National REDD+ policy networks: from cooperation to conflict
Bibliographic record
Abstract
Reducing emissions from deforestation and forest degradation (REDD+) is a financial mechanism aimed at providing incentives to reduce carbon emissions from forests and enhance carbon stocks. In most forest-rich developing countries, policy actors, i.e., state and nonstate as well as international and national, are designing national REDD+ policies. Actors' interests and beliefs shape patterns of interactions, ranging from cooperation to conflict, and these interactions influence a country's direction and progress in REDD+ policy formulation and implementation. We used a comparative policy network approach to analyze the power structures in national REDD+ policy domains in seven countries. We drew on the typology of power structures defined by two dimensions, namely the distribution of power in the policy arena and the dominant type of interaction, cooperative or conflictual, among actors, and we mapped the progress of national REDD+ decision-making processes against these power structures. We tested three hypotheses and found that (1) national ownership over the policy process is a prerequisite for progress. In addition, (2) the level of concentration of power in an actor group can facilitate progress in REDD+; however, particularly when concentration of power is high, progress will be possible only if the interests of the most powerful are aligned with the objectives of REDD+ and address the drivers of deforestation and forest degradation. Furthermore, (3) although cooperation is perceived as ideal in any collective decision-making setting, a certain level of conflict is necessary for progress in REDD+ decision making. This applies particularly in more advanced national REDD+ domains, where, following a honeymoon phase during which most policy actors embrace the broad idea of REDD+, policy decisions must deal with difficult realities associated with negotiating established business-as-usual interests, which entails high political costs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".