A review of fleet-based life-cycle approaches focusing on energy and environmental impacts of vehicles

A review of fleet-based life-cycle approaches focusing on energy and environmental impacts of vehicles

Renewable and Sustainable Energy Reviews 79 (2017) 935–945 Contents lists available at ScienceDirect Renewable and Sustainable Energy Reviews journa...

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Renewable and Sustainable Energy Reviews 79 (2017) 935–945

Contents lists available at ScienceDirect

Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

A review of fleet-based life-cycle approaches focusing on energy and environmental impacts of vehicles

MARK



Rita Garcia , Fausto Freire ADAI/LAETA, Department of Mechanical Engineering, University of Coimbra, Polo II Campus, R. Luís Reis Santos, 3030-788 Coimbra, Portugal

A R T I C L E I N F O

A BS T RAC T

Keywords: Electric vehicles Energy consumption Greenhouse gas emissions (GHG) Life-cycle assessment (LCA) Vehicle fleets

Alternative vehicle propulsion technologies are being promoted to reduce energy consumption and environmental impacts in transportation. Life-cycle assessment (LCA) is often used to assess and compare the environmental impacts of these technologies, but, in its traditional form, it lacks the ability to capture the transient effects as new vehicles displace older vehicles in the fleet. Fleet-based life-cycle (LC) approaches – which combine the LCA methodology with fleet models that describe the stocks and flows associated with a class of products over time – have been proposed to circumvent this issue. This article presents a critical review of the literature addressing fleet-based LC approaches by providing an overview of modeling approaches, its main applications, and an analysis of the key aspects underlying environmental and energy impacts of vehicle fleets (focusing on electrification pathways). Fleet-based LC approaches have been applied with different purposes (e.g., to model recycling processes, to assess trade-offs between manufacturing and use impacts; to optimize product service life). The issue of evaluating the impacts of introducing alternative technologies is appropriately addressed by a fleet-based LC approach, because it allows for the capture of displacement effects, technological improvements over time, and changes in background processes. Several studies have used such an approach to assess scenarios of evolution of the light-duty fleet. The main key aspects are: fleet penetration rate, electricity source, fuel economy improvements, and vehicle weight reduction. Emission reductions were found to be very dependent on the underlying assumptions. Reducing fuel consumption is one of the key ways to reduce fleet GHG emissions, but it needs to be combined with other measures, such as high penetration of advanced technologies, to bring about significant reductions. The electricity generation source has also a large impact on the fleet GHG emissions and increasing renewable energy penetration is key to reduce overall emissions.

1. Introduction Alternative vehicle propulsion technologies (e.g. electric vehicles) are being promoted as a way of reducing energy consumption and environmental impacts in the transportation sector. There is a growing consensus in the scientific literature that the assessment of the environmental impacts of these technologies should be performed considering a life-cycle (LC) perspective [1–4]. This avoids the assumption, for example, that technologies without tailpipe emissions, such as battery electric vehicles (BEVs), have no environmental impacts. In fact, if upstream processes such as fossil-based electricity generation are included in the assessment, BEVs can have higher LC impacts than conventional technologies (i.e. internal combustion engine vehicles [ICEVs]) [3,5]. Moreover, excluding vehicle production and end-of-life of the assessment disregards important sources of impacts [3,5].



Corresponding author. E-mail address: [email protected] (R. Garcia).

http://dx.doi.org/10.1016/j.rser.2017.05.145 Received 26 August 2016; Received in revised form 21 April 2017; Accepted 19 May 2017 1364-0321/ © 2017 Elsevier Ltd. All rights reserved.

The life-cycle assessment (LCA) methodology has been used to assess and compare the environmental impacts of vehicle technologies, but most studies are based on a static analysis of single vehicles [e.g., 1,5–13] not addressing advances in material processing, technology development, and changes in background processes [13]. Another limitation is that transient effects (arising from the fact that the substitution of the older by the newer technology in a fleet does not occur immediately) are not accounted for [14], neither is the scale and timing of adoption of the new technology [15,16], but both should be considered when making strategical environmental decisions [14,15]. The lack of dynamic aspects in LCAs has been pointed out as one main limitation for some systems and environmental impacts. Research on the incorporation of dynamic aspects in LCA has been focused both on the impact assessment and system modeling phases [e.g., 17–20], but the temporal distribution of emissions is not generally taken into account. In most LCAs, emissions occurring during the whole life cycle are aggregated

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mental and energy impacts of vehicle fleets; and (iii) to review the fleetwide impact reduction potential of electric vehicles.

into a single emission and their potential impacts on the environment are assessed as if they occurred at the same time [21]. Recently, this issue has garnered much attention, namely regarding time-dependent greenhouse gas (GHG) emissions impact [e.g., 22,23], and a number of approaches to account for emission timing have been presented [18,19,24–26]. The temporal distribution of emissions is also important when assessing future developments (and related impacts), namely regarding changes in boundary conditions over time, such as legal regulations, technical innovation, changes in technology or changes in used materials or upstream processes [27]. The inclusion of these dynamic aspects in system modeling and life cycle inventory has been investigated [e.g., 14,16,17,27–30]. However, the assessment of new technologies requires also an approach that captures the dynamics (addressing scale and timing) of new products replacing older ones in a fleet, together with indirect impacts on other systems. Field et al. developed a fleet-based approach to LCA that circumvents these issues by considering the “dynamic set of products in use” (i.e. the product fleet), rather than a single-product, with explicit consideration of time [14]. The number of studies using a fleet-based LC approach has been growing (Fig. 1). This article presents a review of the literature addressing fleet-based LC approaches for the assessment of the impacts of the adoption of alternative vehicle technologies or materials. An online search was performed in Web of Knowledge and other scientific search engines (Science Direct, Springer Link, and Wiley Online Library). Additionally, references in the literature identified were used to locate new literature. Peer-reviewed publications and scientific reports written in English were considered. The keywords used for the literature search included a combination of synonyms of the terms “life cycle assessment” (“LCA,” “life cycle analysis,” “life cycle”), and “fleet analysis” (“fleet,” “fleet model,” “product fleet,” “vehicle fleet”). Studies that simultaneously considered a life-cycle perspective (here understood in a broad sense, i.e. which assessed more than just use phase), and a fleet-based approach (i.e. which considered the dynamic set of units in service as the functional unit) were selected for detailed analysis. The reviewed literature covers a total of 29 articles and reports published in 2000–2015 (Table A1 in Appendix A). Firstly, the methodological grounds are presented, followed by a review of the main applications of the fleet-based LC approaches. We then turn to the literature that focuses on the assessment of scenarios of evolution of the transportation system, in particular regarding the introduction of electric vehicles in a light-duty fleet as an increasingly adopted measure to reduce environmental and energy impacts. The main goals are: (i) to discuss the suitability of fleet-based LC approaches to address impacts of introducing alternative technologies in a vehicle fleet; (ii) to analyze the key aspects underlying environ-

2. Fleet-based life-cycle approaches 2.1. Modeling background Life-cycle assessment (LCA) is a methodology to systematically assess the environmental impacts directly and indirectly associated with a product system throughout its entire life cycle, from the ‘cradle’ (i.e. raw material extraction), to the ‘grave’ (i.e. final waste disposal). Because of its holistic approach, LCA is able to shed light on potential trade-offs between different categories of environmental impacts and between different stages of the life cycle. LCA is one of the key tools of Industrial Ecology [34] and has been widely used to support policies and performance-based regulation (e.g., biofuel policies in the EU and EUA), as well as consumer-based information, such as carbon footprint standards (e.g., PAS 2050, GHG Protocol Product Standard, ISO 14067:2013) and environmental product declarations (e.g., ISO 14025:2006, EN 15804:2012). The LCA methodology is well-described elsewhere [e.g., 35–37]. LCA is traditionally a product-centered approach – most LCAs estimate the LC environmental impacts of a single product and assume that the LC model parameters are constant. This means that the interactions between elements of the product system are excluded and only a snapshot of the system behavior is presented – the model may be valid only over a short period of time [16]. Alternatively, Field et al. [14] proposed a new fleet-based approach that circumvents these issues by considering the product fleet (i.e. the dynamic set of products in use, including the transient effects occurring when older products are substituted by newer ones in the fleet) rather than a single-product (Fig. 2). A fleet-based LC approach combines LCA with a fleet model that describes the stocks and flows associated with a class of products over time. It follows a different approach to the functional unit and system boundary: rather than a single unit or a functional unit, it considers “the set of units in service” (i.e. the product stock or fleet). Moreover,

Fig. 1. Histogram of the number of studies published from 2000 to 2015 which apply a fleet-based life-cycle approach and are reviewed in this article. “Material substitution” refers to studies that address the replacement of heavier for lighter materials in vehicles (with the exception of Moura and Viegas [31], which addresses part substitution); “scenarios” refers to studies that address fleet-wide impacts of different scenarios of evolution of the transportation system; “others” refers to the work of Kim et al. [32] and Yokota et al. [33].

Fig. 2. Comparison between single-product and fleet-based LC approaches for vehicle systems (adapted from [38]).

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assess the effects of introducing a new product or technology in an existing fleet.

the notion of time is explicitly introduced in the assessment because the dynamics associated with replacing end-of-life products by new ones are considered in the LC model. Instead of capturing a snapshot in time, a fleet-based LC approach accounts for changes over time in resource consumption and environmental impacts, which is an important feature for assessing the environmental impacts of a technology transition. LC inventories obtained are different from traditional LCA, because process flows change over time. When assessing the environmental impacts of the introduction of a new technology, several dynamic aspects need to be considered: (i) the replacement of an old technology by a new one occurs over time, i.e. it is not an instantaneous process; (ii) the production of the older technology may continue for some time, as it is not expected that the market share of the new technology reaches 100% promptly; (iii) even if the old technology is no longer produced, it does not abruptly disappear from use, especially if it has a long service life [14,30]. As a result, the number of products manufactured, in use and being disposed of changes over time; therefore no steady-state condition exists [14,30]. Moreover, technological improvements may continue to occur, probably at a higher rate for the new technology, but also for the older technology, as long as its market share is significant, and background processes are also likely to change. All these aspects can be captured by a dynamic fleet-based LC approach, although the latter two were not originally addressed in the seminal Field et al. article [14]. Two types of fleet-based scenarios can be applied to compare a product and its alternative: (i) an ab initio scenario (Fig. 3a), in which one fleet with the baseline product and another with the alternative product, growing at the same rate until they reach a steady-state size, are compared; or (ii) a displacement scenario (Fig. 3b), which considers that the fleet of products already in use is gradually displaced by the alternative product fleet [14]. The former is appropriate to compare two alternative new fleets, whereas the latter is suitable to

2.2. Review of applications An overview of the reviewed literature is presented in Table A1 (in Appendix A) classified according to the type of application of the modeling approach. Fleet-based LC approaches have been mostly used to assess environmental and energy impacts of vehicle systems (one exception is [33]). The majority of studies (2/3) addressed fleet-wide impacts of different scenarios of evolution of the transportation system, particularly in the last decade [39–58] (Fig. 1). The second largest group of studies used fleet-based LC approaches for product comparison addressing the replacement of heavier materials for lighter ones in vehicles [14,16,59–62]. Other applications of the modeling approach identified include optimization of product service life [32], modeling recycling processes [14,16], and assessment of the environmental impacts of products on a social scale [33]. 2.2.1. Environmental assessment of scenarios of evolution of the transportation sector Fleet-based LC approaches have been used to assess the environmental impacts of alternative pathways of evolution of the transportation sector. These studies typically involve scenario analysis and, in general, can be divided into two groups as regards its main objective [48]: (i) what if analysis, which assess the overall reduction in environmental impacts resulting from the implementation of different technology or fuel pathways (corresponding to about half of the studies reviewed) [39–51]; and (ii) backcasting analysis, which investigate the pathways that are able to achieve specific policy targets (e.g., emission reduction; fuel economy improvements) [52–55] and their underlying uncertainty [56–58]. What if analyses are more directed to the assessment of the effect of

Fig. 3. Ab initio product scenario (a) and displacement scenario (b) (Source: [14] © Copyright 2001 by the Massachusetts Institute of Technology and Yale University).

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introducing alternative technologies in the fleet, because they allow for the assessment of both the overall environmental impacts resulting from different levels of penetration of a new technology and the change in emissions relative to a baseline scenario. A detailed review of studies that assess scenarios of evolution of vehicle fleets following a what if analysis is presented in Section 3. Backcasting studies, on the other hand, seek to explore the magnitude, combinations, and timing of the changes required to meet policy targets, and to identify the implications and limitations of those pathways [52,53]. The introduction of alternative vehicle technologies can be one of the options analyzed in these studies [52–54], but the main goal is to assess what the level of penetration of the new technology must be to achieve a certain target.

aluminum components in the long-term are usually underestimated. Kim et al. [32] integrated a fleet-based LC approach with dynamic programming to analyze the optimal service life of products and how technology turnover affected their environmental performance. They analyzed tradeoffs between the fact that extending the service life of an existing product avoids the additional resource consumption and environmental impacts associated with the production of the new product, but the replacement of older (inefficient) products with newer (more efficient) ones reduces environmental impacts during the use phase. The authors explored optimal fleet conversion policies for passenger vehicles in the USA by modeling vehicle emissions over their service life as a function of accumulated mileage [32]. Yokota et al. [33] presented a fleet-based approach in which LCA and Population Balance models were integrated to quantitatively assess the total GHG emissions induced by the product population of air conditioners in Japan over time. The model was found to be useful to set targets of product performance (e.g., refrigerant recovery rates) and for policymaking (e.g., to assess compliance with policy targets).

2.2.2. Product comparison Another application of fleet-based LC approaches is the comparison of the use of lightweight materials (e.g., aluminum) with steel in vehicle manufacturing [14,16,59–62]. Field et al. used two models of product fleet growth (exponential and logistics) and considered both ab initio and displacement scenarios to compare the CO2 emissions of vehicles made of steel and aluminum [14]. Cáceres [61] assessed CO2 emissions over time of light alloy substitutions in vehicles, building on Field et al. [14], but considering also the mass efficiency of automotive material substitution. Das [60] considered an ab initio scenario to estimate the energy consumption and CO2 emissions of aluminum versus conventional steel and ultralight steel car bodies-in-white in the U.S, allowing for growth in fleet size. The author found that, in most cases, it would take about twice the time until the benefits of the introduction of aluminum vehicles in the fleet are achieved, compared to a singlevehicle analysis. Using a similar approach, Das [59] estimated energy consumption of an automotive liftgate inner. Stasinopoulos et al. [16] combined LCA and system dynamics to compare the LC energy consumption of car bodies-in-white made from steel and aluminum. Their approach built on Field et al. [14] and Das [60], but considered a gradual adoption of the alternative product. Du et al. [62] assessed the reduction in GHG emissions and energy consumption in the Chinese vehicle fleet by introducing aluminum intensive vehicles considering the increase in fleet size and market share of the lighter vehicles. In general, fleet-based studies show that it takes longer for the energy savings from vehicle operation to compensate for the use of higher energy intensive aluminum components than product-based studies suggest, because the latter do not account for the time necessary for the stock of steel components to be replaced by aluminum ones [14,16]. By accounting for these dynamics, fleet-based LC approaches provide a more comprehensive assessment of the relative environmental benefits of alternative products and materials, and of the time required to achieve those benefits, particularly if the products have a long service life.

3. Fleet-based LC approaches addressing energy and environmental impacts of the introduction of electric vehicles 3.1. Overview of selected literature A review of studies that use a fleet-based LC approach to assess environmental impacts of light-duty vehicle fleets was performed, focusing on those that assess the impacts of introducing electric vehicles in a fleet (following a what if analysis). These studies use a fleet-based approach, consider a LC perspective (here understood in a broad sense, i.e. the studies selected assess more than just tailpipe emissions), and include electric vehicles (i.e. plug-in hybrid electric vehicles [PHEVs] and/or BEVs) within the set of vehicle alternatives addressed. An overview of the selected studies is presented in Table 1. The geographical scope of most studies was the USA or regions within the USA [39,41–45] and the time scope varied between 20 and 45 years. A wide range of technologies was usually addressed, although some studies focused the analysis on PHEVs [43,45] or BEVs [48,47]. Baptista et al. [40] and He and Chen [63] analyses also considered heavy-duty vehicles. All studies assessed GHG emissions (except [46,47], which assessed CO2 emissions only), most assessed energy or petroleum consumption [39–42,44–46,49–51], and some also accounted for other tailpipe emissions [40,43,45,49]. Most studies excluded vehicle manufacturing and end-of-life from the system boundary, and only a few studies have considered the full life cycle [39,40,48]. In general, the contribution of EVs to reduce fleet LC impacts over time was only one of the many issues assessed. Key aspects were addressed through scenario analysis, such as: alternative vehicle penetration rates [e.g., 38,39,42–45]; electricity grid evolution over time e.g., [43,45]; technology improvements [e.g., 39– 41]; and charging profile [e.g., 45]. Table 2 presents a more in depth analysis of key aspects of the selected studies, which are addressed in the following sections.

2.2.3. Others Fleet-based LC approaches can also be used to explore the emergence and availability of scrap material in the system and its implications on recycling, because they can track the flow and accumulation of materials over time [14,16,59,63]. Moreover, they allow for the consideration of time dependencies in the rate of recovery and usage of the products, thus avoiding the simplifying assumptions required in a product-centered approach [14]. For instance, Stasinopoulos et al. [16] used a fleet-based LC approach within a system dynamics framework to compared the LC energy consumption of steel and aluminum car bodies-in-white, incorporating two dynamic processes: the flow of vehicles entering and leaving the fleet, and the recycling of aluminum from end-of-life vehicles back into the production of new car bodies-in-white. They found that, because product-centered approaches do not consider that the availability of recycled aluminum changes over time, the environmental benefits of

3.2. Key aspects 3.2.1. Fuel economy improvements and vehicle weight reduction Fuel economy improvements in new vehicles were generally taken into account, but some studies only considered improvements in conventional technologies (ICEVs) [41,45]. Most studies addressed different scenarios of fuel economy improvement [39,41,42,44,46–48], while some also assessed the isolated effect of this parameter in the results [41,42,46,48,51]. Weight reduction was implicitly considered in

938

ICEV, HEV, PHEV, BEV, FCV

ICEV, HEV, PHEV, BEV, FCHEV IECV, PHEV, BEV, FCHEV

ICEV, HEV, PHEV

ICEV, HEV, PHEV

ICEV, HEV, PHEV, BEV, FCV ICEV, PHEV

ICEV, CNG, BEV, FCHEV

ICEV, HEV, BEV

ICEV, BEV

ICEV, PHEV, BEV

ICEV, HEV, BEV

ICEV, HEV, PHEV, BEV

[39]

[40]

[42]

[43]

[44]

[46]

[47]

[48]

[49]

[50]

[51]

939 EoLb

EoLa

EoL

EoL

EoL

EoL

Excludes vehicle manufacturing and EoL Excludes vehicle manufacturing and EoL Excludes vehicle manufacturing and EoL

Full life cycle

Excludes vehicle manufacturing and Excludes vehicle manufacturing and Excludes vehicle manufacturing and Excludes vehicle manufacturing and Excludes vehicle manufacturing and Excludes vehicle manufacturing and

Excludes vehicle manufacturing and EoL

Full life cycle

Full life cycle

System boundary

China

China

2007–2050 2010–2050

China

Portugal

Greece

Colombia

Michigan, U.S.

U.S.

U.S.

U.S.

U.S.

Portugal

U.S., Europe

Geographical scope

2010–2030

2010–2030

2015–2030

2010–2050

2010–2030

2005–2050

2010–2050

2010–2050

2010–2050

2010–2050

2010–2035

Temporal scope

GHG emissions, fuel conservation

GHG emissions, oil use, tailpipe emissions (CO, PM, NOx, SO2) GHG emissions, fossil energy demand

GHG emissions

CO2 emissions

Energy, GHG emissions, criteria air pollutant emissions (CO, Pb, NOx, PM10, VOC, SOx) Energy, CO2 emissions

Oil use, GHG emissions

GHG emissions, air quality impacts

Petroleum consumption, GHG emissions

Energy, CO2 emissions, tailpipe emissions (HC, CO, PM, NOx) Petroleum consumption, GHG emissions

Fuel consumption, GHG emissions

Environmental impacts

Penetration of alternative technologies; fuel mix (diesel, biofuels, coal-based fuel); GHG intensity of the electricity mix. Penetration of alternative technologies; constrained vehicle registration; VKT; efficiency improvements; vehicle weight reduction.

Penetration of alternative technologies; efficiency improvements; VKT; CO2 intensity of the electricity mix; fleet renewal rate. Penetration of alternative technologies; vehicle weight reduction; efficiency improvements; electricity emission factor. Penetration of alternative technologies.

Powertrain efficiency improvements; lightweighting

PHEV penetration; charging behaviors; electricity mix.

Vehicle costs; fuel prices, government subsidies; and others.

Powertrain efficiency improvements; penetration of advanced technologies; fuel mix (e.g. by including biofuels); vehicle weight. VKT, penetration of advanced technologies, energy source (biofuels, electricity generation mix). ICEVs efficiency improvements; penetration of advanced technologies; electricity mix; hydrogen sources; biofuels utilization. Penetration of alternative technologies; vehicle weight reduction; biofuel feedstock; transportation demand. PHEV penetration; GHG intensity of the electricity mix.

Main scenario variables

ICEV = internal combustion engine vehicles; HEV = Hybrid electric vehicle; PHEV = Plug-in hybrid electric vehicle; BEV = Battery electric vehicle; FCV = Fuel cell vehicle; FCHEV = Fuel cell hybrid electric vehicle; EoL = end-of-life; GHG = greenhouse gas; VKT = vehicle kilometer travelled. a This study addresses vehicle production and end-of-life, but does not present results for CO2 emissions regarding these LC stages. b Fleet-wide GHG emissions are calculated excluding vehicle manufacturing and end-of-life, although the authors present full life cycle results per individual technology.

[45]

[41]

Vehicle technologies

References

Table 1 Overview of selected studies using a fleet-based life-cycle approach to assess environmental impacts of scenarios of evolution of vehicle fleets.

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Average U.S. grid mix.

Two scenarios for average annual electricity mix evolution: 60% and 91% RES in 2050.

Marginal emissions based on the Oak Ridge Competitive Electricity Dispatch Model (22% coal and 78% natural gas). Constant over time as default. Sensitivity analysis considering a linear transition from the default marginal mix to a single electricity source.

Two scenarios: base case and lowcarbon grid (50% non-GHG emitting sources; 15% natural gas; and 35% coal).

Marginal emissions. Three scenarios for the total GHG emissions intensity of the electric sector, based on the electricity sector model NESSIE. Fixed electricity mix comprised of non-renewable sources only.

Considers both average and marginal electricity generation and four electric grid scenarios, varying the amount of renewable generation added, the amount of nuclear capacity added and the number of retirements to existing generation assets. Current Colombian mix.

[39]

[40]

[41]

[42]

[43]

[45]

[46]

[44]

Electricity sector modeling

References

Conventional and lightweighting scenario.

Conventional and lightweighting scenario.

Not considered.

Fixed (for ICEVs only).

Eight scenarios developed by varying charging timing, charging infrastructure and battery size.

No charging profile scenarios.

Implicitly included in fuel economy scenarios.

Different scenarios of fuel economy improvements.

Up to 245 PJ; 19 Tg CO2 (2050).

0.4–10.9% reduction in GHG emissions (0.4–11 Tg CO2 eq) (2030); 2–34 hm3 gasoline (2010– 2030).

3.4–10.3 Pg CO2 eq (2012–2050); 163–612 Tg CO2 eq (in 2050), depending on the PHEV penetration rate and scenario of evolution of the electricity system. Up to 40% reduction in oil use; 13– 47% reduction in GHG emissions (2050).

Not considered.

Fuel economy of ICEVs and HEVs improve 0.5% per year; PHEV with same fuel economy as HEVs when in conventional mode.

Not considered.

Nighttime charging.

Fixed charging profile (74% between10 PM and 6 AM and 26% between 6 AM and 10 PM).

−10% to 65% reduction in GHG emissions (1990–2050); EVs increase reduction potential in 29–42%.

No charging profile scenarios.

Included in efficiency improvement baseline scenario (20%). Additional scenario considering 35% weight reduction.

Three scenarios for gasoline fuel economy improvements (2016 CAFE standards – 28 mpg; 2025 CAFE standards – 44 mpg, and 60 mpg in 2025).

Nighttime charging.

Not considered.

Two scenarios: base case and improvement due to additional weight reduction.

Fixed.

No charging profile scenarios.

Impact reduction potential Up to 40% reduction in fuel use and 35% in GHG emissions; Up to 31% reduction in fuel use and 23% reduction in GHG emissions due to the introduction of alternative technologies. 2–66% reduction in energy use; 7– 73% reduction in GHG emissions (2010–2050); 4–29% reduction in energy use; 10–33% reduction in GHG emissions (2050). 23–77% reduction in GHG emissions (2010–2050); 44–61% due to EVs.

Vehicle weight reduction Implicitly included in fuel economy scenarios.

Different scenarios of fuel economy improvements.

Fuel economy improvements

No charging profile scenarios.

Charging profile

Table 2 Key aspects of the selected fleet-based life-cycle studies (Table 1).

Switching to electric powertrains has larger impacton energy consumption and CO2 emissions than (continued on next page)

Advanced vehicle technologies will need a combination of factors to succeed: high oil prices; significant reductions in technology costs; and strong economic incentives for their purchase. Introduction of PHEVs reduces GHG emissions and gasoline consumption; charging scenarios only modestly affected GHG emissions; increasing RES penetration and retiring old coal PP significantly reduced emissions.

Alternative vehicle technologies can help to lower impacts, but different deployments of alternative technologies may lead to similar impacts. Efficiency improvements according to CAFE standards and alternative technologies operated with current electricity mix and hydrogen production processes alone will not reach the long term reduction target. A combination of efficiency improvements, biofuels and low-GHG fueled alternative technologies is necessary. Changes to vehicle technologies comprise the higher reductions, namely regarding fuel efficiency, weight reduction, and high penetration of PHEVs. Improvements to the electric grid had only a small impact, due to the low penetration of PHEVs and low electric-range considered. The contribution of demand-side reductions declines as the share of advanced technologies increases. GHG emissions are reduced significantly across all scenarios.

Substantial potential to reduce fleet fuel use and GHG emissions exist. Fuel consumption and weight reduction, and high market share of advanced powertrains need to be realized.

Key findings

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Four scenarios of electricity CO2 intensity: high, medium, low, and very low (renewables only).

Four scenarios, including the current mix and different technology scenarios (coal, coal and natural gas, and hydro).

Average Chinese mix.

Two scenarios: base case and coalfired power plants with CCS.

Electricity emission factor decreases along time.

[47]

[48]

[49]

[50]

[51]

No charging profile scenarios.

No charging profile scenarios.

No charging profile scenarios.

Reference scenario with no improvements; 15% reduction in fuel consumption of new vehicles in 2020 compared to 2015 and constant thereafter. Additional reduction with vehicle downsizing (0.42 L/100 km in 2010–2020).

Fuel economy of ICEVs improve 0.3% per year (average) in 2007–2030; constant thereafter.

1.5% per year for diesel-fueled vehicles, 1.3% per year for gasoline-fueled vehicles.

Included for both BEVs and conventional technologies. For the latter, scenarios were based on EU targets. Included for both BEVs and conventional technologies. For the latter, scenarios were based on EU targets.

No charging profile scenarios.

No charging profile scenarios.

Fuel economy improvements

Charging profile

Impact reduction potential

65–668 Gg CO2 yearly emission reduction in 2030 compared to a fleet of conventional vehicles. 1–47% reduction in fleet-GHG emission in 2030 compared to a business-as-usual fleet; −16% to 38% compared to an ICEV improved fleet.

1.7% and 5.2% reduction in energy and GHG emissions considering the entire road transport sector. 15.8% reduction in energy demand and 27.6% in GHG emissions in 2050 compared to a business-asusual fleet.

62% and 73% reduction in GHG emissions in 2030 and 2050, respectively.

Vehicle weight reduction

Not considered.

Included (0.8% per year). Sensitivity analysis to this parameter performed.

Not considered.

Not considered.

Reference scenario with no improvements; Average curb weight of new vehicles decreases to 2000 levels by 2020.

lightweighting; Slow stock turnover and fleet size increment prevent larger reductions. The parameters with higher influence in the results were: fuel consumption, VKT, fleet renewal rate and market penetration of advanced technologies. BEV introduction in the fleet is beneficial compared to an increasingly more efficient ICEV fleet if BEV penetration is high and the electricity emission factor is similar or lower than the current mix. Results were also sensitive to parameters that affect the fleet composition, such as those that change the vehicle stock, the scrappage rate, and the activity level of the fleet. The influence of these parameters also varies over time, becoming more important as time passes. Development of highly efficient EVs is crucial for reducing energy consumption and GHG emissions in China. Development of sustainable biofuel (cellulose BE and BTL) and highly efficient EVs, and promotion of coalbased fuels coupled with CCS technology are viable options to reduce energy demand and GHG emissions in China's transport sector. Reducing the vehicle fleet and improving fuel efficiency are the two most effective measures for fuel conservation and GHG emission reduction. Promoting EVs as a significant effect for fuel conservation in the long term, but not before 2030. The combination of all the measures does not achieve the GHG emission reduction targets set by the Chinese government.

Key findings

GHG = greenhouse gas; RES = renewable energy sources; PHEV = Plug-in hybrid electric vehicle; ICEV = Internal combustion engine vehicle; HEV = Hybrid electric vehicle; BEV = Battery electric vehicle; PP = Power plant; VKT = Vehicle kilometer travelled; CCS = Carbon capture and storage; BE = bioethanol; BTL = biomass to liquid.

Electricity sector modeling

References

Table 2 (continued)

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3.2.4. Other aspects Other key aspects addressed by reviewed studies include biofuel use [39,41,42,50], and demand-side interventions, such as decrease in vehicle fleet size and travel [39,42,47,48,51]. Kromer et al. [42] and Reichmuth et al. [41] found that increasing biofuel use would contribute to long-term GHG emission reduction. Nevertheless, due to constraints in the availability of biomass resources, this measure would need to be combined with others intended to decrease fleet energy demand, such as increasing vehicle fuel economy or introducing more efficient hybrid vehicles, so that significant reductions can be achieved. However, those studies neglect land use change, which is an important and controversial issue in the LCA of biofuels, with notable implications on their GHG intensity [65–69], thus presenting optimistic scenarios for the potential of biofuels to reduce transportation emissions. Regarding demand-side interventions, Garcia et al. estimated a reduction of up to 19% in the Portuguese fleet GHG emissions due to the reduction of the vehicle stock and travel demand [48]. Bandivadekar et al. showed that reducing fleet growth and travel demand has the potential to reduce fuel use by 19% in the U.S. fleet and could even achieve reductions similar to a scenario with high penetration of advanced vehicles (39%) if combined with high improvements in vehicle fuel economy [39]. Because this measure affects all vehicles in the fleet, the authors note that the emission reduction happens sooner, resulting in a higher cumulative emission reduction during the period of analysis. However, the contribution of demandside interventions to emission reduction declines as the share of advanced technologies increases, due to their higher energy efficiency [42].

the fuel economy scenarios in [39,42,44,46], and its effect was also taken into account in the vehicle production impacts in [39,46,48]. Reducing fuel consumption is one of the key ways to reduce fleet GHG emissions. However, it needs to be combined with other measures, such as high penetration of alternative powertrains and biofuels, to bring about significant reductions [39,41,42]. Reichmuth et al. [41] assessed the influence of improving the fuel economy of gasoline ICEVs in the GHG emissions of the U.S. fleet until 2050 and found that increasing fuel economy reduces GHG emissions, but cannot achieve the target emission level unless efficiency increases sharply. Moreover, emissions eventually increase as vehicle fleet growth surpass efficiency improvements. Kromer et al. [42] found that efficiency improvements through weight reduction have higher effect in an ICEV-dominated fleet, than in a fleet dominated by highly efficient hybrid vehicles. Palencia et al. [46] showed that vehicle weight reduction in BEVs only slightly reduced operation emissions, due to the already high efficiency of BEVs. 3.2.2. Electricity modeling Although electricity generation is an important aspect of the environmental assessment of EVs [5,6,11,13,64], some studies assumed a fixed electricity mix throughout the analysis [39,44,46,49]. Others performed a sensitivity analysis considering single electricity technologies [41,48], or scenarios of grid decarbonization [40,42,47,51], and only few modelled the electricity system as part of the fleet assessment [43,45]. EPRI [43] and Keoleian et al. [45] provided the most comprehensive assessment of electricity impacts by using an electric power capacity factor dispatch model to simulate retirement of existing generation capacity, additions of new capacity, and how capacity is dispatched, considering different scenarios for the electricity system evolution and different levels of PHEV penetration. The majority of studies considered the average electricity mix to assess impacts from EV charging [39,40,45,46–51] whereas a few calculated marginal emissions [41,43,45]. The latter studies assumed that EVs are a new load added to the existing load, so that emissions depend on the mix of electricity technologies that would be operating at the margin. Keoleian et al. [45] performed both average and marginal analyses, founding that marginal GHG emissions for the Michigan electricity system tend to be lower than average emissions and vary more along the day. As a result, fleet GHG emissions decrease more with PHEV introduction when marginal emissions are used in the assessment, but the difference is small (below 8%). Nevertheless, different electricity system configurations may lead to different outcomes, so that detailed analysis is needed for any specific energy system. A general conclusion from the reviewed studies is that the electricity generation source has a large impact on the fleet GHG emissions and increasing renewable energy penetration significantly reduces overall emissions [41,45,48].

3.3. Impact reduction potential of electric vehicles Emission reductions are very dependent on the underlying assumptions of the study. Some studies only presented aggregated results for each scenario, which usually comprised a large number of assumptions, making it difficult to discern the main drivers of emission reduction [40,43,47]. Others disaggregated results by main contributors, such as electricity mix, efficiency improvements, and weight reduction, allowing for a more in depth assessment [39,41,42,45,48,51]. In general, the latter studies show that the introduction of alternative technologies have the potential to significantly reduce fleet GHG emissions. Bandivadekar et al. [39] reported that up to 31% reduction in fuel use and 24% in GHG emissions (of 40% of total fuel use and 35% of emission reduction potential) could be achieved with the introduction of alternative technologies in the U.S. fleet in 2035. Reichmuth et al. [41] showed that in 2050, EVs could reduce emissions up to 61%, from a total of 77% reduction potential. Kromer et al. [42] demonstrated that EVs could increase the reduction potential in 2050 relatively to 1990 levels by 29–42%. Keoleian et al. [45] estimated that introducing PHEVs in Michigan could reduce fleet GHG emissions by 0.4–10.9% in 2030. Garcia et al. [48] showed that BEVs could reduce fleet-wide GHG emissions in Portugal by 1–47% in Portugal compared to a businessas-usual ICEV fleet, depending on the BEV penetration rate and electricity emission factor (reductions in the higher end are only achieved with an aggressive penetration of BEVs and a renewablebased electricity mix).

3.2.3. Charging profile The effect of charging time or the temporal variability in emissions from electricity was generally disregarded, with studies ignoring the charging profile of EVs [39,40,42,46–48] or assuming a fixed profile (usually nighttime) [41,43,44]. Only one study assessed different EV charging profiles [45]. The authors found that these only modestly influenced GHG emissions (between 3.5% reduction and 1.6% increase in 2030 compare to the baseline charging), but suggested that the effect would increase as battery size increases (they only assessed PHEVs) [45]. The effect of the charging profile on the overall fleet GHG emissions depends, on the one hand, on the relative share of electricity in the overall fleet energy demand (which, in turn, is dependent on the battery size and EV penetration), and, on the other, on the temporal variability in the marginal electricity mix (a function of each particular electricity system).

4. Conclusions Fleet-based LC approaches have been applied mostly on the automotive sector, focused both on materials or vehicle components and on alternative vehicle propulsion technologies. Different purposes guided these studies: comparing “products that are ‘dirty’ to make and ‘clean’ in use with products that are ‘clean’ to make and ‘dirty’ in use”

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on the results were only occasionally assessed. The majority of these studies only addressed GHG emission reductions, whilst other potentially relevant impact categories (e.g., depletion of abiotic resources, toxicity, air pollutants) were not assessed. Emission reductions were found to be very dependent on the underlying assumptions of the study. Reducing fuel consumption is one of the key ways to reduce fleet GHG emissions, but it needs to be combined with other measures, such as high penetration of alternative technologies, to bring about significant reductions. The electricity generation source was also found to have a large impact on the fleet GHG emissions and increasing renewable energy penetration is key to reduce overall emissions. Nevertheless, different fleet and electricity system configurations may lead to different outcomes, so that detailed analysis is needed for any specific energy and transportation systems.

[14, p.83]; assessing scenarios of evolution of the transport sector; modeling recycling processes; optimizing a product service life; and assessing the society-level impacts of products. When assessing new or changing products or technologies, transient effects need to be considered, as the number of products manufactured, in use and being disposed of changes over time in complex ways. The overall effects of this dynamic behavior, depicted in a fleet-based LC approach, are not accurately described by simply combining single-product life cycles. In fact, the results of a fleet-based LC approach are generally different from a product-centered approach. Therefore, a fleet-based approach is more suitable for a comprehensive evaluation of the impacts of introducing a new technology, because it enables an explicit assessment of changes in technologies and background systems over time and captures the scale and timing necessary for assessing other effects, such as the displacement of an older technology or a change in the energy pathway. Several studies have used fleet-based LC approaches to assess scenarios of evolution of the light-duty transportation sector, with emphasis on the U.S. fleet. Most of the studies did not include all stages of the life cycle, frequently disregarding vehicle production and end-oflife impacts. Only few seek to explicitly assess the effect of introducing a new technology in the fleet GHG emissions (the majority assessed alternative vehicle penetration as one option in many to reduce emissions). Several key aspects were identified, such as: fleet penetration rate, electricity source, fuel economy improvements, and vehicle weight reduction. Nevertheless, the different studies dealt with these aspects with different degrees of comprehensiveness and their effects

Acknowledgements This research was carried out in the framework of the Energy for Sustainability Initiative of the University of Coimbra (Portugal) and the MIT-Portugal Program, and was partly funded by FEDER (Programa Operacional Factores de Competitividade – COMPETE) and Fundação para a Ciência e Tecnologia (FCT) [project POCI-010145-FEDER-016765/PTDC/AAG-MAA/6234/2014]; the Associated Laboratory of Transport Energy and Aeronautics (LAETA) [project LAETA-UID/EMS/50022]; and the University of Coimbra [project “Clean Energy Supply”]. Rita Garcia gratefully acknowledges financial support from FCT through doctoral grant SFRH/BD/51299/2010.

Appendix A See Appendix Table A1 here.

Table A1 Overview of the reviewed literature on fleet-based life-cycle approaches. References

Application

Year

[43] [39] [42] [44] [50] [45] [51] [40] [46] [41] [49] [47] [48] [55] [52] [53] [56] [57] [58] [54] [14] [60] [59] [31] [61] [62] [16] [32] [33]

Scenarios: What if analysis Scenarios: What if analysis Scenarios: What if analysis Scenarios: What if analysis Scenarios: What if analysis Scenarios: What if analysis Scenarios: What if analysis Scenarios: What if analysis Scenarios: What if analysis Scenarios: What if analysis Scenarios: What if analysis Scenarios: What if analysis Scenarios: What if analysis Scenarios: Backcasting analysis Scenarios: Backcasting analysis Scenarios: Backcasting analysis Scenarios: Backcasting analysis Scenarios: Backcasting analysis Scenarios: Backcasting analysis Scenarios: Backcasting analysis Product comparison: material substitution Product comparison: material substitution Product comparison: material substitution Product comparison: part substitution Product comparison: material substitution Product comparison: material substitution Product comparison: material substitution Optimization of product service life Assessment of society-level impacts of products

2007 2008 2009 2009 2010 2011 2011 2012 2012 2013 2013 2014 2015 2008 2011 2011 2012 2012 2012 2013 2000 2000 2005 2009 2009 2010 2011 2004 2003

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