Payments for ecosystem services and conditional cash transfers in a policy mix: Microlevel interactions in Selva Lacandona, Mexico
Bibliographic record
Abstract
Abstract Payments for ecosystem services (PES) programs have been increasingly studied with a policy mix perspective. So far, the focus has been on PES' interplay with other conservation instruments and resulting environmental outcomes at meso‐ and macrolevels. Though PES often operate among “poor” forest‐dwelling communities in the Global South, our knowledge on PES' interactions with poverty alleviation policies is scarce, especially at the microlevel. This article examines PES' interactions—in terms of joint coverage, management, and spending of revenues, and socioeconomic effects of participation—with a conditional cash transfer (CCT) program in a case study of six communities in Selva Lacandona, Chiapas, Mexico. The article builds a dual framework combining policy mix analysis with an actor‐oriented approach focused on participants' microagency, and is based on in‐depth, qualitative research. Results reveal widespread joint PES and CCT coverage, and patterns of specialization between different household members regarding the management and spending of program revenues. Results also show positive, multilevel policy interactions as participants combine resources to pursue individual and collective socioeconomic strategies. The article highlights the creative ways in which local stakeholders integrate individual policies within their broader livelihoods, and how coordination failures among policy‐implementing institutions and deficient public services limit participants' ability to achieve sustained livelihood improvements. The article also highlights how a focus on microlevel policy interactions complements meso‐ and macrolevel analyses for a better understanding of PES' role in a policy mix and concludes by providing some recommendations for building implementation synergies and improving program design.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".