4.7 Article

Renewable energy investments by a combined compromise solution method with stochastic information

期刊

JOURNAL OF CLEANER PRODUCTION
卷 276, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2020.123351

关键词

Renewable energy investments; Compromise solutions; Combined compromise solution (CoCoSo); Stochastic multi-criteria acceptability analysis (SMAA)

资金

  1. National Natural Science Foundation of China [71971145, 71771156]

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Cleaner production dedicates to preventing the production of waste while increasing efficiencies in the uses of resources such as energy. Renewable energy investments, aiming to reduce the dependence on fossil energy and the emission of carbon dioxide, need to consider multiple conflicting criteria. The conflicts among criteria in renewable energy investments bring two difficulties. One is the inaccessibility of utopia alternatives; the other is the appearance of uncertain data. For the former issue, compromise solutions are good alternatives and a Combined Compromise Solution (CoCoSo) method has been devised based on the combinatory perspective and compromise angle. About the latter issue, the SMAA (Stochastic Multi-criteria Acceptability Analysis) is a useful technique to handle imperfect data. The objective of this study is to design an integrated method based on CoCoSo method for renewable energy investments with stochastic information. Firstly, the literature of renewable energy investments with multiple criteria decision-making methods in the last five years is reviewed. Then, the essence of the CoCoSo method is proved by the comparisons of utility-based multiple criteria decision-making methods. Moreover, this paper presents the procedure of the SMAA-CoCoSo method by integrating a feasible and robust aggregation tool in the CoCoSo method when uncertain information appears. A case study about the renewable energy investments is provided to validate the proposed method. Finally, the advantages of the SMAA-CoCoSo method in terms of robustness and flexibility are shown by comparative analyses. (C) 2020 Elsevier Ltd. All rights reserved.

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