Environmental Concern and Destination Choices of Tourists: Exploring the Underpinnings of Country Heterogeneity

Abstract

This article analyzes the main determinants of pro-environmental concern of European tourists when they make holiday decisions. Tourist environmental concern depends not only on individual and travel-related attributes, but also on the characteristics of the tourists’ place of residence. Thus, this article simultaneously applies micro and macro approaches using hierarchical linear models, which can estimate variations in tourists’ environmental concern because of between-country and within-country differences. Specifically, the heterogeneous pattern across European countries in the level of environmental concern is mainly explained by between-country economic, cultural, and environmental differences. Within-country variability in the level of pro-environmental concern is mainly explained by individual and travel-related variables, and particularly by the characteristics of the destination. The results demonstrate that environmental concern varies depending on whether the destination is booked via a last-minute offer or not, whether it is a traditional or emergent destination, and whether the trip is abroad or domestic.

Keywords

tourist environmental concern hierarchical linear models travel-related attributes domestic tourism international tourism country heterogeneity

Introduction

Many national governments and international institutions, such as the World Watch Institute, the International Panel of Climate Change (IPCC), the European Union, and the United Nations, agree on the need for increasing action to protect the global environment. In the particular case of tourism, the World Tourism Organization defines sustainable tourism as “tourism which leads to management of all resources in such a way that economic, social, and aesthetic needs can be filled while maintaining cultural integrity, essential ecological processes, biological diversity, and life support systems” (WTO 2002). As Page and Connell (2009) recognized, there is a symbiotic relationship between tourism and the environment, with each being dependent on the other. The overall management of the environment in the tourism sector needs to take into account not only the environmental regulations imposed on tourism enterprises and destinations but also tourist behavior (Dolnicar and Leisch 2008). The main aim of environmental regulations is to maintain the quality of the natural environment (W. H. Lee and Moscardo 2005; Kasim 2009). One of the main issues in tourist behavior studies is to analyze the main determinants of improvements to the pro-environmental attitudes and behavior of tourists (Dolnicar and Leisch 2008).

Despite the relevance of tourist behavior in relation to the environment, there is little empirical literature on the topic. This lack has been highlighted by Xu and Fox (2014), who justified the need for additional studies on the environmental attitudes and behavior of tourists when they plan their vacations. Passaforo (2019) discussed the complex nature of attitudes, pointed out the limitations of their use, and suggested future lines of research into sustainable tourist behavior. The majority of previous studies on this topic have focused on the socioeconomic characteristics of the individual or the type of tourism (Schultz et al. 2004; Dolnicar and Grün 2009). However, this approach does not address issues such as whether or not tourist behavior is heterogeneous according to the country of origin, the destination, and the characteristics of the trip. Studies that address these issues could be helpful for improving selective target segments at destinations and encourage business initiatives adapted to tourist profiles according to their environmental behavior (Kil, Holland, and Stein 2014).

Thus, this article contributes to the tourism literature on environmental sustainability in different ways. First, the general literature shows that although public concern toward environmental protection is geographically heterogeneous (Franzen and Meyer 2010; Mostafa 2012), this topic has been little analyzed in the field of tourism (Falk and Hagsten 2019). Hence, this article investigated tourists’ pro-environmental concern by analyzing their heterogeneous patterns according to their place of residence using cross-country data sets. Three contextual hypotheses have been used to investigate country heterogeneity in the level of environmental concern. Specifically, we investigated the contribution of between-country economic, cultural, and environmental differences in explaining such heterogeneous patterns. Second, this article contributes to a better understanding of tourist environmental concern by determining the relevance to them of travel and destination characteristics, such as distance from the place of residence, whether destinations are traditional or emergent, and last-minute offers. Specifically, we address the repercussions of tourists’ effects on the environment when traveling domestically and abroad. Juvan and Dolnicar (2014) suggested that people who become involved in protecting the environment in their place of residence could behave differently when they go on holiday, and that such behavior may have negative consequences on the environment. Understanding the causes of such differences in behavior could help to minimize the negative environmental impacts of such tourists. Finally, the proposed methodology and the different databases used in this article allow us to address these objectives with confidence. A representative survey of tourists from EU-27 countries were used that combined micro-data from the Flash Eurobarometer and the European Value Survey, and macro-data from different international sources. This methodology adds the macro-level dimension to the predominantly individual-oriented field of attitude and behavior studies, while taking advantage of hierarchical linear models. The sample data have a hierarchical structure, in which individuals (first level) are grouped into countries (second level), and therefore the random heterogeneity of individuals can be decomposed into within-group and between-group variations. As Train (2009) suggested, this type of methodology applied at the individual level can be aggregated to obtain country-level predictions and can be used for forecasting over time.

The results of this article could be of great interest to policy makers, who not only need macro indicators to assess the economic and environmental impact of this activity but also need complementary indicators. From this perspective, the creation of tourist profiles according to the tourists’ country of origin and their travel and destination characteristics could be of great assistance in identifying environmentally responsible tourists. This approach represents a starting point by which to reduce environmentally unsustainable behavior in the tourist field.

This article is organized as follows: The next section reviews the general literature on environmental attitudes and behavior and the specific literature on these aspects and tourism. It also addresses some widely held contextual and compositional hypotheses; the third section describes the methodology and data sets used; the fourth section discusses the empirical results; and the fifth section presents the main conclusions.

Literature Review

Private Dimension of Sustainable Tourism

The issue of sustainable tourism is of particular relevance to industry and stakeholders, who are committed to protecting the environment because safe and attractive surroundings are part of their core product (Chan and Hsu 2016). In recent decades, the active role of companies has become highly relevant in the promotion of sustainable tourism, not only through the fulfilment of compulsory environmental measures, but also through voluntary initiatives to demonstrate their commitment to sustainability. There has been widespread promotion of formal management systems that proactively incorporate the environmental dimension because of their positive impact on company performance (Claver et al. 2007; Bohdanowicz-Godfrey and Zientara 2014). Furthermore, growing public concern over environmental damage has stimulated many hotels into incorporating the environmental awareness of customers in their management and marketing decision making (Y. Kim and Han 2010). Customers and other stakeholders increasingly expect companies to recognize their social and environmental responsibilities and to adjust business practices to mitigate negative impacts on the environment.

Several studies have demonstrated the growing interest of companies and international organizations in involving customers in a choice of services that are more respectful of the environment, given that this aspect would lead to the development of competitive advantages (J. S. Lee et al. 2010; Oliveira-Brochado, Oliveira-Brochado, and Caldeira 2015). Han, Hsu, and Sheu (2010) found that environmental tourism products had a positive effect on purchase intentions. Sánchez, García, and Marchante (2014) confirmed the positive impact of the environmental concern of tourists on hotel room pricing. Similar results were obtained by J. S. Lee et al. (2010), who analyzed the influence of having a green hotel image on tourists’ recommendations to other clients, intentions regarding revisits, and willingness to pay. In the Nordic hospitality sector, Heikkurinen (2010) found that an environmentally responsible image improved the perceptions of stakeholders. In summary, many studies that have addressed the relationship between the environmental concerns of customers and profitability have reported improved financial outcomes, although this effect varies across industries and destinations. For this reason, a current key marketing strategy is to take into account consumers’ environmental awareness (Kim, Borges, and Chon 2006). These authors suggested that markets punish companies that fail to consider environmental concerns in their marketing strategies.

Public Environmental Concern: Attitudes and Behavior

Dunlap and Jones (2002) defined public environmental concern as the commitment of individuals to the environment in the face of the effects of pollution and resource overexploitation by humans. Public environmental concern has been measured through different dimensions, such as attitudes and behaviors. Social psychological theories that attempt to explain why humans behave in certain ways postulate that attitudes, among other factors, affect behavior (Akintunde 2017). Primitive theories have emphasized the impact of education on environmental issues and the increased amount of information available on ecological problems, both of which may help individuals to alter their behavior in a responsible manner (Hungerford and Volk 1990). This author developed the behavioral change theory, which can be used to understand the links between knowledge about the environment and attitudes, and between attitudes and behavior. Many other models have also supported the link between attitudes and behavior. In the theory of planned behavior developed by Ajzen (1991), attitudes and personal responsibility are also considered as cognitive variables affecting the intention to act, which is a direct determinant of pro-environmental behavior. Several empirical papers on environmental management have confirmed the association between attitudes and behavior (Kaiser, Schultz, and Scheuthle 2007; Levine and Strube 2012), whereas other authors have found no association between these constructs (Cordano, Welcomer, and Scherer 2003). Other studies have found that this association was not consistently strong and that it varied widely according to the sample used (Levine and Strube 2012).

Tourists’ attitudes and behavior have been widely used in the tourism literature to investigate tourist environmental concern. Hall (2013) suggested that it is crucial to encourage pro-environmental consumer attitudes among tourists in order to encourage more responsible behavior. Based on the theory of planned behavior, T. H. Lee and Jan (2018) examined an ecotourism behavioral model using environmental attitudes among tourists visiting different nature-based tourism destinations. Schultz et al. (2004) suggested that tourists’ concern toward the environment is directly related to the type of tourism. In this regard, the degree of environmental commitment of a tourist visiting a natural park or an archaeological excavation is probably much higher than that of a sun and beach tourist. Kyoungjin-Kim and Weiler (2013) demonstrated that nature-based areas tend to attract environmentally responsible visitors notably interested in fossil collection. In another study, Kyoungjin-Kim, Airey, and Szivas (2011) found that specific attitudes toward cliff protection and fossil protection in nature-based tourism areas had a stronger impact on environmental protection than general attitudes. Weaver and Lawton (2004) showed that tourists’ attitudes are key to congestion in natural environments. Similar results were obtained by Tonge et al. (2015), who demonstrated that national area visitors were very concerned about the fragility of the balance of nature. Ballantyne et al. (2018) showed that postvisit environmental behavior increases as a result of a visit to wildlife tourism attractions. Similar differences have been found in individuals who practice outdoor activities, such as hiking or diving. For example, Ong and Musa (2011) found that scuba divers have a high degree of environmental awareness toward marine conservation. Falk and Hagsten (2019) supported this statement, demonstrating that tourists who enjoy outdoor activities have an increased tendency to emphasize environmental aspects when traveling. Studies have also shown that ecotourists favor pro-environmental attitudes: that is, they are more nature oriented than human oriented, although such attitudes widely differ between hard and soft ecotourists (Weaver and Lawton 2002). However, Firth and Hing (1999) found that very few backpackers considered ecofriendly practices to be relevant to their tourism attitudes.

Some studies, although scarce, have also found that environmentally responsible tourism varies geographically (Schultz et al. 2004; Wen and Ximing 2008; Ong and Musa 2011). For example, Wen and Ximing (2008) found a heterogeneous pattern between the eastern and western hemispheres in relation to the perceptions of tourists toward nature. Ong and Musa (2011) suggested that our understanding of tourists’ pro-environmental behavior could be increased by determining the attributes of their place of residence. On the other hand, Falk and Hagsten (2019) investigated this issue among European tourists. However, numerous general studies have analyzed the causes of these heterogeneous patterns across regions and countries in relation to the degree of public concern for environmental protection (Franzen 2003; Franzen and Meyer 2010; Gelissen 2007; Mostafa 2012). Three so-called contextual hypotheses have been used to investigate between-country heterogeneity (Franzen and Meyer 2010).

The first hypothesis states that environmental concern may be determined by economic advancement in a given society. This hypothesis supports the idea that higher income and strong economic growth reinforce public environmental concern in advanced societies (Diekmann and Franzen 1999). Nevertheless, several studies have not found evidence for this hypothesis. Mostafa (2012) suggested that this result was due to the fact that the citizens of high-income countries already support higher tax pressure for improving the environment. Inglehart’s (1995) study, which used the World Values Survey, partially supported this hypothesis, showing that environmental support was much stronger in low-income countries than in high-income countries. In response to this unexpected finding, this author suggested that objective problems and subjective values can act in opposite ways.

The second hypothesis states that objective problems may lead to increased environmental protection by making citizens of poor regions more aware of environmental issues. This hypothesis addresses the issue of whether attitudes toward environmental protection tend to be stronger in countries that have relatively severe objective environmental problems. Although ecological degradation and air and water pollution have become global problems, they are generally worse in developing countries than in advanced industrial societies (Inglehart 1995). Ogunbode (2013) supported this result, demonstrating that ecological degradation in their environment may also encourage individuals to engage in pro-environmental attitudes regardless of personal and economic circumstances. However, as Inglehart (1995) recognized, this hypothesis only partially explains heterogeneity between countries because environmental attitudes are not necessarily formed only in response to objective problems, but also by the adoption of postmaterialist values.

The third hypothesis addresses the issue of whether subjective values, such as those related to cultural and social aspects, have a significant effect on environmental attitudes and whether such attitudes could affect tourism choices (Inglehart 1995). Aspects such as quality of life, liberty, or environmental protection (postmaterialist values) are only of relevance to individuals when they have covered their basic needs, such as food, education, or safety (materialist values). Such cultural changes seem to have at least as much influence on relative support for environmental protection as objective environmental problems. This argument suggests that the term postmaterialism only characterizes advanced high-income countries and that it may be particularly relevant to holiday decisions. Filimonau et al. (2018) showed that there was a correlation between the cultural background of tourists and their pro-environmental attitudes, thus highlighting the relevance of national culture as a key driver for sustainable consumer behavior in the tourism field. In this regard, Wu, Font, and Liu (2020) suggested that an appropriate intervention could be to manage the social context in which tourists are prompted to moderate pro-environmental behaviors.

Given that the majority of previous studies on tourism have used highly focused surveys in specific countries or destinations, this type of contextual hypothesis has been little analyzed. In fact, previous studies have used socioeconomic characteristics (e.g., educational level, age, or gender) as predictors of tourist environmental concern, but have not used the determinants associated with their country or region of residence (Holden and Sparrowhawk 2002; Leonidou et al. 2015; Meric and Hunt 1998). In fact, contextual effects, as proposed in the three hypotheses, should be shaped by the socioeconomic attributes of the individuals. The literature suggests that both kinds of attributes (i.e., contextual and individual characteristics), should be considered simultaneously as determinants of public environmental concern (Franzen and Meyer 2010; Gelissen 2007). As mentioned, changes in economic conditions, the environment, and social values may have a significant effect on environmental attitudes which, in turn, may affect tourism choices. Further research on these aspects will increase the usefulness of this article.

Methodology

Discrete choice models have been widely used in the literature to explain individual attitudes and behavior (Train 2009). In the case of public environmental concern, individuals from the same country share social, cultural, labor, political, and environmental conditions, which may differ from those of other countries. There would have been bias in the treatment of this type of pooled data if a standard regression had been performed, because this approach would not have taken into account the fact that individuals from the same country share common characteristics, and therefore the standard errors could be correlated. Thus, it would be inappropriate to analyze the data using traditional regression methods, or because of the violation of the assumption of independence and the nested nature of the data. These problems are avoided by the use of multilevel modeling. A great advantage of hierarchical linear modeling is that it makes it possible to perform comprehensive analyses of complex policy issues by clarifying the effects between variables measured at different levels (Mostafa 2012). Specifically, this modeling approach is more flexible at the time of analysis because the unobserved heterogeneity of individuals may be decomposed into within-group and between-group variations (Hensher and Greene 2003).

Econometric Modeling

The sample data have a hierarchical structure in which individuals i (first level) are grouped into countries c (second level). We assume a discrete binary response to environmental concern for each individual denoted by $y_{i c}$ , which takes a unitary value if individuals consider environmental issues in their tourism decisions and zero otherwise. Given the random effects, the response is assumed to have a Bernoulli conditional distribution, with the probability of success determined by the logistic cumulative distribution function (c.d.f.). The log likelihood for this model may be expressed as follows:

logit {\Pr (y_{i c} = 1 | x_{i c}, z_{i c}, u_{c}} = β_{0} + β_{1} x_{i c} + β_{2} z_{i c} + u_{c}

(1)

where $β_{0}$ is the overall mean, and $β_{1}$ and $β_{2}$ are vectors of fixed parameters associated with individual variables $x_{i c}$ and context variables $z_{i c}$ , respectively. The random constant for each country, $u_{c}$ , follows a normal distribution with a zero mean and a residual variance between the groups, $σ_{u}^{2}$ , $u_{c} ~ N (0, σ_{u}^{2})$ . For a generalized mixed model that includes fixed and random parameters, the probability, $P (y_{i c} = 1) = π_{i c}$ , is defined as follows:

F^{- 1} (π_{i c}) = β_{0} + β_{1} x_{i c} + β_{2} z_{i c} + u_{c}

(2)

Given that the model is logistic, $F^{- 1} (π_{i c})$ is the log-odds that $y_{i c} = 1$ (i.e., the tourists consider the environment in their vacations). Thus,

\log (\frac{π_{i c}}{1 - π_{i c}}) = β_{0} + β_{1} x_{i c} + β_{2} z_{i c} + u_{c}

(3)

The probability that a tourist will take the environment into consideration can be estimated from equation (3) as follows:

{\overset{\land}{π}}_{i c} = \frac{\exp ({\overset{\land}{β}}_{0} + {\overset{\land}{β}}_{1} x_{i c} + {\overset{\land}{β}}_{2} z_{i c} + {\overset{\land}{u}}_{c})}{1 - \exp ({\overset{\land}{β}}_{0} + {\overset{\land}{β}}_{1} x_{i c} + {\overset{\land}{β}}_{2} z_{i c} + {\overset{\land}{u}}_{c})}

(4)

where “^” indicates that the parameters are those estimated in the adjusted model.

Model Specification

Using a discretized and linearized version, the proposed model in equation (3) can be equivalently specified as a latent formulation:

y_{i c}^{*} = β_{0} + β_{1} x_{i c} + β_{2} z_{i c} + u_{c} + ε_{i c}^{*}

(5)

In equation (5), the individual-level residuals, $ε_{i c}^{*}$ , are assumed to follow a logistic distribution with zero mean and a residual variance $σ_{e}^{2}$ . Thus, the total variance for a multilevel model has two components, $σ_{y_{i c}^{*}}^{2} = σ_{u}^{2} + σ_{e}^{2}$ . In line with previous literature, we proposed different specifications depending on the types of contextual and individual variables considered in the study (Franzen 2003; Mostafa 2012).

We first proposed model 1, which excluded individual-level and country-level regressors: $y_{i c}^{*} = β_{0} + u_{c} + ε_{i c}^{*}$ . This analysis is known as null model and is analogous to a random effects analysis of variance. Models 2 and 3 included a vector of individual characteristics as exogenous variables, $x_{i c}$ , but excluded contextual variables in the regressions: $y_{i c}^{*} = β_{0} + β_{1} x_{i c} + u_{c} + ε_{i c}^{*}$ . Models 2 and 3 were used to investigate the influence of individual-level variables, $x_{i c}$ , on the pro-environmental concern of tourists by incorporating socioeconomic variables, such as gender, age, and education, as well as travel-related characteristics. The need to estimate the model proposed in equation (5) that simultaneously combines micro and macro predictors should be evident. Models 4 and 5 fulfil this requirement by adding a vector of country-level explanatory variables, $z_{i c}$ , to models 2 and 3, respectively. The latter two models allowed us to test the contextual hypotheses proposed. The income of a country as measured by GDP per capita may have significant effects on environmental concern. GDP growth was also included to capture nonlinear patterns. This article used the postmaterialist index to investigate the hypothesis proposed by Inglehart (1995). Finally, the environment index incorporated in the model assesses whether the existence of objective environmental problems confronted by individuals in the country of residence would strengthen their preferences for a better environment.

Data Set and Descriptive Analysis

The model addresses the environmental concern of European tourists. The analysis used micro and macro data. The micro data were drawn from two household surveys conducted in the EU-27 countries. The first micro-data set was obtained from a stratified weighted survey called Flash Eurobarometer 281 (Survey on Attitudes of Europeans Towards Tourism), which was conducted by the European Commission.¹ This micro-data set incorporated information on the socioeconomic characteristics of 21,418 tourists and their decisions about their holidays. Specifically, the endogenous variable, individual-level regressors, and travel-level regressors were obtained from this data set. In the survey, the original question addressing the endogenous variable was as follows: “Do you consider environmental issues when making decisions about your holidays, such as the facilities provided at the destination or offered by service providers (e.g., accommodation with an eco-label, responsible travel, minimising impact on the local environment)?” In this study, the socioeconomic variables were gender, age, and education. Gender is a dummy variable that takes value one for male and zero for female, and Education is a continuous variable that takes the age at which the individual stopped full-time education as the value. This variable only coincides with the age of the tourist if he/she is currently studying.² Travel-related attributes included motivations for traveling, different types of destinations, and environmental quality indicators. Motivations for traveling were represented by a set of dummy variables (i.e., service quality, budget, eco-friendliness, cultural attractiveness, social considerations, and safety and security). Types of destinations were also represented by a set of dummy variables (i.e., destination booked in a last-minute offer or not, traditional vs emerging destinations, and traveling abroad vs domestic). In models 2 and 4, the dummy variable traveling abroad was decomposed into new dummy variables, distinguishing between Europe, America, Africa, Asia, and Other destinations, thus focusing this part of the analysis on the choice of destination. Finally, given their relevance, environmental quality indicators at destinations (see literature review) were added to both models.

The second micro-data set was obtained from the European Value Survey. This source was also a stratified random survey of 66,281 households in EU-27 countries, and included opinions on environmental values among other topics. The postmaterialism index in this data set was created using different items considered by citizens to be the most important in their societies.³ The macro-data were obtained from Eurostat and the Environmental Performance Index (EPI).⁴ Data on GDP in the purchasing power standard and its growth were obtained from Eurostat, whereas information on the environmental quality indicators was obtained from the EPI.

In the Survey on Attitudes of Europeans Towards Tourism, 50.81% of the interviewees represented themselves as environmentally responsible consumers when making holiday decisions. The kernel density estimate shown in Figure 1 represents the density function of the endogenous variable, using Epanechnikov’s (1969) approach. This function is one of the most commonly used because it is optimal in relation to the mean square error. In the same figure, we show the normal density distribution next to this function in order to visually compare them. The figure shows that the relative frequencies of environmental concern at the aggregate level differ by country. These results are also supported by the data shown in Table 1. Eastern and Mediterranean countries, such as Romania, Poland, Portugal, Bulgaria, and Greece, had the highest relative frequencies (75.5%, 66.5%, 64.7%, 62.9%, and 60.4%, respectively), whereas North European countries, such as Denmark, the Netherlands, Ireland, and Sweden had the lowest rates (28.5%, 35.9%, 39.2%, and 39.6%, respectively). As mentioned, we assumed that distance from the place of residence may be a relevant factor in environmental concern. The last two columns in Table 1 show the relative frequencies of the pro-environmental concern of tourists traveling domestically and those traveling abroad. The shaded areas represent all the countries in which tourists have stronger pro-environmental concern when they are traveling domestically than when traveling abroad. As can be seen, most of the countries are characterized by shaded areas. In the cases of Estonia and Austria, this difference exceeded 10 percentage points.

Figure 1.

Kernel distribution of tourists’ environmental support.

Table 1.

Pro-environmental Concern by Country of Origin and Destination Choice.

Country of Origin	Observations	Relative Frequency
Country of Origin	Observations	Total	Domestic	Abroad
Austria	975	0.5541	0.6171	0.5067
Belgium	959	0.4663	0.4923	0.4479
Bulgaria	984	0.6291	0.7738	0.6944
Cyprus	486	0.5665	0.6310	0.5366
Denmark	976	0.2857	0.3241	0.2770
Estonia	487	0.4746	0.5581	0.4563
Finland	966	0.4989	0.5233	0.4764
France	942	0.4199	0.4319	0.3981
Germany	992	0.5032	0.5296	0.4976
Greece	956	0.6040	0.6283	0.6184
Hungary	982	0.4543	0.4741	0.4643
Ireland	952	0.3921	0.3626	0.3848
Italy	982	0.5853	0.6111	0.6458
Latvia	479	0.4826	0.5741	0.5349
Lithuania	484	0.5763	0.5965	0.6439
Malta	478	0.5460	0.5000	0.5702
Poland	985	0.6655	0.7530	0.6835
Portugal	981	0.6472	0.6645	0.6789
Romania	991	0.7550	0.7922	0.6803
Slovakia	986	0.4282	0.5596	0.5000
Slovenia	493	0.5356	0.5946	0.5200
Spain	981	0.5254	0.5196	0.5385
Sweden	965	0.3966	0.4326	0.3520
The Netherlands	977	0.3590	0.3356	0.3727
United Kingdom	979	0.4066	0.4662	0.3762
Total	21,418	0.5081	0.5204	0.4796

Of the study sample, 58.3% were women and 41.7% were men. The average age was 47.8 years. Regarding travel choices, 17.7% of tourists prioritized budget traveling and 14.6% prioritized service quality. The least valued motivation to travel was “Eco-friendliness” (7.1%). Regarding the type of destination, 12.1% chose a last-minute offer and 75.8% chose a traditional destination. A large percentage also chose to travel abroad (around 53%). Of those who traveled abroad, 41.5% did so within Europe (a more detailed descriptive analysis appears in the Annex).

Results

Estimation

Based on the discrete choice models proposed above, we present three types of results: (1) estimates from the null model without explanatory variables, $y_{i c}^{*} = β_{0} + u_{c} + ε_{i c}^{*}$ , which allows us to test whether the intercept for country “c” varied randomly (model 1); (2) estimates of within-country heterogeneity in the level of environmental concern, including a vector of individual characteristics as exogenous variables, $y_{i c}^{*} = β_{0} + β_{1} x_{i c} + u_{c} + ε_{i c}^{*}$ (models 2 and 3); and (3) estimates of between-country heterogeneity by analyzing simultaneously the contribution of individual and contextual regressors, $y_{i c}^{*} = β_{0} + β_{1} x_{i c} + β_{2} z_{i c} + u_{c} + ε_{i c}^{*}$ (models 4 and 5).

These models were estimated using the classic maximum likelihood method. This technique has the advantage of being able to estimate individual parameters and to estimate random effects on the grouping variables in the case of hierarchical models. Classic inference also has advantages, because it is assumed that the parameters can take a fixed value and that the probability can be calculated on all those events for which it is possible to define relative frequencies without having to have a priori information on the distribution of these probabilities. The fixed effects are equivalent to parameters in standard regression and are estimated directly. The random effects, which take the form of random intercepts in this article, were not directly estimated but were summarized according to their estimated variance and covariance matrix. Taking into account this hierarchical data structure, all the estimates from the five models shown in Table 2 were expressed on the odds ratios scale. A parameter less than one indicates a negative effect on the probability of pro-environmental concern in relation to the base category, whereas a parameter more than one indicates a positive effect. In line with the study by Rabe-Hesketh and Skrondal (2012), these estimates are robust against the assumption of non-normal errors.

Table 2.

Estimates from Multilevel Regressions (Odd Ratios).

	Model 1	Model 2	Model 3	Model 4	Model 5
Intercept	0.0433***	0.2577***	0.2635***	3.4770***	3.5052***
Socioeconomic regressors
Gender (male=1)		0.8266***	0.8256***	0.8267***	0.8256***
Age		1.0221***	1.0219***	1.0225***	1.0224***
Age squared		0.9998***	0.9998***	0.9998***	0.9998***
Education		1.0178***	1.0179***	1.0177***	1.0177***
Travel-related characteristics
Motivation for traveling
Service quality		1.1566***	1.1577***	1.1583***	1.1593***
Budget		0.9273*	0.9266*	0.9237*	0.9231*
Eco-friendliness		2.6276***	2.6315***	2.6065***	2.6102***
Social considerations		1.1067*	1.1087*	1.1075*	1.1095*
Safety and security		1.4799***	1.4801***	1.4753***	1.4754***
Type of destination
Last minute		0.9023*	0.9028*	0.9047*	0.9051*
Traditional		0.8577***	0.8581***	0.8593***	0.8597***
Traveling abroad			0.8491***		0.8490***
Europe		0.8438***		0.8440***
America		0.8449***		0.8476***
Africa		0.8698		0.8639
Asia		0.7287**		0.7295**
Other destination		0.9589		0.9519
Environmental quality
Water quality		1.0043**	1.0035*	1.0040*	1.0034*
Climate change		1.0061***	1.0070***	1.0062***	1.0070***
Country-level regressors
GDP pc (PPS)				0.9999*	0.9999*
GDP pc (PPS) squared				1.0000*	1.0000*
Postmaterialism index				1.1941***	1.1987***
Environment index				0.9570***	0.9569***
Var (const.) $σ_{u 0}^{2}$	0.1894	0.2594	0.2612	0.1299	0.1298
ICC (%)	5.44	7.31	7.35	3.80	3.79
Observations	17,114	11,937	11,937	11,937	11,937
Numbers of groups	25	25	25	25	25
Log likelihood	−12,005.89	−7,717.35	−7,718.81	−7,707.46	−7,708.80
LR test	796.77	557.08	564.28	228.12	228.30

Note: The omitted dummy variable for motivation for traveling is “Cultural attractiveness,” and omitted dummy variables for type of destination are “Non–last minute,” “Nontraditional,” and “Traveling domestically.” GDP pc = gross domestic product per capita; PPS = purchasing power standards; ICC = intraclass correlation coefficient; LR = likelihood ratio.

***

Level of significance 1%. **Level of significance 5%. *Level of significance 10%.

From model 1, we obtained the overall mean $β_{0}$ and a random error by country $u_{c}$ . To save space, we show the overall mean in Table 2 and the random error by country in Table 3. According to the estimates from model 1, the “average” estimated probability of being a responsible consumer was 51.08% [1.0443/(1 + 1.0443) = 0.5108]: that is, 51.08% of European tourists consider environmental issues when making holiday decisions. A general finding from this model was that significant variance in the level of pro-environmental concern exists within and between countries. The intraclass correlation coefficient (ICC),⁵ which represents the proportion of total variance between countries, was around 0.0544. Although a relatively small ICC was found, Degenholtz and Bhatnagar (2009) suggested that an $I C C \geq 0.05$ should be used in literature to take into account within-group and between-group correlations. The likelihood ratio (LR) test also demonstrated that variability between countries was statistically significant, and therefore a hierarchical linear model is required. In fact, as shown in Table 3 (second column), the random intercept significantly varied between countries. The intercept for country “c” was calculated as $(β_{0} + u_{c} = 0.0433 u_{c})$ . Several conclusions can be obtained from this table. Tourists across the European countries have heterogeneous levels of pro-environmental concern. There were marked differences between the North European and Mediterranean countries. Inglehart (1995) obtained similar results, finding that environmental concern is particularly weak in the Nordic countries and strong in Eastern Europe. However, countries with the highest probability of pro-environmental support, such as Romania, Poland, and Portugal, also have the highest random intercept (1.0429, 0.6274, and 0.5448, respectively), whereas countries with a lower probability, such as Denmark, the Netherlands, and Ireland, have random intercepts of −0.9334, −0.6076, and −0.4704, respectively. In all these countries, a statistically significant difference was found between the random intercept and the overall mean $β_{0}$ , which again justifies the use of a multilevel model for the estimation.

Table 3.

Predicted Probabilities and Country-Effect Estimations in Rank Order.

Country of Origin	Predicted Probabilities	Country Effects
Country of Origin	Predicted Probabilities	Random Intercept	Level 2 Residual Deviation
Denmark	0.2911	−0.9334	0.0717
The Netherland	0.3626	−0.6076	0.0682
Ireland	0.3948	−0.4704	0.0669
Sweden	0.3993	−0.4518	0.0684
United Kingdom	0.4090	−0.4113	0.0676
France	0.4221	−0.3574	0.0691
Slovakia	0.4304	−0.3234	0.0720
Hungary	0.4559	−0.2203	0.0731
Belgium	0.4679	−0.1721	0.0808
Estonia	0.4765	−0.1376	0.0983
Latvia	0.4841	−0.1070	0.1008
Finland	0.4992	−0.0467	0.0660
Germany	0.5034	−0.0297	0.0650
Spain	0.5250	0.0569	0.0680
Slovenia	0.5345	0.0948	0.0939
Malta	0.5439	0.1328	0.1061
Austria	0.5530	0.1695	0.0689
Cyprus	0.5639	0.2138	0.0943
Lithuania	0.5731	0.2510	0.0970
Italy	0.5832	0.2924	0.0738
Greece	0.6017	0.3693	0.0695
Bulgaria	0.6257	0.4705	0.0754
Portugal	0.6429	0.5448	0.0793
Poland	0.6617	0.6274	0.0711
Romania	0.7477	1.0429	0.0823

Note: Estimates from model 1.

Supplemental Material

Descriptive Statistics.

	Observed	Mean	Standard Deviation	Minimum	Maximum
Socioeconomic regressors
Gender (male=1)	21,418	0.4167	0.4930	0	1
Age	20,599	47.8606	16.8795	15	80
Education	20,874	19.3669	5.4418	1	45
Travel-related characteristics
Motivation for traveling
Service quality	21,418	0.1461	0.3532	0	1
Budget	21,418	0.1771	0.3818	0	1
Eco-friendliness	21,418	0.7059	0.2561	0	1
Social considerations	21,418	0.0806	0.2723	0	1
Safety and security	21,418	0.0842	0.2777	0	1
Type of destination
Last minute	13,235	0.1212	0.3253	0	1
Traditional	12,859	0.7578	0.4284	0	1
Traveling abroad	12,725	0.5300	0.4991	0	1
Environmental quality
Water quality	21,418	77.8777	11.4563	5.2657	97.5534
Climate change	21,418	47.9306	10.8205	9.2630	90.3115
Country-level regressors
GDP pc (PPS)	21,418	22,180.93	8,287.613	6,400	52,500
Postmaterialism index	21,418	0.1285	0.0711	0.0210	0.2368
Environment index	21,418	69.9983	6.6944	56.2636	86.0481

GDP pc = gross domestic product per capita; PPS = purchasing power standards; ICC = intraclass correlation coefficient; LR = likelihood ratio.

Within-Country Differences

Models 2 and 3 were used to investigate the influence of individual-level variables on pro-environmental concern by incorporating socioeconomic characteristics and travel level regressors, although special emphasis was placed on the latter. Before addressing the main results concerning the individual-level variables, we would like to highlight the global fit of the proposed models. Given that the between-country variance in models 2 and 3 was slightly higher than that in the null model, the ICCs were also higher (ICC_model2 = 0.0731; ICC_model3 = 0.0735). However, the results were clear: the empirical evidence showed statistically significant variability between countries (the LR test obtained values of 557.08 and 564.28, respectively). Therefore, a multilevel analysis was required.

As shown in Table 2 (columns 2 and 3), the estimates were very robust in both models, so we restricted our comments to Model 3. The general literature has identified age, gender, and educational attainment as relatively consistent predictors of environmental support (Inglehart 1995; Gelissen 2007). We used specifications that included the linear and quadratic effects of the age of the head of the household. The results show that young tourists are more likely than older tourists to have pro-environmental concern, although this variable has a nonlinear effect over their lifetime. This result is supported by the work of Inglehart (1995), who also found differences between the priorities of young and old people as a consequence of life cycle effects. Gelissen (2007) also found that the oldest people were less willing to pay for environmental protection. However, Mostafa (2012) found no evidence in support of the influence of age on pro-environmental behaviors. All these previous results were related to public environmental support in the place of residence, but were not related to traveling.

Gender was also significantly associated with being an environmental responsible tourist. Consideration for the environment in holiday decisions was less in men than in women. The odds of pro-environmental concern among men were 0.8256; thus, men were 0.8256 times less likely than women to have pro-environmental concern. Falk and Hagsten (2019) obtained similar results, demonstrating that the preference for environmentally friendly destinations was stronger among women than among men. Educational level also made a significant and positive contribution to environmental concern. The probability of considering environmental issues when making tourism decisions increased by 1.79% for every extra year of education. Therefore, most of our results in the tourism field are in line with those obtained in more general settings (Inglehart 1995; Hunter, Hatch, and Johnson 2004; Gelissen 2007). However, other studies have found significant and negative association between education level and pro-environmental behavior (Schultz, Oskamp, and Mainieri 1995; Uyeki and Holland 2000).

As previously mentioned, this study highlights the relevance of travel-related characteristics and to what extent they can affect tourists’ decisions regarding the environment when planning their vacations. For this reason, this study takes into account the motivations for traveling, type of destination, and specific environmental quality indicators. In line with the results obtained by Wurzinger and Johansson (2006) in their study on Swedish tourists, our results corroborated the hypothesis that motivations for traveling play a key role in the environmental concern of European tourists. As would be expected, the probability of pro-environmental concern decreased by 7.34% in households choosing “budget” in relation to the base category (“cultural attractiveness”). Increased pro-environmental concern was associated with the remaining reasons for traveling. Among these reasons, “eco-friendliness” and “safety and security” increased the probability by 163.15% and 48.01%, respectively, in relation to the base category.

Some of the results on the effects of type of destination should be mentioned. For example, tourists who booked their main holiday via a last-minute offer had lower pro-environmental concern. In fact, the estimated probability decreased by 9.72% in relation to those who do not use this type of offer. Similar results were obtained in relation to traditional destinations. In this case, tourists visiting traditional destinations are 0.8581 times less likely to have pro-environmental concern than those visiting emergent destinations. This result may be because the latter type of tourists is sensitive to the degree of saturation and type of activity at the destination. For example, less attention can be paid to environmental parameters in typically crowded traditional sun and beach destinations than in less common destinations where tourists seek other types of experiences. In line with these results, Schultz et al. (2004) suggested that tourist’s decisions regarding the natural environment are directly related to the type of activities they engage in at destinations. The degree of environmental awareness of a tourist visiting a natural park to see exotic plants is probably much higher than that of a sun-and-beach tourist. This suggestion was supported by Ong and Musa (2011) in their article on marine conservation and the underwater behavior of scuba divers. These results may also be due not only to the type of activity at destinations, but also to their degree of congestion and environmental quality. The most saturated and congested destinations tend to have worse indicators of environmental quality. Our results show that environmental quality at destinations is a significant predictor of the pro-environmental concern of tourists who visit it. A 1-point increase in water quality or climate change indicators increased the estimated probabilities by 0.35% and 0.7%, respectively. Our results are in line with those of Ogunbode (2013), who suggested that environmental attitudes may be determined not only by personal characteristics and economic advancement but also by direct experiences of environmental problems.

As mentioned, the literature shows that environmental activists at home display an attitude-behavior gap when they go on vacation (Dolnicar and Leisch 2008; Juvan and Dolnicar 2014). In fact, our results contribute to a better understanding of tourist environmental concern because they show that distance from the place of residence to the destination is relevant to the environmental concern of tourists. The odds of pro-environmental concern among tourists traveling abroad was 0.8491: thus, the estimated probability decreased by 15.09% in relation to those traveling domestically. These results are in line with those obtained by Firth and Hing (1999), who found that backpackers tended to display less environmentally friendly behavior when they were at destinations than when they were at home. However, it could be quite reasonable to assume that someone would give less consideration to environmental issues in the setting of short-haul domestic travel because there are fewer environmental implications when traveling shorter distances, whereas the same person might still have a positive attitude toward minimizing environmental impacts when making other kinds of journeys. This assumption could be supported on two grounds: the decreased impact of the means of transport for short-haul travel; and the possibility of reducing the length of stay or not staying overnight. However, Barr et al. (2010) demonstrated that “ultra-green” travelers regularly use low-cost airlines to fly for short breaks, despite recognizing the environmental impact of their behavior. In this regard, short-haul travel may not have fewer environmental impacts. Furthermore, the increased frequency of short-haul travel for tourists over the year may increase the impact on the environment compared with their main holiday. In fact, Davison, Littleford, and Ryley (2014) highlighted an attitude–behavior gap related to air travel because the largest segment, who recognized the environmental impacts of their air flights, are flight dependent and use air transport more frequently. Although we cannot check these statements using our data set, they are doubtless of interest to future research.

Between-Country Differences

The introduction of contextual-level regressors in models 4 and 5 addressed tourist environmental concern at the country level, while controlling for potentially meaningful individual effects (Table 2, columns 4 and 5). The introduction of such variables decreased the random intercept variances compared to those of the other models, although the variability between countries was still statistically different from zero. Hence, adjusting for both types of effects, the ICCs also decreased (ICC_model4 = 0.0380; ICC_model5 = 0.0379). The coefficient of determination (Snijders and Bosker 2012⁶) shows that the total residual variance in both models was 1.71% less than that of the null model in both cases. Given that no statistically significant differences were found regarding Models 2 and 3 in the effects of the individual variables (socioeconomic and travel-related variables), we restrict our comments to the contribution of the contextual variables. This finding corroborates the robustness of our results.

In line to the hypothesis proposed by Inglehart (1995), our results show that per capita GDP had a negative nonlinear effect on pro-environmental concern. These results are also in line with those obtained in previous general studies in the tourism field. For example, negative relationships have been found between GNP per capita and environmental support (Dunlap and Mertig 1997), national wealth and public environmental concern (Gelissen 2007), and GDP per capita and pro-environmental intentions (Mostafa 2012).

The literature also suggests that greater environmental awareness in the majority of advanced societies may be motivated by a gradual cultural change from materialist to postmaterialist values (Inglehart 1995). This hypothesis was supported by the positive association found between the postmaterialism index and environmental concern. Table 2 shows that the probability of considering the environment in holiday plans increased by 19.87% for every 1-unit increase in the postmaterialist index. These results are in line with those obtained by Lu, Gursoy, and Del Chiappa (2016) in relation to ecotourism products and services. They found a negative association between the materialistic values of individuals and their ecotourism attitudes. Therefore, we suggest that the results concerning the effects of changes in the GDP and in the postmaterialism index on tourist environmental concern are not contradictory, because changes in these variables could independently contribute to environmental concern. Finally, tourist environmental concern should also be affected by the state of the environment at home. Objective environmental problems facing individuals in their country of residence should increase their concern and preferences for a better environment (Ogunbode 2013). Conclusive evidence for this hypothesis was shown by the odds estimated by the environment index (0.9569): thus, the probability of pro-environmental concern in tourists decreased by 4.31% for every 1-unit increase in the quality of the environment in their country of residence.

Conclusions

This article investigated environmental attitudes in tourists from different countries of origin when they make their holiday plans. Previous literature has found that public environmental concern in the place of residence are heterogeneous across regions, but there is a lack of studies on this topic in the setting of tourism. This study found that the pattern of environmental concern among tourists is heterogeneous across European countries and analyzed the underpinnings of this heterogeneity. For this reason, this study addressed the main determinants of pro-environmental concern in the tourism setting.

Modeling the environmental concerns of tourists poses a challenge because they depend not only on individual and travel-related attributes, but also on the characteristics of their place of residence. To meet this challenge, we used hierarchical linear models that simultaneously addressed the macro-level relationship between country-level variables and environmental concern across countries, as well as the effect of individual characteristics on such concerns. This approach divides variations in tourist environmental concern into between-country variations and within-country variations, allowing the intercept to vary randomly across countries. EU-27 countries were analyzed using a combination of micro-data from the Flash Eurobarometer 281 survey, which was conducted by European Commission, micro-data from the European Value Survey, and macro-data from different international sources.

A general finding of this article was that significant variance exists between and within countries in the levels of pro-environmental concern, although the within-countries component explained most of the variability. The study showed that between-country effects varied randomly; thus, environmental concern was heterogeneous across the European countries. There were marked differences between South European as well as East European countries (e.g., Bulgaria, Romania, and Poland), which had the highest pro-environmental concern, and North European countries, such as Denmark, the Netherlands, and Ireland, which had the lowest environmental concern. Such heterogeneity was mainly explained by the economic, cultural, and environmental circumstances that characterized each country. These results in the tourism field are in line with those obtained in more general settings. The estimates also showed that within-country differences in the level of pro-environmental concern were mainly explained by individual and travel-related variables. Among these variables, age, gender, and educational attainment were relevant predictors of environmental concern. However, the characteristics of the destination had a marked effect according to whether the destination was booked via a last-minute offer or not, whether it was a traditional or emergent destination, and its distance from the tourists’ place of residence. The latter variable played a key role, as shown by the finding of a decrease in pro-environmental concern among tourists traveling abroad compared to domestic tourists. Understanding the causes of this gap could help to minimize negative environmental impacts caused by tourists. It is also noteworthy that concern toward the environment was also directly related to the type of activities that tourists engage in at destinations and the quality of their environment.

The results are also of great relevance to assessing the economic and environmental impact of this activity. Based on this perspective, the methodology proposed in this article can be used to analyze spatial heterogeneity in tourist profiles not only according to the tourists’ country of origin, but also in relation to socioeconomic variables, characteristics related to the trip, and the type of destination chosen.

This article strongly contributes to explaining tourist environmental concern across European countries. However, the study has some limitations. First, we investigated the main determinants of pro-environmental support using data from EU-27 nations. It would be of interest to extend this type of research to other regions of the world, which would increase the validity of the contextual hypotheses tested in this article. In this regard, Marquart-Pyatt (2008) suggested that to understand environmental concern globally, it would be relevant to “pursue a systematic, broad ranging investigation that includes a variety of contexts (i.e., industrialised vs industrializing).” Second, we used a cross-sectional approach rather than a longitudinal one. This implies that much more emphasis was placed on observing tourists’ decisions than on observing changes over time. For example, the intensity of tourists’ decision regarding the environment could be affected throughout the economic cycle. Third, we attempted to gain a better understanding of tourist environmental concern, assuming that behavioral changes can occur because of positive behavioral intentions that are in turn underpinned by pro-environmental consumer attitudes. Most traditional attitude theories have attempted to explain why humans behave in certain ways, postulating that attitudes, among other factors, affect behavior. However, some empirical studies have suggested that this relationship is not consistently strong and varies widely according to the sample used. Finally, this article addressed the environmental concern of tourists when they make holiday decisions. These decisions were measured using self-reports, which could be considered a limitation. Furthermore, this approach differs from those commonly used in the literature on this issue, which suggests that attitudes and behavior may be measured according to underlying values by the use of item scales or paradigms. However, the objective of this study was to analyze the main determinants of environmental concern in the tourism field, rather than to obtain an indicator for measuring such concern.

Footnotes

Acknowledgements

The authors would like to thank Elena Bárcena-Martín for their useful suggestions. Any remaining errors are responsibility of the authors. The authors also want to thank the support of University of Málaga (Cátedra de Economía y Finanzas Sostenibles).

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Juan A. Campos-Soria

J. Aníbal Núñez-Carrasco

Notes

Author Biographies

Juan A. Campos-Soria is an Associate Professor at the University of Malaga (Spain). He has participated in international research projects for the Department for Culture, Media and Sport (UK) and the World Travel and Tourism Council (WTTC), as well as numerous national projects for public and private institutions. He has worked on many aspects of economic analysis of tourism, gender economics and labour market, publishing several papers in leading international journals.

J. Aníbal Núñez-Carrasco is a Lecturer at the University of Malaga (Spain). He has published on many aspects of household consumption and savings decisions and economic analysis of tourism.

Alejandro García-Pozo is a Professor at the University of Malaga (Spain). He is currently the head of the Department of Applied Economics (Economic Structure) at this university and the head of labour economics and tourism research group. He has worked on economic analysis of tourism, the return on human capital in tourism sector and sustainable tourism and its impact on labour productivity in hotel establishments, publishing papers in leading international journals.

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