Development and validation of the Emotional Climate Change Stories (ECCS) stimuli set

Abstract

Climate change is widely recognised as an urgent issue, and the number of people concerned about it is increasing. While emotions are among the strongest predictors of behaviour change in the face of climate change, researchers have only recently begun to investigate this topic experimentally. This may be due to the lack of standardised, validated stimuli that would make studying such a topic in experimental settings possible. Here, we introduce a novel Emotional Climate Change Stories (ECCS) stimuli set. ECCS consists of 180 realistic short stories about climate change, designed to evoke five distinct emotions—anger, anxiety, compassion, guilt and hope—in addition to neutral stories. The stories were created based on qualitative data collected in two independent studies: one conducted among individuals highly concerned about climate change, and another one conducted in the general population. The stories were rated on the scales of valence, arousal, anger, anxiety, compassion, guilt and hope in the course of three independent studies. First, we explored the underlying structure of ratings (Study 1; n = 601). Then we investigated the replicability (Study 2; n = 307) and cross-cultural validity (Study 3; n = 346) of ECCS. The collected ratings were highly consistent across the studies. Furthermore, we found that the level of climate change concern explained the intensity of elicited emotions. The ECCS dataset is available in Polish, Norwegian and English and can be employed for experimental research on climate communication, environmental attitudes, climate action-taking, or mental health and wellbeing.

Supplementary Information
The online version contains supplementary material available at 10.3758/s13428-024-02408-1.

Full text

Vol.:(0123456789)
Behavior Research Methods
https://doi.org/10.3758/s13428-024-02408-1
ORIGINAL MANUSCRIPT
Development andvalidation oftheEmotional Climate Change Stories
(ECCS) stimuli set
DominikaZaremba1· JarosławM.Michałowski2· ChristianA.Klöckner3· ArturMarchewka1· MałgorzataWierzba1
Accepted: 18 March 2024
© The Author(s) 2024
Abstract
Climate change is widely recognised as an urgent issue, and the number of people concerned about it is increasing. While
emotions are among the strongest predictors of behaviour change in the face of climate change, researchers have only
recently begun to investigate this topic experimentally. This may be due to the lack of standardised, validated stimuli that
would make studying such a topic in experimental settings possible. Here, we introduce a novel Emotional Climate Change
Stories (ECCS) stimuli set. ECCS consists of 180 realistic short stories about climate change, designed to evoke five dis-
tinct emotions—anger, anxiety, compassion, guilt and hope—in addition to neutral stories. The stories were created based
on qualitative data collected in two independent studies: one conducted among individuals highly concerned about climate
change, and another one conducted in the general population. The stories were rated on the scales of valence, arousal, anger,
anxiety, compassion, guilt and hope in the course of three independent studies. First, we explored the underlying structure
of ratings (Study 1; n = 601). Then we investigated the replicability (Study 2; n = 307) and cross-cultural validity (Study 3;
n = 346) of ECCS. The collected ratings were highly consistent across the studies. Furthermore, we found that the level of
climate change concern explained the intensity of elicited emotions. The ECCS dataset is available in Polish, Norwegian
and English and can be employed for experimental research on climate communication, environmental attitudes, climate
action-taking, or mental health and wellbeing.
Keywords Climate change· Pro-environmental behaviour· Emotion· Climate emotions· Valence· Arousal
Introduction
The climate crisis is recognised as the greatest threat that
humanity has ever faced. The Intergovernmental Panel
on Climate Change’s 2021 report clearly states that ‘it is
unequivocal that human influence has warmed the atmos-
phere, ocean and land’. Widespread and rapid changes
in the atmosphere, ocean, cryosphere and biosphere
have occurred (IPCC, 2021). With the growing aware-
ness of climate change and the associated risks, people
start to experience intense emotions (Leiserowitz etal.,
2021; Marczak etal., 2023; Marks etal., 2021; Zaremba
etal., 2023). In turn, emotions shape one's attitudes and
behaviour in the face of climate change, including cli-
mate change attitudes (Rode etal., 2021), climate change
risk perception (van der Linden, 2015) and willingness to
engage in climate change mitigation (Xie etal., 2019), as
well as endorsement of climate policies (Bouman etal.,
2020; Rees etal., 2015; Wang etal., 2018). According
to the literature, factors such as gender, political views,
Artur Marchewka and Małgorzata Wierzba shareequal senior
contribution.
* Dominika Zaremba
d.zaremba@nencki.edu.pl
* Artur Marchewka
a.marchewka@nencki.edu.pl
* Małgorzata Wierzba
m.wierzba@nencki.edu.pl
1 Laboratory ofBrain Imaging, Nencki Institute
ofExperimental Biology, Polish Academy ofSciences,
Warsaw, Poland
2 Poznan Laboratory ofAffective Neuroscience, SWPS
University, Poznań, Poland
3 Department ofPsychology, Norwegian University ofScience
andTechnology, NTNU, Trondheim, Norway

Behavior Research Methods
understanding of climate change causes and impacts,
social norms, value orientations and personal experiences
with extreme weather events are all recognised as mean-
ingful predictors of climate action (van der Linden, 2015).
Affect and emotions stand out as one of the major deter-
minants (Brosch, 2021). Negative affect toward climate
change was found to be the single largest predictor of all
examined cognitive, experiential and socio-cultural factors
(van der Linden, 2015). In another meta-analysis, negative
affect was identified as one of the largest predictors of
climate action, along with descriptive norms, perceived
self-efficacy and outcome efficacy (van Valkengoed &
Steg, 2019). Interestingly, most of these findings come
from qualitative and questionnaire studies. The results of
experimental studies, however, often yielded inconclusive
results (Brosch, 2021; Reser & Bradley, 2017; Schneider
etal., 2021). The underlying cause can be attributed to
differences in operational definitions and measurement of
climate emotions (Chapman etal., 2017).
In the field of environmental psychology, researchers
often investigate a wide variety of emotions related to cli-
mate change (henceforth: climate emotions), such as guilt,
anger (Bissing-Olson etal., 2016; Harth etal., 2013), com-
passion (Swim & Bloodhart, 2015), anxiety (Whitmarsh
etal., 2022) or hope (Bury etal., 2020). Each of these emo-
tions is associated with a specific appraisal pattern and a spe-
cific context in which it is experienced (Lazarus, 1991). In
fact, failure to provide consistent, replicable findings in this
domain of research was mainly attributed to the lack of focus
on specific appraisals that accompany the emotional experi-
ence of climate change (Brosch, 2021). Therefore, attempts
were made to identify distinct climate emotions (Marczak
etal., 2023; Pihkala, 2022; Zaremba etal., 2023) and cor-
responding appraisals, as well as to understand how they
motivate behaviour (Harth etal., 2013; Landmann, 2021;
Marczak etal., 2023; Zaremba etal., 2023). In the following
paragraphs, we will briefly define emotions commonlyiden-
tified as important motivators of climate action (Brosch,
2021; Reser & Bradley, 2017; Schneider etal., 2021; Shipley
& van Riper, 2022). We also specify how the experience of
each of these emotions translates to a certain behavioural
tendency. In Table1, we propose the formulation of cogni-
tive appraisals related to the investigated climate emotions.
In the context of climate change, anger can be understood
as a moral outrage directed at individuals, people in power
and institutions that deliberately and carelessly contribute
to climate change (Marczak etal., 2023; Zaremba etal.,
2023). In particular, it is experienced upon the realisation
that climate change affects especially those who contribute
the least to global emissions and those who will be most
vulnerable to its consequences (Landmann & Hess, 2017).
It increases arousal and activates behavioural tendencies
to punish those blamed for causing climate change (Harth
etal., 2013). The content of climate anger is relevant for the
type of pro-environmental behaviours it promotes—anger at
politicians and institutions predicts public sphere activism,
while anger directed at general human qualities and actions
predicts individual mitigation behaviours (Gregersen etal.,
2023; Kleres & Wettergren, 2017; Stanley etal., 2021).
Climate anxiety is perhaps the most studied climate emo-
tion and, as such, it has been operationalised in different
ways. Some scholars frame it as disproportionate, debilitat-
ing, intense anxiety (Coffey etal., 2021) that results in active
avoidance of the problem of climate change (Stanley etal.,
2021). However, anxiety can also be defined as an adaptive
reaction, in which climate change is perceived as a real, urgent
and severe threat that needs to be addressed (Marczak etal.,
2023; Zaremba etal., 2023). This type of anxiety is related to
the action tendency to look for solutions and mitigate climate
change (Pihkala, 2020). Thus, such anxiety can predict pro-
environmental behaviours (e.g. Clayton & Karazsia, 2020;
Helm etal., 2018; Hogg etal., 2021; Whitmarsh etal., 2022).
Compassion is experienced as one witnesses the suffer-
ing or undeserved harm of another being and results in the
tendency to approach, help and support (Goetz etal., 2010;
Landmann & Hess, 2017). The mobilising effect of compas-
sion can be explained by the reduced psychological distance
to climate change and its impact on all living things and
beings (McDonald etal., 2015). It increases endorsement of
climate mitigation efforts (Lu & Schuldt, 2016) and climate
change activism (Swim & Bloodhart, 2015).
Guilt arises when one perceives their behaviour as incon-
gruent with their moral standards (Tracy & Robins, 2007), or
when one’s in-group is recognised as collectively responsible
for causing harm (Wohl etal., 2006). It triggers action ten-
dencies such as reparation and compensatory efforts (Harth
Table 1 Example of cognitive appraisals behind selected climate emotions
Emotion Cognitive appraisal
Anger ‘Climate change is a result of harmful actions of individuals or institutions that I don’t identify with.’
Anxiety ‘Climate change is a real, urgent and severe threat to myself and to valued people and places.’
Compassion ‘Climate change results in undeserved and omittable suffering and harm to innocent beings.’
Guilt ‘Climate change is a result of my harmful actions or actions of groups that I identify with.’
Hope ‘Climate change can be limited with collective, coordinated, proactive efforts.’

Behavior Research Methods
etal., 2013; Parkinson etal., 2005; Smith & Ellsworth, 1985).
In general, guilt predicts pro-environmental intentions and
behaviours (Hurst & Sintov, 2022; Shipley & van Riper, 2022)
and is an important moderator between climate change belief
and mitigation behaviours (Ferguson & Branscombe, 2010).
In the case of hope, there is still considerable controversy,
and researchers currently distinguish between the ‘false
hope’ or denial-based hope, related to misguided beliefs
about climate change, and the ‘constructive hope’, related to
trust that climate change can still be halted with collective,
coordinated efforts (Brosch, 2021). The latter type of hope
was positively related to self-reported pro-environmental
behaviour, support of specific policies and political engage-
ment (Feldman & Hart, 2016, 2018; Ojala, 2015), and was
shown to motivate climate action (Chadwick, 2015).
To date, the field of environmental psychology lacks ways
to reliably elicit distinct climate emotions in experimental set-
tings. Most previous studies used ad hoc stimuli, such as news
reports (Chu & Yang, 2019; Nabi etal., 2018; O’Neill etal.,
2013), photos (Gehlbach etal., 2022) or fictitious radio reports
(Gustafson etal., 2020). While such stimuli are ecologically
valid, they are difficult to use in experimental studies, in which
strictly controlled conditions are required to establish a cause-
and-effect relationship between variables. Therefore, there is a
growing need for the development of validated emotional stim-
uli databases. To the best of our knowledge, there are only three
available stimuli sets suitable for studying climate emotions.
The first one, the Affective Climate Images Database (Lehman
etal., 2019) comprises 320 pictures relevant to climate
change. The second one, the Extreme Climate Event Database
(EXCEED; Magalhães etal., 2018), consists of 150 pictures
depicting natural disasters related to climate change. Finally,
Climate Visuals (climatevisuals.org; Chapman etal., 2016)
is a collection of pictures relevant to climate change selected
based on both qualitative and quantitative criteria. Importantly,
stimuli in the abovementioned datasets have been characterised
only in terms of their dimensional properties, such as valence
and arousal (Bradley & Lang, 1994; Stevenson etal., 2007),
while in the context of climate change, there is a clear need to
study discrete emotions (Barrett, 2006; Brosch & Steg, 2021).
Empirical evidence demonstrates that it is important to control
both dimensional and discrete properties of emotional stimuli
(Briesemeister etal., 2014; Harmon-Jones etal., 2017).
Furthermore, despite the long tradition of using images in
emotion research (Lang & Bradley, 2007; Marchewka etal.,
2014; Michałowski etal., 2017), images may not always be
well suited for the task of studying climate emotions. First
and foremost, climate change is a multifaceted phenomenon
that cannot be easily captured in a single frame. Images tend
to be ambiguous, and it is therefore difficult to use them
in emotion research while controlling for the associated
appraisal pattern (Brosch, 2021). Finally, ecologically valid
natural scenes are unsuitable for experimental designs that
require strict control of physical properties (e.g. resolution,
contrast, luminance). In contrast, textual stimuli (e.g. news
reports, personal stories) have been used to convincingly
describe the complex realities of climate change (e.g. Lu &
Schuldt, 2015). Recent research suggests that naturalistic
stimuli such as narratives are especially promising, as they
provide all the relevant context and can be inspiring and
deeply touching, thus inducing strong emotions (Goldberg
etal., 2014; Saarimäki, 2021; Weber, 2006). Such natural-
istic stimuli are frequently used in research, as they enable
the study of many psychological processes, such as emotion
(Jääskeläinen etal., 2021; Saarimäki, 2021), social percep-
tion (Mar, 2011; Redcay & Moraczewski, 2020) and lan-
guage (Hamilton & Huth, 2020), in ecologically valid condi-
tions. Despite their complexity, textual naturalistic stimuli
can be easily edited (e.g. change of characters, change of cul-
tural context) to meet the requirements of a particular study.
On the other hand, naturalistic stimuli pose many meth-
odological challenges. Processing of complex textual stimuli
demands active construction and simulation of the situation
described in the story and involves many processes, such
as linguistic processing, perspective taking, empathy, moral
reasoning and autobiographical memory (Hsu etal., 2015).
Thus, experiments using naturalistic stimuli are challenging
to design and their results more difficult to interpret.
Current study
In this article, we describe the development and validation of
the Emotional Climate Change Stories (ECCS) stimuli set.
ECCS consists of short naturalistic stories in which climate
change is placed in the context of personal experience, rather
than framed as an abstract scientific phenomenon (Harris,
2017; Morris etal., 2019; van der Linden etal., 2015). The
stories were designed to elicit five distinct climate emotions—
anger, anxiety, compassion, guilt and hope—all of which
were indicated as motivating climate action. The ECCS set
was developed in a series of studies conducted in Poland and
Norway, two countries heavily dependent on fossil fuels, but
with different policies towards achieving climate neutrality
(Marczak etal., 2023; Zaremba etal., 2023). The validation
procedure was conducted in line with previous studies (Brad-
ley & Lang, 1994, 1999; Lang & Bradley, 2007; Marchewka
etal., 2014; Wierzba etal., 2015). First, the ratings were col-
lected in Poland from a large opportunity sample (Study 1,
n = 601), as well as from an independent purposive sample
with a demographic profile reflecting the population of Poland
(Study 2, n = 307). This step was crucial to ensuring the high
quality of ECCS ratings and investigating the replicability of
the findings. Next, the ratings were collected in Norway from
a purposive sample with a demographic profile reflecting the
population of Norway (Study 3, n = 346) for the purpose of
validating the ECCS ratings in a different culture.

Behavior Research Methods
Method
Materials
The stories in ECCS were inspired by the material col-
lected in two qualitative studies conducted in Poland. In
each case, we identified excerpts in which participants
reported having experienced five emotion categories of
interest: anger, anxiety, compassion, guilt and hope. In
the first qualitative study (n = 40), we conducted semi-
structured in-depth interviews with people strongly con-
cerned about climate change (Zaremba etal. 2023). In this
study, participants freely described a wide array of emo-
tions experienced in the face of climate change and the
context in which these emotions emerged. The collected
material was analysed in a multi-step thematic analysis: the
content of the interviews was carefully tagged by several
researchers independently, and then used to identify recur-
ring themes (e.g. drought, storm, heatwave, news in social
media, politicians, talking to family). All the details can be
found in the original work by Zaremba etal. (2023). In the
second qualitative study (n = 523), we conducted a short
online survey on a large opportunity sample. In this study,
participants briefly described situations in which they spe-
cifically experienced anger, anxiety, compassion, guilt and
hope in the context of climate change. The survey mate-
rial was reviewed and tagged according to the thematic
structure developed during the interview analysis (Zaremba
etal. 2023). Based on the collected material (the qualitative
interviews and the survey data), we generated several hun-
dred short stories, representing the most commonly recur-
ring themes. Finally, we selected 30 coherent and diverse
stories representing each emotion category (150 stories in
total). Furthermore, 30 neutral stories were developed on
the basis of a standardised list of affect-related life events
(Cohen etal., 2018).
The stories were professionally translated into Nor-
wegian and English. Furthermore, the stories underwent
proofreading to ensure they were adjusted to the cultural
context, avoiding content that might be specific to a par-
ticular culture and could potentially be unfamiliar or less
relevant in other cultural settings. Importantly, the ECCS
stories are of similar length (Polish: M = 307.2, SD = 44.49;
Norwegian: M = 295.2, SD = 49.35; English: M = 304.0,
SD = 51.65). The full list of stimuli included in ECCS can
be found in the Supplementary Materials. The most rep-
resentative stories in each emotion category are presented
in Table2, and the mean story length (number of charac-
ters) for each language version of ECCS is summarised in
TableS1.
Participants
Participants eligible to join the studies were native speakers
of Polish (Study 1, 2) and Norwegian (Study 3). This crite-
rion ensured that the participants had a solid understanding
of their respective languages and minimised the possibility
of inaccuracies or inconsistencies in the data obtained. Fur-
thermore, we collected demographic data about participants’
gender, age, place of residence, education, parenthood sta-
tus, occupation, climate activism and socio-economic sta-
tus, as well as information about their belief in and concern
about climate change (details provided in TableS2 of the
Supplementary Materials).
Table 2 Stories that were found to best represent each emotion category
Type Example story
Anger Lisha is very rich and likes to change her wardrobe frequently. Ever since she has discovered a website with very cheap clothes,
she orders 20–30 items a week. Some turn out to be a poor fit, so she throws them straight into the garbage bin. Still, Lisha does
not consider this wasteful, because the clothes were ridiculously cheap.
Anxiety Ada studies the oceans. Recently she had to double check her calculations, which indicated that melting Arctic ice may submerge
areas inhabited by tens of millions of people. Unfortunately, her calculations turned out to be correct.
Compassion It was a hot day when Emilie was on a bus with her seriously ill rabbit, taking it to the vet. The rabbit had trouble breathing, and
the poor air conditioning was not able to cool the bus sufficiently. Before she could get to the vet, the rabbit died in its cage.
Emilie got out, sat down at an empty bus stop and began to cry.
Guilt By using plastic, we contribute to the growing environmental catastrophe every day. Even if we segregate waste carefully, there
is no guarantee that plastic will be recycled. Patches of plastic garbage float on the surface of the oceans, the largest of which is
three times the size of France.
Hope Yoshida has discovered a new species of bacteria. The bacteria are able to break down plastic that would otherwise be deposited
in landfills. Yoshida's discovery gained so much publicity that he was awarded another research grant. There are reasons to hope
that his solution can be implemented on a large scale.
Neutral Monica entered the room, opened the wardrobe and bent down to reach into one of the lower shelves. She took out her brown
pants and put them on. Then she opened a drawer in her dresser and took out a belt. She passed it through the belt loops on her
pants and fastened the buckle.

Behavior Research Methods
Study 1
A total of 749 Polish residents were recruited via advertisements
on social media and through the SWPS University mailing list.
After a data quality check, data from 601 individuals (504
women, 92 men and five non-binary persons) were retained.
Depending on the recruitment platform, participants received
no remuneration or were remunerated with student credit points.
Study 2
A total of 349 Polish residents were recruited by a profes-
sional company. Purposive sampling was used in order to
reach a diverse group of participants, with a demographic
profile broadly reflecting the population of Poland. After a
data quality check, data from 307 individuals (164 women,
142 men and one non-binary person) were retained. Partici-
pants received remuneration equivalent to €2.
Study 3
A total of 450 Norwegian residents were recruited by a profes-
sional company. Purposive sampling was used in order to reach a
diverse group of participants, with a demographic profile broadly
reflecting the population of Norway. After a data quality check,
data from 346 individuals (181 women and 165 men) were
retained. Participants received remuneration equivalent to €0.50.
Procedure
The procedures used in Studies 1–3 were as closely matched as
possible. Participants completed the procedure remotely, work-
ing on their own devices (e.g. desktops, tablets, mobile phones).
A purpose-built, secure web application was used to collect the
ratings. The participants first read the description of the aims of
the study, and provided their informed consent and demographic
data. Then, participants were informed that they would be asked
to read stories describing different situations and rate each of the
stories on several scales. Participants provided their ratings using
a slider from 0 to 99, with the bounds of the scales explicitly
defined in the following way: valence (from negative emotions,
through no emotions, to positive emotions); arousal (from no
arousal to strong arousal); emotion categories: anger, anxiety,
compassion, guilt, hope (from not at all, to to a large extent).
The participants were able to return to the instruction screen
at any time during the task. The stories to be rated in a given
session were randomly selected from the initial pool of stories.
However, stories with the smallest number of ratings collected
so far had a greater chance of being selected. During the assess-
ment task, each trial began with a display of a story in full-screen
mode. Next, on the following screen, the participants were still
able to see the story in the upper part of the screen, but this time,
they were asked to rate the story in terms of valence, arousal,
as well as the extent to which the story elicited anger, anxiety,
compassion, guilt and hope. As soon as all the responses were
submitted, the next trial would begin. There was no time limit to
complete the task, but the time spent on reading the story and the
time spent providing the ratings was recorded. After completing
the ratings for 10 stories, participants could choose to continue
rating additional stories or finish the task.
Data preprocessing
To ensure data quality, we defined the following criteria: (1)
only adult, native Polish speakers (in the case of Study 1 and
Study 2)/native Norwegian speakers (in the case of Study 3)
were permitted to join the study; (2) their responses regarding
gender, age and the level of climate change concern had to be
consistent with their responses to the same questions in the
screening survey (applicable to Study 2 and Study 3 only);
(3) participants had to complete ratings for at least 10 stories.
Results
Here, we will use the following abbreviations to denote story
types: ANG, anger; ANX, anxiety; COM, compassion; GUI,
guilt; HOP, hope; NEU, neutral. Otherwise, we will use full
terms to denote rating scales: valence, arousal, anger, anxiety,
compassion, guilt and hope.
General information aboutthecollected ratings
A complete list of stories in all available language versions
(Polish, Norwegian and English), together with their mean
ratings, can be found in the Supplementary Materials. On
average, each story was rated by 147.6 people (min = 141,
max = 155), and depending on the study, a participant rated
on average 19.93–23.41 stories. Depending on the study,
the mean story presentation time was 6.8–11.5 seconds, and
the mean story evaluation time was 16.4–19.5 seconds. A
detailed report on the total number of ratings and the number
of stories rated per participant in each study can be found in
TableS3 in the Supplementary Materials.
Dimensional characteristics
First, we investigated the distribution of valence and arousal
ratings, which are commonly used to characterise emotional
stimuli (e.g. Lang & Bradley, 2007; Lehman etal., 2019;
Magalhães etal., 2018; Marchewka etal., 2014; Wier-
zba etal., 2015, 2022). Figure1 presents the pattern of
results obtained in Studies 1–3. In agreement with previous

Behavior Research Methods
research, we observed a non-linear relationship between
valence and arousal ratings. Furthermore, three character-
istic clusters emerged: stories evoking negative emotions
(ANG, ANX, COM and GUI stories, characterised by high
arousal and low valence), neutral stories (NEU stories char-
acterised by low arousal and moderate valence) and stories
evoking positive emotions (HOP stories, characterised by
high arousal and high valence). Thus, stimuli rated more
extreme in terms of valence (either more negative or more
positive) were also rated high in terms of arousal. Although
the dispersion of ratings in the dimensions of valence and
arousal was greater in Study 1 than in Studies 2 and 3, the
general pattern of results seems consistent across the studies.
Comparisons of ratings of each story between studies can be
found in FiguresS1 and S2 in the Supplementary Materials.
Climate emotions
Next, we explored the distribution of ratings in terms of
distinct climate emotions: anger, anxiety, compassion, guilt
and hope. The general distribution of mean ratings for each
rating scale and each study is presented separately in Fig.2.
Furthermore, we statistically examined the effects of story
type and study on the mean ratings of anger, anxiety, com-
passion, guilt and hope. In particular, we were interested in
determining whether stories belonging to a specific story
type were rated similarly on the scale relevant for that story
type (e.g. anger ratings for ANG stories, anxiety ratings for
ANX stories). As the initial step, we performed a MANOVA
with story type and study as factors. We found significant
main effects for story type, F(5, 35,050) = 331.05, p < .001,
Pillai’s trace = .955, and study, F(2, 14,014) = 144.26,
p < .001, Pillai’s trace = .187, revealing that both factors
had a significant overall impact on the collected ratings.
Furthermore, a significant interaction effect was observed,
F(10, 35,050) = 36.61, p < .001, Pillai’s trace = .248). To
further investigate this interaction, ANOVA analyses were
performed for each rating scale separately and corrected
for multiple comparisons. In each case, the ANOVAs were
followed by post hoc comparisons to further investigate
whether the ratings collected in the compared studies dif-
fered. For simplicity, we focused on comparing ratings on
story-type relevant scales.
First, we compared samples with different motivations
to participate in the study (Study 1: opportunity sample,
Study 2: purposive sample). The ANOVAs (with a cor-
rection for multiple comparisons) revealed a significant
interaction effect between story type and study for all of
the rating scales (anger: F(5, 5084) = 18.44, p < .0001, par-
tial η2 = .02; anxiety: F(5, 5084) = 17.14, p < .0001, partial
η2 = .02; compassion: F(5, 5084) = 30.012 p < .0001, partial
η2 = .03; guilt: F(5, 5084) = 7.32, p < .0001, partial η2 =.01;
hope F(5, 5084) = 35.87 p < .0001, partial η2 = .03). Post hoc
comparisons showed that none of the interactions between
story type and study were significant for the relevant scales
(ANG stories on anger scale: p = .64; ANX stories on anxi-
ety scale: p = .93; COM stories on compassion scale: p = .16;
GUI stories on guilt scale: p = .07; HOP stories on hope
scale: p > .99), indicating no significant differences in how
participants from the opportunity and purposive samples
rated stories of each type on the relevant scales (see TableS4
in the Supplementary Materials).
Next, we compared story ratings between samples
from different countries, where different language ver-
sions of ECCS were used (Study 2: Polish sample, Study
3: Norwegian sample). The ANOVAs (with a correction
for multiple comparisons) revealed a significant interac-
tion effect between story type and study for almost all
rating scales (anger: F(5, 3611) = 8.99, p < .0001, par-
tial η2 = .01; anxiety: F(5, 3611) = 4.09, p = .015, par-
tial η2 = .01; compassion: F(5, 3611) = 16.28, p < .0001,
partial η2 = .02; guilt: F(5, 3611) = 2.15, p = .84, par-
tial η2 < .01; hope F(5, 3611) = 5.97, p < .0001, partial
η2 = .01). Similarly, post hoc comparisons showed that
Fig. 1 Mean valence and arousal ratings for each story. Note: Individual stories are represented by dots. Colours denote different story types:
ANG - anger, ANX - anxiety, COM - compassion, GUI - guilt, HOP - hope, NEU - neutral

Behavior Research Methods
Fig. 2 Distribution of mean anger, anxiety, compassion, guilt and hope
ratings for each story type. Note: The results are presented separately
for each rating scale (rows) and each study (columns). Colours denote
different story types: ANG - anger, ANX - anxiety, COM - compassion,
GUI - guilt, HOP - hope, NEU - neutral

Behavior Research Methods
none of the interactions between story type and study
were significant for the relevant scales (ANG stories on
anger scale: p > .99, ANX stories on anxiety scale: p
> .99, COM stories on compassion scale: p = .86, GUI
stories on guilt scale: p = .33, HOP stories on hope scale:
p = .19), indicating no significant differences in how par-
ticipants from Poland and Norway rated stories of each
type on relevant scales (see TableS5 in the Supplemen-
tary Materials).
Impact ofclimate change concern onstory ratings
Finally, we investigated whether the level of concern
about climate change predicts the intensity of emotions
experienced when reading the ECCS stories. To start, we
divided our sample into three groups (of approximately
equal size), representing three levels of concern about
climate change: low (n = 395; score of 1–3 on a five-point
scale), medium (n = 500; score of 4 on a five-point scale)
and high (n = 343; score of 5 on a five-point scale). Next,
for each participant, we calculated their summary emotion
score, representing the intensity of experienced emotions.
The participant’s summary score was calculated for each
story type separately, using the following formula:
where
r
denotes the mean ratings for a given story type. The
summary score was then rescaled to take values between 0
and 1.
The relationship between the summary emotion score
and the level of climate change concern for each story type
is shown in Fig.3. To test this relationship statistically,
we performed a linear regression analysis. The model was
statistically significant, F(2, 6933) = 329.85, p < .001, sug-
gesting that the collected ratings could be predicted based
on participants’ climate change concern. However, the
model accounted for a rather weak proportion of variance
(R2 = 0.09, adj. R2 = 0.09). Results showed that participants
with medium climate concern had significantly higher scores
than those with low concern (β = 0.10, 95% CI [0.09, 0.12],
t(6933) = 15.37, p < .001), as did participants with high cli-
mate concern (β = 0.19, 95% CI [0.17, 0.20], t(6933) = 25.54,
p < .001).
Classification ofstories intoemotion classes
Previous research has demonstrated that the emotional
response to climate change is complex and multifaceted.
Similarly, we expected that realistic personal stories about
climate change would evoke a range of different emotions
simultaneously. For instance, a story about trees damaged
score
=
|
|
r
valence
−50
|
|
∗r
arousal,
by municipal workers could elicit feelings of compassion
towards the victims (the trees) and anger towards those
responsible for causing the harm (the people in power who
issued a permit to cut the trees). Each individual ECCS
story was rated according to the intensity of five emotions:
anger, anxiety, compassion, guilt and hope. In other words,
some stories could be associated mostly with one dominant
emotion (e.g. hope), while others could be related to several
emotions (e.g. anxiety and compassion) or none.
We used the collected data to identify stories that best
represented each emotion category. To achieve this, we
adopted a method introduced in our previous work (Wier-
zba etal., 2015, 2022). Here, we consider a five-dimensional
hypercube, with each axis corresponding to one of the emo-
tions. The ratings of a given story determine its position in
the hypercube. Five of the hypercube's corners represent the
emotion classes: [99 0 0 0 0] anger, [0 99 0 0 0] anxiety, [0 0
99 0 0] compassion, [0 0 0 99 0] guilt and [0 0 0 0 99] hope.
The origin, namely [0 0 0 0 0], represents the neutral class.
The distance of each story from each of the corners can be
calculated using the standard formula:
The distances are first calculated for each participant sep-
arately, based on the individual ratings contributed by each
person. Next, the distances are averaged over all participants
to yield a summary measure of the distance of each story
from each of the corners:
The following conditions must be fulfilled for a story to
be assigned to one of the classes: (1) the story’s distance to
the respective class must be smaller than a chosen threshold;
(2) the story must meet the first condition for one class only;
(3) if the story falls within an area of intersection of two (or
more) classes, it remains unclassified; (4) if the story does
not meet the first condition for any of the classes, it remains
unclassified; (5) the assigned class must match the initial
category label (i.e. the emotion the story was constructed
for).
Importantly, this method can be tailored to one's needs.
One approach is to set a threshold value for each class, which
will determine the size of each class (i.e. the number of sto-
ries it contains). Alternatively, one could set a desired class
size for each class, which would require a particular combi-
nation of threshold values to achieve.
First, we tested different values of thresholds by simul-
taneously increasing the threshold for each of the classes.
FigureS3 in the Supplementary Materials illustrates how
d
(p,q)=
√
(p1−q1)2+(p2−q2)2+... +(pk−qk)
2
d
=
1
n
(
∑
n
i=1
di)=
d
1+
d
1+
...
+
d
n
n

Behavior Research Methods
class sizes change as we gradually increase the thresholds
from 0 to 140 (the minimum and maximum possible distance
between the hypercube's corners, respectively). We observed
that it was especially easy to classify stories into the neutral
class, followed by the hope, anger and compassion classes.
Almost no stories were classified into the anxiety class, and
none were assigned to the guilt class.
Based on these findings, we attempted to create NEU, HOP,
ANG and COM classes of equal size. The threshold values
were determined with the help of a simple genetic algorithm
(the exact implementation of the algorithm is available in the
Supplementary Materials). Figure4 presents the resulting dis-
tribution of classes, each containing nine stories. The list of
classified stories can be found in the Supplementary Materials.
Fig. 3 The impact of climate concern on story ratings. Note: For sim-
plicity, we recoded the climate change concern from a five-point to a
three-point scale (low, medium, high). Colours denote different story
types: ANG - anger, ANX - anxiety, COM - compassion, GUI - guilt,
HOP - hope, NEU - neutral

Behavior Research Methods
Discussion
Recent findings indicate that emotions play a crucial role in
shaping people's perception of climate change. While most of
these findings come from qualitative and questionnaire stud-
ies, experimental studies are of particular importance as they
allow us to establish causal relationships between the studied
variables. Thus, there is a growing need for the development of
validated emotional stimuli databases, suitable for reliably and
effectively eliciting distinct emotions related to climate change
in experimental settings. The employment of such validated
stimuli sets, in contrast to ad hoc stimuli, enables researchers to
exert greater control over their experiments, thereby promoting
a higher level of objectivity in research inferences.
In this article, we describe the development and validation
of the Emotional Climate Change Stories (ECCS) database.
Fig. 4 Distribution of the ratings of stories from ANG, COM, HOP
and NEU classes identified with the classification algorithm. Note:
Here, rows and columns represent different rating scales: valence,
arousal, anger, anxiety, compassion, guilt, and hope. Each subplot
shows the distribution of the ECCS stories in the domain of two rat-
ing scales. For instance, the top-left corner shows the distribution
of the stories in the domain of valence (horizontal axis) and arousal
(vertical axis). Each dot represents one of 180 stories, with colours
denoting different classes identified with the classification algo-
rithm: ANG - anger, COM - compassion, HOP - hope, NEU - neutral.
Unclassified stories are marked in light grey for visualisation pur-
poses

Behavior Research Methods
ECCS is a collection of naturalistic, relatable stories that
place climate change in the context of personal experience,
thus avoiding the framing of this phenomenon solely as
a distant and abstract scientific fact (Morris etal., 2019;
Weber, 2006). For instance, some of the stories describe
people who learn about the consequences of climate change
and make personal choices that prioritise the environment
(Ockwell etal., 2009). This format has been confirmed as
an effective means of eliciting engagement in climate com-
munication research (Gustafson etal., 2020; Jones & Song,
2014; Moezzi etal., 2017). Importantly, the stories were
created based on qualitative in-depth interviews (Zaremba
etal., 2023), in which individuals freely described a wide
array of emotions experienced in the face of climate change
and the context in which these emotions emerged.
The data-driven, narrative origins of ECCS stories allow
for their analysis through the lens of empirical ecocriticism
(Schneider-Mayerson etal., 2020). Even though ECCS are
shorter than most texts investigated by ecocritics (novels,
poetry, children's literature, film, etc.), they definitely reflect
contemporary discourse on climate change. Future research
could use this approach to provide additional insights.
The ECCS stories are characterised in terms of their
dimensional properties, such as valence and arousal (Bradley
& Lang, 1994; Stevenson etal., 2007), as well as in terms of
discrete emotions (Barrett, 2006; Ekman, 1992). Moreover,
we demonstrate that the story ratings can be predicted by the
self-reported level of concern about climate change. This
relationship was consistently observed for all story types
except the neutral stories, providing evidence for the validity
of the collected ratings. In particular, our findings are in line
with the notion that individuals strongly concerned about the
environment should—through their personal, lived experi-
ences of climate change—find the stories more relatable and
thus report higher intensity of emotions (Morris etal., 2019).
Interestingly, although investigated, this relationship was not
observed for other existing datasets of stimuli related to cli-
mate change. Specifically, Lehman and colleagues (Lehman
etal., 2019) examined whether the valence and arousal rat-
ings of climate change images depended on participants’
environmental attitudes, but found no evidence for that.
As for the dimensional properties of ECCS, we observed
a non-linear relationship between valence and arousal. Spe-
cifically, stories that were rated more extreme in terms of
valence (either more negative or more positive) were also
rated as more arousing. This finding is in agreement with
many previous studies (Lang & Bradley, 2007; Magalhães
etal., 2018; Marchewka etal., 2014; Riegel etal., 2015;
Wierzba etal., 2015). Furthermore, negative stories were
rated more arousing than positive stories. Similarly, previ-
ous studies demonstrated that negative, disturbing pictures
of climate change were found most salient (Lehman etal.,
2019; Leiserowitz, 2006).
As for the discrete properties of ECCS, we were able
to identify stories that predominantly represent one emo-
tion category, such as anger, compassion or hope. ECCS
contains stories representing some of the most studied cli-
mate emotions: guilt, anger (Bissing-Olson etal., 2016;
Harth etal., 2013), compassion (Swim & Bloodhart,
2015), anxiety (Whitmarsh etal., 2022) or hope (Bury
etal., 2020). Each of these emotions is associated with a
specific appraisal pattern and a specific context in which
it is experienced (Brosch, 2021), and in turn can lead to
different behavioural tendencies (Böhm, 2003; Landmann,
2021; Marczak etal., 2023; Zaremba etal., 2023). Indi-
viduals from different populations did not differ in terms
of how they rated stories on relevant scales (e.g. ANG sto-
ries on the anger scale). However, they did differ in terms
of how they evaluated stories on the remaining scales (e.g.
ANG stories on the compassion scale). In particular, we
observed significant differences between Studies 1 and
2 (same country, different motivation to participate) in
this regard. In Study 1, both the range and the variance
of responses was greater than in Study 2. This suggests
that populations with different demographic profiles (e.g.
young, mostly student sample with non-financial motiva-
tion to participate vs general population with financial
motivation to participate) might show a slightly different
response pattern. Interestingly, ratings collected in Studies
2 and 3 (same motivation to participate, different country)
were much more similar, suggesting that the collected rat-
ings may be universal across the context of the Global
North. Overall, our results—replicated across different
populations and different cultures—suggest that by fram-
ing climate change in a specific context, one can reliably
elicit distinct climate emotions.
At the same time, our results clearly indicate that ECCS
stories differ in their potential to represent the emotion cat-
egory they were intended to evoke. In other words, some
stories convey a single, pure emotion, while others elicit a
blend of emotions. With the use of the classification method
described in the manuscript, we were able to identify stories
related strongly and specifically to anger, compassion and
hope, but we failed to identify stories related to anxiety and
guilt. ANX stories received comparably high anxiety and
compassion ratings, while also scoring relatively high on
anger and guilt scales. These results are in line with previous
findings, which suggest that these emotions often co-occur
(Hatfield etal., 2009; Marczak etal., 2023). Evoking strong
and specific guilt turned out to be especially challenging.
Cognitive appraisals related to the guilt category revolve
around the locus of responsibility fortaking part in causing
and solving climate change (Wang etal., 2018; Zaremba
etal., 2023). GUI stories were designed to evoke guilt by
promoting identification with the character or the group
that contributes to climate change. They were, however,

Behavior Research Methods
associated with the least specific emotional response and the
lowest overall intensity of emotion compared to other stories.
On average, elicited feelings of guilt were not intense. Per-
haps the fact that most of the ECCS stories were third-person
narratives makes it a less suitable means of eliciting guilt,
for example in comparison to the Ecological Footprint Task
(Mallett etal., 2013). Furthermore, people demonstrate resist-
ance in response to attempts to evoke climate emotions that
threaten self-esteem or sense of security (Ma & Hmielowski,
2022). The low intensity of reported guilt might have also
resulted from the fact that the participants tried to preserve a
positive self-image (Caillaud etal., 2016) and therefore used
a variety of coping strategies (such as disidentifying with
the protagonist or minimising the negative consequences of
climate-unfriendly actions). Similarly, climate anxiety may
also activate maladaptive emotion-focused coping strategies,
such as denial or de-emphasising the threat (Haltinner &
Sarathchandra, 2018; Ojala, 2012). Masking climate emo-
tions as a form of defensive reaction to cognitive dissonance
or a threat to self-concept has been identified in previous
studies (Bercht, 2021). We advise taking these factors into
consideration when using ECCS for research purposes.
The ECCS dataset was created based on the data col-
lected in Poland and Norway, two European countries that
belong to the Global North and are highly reliant on fossil
fuels (Brauers & Oei, 2020; EIA, 2019). While we created
ECCS with the aim that the stories would be as culturally
universal as possible, we acknowledge that the stories are
more representative of the experiences of people living
in the Global North countries. Because the relatability of
such stories may depend on the geopolitical context, we
encourage researchers who plan further ECCS adaptations
to take these limitations into account. Moreover, in-depth
interviews and survey studies that inspired the stories were
conducted between 2020 and 2021. Thus, ECCS narratives
describe contemporary discourse on climate change (e.g.
technological solutions to climate change that are presently
being developed; political and social issues that are currently
prevalent in traditional and social media). Over time, as the
perception of climate change and its impacts evolves, the
stories may become less relevant or relatable.
Future directions
The ECCS database holds the potential for facilitating
research across various disciplines of scientific study. In
particular, it can be used to explore the role of emotions in
motivating pro-environmental behaviour, policy support and
consumer decisions, as well as psychological well-being and
mental health. The collected ratings turned out to be highly
consistent across different populations and different cultural
contexts, which suggests that ECCS can be successfully used
in various cultural contexts (Henrich etal., 2010). Impor-
tantly, the ratings are available both as summary scores and
as individual scores to enable research on specific groups,
such as different generations (Gray etal., 2019), parents
(Schneider-Mayerson & Leong, 2020) or climate activists
(Eide & Kunelius, 2021). Consequently, researchers can
flexibly choose stimuli with desired parameters that best
suit their needs. The choice of emotion categories in ECCS
was based on our focus on the role of emotions inmoti-
vating pro-environmental behaviour. For this reason, our
research was not concerned with many other climate emo-
tions,which are currently widely recognized by the research
community.In future, it may be useful to extend the ECCS
dataset, by including stories representing other climate emo-
tions.Because the ECCS stories have only been investigated
with self-report measures, it would also be beneficial to
investigate their properties using more objective measures
(e.g. physiological reactions, brain activity). The ECCS sto-
ries, together with the accompanying data and code used
for the analysis, are publicly available for scientific, non-
commercial use and can be found at: https:// osf. io/ v8hts/.’
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 3758/ s13428- 024- 02408-1.
Authors' contributions D. Zaremba: Conceptualization, Data curation,
Formal analysis, Investigation, Methodology, Project administration,
Software, Validation, Visualisation, Writing—original draft; Writing—
Review and editing;
J. M.Michałowski: Funding acquisition, Project administration,
Writing—Review and editing;
C. A. Klöckner: Conceptualization, Funding acquisition, Project
administration, Supervision, Writing—Review and editing;
A. Marchewka: Conceptualization, Funding acquisition, Methodol-
ogy, Project administration, Resources, Supervision, Writing—Review
and editing;
M. Wierzba: Conceptualization, Data curation, Formal analysis,
Investigation, Methodology, Resources, Project administration, Soft-
ware, Supervision, Validation, Visualization, Writing—Review and
editing.
Funding This research was conducted under the project ‘Understand-
ing patterns of emotional responses to climate change and their relation
to mental health and climate action taking’ funded by Norway Grants
No. 2019/34/H/HS6/00677.
Availability of data and materials Data and materials can be accessed
at: https:// osf. io/ v8hts/
Code availability Code used for the analyses can be accessed at: https://
github. com/ nencki- lobi/ ECCS
Declarations
Conflicts of interest The authors declare no conflicts of interest.
Ethics approval The research was carried out in compliance with
the principles of the Declaration of Helsinki. The study protocol was
approved by the SWPS University of Social Sciences and Humanities
Research Ethics committee in Poland (approval no. 2021–52-12).

Behavior Research Methods
Consent to participate Informed consent was obtained from all indi-
vidual participants included in the study.
Consent for publication Not applicable.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
References
Barrett, L. F. (2006). Are emotions natural kinds? Perspectives on Psy-
chological Science: A Journal of the Association for Psychologi-
cal Science, 1(1), 28–58. https:// doi. org/ 10. 1111/j. 1745- 6916.
2006. 00003.x
Bercht, A. L. (2021). How qualitative approaches matter in climate
and ocean change research: Uncovering contradictions about
climate concern. Global Environmental Change: Human and
Policy Dimensions, 70, 102326. https:// doi. org/ 10. 1016/j. gloen
vcha. 2021. 102326
Bissing-Olson, M. J., Fielding, K. S., & Iyer, A. (2016). Experiences
of pride, not guilt, predict pro-environmental behavior when pro-
environmental descriptive norms are more positive. Journal of
Environmental Psychology, 45, 145–153. https:// doi. org/ 10.
1016/j. jenvp. 2016. 01. 001
Böhm, G. (2003). Emotional reactions to environmental risks: Conse-
quentialist versus ethical evaluation. Journal of Environmental
Psychology, 23(2), 199–212. https:// doi. org/ 10. 1016/ S0272-
4944(02) 00114-7
Bouman, T., Verschoor, M., Albers, C. J., Böhm, G., Fisher, S. D.,
Poortinga, W., Whitmarsh, L., & Steg, L. (2020). When worry
about climate change leads to climate action: How values, worry
and personal responsibility relate to various climate actions.
Global Environmental Change: Human and Policy Dimensions,
62, 102061. https:// doi. org/ 10. 1016/j. gloen vcha. 2020. 102061
Bradley, M. M., & Lang, P. J. (1994). Measuring emotion: the Self-
Assessment Manikin and the Semantic Differential. Journal of
Behavior Therapy and Experimental Psychiatry, 25(1), 49–59.
https:// doi. org/ 10. 1016/ 0005- 7916(94) 90063-9
Bradley, M. M., & Lang, P. J. (1999). Affective norms for English
words (ANEW): Instruction manual and affective ratings. Tech-
nical report C-1, The Center for Research in Psychophysiology,
University of Florida.https:// pdodds. w3. uvm. edu/ teach ing/ cours
es/ 2009- 08UVM- 300/ docs/ others/ every thing/ bradl ey199 9a. pdf
Brauers, H., & Oei, P.-Y. (2020). The political economy of coal in
Poland: Drivers and barriers for a shift away from fossil fuels.
Energy Policy, 144, 111621. https:// doi. org/ 10. 1016/j. enpol.
2020. 111621
Briesemeister, B. B., Kuchinke, L., & Jacobs, A. M. (2014). Emotion
word recognition: Discrete information effects first, continuous
later? Brain Research, 1564, 62–71. https:// doi. org/ 10. 1016/j.
brain res. 2014. 03. 045
Brosch, T. (2021). Affect and emotions as drivers of climate change
perception and action: A review. Current Opinion in Behavioral
Sciences, 42, 15–21. https:// doi. org/ 10. 1016/j. cobeha. 2021. 02.
001
Brosch, T., & Steg, L. (2021). Leveraging emotion for sustainable
action. One Earth, 4(12), 1693–1703. https:// doi. org/ 10. 1016/j.
oneear. 2021. 11. 006
Bury, S. M., Wenzel, M., & Woodyatt, L. (2020). Against the odds:
Hope as an antecedent of support for climate change action. The
British Journal of Social Psychology/the British Psychological
Society, 59(2), 289–310. https:// doi. org/ 10. 1111/ bjso. 12343
Caillaud, S., Bonnot, V., Ratiu, E., & Krauth-Gruber, S. (2016). How
groups cope with collective responsibility for ecological prob-
lems: Symbolic coping and collective emotions. The British
Journal of Social Psychology/ the British Psychological Society,
55(2), 297–317. https:// doi. org/ 10. 1111/ bjso. 12126
Chadwick, A. E. (2015). Toward a theory of persuasive hope: effects
of cognitive appraisals, hope appeals, and hope in the context of
climate change. Health Communication, 30(6), 598–611. https://
doi. org/ 10. 1080/ 10410 236. 2014. 916777
Chapman, D. A., Corner, A., Webster, R., & Markowitz, E. M. (2016).
Climate visuals: A mixed methods investigation of public percep-
tions of climate images in three countries. Global Environmental
Change: Human and Policy Dimensions, 41, 172–182. https://
doi. org/ 10. 1016/j. gloen vcha. 2016. 10. 003
Chapman, D. A., Lickel, B., & Markowitz, E. M. (2017). Reas-
sessing emotion in climate change communication. Nature
Climate Change, 7(12), 850–852. https:// doi. org/ 10. 1038/
s41558- 017- 0021-9
Chu, H., & Yang, J. Z. (2019). Emotion and the Psychological Distance
of Climate Change. In Science Communication (Vol. 41, Issue 6,
pp. 761–789). https:// doi. org/ 10. 1177/ 10755 47019 889637
Clayton, S., & Karazsia, B. T. (2020). Development and validation of
a measure of climate change anxiety. Journal of Environmental
Psychology, 69, 101434. https:// doi. org/ 10. 1016/j. jenvp. 2020.
101434
Coffey, Y., Bhullar, N., Durkin, J., Islam, M. S., & Usher, K. (2021).
Understanding Eco-anxiety: A Systematic Scoping Review of
Current Literature and Identified Knowledge Gaps. The Journal
of Climate Change and Health, 3, 100047. https:// doi. org/ 10.
1016/j. joclim. 2021. 100047
Cohen, D. J., Barker, K. A., & White, M. R. (2018). A standardized list
of affect-related life events. Behavior Research Methods, 50(5),
1806–1815. https:// doi. org/ 10. 3758/ s13428- 017- 0948-9
EIA. (2019). International - U.S. Energy Information Administration
(EIA). https:// www. eia. gov/ inter natio nal/ ranki ngs/ count ry/ NOR?
pa= 12&u= 0&f= A&v= none&y= 01% 2F01% 2F2019.Accessed
18 March 2024.
Eide, E., & Kunelius, R. (2021). Voices of a generation the communi-
cative power of youth activism. Climatic Change, 169(1–2), 6.
https:// doi. org/ 10. 1007/ s10584- 021- 03211-z
Ekman, P. (1992). Are there basic emotions? Psychological Review,
99(3), 550–553. https:// doi. org/ 10. 1037/ 0033- 295X. 99.3. 550
Feldman, L., & Hart, P. S. (2016). Using political efficacy messages
to increase climate activism: The mediating role of emotions.
Science Communication, 38(1), 99–127. https:// doi. org/ 10. 1177/
10755 47015 617941
Feldman, L., & Hart, P. S. (2018). Is there any hope? how climate
change news imagery and text influence audience emotions and
support for climate mitigation policies. Risk Analysis: An Official
Publication of the Society for Risk Analysis, 38(3), 585–602.
https:// doi. org/ 10. 1111/ risa. 12868
Ferguson, M. A., & Branscombe, N. R. (2010). Collective guilt medi-
ates the effect of beliefs about global warming on willingness to
engage in mitigation behavior. Journal of Environmental Psy-
chology, 30(2), 135–142. https:// doi. org/ 10. 1016/j. jenvp. 2009.
11. 010

Behavior Research Methods
Gehlbach, H., Robinson, C. D., Vriesema, C. C., Bernal, E., & Heise,
U. K. (2022). Worth more than 1000 words: How photographs
can bolster viewers’ valuing of biodiversity. Environmental Con-
servation, 49(2), 99–104. https:// doi. org/ 10. 1017/ S0376 89292
20000 42
Goetz, J. L., Keltner, D., & Simon-Thomas, E. (2010). Compassion:
An evolutionary analysis and empirical review. Psychological
Bulletin, 136(3), 351–374. https:// doi. org/ 10. 1037/ a0018 807
Goldberg, H., Preminger, S., & Malach, R. (2014). The emotion-action
link? Naturalistic emotional stimuli preferentially activate the
human dorsal visual stream. NeuroImage, 84, 254–264. https://
doi. org/ 10. 1016/j. neuro image. 2013. 08. 032
Gray, S. G., Raimi, K. T., Wilson, R., & Árvai, J. (2019). Will Mil-
lennials save the world? The effect of age and generational dif-
ferences on environmental concern. Journal of Environmental
Management, 242, 394–402. https:// doi. org/ 10. 1016/j. jenvm an.
2019. 04. 071
Gregersen, T., Andersen, G., & Tvinnereim, E. (2023). The strength
and content of climate anger. Global Environmental Change:
Human and Policy Dimensions, 82, 102738. https:// doi. org/ 10.
1016/j. gloen vcha. 2023. 102738
Gustafson, A., Ballew, M. T., Goldberg, M. H., Cutler, M. J., Rosenthal,
S. A., & Leiserowitz, A. (2020). Personal stories can shift climate
change beliefs and risk perceptions: The mediating role of emo-
tion. Communication Reports, 33(3), 121–135. https:// doi. org/
10. 1080/ 08934 215. 2020. 17990 49
Haltinner, K., & Sarathchandra, D. (2018). Climate change skepticism
as a psychological coping strategy. Sociology Compass, 12(6),
e12586. https:// doi. org/ 10. 1111/ soc4. 12586
Hamilton, L. S., & Huth, A. G. (2020). The revolution will not be
controlled: Natural stimuli in speech neuroscience. Language,
Cognition and Neuroscience, 35(5), 573–582. https:// doi. org/ 10.
1080/ 23273 798. 2018. 14999 46
Harmon-Jones, E., Harmon-Jones, C., & Summerell, E. (2017). On the
Importance of Both Dimensional and Discrete Models of Emo-
tion. Behavioral Sciences, 7(4). https:// doi. org/ 10. 3390/ bs704 0066
Harris, D. M. (2017). Telling the story of climate change: Geologic
imagination, praxis, and policy. Energy Research & Social Sci-
ence, 31, 179–183. https:// doi. org/ 10. 1016/j. erss. 2017. 05. 027
Harth, N. S., Leach, C. W., & Kessler, T. (2013). Guilt, anger, and pride
about in-group environmental behaviour: Different emotions pre-
dict distinct intentions. Journal of Environmental Psychology,
34, 18–26. https:// doi. org/ 10. 1016/j. jenvp. 2012. 12. 005
Hatfield, E., Rapson, R. L., & Le, Y.-C.L. (2009). Emotional contagion
and empathy. The Social Neuroscience of Empathy., 255, 19–30.
https:// doi. org/ 10. 7551/ mitpr ess/ 97802 62012 973. 003. 0003
Helm, S. V., Pollitt, A., Barnett, M. A., Curran, M. A., & Craig, Z. R.
(2018). Differentiating environmental concern in the context of
psychological adaption to climate change. Global Environmental
Change: Human and Policy Dimensions, 48, 158–167. https://
doi. org/ 10. 1016/j. gloen vcha. 2017. 11. 012
Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest peo-
ple in the world? The Behavioral and Brain Sciences, 33(2–3),
61–83. https:// doi. org/ 10. 1017/ S0140 525X0 99915 2X. discussion
83–135.
Hogg, T., Stanley, S., O’Brien, L., Wilson, M., & Watsford, C. (2021).
The Hogg Eco-Anxiety Scale: Development and Validation of a
Multidimensional Scale. https:// doi. org/ 10. 31219/ osf. io/ rxudb
Hsu, C.-T., Jacobs, A. M., Citron, F. M. M., & Conrad, M. (2015).
The emotion potential of words and passages in reading Harry
Potter–an fMRI study. Brain and Language, 142, 96–114.
https:// doi. org/ 10. 1016/j. bandl. 2015. 01. 011
Hurst, K. F., & Sintov, N. D. (2022). Guilt consistently motivates
pro-environmental outcomes while pride depends on context.
Journal of Environmental Psychology, 80, 101776. https:// doi.
org/ 10. 1016/j. jenvp. 2022. 101776
IPCC. (2021). Climate Change 2021: The Physical Science Basis.
Contribution of Working Group I to the Sixth Assessment
Report of the Intergovernmental Panel on Climate Change.
In Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C.
Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis,
M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. May-
cock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (Eds.).
Cambridge University Press. In press.
Jääskeläinen, I. P., Sams, M., Glerean, E., & Ahveninen, J. (2021).
Movies and narratives as naturalistic stimuli in neuroimag-
ing. NeuroImage, 224, 117445. https:// doi. org/ 10. 1016/j. neuro
image. 2020. 117445
Jones, M. D., & Song, G. (2014). Making sense of climate change:
How story frames shape cognition. Political Psychology, 35(4),
447–476. https:// doi. org/ 10. 1111/ pops. 12057
Kleres, J., & Wettergren, Å. (2017). Fear, hope, anger, and guilt in
climate activism. Social Movement Studies, 1–13. https:// doi.
org/ 10. 1080/ 14742 837. 2017. 13445 46
Landmann, H., & Hess, U. (2017). What elicits third-party anger?
The effects of moral violation and others’ outcome on anger
and compassion. Cognition and Emotion, 31(6), 1097–1111.
https:// doi. org/ 10. 1080/ 02699 931. 2016. 11942 58
Landmann, H. (2021). Emotions in the context of environmental pro-
tection: Theoretical considerations concerning emotion types,
eliciting processes, and affect generalization. https:// doi. org/
10. 31234/ osf. io/ yb2a7
Lang, P., & Bradley, M. M. (2007). The International Affective
Picture System (IAPS) in the study of emotion and attention.
Handbook of Emotion Elicitation and Assessment, 29, 70–73.
Lazarus, R. S. (1991). Progress on a cognitive-motivational-rela-
tional theory of emotion. The American Psychologist, 46(8),
819–834. https:// doi. org/ 10. 1037// 0003- 066x. 46.8. 819
Lehman, B., Thompson, J., Davis, S., & Carlson, J. M. (2019). Affec-
tive Images of Climate Change. Frontiers in Psychology, 10,
960. https:// doi. org/ 10. 3389/ fpsyg. 2019. 00960
Leiserowitz, A. (2006). Climate change risk perception and policy pref-
erences: The role of affect, imagery, and values. Climatic Change,
77(1), 45–72. https:// doi. org/ 10. 1007/ s10584- 006- 9059-9
Leiserowitz, A., Roser-Renouf, C., Marlon, J., & Maibach, E. (2021).
Global warming’s six americas: A review and recommendations
for climate change communication. Current Opinion in Behavio-
ral Sciences, 42, 97–103. https:// doi. org/ 10. 1016/j. cob eha. 2021.
04. 007
Lu, H., & Schuldt, J. P. (2015). Exploring the role of incidental emo-
tions in support for climate change policy. Climatic Change,
131(4), 719–726. https:// doi. org/ 10. 1007/ s10584- 015- 1443-x
Lu, H., & Schuldt, J. P. (2016). Compassion for climate change victims
and support for mitigation policy. Journal of Environmental Psy-
chology, 45, 192–200. https:// doi. or g/ 10. 1016/j. jenvp. 2016. 01. 007
Ma, Y., & Hmielowski, J. D. (2022). Are You Threatening Me? Iden-
tity Threat, Resistance to Persuasion, and Boomerang Effects in
Environmental Communication. Environmental Communication,
16(2), 225–242. https:// doi. org/ 10. 1080/ 17524 032. 2021. 19944 42
Magalhães de Sousa, S., Miranda, D. K., de Miranda, D. M., Mal-
loy-Diniz, L. F., & Romano-Silva, M. A. (2018). The Extreme
Climate Event Database (EXCEED): Development of a picture
database composed of drought and flood stimuli. PloS One,
13(9), e0204093. https:// doi. org/ 10. 1371/ journ al. pone. 02040 93
Mallett, R. K., Melchiori, K. J., & Strickroth, T. (2013). self-con-
frontation via a carbon footprint calculator increases guilt and
support for a proenvironmental group. Ecopsychology, 5(1),
9–16. https:// doi. org/ 10. 1089/ eco. 2012. 0067
Mar, R. A. (2011). The neural bases of social cognition and story
comprehension. Annual Review of Psychology, 62, 103–134.
https:// doi. org/ 10. 1146/ annur ev- psych- 120709- 145406

Behavior Research Methods
Marchewka, A., Żurawski, Ł, Jednoróg, K., & Grabowska, A. (2014).
The Nencki Affective Picture System (NAPS): Introduction to
a novel, standardized, wide-range, high-quality, realistic pic-
ture database. Behavior Research Methods, 46(2), 596–610.
https:// doi. org/ 10. 3758/ s13428- 013- 0379-1
Marczak, M., Winkowska, M., Chaton-Østlie, K., Morote Rios, R.,
& Klöckner, C. A. (2023). “When I say I’m depressed, it’s like
anger. An exploration of the emotional landscape of climate
change concern in Norway and its psychological, social and
political implications. Emotion, Space and Society, 46, 100939.
https:// doi. org/ 10. 1016/j. emospa. 2023. 100939
Marks, E., Hickman, C., Pihkala, P., Clayton, S., Lewandowski,
E. R., Mayall, E. E., Wray, B., Mellor, C., & van Susteren,
L. (2021). Young People’s Voices on Climate Anxiety, Gov-
ernment Betrayal and Moral Injury: A Global Phenomenon.
https:// doi. org/ 10. 2139/ ssrn. 39189 55
McDonald, R. I., Chai, H. Y., & Newell, B. R. (2015). Personal expe-
rience and the “psychological distance” of climate change: An
integrative review. Journal of Environmental Psychology, 44,
109–118. https:// doi. org/ 10. 1016/j. jenvp. 2015. 10. 003
Michałowski, J. M., Droździel, D., Matuszewski, J., Koziejowski,
W., Jednoróg, K., & Marchewka, A. (2017). The Set of Fear
Inducing Pictures (SFIP): Development and validation in fear-
ful and nonfearful individuals. Behavior Research Methods,
49(4), 1407–1419. https:// doi. org/ 10. 3758/ s13428- 016- 0797-y
Moezzi, M., Janda, K. B., & Rotmann, S. (2017). Using stories, nar-
ratives, and storytelling in energy and climate change research.
Energy Research & Social Science, 31, 1–10. https:// doi. org/
10. 1016/j. erss. 2017. 06. 034
Morris, B. S., Chrysochou, P., Christensen, J. D., Orquin, J. L.,
Barraza, J., Zak, P. J., & Mitkidis, P. (2019). Stories vs facts:
triggering emotion and action-taking on climate change.
Climatic Change, 154(1), 19–36. https:// doi. org/ 10. 1007/
s10584- 019- 02425-6
Nabi, R. L., Gustafson, A., & Jensen, R. (2018). Framing Climate
Change: Exploring the Role of Emotion in Generating Advocacy
Behavior. Science Communication, 40(4), 442–468. https:// doi.
org/ 10. 1177/ 10755 47018 776019
O’Neill, S. J., Boykoff, M., Niemeyer, S., & Day, S. A. (2013). On the
use of imagery for climate change engagement. Global Environ-
mental Change: Human and Policy Dimensions, 23(2), 413–421.
https:// doi. org/ 10. 1016/j. gloen vcha. 2012. 11. 006
Ockwell, D., Whitmarsh, L., & O’Neill, S. (2009). Reorienting cli-
mate change communication for effective mitigation: Forcing
people to be green or fostering grass-roots engagement? Science
Communication, 30(3), 305–327. https:// doi. org/ 10. 1177/ 10755
47008 328969
Ojala, M. (2012). How do children cope with global climate change?
Coping strategies, engagement, and well-being. Journal of Envi-
ronmental Psychology, 32(3), 225–233. https:// doi. org/ 10. 1016/j.
jenvp. 2012. 02. 004
Ojala, M. (2015). Hope in the face of climate change: Associations with
environmental engagement and student perceptions of teachers’
emotion communication style and future orientation. The Journal
of Environmental Education, 46(3), 133–148. https:// doi. org/ 10.
1080/ 00958 964. 2015. 10216 62
Parkinson, B., Fischer, A. H., & Manstead, A. S. R. (2005). Emotion in
social relations: Cultural, group, and interpersonal processes.
Psychology Press.
Pihkala. (2020). Anxiety and the ecological crisis: An analysis of eco-
anxiety and climate anxiety. Sustainability: Science Practice and
Policy, 12(19), 7836. https:// doi. org/ 10. 3390/ su121 97836
Pihkala, P. (2022). Toward a Taxonomy of Climate Emotions. Frontiers
in Climate, 3. https:// doi. org/ 10. 3389/ fclim. 2021. 738154
Redcay, E., & Moraczewski, D. (2020). Social cognition in context: A
naturalistic imaging approach. NeuroImage, 216, 116392. https://
doi. org/ 10. 1016/j. neuro image. 2019. 116392
Rees, J. H., Klug, S., & Bamberg, S. (2015). Guilty conscience: moti-
vating pro-environmental behavior by inducing negative moral
emotions. Climatic Change, 130(3), 439–452. https:// doi. org/ 10.
1007/ s10584- 014- 1278-x
Reser, J. P., & Bradley, G. L. (2017). Fear appeals in climate change
communication. In Oxford research encyclopedia of climate sci-
ence. https:// doi. org/ 10. 1093/ acref ore/ 97801 90228 620. 013. 386
Riegel, M., Wierzba, M., Wypych, M., Żurawski, Ł, Jednoróg,
K., Grabowska, A., & Marchewka, A. (2015). Nencki affec-
tive word list (NAWL): The cultural adaptation of the Berlin
Affective Word List-Reloaded (BAWL-R) for Polish. Behavior
Research Methods, 47(4), 1222–1236. https:// doi. org/ 10. 3758/
s13428- 014- 0552-1
Rode, J. B., Dent, A. L., Benedict, C. N., Brosnahan, D. B., Martinez,
R. L., & Ditto, P. H. (2021). Influencing climate change attitudes
in the United States: A systematic review and meta-analysis.
Journal of Environmental Psychology, 76, 101623. https:// doi.
org/ 10. 1016/j. jenvp. 2021. 101623
Saarimäki, H. (2021). Naturalistic stimuli in affective neuroimaging:
A review. Frontiers in Human Neuroscience, 15, 675068. https://
doi. org/ 10. 3389/ fnhum. 2021. 675068
Schneider, C. R., Zaval, L., & Markowitz, E. M. (2021). Positive emo-
tions and climate change. Current Opinion in Behavioral Sci-
ences, 42, 114–120. https:// doi. org/ 10. 1016/j. cobeha. 2021. 04.
009
Schneider-Mayerson, M., & Leong, K. L. (2020). Eco-reproductive
concerns in the age of climate change. Climatic Change, 163(2),
1007–1023. https:// doi. org/ 10. 1007/ s10584- 020- 02923-y
Schneider-Mayerson, M., Weik von Mossner, A., & Małecki, W. P.
(2020). Empirical Ecocriticism: Environmental Texts and Empir-
ical Methods. ISLE: Interdisciplinary Studies in Literature and
Environment, 27(2), 327–336. https:// doi. org/ 10. 1093/ isle/ isaa0
22
Shipley, N. J., & van Riper, C. J. (2022). Pride and guilt predict pro-
environmental behavior: A meta-analysis of correlational and
experimental evidence. Journal of Environmental Psychology,
79, 101753. https:// doi. org/ 10. 1016/j. jenvp. 2021. 101753
Smith, C. A., & Ellsworth, P. C. (1985). Attitudes and social cognition.
Journal of Personality and Social Psychology, 48(4), 813–838.
http:// emoti onres earch er. com/ wp- conte nt/ uploa ds/ 2015/ 08/
SE1985. pdf.
Stanley, S. K., Hogg, T. L., Leviston, Z., & Walker, I. (2021). From
anger to action: Differential impacts of eco-anxiety, eco-depres-
sion, and eco-anger on climate action and wellbeing. The Journal
of Climate Change and Health, 1, 100003. https:// doi. org/ 10.
1016/j. joclim. 2021. 100003
Stevenson, R. A., Mikels, J. A., & James, T. W. (2007). Characteriza-
tion of the affective norms for English words by discrete emo-
tional categories. Behavior Research Methods, 39(4), 1020–
1024. https:// doi. org/ 10. 3758/ bf031 92999
Swim, J. K., & Bloodhart, B. (2015). Portraying the Perils to Polar
Bears: The role of empathic and objective perspective-taking
toward animals in climate change communication. Environ-
mental Communication, 9(4), 446–468. https:// doi. org/ 10. 1080/
17524 032. 2014. 987304
Tracy, J. L., & Robins, R. W. (2007). Self-conscious emotions: Where
self and emotion meet. Self, 364, 187–209. https:// psycn et. apa.
org/ fullt ext/ 2007- 02340- 009. pdf.
van der Linden, S. (2015). The social-psychological determinants
of climate change risk perceptions: Towards a comprehensive
model. Journal of Environmental Psychology, 41, 112–124.
https:// doi. org/ 10. 1016/j. jenvp. 2014. 11. 012

Behavior Research Methods
van der Linden, S., Maibach, E., & Leiserowitz, A. (2015). Improving
Public Engagement With Climate Change: Five “Best Practice”
Insights From Psychological Science. Perspectives on Psycho-
logical Science: A Journal of the Association for Psychological
Science, 10(6), 758–763. https:// doi. org/ 10. 1177/ 17456 91615
598516
van Valkengoed, A. M., & Steg, L. (2019). Meta-analyses of fac-
tors motivating climate change adaptation behaviour. Nature
Climate Change, 9(2), 158–163. https:// doi. org/ 10. 1038/
s41558- 018- 0371-y
Wang, S., Leviston, Z., Hurlstone, M., Lawrence, C., & Walker, I.
(2018). Emotions predict policy support: Why it matters how
people feel about climate change. Global Environmental Change:
Human and Policy Dimensions, 50, 25–40. https:// doi. org/ 10.
1016/j. gloen vcha. 2018. 03. 002
Weber, E. U. (2006). Experience-Based and Description-Based Percep-
tions of Long-Term Risk: Why Global Warming does not Scare
us (Yet). Climatic Change, 77(1), 103–120. https:// doi. org/ 10.
1007/ s10584- 006- 9060-3
Whitmarsh, L., Player, L., Jiongco, A., James, M., Williams, M.,
Marks, E., & Kennedy-Williams, P. (2022). Climate anxiety:
What predicts it and how is it related to climate action? Journal
of Environmental Psychology, 83, 101866. https:// doi. org/ 10.
1016/j. jenvp. 2022. 101866
Wierzba, M., Riegel, M., Wypych, M., Jednoróg, K., Turnau, P.,
Grabowska, A., & Marchewka, A. (2015). Basic emotions in the
Nencki Affective Word List (NAWL BE): New method of clas-
sifying emotional stimuli. PloS One, 10(7), e0132305. https://
doi. org/ 10. 1371/ journ al. pone. 01323 05
Wierzba, M., Riegel, M., Kocoń, J., Miłkowski, P., Janz, A., Klessa,
K., Juszczyk, K., Konat, B., Grimling, D., Piasecki, M., &
Marchewka, A. (2022). Emotion norms for 6000 Polish word
meanings with a direct mapping to the Polish wordnet. Behavior
Research Methods, 54(5), 2146–2161. https:// doi. org/ 10. 3758/
s13428- 021- 01697-0
Wohl, M. J. A., Branscombe, N. R., & Klar, Y. (2006). Collective guilt:
Emotional reactions when one’s group has done wrong or been
wronged. European Review of Social Psychology, 17(1), 1–37.
https:// doi. org/ 10. 1080/ 10463 28060 05748 15
Xie, B., Brewer, M. B., Hayes, B. K., McDonald, R. I., & Newell, B. R.
(2019). Predicting climate change risk perception and willingness
to act. Journal of Environmental Psychology, 65, 101331. https://
doi. org/ 10. 1016/j. jenvp. 2019. 101331
Zaremba, D., Kulesza, M., Herman, A. M., Marczak, M., Kossowski,
B., Budziszewska, M., Michałowski, J. M., Klöckner, C. A.,
Marchewka, A., & Wierzba, M. (2023). A wise person plants a
tree a day before the end of the world: coping with the emotional
experience of climate change in Poland. Current Psychology,
42, 27167–27185. https:// doi. org/ 10. 1007/ s12144- 022- 03807-3
Publisher's Note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.
Open practices statement None of the work described in this paper
was preregistered. All data and code are openly shared at: https:// osf.
io/ v8hts/