The importance of communal forests in carbon storage: Using and destabilizing carbon measurement in understanding Guatemala's payments for ecosystem services
Bibliographic record
Abstract
Payments for ecosystem services (PES) are a conservation initiative that offer payments to people who own or manage lands that provide desired ecosystem services. Utilizing mixed methods, I examine how PES in the form of government‐issued forestry incentives interact with land tenure to affect carbon storage in Guatemala's Western Highlands. Land tenure is a larger determining factor for carbon storage than payments, as communal forests managed by Indigenous Maya K'iche' communities have significantly higher carbon stocks than private landholdings in these same communities. No statistically significant differences were found in carbon stocks between incentivized and non‐incentivized plots, and participants enrolled only a fraction of their land, likely prioritizing enrollment of degraded plots. These results indicate the importance of using both social and physical science methods to understand the physical outcomes and social context of forest management. I also reflect on why carbon storage is often prioritized, drawing on a critical physical geography framework to analyze carbon accounting methods. Measuring carbon storage gives us the tools to describe the success of communal forest management, yet I also caution relying on the quantification of ecosystem services as a method for landscape valuation and suggest avoiding prioritizing carbon storage and sequestration.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".