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The economic impact of tourism on protected natural areas: examining the influence of physical activity intensity on visitors’ spending levels


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In addition to being important tourism attractions that boost local economic development, protected areas also promote healthy habits through engagement in a variety of physical activities (PA). However, little is known about the extent to which PA intensity influences visitors’ spending. Drawing on results from 500 questionnaires collected from visitors in the Alt Pirineu Natural Park, Spain, this study assesses the influence of PA intensity on spending after controlling for sociodemographic, visit, motivational and opinion descriptors to assess the connection between these two factors. Hierarchical regression analysis revealed that PA intensity had a marginal but potentially significant effect on respondents’ expenditure during their visits. When looked at separately, the results indicated that trip and motivational descriptors explained the highest degree of variation in visitor spending. More research is necessary to confirm whether these findings are applicable broadly.
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The economic impact of tourism on protected natural areas: examining the
influence of physical activity intensity on visitors’ spending levels
Estela Inés Farías-Torbidoni & Demir Barić
Keywords: economic impact, physical activity intensity, MET, visitors’ prole, hierarchical multiple regression
In addition to being important tourism attractions that boost local economic de-
velopment, protected areas also promote healthy habits through engagement in a
variety of physical activities (PA). However, little is known about the extent to which PA
intensity influences visitors’ spending. Drawing on results from 500 questionnaires
collected from visitors in the Alt Pirineu Natural Park, Spain, this study assesses the
influence of PA intensity on spending after controlling for sociodemographic, visit,
motivational and opinion descriptors to assess the connection between these two
factors. Hierarchical regression analysis revealed that PA intensity had a marginal
but potentially significant effect on respondents’ expenditure during their visits. When
looked at separately, the results indicated that trip and motivational descriptors
explained the highest degree of variation in visitor spending. More research is neces-
sary to confirm whether these findings are applicable broadly.
Research eco.mont – Volume 12, Number 2, July 2020
ISSN 2073-106X print version – ISSN 2073-1558 online version:
Protected area
Alt Pirineu Natural Park
Mountain range
In addition to preserving biodiversity, protected
natural areas (PNAs) are increasingly recognized as
a driving force for economic regional development
and the sociological prosperity of many adjacent lo-
cal communities (Hammer et al.; 2012; Mayer et al.
2010; Mayer & Job 2014; McDonald & Wilks 1986;
Lintzmeyer & Siegrist 2008; Pröbstl-Haider & Haider
2014; Reinius & Fredman 2007; Schirpke et al. 2018).
They are also seen as promoting healthy lifestyles by
offering engagement in a variety of physical activities
(PA) (Bedimo-Rung et al. 2005; Cohen et al. 2014;
Europarc-España 2013; Lemieux et al. 2012; Maller et
al.2010; Stolton & Dudley 2010).
Attracting more than eight billion visits per year
worldwide, terrestrial protected areas are an important
factor in the growth of nature-based tourism globally
(UNEF-WCMC & UICN). Among others, Eagles et
al. (2000) show for the USA and Canada that nature-
based tourists in national parks create an important
economic impact for the park’s peripheral regions. In
the European context, it is estimated that visitors to
Natura 2000 sites in the EU generate around EUR
50–85 billion / year (European Commission, 2013). In
particular, a study on the economic impact of tourist
spending in the six German national parks revealed
spending ranging from 525 million to 1.9 million euros,
depending on the national park (Mayer et al., 2010).
Several studies have shown that physical activity
carried out in protected areas is generally of a higher
level than exercise done at home, with corresponding-
ly greater physical, psychological, spiritual and social
benets (Bird 2004; Giles-Corti et al. 2005; Godbey
2009; Godbey & Mowen 2010; Oftedal & Schneider
2013; Romgosa et al. 2015; Romagosa 2018). Fur-
thermore, some studies examining characteristics of
visitors to PNAs have demonstrated that the types of
PA available in a PNA are a key pull factor for the
decision to visit the area. Studies have also shown that
differences in PA intensity may reect varieties in visi-
tors’ sociodemographic proles, behavioural charac-
teristics, preferences and motivations (Arnberger et
al. 2019; Barić et al. 2016a; Cordente-Rodríguez 2014;
Broyles et al. 2011; Farías-Torbidoni 2011; Galloway
2002; Mowen et al. 2012) and, indeed, how much they
are willing to spend (Schirpke et al. 2018). Accord-
ing to Jette et al. (1990), PA intensity is dened by its
MET value, which is the ratio of an individual’s work-
ing metabolic rate relative to their resting metabolic
rate. MET is used to express the intensity and energy
expenditure of activities in a way that allows compari-
sons among different physical activities. MET values
are well documented in the Compendium of Physical
Activities and include 4 basic PA intensities: sedentary,
1.5 METs; light intensity, 1.6 to 2.9 METs; moderate
intensity, 3.0 to 5.9 METs; and vigorous intensity, ≥ 6
METs (Ainsworth et al. 2011).
However, in theoretical terms, PNAs and their
managers experience various dilemmas in managing
their territories and in constructing their development
models, which are two increasingly recognized chal-
lenges (Leung et al. 2019). Finding a balance between
protecting the ecological integrity of ecosystems and
satisfying the necessities of growing tourism and rec-
reation demand is increasingly complicated, especially
in PNAs with limited nancial and human resources.
Knowledge of the possible relationship between PA
intensity and visitors’ levels of spending can provide
valuable input data for developing effective and crea-
tive management measures to satisfy the increasing
and varied demands placed on these kinds of area.
Estela Inés Farías-Torbidoni & Demir Barić
The main objectives of this exploratory research
are therefore the following, organized in order of ap-
1. analyse how much visitors spend per visit, includ-
ing on accommodation, food and drink, and local
products and services;
2. group visitors by reported physical activities, using
corresponding MET values;
3. assess the inuence of PA intensity on spending
levels after controlling for sociodemographic, trip,
motivational and opinion descriptors.
Literature review: economic impact of tour-
ism and PA in PNAs
According to Watson et al. (2007), economic im-
pact is dened as the net change in economic activity
associated with an industry, event or policy in an exist-
ing regional economy. A variety of methods, ranging
from pure guesswork to complex mathematical mod-
els, are used to estimate tourism’s economic impacts
(Job 2008; Mayer & Job 2014). Studies vary extensively
in quality and accuracy, as well as in which aspects
of tourism are included (Stynes 1997). According to
Stynes (1999), the economic impact of visitor spend-
ing is typically estimated by the variation of three basic
components: number of tourists, average spending per
visitor and multiplier. However, in the case of PNAs,
the simple consideration of the money visitors spend
on food, accommodation and services during their
visit to an area could be useful rst to assess and then
to track the economic impact of visitors on the re-
gion (Eagles 2002; Mayer et al. 2010; Carlsen & Wood
2004). Moreover, it is interesting to highlight the three
advantages that Alegre and Pou (2004) noted with re-
spect to microeconomic studies. Although macro- and
microeconomic studies serve different purposes, these
authors contend that microeconomics studies allow
little deviation from theoretical economic consumer
models, avoid bias when the analysis is based on ag-
gregated data, and acknowledge the diversity and het-
erogeneity of consumer behaviours that are ignored in
studies using highly aggregated data.
Previous research in the eld of tourism impact in
PNAs encompasses three main topics: i) the role of the
PNA in tourism development and visitor afnity (May-
er et al. 2010; Pröbstl-Haider & Haider 2014; Reinius
& Fredman 2007); ii) the amount of money that a
PNA could generate in the region (Eagles 2002; Per-
son et al. 2000; Zambrano-Monserrate et al. 2018); iii)
the relationship between key visitor characteristics and
visitors’ spending levels (Flix & Loomis 1997; Fredman
2008; Hierpe & Kim 2007 McDonald & Wilks 1986).
Regarding the last topic, several authors have ar-
gued that differences in spending could vary accord-
ing to the prole, needs and preferences of visitors
(Mayer & Voght 2016; Mika et al. 2016; Stynes 1999;
Wanga & Davidson 2010; Watson et al. 2007). Moreo-
ver, although they do not address economic impact
directly, a number of visitor segmentation studies by
specic PNAs have demonstrated that PA and its in-
tensity greatly inuence specic behavioural charac-
teristics (i. e., type of accommodation, length of stay
or party size) and are often responsible for the level of
spending (Farías-Torbidoni & Monserrat 2014; Farías-
Torbidoni et al. 2018; Mayer et al. 2010). For example,
Barić et al. (2016b) and Farías-Torbidoni et al. (2005)
demonstrated that visitors who were more physically
dedicated and active preferred to stay longer at the
chosen destination and visited it repeatedly. Their nd-
ings corroborated signicantly those of Schirpke et al.
(2018), who examined the proles of visitors to ten
Nature 2000 sites in Italy and found that higher-inten-
sity activity visitors such as cyclists (M = 68.77 €) and
mountaineers (M = 58.91 €) spent signicantly more
money per day compared to those who were engaged
in lower-intensity PA such as hiking (M = 46.48 €) and
picking mushrooms (M = 38.75 €). Including travel
costs, this corresponds to a 10.70 € difference in visi-
tors’ average daily spend (48.56 €).
Research methodology
Study area
This study was carried out in the largest natural park
in Catalonia, Spain, located in the Pyrenees. The Alt
Pirineu Natural Park was established by the Catalan
government in 2003. The denition and management
of this protected natural area, which covers 69 850 ha
(172 600 acres), is the responsibility of the Catalonia
Region Government and is equivalent to the IUCN
Protected Area Category V – Landscapes / Seascapes
(Dudley 2008). It stretches over the administrative ar-
eas of Pallars Sobirà and Alt Urgell, and includes the
highest peak in the Catalan Pyrenees. For managerial
purposes, the park is divided into 5 zones and val-
leys: Valls d’Àneu, Vall de Cardós, Vall Ferrera, Vall de
Santa Magdalena and Massís de l’Orri, four of which
attract particularly high numbers of visitors. The num-
ber of park visits is 314 000 per year (data from the
latest visitor report, Farías-Torbidoni & Morera 2017).
Figure 1 shows the 6 main entrances considered
in the eldwork. One of the park’s most important
features for this study is that it has an extensive provi-
sion of trails and managed areas for outdoor activi-
ties such as hiking, mountain biking, snow activities,
and off-road activities. There are 3 different snow
areas and more than 170 trails (permitting off-road
driving) and paths inside the park, 94 of which are
signposted. Thus, this area is representative of PNAs
in Spain generally and of other countries in Europe.
Detailed descriptions of the main characteristics of
the entrances are provided in Table 1.
Data collection
Fieldwork was conducted from June 2017 to De-
cember 2017. The sampling days were one weekend
day monthly during the entire period and one weekday
24 Research
pling days at each entrance and the total number of
questionnaires nally considered in the study.
Data were collected from 10 a. m. until sunset. Re-
spondents were approached on their way out of the
Park through the main entrances because most of the
questions referred to the experience they had just had
(e. g., place visited, activity practised, length of visit,
Figure 1 – Alt Pirineu Natural Park. The different shades of grey distinguish the Park’s main valleys.
each month during the summer season (i. e., from 1
July to 31 August), resulting in 54 eldwork days in
total for the 6 entrances combined. In total, 706 ques-
tionnaires were collected through on-site structured
interviews, carried out at each of the 6 entrances, of
which 500 were considered usable as 206 respondents
were permanent residents within the park borders and
were therefore excluded. Table 2 shows the total sam-
Estela Inés Farías-Torbidoni & Demir Barić
respondent’s foremost activity to be identied cor-
rectly. The list of activities was developed in accord-
ance with park regulations and observations made by
the main author of the present study. The activities
were then related to those listed in the Compendium
of PA (Ainsworth et al. 2011). Activities in the study
area included: activities at the entrances (such as pic-
nics), vehicle touring, recreational hiking (slow walk-
ing), hiking (brisk walking), picking mushrooms (a
variation of slow walking), off-road motocross, snow
activities (snowshoeing, cross-country skiing, down-
hill skiing, snow mountaineering), mountaineering
(scaling a peak), mountain biking, and trail running.
In the third section, individuals were asked to rate the
importance of 12 motivation statements, drawn from
Farías-Torbidoni (2011), for their visit. The statements
were operationalized on a ve-point Likert scale, rang-
ing from 1 (very unimportant) to 5 (very important).
The fourth section aimed to assess how much visitors
spent during their visit. Here, three open-ended ques-
tions were asked to gather information on how much
individuals spent (in euros) on accommodation, food
and drink, and services / products available in the area.
Data analysis
The data collected were transformed and coded us-
ing the Statistical Package for the Social Sciences 18.0
(SPSS). Descriptive statistics including frequencies,
mean values and standard deviations were applied to
assess the basic sample information. An updated ver-
sion of the Compendium of Physical Activities’ Rela-
tive Metabolic Intensity (MET) consumption values
(Ainsworth et al. 2011) was used to identify respond-
ents’ PA intensity (light, moderate or vigorous). To
uncover the underlying dimensions, 12 motivational
statements were factor-analysed using principal com-
ponent analysis (PCA) with Varimax rotation. Reliabil-
ity was established using the Cronbach alpha internal
consistency measure, with values between 0.70 and
0.79 regarded as adequate, values from 0.60 to 0.69
as moderate, and values less than 0.60 as minimal.
Convergent validity was assessed through a minimum
adequate factor loading of 0.50 (Hair et al. 2006). The
following equation was used to calculate the average
value of individual spending during the visit:
Table 1 – The six main entrances of Alt Pirineu Natural
Main entrances Fornet Tavascan La
Tor Sant
Physical activity areas
Path: low
1 2 3 1
1 2 2 1 2
Path: high
1 5 4 1 1 3
Specific MTB trails 1 1 3 1
Cross-Park routes 2 3 3 1
Iconic peaks 3 3 5 2 1 1
Others PA areas*1
Winter activity
2 2
Total 9 16 16 4 11 9
Supporting areas
Parking areas 1 3 4 1 1
1 1 1 1
Picnic areas 1 2 1 2 1
Shelters 2 1 1
Signposts 1 1 1 1 1
Viewpoints 2 1 1 1 1
Total 4 11 8 1 7 5
Recreational and physical activities
Hiking Yes Yes Yes Yes Yes Yes
Mountaineering Yes Yes Yes Yes Yes Yes
Mountain biking Yes Yes Yes Ye s Ye s Ye s
Fishing Yes
Off-road driving Yes Yes Yes Ye s
Downhill skiing Yes
Snow activities** Yes Yes
Total 6 6 4 5 7 4
a The list of PAs and supporting areas is based on the
sectorial maps included on the ofcial web page, http://
* For instance, rivers for shing
** Snowshoeing, snow mountaineering, cross-country skiing.
Table 2 – Distribution of questionnaires administered at the
main entrances.
Entrance Total
Number of
Visitors who
spent money
during their visit
to the park
Fornet 9 115 79
Tavascan 9 204 172
La Farga 9 112 77
Tor 9 53 40
Sant Joan de l’Erm 9 147 90
Os de Civís 9 75 42
Total 54 706 500
The survey was conducted with the assistance of
12 people trained in eld survey techniques. The re-
sponse rate was 95%, and the representativeness of
the whole sample included an error of ±5%.
Survey Instrument
The survey consisted of four sections. In the
rst section, questions were devoted to basic soci-
odemographic and trip characteristics (e. g., place of
residence, age, frequency of visiting). Five age groups
were included: 18 – 25 years, 26 – 36 years, 37 – 47 years,
48 58 years, and older than 58. In the second section,
visitors were asked to select from a predened list the
one recreational activity perceived as the most impor-
tant for their visit. When the type of activity selected
had some associated element of doubt (for instance,
slow or brisk walking), the interviewer continued with
complementary questions related to the itinerary fol-
lowed and time spent on the visit, nally allowing the
26 Research
Table 3 – Descriptive analysis: Visitor sociodemographics and
travel characteristics (n = 500).
Sample characteristics M SD %
Place of residence
Barcelona 54.6
Lleida 16
Tarragona 58
Girona 3
Other provinces 7.2
Foreign countries 8.4
Male 67.2
Female 32.8
18–25 4.7
26–36 21.5
37–47 34.4
48–58 24.4
Over 58 15
Age 46 12.36
Education level
No university degree 46.4
University degree 53.6
Trip characteristics
Park entrance points
Fornet 15.8
Tavascan 34.4
La Farga 15.4
Tor 8
Saint Joan de l’Erm 18
Os de Civís 8.4
Number of visits in last 2 years 3 6.71
Visit duration (days) 3.5 5.57
Spending on accommodation (in ) 238.9 403.02
Spending on food (in ) 81.3 151.83
Spending on services and products (in ) 12.6 33.95
Total spending per visit (in ) 111 149.16
Total spending per day (in ) 31.7 49.72
Total Sp = Sp1+ Sp2 +Sp3
Sp1 – Spending on accommodation
Sp2 Spending on food and drink
Sp3 – Spending on services and products
A one-way analysis of variance (ANOVA) with
a post-hoc Tukey procedure was performed to ex-
plore the differences in visitor spending as related
to entrance points to the park. After controlling for
the effects of sociodemographic, travel and motiva-
tional characteristics, a four-step hierarchical multiple
regression analysis was run to examine the relation-
ship between the independent variable, PA intensity in
which visitors participated (METs), and the depend-
ent variable (individual expenditure during the visit).
All polytomous independent variables were previously
re-coded as dummy variables. Assumptions for nor-
mality, singularity and multicollinearity were checked
(Cohen et al. 2003). The assumption of normality was
assessed by examining the skewness (1.96) and kur-
tosis values (2.56) and visual observation of the Q-Q
plot. Log transformation was performed to reduce a
positive skew of dependent variables. The assumption
of singularity was assessed by conducting a Pearson
correlation analysis to uncover the possible existence
of correlations between the independent variable
above 0.7. The tolerance (values less than 0.10) and
variation ination factor (VIF; values above 10) were
assessed to avoid multicollinearity among the predic-
tor variables.
Descriptive analysis
The total sample showed that more than two-thirds
of the visitors were from Catalonia, of whom the ma-
jority were residents of the city of Barcelona (54.6%).
Male respondents (67.2%) were twice as numerous as
female respondents (32.8%). This proportion is not
exceptional if we take into consideration the latest
results obtained in the national context (Farías et al.
2018; Luque-Gil et al. 2018; Romagosa 2018) or in-
deed the European context (Shirpke et al. 2018). This
kind of area is visited more by men than by women.
Most visitors were in the age range of 37 58 years
(56.8%); 21.5% were 26 to 36; and 15% were aged
over 58. Only 4.7% were aged 18 to 25. The mean
age was 46. More than half of the respondents had a
university degree (53.6%). Tavascan was the most fre-
quent entrance point (34.4%), followed by Sant Joan
de l’Erm (18%). On average, respondents had visited
the park three times in the last two years, usually stay-
ing three and a half days. 111 euros per visit was the
(average) total spend registered by visitors, including
accommodation, food, drink, services and products,
corresponding to 31 euros per day. (See Table 3.)
Visitor spending according to entrance point
A one-way between-groups analysis of variance
(ANOVA) showed statistically signicant differenc-
es in spending with regards to the entrance points:
F (5.494) = 6.148, p < 0.001 (Table 4). Subsequent-
ly, post-hoc comparisons using the Tukey HSD
test indicated that the mean spending for Tavascan
(M = 147.39, SD = 187.05) differed from the mean
for Saint and Os de Civís at a signicance level of
p < 0.001. Visitors who entered the park through La
Farga spent signicantly more money than those who
entered through Sant Joan de l’Erm (p < 0.01).
Grouping procedure
Using the updated version of the Compendium
of Physical Activities’ Relative Metabolic Intensity
(MET) consumption values (Ainsworth et al. 2000),
respondent-reported activities were classied into
three distinct PA intensity groups (Table 5). The rst
group accounted for 21.6% of the sample and com-
prised those visitors who participated in activities with
metabolic consumption between 1.5 and 3 METs
(e. g., light PA intensity). The second (largest) group
included 57.8% of respondents, who carried out mod-
Estela Inés Farías-Torbidoni & Demir Barić
Table 5 – Grouping procedure according to the PA Compen-
dium and corresponding MET consumption a (n = 500).
Reported activities Total sam-
Code MET Grouping
N %
Activities at the
92 18.4 09100 1.8 Light
Vehicle touring 16 3.2 09105 2
Recreational hiking
(slow walking)
129 25.8 17090 3.3 Moderate
Hiking (brisk walking) 130 26 17082 5.3
Picking mushrooms 1 0.2 08246 3.5
Off-road motocross 9 1.8 15470 4
Snow activities 20 4.0 19190 5.3
Mountaineering 49 9.8 17040 7.3 Vigorous
Mountain bike 37 7.4 01009 8.5
Trail running 2 0.4 12140 9
Unclear answers 15 3 -- -- --
Table 4 – ANOVA results: Visitors’ spending with respect to
entrance points (n = 500).
Entrance points n M SD F5.494
a) Fornet 79 116.75 123.05 6.148*
b) Tavascan 172 147.39e,f 187.05
c) La Farga 77 130e192.20
d) Tor 40 105.27 97.37
e) Saint Joan de l'Erm 90 53.5b,c 48.97
f) Os de Civís 42 59.5b65
*Note: p < 0.001; post-hoc signicant differences (Tukey
HSD) are shown as indexes.
For example, spending by visitors who entered via Tavascan
(listed as letter b) differed signicantly only from those visi-
tors who entered via Saint Joan de l'Erm (listed as e) and Os
de Civís (listed as b). Spending by those who entered via
Fornet did not differ signicantly from that of other visitor
Table 6 – Motivation for visiting the park: Descriptive statistics, principal component analysis and factor loadings.
Principal components M SD Item loading Eigenvalue Explained variance Reliability coefficient
Factor 1: Physical activities 3.03 1.35 3.222 35.76 0.74
To do physical activities 0.86
To practise some specific PA or sport 0.86
To improve health 0.55
To visit specific trails 0.52
Factor 2: Nature 4.65 0.65 1.35 14.94 0.63
To relax and disconnect 0.77
To enjoy the scenery 0.70
To be close to nature 0.70
Factor 3: Novelty 3.79 1.13 1.01 11.95 0.63
To enjoy new experiences 0.83
To explore new places 0.80
erate PA intensity, in the range 3–6 METs. The third
group (17.3%) included those individuals who were
engaged in vigorous recreational activities with METs
above 6. Those respondents who did not report their
recreational activities (i. e., other; 1.7%) were excluded
from the grouping procedure.
Visitors’ motivations: factor analysis
A principal component analysis (PCA) with Vari-
max rotation was performed on 12 motivational vari-
ables to reveal underlying motivation factors. First, a
series of basic measures was inspected to justify em-
pirically whether the set of variables tted the pro-
posed statistical technique. Following convention,
only items with no cross-loadings and with loadings
of 0.50 or greater were retained for further analyses
(Hair et al. 2006). Using this criterion, the initial list
was shortened to nine items (Table 6). The Bartlett
test of sphericity was then carried out on the remain-
ing items; the value reached a statistical signicance
of p < 0.001, and the Kaiser-Meyer-Olkin value was
0.45. Therefore, the data revealed a reasonable t for
the proposed statistical procedure for factor analy-
sis. Three factors, all of which had eigenvalues equal
to or greater than 1.0, explained 62.69% of the to-
tal variance. The rst factor, labelled Physical activities,
contained four corresponding variables and yielded
a reliability coefcient of 0.740. The second, Nature,
comprised three items and produced a reliability coef-
cient of 0.635. The third, Novelty, reected two vari-
ables and had an α value of 0.627. Factor two, Nature,
was the most important motivation dimension, with a
grand mean of 4.65.
Hierarchical regression analysis
After controlling for the effects of series of soci-
odemographic, travel and motivational characteristics,
a four-step hierarchical multiple regression analysis
was run to examine the inuence of PA intensity, clas-
sied within MET values, on individual spending dur-
ing the visit, x.
Prior to the regression analysis, a bivariate correla-
tion analysis was conducted, as shown in Table 7. Sev-
en out of the ten independent variables correlated sig-
nicantly with the dependent variable. Among them,
only age and frequency had negative associations.
Correlations between independent variables were pre-
dominately weak and did not exceed 0.4. Additional
preliminary analyses conrmed no violation of the
assumptions of normality and multicollinearity. Four
sociodemographic predictors (place of residence, gen-
der, age and education level) were entered at the rst
28 Research
Table 7 – Correlations among dependent and independent variables.
Individual spending
per visita1 2 3 4 5 6 7 8 9
1. Place of residence 0.123**
2. Gender (Ref: Female) 0.025 0.047
3. Age −0.223*** −0.097*0.071
4. Education level 0.064 0.078*0.146** 0.096*
5. Number of visits in
the last 2 years
−0.142** −0.087*−0.041 0.047 −0.025
6. Visit duration (days) 0.416*** 0.079*0.068 −0.109** 0.055 0.064
7. Physical activities 0.162*** 0.056 −0.005 0.005 0.187*** 0.047 0.055
8. Nature 0.05 0.237*** 0.053 0.015 0.083*−0.013 −0.136** 0.122**
9. Novelty 0.162*** 0.138*** 0.037 0.082*0.032 −0.190*** −0.023 0.107** 0.311***
10. METs 0.197*** 0.069 −0.005 −0.002 0.245*** 0.005 0.131** 0.313*** 0.186*** 0.148***
Signicance level (two-sided): *p < 0.05; **p < 0.01; ***p < 0.001
Note: a Dependent variable
Table 8 – Hierarchical regression analysis for variables predicting total spending per individual during their visit.
ent variable
Model 1 Model 2 Model 3 Model 4
B SE B βB SE B βB SE B βB SE B β
Place of
residence (Ref:
City of Barcelona)
0.088 0.042 0.094*0.054 0.039 0.058 0.023 0.039 0.024 0.024 0.039 0.026
Gender (Ref:
Female) 0.025 0.045 0.025 −0.007 0.041 −0.007 −0.007 0.040 −0.007 −0.002 0.040 −0.002
Age(Year of birth) −0.008 0.002 −0.223** −0.006 0.002 −0.171*** −0.007 0.002 −0.184*** −0.007 0.002 −0.182***
level (Ref: University
0.069 0.043 0.074 0.047 0.039 0.051 0.023 0.038 0.024 0.007 0.039 0.008
Number of
visits in the
last 2 years
−0.011 0.003 –0.154*** −0.010 0.003 −0.137*** −0.010 0.003 −0.138***
Visit duration
(days) 0.033 0.003 0.401*** 0.034 0.003 0.404*** 0.033 0.003 0.393***
activities 0.042 0.014 0.122*** 0.035 0.015 0.101*
Nature 0.031 0.032 0.043 0.022 0.032 0.031
Novelty 0.055 0.018 0.130*** 0.052 0.018 0.123**
METs 0.066 0.033 0.086*
R20.066 0.237 0.276 0.282
F 8.226*** 23.789*** 19.383*** 17.947***
ΔR20.066 0.170 0.039 0.006
ΔF 8.226*** 51.321*** 8.310*** 3.909*
*p < 0.05; **p < 0.01; ***p < 0.001
NOTE: B = Beta of unstandardized coefcients; β = Beta of standardized coefcient; R2 = Variation in the dependent variable
explained by the independent variables; ΔR2 = R square change; F-distribution (F-test); ΔF F-test change
step and accounted for statistically signicant vari-
ance in the dependent variable (R2 = 0.066, F change
(3.462) = 8.226, p < 0.001). Addition of travel descrip-
tors at step two (i. e., number of visits in the last two
years and visit duration), led to a statistically signi-
cant increase in the R2 of 0.170, F (1.460) = 51.321,
p < 0.001. Three motivational dimensions (Physical ac-
tivity, Nature and Novelty) entered at step three resulted
in a statistically signicant increment in R2 of 0.039,
F (2.457) = 8.310, p < 0.001. Finally, by adding the
physical activity intensity in the fourth step, the nal
model reected a weak but statistically signicant
change in R2 of 0.006, F (1.456) = 3.909, p < 0.05. T he
full model comprising all predictor variables was sta-
tistically signicant, R2 = 0.282, F (10.465) = 17.947,
p < 0.001. Here, statistically signicant inuences of
the predictor variables on individual expenditure dur-
ing the visit were found for age = 0.182, p < 0.001),
frequency of visits = 0.138, p < 0.001), visit dura-
tion (β = 0.393, p < 0.001), motivational dimensions
Physical activity = 0.101, p < 0.05), Novelty (β = 0.123,
p < 0.01), and intensity of physical activities (MET;
β = 0.086, p < 0.05).
Discussion of findings and implications
This study is the rst attempt to analyse a compre-
hensive dataset on the microeconomic impact of tour-
ism in a PNA in Spain as linked to visitors’ behaviour.
Where the three main goals of this research are con-
Estela Inés Farías-Torbidoni & Demir Barić
cerned – to analyse how much visitors spent, to group
visitors according to PA intensity, and to assess the
contribution of PA intensity to the level of spending
– the results obtained provide an information base for
detailed discussion.
Visitor spending
Although there were difculties in nding spe-
cic studies that help to put our data in context, the
mean daily spending identied in our study serves as
a rst national reference. Namely, we found that the
mean daily and total spends per person for Alt Pirineu
Natural Park visitors are similar to the national aver-
ages for tourists in Spain; visitors to this park spend
31.7 € per day (national average: 33 €) and 111 € per
trip (national average: 125 €). Despite the different
approach used, the present results corroborate some
of the ndings of the studies referred to earlier. For
example, the daily average spend established in our
study was very similar to that observed in the study
by Shirpke et al. (2018): if we exclude the travel costs
in the Italian study, we nd a difference of 20% be-
tween visitors of Natura sites in both countries (Spain:
31.7 € versus Italy: 37.86 €). Another important nding
was the signicant differences between the various
entrances regarding spending. Results indicated that
visitors who entered the park through Tavascan or La
Farga spent signicantly more than those who entered
through Saint Joan or Os de Civis, which is probably
related to the main characteristics of the different en-
trances. Namely, Tavascan and La Farga offer more
opportunities for engagement in various PAs and are
characterized by a wider range of supporting areas. In
this case, we do not have any specic references with
which to compare these results, but they could also be
connected with the differences identied by Schirpe et
al. in the 10 Natura sites, which ranged from 15.92
(Grigna) to 71.72 € (Fogosa). However, more data are
needed to be able to establish connections between
the characteristics of each site/entrance and spending
PA segmentation
Although the results of the segmentation approach
do not provide empirical evidence in relation to the
issue, this new approach would be easily transfer-
able if we consider that recreational activities are a
common data type collected in studies related to the
identication of visitor proles in this type of area.
Some examples of the approach are to be found in
Farías-Torbidoni et al. (2018), Mowen et al. (2012)
and Walden-Schreiner et al. (2014), who demonstrated
that a metabolic equivalent approach could be used
to categorize the recreational and physical activities
performed by visitors to PNAs. For instance, while
Mowen et al. (2012), who sampled visitors to 6 parks
in Pennsylvania (USA), found similar results (almost
60% of the sample reported participation in moder-
ate-intensity PA), Walden-Schreiner et al. (2014), who
examined visitors in the high-use meadows in Yo-
semite National Park (USA), found that only 44% of
visitors participated in moderate-intensity PA during
their visit. However, the potential of this approach in
connection to promoting health-enhancing physical
activity (HEPA) in PNAs has been argued intensively
(Farías-Torbidoni et al. 2018), not least because these
kinds of data provide a good example of how existing
monitoring programmes may be adapted to incorpo-
rate indicators relevant to PA evaluation point.
Contribution of PA intensity on spending levels
Although the nal model of hierarchical multiple
regression analysis explained a notable 28.2% of total
variance, PA intensity itself made marginal but still sig-
nicant contributions to visitor spending after control-
ling for other descriptors (ΔR2 = 0.006). These ndings
undoubtedly highlight the notion that they should be
perceived holistically and should take into considera-
tion other visitor characteristics. Namely, the ndings
have shown that increasing age was negatively associ-
ated with likelihood of expenditure. In other words,
they revealed that the younger population is willing to
spend more money while visiting the area. In addition,
the results clearly showed that individuals who stayed
longer were more motivated by internal factors, such
as PA and new experiences, and were more likely to
spend more money during their visit. These results are
not surprising and agree with the ndings of other
studies in the eld, which also found a positive asso-
ciation between visitor age (younger to middle-aged),
engagement in activities with higher intensity (e. g.
mountain biking, rock climbing, intensive hiking), and
motivations and variables that reect spending during
the visit (Barić et al. 2016a; Cordente-Rodriguez et al.
2014; Fredman 2008). For instance, Barić et al. (2016a)
found that, compared to general visitors, rock climb-
ers, who were younger and more interested in expe-
riences related to personal achievement, preferred to
stay longer and overnight in local accommodation in
surrounding villages, which indirectly implied greater
spending. Freedman (2008) uncovered similar asso-
ciations. Examining visitor spending in mountain re-
gions, he found that individuals who stayed longer and
participated in higher intensity PA (e. g. downhill ski-
ing) were more likely to spend more at the destination
than those who stayed for shorter times and engaged
in lower-intensity PAs (e. g. snowmobiling). It is there-
fore reasonable to assume that the positive association
between PA intensity and spending found in this study
greatly depends on a range of other behavioural char-
acteristics. However, care should be taken in making
these assumptions as there is little empirical evidence
about the moderating effects of sociodemographic,
trip and motivational descriptors on the association
between PA intensity and total spending.
Overall, the present ndings have important impli-
cations and could be of great importance to park man-
agers, local tourism operators and decision makers in
30 Research
formulating more transparent, accurate and effective
planning strategies and wider marketing programmes.
In short, this study provides holistic insights into the
associations between the inuence of PA intensity on
total spending, considering other relevant characteris-
tics, and may aid managers to better understand visi-
tors’ behavioural patterns, perceiving them not as an
undifferentiated group but more as mutually related
and dependent units who are open to changes ac-
cording to managerial needs. Managers could use this
information to set site-specic strategies for improv-
ing particular physical and social conditions in parks,
widening the range of recreational opportunities for
visitors, and stimulating them to stay longer and spend
more money. Moreover, these ndings might aid park
managers in developing clearer links between inputs
(i. e., facilities and services provided) and outcomes
(visitor spending), which could pave the way for more
rational recreation and tourism strategies.
Conclusion and limitations
Earlier studies have analysed and discussed the im-
portance of the economic impact of tourism in PNAs
and the contribution of these areas to the promotion
of PA and health. However, the relationship between
these two factors has not been examined empirically.
This is the contribution of the present study.
First of all, the results obtained in terms of visitor
spending not only serve as a rst national benchmark,
but also allow us to corroborate the ndings of earlier
studies at both national (Spain) and regional (Europe)
levels. Furthermore, the results obtained indicate, if
inconclusively, a possible connection between park fa-
cilities (PA and supporting areas) and visitor spending
Second, because recreational activities are a com-
mon data type collected in any study related to identi-
fying the proles of visitors to protected areas, the seg-
mentation approach is readily transferable (although
its results do not provide empirical knowledge).
Finally, although the contribution of PA intensity
to the level of expenditure is not conclusive, the re-
sults obtained here show a statistically signicant inu-
ence of predictor variables on individual spending. We
found that age, visit duration, the motivational dimen-
sions of Physical activity and Novelty, and PA intensity
are good predictors of how much a visitor will spend.
This indicates that, by increasing PA intensities, man-
agers and local ofcials could increase visitor spending
and open up a new approach to expand the roles of
PNAs in society. Although the results of this study re-
garding the relationship between the two benets (i. e.
the economic and the health impacts) are not conclu-
sive, they do offer a line of work for future research,
which could create a further segmentation of PA in-
tensities based on market tourism theories. Such data
could help inform policy decisions, aiding managers
to direct and support increasing PA intensity and take
more appropriate decisions to increase the economic
impact on the region.
This project has been possible thanks to the Alt
Pirineu Natural Park, the Ministry of Territory and
Sustainability, Government of Catalunya, and to the
National Institute of Physical Education of Catalonia
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Estela Inés Farías-Torbidoni1,2 – corresponing
is ssociate professor of Health and Sport Manage-
ment at the National Institute of Physical Education
of Catalonia (INEFC) She has a PhD in Sport Sci-
ences (University of Lleida) and a Master’s in Protect-
ed Natural Areas (University of Madrid). Her main
research areas are sport management, sociology of
sport, outdoor activities, and protected natural areas.
Demir Barić1
is a Postdoctoral fellow at the National Institute of
Physical Education of Catalonia (INEFC), in Lleida.
He specializes in visitor monitoring in protected natu-
ral areas and has a particular interest in the develop-
ment of effective visitor management strategies.
1 National Institute of Physical Education of Catalo-
nia (INEFC). University of Lleida (UdL)
2 Grup d’Investigació Social i Educativa de l’Activitat
Física i l’Esport (GISEAFE). Partida la Caparella sn.
25120 Lleida, Spain
... It is projected that the number of foreign arrivals would decline by nearly 20-30 percent, resulting in revenue losses in the worldwide tourist industry of between USD300 and 450 billion. In 2020, a reduction in the number of foreign passengers resulted in revenue losses for airlines of roughly USD252 billion and revenue losses for airports of approximately USD77 billion [7]. Millions of livelihoods and jobs have been eliminated because of travel restrictions and border closures in virtually every country across the world [8]. ...
The pandemic caused by the SARS-CoV-2 virus (COVID-19) has significantly affected the tourism industry. Tourist destinations have adopted emergency measures and restrictions that have affected the mobility of individuals around the world. This study aims to analyze the effects of the COVID-19 pandemic on the tourism industry in Malaysia and its overall economic performance. This research used an extensive set of statistical tests, including a newly constructed Auto-Regressive Neural Network-ADF (ARNN-ADF) test, to determine if foreign visitor arrivals from 10 main source markets in Malaysia will revert to normal. Secondary data from various government published sources were used in this conceptual methodology technique for this study. Based on the research results and exploratory research of the literature, we listed in a synthesizing manner several measures to ensure the resilience of the tourism sector during the COVID-19 pandemic period. This research makes a significant contribution to the literature in terms of validating a new framework that emphasizes the effects of tourists that are largely transitory. In conclusion, this conceptual study will further help the authorities to take precautions and the best policy to be implemented in the future. Doi: 10.28991/esj-2021-SPER-10 Full Text: PDF
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Tourist expenditure is an important measure of international tourism demand. This study is a review of expenditure analyses in a tourism context presenting a range of theoretical factors that could potentially affect tourism demand and expenditure. In addition, a review of 16 tourism expenditure studies that used micro data was conducted, to elicit the sample size, model specification, as well as the dependent and independent variables. The study concluded that greater emphasis should be given to micro-economic modelling of tourism demand and the investigation of the effect of psychological and supply-related factors on tourist expenditure. Keywords: review, tourist expenditure, micro-economic analyses Yes Yes
The study explored whether national-park affinity segments of tourists differ regarding their level of specialization in, attitudes towards, and satisfaction with, national parks between 2013 and 2014. The research categorized 429 overnight tourists to the Bavarian Forest National Park, Germany, into three affinity segments based on the role the national park played in their travel decision. More than the half of the respondents had a high or rather high national-park affinity. Differences in specialization, attitudes and satisfaction between the segments were found. However, differences were only observed between the more national park-interested segments compared to visitors who are not aware of visiting a national park or whose decision to visit the area was not influenced by the national park label, except for satisfaction. Tourists with higher national-park affinity had more positive attitudes towards, and were more specialized in, national parks and were more satisfied with nature conservation management. For satisfaction, most differences were observed between the segment with the lowest and the one with the highest affinity. Study findings may support sustainable park management and park affinity research.
Nature tourism has a strong potential, and as a result of this tourism activity, the environmental concern is considered an important element which guides business and tourism activity. This element also generates the change in the society's behaviour, in order to conciliate economic and political interest with the environmental ones, with the final aim to guarantee the existence of these resources in the future and the tourism competitiveness of these natural areas. To get this purpose of sustainable development, in the case of natural areas where it is difficult to control the carrying capacity because the access is uncontrolled in them, it is important to attract visitors which are identify with the destination, its resources and what it has to offer. In this way the visitors have a behavior at the destination that will help to achieve its sustainability. In this sense, the segmentation has an important role in the development, management and success of a natural area in a competitive tourism environment. Because the segmentation allows knowing visitors, their preference, wishes and needs, and it makes easy the adjustment of supply. At the same time, this activity allows to lead the communication actions of a tourism destination or area toward the visitors who are identified with the destination offers. This guarantees the sustainability of destination over time. The purpose of this paper it is to identify visitors who have a behavior and interest in protected natural areas, and who will be consider as appropriate target to direct the promotion to encourage their visit. This paper includes the analysis of the type of visitors in the area of Serrania Alta de Cuenca, in Spain, according to their features, the type of travel and their opinion about the destination. This study provides relevant information to guide the management of tourism activity in this area. Latent Gold 4.5. is the statistical software used to make the segmentation. This technique is different from other by its strict statistical formulation which puts the visitors into group by the probability of belonging to each segment.
This paper analyses the factors involved in the decision to consume tourism services for leisure purposes. A discrete choice logit model is used, with data drawn from the Spanish Family Expenditure Survey for the period 1985-96. The results suggest that limitations on free time, cultural factors, income (with an income elasticity below one), age and the generation effect are all crucial determining factors in the probability of travel. The use of micro data leads to the detection of a high degree of heterogeneity, both among households and in the different degree to which the aforementioned variables affect travel decisions.
The Compendium of Physical Activities was developed to enhance the comparability of results across studies using self-report physical activity (PA) and is used to quantify the energy cost of a wide variety of PA. We provide the second update of the Compendium, called the 2011 Compendium. The 2011 Compendium retains the previous coding scheme to identify the major category headings and specific PA by their rate of energy expenditure in MET. Modifications in the 2011 Compendium include cataloging measured MET values and their source references, when available; addition of new codes and specific activities; an update of the Compendium tracking guide that links information in the 1993, 2000, and 2011 compendia versions; and the creation of a Web site to facilitate easy access and downloading of Compendium documents. Measured MET values were obtained from a systematic search of databases using defined key words. The 2011 Compendium contains 821 codes for specific activities. Two hundred seventeen new codes were added, 68% (561/821) of which have measured MET values. Approximately half (317/604) of the codes from the 2000 Compendium were modified to improve the definitions and/or to consolidate specific activities and to update estimated MET values where measured values did not exist. Updated MET values accounted for 73% of all code changes. The Compendium is used globally to quantify the energy cost of PA in adults for surveillance activities, research studies, and, in clinical settings, to write PA recommendations and to assess energy expenditure in individuals. The 2011 Compendium is an update of a system for quantifying the energy cost of adult human PA and is a living document that is moving in the direction of being 100% evidence based.
Although regional economic impact studies stop a long way short of providing economic evaluations of alternative uses of open space, they are valuable in quantifying tangible contributions of recreation and tourism to local economic activity. Such studies might be vital in supporting the case for conservation and national parks. Regional economic impact studies predict changes in output, income, and employment stemming from visitor expenditures. A review of the quantitative findings of twenty-nine such studies shows a surprising commonality in regional multiplier estimates in the range 1.25 - 1.89. This is despite wide diversity in the location, context, and methodology of the studies. The second section of the paper is a case study conducted in Cooloola National Park, Australia. Problems in measuring visitor expenditures are discussed in some detail, as it is argued that inaccuracies in this phase of research may be more significant than those arising from multiplier estimation. Markedly different impacts of day-trippers, campers, and commercial tour passengers were found in this area as a result of varying expenditures by visitors and their place of purchase. Campers are typically fully provisioned and contribute little to local business in comparison with commercial tour passengers who patronise local accommodation establishments. Park managers, by controlling access and use densities, choosing facilities and park infrastructure, and supervising visitor activities have a significant influence on the economic impact of a park. Capital investments and maintenance expenditures will also have economic significance to local employment creation and therefore to local economies and their politicians.
Protected Areas in the Alps -The Success Factors of Sustainable Tourism and the Challenge for Regional Policy
Multivariate Data Analysis. 6 th ed. Upper Saddle River, NJ. Hammer, T. & D. Siegrist 2008. Protected Areas in the Alps -The Success Factors of Sustainable Tourism and the Challenge for Regional Policy. GAIA 17/ S1: 152-160
Healthy Parks, Healthy People: The Health Benefits of Contact with Nature in a Park Context
  • Buckley
Buckley (eds.) 2019. Gestión del turismo y de los visitantes en areas protegidas: directrices para la sostenibilidad. Serie Directrices sobre Buenas Prácticas en Áreas Protegidas no. 27, Gland, Suiza: UICN. XII. [In Spanish] Maller, C., M. Townsend, L. St Leger, C. Henderson-Wilson, A. Pryor, L. Prosser & M. Moore 2010. Healthy Parks, Healthy People: The Health Benefits of Contact with Nature in a Park Context. The George Wright Forum 26(2): 51-83.