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

Author

Listed:
  • Asima Razzaque

    (Department of Basic Sciences, King Faisal University Al Ahsa, Al Hofuf 31982, Saudi Arabia)

  • Abdul Razaq

    (Department of Mathematics, Division of Science and Technology, University of Education, Lahore 54770, Pakistan)

  • Afifa Khalid

    (Department of Mathematics, Government College University, Faisalabad 38000, Pakistan)

  • Ibtisam Masmali

    (Department of Mathematics, College of Science, Jazan University, Jazan 45142, Saudi Arabia)

  • Umer Shuaib

    (Department of Mathematics, Government College University, Faisalabad 38000, Pakistan)

  • Ghaliah Alhamzi

    (Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11564, Saudi Arabia)

Abstract

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.

Suggested Citation

  • Asima Razzaque & Abdul Razaq & Afifa Khalid & Ibtisam Masmali & Umer Shuaib & Ghaliah Alhamzi, 2023. "Selecting an Optimal Approach to Reduce Drivers of Climate Change in a Complex Intuitionistic Fuzzy Environment," Sustainability, MDPI, vol. 15(16), pages 1-25, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12300-:d:1215689
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    References listed on IDEAS

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    1. Jeremy Galbreath & David Charles & Eddie Oczkowski, 2016. "The Drivers of Climate Change Innovations: Evidence from the Australian Wine Industry," Journal of Business Ethics, Springer, vol. 135(2), pages 217-231, May.
    2. Hao Bin Liu & Yi Liu & Lei Xu, 2020. "Dombi Interval-Valued Hesitant Fuzzy Aggregation Operators for Information Security Risk Assessment," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-15, May.
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    Cited by:

    1. Yuqi Zang & Jiamei Zhao & Wenchao Jiang & Tong Zhao, 2024. "Advanced Linguistic Complex T-Spherical Fuzzy Dombi-Weighted Power-Partitioned Heronian Mean Operator and Its Application for Emergency Information Quality Assessment," Sustainability, MDPI, vol. 16(7), pages 1-36, April.

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