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Record W2342924510 · doi:10.1111/cjag.12102

Valuing the Willingness‐to‐Pay for Sustainable Seafood: Integrated Multitrophic versus Closed Containment Aquaculture

2016· article· en· W2342924510 on OpenAlexafffundvenue
Winnie Yip, Duncan Knowler, Wolfgang Haider, Ryan Trenholm

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAquacultureFisheryFish farmingWillingness to payAgricultural scienceBiologyEcologyFish <Actinopterygii>Economics

Abstract

fetched live from OpenAlex

Awareness of the environmental problems associated with conventional finfish aquaculture has stimulated interest in more sustainable production methods. For example, integrated multitrophic aquaculture (IMTA) combines the culturing of fish and extractive aquaculture species at one site to simulate a balanced natural system. In contrast, closed containment aquaculture (CCA) separates farming from the natural marine environment using closed water tanks on land or in water. We explore consumer preferences for salmon produced with IMTA or CCA rather than conventional technology and pose two questions: how aware of IMTA and CCA are salmon consumers on the U.S. West Coast and what are they willing to pay for salmon produced with these methods? Using a discrete choice experiment, we estimate marginal willingness‐to‐pay of 39.0% and 15.7% for IMTA and CCA, respectively, as a premium added to the price of conventionally farmed Atlantic salmon. Results using latent class analysis show that consumers with a strong preference for wild salmon have high marginal values for farmed salmon produced with IMTA or CCA, but the average consumer from this group would be unlikely to purchase it. Overall, 44.3% and 16.2% of the respondents preferred IMTA or CCA to conventional salmon farming, respectively, and IMTA was preferred to CCA when respondents were asked to choose one. La sensibilisation aux problèmes environnementaux liés à l’élevage de poissons classique suscite un intérêt pour des méthodes de production plus durables. Par exemple, l'aquaculture multitrophique intégrée (AMTI) combine l’élevage de poissons et d'espèces d'extraction sur un même site afin d'imiter un écosystème naturel équilibré. En revanche, l'aquaculture en parc clos (APC) consiste en un élevage hors du milieu marin naturel à l'aide de réservoirs étanches installés sur le sol ou dans l'eau. Dans la présente étude, nous avons analysé les préférences des consommateurs pour le saumon issu de l'AMTI ou de l'APC et celui issu de l’élevage classique. Nous nous sommes également posé deux questions : À quel point les consommateurs de saumon de la côte Ouest des États‐Unis connaissent‐ils l'AMTI et l'APC, et combien sont‐ils prêts à payer pour obtenir du saumon issu de ces méthodes de production? À l'aide de la méthode des choix discrets, nous avons obtenu un consentement à payer marginal de 39,0 % pour l'AMTI et de 15,7 % pour l'APC, de plus que le prix du saumon de l'Atlantique issu de l’élevage classique. Les résultats de l'analyse des classes latentes ont montré que les consommateurs qui affichaient une préférence marquée pour le saumon sauvage avaient des valeurs marginales élevées pour le saumon issu de l'AMTI ou de l'APC, mais qu'il était peu probable que le consommateur moyen de ce groupe en achète. Dans l'ensemble, les répondants ont indiqué préférer le saumon issu de l'AMTI ou de l'APC (44,3 % et 16,3 % respectivement) au saumon issu de l’élevage classique. De plus, s'ils avaient à choisir entre le saumon issu de l'AMTI ou de l'APC, ils choisiraient le saumon issu de l'AMTI.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.997
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.058
GPT teacher head0.176
Teacher spread0.118 · 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

Citations35
Published2016
Admission routes3
Has abstractyes

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