Probabilistic Cash Rebates and Consumption Choices under Carbon Pricing

Last registered on March 12, 2026

Pre-Trial

Trial Information

General Information

Title
Probabilistic Cash Rebates and Consumption Choices under Carbon Pricing
RCT ID
AEARCTR-0017601
Initial registration date
March 11, 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
March 12, 2026, 4:47 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
Agricultural University of Athens

Other Primary Investigator(s)

PI Affiliation
Texas A&M University

Additional Trial Information

Status
In development
Start date
2026-03-20
End date
2026-04-27
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study uses an online experiment to examine how consumers respond to different types of cash rebates linked to environmentally relevant consumption choices. Participants make repeated decisions about how many units of a product to purchase under a fixed price that reflects a carbon charge, with lower consumption associated with greater environmental benefits. We compare behavior under several treatments inlcuding a no rebate control, a guaranteed cash rebate, and several probabilistic rebate schemes that differ in the likelihood of rebate payment. The study is designed to assess whether probabilistic rebates generate similar behavioral responses to guaranteed rebates, and to examine whether responses vary systematically with the probability of receiving a rebate. This experiment constitutes a new study conducted in response to reviewer comments on a prior preregistration (AEARCTR-0015002, “Lottery incentives and carbon pricing rebates”).
External Link(s)

Registration Citation

Citation
Drichoutis, Andreas and Marco Palma. 2026. "Probabilistic Cash Rebates and Consumption Choices under Carbon Pricing." AEA RCT Registry. March 12. https://doi.org/10.1257/rct.17601-1.0
Experimental Details

Interventions

Intervention(s)
Participants take part in an online decision task in which they choose how many units of a product to purchase at a fixed price that reflects a carbon-related charge. Purchasing fewer units is associated with greater environmental benefits. Depending on the treatment group, participants may receive no cash rebate, a guaranteed cash rebate, or a cash rebate that is awarded with a specified probability. The size of the rebate is fixed, but the likelihood of receiving it varies across groups. Participants make decisions in four rounds and receive feedback about outcomes after each decision round, with one round randomly selected to determine their final payoff.
Intervention Start Date
2026-03-20
Intervention End Date
2026-04-27

Primary Outcomes

Primary Outcomes (end points)
The primary outcome is the number of units purchased in Round 1, Yi1 = {0,1,2,3}, treated as continuous. This measure also provides the participants desired level for the experimenters commitment to retire carbon emission allowances equivalent to 50 pounds of CO2.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary outcomes include the average number of units purchased across all four rounds, Ybar_i = (1/4) * sum_{r=1}^4 Yir, the round-by-round choices Yir, and measures of dynamics such as Yi4 − Yi1.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The study uses a between-subjects experimental design with random assignment to one of five incentive conditions: no rebate, sure rebate, or a probabilistic rebate with a 50%, 25%, or 10% probability. Participants complete a sequence of four decision rounds. In each round, they choose how many units (0–3) of a fictitious consumption good to purchase at a fixed price. Each unit provides a privately induced monetary value to the participant, while consumption is associated with carbon emissions that can be offset by the researchers depending on participants' choices.

In rebate conditions, participants are informed that they may receive a monetary rebate linked to the number of units they choose not to consume. The structure of the rebate varies by treatment in terms of the probability with which it is paid. Participants receive feedback after each round about their own choices and the corresponding carbon offset. At the end of the experiment, one round is randomly selected to determine monetary payoffs.

Randomization is conducted at the individual level, and all participants face the same price, values, and decision environment aside from the rebate probability. The design allows comparison of choices across incentive conditions and across rounds within individuals.
Experimental Design Details
Not available
Randomization Method
Randomization is implemented by the experimental software (Qualtrics or oTree). Participants are randomly assigned to one of the treatment conditions upon entering the study by the experimental software.
Randomization Unit
Randomization is conducted at the individual participant level. Each participant is independently assigned to a treatment condition. There is no cluster-level or group-level randomization.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Not applicable. Randomization is conducted at the individual participant level, and there are no clusters.
Sample size: planned number of observations
Planned Number of Observations: 1,012 individual participants * 4 rounds = 4,048. N = 1,012 is chosen to achieve 80% power at alpha=0.05 for the primary Sure vs 10% contrast with MDE=0.20 under the prespecified unequal allocation.
Sample size (or number of clusters) by treatment arms
No rebate (control): 101 participants
Sure rebate (100%): 355 participants
50% rebate: 101 participants
25% rebate: 100 participants
10% rebate: 355 participants
Total: 1,012 participants
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
See uploaded file in the Analysis Plan section.
IRB

Institutional Review Boards (IRBs)

IRB Name
Human Research Protection Program TAMU
IRB Approval Date
2026-03-10
IRB Approval Number
MOD00003631
Analysis Plan

Analysis Plan Documents

Power analysis

MD5: c3f431d5e44dc5b58d77c65d032dea36

SHA1: 12297bbe80a76bea14afdf3b97745694165156ba

Uploaded At: March 11, 2026