## 3 Results and Discussion  
### Input Values Definition for the Implemented Scenarios  
In the following sections the specific input values used in the model for this study were described. BEV and PHEV fleets forecast over analysis timeframe:  
An historical dataset was needed as a baseline of auxiliary information for the development of the transfer formulas for dataset downscaling; the main sources for data collection are shown in Table 2, with a reference to the area considered. Table 3 highlights the sources used to collect the forecast data for xEV fleets growth. Since only [13; 26; 39] reported separately the contribution of BEV and PHEV, an average value of the allocation suggested by those papers has been used to divide the other forecast data between BEV and PHEV contributions. A total of 23 scenarios, from nine different studies was thus selected. Then these were compared to the maximum assumed turnover for xEV, as described in Section 2.2.1 and only the ones that were proposing forecasts lower than the maximum assumed turnover for xEV were used in the model. After this step, 11 scenarios from seven sources remained; Figure 8 shows the results in terms of xEV shares over the total circulating fleet, for Florence municipality. It also highlights the portion of scenarios range that overcame the selecting threshold. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **EU-28** & **IT** & **BE** & **FI** & **BXL** & **Data** \\ \hline Centro Studi Continental A [35] & - & - & - & X & - & \(\frac{EV_{FI}}{EV_{T}}\) \\ Comune di Firenze [36] & - & - & - & X & - & TOT \\ EAFO [26] & X & X & X & - & - & NR\({}_{\text{EV}}\) \\ ECOSCORE B [37] & - & - & X & - & X & TOT, EV \\ ENEL e-mobility [14] & - & X & - & - & - & EV \\ Eurostat [19] & X & X & X & - & - & N\({}_{\text{TOT}}\), N\({}_{\text{EV}}\) \\ IBSA [38] & - & - & X & & X & TOT, EV \\ UNRAE [39] & - & X & - & - & - & NR\({}_{\text{TOT}}\), NR\({}_{\text{EV}}\) \\ \hline \hline \end{tabular}  
* A: Author’s elaboration on ACI data; B: owned by Vito.be.  
\end{table} 
Table 2: Data sources for historical baseline characterization (FI = Florence, BXL = Bruxelles). Finally, Table 4 highlights the forecasts for BEV and PHEV circulating fleets during the considered time period, used in the model as inputs for this research. Table 5 shows the same data but this time in terms of shares of the total circulating fleets in the two selected areas. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Area**} & \multirow{2}{*}{**Scenario**} & \multicolumn{3}{c}{**BEV**} & \multicolumn{3}{c}{**PHEV**} \\ \cline{3-8}  & & **2020** & **2025** & **2030** & **2020** & **2025** & **2030** \\ \hline \multirow{3}{*}{Florence} & Low & 73 & 441 & 2062 & 90 & 517 & 2544 \\  & Medium & 202 & 1414 & 6344 & 584 & 2980 & 9886 \\  & High & 672 & 3968 & 14,030 & 1141 & 6016 & 21,532 \\ \hline \multirow{3}{*}{Bruxelles} & Low & 663 & 3482 & 10,334 & 984 & 3482 & 12,168 \\  & Medium & 1283 & 6022 & 18,432 & 2655 & 10,266 & 25,451 \\ \cline{1-1}  & High & 2689 & 10,078 & 35,836 & 5152 & 17,591 & 45,337 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Battery electric vehicle (BEV) and plug-in hybrid electric vehicle (PHEV) fleet forecasts for the area of Florence and Bruxelles municipalities—absolute figures. Figure 8: xEV fleet growth forecast—total range of collected data values and highlight of the selected part (light blue), under threshold limit (red line).