Revisiting the Contested Role of Natural Resources in Violent Conflict Risk through Machine Learning

Abstract

The integrated character of the sustainable development goals in Agenda 2030, as well as research in environmental security, flag that sustainable peace requires sustainable and conflict-sensitive natural resource use. The precise relationship between the risk for violent conflict and natural resources remains contested because of the interplay with socio-economic variables. This paper aims to improve the understanding of natural resources’ role in the risk of violent conflicts by accounting for complex interactions with socio-economic conditions. Conflict data was analysed with machine learning techniques, which can account for complex patterns, such as variable interactions. More commonly used logistic regression models are compared with neural network models and random forest models. The results indicate that a country’s natural resource features are important predictors of its risk for violent conflict and that they interact with socio-economic conditions. Based on these empirical results and the existing literature, we interpret that natural resources can be root causes of violent intrastate conflict, and that signals from natural resources leading to conflict risk are reflected in and influenced by interacting socio-economic conditions. More specifically, the results show that variables such as access to water and food security are important predictors of conflict, while resource rents and oil and ore exports are relatively less important than other natural resource variables, contrasting what prior research has suggested. Given the potential of natural resource features to act as an early warning for violent conflict, we argue that natural resources should be included in conflict risk models for conflict prevention.

Identifying Conditioning Factors and Predictors of Conflict Likelihood for Machine Learning Models: A Literature Review

“…Corruption can also weaken the ability of the international community to intervene and assist in resolving conflicts. Corruption as a factor leading to a conflict was mentioned in the article "Revisiting the Contested Role of Natural Resources in Violent Conflict Risk through Machine Learning" [58] and The Global Conflict Risk Index (GCRI) by the JRC [47].…”

“…Extremist groups can also use income inequality to mobilize support and recruit new members. The same is true for the corruption factor; income inequality was mentioned in "Revisiting the Contested Role of Natural Resources in Violent Conflict Risk through Machine Learning" [58] and "The Global Conflict Risk Index" (GCRI) by the JRC [47].…”

“…Crop and agricultural infrastructure loss can also have long-term economic and social repercussions, resulting in food shortages and impeding post-conflict reconstruction challenges. Crops and harvest were used in several scientific papers using machine learning to predict the likelihood of conflicts [40,58]. Data related to harvest and agriculture can be found on the Statistical data website of the Food and Agriculture Organization of the United Nations [61].…”