# Electricity Generation in LCA of Electric Vehicles: A Review
## 4 Conclusions and Recommendations  
The reason for this review stemmed from the nebulous indications that a practitioner approaching the subject, or a decision maker, can encounter reading available literature results. To understand these inconsistencies, all the steps of a life cycle analysis have been analysed, as follows (for a synoptic view see Table S1). Goal and modelling choice: The first hurdle is related to the goal definition, which is often missing or incomplete; the subsequent scope definition and in particular the modelling choice are not justified or inconsistent. Thus, the selection of supplier/technology (the most influential of which is the electricity mix) is not straightforward, and sometimes, in a paradoxical inversion, it becomes the parameter that defines the modelling choice. LCI and data quality: LCI is often inexplicit, thus making the identification of relevant information quite difficult, contravening the dogma of reproducibility of any LCA study. The datasets which most of the background processes are based on are outdated. Considering the influence of the electricity mix, the use of 15-year-old data is found to be an unwise practice. Time inconsistency: Despite the willingness of informing policy makers and thus influencing effect/developments in the long run, most of the scopes are focused on the here and now. Time and fleet prospective are investigated by only a handful of studies, while others apply the results from vehicle technology comparison and extend it to a wider level using a simple scaling factor, thus neglecting time and scale dependent aspects that cannot be detected from a single product analysis. Most of the studies do not even include any penetration scenario in the analysis, instead providing policy makers with a simple vehicle to vehicle comparison. Studies are aimed at informing policy makers but most of the analyses lack a political dimension: No clear time frame, no clear and reliable future scenarios, inconsistency between variables in the scenarios. In the issue of policy information, the selection of short term marginal electricity mixes has to be taken into account. Even though these mixes are useful for the modelling of short term effects of a rapid, albeit unlikely, introduction of EVs, in the authors' opinion they are not the correct instruments to inform policy makers, since they only offer a partial view: Focusing on short term effect is no more than a form of burden shifting in time. Literature results: Range of quantitative results is widening compared to the past. GHG emissions of electric vehicles range from 27.5 g CO\({}_{2}\)-eq/km [40] to 326 g CO\({}_{2}\)-eq/km [32] in the literature. This is due also to the wide range of scopes investigated by scientific papers. The selected papers cover a wide range of scopes: Various vehicle typologies are investigated using different system boundaries and different modelling approaches at various scales. Even with so many variables involved, the selection of the electricity mix has been found to be a key issue. A linear regression between g CO\({}_{2}\)-eq/km and the carbon intensity of the electricity mix considered in the analyses (g CO\({}_{2}\)-eq/kWh). The GHG of the electricity mix was found to explain almost 70% of the variability in the LCA results. Even if the electricity mix has always been seen as an influencing factor, this represent the first attempt to quantify its role. Recommendations from the literature: We found some articles warmly promoting the introduction of EVs as a way to reduce GHG in transport sector, some highly discouraging it and others recommending caution in their adoption. These results could be considered not conflicting if their goal and scope were more detailed and if they explained to the audience how the results should be used. The studies discouraging EVs are works focusing on the short term introduction of EVs (both in present and future scenarios), where the energy system is not able to adjust to the increased demand of electricity.