A comparative assessment of the financial costs and carbon benefits of REDD+ strategies in Southeast Asia
Bibliographic record
Abstract
REDD+ holds potential for mitigating emissions from tropical forest loss by providing financial incentives for carbon stored in forests, but its economic viability is under scrutiny. The primary narrative raised in the literature is that REDD+ will be of limited utility for reducing forest carbon loss in Southeast Asia, while the level of finance committed falls short of profits from alternative land-use activities in the region, including large-scale timber and oil palm operations. Here we assess the financial costs and carbon benefits of various REDD+ strategies deployed in the region. We find the cost of reducing emissions ranges from $9 to $75 per tonne of avoided carbon emissions. The strategies focused on reducing forest degradation and promoting forest regrowth are the most cost-effective ways of reducing emissions and used in over 60% of REDD+ projects. By comparing the financial costs and carbon benefits of a broader range of strategies than previously assessed, we highlight the variation between different strategies and draw attention to opportunities where REDD+ can achieve maximum carbon benefits cost-effectively. These findings have broad policy implications for Southeast Asia. Until carbon finance escalates, emissions reductions can be maximized from reforestation, reduced-impact logging and investing in improved management of protected areas. Targeting cost-efficient opportunities for REDD+ is important to improve the efficiency of national REDD+ policy, which in-turn fosters greater financial and political support for the scheme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".