Analysis of world trade data with machine learning to enhance policies of mineral supply chain transparency-Reference-Cited by-同舟云学术

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

Published:2024-02 Issue: Volume:89 Page:104671
ISSN:0301-4207
Container-title:Resources Policy
language:en
Short-container-title:Resources Policy

Author:

Saka Umut Mete^ORCID,Duzgun Sebnem^ORCID,Bazilian Morgan D.

Funder

National Science Foundation

Publisher

Elsevier BV

Reference43 articles.

1. A Guide to Traceability: A Practical Approach to Advance Sustainability in Global Supply Chains,2014

2. The identification of reporting Accuracies from mirror data;Cate;Jahrb. Natl. Stat.,2014

3. Advancing UN comtrade for physical trade flow analysis: review of data quality issues and solutions;Chen;Resour. Conserv. Recycl.,2022

4. Production of cobalt from copper-cobalt ores on the African Copperbelt – an overview;Crundwell;Miner. Eng.,2020

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Physical and monetary characterization of global nickel flow network;Resources Policy;2024-07

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