How Could Developing Countries Participate in Climate Change Prevention: The Clean Develoment Mechanism and Beyond
Bibliographic record
Abstract
An Agreement on CDM rules in important both for industrialised and developing countries. As a flexibility mechanism, it will allow industrialised countries to benefit from low cost emission reductions but the CDM, as a main goal, should stimulate a more sustainable economic development in DCs. The CDM is the sole instrument, with GEF, proposed for DCs' participation in climate change prevention. This situation satisfies a majority of DCs, but CDM may not offer sufficient perspectives for some countries with rapid industrialisation given the huge economic stakes linked to the creation of a carbon credits market between Annex I countries. The operationality of the CDM is not yet established and important questions, such as environmental additionality, are still unresolved. Here we first examine the rules in order to validate project additionality and its possible consequences on the effectiveness and the scope of the mechanism. The different reactions of major DCs groups on the structure of the mechanism will then be analysed. This will lead us to examine the possibilities to enlarge participation of DCs in climate change prevention according to the apparent wish of semi-industrialised countries.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".