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Development of a Fuzzy optimization model, supporting global warming decision-making

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  • Marian Leimbach

Abstract

An increasing number of models have been developed to support global warming response policies. The model constructors are facing a lot of uncertainties which limit the evidence of these models. The support of climate policy decision-making is only possible in a semi-quantitative way, as presented by aFuzzy model. The model design is based on an optimization approach, integrated in a bounded risk decision-making framework. Given some regional emission-related and impact-related restrictions, optimal emission paths can be calculated. The focus is not only on carbon dioxide but on other greenhouse gases too. In the paper, the components of the model will be described. Cost coefficients, emission boundaries and impact boundaries are represented asFuzzy parameters. TheFuzzy model will be transformed into a computational one by using an approach of Rommelfanger. In the second part, some problems of applying the model to computations will be discussed. This includes discussions on the data situation and the presentation, as well as interpretation of results of sensitivity analyses. The advantage of theFuzzy approach is that the requirements regarding data precision are not so strong. Hence, the effort for data acquisition can be reduced and computations can be started earlier. Copyright Kluwer Academic Publishers 1996

Suggested Citation

  • Marian Leimbach, 1996. "Development of a Fuzzy optimization model, supporting global warming decision-making," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 7(2), pages 163-192, March.
    • Handle: RePEc:kap:enreec:v:7:y:1996:i:2:p:163-192
    DOI: 10.1007/BF00699290
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    References listed on IDEAS

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    1. William R. Cline, 1992. "Economics of Global Warming, The," Peterson Institute Press: All Books, Peterson Institute for International Economics, number 39, October.
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    Cited by:

    1. Tony Prato, 2008. "Accounting for risk and uncertainty in determining preferred strategies for adapting to future climate change," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 13(1), pages 47-60, January.
    2. Xu, Y. & Huang, G.H. & Qin, X.S. & Cao, M.F., 2009. "SRCCP: A stochastic robust chance-constrained programming model for municipal solid waste management under uncertainty," Resources, Conservation & Recycling, Elsevier, vol. 53(6), pages 352-363.
    3. Li, Y.P. & Huang, G.H. & Nie, S.L. & Qin, X.S., 2007. "ITCLP: An inexact two-stage chance-constrained program for planning waste management systems," Resources, Conservation & Recycling, Elsevier, vol. 49(3), pages 284-307.
    4. Giorgio Brajnik & Marji Lines, 1998. "Qualitative Modeling and Simulation of Socio-Economic Phenomena," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 1(1), pages 1-2.
    5. Sun, Y. & Huang, G.H. & Li, Y.P., 2010. "ICQSWM: An inexact chance-constrained quadratic solid waste management model," Resources, Conservation & Recycling, Elsevier, vol. 54(10), pages 641-657.
    6. Li, Y.P. & Huang, G.H. & Nie, X.H. & Nie, S.L., 2008. "A two-stage fuzzy robust integer programming approach for capacity planning of environmental management systems," European Journal of Operational Research, Elsevier, vol. 189(2), pages 399-420, September.

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