Editing IPCC:AR6/WGIII/Chapter-10 (section)

=== 10.4.1 Light-duty Vehicles for Passenger Transport ===

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LDVs represent the main mode of transport for private citizens ( [[#ITF--2019|ITF 2019]] ) and currently represent the largest share of transport emissions globally ( [[#IEA--2019d|IEA 2019d]] ). Currently, powertrains depending on gasoline and diesel fuels remain the dominant technology in the LDV segment ( [[#IEA--2019d|IEA 2019d]] ). HEVs, and fully battery electric vehicles (BEVs), however, have become increasingly popular in recent years ( [[#IEA--2021a|IEA 2021a]] ). Correspondingly, the number of lifecycle assessment (LCA) studies investigating HEVs, BEVs, and fuel cell vehicles have increased. While historically the focus has been on the tailpipe emissions of LDVs, LCA studies demonstrate the importance of including emissions from the entire vehicle value chain, particularly for alternative powertrain technologies.

Figure 10.4 presents the cumulative lifecycle emissions for selected powertrain technologies and fuel chain combinations for compact and mid-sized LDVs. This figure summarises the harmonised findings from the academic literature reviewed and the data submitted through an IPCC data collection effort, as described in Appendix 10.1 ( [[#Hawkins--2013|Hawkins et al. 2013]] ; [[#Messagie--2014|Messagie et al. 2014]] ; [[#Bauer--2015|Bauer et al. 2015]] ; [[#Tong--2015b|Tong et al. 2015b]] ; [[#Ellingsen--2016|Ellingsen et al. 2016]] ; [[#Gao--2016|Gao et al. 2016]] ; [[#Kim--2016|Kim and Wallington 2016]] ; [[#Cai--2017|Cai et al. 2017]] ; [[#Evangelisti--2017|Evangelisti et al. 2017]] ; [[#Ke--2017|Ke et al. 2017]] ; [[#Lombardi--2017|Lombardi et al. 2017]] ; [[#Miotti--2017|Miotti et al. 2017]] ; [[#Valente--2017|Valente et al. 2017]] ; [[#Cox--2018|Cox et al. 2018]] ; [[#de%20Souza--2018|de Souza et al. 2018]] ; [[#Elgowainy--2018|Elgowainy et al. 2018]] ; [[#Luk--2018|Luk et al. 2018]] ; [[#Bekel--2019|Bekel and Pauliuk 2019]] ; [[#Cusenza--2019|Cusenza et al. 2019]] ; [[#Hoque--2019|Hoque et al. 2019]] ; [[#IEA--2019a|IEA 2019a]] ; [[#Rosenfeld--2019|Rosenfeld et al. 2019]] ; [[#Shen--2019|Shen et al. 2019]] ; [[#Wang--2019|Wang et al. 2019]] ; [[#Wu--2019|Wu et al. 2019]] ; [[#Ambrose--2020|Ambrose et al. 2020]] ; [[#Benajes--2020|Benajes et al. 2020]] ; [[#Hill--2020|Hill et al. 2020]] ; [[#Knobloch--2020|Knobloch et al. 2020]] ; [[#Prussi--2020|Prussi et al. 2020]] ; [[#Qiao--2020|Qiao et al. 2020]] ; Wolfram et al. 2020; [[#Zheng--2020|Zheng et al. 2020]] ; [[#Sacchi--2021|Sacchi 2021]] ; [[#Valente--2021|Valente et al. 2021]] ). The values in the figure (and the remaining figures in this section) depend on the 100-year global warming potential (GWP) used in each study, which may differ from the recent GWP updates from WGI. However, it is unlikely that the qualitative insights gained from the figures in this section would change using the update 100-year GWP values.

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'''Figure 10.4 | Life cycle greenhouse gas emissions intensities for mid-sized light-duty vehicle and fuel technologies from the literature.''' The primary x-axis reports units in gCO 2 -eq vkm –1 , assuming a vehicle life of 180,000 km. The secondary x-axis uses units of gCO 2 -eq pkm –1 , assuming a 1.5 occupancy rate. The values in the figure rely on the 100-year GWP value embedded in the source data, which may differ slightly from the updated 100-year GWP values from WGI. The shaded area represents the interquartile range for combined vehicle manufacturing and end-of-life phases. The length of the box and whiskers represent the interquartile range of the operation phase for different fuel chains, while their placement on the x-axis represents the absolute lifecycle climate intensity, that is, includes manufacturing and end-of-life phases. Each individual marker indicates a data point. ‘Advanced biofuels’ refers to the use of second-generation biofuels and their respective conversion and cultivation emission factors. ‘IAM EMF33’ refers to emissions factors for advanced biofuels derived from simulation results from the integrated assessment models EMF33 scenarios. ‘PM’ refers to partial models, where ‘CLC’ is with constant land cover and ‘NRG’ is with natural regrowth. ‘Hydrogen, low-carbon electricity’ is produced via electrolysis using low-carbon electricity. ‘Hydrogen, natural gas SMR’ refers to fuels produced via steam methane reforming of natural gas.

Furthermore, note that the carbon footprint of biofuels used in Figure 10.4 are aggregate numbers not specific to any individual value chain or fuel type. They are derived by combining land use-related carbon emissions from [[IPCC:Wg3:Chapter:Chapter-7|Chapter 7]] with conversion efficiencies and emissions as described in [[#10.3|Section 10.3]] . Specifically, land-use footprints derived from the three modelling approaches employed here are: i) Integrated Assessment Models – Energy Modelling Forum 33 (IAM EMF33); ii) Partial models assuming constant land cover (CLC), and, iii) Partial models using natural regrowth (NRG). The emissions factors used here correspond to scenarios where global production of biomass for energy purposes are 100 EJ/year, with lower emissions factors expected at lower levels of consumption and vice versa. Further details are available in Box 10.2 and Chapter 7.

The tailpipe emissions and fuel consumption reported in the literature generally do not use empirical emissions data. Rather, they tend to report fuel efficiency using driving cycles such as New European Driving Cycle or the US Environmental Protection Agency Federal Test Procedure. As a result, depending on the driving cycle used, operating emissions reported in literature are possibly underestimated by as much as 15–38%, in comparison to real driving emissions ( [[#Fontaras--2017|Fontaras et al. 2017]] ; [[#Tsiakmakis--2017|Tsiakmakis et al. 2017]] ; [[#Triantafyllopoulos--2019|Triantafyllopoulos et al. 2019]] ). The extent of these underestimations, however, varies between powertrain types, engine sizes, driving behaviour and environment.

Current average lifecycle impacts of mid-size ICEVs span from approximately 65 gCO 2 -eq pkm –1 to 210 gCO 2 -eq pkm –1 , with both values stemming from ICEVs running on biofuels. Between this range of values, the current reference technologies are found, with diesel-powered ICEVs having total median lifecycle impacts of 130 gCO 2 -eq pkm –1 and gasoline-fuelled vehicle 160 gCO 2 -eq pkm –1 . Fuel consumption dominates the lifecycle emissions of ICEVs, with approximately 75% of these emissions arising from the tailpipe and fuel chain.

HEVs and plug-in HEVs (PHEVs) vary in terms of degree of powertrain electrification. HEVs mainly rely on regenerative braking for charging the battery. PHEVs combine regenerative braking with external power sources for charging the battery. Operating emissions intensity is highly dependent on the degree to which electrified driving is performed, which in turn is user- and route-dependent. For PHEVs, emissions intensity is also dependent on the source of the electricity for charging. HEV and PHEV production impacts are comparable to the emissions generated for producing ICEVs as the batteries are generally small compared to those of BEVs. Current HEVs may reduce emissions compared to ICEVs by up to 30%, depending on the fuel, yielding median lifecycle intensities varying between 60 gCO 2 -eq pkm –1 (biofuels, EMF33) and 165–170 gCO 2 -eq pkm –1 (biofuels, partial models NRG). Within this wide range, all the combinations of electric and fossil-fuelled driving can be found, as well as the lifecycle intensity for driving 100% on fossil fuel. Because HEVs rely on combustion as the main energy conversion process, they offer limited mitigation opportunities. However, HEVs represent a suitable temporary solution, yielding a moderate mitigation potential, in areas where the electricity mix is currently so carbon intensive that the use of PHEVs and BEVs is not an effective mitigation solution ( [[#Wolfram--2017|Wolfram and Wiedmann 2017]] ; [[#Wu--2019|Wu et al. 2019]] ).

In contrast to HEVs, PHEVs may provide greater opportunities for use-phase emissions reductions for LDVs. These increased potential benefits are due to the ability to charge the battery with low-carbon electricity and the longer full-electric range in comparison to HEVs ( [[#Laberteaux--2019|Laberteaux et al. 2019]] ). Consumer behaviour (e.g., utility factor (UF) and charging patterns), manufacturer settings, and access to renewable electricity for charging strongly influence the total operational impacts ( [[#Wu--2019|Wu et al. 2019]] ). The UF is a weighting of the percentage of distance covered using the electric charge (charge depleting (CD) stage) versus the distance covered using the internal combustion engine (charge sustaining (CS) stage) ( [[#Paffumi--2018|Paffumi et al. 2018]] ). When the PHEV operates in CS mode, the internal combustion engine is used for propulsion and to maintain the state of charge of the battery within a certain range, together with regenerative braking ( [[#Plötz--2018|Plötz et al. 2018]] ; [[#Raghavan--2020|Raghavan and Tal 2020]] ). When running in CS mode, PHEVs have a reduced mitigation potential and have impacts comparable to those of HEVs. On the other hand, when the PHEV operates in CD mode, the battery alone provides the required propulsion energy ( [[#Plötz--2018|Plötz et al. 2018]] ; [[#Raghavan--2020|Raghavan and Tal 2020]] ). Thus, in CD mode, PHEVs hold potential for higher mitigation potential, due to the possibility of charging the battery with low-carbon electricity sources. Consequently, the UF greatly influences the lifecycle emissions of PHEVs. The current peer-reviewed literature presents a wide range of UFs mainly due to varying testing protocols applied for estimating the fuel efficiency and user behaviour ( [[#Pavlovic--2017|Pavlovic et al. 2017]] ; [[#Paffumi--2018|Paffumi et al. 2018]] ; [[#Plötz--2018|Plötz et al. 2018]] ; [[#Plötz--2020|Plötz et al. 2020]] ; [[#Raghavan--2020|Raghavan and Tal 2020]] ; [[#Hao--2021|Hao et al. 2021]] ). These factors make it difficult to harmonise and compare impacts across PHEV studies. Due to the low number of appropriate PHEV studies relative to the other LDV technologies and the complications in harmonising available PHEV results, this technology is omitted from Figure 10.4. However, due to the dual operating nature of PHEV vehicles, one can expect that the lifecycle GHG emissions intensities for these vehicles will lie between those of their ICEV and BEV counterparts of similar size and performance.

Currently, BEVs have higher manufacturing emissions than equivalently-sized ICEVs, with median emissions of 14 tCO 2 -eq per vehicle against approximately 10 tCO 2 -eq per vehicle of their mid-sized fossil-fuelled counterparts. These higher production emissions of BEVs are largely attributed to the battery pack manufacturing and to the additional power electronics required. As manufacturing technology and capacity utilisation improve and globalise to regions with low-carbon electricity, battery manufacturing emissions will likely decrease. Due to the higher energy efficiency of the electric powertrain, BEVs may compensate for these higher production emissions in the driving phase. However, the mitigation ability of this technology relative to ICEVs is highly dependent on the electricity mix used to charge the vehicle. As a consequence of the variety of energy sources available today, current BEVs have a wide range of potential average lifecycle impacts, ranging between 60 and 180 gCO 2 -eq pkm –1 with electricity generated from wind and coal, respectively. The ability to achieve large carbon reductions via vehicle electrification is thus highly dependent on the generation of low-carbon electricity, with the greatest mitigation effects achieved when charging the battery with low-carbon electricity. The literature suggests that current BEVs, if manufactured on low-carbon electricity as well as operated on low-carbon electricity would have footprints as low 22 gCO 2 -eq pkm –1 for a compact-sized car ( [[#Ellingsen--2014|Ellingsen et al. 2014]] ; [[#Ellingsen--2016|Ellingsen et al. 2016]] ). This value suggests a reduction potential of around 85% compared to similarly-sized fossil fuel vehicles (median values). Furthermore, BEVs have a co-benefit of reducing local air pollutants that are responsible for human health complications, particularly in densely-populated areas ( [[#Hawkins--2013|Hawkins et al. 2013]] ; [[#Ke--2017|Ke et al. 2017]] ).

As with BEVs, current HFCVs have higher production emissions than similarly-sized ICEVs and BEVs, generating on average approximately 15 tCO 2 -eq per vehicle. As with BEVs, the lifecycle impacts of FCVs are highly dependent on the fuel chain. To date, the most common method of hydrogen production is steam methane reforming of natural gas ( [[#Khojasteh%20Salkuyeh--2017|Khojasteh Salkuyeh et al. 2017]] ), which is relatively carbon intensive, resulting in lifecycle emissions of approximately 88 gCO 2 -eq pkm –1 . Current literature covering lifecycle impacts of FCVs shows that vehicles fuelled with hydrogen produced from steam methane reforming of natural gas offer little or no mitigation potential over ICEVs. Other available hydrogen fuel chains vary widely in carbon intensity, depending on the synthesis method and the energy source used (electrolysis or steam methane reforming; fossil fuels or renewables). The least carbon-intensive hydrogen pathways rely on electrolysis powered by low-carbon electricity. Compared to ICEVs and BEVs, FCVs for LDVs are at a lower technology readiness level, as discussed in section 10.3.

Two-wheelers, consisting mainly of lower-powered mopeds and higher-powered motorcycles, are popular for personal transport in densely populated cities, especially in developing countries. LCA studies for this class of vehicle are relatively uncommon compared to four-wheeled LDVs. In the available results, however, two-wheelers exhibit similar trends for the different powertrain technologies as the LDVs, with electric powertrains having higher production emissions, but usually lower operating emissions. The lifecycle emissions intensity for two-wheelers is also generally lower than four-wheeled LDVs on a vehicle-kilometre basis. However, two-wheelers generally cannot carry as many passengers as four-wheeled LDVs. Thus, on a passenger-kilometre basis, a fully occupied passenger vehicle may still have lower emissions than a fully occupied two-wheeler. However, today, most passenger vehicles have relatively low occupancy and thus have a correspondingly high emissions intensity on a pkm basis. This points to the importance of utilisation of passenger vehicles at higher occupancies to reduce the lifecycle intensity of LDVs on a pkm basis. For example, the median emissions intensity of a gasoline passenger vehicle is 222 gCO 2 -eq vkm –1 , and 160 gCO 2 -eq vkm –1 for a gasoline two-wheeler ( [[#Cox--2018|Cox and Mutel 2018]] ). At a maximum occupancy factor of four and two passengers, respectively, the transport emissions intensity for these vehicles is 55 and 80 gCO 2 -eq pkm –1 . Under the same occupancy rates assumption, BEV two-wheelers recharged on the average European electricity mix, achieve lower lifecycle GHG intensities than BEV four-wheeled LDVs. On the other hand, FCV two-wheelers with hydrogen produced via steam methane reforming present higher GHG intensity than their four-wheeled counterparts, when compared on a pkm basis at high occupancy rates.

ICEV, HEV, and PHEV technologies, which are powered using combustion engines, have limited potential for deep reduction of GHG emissions. Biofuels offer good mitigation potential if low land-use change emissions are incurred (e.g., the IAM EMF33 and partial models, CLC biofuels pathways shown in Figure 10.4). The literature shows large variability, depending on the method of calculating associated land-use changes. Resolving these apparent methodological differences is important to consolidating the role biofuels may play in mitigation, as well as the issues raised in [[IPCC:Wg3:Chapter:Chapter-7|Chapter 7]] about the conflicts over land use. The mitigation potential of battery and fuel cell vehicles is strongly dependent on the carbon intensity of their production and the energy carriers used in operation. However, these technologies likely offer the highest potential for reducing emissions from LDVs. Prior work on the diffusion dynamics of transport technologies suggests that ‘the diffusion of infrastructure precedes the adoption of vehicles, which precedes the expansion of travel’ ( [[#Leibowicz--2018|Leibowicz 2018]] ). These dynamics reinforce the argument for strong investments in both the energy infrastructure and the vehicle technologies.

To successfully transition towards LDVs utilising low-carbon fuels or energy sources, the technologies need to be accessible to as many people as possible, which requires competitive costs compared to conventional diesel and gasoline vehicles. The lifecycle costs (LCCs) of LDVs depend on the purchasing costs of the vehicles, their efficiency, the fuel costs, and the discount rate. Figure 10.5 shows the results of a parametric analysis of LCC for diesel LDVs, BEVs, and FCVs. The range of vehicle efficiencies captured in Figure 10.5 is the same as the range used for Figure 10.4, while the ranges for fuel costs and vehicle purchase prices come from the literature. The assumed discount rate for this parametric analysis is 3%. Appendix 10.2 includes the details about the method and underlying data used to create this figure.

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'''Figure 10.5 | LCC for light-duty internal combustion engine vehicles, battery electric vehicles, and hydrogen fuel cell vehicles.''' The results for ICEVs represent the LCC of a vehicle running on gasoline. However, these values are also representative for ICEVs running on diesel as the costs ranges in the literature for these two solutions are similar. The secondary y-axis depicts the cost of the different energy carriers normalised in USD per gigajoule for easier cross-comparability.

Figure 10.5 shows the range of LCC, in USD per passenger-kilometre, for different powertrain technologies, and the influence of vehicle efficiency (low or high), vehicle purchase price, and fuel/electricity cost on the overall LCC. For consistency with Figure 10.4, an occupancy rate of 1.5 is assumed. Mid-sized ICEVs have a purchase price of USD20,000–40,000, and average fuel costs are in the range of USD1–1.5 per litre. With these conditions, the LCC of fossil-fuelled LDVs span between USD0.22–0.35 pkm –1 or between USD0.17–0.28 pkm –1 , for low- and high-efficiency ICEVs respectively (Figure 10.5).

BEVs have higher purchase prices than ICEVs, though a sharp decline has been observed since AR5. Due to the rapid development of the lithium-ion battery technology over the years ( [[#Schmidt--2017|Schmidt et al. 2017]] ) and the introduction of subsidies in several countries, BEVs are quickly reaching cost parity with ICEVs. Mid-sized BEVs’ average purchase prices are in the range of USD30,000–50,000 but the levelised cost of electricity shows a larger spread (USD65–200/MWh) depending on the geographical location and the technology (Chapter 6). Therefore, assuming purchase price parity between ICEVs and BEVs, BEVs show lower LCC (Figure 10.5) due to higher efficiency and the lower cost of electricity compared to fossil fuels on a per-gigajoule (GJ) basis (secondary y-axis on Figure 10.5).

FCVs represent the most expensive solution for LDV, mainly due to the currently higher purchase price of the vehicle itself. However, given the lower technology readiness level of FCVs and the current efforts in the research and development of this technology, FCVs could become a viable technology for LDVs in the coming years. The issues regarding the extra energy involved in creating the hydrogen and its delivery to refuelling sites remain, however. The levelised cost of hydrogen on a per GJ basis is lower than conventional fossil fuels but higher than electricity. In addition, within the levelised cost of hydrogen, there are significant cost differences between the hydrogen-producing technologies. Conventional technologies such as coal gasification and steam methane reforming of natural gas, both with and without carbon capture and storage, represent the cheapest options ( [[#Bekel--2019|Bekel and Pauliuk 2019]] ; [[#Parkinson--2019|Parkinson et al. 2019]] ; [[#Khzouz--2020|Khzouz et al. 2020]] ; [[#Al-Qahtani--2021|Al-Qahtani et al. 2021]] ). Hydrogen produced via electrolysis is currently the most expensive technology, but with significant potential cost reductions due to the current technology readiness level.

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