Interval-valued Fermatean fuzzy heronian mean operator-based decision-making method for urban climate change policy for transportation activities

Arunodaya Raj Mishra*, Pratibha Rani, Muhammet Deveci*, Ilgin Gokasar, Dragan Pamucar, Kannan Govindan

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Climate change affects the world. Due to excessive GHG emissions, urban transportation contributes to this threat. Policymakers and authorities want to reduce transportation-related GHG emissions. An imaginary urban area with high transportation-related greenhouse gas emissions, dense, interconnected transportation modes, and a high population density is considered. Istanbul, Turkey meets the criteria of this imaginary place, so the case analysis considers this city. Istanbul's decision-makers are looking for effective strategies to prioritize urban climate change policy alternatives for transportation activities. Four alternative strategies and 13 criteria are presented in this context. Innovative multi-criteria decision-making (MCDM) method with the interval-valued Fermatean fuzzy sets (IVFFSs) strategies is proposed for advantage-prioritization so decision-makers can select the most effective strategies for policies. Utilizing the IVFFSs, the proposed method effectively tackles the qualitative/quantitative data and uncertain information that occurs in realistic applications. In this study, firstly IVFF-heronian mean operators with their desirable characteristics are presented to aggregate the IVFF information. The proposed operators can overcome the drawbacks of existing IVFF information-based operators by considering the relationships between IVFF numbers. Based on IVFF heronian mean operators, a hybrid decision-making framework is proposed by integrating criteria importance through inter-criteria correlation (CRITIC), rank sum (RS), and the double normalization-based multi-aggregation (DNMA) methods with IVFF information. In this method, the CRITIC and RS methods are implemented to derive the objective and subjective weights of the considered evaluation criteria and DNMA is applied to prioritize urban climate change policy alternatives for transportation activities. Sensitivity and comparative analyses with existing studies confirm the proposed framework. The evaluation results show that the integration of transportation sectors, strategies, and innovations across different urban areas in all regions option has the highest overall utility degree (0.731) among a set of four urban climate change policy alternatives for transportation activities.

Original languageEnglish
Article number106603
JournalEngineering Applications of Artificial Intelligence
Volume124
Number of pages23
ISSN0952-1976
DOIs
Publication statusPublished - Sept 2023

Keywords

  • Climate change
  • Heronian mean operator
  • Interval-valued Fermatean fuzzy set
  • Transportation
  • Urban areas

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