Optimal spreading of risks

Advanced Systems Analysis (ASA) Program researchers have developed a framework to produce policy instruments that are robust with respect to potential uncertainties to combat growing risks from natural hazards.

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Optimal design and implementation of proper policy instruments against growing risks from natural hazards is becoming even more important because of more severe climate change and increasing interdependencies among socioeconomic and environmental sectors and countries. In catastrophe management, risk spreading is one of the important measures for increasing societal resilience to disasters. ASA develops and applies methods of stochastic optimization theory to study these questions.

Analysis of the role of economic instruments for environmental regulations, such as emission trading and taxation programs, is essential for designing policy recommendations. For many years ASA has been working on theoretical foundations and applications of optimal robust spreading of risk and insurance against natural hazards. In [1], the researchers addressed the challenge of high carbon market volatility by suggesting a stochastic model of the market-based emission abatement and trade. The model generates robust emission policies under environmental safety constraints, asymmetric information, and other anthropogenic and natural uncertainties. Explicit treatment of uncertainties provides incentives for reducing them before trading. Principles of robust control under natural catastrophes and terroristic attacks were discussed by [2]. The implemented modeling framework was presented by [3] in a decision-support system for improving energy efficiency in public buildings under energy market volatility. This work was carried out in the framework of the European Commission’s FP7-funded project “Energy Efficiency and Risk Management in Public Buildings (EnRiMa).”

References

[1] Ermolieva T, Ermoliev Y, Jonas M, Obersteiner M, Wagner F, Winiwarter W (2014). Uncertainty, cost-effectiveness and environmental safety of robust carbon trading: Integrated approach. Climatic Change, 124(3):633-646

[2] Gaivoronski A, Ermoliev Y, Knopov P, Norkin V (2015). Mathematical modeling of catastrophic and terrorist risks. Journal of Cybernetics and Systems Analysis, 51(1):85-95

[3] Cano EL, Moguerza JM, Ermolieva T, Ermoliev Y (2014). Energy efficiency and risk management in public buildings: Strategic model for robust planning. Computational Management Science, 11(1-2):25-44


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Last edited: 12 March 2015

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