A Review on Energy, Environmental, and Sustainability Implications of Connected and Automated Vehicles

Abstract

Connected and automated vehicles (CAVs) are poised to reshape transportation and mobility by replacing humans as the driver and service provider. While the primary stated motivation for vehicle automation is to improve safety and convenience of road mobility, this transformation also provides a valuable opportunity to improve vehicle energy efficiency and reduce emissions in the transportation sector. Progress in vehicle efficiency and functionality, however, does not necessarily translate to net positive environmental outcomes. Here, we examine the interactions between CAV technology and the environment at four levels of increasing complexity: vehicle, transportation system, urban system, and society. We find that environmental impacts come from CAV-facilitated transformations at all four levels, rather than from CAV technology directly. We anticipate net positive environmental impacts at the vehicle, transportation system, and urban system levels, but expect greater vehicle utilization and shifts in travel patterns at the society level to offset some of these benefits. Focusing on the vehicle-level improvements associated with CAV technology is likely to yield excessively optimistic estimates of environmental benefits. Future research and policy efforts should strive to clarify the extent and possible synergetic effects from a systems level to envisage and address concerns regarding the short- and long-term sustainable adoption of CAV technology.

Forecasting the Impact of Connected and Automated Vehicles on Energy Use: A Microeconomic Study of Induced Travel and Energy Rebound

“…While it is widely understood that automation and connectivity will enable a range of fuel-saving practices at the vehicle level, estimates of the magnitude of associated fuel and time cost changes are rare and largely speculative. Studies collectively suggest 5% to 20% energy efficiency improvement in CAVs compared to conventional counterparts, mainly due to optimal driving cycle, eco-routing, congestion reduction, and improving vehicle electrification 15 attributes [2][3][4][5]24,37].…”

“…The overwhelming 1 CAVs are also referred to as "autonomous", "self-driving", or "driverless" vehicles interchangeably in the literature, though these are not the same. For a disambiguation of definitions, refer to [2]. 2 The rebound effect can refer to the general phenomenon of increased driving after a rise in fuel economy, or it can be mathematically defined as the percent change in miles traveled caused by a one-percent change in fuel economy (or, relatedly, a one-percent change in fuel costs).…”

“…For a disambiguation of definitions, refer to [2]. 2 The rebound effect can refer to the general phenomenon of increased driving after a rise in fuel economy, or it can be mathematically defined as the percent change in miles traveled caused by a one-percent change in fuel economy (or, relatedly, a one-percent change in fuel costs). The empirical investigation of micro-level rebound usually utilizes regression-based approaches with cross-sectional, time series, or panel data [14,19].…”

“…Our contribution is to use the most recent microdata available in the United States to develop empirical estimates of a key parameter governing travel behavior, and to leverage these estimates to provide a glimpse of the possible energy impacts of vehicle connectivity and automation. make up the majority of the variable cost of travel (19% and 45%, respectively [28]), and they are both projected to drop significantly with the diffusion of CAV technology [2,26,29,30].…”

“…Literature estimate of changes in VMT due to CAV technology (list is non-exhaustive). For detailed discussion refer to[2].…”