Providing context to conflict in the Lake Chad Basin: A quantitative assessment of socioeconomic and environmental influences

Over the last two decades, the Lake Chad Basin (LCB) has experienced an increase in violent assaults by insurgency groups and militia clashes, with civilians often bearing the brunt. While numerous mappable factors are assumed to influence when and where conflicts arise, there is limited consensus in the choice of predictors and their meaningfulness for conflict research. This study explores the predictive relevance of socioeconomic and environmental variables for conflict occurrence in the LCB using a machine learning framework. We train and test a random forest regression model on conflict event data from the Armed Conflict Location & Event Data (2003–2020), combined with environmental indicators including air temperature, precipitation, evapotranspiration, soil moisture, surface water extent, and vegetation productivity, as well as socioeconomic variables such as population density, human development index, ethnic claims, and past conflict occurrence. The model achieves a Spearman rank correlation of ρ = 0.45 (p < 0.001) on the test data. Feature importance analysis indicates that anthropogenic and climate-related variables dominate the model’s predictive performance. In particular, conflict dynamics exhibit strong path dependency, consistent with persistent structural conditions rather than isolated causal drivers. Seasonal variations in the predictive relevance of environmental factors align with key phases of the agricultural calendar, highlighting the importance of intra-annual dynamics. The presented approach identifies robust associations and temporal patterns consistent with theoretical accounts of conflict dynamics, while not permitting causal inference. While datasets capturing cooperation and de-escalatory interactions exist, their spatial and temporal resolution currently limit their integration into the present analysis. Incorporating such data at comparable resolution represents an important avenue for future research. Despite these limitations, the results suggest that considering environmental seasonality and socioeconomic context can contribute valuable insights for predictive modeling and inform research on conflict risk in vulnerable regions.