Climate Doom and Policy Support

Last registered on October 06, 2025

Pre-Trial

Trial Information

General Information

Title
Climate Doom and Policy Support
RCT ID
AEARCTR-0016748
Initial registration date
October 01, 2025

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
October 06, 2025, 11:37 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

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Primary Investigator

Affiliation
Norwegian School of Economics

Other Primary Investigator(s)

PI Affiliation
University of Gothenburg
PI Affiliation
University of Gothenburg

Additional Trial Information

Status
In development
Start date
2025-10-31
End date
2027-01-01
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This project uses large-scale surveys with a sample representative of the U.S. population and implements an information experiment to study public beliefs about long-term societal trajectories, including anticipated impacts of climate change and perceptions of scientific projections.
External Link(s)

Registration Citation

Citation
Bilén, David, Mattias Sundemo and Åsa Åsa Löfgren. 2025. "Climate Doom and Policy Support." AEA RCT Registry. October 06. https://doi.org/10.1257/rct.16748-1.0
Experimental Details

Interventions

Intervention(s)
We estimate the causal effects of providing information on (i) climate impacts alone and (ii) climate impacts combined with projections of economic and population growth.
Intervention Start Date
2025-10-31
Intervention End Date
2025-12-31

Primary Outcomes

Primary Outcomes (end points)
We construct measures of policy support, individual behavior, beliefs, and attitudes that range from “moderate” to “more extreme.” Throughout, we use these terms descriptively to contrast lower-cost and less restrictive measures with higher-cost and more restrictive measures, rather than as exact definitions.

Policy support: We measure support for “moderate” policies, such as lower-cost or informational interventions (e.g., labeling, “moderate” carbon taxes), and for “more extreme” policies, such as higher-cost or restrictive interventions (e.g., large carbon taxes, fossil fuel bans).

Individual behavior: We measure willingness to take “moderate” actions, such as lower-cost lifestyle adjustments, and “more extreme” actions, such as higher-cost or more restrictive behavioral changes.

Individual (incentivized) action: We measure choices in one incentivized decision involving a trade-off between keeping a monetary bonus for oneself or using it to reduce carbon emissions.

Emotional responses to climate change: We measure positive emotions (e.g., hope, optimism) and negative emotions (e.g., worry, sadness, anxiety).

Support for civic engagement and climate protests: We measure approval of activities ranging from conventional demonstrations to “more extreme” actions, including civil disobedience.

Beliefs about the future: We measure respondents’ expectations about long-term societal trajectories, including anticipated climate impacts, whether they expect the world to be richer or poorer, and whether they believe the global population will increase or decrease. We also examine respondents’ beliefs about what scientists project for the future.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Relative importance of climate policy: We measure respondents’ perceptions of the importance of climate policy compared to other policy domains.

Heterogeneous treatment effects: We examine whether treatment effects vary according to respondents’ beliefs about scientists’ projections, their own views on climate change and its impacts, political ideology/affiliation, and trust in climate scientists.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Survey Design
Data are collected in two waves: (i) a main wave; and (ii) a follow-up wave.
Experimental Design Details
Not available
Randomization Method
By the survey company.
Randomization Unit
Individual.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
We plan to recruit a total of 10,000 respondents from the United States. Recruitment is made via YouGov, targeting a sample that is representative of the U.S. population on key background variables.
Sample size: planned number of observations
See above.
Sample size (or number of clusters) by treatment arms
We aim for the following sample size per treatment arm:

Control (Impact): 2,000
Treatment (Impact): 2,000

Control (Total): 3,000
Treatment (Total): 3,000
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Primary Hypotheses We preregister three primary hypotheses, together with prior predictions concerning the effects of information treatments on respondents’ policy support, behavior, emotions, and beliefs. In all cases, the null hypothesis states that information treatments have no effect on the specified outcomes. All hypotheses will be evaluated using two-sided statistical tests. H1 (Impact) Null hypothesis: No difference between the Impact treatment and the control group. Prior prediction (“moderate” outcomes): Providing information about IPCC projections of impacts will increase measures of “moderate” outcomes. Prior prediction (“more extreme” outcomes): Providing information about IPCC projections of impacts will not affect measures of “more extreme” outcomes. H2 (Total) Null hypothesis: No difference between the Total treatment and the control group. Prior prediction (“moderate” outcomes): Providing information about IPCC projections of impacts and growth will increase measures of “moderate” outcomes. Prior prediction (“more extreme” outcomes): Providing information about IPCC projections of impacts and growth will reduce measures of “more extreme” outcomes. H3 (Interaction: Impact vs. Total) Null hypothesis: No difference between the Impact and Total treatment groups. Prior prediction (“moderate” outcomes): Measures of “moderate” outcomes will be similar in the Impact and Total groups. Prior prediction (“more extreme” outcomes): Measures of “more extreme” outcomes will be lower in the Total group than in the Impact group. Power calculation: Using a two-sided t-test (α = 0.05; 80% power) with a binary outcome (SD = 0.5), the minimum detectable effect sizes (MDEs) for the respective treatment comparisons are: H1 (Impact vs. Control): With 2,000 observations per group, we can detect a difference of approximately 4.4 percentage points. H2 (Total vs. Control): With 3,000 observations per group, we can detect a difference of approximately 3.6 percentage points. H3 (Impact vs. Total): Comparing treatment effects between the Impact and Total groups (2,000 + 2,000 vs. 3,000 + 3,000 observations), we can detect a difference of approximately 5.7 percentage points.
IRB

Institutional Review Boards (IRBs)

IRB Name
NHH IRB
IRB Approval Date
2025-09-18
IRB Approval Number
NHH-IRB-2025-117