Fires as collateral or means of war: challenges of environmental peacebuilding in the Kurdistan Region of Iraq
Bibliographic record
Abstract
Environmental peacebuilding broadly refers to how sustainable management of natural resources can support prevention, mitigation, and resolution of conflict, as well as recovery after conflict. Shared natural resources constitute a common environmental challenge around which cooperation may be fostered. Environmentally damaging fires in conflict areas have received little attention from the peacebuilding field, especially compared to conflict related to water and oil, despite research that suggests fires may be caused or worsened by armed conflicts. The purpose of our study was twofold: (1) to investigate co-occurrences of armed conflict and fire in the Kurdistan Region of Iraq (KRI), which has seen substantial increases in both fire events and armed conflict in the past decade, and (2) to consider how the environmental peacebuilding framework could apply in this context, potentially offering a shift from conflict to cooperation around mutual environmental issues. Using data for 2016–2022, we analyzed the spatial patterns of fire/burned areas and armed conflict, considering potential connections between the two. Our findings indicated that one-fourth of the conflict hotspot areas coincided with fire hotspots. Two areas stood out as hotspots of both conflict and fire: the Amedi area in the north, dominated by the conflict between Turkey and the Kurdistan Workers’ Party, and the Makhmur area in the south, dominated by the conflict with the Islamic State. Though fires should be seen as a transboundary issue, few peacebuilding initiatives around fire and land resources are found in this conflict-ridden region, indicating a need for a long-term peace ecology approach to overcome the consequences of structural inequalities, conflict, and environmental destruction.
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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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".