Advancing Greenwashing Audits: An Innovative Fuzzy Decision-Making Framework Integrating KochSnowflake Sets and Euclidean Distance-BasedExpert Weighting

This study aims to identify the priority criteria that should be considered for establishing more effective audit processes to prevent green-washing. To achieve this objective, a novel fuzzy multi-criteria decision-making model is developed. Euclidean distance-based expert weighting technique is employed to determine the importance weights of six experts based on their demographic characteristics, thereby reducing subjectivity in expert judgments. CIMAS is applied to compute the importance weights of the criteria. Furthermore, the newly developed Koch Snowflake fuzzy sets are integrated into the model to represent uncertainties more precisely, thereby minimizing infor-mation loss and increasing the robustness of the results. The originality of the study lies in combining these advanced methods to create a more reliable and effective analytical framework that fills a significant methodological gap in the literature on greenwashing audit processes. The findings reveal that compliance with assurance standards is the most significant factor for enhancing audit effectiveness, followed by corporate transparency and the risk level of greenwashing practices. These results not only contribute to the theoretical literature by offering a new methodological approach but also provide practical implications for policymakers and auditors in designing strategies to strengthen audit mechanisms, improve accountability, and reduce the proliferation of greenwashing practices.