The Nikkei reports that East Japan Railway Co. will team up with other Japanese firms, including Kawasaki Heavy Industries Ltd., Sumitomo Corp., Nippon Sharyo Ltd., Hitachi Ltd. and Mitsubishi Heavy Industries Ltd. in bidding for a high-speed railway project in California.

The group will inform the California state government of its intention to participate in the project by March 16. California’s plan involves mixing existing rail lines and those for high-speed trains, so JR East will apply the technology of the so-called mini-bullet train that can run on both regular and high-speed rail lines. The technology is in use on the company’s Yamagata bullet train line.

Central Japan Railway Co., meanwhile, has formed a basic agreement with US construction engineering firm Fluor Corp. and the US arm of major UK builder Balfour Beatty Plc in a high-speed railway project in Texas. Eleven Japanese firms, including Mitsubishi Corp., Toshiba Corp. and the Japan Bank for International Cooperation, have decided to cooperate with JR Tokai.

Net emissions resulting from the use of plug-in hybrid electric vehicles (PHEVs) depend on the efficiency of the conventional vehicle fleet; PHEV CD (all-electric, charge-depleting mode) efficiency; charging strategy; battery pack capacity; driving patterns; and generator mix used for charging.

A team at Carnegie Mellon University has modeled the net emissions in two regional transmission operators (PJM and NYISO) from PHEVs under different scenarios for future power generation; different size battery packs; charging strategies (home, work and smart); and PHEV fleet percentages between 0.4 and 50%. Scott Peterson, J. F. Whitacre and Jay Apt found that compared to 2005 gasoline fleet efficiency levels, all charging strategies and CD mode efficiencies yield reduction of CO₂
emissions.

If a 2020 conventional vehicle fleet efficiency target
of 35 mpg (6.7 L/100km) is compared to the 2020 CD (charge depleting) efficiency, net CO₂
emissions drop significantly in switching from gasoline to electricity in NYISO but less in PJM because of the differences in generation, unless power generation with carbon capture and storage (CCS) is used.

In a paper published in the ACS journal Environmental Science & Technology, they present their results for a 10% PHEV market share of the light-duty vehicle fleet. Other PHEV market shares are included in the paper’s Supporting Information, but the results are similar except for the
lowest 0.45% level (with fewer PHEVs charging, emissions are more sensitive to the specific plant used to charge them).

We estimate that PHEVs are likely to have lower net emissions
of NO_x and CO₂ than a conventional vehicle fleet, given current
(10.7 L/100 km) efficiencies. When compared to 2020 CAFE standards (6.7 L/100 km), net CO₂ emissions in New York are greatly reduced by switching from gasoline to electricity, but
coal-heavy PJM shows lower benefits unless coal units are fitted
with CCS or replaced with lower CO₂ generation. NO_x is
reduced in both RTOs, but SO₂ increases unless a cap binds…A /tonne CO₂ price applied only to
combustion emissions in the electric sector will have a negligible
short-term effects on net CO₂ emissions from PHEVs.

—Peterson et al.

Among the results of their study:

• Home charging occurs near peak system load, smart charging
  near minimum system load, and work charging occurs both near
  peak system load (at the same time as home charging) and earlier
  in the day when most vehicles are arriving at work. These
  differences in timing result in changes in generator mix and thus
  emissions. In PJM, home charging results in the greatest CO₂
  reductions with no CO₂ price and relies more on natural gas
  generation. In NYISO, smart charging results in greater CO₂
  reductions because of the large number of natural gas generators
  predicted to be used to meet demand.

• Battery size results in few qualitative changes. Large batteries increase the magnitude of emissions changes “but do not change the sign” except in the case of NO_x
  emissions in NYISO with work or home charging. Large batteries
  are also more sensitive to charge rate.

• In NYISO home
  charging does not decrease CO₂ emissions as much as smart or
  work charging because it is displacing gasoline with plants near
  the peak, often using oil. Smart charging relies
  on 86% natural gas in NYISO, whereas home charging uses only
  44% natural gas. In NYISO work and smart charging have similar
  CO₂ emissions. PJM shows nearly the opposite result with smart
  charging having significantly lower reductions in CO₂ emissions
  (relying on 98% coal). Home and work charging in PJM exhibit
  similar levels of CO₂ emissions.

• Although without a CO₂ price there is no incentive to
  use a generator with lower CO₂ emissions, adding a /tonne CO₂ price does not significantly alter the
  plants used to meet a given load.

• In the CCS scenario there is little change in NO_x emissions.

• Unlike the other pollutants, net SO₂ emissions
  increase in most scenarios.

There are strong arguments in favor of electrification of the
transportation sector in addition to net emissions. Combining
numerous mobile emission sources into a far small number of
stationary sources offers opportunities for cost-effective emissions
reduction that may not otherwise be feasible in the transportation
sector, and the location of emissions is likely to be moved farther
from densely populated areas. If PHEV cars displace light trucks,
SUVs, and vans from the fleet, emissions will be further reduced
from the values reported here.

Enacting a CO₂ price of /tonne will not be effective at
reducing net CO₂ emissions from a PHEV fleet. PHEVs are likely
to place upward pressure on SO₂ allowance prices if emission
caps bind or to increase emissions if the caps do not bind. PHEVs
will probably reduce net CO₂ and NO_x emissions but are
unlikely to reduce net SO₂ emissions.

—Peterson et al.

Resources

• Scott B. Peterson, J. F. Whitacre, Jay Apt (2011) Net Air Emissions from Electric Vehicles: The Effect of Carbon Price and Charging Strategies Environmental Science & Technology 45 (5), 1792-1797 doi: 10.1021/es102464y