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Sustainability
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Modeling and Managing Catastrophic Risks in Heterogeneous Systems
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Modeling and Managing Catastrophic Risks in Heterogeneous Systems

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Hazards and Sustainability".

Deadline for manuscript submissions: closed (15 August 2022) | Viewed by 12231

Special Issue Editors

Dr. Tatiana Ermolieva

E-Mail Website
Guest Editor
International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria
Interests: spatio-temporal optimization of heterogeneous values and flows in complex dynamic and stochastic systems; analysis and modeling of complex socioeconomic, resources, financial systems with explicit treatment of uncertainties and risks of environment
Special Issues, Collections and Topics in MDPI journals
Dr. Michael Obersteiner

E-Mail Website
Guest Editor
International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria
Interests: biodiversity; food security; sustainability; environmental economics; land-use modeling
Prof. Dr. Yurii Ermoliev

E-Mail Website
Guest Editor
International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria
Interests: nonsmooth stochastic optimization; robust decision-making in the presence of uncertainty and catastrophic risks; optimization of networks; nonlinear dynamics; path-dependent adaptation processes; pollution control problems; energy and agriculture modeling; reliability theory; optimization of discontinuous systems; in particular discrete event systems optimization

Special Issue Information

Dear Colleagues,

The increasing interdependencies among systems involving interactions between man, nature and technology resemble a complex network. The disruption of such a network can trigger systemic catastrophic risks which may be unlike anything that has been experienced in the past. These risks can be induced by human decisions in combination with natural shocks.

Robust decisions for managing the security of systems exposed to systemic extreme events can be essentially different from the oversimplified decisions that ignore such events. Specifically, a proper treatment of rare extreme events requires new performance indicators, new spatio-temporal dimensions for modeling heterogeneous interdependencies, and new representations of feasible risk mitigation and adaptation efforts.

Science-based decision support for effectively coping with the systemic catastrophic risks in complex policy-driven systems needs new approaches and solutions for fundamentally new scientific problems. The aim of the Special Issue on “Modeling and Managing Catastrophic Risks in Heterogeneous Systems” is to provide researchers from diverse scientific areas with a chance to discuss various problems of effectively dealing with uncertainties and risks in heterogeneous natural and anthropogenic systems, including food, energy, water, environmental, social security management, economics, engineering, healthcare, policy-making and management.

The Special Issue is devoted to novel and already existing theoretical and practical approaches to dealing effectively and robustly with systemic catastrophic risks in a cross-disciplinary manner, sharing methods, ideas, and open problems across scientific and practical problem areas.

Contributions to the Special Issue can address, but are not limited to, the following topics: robust decision support in the presence of catastrophic risks; spatio-temporal modeling and the management of catastrophic risks; robust estimation, machine learning, Big Data problems; modeling for the management of food, water, energy and environmental security, etc.

Dr. Tatiana Ermolieva
Dr. Michael Obersteiner
Prof. Dr. Yurii Ermoliev
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • catastrophic risks
  • catastrophe modeling
  • robust preventive and adaptive decisions
  • food–water–energy–environmental security
  • systemic interdependencies
  • robust estimation, machine learning, Big Data problems
  • spatio-temporal heterogeneities
  • etc.

Published Papers (5 papers)

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Research

16 pages, 1150 KiB  
Article
Multi-Objective Water Planning in a Poor Water Data Region: Aragvi River Basin
by Samuel Sandoval-Solis, Jose Pablo Ortiz Partida and Lindsay Floyd
Sustainability 2022, 14(6), 3649; https://0-doi-org.brum.beds.ac.uk/10.3390/su14063649 - 20 Mar 2022
Cited by 1 | Viewed by 2392
Abstract
Water resources planning in regions with sufficient data continuity and quality is complex, but in regions with poor water data, the task is further complicated. In this paper, we share our experience developing a multi-objective technical assessment of water resources in a region [...] Read more.
Water resources planning in regions with sufficient data continuity and quality is complex, but in regions with poor water data, the task is further complicated. In this paper, we share our experience developing a multi-objective technical assessment of water resources in a region with scarce water data. This research is an example of collaborative modeling in which stakeholders were involved during the modeling process to create a model using the Shared Vision collaborative strategy for water planning in the Aragvi River Basin in the country of Georgia. We developed a regional water planning model suitable for evaluating water supply and water demand interaction as well as current and alternative water management strategies. Remarks from scenario development enlightened the need for water efficiency and conservation activities as currently the system is not entirely reliable, and its reliability is expected to decline with population growth and increased hydropower demands. This research is a strong foundation for future water-related projects in the region. Full article
(This article belongs to the Special Issue Modeling and Managing Catastrophic Risks in Heterogeneous Systems)
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18 pages, 988 KiB  
Article
Containing the Risk of Phosphorus Pollution in Agricultural Watersheds
by Matthias Wildemeersch, Shaohui Tang, Tatiana Ermolieva, Yurii Ermoliev, Elena Rovenskaya and Michael Obersteiner
Sustainability 2022, 14(3), 1717; https://0-doi-org.brum.beds.ac.uk/10.3390/su14031717 - 02 Feb 2022
Cited by 3 | Viewed by 2020
Abstract
Phosphorus (P) is an essential nutrient to boost crop yields, but P runoff can cause nutrient over-enrichment in agricultural watersheds and can lead to irreversible effects on aquatic ecosystems and their biodiversity. Lake Erie is one prominent example as this watershed has experienced [...] Read more.
Phosphorus (P) is an essential nutrient to boost crop yields, but P runoff can cause nutrient over-enrichment in agricultural watersheds and can lead to irreversible effects on aquatic ecosystems and their biodiversity. Lake Erie is one prominent example as this watershed has experienced multiple episodes of harmful algal blooms over the last decades. Annual P loads crucially depend on yearly weather variations, which can create the risk of years with high runoff and excessive nutrient loads. Here we apply stochastic modeling to derive sustainable management strategies that balance crop yield optimization with environmental protection, while accounting for weather variability as well as weather trends as a result of climate change. We demonstrate that ignoring annual weather variations results in mitigation efforts for environmental pollution that are largely insufficient. Accounting explicitly for future variations in precipitation allows us to control the risk of emissions exceeding the P target loads. When realistic risk targets are imposed, we find that a package of additional measures is required to avoid P over-enrichment in the Lake Erie watershed. This package consists of a substantial reduction of P inputs (approximately 30% for different accepted risk levels), adoption of cover crops throughout the near- and mid-century, and cultivation of less nutrient-intensive crops (30% more soy at the expense of corn). Although climate change reinforces these conclusions, we find that the accepted risk level of exceeding P target loads is the predominant factor in defining a sustainable nutrient management policy. Full article
(This article belongs to the Special Issue Modeling and Managing Catastrophic Risks in Heterogeneous Systems)
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14 pages, 462 KiB  
Article
A Risk-Informed Decision-Making Framework for Climate Change Adaptation through Robust Land Use and Irrigation Planning
by Tatiana Ermolieva, Petr Havlik, Stefan Frank, Taher Kahil, Juraj Balkovic, Rastislav Skalsky, Yuri Ermoliev, Pavel S. Knopov, Olena M. Borodina and Vasyl M. Gorbachuk
Sustainability 2022, 14(3), 1430; https://0-doi-org.brum.beds.ac.uk/10.3390/su14031430 - 26 Jan 2022
Cited by 5 | Viewed by 2348
Abstract
Uncertainty and variability are key challenges for climate change adaptation planning. In the face of uncertainty, decision-making can be addressed in two interdependent stages: make only partial ex ante anticipative actions to keep options open until new information is revealed, and adapt the [...] Read more.
Uncertainty and variability are key challenges for climate change adaptation planning. In the face of uncertainty, decision-making can be addressed in two interdependent stages: make only partial ex ante anticipative actions to keep options open until new information is revealed, and adapt the first-stage decisions with respect to newly acquired information. This decision-making approach corresponds to the two-stage stochastic optimization (STO) incorporating both anticipative ex ante and adaptive ex post decisions within a single model. This paper develops a two-stage STO model for climate change adaptation through robust land use and irrigation planning under conditions of uncertain water supply. The model identifies the differences between decision-making in the cases of perfect information, full uncertainty, and two-stage STO from the perspective of learning about uncertainty. Two-stage anticipative and adaptive decision-making with safety constraints provides risk-informed decisions characterized by quantile-based Value-at-Risk and Conditional Value-at-Risk risk measures. The ratio between the ex ante and ex post costs and the shape of uncertainty determine the balance between the anticipative and adaptive decisions. Selected numerical results illustrate that the alteration of the ex ante agricultural production costs can affect crop production, management technologies, and natural resource utilization. Full article
(This article belongs to the Special Issue Modeling and Managing Catastrophic Risks in Heterogeneous Systems)
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14 pages, 495 KiB  
Article
Linking Distributed Optimization Models for Food, Water, and Energy Security Nexus Management
by Yuri Ermoliev, Anatolij G. Zagorodny, Vjacheslav L. Bogdanov, Tatiana Ermolieva, Petr Havlik, Elena Rovenskaya, Nadejda Komendantova and Michael Obersteiner
Sustainability 2022, 14(3), 1255; https://0-doi-org.brum.beds.ac.uk/10.3390/su14031255 - 23 Jan 2022
Cited by 7 | Viewed by 2178
Abstract
Traditional integrated modeling (IM) is based on developing and aggregating all relevant (sub)models and data into a single integrated linear programming (LP) model. Unfortunately, this approach is not applicable for IM under asymmetric information (ASI), i.e., when “private” information regarding sectoral/regional models is [...] Read more.
Traditional integrated modeling (IM) is based on developing and aggregating all relevant (sub)models and data into a single integrated linear programming (LP) model. Unfortunately, this approach is not applicable for IM under asymmetric information (ASI), i.e., when “private” information regarding sectoral/regional models is not available, or it cannot be shared by modeling teams (sectoral agencies). The lack of common information about LP submodels makes LP methods inapplicable for integrated LP modeling. The aim of this paper is to develop a new approach to link and optimize distributed sectoral/regional optimization models, providing a means of decentralized cross-sectoral coordination in the situation of ASI. Thus, the linkage methodology enables the investigation of policies in interdependent systems in a “decentralized” fashion. For linkage, the sectoral/regional models do not need recoding or reprogramming. They also do not require additional data harmonization tasks. Instead, they solve their LP submodels independently and in parallel by a specific iterative subgradient algorithm for nonsmooth optimization. The submodels continue to be the same separate LP models. A social planner (regulatory agency) only needs to adjust the joint resource constraints to simple subgradient changes calculated by the algorithm. The approach enables more stable and resilient systems’ performance and resource allocation as compared to the independent policies designed by separate models without accounting for interdependencies. The paper illustrates the application of the methodology to link detailed energy and agricultural production planning models under joint constraints on water and land use. Full article
(This article belongs to the Special Issue Modeling and Managing Catastrophic Risks in Heterogeneous Systems)
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20 pages, 1345 KiB  
Article
A Multicriteria Approach to Modelling Pandemic Response under Strong Uncertainty: A Case Study in Jordan
by Love Ekenberg, Adriana Mihai, Tobias Fasth, Nadejda Komendantova, Mats Danielson and Ahmed Al-Salaymeh
Sustainability 2022, 14(1), 81; https://0-doi-org.brum.beds.ac.uk/10.3390/su14010081 - 22 Dec 2021
Cited by 4 | Viewed by 2491
Abstract
In responding to the COVID-19 pandemic, evidence-based policymaking and risk mitigation have been confronted with limited decision-making mechanisms under conditions of increased uncertainty. Such methods are particularly called for in contexts where reliable data to a large extent are missing and where the [...] Read more.
In responding to the COVID-19 pandemic, evidence-based policymaking and risk mitigation have been confronted with limited decision-making mechanisms under conditions of increased uncertainty. Such methods are particularly called for in contexts where reliable data to a large extent are missing and where the chosen policy would impact a variety of sectors. In this paper, we present an application of an integrated decision-making framework under ambiguity on how to contain the COVID-19 virus spread from a national policy point of view. The framework was applied in Jordan and considered both local epidemiologic and socioeconomic estimates in a multistakeholder multicriteria context. In particular, the cocreation process for eliciting attitudes, perceptions, and preferences amongst relevant stakeholder groups has often been missing from policy response to the pandemic, even though the containment measures’ efficiency largely depends on their acceptance by the impacted groups. For this, there exist several methods attempting to elicit criteria weights, values, and probabilities ranging from direct rating and point allocation methods to more elaborated ones. To facilitate the elicitation, some of the approaches utilise elicitation methods whereby prospects are ranked using ordinal importance information, while others use cardinal information. Methods are sometimes assessed in case studies or more formally by utilising systematic simulations. Furthermore, the treatment of corresponding methods for the handling of the alternative’s values has sometimes been neglected. We demonstrate in our paper an approach for cardinal ranking in policy decision making in combination with imprecise or incomplete information concerning probabilities, weights, and consequences or alternative values. The results of our cocreation process are aggregated in the evaluation of alternative mitigation measures for Jordan, showcasing how a multistakeholder multicriteria decision mechanism can be employed in current or future challenges of pandemic situations, to facilitate management and mitigation of similar crises in the future, in any region. Full article
(This article belongs to the Special Issue Modeling and Managing Catastrophic Risks in Heterogeneous Systems)
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