Financing REDD in developing countries: a supply and demand analysis
Bibliographic record
Abstract
Reducing emissions from deforestation and forest degradation (REDD) in developing countries has been at the centre of negotiations on a renewed international climate regime. Developing countries have made it clear that their ability to engage in REDD activities would depend on obtaining sufficient and stable funding. Two alternative REDD financing options are examined to find possible ways forward: financing through a future compliance market and financing through a non-offset fund. First, global demand for hypothetical REDD credits is estimated. The demand for REDD credits would be highest with a base year of 1990, using gross—net accounting. The key factors determining demand in this scenario are the emission reduction targets and the allowable cap. A proportion of emission reduction targets available for offsets lower than 15% would fail to generate a sufficient demand for REDD. Also examined is the option of financing REDD through a fund. Indirectly linking the replenishment of a REDD fund to the market is a promising mechanism, but its feasibility depends on political will. The example of overseas development assistance for global health indicates the conditions for possible REDD financing. The best financial approach for REDD would be a flexible REDD mechanism with two tracks: a market track serving as a mitigation option for developed countries, and a fund track serving as a mitigation option for developing 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".