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Chapter 3 | Scenario differences and model details
• Allowing for the projected growth in vehicle stocks and total VKT in each major
metropolitan area to increase traffic volumes. When combined with an assumed
rate of future road construction, this gives projections of average network volumeto-capacity
(V/C) ratios for each Australian capital city (where a feedback loop
then calculates the effect on average network speeds, for the V/C ratio increases,
and the consequent increases in average fleet fuel intensity and any dampening
of peak travel demand due to the increased travel times).
• After allowing for the initial feedback of an element of increasing peak traffic
being restrained (through forgone travel or through trip timing decisions leading
to peak-spreading), the next iteration of the model allows for modal choice
changes where the extra delays from the growing congestion can cause some of
the motorists to move to bus or rail commuting. This new set of values for modal
tasks is then passed back to the first stage of the model and the process repeated
until an equilibrium level (for future travel on each mode) is reached.
The modelled equilibrium values for future vehicle travel and average fuel
consumption rates across the networks (derived from the feedback modules) are then
passed onto the emission models (MVEm), which contain all the detailed emission
factors (by vehicle type, age, condition, area of operation, technology type, fuel type,
etc) for converting activity data into emission projection estimates.
The resulting greenhouse gas emission estimates for the road sector (in direct CO 2
equivalent values) are given in Table 1.1 (with more detail provided for projections of
road task levels, road vehicle efficiencies, and emissions by different gas species, in
Appendix Tables A4 to A10, A12, A14, A16, A18 and A21 to A24).
Consistency with the AGO/DCC Inventory values
Many of the emission factor details contained in the BITRE MVEm suite are partially
redundant for this particular projection exercise, since their level of detail is primarily
required for deriving robust estimates of noxious pollutants for urban areas, to
evaluate the impact on urban health and amenity. Greenhouse gas totals for transport,
however, are dominated by the CO 2
output from vehicle fuel use, the estimation of
which is primarily dependent on the carbon content of the various transport fuels
(since full carbon combustion is the standard methodological assumption).
The fuel carbon content values (in terms of grams of CO 2
emitted per megajoule of
fuel consumed), both for this study and for the previous base case projection studies,
have been taken from the National Greenhouse Gas Inventory (NGGI) transport
workbooks—the most recent versions having been published by the AGO in late
2006: NGGIC (2006a), Australian Methodology for the Estimation of Greenhouse Gas
Emissions and Sinks 2005, Energy (Transport); and the DCC in late 2007: NGGIC (2007),
Australian Methodology for the Estimation of Greenhouse Gas Emissions and Sinks
2006, Energy (Transport).
Note that BITRE bottom-up emission aggregates tend to differ to some extent from
the emission estimates contained in the AGO/DCC NGGI publications released
so far. The BITRE estimation methodologies and emission factors are substantially
more detailed than the default methods of the NGGI methodology workbooks, not
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