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Autonomous taxis could greatly reduce greenhouse-gas emissions of US light-duty vehicles


Autonomous vehicles (AVs) are conveyances to move passengers or freight without human intervention. AVs are potentially disruptive both technologically and socially1,2,3, with claimed benefits including increased safety, road utilization, driver productivity and energy savings1,2,3,4,5,6. Here we estimate 2014 and 2030 greenhouse-gas (GHG) emissions and costs of autonomous taxis (ATs), a class of fully autonomous7,8 shared AVs likely to gain rapid early market share, through three synergistic effects: (1) future decreases in electricity GHG emissions intensity, (2) smaller vehicle sizes resulting from trip-specific AT deployment, and (3) higher annual vehicle-miles travelled (VMT), increasing high-efficiency (especially battery-electric) vehicle cost-effectiveness. Combined, these factors could result in decreased US per-mile GHG emissions in 2030 per AT deployed of 87–94% below current conventionally driven vehicles (CDVs), and 63–82% below projected 2030 hybrid vehicles9, without including other energy-saving benefits of AVs. With these substantial GHG savings, ATs could enable GHG reductions even if total VMT, average speed and vehicle size increased substantially. Oil consumption would also be reduced by nearly 100%.

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Figure 1: GHG emissions (coloured bars, left-hand axis) and vehicle efficiencies (symbols, right-hand axis) versus vehicle technology and GHG intensity assumptions.
Figure 2: Total annual cost of LDVs versus annual VMT in 2014 and 2030 for four vehicle technologies.
Figure 3: GHG emissions intensities per mile for CDVs in 2014 and 2030, and ATs in 2030.


  1. Templeton, B. Where Robot Cars (Robocars) Can Really Take Us (2013);

    Google Scholar 

  2. Brown, A., Gonder, J. & Repac, B. in Road Vehicle Automation (eds Meyer, G. & Beiker, S.) 137–153 (Springer, 2014);

    Book  Google Scholar 

  3. Morrow, W. R. et al. in Road Vehicle Automation (eds Meyer, G. & Beiker, S.) 127–135 (Springer, 2014);

    Book  Google Scholar 

  4. Anderson, J. M. et al. Autonomous Vehicle Technology: A Guide for Policymakers (RAND, 2014);

    Google Scholar 

  5. Folsom, T. C. Energy and autonomous urban land vehicles. IEEE Technol. Soc. Mag. 31, 28–38 (2012).

    Article  Google Scholar 

  6. Troppe, W. RMI Outlet (9 September 2014);

    Google Scholar 

  7. National Highway Traffic Safety Administration, Provides Guidance to States Permitting Testing of Emerging Vehicle Technology (US Department of Transportation, 2013);

    Google Scholar 

  8. Emerging Technologies: Autonomous Cars- Not If, But When (IHS Automotive, 2014).

  9. National Academies of Science Transitions to Alternative Vehicles and Fuels (National Academies Press, 2013);

    Google Scholar 

  10. Self-Driving Cars and Insurance (Insurance Information Institute, 2014);

  11. Energy Information Administration Annual Energy Outlook 2014 (US Department of Energy, 2014);

    Google Scholar 

  12. US Environmental Protection Agency Federal Register Vol. 79,, 1429–1519 (National Archives and Records Administration, 2014).

    Google Scholar 

  13. Greenblatt, J. B. Modeling California policy impacts on greenhouse gas emissions. Energy Policy 78, 158–172 (2015).

    Article  Google Scholar 

  14. US Driving Research and Innovation for Vehicle efficiency and Energy Sustainability Hydrogen Production Technical Team Roadmap (US Department of Energy, 2013);

    Google Scholar 

  15. Federal Highway Administration National Household Travel Survey 2009 (US Department of Transportation, 2011).

    Google Scholar 

  16. Anthony, S. ExtremeTech (23 December 2014);

    Google Scholar 

  17. GM Shows Chevrolet EN-V 2.0 Mobility Concept Vehicle (General Motors, 2012);

  18. Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations (Eno Center for Transportation, 2013);

  19. The New York City Taxicab Fact Book (Schaller Consulting, 2006);

  20. Naughton, J. Do autonomous cars need to cost so much? The Guardian (1 June 2013);

    Google Scholar 

  21. Seward, J. Mobileye NV Gains: Tesla Motors Inc. to Use Multiple Suppliers for Self-Driving Car. Benzinga (8 September 2014);

  22. Autonomous Vehicles: Self-Driving Vehicles, Autonomous Parking, and Other Advanced Driver Assistance Systems: Global Market Analysis and Forecasts (Navigant Research, 2013).

  23. Gomes, L. Urban Jungle a Tough Challenge for Googles Autonomous Cars. MIT Technology Review (24 July 2014);

  24. Cabanatuan, M. Northern California at center of driverless car development. San Francisco Chronicle (21 March 2015);

  25. Argonne National Laboratory Welcome to Autonomie (US Department of Energy, 2012);

    Google Scholar 

  26. GREET Mini-Tool and Sample Results from GREET 1 2013 (Argonne National Laboratory, 2013);

  27. Going Green (Metro Taxi Denver, 2013);

  28. Fuel Cell Technologies Office Multi-Year Research, Development, and Demonstration Plan (US Department of Energy, 2014);

  29. Cost of Owning and Operating Vehicle in US Increases Nearly Two Percent According to AAA’S 2013 “Your Driving Costs” Study. AAA NewsRoom (16 April 2013);

  30. US Energy Information Administration Annual Energy Outlook 2013 (US Department of Energy, 2013);

    Google Scholar 

  31. Ayre, J. Nissan LEAF Sets New Annual US Electric Car Sales Record—Yet Again. CleanTechnica (30 October 2014);

  32. Brief specs of 2002 smart Fortwo Coupe Pure. CarSpector (25 November 2014);

  33. US Environmental Protection Agency Code of Federal Regulations Vol. 29, Ch. I (US Government Printing Office, 2006);

    Google Scholar 

  34. 2014 Taxicab Fact Book (New York City Taxi and Limousine Commission, 2014);

  35. Gordon-Bloomfield, N. San Francisco: Twice As Many Taxis Burn Half As Much Gas; Here’s How. Green Car Reports (15 Feb 2012);

  36. Economic Review of the Small Public Service Vehicle Industry (Commission for Taxi Regulation, Goodbody Economic Consultants, 2009);

  37. US Bureau of Labor Statistics Historical Consumer Price Index for All Urban Consumers (CPI-U): US City Average, All Items (US Department of Labor, 2014);

    Google Scholar 

  38. A Look at Historical Car Loan Interest Rates. Car Loan Pal (22 March 2011);

  39. Current Auto Loan Interest Rates. Bankrate (25 September 2014);

  40. Final Technical Support Document: Fuel Economy Labeling of Motor Vehicle Revisions to Improve Calculation of Fuel Economy Estimates EPA420-R-06-017 (US Environmental Protection Agency, 2006);

  41. Methodologies for Estimating Fuel Consumption Using the 2009 National Highway Travel Survey (Energy Information Administration, 2011);

  42. Saxena, S., Gopal, A. R. & Phadke, A. A. Electrical consumption of two-, three- and four-wheel light-duty electric vehicles in India. Appl. Energy 115, 582–590 (2013).

    Article  Google Scholar 

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The authors thank A. Brown, J. Gonder, A. Gopal, D. Millstein, B. Morrow, S. Moura, N. Shah, A. Sturges, R. van Buskirk, J. Ward and T. Wenzel for insights and draft feedback. Special thanks go to C. Scown for analysing FHA data. Work was supported in part by Laboratory Directed Research and Development funding through Lawrence Berkeley National Laboratory, under US Department of Energy Contract No. DE-AC02-05CH11231.

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S.S. performed vehicle powertrain calculations; J.B.G. performed all other calculations and analysis. J.B.G. and S.S. wrote the manuscript and made any appropriate revisions.

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Correspondence to Jeffery B. Greenblatt.

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The authors declare no competing financial interests.

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Greenblatt, J., Saxena, S. Autonomous taxis could greatly reduce greenhouse-gas emissions of US light-duty vehicles. Nature Clim Change 5, 860–863 (2015).

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