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On the global economic potentials and marginal costs of non-renewable resources and the price of energy commodities

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  • Mercure, Jean-François
  • Salas, Pablo

Abstract

A model is presented in this work for simulating endogenously the evolution of the marginal costs of production of energy carriers from non-renewable resources, their consumption, depletion pathways and timescales. Such marginal costs can be used to simulate the long term average price formation of energy commodities. Drawing on previous work where a global database of energy resource economic potentials was constructed, this work uses cost distributions of non-renewable resources in order to evaluate global flows of energy commodities. A mathematical framework is given to calculate endogenous flows of energy resources given an exogenous commodity price path. This framework can be used in reverse in order to calculate an endogenous marginal cost of production of energy carriers given an exogenous carrier demand. Using rigid price inelastic assumptions independent of the economy, these two approaches generate limiting scenarios that depict extreme use of natural resources. This is useful to characterise the current state and possible uses of remaining non-renewable resources such as fossil fuels and natural uranium. The theory is however designed for use within economic or technology models that allow technology substitutions. In this work, it is implemented in the global power sector model FTT:Power. Policy implications are given.

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  • Mercure, Jean-François & Salas, Pablo, 2013. "On the global economic potentials and marginal costs of non-renewable resources and the price of energy commodities," Energy Policy, Elsevier, vol. 63(C), pages 469-483.
    • Handle: RePEc:eee:enepol:v:63:y:2013:i:c:p:469-483
    DOI: 10.1016/j.enpol.2013.08.040
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    5. J.-F. Mercure & A. Lam & S. Billington & H. Pollitt, 2018. "Integrated assessment modelling as a positive science: private passenger road transport policies to meet a climate target well below 2 ∘C," Climatic Change, Springer, vol. 151(2), pages 109-129, November.
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    8. Odenweller, Adrian, 2022. "Climate mitigation under S-shaped energy technology diffusion: Leveraging synergies of optimisation and simulation models," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    9. Hafner, Sarah & Anger-Kraavi, Annela & Monasterolo, Irene & Jones, Aled, 2020. "Emergence of New Economics Energy Transition Models: A Review," Ecological Economics, Elsevier, vol. 177(C).
    10. Mercure, J.-F. & Pollitt, H. & Chewpreecha, U. & Salas, P. & Foley, A.M. & Holden, P.B. & Edwards, N.R., 2014. "The dynamics of technology diffusion and the impacts of climate policy instruments in the decarbonisation of the global electricity sector," Energy Policy, Elsevier, vol. 73(C), pages 686-700.
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