Hostname: page-component-76fb5796d-qxdb6 Total loading time: 0 Render date: 2024-04-25T08:03:06.733Z Has data issue: false hasContentIssue false
  • English
  • Français

Understanding dynamic availability risk of critical materials: The role and evolution of market analysis and modeling

Published online by Cambridge University Press:  04 June 2015

Elsa Olivetti ,
Frank Field  and
Randolph Kirchain
Elsa Olivetti
Affiliation:
Materials Science & Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
Frank Field
Affiliation:
Engineering Systems Division, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
Randolph Kirchain*
Affiliation:
Engineering Systems Division, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
*
a)Address all correspondence to Randolph Kirchain at kirchain@mit.edu
Get access

Abstract

Over the last decade, our understanding regarding the nature and drivers of criticality risk has matured significantly. We review modeling efforts to date, specific to evaluation of future material availability, and identify research gaps.

Many advanced energy technologies are fundamentally “materials-dependent”; they are enabled directly by, or designed around, a particular material or materials. Society's acute dependence on materials has increased in recent years as these technologies tap into an ever broader range of the periodic table and, therefore, into a broader set of underdeveloped and complex supply chains. Ultimately, concern around the supply of materials strategic to energy and security interests has led to the development of a range of systems used to assess criticality—the confluence of vulnerability and risk. Concerning the assessment of criticality risk, this review accomplishes two primary goals. First, through a review of several broad assessments of criticality metrics, we identify those metrics that incorporate assessment of future production and consumption. We review the methods that have been applied to project production and consumption along two axes, one around degree of detail or granularity pursued by the model and the second around the degree to which market function is modeled endogenously. Regarding the second, material projection methods can be broadly classified as (a) those which project material flows only and (b) those which use market modeling to explicitly simulate (endogenously) the associated economic behavior and its implication on material flows.

Keywords

sustainabilityrecyclingeconomicenergy generation
Type
Review
Information
MRS Energy & Sustainability , Volume 2 , 2015 , E5
Copyright
Copyright © Materials Research Society 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Jevons, W.S.: The Coal Question: An Enquiry Concerning the Progress of the Nation, and the Probable Exhaustion of Our Coal-Mines, 2nd ed. (Macmillan, Scotland, 1866).Google Scholar
Malthus, T.R.: An Essay on the Principle of Population, as it Affects the Future Improvement of Society with Remarks on the Speculations of Mr. Godwin, M. Condorcet and Other Writers, 1st ed. (J. Johnson, London, 1798).Google Scholar
Meadows, D.H., Meadows, D.L., Randers, J., and Behrens, W.W. III: The Limits to Growth: A Report for the Club of Rome's Project on the Predicament of Mankind (Universe Books, New York, 1972).Google Scholar
Ricardo, D.: On the Principles of Political Economy, and Taxation (John Murray, London, 1817).Google Scholar
Hotelling, H.: The economics of exhaustible resources. J. Polit. Econ. 39, 137–175 (1931). doi: 10.2307/1822328.CrossRefGoogle Scholar
Barnett, H.J. and Morse, C.: Scarcity and Growth: The Economics of Natural Resource Availability (Published for Resources for the Future by Johns Hopkins Press, Baltimore, MD, 1963).Google Scholar
Slade, M.E.: Trends in natural-resource commodity prices: An analysis of the time domain. J. Environ. Econ. Manag. 9, 122–137 (1982). doi: http://dx.doi.org/10.1016/0095-0696(82)90017-1.Google Scholar
Pindyck, R.S.: The optimal exploration and production of nonrenewable resources. J. Polit. Econ. 86, 841–861 (1978).Google Scholar
Solow, R.M.: Intergenerational equity and exhaustible resources. Rev. Econ. Stud. 41, 29–45 (1974).CrossRefGoogle Scholar
Solow, R.M.: The economics of resources or the resources of economics. Am. Econ. Rev. 64, 1–14 (1974).Google Scholar
Livernois, J.: On the empirical significance of the Hotelling rule. Rev. Environ. Econ. Pol. 3, 22–41 (2009). doi: 10.1093/reep/ren017.CrossRefGoogle Scholar
Economist. The Economist 402, 90 (The Economist Intelligence Unit, London, 2012).Google Scholar
Gleich, B., Achzet, B., Mayer, H., and Rathgeber, A.: An empirical approach to determine specific weights of driving factors for the price of commodities—A contribution to the measurement of the economic scarcity of minerals and metals. Resour. Policy 38, 350–362 (2013). doi: http://dx.doi.org/10.1016/j.resourpol.2013.03.011.Google Scholar
Erdmann, L. and Graedel, T.E.: Criticality of non-fuel minerals: A review of major approaches and analyses. Environ. Sci. Technol. 45, 7620–7630 (2011). doi: 10.1021/Es200563g.Google Scholar
Achzet, B. and Helbig, C.: How to evaluate raw material supply risks—An overview. Resour. Policy 38, 435–447 (2013). doi: http://dx.doi.org/10.1016/j.resourpol.2013.06.003.CrossRefGoogle Scholar
Graedel, T.E., Barr, R., Chandler, C., Chase, T., Choi, J., Christoffersen, L., Friedlander, E., Henly, C., Jun, C., Nassar, N.T., Schechner, D., Warren, S., Yang, M-y., and Zhu, C.: Methodology of metal criticality determination. Environ. Sci. Technol. 46, 1063–1070 (2011). doi: 10.1021/es203534z.CrossRefGoogle Scholar
National Research Council: Minerals, Critical Minerals, and the U.S. Economy (The National Academies Press, Washington, DC, 2008).Google Scholar
United States Department of Energy: Critical Materials Strategy (United States Department of Energy, Washington, D.C., 2010).Google Scholar
Duclos, S.J., Otto, J.P., and Konitzer, D.G.: Design in an era of constrained resources. Mech. Eng. 132, 36–40 (2010).CrossRefGoogle Scholar
Morley, N. and Eatherley, D.: Material Security: Ensuring Resource Availability for the UK Economy (C-Tech Innovation Ltd., Capenhurst, Chester, UK, 2008).Google Scholar
European Commission: Report on Critical Raw Materials for the EU (Enterprise and Industry, European Commission, Brussels, Belgium, 2014).Google Scholar
Thomason, J.S., Atwell, R.J., Bajraktari, Y., Bell, J.P., Barnett, D.S., Karvonides, N.S., Niles, M.F., and Schwartz, E.L.: From National Defense Stockpile (NDS) to Strategic Materials Security Program (SMSP): Evidence and Analytic Support, Vol. 1 (Institute for Defense Analyses, Alexandria, VA, 2010); 120 pp.Google Scholar
Buchert, M., Schüler, D., and Bleher, D.: Critical Metals for Future Sustainable Technologies and Their Recycling Potential (United Nation Environment Programme & United Nations University, Nairobi, Kenya, 2009).Google Scholar
Moss, R.L., Tzimas, E., Kara, H., Willis, P., and Kooroshy, J.: JRC Scientific and Technical Reports (Joint Research Centre - Institute for Energy and Transport, European Commission, Luxembourg, 2011).Google Scholar
Roelich, K., Dawson, D.A., Purnell, P., Knoeri, C., Revell, R., Busch, J., and Steinberger, J.K.: Assessing the dynamic material criticality of infrastructure transitions: A case of low carbon electricity. Appl. Energy 123, 378–386 (2014).Google Scholar
Goe, M. and Gaustad, G.: Identifying critical materials for photovoltaics in the US: A multi-metric approach. Appl. Energy 123, 387–396 (2014). doi: http://dx.doi.org/10.1016/j.apenergy.2014.01.025.Google Scholar
Rosenau-Tornow, D., Buchholz, P., Riemann, A., and Wagner, M.: Assessing the long-term supply risks for mineral raw materials-a combined evaluation of past and future trends. Resour. Policy 34, 161–175 (2009).Google Scholar
European Commission: Critical Raw Materials for the EU (Enterprise and Industry, European Commission, Brussels, Belgium, 2010).Google Scholar
Reuter, M. and Verhoef, E.: A dynamic model for the assessment of the replacement of lead in solders. J. Electron. Mater. 33, 1567–1580 (2004). doi: 10.1007/s11664-004-0100-3.Google Scholar
Wellmer, F-W.: Reserves and resources of the geosphere, terms so often misunderstood. Is the life index of reserves of natural resources a guide to the future? [Reserven und Ressourcen der Geosphäre, Begriffe, die so häufig missverstanden werden. Ist die Reichweite der Reserven von natürlichen Ressourcen ein Hinweis für die Zukunft?]. Zeitschrift der Deutschen Gesellschaft für Geowissenschaften 159, 575–590 (2008). doi: 10.1127/1860-1804/2008/0159-0575.CrossRefGoogle Scholar
Kapilevich, L. and Skumanich, A.: Indium shortage implications for the PV and LCD market: Technology and market considerations for maintaining growth. IEEE Photovoltaic Spec. Conf., 1357-1362 (2009).Google Scholar
Chen, W.Q. and Graedel, T.E.: Anthropogenic cycles of the elements: A critical review. Environ. Sci. Technol. 46, 8574–8586 (2012).Google Scholar
Mueller, E., Hilty, L.M., Widmer, R., Schluep, M., and Faulstich, M.: Modeling metal stocks and flows: A review of dynamic material flow analysis methods. Environ. Sci. Technol. 48, 2102–2113 (2014).Google Scholar
van Vuuren, D.P., Strengers, B.J., and De Vries, H.J.M.: Long-term perspectives on world metal use—A system-dynamics model. Resour. Policy 25, 239–255 (1999).Google Scholar
Alonso, E., Sherman, A.M., Wallington, T.J., Everson, M.P., Field, F.R., Roth, R., and Kirchain, R.E.: Evaluating rare earth element availability: A case with revolutionary demand from clean technologies. Environ. Sci. Technol. 46, 3406–3414 (2012). doi: 10.1021/es203518d.Google Scholar
Sverdrup, H., Koca, D., and Ragnarsdottir, K.V.: Investigating the sustainability of the global silver supply, reserves, stocks in society and market price using different approaches. Resour., Conserv. Recycl. 83, 121–140 (2014).CrossRefGoogle Scholar
Van Vuuren, D.P., Bouwman, A.F., and Beusen, A.H.W.: Phosphorus demand for the 1970–2100 period: A scenario analysis of resource depletion. Global Environ. Change 20, 428–439 (2010).Google Scholar
Hatayama, H., Yamada, H., Daigo, I., Matsuno, Y., and Adachi, Y.: Dynamic substance flow analysis of aluminum and its alloying elements. Mater. Trans. 48, 2518–2524 (2007).Google Scholar
Elshkaki, A. and van der Voet, E.: The consequences of the use of platinum in new technologies on its availability and on other metal cycles. In Conservation and Recycling of Resources: A New Research, Loeffe, C.V. ed.; Nova Publishers: Hauppauge, NY, 2006; 245 pp.Google Scholar
Zuser, A. and Rechberger, H.: Considerations of resource availability in technology development strategies: The case study of photovoltaics. Resour., Conserv. Recycl. 56, 56–65 (2011).CrossRefGoogle Scholar
Busch, J., Steinberger, J.K., Dawson, D.A., Purnell, P., and Roelich, K.: Managing critical materials with a technology-specific stocks and flows model. Environ. Sci. Technol. 48, 1298–1305 (2014).Google Scholar
Hu, M., Pauliuk, S., Wang, T., Huppes, G., van der Voet, E., and Muller, D.B.: Iron and steel in Chinese residential buildings: A dynamic analysis. Resour., Conserv. Recycl. 54, 591–600 (2010).Google Scholar
Bollinger, L.A., Davis, C., Nikolic, I., and Dijkema, G.P.J.: Modeling metal flow systems agents vs. Equations. J. Ind. Ecol. 16, 176–190 (2012).Google Scholar
Daigo, I., Osako, S., Adachi, Y., and Matsuno, Y.: Time-series analysis of global zinc demand associated with steel. Resour., Conserv. Recycl. 82, 35–40 (2014).CrossRefGoogle Scholar
Park, J-a., Hong, S-j., Kim, I., Lee, J-y., and Hur, T.: Dynamic material flow analysis of steel resources in Korea. Resour., Conserv. Recycl. 55, 456–462 (2011).Google Scholar
Yan, L., Wang, A., Chen, Q., and Li, J.: Dynamic material flow analysis of zinc resources in China. Resour., Conserv. Recycl. 75, 23–31 (2013).Google Scholar
Cheah, L., Heywood, J., and Kirchain, R.: Aluminum stock and flows in us passenger vehicles and implications for energy use. J. Ind. Ecol. 13, 718–734 (2009).Google Scholar
Hatayama, H., Daigo, I., Matsuno, Y., and Adachi, Y.: Outlook of the world steel cycle based on the stock and flow dynamics. Environ. Sci. Technol. 44, 6457–6463 (2010).Google Scholar
Hatayama, H., Daigo, I., Matsuno, Y., and Adachi, Y.: Evolution of aluminum recycling initiated by the introduction of next-generation vehicles and scrap sorting technology. Resour., Conserv. Recycl. 66, 8–14 (2012).Google Scholar
Gruber, P.W., Medina, P.A., Keoleian, G.A., Kesler, S.E., Everson, M.P., and Wallington, T.J.: Global lithium availability. J. Ind. Ecol. 15, 760–775 (2011). doi: 10.1111/j.1530-9290.2011.00359.x.Google Scholar
Marwede, M. and Reller, A.: Future recycling flows of tellurium from cadmium telluride photovoltaic waste. Resour., Conserv. Recycl. 69, 35–49 (2012).Google Scholar
Gerst, M.D.: Linking material flow analysis and resource policy via future scenarios of in-use stock: An example for copper. Environ. Sci. Technol. 43, 6320–6325 (2009).Google Scholar
Elshkaki, A. and Graedel, T.E.: Dynamic analysis of the global metals flows and stocks in electricity generation technologies. J. Cleaner Prod. 59, 260–273 (2013).Google Scholar
Gloeser, S., Soulier, M., Espinoza, L.A.T., and Faulstich, M.: Using dynamic stock and flow models for global and regional material and substance flow analysis in the field of industrial ecology: the example of a global copper flow model. In Proceedings of the 31st International Conference of the System Dynamics Society Eberlein, R. and Martínez-Moyano, I.J., eds.; (Cambridge, MA, July 21–25, 2013).Google Scholar
Bouman, M., Heijungs, R., van der Voet, E., van den Bergh, J., and Huppes, G.: Material flows and economic models: An analytical comparison of SFA, LCA and partial equilibrium models. Ecol. Econ. 32, 195–216 (2000).CrossRefGoogle Scholar
Northey, S., Mohr, S., Mudd, G.M., Weng, Z., and Giurco, D.: Modelling future copper ore grade decline based on a detailed assessment of copper resources and mining. Resour., Conserv. Recycl. 83, 190–201 (2014).CrossRefGoogle Scholar
Elshkaki, A., van der Voet, E., Timmermans, V., and Van Holderbeke, M.: Dynamic stock modelling: A method for the identification and estimation of future waste streams and emissions based on past production and product stock characteristics. Energy 30, 1353–1363 (2005).Google Scholar
Zeltner, C., Bader, H.P., Scheidegger, R., and Baccini, P.: Sustainable metal management exemplified by copper in the USA. Reg. Environ. Change 1, 31–46 (1999).Google Scholar
Kapur, A.: The future of the red metal—A developing country perspective from India. Resour., Conserv. Recycl. 47, 160–182 (2006).Google Scholar
Hubbert, M.K.: Nuclear Energy and the Fossil Fuels (American Petroleum Institute & Shell Development, Washington, DC, 1956).Google Scholar
Kuznets, P. and Simon, P.: Economic growth and income inequality. Am. Econ. Rev. 45, 1–28 (1955).Google Scholar
Scholz, R.W. and Wellmer, F-W.: Approaching a dynamic view on the availability of mineral resources: What we may learn from the case of phosphorus? Global. Environ. Change 23, 11–27 (2013).Google Scholar
Sverdrup, H.U., Ragnarsdottir, K.V., and Koca, D.: On modelling the global copper mining rates, market supply, copper price and the end of copper reserves. Resour., Conserv. Recycl. 87, 158–174 (2014).Google Scholar
Seyhan, D., Weikard, H.P., and van Ierland, E.: An economic model of long-term phosphorus extraction and recycling. Resour., Conserv. Recycl. 61, 103–108 (2012).Google Scholar
Knoeri, C., Waeger, P.A., Stamp, A., Althaus, H.-J., and Weil, M.: Towards a dynamic assessment of raw materials criticality: Linking agent-based demand—With material flow supply modelling approaches. Sci. Total Environ. 461, 808–812 (2013).Google Scholar
Elshkaki, A.: An analysis of future platinum resources, emissions and waste streams using a system dynamic model of its intentional and non-intentional flows and stocks. Resour. Policy 38, 241–251 (2013).Google Scholar
Poulizac, C.: Modeling Mining Economics and Materials Markets to Inform Criticality Assessment and Mitigation MS thesis, Massachusetts Institute of Technology (2013).Google Scholar
Alonso, E.: Material scarcity from the perspective of manufacturing firms: case studies of cobalt and platinum PhD thesis, Massachusetts Institute of Technology (2010).Google Scholar
Murto, P., Nasakkala, E., and Keppo, J.: Timing of investments in oligopoly under uncertainty: A framework for numerical analysis. Eur. J. Oper. Res. 157, 486–500 (2004).Google Scholar
Urbance, R., Field, F.R., Kirchain, R.E., Roth, R., and Clark, J.: Market model simulation: The impact of increased automotive interest in magnesium. JOM 54, 25–33 (2002).Google Scholar
Flemings, N.: Metal Price Volatility: A Study of Informative Metrics and the Volatility Mitigating Effects of Recycling MS thesis, Massachusetts Institute of Technology (2011).Google Scholar
Kifle, D., Sverdrup, H.U., Koca, D., and Wibtoe, G.: A simple assessment of the global long term supply of the rare earth elements by using a system dynamics model. Environ. Nat. Resour. Res. 3(1), 77–91 (2013).Google Scholar
Gaustad, G., Olivetti, E., and Kirchain, R.: Toward sustainable material usage: Evaluating the importance of market motivated agency in modeling material flows. Environ. Sci. Technol. 45, 4110–4117 (2011). doi: 10.1021/Es103508u.Google Scholar
Earles, J.M. and Halog, A.: Consequential life cycle assessment: A review. Int. J. Life Cycle Assess. 16, 445–453 (2011). doi: 10.1007/S11367-011-0275-9.Google Scholar
Ekvall, T.: A market-based approach to allocation at open-loop recycling. Resour., Conserv. Recycl. 29, 91–109 (2000). doi: 10.1016/S0921-3449(99)00057-9.CrossRefGoogle Scholar
Ekvall, T. and Weidema, B.P.: System boundaries and input data in consequential life cycle inventory analysis. Int. J. Life Cycle Assess. 9, 161–171 (2004). doi: 10.1065/Lca2004.03.148.Google Scholar
Schmidt, J.H.: System delimitation in agricultural consequential LCA—Outline of methodology and illustrative case study of wheat in Denmark. Int. J. Life Cycle Assess. 13, 350–364 (2008). doi: 10.1007/S11367-008-0016-X.Google Scholar
Dalgaard, R., Schmidt, J., Halberg, N., Christensen, P., Thrane, M., and Pengue, W.A.: LCA of soybean meal. Int. J. Life Cycle Assess. 13, 240–254 (2008). doi: 10.1065/Ica2007.06.342.Google Scholar
Rehl, T., Lansche, J., and Muller, J.: Life cycle assessment of energy generation from biogas-attributional vs. consequential approach. Renewable Sustainable Energy Rev. 16, 3766–3775 (2012). doi: 10.1016/J.Rser.2012.02.072.Google Scholar
Soimakallio, S., Kiviluoma, J., and Saikku, L.: The complexity and challenges of determining GHG (greenhouse gas) emissions from grid electricity consumption and conservation in LCA (life cycle assessment)—A methodological review. Energy 36, 6705–6713 (2011). doi: 10.1016/J.Energy.2011.10.028.Google Scholar
Searchinger, T., Heimlich, R., Houghton, R.A., Dong, F.X., Elobeid, A., Fabiosa, J., Tokgoz, S., Hayes, D., and Yu, T.H.: Use of US croplands for biofuels increases greenhouse gases through emissions from land-use change. Science 319, 1238–1240 (2008). doi: 10.1126/Science.1151861.Google Scholar
Marvuglia, A., Benetto, E., Rege, S., and Jury, C.: Modelling approaches for consequential life-cycle assessment (C-LCA) of bioenergy: Critical review and proposed framework for biogas production. Renewable Sustainable Energy Rev. 25, 768–781 (2013). doi: 10.1016/J.Rser.2013.04.031.Google Scholar
Bustamante, M.L. and Gaustad, G.: Challenges in assessment of clean energy supply-chains based on byproduct minerals: A case study of tellurium use in thin film photovoltaics. Appl. Energy 123, 397–414 (2014).Google Scholar
Xiarchos, I.M. and Fletcher, J.J.: Price and volatility transmission between primary and scrap metal markets. Resour., Conserv. Recycl. 53, 664–673 (2009). doi: 10.1016/J.Resconrec.2009.04.020.Google Scholar