Greenhouse gas emissions from peat extraction in Canada : a life cycle perspective
Bibliographic record
Abstract
This study uses life cycle analysis to examine the net greenhouse gas (GHG) emissions from the activities of the peat industry in Canada for the period 1990 to 2000. GHG accounting is undertaken for (1) land use change, (2) peat extraction and processing, (3) the transport of peat to market by truck, train and ship, and (4) the in situ decomposition of extracted peat. The emission estimates were based on results from GHG accounting models using data derived from scientific literature, government and industry statistics, and the responses to a questionnaire sent to Canada's peat establishments. The questionnaire, designed to obtain information on peat extraction methods, land and fuel use, as well as the transportation of peat, had a response rate representing 69% of Canada's total peat production in the year 2000. Results indicate that 540 600 tonnes of greenhouse gases were emitted in 1990 and 893 300 tonnes were emitted in the year 2000 (emission figures are measured in CO2 equivalents using a 100-year time horizon). Peat decomposition was by far the largest source of GHG emissions, averaging 70.6% of total emissions during the eleven-year period from 1990 to 2000. Greenhouse gases from land use change averaged 14.7%. An average of 10.4% of total emissions resulted from the transport of peat to market, while GHGs from extraction and processing averaged 4.3%. Predictions of the annual GHG emissions from the peat industry, assuming a "business as usual" context, were produced for the years 2001 to 2012. These figures were compared with those resulting from various greenhouse gas reduction scenarios.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".