Projecting armed conflict risk in Africa towards 2050 along the SSP-RCP scenarios: a machine learning approach

Jannis M Hoch,Niko Wanders,Halvard Buhaug,Rens Van Beek,Nina Von Uexkull,Sophie P De Bruin

doi:10.1088/1748-9326/ac3db2

Abstract

In the past decade, several efforts have been made to project armed conflict risk into the future. This study broadens current approaches by presenting a first-of-its-kind application of machine learning (ML) methods to project sub-national armed conflict risk over the African continent along three Shared Socioeconomic Pathway (SSP) scenarios and three Representative Concentration Pathways towards 2050. Results of the open-source ML framework CoPro are consistent with the underlying socioeconomic storylines of the SSPs, and the resulting out-of-sample armed conflict projections obtained with Random Forest classifiers agree with the patterns observed in comparable studies. In SSP1-RCP2.6, conflict risk is low in most regions although the Horn of Africa and parts of East Africa continue to be conflict-prone. Conflict risk increases in the more adverse SSP3-RCP6.0 scenario, especially in Central Africa and large parts of Western Africa. We specifically assessed the role of hydro-climatic indicators as drivers of armed conflict. Overall, their importance is limited compared to main conflict predictors but results suggest that changing climatic conditions may both increase and decrease conflict risk, depending on the location: in Northern Africa and large parts of Eastern Africa climate change increases projected conflict risk whereas for areas in the West and northern part of the Sahel shifting climatic conditions may reduce conflict risk. With our study being at the forefront of ML applications for conflict risk projections, we identify various challenges for this arising scientific field. A major concern is the limited selection of relevant quantified indicators for the SSPs at present. Nevertheless, ML models such as the one presented here are a viable and scalable way forward in the field of armed conflict risk projections, and can help to inform the policy-making process with respect to climate security.

Highlights

Without effective climate change mitigation measures and with continuing human-induced ecological degradation, environmental pressures on livelihoods are expected to worsen in many regions around the world (Adger et al 2014, IPCC 2019)
By comparing fraction of correct predictions (FOPs) and probability of conflict (POC) values obtained by the Shared Socioeconomic Pathways (SSPs) only and SSPRCP run, we find that for the reference period the inclusion of hydro-climatic variables both regionally improves and reduces accuracy as indicated by high FOC values (figure 4(C)) and that eastern Africa and Nigeria are predicted by the SSP-Representative Concentration Pathways (RCPs) run to be more conflict-prone than in the SSP run (figure 4(D))
We compared the relative impact of hydro-climatic variables on conflict occurrence

Summary

Introduction

Without effective climate change mitigation measures and with continuing human-induced ecological degradation, environmental pressures on livelihoods are expected to worsen in many regions around the world (Adger et al 2014, IPCC 2019). A more contested impact of climate change is an increased risk of violent conflict (Hsiang et al 2013, Buhaug et al 2014, Koubi 2019, Mach et al 2019). Political concern as well as scientific and security interests have been rising during the last decades This has resulted in a maturing body of academic literature on climateconflict connections (Von Uexkull and Buhaug 2021), feeding decision-making of intergovernmental institutions, such as the UN Security Council (Scott 2015, Conca 2019). The scientific consensus is still limited regarding the relevance and strength of specific mechanisms linking climate, the environment, and armed conflict risk (Koubi 2019). Recent conclusions differ due to, inter alia, the use of different data proxies, timescales, geographical scales as well as definitions of conflict, and the field is further challenged by concerns about sampling bias in climate-conflict research (Adams et al 2018)

Objectives

Results

Conclusion