Payment for Environmental Services to Promote Agroecology: The Case of the Complex Context of Rural Brazilian
Bibliographic record
Abstract
Modern agriculture has generated complex environmental damages. Sustainable food production models must be encouraged. Agroecology is presented as a more sustainable option, since it brings a holistic view of these complex and interdependent elements: food production and environmental protection. However, this model is challenging to apply, which is intensified by the limitations imposed by environmental command and control instruments. This paper aims to analyze how the economic instrument of Payment for Environmental Services (PES) can be enhanced in order to promote the reproduction of agroecology in Brazil. PES and the main environmental economic theories behind this instrument were briefly analyzed. From the analyses of selected case studies, the core structural and essential issues revolving failures of the current Brazilian PES programs have been identified. The hypothesis states that PES should migrate from the Environmental Neoclassical Economics’ logic and be grounded on the principles of Ecological Economics. Based in our analysis, PES should be able to promote agroecology in Brazil reading 3 key drivers: being mainly non-monetary, public and applying a systemic approach. Following this strategy would mean overcoming the market logic, whilst allowing public participation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".