  Connected and automated vehicles (CAVs) are expected to yield significant
improvements in safety, energy efficiency, and time utilization. However, their
net effect on energy and environmental outcomes is unclear. Higher fuel economy
reduces the energy required per mile of travel, but it also reduces the fuel
cost of travel, incentivizing more travel and causing an energy "rebound
effect." Moreover, CAVs are predicted to vastly reduce the time cost of travel,
inducing further increases in travel and energy use. In this paper, we forecast
the induced travel and rebound from CAVs using data on existing travel
behavior. We develop a microeconomic model of vehicle miles traveled (VMT)
choice under income and time constraints; then we use it to estimate
elasticities of VMT demand with respect to fuel and time costs, with fuel cost
data from the 2017 United States National Household Travel Survey (NHTS) and
wage-derived predictions of travel time cost. Our central estimate of the
combined price elasticity of VMT demand is -0.4, which differs substantially
from previous estimates. We also find evidence that wealthier households have
more elastic demand, and that households at all income levels are more
sensitive to time costs than to fuel costs. We use our estimated elasticities
to simulate VMT and energy use impacts of full, private CAV adoption under a
range of possible changes to the fuel and time costs of travel. We forecast a
2-47% increase in travel demand for an average household. Our results indicate
that backfire - i.e., a net rise in energy use - is a possibility, especially
in higher income groups. This presents a stiff challenge to policy goals for
reductions in not only energy use but also traffic congestion and local and
global air pollution, as CAV use increases.

General Economics;Economics;Applications