# Electricity Generation in LCA of Electric Vehicles: A Review
## 3 Spatial boundary.
[43] and [PERSON] et al. [54] rely on regression results respectively from [PERSON] et al. [44] and [PERSON] et al. [22]. They do not specify the time horizon of their study, but they rely on regression data from 2011 and 2012 respectively. Bottom up approach  
As seen in the aforementioned reviews, there are plenty of system models, with various pro and cons; their level of complexity can range from simple dispatch curves to detailed simulation or optimisation models [46]. The common aspect is that they define how a system is bound to respond to a load profile, due to normative, operational or economic constraints (e.g., ramp rates, plant availability, emissions and transmission constraints, etc.). In order to gain a better understanding of these methods' application for environmental assessments, this study tried to highlight common characteristics influencing the limitations and range of application of the electricity mix obtained, rather than provide details for each method, as this was beside the scope of this analysis. Some relevant aspects of these methods for LCA application are the following:  
* They are suitable to model future power plant scenarios and large load changes [42];
* They have limited scalability [42];
* They could require large number of inputs and their complexity represents a significant hurdle for incorporation in LCA [85];
* The results depend heavily on the input data and on assumptions made by the user [85]. [PERSON] et al. [9] rely on a previous study by [PERSON] et al. [29], which uses a model to determine the optimal long term evolution of the generation set, with and without EV demand. They obtained no significant changes in the two scenarios, because of the limited impact of EV demand, found to be less than 5% of the total 2030 end-use demand, with an EV penetration rate of 25%. A second model has then been used to model the operation of the previously defined generation set, obtaining that all the electricity supplied to EVs will be produced using fossil fuels. [PERSON] and [PERSON] [33] use a simple dispatch model to assess the introduction of EV fleet in 2010; if 1% of Vehicle Miles Travelled is driven with EVs, the increase demand of electricity would be 0.1-0.3%. Their model predicts that marginal mix used to charge EV will be mainly provided with relatively inefficient NGCT plants. Marginal electricity in California is more carbon-intensive than gasoline, but in most cases, the improved efficiency of electric-drive trains outweighs the difference in fuel carbon intensity, and the vehicles considered here reduce GHG emissions compared to HEVs. Both [PERSON] et al. [37] and [PERSON] [47] rely on the marginal mix, determined by [PERSON] and [PERSON] [38], for the introduction of PHEV in the 2020 energy system across all the states of the U.S., under a certain penetration rate and charging condition, and apply it to their own scenarios that include BEVs introduction in the transport system, under unspecified penetration rates. [PERSON] et al. [42] employ the UCED model, a model used to optimise the operation of energy systems. They use data from EPA's NEEDs database to model the 2010 US energy system, while for the future US scenario (2018) they include retirement of power plants predicted by the EPA and a 3% wind penetration. A third scenario includes the power plant retirements predicted by EPA, and 20% wind penetration. [PERSON] et al. [48] use an agent-based electricity market equilibrium model to estimate variable electricity prices and power plant utilisation in a 2030 German scenario. Considerations  
Despite three exceptions, all the marginal mixes identified in the literature are found to be short term marginal mixes, according to the definition by [PERSON].