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Smart city article details

Title	Clean Energy Selection For Sustainable Development By Using Entropy-Based Decision Model With Hesitant Fuzzy Information
ID_Doc	14323
Authors	Krishankumar R.; Pamucar D.; Cavallaro F.; Ravichandran K.S.
Year	2022
Published	Environmental Science and Pollution Research, 29, 28
DOI	http://dx.doi.org/10.1007/s11356-022-18673-6
Abstract	Smart cities development is an ambitious project launched in India in 2015 with around 14 billion USD. Smart city mission program primarily aimed at reducing the carbon footprint and encouraging green and sustainable practices. Under this context, clean energy usage for demand fulfillment became the prime focus. India’s geographic location gifts the nation with diverse clean energy sources (CES). Owing to the multiple sustainable criteria that are both conflicting and correlated, there is an urge for a multi-criteria decision approach. Previously, literatures on CES selection have not been able to grab the hesitation properly and handle uncertainty effectively. Since the human mind is dynamic, hesitation is an integral part of choice making. Hesitant fuzzy set (HFS) is a generic set that captures hesitation better. Driven by these claims, in this work, a new framework for CES selection is developed. Attitude-driven entropy measure is proposed for criteria weight assessment, and a mathematical model is formulated for ranking CESs. Together, these methods constitute a decision framework that (i) considers the attitude of experts and captures hesitation during rating process and (ii) acquires partial personal choices from experts before ranking CESs. To testify the framework, a case study from a smart city within Tamil Nadu (a state in India) is explained. Sensitivity analysis reveals the robustness of the framework, and comparison with other works showcases the novel innovations of the proposal. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Author Keywords	Clean energy; Decision-making; Entropy measure; Hesitant fuzzy set

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