CO2 emission of vehicles and its influence on climate change is a widely discussed topic already for many years. New CO2 emission norms for vehicles have been defined based on the 2021 baseline: 15% reduction resulting in 0.09 kg/km for 2025, and 37.5% reduction resulting in 0.069 kg/km for 2030. Both norms are defined under the Worldwide harmonized Light-duty Test Procedure (WLTP) and apply to light-duty vehicles. In this study a new accurate measure is introduced to determine data related CO2 emissions of vehicle automation. The data related aspects in this study include: sensing components, the computing platform, disks inside the vehicle, wireless communication networks and data centers. These data related emissions are used to compare to the norms, which are based on the propulsion of the vehicle. A computational model is developed based on literature and a survey to obtain the instantaneous CO2 emission rates (kg CO2-e/h) of the data related aspects for varying scenarios. Scenario analysis is applied to define six different scenarios. The scenarios differ based on two varying sensing compositions and three varying energy grids. The two sensing compositions correspond to a Tesla Model S (SAE level 2) and Waymo's Chrysler Pacifica (SAE level 4). The different energy grids correspond to the 2019 Climate Act targets for 2030 and 2050 to reduce CO2 emission, and are referred to as fixed CO2 emission rates in kg CO2-e/Wh. Sensitivity analysis of the instantaneous CO2 emission rate is applied, to obtain the data related aspects that have the strongest influence on the resulting CO2 emission in each scenario. The instantaneous data related CO2 emission rates are translated to the driving cycle of the WLTP to obtain the spatial data related CO2 emission rates (kg CO2-e/km), which are used to compare to the defined propulsion-based CO2 norms of vehicles. From the sensitivity analysis, it is concluded that the energy intensity of wireless communication networks and the data transmission rate from vehicle to data center, are the two data related aspects that have the strongest influence on the instantaneous CO2 emission rate. From the spatial CO2 emission rate of each scenario, it is concluded that the energy grid also significantly affects whether the norms can be met. For high amounts of data transmission, the norms seem to be difficult to achieve in most scenarios and situations. Therefore, the energy consumption of wireless communication networks should be further optimized. Regarding the data transmission rate, further research should be obtained to gain more insights into the values of the data transmission rates dependent on the travel speed. Based on the assumptions made in this study, the CO2 emission from the data related aspects seem to exceed the propulsion-based CO2 norms of vehicles. Future work should investigate if data related aspects of vehicle automation need to be included in the CO2 norms of vehicles.