Challenges for climate change adaptation in Latin America and the Caribbean region
Bibliographic record
Abstract
The limited success of international efforts to reduce global warming at levels established in the Paris Agreement, and the increasing frequency and strength of climate impacts, highlight the urgent need of adaptation, particularly in developing countries. Unfortunately, current levels of adaptation initiatives are not enough to counteract the observed impacts and projected risks from climate change in Latin America and the Caribbean (LAC). In this paper, we review and highlight relevant issues that have limited the capacity to transform climate knowledge and parties’ ambitions into action in the region. Current vulnerabilities and climatic impact-drivers in LAC are diverse, complex, and region-specific and their effects are expected to be exacerbated by climate change. However, the advancement of regional and domestic climate agendas has been hindered by scientific gaps, political support, institutional capacity, and financial, technical, human, and economic limitations that are common to many LAC countries. Transforming climate data into multidimensional metrics with useful thresholds for different sectors and understanding their contribution for feasible adaptation strategies are delayed by regional and local conundrums such as lack of inclusive governance, data availability, equity, justice, and transboundary issues. We discuss ways to move forward to develop local and regional climate resilient development actions and a more sustainable future in LAC. The climate science community in LAC needs to strengthen its local, national, and international connections and with decision/policymakers and society to establish a three-way engagement by proposing suitable adaptation actions and international negotiations to reduce the risks and vulnerability associated with climate extremes, climate variability and climate change in the region. The discussions and insights presented in this work could be extrapolated to other countries in the Global South.
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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 | 0.000 | 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".