Multi-gas abatement analyse van het KyotoProtocol
Bibliographic record
Abstract
This report presents an analysis of the costs and the abatement distribution of the Kyoto Protocol on the basis of a multi-gas approach, accounting for all six Kyoto gases (CO2, CH4, N2O, HFCs, PFCs, and SF6). Results are compared to earlier analyses, in which the Protocol was evaluated taking only CO2 into account. Consistent with earlier analyses, banking of emission allowances is a necessary requirement for the creation of a viable emission trading market, resulting in an international permit price in the range of 15 and 40 euro/tCeq. In such case, of the 490 MtC-eq reduction effort under the Protocol about half in permit demanding regions is achieved through international trading. Approximately 30% of the emission reduction target is realized through implementation of sinks or by the purchase of surplus emission allowances. As several low-costs emission reduction options exist for the non-CO2 emission sources, their share in total abatements is large, while CO2 represents about 30% of the emission reductions. Among the non-CO2 greenhouse gases, the largest contribution comes from CH4, for which most reductions originate in the gas sector, mainly in the Ukraine and the Russian Federation. Other important non-CO2 abatement sources are CH4 emissions from coal production and landfills, and N2O emissions from adipic and nitric acid production, mainly for the EU-25, Japan and Canada. In terms of percentage reduction from the baseline, the reductions for CH4 and the F-gases are much larger than the reductions in the CO2 emission, while in absolute terms, the largest reduction share still comes from CO2 emissions from energy use. Compared to the CO2-only analyses, a decline of both the international permit price and the total costs can be seen along with an increase of reduction in greenhouse gas emissions (in case of banking from 250 to 400 MtC-eq). These gains are somewhat reduced if banking of emission permits is assumed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".