A Machine Learning–Based Multi-Criteria Decision-Making Model for Prioritizing Climate Change Policy Strategies under Spherical Fuzzy Environment

  • Authors

    • Serhat Yüksel School of Business, Istanbul Medipol University, Istanbul, Turkey
    • Hasan Dinçer School of Business, Istanbul Medipol University, Istanbul, Turkey
    • Serkan Eti IMU Vocational School, Istanbul Medipol University, Istanbul, Turkey
    https://doi.org/10.14419/s2d6w261

    Received date: August 28, 2025

    Accepted date: September 27, 2025

    Published date: October 17, 2025

  • machine learning; soft computing; fuzzy decision making; climate change; energy investments

  • Abstract

    The objective of this study is to identify the most prioritized policy strategies that can be implemented to combat climate change, thereby filling the gap in the literature and providing policymakers with a scientifically based roadmap. The proposed model utilizes the opinions of five experts. A machine learning-based method calculates importance weights based on the experts' demographic characteristics. The criteria importance through intercriteria correlation (CRITIC) is used to determine the criteria weights, and the weighted aggregated sum-product assessment (WASPAS) is considered to rank policy alternatives. Furthermore, spherical fuzzy sets are integrated into the model to more effectively manage uncertainties. The study contributes to the literature by proposing a unique decision-making model where expert weights are objectively calculated using machine learning, CRITIC and WASPAS methods are applied in an integrated manner, and uncertainty is managed more flexibly and reliably through spherical fuzzy sets. This model offers an innovative solution to the long-discussed problem of "assuming equal expert weights" in the literature and provides a more robust methodological framework for policy environments with high uncertainty. Research findings indicate that the most critical criteria are technological feasibility and economic feasibility. Moreover, carbon taxes and renewable energy incentives are the most optimal policy strategies.

  • References

    1. Bagdadee, A. H., Dewi, D. A., Varadarajan, V., Mondal, A. U., & Zhang, L. (2025). Assessing the feasibility of climate change-induced energy consumption in the industrial sector of Bangladesh. Environmental Research Communications, 7(3), 035018.
    2. Barr, S., Lemieux, C. J., Hoesen, J., Rushton, B., & Wright, P. (2025). Evaluating the climate change robustness of Canadian protected area man-agement plans. Geography and Sustainability, 6(3), 100280.
    3. Chancel, L., Mohren, C., Bothe, P., & Semieniuk, G. (2025). Climate change and the global distribution of wealth. Nature Climate Change, 1-11.
    4. Cuni-Sanchez, A., Aneseyee, A. B., Baderha, G. K., Batumike, R., Bitariho, R., Imani, G., ... & Zafra-Calvo, N. (2025). Perceived climate change impacts and adaptation responses in ten African mountain regions. Nature Climate Change, 15(2), 153-161.
    5. Duan, Z., de Wilde, P., Attia, S., & Zuo, J. (2025). Challenges in predicting the impact of climate change on thermal building performance through simulation: A systematic review. Applied Energy, 382, 125331.
    6. Dyderski, M. K., Paź-Dyderska, S., Jagodziński, A. M., & Puchałka, R. (2025). Shifts in native tree species distributions in Europe under climate change. Journal of Environmental Management, 373, 123504.
    7. Eyitayo, S., Arbad, N., Okere, C., Gamadi, T., & Watson, M. (2025). Advancing geological storage of carbon dioxide (CO2) with emerging tech-nologies for climate change mitigation. International Journal of Environmental Science and Technology, 22(6), 5023-5056.
    8. Glavina, A., Mišić, K., Baleta, J., Wang, J., & Mikulčić, H. (2025). Economic development and climate change: Achieving a sustainable bal-ance. Cleaner engineering and technology, 100939.
    9. Goodell, J. W., Palma, A., Paltrinieri, A., & Piserà, S. (2025). Firm-level climate change risk and corporate debt maturity. Journal of International Money and Finance, 152, 103275.
    10. Guesmi, K., Makrychoriti, P., & Pyrgiotakis, E. G. (2025). Climate change exposure and green bonds issuance. Journal of International Money and Finance, 152, 103281.
    11. Heikonen, S., Heino, M., Jalava, M., Siebert, S., Viviroli, D., & Kummu, M. (2025). Climate change threatens crop diversity at low lati-tudes. Nature food, 1-12.
    12. Horbach, J., & Rammer, C. (2025). Climate change affectedness and innovation in firms. Research Policy, 54(1), 105122.
    13. Kalra, A. (2025). Introduction to Fuzzy Logic and Its Applications in Machine Learning. In Smart Systems: Engineering and Managing Information for Future Success: Navigating the Landscape of Intelligent Technologies (pp. 1-15). Cham: Springer Nature Switzerland.
    14. Karagiannakis, G., Panteli, M., & Argyroudis, S. (2025). Fragility modeling of power grid infrastructure for addressing climate change risks and adaptation. Wiley Interdisciplinary Reviews: Climate Change, 16(1), e930.
    15. Kumler, A., Kravitz, B., Draxl, C., Vimmerstedt, L., Benton, B., Lundquist, J. K., ... & Tao, L. (2025). Potential effects of climate change and solar radiation modification on renewable energy resources. Renewable and Sustainable Energy Reviews, 207, 114934.
    16. Lang, W., Zhang, Y., Li, X., Meng, F., Liu, Q., Wang, K., ... & Piao, S. (2025). Phenological divergence between plants and animals under climate change. Nature Ecology & Evolution, 9(2), 261-272.
    17. Levy, S. R., Monahan, C., Araiza, A., Ramırez, L., & Palacios‐Espinosa, X. (2025). Reducing Climate Change Denial and Increasing Support for Climate‐Friendly Policies: The Role of Climate Change Education. Journal of Social Issues, 81(1), e12664.
    18. Li, X. (2025). Physical climate change exposure and firms' adaptation strategy. Strategic Management Journal, 46(3), 750-789.
    19. Lu, X., Wang, Z., Zhao, M., Peng, S., Geng, S., & Ghorbani, H. (2025). Data-driven insights into climate change effects on groundwater levels us-ing machine learning. Water Resources Management, 39(7), 3521-3536.
    20. Malik, I. H., & Ford, J. D. (2025). Understanding the impacts of Arctic climate change through the lens of political ecology. Wiley Interdisciplinary Reviews: Climate Change, 16(1), e927.
    21. Mills‐Novoa, M., & Mikulewicz, M. (2025). The promise of resistance: A new lens for climate change adaptation research and practice. Wiley Inter-disciplinary Reviews: Climate Change, 16(1), e922.
    22. Ogier, E. M., Pecl, G. T., Hughes, T., Lawless, S., Layton, C., Nash, K. L., & Morrison, T. H. (2025). Novel marine-climate interventions hampered by low consensus and governance preparedness. Nature Climate Change, 1-10.
    23. Olim, S. T., Nickoloff, A., Moffat, L. J., Weaver, A. J., & Eby, M. (2025). Mitigating anthropogenic climate change with aqueous green ener-gy. Scientific Reports, 15(1), 1700.
    24. Peng, P., Guo, Y., Huang, D., & Wang, H. (2025). Climate change risk and bond risk premium. International Review of Financial Analysis, 97, 103885.
    25. Puig, D., Adger, N. W., Barnett, J., Vanhala, L., & Boyd, E. (2025). Improving the effectiveness of climate change adaptation measures. Climatic Change, 178(1), 7.
    26. Rahim, M., Bajri, S. A., Khan, S., Alqahtani, H., & Khalifa, H. A. E. W. (2025). Innovative multi-criteria group decision making with Interval-valued p, q, r-spherical fuzzy sets: a case study on optimal solar energy investment location. International Journal of Fuzzy Systems, 1-28.
    27. Rasheed, M. W., Y. Saleh, H., Salih, A. A., Karamat, J., & Bilal, M. (2025). An overview of pink eye infection to evaluate its medications: group decision-making approach with 2-tuple linguistic T-spherical fuzzy WASPAS method. Frontiers in Artificial Intelligence, 7, 1496689.
    28. Rasool, Z., Gurmani, S. H., Niazai, S., Zulqarnain, R. M., Alballa, T., & Khalifa, H. A. E. W. (2025). An integrated CRITIC and EDAS model us-ing linguistic T spherical fuzzy Hamacher aggregation operators and its application to group decision making. Scientific Reports, 15(1), 6122.
    29. Shobanke, M., Bhatt, M., & Shittu, E. (2025). Advancements and future outlook of Artificial Intelligence in energy and climate change model-ing. Advances in Applied Energy, 100211.
    30. Singer, M. E., Rosenthal, A., Shapira, S., Natour, S., Porat, I., & Negev, M. (2025). Exploring the climate change, infrastructure, health nexus and everyday adaptation among vulnerable populations. Habitat International, 164, 103535.
    31. Tank, L., Voget-Kleschin, L., Garschagen, M., Boettcher, M., Mengis, N., Holland-Cunz, A., ... & Baatz, C. (2025). Distinguish between feasibility and desirability when assessing climate response options. Npj Climate Action, 4(1), 34.
    32. Verma, K. K., Song, X. P., Kumari, A., Jagadesh, M., Singh, S. K., Bhatt, R., ... & Li, Y. R. (2025). Climate change adaptation: challenges for ag-ricultural sustainability. Plant, Cell & Environment, 48(4), 2522-2533.
    33. Wu, S., Hu, F., & Zhang, Z. (2025). Climate change and energy poverty: Evidence from China. World Development, 186, 106826.
    34. Yang, W., Schmidt, C., Wu, S., Zhao, Z., Li, R., Wang, Z., ... & Zhang, J. (2025). Exacerbated anthropogenic water pollution under climate change and urbanization. Water Research, 280, 123449.
    35. Yi, J., Tian, Y., Simpson, N. P., Du, Y., Ma, T., Yu, C., ... & Xu, R. (2025). Low perception of climate change by farmers and herders on Tibetan Plateau. Global Environmental Change, 91, 102970.

  • Downloads

  • How to Cite

    Yüksel, S., Dinçer, H., & Eti, S. (2025). A Machine Learning–Based Multi-Criteria Decision-Making Model for Prioritizing Climate Change Policy Strategies under Spherical Fuzzy Environment. International Journal of Basic and Applied Sciences, 14(6), 337-344. https://doi.org/10.14419/s2d6w261