Energy Transition Metals

Boer, Lukas and Pescatori, Andrea and Stuermer, Martin (2021): Energy Transition Metals.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Conditional forecasts, structural vector autoregression, structural scenario analysis, energy transition, metals, fossil fuels, prices, climate change
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications; L - Industrial Organization > L7 - Industry Studies: Primary Products and Construction > L72 - Mining, Extraction, and Refining: Other Nonrenewable Resources; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q3 - Nonrenewable Resources and Conservation; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics

References

Acemoglu, D., Aghion, P., Bursztyn, L., and Hemous, D.(2012). The environment and directed technical change. American Economic Review, 102(1):131{66.

Antolin-Diaz, J., Petrella, I., and Rubio-Ramirez, J. F. (2021). Structural scenario analysis with SVARs. Journal of Monetary Economics, 117(C):798{815.

Antolin-Diaz, J. and Rubio-Ramirez, J. F. (2018). Narrative Sign Restrictions for SVARs. American Economic Review, 108(10):2802{2829.

Basher, S. A., Haug, A. A., and Sadorsky, P. (2018). The impact of oil-market shocks on stock returns in major oil-exporting countries. Journal of International Money and Finance, 86:264{280.

Baumeister, C. and Hamilton, J. D. (2015). Sign restrictions, structural vector autoregressions, and useful prior information. Econometrica, 83(5):1963{1999.

Baumeister, C. and Hamilton, J. D. (2020). Drawing conclusions from structural vector autoregressions identi�ed on the basis of sign restrictions. Journal of International Money and Finance, 109(C).

Baumeister, C. and Hamilton, J. D. (2021). Estimating structural parameters using vector autoregressions. Mimeo.

Baumeister, C. and Peersman, G. (2013). The role of time-varying price elasticities in accounting for volatility changes in the crude oil market. Journal of Applied Econometrics, 28(7):1087{1109.

Canova, F. and Nicolo, G. D. (2002). Monetary disturbances matter for business fluctuations in the G-7. Journal of Monetary Economics, 49(6):1131{1159.

Dahl, C. A. (2020). Dahl Mineral Elasticity of Demand and Supply Database (MEDS). Working Papers 2020-02, Colorado School of Mines, Division of Economics and Business.

Doan, T., Litterman, R., and Sims, C. (1984). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1):1{100.

Fally, T. and Sayre, J. (2018). Commodity Trade Matters. NBER Working Papers 24965, National Bureau of Economic Research, Inc.

Faust, J. (1998). The robustness of identi�ed VAR conclusions about money. In Carnegie-Rochester Conference Series on Public Policy, volume 49, pages 207{244.

Giannone, D., Lenza, M., and Primiceri, G. (2015). Prior selection for vector autoregressions. The Review of Economics and Statistics, 97(2):436{451.

Golosov, M., Hassler, J., Krusell, P., and Tsyvinski, A. (2014). Optimal taxes on fossil fuel in general equilibrium. Econometrica, 82(1):41{88.

Hassler, J. and Krusell, P. (2012). Economics and climate change: Integrated assessment in a multi-region world. Journal of the European Economic Association, 10(5):974{1000.

Herrera, A. M. and Rangaraju, S. K. (2020). The e�ect of oil supply shocks on US economic activity: What have we learned? Journal of Applied Econometrics, 35(2):141{159.

IEA (2021a). Net zero by 2050. A roadmap for the global energy sector. International Energy Agency. Paris, France.

IEA (2021b). The role of critical minerals in clean energy transitions. world energy outlook special report. International Energy Agency. Paris, France.

Inoue, A. and Kilian, L. (2020). The role of the prior in estimating VAR models with sign restrictions. Manuscript, Federal Reserve Bank of Dallas.

Inoue, A. and Kilian, L. (2021). Joint Bayesian inference about impulse responses in var models. Journal of Econometrics, forthcoming.

Jacks, D. S. and Stuermer, M. (2020). What drives commodity price booms and busts? Energy Economics, 85:104035.

Jorda, O., Knoll, K., Kuvshinov, D., Schularick, M., and Taylor, A. M. (2019). The Rate of Return on Everything, 1870{2015. The Quarterly Journal of Economics, 134(3):1225{ 1298.

Kilian, L. (2009). Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market. The American Economic Review, 99(3):1053{1069.

Kilian, L. and Murphy, D. P. (2012). Why agnostic sign restrictions are not enough: Understanding the dynamics of oil market var models. Journal of the European Economic Association, 10(5):1166{1188.

Kilian, L. and Murphy, D. P. (2014). The role of inventories and speculative trading in the global market for crude oil. Journal of Applied Econometrics, 29(3):454{478.

Ludvigson, S. C., Ma, S., and Ng, S. (2017). Shock restricted structural vectorautoregressions. NBER Working Paper, (w23225).

Lutkepohl, H. (2005). New introduction to multiple time series analysis. Cambridge UnivPress, Cambridge.

Mertens, K. and Ravn, M. (2013). The dynamic e�ects of personal and corporate income tax changes in the united states. American Economic Review, 103(4):1212{47.

Nordhaus, W. D. and Boyer, J. (2000). Warming the World: Economic Models of Global Warming. Cambridge, MA, MIT Press.

Rubio-Ramirez, J. F., Waggoner, D. F., and Zha, T. (2010). Structural vector autoregressions: Theory of identi�cation and algorithms for inference. The Review of Economic Studies, 77(2):665{696.

Sims, C. A. (1993). A nine-variable probabilistic macroeconomic forecasting model, in business cycles, indicators and forecasting. NBER Studies in Business Cycles, In J.H. Stock and M.W. Watson (eds.) University of Chicago, pages 179{212.

Stock, J. H. and Watson, M. (2012). Disentangling the channels of the 2007-09 recession. Brookings Papers on Economic Activity, 43(1 (Spring)):81{156.

Stuermer, M. and Schwerhoff, G. (2015). Non-renewable resources, extraction technology, and endogenous growth. Dallas Fed Working Papers 1506 (Updated version: August 2020), Federal Reserve Bank of Dallas.

Uhlig, H. (2005). What are the effects of monetary policy on output? Results from an agnostic identi�cation procedure. Journal of Monetary Economics, 52(2):381{419.

USGS (2013). Metal prices in the United States through 2010. Scienti�c investigations report, U.S. Geological Survey.

Waggoner, D. and Zha, T. (1999). Conditional forecasts in dynamic multivariate models. The Review of Economics and Statistics, 81(4):639{651.

Watson, M. (2019). Comment on The empirical (ir) relevance of the zero lower bound constraint". NBER Macroeconomics Annual, 34.

World Bank (2020). Minerals for climate action: The mineral intensity of the clean energy transition. World Bank, Washington D.C.