Quebec hog producers' willingness to accept carbon credit revenue for adopting management practices that reduce greenhouse gas emissions
Bibliographic record
Abstract
Canada's commitment to the Kyoto Protocol provides agricultural producers with an opportunity to supply carbon offset credits to a domestic carbon market and receive revenue from the sale of these credits. This study employed the multiple bounded discrete choice method to estimate Quebec hog producers' willingness to accept compensation to adopt two management practices that reduce carbon emissions; i.e. reduced protein feeding and adopting a manure storage cover. The average willingness to accept compensation for reduced protein feeding was $46.71 per tonne of CO2 equivalent and for the manure storage cover was $40.40 per tonne of CO2 equivalent. In addition, hog producers were asked what cost they would be willing to bear if they received $20 per animal unit in carbon offset credit revenue. The average cost they were willing to bear was $11.88. Key factors that influenced producers' decisions were identified. Results can be used to improve the institutional rules and public policy associated with developing a domestic carbon emission trading mechanism. Starting-point and sequencing bias were tested for with the convolution approach. Starting-point bias was found in all the hypothetical situations; while sequencing bias was not found.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".