10.1.5 Robust Decision-making
Uncertainty is a feature that pervades discussions on climate change issues.
IPCC SAR covered main areas of uncertainties, especially those related to:
- atmospheric concentrations of GHGs and their impact on meteorological phenomena
- the potential of technological options and the relationships between climate
change and the dynamics of natural systems (IPCC, 1996b); and
- socio-economic dimensions of climate change (IPCC, 1996c).
Several sections in this report (1.5; 2.2;
7.2; 10.1) review new and complementary
perspectives that facilitate a better understanding of the tensions between
the limited capacity to predict and the urgent need to act in a situation faced
with high stakes of risk.
The implications of uncertainty are global in scale and long-term in their
impact; quantitative data for baselines and the consequences of climate change
are inadequate for decision making. In recent years, researchers and policymakers
have become increasingly concerned about the high levels of inherent uncertainty,
and the potentially severe consequences of decisions that have to be made.
Conventional frameworks for decision making on climate change policies presume
that relevant aspects of the contextual environment are to some extent predictable;
therefore uncertainty can be reduced to provide decision makers with appropriate
information within appropriate time frames.
This anticipatory management approach is based on the premise that it is possible
to predict and anticipate the consequences of decisions and hence to make a
proper decision once all the necessary information is gathered to make a scientific
forecast. The prevailing image is that given enough information and powerful
enough computers it is possible to predict with certainty, in a quantitative
form, which in turn makes it possible to control natural systems (Tognetti,
Anticipatory approaches have successfully managed a wide range of decision
problems in which the relative uncertainties are reducible, and the stakes or
outcomes associated with the decisions to be made are modest (Kay et al., 1999).
A number of uncertainty analysis techniques, such as Monte Carlo sampling, Bayesian
methods, and fuzzy set theory, have been designed to perform sensitivity and
uncertainty analysis related to the quality and appropriateness of the data
used as inputs to models. However, these techniques, suitable for addressing
technical uncertainties, ignore those uncertainties that arise from an incomplete
analysis of the climate change phenomena, or from numerical approximations used
in their mathematical representations (modelling uncertainties), as well as
uncertainties that arise from omissions through lack of knowledge (epistemological
uncertainties). Current methods thus give decision makers limited information
regarding the magnitude and sources of the underlying uncertainties and fail
to provide them with straightforward information as input to the decision-making
process (Rotmans and de Vries, 1997).
The management of uncertainties is not just an academic issue but an urgent
task for climate change policy formulation and action. Various vested interests
may inhibit, delay, or distort public debate with the result that procrastination
is as real a policy option as any other, and indeed one that is traditionally
favoured in bureaucracies; and inadequate information is the best excuse for
delay (Funtowicz and Ravetz, 1990).
Funtowicz and Ravetz have proposed a highly articulated and operational scheme
for dealing with the problems of uncertainty and quality of scientific information
in the policy context. By displaying qualifying categories of the informationnumeral,
unit, spread, assessment, and pedigree (NUSAP)the NUSAP scheme provides
a framework for the inquiry and elicitation required to evaluate information
quality. By such means it is possible to convey alternative interpretations
of the meaning and quality of crucial quantitative information with greater
quality and coherence, and thus reduce distortion of its meaning.
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