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 (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.
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: | P - Economic Systems > P0 - General |
Item ID: | 122044 |
Depositing User: | Prof. Phoebe Koundouri |
Date Deposited: | 02 Oct 2024 06:50 |
Last Modified: | 02 Oct 2024 06:50 |
References: | [1] Alkema, L., Raftery, A. E., Gerland, P., Clark, S. J., Pelletier, F., Buettner, T., & Heilig, G. K. (2011). Probabilistic projections of the total fertility rate for all countries. Demography, 48(3), 815-839. [2] Azose, J. J., & Raftery, A. E. (2015). Bayesian probabilistic projection of international migration. Demography, 52(5), 1627-1650. [3] Follmer, H., & Schied, A. (2010). Convex and coherent risk measures. Encyclopedia of Quantitative Finance, 355–363 [4] Four´e, J., B´enassy-Qu´er´e, A., & Fontagn´e, L. (2013). Modelling the world economy at the 2050 horizon. Economics of Transition, 21(4), 617-654. [5] Meinshausen, M., Nicholls, Z. R., Lewis, J. et al. (2020). The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. Geoscientific Model Development, 13(8), 3571-3605. [6] Lutz, W., Butz, W. P., & Samir, K. E. (Eds.). (2014). World population and human capital in the twenty-first century. Oxford University Press. [7] Lutz, W., Goujon, A., Kc, S., Stonawski, M., & Stilianakis, N. (2018). Demographic and human capital scenarios for the 21st century: 2018 assessment for 201 countries. Publications Office of the European Union. [8] G. Papayiannis and A. N. Yannacopoulos, Convex risk measures for the aggregation of multiple information sources and applications in insurance, Scandinavian Actuarial Journal, 2018 (9), 792-822. [9] E. V. Petracou, A. Xepapadeas & A. N. Yannacopoulos. (2021). Decision Making Under Model Uncertainty: Fr´echet-Wasserstein Mean Preferences, Management Science (in press) ISSN 0025-1909 (print), ISSN 1526-5501 (online) [10] Raftery, A. E., Li, N., Sevcikov´a, H., Gerland, P., & Heilig, G. K. (2012). Bayesian probabilistic population projections for all countries. Proceedings of the National Academy of Sciences, 109(35), 13915-13921. [11] Raftery, A. E., Chunn, J. L., Gerland, P., & Sevcikov´a, H. (2013). Bayesian probabilistic projections of life expectancy for all countries. Demography, 50(3), 777-801. [12] Sevcikov´a, H., & Raftery, A. E. (2016). bayesPop: probabilistic population projections. Journal of Statistical Software, 75. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/122044 |