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Selecting the optimal renewable energy using multi criteria decision making

Author

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  • Abdolreza Yazdani-Chamzini
  • Mohammad Majid Fouladgar
  • Edmundas Kazimieras Zavadskas
  • S. Hamzeh Haji Moini

Abstract

Renewable energies are well-known as one of the most important energy resources not only due to limited other energy resources, but also due to environmental problems associated with air pollutants and greenhouse gas emissions. Renewable energy project selection is a multi actors and sophisticated problem because it is a need to incorporate social, economic, technological, and environmental considerations. Multi criteria decision making (MCDM) methods are powerful tools to evaluate and rank the alternatives among a pool of alternatives and select the best one. COPRAS (COmplex PRoportional ASsessment) is an MCDM technique which determines the best alternative by calculating the ratio to the ideal solution and the negative ideal solution. On the other hand, analytical hierarchy process (AHP) is widely used in order to calculate the importance weights of evaluation criteria. In this paper an integrated COPRAS-AHP methodology is proposed to select the best renewable energy project. In order to validate the output of the proposed model, the model is compared with five MCDM tools. The results of this paper demonstrate the capability and effectiveness of the proposed model in selecting the most appropriate renewable energy option among the existing alternatives.

Suggested Citation

  • Abdolreza Yazdani-Chamzini & Mohammad Majid Fouladgar & Edmundas Kazimieras Zavadskas & S. Hamzeh Haji Moini, 2013. "Selecting the optimal renewable energy using multi criteria decision making," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 14(5), pages 957-978, November.
    • Handle: RePEc:taf:jbemgt:v:14:y:2013:i:5:p:957-978
    DOI: 10.3846/16111699.2013.766257
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    References listed on IDEAS

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    1. Yoram Wind & Thomas L. Saaty, 1980. "Marketing Applications of the Analytic Hierarchy Process," Management Science, INFORMS, vol. 26(7), pages 641-658, July.
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