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Energy Transition Metals

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  • Boer, Lukas
  • Pescatori, Andrea
  • Stuermer, Martin

Abstract

The energy transition requires substantial amounts of metals, including copper, nickel, cobalt, and lithium. Are these metals a key bottleneck? We identify metal-specific demand shocks with an ``anchor'' variable, estimate supply elasticities, and pin down the price impact of the energy transition in a structural scenario analysis. Metal prices would reach historical peaks for an unprecedented, sustained period in a net-zero emissions scenario. The total production value of these four metals alone would rise more than four-fold to USD 13 trillion for the period 2021 to 2040, rivaling the estimated total value of crude oil production. These metals could potentially become as important to the global economy as crude oil.

Suggested Citation

  • Boer, Lukas & Pescatori, Andrea & Stuermer, Martin, 2021. "Energy Transition Metals," MPRA Paper 110364, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:110364
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    as
    1. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.
    2. Helmut Lütkepohl, 2005. "New Introduction to Multiple Time Series Analysis," Springer Books, Springer, number 978-3-540-27752-1.
    3. Daron Acemoglu & Philippe Aghion & Leonardo Bursztyn & David Hemous, 2012. "The Environment and Directed Technical Change," American Economic Review, American Economic Association, vol. 102(1), pages 131-166, February.
    4. Christiane Baumeister & Gert Peersman, 2013. "The Role Of Time‐Varying Price Elasticities In Accounting For Volatility Changes In The Crude Oil Market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(7), pages 1087-1109, November.
    5. Òscar Jordà & Katharina Knoll & Dmitry Kuvshinov & Moritz Schularick & Alan M Taylor, 2019. "The Rate of Return on Everything, 1870–2015," The Quarterly Journal of Economics, Oxford University Press, vol. 134(3), pages 1225-1298.
    6. Juan F. Rubio-Ramírez & Daniel F. Waggoner & Tao Zha, 2010. "Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference," Review of Economic Studies, Oxford University Press, vol. 77(2), pages 665-696.
    7. Juan Antolín-Díaz & Juan F. Rubio-Ramírez, 2018. "Narrative Sign Restrictions for SVARs," American Economic Review, American Economic Association, vol. 108(10), pages 2802-2829, October.
    8. James H. Stock & Mark W. Watson, 2012. "Disentangling the Channels of the 2007-09 Recession," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 43(1 (Spring), pages 81-156.
    9. Daniel F. Waggoner & Tao Zha, 1999. "Conditional Forecasts In Dynamic Multivariate Models," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 639-651, November.
    10. Atsushi Inoue & Lutz Kilian, 2020. "The Role of the Prior in Estimating VAR Models with Sign Restrictions," Working Papers 2030, Federal Reserve Bank of Dallas.
    11. Christopher A. Sims, 1993. "A Nine-Variable Probabilistic Macroeconomic Forecasting Model," NBER Chapters, in: Business Cycles, Indicators, and Forecasting, pages 179-212, National Bureau of Economic Research, Inc.
    12. Baumeister, Christiane & Hamilton, James D., 2020. "Drawing conclusions from structural vector autoregressions identified on the basis of sign restrictions," Journal of International Money and Finance, Elsevier, vol. 109(C).
    13. Fally, Thibault & Sayre, James E., 2018. "Commodity Trade Matters," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt9121v3rt, Department of Agricultural & Resource Economics, UC Berkeley.
    14. Canova, Fabio & Nicolo, Gianni De, 2002. "Monetary disturbances matter for business fluctuations in the G-7," Journal of Monetary Economics, Elsevier, vol. 49(6), pages 1131-1159, September.
    15. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    16. Juan Antolin-Diaz & Juan F. Rubio-Ramirez, 2016. "Narrative Sign Restrictions for SVARs," FRB Atlanta Working Paper 2016-16, Federal Reserve Bank of Atlanta.
    17. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    18. Faust, Jon, 1998. "The robustness of identified VAR conclusions about money," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 49(1), pages 207-244, December.
    19. Mark W. Watson, 2019. "Comment on "On the Empirical (Ir)relevance of the Zero Lower Bound Constraint"," NBER Chapters, in: NBER Macroeconomics Annual 2019, volume 34, pages 182-193, National Bureau of Economic Research, Inc.
    20. Sydney C. Ludvigson & Sai Ma & Serena Ng, 2017. "Shock Restricted Structural Vector-Autoregressions," NBER Working Papers 23225, National Bureau of Economic Research, Inc.
    21. Christiane Baumeister & James D. Hamilton, 2015. "Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information," Econometrica, Econometric Society, vol. 83(5), pages 1963-1999, September.
    22. Karel Mertens & Morten O. Ravn, 2013. "The Dynamic Effects of Personal and Corporate Income Tax Changes in the United States," American Economic Review, American Economic Association, vol. 103(4), pages 1212-1247, June.
    23. John Hassler & Per Krusell, 2012. "Economics And Climate Change: Integrated Assessment In A Multi-Region World," Journal of the European Economic Association, European Economic Association, vol. 10(5), pages 974-1000, October.
    24. Lutz Kilian & Daniel P. Murphy, 2014. "The Role Of Inventories And Speculative Trading In The Global Market For Crude Oil," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 454-478, April.
    25. Mikhail Golosov & John Hassler & Per Krusell & Aleh Tsyvinski, 2014. "Optimal Taxes on Fossil Fuel in General Equilibrium," Econometrica, Econometric Society, vol. 82(1), pages 41-88, January.
    26. Antolín-Díaz, Juan & Petrella, Ivan & Rubio-Ramírez, Juan F., 2021. "Structural scenario analysis with SVARs," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 798-815.
    27. Jacks, David S. & Stuermer, Martin, 2020. "What drives commodity price booms and busts?," Energy Economics, Elsevier, vol. 85(C).
    28. Gregor Schwerhoff & Martin Stuermer, 2015. "Non-renewable resources, extraction technology, and endogenous growth," Working Papers 1506, Federal Reserve Bank of Dallas.
    29. Ana María Herrera & Sandeep Kumar Rangaraju, 2020. "The effect of oil supply shocks on US economic activity: What have we learned?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(2), pages 141-159, March.
    30. Lutz Kilian & Daniel P. Murphy, 2012. "Why Agnostic Sign Restrictions Are Not Enough: Understanding The Dynamics Of Oil Market Var Models," Journal of the European Economic Association, European Economic Association, vol. 10(5), pages 1166-1188, October.
    31. Jon Faust, 1998. "The robustness of identified VAR conclusions about money," International Finance Discussion Papers 610, Board of Governors of the Federal Reserve System (U.S.).
    32. Basher, Syed Abul & Haug, Alfred A. & Sadorsky, Perry, 2018. "The impact of oil-market shocks on stock returns in major oil-exporting countries," Journal of International Money and Finance, Elsevier, vol. 86(C), pages 264-280.
    33. Carol A. Dahl, 2020. "Dahl Mineral Elasticity of Demand and Supply Database (MEDS)," Working Papers 2020-02, Colorado School of Mines, Division of Economics and Business, revised Apr 2020.
    34. James H. Stock & Mark W. Watson, 1993. "Business Cycles, Indicators, and Forecasting," NBER Books, National Bureau of Economic Research, Inc, number stoc93-1, January.
    35. Stock, James H. & Watson, Mark W. (ed.), 1993. "Business Cycles, Indicators, and Forecasting," National Bureau of Economic Research Books, University of Chicago Press, edition 1, number 9780226774886, February.
    36. James H. Stock & Mark W. Watson, 2012. "Disentangling the Channels of the 2007-09 Recession," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 44(1 (Spring), pages 81-156.
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    2. Martin Stuermer, 2022. "Non-renewable resource extraction over the long term: empirical evidence from global copper production," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 35(3), pages 617-625, December.

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    More about this item

    Keywords

    Conditional forecasts; structural vector autoregression; structural scenario analysis; energy transition; metals; fossil fuels; prices; climate change;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • L72 - Industrial Organization - - Industry Studies: Primary Products and Construction - - - Mining, Extraction, and Refining: Other Nonrenewable Resources
    • Q3 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics

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