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    ANALYZING CLIMATE CHANGE PERFORMANCE OVER THE LAST FIVE YEARS OF G20 COUNTRIES USING A MULTI-CRITERIA DECISION-MAKING FRAMEWORK
    (2023) Keleş, Nuh; Ersoy, Nazli
    Today, limited resources are decreasing/depleting with the increase in the human population living on Earth. The increased human population brings with it various problems. Different events cause important climate events at the global level, such as the decrease or depletion of water resources with the increase in demand, damage to the ecosystem, health risks, and deterioration of biological diversity. Due to the use of fossil fuels, the formation of GHG (greenhouse gas) emissions and global warming cause significant climate changes. Climate change causes the restriction of environmental and vital activities, the increase of natural disasters, and the extinction of species. This study aimed to evaluate the climate change performance of G20 countries which emit more than 75% of the world’s GHG emissions from 2019 to 2023, using MCDM methods. An objective method, LOPCOW, was used to assign weights while SPOTIS, WISP, and RSMVC methods were used to determine the climate change performances of G20 countries. The findings showed that among G20 countries, the highest performance was found in the United Kingdom and India, while the United States, Canada and Saudi Arabia were found in the last ranks.
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    Öğe
    Comparison of multi-criteria decision-making methods with the same normalization procedure based on real-life applications
    (Wroclaw Univ Science & Technology, Fac Management, 2024) Ersoy, Nazli; Kele, Nuh
    The ranking of a set of objects defined by a single data set may vary due to differences in multi-criteria decision-making (MCDM) procedures. One of these procedural differences is normalization, which is an important step in data analysis and MCDM methods. In terms of demonstrating the impact of the normalization process on the results, this study aims to compare MCDM methods with a linear normalization process. This study works on eight ranking methods (WASPAS, SECA, SAW, OWA, CODAS, MARCOS, PSI, and WPM), and three weighting methods (Entropy, EW, LOPCOW) based on three reallife applications. The study primarily explains the differences in rankings by the MCDM methods. Additionally, it is also important to demonstrate the impact of different weights on the results. The study found that the MCDM rankings obtained with the same normalization process differed, and it also observed that different criterion weights had an impact on the ranking results. This study contributes to the literature as it is the first to compare MCDM methods using linear normalization processes based on real-life applications.