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Eurofuse 2011 pp 209-220 | Cite as

A Heterogeneous Evaluation Model for the Assessment of Sustainable Energy Policies

  • M. Espinilla
  • R. de Andrés
  • F. J. Marténez
  • L. Martínez
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 107)

Abstract

Decision makers are increasingly involved in complex real decisions that require multiple viewpoints. A specific case of this fact is the evaluation of sustainable policies related to environment and energy sectors. In this evaluation process, multiple experts are involved to assess a set of scenarios, according to multiple criteria that might have different nature. These evaluation processes aim to achieve an overall value for each scenario to obtain a ranking among them with the goal of identifying the best one. In this evaluation process a key issue is the treatment of experts’ assessments for each criterion. Due to the uncertainty and vagueness in the judgments of the experts and the nature of the criteria, these assessments can be expressed in different information formats, generating an heterogeneous framework. There are diverse approaches to deal with this type of framework, the use of one approach or another could be crucial in the evaluation process, according to the necessities and requirements of the evaluation models regarding the expected results. In this contribution, we propose an evaluation model applied to energy policy selection based on the decision analysis which may use different approaches to deal with heterogeneous information. We present a comparative study of the proposed model using two different approaches to deal with heterogeneous information. Finally, we show the strengths and weaknesses of the evaluation model depending on the approach used to manage heterogeneous information

Keywords

Ideal Solution Linguistic Term Collective Evaluation Heterogeneous Information Negative Ideal Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • M. Espinilla
    • 1
  • R. de Andrés
    • 2
  • F. J. Marténez
    • 1
  • L. Martínez
    • 1
  1. 1.Department of Computer SciencesUniversity of JaénJaénSpain
  2. 2.Department of Economic and Economic HistoryUniversity of SalamancaSalamancaSpain

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