Purpose
Platinum-group metals (PGM) constitute an extreme example of a joint production activity since mines, mainly in Africa and Russia, co-produce up to eight valuable metals simultaneously. While this multifunctionality has been traditionally dealt with by allocation (partitioning), in this article we aim at developing a life cycle inventory (LCI) model following consequential modelling principles, covering supply of five of the six metals in the group (platinum, palladium, rhodium, ruthenium, iridium).
Methods
An analysis of data from 33 mines around the world shows that PGM production can be considered a case of joint production with more than one determining product. Following this finding, we develop a consequential LCI model with a cradle-to-gate scope, considering supply from mines in South Africa, Russia and Zimbabwe, based on existing data for PGM mining in the ecoinvent database, together with public statistics on global PGM production trends and prices in the period 2019–2023.
Results and discussion
The model is evaluated at the impact assessment level, focusing only on greenhouse-gas (GHG) emissions per kg metal. The ranking of metals, from higher to lower emissions is rhodium, platinum, palladium, iridium and ruthenium. Sensitivity analyses show that results are strongly influenced by fluctuating market prices, the marginal supplying countries, and the identification of joint products being either fully utilised or not fully utilised. When the model is evaluated for the period 2014–2018, GHG emissions per kg metal are substantially different for rhodium, platinum and palladium.
Conclusions
A key finding of this research is that, from a consequential modelling standpoint, PGM mining constitutes a situation of joint production, where platinum, palladium and rhodium are co-determining products in the mines, and ruthenium and iridium in the further refining of the refinery residual. The main limitation of the developed model arises from the need to address the intrinsic variability in prices associated with the global PGM market, as a result of imbalances in supply and demand. While our model has been based on historical 5-year time series on production and prices, an alternative for future improvement would be to use longer time series, or more advanced forecasting such as Autoregressive integrated moving average (ARIMA) techniques for the future mine supply and metal prices.
