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A holistic analysis of passenger travel energy and greenhouse gas intensities

Nature Sustainability volume 3, pages 459–462 (2020)Cite this article

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Abstract

Transportation is a major energy consumer and emitter of greenhouse gases (GHGs). Exploring the opportunities for energy savings and GHG emissions reductions requires understanding transportation energy or GHG intensity, which is defined as energy use or GHG emissions per unit activity, here passenger-kilometres travelled. This aggregate indicator quantifies the amount of energy required or GHGs emitted to provide a generic transportation service. We show that the range of observed energy and GHG intensities of major transportation modes is remarkably similar and that occupancy explains about 70–90% of the variation around the mean; only the remaining 10–30% is explained by differences in trip distances and other factors such as technology and operating conditions. Whereas average occupancy levels differ vastly, they translate into roughly similar levels of energy and GHG intensity for nearly all major transportation modes.

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Fig. 1: Energy intensity versus vehicle occupancy for light-duty vehicles, buses, railways and fixed-wing aircraft.
Fig. 2: Life-cycle GHG intensity versus vehicle occupancy for light-duty vehicles, buses, railways and fixed-wing aircraft based on data in Fig. 1.
Fig. 3: Energy intensity versus average trip distance for the transport modes shown in Figs. 1 and 2.

Data availability

All data were derived from public databases. The datasets used in the analysis have been deposited at https://doi.org/10.5281/zenodo.3727541.

References

  1. Deloitte Efficacités Énergetique et Environnementale des Modes de Transport: Synthèse Publique (ADEME, 2008).

  2. Transport, Energy and CO 2: Moving Toward Sustainability (IEA/OECD, 2009).

  3. Sims, R. et al. in Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) Ch. 8 (IPCC, Cambridge Univ. Press, 2014).

  4. M. J. Bradley & Associates Updated Comparison of Energy Use & CO 2 Emissions from Different Transportation Modes (American Bus Association, 2008).

  5. TranSys Research Ltd., Rail Tec, CPCS Transcom & Lawson Economics Research Ltd Comparison of Passenger Rail Energy Consumption with Competing Modes (National Cooperate Rail Research Program, 2015).

  6. Energy Efficiency (European Environment Agency, 2008).

  7. Railway Handbook, 2017—Energy Consumption and CO 2 Emissions (International Energy Agency and International Union of Railways, 2017) .

  8. Transport for London International Benchmarking Report (Rail and Underground Panel, 2016).

  9. Gucwa, M. & Schäfer, A. The impact of scale on energy intensity in freight transportation. Transp. Res. Pt D 23, 41–49 (2013).

    Article  Google Scholar 

  10. Cramer, J. & Krueger, A. B. Disruptive change in the taxi business: the case of Uber. Am. Econ. Rev. 106, 177–182 (2016).

    Article  Google Scholar 

  11. Henao, X. Impacts of Ridesourcing—Lyft and Uber—on Transportation Including VMT, Mode Replacement, Parking, and Travel Behavior. PhD thesis, Univ. Colorado (2017).

  12. ITF/OECD Transition to Shared Mobility (International Transport Forum, 2017).

  13. Gurumurthy, K. M. & Kockelman, K. M. Analyzing the dynamic ride-sharing potential for shared autonomous vehicle fleets using cellphone data from Orlando, Florida. Comput. Environ. Urban Syst. 71, 177–185 (2018).

    Article  Google Scholar 

  14. Moreno, A. T., Michalski, A., Llorca, C. & Moeckel, R. Shared autonomous vehicles effect on vehicle-km traveled and average trip duration. J. Adv. Transp. 2018, 8969353 (2018).

    Article  Google Scholar 

  15. Loeb, B., Kockelman, K. M. & Liu, J. Shared autonomous electric vehicle (SAEV) operations across the Austin, Texas network with charging infrastructure decisions. Transp. Res. Pt C 89, 222–233 (2018).

    Article  Google Scholar 

  16. Truong, L. T., De Gruyter, C., Currie, G. & Delbosc, A. Estimating the trip generation impacts of autonomous vehicles on car travel in Victoria, Australia. Transportation 44, 1279–1292 (2017).

    Article  Google Scholar 

  17. Schäfer, A., Heywood, J. B., Jacoby, H. D. & Waitz, I. A. Transportation in a Climate-Constrained World (MIT Press, 2009).

  18. Lave, C. Things Won’t Get a Lot Worse: the Future of US Traffic Congestion (Univ. California, Berkeley, 1991)..

  19. Grewe, V. et al. Mitigating the climate impact from aviation: achievements and results of the DLR WeCare project. Aerospace 4, 34 (2017).

    Article  Google Scholar 

  20. Wang, M. et al. Summary of Expansions, Updates, and Results in GREET® 2017 Suite of Models (Argonne National Laboratory, 2017).

Download references

Acknowledgements

We thank D. Daniels and M. Schipper (both US Energy Information Administration) for providing the NHTS 2009 raw dataset with estimates of household vehicle energy use.

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Authors and Affiliations

  1. UCL Energy Institute, University College London, London, UK

    Andreas W. Schäfer

  2. Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden

    Sonia Yeh

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  1. Andreas W. Schäfer
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Contributions

A.W.S. led the data collection, data analysis and preparation of the manuscript. S.Y. contributed to data collection, data analysis and preparation of the manuscript.

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Correspondence to Andreas W. Schäfer.

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Extended data

Extended Data Fig. 1 Energy intensity versus occupancy for railways in Canada, India, and the US.

Four electric commuter railways in the US are added to the figure. The energy intensities of electric railways are 0.49, 0.91, 0.55 and 0.45 MJ electricity/pkm for NY, PA, PA and IN, respectively. To ensure consistent comparisons, the final energy (electricity consumed) is converted to the secondary energy required to generate electricity in the corresponding US state, and multiplied by the final-to-secondary energy ratio for diesel fuel, which is 0.83 according to the latest GREET model. For example, the fossil-fuel equivalent EI of 0.55 MJ electricity/pkm is 0.55 /0.36 x 0.83 = 1.26 MJ/pkm, given the secondary-to-final energy efficiency of 36% for the electric power system in PA. The dotted lines represent a hypothetical 1:1 decline in energy intensity with vehicle occupancy, the limiting case of zero-weight passengers. All trajectories thus need to decline at a lower than 1:1 ratio.

Extended Data Fig. 2 Energy intensity versus occupancy for fixed-wing aircraft operating in US domestic travel.

All data relate to US domestic air travel in 2015. The dotted lines represent a hypothetical 1:1 decline in energy intensity with vehicle occupancy, the limiting case of zero-weight passengers. All trajectories thus need to decline at a lower than 1:1 ratio.

Extended Data Fig. 3 Energy intensity versus occupancy for intercity, regional, and urban buses in Australia, Canada, European cities, Sweden, Taiwan, and the US.

The dotted lines represent a hypothetical 1:1 decline in energy intensity with vehicle occupancy, the limiting case of zero-weight passengers. All trajectories thus need to decline at a lower than 1:1 ratio.

Extended Data Fig. 4 Energy intensity versus occupancy for light-duty vehicles.

The dotted lines represent a hypothetical 1:1 decline in energy intensity with vehicle occupancy, the limiting case of zero-weight passengers. All trajectories thus need to decline at a lower than 1:1 ratio.

Supplementary information

Supplementary Information

Supplementary description of data sources.

Reporting Summary

Supplementary Data

Data underlying Figs. 1–3 and econometric analysis.

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Schäfer, A.W., Yeh, S. A holistic analysis of passenger travel energy and greenhouse gas intensities. Nat Sustain 3, 459–462 (2020). https://doi.org/10.1038/s41893-020-0514-9

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