CO2 Emissions from Asheville’s Craft Brewing Industry
Bibliographic record
Abstract
This study examined the relationship between two foundational identities of Asheville, North Carolina — its environmentally mindful community and the craft brewing industry. We quantified CO2 emissions from the fermentation process of brewing beer at several local breweries in Asheville. Additionally, this project determined whether emissions from fermentation were substantial compared to CO2 emissions from the breweries’ electricity usage. We analyzed data from four breweries of varying size. Our results showed that the emissions from fermentation were small compared to emissions from electricity usage. Total CO2 emissions from electricity usage from all four breweries were slightly less than 180,000 tonnes compared to just over 600 tonnes from the fermentation process. Emissions from fermentation were less than 0.5% of emissions from electricity usage at all four breweries. While 600 tonnes of CO2 may not seem substantial, this study was limited to just four of the more than 35 breweries in Asheville as of 2016. Given the size and rate of growth of the craft brewing industry in the region, it is too soon to dismiss fermentation emissions as unimportant to Asheville’s total carbon footprint.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".