Urban–industrial symbiosis recommendation platform for urban factories: Leveraging historical exchange patterns through feature analysis for real‐world applications

Abstract Since the beginning of industrialization, the economic viability of manufacturing companies relied on the exploitation of natural resources. From 2000 to 2019, the global consumption of raw materials surged by 65%, with 70% of these materials being non‐renewable. Addressing this unsustainable trajectory, the United Nations emphasizes “Responsible consumption and production” as a sustainable development goal (SDG 12), advocating for resource efficiency, circularity, and dematerialization of economic growth. In that context, industrial symbiosis (IS) emerges as a key strategy for sustainable industrial development. IS networks have demonstrated substantial environmental and economic benefits in supply chains. Urban areas, such as the Braunschweig region in Germany or Singapore, characterized by its diverse industries in close geographical proximity, present unique opportunities for IS. In parallel, there is a special demand to enhance the circular economy due to the high density of resource flows and the high dependency on external material supply and disposal. Against this background, this research introduces an IS recommendation system that relies on a knowledge database containing reported material exchange data from various industries. This system incorporates a knowledge‐based matching methodology, which identifies potential symbiotic relationships by evaluating the suitability of different waste stream patterns. Additionally, a hierarchical matching method is developed to suggest potential IS partners based on multicriteria decision support. The proposed method is implemented, tested, and validated through a case study in Braunschweig and Singapore. Finally, recommendations for action are derived, and the methodology is critically reviewed for its effectiveness and applicability.

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Publication Details

Journal
Journal of Industrial Ecology
Published
2025-04-11
DOI
https://doi.org/10.1111/jiec.70015
Citations
2
Primary Topic
Sustainable Industrial Ecology
Type
article
Field-Weighted Citation Impact
2.48

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article

Urban–industrial symbiosis recommendation platform for urban factories: Leveraging historical exchange patterns through feature analysis for real‐world applications

Yajuan Sun, Mark Mennenga, Chuan Fu Tan, Christoph Herrmann et al.
2 citations
Journal of Industrial Ecology
Sustainable Industrial Ecology
2.48
article

Urban–industrial symbiosis recommendation platform for urban factories: Leveraging historical exchange patterns through feature analysis for real‐world applications

Yajuan Sun, Mark Mennenga, Chuan Fu Tan, Christoph Herrmann, Vanessa Miriam Carlow, Philipp Grimmel, Yang Zhao, Zhiquan Yeo, Jan Felix Niemeyer, Nils Schöling
article en
2 citations

Abstract

Abstract Since the beginning of industrialization, the economic viability of manufacturing companies relied on the exploitation of natural resources. From 2000 to 2019, the global consumption of raw materials surged by 65%, with 70% of these materials being non‐renewable. Addressing this unsustainable trajectory, the United Nations emphasizes “Responsible consumption and production” as a sustainable development goal (SDG 12), advocating for resource efficiency, circularity, and dematerialization of economic growth. In that context, industrial symbiosis (IS) emerges as a key strategy for sustainable industrial development. IS networks have demonstrated substantial environmental and economic benefits in supply chains. Urban areas, such as the Braunschweig region in Germany or Singapore, characterized by its diverse industries in close geographical proximity, present unique opportunities for IS. In parallel, there is a special demand to enhance the circular economy due to the high density of resource flows and the high dependency on external material supply and disposal. Against this background, this research introduces an IS recommendation system that relies on a knowledge database containing reported material exchange data from various industries. This system incorporates a knowledge‐based matching methodology, which identifies potential symbiotic relationships by evaluating the suitability of different waste stream patterns. Additionally, a hierarchical matching method is developed to suggest potential IS partners based on multicriteria decision support. The proposed method is implemented, tested, and validated through a case study in Braunschweig and Singapore. Finally, recommendations for action are derived, and the methodology is critically reviewed for its effectiveness and applicability.

Journal of Industrial EcologyVol. 29(3)
Agency for Science, Technology and Research (SG), Singapore Institute of Manufacturing Technology (SG), Cycle (Germany) (DE), Technische Universität Braunschweig (DE)
Niedersächsische Ministerium für Wissenschaft und Kultur, Agency for Science, Technology and Research, Volkswagen Foundation, Bundesministerium für Bildung und Forschung
Openalex Percentile: Top 11%
Sustainable Industrial Ecology
2.48
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