Estimation of global final-stage energy-return-on-investment for fossil fuels with comparison to renewable energy sources

Abstract

Under many scenarios, fossil fuels are projected to remain the dominant energy source until at least 2050. However, harder-to-reach fossil fuels require more energy to extract and, hence, are coming at an increasing ‘energy cost’. Associated declines in fossil fuel energy-return-on-investment ratios at first appear of little concern, given that published estimates for oil, coal and gas are typically above 25:1. However, such ratios are measured at the primary energy stage and should instead be estimated at the final stage where energy enters the economy (for example, electricity and petrol). Here, we calculate global time series (1995–2011) energy-return-on-investment ratios for fossil fuels at both primary and final energy stages. We concur with common primary-stage estimates (~30:1), but find very low ratios at the final stage: around 6:1 and declining. This implies that fossil fuel energy-return-on-investment ratios may be much closer to those of renewables than previously expected and that they could decline precipitously in the near future.

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Fig. 1: Conceptual framework for global fossil fuels EROI estimation.
Fig. 2: Basic MRIO structure with energy extensions vector.
Fig. 3: Global primary-stage fossil fuel EROI ratios from 1995–2011.
Fig. 4: Global final-stage fossil fuel EROI ratios from 1995–2011.
Fig. 5: Components of the EROI calculations.
Fig. 6: Analysis results superimposed on the ‘net energy cliff’.

Data availability

The extended energy input datasets were obtained under licence from the IEA. The IEA World Energy Statistics and Balances can be downloaded with institutional or other user licence from https://doi.org/10.1787/enestats-data-en. The EXIOBASE 3.4 database is available at http://exiobase.eu/index.php/data-download/exiobase3mon. The concordance matrices used in the EXIOBASE-based calculations are available in the associated University of Leeds data repository47. The aggregate EROI datasets generated are available from the corresponding author upon reasonable request.

Code availability

The MATLAB code written for generating EiE in the EROI calculations is available at GitHub at the following link: https://github.com/earao/EROI.

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Acknowledgements

This research was primarily funded by the UK Energy Research Centre, supported by the UK Research Councils under EPSRC award EP/L024756/1. We also acknowledge support for P.E.B. under EPSRC Fellowship award EP/R024251/1. The contributions of A.O. were also supported by the Centre for Industrial Energy, Materials and Products (EPSRC award EP/N022645/1), and under EPSRC Fellowship award EP/R005052/1. L.I.B.-C. was supported by the Living Well Within Limits project funded by the Leverhulme Trust.

Author information

P.E.B., L.I.B.-C., A.O. and L.H. jointly designed the study and wrote the paper. P.E.B. and L.H. sourced IEA data and undertook calculations to calculate the total energy produced and direct energy consumed. L.I.B.-C. and A.O. performed the MRIO calculations to obtain indirect energy estimates.

Correspondence to Paul E. Brockway.

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