An MIT economist publishes a paper explaining that key economic modeling of climate change is wrong primarily because the models do not account for the risk of catastrophic climate chaos, but essentially have linear analysis that fosters a greatly understated accounting of the risks that climate chaos will devastate not just human prospects in the decades/centuries to come but the economy.
The climate risk downplaying and dismissing Institute for Energy Research (IER) leverages this study in support of its generalized opposition to any and all climate mitigation and investments to move toward a clean energy future. IER’s perspective: since the modeling is questionable, we should totally discount it and not include it to support any decision-making.
A fair read of the paper suggests something far different: be careful of taking the results from these models as perfectly accurate because they are almost certainly wrong by being too optimistic about plausible risks and plausible outcomes. And, thus, the results could be used as a low threshold for consideration even as we should seek to develop better modeling approaches to assessing economic costs and economic risks from linearly developing climate change and from potential catastrophic climate chaos.
First, a short reminder about modeling from George Box:
Essentially, all models are wrong, but some are useful.
Remember(ing) that all models are wrong; the practical question is how wrong do they have to be to not be useful.
No one should have any pretense that one can be perfect with models which, after all, are an attempt to describe and understand via a simplification of reality. Our challenge is whether they can be done well enough to support policy making.
Professor Robert Pindyck has just put out Climate Change Policy: What do the models tell us? In this, he accurately informs us that:
integrated assessment models (IAMs) based analyses of climate policy create a perception of knowledge and precision, but that perception is illusory and misleading
What is most seriously wrong with that perception?
the models can tell us nothing about the most important driver of the social cost of carbon (SCC), the possibility of a catastrophic climate outcome
Pindyck does not necessarily see a path to rescuing IAMs toward greater accuracy (including incorporating the risks of catastrophe) and, with that, thus lays down that the approach should follow the cautionary policy:
My criticism of IAMs should not be taken to imply that because we know so little, nothing should be done about climate change right now, and instead we should wait until we learn more. Quite the contrary. One can think of a GHG abatement policy as a form of insurance: society would be paying for a guarantee that a low-probability catastrophe will not occur (or is less likely).
IER’s Robert Murphy recently testified on the Hill. He, too, accurately stated that the economic modeling related to climate change’s economic impact is questionable in how it is done. As opposed to Pindyck, Murphy asserts that since it is difficult to assess costs and risks with precision, we simply shouldn’t include those risks and costs in any decision-making process. There is an old adage about the failures of economics, what can’t be counted doesn’t matter. That adage seems to fit Murphy’s prescription as to including climate risks in the decision-making process. Murphy gleefully cites Pindyck as to the faults and challenges of IAM since he is challenging their use in supporting government policy making. Murphy, however, simply thrusts aside the most important element of Pindyck’s work — the highlighting of the the modeler’s inability to incorporate risk into their analytical framework.
Murphy wishes to project a risk-less future when the scientific analysis of climate change highlights risk after risk after risk …