Robust Decision-Making in the Water Sector

A Strategy for Implementing Lima's Long-Term Water Resources Master Plan

Published in: Policy Research Working Paper No. WPS 7439 (Washington, D.C.: World Bank Group, Oct. 2015), 47 p

Posted on RAND.org on November 11, 2015

by Nidhi Kalra, David G. Groves, Laura Bonzanigo, Edmundo Molina-Perez, Cayo Ramos, Carter Brandon, Ivan Rodriguez Cabanillas

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How can water resource agencies make smart investments to ensure long-term water reliability when the future is fraught with deep climate and economic uncertainty? This study helped SEDAPAL, the water utility serving Lima, Peru, answer this question by drawing on state of the art methods for decision making under deep uncertainty. These methods provide techniques for evaluating the performance of a water system over a wide range of plausible futures and then developing strategies that are robust across these futures. Rather than weighting futures probabilistically to define an optimal strategy, these methodologies identify the vulnerabilities of a system and then evaluate the key trade-offs among different adaptive strategies. Through extensive iteration and collaboration with SEDAPAL, the study used these methods to define an investment strategy that is robust, ensuring water reliability across as wide a range of future conditions as possible while also being economically efficient. First,on completion, the study helped SEDAPAL realize that not all projects included in the Master Plan were necessary to achieve water reliability, and the utility could save 25 percent (more than $600 million) in investment costs. Second, the study helped focus future efforts on demand-side management, pricing, and soft infrastructure, a refocusing that is difficult to achieve in traditional utility companies. Third, the study helped SEDAPAL gain the support of regulatory and budget agencies through the careful analysis of alternatives. Fourth, the study allowed the utility to postpone lower priority investments, and to analyze future options based on climate and demand information that simply is not available now.

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