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Learning from the COVID-19 pandemic to combat climate change: comparing drivers of individual action in global crises

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The COVID-19 pandemic and climate change are two global crises that require collective action. Yet, the inertia typically associated with behavior change to limit climate change stands in contrast to the speed associated with behavior change to stop the spread of COVID-19. Identifying the roots of these differences can help us stimulate climate-friendly behaviors. We assessed the extent to which a number of theory-based drivers underlie behaviors aiming to counter COVID-19 and climate change with an online survey ( N = 534). We focused on the role of a number of drivers derived from prominent behavior change theories and meta-analyses in the field, namely, personal threat, threat to close others, threat to vulnerable others, fear, participative efficacy, injunctive and descriptive social norms, and governmental policy perceptions. We investigated (1) what drivers people perceived as most important to engage in behaviors that limit the spread of the COVID-19 pandemic and climate change and (2) the strength of the associations between these drivers and engaging in behaviors that limit the spread of the pandemic and climate change. Results highlight three key drivers for climate change action: changing perceptions of governmental policy and perceptions of threat to close others and priming participative efficacy beliefs.
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Journal of Environmental Studies and Sciences
https://doi.org/10.1007/s13412-021-00727-9
RESEARCH BRIEF
Learning fromtheCOVID‑19 pandemic tocombat climate change:
comparing drivers ofindividual action inglobal crises
MarijnH.C.Meijers1· ChristinScholz1· Ragnheiður“Heather”Torfadóttir2· AnkeWonneberger1· MarkoMarkov2
Accepted: 4 October 2021
© The Author(s) 2021
Abstract
The COVID-19 pandemic and climate change are two global crises that require collective action. Yet, the inertia typically
associated with behavior change to limit climate change stands in contrast to the speed associated with behavior change to
stop the spread of COVID-19. Identifying the roots of these differences can help us stimulate climate-friendly behaviors. We
assessed the extent to which a number of theory-based drivers underlie behaviors aiming to counter COVID-19 and climate
change with an online survey (N = 534). We focused on the role of a number of drivers derived from prominent behavior
change theories and meta-analyses in the field, namely, personal threat, threat to close others, threat to vulnerable others,
fear, participative efficacy, injunctive and descriptive social norms, and governmental policy perceptions. We investigated (1)
what drivers people perceived as most important to engage in behaviors that limit the spread of the COVID-19 pandemic and
climate change and (2) the strength of the associations between these drivers and engaging in behaviors that limit the spread
of the pandemic and climate change. Results highlight three key drivers for climate change action: changing perceptions of
governmental policy and perceptions ofthreat to close others and priming participative efficacy beliefs.
Keywords Climate change· Behavior change· COVID-19· Pro-environmental behavior
COVID-19 has changed our world rapidly. Individuals,
organizations, and governments have shown willingness
and capacity to make profound changes to public life, sur-
prisingly quickly (Cova 2020; Johns Hopkins University &
Medicine, 2020). This stands in contrast to the inertia that is
typically exhibited in the response to climate change (Munck
af Rosenschöld etal. 2014; Whitmarsh etal. 2013). This
difference in the speed of reactions to climate change and
COVID-19 has surprised scientists (Galbraith & Otto 2020)
and journalists alike (Segalov 2020). Both the COVID-19
pandemic and climate change constitute global crises that
require collective action. Given these similarities, what
drives individuals to act swiftly and drastically to address
one crisis but not the other? We argue that similarities of
the COVID-19 pandemic to the climate change crisis pre-
sent a unique opportunity to draw lessons from this intense
moment of public involvement, which can be utilized to
stimulate climate-friendly behavior (Galbraith & Otto 2020;
Schmidt 2021). To this end, we explore differences and
similarities in key drivers of actions in response to COVID-
19 and climate change. Stimulating key drivers of swift
COVID-19 responses in the context of climate change may
be a vital first step to counter climate change.
Based on prominent theories of behavior change (Hornik
& Woolf 1999), we argue that (1) drivers which are central
to COVID-19, but not (yet) to climate change action, and (2)
drivers that are already important in both crises are promis-
ing levers for future interventions. Based on the reasoning
put forward by Fishbein and Cappella (2006), the former
are candidates for change: Introducing and fostering novel
drivers in the climate change context that are known to be
associated with rapid behavior change during the COVID-
19 crisis may help also increase climate change action
readiness. The latter, successful COVID-19 drivers that are
already present in the climate change context, are candidates
for priming: Emphasizing and reinvigorating existing drivers
in the climate change context that are known to play a role in
* Marijn H. C. Meijers
M.H.C.Meijers@uva.nl
1 Amsterdam School ofCommunication Research,
Department ofCommunication Science, University
ofAmsterdam, PO BOX 15791, 1001NGAmsterdam,
TheNetherlands
2 Department ofCommunication Science, University
ofAmsterdam, PO BOX 15791, 1001NGAmsterdam,
TheNetherlands
Journal of Environmental Studies and Sciences
1 3
the rapid COVID-19 response can selectively leverage those
bases of climate change behavior that are already present in
the target group.
Drivers ofbehavioral change andcollective
action
We evaluate these two criteria for a set of behavioral drivers
from well-established behavioral change models, collective
action theories, extensive previous research, and meta-anal-
yses on behavioral change (Bergquist etal. 2019; Rainear &
Christensen 2017; Rogers 1975; Urbanovich & Bevan 2020;
van Valkengoed & Steg 2019; Witte & Allen 2000). Spe-
cifically, the Extended Parallel Process Model (Witte 1992)
highlights the importance of perceived threat and efficacy.
Furthermore, recent meta-analyses regarding collective
action emphasize the central role of social norms (Bergquist
etal. 2019; van Valkengoed & Steg 2019). Finally, we exam-
ine perceptions of governmental policy, following insights
from the Behavior Change Wheel (Michie etal. 2011) and
several scholars arguing that governmental policy plays a
significant role in large-scale issues like pandemics and cli-
mate change (Cooper & Nagel 2021; Jagers etal. 2020).
Threat andefficacy
Behavior change models like the Extended Parallel Pro-
cess Model (Witte 1992) and Protection Motivation Theory
(Rogers 1975) have proven valuable in predicting behav-
iors in response to threats, like diseases or climate change
(Hartmann etal. 2014; Homburg & Stolberg 2006; Witte
1992; Witte & Allen 2000). According to these theories,
the likelihood of an adaptive threat response (e.g., adhering
to COVID-19 or climate change-related recommendations)
increases with the extent to which a person perceives the
situation as threatening (threat appraisal) and, subsequently,
judges that they are able to cope with the threat (efficacy
beliefs; Witte & Allen 2000).
Prior empirical work confirms the importance of threat
appraisals for predicting responses to both public health
issues and climate change (Harper etal. 2020; Hartmann
etal. 2014; Jørgensen etal. 2021; Witte & Allen 2000).
Here, we examine whether there are differences in the cog-
nitive (perceived threat) and affective component (fear) of
threat appraisal for COVID-19 and climate change. Within
the cognitive component, we differentiate personal threat,
threat to close others, and threat to vulnerable others. For
global crises, like COVID-19 and climate change, both
perceived threat to one’s own well-being (personal threat)
and threat to others can motivate action (Corner etal. 2014;
Ortega-Egea etal. 2014; Slater etal. 2015; Van der Linden
etal. 2015). When it concerns others, a distinction can be
made between close others who are important to people
personally and vulnerable others; those who are especially
vulnerable to a certain threat such as the elderly when it
concerns COVID-19 (Christner etal. 2020).
Similarly, there is strong evidence for the importance of
efficacy beliefs in motivating adaptive responses to threats
(beliefs of being able to effectively respond to a threat,
Bandura 1977; Chen 2015; Homburg & Stolberg 2006; Jør-
gensen etal. 2021; Witte & Allen 2000). Previous research
highlights that large-scale global threats, which cannot be
solved by an individual but require collective effort, may
require beliefs about the efficacy of the collective in addi-
tion to personal efficacy beliefs (Chen 2015; Homburg &
Stolberg 2006; Jugert etal. 2016). Here, we focus on par-
ticipative efficacy beliefs, the belief that one can personally
make an incremental difference in achieving the collective
goal (Van Zomeren etal. 2013). Participative efficacy is
an important predictor of collective action (Bamberg etal.
2015; Van Zomeren etal. 2013) and bridges the concepts of
personal and collective efficacy, by taking into account the
importance and indispensability of the individual’s actions
towards achieving the collective goal. Prior work shows a
lack of perceived efficacy in the context of climate change
(Doherty & Webler 2016; Lorenzoni etal. 2007).
Social norms
Both health and climate change behaviors are strongly
influenced by beliefs about what others deem appropriate
behavior (injunctive social norms) and about what others
actually do (descriptive social norms, Bergquist etal. 2019;
Cialdini etal. 1990; Mollen etal. 2013). Furthermore, given
the collective nature of the COVID-19 pandemic and climate
change crises, social norms might be especially important
according to collective action theories (Fritsche etal. 2018;
Reese etal. 2020).
Perceptions ofgovernmental policy
An effective, organized response to collective threats like
COVID-19 and climate change also requires sensible gov-
ernment regulations and policies (Doherty & Webler 2016;
Hart & Feldman 2016; Lubell 2002). In modern democ-
racies, the success of governmental policies depends on
public support and widespread motivation to act upon those
policies. Governmental policy and recommendations have
proven to make a great difference in combatting COVID-
19 (Van Uffelen etal. 2020; Walker & Smith 2020). Simi-
larly, governmental actions and policy have been shown to
be related to climate change behaviors (Feldman & Hart
2016; Hart & Feldman 2016; Jamelske etal. 2013; Lubell
Journal of Environmental Studies and Sciences
1 3
2002). Here, we therefore compare to what extent percep-
tions of governmental policy influence individual responses
to COVID-19 and climate change.
Current research
We investigate similarities and differences in the extent
to which people perceive certain drivers as important for
engaging in COVID-19 vs climate-friendly behaviors and in
the relationship between these drivers and behaviors aiming
to curb COVID-19 and climate change. Our aim is to inform
climate-related interventions using insights about rapid and
effective responses to the COVID-19 pandemic.
Method
Sample anddesign
We conducted an online survey among a snowball conveni-
ence sample recruited through the social media channels of
a Dutch university (N = 536, 95% power to detect Cohen’s
dz = 0.14 and f2 = 0.04). Two participants showed no variance
in all self-report responses, and were excluded from analy-
ses, leaving a total of 534 participants (69.7% female, 27.9%
male, 2.4% other/ “rather not say”; age ranged from 17 to
82, Mage = 38.10, SD = 14.03, n = 9 “rather not say”). The
vast majority had a university degree (84.5%) and lived in
Europe (94.8%, 68.7% in the Netherlands, n = 9 "rather not
say"). The sample was thus highly educated in comparison
to a more general sample.
Procedure
Participants completed a survey hosted on Qualtrics which
was available in Bulgarian, Dutch, English, German, and
Icelandic. After providing informed consent, participants
answered two blocks of identical questions rating the per-
ceived importance of key drivers of behavior as well as
the extent to which they themselves engage in COVID-19
and climate change-related behaviors. Block order was
randomized. Lastly, participants had the opportunity to
leave comments. All study procedures were approved by
our university’s ethical review board (reference number:
2020-PC-12051).
Measures
Each question block introduced the study topic as par-
ticipants’ opinions, motivations, and behavior related to
COVID-19 [climate change]. We further provided examples
of each behavior category (e.g., COVID-19: social distanc-
ing, washing hands frequently; climate change: consuming
less meat/dairy, lowering the thermostat) so that each par-
ticipant could envision behaviors within each category
that were most relevant to their personal life. Hereafter,
we asked participants “To what extent do you engage in
behavior to help in the fight against the coronavirus [climate
change]?” (Not at all (1) to A great deal (5)), thus referring
to behaviors in general rather than the examples mentioned.
Because behavior was not normally distributed, we created
a categorical variable with three categories for the COVID-
19 and climate change behavior scales respectively, based
on where on the scale significant clusters of participants
were located.1 The three categories contain participants who
reported relatively low, medium, and high frequencies of a
given behavior. Next, participants were asked to indicate the
importance of eight drivers of behaviors related to COVID-
19 [climate change] (Not at all important (1) to Extremely
important (5) – 1 item each) in randomized order: govern-
mental policy, injunctive and descriptive social norms, par-
ticipative efficacy, perceived threat (to me, to close others,
and to vulnerable others), and fear (see Table1 for items
in the Appendix). Materials and data are available on OSF.
Results
Importance ofbehavioral drivers
First, we examined the perceived importance of each
behavioral driver in the context of COVID-19 and climate
change (Fig.1, Table2 in the Appendix). Paired-sample
t tests showed significant differences for the importance
of all drivers when comparing COVID-19 and climate
change. Most drivers were rated to be more important for
acting to counter COVID-19. Only personal threat and
fear of the potential impact of the crisis were rated to be
more important for climate change, although effect sizes
were small (t(533) = − 2.00, p = 0.046, Cohen’s dz = 0.09
and t(533) = − 5.15, p < 0.001, Cohen’s dz = 0.23 respec-
tively). The greatest difference emerged for perceptions of
1 For COVID-19-related behaviors, participants who indicated to
engage “not at all,” “slightly,” or “moderately” in COVID-19-related
behavior were grouped in the “low” frequency group (14.0%). Partici-
pants who indicated to engage “considerably” in COVID-19-related
behavior were grouped in the “medium” frequency group (46.1%),
whereas participants who indicated to engage “a great deal” in
COVID-19-related behavior were grouped in the “high” frequency
group (39.9%). For climate change-related behaviors, participants
who indicated to engage “not at all” or “slightly” in climate change-
related behavior were grouped in the “low” frequency group (26.8%).
Participants who indicated to engage “moderately” in climate
change-related behavior were grouped in the “medium” frequency
group (42.3%), whereas participants who indicated to engage “con-
siderably” or “a great deal” in climate change-related behavior were
grouped in the “high” frequency group (30.9%).
Journal of Environmental Studies and Sciences
1 3
governmental policy, which was rated to be more impor-
tant for engaging in COVID-19 than climate change-
related behaviors, t(533) = 27.76, p < 0.001, Cohen’s
dz = 1.20.2
Relationship betweenbehavioral drivers
andself‑reported behavior
Next, we investigated to what extent perceptions of the
importance of behavioral drivers were related to actual, self-
reported behavior. We conducted multinomial regressions,
regressing behavior on each motive’s importance (Tables3
and 4 in the Appendix). We investigated which drivers dif-
ferentiated participants in the low and high compared to the
medium frequency (reference) group, respectively control-
ling for gender, age, and education(for correlations between
drivers and behaviors, seeTables5 and 6in the Appendix).
For COVID-19-related behaviors, participants who indi-
cated the following drivers to be more important were more
likely to report high (vs medium) frequency of behavior:
participative efficacy (B = 0.53, SE = 0.17, Wald = 10.03,
p = 0.002), threat to close others (B = 0.31, SE = 0.11,
Wald = 7.51,p = 0.006), and governmental policy (B = 0.21,
SE = 0.11, Wald = 3.98,p = 0.046). Participants, who indi-
cated descriptive norms to be a more important driver
(B = − 0.49, SE = 0.17, Wald = 8.16, p = 0.004), were less
likely to report low (vs medium) frequency behavior. In con-
trast to the literature, participants who indicated injunctive
norms to be a less important driver (B = 0.33, SE = 0.16,
Wald = 4.16, p = 0.041) were less likely to report low (vs
medium) frequency behavior.In other words, participants
who indicated that injunctive norms are an important driver
reported a lower behavioral frequency.
For climate-friendly behaviors, participants who indi-
cated that threat to vulnerable others (B = 0.37, SE = 0.11,
Wald = 11.65,p = 0.001) and participative efficacy (B = 0.37,
SE = 0.15, Wald = 5.90, p = 0.015) were important drivers
and were more likely to report high (vs medium) frequency
behavior. Participants who indicated that participative effi-
cacy was a more important driver (B = − 0.42, SE = 0.14,
Wald = 9.60, p = 0.002) were less likely to report low (vs
medium) frequency behavior.
Discussion
In this study, we aimed to identify drivers that may help
explain the stark difference in the behavioral response to the
COVID-19 and climate change crises. Specifically, guided
by behavioral change theories (Fishbein & Cappella 2006;
Hornik & Woolf 1999), we aimed to identify (1) drivers
that are important in instigating high-frequency behavior
regarding the COVID-19 pandemic but not regarding cli-
mate change (candidates for change) and (2) drivers that are
already important for behavior addressing both crises (can-
didates for priming). Results highlight governmental policy,
threat to close others, and participative efficacy as key levers
for further research and future interventions.
Government policy emerged as a candidate for change as
it was perceived to be a more important behavioral driver
in the COVID-19 than the climate change context. Further-
more, when investigating the relationship between drivers
and self-reported behaviors, results showed that partici-
pants engaging in high (vs medium) frequency COVID-19
Fig. 1 Average importance
of theory-driven drivers for
COVID-19 vs climate change.
Note: Results of paired samples
t-test
3.22
2.58
2.74
4.40
2.58
4.01
4.26
3.43
2.00
2.03
2.21
4.05
2.71
3.10
3.36
3.74
1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00
Governmental policy
Social norms: injuncve
Social norms: descripve
Parcipatory efficacy
Threat: personal
Threat: people close to me
Threat: vulnerable others
Fear
Movaons for behaviour
Covid19 Climate Change
2 Checking for order effects (whether the questions pertaining cli-
mate change or COVID-19 were asked first) revealed no meaningful
differences.
Journal of Environmental Studies and Sciences
1 3
behaviors were more likely to indicate governmental policy
as an important driver. This was not the case in the climate
change context. Governments around the globe have acted
quickly to address COVID-19. In contrast, governmental
responses to climate change have been hesitant at best (e.g.,
although promising, the implementation of the Paris Agree-
ment has remained insufficient). Interestingly, 22% of the
participants who left a comment in the survey pointed out
that the government should take a more leading role in the
response to climate change. Consequently, if governments
were seen as more supportive of climate-friendly policies
and behaviors, this may stimulate individual climate-friendly
behavior (Ockwell etal. 2009). Qualitative data from this
study suggests that such policies would likely receive sup-
port from the population we sampled here, though future
work should probe a broader population.
Additionally, threat to close others also emerged
as a candidate for change as it was rated as a more
important behavioral driver in the COVID-19 than
the climate change context. Multinominal regres-
sions investigating the relationships between driv-
ers and self-reported behaviors furthermore showed
that threat to close others was an important driver
for high (vs medium) frequency behaviors related
COVID-19, but not for climate change. Some par-
ticipants also commented on the more pressing
nature of COVID-19 versus climate change. People
seemingly do not perceive climate change as threat-
ening enough, which is in line with climate change
being perceived as a rather distant threat (Ockwell
etal. 2009; Weber 2006). This might also explain
why personal threat and fear of the potential impact
of the crisis were perceived as important drivers
for climate-friendly behavior (more than those for
COVID-19), but did not show a relationship with
the engagement in behaviors that help limit climate
change; the threat and fear might be too abstract
and distant.
Furthermore, as a candidate for priming, we found that
for both COVID-19 and climate change, participative
efficacy beliefs were strongly related to high-frequency
behavior (cf. Jørgensen etal. 2021, regarding COVID-
19 behaviors). In line with the importance of participa-
tive efficacy for climate-friendly behavior, an Ipsos poll
among > 28,000 adults (April, 2020) has shown that two-
thirds of participants believed that the issue of climate
change is as pressing as COVID-19, but also that people
are not changing their behavior because they believe they
are unable to make an impact (Gray & Jackson 2020).
Priming participative efficacy beliefs for climate change
by stressing the importance of everyone chipping in via
environmental communication may thus accelerate cli-
mate-friendly behaviors.
This study provides a snapshot of key perceptions
and action readiness at the height of the COVID-19 pan-
demic but is also limited in several ways. In order to
capture perceptions at the right time during the develop-
ing COVID-19 pandemic, we recruited a cross-sectional
convenience sample with limited generalizability. Repli-
cation of the current findings is desirable. For example,
investigating whether and how drivers might differ for
residents of developed vs developing countries (Thomas
& Benjamin 2018), for different intrapersonal stages of
behavior change (Prochaska etal. 2014), and among a
sample that better reflects the general population as the
current sample was, on average, more highly educated
than the general population. Furthermore, to reduce par-
ticipant burden, we used single item measures in line
with common research practices (e.g., Boerman etal.
2012; Du etal. 2011; Swim & Geiger 2017). This raises
potential concerns about reliability, but please note that
research shows that single and multiple item measures
perform similarly (Bergkvist & Rossiter 2007; Gardner
etal. 1998). Furthermore, when measuring engagement
in COVID-19 vs climate change behaviors, we allowed
participants to focus on top-of-mind behaviors rele-
vant to their own lives but provided guiding examples.
Because drivers may vary between specific behaviors
within a crisis category, follow-up research should dis-
tinguish between different behaviors.
Future research could also address the causal relation-
ships between COVID-19 and climate change behaviors.
While here we consider differences in the reasons behind
engaging in behaviors that help in combatting climate
change and COVID-19, prior work has highlighted that,
under certain circumstances, the COVID-19 pandemic
might positively influence pro-environmental actions
(Tchetchik etal. 2021; butsee also Ecker etal. 2020).
A fruitful future avenue of work lies in the exploration
of relationships between reasons motivating each behav-
ior and subsequent effects on the respective other type
of behavior. Lastly, it would be interesting to conduct a
similar study when climate change is more prominently
in the news than the COVID-19 pandemic. When the
study was conducted, the seriousness of the COVID-19
pandemic only just sank in and was surrounded by great
uncertainty. While there were also climate change-related
natural disasters, for example, the aftermath of the Aus-
tralian bushfires; floods in South America, Africa, and
Asia; and bushfires in Siberia, COVID-19 was more
prominent in the media in Europe. It would be interesting
to investigate how this media prominence might influ-
ence COVID-19 and climate change-related behaviors
(cf. Wonneberger etal. 2020) and how differential knowl-
edge regarding governmental policies on COVID-19 vs
climate change plays a role in this.
Journal of Environmental Studies and Sciences
1 3
The current study exploratively investigated the
drivers that play a role in behavior to combat the
COVID-19 pandemic and climate change. We there-
fore recommend further exploration of our current
findings (while keeping in mind the aforementioned
limitations). Regarding practical implications, three
strategies can be cautiously derived to stimulate cli-
mate-friendly behavior, although we commend these
should first be tested in more detail. First, interven-
tions focused on leveraging drivers that are related
to COVID-19, but not to climate-friendly behavior.
The results indicate that this can be done by chang-
ing perceptions regarding governmental policies and
threat to close others. Changing perceptions regard-
ing governmental policies could be done by devel-
oping environmental communication campaigns on
changing the perception of governmental policies
for the better. Additionally, this could be achieved
by stimulating people to engage in public-sphere
pro-environmental behavior (e.g., taking part in
climate marches) to enhance the chance that envi-
ronmental governmental policy will change. Threat
to close others could be made more salient by, for
example, using virtual reality experiences showing
how climate change can also affect people’s own
environment, showing that also their close others
are susceptible to the threat of climate change. Sec-
ond, interventions focusing on already important
drivers by priming these motives, like participative
efficacy, could be highly effective. Therefore, com-
municating to people that their individual actions
are indispensable to reach the goal of combating
climate change could be an effective way of stimu-
lating climate-friendly behavior.
Table 1 Items to measure drivers for engaging in behaviors that help fight COVID-19 and climate change
Note.The references indicate research the item was based on.
Governmental policy Because the government is advocating for these behaviors
Injunctive social norms (Árnadóttir, Kok, Van Gils, & Ten
Hoor, 2019)Because people who are important to me think I should engage in these behaviors
Descriptive social norms (Árnadóttir et al. 2019)Because most people who are important to me are engaging in these behaviors
Participative efficacy (Van Zomeren etal. 2013)Because if I engage in these behaviors, we can together limit [the spread of the coro-
navirus/the harm caused by climate change]
Perceived threat to me (Slater etal. 2015)Because [the coronavirus/climate change] poses a serious threat to me personally
Perceived threat to those close others (Slater etal. 2015)Because [the coronavirus/climate change] poses a serious threat to people who are
important to me
Perceived threat to vulnerable others (Slater etal. 2015)Because [the coronavirus/climate change] poses a serious threat to vulnerable others
in society
Fear (Hartmann etal. 2014)Because I am scared of the potential impact of [the coronavirus/climate change]
Table 2 Means and standard
deviations for the drivers for
engaging in behaviors that help
fight COVID-19 and climate
change, and results of paired
samples t-test comparing the
drivers for the two crises
COVID-19 Climate change t p Cohen’s dz
Mean SD Mean SD
Governmental policy 3.22 (1.03) 2.00 (0.90) 27.76 < .001 1.20
Social norms – injunctive 2.58 (1.13) 2.03 (0.96) 11.11 < .001 0.48
Social norms – descriptive 2.74 (1.12) 2.21 (0.94) 11.13 < .001 0.48
Participative efficacy 4.40 (0.78) 4.05 (0.97) 7.80 < .001 0.34
Personal threat 2.58 (1.23) 2.71 (1.17) − 2.00 .046 0.09
Threat to close others 4.01 (1.10) 3.10 (1.22) 14.47 < .001 0.63
Threat to vulnerable others 4.26 (0.88) 3.36 (1.21) 17.14 < .001 0.74
Fear 3.43 (1.17) 3.74 (1.10) − 5.15 < .001 0.23
Appendix
Journal of Environmental Studies and Sciences
1 3
Table 3 Results of multinomial
regressions from self-reported
behaviors that help fight
COVID-19 on the behavioral
drivers
“Medium” is the specified reference category. For comparing low vs medium frequency behaviors, this
means that minus values should be interpreted as drivers being more important in the medium compared to
the low category, whereas for comparing medium vs high frequency behaviors, this means that minus val-
ues should be interpreted as drivers being more important in the medium compared to the high category(n
= 525).
R2 = 0.14 (Cox and Snell), R2 = 0.16 (Nagelkerke). Model χ2 (22) = 78.33, p < 0.001
bSE Wald pOdds ratio 95% CI for odds
ratio
Lower Upper
Low vs. medium
Intercept − 0.08 1.03 0.01 .934
Governmental policy 0.05 0.14 0.14 .705 1.06 0.80 1.40
Social norms – injunctive 0.33 0.16 4.16 .041 1.40 1.01 1.92
Social norms – descriptive − 0.49 0.17 8.16 .004 0.61 0.44 0.86
Participative efficacy 0.02 0.19 0.01 .919 1.02 0.70 1.49
Personal threat − 0.12 0.15 0.70 .404 0.88 0.66 1.18
Threat to close ones 0.20 0.14 2.07 .150 1.22 0.93 1.60
Threat to vulnerable others − 0.04 0.18 0.04 .840 0.97 0.68 1.36
Fear − 0.12 0.15 0.65 .421 0.89 0.67 1.19
Gender (female vs. rest) 0.18 0.30 0.35 .555 1.19 0.66 2.14
Age − 0.02 0.01 2.73 .099 0.98 0.96 1.00
Education level: university vs.
rest − 0.30 0.36 0.71 .399 0.74 0.36 1.50
High vs. medium
Intercept − 4.39 0.92 22.58 < .001
Governmental policy 0.21 0.11 3.98 .046 1.24 1.00 1.52
Social norms – injunctive − 0.13 0.12 1.13 .288 0.88 0.69 1.12
Social norms – descriptive − 0.24 0.12 3.66 .056 0.79 0.62 1.01
Participative efficacy 0.53 0.17 10.02 .002 1.70 1.22 2.36
Personal threat 0.15 0.10 2.22 .136 1.16 0.95 1.42
Threat to close ones 0.31 0.11 7.51 .006 1.36 1.09 1.70
Threat to vulnerable others 0.06 0.14 0.18 .671 1.06 0.81 1.39
Fear 0.02 0.11 0.05 .822 1.03 0.83 1.27
Gender (female vs. rest) 0.23 0.22 1.08 .299 1.26 0.81 1.96
Age 0.00 0.01 0.21 .651 1.00 0.99 1.02
Education level: university vs.
rest − 0.05 0.29 0.04 .851 0.95 0.54 1.66
Journal of Environmental Studies and Sciences
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Table 4 Results of multinomial
regressions from self-reported
behaviors that help fight climate
change on the behavioral drivers
“Medium” is the specified reference category. For comparing low vs medium frequency behaviors, this
means that minus values should be interpreted as drivers being more important in the medium compared to
the low category, whereas for comparing medium vs high frequency behaviors, this means that minus val-
ues should be interpreted as drivers being more important in the medium compared to the high category(n
=525).
R2= 0.22 (Cox and Snell), R2 = 0.25 (Nagelkerke). Model χ2 (22) = 132.58, p < 0.001
b SE Wald pOdds ratio 95% CI for odds
ratio
Lower Upper
Low vs. medium
Intercept 2.65 0.74 12.69 < .001
Governmental policy 0.12 0.14 0.77 .379 1.13 0.86 1.49
Social norms – injunctive − 0.13 0.17 0.56 .454 0.88 0.63 1.23
Social norms – descriptive 0.02 0.17 0.01 .929 1.02 0.73 1.42
Participative efficacy − 0.42 0.13 9.60 .002 0.66 0.51 0.86
Personal threat − 0.08 0.12 0.45 .501 0.92 0.72 1.17
Threat to close ones − 0.04 0.12 0.15 .698 0.96 0.76 1.20
Threat to vulnerable others − 0.18 0.11 2.57 .109 0.84 0.67 1.04
Fear − 0.21 0.13 2.78 .096 0.81 0.63 1.04
Gender (female vs. rest) 0.12 0.25 0.21 .649 1.12 0.68 1.85
Age − 0.01 0.01 2.29 .130 0.99 0.97 1.00
Education level (university vs.
rest) 0.68 0.35 3.92 .048 1.98 1.01 3.90
High vs. medium
Intercept − 2.49 0.84 8.88 .003
Governmental policy − 0.03 0.13 0.04 .844 0.97 0.75 1.26
Social norms – injunctive 0.00 0.16 0.00 .979 1.00 0.74 1.36
Social norms – descriptive − 0.28 0.16 3.14 .076 0.75 0.55 1.03
Participative efficacy 0.37 0.15 5.90 .015 1.45 1.07 1.96
Personal threat − 0.08 0.11 0.51 .474 0.92 0.74 1.15
Threat to close ones 0.02 0.11 0.05 .824 1.02 0.83 1.27
Threat to vulnerable others 0.37 0.11 11.65 .001 1.45 1.17 1.80
Fear 0.16 0.13 1.70 .192 1.18 0.92 1.51
Gender (female vs. rest) 0.09 0.25 0.14 .710 1.10 0.67 1.80
Age − 0.02 0.01 3.45 .063 0.99 0.97 1.00
Education level (university vs.
rest) − 0.09 0.29 0.09 .769 0.92 0.52 1.63
Journal of Environmental Studies and Sciences
1 3
Table 5 Correlations between COVID-19 behavior and the perceived importance of behavioral drivers
The upper right half of the table represents the bivariate correlations(n = 534), the lower left half represents the partial correlations(df = 484)
— controlled for demographics(gender, age, education, country of residence) and political affiliation. All items were measured on a 1–5 scale.
The first number represents the correlation; between brackets, the exact p-values are reported.
Behavior Governmental
policy
Social norms
– injunctive
Social norms
– descriptive
Participative
efficacy
Personal
threat
Threat to
close ones
Threat to
vulnerable
others
Fear
Behavior 0.100 (0.021) − 0.073
(0.093)
0.006 (0.883) 0.226
(< 0.001)
0.177
(< 0.001)
0.177
(< 0.001)
0.146 (0.001) 0.186 (< 0.001)
Governmental
policy
0077 (0.088) 0.253
(< 0.001)
0.301
(< 0.001)
0.185
(< 0.001)
0.126 (0.003) 0.020 (0.651) 0.187
(< 0.001)
0.112 (0.009)
Social norms
– injunctive
− 0.106
(0.020)
0.256
(< 0.001)
0.667
(< 0.001)
-0.004 (0.927) 0.185
(< 0.001)
0.124 (0.004) 0.063 (0.149) 0.150 (0.001)
Social norms
– descrip-
tive
-0.032 (0.482) 0.286
(< 0.001)
0.653
(< 0.001)
0.069 (0.112) 0.192
(< 0.001)
0.157
(< 0.001)
0.119 (0.006) 0.226 (<
0.001)
Participative
efficacy
0.190
(< 0.001)
0.176
(< 0.001)
0.011 (0.815) 0.063 (0.167) 0.128 (0.003) 0.275
(< 0.001)
0.461
(< 0.001)
0.349 (< 0.001)
Personal
threat
0.130 (0.004) 0.115 (0.011) 0.170
(< 0.001)
0.162
(< 0.001)
0.129 (0.004) 0.396
(< 0.001)
0.109 (0.012) 0.512 (<
0.001)
Threat to
close ones
0.134 (0.003) 0.010 (0.833) 0.108 (0.017) 0.120 (0.008) 0.239
(< 0.001)
0.361
(< 0.001)
0.362
(< 0.001)
0.422 (<
0.001)
Threat to
vulnerable
others
0.126 (0.005) 0.177
(< 0.001)
0.049 (0.281) 0.112 (0.013) 0.429
(< 0.001)
0.110 (0.015) 0.346
(< 0.001)
0.273 (< 0.001)
Fear 0.145 (0.001) 0.067 (0.140) 0.153 (0.001) 0.205
(< 0.001)
0.333
(< 0.001)
0.475
(< 0.001)
0.394
(< 0.001)
0.272
(< 0.001)
Table 6 Correlations between climate-friendly behavior and the perceived importance of behavioral drivers
The upper right half of the table represents the bivariate correlations (n = 534); the lower left half represents the partial correlations(df = 484)
—controlled for demographics (gender, age, education, country of residence)and political affiliation. All items were measured on a 1–5 scale.
The first number represents the correlation; between brackets, the exact p-values are reported.
Behavior Governmen-
tal policy Social
norms –
injunctive
Social
norms –
descriptive
Participative
efficacy Personal
threat Threat to
close ones Threat to
vulnerable
others
Fear
Behavior -0.013
(0.758) 0.016
(0.704) -0.040
(0.352) 0.388
(< 0.001) 0.173
(< 0.001) 0.219
(< 0.001) 0.363
(< 0.001) 0.309
(< 0.001)
Governmen-
tal policy − 0.020
(0.655) 0.340
(< 0.001) 0.362
(< 0.001) 0.065
(0.136) 0.194
(< 0.001) 0.199
(< 0.001) 0.102
(0.018) 0.159
(< 0.001)
Social
norms –
injunctive
− 0.004
(0.937) 0.323
(< 0.001) 0.674
(< 0.001) 0.059
(0.171) 0.247
(< 0.001) 0.175
(< 0.001) 0.138
(0.001) 0.240
(< 0.001)
Social
norms –
descriptive
− 0.068
(0.137) 0.352
(< 0.001) 0.664
(< 0.001) 0.065
(0.135) 0.222
(< 0.001) 0.183
(< 0.001) 0.119
(0.006) 0.223
(< 0.001)
Participative
efficacy 0.339
(< 0.001) 0.077
(0.091) 0.046
(0.309) 0.052
(0.249) 0.228
(< 0.001) 0.310
(< 0.001) 0.380
(< 0.001) 0.458
(< 0.001)
Personal
threat 0.131
(0.004) 0.178
(< 0.001) 0.214
(< 0.001) 0.189
(< 0.001) 0.195
(< 0.001) 0.451
(< 0.001) 0.341
(< 0.001) 0.436
(< 0.001)
Threat to
close ones 0.174
(< 0.001) 0.214
(< 0.001) 0.173
(< 0.001) 0.160
(< 0.001) 0.277
(< 0.001) 0.441
(< 0.001) 0.432
(< 0.001) 0.319
(< 0.001)
Threat to
vulnerable
others
0.289
(<0.001) 0.122
(0.007) 0.131
(0.004) 0.102
(0.024) 0.314
(< 0.001) 0.322
(< 0.001) 0.394
(< 0.001) 0.348
(< 0.001)
Fear 0.234
(< 0.001) 0.165
(< 0.001) 0.246
(< 0.001) 0.236
(< 0.001) 0.417
(< 0.001) 0.428
(< 0.001) 0.300
(< 0.001) 0.286
(< 0.001)
Journal of Environmental Studies and Sciences
1 3
Funding Theresearch was funded by ASCoR, the Amsterdam
School of Communication Science at the University of Amsterdam,
the Netherlandsandby a Netherlands Organisation for Scientific
Research grant awarded to the first [grantnumber VI.Veni.201S.075]
and second author [grant number VI.Veni.191G.034].
Data availability The data that support the findings of this study are
openly available on the Open Science Framework via this link https://
osf. io/ bcuqh/.
Code availability NA.
Declarations
Ethics approval All study procedures were approved by our univer-
sity’s ethical review board (reference number: 2020-PC-12051); for
the consent and other information, please see OSF via this link https://
osf. io/ bcuqh/.
Conflict of interest The authors declare no competing interests.
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
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... However, international collaborations to manage these crises have been limited (Cole and Dodds, 2021;Klenert et al., 2020). To this end, inclusive regulations and policies should be adopted that foster environmental communication campaigns and sustainable strategies (Meijers et al., 2022;Rahman et al., 2021). These policies should be durable, easy to adjust, contain a broad range of global efforts (e.g., targeted policy reforms and facilitated international coordination) (Auld et al., 2021;Di Ciaula et al., 2021;Mai, 2021), and be based on evidence and expert advice globally (Baldwin and English, 2020;Obergassel et al., 2021;Phillips et al., 2020). ...
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In the current context of the global pandemic of coronavirus disease-2019 (COVID-19), health professionals are working with social scientists to inform government policy on how to slow the spread of the virus. An increasing amount of social scientific research has looked at the role of public message framing, for instance, but few studies have thus far examined the role of individual differences in emotional and personality-based variables in predicting virus-mitigating behaviors. In this study we recruited a large international community sample (N = 324) to complete measures of self-perceived risk of contracting COVID-19, fear of the virus, moral foundations, political orientation, and behavior change in response to the pandemic. Consistently, the only predictor of positive behavior change (e.g., social distancing, improved hand hygiene) was fear of COVID-19, with no effect of politically-relevant variables. We discuss these data in relation to the potentially functional nature of fear in global health crises.
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