Enciclopédia da Energia Natural   CPMA.COMUNIDADES.NET
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Enciclopédia da Energia Natural CPMA.COMUNIDADES.NET

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tied to climate change. How will temperatures change after a
doubling of CO2 levels? What economic impact will result
from an increase in global mean temperatures of 3\ufffdC? These
are extreme events, and extreme events have a low sample size.
There is little or no data for these observations, and it is
difficult to extrapolate the outcomes based on the available
data because there are likely highly nonlinear mechanisms at
work, and, furthermore, the extent of these nonlinearities is
unknown, again because of lack of sufficient data in the rele-
vant areas.
Climate Change and Policy | Dealing with the Uncertainty About Climate Change 33
One popular method of addressing the uncertainty inher-
ent in climate change (used by Christopher Hope and Nicholas
Stern, for example) is the use of Monte Carlo analysis, in which
the model is solved numerous times using a variety of param-
eter values that are drawn from assumed distributions. The
shortcoming here, as can be determined by the preceding
discussion, is that the result of this analysis depends largely
on the choice of distribution, which is precisely what one does
not know enough about. Assuming a normal distribution can
give us precise probability measures for extreme events, but
these measures are only as good as the normality assumption
that drives them. Furthermore, some authors tend to \u2018bound\u2019
their distributions by ignoring high-impact, very low probabil-
ity outcomes. The major argument in favor of this approach is
that one has such a weak understanding of the likelihood of
these events that taking them into account and having them
overtly influence policy would be foolish. Indeed, it might lead
to extreme risk aversion and policy actions that may cripple the
economy. In further support of this approach, William
Nordhaus found that his optimal policy in a model which
focused on the \u2018best guesses,\u2019 or mean estimates for parameter
values, was a good approximation of the policy for the
expected value model, which would introduce some risk pre-
mium to avoid extreme events. However, other authors have
found that the risk premium associated with extreme events
would indeed be substantial, including Richard Tol, Megan
Ceronsky, David Anthoff, and Cameron Hepburn.
Numerous authors, from Nordhaus and Weitzman to
Robert Pindyck, have put forth arguments for why the distri-
bution of climate change outcomes is unlikely to have a nor-
mal \u2018thin-tailed\u2019 distribution. They argue that the distribution
is likely to possess \u2018fat tails,\u2019 meaning that the probability de-
creases at a less than exponential rate. For fat-tailed distribu-
tions, the probability assigned to events located many standard
deviations from the mean are much higher than what would be
predicted in a thin-tailed distribution. Figures 3 and 4 present
a comparison of two probability density functions (PDFs). In
the figures, both distributions possess the same mean (1.667)
and variance (2.222), but one distribution is fat tailed and the
other is thin tailed. The thin-tailed PDF is a normal distribu-
tion and the thick-tailed PDF is a generalized Pareto distribu-
tion with K¼0.4, s¼0.4, and m¼1.
There are several reasonable motivations for assuming a fat-
tailed distribution to key IAM parameters like climate sensitiv-
ity and economic damages. One plausible reason is the largely
unknown extent to which feedback loops could create large
nonlinearities in climate sensitivity, species extinction, and the
like. Even if it is believed that the parameter in question follows
a thin-tailed distribution, uncertainty about the variance may
lead one to treat the distribution as fat tailed. This is put
succinctly by Pindyck:
. . . suppose we don\u2019t know the variance of the distribution, and
therefore we estimate the variance using all available data, with
Bayesian updating of our estimate as new data becomes available.
In that case the posterior distribution for [the variable] (i.e., the distri-
bution conditional on our estimation process for the variance) is
necessarily fat-tailed (from \u2018Fat Tails, Thin Tails, and Climate Change
Policy\u2019 in the Review of Environmental Economics and Policy volume 5,
p. 259).
Weitzman goes further, arguing that the combination of fat-
tailed distributions and typical formulations of utility that are
used in IAMs can lead to the unpalatable conclusion that
governments should devote almost 100% of GDP toward
curbing the effects of climate change (this is Weitzman\u2019s \u2018dismal
theorem\u2019). When low levels of consumption possess marginal
utilities that approach infinity, the marginal utility of avoiding
damages from extreme events can approach infinity. When the
distribution of climate change outcomes is fat tailed, the rising
marginal utility of avoiding extreme events outpaces the falling
probability of these extreme events occurring, creating infinite
or near-infinite returns to climate change policy.
This can be viewed as a call to far greater mitigation efforts,
although it is more widely regarded as validation for bounding
marginal utility in IAMs. This is the perspective Nordhaus takes
when he provides several examples, from asteroids to nuclear
weapons, suggesting that one does not treat \u2018zero consump-
tion\u2019 as an outcome yielding infinite disutility. He also argues
that the dismal theorem applies most directly for issues where
no learning can take place (the creation of strangelets in parti-
cle colliders, for example), whereas climate change is a more
gradual process in which learning can occur. Pindyck acknowl-
edges the importance of distributional assumptions regarding
climate change parameters but also argues that tail properties
1 2 3 4 5 6
Standard deviations from mean
7 8 9
Thin tail
Fat tail
Figure 3 Example of fat- and thin-tailed distributions.
34 Climate Change and Policy | Dealing with the Uncertainty About Climate Change
alone do not dictate the level of optimal intervention. He notes
that \u201cWhat matters here is the entire distribution for outcomes,
and not necessarily whether that distribution has fat or thin
tails (from \u2018Fat Tails, Thin Tails, and Climate Change Policy\u2019
in the Review of Environmental Economics and Policy volume 5,
p. 276).\u201d
The Implications of Uncertainty
The implications of uncertainty are somewhat unclear. Uncer-
tainty often can be resolved (or lessened) over time using a
\u2018wait and see\u2019 attitude. As Pindyck has noted, the attractiveness
of this position depends on the relative weight of two distinct
irreversibilities. Working against the \u2018wait and see\u2019 attitude is
the potential irreversibility of climate damage or the existence
of tipping points regarding climate damage. If the impacts of
climate change are largely irreversible, waiting can have severe
consequences. Uncertainty regarding potential environmental
irreversibilities can urge policymakers into greater and earlier
intervention through the \u2018precautionary principle.\u2019 The other
irreversibility which favors the \u2018wait and see\u2019 approach relates
to the irreversibility of investment decisions. Investing in mit-
igation and adaptation may prove ineffective or unnecessary
due to the high uncertainty faced when making policy deci-
sions. As an example, investment in sea walls will not yield
tangible benefits if sea level rise is inconsequential, or if sea
level rise is well beyond what the sea walls can control. In both
cases, investment in the sea walls will be largely lost and
useless. Uncertainty regarding the effectiveness and necessity
of costly mitigation and adaptation investments suggest that
these investments