Financing the agrarian transition? The Clean Development Mechanism and agricultural change in Latin America
Bibliographic record
Abstract
The food crisis of 2007–08 generated widespread global concerns about land consolidation and agricultural transition, with renewed attention on foreign land investments and growing global markets for meat and biofuels. As part of and alongside this process, agriculture and land-use projects registered in the Kyoto Protocol's Clean Development Mechanism (CDM) continued to rise, representing almost a third of global projects and almost 50% of projects in Latin America. In this paper we conduct an analysis of the sustainable development claims of Latin American CDM projects, focusing particularly on their implications for land consolidation, regional food security, and agrarian justice. Our analysis suggests that in Latin America those benefiting most from the development and sale of carbon-offset projects have, to date, been large-scale corporations investing in industrial carbon projects such as large tree plantations, sugarcane, and large-scale, export-oriented livestock production. As such, we argue that the carbonization of agriculture through the CDM serves as a driver of ‘global green grabbing’ and that the scope and financialization of CDM projects in the agriculture and forestry sectors in Latin America may contribute to the maintenance of an agrarian system of ‘climate injustice’ rather than foster sustainable development across the region.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".