REDD+ Contribution to Well-Being and Income Is Marginal: The Perspective of Local Stakeholders
Bibliographic record
Abstract
In addition to being a global strategy for reducing greenhouse gas emissions from tropical deforestation, Reducing Emission from Deforestation and Degradation (REDD+) intends to protect and improve the well-being and income of local stakeholders. The intention is to provide livelihood support in exchange for local stakeholder involvement in protecting forests. Eleven years after the launch of REDD+ at COP 11 in Montreal, the degree of success in meeting well-being and income goals is examined in six countries (Brazil, Peru, Cameroon, Tanzania, Indonesia, Vietnam) at 22 initiatives, 149 villages, and approximately 4000 households through a counter-factual approach. Half the villages and households are inside and half are outside the sphere of REDD+. Measurements are made at two points in time (2010–2012, and 2013–2014). This paper focuses on measurement of the subjective perception of local stakeholders. The study finds that REDD+ has not contributed significantly to perceived well-being and income sufficiency, in spite of the fact that most households have not only engaged with REDD+ interventions, but view them favorably. REDD+’s limited achievement to date is due to unavailability of funding, among other obstacles. Recommendations are made for enhanced attention to well-being and income sufficiency in the event that REDD+ eventually takes off.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".