Endogenous Learning in Climate-Energy-Economic Models - An Inventory of Key Uncertainties

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 modeling, methodological and parameter uncertainties. We emphasize 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 modeling 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 recognized, classified, quantified and reported.