Full length articleA system dynamics model for platinum group metal supply, market price, depletion of extractable amounts, ore grade, recycling and stocks-in-use
Introduction
It is a known fact that the platinum group metals (PGM) platinum, palladium and rhodium are scarce natural resources and that these metals are of great importance for modern technologies and modern advanced chemistry. The platinum group metals (PGM) comprise the metals platinum, palladium, rhodium, ruthenium, iridium and osmium. The platinum group metals production is dominated by five major actors; South Africa, Russia, United States, Zimbawe and Canada. South Africa is the dominant producer of platinum (92%). For palladium, Russia (40%) and South Africa (37%) together share and dominate the market (77% of the total). South Africa dominates the rhodium market (80%), where the other big three producers have minor contributions. Several authors have previously expressed concerns about a potential scarcity and a future peak in platinum group metals production from mining and discussed the associated challenges and possibilities for the platinum group metals mining industry and the supply to the market (Jochens, 1980, Allen and Behamanesh, 1994, Cawthorn, 1999, Cawthorn, 2010, Wagner and Fettweiss, 2001, Heinberg, 2001, Babakina and Graedel, 2005, Hagelüken et al., 2005, Gordon et al., 2006, Alonso, 2010, Alonso et al., 2007a, Alonso et al., 2007b, Alonso et al., 2009, Alonso et al., 2012, Geoscience Australia, 2009, Thomas, 2009, Yang, 2009, Glaister and Muss, 2010, Graedel et al., 2011, Mudd, 2010, Mudd, 2012a, Mudd, 2012b, Schooldermann and Martlehner, 2011, Wäger et al., 2011, Wäger et al., 2012; Gordon et al., 2011; Graedel and Erdmann, 2012, Nuss et al., 2014, Nuss et al., 2014, Nuss and Eckelmann, 2014, Radetzki, 2012, Elshkaki, 2013, UNEP, 2013b, Bardi, 2013, Eliott et al., 2013, Eliott et al., 2014, Nansai et al., 2014, Sverdrup and Ragnarsdottir, 2014). These have addressed different parts of the system, but not really taken a systemic grip and modelled the whole system and how platinum group metals circulate in the system. For that, this study is the first to put it all together and assess it as a dynamic system with feedback.
Section snippets
Objectives and scope
Our objective is to model the price of platinum and the physical availability, and to do this based on a systemic mapping of platinum group metals flows in the world platinum group metal trade system. The model we have developed has a wider scope than earlier developed models, and is based on a careful mapping of the whole system of causal feedback links. The wider scope implies that the causal links connect the physical flow dynamics to the market dynamics of supply, demand and market price
Background information
Data was taken from a number of sources (von Gruenewaldt 1973; von Gruenewaldt, 1976; Hulbert and von Gruenewaldt, 1982, Sutphin and Page, 1986, Prendergast, 1988, Prendergast and Wilson, 1989, Quiring, 1962, Kunilov, 1994, Cawthorn, 1999, Cawthorn, 2010, Hilliard, 2001, Williamson, 2003, Wilburn and Bleiwas, 2004, Gauthier et al., 2004, Dalvi et al., 2004, Cailteaux et al., 2005, Gotthelf, 2005, Alonso et al., 2007a, Alonso et al., 2007b, Alonso et al., 2009, Alonso et al., 2012, Papp et al.,
Earlier modelling of the global platinum system
TIAX LLC (2003) produced a report on platinum sufficiency for fuel cells to the United States Department of Energy, based on an econometric model. The model used calibrated polynomials based on observed time-series of fluxes. The model involved no causalities or mass balances, and no systemic feedbacks. Saurat and Bringezu, 2009a, Saurat and Bringezu, 2009b made a Material Flow Analysis model simulation based on econometric principles for platinum group metals supply and evaluated it for
General methods
The methods are those of systems analysis and systems dynamics, combined with a review of the literature, complemented by unpublished sources from within the platinum group metals industry. Many of these information sources are sometimes inconsistent between them and we therefore make expert judgement on what we think are the most likely estimate. We use different types of methods in order to estimate the scarcity time horizon for platinum group metals (Ragnarsdottir et al., 2012, Sverdrup and
Peak prospecting estimate
The timing of platinum group metals peak extraction and peak supply was taken from the model outputs. The platinum group metals discovery peaked in the period 19801985 according to available data records, and prospecting yields have declined ever since. This points to a peak in platinum group metals production in 20202025, when we add 40 ± 3 years to the peak discovery years. This appears to be consistent with the observations and the model results described below.
Dynamic modelling
Results from the PGM model
Sensitivity runs
A sensitivity analysis was made to investigate the effect of different demands in the platinum group metals market. Fig. 16af shows the results from a sensitivity run with the PGM-model. The standard scenario demand was multiplied with a factor rising in a scale: 0.4, 1.0, 1.6, 2.2, 2.8. In the model, increased demand push on the mining rate, mainly through the price (See the causal loop diagram in Fig. 6). The sensitivity run shows that the model is sensitive to demand across ore grades,
Conclusions
The platinum group metals are among the rarest metals on Earth. For these metals, platinum, palladium, rhodium, iridium, ruthenium, and osmium, has always been scarce, reflected in the constantly very high price. The price volatility is typical of a scarce metal with a small stock-in-use in society and low flexibility for accommodating rapid changes in demand. Our result is that the extractable resources are twice as big as earlier anticipated and estimated. This result does not affect the fact
Disclaimer
It is important to understand that model based predictions do not tell what the future will be, but what the future could look like, and perhaps some indication of the likelihood of that potential future. We can analyse different scenarios, and then pass value judgement on which scenarios to put more weight on and which to use less. Thus any prediction depends on the assumptions the model-user makes and any use of the output will be fully at the responsibility of the model-user. The accuracy of
Acknowledgements
This study contributed to the SimRess project (Models, potential and long-term scenarios for resource efficiency), funded by the German Federal Ministry for Environment and the German Environmental Protection Agency (FKZ 3712 93 102). Other partners to the SIMRESS project are CEC, Lund University, Lund, Sweden, Ecologic Institute, Berlin, Germany; the Institute of Economic Structures Research, GWS, Osnabrück, Germany; European School of Governance, EUSG, Berlin, Germany. Ullrich Lorenz is
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