On data-induced CO2 emissions of vehicle automation: An overlooked emission source

Abstract

CO${}_2$ emission of vehicles and its influence on climate change is a widely discussed topic already for many years. New CO${}_2$ emission norms for vehicles have been introduced based on the propulsion of the vehicle, to reduce future CO${}_2$ emissions. Automated vehicles (AVs) have potential in reducing emissions by optimizing routes and speed profiles. However, they also generate extra emissions due to large data involved. Whether the norms can be met with these extra data-induced emissions of AVs remains an open question. This paper provides an approach to determine the CO${}_2$ emissions of these data related aspects. The approach dissects data-induced emissions stemming from energy consumption of the sensing components, the computing platform, disks inside the vehicle, wireless communication networks and data centers. We apply the approach to estimate CO${}_2$ emissions for varying scenarios of technology composition and energy mix. Sensitivity analysis shows that the energy intensity of wireless communication networks and the data transmission rate from vehicle to data center have the strongest influence on the resulting CO${}_2$ emissions. The energy mix also significantly affects whether the norms can be met. For high amounts of data transmission, compliance with the norms seem to be difficult in most scenarios. We recommend that the energy consumption of wireless communication networks and data transmission from vehicle to data center should be further optimized. Future work should focus on empirical evidence to validate/falsify the key assumptions in this paper, which will lead to a more accurate estimate of automation-induced emissions.