Abstract
No matter what one’s views on global climate change, it is easy to agree that there is great uncertainty and that our models should reflect that uncertainty. The technical difficulty is that uncertainty can lead to an enormous increase in dimensionality. In this chapter, we will explore an alternative approach to dealing with the problem of dimensionality in large multiregion, multiperiod models, where the regions are aggregated so that we solve a “one-world” model in the later time periods, because discounting limits the importance of distant-future uncertainties for near-future decisions.
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References
Andronova, N., Schlesinger, M.E.: Objective estimation of the probability density function for climate sensitivity. J. Geophys. Res. D19(106), 22605–22611 (2001)
Chang, D.: Solving large-scale intertemporal CGE models with decomposition methods. PhD thesis, Department of Operations Research, Stanford University, Stanford, CA (1997)
Energy Modeling Forum. EMF 22: Climate change control scenarios, Energy Modeling Forum, Stanford University, Stanford, CA (2004–2006)
Manne, A.S., Stephan, G.: Global climate change and the equity-efficiency puzzle. Working Paper (2003)
Rutherford, T.: Sequential joint maximization. In: Weyant, J.P., (ed.) Energy and Environmental Policy Modeling. Kluwer, Norwell, MA (1999)
Yohe, G.: Second round exercise description and instructions. EMF report, Energy Modeling Forum, Stanford University, Stanford, CA (2005)
Acknowledgments
For helpful comments, the author is indebted to John Rowse and Thomas Rutherford.
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Manne, A.S. (2010). Global Climate Decisions Under Uncertainty. In: Infanger, G. (eds) Stochastic Programming. International Series in Operations Research & Management Science, vol 150. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-1642-6_15
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DOI: https://doi.org/10.1007/978-1-4419-1642-6_15
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