Mitigation scenarios in a world oriented at sustainable development: the role of technology, efficiency and timing
Bibliographic record
Abstract
Two different mitigation scenarios for stabilising carbon dioxide concentration at 450 ppmv by 2100 have been developed, based on the recently developed B1 baseline scenario (part of the IPCC Special Report on Emission Scenarios). In both mitigation scenarios, a global uniform carbon tax has been applied as a proxy of pressure on the system to induce a variety of mitigation measures—assuming the presence of some international mechanism for globally cost-efficient implementation of such measures. 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 from the B1 baseline scenario is technically feasible. In the first quarter/second quarter of this century most of the reduction will come from energy-efficiency and fuel switching options; later on the introduction of carbon-free supply options will account for the bulk of the required reductions. Second, postponing measures foregoes the benefits of learning-by-doing, and, as a result, an early action strategy will at low discount rates lead to reduced mitigation costs compared to delayed response. 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, while overall costs seems to be limited, there are large differences in costs and benefits for individual regions and sectors for instance in terms of redirection of investments, changing fuel trade patterns and changing energy expenditures.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".