Building a Latin American Coalition on Forests: Negotiation Barriers and Opportunities
Bibliographic record
Abstract
Reducing emissions from deforestation and forest degradation (REDD) is one of the most important and most contentious issues in global climate talks. Deforestation talks have been fraught with many of the problems that plague international negotiations involving complex geopolitical and scientific components. In these situations, small developing countries experience particular difficulties preparing and organizing to best pursue their interests and ensure a sustainable outcome. The fact that many Latin American countries find themselves in this position should concern all parties involved, because the region is home to more than 20 percent of the world’s forests and has the world’s highest rate of deforestation. This Article looks at how these difficulties might be remedied to allow Latin America to lead the world to a robust anti-deforestation agreement. Part I provides a concise background on the REDD talks at the 2009 Copenhagen Climate Conference, which serves as a useful illustration of the substantive and procedural challenges in the ongoing deforestation negotiations. It identifies the main stakeholders at the climate talks and the camps in which national delegations organized themselves at Copenhagen. It then reviews the major substantive roadblocks in the REDD negotiations and identifies a series of analytical, ideological, and structural barriers that impeded significant progress on forests at Copenhagen. We suggest that these barriers necessitate the formation of an active and forward-looking Latin American coalition on forests. Part II discusses the reasons why forming a coalition is in Latin American countries’ interest. Beyond the general advantages obtained by pooling resources and negotiating strength, Part II explores the regional ecology, global politics, potential first-mover advantage, and the possibility of capacity building within the region. We conclude that the establishment of an authoritative regional coalition would ensure that, when the REDD framework is decided, Latin America is present at the table and able to maximize the benefits that the region receives. Part III suggests a two-pronged approach, including a specialized, high-level facilitator to aid Latin American countries in forming a coalition and a three- step model of coalition building. For the coalition-building model, we argue that Latin American countries should form a regional “Core Group” dedicated to forest protection, manage any potential obstructionists or spoilers, and deepen the Core Group’s commitment through relationship-building and knowledge-transfer measures. The Article concludes with a discussion of the payoffs for the region and the global climate talks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".