Evaluating the greenhouse gas emissions of the Ontario craft beer industry: an assessment of challenges and benefits of greenhouse gas accounting
Bibliographic record
Abstract
Ontario, Canada’s cap and trade program, a provincial tool for carbon regulation, came into effect January 1, 2017. While larger companies are targeted from this policy, both large and small companies have a responsibility to reduce their greenhouse gas emissions (GHGs). Craft brewing in Ontario is growing, however industry GHGs have not been comprehensively studied. The purpose of this research is to measure the GHGs of an Ontario craft brewery, investigate the challenges and benefits to calculating GHGs, and evaluate Ontario craft brewers’ perceptions of carbon pricing policy. This research found that indirect sources account for the majority of GHGs, particularly from barley agriculture, malted barley transportation, and bottle production. Direct emissions account for the least GHGs. This research found that the main challenges in calculating Ontario brewery GHGs are secondary data availability, technical knowledge, and finances. The main benefits for breweries include sustainability marketing, and preserving the environment.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".