Robust downscaling and models’ linkage procedures for integrated modeling and management of systemic risks, resilience, and food-energy-water-environmental nexus security [in Russian]

Zagorodny, A., Bogdanov, V., Schellnhuber, H.J. ORCID: https://orcid.org/0000-0001-7453-4935, Ermolieva, T., Havlik, P. ORCID: https://orcid.org/0000-0001-5551-5085, & Komendantova, N. ORCID: https://orcid.org/0000-0003-2568-6179 (2026). Robust downscaling and models’ linkage procedures for integrated modeling and management of systemic risks, resilience, and food-energy-water-environmental nexus security [in Russian]. Кібернетика та системний аналіз 62 (3) 68-88.

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Abstract

The paper makes a short overview of advanced systems analysis methods, models and modeling tools being developed at IIASA (International Institute for Applied Systems Analysis, Laxenburg, Austria) and within NASU (National Academy of Sciences, Ukraine) and IIASA joint project “Integrated modeling for robust management of food–energy–water–social–environmental nexus security and sustainable development”. Emerging systemic risks in interdependent Food–Energy–Water–Environment (FEWE) systems can be managed through a two-stage coherent decision-making framework: ex-ante (anticipatory) and ex-post (adaptive), using integrated models to balance proactive risk reduction (e.g., resilient infrastructure, diversified resources) with reactive crisis response (e.g., emergency planning, technological and financial backstops) for increased resilience, as highlighted by IIASA and NASU joint research. This approach, using two-stage stochastic optimization, aims for robust management by keeping options open while preparing for inevitable uncertainties in these complex, interconnected systems, notes researchers in papers like those from Springer Nature book on FEWE nexus security [1-2]. Truly integrated modeling often requires rescaling (down- and up-scaling) of models’ data and results. The mismatch of scales creates a major source of uncertainties, which calls for the identification of proper indicators, new measures of uncertainties and risks, and goodness criteria for disaggregation and aggregation. To represent information in locations, the procedures rely on an appropriate optimization principle, e.g., generalized cross-entropy maximization, and combine the available samples of real observations in the locations with other “prior” hard and soft data (expert opinion, scenarios), pseudo-sampling models, and evidences on the related variables that exist in the form of equations and constraints. A key issue is the treatment of uncertainties in priors and parameters of available constraints. Approaches to down-scaling in the presence of uncertain priors are outlined. The approaches are being further developed at IIASA and the NASU-IIASA joint project. Distributed models’ optimization and linkage methods enable the establishment of relationships and dialogues between separate models of FEWE systems for the analysis of coordinated solutions without requiring the sharing or revealing of systems-specific information, i.e., under asymmetric information
(ASI). The problem is illustrated with an example of linking models of individual producers emitting GHGs (emitting entities or parties) into a prototype model of an emission trading market, when information about parties may not be available, and joint safety constraints on emissions (when individual parties’ emissions are uncertain) have to be fulfilled. The outlined methods and tools pursue the goal to develop and implement advanced systems analysis and integrated modeling approaches, allowing coherent planning of FEWE systems under joint constraints, asymmetric information, and uncertainties about the sectoral models. Explicit modeling of linkages allows evaluation and treatment of such risks under standard independent planning of sectors. Therefore, the models and methods aim for systems analysis of FEWE nexus security under exogenous risks and risks affected (intentionally and unintentionally) by decisions of various agents. The methods and tool involve the concept of robustness and robust solutions, which are, in a sense, optimal for any scenario of potential uncertainties.

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