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Hypothetical extraction, betweenness centrality, and supply chain complexity

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  • Shohei Tokito
  • Shigemi Kagawa
  • Tesshu Hanaka

Abstract

Two frameworks, hypothetical extraction and betweenness centrality analysis, can be used to identify environmentally important sectors in complex supply chains. This study derives an analytic expression for the relationship between hypothetical extraction and betweenness centrality analysis. Second, using the Eora and WIOD, this study analyzes the degree of difference in ‘important’ sectors identified by hypothetical extraction and betweenness centrality analysis. While the results obtained by rank correlation yield similarities, both methods have advantages. This study demonstrates that estimating betweenness centrality is meaningful and less computationally expensive, and can help us to understand the structural positions in the global supply chain network. The hypothetical extraction indicators can be easily computed using the betweenness centrality indicators’ mathematical relationship. We conclude that the implementation of effective CO2-reduction polices through greener global supply chain engagement center around two key sectors, chemical and metal products from China, and their higher betweenness centrality should be strengthened.

Suggested Citation

  • Shohei Tokito & Shigemi Kagawa & Tesshu Hanaka, 2022. "Hypothetical extraction, betweenness centrality, and supply chain complexity," Economic Systems Research, Taylor & Francis Journals, vol. 34(1), pages 111-128, January.
    • Handle: RePEc:taf:ecsysr:v:34:y:2022:i:1:p:111-128
    DOI: 10.1080/09535314.2020.1848807
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    Cited by:

    1. Maeno, Keitaro, 2023. "Identifying critical sectors in the restructuring of low-carbon global supply chains," Energy Economics, Elsevier, vol. 127(PA).
    2. Zhu, Qingyuan & Xu, Chengzhen & Pan, Yinghao & Wu, Jie, 2024. "Identifying critical transmission sectors, paths, and carbon communities for CO2 mitigation in global supply chains," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    3. Yelai Feng & Huaixi Wang & Chao Chang & Hongyi Lu, 2022. "Intrinsic Correlation with Betweenness Centrality and Distribution of Shortest Paths," Mathematics, MDPI, vol. 10(14), pages 1-18, July.
    4. Shohei Tokito & Tesshu Hanaka & Fumiya Nagashima, 2023. "Structural attribution of emissions along the global supply chain and implications for climate policy," Journal of Industrial Ecology, Yale University, vol. 27(6), pages 1488-1499, December.

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