Original Research
13 February 2023
10.3389/frvir.2023.897034
TYPE
PUBLISHED
DOI
OPEN ACCESS
EDITED BY
Carolin Wienrich,
Julius Maximilian University of Würzburg,
Germany
REVIEWED BY
Madis Vasser,
University of Tartu, Estonia
Alexander Klippel,
Wageningen University and Research,
Netherlands
Increasing awareness of climate
change with immersive virtual
reality
Stefan P. Thoma 1,2†, Matthias Hartmann 2†, Jonas Christen 3,
Boris Mayer 1,2, Fred W. Mast 1 and David Weibel 1,2*
1
Department of Psychology, University of Bern, Bern, Switzerland, 2Faculty of Psychology, UniDistance
Suisse, Brig, Switzerland, 3Department of Design, Zurich University of the Arts, Zurich, Switzerland
*CORRESPONDENCE
David Weibel,
david.weibel@unibe.ch
†
These authors have contributed equally to
this work and share first authorship
SPECIALTY SECTION
This article was submitted to Virtual Reality
and Human Behaviour,
a section of the journal
Frontiers in Virtual Reality
15 March 2022
24 January 2023
PUBLISHED 13 February 2023
RECEIVED
ACCEPTED
CITATION
Thoma SP, Hartmann M, Christen J,
Mayer B, Mast FW and Weibel D (2023),
Increasing awareness of climate change
with immersive virtual reality.
Front. Virtual Real. 4:897034.
doi: 10.3389/frvir.2023.897034
COPYRIGHT
© 2023 Thoma, Hartmann, Christen,
Mayer, Mast and Weibel. This is an openaccess article distributed under the terms
of the Creative Commons Attribution
License (CC BY). The use, distribution or
reproduction in other forums is permitted,
provided the original author(s) and the
copyright owner(s) are credited and that
the original publication in this journal is
cited, in accordance with accepted
academic practice. No use, distribution or
reproduction is permitted which does not
comply with these terms.
Previous research has shown that immersive virtual reality (VR) is a suitable tool for
visualizing the consequences of climate change. The aim of the present study was to
investigate whether visualization in VR has a stronger influence on climate change
awareness and environmental attitudes compared to traditional media. Furthermore,
it was examined how realistic a VR experience has to be in order to have an effect.
The VR experience consisted of a model of the Aletsch glacier (Switzerland) melting
over the course of 220 years. Explicit measurements (new environmental paradigm
NEP, climate change scepticism, and nature relatedness) and an implicit
measurement (implicit association test) were collected before and after the VR
intervention and compared to three different non-VR control conditions (video,
images with text, and plain text). In addition, the VR environment was varied in terms
of degrees of realism and sophistication (3 conditions: abstract visualization, less
sophisticated realistic visualization, more sophisticated realistic visualization). The six
experimental conditions (3 VR conditions, three control conditions) were modeled as
mixed effects, with VR versus control used as a fixed effect in a mixed effects
modeling framework. Across all six conditions, environmental awareness (NEP) was
higher after the participants (N = 142) had been confronted with the glacier melting,
while no differences were found for nature relatedness and climate change
scepticism before and after the interventions. There was no significant difference
between VR and control conditions for any of the four measurements. Nevertheless,
contrast analyses revealed that environmental awareness increased significantly only
for the VR but not for the control conditions, suggesting that VR is more likely to lead
to attitude change. Our results show that exposure to VR environments successfully
increased environmental awareness independently of the design choices, suggesting
that even abstract and less sophisticated VR environment designs may be sufficient
to increase pro-environmental attitudes.
KEYWORDS
virtual reality, environmental attitude, changing attitude, climate change, realism, IAT,
immersion, presence
1 Introduction
One of the greatest challenges faced by humanity is climate change, which has already led to
dramatic consequences for the environment (e.g., sea ice and mountain glaciers are melting,
wildlife populations and habitats are changing, extreme weather conditions are becoming more
frequent). Climate change is caused by increased emissions of greenhouse gases (Pachauri et al.,
2014). In order to counteract climate change it is therefore important to make people aware of
the origins of climate change and to alter their attitudes and behavior. However, this endeavor
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open. First, most of these results are based on a comparison of
environmental awareness before and after exposure to immersive
VR, without comparing the effects to a non-immersive control
condition (Hsu et al., 2018; Markowitz et al., 2018; Petersen et al.,
2020; Chirico et al., 2021). One of the studies that did make such a
comparison found that watching a nature video through a headmounted display (immersive technology) enhanced nature
relatedness, but not to a higher extent than watching the same
content on a conventional computer monitor (Soliman et al.,
2017). Moreover, in the context of learning, it has been shown that
high immersion does not always improve learning, and it has been
suggested that there are also distracting effects of immersion on
cognitive processing (e.g., attending to irrelevant information; Craig
et al., 2002; Cummings and Bailenson, 2016; Makransky et al., 2019;
Mania and Chalmers, 2001; Mayer, 2014; Pollard et al., 2020). Thus, it
remains an open question to what extent immersive VR is more
effective at increasing environmental awareness compared to
traditional communication media (e.g., text, 2D desktop
demonstration).
Furthermore, there is often no manipulation of design aspects
within immersive VR. When setting up an immersive VR
environment, VR designers are confronted with numerous
decisions about the specific VR design, such as the degree of visual
realism (i.e., how graphically realistic the virtual environment is), the
level of interactivity with the virtual world, or the use of avatars. From
the developers’ perspective, it would be important to know which of
the various aspects of a VR environment contribute most to the
positive effects on attitude change. Previous studies have shown
that visual realism is an important determinant of presence
(IJsselsteijn et al., 2001; Schubert et al., 2001; Baños et al., 2004;
Hvass et al., 2017; Weber et al., 2021). Presence refers to the subjective
feeling of being in the virtual world (Slater and Wilbur, 1997). For
example, in scenarios designed for participants with fear of height or
other anxieties, more visual realism resulted in increased presence and
affective responses (Hvass, 2018; Gromer et al., 2019). Increased
presence is also associated with enhanced interest and personal
involvement in immersive VR experiences (Makransky and
Petersen, 2021). Based on these results, it can be hypothesized that
immersive VR is most effective with high levels of visual realism.
However, the role of visual realism in increasing environmental
awareness is largely unexplored (Weber et al., 2021). There is some
evidence in the context of learning that visual realism is not the most
important factor: Rather, it seems more important that virtual
representations are authentic, coherent, plausible, and functionally
correspond to the applied context (Witmer and Singer, 1998; Hamstra
et al., 2014; Jacobson, 2017).
Manipulating visual realism within immersive VR can serve as a
more fine-tuned means of increasing (or reducing) the sense of
presence. We therefore argue that such a manipulation helps to
better understand the complex interrelationship between
immersion, presence, and attitude change beyond simple
comparisons between media (immersive VR vs. non-VR). In
addition to these considerations, the focus on visual realism as a
design aspect is also relevant from a practical perspective. Inspired by
ever-improving technology, there is a tendency to strive for the highest
possible degree of visual realism when developing immersive VR
(Bowman and McMahan, 2007; Hvass et al., 2017; Erolin et al.,
2019). Remarkably, this design choice significantly impacts
development costs: To increase visual realism, 3D modellers and
has turned out to be challenging, and people’s level of concern does not
appropriately reflect the scope of the problem (Carmichael and Brulle,
2017).
One source of the problem is that climate change is a relatively
abstract phenomenon, in the sense that the effects of greenhouse gas
emissions are not immediately visible but take place over many years
(temporal distance), and the consequences do not necessarily occur at
the same place as the causing behavior (spatial distance). Temporal
and spatial distance can - at least partially - explain why it is so hard to
bring this issue into awareness to the extent that it changes behavior in
a sustainable manner (Trope and Liberman, 2010). Using traditional
media such as films or illustrated brochures can be used to explain the
mechanisms of climate change and visualize its consequences.
However, providing an immersive realistic experience of the
consequences of climate change may have additional psychological
effects that increase environmental awareness, as elaborated next.
Researchers and media developers are now able to create rich
computer-generated 3D environments that enable participants to
experience virtual environments as if they were real (Gonçalves
et al., 2022). Immersive VR is typically experienced using a headmounted display that digitally recreates user’s movements in their
physical space and creates depth perception by rendering a different
image for each eye. Compared to conventional computer screens, head
mounted displays shut out the outside world, and typically occupy a
larger part of the field of view and have a larger field of regard, allowing
users to experience an illusory sense of presence in the threedimensional virtual world. Immersive VR thus enables a direct
personal and embodied experience of climate change consequences
that may bring participants psychologically closer to the issue when
compared to simply watching a video (Markowitz et al., 2018;
Markowitz and Bailenson, 2021). In particular, immersive VR can
be used to visualize effects of climate change that can otherwise not be
experienced directly due to large time spans (e.g., glacier melting) in a
realistic way. Thus, the immersive experience can impact both the
cognitive and affective processing of climate change. At the cognitive
level, it might help users to establish a mental representation of the
mechanisms that underlie climate change and its consequences. This,
in turn, may raise the awareness of the risks associated with climate
change (Ahn S. J. et al., 2014a; Kim, 2020). At the affective level, it can
enhance the sense of being related and emotionally connected to the
environment, thus making climate change a personally relevant matter
(Akerlof et al., 2013; Ahn S. J. et al., 2014a).
While the potential of immersive VR as a persuasive tool (i.e., as
means to change attitudes or behaviors; see Wienrich et al., 2021) has
been recognized in other domains such as prejudice (Peck et al., 2013;
Hasler et al., 2017), empathy (van Loon et al., 2018), or human rights
(Bujić et al., 2020), its effect on environmental attitudes has not yet
been extensively studied. Most previous studies using immersive VR in
the context of climate change have focused on knowledge transfer
(Markowitz and Bailenson, 2021). Nevertheless, there is some
evidence that immersive VR can increase environmental awareness
and pro-environmental behavior (Ahn S. J. et al., 2014a; Hsu et al.,
2018; Markowitz et al., 2018; Fauville et al., 2020; Petersen et al., 2020;
Chirico et al., 2021). For example, a visualization of the tree-cutting
process had an impact on paper conservation (Ahn S. J. et al., 2014a),
an underwater world helped to demonstrate the impact of rising
seawater (Markowitz et al., 2018), or a landscape of Greenland served
to illustrate the melting ice sheet (Petersen et al., 2020). Although these
studies provide promising results, several crucial questions remain
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FIGURE 1
Three views each taken from the three different VR environments.
environmental attitudes. In doing so, we aim to take into consideration
some of the limitations of previous research outlined above, including
(1) comparing immersive VR to non-VR interventions, (2)
manipulating the design aspect of visual realism within the
immersive VR experience, and (3) incorporating implicit
measurements of environmental attitudes. To this end, we created
different visualisations of the Aletsch glacier to represent the negative
effect of climate change. The Aletsch glacier is the largest and best
known glacier in Switzerland and is featured in many newspaper
articles about climate change as the glacier is expected to shrink
substantially within the next 80–100 years (Jouvet et al., 2011;
Linsbauer et al., 2012). By demonstrating the effects of climate
change via melting glaciers, and by simulating many years in a
duration of only a few minutes, we aimed to make the invisible
effects of climate change visible and directly tangible to the
participants. Importantly, we created different virtual environments
(VE), all showing the same narrated sequence of the Aletsch glacier
melting but with different degrees of visual realism. Visual realism was
varied by manipulating the parameters of texture resolution and
repetition, glacier topography, atmospheric perspective and color
adjustments (cf. methods). By manipulating these parameters, three
conditions were created: a well-designed abstract condition, a welldesigned realistic condition, and a less sophisticated realistic condition
(see Figure 1).
Due to the reduced psychological distance (in space and time) to
climate change consequences during the immersive VR experience
(Trope and Liberman, 2010), we expected that the immersive VR
experience would increase climate change awareness and
environmental attitudes. Specifically, we expected these changes to
be stronger in the immersive VR conditions compared to three nonVR control conditions (watching a video of the virtual application,
reading a text with screenshots of the virtual application, or just
reading a text). Within the immersive VR conditions, we expected that
the well-designed realistic condition would lead to the highest levels of
presence and therefore would have the strongest impact on climate
developers may have to invest hundreds of working hours. Creating
less realistic but visually coherent and authentic environments could
emphasize the focus on educational practices, which in turn could
achieve equal or even better effects. While the pursuit of photorealistic
graphics may be the standard for most gaming applications, the role of
visual realism for other immersive VR goals, such as increasing
environmental awareness, will need to be thoroughly explored in
order to create efficient immersive VR applications.
Another limitation of previous studies is that they have mostly
relied on explicit measurements of environmental attitudes. In such
measures, participants typically rate their agreement with certain
environment-related statements on a predefined scale (e.g., likert
scale ranging from 1–5; Ahn et al., 2014a; Ahn et al., 2014b;
Markowitz et al., 2018; Petersen et al., 2020)1. In the indirect
measurement, they counted how many napkins participants used
to help clean up spilled water. Such an approach can be prone to
social desirability or expectancy effects (Beattie and Sale, 2011). For
example, people want to present themselves “greener” than they
actually are (Beattie and McGuire, 2012), or they adapt their
responses because they think that an increase in environmental
awareness is expected from them after the virtual intervention.
Such biases can lead to misleading results, especially when changes
in questionnaire scores are not compared to a non-VR control
condition. Moreover, attitudes have explicit and implicit
components, both of which can influence behavior (Wilson et al.,
2000; Evans, 2008). Thus, explicit measures alone may not be sufficient
to assess the success of immersive VR in changing attitudes (Beattie
and McGuire, 2012).
The aim of the present study is to explore the effectiveness of an
immersive VR experience in increasing climate change awareness and
1 Note that Ahn S. J. et al. (2014a); Ahn S. J. G. et al. (2014b) also used an
indirect measurement in addition to the explicit measurement.
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change awareness and environmental attitudes. Finally, we
supplemented well-established explicit measurements (new
environmental paradigm, climate change scepticism, and nature
relatedness) with a self-created version of an implicit association
test (IAT; Greenwald et al., 1998) to measure environmental
attitudes. The IAT uses response times in binary classification tasks
to measure implicit attitudes toward concepts. The responses are
facilitated when a target concept (e.g., “environmentally-friendly”)
and a matching affective concept (e.g., “pleasant”) are allocated to the
same response key (Schultz et al., 2004; Wang et al., 2019). IATs have
been criticized (Mierke and Klauer, 2003; Rothermund and Wentura,
2004) but have nevertheless often been shown to be sensitive to
cognitive interventions (Friese et al., 2006; Slabbinck et al., 2011;
Rihs et al., 2022). For example, Beattie and McGuire (2012) found that
the IAT measurement of implicit attitude toward low (vs. high) carbon
footprint products, but not an explicit measurement of the same issue,
was associated with an attentional focus toward negative images of
climate change. We therefore expected to find effects of the immersive
VR interventions on the implicit measure.
TABLE 1 Correlation table of the four environmental attitudes measures.
Computed on the untranformed CCS scale.
IAT
CCS
NR
IAT
1
CCS
−0.05
1
NR
0.14
−0.2
1
NEP
0.14
−0.38
0.36
NEP
1
1. Baseline a: All dependent variables except the IAT were first
measured via online questionnaire between one and 2 weeks
before the experiment in order to reduce reactive effects. The
order of the three scales measuring explicit environmental
awareness was randomized, as were the items within the scales.
2. Baseline b: At the beginning of the experiment the baseline IATmeasure was conducted.
3. VR or control intervention: Participants gave informed written
consent and were reminded of their right to cancel the experiment
without cause. VR: Participants were informed of possible dangers
of the VR-experience, such as feeling dizzy or nauseous, and were
given a bottle of water. Then, participants entered the VR
environment. Control: Participants remained seated at the desk
and were presented with one of the three control conditions.
4. Post intervention: After the VR-experience, participants first filled
out a survey starting with a suspension of disbelief and a presence
questionnaire and the evaluation of the stimuli-experience followed
by the three scales measuring explicit environmental attitude.
Then, the IAT was administered again followed by the
debriefing of the participants.
2 Material and methods
2.1 Participants
A total of 142 participants were recruited (age: M = 27.21, SD =
9.47) of which 82 were women and 60 were men. Most participants
were students and received course credit in exchange for their
participation. The remaining participants did not receive any
compensation. Participants were treated in accordance with the
Code of Ethics of the World Medical Association (Declaration of
Helsinki) and the study was approved by the local Ethics Committee.
2.4 Materials
2.2 Design
Four measures aimed to capture the multidimensional construct
environmental attitudes. The New ecological paradigm questionnaire
(NEP) measured pro-environmental sentiment most generally
(Dunlap et al., 2000). The climate change scepticism scale (CCS)
measured the extent to which participants questioned the severity
of climate change (Whitmarsh, 2013). An affective component of
environmental attitude was captured by the nature relatedness (NR)
questionnaire (Nisbet et al., 2008). To measure implicit attitudes
towards the environment we created an implicit association test
(IAT). Correlations of the measures are reported in Table 1.
For the VR and the video conditions secondary measures included
the Igroup Presence Questionnaire (IPQ, Igroup, 2016), and the
suspension of disbelief scale (SOD, Vorderer et al., 2004). With the
exception of members of the plain text condition, participants were
further asked two qualitative questions about the graphics (realism
and graphical pleasantness). All participants were asked to rate the
subjective experience of the stimuli with respect to excitement,
pleasantness, and enjoyment (single item measures).
The study employed a between subjects design measuring all
dependent variables both before and after the exposure to the
condition. The three VR conditions were: Abstract and well
designed (abstract, n = 23), realistic and well designed (real+, n =
23), or realistic with a less sophisticated design (real-, n = 23). The
three control conditions were watching a screencast of the real +
experience (video, n = 24), reading a text describing the process of the
glacier melting (text, n = 25), and reading a shorter version of text but
including some screenshots of real+ (text and picture, n = 24). While
the three VR conditions varied in realism and in sophistication the
three control conditions varied in the degree of visualisation, with
video being the most visual and text the least visual control condition.
The VR conditions were tested in a first step of data collection,
followed by the control conditions. Thus, participants were not
randomly assigned to VR or control, but they were randomly
assigned to a specific condition within the VR or the control
conditions. All interventions lasted approximately five and a half
minutes.
2.4.1 Stimuli
The three VR environments were all based on the same underlying
3D model of the Aletsch glacier melting created for a museum
experience (Jouvet et al., 2011; Linsbauer et al., 2012). In both the
virtual and the real environment, participants were standing on a
2.3 Procedure
The experiment consisted of four stages.
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FIGURE 2
Manipulation check. Experience variables are based on one item each. IPQ and SOD represent average questionnaire scores.
2.4.2 New ecological paradigm (NEP)
platform surrounded by railing. This added a level of security to the
experience and simultaneously limited the space participants could
explore in a sensible way. Participants heard a narrator speaking
German with a slight British accent who was introduced as John
Tyndall, a British physicist who studied greenhouse gases and the
Aletsch glacier in the 19th century. The narrator guided participants
through 220 years, ending with how the glacier would look like in the
year 2070 if global warming continues as it has. In the background, a
realistic sound of the melting glacier (recorded with microphones in
the actual glacier) set the mood and the sound stage.
The three different VR environments varied with respect to
realism and visual coherence/sophistication. The conditions were:
A well-designed abstract condition abstract, a well-designed
realistic condition text, and a less sophisticated realistic condition
real-. The VR environments were not designed to vary in image
resolution, audio, or other factors affecting the quality of experience.
In the video condition (video) participants watched a screencast of a
typical experience in the well designed realistic VR condition.
Participants in the text and picture condition (text and picture)
read a text interlaced with pictures of the screencast describing the
information content of the VR experience. The text was a slightly
adapted transcript of the narrator’s words. In the text condition (text)
participants read a plain text version of the stimuli in the text and
picture condition. The informational content and the duration of all
six interventions were comparable.
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The NEP scale is a widely used measure of pro-environmental
orientation and environmental concern (Xiao et al., 2019). Its 15 items,
some of which are reverse-coded, capture various aspects of
environmental awareness. Even though some studies found two or
more subscales, the NEP retains an internal consistency high enough
to be used as a single measure of endorsement of an ecological
worldview with a Cronbach’s α = 0.83 (Dunlap et al., 2000). In our
sample the Cronbach’s Alpha estimated reliability of the NEP was α =
0.686, 95% CI [0.632, 0.739].
2.4.3 Climate change scepticism (CCS)
The CCS scale is a multidimensional measure of scepticism and
uncertainty about climate change. Its high internal consistency (α =
0.92) enables the CCS to be used as a single measure of scepticism
(Whitmarsh, 2013). The scale administered consisted of 12 items with
five point Likert scale response options. CCS has been shown to be
negatively predicted by NEP scores (Dunlap et al., 2000). The two
scores were also substantially negatively correlated in our sample (see
Table 1), r = −0.38. CCS was shown to be reliable in the current sample
with Cronbach’s α = 0.845, 95% CI [0.819, 0.871].
2.4.4 Nature relatedness (NR)
The NR scale assesses different aspects of an individual’s
connectedness to nature–a relevant affective component of
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FIGURE 3
Conditional mean and CI of all four DV’s for VR vs. control conditions. CCS has been transformed back to its original scale.
Blume, Tier, Stein; EN: Tree, water, flower, animal, stone). The
affective-words were either positive (DE: Ausgezeichnet,
einwandfrei, hervorragend, positiv, gut; EN: Excellent, impeccable,
outstanding, positive, good) or negative (DE: Schlimm, schlecht,
negativ, schädlich, schwach; EN: Bad, poor (as in: poorly worded),
harmful, weak). The PsychoPy script defining the IAT procedure can
be found on the osf. io materials page (https://osf.io/n28cj/).
The raw data was adjusted according to the improved scoring algorithm
and scores were computed so that higher scores represented higher implicit
pro-environmental attitudes and vice versa (Greenwald et al., 2003).
environmental awareness (Littledyke, 2008; Nisbet et al., 2008). It is
comprised of 21 items with five point Likert scale response options, has
good psychometric properties and is positively correlated with NEP scores
(r = 0.54, Nisbet et al., 2008). In our sample NR and NEP correlated
positively with r = 0.36. The measure showed a high internal consistency
in our sample with Cronbach’s α = 0.840, 95% CI [0.813, 0.866].
2.4.5 Implicit association test (IAT)
We included an IAT as a measure of implicit attitudes to capture
an additional dimension of environmental awareness. The IAT is a
standard paradigm used in various studies and is suitable for
investigating the strength of associations (cf. Jost, 2019). One
advantage of the IAT is its robustness to manipulation from
explicit attitudes and social influences as it measures reaction time
ratios between different categorisation tasks (Greenwald et al., 1998;
Nosek et al., 2005). While IAT scores have been criticised in recent
years–mostly for unverified psychometric properties–they remain
useful as an additional measure to capture environmental
awareness more thoroughly (Karpinski and Hilton, 2001; Fiedler
et al., 2006; Wilson and Smith, 2017). We constructed the IAT in
PsychoPy® analoguous to Wilson and Smith (2017) but using words
only (Peirce et al., 2019).
The object-words included in the IAT were either man-made (DE:
Gebäude, Stadt, Kleider, Computer, Lampe; EN: building, city, clothes,
computer, lamp) or typically occurring in nature (DE: Baum, Wasser,
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2.4.6 Igroup presence questionnaire (IPQ)
The IPQ was designed to measure presence in virtual environments
(including VR). Its validity, reliability and practicability have been
confirmed in numerous studies (Schubert et al., 2001; Schuemie et al.,
2002; Ling et al., 2014). The scale consists of 14 items with seven point
Likert scale response options. Within our study, internal consistency was
good with Cronbach’s α = 0.826, 95% CI [0.785, 0.867].
2.4.7 Suspension of disbelief (SOD)
SOD describes a state of reduced scrutiny towards an artificial
environment which mediates presence (Tussyadiah et al., 2018). The
SOD scale was developed by Vorderer et al. (2004) as a subscale to the
MEC Spacial Presence Questionnaire. The scale was chosen for its
good psychometric properties (Vorderer et al., 2004). We used the full
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TABLE 2 Models comparing VR to control conditions. Type is deviation coded, the coefficient type vr represents the difference between the VR condition compared to
the average value at the first measurement.
DV
Coef
IAT
(Intercept)
CCS
NR
CI
t value
df
p-value
0.23
[0.106; 0.355]
3.63
225.16
0.00
time
−0.01
[-0.085; 0.066]
−0.23
20.40
0.82
type vr
−0.15
[-0.276; −0.028]
−2.39
225.16
0.02
time:type1
0.07
[-0.001; 0.15]
1.83
20.40
0.08
(Intercept)
0.23
[0.199; 0.27]
12.84
278.36
0.00
time
−0.01
[-0.029; 0.006]
−1.32
140.00
0.19
type vr
−0.00
[-0.038; 0.034]
−0.12
278.36
0.91
time:type1
−0.00
[-0.02; 0.015]
−0.32
140.00
0.75
(Intercept)
3.82
[3.71; 3.925]
69.64
268.10
0.00
time
0.04
[-0.009; 0.085]
1.51
9.57
0.16
−0.01
[-0.118; 0.096]
−0.20
268.10
0.84
time:type1
0.04
[-0.008; 0.086]
1.56
9.57
0.15
(Intercept)
3.76
[3.663; 3.849]
79.18
276.21
0.00
time
0.07
[0.03; 0.117]
3.23
14.89
0.01
type vr
NEP
Estimate
type vr
time:type1
−0.04
[-0.128; 0.058]
−0.74
276.21
0.46
0.02
[-0.025; 0.063]
0.83
14.89
0.42
awareness variables. Lastly, we took a closer look at the differences
among the three VR conditions.
The manipulation checks relied on simple linear models using the
dummy-coded condition (contrasting the three VR conditions) or type
(contrasting VR and control conditions) as the relevant predictor,
depending on the respective question/model. Setting real + as the
reference condition for condition allowed for a direct comparison of
real- and abstract to real+.
In a second step, we compared all VR conditions to all the control
conditions using random effects both for the subjects and for the (six)
conditions. Models with the predictors time (with levels before and
after), type, and their interaction term were used to compare how VR
and control conditions differ in their efficacy to increase pro
environmental attitudes. The variable type was included with
deviation-coded contrasts (UCLA: Statistical Consulting Group,
2022, see). This meant that the simple slope of time could be
interpreted as the average increase over time across all (VR and
control) conditions. To control the false discovery rate for multiple
contrast analyses we used the Benjamini and Hochberg (1995) method
implemented in the stats package in R (R Core Team, 2018). The
interaction coefficient represents the difference in slope (before vs.
after) of the VR group as compared to the average time slope. The
p-value corresponding to the interaction term tests whether the time
slopes differed between VR and control conditions.
A random intercept per participant accounted for the repeated
measures structure. Additionally including a random time slope for
the subject variable participant (nested in condition) would have lead
to a saturated model and resulted in an identifiability error for all
analyses. A random intercept and a random time slope for condition
were introduced to allow for random variation due to the inherent
randomness of our choice of VR and control conditions. Our analysis
(8-item) version of the scale, which showed an internal consistency of
α = 0.805, 95% CI [0.759, 0.852].
2.4.8 Experience, enjoyment and demographics
As a manipulation check, participants rated the experienced
stimuli with regard to their realism, design quality, excitement,
pleasantness and overall enjoyment on a 7-point Likert scale from
do not agree at all to totally agree. The items included statements
such as “The VR experience was graphically pleasing”, which were
adjusted for the control conditions. Previous experience with VR
was captured as well. Demographic information was collected
with regard to age, gender, education, as well as a onedimensional political orientation measure with seven options
from left to right.
2.5 Equipment
The study used both the HTC Vive and the HTV Vive Pro
(Wireless) in conjunction with a computer using an Intel® Core
i7-7700K CPU at 4.20 GHz, 16.0 GB RAM, and an NVIDIA GeForce
GTX 1080 Ti graphics processing unit.
™
2.6 Analysis
The analysis consisted of three steps: First, we used a manipulation
check to confirm the intended perception of the stimuli in the different
conditions, including the perceived level of realism. Then, the VR
conditions were compared to the control conditions with a focus on
differences in before-after changes in the different environmental
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FIGURE 4
Conditional mean and CI of all four DV’s comparing the three VR conditions. CCS has been transformed back to its original scale.
represented the average increase over time across all three VR
conditions. Interaction coefficients now represent the difference in
slope of a particular condition compared to the average time slope. We
conducted contrast analyses–again controlling the false discovery
rate–to detect whether a dependent variable increased over time in
any condition. The interaction coefficient indicated whether the
increase over time differed between the respective condition and
the average increase across all three conditions. Comparing to a
model with only main effects (and no interaction effect) provided a
statistical test of the interaction effect.
The analysis was conducted in the open-source statistical software
program R (R Core Team, 2018). All mixed effects models were fit
using the lme4 and the lmerTest packages (Bates et al., 2015;
Kuznetsova et al., 2017).
strategy was that a singular fit resulting from the model described
would lead to first the removal of the random intercept per
condition–this happened for IAT, NR and NEP–and if still singular
to the removal of the random time slope for condition–this happened
for CCS.
Model diagnostics revealed substantive violations of model
assumptions for CCS (heteroscedasticity and non-normality of
residuals). A Box-Cox transformation of the CCS measure resulted
in a much better model appropriateness/no violations of assumptions
and did not change inference results. The Box-Cox transformation
parameter (λ = −1.596) was estimated based on a linear model
disregarding the repeated measure structure. All reported CCS
results are therefore based on the transformed variable. All other
scales did not reveal any violations of model assumptions or outliers
and therefore did not require any transformations.
To compare the efficacy of the intervention between the three VR
conditions, models with the predictors time, condition and their
interaction term were used. In this model, a random intercept per
participant accounted for the repeated measures structure. Further
including a random time slope for each participant resulted in an
identifiability error due to the fact that there was only one observation
per time point (saturated model) and was thus not included in the final
model.
Similarly to the variable type in the above analysis, condition was
deviation contrast coded. This meant that the simple slope of time
Frontiers in Virtual Reality
3 Results
3.1 Manipulation check
Suspension of Disbelief and presence were measured in all but the
two text conditions. Participants of the plain text condition (text) did
not rate the experience on graphical pleasantness or visual realism
either (see Figure 2) The comparison between the three VR conditions
and the three control conditions showed that the VR conditions were
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TABLE 3 Models comparing the three VR conditions. Condition is deviation coded, i.e. the coefficient condition_real + represents the difference between the realistic VR
condition compared to the average value over all three conditions at the first measurement.
DV
Coef
t value
df
p-value
IAT
(Intercept)
0.08
[-0.109; 0.266]
0.81
106.86
0.42
condition_real+
0.10
[-0.166; 0.364]
0.72
106.86
0.47
−0.23
[-0.491; 0.039]
−1.65
106.86
0.10
0.07
[-0.043; 0.173]
1.17
66.00
0.24
−0.08
[-0.23; 0.076]
−0.98
66.00
0.33
condition_abstract:time
0.04
[-0.114; 0.192]
0.50
66.00
0.62
(Intercept)
0.23
[0.181; 0.283]
8.84
131.56
0.00
−0.06
[-0.129; 0.014]
−1.54
131.56
0.12
0.01
[-0.057; 0.086]
0.38
131.56
0.70
−0.01
[-0.038; 0.009]
−1.21
66.00
0.23
condition_real+:time
0.01
[-0.018; 0.048]
0.88
66.00
0.38
condition_abstract:time
0.01
[-0.026; 0.04]
0.41
66.00
0.68
(Intercept)
3.81
[3.674; 3.939]
55.49
129.34
0.00
condition_real+
0.10
[-0.091; 0.283]
0.99
129.34
0.33
−0.29
[-0.476; −0.102]
−2.98
129.34
0.00
condition_abstract
time
condition_real+:time
CCS
condition_real+
condition_abstract
time
NR
condition_abstract
NEP
Estimate
CI
time
0.08
[0.009; 0.144]
2.21
66.00
0.03
condition_real+:time
0.01
[-0.081; 0.11]
0.30
66.00
0.77
condition_abstract:time
0.04
[-0.058; 0.132]
0.76
66.00
0.45
(Intercept)
3.72
[3.591; 3.85]
55.49
132.00
0.00
condition_real+
−0.04
[-0.228; 0.138]
−0.48
132.00
0.63
condition_abstract
−0.06
[-0.243; 0.123]
−0.63
132.00
0.53
time
0.09
[0.031; 0.154]
2.94
66.00
0.01
condition_real+:time
0.02
[-0.064; 0.11]
0.52
66.00
0.60
condition_abstract:time
0.03
[-0.055; 0.119]
0.71
66.00
0.48
presence (IPQ) nor suspension of disbelief (SOD) varied
significantly between the three VR conditions, SOD: Fcondition =
0.58, p = 0.562; IPQ: Fcondition = 1.63, p = 0.204.
To summarize, the three VR environments varied in realism
and to some degree in graphical pleasantness while the overall
experience (IPQ, SOD) remained unchanged (as visualized in
Figure 2). Overall, participants found the VR experience more
exciting and enjoyable than the control conditions, and presence
was higher when comparing the VR conditions to the video
condition. We therefore conclude that the manipulation of the
VR environments was successful.
rated as more exciting (Mtype = 6.38, Mcontrol = 5.53) and more
enjoyable (Mtype = 6.38, Mcontrol = 6.45) than the control
conditions, excitement: F = 21.22, p < 0.001; enjoyment: F = 17.37,
p = 0.013. Pleasantness of the experience was not affected, neither was
realism nor graphical pleasantness when comparing the VR conditions
to the video condition. While presence (IPQ) was significantly higher
in the VR conditions compared to the video condition, suspension of
disbelief (SOD) was not; IPQ: βtype = 1.085, F = 37.323, p < 0.001; SOD:
βtype = 0.354, F = 1.886, p = 0.173. As intended, perceived realism of the
environment differed between the three VR conditions with the real +
environment being most and the abstract environment being the least
realistic, Fcondition = 4.49, p = 0.0148; Mreal+ = 5.43, Mreal− = 5.087,
Mabstract = 4.43. We designed the VR environments to differ in
graphical pleasantness, with real+ and abstract aiming to be the
more pleasant environments. Yet, graphical pleasantness was
higher in both realistic conditions compared to the abstract
condition, Fcondition = 3.21, p = 0.047, Mreal+ = 5.96, Mreal− = 5.35,
Mabstract = 4.91. The different VR conditions had no significant
influence on enjoyment, pleasantness, and excitement. Neither
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3.2 VR vs. control
In Figure 3 we display the estimated means of each dependent
variable including bootstrapped confidence intervals for participants in
the VR conditions and the participants in the control conditions over
both time points. Estimated model parameters can be found in Table 2.
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Overall, CCS scores did not decrease significantly over time,
βtime = −0.015 95% CI [−0.038, 0.009], p = 0.229. However, this
effect did not differ between the conditions, χ2(df = 2) = 1.806, p =
0.4052. Contrast analysis provided some further insight: The CCS
score did not decrease significantly in any of the conditions. Contrast
analysis revealed a trend for decrease of CCS over time in real-only,
real-: βtime = −0.018, t(66) = −1.758, p = 0.083; real+:βtime = 0.000, t
(66) = 0.021, p = 0.983; abstract: βtime = −0.003, t(66) = −0.365,
p = 0.716.
NR increased significantly over time across all VR conditions,
βtime = 0.077, 95% CI [0.009, 0.144], p = 0.030. The effect did not differ
between the conditions, though, χ2(df = 2) = 1.236, p = 0.539. When
looking at the time effect within each condition separately none of the
slopes remained significant – likely due to a reduction of power and
multiple testing adjustment. Only in the abstract condition the time
effect bordered on significance, abstract: βtime = 0.0569, t(66) = 1.901,
p = 0.0616; real+ and real-: p > 0.1. On a further note, it was evident
that - across both time points - participants in the abstract condition
scored much lower in NR compared to the average score,
βcondition−abstract = −0.289, 95% CI [−0.476, −0.102], p = 0.003.
IAT scores did not increase significantly over time across all
conditions, βtime = 0.065, 95% CI [−0.043; 0.173], p = 0.245. The
time slopes did not differ between the conditions either, χ2(df = 2) =
1.000, p = 0.606. Furthermore, contrast analyses also did not reveal a
significant increase of IAT scores over time in any of the three
conditions, all effects p > 0.1.
Taken together, our results show that real, real+, and abstract
interventions did have a similar effects on changes (or no changes) of
environmental awareness over time. Specifically, while the VR
environments increased NEP and NR there was no evidence that
this effect varied between the VR conditions.
NEP increased significantly across all groups (VR and control),
βtime = 0.074, 95% CI [0.030, 0.117], p = 0.005. When looking at the
individual slopes per group (i.e. VR or control) a (simple slope)
contrast analysis revealed that while the time slope of the VR
group was significant, the slope of the control group was not, VR:
β = 0.046, t (14.4) = 2.83, p = 0.013; control: β = 0.027, t (13) = 1.72, p =
0.109. However, the interaction term was not significant, so the time
slopes did not significantly differ between the two groups, βtime:type =
0.002, 95% CI [−0.025, 0.063], p = 0.152.
Overall, CCS did not decrease significantly over time,
βtime = −0.012, 95% CI [−0.029, 0.006], p = 0.189 and VR and
control groups did also not differ in this regard as revealed in the
non-significant interaction effect, βtime:type = 0.003, 95% CI [−0.02,
0.015], p = 0.753.
There was no overall NR increase over time, βtime = 0.038, 95% CI
[−0.009, 0.085], p = 0.164. Further contrast analyses revealed that
while the time slope was essentially horizontal in the control group
there was a trend towards significance for the time slope in the VR
group, VR: β = 0.038, t(10.66) = 2.142, p = 0.056; control: β = −0.001, t
(9.67) = .035, p = 0.973. However, the difference in slopes between the
control group and the VR group was not significant, βtime:type = 0.08,
95% CI [−0.017, 0.172], p = 0.152.
Across all conditions, IAT scores did not change over time, βtime =
0.009, 95% CI [−0.085, 0.066], p = 0.822, but there was a trend for an
interaction between time and condition, βtime:type = 0.075, 95% CI
[−0.001, 0.150], p = 0.0824: IAT scores decreased in the control
conditions but increased in the VR conditions, as depicted in
Figure 3. However, further contrast analyses showed that the time
slope was also non-significant for both groups separately, VR: β =
0.032, t(23) = 1.118, p = 0.275; control: β = −0.042, t(21.2) = −1.47,
p = 0.156.
In summary, the directions of all estimates were coherent with our
hypothesis. NEP increased significantly across all groups. However,
even though interaction did not turn out to be significant, the contrast
analysis show that the difference is significant in the VR conditions but
not in the control conditions. There was a trend for an increase of NR
across the VR groups, but not in the control groups. However, none of
the results showed any significant difference between the time slopes in
the VR groups versus the control groups.
4 Discussion
The aim of this study was to examine whether an immersive
experience of climate change consequences in VR increases climate
change awareness. The visualization showed in time lapse how the
Swiss Aletsch glacier melts over a period of 220 years. Thus, by
providing an embodied experience that overcomes the temporal
distance of climate change consequences, we expected a stronger
impact of the immersive VR experience when compared to less
immersive 2D control conditions. Indeed, participants experienced
the VR conditions as being more exciting and enjoyable, and they had
a higher sense of presence when compared to the control conditions.
This confirms the general usefulness of VR interventions to convey
pro-environmental messages. Across all conditions, environmental
awareness (as measured by NEP) increased after the intervention. This
indicates that glacier melting is well suited to illustrate climate change
and that it affects peoples’ attitudes towards it. Not only is glacier
melting compelling evidence of global warming, it can also serve as a
symbol of climate change showing how dramatic changes of climate
are affecting our environment. Our results show that confronting
people with this phenomenon can positively influence environmental
awareness. Although the change in NEP did not differ between
immersive VR and the control conditions (non-significant time:type
interaction), the increase in environmental awareness was only
significant for the VR conditions, while there was no change for
the control conditions. While a straight-forward interpretation is not
3.3 Comparing VR conditions
The estimated means of the four environmental attitude measures
by condition are visualised in Figure 4. The parameters of the models
used can be found in Table 3. Whether the time slope differed
significantly between conditions was tested by comparing the full
model to a model without the time:condition interaction term.
Contrast analyses were used to get further insight into the time
slopes of each condition. Care must be taken not to overinterpret
contrast results when the interaction effect is not significant.
NEP increased significantly over time across all VR conditions,
βtime = 0.093 95% CI [0.031, 0.154], p = 0.005. The assigned VR
environment had no significant impact on the time slope as the model
comparison testing the time:condition interaction was non-significant,
χ2(df = 2) = 1.586, p = 0.452. Contrast analysis further showed that
NEP increased significantly in both real+ and abstract (βtime = 0.058, t
(66) = 2.121, p = 0.038 and βtime = 0.062, t(66) = 2.280, p = 0.026,
respectively), but not in real- (βtime = 0.019, t(66) = 0.689, p = 0.493).
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but not the control conditions, counteracted the decrease in the IAT
score due to repeated measurement. Thus, although the present results
do not allow to draw any firm conclusions, they nevertheless point to a
possible value of using implicit measurements for the evaluation of the
effectiveness of VR interventions on attitude change.
Another aim of the present study was to explore whether specific
VR design choices with regard to visual realism would affect climate
change awareness. Therefore, we varied the degree of visual realism
and sophistication of the VR environment and compared an abstract
and two realistic depictions of the Aletsch glacier melting. The results
show that our manipulations did work in the intended way: The
sophisticated realistic environment was perceived as the most realistic,
the abstract environment was perceived as least realistic. Crucially, we
found no evidence that realism in VR depictions favours presence or
any of the environmental measurements. The only hint that we found
was that environmental awareness (NEP) only increased for the welldesigned abstract and realistic condition, but not for the less
sophisticated realistic conditions. This result should be taken with
caution, however, because baseline values before VR exposure in the
real-condition were higher than in the other two conditions, whereas
values after the intervention were similar. Our results suggest that
visual realism per se does not necessarily foster changes in attitudes.
This has important implications. New consumers of VR experiences
base their expectations regarding visual fidelity on high-end graphics
in the gaming industry. However, it is expensive and time-intensive to
create high visual realism and budgets for non-profit and educational
applications are usually multiple orders of magnitude smaller when
compared to the gaming industry. We show that a less sophisticated
and less realistic, but visually coherent and authentic environment (the
abstract condition) exerts the same positive impact on climate change
awareness as the realistic environments (real- and real +). This is
consistent with previous studies that indicate, on the one hand, that
authenticity, coherence, and functionality of virtual environments are
as important or even more important than realism in achieving the
desired effect (Witmer and Singer, 1998; Hamstra et al., 2014;
Jacobson, 2017) and that, on the other hand, cartoon-like
visualizations can have the same effect as realistic ones (Huang,
2021). Our findings provide practitioners in the field of
environmental VR with an argument to shift scarce resources from
the realistic visual reconstruction to other aspects of production like
coherence, both in regards to look and content. Which of the various
other aspects of VR design ultimately influence attitude changes needs
to be investigated in future studies.
A few limitations should be considered. First of all, various
subjective decisions were made in the design of the VR
environments. We therefore cannot conclusively state that visual
realism plays no role in terms of presence or attitude change. It is
possible that our well designed and realistic condition was not realistic
enough or that the design of the less realistic condition (real-) was still
too well designed to induce differences in experiential effects between
the environments. Furthermore, we explored the effect of visual
realism in a specific context (glacier melting, attitude change),
which limits the generalizability of our results to other contexts
(e.g., learning about climate change). Moreover, the implicit
measurement that we developed in this study (IAT) can be
regarded as a promising starting point, but more effort is required
to develop and validate such implicit measurements.
Further, our sample size was not representative of the general
population. Consequently, there was limited variance in some of our
possible, we think that this result is of interest in light of a number of
previous studies that have shown that virtual realities have persuasive
potential because they are immersive and thus enable a presence
experience (e.g., Weibel et al., 2011; Weibel and Wissmath, 2011;
Makransky and Petersen, 2021). Because presence was higher in the
VR conditions when compared to the control conditions (i.e. the video
condition), it may be that presence accounts for at least part of the
increased environmental awareness. We therefore interpret these
results in favour of the potential of immersive VR in increasing
climate change awareness (Ahn S. J. et al., 2014a; Hsu et al., 2018;
Markowitz et al., 2018; Fauville et al., 2020; Petersen et al., 2020;
Chirico et al., 2021).
Since presence can contribute to something not only being
comprehended but actually experienced (cf. Weber et al., 2021, or
Tussyadiah et al., 2018, in the field of tourism), it can be assumed that
this very fact provides a benefit in the VR conditions and distinguishes
VR from the other conditions. In this sense, Wirth et al. (2007), for
example, assumes that presence is an “amplifier of media effects”
(p. 519).
We also expected a stronger increase in nature relatedness for
immersive VR (vs. control). There was no overall increase in nature
relatedness across all conditions, although there was a trend for an
increase exclusively in the VR conditions. The absence of a clear effect
of VR on nature relatedness is in line with other recent studies
(Soliman et al., 2017; Spangenberger et al., 2022). For example,
Spangenberger et al. used VR to “embody” participants with a tree
(participants could do small branch movements with their arms).
They found that the increased level of immersion in VR, when
compared to a video watching control condition, did not translate
into stronger effects on nature relatedness. These and our own results
suggest that nature relatedness may be difficult to modulate with
short-time interventions, particularly when using traditional media.
As pointed out by others, interactivity with the environment and
movements of the person’s own body resembling the virtual body
when interacting with the environment may be important factors in
increasing nature relatedness, which was not the case in the present
study (Ahn et al., 2016; Spangenberger et al., 2022).
In a similar vein, there was no change in climate change scepticism
(CCS). The absence of an effect on this measure might not be
surprising because of a floor effect: Values were already very low
for the vast majority of participants before the interventions (M =
1.5 on a scale from 1–5), leaving less room for changes to even lower
scores after the respective intervention. Moreover, people who are
sceptic about climate change may not be prone to changing their
minds as a result of interventions since their firm opinion may be
related to stable traits and political/ideological orientations (McCright
et al., 2016; Trémolière and Djeriouat, 2021). In addition to these
established measurements, we introduced an implicit measurement of
pro-environmental attitudes (Implicit Association Test - IAT) in this
study. There was no effect across all conditions and within each
condition separately (VR vs. control) on the IAT score. Noteworthy, in
a previous study, it has been found that the IAT score decreased in
both an active and passive control condition from the first to the
second measurement, suggesting that there might be an interventionindependent decrease in IAT effects due to practice (Rihs et al., 2022).
In this regard, not the absolute change but rather the relative change
between conditions might be relevant, and there was indeed a trend for
a differential change in the IAT score in this study (increase for VR vs.
decrease for control). This pattern of results may indicate that the VR,
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visualizations that show more drastic consequences of climate
change. It is possible that these would have an even stronger
impact on attitudes.
To conclude, this study shows that climate change consequences
can successfully be visualized in immersive VR, and that such
visualizations have the potential to change climate change
awareness, although the results need to be replicated and extended
with a larger and more diverse sample and validated implicit
measurements. The comparisons between immersive VR and the
control conditions showed a nuanced picture, with more
similarities than differences, highlighting that media comparison is
an important step for a critical assessment of the effectiveness of
immersive VR interventions.
measurements, which might have reduced the probability of finding a
change due to the VR interventions (see discussion above). In a similar
vein, our sample size was rather small, so that possible moderating
variables for the effect of the VR intervention on environmental
awareness could not be assessed. For example, Sajjadi et al. (2022)
found that serious games about system thinking had the strongest
effects on pro-environmental attitudes for those participants with less
past science education. Future studies should take such variables into
account.
The measurements that were used in this study were limited to
attitudes toward climate change and towards the environment.
When using VR as a persuasive tool to counteract climate change, it
would be promising for future research to also measure real (or at
least hypothetical) pro-environmental behaviour (Deringer and
Hanley, 2021). Moreover, future VR experiences could be more
interactive and may for example allow participants to learn more
about actions they can take to reduce greenhouse gas emissions and
other pro-environmental behaviour. This concurs with recent
claims that effective climate change interventions should also
focus on solutions rather than solely highlight impacts
(Markowitz et al., 2014). In addition, future studies could
include other indirect methods of measuring environmental
attitudes besides the IAT, which might have a more direct
behavioral link. One possibility would be to incorporate the
concept of System Thinking. According to Arnold and Wade
(2015) systems thinking involves synergistic and analytical skills
to identify and understand different components of a system as well
as their causal relationships. Lezak and Thibodeau (2016) were able
to show that systems thinkers tend to support policies on climate
change. Sajjadi et al. (2022) could show that a learning experience
can increase systems thinking and that systems thinking in turn
leads to policy support regarding measures concerning foodenergy-water nexus. The authors have developed an interesting
method to indirectly measure system thinking by assessing
different scenarios and their impacts. We consider it promising
to use this method in future studies similar to ours.
It should be clear that interventions such as the one assessed in this
study cannot solve current climate change problems. Exploring means
to increase environmental awareness is a step into the right direction,
but an urgent change in societal attitudes and behavioural adaptations
at the institutional and personal levels are required. In the future,
immersive VR applications might not only be used to visualize climate
change consequences, but maybe also to increase the awareness of
planetary limits, energy limits, and other contents that fosters the
critical reflection of consumption behaviour and economic growth
(Nardi et al., 2018).
According to various experts, glacier melt is the most tangible
manifestation of climate change, as it can impressively demonstrate
its effects (e.g., Price, 2009; Carey, 2010; Jackson, 2015). In our
study, we were able to show that the visualization of glacier melt
can have an influence on our attitude. However, it should be
mentioned that melting glaciers do not have a direct impact on
our personal lives unlike, for example, heat waves or floods.
Accordingly, Whitmarsh (2008) was able to show that personal
experience of air pollution influences perceptions and behaviors
related to climate change. Future studies could therefore draw on
Frontiers in Virtual Reality
Data availability statement
The analyzed data sets, the analysis script, and additional materials
for this study (e.g. stimuli) can be found on the Open Science
Framework repository: https://osf.io/y7nz5/.
Ethics statement
The studies involving human participants were reviewed and
approved by Ethics Committee of the Faculty of Human Sciences,
University of Bern. The patients/participants provided their written
informed consent to participate in this study.
Author contributions
DW and JC developed the idea for the study. The study was
designed by DW, JC, and ST with inputs regarding implementation
and experimental design from FM. JC created the three virtual
environments while DW created the stimuli of the three control
conditions. BM and ST were responsible for the data analysis and
inference. MH and ST wrote the manuscript with support from DW,
FM, JC, and BM.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
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