Payments for ecosystem services in Mexico: Two decades of progress and challenges between research and practice
Bibliographic record
Abstract
• We review two decades of research and practice of Mexico’s PES schemes. • Mexico’s federal PES reached 7.4 million hectares from 2003 to 2022. • We found 140 peer-reviewed publications focused on Mexico’s PES. • Most studies show Mexico’s PES produced positive ecological outcomes. • Mexico-based scholars led half of publications but are less cited than foreign ones. As some of the world’s largest, longest lasting and most researched initiatives that reward individual and communal landowners for conserving forests and associated ecosystem services, Mexico’s Payments for Ecosystem Services (PES) programmes provide a significant opportunity to examine questions of how, where, and by whom scholarship has been produced and the potential gaps revealed when comparing research insights with implementation patterns. To address these questions, we assembled the most up-to-date and comprehensive database of PES peer-reviewed publications and programme data in a single country. Our study includes a systematic analysis of relevant scientific literature in English and Spanish through 2022 (N = 140) and an assessment of the spatial and temporal distribution, timing, focus, and scope of all federally funded PES programmes at national, subnational, and local levels between 2003 and 2022. We find that variations in the spatial coverage of programme implementation have been associated with proportional levels of research interest over time and that studies represent multiple themes, spatiotemporal scales, and disciplinary and methodological approaches. With some variation, there is congruence among research findings that programmes have produced mostly positive ecological effects and mixed social effects. However, research has been disproportionately concentrated in specific geographic regions and Mexican scholarship has had considerably less global visibility and impact than European and U.S.-based research. By focusing our analysis on PES research and practice within a country-specific context and including literature produced in the local language, our analysis provides greater nuance than previous PES reviews regarding how knowledge is produced and by whom. We identify permanence of programme effects in Mexico as a key emerging issue for future research and, at a global scale, for the need to conduct such nuanced and inclusive assessments of other specific PES programmes to help identify and address key drivers of knowledge gaps in incentive-based environmental policies.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".