Martin Weitzman’s paper on the economics of catastrophic climate change is rather like Moby Dick – something we all nod to knowing references while we’re secretly saying to ourselves, “WTF? Did you actually understand this?” I actually went so far as to enlist an environmental economist of my acquaintence (being a journalist has its advantages). He used a white board. It was fun.
The nut of the argument is that climate change involves uncertainty about low probability but potentially very high consequence events, making ordinary cost-benefit analysis difficult.
So I was happy to see in Daniel Hall’s post today on the subject that I am not alone:
Nobel prize winner Tom Schelling, one of the discussants at the event, noted that he read the paper 5 or 6 times without ever feeling that he was sure what it was saying.
The problem is that Weitzman doesn’t really tell us what we ought to do. But it nevertheless is a very significant framework for thinking about the issue.
FWIW I think that Marty’s paper is fundamentally wrong. However I’ve had lengthy email discussions with it and failed to convince him of my point of view, so there’s little point trying to expand on it here 🙂
I’ve tried to get some experts in Bayesian statistics to read and comment on it, to no avail. So far the climate scientists seem to be lapping it up, but that hardly means anything I’m afraid.
The nut in ecology people’s minds is that the increasing…erm…chaoticality of the system in AGW regimes makes analysis and prediction/projection more difficult, making decision-making more risky.
You might read Oreskes paper on the Nierenberg report to see how Schelling invested early in denial. Another example of how only those who are wrong can be considered serious.
0) james: can you say a few words more? That’s a teaser.
1) This reminds me a bit of the Cauchy Distribution: r = x/y, x & y independent & normally distributed: it has no mean of variance, and increasing a sample size from such a distribution doesn’t help.
Effective infinities are troublesome anywhere.
2) Personally, I think the killer-asteroid problem is even more intractable to predict. There, the probability that a killer-asteroid will come by is ~1.0, the question is when? And really-low-frequency events are even more unpredictable that AGW. The real question there is: when it comes, will there be technical civilization capable of fending it off?
3) So far, I’ve read Stern and looked at some other climate-change economics arguments … and am nervous about them all. IPCC, and Stern, and others seem to assume a CAGR of several % in GDP growth rate, indefinitely, together with various assumptions about discount rates. A big discount rate, and you assume that descendants will be rich, and can there fore easily pay for any adaptation needed.
4) From now, GDP CAGRs and result in 2100:
1% 2.5X bigger GDP
2% 6.2X bigger GDP
3% 15.2X bigger GDP
5) But, this seems really, really bogus. I’m no economist, but the work Charlie Hall and Robert Ayres&Benjamin Warr do in modeling energy (or rather efficiency* energy) makes more sense to me than most of what I see. For example, see the last slide in:
It projects US GDP with several different assumptions on efficiency improvements, in the light of Peak Oil. Basically, GDP flattens, and then declines [maybe, depend on how aggressive we get].
This is a paper with some explanations:
And to see how far we have to go building solar & windmills:
6) Anyway, has anyone seen any climate change economics paper that doesn’t make the happy 1-3% CAGR assumption?
7) As for the Weitzman paper, I actually think it’s a moot point. If Hall/Ayres/Warr viewpoint is right, there’s NO WAY people in 2100 are going to be vastly richer, especially given nonsubstitutability of goods. i.e., Terabyte iPods may be cheap, but things that require lots of energy are going to be *very* expensive, like:
– moving earth for dikes
– making steel & concrete for seawalls
– doing water engineering
Oh, I see that I’ve already blogged it here:
I might do a follow-up to explain the main point in more detail (drawing samples from “the pdf of S” versus making imprecise observations of an unknown but fixed parameter S).