Economics of afforestation for carbon sequestration in western Canada
Bibliographic record
Abstract
The Kyoto Accord on climate change requires developed countries to achieve CO 2 -emissions reduction targets, but permits them to charge uptake of carbon (C) in terrestrial (primarily forest) ecosystems against emissions. Countries such as Canada hope to employ massive afforestation programs to achieve Kyoto targets. One reason is that foresters have identified large areas that can be afforested. In this paper, we examine this forestry option, focusing on the economics of afforestation in western Canada. In particular, we develop marginal C uptake curves and show that much less land is available for afforestation than would be the case if economics is ignored. We conclude that, while afforestation is a feasible weapon in the greenhouse policy arsenal, it might not be as effective on an economic basis as many forest-sector analysts make out. Key words: Climate change, economics of afforestation, Kyoto Accord
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".