Assessing populations exposed to climate change: a focus on Africa in a global context
Bibliographic record
Abstract
Abstract The recent debate on population dynamics and climate change has highlighted the importance of assessing and quantifying disparities in populations’ vulnerability and adopting a forward-looking manner when considering the potential impacts of climate change on different communities and regions. In this article, we overlay demographic projections based on the Shared Socioeconomic Pathways and climate change projections derived from the Representative Concentration Pathways. We focus on populations that are likely to be the most exposed to climate change in the future, namely, African populations in a comparative global context. First, we estimate the share of populations living in rural areas, who would be more dependent on agriculture, as one of the economic sectors mostly affected by climate change. Second, we explore how climate change would worsen the condition of populations living below the poverty line. Finally, we account for low levels of education, as further factors limiting people’s adaptation ability to increasingly adverse climate circumstances. Our contribution to the literature on population, agriculture, and environmental change is twofold. Firstly, by mapping the potential populations exposed to climate change, in terms of declining agricultural yields, we identify vulnerable areas, allowing for the development of targeted strategies and interventions to mitigate the impacts, ensure resilience, and protect the population living in the most affected areas. Secondly, we assess differentials in the vulnerability of local populations, showing how African regions would become among one of the most exposed to climate change by the end of the century. The findings support the targeting of policy measures to prevent increased vulnerability among already disadvantaged populations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".