Finding Effective Factor for Circular Economy Using Uncertain MCDM Approach
Year: 2024
Type: article
Abstract: This study investigates the interrelationships among key criteria that support the adoption and implementation of circular economy practices under uncertainty. Five criteria are considered: sustainability, resource management, product lifespan, collaboration and partnerships, and technology and innovation. Because the influence among these criteria may be difficult to express through precise numerical judgments, an interval-valued Decision-Making Trial and Evaluation Laboratory (DEMATEL) framework is employed to represent expert assessments and preserve uncertainty during the evaluation process. Linguistic evaluations provided by two decision-makers are transformed into interval numbers and aggregated to construct the direct-relation matrix. The subsequent DEMATEL procedure is used to derive the total-relation matrix, prominence and relation measures, and threshold values that describe both the overall importance of each criterion and its causal role within the system. The results indicate that Product Lifespan has the strongest overall relationship with the remaining circular economy criteria, whereas Collaboration and Partnerships exhibits the lowest overall relation. The cause-effect analysis further classifies Collaboration and Partnerships and Technology and Innovation as causal or dispatcher criteria, while Sustainability, Resource Management, and Product Lifespan are identified as effect or receiver criteria. A scatter-based representation is additionally used to visualize the dependency structure and the relevant relationships among the criteria. The proposed interval-valued DEMATEL analysis therefore provides a transparent way to identify influential circular economy factors when expert information is imprecise. The findings can support decision-makers in prioritizing interconnected actions for circular economy development while explicitly accounting for uncertainty in expert judgments.
Source: Management Science Advances.
DOI: 10.31181/msa1120245
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4404892843