Climate Change Adaptation in the British Columbia Wine Industry Can Carbon Sequestration Technology Lower the BC Wine Industry’s Greenhouse Gas Emissions?
Bibliographic record
Abstract
This paper measures the benefits and costs of using biochar, a carbon sequestration technology in the British Columbia (BC) wine industry. It was found that the use of biochar, produced from wine industry waste, can reduce greenhouse gas emissions, and make a significant economic contribution to the BC wine industry. An economic model was developed to calculate the value-added from each of the three sectors that comprise the BC Wine industry value chain. The model uses biochar, produced from grape prunings and pomace, as a soil amendment in the vineyards. Grapes produced from these vineyards are used to produce wine. The assumptions for each variable used in this study are drawn from the literature and prior research by the authors. In addition to achieving the industry’s sustainability goals, each sector of the wine value chain is potentially profitable, however producing biochar as a profitable independent business is likely minimal compared to what could be achieved along the value chain with increased yields of the same quality. Biochar as a soil amendment is a long-term investment for farmers with results best assessed after multiple years. Future research is needed to better understand the biochar production process as an integral part of the BC wine industry, the carbon sequestration benefits, the specific increases in long-term grape yields and wine production. Also, the industry willingness to re-evaluate and change present industry practices, and other important benefits that can be derived from marketing climate friendly wine to BC consumers needs to be understood.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".