Does Collective Action Sequester Carbon? The Case of the Nepal Community Forestry Program
Bibliographic record
Abstract
This paper estimate the effects of collective action in Nepal’s community forests on four ecological measures of forest quality. Forest user group collective action is identified through membership in the Nepal Community Forestry Programme, pending membership in the program, and existence of a forest user group whose leaders can identify the year the group was formed. This last, broad category is important, because many community forest user groups outside the program show significant evidence of important collective action. The study finds that presumed open access forests have only 21 to 57 percent of the carbon of forests governed under collective action. In several models, program forests sequester more carbon than communities outside the program. This implies that paying new program groups for carbon sequestration credits under the United Nations Collaborative Programme on Reducing Emissions from Deforestation and Degradation in Developing may be especially appropriate. However, marginal carbon sequestration effects of program participation are smaller and less consistent than those from two broader measures of collective action. The main finding is that within the existing institutional environment, collective action broadly defined has very important, positive, and large effects on carbon stocks and, in some models, on other aspects of forest quality.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".