Prioritizing Climate Change Contributing Factors via the VIKOR Method under Q-Rung Orthopair Fuzzy Environment

Authors

  • Lazim Abdullah * Departmant of Computer Science and Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu Malaysia, Malaysia. https://orcid.org/0000-0002-6646-4751
  • Norsyahida Zulkifl Departmant of Computer Science and Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu Malaysia, Malaysia.
  • Ahmad Termimi Ab Ghani Departmant of Computer Science and Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu Malaysia, Malaysia.

https://doi.org/10.48314/ramd.vi.60

Abstract

Climate change, characterized by long-term shifts in temperature and weather patterns, is predominantly driven by human activities. Although numerous factors, such as carbon dioxide concentration, changes in the Earth’s orbit, ocean currents, greenhouse gas emissions, and variations in solar energy reflection or absorption, are recognized as contributors, the degree of their individual impacts remains unclear and uncertain. This study aims to prioritize the contributing factors to climate change by employing the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method integrated with Q-Rung Orthopair Fuzzy Sets (Q-ROFS), a robust approach to handle vagueness and uncertainty in expert assessments. Five domain experts provided linguistic evaluations regarding the importance of each contributing factor. The aggregated linguistic data were analysed using the Q-ROFS-VIKOR model, revealing that two primary factors, identified as R1 and R2, are the most significant contributors to climate change. Interestingly, the factor 'carbon dioxide concentration' was ranked lowest, suggesting a relatively negative impact compared to other factors considered. The findings provide a clearer perspective on the relative significance of various climate change factors, offering valuable insights for policymakers and researchers in designing effective mitigation strategies.

Keywords:

Decision making, Climate change, Q-rung orthopair fuzzy sets, VlseKriterijumska optimizacija i kompromisno resenje method

References

  1. [1] Intergovernmental Panel on Climate Change (IPCC). (2022). Climate change 2022: Impacts, adaptation and vulnerability. https://www.ipcc.ch/report/ar6/wg2/

  2. [2] United Nations Environment Programme (UNEP). (2022). Global environment outlook for cities. https://B2n.ir/bd8424

  3. [3] NASA. (2023). Global climate change: Vital signs of the planet. https://climate.nasa.gov/

  4. [4] Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., ... & Zhou, B. (2021). Climate change 2021: The physical science basis. Contribution of working group i to the sixth assessment report of the intergovernmental panel on climate change, 2(1), 2391. https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_FrontMatter.pdf

  5. [5] Hausfather, Z., & Peters, G. P. (2020). Emissions-the ‘business as usual’story is misleading. Nature, 577(7792), 618–620. https://www.nature.com/articles/d41586-020-00177-3

  6. [6] Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., ... & Zelinka, M. D. (2020). An assessment of Earth’s climate sensitivity using multiple lines of evidence. Reviews of geophysics, 58(4), e2019RG000678. https://doi.org/10.1029/2019RG000678

  7. [7] Sivakumar, M. V. K. (2005). Impacts of natural disasters in agriculture, rangeland and forestry: An overview. In Natural disasters and extreme events in agriculture: Impacts and mitigation (pp. 1–22). Springer. https://doi.org/10.1007/3-540-28307-2_1

  8. [8] Janzen, H. H. (2004). Carbon cycling in earth systems—a soil science perspective. Agriculture, ecosystems & environment, 104(3), 399–417. https://doi.org/10.1016/j.agee.2004.01.040

  9. [9] Smith, P., Martino, D., Cai, Z., Gwary, D., Janzen, H., Kumar, P., ... & Smith, J. (2008). Greenhouse gas mitigation in agriculture. Philosophical transactions of the royal society b: Biological sciences, 363(1492), 789–813. https://doi.org/10.1098/rstb.2007.2184

  10. [10] Amien, I., Rejekiningrum, P., Pramudia, A., & Susanti, E. (1996). Effects of interannual climate variability and climate change on rice yield in Java, Indonesia. Water, air, and soil pollution, 92(1), 29–39. https://doi.org/10.1007/BF00175550

  11. [11] World Bank. (2021). Climate-smart development: Policies and programs for advancing economic growth and sustainable development. https://openknowledge.worldbank.org/entities/publication/ee8a5cd7-ed72-542d-918b-d72e07f96c79

  12. [12] Meng, M., Dabrowski, M., & Stead, D. (2020). Enhancing flood resilience and climate adaptation: The state of the art and new directions for spatial planning. Sustainability, 12(19), 7864. https://doi.org/10.3390/su12197864

  13. [13] McGrath, H., & Gohl, P. N. (2022). Accessing the impact of meteorological variables on machine learning flood susceptibility mapping. Remote sensing, 14(7), 1656. https://doi.org/10.3390/rs14071656

  14. [14] Mirza, M. M. Q. (2011). Climate change, flooding in South Asia and implications. Regional environmental change, 11(1), 95–107. https://doi.org/10.1007/s10113-010-0184-7

  15. [15] Lee, J. S., & Choi, H. Il. (2018). Comparison of flood vulnerability assessments to climate change by construction frameworks for a composite indicator. Sustainability, 10(3), 768. https://doi.org/10.3390/su10030768

  16. [16] Lee, E. H., & Kim, J. H. (2018). Development of a flood-damage-based flood forecasting technique. Journal of hydrology, 563, 181–194. https://doi.org/10.1016/j.jhydrol.2018.06.003

  17. [17] Chang, L. F., Lin, C. H., & Su, M. D. (2008). Application of geographic weighted regression to establish flood-damage functions reflecting spatial variation. Water sa, 34(2), 209–216. https://doi.org/10.4314/wsa.v34i2.183641

  18. [18] Keynes, J. M. (2017). The economic consequences of the peace. Routledge. https://doi.org/10.4324/9781351304641

  19. [19] Banholzer, S., Kossin, J., & Donner, S. (2014). The impact of climate change on natural disasters. In Reducing disaster: Early warning systems for climate change (pp. 21–49). Springer. https://doi.org/10.1007/978-94-017-8598-3_2

  20. [20] Yildiz, V., Hatipoglu, M. A., & Kumcu, S. Y. (2022). Climate change impacts on water resources. In Water and wastewater management: Global problems and measures (pp. 17–25). Springer. https://doi.org/10.1007/978-3-030-95288-4_2

  21. [21] Zhang, K., Yao, L., Meng, J., & Tao, J. (2018). Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. Science of the total environment, 634, 1326–1334. https://doi.org/10.1016/j.scitotenv.2018.04.112

  22. [22] Milly, P. C. D., Dunne, K. A., & Vecchia, A. V. (2005). Global pattern of trends in streamflow and water availability in a changing climate. Nature, 438(7066), 347–350. https://doi.org/10.1038/nature04312

  23. [23] Scholze, M., Knorr, W., Arnell, N. W., & Prentice, I. C. (2006). A climate-change risk analysis for world ecosystems. Proceedings of the national academy of sciences, 103(35), 13116–13120. https://doi.org/10.1073/pnas.0601816103

  24. [24] Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European journal of operational research, 156(2), 445–455. https://doi.org/10.1016/S0377-2217(03)00020-1

  25. [25] Zimonjić, S., Đekić, M., & Kastratović, E. (2018). Application of vikor method in ranking the investment projects. International journal of economics & law, 8, 125–134. https://www.ceeol.com/search/article-detail?id=712790

  26. [26] Campbell, J. E., Berry, J. A., Seibt, U., Smith, S. J., Montzka, S. A., Launois, T., …& Laine, M. (2017). Large historical growth in global terrestrial gross primary production. Nature, 544(7648), 84–87. https://doi.org/10.1038/nature22030

  27. [27] Vose, J. M., Peterson, D. L., Domke, G. M., Fettig, C. J., Joyce, L. A., Keane, R. E., ... & Halofsky, J. E. (2018). Forests. In: Reidmiller, DR; Avery, CW; Easterling, DR; Kunkel, KE; Lewis, KLM; Maycock, TK; Stewart, BC, eds. 2018. (pp. 232-267). Washington, DC: US Global Change Research Program. https://doi.org/10.7930/SOCCR2.2018.Ch9

  28. [28] Graven, H. D., Keeling, R. F., Piper, S. C., Patra, P. K., Stephens, B. B., Wofsy, S. C., ... & Bent, J. D. (2013). Enhanced seasonal exchange of CO2 by northern ecosystems since 1960. Science, 341(6150), 1085–1089. https://doi.org/10.1126/science.1239207

  29. [29] Cox, P. M. (2019). Emergent constraints on climate-carbon cycle feedbacks. Current climate change reports, 5(4), 275–281. https://doi.org/10.1007/s40641-019-00141-y

  30. [30] Change, N. C. (2016). Greening of the Earth and its drivers. http://dx.doi.org/10.1038/nclimate3004

  31. [31] Chen, S. M., Lee, L. W., Liu, H. C., & Yang, S. W. (2012). Multiattribute decision making based on interval-valued intuitionistic fuzzy values. Expert systems with applications, 39(12), 10343–10351. https://doi.org/10.1016/j.eswa.2012.01.027

  32. [32] Chen, S. J., & Hwang, C.-L. (1992). Fuzzy ranking methods. In Fuzzy multiple attribute decision making: Methods and applications (pp. 101–288). Springer. https://doi.org/10.1007/978-3-642-46768-4_4

Published

2025-06-19

How to Cite

Abdullah, L. ., Zulkifl, N. ., & Termimi Ab Ghani, A. . (2025). Prioritizing Climate Change Contributing Factors via the VIKOR Method under Q-Rung Orthopair Fuzzy Environment. Risk Assessment and Management Decisions, 2(2), 118-130. https://doi.org/10.48314/ramd.vi.60

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