GHG energy intensities: EIA, US Energy Information Administration for gasoline, electricity and natural gas-produced hydrogen; EPA, US Environmental Protection Agency proposed rule for electricity; CA, California policy for electricity. Electricity-produced hydrogen was assumed for EPA and CA.
Overview.
Supplementary Table 12 presents parameter assumptions. Ref. 9 provided current and future efficiencies of ICEVs, HEVs, HFCVs, and BEVs. Hydrogen- and electricity-based vehicle efficiencies were converted to equivalent mpg of gasoline. Refs 9, 11, 12, 26 provided current and projected future US GHG emissions (including upstream emissions) for gasoline, natural gas and electricity. Ref. 13 provided 2030 California electricity GHG emissions estimates that were used to estimate best-case US electricity GHG reductions. Hydrogen was assumed produced by natural gas steam reforming or electrolysis, using conversion efficiencies from ref. 9. US occupancy by fraction of total VMT came from ref. 15. Validated models within the powertrain simulator Autonomie25 estimated the energy use of hypothetical small-occupancy BEVs based on a five-seat Nissan LEAF reference, but with 40% reduced frontal area corresponding to single-seat width, and vehicle mass, engine power, battery capacity and accessory loads reduced by smaller amounts. VMT of 12,000 mi. yr−1 was assumed11 for CDVs, and 40,000–70,000 mi. yr−1 for ATs based on New York City19 and Denver27 taxis. To estimate total vehicle ownership costs, we developed a model using capital costs from ref. 9, fuel costs from refs 11, 28, maintenance and insurance costs from ref. 29, and longevity from ref. 19.
GHG intensities.
The National Academy of Sciences (NAS) provided 2010 reference greenhouse gas (GHG) energy intensities for gasoline, natural gas and electricity9. We used data from the Energy Information Administration (EIA; refs 11, 30) to estimate 2014 and projected 2030 GHG intensities from gasoline and electricity (GHG intensities for natural gas were projected to change by <1%, so were held constant). The US Environmental Protection Agency (EPA) proposed rule GHG energy intensity target for 2030 (30% reduction from 2005) was provided by ref. 12, whereas projections for 2030 California gasoline and electricity were obtained from scenario S2 in Greenblatt13. All GHG emissions included upstream estimates provided by NAS (ref. 9) or Greenblatt13. Argonne National Laboratory (ANL) provided confirmatory life-cycle GHG emission estimates26. GHG intensities of hydrogen were obtained using conversion efficiencies from the US Department of Energy (DOE; ref. 28), based on natural gas steam reforming and electrolysis. For the latter, both EPA and California (CA) GHG electricity intensities were analysed, but only CA electricity resulted in a lower overall GHG intensity of hydrogen than natural gas-based hydrogen. Hydrogen GHG intensities based on EPA electricity were included in Fig. 1 in the main text, but GHG intensities based on EIA data were omitted from analysis because they were much higher, comparable to those of a 2030 hybrid-electric vehicle (HEV).
Vehicle occupancy.
We used data from the Federal Highway Administration (FHA; ref. 15) to estimate the fraction of total US vehicle-miles travelled (VMT) by number of passengers (occupancy); this data was provided by state, and aggregated to US totals. Results of this analysis are presented in Table 1 in the main text.
Right-sizing.
We used the powertrain simulation tool Autonomie25 to model hypothetical small-occupancy battery-electric vehicles (BEVs). The modelled reference vehicle was a Nissan LEAF, the top-selling, five-seat BEV introduced in 2010, with more than 142,000 vehicles sold worldwide31. One- and two-seat vehicle models were constructed based on LEAF parameters, but reducing the frontal area by 40% to accommodate a one-seat width. Reduction was less than 50%, owing to the assumption that a portion of the vehicle’s width remained constant to provide a sufficient safety margin. Vehicle mass, engine power, battery capacity and electrical accessory loads were also reduced by smaller amounts; see Supplementary Table 13. For comparison, the two-seat Smart BEV has approximately the same mass, motor power and battery capacity as the two-seat simulated vehicle shown here, but the frontal area is intermediate between the two- and five-seat versions. Specifically, the Smart Electric Drive Coupe has a curb mass of 950 kg, peak power of 55 kW, and battery capacity of 17.6 kWh (ref. 32); the estimated frontal area of the 2002 model was 2.02 m2 (ref. 32); the current model may be somewhat larger.
Using these input parameters, energy consumption for each vehicle model was calculated for three different EPA test drive cycles: the Urban Dynamometer Driving Schedule (UDDS), simulating an urban route with frequent stops; the Highway Fuel Economy Test (HWFET), simulating the higher speeds of highway driving; and the US06 Supplemental Federal Test Procedure, used to represent aggressive, high-speed and/or high-acceleration driving behaviour, rapid speed fluctuations, and driving behaviour following startup. A weighted sum of the UDDS (55%) and HWFET (45%) results yielded the standard EPA efficiency rating33.
BEV efficiencies relative to an average light-duty vehicle (LDV) were estimated assuming 56% passenger cars and 44% light trucks in 2030 (ref. 11).
For the largest size class in Table 1 in the main text (6.9 passengers), average efficiencies of large light trucks in NAS (ref. 9) were used: Dodge Grand Caravan minivan (seating for seven) and Ford F-150 pick-up truck (seating for six in ‘Super Cab’ model). (The Saturn Vue sport-utility vehicle included in NAS (ref. 9) is also considered a light truck, but was omitted from our analysis because it seats only five.)
Annual VMT.
Annual VMT estimates for CDVs were provided by EIA (ref. 11), whereas annual VMT for taxis in New York City and Denver were provided by Schaller19 and Metro Taxi27, respectively, and ranged from 39,410 to 72,000 mi. yr−1. The New York City Taxi and Limousine Commission34 also provided an estimate for New York City taxis (70,000 mi. yr−1) that was similar to the Schaller19 average of 64,600 mi. yr−1. Although we expect that autonomous taxis (ATs) will be more efficient than human-driven taxis in identifying and driving to passengers, thus possibly driving VMT even higher, we explored two AT cases in our analysis (40,000 and 70,000 mi. yr−1), along with a CDV reference case (12,000 mi. yr−1).
A San Francisco taxi estimate from Gordon-Bloomfield35 was higher (90,000 mi. yr−1), but increasing the VMT range was deemed unimportant, as all significant conclusions were observed at 70,000 mi. yr−1. Although not directly comparable, the average annual VMT for Irish taxis and limousines in 2008 (35,602 mi. yr−1; ref. 36) was below the low end of this range; however, 40% of Irish taxis and limousines travel 40,000 mi. yr−1 or more, consistent with our estimate.
AV and taxi economics.
We used estimates from Naughton20 and Troppe6 for the current incremental cost of AV technology. IHS (ref. 8) provided estimates of the eventual cost of this technology through 2035. Schaller19 provided an estimate of driver revenue for New York City taxis, adjusted to 2012 dollars using the historical consumer price index published by the US Bureau of Labor Statistics (BLS; ref. 37). This index was also used to adjust other cost data reported for years prior to 2012.
For vehicle loan rates, Car Loan Pal38 provided historical rates of five-year new car loans between 1980 and 2011; BankRate39 provided a rate estimate for 2014. Based on this data, we assumed a long-term average interest rate of 8.0% for five-year loans, corresponding to an annual capital recovery factor of 24.33%, assuming monthly payments. This factor was used to estimate annual capital costs of both CDVs and AV technology.
Vehicle costs, fuel costs and fuel efficiencies.
We developed a model of total ownership cost of vehicles with potentially high annual VMT. Using cost and efficiency estimates from NAS (ref. 9), fuel cost estimates from EIA (ref. 11) and DOE (ref. 14) and maintenance and insurance estimates from the American Automobile Association29, we calculated the annual total cost of ownership Ctotal (US$/yr) as:
where CRF = capital recovery factor (%/yr), Ccapital = cost of vehicle capital (US$), VMT = annual vehicle-miles travelled (mi. yr−1), Evehicle = vehicle energy efficiency (gal/mi. or kWh/mi. as appropriate), Cenergy = cost of energy (US$/gal or US$/kWh), Cmaint = cost of maintenance (US$/mi.), Cins = cost of insurance (US$/mi.).
NAS (ref. 9) provided estimates of a wide variety of passenger car and light truck vehicle technologies in 2010 and 2030. Technologies included internal combustion engine vehicles (ICEV), HEVs, hydrogen fuel-cell vehicles (HFCV) and BEVs. Hydrogen- and electricity-based vehicle efficiencies were converted to equivalent mpg of gasoline, using final energy lower heating values of gasoline and hydrogen, and final energy content of electricity. Further efficiency improvements included for 2030 were increased rolling resistance (RR) tyres, vehicle weight reductions (WR) and improved aerodynamics (AERO). All fuel efficiency estimates provided by NAS were expressed as EPA ratings40, but we have reduced these fuel efficiencies for ICEVs by 15% according to guidance published by EIA (ref. 41). For BEVs, which have idle shutoff, regenerative braking and high efficiency across a wide range of tractive loads, we have found evidence for less difference between EPA rated and real-world fuel economy compared with ICEVs (ref. 42). We also expect this to be the case for other advanced powertrains, including HEVs and HFCVs. Therefore, for this analysis we retained the EPA ratings for all of these powertrains.
We compared efficiency estimates against those of EIA (ref. 11) for 2014 new vehicles and 2030 new vehicles and fleet averages. This source was also used to estimate the number of LDVs in 2030 and the fraction of passenger cars and light trucks composing the 2030 fleet.