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Endogenous learning in climate-energy-economic models – an inventory of key uncertainties

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  • Bob van der Zwaan
  • Ad Seebregts

Abstract

This paper gives an overview of uncertainties related to endogenous learning as observed in integrated assessment models (IAMs) of global warming, both for bottom-up and top-down climate-energy-economic models. A classification is formulated by which uncertainties can be evaluated, and through which one can distinguish between modelling, methodological and parameter uncertainties. We emphasise that the analysis of uncertainties in IAM exercises of global warming is essential for both scientific and policy-making related reasons. At present, proper analyses of the sensitivity and robustness characteristics of modelling results are often omitted. Our main conclusion, and recommendation, is that in future IAM analyses of climate change, both for the benefit of scientists and public policy decision makers, the presence of different kinds of uncertainties should be appropriately recognised, classified, quantified and reported.

Suggested Citation

  • Bob van der Zwaan & Ad Seebregts, 2004. "Endogenous learning in climate-energy-economic models – an inventory of key uncertainties," International Journal of Energy Technology and Policy, Inderscience Enterprises Ltd, vol. 2(1/2), pages 130-141.
    • Handle: RePEc:ids:ijetpo:v:2:y:2004:i:1/2:p:130-141
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    Cited by:

    1. Reinhard Haas & Marlene Sayer & Amela Ajanovic & Hans Auer, 2023. "Technological learning: Lessons learned on energy technologies," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(2), March.
    2. Giuseppe Travaglini, 2010. "The dynamic effects of technological and non technological shocks in the energy sector: a case study for Italy," Working Papers 1001, University of Urbino Carlo Bo, Department of Economics, Society & Politics - Scientific Committee - L. Stefanini & G. Travaglini, revised 2010.
    3. Benjamin D. Leibowicz & Maria Roumpani & Peter H. Larsen, 2013. "Carbon Emissions Caps and the Impact of a Radical Change in Nuclear Electricity Costs," International Journal of Energy Economics and Policy, Econjournals, vol. 3(1), pages 60-74.
    4. Bossink, Bart, 2020. "Learning strategies in sustainable energy demonstration projects: What organizations learn from sustainable energy demonstrations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    5. Sagar, Ambuj D. & van der Zwaan, Bob, 2006. "Technological innovation in the energy sector: R&D, deployment, and learning-by-doing," Energy Policy, Elsevier, vol. 34(17), pages 2601-2608, November.
    6. Odam, Neil & de Vries, Frans P., 2020. "Innovation modelling and multi-factor learning in wind energy technology," Energy Economics, Elsevier, vol. 85(C).

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