Improving citizens' support for carbon taxes in China

Last registered on January 03, 2023


Trial Information

General Information

Improving citizens' support for carbon taxes in China
Initial registration date
December 20, 2022

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
January 03, 2023, 4:54 PM EST

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



Primary Investigator

Ruhr University Bochum

Other Primary Investigator(s)

PI Affiliation
Ruhr University Bochum
PI Affiliation
Ruhr University Bochum

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
We aim to understand how to communicate carbon tax and redistribution policies in order to improve citizens' support for carbon taxes. Our experiment design consists of two parts: (i) the explanation of carbon taxes (a basic explanation, the externality nature of carbon emission, the efficiency, and re-distribution), and (ii) information on the gains and losses of a household under a certain redistribution scheme depending on the per capita income and carbon footprint. A random set of information on the explanation of carbon taxes and the information on the gains and losses would be delivered to the respondents, before they decide whether they accept such a policy. The answers are incentivized to reduce measurement errors.
External Link(s)

Registration Citation

Feldhaus, Christoph, Andreas Loeschel and Yuanwei Xu. 2023. "Improving citizens' support for carbon taxes in China." AEA RCT Registry. January 03.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Citizens' acceptance of carbon taxes via self-reports in an online survey.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Experiment 1 - how to explain carbon taxes. There would be four groups, with the first group receiving the basic explanation of carbon taxes from the IPCC, the second group receiving the basic information + the externality nature of carbon emission, the third group receiving the basic + externality + the efficiency of carbon taxes, and the last group receiving the basic + externality + efficiency + the redistribution of carbon tax revenues. The four pieces of information regarding carbon taxes are extracted directly from the IPCC and the World Bank. We expect that more detailed information increases people's acceptance of carbon taxes.

Experiment 2 - how to inform people gains and losses under a certain redistribution scheme when carbon taxes are introduced. We design three scenarios of redistribution of carbon taxes: (i) per capita uniform redistribution, (ii) per capita uniform redistribution to the 70% poorest households, and (iii) equity-based redistribution, that 80% of the household carbon emissions in the previous year would be compensated for free, and carbon taxes are charged regarding the exceeding amount of emission. Under each scenario, there will be 3 groups. One group serves as the control group only receiving information on the redistribution scheme, another one would receive information on the gains, and the last one would receive information on both gains and losses (depending on the per capita household income and carbon footprint). We expect that households would usually believe that when the tax is introduced, the household expenditure would rise. However, by providing information on the gains that the households would receive some tax revenue back, some of them could be net winners from the tax. Thus, by providing more detailed information on the personalized gains and/or losses, the support for carbon taxes could be increased.
Experimental Design Details
Randomization Method
Randomizaton done in the survey by a computer.
Randomization Unit
Randomization at the individual level.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
3,000 individuals
Sample size (or number of clusters) by treatment arms
Experiment 1, 750 basic, 750 basic+externality, 750 basic+externality+efficiency, 750 basic+externality+efficiency+redistribution.
Experiment 2, 1000 control, 1000 gains, 1000 gains+losses.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials