A general framework for the generation of probabilistic socioeconomic scenarios and risk quantification concerning food security with application in the Upper Nile river basin

Food security is a key issue in sustainability studies. In this paper we propose a general framework for providing detailed probabilistic socioeconomic scenarios as well as predictions across scenarios, concerning food security. Our methodology is based on the Bayesian probabilistic prediction model of world population (Raftery et al [10]) and on data driven prediction models for food demand and supply and its dependence on key drivers such as population and other socioeconomic and climate indicators(e.g. GDP, temperature, etc). For the purpose of risk quantification, concerning food security, we integrate the use of recently developed convex risk measures involving model uncertainty (Papayiannis et al [8], [9]) and propose a methodology for providing estimates and predictions across scenarios, i.e. when there is uncertainty as to which scenario is to be realized. Our methodology is illustrated by studying food security for the 2020-2050 horizon in the context of the SSP-RCP scenarios, for Egypt and Ethiopia.