Content uploaded by Roman Briker
Author content
All content in this area was uploaded by Roman Briker on Dec 08, 2023
Content may be subject to copyright.
Manuscript accepted at Science Advances. 1
Addressing Climate Change with Behavioral Science:
A Global Intervention Tournament in 63 Countries
Madalina Vlasceanu1*, Kimberly C. Doell1,2*, Joseph B. Bak Coleman3,4*, Boryana Todorova2, Michael M.
Berkebile-Weinberg1, Samantha J. Grayson5, Yash Patel1, Danielle Goldwert1, Yifei Pei1, Alek Chakroff6,
Ekaterina Pronizius2, Karlijn L. van den Broek7, Denisa Vlasceanu8, Sara Constantino9,10, Michael J.
Morais11, Philipp Schumann12, Steve Rathje1, Ke Fang1, Salvatore Maria Aglioti13,14, Mark Alfano15, Andy
J. Alvarado-Yepez16, Angélica Andersen17, Frederik Anseel18, Matthew A. J. Apps19, Chillar Asadli20,
Fonda Jane Awuor21, Flavio Azevedo22, Piero Basaglia23, Jocelyn J. Bélanger24, Sebastian Berger25, Paul
Bertin26,27, Michał Białek28, Olga Bialobrzeska29, Michelle Blaya-Burgo30, Daniëlle N. M. Bleize31, Simen
Bø32, Lea Boecker33, Paulo S. Boggio34, Sylvie Borau35, Björn Bos36, Ayoub Bouguettaya37, Markus
Brauer38, Cameron Brick39,40, Tymofii Brik41, Roman Briker42, Tobias Brosch43, Ondrej Buchel44, Daniel
Buonauro45, Radhika Butalia46, Héctor Carvacho47, Sarah A. E. Chamberlain48, Hang-Yee Chan49, Dawn
Chow50, Dongil Chung51, Luca Cian52, Noa Cohen-Eick53,54, Luis Sebastian Contreras-Huerta19,55, Davide
Contu56, Vladimir Cristea57, Jo Cutler19, Silvana D'Ottone58, Jonas De keersmaecker59,60, Sarah Delcourt61,
Sylvain Delouvée62, Kathi Diel63, Benjamin D. Douglas38, Moritz A. Drupp23,64, Shreya Dubey65, Jānis
Ekmanis66, Christian T. Elbaek67, Mahmoud Elsherif68,69, Iris M. Engelhard70, Yannik A. Escher71, Tom W.
Etienne57,72, Laura Farage73, Ana Rita Farias74, Stefan Feuerriegel75, Andrej Findor76, Lucia Freira77, Malte
Friese63, Neil Philip Gains78, Albina Gallyamova79, Sandra J. Geiger80, Oliver Genschow81, Biljana
Gjoneska82, Theofilos Gkinopoulos83, Beth Goldberg84, Amit Goldenberg85,86,87, Sarah Gradidge88, Simone
Grassini89,90, Kurt Gray91, Sonja Grelle92, Siobhán M. Griffin93, Lusine Grigoryan94, Ani Grigoryan95,
Dmitry Grigoryev79, June Gruber96, Johnrev Guilaran97, Britt Hadar98, Ulf J.J. Hahnel99, Eran Halperin53,
Annelie J. Harvey88, Christian A. P. Haugestad100, Aleksandra M. Herman101,102, Hal E. Hershfield103,
Toshiyuki Himichi104, Donald W. Hine105, Wilhelm Hofmann92, Lauren Howe106, Enma T. Huaman-
Chulluncuy107, Guanxiong Huang108, Tatsunori Ishii109, Ayahito Ito110, Fanli Jia111, John T. Jost1, Veljko
Jovanović112, Dominika Jurgiel113, Ondřej Kácha114, Reeta Kankaanpää115,116, Jaroslaw Kantorowicz117,
Elena Kantorowicz-Reznichenko118, Keren Kaplan Mintz119,120, Ilker Kaya121, Ozgur Kaya121, Narine
Khachatryan95, Anna Klas122, Colin Klein123, Christian A. Klöckner124, Lina Koppel125, Alexandra I.
Kosachenko126, Emily J. Kothe122, Ruth Krebs127, Amy R. Krosch128, Andre P.M. Krouwel129, Yara
Kyrychenko130, Maria Lagomarsino131, Claus Lamm2, Florian Lange61, Julia Lee Cunningham132, Jeffrey
Lees133,134, Tak Yan Leung135, Neil Levy136, Patricia L. Lockwood19, Chiara Longoni137, Alberto López
Ortega138, David D. Loschelder139, Jackson G. Lu140, Yu Luo141, Joseph Luomba142, Annika E. Lutz143,
Johann M. Majer144, Ezra Markowitz145, Abigail A. Marsh146, Karen Louise Mascarenhas147,148, Bwambale
Mbilingi149, Winfred Mbungu150, Cillian McHugh151, Marijn H.C. Meijers152, Hugo Mercier153, Fenant
Laurent Mhagama154, Katerina Michalaki155, Nace Mikus156,157, Sarah Milliron128, Panagiotis Mitkidis67,
Fredy S. Monge-Rodríguez158, Youri L. Mora159,160, David Moreau161, Kosuke Motoki162, Manuel
Moyano163, Mathilde Mus164, Joaquin Navajas165,166, Tam Luong Nguyen167, Dung Minh Nguyen168, Trieu
Nguyen168, Laura Niemi169, Sari R. R. Nijssen170, Gustav Nilsonne171,172, Jonas P. Nitschke2, Laila
Nockur173, Ritah Okura149, Sezin Öner174, Asil Ali Özdoğru175,176, Helena Palumbo177, Costas
Panagopoulos178, Maria Serena Panasiti14,179, Philip Pärnamets180, Mariola Paruzel-Czachura181,182, Yuri G.
Pavlov183, César Payán-Gómez184, Adam R. Pearson45, Leonor Pereira da Costa185, Hannes M. Petrowsky186,
Stefan Pfattheicher173, Nhat Tan Pham187, Vladimir Ponizovskiy92, Clara Pretus188, Gabriel G. Rêgo189,
Ritsaart Reimann136, Shawn A. Rhoads190,191, Julian Riano-Moreno192, Isabell Richter193, Jan Philipp
Röer194, Jahred Rosa-Sullivan195, Robert M. Ross136, Anandita Sabherwal196, Toshiki Saito197,198, Oriane
Sarrasin199, Nicolas Say200, Katharina Schmid201, Michael T. Schmitt143, Philipp Schoenegger202,203, Christin
Scholz204, Mariah G. Schug205, Stefan Schulreich206,207, Ganga Shreedhar196, Eric Shuman1,208, Smadar
Sivan209, Hallgeir Sjåstad32, Meikel Soliman210, Katia Soud211,212, Tobia Spampatti213,214, Gregg
Sparkman215, Ognen Spasovski216,217, Samantha K. Stanley218, Jessica A. Stern219, Noel Strahm25, Yasushi
Suko220, Sunhae Sul221, Stylianos Syropoulos222, Neil C. Taylor223, Elisa Tedaldi224, Gustav Tinghög125, Luu
Duc Toan225, Giovanni Antonio Travaglino226, Manos Tsakiris155, İlayda Tüter227, Michael Tyrala228, Özden
Melis Uluğ229, Arkadiusz Urbanek230, Danila Valko231,232, Sander van der Linden233, Kevin van Schie234,
Aart van Stekelenburg235, Edmunds Vanags236, Daniel Västfjäll237, Stepan Vesely124, Jáchym Vintr114,
Marek Vranka238, Patrick Otuo Wanguche239, Robb Willer240, Adrian Dominik Wojcik241, Rachel Xu84,
Anjali Yadav242,243, Magdalena Zawisza88, Xian Zhao244, Jiaying Zhao141,245, Dawid Żuk246, Jay J. Van
Bavel247,248
*Marks equal contribution authors. Address correspondence to Madalina Vlasceanu
[vlasceanu@nyu.edu] or Kimberly Doell [kimberlycdoell@gmail.com]
Abstract
Effectively reducing climate change requires dramatic, global behavior change. Yet it is unclear
which strategies are most likely to motivate people to change their climate beliefs and behaviors.
Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes:
beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral
task. Across 59,440 participants from 63 countries, the interventions’ effectiveness was small,
largely limited to non-climate-skeptics, and differed across outcomes: Beliefs were strengthened
most by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future
generation member (2.6%), information sharing by negative emotion induction (12.1%), and no
intervention increased the more effortful behavior–several interventions even reduced tree
planting. Finally, the effects of each intervention differed depending on people’s initial climate
beliefs. These findings suggest that the impact of behavioral climate interventions varies across
audiences and target behaviors.
One sentence summary
Climate interventions increase beliefs, policy support, and willingness to share information, but
not higher effort action.
Keywords
Climate interventions; megastudy; climate change; behavior change; international research
Introduction
The climate crisis is one of humanity’s most consequential and challenging problems (1).
Successfully rising to the challenge depends on both “top-down” structural changes (e.g.,
regulation, investment) as well as “bottom-up” changes (e.g., individuals’ and collectives’ beliefs
and behaviors). These bottom-up processes require wide-spread belief in climate change, support
for climate change policy, and willingness to engage in climate action (2-4). The behavioral
sciences have been seen as a crucial component in promoting bottom-up change, through the
development of large-scale interventions that can shift public opinion and enable and support top-
down governmental climate policies (5-7). Yet it is unclear which strategies are most likely to
motivate people to change their climate change beliefs and climate mitigation behaviors. Here, we
assess the effectiveness of expert-crowdsourced, theoretically-derived interventions at promoting
a range of climate change mitigation behaviors in a large and diverse global sample.
A growing body of research across the behavioral sciences has been investigating
intervention strategies aimed at boosting sustainable intentions and behaviors such as recycling,
public transportation use, and household energy saving (3, 8, 9). For instance, communications
aimed at reducing the psychological distance of climate change, by making it feel more
geographically, socially, and temporally close, were effective at increasing climate concern, and
amplifying self-reported intentions to engage in mitigating behaviors, such as reducing energy
consumption (10). Similarly, normative appeals that include an invitation to work together and
“join in” were found effective at influencing behaviors such as charitable giving (11). These are
only two examples in a growing list of behavioral interventions designed to mitigate climate
change. As such, there are numerous competing theories in the behavioral sciences about how to
stimulate climate change beliefs and pro-environmental behaviors.
While many of these theories–and their corresponding interventions–are promising, they
have been tested independently with different samples, and on separate outcomes, making it
impossible to directly compare their effectiveness. Additionally, assessing interventions on a
single outcome renders it difficult to understand their effects on multiple facets of climate
mitigation, which are all necessary to significantly reduce climate change (e.g., support for climate
mitigation policy and sustainable behavior). These limitations are a major barrier to resolving
theoretical debates within the scientific community (12, 13) and to translating scientific findings
into impactful policies (14, 15). Moreover, traditional attempts to compare interventions (e.g.,
meta-analyses) (16)) are limited by differences in experimental protocols, outcome variables,
samples, and operationalizations (17, 18, 19). These differences hinder evaluations of the relative
effectiveness of different theories and interventions (15). To address these concerns, we used the
megastudy approach – an experimental paradigm similar to a randomized controlled trial, but
designed to evaluate the efficacy of many interventions on several outcome variables, in the same
large-scale experiment (18). This provides a rigorous direct comparison of competing approaches
to climate change mitigation.
Another challenge is that most prior work across the behavioral sciences (including the
megastudy approach) has been mainly conducted on Western, Educated samples from
Industrialized, Rich, and Developed countries (i.e., WEIRD) (20)). Results from such samples may
not generalize to other nations, restricting the ability to apply findings beyond WEIRD
populations. This is a particular problem for a topic like climate change where the social and
political dynamics, and exposure to the impacts of climate change, vary across countries (21, 22).
While wealthier nations are disproportionately responsible for causing climate change (23), it is
still important to understand which interventions work across a diversity of cultures since the most
effective mitigation strategies will likely require global cooperation. Accordingly, we leveraged
the many labs approach, in which the same study is being conducted by many research labs around
the world, aggregating the results in the same international dataset (17, 24).
In this global megastudy, we crowdsourced interventions previously found to stimulate
climate mitigation, from behavioral science experts (Figure S5). We used a crowdsourcing
approach to determine which interventions to test, given recent evidence that crowdsourcing can
improve the quality of scientific investigations by promoting ideation, inclusiveness, transparency,
rigor, and reliability (25). This resulted in the identification of 11 behavioral interventions based
on competing theoretical frameworks in the behavioral sciences (Fig. 1).
Figure 1. Interventions, theoretical frameworks, and brief descriptions.
We tested these interventions in a global tournament spanning 63 countries, on four
outcome variables, which were also crowdsourced and selected based on their theoretical and
practical relevance to climate mitigation. The first outcome on which we assessed each
intervention was belief in climate change (4-items; e.g., “Climate change poses a serious threat to
humanity”). Given that belief is a key antecedent of pro-environmental intentions, behavior, and
Intervention Theoretical framework Description
Dynamic Social Norms Sparkman & Walton, 2017 Informs participants of how country-
level norms are changing and “more and more people are becoming concerned about
climate change”, suggesting that people should take action.
Work Together Norm Howe, Carr, & Walton, 2021
Combines referencing a social norm (i.e., “a majority of people are taking steps to reduce the ir carbon footprint”) with an
invitation to “join in” and work together with fellow citizens toward this common goal.
Effective Collective
Action
Goldenberg et al., 2018;
Lizzio-Wilson et al., 2021
Features examples of successful collective action that have had meaningful effects on climate policies (e.g., protests) or
have solved past global issues (e.g., the restoration of the ozone layer).
Psychological Distance Jones, Hine, & Marks, 2017 Frames climate change as a proximal risk by using examples of recent natural disasters caused by clim ate change in each
participants’ nation and prompts them to write about the climate impacts on their community.
System Justification Feygina, Jost, & Goldsmith,
2010
Frames climate change as threatening to the way of life to each participant’s nation, and makes an appeal to clima te
action, as the patriotic response.
Future-Self Continuity Hershfield, Cohen, &
Thompson, 2012
Emphasizes the future self-continuity by asking each participant to project themselves int o the future and write a letter
addressed to themselves in the present, describing the actions they would have wanted to take regarding cli mate change.
Negative Emotions Chapman, Lickel, &
Markowitz, 2017
Exposes participants to ecologically valid scientific facts regarding the impact s of climate change framed in a ‘doom and
gloom’ style of messaging that were drawn from different real-world news and media sources.
Pluralistic Ignorance Geiger & Swim, 2016 Presents real public opinion data collected by the United Nations that shows what percentage of people in e ach
participant’s country agree that climate change is a global emergency.
Letter to Future
Generation
Shrum, 2021; Wickersham,
Zaval, Pachana, & Smyer, 2020
Emphasizes how one’s current actions impact future generations by asking participants to write a letter to a socially close
child who will read it in 25 years when they are an adult, describing current actions towards ensuring a habitable planet.
Binding Moral
Foundations
Wolsko, Ariceaga, & Seiden,
2016
Invokes authority (e.g., “From scientists to experts in the military, there is near universal agreement”), purity (e.g., keep
our air, water, and land pure), and ingroup-loyalty (e.g., “it is the American solution”) moral foundations.
Scientific Consensus van der Linden et al., 2015,
2021; Rode et al., 2021
Informs participants that “99% of expert climate scientists agree that the Earth is warm ing, and climate change is
happening, mainly because of human activity”.
policy support (26), we examined how the interventions would impact these outcomes for different
people along the belief continuum ranging from skeptics to true believers.
The second outcome was support for climate change mitigation policy (9-items; e.g., “I
support raising carbon taxes on gas/ fossil fuels/coal”). Given that successful climate mitigation
requires large-scale policy reform (1), and the public’s support for climate policies is the top
predictor of policy adoption (27), this outcome variable reflects the importance of impactful
systemic change, rather than private mitigation efforts based on individual decision-making (28-
30). Indeed, recent work argues that individual-level behaviors should be targeted alongside
structural changes (31), especially since framing climate change as an individual level problem
can backfire, leading to feelings of helplessness and concerns about free-riding (32, 33).
To target more ecologically valid behavior and climate activism (34), the third outcome
was willingness to share climate mitigation information on social media (i.e., “Did you know that
removing meat and dairy for only two out of three meals per day could decrease food-related
carbon emissions by 60%?”). While this behavior is relatively low-effort, recent work suggests
climate information sharing with one’s community as an essential step in addressing the climate
crisis (35).
Finally, given the large gap between self-reported measures and objective pro-
environmental behavior (36), the fourth outcome we targeted was a more effortful behavior of
contributing to a real tree-planting initiative by engaging in a cognitively demanding task (i.e., a
modified version of the Work for Environmental Protection Task or WEPT; 37). The WEPT is a
multi-trial, web-based procedure in which participants choose to exert voluntary effort screening
stimuli for specific numerical combinations (i.e., an even first digit and odd second digit) in
exchange for donations to a tree-planting environmental organization. Thus, they had the
opportunity to produce actual environmental benefits, at actual behavioral costs, mimicking classic
sustainable-behavior tradeoffs (38-40).
Participants (N = 59,440, from 63 counties; Table 1) were mostly recruited through online
data collection platforms (80.8%) or via convenience/snowball sampling (19.1%; Fig 5; Table 1).
They were randomly assigned to one of 11 experimental interventions (Fig. 1), or a no-intervention
control condition in which they read a passage from a literary text. Then, in a randomized order,
participants indicated their climate beliefs, climate policy support, and willingness to share
climate-related information on social media. Finally, participants were able to opt into completing
up to 8 pages of a tree-planting task, each completed page resulting in the real planting of a tree
through a donation to The Eden Reforestation Project. As a result of participants’ behavior, our
team actually planted 333,333 trees. Assuming that the average fully-grown tree absorbs between
10 and 40 kg of carbon dioxide per year, in 5-10 years when all trees are fully grown, the efforts
from this project will result in approximately 9,999,990 kg of carbon dioxide sequestered per year
which is the equivalent amount of carbon dioxide used to produce energy for 1,260 US homes.
Results
1. Main effects of intervention
First, we examined the effect of each intervention on each of the four outcomes, estimated using a
series of Bayesian regressions (see Methods). As the goal of this study is to estimate the relative
effectiveness of treatments, in contrast to establishing non-null effects or differences, Bayesian
estimation is preferable to classical Null Hypothesis Significance Testing. Bayesian techniques
produce posterior distributions for parameters (here, treatment effects) that characterizes their
magnitude and associated uncertainty. We summarize this distribution in the main text using a
point estimate corresponding to the mean, and a 94% credible region, which differs from a
confidence interval in that it indicates a region with a 94% chance of containing the unobserved
parameter value (41). Moreover, we also conducted similar frequentist analyses (hierarchical
mixed models) and found converging results (see Supplement for details).
We began by assessing the main intervention effects on each outcome. For belief in climate
change (measured on a scale from 0 to 100), the top-performing intervention, decreasing
psychological distance, increased beliefs by an absolute effect size of 2.3% [1.6, 2.9] (94%
Credible Region) compared to the control condition. Consistent with prior work (10), some
interventions slightly increased beliefs. However, other interventions had near-zero effect, failing
to replicate prior research (11) (Fig. 2A).
Fig 2. Average effects (i.e., posterior estimates using Bayesian regressions) by intervention for each outcome.
Dots indicate the mean, with error bars indicating the 94% credible region (C.R.). Thicker error bars indicate the
interquartile range (IQR). Vertical lines indicate control average. A) Belief, B) Support for Policy, C) Willingness to
share climate-change information on social media, D) Number of trees planted in the WEPT. Estimates reported in
Tables S1-S4.
For climate policy support (measured on a scale from 0 to 100), the intervention with the
largest average effect was writing a letter to a member of the future generation, which increased
policy support by 2.6% [2.0, 3.2]. Similar to belief, all interventions produced either more policy
support or no discernible differences from the control condition (Fig. 2B).
For willingness to share climate change information on social media (measured as a binary
choice), all interventions generally increased intentions to share. The largest gains were exhibited
in the negative emotion induction condition, which led to 12.1% [9.8, 14.6] more sharing compared
to the control condition (Fig. 2C).
67 68 69 70 71 72 73 74 75 76 77
38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68
4.0 4.4 4.8 5.2 5.6 6.0
Belief in climate change (%)
Work Together Norm
Pluralistic Ignorance
Negative Emotions
Dynamic Soc. Norm
Binding Moral Found.
Scientific Consensus
System Justification
Letter to Future Gen.
Future-Self Continuity
Collective Action
Psychological Distance
A
Climate policy support (%)
B
Willingness to share climate info (%)
C
Number of trees planted
D
Belief Action
Policy Share
82 83 84 85 86 87 88 89 90 91 92
For the number of pages completed on the WEPT tree planting task (from 0 to 8), no
intervention was better than the control condition, and some interventions (i.e., decreasing
psychological distance, inducing negative emotions, work-together normative appeals, and writing
a letter to a future generation member) appeared to reduce tree-planting (Fig. 2D). These results
held regardless of the operationalization of a tree planted as participants’ confirmation that they
wanted to complete another WEPT page, or their accuracy in the task (Table S24).
The interventions that produced negative effects on the WEPT were also those that took
the most time to complete (Supplement Analyses). Assuming participants have a limited budget
of time for completing surveys, and given the tree planting task requires time, it is unsurprising we
observed a tradeoff between the time spent on the intervention and on the outcome task. Therefore,
in an exploratory analysis (Tables S22, S23) we assessed the effects of the interventions when
adjusting for the time spent on each intervention. While we still observed the negative effects of
some interventions on tree planting, we now also observed positive effects of five interventions.
That is, when controlling for intervention length, Binding Moral Foundations, Scientific
Consensus, Dynamic Norms, Pluralistic Ignorance, and System Justification all increased the
number of trees planted compared to the control condition. Thus, in the absence of time constraints,
such interventions might increase pro-environmental behavior. However, the degree to which these
findings actually generalize to pro-environmental behaviors that do not hinge on time (e.g.,
donations) should be assessed in future studies.
For further assessing the average effects of each intervention on each outcome within any
subsample of interest varying along demographics such as nationality, political ideology, age,
gender, education, or income level, we provide an easy to use and disseminate webtool:
https://climate-interventions.shinyapps.io/climate-interventions/.
2. Heterogeneous intervention effects along initial belief continuum
We found a high level of belief in climate change (i.e., 85.7% [85.2, 86.2], an estimate computed
using the ratings of belief in the control participants and estimated pre-intervention levels of belief
from all other participants). This could raise two potential concerns when evaluating the main
effects of the interventions mentioned above: On the one hand, at this high level of belief,
participants may be particularly receptive to interventions. As a result, average effects may tend
to overestimate the effectiveness of interventions in applied contexts where the aim is to increase
belief or policy support in skeptical participants that do not already believe in climate change. On
the other hand, as our outcomes are bounded, these high levels of belief may lead to ceiling effects
in the estimation of the average effects, which may undervalue the true effectiveness of the
interventions. To address this concern, we conducted an additional analysis where we modeled
heterogeneous effects as a function of unobserved pre-intervention belief (see Methods, SI). This
analysis allowed us to visualize how effective interventions were across the continuum from
climate change skeptics (i.e., those with initial beliefs less than 35%) to true believers (i.e., those
with initial beliefs higher than 65%; Fig 3).
For the impact of interventions on belief (Fig 3A), we found clear indications of ceiling
effects with many interventions being maximally impactful among uncertain participants, even
those with low to moderate levels of initial belief. Even in participants with low levels of pre-
existing climate change belief (i.e., less than 35%), interventions like reducing psychological
distance, future self continuity, and effective collective action are all viable ways to increase belief
in climate change.
For policy support, a different pattern emerged. Interventions like writing a letter to a
member of the future generation, collective action efficacy, future-self continuity, and decreasing
psychological distance all increased support for climate policy (Fig 3B). Those same interventions
appear to function well on individuals with modest to high levels of initial climate change belief
(i.e., at approximately 35-90%; Fig 3B). However, they were relatively ineffectual amongst those
that were low in initial belief (i.e., climate skeptics). The main exception is in writing a letter to a
member of the future generation intervention, which worked across nearly the entire spectrum of
initial belief. Additionally, for those that were very low to moderate (i.e., 0-65%) on initial belief,
the negative emotion intervention appeared to backfire, reducing support for climate change
policies. Similar to belief, the work together normative appeal also slightly backfired in
participants with moderate levels of initial belief.
Regarding social media sharing, nearly all interventions (i.e., 9 out of 11) increased
willingness to share even at moderate levels of initial belief (i.e., those greater than approximately
35-60%). Moreover, the increase in willingness to share by inducing negative emotions extended
into individuals who generally do not believe in climate change. Finally, the work-together
normative appeal intervention backfired amongst those who are very low on initial belief (i.e.,
approximately 0-15%), reducing their willingness to share information on social media by up to
12%.
Finally, for the tree planting task, more than half of the interventions decreased the number
of pages completed on the WEPT across all levels of initial belief (Fig 3D).
Fig 3. Marginal effects (as the difference between interventions and control) as a function of estimated pre-
intervention belief in climate change. Lines indicate the average effect size, with shaded regions indicating the 94%
credible region (C.R.). For visual clarity, regions in which the 94% C.R. overlap zero are omitted from the figure.
Where interventions have positive and negative effects that meet these criteria, a dashed line is used to connect these
regions. The dashed vertical line indicates average belief, where effects in Figure 1 are estimated. A) Climate change
Belief, B) Policy Support, C) Sharing information on social media, D) Trees planted via the WEPT.
3. Country-level main effects
Finally, we examined the country-level main effects for each of our key outcome variables. We
found that average belief in climate change, across all countries surveyed, was high (85.7% [85.2,
86.2]. This includes both ratings of belief in the control participants and estimated pre-intervention
levels of belief from all other participants). Importantly, there was very little variation between
countries (Fig 4A & Fig S4A; Table S5) indicating a clear majority belief in climate change.
Similar patterns were observed for policy support (Fig. 4B), with all countries indicating clear
majority support for a variety of climate change policies (72.2% [71.6, 72.8]). These results
suggest that there is clear and consistent global consensus regarding the dangers posed by climate
change and the importance of enacting climate change mitigation.
Other outcome variables exhibited larger variation across countries. Willingness to share
climate–change related information on social media was more modest (56.9 [56.4,57.5]) and
variable, ranging from a low in Latvia of 17.6% [14.3,21.4] to a high of 93.3% [90.4, 95.7] in
Kenya (Fig 4C). These results suggest that observations of climate-change discussion online may
not accurately reflect global sentiments about the reality of climate change, but rather different
local norms.
Finally, half of all participants (50.7% of total sample; 53.1% of control condition sample)
completed all eight pages of the WEPT, earning the maximum number of trees possible, with an
overall average of 5.2 [5.1, 5.3] pages completed (Fig. 4D).
Fig 4. Country-level means of each outcome variable. Countries without available data are
shown in gray. Statistics shown in Tables S5-S8. A) Climate change Belief, B) Policy Support,
C) Sharing information on social media, D) Trees planted via the WEPT.
Discussion
In a global megastudy conducted on a sample of 59,440 people from 63 countries, we empirically
assessed the relative effectiveness of 11 expert-crowdsourced, theoretically-derived behavioral
interventions at stimulating climate mitigation beliefs and behaviors (i.e., climate change beliefs,
policy support, willingness to share information, and tree planting contributions). We found that
different interventions tended to have small global effects, which varied across outcomes and
largely impacted non-skeptics, emphasizing the importance of examining the impact of climate
interventions on a range of outcomes before drawing conclusions regarding their overarching
relative efficacy. These findings suggest that the impact of behavioral climate interventions varies
across audiences’ characteristics and target behaviors.
Here, climate change beliefs were strengthened most by decreasing the psychological
distance of climate change. Support for climate change mitigation policy was increased most by
writing a letter to be read in the future by a socially close child, describing one’s current climate
change mitigation actions. Willingness to share climate change information on social media was
increased most by inducing negative emotions through “doom and gloom” styled messaging about
the consequences of climate change. Finally, while half of the tested interventions had no effect
on the effortful tree-planting behavior, the other half of the interventions reduced the number of
trees participants planted. Beyond revealing the utility of harnessing a multi-outcome approach,
these results also highlight the need for tailoring interventions to target outcomes.
Our findings extend prior work and are theoretically informative in several ways. Notably,
these findings help reconcile several theoretical debates in the literature. For example, some have
argued in favor of employing a “doom-and-gloom” messaging style in climate communications
(i.e., induce negative emotions) as a way to stimulate climate mitigation behaviors (43). For
instance, recent work found that online news consumption is largely driven by the negative content
of the news (44). However, others have warned that such messaging may have no impact on
behavior (45), or worse, that it may depress and demoralize the public into inaction (46). Here, we
found empirical support for both accounts on different outcomes: while negative emotion
messaging was highly effective at stimulating climate information sharing intentions (a relatively
low-effort behavior), it decreased tree planting efforts. Further, the negative emotion induction
intervention appeared to backfire on policy support among participants with low initial climate
beliefs. These results suggest that climate scientists should carefully consider the differential
effects of the prevalent fear-inducing writing styles on different pro-climate outcomes. Moreover,
it suggests that theoretical models need to explain divergent patterns across outcomes.
The results also indicate the impact of the interventions on each outcome depends on
peoples’ pre-existing belief in climate change, supporting the claim that interventions need to be
tailored to the characteristics of their audience (45, 46). For belief, the effectiveness of several
interventions (e.g., decreasing the psychological distance, and collective action efficacy) was
maximized among the uncertain, with lesser effects among believers and skeptics. For policy
support, however, interventions were generally only effective among those with high initial levels
of belief, with negative emotions backfiring among skeptics. Similarly, the robust increases in
willingness to share on social media were largely restricted to people who already believed in
climate change—with negative emotions increasing sharing intentions even among skeptics. For
the higher effort behavior, however, interventions appeared to uniformly reduce tree planting
across all levels of initial belief.
Given the heterogeneity of these results across outcomes, we created a web tool resource
(https://climate-interventions.shinyapps.io/climate-interventions/) that can easily and rapidly
assess intervention efficacy across each of the four outcomes and across a range of variables,
including country, political ideology, gender, age, socioeconomic status, income, and education.
While we caution that users must take into account the sample sizes when exploring subsamples
of the data, and the fact that they are looking at percentage of change compared to the control
condition, this web tool can be used as a rapid and intuitive way to query intervention efficacy
within subsamples of interest. For example, for highly educated conservatives in the United States,
the top intervention to increase climate policy support was the future-self continuity intervention,
increasing support by 18%. This intervention also increased climate beliefs in Russian participants
by 9%. The scientific consensus intervention increased climate policy support by 9% in Romania,
but decreased it by 5% in Canada. The binding moral foundations intervention increased the
number of trees planted by Australians under the age of 40 by 40%, and by Gambians by 35%, but
this intervention decreased the number of trees planted by wealthy Japanese participants by 24%.
Such results can inform the development of local intervention strategies, which should then be
empirically validated. Critically, these results also bolster the message that interventions need to
be tailored to the characteristics of the target audience, nationality being an important factor. The
accompanying data exploration web tool and the open-source raw dataset, contribute to the data-
as-public-good trend emerging in the spirit of open science, thus facilitating the testing of
additional hypotheses and advancement of science.
Importantly, in a linked forecasting experiment (42), academics (e.g., behavioral scientists)
and the general public were asked to predict how each intervention would impact belief, policy
support, and the tree-planting behavior in a subset of participants from this study (i.e., those from
The United States). While academics were better than the general public at predicting the efficacy
of these interventions on beliefs and policy support, when compared to statistical models using
simple heuristics like “interventions would have no effect”, no group was able to accurately predict
how interventions would impact behavior. These results suggest that our findings here, reflect an
important departure from the expectations within the academic community.
There are also several limitations and future directions that should be emphasized. First,
the sampling procedures differed between countries (e.g., the U.S., and Israel samples matched the
census on age, gender, region, ethnicity; the Norway sample matched on age, gender, ethnicity;
etc; Table 1). It should be noted that 73.6% of the entire sample were matched for at least one
variable. However, despite these differences, recent work has found that representative samples
are not required to obtain generalizable estimates of effect sizes within countries (47, 48). Indeed,
various analyses have highlighted that convenience samples are adequate for estimating treatment
effects (49, 50). As such, given that our paper is primarily concerned with the effects of these
interventions rather than with estimating levels of opinion within each country, our sampling
procedures were appropriate for the analyses and conclusions drawn here. However, while
realizing it will be a challenge, we encourage future work to examine these processes using larger,
more representative samples from an even broader sample of countries.
Second, we leveraged an online survey-based approach, which means that we were able to
capture a limited set of contextual factors that may have influenced our results. This approach was
the most effective way to measure and compare intervention efficacy in such a diverse global
sample. However, one important and potentially impactful avenue for future research could be to
leverage these findings to conduct local field experimentation in targeted samples.
One of the major strengths of our tournament was testing eleven different interventions
simultaneously in a large global sample across multiple outcomes. Given the heterogeneity in the
effectiveness of the interventions across the outcomes, future work should likewise prioritize
testing promising interventions on even more climate-relevant antecedents and outcomes, for a
more comprehensive assessment of climate interventions and their underlying theoretical
frameworks. One constraint we faced when attempting to test additional theories was the decision
to not use deception in our interventions. For example, descriptive or injunctive norm based
interventions would have needed to be based on deception to be included in a deployed at this
global scale, given the unavailability of the empirical information critical to creating these
interventions. We hope the current dataset can provide this information for future research in
international contexts. Future work should also investigate additional pro-environmental
behaviors, such as investment decisions, activism, advocacy, or civic participation, critical to
climate change mitigation.
Future research should also assess the processes behind the negative effects we observed
on the tree planting task. Here, we find evidence for a tradeoff between time spent on the
intervention and in the behavioral task, but additional processes may also be at play. For instance,
the negative effects observed might suggest a negative spillover process, by which increasing some
mitigation actions (e.g., policy support, social media sharing, etc.) could have decreased other
mitigation actions (e.g., contributing to tree planting). Given that the tree planting task was also
the last outcome variable completed by participants in, such a process could be plausible. However,
each of the first three outcomes (i.e., climate belief, climate policy support, and information
sharing willingness) were positively associated with the last outcome (i.e., WEPT; Fig S2, Tables
S13–S15). These positive associations at the study level also held within each of the 12 conditions
(Tables S16–S18). That is, the more a participant supported climate policy the more trees they
planted, a pattern found in each condition (Table S17). Similarly, participants who were willing to
share climate information on social media also planted more trees, again a pattern found within
each condition (Table S18). These positive associations are more consistent with a positive
spillover.
An alternative explanation for the intervention effects on the tree planting task could be
that current behavioral science theories and their corresponding interventions are more effective
at targeting conceptual processes compared to more effortful and time-consuming behavioral
signatures, especially in such a heterogenous global sample. Yet another explanation could be that
interventions that made the negative consequences of climate change more salient (e.g., negative
emotions, decreasing of psychological distance, future-self continuity), triggered the perception
that individual-level solutions (e.g., planting trees) may be futile in the face of such an
insurmountable phenomenon, in line with the learned helplessness hypothesis (46). Or perhaps, a
combination of these explanations gave rise to the effects observed. Future research is needed to
clarify these processes, and identify interventions that increase more effortful climate actions
around the world, as well as actions that are more effective solutions to the climate crisis (30).
Finally, while in this global study we tested the effects of several theoretically-derived
behavioral interventions on people’s beliefs and actions in the context of climate change, our
findings provide meaningful insights to the broader fields of social and behavioral sciences. For
instance, the average global effects of the interventions tested ranged from effectively zero to very
small in the conceptual outcomes (beliefs, policy support), and near-zero to negative in the
behavioral outcome (tree planting). These findings point to critical limitations in these theories’
utility and generalizability beyond the contexts in which they were developed. The most extreme
example is the correcting pluralistic ignorance intervention, which had no effect on beliefs, policy
support, or willingness to share information on social media, and even reduced tree planting
efforts. Indeed, theories are often tested and evaluated mainly on their ability to account for
decontextualized patterns of data in laboratory settings, rather than their ability to help solve
societal problems (51). In response to this limitation, researchers have recently proposed reverting
the scientific paradigm to an impact-oriented theoretical and empirical research agenda (30).
The small effect sizes we observed in this global sample might also be partly interpreted
through the lens of recent work reporting that over 60% of studies in the most prestigious journals
in psychology have only focused on 11% of the world’s population (52). Indeed, in our data
collected in the US or other WEIRD nations, the effects of the top interventions on belief and
policy support were much stronger than at the global level. The skewed representation in the field
may pose another significant obstacle in addressing societal problems that depend on global
cooperation and a diversity of solutions for different cultural contexts, as is the case in climate
change among numerous others global crises. One promising solution to these generalizability and
practicality limitations in the behavioral sciences relies on embracing international collaborative
science. Indeed, large global scientific projects can benefit from access to a wider range of
populations, but also from a diversity of scientific perspectives. For example, crowdsourcing has
been found to improve the quality of scientific investigations by promoting ideation, inclusiveness,
transparency, rigor, reliability among other factors (25). Thus, crowdsourcing decisions related to
the experimental design from experts more widely representative of the global scientific
community might increase the impact and generalizability of scientific investigations. For
example, the crowdsourcing of the theories tested from our large international team, has led us to
include less established interventions, such as “letter to future generation”, which ended up being
one of the top interventions tested. Future work could also consider extending this crowdsourcing
paradigm to include non-experts (e.g., lay audiences), as recent work suggests that there may be
unique benefits (e.g., increased interdisciplinarity), sometimes even producing research questions
that outperform experts’ suggestions (53). Finally, combining this “many labs” approach (24) with
the megastudy approach (18), promises to push the limits of conventional scientific practices, and
overcome some of the main barriers of science generalization and implementation (54).
Overall, we tested the effectiveness of 11 expert-crowdsourced behavioral interventions,
at increasing climate awareness and action in 63 countries. Our findings provide theoretical
support for many of the tested interventions. However, variation in effectiveness across outcomes,
between countries, and along the spectrum of climate beliefs, suggest significant gaps in our
current theoretical understanding of climate change behavior. Moreover, the high pre-existing
levels of belief and policy support, alongside the small effect sizes observed here, raise critical
questions about the practical capacity to facilitate bottom-up change at a global level, suggesting
that top-down change might need to be prioritized to achieve the emissions reduction necessary to
stay within safe planetary limits for human civilization. Practically, these findings provide critical
information to policymakers considering climate solution implementations, streamlining the
behavioral sciences’ response to the climate crisis.
Materials and Methods
Participants. The data were collected between July 2022 and May 2023. A total of 83,927
completed the study. Of them, 59,440 participants (Mage=39.13, SDage =15.76; 50% women, 46%
men) from 63 countries (Fig 5; Table 1) who passed the two attention checks (i.e., Please select
the color “purple” from the list below.” and “To indicate you are reading this paragraph, please
type the word sixty in the text box below.”) and correctly completed the WEPT demo, were
included in the analyses. Although removing participants who failed these preregistered attention
checks risks contributing to a selection bias in the sample (55), we a priori determined we would
screen participants according to these criteria to ensure data quality.
Fig 5. The number of participants in each of the 63 countries represented in the sample (Ntotal=59,440).
Ethics approval was obtained independently by each data collection team from their
corresponding Institutional Review Board (IRB). Only datasets submitted along with IRB
approval were included in the analysis.
Table 1. Variables on which the samples in each country were matched to the population. Countries in which
no demographic variable was census matched are marked as “N/A” in the “Matched Variables” column.
Sample
Matched Variables
N
Sample
Matched Variables
N
Algeria
N/A
528
Philippines
N/A
145
Armenia
N/A
492
Poland_1
Age, Gender, Education
1883
Australia
Gender
979
Poland_2
N/A
463
Austria
Age, Gender
502
Portugal
N/A
499
Belgium_1
Age, Gender
522
Romania
N/A
411
Belgium_2
Age, Gender
512
Russia_1
N/A
718
Brazil
Age, Gender, Education
1261
Russia_2
Region, Ethnicity
395
Bulgaria
Age, Gender
778
Russia_3
N/A
322
Canada_1
N/A
858
Saudi Arabia
N/A
489
Canada_2
Age, Gender
303
Serbia
N/A
337
Chile
Age, Gender, Region, SES
1992
Singapore
N/A
500
China
N/A
896
Slovakia
Age, Gender, Region, Municipality
Size
1027
Czechia
N/A
547
Slovenia
Age, Gender
501
Denmark
Age, Gender, Region
792
South Africa
Age, Gender
496
Ecuador
Age, Gender, Region
679
South Korea
Age, Gender
639
Finland
Age, Gender
625
Spain_1
N/A
110
France
Age, Gender
1480
Spain_2
Age, Gender, Region
434
Gambia
N/A
527
Sri Lanka
N/A
413
Germany
Age, Gender, Region
1545
Sudan
Age, Gender
623
Ghana
Age, Gender
522
Sweden
Age, Gender
2393
Greece
Age, Gender
597
Switzerland_1
Age, Gender
512
India
N/A
688
Switzerland_2
Age, Gender
531
Ireland
N/A
753
Taiwan
N/A
206
Israel
Age, Gender, Region, Ethnicity
1384
Tanzania
Age, Gender
104
Italy_1
Age, Gender, Region
591
Thailand
N/A
586
Italy_2
Gender
993
Turkey_1
N/A
359
Japan_1
N/A
653
Turkey_2
Age, Gender
347
Japan_2
Income, Education, Region,
Ethnicity
802
Uganda
Age, Gender
476
Kenya
Age, Gender
409
UK_1
N/A
220
Latvia
Income, Education, Ethnicity
485
UK_2
Age, Gender
952
Mexico
Age, Gender
490
UK_3
N/A
234
Morocco
Age, Gender
474
UK_4
Gender
501
Netherlands_1
Age, Gender
854
Ukraine
N/A
496
Netherlands_2
Age, Gender
510
UAE
Broadly representative
554
Netherlands_3
N/A
500
Uruguay
N/A
838
New Zealand
Gender
1005
USA_1
Age, Gender
2360
Nigeria
Age, Gender
1513
USA_2
Age, Gender, Region, Ethnicity
5055
North Macedonia
N/A
878
USA_3
Age, Gender
497
Norway
Age, Gender, Ethnicity
997
Venezuela
N/A
110
Peru
Age, Gender
405
Vietnam
N/A
383
Collaboration Procedure. Following procedures from Van Bavel and colleagues (24), the
organizational team submitted a call for collaboration
(https://manylabsclimate.wordpress.com/call-for-collaboration/) in November 2021 on social
media (i.e., Twitter), via personal networks, and by posting on various mailing lists. We asked
researchers from around the world to join our project by contributing in one of three ways: (1)
collecting data (i.e., >500 responses) from a new country, (2) propose an intervention that
becomes included in the final study, and/or (3) fund data collection (i.e., >500 responses) from a
new country and support a local team who lacks funding. The collaborators who proposed an
intervention were asked to keep in mind time constraints (i.e., each intervention had to take on
average at most 5 minutes) and the targeted outcome variables (i.e., climate beliefs, policy
support, social media sharing, and tree planting contributions). We received a total of 36
proposed interventions, which were coded by the first authors (who were blinded to the
intervention authors). The coding procedure involved screening the proposed interventions for
feasibility in an international context, relevance for the dependent variables, and theoretical
support from prior work (quantified by previously reported effect sizes). We also aggregated
similar interventions and duplicates. Following this procedure, we identified 11 unique and
feasible interventions, which we then asked all collaborators to rate on perceived efficacy
(practical support) and theoretical value (theoretical support), initially aiming to select the top
five interventions. We obtained 188 responses from our collaborators in January 2022 (Fig. S5).
Given high support for all interventions, we decided to test all 11 interventions in the main study.
We then contacted the collaborators whose interventions had been selected to be included in the
main study, to coordinate the intervention implementation and programming on the Qualtrics
survey platform (https://www.qualtrics.com/). After obtaining the programmed interventions, we
gave our collaborators feedback on their submissions and allowed them time to address our
comments. After receiving the revised interventions, we contacted expert researchers who had
published theoretical work relevant to each intervention, asking them to critically review each
intervention’s implementation. For example, Professor John Jost reviewed the System
Justification intervention (57) and Professor Sander van der Linden reviewed the Scientific
Consensus intervention (58). This process was iterated for each of the 11 interventions. After
receiving critical suggestions from these experts we engaged in another round of revisions.
Finally, in an attempt to reduce American-centric researcher biases, we asked all collaborators
from around the world for additional feedback on the entire survey, including all interventions,
demographics, and independent variables. This process lasted until the end of May 2022, when
we started piloting the final version of the study, on a sample of 723 participants (Mage=43.6;
SDage=15.7; 52% women, 46% men, <2% non-binary), collected in the United States. Using the
pilot data, we wrote our analysis scripts and the pre-registration (available here:
https://aspredicted.org/blind.php?x=W83_WTL). After the piloting was completed (July 2022),
we sent our collaborators the final version of the study in Qualtrics along with an in-depth
instructions manual (https://osf.io/ytf89/files/osfstorage/6454f8e3b30b49156cb9dd79/) on how
to translate and adapt the study to each country. We also instructed our collaborators to obtain
ethics approval from their institutions’ review boards before launching data collection. All
collaborators were given 10 months (until May 2023) to submit their data.
Experimental design. Participants signing up to complete the study (expected to take 15
minutes to complete) were first asked to read and sign the informed consent. They were then
exposed to the first attention check (“Please select the color “purple” from the list below. We
would like to make sure that you are reading these questions carefully.”), which removed from
the experiment any participants choosing an incorrect answer. Then, participants were then given
a definition of climate change: “Climate change is the phenomenon describing the fact that the
world’s average temperature has been increasing over the past 150 years and will likely be
increasing more in the future.” After reading this definition, participants were randomly assigned
to one of 12 conditions: 11 experimental interventions (Fig. 1), or a no-intervention control
condition, in a between-subjects design. Participants in the control condition were then exposed
to a short, thematically unrelated text from the novel “Great Expectations” by Charles Dickens,
while participants in the experimental conditions were exposed to an intervention. Then, all
participants were directed to the outcome variable phase, in which they rated (in random order)
their (1) climate beliefs, (2) climate policy support, (3) willingness to share climate information
on social media. Finally, participants were given the chance to contribute to tree planting efforts
by completing the WEPT. Then, participants in the control condition were asked to complete an
additional set of variables. Finally, all participants were asked to fill out a series of demographic
variables, which included another attention check (“In the previous section you viewed some
information about climate change. To indicate you are reading this paragraph, please type the
word sixty in the text box below.”). Of note, participants filled out the entire survey in the
primary language of their country of residence.
Outcome variables.
Climate beliefs. Climate beliefs were measured by participants’ answer to the question “How
accurate do you think these statements are?” from 0=Not at all accurate to 100=Extremely
accurate. The four statements were: “Taking action to fight climate change is necessary to avoid
a global catastrophe”, “Human activities are causing climate change”, “Climate change poses
a serious threat to humanity”, and “Climate change is a global emergency”. The Cronbach’s
alpha measure of internal consistency of this 4-item scale in this dataset was 0.934.
Climate policy support. This dependent variable consisted of participants’ level of agreement
from 0=Not at all to 100=Very much so, with the following nine statements: “I support raising
carbon taxes on gas/fossil fuels/coal?”, “I support significantly expanding infrastructure for
public transportation.”, “I support increasing the number of charging stations for electric
vehicles.”, “I support increasing the use of sustainable energy such as wind and solar energy.”,
“I support increasing taxes on airline companies to offset carbon emissions.”, “I support
protecting forested and land areas.”, “I support investing more in green jobs and businesses.”,
“I support introducing laws to keep waterways and oceans clean.”, and “I support increasing
taxes on carbon intense foods (for example meat and dairy).” The Cronbach’s alpha measure of
internal consistency of this 9-item scale in this dataset was 0.876.
Social media sharing. Participants were first presented with the text, “Did you know that
removing meat and dairy for only two out of three meals per day could decrease food-related
carbon emissions by 60%? It is an easy way to fight #ClimateChange
#ManyLabsClimate${e://Field/cond} source: https://econ.st/3qjvOnn” (where “{e://Field/cond}”
was replaced with the condition code for each group). Participants were then asked “Are you
willing to share this information on your social media?”, the answer options being “Yes, I am
willing to share this information”, “I am not willing to share this information”, and “I do not use
social media”. Participants who indicated they do not use social media were excluded from this
analysis (i.e., a third of the sample). Moreover, participants were asked to indicate the platform
(e.g., Facebook, Twitter, Instagram) on which they posted the information.
WEPT Tree planting efforts. To measure an action with a real-world impact performed at an
actual cost to participants, we used a modified version of the work for environmental protection
task (WEPT) (37)). This task is a multi-trial web-based procedure that detects consequential pro-
environmental behavior by allowing participants the opportunity of engaging in voluntary
cognitive effort (i.e., screen numerical stimuli) in exchange for donations to an environmental
organization. This measure has been validated and has been found to correlate with well-
established scales for the assessing pro-environmental behavioral intentions (e.g., General
Ecological Behavior scale, GEB, 59) and with direct donation behaviors (e.g., the donation of a
part of their payment to an environmental organization; 39).
Participants were first exposed to a demonstration of the WEPT, in which they were
instructed to identify all target numbers for which the first digit is even and the second digit is
odd (4 out of 18 numbers were target numbers on the demonstration page). Participants were not
allowed to advance the page until they correctly completed the WEPT demonstration. Then, they
were told that planting trees is one of the best ways to combat climate change, and that they
would have the opportunity to plant up to 8 trees if they chose to engage in additional pages of
the item identification task (one tree per page of WEPT completed). These pages contained 60
numbers per page, which participants had to screen for target numbers. Alongside these
instructions participants were shown a pictogram of 8 trees, one of which was colored green to
mark their progress in the task. Participants were allowed to exit the task at any point with no
penalty.
Demographics. Participants were asked to indicate their gender, age, education level, political
orientation for economic and social issues, and household income.
Experimental Conditions (Interventions)
Working-Together Norms (submitted by Madalina Vlasceanu and Jay Van Bavel). This
intervention was adapted from Howe, Carr, and Walton (11) and it combines referencing a social
norm with an invitation to work with others toward a common goal. This working-together
normative appeal invites people to “join in” and “do it together,” and has been found to increase
interest in and actual charitable giving, reduce paper-towel use in public restrooms, and increase
interest in reducing personal carbon emissions (11). Mediation analyses in prior work also
suggested that working-together normative appeals are effective because they foster a feeling in
participants that they are working together with others, which can increase motivation while
reducing social pressure. Participants in this condition were exposed to a flier adapted from Howe
and colleagues (11), after which they were asked 15 questions about the flier, serving as
manipulation checks that were also meant to reinforce the manipulation (e.g., “If you are taking
steps towards reducing your carbon footprint, to what extent would you feel like you are doing so
together with other Americans [or participants’ group, adapted for each country]?” on a scale
from 0=Not at all to 100=Extremely, or “How strongly do you identify with your fellow Americans
[or participants’ group, adapted for each country]?” on a scale from 0=Not at all to
100=Extremely).
System justification (submitted by Ondrej Buchel, Michael Tyrala, Andrej Findor). This
intervention is situated at the intersection of social identity, collective narcissism, and system
justification approaches (based on 60), and consists of framing climate change as uniquely
threatening the way of life of participants’ nationality (e.g., the American way of life). Participants
were asked to read a text emphasizing the importance of nature and the environment to one’s life
(e.g., “(..) the food you eat, the sports you enjoy, the customs you observe, how you spend your
free time, or even how you imagine growing old, all are likely impacted by where you live”),
followed by examples of the effect of climate change on the local environment of participants’
nation (e.g., “(..) we can already see the consequences of climate change in the United States. For
example, floods are becoming more and more frequent, putting a quarter of Americans at risk of
losing their homes. Similarly, wildfires are becoming more frequent and more intense, threatening
millions of Americans.”). The text ends with an appeal to being pro-environmental as a patriotic
gesture that will protect one’s way of life (e.g., “Being pro-environmental allows us to protect and
preserve the American way of life. It is patriotic to conserve the country’s natural resources. It is
important to protect and preserve our environment so that the United States remains the United
States.”). This narrative was also intertwined with representative images of participants’ country
of residence.
Binding moral foundations (submitted by Benjamin Douglas & Markus Brauer). This
intervention relies on evoking ingroup-loyalty and authority moral foundations, which has been
shown to increase support for pro-environmental behavior and attitudes (61, 62). Participants were
asked to read the following text “We are Americans [or participants’ nationality, adapted for each
country]. This means we can rise to any challenge that faces our country. From scientists to experts
in the military, there is near universal agreement that climate change is real. The time to act is
now. Using clean energy will help to keep our air, water, and land pure. It is the American [or
participants’ nationality, adapted for each country] solution to the climate crisis.”, after which
they were exposed to an image of a person holding the national flag of participants’ country of
residence.
Exposure to effective collective action (Eric Shuman, Amit Goldenberg). This intervention
features examples of successful collective action that have had meaningful effects on climate
policies, building on prior work showing that exposure to nonviolent action can increase
willingness to join and maintain support (63, 64). In addition, prior work also found that
highlighting the possibility of making real concrete changes through collective action can increase
hope, efficacy, and collective action (63). Participants were exposed to a text explaining the impact
people’s actions can have on curbing the effects of climate change, citing research indicating there
is still “a window of opportunity” to make a difference. Then participants were informed that the
effectiveness of people’s actions to fight climate change depends on their ability to “come together
and demand systemic change”. Participants were then exposed to several successful examples in
which people solved global issues, such as the restoration of the ozone layer in 1987. Then
participants were exposed to examples of climate activism initialized by individual people and
leading to large scale movements or policy implementation (e.g., protests by locals from the
American Midwest against fossil fuels pressured the governors of Illinois, Indiana, Michigan,
Minnesota, and Wisconsin to build a new network for charging electric vehicles.). Images of
concepts described in the text were displayed throughout.
Future-self-continuity (submitted by Vladimir Ponizovskiy, Lusine Grigoryan, Sonja Grelle, &
Wilhelm Hofmann). This intervention consists of emphasizing the future-self which has been
found in prior work to motivate future-oriented behaviors, such as academic performance, ethical
decision making, and pro-environmental behavior (65-67). Participants were asked to read a text
emphasizing the importance of engaging in climate action (i.e., “If no changes are made, the
average temperature can increase by up to 6.5°C (12°F) by the year 2100 (IPCC, 2022). This
would be extremely dangerous as super hurricanes, gigantic wildfires, and extreme food and water
shortages would become commonplace.”). They were then presented with a series of causes for
this phenomenon (i.e., “Human behaviors like energy production from fossil fuels, excessive meat
consumption, and car driving increase the concentrations of greenhouse gasses in Earth’s
atmosphere. Over 90% of the increase in the world's temperature is caused by human activity.”).
Finally, participants were asked to imagine their 2030 self is writing a letter to their present self,
in which their future self is describing the actions they would have wanted to take regarding climate
change (i.e., “Please put yourself in the year 2030 - eight years from now. Take a few moments to
imagine your life in that future. Imagine how you will look, where you will be, and who you are
with. In the year 2030, it will be clear whether keeping climate change under 2°C is still possible.
It will be clear whether the necessary change occurred fast enough to match the speed of the
changing climate. As the Earth’s atmosphere continues to heat up, the effects of climate change
will be more apparent: the “highest observed temperature” records will keep being updated,
heatwaves and the draughts will become more common, species will continue to become extinct.
Now please write yourself a “letter from the future”. This should be a letter you are writing in the
year 2030, to your past self. As the person that you will be in 2030, what role would you think
would be appropriate for you in respect to climate change? What would you want to tell yourself
in the past? What would you like your past self to do? Please spend a bit of time on this task and
try to write at least 100 words (5 sentences), or more, if possible.”).
Scientific consensus (submitted by Aart van Stekelenburg, Christian Klöckner, Stepan Vesely,
Danielle Bleize). This intervention consists of a message suggesting climate scientists are in
agreement with each other that climate change is real and primarily caused by human action. Such
messaging has been found to increase people’s belief in climate change and support for climate
mitigation policy (58, 68). Participants were exposed to the following text “Did you know that
99% of expert climate scientists agree that the Earth is warming and climate change is happening,
mainly because of human activity (for example, burning fossil fuels)? (Myers et al., 2021,
Environmental Research Letters; Lynas et al., 2021, Environmental Research Letters; Doran et
al., 2009, EOS)”. The text was accompanied by a pie chart with 99% of the surface area shaded.
Decreasing psychological distance (designed by Sarah Chamberlain, Don Hine, Guanxiong
Huang). This intervention is based on prior work finding that many perceive climate change as
psychologically distant (i.e., “as a set of uncertain events that may occur far in the future,
impacting distant places and affecting people dissimilar to themselves”) (10)). Thus, framing
climate change as a psychologically proximal risk issue (e.g., geographic) is expected to reduce
psychological distance and increase public engagement. Participants were exposed to a paragraph
emphasizing the impact of climate change (i.e., “There is no doubt that humans are the main driver
of climate change. Human influence has warmed the atmosphere, ocean, and land. Climate change
is already affecting every region across the world. It has resulted in more frequent and intense
extreme weather events, causing widespread harm and damage to people, wildlife and ecosystems.
Human systems are being pushed beyond their ability to cope and adapt.”). They were then
exposed to two examples of recent natural disasters caused by climate change in participants’
region (e.g., US participants will be exposed to information about the 2021 record-breaking heat
wave in North America causing the Lytton wildlife, and to information about the 2017 Hurricane
Harvey in Texas and Hurricane Irma in Florida, killing 232 people and causing $175 billion in
damage). Participants were then asked to select the aspects of their lives impacted by climate
change from a list including: food production, farming and crop production, health and wellbeing,
infectious disease, heat related harm and deaths, lack of, mental health issues, flooding and storms,
changed land, freshwater and ocean environments, damaged infrastructure and economy. After
making the selections, participants were provided the correct answers based on current scientific
estimates (i.e., all the possible options). Finally, participants were asked to write about how climate
change will affect them and their community (i.e., “Please write in a few sentences: how those
climate consequences will affect you, your friends and family, and your community. Try to imagine
these things happening today so you can be specific and describe what it will be like.”).
Dynamic social norms (submitted by Oliver Genschow, David Loschelder, Gregg Sparkman, &
Kimberly C. Doell). This intervention is based on work showing that dynamic norms (i.e., how
other people’s behavior is changing over time) are even more impactful at changing behavior than
static social norms (69). Participants in this intervention first read a paragraph emphasizing that
“People in the United States and around the world are changing: more and more people are
concerned about climate change, and are now taking action across multiple fronts”, accompanied
by an image featuring relevant data in support of this claim. Then participants were given examples
of actions people are starting to take to mitigate the changing climate (i.e., “Since 2013, concerns
about climate change have increased in most countries surveyed. What kinds of actions are people
taking right now? More than ever before, people are making changes to their lifestyles, supporting
policies to address climate change, and are giving the issue more time and attention. For example,
more and more people from around the world are now: cutting back on personal consumption,
especially meat and dairy products, spending time, effort, and money on initiatives to mitigate
climate change (for example, planting trees, offsetting carbon emissions), switching to low carbon
modes of transportation (for example, taking bicycles). There’s also been a notable increase in
support for climate change mitigation policy–some of the most popular policies include:
attempting to conserve forests and land, transitioning to solar, wind, and other renewable energy
sources, creating/raising carbon taxes on fossil fuels, coal, gas, etc.”).
Correcting pluralistic ignorance (submitted by Michael Schmitt, Annika Lutz, & Jeff Lees). This
intervention builds on work reporting that people substantially underestimate the climate change
concern of others, a phenomenon labeled as “pluralistic ignorance” (70). Accordingly, collective
action might be limited by people’s misperception that not many people are concerned. This
intervention presented real public opinion data that shows majorities around the world are
concerned about climate change. Participants were first asked to predict the percent of people in
their country who hold the belief that climate change is a global emergency (i.e., Researchers
recently conducted the "People's Climate Vote", which is the World's largest survey of public
opinion on climate change (“global warming”). 1.2 million people completed the survey from 50
different countries around the globe. The survey included people from the United States. Think for
a moment about Americans and their views on climate change. How many Americans do you think
would agree with the statement “Climate change is a global emergency”?). After providing a
prediction, participants were shown the actual percentage of people in their country who hold the
belief in question, according to The Peoples’ Climate Vote (71). For example, participants in the
United States will be told that “The People's Climate Vote found that 65% of Americans agree that
climate change is a global emergency". For countries where the People’s Climate Vote does not
report national level results, participants were presented with the climate opinion of people in their
region.
Letter to future generations (submitted by Stylianos Syropoulos, & Ezra Markowitz). This
intervention involves writing a letter to a member of the future generation, which has been shown
to reduce the psychological distance between one’s current choices and their consequences on
future generations (72, 73). Participants were asked to write a letter to a child who will read it in
the future (i.e., “Please think of a child that is currently less than 5 years old (..) Now imagine that
child is a 30 year old adult. It is approximately the year 2055, they have started a family of their
own, and they are finding their own way in the world. Whether they recognize it or not, they live
in a world that is powerfully shaped by the decisions we are all making now, in 2022. One day,
(..) they find a letter written today, in 2022, which is a message from you.”). In this letter,
participants are encouraged to write about their actions toward ensuring an inhabitable plant
(i.e.,”In it, you tell this family about all of the things you have done and want to do in the future
to ensure that they will inherit a healthy, inhabitable planet. You tell them about your own personal
efforts—however small or large—to confront the complex environmental problems of your time,
from habitat loss to water pollution to climate change. In this letter you also tell this family in
2055 about how you want to be remembered by them and future generations as someone who did
their best to ensure a safe, flourishing world.”). Participants were allowed to write for 3 minutes
and encouraged to write at least 100 words or 5 sentences.
Negative emotion (submitted by Kimberly Doell & Clara Pretus). This intervention involves
exposure to scientific facts regarding the impacts of climate change in a ‘doom and gloom’
messaging style typically employed by climate communicators to induce negative emotions as a
way of stimulating mitigation behaviors (60). Participants were first asked to report their baseline
levels of emotions related to climate change, (e.g., hopeful, anxious, depressed, scared, indifferent,
angry, helpless, guilty). They were then exposed to information about the consequences of climate
change alongside representative images (e.g., “Climate change is happening much more quickly,
and will have a much greater impact, than climate scientists previously thought, according to the
latest report by the Intergovernmental Panel on Climate Change (IPCC, 2022). If your anxiety
about climate change is dominated by fears of starving polar bears, glaciers melting, and sea
levels rising, you are barely scratching the surface of what terrors are possible, even within the
lifetime of a young adult today. And yet the swelling seas — and the cities they will drown — have
so dominated the picture of climate change/global warming that they have blinded us to other
threats, many much closer at hand and much more catastrophic (...)”). Finally, participants were
asked to report their levels of emotions related to climate change again.
Control condition. Participants in the control condition were instructed to read a text retrieved
from the novel Great Expectations by Charles Dickens (i.e., “As soon as the great black velvet pall
outside my little window was shot with grey, I got up and went downstairs; every board upon the
way, and every crack in every board calling after me (…) I took it in the hope that it was not
intended for early use, and would not be missed for some time.”). Participants were required to
spend at least 10 seconds reading this text. This was to ensure participants exerted some level of
cognitive effort before being exposed to the dependent variable phase, to mirror the experience of
participants in the experimental conditions. We chose a fiction text to prevent priming participants
in any relevant way that could influence the dependent variables. After reading the excerpt,
participants in the control condition were directed to the dependent variable phase, followed by
the demographics phase. Finally, participants in the control condition were also directed to an
additional independent variable phase, exclusive to participants in this condition.
Additional Variables Collected. These variables were only displayed to participants in the
control condition, after they completed all dependent variables. First, participants were asked to
rate the competence of climate scientists (“On average, how competent are climate change
research scientists?”on a scale from 0=Not at all to 100=Very much so), their trust in scientific
research about climate change (“On average, how much do you trust scientific research about
climate change?” on a scale from 0=Not at all to 100=Very much so), their trust in their
government (“On average, how much do you trust your government?” on a scale from 0=Not at
all to 100=Very much so), their attitudes towards human welfare (“To what degree do you see
yourself as someone who cares about human welfare?” on a scale from 0=Not at all to 100=Very
much so), their global citizenship identity (“To what degree do you see yourself as a global
citizen?” on a scale from 0=Not at all to 100=Very much so), their environmental identification
(e.g., “To what degree do you see yourself as someone who cares about the natural
environment?” on a scale from 0=Not at all to 100=Very much so), their extrinsic environmental
motivation (e.g., “Because of today’s politically correct standards, I try to appear pro-
environmental.” on a scale from 0=Strongly disagree to 100=Strongly agree). Then they were
asked to estimate the percentage of people in their country who believe that climate change is a
global emergency.
Statistical Methods
Our dependent variables have distributional properties (Fig S6) that preclude unbiased estimation
with common, off-the-shelf, regression tools (such as the pre-registered analyses). To address
this, estimates presented in the main text relied on Bayesian methods and custom likelihood
functions. Full mathematical descriptions of all models can be found in the supplied code
(https://github.com/josephbb/ManyLabsClimate). Additional analyses can be found at:
https://github.com/mvlasceanu/ClimateTournament
Belief was estimated using a hierarchical Zero-One-Inflated Beta (ZOIB) model. This
model was further used to derive adjusted participant-level estimates of pre-intervention belief,
to avoid post-intervention bias in subsequent models. Sharing on social media was evaluated
with a logistic regression. For WEPT, we used a geometric regression with a customized
likelihood function to account for truncation and over-inflation for the maximum number of trees
planted. Priors were selected using prior-predictive simulation, with model structure iteratively
developed through analysis of the prior predictive distribution and validated through model
comparison using posterior predictive simulation. Posteriors were sampled using a No-U-Turn
Sampler (NUTS) implemented on a GPU with PyMC/NumPyro.
We note that these modeling choices are different from our pre-registered analysis, which
specified linear (Belief, Policy), ordinal (WEPT), and logistic (Sharing) mixed-effects models.
Plots of residuals from pre-registered models suggested moderate to severe violations of
distributional assumptions. For this reason, p-values and estimates of effect sizes for these
models may be unreliable. Despite these issues, we note the findings from pre-registered
analyses are qualitatively similar to those from the Bayesian analyses. Overall, similarities
between the pre-registered and Bayesian analyses suggest effects that are remarkably robust to
analysis decisions.
For completeness, we include the results as pre-registered in Tables S9-S12 and Figure
S1. Belief and Policy support were modeled using a linear mixed effects model with climate
policy support, as the dependent variable, condition as the fixed effect, including item (9
policies), participant, and country as random effects. WEPT was modeled using an ordinal mixed
effects model with climate action (WEPT), as the dependent variable, condition as the fixed
effect, including country as random effects. Sharing was modeled using an ordinal mixed effects
model with climate action (WEPT), as the dependent variable, condition as the fixed effect,
including country as random effects.
To develop and evaluate our Bayesian models, we adapted an established Principle
Bayesian Workflow (74). This process begins by identifying inference goals, domain knowledge,
and features of the dataset. Candidate statistical models are proposed, with prior predictive
checks are used to identify reasonable priors. Data are simulated from the prior predictive
distribution, and the statistical model is fit to this simulated data. This allows for evaluation of
computational properties of the model, tuning of the sampler, adjustment of the model or priors,
and refinement. Key insight was gained through visual inspection of the posterior z-score vs.
posterior contraction, which can indicate issues with overfit, underfit, bad prior models, or
poorly identified model specification. This process was iterated on until a suitable candidate
model and priors were identified. Finally, posterior predictive checks were used to verify that
models adequately reconstructed broad properties of the data without regard to the estimands of
interest (i.e., country/treatment effects). Failures here lead to adjustment of the underlying
model. Once all model development criteria were satisfied, final analysis of the dataset was used
to generate estimates of treatment and country level effects as well as all relevant figures. We
note that priors for similar parameters across models may differ as a result of this iterative
process, owing to distinct link functions and differing computational constraints. However, the
impact of the prior on posterior samples is unlikely to be meaningful, given the volume of data.
We fit the selected model to the study data using PYMC (75) with a No U-Turn Sampler
implemented on the GPU in NumPyro. We evaluated the model fit, ensuring the absence of
divergent transitions, sufficient mixing of the (4) Markov chains, a large enough effective sample
size, and an acceptable Estimated Bayesian Fraction of Missing Information (eBFMI). Finally,
data were simulated from the posterior distribution and visual inspection of these posterior
retrodictive checks were used to assess model fit. Sampling parameters were largely default, and
can be found in the supplied code. .
Belief. Belief was indicated for four items on a scale from 0 to 100, inclusive. We scaled the
outcome variable for each item to 0-1, to facilitate the use of common bound distributions.
However, as both 0 and 1 were possible values, our likelihood function needed to account for
possible inflation. As such, we implemented a hierarchical Zero-One-Inflated Beta (ZOIB)
regression. We developed a generative model in which participants were estimated to have an
unobserved pre-intervention belief, defined by their observed belief minus the estimated pre-
intervention effect for their level of belief (i.e., as though they had been in the control condition).
that was partially pooled by country, which in turn was partially pooled via a hyperparameter for
average belief. Interventions were modeled with an intercept, corresponding to the average
effect, and an effect of the estimated pre-intervention belief. The intervention effect and intercept
for the control condition was fixed at zero. Otherwise, we modeled intervention effects using a
multivariate normal distribution, to account for covariance between intercepts and interventions.
Further, we included partially pooled intercepts for item-specific effects. Where necessary, non-
centered parameterizations were used to improve model fit.
Finally, we extracted the posterior average pre-intervention belief for each participant, to use in
modeling Policy Support, Social Media Sharing, and WEPT. This reflects the observed level of
belief, after adjusting for intervention effects on belief. As the treatment effects are small, these
adjustments are minimal. Ideally, one would jointly model belief and other outcomes, however
the large sample sizes inherent to a megastudy impose computational constraints, a particular
issue with model development and evaluation. Extracting intervention-adjusted estimates of
initial belief enables us to examine heterogeneous intervention effects for each of these
outcomes, at a tractable degree of model complexity. We chose to focus on belief for evaluating
heterogenous intervention effects under the assumption that belief is more likely to be a cause of
support for policy, social media sharing, and investment in tree-planting activities than a
consequence. Full mathematical descriptions of the model can be found in the supplied code.
Policy Support. Support for policy was indicated for nine items on a scale from 0 to 100,
inclusive. Owing to computational constraints with the full dataset, we examined the average of
these items. As with belief, this outcome was scaled from 0-1 and a ZOIB was used to model the
data. Policy support was modeled with an intercept, an effect of adjusted belief, with intercept
and belief effects modeled for interventions and countries. Intervention and country effects were
modeled as separate zero-centered normal distributions.
Social Media Sharing. Sharing was a binary outcome, restricted to users who used social media.
To analyze the impact on sharing, we relied on a Bayesian logistic regression. The probability of
sharing was modeled with an intercept, an effect of adjusted belief, with intercept and belief
effects modeled for interventions and countries. Intervention and country effects were modeled
as separate zero-centered normal distributions.
WEPT Participants were able to plant between 1 and 8 trees. We began by modeling this as a
truncated geometric distribution, assuming participants have a per-timestep chance of giving up
and are forced to stop at 8. However, we noticed an over-abundance of planting eight trees
consistent with some participants committing to planting all eight. Accordingly, we modified our
likelihood to include inflation at 8 trees. Posterior predictive fits confirmed adequate model fit.
With this likelihood, we constructed a Bayesian Hierarchical with an intercept, an effect of
adjusted belief, and intercepts and belief effects modeled for interventions and countries.
References
1. IPCC, 2023, “Climate Change 2023: Synthesis Report. A Report of the Intergovernmental Panel on
Climate Change. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the
Intergovernmental Panel on Climate Change” (IPCC, Geneva, Switzerland, 2023).
2. D. Ivanova, J. Barrett, D. Wiedenhofer, B. Macura, M. Callaghan, F. Creutzig, Quantifying the potential for
climate change mitigation of consumption options. Environmental Research Letters 15, 093001 (2020).
3. R. Gifford, C. Kormos, A. McIntyre, Behavioral dimensions of climate change: drivers, responses, barriers,
and interventions. WIREs Climate Change. 2, 801–827 (2011).
4. I. M. Otto, J. F. Donges, R. Cremades, A. Bhowmik, R. J. Hewitt, W. Lucht, J. Rockström, F. Allerberger,
M. McCaffrey, S. S. P. Doe, A. Lenferna, N. Morán, D. P. van Vuuren, H. J. Schellnhuber, Social tipping
dynamics for stabilizing Earth’s climate by 2050. Proceedings of the National Academy of Sciences. 117,
2354–2365 (2020).
5. H. Allcott, S. Mullainathan, Behavior and Energy Policy. Science. 327, 1204–1205 (2010).
6. S. Carattini, S. Kallbekken, A. Orlov, How to win public support for a global carbon tax. Nature. 565, 289–
291 (2019).
7. M. A. Jenny, C. Betsch, Large-scale behavioural data are key to climate policy. Nature Human Behavior. 6,
1444–1447 (2022).
8. J. W. Composto, E. U. Weber, Effectiveness of behavioural interventions to reduce household energy
demand: A scoping review. Environmental Research Letters. 17, 063005 (2022).
9. C. F. Nisa, J. J. Bélanger, B. M. Schumpe, D. G. Faller, Meta-analysis of randomised controlled trials
testing behavioural interventions to promote household action on climate change. Nature Communications.
10, 4545 (2019).
10. C. Jones, D. W. Hine, A. D. G. Marks, The future is now: Reducing psychological distance to increase
public engagement with climate change. Risk Analysis. 37, 331–341 (2017).
11. L. C. Howe, P. B. Carr, G. M. Walton, Normative appeals motivate people to contribute to collective action
problems more when they invite people to work together toward a common goal. Journal of Personality
and Social Psychology. 121, 215–238 (2021).
12. C. J. Clark, T. Costello, G. Mitchell, P. E. Tetlock, Keep your enemies close: Adversarial collaborations
will improve behavioral science. Journal of Applied Research in Memory and Cognition. 11, 1–18 (2022).
13. D. Kahneman, Experiences of collaborative research. American Psychologist. 58, 723–730 (2003).
14. S. Benartzi, J. Beshears, K. L. Milkman, C. R. Sunstein, R. H. Thaler, M. Shankar, W. Tucker-Ray, W. J.
Congdon, S. Galing, Should Governments Invest More in Nudging? Psychological Science. 28, 1041–1055
(2017).
15. A. L. Duckworth, K. L. Milkman, A guide to megastudies. PNAS Nexus. 1, pgac214 (2022).
16. M. Bergquist, M. Thiel, M. H. Goldberg, S. van der Linden, Field interventions for climate change
mitigation behaviors: A second-order meta-analysis. Proceedings of the National Academy of Sciences.
120, e2214851120 (2023).
17. K. C. Doell, Megastudies to test the efficacy of behavioural interventions. Nature Reviews Psychology. 2,
263–263 (2023).
18. K. L. Milkman, D. Gromet, H. Ho, J. S. Kay, T. W. Lee, P. Pandiloski, Y. Park, A. Rai, M. Bazerman, J.
Beshears, L. Bonacorsi, C. Camerer, E. Chang, G. Chapman, R. Cialdini, H. Dai, L. Eskreis-Winkler, A.
Fishbach, J. J. Gross, S. Horn, A. Hubbard, S. J. Jones, D. Karlan, T. Kautz, E. Kirgios, J. Klusowski, A.
Kristal, R. Ladhania, G. Loewenstein, J. Ludwig, B. Mellers, S. Mullainathan, S. Saccardo, J. Spiess, G.
Suri, J. H. Talloen, J. Taxer, Y. Trope, L. Ungar, K. G. Volpp, A. Whillans, J. Zinman, A. L. Duckworth,
Megastudies improve the impact of applied behavioural science. Nature. 600, 478–483 (2021).
19. A. M. van Valkengoed, W. Abrahamse, L. Steg, To select effective interventions for pro-environmental
behaviour change, we need to consider determinants of behaviour. Nature Human Behavior. 6, 1482–1492
(2022).
20. J. Henrich, S. J. Heine, A. Norenzayan, Most people are not WEIRD. Nature. 466, 29–29 (2010).
21. K. C. Doell, P. Pärnamets, E. A. Harris, L. M. Hackel, J. J. Van Bavel, Understanding the effects of
partisan identity on climate change. Current Opinion in Behavioral Sciences. 42, 54–59 (2021).
22. M. J. Hornsey, E. A. Harris, K. S. Fielding, Relationships among conspiratorial beliefs, conservatism and
climate scepticism across nations. Nature Climate Change. 8, 614–620 (2018).
23. L. Chancel, Global carbon inequality over 1990–2019. Nature Sustainability. 5, 931–938 (2022).
24. J. J. Van Bavel, A. Cichocka, V. Capraro, H. Sjåstad, J. B. Nezlek, T. Pavlović, M. Alfano, M. J. Gelfand,
F. Azevedo, M. D. Birtel, A. Cislak, P. L. Lockwood, R. M. Ross, K. Abts, E. Agadullina, J. J. B. Aruta, S.
N. Besharati, A. Bor, B. L. Choma, C. D. Crabtree, W. A. Cunningham, K. De, W. Ejaz, C. T. Elbaek, A.
Findor, D. Flichtentrei, R. Franc, B. Gjoneska, J. Gruber, E. Gualda, Y. Horiuchi, T. L. D. Huynh, A.
Ibanez, M. A. Imran, J. Israelashvili, K. Jasko, J. Kantorowicz, E. Kantorowicz-Reznichenko, A. Krouwel,
M. Laakasuo, C. Lamm, C. Leygue, M.-J. Lin, M. S. Mansoor, A. Marie, L. Mayiwar, H. Mazepus, C.
McHugh, J. P. Minda, P. Mitkidis, A. Olsson, T. Otterbring, D. J. Packer, A. Perry, M. B. Petersen, A.
Puthillam, J. C. Riaño-Moreno, T. Rothmund, H. Santamaría-García, P. C. Schmid, D. Stoyanov, S.
Tewari, B. Todosijević, M. Tsakiris, H. H. Tung, R. G. Umbreș, E. Vanags, M. Vlasceanu, A. Vonasch, M.
Yucel, Y. Zhang, M. Abad, E. Adler, N. Akrawi, H. A. Mdarhri, H. Amara, D. M. Amodio, B. G. Antazo,
M. Apps, F. C. Ay, M. H. Ba, S. Barbosa, B. Bastian, A. Berg, M. P. Bernal-Zárate, M. Bernstein, M.
Białek, E. Bilancini, N. Bogatyreva, L. Boncinelli, J. E. Booth, S. Borau, O. Buchel, C. D. Cameron, C. F.
Carvalho, T. Celadin, C. Cerami, H. N. Chalise, X. Cheng, L. Cian, K. Cockcroft, J. Conway, M. A.
Córdoba-Delgado, C. Crespi, M. Crouzevialle, J. Cutler, M. Cypryańska, J. Dabrowska, M. A. Daniels, V.
H. Davis, P. N. Dayley, S. Delouvee, O. Denkovski, G. Dezecache, N. A. Dhaliwal, A. B. Diato, R. Di
Paolo, M. Drosinou, U. Dulleck, J. Ekmanis, A. S. Ertan, T. W. Etienne, H. H. Farhana, F. Farkhari, H.
Farmer, A. Fenwick, K. Fidanovski, T. Flew, S. Fraser, R. B. Frempong, J. A. Fugelsang, J. Gale, E. B.
Garcia-Navarro, P. Garladinne, O. Ghajjou, T. Gkinopoulos, K. Gray, S. M. Griffin, B. Gronfeldt, M.
Gümren, R. L. Gurung, E. Halperin, E. Harris, V. Herzon, M. Hruška, G. Huang, M. F. C. Hudecek, O.
Isler, S. Jangard, F. J. Jørgensen, F. Kachanoff, J. Kahn, A. K. Dangol, O. Keudel, L. Koppel, M. Koverola,
E. Kubin, A. Kunnari, Y. Kutiyski, O. Laguna, J. Leota, E. Lermer, J. Levy, N. Levy, C. Li, E. U. Long, C.
Longoni, M. Maglić, D. McCashin, A. L. Metcalf, I. Mikloušić, S. El Mimouni, A. Miura, J. Molina-
Paredes, C. Monroy-Fonseca, E. Morales-Marente, D. Moreau, R. Muda, A. Myer, K. Nash, T. Nesh-Nash,
J. P. Nitschke, M. S. Nurse, Y. Ohtsubo, V. Oldemburgo de Mello, C. O’Madagain, M. Onderco, M. S.
Palacios-Galvez, J. Palomäki, Y. Pan, Z. Papp, P. Pärnamets, M. Paruzel-Czachura, Z. Pavlović, C. Payán-
Gómez, S. Perander, M. M. Pitman, R. Prasad, J. Pyrkosz-Pacyna, S. Rathje, A. Raza, G. G. Rêgo, K.
Rhee, C. E. Robertson, I. Rodríguez-Pascual, T. Saikkonen, O. Salvador-Ginez, W. M. Sampaio, G. C.
Santi, N. Santiago-Tovar, D. Savage, J. A. Scheffer, P. Schönegger, D. T. Schultner, E. M. Schutte, A.
Scott, M. Sharma, P. Sharma, A. Skali, D. Stadelmann, C. A. Stafford, D. Stanojević, A. Stefaniak, A.
Sternisko, A. Stoica, K. K. Stoyanova, B. Strickland, J. Sundvall, J. P. Thomas, G. Tinghög, B. Torgler, I.
J. Traast, R. Tucciarelli, M. Tyrala, N. D. Ungson, M. S. Uysal, P. A. M. Van Lange, J.-W. van Prooijen,
D. van Rooy, D. Västfjäll, P. Verkoeijen, J. B. Vieira, C. von Sikorski, A. C. Walker, J. Watermeyer, E.
Wetter, A. Whillans, R. Willardt, M. J. A. Wohl, A. D. Wójcik, K. Wu, Y. Yamada, O. Yilmaz, K.
Yogeeswaran, C.-T. Ziemer, R. A. Zwaan, P. S. Boggio, National identity predicts public health support
during a global pandemic. Nature Communications. 13, 517 (2022).
25. E. L. Uhlmann, C. R. Ebersole, C. R. Chartier, T. M. Errington, M. C. Kidwell, C. K. Lai, R. J. McCarthy,
A. Riegelman, R. Silberzahn, B. A. Nosek, Scientific utopia III: Crowdsourcing science. Perspectives on
Psychological Science. 14, 711–733 (2019).
26. M. J. Hornsey, E. A. Harris, P. G. Bain, K. S. Fielding, Meta-analyses of the determinants and outcomes of
belief in climate change. Nature Climate Change. 6, 622–626 (2016).
27. A. J. Yeganeh, A. P. McCoy, T. Schenk, Determinants of climate change policy adoption: A meta-analysis.
Urban Climate. 31, 100547 (2020).
28. N. Chater, G. Loewenstein, The i-frame and the s-frame: How focusing on individual-level solutions has
led behavioral public policy astray. Behavioral and Brain Sciences, 1–60 (2022).
29. M. Fairbrother, Public opinion about climate policies: A review and call for more studies of what people
want. PLOS Climate. 1, e0000030 (2022).
30. Nielsen, K. S., Cologna, V., Lange, F., Brick, C., & Stern, P. C. (2021). The case for impact-focused
environmental psychology. Journal of Environmental Psychology, January, 10–12.
https://doi.org/10.1016/j.jenvp.2021.101559
31. F. Creutzig, L. Niamir, X. Bai, M. Callaghan, J. Cullen, J. Díaz-José, M. Figueroa, A. Grubler, W. F.
Lamb, A. Leip, E. Masanet, É. Mata, L. Mattauch, J. C. Minx, S. Mirasgedis, Y. Mulugetta, S. B. Nugroho,
M. Pathak, P. Perkins, J. Roy, S. de la Rue du Can, Y. Saheb, S. Some, L. Steg, J. Steinberger, D. Ürge-
Vorsatz, Demand-side solutions to climate change mitigation consistent with high levels of well-being.
Nature Climate Change. 12, 36–46 (2022).
32. E. Salomon, J. L. Preston, M. B. Tannenbaum, Climate change helplessness and the (de)moralization of
individual energy behavior. Journal of Experimental Psychology: Applied. 23, 15–28 (2017).
33. Simpson, B. (2006). Social identity and cooperation in social dilemmas. Rationality and society, 18(4), 443-
470.
34. W. J. Brady, J. A. Wills, J. T. Jost, J. A. Tucker, J. J. Van Bavel, Emotion shapes the diffusion of moralized
content in social networks. Proceedings of the National Academy of Sciences. 114, 7313–7318 (2017).
35. K. Hayhoe, Saving Us (Atria/One Signal Publishers, 2022).
36. C. Kormos, R. Gifford, The validity of self-report measures of proenvironmental behavior: A meta-analytic
review. Journal of Environmental Psychology. 40, 359–371 (2014).
37. F. Lange, S. Dewitte, The Work for Environmental Protection Task: A consequential web-based procedure
for studying pro-environmental behavior. Behavior Research Methods. 54, 133–145 (2022).
38. L. R. Glasman, D. Albarracín, Forming attitudes that predict future behavior: a meta-analysis of the
attitude-behavior relation. Psychological Bulletin. 132, 778–822 (2006).
39. F. Lange, Behavioral paradigms for studying pro-environmental behavior: A systematic review. Behavior
Research Methods. 55, 600–622 (2023).
40. P. Sheeran, T. L. Webb, The Intention–Behavior Gap. Social and Personality Psychology Compass. 10,
503–518 (2016).
41. L. Hespanhol, C. S. Vallio, L. M. Costa, B. T. Saragiotto, Understanding and interpreting confidence and
credible intervals around effect estimates. Brazilian Journal of Physical Therapy. 23, 290-301 (2019).
42. K. C. Doell, L. Lengersdorff, S. A. Rhoads, B. Todorova, J. P. Nitschke, J. Druckman, M. Vlasceanu,
Manylabs Climate Consortia, C. Lamm, J. J. Van Bavel, Academics are more accurate at predicting the
efficacy of climate action interventions for outcomes with more recent scientific investigation. [Manuscript
in prep].
43. T. Toles, Opinion | Doomsday scenarios are as harmful as climate change denial. Washington Post (2017),
(available at https://www.washingtonpost.com/opinions/doomsday-scenarios-are-as-harmful-as-climate-
change-denial/2017/07/12/880ed002-6714-11e7-a1d7-9a32c91c6f40_story.html).
44. C. E. Robertson, N. Pröllochs, K. Schwarzenegger, P. Pärnamets, J. J. Van Bavel, S. Feuerriegel,
Negativity drives online news consumption. Nature Human Behavior, 1–11 (2023).
45. K. C. Doell, B. Conte, T. Brosch, Interindividual differences in environmentally relevant positive trait
affect impacts sustainable behavior in everyday life. Scientific Reports. 11, 20423 (2021).
46. D. A. Chapman, B. Lickel, E. M. Markowitz, Reassessing emotion in climate change communication.
Nature Climate Change. 7, 850–852 (2017).
47. A. Coppock, T. J. Leeper, K. J. Mullinix, Generalizability of heterogeneous treatment effect estimates
across samples. Proceedings of the National Academy of Sciences. 115, 12441-12446 (2018).
48. A. Coppock, Persuasion in Parallel: How Information Changes Minds about Politics (Chicago: The
University of Chicago Press, 2022).
49. K. J. Mullinix, T. J. Leeper, J. N. Druckman, J. Freese, The Generalizability of Survey Experiments.
Journal of Experimental Political Science. 2, 109-138, (2015).
50. J. D. Weinberg, J. Freese, D. McElhattan, Comparing Data Characteristics and Results of an Online
Factorial Survey between a Population-Based and a Crowdsource-Recruited Sample. Sociological Science.
1, 292-310 (2014).
51. E. T. Berkman, S. M. Wilson, So useful as a good theory? The practicality crisis in (social) psychological
theory. Perspectives on Psychological Science. 16, 864–874 (2021).
52. A. G. Thalmayer., C. Toscanelli, J. J. Arnett, The neglected 95% revisited: Is American psychology
becoming less American? American. Psychologist. 76, 116–129 (2021).
53. S. Beck, T. Brasseur, M. Poetz, H. Sauermann, Crowdsourcing research questions in science. Research
Policy. 51, 104491 (2022).
54. K. C. Doell, Megastudies to test the efficacy of behavioural interventions. Nature Reviews Psychology. 2,
263 (2023).
55. A. J. Berinsky, M. F. Margolis, &M. W. Sances, Separating the Shirkers from the Workers? Making Sure
Respondents Pay Attention on Self-Administered Surveys. American Journal of Political Science. 58, 739–
753 (2014).
56. A. Baranzini, J. C. J. M. van den Bergh, S. Carattini, R. B. Howarth, E. Padilla, J. Roca, Carbon pricing in
climate policy: seven reasons, complementary instruments, and political economy considerations. WIREs
Climate Change. 8, e462 (2017).
57. J. T. Jost, A Theory of System Justification (Harvard University Press, 2020).
58. S. L. van der Linden, A. A. Leiserowitz, G. D. Feinberg, E. W. Maibach, The Scientific Consensus on
Climate Change as a Gateway Belief: Experimental Evidence. PLOS ONE. 10, e0118489 (2015).
59. F. G. Kaiser, M. Wilson, Goal-directed conservation behavior: The specific composition of a general
performance. Personality and Individual Differences. 36, 1531-1544 (2004).
60. I. Feygina, J. T. Jost, R. E. Goldsmith, System Justification, the Denial of Global Warming, and the
Possibility of “System-Sanctioned Change.” Personality and Social Psychology Bulletin. 36, 326–338
(2010).
61. M. Feinberg, R. Willer, Moral reframing: A technique for effective and persuasive communication across
political divides. Social and Personality Psychology Compass. 13 (2019), doi:10.1111/spc3.12501.
62. C. Wolsko, H. Ariceaga, J. Seiden, Red, white, and blue enough to be green: Effects of moral framing on
climate change attitudes and conservation behaviors. Journal of Experimental Social Psychology. 65, 7–19
(2016).
63. A. Goldenberg, S. Cohen-Chen, J. P. Goyer, C. S. Dweck, J. J. Gross, E. Halperin, Testing the impact and
durability of a group malleability intervention in the context of the Israeli–Palestinian conflict. PNAS
Proceedings of the National Academy of Sciences of the United States of America. 115, 696–701 (2018).
64. E. Shuman, T. Saguy, M. van Zomeren, E. Halperin, Disrupting the system constructively: Testing the
effectiveness of nonnormative nonviolent collective action. Journal of Personality and Social Psychology.
121, 819–841 (2021).
65. R. M. Adelman, S. D. Herrmann, J. E. Bodford, J. E. Barbour, O. Graudejus, M. A. Okun, V. S. Y. Kwan,
Feeling Closer to the Future Self and Doing Better: Temporal Psychological Mechanisms Underlying
Academic Performance. Journal of Personality. 85, 398–408 (2017).
66. H. E. Hershfield, T. R. Cohen, L. Thompson, Short horizons and tempting situations: Lack of continuity to
our future selves leads to unethical decision making and behavior. Organizational Behavior and Human
Decision Processes. 117, 298–310 (2012).
67. C. Nurra, D. Oyserman, From future self to current action: An identity-based motivation perspective. Self
and Identity. 17, 343–364 (2018).
68. J. B. Rode, S. Iqbal, B. J. Butler, P. H. Ditto, Using a News Article to Convey Climate Science Consensus
Information. Science Communication. 43, 651–673 (2021).
69. G. Sparkman, G. M. Walton, Dynamic Norms Promote Sustainable Behavior, Even if It Is
Counternormative. Psychological Science. 28, 1663–1674 (2017).
70. N. Geiger, J. K. Swim, Climate of silence: Pluralistic ignorance as a barrier to climate change discussion.
Journal of Environmental Psychology. 47, 79–90 (2016).
71. The Peoples’ Climate Vote | United Nations Development Programme. UNDP, (available at
https://www.undp.org/publications/peoples-climate-vote).
72. T. R. Shrum, The salience of future impacts and the willingness to pay for climate change mitigation: an
experiment in intergenerational framing. Climatic Change. 165, 1–20 (2021).
73. R. H. Wickersham, L. Zaval, N. A. Pachana, M. A. Smyer, The impact of place and legacy framing on
climate action: A lifespan approach. PLOS ONE. 15, e0228963 (2020).
74. D. J. Schad, M. Betancourt, S. Vasishth, Toward a principled Bayesian workflow in cognitive science.
Psychological Methods. 26, 103–126 (2021).
75. J. Salvatier, T. V. Wiecki, C. Fonnesbeck, Probabilistic programming in Python using PyMC3. PeerJ
Computer Science. 2, e55 (2016).
Acknowledgements
Funding
Google Jigsaw grant (Madalina Vlasceanu; Kimberly C. Doell; Jay J. Van Bavel)
Swiss National Science Foundation P400PS_190997 (Kimberly C. Doell)
Dutch Research Council grant 7934 (Karlijn L. van den Broek)
John Templeton Foundation grant 61378 (Mark Alfano)
The National Council for Scientific and Technological Development grant (Angélica Andersen)
Christ Church College Research Centre grant (Matthew A. J. Apps)
David Phillips Fellowship grant BB/R010668/2 (Matthew A. J. Apps)
Jacobs Foundation Fellowship (Matthew A. J. Apps)
"DFG grant project no. 390683824 (Moritz A. Drupp; Piero Basaglia; Björn Bos)"
NYUAD research funds (Jocelyn J. Bélanger)
"The Swiss Federal Office of Energy through the ""Energy, Economy, and Society"" program
grant number: SI/502093-01 (Sebastian Berger)"
The Belgian National Fund for Scientific Research (FRS-FNRS) PDR 0253.19 (Paul Bertin)
Fund for scientific development at the Faculty of Psychology at SWPS University in Warsaw
(Olga Bialobrzeska)
Radboud University Behavioural Science Institute (Daniëlle N. M. Bleize)
"Leuphana University Lüneburg research fund (David D. Loschelder; Lea Boecker; Yannik A.
Escher; Hannes M. Petrowsky; Meikel Soliman)"
University of Birmingham Start up Seed Grant (Ayoub Bouguettaya)
Prime-Pump Fund from University of Birmingham (Ayoub Bouguettaya; Mahmoud Elsherif)
University of Geneva Faculty Seed Funding (Tobias Brosch)
"Pomona College Hirsch Research Initiation Grant (Adam R. Pearson)"
Center for Social Conflict and Cohesion Studies grant ANID/FONDAP #15130009 (Héctor
Carvacho; Silvana D'Ottone)
Center for Intercultural and Indigenous Research grant ANID/FONDAP #15110006 (Héctor
Carvacho; Silvana D'Ottone)
National Research Foundation of Korea NRF-2020S1A3A2A02097375 (Dongil Chung; Sunhae
Sul)
Darden School of Business (Luca Cian)
Kieskompas - Election Compass (Tom W. Etienne; Andre P. M. Krouwel; Vladimir Cristea;
Alberto López Ortega)
The National Agency of Research and Development, National Doctoral Scholarship 24210087
(Silvana D'Ottone)
Dutch Science Foundation (NWO) grant VI.Veni.201S.075 (Marijn H.C. Meijers)
The Netherlands Organization for Scientific Research (NWO) Vici grant 453-15-005 (Iris
Engelhard)
Foundation for Science and Technology – FCT (Portuguese Ministry of Science, Technology
and Higher Education) grant UIDB/05380/2020 (Ana Rita Farias)
The Slovak Research and Development Agency (APVV) contract no. APVV-21-0114 (Andrej
Findor)
The James McDonnell Foundation 21st Century Science Initiative in Understanding Human
Cognition—Scholar Award grant 220020334 (Lucia Freira; Joaquin Navajas)
Sponsored Research Agreement between Meta and Fundación Universidad Torcuato Di Tella
grant INB2376941 (Lucia Freira; Joaquin Navajas)
Thammasat University Fast Track Research Fund (TUFT) 12/2566 (Neil Philip Gains)
HSE University Basic Research Program (Dmitry Grigoryev; Albina Gallyamova)
ARU Centre for Societies and Groups Research Centre Development Funds (Sarah Gradidge;
Annelie J. Harvey; Magdalena Zawisza)
University of Stavanger faculty of Social Science research activities grant (Simone Grassini)
Center for the Science of Moral Understanding (Kurt Gray)
University of Colorado Boulder Faculty research fund (June Gruber)
Swiss National Science Foundation grant 203283 (Ulf J.J. Hahnel)
Kochi University of Technology Research Funds (Toshiyuki Himichi)
RUB appointment funds (Wilhelm Hofmann)
Dean’s Office, College of Arts and Sciences at Seton Hall University (Fanli Jia)
Nicolaus Copernicus University (NCU) budget (Dominika Jurgiel; Adrian Dominik Wojcik)
Sectorplan Social Sciences and Humanities, The Netherlands (Elena Kantorowicz-Reznichenko)
Erasmus Centre of Empirical Legal Studies (ECELS), Erasmus School of Law, Erasmus
University Rotterdam, The Netherlands (Elena Kantorowicz-Reznichenko)
American University of Sharjah Faculty Research Grant 2020 FRG20-M-B134 (Ozgur Kaya;
Ilker Kaya)
Centre for Social and Early Emotional Development SEED grant (Anna Klas; Emily J. Kothe)
ANU Futures Grant (Colin Klein)
Research Council of Norway through Centres of Excellence Scheme, FAIR project No 262675
(Hallgeir Sjåstad and Simen Bø)
Aarhus University Research Foundation grant AUFF-E-2021-7-16 (Ruth Krebs; Laila Nockur)
Social Perception and Intergroup Inequality Lab at Cornell University (Amy R. Krosch)
COVID-19 Rapid Response grant, University of Vienna (Claus Lamm)
Austrian Science Fund FWF I3381 (Claus Lamm)
FWO Postdoctoral Fellowship 12U1221N (Florian Lange)
National Geographic Society (Julia Lee Cunningham)
University of Michigan Ross School of Business Faculty Research Funds (Julia Lee
Cunningham)
The Clemson University Media Forensics Hub (Jeffrey Lees)
John Templeton Foundation grant 62631 (Neil Levy; Robert M. Ross)
ARC Discovery Project DP180102384 (Neil Levy)
Medical Research Council Fellowship grant MR/P014097/1 (Patricia L. Lockwood)
Medical Research Council Fellowship grant MR/P014097/2 (Patricia L. Lockwood)
Jacobs Foundation (Patricia L. Lockwood)
Wellcome Trust and the Royal Society Sir Henry Dale Fellowship grant 223264/Z/21/Z (Patricia
L. Lockwood)
JFRAP grant (Jackson G. Lu)
Social Sciences and Humanities Research Council (SSHRC) Doctoral Fellowship (Yu Luo)
Simon Fraser University Psychology Department Research Grant (Annika E. Lutz; Michael T.
Schmitt)
GU internal funding (Abigail A. Marsh; Shawn A. Rhoads)
FAPESP 2014/50279-4 (Karen Louise Mascarenhas)
FAPESP 2020/15230-5 (Karen Louise Mascarenhas)
Shell Brasil (Karen Louise Mascarenhas)
Brazil’s National Oil, Natural Gas and Biofuels Agency (ANP) through the R&D levy regulation
(Karen Louise Mascarenhas)
ANR grant SCALUP, ANR-21-CE28-0016-01 (Hugo Mercier)
NOMIS Foundation grant for the Centre for the Politics of Feelings (Katerina Michalaki; Manos
Tsakiris)
"Applied Moral Psychology Lab at Cornell University (Sarah Milliron; Laura Niemi; Magdalena
Zawisza)"
Universidad Peruana Cayetano Heredia Project 209465 (Fredy S. Monge-Rodríguez)
Belgian National Fund for Scientific Research (FRS-FNRS) grant PDR 0253.19 (Youri L. Mora)
Riksbankens Jubileumsfond grant P21-0384 (Gustav Nilsonne)
European Research Council funded by the UKRI Grant EP/X02170X/1 (Maria Serena Panasiti;
Giovanni Antonio Travaglino)
Statutory Funding of Institute of Psychology, University of Silesia in Katowice (Mariola
Paruzel-Czachura)
Aarhus University Research Foundation AUFF-E-2018-7-13 (Stefan Pfattheicher)
São Paulo Research Foundation (FAPESP) grant 2019/26665-5 (Gabriel G. Rêgo)
Mistletoe Unfettered Research Grant, National Science Foundation GRFP Award 1937959
(Shawn A. Rhoads)
Japan Society for the Promotion of Science grant 21J01224 (Toshiki Saito)
Institute of Psychology & the Faculty of Social and Political Sciences, University of Lausanne
(Oriane Sarrasin)
Universitat Ramon Llull, Esade Business School (Katharina Schmid)
University of St Andrews (Philipp Schoenegger)
Dutch Science Foundation (NWO) VI.Veni.191G.034 (Christin Scholz)
Universität Hamburg (Stefan Schulreich)
Faculty of Health PhD fellowship, Aarhus University (Katia Soud)
School of Medicine and Psychology, Australian National University (Samantha K. Stanley)
Swedish Research Council grant 2018-01755 (Gustav Tinghög)
Russian Federation Government grant project 075-15-2021-611 (Danila Valko)
Swedish Research Council (Daniel Västfjäll)
Cooperatio Program MCOM (Marek Vranka)
Stanford Center on Philanthropy and Civil Society (Robb Willer)
Canada Research Chairs program (Jiaying Zhao)
Author Contributions
Conceptualization: MV, KCD, JJVB
Methodology: MV, KCD, JBC, JJVB
Investigation: all
Visualization: JBC, MV
Funding acquisition: MV, KCD, JJVB, AC
Project administration: MV, KCD, SG, YP, EP, DV
Supervision: MV, KCD, JJVB
Writing – original draft: MV, KCD, JJVB, JBC, SC
Writing – review & editing: all
Competing Interests
André Krouwel (Departments of Political Science and Communication Science at Vrije
Universiteit Amsterdam) is founder and stockholder of Kieskompas (data collection service), but
has not financially benefited from this data collection or study.
Data and Materials Availability
All data and code can be found on GitHub: https://github.com/josephbb/ManyLabsClimate and
https://github.com/mvlasceanu/ClimateTournament
The interventions (in each language) can be accessed as qsf files (to be imported in Qualtrics):
https://osf.io/ytf89/files/osfstorage/6454f8d771778511d9b0f48f.
A webtool for rapidly assessing which intervention is most likely to be effective at increasing
climate change beliefs, policy support, information sharing, and tree planting efforts, for any
subsample target of interest, varying along demographics such as nationality, political ideology,
age, gender, education, or income level can be found here: https://climate-
interventions.shinyapps.io/climate-interventions/
Supplemental Analyses and Figures
Figure S1: Coefficient estimates and 95% confidence intervals average treatment effects in the
pre-registered analysis. A) Belief B) Policy Support C) Social Media Sharing D) Trees Planted
(N=59,440 participants in 63 countries).
Figure S2: Number of trees planted in the WEPT as a function of belief in climate change,
climate policy support, and willingness to share climate information. Statistics of the mixed
models conducted are reported in tables S13-S18. The results reveal positive associations
between the first three (lower effort behavioral) outcomes and the higher effort action (WEPT).
Fig S3: Average intervention effects for WEPT as a function of median intervention time.
Fig S4: Country-level effects. Marginal, country-level posterior estimates for each of the key
dependent variables. Dots indicate the mean, with error bars indicating the 94% credible region
(C.R.). Thicker bars, when visible, indicate the interquartile range (IQR). Vertical lines and
shading indicate the overall average across countries and 94% C.R., respectively. A) Belief, B)
Support for Policy, C) Willingness to share climate-change information on social media, D)
Number of trees planted in the WEPT task. Estimates shown in Tables S5-S8.
Figure S5. Average support of each crowdsourced intervention from a sample of 188 behavioral
scientists (coauthors on the current paper) who were asked to rate the interventions on perceived
efficiency (practical support) and theoretical value (theoretical support).
Figure S6. Frequency plots of A) belief, B) policy support, C) climate information sharing, and
D) number of trees planted) in the control condition (N=5,086, from 63 countries), emphasizing
the distributions of these dependent variables at baseline.
Table S1. Bayesian estimates of belief in climate change in each intervention, compared to the
control condition.
Intervention
mean
sd
1.5%
3%
median
97%
98.5%
Psych Distance
2.255
0.332
1.524
1.619
2.263
2.865
2.954
Collective Action
1.454
0.338
0.712
0.816
1.457
2.07
2.177
Fut. Self Cont.
1.257
0.351
0.547
0.637
1.249
1.922
2.037
Letter Fut. Gen.
1.206
0.341
0.471
0.553
1.203
1.863
1.952
System Justif.
0.847
0.329
0.112
0.218
0.852
1.458
1.555
Sci. Consensus
0.426
0.345
-0.341
-0.252
0.437
1.052
1.134
Binding Moral
0.348
0.347
-0.385
-0.295
0.352
0.994
1.087
Dyn. Soc. Norm
0.343
0.342
-0.421
-0.324
0.35
0.969
1.073
Neg. Emotions
0.244
0.341
-0.488
-0.404
0.238
0.907
0.997
Plural Ignorance
-0.263
0.348
-0.976
-0.894
-0.274
0.404
0.507
Work Together
Norm
-1.195
0.359
-1.947
-1.839
-1.198
-0.5
-0.374
Table S2. Bayesian estimates of policy support in each intervention, compared to the control
condition.
Intervention
mean
sd
1.5%
3%
median
97%
98.5%
Letter Future Gen.
2.552
0.318
1.847
1.95
2.55
3.155
3.236
CollectAction
2.406
0.289
1.794
1.863
2.405
2.967
3.046
FutureSelfCont
2.111
0.308
1.464
1.545
2.111
2.696
2.804
PsychDistance
1.066
0.302
0.388
0.486
1.07
1.64
1.722
DynamicNorm
0.93
0.294
0.295
0.385
0.927
1.492
1.581
SystemJust
0.741
0.287
0.115
0.201
0.739
1.293
1.368
BindingMoral
0.511
0.298
-0.133
-0.051
0.506
1.062
1.159
SciConsens
0.471
0.309
-0.226
-0.098
0.468
1.041
1.135
NegativeEmotions
0.148
0.296
-0.501
-0.411
0.152
0.708
0.797
Work Together Norm
0.145
0.3
-0.485
-0.396
0.142
0.711
0.795
PluralIgnorance
0.096
0.299
-0.557
-0.463
0.094
0.666
0.737
Table S3. Bayesian estimates of sharing intentions in each intervention, compared to the control
condition.
Intervention
mean
sd
1.5%
3%
median
97%
98.5%
Neg. Emotions
12.106
1.293
9.325
9.671
12.079
14.593
14.966
Letter Fut. Gen.
10.754
1.419
7.817
8.099
10.749
13.535
13.893
Collective Action
10.527
1.319
7.611
8.108
10.536
13.014
13.421
Psych Distance
9.085
1.338
6.288
6.631
9.074
11.611
11.974
Fut. Self Cont.
8.255
1.41
5.271
5.64
8.249
10.876
11.254
Dyn. Soc. Norm
7.978
1.317
5.085
5.465
7.982
10.455
10.847
Work Together Norm
7.535
1.331
4.739
5.111
7.515
10.078
10.395
System Justif.
5.812
1.304
2.966
3.367
5.801
8.301
8.639
Binding Moral
4.455
1.326
1.656
2.009
4.432
6.964
7.338
Sci. Consensus
4.444
1.352
1.527
1.851
4.449
6.964
7.388
Plural Ignorance
2.309
1.32
-0.51
-0.1
2.276
4.82
5.259
Table S4. Bayesian estimates of number of trees planted in each intervention, compared to the
control condition.
Intervention
mean
sd
1.5%
3%
median
97%
98.5%
Binding Moral
0.038
0.082
-0.14
-0.117
0.039
0.192
0.213
Sci. Consensus
0.003
0.081
-0.17
-0.146
0.004
0.158
0.184
Dyn. Soc. Norm
-0.009
0.081
-0.182
-0.161
-0.008
0.144
0.171
System Justif.
-0.12
0.081
-0.295
-0.272
-0.12
0.031
0.057
Plural Ignorance
-0.165
0.081
-0.339
-0.321
-0.165
-0.014
0.008
Collective Action
-0.185
0.083
-0.361
-0.337
-0.187
-0.028
-0.005
Fut. Self Cont.
-0.229
0.086
-0.413
-0.391
-0.229
-0.061
-0.038
Letter Fut. Gen.
-0.365
0.087
-0.552
-0.526
-0.365
-0.199
-0.174
Work Together Norm
-0.438
0.08
-0.615
-0.593
-0.44
-0.283
-0.263
Psych Distance
-0.51
0.083
-0.689
-0.664
-0.509
-0.349
-0.326
Neg. Emotions
-0.536
0.082
-0.725
-0.695
-0.535
-0.377
-0.357
Table S5. Belief by country.
Country
mean
sd
1.5%
3%
median
97%
98.5%
Philippines
96.747
0.46
95.708
95.843
96.773
97.553
97.635
Portugal
94.055
0.451
93.031
93.157
94.072
94.89
94.99
Uganda
93.387
0.481
92.302
92.447
93.39
94.278
94.404
Ghana
92.957
0.489
91.872
92.014
92.963
93.867
93.989
Tanzania
92.854
1.084
90.229
90.67
92.915
94.733
94.991
Brazil
92.607
0.355
91.792
91.912
92.617
93.239
93.328
Turkey
92.478
0.447
91.522
91.629
92.477
93.293
93.402
Gambia
92.463
0.3
91.828
91.905
92.466
93.007
93.081
Chile
92.452
0.524
91.247
91.434
92.479
93.395
93.53
Serbia
92.398
0.642
90.878
91.134
92.411
93.626
93.816
Venezuela
91.975
1.179
89.143
89.564
92.049
94.006
94.291
Uruguay
91.697
0.578
90.342
90.559
91.723
92.707
92.883
Vietnam
91.631
0.676
90.067
90.339
91.654
92.847
93.026
Mexico
91.399
0.578
90.128
90.277
91.388
92.485
92.636
Italy
91.213
0.369
90.347
90.48
91.228
91.897
92.003
Kenya
91.167
0.698
89.535
89.759
91.208
92.367
92.525
North Macedonia
90.815
0.498
89.665
89.846
90.835
91.675
91.79
Peru
90.601
0.73
88.921
89.194
90.617
91.9
92.056
Ecuador
90.524
0.553
89.267
89.446
90.545
91.505
91.675
Nigeria
90.129
0.411
89.234
89.343
90.135
90.893
91.006
Czechia
89.293
0.7
87.628
87.889
89.314
90.554
90.792
Armenia
89.19
0.716
87.653
87.861
89.188
90.56
90.749
Canada
89.135
0.52
87.976
88.102
89.159
90.058
90.214
South Africa
89.102
0.767
87.409
87.612
89.127
90.537
90.681
India
88.96
0.664
87.432
87.654
88.98
90.168
90.389
Sri Lanka
88.652
0.865
86.66
86.929
88.682
90.198
90.408
Ukraine
88.368
0.759
86.643
86.881
88.406
89.702
89.894
Spain
88.212
0.781
86.348
86.653
88.233
89.699
89.897
Romania
88.046
0.883
86.064
86.301
88.058
89.647
89.865
Ireland
87.15
0.666
85.624
85.842
87.154
88.367
88.588
South Korea
85.666
0.812
83.785
84.024
85.699
87.144
87.398
France
85.492
0.592
84.076
84.292
85.506
86.551
86.707
Algeria
85.084
0.919
83.045
83.311
85.09
86.747
86.995
Morocco
84.872
0.971
82.576
82.881
84.926
86.581
86.805
China
84.692
0.709
83.101
83.32
84.711
85.955
86.16
Bulgaria
84.629
0.771
82.955
83.208
84.628
86.073
86.284
Switzerland
84.515
0.684
82.977
83.163
84.53
85.741
85.911
Thailand
83.984
0.967
81.835
82.115
84.022
85.761
85.979
Singapore
83.941
0.867
82.046
82.288
83.938
85.568
85.934
Poland
83.593
0.518
82.443
82.566
83.615
84.527
84.672
Germany
83.33
0.632
81.981
82.169
83.321
84.533
84.705
UK
82.663
0.608
81.265
81.516
82.674
83.764
83.941
Austria
82.254
1.04
79.734
80.151
82.305
84.075
84.398
UAE
81.942
1.016
79.602
79.966
81.958
83.751
84.045
Greece
81.318
1.051
78.973
79.264
81.332
83.301
83.531
Australia
81.316
0.82
79.469
79.7
81.345
82.823
83.037
Russia
81.226
0.708
79.681
79.887
81.235
82.584
82.728
Slovenia
80.135
1.18
77.379
77.743
80.189
82.23
82.445
Netherlands
80.092
0.677
78.589
78.784
80.099
81.354
81.533
Denmark
79.843
0.972
77.656
77.993
79.844
81.63
81.926
Finland
79.436
1.127
77.014
77.333
79.425
81.454
81.737
Slovakia
79.396
0.89
77.342
77.609
79.438
80.904
81.136
Belgium
79.384
0.875
77.393
77.659
79.424
80.966
81.165
Sweden
79.343
0.648
77.842
78.062
79.333
80.527
80.699
Taiwan
79.281
1.756
75.177
75.899
79.342
82.588
83.03
Sudan
79.217
1.058
76.913
77.229
79.241
81.058
81.214
New Zealand
79.152
0.859
77.212
77.488
79.169
80.748
80.977
Japan
79.148
0.733
77.501
77.759
79.147
80.443
80.629
Latvia
76.918
1.378
73.982
74.384
76.929
79.475
79.801
USA
76.861
0.467
75.819
75.945
76.882
77.687
77.8
Norway
76.248
0.898
74.234
74.541
76.256
77.931
78.188
Saudi Arabia
73.952
1.469
70.969
71.339
73.941
76.716
77.138
Israel
73.439
0.891
71.426
71.688
73.468
75.037
75.294
Table S6. Policy support by country.
Country
mean
sd
1.5%
3%
median
97%
98.5%
Turkey
80.201
0.635
78.839
79.014
80.185
81.407
81.573
Brazil
78.445
0.565
77.204
77.35
78.442
79.503
79.643
Chile
78.425
0.469
77.42
77.547
78.429
79.31
79.438
Uganda
78.278
0.893
76.328
76.582
78.272
79.967
80.232
India
77.921
0.647
76.479
76.687
77.926
79.164
79.342
Sri Lanka
77.858
0.767
76.13
76.388
77.849
79.3
79.52
Ghana
77.564
0.744
75.985
76.178
77.554
78.958
79.201
Taiwan
77.336
1.48
74.05
74.497
77.335
80.053
80.405
Ukraine
76.983
0.724
75.392
75.62
76.997
78.318
78.554
Uruguay
76.859
0.763
75.198
75.402
76.867
78.313
78.496
Nigeria
76.617
0.512
75.497
75.653
76.619
77.569
77.714
Venezuela
76.565
1.584
73.061
73.495
76.589
79.495
79.863
Mexico
76.361
0.827
74.582
74.852
76.365
77.92
78.152
Philippines
76.353
1.293
73.538
73.903
76.38
78.786
79.061
Vietnam
76.083
0.932
73.989
74.3
76.096
77.784
78.084
Gambia
75.981
0.793
74.187
74.467
75.989
77.488
77.73
China
75.82
0.649
74.416
74.624
75.808
77.069
77.271
Serbia
75.646
0.934
73.624
73.868
75.656
77.348
77.657
Portugal
75.578
0.809
73.839
74.051
75.566
77.121
77.359
Algeria
75.326
0.759
73.669
73.888
75.319
76.722
76.976
Spain
75.11
0.838
73.305
73.558
75.125
76.663
76.945
Sudan
75.01
0.809
73.256
73.441
75.021
76.491
76.666
Morocco
74.933
0.892
73.007
73.267
74.925
76.637
76.907
Ecuador
74.365
0.76
72.765
72.986
74.358
75.799
75.993
Bulgaria
73.86
0.764
72.147
72.436
73.864
75.284
75.522
Thailand
73.766
0.932
71.752
71.954
73.781
75.476
75.78
Italy
73.757
0.543
72.576
72.763
73.755
74.782
74.927
South Africa
73.328
0.895
71.409
71.653
73.328
75.024
75.341
Ireland
73.256
0.735
71.7
71.859
73.255
74.615
74.779
Kenya
73.196
0.885
71.283
71.53
73.205
74.859
75.06
UK
72.706
0.61
71.391
71.559
72.71
73.877
74.085
South Korea
72.512
0.829
70.713
70.914
72.52
74.064
74.302
Canada
72.018
0.661
70.594
70.774
72.007
73.262
73.445
Singapore
71.941
0.887
70.005
70.292
71.936
73.608
73.864
North Macedonia
71.941
0.652
70.468
70.75
71.963
73.132
73.365
Peru
71.87
0.93
69.824
70.114
71.865
73.592
73.83
Australia
71.744
0.748
70.123
70.312
71.731
73.187
73.399
Romania
71.679
0.983
69.539
69.809
71.701
73.531
73.792
Tanzania
71.283
1.955
66.959
67.596
71.282
74.866
75.397
France
70.641
0.612
69.278
69.475
70.646
71.785
71.944
Netherlands
70.631
0.586
69.356
69.554
70.639
71.729
71.892
UAE
70.519
0.928
68.478
68.759
70.536
72.248
72.512
Greece
70.466
0.803
68.725
68.978
70.471
71.986
72.194
Denmark
70.356
0.84
68.5
68.761
70.359
71.915
72.177
Poland
70.223
0.509
69.155
69.301
70.212
71.199
71.354
Armenia
69.95
0.812
68.195
68.479
69.932
71.495
71.768
Switzerland
69.881
0.725
68.28
68.481
69.878
71.207
71.431
Slovenia
69.713
0.896
67.822
68.03
69.709
71.384
71.599
Czechia
68.975
0.906
66.989
67.269
68.965
70.681
70.886
Israel
68.435
0.591
67.119
67.298
68.437
69.543
69.756
Austria
68.046
0.949
66.023
66.281
68.058
69.777
70.017
USA
67.88
0.431
66.916
67.036
67.887
68.671
68.771
Germany
67.674
0.643
66.299
66.474
67.67
68.908
69.099
Belgium
67.118
0.746
65.512
65.722
67.098
68.528
68.735
New Zealand
67.028
0.767
65.367
65.599
67.027
68.481
68.697
Norway
67.013
0.758
65.327
65.539
67.009
68.436
68.66
Sweden
66.273
0.58
64.976
65.172
66.268
67.352
67.507
Russia
64.992
0.579
63.756
63.92
64.986
66.103
66.259
Finland
64.545
0.994
62.353
62.61
64.544
66.362
66.646
Saudi Arabia
64.444
1.093
62.171
62.508
64.416
66.544
66.834
Slovakia
64.362
0.703
62.865
63.038
64.358
65.689
65.858
Latvia
62.491
0.983
60.326
60.591
62.512
64.358
64.592
Japan
58.939
0.616
57.613
57.798
58.945
60.097
60.275
Table S7. Sharing intentions by country.
Country
mean
sd
1.5%
3%
median
97%
98.5%
Kenya
93.31
1.437
89.913
90.418
93.426
95.709
95.969
Uganda
91.743
1.88
87.214
87.855
91.873
94.834
95.339
Mexico
90.518
1.53
86.866
87.42
90.596
93.149
93.489
Taiwan
88.544
2.757
81.7
83.04
88.705
93.337
94.025
Ecuador
88.295
1.408
85.068
85.514
88.361
90.728
91.051
Gambia
87.53
1.656
83.911
84.365
87.581
90.524
91.035
Nigeria
86.151
0.977
83.916
84.251
86.166
87.89
88.244
Ghana
85.086
1.758
81.183
81.675
85.133
88.257
88.665
Tanzania
82.466
4.43
72.229
73.671
82.664
90.198
90.939
UAE
81.872
1.717
78.087
78.663
81.876
84.983
85.388
Vietnam
81.416
2.309
76.107
76.954
81.581
85.437
86.051
Thailand
81.093
2.097
76.417
77.093
81.121
84.968
85.517
China
80.925
1.44
77.75
78.191
80.932
83.51
83.837
Peru
80.692
2.314
75.356
76.058
80.749
84.931
85.465
Saudi Arabia
80.387
1.778
76.361
76.968
80.417
83.59
84.102
South Korea
79.702
2.197
74.722
75.38
79.756
83.727
84.351
India
78.964
1.608
75.443
75.878
78.991
81.939
82.332
South Africa
77.534
2.145
72.813
73.381
77.549
81.466
82.084
Morocco
76.654
2.116
71.898
72.58
76.713
80.525
81.113
Chile
74.184
1.191
71.658
71.97
74.167
76.464
76.76
Brazil
73.233
1.409
70.123
70.521
73.256
75.824
76.273
Algeria
71.205
2.15
66.353
67.162
71.19
75.038
75.525
Singapore
68.836
2.176
64.203
64.819
68.883
72.738
73.444
Uruguay
68.78
2.526
63.148
63.917
68.883
73.361
74.062
Greece
66.537
2.305
61.533
62.161
66.559
70.846
71.454
Sudan
66.491
1.941
62.435
62.844
66.5
70.158
70.738
Venezuela
66.344
4.934
55.454
56.782
66.413
75.446
77.046
Philippines
65.902
4.414
56.219
57.384
65.891
73.895
75.357
France
64.752
1.5
61.54
61.94
64.755
67.583
67.945
Sri Lanka
64.328
2.918
57.922
58.69
64.353
69.722
70.551
Bulgaria
64.26
2.122
59.538
60.124
64.339
68.201
68.753
Ireland
62.759
2.204
57.926
58.503
62.775
66.783
67.301
USA
61.391
0.682
59.916
60.119
61.385
62.695
62.902
Italy
57.09
1.491
53.891
54.306
57.102
59.856
60.285
UK
55.063
1.393
52.023
52.497
55.092
57.584
57.986
Spain
54.985
3.029
48.591
49.382
54.984
60.563
61.501
Slovenia
53.929
2.686
48.062
48.817
53.924
59.043
59.99
Sweden
53.058
1.234
50.478
50.752
53.076
55.32
55.776
Denmark
51.981
2.146
47.428
47.898
51.99
56.016
56.542
Turkey
48.186
2.195
43.447
44.117
48.156
52.302
52.844
Australia
45.53
2.075
40.871
41.597
45.541
49.522
50.215
New Zealand
37.913
1.782
33.92
34.598
37.917
41.228
41.754
Portugal
36.846
2.624
31.254
32.018
36.884
41.748
42.542
Finland
35.997
2.217
31.283
31.857
35.971
40.224
40.93
Canada
35.824
1.684
32.27
32.678
35.782
38.918
39.367
Poland
35.555
1.239
32.906
33.248
35.562
37.891
38.317
Belgium
35.172
1.781
31.455
31.861
35.137
38.561
39.07
Ukraine
34.285
2.412
29.372
29.887
34.258
38.955
39.714
Armenia
32.677
2.393
27.546
28.264
32.635
37.313
37.996
Germany
31.981
1.466
28.809
29.274
31.954
34.732
35.24
Israel
31.629
1.68
28.09
28.442
31.617
34.834
35.395
Slovakia
30.655
1.647
27.065
27.637
30.641
33.806
34.307
North Macedonia
28.421
1.871
24.443
24.903
28.461
31.937
32.364
Austria
27.791
2.325
22.971
23.578
27.753
32.242
32.957
Switzerland
26.868
1.743
23.171
23.71
26.854
30.263
30.85
Serbia
26.537
2.713
21.029
21.639
26.433
31.747
32.44
Russia
26.052
1.41
23.027
23.445
26.03
28.729
29.216
Japan
24.205
1.415
21.274
21.618
24.199
26.888
27.234
Czechia
20.411
1.909
16.319
16.887
20.369
24.17
24.672
Romania
19.597
2.24
15.141
15.559
19.52
24.07
24.782
Netherlands
19.335
1.113
17.006
17.313
19.325
21.478
21.761
Norway
19.178
1.467
16.118
16.46
19.168
22.081
22.577
Latvia
17.684
1.9
13.816
14.27
17.629
21.378
22.103
Table S8. Number of trees planted by country.
Country
mean
sd
1.5%
3%
median
97%
98.5%
Kenya
6.958
0.42
6.048
6.167
6.978
7.678
7.745
Serbia
6.851
0.346
6.188
6.268
6.824
7.55
7.636
Venezuela
6.815
0.485
5.819
5.939
6.796
7.734
7.811
Philippines
6.447
0.459
5.606
5.712
6.393
7.52
7.676
Chile
6.418
0.09
6.222
6.245
6.418
6.583
6.613
Slovakia
6.348
0.108
6.098
6.14
6.349
6.549
6.585
Italy
6.33
0.129
6.062
6.101
6.327
6.582
6.616
China
6.204
0.161
5.878
5.917
6.198
6.525
6.587
UAE
6.126
0.145
5.817
5.853
6.126
6.4
6.435
South Korea
6.126
0.177
5.761
5.808
6.122
6.481
6.55
Thailand
6.085
0.16
5.744