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Analysis of world trade data with machine learning to enhance policies of mineral supply chain transparency

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  • Saka, Umut Mete
  • Duzgun, Sebnem
  • Bazilian, Morgan D.

Abstract

The increasing integration of supply chains worldwide and the establishment of resilient material flows emphasize the significance of transparency. As regulations and policies around mineral supply become more stringent, organizations are actively seeking effective tools to assess the transparency of their supply chains. Ensuring supply chain transparency plays a vital role in international trade data since it addresses the issue of inconsistent reporting by two parties involved in a transaction, sometimes referred to as bilateral asymmetries. Nevertheless, bilateral asymmetries might be utilized as a proxy to examine discrepancies in the transparency of supply chains.

Suggested Citation

  • Saka, Umut Mete & Duzgun, Sebnem & Bazilian, Morgan D., 2024. "Analysis of world trade data with machine learning to enhance policies of mineral supply chain transparency," Resources Policy, Elsevier, vol. 89(C).
    • Handle: RePEc:eee:jrpoli:v:89:y:2024:i:c:s0301420724000382
    DOI: 10.1016/j.resourpol.2024.104671
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    References listed on IDEAS

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    Cited by:

    1. Sun, Xin & Jiao, Yang & Hao, Han & Liu, Zongwei & Zhao, Fuquan, 2024. "Physical and monetary characterization of global nickel flow network," Resources Policy, Elsevier, vol. 94(C).

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