Renewable energy source technology selection considering the empathetic preferences of experts in a cognitive fuzzy social participatory allocation network

Yilu Long, Ming Tang and Huchang Liao

Technological Forecasting and Social Change, 2022, vol. 175, issue C

Abstract: Renewable energy sources (RESs) have become increasingly significant in optimising the energy structure, protecting the ecological environment, and achieving sustainable economic and social developments. The residential sector is one of the major energy consumption industries. Choosing an appropriate RES technology for a residential building can be regarded as a multi-criteria group decision-making (MCGDM) problem, and the preferences of inhabitants regarding the renewable energy alternatives of a residential building should be considered. However, the previous literature on MCGDM did not consider the social relationships between experts. To address these challenges, we propose a cognitive fuzzy social participatory allocation network (SPAN) method considering the empathetic preferences (EPs) of experts. The cognitive fuzzy set is used by experts to depict fuzzy and uncertain information. Given that the traditional SPAN method is easily influenced by personal self-evaluations when aggregating evaluation information, we incorporate the traditional SPAN method with the EPs of experts to make the decision results more objective. A case study concerning the evaluation of RES technologies is presented to verify the practicability of the proposed model. The method is efficient, since the economic, environmental, technological, and social factors regarding the selection of RES technologies for residential buildings are included.

Keywords: Renewable energy sources; Empathetic preferences; Multiple criteria group decision-making; Social participatory allocation network; Cognitive fuzzy set (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:175:y:2022:i:c:s0040162521007484

DOI: 10.1016/j.techfore.2021.121317

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