Changes in Quebec Cropping Practices in Response to a Carbon Offset Market: A Simulation*
Bibliographic record
Abstract
This paper investigates the response of the Quebec cropping sector to the introduction of carbon credit revenue made available through the implementation of a domestic emission trading and offset system in Canada. Eligible carbon sequestering practices investigated include adoption of moderate tillage and no‐till as well as conversion to a permanent cover crop. Monetary demand for greenhouse gas emissions offsets from the cropping sector is endogenized in the objective function of the Canadian Regional Agriculture Model (CRAM). Changes in cropping sector practices induced by the introduction of carbon prices ranging from $5 to $50 per tonne CO 2 e are compared to a baseline. Results indicate that almost all of the potential to sequester carbon in agricultural soils in Quebec would lie in the conversion to permanent cover. Le présent article a examiné la réaction du secteur des cultures du Québec à l'introduction de revenus qui proviendraient de la vente de crédits carbone à la suite de la mise en place de systèmes d'échange de droits d'émissions et de compensations pour les gaz à effet de serre (GES) au Canada. Les pratiques de séquestration du carbone admissibles analysées comprenaient le travail réduit du sol, le semis direct et l'établissement d'une couverture végétale permanente. La demande monétaire pour l'obtention de crédits compensatoires de la part du secteur des cultures a été endogénéisée dans la fonction‐objective du modèle d'analyse régionale de l'agriculture du Canada (CRAM). Les modifications de pratiques du secteur des cultures suscitées par l'introduction de prix du carbone, variant entre 5 $ et 50 $ la tonne d'équivalent‐CO 2 , ont été comparées à une base de référence. Les résultats ont montré que la quasi‐totalité du potentiel des sols agricoles du Québec à séquestrer le carbone réside dans l'établissement de couvertures végétales permanentes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".