Abstract

Climate-related disasters are on the rise, with a 44% increase between 1994 and 2013, and the population at risk is ever growing. The need to help people protect their well-being, families, and homes is of utmost importance. We surveyed individuals impacted by Hurricane Matthew in real time in October 2016 to explore the role of mental health, self-efficacy, social support, and evacuation and attitudinal factors on disaster response. We asked, “How much do 1) evacuation-relevant factors (reported evacuation zone, awareness of risk, and source of warning); 2) attitudes (climate- and environment-related perceptions and intentions); and 3) psychosocial factors (mental health, self-efficacy, and social support) contribute to engagement in protective behaviors (evacuation and preparation)?” We found 1) greater immediate exposure to risk increases protective behaviors; 2) climate and environmental concern increase preparation, but not evacuation; and 3) people with greater mental health and self-efficacy respond in ways commensurate with risk, taking protective actions if they live within a reported evacuation zone and not if they are not at risk, while those with lower mental health and self-efficacy do not respond in line with risks. These findings paint a complex picture of disaster response and suggest that preparedness efforts need to go beyond simple policy prescriptions (e.g., mandated evacuations) or improved messaging toward a focus on developing comprehensive programs that build human capital and provide people with psychological and social resources in advance of, during, and after an extreme weather event.

1. Introduction

Between 1994 and 2013, natural and anthropogenic disasters claimed 1.35 million lives, with an additional 218 million people affected worldwide (CRED 2015). While the frequency of geophysical disasters (such as earthquakes, tsunamis, volcanic eruptions, and landslides) remained constant, the number of climate-related events (such as floods, storms, droughts, and heat waves) increased by 44% during that time (IPCC 2013). This trend is expected to continue into the foreseeable future. With the population at risk ever rising, the need to help people protect themselves, their families, and their homes is of utmost importance.

a. Disaster response

There are two types of protective behaviors people can take to mitigate risk for many types of environmental disaster hazards: evacuation (when appropriate) and preparation. Evacuation is an effective protective behavior in the face of many imminent disaster risks, including volcanic eruptions, hurricanes, floods, tsunamis, and firestorms. To illustrate, in 1999, Tropical Cyclone 05B struck the rural state of Odisha, India, resulting in 10 000 deaths (Thompson et al. 2017). In response to this tragedy, the Indian government implemented a “zero casualty” policy, which entailed timely early warnings and targeted evacuations (World Bank 2014). Largely due to this policy, when Tropical Cyclone Phailin made landfall in the same area 4 years later, there were only 14 casualties. In another example, during the 2007 firestorms in California, nearly 1 million people were evacuated. Although hundreds of thousands of acres burned, only 14 lives were lost. Although not all evacuation attempts are as successful (e.g., Hurricanes Rita and Katrina in 2005; Litman 2006; Waugh 2006), when implemented correctly, evacuations can save lives and reduce injury.

Evacuation is a response to a primary risk—an imminent disaster that requires immediate action. Additional risks that can take place during or after a disaster include secondary risks, such as injury and/or disease, or damage to one’s home, as well as tertiary risks, such as disability and/or death after injury/disease (Keim 2016). Mitigating these risks involves a different, although potentially related, set of behaviors: preparation. Murakami et al. (2015) surveyed patients receiving dialysis for treatment of end-stage renal disease in areas affected by Superstorm Sandy and found that those who had prepared (i.e., made plans to get to safety if disaster is imminent and stored extra medicine and food) were significantly less likely to have missed treatments after Sandy occurred. Other measures, such as retrofits to make homes more resilient to extreme weather events or earthquakes, have been shown to result in reduced damage and financial losses (Kreibich et al. 2005; Shreve and Kelman 2014). Thus, preparedness-related actions can be instrumental in mitigating secondary and tertiary risks from disasters.

b. Contextual and attitudinal predictors of disaster response

Despite the clear benefit of taking action to protect against and prepare for disasters, many people do little or nothing in the face of natural and anthropogenic hazards. To better understand the reasons for this, researchers have examined potential influential factors and developed frameworks and models to account for failure to respond to disasters (Ajzen 1991; Lindell et al. 2005; Lindell and Perry 2000, 2012; Prentice-Dunn and Rogers 1986). Overall, findings suggest that evacuations are higher among people who are located in an evacuation zone; perceive greater risk and/or are in actual danger; learn about the risk from a trusted source, such as a first responder; have evacuated previously and have evacuation experience; receive an official notification (watches, warnings, and evacuation orders); and live in vulnerable housing (e.g., mobile homes). Demographics also play a role—being female, younger, and having lower income are associated with greater intent to evacuate and actual evacuation behavior (findings for other demographic variables, such as race, are mixed) (Baker 1991; Bowser and Cutter 2015; Dow and Cutter 2000, 1998; DeYoung et al. 2016; Huang et al. 2017; Jungermann et al. 1996; Meyer et al. 2018; Mileti et al. 2006; Wray et al. 2006; Wong-Parodi et al. 2017). These findings are largely echoed in the empirical literature examining factors predictive of preparedness-related behaviors. However, the extent to which these types of factors affect preparation behavior is less well understood and is worthy of further investigation.

Moreover, perceptions about the contribution of climate change or other environmental factors to increasing the risk of natural hazards (e.g., coastal flooding) have been shown to contribute to willingness to prepare and respond to disaster (Botzen and van den Bergh 2012; Botzen et al. 2009). However, research in this area is limited. Taken together, these findings suggest that evacuation-relevant factors (evacuation zone, awareness of risk, and source of warning) and attitudes (climate- and environmental-related perceptions) may play an important role in whether people take action to mitigate risk.

c. Psychosocial predictors of disaster response

In addition to the factors outlined above, social support, mental health, and self-efficacy have been identified as potentially important predictors of risk mitigation. The public health literature suggests a strong positive association of better mental health, a stronger sense of self-efficacy, and higher levels of social support with greater engagement in prohealth behaviors and reduction of health risks (Bandura 1977; Cohen and Wills 1985; Faulkner and Taylor 2005; Keyes 2002; Prochaska and Velicer 1997; Strecher et al. 1986). These factors are thus important to investigate as predictors of protective decision-making in the face of environmental risk.

Yet, these psychosocial factors have been understudied, and extant research findings are mixed. For mental health, most research has focused on the relationship of disasters on mental health, and the influence of being impacted by a disaster on future preparation behaviors (Clay et al. 2014; Eisenman et al. 2009; Gershon et al. 2016; Gargano et al. 2015; Sattler et al. 2000). The few studies that have examined the relationship of mental health with preparedness have reported mixed findings (Clay et al. 2014; Eisenman et al. 2009; Gargano et al. 2015). Clay et al. (2014) found that people experiencing greater mental distress were less likely to prepare for a disaster and more at risk for adverse consequences after a disaster. On the other hand, Gargano et al. (2015) found no difference in preparation for Superstorm Sandy between those who were experiencing 9/11-related post-traumatic stress disorder and those who were not.

The findings for self-efficacy are also mixed. Samaddar et al. (2012) found that greater self-efficacy predicted a stronger intent to evacuate in response to flood risk, whereas others found no relationship between self-efficacy and evacuation- or preparedness-related behaviors (Bubeck et al. 2012).

Social support has been found, on balance, to be positively related with preparation for disasters (Gargano et al. 2015; Poussin et al. 2014). Social support appears to be of particular importance among older adults in preparing for a disaster and coping with its aftermath (Kim and Zakour 2017; Gershon et al. 2016). However, Wong-Parodi et al. (2017) found that among coastal residents impacted by Superstorm Sandy, those with higher levels of social support reported greater tolerance for flood risks as assessed by their willingness to live in flood-prone areas. Hence, having social support may result in greater preparation and resilience, but may also result in greater willingness to accept risks. These results suggest a complex relationship between social support and risk perception and response (Wong-Parodi et al. 2017). [For more on the role of social support, capital, and networks, see, e.g., Aldrich and Meyer (2015), Bowser and Cutter (2015), Cutter et al. (2003), Mathbor (2007), and Nakagawa and Shaw (2004).]

Overall, it appears that research into the impacts of mental health, self-efficacy, and social support on risk response is limited, and its findings are mixed. Moreover, most studies are retrospective and hence subject to recall bias (Thompson et al. 2017). To address these shortcomings, we propose a research approach that systematically examines key psychosocial predictors of evacuation and preparation behaviors and does so in real time—during the occurrence of an extreme weather event—so as to assess current mental health, self-efficacy, and social support and to determine their dynamic impact on decision-making in the face of risk. This approach can yield important insights into the role these key psychological factors play in evacuation and preparation and suggest pathways for developing programs and policies that can improve disaster response among those at risk.

d. Study design

We surveyed individuals living within and just outside of Hurricane Matthew self-reported evacuation zones in early October 2016, in the days leading up to and during the hurricane’s landfall. Our objective was to understand the role of evacuation-relevant, attitudinal, and psychosocial factors on evacuation and preparedness-related behaviors. Here, we treat each category separately because the literature on natural hazards, public health, and psychology suggests that they are likely to make unique contributions to engaging in protective actions in response to risk. Even within our first category, evacuation-relevant factors, which are external to the individual (evacuation zone, information source, and official warning), prior research has shown that each factor uniquely contributes to evacuation decisions, in line with extant models of decision-making, such as the Protective Action Decision Model (Lindell and Perry 2012).

With respect to the attitudinal factors, psychological literature on attitudes and behaviors suggests a complex relationship. While in some settings attitudes are strong predictors of behavior, attitudes may not always give rise to corresponding actions due to normative and contextual barriers, and in some cases it is behaviors that give rise to corresponding attitudes [see, e.g., the theory of planned behavior (Ajzen 1991) and the theory of reasoned action (Fishbein 1980)]. Attitudinal factors are, therefore, worth investigating in their own right in the context of decision-making in response to risk. Finally, the public health literature suggests that psychosocial factors, which are intrinsic to the individual, and/or the psychosocial resources available during times of crises might also play a unique role in people’s behavior during a natural hazard such as a hurricane.

Therefore, in this study, we examine each of these potential contributors to risk response and ask the following: How much do real-time 1) evacuation-relevant factors (reported evacuation zone, information source, and official warning); 2) attitudes (climate- and environmental-related perceptions and intentions); and 3) psychosocial factors (mental health, self-efficacy, and social support) contribute to protective action-taking behaviors? In so doing, we highlight the important role of people’s psychological experiences and contextual factors in their ability to make adaptation decisions during a time of potential personal and community crisis.

2. Methods

a. Recruitment

Respondents were recruited for this study through Amazon’s Mechanical Turk (MTurk), an online data-gathering service (Buhrmester et al. 2011). The researchers posted an advertisement for this study titled “Your Attitudes and Beliefs,” specifically targeting individuals residing in states impacted by Hurricane Matthew. Interested MTurk “workers” clicked on the study to learn more about it. They were informed that they would receive $3 in compensation for a 15–20-min survey (or $9–$12 per hour) and entered into a lottery for the chance to win an additional $100 (see  appendix A for the full advertisement) for their participation. Those who were interested in participating took the online survey immediately. Comparison of people’s responses to classical psychological experiments (i.e., Stroop, subliminal priming) among those recruited through MTurk versus more traditional methods have found few differences (Crump et al. 2013). In addition, recent research suggests that there are few demographic differences between MTurk and probabilistic telephone samples, with the exception of those who hold no more than a high school diploma or who are over the age of 50 (Redmiles et al. 2017).

The target population consisted of adults (ages 18 or older) residing in states affected by Hurricane Matthew: Florida, Georgia, South Carolina, and North Carolina. Evacuation orders were given for some counties in each of these states by 6 October 2016; hence, data were collected between 6 and 13 October 2016 (for more details on data collection dates by state, see  appendix B). A total of 766 people started the survey, and 650 people completed it; we excluded individuals who took the survey more than once or failed to complete it properly, bringing the total to 521 valid responses. We do not have estimates of how many people saw the advertisement or whether they clicked on it but elected not to participate, as this type of information about MTurk workers is proprietary. We do know that there are between 500 000 and 750 000 registered “workers” worldwide (Hitlin 2016), though not all of their accounts are active. The Institutional Review Board of Solutions (IRB) approved all procedures. All participants provided informed consent.

b. Survey protocol

Participants were first informed, “Currently, Hurricane Matthew is impacting many people and places by bringing severe rains, winds, and flooding. We’d like to know about your personal experience with Hurricane Matthew.” They reported 1) if they were in an evacuation zone; 2) whether they chose to evacuate, including open-ended responses explaining the reason for their choice, where they evacuated to, and what they expect will happen; 3) whether they received a watch/warning about the impending hurricane; and 4) whether information about Hurricane Matthew came from a first responder. They then reported on their 5) mental health, 6) current preparation behaviors, 7) climate change worry, 8) climate change cause, 9) future adaptation intentions, 10) environmental intentions, 11) climate change policy support, 12) self-efficacy, 13) social support, and 14) standard demographics questions. To preserve the validity of the scales that are widely used and evaluated, we maintained their original response options. Hence, some variables may have four response options, whereas other may have seven. In responses not analyzed here, participants also reported on climate change acceptance, what they thought caused events like Hurricane Matthew, and psychological proximity to climate change impacts. All surveys were conducted online, and participants did not have the option to go back and revise previously answered questions. See  appendix C for a complete copy of the survey.

c. Variables

1) Reported evacuation zone

Participants indicated whether they were located in an evacuation zone by answering, “Are you in an evacuation zone?” with responses of “yes” coded as 1, “maybe” as 2, and “no” as 0. Most reported residing in an evacuation zone (n = 313), fewer than 1/3 did not reside in an evacuation zone (n = 147), and a small group was unsure (n = 36).

2) Evacuate

Participants indicated evacuation behavior by responding to one of two questions: “Did you evacuate?” (n = 164), with responses of “yes” coded as 1 and responses of “no” coded as 0. We excluded those who were intending to evacuate but had not done so yet (n = 22) from our analysis. Thus, we observe that most people decided not to evacuate (335 out of 499).

3) Watch/warning

Participants indicated whether they received a watch and/or warning about Hurricane Matthew by answering, “Did you receive any of the following about Hurricane Matthew?” with responses of “hurricane watch,” “hurricane warning,” or “other” coded as 1 and “I did not receive any of these” coded as 0.

4) First responder

Participants indicated whether they received hurricane information from a first responder (police officer, firefighter, emergency personnel), with responses of “yes” coded as 1 and “no” coded as 0.

5) Mental health

Participants answered five questions from the Rand Corporation’s 36-item 1993 Short Form Health Survey to assess mental health by indicating the extent to which they were currently feeling 1) “very nervous,” 2) “calm and peaceful,” 3) “downhearted and blue,” 4) “happy,” and 5) “so down in the dumps that nothing could cheer you up,” with 1 = not at all and 4 = a lot. Questions 1, 3, and 5 were reverse coded, and responses were averaged to create a mean index of mental health, with Cronbach’s α = 0.82.

6) Preparation

Participants read, “Below is a list of actions that people can take to protect themselves from the impacts of extreme weather and natural disasters, including flooding from extreme rain or sea level rise, as well as severe wind, rain, and hail from hurricanes.” They were then asked to “please check all that you have done or are doing to prepare for the impacts of Hurricane Matthew (or similar events)” and provided with a list of 30 preparedness behaviors, such as “practice drills of evacuation plan” and “make your house more resilient to extreme weather (roof, fire proofing).” We summed the number of behaviors performed to create a 0–30 count variable of preparation.

7) Climate change worry

Participants indicated concern about climate change by answering, “How worried are you about climate change?” where 1 = “not worried at all” and 4 = “very worried.”

8) Climate change cause

Participants were asked whether, if we assume that climate change is happening, they thought it was 1 = “caused by mostly human activities,” 2 = “caused mostly by natural changes in the environment,” 3 = “other,” or 4 = “none of the above because climate change isn’t happening.” Most reported thinking it was caused by humans (63.9%), followed by natural changes (23.3%), other reasons (9.6%), and none of the above (3.3%).

9) Adaptation intentions

Participants indicated their intention to adapt to climate change and extreme weather events by rating their agreement with three statements: “I intend to prepare for the impacts of climate change in my daily life,” “I intend to learn about the ways a warming climate will change the environment around me,” and “I intend to make a plan for what to do in the case of an extreme weather event,” where 1 = “completely disagree” and 7 = “completely agree.” We averaged the responses to create a mean index of adaptation intentions (Cronbach’s α = 0.85).

10) Environmental intentions

Participants indicated their environmental intentions by rating their agreement with eight statements: “I intend to use primarily recyclable and reusable products from now on,” “I intend to stop supporting organizations and politicians that harm the environment,” “I intend to join and provide financial support to pro-environmental organizations in the near future,” “I intend most of my purchases and food choices to reflect environmental concerns,” “I intend to actively rally for policies that are good for the environment,” “I intend to cut down on using electricity and driving by at least 30%,” “I intend to personally push for greater environmental regulation of industry practices,” and “I intend to devote more money to purchase products that are environmentally friendly,” with 1 = “completely disagree” and 7 = “completely agree.” Responses were averaged to create a mean index of environmental intentions (Cronbach’s α = 0.94).

11) Policy support

Participants indicated their support for climate-related policies by rating their agreement with four statements: “I support the use of U.S. tax dollars to address climate change,” “The reduction of destructive carbon emissions should become a national priority,” “I support implementing a carbon tax to reduce carbon pollution,” and “I believe leaders in Congress and in the White House should prioritize finding and implementing solutions to climate change,” with 1 = “completely disagree” and 7 = “completely agree.” Responses were averaged to create a mean index of policy support (Cronbach’s α = 0.96).

12) Self-efficacy

Participants answered four questions from Schwarzer and Jerusalem’s (1995) 10-item Generalized Self-Efficacy Scale by indicating how true each of the following states is for them: “I am confident that I could deal efficiently with unexpected events,” “When I am confronted with a problem, I can usually find several solutions,” “I can always manage to solve difficult problems if I try hard enough,” and “I can usually handle whatever comes my way,” with 1 = “not at all true” and 4 = “exactly true.” Responses were averaged to create a mean index of self-efficacy (Cronbach’s α = 0.84).

13) Social support

Participants answered two questions from Krause’s (1997) 11-item Social Support scale: “If you needed to talk about your problems and private feelings, how much would people around you be willing to listen?” and “If you needed help with a practical problem, how much would people around you be willing to help?” with 1 = “never” and 4 = “often.” Responses were averaged to create a mean index of social support (Cronbach’s α = 0.86).

d. Participant characteristics

Participants were 35.3 years old, on average [standard deviation (SD) = 11.7], and 58.9% were female. Participants reported their race as 71.7% white or European American, 16.0% black or African-American, 5.2% Latino or Hispanic, 3.8% Asian or Asian-American, 2.4% more than one race, 0.6% Native American or First Nations, and 0.2% Pacific Islander. Of our participants, 55.5% earned $50,000 or less, 54.7% had a college degree or higher, and 58.8% reported no children under the age of 18 living at home. On average, participants reported being somewhat liberal leaning overall (M = 3.58, median = 4.00, SD = 1.53, range = 1–7). Specifically, approximately 41.7% were liberal or liberal leaning (1–3), 32.7% were moderates (4), and 25.7% were conservative or conservative leaning (5–7). Most participants were residents of Florida (n = 207), followed by Georgia (n = 107), North Carolina (n = 101), and South Carolina (n = 84).

e. Data analytic plan

Statistical analyses were conducted using Stata (version 14; Stata Corp, College Station, Texas). We conducted three separate regressions to analyze three sets of predictors of evacuation and preparation behavior. Binomial distributions were used for binary outcomes (i.e., evacuation) and Poisson distributions for count outcomes (i.e., preparation). Model 1 included evacuation-relevant variables: reported evacuation zone, watch/warning, and first responder. Model 2 included attitudinal variables: adaptation intentions, environmental intentions, policy support, climate change worry, and climate change cause. Model 3 included psychosocial variables: mental health, social support, and self-efficacy. Model 4 included all variables. All models adjusted for demographics: age, gender, income, and political ideology.

It is likely that the effect of evacuation-relevant, attitudinal, and psychosocial factors on the decision to evacuate or implement preparation measures would differ depending on the situation people are facing, such as being in a reported evacuation zone. Thus, we examined the interactions between reported evacuation zone and evacuation-relevant, attitudinal, and psychosocial variables on evacuation and preparation behaviors, respectively, adjusting for demographics. Finally, we present significant patterns of association between demographics and our outcomes of interest.

3. Results

a. How much do evacuation-relevant factors contribute to protective action-taking behaviors?

1) Evacuation

Participants who reported being located in an evacuation zone were significantly more likely to evacuate than those who were not or were uncertain about the evacuation status of their location (Table 1). Moreover, people who were informed about Hurricane Matthew by a first responder were nearly twice as likely to evacuate than those who were not. Follow-up analyses found no significant interaction between reported evacuation zone and being informed by a first responder or receiving a watch/warning on evacuation behavior (p > 0.05).

Table 1.

Logistic regression predicting evacuation behavior (reporting odds ratios). For odds ratios, values greater than 1 indicate greater likelihood, whereas values less than 1 indicate less likelihood. CI stands for confidence interval, and SE is standard error.

Logistic regression predicting evacuation behavior (reporting odds ratios). For odds ratios, values greater than 1 indicate greater likelihood, whereas values less than 1 indicate less likelihood. CI stands for confidence interval, and SE is standard error.
Logistic regression predicting evacuation behavior (reporting odds ratios). For odds ratios, values greater than 1 indicate greater likelihood, whereas values less than 1 indicate less likelihood. CI stands for confidence interval, and SE is standard error.

2) Preparation

The same pattern of results was observed for storm preparation-taking behaviors. In addition to reportedly being in an evacuation zone and being informed about Hurricane Matthew by a first responder, those who reported receiving some advanced watch/warning of Hurricane Matthew were more likely to take preparation measures than those who did not (Table 2). Moreover, reported evacuation zone status moderated the relationship between being informed by a first responder and preparation behavior (b = 0.26, p < 0.01; Fig. 1). Participants informed about Hurricane Matthew by a first responder were equally likely to take preparation measures irrespective of whether they reported being located inside or outside of an evacuation zone [χ2(1) = 0.01, p = 0.92]. Conversely, those who were not informed by a first responder were significantly more likely to take preparation measures if they reported being inside an evacuation zone than if not [χ2(1) = 44.14, p < 0.001]. Receiving a watch/warning did not moderate the relationship between reported evacuation status and preparation behaviors (p > 0.05).

Table 2.

Poisson regression predicting preparation behavior.

Poisson regression predicting preparation behavior.
Poisson regression predicting preparation behavior.
Fig. 1.

Interaction between evacuation status and being informed by a first responder predicting preparation measures taken.

Fig. 1.

Interaction between evacuation status and being informed by a first responder predicting preparation measures taken.

b. How much do attitudes contribute to protective action-taking behaviors?

1) Evacuation

No significant relationships were observed among adaptation intentions, environmental intentions, or climate policy support attitudes and evacuation behavior (p > 0.05). Follow-up analyses found no significant interactions among reported evacuation zone and adaptation intentions, environmental intentions, or climate policy support on evacuation behavior (p > 0.05).

2) Preparation

Participants who expressed more intent to adapt to climate change and to take actions to protect the environment, and who worried more about climate change, were also significantly more likely to report having taken preparation measures to protect against Hurricane Matthew, as well as future disasters (Table 2). Participants who think climate change is not anthropogenic were significantly less likely to prepare. Surprisingly, we found those who reported greater climate policy support were less likely to have taken preparation measures. One possible explanation is that those who see government as being responsible for managing the risks of natural disasters may see less of a need to prepare themselves.

Follow-up analyses found that reported evacuation zone status moderated the relationships of intention to adapt to climate change (b = 0.13, p < 0.001), intentions to take action to protect the environment (b = 0.12, p < 0.001), support for climate policy (b = 0.07, p < 0.001), and worry about climate change (b = 0.10, p < 0.01) and preparation behavior. As shown in Fig. 2, adaptation intentions were less strongly related to taking preparation measures among those who reported living in an evacuation zone (dy/dx = 0.58, p < 0.01) and more strongly related among those who reported living outside of an evacuation zone (dy/dx = 1.52, p < 0.001). Similarly, as shown in Fig. 3, environmental intentions were not related to taking preparation measures for participants who reported living in an evacuation zone (dy/dx = 0.14, p = 0.50), but were positively related for those who reported living outside an evacuation zone (dy/dx = 1.16, p < 0.001). As shown in Fig. 4, greater support for climate policy was negatively related to taking preparation measures for those who reported being located inside an evacuation zone (dy/dx = −0.90, p < 0.001), but was not related to preparation for those who reported living outside an evacuation zone (dy/dx = 0.01, p = 0.97). Finally, as shown in Fig. 5, worry about climate change was not related to preparation among those who reported being inside an evacuation zone (dy/dx = 0.03, p = 0.94), but was strongly positively related to preparation among those who reported living outside an evacuation zone (dy/dx = 0.87, p < 0.01).

Fig. 2.

Interaction between evacuation status and adaptation intentions predicting preparation measures taken.

Fig. 2.

Interaction between evacuation status and adaptation intentions predicting preparation measures taken.

Fig. 3.

Interaction between evacuation status and environmental intentions predicting preparation measures taken.

Fig. 3.

Interaction between evacuation status and environmental intentions predicting preparation measures taken.

Fig. 4.

Interaction between evacuation status and climate policy support predicting preparation measures taken.

Fig. 4.

Interaction between evacuation status and climate policy support predicting preparation measures taken.

Fig. 5.

Interaction between evacuation status and climate change worry predicting preparation measures taken.

Fig. 5.

Interaction between evacuation status and climate change worry predicting preparation measures taken.

Reported evacuation status did not moderate the relationships between views on the cause of climate change and preparation behaviors (p > 0.05).

c. How much do psychosocial factors contribute to protective action-taking behaviors?

1) Evacuation

Greater mental well-being was related to lower likelihood of evacuating (Table 1), whereas no relationship was observed between self-efficacy or social support and evacuation behavior (p > 0.05). Follow-up analyses found no significant interactions among reported evacuation zone and mental health, social support, or self-efficacy on evacuation behavior (p > 0.05).

2) Preparation

As for evacuation behavior, we found that greater mental health was related to taking fewer preparation measures (Table 2). A follow-up Poisson logistic regression interaction analysis revealed that evacuation zone moderated the relationship between mental health and preparation behaviors (b = −0.26, p < 0.001). Participants who reportedly lived in an evacuation zone were equally likely to take preparation measures irrespective of their mental health (dy/dx = −0.54, p = 0.07), while for those who reportedly lived outside of an evacuation zone, mental health mattered: participants with greater mental health were much less likely to take preparation measures, compared to participants with poorer mental health (dy/dx = −2.78, p < 0.001; see Fig. 6).

Fig. 6.

Interaction between evacuation status and mental health predicting preparation measures taken.

Fig. 6.

Interaction between evacuation status and mental health predicting preparation measures taken.

Greater sense of self-efficacy, on the other hand, was related to taking more preparation measures. As for evacuation, social support was not a significant predictor of taking preparation measures (p > 0.05; Fig. 7).

Fig. 7.

Reported preparation measures taken (“more preparation” vs “less preparation”) among levels of mental health (“lower mental health” vs “greater mental health”) for those living inside a reported evacuation zone (“at risk”) or outside a reported evacuation zone (“not at risk”).

Fig. 7.

Reported preparation measures taken (“more preparation” vs “less preparation”) among levels of mental health (“lower mental health” vs “greater mental health”) for those living inside a reported evacuation zone (“at risk”) or outside a reported evacuation zone (“not at risk”).

d. How much do demographic factors contribute to protective action-taking behaviors?

1) Evacuation

We found older individuals were less likely to evacuate than younger individuals (Table 1). No other significant associations were observed between demographic variables and evacuation behavior.

2) Preparation

We found that older compared to younger individuals and those earning between $25,000 and $100,000 compared to those earning under $25,000 were significantly more likely to take preparation measures (Table 2). We also found that greater ideological conservatism was related to taking more preparation measures. (See  appendix D for associations between demographics and evacuation-relevant, attitudinal, and psychosocial factors.)

4. Discussion

This study sought to understand the role of evacuation-relevant, attitudinal, and psychosocial factors in preparing for and responding to environmental disasters, in order to inform strategies to encourage people at risk to take protective action. Specifically, we posed three questions: How much do real-time 1) evacuation-relevant factors (reported evacuation zone, awareness of risk, and source of warning), 2) attitudes (climate- and environmental-related perceptions and intentions), and 3) psychosocial factors (social support, mental health, and self-efficacy) contribute to protective action-taking behaviors?

Overall, our results indicate that evacuation-relevant factors play a large role in whether people decide to evacuate or implement preparation measures in advance of an imminent extreme weather event. The two important predictors are 1) being within a reported evacuation zone and 2) being informed by a first responder of the risk. Both are consistent with prior findings (Baker 1991; Bowser and Cutter 2015; Bubeck et al. 2012; Dash and Gladwin 2007; DeYoung et al. 2016; Dow and Cutter 2000; Huang et al. 2017; Jungermann et al. 1996; Meyer et al. 2018; Mileti et al. 2006; Thompson et al. 2017; Wray et al. 2006; Wong-Parodi et al. 2017). Research suggests that people in evacuation zones are more likely to evacuate for a number of reasons, including higher perceived risk (Weinstein 1987; Zhang et al. 2004) and actual danger (i.e., from environmental cues) and past experience (Lindell et al. 2005; Thompson et al. 2017). Research also suggests that first responders are trusted by community members during times of crisis, more so than other official sources, as they are perceived to be more competent and trustworthy (Jungermann et al. 1996; Mileti et al. 2006; Wray et al. 2006; Wong-Parodi et al. 2017). Indeed, we found those who reported living in evacuation zones, irrespective of whether they learned about Hurricane Matthew risks from a first responder, acted in ways commensurate with the risks they face: they prepared. As reasoned by one participant, “We evacuated because the governor declared our county a mandatory evacuation zone, and it was highly recommended by our emergency services and police departments” (Participant 205). Moreover, we found that individuals who reported living outside an evacuation zone were much more likely to take preparation measures if they learned about the risks from a trusted first responder. Hence, people may be more willing to take action when the information or request is coming from a trusted source, given that the request is appropriate to the risks being faced.

We also found that people’s climate and environmental attitudes play a role in preparation, but not in whether they evacuate, in response to an imminent extreme weather event. Those who reported stronger intentions to adapt to a changing climate and to protect the environment were more likely to prepare for Hurricane Matthew. This is consistent with prior research examining adaptation to natural hazards, such as coastal flooding, which has found that people who perceive greater flood risk from climate change are more likely to prepare for future flooding risk (Botzen and van den Bergh 2012; Botzen et al. 2009). It is possible that those who accept climate change are also more aware of its impacts (Reynolds et al. 2010), including the increasing frequency and intensity of natural hazards, such as hurricanes, which may encourage people to think long term and prepare for such events. In support of this reasoning, we found that people with stronger climate and environmental attitudes were much more likely to take steps to prepare for the imminent hurricane threat, even if they were not in direct danger (reported not living in an evacuation zone).

Next, we found that psychosocial factors—specifically, mental health and self-efficacy—played a role in evacuation and preparation decisions. Overall, experiencing better mental health was related to lower likelihood of evacuating or preparing for Hurricane Matthew. This is contrary to prior findings of a positive association between mental health and protective behavior taking (Clay et al. 2014; Eisenman et al. 2009), although findings have been mixed (Gargano et al. 2015). However, closer inspection of the data showed that individuals with good mental health responded in ways commensurate with risk and prepared more if they reported residing within an evacuation zone. However, individuals with poor mental health always prepared, irrespective of whether they reported living within or outside of an evacuation zone. This finding has been reported elsewhere in the literature. For example, in the aftermath of devastating flooding and mudslides in Tezuitlán, Mexico, that took the lives of 350 people and destroyed 200 000 homes, those experiencing more post-traumatic stress, physical health symptoms, and depressive symptoms were more likely to have a rescue plan for future flood/mudslide events than those who experienced less psychological impacts (Tobin et al. 2011). This protective response may work well during times of crisis, but may be maladaptive if it results in undue worry and stress when action is not necessary, possibly exacerbating negative affect, distress, and poor health outcomes (Jackson et al. 2010; Thoits 2010). Similarly, people who reported greater self-efficacy were overall less likely to evacuate but were more likely to prepare, in line with previous findings of a positive or null relationship between self-efficacy and preparation for flood risk (Samaddar et al. 2012). However, we did not find a relationship between social support and evacuation or preparation behavior or a moderating role of being located in an evacuation zone on the relationship between self-efficacy or social support and evacuation or preparation, as we did for mental health.

Our demographic findings largely correspond with previous literature (Baker 1991; Bowser and Cutter 2015; Bubeck et al. 2012; Dash and Gladwin 2007; DeYoung et al. 2016; Dow and Cutter 2000; Huang et al. 2017; Jungermann et al. 1996; Meyer et al. 2018; Mileti et al. 2006; Wray et al. 2006; Wong-Parodi et al. 2017). Younger individuals were more likely to evacuate but less likely to take measures to prepare for the hurricane. Individuals who have more resources were more likely to prepare. Finally, those expressing greater political conservatism were more likely to prepare, in line with findings that conservatives are more likely to see individuals as responsible for their plight should disaster strike (Arceneaux and Stein 2006; Skitka 1999).

The findings of this study paint a more complex picture of disaster response and suggest that preparedness efforts need to go beyond simple policy prescriptions (e.g., mandated evacuations) or improved messaging toward a focus on developing comprehensive programs that build human capital and provide people with the psychological and social resources they need in advance of, during, and after an extreme weather event. The findings also highlight the importance of locally trusted communicators during a time of crisis, and of the beliefs and expectation of those receiving the communication.

a. Limitations

While our study has a number of strengths, including its real-time design and large sample size, it is not without limitations. First, we conducted this study using Amazon’s Mechanical Turk, which does not guarantee a representative sample. Indeed, users of MTurk have been found, in general, to be more liberal, urban, and younger than the general population (Huff and Tingley 2015). However, studies replicating experimental behavioral research find few differences in responses between participants recruited via MTurk versus traditional methods (Buhrmester et al. 2011). Given the exploratory nature of our study and the nimbleness offered by MTurk to quickly and efficiently deploy a survey in the field, it offered a good choice for this study. Importantly, we find little difference between the demographics of our sample and that of the U.S. Census South Atlantic division’s population estimates (see  appendix E).

A second potential limitation is the collection of self-reported behavior rather than actual measurable behavior. Here, we mitigate the risk of recall bias by asking participants several open-ended questions about their decision-making with respect to evacuation and preparation behaviors in order to enhance recall and to potentially be more honest in their responses.

Third, individuals who responded to the survey had to have internet access in the days leading up to and during Hurricane Matthew. Among those who reported being in evacuation zones, this suggests an ability to successfully shelter in place, relocate to a safe place, and/or avoid impacts that would disrupt their internet connectivity. Hence, we may be underrepresenting people who were less advantaged or more affected. However, given that Hurricane Matthew did not result in major damage along coastal areas (where the reported evacuation zones were located), it is likely that many people were able to access the internet in the days before and during the hurricane.

A fourth potential limitation is that our measure of social support included two questions from Krause’s 11-item Social Support scale and, hence, may have made it a less sensitive predictor than if we had used the whole scale. As we did not want to burden our participants with an unduly long survey, we chose to reduce the length of scales such as the Social Support scale. It would be instructive in future studies to include the whole scale to see if the same pattern of results emerges, or if social support becomes a stronger predictor of evacuation and preparation behaviors.

b. Future directions

Understanding the role of psychosocial factors, such as mental health, self-efficacy, and social support, would benefit from prospective longitudinal research that examines their influence on decision-making and subsequent resilience in the face of disaster, and should be an essential focus of future research. Further studies should also investigate the relationship between climate and environment-related perceptions vis-à-vis natural and anthropogenic hazards and protective behaviors. For example, does better understanding of the connection between climate change and local extreme weather events affect preparation behaviors, both in the short term and long term? Studies should also investigate how the nature of the hazard affects protective-action behaviors. For example, are people more responsive to preparing for acute events, such as hurricanes, than longer-term trends, such as drought? Hence, differing abilities to think about long-term problems may affect whether or not they prepare or respond to those long-term risks.

5. Conclusions

Overall, there is much to be encouraged by in our findings. People who live in evacuation zones and who are informed by trusted sources, such as first responders, are likely to evacuate and take preparation measures to mitigate injury, disease, and death in the aftermath of a disaster. However, while people with better mental health and higher levels of self-efficacy take preparedness measures commensurate with risk, those with poorer mental health and lower self-efficacy may be engaging in maladaptive behaviors. One potential implication is the need for interventions, whether policies or programs, that help people cope with the threat of hazards in a way that is sensitive to who they are and what they are going through. Thus, building resources within the community that bolster individual decision-making through improving mental health and self-efficacy may be a powerful way to encourage resilience in the face of increasing anthropogenic and natural disasters.

Acknowledgments

This work was supported by the Rita Allen Foundation and the Environmental Defense Fund.

APPENDIX A

Recruitment Advertisement

You will be asked to fill out a survey in which you will be asked questions about your attitudes and beliefs. Estimated duration: about 15 to 30 min. Payment: $3; all participants will be entered into a random drawing for a $100 bonus.

APPENDIX B

Evacuation Status by State

Table B1 shows participant evacuation status by state.

Table B1.

Participant evacuation status by state.

Participant evacuation status by state.
Participant evacuation status by state.

APPENDIX C

Survey

Welcome to the study!

You are invited to take part in a study named Your Attitudes and Beliefs. The study is designed to learn more about what people believe about themselves and the world. You must be a United States citizen and 18 years or older to participate. If you agree to participate in this study, you will be asked to read an article and reply to questions about your beliefs and attitudes about yourself and the world. Your participation will take about fifteen to thirty minutes. You will be paid $3 for completing this study and will be entered into a lottery for a $100 bonus. A written explanation of the research will be provided at the completion of the study, and we are available to discuss any questions you may have via e-mail. There are no known risks associated with your participation in this research beyond those of everyday life. Although you will receive no direct benefits for participation in this study, it may make you more aware of how knowledge is discovered and help the researchers better understand people’s attitudes about their world. Taking part in this study is voluntary. You have the right to decline to participate and withdraw from the research now or at any time after the study has begun. You have the right to skip or not answer any questions you prefer not to answer. Confidentiality of your research records will be strictly maintained. We will not collect any personally identifying information and will assign random codes to your responses to prevent linking of any responses to your identity. Results will only be analyzed and discussed in aggregate. If there is anything about participating in this study that is unclear or if you wish to report a research-related problem, you may contact the principal investigator, Dr. Irina Feygina, at ifeygina@climatecentral.org.

○ I consent to participate in this study

HURRICANE MATTHEW EXPERIENCE

Currently, Hurricane Matthew is impacting many people and places by bringing severe rains, winds, and flooding.

Wed like to know about your personal experience with Hurricane Matthew.

  • Are you in an evacuation zone due to Hurricane Matthew? (1 = yes, 2 = maybe, 3 = no)

  • Did you evacuate? (1 = yes, 2 = no)

  • [IF YES]

  • In 1 or 2 sentences, tell us why you evacuated. [Open ended]

  • Where did you evacuate to? [Open ended]

  • [IF NO]

  • Are you planning on evacuating? (1 = yes, 2 = no)

  • [IF YES]

  • In 1 or 2 sentences, tell us why you are planning on evacuating. [Open ended]

  • [IF NO]

  • In 1 or 2 sentences, tell us why you aren’t evacuating.

  • What do you expect to experience once Hurricane Matthew makes landfall? [Open ended]

  • Did you receive any of the following about Hurricane Matthew? (1 = hurricane watch, 2 = hurricane warning, 3 = other______, 4 = I did not receive any of these)

  • How did you receive information about Hurricane Matthew? (1 = radio, 2 = social media, 3 = television, 4 = Internet, 5 = phone call, text message, 6 = newspaper, 7 = sirens, 8 = loudspeaker, 9 = in person, 10 = other______)

  • From whom did you receive information about Hurricane Matthew? (1 = meteorologist, 2 = newspaper or anchor, 3 = friend, family, neighbor, 4 = local public official, 5 = state or national public official, 6 = police officer or firefighter, 7 = emergency personnel, 8 = other______)

  • Please share any other thoughts you may have about how Hurricane Matthew is impacting you or how you expect it to impact you, and what you are doing to prepare and respond to these impacts. [Open ended]

MENTAL HEALTH

For each of the following questions, please mark the circle for the one answer that comes closest to the way you feel right now.

  • Right now, to what extend do you feel (1 = not at all, 2 = a little, 3 = somewhat, 4 = a lot):

  • Very nervous?

  • Calm and peaceful?

  • Downhearted and blue?

  • Happy?

  • So down in the dumps that nothing could cheer you up?

ADAPTATION ACTIONS

Below is a list of actions that people can take to protect themselves from the impacts of extreme weather and natural disasters, including flooding from extreme rain or sea level rise as well as severe wind, rain, and hail from hurricanes.

Please check all that you have done or are doing to prepare for the impacts of Hurricane Matthew (or similar events):

  • Understand how extreme weather may affect your neighborhood and home.

  • Determine the hazards you and your family could face due to extreme weather.

  • Identify the safest area of your home during a natural disaster.

  • Create an evacuation plan.

  • Practice drills of evacuation plan.

  • Know the location of a shelter or place to evacuate.

  • Identify escape routes from your home or neighborhood.

  • Have an out-of-state contact that all in your family know to call.

  • Get a battery-powered AM/FM weather radio and extra batteries.

  • Have multiple modes of communication (landline, cell phone, etc.).

  • Get emergency supplies (e.g., first aid kit, etc.).

  • Get extra water.

  • Get candles.

  • Get flashlights, extra batteries, and extra bulbs.

  • Have extra cash in the house.

  • Get nonperishable food.

  • Get extra gasoline.

  • Make copies of important documents.

  • Get emergency blankets and shelter.

  • Get an emergency source of power (e.g., generator or solar).

  • Install a woodstove and keep backup wood.

  • Move vehicle to safe location.

  • Get a safe box to protect valuables.

  • Secure home to prevent looting.

  • Perform general household maintenance.

  • Buy flood insurance.

  • Install flood vents.

  • Put in protective landscaping and maintain yard.

  • Make your house more resilient to extreme weather (roof, fire-proofing).

  • Make inside of home more resilient to extreme weather (windows, secure heavy furniture).

CLIMATE CHANGE

  • What do you think? Do you think that climate change is happening? (1 = yes, 2 = no, 3 = I do not know)

  • How sure do you feel of this? (1 = I’m extremely sure, 2 = I’m very sure, 3 = I’m somewhat sure, 4 = I’m not at all sure)

  • Assuming climate change is happening, do you think it is ... (1 = caused mostly by human activities, 2 = caused mostly by natural changes in the environment, 3 = other______, 4 = none of the above because climate change isn’t happening)

  • How worried are you about climate change? (1 = very worried, 2 = somewhat worried, 3 = not very worried, 4 = not at all worried)

ADAPTION AND MITIGATION BEHAVIORAL INTENTIONS

  • Please let us know how much you agree with each of the statements presented below (1 = strongly disagree, 7 = strongly agree).

  • I intend to prepare for the impacts of climate change in my daily life.

  • I intend to learn about the ways a warming climate will change the environment around me.

  • I intend to make a plan for what to do in the case of an extreme weather event.

  • I intend to use primarily recyclable and reusable products from now on.

  • I intend to stop supporting organizations and politicians that harm the environment.

  • I intend to join and provide financial support to proenvironmental organizations in the near future.

  • I intend most of my purchases and food choices to reflect environmental concerns.

  • I intend to actively rally for policies that are good for the environment.

  • I intend to cut down on using electricity and driving by at least 30%.

  • I intend to personally push for greater governmental regulation of industry practices.

  • I intend to devote more money to purchase products that are environmentally friendly.

POLICY

  • I support the use of U.S. tax dollars to address climate change.

  • The reduction of destructive carbon emissions should become a national priority.

  • I support implementing a carbon tax to reduce carbon pollution.

  • I believe leaders in Congress and in the White House should prioritize finding and implementing solutions to climate change.

SELF-EFFICACY

  • How true is each of the following states for you (1 = not at all true, 2 = hardly true, 3 = moderately true, 4 = exactly true)?

  • I am confident that I could deal efficiently with unexpected events.

  • When I am confronted with a problem, I can usually find several solutions.

  • I can always manage to solve difficult problems if I try hard enough.

  • I can usually handle whatever comes my way.

SOCIAL SUPPORT

  • If you needed to talk about your problems and private feelings, how much would people around you be willing to listen (1 = never, 2 = once in a while, 3 = fairly often, 4 = often)?

  • If you needed help with a practical problem, how much would people around you be willing to help?

DEMOGRAPHICS

  • What is your gender? (1 = male, 2 = female, 3 = other)

  • How old are you? (18–100)

  • What is your racial or ethnic background? (1 = Latino or Hispanic, 2 = Black or African-American, 3 = Asian or Asian-American, 4 = White or European-American, 5 = Native American or First Nations, 6 = Pacific Islander, 7 = More Than One Race)

  • How many children under the age of 18 live at home? (0–10)

  • Approximately what is your family’s annual income? (1 = under $25K, 2 = between $25–$50K, 3 = between $50–$100K, 4 = between $100–$250K, 5 = more than $250K)

  • What is the highest level of education that you have completed? (1 = grade school, 2 = high school, 3 = associated degree/some college, 4 = college, 5 = graduate/professional)

  • What zip code do you live in?

  • Where on the following scale of political orientation would you place yourself? (1 = extremely liberal, 7 = extremely conservative)

FEEDBACK

If you are affected by Hurricane Matthew, we would love to learn more about your experience, and how you are doing right now. And if you have any reflections on extreme weather and our climate more broadly, please share them with us! We really appreciate your thoughts. [Open ended]

Your feedback is very important to us as we strive to improve this study. Was there anything that you did not like about the questionnaire, was not clear to you, or you feel needs to be changed? Was there anything you liked or found to be informative? Please feel free to leave any additional comments you would like to share with us. [Open ended]

DEBRIEF AND CODE

Debriefing for Study on Your Attitudes and Beliefs

Thank you very much for taking the time to complete this study!

This research involves understanding people’s beliefs and attitudes toward a variety of issues that are studied by science and touch people’s lives, with a special emphasis on climate change–related beliefs. The goal of this research is to inform our understanding of how to present objective scientific analyses of climate science in clear and understandable ways, and how to communicate about scientific issues around which there is a diversity of beliefs and attitudes in society. Please do not hesitate to contact Dr. Irina Feygina at ifeygina@climatecentral.org if you have any further questions about this research.

Your Mechanical Turk Completion Code is: ${e://Field/RandomID} ${rand://int/<00000>:<99999>}

APPENDIX D

Associations between Predictor Factors and Demographics

Tables D1D9 show associations between demographics and evacuation-relevant, attitudinal, and psychosocial factors.

Table D1.

Results of chi-square test of independence between 1) evacuation zone and notification, 2) evacuation zone and first responder, and 3) first responder and notification.

Results of chi-square test of independence between 1) evacuation zone and notification, 2) evacuation zone and first responder, and 3) first responder and notification.
Results of chi-square test of independence between 1) evacuation zone and notification, 2) evacuation zone and first responder, and 3) first responder and notification.
Table D2.

Results of Pearson correlation among adaptation intentions, environmental intentions, policy support, and worry. Results of Kruskall–Wallis equality of populations rank test among climate cause and adaptation intentions, environmental intentions, policy support, and worry. Note that *** indicates p < 0.001, ** indicates p < 0.01, and * indicates p < 0.05.

Results of Pearson correlation among adaptation intentions, environmental intentions, policy support, and worry. Results of Kruskall–Wallis equality of populations rank test among climate cause and adaptation intentions, environmental intentions, policy support, and worry. Note that *** indicates p < 0.001, ** indicates p < 0.01, and * indicates p < 0.05.
Results of Pearson correlation among adaptation intentions, environmental intentions, policy support, and worry. Results of Kruskall–Wallis equality of populations rank test among climate cause and adaptation intentions, environmental intentions, policy support, and worry. Note that *** indicates p < 0.001, ** indicates p < 0.01, and * indicates p < 0.05.
Table D3.

Results of Pearson correlation among mental health, self-efficacy, and social support. Note that *** indicates p < 0.001, ** indicates p < 0.01, and * indicates p < 0.05.

Results of Pearson correlation among mental health, self-efficacy, and social support. Note that *** indicates p < 0.001, ** indicates p < 0.01, and * indicates p < 0.05.
Results of Pearson correlation among mental health, self-efficacy, and social support. Note that *** indicates p < 0.001, ** indicates p < 0.01, and * indicates p < 0.05.
Table D4.

Results of Kruskall–Wallis equality of populations rank test between 1) age and evacuation zone, first responder, and notification and 2) political ideology and evacuation zone, first responder, and notification.

Results of Kruskall–Wallis equality of populations rank test between 1) age and evacuation zone, first responder, and notification and 2) political ideology and evacuation zone, first responder, and notification.
Results of Kruskall–Wallis equality of populations rank test between 1) age and evacuation zone, first responder, and notification and 2) political ideology and evacuation zone, first responder, and notification.
Table D5.

Results of chi-square test of independence between 1) gender and evacuation zone, 2) gender and first responder, and 3) gender and notification.

Results of chi-square test of independence between 1) gender and evacuation zone, 2) gender and first responder, and 3) gender and notification.
Results of chi-square test of independence between 1) gender and evacuation zone, 2) gender and first responder, and 3) gender and notification.
Table D6.

Results of chi-square test of independence between 1) income and evacuation zone, 2) income and first responder, and 3) income and notification.

Results of chi-square test of independence between 1) income and evacuation zone, 2) income and first responder, and 3) income and notification.
Results of chi-square test of independence between 1) income and evacuation zone, 2) income and first responder, and 3) income and notification.
Table D7.

Results of Pearson correlation of adaptation intentions, environmental intentions, policy support, and worry with age and political ideology. Results of Kruskall–Wallis equality of populations rank test between climate cause and age and political ideology. Note that *** indicates p < 0.001, ** indicates p < 0.01, and * indicates p < 0.05.

Results of Pearson correlation of adaptation intentions, environmental intentions, policy support, and worry with age and political ideology. Results of Kruskall–Wallis equality of populations rank test between climate cause and age and political ideology. Note that *** indicates p < 0.001, ** indicates p < 0.01, and * indicates p < 0.05.
Results of Pearson correlation of adaptation intentions, environmental intentions, policy support, and worry with age and political ideology. Results of Kruskall–Wallis equality of populations rank test between climate cause and age and political ideology. Note that *** indicates p < 0.001, ** indicates p < 0.01, and * indicates p < 0.05.
Table D8.

Results of chi-square test of independence between 1) gender and climate cause and 2) income and climate cause. Results of Kruskall–Wallis equality of populations rank test among adaptation intentions, environmental intentions, and climate policy and both gender and income.

Results of chi-square test of independence between 1) gender and climate cause and 2) income and climate cause. Results of Kruskall–Wallis equality of populations rank test among adaptation intentions, environmental intentions, and climate policy and both gender and income.
Results of chi-square test of independence between 1) gender and climate cause and 2) income and climate cause. Results of Kruskall–Wallis equality of populations rank test among adaptation intentions, environmental intentions, and climate policy and both gender and income.
Table D9.

Results of Pearson correlation of mental health, self-efficacy, and social support with age and political ideology. Results of Kruskall–Wallis equality of populations rank test among mental health, self-efficacy, and social support and both gender and income. Note that *** indicates p < 0.001, ** indicates p < 0.01, and * indicates p < 0.05.

Results of Pearson correlation of mental health, self-efficacy, and social support with age and political ideology. Results of Kruskall–Wallis equality of populations rank test among mental health, self-efficacy, and social support and both gender and income. Note that *** indicates p < 0.001, ** indicates p < 0.01, and * indicates p < 0.05.
Results of Pearson correlation of mental health, self-efficacy, and social support with age and political ideology. Results of Kruskall–Wallis equality of populations rank test among mental health, self-efficacy, and social support and both gender and income. Note that *** indicates p < 0.001, ** indicates p < 0.01, and * indicates p < 0.05.

APPENDIX E

U.S. Census Data for South Atlantic Division

The median age of people in the South Atlantic division of the United States is 38.8 years old, with 51.2% being female, 69.1% being white or European American, 48.4% earning $50,000 or less, and 29.9% having a college degree or higher (U.S. Census 2015).

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Footnotes

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