The status of climate change adaptation in Africa and Asia
Bibliographic record
Abstract
Adaptation is a key component of climate policy, yet we have limited and fragmented understanding of if and how adaptation is currently taking place. In this paper, we document and characterize the current status of adaptation in 47 vulnerable ‘hotspot’ nations in Asia and Africa, based on a systematic review of the peer-reviewed and grey literature, as well as policy documents, to extract evidence of adaptation initiatives. In total, 100 peer-reviewed articles, 161 grey literature documents, and 27 United Nations Framework Convention on Climate Change National Communications were reviewed, constituting 760 adaptation initiatives. Results indicate a significant increase in reported adaptations since 2006. Adaptations are primarily being reported from African and low-income countries, particularly those nations receiving adaptation funds, involve a combination of groundwork and more concrete adaptations to reduce vulnerability, and are primarily being driven by national governments, NGOs, and international institutions, with minimal involvement of lower levels of government or collaboration across nations. Gaps in our knowledge of adaptation policy and practice are particularly notable in North Africa and Central Asia, and there is limited evidence of adaptation initiatives being targeted at vulnerable populations including socioeconomically disadvantaged populations, children, indigenous peoples, and the elderly.
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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 | 0.025 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".