## 2 Method
evaluated two different charging scenarios (an at home charger and an at home plus at workplace charger) and they determined the optimal allocation of workplace charging infrastructures. They found that the impact of the infrastructure is less than 3%, as can be seen in their figures [30]. [PERSON] et al. assessed the environmental impact of different charging systems, expressed in terms of kWh of delivered electricity. In the case of e-roads, the charging infrastructure and the roads are merged into a single system, whose role in the system level LCA of electric mobility still has to be fully investigated. The LCAs of e-roads are reported in Table 1. It follows the same structure as Figure 1 in order to show which aspects were considered by each author. The focus of the present paper has been to provide a full LCA of an e-road, in which the construction and maintenance of the road have been included as well as the WPT components. The results of this analysis are expected to be used in a future system level LCA analysis. Due to the collaboration with the test site in Susa (Italy), this study is able to provide primary data for the WPT components and construction technique. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper** & **Well-to-Wheel** & **Equipment** & **Infrastructure** \\ \hline [PERSON] et al. [9] & - & - & Construction and maintenance (WPT components excluded) \\ \hline [PERSON] et al. [10] & Electricity to recharge & & WPT components (off board battery chargers) \\  & included (at a fleet level) & & (installation and maintenance excluded) \\ \hline [PERSON] et al. [11] & Electricity to recharge & Reduced & WPT components (off board battery chargers) \\  & included (at a bus level) & battery size & (installation and maintenance excluded) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The papers that have conducted LCAs of dynamic wireless power transfer technology (DWPT) solutions. There are a variety of relationships between the three axes in Figure 1 and they can also have an effect from an environmental point of view. Each axis not only contributes to the system level LCA, but also significantly affects the performance of the other ones. The emissions due to the energy carrier directly affect the performance of the vehicle use phase, but, on the other hand, the EVs at a system level can also affect the performance of the grid. Many studies have dealt with the role played by the EV fleet in integrating renewable energy sources (RES) and in absorbing an intermittent supply due to a more flexible demand [12] and the option of smart charging [31]. The roads directly affect the performance of a vehicle through the rolling resistance [32]. This aspect was analyzed by [PERSON] et al. [17] who found that surface course materials have a great influence on rolling resistance and therefore affect the performance of a vehicle during the use phase. According to these authors, the reductions that can be obtained by applying a surface course with a lower rolling resistance may be substantially higher than those of the construction phase. On the other hand, vehicles play an important role in determining the performance and in particular, the maintenance operations of a road. [PERSON] et al. [33] highlighted the effect of autonomous driving (AD) and the consequent reduced wheel wander in the accelerated cracking of the road, and in the increased frequency of maintenance operations. [PERSON] et al. [9] analysed similar aspects pertaining to the increased load in the central lane due to the specificity of dynamic charging. As transmission efficiency is related directly to the alignment of the vehicle and the centre of the lane, it affects the maintenance operations. An e-road infrastructure can have various effects on electric mobility as a system.