Consumer preferences and willingness to pay for methane reduction in the dairy sector

Last registered on June 15, 2026

Pre-Trial

Trial Information

General Information

Title
Consumer preferences and willingness to pay for methane reduction in the dairy sector
RCT ID
AEARCTR-0018877
Initial registration date
June 06, 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
June 15, 2026, 1:50 PM 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

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2026-06-15
End date
2026-07-15
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Enteric fermentation in the rumen accounts for approximately 50 percent of methane emissions
from dairy cattle, making it a primary target for mitigation efforts (Dijkstra et al., 2018; Hristov
et al., 2022). Among the most promising intervention strategies are feed additives, including
3-Nitrooxypropanol (3-NOP). However, the reported methane reduction potential varies across
studies as a function of dosage, diet composition, and duration of treatment. As a result, the
reported effectiveness in reports about methane reduction potentials tends to reflect a range
of possible methane reductions with substantial uncertainty (de Oliveira et al., 2025; Kebreab
et al., 2023, 2025).
Emerging evidence suggests that consumers exhibit a positive WTP for low methane food.
Davidson et al. (2025) find that consumers are willing to pay a premium for seaweed-based over 3-
NOP-based ground beef, yet associate each additive with only a single point estimate of methane
reduction potential (95 and 40 percent, respectively), leaving the effect of variability of methane
reductions unexamined. A related concern arises from another study design: Luke & Tonsor
(2025) report the strongest relative preference for seaweed as a methane reduction strategy, but
describe 3-NOP as synthetic and seaweed as natural, precluding identification of whether the
observed preference reflects genuine beliefs about methane reduction efficacy or a naturalness
halo effect. Disentangling these mechanisms is essential for drawing reliable conclusions for the
market potential of feed additives. On the supply side, Gottlieb & Rommel (2025) demonstrate
that the probability of adoption of 3-NOP increases with perceived effectiveness and advise from
farm advisors for dairy farmers in Sweden.
The range in scientific estimates of 3-NOP’s effectiveness, combined with the spread of
misinformation across media platforms and varying levels of trust in dairy stakeholders, may
collectively shape how consumers value methane reducing feed additives. To explore this, we
examine whether exposure to varying efficacy information, media consumption habits, and trust
in relevant information providers influence consumer WTP for milk produced with 3-NOP. We
adapt the framework of Kumar et al. (2023), who identify the causal effect of uncertainty on
economic decisions by measuring prior and posterior beliefs around randomized information
treatments. Belief updating is measured by regressing posterior beliefs on prior beliefs and
treatment assignment, where the coefficient on the treatment indicator captures the shift in
posteriors conditional on the prior (Haaland et al., 2023; Coibion et al., 2024; Dietrich et al.,
2024). We apply a Best Worst Scaling approach for examining relative preference shares for
emission reducing policies. Due to substantial differences of public preferences and trust in
relevant stakeholders across high income countries, we intend to compare findings of selected
survey parts with researcher teams in other high income countries (Marette et al., 2021; Fesenfeld
et al., 2020).
Accordingly, we formulate the following research questions:
1. How does information provision about methane reduction effectiveness shape consumer
WTP for 3-NOP milk?
2. To what extend do consumers update their beliefs when provided with scientific informa-
tion about methane reduction effectiveness? How does media consumption and trust in
information providers influence the degree of updating beliefs?
3. What are relative preferences for strategies to reduce enteric methane emissions from the
U.S. dairy industry, and how do these preferences align with the subjective priorities of
key U.S. dairy industry stakeholders?
4. Do prior beliefs about the effectiveness in methane reducing technologies vary by countries?
External Link(s)

Registration Citation

Citation
Wehner, Jasmin. 2026. "Consumer preferences and willingness to pay for methane reduction in the dairy sector." AEA RCT Registry. June 15. https://doi.org/10.1257/rct.18877-1.0
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2026-06-15
Intervention End Date
2026-07-15

Primary Outcomes

Primary Outcomes (end points)
RQ1: WTP for 3-NOP
RQ2: Measure of belief updating
RQ3: Preference Share
Primary Outcomes (explanation)
RQ1: Feed additive, Animal Welfare certification, Farm Type
RQ2: Prior beliefs, information treatments, media consumption,
trust in organizations, Knowledge questions
RQ3: Demographics, Media consumption, and trust in organizations

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The data collection will start in June 2026. The survey has a between-subjects design. Re-
spondents will be randomly assigned to one out of the two treatment sets that vary only by
the inclusion of a reminder in the choice experiment. Each information set consists of three
information treatments. Respondents will receive an introduction that is common to all.
Experimental Design Details
Not available
Randomization Method
Randomization done in online survey tool
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1800 individuals
Sample size: planned number of observations
1800 individuals
Sample size (or number of clusters) by treatment arms
250 per information treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Institutional Review Board for Human Participants Cornell University
IRB Approval Date
2026-06-03
IRB Approval Number
IRB0150765