Exploring Indicators of Circular Economy Adoption Framework through a Hybrid Decision Support Approach

Yadav, Gunjan, Mangla, Sachin Kumar, Bhattacharya, Arijit and Luthra, Sunil (2020) Exploring Indicators of Circular Economy Adoption Framework through a Hybrid Decision Support Approach. Journal of Cleaner Production. ISSN 0959-6526

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Abstract

Circular economy (CE) focuses on a circular approach to energy and material resources, which provides economic, environmental and social benefits for manufacturing organisations. CE adoption in emerging economies facilitates in substantial economic growth through appropriate utilisation of energy and material resources across manufacturing industries. This study identifies CE indicators in the context of an emerging economy. The study further develops a framework for the adoption of CE and tests it through a hybrid Best Worst Method and Decision-Making Trial and Evaluation Laboratory approach. The framework is validated through an Indian manufacturing case organisation. While Best Worst Method computes the CE related indicator weights, Decision-Making Trial and Evaluation Laboratory analyses the inter-relationship among indicators. Disparate CE related indicators, e.g. strategic, managerial, informational and technological, supply chain and organisational, influence the CE adoption in an emerging economy context. The findings reveal that the strategic and managerial indicators have the strongest influence on developing other indicators. The causal digraph and relationship diagram assist the practitioners in predicting the inter-relationship of indicators in CE adoption. The study outcomes will help the practitioners, policymakers and researchers to draw a framework for adoption of circular and green practices and usage of resources sustainably.

Item Type: Article
Faculty \ School: Faculty of Social Sciences > Norwich Business School
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 15 Sep 2020 23:56
Last Modified: 21 Sep 2020 00:04
URI: https://ueaeprints.uea.ac.uk/id/eprint/76892
DOI: 10.1016/j.jclepro.2020.124186

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