Traveling in haze: How air pollution inhibits tourists' pro-environmental behavioral intentions

STOTEN-135569; No of Pages 11 Science of the Total Environment xxx (xxxx) xxx

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Traveling in haze: How air pollution inhibits tourists' pro-environmental behavioral intentions Zhongda Wu, Liuna Geng ⁎ School of Social and Behavioral Science, Nanjing University, Nanjing, China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Air pollution inhibited tourists' proenvironmental behavioral intentions (TPEBIs). • Air pollution and mental factors were integrated to explore sustainable tourism. • Anxiety and nostalgia were investigated in the nexus of air pollution and TPEBIs. • We used multiple methods, including experiment and field study techniques.

a r t i c l e

i n f o

Article history: Received 24 July 2019 Received in revised form 19 October 2019 Accepted 15 November 2019 Available online xxxx Editor: Pavlos Kassomenos Keywords: Air pollution Sustainable tourism Pro-environmental behavior Nostalgia Anxiety

a b s t r a c t Although air pollution is an important environmental concern in tourism, it is rarely studied in the field of sustainable tourism. Thus, we investigated how air pollution influences tourists' pro-environmental behavioral intentions (TPEBIs) through two laboratory experiments (studies 1 and 2) and one field study (study 3). Study 1 (n = 104) revealed the negative influence of air pollution on TPEBIs, both explicitly and implicitly. Study 2 (n = 108) further explored the mediating effect of state anxiety on the relationship between air pollution and TPEBIs. Furthermore, study 3 (n = 350) investigated a real sample of traveling tourists. Study 3's results confirmed the laboratory findings of studies 1 and 2, indicating the buffering effect of tourism nostalgia (i.e., a moderated mediation model) in a real traveling context. These findings advance the understanding of air pollution's impact on TPEBIs and can serve as practical advice for sustainable tourism management. © 2018 Elsevier B.V. All rights reserved.

1. Introduction With the rapid development of the tourism industry, air pollution has become a concerning environmental issue that poses serious ⁎ Corresponding author at: School of Social and Behavioral Sciences, Nanjing University, Nanjing, 210023, China. E-mail addresses: [email protected] (Z. Wu), [email protected] (L. Geng).

environmental risks to the sustainable development of tourism (Bohm and Pfister, 2011). In recent years, studies have found that air pollution can influence tourist traveling behaviors, such as outbound travel (Wang et al., 2018) and domestic travel (Sun et al., 2019). However, how air pollution influences tourists' pro-environmental behavioral intentions (TPEBIs) remains an interesting question yet to be answered. Does traveling in haze make tourists care more about environmental protection? Or, does degraded air quality somehow reduce their

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intention to adopt pro-environmental behaviors? Focusing on the relationship between air pollution and TPEBIs, we attempted to develop a comprehensive framework to answer these questions. The signing of the “2030 Agenda for Sustainable Development” by the member states of the United Nations (UN) in 2015 led to the formation of a global framework in pursuit of meeting 17 sustainable development goals (SDGs) before 2030. As an important part of sustainable development, tourism has both direct and indirect impacts on the environment (Becken and Patterson, 2006; Scott et al., 2010), and appears to be an influential sector with the potential to advance all the SDGs (World Tourism Organization, 2017). Sustainable tourism, defined by World Tourism Organization (http://sdt.unwto.org/content/about-us5) as “tourism that takes full account of its current and future economic, social and environmental impacts, addressing the needs of visitors, the industry, the environment and host communities,” has become an important, multi-dimensional topic involving the economy, environment, culture, and society (Pan et al., 2018). On the road toward sustainable tourism (similar concepts include ecotourism, green tourism, and so on), besides just focusing on how to build a more effective regulatory and economic system on a macrolevel, understanding micro-level tourist psychology and behavior has also been emphasized by numerous scholars as a key component of sustainable tourism (Cheng et al., 2018; Han, 2015; Kiatkawsin and Han, 2017; Lee and Jan, 2018). In fact, tourists' anti-environmental behaviors, such as wasting energy resources and destroying historical relics, can be detrimental to the sustainable development of tourism. To understand tourists' pro-environmental behaviors, previous researchers endeavored to apply psychological theories to tourism practices that are of significance to sustainable management. For example, Kiatkawsin and Han (2017) merged the value-belief-norm theory and the expectancy theory to understand TPEBIs. As the starting point of this research, we attempted to achieve a deeper and further understanding of TPEBIs. Specifically, we aimed to explore whether and how air pollution impacts TPEBIs through two laboratory experiments and a field study. The current research bears both theoretical and practical significance: (1) for the first time (to the best of our knowledge), the relationship between air pollution and tourists' pro-environmental behavior was explored; (2) it enriched the research scope of air pollution and extended the effects of air pollution to tourism; (3) it deepened the understanding of the psychological and behavioral mechanisms regarding environmental protections; (4) it adopted a mixed research approach combining laboratory experiments and a field experiment, self-report measures and an implicit association test, thus making the findings more reliable; and (5) given its multi-disciplinary perspective (combining behavioral science and environmental management), it provided practical insights on how to enhance tourists' pro-environmental behavior, thus contributing to more effective environmental management and sustainable tourism. 2. Literature review 2.1. Air pollution and tourists' pro-environmental behavior Tourists' pro-environmental behavior is defined as proenvironmental individual behavior that protects the environment or minimizes negative influence on the environment in a tourism situation (Miller et al., 2015). Although researchers have investigated the effects of air pollution on people's cognitive performance (Zhang et al., 2018), emotion (Zheng et al., 2019), health risk (Chen et al., 2019; Duan et al., 2018), perception bias (Geng et al., 2019), and even corruption perception (Huang et al., 2016), few have extended their work into the field of sustainable tourism (e.g., tourists' pro-environmental behavior). The famous broken windows theory describes the negative behavioral tendency caused by environmental disorder (Wilson and Kelling, 1982). An air-polluted environment may be perceived as disordered, a fact that inspired us to

focus on the negative effects of air pollution on tourist behavior. Viewing relevant literature, we found that it is well established that air pollution can induce negative emotions like depression, pressure, and stress (Bakian et al., 2015; Kim et al., 2010; Kondo et al., 2014; Sass et al., 2017), and these negative emotions can suppress people's social behavior and self-efficacy (Carlo et al., 2011; Laible et al., 2014; Mesurado et al., 2018; Roberts et al., 2014; Verona et al., 2002). Researchers even found that air pollution brought about unethical behavior (Lu et al., 2018). Since pro-environmental behavior is correlated with pro-social behavior and ethical behavior (Bamberg and Moser, 2007; Steg and Vlek, 2009), and considering all the aforementioned studies, we hypothesized that air pollution reduces TPEBIs. Hypothesis 1 was as follows: H1. Air pollution inhibits TPEBIs. H1a. People experiencing clean air intend to behave in a more proenvironmental manner than people experiencing polluted air. H1b. People implicitly intend to behave in a pro-environmental manner when experiencing clean air versus polluted air.

2.2. Effects of anxiety According to Spielberger and Reheiser (2009), anxiety can be divided into state anxiety (i.e., subjective feelings of anxiety/stress at a particular time) and trait anxiety (i.e., the tendency to feel anxiety/stress). Previous research has shown that air pollution can arouse state anxiety (Lu et al., 2018; Power et al., 2015). Moreover, the anxiety aroused by air pollution could have an impact on people's behavioral tendencies. As a matter of fact, previous research has indicated that those who feel anxious might care less about the public or environmental interests (Berger et al., 2018; Davis et al., 2018a; Davis et al., 2018b; George, 1991; Larson and Moses, 2014; McGinley et al., 2009; Sollberger et al., 2016). Therefore, we hypothesized that air pollution induces state anxiety (a mediator) and reduces TPEBIs. Hypotheses 2 and 3 were as follows: H2. People experiencing polluted air have a higher level of state anxiety and a lower level of TPEBI compared with people experiencing clean air. H3. State anxiety mediates the relationship between air pollution and TPEBIs (i.e., air pollution inhibits TPEBIs by arousing state anxiety).

2.3. Tourism nostalgia The pursuit of nostalgia is a distinct characteristic of “postmodern tourism” (Uriely, 2005); it plays an important role in the development of modern tourism (S. Kim et al., 2019; Leong et al., 2015; Prentice et al., 1998). From the perspective of psychology, nostalgia, defined as a “positively toned evocation of a lived past” (Davis, 1979, p. 18), is mainly a positive emotional process. The beneficial functions of nostalgia have been verified in various fields; for example, in raising positive emotions (van Tilburg et al., 2018), promoting social connections (Sedikides et al., 2008), and de-emphasizing the value of money (Lasaleta et al., 2014). In the present research, we aimed to stretch the beneficial functions of nostalgia into sustainable tourism, hypothesizing that the nostalgia tourists feel in a heritage destination modulates the anxiety caused by air pollution and indirectly promotes TPEBIs. Hypothesis 4 was as follows: H4. The mediation effect of air pollution on TPEBIs through state anxiety is moderated by tourism nostalgia (see Fig. 1).

3. Study 1: air pollution and TPEBIs Study 1 aimed to explore the relationship between air pollution and TPEBIs. A laboratory experiment was conducted to both explicitly and

Please cite this article as: Z. Wu and L. Geng, Traveling in haze: How air pollution inhibits tourists' pro-environmental behavioral intentions, Science of the Total Environment, https://doi.org/10.1016/j.scitotenv.2019.135569

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Fig. 1. Moderated mediation model. Note. A, Air pollution; S, State anxiety; P1, Consumption of TPEBIs; P2, General behavior of TPEBIs; N, Tourism nostalgia; AN, A*N;

implicitly test Hypothesis 1. In study 1, experimental materials involved uniform desktop PC computers with 19″ Lenovo LCD screens (Lenovo Corporation, Beijing, China), A4 papers, and 2B pencils. 3.1. Methods 3.1.1. Participants and procedures G*Power version 3.1 (Faul et al., 2007) was adopted to determine the priori sample size for a medium effect (i.e., Cohen's d = 0.5), and at least 51 participants per condition were required to achieve an 80% power in our study (the same priori analyses were conducted in the subsequent studies to determine the sample size). Then, 104 participants (51 males, 53 females, Mage = 20.76, Rangeage = 18 to 25) were voluntarily recruited from the study pool of undergraduate students at Nanjing University in China whose grade, gender, and other socioeconomic characteristics were balanced, via posting online flyers. Participants were randomly assigned to one of two experimental groups (n = 52/group). Participants first signed an informed consent form and were told that the experiment concerned feelings and experiences during travel and would be carried out in two steps. An inquiry done at the end of the experiment showed that no participant surmised the actual research purpose. In the first step, participants were shown a picture of a polluted-air (pollution group) or clean-air tourist site (non-pollution group). They were then instructed to imagine traveling in this tourist site, and to spend 3 to 5 min writing a diary (e.g., their experiences or feelings when traveling in this tourist site); sequentially participants took measurements of the perceived air pollution and TPEBIs. After this first step, all participants rested for 5 min before proceeding to the next step of the experiment. In step 2, we measured implicit attitudes toward air pollution and tourists' pro-environmental behavior using a Brief Implicit Association Test (BIAT). Participants conducted the BIAT on a computer and provided their socioeconomic information. The same experiment was conducted for each participant in a private and quiet room. All participants were debriefed and thanked with a small gift worth RMB 15. This study and the following studies were approved by the Institutional Review Board of Nanjing University. 3.1.2. Measures 3.1.2.1. Perceived air pollution. Perceived air pollution was measured through a single question (i.e., “How severe do you feel the air pollution is today?”) rated on a 5-point Likert scale from 1 (“no pollution”) to 5 (“very severe pollution”). 3.1.2.2. TPEBI. The TPEBI scale adopted in our experiment was a Chinese version of the scale used in Kiatkawsin and Han's (2017) research and based on previous literature (Dolnicar and Grun, 2009; Miller et al.,

2015; Stern et al., 1999). The TPEBI scale consisted of seven items that measure various dimensions of people's TPEBIs during a tourism situation (7-point Likert scale; 1 “totally disagree” to 7 “totally agree”; Cronbach's α = 0.72 in study 1). Specifically, the TPEBI scale mainly measured two aspects of TPEBIs in a tourism situation: the consumption aspect (three items, for example, “I would buy ‘eco-’ or ‘organic-’ products if possible when traveling”) and the general behavior aspect (four items, for example, “I would try to protect local resources as much as I could when traveling”).1

3.1.2.3. BIAT. The Implicit Association Test (IAT) is a cognitive– behavioral paradigm that reveals the implicit associations between concepts (targets) and attributes by latency measures of reaction time (Greenwald et al., 1998). In the current study, the Brief Implicit Association Test (BIAT, Sriram and Greenwald, 2009) with Inquisit 4.0 software (Millisecond Software Company, Seattle, WA, USA) was used to measure participants' implicit association toward air pollution and tourists' pro-environmental behavior. BIAT has been applied to a variety of fields and has been confirmed to have good psychometric properties (Yang et al., 2014). Here, we provide a detailed introduction about the BIAT employed in study 1. The BIAT measure involved four blocks (with 20 trials for each block) containing mixed stimuli of attribute words (Me vs. Others) and target images (tourists' pro-environmental behavior in a polluted-air place vs. a clean-air place). Participants were timed (ms) while conducting a simple sorting task (sorting the attribute words or target images one by one into the corresponding categories). Only one stimulus (a word or an image) was presented in a trail, and participants were required to react using the computer keyboard as quickly as possible (e.g., for a word or picture representing either “Me” or “tourists' pro-environmental behavior in a polluted-air place,” press the “E” key; for a word or picture representing neither “Me” nor “tourists' pro-environmental behavior in a polluted-air place,” press the “I” key). The order of trials was counterbalanced across subjects. Then, the IAT-D scores were calculated using the improved scoring algorithm (Greenwald et al., 2003), with a standardized mean difference score of the “hypothesis-consistent” pairing (“Me” and “tourists' proenvironmental behavior in a clean-air place”) and “hypothesis-inconsistent” pairing (“Me” and “tourists' pro-environmental behavior in a polluted-air place”). A significant positive IAT-D score demonstrated a stronger implicit association between “Me” and “tourists' proenvironmental behavior in a clean-air place” than for “Me” and “tourists' pro-environmental behavior in a polluted-air place.”

1 A two-factor model (consumption and general behavior of TPEBIs) was established to construct TPEBIs in the third study. A detailed description about it can be found in the Section 5.2.2 Part 2: Test of the moderated mediation model (Confirmative factor analysis).

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3.2. Results We conducted t-test2 to confirm the hypothesized differences using SPSS 22.0 (IBM Corporation, New York, USA). Data in our study conformed to a normal distribution (examined by the Shapiro–Wilks test with a significance level α of 0.05, QQ plot, and PP plot). The results are shown below in the form of manipulation checks, a test of group differences, and a test of implicit associations.

stronger implicit association for “Me”– “tourists' pro-environmental behavior in a clean-air place” than for “Me”–“tourists' pro-environmental behavior in a polluted-air place,” thus indicating that participants implicitly associated themselves more with adopting pro-environmental behavior when traveling in a clean-air place than when traveling in a polluted-air place (M = 0.15, SD = 0.34, t(103) = 4.33, p b .001, Cohen's d = 0.44). Therefore, H1b was supported. 4. Study 2: the mediating role of state anxiety

3.2.1. Manipulation checks (1) We checked the manipulation of the experimental materials in the first part of study 1. The results of an independent sample t-test revealed that participants in the pollution group (M = 4.00, SD = 0.77) felt that there was a significantly higher level of air pollution than those in the non-pollution group (M = 1.56, SD = 0.54), t(102) = 18.79, p b .001, Cohen's d = 3.67. In addition, two master students at the authors' university, who did not previously touch our research, checked the diaries to confirm that participants in the pollution group did perceive the presented tourist site as hazy, smoggy, or pollution-filled rather than other weather states like rain or fog; they also confirmed that the participants in the nonpollution group did not perceive pollution. All the participants in the polluted-air condition mentioned air pollution in their diaries, whereas none in the clean-air condition did so. Thus, the significant difference in perceived air quality and the diary content check both confirmed the effective priming of a polluted/clean-air tourism situation. (2) For the manipulation of the picture set in the second part of study 1 (measures using BIAT), an independent pilot study was conducted to test the effectiveness of the images to represent the tourists' pro-environmental behavior in a pollutedair or clean-air travel situation. Twenty participants (Mage = 23.56, SD = 2.73, none having participated in our formal experiments) were voluntarily recruited from the study pool of Nanjing University and were invited to evaluate 21 pictures (photos taken by the authors or collected from publicly available online resources) that presented tourists' proenvironmental behaviors in a clean-air situation. The evaluation results screened out the four most representative pictures for tourists' pro-environmental behavior. Photoshop 6.0 (Adobe Corporation, San Jose, CA, USA) was used to simulate a haze effect by adding a 50% transparency gray layer to each of these four pictures to represent tourists' proenvironmental behavior in a polluted-air situation. Another group of participants was voluntarily recruited (n = 20, Mage = 22.21, SD = 2.10) from the same study pool and invited to evaluate the extent of air pollution among the eight pictures. We confirmed a significant difference of perceived air pollution between the four haze-added pictures (M = 3.96, SD = 1.87) and the four original pictures (M = 2.31, SD = 1.02), t(19) = 3.92, p = .001, Cohen's d = 0.88. 3.2.2. Test of group differences An independent sample t-test revealed that the level of TPEBIs in the non-pollution group (M = 5.42, SD = 0.60) was significantly higher than that in the pollution group (M = 5.04, SD = 0.74), t(102) = 2.94, p = .004, Cohen's d = 0.58. Therefore, H1a was supported. 3.2.3. Test of implicit association A single-sample t-test was conducted for the IAT-D scores (data for the two groups were pooled, n = 104), and the results showed a 2 Great and sincere thanks are extended to anonymous reviewer's comments and suggestions on the analytical method, which not only helped us to the improve the quality of this paper, but also enlightened us to make careful work in our further research.

Mediation analysis is an effective way to explain the mechanism of how one variable affects another (MacKinnon et al., 2007). In study 2, we aimed to construct a mediation model for the relationship between air pollution and TPEBIs by introducing the mediator of state anxiety. The method of study 2 was similar to study 1's. Data from study 2 were confirmed as normally distributed (the same examinations were done with study 1). 4.1. Method 4.1.1. Participants and procedures In total, 108 participants (55 males, 53 females; M = 22.78 years, Rangeage = 18 to 26) were recruited from the same study pool as that used in study 1 (but none of them participated in study 1). Half of the recruited participants were randomly assigned to the pollution group (n1 = 54), and the other half were assigned to the non-pollution group (n2 = 54). The procedure was similar to that in study 1. Participants were instructed to imagine traveling in a polluted-air (vs. cleanair) tourist site, spend 5 min writing a diary entry, fill out questionnaires, and finally provide their socioeconomic information. All participants were debriefed and thanked with RMB 5. 4.1.2. Measures 4.1.2.1. Perceived air pollution. The perceived air pollution in study 2 was measured in the same way as in study 1. 4.1.2.2. TPEBI. The TPEBI scale adopted in study 2 was the same as in study 1. Cronbach's α of TPEBI in study 2 was 0.77. 4.1.2.3. State anxiety. The state anxiety scale was adapted from the short Spielberger State-Trait Anxiety Inventory (STAI) (Marteau & Bekker, 1992). It included six items (5-point Likert scale, with strength increasing from 1 to 5). The Cronbach's α of state anxiety in study 2 was 0.88. 4.1.2.4. Control variables. Considering the possible interaction between state anxiety and trait anxiety (Endler and Kocovski, 2001), trait anxiety was measured through a short-form trait scale of the STAI (Spielberger, 1979) with 10 items (Cronbach's α = 0.86 in study 2). 4.2. Results 4.2.1. Manipulation check The results of an independent sample t-test revealed a significant difference in the tourists' perceived air pollution between the pollution group and non-pollution group (t(106) = 10.38, p b .001, Cohen's d = 2.00). Specifically, participants in the pollution group (M = 4.30, SD = 0.63) felt that there was a significantly higher level of air pollution than those in the non-pollution group (M = 2.63, SD = 1.00). Like in study 1, the diary entries were checked, and all the participants of the polluted-air condition mentioned air pollution, whereas none of those in the clean-air condition mentioned air pollution. Additionally, trait anxiety was confirmed to be balanced between the pollution group (M = 2.13, SD = 0.57) and non-pollution group (M = 2.15, SD = 0.46), t(85) = 0.20, p = .84.

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4.2.2. Test of differences in state anxiety and TPEBI Results of an independent sample t-test revealed that the level of state anxiety in the pollution group (M = 2.76, SD = 0.70) was significantly higher than that in the non-pollution group (M = 2.25, SD = 0.39), t(106) = 4.31, p b .001, Cohen's d = 0.83; the level of TPEBI in the pollution group (M = 5.32, SD = 0.82) was significantly lower than that in the non-pollution group (M = 5.72, SD = 0.55), t (106) = 2.96, p = .004, Cohen's d = 0.57. Therefore, H2 was supported.

4.2.3. Test of indirect effects Using Mplus 7.4 (Muthén and Muthén, 2015), bootstrapping analyses were conducted (a dummy variable was created to represent the extent of air pollution; the pollution group was coded as “1” and the nonpollution group was coded as “0”). Results revealed that state anxiety mediated the effect of air pollution on the TPEBIs (mediation effect = −0.32, bias-corrected 95% CI = [−0.53, −0.17]). Therefore, H3 was supported.

5. Study 3: a field study Previous studies have confirmed the negative effects of air pollution on tourists' pro-environmental behavior and the mediating role of state anxiety using laboratory experimental designs. As a field experiment, study 3 aimed to further examine these effects and the moderating role of tourism nostalgia in a real tourism context. Data were collected at a famous heritage site in China under two air quality conditions.

5.1. Methods 5.1.1. Participants and procedures Data for study 3 were collected at the Giant Wild Goose Pagoda (also called Dayan Pagoda), one of the most famous tourist destinations in Xi'an, China. Originally built in 652 (Tang dynasty), the Giant Wild Goose Pagoda has been a World Heritage site since 2014 and has become a national AAAAA scenic area (the highest level in the rating categories assessed by the Ministry of Culture and Tourism of China), attracting a large number of visitors. We invited tourists to fill out questionnaires during their visit to this scenic area (we set eight locations covering different directions in the Giant Wild Goose Pagoda, trying to randomly select respondents). Moreover, to examine the effect of different levels of air pollution, we collected our data on two independent days, namely, daytime (from 8:00 to 17:00) on Jan 12 and Jan 15, 2019. According to Air Quality Index (AQI) data from China's Ministry of Ecological Environment (http://m.mee.gov.cn), the air quality on Jan 12 was 265 (heavy pollution), and the air quality on Jan 15 was 69 (no pollution). Therefore, data collected on Jan 12 were put into the pollution group, and data collected on Jan 15 were put into the nonpollution group. The data were collected from a total of 366 tourist responses. There were two stages for data screening. First, cases exhibiting over 10% of missing data were unusable and removed (Hair et al., 2014). As a result, seven responses were removed according to this criterion. Second, unengaged responses were detected using an item set in the medium part of the questionnaire to test the effort and attention the respondents devoted to the study (i.e., “This is an item serving for testing your attention/concentration. In this item, please directly choose 5 as the answer”). Thus, an additional nine responses were removed for exhibiting unengaged responses (seven of them chose other options than the required one, and two of them did not answer the item). Finally, the total number of usable responses was 350 (182 in the pollution group, 168 in the non-pollution group; 162 males, 188 females; Mage = 24.25, Rangeage = 13 to 46).

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5.1.2. Measures 5.1.2.1. Perceived air pollution. The perceived air pollution was measured in the same way as in studies 1 and 2. 5.1.2.2. TPEBI. The TPEBI scale adopted in study 3 was the same as that in studies 1 and 2 (Cronbach's α = 0.76). 5.1.2.3. State anxiety. The state anxiety scale adopted in study 3 was the same as that used in study 2 (Cronbach's α = 0.87). 5.1.2.4. Tourism nostalgia. The tourism nostalgia scale was adapted from previous psychological research regarding nostalgia (Hepper et al., 2012; Wildschut et al., 2006) in tourism situations. It consisted of three items (7-point Likert scale, with strength increasing from 1 to 7). In the current study, tourism nostalgia was primed through traveling within the heritage site, and the Cronbach's α was 0.93. 5.1.2.5. Tourists' willingness-to-pay. Tourists' willingness-to-pay was a single-item measure for how tourists were willing to pay for a fictitious project to promote environmental protection of a tourist site, i.e., “If this scenic site is promoting a project to protect the tourism environment (e.g., adding garbage cans and recycling bins to this scenic site), how willing are you to donate money to the project without considering other factors such as income?”. A 7-point Likert scale was adopted for the measure (the willingness increased from 1 to 7). Tourists' willingness-to-pay was not a main focus in our analysis, thus, we only compared its group difference in order to further confirm H1 (see Section 5.2.1). 5.1.2.6. Covariates. Trait anxiety, perceived air pollution at the tourist's origin, perceived importance of air quality, and sociodemographic characteristics (i.e., age, gender, education, marital status, income, and the number of traveling days) were involved in our analysis as covariates (control variables). Among them, trait anxiety was measured using the same scale used in study 2; following previous research (Gu et al., 2015), perceived air pollution at the tourist's origin (i.e., the daily perception of air pollution in the place where the tourist was from) was measured through a four-item scale adapted from the Global Warming Index (Heath and Gifford, 2006), and the perceived importance of air quality was measured by a three-item scale (i.e., how important good air quality was perceived to be by individuals). The Cronbach's α of trait anxiety, perceived air pollution at the tourist's origin, and perceived importance of air quality were 0.77, 0.89, and 0.91, respectively, in study 3. 5.1.3. Statistical approaches The results of study 3 were analyzed in two parts: a test between the pollution group and the non-pollution group, and a test of the hypothesized moderated mediation model. 5.2. Results 5.2.1. Part 1: test of group difference SPSS 22.0 was implemented for statistical analysis in study 3. Data's linearity, homoscedasticity, multicollinearity, and normality were also assessed without evidence of any major violations or problems (all the variables were within the required range from −2.00 to +2.00 for skewness values and kurtosis values, Curran et al., 1996, Gravetter and Wallnau, 2014). 5.2.1.1. Common method bias analysis. Harman's single-factor method was used to test the common method bias (Harman, 1976). Conducting an unrotated explorative factor analysis for all items, the results showed that nine factors' eigenvalues were N1. Moreover, the first factor accounted for 22.31%, which was much lower than the 40% standard.

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Therefore, the problem of a serious common method bias did not emerge in the current research (Podsakoff et al., 2003). 5.2.1.2. Manipulation checks. The results of an independent sample t-test revealed a significant difference in tourists' perceived air pollution, t (301) = 6.11, p b .001, Cohen's d = 0.66. Specifically, participants in the pollution group (M = 4.04, SD = 0.81) felt that there was a significantly higher level of air pollution than those in the non-pollution group (M = 3.40, SD = 1.11). Thus, both the AQI index and the perceived air quality difference confirmed the manipulated group difference in study 3. Moreover, there was no significant difference in tourists' trait anxiety between the pollution group (M = 2.09, SD = 0.41) and the non-pollution group (M = 2.11, SD = 0.41), t(338) = 0.35, p = .73. A single-sample t-test of tourism nostalgia (compared with 4, the neutral point of the 7-point Likert scale) was conducted to test the nostalgic features of the experimental tourist site (data of the two groups were pooled). Results showed that the tourist site in study 3 effectively aroused tourists' feelings of nostalgia, as participants showed a significantly higher level of tourism nostalgia than the neutral point, M = 4.79, SD = 1.24, t(348) = 11.94, p b .001, Cohen's d = 0.64. Besides, according to the distribution of the tourism nostalgia scores (combining the data from two groups), up to 84% of respondents reported a level ≥ 4 (the neutral point in the 7-point Likert scale), compared with only 16% of respondents who reported a level lower than 4. This also supported the idea that heritage sites could raise visitors' nostalgia. In addition, the difference in tourism nostalgia was insignificant between the pollution group (M = 4.86, SD = 1.16) and the non-pollution group (M = 4.72, SD = 1.32), t(347) = 1.10, p = .27. This demonstrated that the tourism nostalgia was felt consistently by participants who visited this tourist site on two separate days. 5.2.1.3. Test of differences in state anxiety and TPEBI. An independent sample t-test revealed that the level of state anxiety of the pollution group (M = 2.72, SD = 0.91) was significantly higher than that of the nonpollution group (M = 2.10, SD = 0.70), t(336) = 7.20, p b .001, Cohen's d = 0.76; meanwhile, the level of TPEBI in the pollution group (M = 5.17, SD = 0.62) was significantly lower than that of the nonpollution group (M = 5.55, SD = 0.64), t(346) = 5.63, p b .001, Cohen's d = 0.60. In addition, participants in the pollution group (M = 4.32, SD = 1.16) reported a significantly lower level of tourists' willingness-topay than those in the non-pollution group (M = 5.01, SD = 1.04), t (346) = 5.83, p b .001, Cohen's d = 0.63. Therefore, H1 and H2 were further supported. 5.2.2. Part 2: test of the moderated mediation model To further test the hypothesized model (see Fig. 1), the current study adopted Mplus 7.4 for statistical analysis. Five commonly used fitness indices were chosen for the evaluation of the fitness of the measurement model, namely, χ2/df, CFI, TLI, RMSEA, and SRMR (Hu and Bentler, 1999). Similar to previous research, the acceptable values for these indices were as follows: χ2/df ≤ 3, CFI ≥ 0.90, TLI ≥ 0.90, RMSEA ≤ 0.08, and SRMR ≤ 0.10 (Kline, 2010). The index of moderated mediation (MacKinnon, 2008; Hayes, 2015) and the mediation effect difference (MacKinnon et al., 2002; Preacher et al., 2007; Edwards and Lambert, 2007) were combined to examine the moderator's role in the mediation effect. Considering the advantages of bootstrapping analysis in testing the moderated mediation model (Edwards and Lambert, 2007; Preacher et al., 2007), the following analysis was conducted by bootstrapping 5000 samples and calculating 95% bias-corrected confidence intervals. The effects were considered statistically significant if the corresponding confidence intervals excluded zero. Note that the control variables were involved in our analysis. 5.2.2.1. Confirmatory factor analysis. The TPEBI scale was split into two factors (i.e., consumption and general behavior) in the SEM analysis for three reasons: (1) the first three items of the TPEBI scale were specific

measurements for tourists' pro-environmental consumption behavior, whereas the other four items represented more general proenvironmental behavior; (2) the fitness of the two-factor model for the TPEBI scale (e.g., χ2 = 17.43, df = 10, CFI = 0.99, TLI = 0.98, RMSEA = 0.05, SRMR = 0.03) was better than the fitness of the onefactor model (e.g., χ2 = 59.95, df = 11, CFI = 0.94, TLI = 0.89, RMSEA = 0.11, SRMR = 0.06), which meant that the two-factor model provided a better data fit; and (3) the original users of the TPEBI scale (Kiatkawsin and Han, 2017) excluded the first three items in their SEM study because of relatively low loadings (this was consistent with our data). This suggested that a two-factor model might be more appropriate for this scale. A confirmatory factor analysis was made to test the reliability and validity of the current constructs (see Appendix A; for the Pearson's correlations of main variables, see Table 1). The measurement model yielded a good model fit (e.g., χ2 = 441.22, df = 212, CFI = 0.95, TLI = 0.94, RMSEA = 0.06, SRMR = 0.06). For all the factors in our study, the CR values ranged from 0.75 to 0.93, showing a good composite reliability (larger than the cutoff of 0.60; Bagozzi and Yi, 1988). The results of average variance extracted (AVE) also revealed good convergent validity and discriminant validity for all the factors (larger than the cutoff of 0.50, Fornell and Larcker, 1981). Moreover, the Cronbach's α coefficients of all factors were larger than 0.7; this showed an acceptable internal consistency for all the scales used in study 3 (Kline, 2000, p. 13). Overall, the measurement model was satisfactory in its reliability and validity. 5.2.2.2. Indirect effect analysis. Based on the measurement model, we ran an indirect effect analysis, including a mediation test (H3) and a moderation test (H4). That is, we examined the mediating effect of state anxiety between air pollution and TPEBIs (same as in study 2), as well as the moderating role of tourism nostalgia in the effect's first half path (path “A to S”, see Fig. 2). The model's fit was good (e.g., χ2 = 614.83, df = 347, CFI = 0.92, TLI = 0.91, RMSEA = 0.05, SRMR = 0.06). Results of the path analysis involving all variables are shown in Table 2. In testing H3, the mediating effect of state anxiety on air pollution and TPEBIs was confirmed for both path A to S to P1 (a1b1 = −0.23, CI = [−0.37, −0.11]) and path A to S to P2 (a1b2 = −0.28, CI = [−0.43, −0.17]). That is to say, air pollution inhibited the consumption and general behavior of TPEBIs by arousing the mediating role of state anxiety. Therefore, H3 was supported. To test H4, the moderating effect of tourism nostalgia was investigated. As presented in Table 2, the indices of moderated mediation were significant both for the path to P1 (a3b1 = 0.06, CI = [0.02, 0.13]) and for the path to P2 (a3b2 = 0.08, CI = [0.03, 0.15]). Table 3 shows that the mediation effect (air pollution on TPEBI through state anxiety) varied significantly across different levels of tourism nostalgia (±1 SD from the mean) for both the path to P1 (Dif1 = −0.16, CI = [−0.33, −0.05]) and the path to P2 (Dif2 = −0.20, CI = [−0.38, −0.07]). More concretely, for the path to P1, the mediation effect of air pollution on TPEBI through state anxiety was weaker, with a high level of tourism nostalgia (βH1 = −0.15, CI = [−0.28, −0.07]), than with a low level of tourism nostalgia (βL1 = −0.30, CI = [−0.51, −0.14]). Additionally, for the path to P2, the same effects were observed (βH2 = −0.19, CI = [−0.32, −0.10]; βL2 = −0.38, CI = [−0.59, −0.22]). Hence, H4 was supported. 6. Discussion Across two laboratory experiments (studies 1 and 2) and a field experiment (study 3), we aimed to explore the relationship between air pollution and TPEBIs and to determine the underlying mechanisms. In study 1, we found both explicit and implicit evidence for the idea that air pollution inhibited TPEBIs (Hypothesis 1). Consistent with previous research (Bakian et al., 2015; C. Kim et al., 2010; Min et al., 2018; Zheng et al., 2019), study 1 showed the negative effects of air pollution;

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Table 1 Pearson's correlations of main variables in the proposed model.

A S N P1 P2 PA PI TA

A

S

N

1 0.44 b0.01 −0.35 −0.21 b0.01 b0.01 −0.04

1 −0.54 −0.43 −0.45 −0.07 −0.03 −0.01

1 0.18 0.26 0.13 0.06 b0.01

P1

P2

1 0.31 0.04 0.07 0.10

PA

1 0.31 0.27 b0.01

1 0.78 b0.01

PI

1 b0.01

TA

Mean (SD)

Range

Coefficient of variation

1

2.42 (0.87) 4.79 (1.24) 4.62 (0.94) 5.90 (0.74) 5.62 (1.20) 6.09 (1.06) 2.10 (0.41)

3.83 6.00 5.00 3.25 6.00 6.33 2.30

0.36 0.26 0.20 0.13 0.21 0.17 0.20

Note. A, air pollution (the pollution condition was coded into 1; the non-pollution condition was coded into 0); S, State anxiety; N, Tourism nostalgia; P1, Consumption of TPEBIs; P2, General behavior of TPEBIs; PA, Perceived air pollution at the tourist's origin; PI, Perceived importance of air quality; TA, Trait anxiety.

that is, polluted air led to “polluted ecotourism.” As indicated by this finding, there seems to be a vicious cycle among air pollution, tourism, and the environment: tourism induces air pollution (Saenz-de-Miera and Rossello, 2014), and air pollution adds an even heavier environmental burden (by decreasing tourists' pro-environmental behavior), which, in turn, harms the sustainable development of tourism. In order to break this vicious cycle, studies 2 and 3 explored a deeper and more comprehensive understanding of the framework of air pollution and TPEBIs. Results of study 2 supported the findings of study 1, and further confirmed Hypothesis 2 and 3, as state anxiety mediated the relationship between air pollution and TPEBIs. In line with previous research indicating that air pollution raises individuals' anxiety (Power et al., 2015), and anxiety brings about negative effects (Lu et al., 2018), we found that state anxiety was increased by air pollution and predicted a lower level of TPEBIs. Study 3 further explored the impact mechanisms regarding air pollution and TPEBIs, confirming Hypotheses 1, 2, and 3 at a real tourism attraction. Moreover, study 3 introduced tourism nostalgia into ecotourism, and we built a moderated mediation model to clearly explain how air pollution influenced TPEBIs. Tourism nostalgia was found to alleviate the state anxiety caused by air pollution and indirectly promote TPEBIs (Hypothesis 4). The findings supported previous research that showed nostalgia's positive effects on individuals' emotions and behaviors (van Tilburg et al., 2018; Sedikides et al., 2008). Gender was found to be a significant predictor of the general behavior of TPEBIs (female tourists showed a higher level of general TPEBIs compared with male tourists). This was consistent with previous research regarding pro-environmental behavior (Liu et al., 2019) and offered new insights for the gender difference into tourists' proenvironmental behavior. This research, to the best of our knowledge, was the first to extend the negative effects of air pollution to tourists' pro-environmental behavior. As one of the world's most concerning environmental problems, we proposed that air pollution's threats to tourism development should

N -.24**(.08)

AN

.89***(.09)a

-.43***(.04)

-.33**(.09)

S -.33*(.15)

A

P1

-.43***(.08)

P2 .04(.14)

Fig. 2. Results of path analysis. Note 1. A, Air pollution (the pollution condition was coded into 1; the non-pollution condition was coded into 0); S, State anxiety; P1, Consumption of TPEBIs; P2, General behavior of TPEBIs; N, Tourism nostalgia; AN, A*N. Note 2. Control variables were included in the model (i.e., age, gender, education, marital status, income, the number of traveling days, trait anxiety, perceived air pollution of tourist's origin, and perceived importance of air quality). The results of the control variables can be found in Table 2. Note 3. a Standardized path coefficients (SE); ⁎p b .05, ⁎⁎p b .01, ⁎⁎⁎p b .001.

not be ignored. Take China as an example; although air quality has improved significantly in recent years, great challenge remains in reducing the negative effects of air pollution (Zeng et al., 2019). It is noteworthy that 239 of 338 major cities in China (up to 70.71%) were recognized as having poor air quality in 2017 (SO2, NO2, PM10, PM2.5, CO, and O3 were involved in the indicators for air quality, according to data from China's Ministry of Ecological Environment). Among the most severely polluted cities in China are world-famous tourist destinations, like Beijing, Tianjin, and Xi'an. Therefore, we emphasized the importance of air pollution in the tourism context. In addition, the current research attempted to shed light on the relationship between air pollution and TPEBIs. A comprehensive framework was built to explain the corresponding mechanisms through the moderated mediation analysis. The framework offered an explanation regarding a variety of internal and external factors on tourists' pro-environmental behaviors, which helps fill the existing gap in deep analysis on the formation of environmental behaviors (Li et al., 2019). When putting this research into practice, policy makers and tourism managers should focus on air pollution at tourist destinations and design efficient strategies to prevent or control its occurrence (e.g., by improving the energy structure and strategy, minimizing dependence on fossil fuels, and expanding the use of renewable energy resources). Tourism industry stakeholders should also aim to promote sustainable tourism (e.g., by supporting low-carbon policies and advocating recycling), as tourism growth relies on environmental sustainability (Pulido-Fernández et al., 2019). Moreover, this research hinted at the practical values of understanding tourists' psychological and behavioral processes on the road to meeting the UN's SDGs. Encouraging tourists' pro-environmental behavior is of great significance for the sustainable management of tourism, as individuals behave in a less proenvironmental manner when they become tourists (Miller et al., 2015). In addition, the findings in this research indicated the value of integrating nostalgia into the sustainable tourism agenda. Thus, high priority should be given to conserving the original scenery and resources of heritage sites. Developing countries like China, India, and Mexico have a large number of World Heritage Sites (http://whc.unesco.org/en/list/) but lack resources for heritage protection (thus impairing the nostalgic features). Global organizations may need to lend these developing countries a hand in nostalgic tourism development. Furthermore, the psychological benefits of nostalgia should be considered in the construction of attractions. Specifically, nostalgic elements could be added to scenic sites to nudge the positive influence of tourism nostalgia on tourists' sustainable behaviours (e.g., decorating constructions in nostalgic styles, playing nostalgic music in specific places, presenting nostalgic souvenirs, and so on), and this approach is suggested to be practical even at the non-heritage sites. The limitations of this study should be considered when generalizing the current findings. First, there may have been some potential covariates other than air pollutant levels differing between the two separate study days (with a two-day interval) in study 3. Therefore, longitudinal studies are encouraged (e.g., by collecting data on several days with different air pollutant levels to observe a more comprehensive exposure-

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Table 2 Path coefficients and indirect effects test on TPEBIs in study 3. Path a1 Air pollution b1 State anxiety b2 State anxiety c1′ Air pollution c2′ Air pollution a1b1 Mediation1 a1b2 Mediation2 a3b1 Moderation1 a3b2 Moderation2 a3 Interaction of Air pollution and nostalgia Age Gender Education Number of traveling days Marital status Income Perceived air pollution at the tourist's origin Perceived importance of air quality Tait anxiety

on on on on on on on on on on on on on on on on on on on on on on on on on on on on

State anxiety Consumption (P1) General behavior (P2) Consumption (P1) General behavior (P2) Consumption (P1) General behavior (P2) Consumption (P1) General behavior (P2) State anxiety Consumption (P1) General behavior (P2) Consumption (P1) General behavior (P2) Consumption (P1) General behavior (P2) Consumption (P1) General behavior (P2) Consumption (P1) General behavior (P2) Consumption (P1) General behavior (P2) Consumption (P1) General behavior (P2) Consumption (P1) General behavior (P2) Consumption (P1) General behavior (P2)

Estimate

SE

95%CI

Hypothesesa

0.89⁎⁎⁎ −0.33⁎⁎⁎ −0.43⁎⁎⁎ −0.33⁎ 0.04 −0.23⁎⁎⁎ −0.28⁎⁎⁎ 0.06⁎ 0.08⁎ −0.24⁎⁎

0.09 0.09 0.08 0.15 0.14 0.07 0.07 0.03 0.03 0.08 0.02 0.01 0.14 0.13 0.07 0.07 0.03 0.02 0.21 0.19 0.05 0.05 0.12 0.14 0.12 0.15 0.09 0.06

[0.71, 1.06] [−0.50, −0.15] [−0.57, −0.27] [−0.62, −0.01] [−0.21, 0.31] [−0.37, −0.11] [−0.43, −0.17] [0.02, 0.13] [0.03, 0.15] [−0.38, −0.09] [−0.04, 0.02] [−0.02, 0.04] [−0.26, 0.28] [0.11, 0.63] [−0.12, 0.17] [−0.14, 0.15] [0.01, 0.11] [−0.01, 0.08] [−0.43, 0.37] [−0.38, 0.36] [−0.10, 0.11] [−0.11, 0.09] [−0.29, 0.18] [−0.05, 0.51] [−0.15, 0.34] [−0.24, 0.35] [−0.10, 0.50] [−0.38, 0.29]

H2 supported

−0.01 0.01 0.01 0.36⁎⁎ 0.02 0.01 0.06⁎ 0.03 −0.04 −0.01 0.01 −0.01 −0.07 0.21 0.11 0.09 0.21 −0.02

H3 supported H3 supported H4 supported H4 supported

n = 350; Mplus programs were used for these analyses (Muthén and Muthén, 2015; see the introduction to Mplus programs at http://www.statmodel.com/programs.shtml); all variables listed were considered simultaneously. a H2: People experiencing polluted air had a higher level of state anxiety and a lower level of TPEBIs compared with people experiencing clean air; H3: State anxiety mediated the relationship between air pollution and TPEBIs (i.e., air pollution inhibited TPEBIs through arousing state anxiety); H4: The mediation effect of air pollution on TPEBIs through state anxiety was moderated by tourism nostalgia. ⁎ p b .05. ⁎⁎ p b .01. ⁎⁎⁎ p b .001.

response relationship) in future research. In addition, proper utilization of big data (with a large sum of longitudinal data) can help avoid this limiting point in future air pollution and tourism research (e.g., daily pollution data from regulators or tourist data from online travel agencies). Second, the interaction effects of tourism nostalgia with air pollution on individual outcomes may have been confounded by other factors such as the illumination condition. Future researchers can overcome this limitation by additionally considering interaction terms of air pollution with other factors and then testing the stability of the interaction between air pollution and nostalgia. Additionally, there might be alternative mechanisms linking air pollution and TPEBIs besides anxiety and nostalgia, which also await further examination. Third, the BIAT used in study 1 was limited, as we did not involve the opposite side of

pro-environmental behavior (i.e., “non-pro-environmental behavior”) in the experimental stimuli. Thus, we cannot rule out the possibility that participants may have made their choices based on the concepts of clean and polluted air more so than based on the behaviors in these two settings. Therefore, results of the implicit association between air pollution and TPEBIs should be taken with caution, and it will be fruitful to carry out a deeper implicit exploration on air pollution and TPEBIs (e.g., by adding the “non-pro-environmental behavior” targets in the BIAT/IAT and comparing the implicit responses under different behavioral situations). In sum, there are a range of interesting and meaningful directions on the topic of air pollution and tourists' pro-environmental behavior for future studies, and more investigations will be important on the road to sustainable development.

Table 3 Conditional mediation effect at two values of the moderator. n = 350. Moderator variablea Low tourism nostalgia High tourism nostalgia Differences

βL1 βL2 βH1 βH2 Dif1 Dif2

Dependent variable

Mediation effects

SE

95%CI

Consumption (P1) General behavior (P2) Consumption (P1) General behavior (P2) Consumption (P1) General behavior (P2)

–0.30⁎⁎⁎ –0.38⁎⁎⁎ –0.15⁎⁎ –0.19⁎⁎⁎ –0.16⁎ –0.20⁎

0.10 0.09 0.05 0.06 0.07 0.08

[–0.51, [–0.59, [–0.28, [–0.32, [–0.33, [–0.38,

–0.14] –0.22] –0.07] –0.10] –0.05] –0.07]

a Two conditional values of the moderator (tourism nostalgia) were determined by ± 1 SD from the mean (low values of tourism nostalgia were coded as one SD below the mean, whereas high values of tourism nostalgia were coded as one SD above the mean). This approach was described in detail in the previous literature (Aiken and West, 1991; Edwards and Lambert, 2007; Preacher et al., 2007). Thus, the conditional mediating effects were calculated at each of the dichotomous values of tourism nostalgia. ⁎ p b .05. ⁎⁎ p b .01. ⁎⁎⁎ p b .001.

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7. Conclusion

Funding

This research utilized two laboratory experiments and a field study to examine the effect of air pollution on TPEBIs and the underlying mechanisms. In study 1, we found both explicit and implicit evidence for the idea that air pollution inhibits TPEBIs. Study 2 was a follow-up investigation of study 1, and we revealed the mediating role of state anxiety between air pollution and TPEBIs. Study 3 further confirmed the findings of studies 1 and 2 in a real tourism situation, and pointed out the moderating role of tourism nostalgia in the inhibitive effect of air pollution on TPEBIs through state anxiety. These findings contribute to a better understanding of air pollution's impact on TPEBIs and provide practical implications for environmental policymakers and managers.

The study described in this paper was supported by the National Social Science Fund of China (No. 18BSH122). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Declaration of competing interest All co-authors have declared that there are no financial/nonfinancial competing interests related to the research, authorship, and/ or publication of this article.

Appendix A. Results of confirmative factor analysis and reliability analysis Constructs and items Consumption of TPEBIs 1. I would prefer to buy local products when traveling. 2. I would buy “eco-friendly” or “organic” products if possible when traveling. 3. I would buy products in eco-friendly packaging if possible (i.e. avoid plastic shopping bags, plastic bottles, and try to reuse bottles and bags) when traveling. General behavior of TPEBIs 1. I would try to save water and electricity (i.e. turn off the tap while washing/brushing teeth, turning off the lights if I leave the room for N10 min, walking up the stairs if I only need to go one floor up, and using hotel towels more than once) when traveling. 2. I would try to learn about the recycling facilities and actions of the locals for sustainable tourism. 3. I would try to dispose garbage properly if possible (i.e. sort my garbage into separate containers for paper, plastic, and glass) when traveling. 4. I would try to protect local resources as much as I could (I would voluntarily stop visiting a famous spot if it needed to recover from environmental damage, and I would not disturb any creatures and vegetation, e.g., feeding fish and birds or picking flowers) when traveling. State Anxiety 1. I feel calm (reverse corded). 2. I am relaxed (reverse corded). 3. I feel content (reverse corded). 4. I feel tense. 5. I feel upset. 6. I am worried. Tourism Nostalgia 1. Traveling here makes me feel quite nostalgic. 2. Traveling here gives me nostalgic feelings. 3. Traveling here makes feel nostalgia at the moment. Perceived Air Pollution at the Tourist's Origin 1. I have already noticed some signs of air pollution. 2. It seems to me that air quality is worse now than in years prior. 3. It seems to me that air quality is worse compared to when I was a child. 4. I am quite sure that air pollution is occurring now. Perceived Importance of Air Quality 1. I think air quality is very important to my life. 2. I think air pollution will damage my physical health. 3. I think air pollution will damage my mental health.

Standardized factor loading

SE

0.63⁎⁎⁎ 0.85⁎⁎⁎ 0.63⁎⁎⁎

0.07 0.08 0.07

0.75⁎⁎⁎

0.04

0.81⁎⁎⁎ 0.67⁎⁎⁎

0.04 0.05

0.60⁎⁎⁎

0.06

0.60⁎⁎⁎ 0.62⁎⁎⁎ 0.63⁎⁎⁎ 0.77⁎⁎⁎ 0.84⁎⁎⁎ 0.82⁎⁎⁎

0.05 0.05 0.04 0.04 0.03 0.03

0.90⁎⁎⁎ 0.93⁎⁎⁎ 0.88⁎⁎⁎

0.02 0.02 0.02

0.70⁎⁎⁎ 0.73⁎⁎⁎ 0.82⁎⁎⁎ 0.92⁎⁎⁎

0.04 0.05 0.04 0.03

0.93⁎⁎⁎ 0.94⁎⁎⁎ 0.78⁎⁎⁎

0.02 0.02 0.03

Cronbach's α

CR

AVE

0.73

0.75 0.51

0.83

0.80 0.51

0.87

0.86 0.52

0.93

0.93 0.82

0.89

0.87 0.64

0.91

0.92 0.79

Note. Model fit information: χ2 = 441.22, df = 212, CFI = 0.95, TLI = 0.94, RMSEA = 0.06, SRMR = 0.06; ⁎p b .05, ⁎⁎p b .01, ⁎⁎⁎p b .001.

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Zhang, X., Chen, X., Zhang, X., 2018. The impact of exposure to air pollution on cognitive performance. Proc. Natl. Acad. Sci. U. S. A. 115, 9193–9197. https://doi.org/10.1073/ pnas.1809474115. Zheng, S.Q., Wang, J.H., Sun, C., Zhang, X.N., Kahn, M.E., 2019. Air pollution lowers Chinese urbanites' expressed happiness on social media. Nat. Hum. Behav. 3, 237–243. https://doi.org/10.1038/s41562-018-0521-2. Zhongda Wu is a master student at Nanjing University. His research interests are sustainable development, environmental management, pro-environmental attitudes and behaviors, as well as judgment and decision making. Liuna Geng is a professor in School of Social and Behavioral Sciences at Nanjing University. Her research interests lie in sustainable management in a range of environmental and social issues. Her current work focuses on sustainable behaviors, climate change, and mindfulness.

Please cite this article as: Z. Wu and L. Geng, Traveling in haze: How air pollution inhibits tourists' pro-environmental behavioral intentions, Science of the Total Environment, https://doi.org/10.1016/j.scitotenv.2019.135569