The dynamics of social conflict and deforestation: Empirical evidence from the refugee crisis in southeast Bangladesh
Bibliographic record
Abstract
This study explores the complex interplay between social conflict and forest resource exploitation in the context of the Rohingya refugee crisis in Bangladesh. While previous research has largely treated deforestation as a cause and social conflict as a consequence, our study proposes a cyclical model in which these two dynamics reinforce each other. Based on empirical data collected from 398 refugee households in the Kutupalong camp, we find that fuelwood collection is primarily driven by low adaptive capacity, unstable income, limited access to alternative energy, and lack of livelihood options. The influx of refugees has intensified pressure on already-degraded forests, leading to competition with host communities and escalating tensions. Our binary logistic regression analysis identifies key socioeconomic predictors of forest dependency. We propose a conceptual framework highlighting both “enforce drivers” and “break drivers” that sustain or disrupt the conflict-deforestation cycle. In the cycle, the social conflict causing deforestation was empirically shown, while deforestation causing social conflict was discussed based on relevant theories. Addressing this vicious cycle requires an integrated approach involving clean energy access, sustainable livelihood development, and participatory forest governance. These findings provide critical insights for policymakers and practitioners working on humanitarian and environmental issues in conflict-affected regions, offering a scalable framework for mitigating the socio-ecological impacts of forced migration and resource scarcity. • Identifies socioeconomic drivers of forest dependence among Rohingya refugees. • Fuelwood collection emerges as the primary form of forest exploitation. • Low adaptive capacity and limited clean energy access intensify forest pressure. • Proposes a reinforcing cycle between social conflict and deforestation. • Social conflict leading to deforestation was established based on empirical evidence, and the opposite was suggested based on relevant theories.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".