The economic sequestration potential of agricultural soils in Canada in response to a carbon market
Bibliographic record
Abstract
The Canadian greenhouse gas offset system was proposed and developed with the objective of assisting Canada in achieving its Kyoto target by means of low cost emission reduction. This study estimates the potential of agricultural soils in Canada to provide carbon credits. Carbon sequestration practices such as moderate till, no-till and perennial crop activities were considered in the analysis. Crops under different tillage regimes, hay and alfalfa were also included in the study. Simulation analysis was undertaken using the Canadian Regional Agricultural Model (CRAM) for carbon prices ranging from $5 to $100/t of CO2e. Carbon credits generated as a result of the sequestration activities were estimated by endogenizing a carbon price for the sequestration activity into the CRAM model. The analysis was done regionally, provincially, and nationally. Two scenarios were investigated; one that included tillage practices and perennial crops (Policy All) and the other that only included tillage practices (Policy Till). Cropping pattern changes, carbon sequestration levels, carbon revenues, and adoption rates were estimated in the simulation. In addition, the role of transaction costs in the offset system was also examined. The results of the simulation indicated that crop shifts towards hay and alfalfa occurred in the Policy All scenario, while practice shifts towards moderate and no-till occurred in the Policy Till scenario. Simulation analysis indicated that carbon sequestration levels vary by province and region. Among the provinces, the Prairie provinces had the highest carbon sequestration levels ranging from 50 percent under the Policy Till scenario, while under the Policy All scenario it was close to 97 percent. Nationally at a medium price of $15/t of CO2 approximately 1.08 Mt of CO2 and 0.11 Mt of CO2 were sequestered under Policy All and Policy Till scenario. When transaction costs were included in the analysis, approximately 30 to 40 percent less sequestration from the baseline was estimated. The results varied by province and region.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".