hypothesis for natural disaster

Facing Hazards and Disasters: Understanding Human Dimensions (2006)

Chapter: 4 research on disaster response and recovery, 4 research on disaster response and recovery.

T his chapter and the preceding one use the conceptual model presented in Chapter 1 (see Figure 1.1 ) as a guide to understanding societal response to hazards and disasters. As specified in that model, Chapter 3 discusses three sets of pre-disaster activities that have the potential to reduce disaster losses: hazard mitigation practices, emergency preparedness practices, and pre-disaster planning for post-disaster recovery. This chapter focuses on National Earthquake Hazards Reduction Program (NEHRP) contributions to social science knowledge concerning those dimensions of the model that are related to post-disaster response and recovery activities. As in Chapter 3 , discussions are organized around research findings regarding different units of analysis, including individuals, households, groups and organizations, social networks, and communities. The chapter also highlights trends, controversies, and issues that warrant further investigation. The contents of this chapter are linked to key themes discussed elsewhere in this report, including the conceptualization and measurement of societal vulnerability and resilience, the importance of taking diversity into account in understanding both response-related activities and recovery processes and outcomes, and linkages between hazard loss reduction and sustainability. Although this review centers primarily on research on natural disasters and to a lesser degree on technological disasters, research findings are also discussed in terms of their implications for understanding and managing emerging homeland security threats.

The discussions that follow seek to address several interrelated questions: What is currently known about post-disaster response and recovery,

and to what extent is that knowledge traceable to NEHRP-sponsored research activities? What gaps exist in that knowledge? What further research—both disciplinary and interdisciplinary—is needed to fill those gaps?

RESEARCH ON DISASTER RESPONSE

Emergency response encompasses a range of measures aimed at protecting life and property and coping with the social disruption that disasters produce. As noted in Chapter 3 , emergency response activities can be categorized usefully as expedient mitigation actions (e.g., clearing debris from channels when floods threaten, containing earthquake-induced fires and hazardous materials releases before they can cause additional harm) and population protection actions (e.g., warning, evacuation and other self-protective actions, search and rescue, the provision of emergency medical care and shelter; Tierney et al., 2001). Another common conceptual distinction in the literature on disaster response (Dynes et al., 1981) contrasts agent-generated demands , or the types of losses and forms of disruption that disasters create, and response-generated demands , such as the need for situation assessment, crisis communication and coordination, and response management. Paralleling preparedness measures, disaster response activities take place at various units of analysis, from individuals and households, to organizations, communities, and intergovernmental systems. This section does not attempt to deal exhaustively with the topic of emergency response activities, which is the most-studied of all phases of hazard and disaster management. Rather, it highlights key themes in the literature, with an emphasis on NEHRP-based findings that are especially relevant in light of newly recognized human-induced threats.

Public Response: Warning Response, Evacuation, and Other Self-Protective Actions

The decision processes and behaviors involved in public responses to disaster warnings are among the best-studied topics in the research literature. Over nearly three decades, NEHRP has been a major sponsor of this body of research. As noted in Chapter 3 , warning response research overlaps to some degree with more general risk communication research. For example, both literatures emphasize the importance of considering source, message, channel, and receiver effects on the warning process. While this discussion centers mainly on responses to official warning information, it should be noted that self-protective decision-making processes are also initiated in the absence of formal warnings—for example, in response to cues that people perceive as signaling impending danger and in disasters that occur without warning. Previous research suggests that the basic deci-

sion processes involved in self-protective action are similar across different types of disaster events, although the challenges posed and the problems that may develop can be agent specific.

As in other areas discussed here, empirical studies on warning response and self-protective behavior in different types of disasters and emergencies have led to the development of broadly generalizable explanatory models. One such model, the protective action decision model, developed by Perry, Lindell, and their colleagues (see, for example, Lindell and Perry, 2004), draws heavily on Turner and Killian’s (1987) emergent norm theory of collective behavior. According to that theory, groups faced with the potential need to act under conditions of uncertainty (or potential danger) engage in interaction in an attempt to develop a collective definition of the situation they face and a set of new norms that can guide their subsequent action. 1 Thus, when warnings and protective instructions are disseminated, those who receive warnings interact with one another in an effort to determine collectively whether the warning is authentic, whether it applies to them, whether they are indeed personally in danger, whether they can reduce their vulnerability through action, whether action is possible, and when they should act. These collective determinations are shaped in turn by such factors as (1) the characteristics of warning recipients , including their prior experience with the hazard in question or with similar emergencies, as well as their prior preparedness efforts; (2) situational factors , including the presence of perceptual cues signaling danger; and (3) the social contexts in which decisions are made—for example, contacts among family members, coworkers, neighborhood residents, or others present in the setting, as well as the strength of preexisting social ties. Through interaction and under the influence of these kinds of factors, individuals and groups develop new norms that serve as guidelines for action.

Conceptualizing warning response as a form of collective behavior that is guided by emergent norms brings several issues to the fore. One is that far from being automatic or governed by official orders, behavior undertaken in response to warnings is the product of interaction and deliberation among members of affected groups—activities that are typically accompanied by a search for additional confirmatory information. Circumstances that complicate the deliberation process, such as conflicting warning information that individuals and groups may receive, difficulties in getting in touch with others whose views are considered important for the decision-making process, or disagreements among group members about any aspect of the

threat situation, invariably lead to additional efforts to communicate and confirm the information and lengthen the period between when a warning is issued and when groups actually respond.

Another implication of the emergent norm approach to protective action decision making is the recognition that groups may collectively define an emergency situation in ways that are at variance from official views. This is essentially what occurs in the shadow evacuation phenomenon, which has been documented in several emergency situations, including the Three Mile Island nuclear plant accident (Zeigler et al., 1981). While authorities may not issue a warning for a particular geographic area or group of people, or may even tell them they are safe, groups may still collectively decide that they are at risk or that the situation is fluid and confusing enough that they should take self-protective action despite official pronouncements.

The behavior of occupants of the World Trade Center during the September 11, 2001 terrorist attack illustrates the importance of collectively developed definitions. Groups of people in Tower 2 of the World Trade Center decided that they should evacuate the building after seeing and hearing about what was happening in Tower 1 and after speaking with coworkers and loved ones, even when official announcements and other building occupants indicated that they should not do so. Others decided to remain in the tower or, perhaps more accurately, they decided to delay evacuating until receiving additional information clarifying the extent to which they were in danger. Journalistic accounts suggest that decisions were shaped in part by what people could see taking place in Tower 1, conversations with others outside the towers who had additional relevant information, and directives received from those in positions of authority in tenant firms. In that highly confusing and time-constrained situation, emergent norms guiding the behavior of occupants of the second tower meant the difference between life and death when the second plane struck (NIST, 2005).

The large body of research that exists regarding decision making under threat conditions points to the need to consider a wide range of individual, group, situational, and resource-related factors that facilitate and inhibit self-protective action. Qualitatively based decision-tree models developed by Gladwin et al. (2001) demonstrate the complexity of self-protective decisions. As illustrated by their work on hurricane evacuation, a number of different factors contribute to decisions on whether or not to evacuate. Such factors range from perceptions of risk and personal safety with respect to a threatened disaster, to the extent of knowledge about specific areas at risk, to constraining factors such as the presence of pets in the home that require care, lack of a suitable place to go, counterarguments by other family members, fears of looting (shown by the literature to be unjustified; see, for example, Fischer, 1998), and fear that the evacuation process may

be more dangerous than staying home and riding out a hurricane. Warning recipients may decide that they should wait before evacuating, ultimately missing the opportunity to escape, or they may decide to shelter in-place after concluding that their homes are strong enough to resist hurricane forces despite what they are told by authorities.

In their research on Hurricane Andrew, Gladwin and Peacock describe some of the many factors that complicate the evacuation process for endangered populations (1997:54):

Except under extreme circumstances, households cannot be compelled to evacuate or to remain where they are, much less to prepare themselves for the threat. Even under extraordinary conditions many households have to be individually located and assisted or forced to comply. Segments of a population may fail to receive, ignore, or discount official requests and orders. Still others may not have the resources or wherewithal to comply. Much will depend upon the source of the information, the consistency of the message received from multiple sources, the nature of the information conveyed, as well as the household’s ability to perceive the danger, make decisions, and act accordingly. Disputes, competition, and the lack of coordination among local, state, and federal governmental agencies and between those agencies and privately controlled media can add confusion. Businesses and governmental agencies that refuse to release their employees and suspend normal activities can add still further to the confusion and noncompliance.

The normalcy bias adds other complications to the warning response process. While popular notions of crisis response behaviors seem to assume that people react automatically to messages signaling impending danger—for example, by fleeing in panic—the reality is quite different. People typically “normalize” unusual situations and persist in their everyday activities even when urged to act differently. As noted earlier, people will not act on threat information unless they perceive a personal risk to themselves. Simply knowing that a threat exists—even if that threat is described as imminent—is insufficient to motivate self-protective action. Nor can people be expected to act if warning-related guidance is not specific enough to provide them with a blueprint for what to do or if they do not believe they have the resources required to follow the guidance. One practical implication of research on warnings is that rather than being concerned about panicking the public with warning information, or about communicating too much information, authorities should instead be seeking better ways to penetrate the normalcy bias, persuade people that they should be concerned about an impending danger, provide directives that are detailed enough to follow during an emergency, and encourage pre-disaster response planning so that people have thought through what to do prior to being required to act.

Other Important Findings Regarding the Evacuation Process

As noted earlier, evacuation behavior has long been recognized as the reflection of social-level factors and collective deliberation. Decades ago, Drabek (1983) established that households constitute the basic deliberative units for evacuation decision making in community-wide disasters and that the decisions that are ultimately made tend to be consistent with pre-disaster household authority patterns. For example, gender-related concerns often enter into evacuation decision making. Women tend to be more risk-averse and more inclined to want to follow evacuation orders, while males are less inclined to do so (for an extensive discussion of gender differences in vulnerability, risk perception, and responses to disasters, see Fothergill, 1998). In arriving at decisions regarding evacuation, households take official orders into account, but they weigh those orders in light of their own priorities, other information sources, and their past experiences. Information received from media sources and from family and friends, along with confirmatory data actively sought by those at risk, generally has a greater impact on evacuation decisions than information provided by public officials (Dow and Cutter, 1998, 2000).

Recent research also suggests that family evacuation patterns are undergoing change. For example, even though families decide together to evacuate and wish to stay together, they increasingly tend to use more than one vehicle to evacuate—perhaps because they want to take more of their possessions with them, make sure their valuable vehicles are protected, or return to their homes at different times (Dow and Cutter, 2002). Other social influences also play a role. Neighborhood residents may be more willing to evacuate or, conversely, more inclined to delay the decision to evacuate if they see their neighbors doing so. Rather than becoming more vigilant, communities that are struck repeatedly by disasters such as hurricanes and floods may develop “disaster subcultures,” such as groups that see no reason to heed evacuation orders since sheltering in-place has been effective in previous events.

NEHRP-sponsored research has shown that different racial, ethnic, income, and special needs groups respond in different ways to warning information and evacuation orders, in part because of the unique characteristics of these groups, the manner in which they receive information during crises, and their varying responses to different information sources. For example, members of some minority groups tend to have large extended families, making contacting family members and deliberating on alternative courses of action a more complicated process. Lower-income groups, inner-city residents, and elderly persons are more likely to have to rely on public transportation, rather than personal vehicles, in order to evacuate. Lower-income and minority populations, who tend to have larger families, may

also be reluctant to impose on friends and relatives for shelter. Lack of financial resources may leave less-well-off segments of the population less able to afford to take time off from work when disasters threaten, to travel long distances to avoid danger, or to pay for emergency lodging. Socially isolated individuals, such as elderly persons living alone, may lack the social support that is required to carry out self-protective actions. Members of minority groups may find majority spokespersons and official institutions less credible and believable than members of the white majority, turning instead to other sources, such as their informal social networks. Those who rely on non-English-speaking mass media for news may receive less complete warning information, or may receive warnings later than those who are tuned into mainstream media sources (Aguirre et al., 1991; Perry and Lindell, 1991; Lindell and Perry, 1992, 2004; Klinenberg, 2002; for more extensive discussions, see Tierney et al., 2001).

Hurricane Katrina vividly revealed the manner in which social factors such as those discussed above influence evacuation decisions and actions. In many respects, the Katrina experience validated what social science research had already shown with respect to evacuation behavior. Those who stayed behind did so for different reasons—all of which have been discussed in past research. Some at-risk residents lacked resources, such as automobiles and financial resources that would have enabled them to escape the city. Based on their past experiences with hurricanes like Betsey and Camille, others considered themselves not at risk and decided it was not necessary to evacuate. Still others, particularly elderly residents, felt so attached to their homes that they refused to leave even when transportation was offered.

This is not to imply that evacuation-related problems stemmed solely from individual decisions. Katrina also revealed the crucial significance of evacuation planning, effective warnings, and government leadership in facilitating evacuations. Planning efforts in New Orleans were rudimentary at best, clear evacuation orders were given too late, and the hurricane rendered evacuation resources useless once the city began to flood.

With respect to other patterns of evacuation behavior when they do evacuate, most people prefer to stay with relatives or friends, rather than using public shelters. Shelter use is generally limited to people who feel they have no other options—for example, those who have no close friends and relatives to take them in and cannot afford the price of lodging. Many people avoid public shelters or elect to stay in their homes because shelters do not allow pets. Following earthquakes, some victims, particularly Latinos in the United States who have experienced or learned about highly damaging earthquakes in their countries of origin, avoid indoor shelter of all types, preferring instead to sleep outdoors (Tierney, 1988; Phillips, 1993; Simile, 1995).

Disaster warnings involving “near misses,” as well as concerns about the possible impact of elevated color-coded homeland security warnings,

raise the question of whether warnings that do not materialize can induce a “cry-wolf” effect, resulting in lowered attention to and compliance with future warnings. The disaster literature shows little support for the cry-wolf hypothesis. For example, Dow and Cutter (1998) studied South Carolina residents who had been warned of impending hurricanes that ultimately struck North Carolina. Earlier false alarms did not influence residents’ decisions on whether to evacuate; that is, there was little behavioral evidence for a cry-wolf effect. However, false alarms did result in a decrease in confidence in official warning sources, as opposed to other sources of information on which people relied in making evacuation decisions—certainly not the outcome officials would have intended. Studies also suggest that it is advisable to clarify for the public why forecasts and warnings were uncertain or incorrect. Based on an extensive review of the warning literature, Sorensen (2000:121) concluded that “[t]he likelihood of people responding to a warning is not diminished by what has come to be labeled the ‘cry-wolf’ syndrome if the basis for the false alarm is understood [emphasis added].” Along those same lines, Atwood and Major (1998) argue that if officials explain reasons for false alarms, that information can increase public awareness and make people more likely to respond to subsequent hazard advisories.

PUBLIC RESPONSE

Dispelling myths about crisis-related behavior: panic and social breakdown.

Numerous individual studies and research syntheses have contrasted commonsense ideas about how people respond during crises with empirical data on actual behavior. Among the most important myths addressed in these analyses is the notion that panic and social disorganization are common responses to imminent threats and to actual disaster events (Quarantelli and Dynes, 1972; Johnson, 1987; Clarke, 2002). True panic, defined as highly individualistic flight behavior that is nonsocial in nature, undertaken without regard to social norms and relationships, is extremely rare prior to and during extreme events of all types. Panic takes place under specific conditions that are almost never present in disaster situations. Panic only occurs when individuals feel completely isolated and when both social bonds and measures to promote safety break down to such a degree that individuals feel totally on their own in seeking safety. Panic results from a breakdown in the ongoing social order—a breakdown that Clarke (2003:128) describes as having moral, network, and cognitive dimensions:

There is a moral failure, so that people pursue their self interest regardless

of rules of duty and obligation to others. There is a network failure, so that the resources that people can normally draw on in times of crisis are no longer there. There is a cognitive failure, in which someone’s understanding of how they are connected to others is cast aside.

Failures on this scale almost never occur during disasters. Panic reactions are rare in part because social bonds remain intact and extremely resilient even under conditions of severe danger (Johnson, 1987; Johnson et al., 1994; Feinberg and Johnson, 2001).

Panic persists in public and media discourses on disasters, in part because those discourses conflate a wide range of other behaviors with panic. Often, people are described as panicking because they experience feelings of intense fear, even though fright and panic are conceptually and behaviorally distinct. Another behavioral pattern that is sometimes labeled panic involves intensified rumors and information seeking, which are common patterns among publics attempting to make sense of confusing and potentially dangerous situations. Under conditions of uncertainty, people make more frequent use of both informal ties and official information sources, as they seek to collectively define threats and decide what actions to take. Such activities are a normal extension of everyday information-seeking practices (Turner, 1994). They are not indicators of panic.

The phenomenon of shadow evacuation, discussed earlier, is also frequently confused with panic. Such evacuations take place because people who are not defined by authorities as in danger nevertheless determine that they are—perhaps because they have received conflicting or confusing information or because they are geographically close to areas considered at risk (Tierney et al., 2001). Collective demands for antibiotics by those considered not at risk for anthrax, “runs” on stores to obtain self-protective items, and the so-called worried-well phenomenon are other forms of collective behavior that reflect the same sociobehavioral processes that drive shadow evacuations: emergent norms that define certain individuals and groups as in danger, even though authorities do not consider them at risk; confusion about the magnitude of the risk; a collectively defined need to act; and in some cases, an unwillingness to rely on official sources for self-protective advice. These types of behaviors, which constitute interesting subjects for research in their own right, are not examples of panic.

Research also indicates that panic and other problematic behaviors are linked in important ways to the manner in which institutions manage risk and disaster. Such behaviors are more likely to emerge when those who are in danger come to believe that crisis management measures are ineffective, suggesting that enhancing public understanding of and trust in preparedness measures and in organizations charged with managing disasters can lessen the likelihood of panic. With respect to homeland security threats, some researchers have argued that the best way to “vaccinate” the public

against the emergence of panic in situations involving weapons of mass destruction is to provide timely and accurate information about impending threats and to actively include the public in pre-crisis preparedness efforts (Glass and Shoch-Spana, 2002).

Blaming the public for panicking during emergencies serves to diffuse responsibility from professionals whose duty it is to protect the public, such as emergency managers, fire and public safety officials, and those responsible for the design, construction, and safe operation of buildings and other structures (Sime, 1999). The empirical record bears out the fact that to the extent panic does occur during emergencies, such behavior can be traced in large measure to environmental factors such as overcrowding, failure to provide adequate egress routes, and breakdowns in communications, rather than to some inherent human impulse to stampede with complete disregard for others. Any potential for panic and other problematic behaviors that may exist can, in other words, be mitigated through appropriate design, regulatory, management, and communications strategies.

As discussed elsewhere in this report, looting and violence are also exceedingly rare in disaster situations. Here again, empirical evidence of what people actually do during and following disasters contradicts what many officials and much of the public believe. Beliefs concerning looting are based not on evidence but rather on assumptions—for example, that social control breaks down during disasters and that lawlessness and violence inevitably result when the social order is disrupted. Such beliefs fail to take into account the fact that powerful norms emerge during disasters that foster prosocial behavior—so much so that lawless behavior actually declines in disaster situations. Signs erected following disasters saying, “We shoot to kill looters” are not so much evidence that looting is occurring as they are evidence that community consensus condemns looting.

The myth of disaster looting can be contrasted with the reality of looting during episodes of civil disorder such as the riots of the 1960s and the 1992 Los Angeles unrest. During episodes of civil unrest, looting is done publicly, in groups, quite often in plain sight of law enforcement officials. Taking goods and damaging businesses are the hallmarks of modern “commodity riots.” New norms also emerge during these types of crises, but unlike the prosocial norms that develop in disasters, norms governing behavior during civil unrest permit and actually encourage lawbreaking. Under these circumstances, otherwise law-abiding citizens allow themselves to take part in looting behavior (Dynes and Quarantelli, 1968; Quarantelli and Dynes, 1970).

Looting and damaging property can also become normative in situations that do not involve civil unrest—for example, in victory celebrations following sports events. Once again, in such cases, norms and traditions governing behavior in crowd celebrations encourage destructive activities

(Rosenfeld, 1997). The behavior of participants in these destructive crowd celebrations again bears no resemblance to that of disaster victims.

In the aftermath of Hurricane Katrina, social scientists had no problem understanding why episodes of looting might have been more widespread in that event than in the vast majority of U.S. disasters. Looting has occurred on a widespread basis following other disasters, although such cases have been rare. Residents of St. Croix engaged in extensive looting behavior following Hurricane Hugo, and this particular episode sheds light on why some Katrina victims might have felt justified in looting. Hurricane Hugo produced massive damage on St. Croix, and government agencies were rendered helpless. Essentially trapped on the island, residents had no idea when help would arrive. Instead, they felt entirely on their own following Hugo. The tourist-based St. Croix economy was characterized by stark social class differences, and crime and corruption had been high prior to the hurricane. Under these circumstances, looting for survival was seen as justified, and patterns of collective behavior developed that were not unlike those seen during episodes of civil unrest. Even law enforcement personnel joined in the looting (Quarantelli, 2006; Rodriguez et al., forthcoming).

Despite their similarities, the parallels between New Orleans and St. Croix should not be overstated. It is now clear that looting and violent behavior were far less common than initially reported and that rumors concerning shootings, rapes, and murders were groundless. The media employed the “looting frame” extensively while downplaying far more numerous examples of selflessness and altruism. In hindsight, it now appears that many reports involving looting and social breakdown were based on stereotyped images of poor minority community residents (Tierney et al., forthcoming).

Extensive research also indicates that despite longstanding evidence, beliefs about disaster-related looting and lawlessness remain quite common, and these beliefs can influence the behavior of both community residents and authorities. For example, those who are at risk may decide not to evacuate and instead stay in their homes to protect their property from looters (Fischer, 1998). Concern regarding looting and lawlessness may cause government officials to make highly questionable and even counterproductive decisions. Following Hurricane Katrina, for example, based largely on rumors and exaggerated media reports, rescue efforts were halted because of fears for the safety of rescue workers, and Louisiana’s governor issued a “shoot-to-kill” order to quash looting. These decisions likely resulted in additional loss of life and also interfered with citizen efforts to aid one another. Interestingly, recent historical accounts indicate that similar decisions were made following other large-scale disasters, such as the 1871 Chicago fire, the 1900 Galveston hurricane, and the 1906 San Francisco earthquake and firestorm. In all three cases, armed force was used to stop

looting, and immigrant groups and the poor were scapegoated for their putative “crimes” (Fradkin, 2005). Along with Katrina, these events caution against making decisions on the basis of mythical beliefs and rumors.

As is the case with the panic myth, attributing the causes of looting behavior to individual motivations and impulses serves to deflect attention from the ways in which institutional failures can create insurmountable problems for disaster victims. When disasters occur, communications, disaster management, and service delivery systems should remain sufficiently robust that victims will not feel isolated and afraid or conclude that needed assistance will never arrive. More to the point, victims of disasters should not be scapegoated when institutions show themselves to be entirely incapable of providing even rudimentary forms of assistance—which was exactly what occurred with respect to Hurricane Katrina.

Patterns of Collective Mobilization in Disaster-Stricken Areas: Prosocial and Helping Behavior

In contrast to the panicky and lawless behavior that is often attributed to disaster-stricken populations, public behavior during earthquakes and other major community emergencies is overwhelmingly adaptive, prosocial, and aimed at promoting the safety of others and the restoration of ongoing community life. The predominance of prosocial behavior (and, conversely, a decline in antisocial behavior) in disaster situations is one of the most longstanding and robust research findings in the disaster literature. Research conducted with NEHRP sponsorship has provided an even better understanding of the processes involved in adaptive collective mobilization during disasters.

Helping Behavior and Disaster Volunteers. Helping behavior in disasters takes various forms, ranging from spontaneous and informal efforts to provide assistance to more organized emergent group activity, and finally to more formalized organizational arrangements. With respect to spontaneously developing and informal helping networks, disaster victims are assisted first by others in the immediate vicinity and surrounding area and only later by official public safety personnel. In a discussion on search and rescue activities following earthquakes, for example, Noji observes (1997:162)

In Southern Italy in 1980, 90 percent of the survivors of an earthquake were extricated by untrained, uninjured survivors who used their bare hands and simple tools such as shovels and axes…. Following the 1976 Tangshan earthquake, about 200,000 to 300,000 entrapped people crawled out of the debris on their own and went on to rescue others…. They became the backbone of the rescue teams, and it was to their credit that more than 80 percent of those buried under the debris were rescued.

Thus, lifesaving efforts in a stricken community rely heavily on the capabilities of relatively uninjured survivors, including untrained volunteers, as well as those of local firefighters and other relevant personnel.

The spontaneous provision of assistance is facilitated by the fact that when crises occur, they take place in the context of ongoing community life and daily routines—that is, they affect not isolated individuals but rather people who are embedded in networks of social relationships. When a massive gasoline explosion destroyed a neighborhood in Guadalajara, Mexico, in 1992, for example, survivors searched for and rescued their loved ones and neighbors. Indeed, they were best suited to do so, because they were the ones who knew who lived in different households and where those individuals probably were at the time of the disaster (Aguirre et al., 1995). Similarly, crowds and gatherings of all types are typically comprised of smaller groupings—couples, families, groups of friends—that become a source of support and aid when emergencies occur.

As the emergency period following a disaster lengthens, unofficial helping behavior begins to take on a more structured form with the development of emergent groups—newly formed entities that become involved in crisis-related activities (Stallings and Quarantelli, 1985; Saunders and Kreps, 1987). Emergent groups perform many different types of activities in disasters, from sandbagging to prevent flooding, to searching for and rescuing victims and providing for other basic needs, to post-disaster cleanup and the informal provision of recovery assistance to victims. Such groupings form both because of the strength of altruistic norms that develop during disasters and because of emerging collective definitions that victims’ needs are not being met—whether official agencies share those views or not. While emergent groups are in many ways essential for the effectiveness of crisis response activities, their activities may be seen as unnecessary or even disruptive by formal crisis response agencies. In the aftermath of the attack on the World Trade Center, for example, numerous groups emerged to offer every conceivable type of assistance to victims and emergency responders. Some were incorporated into official crisis management activities, while others were labeled “rogue volunteers” by official agencies (Halford and Nolan, 2002; Kendra and Wachtendorf, 2002). 2

Disaster-related volunteering also takes place within more formalized organizational structures, both in existing organizations that mobilize in response to disasters and through organizations such as the Red Cross,

which has a federal mandate to respond in presidentially declared disasters and relies primarily on volunteers in its provision of disaster services. Some forms of volunteering have been institutionalized in the United States through the development of the National Voluntary Organizations Active in Disaster (NVOAD) organization. NVOAD, a large federation of religious, public service, and other groups, has organizational affiliates in 49 states, the District of Columbia, Puerto Rico, and U.S. territories. National-level NVOAD affiliates include organizations such as the Salvation Army, Church World Service, Church of the Brethren Disaster Response, and dozens of others that provide disaster services. Organizations such as the Red Cross and the NVOAD federation thus provide an infrastructure that can support very extensive volunteer mobilization. That infrastructure will likely form the basis for organized volunteering in future homeland security emergencies, just as it does in major disasters.

Helping behavior is very widespread after disasters, particularly large and damaging ones. For example, NEHRP-sponsored research indicates that in the three weeks following the 1985 earthquake in Mexico City, an estimated 1.7 to 2.1 million residents of that city were involved in providing volunteer aid. Activities in which volunteers engaged after that disaster included searching for and rescuing victims trapped under rubble, donating blood and supplies, inspecting building damage, collecting funds, providing medical care and psychological counseling, and providing food and shelter to victims (Wenger and James, 1994). In other research on post-earthquake volunteering, also funded by NEHRP, O’Brien and Mileti (1992) found that more than half of the population in San Francisco and Santa Cruz counties provided assistance to their fellow victims after the 1989 Loma Prieta earthquake—help that ranged from assisting with search and rescue and debris removal activities to offering food, water, and shelter to those in need. Thus, the volunteer sector responding to disasters typically constitutes a very large proportion of the population of affected regions, as well as volunteers converging from other locations.

Social science research, much of it conducted under NEHRP auspices, highlights a number of other points regarding post-disaster helping behavior. One such insight is that helping behavior in many ways mirrors roles and responsibilities people assume during nondisaster times. For example, when people provide assistance during disasters and other emergencies, their involvement is typically consistent with gender role expectations (Wenger and James, 1994; Feinberg and Johnson, 2001). Research also indicates that mass convergence of volunteers and donations can create significant management problems and undue burdens on disaster-stricken communities. In their eagerness to provide assistance, people may “overrespond” to disaster sites, creating congestion and putting themselves and others at risk or insisting on providing resources that are in fact not needed. After disas-

ters, communities typically experience major difficulties in dealing with unwanted and unneeded donations (Neal, 1990).

Research on public behavior during disasters has major implications for homeland security policies and practices. The research literature provides support for the inclusion of the voluntary sector and community-based organizations in preparedness and response efforts. Initiatives that aim at encouraging public involvement in homeland security efforts of all types are clearly needed. The literature also provides extensive evidence that members of the public are in fact the true “first responders” in major disasters. In using that term to refer to fire, police, and other public safety organizations, current homeland security discourse fails to recognize that community residents themselves constitute the front-line responders in any major emergency

One implication of this line of research is that planning and management models that fail to recognize the role of victims and volunteers in responding to all types of extreme events will leave responders unprepared for what will actually occur during disasters—for example, that, as research consistently shows, community residents will be the first to search for victims, provide emergency aid, and transport victims to health care facilities in emergencies of all types. 3 Such plans will also fail to take advantage of the public’s crucial skills, resources, and expertise. For this reason, experts on human-induced threats such as bioterrorism stress the value of public engagement and involvement in planning for homeland security emergencies (Working Group on “Governance Dilemmas” in Bioterrorism Response, 2004).

These research findings have significant policy implications. To date, Department of Homeland Security initiatives have focused almost exclusively on providing equipment and training for uniformed responders, as opposed to community residents. Recently, however, DHS has begun placing more emphasis on its Citizen Corps component, which is designed to mobilize the skills and talents of the public when disasters strike. Public involvement in Citizen Corps and Community Emergency Response Team (CERT) activities have expanded considerably since the terrorist attacks of

9/11—a sign that many community residents around the nation wish to play an active role in responding to future disasters. The need for community-based preparedness and response initiatives is more evident than ever follow-ing the Katrina disaster.

Organizational, Governmental, and Network Responses. The importance of observing disaster response operations while they are ongoing or as soon as possible after disaster impact has long been a hallmark of the disaster research field. The quick-response tradition in disaster research, which has been a part of the field since its inception, developed out of a recognition that data on disaster response activities are perishable and that information collected from organizations after the passage of time is likely to be distorted and incomplete (Quarantelli, 1987, 2002). NEHRP funds, provided through grant supplements, Small Grants for Exploratory Research (SGER) awards, Earthquake Engineering Research Institute (EERI) reconnaissance missions, earthquake center reconnaissance funding, and small grants such as those provided by the Natural Hazards Research and Applications Information Center, have supported the collection of perishable data and enabled social science researchers to mobilize rapidly following major earthquakes and other disasters.

NEHRP provided substantial support for the collection of data on organizational and community responses in a number of earthquake events, including the 1987 Whittier Narrows, 1989 Loma Prieta, and 1994 Northridge earthquakes (see, for example, Tierney, 1988, 1994; EERI, 1995), as well as major earthquakes outside the United States such as the 1985 Mexico City, 1986 San Salvador, and 1988 Armenia events. More recently, NEHRP funds were used to support rapid-response research on the September 11, 2001 terrorist attacks and Hurricanes Katrina and Rita. Many of those studies focused on organizational issues in both the public and private sectors. (For a compilation of NEHRP-sponsored quick-response findings on the events of September 11, see Natural Hazards Research and Applications Information Center, 2003).

In many cases, quick-response research on disaster impacts and organizational and governmental response has led to subsequent in-depth studies on response-related issues identified during the post-impact reconnaissance phase. Following major events such as Loma Prieta, Northridge, and Kobe, insights from initial reconnaissance studies have formed the basis for broader research initiatives. Recent efforts have focused on ways to better take advantage of reconnaissance opportunities and to identify topics for longer-term study. A new plan has been developed to better coordinate and integrate both reconnaissance and longer-term research activities carried out with NEHRP support. That planning activity, outlined in the report The Plan to Coordinate NEHRP Post-earthquake Investigations (Holzer et

al., 2003), encompasses both reconnaissance and more systematic research activities in the earth sciences, engineering, and social sciences.

Through both initial quick-response activities and longer-term studies, NEHRP research has added to the knowledge base on how organizations cope with crises. Studies have focused on a variety of topics. A partial list of those topics includes organizational and group activities associated with the post-disaster search and rescue process (Aguirre et al., 1995); intergovernmental coordination during the response period following major disaster events (Nigg, 1998); expected and improvised organizational forms that characterize the disaster response milieu (Kreps, 1985, 1989b); strategies used by local government organizations to enhance interorganizational coordination following disasters (Drabek, 2003); and response activities undertaken by specific types of organizations, such as those in the volunteer and nonprofit sector (Neal, 1990) and tourism-oriented enterprises (Drabek, 1994).

Focusing specifically at the interorganizational level of analysis, NEHRP research has also highlighted the significance and mix of planned and improvised networks in disaster response. It has long been recognized that post-disaster response activities involve the formation of new (or emergent) networks of organizations. Indeed, one distinguishing feature of major crisis events is the prominence and proliferation of network forms of organization during the response period. Emergent multiorganizational networks (EMON) constitute new organizational interrelationships that reflect collective efforts to manage crisis events. Such networks are typically heterogeneous, consisting of existing organizations with pre-designated crisis management responsibilities, other organizations that may not have been included in prior planning but become involved in crisis response activities because those involved believe they have some contribution to make, and emergent groups. EMONs tend to be very large in major disaster events, encompassing hundreds and even thousands of interacting entities. As crisis conditions change and additional resources converge, EMON structures evolve, new organizations join the network, and new relationships form. What is often incorrectly described as disaster-generated “chaos” is more accurately seen as the understandable confusion that results when mobilization takes place on such a massive scale and when organizations and groups that may be unfamiliar with one another attempt to communicate, negotiate, and coordinate their activities under extreme pressure. (For more detailed discussions on EMONs in disasters, including the 2001 World Trade Center attack, see Drabek, 1985, 2003; Tierney, 2003; Tierney and Trainor, 2004.)

This is not to say that response activities always go smoothly. The disaster literature, organizational after-action reports, and official investigations contain numerous examples of problems that develop as inter-

organizational and intergovernmental networks attempt to address disaster-related challenges. Such problems include the following: failure to recognize the magnitude and seriousness of an event; delayed and insufficient responses; confusion regarding authorities and responsibilities, often resulting in major “turf battles;” resource shortages and misdirection of existing resources; poor organizational, interorganizational, and public communications; failures in intergovernmental coordination; failures in leadership and vision; inequities in the provision of disaster assistance; and organizational practices and cultures that permit and even encourage risky behavior. Hurricane Katrina became a national scandal because of the sheer scale on which these organizational pathologies manifested. However, Katrina was by no means atypical. In one form or another and at varying levels of severity, such pathologies are ever-present in the landscape of disaster response (for examples, see U.S. President’s Commission on the Accident at Three Mile Island, 1979; Perrow, 1984; Shrivastava, 1987; Sagan, 1993; National Academy of Public Administration, 1993; Vaughan, 1996, 1999; Peacock et al., 1997; Klinenberg, 2002; Select Bipartisan Committee to Investigate the Preparations for and Response to Hurricane Katrina, 2006; White House, 2006).

Management Considerations in Disaster Response

U.S. disaster researchers have identified two contrasting approaches to disaster response management, commonly termed the “command-and-control” and the “emergent human resources,” or “problem-solving,” models. The command-and-control model equates preparedness and response activities with military exercises. It assumes that (1) government agencies and other responders must be prepared to take over management and control in disaster situations, both because they are uniquely qualified to do so and because members of the public will be overwhelmed and will likely engage in various types of problematic behavior, such as panic; (2) disaster response activities are best carried out through centralized direction, control, and decision making; and (3) for response activities to be effective, a single person is ideally in charge, and relations among responding entities are arranged hierarchically.

In contrast, the emergent human resources, or problem-solving, model is based on the assumption that communities and societies are resilient and resourceful and that even in areas that are very hard hit by disasters, considerable local response capacity is likely to remain. Another underlying assumption is that preparedness strategies should build on existing community institutions and support systems—for example by pre-identifying existing groups, organizations, and institutions that are capable of assuming leadership when a disaster strikes. Again, this approach argues against

highly specialized approaches that tend to result in “stovepiped” rather than well-integrated preparedness and response efforts. The model also recognizes that when a disaster occurs, responding entities must be flexible if they are to be effective and that flexibility is best achieved through a decentralized response structure that seeks to solve problems as they arise, as opposed to top-down decision making. (For more extensive discussions of these two models and their implications, see Dynes, 1993, 1994; Kreps and Bosworth, forthcoming.)

Empirical research, much of which has been carried out with NEHRP support, finds essentially no support for the command-and-control model either as a heuristic device for conceptualizing the disaster management process or as a strategy employed in actual disasters. Instead, as suggested in the discussion above on EMONs, disaster response activities in the United States correspond much more closely to the emergent resources or problem-solving model. More specifically, such responses are characterized by decentralized, rather than centralized, decision making; by collaborative relationships among organizations and levels of government, rather than hierarchical ones; and, perhaps most important, by considerable emergence—that is, the often rapid appearance of novel and unplanned-for activities, roles, groups, and relationships. Other hallmarks of disaster responses include their fluidity and hence the fast pace at which decisions must be made; the predominance of the EMON as the organizational form most involved in carrying out response activities; the wide array of improvisational strategies that are employed to deal with problems as they manifest themselves; and the importance of local knowledge and situation-specific information in gauging appropriate response strategies. (For empirical research supporting these points, see Drabek et al., 1982; Stallings and Quarantelli, 1985; Kreps, 1985, 1989b; Bosworth and Kreps, 1986; Kreps and Bosworth, 1993; Aguirre et al., 1995; Drabek and McEntire, 2002; Waugh and Sylves, 2002; Webb, 2002; Drabek, 2003; Tierney, 2003; Tierney and Trainor, 2004; Wachtendorf, 2004.)

NEW WAYS OF FRAMING DISASTER MANAGEMENT CHALLENGES: DEALING WITH COMPLEXITY AND ACCOMMODATING EMERGENCE

Advancements brought about through NEHRP research include new frameworks for conceptualizing responses to extreme events. In Shared Risk: Complex Systems in Seismic Response , a NEHRP-supported comparative study of organized responses to 11 different earthquake events, Comfort argues that the major challenge facing response systems is to use information in ways that enhance organizational and interorganizational learning and develop ways of “integrating both technical and organiza-

tional components in a socio-technical system to support timely, informed collective action” (Comfort, 1999:14). Accordingly, effective responses depend on the ability of organizations to simultaneously sustain structure and allow for flexibility in the face of rapidly changing disaster conditions and unexpected demands. Response networks must also be able to accommodate processes of self-organization —that is, organized action by volunteers and emergent groups. This approach again contrasts with command-and-control notions of how major crises are managed (Comfort, 1999:263-264):

A socio-technical approach requires a shift in the conception of response systems as reactive, command-and-control driven systems to one of inquiring systems , activated by processes of inquiry, validation, and creative self-organization…. Combining technical with organizational systems appropriately enables communities to face complex events more effectively by monitoring changing conditions and adapting its performance accordingly, increasing the efficiency of its use of limited resources. It links human capacity to learn with the technical means to support that capacity in complex, dynamic environments [emphasis added].

Similarly, research stressing the importance of EMONs as the predominant organizational form during crisis response periods points to the importance of improving strategies for network management and of developing better methods to take advantage of emergent structures and activities during disasters. Planning and management approaches must, in other words, support rather than interfere with the open and dynamic qualities of disaster response activities. Indicators of improved capacity to manage emergent networks could include the diversity of organizations and community sectors involved in pre-crisis planning; plans and agreements facilitating the incorporation of the voluntary sector and emergent citizen groups into response activities; plans and tools enabling the rapid expansion of crisis communication and information-sharing networks during disasters to include new organizations; and protocols, such as mutual aid agreements, making it possible for new actors to more easily join response networks (Tierney and Trainor, 2004).

In the wake of the Katrina disaster, the need for disaster management by command-and-control-oriented entities has once again achieved prominence. For example, calls have increased for greater involvement on the part of the military in domestic disaster management. Such recommendations are not new. Giving a larger role in disaster management to the military was an idea that was considered—and rejected—following Hurricane Andrew (National Academy of Public Administration, 1993). Post-Katrina debates on needed policy and programmatic changes will likely continue to focus on how to most effectively deploy military assets while ensuring that disaster management remains the responsibility of civilian institutions.

Additional Considerations: Do Responses to Natural, Technological, and Human-Induced Events Differ?

One issue that has come to the fore with the emergence of terrorism as a major threat involves the extent to which findings from the field of disaster research can predict responses to human-induced extreme events. Although some take the position that terrorism and bioterrorism constitute such unique threats that behavioral and organizational responses in such events will differ from what has been documented for other types of extreme events, others contend that this assumption is not borne out by social science disaster research.

The preponderance of evidence seems to suggest that there is more similarity than difference in response behaviors across different types of disaster agents. Regarding the potential for panic, for example, there is no empirical evidence that panic was a problem during the influenza pandemic of 1918, among populations under attack during World War II (Janis, 1951), in catastrophic structure fires and crowd crushes (Johnson, 1987; Johnson et al., 1994; Feinberg and Johnson, 2001), or in the Chernobyl nuclear disaster (Medvedev, 1990). Nor was panic a factor in the 1993 bombing of the World Trade Center (Aguirre et al., 1998), the 1995 Tokyo subway sarin attack (Murakami, 2000), or the terrorist attacks of September 11, 2001 (NIST, 2005; National Commission on Terrorist Attacks upon the United States, 2004). The failure to find significant evidence of panic across a wide range of crisis events is a testimony to the resilience of social relationships and normative practices, even under conditions of extreme peril.

Similarly, as noted earlier, research findings on challenges related to risk communication and warning the public of impending extreme events are also quite consistent across different types of disaster events. For individuals and groups, there are invariably challenges associated with understanding what self-protective actions are required for different types of emergencies, regardless of their origin.

In all types of disasters, organizations must likewise face a common set of challenges associated with situation assessment, the management of primary and secondary impacts, communicating with one another and with the public, and dealing with response-related demands. The need for more effective communication, coordination, planning, and training transcends hazard type. Although recent government initiatives such as the National Response Plan will result in the incorporation of new organizational actors into response systems for extreme events, most of the same local-, state-, and federal-level organizations will still be involved in managing extreme events of all types, employing common management frameworks such as

the Incident Command System and now the National Incident Management System (NIMS).

Social scientific studies on disasters have long shown that general features of extreme events, such as geographic scope and scale, impact severity, and speed of onset, combined with the overall quality of pre-disaster preparedness, have a greater influence on response patterns than do the specific hazard agents that trigger response activities. Regardless of their origins, very large, near-catastrophic, and catastrophic events all place high levels of stress on response systems.

In sum, social science disaster research finds little justification for the notion that individual, group, and community responses to human-induced extreme events, including those triggered by weapons of mass terror, will differ in important ways from those that have been documented in natural and technological disasters. Instead, research highlights the importance of a variety of general factors that affect the quality and effectiveness of responses to disasters, irrespective of the hazard in question. With respect to warning the public and encouraging self-protective action, for example, warning systems must be well designed and warning messages must meet certain criteria for effectiveness, regardless of what type of warning is issued. Members of the public must receive, understand, and personalize warning information; must understand what actions they need to take in order to protect themselves; and must be able to carry out those actions, again regardless of the peril in question. Community residents must feel that they can trust their leaders and community institutions during crises of all types. For organizations, training and exercises and effective mechanisms for interorganizational communication and coordination are critical for community-wide emergencies of all types. When such criteria are not met, response-related problems can be expected regardless of whether the emergency stems from a naturally occurring event, a technological accident, or an intentional act.

Individual and group responses, as well as organizational response challenges, are thus likely to be consistent across different types of crises. At the same time, however, it is clear that there are significant variations in the behavior of responding institutions (as opposed to individuals, groups, and first responders) according to event type. In most technological disasters, along with the need to help those affected, questions of negligence and liability typically come to the fore, and efforts are made to assign blame and make responsible parties accountable. In terrorist events, damaged areas are always treated as crime scenes, and the response involves intense efforts both to care for victims and to identify and capture the perpetrators. Further, although as noted earlier, scapegoating can occur in disasters of all types, the tendency for both institutions and the public to assign blame to

particular groups may be greater in technological and terrorism-related crises than in natural disasters. 4

Finally, with respect to responses on the part of the public, even though evidence to the contrary is strong, the idea that some future homeland security emergencies could engender responses different from those observed in past natural, technological, and intentional disasters cannot be ruled out entirely. The concluding section of this chapter highlights the need for further research in this area.

Research on Disaster Recovery

Like hazards and disaster research generally, NERHRP-sponsored research has tended to focus much more on preparedness and response than on either mitigation or disaster recovery. This is especially the case with respect to long-term recovery, a topic that despite its importance has received very little emphasis in the literature. However, even though the topic has not been well studied, NEHRP-funded projects have done a great deal to advance social science understanding of disaster recovery. As discussed later in this section, they have also led to the development of decision tools and guidance that can be used to facilitate the recovery process for affected social units.

It is not an exaggeration to say that prior to NEHRP, relatively little was known about disaster recovery processes and outcomes at different levels of analysis. Researchers had concentrated to some degree on analyzing the impacts of a few earthquakes, such as the 1964 Alaska and 1971 San Fernando events, as well as earthquakes and other major disasters outside the United States. Generally speaking, however, research on recovery was quite sparse. Equally important, earlier research oversimplified the recovery process in a variety of ways. First, there was a tendency to equate recovery, which is a social process, with reconstruction, which involves restoration and replacement of the built environment. Second, there was an assumption that disasters and their impacts proceed in a temporal, stage-like fashion, with “recovery” following once “response” activities have

been concluded. 5 Earlier research also underemphasized the extent to which recovery may be experienced differently by different sectors and subpopulations within society. Some of these problems were related to the fact that at a more abstract level, earlier work had not sufficiently explored the concept of recovery itself—for example, whether recovery should be equated with a return to pre-disaster circumstances and social and economic activities, with the creation of a “new normal” that involves some degree of social transformation, or with improvements in community sustainability and long-term disaster loss reduction. Since the inception of NEHRP and in large measure because of NEHRP sponsorship, research has moved in the direction of a more nuanced understanding of recovery processes and outcomes that has not entirely resolved but at least acknowledges many of these issues.

The sections that follow discuss significant contributions to knowledge and practice that have resulted primarily from NEHRP-sponsored work. Those contributions can be seen (somewhat arbitrarily) as falling into four categories: (1) refinements in definitions and conceptions of disaster recovery, along with a critique and reformulation of stage-like models; (2) contributions to the literature on recovery processes and outcomes across different social units; (3) the development of empirically based models to estimate losses, anticipate recovery challenges, and guide decision making; and (4) efforts to link disaster recovery with broader ideas concerning long-term sustainability and environmental management.

Conceptual Clarification. Owing in large measure to NEHRP-sponsored efforts, the disaster field has moved beyond equating recovery with reconstruction or the restoration of the built environment. More usefully, research has moved in the direction of making analytic distinctions among different types of disaster impacts, recovery activities undertaken by and affecting different social units , and recovery outcomes. Although disaster impacts can be positive or negative, research generally tends to focus on various negative impacts occurring at different levels of analysis. As outlined in Chapter 3 , these impacts include effects on the physical and built environment, including residential, commercial, and infrastructure damage as well as disaster-induced damage to the environment; other property losses; deaths and injuries; impacts on social and economic activity; effects at the community level, such as impacts on community cohesiveness and urban

form; and psychological, psychosocial, and political impacts. Such impacts can vary in severity and duration, as well as in the extent to which they are addressed effectively during the recovery process. An emphasis on recovery as a multidimensional concept calls attention to the fact that physical and social impacts, recovery trajectories, and short- and longer-term outcomes in chronological and social time can vary considerably across social units.

Recovery activities constitute measures that are intended to remedy negative disaster impacts, restore social units as much as possible to their pre-disaster levels of functioning, enhance resilience, and ideally, realize other objectives such as the mitigation of future disaster losses and improvements in the built environment, quality of life, and long-term sustainability. 6 Recovery activities include the provision of temporary and replacement housing; the provision of resources (government aid, insurance payment, private donations) to assist households and businesses with replacement of lost goods and with reconstruction; the provision of various forms of aid and assistance to affected government units; the development and implementation of reconstruction and recovery plans in the aftermath of disasters; coping mechanisms developed by households, businesses, and other affected social units; the provision of mental health and other human services to victims; and other activities designed to overcome negative disaster impacts. In some circumstances, recovery activities can also include the adoption of new policies, legislation, and practices designed to reduce the impacts of future disasters.

Recovery processes are significantly influenced by differential societal and group vulnerability; by variations in the range of recovery aid and support that is available; and by the quality and effectiveness of the help that is provided. The available “mix” of recovery activities and post-disaster coping strategies varies across groups, societies, and different types of disasters. For example, insurance is an important component in the reconstruction and recovery process for some societies, some groups within society, and some types of disasters, but not for others.

Recovery outcomes —or the extent to which the recovery activities are judged, either objectively or subjectively, as “complete” or “successful”—also show wide variation across societies, communities, social units, and disaster events. Outcomes can be assessed in both the short and the longer terms, although, as noted earlier, the literature is weak with respect to empirical studies on the outcomes of longer-term disasters. Additionally,

outcomes consist not only of the intended effects of recovery programs and activities, but also of their unintended consequences. For example, the provision of government assistance or insurance payments to homeowners may make it possible for them to rebuild and continue to live in hazardous areas, even though such an outcome was never intended.

Keeping in mind the multidimensional nature of recovery, post-disaster outcomes can be judged as satisfactory along some dimensions, or at particular points in time, but unsatisfactory along others. Outcomes are perceived and experienced differently, when such factors as level of analysis and specific recovery activities of interest are taken into account. With respect to units of aggregation, for example, while a given disaster may have few discernible long-term effects when analyzed at the community level, the same disaster may well be economically, socially, and psychologically catastrophic for hard-hit households and businesses. A community may be considered “recovered” on the basis of objective social or economic indicators, while constituent social units may not be faring as well, in either objective or subjective terms. The degree to which recovery has taken place is thus very much a matter of perspective and social position.

In a related vein, research has also led to a reconsideration of linear conceptions of the recovery process. Past research tended to see disaster events as progressing from the pre-impact period through post-impact emergency response, and later recovery. In a classic work in this genre— Reconstruction Following Disaster (Haas et al., 1977:xxvi), for example—the authors argued that disaster recovery is “ordered, knowable, and predictable.” Recovery was characterized as consisting of four sequential stages that may overlap to some degree: the emergency period; the restoration period; the replacement reconstruction period; and the commemorative, betterment, and developmental reconstruction period. In this and other studies, the beginning of the recovery phase was generally demarcated by the cessation of immediate life saving and emergency care measures, the resumption of activities of daily life (e.g., opening of schools), and the initiation of rebuilding plans and activities. After a period of time, early recovery activities, such as the provision of temporary housing, would give way to longer-term measures that were meant to be permanent. Kates and Pijawka’s (1977) frequently cited four-phase model begins with the emergency period, lasting for a few days up to a few weeks, and encompassing the period when the emergency operations plan (EOP) is put into operation. Next comes the restoration period—when repairs to utilities are made; debris is removed; evacuees return; and commercial, industrial, and residential structures are repaired. The third phase, the reconstruction replacement period, involves rebuilding capital stocks and getting the economy back to pre-disaster levels. This period can take some years. Finally, there is the development phase, when commemorative structures are built, memo-

rial dates are institutionalized in social time, and attempts are made to improve the community.

In another stage-like model focusing on the community level, Alexander (1993) identified three stages in the process of disaster recovery. First, the rehabilitation stage involves the continuing care of victims and frequently is accompanied by the reemergence of preexisting problems at the household or community level. During the temporary reconstruction stage, prefabricated housing or other temporary structures go up, and temporary bracing may be installed for buildings and bridges. Finally, the permanent reconstruction stage was seen as requiring good administration and management to achieve full community recovery.

Later work sees delineations among disaster phases as much less clear, showing, for example, that decisions and actions that affect recovery may be undertaken as early as the first days or even hours after the disaster’s impact—and, importantly, even before a disaster occurs. The idea that recovery proceeds in an orderly, stage-like, and unitary manner has been replaced by a view that recognizes that the path to recovery is often quite uneven. While the concept of disaster phases may be a useful heuristic device for researchers and practitioners, the concept may also mask both how phases overlap and how recovery proceeds differently for different social groups (Neal, 1997). Recovery does not occur at the same pace for all who are affected by disasters or for all types of impacts. With respect to housing, for example, owing to differences in the availability of services and financing as well as other factors, some groups within a disaster-stricken population may remain in “temporary housing” for a very long time—so long, in fact, that those housing arrangements become permanent—while others may move rapidly into replacement housing (Bolin, 1993a). Put another way, as indicated in Chapters 1 and 3 , while stage-like approaches to disasters are framed in terms of chronological time, for those who experience them, disasters unfold in social time.

Researchers studying recovery continue to contend with a legacy of conceptual and measurement difficulties. One such difficulty centers on the question of how the dependent variable should be measured. This problem itself is multifaceted. Should recovery be defined as a return to pre-disaster levels of psychological, social, and economic well-being? As a return to where a community, business, or household would have been were it not for the occurrence of the disaster? The study of disaster recovery also tends to overlap with research on broader processes of social change. Thus, in addition to focusing on what was lost or affected as a consequence of disaster events and on outcomes relative to those impacts, recovery research also focuses on more general post-disaster issues, such as the extent to which disasters influence and interact with ongoing processes of social change, whether disaster impacts can be distinguished from those resulting

from broader social and economic trends, whether disasters simply magnify and accelerate those trends or exert an independent influence, and the extent to which the post-disaster recovery period represents continuity or discontinuity with the past. Seen in this light, the study of recovery can become indistinguishable from the study of longer-term social change affecting communities and societies. While these distinctions are often blurred, it is nevertheless important to differentiate conceptually and empirically between the recovery process, specific recovery outcomes of interest, and the wide range of other changes that might take place following (or as a consequence of) disasters.

Analyzing Impacts and Recovery Across Different Social Units. Following from the discussions above, it is useful to keep in mind several points about research on disaster recovery. First, studies differ in the extent to which they emphasize the objective, physical aspects of recovery—restoration and reconstruction of the built environment—or subjective, psychosocial, and experiential ones. Second, studies generally focus on particular units of analysis and outcomes, such as household, business, economic, or community recovery, rather than on how these different aspects of recovery are interrelated. This is due partly to the fact that researchers tend to specialize in particular types of disaster impacts and aspects of recovery, which has both advantages and disadvantages. While allowing for the development of in-depth research expertise, such specialization has also made it more difficult to formulate more general theories of recovery. Third, the literature is quite uneven. Some aspects of recovery are well understood, while there are others about which very little is known.

Even with these limitations, more general theoretical insights about recovery processes and outcomes have begun to emerge. Key among these is the idea that disaster impacts and recovery can be conceptualized in terms of vulnerability and resilience . As noted in Chapters 2 and 3 , vulnerability is a consequence not only of physical location and the “hazardousness of place,” but also of social location and of societal processes that advantage some groups and individuals while marginalizing others. The notion of vulnerability applies both to the likelihood of experiencing negative impacts from disasters, such as being killed or injured or losing one’s home or job, and to the likelihood of experiencing recovery-related difficulties, such as problems with access to services and other forms of support. Social vulnerability is linked to broader trends within society, such as demographic trends (migration to more hazardous areas, the aging of the U.S. population) and population diversity (race, class, income, and linguistic diversity). Similarly, resilience , or the ability to survive and cope with disaster impacts and rebound after those events, is also determined in large measure by social factors. According to Rose (2004), resilience can be conceptualized

as both inherent and adaptive, where the former term refers to resilience that is based on resources and options for action that are typically available during nondisaster times, and the latter refers to the ability to mobilize resources and create new options following disasters. 7 As discussed in Chapter 6 , resilience stems in part from factors commonly associated with the concept of social capital, such as the extensiveness of social networks, civic engagement, and interpersonal, interorganizational, and institutional trust. (For an influential formulation setting out the vulnerability perspective, see Blaikie et al., 1994). As subsequent discussions show, the concepts of vulnerability and resilience are applicable to individuals, households, groups, organizations, economies, and entire societies affected by disasters. The sections that follow, which are organized according to unit of analysis, discuss psychosocial impacts and recovery; impacts and recovery processes for housing and businesses; economic recovery; and community-level and societal recovery.

Psychological Impacts and Recovery. There is no disagreement among researchers that disasters cause genuine pain and suffering and that they can be deeply distressing for those who experience them. Apart from that consensus, however, there have been many debates and disputes regarding the psychological and psychosocial impacts of disasters. One such debate centers on the extent to which disasters produce clinically significant symptoms of psychological distress and, if so, how long such symptoms last. Researchers have also struggled with the questions of etiology, or the causes of disaster-related psychological reactions. Are such problems the direct result of trauma experienced during disaster, the result of disaster-induced stresses, a reflection of a lack of coping capacity or weak social support networks, a function of preexisting vulnerabilities, or a combination of all these factors? Related concerns center on what constitute appropriate forms of intervention and service delivery strategies for disaster-related psychological problems. Do people who experience problems generally recover on their own, without the need for formally provided assistance, or does such assistance facilitate more rapid and complete recovery? What types of assistance are likely to be most efficacious and for what types of problems?

Research has yielded a wide array of findings on questions involving disaster-related psychological and psychosocial impacts and recovery. Findings tend to differ depending upon disaster type and severity, how disaster victimization is defined and measured, how mental health outcomes are measured, the research methodologies and strategies used (e.g., sampling,

timing, variables of interest), and not inconsequentially, the discipline-based theoretical perspectives employed (Tierney, 2000). With respect to the controversial topic of post-traumatic stress disorder (PTSD), for example, well-designed epidemiological studies have estimated the lifetime prevalence of PTSD at around 5.4 percent in the U.S. population. An important epidemiologic study on the incidence of trauma and the subsequent risk of developing PTSD after various types of traumatic events estimates the risk at about 3.8 percent for natural disasters (Breslau et al., 1998; Kessler and Zhao, 1999). NEHRP-sponsored surveys following recent earthquakes in California found PTSD to be extremely rare among affected populations and not significantly associated with earthquake impacts (Seigel et al., 2000). Other studies show immense variation, with estimates of post-disaster PTSD ranging from very low to greater than 50 percent. Such variations could reflect real differences in the traumatic effects of different events, but it is equally likely that they are the result of methodological, measurement, and theoretical differences among investigators.

One key debate centers on the clinical significance of post-disaster emotional and mental health problems. Research is clear on the point that it is not unusual for disaster victims to experience a series of problems, such as headaches, problems with sleeping and eating, and heightened levels of concern and anxiety, that can vary in severity and duration (Rubonis and Bickman, 1991; Freedy et al., 1994). Perspectives begin to diverge, however, on the extent to which these and other disaster-induced symptoms constitute mental health problems in the clinical sense. In other words, would disaster victims, presenting their symptoms, be considered candidates for mental health counseling or medication if those symptoms were present in a nondisaster context? Do their symptoms correspond to survey based or clinically based measures of what constitutes a “case” for psychiatric diagnostic purposes? Again, as with PTSD, findings differ. While noting that many studies do document a rise in psychological distress following disasters, Shoaf et al. (2004:320) conclude that “those impacts are not of a nature that would significantly increase the rates of diagnosable mental illness.” With respect to severe psychological impacts, these researchers found that suicide rates declined in Los Angeles County following the Northridge earthquake—a continuation of a trend that had already begun before that event. They also note that these findings are consistent with research on suicide following the Kobe earthquake, which showed that the suicide rate in the year following that quake was less than the average rate for the previous 10 years (Shoaf et al., 2004). Yet many researchers and practitioners rightly contend that psychosocial interventions are necessary following disasters, both to address clinically significant symptoms and to prevent more serious psychological sequelae.

There is also the question of whether some types of disasters are more

likely than others to cause negative psychological impacts. Some researchers argue that certain types of technological hazards, such as nuclear threats and chronic exposures to toxic substances, are more pernicious in their effects than natural disasters because they persist longer and create more anxiety among potential victims, and especially because they tend to result in community conflict, causing “corrosive” rather than “therapeutic” communities to develop (Erikson, 1994). Events such as the Oklahoma City bombing, the Columbine school shootings, and the events of September 11, 2001 lead to questions about whether intentional attacks engender psychological reactions that are distinctive and different from those that follow other types of community crisis events. Some studies have suggested that the psychological impacts of terrorist attacks are profound, at least in the short term (North et al., 1999). Other research, focusing specifically on the short-term impacts of the September 11, 2001 terrorist attacks, indicates that the psychological impacts resulting from the events of 9/11 “are consistent with prior estimates of the impact of natural disasters and other terrorist events” (Miller and Heldring, 2004:21). Again, drawing conclusions about the relative influence of agent characteristics—as opposed to other factors—is difficult because studies vary so much in their timing, research designs, methodological approaches, and procedures for defining disaster victimization.

Another set of issues concerns factors associated with risk for poor psychological outcomes. Perilla et al. (2002) suggest that such outcomes can vary as a consequence of both differential exposure and differential vulnerability to extreme events. With respect to differential exposure, factors such as ethnicity and social class can be associated with living in substandard and vulnerable housing, subsequently exposing minorities and poor people to greater losses and disaster-related trauma. Regarding differential vulnerability, minorities and the poor, who are more vulnerable to psychosocial stress during nondisaster times, may also have fewer coping resources upon which to draw following disasters.

In a comprehensive and rigorous review of research on the psychological sequelae of disasters, Fran H. Norris and her colleagues (Norris et al., 2002a,b) carried out a meta-analysis of 20 years of research, based on 160 samples containing more than 60,000 individuals who had experienced 102 different disaster events. These data sets included a range of different types of surveys on both U.S. disaster victims and individuals in other countries, on various subpopulations, and on disasters that differed widely in type and severity. Impacts documented in these studies included symptoms of post-traumatic stress, depression, and anxiety; other forms of nonspecific distress not easily related to specific syndromes such as PTSD; health problems and somatic complaints; problems in living, including secondary stressors such as work-related and financial problems; and “psychosocial resource

loss,” a term that refers to negative effects on coping capacity, self-esteem, feelings of self-efficacy, and other attributes that buffer the effects of stress. According to their interpretation, which was based on accepted methods for rating indicators of psychological distress, the symptoms reported by as many as 39 percent of those studied reached clinically significant levels. However—and this is an important caveat—they found negative psychological effects to be much more prevalent in disasters occurring outside the United States. Generally, symptoms were most severe in the year following disaster events and declined over time.

Norris et al. (2002a, 2002b) classified U.S. disasters as low, moderate, and high in their psychosocial impacts, based on empirical data on post-disaster distress. The Loma Prieta and Northridge earthquakes were seen as having relatively few adverse impacts, and Hurricane Hugo and Three Mile Island were classified as moderate in their effects. Hurricane Andrew, the Exxon oil spill, and the Oklahoma City bombing were classified as severe with respect to their psychological impacts. As these examples suggest, the researchers found no evidence that natural, technological, and human-induced disasters necessarily differ in their effects.

This research review uncovered a number of vulnerability and protective factors that were associated with differential psychological outcomes following disasters. Broadly categorized, those risk factors most consistently shown to be negatively associated with post-disaster psychological well-being include severity of disaster exposure at both the individual and the community levels; being female; being a member of an ethnic minority; low socioeconomic status; experiencing other stressors or chronic stress; having had other mental health problems prior to the disaster; employing inappropriate coping strategies (e.g., withdrawal, avoidance); and reporting problems with both perceived and actual social support.

Overall, these findings are very consistent with perspectives in disaster research that emphasize the relationship between systemically induced vulnerability, negative disaster impacts, lower resilience, and poor recovery outcomes. Recent research situates disasters within the context of other types of stressful events (e.g., death of a loved one or other painful losses) that disproportionately affect those who are most vulnerable and least able to cope. At the same time, studies—many conducted under NEHRP auspices—show how social inequality and vulnerability both amplify the stress that results directly from disasters and complicate the recovery process over the longer term. For example, Fothergill (1996, 1998, 2004) and Enarson and Morrow (1998) have documented the ways in which gender is associated both with the likelihood of becoming a disaster victim and with a variety of subsequent post-disaster stressors. Peacock et al. (1997) and Bolin and Stanford (1998) have shown how pre-disaster conditions such as income disparities and racial and ethnic discrimination contribute both to

disaster losses and to subsequent psychosocial stress and make recovery more difficult for vulnerable groups. Perilla et al. (2002), who studied ethnic differences in post-traumatic stress following Hurricane Andrew, also note that ethnicity can be associated with variations in personality characteristics such as fatalism, which tends to be associated with poor psychosocial outcomes resulting from stressful events, as well as with additional stresses associated with acculturation. 8

Hurricane Katrina represents a critical test case for theories and research on psychosocial vulnerability and resilience. If, as Norris and her collaborators indicate, Hurricane Andrew resulted in relatively high levels of psychosocial distress, what will researchers find with respect to Katrina? For many victims, Katrina appears to contain all of the ingredients necessary to produce negative mental health outcomes: massive, catastrophic impacts; high property losses resulting in financial distress; exposure to traumas such as prolonged physical stress and contact with dead and dying victims; disruption of social networks; massive failures in service delivery systems; continual uncertainty about the future; and residential dislocation on a scale never seen in a U.S. disaster. Over time, research will result in important insights regarding the psychosocial dimensions of truly catastrophic disaster events.

Household Impacts and Recovery. Within the disaster recovery area, households and household recovery have been studied most often, with a significant proportion of that work focusing on post-earthquake recovery issues. Although this line of research predates NEHRP, many later studies have been undertaken with NEHRP support. Studies conducted prior to NEHRP include Bolin’s research on household recovery processes following the Managua earthquake and the Rapid City flood, both of which occurred in 1972 (Bolin, 1976). Drabek and Key and their collaborators had also examined disaster impacts on families and the household recover process (Drabek et al., 1975; Drabek and Key, 1976, 1984). With NEHRP support, Bolin and Bolton studied household recovery following tornadoes in Wichita Falls, Vernon, and Paris, Texas; a hurricane in Hawaii; flooding in Salt Lake City; and the Coalinga earthquake (Bolin, 1982; Bolin and Bolton, 1986). Bolin’s monograph Household and Community Recovery after

Earthquakes was based on research on the 1987 Whittier Narrows and 1989 Loma Prieta events (Bolin, 1993b). Households have also been the focus of more recent studies on the impacts of Hurricane Andrew (Peacock et al., 1997) and the 1994 Northridge earthquake (Bolin and Stanford, 1998). Other NEHRP-sponsored work has focused more specifically on issues that are important for household recovery, such as post-disaster sheltering processes (Phillips, 1993, 1998) and housing impacts and recovery (Comerio, 1997, 1998). As Bolin (1993a:13) observes

[d]isasters can have a multiplicity of effects on a household, including physical losses to property, injury and/or death, loss of job or livelihood, disruption of social and personal relations, relocation of some or all members of a family, physical disruption or transformation of community and neighborhood, and increased household indebtedness.

Accordingly, the literature has explored various dimensions of household impacts and recovery, including direct impacts such as those highlighted by Bolin; changes in the quality and cohesiveness of relationships among household members; post-disaster problems such as conflict and domestic violence; stressors that affect households during the recovery process; and coping strategies employed by households, including the use of both formal and informal sources of post-disaster support and recovery aid.

The literature also points to a number of factors that are associated with differences in short- and longer-term household recovery outcomes. Housing supply is one such factor—as indicated, for example, by housing costs, other real estate market characteristics, and rental vacancy rates Temporary housing options are affected by such factors as the proximity of friends and relatives with whom to stay, although use of this housing option is generally only a short-term strategy. Extended family members may not be able to help if they also are victims (Morrow, 1997). Such problems may be more prevalent in lower-income groups that have few alternative resources and when most members of an extended family live in the same affected community.

Availability of temporary and permanent housing generally is limited by their pre-impact supply in and near the impact area. In the U.S., in situations in which there is an insufficient supply of housing for displaced disaster victims, FEMA provides mobile homes, but even this expedient method of expanding the housing stock takes time. Even when houses are only moderately damaged, loss of housing functionality may be a problem if there is massive disruption of infrastructure. In such cases, tent cities may be necessary if undamaged housing is beyond commuting range (e.g., Homestead, Florida after Hurricane Andrew, as discussed in Peacock et al., 1997).

In the longer term, household recovery is influenced by such factors as household financial resources, the ability to obtain assistance from friends and relatives, insurance coverage, and the mix of housing assistance pro-

grams available to households. Typically, access to and adequacy of recovery resources are inversely related to socioeconomic status. Those with higher incomes are more likely to own their own homes, to be adequately insured, and to have savings and other financial resources on which to draw in order to recover—although disasters can also cause even better-off households to take on additional debt. With respect to formal sources of aid, the assistance process generally favors those who are adept at responding to bureaucratic requirements and who are able to invest time and effort to seek out sources of aid. The aid process also favors those living in more conventional, nuclear family living arrangements, as opposed to extended families or multiple households occupying the same dwelling unit (Morrow, 1997). Recovery may be particularly difficult for single-parent households, especially those headed by women (Enarson and Morrow, 1998; Fothergill, 2004).

The picture that emerges from research on household recovery is not that of a predictable and stage-like process that is common to all households, but rather of a multiplicity of recovery trajectories that are shaped not only by the physical impacts of disaster but also by axes of stratification that include income, race, and ethnicity, as well as such factors as the availability of and access to different forms of monetary aid, other types of assistance, and informal social support—which are themselves associated with stratification and diversity. Disaster severity matters, both because disasters that produce major and widespread impacts can limit recovery options for households and because they tend to be more damaging to the social fabric of the community. As Comerio’s extensive research on housing impacts and issues following earthquakes and other disasters in different societal contexts illustrates, household recovery processes are also shaped by societal-level policy and institutional factors—which themselves have differential impacts (Comerio, 1998). 9

Large-Scale Comparative Research on Household Recovery. Although there is clearly a need for such research, few studies exist that compare household recovery processes and outcomes across communities and disaster events. With NEHRP funding, Frederick Bates and his colleagues carried out what may well be the largest research efforts of this kind: a multicommunity

longitudinal study on household and community impacts and recovery after the 1976 Guatemala earthquake and a cross-national comparative study on household recovery following six different disaster events. The Guatemala study, designed as a quasi-experiment, included households in 26 communities that were carefully selected to reflect differences in the severity of earthquake impacts, size, population composition, and region of the country. That study focused on a broad spectrum of topics, including changes over time in household composition and characteristics; household economic activity; housing characteristics and standards of living; household experiences with relief and reconstruction assistance; and fertility, health, and nutrition. Never replicated for any other type of disaster, the study provided detailed information on these topics, focusing in particular on how different forms of aid provision either facilitated or hampered household recovery (for detailed discussions, see Bates, 1982; Hoover and Bates, 1985; Bates et al., 1979).

The second study carried out by Bates and his colleagues extended methods developed to assess household recovery following the Guatemala earthquake to measure household recovery in disaster-stricken communities in six different countries. The tool used to measure disaster impacts and household recovery across different events and societies, the Domestic Assets Scale, made possible systematic comparisons with respect to one dimension of household recovery—the restoration of household possessions, tools, and technologies (Bates and Peacock, 1992, 1993).

Vulnerability, Resilience, and Household Recovery. Like the other aspects of recovery discussed here, what happens to households during and after disasters can be conceptualized in terms of vulnerability and resilience. With respect to vulnerability, social location is associated with the severity of disaster impacts for households. Poverty often forces people to live in substandard or highly vulnerable housing—manufactured housing is one example—leaving them more vulnerable to death, injury, and homelessness. As discussed in Chapter 3 with respect to disaster preparedness, factors such as income, education, and homeownership influence the ability of households to mitigate and prepare for disasters. Social-structural factors also affect the extent to which families can accumulate assets in order to achieve higher levels of safety, as well as their recovery options and access to resources after disasters strike—for example the forms of recovery assistance for which they are eligible. Households are thus differentially exposed to disasters, differentially vulnerable during the recovery period, and diverse in terms of both inherent and adaptive resilience.

ECONOMIC AND BUSINESS IMPACTS AND RECOVERY: THE CHALLENGE OF ASSESSING DISASTER LOSSES

As discussed in Chapter 3 , assessing how much disasters cost the nation and its communities has proven to be a major challenge. A National Research Council (NRC, 1999c) study concluded that such calculations are difficult in part because different agencies and entities calculate costs and losses differently. Moreover, no universally accepted standards exist for calculating economic impacts resulting from disasters, and there is no single agency responsible for keeping track of disaster losses. For any given disaster event, assessments of economic impacts may vary widely depending on which statistics are used—for example, direct or insured losses versus total losses.

NEHRP-sponsored research has addressed these problems to some degree. For example, as part of the NEHRP-sponsored “Second Assessment of Research on Natural Hazards,” researchers attempted to estimates losses, costs, and other impacts from a wide array of natural and technological hazards. 10 For the 20 year period 1975–1994, they estimated that dollar losses from disasters amounted to $.5 billion per week, with climatological hazards accounting for about 80 percent of those losses; since 1989, losses have totaled $1 billion per week (Mileti, 1999a). Through work undertaken as part of the Second Assessment, data on losses from natural hazard events from the mid-1970s to 2000 are now available at the county level in geocoded form for the entire United States through the Spatial Hazard Events and Losses Database for the United States (SHELDUS). This data collection and database development effort has made it possible to analyze different types of losses, at different scales, using different metrics, and to assess locations in terms of their hazard proneness and loss histories. (For discussions of the data used in the SHELDUS database and associated challenges see Cutter, 2001.) What is still lacking is a national program to continue systematically collecting and analyzing impact and loss data.

Studies on economic impacts and recovery from earthquakes and other disasters can be classified according to the units of analysis on which they focus. Most research concerns economic losses and recovery at the community or, more frequently, the regional level. A smaller set of studies has analyzed economic impacts and recovery at the firm or facility level. There is even less research documenting national-level and macroeconomic impacts.

Community-Level and Regional Studies

Studies on the economics of natural disasters at the community and regional levels of analysis differ significantly in methods, topics of interest, and conclusions. Some researchers, such as Rossi et al. (1978) and Friesema et al. (1979) have argued that at least in the United States, natural disasters have no discernible social or economic effects at the community level and that nondisaster-related trends have a far more significant influence on long-term outcomes than disasters themselves. This position has also been argued at the macroeconomic level, with respect to other developed and developing countries (Albala-Bertrand, 1993). 11 Dacy and Kunreuther (1969:168) even argued (although more than 30 years ago) that “a disaster may actually turn out to be a blessing in disguise” because disasters create reconstruction booms and allow community improvements to be made rapidly, rather than gradually. However, most research contradicts the idea that disasters constitute economic windfalls, emphasizing instead that economic gains that may be realized at one level (e.g., the community, particular economic sectors) typically constitute losses at another (e.g., the national tax base). One analyst has called the idea that disasters are beneficial economically “one of the most widely held misbeliefs in economics” (DeVoe, 1997:188).

Other researchers take the position that post-disaster economic and social conditions are generally consistent with pre-disaster trends, although disasters may amplify those changes (Bates and Peacock, 1993). Disasters may further marginalize firms and sectors of the economy that were already in decline, or they may speed up processes that were already under way prior to their occurrence. For example, Homestead Air Force Base was already slated for closure before Hurricane Andrew despite ongoing efforts to keep the base opened. When Andrew occurred, the base sustained damage and was closed for good. The closure affected businesses that had depended on the base and helped lead to the exodus of many middle-class families from the area, which in turn affected tax revenues in the impact region. These changes would have taken place eventually, but they were accelerated by Hurricane Andrew.

Related research has analyzed the distributive effects of earthquakes and other disasters. In an early formulation, Cochrane (1975) observed that lower-income groups consistently bear a disproportionate share of disaster losses, relative to higher-income groups. This theme continues to be promi-

nent in the disaster literature; the notion that disasters create economic “winners and losers” has been borne out for both households and businesses (Peacock et al., 1997:Chapter 11; Tierney and Webb, forthcoming).

Another prominent research emphasis at the community and regional levels of analysis has grown out of the need to characterize and quantify the economic impacts of disasters (as well as other impacts) in order to be better able to plan for and mitigate those impacts. A considerable amount of NEHRP research on economic impacts and recovery has been driven by concern about the potentially severe economic consequences of major earthquakes, particularly those that could occur in highly populated urban areas. That concern is reflected in a number of NRC reports (1989, 1992, 1999c) on projected losses and potential economic impacts. Within the private sector, the insurance industry has also committed significant resources in an effort to better anticipate the magnitude of insured losses in future disaster events. (For new developments in research on the management of catastrophic insurance risk, see Grossi et al., 2004.)

Stimulated in large measure by NEHRP funding, new tools have been developed for both pre-disaster estimation of potential losses and post-disaster impact assessments, particularly for earthquakes. HAZUS, the national loss estimation methodology, which was originally developed for earthquakes and which has now been extended to flood and wind hazards, was formulated under FEMA’s supervision with NEHRP funding. NEHRP funds have also supported the development of newer and more sophisticated modeling approaches through research undertaken at earthquake centers sponsored by the National Science Foundation (NSF).

The framework for estimating losses from natural hazards was initially laid out more than 20 years ago in publications such as Petak and Atkisson’s Natural Hazard Risk Assessment and Public Policy (1982) and in applied studies such as the PEPPER (Pre-Earthquake Planning for Post-Earthquake Rebuilding) project (Spangle, 1987), which analyzed potential earthquake impacts and post-disaster recovery strategies for Los Angeles. According to the logic developed in these and other early studies (see, for example, NRC, 1989) and later through extensive NEHRP research, loss estimation consists of the analysis of scenario or probabilistic models that include data on hazards; exposures , or characteristics of the built environment at risk, including buildings and infrastructural systems; fragilities , or estimates of damage likelihood as a function of one or more parameters, such as earthquake shaking intensity; direct losses , such as deaths, injuries, and costs associated with damage; and indirect losses and ripple effects that result from disasters. Within this framework, recent research has focused on further refining loss models and reducing uncertainties associated with both the components of loss estimation models and their interrelationships (for

representative work, see theme issue in Earthquake Spectra, 1997; Tierney et al., 1999; Okuyama and Chang, 2004).

This line of research has led both to advances in basic science knowledge and to a wide range of research applications. At the basic science level, loss modeling research—particularly studies supported through NEHRP—has helped distinguish and clarify relationships among such factors as physical damage, direct economic loss, business interruption effects, and indirect losses and ripple effects. For example, it is now more possible than ever before to disaggregate and analyze separately different types of economic effects and to understand how particular types of damage (e.g., damage to electrical power or transportation systems) contribute to overall economic losses. This research has shed light on factors that contribute to the resilience of regional economies, both during normal times and in response to sudden shocks. It has also shown how the application of newer economic modeling techniques, such as computable general equilibrium modeling and agent-based modeling, constitute improvements over more traditional input-output modeling, particularly for the study of extreme events (for discussions, see Rose et al., 2004; Chang, 2005; Rose and Liao, 2005). Econometric modeling provides another promising approach at both the micro and the regional levels (see West and Lenze, 1994), but this potential remains largely untapped.

At the applications level, loss estimation tools and products have proven useful for raising public awareness of the likely impacts of disaster events and for enhancing community preparedness efforts and mitigation programs. They have also made it possible to assess mitigation alternatives, not only in light of the extent to which those measures reduce damage, but also in terms of their economic costs and benefits. When applied in the disaster context, rapid economic loss estimates have also formed the basis for requests for federal disaster assistance. For the insurance industry, loss models provide important tools to improve risk management decision making, particularly with regard to catastrophic risks.

As noted earlier, loss modeling originally was driven by the need to better understand the economic impacts of earthquakes. In addition to economic losses, earthquake loss models are increasingly taking into account other societal impacts such as deaths, injuries, and residential displacement, as well as secondary effects such as earthquake-induced fires. The methodological approach developed to study earthquakes was first extended to other natural hazards and is now being used increasingly to assess potential impacts from terrorism. The nation is now better able to address the issue of terrorism-related losses because of the investments that had been made earlier for earthquakes and other natural hazards. Significantly, when the Department of Homeland Security decided in 2003 to begin funding

university-based “centers of excellence” for terrorism research, the first topic that was selected for funding was risk and economic modeling for terrorist attacks in the United States. 12 Many of the investigators associated with that center had previously worked on loss modeling for earthquakes.

Business and Facility-Level Impacts and Recovery. Most research on recovery processes and outcomes has focused on households and communities. Prior to the 1990s, most research on the economic aspects of disasters focused not on individual businesses but rather on community-wide and regional impacts. Almost nothing was known about how private sector organizations are affected by and recover from disasters. Since then, a small number of studies have focused on business firms or, in some cases, commercial facilities, as units of analysis. Much of this work, including studies on large, representative samples of businesses, has been carried out with NEHRP support. Business impacts and recovery have been assessed following the Whittier Narrows, Loma Prieta, Northridge, and Kobe earthquakes; the 1993 Midwest floods; Hurricane Andrew; and other flood and hurricane events (for representative studies and findings, see Dahlhamer, 1998; Chang, 2000; Webb et al., 2000; Alesch et al., 2001). Long-term business recovery has been studied in the context of only two disaster events—the Loma Prieta earthquake and Hurricane Andrew (Webb et al., 2003).

These studies have shown that disasters disrupt business operations through a variety of mechanisms. Direct physical damage to buildings, equipment, vehicles, and inventories has obvious effects on business operation. It might be less obvious that disruption of infrastructure such as water/sewer, electric power, fuel (i.e., natural gas), transportation, and telecommunications frequently forces businesses to shut down in the aftermath of a disaster (Alesch et al., 1993; Tierney and Nigg, 1995; Tierney, 1997a, b; Webb et al., 2000). For example, Tierney (1997b) reported that extensive electrical power service interruption after the 1993 Midwest floods caused a large number of business closures in Des Moines, Iowa, even though the physical damage was confined to a relatively small area.

Other negative disaster effects include population dislocation, losses in discretionary income among those victims who remain in the impact area—which can weaken market demand for many products and services—and competitive pressure from large outside businesses. These kinds of impacts can cause small local businesses to experience major difficulties recovering from the aftermath of a disaster (Alesch et al., 2001). Indeed, such factors

can produce business failures long after the precipitating event, especially if the community was already in economic decline before the disaster occurred (Bates and Peacock, 1993; Webb et al., 2003).

It is difficult to generalize on the basis of so few studies, particularly when the issues involved and the methodological challenges are so complex. However, studies to date have uncovered a few consistent patterns with respect to business impacts and recovery. First, studies show that most businesses do recover, and do so relatively quickly. In other words, typical businesses affected by disasters show a good deal of resilience in the face of major disruption.

Second, some businesses do tend to fare worse than others in the aftermath of disasters; clearly, not all businesses are equally vulnerable or equally resilient. Although findings from individual studies differ, the factors that seem to contribute most to vulnerability include small size; poor pre-disaster financial condition; business type, with wholesale and retail trade appearing to be especially vulnerable, while manufacturing and construction businesses stand to benefit most from disasters; and severity of disaster impacts. With regard to this last-mentioned factor, studies show that negative impacts on businesses include not only direct physical damage, lifeline-related problems, and business interruption, but also more long-lasting operational problems that businesses may experience following disasters, such as employee absenteeism and loss of productivity, earthquake-induced declines in demands for goods and services, and difficulties with shipping or receiving products and supplies.

Third, business recovery is affected by many factors that are outside the control of the individual business owner. For example, businesses located in highly damaged areas may experience recovery difficulties independent of whether or not they experience losses. In this case, recovery is complicated by the fact that disasters disrupt local ecologies on which individual businesses depend. Business recovery processes and outcomes are also linked to community-level decision making. After the Loma Prieta earthquake, for example, the City of Santa Cruz offered extensive support to businesses and used the earthquake as an opportunity to reinvent itself and to revitalize a business district that had fallen short of realizing its potential prior to the disaster (Arnold, 1998). Actions that communities take with respect to land-use, structural mitigation, infrastructure protection, community education, and emergency response planning also affect how businesses and business districts fare during and after disasters.

Fourth, recovery outcomes following disasters are linked to pre-disaster trends and broader market forces. For example, focusing on an important transport facility, the Port of Kobe, Chang (2000) showed that the port’s inability to recover fully after the 1995 earthquake was due in part to losses in one part of the port’s business—trans-shipment cargo—that had already

been declining before the earthquake owing to severe competition from other ports in the region. Similarly, Dahlhamer (1998) found that businesses in the wholesale and retail trade sectors were more vulnerable to experiencing negative economic outcomes following the Northridge earthquake, perhaps because they constitute crowded and highly competitive economic niches and because turnover is high in those sectors during normal times. He also found that firms in industries that had been experiencing growth in the two-year period just before the earthquake were less likely than firms in declining industries to report being worse off following the Northridge event. Such findings are consistent with a more general theme in recovery research discussed earlier—that disasters do not generate change in and of themselves, but rather intensify or accelerate preexisting patterns.

Community Recovery. Although the topic of community recovery is still not well studied, significant progress has been made in understanding both recovery processes and factors that are associated with recovery outcomes for communities. Earlier research indicated that communities rebound well from disasters and that, at the aggregate level and net of other factors, the impacts of disasters are negligible (Friesema et al., 1979; Wright et al., 1979). However, other more recent research suggests that such findings paint an overly simplified and perhaps overly optimistic picture of post-disaster recovery. This may have been due to methodological shortcomings—for example, the tendency to aggregate data and to group together both more damaging disasters and those that did comparatively little damage—or because such studies were based on “typical” disasters in the United States, rather than catastrophic or near-catastrophic ones. 13 In contrast, in a methodologically sophisticated study focusing on a much more severe disaster, the 1995 Kobe event, Chang (2001) analyzed a number of recovery indicators, including measures of economic activity, employment in manufacturing, changes in the spatial distribution of work activities, and differences in recovery indicators among different districts within the city. She found that the earthquake did have lasting and significant negative effects on the City of Kobe. Equally important, poor recovery outcomes were more pronounced in some parts of the city than in others—specifically those areas that had already been experiencing declines. This study provides yet another illustration of how disasters exploit existing vulnerabilities. It also cautions against making blanket statements about disaster impacts and recovery.

Another limitation of earlier work on community recovery was that it provided too little information on what actually happens in communities during the recovery process or what communities can do to ensure more rapid and satisfactory recovery outcomes. Later research, much of which has been undertaken with NEHRP support, has addressed these issues. For example, in Community Recovery from a Major Natural Disaster , Rubin et al. (1985) developed a set of propositions regarding factors that affect community recovery outcomes. That monograph, which was based on case study analyses of recovery following 14 disasters that occurred in the early 1980s, emphasized the importance of three general constructs—personal leadership, knowledge of appropriate recovery actions, and ability to act—as well as the influence of intergovernmental (state and federal) policies and programs. This work highlighted the effects of both government decision making and broader societal policies on community recovery.

Some more recent research has more explicitly incorporated community and population vulnerability as factors affecting community-level recovery. Bolin and Stanford (1998) traced how the post-Northridge recovery experiences of Los Angeles and smaller outlying towns differed as a function of such factors as political expertise and influence, preexisting plans, institutional capacity, involvement of community organizations, and interest group competition. In these diverse communities, the needs of more vulnerable and marginalized groups were sometimes addressed during the recovery process. However, recovery programs ultimately did little to improve the safety of those groups, because they failed to address the root causes of vulnerability (Bolin and Stanford, 1998:216):

[s]ince vulnerability derives from political, economic, and social processes that deny certain people and groups access or entitlements to incomes, housing, health care, political rights, and, in some cases, even food, then post-disaster rebuilding by itself will have little effect on vulnerability.

Societal-Level and Comparative Research on Disaster Recovery. International research on disasters is discussed in greater detail in Chapter 6 . This chapter focuses in a more limited way on what little research exists on disaster impacts and post-disaster change at the societal level. Regarding long-term societal impacts, researchers have generally found that disasters, even very large ones, typically do not in and of themselves result in significant change in the societies they affect. Instead, the broad consensus has been that to the extent disasters do have lasting effects, it is because they interact with other factors to accelerate changes that were already under way. Albala-Bertrand, for example has argued that while disasters can highlight preexisting political conflicts, whether such effects are sustained over time “has little to do with the disaster itself, but with preexisting economic and sociopolitical

conditions” (1993:197). This research found that the potential for such changes was generally greater in developing countries than developed ones, although not great in any case.

With respect to the political impacts of disasters at the societal level, comparing very large disasters that occurred between 1966 and 1980, political scientist Richard Olson found that that major disasters can result in higher levels of political unrest, particularly in developing countries that are already politically unstable (Olson and Drury, 1997). In other research, Olson argues that under certain (and rare) circumstances, disasters can constitute “critical junctures,” or crises that leave distinctive legacies within those societies. The 1972 earthquake in Managua, Nicaragua, was one such case. Following that devastating event, the corrupt and dictatorial Somoza regime took a large share of post-disaster aid for itself and mismanaged the recovery, in the process alienating Nicaraguan elites, the business establishment, and finally the middle class, and paving the way for the Sandanistas to assume power in 1979. The 1985 Mexico City earthquake also affected the political system of that nation by, among other things, helping to weaken the hegemony of the Institutional Revolutionary Party. However, rather than having a direct and independent influence on subsequent political changes, that earthquake interacted with factors and trends that were already beginning to affect Mexican society before it occurred. That disaster, which was not well managed by the ruling government, provided the Mexican people with a sharp contrast between the vibrancy and the capability of civil society and the government’s lack of preparedness. Grass-roots response and recovery efforts also facilitated broader mobilization by groups that had been pressing for change. Although not a “critical juncture” in its own right, the earthquake did play a role in moving the political system in the direction of greater pluralism and strengthened the power of civil society institutions vis-à-vis the state (Olson and Gawronski, 2003).

Such findings assume particular significance in the aftermath of the December 2004 Indian Ocean earthquake and tsunami. The impacts of that catastrophe span at least 12 different nations and a number of semi-autonomous subnational units, each with its own distinctive history, mode of political organization, internal cleavages, and preexisting problems. Research is needed to better understand both recovery processes and outcomes and the longer-term societal effects of this devastating event.

OTHER DISASTER RECOVERY-RELATED ISSUES

Disaster experience and the mitigation of future hazards.

Social science research has also focused in various ways on the question of whether the positive informational effects of disasters constitute learning

experiences for affected social units by encouraging the adoption of mitigation measures and stimulating preparedness activity. While this idea seems intuitively appealing, the literature is in fact quite equivocal with regard to the extent to which disasters actually promote higher levels of safety. On the one hand, at the community and societal levels, there is considerable evidence to suggest that disasters constitute “windows of opportunity” for those seeking to enact loss reduction programs, making it possible to achieve policy victories that would not have been possible prior to those events (Alesch and Petak, 1986). Disasters have the potential to become “focusing events” (Birkland, 1997) that can alter policy agendas through highlighting areas in which current policy has failed, energizing advocates, and raising public awareness. On the other hand, many disasters fail to become focusing events and have no discernible impacts on the adoption and implementation of loss-reduction measures. For example, Burby et al., (1997), who studied communities in five different states, found no relationship between disaster experience and adoption of mitigation measures. Birkland (1997) suggests that these differences are related in part to the extent to which advocacy coalitions exist, are able to turn disaster events to their advantage, and are able to formulate appropriate policy responses.

Further complicating matters, policies adopted in the aftermath of disasters, like other policies, may meet with resistance and be only partially implemented—or implemented in ways that were never intended. While it is possible to point to examples of successful policy adoption and implementation in the aftermath of disasters, such outcomes are by no means inevitable, and when they do occur, they are typically traceable to other factors, not just to disaster events themselves.

Research does suggest that households, businesses, and other entities affected by disasters learn from their experiences and take action to protect themselves from future events. Those who have experienced disasters may, for example, step up their preparedness for future events or be more likely to heed subsequent disaster warnings. At the same time, it is also clear that there is considerable variability in the relationship between experience and behavioral change. While some studies document the positive informational effects of experience, others show no significant impact, and some research even indicates that repeated experiences engender complacency and lack of action (for a review of the literature, see Tierney et al., 2001).

Role of Prices and Markets

Mainstream economic theory, models, and analytical tools (e.g., benefit-cost analysis) assume that markets generally function efficiently and equilibrate. Barring various situations of market failure, prices serve a key role as signals of resource scarcity. In this context, two broad areas of research

needs can be identified. One is the role of prices and markets in pre-disaster mitigation (see also Chapter 3 ). Market-based approaches to reducing disaster risk involve such questions as how prices can serve as better signals of risk taking and risk protection, and the potential for new approaches to risk sharing (e.g., catastrophe bonds). At the same time, better understanding is also needed of market failures in mitigation (e.g., externalities in risk taking and risk protection). The second broad research need concerns markets in post-disaster loss and recovery. Little empirical research has been conducted on the degree to which assumptions of efficient markets actually hold in disasters, especially those having catastrophic impacts, and the degree to which markets are resilient in the face of disasters. Research is also needed on how economic models can capture the adjustment processes and disequilibria that are important as economies recover from disasters, and how economic recovery policies can influence recovery trajectories.

Disaster Recovery and Sustainability

As discussed in more detail in Chapter 6 , which focuses on international research, disaster theory and research have increasingly emphasized the extent to which vulnerability to disasters can be linked to unsustainable development practices. Indeed, the connection between disaster loss reduction and sustainability was a key organizing principle of the NEHRP-sponsored Second Assessment of Research on Natural Hazards. The title of the summary volume for the Second Assessment, Disasters by Design (Mileti, 1999b), was chosen to emphasize the idea that the impacts produced by disasters are the consequence of prior decisions that put people and property at risk. A key organizing assumption for the Second Assessment was the notion that societies and communities “design” the disasters of the future by failing to take hazards into account in development decisions; pursuing other values, such as rapid economic growth, at the expense of safety; failing to take decisive action to mitigate risks to the built environment; and ignoring opportunities to enhance social and economic resilience in the face of disasters. Conversely, communities and societies also have the ability to design safer futures by better integrating hazard reduction into their ongoing policies and practices in areas such as land-use and development planning, building codes and code enforcement, and quality-of-life initiatives.

Just as disasters dramatically highlight failures to address sources of vulnerability, the post-disaster recovery period gives affected communities and societies an opportunity to reassess pre-disaster plans, policies, and programs, remedy their shortcomings, and design a safer future (Berke et al., 1993). The federal government seeks to promote post-disaster mitigation through FEMA’s Hazard Mitigation Grant Program, as well as programs

that seek to reduce repetitive flood losses through relocating flood-prone properties. The need to weave a concern with disaster loss reduction into the fabric of ongoing community life has also guided federal initiatives such as Project Impact, FEMA’s Disaster Resistant Communities program.

Yet the research record suggests that those opportunities are often missed. While it is clear that some disaster-stricken communities do act decisively to reduce future losses, for others the recovery period brings about a return to the status quo ante, marked at most by gains in safety afforded by reconstruction to more stringent building codes. The section above noted that disasters create “windows of opportunity” for loss reduction advocates, in part by highlighting policy failures and temporarily silencing opponents. At the same time, however, research evidence suggests that even under those circumstances, it is extremely difficult to advance sustainability goals in the aftermath of disasters. Changes in land use are particularly difficult to enact, both during nondisaster times and after disasters, despite the fact that such changes can significantly reduce vulnerability. Land use decision making generally occurs at the local level, but local jurisdictions have great difficulty enacting controls on development in the absence of enabling legislation from higher levels of government. Even when land-use and zoning changes and other mitigation measures are seen as desirable following disasters, community leaders may lack the political will to promote such efforts over the long term, allowing opponents to regroup and old patterns to reassert themselves (see, for example, Reddy, 2000; for more detailed discussions on land-use and hazards, see Burby, 1998). Assessing reconstruction following recent U.S. disasters, Platt (1998:51) observed that “[d]espite all the emphasis on mitigation of multiple hazards in recent years, political, social and economic forces conspire to promote rebuilding patterns that set the stage for future catastrophe.” Overall, the research record suggests that while the recovery period should ideally be a time when communities take stock of their loss reduction policies and enact new ones, post-disaster change tends to be incremental at best and post-disaster efforts to promote sustainability are rare.

RESEARCH RECOMMENDATIONS

This chapter closes by making recommendations for future research on disaster response and recovery. As the foregoing discussions have indicated, existing research has raised numerous questions that need to be addressed through future research. This concluding section highlights general areas in which new research is clearly needed, both to test the limits of current social science knowledge and to take into account broad societal changes and issues of disaster severity and scale.

Recommendation 4.1: Future research should focus on further empirical explorations of societal vulnerability and resilience to natural, technological, and willfully caused hazards and disasters.

Discussions of factors associated with differential vulnerability and resilience in the face of disasters appear in many places in this report. What these discussions reveal is that researchers have only begun to explore these two concepts and much work remains to be done. It is clear that vulnerability is produced by a constellation of psychological, attitudinal, physical, social, and economic factors. However, the manner in which these factors operate and interact in the context of disasters is only partially understood. For example, while sufficient evidence exists to indicate that race, gender, and ethnicity are important predictors of hazard vulnerability and disaster-related behavior, research has yet to fully explore such factors, their correlates, and their interactions across different hazard and disaster contexts. In many cases age is associated with vulnerability to disasters (see Ngo, 2001; Anderson, 2005), but other factors such as ethnicity and socioeconomic status have differential effects within particular age groups (Bolin and Klenow, 1988), and the vulnerability of elderly persons may be related not only to age but also to other factors that are correlated with age, such as social isolation, which can cut off older adults from sources of lifesaving aid under disaster conditions (Klinenberg, 2002).

Even less is known about how to conceptualize, measure, and enhance resilience in the face of disasters—whether that concept is applied to the psychological resilience of individuals or to the resilience of households, communities, local and regional economies, or other units of analysis. Resilience can be conceptualized as the ability to survive disasters without significant loss, disruption, and stress, combined with the ability to cope with the consequences of disasters, replace and restore what has been lost, and resume social and economic activity in a timely manner (Bruneau et al., 2003). Other dimensions of resilience include the ability to learn from disaster experience and change accordingly.

The large volume of literature on psychological resilience and coping offers insights into factors that facilitate resilient responses by individual disaster victims. Other work, such as research on “high-reliability organizations,” organizational adaptation and learning under crisis conditions, and organizational effectiveness (Roberts, 1989; La Porte and Consolini, 1998; Comfort, 1999; Drabek, 2003) also offers insights into correlates of resilience at the organizational and interorganizational levels. As suggested in Chapter 6 , the social capital construct and related concepts such as civic engagement and effective collective action are also related to resilience. The challenge is to continue research on the resilience concept while synthesizing theoretical insights from these disparate literatures, with the ultimate objective of developing an empirically grounded

theory of resilience that is generalizable both across different social units and across different types of extreme events.

Recommendation 4.2: Future research should focus on the special requirements associated with responding to and recovering from willful attacks and disease outbreaks.

A better understanding is needed of likely individual, group, and public responses to intentional acts of terrorism, as well as disease outbreaks and epidemics. As indicated in this chapter, there appears to be no strong a priori reason for assuming that responses to natural, technological, or intentionally caused disasters and willful or naturally occurring disease outbreaks will differ. However, research on hazards and disasters also calls attention to factors that could well prove to be important predictors of responses to such occurrences, particularly those involving unique hazards such as chemical, biological, nuclear, and radiological agents. Research on individual and group responses to different types of disasters has highlighted the importance of such factors as familiarity, experience, and perceptual cues; perceptions about the characteristics of hazards (e.g., their dread nature, lethality and other harms); the content, clarity, and consistency of crisis communications; knowledge of appropriate self-protective actions; and feelings of efficacy with respect to carrying out those measures (see, for example, classic work on risk perception, discussed in Slovic, 2000, as well as Lindell and Perry, 2004).

Recent research has also highlighted the importance of emotions in shaping perceptions of risk. Hazards that trigger vivid images of danger and strong emotions may be seen as more likely to occur, and more likely to produce harm, even if their probability is low (Slovic et al., 2004). If willful acts engender powerful emotions, they could potentially also engender unusual responses among threatened populations.

The potential for ambiguity and confusion with respect to public communications may also be greater for homeland security threats and public health hazards such as avian flu than for other hazards. For example, warning systems and protocols are more institutionalized and more widely understood for natural hazards than for homeland security and public health threats. While it is generally recognized that organizations such as the National Hurricane Center and the U.S. Geological Survey constitute reliable sources of information on hurricanes and earthquakes, respectively, members of the public may be less clear regarding responsibilities and authorities with respect to other risks, particularly since such threats and the expertise needed to assess them are so diverse.

These kinds of differences could translate into differences in public perceptions and subsequent responses. Research is needed on the manner in which the distinctive features of particular homeland security and public

health threats, such as those highlighted here, as well as official plans and management strategies, could affect responses during homeland security emergencies.

Recommendation 4.3: Future research should focus on the societal consequences of changes in government organization and in emer gency management legislation, authorities, policies, and plans that have occurred as a result of the terrorist attacks of September 11, 2001, as well as on changes that will almost certainly occur as a result of Hurricane Katrina.

The period since the 2001 terrorist attacks has been marked by major changes in the nation’s emergency management system and its plans and programs. Those changes include the massive government reorganization that accompanied the creation of the Department of Homeland Security (DHS); the transfer of FEMA, formerly an independent agency, into DHS; the shifting of many duties and responsibilities formerly undertaken by FEMA to DHS’s Office of Domestic Preparedness, which was formerly a part of the Justice Department; the development of the National Response Plan, which supercedes the Federal Response Plan; Presidential Homeland Security Directives 5 and 8, which make the use of the National Incident Management System (NIMS) mandatory for all agencies and organizations involved in responding to disasters and also mandate the establishment of new national preparedness goals; and increases in funding for special homeland security-related initiatives, particularly those involving “first responders.” Other changes include a greater emphasis on regionalized approaches to preparedness and response and the growth at the federal, state, and local levels of offices and departments focusing specifically on homeland security issues—entities that in many cases exist alongside “traditional” emergency management agencies. While officially stressing the need for an “all-hazards” approach, government initiatives are concentrating increasingly on preparedness, response, and recovery in the context of willful attacks. These changes, all of which have taken place within a relatively short period of time, represent the largest realignment of emergency management policies and programs in U.S. history.

What is not known at this time—and what warrants significant research—is how these changes will affect the manner in which organizations and government jurisdictions respond during future extreme events. Is the system that is evolving more centralized and more command-and-control oriented than before September 11? If so, what consequences will that have for the way organizations and governmental entities respond? What role will the general public and emergent groups play in such a system? How will NIMS be implemented in future disasters, and to what effect? What new forms will emergent multiorganizational networks assume in future

disasters? Which agencies and levels of government will be most central, and how will shifts in authority and responsibility affect response and recovery efforts? Will the investment in homeland security preparedness translate into more rapid, appropriate, and effective responses to natural and technological disasters, or will the new focus on homeland security lead to an erosion in the competencies required to manage other types of emergencies? A major research initiative is needed to analyze the intended and unintended consequences in social time and space of the massive changes that have taken place in the nation’s emergency management system since September 11, 2001.

These concerns loom even larger in the aftermath of Hurricane Katrina. That disaster revealed significant problems in virtually every aspect of intergovernmental preparedness and response. The inept management of the Katrina disaster was at least in part a consequence of the myopic institutional focus on terrorism that developed in the wake of the September 11, 2001 attacks—a focus that included marginalizing and underfunding FEMA and downplaying the challenges associated with responding to large-scale natural disasters (Tierney, 2006, forthcoming). Katrina is certain to bring about further efforts at reorganizing the nation’s response system, particularly at the federal level. These reorganizations and their consequences merit special attention.

Recommendation 4.4: Research is needed to update current theories and findings on disaster response and recovery in light of chang ing demographic, economic, technological, and social trends such as those highlighted in Chapter 2 and elsewhere in this report.

It is essential to keep knowledge about disaster response and recovery current. The paragraphs above highlight the need for new research on homeland security threats and institutional responses to those threats. Research is also needed to update what is known about disaster response and recovery in light of other forms of social change and to reassess existing theories. Technological change is a case in point. Focusing on only one issue—disaster warnings—the bulk of the research that has been conducted on warning systems and warning responses was carried out prior to the information technology and communications revolutions. With the rise of the Internet and interactive Web-based communication, the proliferation of cellular and other wireless media, and the growing potential for ubiquitous communications, questions arise regarding the applicability of earlier research findings on how members of the public receive, interpret, and act on warnings. Changes in the mass media, including the rise of the 24-hour news cycle and the trend toward “narrowcasting” and now “podcasting” for increasingly specialized audiences, also have implications for the ways in which the public learns about hazards and receives warning-related

information. In many respects, warning systems reflect a preference for “push-oriented” information dissemination approaches. However, current information collection practices are strongly “pull oriented.” These and other trends in communications technology introduce additional complexity into already complex processes associated with issuing and receiving warnings, decision making under uncertainty, and crisis-related collective behavior. New research is needed both to improve theories and models and to serve as the basis for practical guidance.

Much the same can be said with respect to organizations charged with responding during disaster events. Along with being affected by policy and programmatic changes such as those discussed above, crisis-relevant agencies are also being influenced by the digital and communications revolution and by the diffusion of technology in areas such as remote sensing, geographic information science, data fusion, decision support systems, and visualization. In the more than 15 years since Drabek (1991b) wrote Microcomputers and Emergency Management , which focused on the ways in which computers were affecting the work of local emergency management agencies, technological change has been rapid and massive. How such changes are affecting organizational performance and effectiveness in disasters is not well understood and warrants extensive systematic study.

Recommendation 4.5: More research is needed on response and recovery for near-catastrophic and catastrophic disaster events.

Chapter 1 discusses issues of determining thresholds of disastrous conditions. NEHRP-sponsored social science research indicates that, in the main, U.S. communities have shown considerable resilience even in the face of major disasters. Similarly, at the individual level, U.S. disasters have produced a range of negative psychosocial impacts, but such impacts appear to have been neither severe nor long-lasting. While recognizing that disasters disproportionately affect the most vulnerable in U.S. society and acknowledging that recovery is extremely difficult for many, disasters have been less devastating in the United States and other developed societies than in the developing world. Disaster-related death tolls have also been lower by orders of magnitude, and economic losses, although often large in absolute terms, have also been lower relative to the size of the U.S. economy. At least that was the case until Hurricane Katrina, a catastrophic event that has more in common with disasters in the developing world than with the typical U.S. disaster.

The vast majority of empirical studies on which such generalizations are based have not focused on truly catastrophic disasters, and therefore research results may not be “scalable” to such events. Katrina clearly demonstrates that the nation is at risk for events that are so large that they overwhelm response systems and produce almost insurmountable post-

disaster recovery challenges. What kinds of social and economic impacts and outcomes would result from a large earthquake under downtown Los Angeles, a 7.0 earthquake event on the Hayward Fault in the San Francisco Bay area, a repeat of Hurricane Andrew directly striking Miami, or another hurricane landfall in the already devastated Gulf Coast region? What about situations involving multiple disaster impacts, such as the 2004 hurricane season in Florida and multiple disaster events that produce protracted impacts over time, such as the large aftershocks that are now occurring after the Indian Ocean earthquake and tsunami? To move into the realm of worst cases, what about an attack involving weapons of mass destruction, or simultaneous terrorist attacks in different cities around the United States? Such events are not outside the realm of possibility. There is a need to envision the potential social and economic effects of very large disasters, to learn from catastrophic events such as Hurricane Katrina, and to analyze historical and comparative cases for the insights they can provide.

Recommendation 4.6: More cross-societal research is needed on natural, technological, and willfully caused hazards and disasters.

Most of the research discussed in this chapter has focused on studies conducted within the United States, but it is important to recognize that findings from U.S. research cannot be overgeneralized to other societies. Disaster response and recovery challenges are greater by many orders of magnitude in smaller and less developed societies than in larger and more developed ones.

Disaster impacts, disaster responses, and recovery processes and outcomes clearly vary across societies. Although the earthquakes that struck Los Angeles in 1994, Kobe in 1995, and Bam, Iran, in 2003 were roughly equivalent in size, they differed in almost every other way: lives lost, injuries, extent of physical damage, economic impacts, and subsequent response and recovery activities. Research suggests that such cross-societal differences are attributable to many factors, including differences in physical and social vulnerability; governmental and institutional capacity; government priorities with respect to loss reduction; and response and recovery policies and programs (see, for example, Davis and Seitz, 1982; Blaikie et al., 1994; Berke and Beatley, 1997; Olson and Gawronski, 2003). NEHRP has made significant contributions to cross-societal research through initiatives such as the U.S.-Japan research program on urban earthquake hazards, which was launched following the Northridge and Kobe earthquakes, as well as a similar initiative that was developed after the 1999 Turkey and Taiwan earthquakes. In some cases, these initiatives have led to longer-term research partnerships; Chapter 6 contains information on one such collaboration, involving the Texas A&M University Hazard Reduction and Recovery Center and the National Center for Hazards Mitigation at the National

Taiwan University. Significantly more cross-national and comparative research is needed to further document and explain cross-societal variations in response and recovery processes and outcomes across different scales and different disaster events. Disasters such as the Indian Ocean earthquake and tsunami merit intensive study because they allow for rich comparisons at various scales (individuals, households, communities, and institutional and societal levels), providing an opportunity to greatly expand existing social science knowledge.

Recommendation 4.7: Taking into account both existing research and future research needs, sustained efforts should be made with respect to data archiving, sharing, and dissemination.

As noted in detail in Chapter 7 , attention must be paid to issues related to data standardization, data archiving, and data sharing in hazards and disaster research. NEHRP has been a major driving force in the development of databases on response and recovery issues. However, vast proportions of these data have yet to be fully analyzed. For social scientists to be able to fully exploit the data that currently exist, let alone the volume of data that will be collected in the future, specific steps have to be taken to make available and systematically collect, preserve, and disseminate such data appropriately within the research community. As recommended in Chapter 7 , information management strategies must be well coordinated, formally planned, and consistent with federal guidelines governing the protection of information on human subjects. Assuming that these foundations are established, the committee supports the creation of a Disaster Data Archive organized in ways that would encourage broader use of social science data on disaster response and recovery. Contents of this archive would include (but not be limited to) survey instruments; cleaned databases in common formats; code books, coding instructions and other forms of documentation; descriptions of samples and sampling methods; collections of papers containing analyses using those databases; photographs and Internet links (where applicable); and related research materials. Procedures for data archiving and sharing would build on existing protocols set out by organizations such as the Inter-University Consortium for Political and Social Research (e.g., ICPSR, 2005).

The distributed Disaster Data Archive would perform a number of important functions for social science hazards and disaster research and for the nation. The existence of the archive would make it much more likely that existing data sets will be used to their full potential by greatly improving accessibility. The archive would serve as an important tool for undergraduate and graduate education by making data more easily available for course projects, theses, and dissertations. By enabling researchers to access instruments used in previous research and incorporate past survey and

interview items into their own research, the archive should help make social science research on disasters more cumulative and replicable. An archive would also make it easier for newcomers to the field of disaster research to become familiar with existing research and enable researchers to identify gaps in past research and avoid unnecessary duplication. The archive would also serve an important function in preserving data that might otherwise be lost. Finally, such an archive would enable social science disaster research to better respond to agency directives regarding the desirability of data sharing.

For an effort of this kind to succeed, a number of conditions must be met. Funds will be needed to support the development and maintenance of the archive, and researchers must be willing to make their data sets and all relevant documentation available. This second condition is crucial, because the committee is aware of a number of important data sets that are not currently being shared, and the archive cannot succeed without broad researcher support. Challenges related to human subjects review requirements, confidentiality protections, and disclosure risks must be fully explored and addressed. Other issues include challenges associated with the development and enforcement of quality control standards, rules and standards for data sharing, procedures to ensure that proper acknowledgment is given to project sponsors and principal investigators, and questions about long-term management of the archive.

Related to the need for better data archiving, sharing, and dissemination strategies, social scientists must be poised to take advantage of new capabilities for data integration and fusion. Strategies are needed to integrate social science data with other types of data collected by both pervasive in situ and mobile ad hoc sensor networks (Estrin et al., 2003), such as networks that collect data on environmental and ecological changes and disaster impacts. In light of the availability of such a wide array of data, the hazards and disasters research community must recognize that hazards and disaster informatics—the application of information science and technology to disaster research, education, and practice—is an emerging field.

To realize this potential, and with the foundation established through implementing recommendations in Chapter 7 , the committee further supports the creation of a Data Center for Social Science Research on Hazards and Disasters. In addition to maintaining the Disaster Data Archive, this center would conduct research on automated information extraction from data, including the development of efficient and effective methods for storing, querying, and maintaining both qualitative and quantitative data from disparate and heterogeneous sources.

Social science research conducted since the late 1970's has contributed greatly to society's ability to mitigate and adapt to natural, technological, and willful disasters. However, as evidenced by Hurricane Katrina, the Indian Ocean tsunami, the September 11, 2001 terrorist attacks on the United States, and other recent events, hazards and disaster research and its application could be improved greatly. In particular, more studies should be pursued that compare how the characteristics of different types of events—including predictability, forewarning, magnitude, and duration of impact—affect societal vulnerability and response. This book includes more than thirty recommendations for the hazards and disaster community.

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Impact of Natural Disasters on Mental Health: A Cross-Sectional Study Based on the 2014 China Family Panel Survey

1 Department of Economics, Jinan University, Guangzhou 510632, China; moc.361@omiteujn

Yunzhi Zhang

2 Faculty of Law, Economic, and Management, LEO-University of Orléans, 45067 Orléans, France

3 Law School, Jinan University, Guangzhou 510632, China; moc.621@gnikehziad

Associated Data

Data used in this paper can be found from the China Family Panel Survey, http://www.isss.pku.edu.cn/cfps/ (accessed on 30 December 2021).

Mental health problems are a leading cause of disability in both developed and developing countries, and the consequences of mental health disorders for individuals, families, and society as a whole could be severe and costly. To supplement relevant research and provide insightful policy suggestions to families, government and societies, this study investigates the nexus between natural disasters and mental health for middle-aged and older adults in rural China. Based on data of 8721 observations from the 2014 China Family Panel Studies, we estimate the effects of natural disasters on mental health using ordinary least squares and propensity score matching. Our findings suggest that natural disasters have a significant negative effect on middle-aged and older adults’ mental health in the case of rural China. This effect is heterogeneous depending on individuals’ education level and their agricultural production status. Finally, individuals’ happiness and life satisfaction are shown to be the potential mechanism through which the effect of natural disasters on mental health operates.

1. Introduction

Natural disasters, which are a possible result of global warming, play a crucial role in the relationship between humans and nature. For this reason, natural disasters have been widely studied, with researchers exploring their impact on society and aspects such as household finance, poverty, family violence, the macroeconomy, and energy consumption [ 1 , 2 , 3 , 4 , 5 , 6 ]. It is obvious that natural disasters threaten people’s lives and physical health; however, little attention has been paid to their impact on mental health. Natural disasters can cause anxiety, sleep disturbances, impaired interpersonal relationships, and depression, among other mental problems [ 7 , 8 , 9 ].

The importance of mental health is confirmed by the World Health Organization [ 10 ], which states that: “Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity”. Mental health is closely related to daily life and work, and it affects our attitude to life and work productivity. Research also indicates that mental illness can be costly for individuals and society [ 11 , 12 , 13 , 14 , 15 , 16 ]. Serious mental illness can even lead to suicide. Globally, around 703,000 people die by suicide every year (World Health Organization, 2021) [ 10 ]. Hence, exploring the causes of mental health problems is crucial for the well-being of individuals, their families, and society as a whole.

Given the importance of mental health, the determinants of mental health problems attract scholars’ interests. Most of the research focuses on the impact of human activity on mental health. For example, Ettner [ 17 ] found that an increase in individuals’ income can improve their mental health. In contrast, unemployment has a negative impact on mental health, a relationship that has been investigated by Scutella and Wooden [ 18 ]. Furthermore, Chen and Fang [ 19 ] reveal that China’s one-child policy has a negative impact on elderly people’s mental health. Apart from the abovementioned aspects, external shocks—such as economic shocks and war—can also affect mental health [ 20 , 21 , 22 ]. Recent trends have led to a proliferation of studies about the link between environment and health.

Environmental problems represent one of the most pressing concerns for global health in the 21st century. Specifically, it has been shown that air pollution can exacerbate respiratory or heart disease, among others [ 23 , 24 , 25 , 26 ]. Employing two-stage least squares estimation with data from the China Migrant Dynamic Survey, Gu et al. [ 27 ] found that poor air quality could cause tension, depression, and irritability, which could further harm mental health. Compared with air pollution, natural disasters are difficult to control and deal with for human beings. Some studies investigate the possible impact of specific natural disasters on mental health. For example, Yokoyama et al. [ 28 ] found that earthquakes and tsunamis have a negative impact on disaster survivors. The study of Gissurardóttir et al. [ 29 ] indicates that exposure to a volcanic eruption may cause mental health disorders. After the outbreak of COVID-19, most analyses focused on the impact of the pandemic on mental health. Pfefferbaum and North [ 30 ] found that the COVID-19 pandemic may result in a negative impact on individuals’ mental health. This result was also confirmed by Zhang and Ma [ 31 ], Yao et al. [ 32 ] and Li et al. [ 33 ]. Furthermore, many studies explore temperature as a factor that can impact mental health [ 16 , 34 , 35 , 36 ] or even lead to suicide [ 37 ]. Although the existing literature has investigated the determinants of mental health from different perspectives, little attention has been paid to the essential role of natural disasters affecting humans’ mental health in rural China.

This paper seeks to fill a gap in the related literature and to understand the relationship between natural disasters and human beings. We employ data from the China Family Panel Studies (CFPS) [ 38 ] in 2014 to identify the impact of natural disasters on mental health for middle-aged and older adults in rural China. We focus on rural China for three reasons. First, given the vulnerability of the infrastructure in China, natural disasters have a more devasting potential to affect this country. Over the years, economic development has been China’s main goal, with individual interests being subordinated to the collective interest. The huge population base detracts from the value of the individual, not to mention the importance of their health. Second, natural disasters have a longer and more persistent destructive impact on rural regions’ infrastructure than in urban regions. Third, natural disasters directly influence farmers’ daily life and work. Moreover, middle-aged, and older people constitute the main source of labor for most families in China. Another reason that we focus on this cohort is that China is rapidly aging. The seventh Population Census (2021) [ 39 ] shows that the share of people over 60 in the total population is 18.7%, accounting for 264 million people. This number has increased by 5.44%, compared to 2010. The mental health problems of middle-aged and older adults can affect the quality of development in China.

The novelty of this paper is four-fold. First, to the best of our knowledge, this is the first paper to investigate the impact of natural disasters on mental health for the case of a developing country: China. Previous studies focused on the impact of a specific disaster, such as heat, floods, hurricanes, and earthquakes on mental health [ 9 , 40 , 41 , 42 ]. Our research investigates the general impact of natural disasters as an external shock on middle-aged and older adults’ mental health. Secondly, we address the impact on a particular cohort; specifically, middle-aged and older people in a rural region, which is important to discuss regarding this issue. Third, we examine the heterogeneity of effects by splitting the sample into different education levels and agricultural production status. These results help us to understand the heterogeneity of the impact of natural disasters on mental health. Finally, existing studies fail to provide mechanisms as to how the natural disaster could impact mental health [ 43 , 44 ]. This paper reveals that natural disasters could affect mental health through the influence of happiness and life satisfaction.

The remainder of this paper is structured as follows. In Section 2 , we describe the study design, statistical analysis, data, and methodology. The empirical analysis is reported in Section 3 . In Section 4 we discuss the results and provide the policy implications. Conclusions are drawn in Section 5 .

2. Materials and Methods

2.1. study design.

A cross-sectional study was performed by using the 2014 China Family Panel Studies (CFPS) [ 38 ], a nationwide, comprehensive, longitudinal survey in mainland China. Five follow-up sampling waves were conducted in 2010, 2012, 2014, 2016, and 2018. However, only the 2014 CFPS has complete information on natural disasters and mental health. Thus, only the 2014 baseline survey is used for the analysis in this study. From July 2014 to May 2015, the CFPS project team collected data at individual, family, and community levels through face-to-face interviews and telephone surveys. CFPS sampling adopts implicit stratified, multi-stage, multi-level, and proportional probability sampling. The administrative division and socio-economic level are the main hierarchical variables. The samples of each sub-sample box of CFPS are extracted through three stages. The first stage sample is the administrative district/county. The second stage sample is administrative village/neighborhood committee, and the third stage (terminal) sample is household. Twenty-five provinces or their administrative equivalents were surveyed: Beijing, Chongqing, Shanghai, Tianjin, Zhejiang, Liaoning, Fujian, Sichuan, Shandong, Guizhou, Gansu, Hebei, Hubei, Hunan, Guangdong, Guangxi, Yunnan, Heilongjiang, Jilin, Shanxi, Anhui, Jiangxi, Shaanxi, Henan, and Jiangsu. The data included individual, family, and community levels; that is, individual psychological and physiological status, education outcomes, natural disaster, demographic characteristics, and family economic characteristics.

In 2014, the number of middle-aged and older adults (“middle-aged and older adults” refers to individuals older than 44 years old) in the sample was 18,607. Exclusion of outliers, urban, and missing data yields 8721 observations. An econometric analysis using ordinary least squares (OLS) was conducted to investigate the effect of natural disasters on middle-aged and older adults’ mental health. This study was approved by the Ethics Committee of the Institute of Social Science Survey of Peking University, and ethical clearance or equivalent approval to conduct the study was granted in each country.

Our paper not only investigated the impact of natural disasters on middle-aged and older adults’ mental health, but also considered the heterogeneous effects and mechanisms of natural disasters on mental health. Hence, five hypotheses were proposed for our study.

Natural disasters may influence individuals’ life through different aspects. For example, natural disasters may damage individuals’ houses and crops, resulting in huge financial stress for disaster survivors. Furthermore, anxiety, impaired interpersonal relationships, food insecurity, and numerous other potential triggers for stress response may all have been intensified due to natural disasters [ 7 , 8 , 9 ]. Based on the above analysis, we propose Hypothesis 1.

Natural disasters have a significant negative effect on middle-aged and older adults’ mental health.

Belo et al. [ 44 ] found that well-educated people tend to have a higher income, a healthy diet, and an optimistic attitude towards life. Natural disasters might destroy immovables, cause massive loss of human life, and destruction of resources. Compared with less-educated individuals, well-educated people own more social and economic resources. Those well-educated individuals could better cope with the negative impact of natural disasters. The effect of natural disasters on mental health might not be homogeneous for people at different education levels. Hence, Hypothesis 2 arises.

The impact of natural disasters for well-educated individuals is less strong than it is for their less-educated counterparts.

Most individuals have sustained heavy financial losses due to natural disasters. People involved in agricultural production suffer more losses from natural disasters [ 45 ]. Furthermore, property loss induces anxiety or other mental health problems in these people. Second, compared with the individuals who are not involved in agricultural production, natural disasters can be more devastating for those who are. Since the damage affects not only financial property, but also people’s agricultural livelihoods [ 3 ], the double loss might result in mental health problems. The effect of natural disasters on mental health might also vary depending on the family’s agricultural production status. Hence, we propose Hypothesis 3.

Individuals show a stronger response to natural disasters if they have a family member involved in agricultural production, compared to those who do not.

Individuals with a higher level of happiness have more positive emotions and attitude to life than the ones with a lower level. Previous studies have recognized the important role of happiness in an individual’s mental health [ 46 ]. Furthermore, the existing literature indicates that natural disasters have a significant negative impact on individuals’ happiness [ 47 , 48 ]. Hence, we propose Hypothesis 4.

Natural disasters have an impact on mental health through their effects on happiness.

Natural disasters are linked with reduced satisfaction. Effects of natural disasters on life satisfaction fall into two broad categories: psychic costs and financial losses. Luechinger and Raschky [ 49 ] found that flood disasters have a negative effect on individuals’ life satisfaction. Individuals’ life satisfaction scores embody specific information on a subjective assessment of their daily life. Respondents with a higher score of life satisfaction are less likely to experience a psychological problem. Hence, Hypothesis 5 is proposed.

Natural disasters can harm mental health through their effects on life satisfaction.

2.2. Statistical Analysis

Statistical analysis was conducted using econometric software STATA version 15.1 (StataCorp, College Station, TX, USA). We report the mean, standard deviation, minimum, and maximum of variables in Table 1 . Given mental health is a continuous variable, OLS was constructed to investigate the causal relationship between natural disasters and middle-aged and older adults’ mental health. (We used the STATA package “regress” for the OLS regression). In our robustness check, we estimate the effect of natural disasters on mental health using propensity score matching (PSM). (We used the STATA package “psmatch2” to calculate the average treatment effect on the treated (ATT) of the various propensity score matching methods). To investigate the mechanisms, we estimate the impact of natural disasters on individuals’ happiness and life satisfaction using OLS and the ordered probit model. (We used the STATA package “oprobit” for the ordered probit model). All reported p -values were two-tail. The level of statistical significance was set at p < 0.1 .

Descriptive statistics of the key variables.

2.3. Variables and Descriptive Statistics

Outcome variable: middle-aged and older adults’ mental health

The main outcome variable in this paper is the mental health of middle-aged and older adults in rural China. Following existing studies [ 43 , 50 ], the mental health index is derived from the 6-item short form of the Center for Epidemiologic Studies of Depression (CES-D) in the CFPS. (CES-D questions: 1. How often did you feel depressed that nothing could cheer you up during the past 30 days? 2. How often did you feel nervous during the past days? 3. How often did you feel restless or fidgety during the past 30 days? 4. How often did you feel hopeless during the past 30 days? 5. How often did you feel that everything was an effort during the past 30 days? 6. How often did you feel that life was meaningless during the past 30 days? Individuals were asked to indicate the frequency of their feelings on a five-scale metric—“Almost daily”, “Often”, “Half of the time”, “Sometimes”, and “Never”. These responses are coded from 1 to 5, respectively). The response for each question is coded from 1 to 5. There are six questions to assess mental state in the survey, and each one is constructed and standardized to have a mean of zero and a standard deviation of one. The final score is calculated by aggregating the multiple measures into indices. The higher the index value, the better the individual’s mental health.

Independent variable: natural disaster

We consider two measures of natural disaster as the independent variable. The first one is captured by a dummy variable (Disaster_d). It equals 1 if the middle-aged or older adult has experienced at least one type of natural disaster, and otherwise 0. (The types of natural disasters include typhoons, floods, storm surges, forest fires, frost, hail, landslides, debris flow, earthquakes, infectious diseases, agricultural and forestry pests, etc.). The second is constructed as a continuous variable (Disaster_n), which measures the number of types of natural disaster that the middle-aged or older adult has experienced.

Control variables and descriptive statistics

We include the following control variables: age, a dummy variable for sex, education level, marital status, cognitive abilities, income, medical insurance, and a dummy variable for agricultural production. In addition, we control for family size, house value, and family expenditure. Descriptive statistics of the variables used in the paper are reported in Table 1 , where it can be seen that the sampled middle-aged and older adults were 58.41 years old on average, and 50.4 percent of them were male. The average mental health score is −0.339. About 75 percent of middle-aged and older adults have experienced at least one type of natural disaster. The value of Disaster_n varies from 0 to 5. That is to say, the most types of disasters that have been experienced by a person is 5, and the least is 0 in our sample.

2.4. Empirical Methodologies

The effect of natural disasters on middle-aged and older adults’ mental health is estimated using ordinary least squares, as follows:

where m e n t a l i represents the dependent variable (middle-aged and older adults’ mental health), d i s a s t e r _ d represents the natural disaster dummy variable (dummy variable equal to 1 if the middle-aged or older adult experienced at least one type of natural disaster, and otherwise 0), d i s a s t e r _ n represents the number of times a natural disaster was experienced, and c o n t r o l i is a vector of observable determinants of middle-aged and older adults’ mental health.

3. Empirical Results

3.1. the basic correlation.

The basic relationship between natural disasters and mental health is presented in Figure 1 . The graph indicates that a negative correlation exists between natural disasters and middle-aged and older adults’ mental health.

Graph of natural disasters and mental health.

3.2. Baseline Results

When investigating the causal relationship between natural disasters and mental health, an individual’s math and language abilities are generally highly correlated. High correlation among variables gives rise to concerns about multicollinearity, which may lead to considerable bias in the estimation. We use the variable inflation factor (VIF) to check for multicollinearity in our model. Table 2 reports the VIF of each variable. In each case, the VIF is less than the rule-of-thumb value of 10, indicating that multicollinearity is not a major issue.

The variance inflation factor of each variable.

Note: VIF represents variable inflation factor.

Table 3 reports the baseline results on the effects of natural disasters on middle-aged and older adults’ mental health. Columns (1) and (3) include only the dummy of natural disasters and the intensity of natural disasters, respectively. A set of control variables affecting middle-aged and older adults’ mental health is included in columns (2) and (4). The effects in columns (1) and (3) suggest a salient negative effect of natural disasters on mental health for middle-aged and older people. When controlling for a set of covariates in columns (2) and (4), results from OLS models indicate that natural disasters are a significant predictor of middle-aged and older adults’ mental health, showing a negative correlation. Those results verify Hypothesis 1. In addition, sex shows a positive sign in columns (1) and (3). This indicates that males have better mental health than females. The results for education report positive signs, indicating that education has a positive impact on mental health. The coefficients of marital status are positive and statistically significant. The results indicate that the mental health status of married adults is higher than in their unmarried counterparts. Math abilities, income, insurance, and house value show a salient positive impact on mental health.

OLS results of the effects of natural disasters on middle-aged and older adults’ mental health.

Note: *** p < 0.01, ** p < 0.05, * p < 0.10. Standard errors clustered at the individual level are reported in parentheses. OLS represents ordinary least squares.

3.3. Endogeneity

Bearing selection bias in mind, we estimate the causal effect of natural disasters on mental health using the propensity score matching (PSM) technique. In this case, we use a dummy variable equal to 1 if the middle-aged or older adult experienced at least one type of the natural disaster (treatment group), or otherwise 0 (control group).

An important step when applying PSM is to check the covariate balance of the treatment and control group, which is achieved if both groups have similar observable covariates. This paper uses two methods to check the covariate balance of the two groups. The first one is essentially based on comparing the mean (after matching) of observable covariates in the two groups. The second one is based on the standardized bias. Table 4 reports the results of the mean of the observable covariates in the two groups. The results in column (5) indicate that the p -values (after matching) are larger than 0.1 in most of the cases. Additionally, we report the standardized bias in Figure 2 . The standardized bias reduction is below 5%, providing evidence that the covariates are balanced in the two groups.

Standardized bias before and after matching.

The mean of covariates in treatment and control groups.

According to Heckman et al. [ 51 ], a crucial step when applying PSM is to examine the overlap and region of common support between treatment and control groups. Figure 3 and Figure 4 report the estimation of the density distribution in the two groups, indicating that most samples fall into the region of common support.

Density distribution of the propensity score (before matching).

Density distribution of the propensity score (after matching).

Following Rosenbaum and Rubin [ 52 ], this paper presents different types of matching estimators, including kernel matching, local linear matching, radius matching, and nearest-neighbor matching (k = 1, k = 4). Table 5 presents the results of the different matching strategies. The results indicate that the average treatment effect on the treatment of the various matching methods is all negative and significant, indicating a negative correlation between natural disaster and mental health in our sample.

PSM analysis of the effects of natural disasters on middle-aged and older adults’ mental health.

Note: *** p < 0.01, ** p < 0.05.

3.4. Robustness Check

Two lines of the robustness check are conducted to confirm the reliability of the results in the previous section. In the first, we use an alternative measure of mental health. In the second, we employ another methodology to estimate the effect of natural disasters on middle-aged and older adults’ mental health.

One concern may be that our findings might be driven by the measurement of mental health. To analyze this, we construct an alternative index to measure mental health in our sample. We adopt a factor analysis approach to assess the individual’s mental health. The results show that the KMO-statistics are all larger than 0.8, Cronbach’s alpha is 0.86, and the p -values of the Bartlett test of sphericity are all less than 0.01, confirming that exploratory factor analysis fits well as a method to measure mental health. We also use this index to regress our model using OLS, and the results are reported in Table 6 . The results indicate that natural disasters have a negative effect on middle-aged and older adults’ mental health, consistent with the OLS results of Table 3 .

Robustness test results.

Note: *** p < 0.01, ** p < 0.05. OLS represents ordinary least squares.

In the second robustness check, we apply the probit methodology to estimate the effect of natural disasters on middle-aged and older adults’ mental health. To do this, we replace individuals’ mental health by using a dummy. This dummy takes 1 if the value of mental health is more than −0.339, and otherwise 0. Columns (3)–(4) of Table 6 show the estimated results, which indicate that natural disasters have a negative effect on mental health. We also report the margin effects in columns (5)–(6) of Table 6 . The marginal effects are all negative and statistically significant. All in all, the results in Table 6 are consistent with the results in Table 3 .

3.5. Heterogeneity

To better understand the relationship between natural disasters and mental health, we examine the heterogeneity of effects by splitting the sample into different education levels and agricultural production status.

In order to check Hypothesis 2, Table 7 presents the results of the heterogeneous effect of natural disasters on mental health for different education levels. The results suggest that less-educated adults show a stronger response to natural disasters than well-educated ones. These results verify Hypothesis 2.

Heterogeneous effects of natural disaster by education level.

Note: *** p < 0.01, ** p < 0.05, * p < 0.10.

To check Hypothesis 3, we include a dummy variable to measure the agricultural production status in a family. The dummy equals 1 if the individual belongs to a family involved in agricultural production, and otherwise 0. Table 8 reports the effect of natural disasters considering the family’s agricultural production status. The results indicate that middle-aged and older adults have a stronger response to natural disasters if they have a family member involved in agricultural production, compared to those that do not. The results in Table 8 verify Hypothesis 3.

Heterogeneous effects of natural disaster by agricultural production status.

3.6. Mechanisms

To explore the mechanisms through which natural disasters affect middle-aged and older adults’ mental health, two channels are studied in this section: happiness and life satisfaction.

To test Hypothesis 4, we estimate the impact of natural disasters on happiness for middle-aged and older adults by means of OLS. (The happiness index ranges from 1–10, where 0 is the least happy and 10 is the most happy). Given that happiness is reported on an ordinal scale, we also employ the ordered probit model to investigate the impact of natural disasters on mental health. Table 9 reports the results for the effect of natural disasters on middle-aged and older adults’ happiness, revealing negative and statistically significant coefficients. This indicates that natural disasters have an impact on middle-aged and older adults’ mental health through their effects on happiness. The results in Table 9 verify Hypothesis 4.

Natural disasters and happiness.

Note: *** p < 0.01. OLS represents ordinary least squares.

In order to test Hypothesis 5, we investigate whether natural disasters can affect life satisfaction. This indicator is also available in the CFPS survey, with a higher value meaning higher life satisfaction. (The life satisfaction index ranges from 1–5). The corresponding estimates are presented in Table 10 . Columns (1)–(2) of Table 10 report the results of the OLS. Life satisfaction is reported on an ordinal scale, which allows us to estimate the effects of natural disasters on mental health with the ordered probit model. The results show that the coefficients of the natural disasters are negative and statistically significant, indicating that natural disasters can harm mental health through their effects on life satisfaction. These results verify Hypothesis 5.

Natural disasters and life satisfaction.

4. Discussion

There are a number of studies that are related to what we have examined in this paper, but they are in the spirit of focusing on a specific disaster. For instance, Kovats and Hajat [ 40 ] conducted a meta-analysis of previous studies and found that extreme hot weather threatens public health and can also be a cause of mortality. Furthermore, studies by Rataj et al. [ 41 ] and Weilnhammer et al. [ 53 ] show that extreme weather has a negative impact not only on physical health, but also on mental health. However, the abovementioned research is based on descriptive studies and lacks empirical support. Our research employs ordinary least squares and propensity score matching to investigate the causal impact of natural disasters on middle-aged and older adults’ mental health and provides empirical evidence on the effects of natural disasters on mental health. The baseline results are in line with previous studies [ 54 , 55 ]. Moreover, most of the research is derived from studies of flood-exposed regions. Unlike the research of Fernandez et al. [ 56 ], our research is derived from large-scale micro population survey data (CFPS). Based on Adult Psychiatric Morbidity Survey data in England, Graham et al. [ 42 ] investigated the impact of storms and floods on individuals’ mental health, but their paper makes no attempt to provide the mechanism analysis. Our research not only investigates the impact of natural disasters on individuals’ mental health, but also strives to ascertain the mechanism between natural disasters and mental health. Furthermore, previous studies found that experiencing an earthquake may influence sleep quality and interpersonal relationships, or even lead to suicide [ 7 , 8 ].

Despite mounting evidence indicating that heat, floods, and hurricanes might cause a negative impact on individuals, little has been said to discuss the impact of all kinds of natural disasters as an external shock on middle-aged and older adults’ mental health. Furthermore, our research also indicates that the impact of natural disasters on middle-aged and older adults’ mental health is heterogeneous depending on individuals’ education level and their agricultural production status. Our study found that well-educated individuals have a weaker response to natural disasters than their less-educated counterparts. Middle-aged and older adults show a stronger response to natural disasters if they have a family member involved in agricultural production, compared to those that do not. Last but not least, our findings provide new evidence on the causal mechanism between natural disasters and middle-aged and older adults’ mental health.

However, this paper is limited in some facets. First, we estimate the short-run effects of natural disasters on middle-aged and older adults’ mental health. Regretfully, due to data constraints, we fail to consider the long-term effects of natural disasters. Second, as well as the data limitations, we measure mental health in a very general way. For instance, post-traumatic stress disorder (PTSD) is highly related to disaster survivors [ 29 , 55 ]. Given the lack of relevant data to PTSD, we do not investigate the impact of natural disasters on PTSD. Third, although we try our best to include the factors that might affect individuals’ mental health, the model could not include some further external factors that affect individuals’ mental health, which are difficult to measure. An interesting future research avenue could be projected on the long-term and dynamic effects of natural disasters on middle-aged and older adults’ mental health. Furthermore, research could also investigate the causal effect between natural disasters and a specific mental problem, such as PTSD.

Several policy implications can be derived from this analysis. First, our study suggests that natural disasters have a notable adverse impact on middle-aged and older adults’ mental health. Thus, the government and society as a whole might need to provide aid to the middle-aged and older adults who have suffered from natural disasters. This help should be targeted not only at infrastructure reconstruction and financial subsidies but also at effective mental health care. Particular attention should be paid to those people who have a low level of education and are involved in agricultural production. Finally, the government might also focus more on helping the middle-aged and older adults of disaster-stricken regions by improving their happiness and life satisfaction.

5. Conclusions

Given the importance of mental health in daily life, there has been a growing amount of research on this topic. In this paper, we investigate the causal relationship between natural disasters and mental health in the case of middle-aged and older adults in rural China by using 8721 observations from 2014 CFPS survey data. One of the most important findings to emerge from this paper is that natural disasters have a negative impact on mental health.

Further analysis on heterogeneous effects is conducted by splitting the sample according to educational level and family agricultural production status. On the one hand, the results show that natural disasters have a slightly stronger impact on less-educated people than their better-educated counterparts. On the other hand, compared with those whose family members are not involved in agricultural production, those who are involved in agriculture show a stronger response to natural disasters. Our study also investigates the mechanisms through which natural disasters can have an impact on mental health, indicating that they influence mental health through their effect on the individual’s level of happiness and life satisfaction.

Acknowledgments

We are grateful to the anonymous referees for their constructive and valuable comments and suggestions that helped us to greatly improve the quality of this paper. We would like to thank Camelia Turcu, Ruidong Sun, and Wenxuan Tan for their helpful remarks.

Author Contributions

Conceptualization, R.Z. and Y.Z.; methodology, R.Z.; software, R.Z.; validation, R.Z., Y.Z. and Z.D.; formal analysis, R.Z.; investigation, Y.Z. and Z.D.; resources, R.Z.; data curation, R.Z. and Y.Z.; writing—original draft preparation, R.Z. and Y.Z.; writing—review and editing, Y.Z.; visualization, R.Z.; supervision, R.Z. and Y.Z.; project administration, R.Z., Y.Z. and Z.D.; funding acquisition, Z.D. All authors have read and agreed to the published version of the manuscript.

This research was funded by the National Social Science Foundation Youth Project, grant number 20CFX054.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Patient consent was waived due to publicly open data from the China Family Panel Survey.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Introduction to Geographic Science

1.5 Understanding Natural Disasters

Science of natural disasters.

Because of the scientific method, we now understand where and why most natural disasters occur. For example, because of the theory of plate tectonics, we know why 90 percent of all earthquakes and volcanoes occur along the Pacific Ocean’s outer edges, called the Ring of Fire . The plate tectonics theory has also helped explain why some volcanoes are more explosive and active than others. We also understand that different tectonic plate boundaries produce different fault lines and, thus, several types of earthquakes.

Many natural hazards have seasons, especially those controlled by external forces. The United States has more tornadoes than the rest of the world combined, yet it occurs in the spring and early fall. Landslides are more prone in the spring when snow begins to melt, and the saturated ground causes unstable slopes to slide. Wildfires are frequent in the middle of the summer and early fall when the land is dry, and afternoon thunderstorms in arid climates produce lightning without any precipitation. Furthermore, hurricane season in the Northern Hemisphere peaks between August and September when the Atlantic Ocean is warmest.

Since hazards are statistically predictable in some manner, it becomes essential to develop a warning system. Predictions like weather predictions state that they will occur at a specified time, date, and intensity. It is like saying, “A major snowstorm will reach Salt Lake City at 4:30 PM for the commute home.” A forecast states a probability of something occurring, such as “40 percent of showers today.” Forecasts are much broader than predictions.

When a natural disaster event is about to happen or has occurred, a system has been set up to alert the public. A watch is issued when the conditions for an event are right. A tornado may form if a severe thunderstorm is strong enough and rotating. Alternatively, if an earthquake with a magnitude of 7.5 strikes somewhere in the ocean, a tsunami watch may be issued because it was strong enough to generate one. However, a watch does not necessarily mean that it will occur. A warning is sent to the areas that could be impacted if a tornado is spotted on the ground or an ocean sensor records an approaching tsunami.

Determining Risk

A risk assessment must be conducted for a specific geographic area to understand how to prepare for a natural hazard. The risk of a potential hazard is defined as the probability of a disaster multiplied by the consequence to the human environment.

• Risk = Probability of Disaster x Consequence of Disaster

It is essential to determine the potential risk a location has for any disaster to know how to prepare for one. Referring to Salt Lake City again, the probability of an earthquake occurring anytime soon is small, but the consequences to human lives and destruction are exceedingly high. There is a moderately increased risk of an earthquake striking Salt Lake City. One of the limiting factors of risk is knowing the probability of a disaster. Too often, scientific data lacks enough information to determine how a disaster usually occurs in a particular location. This is particularly true with geologic hazards, where geologic time is vastly more extensive than the age of scientific reasoning.

Hazards, Disasters, and Catastrophes

What is the difference between natural hazards, disasters, or catastrophes? A hazard is any natural process or event directly threatening the human environment. The event itself is not a hazard; a function or event becomes a hazard when it threatens human interests. A disaster is the effect of a hazard on society, usually an event that occurs over a limited time in a defined geographic area. The term disaster is used when the interaction between humans and a natural process results in significant property damage, injuries, or loss of life. Finally, a catastrophe is a massive disaster that significantly impacts the human environment and requires considerable time, money, and resources for response and recovery.

Click on the story map below, titled US Hazards, to spatially understand the variety of natural hazards across the United States and their potential impact on society.

Currently, the earthquake that is expected to strike Salt Lake City is just a hazard, a natural process that poses a potential threat to the human environment because it has not occurred yet. If that earthquake turns out to be a moderate 5.0 magnitude earthquake, it will be considered a disaster. However, if the expected 7.0 to 7.5 magnitude earthquake were to occur, it would be viewed as a catastrophe because thousands of people would perish, tens of thousands would be injured, and the economic cost would be billions of dollars. An article by NASA titled The Rising Costs of Natural Hazards talks about how the financial and human cost of natural disasters is rising. Better mitigation efforts will be required to help prepare for these disasters, such as proper building and zoning codes, first responder preparedness, and public education.

In the summer of 2008, China was rocked by a magnitude 8.0 earthquake that killed over 80,000 people. A week earlier, a cyclone struck Burma , killing 130,000. On January 12, 2010, a magnitude 7.0 earthquake killed nearly 300,000 people and leveled the capital city of Port-a-Prince in Haiti . On March 11, 2011, a magnitude 9.0 earthquake generated a tsunami off the coast of eastern Japan , killing 30,000 people. Are natural disasters getting worse? Not really. Humans are overpopulating the Earth and living in more hazard-prone areas. Over the last 70 years, the world’s population has tripled to 6.7 billion. World population projections suggest that the human population will reach 9 billion by 2050. exponentially grow, and 2050 the world’s population will reach 9 billion. Exponential growth means the world’s population will not grow linearly (in a straight line) but rather as a percentage. Our increased population has caused air quality to suffer, reduced the availability of clean drinking water, raised the world’s extreme poverty rate, and made us more prone to natural hazards.

There is also a relationship between the magnitude of an event (energy released) and its frequency (intervals between episodes). The more earthquakes that occur in a particular location, the weaker they tend to be. That is because built-up energy is slowly being released constantly. However, if there are long intervals between one earthquake and the next, the energy can build and produce a stronger earthquake. That is the problem with earthquakes along the Wasatch Front of Utah. The interval or frequency between earthquakes tends to be 1,500 years, so the magnitude tends to be high because of the built-up energy. We will want to get this earthquake over with at some point because the longer it waits, the worse it will be.

Primary and Secondary Effects

​Natural disasters cause two types of effects: direct and indirect . Direct effects, also called primary effects , include destroyed infrastructure and buildings, injuries, separated families, and even death. Indirect , called secondary effects , are things like contaminated water, disease, and financial losses. In other words, indirect effects are things that happen after the disaster has occurred.

How we build our cities will significantly determine how many lives are saved in a disaster. For example, we should not be building homes in areas that are prone to landslides, liquefaction, or flash floods. Instead, these places should be left as open spaces such as parks, golf courses, or nature preserves. This is a matter of proper zoning laws, which the local government controls. Another way we can reduce the impact of natural disasters is by having evacuation routes, disaster preparedness and education, and building codes so that our buildings do not collapse on people.

Internal and External Forces

Two forces generate natural hazards: internal forces and external forces . The first is internal forces generated by the Earth’s internal heat, creating geologic hazards like earthquakes, volcanoes, and tsunamis. The theory of plate tectonics proposes that internal heating from the Earth’s core causes large tectonic plates that make up the planet’s continents and oceans to move around like bumper cars, where they either slam into each other or pull apart.

External forces influence weather, climate, and landslides. Heating from the Sun causes differential heating on the surface, creating our weather and all its associated hazards. These external forces generate flash floods, tornadoes, hurricanes, supercells, and climatic disasters such as droughts and famines.

Human Population

Sometimes, people will ask if natural disasters are getting worse. This apocalyptic concern has only increased because of climate change or COVID-19. From a geologic perspective, the data suggests that natural events are not continuing to get worse. That does not mean that issues such as climate change should be discounted, far from it. But one consistent variable is that human population growth is causing humans to be more in the way of natural events.

Demography is the study of how human populations change over time and space. It is a branch of human geography related to population geography , which examines the spatial distribution of human populations. Geographers study how populations grow and migrate, how people are distributed worldwide, and how these distributions change over time.

For most of human history, few people lived on Earth, and the world population grew slowly. Only about five hundred million people lived on the entire planet in 1650 (less than half of India’s population in 2000). Things changed dramatically during Europe’s Industrial Revolution in the late 1700s and 1800s, when declining death rates due to improved nutrition and sanitation allowed more people to survive to adulthood and reproduce. The population of Europe increased. However, by the middle of the twentieth century, birth rates in developed countries declined, as children had become a financial liability rather than an economic asset to families. Fewer families worked in agriculture, more families lived in urban areas, and women delayed the age of marriage to pursue education, resulting in a decline in family size and a slowing of population growth. The population is declining in some countries (e.g., Russia and Japan), and the average age in developed countries has been rising for decades. The process just described is called the demographic transition .

At the beginning of the twentieth century, the world’s population was about 1.6 billion. One hundred years later, there were roughly six billion people worldwide, and as of 2011, the number was approaching seven billion. This rapid growth occurred as the demographic transition spread from developed countries to the rest of the world. During the twentieth century, death rates due to disease and malnutrition decreased in every corner of the globe. In developing countries with agricultural societies, however, birth rates remained high. Low death rates and high birth rates resulted in rapid population growth.

Meanwhile, birth rates and family sizes have declined in most developing countries as people leave agricultural professions and move to urban areas. This means that population growth rates, while still higher in the developing world than in the developed world, are declining. Although the exact figures are unknown, demographers expect the world’s population to stabilize by 2100 and decline.

The world’s population growth rate has primarily occurred in developing countries, whereas populations are stable or declining in Europe and North America. The world’s population increase is pronounced on the continent of Asia: China and India are the most populous countries, each with more than a billion people, and Pakistan is an emerging population giant with a high population growth rate. The continent of Africa has the highest fertility rates in the world. The most striking paradox within population studies is that while there has been a decline in fertility (a declining family size) in developing countries, the world’s population will grow by 2030 because of the compounding effect of many people already in the world. Even though population growth rates are in decline in many countries, the population is still growing. A small growth rate on a broad base population still results in the birth of many millions of people.

As of May 2020, the United States Census Bureau estimates the world population is nearly 7.65 billion, with a growth rate of roughly 1.07 percent, or approximately 82 million people annually. The world population reached 6 billion in 1999 and 7 billion in 2011. If the current growth rate continues, the human population will reach 8 billion by 2023 and hopefully level off at roughly 10 billion by 2055. Between 2010 and 2050, world population growth will be generated exclusively in developing countries.

The world’s three most significant population clusters are the regions of eastern China, South Asia, and Europe. Southeast Asia also has large population clusters. Additionally, large population centers exist in various countries with high urbanization. An example is the urbanized region between Boston and Washington, DC, including New York City, Philadelphia, Baltimore, and neighboring metropolitan areas, often called megalopolis. The coastal country of Nigeria in West Africa and the island of Java in Indonesia are good examples of large population clusters centered in the tropics.

Social dynamics and geography will determine where the new additions to the human family will live. Providing food, energy, and materials for these additional humans will tax many countries, and poverty, malnutrition, and disease are expected to increase in regions with poor sanitation, limited clean water, and a lack of economic resources. In 2010, more than two billion people (one-third of the planet’s population) lived in abject poverty and earned less than the equivalent of two US dollars per day. The carrying capacity of the world is not and cannot be known. How many humans can the Earth sustain indefinitely? There is the possibility that we have already reached the threshold of its carrying capacity .

The human population will continue to grow until it either crashes due to the depletion of resources or stabilizes at a sustainable carrying capacity. Population growth takes a toll on the Earth as more people use more environmental resources. The areas most immediately affected by increased populations include forests (a fuel resource and a source of building material), freshwater supplies, and agricultural soils. These systems get overtaxed, and their depletion has profound consequences. Type C climates, which are moderate and temperate, are usually the most productive and are already vulnerable to severe deforestation, water pollution, and soil erosion. Maintaining adequate food supplies will be critical to supporting a sustainable carrying capacity. The ability to transport food supplies quickly and safely is a significant component of managing the conservation of resources. Deforestation by humans using wood for cooking fuel is already a severe concern in arid type B climates.

Population Demographics

The Industrial Revolution, which prompted the shift in population from rural to urban, also encouraged market economies, which have evolved into modern consumer societies. Various theories and models have been developed to help explain these changes. For example, in 1929, the American demographer Warren Thompson developed the Demographic Transition Model (DTM) to explain population growth based on an interpretation of demographic history. A revised version of Thomson’s model outlines five stages of the demographic transition from traditional rural to modern urban societies.

Stage 1: Low Growth Rate

Humans have lived in the first stage of the DTM for most of our existence. In this first stage, CBRs and CDRs fluctuated regionally, globally, and over time because of living conditions, food output, environmental conditions, war, and disease. The natural increase of the world was stable because CBRs and CDRs were about equal. However, around 8,000 BC, the world’s population grew dramatically due to the agricultural revolution. During this time, humans learned to domesticate plants and animals for personal use and became less reliant on hunting and gathering for sustenance. This allowed for more stable food production and allowed village populations to grow. War and disease prevented population growth from occurring on a global scale.

Stage 2: High Growth Rate

Around the mid-1700s, global populations grew ten times faster than in the past because of the Industrial Revolution. The Industrial Revolution brought with it a variety of technological improvements in agricultural production and food supply. Increased wealth in Europe and later North America because the Industrial Revolution meant more money and resources could be devoted to medicine, medical technology, water sanitation, and personal hygiene. Sewer systems were installed in cities; thus, public health improved. All this dramatically caused CDRs to drop around the world. At first, CBRs stayed high as CDRs dropped, causing populations to increase in Europe and North America. Over time, this would change.

Africa, Asia, and Latin America moved into Stage 2 of the demographic transition model two hundred years later for more varied reasons than those of their European and North American counterparts. The medicine created in Europe and North America was brought into these developing nations, creating what is now called the medical revolution. This revolution or diffusion of medicine to this region caused death rates to drop quickly. While the medical revolution reduced death rates, it did not bring with it the wealth and improved living conditions and development that the Industrial Revolution created. Global population growth is most significant in the regions still in Stage 2.

Stage 3: Moderate Growth Rate

Europe and North America have moved to Stage 3 of the demographic transition model. A nation moves from Stage 2 to Stage 3 when CBRs begin to drop while CDRs remain low or even continue to fall. It should be noted that the natural rate of increase in nations within Stage 3 is moderate because CBRs are higher than CDRs. The United States, Canada, and European nations entered this stage in the early 20th century. Latin American nations entered this stage later in the century.

Advances in technology and medicine cause a decrease in IMR and overall CDR during Stage 2. Social and economic changes bring about a decrease in CBR during Stage 3. Countries that begin to acquire wealth tend to have fewer children as they move away from rural-based development structures toward urban-based structures because more children survive in childhood. The need for large families for agricultural work decreases. Additionally, women gained more legal rights and chose to enter the workforce, own property, and have fewer children as nations moved into Stage 3.

Stage 4: Return to Low Growth Rate

A country enters Stage 4 of the demographic transition model when CBRs are equal to or become less than CDRs. When CBRs are equal to CDRs, a nation will experience zero population growth (ZPG). This occurs in many countries where girls do not live as long as they reach their childbearing age due to gender inequality.

A country in the first two stages of the transition model will have a broad base of young people and a smaller proportion of older people. A country in Stage 4 will have a much smaller base of young people (fewer children) but a much larger population of elderly (decreased CDR). A country with a large youth population is more likely to be rural, with high birthrates and death rates, helping geographers analyze a nation’s health care system. Moreover, a country in Stage 4 with a large elderly population will have fewer young people supporting the economy. These two examples represent the dependency ratio mentioned earlier in this chapter. This ratio is the number of young and older adults dependent on the working force.

Human geographers like to focus on the following demographic groups: 0-14 years old, 15-64 years old, and 65 and older. Individuals 0-14 and over sixty-five are considered dependents (though this is changing in older generations). One-third of all young people live in developing nations. Moreover, this places considerable strain on those nations’ infrastructure, such as schools, hospitals, and daycare. Older individuals in more developed countries (MDL) benefit from health care services but require more help and resources from the government and the economy.

Another ratio geographers look at is the number of males compared to females, called the sex ratio . Globally, more males are born than females, but males have a higher death rate than females. However, understanding a country’s sex and dependency ratios helps human geographers analyze fertility rates and natural increases.

As noted earlier, population growth has increased dramatically in the last century. No country is still in Stage 1; very few have moved into Stage 4. Most of the world is either in Stage 2 or 3, which both have higher CBRs than CDRs, creating a human population of over 7.5 billion today.

Stage 5: Population Decline

Many demographers believe a new stage in the DTM should be added to address issues starting to develop in countries within Europe and Japan. CBR would be extremely low, and CDR would increase in this final stage. This would cause the area’s NIR to be negative, leading to declining population growth. This may strain a country’s social safety net programs as it tries to support older citizens who are no longer working and contributing to the economy.

Unnatural Disasters

Former UN Security General Kofi Annan has said, “The term natural disaster has become an increasingly misnomer. Human behavior transforms natural hazards into unnatural disasters.” Most deaths from natural disasters occur in less developed countries. According to the United Nations, a less developed country (LDC) is a country that exhibits the lowest indicators of socioeconomic development and is ranked among the lowest on the Human Development Index . Those who live in low-income environments tend to have the following characteristics:

• Live in areas at a higher risk of geologic, weather, and climate-related disasters.
• They live in areas that lack the economics and resources to provide a safe living infrastructure for their people.
• Tend to have few social and economic assets and a weak social safety net
• Lack of the technological infrastructure to provide early warning systems

As human populations have grown and expanded, and technology has allowed us to manipulate the environment, natural disasters have become more complex and “unnatural.” Humans have not only influenced but magnified the impacts of disasters on society in numerous ways. For simplification, this book will narrow it down to four: human population growth, poverty and inequality, environmental degradation, and climate change.

Physical Geography and Natural Disasters Copyright © 2020 by R. Adam Dastrup, MA, GISP is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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1.4 The Science of Natural Disasters

Carolina Londono Michel

Science and Natural Disasters

Thanks to the scientific method, we now understand where and why most natural disasters occur. For example, because of the theory of plate tectonics, we understand why nearly 90% of all earthquakes and volcanoes on Earth occur along the Pacific Ocean’s outer edges, called the Ring of Fire . The theory of plate tectonics has also helped to explain why some volcanoes are more explosive and dangerous than others. We will dedicate a Chapter to study the theory of plate tectonics since it is so fundamental to explain the main geologic hazards.

Internal and External Forces

The forces that generate natural hazards can be internal or   external to the Earth .  The internal forces arise within Earth. Examples are the internal heat of the Earth, the movement of the Earth’s plates that produces compression or tension and the movement of magma in the crust. External forces come from the Sun or the moon and influence weather and climate. For example, uneven heating from the Sun causes wind circulation around the atmosphere and different pressures, which can generate weather patterns and extreme weather events. The gravity between the moon and the Earth causes tides. The interactions between these forces and the Earth’s spheres control most natural processes.

Natural hazards controlled by external forces can show seasonality, a correlation with seasons. The United States has more tornadoes than the rest of the world combined, yet they most only occur in the spring and early fall. Landslides are more prone in the spring when the snow melts, and the saturated ground causes unstable slopes to slide. Wildfires are frequent in the middle of the summer and early fall when the land is dry, and afternoon thunderstorms in arid climates produce lightning with no precipitation. Hurricane season in the Northern Hemisphere peaks between August and September when the Atlantic Ocean is warmest.

Understanding Natural Hazards

Earth hazards are natural phenomena capable of causing harm to humans. These hazards include earthquakes, tsunamis, hurricanes, floods, droughts, landslides, volcanic eruptions, extreme weather, lightning-induced fires, sinkholes, coastal erosion, and comet and asteroid impacts, among others. But why do they exist in the first place? Can natural disasters serve a purpose in nature?

Video 1.4.1. Think: Can natural disasters be good for nature? (6:01)

Our human history has been shaped by nature, perhaps by natural disasters. Hazardous events can significantly alter human populations and drive human migrations, that is, they pose risk. A risk is the likelihood and cost of a hazard or group of hazards. When considering risks we talk about two factors:

• the cost in terms of human life and damage to property and infrastructure
• the probability of the event, which depends on the location and the magnitude of the event.

The risks increase as populations expand into hazardous areas or concentrate in already inhabited areas. Risks are also connected to socio-economic variables. Usually, the most vulnerable populations are historically underserved. The occurrence of natural disasters in an underprivileged community can exacerbate inequitable social and economic conditions, this has given rise to the term environmental justice. A related concept is climate justice, the acknowledgment that the impacts of climate change are not felt equally around the globe. The social, economic, public health, and other adverse impacts are harder for underserved or marginalized populations (e.g., low-income communities, people of color, indigenous peoples, people with disabilities, older or very young people, women, etc.) Advocates for climate justice strive to have these inequities addressed head-on through long-term mitigation and adaptation strategies.

For more on these topics read What is climate justice and How inequality grows in the aftermath of hurricanes , both published on the Yale Climate Connections Website.

Determining Risk

To understand how to prepare for a natural hazard, scientists conduct a risk assessment for a specific geographic area. From an environmental perspective, we define the risk of a potential hazard as the probability of a disaster, or the likelihood, multiplied by the consequence to the human environment, or cost.

It is essential to determine the potential risk a location has for any particular disaster to know how to prepare for one. One of the limiting factors of risk is knowing the probability of a disaster. Too often, scientific data is lacking enough information to determine how often a disaster occurs in a particular location. This is true with geologic hazards, where geologic time is vastly more extensive than the age of scientific reasoning.

There is also an inverse relationship between the magnitude of an event (energy released) and its frequency (intervals between episodes). For example, the more earthquakes that occur in a particular location, the weaker they tend to be. That is because built-up energy is slowly being released at a relatively constant rate. However, if there are long intervals between one earthquake and the next, the energy can build and can ultimately produce a stronger earthquake. That is the problem with earthquakes along the Wasatch Front of Utah. The interval or frequency between earthquakes tends to be 1,500 years, so the magnitude tends to be high because of the built-up energy. At some point, we are going to want to get this earthquake over with because the longer it waits, the worse it will be.

Hazards, Disasters, and Catastrophes

What is the difference between a natural hazard, a disaster, or a catastrophe? A hazard is any natural process that poses a direct threat to the human environment. The event itself is not a hazard; instead, a process or event becomes a hazard when it threatens human interests. A disaster is the effect of a hazard on society, usually as an event that occurs over a limited time in a defined geographic area. We use the term disaster when the interaction between humans and a natural process results in significant property damage, injuries, or loss of life. Finally, a catastrophe is a massive disaster that significantly impacted the human environment and requires a significant expenditure of time, money, and resources for response and recovery.

Disaster Forecast, Prediction, and Warning

Humans cannot eliminate natural hazards but can reduce their impacts. We can reduce the loss of life, property damage, and economic costs by identifying high-risk locations and minimizing human habitation and societal activities in them, improving construction methods, developing warning systems, and recognizing how human behavior influences preparedness and response. This is the function of forecasting, predicting, and warning when a disaster is coming. Another way in which we can reduce the impacts of disasters is by refining our knowledge about them.

Earth scientists are continually improving estimates of when and where natural hazards occur. Since hazards are statistically predictable, it becomes essential to develop a warning system. Predictions , such as weather predictions, state that it will occur at a specified time, date, and intensity. It is like saying, “a major dust storm will reach Phoenix at 4:30 PM for the commute home.” A forecast states a probability of something occurring, such as “5 percent of showers today.” Forecasts are much broader than predictions.

When a natural disaster event is about to happen or has occurred, we have set up a system up to alert the public. A watch is issued when the conditions for an event are right. If a severe thunderstorm is strong enough and is rotating, a tornado may form. Alternatively, if an earthquake with a magnitude of 7.5 strikes somewhere in the ocean, a tsunami watch may be issued because it was strong enough to generate one. However, a watch does not necessarily mean that it will occur. A warning is sent out to the areas that could be impacted if a tornado is spotted on the ground or an ocean sensor records an approaching tsunami.

Video 1.4.2  The science of natural disasters (1:43)

The financial and human cost of natural disasters is rising.

To help prepare for these disasters, better mitigation efforts will be required, such as proper building and zoning codes, first responder preparedness, and public education.

As humans over-populate the Earth and live in more hazard-prone areas, disasters seem to be getting worst: affecting more people and costing more money. Over the last 70 years, the world’s population has tripled to 7.97 billion. World population projections suggest that the human population will reach 9.7 billion by 2050 (Worldmeter.info).  Our increased population has caused air quality problems, reduced availability of clean water, increased the world’s extreme poverty rate and has made us more prone and vulnerable to natural hazards.

How we choose to build our cities will significantly determine how many lives we save in a disaster. For example, we should not be building homes in areas that are prone to landslides, liquefaction, or flash floods. Instead, these places should be open space, such as parks, golf courses, or nature preserves. This is a matter of proper zoning laws which are controlled by the local government. Another way we can reduce the impact of natural disasters is by having evacuation routes, disaster preparedness and education, and building codes so that our buildings do not collapse on people.

Natural Hazards in Arizona

Natural hazards abound in Arizona. At the top of list: flash floods, severe weather, landslides and debris flows, earthquakes, and earth fissures. Other hazards, include: problem soils – a multi-billion dollar problem annually in the U.S.; volcanism – Arizona has three active volcanic fields and 1000s of extinct volcanoes, some of which are prone to collapse; locally, radon and arsenic can threaten health and human life. (Source: AZGS https://azgs.arizona.edu/center-natural-hazards)

You can visualize the nature and location of natural hazards in Arizona thanks to the interactive map  developed by the Arizona Geological Survey. The interactive map Interactive map highlights fissures, floods, fires, earthquakes, and fault lines in Arizona. Visit the viewer here .

Visit the Center for Natural Hazards to learn more about Arizona’s hazards.

You may also want to check out the Home buyers guide to geologic hazards for Arizona

an extreme natural event that is a threat to life and property

Statement that under specific circumstances something will occur, leading to recognition of cause and effect.

Description of what may happen in the future, usually stated in terms of propability

The identification of actions that will avoid, lessen, or compensate for anticipated adverse environmental impacts

Dynamic Planet: Exploring Geological Disasters and Environmental Change Copyright © 2021 by Carolina Londono Michel is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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News from the Columbia Climate School

Citizen Scientists Can Help Predict and Prepare for Disasters

By Jackie Ratner

“Why don’t we know this already?” “Why hasn’t science figured this out yet?” “We have  talking fridges  but something as important as this is still unsolved?” These are some of the questions people ask every time there’s a disaster. (OK, maybe not the fridges  specifically… )

We collectively have trouble understanding why disaster science, especially predictive science, is so far behind the tech curve, given its outsized importance to our lives. Part of the reason is that disaster science is incredibly complex! Whether we’re trying to harness atmospheric physics to understand the inner workings of a hurricane, or trying to understand the sociological complexities of keeping people safe in crises, there’s just no easy way to “predict” disasters.

But what about that old saying, “It takes a village to raise a child”? It occurred to me, as early as the 2004 Boxing Day Tsunami (also known as the Indian Ocean Tsunami), that a village could share the load of figuring this mess out instead of wholly relying on scientists and experts, especially in regions where scientists and experts might seldom be found. After all, when it comes to disasters, we’re definitely all in this together, like it or not.

Fifteen years later, along with co-authors Jonathan Sury, Mike James, Tamsin Mather and David Pyle, I’m proud to announce the publication of my first paper presenting evidence of what I’ve suspected all along:  the “village” can indeed help predict disasters .

To find this out, we blended a few research methods in computer science, geography, and geohazards, and applied them to a hypothetical disaster situation to see if citizen scientists or the general public could help collect valuable terrain data, and whether that data would be any good for the purposes of disaster prediction.

Terrain models (3D digital representations of the Earth’s surface) are important for predicting potential disaster scenarios. Without terrain models, we can’t know where it might flood, how severe a landslide might be, or how quickly an avalanche can move.

The historic issue has been that terrain models are costly to make. Data for even a small study area can cost thousands, or likely tens of thousands, of dollars. Sometimes you can get terrain data for free, but often the tradeoff is that it’s lower quality and usually it’s not precise enough for predicting a disaster scenario. The commonly used free datasets derived from satellite missions would need to be 3 to 9 times more precise to give a good prediction for many disasters, such as landslides, avalanches, most volcanic phenomena, and flash floods. Some kinds of floods over large, flat areas can still be predicted reasonably well from lower resolution datasets, and little of the discussion here applies to disasters that happen below the Earth’s surface, such as earthquakes, or in its atmosphere, such as hurricanes. That said, it does apply to hazards that are  related to and  caused by earthquakes (landslides, rockfalls, etc.) or hurricanes (floods). The important distinction is to recognize where the hazard occurs: if it occurs at terrain level, then  our article is relevant.

Typically as scientists, we operate under the assumption that we want the highest quality data we can get. However, for disasters, that’s not always the case. Sometimes we just have to make do with what’s available, or if time is of the essence then we need to just get moving even if everything’s not quite perfect yet. That’s where citizen science or crowdsourcing comes in: you utilize other people to help accomplish an otherwise laborious task in less time, even if they’re not highly trained in how to do it.

For our study we crowdsourced the task of data collection — in this case, collecting digital photos of terrain. Our experiment took place at Agios Georgios volcanic crater in Greece, part of the Santorini caldera volcano. Our citizen scientists were a group of Oxford undergraduates visiting the site to learn and improve field techniques, and thanks to a prior Oxford study on deformation at Santorini, we already had access to Lidar for the area to compare with the crowdsourced data.

Our crowdsourcing would not have been possible even a few years ago, because this work depends entirely on a relatively new technology called structure from motion, or SFM (which I did not develop, I only opportunistically co-opted it for the benefit of humankind, which seemed fair). It’s a kind of computer science that uses digital photographs as input data to yield 3D digital models of whatever was in the photos. It does this by treating the pixels in the photos as data points, and looking for similar clumps of pixels across different photos. It makes best guesses as to which pixel clumps represent the same thing in different photos, and uses it to reconstruct that object from multiple perspectives. The basic procedure is called photogrammetry and there are a number of other ways to do it, including pen and paper, but you can see how computers aid in getting it done much faster!

Depending on the algorithms employed in the structure from motion process, it can be incredibly good at matching pixel clumps to each other and throwing out false matches (95% confidence or better). With such high fidelity, it didn’t seem entirely necessary for the photos to be expertly sourced. But although this had already been shown to work well for things like architecture, it hadn’t yet been applied in the geosciences.

Terrain presents unique challenges when we’re talking photography: among 10 pictures of a bunch of rocks and dirt, could you spot the same rock from a different angle? You could probably do that pretty quickly if somebody showed you 10 pictures of a building from different perspectives. Because of these unique challenges, we couldn’t assume that structure from motion with crowdsourced data would work in geoscience the same way it works in architecture. We had to test it to be sure.

The 17 undergraduates, with no prior experience in photogrammetry or structure from motion, were split into 3 groups with different directives and given one hour to collect photos. Group A wasn’t informed about the project at all; they were asked to role play as people who would possess “incidental” photos of terrain that, in a real world scenario, could be mined from the internet. Such roles included tourists, travel bloggers, and photographers. Group B was given a one-sentence directive on how to capture quality photos for the project, and were asked to approach it from the perspective of a concerned citizen, observatory intern, or photogrammetry hobbyist — people who, in the real world, might have interest in disaster risk reduction or community engagement. Group C, the “experts,” was given a four-page manual explaining the project, its objectives, and the nitty gritty details of structure from motion data collection, as well as a mapped route for taking photos. We wanted to find out whether the data sets would progressively improve in quality if people were more informed before taking photos.

Once the photos were collected and randomly downsampled to data sets of equivalent size, we ran the photos through the free structure from motion software Visual SFM, and refined the resultant point clouds using the free software Cloud Compare. We were able to use the previously collected Lidar as reference data, and mapped our SFM point clouds onto it. We also created another SFM data set combining photos from all three groups into Group ALL, a mixture of the photos.

We were mildly surprised to see that with the exception of Group A, the other three SFM data sets actually outperformed the Lidar in terms of data density for the study area, by up to a factor of 10. There were some differences in the density distribution of data points, which is a quirk of structure from motion that makes the result look uneven when compared to Lidar. But each data set was accurate to within a meter of the Lidar, which is more than adequate for disaster modeling. With the low cost, speed of processing, and low barriers to accessibility (computers and camera phones are ubiquitous; Lidar rigs are not), it was easy to conclude that crowdsourced SFM indeed produces a viable alternative to traditional terrain modeling techniques.

And now we’re pretty reasonably sure* that there’s a better (more precise), cheaper, and faster way to create digital models of Earth’s terrain, which is important because we need terrain models to predict disasters. All you need are a basic computer set up and digital photos collected via citizen science or crowdsourcing.

Check out the study in Progress in Physical Geography !

The next step is to compare Lidar and SFM point clouds again, but in a less ideal environment. A valley in Cornwall, UK, that is prone to flash floods provides a smallish (1 kilometer) field area with vegetation and human activity that would be expected in a real-life disaster area. After we compare Lidar to SFM for point clouds and flood models in this location, our next study will enlarge the field area to several kilometers, to a hazard-prone valley in rural Ecuador that doesn’t have Lidar or other high quality reference data available. We’ll assess how well SFM-derived terrain models work to predict the flow of volcanic mudslides (called lahars) in this area. With these more realistic scenarios, we hope to bring this technique closer to being utilized in the real world, to build terrain models and save lives.

*Classic scientist/academic hedging, right?

Jackie Ratner is senior project manager at Columbia University’s National Center for Disaster Preparedness.

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Climate and weather related disasters surge five-fold over 50 years, but early warnings save lives - WMO report

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Climate change and increasingly extreme weather events, have caused a surge in natural disasters over the past 50 years disproportionately impacting poorer countries, the World Meteorological Organization (WMO) and UN Office for Disaster Risk Reduction (UNDRR) said on Wednesday.

According to the agencies' Atlas of Mortality and Economic Losses from Weather, Climate and Water Extremes , from 1970 to 2019, these natural hazards accounted for 50 per cent of all disasters, 45 per cent of all reported deaths and 74 per cent of all reported economic losses.

There were more than 11,000 reported disasters attributed to these hazards globally, with just over two million deaths and $3.64 trillion in losses. More than 91 per cent of the deaths occurred in developing countries.

Lifesaving early warning boost

But the news is far from all bad. Thanks to improved early warning systems and disaster management, the number of deaths decreased almost threefold between 1970 and 2019 - falling from 50,000 in the 1970s to less than 20,000 in the 2010s. the report explains.

“Economic losses are mounting as exposure increases. But, behind the stark statistics, lies a message of hope. Improved multi-hazard early warning systems have led to a significant reduction in mortality. Quite simply, we are better than ever before at saving lives ”, said WMO Secretary-General Petteri Taalas.

Statistics tell the story

Of the top 10 disasters, droughts proved to be the deadliest hazard during the period, causing 650,000 deaths, followed by storms that led to 577,232 deaths; floods, which took 58.700 lives; and extreme temperature events, during which 55,736 died.

Costs spiralling

Meanwhile, economic losses have increased sevenfold from the 1970s to the 2010s, going from an average of $49 million, to a whopping $383 million per day globally.

Storms, the most prevalent cause of damage, resulted in the largest economic losses around the globe.

Three of the costliest 10 disasters, all hurricanes that occurred in 2017, accounted for 35 per cent of total economic disaster losses around the world from 1970 to 2019.

In the United States, Hurricane Harvey caused $96.9 billion in damage, Maria in the Caribbean 69.4 billion, and Irma $58.2 billion in Cape Verde.

Climate change footprints

“The number of weather, climate and water extremes are increasing and will become more frequent and severe in many parts of the world as a result of climate change”, said Mr. Taalas. “That means more heatwaves, drought and forest fires such as those we have observed recently in Europe and North America”.

World Meteorological Organization

More water vapor in the atmosphere has exacerbated extreme rainfall and flooding, and the warming oceans have affected the frequency and extent of the most intense tropical storms , the WMO chief explained.

WMO cited peer-reviewed studies in the Bulletin of the American Meteorological Society,  showing that over the period 2015 to 2017, 62 of the 77 events reported, revealed a major human influence at play. Moreover, the probability of heatwaves has been significantly increased due to human activity, according to several studies done since 2015.

The Atlas clarifies that the attribution of drought events to anthropogenic, or human, factors, is not as clear as for heatwaves because of natural variability caused by large oceanic and atmospheric oscillations, such as El Niño climate pattern. However, the 2016-2017 East African drought was strongly influenced by warm sea-surface temperatures in the western Indian Ocean to which human influence contributed.

Climate change has also increased extreme sea level events associated with some tropical cyclones , which have increased the intensity of other extreme events such as flooding and associated impacts. This has augmented the vulnerability of low-lying megacities, deltas, coasts and islands in many parts of the world.

Moreover, an increasing number of studies are also finding human influence exacerbating extreme rainfall events, sometimes in conjunction with other major climate influences. Examples include the extreme rainfall in eastern China in June and July 2016 and Hurricane Harvey, which hit Houston in 2017.

The need for adaptability

Only half of WMO’s 193 member countries have multi-hazard early warning systems and severe gaps in weather and hydrological observing networks exist in Africa, some parts of Latin America and in Pacific and Caribbean island States, the report warns.

“More lives are being saved thanks to early warning systems, but it is also true that the number of people exposed to disaster risk is increasing due to population growth in hazard-exposed areas and the growing intensity and frequency of weather events.  More international cooperation is needed to tackle the chronic problem of huge numbers of people being displaced each year by floods, storms and drought ”, said Mami Mizutori, UN Special Representative and head of the Office for Disaster Risk Reduction ( UNDRR ).

Ms. Mizutori called for a greater investment in comprehensive disaster risk management to ensure that climate change adaptation is integrated in national and local disaster risk reduction strategies.

The UNDRR chief warned that the failure to reduce disasters losses as set out in the 2015 Sendai Framework is putting at risk the ability of developing countries to eradicate poverty and to achieve other important Sustainable Development Goals (SDGs).

The Atlas further recommends countries to review hazard exposure and vulnerability considering a changing climate to reflect that tropical cyclones may have different tracks, intensity and speed than in the past.

It also calls for the development of integrated and proactive policies on slow-onset disasters such as drought.

The Atlas by region from 1970 to 2019

• 1,695 recorded disasters caused the loss of 731,747 lives and $5 billion in economic losses.
• The continent accounts for 15 per cent of weather, climate, and water-related disasters; 35 per cent of associated deaths and one per cent of economic losses reported globally.
• Although disasters associated with floods were the most prevalent, at 60 per cent, droughts led to the highest number of deaths, accounting for 95 per cent of all lives lost in the region, withmost occurring in Ethiopia, Mozambique and Sudan
• 3,454 disasters were recorded, with 975,622 lives lost and $2 trillion reported in economic damages.
• Asia accounts for nearly one third, or 31 per cent of weather, climate, and water-related disasters globally, for nearly half of all deaths and one-third of associated economic losses.
• Forty-five per cent of these disasters were associated with floods and 36 per cent with storms .
• Storms took 72 per cent of of lives lost, while floods led to 57 per cent of economic losses

South America

• The top 10 recorded disasters in the region accounted for 60 per cent of the 34,854 lives lost 38 per cent of economic losses equalling $39.2 billion.
• Floods represented 90 per cent of events in the top 10 list of disasters by death toll and 41 per cent of the top ten list by economic losses.
• Floods were responsible for 59 per cent of disasters, 77 per cent for lives lost and 58 per cent of economic loss for the region.

North America, Central America & the Caribbean

• The region suffered 74,839 deaths and $1.7 trillion economic losses.
• The region accounted for 18 per cent of weather-, climate- and water-related disasters, four per cent of associated deaths and 45 per cent of associated economic losses worldwide.
• Storms were responsible for 54 per cent and floods, 31 per cent of recorded disasters., with the former linked to 71 per cent of deaths and the latter to 78 per cent of economic losses.
• The United States accounts for 38 per cent of global economic losses caused by weather, climate and water hazards.

South West Pacific

• The region recorded 1,407 disasters, 65,391 deaths, and $163.7 billion in economic losses.
• 45 per cent of these disasters were associated with storms and 39 per cent with floods.
• Storms accounted for 71 per cent of disaster-related deaths.
• Disasters resulting from weather, climate and water hazards in Australia accounted for 54 per cent or $88.2 billion in economic losses in the entire region.
• 1,672 recorded disasters took 159,438 lives and $476.5 billion in economic damages.
• Although 38 per cent were attributed to floods and 32 per cent to storms, extreme temperatures accounted for 93 per cent of deaths, with 148,109 lives lost.
• Extreme heatwaves of 2003 and 2010 were responsible for 80 per cent of all deaths, with 127,946 lives lost in the two events.
• climate change
• global warming
• climate action

Effects of Risk Perception on Disaster Preparedness Toward Typhoons: An Application of the Extended Theory of Planned Behavior

• Open access
• Published: 15 February 2022
• Volume 13 , pages 100–113, ( 2022 )

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• Sai Leung Ng 1  

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This study adopted an extended theory of planned behavior to understand how risk perception affected disaster preparedness behavior. An intercept survey (N = 286) was conducted at a typhoon-prone district of Hong Kong, China in 2019, then the data were analyzed using structural equation modeling. The results indicated that risk perception and intention of preparedness were predictors of disaster preparedness behavior. Risk perception significantly affected intention of preparedness and the effect was partially mediated by subjective norm. Risk perception also significantly affected attitude and perceived behavioral control, but attitude and perceived behavioral control were not significantly correlated with intention of preparedness. Not only may this study supplement the existing literature of disaster preparedness toward typhoons, but also it provides insights for the planning and management of natural hazards and disaster risk reduction in Hong Kong.

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1 Introduction

Tropical cyclones, also known as typhoons in Asia or hurricanes in North America, refer to intense low-pressure systems observed in tropical and subtropical oceans. Tropical cyclones bring strong winds and torrential rains that may directly result in physical destruction, they may further cause flooding, landslides, and storm surge that lead to sequential impacts on the affected areas. Globally, tropical cyclones are responsible for the largest proportion of mortality and economic loss among various meteorological hazards (Fok and Cheung 2012 ), although a decreasing trend of mortality was observed in the past 70 years (Doocy et al. 2013 ).

Typhoons are the most common natural hazard in subtropical East Asia (Fok and Cheung 2012 ). On average, Hong Kong is affected by approximately five typhoons every year (Hong Kong Observatory 2020 ). In Hong Kong, typhoons have caused the most casualties and damages among various natural hazards (Johnson et al. 2016 ). From 1980 to 2010, on average, the annual mortality and economic damage are 2.6 people and USD 8.1 million, respectively (Fok and Cheung 2012 ).

Hong Kong, like many coastal cities in China and around the world, is predicted to be at risk of climate change (Sundermann et al. 2013 ). Specifically, global warming is expected to increase the frequency and intensity of typhoons in the West Pacific (Webster et al. 2005 ), and Hong Kong has already experienced an increasing trend of extreme typhoons brought by global warming. In the past 40 years, only two signal no. 10 typhoons Footnote 1 occurred from 1980 to 2010. However, there were three signal no. 10 typhoons in the last decade (that is, 2012, 2017, and 2018).

Facing the challenges of typhoons, a considerable number of studies have examined their physical attributes, including number, duration, and intensity (Webster et al. 2005 ; Lam and To 2009 ). These studies help predict the future occurrence of typhoons in Hong Kong so that possible mitigation plans and measures can be formulated. However, the need for proactive strategies and measures of risk reduction aiming to reduce hazard vulnerability in the process of disaster management is equally important (UNISDR 2015 ; Paton 2019 ).

Disaster preparedness refers to activities and measures taken in advance to ensure an effective response to the impact of hazards (Paton 2019 ; Dasgupta et al. 2020 ). Preparedness increases people’s capacity to cope, adapt, respond, and recover when disaster strikes. Consequently, the costs of natural hazard-related disasters can be reduced (Paton 2019 ). Yet, disaster preparedness is one of the weakest links in the risk management system of Hong Kong. One local study indicated that 69% of residents took no precautions even when they were aware of a severe weather warning (Wong and Yan 2002 ). Another local study indicated that only 22.4% of the respondents were prepared for natural hazard-related disasters (Loke et al. 2010 ).

Although these studies are valuable in understanding the situation of personal preparedness toward typhoons in Hong Kong, our knowledge is limited to the description of the phenomena. To promote personal preparedness in society, it is necessary to understand the factors that motivate or inhibit disaster preparedness behavior (Najafi et al. 2017 ). However, the majority of previous studies of disaster preparedness behavior were lack of underpinned theory. Furthermore, vulnerable people deserve more attention as they may need extra assistance during disasters, but very few studies have been conducted to investigate them (Kuran et al. 2020 ).

With the above considerations in mind, this study adopted an extended theory of planned behavior (Ajzen 1991 ) to investigate the disaster preparedness behavior of typhoon-vulnerable people in Hong Kong, using first-hand data from an intercept survey conducted on the streets of Kwun Tong district from December 2018 to May 2019. Not only may this study supplement the body of knowledge on disaster preparedness toward typhoons, but also it provides a reference for the development of effective management of typhoon disasters in Hong Kong and other coastal cities in Asia.

2 Literature Review

The theory of planned behavior and risk perception have been used to explain goal-directed behaviors (Ajzen 2011 ) and disaster preparedness, respectively. To the best of my knowledge, there is no attempt to incorporate risk perception into the theory of planned behavior to understand an individual’s disaster preparedness behavior toward typhoons. To lay the theoretical foundation, the key concepts and related studies are first summarized by reviewing the existing literature of behavioral sciences and hazard management. Along with the formulation of hypotheses, the integration of the theory of planned behavior and risk perception is elaborated.

2.1 Disaster Preparedness Behavior and the Theory of Planned Behavior

Disaster preparedness behavior refers to the personal undertaking of activities or measures before a hazard event in order to mitigate the severity of disaster impacts (Dasgupta et al. 2020 ). Although the connotations of disaster preparedness behavior are varied in time, place, and type of natural hazard (Fung and Loke 2010 ), two common components can be found in the majority of existing literature: preparing an emergency kit and making an emergency plan (Paul and Bhuiyan 2010 ; Kohn et al. 2012 ; Lam et al. 2017 ). The emergency kit usually refers to a package of items for survival, such as clean water, food, and first-aid supplies (Fung and Loke 2010 ). The emergency plan refers to specific procedures for handling sudden or unexpected situations (Bhanumurthy et al. 2015 ). Considering the urban context of Hong Kong, a simple first-aid kit is sufficient for typhoon preparedness (Chan et al. 2016 ). While formal emergency plans may not be necessary (Lam et al. 2017 ), the plan may refer to the consent of family members, for example, going out with an umbrella or simply canceling the trip according to the weather condition.

The performance of disaster preparedness behavior, like other environmental behaviors, is controlled by various factors, but the process is still not well understood (Najifi et al. 2017 ). Therefore, it is preferable to adopt a behavioral model to guide the research (Najafi et al. 2018 ). Many behavioral models have been developed to understand and predict human behavior. Among them, the theory of planned behavior is the most influential and widely used model (Ajzen 2011 ). The theory of planned behavior has two central propositions. First, an individual’s intention is the immediate cause for the performance of a given behavior. Second, intention is determined by three preceding motivational factors, namely attitude, subjective norm, and perceived behavioral control (Ajzen 1991 ).

Intention refers to the voluntary decision to perform a particular behavior or take an action (Sheeran 2002 ). In a meta-analysis that included 422 studies of intention and behavior relations in various contexts, the mean correlation between intention and behavior was 0.53 (Sheeran 2002 ). Another meta-analysis including 206 independent studies reported a mean correlation of 0.43 (McEachan et al. 2011 ). Because the predictive power of intention was usually higher than socio-demographic and other behavioral factors, many studies considered intention as a proxy measure of the actual behavior (for example, Jang et al. 2016 ).

In the theory of planned behavior, attitude is the first construct affecting intention. Attitude refers to the extent to which a person develops a positive or negative perception toward a given behavior (Ajzen 1991 ). Attitude may be categorized as cognitive (that is, beliefs or knowledge about an attitude object), affective (that is, the feelings or emotions toward an object), and behavioral (that is, the way that a person has influenced his or her behavior) (Eagly and Chaiken 2007 ). Significant associations between attitude and intention can be found in various settings and contexts, which is evident from a large number of published works.

The second construct is subjective norm, which reflects a person’s perceptions of how others expect him or her to behave (Ajzen 1991 ). Subjective norm consists of injunctive (that is, how the social network wants this person to behave) and descriptive (that is, the behavior of the social network) components (Daellenbach et al. 2018 ).

The last construct, perceived behavioral control, is the volitional factor in the theory of planned behavior. It incorporates a person’s perception of his or her capacity or control over the behavior (Ajzen 1991 ). Perceived behavioral control consists of internal (that is, self-efficacy; the belief for a person to be capable of performing a given behavior) and external (that is, perceived controllability; the barriers to performing a given behavior) components (Ajzen 2002 ). Manstead and van Eekelen ( 1998 ) indicated that self-efficacy mainly affected intention and perceived controllability influenced behavior, respectively. Therefore, perceived behavioral control affects both behavioral intention and actual behavior (Ajzen 1991 , 2002 ).

In a meta-analysis that included 185 independent studies using the theory of planned behavior to predict human behaviors in various contexts, the mean correlation between intention and attitude was 0.49, that of subjective norm was 0.34, and that of perceived behavioral control was 0.43, respectively (Armitage and Conner 2001 ).

In the context of hazard studies, the theory of planned behavior had been successfully used to explain the behavioral adjustments related to natural hazards (for example, earthquakes (Najafi et al. 2017 ), and typhoons (Dasgupta et al. 2020 )), and threats of anthropogenic origins (for example, terrorist attacks (Tan et al. 2020 )).

Based on the theory of planned behavior, this study formulated five hypotheses:

Hypothesis 1

Intention of typhoon preparedness positively affects disaster preparedness behavior toward typhoons.

Hypothesis 2

Attitude toward typhoon preparedness positively affects intention of typhoon preparedness.

Hypothesis 3

Subjective norm of typhoon preparedness positively affects intention of typhoon preparedness.

Hypothesis 4

Perceived behavioral control of typhoon preparedness positively affects intention of typhoon preparedness.

Hypothesis 5

Perceived behavioral control of typhoon preparedness positively affects disaster preparedness behavior toward typhoons.

2.2 Risk Perception and Disaster Preparedness Behavior

Despite the success of the theory of planned behavior, some researchers, for example, Sommestad et al. ( 2015 ), questioned whether the three variables in the model—attitude, subjective norm, and perceived behavioral control—were sufficient to predict intention. As a response to the challenge, Ajzen ( 1991 ) indicated that the theory of planned behavior was open to the inclusion of additional variables, when they made significant and distinct contributions.

In the context of disaster preparedness behavior, risk perception is central to a large number of previous studies of disaster preparedness (Paul and Bhuiyan 2010 ; Shreve et al. 2016 ). The popularity of risk perception speaks for its potential to extend the theory of planned behavior for predicting disaster preparedness behavior. Existing literature indicated that humans adopted preparedness measures and behaviors only when they perceived that they were under the threat of a disaster (Lazo et al. 2015 ).

Risk perception refers to personal judgment about the uncertainty associated with the disaster (Paul and Bhuiyan 2010 ; Bourque et al. 2012 ). It is not the objective reality but a subjective evaluation of risk (Xu et al. 2016 ). Most researchers adopted a three-factor model of risk perception: (1) perceived likelihood (that is, the probability of a disaster to occur); (2) perceived severity (that is, the potential damage caused by the disaster); and (3) perceived susceptibility (that is the individual’s constitutional vulnerability to a hazard) (Brewer et al. 2007 ; Shreve et al. 2016 ).

A meta-analysis of several empirical studies of risk perception reported significant associations between risk perception and risk-taking behavior; the overall weighted effect size was −0.70 (Cooper and Faseruk 2011 ). Another meta-analysis of 34 risk perception studies also reported significant correlations between risk perception and behavior; the correlation ranged from 0.16 to 0.26 (Brewer et al. 2007 ). Bourque et al. ( 2012 ) indicated that risk perception was a necessary predictor of preparedness, but it might not be a sufficient predictor.

Based on the findings in the literature, two hypotheses are formulated:

Hypothesis 6

Risk perception of typhoons positively affects disaster preparedness behavior.

Hypothesis 7

Risk perception of typhoons positively affects intention of typhoon preparedness.

2.3 Risk Perception and the Theory of Planned Behavior

Previous studies also reported relations between risk perception and a person’s attitude, subjective norm, and perceived behavioral control of natural hazards, prompting the possibility that these variables mediated the effects of risk perception on disaster preparedness behavior.

First, relations between risk perception and attitude have long been identified by previous literature of natural hazards (for example, Marti et al. 2017 ). The Risk Perception Attitude framework describes the effects of risk perception on behaviors that form different attitude scenarios (Rimal and Real 2003 ). The Risk Perception Attitude framework recently was applied in risk management (for example, Liu-Lastres et al. 2019 ).

Second, both risk perception and subjective norm are socially and culturally shaped by society (Najafi et al. 2017 ). While risk perception provides values or meanings for the potential disaster (McIvor and Paton 2007 ), the internalization of these values forms subjective norms (Khalil et al. 2014 ).

Third, an association is believed to exist between risk perception and perceived behavioral control because both internal and external controls of behavior are related to people’s perception of the context of the issue (Liu-Lastres et al. 2019 ).

Based on the findings in the literature, three hypotheses are formulated:

Hypothesis 8

Risk perception of typhoons positively affects attitude toward typhoon preparedness.

Hypothesis 9

Risk perception of typhoons positively affects subjective norm of typhoon preparedness.

Hypothesis 10

Risk perception of typhoons positively affects perceived behavioral control of typhoon preparedness.

Combining the above observations, this study proposed a conceptual framework that extended the theory of planned behavior by adding risk perception as a new variable for predicting disaster preparedness behavior of typhoon vulnerable people in Hong Kong (Fig. 1 ).

Conceptual framework used in this study

This study conducted an intercept survey at a typhoon-prone district of Hong Kong. The survey period was from December 2018 to May 2019, before the start of a typhoon season, so that the respondents expressed general opinions toward typhoon preparedness without the interference of recent typhoon events.

3.1 Questionnaire

A structured questionnaire was developed according to the conceptual framework presented in Fig. 1 . Excluding the question items to determine the socio-demographic characteristics of respondents, there were 17 items that belonged to six sections (Table 1 ). All instrument items were adopted from previous literature and modified to fit the current research context. Except for the question items of attitude, which used a 5-point bipolar semantic differential scale ranging from −2 to 2, all questions were set with a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Two independent experts in the field were invited to review the questionnaire and a pilot survey was conducted for the revision of ambiguous or unclear wording.

The measurement items of attitude, subjective norm, perceived behavioral control, and intention of disaster preparedness were adapted from Ajzen ( 1991 ), Najafi et al. ( 2017 ), Daellenbach et al. ( 2018 ), Tan et al. ( 2020 ), and Ng ( 2021 ). Attitude was measured by asking respondents to rate three pairs of adjectives: ineffective–effective, useless–useful, harmful–beneficial. Three dimensions of subjective norm were measured: family or friends; people who are important to the respondent, and social pressure. Three items were used to measure perceived behavioral control: confident to do, up to the respondent, and easy to do. Intention was measured using three items: expect to do, plan to do, and will do. The measurement items of risk perception were adopted from Brewer et al. ( 2007 ), Paul and Bhuiyan ( 2010 ), and Miceli et al. ( 2008 ). Disaster preparedness behavior was measured by asking whether respondents prepared a first-aid kit and made an emergency plan before the arrival of imminent typhoons. Previous studies commonly considered these two items as the principal constituents of the operationalization of personal preparedness (Paul and Bhuiyan 2010 ; Kohn et al. 2012 ; Lam et al. 2017 ). Five items of demographic variables (that is, gender, age, education, income, and housing type) of respondents were asked to obtain their background information.

3.2 Data Collection

The research was based on face-to-face interviews conducted on the streets in Kwun Tong. Potential streets, located within a distance of two blocks to the coastline and highly affected by strong winds and heavy rainstorms during typhoons, were first identified from the map, then their suitability for conducting the interview was checked in the field.

There were two reasons for conducting the survey in Kwun Tong. First, Kwun Tong is a typhoon-prone district in Hong Kong. Kwun Tong has high exposure to the impacts of typhoons because of its location and aspects. It also experiences multiple impacts caused by typhoons, including storm surges and landslides because of the local topography and geology (Johnson et al. 2016 ). Therefore, Kwun Tong residents are generally more exposed to hazards than residents of other districts in Hong Kong. Second, Kwun Tong residents may represent a typhoon vulnerable community, as Kwun Tong is the poorest district in Hong Kong. Its median monthly household income is HK$15,960, significantly lower than the median of Hong Kong as a whole (that is, HK$20,500) (Census and Statistics Department 2011 ). Kwun Tong also has the highest population density (57,530 persons per km 2 ) and the largest number of households (227,168) among all Hong Kong districts (Census and Statistics Department 2016 ).

To approach suitable respondents for this study, a street intercept survey was believed to be more effective than conventional methods of population survey in Hong Kong because of three reasons: (1) Hong Kong’s urban fabric is dominated by high-rise buildings guarded by security checkpoints that deny unsolicited visits (Lo et al. 2017 ); (2) respondents generally feel more comfortable with interviews in public areas than in the household units (Lo and Jim 2012 ); and (3) the interviewers can select and check suitable samples before engaging in the interview.

Pedestrians who were visually impaired or illiterate were not invited. Appropriate pedestrians who were Kwun Tong residents above the legal age of 18 and were capable of communication were invited to attend face-to-face interviews. After gaining the consent of suitable respondents, the interviewer engaged with them to complete the questionnaire. Each interview took approximately 15–20 minutes to complete.

To enhance the variability of samples, interviews were conducted on both weekdays and weekends. Furthermore, only one individual was selected from a group of pedestrians. This street survey naturally excluded those people who did not appear on the street for various reasons, such as mobility problems and family roles. However, this did not create a sampling bias because less mobile people are usually less exposed to typhoons.

A total of 300 participants were successfully interviewed. The sample size was comparable to many social surveys in Hong Kong (for example, Wong and Yan 2002 ) and hazard studies in foreign countries (for example, Shapira et al. 2018 ).

3.3 Statistical Analysis

All questionnaire data were checked for completeness and unresponsive results were removed. Descriptive statistics were used to categorize the socio-demographic characteristics of respondents.

The normality of numerical data was checked and skewed data were log-transformed. Multivariate outliers were identified by calculating the Mahalanobis Distance ( p < 0.01). Eventually, a total of 286 cases were secured for statistical analysis. The sample size was considered effective for structural equation modeling (Kline 2010 ). The reliability of the data was tested using Cronbach’s alpha. The common method bias of the data was evaluated by the Common Latent Factor method. The above analyses were performed using IBM SPSS Statistics 26.0.

The structural equation modeling was performed to evaluate the theoretical model (that is, the extended theory of planned behavior in this study) based on its consistency with actual data. It allows the examination of causal relations among multiple variables of different levels in a single analysis (Kline 2010 ). The procedure of structural equation modeling consists of two steps: (1) confirmatory factor analysis assesses the validity of the measurement model by testing the relationships between latent variables and the corresponding items; and (2) path analysis tests the structural model by determining the correlations between latent variables. Based on the results of path analysis, the indirect effects of risk perception on intention and disaster preparedness behavior (that is, the mediations via the constructs of the theory of planned behavior) were assessed using 95% confidence intervals from 2000 bootstrap samples. The structural equation modeling was performed using IBM SPSS AMOS 26.

This section first provides a summary of descriptive statistics. After presenting the results of statistical analysis, the effects of risk perception, attitude, social norm, and perceived behavior control on intention of preparedness and disaster preparedness behavior are elaborated.

4.1 Descriptive Statistics

The socio-demographic characteristics of the respondents are outlined in Table 2 . The numbers of male (49%) and female (51%) respondents were almost equal. Of the respondents, 57% were youth between the ages of 18–35. The seniors (3.5%) were very few. In terms of education, 63.3% had received a qualification from a university or college. More than two-thirds of the respondents (68.5%) reported a gross monthly household income in the range of HK$5000 to $39,999; 3.5% and 28% of the respondents monthly earned < HK$4999 and > HK$40,000, respectively. Over half of the respondents (59.1%) lived in public housing, and the rest lived in either private housing (30.8%) or other types of housing (10.1%).

The survey reported high levels of attitude (mean = 3.78 ± 0.646, out of 5 marks), subjective norm (mean = 3.48 ± 0.694, out of 5 marks), perceived behavioral control (mean = 3.60 ± 0.590, out of 5 marks), risk perception (mean = 3.73 ± 0.637, out of 5 marks), and intention of preparedness (mean = 3.54 ± 0.692, out of 5 marks). However, the level of disaster preparedness behavior (mean = 2.59 ± 0.869, out of 5 marks) was comparatively low (Table 3 ). Relatively low levels of preparedness were also reported by a few local studies, for example, Chan et al. ( 2016 ) reported that 49.4% of the respondents had a first-aid kit, and 57.4% prepared non-perishable food and drinking water; Fung and Loke ( 2010 ) reported that 60.6% of the respondents kept a first-aid kit at home.

4.2 Testing the Extended Theory of Planned Behavior

After items SN3 and Risk3 had been deleted, all constructs achieved satisfactory reliability as the Cronbach’s α values were higher than the accepted value of 0.7. The measurement model was assessed by confirmatory factor analysis and the results are also included in Table 3 . The construct validity of all constructs was acceptable as the values of loading were higher than the accepted value of 0.6. The composite reliability of all constructs was excellent as the values of construct reliability were higher than the accepted value of 0.6. Discriminant validity was achieved as the values of average variances extracted were higher than the accepted value of 0.50, and all the correlations between constructs were lower than the square roots of the values of average variances extracted.

The confirmatory factor analysis generated various indices of fit to reflect the fit between the measurement model and the data set. Important indices of fit are stated as follows: Chi-square to degree of freedom (χ 2 /df) = 1.87, comparative fit index (CFI) = 0.965, Tucker-Lewis index (TLI) = 0.949, goodness of fit index (GFI) = 0.940, normed fit index (NFI) = 0.929, incremental fit index (IFI) = 0.965, and root mean square error of approximation (RMSEA) = 0.055. The compliance of indices of fit with recommended values indicated a good fit of the measurement model (Schreiber et al. 2006 ; Hair et al. 2010 ).

The same set of indices of fit was generated for the structural model. The results show that the structural model was a good fit, with χ 2 /df = 2.351, CFI = 0.939, TLI = 0.920, GFI = 0.918, NFI = 0.900, IFI = 0.940, and RMSEA = 0.069. The structural model was a good fit because all indices of fit complied with recommended values.

The path analysis evaluated causal relations among the constructs of the structural model (Fig. 2 ). The correlation between two variables was indicated by the standardized path coefficient. Critical ratio (CR) was calculated to indicate the significance of the path, where significance at 0.05 level if critical ratio > 1.96, and significance at 0.01 level if critical ratio is > 2.576. Hypotheses were tested by evaluating the significances of path coefficients. Therefore, hypotheses 3, 8, 9 and 10 were accepted at the significance level of 0.01, and hypotheses 1, 6, and 7 were accepted at the significance level of 0.05. Hypotheses 2, 4, and 5 were rejected (Table 4 ). The r 2 values were 0.335 and 0.714 for the constructs of behavior and intention, indicating that the structural model explained 33.5% and 71.4% of variances in these two variables, respectively.

Structural paths and path coefficients of the structural equation modeling in this study

4.3 The Theory of Planned Behavior and Disaster Preparedness Behavior

The results of structural equation modeling indicated that intention was a significant predictor of behavior (r = 0.343, CR = 2.485, p < 0.05). Therefore, H1 was accepted. Significant correlations between intention and behavior were reported by previous studies of disaster preparedness behavior (for example, Tan et al. 2020 ). The level of intention (mean = 3.54 ± 0.80) was higher than that of behavior (mean = 2.59 ± 0.96), implying that not all individuals would carry out their intention to perform the behavior (that is, intention-behavior gap). Martins et al. ( 2019 ) indicated that situational facilitators and impediments affected the execution of the decision for disaster preparedness.

Although significant associations between attitude and intention were found in various settings and contexts (Kraus 1995 ), attitude was not significantly correlated with intention (r = −0.060, CR = 0.604, p > 0.05) in this study. Therefore, H2 was rejected. It is probably because attitude does not well explain behavior under extreme conditions (Turaga et al. 2010 ). Glasman and Albarracín ( 2006 ) indicated that attitude was a more reliable predictor of behavior if it was easy to recall and was stable over time. Since disaster is not a matter of daily life, residents may not have stable attitudes toward disaster preparedness.

Among the three basic constructs of theory of planned behavior, subjective norm was the only significant predictor of intention of preparedness (r = 0.483, CR = 3.843, p < 0.01). The acceptance of H3 indicated that society played an important role in a person’s decision to take action (for example, Najafi et al. 2017 ; Tan et al. 2020 ). When the residents were aware of the expectation of preparedness from family, friends, and society, they were more willing to prepare for typhoons. It is because people interact with others (such as friends and family members) to form a social environment that gives meaning (value, benefit, and so on) to the decision for action (Becker et al. 2012 ).

H4 and H5 were rejected because perceived behavioral control was not significantly correlated with intention (r = 0.072, CR = 0.758, p > 0.05) and behavior (r = −0.118, CR = 1.037, p > 0.05), respectively. Similar findings were reported by a few studies of disaster preparedness (for example, Najafi et al. 2017 ; Tan et al. 2020 ). Because the impacts of a disaster are often insurmountable and beyond human imagination, people cannot control the outcome even with preparedness. The low outcome expectancy cuts off the associations between perceived behavioral control, intention, and behavior (Artistico et al. 2014 ). Consequently, people become reluctant to prepare and/or transfer the responsibility of preparedness from themselves to other parties, for instance, the government (Paton 2019 ). Fung and Loke ( 2010 ) reported that nearly half of the surveyed households were confident that the government could manage disastrous situations.

4.4 Risk Perception and Disaster Preparedness

Interestingly, significant correlations were found between risk perception and all studied variables in this study. Risk perception was significantly correlated with disaster preparedness behavior (r = 0.353, CR = 1.980, p < 0.05) and intention (r = 0.406, CR = 1.972, p < 0.05), respectively. Therefore, both H6 and H7 were accepted. Previous studies reported that risk perception was significantly correlated with disaster preparedness behavior and intention in various hazard contexts and settings, for example, landslides (Xu et al. 2016 ), floods (Miceli et al. 2008 ), earthquakes (Becker et al. 2012 ), and hurricanes (Martins et al. 2019 ).

Risk perception was also a significant predictor of the three constructs of the theory of planned behavior. Risk perception was correlated with attitude (r = 0.717, CR = 7.858, p < 0.01), subjective norm (r = 0.762, CR = 8.502, p < 0.01), and perceived behavior control (r = 0.694, CR = 7.056, p < 0.01), respectively. Therefore, H8, H9, and H10 were accepted. These findings generally are consistent with existing studies of attitude (for example, Marti et al. 2017 ), subjective norm (for example, Najafi et al. 2017 ; Tan et al. 2020 ), and perceived behavioral control (for example, Liu-Lastres et al. 2019 ).

The above findings confirm that risk perception generates a multitude of effects on a person who decides to perform disaster preparedness behavior. Risk perception influences intention of preparedness and disaster preparedness behavior via two types of channels. The first type is the “direct” channels, as indicated by the significant correlations between risk perception, intention, and disaster preparedness behavior. The second type is the “indirect” channels via subjective norm. Table 5 summarizes the important statistics of the indirect effects of risk perception on intention and behavior.

5 Discussion

This study has a few theoretical and practical implications. For theoretical implications, this study confirmed the value of adding risk perception to the theory of planned behavior. The extended theory of planned behavior effectively predicted intention of disaster preparedness and disaster preparedness behavior. As the values of r 2 exceed the threshold of 0.26, the model is considered substantial (Cohen 1988 ). Specifically, the extended theory of planned behavior can explain 33.5% of the variances in behavior, and 71.4% of the variances in intention, respectively, performing better than the original theory of planned behavior. A meta-analysis of 206 independent studies reported that, on average, the theory of planned behavior explained 19.3% of the variances in behavior and 44.3% of the variances in intention, respectively (McEachan et al. 2011 ). Second, this study presented a roadmap to show how risk perception and behavioral variables affected intention of disaster preparedness and disaster preparedness behavior. Although risk perception is believed to generate a multitude of effects on disaster preparedness behavior, the process of how risk perception affects disaster preparedness behavior has not yet been clarified by the existing literature. Whereas Miceli et al. ( 2008 ) indicated that risk perception encompassed both cognitive and affective impacts on a person’s decision on preparedness, Loewenstein et al. ( 2001 ) indicated that risk perception exerted both direct and indirect influences on behavior. This study demonstrated that, apart from the direct effect on intention of preparedness and disaster preparedness behavior, the indirect effects of risk perception was exerted via subjective norm.

This study also offers practical insights that enhance personal and household preparedness toward typhoons. Due to the importance of risk perception for disaster preparedness, educational and promotional programs are always necessary to enhance risk perception and awareness in society (Chan et al. 2016 ). Equally important is to identify and understand the factors that distort risk perception, and hence lead to inappropriate decisions for disaster preparedness behavior. Only when people realize the risks associated with typhoons, they become motivated to prepare accordingly (Lazo et al. 2015 ). Significantly, subjective norm was the only construct of the theory of planned behavior that had a significant correlation with intention of preparedness, highlighting the importance of social influence on a person’s disaster preparedness. While conventional initiatives to promote preparedness target the individual’s decision, they often neglect the social context of that decision (Becker et al. 2012 ). Because people are more likely to adopt preparedness measures if they observe or believe that others have prepared, it is important to cultivate the preparedness culture in local communities.

This study has a few noted limitations. The first limitation is the reporting bias associated with the self-reported questionnaire. What the respondents had reported might not be accurate and objective measures of what they thought and how they behaved. However, validating the opinions collected from the respondents is impossible. Second, although this study had developed the survey protocol that aimed at a good control of data quality, younger and well-educated respondents were over-represented, which might have biased the results. Third, this study had only interviewed respondents from one district in Hong Kong, so the samples did not represent the general population of Hong Kong. Hence, the findings should be interpreted with caution. Fourth, relations identified by the structural equation modeling were limited to statistical inferences and could not be recognized as causation. Nevertheless, these findings cast light on developing research questions and hypotheses that inform future studies. Qualitative methods, such as in-depth interviews, are useful to understand the causal relations between disaster preparedness behavior and its predictors. Despite the above limitations, this study was able to integrate risk perception and the theory of planned behavior into a united model that can be used to predict the disaster preparedness behavior of typhoon vulnerable people in Hong Kong.

6 Conclusion

Facing the challenges brought by typhoons, a robust body of research has explored various options for reducing the risks of typhoon impacts. Social scientists emphasize the importance of personal disaster preparedness for reducing hazard vulnerability in the process of disaster management. This study adopted an extended theory of planned behavior to predict the disaster preparedness behavior of typhoon-vulnerable people in Hong Kong by using the data acquired from an intercept survey. Confirmatory factor analysis affirmed the validity of the model and the final structural equation model adequately fits the data. The results indicated that risk perception directly affected intention of preparedness and disaster preparedness behavior, while generating indirect effects via subjective norm. Although risk perception changed attitude and perceived behavioral control, the changes had no significant effects on intention of preparedness and disaster preparedness behavior. This study demonstrated the value of extending the original theory of planned behavior by adding risk perception as the new variable for predicting personal typhoon preparedness. Educational and promotional programs are necessary to enhance risk perception and cultivate a preparedness culture in society.

A warning signal is hoisted if a tropical cyclone approaches within a distance of 800 km to Hong Kong. Signal no. 10 represents the highest level of typhoon intensity.

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Acknowledgements

The author is grateful to Ms. Joni Fung Mei Wong for organizing the questionnaire survey. The author is also grateful to Ms. Joey Cheuk Yee Chan for carrying out the field interview. Thanks are given to Mr. Andrew Yan To Ng for polishing and editing the manuscript.

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Ng, S.L. Effects of Risk Perception on Disaster Preparedness Toward Typhoons: An Application of the Extended Theory of Planned Behavior. Int J Disaster Risk Sci 13 , 100–113 (2022). https://doi.org/10.1007/s13753-022-00398-2

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Evolution of natural disaster terminologies, with a case study of the covid-19 pandemic

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Disaster, catastrophe, and cataclysm are some English terminologies that describe the severity of adverse events. Civilians, reporters, and professionals often use these terminologies to communicate and report any event’s severity. This linguistic method is the most practical way to rapidly reach all levels of local/regional/national, and international stakeholders during disasters. Therefore, disaster terminologies play a significant role in disaster management. However, attaining the actual magnitude of a disaster’s severity cannot be comprehended simply by using these terminologies because they are used interchangeably. Unfortunately, there is no consistent method to differentiate disaster terminologies from one another. Additionally, no globally accepted standard technique exists to communicate the severity level when disasters strike; one observer’s ‘disaster’ can be another’s ‘catastrophe’. Hence, a nation’s ability to manage extreme events is difficult when there are no agreed terminologies among emergency management systems. A standard severity classification system is required to understand, communicate, report, and educate stakeholders. This paper presents perceptions of people about disaster terminologies in different geographical regions, rankings and differences in disaster lexical and lexicon. It explores how people perceive major events (e.g., the Covid-19 pandemic), and proposes a ranking of disaster terminologies to create a severity classification system suitable for global use.

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Introduction.

The linguistic method, which involves using generic terminology such as ‘emergency,’ ‘disaster,’ and ‘catastrophe’, plays a vital role in communicating the severity of natural disasters—events triggered by nature, such as biological, climatological, extraterrestrial, geophysical, hydrological, and meteorological disasters. Within a language community, terminologies serve to convey information and inter-subjective messages. Like all forms of written or oral communication, the meaning of a term (or sentence) carries a message and information from one person to another. The linguistic method represents the oldest and most practical method of communication and reporting during disasters of varying severity levels. It rapidly disseminates information to all levels of stakeholder groups (local, provincial, regional, national, continental, or international). Additionally, it is commonly employed for educational purposes due to its wide-reaching effectiveness.

The need for a common understanding of terminologies to communicate about the severity of an event is essential in disaster situations 1 , as they often require many stakeholders to work together toward a common goal. This need is compounded by the fact that many stakeholders involved in disaster relief may speak different languages and might not fully understand English words such as ‘disaster,’ ‘catastrophe,’ and ‘cataclysm.’ As a result, confusion and breakdowns in communication may occur because some stakeholders might not comprehend the exact meaning of the terminologies being used 2 . Yet, establishing a common understanding of these terminologies is vital to reduce confusion among stakeholders and create a well-understood framework for providing disaster relief. However, for this to happen, policymakers, academics, and practitioners must initiate the process of redefining terminology while simultaneously developing appropriate measures and scales that distinguish each term and its representation of disaster severity.

Similarly, although disasters are not universally understood in the same way 3 , reaching a common understanding—such as a universally agreed-upon approach about what category a disaster falls under–to classify disaster severity is essential 4 in disaster management and disaster risk management. This common understanding is crucial in situations where many stakeholders worldwide come together for a common cause. To initiate this process, English is the most suitable language for classifying natural disasters globally, given its predominance as the most widely spoken and used official language (see Supplementary A online). The disaster terminologies in English are even adapted with modifications when creating severity scales (for both individual and common classification systems, as shown in Supplementary B online) to emphasize the degree of impact an event has 5 . When an acceptable point of reference exists within a globalized language (i.e., a guideline or standard criterion) to classify disasters, it can be adapted to any particular language through translation and standard colour and number coding, as described in Section “ Limitations and future extensions ”.

Due to inconsistency in how stakeholders perceive various terminologies, the lack of agreed terminology poses a global challenge in formulating legislation and policies regarding disaster response 6 . Failing to identify a potential hazard during disaster communication can lead to devastating consequences 7 . For example, Hurricane Katrina’s devastating impacts were exacerbated by an ineffective government response and a failure to recognize the severity of the situation 8 . Another example is the series of decision-making errors that compounded disaster relief efforts during the 2004 Indian Ocean tsunami. These errors contributed to the Indian Ocean Tsunami becoming one of the world’s deadliest natural disasters, resulting in approximately 230,000 deaths and leaving over a million homeless in 14 countries. For instance, in India, word of the disaster went to the wrong official 9 . With no warning, coastal populations were caught off guard by the immense waves from the tsunami. The lack of communication was made worse because officials did not recognize or adequately classify the severity of the event. Consequently, coastal residents, tourists, and governments did not know the tsunami’s severity, so they did not effectively respond to the disaster 10 . In addition, inconsistent identification of disaster impacts results in overcompensation or under-compensation in assigning resources for mitigation 11 . Overcompensation may waste resources, while under-compensation could increase the impact severity. Properly and promptly identifying the disaster impact is crucial because lives depend on these decisions 12 .

These examples make it imperative that a standard severity classification system is required to understand, communicate, report, and educate stakeholders during a disaster, including in both the pre- and post-disaster period. Moreover, these natural phenomena have no national boundaries when they strike. The impacts of a disaster in a region, if not managed properly, can produce political and social instability, and affect international security and relations 13 . The recent Covid-19 pandemic is a good example of these consequences. A common communication platform for disaster severity is therefore needed to convey vital information in a standard format that a global audience understands.

Selecting specific terminologies, even within the English language, to represent varying levels of severity for a global audience is a challenging task that demands careful consideration. This challenge arises because the terminology we employ is not universally understood. The lexicon (dictionary) meaning and the lexical (verbal) meaning of these terminologies can vary based on factors such as time/era, location, individual experience, and situation (see Supplementary C online). Although the severity scales mentioned in Supplementary B online are developed using the linguistic method to categorize the different levels of severity, in all but the two common scales, the proposed labeling appears to be arbitrary, particularly in all individual and common scales. The two scales, Fatality-Based Disaster Scale 14 and Universal Disaster Severity Classification 15 , paid some attention when selecting the terminologies to label the levels of severity; however, they did not consider the current views of the people who are going to use these scales. Proposing a clear definition and criteria for disaster terminologies is important, but people often do not refer to the definition, especially in disaster situations, and they assume the lexical (verbal) meaning of the word. Therefore, it is important to consider the users’ perspectives when selecting labels/terminologies for unification to categorize disaster severity levels.

This research aims to propose a universal classification framework for defining disaster severity regardless of the geographical location of the disaster and the linguistic, lexical, and semantic nuances that can affect the interpretation of terminology. Moreover, the focus of this universal disaster framework will be measured specifically in terms of the adverse effects the event has on a community or an environment and not the degree of severity it has on an individual.

Methodology

The terminologies that describe the magnitude of a natural phenomenon, including calamity, cataclysm, catastrophe, disaster, and emergency, were selected for investigation. The aim was to determine whether significant differences exist in the seriousness levels among these terms or if people perceive them as synonyms and use them randomly. The term ‘Armageddon,’ which describes “a usually vast decisive conflict or confrontation” or “a terrible war that could destroy the world” 16 , was excluded from consideration due to its relevance to human-caused catastrophes rather than natural events. Similarly, the term ‘apocalypse’ was excluded from the analysis due to its religious connotations, as some religious beliefs associate it with the destruction or end of the world.

Surveys have been conducted to investigate people’s perceptions of natural disaster terminologies and how they utilize these terms to indicate the severity levels of an event using a case study. The primary objective of these surveys was to determine whether differences exist in ranking disaster terminologies among individuals. The research question addressed was: Are there any differences in the ranking of disaster terminologies among people? The hypothesis posited is that there are no differences in the ranking of disaster terminologies among people. Therefore, the independent variables in this study were the disaster terminologies, and the dependent variable was the respondents’ rankings of the disaster terminologies. These surveys were approved by the University of Calgary Conjoint Faculties Research Ethics Board.

To examine the previous research question, two web-based surveys were conducted. All five terminologies (calamity, cataclysm, catastrophe, disaster, and emergency) were presented in alphabetical order to each respondent. Subsequently, the respondents were asked to rank the five terminologies based on their understanding of the term’s severity level, ranging from the lowest (Level 1) to the highest (Level 5). Respondents were not allowed to assign the same rank to two different terminologies within these surveys. The first survey, conducted from August 2015 to December 2020, involved presenting the terminologies without providing their definitions, resulting in rankings based on lexical (verbal) meaning. In the second survey (conducted from September 2020 to June 2021), respondents were given the definitions from the Oxford dictionary, resulting in rankings based on lexicon (dictionary) meaning. The study seeks to rank the severity of disaster terminologies for global audiences who typically rely on dictionary definitions rather than disaster literature when referencing meanings. It is noteworthy that none of the disaster literature, except for the literature related to the continuation of this research, provides definitions for all five terms considered in this study. Consequently, the current definitions from the Oxford English Dictionary for the aforementioned five terms are presented to the respondents to ensure consistency in the analysis. During the second survey, respondents were also queried about the single terminology they would use to describe the ongoing Covid-19 pandemic. Additionally, real-time Covid-19 statistics, including global confirmed cases, global deaths, and global recovered cases, were presented to the respondents while answering this question.

Web-based international surveys were conducted to provide access to large and geographically dispersed populations cost-effectively and efficiently. These web surveys were launched on the Alchemer platform (formerly known as SurveyGizmo) to reach participants globally. As this study was conducted in English, the target population comprised English-speaking adults aged 18 years or older who were internet users, amounting to fewer than 1 billion people. Approximately 1.4 billion out of 7.8 billion people spoke English, with around 26% of the global population being under 15 years of age 17 . Additionally, there were 4.914 billion active internet users worldwide, constituting 63% of the global population in 2021 18 . The survey was designed as an international web-based survey due to its focus on the adult English-speaking population (aged 18 and above). While no subgroups were identified within the global population analysis, subgroups were considered for geographical areas, such as the six populated continents. A general statistical guideline suggests that approximately 30 participants are needed in each group 19 . However, the sample size requirement for non-parametric tests was 1.15% of the parametric test’s sample size 20 . Consequently, a sample size of about 242 was necessary to represent all continents in non-parametric tests.

In this study, non-probabilistic sampling techniques are employed because the research focuses on the entire population of English-speaking adult internet users, a group too vast to be comprehensively examined. The study aims to establish a ranking suitable for a global audience, rendering it impractical to utilize random probability sampling, which would grant each population member a known (or equal) chance of participation. Consequently, a combination of convenience sampling (recruiting readily available and willing participants) 19 and snowball sampling (recruiting participants through existing participants) 19 was employed to gather respondents. Potential survey participants were invited through both professional and personal networks, including contacts gathered from the 3rd United Nations World Conference on Disaster Risk Reduction. Survey links were disseminated through various means, including emails, short message services, social media platforms (such as Facebook and LinkedIn), newsletters (e.g., the e-PEG newsletter of the Association of Professional Engineers and Geoscientists of Alberta, and the electronic newsletter of the World Federation of Engineering Organizations—Committee on Disaster Risk Management), websites, online discussion platforms (such as Catastrophe Indices and Quantification Inc. (CatIQ), and the Canadian Risk and Hazards Network (CRHNet)), and by distributing handouts at conferences (including the 12th and 13th Annual CRHNet Symposiums, the 5th International Natural Disaster Mitigation Specialty Conference of the Canadian Society for Civil Engineering, the 12th International Conference of the International Institute for Infrastructure Resilience and Reconstruction, and the Canadian Catastrophe Conference of the CatIQ).

Since the study was based on ordinal data (ranking/ordering values) 20 , it was better suited for representation by the median rather than the mean. Consequently, non-parametric tests were employed. The preference for the median over the mean stemmed from the skewed distribution nature of the study. The median better captures the center of the distribution, signifying that 50% of the values lie above it while 50% lie below. For instance, consider a scenario where most individuals assign higher rankings to a particular term, and very few assigns lower rankings (resulting in a few outliers) to that same term. In such cases, the mathematical mean may decrease even though the median remains stable. In situations where the distribution is significantly skewed, extreme values in the distribution’s tail can substantially affect the mean. Conversely, the median remains a more robust indicator of the distribution’s center.

In these surveys, the five samples of each terminology are interconnected, as respondents were unable to assign the same rank to two different terminologies. As a result, the ranks received for the five terminologies were interdependent. The non-parametric tests below 21 were conducted to address the following hypotheses:

The Friedman test was employed to determine whether people utilize these five terminologies randomly or if there exists a significant difference in the ranking of each terminology.

There is no significant difference between the mean ranks of the disaster terminologies.

If the ranks were not randomly distributed, Kendall’s W Test was performed to ascertain the agreement between respondents’ rankings.

There was no agreement among the respondents in ranking different terminologies (W = 0).

In cases where agreement among respondents’ ranking was observed, the Wilcoxon signed-rank test was conducted for each pair of terminologies. This aimed to identify terminologies with differing rankings and terminologies showing similar rankings, shedding light on peoples’ ranking of these natural disaster terminologies. Further details about the Wilcoxon signed-rank test are available in Supplementary D online.

The median difference (M A  - M B ) was equal to zero. For instance: H 0 : M Cataclysm  − M Calamity = 0.

Analysis of perception about natural disaster terminologies

To gauge public perceptions of commonly used severity terminologies, an initial survey collected 1170 responses. However, only 624 respondents (approximately 54%) completed rankings for all five terminologies based on their lexical (verbal) meanings. Notably, many respondents omitted rankings for ‘cataclysm’ or ‘calamity’ compared to the other three terminologies. ‘Emergency,’ ‘disaster,’ and ‘catastrophe’ were more widely recognized, likely due to their prevalence in governmental and insurance-related contexts, while ‘calamity’ and ‘cataclysm’ were viewed as more colloquial 15 . Some respondents may have refrained from ranking these terminologies due to their perceived subjectivity, favouring a more objective approach to assessing disaster severity. The initial assumption that ‘emergency,’ ‘disaster,’ ‘calamity,’ ‘catastrophe,’ and ‘cataclysm’ represent a perceived hierarchy of seriousness among disaster terminologies was derived from the frequency of completed survey rankings (see Fig.  1 ).

Frequency distribution of respondents’ rankings (from 1 to 5) for natural disaster terminologies.

Based on the two-survey analysis (see Supplementary E online), Table 1 compares respondents’ rankings of disaster terminologies with and without the respective terminology’s definitions. In the global sample, the absence of the terminology’s definitions (i.e., lexical (verbal) meaning) resulted in four distinct levels of ranking for ‘emergency,’ ‘calamity,’ ‘disaster,’ and ‘catastrophe/cataclysm.’ However, when the definitions of the terminologies were provided (i.e., lexicon (dictionary) meaning), respondents did not differentiate between terminologies with more than two levels, specifically ‘emergency/calamity’ and ‘disaster/catastrophe/cataclysm.’ Similarly, for North Americans and Asians, the absence of terminology definitions led to clear differentiation among ranks, creating three distinct levels: ‘emergency,’ ‘calamity/disaster,’ and ‘catastrophe/cataclysm.’ However, with the presence of definitions, they did not differentiate terminologies with more than two levels. Additionally, Oceania respondents, who exhibited two distinct levels without the presence of disaster terminology definitions, showed only one level when definitions were provided (i.e., they randomly ranked the five terminologies). Europeans maintained the same rankings with or without definitions. However, it is important to note that the European and Oceania continents might not have yielded accurate results due to insufficient data (n < 60) for the Wilcoxon signed-rank test. Nevertheless, a clear differentiation between respondent rankings for lexicon (dictionary) meaning and lexical (verbal) meaning was evident among the global, North American, and Asian respondents.

The summary of the results in Supplementary E online and “ Analysis of perception about natural disaster terminologies ” section can be outlined as follows: Firstly, there is a consensus among global respondents regarding the representation of severity order for disaster terminologies in both their lexical (verbal) and lexicon (dictionary) meanings. Similarly, North Americans, Asians, and Europeans share a common perspective on the severity order representation of disaster terminologies for their lexical (verbal) and lexicon (dictionary) meanings. However, for Oceania respondents, agreement is observed only in the lexical (verbal) meaning, not in the lexicon (dictionary) meaning. In essence, an agreed-upon order of seriousness exists rather than random usage of these terms. Secondly, a slight variation exists in the understanding of these terminologies based on the geographical locations of English speakers, particularly in their lexical (verbal) meaning. Nonetheless, such differences are not significant when it comes to the lexicon (dictionary) meaning. In other words, the inclusion of definitions can lead to a general agreement among people, reducing the variance in the severity order representation based on geographical regions. Thirdly, a distinction is evident in perceptions about the order of severity for disaster terminologies between their lexical (verbal) meaning and their lexicon (dictionary) meaning. While a clear differentiation across four severity levels existed for lexical (verbal) meaning, the differentiation was limited to two levels for lexicon (dictionary) meaning. The provided disaster definitions (Oxford Dictionary definitions) did not facilitate differentiation among the disaster terminologies 22 , 23 . The analysis underscores that these provided definitions did not enhance understanding; rather, they introduced further confusion. Consequently, if these terminologies are to be employed for distinguishing severity levels within a standard classification system, precise definitions for each disaster terminology are imperative.

Dynamic nature of severity classification

Understanding the usage of disaster terminologies and how global respondents employ them in disaster situations is crucial, particularly when integrating them into a global severity classification system encompassing all types of disasters. In general, it is anticipated that global respondents comprehend and utilize the terminology accurately, and their classifications shift as the severity of an event changes. Within this context, a widespread understanding of disaster terminologies can be inferred. Consequently, these terminologies can be leveraged to delineate severity levels within a global severity classification system, provided that precise definitions are established to enhance people’s comprehension. To examine the hypothesis about how global respondents employ disaster terminologies to convey the severity of an event, a significant event characterized by its diffusion across space and time becomes a more suitable subject for analysis.

The Covid-19 pandemic, which originated in Wuhan, China, in December 2019, swiftly evolved from an endemic to an epidemic, eventually reaching global pandemic status within months. As of March 10, 2023, the pandemic has resulted in over 676.6 million confirmed cases and 6.8 million reported fatalities globally 24 , with new cases reported daily. Covid-19’s far-reaching impact, profoundly affecting various aspects of life worldwide from fatalities to financial crises, makes it a compelling example for this study. To understand public perceptions of major events, an investigation into individuals’ perceptions of the Covid-19 pandemic was conducted.

During the pandemic, a second survey was conducted to assess respondents’ choice of terminology to describe Covid-19’s severity. Respondents selected a single terminology from five options, with real-time Covid-19 statistics provided alongside. Out of 848 respondents, 674 (79.5%) chose one of the five terminologies to describe Covid-19’s severity. The majority described it as a disaster, followed by catastrophe, and emergency (see Fig.  2 ).

Frequency distribution of people’s perceptions regarding the ongoing Covid-19 pandemic.

Respondents’ perceptions of Covid-19 may have shifted due to its increasing impact. The analysis of respondents’ choices over time revealed that each terminology displayed initial randomness in 2020, followed by a stable pattern emerging in the first half of 2021, and subsequently showed an upward trend for disaster and catastrophe and a downward trend for calamity, cataclysm, and emergency (see Fig.  3 ).

Change in perception about the ongoing Covid-19 pandemic.

This shift coincided with the designation of variants of concerns (VOCs) (see Fig.  3 and Table 2 ). With the designation of Alpha and Beta variants, there was a gradual increase in describing Covid-19 as an emergency, calamity, disaster, and cataclysm, while labeling it as a catastrophe decreased. By January 2021, with the designation of the Gamma variant and total confirmed cases surpassing 100 million, with over two million fatalities, respondents consistently applied a variety of labels to the pandemic. Post-April 2021, as the Delta variant emerged and total confirmed cases surpassed 150 million, with over three million fatalities, the trend shifted towards identifying Covid-19 as a disaster or catastrophe, with a decrease in labeling it as an emergency, calamity, or cataclysm. By the survey’s end in June 2021, 50% of respondents characterized Covid-19 as a disaster, 33.3% as a catastrophe, and the remainder as an emergency; none used calamity or cataclysm. Throughout the survey period, the usage of calamity or cataclysm remained low compared to disaster, catastrophe, and emergency.

This case study provides valuable insights into how individuals reference major events and how their perceptions evolve with changing circumstances. As the severity of Covid-19 rapidly increased during the first four months of 2021, reaching over 0.1 to 0.2 billion global confirmed cases and over 2 to 3 million fatalities, people’s choice of terminology became more stable. Their preferences shifted towards terms indicating a higher order of seriousness rather than those with lower levels. As the severity of the pandemic continued to escalate, surpassing 0.2 billion global confirmed cases and 3 million fatalities, people’s usage of terms decreased for those with lower levels of seriousness, while there was an increase in the usage of terms indicating higher levels of severity. This suggests that individuals globally possess a general understanding of disaster terms, and their utilization of these terminologies is guided by their comprehension of the hierarchy of seriousness and events’ severity levels. Therefore, these terminologies can effectively define severity levels within a global classification system, contingent upon establishing precise definitions that enhance people’s comprehension.

Proposed qualitative universal disaster severity classification

Integrating descriptive terminologies within an emergency management system enhances mutual understanding and simplifies management, minimizing confusion. For instance, using terminologies with escalating severity levels such as ‘emergency,’ ‘disaster,’ and ‘catastrophe’ as descriptive headings aligns with increasing severity, rather than only employing headings like ‘Type 1,’ ‘Type 2,’ or ‘Type 3.’ This approach helps to avoid ambiguity regarding whether Type 1 or Type 5 holds greater significance, as it does for Incident Management Teams Typing (IMTs), a classification used by disaster managers and emergency responders 25 , 26 . Consequently, a universal linguistic approach that integrates existing severity classification systems becomes imperative. However, the selection of appropriate terminologies for distinct severity levels should be undertaken with meticulous evaluation 15 .

Proposed sequence of natural disaster terminologies for a global audience

Based on “ Analysis of perception about natural disaster terminologies ” section and Supplementary C and E online, the order of seriousness for the current dictionary definitions, etymological definitions, and people’s perceptions of natural disaster terminologies is presented in Table 3 . This order is being proposed to establish a hierarchy of seriousness for the considered terminologies, tailored specifically for a global audience. As previously mentioned, ‘apocalypse’ is unsuitable for representing severity levels for global audiences due to its religious bias. When determining this order, greater importance was given to the sequence of lexical (verbal) meanings (Column 4 in Table 3 ) compared to the lexicon (dictionary) meanings (Column 5 in Table 3 ), as perceived by individuals. This differentiation stems from the fact that the intended order of seriousness is meant for a worldwide audience, where people generally understand a term’s lexical (verbal) meaning without necessarily referring to the provided lexicon (dictionary) meaning. Consequently, the suggested sequence is as follows: emergency, calamity, and disaster for Levels 1, 2, and 3, respectively. However, both catastrophe and cataclysm are placed at the same level based on the convergence of people’s perceptions regarding the lexical (verbal) and lexicon (dictionary) meanings. Nonetheless, when considering the overall mean rank order obtained from respondents’ rankings (as depicted in Fig.  1 and Supplementary Table S6 online), catastrophe and cataclysm are recommended for Levels 4 and 5, respectively. Therefore, based on the analysis of both lexical (verbal) and lexicon (dictionary) meanings, the proposed sequence of the five terminologies from lowest to highest seriousness is as follows: emergency, calamity, disaster, catastrophe, and cataclysm. This arrangement is not arbitrary; it is substantiated by the data and reflects the contemporary viewpoints of individuals on a global scale. Consequently, this sequence is well-suited for a global audience, and these designations effectively function as categories within a comprehensive global severity classification system.

Proposed qualitative global severity classification system

To establish a universally accepted method of communicating disaster severity levels using a linguistic approach, we have applied the aforementioned proposed order of disaster terminologies to the Qualitative Universal Disaster Severity Classification (QUDSC) developed by Caldera and Wirasinghe 27 , incorporating certain modifications. The selection of QUDSC for this application is primarily attributed to five key factors as described in Supplementary F online.

Table 4 presents Advanced Qualitative Universal Disaster Severity Classification (AQUDSC), a comprehensive system for categorizing all types of natural disasters across stakeholder groups. Five modifications have been introduced to the existing QUDSC. Firstly, the order of seriousness for terminologies has been adjusted, incorporating ‘emergency,’ ‘calamity,’ ‘disaster,’ ‘catastrophe,’ ‘cataclysm,’ and ‘partial or full extinction’ aligning with the general understanding of the global audience as analysed above. Secondly, each level is now assigned a name and definition to create a complete 0–10 level system, including the addition of ‘Emergency Level 1’ to maintain consistency with sub-levels. Thirdly, ‘Type 1’ and ‘Type 2’ terms have been replaced by ‘Level 1’ and ‘Level 2’ to enhance clarity with hierarchical connotation. Fourthly, the definition of ‘emergency’ has been revised to accommodate disasters without human fatalities but substantial damage. Lastly, colour-coding has been adjusted to maintain consistency and aid memorization, with each term assigned a unique color: blue for ‘Emergency,’ green for ‘Calamity,’ yellow for ‘Disaster,’ red for ‘Catastrophe,’ gray for ‘Cataclysm,’ and black for ‘Partial or Full Extinction.’ Lower levels represent light colours, while upper levels represent dark colours. These modifications aim to enhance clarity and facilitate disaster management across all levels (see Supplementary G online for more details).

The QUDSC was used to create the Initial Universal Disaster Severity Classification (IUDSC) 27 . Subsequently, adjustments were made to the IUDSC to align with the AQUDSC. The resulting Modified Universal Disaster Severity Classification (MUDSC) is presented in Table 5 . These modifications have led to improvements to the QUDSC/IUDSC:

The ranking of disaster terminologies in AQUDSC/MUDSC is suitable for a global audience, as it considers the general understanding and lexical (verbal) meaning of users.

AQUDSC/MUDSC comprehensively represents the complete range of severity including disasters that lack direct fatalities but cause significant damage to communities, such as the 2016 Fort McMurray fire.

The system provides a clear labeling strategy to distinguish each level without causing confusion about their respective criticality. Additionally, a consistent colour-coded system facilitates broader communication between the public, emergency services, and media organizations, enabling easy adaptation for any language, country, or culture.

Therefore, AQUDSC/MUDSC enhances the differentiation of disaster severity levels for the global audience, offering a clear understanding of severity along the disaster continuum.

AQUDSC/MUDSC provides standardized terminologies and clear definitions for a global audience to describe the impact of natural disasters. Standardized definitions have significant implications, as outlined in the Technical Report on Hazard Definition and Classification Review 2020 28 . Clear definitions facilitate effective measurement and reporting of risks, thereby contributing to the development of appropriate disaster risk management measures and long-term planning. Standardization supports all aspects of risk management, including multi-hazard risk assessments, warnings and alerts, disaster response and recovery, long-term planning, and public awareness efforts. Furthermore, standardized definitions form the foundation for a uniform database of loss data and information, which makes a valuable contribution to forecasting future events. With consistent, standardized definitions and global-scale risk information, communities at local and national levels can determine the most effective strategies for mitigating the impacts of future events.

MUDSC serves as a global severity classification system for post-event assessment, accommodating various natural events irrespective of the disaster type, location, or occurrence time. It allows for the evolution of severity classifications over time as reports on impacts are updated, aiding responders, and informing public planning and relief efforts. Additionally, it is a comprehensive tool to describe, measure, categorize, compare, assess, rate, and rank the impact of various natural events, ranging from a lightning strike to a super volcanic eruption. Thus, MUDSC simplifies impact assessments, enhances disaster preparedness, and facilitates multi-hazard management by offering a unified classification for regions prone to multiple disasters.

MUDSC enhances warning communications by employing plain language to categorize disasters, ensuring the public comprehends the severity and urgency of evacuation. Plain language communication of warning indications ensures mutual understanding between emergency management systems and the general public. Populations are most sensitive to disasters with high human impacts 7 , and MUDSC explicitly establishes a direct relationship between a disaster and its human impacts. Employing MUDSC for preparedness and mitigation methods, including public awareness campaigns, disaster education, and disaster drills, helps reshape public opinion, capturing the public’s attention and fostering trust and response rates to warnings, minimizing fatalities and injuries during disasters. Its integration into multi-hazard early warning systems contributes to achieving Sendai Framework targets, specifically Target G 29 .

MUDSC enhances disaster preparedness and management globally by providing standardized severity levels. Its implementation is expected to eliminate inconsistencies, facilitate mutual communication among stakeholders, and assess the need for regional, national, and international assistance in managing global disasters. Additional detailed descriptions regarding the significance of the proposed AQUDSC/MUDSC are available in Supplementary H online.

AQUDSC and its application version, MUDSC, were developed to provide a common language for communicating the severity of natural disasters globally. This system aims to facilitate easier communication and management at all levels by selecting appropriate terminologies and using plain language to describe the magnitude of a disaster’s impact, considering the general public’s perception of disaster terminology.

The main advantage of the MUDSC is its ability to provide a standardized method for comparing natural disasters. It allows for the quantitative and qualitative description, measurement, categorization, comparison, assessment, rating, and ranking of a wide range of natural disasters occurring anywhere in the world. The system covers disasters resulting from various types of events, including those that are diffuse in space and time as well as events with less clear start and endpoints, such as droughts, pollution, and epidemics. It also encompasses conditions that could lead to extinction events or massive phenomena, such as super volcanoes or meteoroid impacts. Furthermore, by facilitating multi-hazards management, disaster risk reduction, and preparedness at all levels and within/across all sectors, MUDSC aligns with the goals of the Sendai Framework.

Importantly, the AQUDSC/MUDSC serves as a common categorization system for all stakeholder groups involved in disaster management and response, including civilians, emergency responders, disaster managers, relief agencies, international/regional/national/local government entities, non-governmental organizations, media, insurance managers/estimators, academics, researchers, and policymakers. By offering a comprehensive view of disaster severity, the system aids in public education, assessment purposes, and decision-making for resource allocation, mitigation, and recovery efforts.

Overall, the AQUDSC/MUDSC is expected to establish a universal standard severity classification system that promotes mutual understanding among different countries’ emergency management systems, eliminates inconsistencies, and provides a common language for describing the impact of disasters worldwide.

Ethical approval and informed Consents

Informed consent was obtained from all participants before data collection. Consents were granted only for the inclusion of group information in any presentation or publication of results. Please note that the dataset was collected following the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans 2010 (TCPS 2) and the University of Calgary Guidelines. Ethics approval was granted under certificate ID REB15-0031 and modification ID REB15-0031_MODI for additional questions added to the questionnaire using Covid-19 as a case study.

Limitations and future extensions

This ongoing research project aims to develop an advanced multidimensional Universal Disaster Severity Classification System (UDSCS) to comprehensively assess the disaster continuum both qualitatively and quantitatively 61 . The paper introduces an AQUDSC/MUDSC for global comparison of various natural disasters’ impacts. Initially focusing on fatalities alone, MUDSC’s limitation prompted the need for a multidimensional quantitative scale incorporating influential factors like fatalities and damage costs using a disutility function 30 .

The analysis explores the disparity between perceived severity and actual impact, demonstrating the dynamics of community communication. However, the non-random and restricted sample, especially in linguistically diverse regions, may result in deviations in severity perception. Covid-19 serves as an illustrative case study due to its global impact and evolving severity, although perceptions of epidemics and pandemics differ from other disasters. Consequently, there might be disparities in the perception of event severity.

The provided definitions and criteria in AQUDSC offer guidance for adapting the classification to different languages, aiming for equivalence in meaning rather than exact word translations. Future language adaptations may involve proposing suitable terminologies by bilingual experts, followed by surveys to select the most appropriate terms. However, this adaptation process falls beyond the current research scope and remains a potential avenue for future studies.

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Acknowledgements

The authors would like to thank Professor Emeritus R. B. Bond for his guidance, input, and comments on the disaster terminology section of this paper. The authors also thank all the respondents of the survey and those who assisted in distributing it worldwide. This research was funded in part by the Natural Sciences and Engineering Research Council of Canada, Alberta Innovates—Technology Futures, Alberta Motor Association, the University of Calgary, the Catastrophe Indices and Quantification Incorporated, the Canadian Risk Hazard Network, and the Ministry of Culture and Status of Women, Government of Alberta.

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HJC designed, developed, and conducted the survey, processed and analysed the data, interpreted the results, and wrote the manuscript. SCW supervised the project, reviewed the results and the manuscript, and granted approval for publication. Both authors collaborated in distributing the survey, discussing the results, and enhancing the manuscript.

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Correspondence to H. Jithamala Caldera .

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Caldera, H.J., Wirasinghe, S.C. Evolution of natural disaster terminologies, with a case study of the covid-19 pandemic. Sci Rep 14 , 14616 (2024). https://doi.org/10.1038/s41598-024-64736-8

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Received : 24 October 2023

Accepted : 12 June 2024

Published : 25 June 2024

DOI : https://doi.org/10.1038/s41598-024-64736-8

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