# Electricity Generation in LCA of Electric Vehicles: A Review
## 3 Spatial boundary.
### Results  
#### 3.4.1 Quantitative Results  
Current quantitative results have a wider variability than older ones: [PERSON] and [PERSON] were the first to pinpoint this aspect; they found that the results of the impact of electric vehicles--expressed in the most common indicator g CO\({}_{2}\)-eq/km--ranged from 95 to 240 g CO\({}_{2}\)-eq/km [100]. In the present review results are even more spread. By excluding results obtained from stylised states, whose role is exactly to denote an extreme state rather than a situation that is likely to happen, the aforementioned indicator spans from 27.5 g CO\({}_{2}\)-eq/km, obtained by [PERSON] et al. [40] presenting the WtW results of an EV in the Belgium environment (data from 2011), to 326 g CO\({}_{2}\)-eq/km, obtained by [PERSON] et al. [32] when assessing EV introduction in the UK market in 2015, using short term marginal electricity mix (see Figure 8). In Figure 8, the GHG of electric vehicles found in the literature are listed. Since results in the literature are expressed according to different functional units, in this review they have been transformed in the most common functional unit (g CO\({}_{2}\)-eq/kWh) when enough information allows for the conversion. If more than a single value is provided, bars represent the average value, while the error bars delimit the upper and lower value (see Figure S1 for the value with the associated electricity mix carbon intensity). For the numeric values see Table 1.  
The reason behind this spread is due to wider scopes and more detailed LCI and does not necessarily entail weaker studies. However, such is the variety of study types and scopes that the use of a framework and a rigid application of the ISO guidelines should be applied more than ever. A tentative framework for the main applications found in literature can be find in Table S2. Influence of Electricity Mixes in the Results  
Within this plethora of results, the focus of this literature review was to understand the role of a specific data, the electricity mix, because its influence has been highlighted by many authors [101] but never quantified. Among studies that present a level of detail fair enough to obtain homogeneous results (expressed in the most diffuse functional unit CO\({}_{2}\)-eq/km) and CO\({}_{2}\)-eq emissions for kWh, correlation has been investigated. These articles are represented in Figure S2 along with the aforementioned values. Some studies present results for more than one time-horizon and for more than one country, others present results according to different stylised states. Thus, in Figure S2 studies are labelled with their peculiarity: Country mix, time-horizon (or the year of the data of the electricity mix) or stylised state, depending whether the information was available. In Figure 9 a linear regression between g CO\({}_{2}\)-eq/km and g CO\({}_{2}\)-eq/kWh shows the correlation between the selection of the electricity mix and final result. The pool includes all the data available in the literature, and no normalisation has been applied to them. This means that in the graph are reported values from WtW analysis, and complete life cycle assessments, with different system boundaries and modelling approaches, with different vehicle lifetimes and energy consumptions per km (PHEV results have been excluded from the regression, since their use phase relies also on other fuels in addition to electricity). The purpose of this data collection is not to fully harmonise the studies and create comparable results, but rather aims at showing how influential the electricity mix selection is, despite the extent of the scopes available in the literature. Figure 8: Literature results normalised at the most common indicator \"g CO\({}_{2}\)-eq/km\({}^{\prime}\) (blue bars). If a study presents more than one value, the average value has been reported (blue bar) with the associated range. The coefficient of determination R\({}^{2}\) is 0.57.