Eli has been going back through the Rabett Run archives fishing out some old drafts and finding this and that. Here is one of the thats.
THE USE AND MISUSE OF MODELS FOR CLIMATE POLICY * by Robert S. Pindyck
In a recent article, I argued that integrated assessment models (IAMs) “have crucial flaws that make them close to useless as tools for policy analysis.” In fact, I would argue that calling these models “close to useless” is generous: IAM-based analyses of climate policy create a perception of knowledge and precision that is illusory, and can fool policy-makers into thinking that the forecasts the models generate have some kind of scientific legitimacy. IAMs can be misleading – and are inappropriate – as guides for policy, and yet they have been used by the government to estimate the social cost of carbon (SCC) and evaluate tax and abatement policies.Pindyck's is indeed an argument for ignorance. He is quite pessimistic that anybunny, economist or climate scientist knows anything, from discount rate to climate sensitivity to damage functions. Choice of discount rate, of course can yield any answer the mythical anybunny might wish, but according to Pindyke it is worse because even probability distributions for any of these are improbable. Thus IAM's become computer driven fantasy
So what to do. Well, really really bad outcomes are so really bad that it doesn't matter what discount rate you chose if you lose the economy. Pindyck is an economist.
So Pindyck's idea is get a bunch of wise heads together and figure out what the most probable really really bad thing that might happen is and figure out how bad it really would be.
I have argued that the problem is somewhat simplified by the fact that what matters for policy is the possibility of a catastrophic climate outcome. How probable is such an outcome (or set of outcomes), and how bad would they be? And by how much would emissions have to be reduced to avoid these outcomes? I have argued that the best we can do at this point is come up with plausible answers to these questions, perhaps relying at least in part on consensus numbers supplied by climate scientists and environmental economists. This kind of analysis would be simple, transparent, and easy-to-understand. It might not inspire the kind of awe and sense of scientific legitimacy conveyed by a large-scale IAM, but that is exactly the point. It would draw back the curtain and clarify our beliefs about climate change and its impact.Discuss