Research ArticleECONOMICS

Evaluating the mineral commodity supply risk of the U.S. manufacturing sector

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Science Advances  21 Feb 2020:
Vol. 6, no. 8, eaay8647
DOI: 10.1126/sciadv.aay8647
  • Fig. 1 Assessing the EV of aluminum by application for the year 2008.

    Each of the 21 aluminum applications is represented by an individual column, with height depicting the ratio of EXP to OP and width representing the ratio of VA to GDP. The area of each column represents the application’s vulnerability, with darker shades indicating a greater contribution to aluminum’s overall vulnerability.

  • Fig. 2 Assessment of SR for year 2016.

    DP (horizontal axis), EV (vertical axis), TE (point size), and SR (point shade) are shown. For some commodities, indicator scores are rounded to avoid disclosing company proprietary data.

  • Fig. 3 SR by indicator for years 2007–2016.

    DP (A), TE (B), EV (C), and SR (D) scores for all commodities examined for the years 2007–2016 are shown. For each box, the vertical axis represents scores ranging from 0 to 1, while the horizontal axis represents the years 2007–2016. No results are available for tellurium (Te) before 2011 or neodymium (Nd), praseodymium (Pr), samarium (Sm), and dysprosium (Dy) before year 2015, as indicated by “NA” in their box. For some commodities, indicator scores are rounded to avoid disclosing company proprietary data.

  • Fig. 4 Dynamic SR indicators for selected commodities.

    DP (horizontal axis), EV (vertical axis), TE (point size), and SR (point shade) for the years 2007–2016 for selected commodities are shown. For some commodities, indicator scores are rounded to avoid disclosing company proprietary data.

  • Fig. 5 Heat map displaying the SR for all commodities examined for years 2007–2016.

    Warmer (i.e., orange to red) shades indicate a greater degree of SR. Commodities are listed in descending order of their 2007–2016 average SR and identified by cluster based on a hierarchical cluster analysis. Leading producing countries, based on primary production, are identified, and their share of world production from 2007 to 2016 is displayed in the stacked blue bars. The most vulnerable applications in 2016 are identified, and their contribution and the contribution of all other applications to a commodity’s overall EV are depicted in the stacked teal and dark teal bars, respectively.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/6/8/eaay8647/DC1

    Supplementary Materials and Methods

    Fig. S1. WSI and constituent components by year.

    Fig. S2. SR, DP, EV, and TE scores for years 2007–2015.

    Fig. S3. Hierarchical cluster analysis based on 2016 DP, EV, and TE scores.

    Fig. S4. Hierarchical cluster analysis based on 2007–2016 average SR scores.

    Table S1. Description of data for world primary and secondary production and prices for each commodity.

    Table S2. Description of data for U.S. apparent consumption calculation by component for each commodity.

    Table S3. Estimated elemental content of various steel alloys.

    Table S4. Description of applications, associated NAICS codes, and U.S. demand fractions for each commodity.

    Table S5. Rare earth oxide distribution (in percent of total) for various world regions.

    Table S6. Rare earth oxide distribution (in percent of total) for various regions in China.

  • Supplementary Materials

    This PDF file includes:

    • Supplementary Materials and Methods
    • Fig. S1. WSI and constituent components by year.
    • Fig. S2. SR, DP, EV, and TE scores for years 2007–2015.
    • Fig. S3. Hierarchical cluster analysis based on 2016 DP, EV, and TE scores.
    • Fig. S4. Hierarchical cluster analysis based on 2007–2016 average SR scores.
    • Table S1. Description of data for world primary and secondary production and prices for each commodity.
    • Table S2. Description of data for U.S. apparent consumption calculation by component for each commodity.
    • Table S3. Estimated elemental content of various steel alloys.
    • Table S4. Description of applications, associated NAICS codes, and U.S. demand fractions for each commodity.
    • Table S5. Rare earth oxide distribution (in percent of total) for various world regions.
    • Table S6. Rare earth oxide distribution (in percent of total) for various regions in China.

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