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While agreeing on the choice of an
optimal investment decision is already difficult for any
diverse group of actors, priorities, and world views, the
presence of deep uncertainties further challenges the
decision-making framework by questioning the robustness of
all purportedly optimal solutions. This paper summarizes the
additional uncertainty that is created by climate change,
and reviews the tools that are available to project climate
change (including downscaling techniques) and to assess and
quantify the corresponding uncertainty. Assuming that
climate change and other deep uncertainties cannot be
eliminated over the short term (and probably even over the
longer term), it then summarizes existing decision-making
methodologies that are able to deal with climate-related
uncertainty, namely cost-benefit analysis under uncertainty,
cost-benefit analysis with real options, robust decision
making, and climate informed decision analysis. It also
provides examples of applications of these methodologies,
highlighting their pros and cons and their domain of
applicability. The paper concludes that it is impossible to
define the "best" solution or to prescribe any
particular methodology in general. Instead, a menu of
methodologies is required, together with some indications on
which strategies are most appropriate in which contexts.
This analysis is based on a set of interviews with
decision-makers, in particular World Bank project leaders,
and on a literature review on decision-making under
uncertainty. It aims at helping decision-makers identify
which method is more appropriate in a given context, as a
function of the project's lifetime, cost, and vulnerability.
