Who gains from credited forest carbon sinks: Finland and other Annex I countries in comparison
Bibliographic record
Abstract
In the Kyoto Protocol carbon sinks became a tool for releasing the economic burden of achieving the emission target. For Finland, credits from carbon sinks might be important since the amount of carbon sequestered in total forest area has been large relative to total emissions. It was agreed in Bonn, however, that only part of the sinks resulting from forest management is allowed to be credited. Here we use the multi-region computable general equilibrium model GTAP-E to analyse (i) which countries benefit from carbon sinks, (ii) how benefits are distributed within the economy, (iii) whether carbon sinks reduce the economic burden for Finland as such and relative to other countries and (iv) what is the economic importance of the larger sinks allowed for Japan and Canada, both for themselves and for other countries. For Finland, where the costs of achieving the emission target were already originally high, the inclusion of credited forest carbon sinks provides only a slight release from economic burden in the first commitment period. The credited carbon sink decrease the necessary emission reduction only slightly because the amount to be credited in the first commitment period is low, and a part of that is used to compensate the source of carbon under Article 3.3. New Zealand gains most from the inclusion of sinks; but Sweden, Canada and Japan also benefit considerably. Of these countries, only Canada has high costs without sinks. Thus credited sinks only partly reduce the difference in economic burden of achieving the Kyoto target among countries. Even though country-specific sinks clearly benefit Canada and Japan, their effect on other countries, either on the economywide or on the sectoral level, remains marginal. For example, paper and pulp industry in Finland does not seem to lose competitiveness. Sectors that are fossil fuel intensive, like the iron and steel or the chemical industry, benefit from the inclusion of sinks while the other sectors, like machinery, may suffer.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".