title: Ore grade decrease as life cycle impact indicator for metal scarcity: the case of copper.
abstract: In the life cycle assessment (LCA) of products, the increasing scarcity of metal resources is currently addressed in a preliminary way. Here, we propose a new method on the basis of global ore grade information to assess the importance of the extraction of metal resources in the life cycle of products. It is shown how characterization factors, reflecting the decrease in ore grade due to an increase in metal extraction, can be derived from cumulative ore grade-tonnage relationships. CFs were derived for three different types of copper deposits (porphyry, sediment-hosted, and volcanogenic massive sulfide). We tested the influence of the CF model (marginal vs average), mathematical distribution (loglogistic vs loglinear), and reserve estimate (ultimate reserve vs reserve base). For the marginal CFs, the statistical distribution choice and the estimate of the copper reserves introduce a difference of a factor of 1.0-5.0 and a factor of 1.2-1.7, respectively. For the average CFs, the differences are larger for these two choices, i.e. respectively a factor of 5.7-43 and a factor of 2.1-3.8. Comparing the marginal CFs with the average CFs, the differences are higher (a factor 1.7-94). This paper demonstrates that cumulative grade-tonnage relationships for metal extraction can be used in LCA to assess the relative importance of metal extractions.
authors.affiliation: PRé Consultants bv, Printerweg 18, NL-3821 AD Amersfoort, The Netherlands. vieira@pre-sustainability.com
authors.forename: Marisa D M
authors.initials: MD
authors.lastname: Vieira
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Goedkoop
authors.forename: Per
authors.initials: P
authors.lastname: Storm
authors.forename: Mark A J
authors.initials: MA
authors.lastname: Huijbregts
mesh_terms: D003247:Conservation of Natural Resources / Q000706:statistics & numerical data; D003300:Copper / Q000032:analysis / Q000600:supply & distribution*; D004784:Environmental Monitoring / Q000706:statistics & numerical data; D015233:Models, Statistical; D016008:Statistical Distributions
chemical_list: D003300:Copper