Large inequality in international and intranational energy footprints between income groups and across consumption categories

Abstract

Inequality in energy consumption, both direct and indirect, affects the distribution of benefits that result from energy use. Detailed measures of this inequality are required to ensure an equitable and just energy transition. Here we calculate final energy footprints; that is, the energy embodied in goods and services across income classes in 86 countries, both highly industrialized and developing. We analyse the energy intensity of goods and services used by different income groups, as well as their income elasticity of demand. We find that inequality in the distribution of energy footprints varies across different goods and services. Energy-intensive goods tend to be more elastic, leading to higher energy footprints of high-income individuals. Our results consequently expose large inequality in international energy footprints: the consumption share of the bottom half of the population is less than 20% of final energy footprints, which in turn is less than what the top 5% consume.

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Fig. 1: Energy footprints versus expenditure.
Fig. 2: Energy footprint inequality versus expenditure inequality for 2011.
Fig. 3: Energy intensity and elasticity spectra.
Fig. 4: Elasticity versus energy intensity.
Fig. 5: International Lorenz curves.
Fig. 6: Business-as-usual trends for household energy footprints.

Data availability

The expenditure data used is available at http://datatopics.worldbank.org/consumption/ and https://ec.europa.eu/eurostat/data/database. The IEA data can be downloaded under institutional license from the UK data service at https://stats2.digitalresources.jisc.ac.uk/ and https://doi.org/10.5257/iea/web/2018-10. The underlying GTAP 9 database can be purchased from https://www.gtap.agecon.purdue.edu/databases/v9/default.asp. The concordance matrices used in the footprint calculations are depicted in the Supplementary Tables 3 and 4. The final energy footprint data per consumption category, nation and income group as well as energy intensities, elasticities and scenario parameters are available from the corresponding author on reasonable request. Source data for Figures 1 to 6 are provided with the paper.

Code availability

MATLAB code for obtaining final energy footprints from the MRIO and calculating elasticities and the Gini coefficient is available at https://github.com/eeyouol.

Change history

  • 30 March 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Acknowledgements

Y.O. is supported by the Leverhulme Trust’s Research Leadership Award ‘Living Well Within Limits’ (RL2016–048) project awarded to J.K.S. J.K.S was partly supported by an International Academic Fellowship of the Leverhulme Trust (IAF-2018–018). The contributions of A.O. were supported by EPSRC Fellowship award EP/R005052/1. We also would like to thank M. Baltruszewicz, J. Vogel, J. Millward-Hopkins, P. Brockway and L. Hardt for helpful discussions.

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Authors

Contributions

Y.O., J.K.S. and A.O. jointly designed the study, sourced the data, designed the analysis and wrote the paper. Y.O. conducted the analysis.

Corresponding author

Correspondence to Yannick Oswald.

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The authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary Notes 1–15, Tables 1–9, Figs. 1–9 and refs. 1–5.

Source data

Source Data Fig. 1

Energy footprints per capita, expenditure per capita.

Source Data Fig. 2

Gini coefficients of countries in energy and expenditure.

Source Data Fig. 3

Elasticities and energy intensities per country and consumption category.

Source Data Fig. 4

Population-weighted average of elasticities and energy intensities.

Source Data Fig. 5

Cumulative population and cumulative energy resources needed to generate Lorenz curves, Raw consumption-based energy accounts per country and consumption category.

Source Data Fig. 6

Share of aggregated consumption categories in energy over time, Raw consumption-based energy accounts per country and consumption category with additional business as usual forecasts, OECD given and applied growth forecasts.

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Oswald, Y., Owen, A. & Steinberger, J.K. Large inequality in international and intranational energy footprints between income groups and across consumption categories. Nat Energy 5, 231–239 (2020). https://doi.org/10.1038/s41560-020-0579-8

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