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Uncertainty in climate change modelling: can global sensitivity analysis be of help?

Author

Listed:
  • Barry ANDERSON

    ()

  • Emanuele BORGONOVO

    ()

  • Marzio GALEOTTI

    ()

  • Roberto ROSON

    ()

Abstract

The complexity of integrated assessment models (IAMs) prevents the direct appreciation of the impact of uncertainty on the model predictions. However, for a full understanding and corroboration of model results, analysts might be willing, and ought to identify the model inputs that influence the model results the most (key drivers), appraise the relevance of interactions and the direction of change associated with the simultaneous variation of the model inputs. We show that such information is already contained in the data set produced by Monte Carlo simulations and that it can be extracted without additional calculations. Our discussion is guided by an application of the proposed methodologies to the well-known DICE model of William Nordhaus (2008). A comparison of the proposed methodology to approaches previously applied on the same model shows that robust insights concerning the dependence of future atmospheric temperature, global emissions and current carbon costs and taxes on the model’s exogenous inputs can be obtained. The method avoids the fallacy of a priori deeming the important factors based on sole intuition.

Suggested Citation

  • Barry ANDERSON & Emanuele BORGONOVO & Marzio GALEOTTI & Roberto ROSON, 2012. "Uncertainty in climate change modelling: can global sensitivity analysis be of help?," Departmental Working Papers 2012-18, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
  • Handle: RePEc:mil:wpdepa:2012-18
    as

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    File URL: http://wp.demm.unimi.it/files/wp/2012/DEMM-2012_018wp.pdf
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    References listed on IDEAS

    as
    1. Borgonovo, E. & Gatti, S. & Peccati, L., 2010. "What drives value creation in investment projects? An application of sensitivity analysis to project finance transactions," European Journal of Operational Research, Elsevier, vol. 205(1), pages 227-236, August.
    2. David Anthoff & Richard Tol, 2013. "The uncertainty about the social cost of carbon: A decomposition analysis using fund," Climatic Change, Springer, vol. 117(3), pages 515-530, April.
    3. Baker, Erin & Solak, Senay, 2011. "Climate change and optimal energy technology R&D policy," European Journal of Operational Research, Elsevier, vol. 213(2), pages 442-454, September.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Simon Dietz & Anca N. Matei, 2013. "Is there space for agreement on climate change? A non-parametric approach to policy evaluation," GRI Working Papers 136, Grantham Research Institute on Climate Change and the Environment.

    More about this item

    Keywords

    OR in Environment; Robustness and Sensitivity; Climate change; Global sensitivity analysis; Integrated Assessment Modelling;

    JEL classification:

    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth

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