Households’ Willingness-to-Pay for Fish Product Attributes and Implications for Market Feasibility of Wastewater-Based Aquaculture Businesses in Hanoi, Vietnam
Bibliographic record
Abstract
A choice experiment was used to assess households’ willingness-to-pay (WTP) for informational attributes (sources of water used to rear fish, and certification) of fish products in Hanoi, Vietnam. The study showed that households’ purchasing decisions are influenced by their access to information of food product attributes and ascribe an economic value to it. The results indicated that households are willing to pay 51% (USD 1.11 per kg) above the prevailing market price of fish for information to know if wastewater is used to rear the fish they consume. Similarly, they are willing to pay 20% above the prevailing market price of fish (USD 0.43 per kg) to know if freshwater is used as a rearing medium. It is important to note that the increased marginal WTP is for information on whether the fish they consume is raised in wastewater over freshwater. This supports the notion of households’ concern over the safety of consuming wastewater-raised fish. Households are also willing to pay 65% (USD 1.42 per kg) above the prevailing market price for certified fish. Based on the cost of fish certification and WTP estimates, we found a total economic benefit of USD 172 million for the implementation of a wastewater-raised fish business model in Hanoi. The demand for wastewater-raised fish is likely to be affected by households’ perception of certification by a trusted government agency, source of water used to raise the fish, age, income and household size.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".