In this work a general framework for providing detailed probabilistic socioeconomic scenarios as well as estimates concerning country-level food security risk is proposed. Our methodology builds on (a) the Bayesian probabilistic version of the world population model and (b) on the interdependencies of the minimum food requirements and the national food system capacities on key drivers, such as: population, income, natural resources, and other socioeconomic and climate indicators. Model uncertainty plays an important role in such endeavours. In this perspective, the concept of the recently developed convex risk measures which mitigate the model uncertainty effects, is employed for the development of a framework for assessment, in the context of food security. The proposed method provides predictions and evaluations for food security risk both within and across probabilistic scenarios at country level. Our methodology is illustrated through its implementation for the cases of Egypt and Ethiopia, for the time period 2019-2050, under the combined context of the Shared Socioeconomic Pathways (SSPs) and the Representative Concentration Pathways (RCPs).

翻译：本研究提出一个通用框架，用于提供详细的概率社会经济情景以及国家层面粮食安全风险的估计。我们的方法基于：(a) 世界人口模型的贝叶斯概率版本，(b) 最低粮食需求与国家粮食系统能力对关键驱动因素（如人口、收入、自然资源及其他社会经济和气候指标）的相互依赖性。模型不确定性在此类工作中具有重要作用。基于此，我们采用近期发展的凸风险度量概念来减轻模型不确定性影响，从而构建粮食安全评估框架。所提出的方法可在国家层面的概率情景内及情景间提供粮食安全风险的预测与评估。我们通过将该方法应用于2019-2050年期间埃及和埃塞俄比亚的案例，并结合共享社会经济路径（SSPs）与典型浓度路径（RCPs）的联合背景，对方法论进行实证检验。