## Appendix A Monte Carlo Analysis of the Impact Assessment Results  
Uncertainty in LCA is pervasive, and it is widely acknowledged that uncertainty analyses should be carried out in LCA to grant a more rigorous status to the conclusions of a study [55]. The Monte Carlo technique has here been used to measure the effects that the data and process uncertainties have on the LCA results [56]. Monte Carlo analysis is a numerical way of processing uncertainty data and establishing an uncertainty range in the calculated results [47]. In order to use Monte Carlo analysis to estimate the overall uncertainty of a life cycle system, a sufficient number of iterations of the model must be run to converge on a mean value [56]. In order to ensure an adequate convergence with real systems [56], 10,000 iterations were used for this study.  
In this study, NMC111-BEV and ZEBRA-BEV were first assessed in a stand-alone analysis (Figures S5-S7) and then a comparative analysis of the two BEV alternative systems was conducted.  
Figure A1 compares the LCA impacts of the two versions of the electric LDCV, i.e., equipped with two NMC battery packs (NMC-BEV) and two ZEBRA battery packs (ZEBRA-BEV). The impacts were normalised to the highest score in each category. The vertical line shows the uncertainty range for each vehicle and the impact category. The range expresses 95% confidence interval [47]. Considering all the unit processes, about 65% of the values contain uncertainty data. About 90-100% of the Monte Carlo runs have shown that NMC-BEV has lower impacts than ZEBRA-BEV for all the categories (Tables S30-S32), i.e., according to the guidelines reported in [47], the impact of NMC-BEV can be considered significantly lower than that of ZEBRA-BEV.  
The standard error of the mean (SEM) indicates how much the mean value is changed by the previous Monte Carlo run. The lower the SEM, the more reliable the results are (a SEM below 0.01 is acceptable) [47]. In this study, SEM is acceptable (\(\leq\)0.01) for all the vehicle configurations and all the impact categories, except for Marine Aquatic Ectotoxicity, for which SEM ranges from 0.82 \(\div\) 1.67 were obtained.  
A second scenario, in which three NMC battery packs are mounted onto the vehicle, has been considered to increase the driving range of the electric LDV. In this case, the impacts of the NMC-BEV increase and, when the Monte Carlo analysis is conducted, the difference is not significant for only four effects: ADP, GWP, POP, and AP. This is shown in Figure 14, where the brighter colours represent a significant difference between the two vehicles, while the lighter colours represent no significant differences.  
The ZEBRA-BEV vehicle has higher ozone layer depletion, eutrophication, and cumulative energy demand results than NMC-BEV in 100% of the runs.  
The deterministic results show that NMC-BEV has the lowest GWP result of the three alternatives. However, according to the Monte Carlo analysis, NMC-BEV is lower than ZEBRA-BEV, for 83% of the runs, but there is a 17% likelihood of obtaining the opposite result.  
A comparative uncertainty analysis was also carried out between the ZEBRA-BEV vehicle and the second scenario of the NMC-BEV vehicle by varying the production location of the NMC battery, that is, assuming it is produced in Europe using the European average electricity mix instead of in China. Figure 15 shows that, in this case, the GWP of the NMC-BEV is lower than ZEBRA-BEV for 100% of the runs, while the difference remains insignificant for Abiotic depletion, Acidification, and Photochemical Oxidation (further details are available in the supporting material section).