Climate change adaptation in conflict-affected countries: A systematic assessment of evidence
Bibliographic record
Abstract
People affected by conflict are particularly vulnerable to climate shocks and climate change, yet little is known about climate change adaptation in fragile contexts. While climate events are one of the many contributing drivers of conflict, feedback from conflict increases vulnerability, thereby creating conditions for a vicious cycle of conflict. In this study, we carry out a systematic review of peer-reviewed literature, taking from the Global Adaptation Mapping Initiative (GAMI) dataset to documenting climate change adaptation occurring in 15 conflict-affected countries and compare the findings with records of climate adaptation finance flows and climate-related disasters in each country. Academic literature is sparse for most conflict-affected countries, and available studies tend to have a narrow focus, particularly on agriculture-related adaptation in rural contexts and adaptation by low-income actors. In contrast, multilateral and bilateral funding for climate change adaptation addresses a greater diversity of adaptation needs, including water systems, humanitarian programming, and urban areas. Even among the conflict-affected countries selected, we find disparity, with several countries being the focus of substantial research and funding, and others seeing little to none. Results indicate that people in conflict-affected contexts are adapting to climate change, but there is a pressing need for diverse scholarship across various sectors that documents a broader range of adaptation types and their results.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".