Assessing “Results-Based” Payment Determinants in Forest Carbon Emission Reduction Initiatives: Case of Forest Carbon Projects in Cameroon
Bibliographic record
Abstract
The international community has acknowledged the critical role of results-based avoided deforestation and forest degradation, sustainable management of forest, conservation and enhancement of carbon stocks (REDD+) activities in curbing climate change. However, ensuring that REDD+ programs and projects deliver carbon and non-carbon results, remains a challenge. This paper analyses results-based determinants in REDD+ projects in Cameroon. Experiences from these projects are expected to inform the design and implementation of sustainable and effective REDD+ projects. It draws on data collected from feasibility study reports, project design documents, project evaluation reports and the opinions and perspectives of 86 REDD+ stakeholders. Findings indicate that projects employed a combination of incentives, disincentives and enabling measures towards achieving the intended REDD+ results. However, none of the projects proposed conditional incentives (direct payments) to land owners and users, the key innovation brought by REDD+. Despite the fact that these projects are branded REDD+ projects, they offer little or no experiences on the relationship between REDD+ payments and carbon and non-carbon outcomes. Achieving results from REDD+ projects depend on how effective choices are made by stakeholders in relation to the type of instruments/interventions and the location of projects, and the ability to make choices further depends on the technical capacity of stakeholders. Thus, the capacity of stakeholders to be involve in REDD+ project design and implementation should be strengthened, in order for them to better appraise the results-based requirements of REDD+.
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".