Paying for ecological services in Ecuador: The political economy of structural inequality
Bibliographic record
Abstract
Abstract Paying Indigenous communities to conserve land for carbon sequestration is a controversial way of tackling climate change. Critics argue that paying for ecological services (or ‘PES’) in the form of carbon offset programmes reduces land and social relations to an economic transaction that devalues Indigenous livelihoods and communities. At the same time, empirical studies have shown that Indigenous communities have accepted and even embraced the idea of being paid to conserve land for climate change mitigation. This paper explores this apparent contradiction by investigating the implementation of Programa Socio Bosque (PSB), a PES carbon sequestration programme in Ecuador. Drawing upon primary fieldwork in the highland province of Chimborazo, it makes the case that PES programmes need to be understood as form of state power that reconfigures and reinforces the ways in which Indigenous peoples engage with the state. Particularly important in this regard is the role of the state in reinforcing the agrarian conditions under which Indigenous communities use and interpret PES payments while at the same time reconfiguring new forms of land conservation. Empirically, the research reveals important complementarities between the goals of carbon sequestration PES programmes and Indigenous land‐use practices. Methodologically, it highlights the importance of situating the study of PES programmes in a context of land struggles, community–state relations and agrarian change.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".