Koundouri, Phoebe and Papayiannis, Georgios and Vassilopoulos, Achilleas and Yannacopoulos, Athanasios (2022): A general framework for the generation of probabilistic socioeconomic scenarios and risk quantification concerning food security with application in the Upper Nile river basin. Published in:
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Abstract
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 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 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.
Item Type: | MPRA Paper |
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Original Title: | A general framework for the generation of probabilistic socioeconomic scenarios and risk quantification concerning food security with application in the Upper Nile river basin |
Language: | English |
Keywords: | food security, probabilistic projections, risk quantification, shared socioeconomic pathways scenarios |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling |
Item ID: | 122117 |
Depositing User: | Prof. Phoebe Koundouri |
Date Deposited: | 26 Sep 2024 13:26 |
Last Modified: | 26 Sep 2024 13:26 |
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URI: | https://mpra.ub.uni-muenchen.de/id/eprint/122117 |