Presented at the “State of the science of “political futures”: Exploring how to improve the representation and usability of socio-political factors in the SSPs” workshop at the German Institute of Development and Sustainability (IDOS) in Bonn, Germany, 24-25 October 2022.

Title: Predicting the impact of armed conflict on vulnerability: a Machine Learning approach

Authors: Mariagrazia D’Angeli & Paola Vesco

Date: 2022

Series: VIEWS Working Paper Series, no. 1

Publisher: Uppsala University

Abstract: Armed conflicts have been associated with a variety of detrimental impacts on humansecurity and development, and represent a crucial vector of societal vulnerability to sub-sequent climate hazards. The burgeoning literature on climate security has highlighted that climate variability and natural disasters may indirectly increase conflict risk invulnerable locations. However, scientifically sound knowledge of the impacts of armedconflicts on socio-economic vulnerability remains sparse, and more research is needed to understand the complex linkages between natural disasters, armed conflict, and societal vulnerability. This study fills the gap by empirically investigating the impacts of armed conflicts and natural disasters on subsequent levels of societal vulnerability to climate hazards. The paper uses global, time-varying data for 189 countries between 1995 and2019, combining information on natural disasters, armed conflict, and socio-economic vulnerability. We apply a leave-the-future-out cross validation and an extreme gradient boosting algorithm to test the out-of-sample performance of armed conflict, alone or in combination with natural disasters, as a predictor of vulnerability. This machine learn-ing approach enables us to overcome some of the empirical challenges that traditional statistical methods relying on reduced form regressions fail to solve.

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