Potential Impacts of High Oil Prices on TransportationFor the transportation sector, six major types of interdependent
impacts of high oil prices can be expected:
Usage level. This trend is clear and straightforward
as users of a specific mode generally respond to higher prices by
limiting or rationalizing (e.g. speed) their usage level. Transport
operators, such as airline companies, can reduce the frequency of
their services. It is a matter of price elasticity where an increase
of price P will result in a usage level change of Q.
This function is rarely linear. At first, price increases may have
limited effects as they are simply absorbed with the expectation
that they are a temporary condition. Once a specific price threshold
is reached, then significant changes will result as marginal and
extraneous usage will be cut until a new equilibrium is reached.
Usage for this mode is said to have reached a paradigm shift. For
instance, in 2008 the amount of driving in the United States fell
sharply as well as the number of flights, on par with an increase
in oil prices.
Modal shift. In conjunction with a drop in usage level
for a mode, an alternative mode may capture the traffic of that
change, in whole or in part, through a modal shift. Again, this
process is commonly not linear and a modal balance (A/B)
can shift rapidly once a price threshold is reached. Thus, an increase
of price P may result in a substantial shift, Q(A/B),
in the modal balance. A modal shift commonly takes place towards
a mode which is less energy intensive (less elasticity) than the
other. It can thus be expected that with higher oil prices some
trucking will shift towards rail and that public transit will gain
in market share.
Service area changes. Under a specific price level, each
mode has an optimum service area; a distance at which it provides
mobility in a cost effective fashion. Since each mode has a different
elasticity, an increase in prices will have different impacts on
the cost / distance function. For two modes, A and B,
the same increase in energy price would create a different inflection
of the cost / distance function where the range of mode B
would reduced by R(B). Thus, mode A gains in market
share. An example is trucking versus rail in North America where
about 700 miles (1120 km) was considered to be a standard cutting
point, but a rise in fuel prices has placed this range around 500
miles (800 km).
Gateway / Hub selection. A gateway is a point of interface
between two systems of circulation. Since these systems have different
elasticity, a rise in energy prices can eventually change their
relationships, particularly the locations where intermodal transportation
takes place. A shipping service using gateway port A and
taking advantage of faster (but more energy intensive) hinterland
connections may instead switch to gateway port B which is
closer to customers. Although this change may result in longer total
shipping times, the cost trade-off would make it acceptable. Higher
energy prices are thus likely to reinforce gateways that have the
most efficient hinterland connections, notably in terms of modal
choice.
Network configuration. Enduring high energy prices are
likely to trigger shifts in the configuration of transportation
networks in terms gateways, hubs, routing and corridors. For instance
an inland corridor may experience a change of the linkages with
inland load centers that minimize road use and maximize rail. An
airline may decide to abandon less profitable routes an offer more
direct (point to point) services. An air transport network may experience
a reconfiguration and an abandonment of marginal services, namely
at small airports.
Supply chain propagation. A supply chain is composed
of a series of inputs and outputs having a complex geographical
and functional structure. Rising energy prices imply a wide variety
of changes in the cost structure within a supply chain, namely a
propagation of those costs. Procurement, manufacturing and distribution
costs are all impacted to various degrees. For instance an
increase in the density of packing of parts for a better level
of transport asset utilization may involve the delay of assembly
tasks along the supply chain. It is possible for some
of these costs to be absorbed through reduced profit margins and
higher efficiencies, but they do eventually propagate and end up
in higher consumer prices. The functional and geographical structure
of a supply chain is a key element of the nature and extent of its
costs propagation. At certain price level, some supply chains cease
to be profitable and a reconfiguration becomes necessary.
It is assumed here that the origins and destinations remain relatively
constant. However, it is clear that locational choices are significantly
impacted as well. For instance, many comparative advantages in global
trade are mainly based on low transport costs. In a higher energy prices
environment, locational practices may change in several manufacturing
sectors with sites closer to final markets, even if characterized by
higher labor (or input) costs, may be advantaged.
