Refugees and host communities’ vulnerability to climate and disaster risks in Rwanda
Bibliographic record
Abstract
Climate change hazards pose significant risks to vulnerable populations, particularly refugees residing in camps within environmentally sensitive areas. This study assesses climate and disaster risks in refugee-hosting districts in Rwanda using GIS-based risk mapping, decision science tools (AHP), remote sensing, and econometric analysis. The findings reveal spatial variability in hazard exposure across camps, with Mahama and Mugombwa refugee camps experiencing the highest flood risks, while Gihembe, Kiziba, and Kigeme camps are most susceptible to landslides. Between 2013 and 2021, landslides resulted in the damage of 324 hectares of cropland in Gihembe district and caused 110 fatalities in Kiziba’s district in 2016 alone. The study also finds that Mahama camp is highly vulnerable to drought, reflecting national data indicating that 4.2 million Rwandans were affected by droughts between 1974 and 2018. In addition, Kiziba camp exhibits severe soil erosion, with up to 19 million tons of annual soil loss in its watershed area. This erosion, exacerbated by deforestation due to firewood harvesting and construction material collection, weakens slope stability, intensifying landslide risks and increasing sediment transport into local water sources, thereby impacting water quality. Our results support recent disaster management decisions by the Government of Rwanda and UNHCR, including the closure of Gihembe camp in 2021 due to landslide risks and the relocation of vulnerable populations from Kigeme camp due to erosion-induced ravine formation. While these interventions reduce immediate risks, continued efforts are needed to enhance camp resilience, strengthen early warning systems, and integrate nature-based solutions into long-term disaster risk management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".