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This study compares the effectiveness of COVID-19 control policies on the virus’s spread and on the change of the infection dynamics in China, Germany, Austria, and the USA relying on a regression discontinuity in time and ‘earlyR’ epidemic models. The effectiveness of policies is measured by real-time reproduction number and cases counts. Comparison between the two lockdowns within each country showed the importance of people's risk perception for the effectiveness of the measures. Results suggest that restrictions applied for a long period or reintroduced later may cause at-tenuated effect on the circulation of the virus and the number of casualties.
Shangjun Liu; Tatiana Ermolieva; Guiying Cao; Gong Chen; Xiaoying Zheng. Analyzing the Effectiveness of COVID-19 Lockdown Policies Using the Time-Dependent Reproduction Number and the Regression Discontinuity Framework: Comparison between Countries. Engineering Proceedings 2021, 5, 8 .
AMA StyleShangjun Liu, Tatiana Ermolieva, Guiying Cao, Gong Chen, Xiaoying Zheng. Analyzing the Effectiveness of COVID-19 Lockdown Policies Using the Time-Dependent Reproduction Number and the Regression Discontinuity Framework: Comparison between Countries. Engineering Proceedings. 2021; 5 (1):8.
Chicago/Turabian StyleShangjun Liu; Tatiana Ermolieva; Guiying Cao; Gong Chen; Xiaoying Zheng. 2021. "Analyzing the Effectiveness of COVID-19 Lockdown Policies Using the Time-Dependent Reproduction Number and the Regression Discontinuity Framework: Comparison between Countries." Engineering Proceedings 5, no. 1: 8.
Uncertainties, risks, and disequilibrium are pervasive characteristics of modern socio-economic, technological, and environmental systems involving interactions between humans, economics, technology and nature. The systems are characterized by interdependencies, discontinuities, endogenous risks and thresholds, requiring nonsmooth quantile-based performance indicators, goals and constraints for their analysis and planning. The paper discusses the need for the two-stage stochastic optimization and the stochastic quasigradient (SQG) procedures to manage such systems. The two-stage optimization enables designing a robust portfolio of interdependent precautionary strategic and adaptive operational decisions making the systems robust with respect to potential uncertainty and risks. The SQG iterative algorithms define a “searching” process, which resembles a sequential adaptive learning and improvement of decisions from data and simulations, i.e. the so-called Adaptive Monte Carlo optimization. The SQG methods are applicable in cases when traditional stochastic approximation, gradient or stochastic gradient methods do not work, in particular, to general two-stage problems with implicitly defined goals and constraints functions, nonsmooth and possibly discontinuous performance indicators, risk and uncertainties shaped by decision of various agents. Stylized models from statistics, machine learning, robust decision making are presented to illustrate the two-stage (strategic-adaptive) modeling concept and the SQG procedures. The stylized models are parts of larger integrated assessment models developed at IIASA, e.g. Global Biosphere Management model (GLOBIOM) and Integrated Catastrophe Risk Management model (ICRIM).
Tatiana Ermolieva; Yuri Ermoliev; Michael Obersteiner; Elena Rovenskaya. Chapter 4 Two-Stage Nonsmooth Stochastic Optimization and Iterative Stochastic Quasigradient Procedure for Robust Estimation, Machine Learning and Decision Making. Transactions on Petri Nets and Other Models of Concurrency XV 2021, 45 -74.
AMA StyleTatiana Ermolieva, Yuri Ermoliev, Michael Obersteiner, Elena Rovenskaya. Chapter 4 Two-Stage Nonsmooth Stochastic Optimization and Iterative Stochastic Quasigradient Procedure for Robust Estimation, Machine Learning and Decision Making. Transactions on Petri Nets and Other Models of Concurrency XV. 2021; ():45-74.
Chicago/Turabian StyleTatiana Ermolieva; Yuri Ermoliev; Michael Obersteiner; Elena Rovenskaya. 2021. "Chapter 4 Two-Stage Nonsmooth Stochastic Optimization and Iterative Stochastic Quasigradient Procedure for Robust Estimation, Machine Learning and Decision Making." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 45-74.
Food, energy, and water (FEW) are interconnected pillars that underpin the security of people’s livelihoods. In this paper, we propose a decision-support model to better understand and aid management of regional FEW nexus systems under uncertainty. We apply the model to a case study focusing on fluctuations in water supply, which significantly affect production in the agriculture and energy sectors in Shanxi Province, China. We use a two-stage, stochastic, chance-constrained programming approach to the proposed spatially detailed cost-minimizing FEW nexus model under demand and natural resource (land and water) constraints. This approach translates the target reliability level (i.e., the probability that the devised solution can satisfy all constraints) into a penalty that has to be paid in the case of their non-fulfillment. On this basis, robust decisions (i.e., production options suitable for a broad variation in certainty of water supply) are derived. Using this approach, we estimate the penalties required to achieve given levels of reliability by incentivizing the deployment of water-saving technologies. For example, our model predicts that water storage would become cost-effective if the penalty for exceeding the available water supply were 2.5 times higher than the current price for industrial water; this would enable at least 40% reliability compared to 18% if the penalty were at the current water price level. Taking advantage of the differences in water intensity of crops in different sites, our model optimizes the reservoir location, which allows water withdrawal by agriculture to be reduced by 1.23%. We also evaluate the benefits of incorporating uncertainty and missed opportunity due to a lack of perfect information. In the case study, we show that the benefits of including uncertainty in the form of the two-stage stochastic programming approach appear to be quite significant, reaching 4% of the total solution costs. Water-importing costs, taxes, and subsidies are instruments that translate into the penalty in this model; the modeling approach presented here can thus be used to inform cost-effective and robust management of the FEW nexus in Shanxi Province, China, and other water-scarce regions around the world.
Junlian Gao; Xiangyang Xu; Guiying Cao; Yurii M. Ermoliev; Tatiana Y. Ermolieva; Elena A. Rovenskaya. Strategic decision-support modeling for robust management of the food–energy–water nexus under uncertainty. Journal of Cleaner Production 2021, 292, 125995 .
AMA StyleJunlian Gao, Xiangyang Xu, Guiying Cao, Yurii M. Ermoliev, Tatiana Y. Ermolieva, Elena A. Rovenskaya. Strategic decision-support modeling for robust management of the food–energy–water nexus under uncertainty. Journal of Cleaner Production. 2021; 292 ():125995.
Chicago/Turabian StyleJunlian Gao; Xiangyang Xu; Guiying Cao; Yurii M. Ermoliev; Tatiana Y. Ermolieva; Elena A. Rovenskaya. 2021. "Strategic decision-support modeling for robust management of the food–energy–water nexus under uncertainty." Journal of Cleaner Production 292, no. : 125995.
Critical imbalances and threshold exceedances can trigger a disruption in a network of interdependent systems. An insignificant-at-first-glance shock can induce systemic risks with cascading catastrophic impacts. Systemic risks challenge traditional risk assessment and management approaches. These risks are shaped by systemic interactions, risk exposures, and decisions of various agents. The paper discusses the need for the two-stage stochastic optimization (STO) approach that enables the design of a robust portfolio of precautionary strategic and operational adaptive decisions that makes the interdependent systems flexible and robust with respect to risks of all kinds. We established a connection between the robust quantile-based non-smooth estimation problem in statistics and the two-stage non-smooth STO problem of robust strategic–adaptive decision-making. The coexistence of complementary strategic and adaptive decisions induces systemic risk aversion in the form of Value-at-Risk (VaR) quantile-based risk constraints. The two-stage robust decision-making is implemented into a large-scale Global Biosphere Management (GLOBIOM) model, showing that robust management of systemic risks can be addressed by solving a system of probabilistic security equations. Selected numerical results emphasize that a robust combination of interdependent strategic and adaptive solutions presents qualitatively new policy recommendations, if compared to a traditional scenario-by-scenario decision-making analysis.
Tatiana Ermolieva; Petr Havlik; Yuri Ermoliev; Nikolay Khabarov; Michael Obersteiner. Robust Management of Systemic Risks and Food-Water-Energy-Environmental Security: Two-Stage Strategic-Adaptive GLOBIOM Model. Sustainability 2021, 13, 857 .
AMA StyleTatiana Ermolieva, Petr Havlik, Yuri Ermoliev, Nikolay Khabarov, Michael Obersteiner. Robust Management of Systemic Risks and Food-Water-Energy-Environmental Security: Two-Stage Strategic-Adaptive GLOBIOM Model. Sustainability. 2021; 13 (2):857.
Chicago/Turabian StyleTatiana Ermolieva; Petr Havlik; Yuri Ermoliev; Nikolay Khabarov; Michael Obersteiner. 2021. "Robust Management of Systemic Risks and Food-Water-Energy-Environmental Security: Two-Stage Strategic-Adaptive GLOBIOM Model." Sustainability 13, no. 2: 857.
The paper describes an agricultural crop diversification model on the basis of simulation modeling and robust solutions. The model is intended for the design and development of the optimal structure of crop areas in order to combine the agricultural crops in a compatible manner, and to gradually transition to the principles of sustainable farming in domestic agriculture. A detailed analysis of the modern practice of monoculture farming was conducted. Based on these calculations, a diversified structure of ten agricultural crops is proposed, which will help strengthen the internal food security and harmonize agricultural development in its ecological, social, and economic aspects.
O. M. Borodina; S. V. Kyryziuk; O. V. Fraier; Y. M. Ermoliev; T. Y. Ermolieva; P. S. Knopov; V. M. Horbachuk. Mathematical Modeling of Agricultural Crop Diversification in Ukraine: Scientific Approaches and Empirical Results*. Cybernetics and Systems Analysis 2020, 1 -10.
AMA StyleO. M. Borodina, S. V. Kyryziuk, O. V. Fraier, Y. M. Ermoliev, T. Y. Ermolieva, P. S. Knopov, V. M. Horbachuk. Mathematical Modeling of Agricultural Crop Diversification in Ukraine: Scientific Approaches and Empirical Results*. Cybernetics and Systems Analysis. 2020; ():1-10.
Chicago/Turabian StyleO. M. Borodina; S. V. Kyryziuk; O. V. Fraier; Y. M. Ermoliev; T. Y. Ermolieva; P. S. Knopov; V. M. Horbachuk. 2020. "Mathematical Modeling of Agricultural Crop Diversification in Ukraine: Scientific Approaches and Empirical Results*." Cybernetics and Systems Analysis , no. : 1-10.
This paper aimed to estimate health risks focusing on respiratory diseases from exposure to gaseous multi-pollutants based on new data and revealed new evidence after the most stringent air pollution control plan in Beijing which was carried out in 2013. It used daily respiratory diseases outpatient data from a hospital located in Beijing with daily meteorological data and monitor data of air pollutants from local authorities. All data were collected from 2014 to 2016. Distributed lag non-linear model was employed. Results indicated that NO2 and CO had positive association with outpatients number on the day of the exposure (1.045 (95% confidence interval (CI): 1.003, 1.089) for CO and 1.022 (95% CI: 1.008, 1.036) for NO2) (and on the day after the exposure (1.026 (95% CI: 1.005, 1.048) for CO and 1.013 (95% CI: 1.005, 1.021) for NO2). Relative risk (RR) generally declines with the number of lags; ozone produces significant effects on the first day (RR = 0.993 (95% CI: 0.989, 0.998)) as well as second day (RR = 0.995 (95% CI: 0.991, 0.999)) after the exposure, while particulate pollutants did not produce significant effects. Effects from the short-term exposure to gaseous pollutants were robust after controlling for particulate matters. Our results contribute to a comprehensive understanding of the dependencies between the change of air pollutants concentration and their health effects in Beijing after the implementation of promising air regulations in 2013. Results of the study can be used to develop relevant measures minimizing the adverse health consequences of air pollutants and supporting sustainable development of Beijing as well as other rapidly growing Asian cities.
Yunfei Cheng; Tatiana Ermolieva; Gui-Ying Cao; Xiaoying Zheng. Health Impacts of Exposure to Gaseous Pollutants and Particulate Matter in Beijing—A Non-Linear Analysis Based on the New Evidence. International Journal of Environmental Research and Public Health 2018, 15, 1969 .
AMA StyleYunfei Cheng, Tatiana Ermolieva, Gui-Ying Cao, Xiaoying Zheng. Health Impacts of Exposure to Gaseous Pollutants and Particulate Matter in Beijing—A Non-Linear Analysis Based on the New Evidence. International Journal of Environmental Research and Public Health. 2018; 15 (9):1969.
Chicago/Turabian StyleYunfei Cheng; Tatiana Ermolieva; Gui-Ying Cao; Xiaoying Zheng. 2018. "Health Impacts of Exposure to Gaseous Pollutants and Particulate Matter in Beijing—A Non-Linear Analysis Based on the New Evidence." International Journal of Environmental Research and Public Health 15, no. 9: 1969.
Across the world, human activity is approaching planetary boundaries. In northwest China, in particular, the coal industry and agriculture are competing for key limited inputs of land and water. In this situation, the traditional approach to planning the development of each sector independently fails to deliver sustainable solutions, as solutions made in sectorial ‘silos’ are often suboptimal for the entire economy. We propose a spatially detailed cost-minimizing model for coal and agricultural production in a region under constraints on land and water availability. We apply the model to the case study of Shanxi province, China. We show how such an integrated optimization, which takes maximum advantage of the spatial heterogeneity in resource abundance, could help resolve the conflicts around the water–food–energy (WFE) nexus and assist in its management. We quantify the production-possibility frontiers under different water-availability scenarios and demonstrate that in water-scarce regions, like Shanxi, the production capacity and corresponding production solutions are highly sensitive to water constraints. The shadow prices estimated in the model could be the basis for intelligent differentiated water pricing, not only to enable the water-resource transfer between agriculture and the coal industry, and across regions, but also to achieve cost-effective WFE management.
Junlian Gao; Xiangyang Xu; Guiying Cao; Yurii M. Ermoliev; Tatiana Y. Ermolieva; Elena A. Rovenskaya. Optimizing Regional Food and Energy Production under Limited Water Availability through Integrated Modeling. Sustainability 2018, 10, 1689 .
AMA StyleJunlian Gao, Xiangyang Xu, Guiying Cao, Yurii M. Ermoliev, Tatiana Y. Ermolieva, Elena A. Rovenskaya. Optimizing Regional Food and Energy Production under Limited Water Availability through Integrated Modeling. Sustainability. 2018; 10 (6):1689.
Chicago/Turabian StyleJunlian Gao; Xiangyang Xu; Guiying Cao; Yurii M. Ermoliev; Tatiana Y. Ermolieva; Elena A. Rovenskaya. 2018. "Optimizing Regional Food and Energy Production under Limited Water Availability through Integrated Modeling." Sustainability 10, no. 6: 1689.
In order to conduct research at required spatial resolution, we propose a model fusion involving interlinked calculations of regional projections by the global dynamic model GLOBIOM (Global Biosphere Management Model) and robust dynamic downscaling model, based on cross-entropy principle, for deriving spatially resolved projections. The proposed procedure allows incorporating data from satellite images, statistics, expert opinions, as well as data from global land use models. In numerous case studies in China and Ukraine, the approach allowed to estimate local land use and land use change projections corresponding to real trends and expectations. The disaggregated data and projections were used in national models for planning sustainable land use and agricultural development.
T. Y. Ermolieva; Y. M. Ermoliev; Petr Havlik; Aline Mosnier; David Leclere; S. Fritz; Hugo Valin; M. Obersteiner; S. V. Kyryzyuk; O. M. Borodina. Dynamic Merge of the Global and Local Models for Sustainable Land Use Planning with Regard for Global Projections from GLOBIOM and Local Technical–Economic Feasibility and Resource Constraints*. Cybernetics and Systems Analysis 2017, 53, 176 -185.
AMA StyleT. Y. Ermolieva, Y. M. Ermoliev, Petr Havlik, Aline Mosnier, David Leclere, S. Fritz, Hugo Valin, M. Obersteiner, S. V. Kyryzyuk, O. M. Borodina. Dynamic Merge of the Global and Local Models for Sustainable Land Use Planning with Regard for Global Projections from GLOBIOM and Local Technical–Economic Feasibility and Resource Constraints*. Cybernetics and Systems Analysis. 2017; 53 (2):176-185.
Chicago/Turabian StyleT. Y. Ermolieva; Y. M. Ermoliev; Petr Havlik; Aline Mosnier; David Leclere; S. Fritz; Hugo Valin; M. Obersteiner; S. V. Kyryzyuk; O. M. Borodina. 2017. "Dynamic Merge of the Global and Local Models for Sustainable Land Use Planning with Regard for Global Projections from GLOBIOM and Local Technical–Economic Feasibility and Resource Constraints*." Cybernetics and Systems Analysis 53, no. 2: 176-185.
The authors develop a three-dimensional model to schedule a sustainable development of energy and agricultural industries in China under competition for land and water resources. The numerical experiments confirm the role of natural resources as factors that determine secure supply of energy and food and emphasize the important role of systems analysis of interdependences among industries and resources.
Xu Xiangyang; Junlian Gao; Gui-Ying Cao; Yu. M. Ermoliev; T. Yu. Ermolieva; A. V. Kryazhimskii; E. A. Rovenskaya. Systems Analysis of Coal Production and Energy-Water-Food Security in China. Cybernetics and Systems Analysis 2015, 51, 370 -377.
AMA StyleXu Xiangyang, Junlian Gao, Gui-Ying Cao, Yu. M. Ermoliev, T. Yu. Ermolieva, A. V. Kryazhimskii, E. A. Rovenskaya. Systems Analysis of Coal Production and Energy-Water-Food Security in China. Cybernetics and Systems Analysis. 2015; 51 (3):370-377.
Chicago/Turabian StyleXu Xiangyang; Junlian Gao; Gui-Ying Cao; Yu. M. Ermoliev; T. Yu. Ermolieva; A. V. Kryazhimskii; E. A. Rovenskaya. 2015. "Systems Analysis of Coal Production and Energy-Water-Food Security in China." Cybernetics and Systems Analysis 51, no. 3: 370-377.
Carbon markets, like other commodity markets, are volatile. They react to stochastic “disequilibrium” spot prices, which may be affected by inadequate policies, speculations and bubbles. The market-based emission trading, therefore, does not necessarily minimize abatement costs and achieve emission reduction goals. We introduce a basic stochastic model integrating emissions reduction, monitoring and trading costs allowing us to analyze the robustness of emission and uncertainty reduction policies under environmental safety constraints asymmetric information and other multiple anthropogenic and natural uncertainties. Explicit treatment of uncertainties provides incentives for reducing them before trading. We illustrate functioning of the robust market with numerical results involving such countries as the US, Australia, Canada, Japan, EU27, Russia, Ukraine. In particular, we analyze if the knowledge about uncertainties may affect portfolios of technological and trade policies or structure of the market and how uncertainty characteristics may affect market prices and change the market structure.
T. Ermolieva; Y. Ermoliev; Matthias Jonas; M. Obersteiner; F. Wagner; W. Winiwarter. Uncertainty, cost-effectiveness and environmental safety of robust carbon trading: integrated approach. Uncertainties in Greenhouse Gas Inventories 2015, 183 -196.
AMA StyleT. Ermolieva, Y. Ermoliev, Matthias Jonas, M. Obersteiner, F. Wagner, W. Winiwarter. Uncertainty, cost-effectiveness and environmental safety of robust carbon trading: integrated approach. Uncertainties in Greenhouse Gas Inventories. 2015; ():183-196.
Chicago/Turabian StyleT. Ermolieva; Y. Ermoliev; Matthias Jonas; M. Obersteiner; F. Wagner; W. Winiwarter. 2015. "Uncertainty, cost-effectiveness and environmental safety of robust carbon trading: integrated approach." Uncertainties in Greenhouse Gas Inventories , no. : 183-196.
The paper focuses on the development of adequate approaches to the systems analysis of geographically-explicit robust decisions for long-term consistent management of interdependent land use systems. We introduce a stochastic partial equilibrium Global Biosphere Management Model (GLOBIOM) enabling to analyze secure food, energy, and water provision accounting for interdependencies between countries and global diversification of systemic risks.
T. Yu. Ermolieva; Yu. M. Ermoliev; P. Havlik; A. Mosnier; D. LeClere; F. Kraksner; N. Khabarov; M. Obersteiner. Systems Analysis of Robust Strategic Decisions to Plan Secure Food, Energy, and Water Provision Based on the Stochastic Globiom Model. Cybernetics and Systems Analysis 2015, 51, 125 -133.
AMA StyleT. Yu. Ermolieva, Yu. M. Ermoliev, P. Havlik, A. Mosnier, D. LeClere, F. Kraksner, N. Khabarov, M. Obersteiner. Systems Analysis of Robust Strategic Decisions to Plan Secure Food, Energy, and Water Provision Based on the Stochastic Globiom Model. Cybernetics and Systems Analysis. 2015; 51 (1):125-133.
Chicago/Turabian StyleT. Yu. Ermolieva; Yu. M. Ermoliev; P. Havlik; A. Mosnier; D. LeClere; F. Kraksner; N. Khabarov; M. Obersteiner. 2015. "Systems Analysis of Robust Strategic Decisions to Plan Secure Food, Energy, and Water Provision Based on the Stochastic Globiom Model." Cybernetics and Systems Analysis 51, no. 1: 125-133.
The chapter analyzes effects of catastrophes on economic growth and stagnation. The economy is a complex system constantly facing shocks and changes with possible catastrophic impacts. A shock is understood as an event removing from the economy a part of the capital. We show that even in the case of well-behaving economies defined by the Harrod-Domar model, persistent in time shocks implicitly modify the economy and may lead to various traps and thresholds triggering stagnation and shrinking. The stabilization of the growth must then rely on ex-ante risk reduction and risk transfer options, such as hazard mitigation and the purchase of catastrophic insurance, as well as ex-post borrowing. The coexistence of ex-ante (risk averse) and ex-post (risk prone) options in the proposed model generates a strong risk aversion even in the case of linear utility functions. In contrast to the traditional expected utility theory, it assesses and explains trade-offs and benefits of ex-ante and ex-post management options
Yuri Ermoliev; Tatiana Ermolieva. Economic Growth Under Catastrophes. Landslides in Sensitive Clays 2012, 103 -117.
AMA StyleYuri Ermoliev, Tatiana Ermolieva. Economic Growth Under Catastrophes. Landslides in Sensitive Clays. 2012; ():103-117.
Chicago/Turabian StyleYuri Ermoliev; Tatiana Ermolieva. 2012. "Economic Growth Under Catastrophes." Landslides in Sensitive Clays , no. : 103-117.
Planning dams for regional economic developments and social welfare without addressing issues related to catastrophic risks may lead to dangerous clustering of people, production facilities, and infrastructure in hazard-prone areas. The concerned region may be exposed to very large losses from the low probability-high consequence event of a dam break. Endogeneity of risks on land use decisions represents new challenges for dam development planning. In this chapter we discuss an integrated risk management model that allows the planners to deal in a consistent way with the multiple aspects, views and objectives of dam projects. We introduce the notion of robust decisions, which are considered safe, flexible, and optimal because they account for multiple criteria, risks and heterogeneities of locations and stakeholders. Specific attention is paid to the choice of proper discount factors to address long-term planning perspectives of dam construction and maintenance. We illustrate how the misperception of proper discounting in the presence of potential catastrophic events may overlook the need for dam maintenance and undermine regional safety. The proposed model can be used as a learning-by-simulation tool for designing robust regulations and policies
Tatiana Ermolieva; Yuri Ermoliev; Michael Obersteiner; Marek Makowski; Günther Fischer. Dams and Catastrophe Risk: Discounting in Long Term Planning. Landslides in Sensitive Clays 2012, 73 -92.
AMA StyleTatiana Ermolieva, Yuri Ermoliev, Michael Obersteiner, Marek Makowski, Günther Fischer. Dams and Catastrophe Risk: Discounting in Long Term Planning. Landslides in Sensitive Clays. 2012; ():73-92.
Chicago/Turabian StyleTatiana Ermolieva; Yuri Ermoliev; Michael Obersteiner; Marek Makowski; Günther Fischer. 2012. "Dams and Catastrophe Risk: Discounting in Long Term Planning." Landslides in Sensitive Clays , no. : 73-92.
In catastrophe management, risk spreading is one of the important measures for increasing societal resilience to disasters. In this paper we discuss an integrated catastrophe management model which explores alternative risk spreading options. As a case study we consider the seismic prone Tuscany region of Italy. Special attention is given to the evaluation of a public loss-spreading program involving partial compensation to victims by the central government and the spreading of risks through a pool of insurers on the basis of location-specific exposures. GIS-based catastrophe models and stochastic optimization methods are used to guide policy analysis with respect to location-specific risk exposures. The use of economically sound risk indicators lead to convex stochastic optimization problems strongly connected with nonconvex insolvency constraint and Conditional Value-at-Risk (CVaR)
Tatiana Ermolieva; Yuri Ermoliev. Modeling Catastrophe Risk for Designing Insurance Systems. Landslides in Sensitive Clays 2012, 29 -52.
AMA StyleTatiana Ermolieva, Yuri Ermoliev. Modeling Catastrophe Risk for Designing Insurance Systems. Landslides in Sensitive Clays. 2012; ():29-52.
Chicago/Turabian StyleTatiana Ermolieva; Yuri Ermoliev. 2012. "Modeling Catastrophe Risk for Designing Insurance Systems." Landslides in Sensitive Clays , no. : 29-52.
Catastrophe models that combine data on past occurrences with future simulations of the hazard, exposure and vulnerability, and that take account of the dynamic environment as well as correlated loss distributions, are becoming increasingly important for assessing the risks of extreme events. This volume demonstrates innovative ways for adapting catastrophe models to aid risk management policy processes via a number of wide ranging applications. These are grouped into three parts, according to whether they inform local or regional risk management policy (Part I); the management of country-wide catastrophe risk and its implication on development (Part II); and the participatory design of a national insurance program (Part III). After discussion of the rational for the proposed approaches, which integrate across multiple disciplines and take into account the diverse values and preferences of stakeholders, this chapter introduces Part I of the volume, including cases on the management of flash flood risk in Vienna, Austria, an earthquake insurance program for the Tuscany region in Italy, balancing stakeholder concerns in establishing flood risk management strategies in northern Vietnam, and the choice of appropriate discounting factors in the design of infrastructures under consideration of catastrophe risk
Aniello Amendola; Tatiana Ermolieva; Joanne Linnerooth-Bayer; Reinhard Mechler. Catastrophe Models for Informing Risk Management Policy: An Introduction. Landslides in Sensitive Clays 2012, 3 -12.
AMA StyleAniello Amendola, Tatiana Ermolieva, Joanne Linnerooth-Bayer, Reinhard Mechler. Catastrophe Models for Informing Risk Management Policy: An Introduction. Landslides in Sensitive Clays. 2012; ():3-12.
Chicago/Turabian StyleAniello Amendola; Tatiana Ermolieva; Joanne Linnerooth-Bayer; Reinhard Mechler. 2012. "Catastrophe Models for Informing Risk Management Policy: An Introduction." Landslides in Sensitive Clays , no. : 3-12.
In this chapter, different concepts of risk and uncertainty are applied to the analysis and management of the risk of flooding along the Vienna River in Vienna, Austria. The methodology illustrates how, by the use of catastrophe models, it is possible to extend traditional engineering-based approaches to flood risk management to integrate loss spreading techniques (such as the purchase of flood insurance or the maintenance of a catastrophe fund) with traditional loss-reduction techniques (such as the construction of levees, floodwalls, or detention basins) and to give a full account of uncertainty. The results show that the greatest risk from flash flooding is to the Vienna city subway system, and suggest that combining available measures in an overall mitigation strategy results in decreasing total costs and reducing the likelihood and uncertainties of catastrophic financial loss
Keith L. Compton; Tatiana Ermolieva; Joanne Linnerooth-Bayer; Aniello Amendola; Rudolf Faber; Hans-Peter Nachtnebel. Modeling Risk and Uncertainty: Managing Flash Flood Risk in Vienna. Landslides in Sensitive Clays 2012, 13 -28.
AMA StyleKeith L. Compton, Tatiana Ermolieva, Joanne Linnerooth-Bayer, Aniello Amendola, Rudolf Faber, Hans-Peter Nachtnebel. Modeling Risk and Uncertainty: Managing Flash Flood Risk in Vienna. Landslides in Sensitive Clays. 2012; ():13-28.
Chicago/Turabian StyleKeith L. Compton; Tatiana Ermolieva; Joanne Linnerooth-Bayer; Aniello Amendola; Rudolf Faber; Hans-Peter Nachtnebel. 2012. "Modeling Risk and Uncertainty: Managing Flash Flood Risk in Vienna." Landslides in Sensitive Clays , no. : 13-28.
This chapter summarizes studies on the development of a financial risk management model for floods in the Upper Tisza river region, Hungary. We focus on the evaluation of a multi-pillar flood loss-spreading program involving partial compensation to flood victims by the central government, the pooling of risks through a mandatory public-private insurance on the basis of location-specific exposures, and a contingent ex-ante credit to reinsure the pool's liabilities. Policy analysis is guided by GIS-based catastrophe models and stochastic optimization methods with respect to location-specific risk exposures. We use economically sound risk indicators leading to convex stochastic optimization problems strongly connected with non-convex insolvency constraint and Conditional Value-at-Risk (CVaR)
Yuri Ermoliev; Tatiana Ermolieva; Istvan Galambos. Optimizing Public Private Risk Transfer Systems for Flood Risk Management in the Upper Tisza Region. Landslides in Sensitive Clays 2012, 245 -262.
AMA StyleYuri Ermoliev, Tatiana Ermolieva, Istvan Galambos. Optimizing Public Private Risk Transfer Systems for Flood Risk Management in the Upper Tisza Region. Landslides in Sensitive Clays. 2012; ():245-262.
Chicago/Turabian StyleYuri Ermoliev; Tatiana Ermolieva; Istvan Galambos. 2012. "Optimizing Public Private Risk Transfer Systems for Flood Risk Management in the Upper Tisza Region." Landslides in Sensitive Clays , no. : 245-262.
In Ukraine, the growth of intensive agricultural enterprises that focus on fast profits contribute considerably to food insecurity and increasing socio-economic and environmental risks. Ukraine has important natural and labor resources for effective rural development; more than 50% of food production is still contributed by small and medium farms, despite the difficulties associated with economic instabilities and the lack of proper policy support. Currently, the main issue for the agro-policy is to use these resources in a sustainable way, enforcing robust long term development of rural communities and agriculture. In this chapter, we introduce a stochastic, geographically explicit model for designing forward-looking policies regarding robust resources allocation and composition of agricultural production, in order to enhance food security and rural development. In particular, we investigate the role of investments into rural facilities to stabilize and enhance the performance of the agrofood sector in view of uncertainties and incomplete information. The security goals are introduced in the form of multidimensional risk indicators
Oleksandra Borodina; Elena Borodina; Tatiana Ermolieva; Yuri Ermoliev; Günther Fischer; Marek Makowski; Harrij Van Velthuizen. Sustainable Agriculture, Food Security, and Socio-Economic Risks in Ukraine. Multiple Criteria Decision Making 2011, 169 -185.
AMA StyleOleksandra Borodina, Elena Borodina, Tatiana Ermolieva, Yuri Ermoliev, Günther Fischer, Marek Makowski, Harrij Van Velthuizen. Sustainable Agriculture, Food Security, and Socio-Economic Risks in Ukraine. Multiple Criteria Decision Making. 2011; ():169-185.
Chicago/Turabian StyleOleksandra Borodina; Elena Borodina; Tatiana Ermolieva; Yuri Ermoliev; Günther Fischer; Marek Makowski; Harrij Van Velthuizen. 2011. "Sustainable Agriculture, Food Security, and Socio-Economic Risks in Ukraine." Multiple Criteria Decision Making , no. : 169-185.
In this chapter we present an integrated model for long term and geographically explicit planning of agricultural activities to meet demands under resource constraints and ambient targets. Environmental, resource and production feasibility indicators permit estimating impacts of agricultural practices on environment to guide agricultural policies regarding production allocation, intensification, and fertilizer application while accounting for local constraints. Physical production potentials of land are incorporated in the model, together with demographic and socio-economic variables and behavioral drivers to reflect spatial distribution of demands and production intensification levels. The application of the model is demonstrated with a case study of nitrogen accounting at the level of China counties. We discuss current intensification trends and estimate the ranges of agricultural impacts on China's environment under plausible pollution mitigation scenarios with a particular focus on nitrogen sources and losses
Günther Fischer; Wilfried Winiwarter; Tatiana Ermolieva; Gui-Ying Cao; Harrij Van Velthuizen; Zbigniew Klimont; Wolfgang Schoepp; Wim Van Veen; David Wiberg; Fabian Wagner. Sustainable Agriculture in China: Estimation and Reduction of Nitrogen Impacts. Multiple Criteria Decision Making 2011, 327 -350.
AMA StyleGünther Fischer, Wilfried Winiwarter, Tatiana Ermolieva, Gui-Ying Cao, Harrij Van Velthuizen, Zbigniew Klimont, Wolfgang Schoepp, Wim Van Veen, David Wiberg, Fabian Wagner. Sustainable Agriculture in China: Estimation and Reduction of Nitrogen Impacts. Multiple Criteria Decision Making. 2011; ():327-350.
Chicago/Turabian StyleGünther Fischer; Wilfried Winiwarter; Tatiana Ermolieva; Gui-Ying Cao; Harrij Van Velthuizen; Zbigniew Klimont; Wolfgang Schoepp; Wim Van Veen; David Wiberg; Fabian Wagner. 2011. "Sustainable Agriculture in China: Estimation and Reduction of Nitrogen Impacts." Multiple Criteria Decision Making , no. : 327-350.
Agriculture is a major source of water, air and soil pollution. Pollution rates are often linked to intensification of agricultural activities, i.e., large-scale livestock production and excessive fertilization of crops. In this paper, we develop a concept of a spatially and temporally explicit model for sustainable agriculture production planning, which operates at a detailed spatial scale and integrates demand and agricultural production activities at national and sub-national levels. It distinguishes environmental loads and sources of agricultural pollution originating from two main agricultural activities: livestock raising and crop production. The indicators used to reflect the impacts of agricultural production comprise of fluxes to air (emissions of ammonia and nitrous oxide) and water (nitrate leaching). Possible domains of the indicator variables are subdivided into sub-domains of different impact severity classes. Risk functions indicate the degree of risk associated with agricultural production, similar to fuzzy logic approaches. Alternative production scenarios are compared in terms of human exposure to risk, i.e., the number of people in different risk classes. In this way the model allows for assessment and testing of strategies for mitigating agricultural pollution. Application of the integrated model is illustrated by a case study of agricultural production development in China. China is of global importance due to its huge size and is of scientific interest due to its heterogeneity. Experience gained from the case study can be used for similar research in other regions.
G. Fischer; W. Winiwarter; T. Ermolieva; G.-Y. Cao; H. Qui; Zbigniew Klimont; D. Wiberg; F. Wagner. Integrated modeling framework for assessment and mitigation of nitrogen pollution from agriculture: Concept and case study for China. Agriculture, Ecosystems & Environment 2010, 136, 116 -124.
AMA StyleG. Fischer, W. Winiwarter, T. Ermolieva, G.-Y. Cao, H. Qui, Zbigniew Klimont, D. Wiberg, F. Wagner. Integrated modeling framework for assessment and mitigation of nitrogen pollution from agriculture: Concept and case study for China. Agriculture, Ecosystems & Environment. 2010; 136 (1-2):116-124.
Chicago/Turabian StyleG. Fischer; W. Winiwarter; T. Ermolieva; G.-Y. Cao; H. Qui; Zbigniew Klimont; D. Wiberg; F. Wagner. 2010. "Integrated modeling framework for assessment and mitigation of nitrogen pollution from agriculture: Concept and case study for China." Agriculture, Ecosystems & Environment 136, no. 1-2: 116-124.