Cost to produce carbon credits through fluctuating harvest levels in British Columbia, Canada
Bibliographic record
Abstract
The forest inventory of an actively managed forest estate in the Coast Forest Region of British Columbia was used to investigate the potential of fluctuating harvest levels to produce carbon credits. Fluctuating harvest levels allowed the target harvest level to fluctuate between the baseline and a starting target harvest level (set at a lower level than the baseline) over the 25 year life of the carbon project. Carbon credits continued to be produced for 4–15 years following the harvest adjustment from the starting level to the baseline level. The production of carbon credits for the fluctuating harvest schedules was highest when the starting harvest level was held for 10–15 years, and the baseline level was held for the remainder of the carbon project life. Carbon credit production was sensitive to the initial age class structure of the forest estate, the harvest priority algorithm, the starting target harvest level, and the timing of harvest adjustment from the starting level to the baseline level. The cost to produce carbon credits using fluctuating harvest levels for the studied forest estate varied between $32.2 and $41.1 t of CO2e−1 (at 0% discount rate), which is 14%–17% lower than using a constant reduced harvest level.
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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 | 0.003 |
| 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 | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".