Influence of built environment and risk perception on seismic evacuation behavior: Evidence from rural areas affected by Wenchuan earthquake

Journal Pre-proof Influence of built environment and risk perception on seismic evacuation behavior: Evidence from rural areas affected by Wenchuan earthquake Yibin Ao, Kun Huang, Yan Wang, Qiongmei Wang, Igor Martek PII:

S2212-4209(19)31350-0

DOI:

https://doi.org/10.1016/j.ijdrr.2020.101504

Reference:

IJDRR 101504

To appear in:

International Journal of Disaster Risk Reduction

Received Date: 29 September 2019 Revised Date:

22 January 2020

Accepted Date: 23 January 2020

Please cite this article as: Y. Ao, K. Huang, Y. Wang, Q. Wang, I. Martek, Influence of built environment and risk perception on seismic evacuation behavior: Evidence from rural areas affected by Wenchuan earthquake, International Journal of Disaster Risk Reduction (2020), doi: https://doi.org/10.1016/ j.ijdrr.2020.101504. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

Influence of built environment and risk perception on seismic evacuation behavior: Evidence from rural areas affected by Wenchuan earthquake ABSTRACT During the decade after the 2008 Wenchuan earthquake, numerous earthquakes of different magnitudes have occurred in the affected areas. The need for immediate emergency evacuation following the occurrence of disaster was unanimously recognized by experts as the safest and most effective response of residents. Moreover, this behavior is directly influenced by residents’ perceptions of disaster risk and built environment (BE). To explore the relationship between seismic evacuation behavior and perceptions of risk and BE, this study utilized a random survey in the form of a sample questionnaire in the areas most affected by the Wenchuan earthquake in 2008, combined with exploratory factor analysis and binary logistic regression analysis. Results show that residents’ BE and risk perceptions positively affected their evacuation choice behavior. Specifically, when rural residents perceived a reasonable evacuation route and good quality of village roads, they would flee their homes as soon as an earthquake struck. With regard to seismic risk perception, if the residents perceived highly negative consequences from earthquakes, they would escape immediately as soon as an earthquake occurred. This conclusion highlights the importance of strengthening the construction of BE in disaster-prone areas, and raising residents’ awareness and risk perception of earthquake disasters. This study has practical significance in further construction of earthquake-stricken areas. Keywords Built environment perception, Risk perception, Evacuation behavior, Wenchuan earthquake, Exploratory factor analysis, Binary logistic regression 1. Introduction The Wenchuan earthquake that occurred on May 12, 2008 caused an enormous loss to China. This phenomenon, which had a 8.0 magnitude, was the most destructive earthquake since the founding of new China in 1949 and the deadliest since the 1976 Tangshan earthquake. Besides damage to land resources and infrastructure, 7.79 million houses collapsed in 14,565 villages and 24.59 million houses were damaged. The direct economic losses are approximately 845 billion Chinese yuan (CNY) and the indirect losses cost more [1]. A report issued by the Chinese Ministry of Civil Affairs in 2008 stated that a total of 69,227 people were killed; 374,643 were injured; another 17,923 were listed as missing; and approximately 4.8 million were left homeless as a result of the Wenchuan earthquake [2]. During the decade after the Wenchuan earthquake in 2008, scholars studied this disaster from different perspectives: (1) assessment of damage level [3]; (2) assessment of natural disasters (such as landslides, dammed lakes, and mudslides) caused by the earthquake [4]; (3) emergency response process such as emergency rescue, rescue lifeline, resource mobilization, mass medical care, public management, decision-making behavior for emergency response, and others [5, 6]; (4) earthquake resistance of buildings [7]; (5) social and environmental vulnerability of the affected areas [8]; (6) models and resource allocation for post-disaster reconstruction [9, 10]; and (7) psychological resilience of affected residents [11, 12]. Although scholars have explored various problems caused by the Wenchuan earthquake, they have

ignored the influences of risk and built environment (BE) perceptions on the evacuation behavior of residents in disaster areas, although numerous studies have been conducted on risk perception [13-15]. All of these references on risk perception conclude that the most effective means to create awareness of potential disasters is to enhance trust in public authorities and encourage citizens to take greater personal responsibility for protection and disaster preparedness. The term “built environment” has an urban form. In the middle to late 1990s, the connotation and extension of BE was developed to emphasize the urban physical environment to which the spatial, temporal, and sociocultural backgrounds centered on human activities are attached, including land use patterns, the BE associated with their human activities, transportation systems, positive urban design, and organization of physical elements [16, 17]. Moreover, the enormous losses resulting from the Wenchuan earthquake were largely attributed to the failures in BE [18], such as improper land use planning, lack of strict implementation of seismic design codes, and unqualified construction. Furthermore, Lindell [19] stated that these failures in BE severely affected local and external emergency responses. The areas affected ruinously by the Wenchuan earthquake are also located in villages, towns, and other areas at the foot of the mountain or in the mountains. The BE in these areas are generally backward compared with that in cities, and the seismic resistance is relatively weak. After the Wenchuan earthquake, the government paid special attention to the post-disaster reconstruction work in the affected areas. Compared with the past BE of the affected areas, the BE improved considerably [20]. However, we should know how to effectively help the victims escape and start reconstruction after the disaster. The importance of post-disaster response is highlighted in the existing literature on post-disaster rehabilitation or reconstruction modes [10, 21]. We aim to explore whether residents’ perception of the new BE after the post-disaster reconstruction affects their choice of emergency evacuation behavior in areas affected by the Wenchuan earthquake. Risk perception (RP) is a psychological process that describes a subjective (conscious and unconscious) assessment of the likelihood of the effect of an impending undesirable event (as opposed to an objective risk assessment) in a specific situation as well as an assessment of one’s own perceived vulnerability and coping resources [22]. RP is usually regarded as an important predictor of disaster evacuation behavior [23]. When humans face an emergency natural disaster, the perception and assessment of disaster risk is a precursor to emergency escape or protection measure. The risk assessment includes the individual’s likelihood estimation of a disaster occurrence, severity and urgency of the hazard, extent of impact (such as physical injury, property damage, and interruptions in daily life), and level of concern for the hazard [24]. In addition, Lindell et al. [25] found a close relationship between risk perception and earthquake response measures; that is, actions related to mitigating the potential consequences of people and properties are also related to individuals’ risk perception during earthquake events. After the Wenchuan earthquake, farmers’ risk perception of concentrated rural settlements was investigated by Peng et al. [26] using survey data on the hardest earthquake-hit area, and critical risk factors were also identified. Human behavior is difficult to predict at all times, especially in emergencies, which are characterized by stress and chaos [27]. During an earthquake, most people’s first reaction is to search for furniture and chairs for safety. People have been observed holding on to walls and/or other individuals in corridors and areas with no tables or other furniture [28]. Shapira et al. [29] found that the main danger comes from falling objects that could cause injury or even death during

an earthquake. In addition, they conducted a questionnaire survey of 306 residents in the earthquake disaster area, with mean age of 35 (SD = 11.5). They found that when asked how to choose disaster emergency measures, 43% of the respondents opted to evacuate buildings during the earthquake, 19% chose to hide in apartment shelters, 13% hid under heavy furniture, 8% climbed the stairs, 5% sat against the wall, and 12% did not know what to do. Regarding evacuation behavior during other disaster events, Cahyanto et al. [30], Bowser et al. [31], and Koshiba et al. [32] studied the evacuation behavior strategies used in the occurrence of hurricanes and floods. The consensus is that the safest action is to evacuate from a house or low-lying area to a higher safety zone. Therefore, compared with any other proposed refuge behavior, whether an earthquake or another disaster, evacuating from the building to the safe zone in time is the safest and most survivable emergency behavior. The Wenchuan disaster has caused people, especially those living in disaster-prone areas, to pay close attention to the emergency protection behavior during earthquakes. However, in areas with frequent earthquakes, people’s first choice to evacuate is the best self-protection behavior to ensure their safety. Developed Western countries have conducted relatively mature research on this subject, whereas in China, the research is mainly carried out from the perspective of natural science and rarely from the perspective of social science. Limited studies explore the impact of BE and risk perceptions on evacuation behavior. To fill this gap, the present study aims to explore the influence of BE and risk perceptions on evacuation behavior after control of socio-demographic information. This paper is expected to provide a perspective for the further development of rural areas with low level of economy and construction, and improves the disaster resilience of residents in rural areas. 2. Materials and methodology 2.1 Questionnaire design The questionnaire design involves four steps: (1) The first draft was completed based on the literature review. (2) After the questionnaire was discussed with 16 undergraduates from the Wenchuan earthquake disaster area, the questionnaire was modified so that it conforms to the actual situation of target sample villages. (3) Trial investigation was conducted in random selected sample villages to test the readability and completeness of the questionnaire. (4) The questionnaire was improved according to feedback on the trial investigation. Lastly, the final questionnaire consisted of four parts focusing on the respondents’ (1) basic information, (2) BE perception, (3) disaster risk perception, and (4) seismic evacuation behavior choice. The basic information involves demographic and socioeconomic factors based on the study by Shapira et al. [29] and Li et al. [33], which includes gender, age, education [21], occupation [24], physical condition, and marital status. Family structure, such as number of children under age 12, [34] and earthquake experiences [35, 36] are also considered. In addition to the basic information considered by existing research, this study also considers number of floors of the house and housing type factors according to the trail investigation and specific situation in rural Sichuan. In the areas affected by the Wenchuan earthquake, the main housing types are single and non-detached buildings. Further details on the basic information are shown in Table 1. BE refers to the man-made environment formed by human activities, which are different from the natural environment [37]. Each person’s perception of BE is different, resulting in the choice of emergency behavior [38]. Therefore, BE perception was considered to explore BE’s

influences on seismic evacuation behavior. Respondents were asked to judge the degree of recognition of various BE perception items. A five-point Likert scale [39] was used to represent the degree of recognition of different BE perception items from non-acceptance to full acceptance. The items of BE perception are shown in Table 3. In accordance with existing natural hazards, risk perception is defined as a subjective belief held by an individual residing in a natural hazard zone regarding the potential harm or possibility of loss due to an earthquake or related event assessed in terms of one’s evaluation of the characteristics, probability, and severity of such risk [15]. Disaster risk perception was derived from four studies that considered Chinese as their sample. Specifically, respondents were asked to assess the threat (RP1), fear (RP2), harm (RP3), and concerns (RP4) experienced regarding earthquakes [40, 41]. In our questionnaire, we also introduce a three-item measure (individual’s likelihood estimation of a disaster occurrence, severity and urgency of hazard, and extent of own impact) that deals with dimensions associated with environmental hazards [24]. In terms of earthquake evacuation, respondents were asked to select whether to escape from home during the earthquake when an earthquake occurs again; this question was also found in the study by Shapira et al. [29]. 2.2 Sample and data collection 2.2.1 Sample village selection The purpose of this study is to explore the impact of BE and risk perception on the seismic evacuation behavior of residents from the areas affected by the Wenchuan earthquake after a decade. Wen Chuan, Qing Chuan, Bei Chuan, Mian Zhu, and Du Jiangyan are the most affected areas in 2008 [2]. Historical records show that since the founding of the People’s Republic of China in 1949, more than 90% of the devastating earthquakes with magnitudes above 7 on the Richter scale occurred in rural areas [42]. Therefore, the research focuses on the rural areas that were the most seriously affected by the Wenchuan earthquake in 2008. Then, among the seriously affected areas, 10 villages were randomly selected for representation (Wen Chuan: Guo Jiaba and Lian Shanpo villages; Mian Zhu: Ji Xian community and Hong Ming village; Du Jiangyan: He Ming and He Jia villages; Qing Chuan: Dong Fang and Yin Ping villages; and Bei Chuan: Lao Chang and Qing Lin villages). These villages are all located on the Longmenshan earthquake zone, where the majority of the villages are surrounded by or located in the mountains. The high frequency of earthquakes has a typical research value. Figure 1 shows the location and distribution of the sample villages.

Fig. 1. Location of sample villages

2.2.2 Data collection To collect valid data, we recruited a total of 32 experienced researchers who have participated in face-to-face surveys twice in rural areas; these researchers include 1 teacher, 11 graduates, and 20 undergraduates. All the researchers are from rural areas in Sichuan; thus, they are familiar with the rural environment and can communicate effectively with rural residents. The face-to-face questionnaire survey was conducted from December 28, 2018 to January 5, 2019. The respondents were randomly selected to complete the survey in the 10 villages. A total of 502 out of 600 questionnaires were completed effectively. Then, another 34 out of 502 questionnaires were excluded according to a consistency check. Finally, a total of 468 valid questionnaires were obtained with an effective questionnaire rate of 93.23% (see Table 1). Table 1. Distance to epicenter and number of valid questionnaires Distance from village center to Sample villages epicenter (km)a Guo Jiaba 17 Wen Chuan Lian Shanpo 22 Du Jiangyan

Bei Chuan

Mian Zu

Qing Chuan

Number of valid questionnaires 49 42

He Ming

52

52

He Jia

49

36

Lao Chang

208

53

Qing Lin

213

45

Ji Xian

132

48

Hong Ming

121

53

Dong Fang

328

55

Yin Ping

331

35

a. This distance is the driving distance measured by Baidu navigation application. 2.3 Variable specification According to the research design, this study mainly considers three types of explanatory variables to explore their effects on earthquake evacuation behavior. These variables are basic population information, BE perception, and earthquake risk perception. Respondents were asked to answer the following question about a dependent variable with a binary alternative of yes or no: Will you evacuate from your home during the earthquake? According to the statistics of 468 valid questionnaires, 277 (59.2%) of the respondents opted to escape from their house, whereas the remaining 191 (40.8%) prefer to stay at home during the earthquake. 2.3.1 Basic information of respondents Gender, age, education background, physical condition, marital status of the respondent, and presence or absence of casualties and property loss in the household were considered in this study. Whether the family of the respondent had children under the age of 12, the number of residential floors, and the type of residential buildings were also considered. Table 2 shows the basic information of the respondents. A total of 211 males and 257 females were surveyed in this study. The number of surveyed men and women was not much disparate, and the distribution of men and women in the questionnaire was relatively uniform. The age of the surveyed population ranges from 18 to 65 years. However, according to actual research, most of

the rural residents are mainly middle-aged people over the age of 40 and children under the age of 12. Young people are more likely to go out to study or work. The division on the basis of educational background is based on the general classification of Chinese academic background. Residents with income from farming account for 33.3% of the research population, and other residents have other jobs. Moreover, 24.8% of the research population account for residents without work. The common reason is that in Chinese rural areas, elderly people and women raise their children at home without jobs and income. Table 2. Basic information on individuals and their households Basic information Options Male Gender Female

Age

Education background

Physical condition

Marital status

With children under 12 years old

With or without casualties

With or without property loss

Number of housing floors

Housing type

n 211

％ 45.09

257

54.91

Less than 20

11

2.35

20–30

52

11.11

30–40

54

11.54

40–50

108

23.08

50–60

104

22.22

More than 60

139

29.70

No education at all

67

14.32

Compulsory education

291

62.18

Senior high school

69

14.74

Bachelor’s degree or above

41

8.76

Health

430

91.88

Severe disability

11

2.35

Slight disability

27

5.77

Single

87

18.59

Married

381

81.41

Yes

271

57.91

No

197

42.09

Yes

226

48.2

No

242

51.6

Yes

426

91

No

42

9

Below 2

395

84.40

2 or more

73

15.60

Single building

266

56.84

Non-detached building

202

43.16

2.3.2 BE perception The questionnaire design of the BE was mainly considered from the perspective of infrastructure and combined with previous relevant studies and field conditions of the research area. Many of the affected areas in the 2008 Wenchuan earthquake were mountainous areas. Considering that mountainous areas are not only directly affected by the earthquake but also have other derived disasters, we also designed a question from the perspective of geographical space. The main items of BE perception and their sources are shown in Table 3.

Table 3. BE perception items and sources Item The current terrain environment has a strong effect on housing earthquake resistance.

Source Authors

The current residential infrastructure is well planned.

[29]

The residence is convenient for emergency evacuation in the area where you currently reside.

[43]

A reasonable shelter exists in the place where you currently reside.

[43]

Reasonable spacing is conducive to escape and evacuation during earthquakes.

[43]

The current house is strong and earthquake resistant.

[29]

The interior design of the house has a reasonable emergency shelter.

[29, 43]

The interior design of the house has a reasonable emergency escape route.

[29, 43]

The quality of building materials is guaranteed.

[29]

Good roads exist from villages to other villages or towns.

[29]

Roads from villages to other villages or towns are not easily damaged or congested in the event of an earthquake.

[29]

2.3.3 Earthquake risk perception We considered the effect of earthquake disasters on individuals and the entire family in the questionnaire design of earthquake risk perception. Earthquake risk perception items were combined with the relevant research literature, sorted out, and shown in Table 4. Table 4. Earthquake risk perception items and sources Items

Source

You are very sensitive to shaking things.

[24]

Earthquakes happen easily in this area.

[24]

The risk of a serious earthquake in the future will be greater.

[24]

You think you will be directly affected.

[40, 41]

You think that an extreme earthquake will have a long-term negative effect.

[40, 41]

An earthquake is catastrophic.

[40, 41]

After the earthquake, you will always be vigilant.

[40, 41]

After the earthquake, you feel that aftershocks will always occur.

[40, 41]

The number of earthquake disasters has decreased at present.

[33, 44]

The earthquake will not cause devastation to the house.

[33, 44]

The effect of the earthquake on you is very serious.

[40, 41]

The effect of the earthquake on your family is very serious.

[40, 41]

You are not very scared of the earthquake at present.

[33, 44]

A minor earthquake will cause damage to your house.

[33, 44]

The occurrence of an earthquake will cause further damage to your house.

[33, 44]

2.4 Model specification 2.4.1 Exploratory factor analysis Exploratory factor analysis (EFA) was first proposed by Spearman [46] in 1904. The basic purpose of this approach is to use a few unrelated factors to describe the relationship between multiple variables, namely, how to condense the measured variables into a few factors with minimal information loss. The general form of EFA is as follows:

where 


 =   +   + ⋯ +   +  = 1,2, ⋯ , is a factor load; its essence is the correlation coefficient between common factors 

and special factor variables
 .  represents factors other than the common factor [47]. 2.4.2 Binary Logistic Regression Analysis Logistic regression belongs to probabilistic nonlinear regression. Logistic regression analysis is divided into binary logistic regression and multiple logistic regression analyses according to the different types of dependent variables. The difference is that the dependent variable of binary logistic regression analysis can only take two values, namely, 0 and 1 (virtual dependent variable), whereas the dependent variable of multivariate logistic regression analysis can be multiple values. The probability of the event being studied in the formula is   = 1|
  =  . The probability formulas for the occurrence  and non-occurrence 1 −  of the event are 1 e ∑i=1 ι i , = pi = m m − (α + β +x ) α+ β +x 1 + e ∑i=1 ι i 1 + e ∑i=1 ι i α+

1 - pi = 1 -

m

β +x

1 1 = , m m −(α + ∑ βι + xi ) α + ∑ βι + xi i=1 i =1 1+ e 1+ e

where
 is an independent variable,  is a regression intercept, and  represents regression coefficients. They are all nonlinear functions consisting only of independent variables
 . The ratio of the probability of occurrence of an event to the probability of non-occurrence is called the ratio of occurrence of an event:  ⁄ 1 −  . A linear model of the logistic regression model can be obtained by comparing the event to logarithmic transformation as follows [48]:

 p ln  i  1 − pi

 m  = α + ∑ i =1 β i xi  .

In this study, the seven common factors derived from EFA combined with the basic information of respondents were the independent variables, whereas the binary alternative of evacuating from home during the earthquake was the dependent variable, where 0 represents the choice to stay at home and 1 represents the choice to escape from home during the earthquake. 3 Results and discussion 3.1 Reliability test A reliability analysis for all the aggregated data was performed using SPSS 24.0 software. We conducted reliability analysis on 35 questions designed in this questionnaire (9 basic information, 11 BE perception, and 15 disaster risk perception). The Cronbach’s alpha and number are shown in Table 5. In general, Cronbach’s alpha values of <0.60 are considered as unsatisfactory, whereas values of >0.70 are regarded as satisfactory [49]. Therefore, the sample data used in this study passed the reliability test. Table 5. Reliability statistics BE perception

Risk perception

Total items

Cronbach’s alpha

0.843

0.768

0.791

Number

11

15

26

3.2 Explore factor analysis The maximum variance method was used to analyze the independent variables in factor analysis. Kaiser–Meyer–Olkin (KMO) and Bartlett’s tests were performed before EFA of BE and disaster risk perception. In general, if the KMO value, which varies from 0 to 1, is ≥0.70 and the p value for the Bartlett’s test for homogeneity of variances is <0.05, then the data are considered to be suitable for EFA [50]. The test results are shown in Table 6. The KMO statistic is 0.822 and 0.797, and the two p values are 0.000. The test results are significant, which indicates a certain correlation between the items of BE and risk perception, and is suitable for EFA. Table 6. KMO and Bartlett’s test results KMO and Bartlett’s test

BE perception

Risk perception

KMO

0.822

0.797

Chi-square

2066.480

2162.960

Degrees of freedom

55

105

P

0.000

0.000

Bartlett’s test

The EFA of BE perception is shown in Table 8. Finally, the 11 BE perception variables were reduced to 3 common variables (number of variables decreased by 72.727%), whereas the 3 common factors can explain 63.543% of the overall information of the 11 variables. In addition, the 16 risk perception variables were reduced to 4 common factors (number of variables decreased by 75.000%), where the 4 common factors can explain 58.263% of the overall information of the 16 variables (see Table 9). Therefore, EFA has less information loss and good analysis results.

Table 7. EFA results of BE perception: factor component matrix

The residence is convenient for emergency evacuation in the area where you currently reside.

Reasonable outdoor evacuation route and shelter planning 0.849

A reasonable shelter exists in the place where you currently reside.

0.837

Reasonable spacing is conducive to escape and evacuation during earthquakes.

0.801

Good roads exist from villages to other villages or towns. Roads from villages to other villages or towns are not easily damaged or congested in the event of an earthquake. The quality of building materials is guaranteed.

Component Good quality of building and village roads

Reasonable indoor evacuation route

0.761 0.709 0.699

The current house is strong and earthquake resistant.

0.654

The current residential infrastructure is well planned.

0.491

The current terrain environment has a strong impact on housing earthquake resistance. * The interior design of the house has a reasonable emergency shelter.

0.838

The interior design of the house has a reasonable emergency escape route.

0.815

Eigenvalues

2.639

2.624

1.728

Proportion of variance explained (%)

23.989

23.850

15.705

Cumulative variance explained (%)

23.989

47.839

63.543

Extraction method: principal component analysis (PCA). Rotation method: Caesar normalized maximum variance method. The rotation converged after five iterations. * The results were all less than 0.4, and the factor was not classified.

Table 8. EFA results of disaster risk perception: factor component matrix

Items

The impact of the earthquake on you is very serious.

Continuous negative psychological effects of earthquake 0.810

The impact of the earthquake on your family is very serious.

0.779

An earthquake is catastrophic.

0.661

You think you will be directly affected.

0.653

You think that an extreme earthquake will have a long-term negative impact.

0.625

After the earthquake, you feel that aftershocks will always occur.

0.577

After the earthquake, you will always be vigilant.

0.563

Very sensitive to earthquakes

Earthquakes happen easily in this area.

0.817

The risk of a serious earthquake in the future will be greater.

0.737

You are very sensitive to shaking things.

0.575

Component Housing damage caused by earthquake

A minor earthquake will cause damage to your house.

0.785

The occurrence of an earthquake will cause further damage to your house.

0.689

Number and impact of earthquakes are expected to decrease

The number of earthquake disasters has decreased at present.

0.742

The earthquake will not cause devastation to the house.

0.717

You are not so scared of the earthquake at present.

0.597

Eigenvalues

3.458

2.180

1.563

1.538

Proportion of variance explained (%)

23.052

14.534

10.422

10.255

Cumulative variance explained (%)

23.052

37.586

48.008

58.263

Extraction method: PCA. Rotation method: Caesar normalized maximum variance method. The rotation converged after five iterations.

Tables 7 and 8 show the component rotation matrix of EFA on BE and risk perception, respectively. The three common factors of built environment perception can be concluded from Table 8, namely, BEP1 (reasonable outdoor evacuation route and shelter planning), BEP2 (good quality of building and village roads), and BEP3 (reasonable indoor evacuation route). Moreover, four disaster risk perception common factors are concluded from Table 9, namely, DRP1 (continuous negative psychological effects of earthquake), DRP2 (very sensitive to earthquakes), DRP3 (housing damage caused by earthquake), and DRP4 (number and impact of earthquakes are expected to decrease). 3.3 Multicollinearity of variables When the multicollinearity problem exists, it will seriously affect the fitting effect of the model [45]. In this study, the variance inflation factor (VIF) was used to test multicollinearity, and the results are shown in Table 9. All VIF values of the variables used in this study were less than 2. Therefore, no multicollinearity problem exists between all explanatory variables. Table 9. Multicollinearity analysis Coefficienta Model

Collinearity statistics Tolerance

VIF

Gender

0.946

1.057

Age

0.535

1.869

Educational background

0.583

1.714

Physical condition

0.960

1.042

Marital status

0.870

1.149

With or without children under 12 years old

0.915

1.093

With or without casualties

0.924

1.083

With or without property loss

0.967

1.034

Number of building floors

0.980

1.020

Housing construction type

0.916

1.092

Reasonable outdoor evacuation route and shelter planning

0.913

1.095

Good quality of building and village roads

0.889

1.125

Reasonable indoor evacuation route

0.889

1.125

Adverse continuous psychological effects of earthquake

0.865

1.156

Very sensitive to earthquakes

0.970

1.031

Housing damage caused by earthquake

0.956

1.046

Number and effect of earthquakes are expected to decrease

0.909

1.100

a. Dependent variable: whether to escape from home during the earthquake 3.4 Binary logistic regression analysis Binary logistic regression was used to explore the influences of BE and disaster risk perception on seismic evacuation behavior after controlling the social demographic information. SPSS 24.0 software was used for binary logistic regression analysis estimation. Stepwise method was used to explore the contribution of each type of variable to the proposed model when running binary logistic regression. Basic information, three BE perception

common factors, and four disaster risk perception common factors were added into the binary logistic regression model step by step. The Cox–Snell R2 values of the three models were 0.051, 0.072, and .0126. Finally, the Cox–Snell R2 value of the final binary logistic regression model was 0.126, showing that the data used in this study fit the binary logistic regression model well. Beta value represents the degree of influence of independent variables on dependent variables; positive and negative values indicate positive or negative influences, respectively. The greater the absolute value of beta is, the greater the effect on the dependent variable. The results of the binary regression model are shown in Table 10 and will be further explained. Cross validation was performed using SPSS 24.0. Overall, 59.7% of the respondents who will stay at home were predicted as staying at home, whereas 40.3% of the respondents who will stay at home were predicted as escaping from home during the earthquake. More information about the cross-validation results are presented in Table 11.

Table 10. Results of Binary Logistic Regression Analysis 95%CI Variable

Beta

S.E.

Wald

P

OR

Gender

0.153

0.208

0.544

0.461

1.165

0.776

1.750

Age

-0.071

0.097

0.542

0.461

0.931

0.770

1.126

Educational background

0.488

0.178

7.544

0.006

1.629

1.150

2.308

Physical condition

0.131

0.210

0.386

0.534

1.140

0.755

1.721

Marital status

0.029

0.281

0.011

0.917

1.030

0.594

1.784

With or without children under 12 years old

-0.168

0.210

0.637

0.425

0.846

0.560

1.276

With or without casualties

-0.480

0.364

1.742

0.187

0.619

0.303

1.262

With or without property loss

-0.303

0.207

2.134

0.144

0.739

0.492

1.109

Number of building floors

-0.278

0.279

0.992

0.319

0.757

0.438

1.309

Housing construction type

0.448

0.215

4.359

0.037

1.566

1.028

2.385

BEP1 (reasonable outdoor evacuation route and shelter planning)

0.176

0.105

2.800

0.094

1.193

0.970

1.467

BEP2 (good quality of building and village roads)

0.277

0.108

6.614

0.010

1.320

1.068

1.630

BEP3 (reasonable indoor evacuation route)

0.233

0.108

4.685

0.030

1.263

1.022

1.560

DRP1 (bad continuous psychological effects of earthquake)

0.204

0.108

3.549

0.060

1.226

0.992

1.515

DRP2 (very sensitive to earthquakes)

0.227

0.104

4.817

0.028

1.255

1.025

1.538

DRP3 (housing damage caused by earthquake)

0.471

0.108

18.858

0.000

1.601

1.295

1.980

DRP4 (number and impact of earthquakes are expected to decrease)

0.080

0.106

0.572

0.450

1.083

0.880

1.334

Constant

0.045

1.212

0.001

0.970

1.047

Lower

Upper

Table 11. Leave-one-out Cross Validation Resultsa,c Predicted Group Membership

Whether to escape from home during the earthquake Count Original

%

Count Cross validatedb

%

Total No

Yes

No

116

75

191

Yes

99

178

277

No

60.7

39.3

100.0

Yes

35.7

64.3

100.0

No

114

77

191

Yes

103

174

277

No

59.7

40.3

100.0

Yes 37.2 62.8 100.0 a. A total of 62.8% of original grouped cases was correctly classified. b. Cross validation was performed only for those cases in the analysis. In cross validation, each case is classified by the functions derived from all cases other than that case. c. A total of 61.5% of cross-validated grouped cases was correctly classified.

According to the results of binary logistical regression (Table 10), BE and risk perception have a significant influence on the seismic evacuation behavior of residents in the areas affected by the Wenchuan earthquake in 2008, whereas the impact of demographic characteristic variables is limited. The conclusions are analyzed and discussed in the following. According to the results in Table 10, educational background positively affects the seismic evacuation behavior (OR = 1.629, 95% CI: 1.150–2.308, p = 0.006 < 0.01). That is, people with a higher education level feel that escaping from the building is the safest way to seek refuge. Moreover, these people are more inclined to escape from their residences when an earthquake occurs. This result is consistent with the findings of Koshiba et al. [21] that people with higher academic qualifications are more rational about their choice of emergency behavior when disasters occur. Alexander and Magni [51] also found that participants with a higher education background were more inclined to implement positive behavioral strategies during the earthquake. Although the researchers referred to a number of behavioral patterns rather than just evacuating to the outdoors, their conclusions are similar in a sense to our findings that educational level has a positive influence on seismic evacuation behavior. Building construction type has a significant positive influence on evacuation behavior of the affected residents when an earthquake occurs. Residents living in non-detached houses are more inclined to choose to escape from their residence, whereas those living in single-family houses do not choose to escape out. Although this result is consistent with the research conclusion of Alexander and Magni [51] that residents living in private single-family houses are more willing to stay at home when an earthquake occurs, the reasons why they do not escape are different. According to Alexander and Magni [51], the residents who lived in private single-family houses have more options for emergency evacuation during a disaster, and may be safer and more effective in avoiding hazards than those who directly escape from their residence. On the contrary, we found through our field household survey that due to imperfect evacuation facilities, residents who lived in single-family buildings in Chinese villages have to stay at home when an earthquake occurs. Therefore, the earthquake disaster evacuation facilities need to be further improved in the earthquake zones, especially in remote rural areas. BEP1 (reasonable outdoor evacuation route and shelter planning) shows a significant positive

correlation with seismic evacuation behavior in the binary logistic regression (see Table 10) (P = – 0.094). If residents believe that reasonable outdoor evacuation route and shelter are available in their area, then they will immediately escape from the residence to the safe shelter when an earthquake disaster occurs to avoid the possible danger caused by the earthquake. Anbarci et al. [52] also found that well-planned evacuation routes and shelters are more secure, which can help residents escape when an earthquake occurs. Thus, the development level of the local economy directly affects the improvement of earthquake escape facilities. BEP2 (good quality of buildings and village roads) has a significant positive influence on seismic evacuation behavior (P = 0.010, B = 0.277). That is, if residents have knowledge on the good quality of buildings and village roads in the area where they currently reside, then they are inclined to escape from their residence and wait for external rescue. Previous studies show that the enormous losses resulting from the Wenchuan earthquake in 2008 were largely attributed to failures of BE, which also severely affected local emergency response and external aid [18]. Therefore, the quality improvement of rural roads and rural buildings is necessary to protect the lives and properties of rural residents. BEP3 (reasonable indoor evacuation route) (P = 0.03 < 0.05, B = 0.233) shows that if the interior design of the house has a good emergency escape route or a good escape environment, then the residents will also opt to evacuate the building with good escape conditions. DRP1 (continuous negative psychological effects of earthquake, P = 0.060, B = 0.205) and DRP2 (very sensitive to earthquakes, P = 0.028 < 0.05, B = 0.227) are both positively correlated to seismic evacuation behavior. In other words, the more the negative impacts caused by the earthquake disasters on residents’ psychology and their own risk sensitivity, the greater is their possibility of choosing to escape from buildings to avoid disaster and injury. The results of these two risk perception factors are in accordance with the research findings of Ruin I and Hung HV [53, 54]. Individuals with low risk perception are less likely to effectively make emergency evacuation behaviors than those with higher risk perception. Therefore, residents’ earthquake disaster risk psychology can effectively predict their evacuation behavior. A noteworthy point is that the factor of DRP3 (housing damage caused by earthquake) is positively correlated with the evacuation behavior of local residents during the earthquake disaster (p = 0.000, B = 0.471). When residents perceive greater risk and damage to their homes, they have a greater likelihood of fleeing when an earthquake strikes. The larger absolute value of the coefficient also indicates that housing damage risk perception of the residents largely determines their evacuation behavior during the earthquake disaster [55]. In sum, BE and disaster risk perception have more significant effects on seismic evacuation behavior than social demographic variables. All the three common factors of BE perception significantly affect the escape behavior of residents during an earthquake disaster. Moreover, three out of four common factors of disaster risk perception also significantly affect the seismic evacuation behavior of local residents. According to Burnside et al. [56], the residents who chose to evacuate during the previous earthquake were likely to have the same evacuation behavior when another earthquake occurred. This result is also related to the psychological impact on the affected residents during the previous earthquake. Thus, the results of the present study are consistent with the existing research conclusions. 4 Conclusion and policy recommendations

The main purpose of this study is to investigate the influence of BE and disaster risk perception on the seismic evacuation behavior of residents in earthquake-prone areas. A total of 10 villages located within the worst-hit areas of the Wenchuan earthquake in 2008 were randomly selected as samples for empirical research. Exploratory factor analysis and binary logistic regression were used in this study. Therefore, the following conclusions were drawn: (1) The demographic characteristics of residents have less influence on the seismic evacuation behavior than their subjective perception factors. The more educated the residents are, the more likely they are to flee their homes when an earthquake occurs. (2) When an earthquake occurs, residents living in non-detached buildings are more likely to decide to leave their residences than those living in single-family buildings due to the difference in infrastructures. The areas of non-detached buildings are more perfect than those of single-family buildings. Alexander and Magni [51] also concluded this finding in the foreign rural villages, which are totally different from Chinese villages. However, a similarity exists in the notion that the building type affects the evacuation choice of residents all over the world. Therefore, to better protect the lives of residents during an earthquake disaster, some of the most effective methods are to improve the residents’ education level, regional infrastructures, and building quality, especially in remote rural areas. (3) The three common factors of BE perception from the micro-building to the macro area have a positive influence on the seismic evacuation behavior of the local residents. Compared with cities, rural areas’ social and economic fragility and inequality in the built environment may further aggravate the negative consequences of disasters [57]. Therefore, for earthquake-prone areas, planning and constructing residential and regional evacuation facilities based on the perspective of disaster prevention and mitigation is particularly important. Moreover, enabling residents to intuitively perceive the convenience of escape during the occurrence of an earthquake is also necessary. (4) Risk perception plays a major role in various effective responses to disasters, and facilitates decision-making in risk management and disaster mitigation [58]. A total of three out of four common factors in disaster risk perception positively affect the seismic evacuation behavior. Although the frequency of major earthquakes is lower than that of minor earthquakes, the consequences can also be fatal [59]. Therefore, improving residents’ disaster risk perception is crucial. Moreover, preparing for future earthquake disasters is necessary. In earthquake-prone areas, strengthening awareness and education on earthquake disaster risk prevention is essential to protect the safety of residents’ lives and properties. 5 Limitations and future work The main limitation of this study is that it only conducts binary logistic regression analysis based on the dependent variable of whether to evacuate buildings or not but does not comprehensively analyze the effect of BE perception and disaster risk perception on various emergency behaviors. Earthquakes usually kill or injure people in areas with similar structures and vulnerability, mostly in developing countries [60]. Our findings may help reduce the effect of disasters in less-developed areas such as the mountainous countryside near China’s seismic belt. In future studies, we can analyze the diverse choices of people’s emergency behaviors under various conditions that are not limited to evacuation. For example, in the study by Koshiba et al. [32], respondents were asked about their behavior choices in 13 scenarios, such as in cold weather

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