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Abstract:
Quantitative climate and conflict research has thus far considered the role of biophysical extreme weather impacts in conflict dynamics only to a limited extent. We do not fully understand if and if so how, extreme weather impacts can improve conflict predictions. Addressing this gap, we use the Generalized Random Forest (GRF) algorithm to evaluate whether detailed information on extreme weather impacts improves conflict forecasts made with well known conflict predictors such as socio-economic, governance, and history of conflict indicators. We integrate data on biophysical extreme weather impacts such as droughts, floods, crop production shocks, and tropical cyclones from the Inter-Sectoral Impact Model Intercomparison Project 2a (ISIMIP2a) project into predictive models of conflict in mainland Africa between 1994 and 2012. While we find that while extreme weather impacts alone predict violent conflicts modestly well, socio-economic and conflict history indicators remain the strongest individual predictors of conflicts. Finally, fully specified forecast models including conflict history, governance, and socio-economic variables are not improved by adding extreme weather impacts information. Some part of this can be explained by spatial correlations between extreme weather impacts and other socioeconomic and governance conditions. We conclude that extreme weather impacts do not contain any unique information for forecasting annual conflict incidence in Africa, which calls into question its usefulness for early warning.
