Scenario's gericht op koolstof dioxide emissiereductie: de rol van technology, efficientie en het tijdstip van actie
Bibliographic record
Abstract
Two different mitigation scenarios for stabilising carbon dioxide concentration at 450 ppmv by 2100 have been developed, founded on the recently developed IPCC-SRES B1 baseline scenario. In both, a global uniform carbon tax was used to induce a variety of mitigation measures - assuming the presence of an international mechanism for cost-efficient implementation of measures (for instance emission trading or the Clean Development Mechanism). The two scenarios differ in the timing of mitigation action (early action versus delayed response). Analysis of the scenarios has led to the following findings. First, stabilisation at a carbon dioxide concentration of 450 ppmv is technically feasible (requiring a 40% reduction of cumulative emissions between 2000 and 2100 compared to the baseline). Second, in the first quarter/second quarter of this century most of the reduction will come from energy efficiency and fuel-switching options, while the introduction of carbon-free supply options will later account for the bulk of the required reductions. Third, postponing measures foregoes the benefits of learning-by-doing, and, as a result, early-action seems to be a more attractive strategy for stabilisation at 450 ppmv than delayed response (about 30% lower cumulative investments between 2000 and 2100). Fourth, the most difficult period for the mitigation scenarios is the 2010-2040 period (exact timing depends on early action or delayed response), when 'bending the curve' towards a lower carbon emission system will have to be initiated. Finally, the real obstacles for implementing mitigation policies are related to (the large differences in) costs and benefits for individual countries and sectors. Hence, we believe that much political ingenuity will be required to find a method for fair burden sharing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".