sustainability

Article Life-Cycle Assessment of Brazilian Transport Biofuel and Electrification Pathways

Kain Glensor * and María Rosa Muñoz B.

Mobility and International Cooperation, Wuppertal Institut für Klima, Umwelt, Energie gGmbH, 10178 Berlin, Germany; [email protected] * Correspondence: [email protected]



Received: 26 September 2019; Accepted: 7 November 2019; Published: 11 November 2019


Abstract: Biofuels and electrification are potential ways to reduce CO2 emissions from the transport sector, although not without limitations or associated problems. This paper describes a life-cycle analysis (LCA) of the Brazilian urban passenger transport system. The LCA considers various scenarios of a wholesale conversion of car and urban bus fleets to 100% electric or biofuel (bioethanol and biodiesel) use by 2050 compared to a business as usual (BAU) scenario. The LCA includes the following phases of vehicles and their life: fuel use and manufacturing (including electricity generation and land-use emissions), vehicle and battery manufacturing and end of life. The results are presented in terms of CO2, nitrous oxides (NOx) and particulate matter (PM) emissions, electricity consumption and the land required to grow the requisite biofuel feedstocks. Biofuels result in similar or higher CO2 and air pollutant emissions than BAU, while electrification resulted in significantly lower emissions of all types. Possible limitations found include the amount of electricity consumed by electric vehicles in the electrification scenarios.

Keywords: life-cycle analysis (LCA); transport; mobility; Brazil; biofuel; ethanol; electrification

1. Introduction This article describes a life-cycle analysis (LCA) performed for urban passenger transport (cars, urban buses and bus rapid transit (BRT) buses) in Brazil for the years 2015–2050 in five-year steps, calculated in a spreadsheet. The LCA compares the effect of a gradual but complete shift to the use of either biofuels or battery electric vehicles (BEVs) from a 2015 starting point and compared to a business as usual (BAU) scenario. The scenarios assume that the only change made is the propulsion technology/fuel used and that the distance driven by all vehicles (within each mode) per year is the same for all scenarios. The authors acknowledge a wholesale conversion to a particular powertrain type is neither realistic or necessarily desirable, but the intention of this LCA is not to provide a forecast of greenhouse gas (GHG) and air pollution emissions or other factors; the system is too complex and the uncertainties too great. Instead, the intention is to compare the effect of applying the various technologies/fuels in extremis in order to discern their effect in a simplified manner and identify any possible limitations and allow further discussions on the ideal policy strategy.

1.1. Literature Review Changing from the use of internal combustion engine vehicles (ICEVs, i.e., conventional petrol- or diesel- fueled vehicles) offers potential GHG emissions savings. The literature contains a wide range of estimates on how much this potential is, and what is important in determining it. Hawkins et al. [1] find the advantage of EVs to be 9%–29% less than petrol vehicles (EVs using EU-average electricity), while Garcia et al. [2] find it to be 30%–39% in Portugal. Abdul-Manan [3] quotes a 10%–60% advantage

Sustainability 2019, 11, 6332; doi:10.3390/su11226332 www.mdpi.com/journal/sustainability Sustainability 2019, 11, 6332 2 of 31 in other studies, and also finds a 40% reduction in GHGs, along with a 60% likelihood that GHG emissions would increase if hybrid electric vehicles (HEVs) were displaced by BEVs. From an LCA perspective, the major difference between a BEV and its ICEV equivalent is a shift of emissions from those produced in the production and use of fossil fuel to those from the production of batteries and the electricity used to power the vehicle. It follows that the major determinants of the overall effects of BEVs relate to the production of vehicles and their batteries, and the electricity used to power them. Given the production-heavy emissions profile of BEVs, the lifetime of the vehicles and their batteries significantly affects the lifetime CO2 emissions [1,2,4–10], i.e., how many driven km before either must be replaced; this need not be the same value, as a vehicle’s battery may be replaced before the end of the vehicle’s life. Peters et al. [10] point out that most of the LCA studies reviewed focus primarily on assessing impacts through storage capacity basis rather than accounting for the battery lifetime, which they suggest might lead to misleading conclusions. Battery manufacturing, and in particular the cell chemistry adopted to manufacture the battery, is another significant determinant of the overall CO2 emissions. Several studies examine various effects of different cell chemistries [7,11–13]. Depending on the electricity used to power the EV in its use phase, battery manufacturing was found to contribute between 8% and 38% of the total life-cycle emissions (Poland and Sweden, with electricity CO2 emissions of with 650 and 20 g/kWh, respectively), or 15% at the EU-average electricity CO2 emissions (300 g/kWh) [14]. Notter et al. [7] estimate that batteries cause 7%–15% of the environmental impacts of e-mobility, while Ambrose and Kendall [15] estimate that the battery production phase accounts for 5%–15% of the fuel cycle GHGs of plug-in electric vehicles. Peters et al. [10] find equivalent CO2 emissions for battery manufacturing of 110 kg/kWh, while Ambrose and Kendall [15] find 256–261 kg/kWh. Hall and Lutsey [16], in a wide-ranging review (including the two sources mentioned here), find a range of battery manufacturing emissions of 30–494 g/kWh. Finally, Peters and Weil [17], however, urge caution, concluding that the discrepancies in many of these results are primarily due to the differences in assumptions rather than the particular cell chemistries. Related to the high production emissions, material recycling is another significant contributor to the overall CO2 emissions [4,6]. The most commonly noted determinant of the overall CO2 emissions of BEVs is the carbon intensity of the electricity used to power the vehicles [2,5,7,15,18–20]. For example, Onat et al. [21] find different optimum vehicle types in different US states based on the states’ electricity generation (and driving patterns). Hawkins et al. [8] find that BEVs generally outperform ICEVs, although not for very efficient ICEVs compared to BEVs running on coal-fired electricity. Similarly, Varga [22] concludes that increasing the use of EVs will have no effect on GHG emissions in Romania given the country’s carbon-intensive electricity generation. In Malaysia, Onn et al. [23] conclude that EVs produced higher well-to-wheel (WTW, i.e., all impacts from fuel production to delivery to the vehicle and final use in the vehicle) environmental impacts in seven out of 15 categories than ICEVs, primarily due to the composition of the electricity grid (40% coal). Similar conclusions for China were put forth by Hofmann et al. [24] and Zhou et al. [25]. Finally, in Beijing, Ke et al. [26] show that BEVs can significantly reduce CO2 emissions, unlike previous assessments, primarily due to the shift from coal-based electricity generation to gas. Similar results are presented by Shi et al. [27] who analyze the impacts of the electric taxi fleets in Beijing. Given the significance of electricity emissions on the overall results and the temporal variation of electricity emissions, at what time of day BEVs are charged is significant [28]. Faria et al. [29] warn that a renewable-dependent grid may not necessarily directly translate to low GHGs for EVs due to the high variability of such systems. Rangaraju et al. [30] point to the importance of charging during off-peak hours in terms of reducing the impacts of BEVs. However, a study performed by EPRI (as quoted in [31]) warns that an off-peak charging scheme might increase emissions of EVs, particularly if the grid is coal based, by increasing the base-load (often coal-fired) electricity demand. Sustainability 2019, 11, 6332 3 of 31

While electric motors are much quieter than their internal combustion engine (ICE) equivalents, above approximately 40 km/h, other sources of noise (tyre and aerodynamic) begin to become dominant, leading to little difference in the noise emissions of BEVs and ICEVs from that speed and above [28]. Garcia and Freire [32] discuss the different fleet-based LCAs that have been conducted and observe that many of these studies have not integrated all stages of the life cycle. In particular, end-of-life treatment (i.e., recycling and/or reuse) is another influential, and oft-overlooked aspect of an LCA of vehicle technologies [4–6,32]. While BEVs have definite benefits in terms of urban air pollutant emissions, and electricity-dependent benefits in terms of GHG emissions, these benefits are also accompanied by negative effects, such as human toxicity, water eco-toxicity, freshwater eutrophication, acidification, and metal depletion, amongst others [1,33], with Notter et al. [7] estimating that human health damage accounts for 43% of the complete environmental burden caused by the production of a Li-ion battery. Few studies could be found directly comparing electrification with biofuels. Choma and Ugaya [33] examine the effect of increasing the number of electric vehicles in Brazil, finding that EVs offer reduced global warming potential compared to ICEVs (using E25 fuel: 25% ethanol and 75% petrol). While Meier et al. [34], in their high electrification scenario (40% of distance and 26% of fuel use) in the USA, find that meeting an 80% GHG reduction target would require “significant quantities of low-carbon liquid fuel”. The calculation of the overall GHG emissions from biofuel production is particularly reliant on the inclusion (or not) of direct (the effect of changing land from one use to growing biofuel feedstocks) indirect (increased land use for biofuel feedstocks causes land elsewhere to be converted to other uses) land-use change emissions, and how those are calculated. Particularly the inclusion (and calculation method) of LUC, especially indirect, emissions is difficult and subject to significant controversy [35]. However, the degree to which including LUC reverses the GHG advantages of biofuels depends on a great deal of factors and conditions [36–39].

1.2. Brazilian Transport and Biofuel Industries The establishment of the National Alcohol Program (PROALCOOL by its Portuguese acronym) in 1975 marked the beginning of a unique model that promotes the large-scale production and use of biofuels for transport. PROALCOOL was created with the aim of promoting the production of ethanol and thus reducing the economic and environmental impacts of imported petrol. The program started with a required blend of 10% anhydrous ethanol with petrol in 1979, and this reached 27% by March 2015 [40]. In addition to ethanol, in 2005, Brazil started to promote the production of biodiesel as an alternative for petroleum-derived diesel fuel. Brazil’s Federal Law No. 11,097/2005 established the legal requirement for blending biodiesel with petroleum-derived diesel, which started with a blend ratio of 2% by volume and has subsequently been increased to 7%. In March 2016, new mandatory blend ratios were set for 2017 (8%), 2018 (9%) and 2019 and onward (10%) [40]. As a result of these policies and programs, Brazil is the second largest producer of biofuels in the world after the USA and more than 90% of all new light vehicles sold in Brazil run on any combination of ethanol and petrol (flex-fuel engines) [41]. Sugarcane ethanol plays a key role in terms of energy security as it represents 18% of primary energy production, 34% of energy consumption in the transportation sector for light vehicles, and 5.5% of the electricity generation in Brazil [42]. In the Brazilian transport sector, 49% of emissions are due to the combustion of diesel fuel and 33% due to the combustion of petrol. In addition, it is important to bear in mind that sugarcane production is associated with the following environmental and social impacts [43]: Land degradation and deforestation and direct and indirect land-use change (LUC); • Soil and water pollution; • Loss of biodiversity due to monocultures and straw burning; • Carcinogenic air emissions from sugarcane straw burning; • Sustainability 2019, 11, 6332 4 of 31

Worsened inequality in the countryside and poor working conditions (overworking, low wages, • the use of temporary and seasonal labor, and even child and slave labor).

In 2015, agriculture accounted for 31% and LUC for 24% of the GHG emitted, adding up to more than half of the total GHG emissions of Brazil [44].

1.3. Brazilian Electricity Sector As shown in Table1, Brazil’s electricity system is low carbon, with low-emission sources (hydro, wind and other renewable) making up 84% of the total generation in 2018. Moreover, according to the Strategic Electricity Plan, by 2030, Brazil will double its electricity generation in relation to 2014 levels [45].

Table 1. Electricity generation by source in 2018 [46].

Source GWh % Hydro 399,109 73.6% Thermal—fossil 66,467 12.3% Wind 44,506 8.2% Thermal—other 13,816 2.5% Thermal—renewable 10,723 2.0% Nuclear 4974 0.9% Solar 2492 0.5% Total 542,087

2. Methodology The LCA presented here examines the effect of a gradual but complete adoption of either electric vehicles or biofuels by 2050 in Brazilian (urban) passenger transport. This means 100% of vehicles would be BEVs, or 100% of all fuel sold would be biofuel (sugarcane ethanol or soy-based biodiesel) in conjunction with 100% biofuel-capable vehicles, respectively, in 2050. The results are calculated in terms of CO2 emissions, urban air pollution (nitrous oxides (NOx) and particulate matter (PM)), electricity consumption and the land area required to grow sufficient biofuel feedstocks. The LCA considers passenger cars, urban buses and bus rapid transit (BRT) buses. However, because the magnitude of results of BRT buses is negligible in comparison to the other modes (see Figure 4), the results for BRT are omitted for brevity. The LCA is calculated in a spreadsheet for the years 2015–2050 in five-year steps. The results for each year are for only the year in question as a ‘snapshot’ (i.e., the intervening years are ignored). An overview of the aspects and life phases considered in the LCA is shown in Figure1, and further described in the section below. The spreadsheet is structured to allow for the comparison of sets of five scenarios, allowing a comparison of the BAU scenario with four alternatives. AppendixA contains tables of the input values used in the LCA. SustainabilitySustainability 20192019, ,1111, ,x 6332 FOR PEER REVIEW 5 5of of 32 31 Sustainability 2019, 11, x FOR PEER REVIEW 5 of 32

Figure 1. Schematic of the life-cycle analysis (LCA) methodology. WtT: well to tank; TtW: tank to FigureFigure 1. 1.Schematic Schematic of of the the life-cycle life-cycle analysis analysis (LCA) wheel.(LCA) methodology. methodology. WtT: WtT: well well to tank; to tank; TtW: TtW: tank totank wheel. to wheel. 2.1.2.1. Vehicle Vehicle Use Use and and Characteristics Characteristics 2.1. Vehicle Use and Characteristics 2.1.1.2.1.1. Total Total National National Vehicle Vehicle Use Use 2.1.1.ToTo Total allow allow National free free selection selection Vehicle of of Usevarious various powertrain powertrain types types (see (see below) below) in in the the scenarios, scenarios, the the total total distance distance driven in Brazil by all vehicles of each mode (cars, urban buses) (Figure2) per year in vehicle kilometers drivenTo in allow Brazil free by selection all vehicles of various of each powertrain mode (cars, types urban (see below) buses) in (Figure the scenarios, 2) per theyear total in distancevehicle (vkm) must be calculated. It is calculated from the total projected vehicle stock of each mode multiplied kilometersdriven in Brazil(vkm) mustby all be vehicles calculated. of each It is modecalculat (cars,ed from urban the buses) total projected (Figure 2)vehicle per year stock in ofvehicle each by the corresponding average distance driven per vehicle, using figures for both from International modekilometers multiplied (vkm) by must the becorresponding calculated. It average is calculat distedance from driven the total per vehicle,projected using vehicle figures stock for of botheach Energy Agency (IEA) modeling [47,48] (Tables A4 and A5). frommode International multiplied byEnergy the corresponding Agency (IEA) modelingaverage dist [47,48]ance (Tabledriven A4 per and vehicle, Table usingA5). figures for both from International Energy Agency (IEA) modeling [47,48] (Table A4 and Table A5). Total na onal distance driven annually (billion vkm) - by mode 1,200 12 Total na onal distance driven annually (billion vkm) - by mode BRT

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2015 2020 2025 2030 2035 2040 2045 2050 Distance 0 0 Cars Urban buses BRT 2015 2020 2025 2030 2035 2040 2045 2050 Cars Urban buses BRT FigureFigure 2. 2. Total distancedistance drivendriven by by the the respective respective modes modes annually annually (billion (billion vkm). vkm). Cars (green)Cars (green) are shown are shownonFigure left on y-axis,2. leftTotal whiley-axis, distance urban while driven (red) urban and by (red) busthe rapidandrespective bus transit ra pidmodes (BRT) transit busesannually (BRT) (blue) buses(billion are shown(blue) vkm). are on Cars theshown right(green) on y-axis. theare rightshown y-axis. on left y-axis, while urban (red) and bus rapid transit (BRT) buses (blue) are shown on the right y-axis.

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2.1.2. Fleet and Fuel Composition In each scenario, for each year (Tables A2 and A3), the composition of the fleets (cars and urban buses) is defined from the following powertrain types: flex, flex hybrid, diesel, diesel hybrid, compressed natural gas (CNG) and electric. Hybrids are assumed to be ‘standard’ hybrids without the facility to be charged from external sources, while electric is assumed to mean fully electric vehicles with only onboard batteries as energy storage. In parallel, for each year, the proportion of biofuels sold is defined from 0%–100% (Table A1). Values for the volume of fuels sold are available for 2015 and 2018 [49,50]; the 2020 values are calculated by extrapolating the development from 2015 to 2018. For ethanol, both hydrated and anhydrous are considered together; hence, the proportion in 2015 (49.6%) being significantly greater than the 27% ethanol as mandated in pump petrol (‘gasohol’). For biodiesel, the extrapolated 2020 value was above the mandated 10%; so, 10% has been used from 2020 onwards under the assumption that there would be no overshoot. This model assumes all relevant vehicles are able to run on any proportion of bio or fossil fuel. For petrol, flex vehicles are near ubiquitous in new registrations in Brazil [41] (and ethanol is sold in similar volumes to petrol in 2015) and so this assumption is almost already reality. For diesel, however, Brazil’s pump diesel currently contains only 10% biodiesel, so this assumption must be considered more speculative, though it is assumed to be possible given sufficient research and development for the purposes of this LCA.

2.2. Fuel Production and Use Phase

2.2.1. Total Fuel Use Per Vehicle Type and Fuel Type The total vkm per mode and powertrain type is calculated based on the overall vkm per mode and the powertrain proportions as entered in the scenarios. The total use-phase energy, and thus fuel, consumed by each vehicle type is calculated from the vkm and the corresponding on-road average fuel economy for each type. All relevant values here are provided by the IEA [47] (Tables A4 and A5). The total amounts of fuel used are multiplied by emission factors for well-to-tank (WtT) (including electricity) and tank-to-wheel (TtW) emissions (more details of both below) to determine the emissions of each vehicle (and fuel) type and thus scenario. For electric vehicles, the round-trip efficiency for electric vehicles (buses) of 88%, from Lajunen [51], is applied to the stated fuel economy to reflect the inefficiency in charging and discharging the battery.

2.2.2. Well-to-Tank (WtT) Emissions

For each fuel type, the well-to-tank CO2 emissions (per liter) are given by Edwards et al. [52] (Table A7). The figures provided are calculated for Europe and modified for Brazil (see below), as consistent figures for all fuel types for Brazil could not be found. For fossil fuels, the figures have been used as found. For biofuels, the European figures for Brazilian-produced ethanol and biodiesel are modified by deducting the value listed for ‘transport to Europe’. These figures do not contain land-use emissions. The WtT factor for electric vehicles is provided by the relevant average emission intensity of the electricity system in that year (see below).

2.2.3. Tank-to-Wheels (TtW) Emissions

TtW emissions are calculated using the energy and CO2 content of each fuel type [47,53], along with NOx and PM exhaust emissions intensities from Agência Nacional de Transportes Terrestres [54], supplemented with NOx exhaust emissions intensities for LPG/CNG vehicles from Murrells and Pang [55] (Table A7). The TtW CO2 exhaust emission intensity of ethanol and biodiesel is considered to be zero as this carbon is considered to have been sequestered in the production phase. For NOx and PM, the values for 2012 (the latest year available) are applied for all years (Table A6), or an annual reduction in air pollution emission intensity of 8.67% (from 2012), derived from Miller et al. [56] is applied. Sustainability 2019, 11, 6332 7 of 31

2.2.4. Electricity System Best- (mostly renewables) and worst-case (mostly fossil fuels) scenarios for both total generation (in TWh) and CO2 emission intensity (in kilotonne/TWh) are provided by the online calculator provided by the Energy Research Office [57] (EPE in Portuguese) for decadal years (2020, 2030, etc.) (Table A8), extrapolated to other years. The worst-case scenario is the default used in the scenarios. The model does not account for any effects of large additional loads in the short or long term, and the total generation capacity is used only as a comparison; a more complicated model accounting for the characteristics of the electricity generation system is beyond the scope of this study.

2.2.5. Land-Use Change Emissions A direct land-use change (LUC) emission model is used to calculate the LUC emissions. It is assumed that the land area for all other crops remains the same, with all changes in biofuel use directly affecting the land used to grow the feedstocks, and that this land comes from (or returns to) various forest types or Cerrado/savannah according to the volume of biofuel needed in each year. A more complicated model involving intermediate steps and economic elasticities is beyond the scope of this analysis. Firstly, the land area required to grow the requisite fuel feedstocks is calculated using figures for the yield of ethanol (sugarcane) or biodiesel (assumed to be all derived from soyabeans) per land area, using values from Valin et al. [58] (Table A10). The values used are constant for all land types and locations. From this and the volume of fuel required each year, the yearly change in cropland area from the previous period is calculated. The National Energy Policy Council (CNPE) has mandated, as part of the RenovaBio programme, a decrease in the CO2 emissions of each unit of energy consumed by Brazilians from 74.3 g/MJ (2017) to 66.1 g/MJ in 2029; 1.07% per year [59], assumed to be achieved through increased biofuel yields (L/ha) and from increasing the proportion of biofuels used in fuels, though no statement of how much each contributes is given, and so it is assumed that each contributes half of the 1.07% (0.535% pa). The improved biofuel yields are thus increased at the rate of 0.535% per annum, although even this may be overly favorable to biofuels as the possibility of increased yields is questioned by Bernardo, Lourenzani, Satolo, and Caldas [60]. Furthermore, this would almost certainly cause the embedded CO2 in biofuel to increase through, e.g., increased tractor and fertilizer use, and because these changes cannot be quantified, they are assumed to be negligible. The assumption that increased biofuel use decreases emissions presents a difficulty, as this is the opposite finding to this LCA, and so must be assumed to be based on a different methodology; as such, the other half of the mandated 1.07% decrease from increased biofuel use is omitted. For biodiesel, 20% of the land-use change emissions are attributed to transport use, according to the 20% oil portion of the beans which can be used to make biodiesel: the other 80% would be ≈ attributed to the other uses of the soyabeans (e.g., soyameal for animal feed), outside this LCA. Secondly, the carbon dioxide emitted (or absorbed) from the change in land-use is calculated using values of the carbon content of various Brazilian forest types (including plantation) and Cerrado (Brazilian Savannah) (Table A9). The values for plantation forest and Cerrado are used as found, whereas a numerical mean and standard deviation is calculated for the forest values, with the low and high values representing one standard deviation from the mean value. The conversion from biomass carbon to CO2 emissions is performed using the average ratio between “Average forest AGB [Above Ground Biomass]” and “Primary forest (instantaneous, non-process) emissions” (primary is selected assuming that the recropping of the land for biofuel is part of the secondary stage), from Aguiar et al. [61], giving a value for CO2 emissions for each hectare of land changed. The emissions from land-use change are spread over a 20 year period. LUC emissions in 2015 are zero as the LCA captures only change in biofuel use (and thus land use) to generate LUC emissions, and so for example the 2020 value includes any change from 2015 to 2020. Sustainability 2019, 11, 6332 8 of 31

2.3. Vehicle and Battery Manufacturing Phase For computational efficiency and because of a lack of equivalent model data pre-2015, a fleet model could not be implemented, so the number of vehicles manufactured per year is calculated by assuming each year is closed regarding vehicles, i.e., all vehicles used in a year would be manufactured in that year and disposed of at the end of the same year. The scenarios thus prescribe a fleet proportion, not sales proportions of the various powertrain types. The number of vehicles for each year is calculated by dividing the total vkm for each powertrain type by the total lifetime (in km) of that type. The emissions for vehicle manufacturing are constant per vehicle, independent of changes to the electricity emission intensity applied elsewhere in the LCA. In addition, the values are accounted for in Brazil, even if the parts actually originate elsewhere. For cars, the lifetime (200,000 km) is provided by Messagie [14] and assumed to be the same for all vehicle types (see Table A11). In addition, with the exception of the batteries for hybrid and electric vehicles, the non-battery components are assumed to result in the same CO emissions ( 3.2 tonnes); 2 ≈ see Table A12. For buses, the lifetimes (different by powertrain type) are provided by Lajunen [51] with a correction factor for the lifetime of non-diesel buses in line with the calculations by WRI [62] (Tables A13 and A14). Battery manufacturing emissions are calculated from the overall capacity of batteries required for all hybrid and electric vehicles and their respective battery capacities (and the rate of battery replacement) as shown in Tables A11 and A13. A range of current battery manufacturing emissions intensities (in g/kWh) is provided in Hall and Lutsey [16], from which the average (default) minimum or maximum values (see Table A15) can be applied. The default assumption for the battery and vehicle manufacturing calculations is that both are manufactured from raw materials and disposed of without reuse or recycling at the end of life. As such, if the scenarios dictate that the materials (and thus CO2 emissions) are recovered or reused at the end of life, this results in a ‘credit’, covered in more detail in the end-of-life section, below.

2.4. End-of-Life Phase As mentioned above, the LCA treats each year as a closed system, so all vehicles used in any year also come to the end of their lives in that year. Five aspects of the end of life are considered: non-battery recycling emissions, non-battery materials credit, battery recycling emissions and materials credit and a battery reuse credit (values and sources given in Table A16). Reusing batteries and recovering materials through recycling effectively reduces the manufacturing emissions, but they are considered as credits in this manner to allow separate (calculation) and presentation of these and the default manufacturing emissions. Recycling emissions considers the energy required to recycle the respective parts and is calculated by multiplying the total mass of vehicles and capacity of batteries to be recycled by the respective factors for the energy required to recycle them. It is assumed the energy for this comes from the electricity network, so the relevant CO2 emission intensity of the electricity system for that year is applied. The vehicle recycling materials credit is included at current levels of recycling and use of recycled materials. The credit is zero as it is assumed to be included in current calculations of vehicle manufacturing emissions. The battery recycling materials credit is calculated according to the 10%–17% and 23%–43% reductions (Table A16) given for the use of recycled materials in battery manufacturing. The base assumed reduction is 26.5%—the mid-point between the 10% and 43% outer figures. The actual emission credit figure is calculated from the total battery manufacturing emissions in the relevant year, multiplied by the 26.5% and the assumed proportion of battery recycling (details below). The battery reuse credit is calculated using the assumed extra life (72%) batteries can be used for after their vehicular use, i.e., they are used for 58.1% of their lives in vehicles and, as such, the remaining 41.9% counted as a credit for this LCA, as that proportion of the manufacturing emissions can be attributed to subsequent use. Sustainability 2019,, 11,, 6332x FOR PEER REVIEW 99 of of 32 31

The calculation of end-of-life credits applies an assumed proportion of battery reuse and recycling.The calculation Both are assumed of end-of-life to increase credits linearly applies to an 100% assumed by 2050, proportion from 5% of and battery 0% in reuse 2015 and for recycling recycling. andBoth reuse, are assumed respectively to increase (see linearlyTable A17). to 100% This by assu 2050,mes from that 5% the and greater 0% in 2015use of for batteries recycling as and per reuse, the scenariosrespectively will (see create Table an A17 ever-greater). This assumes economic that an thed environmental greater use of batteries imperative as peron the more scenarios efficient will usecreate of anbatteries ever-greater and materials. economic As and batteries environmental can be imperativeboth reused on and the recycled, more effi cientboth usecredits of batteries can be applied.and materials. As batteries can be both reused and recycled, both credits can be applied.

3. Scenarios Tested Figure3 3 shows shows the the sets sets of of scenarios scenarios examined examined (for (for each each mode), mode), consisting consisting of a mainof a main LCA LCA and fourand foursensitivity sensitivity analyses, analyses, considering considering LUC emissions,LUC emissions, battery battery manufacturing manufacturing emissions, emissions, vehicle vehicle and battery and batterylives and lives electricity and electricity system developments system developments vs. a best-case vs. a best-case biofuel scenario, biofuel respectively.scenario, respectively. The content The of contentthe scenarios of the was scenarios defined was based defined on a combinationbased on a combination of the primary of the purpose primary of thepurpose study of (comparing the study (comparingbiofuels to electrification), biofuels to electrification), the main factors the aff ectingmain factors the results affecting as described the results in the introductionas describedand in the introductionintermediate resultsand the of intermediate the LCA itself. results These of are the outlined LCA initself. the followingThese are section,outlined and in thethe component following partssection, are and described the component in detail parts in Appendix are describedA. in detail in Appendix A.

FigureFigure 3. Scenarios3. Scenarios examined examined in thein the main ma LCAin LCA and and thethree the three sensitivity sensitivity analyses. analyses. LUC: LUC: land-use land-use change. change.

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For all scenario values, where the user-input (i.e., non-modeled) values change between 2015 and 2050, the values for intervening years are changed linearly. All parameters for all scenarios in 2015 are set to the 2015 BAU values. All sub-LCAs contain the BAU as described in the main LCA.

3.1. Main LCA An overview of the scenarios used for the LCA of cars is shown in Table2 (details in Tables A1–A3). The main LCA compares BAU to biofuel scenarios with and without hybridization, and electrification for the best- and worst-case electricity system development.

Table 2. Overview of the scenarios tested in the main LCA (values for 2015/20–2050, interpolated for intermediate years).

Biofuel with Biofuel without Electrification, Electrification, BAU Hybrid Hybrid Best Case Worst Case Ee50/56-Ee56 Ee50/57-Ee100 Ee50/56-Ee56 Fuels * B0/10-B10 B0/14-B100 B0/10-B10 0%–100% Vehicles Model 0%–0% hybrid 0%–100% electric hybrid Electricity Worst case Best case Worst case Battery manuf. Average LUC Average forest * Analogous to the common naming convention whereby, e.g., E85 represents an 85%/15% ethanol/petrol mix as sold; ‘Ee’ (ethanol effective) represents the overall proportion of ethanol (hydrated and anhyrdous) to petrol, as if these were sold as a single mixed fuel, though in fact hydrated ethanol is sold from separate pumps by itself. B10 denotes 10% biodiesel, 90% fossil-diesel.

For the BAU scenario, the 2015/2020 proportions of ethanol (50% in 2015, 57% subsequently) and biodiesel (0% in 2015, 10% from 2020 onwards) are applied, while the vehicle proportions are provided from the IEA model [47]. The biofuel scenarios are based on the complete conversion of cars to ethanol and buses to biodiesel by 2050. As such, the proportions of biofuel in pump fuels are linearly increased to 100% in 2050 from the 2020 values: 57% for ethanol and 10% for biodiesel. In addition, the proportion of flex vehicles (for cars) or diesels (for buses) is linearly increased to 100% by 2050. Furthermore, the proportion of hybrid vehicles is increased to 100% in the biofuel and hybrid scenario but maintained at 0% in the biofuel without hybrid scenario. The few other vehicles are phased out by 2020. Both electrification scenarios are based on the gradual shift toward complete electrification of cars and buses by 2050. The biofuel components of pump fuels as per BAU are used. The proportion of flex or diesel vehicles is linearly decreased to 0% by 2050, with hybrids contributing an ever-greater proportion of the total proportion allocated to flex or diesel vehicles. Other vehicle types (CNG, non-diesel buses) are phased out by 2020. Fully electric vehicles make up the remainder. The sole difference in the two electrification scenarios is the use of either the EPE best- or worst-case scenarios concerning total electricity generation and CO2 emission intensity.

3.2. Sensitivity Analysis #1: Land-Use Change Emissions This sensitivity analysis (Table3) considers the e ffect of the biome used as the basis of land-use change (LUC) emissions on biofuel scenarios, i.e., the source of land used for extra land for biofuel feedstocks (or the inverse for diminishing demand). The scenarios are calculated (and presented) in two halves due to scenario-number limitations of the calculation tool. Sustainability 2019, 11, 6332 11 of 31

Table 3. Overview of the scenarios tested in the sensitivity analysis on LUC (values for 2015/20–2050, interpolated for intermediate years).

1 BAU Biofuel 2 Hybrid with without No Forest Density Cerrado LUC LUC LUC Plantation Low Avg High Ee50/56-Ee56 Ee50/57-Ee100 Fuels B0/10-B10 B0/14-B100 Vehicles Model 0%–50% hybrid Electricity Worst case Battery manuf. Average Avg Plantation Low Avg High LUC None None Cerrado forest forest forest * forest * forest * * Refers to the carbon density of the forest per hectare.

The BAU scenario is used as per the main LCA, along with a BAU without LUC scenario, in which the LUC component is omitted. These are shown on both graphs for comparison. Otherwise, six biofuel scenarios are calculated assuming the use of the biomes Cerrado, plantation forest, and low-, average- and high-density virgin forest, along with a scenario in which LUC emissions are omitted. These latter six LUC biome scenarios are applied to a biofuel scenario, splitting the difference between the ‘with’ and ‘without hybrid’ scenarios from the main LCA: the proportion of hybrids is increased linearly to 50% in 2050 (see Tables A2 and A3).

3.3. Sensitivity Analysis #2: Battery Manufacturing Emissions This sensitivity analysis (see Table4) considers the e ffect on the electrification scenario of different values for battery manufacturing emissions. The values used are the average, minimum and maximum as presented in Hall and Lutsey [16]. These are applied to the (worst-case) electrification scenario from 1 the main LCA and compared to the BAU and biofuel 2 hybrid scenarios from the main LCA and the sensitivity analysis, respectively.

Table 4. Overview of the scenarios tested in the sensitivity analysis on battery manufacturing emissions (values for 2015/20–2050, interpolated for intermediate years).

Electrification 1 BAU Biofuel, 2 Hybrid Avg Battery Min Battery Max Battery Manuf. Manuf. Manuf. Ee50/56-Ee56 Ee50/57-Ee100 Ee50/56-Ee56 Fuels B0/10-B10 B0/14-B100 B0/10-B10 Vehicles Model 0%–50% hybrid 0%–100% electric Electricity Worst case Battery manuf. Average Average Average Min Max LUC Avg forest

3.4. Sensitivity Analysis #3: Vehicle and Battery Lives This sensitivity analysis (see Table5) considers the e ffect on the electrification scenarios of changes to the effective life of both vehicles and batteries. Specifically, the lifetime of the vehicles and batteries (both expressed in km) is increased or decreased by 20%. Sustainability 2019, 11, 6332 12 of 31

Table 5. Overview of the scenarios tested in the sensitivity analysis on battery manufacturing emissions (values for 2015/20–2050, interpolated for intermediate years).

Electrification BAU Biofuel, 1 Hybrid 2 Default Lives 20% Lives +20% Lives − Ee50/56-Ee56 Ee50/57-Ee100 Ee50/56-Ee56 Fuels B0/10-B10 B0/14-B100 B0/10-B10 Vehicles Model 0%–50% hybrid 0%–100% electric Electricity Worst case Battery manuf. Average Average Average Min Max LUC Avg forest

VehicleSustainability and battery 2019, 11 lives, x FOR PEER REVIEW Default Default Default 20% Default 12 of+20% 32 −

Electricity Worst case 3.5. SensitivityBattery Analysis manuf. #4: Best-Case Average Biofuel Vs. Average Electricity System Average Development Min Max This sensitivityLUC analysis (see Table6) compares a best-caseAvg forest biofuel scenario (full hybridization, Vehicle and battery no LUC emissions) with electrificationDefault scenarios under di Defaultfferent electricity Default -20% system Default developments +20% lives regarding CO2 emission intensity. 3.5. Sensitivity Analysis #4: Best-Case Biofuel vs. Electricity System Development Table 6. Overview of the scenarios tested in the sensitivity analysis on the best-case biofuel scenario vs. electrificationThis sensitivity under electricity analysis (see system Table development 6) compares scenariosa best-case (values biofuel forscenario 2015/ 20–2050,(full hybridization, interpolated forno intermediate LUC emissions) years). with electrification scenarios under different electricity system developments regarding CO2 emission intensity. Electrification BAU Biofuel with Hybrid Table 6. Overview of the scenarios tested in the sensitivity analysis2015/17 on the best-case Best Case biofuel scenario Worst Case vs. electrification under electricity system development scenarios (values for 2015/20–2050, Ee50/56-Ee56 Ee50/57-E100 Ee50/56-Ee56 Fuelsinterpolated for intermediate years). B0/10-B10 B0/14-B100 B0/10-B10 Electrification Vehicles Model BAU 0%–100% Biofuel hybrid with Hybrid 0%–100% electric 2015/17 Best case Worst case ElectricityEe50/56-Ee56 Worst case Ee50/57-E100 2015/17 Ee50/56-Ee56 Best case Worst case Fuels Battery manuf.B0/10-B10 B0/14-B100 Average B0/10-B10 LUCVehicles Avg forest Model None 0%–100% hybrid 0%–100% Avg electric forest Electricity Worst case 2015/17 Best case Worst case Battery manuf. Average 4. Results LUC Avg forest None Avg forest The following contains an overview of the results of the various LCAs tested as described above. 4. Results First, the main LCA results are shown, split into separate sections for cars and urban buses. This is followed byThe a section following for contains the relevant an overview results of of the each results of the of the sensitivity various LCAs analysis tested for as described both modes above. combined. First, the main LCA results are shown, split into separate sections for cars and urban buses. This is 4.1. Mainfollowed LCA by a section for the relevant results of each of the sensitivity analysis for both modes combined. Figure4 shows the CO 2 emissions of the BAU scenarios of cars, urban buses and BRT buses, respectively,4.1. Main in theLCA main LCA. Cars are by far the dominant mode, while BRT buses are insignificant.

Annual & average total CO2 emissions (Mt) - by mode 140

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0 2015 2020 2025 2030 2035 2040 2045 2050 Average - BAU - Cars BAU - Urban buses BAU - BRT

Figure 4.FigureAnnual 4. Annual and and average average (for (for all all years) years) CO CO2 2emissionsemissions for the for BAU the scena BAUrios scenarios in the main in theLCA main by LCA by mode.mode.

Figure 4 shows the CO2 emissions of the BAU scenarios of cars, urban buses and BRT buses, respectively, in the main LCA. Cars are by far the dominant mode, while BRT buses are insignificant.

Sustainability 2019, 11, 6332 13 of 31 Sustainability 2019, 11, x FOR PEER REVIEW 13 of 32

4.1.1.4.1.1 Cars Cars TheThe results results for for the the main main LCA LCA scenarios scenarios in Figu Figurere 5 show that that the the biofuel biofuel scenario scenario without without hybridizationhybridization of theof the fleet fleet has has greater greater emissions than than BAU BAU on onaverage, average, while while biofuel biofuel with hybrid with hybridis approximately equal on average. Both electrification scenarios (best and worst case) have lower is approximately equal on average. Both electrification scenarios (best and worst case) have lower emissions than BAU throughout and on average. emissions than BAU throughout and on average.

Annual & average total CO2 emissions (Mt) - cars 200

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0 emissions 2015 2020 2025 2030 2035 2040 2045 2050 Average - CO2 -50

-100 BAU Biofuel & hybrid Biofuel w/o hybrid Electrifica on, EPE best case Electrifica on, EPE worst case

FigureFigure 5. Annual 5. Annual and and average average (for (for all all years) years) COCO2 emissionsemissions for for the the scenarios scenarios examined examined for cars for in cars the in the mainmain LCA. LCA. EPE: EPE: Energy Energy Research Research O Office.ffice.

FigureFigure6 shows 6 shows the the composition composition of of the the scenarios scenarios in the the main main LCA LCA according according to the to thedifferent di fferent life life phasesphases in 2020, in 2020, 2035 2035 and and 2050. 2050. The The BAU BAU scenarioscenario is is predominated predominated by by exhaust exhaust emissions emissions (grey), (grey), while while the biofuel scenarios have large portions from LUC emissions (red), especially in 2035 and 2050. The the biofuel scenarios have large portions from LUC emissions (red), especially in 2035 and 2050. The electrification scenarios have large portions for manufacturing emissions (blue) in 2050, but in the electrificationbest-case scenario, scenarios a great have deal large of those portions would for be manufacturing offset by the end-of-life emissions credit (blue) for increased in 2050, reuse but in the best-caseand recycling scenario, of a greatthe parts deal and of thosematerials would (black). be o ffInset addition, by the end-of-life this scenario credit is notable for increased for the large reuse and recyclingnegative of theportion parts of and LUC materials emissions (black). as the Inuse addition, of biofuel this decreases scenario and is notablethe land for is theswitched large negativeto portionsequestration. of LUC emissions In the worst-case as the use electrification of biofuel decreasesscenario, electricity and the landgeneration is switched (green) to is sequestration.a significant In the worst-casecontributor electrificationto the overall scenario,emissions electricityin 2050 due generation to the greater (green) emission is a significant intensity contributorof electricity to the overallgeneration emissions in this in 2050 scenario. due toFor the the greater same reason emission the end-of-life intensity credit of electricity is diminished. generation in this scenario. For theSustainability same reason 2019, 11, thex FOR end-of-life PEER REVIEW credit is diminished. 14 of 32

FigureFigure 6. Composition 6. Composition of of the the main LCA LCA scenarios scenarios accordin accordingg to life-phase to life-phase for the foryears the 2020, years 2035 2020, and 2035 and 2050.2050.

Figure 7 shows the result for air pollutant emissions in the main LCA scenarios. It shows electrification has a clear advantage over BAU. For NOx, the biofuel scenarios have similar emissions to the (fossil-dominated) BAU scenario, while the air pollutant emissions for the electrification scenarios reduce to zero. For PM emissions, all alternatives to BAU have lower emissions. However, as shown in Figure 8, if policies and technologies are applied which contribute to an 8.67% annual improvement, as suggested by Miller et al. [56], the differences in powertrain technology become much less pronounced and all scenarios reduce to much lower levels by 2050.

Annual total combined NOx emissions (tonnes) - cars Annual total combined PM emissions (tonnes) - cars 35,000 500 450 30,000 400

25,000 350

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15,000 200 150 10,000 emissions

emissions 100

5,000 PM 50 NOx 0 0 2015 2020 2025 2030 2035 2040 2045 2050 2015 2020 2025 2030 2035 2040 2045 2050 BAU Biofuel & hybrid BAU Biofuel & hybrid Biofuel w/o hybrid Electrifica on, EPE best case Biofuel w/o hybrid Electrifica on, EPE best case Electrifica on, EPE worst case Electrifica on, EPE worst case

Figure 7. Total air pollutant (nitrous oxides (NOx) and particulate matter (PM)) emissions (2012 levels) of cars in the main LCA scenarios.

Sustainability 2019, 11, x FOR PEER REVIEW 14 of 32

Sustainability 2019, 11, 6332 14 of 31 Figure 6. Composition of the main LCA scenarios according to life-phase for the years 2020, 2035 and 2050. Figure7 shows the result for air pollutant emissions in the main LCA scenarios. It shows electrificationFigure has 7 shows a clear the advantage result for over air BAU. pollutant For NOemxissions, the biofuel in the scenarios main LCA have scenarios. similar It emissions shows to the (fossil-dominated)electrification has a clear BAU advantage scenario, whileover BAU. the airFor pollutant NOx, the biofuel emissions scenarios for the have electrification similar emissions scenarios reduceto the to zero. (fossil-dominated) For PM emissions, BAU allscen alternativesario, while tothe BAU air havepollutant lower em emissions.issions for However, the electrification as shown in scenarios reduce to zero. For PM emissions, all alternatives to BAU have lower emissions. However, Figure8, if policies and technologies are applied which contribute to an 8.67% annual improvement, as as shown in Figure 8, if policies and technologies are applied which contribute to an 8.67% annual suggested by Miller et al. [56], the differences in powertrain technology become much less pronounced improvement, as suggested by Miller et al. [56], the differences in powertrain technology become andmuch all scenarios less pronounced reduce to and much all scenarios lower levels reduce by to 2050. much lower levels by 2050.

Annual total combined NOx emissions (tonnes) - cars Annual total combined PM emissions (tonnes) - cars 35,000 500 450 30,000 400

25,000 350

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15,000 200 150 10,000 emissions

emissions 100

5,000 PM 50 NOx 0 0 2015 2020 2025 2030 2035 2040 2045 2050 2015 2020 2025 2030 2035 2040 2045 2050 BAU Biofuel & hybrid BAU Biofuel & hybrid Biofuel w/o hybrid Electrifica on, EPE best case Biofuel w/o hybrid Electrifica on, EPE best case Electrifica on, EPE worst case Electrifica on, EPE worst case Figure 7. Total air pollutant (nitrous oxides (NOx) and particulate matter (PM)) emissions (2012 levels) SustainabilityFigure 2019 7., 11 Total, x FOR air PEERpollutant REVIEW (nitrous oxides (NOx) and particulate matter (PM)) emissions (2012 15 of 32 Sustainabilityof cars in the2019 main, 11, x FOR LCA PEER scenarios. REVIEWlevels) of cars in the main LCA scenarios. 15 of 32

Annual total combined NOx emissions (tonnes) - cars Annual total combined PM emissions (tonnes) - cars 16,000 Annual total combined NOx emissions (tonnes) - cars 350 Annual total combined PM emissions (tonnes) - cars 16,000 350 14,000 300 14,000 300 12,000

250 12,000

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50 emissions PM 2,000 NOx 50 2,000 PM NOx 0 0 0 2015 2020 2025 2030 2035 2040 2045 2050 02015 2020 2025 2030 2035 2040 2045 2050 2015 2020 2025 2030 2035 2040 2045 2050 2015 2020 2025 2030 2035 2040 2045 2050 BAU Biofuel & hybrid BAU Biofuel & hybrid BAUBiofuel w/o hybrid BiofuelElectrifica & hybridon, EPE best case BAUBiofuel w/o hybrid BiofuelElectrifica & hybridon, EPE best case BiofuelElectrif icaw/oon, hybrid EPE worst case Electrifica on, EPE best case BiofuelElectrif icaw/oon, hybrid EPE worst case Electrifica on, EPE best case Electrifica on, EPE worst case Electrifica on, EPE worst case

FigureFigure 8. Total 8. Total air air pollutant pollutant (NO (NOx xand andPM) PM) emissionsemissions (2012 (2012 levels, levels, 8.67% 8.67% annual annual reduction) reduction) of cars of carsin in Figure 8. Total air pollutant (NOx and PM) emissions (2012 levels, 8.67% annual reduction) of cars in the mainthe main LCA LCA scenarios. scenarios. the main LCA scenarios. FigureFigure9 shows 9 shows the the results results of of the the electricity electricity consumption consumption (left) (left) and and land land use use (right). (right). The The amount amount Figure 9 shows the results of the electricity consumption (left) and land use (right). The amount of electricityof electricity consumed consumed steadily steadily increases, increases, approachingapproaching 9% 9% of of the the worst-case worst-case generation generation capacity. capacity. The The of electricity consumed steadily increases, approaching 9% of the worst-case generation capacity. The proportionproportion of theof the arable arable land land area area required required toto meetmeet the demand demand for for ethanol ethanol increases increases steadily steadily to over to over proportion of the arable land area required to meet the demand for ethanol increases steadily to over 13% and 16% in 2050 for the hybrid and non-hybrid scenarios, respectively. 13%13% and and 16% 16% in 2050in 2050 for for the the hybrid hybrid and and non-hybrid non-hybrid scenarios, respectively. respectively.

Annual electricity use by BEVs as propor on of EPE worst case Propor on of total arable land required to grow biofuel Annual electricity use by BEVsgenera as proporon - on of EPE worst case Propor on of total arablefeedstocks land -required cars to grow biofuel generacars on - 18% feedstocks - cars 18% 10% cars 16%

10%9% 16%

14% (%)

(%) 9%

8% 14% (%)

(%) on 12% 8%

7% land

on 12% 7%6% land

10% 6%5% 10% arable genera 8%

5% of arable of

genera 4%

8% 6% of on of

on 4%

3% 6% on on 3%2% 4% 4% 2%1% 2% Propor Propor 1% 2%

0% Propor Propor 0% 0% 2015 2020 2025 2030 2035 2040 2045 2050 0%2015 2020 2025 2030 2035 2040 2045 2050 BEV2015 use - BAU2020 2025 2030 BEV2035 use - Biofuel2040 & hybrid2045 2050 2015BAU 2020 2025 2030 2035Biofuel &2040 hybrid 2045 2050 BEV use -- BAUBiofuel w/o hybrid BEV use -- BiofuelElectrifica & hybridon, EPE best case BAUBiofuel w/o hybrid BiofuelElectrifica & hybridon, EPE best case BEV use -- BiofuelElectrif icaw/oon, hybrid EPE worst case BEV use - Electrifica on, EPE best case BiofuelElectrif icaw/oon, hybrid EPE worst case Electrifica on, EPE best case BEV use - Electrifica on, EPE worst case Electrifica on, EPE worst case Figure 9. Proportion of worst-case electricity generation consumed (left) and the proportion of 2016- FigureFigure 9. 9.Proportion Proportion of worst-case worst-case electricity electricity generati generationon consumed consumed (left) and (left) the and proportion the proportion of 2016- of level arable land required to grow biofuel feedstocks (right) for the scenarios in the main LCA for 2016-levellevel arable arable land land required required to togrow grow biofuel biofuel feedst feedstocksocks (right) (right) for forthe thescenar scenariosios in the in main the main LCA LCA for for cars. BEVs: battery electric vehicles. cars.cars. BEVs: BEVs: battery battery electric electric vehicles. vehicles. 4.1.2 Urban Buses 4.1.2 Urban Buses The results of the main LCA for urban buses (Figure 10) follow a similar pattern (between The results of the main LCA for urban buses (Figure 10) follow a similar pattern (between scenarios) as for cars, above. The electrification of urban buses would result in the lowest CO2 scenarios) as for cars, above. The electrification of urban buses would result in the lowest CO2 emissions, with the two biofuel scenarios resulting in the greatest emissions. However, on a yearly emissions, with the two biofuel scenarios resulting in the greatest emissions. However, on a yearly basis, the hybrid biofuel scenario becomes lower than BAU by 2050, as the overall use of fuel reduces basis, the hybrid biofuel scenario becomes lower than BAU by 2050, as the overall use of fuel reduces and much of the land previously used to grow biofuel feedstocks switches to sequestration. The and much of the land previously used to grow biofuel feedstocks switches to sequestration. The biofuel scenarios are sensitive to the gradient of vehicle use. This holds for cars also, but there are biofuel scenarios are sensitive to the gradient of vehicle use. This holds for cars also, but there are several differences which account for the greater variation for urban buses. several differences which account for the greater variation for urban buses. • Urban bus use increases in the early stages of the analysis, where car use does not. • Urban bus use increases in the early stages of the analysis, where car use does not. • Biodiesel results in greater LUC emissions per liter because of the lower yield per hectare • Biodiesel results in greater LUC emissions per liter because of the lower yield per hectare of land. of land. • The scenarios dictate a greater increase per slot for biodiesel, which starts at zero and • The scenarios dictate a greater increase per slot for biodiesel, which starts at zero and increases to 100% (≈14% per 5-year slot), whereas ethanol starts from ≈50%. increases to 100% (≈14% per 5-year slot), whereas ethanol starts from ≈50%.

Sustainability 2019, 11, 6332 15 of 31

4.1.2. Urban Buses The results of the main LCA for urban buses (Figure 10) follow a similar pattern (between scenarios) as for cars, above. The electrification of urban buses would result in the lowest CO2 emissions, with the two biofuel scenarios resulting in the greatest emissions. However, on a yearly basis, the hybrid biofuel scenario becomes lower than BAU by 2050, as the overall use of fuel reduces and much of the land previously used to grow biofuel feedstocks switches to sequestration. The biofuel scenarios are sensitive to the gradient of vehicle use. This holds for cars also, but there are several differences which account for the greater variation for urban buses.

Urban bus use increases in the early stages of the analysis, where car use does not. • Biodiesel results in greater LUC emissions per liter because of the lower yield per hectare of land. • The scenarios dictate a greater increase per slot for biodiesel, which starts at zero and increases to • 100% ( 14% per 5-year slot), whereas ethanol starts from 50%. Sustainability 2019≈ , 11, x FOR PEER REVIEW ≈ 16 of 32

Annual & average total CO2 emissions (Mt) - urban buses 25

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(Mt) 10

5 emissions

CO2 0 2015 2020 2025 2030 2035 2040 2045 2050 Average - -5 BAU Biofuel & hybrid Biofuel w/o hybrid Electrifica on, EPE best case Electrifica on, EPE worst case

Figure 10. AnnualAnnual and and average (for all years) CO 2 emissionsemissions for for the scenarios examined for urban buses in the main LCA.

The composition ofof thethe total total emissions emissions according according to to life life phases phases for for urban urban buses buses (Figure (Figure 11) is11) also is alsosimilar similar in pattern in pattern to cars. to Thecars. biofuel The biofuel scenarios scenario haves largehave land-uselarge land-use change change emissions, emissions, especially especially in 2035, inand 2035, the worst-caseand the worst-case electrification electrification scenario has scenar large electricityio has large generation electricity emissions. generation Manufacturing emissions. Manufacturingand end-of-life emissionsand end-of-life make onlyemissions very small make or only insignificant very small contributions. or insignificant This figure contributions. also shows This the figuresource ofalso the shows negative the overall source emissions of the calculatednegative foroverall the best-caseemissions electrification calculated scenario:for the primarily,best-case electrificationthe large negative scenario: emissions primarily, from LUC,the large as biofuel negative use emissions is reduced. from LUC, as biofuel use is reduced. Also regarding air pollutant emissions, the same pattern emerges as for cars, as shown in Figure 12. The results and expected trends are similar to those of the car scenarios, with the biofuel scenarios without and with hybrid close to, but above and below, BAU, respectively, while the electrification scenarios steadily decrease to zero. However, as shown in Figure 13, if policies and technologies are applied which contribute to a 8.67% annual improvement, as suggested by Miller et al. [56], the differences in powertrain technology become much less pronounced and the emissions from all scenarios diminish to much lower levels by 2050.

Figure 11. Composition of the main LCA scenarios according to life-phase for the years 2020, 2035 and 2050.

Also regarding air pollutant emissions, the same pattern emerges as for cars, as shown in Figure 12. The results and expected trends are similar to those of the car scenarios, with the biofuel scenarios without and with hybrid close to, but above and below, BAU, respectively, while the electrification scenarios steadily decrease to zero. However, as shown in Figure 13, if policies and technologies are applied which contribute to a 8.67% annual improvement, as suggested by Miller et al. [56], the differences in powertrain technology become much less pronounced and the emissions from all scenarios diminish to much lower levels by 2050.

Sustainability 2019, 11, x FOR PEER REVIEW 16 of 32

Annual & average total CO2 emissions (Mt) - urban buses 25

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CO2 0 2015 2020 2025 2030 2035 2040 2045 2050 Average - -5 BAU Biofuel & hybrid Biofuel w/o hybrid Electrifica on, EPE best case Electrifica on, EPE worst case

Figure 10. Annual and average (for all years) CO2 emissions for the scenarios examined for urban buses in the main LCA.

The composition of the total emissions according to life phases for urban buses (Figure 11) is also similar in pattern to cars. The biofuel scenarios have large land-use change emissions, especially in 2035, and the worst-case electrification scenario has large electricity generation emissions. Manufacturing and end-of-life emissions make only very small or insignificant contributions. This figureSustainability also 2019shows, 11, 6332the source of the negative overall emissions calculated for the best-case16 of 31 electrification scenario: primarily, the large negative emissions from LUC, as biofuel use is reduced.

FigureFigure 11. 11. CompositionComposition of ofthe the main main LCA LCA scenarios scenarios ac accordingcording to tolife-phase life-phase for for the the years years 2020, 2020, 2035 2035 andSustainabilityand 2050. 2050. 2019, 11, x FOR PEER REVIEW 17 of 32 Sustainability 2019, 11, x FOR PEER REVIEW 17 of 32

Also regardingAnnual total aircombined pollutant NOx emissions emissions, (tonnes) - urban the buses same patternAnnual emerges total combined as PMfor emissions cars, (tonnes) as shown - urban buses in Figure 25,000 Annual total combined NOx emissions (tonnes) - urban buses 250 Annual total combined PM emissions (tonnes) - urban buses 12. The results25,000 and expected trends are similar to those250 of the car scenarios, with the biofuel scenarios without 20,000and with hybrid close to, but above and below,200 BAU, respectively, while the electrification

20,000 200

scenarios15,000 steadily decrease to zero. However, as shown 150 in Figure 13, if policies and technologies are

15,000 150 (tonnes)

(tonnes) applied 10,000which contribute to a 8.67% annual improvement,100 as suggested by Miller et al. [56], the (tonnes)

(tonnes) differences 10,000 in powertrain technology become much 100less pronounced and the emissions from all emissions 5,000 50 emissions

PM emissions 5,000 50 emissions

scenarios diminish to much lower levels by 2050. NOx

0 PM 0 NOx 2015 2020 2025 2030 2035 2040 2045 2050 02015 2020 2025 2030 2035 2040 2045 2050 0 2015BAU 2020 2025 2030 2035Biofuel & 2040hybrid 2045 2050 2015BAU 2020 2025 2030 2035Biofuel &2040 hybrid 2045 2050 Biofuel w/o hybrid Electrifica on, EPE best case Biofuel w/o hybrid Electrifica on, EPE best case BAU Biofuel & hybrid BAU Biofuel & hybrid Electrifica on, EPE worst case Electrifica on, EPE worst case Biofuel w/o hybrid Electrifica on, EPE best case Biofuel w/o hybrid Electrifica on, EPE best case Electrifica on, EPE worst case Electrifica on, EPE worst case Figure 12. Total air pollutant (NOx and PM) emissions (2012 levels) of urban buses in the main LCA Figure 12. Total air pollutant (NOx and PM) emissions (2012 levels) of urban buses in the main scenarios.Figure 12. Total air pollutant (NOx and PM) emissions (2012 levels) of urban buses in the main LCA LCAscenarios. scenarios.

Annual total combined NOx emissions (tonnes) - urban buses Annual total combined PM emissions (tonnes) - urban buses 20,000 Annual total combined NOx emissions (tonnes) - urban buses 200 Annual total combined PM emissions (tonnes) - urban buses 18,00020,000 180200 16,00018,000 160180

14,00016,000 140160

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PM emissions 2,0004,000 2040 emissions

NOx

2,0000 PM 200 NOx 2015 2020 2025 2030 2035 2040 2045 2050 02015 2020 2025 2030 2035 2040 2045 2050 0 2015BAU 2020 2025 2030 2035Biofuel & 2040hybrid 2045 2050 2015BAU 2020 2025 2030 2035Biofuel &2040 hybrid 2045 2050 Biofuel w/o hybrid Electrifica on, EPE best case Biofuel w/o hybrid Electrifica on, EPE best case BAU Biofuel & hybrid BAU Biofuel & hybrid Electrifica on, EPE worst case Electrifica on, EPE worst case Biofuel w/o hybrid Electrifica on, EPE best case Biofuel w/o hybrid Electrifica on, EPE best case Electrifica on, EPE worst case Electrifica on, EPE worst case FigureFigure 13. 13.Total Total air air pollutant pollutant (NO (NOx xand and PM)PM) emissions (2012 (2012 levels, levels, 8.67% 8.67% annual annual reduction) reduction) of urban of urban busesbusesFigure in the in 13. the main Total main LCA air LCA pollutant scenarios. scenarios. (NO x and PM) emissions (2012 levels, 8.67% annual reduction) of urban buses in the main LCA scenarios. FigureFigure 14 shows14 shows the the results results of of proportional proportional electricity consumptio consumptionn (left) (left) and and proportional proportional land land useuse (right). (right).Figure Regarding Regarding14 shows electricity the electricity results use, ofuse, pr electricelectricoportional urban electricity bus bus electricity electricity consumptio consumption consumptionn (left) and peaks proportional peaks at below at below 0.5%, land 0.5%, presumablypresumablyuse (right). an Regarding irrelevantan irrelevant electricity amount amount fromuse, from electric a grid-operationala grid-operational urban bus electricity perspective.perspective. consumption The amount amount peaks of of atland landbelow required required 0.5%, to presumably an irrelevant amount from a grid-operational perspective. The amount of land required growto sugrowfficient sufficient biofuel biofuel feedstocks feedst forocks urban for urban buses buses peaks peaks at below at be 1%low for 1% the for highest the highest scenario: scenario: biofuels to grow sufficient biofuel feedstocks for urban buses peaks at below 1% for the highest scenario: withoutbiofuels hybrid, without while hybrid, the biofuelwhile the with biofuel hybrid with scenario hybrid scenario peaks at peaks below at 0.8%.below 0.8%. biofuels without hybrid, while the biofuel with hybrid scenario peaks at below 0.8%. Annual electricity use by BEVs as propor on of EPE worst case Propor on of total arable land required to grow biofuel Annual electricity use by generaBEVs as onpropor - on of EPE worst case Propor on of totalfeedstocks arable land- urban required buses to grow biofuel

urbangenera buseson - 1.0% feedstocks - urban buses 0.6% urban buses 0.9%1.0% 0.6% 0.5% 0.8%0.9% (%)

(%)

0.7%0.8% on 0.5% (%)

0.4% (%)

land 0.6%0.7%

on 0.4%

land 0.6% 0.3% 0.5% genera

arable

of 0.3% 0.4%0.5% genera of

0.2% arable on

of

0.3%0.4% on of 0.2% on 0.3% 0.1% on 0.2%

Propor 0.1% 0.1%0.2% 0.0% Propor Propor 0.0%0.1% 0.0%2015 2020 2025 2030 2035 2040 2045 2050 Propor 2015 2020 2025 2030 2035 2040 2045 2050 BEV2015 use - BAU 2020 2025 2030 BEV2035 use - Biofuel2040 & hybrid2045 2050 0.0% BAU Biofuel & hybrid BEV use - Biofuel w/o hybrid BEV use - Electrifica on, EPE best case Biofuel w/o hybrid Electrifica on, EPE best case BEV use - BAU BEV use - Biofuel & hybrid 2015BAU 2020 2025 2030 2035Biofuel &2040 hybrid 2045 2050 BEV use - Electrifica on, EPE worst case Electrifica on, EPE worst case BEV use - Biofuel w/o hybrid BEV use - Electrifica on, EPE best case Biofuel w/o hybrid Electrifica on, EPE best case BEV use - Electrifica on, EPE worst case Electrifica on, EPE worst case Figure 14. Proportion of worst-case electricity generation consumed (left) and the proportion of 2016- levelFigure arable 14. Proportion land required of worst-case to grow biofuel electricity feedstoc generatiks (right),on consumed both for (left) the andscenarios the proportion in the main of 2016-LCA forlevel urban arable buses. land required to grow biofuel feedstocks (right), both for the scenarios in the main LCA for urban buses.

Sustainability 2019, 11, x FOR PEER REVIEW 17 of 33

Annual total combined NOx emissions (tonnes) - urban buses Annual total combined PM emissions (tonnes) - urban buses 25,000 250

20,000 200

15,000 150 (tonnes)

(tonnes) 10,000 100 emissions 5,000 50 emissions

PM NOx 0 0 2015 2020 2025 2030 2035 2040 2045 2050 2015 2020 2025 2030 2035 2040 2045 2050 BAU Biofuel & hybrid BAU Biofuel & hybrid Biofuel w/o hybrid Electrifica on, EPE best case Biofuel w/o hybrid Electrifica on, EPE best case Electrifica on, EPE worst case Electrifica on, EPE worst case

Figure 12. Total air pollutant (NOx and PM) emissions (2012 levels) of urban buses in the main LCA scenarios.

Annual total combined NOx emissions (tonnes) - urban buses Annual total combined PM emissions (tonnes) - urban buses 20,000 200 18,000 180 16,000 160

14,000 140

12,000 120 10,000 100 (tonnes)

(tonnes) 8,000 80 6,000 60 emissions 4,000 40 emissions

2,000 PM 20 NOx 0 0 2015 2020 2025 2030 2035 2040 2045 2050 2015 2020 2025 2030 2035 2040 2045 2050 BAU Biofuel & hybrid BAU Biofuel & hybrid Biofuel w/o hybrid Electrifica on, EPE best case Biofuel w/o hybrid Electrifica on, EPE best case Electrifica on, EPE worst case Electrifica on, EPE worst case

Figure 13. Total air pollutant (NOx and PM) emissions (2012 levels, 8.67% annual reduction) of urban buses in the main LCA scenarios.

Figure 14 shows the results of proportional electricity consumption (left) and proportional land use (right). Regarding electricity use, electric urban bus electricity consumption peaks at below 0.5%, presumably an irrelevant amount from a grid-operational perspective. The amount of land required to grow sufficient biofuel feedstocks for urban buses peaks at below 1% for the highest scenario: Sustainability 2019, 11, x FOR PEER REVIEW 18 of 32 biofuels without hybrid, while the biofuel with hybrid scenario peaks at below 0.8%. Sustainability4.2. Sensitivity2019, 11, Analysis 6332 #1: Land-Use Change Emissions (Biofuel) 17 of 31

The total annual CO2 emissions for the sensitivity analysis of various LUC emission scenarios

are shownAnnual electricityin Figure use by 15,BEVs below.as propor onAlso of EPE included,worst case for perspective, are the results for BAU if the LUC emissions were disregarded.genera on - urban buses 0.6% For both cars and urban buses, the results are quite sensitive to the LUC scenario selected, with

0.5% (%) a considerable difference between the biofuel scenarios with no-LUC (blue, left) and high-density on forest0.4% (grey, right), in line with the overall contribution of LUC emissions to the overall result and 0.3% genera 2

theof CO embedded in the various biomes (values in t/ha), Cerrado (122, green, left), plantation forest

0.2% (212,on grey, left), then low- (284, blue, right), average- (459, green, right) and high-density forest (635, 0.1%

grey,Propor right). 0.0% 2015For cars,2020 on2025 average, 2030 the2035 plantation 2040 2045 forest, 2050 Cerrado and low-density forest biomes result in lower BEV use - BAU BEV use - Biofuel & hybrid overallBEV use - Biofuelemissions w/o hybrid than BAU;BEV use the - Electrifica average-on, EPE best caseand high-density forest biomes result in greater overall BEV use - Electrifica on, EPE worst case emissions than BAU. FigureFor 14. buses,Proportion if the land of worst-case for biofuel electricity feedstocks generation is assumed consumed to come (left) from/return and the proportionto Cerrado ofor plantation2016-level arableforest, landon average, required the to grow results biofuel are lower feedstocks than (right),BAU, bothwith forall theremaining scenarios forest in the biomes main resultingLCA for urbanin greater buses. emissions than BAU. Furthermore, the difference between BAU with (black) and without (red) LUC emissions can be 4.2. Sensitivityseen; for cars, Analysis BAU without #1: Land-Use LUC is Changeconsistently Emissions below (Biofuel)BAU, but for urban buses, the same holds until between 2035 and 2040, where the lines cross due to the LUC emissions switching to sequestration in The total annual CO2 emissions for the sensitivity analysis of various LUC emission scenarios are the BAU scenario. shown in Figure 15, below. Also included, for perspective, are the results for BAU if the LUC emissions In addition, in general, it can be seen that the results are also quite sensitive to year-to-year were disregarded. changes of fuel use.

Annual & average total CO2 emissions (Mt) - cars Annual & average total CO2 emissions (Mt) - cars 200 200 180 180 160 160 140 140

120 120 (Mt) (Mt) 100 100 80 80 60 60 emissions emissions

40 40 CO2 CO2 20 20 0 0 2015 2020 2025 2030 2035 2040 2045 2050 Average - 2015 2020 2025 2030 2035 2040 2045 2050 Average - BAU BAU - no LUC BAU BAU - no LUC Biofuel, ½ hybrid, no LUC Biofuel, ½ hybrid, Cerrado LUC Biofuel, ½ hybrid, low forest LUC Biofuel, ½ hybrid, avg forest LUC Biofuel, ½ hybrid, planta on forest LUC Biofuel, ½ hybrid, high forest LUC

Annual & average total CO2 emissions (Mt) - urban buses Annual & average total CO2 emissions (Mt) - urban buses 30 30

25 25

20 20

(Mt) (Mt)

15 15

10 10 emissions emissions

5 5 CO2 CO2

0 0 2015 2020 2025 2030 2035 2040 2045 2050 Average - 2015 2020 2025 2030 2035 2040 2045 2050 Average - BAU BAU - no LUC BAU BAU - no LUC Biofuel, ½ hybrid, no LUC Biofuel, ½ hybrid, Cerrado LUC Biofuel, ½ hybrid, low forest LUC Biofuel, ½ hybrid, avg forest LUC Biofuel, ½ hybrid, planta on forest LUC Biofuel, ½ hybrid, high forest LUC

FigureFigure 15. Annual15. Annual and and average average (for (for all all years) years) COCO22 emissions for for the the scenarios scenarios examined examined for forcars cars (left) (left) andand urban urban buses buses (right) (right) in in the the sensitivity sensitivity analysis analysis regardingregarding LUC emissions. emissions.

For both cars and urban buses, the results are quite sensitive to the LUC scenario selected, with a considerable difference between the biofuel scenarios with no-LUC (blue, left) and high-density forest (grey, right), in line with the overall contribution of LUC emissions to the overall result and the CO2 embedded in the various biomes (values in t/ha), Cerrado (122, green, left), plantation forest (212, grey, left), then low- (284, blue, right), average- (459, green, right) and high-density forest (635, grey, right). For cars, on average, the plantation forest, Cerrado and low-density forest biomes result in lower overall emissions than BAU; the average- and high-density forest biomes result in greater overall emissions than BAU. Sustainability 2019, 11, 6332 18 of 31

For buses, if the land for biofuel feedstocks is assumed to come from/return to Cerrado or plantation forest, on average, the results are lower than BAU, with all remaining forest biomes resulting in greater emissions than BAU. Furthermore, the difference between BAU with (black) and without (red) LUC emissions can be seen; for cars, BAU without LUC is consistently below BAU, but for urban buses, the same holds until between 2035 and 2040, where the lines cross due to the LUC emissions switching to sequestration in the BAU scenario. In addition, in general, it can be seen that the results are also quite sensitive to year-to-year changes of fuel use. Sustainability 2019, 11, x FOR PEER REVIEW 19 of 32 Sustainability 2019, 11, x FOR PEER REVIEW 19 of 32 4.3. Sensitivity Analysis #2: Battery Manufacturing Emissions 4.3. Sensitivity Analysis #2: Battery Manufacturing Emissions 4.3. Sensitivity Analysis #2: Battery Manufacturing Emissions This sensitivity analysis examines the effect of battery manufacturing emissions on a CO2 per This sensitivity analysis examines the effect of battery manufacturing emissions on a CO2 per kWhkWh of batteryThis of battery sensitivity capacity capacity analysis basis. basis. Theexamines The results results the are areeffect shown show of nbattery in in Figure Figure manufacturing 16 16,, showing showing emissions thatthat thethe overall on a CO results2 per are not particularlyarekWh not of particularlybattery sensitive capacity sensitive to basis. this to change Thethis resultschange for cars, arefor showcars, and andn practically in practicallyFigure 16, not notshowing at at all all for forthat urban urban the overall buses. buses. results are not particularly sensitive to this change for cars, and practically not at all for urban buses. Annual & average total CO2 emissions (Mt) - cars Annual & average total CO2 emissions (Mt) - urban buses 200 Annual & average total CO2 emissions (Mt) - cars 25 Annual & average total CO2 emissions (Mt) - urban buses 200 25 150 20 150 20 100 15

100 15

(Mt)

(Mt)

50 10 (Mt)

(Mt)

50 10 0 5 emissions

2015 2020 2025 2030 2035 2040 2045 2050 Average - emissions 0 5

emissions -50 0 2015 2020 2025 2030 2035 2040 2045 2050 Average - emissions CO2

CO2 -50 02015 2020 2025 2030 2035 2040 2045 2050 Average - CO2 -100 CO2 -52015 2020 2025 2030 2035 2040 2045 2050 Average - -100 -5 BAU Biofuel, ½ hybrid Elec., avg ba ery manuf. BAU Biofuel, ½ hybrid Elec., avg ba ery manuf. Elec.,BAU min ba ery manuf. Elec.,Biofuel, max ½ hybridba ery manuf. Elec., avg ba ery manuf. Elec.,BAU min ba ery manuf. Elec.,Biofuel, max ½ hybridba ery manuf. Elec., avg ba ery manuf. Elec., min ba ery manuf. Elec., max ba ery manuf. Elec., min ba ery manuf. Elec., max ba ery manuf. Figure 16. Annual and average (for all years) CO emissions for the scenarios examined for cars (left) Figure 16. Annual and average (for all years) CO2 emissions for the scenarios examined for cars (left) andandFigure urban urban 16. buses Annualbuses (right) (right) and in average in the the sensitivity sensitivity (for all years)analysis analys COis2 regardingemissions batteryfor battery the scenariosmanufacturing manufacturing examined emissions. emissions. for cars (left) and urban buses (right) in the sensitivity analysis regarding battery manufacturing emissions. 4.4. Sensitivity Analysis #3: Vehicle and Battery Lives 4.4. Sensitivity Analysis #3: Vehicle and Battery Lives 4.4. Sensitivity Analysis #3: Vehicle and Battery Lives ThisThis sensitivity sensitivity analysis analysis examines examines the effect effect of of a athis this change change in the in thelife-length life-length (both (bothexpressed expressed in in km) ofThis both sensitivity vehicles analysis and their examines batteries the ineffect the of electrification a this change in scenarios. the life-length For example,(both expressed in the in 20% km) of both vehicles and their batteries in the electrification scenarios. For example, in the −20%− scenario,scenario,km) of the both the life lifevehicles of of a cara car and is is reduced reducedtheir batteries from 200,000 200,000in the electrificationkm km to to160,000 160,000 km,scenarios. km, in conjunction in For conjunction example, with a within decrease the a − decrease20% in in batterybatteryscenario, lifelife the fromfrom life of ≈ 133,000a car is reducedkm km to to ≈ 107,000from107,000 200,000 km. km. This km This tohas 160,000 has the the effect km, eff ectinof conjunction needing of needing a greaterwith a greater a decrease(or lower) (or lower)in battery life from ≈≈133,000 km to ≈≈107,000 km. This has the effect of needing a greater (or lower) numbernumber of vehiclesof vehicles and and batteries batteries to to be bemanufactured manufactured each each year, year, with with a corresponding a corresponding change change in the in the number of vehicles and batteries to be manufactured each year, with a corresponding change in the overalloverall proportion proportion of of manufacturing manufacturing emissions. emissions. Figure 1717 shows shows the the results results for for CO CO2 emissions2 emissions with with a a overall proportion of manufacturing emissions. Figure 17 shows the results for CO2 emissions with a 20%±20% change change in in the the assumed assumed vehicle vehicle andand batterybattery life. life. For For cars, cars, this this has has a comparatively a comparatively small small effect, eff ect, ± ±20% change in the assumed vehicle and battery life. For cars, this has a comparatively small effect, especiallyespecially when when compared compared to to the the di differencefference toto thethe BAU and and biofuel biofuel scenarios scenarios shown. shown. For Forurban urban buses, buses, theespecially effect is when even compared smaller; it to is the practically difference invisible. to the BAU and biofuel scenarios shown. For urban buses, the etheffect effect is even is even smaller; smaller; it isit is practically practically invisible. invisible. Annual & average total CO2 emissions (Mt) - cars Annual & average total CO2 emissions (Mt) - urban buses 180 Annual & average total CO2 emissions (Mt) - cars 25 Annual & average total CO2 emissions (Mt) - urban buses 160180 25 140160 20 20 120140

15 100120

(Mt)

(Mt) 15 10080 (Mt)

(Mt)

10 80 60 10 emissions

emissions

60 40 5 emissions

emissions CO2

2040 CO2 5 CO2 200 CO2 0 02015 2020 2025 2030 2035 2040 2045 2050 Average - 02015 2020 2025 2030 2035 2040 2045 2050 Average - 2015 2020 2025 2030 2035 2040 2045 2050 Average - 2015 2020 2025 2030 2035 2040 2045 2050 Average - BAU Biofuel, ½ hybrid Elec., -20% life Elec., default life Elec., +20% life BAU Biofuel, ½ hybrid Elec., -20% life Elec., default life Elec., +20% life BAU Biofuel, ½ hybrid Elec., -20% life Elec., default life Elec., +20% life BAU Biofuel, ½ hybrid Elec., -20% life Elec., default life Elec., +20% life

Figure 17. Annual and average (for all years) CO2 emissions for the scenarios examined for cars (left) Figure 17. Annual and average (for all years) CO2 emissions for the scenarios examined for cars (left) andFigure urban 17. Annualbuses (right) and average in the sensitivity (for all years) analysis CO2 regardingemissions vehiclefor the scenariosand battery examined lives. for cars (left) andand urban urban buses buses (right) (right) in in the the sensitivity sensitivityanalysis analysis regarding vehicle vehicle and and battery battery lives. lives. The final sensitivity analysis compares a best-case biofuel scenario with the three electrification scenarios,The final the resultssensitivity of which analysis are compares shown in a Figure best-cas 18.e biofuelAs this scenarioshows, all with alternative the three scenarios electrification have lowerscenarios, emissions the results than ofBAU, which throughout are shown and in on Figure averag 18.e. As Furthermore, this shows, on all average, alternative for scenarioscars the worst- have caselower electrification emissions than scenario BAU, throughoutresults in lower and emissionson averag e.than Furthermore, the best-case on biofuel average, scenario, for cars though the worst- the twocase doelectrification converge somewhat scenario results by 2050. in Forlower urban emissions buses, thanall electrification the best-case scenariosbiofuel scenario, initially though rise above the thetwo best-case do converge biofuel somewhat scenario by bu 2050.t subsequently For urban dropbuses, below. all electrification scenarios initially rise above the best-case biofuel scenario but subsequently drop below.

Sustainability 2019, 11, 6332 19 of 31

The final sensitivity analysis compares a best-case biofuel scenario with the three electrification scenarios, the results of which are shown in Figure 18. As this shows, all alternative scenarios have lower emissions than BAU, throughout and on average. Furthermore, on average, for cars the worst-case electrification scenario results in lower emissions than the best-case biofuel scenario, though Sustainability 2019, 11, x FOR PEER REVIEW 20 of 32 the two do converge somewhat by 2050. For urban buses, all electrification scenarios initially rise above4.5. the Sensitivity best-case Analysis biofuel #4: scenario Best-Case but Biofuel subsequently vs. Electricity drop System below. Development

Annual & average total CO2 emissions (Mt) - cars Annual & average total CO2 emissions (Mt) - urban buses 140 18 120 16 100 14 80 12

10 60 8 (Mt)

(Mt)

40 6 20 4 0

emissions 2

emissions 2015 2020 2025 2030 2035 2040 2045 2050 Average - -20 0 CO2 -40 CO2 -22015 2020 2025 2030 2035 2040 2045 2050 Average - -60 -4

BAU Best case biofuel (hybrid, no LUC) BAU Best case biofuel (hybrid, no LUC) Electrifica on, 2015/17 Electrifica on, EPE best case Electrifica on, 2015/17 Electrifica on, EPE best case Electrifica on, EPE worst case Electrifica on, EPE worst case

FigureFigure 18. 18.Annual Annual and and average average (for (for all all years)years) COCO2 emissionsemissions for for the the scenarios scenarios examined examined for cars for cars(left) (left) andand urban urban buses buses (right) (right) in in the the sensitivity sensitivity analysis analysis regarding electricity electricity system system development. development.

4.5. SensitivityOverall, Analysis this sensitivity #4: Best-Case analysis Biofuel shows vs. Electricitythe advantage System of Developmentthe electrification scenarios over biofuels:Overall, even this in sensitivity what could analysis be regarded shows as the extrem advantagee and unrealistic of the electrification scenarios, (biofuel scenarios disregarding over biofuels: evenLUC in whatemissions could altogether, be regarded and electrification as extreme anddominated unrealistic by fossil scenarios, fuels), for (biofuel cars, electrification disregarding still LUC has lower emissions on average and throughout, and for urban buses, on average the result is only emissions altogether, and electrification dominated by fossil fuels), for cars, electrification still has slightly in favor of the biofuel scenario. As such, under more realistic scenarios, it could be expected lower emissions on average and throughout, and for urban buses, on average the result is only slightly that electrification would have lower emissions. in favor of the biofuel scenario. As such, under more realistic scenarios, it could be expected that Finally, the results presented here show that if the current overall CO2 emission intensity of electrificationBrazilian electricity would have generation lower emissions.is maintained into the future (i.e., as per the 2015/17 scenario), the resultingFinally, emissions the results would presented be between here the show EPE that scenario if thes, currentbut closer overall to the CObest2 case,emission i.e., the intensity worst- of Braziliancase scenario electricity is significantly generation more is maintained CO2 emission-intensive into the future than (i.e., the ascurrent per thescenario, 2015/ 17and scenario), the best- the resultingcase scenario emissions is somewhat would be less between CO2 emission-intensive. the EPE scenarios, but closer to the best case, i.e., the worst-case scenario is significantly more CO2 emission-intensive than the current scenario, and the best-case 5. Findings and Discussion scenario is somewhat less CO2 emission-intensive. 1. Cars are the largest contributor to the effects examined in this LCA. 5. FindingsOverall, and of Discussion the modes examined in this LCA, cars have the greatest effect on the results—CO2 emissions, air pollutant emissions, electricity consumption and land-use for biofuel feedstocks— 1. Carsalbeit are to thediffering largest degrees: contributor the results to the for e airffects pollutant examined emissions, in this for LCA example, between cars and urban buses are comparatively close. Despite cars being smaller and having lower emissions per distance drivenOverall, than of buses, the modes their collective examined total in thisdistance LCA, driven cars haveis much, the much greatest higher, effect accounting on the results—CO for their 2 emissions,greater airoverall pollutant effect. emissions, electricity consumption and land-use for biofuel feedstocks—albeit to differing degrees: the results for air pollutant emissions, for example, between cars and urban buses are comparatively2. Electrification close.results Despite in the lowest cars beingCO2 emissions. smallerand having lower emissions per distance driven than buses,This their LCA collectivefinds that the total lowest distance CO2 drivenemissions is much,would be much reached higher, in the accounting electrification for scenarios their greater overallexamined. effect. Even if the worst-case electrification scenario is compared to the best-case biofuel scenario—both of which are probably unlikely given the extremity of their underlying premises—the 2. Electrificationelectrification resultsscenario in results the lowest in lower CO (cars)2 emissions or only slightly higher (urban buses) emissions. In the main LCA, the electrification scenarios offer 65%–89% CO2 savings over BAU on average (note that theThis electrification LCA finds scenarios that the lowestinclude CO negative2 emissions LUC emissions). would be A reached comparison in the of electrificationthis finding with scenarios the examined.other electrification-biofuel Even if the worst-case comparison electrification found [34] scenariois near-impossible is compared due toto thethe best-casevery different biofuel scenario—bothmethodologies of whichapplied are (including probably an unlikely apparent given exclusion the extremity of LUC of emissions) their underlying and the premises—the different electrificationcountries in scenario question. results in lower (cars) or only slightly higher (urban buses) emissions. In the main LCA,Not the shown electrification above is a further scenarios expansion offer 65%–89% of sensitivity CO2 analysissavings #4, over in BAUwhich on the average electrification (note that the electrificationscenarios were calculated scenarios without include LU negativeC emissions. LUC For emissions). cars, the average A comparison CO2 emissions of this were finding almost with identical between the biofuel (with hybrid) and electrification (2015/17) scenarios. For urban buses, the biofuel scenario resulted in near-identical CO2 emissions to the worst-case electrification scenario, i.e., without LUC at all, the BEVs offer improved CO2 emissions, assuming the overall generation

Sustainability 2019, 11, 6332 20 of 31 the other electrification-biofuel comparison found [34] is near-impossible due to the very different methodologies applied (including an apparent exclusion of LUC emissions) and the different countries in question. Not shown above is a further expansion of sensitivity analysis #4, in which the electrification scenarios were calculated without LUC emissions. For cars, the average CO2 emissions were almost identical between the biofuel (with hybrid) and electrification (2015/17) scenarios. For urban buses, the biofuel scenario resulted in near-identical CO2 emissions to the worst-case electrification scenario, i.e., without LUC at all, the BEVs offer improved CO2 emissions, assuming the overall generation profile remains similar to the present. While there is controversy regarding the methodology of including LUC emissions, we consider it unlikely that LUC would be zero; however, even if it were, BEVs under reasonable assumptions would result in similar CO2 emissions to biofuel with full hybridization.

3. Electrification results in lower air pollutant emissions

As with the CO2 emissions, the results suggest that the electrification of cars and buses would be the ideal path as it would lead to the highest reduction in both NOx and PM emissions. Biofuels, on the other hand, in most cases, do not reduce air pollution significantly from BAU (except for car PM emissions from ethanol). The magnitude of this result is greatly diminished; however, if a yearly improvement of 9% in emission intensity is achieved, the difference between scenarios becomes ≈ much smaller.

4. Land-use change emissions are a major contributor to the results for the biofuel scenarios

LUC emissions as calculated here are a major contributor to the overall CO2 emissions of the biofuel scenarios, and there is a great difference between the carbon content of the biomes selected, making the results very sensitive to whether LUC emissions are included or how they are calculated, and the rate of change of vehicle/biofuel use. If the Cerrado or plantation forest biomes are used to calculate LUC emissions, the result is emissions below BAU level, while using average- or high-density forest results in higher-than-BAU emissions, with low-density forest being below BAU for cars, above for urban buses. LUC emissions are delayed and for a limited time (20 years), but the other emissions occur on a year-by-year basis. An interesting aspect of this analysis, covering a long timeframe, is that it shows the way in which LUC emissions fluctuate in relation to the other emissions that occur on a year-to-year basis, which LCAs of a single point in time would be unable to show well. Related to this point is the land that would be required to grow sufficient feedstocks for biofuels. For cars, assuming the worst case of the full use of biofuels (without hybridization), up to 16% of Brazil’s arable land would be required to grow sufficient sugarcane.

5. Electrification is sensitive to electricity system characteristics While the electrification scenarios can potentially yield significant benefits in terms of emission reduction, the amount of benefit depends greatly on the electricity system’s average emission intensity. However, the analysis suggests capacity can be added with higher than current emission intensity (as per the EPE worst case) while still maintaining a significant benefit over BAU. This is in line with the findings of previous studies described in the introduction. Another characteristic of the electricity system of note is the total generation capacity. This analysis shows that cars would result in significant electricity demand: the full-scale electrification of cars is calculated to require up to 9% (in 2050) of the projected total generation capacity of Brazil (38% of ≈ 2017-level generation capacity). Intuitively, this would also be significant in terms of peak loads. The electrification of urban buses is calculated to result in urban bus electricity demand that would remain below 0.6% of the total expected national generation capacity. Sustainability 2019, 11, 6332 21 of 31

Other aspects Similar patterns were found for the sensitivity of the results to battery manufacturing emission factors and the lifetime of vehicles and batteries. Namely, for cars, the overall result was not particularly sensitive to changes, while for urban buses, the overall result was hardly sensitive at all. Presumably, this is due to the relatively lower proportion of the manufacturing phase for urban buses. This is particularly interesting because it is in contrast to many sources in the introduction, suggesting both of these factors are strong determinants of the overall result. The other effects listed in the literature review: charging time of day, noise, and other effects were beyond the scope of this study.

5.1. Policy Recommendations

Cars

From a CO2 and air pollutant emissions standpoint, ideally, Brazil should implement policies to encourage the increased adoption of electric cars. These could include taxation or feebate schemes based on exhaust CO2 emissions, subsidies on the purchase of electric vehicles, or free parking or use of carpooling lanes. As included in Rota 2030, manufacturers could be forced to include and encourage EVs by setting average emission levels targets (which decrease year by year) for all vehicles they sell each year, such as applies in the EU. Subsidies on locally manufactured fuels (including biofuels) should be removed or reduced If electrification is not pursued, the use of ethanol should not be increased, by (at least) maintaining the current 50% proportion of ethanol (to petrol) sold. ≈ Urban buses The recommended course of action for urban buses is, superficially, the same as for cars: from a CO2 and air pollution emissions standpoint, urban buses should be electrified. Electrification of urban buses may be easier than cars because biodiesel is not as well established or widespread in Brazil, and there are far fewer vehicles, and those vehicles tend to be owned and operated by organizations with many vehicles each, limiting the number of affected stakeholders. Given the far lower consumption of urban buses combined, less regard needs to be paid to considerations of the grid or overall generation capacity; though given that buses may need (very) high power, some local improvements may be necessary. Electrification of urban buses could be encouraged by providing direct funding to operators or local authorities to buy electric buses or the necessary charging infrastructure. Furthermore, where routes are put to tender, these could have the condition of being operated by electric vehicles.

General Several aspects not covered directly by this LCA are nevertheless important regarding the discussion on reducing the emissions from the transport sector. These include measures to reduce the per km fuel consumption (and air pollution emissions) of individual vehicles, reducing the use of vehicles through improved urban planning and improved cycling and walking infrastructure and encouraging public transport use rather than individual vehicles, which would have positive effects on all aspects considered in this LCA, as well as in other dimensions not covered (e.g., health, noise, economics, etc.). Another aspect to consider is the international aspect of technological development. Brazil’s biofuel system is very well developed, with arguably significantly better environmental outcomes than other countries, especially those using corn-ethanol. If all other countries shift away from liquid-fueled, internal-combustion vehicles to electric vehicles, this could make Brazil a technology island; potentially cut off from foreign sources of major developments such as autonomous vehicles, forcing Brazil to either develop those technologies themselves or adapt them to local vehicles. Sustainability 2019, 11, 6332 22 of 31

Moreover, it is important to bear in mind the economic relevance of the biofuel industry in Brazil and its linkages to other sectors such as automotive. As such, if an electrification strategy for the automotive sector were implemented, consideration should be paid to the associated conversion of the biofuel industry.

Biofuel feedstocks and land In all cases, but especially where the policy focus remains on biofuels, it should be ensured that any additional land used to grow biofuel feedstocks should not have high stored carbon, i.e., should be Cerrado or plantation forest, or similar biomes, or even better that it must come from already cleared land, such as farmland used for other crops, degraded pastureland or brownfield sites. However, steps must be taken to ensure any resulting pressure on farmland does not result in land cleared for other crops, and thus indirect land-use change emissions and productive ecosystems and the services they offer are not degraded. As included in the yearly increase in biofuel feedstock yields, efforts to increase the yield of biofuels per area of land should be undertaken, such as investing in technological and capacity building. It must be noted though that this study does not include the assessment of impacts on other parameters such as soil biodiversity and sustainability, which might be important to consider if higher yield intensities are sought for existing areas of feedstock cultivation.

Electricity system If electrification is pursued, especially for cars, the country’s electricity generation capacity should be increased in line with the consumption of BEVs. This added capacity should be as low emission as possible. Additionally, policies to address the possible high load on the grid of vehicles being charged should be implemented. At their simplest, these could involve simple grid/generation capacity upgrades. Preferential tariffs for off-peak charging could be introduced, possibly also in conjunction with technologies to allow vehicles to take advantage of those tariffs and also potentially for them to be utilized for grid balancing. Furthermore, the availability of charging infrastructure away from home would encourage people to buy EVs in the first place by easing their range anxiety, but also to charge their cars during the day away from the morning and evening peaks.

5.2. Suggested Improvements to the LCA Methodology Applied The following areas have been identified as areas that would improve the LCA. A more complicated LUC calculation including the agricultural land-use elasticities and yields of • biofuels differentiated by land type. Inclusion of modal/behavioral shift aspects to allow the effects of reduced travel or modal shifts to • be included in scenarios. Inclusion of electricity emissions intensities in all aspects of the LCA (e.g., manufacturing) to • better reflect changes to the system, along with better representation of the place of manufacture (and CO2 emissions) of vehicle components. Consideration of the economic effects of hybrids and BEVs in Brazil, and of the expansion in • electricity generation necessary for the electrification scenarios. Include a fleet model for vehicles. • Inclusion of further effects such as water use (especially for biofuel feedstock production), toxicity, • and the use of other key materials

Author Contributions: Conceptualization, K.G.; Formal analysis, K.G.; Investigation, M.R.M.B.; Methodology, K.G.; Writing—original draft, K.G.; Writing—review and editing, M.R.M.B. Funding: The work described in this article was completed as part of the FUTURE-RADAR project, a research project funded by the European Union’s Horizon 2020 research and innovation programme (grant agreement no 723970). Sustainability 2019, 11, 6332 23 of 31

Acknowledgments: Thanks to our colleague Alvin Mejia for commenting on the methodology. Conflicts of Interest: The authors declare no conflict of interest.

Appendix A. Inputs and Assumptions

Table A1. Proportion of biofuels sold in Brazil in the various scenarios [49,50]. Fossil diesel and petrol make up the remaining portion.

2015 2020 2025 2030 2035 2040 2045 2050 Proportion of biodiesel BAU/electrification 0.100 0.100 0.100 0.100 0.100 0.100 0.100 Biofuel 0.143 0.286 0.429 0.571 0.714 0.857 1.000 Proportion of ethanol BAU/electrification 0.496 0.560 0.560 0.560 0.560 0.560 0.560 0.560 Biofuel 0.496 0.568 0.640 0.712 0.784 0.856 0.928 1.000

Table A2. Proportion of cars in the fleet by powertrain type for the various scenarios.

2015 2020 2025 2030 2035 2040 2045 2050 BAU Flex internal combustion engine (ICE) 0.95 0.96 0.98 0.97 0.95 0.92 0.88 0.82 Flex hybrid 0.01 0.03 0.05 0.07 0.11 0.16 Compressed natural gas (CNG) 0.05 0.04 0.01 BEV 0.01 0.01 0.02 Biofuel w/o hybrid Flex ICE 0.95 1 1 1 1 1 1 1 Flex hybrid CNG 0.05 BEV 1 Biofuel 2 hybrid Flex ICE 0.95 0.9 0.84 0.77 0.7 0.63 0.56 0.5 Flex hybrid 0.1 0.16 0.23 0.3 0.37 0.44 0.5 CNG 0.05 BEV Biofuel with hybrid Flex ICE 0.95 0.81 0.68 0.54 0.4 0.26 0.12 Flex hybrid 0.19 0.32 0.46 0.6 0.74 0.88 1 CNG 0.05 BEV Electrification Flex ICE 0.95 0.82 0.67 0.54 0.4 0.27 0.14 Flex hybrid CNG 0.05 BEV 0.18 0.33 0.46 0.6 0.73 0.86 1 Sustainability 2019, 11, 6332 24 of 31

Table A3. Proportion of urban buses in the fleet by powertrain type for the various scenarios.

2015 2020 2025 2030 2035 2040 2045 2050 BAU Diesel ICE 1 1 1 0.98 0.95 0.89 0.8 0.7 Diesel hybrid 0.01 0.03 0.06 0.1 0.15 CNG 0.01 0.01 0.01 BEV 0.01 0.02 0.04 0.09 0.14 Biofuel w/o hybrid Diesel ICE 1 1 1 1 1 1 1 1 Diesel hybrid CNG BEV 1 Biofuel 2 hybrid Diesel ICE 1 0.93 0.85 0.78 0.71 0.64 0.57 0.5 Diesel hybrid 0.07 0.15 0.22 0.29 0.36 0.43 0.5 CNG BEV Biofuel with hybrid Diesel ICE 1 0.86 0.71 0.57 0.43 0.29 0.14 Diesel hybrid 0.14 0.29 0.43 0.57 0.71 0.86 1 CNG BEV Electrification Diesel ICE 1 0.85 0.71 0.57 0.42 0.28 0.14 Diesel hybrid CNG BEV 0.15 0.29 0.43 0.58 0.72 0.86 1

Table A4. Overall annual distance driven and fuel economy (liters of gasoline equivalent (LGe), per 100 km) for cars. Data from [47].

2015 2020 2025 2030 2035 2040 2045 2050 Vehicle stock (million) 35.7 37.8 41.2 45.7 54.3 62,6 68.6 72.7 Average travel per vehicle 13,899 13,422 13,108 13,196 13,385 13,578 13,717 13,797 Total vkm (billion) 496.6 501.1 503.3 505.4 507.4 539.7 603.5 726.8 Fuel economy (LGe/100 km) Diesel ICE 7.6 7.2 6.8 6.4 6.0 5.8 5.7 5.5 Diesel hybrid 6.1 4.8 4.9 4.9 4.9 4.9 4.8 4.8 Flex ICE 9.4 8.9 8.3 7.5 6.8 6.5 6.2 6.0 Flex hybrid 6.3 5.5 5.3 5.2 5.1 5.1 4.9 4.8 CNG/LPG 9.9 9.8 9.7 8.4 7.0 6.6 6.2 6.0 Electric 2.3 2.2 2.2 2.1 2.0 2.0 2.0 1.9 Sustainability 2019, 11, 6332 25 of 31

Table A5. Overall annual distance driven and fuel economy (liters of gasoline equivalent (LGe), per 100 km) for urban buses. Data from [47].

2015 2020 2025 2030 2035 2040 2045 2050 Vehicle stock (thousand) 273.6 293.4 315.1 300.1 277.1 251.4 229.7 210.2 Average travel per vehicle 32,715 32,715 32,715 32,715 32,715 32,715 32,715 32,408 Total vkm (billion) 8.95 9.60 10.31 9.82 9.07 8.22 7.52 6.81 Fuel economy (LGe/100 km) Diesel ICE 44.7 43.1 42.1 41.0 40.0 39.0 38.0 37.1 Diesel hybrid 34.7 33.6 32.7 31.9 31.1 30.3 29.6 28.8 Flex ICE 49.6 47.9 46.7 45.6 44.4 43.3 42.2 41.2 Flex hybrid 37.2 36.0 35.1 34.2 33.3 32.5 31.7 30.9 CNG/LPG 49.6 47.9 46.7 45.6 44.4 43.3 42.2 41.2 Electric 14.9 14.4 14.0 13.7 13.3 13.0 12.7 12.4

Table A6. NOx and PM emissions intensities (g/km) for 2012. [54].

Pollutant Petrol Ethanol Diesel CNG

NOx 0.0300 0.0300 2.1020 0.0310 PM 0.0011 0.0000 0.0200 0.0011

Table A7. Well to Tank (WtT) and Tank to Wheels (TtW) CO2 emission factors for various fuels.

Fuel Well to Tank (g/MJ) Tank to Wheels (g/MJ) Petrol 13.8 69.8 Ethanol 18.1 0.0 Diesel 15.4 74.4 Biodiesel 34.6 0.0 CNG 19.6 62.2

Table A8. Total Brazilian electricity generation and CO2 emissions intensities for 2015/17 [46] and EPE scenarios for the best and worst case [57] for 2020–2050.

CO Emissions Intensities Year Generation (TWh) 2 (kiloton/TWh) 2015/17 Best Case Worst Case 2015/17 Best Case Worst Case 2015 531 * 531 * 531 * 122 * 122 * 122 * 2020 547 ** 857 837 94 ** 88 147 2030 547 ** 1197 1246 94 ** 47 257 2040 547 ** 1656 1743 94 ** 37 317 2050 547 ** 2260 2298 94 ** 29 354 * Actual values from 2015; ** actual values from 2017.

Table A9. Description of various methods and sources of carbon (not CO2) content of Brazilian biomes.

Description Value (Mg/ha) Source Author’s judgement of map of values for Amazon 360 Average of given values for Atlantic forest 230.8 [63] Author’s judgement of map of values for Cerrado 60 “national-level forest AGB [above-ground biomass] density” with errors. 306.79 36.1 [64] ± “Estimates of forest carbon stocks” per canopy cover threshold (10%, 25% 102, 116, 123 [65] and 30%) Plantation forest land area and AGB for 2016 104.3 [66] Average forest AGB for four submodels (1990–2009 average) 199, 266, 200, 196 [61] Sustainability 2019, 11, 6332 26 of 31

Table A10. Biofuel yields [58] and applied values for LUC emissions of CO2 for various biomes (from Table A9).

Aspect Value Biofuel yields per land area of Ethanol 5570 cropland (L/ha) Biodiesel 530 Cerrado (savannah) 121,845,050 Plantation forest 211,755,493 CO2 emissions from LUC (g/ha) Low forest 283,641,324 Avg forest 459,142,610 High forest 634,643,895

Table A11. Vehicle and battery life and number of batteries required in cars’ lives.

Battery Size Batteries in Battery Round Vehicle Type Vehicle Life (km) (kWh) Vehicle Life Trip Efficiency Flex ICE Flex hybrid 1.5 1.5 200,000 CNG BEV 30.0 1.5 0.88

Table A12. Total lifetime CO2 emissions for cars (including fuel use) and the proportion of the total from manufacturing of the glider and powertrain (i.e., everything but the battery).

Component Proportions Proportion CO2 (g) Total lifetime emissions (estimated, incl. fuel use) 17,680,000 − Glider 0.15 2,652,000 Powertrain 0.03 530,400

Table A13. Vehicle and battery life and number of batteries required in urban bus lives.

Vehicle Life Battery Size Battery Life Batteries in Battery Round Vehicle Type (km) (kWh) (km) Life Trip Efficiency Diesel ICE 1,000,000 Diesel hybrid 1,200,000 14.5 190,000 6.3 Flex ICE 1,000,000 Flex hybrid 1,200,000 14.5 190,000 6.3 CNG/LPG 1,000,000 Electric 1,500,000 99.5 600,000 2.5 0.88

Table A14. Total manufacturing emissions for various bus types and their associated non-battery component where applicable.

Vehicle Type Total Total Non-Battery Diesel ICE 45,400,000 45,400,000 Diesel hybrid 48,600,000 45,600,000 Flex ICE 45,400,000 45,400,000 Flex hybrid 48,600,000 45,600,000 CNG 45,400,000 45,400,000 Electric 56,900,000 40,800,000 Sustainability 2019, 11, 6332 27 of 31

Table A15. CO2 emissions intensities for battery manufacturing (in g/kWh).

Scenario Manufacturing CO2 Emissions (g/kWh) Minimum 30,000 Average 152,063 Maximum 494,000

Table A16. End-of-life data and sources.

Aspect Figure Source Vehicle 0.43 MJ/kg Energy for recycling [67] Battery 469 MJ/kWh Car 1200 [40] Mass (kg) Bus 7491 [51] Extra emissions for all-virgin materials vehicle production 15–20% [68] 23–43% [69] Reduction in emissions by using recycled battery materials 10–17% [68] Current battery recycling rate 5% [70] Vehicle-use portion of battery life 58.1% [69]

Table A17. Battery recycling and reuse rates as applied in the LCAs.

Aspect 2015 2020 2025 2030 2035 2040 2045 2050 Battery recycling rate 0.05 0.19 0.32 0.46 0.59 0.73 0.86 1.00 Battery reuse rate 0.17 0.33 0.46 0.60 0.73 0.87 1.00

Table A18. Settings used to create the best- and worst-case scenarios in the EPE electricity system characteristic calculator [57].

Portuguese English Worst Case Best Case Demanda de energia Energy demand Transporte de Passenger transport—modal 1 1 passageiros—escolha modal choice Transporte de Passenger transport—efficient 1 3 passageiros—veículos eficientes vehicles Preferência de uso do etanol em Preference to use ethanol in 1 1 veículos flex flex-fuel vehicles Transporte de carga—distribuição Freight transport—modal split 1 1 modal Transporte de carga—eficiência Cargo transport—efficiency 1 1 Conteúdo de biodiesel no diesel Biodiesel content in diesel 1 1 Setor residencial—eficiência Residential sector—energy 1 3 energética efficiency Setor comercial e Commercial and public 1 3 público—eficiência energética sector—energy efficiency Composição da indústria Composition of the industry B C Eficiência energética na indústria Energy efficiency in industry 1 3 Setor agropecuário—eficiência Agricultural sector—efficiency 1 1 Sustainability 2019, 11, 6332 28 of 31

Table A18. Cont.

Portuguese English Worst Case Best Case Demanda de energia Energy demand Oferta Interna de Energia Internal Electricity Supply Elétrica Termelétricas a gás Natural gas-fired power 1 1 natural—potência instalada stations—installed capacity Natural gas thermal power Termelétricas a gás natural—CCS 1 1 plants—CCS Termelétricas a carvão—potência Coal-fired power plants—installed 3 1 instalada capacity Termelétricas a carvão—CCS Coal-fired power plants—CCS 1 1 Termelétricas a derivados de Thermal power plants using 3 1 petróleo petroleum products Aproveitamento da biomassa e do Use of biomass and biogas 1 3 biogás Aproveitamento do excedente de Utilization of surplus bagasse 1 3 bagaço Prioridade de uso do biogás Priority of biogas use A A Eficiência das usinas a Efficiency of biofuel plants 1 3 biocombustível Energia nuclear Nuclear energy 1 3 Energia eólica onshore Onshore wind energy 1 3 Energia eólica offshore Offshore wind energy 1 2 Energia dos oceanos Ocean energy 1 3 Energia hidráulica Hydraulic power 1 3 Energia solar fotovoltaica Photovoltaic solar energy 1 3 Energia solar heliotérmica (CSP) Solar heliothermal energy (CSP) 1 3 Importação de hidrelétricas Importation of binational 1 3 binacionais hydroelectric plants Segurança do sistema elétrico Electrical system safety 1 1 Production of oil and associated Produção de óleo e gás associado DA gas Produção de gás natural não Non-associated natural gas 1 1 associado production Links to the calculator with settings:.Best case:.http://calculadora2050.epe. gov.br/pathways/111111133133132333331111113111111131313311/electricity/comparator/ 011011111111011111111110011101101010101101. Worst case:.http://calculadora2050. epe.gov.br/pathways/111131311111111111111411111111111111112111/electricity/comparator/ 011011111111011111111110011101101010101101.

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