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Ho Chi Minh City faces significant and growing flood risk. Recent risk reduction efforts may be insufficient as climate and socio-economic conditions diverge from projections made when those efforts were initially planned. This study demonstrates how robust decision making can help Ho Chi Minh City develop integrated flood risk management strategies in the face of such deep uncertainty. Robust decision making is an iterative, quantitative, decision support methodology designed to help policy makers identify strategies that are robust, that is, satisfying decision makers' objectives in many plausible futures, rather than being optimal in any single estimate of the future. This project used robust decision making to analyze flood risk management in Ho Chi Minh City's Nhieu Loc-Thi Nghe canal catchment area. It found that the soon-to-be-completed infrastructure may reduce risk in best estimates of future conditions, but it may not keep risk low in many other plausible futures. Thus, the infrastructure may not be sufficiently robust. The analysis further suggests that adaptation and retreat measures, particularly when used adaptively, can play an important role in reducing this risk. The study examines the conditions under which robust decision making concepts and full robust decision making analyses may prove useful in developing countries. It finds that planning efforts in developing countries should at minimum use models and data to evaluate their decisions under a wide range of conditions. Full robust decision making analyses can also augment existing planning efforts in numerous ways.

Adaptation --- Climate change --- Decision-making under uncertainty --- Flood risk management --- Long-lived infrastructure investments --- Macroeconomics and Economic Growth

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Governments invest billions of dollars annually in long-term projects. Yet deep uncertainties pose formidable challenges to making near-term decisions that make long-term sense. Methods that identify robust decisions have been recommended for investment lending but are not widely used. This paper seeks to help bridge this gap and, with a demonstration, motivate and equip analysts better to manage uncertainty in investment decisions. The paper first reviews the economic analysis of ten World Bank projects. It finds that analysts seek to manage uncertainty but use traditional approaches that do not evaluate options over the full range of possible futures. Second, the paper applies a different approach, Robust Decision Making, to the economic analysis of a 2006 World Bank project, the Electricity Generation Rehabilitation and Restructuring Project, which sought to improve Turkey's energy security. The analysis shows that Robust Decision Making can help decision makers answer specific and useful questions: How do options perform across a wide range of potential future conditions? Under what specific conditions does the leading option fail to meet decision makers' goals? Are those conditions sufficiently likely that decision makers should choose a different option? Such knowledge informs rather than replaces decision makers' deliberations. It can help them systematically, rigorously, and transparently compare their options and select one that is robust. Moreover, the paper demonstrates that analysts can use the same data and models for Robust Decision Making as are typically used in economic analyses. Finally, the paper discusses the challenges in applying such methods and how they can be overcome.

Climate Change Economics --- Climate Change Mitigation and Green House Gases --- Debt Markets --- Decision Making Under Uncertainty --- Economic Analysis --- Energy --- Energy Production and Transportation --- Environment --- Finance and Financial Sector Development --- Long-Lived Infrastructure Investments --- Macroeconomics and Economic Growth --- Non Bank Financial Institutions --- Robust Decision Making