# Electricity Generation in LCA of Electric Vehicles: A Review
## 3 Literature Results
### Marginal Mixes Selection  
Among the selected articles, 17 use marginal mix when assessing EVs from a life cycle point of view. 12 are complete LCAs, 4 are WtW analysis, and the last one is a battery-LCA accounting also for the use phase (see Figure 2).  
The interesting aspect is that among these, seven studies use marginal mixes along with average mixes. Some of these consider alternatively one mix or the other as a matter of sensitivity analysis [22], others for testing results with the mix of higher GHG intensity [10], others use different mixes for different time horizon, due to difficulties in determining the marginal mix in future energy systems [22, 37].  
This aspect is crucial in demonstrating the detachment of the inventory from the methodological choice, as our work previously stated after highlighting a diffuse weakness in the definition of goal and scope phases.  
Since the use of marginal mixes is no longer linked to the system modelling choice as it was expected, the reasons to adopt marginal mixes have been investigated. Among the literature, these reasons are:  
* It is a way of testing the robustness of results [22];
* It is used as a sensitivity analysis [10, 25, 37, 69];
* It is considered conceptually more appropriate [22, 25, 28, 33, 46];
* It is the result of a consequential modelling choice [20, 42];
* It is suggested from previous studies [10];
* It is required from GHG protocol [47];
* Not specified [43, 49];
* EVs are considered the marginal consumer [9].  
The last point, made explicit by few studies, is actually the implicit assumption made by every study that uses a marginal mix when assessing EVs.  
Some studies state that using the marginal mix is more appropriate, but their explanations barely offer any insight as to why. Some of them read as follows:  
* _\"... small changes in the composition of the vehicle stock, replacing an ICE with an EV will represent an increase on the margin of electricity generation...\" [22]_
* _\"Marginal grid GHG intensity gives a more realistic measure of the GHG impact of the growth of electric vehicles than does average grid GHG intensity.\" [32, 70]_
* _\"... assessing a technology that entails a change in electricity consumption require MEF...\" [28]_
* _\"It better represents the effects of the EV adoption in the near future...\" [22]_
* _\"It is useful for short term forecasts of electricity demand...\" [25]_  
The widespread lack of specification and justification of the modelling choices made in published LCA studies has been already pointed out by [PERSON] et al. [71] and it is confirmed by our selection  
Figure 2: Number of LCAs sorted per study type and electricity mix selected.  
of articles. Only two studies actively describe their choice in the framework of the CLCA methodology, either adopting it [20], or refusing it [9].  
The study by [PERSON] et al. [20] explains the reason for assessing a consequential LCA. It estimates the impacts of transitioning to an electric fleet in the Copenhagen urban area from 2016 to 2030. The decision context is ascribed to the macro level decision support or case B according to the ILCD handbook (see Table 2) and the consequential approach is chosen [19]. Therefore the consequential EcoInvent dataset is selected and the \"medium-term\" marginal mix for Denmark is used. This is derived from EcoInvent database 3.1, and adapted with projections from Danish government [72] and goals set by the European Union [73].  
On the other hand, [PERSON] et al. [9] do not define their study as a CLCA, since their system boundary does not include \"processes to the extent of their expected change caused by a demand (affected processes) and do not solve multifunctionality through system expansion\". Instead, they adopt the approach by [PERSON] et al. [74]. According to the authors, CLCA is not strictly a methodology but an approach to \"deepen LCA\" taking into account market mechanisms, rather than a modelling principle with defined rules.