Application of fuzzy credibility graph in climate mitigation strategy assessment

• Published in: Scientific reports Vol. 15; no. 1; pp. 29925 - 18
• Main Authors: Ullah, Ihsan, Abdullah, Saleem, Nawaz, Marya, Ahmadzai, Hameed Gul
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.08.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/705; Climate change; Climate change mitigation; Climate change mitigation strategies; Comparative analysis; Correlation coefficient; Credibility; Decision making; Decision making model; Decision making models; Emissions; Energy consumption; Environmental impact; Fuzzy credibility graph; Fuzzy sets; Graphs; Greenhouse gases; Humanities and Social Sciences; Industrial plant emissions; multidisciplinary; Multiple criteria decision making; Natural resources; Power plants; Science; Science (multidisciplinary); Sustainable development; Weather patterns; Decision making model; Fuzzy credibility graph; Climate change mitigation strategies
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-15775-2

The challenges posed to our environment by climate change are immense and are increasing every day. Its impacts on ecosystems, weather patterns, and human health are extensive. It is imperative that we tackle climate change to save the environment and ensure a sustainable future. It is essential to implement effective climate change mitigation and control strategies to conserve natural resources and improve global well-being. Therefore, we develop a novel decision making model based on the fuzzy credibility graph to select the best climate change mitigation strategies. In this article, the fuzzy credibility graph, the direct product of fuzzy credibility graphs, the degree of a vertex, and the total degree of a vertex are defined first. After that, we apply the proposed decision making model to select the best climate change mitigation strategy. For this, we collect the expert information and the fuzzy credibility edges information about the climate change mitigation strategies, and process the proposed model to compute the relative closeness of the alternatives and identify the most suitable climate change mitigation strategy. To evaluate the performance of the proposed model, we compare it with existing decision making methods. The results demonstrate that our model provides accurate and effective decision support. Additionally, we use Spearman’s correlation coefficient to verify the consistency of the rankings. The comparative analysis confirms the validity and reliability of the proposed model in supporting climate mitigation decisions.