Macroeconomic impacts of reducing greenhouse gas emissions from Canadian agriculture
Bibliographic record
Abstract
Abstract Canada's commitment under the Kyoto Protocol is to reduce its greenhouse gas (GHG) emissions by 6% of its 1990 levels. Each industrial sector is investigating alternative technologies, production and management practices that can decrease their GHG emissions. The macroeconomic impacts of four mitigation strategies to reduce GHG emissions from Canada's agriculture sectors were measured using an input-output model. The size of the GHG reduction from each mitigation strategy depended on whether agricultural soils were included as a carbon (C) sink. Including agricultural soils as a C sink impacts on the absolute amount of GHG emissions that must be reduced and the relative importance of the various mitigation strategies. This will be a key factor in policy development. Only one strategy, improving forage quality by 15%, had positive macroeconomic impacts in all situations. It was projected that this strategy would increase industrial output by $106.97 M (M = million; all $ Canadian), gross domestic product at factor cost (GDP) by $45.51 M and employment by 689 jobs. This strategy decreased GHG emissions by 0.07% below the ‘business as usual’ (BAU) situation when sinks were included. Increasing the adoption of zero-till farming had a positive macroeconomic impact only when the industrial sector effects were included. However, when household and industrial-sector impacts were combined, the results were decreases in industrial output of $286.90 M, GDP of $55.98 M and employment by 769 jobs. The mitigation strategy decreased GHG emissions by 3.06% below the BAU situation when sinks were included in the estimate. Improved soil nutrient management through more efficient use of N fertilizer had a negative net impact on the economy. This mitigation strategy had a direct impact on the agriculture and the fertilizer sectors, resulting in net decreases in industrial output of $70.76 M, GDP of $43.38 M and employment of 518 jobs. It was estimated that this mitigation strategy would decrease GHG emissions by 1.37% below the BAU situation. The last mitigation strategy was a permanent plant cover program. This generated the largest negative impact on the economy. It was projected to decrease industrial output by $1192.63 M, GDP by $392.17 M and employment by 6155 jobs. The strategy decreased GHG emissions by 1.73% below the BAU situation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".