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Chapter 5
Projection and estimation
uncertainties
The sensitivity tests in the preceding chapter give some idea of the uncertainty
associated with the projections, where even if the models are well formulated the
difficulties inherent in accurately forecasting all the model input data and parameters
mean that there will always be a certain range of fundamental variability in the results.
The sensitivity tests suggest this underlying variability of the projections to be of the
order of a 5 per cent variation (in the projection period endpoint) for the sensitivity
range in any one particular major parameter. Assuming the model structure is an
accurate reflection of the transport sector’s dynamics and that current travel behaviour
trends continue, the results imply that 2020 aggregate emissions, in the absence of
further abatement measures, are unlikely to lie more than 15–20 per cent from the
base case values, unless there is major structural change to the sector (or there are
other major unforeseen events impacting on transport activity, such as severe fuel
shortages) before 2020. Of course, future Australian transport emissions could be
significantly different from the levels projected here, depending on which greenhouse
gas abatement policies are pursued (both domestically and internationally) over the
coming years. For example, the establishment of the Australian Government’s CPRS
(along with any complementary measures also introduced) will doubtless have a
range of impacts on transport choices.
Regarding structural change, there could be some projection uncertainty associated
with the modelled saturation effects. The BITRE models have saturation constraints
imposed on a variety of underlying transport trends (such as for future average per
capita car travel). Deciding on the best value for such asymptotic saturation levels is
often reasonably approximate, and partly dependent on the particular mathematical
form chosen to fit or explain the trend data. Hypothetically, given the right conditions,
even behavioural patterns currently following saturating or logistic trends could,
in the future, leave such trend curves. That is, large enough economic changes,
or technological innovations, could be capable of generating significant demand
growth opportunities, perhaps such that the modelled saturation levels could then
be exceeded within projection timeframes.
For example, very high fuel efficiency in future generations of hybrid motor vehicles
(including plug-in hybrids, that can run for reasonable portions of their daily
travel on battery power, or hydrogen fuel cell hybrids) could possibly offer such
large reductions in motoring costs that substantial extra travel (‘rebound’ travel)
is generated. Yet even if current technology bottlenecks could be overcome quite
swiftly, and such highly efficient vehicles start being introduced in the near future,
their overall penetration into the Australian vehicle fleet would probably still be
relatively slight before 2020. Another hypothetical example of a structural change
that could be significant enough (i.e. to have the capability of altering the shape of
the expected saturation path) concerns the possible introduction, over the longer
term, of driverless road vehicles. The availability of technology and infrastructure
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