Transactions on Data Analysis in Social Science

Transactions on Data Analysis in Social Science

A Symbiosis-Based Life Cycle Management Approach for Sustainable Resource Flows in the Industrial Ecosystem

Document Type : Original Article

Authors
1 Ph.D. Candidate, Department of Industrial Engineering, Eyvan-Key Non-Profit University, Semnan, Iran.
2 Assistant Professor, Department of Applied Mathematics, Imam Hossein University, Tehran, Iran.
Abstract
The foundation of sustainable industrial development lies in maintaining a continuous flow of resources from nature to the industrial ecosystem and their return to life cycles. Traditional management approaches, characterized by linear flows, lead to unstable resource streams, which are the primary causes of severe environmental degradation and resource scarcity. Shifting the resource flow paradigm from linear or intermittent patterns toward continuous and circular streams constitutes a major challenge for sustainable resource management in industrial ecosystems. Existing research has primarily focused on improving resource efficiency or reducing waste; however, systematic management studies on resource flows from the perspective of the industrial ecosystem remain scarce. To address this gap, this paper proposes a life cycle management approach grounded in the concepts and mechanisms of industrial symbiosis. By analyzing the life cycle system of resource flows and the symbiosis model, a framework for circular resource flows is developed. Based on this framework, a life cycle model is introduced that leverages the symbiosis of existing resource streams, coupled with an integrated assessment method that supports goal-oriented life cycle management while also addressing environmental impacts and sustainable utilization prospects. The case study demonstrates the capability of this approach to assist decision-makers in identifying key issues and formulating integrated, targeted strategies to facilitate sustainable resource flows within industrial ecosystems. To illustrate the evaluation and outcomes, the case study employs Nash theory.
Keywords

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Volume 7, Issue 1
Winter 2025
Pages 16-29

  • Receive Date 08 December 2024
  • Revise Date 14 January 2025
  • Accept Date 02 March 2025