Greenhouse Gas Emissions from Canadian Agriculture: Policies and Reduction Measures
Bibliographic record
Abstract
Despite numerous national and international climate conferences, meetingsand workshops leading to various greenhouse gas (GHG) emission targets and agreements since the 1970s, total GHG emissions in Canada continue to increase. They reached 729 megatonnes of carbon dioxide equivalent (Mt CO2 eq) in 2018, with the Canadian agricultural sector contributing approximately 10 per cent of total GHGs emitted. Different regions of the country contribute different levels, face different challenges and have different capacities to address their GHG emissions. Designing climate guidelines, programs, policies and adopting best management practices (BMPs) that promote relevant local and regional adaptation and mitigation efforts is important. Mechanisms such as setting a carbon price, cap- and-trade systems and tax-based policies contribute to decreased GHG emissions. GHG emissions in Canada are regulated at the federal level via a national carbon pricing policy and provinces have set limitations on GHG emissions via pricing or taxation. Agriculture has the potential to mitigate GHG emissions by applying BMPs that reduce emissions and increase carbon storage in soils. Meanwhile, the pressure is increasing on the agricultural sector to increase production, both for local commodities and those destined for export, to feed a growing population. This paper explores agricultural policies and measures that encourage farmers and producers across Canada to reduce their GHG emissions. Specifically, national and provincial measures and implications are presented and compared to international measures and outcomes. Finally, recommendations are made for future climate policy research and adoption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".