Improving Regional Climate Projections by Prioritized Aggregation via Ordered Weighted Averaging Operators

Llopis-Albert, Carlos; Merigó, José M.; XuYejun,; Liao, Huchang

doi:10.1089/ees.2016.0546

Cited by 5 publications

(3 citation statements)

References 34 publications

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“…Finally, in response to other studies [43][44][45], the OWA operator was shown to be appropriate for evaluating, prioritizing, and comparing data across countries to SDG13. Determining weights for each indicator based on official data and scientific studies [4,18,25] allowed for prioritizing the most relevant climate change indicators and presenting a more realistic ranking of countries.…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

“…In the second research [44] conducted in Spain, the results showed the OWA operator as a robust decision-making tool to assess the performance of future climate projections and to design sustainable policies under uncertainty and risk tolerance [44]. In addition, the methodology made it possible to address stakeholder attitudes and risk preferences regarding actions to be taken and to minimize uncertainties associated with other climate projection methods [44].…”

Section: Climate Change and Owa Operatormentioning

confidence: 99%

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Algorithm Applied to SDG13: A Case Study of Ibero-American Countries

2023

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Scientific studies confirm the existence of a crisis caused by climate change, in which global causes produce local effects. Despite climate agreements, greenhouse gas emissions continue to fall short of targets to limit global warming. There is still a need for comparable data for Sustainable Development Goal (SDG) 13—Climate Action. The motivation of the research is to provide data for decision-making and to propose solutions to address the climate crisis. The article aims to propose a Fuzzy Logic algorithm to evaluate the SDG13 indicators and to deepen the discussion on climate change. The research is applied explanatory with a combined approach (quantitative-qualitative) through modeling, simulation, and case studies. As a result, the OWA operator ranks 10 Ibero-American countries to SDG13, indicating Colombia, Peru, and Cuba in the first positions. The main contributions are the reduction of identified knowledge gaps and proposals for action for policy and decision-makers. A limitation of this study would be the number of participating countries. The authors indicate future lines of research.

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Section: Discussion Of the Resultsmentioning

confidence: 99%

Section: Climate Change and Owa Operatormentioning

confidence: 99%

Algorithm Applied to SDG13: A Case Study of Ibero-American Countries

2023

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“…They are different from the normalization and aggregation techniques. It is known that various normalization procedures have different advantages and disadvantages (Jahan & Edwards, 2015 ), and several “Aggregation Operators (AOs)” have different functions (Llopis-Albert et al, 2017 ; Liao & Wu, 2020 ; Mishra et al, 2019b ). However, the classical utility value-based methods apply only one normalization technique, which would limit their applications.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessment of healthcare waste treatment methods using an interval-valued intuitionistic fuzzy double normalization-based multiple aggregation approach

Al-Barakati

Rani²

2023

Environ Dev Sustain

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Healthcare waste management has been an extensively attractive topic recently since it is one of the key concerns regarding both environment and public health, predominantly in developing nations. The optimization of the treatment procedure for healthcare waste is indeed a complex “multi-criteria decision-making (MCDM)” problem that involves contradictory and interweaved critical criteria. To successfully handle this issue, this study extends the original method, named the “double normalization-based multi-aggregation (DNMA)” approach, with “interval-valued intuitionistic fuzzy sets (IVIFSs)” for decision-making problems taking criteria in terms of benefit or cost types. This method involves two target-based normalizations and three subordinate utility models. To estimate the criteria weights, we propose a new parametric divergence measure and discuss the feasibility of the developed divergence measure based on existing divergence measures for IVIFSs. Further, the developed framework is implemented to elucidate the “healthcare waste treatment (HCWT)” problem. The comparative and sensitivity analyses of the outcomes indicate that the proposed approach efficiently tackles the problem of HCWT selection. The outcomes show that steam sterilization (0.462) is the optimal one for HCWT. The prioritization options, obtained by presented approach, are dependable and suitable, which are steam sterilization ≻ microwave ≻ incineration ≻ landfilling.

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