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Explora: Environment
and Resource WTW emissions of road and rail transport

of weighted EIs for electricity production. Projected average 2.7. Annualization
EIs by state are shown in Table 3. It was assumed that 50% Total annual emissions were determined for each transport
of charging was in NSW, with 25% in both Queensland and unit, taking into account the corresponding travel distance
Victoria. This led to EIs of 840, 280, and 168 g CO -e/kWh and activity. It should be noted that, for each mode,
2
for average grid-loss-corrected electricity generation in the annualized emissions were calculated on the basis that the
three states, and for 2019, 2030, and 2050, respectively. The mode would be responsible for all the transport activity
associated normal distributions (ε , g CO -e/kWh) were between Brisbane and Melbourne (i.e., there was no
grid
2
adopted from another study, that is, N: 840, 13 for 2019, distribution of activity across the modes).
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truncated at 801 and 878, N: 280, 4 for 2019, truncated at 267
and 293, and N: 168, 3 for 2019, truncated at 160 and 176. For passenger transport, in the absence of detailed
statistics for travel by road and rail between Brisbane and
A further consideration was the impact of battery Melbourne, the annual activity was based on statistics for
charging losses (%), which were not yet included in the air travel between the two cities. In this hypothetical case,
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electricity EIs. Triangular distributions were assumed for the total two-way activity between the two cities was taken
battery charging efficiency (η rech ), T: 0.80, 0.95, 0.90 in to be 3,608,500 passengers/year in 2019. For future years,
2019 and 2030, and T: 0.90, 0.96, 0.93 in 2050. 8,9
this number was multiplied with a VKT growth factor
The GHG emission impacts of H use in transport derived from AFM for Australian PVs (1.13 for 2030 and
2
depend critically on the production and supply methods. 1.43 for 2050), that is, 4,077,605 passengers/year in 2030
Input distributions were adopted from another study. It and 5,160,155 passengers/year in 2050.
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was assumed for 2019 and 2030 that about 75% of H was For freight transport, the total two-way annual activity
2
produced and distributed using fossil fuels (steam methane for the Brisbane-Melbourne route was estimated from
reforming, [SMR]) and about 25% was produced using ARTC to be 3,889,600 tonnes/year in 2019 and 2030 and
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renewables (electrolysis), which yielded the distribution 6,406,400 tonnes/year in 2050.
(U: 9.3, 13.2 g CO -e/g H ). It was assumed for 2050 that
2
2
10% of H was produced and distributed using fossil 3. Results and discussion
2
fuels (SMR), and 90% was produced using renewables
(electrolysis), which gave the distribution (U: 2.3, 5.4 g The WTW emission intensity results are presented in
CO -e/g H ). Section 3.1, with a probabilistic assessment of the emissions
2 2 performance of the different transport modes: passenger
For the impact of H distribution and refueling losses transport by road or by electric train, and freight transport
2
(%), the input distributions were taken from an external by road, by diesel train or by electric train. Section 3.2
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study. A triangular distribution was assumed for H considers the potential impacts on annual emissions of a
2
distribution efficiency (η h2dist ), (T: 0.900, 0.999, 0.980) shift of activity from road to rail, and Section 3.3 places the
for 2019 and 2030, with improved performance in 2050 results in an international context.
(T: 0.950, 0.999, 0.990). A triangular distribution was
also assumed for H vehicle refueling efficiency (η h2refuel ) 3.1. WTW emission intensity
2
in 2019 and 2030 (T: 0.990, 0.999, 0.998), with improved
performance in 2050 (T: 0.995, 0.999, 0.998). 3.1.1. Passenger transport
Figure 6 shows the PDFs of the WTW emission intensity
2.6.2. Rail transport for the passenger transport units. Each PDF quantifies
For diesel-fueled trains, upstream emissions were modeled the probability that the variable falls within a particular
in a similar way to fossil-fueled road transport, that is, as an range of values, with the area under the function being
energy penalty (λ, U: 0.14, 0.28). In the WTW simulation, equal to one. The position and shape of the distribution
this distribution was combined with the distributions for provide information on its typical values (e.g., mean and
TTW emissions. median) and the associated variability and uncertainty.

The electric freight train was assumed to have the A wider distribution suggests a higher level of uncertainty
same characteristics as the diesel train, but using a factor and variability, whereas a narrower distribution suggests
(γ) for reduced energy consumption to reflect the greater a more robust emissions performance. Table 5 provides
efficiency of electric drive and the possibility of regenerative summary statistics for the passenger transport units.
braking, as discussed in Section 2.5.3. Indirect emissions The emission intensity for electric rail transport was
were captured by combining electricity consumption considerably lower than that for road transport (74%
with average grid-loss-corrected emissions for electricity lower in 2019, and around 90% lower in 2030 and 2050).
generation (Table 3). Moreover, the uncertainty in the WTW emission intensity

Volume 1 Issue 1 (2024) 10 doi: 10.36922/eer.3470