How regional predictions of heat events in the coming summer and their forward-attribution to climate change affect support for adaptation and mitigation measures as well as attribution of guilt across generations. (second wave)

Last registered on October 28, 2024
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Pre-Trial

Trial Information

General Information

Title
How regional predictions of heat events in the coming summer and their forward-attribution to climate change affect support for adaptation and mitigation measures as well as attribution of guilt across generations. (second wave)
RCT ID
AEARCTR-0014419
Initial registration date
October 21, 2024

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 28, 2024, 12:55 PM EDT

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

Locations

Country
Germany
Region

Primary Investigator

Name
Enrico Longo
Affiliation
University of Hamburg
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Other Primary Investigator(s)

PI Name
Grischa Perino
PI Affiliation
University of Hamburg
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PI Name
Andreas Lange
PI Affiliation
University of Hamburg
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PI Name
Moritz Drupp
PI Affiliation
University of Hamburg
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PI Name
Mrunali Damania
PI Affiliation
University of Hamburg
Contact Investigator
PI Name
Johanna Baehr
PI Affiliation
University of Hamburg
Contact Investigator

Additional Trial Information

Status
In development
Start date
2024-10-22
End date
2024-11-05
Keywords
Behavior, Environment & Energy, Health
Additional Keywords
Climate change, Adaptation, Mitigation, Attribution
JEL code(s)
Q54, Q58, D91
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
In this second, ex-post wave, we re-contact participants of all experimental conditions after having experienced the summer (June - August) of 2024. We investigate how heterogeneity in experience and in the accuracy of prediction shape willingness-to-pay (revealed preferences) for seasonal and trust in long-run climate prediction, as well as revealed-preference support for adaptation and mitigation measures.
Design: All (including the control group) are given the prediction/risk-assessment (75th percentile of the number of tropical nights) and the observation according to official records for their region as well as information on the nation-wide accuracy of the risk-assessment. Half of participants are in addition presented with a graphical representation (on a map) of how the risk-assessment performed relative to observations. With respect to the elicitation of the willingness-to-pay (WTP) for seasonal predictions for summer 2025, we elicit WTP of the same risk assessment as used in the current study for half of participants. The other half is first asked to choose their preferred risk assessment (tropical nights vs. hot days / three levels of risk) before the WTP for the selected seasonal prediction is elicited.
In an independent random draw, half of participants is confronted with a statement that seasonal and long-run climate predictions are based on the same basic principles and similar models. The latter is inteded to exogenously vary the degree to which trust in seasonal predictions spills over to long-run predictions.
External Link(s)

Registration Citation

Citation
Baehr, Johanna et al. 2024. "How regional predictions of heat events in the coming summer and their forward-attribution to climate change affect support for adaptation and mitigation measures as well as attribution of guilt across generations. (second wave)." AEA RCT Registry. October 28. https://doi.org/10.1257/rct.14419-1.0
Sponsors & Partners

Sponsors

Experimental Details

Interventions

Intervention(s)
Assignments of all interventions/treatments are independent of each other and of interventions in wave 1.

Intervention 1 "Map" (Text+Map vs Text group):
Half of participants is presented with a map that details the geographical distribution of the nation-wide accuracy of the risk-assessment (75th percentile of seasonal prediction).

Intervention 2 "Preferred risk assessment" (PredefinedChoice vs. FreeChoice group):
Half of participants can choose from a set of six (2 x 3) different risk assessments to be ordered for 1st of June to 31st of August 2025 (delivery in May 2025) rather than stating willingness-to-pay for the risk assessment used in waves 1 and 2 (75th percentile of number of tropical nights).

Intervention 3 "Same science" (WithNotice vs. WithoutNotice group):
Half of participants is presented with a statement that seasonal and long-run climate predictions are based on the same scientfic understanding of the climate system and similar models.
Intervention (Hidden)
Intervention Start Date
2024-10-22
Intervention End Date
2024-11-05

Primary Outcomes

Primary Outcomes (end points)
The following revealed-preference outcomes are elicitied:
Willingness-to-pay for risk assessment for summer 2025 (June 1 to August 31) to be delivered in May 2025 either for the risk assessment used in waves 1 and 2 or the risk assessment preferred by the participant (see intervention 2).
Participants are randomly presented with a monetary payment (ranging from 0.10 € to 5 €) that they would forgo when opting to receive the risk assessment (seasonal prediction) for summer 2025 in May 2025.

Willingness-to-donate to support adaptation in the form of plant trees in a large German city.
Participants are enroled in a lottery (chance of winning is 1 in 200) and are asked to specify how much of the 100 € prize they would be willinging to donate to plant trees in a major German city. They are informed that urban trees improve air quality and provide cooling services.

Willingness-to-support mitigation in the form of a costly vote in favor of retiring emission allowances from the EU Emission Trading System.
Participants can choose between a certain payment of 0.70 € and voting in favor of retiring allowances corresponding to 10 tons of CO2 from the European Union Emission Trading System. If at least half of the valid votes is in favor of retiring, then allowances will be retired and no payments made to participants. We use intervention 3 (climate modeling) to separate participants in two groups for which we count votes separately.

For more details on all three primary outcomes see survey.
Primary Outcomes (explanation)
Further outcomes will be computed by linking the first and the second wave of the survey.
The revealed-preference support for mitigation measure is the same in both waves and a discrete variable (yes/no/abstain) in both cases. We construct changes between waves as a further primary outcome variable.

Secondary Outcomes

Secondary Outcomes (end points)
Stated-preference:
Measure of trust in seasonal prediction/risk assessment identical to the one used in waves 1 and 2. (5-point Likert scale)
Yes/No question about whether participant has bought an air fan for the summer of 2024.
Support for five adaptation measures elicited on 5-point Likert scales
a) mandatory insurance against natural hazards for all landlords
b) more space for and planting of urban trees to reduce heat in cities
c) Subsidies for the installation of air conditioning in private homes and offices
d) provision of reuasable water bottles for homeless people free of charge
f) Heat-warning app that also provides advice on how to protect oneself

Further variables are:
Beliefs regarding climate change, its origin and severity
recollected number of tropical nights (>20°C) and hot days (>30°C) for June 1 and August 31, 2024
Experienced extreme weather events (flooding, strong rainfalls, storms, draughts, bushfires, heat waves, other)
Experienced health impacts
Secondary Outcomes (explanation)
We build an aggregate support measure average of the five adaptation support measures described above (wave 2).
We build differences in measures between wave 1 and 2 as further secondary outcome variables: trust in prediction, trust in government, willingness to participate in climate protest, party preferences, beliefs regarding climate change, its origin and severity

Experimental Design

Experimental Design
All participants (including the control group) are given the prediction/risk-assessment (75th percentile of the number of tropical nights) and the observation according to official records for their region as well as information on the nation-wide accuracy of the risk-assessment used in wave 1.
Half of participants are in addition presented with a graphical representation (on a map) of how the risk-assessment performed relative to observations.
With respect to the elicitation of the willingness-to-pay (WTP) for seasonal predictions for summer 2025, we elicit WTP of the same risk assessment as used in the current study for half of participants. The other half is first asked to choose their preferred risk assessment (tropical nights vs. hot days / three levels of risk) before the WTP for the selected seasonal prediction is elicited.
In an independent random draw, half of participants is confronted with a statement that seasonal and long-run climate predictions are based on the same basic principles and similar models. The latter is inteded to exogenously vary the degree to which trust in seasonal predictions spills over to long-run predictions.
Experimental Design Details
For more details regarding the design, see the attached copy of the survey (incl. advice for programming).

Hypotheses
1. The “map” intervention (Text+Map vs Text group) increases trust in and WTP for the seasonal prediction both in the PredefinedChoice and the FreeChoice.
2. WTP for the seasonal prediction is higher in the FreeChoice (averaged over all risk assessments) than in the PredefinedChoice.
3. Alignment of prediction and observation in the region the participant spent the summer increases trust in and WTP for the seasonal prediction.
4. The „same science“ intervention (WithNotice vs. WithoutNotice group) increases the spillover of trust from the seasonal to the long-run modelling. Given the pre-treatment attitude towards anthropogenic climate change (from wave 1 to make sure it is not affected by seasonal prediction or experience of the summer 2024), a change in trust in the seasonal prediction contributes to explaining trust in long-run predictions more so for the WithNotice treatment than for the WithoutNotice group.
5. Trust in long-run prediction (instrumented by Text+Map and WithNotice interventions) increases willingness to support adaptation and mitigation measures (revealed preference) as well as stated support for adaptation measures.
Randomization Method
all randomizations (experimental conditions, assignment of cash payment levels, and selection of winners in lottery) are random draws done by a computer
Assignment of experimental conditions and cash payment levels is done in real time by the survey company.
Drawing of winners is done after completion of the study by the authors.
Randomization Unit
Experimental conditions (treatments/interventions), assigment of cash payments for willingness-to-pay elicitations and drawing of winners of the lottery randomizes at the level of all participants.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
none
Sample size: planned number of observations
about 3,000 individuals - as many as we can attract from those having completed wave 1 (4.251 individuals).
Sample size (or number of clusters) by treatment arms
All interventions are independent random draws that split the group in half.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
As this is the first study we are aware of that uses this type of interventions and outcome variables, we have no priors for the variance in the outcome variables.
Supporting Documents and Materials

Documents

Document Name
Survey questionnaire (German original)
Document Type
survey_instrument
Document Description
File
Survey questionnaire (German original)

MD5: 4201993e307e59ab96a200b5244975ed

SHA1: 344a268a0a547c442278760bdb9f1fba7366d1ee

Uploaded At: October 21, 2024

IRB

Institutional Review Boards (IRBs)

IRB Name
Ethics committee for the Faculty of Business, Economics and Social Sciences at Universität Hamburg
IRB Approval Date
2024-05-17
IRB Approval Number
2024-015
Analysis Plan

Post-Trial

Post Trial Information

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials