Climate change, mitigation and adaptation with uncertainty and learning

One of the major issues in climate change policy is how to deal with the considerable uncertainty that surrounds many of the elements. Some of these uncertainties will be resolved through the process of further research. This process of learning raises a crucial timing question: should society delay taking action in anticipation of obtaining better information, or should it accelerate action, because we might learn that climate change is much more serious than expected. Much of the analysis to date has focussed on the case where the actions available to society are just the mitigation of emissions, and where there is little or no role for learning. We extend the analysis to allow for both mitigation and adaptation. We show that including adaptation weakens the effect of the irreversibility constraint and so, for this model, makes it more likely that the prospect of future learning should lead to less action now to deal with climate change. We review the empirical literature on climate change policy with uncertainty, learning, and irreversibility, and show that to date the effects on current policy are rather small, though this may reflect the particular choice of models employed.