Weiller's study is based on historical driving patterns of gasoline and diesel vehicles, not EVs or PHEVs. Thus the electric range implied could actually underestimate real electric range if the driving habits of EV and PHEV owners are different from those of the average driver of regular motor vehicles. As well, since American drivers tend to travel much farther than those in many other countries, the estimates used here for electric driving share may be below the actual electric-driving share in countries where average daily driving is less. Further, if public charging stations at workplaces and commercial centres were installed and used in addition to home recharging (not considered here), the percentage of electric driving would become even higher.

Weiller's analysis shows that, starting with vehicles with short battery driving range, increasing battery size is strongly beneficial. A PHEV-10 will travel 30% of its annual distance on electricity, while a PHEV-20 travels around 47% on electricity. But marginal gains decrease with further range increases: an additional doubling to a PHEV with 40 kilometres of range will increase electric travel only by about a third (from 47% to 64%); this simply reflects that the first 40 kilometres of daily driving account for, on average, 64% of total driving in the United States. Thus, using just a night-time charge, these percentages should be achievable.

For fully electric vehicles, three types are assumed in the EV module, with 150, 200 and 400 kilometres of range. Because the share of electric travel of all EV types is 100%, the type of EV does not directly influence electricity demand or CO2 emissions.

EV type would, however, affect driving range and vehicle cost, so could influence driving patterns and market share. Because these aspects are beyond the scope of this paper, the market shares are simply assumed within the definition of the scenarios.

Electricity: fuel mix and CO2 emissions

Grid-electricity demand is calculated as total electricity charged from the grid in kilowatt hours (kWh), excluding transmission and distribution losses, in a given region. In this calculation, the number of vehicles in stock and average annual distance driven per vehicle (in kilometres) are taken from the MoMo database, which is frequently updated with travel-survey results (IEA, 2009). The annual vehicle distance is assumed to be the same across vehicle categories (cars or light trucks, EVs or PHEVs).

When a PHEV runs in electric mode, it is considered to have the same efficiency, or rate of electric consumption, as a pure EV. Electricity usage per kilometre of travel in the EV module is projected to decrease slightly over time, from around 0.25 kWh/km in 2010 to 0.23 kWh/km in 2050 in the BLUE Map scenario, thanks to improvements in technology. These figures, which depend on the weight and other characteristics of vehicles, are averages for passenger cars (not light-truck electric consumption rates) as found in MoMo. The total kilometres of travel for PHEVs and EVs can thus be translated into annual and daily electricity demand.

As electricity is produced from a mix of sources that have different specific emissions levels, the share of different fuels in the electricity mix determines the CO2 emissions rate of each unit of electricity produced at a given time.

The energy sources considered in ETP 2010 which have very low, almost zero emissions are hydro, nuclear, wind, solar, geothermal and tidal. Fossil fuel-based electricity generated from coal is the most CO2-emissions intensive; oil and gas generation produce intermediate levels of CO2 emissions. These levels are also affected by the use of carbon capture and storage technologies.

The two scenarios of electricity-sector evolution considered here correspond to the Baseline and BLUE Map scenarios. The BLUE Map scenario forecasts efficiency improvements and a large share of renewable energies in the electricity 46y sector (48%) by 2050 while in the Baseline case, business-as-usual policies lead to a small increase of renewables from 18% of the mix in 2007 to 22% of the mix in 2050 (IEA, 2010). Coal and gas contribute 28% of total electricity generation in 2050 in the BLUE Map scenario, compared with 67% in the Baseline scenario. More than 90% of coal-fired generation stations and one-third of gas-fired facilities are fitted with carbon capture and storage (CCS) in the BLUE Map scenario (IEA, 2010). While the Baseline plan implies that new capacity additions are made without considering environmental consequences, in the BLUE Map scenario they are consistent with a 50% reduction in CO2 emissions from 2007 to 2050 across all energy sectors.

Total electricity demand from PHEVs jumps from negligible levels in 2020 to 1 398 terawatt hours (TWh) and 2 147 TWh for the two respective PHEV scenarios (low-range and high-range) by 2050, or 54% higher demand in the high-range PHEV scenario than in the low-range (Figure 3). As a percentage of world total electricity demand (40 1 37 TWh according to the ETP BLUE Map 2050), the low-range PHEV scenario represents a 3% increase in demand and the high-range scenario represents a 5% increase. If EVs were included, world total electricity demand would increase 7%-9% depending on the PHEV scenario.

Although one sees clear differences when looking at CO2 emissions from electricity generation by region (Figure 4), the emerging picture shows that by 2050 CO2 intensity will be so low (near or much below 100 grammes/kWh in most regions compared to a world average of about 400 in 2020, and close to 500 today) that increased demand from PHEVs and EVs will result in relatively small increases in CO2 emissions. In 2020, China and India will have relatively high CO2 emissions intensities, over 500 g/kWh, but by 2050 they are projected to decrease by 79% and 87% respectively. Shifting to electricity use in vehicles will actually bring about greater CO2 emissions reductions over time.

Figure 4

Regional CO2 intensities of electricity

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