Feasibility assessment of climate change adaptation options across Africa: an evidence-based review
Bibliographic record
Abstract
Abstract Considering the feasibility and effectiveness of adaptation options is essential for guiding responses to climate change that reduce risk. Here, we assessed the feasibility of adaptation options for the African context. Using the Global Adaptation Mapping Initiative, a stocktake of adaptation-related responses to climate change from the peer-reviewed literature in 2013–2020, we found 827 records of adaptation actions in Africa. We categorised and evaluated 24 adaptation options and for each option, six dimensions of feasibility were considered: economic, environmental, social, institutional, technological, and evidence of effectiveness. Over half (51%) of all adaptation actions were reported in the food sector where sustainable water management (SWM) was the most reported option. The fewest actions were reported for cities (5%). The majority of actions (53%) were recorded in just six countries: Ghana, Ethiopia, Kenya, Tanzania, Nigeria and South Africa. Encouragingly, effectiveness was assessed as medium or high for 95% of adaptation options. However, no options had high feasibility on any other dimension. Technological and institutional factors present major barriers to implementation. Crop management, SWM, sustainable agricultural practices, agroforestry, livelihood diversification, ecosystem governance and planning, health governance and planning, infrastructure and built environment, all had moderate feasibility across three or more dimensions. Human migration has low feasibility but high potential for risk reduction. Major knowledge gaps exist for environmental feasibility, for assessing adaptation limits at increasing levels of climate hazard, for economic trade-offs and synergies, and for Central and Northern Africa. Our results highlight sectors where enablers for adaptation can be increased. Future assessments can apply the method established here to extend findings to other national and local levels.
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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 | 0.031 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".