# Electricity Generation in LCA of Electric Vehicles: A Review
## 3 Spatial boundary.
#### 3.2.2. Attributed System Modelling
found the use of state boundaries versus NERC region boundaries leads to estimates that differ by as much as 120% for the same location (using average emission factors) [46]. Choosing the adequate regions for the accounting of electricity emissions is a trade-off between including spatial heterogeneity and modelling cross-boundary electricity flows. This is naturally linked to the problem of defining the consumption mix of a country, rather than using its production mix, as highlighted in [37,88]. In EcoInvent, the supply is created as the sum of the production and the import, which is modelled as a percentage of the production of the importing country. However, this simplification can represent a problem in countries where the trades are a significant percentage of the domestic demand, such as Switzerland, where the traded electricity volume is about 85% of the domestic demand [89]. Modelling electricity trades is complicated: A net importer could export part of the electricity, and since it is unknown which electrons have been produced by which plant, this generates a recursive problem. [PERSON] et al. [89] present a model to deal with this aspect: The electricity mix of the domestic supply is modelled according to the integration of the electricity declarations of all electric utilities in a country. The declaration includes a differentiation according to technology and whether or not the electricity is produced domestically or is imported. It usually includes a share of \"unknown\" electricity, which in that study is represented by the ENTSO-E electricity mix. Figure 4: Climate change of 1 kWh of different electricity mixes provided at EcoInvent database. Characterisation model: IPCC 2007; Characterisation factor: GWP 100a. Time period: 2004 for EcoInvent 2.2 series, 2014 for EcoInvent 3.4 series. Temporal Boundary  
Attributional studies rarely present the time horizon of their analysis (see Figure 5). The lack of time horizon in the scope definition has been already highlighted in [PERSON] et al. [2]. It makes it difficult to determine the temporal validity of both results and conclusions and, as far as electricity generation is concerned, also to determine whether the dataset used is suitable to the goal and scope. A relevant exception is represented by the article by [PERSON] et al. [23]. In their parametric study on environmental performance of current and future mid-size passenger vehicles, every input varies with time. Vehicle mass, aerodynamic drag, tire rolling, performances of powertrain components are expected to change due to increasing know how and to compelling emission standards. However, despite the clear time scope (2012 for current fleet), the electricity mix used--from EcoInvent 2.2--is still based on data from 2004. Despite the temporal granularity discussed in the paragraph \"Short Term Marginal Mix--Calculation Method\" (in Section 3.2.1), even temporal aggregated data can present some inconvenient. The composition of the country mix can differ a lot from one year to another, especially when a relevant share of the country generation capacity relies on renewable energy sources (RES). [PERSON] and [PERSON] [27] detailed how GHG intensity of Portuguese mix reduced from 560 to 390 g CO\({}_{2}\)-eq/kWh from 2009 to 2010, due to unusual meteorological condition in 2010: 1374 mm of rain compared to 950 mm of the previous year [90]. [PERSON] et al. [59] reported that GHG of French mix varied from one year to another, due to economic and meteorological hazards, thus suggesting the need to define a reference year to mitigate these variations and to convey more representative average impacts. They also highlight that using EcoInvent v3.1 data for low voltage supply mix in order to evaluate the impact of the average mix for year 2014 leads to an overestimation of the carbon footprint by 25%.