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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.
Adaptation --- Climate change --- Climate Change Economics --- Climate Change Mitigation and Green House Gases --- Decision-making under uncertainty --- Global Environment Facility --- Investment --- Macroeconomics and Economic Growth --- Science of Climate Change --- Water Supply and Sanitation Governance and Institutions
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Investment decision making is already difficult for any diverse group of actors with different priorities and views. But the presence of deep uncertainties linked to climate change and other future conditions further challenges decision making by questioning the robustness of all purportedly optimal solutions. While decision makers can continue to use the decision metrics they have used in the past (such as net present value), alternative methodologies can improve decision processes, especially those that lead with analysis and end in agreement on decisions. Such "Agree-on-Decision" methods start by stress-testing options under a wide range of plausible conditions, without requiring us to agree ex ante on which conditions are more or less likely, and against a set of objectives or success metrics, without requiring us to agree ex ante on how to aggregate or weight them. As a result, these methods are easier to apply to contexts of large uncertainty or disagreement on values and objectives. This inverted process promotes consensus around better decisions and can help in managing uncertainty. Analyses performed in this way let decision makers make the decision and inform them on (1) the conditions under which an option or project is vulnerable; (2) the tradeoffs between robustness and cost, or between various objectives; and (3) the flexibility of various options to respond to changes in the future. In doing so, they put decision makers back in the driver's seat. A growing set of case studies shows that these methods can be applied in real-world contexts and do not need to be more costly or complicated than traditional approaches. Finally, while this paper focuses on climate change, a better treatment of uncertainties and disagreement would in general improve decision making and development outcomes.
Adaptation --- Climate Change --- Climate Change Economics --- Climate Change Mitigation and Green House Gases --- Debt Markets --- Decision-Making Under Uncertainty --- Environment --- Finance and Financial Sector Development --- Investment --- Macroeconomics and Economic Growth --- Science and Technology Development --- Science of Climate Change --- Transport --- Transport Economics Policy and Planning
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