Economics of Forest Ecosystem Carbon Sinks: A Review
Bibliographic record
Abstract
Carbon terrestrial sinks are seen as a low-cost alternative to fuel switching and reduced fossil fuel use for lowering atmospheric CO2. In this study, we review issues related to the use of terrestrial forestry activities to create CO2 offset credits. To gain a deeper understanding of the confusing empirical studies of forest projects to create carbon credits under Kyoto, we employ meta-regression analysis to analyze conditions under which forest activities generate CO2-emission reduction offsets at competitive "prices." In particular, we examine 68 studies of the costs of creating carbon offsets using forestry. Baseline estimates of costs of sequestering carbon are some US$3–$280 per tCO2, indicating that the costs of creating CO2-emission offset credits through forestry activities vary wildly. Intensive plantations in the tropics could potentially yield positive benefits to society, but in Europe similar projects could cost as much as $195/tCO2. Indeed, Europe is the highest cost region, with costs in the range of $50–$280 per tCO2. This might explain why Europe has generally opposed biological sinks as a substitute for emissions reductions, while countries rush to finance forestry sector clean development mechanism projects. In Canada and the U.S., carbon sequestration costs range from a low of about $2 to nearly $80 per tCO2. One conclusion is obvious: some forestry projects to sequester carbon are worthwhile undertaking, but certainly not all.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".