Endogenous Risks and Learning in Climate Change Decision Analysis

We analyze the effects of risks and learning on climate change decisions. A two-stage, dynamic, climate change stabilization problem is formulated. The explicit incorporation of ex-post learning induces risk aversion among ex-ante decisions, which is characterized in linear models by VaR- and CVaR-type risk measures. Combined with explicit introduction of "safety" constraints, it creates a "hit-or-miss" type decision-making situation and shows that, even in linear models, learning may lead to either less-or more restrictive ex-ante emission reductions. We analyze stylized elements of the model in order to identify the key factors driving outcomes, in particular, the critical role of quantiles of probability distributions characterizing key uncertainties