REWAS 2019 pp 333-336 | Cite as

Exploring Drivers of Copper Supply and Demand Using a Dynamic Market Simulation

  • Jingshu Zhang
  • Omar Swei
  • Richard Roth
  • Randolph KirchainEmail author
Conference paper
Part of the The Minerals, Metals & Materials Series book series (MMMS)


Several authors have suggested that supplies for key non-renewable resources may soon cease to expand at the same rate as demand. Inevitably this would lead to some peak and then decline in production. This work describes the development and application of a fully dynamic model of the copper market. This model includes both a probabilistic simulator of future supply expansion based on published data on copper deposits and a novel model of copper demand that includes both short-term and long-term elasticity of demand—both due to self-price and aluminum price. Using this model in long-term simulations suggests significant copper reserve depletion would not be expected to occur in this century. The model predicts a time frame to significant depletion that is more than 50% further out in the future than previously reported results that did not include these effects.


Price elasticity Economics Criticality 


  1. 1.
    Kelly TD, Matos GR (2018) Historical statistics for mineral and material commodities in the United States (2016 version): U.S. Geological Survey Data Series 140 (Accessed 20 Oct 2018)
  2. 2.
    Graedel TE, Harper EM, Nassar NT, Reck BK (2015) On the materials basis of modern society. Proc Natl Acad Sci 112(20):6295–6300CrossRefGoogle Scholar
  3. 3.
    Reck BK, Graedel TE (2012) Challenges in metal recycling. Science (80-.) 337(6095):690–695CrossRefGoogle Scholar
  4. 4.
    Erdmann L, Graedel TE (2011) Criticality of non-fuel minerals: a review of major approaches and analyses. Environ Sci Technol 45(18):7620–7630CrossRefGoogle Scholar
  5. 5.
    Aguirregabiria V, Luengo A (2016) A microeconometric dynamic structural model of copper mining decisionsGoogle Scholar
  6. 6.
    Northey S, Mohr S, Mudd GM, Weng Z, Giurco D (2014) Modelling future copper ore grade decline based on a detailed assessment of copper resources and mining. Resour Conserv Recycl 83:190–201CrossRefGoogle Scholar
  7. 7.
    Glöser S, Soulier M, Tercero Espinoza LA (2013) Dynamic analysis of global copper flows. global stocks, postconsumer material flows, recycling indicators, and uncertainty evaluation. Environ Sci Technol 47(12):6564–6572CrossRefGoogle Scholar
  8. 8.
    Glöser-Chahoud S, Hartwig J, Wheat ID, Faulstich M (2016) The cobweb theorem and delays in adjusting supply in metals’ markets. Syst Dyn Rev 32(3–4):279–308CrossRefGoogle Scholar
  9. 9.
    Mosier D, Singer V, Donald A (2009) Volcanogenic massive sulfide deposits of the world: database and grade and tonnage modelsGoogle Scholar
  10. 10.
    Cox D, Lindsey D, Singer D, Diggles M (2003) Sediment-hosted copper deposits of the world: deposit models and databaseGoogle Scholar
  11. 11.
    Singer D, Berger V, Moring B (2005) Porphyry copper deposits of the world: database, map, and grade and tonnage modelsGoogle Scholar

Copyright information

© The Minerals, Metals & Materials Society 2019

Authors and Affiliations

  • Jingshu Zhang
    • 1
  • Omar Swei
    • 2
  • Richard Roth
    • 1
  • Randolph Kirchain
    • 1
    Email author
  1. 1.Materials Systems LaboratoryMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Department of Civil EngineeringUniversity of British ColumbiaVancouverCanada

Personalised recommendations