Jukuri, open repository of the Natural Resources Institute Finland (Luke)               
   
 
 
All material supplied via Jukuri is protected by copyright and other intellectual property rights. 
Duplication or sale, in electronic or print form, of any part of the repository collections is prohibited. 
Making electronic or print copies of the material is permitted only for your own personal use or for 
educational purposes. For other purposes, this article may be used in accordance with the publisher’s 
terms. There may be differences between this version and the publisher’s version. You are advised to 
cite the publisher’s version. 
This is an electronic reprint of the original article.  
This reprint may differ from the original in pagination and typographic detail. 
 
Author(s): Craig W. McDougall, Lewis R. Elliott, Mathew P. White, James Grellier, Simon Bell, 
Gregory N. Bratman, Mark Nieuwenhuijsen, Maria L. Lima, Ann Ojala, Marta Cirach, 
Anne Roiko, Matilda van den Bosch, Lora E. Fleming 
Title: What types of nature exposure are associated with hedonic, eudaimonic and 
evaluative wellbeing? An 18-country study 
Year: 2024 
Version: Published version 
Copyright: The Author(s) 2024 
Rights: CC BY 4.0 
Rights url: https://creativecommons.org/licenses/by/4.0/  
 
Please cite the original version: 
Craig W. McDougall, Lewis R. Elliott, Mathew P. White, James Grellier, Simon Bell, Gregory N. Bratman, 
Mark Nieuwenhuijsen, Maria L. Lima, Ann Ojala, Marta Cirach, Anne Roiko, Matilda van den Bosch, 
Lora E. Fleming, What types of nature exposure are associated with hedonic, eudaimonic and 
evaluative wellbeing? An 18-country study, Journal of Environmental Psychology, Volume 100, 2024, 
102479, ISSN 0272-4944, https://doi.org/10.1016/j.jenvp.2024  
What types of nature exposure are associated with hedonic, eudaimonic
and evaluative wellbeing? An 18-country study
Craig W. McDougall a,b,*, Lewis R. Elliott a, Mathew P. White c, James Grellier a,d, Simon Bell e,f,
Gregory N. Bratman g, Mark Nieuwenhuijsen h,i,j, Maria L. Lima k, Ann Ojala l, Marta Cirach h,
Anne Roikom, Matilda van den Bosch h,i,j,n,o,p, Lora E. Fleming a
a European Centre for Environment and Human Health, University of Exeter, UK
b Scottish Collaboration for Public Health Research and Policy, University of Edinburgh, UK
c Vienna Cognitive Science Hub, University of Vienna, Austria
d Institute of Psychology, Jagiellonian University, Poland
e Estonian University of life Sciences, Tartu, Estonia
f Edinburgh School of Architecture and Landscape Architecture, Edinburgh College of Art, University of Edinburgh, UK
g School of Environmental and Forest Sciences, University of Washington, USA
h ISGlobal, Barcelona, Spain
i Universitat Pompeu Fabra (UPF), Barcelona, Spain
j CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
k Department of Social and Organizational Psychology, University Institute of Lisbon, Portugal
l Natural Resources Institute Finland (Luke), Finland
m School of Medicine, Griffith University, Australia
n School of Population and Public Health, University of British Columbia, Canada
o Department of Forest and Conservation Sciences, University of British Columbia, Canada
p Biocities Facility, European Forest Institute, Rome, Italy
A R T I C L E I N F O
Keywords:
Nature
Blue space
Public health
Life satisfaction
Happiness
A B S T R A C T
Although spending time in nature can improve subjective wellbeing (SWB), little is known about how different
types of nature exposure are associated with different dimensions of SWB or the consistency of associations
across national/cultural contexts. Using data from 18 countries, associations between green, coastal and fresh-
water blue space exposures (including residential availability, visits ‘yesterday’ and visits in the previous four
weeks) and hedonic, eudaimonic, and evaluative wellbeing were estimated. Overall, residential nature avail-
ability showed little association with any wellbeing outcome, whereas visiting green and coastal locations
‘yesterday’ was associated with better hedonic wellbeing. Although frequently visiting green, coastal and
freshwater spaces were all associated with greater evaluative wellbeing, greater eudaimonic wellbeing was only
associated with frequent visits to green and freshwater spaces. Variations existed across countries. Results
suggest that different types of nature exposure vary in their association with different dimensions of SWB. Un-
derstanding these differences may help us maximise the potential of natural environments as SWB-promoting
resources.
1. Introduction
Societal wellbeing is often measured by gross domestic product
(GDP) and GDP per capita, although these metrics have a range of
limitations (Giannetti et al., 2015). GDP has been criticised for failing to
account for individuals’ perceptions of their own wellbeing (Voukelatou
et al., 2021) and relying solely on GDP as an indicator of societal
wellbeing can lead to ill-advised national policies (Diener et al., 2018). A
range of metrics have been suggested as more appropriate, or at least
complementary, alternatives, better suited to measuring wellbeing
(Cavalletti& Corsi, 2018). It is now recognised that subjective wellbeing
(SWB) is an important indicator of societal performance (Das et al.,
2020) and monitoring and promoting SWB has become an established
practice in many countries (Austin, 2016; Diener, 2013).
* Corresponding author. European Centre for Environment and Human Health, University of Exeter, UK.
E-mail address: cmcdoug3@ed.ac.uk (C.W. McDougall).
Contents lists available at ScienceDirect
Journal of Environmental Psychology
journal homepage: www.elsevier.com/locate/jep
https://doi.org/10.1016/j.jenvp.2024.102479
Received 8 April 2024; Received in revised form 4 November 2024; Accepted 4 November 2024
Journal of Environmental Psychology 100 (2024) 102479 
Available online 5 November 2024 0272-4944/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). 
SWB is broadly defined as how people experience and evaluate their
lives (Diener, 2000). Despite much debate, there is some agreement on
what constitutes SWB. It is generally accepted that SWB is a
multi-faceted and multi-dimensional construct that includes aspects of
both the hedonic, and eudaimonic conceptions of wellbeing (Ryan and
Deci, 2001) and there is now growing consensus that there are four key
components of SWB that should be measured: a) positive and b) negative
hedonicwellbeing, i.e. the emotions of pleasure (e.g. happiness) and pain
(e.g. anxiety) individuals regularly experience; (c) eudaimonic well-
being, i.e. how meaningful/worthwhile individuals think their behav-
iours/activities are; and (d) evaluative wellbeing, i.e. how well
individuals think their life is going overall (ONS, 2011; OECD, 2013).
SWB can be influenced by a range of factors which typically fall
under the remit of national or local governments including: crime
(Krulichova, 2021), social capital (e.g., community cohesion) (Maass
et al., 2016), housing quality (Badland et al., 2017), transport (Bokhari
& Sharifi, 2022), public service provision (Isaacs et al., 2022), and urban
planning (Mouratidis, 2021). Policy and decision-makers, globally, have
attempted a range of strategies to improve SWB among populations. In
recent years, provision of accessible urban nature has been proposed as a
tool for increasing human health and wellbeing, including by the World
Health Organisation (WHO) and the United Nations (UN) (Douglas et al.,
2017).
A variety of studies have demonstrated the potential of natural en-
vironments to provide benefits for SWB (Huerta & Utomo, 2021; Mavoa
et al., 2019; White et al., 2017; McDougall et al., 2022; Wood et al.,
2017). For example, greater levels of green space surrounding a person’s
residence are associated with greater likelihood of reporting feelings of
happiness (hedonic wellbeing) compared to lower levels/amounts
(Patino et al., 2023). Measures of hedonic wellbeing often focus on the
feelings of yesterday as asking about momentary wellbeing is imprac-
tical in most survey scenarios and as recalled wellbeing after a longer
duration than 24 h may lead to memory accuracy fading (Kahneman
et al., 2004; White & Dolan, 2009). For instance, Fisher et al. (2021)
found individuals were more likely to report feelings of happiness
yesterday if they had visited a green space for more than 25 min in the
week prior to surveying. Living in greener areas (Mavoa et al., 2019) and
visiting green space (Jiang & Huang, 2022) have also been associated
with higher self-reported life satisfaction (evaluative wellbeing) relative
to living in less green areas and not visiting green space, respectively.
Despite growing evidence suggesting exposure to natural environ-
ments can promote SWB, there are opportunities to progress this evi-
dence as most studies overlook (a) actual exposure (e.g. visitation): to
natural environments in favour of proxies for exposure; (b) distinctions
between different types of natural environment; (c) distinctions between
different components of SWB (e.g. hedonic, eudaimonic and evaluative);
and (d) how each of the above is impacted by differing national and
cultural contexts.
These limitations are important for several reasons. Firstly, epide-
miological nature-wellbeing studies often utilise residential availability
or proximity as a proxy to measure contact with natural environments
(Gascon et al., 2017). Such metrics may represent reasonable proxies
given that usage of natural environments is likely to decrease with
increasing distance from the residence (Coombes et al., 2010; Elliott
et al., 2020) and reduced accessibility (Ekkel & de Vries, 2017). How-
ever, it has also been argued that access metrics and contact with nature
are weakly correlated (Jarvis et al., 2020) and a range of other factors
also influence the likelihood of visiting nature such as home and work
commitments and long-term illness (Boyd et al., 2018). Furthermore,
access metrics are limited in their capability to quantify contact
(Helbich, 2018) or to establish varying levels of contact with different
natural environment types.
Self-reported accounts of nature exposure, such as recalled visit
frequency, offer an opportunity to quantify an individual’s exposure to
different types of nature in a way that captures the heterogeneity of
individual exposure. It should also be noted that nature can provide
indirect benefits to SWB, which do not require visitation such as benefits
related to reduction of air pollution, noise and heat (White et al., 2020).
Despite progress in understanding the SWB-promoting potential of
natural environments, untangling the unique SWB impacts of different
types and levels of nature exposure remains a key research gap
(McDougall et al., 2022).
Most studies on nature contact and SWB focus on only one of the
dimensions of SWB (e.g., evaluative wellbeing (e.g. Knight et al., 2022;
Patino et al., 2023), hedonic wellbeing (e.g. de Vries et al., 2021;
MacKerron & Mourato, 2013) or eudaimonic wellbeing (see Lima &
Mariano, 2022 for a review); and relatively few studies explore multiple
dimensions in parallel (Fisher et al., 2021; Richardson et al., 2021;
White et al., 2017). In one study, White et al. (2017) found that visiting
nature yesterday (the day prior to surveying) was associated with
greater happiness (positive hedonic wellbeing) and lower anxiety
(negative hedonic wellbeing) yesterday; and visiting nature frequently
in the last 12 months was associated with greater eudaimonic wellbeing.
However, this study could not distinguish between different types of
nature (e.g. green vs. blue), and thus was unable to investigate whether
different settings might impact different dimensions of SWB.
Most research on natural environments and SWB has focused on
green space (e.g. parks, woodlands and other vegetated areas) (Hong
et al., 2021; Patino et al., 2023; Fisher et al., 2021; Jiminez et al., 2021).
There is also growing interest in the wellbeing-promoting potential of
other elements of the natural environment, such as water bodies or ‘blue
spaces’. Blue spaces are defined as ‘outdoor environments – either nat-
ural or manmade – that prominently feature water and are accessible to
humans’ (Grellier et al., 2017).
Understanding the relative wellbeing-promoting potential from
different natural environments can assist evidence-based policymaking
and is particularly important given that blue and green space may have
differing associations with SWB (McDougall et al., 2022). For example,
higher neighbourhood greenness and more frequent green space visi-
tation, but not coastal proximity or freshwater blue space availability,
were associated with greater SWB in Melbourne, Australia (Mavoa et al.,
2019). Contrastingly, McDougall et al. (2022) found frequently visiting
coastal and freshwater blue space, but not public green space, was
associated with higher SWB. Indeed, it has been hypothesised that blue
spaces offer a range of unique therapeutic and health-promoting prop-
erties (Foley & Kistemann, 2015; Volker & Kistemann, 2011), and may
be more suited to wellbeing-promotion than green space (McDougall
et al., 2022)
The majority of blue space and SWB research has focused on the
wellbeing benefits of visiting or living in close proximity to the sea
(coastal blue space), with freshwater blue space often overlooked
(Gascon et al., 2017). These two environments substantially differ in
their physical and hydrological properties and the ecosystem services
and amenity values they provide (McDougall et al., 2020). Furthermore,
experiences at freshwater blue space are likely to consist of different
scenery, smells, sounds, and recreation opportunities than experiences
in coastal environments (Mavoa et al., 2019).
Emerging research suggests that spending time in and around
freshwater blue space is associated with higher reports of happiness (de
Vries et al., 2021). Visiting freshwater blue space has been associated
with greater SWB (McDougall et al., 2022), lower perceived stress
(Poulsen et al., 2022) and psychological benefits (de Bell et al., 2017).
Although interest in the wellbeing-promoting potential of freshwater
blue space is growing, empirical studies of the topic remain sparse; and
there is a need for a clearer distinction between freshwater and coastal
blue space and greater exploration of causation in future research
(McDougall et al., 2020).
Lastly, a further criticism of nature-wellbeing research has been a
lack of geographic and cultural diversity in study sites and populations
(Gallegos-Riofrío et al., 2022); and it has been suggested that climatic
and cultural differences may impact the influence of natural environ-
ments on SWB (Benita et al., 2019). Nature and SWB studies have been
C.W. McDougall et al. Journal of Environmental Psychology 100 (2024) 102479 
2 
primarily limited to single cities or countries (Gascon et al., 2017).
Broadening the nature-wellbeing evidence-base to consider a wider
variety of cultures, geographies, and climates is a key step in advancing
current understanding, particularly as SWB (Diener, 2021), the role of
nature in culture (Wheaton et al., 2020) and access to nature (Kabisch
et al., 2016) vary across countries. Although comparisons of
single-location studies and meta-analyses offer some value, ideally
multi-location single studies are preferred to ensure comparability of
wellbeing, exposure, and covariate variables.
The aim of the present study was thus to quantify the relationship
between proximity and visits to green space, coastal and freshwater blue
space, and multiple dimensions of SWB across 18 countries using
harmonised survey data from the Horizon 2020 BlueHealth project
(Grellier et al., 2017; Elliott & White, 2020). Our specific objectives
were to investigate: (a) associations between residential availability of
green, freshwater, and coastal space together with visits to these spaces
in the previous day and positive hedonic wellbeing; (b) the same expo-
sures’ associations with negative hedonic wellbeing; (c) associations
between residential availability of green, freshwater, and coastal space
together with visits to these spaces in the previous four weeks and
eudaimonic wellbeing; (d) the same exposures’ associations with eval-
uative wellbeing; and (e) objectives a-d across 18 countries individually.
2. Methods
2.1. Overview
Data were obtained from the BlueHealth International Survey (BIS)
(Elliott&White, 2020). BIS is an 18-country survey of health, wellbeing,
and natural environment engagement carried out within the BlueHealth
project (Grellier et al., 2017). BIS data have been used in studies
focusing on different types of nature exposure including: childhood
(Vitale et al., 2022), visit (Garrett et al., 2023) and neighbourhood ex-
posures (Pasanen et al., 2023); for different mental health outcomes
(White et al., 2021), including sub-samples with common mental health
disorders (Tester-Jones et al., 2020); and more generic health outcomes
(Elliott et al., 2023). However, to date, the BIS data have not been used
to explore relations with different dimensions of SWB across different
exposure types. Ethical approval for this research was granted by the
University of Exeter’s College of Medicine and Health Research Ethics
Committee (Ref: Aug16/B/099).
2.2. Survey instrument and sampling
The BIS consisted of seven modules which focused on the following
domains; (1) SWB; (2) natural environment visits; (3) perceptions of
natural environments; (4) specific blue space visits; (5) economic valu-
ation; (6) physical and mental health; and (7) demographics. The BIS
was distributed in 18 countries/regions: Bulgaria, California (USA),
Canada, Czechia, Estonia, Finland, France, Germany, Greece, Hong
Kong (China), Ireland, Italy, Netherlands, Portugal, Queensland
(Australia), Spain, Sweden, and the United Kingdom (UK). The BIS was
distributed and administered via YouGov, an online panel provider who
develop and maintain (or subcontract) a panel of participants in each of
the countries/regions listed. In 14 countries, samples were recruited to
reflect representative samples of the adult population based on region of
residence and combined age/sex composition. In Estonia, Queensland
(AU), and Hong Kong (CN), only combined age/sex composition was
used, while in California (US), only age and sex (separately) were used.
These decisions were based on the feasibility of recruiting such samples
using the online panels. More specific information is provided in the
technical report (Elliott & White, 2020).Sampling took place between
June 2017 and April 2018 in four seasonal waves of data collection. Full
details of the survey instrument and sampling process are available in
the BlueHealth International Survey Methodology and Technical Report
(Elliott & White, 2020).
2.3. Subjective wellbeing
SWB was measured using four questions developed and extensively
tested by the UK’s Office for National Statistics (ONS, 2011) and sub-
sequently adopted by the Organisation of Economic Cooperation and
Development (OECD, 2013). These SWB questions encompass hedonic,
eudaimonic, and evaluative dimensions of SWB; and are, therefore,
well-suited to distinguishing any unique variance applicable to each
dimension of SWB and green/blue space proximity and visit metrics.
The four questions were: (1) ‘Overall, how happy did you feel
yesterday?’ (Positive hedonic); (2) ‘Overall, how anxious did you feel
yesterday?’ (Negative hedonic); (3) ‘Overall, to what extent do you feel
that the things you do in your life are worthwhile?’ (Eudaimonic); and
(4) ‘Overall how satisfied are you with life nowadays?’ (Evaluative).
Responses were reported on an 11-point scale ranging from 0 (not at all)
to 10 (completely).
Hedonic wellbeing questions asked participants about their feelings
yesterday (the day prior to surveying) as recollections of wellbeing after
a longer duration than 24 h may lead to memory accuracy fading
(Kahneman et al., 2004; White & Dolan, 2009). Each SWB question has
shown predictive validity for a range of outcomes of policy relevance
(Hicks et al., 2013), as well as showing consistent patterns in previous
nature-wellbeing research (de Bell et al., 2020; White et al., 2017).
2.4. Proximity and exposure to green and blue space
Three metrics of exposure to green space, coastal blue space and
freshwater blue space were adopted; (1) objective residential avail-
ability of each; (2) self-reported visits to each in the day prior to survey
completion (yesterday); and (3) self-reported visit frequency in the last
4-weeks.
2.4.1. Residential availability
The availability of green space, coastal and freshwater blue space
within 1,000m of each participant’s residence was quantified
(Hogendorf et al., 2020; Klompmaker et al., 2018). Participants pro-
vided the location of their residence using a Google Maps application
programming interface, with coordinates rounded to three decimal de-
grees to preserve anonymity, resulting in 55m error on average. Natural
environment coverage was captured via the GlobeLand30 land cover
map (Chen et al., 2015), derived from globally consistent 30m resolution
imagery, containing ten different land cover classes in the baseline year
of 2010.
Green space was defined as land cover classes classified as “forests”,
“grassland”, “shrub land” and “cultivated land” with a Minimum Map-
ping Unit (MMU) between 8 and 10 square pixels each, meaning that the
smallest feature represented in a map is 240–300 m2. Freshwater blue
space was defined as land classified as “water bodies” representing lakes
and rivers or “wetlands” with an MMU of around 270 m2. The presence
of coastal blue space was captured via the Global Self-consistent Hier-
archical High-resolution Geography shoreline database from the Na-
tional Oceanic and Atmospheric Administration (Wessel & Smith,
1996).
Buffer zones of 1,000m around participant residences were chosen to
represent a 10–15 min walk (Hogendorf et al., 2020; Klompmaker et al.,
2018; Smith et al., 2017). The percentage of residential green space
coverage within the buffer zone was calculated and divided into quar-
tiles. Freshwater and coastal availability were operationalised as the
presence or absence of each within a 1,000m buffer surrounding a
participant’s home (White et al., 2021). Spatial analyses were performed
using Python and PostGIS extension (PostgreSQL).
2.4.2. Visits yesterday
Participants were asked whether they had visited a series of green,
coastal, and freshwater environments in the day prior to surveying
(Garrett et al., 2023). Responses to 12 greenspace types, six freshwater
C.W. McDougall et al. Journal of Environmental Psychology 100 (2024) 102479 
3 
blue space types and, eight coastal blue space types were collapsed into
green, coastal, and freshwater variables. The resulting three variables
were operationalised as whether a participant did or did not (reference
category) visit any of the natural environment types yesterday. Focusing
on specific visits yesterday minimised recall bias, memory fading and
aligned with the recall period of the positive and negative hedonic
wellbeing questions adopted in this study.
2.4.3. Visit frequency
Visit frequency to the same series of natural environment types was
self-reported for the previous four weeks (McDougall et al., 2022; White
et al., 2021). Visit frequency was operationalised as four categories: zero
visits (reference category); visiting less than weekly (<4 visits); visiting
weekly (4 visits); and more than weekly (>4 visits). The groupings of
natural environment types were collapsed as above.
2.5. Covariates
A number of demographic and socioeconomic covariates which have
been shown to influence SWB and/or nature visits were captured in the
survey and utilised in the analysis including: age (Steptoe et al., 2015),
sex (Graham & Chattopadhyay, 2013), disability or long-term limiting
illness (Boyd et al., 2018; van Campen and van Santvoort, 2013), the
number of days participants completed more than 30 min of physical
activity (Roman et al., 2023), education level, perceived household in-
come status, employment status, relationship status and the number of
adults and children in the household (Andrade et al., 2022; Das et al.,
2020; Diener et al., 2018; Lamu & Olsen, 2016), dog ownership (White
et al., 2021; Barcelos et al., 2020) and car ownership (Gan et al., 2018),
Whether the day prior to surveying was during the week or weekend was
also included as a covariate to account for potential day-of-the week
related variations in SWB reporting (Csikszentmihalyi & Hunter, 2003).
Following White et al. (2017), the analysis also controlled for different
dimensions of SWB, which were not the dependent variable, to fully
capture the unique variance attributable to hedonic, eudaimonic and
evaluative wellbeing respectively (see section 2.6 for full details).
The covariate variables were operationalised as follows: sex (female
ˆ ref; male); age (16–29 years ˆ ref; 30–39 years; 40–49 years; 50–59
years; 60 years); longstanding illness or disability (no ˆ ref, yes);
educational level (no higher education ˆ ref; completed higher educa-
tion); perceived income (struggling ˆ ref; coping; comfortable);
employment status (employed; unemployed ˆ ref); relationship status
(single ˆ ref; married or cohabiting); number of children in household
(zero ˆ ref; 1; 2); number of adults in household (1 ˆ ref; 2; >2); dog
ownership (no ˆ ref; yes); car ownership (no ˆ ref; yes); weekly days of
physical activity 30 min (0 ˆ ref; 1–4, 5); and day of surveying
(weekday ˆ ref; weekend).
2.6. Statistical analysis
A total of 16,309 participants across eighteen countries/regions were
used in the current analysis. The original sample consisted of 18,484
participants and 2175 were removed as one or more variables (primarily
home locations using the mapping tool) were missing. Statistical ana-
lyses were carried out in Stata version 17.0 (StataCorp LLC, College
Station, TX) and visualisation in R v4.1.3 (R Core Team, 2022). A series
of linear mixed effects models were developed to quantify the rela-
tionship between proximity and exposure to green space, coastal and
freshwater blue space and each dimension of SWB. All models included
participant country/region of residence as a random intercept term to
account for possible measurement invariance with regards to SWB
outcomes (White et al., 2021).
Associations between each dimension of SWB and covariates were
reported as model coefficients with 95% confidence intervals. Exami-
nation of variance inflation factors (VIFs) suggested multicollinearity
was not present in the data and covariates were sufficiently independent
of each other. Four models were developed for each dimension of SWB
(positive and negative hedonic, eudaimonic and evaluative) to deter-
mine the unique variance applicable to each wellbeing dimension and
exposure metric. A summary of each model is provided in Table 1.
The models for positive and negative hedonic wellbeing included:
(Model 1/Model 5) residential availability of each natural environment
type; (Model 2/Model 6) residential availability and visits yesterday to
each natural environment type; (Model 3/Model 7) residential avail-
ability and visits yesterday to each natural environment type, control-
ling for all listed covariates; and (Model 4/Model 8) residential
availability and visits yesterday to each natural environment type,
controlling for all listed covariates and the alternative SWB dimension
(e.g. negative hedonic in the positive hedonic model and positive he-
donic in the negative hedonic model).
The models for eudaimonic and evaluative wellbeing included:
(Model 9/Model 13) residential availability of each natural environment
type; (Model 10/Model 14) residential availability and visit frequency to
each natural environment type; (Model 11/Model 15) residential
availability and visit frequency to each natural environment type, con-
trolling for all listed covariates; and (Model 12/Model 16) residential
availability and visit frequency to each natural environment type, con-
trolling for all listed covariates and the alternative wellbeing dimension
(e.g. eudaimonic in the evaluative model and evaluative in the eudai-
monic model).
Table 1
Summary of variables included in each model. ( ) indicates variables not
included.
Model
Name
Wellbeing
Outcome
Residential
Availability
Visits
Type
Covariates Wellbeing
Adjustment
M1 Positive
Hedonic
Yes – – –
M2 Positive
Hedonic
Yes Visit
Yesterday
– –
M3 Positive
Hedonic
Yes Visit
Yesterday
Yes –
M4 Positive
Hedonic
Yes Visit
Yesterday
Yes Negative
Hedonic
M5 Negative
Hedonic
Yes – – –
M6 Negative
Hedonic
Yes Visit
Yesterday
– –
M7 Negative
Hedonic
Yes Visit
Yesterday
Yes –
M8 Negative
Hedonic
Yes Visit
Yesterday
Yes Positive
Hedonic
M9 Eudaimonic Yes – – –
M10 Eudaimonic Yes Visits in
Previous
Week
– –
M11 Eudaimonic Yes Visits in
Previous
Week
Yes –
M12 Eudaimonic Yes Visits in
Previous
Week
Yes Evaluative
M13 Evaluative Yes – – –
M14 Evaluative Yes Visits in
Previous
Week
– –
M15 Evaluative Yes Visits in
Previous
Week
Yes –
M16 Evaluative Yes Visits in
Previous
Week
Yes Eudaimonic
C.W. McDougall et al. Journal of Environmental Psychology 100 (2024) 102479 
4 
Fully-adjusted models were then stratified by country so that the
relationships could be examined for each of the 18 countries/regions
separately. Due to substantial reductions in sample size in these models,
the four-week visit frequency categories used in the eudaimonic and
evaluative wellbeing models were collapsed into two groups: (1) zero
visits or visiting less than weekly (reference category); and (2) visiting
weekly or more than weekly. Of note, visiting nature weekly has been
used as a key threshold for health benefits in previous work (Shanahan
et al., 2016).
3. Results
3.1. Descriptive statistics
Table 2 summarises the SWB responses and socioeconomic and de-
mographic characteristics of the sample. The analytical sample was
composed of 8303 females (51%) and 8006 males (49%) and was spread
consistently across the five collected age categories (suggesting the loss
of participants to missing data was not systematic). Mean positive he-
donic wellbeing and negative hedonic wellbeing were 6.85 (SD: 2.18)
and 3.97 (SD: 2.81), respectively. Mean eudaimonic wellbeing was 7.37
(SD: 1.87) and mean evaluative wellbeing was 7.01 (SD: 1.82).
Fig. 1 shows the mean wellbeing values for each of the countries and
regions included in the analysis. Canada (7.27) exhibited the highest
mean value of positive hedonic wellbeing and Greece (5.56) exhibited
the highest mean value of negative hedonic wellbeing. The lowest mean
positive hedonic wellbeing was reported in Hong Kong (6.23) and the
lowest mean negative hedonic wellbeing was reported in the
Netherlands (2.68). The Netherlands (7.44) exhibited the highest mean
evaluative wellbeing and Portugal exhibited the highest mean eudai-
monic wellbeing (7.72). The lowest mean evaluative wellbeing was re-
ported in Greece (6.15) and the lowest mean eudaimonic wellbeing was
reported in Hong Kong (6.70).
Table 2 also summarises residential availability, visits in the last four
weeks and visits in the day prior to surveying for green space and coastal
and freshwater blue space. Approximately 11% and 38% of the sample
resided within 1,000m of coastal and freshwater blue space respectively.
Green space was the most visited natural environment category. In the
last four weeks, only 12% of the sample did not visit a green space.
Contrastingly, 48% of the sample did not visit coastal blue space in the
last four weeks and 27% did not visit freshwater blue space.
Green spaces were also visited more frequently (more than weekly)
than both types of blue space in the last four weeks. Approximately 34%
of the sample visited green spaces frequently, while coastal and fresh-
water blue space were visited frequently by 11% and 18% of the sample,
respectively. A similar trend was present for visits taken yesterday.
Around half of participants (50.25%) reported visiting a green space
yesterday compared to only 14% for coastal blue space and 22% for
freshwater blue space.
3.2. Inferential statistics
Fig. 2 displays the fully adjusted relationships between all four di-
mensions of SWB and proximity (residential availability) and exposure
(visits yesterday and in the last four weeks) to green space, coastal blue
space and freshwater blue space. Unadjusted and adjusted model results
(without results for each covariate) are displayed for positive hedonic
wellbeing (Table 3), negative hedonic wellbeing (Table 4), eudaimonic
wellbeing (Table 5) and evaluative wellbeing (Table 6). Supplementary
Tables 1–16 show the same models including results for all covariates in
full.
3.2.1. Positive hedonic wellbeing
3.2.1.1. Residential availability. Positive hedonic wellbeing models are
displayed in Table 3. No significant associations were observed between
residing in areas with higher coverage of green space or near coastal or
freshwater blue space and positive hedonic wellbeing (self-reported
feelings of happiness in the day prior to surveying) in any of the final
adjusted models.
3.2.1.2. Visits (yesterday). Mixed associations were observed between
visiting each natural environment type in the day prior to surveying and
positive hedonic wellbeing. Visiting green space in the day prior to
completing the survey was significantly associated with higher reports
Table 2
Summary statistics of subjective wellbeing (SWB), residential availability, visi-
tation and covariates.
Variable mean SD Variable n %
SWB Sex
Positive hedonic 6.85 2.18 Female 8303 50.91
Negative hedonic 3.97 3.97 Male 8006 49.09
Eudaimonic wellbeing 7.37 1.87 Disability status
Evaluative wellbeing 7.01 1.82 No 10,344 63.43
Yes (to some
extent)
5965 36.57
​ n % Physical activity
Residential availability Zero PA 4195 25.72
Green space [Q1] 4079 25.01 0-4 PA days 8846 54.24
Green space [Q2] 4077 25 5 PA days 3268 20.04
Green space [Q3] 4075 24.99 Perceived income status
Green space [Q4] 4078 25 Struggling 4091 25.08
Coastal blue space
[No]
14,513 88.99 Coping 7648 46.89
Coastal blue space
[Yes]
1796 11.01 Comfortable 4570 28.02
Freshwater blue space
[No]
10,148 62.22 Education status
Freshwater blue space
[Yes]
6161 37.78 No higher
education
7934 48.65
Visit yesterday Yes higher
education
8375 51.35
Green space [No] 8114 49.75 Unemployment status
Green space [Yes] 8195 50.25 Unemployed 1025 6.28
Coastal [No] 14,095 86.42 Not unemployed 15,284 93.72
Coastal [Yes] 2214 13.58 Relationship status
Freshwater [No] 12,740 78.12 Single 6634 40.68
Freshwater [Yes] 3569 21.88 Married or
cohabiting
9675 59.32
Visits in last four weeks Children in household
Green space [zero] 1913 11.73 0 11,704 72
Green space [less than
weekly]
4673 28.65 1 2496 15
Green space [weekly] 4127 25.31 2 2109 12.93
Green space [more
than weekly]
5596 34.31 Adults in household
Coastal [zero] 7851 48.14 1 3262 20
Coastal [less than
weekly]
4757 29.17 2 5577 34.2
Coastal [weekly] 1834 11.25 >2 7470 45.8
Coastal [more than
weekly]
1867 11.45 Dog ownership
Freshwater [zero] 4423 27.12 No 11,310 69.35
Freshwater [less than
weekly]
5749 35.25 Yes 4999 30.65
Freshwater [weekly] 3168 19.42 Car ownership
Freshwater [more
than weekly]
2969 18.2 No 2846 17.45
Age Yes 13,463 82.55
18–29 3088 18.93 Day prior to surveying
30–39 2997 18.38 Weekday 13,252 81.26
40–49 3045 18.67 Weekend 3057 18.74
50–59 2959 18.14 ​ ​ ​
60‡ 4220 25.88 ​ ​ ​
C.W. McDougall et al. Journal of Environmental Psychology 100 (2024) 102479 
5 
Fig. 1. Mean values of each SWB dimension for each country/region included in the analysis.
Fig. 2. Summary of fully adjusted models of all dimensions of SWB and residential availability and visits to green space, coastal blue space and freshwater blue space.
(For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
C.W. McDougall et al. Journal of Environmental Psychology 100 (2024) 102479 
6 
of positive hedonic wellbeing (∠0.42, 95% CIˆ 0.35–0.48), relative to
not visiting green space in the day prior. Visiting coastal blue space
yesterday was also associated with reporting higher positive hedonic
wellbeing yesterday, compared to not visiting (⠈ 0.25, 95% CI ˆ
0.16–0.34). Visiting freshwater blue space in the day prior was not
significantly associated with positive hedonic wellbeing.
3.2.2. Negative hedonic wellbeing
3.2.2.1. Residential availability. Negative hedonic wellbeing models are
displayed in Table 4. Residing in areas withmore green space (Quartile 3
⠈  0.13, 95% CI ˆ  0.24 to  0.01) was significantly associated with
lower self-reported negative hedonic wellbeing (feelings of anxiety) in
the day prior to surveying compared to areas with lower levels of green
space (Quartile 1). Residing in close proximity to freshwater blue space
was also significantly associated with lower self-reported feelings of
anxiety in the day prior to surveying (β  0.09, 95% CI ˆ  0.17 - 0.00).
3.2.2.2. Visits (yesterday). Visiting green space (⠈ 0.31, 95% CI ˆ
0.23 0.40) and coastal blue space (⠈ 0.14, 95% CI ˆ 0.02–0.26) in the
day prior to completing the survey were both significantly associated
with reporting greater feelings of anxiety (negative hedonic wellbeing).
Visiting freshwater blue space in the day prior to surveying was not
significantly associated with negative hedonic wellbeing.
3.2.3. Eudaimonic wellbeing
3.2.3.1. Residential availability. Eudamonic wellbeing models are dis-
played in Table 5. No significant associations were identified between
living in areas with high green space or living in close proximity to
coastal or freshwater blue space and eudaimonic wellbeing.
3.2.3.2. Visits (last four weeks). Each category of green space visits in
the last four weeks was significantly associated with higher reports of
eudaimonic wellbeing relative to zero visits. Visiting green spaces more
than weekly (⠈ 0.28, 95% CI ˆ 0.19–0.37) had a larger coefficient
value than visiting weekly (⠈ 0.16, 95% CI ˆ 0.07–0.25) and less than
weekly (⠈ 0.16, 95% CI ˆ 0.08–0.25). Although visiting coastal blue
space in the last four weeks was significantly associated with higher
reports of eudaimonic wellbeing in unadjusted models (M10 and M11),
no significant association was observed for any frequency of coastal blue
space visits in the last four weeks and eudaimonic wellbeing once con-
trols for evaluative wellbeing were included (M4). Visiting freshwater
blue space weekly (⠈ 0.08, 95% CI ˆ 0.00–0.15) and more than
Table 3
Models of residential availability and visits ‘yesterday’ to green space, coastal and freshwater blue space and positive hedonic wellbeing displayed as regression
coefficients (β) with Upper (U) and Lower (L) 95% Confidence Intervals (CI) and Intraclass Correlation Coefficient (ICC). ***p < 0.001, **p.0.01, *p < 0.05.
Positive hedonic wellbeing
(happiness yesterday)
M1 M2 M3 M4
β U. 95%
CI
L. 95%
CI
β U. 95%
CI
L. 95%
CI
В U. 95%
CI
L. 95%
CI
β U. 95%
CI
L. 95%
CI
Green space within 1000m
Q1 REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Q2 0.07  0.02 0.17 0.07  0.03 0.16 0.06  0.03 0.15 0.04  0.05 0.12
Q3 0.07  0.03 0.16 0.03  0.07 0.12 0.00  0.09 0.09  0.03  0.11 0.06
Q4 0.14*** 0.04 0.24 0.07  0.02 0.17 0.02  0.07 0.11 0.01  0.08 0.10
Coast within 1000m
No REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Yes 0.15** 0.03 0.26 0.09  0.02 0.21 0.05  0.06 0.16 0.04  0.06 0.14
Freshwater within 1000m
No REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Some 0.00  0.08 0.07  0.02  0.09 0.06 0.01  0.06 0.08  0.01  0.08 0.05
Green space yesterday
No REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Yes NA ​ ​ 0.53*** 0.46 0.61 0.38*** 0.31 0.45 0.42*** 0.35 0.48
Coastal yesterday
No REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Yes NA ​ ​ 0.32*** 0.22 0.43 0.24*** 0.14 0.33 0.25*** 0.16 0.34
Freshwater yesterday
No REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Yes NA ​ ​ 0.09* 0.00 0.17 0.07  0.01 0.15 0.07*  0.01 0.15
Covariates NA ​ ​ NA ​ ​ Yes ​ ​ Yes ​ ​
Negative hedonic wellbeing
(anxiety yesterday)
NA ​ ​ NA ​ ​ NA ​ ​  0.22***  0.24  0.21
Constant 6.76*** 6.60 6.91 6.46*** 6.29 6.63 5.00*** 4.73 5.12 6.06*** 5.84 6.28
R2 0.0006 ​ ​ 0.0166 ​ ​ 0.1547 ​ ​ 0.2262 ​ ​
ICC 0.01 ​ ​ 0.04 ​ ​ 0.02 ​ ​ 0.02 ​ ​
C.W. McDougall et al. Journal of Environmental Psychology 100 (2024) 102479 
7 
weekly (⠈ 0.10, 95% CI ˆ 0.02–0.18) were both significantly associ-
ated with reports of higher eudaimonic wellbeing.
3.2.4. Evaluative wellbeing
3.2.4.1. Residential availability. Evaluative wellbeing models are dis-
played in Table 6. Residing in areas with high green space coverage
(Quartile 4) was significantly associated with higher reports of evalua-
tive wellbeing (⠈ 0.09, 95% CI ˆ 0.03–0.15), relative to residing in
areas with low green space coverage (Quartile 1). No significant dif-
ferences in evaluative wellbeing were observed between areas of low
(Quartile 1) and moderate (Quartile 2 and Quartile 3) green space
coverage. Similarly, no significant associations were observed between
residing in close proximity to coastal nor freshwater blue space and
evaluative wellbeing.
3.2.4.2. Visits (last four weeks). Visiting any type of natural environ-
ment in the last four weeks was associated with reporting higher eval-
uative wellbeing. For each natural environment type, larger coefficient
values were apparent for higher visit frequencies. Frequently visiting
green space (⠈ 0.17, 95% CI ˆ 0.09–0.25), coastal blue space (⠈
0.29, 95% CI ˆ 0.21–0.36) and freshwater blue space (⠈ 0.08, 95% CI
ˆ 0.01–0.15) were all significantly associated with higher reports of
evaluative wellbeing compared to not visiting at all in the last four
weeks.
A reduction in coefficient values was observed in the fully adjusted
model, which included controls for eudaimonic wellbeing (M16). For
example, the association between evaluative wellbeing and frequent
coastal blue space visits was greater without a eudaimonic control (⠈
0.44, 95% CI ˆ 0.36–0.54) (M15), relative to the final model which
included a eudaimonic control (⠈ 0.29, 95% CI ˆ 0.21–0.36) (M16).
3.3. Country stratification
Fig. 3 displays the fully adjusted relationships between all four di-
mensions of SWB and exposure (visits yesterday for hedonic wellbeing
and in the last four weeks for evaluative and eudaimonic wellbeing) to
green space, coastal blue space and freshwater blue space for each of the
18 countries/regions included in the analysis.
A significant positive association (minimum p < 0.05) was observed
between visiting green space yesterday and positive hedonic wellbeing
in 13/18 countries. Only Italy and France reported a significant positive
association between positive hedonic wellbeing and visiting the coast
yesterday. Italy was the only country where a significant association was
reported for visiting freshwater in the day prior to surveying and posi-
tive hedonic wellbeing.
Significant positive associations (minimum p < 0.05) were observed
Table 4
Models of residential availability and visits ‘yesterday’ to green space, coastal and freshwater blue space and negative hedonic wellbeing displayed as regression
coefficients (β) with Upper (U) and Lower (L) 95% Confidence Intervals (CI) and Intraclass Correlation Coefficient (ICC). ***p < 0.001, **p.0.01, *p < 0.05.
Negative hedonic wellbeing
(anxiety yesterday)
M5 M6 M7 M8
β U. 95%
CI
L. 95%
CI
β U. 95%
CI
L. 95%
CI
β U. 95%
CI
L. 95%
CI
β U. 95%
CI
L. 95%
CI
Green space within 1000m
Q1 REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Q2  0.11  0.23 0.01  0.11  0.23 0.01  0.10  0.21 0.02  0.07  0.18 0.04
Q3 ¡0.15*  0.27  0.03 ¡0.16**  0.28  0.04 ¡0.13*  0.24  0.01 ¡0.13*  0.24  0.01
Q4  0.12  0.24 0.00 ¡0.13*  0.26  0.01  0.03  0.15 0.09  0.02  0.14 0.09
Coast within 1000m
No REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Yes  0.13  0.28 0.01  0.12  0.27 0.02  0.05  0.19 0.09  0.03  0.16 0.10
Freshwater within 1000m
No REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Some  0.06  0.16 0.03  0.07  0.16 0.03 ¡0.09*  0.18 0.00 ¡0.09*  0.17 0.00
Green space yesterday
No REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Yes NA ​ ​ 0.16*** 0.07 0.25 0.16*** 0.07 0.25 0.31*** 0.23 0.40
Coastal yesterday
No REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Yes NA ​ ​  0.03  0.17 0.10 0.04  0.08 0.17 0.14* 0.02 0.26
Freshwater yesterday
No REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Yes NA ​ ​  0.01  0.12 0.09 0.01  0.09 0.11 0.04  0.06 0.14
Covariates NA ​ ​ NA ​ ​ Yes ​ ​ Yes ​ ​
Positive hedonic wellbeing
(happiness yesterday)
NA ​ ​ NA ​ ​ NA ​ ​ ¡0.39***  0.41  0.37
Constant 4.16*** 3.83 4.49 4.09*** 3.76 4.42 4.92*** 4.53 5.31 6.84*** 6.45 7.24
R2 0.0061 ​ ​ 0.0081 ​ ​ 0.0899 ​ ​ 0.1668 ​ ​
ICC 0.06 ​ ​ 0.06 ​ ​ 0.06 ​ ​ 0.06 ​ ​
C.W. McDougall et al. Journal of Environmental Psychology 100 (2024) 102479 
8 
between visiting green space and negative hedonic wellbeing (greater
feelings of anxiety) in half (9/18) of the studied countries. Queensland,
Australia was the only location where coastal visitation yesterday was
significantly negatively associated with negative hedonic wellbeing
(reduced feelings of anxiety). Significant associations were reported
between visiting the coast yesterday and greater feelings of anxiety
(negative hedonic wellbeing) in Canada and France. California was the
only location where significant association was reported for visiting
freshwater in the day prior to surveying and negative hedonic wellbeing.
Significant associations between at least weekly visits to green space
in the last four weeks and eudaimonic wellbeing were observed in three
countries (Bulgaria, Germany and Hong Kong) and between at least
weekly visits to freshwater blue space and eudaimonic wellbeing in
three countries (Czechia, Italy, and Portugal). No significant associa-
tions were found between visiting the coast in the last four weeks and
eudaimonic wellbeing in any country.
Half (9/18) countries/regions showed significant positive associa-
tions between evaluative wellbeing and visits to at least one type of
natural environment in the last four weeks. Significant positive associ-
ations were observed between evaluative wellbeing and at least weekly
visits to green space (Italy, California, Portugal, Queensland), coastal
blue space (France, Ireland, Italy, the Netherlands, Queensland and
Spain) and freshwater blue space (Canada and Italy).
4. Discussion
This study aimed to quantify the relationship between proximity and
exposure to green space, coastal and freshwater blue space and hedonic,
eudaimonic, and evaluative dimensions of SWB across 18 countries.
Collectively, our findings align with previous suggestions of a positive
and multidimensional relationship between spending time in and
around natural environments and SWB (Fisher et al., 2021; Huerta &
Utomo, 2021; Mavoa et al., 2019; McDougall et al., 2022; Sharifi et al.,
2021; White et al., 2017; Wood et al., 2017). However, our results also
indicate that these relationships can differ by: (1) natural environment
type; (2) dimension of SWB; and (3) country/region.
Table 5
Models of residential availability and visit frequency to green space, coastal and freshwater blue space and eudaimonic wellbeing displayed as regression coefficients
(β) with Upper (U) and Lower (L) 95% Confidence Intervals (CI) and Intraclass Correlation Coefficient (ICC). ***p < 0.001, **p.0.01, *p < 0.05.
Eudaimonic
wellbeing
M9 M10 M11 M12
В U. 95% CI L. 95%
CI
β U. 95% CI L. 95% CI β U. 95% CI L. 95%
CI
β U. 95% CI L. 95%
CI
GS within 1000m
Q1 REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Q2 0.03  0.05 0.11 0.01  0.07 0.09 0.01  0.07 0.08  0.01  0.08 0.05
Q3 0.07  0.01 0.15 0.03  0.05 0.11  0.01  0.09 0.07  0.04  0.10 0.03
Q4 0.17*** 0.09 0.26 0.10 0.02* 0.19 0.03  0.04 0.11  0.04  0.10 0.03
Coast within 1000m
No REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Yes 0.16*** 0.06 0.26 0.05  0.05 0.15 0.04  0.05 0.13 0.06  0.02 0.14
Freshwater within 1000m
No REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Some  0.06  0.12 0.01 ¡0.07*  0.14  0.01  0.05  0.10 0.01  0.04  0.09 0.01
Green space visits
Zero visits REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Less than weekly NA ​ ​ 0.41*** 0.30 0.51 0.30*** 0.20 0.40 0.16*** 0.08 0.25
Weekly NA ​ ​ 0.55*** 0.44 0.66 0.36*** 0.25 0.46 0.16*** 0.07 0.25
More than weekly NA ​ ​ 0.80*** 0.69 0.91 0.57*** 0.46 0.67 0.28*** 0.19 0.37
Coastal visits
Zero visits REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Less than weekly NA ​ ​ 0.17*** 0.10 0.24 0.10*** 0.04 0.17  0.01  0.06 0.05
Weekly NA ​ ​ 0.16*** 0.06 0.26 0.15*** 0.05 0.24  0.02  0.10 0.06
More than weekly NA ​ ​ 0.42*** 0.31 0.52 0.31*** 0.21 0.41 0.04  0.05 0.12
Freshwater visits
Zero visits REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Less than weekly NA ​ ​ 0.23*** 0.15 0.30 0.15*** 0.07 0.22 0.06 0.00 0.12
Weekly NA ​ ​ 0.25*** 0.16 0.34 0.19*** 0.10 0.28 0.08* 0.00 0.15
More than weekly NA ​ ​ 0.27*** 0.17 0.37 0.22*** 0.13 0.32 0.10** 0.02 0.18
Covariates NA ​ ​ NA ​ ​ Yes ​ ​ Yes NA ​
Evaluative wellbeing NA ​ ​ NA ​ ​ NA ​ ​ 0.62*** 0.61 0.63
Constant 7.29*** 7.17 7.42 6.52*** 6.37 6.67 5.30*** 5.11 5.49 2.31*** 2.14 2.48
R2 0.0007 ​ ​ 0.0379 ​ ​ 0.1494 ​ ​ 0.4236 ​ ​
ICC 0.02 ​ ​ 0.02 ​ ​ 0.01 ​ ​ 0.01 ​ ​
C.W. McDougall et al. Journal of Environmental Psychology 100 (2024) 102479 
9 
4.1. Residential availability and SWB
Residing in close proximity to natural environments had limited
associations with hedonic and eudaimonic wellbeing; and a significant
association with evaluative wellbeing, in the case of individuals living in
the greenest areas. Our findings, support the notion that simply residing
in close proximity to natural environments is less important than contact
with or visits to natural environments when considering pathways be-
tween nature and hedonic, eudaimonic, and evaluative wellbeing. These
findings were somewhat anticipated as both proximity and access to
blue and green space are important determinants of visits (Ekkel & de
Vries, 2017) and because usage decreases with increasing distance
(Coombes et al., 2010). Previous studies have also shown visits to green
space, coastal and freshwater blue space to be more associated with SWB
than other metrics such as accessibility or residential availability (Elliott
et al., 2023; Geiger et al., 2023; McDougall et al., 2022).
In our data, eudaimonic wellbeing was unrelated to green space
quantities around the residence and proximity to coastal or freshwater
blue space. This largely replicates White et al. (2017) who only found an
association for the highest (vs. lowest) quantities of green space and also
found no association between proximity to coastal blue space and
eudaimonic wellbeing. The mere availability of nature is unlikely to
stimulate a eudaimonic response given the eudaimonic survey question
concerns ‘activities’ of daily life, and should therefore theoretically be
better determined by behaviours and actions, rather than one’s
surroundings.
Residing in highly green areas (Quartile 4) was positively associated
with evaluative wellbeing, even when a control for visits was included.
Residing in areas with higher coverage of green space surrounding the
residence has been associated with greater evaluative wellbeing in both
cross-sectional (Houlden et al., 2019) and longitudinal studies (White
et al., 2013). Residential green space availability may be more important
for evaluative wellbeing compared to hedonic and eudaimonic well-
being, because the presence of neighbourhood green space can influence
neighbourhood satisfaction and overall life satisfaction (Houlden et al.,
2019). Residing in highly green areas may also increase the likelihood of
indirect (e.g. via household views) or incidental (passing by) exposure to
green space (Garrett et al., 2019) and these exposures may not be
Table 6
Models of residential availability and visit frequency to green space, coastal and freshwater blue space and evaluative wellbeing displayed as regression coefficients (β)
with Upper (U) and Lower (L) 95% Confidence Intervals (CI) and Intraclass Correlation Coefficient (ICC). ***p < 0.001, **p.0.01, *p < 0.05.
M13 M14 M15 M16
В U. 95% CI L. 95%
CI
β U. 95% CI L. 95%
CI
β U. 95% CI L. 95%
CI
β U. 95% CI L. 95%
CI
GS within 1000m
Q1 REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Q2 0.04  0.04 0.12 0.03  0.05 0.10 0.03  0.04 0.10 0.03  0.03 0.09
Q3 0.10* 0.02 0.18 0.05  0.02 0.13 0.04  0.03 0.12 0.05  0.01 0.11
Q4 0.21*** 0.13 0.29 0.14*** 0.06 0.22 0.11*** 0.04 0.18 0.09*** 0.03 0.15
Coast within 1000m
No REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Yes 0.13** 0.04 0.23  0.02  0.12 0.07  0.02  0.11 0.06  0.05  0.12 0.02
Freshwater within 1000m
No REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Some  0.03  0.09 0.03  0.05  0.11 0.01  0.02  0.07 0.04 0.01  0.04 0.05
​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​
Green space visits
Zero visits REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Less than weekly NA ​ ​ 0.35*** 0.25 0.45 0.21*** 0.12 0.31 0.06  0.01 0.13
Weekly NA ​ ​ 0.55*** 0.44 0.65 0.32*** 0.23 0.42 0.14*** 0.06 0.22
More than weekly NA ​ ​ 0.72*** 0.61 0.82 0.46*** 0.37 0.56 0.17*** 0.09 0.25
Coastal visits
Zero visits REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Less than weekly NA ​ ​ 0.27*** 0.21 0.34 0.18*** 0.12 0.24 0.13*** 0.08 0.18
Weekly NA ​ ​ 0.32*** 0.23 0.42 0.27*** 0.18 0.36 0.19*** 0.12 0.27
More than weekly NA ​ ​ 0.60*** 0.49 0.70 0.45*** 0.36 0.54 0.29*** 0.21 0.36
Freshwater visits
Zero visits REF ​ ​ REF ​ ​ REF ​ ​ REF ​ ​
Less than weekly NA ​ ​ 0.26*** 0.19 0.34 0.14*** 0.07 0.21 0.07* 0.01 0.12
Weekly NA ​ ​ 0.30*** 0.21 0.39 0.19*** 0.11 0.27 0.09** 0.02 0.16
More than weekly NA ​ ​ 0.28*** 0.19 0.38 0.19*** 0.11 0.28 0.08* 0.01 0.15
Covariates NA ​ ​ NA ​ ​ Yes ​ ​ Yes ​ ​
Eudaimonic
wellbeing
NA ​ ​ NA ​ ​ NA ​ ​ 0.52*** 0.51 0.53
Constant 6.90*** 6.74 7.07 6.08*** 5.89 6.28 4.83*** 4.63 5.03 2.08*** 1.91 2.25
R2 0.0021 ​ ​ 0.0423 ​ ​ 0.2371 ​ ​ 0.4830 ​ ​
ICC 0.03 ​ ​ 0.04 ​ ​ 0.03 ​ ​ 0.03 ​ ​
C.W. McDougall et al. Journal of Environmental Psychology 100 (2024) 102479 
10 
captured in our visit metrics. Furthermore, high quantities of green
space can provide wellbeing benefits by improving environmental
conditions which do not require visits e.g. urban noise masking
(Axelsson et al., 2014), improved thermal comfort (Sun et al., 2017) and
air quality (Moradpour & Hosseini, 2020), which have been associated
with SWB ().
When a control for visit frequency was added to our models, residing
in close proximity to freshwater or coastal blue space was not associated
with greater evaluative wellbeing. It should be noted that our blue space
measures only accounted for the presence or absence of freshwater or
coastal blue space within 1,000m of a participant’s household; and were
unable to capture the quality or quantity of nearby blue space in the
area, which may be more strongly associated with wellbeing than the
presence or absence of blue space alone (McDougall et al., 2022).
4.2. Visits and hedonic wellbeing
Visits to natural environments were associated with improved SWB,
although these associations varied depending on the dimension of SWB
considered. Visiting green space and coastal blue space yesterday were
significantly associated with improved positive hedonic wellbeing
yesterday. These relationships remained once negative hedonic well-
being was controlled for, suggesting the observed hedonic benefits were
independent of any negative hedonic effects. This finding supports key
theoretical debates surrounding the connection between positive and
negative hedonic wellbeing by demonstrating that hedonic dimensions
of wellbeing are not merely two ends of a spectrum as sometimes
assumed (McMahan & Estes, 2015).
Our findings align with various studies that show people are happier
when visiting green and blue spaces (de Vries et al., 2021; MacKerron &
Mourato, 2013) and recall being happier even a day after visiting these
environments (Garrett et al., 2023). Research conducted by de Vries
et al. (2021) found people were happier when in or around freshwater
blue space, relative to other environment types. However, our results
extend these findings as visits to freshwater blue space specifically, were
not significantly associated with positive hedonic wellbeing in our
multi-country models; and a significant positive association was only
observed in 1/18 countries (Italy) in our country-stratified analysis.
Momentary evaluations of happiness, as adopted by de Vries et al.
(2021), are likely to be more sensitive to changes in happiness than the
recalled happiness in the previous day approach adopted in our study
and this may partly explain differences in our findings.
Interestingly, our findings also show an association between visiting
green space and coastal blue space and greater feelings of anxiety
(negative hedonic wellbeing). Although our data do not allow detailed
analysis explaining this association, a number of possibilities exist.
Firstly, the association between visiting green and coastal blue space and
negative hedonic wellbeing may represent causal direction ambiguity
and reflect a ‘self-medicating’ effect whereby individuals choose, or are
recommended to, visit these environments to improve their wellbeing
(Tester-Jones et al., 2020). Prescribed or self-prescribed visits are likely
to increase as nature-based prescriptions become more popular (Kondo
et al., 2020) and awareness of the health benefits of spending time in and
around nature increases. Qualitative research often depicts nature visits
for self-medicating purposes whereby individuals suffering from stress,
physical discomfort, or mental ill-health are drawn to the restorative
benefits of nature or particular green and blue spaces associated with
healing (Bell et al., 2015; Korpela et al., 2010).
Secondly, these findings may be indicative of negative experiences of
blue and green space within the study population. Large scale epide-
miological studies often focus primarily on the net population health
impact of exposure to nature; however, this may oversimplify the rela-
tionship between nature and SWB and fail to account for nuances of the
individual experience. Consequently, negative experiences in nature,
Fig. 3. Summary of fully adjusted models of all dimensions of SWB and visits to green space, coastal blue space and freshwater blue space stratified for each of the 18
countries/regions included in the analysis. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
C.W. McDougall et al. Journal of Environmental Psychology 100 (2024) 102479 
11 
which have been demonstrated in poor quality environments and for
particular minority groups, may be overlooked (Phoenix et al., 2021;
Milligan & Bingley, 2007). By controlling for positive hedonic well-
being, our analysis may be more suited to capturing negative hedonic
effects, which could be overlooked in study designs which do not include
these controls.
Thirdly, it is plausible that the relationship between visits to nature
and hedonic wellbeing is complex and can induce both positive and
negative hedonic experiences within a single visit. Such complexity is
often described in the experiences of adventurous engagement with
nature such as surfing and open water swimming, whereby the activity
provides both happiness and anxiety from risk (Costello et al., 2019).
Despite only being able to speculate as to why visitation to natural
environments can be associated with negative hedonic wellbeing, re-
searchers and policymakers should be aware of these potentially nega-
tive impacts. This issue is particularly important in the development of
nature-based wellbeing interventions or nature-based prescriptions
aimed at improving wellbeing among groups who may be particularly
vulnerable to negative hedonic experiences. Indeed, our findings rein-
force the value of considering positive and negative hedonic wellbeing
independently rather than merely as two ends of a single spectrum in
nature-wellbeing research (McMahan & Estes, 2015).
4.3. Visits and eudaimonic and evaluative wellbeing
Visiting both green space and freshwater blue space in the last four
weeks was associated with greater eudaimonic wellbeing, even after
controlling for evaluative wellbeing. Previous studies of green space and
wellbeing often fail to consider eudaimonic dimensions of wellbeing
(Houlden et al., 2018); and the eudaimonic wellbeing potential of blue
space has also been largely ignored in previous research. Qualitative
research suggests eudaimonic wellbeing is promoted in natural envi-
ronments, more generally, by increasing connections to one’s self (e.g.
cultivating a sense of belonging and developing self-discovery), or
increasing connections to others, which can stimulate feelings of pur-
pose (Saint-Onge et al., 2022). A recent meta-analysis suggests greater
feelings of nature connection could partly explain improved eudaimonic
wellbeing as a result of visiting nature (Pritchard et al., 2020).
Visiting the coast was not associated with improved eudaimonic
wellbeing in the overall or any of the stratified country-specific models,
unlike other natural environments. Vert et al. (2020) did not find any
significant differences in eudaimonic wellbeing after undertaking 20
min of walking along a coastal blue space route, on 4 days each week, for
3 weeks. Garrett et al. (2023) also found visits to a range of coastal blue
space categories (e.g. open seas, harbours or marinas) were not associ-
ated with eudaimonic wellbeing.
People are often willing to travel further to visit coastal blue spaces
than other natural environment types (Wilczynska et al., 2023). Liter-
ature on coastal engagement and interactions often depict a deep and
unique relationship between humans and coastal environments (Bell
et al., 2015; Severin et al., 2022). Coastal visits are often passive and
motivated by relaxation, whereas evidence from the UK suggests
freshwater blue space visits are more likely to be purposeful, i.e. to
attain health benefits (Elliott et al., 2018). This may partly explain why
only associations between costal visits and evaluative wellbeing (and
not eudaimonic wellbeing) were observed in our data. Indeed, people
tend to report being happier, in and around coastal rather than fresh-
water blue space (MacKerron and Mourato, 2013) and passive activities
or soft leisure in blue space may be less likely to produce eudaimonic
wellbeing benefits than other activities (Prochniak & Prochniak, 2023).
Although, our data only allow speculation on the relationship be-
tween coastal blue space and eudaimonic wellbeing, a unique relation-
ship is apparent relative to other natural environments and this may be
at least partly due to the types of visits that occur at the coast (e.g.
Garrett et al., 2023; Wilczynska et al., 2023). Understanding these
differing wellbeing impacts is important, particularly as nature-based
health interventions often rely on eudaimonic wellbeing-promotion (e.
g. by promoting feelings of purpose or promoting vitality) as mecha-
nisms for wider health and wellbeing benefits (Coventry et al., 2021;
Ryan et al., 2010).
Visiting all types of natural environment in the last four weeks was
associated with greater evaluative wellbeing (or life satisfaction) as has
been demonstrated in a range of other studies (Jiang & Huang, 2022;
Mavoa et al., 2019). Advancing previous research, our findings addi-
tionally show that such an association is not driven by an overlap (or
shared variance) with eudaimonic wellbeing. Generally, associations
with evaluative wellbeing were larger with higher visit frequency,
suggesting spending more time in nature can provide greater wellbeing
benefits (White et al., 2019; McDougall et al., 2022).
Evaluative wellbeing benefits were somewhat expected given the
hedonic benefits associated with individual visits, which are likely to
cumulative contribute to a wider life satisfaction over a several week
period (White et al., 2017), particularly if visitation occurs frequently.
Indeed, Foley (2017) suggests an accretive process whereby visits to
natural environments can progressively provide benefits and contribute
to a growing wellbeing over time.
4.4. Country stratification
The random intercept in our non-stratified model and country-
stratification indicate heterogeneity between countries when consid-
ering associations between visiting natural environments and different
dimensions of wellbeing. Our data may not be particularly well suited
for identifying particular country trends as stratification by country,
natural environment type and wellbeing dimension reduced statistical
power considerably. However, some tentative conclusions can be
drawn.
When all wellbeing dimensions were considered, at least one sig-
nificant positive association between one type of natural environment
visit frequency and one dimension of wellbeing was identified for 15/18
countries, despite the substantively reduced power. Generally, associa-
tions between improved wellbeing and coastal visit frequency appear
greater in countries and regions with warmer climates and strong cul-
tures of coastal engagement, such as Queensland, Italy, and Spain. This
was also suggested in a previous study, which indicated that Mediter-
ranean countries may benefit particularly from coastal blue space
(White et al., 2021).
Only Queensland, Australia, was associated with reductions in feel-
ings of anxiety (negative hedonic wellbeing) as a result of visiting
coastal blue space. This is somewhat surprising given the wide publicity
and public health warnings on the risks of beach visits in Australia – e.g.
sunburn, shark attacks, and riptides (Costello et al., 2019). Perhaps it is
due to the fact that the marine environment is a much more familiar
feature of Australian life, not least because much of the population live
relatively close to the coast (Maguire et al., 2011).
The wellbeing benefits of visiting green space appeared to be more
homogenous than those of freshwater and coastal blue space. These
findings may point to climate and cultural differences playing a greater
role in the facilitation of wellbeing benefits via blue space than green
space, potentially as climate and weather conditions can have major
impacts on the suitability of freshwater and coastal environments for
recreation and the ecosystem services these environments provide
(Elliott et al., 2019). Our study, therefore, reinforces the need for wider
considerations of climates and cultures in future blue space and health
research.
4.5. Strengths, limitations, and future research
Our study design and data offer a number of strengths allowing it to
overcome common limitations of nature-wellbeing research and address
a number of key gaps in the current literature. Our sample (>16,000
individuals across 18 countries) provided a number of benefits including
C.W. McDougall et al. Journal of Environmental Psychology 100 (2024) 102479 
12 
cultural/geographic diversity (Gascon et al., 2017). A key strength of the
multi-country approach adopted in our study was the capability to
establish which patterns generalise across countries and which patterns
do not, something that can help inform efforts to promote nature contact
more broadly.
By considering green space and freshwater and coastal blue space
independently, our findings deepen our understanding of how different
elements of nature may contribute to wellbeing (Frumkin et al., 2017).
By considering hedonic, eudaimonic, and evaluative wellbeing, our
approaches allowed key theoretical debates regarding different di-
mensions of SWB to be considered and accounted for in our statistical
analysis. Our findings, therefore, allowed us to distinguish the unique
variance applicable to each wellbeing concept and exposure metric,
which has been deemed an important step for policy and research
(White and Dolan, 2009; Fisher et al., 2021). The adoption of the
ONS/OECD wellbeing indicators means our results are highly compa-
rable and aligned with a key set of global policy tools.
Our study also has a number of limitations, offering valuable op-
portunities to build upon the research outlined here. The study design
was cross sectional and our ability to determine causation or understand
temporal effects of nature visits on wellbeing is therefore limited. Lon-
gitudinal studies offer an opportunity to draw causal inference more
comprehensively and funding to conduct these kinds of more resource
demanding studies should be encouraged (Geary et al., 2023).
Our analysis was also unable to account for variation in green and
blue space quality, which is likely to play an important role in for health
and wellbeing benefits (Nguyen et al., 2021). Although a range of green
and blue space quality indicators exist, there is a dearth of suitable in-
dicators to apply at national (or multi-national) scale as necessary for
this research (McDougall et al., 2020).
Although our study was able to advance current understanding of the
impact of green and blue space visits on different dimensions of well-
being, our analysis was unable to explore the mechanisms by which
contact with nature could come to impact SWB as such analysis would
require a more thorough understanding of the motivations behind such
visits and their characteristics, which exceeded the scope of this study.
Mediation analysis, including for example variables related to motiva-
tion or characteristics, seeking to understand the relationship between
hedonic, eudaimonic and evaluative wellbeing in the context of blue and
green space exposure offers opportunities to advance current under-
standing considerably.
Our data may also be subject to a range of reporting and memory
biases. For example, following global practice for over a decade (OECD,
2013; www.oecdbetterlifeindex.org) our four measures of SWB were
single items and thus potentially subject to mono-operation bias. Given
the size of the survey this was required to enable sufficient topics to be
included, but future studies could use more complex multi-item mea-
sures of each SWB sub-dimension. Further, self-reported visit frequency
may be subject to recall biases or errors, and responses to wellbeing or
covariates may be subject to social desirability bias. Realistically col-
lecting more objective data from such large samples across multiple
countries will remain a challenge. Although modern tracking technol-
ogies may begin to be able to overcome some of these issues, whether or
not this is acceptable to participants, and is feasible for large samples in
multiple locations, remains an open question.
5. Conclusion
This study estimated the relationships between proximity and visit-
related exposure to green space, coastal and freshwater blue space and
hedonic, eudaimonic and evaluative dimensions of SWB across 18
countries, whilst controlling for a range of socioeconomic and de-
mographic variables. The findings suggest visiting green space and
coastal blue space was associated with both positive and negative he-
donic wellbeing outcomes. Frequently visiting green space and fresh-
water blue space, but not coastal, was associated with greater
eudaimonic wellbeing and frequently visiting green space, coastal and
freshwater blue space was associated with greater evaluative wellbeing.
Though likely underpowered due to stratification, our results allow
us to begin to understand the generalisability of some of these associa-
tions across countries. Our results contribute to a growing body of
literature that suggests spending time in and around nature can provide
SWB benefits. Importantly, our data suggest that different types of nat-
ural environment may – but not always – vary in their associations with
different dimensions of SWB. Understanding these potential differences
and potentially negative wellbeing outcomes is an important consider-
ation for future research; and is a prerequisite for evidence-based public
health and environmental policy making to maximise the wellbeing-
promoting potential of different natural environments.
CRediT authorship contribution statement
Craig W. McDougall:Writing – review & editing, Writing – original
draft, Methodology, Investigation, Formal analysis, Conceptualization.
Lewis R. Elliott: Writing – review & editing, Writing – original draft,
Visualization, Validation, Supervision, Software, Investigation, Formal
analysis, Conceptualization. Mathew P. White: Writing – review &
editing, Writing – original draft, Visualization, Validation, Supervision,
Software, Resources, Methodology, Investigation, Funding acquisition,
Data curation, Conceptualization. James Grellier: Writing – review &
editing, Funding acquisition, Data curation, Conceptualization. Simon
Bell: Writing – review & editing, Investigation, Funding acquisition,
Conceptualization. Gregory N. Bratman: Writing – review & editing,
Validation, Supervision, Investigation, Funding acquisition, Data cura-
tion, Conceptualization. Mark Nieuwenhuijsen: Writing – review &
editing, Validation, Supervision, Funding acquisition, Conceptualiza-
tion. Maria L. Lima: Writing – review & editing, Validation, Supervi-
sion, Funding acquisition, Data curation, Conceptualization. Ann Ojala:
Writing – review & editing, Validation, Supervision, Funding acquisi-
tion.Marta Cirach:Writing – review& editing, Validation, Supervision,
Funding acquisition. Anne Roiko: Writing – review & editing, Valida-
tion, Supervision, Funding acquisition.Matilda van den Bosch:Writing
– review & editing, Validation, Supervision, Funding acquisition. Lora
E. Fleming: Writing – review & editing, Validation, Supervision,
Funding acquisition.
Acknowledgments
We thank Ben Butler, Gavin Ellison, and Tom Powell at YouGov for
managing the data collection pertaining to this study. This project
received support from the European Union’s Horizon 2020 research and
innovation programme under grant agreement No. 666773 (Blue-
Health). MvdB and MN acknowledge support from the grant CEX2018-
000806-S funded by MCIN/AEI/10.13039/501100011033, and support
from the Generalitat de Catalunya through the CERCA Program.The
funders had no role in the conceptualisation, design, analysis, decision
to publish or preparation of the manuscript.Data collection in California
was supported by the Center for Conservation Biology, Stanford Uni-
versity. Data collection in Canada was supported by the Faculty of
Forestry, University of British Columbia. Data collection in Finland was
supported by the Natural Resources Institute Finland. Data collection in
Australia was supported by Griffith University and the University of the
Sunshine Coast. Data collection in Portugal was supported by ISCTE-
—University Institute of Lisbon. Data collection in Ireland was sup-
ported by the Environmental Protection Agency, Ireland. Data collection
in Hong Kong was supported by an internal University of Exeter—Chi-
nese University of Hong Kong international collaboration fund.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.jenvp.2024.102479.
C.W. McDougall et al. Journal of Environmental Psychology 100 (2024) 102479 
13 
References
Andrade, V., Quarta, S., Tagarro, M., Miloseva, L., Massaro, M., Chervenkov, M.,
Ivanova, T., Jorge, R., Maksimova, V., Smilkov, K., & Ackova, D. G. (2022).
Exploring hedonic and eudaimonic items of well-being in Mediterranean and non-
Mediterranean countries: Influence of sociodemographic and lifestyle factors.
International Journal of Environmental Research and Public Health, 19(3), 1715.
Austin, A. (2016). On well-being and public policy: Are we capable of questioning the
hegemony of happiness? Social Indicators Research, 127(1), 123–138.
Axelsson, O., Nilsson, M. E., Hellstrom, B., & Lunden, P. (2014). A field experiment on
the impact of sounds from a jet-and-basin fountain on soundscape quality in an
urban park. Landscape and Urban Planning, 123, 49–60.
Badland, H., Foster, S., Bentley, R., Higgs, C., Roberts, R., Pettit, C., & Giles-Corti, B.
(2017). Examining associations between area-level spatial measures of housing with
selected health and wellbeing behaviours and outcomes in an urban context. Health
& Place, 43, 17–24.
Barcelos, A. M., Kargas, N., Maltby, J., Hall, S., & Mills, D. S. (2020). A framework for
understanding how activities associated with dog ownership relate to human well-
being. Scientific Reports, 10(1), Article 11363.
Bell, S. L., Phoenix, C., Lovell, R., & Wheeler, B. W. (2015). Using GPS and geo-
narratives: A methodological approach for understanding and situating everyday
green space encounters. Area, 47(1), 88–96.
Benita, F., Bansal, G., & Tunçer, B. (2019). Public spaces and happiness: Evidence from a
large-scale field experiment. Health & Place, 56, 9–18.
Bokhari, A., & Sharifi, F. (2022). Public transport and subjective well-being in the just
city: A scoping review. Journal of Transport & Health, 25, Article 101372.
Boyd, F., White, M. P., Bell, S. L., & Burt, J. (2018). Who doesn’t visit natural
environments for recreation and why: A population representative analysis of
spatial, individual and temporal factors among adults in England. Landscape and
Urban Planning, 175, 102–113.
Cavalletti, B., & Corsi, M. (2018). “Beyond GDP” effects on national subjective well-being
of OECD countries. Social Indicators Research, 136, 931–966.
Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G., Peng, S., Lu, M., &
Zhang, W. (2015). Global land cover mapping at 30 m resolution: A POK-based
operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103,
7–27.
Coombes, E., Jones, A. P., & Hillsdon, M. (2010). The relationship of physical activity
and overweight to objectively measured green space accessibility and use. Social
science & medicine, 70(6), 816–822.
Costello, L., McDermott, M. L., Patel, P., & Dare, J. (2019). A lot better than medicine’-
Self-organised ocean swimming groups as facilitators for healthy ageing. Health &
Place, 60, 102212.
Coventry, P. A., Brown, J. E., Pervin, J., Brabyn, S., Pateman, R., Breedvelt, J., …
White, P. L. (2021). Nature-based outdoor activities for mental and physical health:
Systematic review and meta-analysis. SSM-Population Health, 16, 100934.
Csikszentmihalyi, M., & Hunter, J. (2003). Happiness in everyday life: The uses of
experience sampling. Journal of Happiness Studies, 4, 185–199.
Das, K. V., Jones-Harrell, C., Fan, Y., Ramaswami, A., Orlove, B., & Botchwey, N. (2020).
Understanding subjective well-being: Perspectives from psychology and public
health. Public Health Reviews, 41(1), 1–32.
de Bell, S., Graham, H., Jarvis, S., & White, P. (2017). The importance of nature in
mediating social and psychological benefits associated with visits to freshwater blue
space. Landscape and Urban Planning, 167, 118–127.
de Bell, S., White, M., Griffiths, A., Darlow, A., Taylor, T., Wheeler, B., & Lovell, R.
(2020). Spending time in the garden is positively associated with health and
wellbeing: Results from a national survey in England. Landscape and Urban Planning,
200, Article 103836.
De Vries, S., Nieuwenhuizen, W., Farjon, H., Van Hinsberg, A., & Dirkx, J. (2021). In
which natural environments are people happiest? Large-Scale experience sampling
in The Netherlands. Landscape and Urban Planning, 205, Article 103972.
Diener, E. (2000). Subjective well-being: The science of happiness and a proposal for a
national index. American Psychologist, 55(1), 34.
Diener, E. (2013). The remarkable changes in the science of subjective well-being.
Perspectives on Psychological Science, 8(6), 663–666.
Diener, E. (2021). Subjective well-being in cross-cultural perspective. In Key issues in
cross-cultural psychology (pp. 319–330). Garland Science.
Diener, E., Oishi, S., & Tay, L. (2018). Advances in subjective well-being research. Nature
Human Behaviour, 2(4), 253–260.
Douglas, O., Lennon, M., & Scott, M. (2017). Green space benefits for health and well-
being: A life-course approach for urban planning, design and management. Cities, 66,
53–62.
Ekkel, E. D., & de Vries, S. (2017). Nearby green space and human health: Evaluating
accessibility metrics. Landscape and Urban Planning, 157, 214–220.
Elliott, L. R., Pasanen, T., White, M. P., Wheeler, B. W., Grellier, J., Cirach, M.,
Bratman, G. N., van den Bosch, M., Roiko, A., Ojala, A., & Nieuwenhuijsen, M.
(2023). Nature contact and general health: Testing multiple serial mediation
pathways with data from adults in 18 countries. Environment International, 178,
Article 108077.
Elliott, L. R., & White, M. P. (2020). BlueHealth international survey methodology and
technical report. Cornwall, UK: BlueHealth.
Elliott, L. R., White, M. P., Grellier, J., Garrett, J. K., Cirach, M., Wheeler, B. W.,
Bratman, G. N., van den Bosch, M. A., Ojala, A., Roiko, A., & Lima, M. L. (2020).
Research Note: Residential distance and recreational visits to coastal and inland blue
spaces in eighteen countries. Landscape and Urban Planning, 198, Article 103800.
Elliott, L. R., White, M. P., Grellier, J., Rees, S. E., Waters, R. D., & Fleming, L. E. (2018).
Recreational visits to marine and coastal environments in England: Where, what,
who, why, and when? Marine Policy, 97, 305–314.
Elliott, L. R., White, M. P., Sarran, C., Grellier, J., Garrett, J. K., Scoccimarro, E., …
Fleming, L. E. (2019). The effects of meteorological conditions and daylight on
nature-based recreational physical activity in England. Urban Forestry & Urban
Greening, 42, 39–50.
Fisher, J. C., Bicknell, J. E., Irvine, K. N., Fernandes, D., Mistry, J., & Davies, Z. G. (2021).
Exploring how urban nature is associated with human wellbeing in a neotropical
city. Landscape and Urban Planning, 212, Article 104119.
Foley, R. (2017). Swimming as an accretive practice in healthy blue space. Emotion, Space
and Society, 22, 43–51.
Foley, R., & Kistemann, T. (2015). Blue space geographies: Enabling health in place.
Health & Place, 35, 157–165.
Frumkin, H., Bratman, G. N., Breslow, S. J., Cochran, B., Kahn, P. H., Jr., Lawler, J. J.,…
Wood, S. A. (2017). Nature contact and human health: A research agenda.
Environmental Health Perspectives, 125(7), 075001.
Gallegos-Riofrío, C. A., Arab, H., Carrasco-Torrontegui, A., & Gould, R. K. (2022).
Chronic deficiency of diversity and pluralism in research on nature’s mental health
effects: A planetary health problem. Current Research in Environmental Sustainability,
4, 100148.
Gan, Z., Feng, T., & Yang, M. (2018). Exploring the effects of car ownership and
commuting on subjective well-being: A nationwide questionnaire study.
Sustainability, 11(1), 84.
Garrett, J. K., White, M. P., Elliott, L. R., Grellier, J., Bell, S., Bratman, G. N.,
Economou, T., Gascon, M., L~ohmus, M., Nieuwenhuijsen, M., & Ojala, A. (2023).
Applying an ecosystem services framework on nature and mental health to
recreational blue space visits across 18 countries. Scientific Reports, 13(1), 2209.
Garrett, J. K., White, M. P., Huang, J., Ng, S., Hui, Z., Leung, C., Tse, L. A., Fung, F.,
Elliott, L. R., Depledge, M. H., & Wong, M. C. (2019). Urban blue space and health
and wellbeing in Hong Kong: Results from a survey of older adults. Health & Place,
55, 100–110.
Gascon, M., Zijlema, W., Vert, C., White, M. P., & Nieuwenhuijsen, M. J. (2017). Outdoor
blue spaces, human health and well-being: A systematic review of quantitative
studies. International Journal of Hygiene and Environmental Health, 220(8),
1207–1221.
Geary, R. S., Thompson, D., Mizen, A., Akbari, A., Garrett, J. K., Rowney, F. M., …
Nieuwenhuijsen, M. (2023). Ambient greenness, access to local green spaces, and
subsequent mental health: a 10-year longitudinal dynamic panel study of 2⋅ 3 million
adults in Wales. The Lancet Planetary Health, 7(10), e809–e818.
Geiger, S. J., White, M. P., Davison, S. M., Zhang, L., McMeel, O., Kellett, P., &
Fleming, L. E. (2023). Coastal proximity and visits are associated with better health
but may not buffer health inequalities. Communications Earth & Environment, 4(1),
166.
Giannetti, B. F., Agostinho, F., Almeida, C. M., & Huisingh, D. (2015). A review of
limitations of GDP and alternative indices to monitor human wellbeing and to
manage eco-system functionality. Journal of Cleaner Production, 87, 11–25.
Graham, C., & Chattopadhyay, S. (2013). Gender and well-being around the world.
International Journal of Happiness and Development, 1(2), 212–232.
Grellier, J., White, M. P., Albin, M., Bell, S., Elliott, L. R., Gascon, M., Gualdi, S.,
Mancini, L., Nieuwenhuijsen, M. J., Sarigiannis, D. A., & Van Den Bosch, M. (2017).
BlueHealth: A study programme protocol for mapping and quantifying the potential
benefits to public health and well-being from europe’s blue spaces. BMJ Open, 7(6),
Article e016188.
Helbich, M. (2018). Toward dynamic urban environmental exposure assessments in
mental health research. Environmental Research, 161, 129–135.
Hicks, S., Tinkler, L., & Allin, P. (2013). Measuring subjective well-being and its potential
role in policy: Perspectives from the UK office for national statistics. Social Indicators
Research, 114, 73–86.
Hogendorf, M., Groeniger, J. O., Noordzij, J. M., Beenackers, M. A., & Van Lenthe, F. J.
(2020). Longitudinal effects of urban green space on walking and cycling: A fixed
effects analysis. Health & Place, 61, Article 102264.
Hong, A., Martinez, L., Patino, J. E., Duque, J. C., & Rahimi, K. (2021). Neighbourhood
green space and health disparities in the global South: Evidence from Cali, Colombia.
Health & Place, 72, Article 102690.
Houlden, V., de Albuquerque, J. P., Weich, S., & Jarvis, S. (2019). A spatial analysis of
proximate greenspace and mental wellbeing in London. Applied Geography, 109,
102036.
Houlden, V., Weich, S., Porto de Albuquerque, J., Jarvis, S., & Rees, K. (2018). The
relationship between greenspace and the mental wellbeing of adults: A systematic
review. PLoS One, 13(9), Article e0203000.
Huerta, C. M., & Utomo, A. (2021). Evaluating the association between urban green
spaces and subjective well-being in Mexico city during the COVID-19 pandemic.
Health & Place, 70, Article 102606.
Isaacs, A., Halligan, J., Neve, K., & Hawkes, C. (2022). From healthy food environments
to healthy wellbeing environments: Policy insights from a focused ethnography with
low-income parents’ in England. Health & Place, 77, Article 102862.
Jarvis, I., Gergel, S., Koehoorn, M., & van den Bosch, M. (2020). Greenspace access does
not correspond to nature exposure: Measures of urban natural space with
implications for health research. Landscape and Urban Planning, 194, Article 103686.
Jiang, Y., & Huang, G. (2022). Urban residential quarter green space and life satisfaction.
Urban Forestry & Urban Greening, 69, 127510.
Jimenez, M. P., DeVille, N. V., Elliott, E. G., Schiff, J. E., Wilt, G. E., Hart, J. E., &
James, P. (2021). Associations between nature exposure and health: A review of the
evidence. International Journal of Environmental Research and Public Health, 18(9),
4790.
C.W. McDougall et al. Journal of Environmental Psychology 100 (2024) 102479 
14 
Kabisch, N., Strohbach, M., Haase, D., & Kronenberg, J. (2016). Urban green space
availability in European cities. Ecological Indicators, 70, 586–596.
Kahneman, D., Krueger, A. B., Schkade, D. A., Schwarz, N., & Stone, A. A. (2004).
A survey method for characterizing daily life experience: The day reconstruction
method. Science, 306(5702), 1776–1780.
Klompmaker, J. O., Hoek, G., Bloemsma, L. D., Gehring, U., Strak, M., Wijga, A. H., van
den Brink, C., Brunekreef, B., Lebret, E., & Janssen, N. A. (2018). Green space
definition affects associations of green space with overweight and physical activity.
Environmental Research, 160, 531–540.
Knight, S. J., McClean, C. J., & White, P. C. (2022). The importance of ecological quality
of public green and blue spaces for subjective well-being. Landscape and Urban
Planning, 226, Article 104510.
Kondo, M. C., Oyekanmi, K. O., Gibson, A., South, E. C., Bocarro, J., & Hipp, J. A. (2020).
Nature prescriptions for health: A review of evidence and research opportunities.
International Journal of Environmental Research and Public Health, 17(12), 4213.
Korpela, K. M., Ylen, M., Tyrvainen, L., & Silvennoinen, H. (2010). Favorite green,
waterside and urban environments, restorative experiences and perceived health in
Finland. Health Promotion International, 25(2), 200–209.
Krulichova, E. (2021). Changes in crime-related factors and subjective well-being over
time and their mutual relationship. International Journal of Law, Crime and Justice, 65,
Article 100457.
Lamu, A. N., & Olsen, J. A. (2016). The relative importance of health, income and social
relations for subjective well-being: An integrative analysis. Social Science &Medicine,
152, 176–185.
Lima, P. A. B., & Mariano, E. B. (2022). Eudaimonia in the relationship between human
and nature: A systematic literature review. Cleaner Production Letters, 2, Article
100007.
Maass, R., Kloeckner, C. A., Lindstrøm, B., & Lillefjell, M. (2016). The impact of
neighborhood social capital on life satisfaction and self-rated health: A possible
pathway for health promotion? Health & Place, 42, 120–128.
MacKerron, G., & Mourato, S. (2013). Happiness is greater in natural environments.
Global Environmental Change, 23(5), 992–1000.
Maguire, G. S., Miller, K. K., Weston, M. A., & Young, K. (2011). Being beside the seaside:
Beach use and preferences among coastal residents of south-eastern Australia. Ocean
& Coastal Management, 54(10), 781–788.
Mavoa, S., Davern, M., Breed, M., & Hahs, A. (2019). Higher levels of greenness and
biodiversity associate with greater subjective wellbeing in adults living in
Melbourne, Australia. Health & Place, 57, 321–329.
McDougall, C. W., Hanley, N., Quilliam, R. S., & Oliver, D. M. (2022). Blue space
exposure, health and well-being: Does freshwater type matter? Landscape and Urban
Planning, 224, Article 104446.
McDougall, C. W., Quilliam, R. S., Hanley, N., & Oliver, D. M. (2020). Freshwater blue
space and population health: An emerging research agenda. Science of the Total
Environment, 737, Article 140196.
McMahan, E. A., & Estes, D. (2015). The effect of contact with natural environments on
positive and negative affect: A meta-analysis. The Journal of Positive Psychology, 10
(6), 507–519.
Milligan, C., & Bingley, A. (2007). Restorative places or scary spaces? The impact of
woodland on the mental well-being of young adults. Health & Place, 13(4), 799–811.
Moradpour, M., & Hosseini, V. (2020). An investigation into the effects of green space on
air quality of an urban area using CFD modeling. Urban Climate, 34, Article 100686.
Mouratidis, K. (2021). Urban planning and quality of life: A review of pathways linking
the built environment to subjective well-being. Cities, 115, Article 103229.
Nguyen, P. Y., Astell-Burt, T., Rahimi-Ardabili, H., & Feng, X. (2021). Green space quality
and health: a systematic review. International Journal of Environmental Research and
Public Health, 18(21), 11028.
OECD. Publishing and Organisation for Economic Co-operation and Development.
(2013). OECD guidelines on measuring subjective well-being. OECD publishing.
Office of National Statistics (ONS). (2011). Measuring subjective wellbeing. Office of
national statistics, london.
Pasanen, T. P., White, M. P., Elliott, L. R., van den Bosch, M., Bratman, G. N., Ojala, A.,
Korpela, K., & Fleming, L. E. (2023). Urban green space and mental health among
people living alone: The mediating roles of relational and collective restoration in an
18-country sample. Environmental Research, Article 116324.
Patino, J. E., Martinez, L., Valencia, I., & Duque, J. C. (2023). Happiness, life satisfaction,
and the greenness of urban surroundings. Landscape and Urban Planning, 237, Article
104811.
Phoenix, C., Bell, S. L., & Hollenbeck, J. (2021). Segregation and the sea: Toward a
critical understanding of race and coastal blue space in greater Miami. Journal of
Sport and Social Issues, 45(2), 115–137.
Poulsen, M. N., Nordberg, C. M., Fiedler, A., DeWalle, J., Mercer, D., & Schwartz, B. S.
(2022). Factors associated with visiting freshwater blue space: The role of
restoration and relations with mental health and well-being. Landscape and Urban
Planning, 217, Article 104282.
Pritchard, A., Richardson, M., Sheffield, D., & McEwan, K. (2020). The relationship
between nature connectedness and eudaimonic well-being: A meta-analysis. Journal
of Happiness Studies, 21, 1145–1167.
Prochniak, P., & Prochniak, A. (2023). Adventure recreation in blue spaces and the
wellbeing of young polish adults. International Journal of Environmental Research and
Public Health, 20(5), 4472.
R Core Team. (2022). R: A language and environment for statistical computing. Vienna,
Austria: R Foundation for Statistical Computing. URL https://www.R-project.org/.
Richardson, M., Passmore, H. A., Lumber, R., Thomas, R., & Hunt, A. (2021). Moments,
not minutes: The nature-wellbeing relationship. International Journal of Wellbeing, 11
(1).
Roman, J. E. I., Ekholm, O., Algren, M. H., Koyanagi, A., Stewart-Brown, S., Hall, E. E.,
Stubbs, B., Koushede, V., Thygesen, L. C., & Santini, Z. I. (2023). Mental wellbeing
and physical activity levels: A prospective cohort study. Mental Health and Physical
Activity, 24, Article 100498.
Ryan, R. M., & Deci, E. L. (2001). On happiness and human potentials: A review of
research on hedonic and eudaimonic well-being. Annual Review of Psychology, 52(1),
141–166.
Ryan, R. M., Weinstein, N., Bernstein, J., Brown, K. W., Mistretta, L., & Gagne, M. (2010).
Vitalizing effects of being outdoors and in nature. Journal of Environmental
Psychology, 30(2), 159–168.
Saint-Onge, K., Coulombe, S., Philibert, M., Wiesztort, L., & Houle, J. (2022). How urban
parks nurture eudaimonic and hedonic wellbeing: An explorative large scale
qualitative study in Quebec, Canada. Wellbeing. Space and Society, 3, 100095.
Severin, M. I., Raes, F., Notebaert, E., Lambrecht, L., Everaert, G., & Buysse, A. (2022).
A qualitative study on emotions experienced at the coast and their influence on well-
being. Frontiers in Psychology, 13, 902122.
Shanahan, D. F., Bush, R., Gaston, K. J., Lin, B. B., Dean, J., Barber, E., & Fuller, R. A.
(2016). Health benefits from nature experiences depend on dose. Scientific Reports, 6
(1), Article 28551.
Sharifi, F., Nygaard, A., & Stone, W. M. (2021). Heterogeneity in the subjective well-
being impact of access to urban green space. Sustainable Cities and Society, 74,
103244.
Smith, G., Cirach, M., Swart, W., D _edel _e, A., Gidlow, C., van Kempen, E., Kruize, H.,
Grazulevicien _e, R., & Nieuwenhuijsen, M. J. (2017). Characterisation of the natural
environment: Quantitative indicators across europe. International Journal of Health
Geographics, 16, 1–15.
Steptoe, A., Deaton, A., & Stone, A. A. (2015). Subjective wellbeing, health, and ageing.
The Lancet, 385(9968), 640–648.
Sun, S., Xu, X., Lao, Z., Liu, W., Li, Z., García, E. H., … Zhu, J. (2017). Evaluating the
impact of urban green space and landscape design parameters on thermal comfort in
hot summer by numerical simulation. Building and Environment, 123, 277–288.
Tester-Jones, M., White, M. P., Elliott, L. R., Weinstein, N., Grellier, J., Economou, T.,
Bratman, G. N., Cleary, A., Gascon, M., Korpela, K. M., & Nieuwenhuijsen, M. (2020).
Results from an 18 country cross-sectional study examining experiences of nature for
people with common mental health disorders. Scientific Reports, 10(1), Article
19408.
Volker, S., & Kistemann, T. (2011). The impact of blue space on human health and well-
being–Salutogenetic health effects of inland surface waters: A review. International
Journal of Hygiene and Environmental Health, 214(6), 449–460.
Van Campen, C., & Van Santvoort, M. (2013). Explaining low subjective well-being of
persons with disabilities in Europe: The impact of disability, personal resources,
participation and socio-economic status. Social Indicators Research, 111, 839–854.
Vert, C., Gascon, M., Ranzani, O., Marquez, S., Triguero-Mas, M., Carrasco-Turigas, G.,…
Elliott, L. R. (2020). Physical and mental health effects of repeated short walks in a
blue space environment: A randomised crossover study. Environmental Research, 188,
109812.
Vitale, V., Martin, L., White, M. P., Elliott, L. R., Wyles, K. J., Browning, M. H., Pahl, S.,
Stehl, P., Bell, S., Bratman, G. N., & Gascon, M. (2022). Mechanisms underlying
childhood exposure to blue spaces and adult subjective well-being: An 18-country
analysis. Journal of Environmental Psychology, 84, Article 101876.
Voukelatou, V., Gabrielli, L., Miliou, I., Cresci, S., Sharma, R., Tesconi, M., &
Pappalardo, L. (2021). Measuring objective and subjective well-being: Dimensions
and data sources. International Journal of Data Science and Analytics, 11, 279–309.
Wessel, P., & Smith, W. H. (1996). A global, self-consistent, hierarchical, high-resolution
shoreline database. Journal of Geophysical Research: Solid Earth, 101(B4), 8741–8743.
Wheaton, B., Waiti, J., Cosgriff, M., & Burrows, L. (2020). Coastal blue space and
wellbeing research: Looking beyond western tides. Leisure Studies, 39(1), 83–95.
White, M. P., Alcock, I., Grellier, J., Wheeler, B. W., Hartig, T., Warber, S. L., …
Fleming, L. E. (2019). Spending at least 120 minutes a week in nature is associated
with good health and wellbeing. Scientific Reports, 9(1), 1–11.
White, M. P., Alcock, I., Wheeler, B. W., & Depledge, M. H. (2013). Would you be happier
living in a greener urban area? A fixed-effects analysis of panel data. Psychological
Science, 24(6), 920–928.
White, M. P., & Dolan, P. (2009). Accounting for the richness of daily activities.
Psychological science, 20(8), 1000–1008.
White, M. P., Elliott, L. R., Gascon, M., Roberts, B., & Fleming, L. E. (2020). Blue space,
health and well-being: A narrative overview and synthesis of potential benefits.
Environmental Research, 191, Article 110169.
White, M. P., Elliott, L. R., Grellier, J., Economou, T., Bell, S., Bratman, G. N., …
Nieuwenhuijsen, M. (2021). Associations between green/blue spaces and mental
health across 18 countries. Scientific Reports, 11(1), 8903.
White, M. P., Pahl, S., Wheeler, B. W., Depledge, M. H., & Fleming, L. E. (2017). Natural
environments and subjective wellbeing: Different types of exposure are associated
with different aspects of wellbeing. Health & Place, 45, 77–84.
Wilczynska, A., Niin, G., Vassiljev, P., Myszka, I., Bell, S., & Plymouth. (2023).
Perceptions and patterns of use of blue spaces in selected European cities.
Sustainability, 15(9), 7392.
Wood, L., Hooper, P., Foster, S., & Bull, F. (2017). Public green spaces and positive
mental health–investigating the relationship between access, quantity and types of
parks and mental wellbeing. Health & Place, 48, 63–71.
C.W. McDougall et al. Journal of Environmental Psychology 100 (2024) 102479 
15