In order to understand the composition of a small ICEV and an EV, we found it necessary to create our inventory with more detail than can be readily obtained from present public inventories. This study thus contributes a transparent comparison of an ICEV and an EV to the publicly available literature. The material content of vehicle components and the processes used to produce them are estimated based on secondary data and well‐reasoned assumptions. With respect to prior EV LCAs, our study offers significantly more resolution regarding the manufacture of vehicle components, full transparency, consideration of a range of battery technologies, and includes a broader array of environmental impacts. In this way, it provides a basis upon which the next generation of LCA studies of generic vehicles can be built and a context within which proprietary LCA studies can be placed.

The primary objective of this LCA is to provide an appropriate comparison of an EV and an ICEV over their entire life cycle. A second objective is to provide a transparent inventory that can be used for assessing other vehicle and fuel options. Results are presented for a suite of ten relevant environmental impact categories, including GWP, toxicity impacts, and metal depletion. To address uncertainty and the difficulty of predicting aspects of technological development, results of a sensitivity analysis with respect to key parameters are presented.

A few studies consider battery and/or EV production explicitly, at varied levels of detail and transparency. Samaras and Meisterling ( 2008 ) focus on energy and GWP, providing an inventory based primarily on energy consumption within life cycle stages. Burnham and colleagues ( 2006 ) provide a stylized representation of vehicle production, relying on material content to estimate GWP criteria, air pollution, and energy use to give a basis for comparing EVs with other technologies within the Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model. Van den Bossche and colleagues ( 2006 ) and Matheys and colleagues (2008) perform a more complete assessment of traction batteries within the EU‐sponsored Sustainable Batteries (SUBAT) project. Their results are generally presented as EcoIndicator points and are based on confidential inventories. Daimler AG ( 2009 ) presents results from a comparative study of a hybrid and a conventional version of the same car from a full LCA perspective. This is likely the most complete life cycle inventory (LCI) of an EV; however, it is for a hybrid rather than a full‐battery EV. Zackrisson and colleagues ( 2010 ) provide a well‐documented inventory for comparison of two prospective production processes for lithium iron phosphate (LiFePO 4 ) next‐generation batteries. Notter and colleagues ( 2010 ) present one of the most transparent LCA studies of an EV based on a lithium manganese oxide (LiMn 2 O 4 ) battery. Their inventory focuses on battery production and places these results in the context of the EV life cycle. Majeau‐Bettez and colleagues ( 2011 ) provide another transparent inventory for production of nickel metal hydride (NiMH), lithium nickel cobalt manganese (LiNCM), and LiFePO 4 batteries designed to be adapted into a more complete study of the full EV life cycle.

In an earlier stage of this research, we reviewed life cycle assessment (LCA) studies of EVs (Hawkins et al. 2012 ). For conventional ICEVs, although the use phase accounts for the majority of global warming potential (GWP) impact, vehicle production is not insignificant, contributing on the order of 10% to the life cycle GWP. When considering a suite of environmental impacts of ICEVs, the need for a full LCA including manufacturing is well documented (Daimler AG 2005 , 2007a , 2007b b, 2008a a, 2008b b). Accounting for production impacts is even more important when comparing technologies with significantly different powertrains such as ICEVs and EVs. In particular, the production of electronic equipment requires a variety of materials, which poses a challenge for recycling and raises concerns about toxicity (Johnson et al. 2007).

EVs offer advantages in terms of powertrain efficiency, maintenance requirements, and zero tailpipe emissions, the last of which contributes to reducing urban air pollution relative to conventional internal combustion engine vehicles (ICEVs) (Wang and Santini 1993 ). This has led to a general perception of EVs as an environmentally benign technology. The reality is more complex, requiring a more complete account of impacts throughout the vehicle's life cycle. Consistent comparisons between emerging technologies such as EVs and their conventional counterparts are necessary to support policy development, sound research, and investment decisions.

Among available transport alternatives, electric vehicles (EVs) have reemerged as a strong candidate. The European Union (EU) and the United States, among others, have provided incentives, plans, and strategies, at different levels of ambition, for the introduction of EVs (European Commission 2010 ; Greater London Authority 2009 ; IEA 2009 ; U.S. Department of Energy 2011 ). One of the more ambitious targets is proposed by a consortium of the International Energy Agency (IEA) ( 2009 ) and eight countries (China, France, Germany, Japan, South Africa, Spain, Sweden, and the United States), which aims to reach a combined total of 20 million full and plug‐in hybrid EVs by 2020. Meanwhile, battery‐powered EVs are becoming an important component of automotive manufacturers’ strategies. Both Mercedes and Ford have clear ambitions in this area (Daimler AG 2010b ; Ford Motor Company 2007 ). The first generation of mass‐produced EVs has just entered the market (e.g., the Mitsubishi i‐MiEV, Nissan Leaf, Renault Kangoo, GM Volt, and Ford Electric Focus).

Our global society is dependent on road transport, and development trends project substantial growth in road transport over the coming decades. According to a study commissioned by the World Business Council for Sustainable Development ( 2004 ), light‐duty vehicle 1 ownership could increase from roughly 700 million to 2 billion over the period 2000–2050. Globally, light‐duty vehicles account for approximately 10% of global energy use and greenhouse gas (GHG) emissions (Solomon et al. 2007 ). These patterns forecast a dramatic increase in gasoline and diesel demands, with associated energy security concerns as well as implications for climate change and urban air quality.

Vehicle and battery lifetimes are assumed to be 150,000 km driven, which is well aligned with typical lifetime assumptions used by the automotive industry (Daimler AG 2008a ; Volkswagen AG 2008b ; Ford Motor Company 2011 ), although lifetimes found in the literature range between 150,000 and 300,000 km (Hawkins et al. 2012 ). Results for alternative lifetimes are presented in the sensitivity analysis section. End‐of‐life vehicle treatment is based on Ecoinvent v2.2 (Burnham et al. 2006 ). Battery treatment consists of dismantling and a cryogenic shattering process. The impacts associated with material recovery and disposal processes are allocated to the vehicle life cycle.

Use phase gasoline, diesel, and electricity inputs to vehicles are representative of average European conditions and import mixes. Results for natural gas and coal electricity use by the LiNCM EV are also provided, and additional electricity sources are represented in the sensitivity analysis. Brake wear is estimated based on work by Garg and colleagues ( 2000 ) and tire wear is based on work by Röder ( 2001 ). Maintenance and parts replacement is estimated based on available reports and our own assumptions documented in the supporting information on the Web.

To ensure the comparability of these test results, we checked that the energy at wheel was similar for the different vehicles, taking into account typical battery, engine, and transmission losses (see sheet 24 of supporting information S2 on the Web) (Åhman 2001 ; Karden et al. 2007 ; Larminie and Lowry 2003 ; Matheys et al. 2008 ; Tanaka et al. 2001 ; Van den Bossche et al. 2006 ) and found our use phase energy requirements imply energy delivered to the wheel to be 0.42 MJ/km for the ICEVs and 0.48 MJ/km for the EV. The slight increase in energy use is consistent with simulation results considering battery and structural mass differences between an ICEV and an EV (Shiau et al. 2009 ).

All use phase energy requirements were based on industry performance tests with the New European Driving Cycle, following the UNECE 101 regulation (UNECE 2005 ). These tests combine four elementary urban driving cycles and one extra‐urban driving cycle, with regenerative charging and energy losses during overnight charging included for EVs. Use phase energy requirements were assumed to be 0.623 megajoules/kilometer (MJ/km) 4 for the EV, 68.5 milliliter/kilometer (mL/km) 5 for the gasoline ICEV, and 53.5 mL/km for the diesel ICEV, based on the Nissan Leaf (Nissan 2010a ), the Mercedes A‐170, and an average of the Mercedes CDI A‐160 and A‐180 results (Daimler AG 2008a ). These vehicles were selected because of their comparable sizes, masses, and performance characteristics (0 to 100 kilometer/hour [km/h] acceleration between 11.5 and 13.5 seconds).

The GREET 2.7 vehicle cycle model (Burnham et al. 2006 ) served as a starting point for modeling the glider and ICEV powertrain. It was rescaled and adapted to match the characteristics of the Mercedes A‐Class (Daimler AG 2008a ), further subdivided to gain additional component‐level detail, and then supplemented by data from detailed industry inventories and reports. Notably, the engine composition is based on the Volkswagen A4 (Schweimer and Levin 2000 ). The EV powertrain configurations were modeled roughly after that of the Nissan Leaf EV (Nissan 2010b ). Battery inventories were adapted in full resolution from Majeau‐Bettez and colleagues ( 2011 ). Battery masses of 214 and 273 kilograms (kg) were selected for LiNCM and LiFePO 4 , respectively, so as to have equal charge capacities of 24 kilowatt‐hours (kWh). 3

We first established the inventory of a generic vehicle glider, which was devoid of any component specific to ICEVs or EVs. We then added the ICEV and EV powertrains. In the case of the EV, two battery types were investigated (i.e., LiFePO 4 and LiNCM). Table 1 provides a list of the different vehicles’ components, which are comprised of roughly 140 subcomponents. The detailed inventories and vehicle properties are provided in supporting information S2 on the Web.

Our inventory was compiled as a technical requirement and a stressor intensity matrix. The requirement matrix was built in a triangularized hierarchical manner, following Nakamura and colleagues ( 2008 ). Material and processing requirements were tracked in matrices for each vehicle component with columns representing subcomponents and rows representing production requirements based on original source data. A second matrix was then developed for each component to associate production requirements based on original source data to the closest matching Ecoinvent v2.2 processes (Ecoinvent Centre 2010 ). It was always possible to find a good match or an appropriate proxy such that we are confident that our results offer a decent scoping‐level life cycle representation of material and process requirements. Further details on system definitions, component matrices, correspondence matrices, variables, and calculations are provided in supporting information S2 available on the Journal's Web site.

The functional unit is 1 kilometer (km) 2 driven under European average conditions. Our LCA is attributional and process based. The foreground LCI was compiled using secondary data sources. We put a premium on transparency and thereby sacrificed the additional detail associated with confidential, manufacturer‐specific data. Detailed industry inventories and reports regarding materials, masses, and processes were used whenever these were publicly available, but we avoid the use of rolled‐up LCIs. Different modeling assumptions of vehicle composition, efficiency, lifetime, and fuel use are assessed in a sensitivity analysis. Ecoinvent v2.2 (Ecoinvent Centre 2010 ) was used as a background dataset, and impact assessment was performed using the ReCiPe characterization method for midpoint indicators, from the hierarchical perspective (Goedkoop et al. 2009 ). Sensitivity analysis was performed to test the effect of modeling assumptions regarding vehicle composition, efficiency, lifetime, and fuel use.

An appropriate comparison of an EV and a conventional ICEV requires that the system boundary be set to include all relevant differences between the two alternatives. Our scope includes vehicle production, use, and end of life together with all relevant supply chains. To ensure the comparability of the EVs and ICEVs, we established a common generic vehicle glider (vehicle without a powertrain; see glider components in Table 1 ) and customized powertrains for gasoline, diesel, and EVs. The assumption of a common glider platform for multiple drivetrains seems reasonable considering industry signals regarding forthcoming generations of vehicles (Daimler AG 2010a ). In the use phase we tracked electricity and fuel consumption, together with their full supply chains. Use phase energy requirements are based on the performance of the Mercedes A‐series ICEV and the Nissan Leaf EV, vehicles of comparable size, mass, and power. Performing the analysis in this way guaranteed the comparability of our case vehicles during the production, use, and disposal phases of their lives, thereby isolating the core differences. For the end of life, we model treatment and disposal of the vehicle and batteries.

LCA involves compiling an inventory of the environmentally relevant flows associated with all processes involved in the production, use, and end of life of a product and translating this inventory into impacts of interest (Curran 1996 ; Guinée et al. 2002). The goal of this study is to provide a scoping‐level comparative LCA of a conventional ICEV and a first‐generation battery EV representative of a typical small European car, including all relevant processes and a cross section of relevant impacts.

Results

Overview Figure 1 compares six transportation technologies in terms of ten life cycle environmental impact categories. Detailed numerical results are presented in section I of supporting information S1 on the Web. The cases represent an LiNCM or LiFePO 4 EV powered by European average electricity (Euro), an LiNCM EV powered by either natural gas (NG) or coal (C) electricity, and an ICEV powered by either gasoline (G) or diesel (D). Impacts are broken down in terms of life cycle stages and normalized to the greatest impact. Differences between the impacts of the two EV options arise solely from differences in the production of the batteries. Figure 1 Open in figure viewer PowerPoint Normalized impacts of vehicle production. Results for each impact category have been normalized to the largest total impact. Global warming (GWP), terrestrial acidification (TAP), particulate matter formation (PMFP), photochemical oxidation formation (POFP), human toxicity (HTP), freshwater eco‐toxicity (FETP), terrestrial eco‐toxicity (TETP), freshwater eutrophication (FEP), mineral resource depletion (MDP), fossil resource depletion (FDP), internal combustion engine vehicle (ICEV), electric vehicle (EV), lithium iron phosphate (LiFePO 4 ), lithium nickel cobalt manganese (LiNCM), coal (C), natural gas (NG), European electricity mix (Euro). For all scenarios, human toxicity potential (HTP), mineral depletion potential (MDP), and freshwater eco‐toxicity potential (FETP) are caused primarily by the supply chains involved in the production of the vehicles. On the other hand, the use phase dominates for GWP, terrestrial eco‐toxicity potential (TETP), and fossil depletion potential (FDP). End‐of‐life treatment adds only a marginal contribution across all impact categories. The EV production phase is more environmentally intensive than that of ICEVs for all impact categories with the exception of terrestrial acidification potential (TAP). The supply chains involved in the production of electric powertrains and traction batteries add significantly to the environmental impacts of vehicle production. For some environmental impact categories, lower emissions during the use phase compensate for the additional burden caused during the production phase of EVs, depending on the electricity mix. However, this is not always the case.

Global Warming Potential For all scenarios analyzed, the use phase is responsible for the majority of the GWP impact, either directly through fuel combustion or indirectly during electricity production. When powered by average European electricity, EVs are found to reduce GWP by 20% to 24% compared to gasoline ICEVs and by 10% to 14% relative to diesel ICEVs under the base case assumption of a 150,000 km vehicle lifetime. When powered by electricity from natural gas, we estimate LiNCM EVs offer a reduction in GHG emissions of 12% compared to gasoline ICEVs, and break even with diesel ICEVs. EVs powered by coal electricity are expected to cause an increase in GWP of 17% to 27% compared with diesel and gasoline ICEVs. In contrast with ICEVs, almost half of an EV's life cycle GWP is associated with its production. We estimate the GWP from EV production to be 87 to 95 grams carbon dioxide equivalent per kilometer (g CO 2 ‐eq/km), which is roughly twice the 43 g CO 2 ‐eq/km associated with ICEV production. Battery production contributes 35% to 41% of the EV production phase GWP, whereas the electric engine contributes 7% to 8%. Other powertrain components, notably inverters and the passive battery cooling system with their high aluminum content, contribute 16% to 18% of the embodied GWP of EVs. Under the assumption of identical life expectancies, LiNCM EVs cause slightly less GWP impact than LiFePO 4 EVs due to the greater energy density of their batteries. With the European electricity mix, the LiNCM and LiFePO 4 vehicles present life cycle GWP intensities of 197 and 206 g CO 2 ‐eq/km, respectively. Because production impacts are more significant for EVs than conventional vehicles, assuming a vehicle lifetime of 200,000 km exaggerates the GWP benefits of EVs to 27% to 29% relative to gasoline vehicles or 17% to 20% relative to diesel because production‐related impacts are distributed across the longer lifetime. An assumption of 100,000 km decreases the benefit of EVs to 9% to 14% with respect to gasoline vehicles and results in impacts indistinguishable from those of a diesel vehicle. Although not discussed in detail due to space constraints, the sensitivity to lifetime assumption follows a similar pattern for other impact categories as well, with impacts associated with vehicle production being effected more significantly than those more closely associated with the use phase.

Other Potential Impacts The TAP impacts caused by the production phase of the EVs and ICEVs are similar, but their underlying causes differ. With structural path analysis (Defourny and Thorbecke 1984; Treloar 1997; Peters and Hertwich 2006), the acidification impact of EV production can be traced back to the nickel, copper, and, to a lesser extent, aluminum requirements of the battery and the motor (see section IV of supporting information S1 on the Web). On the other hand, more than 70% of the production phase TAP of the ICEV is caused by the production of platinum‐group metals for the exhaust catalyst. It should be noted that there is significant variability between the LCIs of primary platinum‐group metals (Classen et al. 2009). The acidifying emissions reported for Russian and South African production processes differ by more than an order of magnitude. Our study uses a European consumption mix of these two sources and secondary platinum‐group metals. As more than 70% of the life cycle TAP is caused by sulfur dioxide (SO 2 ) emissions, the sulfur intensity of the use phase fuel largely determines the relative performances of the different transportation technologies in terms of TAP. Because of its share of hard coal and lignite combustion, the use of average European electricity for EV transportation does not lead to significant improvements relative to ICEVs. Significant benefits may only be expected for EVs using electricity sources with sulfur intensities comparable to or lower than that of natural gas. Particulate matter formation potential (PMFP) follows a trend similar to that of TAP. Structural path analysis identifies the same metal supply chains—nickel, copper, and aluminum—as the dominant sources of emissions from the production phase, and SO 2 emissions are the leading cause of PMFP for all life cycle transportation scenarios (35% to 46% of impact). EVs using natural gas electricity perform best with regard to PMFP due to the relative purity of natural gas and the completeness of its combustion. The use of average European or coal‐based electricity leads to a potential increase in PMFP relative to ICEVs, though this impact is spatially and to some extent temporally distanced from the use phase. The photochemical oxidation formation potential (POFP), or smog formation potential, is one of the environmental impact categories for which EVs perform best, with European and natural gas electricity mixes allowing for reductions of 22% to 33% relative to ICEVs. For all scenarios, releases of nitrogen oxides are the predominant cause of impact. These are mostly caused by combustion activities, but also from blasting in mining activities. Human toxicity potential (HTP) stands out as a potentially significant category for problem‐shifting associated with a shift from ICEVs to EVs. We estimate that HTP increases for EVs relative to ICEVs both in the production and the use phase. The different EV options have 180% to 290% greater HTP impacts compared to the ICEV alternatives. The additional production phase toxicity impacts of EVs stem mostly from additional copper requirements and, in the case of NCM EVs, nickel requirements. Toxic emissions from the production chain of these metals mostly occur in the disposal of the sulfidic mine tailings, which accounts for roughly 75% of the HTP from the production phase. The rest of the impact is caused predominantly by the disposal of spoils from lignite and coal mining, which are important sources of energy throughout the life cycle of the EV. Freshwater ecotoxicity potential (FETP) and eutrophication potential (FEP) impacts demonstrate patterns similar to HTP. In fact, these three impact categories are dominated by the same processes (i.e., disposal of sulfidic tailings and spoils from coal and lignite mining). For all three impacts, the use of electricity from natural gas yields substantial benefits relative to the other electricity mixes. Terrestrial ecotoxicity potential (TETP) is dominated by the use phase emissions of zinc from tire wear (approximately 40%), and copper and titanium from brake wear (25%). Given the uncertainty of the characterization of this impact (Huijbregts et al. 2000; Lenzen 2006), there is no clear difference among the vehicle options considered. Metal depletion potential (MDP) is a commonly cited concern with EVs (e.g., Gaines and Nelson 2009, 2010), due to their reliance on metals of differing scarcities. This analysis suggests that the MDP of EVs is roughly three times that of ICEVs. However, as this investigation was not specifically focused on MDP, results are more uncertain than for other impact categories. Depending on the component and the metal, our inventory either relies on primary sources or on average consumption mixes of primary and secondary sources (see sheets 6‐19 of supporting information S2 on the Web). It should be noted that the ReCiPe method does not include MDP characterization factors for lithium. Fossil depletion potential (FDP) may be decreased by 25% to 36% with electric transportation relying on average European electricity. EVs with natural gas or coal electricity, however, do not lead to significant reductions.