Comparing carbon offsets and livelihood benefits in a long‐term reforestation project: Agroforestry versus native timber versus enrichment planting
Bibliographic record
Abstract
Abstract Indigenous People and Local Communities (IPLC) can play an important role in reforestation for climate change mitigation while providing environmental and livelihood benefits. However, there is a lack of direct comparison between different reforestation design's carbon uptake potential and their relevance to community reforestation. We evaluate the carbon capture and survival of the four most common reforestation designs in a 14‐year‐long project in an Emberá community in eastern Panama. We look at native timber mixtures and monocultures, agroforestry, and natural regrowth with enrichment planting. To explain the differences between plots, we compare design, management and environmental characteristics using redundancy analyses (RDA). We then contextualize our results with the project leader's perspectives of success using interviews and workshops. In the first decade, mixtures and monocultures of timber stored on average roughly 3× more carbon than agroforestry and enrichment planting (140 tCO 2 /ha compared to 40 tCO 2 /ha and 53 tCO 2 /ha). The project design had the largest influence on carbon storage and survival with an explanatory power of 31% followed by management and environmental characteristics (14% and 2% respectively). The largest threat to the survival and growth was the risk of fire which caused mortality in more than 2/3 of the plots. Some damage was offset by natural regrowth which accounted for 34% of the total carbon, but the participants generally perceived natural regrowth as being “dirty.” They had a strong preference for agroforestry, particularly coffee ( Coffea spp.), a crop with negligible carbon value, but high economic value. ‘Practical implication’ : The findings underscore the need to balance carbon sequestration with local economic preferences, integrate community input, mitigate environmental risks and adopt long‐term, holistic approaches for effective reforestation projects.
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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) | 0.000 | 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".