Assessment of Climate Change Factors and Impacts Using the Pythagorean Fuzzy AHP Approach

Year 2025, Volume: 6 Issue: 2, 130 - 152, 31.12.2025

Buse Aydın , Aytaç Yildiz

https://doi.org/10.54559/amesia.1780230

Abstract

Today, climate change has become a global crisis threatening not only the environment but also economic, social, and political systems worldwide. This situation necessitates the systematic analysis of both the drivers contributing to climate change and the areas affected by it. Given the high level of uncertainty in environmental decision-making processes, the use of fuzzy logic-based multi-criteria decision-making approaches has become essential. In this study, the Pythagorean Fuzzy Analytic Hierarchy Process (AHP), developed by utilizing Pythagorean fuzzy sets, is applied to prioritize the factors influencing climate change and the elements impacted by it. Accordingly, the relevant factors and elements were identified through literature review and expert opinions, and the methodological steps were followed to assign their respective weights. The results of the study indicate that among the factors contributing to climate change, fossil fuel use and carbon emissions was identified as the most critical factor. This was followed by animals and industrialization, while tourism activities and the health sector received the lowest priority levels. On the other hand, among the elements affected by climate change, biodiversity emerged as the most critical domain, with natural disasters and insects also assigned high importance levels. In contrast, destruction of cultural and historical sites and economy and socio-politics were ranked lower due to their more indirect effects. This study is the first to examine climate change drivers and impacts, ranking them under uncertainty. The findings provide decision-makers with a roadmap to identify strategic focal points in combating climate change.

Keywords

Climate change , fuzzy set , multi-criteria decision making , Pythagorean fuzzy AHP , sustainability

Supporting Institution

This study was supported by TÜBİTAK under the 2209-A Research Project Support Programme for Undergraduate Students.

Thanks

The authors would like to thank TÜBİTAK for its support

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