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


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Likewise it is known that
teams typically perform better with their star player than with-
out him or her. Each additional bit of information expands
the sample of previous outcomes that is believed to be relevant
or hold some expository power for this new sporting event and
so it improves the confidence in the admittedly subjective
probability distribution assigned to potential outcomes. The
30 Encyclopedia of Energy, Natural Resource and Environmental Economics http://dx.doi.org/10.1016/B978-0-12-375067-9.00079-6
coin flip may not be viewed in this way since the probability of
50% for heads is more likely derived mathematically than
probabilistically, but one can also come to the realization
that a coin flip is a 50\u201350 proposition because one is aware
of an untold and uncountable number of past coin flips attest-
ing to this fact. In summary, the best way to reduce uncertainty
is through increased sampling of the population in question.
At the limit in which the entire population of possible out-
comes has been sampled, uncertainty disappears and one is left
with only risk.
Specifically turning to climate change, the Intergovernmen-
tal Panel on Climate Change (IPCC) distinguishes between
two sources of what they call uncertainty: \u201cQuantified mea-
sures of uncertainty in a finding expressed probabilistically
(based on statistical analysis of observations or model results,
or expert judgment) (IPCC 2010, p. 1),\u201d which reflects measur-
able uncertainty or risk, and \u201cConfidence in the validity of a
finding, based on the type, amount, quality, and consistency of
evidence (IPCC 2010, p. 1),\u201d which reflects the degree to which
one has confidence in the probabilities assigned. Put another
way, this second concept gauges to what extent one believes
these probabilities are known, and consequentially the degree
to which uncertainty (as defined here) impacts the model. The
probability estimates used by the IPCC are generally subjec-
tively determined by the authors of various studies. These
studies are validated to some extent by virtue of being through
the peer-review process, but they are generally not subject to
broader consensus development.
This article will begin with a brief elaboration on the many
different sources of uncertainty in climate change analysis. The
work of several prominent economists who have addressed
these issues and the policy implications of uncertainty related
to climate change will be discussed next.
Sources of Uncertainty
There is high and increasing consensus among scientists that
increasing atmospheric carbon content will lead to an increase
in average global temperatures. In addition to average tempera-
ture rise, changes in other facets of climate, from precipitation
levels to the probability of extreme weather outcomes like
droughts and hurricanes, are also likely. According to the
IPCC\u2019s Fourth Assessment Report, this climate change has al-
readybegun andwill likely continueover the proceedingdecades
and centuries. The rise of global mean temperatures will inflict
economic damages that result from adverse impacts on many
natural and human systems, including but not limited to sea
level rise, loss of agricultural productivity, increased storm sever-
ity, and increased incidence ofdroughts and floods. The question
facing policymakers regarding climate change is whether one
should incur costs today (in the form of investments in adapta-
tion,mitigation, carbon sequestration, geoengineering, research,
etc.) in order to avoid future damages by limiting either the
extent or the economic impact of climate change.
Economists have used integrated assessment models
(IAMs), which combine details of climate science and eco-
nomic models, to assess the economic impact of greenhouse
gas (GHG) emissions in an attempt to determine optimal
investment levels or the optimal price of carbon. This is a
challenging task for many reasons, but the main focus here
will be the high levels of uncertainty regarding the physiolog-
ical and economic processes that must be understood to eval-
uate potential investment options. The inability to make
confident statements regarding climate change is, primarily, a
sampling problem. Predicting climate change is profoundly
challenging precisely because it is an unprecedented event.
We have no pool of past experience or data to consult when
determining how to properly and accurately derive distribu-
tions for many of the key parameters in IAMs, but more on this
later.
To assess the impact of investments, researchers must de-
velop a baseline or business as usual (BAU) estimate outcome
that occurs in the absence of policy intervention. The value of
the intervention lies in the improvement in welfare achieved
compared to this BAU scenario. High degrees of uncertainty
persist for this type of assessment, most notably in the follow-
ing areas. The first three areas deal primarily with issues faced
by climate scientists, the later three are economic in nature.
GHG Emissions and Atmospheric Carbon
In order to produce estimates of future atmospheric carbon,
one must project the path of anthropogenic GHG emissions.
This estimate is dependent on accurately assessing the path of
global economic growth (and the distribution of this growth,
since increased incomes in China, the Sudan, and the United
States will not yield identical GHG emissions) as well as the
advancement and implementation of technologies in the BAU
world. Furthermore, this path is subject to potential nonline-
arities, driven mainly by feedback loops. These loops could be
positive, as would be the case should carbon sinks become
saturated or if melting permafrost lead to the emission of
large quantities of methane. The loops could also be negative,
as in the case of higher carbon concentrations leading to more
rapid natural growth of forestry and subsequent carbon seques-
tration. The point is that predicting how these effects will
combine and influence atmospheric carbon levels in 10 years
with confidence is a challenge; predicting values that will occur
in 100 years is far worse.
The Impact of Atmospheric Carbon on Temperature
Even if climate scientists were able to accurately and confi-
dently predict an atmospheric carbon growth path, it is
unclear how changes in carbon will translate into changes
in mean annual temperature. This relationship between at-
mospheric carbon and temperature changes is called climate
sensitivity and is frequently modeled as the change in de-
grees centigrade that results from a doubling of atmospheric
carbon concentrations. Figures 1 and 2 outline the 90%
confidence interval for climate sensitivity generated in nu-
merous studies.
Biological, Geophysical, and Social Impacts of Temperature
Changes
What sort of changes to ocean levels and the hydrological cycle
will accompany a given temperature change? How will species
Climate Change and Policy | Dealing with the Uncertainty About Climate Change 31
be able to adapt to these ecosystem changes? Even in cases
where scientists are fairly certain of the direction of geophysical
changes (e.g., that sea levels will likely rise as a result of warm-
ing), what magnitudes correspond to various temperature
changes is more difficult to specify. Making estimates and
placing reasonable bounds on the impact to ecosystems and
human systems (coastal settlements, agriculture, etc.) is even
more challenging. This difficulty is confounded by the notion
that these effects are dependent on the time frame in question,
as human systems especially will show greater adaptation
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