable to commute to work, and 73% were unable to purchase groceries for their families during this period. We incorporated the household survey responses into the Dynamic Inoperability Input-Output Model (DIIM) to estimate inoperability and economic losses across interconnected sectors. The projected economic loss was estimated to be in the range of $6.7- $9.7 billion when sensitivity analysis is performed with respect to the number of working days. Understanding the resilience of each sector and the inherent interdependencies among them can provide beneficial insight to policymakers for disaster risk management, notably preparedness and recovery planning for future events. 

Critical infrastructures are ubiquitous and their interdependencies have become more complex leading to their uncertain behaviors in the aftermath of disasters. The article develops an integrated economic input–output model that incorporates household‐level survey data from Hurricane Sandy, which made its landfall in 2012. In this survey, 427 respondents who were living in the state of New Jersey during Hurricane Sandy were used in the study. The integration of their responses allowed us to show the probability and duration of various types of critical infrastructure failures due to a catastrophic hurricane event and estimate the economic losses across different sectors. The percentage of disruption and recovery period for various infrastructure systems were extracted from the survey, which were then utilized in the economic input–output model comprising of 71 economic sectors. Sectors were then ranked according to: (i) inoperability, the percentage in which a sector is disrupted relative to its ideal level, and (ii) economic loss, the monetary worth of business interruption caused by the disaster. With the combined infrastructure disruptions in the state of New Jersey, the model estimated an economic loss of $36 billion, which is consistent with published estimates. Results from this article can provide insights for future disaster preparedness and resilience planning.

 

Climate change by its very nature epitomizes the necessity and usefulness of the global-to-local-to-global (GLG) paradigm. It is a global problem with the potential to affect local communities and ecosystems. Accumulation of local impacts and responses to climate change feeds back to regional and global systems creating feedback loops. Understanding these complex impacts and interactions is key to developing more resilient adaptation measures and designing more efficient mitigation policies. To this date, however, GLG interactions have not yet been an integrative part of the decision-support toolkit. The typical approach either traces the impacts of global action on the local level or estimates the implications of local policies at the global scale. The first approach misses cumulative feedback of local responses that can have regional, national or global impacts. In the second case, one undermines a global context of the local actions most likely misrepresenting the complexity of the local decision-making process. Potential interactions across scales are further complicated by the presence of cascading impacts, connected risks and tipping points. Capturing these dimensions is not always a straightforward task and often requires a departure from conventional modeling approaches. In this paper, we review the state-of-the-art approaches to modeling GLG interactions in the context of climate change. We further identify key limitations that drive the lack of GLG coupling cases and discuss what could be done to address these challenges.

 

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. 

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.