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Record W4407341489 · doi:10.1787/0587c663-en

How do consumers interact with environmental sustainability claims on food?

2025· report· en· W4407341489 on OpenAlexfundno aff
Koen Deconinck, Céline Giner, May Hobeika, Céline Nauges

Bibliographic record

VenueOECD food, agriculture and fisheries working papers · 2025
Typereport
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsnot available
FundersGovernment of Canada
KeywordsSustainabilityBusinessEcologyBiology

Abstract

fetched live from OpenAlex

This paper presents new evidence on how consumers interact with sustainability claims on food products, based on a survey of 37 000 consumers in 40 countries. Respondents are generally most interested in natural, locally produced, eco-friendly and organic claims. Trust and broader attitudes and beliefs regarding the environment play an important role in shaping consumers’ willingness to pay more for products with a sustainability claim. For most claims, willingness to pay is also higher among consumers with higher incomes and education. Even after controlling for these factors, there are significant differences among countries. In some countries, people are generally distrustful of claims, while in others people have greater trust. This suggests that consumer trust may be shaped less by the specifics of a claim and more by country-specific factors. This interpretation is consistent with data suggesting that consumers have only a limited understanding of what different claims mean.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0000.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.180
Teacher spread0.170 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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