A meta-regression analysis of forest carbon offset costs
Bibliographic record
Abstract
The main focus of efforts to mitigate climate change is on the avoidance of fossil fuel emissions. However, the Kyoto Protocol rules permit the use of forestry activities that create carbon offset credits. These could obviate the need for lifestyle-changing reductions in fossil fuel use. Therefore, it is necessary for policy purpose to determine the cost effectiveness of creating forest sink carbon credits. In this study, meta-regression analyses with 1047 observations from 68 studies are used to determine factors that affect carbon sequestration costs. Results indicate that forest plantations and use of harvested biomass for energy make forestry activities more attractive. It also turns out that forestry activities are competitive with emissions reduction in tropical regions and, perhaps, boreal regions but certainly not in Europe. Finally, the regression estimates are used to project the potential costs of carbon uptake for various forest management scenarios — costs range from 0 to over 200 US$/t CO 2 .
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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 | 0.055 | 0.091 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.058 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".