Bibliographic record
Abstract
The Pan-Canadian Framework was implemented in 2016 to help meet emission reduction targets set out by the Paris Agreement. Carbon pricing is at the foundation of this framework, where Alberta has used a carbon-credit market to reduceemissions from large-scale emitters. Agricultural producers voluntarily participate in these markets through agricultural carbon offset protocols; regulated emitters can purchase agricultural carbon credits to meet their emission reduction requirements. The main goal of these agricultural protocols is to reduce on-farm emissions through the adoption of best management practices (BMPs), alongside providing producers with the potential benefit of earning additional revenue by selling carbon credits on the market. While producers have participated in the market for quite some time, the impact of the market on Alberta agricultural producers is unknown. The main objective of this paper is to understand this impact by analyzing two considerations: 1) emission reductions (or removals) from agricultural protocols; and 2) economic benefits to producers from participating in the carbon offset market. After a case study ofcurrent agricultural carbon offset protocols, the results suggest producers are mainly participating for the economic benefits stemming from the adoption of BMPs, rather than the potential revenue from selling carbon credits on the market. Results also show protocols have high emission reduction potential, but this analysis was limited due to a lack of publicly available data. The most significant observation is that the majority of protocol emission reductions come from one protocol, the Conservation Cropping Protocol. The remaining agricultural protocols have seen minimal uptakein participation, specifically from livestock producers, which is concerning given the retirement of the Conservation Cropping Protocol on December 31, 2021. The main consideration will be addressing current protocol shortcomings to ensure producers are willing and able to participate in the market.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".