Measuring leakage from carbon projects in open economies: a stop timber harvesting project in Bolivia as a case study
Bibliographic record
Abstract
This paper develops methods for estimating leakage from forest-based carbon projects that seek to reduce carbon emissions from timber harvesting in tropical forests. A theoretical framework is presented in which a specific country, in this case Bolivia, is treated as a supplier to the global timber market. Leakage is measured, over a 30- to 50-year time period, as the difference in net national carbon emissions from timber harvesting between the baseline case and a scenario in which some of the land is removed from the concession base. Estimates of timber leakage are made for several different assumptions about future global sequestration policies, capital constraints, demand elasticity, and deadwood decomposition rates. The results suggest that leakage could range from 5% to 42% without discounting carbon, and from 2% to 38% when carbon is discounted. Demand elasticity and wood decomposition rates have the largest effects on the leakage calculation. Leakage is lowest when demand is more elastic and wood decomposition rates are faster, and vice-versa when these conditions are reversed. Leakage appears to be sensitive to capital constraints only when project benefits are measured over a shorter time period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".