Credited forest carbon sinks: how the cost reduction is allocated among countries and sectors
Bibliographic record
Abstract
Forest carbon sinks have been included in the Kyoto Protocol as one of the mechanisms for mitigating climate change. Consequently, credited sinks decrease the need to reduce emissions.We analyse in detail both the economywide and the sectoral effects of inclusion of carbon sinks as agreed upon in Bonn and Marrakesh for the first commitment period of 2008–2012. The focus of our analysis is the special treatment for Canada and Japan that allows them larger sinks. The analysis is performed with the multi-region computable general equilibrium (CGE) model GTAP-E. New Zealand benefits most from the inclusion of sinks as it gains large carbon sinks from afforestation. Also in Sweden, Canada and Japan the costs of achieving the emission target are considerably reduced. Of these countries, only Canada has high costs without sinks. Thus credited sinks partly reduce the difference in economic burden of achieving the Kyoto target among countries. Even though larger sinks clearly benefit Canada and Japan, their effect on other countries, either on the economywide or on the sectoral level, remains marginal. Allowing larger sinks is, indeed, of relatively minor importance for the world economy and emission reduction, compared to the US withdrawal from the Kyoto Protocol.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".