"Very little," according to Robert Pindyck in a new working paper.
Integrated assessment models (IAMs to practitioners) stitch together projections from climate models, energy sector models, agronomic crop models, models of other sectors of the economy, and partial or general equilibrium models that account for price and interactions with the broader economy to derive a more comprehensive evaluation of costs and benefits from climate change.
Pindyck is understandably frustrated with the false sense of precision these models can impart. As he explains, a few reasonable tweaks of any of these models can give very different estimates about the social cost of carbon---the price we should pay, but typically don't, for emitting CO2.
Pindyck raises some good criticisms about IAMs, or at least says out loud a lot of things that many economists have quietly said to each other. I'm glad he's bringing our varying assumptions and wildly varying cost-of-carbon estimates out into the open for all to see. Perhaps it will push us to make our modeling efforts a little more useful, or at least more transparent.
He's right to pick on false precision. But I wonder: has anyone really been fooled? My sense is no. ...
Integrated assessment models tell us more than nothing. But, like all sorts of research, the uncertainty inherent in the estimates needs to be presented as well. This is usually done in the fine print and is not what is communicated to the general public. So, my sense is that critics of climate change economics read the fine print and harp on the uncertainty in the results as if the researcher is trying to bury it. I suggest taking the uncertainty out of the fine print and putting it front and center so that we can avoid the red herring criticism.