[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/110364.html
   My bibliography  Save this paper

Energy Transition Metals

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/110364/1/MPRA_paper_110364.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    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, December.
    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, President and Fellows of Harvard College, 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," The Review of Economic Studies, Review of Economic Studies Ltd, 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. Thibault Fally & James Sayre, 2018. "Commodity Trade Matters," NBER Working Papers 24965, National Bureau of Economic Research, Inc.
    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. 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.
    17. 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.
    18. 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.
    19. Sydney C. Ludvigson & Sai Ma & Serena Ng, 2017. "Shock Restricted Structural Vector-Autoregressions," NBER Working Papers 23225, National Bureau of Economic Research, Inc.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    26. Jacks, David S. & Stuermer, Martin, 2020. "What drives commodity price booms and busts?," Energy Economics, Elsevier, vol. 85(C).
    27. Gregor Schwerhoff & Martin Stuermer, 2015. "Non-renewable resources, extraction technology, and endogenous growth," Working Papers 1506, Federal Reserve Bank of Dallas.
    28. 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.
    29. 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.
    30. 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.).
    31. 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.
    32. 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.
    33. James H. Stock & Mark W. Watson, 1993. "Business Cycles, Indicators, and Forecasting," NBER Books, National Bureau of Economic Research, Inc, number stoc93-1.
    34. 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, August.
    35. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hugh Miller & Simon Dikau & Romain Svartzman & Stéphane Dees, 2023. "The Stumbling Block in ‘the Race of our Lives’: Transition-Critical Materials, Financial Risks and the NGFS Climate Scenarios," Working papers 907, Banque de France.
    2. Thomas Allen & Mathieu Boullot & Stéphane Dées & Annabelle de Gaye & Noëmie Lisack & Camille Thubin & Oriane Wegner, 2023. "Using Short-Term Scenarios to Assess the Macroeconomic Impacts of Climate Transition," Working papers 922, Banque de France.
    3. Alessi, Lucia & Ossola, Elisa & Panzica, Roberto, 2023. "When do investors go green? Evidence from a time-varying asset-pricing model," International Review of Financial Analysis, Elsevier, vol. 90(C).
    4. Etienne ESPAGNE & Hugo LAPEYRONIE, 2023. "Energy transition minerals and the SDGs. A systematic review," Working Paper ebe0968c-fce0-4ce9-b3b6-b, Agence française de développement.
    5. Committeri, Marco & Brüggemann, Axel & Kosterink, Patrick & Reininger, Thomas & Stevens, Luc & Vonessen, Benjamin & Zaghini, Andrea & Garrido, Isabel & Van Meensel, Lena & Strašuna, Lija & Tiililä, Ne, 2022. "The role of the IMF in addressing climate change risks," Occasional Paper Series 309, European Central Bank.
    6. George Yunxiong Li & Simona Iammarino, 2024. "Critical Raw Materials and Renewable Energy Transition: The Role of Domestic Supply," Discussion Paper series in Regional Science & Economic Geography 2024-04, Gran Sasso Science Institute, Social Sciences, revised Oct 2024.
    7. Francisco Ríos Muñoz & Camilo Peña Ramírez & José Meza & Tenzin Crouch, 2024. "Platinum Group Metals Extraction from Asteroids vs Earth: An Overview of the Industrial Ecosystems, Technologies and Risks," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 37(3), pages 681-700, September.
    8. Ghosh, Bikramaditya & Pham, Linh & Teplova, Tamara & Umar, Zaghum, 2023. "COVID-19 and the quantile connectedness between energy and metal markets," Energy Economics, Elsevier, vol. 117(C).
    9. Agnese, Pablo & Rios, Francisco, 2024. "Spillover effects of energy transition metals in Chile," Energy Economics, Elsevier, vol. 134(C).
    10. 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.
    11. Alberto Prina Cerai, 2024. "Geography of control: a deep dive assessment on criticality and lithium supply chain," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 37(3), pages 499-546, September.
    12. Zhang, Hongwei & Zhang, Yubo & Gao, Wang & Li, Yingli, 2023. "Extreme quantile spillovers and drivers among clean energy, electricity and energy metals markets," International Review of Financial Analysis, Elsevier, vol. 86(C).
    13. Vlado Vivoda & Ron Matthews, 2024. "“Friend-shoring” as a panacea to Western critical mineral supply chain vulnerabilities," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 37(3), pages 463-476, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Robin Braun & Ralf Brüggemann, 2017. "Identification of SVAR Models by Combining Sign Restrictions With External Instruments," Working Paper Series of the Department of Economics, University of Konstanz 2017-07, Department of Economics, University of Konstanz.
    3. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    4. Dominik Bertsche, 2019. "The effects of oil supply shocks on the macroeconomy: a Proxy-FAVAR approachThe effects of oil supply shocks on the macroeconomy: a Proxy-FAVAR approach," Working Paper Series of the Department of Economics, University of Konstanz 2019-06, Department of Economics, University of Konstanz.
    5. Laumer, Sebastian, 2020. "Government spending and heterogeneous consumption dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 114(C).
    6. Kilian, Lutz, 2022. "Facts and fiction in oil market modeling," Energy Economics, Elsevier, vol. 110(C).
    7. Canova, Fabio & Ferroni, Filippo, 2020. "A hitchhiker guide to empirical macro models," CEPR Discussion Papers 15446, C.E.P.R. Discussion Papers.
    8. 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).
    9. Baumeister, Christiane & Hamilton, James, 2020. "Advances in Structural Vector Autoregressions with Imperfect Identifying Information," CEPR Discussion Papers 14603, C.E.P.R. Discussion Papers.
    10. Christiane Baumeister & James D. Hamilton, 2020. "Advances in Using Vector Autoregressions to Estimate Structural Magnitudes," NBER Working Papers 27014, National Bureau of Economic Research, Inc.
    11. David S. Jacks & Martin Stuermer, 2021. "Dry bulk shipping and the evolution of maritime transport costs, 1850–2020," Australian Economic History Review, Economic History Society of Australia and New Zealand, vol. 61(2), pages 204-227, July.
    12. Ramey, V.A., 2016. "Macroeconomic Shocks and Their Propagation," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 71-162, Elsevier.
    13. Herwartz, Helmut & Rohloff, Hannes & Wang, Shu, 2022. "Proxy SVAR identification of monetary policy shocks - Monte Carlo evidence and insights for the US," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
    14. Inoue, Atsushi & Kilian, Lutz, 2013. "Inference on impulse response functions in structural VAR models," Journal of Econometrics, Elsevier, vol. 177(1), pages 1-13.
    15. Paul Carrillo‐Maldonado, 2023. "Partial identification for growth regimes: The case of Latin American countries," Metroeconomica, Wiley Blackwell, vol. 74(3), pages 557-583, July.
    16. 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.
    17. 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.
    18. Christiane Baumeister & Gert Peersman, 2013. "Time-Varying Effects of Oil Supply Shocks on the US Economy," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(4), pages 1-28, October.
    19. Victor Pontines, 2021. "The real effects of loan-to-value limits: empirical evidence from Korea," Empirical Economics, Springer, vol. 61(3), pages 1311-1350, September.
    20. Braun, Robin, 2021. "The importance of supply and demand for oil prices: evidence from non-Gaussianity," Bank of England working papers 957, Bank of England.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:110364. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.