Symposium:Fat Tails and the Economics of Climate ChangeFat-Tailed Uncertainty in theEconomics of Catastrophic ClimateChangeMartin L. Weitzman*IntroductionI believe that the most striking feature of the economics of climate change is that its extremedownside is nonnegligible. Deep structural uncertainty about the unknown unknowns ofwhat might go very wrong is coupled with essentially unlimited downside liability on possibleplanetary damages. This is a recipe for producing what are called ‘‘fat tails’’ in the extremes ofcritical probability distributions. There is a race being run in the extreme tail between howrapidly probabilities are declining and how rapidly damages are increasing. Who wins thisrace, and by how much, depends on how fat (with probability mass) the extreme tails are. It isdifficult to judge how fat the tail of catastrophic climate change might be because it representsevents that are very far outside the realm of ordinary experience.In this article, which is part of a symposium on Fat Tails and the Economics of ClimateChange, I address some criticisms that have been leveled at previous work of mine on fattails and the so-called ‘‘dismal theorem.’’1At first, I was inclined to debate some of the criticsand their criticisms more directly. But, on second thought, I found myself anxious not to bedrawn into being too defensive and having the main focus be on technical details. Instead, Iam more keen here to emphasize anew and in fresh language the substantive concepts that, I
think, may be more obscured than enlightenedby a debate centered on technicalities. I am
far more committed to the simple basic ideas that underlie my approach to fat-tailed uncertainty and the economics of catastrophic climate change than I am to the particular
*Department of Economics, Harvard University; e-mail: mweitzman@harvard.edu
Without blaming them for the remaining deficiencies in this article, I am extremely grateful for the constructive comments on an earlier version by James Annan, Daniel Cole, Stephen DeCanio, Baruch Fischoff,
Don Fullerton, John Harte, William Hogan, Matthew Kahn, David Kelly, Michael Oppenheimer, Robert
Pindyck, Joseph Romm, and Richard Tol.
1
This symposium also includes articles byNordhaus (2011)andPindyck (2011). The ‘‘dismal theorem,’’
introduced in Weitzman (2009a), will be discussed later in this article.
Review of Environmental Economics and Policy,volume 5, issue 2, summer 2011, pp. 275–292
doi:10.1093/reep/rer006
The Author 2011. Published by Oxford University Press on behalf of the Association of Environmental and Resource
Economists. All rights reserved. For permissions, please email: journals.permissions@oup.com
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mathematical form in which I have chosen to express them. These core concepts could have
been wrapped in a variety of alternative mathematical shells—and the particular one that I
chose to use previously is somewhat arbitrary. The implications are roughly similar, irrespective of formalization. Some technical details are unavoidable, but if I can give the underlying concepts greater intuitive plausibility, then I believe that this set of ideas will
become more self-evident and more self-evidently resistant to several of the criticisms that
have been leveled against it.
In the next section, I present an intuitive–empirical argument that deep structural uncertainty lies at the heart of climate change economics. Then, in the following section, I try
to explain some of the theory behind fat-tailed extreme events and discuss some possible
implications for the analysis of climate change. I offer a few summary remarks in the final
section.
Some Empirical Examples of Deep Structural Uncertainty
about Climate Extremes
In this section I try to make a heuristic–empirical case (in the form of five ‘‘stylized facts’’) for
there being big structural uncertainties in the economics of extreme climate change. I will argue
on intuitive grounds that the way in which this deep uncertainty is conceptualized and formalized should influence the outcomes of any reasonable benefit–cost analysis (BCA) of climate
change. Furthermore, I will argue that the seeming immunity of the ‘‘standard’’ BCA to the
possibility of extreme outcomes is both peculiar and disturbing. My arguments in this section
are not intended to be airtight or rigorous. Rather, this is an intuitive presentation based on some
very rough stylized facts.
By BCA of climate change, I mean, in the widest sense, some overall economic analysis
centered on maximizing (or at least comparing) welfare. My notion of BCA in the present
context is so broad that it overlaps with an integrated assessment model (IAM), and here I
treat the two as essentially interchangeable. I begin by setting up a straw man that I will label
the ‘‘standard BCA of climate change.’’ Of course, there is no ‘‘standard BCA of climate
change,’’ but I think this is an allowable simplification for purposes of exposition here.
We all know that computer-driven simulations are dependent upon the core assumptions
of the underlying model. The intuitive examples presented below are frankly aimed at sowing
a few seeds of doubt that the ‘‘standard BCA of climate change’’ fairly represents structural
uncertainties about extreme events, and that therefore its conclusions might be less robust
than is commonly acknowledged. I argue not that the standard model is wrong or even
implausible, but rather that it may not be robust with respect to the modeling of catastrophic
outcomes. I will try to make my case by citing five aspects of the climate science and economics
that do not seem to me to be adequately captured by the standard BCA. The five examples—
which I call Exhibits 1, 2, 3, 4 and 5—are limited to structural uncertainty concerning the
modeling of climate disasters. While other important aspects of structural uncertainty might
also be cited, I restrict my stylized facts to these five examples. In the spirit of performing
a kind of ‘‘stress test’’ on the standard BCA, I naturally concentrate on things that might go
wrong rather than things that might go right.
276 M. L. Weitzman
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Exhibit 1: Past and Present Greenhouse Gas Concentrations
Exhibit 1 concerns the atmospheric level of greenhouse gases (GHGs) over time. Ice-core
drilling in Antarctica began in the late 1970s and is still ongoing. The record of carbon dioxide
(CO2) and methane (CH4
) trapped in tiny ice-core bubbles currently spans 800,000 years.
2
It is important to recognize that the numbers in this unparalleled 800,000-year record of
GHG levels are among the very best data that exist in the science of paleoclimate. Almost
all other data (including past temperatures) are inferred indirectly from proxy variables,
whereas these ice-core GHG data are directly observed.
The preindustrial revolution level of atmospheric CO2(about two centuries ago) was 280
parts per million (ppm). The ice-core data show that CO2
varied gradually during the last
800,000 years within a relatively narrow range roughly between 180 and 280 ppm and has
never been above 300 ppm. Currently, CO2is over 390 ppm and climbing steeply. In 800,000
years, methane has never been higher than 750 parts per billion (ppb), but now this extremely
potent GHG, which is twenty-two times more powerful than CO2
(per century), is at about
1,800 ppb. The sum total of all carbon dioxide equivalent (CO2
e) GHGs is currently at about
450 ppm. An even more startling contrast with the 800,000-year record is the rate of change of
GHGs: increases in CO2
were below (and typically well below) 25 ppm within any past
subperiod of 1,000 years, while now CO2
has risen by over 25 ppm in just the last ten years.
Thus, anthropogenic activity has very rapidly elevated atmospheric CO2and CH4
to levels
very far outside their natural range. The unprecedented scale and speed of GHG increases
brings us into uncharted territory and makes predictions of future climate change
very uncertain. Looking ahead a century or two, the levels of atmospheric GHGs that
may ultimately be attained (unless decisive measures are undertaken) have likely not existed
for tens of millions of years, and the speed of this change may be unique on a time scale of
hundreds of millions of years.
Remarkably, the ‘‘standard BCA of climate change’’ takes little account of the magnitude
of the uncertainties involved in extrapolating future climate change so far beyond past
experience. Perhaps even more surprising, the gradual tightening of GHG emissions, which
emerges as optimal policy from the ‘‘standard’’ BCA, typically attains stabilization at levels of
CO2that approach 700 ppm (and levels of CO2e that are even higher). The ‘‘standard’’ BCA
thus recommends subjecting the Earth’s system to the unprecedented shock of instantaneously (in geological terms) jolting atmospheric stocks of GHGs up to two-and-a-half times
above their highest level over the past 800,000 years—without mentioning the unprecedented
nature of this unique planetary experiment. This is my Exhibit 1.
Exhibit 2: The Uncertainty of the Climate Change Response
Exhibit 2 concerns the highly uncertain climate change response to the kind of unprecedented
increases in GHGs that were described in Exhibit 1. For specificity, I focus on the uncertainty
of so-called ‘‘equilibrium climate sensitivity,’’ which is a key macro-indicator of theeventual
temperature response to GHG changes. This is a good example of a ‘‘known unknown.’’
2
My numbers are taken fromDieter et al. (2008)and supplemented by data from the Keeling curve for more
recent times (available online at: ftp://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_mm_mlo.txt).
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