We are excited to announce that the repository will soon undergo an upgrade, featuring a new look and feel along with several enhanced features to improve your experience. Please be on the lookout for further updates and announcements regarding the launch date. We appreciate your support and look forward to unveiling the improved platform soon.
dc.contributor.author | Effatpanah, Saeed Khojaste![]() |
|
dc.contributor.author | Ahmadi, Mohammad Hossein![]() |
|
dc.contributor.author | Aungkulanon, Pasura![]() |
|
dc.contributor.author | Maleki, Akbar![]() |
|
dc.contributor.author | Sadeghzadeh, Milad![]() |
|
dc.contributor.author | Sharifpur, Mohsen![]() |
|
dc.contributor.author | Chen, Lingen![]() |
|
dc.date.accessioned | 2023-03-02T05:21:58Z | |
dc.date.available | 2023-03-02T05:21:58Z | |
dc.date.issued | 2022-01-26 | |
dc.description.abstract | Over the last decade, the total primary energy consumption has increased from479 1015 BTU in 2010 to 528 1015 BTU in 2020. To address this ever-increasing energy demand, as well as prevent environmental pollution, clean energies are presented as a potential solution. In this regard, evaluating and selecting the most appropriate clean energy solution for a specific area is of particular importance. Therefore, in this study, a comparative analysis in Jiangsu province in China was performed by describing and implementing five prominent multi-criteria decision-making methods in the field of energy technology selection, including SAW, TOPSIS, ELECTRE, VIKOR, and COPRAS. The decision problem here consists of four clean energy options, including solar photovoltaic, wind, nuclear, and biomass, which have been evaluated by twelve basic and important criteria for ranking clean energy options. The obtained results, according to all five MCDM methods, indicate that solar photovoltaic was the optimal option in this study, followed by wind energy. The nuclear and biomass options placed third and fourth, respectively, except in the ELECTRE method ranking, in which both options scored the same and thus neither was superior. Finally, by conducting a comprehensive two-stage sensitivity analysis, in the first stage, it was found that changes in the weights of land use and water consumption criteria had the greatest impact on the performance of options, among which biomass and nuclear showed high sensitivity to variations in criteria weights. In the second stage, by defining five scenarios, the ranking of options was evaluated from different aspects so that the decision maker/organization would be able to make appropriate decisions in different situations. | en_US |
dc.description.department | Mechanical and Aeronautical Engineering | en_US |
dc.description.librarian | am2023 | en_US |
dc.description.uri | https://www.mdpi.com/journal/sustainability | en_US |
dc.identifier.citation | Effatpanah, S.K.; Ahmadi, M.H.; Aungkulanon, P.; Maleki, A.; Sadeghzadeh, M.; Sharifpur, M.; Chen, L. Comparative Analysis of Five Widely-Used Multi-Criteria Decision-Making Methods to Evaluate Clean Energy Technologies: A Case Study. Sustainability 2022, 14, 1403. https://DOI.org/10.3390/su14031403. | en_US |
dc.identifier.issn | 2071-1050 (online) | |
dc.identifier.other | 10.3390/su14031403 | |
dc.identifier.uri | https://repository.up.ac.za/handle/2263/89911 | |
dc.language.iso | en | en_US |
dc.publisher | MDPI | en_US |
dc.rights | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. | en_US |
dc.subject | Clean energy | en_US |
dc.subject | Multi-criteria decision-making | en_US |
dc.subject | Comparative analysis | en_US |
dc.subject | Sensitivity analysis | en_US |
dc.subject | Jiangsu province | en_US |
dc.subject | Simple additive weighting (SAW) | en_US |
dc.subject | Technique for order of preference by similarity to ideal solution (TOPSIS) | en_US |
dc.subject | Elimination et choix traduisant la realité (ELECTRE) | en_US |
dc.subject | Viekriterijumsko kompromisno rangiranje (VIKOR) | en_US |
dc.subject | Preference ranking organization method for enrichment evaluations (PROMETHEE) | en_US |
dc.subject | Complex proportional assessment (COPRAS) | en_US |
dc.subject | Step-wise weight assessment ratio analysis (SWARA) | en_US |
dc.subject.other | Engineering, built environment and information technology articles SDG-04 | |
dc.subject.other | SDG-04: Quality education | |
dc.subject.other | Engineering, built environment and information technology articles SDG-07 | |
dc.subject.other | SDG-07: Affordable and clean energy | |
dc.subject.other | Engineering, built environment and information technology articles SDG-09 | |
dc.subject.other | SDG-09: Industry, innovation and infrastructure | |
dc.subject.other | Engineering, built environment and information technology articles SDG-13 | |
dc.subject.other | SDG-13: Climate action | |
dc.title | Comparative analysis of five widely-used multi-criteria decision-making methods to evaluate clean energy technologies | en_US |
dc.type | Article | en_US |