A Decision Support System for Prioritizing Investments in an Energy Efficiency Program in Favelas in the City of Rio de Janeiro

Abstract

AbstractThis study proposes an integrated qualitative and quantitative assessment of expert opinions aiming at ranking a set of five shantytowns (favelas) located in the city of Rio de Janeiro. These communities are candidates for investments in an energy efficiency program implemented by the local electric utility company. The city, state, and federal governments want to eliminate domination of these areas by organized criminal gangs and present the city as a peaceful metropolis while hosting two big sports events: the soccer World Cup in 2014 and the Olympic Games in 2016. In recent years, some favelas were chosen to be prototypes for an ambitious project to reshape Rio de Janeiro. This involves first sending in special tactics police to drive drug gangs out, then installing the Pacifying Police Unit in the favela. Once security has been established, it is possible to improve general living conditions in these areas, including by providing public services such as health clinics, formal electricity connections and cable television. The core of the energy efficiency program was to convert informal customers to formal ones, because such communities were responsible for approximately 40% of the commercial losses (stolen energy) in the city. The model specification presented in this paper was set up with ten relevant criteria for decision making, identified through an in‐depth interview with the decision maker. The relative importance of the criteria and the performance of each favela regarding each criterion were measured by the Simos method. The preferences resulting from this method were translated into a nine‐point scale. The imprecision of subjective judgment was partially compensated by using a fuzzy analytical hierarchy process. Some criteria were ordinal, such as ‘Fair Relationship with the Community’ and ‘Complexity to Rebuild the Distribution Lines’, whereas other were cardinal, like ‘Percentage of Clients in Default’ and ‘Commercial Loss Due to Energy Theft’. At the end, the model was efficient in ranking the five favelas, therefore contributing to a rational and transparent approach for capital investment in social projects. Copyright © 2014 John Wiley & Sons, Ltd.