Carbon tax design, emissions-inequality information, and public support for progressive climate taxation: a survey experiment in the United States

Last registered on September 21, 2026

Pre-Trial

Trial Information

General Information

Title
Carbon tax design, emissions-inequality information, and public support for progressive climate taxation: a survey experiment in the United States
RCT ID
AEARCTR-0019512
Initial registration date
September 07, 2026

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 21, 2026, 7:47 AM EDT

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

Locations

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

Primary Investigator

Affiliation
UB

Other Primary Investigator(s)

PI Affiliation
PI Affiliation

Additional Trial Information

Status
On going
Start date
2026-09-07
End date
2026-12-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Greenhouse-gas (GHG) emissions are highly concentrated at the top of the economic distribution, yet the public systematically underestimates how concentrated they are. A less appreciated point is that "how concentrated" has no single answer: the emissions attributed to the richest 1% of Americans range from roughly 6% to roughly 39% of the national total depending on whether responsibility is assigned through consumption, income, or ownership of productive assets. We exploit this variation as an experimental design feature. In a pre-registered information-provision experiment on a US online sample (N ≈ 700 per arm), respondents are randomly assigned to receive one accurate, peer-reviewed statistic on the top-1% emission share under one of three accounting frames, or to a pure control. Policy preferences are then elicited through an eight-task conjoint experiment in which carbon-tax packages vary in household cost, revenue use, and whether an additional tax targets the richest households. This design allows us to test whether information dampens cost sensitivity, shifts demand toward high-emitter-targeted designs, and does so most strongly under the ownership frame. We also examine heterogeneity by prior beliefs, self-interest and partisanship, potential backlash on generic carbon-tax support, and an incentivised donation task.
External Link(s)

Registration Citation

Citation
Li Donni, Paolo , Maria Marino and Maria Teresa Silvi . 2026. "Carbon tax design, emissions-inequality information, and public support for progressive climate taxation: a survey experiment in the United States." AEA RCT Registry. September 21. https://doi.org/10.1257/rct.19512-1.0
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2026-09-07
Intervention End Date
2026-12-31

Primary Outcomes

Primary Outcomes (end points)
The primary outcome is policy preference measured by a conjoint experiment. Respondents see 8 pairs of climate-policy packages built from randomized attributes, choose the preferred package (forced choice). Choice is the primary dependent variable.

Candidate attributes drawn from prior work on carbon-tax, climate-policy design and real policy proposal in the US or at global level (Beiser-McGrath & Bernauer, 2019; Dechezleprêtre et al., 2025; Ahrens et al., 2025; Chancel & Rehm, 2026). The first attribute, carbon price level, takes five candidate levels: $0/yr (status quo), $150/yr, $600/yr, $1,500/yr and $2,500/yr. The second, use of revenue, likewise takes five: none collected (status quo), climate investments (renewables, public transit, adaptation), equal yearly cash payments to all households, yearly cash payments to the lowest-income 10%, and yearly cash payments to the lowest-income 50%. The third, additional tax, takes the following levels: no additional tax; annual tax of $150 for every ton of CO₂ emitted by the luxury, high-emission goods an individual owns or uses (e.g., private jets, yachts); annual 10% income surtax on individuals with income above $1 million; annual 2% wealth tax on individuals with wealth above $50 millions; and annual tax of $150 for every ton of CO₂ emitted by the companies and assets an individual owns.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary outcomes
• Policy-builder Respondents design their own carbon tax by selecting one level per attribute; used for the convergent-validity check
• General support — Likert (EXTRA POLICIES). Two 1–5 items: support for (a) a federal carbon tax, (b) a federal carbon tax with an extra tax on the richest households.
• Donation split. Incentivized allocation of a $50 lottery prize among self, Feeding America, and The Nature Conservancy; a real-stakes behavioral proxy for redistribution/climate preferences
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This is a pre-registered information-provision experiment with an embedded conjoint policy-choice task, fielded on a sample of US adults recruited online.

Respondents first report their prior belief about the share of total annual US greenhouse-gas emissions generated by the richest 1%, on a 0–100 slider, followed by a confidence item. They are then randomly assigned, with equal probability, to one of three arms: a pure control, which receives no statistic, and two treatment arms, each of which receives one accurate statistic on the top-1% emission share drawn from a published, peer-reviewed study, together with its source. The two treatments differ in the accounting frame used to attribute emissions to households: a consumption-based frame (Starr et al., Ecological Economics, 2023) and an ownership-based frame (Chancel & Rehm, Nature Climate Change, 2026). Treatment texts are harmonised across arms in length, structure and register: each names the source, states the attribution rule in one sentence, and gives a concrete household-level example.

A third treatment arm, using an income-based accounting frame (Starr et al., PLOS Climate, 2023), is specified in the pre-analysis plan but is not fielded in this wave, as its implementation depends on funding beyond the committed base budget. It will be added in a subsequent wave if that funding is secured.

All respondents then complete a conjoint policy-choice task. They see eight pairs of federal carbon-tax packages built from randomised attributes — annual household cost, use of revenue, and whether an additional tax applies to the richest households — and select their preferred package or the status quo of no carbon tax. Choice is the primary dependent variable.

After the conjoint, all respondents report a posterior belief. To limit anchoring, the posterior is framed as the share attributable to the remaining 99% rather than to the top 1%. Secondary outcomes include a policy-builder task in which respondents assemble their own preferred carbon tax, two Likert items on general support for a federal carbon tax with and without an additional tax on the richest households, and an incentivised allocation of a $50 lottery prize between the respondent and two charities.

Within the control arm, the timing of prior-belief elicitation is itself randomised: priors are elicited either before or after the conjoint task. This allows us to assess whether eliciting priors primes attention to inequality independently of any informational content.
Experimental Design Details
Not available
Randomization Method
Randomization is performed by computer, using the built-in randomizer of the survey software (Qualtrics), at the point of survey administration. No researcher is involved in assignment.
Randomization Unit
The unit of randomization is the individual respondent. There is no clustering: respondents are randomized independently of one another, and no group-level or session-level randomization is used.

Two levels of randomization are present. Assignment to the information treatment (and, within the control arm, assignment to the timing of prior-belief elicitation) is at the individual respondent level. Randomization of conjoint attribute levels operates at the level of the individual policy profile, nested within choice task, within respondent: each respondent completes eight choice tasks, each containing two policies, and attribute levels are drawn independently for each profile.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No clustering; the individual respondent is both the unit of randomization and the unit of analysis
Sample size: planned number of observations
700 individuals per treatment
Sample size (or number of clusters) by treatment arms
700 individuals per treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Ghent University COMMISSIE ETHIEK
IRB Approval Date
2026-08-18
IRB Approval Number
UG-EB 2026-BL