Nexus between food insecurity, poverty, and climate change: a cross-regional multifactorial analysis
Bibliographic record
Abstract
Climate change, by exacerbating poverty and food insecurity, creates complex dynamics that threaten global food security. This study aims to examine the complex interactions between these phenomena (climate change, poverty and food security), adopting a multidimensional approach to understand their direct and indirect relationships. The study focused on eight countries representing the African, Asian, American and European continents, selected according to their Human Development Index (HDI). Data is collected for the period 1990–2020 from platforms such as FAO, World Bank, UNDP and Our World in Data, based on indicators of poverty, climate change and food insecurity (qualitative approach). Data analysis is based on time series (temporal evolutions in climate and poverty variables) as well as thematic content analysis. Analysis of evolutions in poverty indicators and the HDI reveals marked disparities between regions, with notable progress in Asia and Europe, but persistent challenges in Africa and Yemen. Similarly, these disparities are also observed for climate evolutions and changes in land, particularly in Africa and Asia. Regarding food insecurity evolutions, there is a considerable increase with marked regional disparities, where Africa and Latin America are the most affected. Direct arable effects include reduced agricultural productivity, crop and livestock yield, increased undernourishment, reduced livelihoods and producer incomes. Indirectly, these changes reduce crop quality, disrupt ecosystem services, exacerbate resource conflicts and increase production costs. These findings provide guidance for policymakers and researchers in developing integrated strategies that address not only food security and poverty, but also climate change.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".