How Automated Vehicles affects Energy and Environment

Some researchers indicate that environmental impacts of automated vehicles (AVs) strongly depend on the connectivity and market penetration rates [1], [2], [3], [4]. For example, Mattas et al., [5] shows that with dense traffic, AVs that lack interconnectivity are likely to reduce speed in adherence to safety and comfort guidelines, consequently producing an additional 11 percent in emissions. Wadud et al. [6] developed an energy decomposition framework and quantified the potential percentage change of greenhouse gas (GHG) emissions from AVs depending on energy intensity effect, travel demand effects and net effects of automation. Wadud et al. [6] concluded that vehicle automation offers the potential to reduce light-duty energy consumption by nearly half, but this decrease is dependent on several factors including the degree to which energy-saving algorithms and design changes are implemented into practice and policy responses at federal, state, and local agencies, among others.

While AVs could induce demand due to easier travel and the empty travel generated from shared AV fleets [7], [8], [9], most studies show energy savings despite the Vehicle-Miles-Traveled (VMT) increase [10], [11], [12]. For example, Fagnant and Kockleman [11] estimated that shared autonomous vehicles (SAVs) may save 10 times the number of cars needed for personally owned vehicles travel but increase daily VMT by about 11 percent from empty vehicle travel. The energy use and GHG emissions could be reduced by 12 percent and 5.6 percent respectively, owing to changes in total number of vehicle starts, lower proportion of cold starts, and reduced parking needs. However, some studies also indicated an increase of emissions considering different AV penetration rates [13], [14], [15]. For example, Harper et al. [16] estimated that privately owned AVs searching for cheaper parking could increase light-duty energy use in Seattle by up to 2 percent.

In general, most studies conclude that AVs would reduce energy consumption and GHG emissions per mile driven due to improvements in operational efficiencies such as automated eco-driving, changes in vehicle size, and traffic smoothing, but there is not a clear consensus that these efficiency improvements will reduce total energy use and emissions. Current areas for future research include: 1) studying the full lifecycle environmental impacts of AVs, 2) investigating models that capture the full complexity of real-world scenarios such as dynamic traffic patterns, diverse weather conditions, varying road types, and unpredictable human behavior, 3) exploring how a fleet of electric AVs might interact with power grids, especially concerning charging demands and renewable energy integration, 4) exploring if the operational efficiencies gained from AVs, lower emissions and energy use remain as trip making and VMT increases due to empty, longer, and/or easier travel [17], [18].

References

  1. R. E. Stern et al., “Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments,” Transp. Res. Part C Emerg. Technol., vol. 89, pp. 205–221, Apr. 2018, doi: 10.1016/j.trc.2018.02.005.

  2. J. M. Bandeira, E. Macedo, P. Fernandes, M. Rodrigues, M. Andrade, and M. C. Coelho, “Potential Pollutant Emission Effects of Connected and Automated Vehicles in a Mixed Traffic Flow Context for Different Road Types,” IEEE Open J. Intell. Transp. Syst., vol. 2, pp. 364–383, 2021, doi: 10.1109/OJITS.2021.3112904.

  3. M. Makridis, K. Mattas, C. Mogno, B. Ciuffo, and G. Fontaras, “The impact of automation and connectivity on traffic flow and CO2 emissions. A detailed microsimulation study,” Atmos. Environ., vol. 226, p. 117399, Apr. 2020, doi: 10.1016/j.atmosenv.2020.117399.

  4. L. Huang, C. Zhai, H. Wang, R. Zhang, Z. Qiu, and J. Wu, “Cooperative Adaptive Cruise Control and exhaust emission evaluation under heterogeneous connected vehicle network environment in urban city,” J. Environ. Manage., vol. 256, p. 109975, Feb. 2020, doi: 10.1016/j.jenvman.2019.109975.

  5. K. Mattas et al., “Simulating deployment of connectivity and automation on the Antwerp ring road,” IET Intell. Transp. Syst., vol. 12, no. 9, pp. 1036–1044, Nov. 2018, doi: 10.1049/iet-its.2018.5287.

  6. Z. Wadud, D. MacKenzie, and P. Leiby, “Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles,” Transp. Res. Part Policy Pract., vol. 86, pp. 1–18, Apr. 2016, doi: 10.1016/j.tra.2015.12.001.

  7. T. D. Chen, K. M. Kockelman, and J. P. Hanna, “Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions,” Transp. Res. Part Policy Pract., vol. 94, pp. 243–254, Dec. 2016, doi: 10.1016/j.tra.2016.08.020.

  8. W. Zhang, S. Guhathakurta, and E. B. Khalil, “The impact of private autonomous vehicles on vehicle ownership and unoccupied VMT generation,” Transp. Res. Part C Emerg. Technol., vol. 90, pp. 156–165, May 2018, doi: 10.1016/j.trc.2018.03.005.

  9. D. J. Fagnant and K. Kockelman, “Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations,” Transp. Res. Part Policy Pract., vol. 77, pp. 167–181, Jul. 2015, doi: 10.1016/j.tra.2015.04.003.

  10. J. Liu, K. Kockelman, and A. Nichols, “Anticipating the Emissions Impacts of Smoother Driving by Connected and Autonomous Vehicles, Using the MOVES Model,” in Smart Transport for Cities & Nations: The Rise of Self-Driving & Connected Vehicles, Austin, TX: The University of Texas at Austin, 2018. [Online]. Available: http://www.caee.utexas.edu/prof/kockelman/public_html/CAV_Book2018.pdf

  11. D. J. Fagnant and K. M. Kockelman, “The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios,” Transp. Res. Part C Emerg. Technol., vol. 40, pp. 1–13, Mar. 2014, doi: 10.1016/j.trc.2013.12.001.

  12. J. B. Greenblatt and S. Saxena, “Autonomous taxis could greatly reduce greenhouse-gas emissions of US light-duty vehicles,” Nat. Clim. Change, vol. 5, no. 9, pp. 860–863, Sep. 2015, doi: 10.1038/nclimate2685.

  13. C. D. Harper, C. T. Hendrickson, and C. Samaras, “Exploring the Economic, Environmental, and Travel Implications of Changes in Parking Choices due to Driverless Vehicles: An Agent-Based Simulation Approach,” J. Urban Plan. Dev., vol. 144, no. 4, p. 04018043, Dec. 2018, doi: 10.1061/(ASCE)UP.1943-5444.0000488.

  14. M. Lu, M. Taiebat, M. Xu, and S.-C. Hsu, “Multiagent Spatial Simulation of Autonomous Taxis for Urban Commute: Travel Economics and Environmental Impacts,” J. Urban Plan. Dev., vol. 144, no. 4, p. 04018033, Dec. 2018, doi: 10.1061/(ASCE)UP.1943-5444.0000469.

  15. [15] S. Rafael et al., “Autonomous vehicles opportunities for cities air quality,” Sci. Total Environ., vol. 712, p. 136546, Apr. 2020, doi: 10.1016/j.scitotenv.2020.136546.

  16. C. D. Harper, C. T. Hendrickson, S. Mangones, and C. Samaras, “Estimating potential increases in travel with autonomous vehicles for the non-driving, elderly and people with travel-restrictive medical conditions,” Transp. Res. Part C Emerg. Technol., vol. 72, pp. 1–9, Nov. 2016, doi: 10.1016/j.trc.2016.09.003.

  17. Ó. Silva, R. Cordera, E. González-González, and S. Nogués, “Environmental impacts of autonomous vehicles: A review of the scientific literature,” Sci. Total Environ., vol. 830, p. 154615, Jul. 2022, doi: 10.1016/j.scitotenv.2022.154615.

  18. Md. M. Rahman and J.-C. Thill, “Impacts of connected and autonomous vehicles on urban transportation and environment: A comprehensive review,” Sustain. Cities Soc., vol. 96, p. 104649, Sep. 2023, doi: 10.1016/j.scs.2023.104649.

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Note: Mobility COE research partners conducted this literature review in Spring of 2024 based on research available at the time. Unless otherwise noted, this content has not been updated to reflect newer research.

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