Selecting an Optimal Approach to Reduce Drivers of Climate Change in a Complex Intuitionistic Fuzzy Environment

The sustainability of the climate is a critical concern in the modern world. A variety of acts are included in sustainability that strive to lessen our carbon footprint and maintain the fragile balance of our world. To preserve a sustainable future for future generations, we must cooperate in adopti...

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Bibliographic Details
Published inSustainability Vol. 15; no. 16; p. 12300
Main Authors Razzaque, Asima, Razaq, Abdul, Khalid, Afifa, Masmali, Ibtisam, Shuaib, Umer, Alhamzi, Ghaliah
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2023
Subjects
Automobile drivers
Climate change
Decision making
Environmental aspects
Fuzzy sets
Global temperature changes
Linear programming
Sustainability
Iran
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Summary:The sustainability of the climate is a critical concern in the modern world. A variety of acts are included in sustainability that strive to lessen our carbon footprint and maintain the fragile balance of our world. To preserve a sustainable future for future generations, we must cooperate in adopting renewable energy sources, supporting green transportation, and implementing responsible land use. In this article, we propose the concepts of complex intuitionistic fuzzy Dombi hybrid averaging (CIFDHA) and complex intuitionistic fuzzy Dombi hybrid geometric (CIFDHG) operators within the framework of a complex intuitionistic fuzzy environment. Furthermore, we explore several additional important features of these operators. To overcome the limitations of the existing score function within the CIF knowledge context, we present a new and improved score function. Additionally, we apply the proposed score function and newly defined operators to select an optimal strategy for mitigating the drivers of climate change and saving the planet’s valuable resources for a more livable and resilient planet. In order to demonstrate the validity and practicality of the suggested strategies, we conducted a comparative study of these novel techniques with existing methods.
ISSN:2071-1050
2071-1050
DOI:10.3390/su151612300