## 2 Model and Methodology
### Model Structure  
#### 2.2.1 Data Collection  
This section describes all the necessary inputs, as well as the data collection methods, while Section 3.1 reports the specific values of the actual data used during model implementation, together with a description of the data sources. The data collected can be classified under five macro-categories, spanning from BEV and PHEV fleets size forecast, to their average range and consumption; from parking spaces availability to users' behaviors and finally to CS characteristics and costs. BEV and PHEV fleets forecast over the analysis timeframe:  
As already specified in the introduction, only the M1 category light passenger car sector would be considered in this paper. According to [24], M1 category vehicles are designed and constructed for the carriage of passengers, comprising no more than eight seats in addition to the driver's seat. A literature research was then carried out for forecasts on BEV and PHEV car fleets size, over the 2020-2030 period, with a specific focus in years 2020, 2025 and 2030 and referring to the two evaluated areas. Unfortunately, no municipality-level results were publicly available, thus the research was further extended to forecasts evaluating single EU countries, as well as the EU-28 region as a whole. Following this decision, a methodology to scale down National and EU-28-level data back to the context of a municipality was then developed. In order to check dataset soundness for model's scopes, the methodology also compares the collected forecasts to the existing market conditions, using the EU-28 global passenger car fleet turnover rate as a threshold for xEV fleet forecast growth rate. Being the xEV market relatively new and still evolving, it presents different penetration levels across EU countries; this situation can be related to local factors such as the current development of the charging infrastructures and the existence (or the lack) of active support schemes and subsidies. On the other hand, the total passenger vehicle market--mainly composed by ICEV--is well-developed and with a relatively stable trend in terms of number of circulating vehicles. Following the previous considerations, the methodology has been developed under two main assumptions:  
* Within 2050, the xEV distribution across EU-28 countries will follow that of total passenger fleet. * The forecasts downscaling is realized sequentially: from EU-28 level to national level, then from the national level to municipality level. As the first step of the process, a baseline of auxiliary information is defined for each of the three geographical levels, to be used by the transfer formulas during dataset downscaling. It is composed by an historical dataset, evaluated over the 2012-2017 period and composed by four specific entries:  
* Total circulating fleet (ICEV, BEV and PHEV): TOT;
* Total circulating xEV (BEV and PHEV): xEV;
* Total new vehicles registrations (ICEV, BEV and PHEV): NR\({}_{\text{TOT}}\);
* Total new xEV registrations: NR\({}_{\text{EV}}\). The last two variables, namely new vehicles and new xEV registrations, were selected as control parameter to check the soundness of the total circulating fleet and total circulating xEV data. Moreover, the auxiliary baseline comprises a forecast value of 2050 total circulating fleet (TOT), obtained by literature research. After this preparation phase, the downscaling process of the xEV fleet forecasts was performed with the following procedure (the 'input' subscript refers to the geographical area analyzed by the forecast; the 'output' subscript refers to the geographical area to which the forecast was being scaled to):  
* At the starting year, i.e., 2017, the historical value of \(\left(\frac{xEV_{output}}{xEV_{input}}\right)_{2017}\), which expresses the ratio between the xEV fleet circulating in the two considered areas (i.e., EU-28 and Italy), was assumed.