Abstract
We analyze the effects of risks and learning on climate change decisions. Using a new two-stage, dynamic, climate change stabilization model with random time horizons, we show that the explicit incorporation of ex-post learning and safety constraints induces risk aversion in ex-ante decisions. This risk aversion takes the form in linear models of VaR- and CVaR-type risk measures. We also analyze extensions of the model that account for the possibility of nonlinear costs, limited emissions abatement capacity, and partial learning. We find that in all cases, even in linear models, any conclusion about the effect of learning can be reversed. Namely, learning may lead to either less- or more restrictive ex-ante emission reductions depending on model assumptions regarding costs, the distributions describing uncertainties, and assumptions about what might be learned. We analyze stylized elements of the model in order to identify the key factors driving outcomes and conclude that, unlike in most previous models, the quantiles of probability distributions play a critical role in solutions.
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O’Neill, B., Ermoliev, Y., Ermolieva, T. (2006). Endogenous Risks and Learning in Climate Change Decision Analysis. In: Coping with Uncertainty. Lecture Notes in Economics and Mathematical Systems, vol 581. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-35262-7_16
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DOI: https://doi.org/10.1007/3-540-35262-7_16
Publisher Name: Springer, Berlin, Heidelberg
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