AEA RCT Registry

Trial Information

General Information

Title

Acceptance of Different Revenue Recycling Schemes for Carbon Pricing

RCT ID

AEARCTR-0014418

Initial registration date

September 23, 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

September 26, 2024, 12:30 PM 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

Name

Daniela Flörchinger

Affiliation

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

PI Affiliation

PI Name

PI Affiliation

Additional Trial Information

Status

In development

Start date

2024-09-25

End date

2025-06-30

Keywords

Environment & Energy

Additional Keywords

carbon pricing, information provision

JEL code(s)

D83, H23, H30, Q58

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

Carbon pricing is an effective and efficient measure to reduce carbon emissions. Previous research has shown that acceptance of carbon pricing is highest when its revenues are earmarked for green spending. This is because many people believe that the effectiveness in reducing carbon emissions depends on revenue use. In this study, we test whether explaining that carbon pricing increases the relative price of carbon-intensive products and thereby stirs demand towards more climate-friendly alternatives can increase acceptance and perceived effectiveness of revenue recycling schemes other than green spending. Our randomized experiment is embedded in a large-scale online survey (N=4,000).

External Link(s)

Registration Citation

Citation

Eßer, Jana, Daniela Flörchinger and Manuel Frondel. 2024. "Acceptance of Different Revenue Recycling Schemes for Carbon Pricing." AEA RCT Registry. September 26. https://doi.org/10.1257/rct.14418-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

This study investigates whether explaining how carbon pricing works can increase support and perceived effectiveness of revenue recycling schemes other than green spending. To this end, we conduct an online survey-experiment among 4,000 respondents.
The sample is randomly split into a treatment group and a control group of equal size.

At the beginning of the experiment, both groups are asked about their familiarity with German carbon pricing, which was introduced in 2021.
After that, the treatment group receives an information treatment about the functioning and goals of German carbon pricing in the form of a quiz: In a set of multiple-choice questions, respondents are asked to select the correct answers. After each question, they are then provided with an explanation of the correct answer. By structuring the information treatment as a quiz, we aim to increase the respondents' engagement with the information provided, compared to simply providing it as a text.

Subsequently, we present all respondents of the treatment and control group with six suggestions of how the revenues generated by carbon pricing can be used. The suggestions (presented in random order) are:
- The expansion of renewable energies or climate-friendly transportation systems (green spending)
- An annual lump-sum repayment
- Support of low-income households
- Reduction of electricity prices by lowering the grid charge
- Integration into the overall government budget
- Reduction of government debt.

Afterwards, study participants are asked to state on a five-point Likert scale whether they agree with the suggested revenue use and whether they consider it effective.

Intervention Start Date

2024-09-25

Intervention End Date

2024-10-15

Primary Outcomes

Primary Outcomes (end points)

Difference between agreement of revenue use for green spending and annual lump-sum repayment, support of low-income households, reduction of electricity prices, integration into the overall government budget, reduction of government debt.

Difference in perceived effectiveness of carbon pricing when its revenue is used for green spending and annual lump-sum repayment, support of low-income households, reduction of electricity prices, integration into the overall government budget, reduction of government debt.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

This study investigates whether explaining how carbon pricing works can increase support and perceived effectiveness of revenue recycling schemes other than green spending. To this end, we conduct an online survey-experiment among 4,000 respondents.
The sample is randomly split into a treatment group and a control group of equal size.

At the beginning of the experiment, both groups are asked about their familiarity with German carbon pricing, which was introduced in 2021.
After that, the treatment group receives an information treatment about the functioning and goals of German carbon pricing in the form of a quiz: In a set of multiple-choice questions, respondents are asked to select the correct answers. After each question, they are then provided with an explanation of the correct answer. By structuring the information treatment as a quiz, we aim to increase the respondents' engagement with the information provided, compared to simply providing it as a text.

Subsequently, we present all respondents of the treatment and control group with six suggestions of how the revenues generated by carbon pricing can be used. The suggestions (presented in random order) are:
- The expansion of renewable energies or climate-friendly transportation systems (green spending)
- An annual lump-sum repayment
- Support of low-income households
- Reduction of electricity prices by lowering the grid charge
- Integration into the overall government budget
- Reduction of government debt.

Afterwards, study participants are asked to state on a five-point Likert scale whether they agree with the suggested revenue use and whether they consider it effective.

Experimental Design Details

Not available

Randomization Method

randomization by computer

Randomization Unit

individual

Was the treatment clustered?

No

Experiment Characteristics

Sample size: planned number of clusters

0

Sample size: planned number of observations

4,000 individuals

Sample size (or number of clusters) by treatment arms

2,000 individuals in the treatment group and 2,000 individuals in the control group

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

see pre-analysis plan for an extensive description of the minimum detectable effect size.

Supporting Documents and Materials

IRB

Institutional Review Boards (IRBs)

IRB Name

IRB Approval Date

IRB Approval Number

Analysis Plan