Estimating Carbon Footprint and Environmentally Adjusted Productivity of Ontario Dairy Farms
Bibliographic record
Abstract
Even though the Canadian dairy industry has one of the lowest carbon footprints globally, it still comprises a significant percentage of the total emissions from agriculture. Productivity based on conventional production functions does not account for the socially undesired Greenhouse Gas emitted in dairy farming. Using the input-oriented directional distance function, I estimated the environmentally adjusted efficiency and productivity of Ontario dairy farms from 2000 to 2020. First, I estimated the annual GHG emission from each dairy farm based on the life cycle assessment methodology guidelines suggested by the International Dairy Federation. I found that the carbon footprint for Ontario dairy farms decreased by 14% over the study period. Second, I estimated the standard and environmentally adjusted efficiency and productivity. Productivity growth was decomposed into two sources: efficiency change and technical change. The results indicated that technological change had a higher contribution than efficiency change to overall productivity growth.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".