Considering electrification, autonomy and sharing (the “Three Revolutions”), a team from Boston University and the University of California, Berkeley projects that electricity use from light duty vehicle (LDV) transport will likely be in the 570–1140 TWh range—13–26%—respectively, of total electricity demand in 2050. Their open-access study appears in the journal Energy Policy.

The researchers also project that, depending upon the pace at which the electric sector decarbonizes, this increase in electric demand could correspond to a decrease in LDV greenhouse gas emissions of up to 80%.

For their study, the team used an expanded Kaya identity framework, and modeled vehicle stock, energy intensity, and vehicle miles traveled, progressively considering the effects of each of the three revolutions.

Research approach.

The conventional Kaya identity used in transport energy and emissions forecasting considers vehicle miles travelled (VMT) and average energy density in kWh/mile to calculate total energy use.

This approach is useful when models predicting aggregate total travel are stable enough to perform well over long forecast periods and fleetwide average energy intensity can also be projected with confidence. Unfortunately, few of the conditions that make this aggregate approach useful hold today. Traditional forecasts of aggregate VMT began losing accuracy following the Great Recession of 2008, well before the sharing and autonomy disruptions had much of an effect. Autonomy is expected to greatly disrupt these forecasts, possibly along with new preferences for walkable urbanism, ridesharing, and other changes.

There is no silver bullet to address these difficulties, but we gain a little tractability with a conceptual framework based on an expanded identity of the following form.

Σ_i [v_i,t * ĸ_i,t * e_i,t] = Φ_l,t

where the stock in year t of EVs of a motorized vehicle type i is denoted by ĸ_i,t, v_i,t is the average miles traveled by that vehicle type in year t, and e_i,t is the average electricity use of the vehicle type i per mile traveled during year t, which we refer to as electric intensity (EI).

… The uncertainties and potential errors in this approach remain large, but at least they are disaggregated within a more flexible and transparent framework. For example, this framework allows us to treat electric non-autonomous and autonomous cars and light trucks all separately, adjusting use intensity for vehicle type as well as allowing the composition of the fleet to migrate from one type to another.

—Fox-Penner et al.

Based on their modeling, the researchers propose that while electric and autonomous passenger vehicles will represent a large and important new demand driver for the electricity sector, it should not be difficult to supply.

The team suggests that rapid and complete transport electrification with a carbon-free grid should remain the cornerstones of transport decarbonization policy in the near term.

However, they add, long-term policy should also aim to mitigate autonomous vehicles’ potential to increase driving mileage, urban and suburban sprawl, and traffic congestion while incentivizing potential energy efficiency improvements through both better system management and the lightweighting of an accident-free vehicle fleet.

The realm of potential transportation futures is still highly uncertain. Researchers have just begun to estimate the true impacts of automation and transportation as a service on transportation energy demand. Given the significant implications of electric and automated vehicles on greenhouse gas emissions, infrastructure development, and social systems, there is an obvious need for further work. Future work should aim to condense the available evidence of the impact of these emerging travel modes while understanding their adoption trajectory. The results of such work could then be integrated into our modeling framework. Research that would inform such work is currently ongoing or has yet to be done.

—Fox-Penner et al.

Resources

• Peter Fox-Penner, Will Gorman, Jennifer Hatch (2018) “Long-term US transportation electricity use considering the effect of autonomous-vehicles: Estimates & policy observations,” Energy Policy Volume 122, Pages 203-213 doi: 10.1016/j.enpol.2018.07.033.