The use of a preference disaggregation method in energy analysis and policy making

University of Crete, Retimo, Crete, Greece
Energy (Impact Factor: 4.84). 02/1999; 24(2):157-166. DOI: 10.1016/S0360-5442(98)00081-4


Following the oil crisis, most developed countries have increasingly implemented measures for energy conservation and fuels substitution aimed at decreasing the energy intensities of their economies. These efforts have been further augmented during the eighties due to growing awareness of adverse effects of energy use on the environment. The measures and their effectiveness differ greatly from country to country, without clear identification of the relevant cause–effect relations. We examine this issue by using a multicriteria decision aid (MCDA) method based on preference disaggregation analysis. The method used is the UTADIS (UTilités Additives DIScriminantes) method that has already been widely applied for financial management. The problem examined in this paper has been formulated following the segmentation approach where a number of countries are grouped into a set of predefined classes according to their energy intensities. The UTADIS method proceeds to the estimation of a set of additive utility functions referring to various indices characterizing the economic and energy structure of each country. The analysis is performed at 3 distinct points in time in order to check for consistency of results and investigate time-dependent phenomena. The results show to what extent each of the examined characteristics influences the countries' energy effectiveness and may be further exploited in energy-policy making. They confirm that the UTADIS method is a powerful tool for examination of a wide range of real decision situations.

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    • "MCDA is involved with decision problems under the presence of multiple (conflicting) decision criteria, which require the selection of the best alternatives , the ranking of the alternatives according to their overall performance, or their classification into predefined performance groups. Diakoulaki et al. (1999) used a multicriteria methodology to determine the relative contribution of different factors such as socio-economic indices, structural characteristics, and energy mix of countries in reaching a desired level of energy efficiency. The authors' analysis focused on 13 EU countries and the United States in three points in time, namely, 1983, 1988, and 1993, using data on economic growth, energy consumption, and its breakdown into energy forms and sectors. "
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    ABSTRACT: his paper evaluates the energy efficiency of EU countries over the period 2000–2010. At the first stage, Data Envelopment Analysis (DEA) is employed, combining multiple energy consumption data, economic outputs, structural indicators, and environmental factors. The efficiency estimates obtained from the analysis are evaluated in a second stage through a multiple criteria decision aiding methodology (MCDA). The proposed non-parametric approach combining DEA with MCDA enables the modeling of the problem in an integrated manner, providing not only energy efficiency estimates, but also supporting the analysis of the main contributing factors, as well as the development of a benchmarking model for energy efficiency evaluation in country level.
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    • "Detailed description of the mathematical programming formulation used in the UTADIS method can be found in the works of Zopounidis and Doumpos (1999) and Doumpos and Zopounidis (2002). The UTADIS method has been successfully used in several fields, such as bankruptcy prediction and credit rating (Zopounidis and Doumpos 1999), stock selection (Zopounidis et al. 1999), auditing (Spathis et al. 2003), environmental management (Diakoulaki et al. 1999), etc. "
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    ABSTRACT: The purpose of this study is to gain insight into criteria that contribute to the success in new service development (NSD) projects in the hospitality economy. The results of the exploratory study are conducted in a precise predictive model for the successful hotel services. The analysis is based on data collected via in depth structured interview with questionnaires from hotel managers knowledgeable about NSD in their organization. A multicriteria methodology is used to examine the potential of a predictive model for successful NSD projects in the hotel sector. A comparative analysis with other popular classification methods is also performed.
    Operational Research 05/2009; 9(1):17-33. DOI:10.1007/s12351-008-0025-3 · 0.31 Impact Factor
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