# Greenhouse Gas Emissions Performance of Electric, Hydrogen and Fossil-Fuelled Freight Trucks with Uncertainty Estimates Using a Probabilistic Life-Cycle Assessment (pLCA)

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## Abstract

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## 1. Introduction

#### 1.1. Life-Cycle Analysis

#### 1.2. Background and Purpose of This Study

## 2. Materials and Methods

#### 2.1. Probabilistic LCA (pLCA)

_{1}, X

_{2}, …, X

_{n}(Section 2.3) collectively explain and quantify the magnitude of and the variability and uncertainty in the response variable. In the analysis phase, a range of methods are used to define the input distributions for each predictor variable, where the choice of method(s) is guided by the available information and data (Section 2.3 and Section 3). The probabilistic definition of input variables is based on the statistical analysis of empirical data, software simulation, results from peer-reviewed scientific studies or expert judgement wherever available and in this order of preference.

#### 2.2. Model Definition

_{2}-e/vehicle.km. The computation of carbon dioxide equivalent (CO

_{2}-e) emissions involves multiplying the emissions of a particular greenhouse gas by its global-warming potential (GWP) and aggregating these emissions.

_{ICEV}, e

_{BEV}and e

_{FCEV}, are computed using three basic additive models and sub-models (if applicable), as shown in Table 1. In Equations (1)–(3), e

_{i,j}is used to represent a GHG emission factor (CO

_{2}-e/km) for a life-cycle aspect i and vehicle type j. This research focuses on assessing the fleet’s average impact. Therefore, fleet-averaged input data are generally used, such as mean vehicle mass and the associated probability distribution of this mean value. While the pLCA method can be employed for individual vehicles if desired, this falls outside the scope of this study. Such an analysis would require the utilization of vehicle-specific input data instead of the aggregated data used for fleet averages.

#### 2.3. Developing Input Distributions

e_{ICEV} = e_{vehicle,ICEV} + e_{infra,ICEV} + e_{fuel,ICEV} + e_{road,ICEV} + e_{disposal,ICEV} | (1) |

e_{vehicle,ICEV} = w_{ICEV} φ_{v,ICEV}/M_{ICEV} | |

e_{BEV} = e_{vehicle,BEV} + e_{infra,BEV} + e_{fuel,BEV} + e_{road,BEV} + e_{disposal,BEV} | (2) |

e_{vehicle,BEV} = ((M_{BEV} − M_{BAT}) φ_{v,BEV} + (Γ_{BAT} φ_{BAT} θ_{BAT}))/M_{BEV} | |

e_{infra,BEV} = ε σ_{elec}/(η_{g} η_{b}) | |

e_{fuel,BEV} = ε ϕ_{elec}/η_{b} | |

e_{road,BEV} = ε ω_{elec}/(η_{g} η_{b}) | |

e_{FCEV} = e_{vehicle,FCEV} + e_{infra, FCEV} + e_{fuel,FCEV} + e_{road,FCEV} + e_{disposal, FCEV} | (3) |

e_{vehicle,FCEV} = ((M_{FCEV} − M_{BAT} − M_{FCL}) φ_{v,FCEV} + (Γ_{BAT} φ_{BAT} θ_{BAT}) + (Γ_{FCL} φ_{FCL} ρ_{FCL}))/M_{FCEV} | |

e_{infra,FCEV} = H σ_{H2,p}/η_{h} | |

e_{fuel,FCEV} = H ϕ_{H2,p}/η_{h} | |

e_{road,FCEV} = H ω_{H2,p}/η_{r} |

**U**: a, b), Triangular (

**T**: a, b, c), Normal (

**N**: m, s), Lognormal (

**L**: m, s), Weibull (

**W**: s, s), Gamma (

**G**: s, r), Exponential (

**E**: s), non-standard beta distribution (

**B**: s, s), the location-scale t-distribution (

**O**: m, s, df) and the skew t-distribution (

**S**: m, s, a, df). The Dirac Delta function (

**D**: m) was used to describe a constant value. Appendix A provides further information for the distributions. The “truncdist” R package [28] was deployed to apply truncations to the fitted distributions (setting a lower limit (a) and an upper limit (b)). The plausible range in the input data is defined as the 99.7% confidence interval (equivalent to ±3 SD in a normal distribution), which prevents the use of unrealistic values in the pLCA. In the optimised fitting process, the R packages “fitdistr” and “fitdistrplus” [29], “extraDistr” [26], “sn” [30] and “truncdist” were used.

**U**: a, b), signifies equal probability between these endpoints. This distribution is suitable when information is only available for the lower and upper limit values [32]. The triangular probability distribution (

**T**: a, b, c) is continuous and characterized by a lower limit (a), an upper limit (b), and the most plausible estimate (c). It is appropriate for situations where the exact form of a distribution is uncertain, but values toward the middle of the range are deemed more likely than those near the extremes [33]. The triangular probability distribution can be asymmetrical.

_{ICEV}, e

_{BEV}and e

_{FCEV}). The resulting probability density functions (PDFs) not only indicate central tendencies, but also capture the variability and uncertainty in the output variables arising from variations in the input variables. The uncertainty in the outputs is defined as a 99.7% confidence interval (CI) of the mean value, presented either as a value range (asymmetric confidence interval) or a percentage (symmetric confidence interval).

#### 2.4. Scenario Definitions and Heavy-Duty Vehicle Classification

- Medium commercial (rigid) vehicles (MCV); GVM 3.5–12.0 t;
- Heavy commercial (rigid) vehicles (HCV); GVM 12.0–25.0 t;
- Articulated trucks (AT), gross vehicle mass; GVM > 25.0 t.

- The Recent Past Scenario (2019) reflects the Australian electricity mix and hydrogen production pathways in 2019. A mix of two hydrogen production pathways were considered: steam–methane reforming and green hydrogen production with electrolysis (Table 3).
- The Future Scenario (~2050) is a more decarbonised Australian scenario, loosely allocated to the year 2050, which assumes the Australian electricity generation mix and hydrogen-production pathways shown in Table 3. This assumption is in line and consistent with a similar pLCA study for passenger vehicles [17]. It is noted, however, that this scenario is not necessarily restricted to 2050. It would apply to any current situation where renewable low-carbon energy is used for the different life-cycle aspects. Examples are the use of solar panels to charge batteries or the use of grid electricity that is currently generated in Tasmania with almost 95% renewables [17].

## 3. Input Distributions

#### 3.1. Lifetime Mileage and System Durability

_{ICEV}input distributions are therefore (

**N**: 500,000; 33,000) truncated at 400,000 and 600,000 for MCVs and HCVs and (

**N**: 2,000,000; 62,000) truncated at 1,800,000 and 2,200,000 for ATs. These Australian values are different from those reported in other studies. For instance, O’Connell et al. [2] assumed a lifetime mileage of 900,000 km for MCVs and 1,300,000 km for ATs for the EU truck fleet.

**N**: 13,250; 875) truncated at 10,500 and 16,000 h for MCVs and HCVs and (

**N**: 27,667; 858) truncated at 24,900 and 30,450 h for ATs.

_{BAT}and Γ

_{FCL}), which estimate how often these vehicle components need to be replaced over the full lifetime of a truck. The replacement factor is a function of durability (hours) and HDV lifetime mileage, which are both variable. In the simulation, values are rounded up to the nearest integer. This means that values < 1 are set to unity, where a value of 1 means that a battery or fuel-cell system is not replaced during a vehicle’s lifetime (i.e., the original system is used). A value of two means that a battery or fuel-cell system is replaced once, a value of three means a battery or fuel-cell system is replaced twice, and so forth.

**U**: 400,000, 600,000), which, on average, corresponds to a battery replacement of 0 to 1 time for an MCV and a HCV and, with a proper technical design for articulated truck use, theoretically 3 to 5 times for an AT. Improved durability of battery technology is expected to double the battery lifetime mileage (

**U**: 800,000, 1,200,000), which, on average, corresponds to a battery replacement of 0 times for an MCV and a HCV and 1 to 2 times for an AT.

- Alternative usage of the ageing truck (e.g., shifting to shorter-distance transport missions) may allow for a lower SOC and, therefore, longer battery durability. This may include the use of ageing trucks along freight corridors with a high density of fast-charging stations and therefore more regular (fast) charging opportunities, or different types of and shorter distance missions, both making a lower SOC acceptable.
- The use of shared and externally charged batteries (battery swapping) in either OEM or retrofitted long-distance truck operations may increase the battery durability (slow charging) and may also allow for a lower SOC, although a larger number of batteries (spare for charging) would be required in this setup, impacting life-cycle emissions.
- The secondary use of truck batteries in non-transport applications, in which the GHG emission impacts of battery production should at least partly be passed on to the non-transport application. In this case, one to two battery replacements should likely sufficiently account for the GHG emission impacts of battery production on transport emissions.

**U**: 4000, 14,000), which, on average, corresponds to a fuel-cell system replacement of 0 to 3 times for an MCV and a HCV and 2 to 7 times for an AT. For 2050, improved durability of fuel-cell technology is expected to approximately double the lifetime and shift the average fuel-cell system durability to 8000–30,000 h (

**U**: 8000, 30,000), which, on average, corresponds to a system replacement of 0 to 1 time for an MCV and a HCV and 0 to 3 times for an AT. Similar to BEVs, it is unlikely that more than three (partial) fuel-system replacements in trucks will be acceptable and feasible (cost-wise) in practice. However, the available evidence and elevated uncertainty do not justify capping the replacement factors of current fuel-cell systems to a maximum of four in the simulation, and the replacement factors were kept as estimated. It is noted that for future fuel-cell systems, replacement factors do no longer exceed a value of 4, reflecting expected durability improvements.

- BEV: 1.5 for MCV/HCV in 2019 and 1.0 in 2050;
- BEV: 4.0 for AT in 2019 and 2.5 in 2050;
- FCEV: 2.2 for MCV/HCV in 2019 and 1.2 in 2050;
- FCEV: 4.0 for AT in 2019 and 2.2 in 2050.

#### 3.2. Mass of Vehicle, Battery and the Fuel-Cell System

**N**: 3.1, 0.12) for MCVs, (

**N**: 9.2, 0.37) for HCVs and (

**N**: 24.3, 0.65) for ATs.

**T**: 100, 250, 200), HCVs (

**T**: 150, 400, 340) and ATs (

**T**: 150, 1000, 600). FCEVs use a smaller (system support) battery than BEVs, and their battery capacity is defined as the triangular input distributions for MCVs (

**T**: 40, 100, 60), HCVs (

**T**: 50, 150, 80) and ATs (

**T**: 100, 200, 150). For battery-electric HDV applications, a plausible range for the battery energy density at the pack level is assumed to be at the higher end (to maximise the mass reduction of high-capacity batteries) and is assumed to lie between 0.15 to 0.20 kWh per kg of battery, with a typical value of 0.16 kWh/kg (

**T**: 0.15, 0.20, 0.16) for the current situation. A significant increase is expected in the battery energy density. The question is whether this improvement in energy density will translate into an increase in the electric range or a reduction in battery mass. For the future situation, the nominal battery density is expected to (at least) improve to 0.20 to 0.30 kWh per kg of battery, with a typical value of 0.27 kWh/kg (

**T**: 0.20, 0.30, 0.27) [2,41,42,44,45,46,47,48,49].

**T**: 100, 200, 140), HCVs (

**T**: 150, 250, 200) and ATs (

**T**: 200, 600, 400).

**T**: 0.5, 0.6, 0.9). For future years, an improvement in fuel-cell energy efficiency is expected from the current 50–60% to 65% in the near term and up to 70% in the long term [49,54,55]. It has been assumed in this study that this will largely translate into a range and power increase, leaving the fuel-cell mass approximately the same in the future. Thus, effectively the same fuel-cell power density distribution is used for both 2019 and 2050. Using these distributions in a Monte Carlo simulation and subsequent distribution fitting, the fuel-cell mass distributions can be estimated. They are presented in Table 4. Similarly to the battery mass simulation, a more restricted truncation of ±1 SD is applied specifically to the fuel-cell system mass to prevent the use of unreasonably low or high values.

#### 3.3. Electricity Production, Distribution and Recharging Losses

**T**: 1.05, 1.10, 1.06). Efficiency is computed as 100% minus the loss (%). The distribution definitions are shown in Table 5. It is noted that the future scenario (2050) assumes a 10% fossil fuel use in electricity generation (Section 2.4).

**T**: 0.80, 0.95, 0.90), with an improved performance in 2050 (

**T**: 0.90, 0.96, 0.93).

#### 3.4. Hydrogen Production, Distribution and Refuelling Losses

_{2}per ton of hydrogen produced [48,63], as well as emissions of air pollutants such as NO

_{x}and PM. This estimate includes SMR-related emissions but does not yet include emissions related to hydrogen distribution. A range of 100–134 g CO

_{2}-e/MJ H

_{2}has been estimated for the SMR hydrogen pathway [48,64], which corresponds to (

**U**: 100, 135). To reduce the GHG impacts of hydrogen production from fossil fuels, green hydrogen can be used (electrolysis using renewable energy). The scientific literature e.g., [48,64] shows a range of about 10–35 g CO

_{2}-e/MJ H

_{2}(

**U**: 10, 35) for the renewable hydrogen pathway using wind or solar power.

**U**: 80, 110). For consistency with electricity generation (Section 3.3), it is assumed that for 2050, 10% of hydrogen is produced and distributed by fossil fuels (SMR) and 90% is produced by renewables (electrolysis), i.e., (

**U**: 20,45). Table 6 shows the results after conversion to mass units.

**T**: 0.900, 0.999, 0.980) for 2019, with an improved performance in 2050 (

**T**: 0.950, 0.999, 0.990).

**T**: 0.990, 0.999, 0.998), with an improved performance in 2050 (

**T**: 0.995, 0.999, 0.998).

#### 3.5. Future Improvements in LCA Input Variables

**U**: 0.10, 0.50). The emission intensities for vehicle, battery and fuel-cell production (φ) will also improve over time, and these are discussed in Section 3.6. Real-world fuel and energy consumption (ε, H) will change over time, and these are discussed in Section 3.7.

#### 3.6. Truck Manufacturing

_{2}-e/kg of vehicle, with a typical value of 3.0 kg CO

_{2}-e/kg of vehicle, which is lower than those used for passenger vehicles [2,17,48,67,68,69], i.e., (

**T**: 2.0, 3.5, 3.0). For future vehicle manufacturing, this emission intensity is expected to drop significantly due to the increased use of recycling practices and the general decarbonisation of energy systems [14,70]. The reduction factor in 2050 is expected to vary between 10% and 50% of the 2019 values (

**U**: 0.10, 0.50). For electric vehicles, the same distribution was assumed for non-battery and non-fuel-cell vehicle components. The GHG emissions for battery and fuel-cell production need to be estimated separately and added.

_{2}-e per kWh of battery capacity, with a current average of about 90 kg CO

_{2}-e per kWh [2,7,47,48,71], so (

**T**: 35, 160, 90). For future battery manufacturing, this emission intensity is expected to drop significantly for the same reasons mentioned before. For batteries, specifically, there is also the possibility of the second-life use of spent batteries, which can further decrease the vehicle’s battery carbon footprint by 50% [7]. Battery manufacturing emissions in 2050 are assumed to fall between 9 and 42 kg CO

_{2}-e per kWh of battery capacity [47], with a typical value of about 20 kg CO

_{2}-e per kWh, so (

**T**: 10, 40, 20). The reduction factor in 2050 is therefore expected to vary between 10% and 25% of the 2019 values (

**U**: 0.10, 0.25). The assumed distributions for the BEV battery capacity, battery mass and battery replacement factor have already been discussed in Section 3.2 and Section 3.3.

_{2}-e per kW of rated power [2,37,48] (

**U**: 60, 350). Similarly to batteries, the reduction factor in 2050 is assumed to vary between 10% and 25% of the 2019 values (

**U**: 0.10, 0.25). The assumed distributions for the fuel-cell system mass, rated power and replacement factor have already been discussed in Section 3.2 and Section 3.3.

_{BAT}and Γ

_{FCL}). Manufacturing of electric vehicles “naturally” has a higher carbon footprint than that of conventional vehicles, and the only way to reduce this difference is through the further decarbonisation of battery and fuel-cell production processes and significantly increased (second) use and recycling. Nevertheless, these increased normalised emissions (g/km) can be more than compensated for in the use phase, leading to an overall emissions improvement, as will be discussed later.

#### 3.7. Operational Electricity Use, Fuel Consumption and Emissions (On-Road Driving)

_{2}, CH

_{4}, N

_{2}O, BC, CO

_{2}-e), fuel consumption (petrol, E10, diesel, LPG, hydrogen) and energy/electricity use (kWh consumed). About two million emission factors (g/km), fuel use factors (g/km, MJ/km) and electricity/energy use factors (Wh/km) are generated by n0vem. These factors are generated for different operational (driving) conditions and different emission types. They include vehicle speed dependencies (driving behaviour and congestion level), hot-running emissions and additional GHG emissions due engine start, air-conditioning use, engine oil and NO

_{x}emission control (SCR).

_{2}-e emissions due to fuel combustion, engine oil losses and SCR operation. Meteorological input data (ambient temperature, humidity) for Australia was sourced from previous research work [75]. The default inputs for the Australian mean network speed and VKT shares were used in the simulation, which are for MCVs/HCVs and ATs, respectively:

- 70% and 15% VKT share of urban driving (30 km/h);
- 5% and 10% VKT share of rural driving (75 km/h);
- 25% and 75% VKT share of highway driving (100 km/h).

_{2}/g fuel was used to convert units from l/100 km to g CO

_{2}/km. The relative uncertainty in the converted ABS figures is assumed to be ±3 RSE and follows a truncated normal distribution. The analysis shows that the plausible range for truncation is ±6% for MCVs and HCVs and ±3% for ATs, respectively. This distribution and the plausible ranges are assumed to apply to all powertrains. The results are shown in Table 8, Table 9 and Table 10. It is noted that the GHG emission factor for the Australian AT fleet derived from the SMVU (1457 g CO

_{2}/km) is 3% higher than the value predicted by n0vem (1420 g CO

_{2}-e/km). The SMVU-derived emission factor for rigid trucks (755 g CO

_{2}/km) lies in between the more refined classification used in n0vem for MCVs (644 g CO

_{2}-e/km) and HCVs (860 g CO

_{2}-e/km).

- Specifically, for compression-ignition (diesel) ICEVs, further technological “system approach” engine improvements are expected to lead to an overall 10–20% fuel efficiency improvement in 2050 (
**U**: 0.80, 0.90). Measures to achieve this may include, but are not limited to, advanced systems for valve-train control, use of low-viscosity lubricants, variable compression ratios, re-use of waste heat and engine downsizing [49,77,78]. - BEV energy improvement is expected to be larger and is expected to occur sooner than that for FCEVs. One of these expected improvements is a significant increase in battery energy density, as was discussed in Section 3.2. It has been assumed in this study that this will largely translate into a range and power increase without affecting battery mass significantly. There are several potential improvements that will lead to significant efficiency improvements for BEVs, for instance, purpose design, in-wheel or wheel-hub electric motors rather than central engines, improved energy recuperation, decreased coasting resistance and the application of lightweight chassis components [50]. The expected improvement in energy efficiency for BEVs is assumed to be in the order of 20–30% in 2050 (
**U**: 0.70, 0.80). - Although some studies assume zero improvement for FCEVs [76], further improvement in the fuel-cell energy efficiency is expected from the current 50–60% to 65% in the near term and up to 70% in the long term [49,54,55]. This leads to an estimated improvement in energy efficiency for FCEVs of 15–25% in 2050 (
**U**: 0.75, 0.85). It has been assumed in this study that this will largely translate into a range and power increase.

_{2}-e/kWh consumed) for the grid-loss-corrected electricity generation in Australia in 2019 and 2050 (Table 5) in a Monte Carlo simulation and subsequent distribution fitting. The results are shown in Table 11.

_{2}-e/g H

_{2}) for hydrogen production in Australia in 2019 and 2050 (Table 6) and for hydrogen losses (distribution and refuelling) in a Monte Carlo simulation and subsequent distribution fitting. The results are shown in Table 12.

#### 3.8. Truck Maintenance

_{2}-e/km for all power trains and vehicle classes [2], i.e., (

**U**: 4, 7).

#### 3.9. Energy Infrastructure

_{2}-e per kg of fossil-fuel produced (

**U**: 2, 30). This distribution was combined in a Monte Carlo simulation with the distributions for real-world fuel consumption, which were derived from Table 8 after conversion from GHG emissions to real-world (diesel) fuel consumption. The sampling distributions were used to determine the best theoretical distribution through a maximum-likelihood fit. The results are shown in Table 13.

**T**: 1.05, 1.10, 1.06) and BEV real-world energy consumption distributions (Table 9), which account for battery charging losses. The fuel-type percentages from Table 3 were used as weights in this process. The resulting sampling distributions were employed to identify the optimal theoretical distribution through a maximum-likelihood fit, and the outcomes are presented in Table 15.

#### 3.10. Upstream Emissions (Fuel/Energy)

**U**: 0.14, 0.28) [6,7,8,44,46,82,83]. This distribution was combined in a Monte Carlo simulation with the distributions for real-world fuel consumption derived from Table 8, after conversion from GHG emissions to real-world (diesel) fuel consumption. The sampling distributions were employed to identify the optimal theoretical distribution using the maximum-likelihood fit, and the outcomes are presented in Table 16.

_{2}-e/kWh consumed), published as Scope 3 NGA GHG emission factors (National Greenhouse Accounts) for Australia, and a skewed t distribution to quantify the uncertainty (

**S**: 0.94, 0.09, 1.49, 216.93), truncated at 0.75 and 1.30, in a Monte Carlo simulation and subsequent distribution fitting. For BEVs in 2050, real-world electricity consumption (Table 9) was combined with the upstream GHG emission intensities for electricity generation using a range of renewables and fossil fuels (Table 9 in [17]) and the grid-loss distribution (

**T**: 1.05, 1,10, 1.06) in a Monte Carlo simulation and subsequent distribution fitting. The results are shown in Table 17. It has conservatively been assumed that the proportion of battery electric truck operators that generate their own sustainable electricity (solar panels) for battery recharging is zero.

**U**: 0.14, 0.28). Assuming that about 3.0 to 3.5 times the amount of natural gas is required to produce 1 kg of hydrogen, reflecting the LHV ratio and an assumed 70 to 80% SMR conversion efficiency (

**U**: 3.0, 3.5), the two uniform distributions were combined in a Monte Carlo simulation with the distributions for on-road hydrogen consumption (Table 9). As mentioned before, it was assumed that about 75% and 10% of hydrogen is produced and distributed with fossil fuels (SMR) in 2019 and 2050, respectively. The carbon intensity of natural gas is assumed to be 2.74 g CO

_{2}-e/g fuel.

#### 3.11. Vehicle Disposal and Recycling

**U**: 0.10, 0.50).

## 4. Results and Discussion

#### 4.1. Average Life-Cycle GHG Emission Factors for Trucks

_{2}-e/km. In comparison, the range is 139–235 g CO

_{2}-e/km for ICEVs and 65–569 g CO

_{2}-e/km for FCEVs. The uncertainty is high for hydrogen trucks due to the propagation of relatively large uncertainty and variability in the inputs.

#### 4.2. The Relevance of Different Life-Cycle Aspects

- First, it is clear that life-cycle emission factor distributions for trucks vary widely in magnitude, range and shape. They all depend on the year of assessment, truck vehicle class and powertrain technology.
- Second, any cost-effective emission reduction policies would naturally favour truck types that have narrow life-cycle GHG emissions distributions, which are as close to zero emissions as possible. They represent the maximum potential for emissions reduction and are the most robust and least uncertain technology choice. Whereas diesel trucks appear to have had the best life-cycle emissions performance in 2019, the situation is the inverse in 2050 (or in a more decarbonised situation). The simulation suggests that battery electric trucks are expected to achieve the greatest and most reliable and robust reductions in life-cycle GHG emissions.
- Third, the contribution of different life-cycle aspects is quite different, and this is particularly clear when diesel trucks are compared with electric trucks.

#### 4.3. Comparison to Other Studies

- First, this study estimates low emission intensities (per tonne km) for heavy articulated trucks (AT), but it is unclear if these heavy vehicles were included in the IPCC data. Medium-duty trucks (MDT, Figure 9) in IPCC [20] refer to the US American classification for trucks with gross vehicle masses between 7 tonnes and 13 tonnes, while heavy-duty trucks (HDT) refer to trucks with vehicle masses > 16.5 tonnes [86]. In this study, articulated trucks are defined as trucks > 25 tonnes, and the AT modelled in this study has a gross vehicle mass of 90 tonnes (Table 2).
- Second, this study estimates a significantly wider plausible range in the life-cycle emissions performance of hydrogen trucks (FCEV), whereas the IPCC estimates quite a narrow range. This is an interesting result. Our study suggests that the elevated uncertainty in several life-cycle aspects for FCEVs can produce the highest emission intensities for all technology classes.

## 5. Conclusions

_{2}-e/km. In comparison, the spread or range is 139–235 g CO

_{2}-e/km for diesel trucks and 65–569 g CO

_{2}-e/km for hydrogen trucks, as predicted for 2050. This suggests that, out of the three powertrain options, battery electric trucks are expected to deliver the largest and most robust emission reductions for all vehicle classes, as long as the electricity mix is largely generated from renewables. They carry the lowest risk of not delivering the anticipated reductions in life-cycle GHG emissions.

## 6. Recommendations for Future Work and Refinement

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Glossary of Terms

ABS | Australian Bureau of Statistics |

AFM | Australian Fleet Model |

AT | Articulated truck |

B | Non-standard beta distribution |

BE(V) | Battery electric (vehicle) |

CDF | Cumulative distribution function |

CI | Confidence interval |

D | Dirac Delta function |

e_{ICEV}, e_{BEV}, and e_{FCEV} | Life-cycle GHG emission factor |

E | Exponential distribution |

EV | Electric vehicle |

G | Gamma distribution |

GHG | Greenhouse gas |

GWP | Global-warming potential |

GVM | Gross vehicle mass |

FC | Fuel consumption |

FCE(V) | Fuel-cell electric (vehicle) |

HCV | Heavy commercial (rigid) vehicle |

HDT | Heavy-duty truck |

HDV | Heavy-duty vehicle |

HEV | Hybrid electric vehicle |

ICE(V) | Internal combustion engine (vehicle) |

IPCC | Intergovernmental Panel on Climate Change |

L | Lognormal distribution |

LCA | Life-cycle assessment |

MCV | Medium commercial (rigid) vehicle |

MDT | Medium-duty truck |

N | Normal distribution |

NGA | National greenhouse accounts (factors) |

PHEV | Plug-in hybrid electric vehicle |

pLCA | Probabilistic LCA |

Probability density function | |

PV | Passenger vehicle |

Quantile–quantile (plot) | |

RSE | Relative standard error |

SOC | State of charge |

SCR | Selective catalytic reduction |

SMR | Steam–methane reforming |

SMVU | Survey of Motor Vehicle Use |

T | Triangular distribution |

U | Uniform distribution |

VKT | Vehicle kilometres travelled |

W | Weibull distribution |

## Appendix A

Name | Range | Parameters | Probability Density Function (PDF) |
---|---|---|---|

Uniform—U(x:a,b) | a ≤ x ≤ b | $a$: Minimum, $-\mathrm{\infty}<a<b$ $b$: Maximum, $a<b<-\mathrm{\infty}$ | $\frac{1}{b-a}$ |

Triangular—T(x:a,b,c) | a ≤ x ≤ b | $a$: Minimum, $-\mathrm{\infty}<a<b$ $b$: Maximum, $a<b<-\mathrm{\infty}$ $c$: Mode, $a\le c\le b$ | $\left\{\right)separators="|">\begin{array}{cc}\frac{2\left(x-a\right)}{\left(b-a\right)\left(c-a\right)},& \text{}x\le c\\ \frac{2\left(b-x\right)}{\left(b-a\right)\left(c-a\right)},& xc\end{array}$ |

Normal—N(x:m,s) | −∞ ≤ x ≤ +∞ | $m$: Mean, $-\mathrm{\infty}<m<\mathrm{\infty}$ $s$: Standard deviation, $0<\mathrm{s}<\mathrm{\infty}$ | $\frac{1}{\sqrt{2\pi}s}\mathrm{e}\mathrm{x}\mathrm{p}\left(-\frac{1}{2{s}^{2}}{\left(x-m\right)}^{2}\right)$ |

Lognormal—L(x:m,s) | 0 ≤ x ≤ +∞ | $m$: Log-mean, $-\mathrm{\infty}<m<\mathrm{\infty}$ $s$: Scale, $0<\mathrm{s}<\mathrm{\infty}$ | $\frac{1}{x\sqrt{2\pi}s}\mathrm{e}\mathrm{x}\mathrm{p}\left(-\frac{1}{2{s}^{2}}{\left(ln\left(x\right)-m\right)}^{2}\right)$ |

Weibull—W(x:s,k) | 0 ≤ x ≤ +∞ | $s$: Scale, $0<\mathrm{s}<\mathrm{\infty}$ $k$: Shape, $0<\mathrm{s}<\mathrm{\infty}$ | $\frac{k}{s}{\left(\text{}\frac{x}{s}\text{}\right)}^{k-1}\mathrm{exp}\left(-{\left(\text{}\frac{x}{\mathrm{s}}\text{}\right)}^{k}\right)$ |

Gamma—G(x:s,k) | 0 ≤ x ≤ +∞ | $s$: Scale, $0<\mathrm{s}<\mathrm{\infty}$ $r$: Rate, $0<\mathrm{s}<\mathrm{\infty}$ | $\frac{{r}^{s}}{\mathsf{\Gamma}\left(s\right)}{x}^{s-1}\mathrm{exp}\left(-rx\right)$ |

Exponential—E(x:s) | 0 ≤ x ≤ +∞ | $s$: Scale, $0<\mathrm{s}<\mathrm{\infty}$ | $r\mathrm{exp}\left(-rx\right)$ |

Non-Standard Beta—B(x:s,k,a,b) | a ≤ x ≤ b | $s$: Scale, $0<\mathrm{s}<\mathrm{\infty}$ $k$: Shape, $0<\mathrm{k}<\mathrm{\infty}$ $a$: Minimum, $-\mathrm{\infty}<a<b$ $b$: Maximum, $a<b<-\mathrm{\infty}$ | $\frac{\mathsf{\Gamma}\left(s+k\right)}{\mathsf{\Gamma}\left(s\right)\mathsf{\Gamma}\left(k\right)}{\left(\text{}\frac{x-a}{b-a}\text{}\right)}^{s-1}{\left(1-\frac{x-a}{b-a}\text{}\right)}^{k-1}$ |

Skew t—S(x:m,s,a,d) | -∞ ≤ x ≤ +∞ | $m$: Mean, $-\mathrm{\infty}<m<\mathrm{\infty}$ $s$: Scale, $0<\mathrm{s}<\mathrm{\infty}$ $a$: Skew, $0<\mathrm{a}<\mathrm{\infty}$ $d$: Degrees of freedom, $0<\mathrm{d}<\mathrm{\infty}$ | $2t\left(x:m,s,d\right)T\left(az\sqrt{\frac{d+1}{d+{z}^{2}}}:\mathrm{0,1},d\right),$ where $t\left(x:m,s,d\right)=\frac{\mathsf{\Gamma}\left(\frac{1}{2}\left(d+1\right)\right)}{\frac{\sqrt{\pi d}1}{2}d}{\left(1+{\left(\frac{x-m}{s}\right)}^{2}\right)}^{-\frac{v+1}{2}}$ $z=\left(x-m\right)/s),$ and $T(x:m,s,d)$ is the cumulative distribution function. |

Dirac Delta—D(x:m) | -∞ ≤ x ≤ +∞ Practically x = m | $m$: Location, $-\mathrm{\infty}<m<\mathrm{\infty}$ | $\left\{\right)separators="|">\begin{array}{cc}\mathrm{\infty},& \text{}x=m\\ 0,& x\ne m\end{array}$ |

## Appendix B

**Figure A1.**Box plot of the percentage contribution of on-road operation to life-cycle GHG emissions (CO

_{2}-e/km) by vehicle class, powertrain category and year of assessment.

**Figure A2.**Box plot of the percentage contribution of vehicle manufacturing to life-cycle GHG emissions (CO

_{2}-e/km) by vehicle class, powertrain category and year of assessment.

**Figure A3.**Box plot of the percentage contribution of fuel and energy infrastructure to life-cycle GHG emissions (CO

_{2}-e/km) by vehicle class, powertrain category and year of assessment.

**Figure A4.**Box plot of the percentage contribution of upstream fuel and energy production to life-cycle GHG emissions (CO

_{2}-e/km) by vehicle class, powertrain category and year of assessment.

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**Figure 1.**Flow diagram showing the method and steps followed in the probabilistic LCA (pLCA) process applied in this study.

**Figure 2.**Correction of operational on-road energy, fuel consumption and emissions for changes in on-road vehicle mass for different driving conditions. The figure shows the 99.7% prediction and confidence intervals with light grey and dark grey polygons.

**Figure 3.**PDFs of life-cycle GHG emission factors by vehicle class and powertrain category. The boxes with dotted lines define the 99.7% confidence intervals (x-axis) and the maximum probability density (y-axis) for each distribution.

**Figure 4.**The confidence interval of the mean life-cycle emission factor by year, vehicle class and powertrain category. (Note: the scope of this study was restricted to a recent past year and future decarbonised year, so a white shaded area has been added to highlight that the progression over time is likely non-linear rather than linear; refer to Section 6).

**Figure 5.**Box plot of the percentage share of the vehicle-cycle (vehicle manufacturing + disposal/recycling) to life-cycle GHG emissions (CO

_{2}-e/km) by vehicle class, powertrain category and year of assessment.

**Figure 6.**Box plot of the percentage share of the fuel-cycle (upstream emissions + operational emissions) to life-cycle GHG emissions (CO

_{2}-e/km) by vehicle class, powertrain category and year of assessment.

**Figure 7.**Cumulative life-cycle emission factor distributions, including shares of the main life-cycle aspects, presented by vehicle class and powertrain categories for 2019.

**Figure 8.**Cumulative life-cycle emission factor distributions, including shares of main life-cycle aspects, presented by vehicle class and powertrain categories for 2050.

**Figure 9.**Life-cycle GHG intensity (g CO

_{2}-e per tonne-km) for freight transport. Results for freight trucks from this study (

**top**) compared to the literature data reviewed by IPCC [20] (

**bottom**), where MDT = Medium-Duty Trucks and HDT = Heavy-Duty Trucks, with MDT and HDT data for 50% payload each. Red = ICEV, Green = BEV, Blue = FCEV.

Notation | Description and Units | Time Variable |
---|---|---|

M_{x} | lifetime mileage for technology x (x = ICEV, BEV, FCEV) (km) | No (S.3.1) |

Γ_{BAT} | battery replacement factor (−) | Yes (S.3.1) |

Γ_{FCL} | fuel-cell replacement factor (−) | Yes (S.3.1) |

M_{x} | vehicle tare mass for technology x (x = ICEV, BEV, FCEV) (kg) | No (S.3.2) |

M_{BAT} | battery mass for BEV or FCEV (kg) | No (S.3.2) |

M_{FCL} | fuel-cell mass (kg) | No (S.3.2) |

θ_{BAT} | battery capacity (kWh) | Yes (S.3.2) |

ρ_{FCL} | fuel-cell rated power (kW) | Yes (S.3.2) |

ω_{elec} ^{(1)} | GHG emission-intensity electricity generation (g CO_{2}-e/kWh generated) | Yes (S.3.3) |

η_{b} | battery recharging efficiency (−) | Yes (S.3.3) |

η_{g} | grid transmission efficiency (−) | Yes (S.3.3) |

ω_{H2,P} | GHG emission-intensity hydrogen production (g CO_{2}-e/g fuel) for production pathway P | Yes (S.3.4) |

σ_{elec} | GHG emission-intensity electricity infrastructure (g CO_{2}-e/kWh generated) | Yes (S.3.5) |

η_{h} | hydrogen distribution efficiency (−) | Yes (S.3.4) |

η_{r} | hydrogen refuelling efficiency (−) | Yes (S.3.4) |

σ_{H2,P} | GHG emission-intensity H_{2} production infrastructure (g CO_{2}-e/g fuel) production pathway P | Yes (S.3.5) |

ϕ_{elec} ^{(2)} | GHG emission-intensity upstream fuels for electricity generation (g CO_{2}-e/kWh consumed) | Yes (S.3.5) |

ϕ_{H2,p} ^{(2)} | GHG emission-intensity upstream H_{2} production (g CO_{2}-e/g fuel) for production pathway P | Yes (S.3.5) |

φ_{v,ICEV} | GHG emission-intensity ICEV production (kg CO_{2}-e/kg vehicle) | Yes (S.3.6) |

φ_{v,BEV} | GHG emission-intensity BEV production without battery (kg CO_{2}-e/kg vehicle) | Yes (S.3.6) |

φ_{v,FCEV} | GHG emission-intensity FCEV production without battery/fuel cell (kg CO_{2}-e/kg vehicle) | Yes (S.3.6) |

φ_{BAT} | GHG emission-intensity battery production (kg CO_{2}-e/kWh battery capacity) | Yes (S.3.6) |

φ_{FCL} | GHG emission-intensity fuel-cell production (kg CO_{2}-e/kW fuel-cell-rated power) | Yes (S.3.6) |

ε | real-world electricity consumption BEV (kWh/km) | Yes (S.3.7) |

H | real-world hydrogen consumption FCEV (g/km) | Yes (S.3.7) |

^{(1)}Direct emissions due to the activity;

^{(2)}Indirect emissions from upstream activities (extraction, production, etc.).

Vehicle Class | Powertrain | GVM * (t) | Tare Mass ** (t) | Payload *** (t) | Typical Rated Power (kW) | Typical Battery Capacity (kWh) |
---|---|---|---|---|---|---|

MCV | ICEV | 7.5 | 3.1 | 2.2 | 125 | - |

BEV | 7.5 | Variable | 2.2 | 115 | 200 | |

FCEV | 7.5 | Variable | 2.2 | 140 | 60 (40 ****) | |

HCV | ICEV | 17.2 | 9.2 | 4.0 | 247 | - |

BEV | 17.2 | Variable | 4.0 | 220 | 340 | |

FCEV | 17.2 | Variable | 4.0 | 200 | 80 (40 ****) | |

AT | ICEV | 90.0 | 24.3 | 32.9 | 510 | - |

BEV | 90.0 | Variable | 32.9 | 394 | 600 | |

FCEV | 90.0 | Variable | 32.9 | 400 | 150 (80 ****) |

Scenario, Jurisdictions | Coal | Gas | Oil | Nuclear | Hydro | Wind | Biomass | Solar |
---|---|---|---|---|---|---|---|---|

Electricity, Australia, Past (2019) | 58.4% | 20.0% | 1.9% | 0.0% | 6.0% | 6.7% | 1.3% | 5.6% |

Electricity, Australia, Future (2050) | 5.0% | 5.0% | 0.0% | 0.0% | 30.0% | 25.0% | 5.0% | 30.0% |

Hydrogen, Australia, Past (2019) | - | 75.0% * | - | - | - | 25.0% ** | - | - |

Hydrogen, Australia, Future (2050) | - | 10.0% * | - | - | - | 90.0% ** | - | - |

Year | Vehicle Technology | LCA Model Input Variable | Distribution | Typical Value (kg) | Plausible Min–Max Value (kg) |
---|---|---|---|---|---|

2019 | MC-BEV | M_{BAT,BEV,MCV} | Weibull, W (6.22, 1161.68) | 1080 | 868–1283 |

2019 | HC-BEV | M_{BAT,BEV,HCV} | Weibull, W (6.07, 1891.85) | 1755 | 1384–2101 |

2019 | AT-BEV | M_{BAT,BEV,AT} | Non-standard beta, B (2.91, 3.21) | 3454 | 2352–4541 |

2050 | MC-BEV | M_{BAT,BEV,MCV} | Normal, N (719, 136) | 719 | 578–854 |

2050 | HC-BEV | M_{BAT,BEV,HCV} | Weibull, W (5.69, 1258.83) | 1165 | 915–1395 |

2050 | AT-BEV | M_{BAT,BEV,AT} | Weibull, W (3.54, 2547.64) | 2292 | 1533–3027 |

2019 | MC-FCEV | M_{BAT,FCEV,MCV} | Non-standard beta, B (3.51, 6.28) | 394 | 315–477 |

2019 | HC-FCEV | M_{BAT,FCEV,HCV} | Non-standard beta, B (3.17, 5.98) | 551 | 422–690 |

2019 | AT-FCEV | M_{FCL,FCEV,AT} | Non-standard beta, B (3.68, 4.72) | 888 | 748–1027 |

2050 | MC-FCEV | M_{BAT,FCEV,MCV} | Non-standard beta, B (3.31, 8.19) | 261 | 206–317 |

2050 | HC-FCEV | M_{BAT,FCEV,HCV} | Non-standard beta, B (3.16, 7.83) | 367 | 278–460 |

2050 | AT-FCEV | M_{BAT,FCEV,HCV} | Non-standard beta, B (5.32, 9.75) | 589 | 492–684 |

2019 + 2050 | MC-FCEV | M_{FCL,FCEV,MCV} | Non-standard beta, B (4.82, 10.14) | 223 | 181–265 |

2019 + 2050 | HC-FCEV | M_{FCL,FCEV,HCV} | Non-standard beta, B (4.79, 7.61) | 305 | 255–356 |

2019 + 2050 | AT-FCEV | M_{FCL,FCEV,AT} | Non-standard beta, B (4.76, 9.04) | 608 | 459–756 |

**Table 5.**GHG emission-intensity distributions for grid-loss-corrected electricity generation in Australia by year (g CO

_{2}-e/kWh consumed).

Year | Input Distribution | Typical Value | Plausible Min–Max Value |
---|---|---|---|

2019 | Normal, N (760, 11.4) | 760 | 725–794 |

2050 | Skewed t, S (78.66, 4.52, 2.87, 27.08) | 82 | 74–96 |

**Table 6.**GHG emission-intensity distributions for hydrogen production in Australia by year (g CO

_{2}-e/g H

_{2}).

Year | Input Distribution | Typical Value | Plausible Min–Max Value |
---|---|---|---|

2019 | Uniform, U (9.3, 13.2) | 11.2 | 9.3–13.2 |

2050 | Uniform, U (2.3, 5.4) | 3.8 | 2.3–5.4 |

Life-Cycle Aspect | Vehicle Technology | LCA Model Input Variable | Distribution | Typical Value | Plausible Min–Max Value |
---|---|---|---|---|---|

M 2019 | MC-ICEV | e_{vehicle,ICEV,MCV} | Non-standard beta, B (8.82, 10.47) | 18 | 12–25 |

M 2019 | HC-ICEV | e_{vehicle,ICEV,HCV} | Non-standard beta, B (9.66, 11.63) | 52 | 34–72 |

M 2019 | AT-ICEV | e_{vehicle,ICEV,AT} | Non-standard beta, B (5.15, 4.170) | 35 | 24–44 |

M 2050 | MC-ICEV | e_{vehicle,ICEV,MCV} | Non-standard beta, B (2.66, 3.56) | 6 | 1–12 |

M 2050 | HC-ICEV | e_{vehicle,ICEV,HCV} | Weibull, W (2.25, 18.54) | 16 | 4–35 |

M 2050 | AT-ICEV | e_{vehicle,ICEV,AT} | Weibull, W (2.28, 12.20) | 11 | 3–22 |

M 2019 | MC-BEV | e_{vehicle,BEV,MCV} | Non-standard beta, B (3.15, 39.66) | 68 | 28–150 |

M 2019 | HC-BEV | e_{vehicle,BEV,HCV} | Lognormal, L (4.86, 0.29) | 134 | 63–277 |

M 2019 | AT-BEV | e_{vehicle,BEV,AT} | Gamma, G (10.37, 0.07) | 145 | 57–300 |

M 2050 | MC-BEV | e_{vehicle,BEV,MCV} | Non-standard beta, B (4.94, 10.40) | 14 | 5–25 |

M 2050 | HC-BEV | e_{vehicle,BEV,HCV} | Non-standard beta, B (4.02, 7.08) | 30 | 11–56 |

M 2050 | AT-BEV | e_{vehicle,BEV,AT} | Non-standard beta, B (5.56, 22.64) | 28 | 8–62 |

M 2019 | MC-FCEV | e_{vehicle,FCEV,MCV} | Non-standard beta, B (2.42, 13.12) | 169 | 43–474 |

M 2019 | HC-FCEV | e_{vehicle,FCEV,HCV} | Non-standard beta, B (2.76, 11.81) | 260 | 84–658 |

M 2019 | AT-FCEV | e_{vehicle,FCEV,AT} | Lognormal, L (5.34, 0.44) | 228 | 73–657 |

M 2050 | MC-FCEV | e_{vehicle,FCEV,MCV} | Lognormal, L (2.97, 0.41) | 21 | 6–64 |

M 2050 | HC-FCEV | e_{vehicle,FCEV,HCV} | Gamma, G (7.50, 0.20) | 38 | 11–96 |

M 2050 | AT-FCEV | e_{vehicle,FCEV,AT} | Lognormal, L (3.37, 0.38) | 31 | 9–84 |

**Table 8.**ICEV GHG emission factor (g CO

_{2}-e/km) distributions for on-road driving (operational, O).

Life-Cycle Aspect | Vehicle Technology | LCA model Input Variable | Distribution | Typical Value | Plausible Min–Max Value |
---|---|---|---|---|---|

O 2019 | MC-ICEV | e_{road,ICEV,MCV} | Normal, N (644, 13) | 644 | 605–682 |

O 2019 | HC-ICEV | e_{road,ICEV,HCV} | Normal, N (860, 17) | 860 | 808–911 |

O 2019 | AT-ICEV | e_{road,ICEV,AT} | Normal, N (1420, 14) | 1420 | 1377–1462 |

O 2050 | MC-ICEV | e_{road,ICEV,MCV} | Non-standard beta, B (3.57, 3.91) | 547 | 496–603 |

O 2050 | HC-ICEV | e_{road,ICEV,HCV} | Non-standard beta, B (3.52, 3.77) | 731 | 663–804 |

O 2050 | AT-ICEV | e_{road,ICEV,AT} | Triangular, T (1111, 1309, 1198) | 1207 | 1111–1309 |

**Table 9.**Electricity (Wh/km) consumption distributions for on-road driving (operational, O), including battery recharging losses.

Life-Cycle Aspect | Vehicle Technology | LCA Model Input Variable | Distribution | Typical Value | Plausible Min–Max Value |
---|---|---|---|---|---|

O 2019 | MC-BEV | ε_{MCV} | Non-standard beta, B (5.77, 11.49) | 908 | 821–1021 |

O 2019 | HC-BEV | ε_{HCV} | Non-standard beta, B (5.53, 11.05) | 1229 | 1113–1384 |

O 2019 | AT-BEV | ε_{AT} | Non-standard beta, B (3.81, 7.65) | 3343 | 3080–3695 |

O 2050 | MC-BEV | ε_{MCV} | Non-standard beta, B (4.14, 4.58) | 646 | 574–720 |

O 2050 | HC-BEV | ε_{HCV} | Non-standard beta, B (4.17, 4.68) | 875 | 778–979 |

O 2050 | AT-BEV | ε_{AT} | Non-standard beta, B (2.92, 3.17) | 2380 | 2163–2622 |

**Table 10.**Hydrogen consumption (g H

_{2}/km) distributions for on-road driving (operational, O), including hydrogen refuelling losses.

Life-Cycle Aspect | Vehicle Technology | LCA Model Input Variable | Distribution | Typical Value | Plausible Min–Max Value |
---|---|---|---|---|---|

O 2019 | MC-FCEV | H_{MCV} | Non-standard beta, B (11.42, 11.82) | 45 | 43–48 |

O 2019 | HC-FCEV | H_{HCV} | Non-standard beta, B (71.83, 62.17) | 61 | 58–64 |

O 2019 | AT-FCEV | H_{AT} | Gamma, G (8168.15, 45.95) | 178 | 173–183 |

O 2050 | MC-FCEV | H_{MCV} | Gamma, G (564.18, 15.63) | 36 | 33–40 |

O 2050 | HC-FCEV | H_{HCV} | Non-standard beta, B (3.07, 3.35) | 49 | 45–54 |

O 2050 | AT-FCEV | H_{AT} | Triangular, T (130, 155, 140) | 142 | 131–154 |

**Table 11.**BEV GHG emission factor (g CO

_{2}-e/km) distributions for on-road driving (operational, O), including grid losses and battery charging losses.

Life-Cycle Aspect | Vehicle Technology | LCA Model Input Variable | Distribution | Typical Value | Plausible Min–Max Value |
---|---|---|---|---|---|

O 2019 | MC-BEV | e_{road,BEV,MCV} | Non-standard beta, B (6.47, 12.41) | 690 | 618–780 |

O 2019 | HC-BEV | e_{road,BEV,HCV} | Non-standard beta, B (6.64, 13.03) | 934 | 837–1059 |

O 2019 | AT-BEV | e_{road,BEV,AT} | Non-standard beta, B (5.01, 9.58) | 2541 | 2305–2837 |

O 2050 | MC-BEV | e_{road,BEV,MCV} | Non-standard beta, B (9.71, 23.05) | 53 | 46–64 |

O 2050 | HC-BEV | e_{road,BEV,HCV} | Lognormal, L (4.27, 0.06) | 72 | 62–86 |

O 2050 | AT-BEV | e_{road,BEV,AT} | Non-standard beta, B (8.24, 19.56) | 195 | 170–232 |

**Table 12.**FCEV GHG emission factor (g CO

_{2}-e/km) distributions for on-road driving (operational, O), including hydrogen distribution and refuelling losses.

Life-Cycle Aspect | Vehicle Technology | LCA Model Input Variable | Distribution | Typical Value | Plausible Min–Max Value |
---|---|---|---|---|---|

O 2019 | MC-FCEV | e_{road,FCEV,MCV} | Non-standard beta, B (2.51, 2.60) | 529 | 446–623 |

O 2019 | HC-FCEV | e_{road,FCEV,HCV} | Gamma, G (157.57, 0.22) | 718 | 611–834 |

O 2019 | AT-FCEV | e_{road,FCEV,AT} | Non-standard beta, B (1.57, 1.71) | 2083 | 1795–2398 |

O 2050 | MC-FCEV | e_{road,FCEV,MCV} | Location-scale t, O (2,289,788, 142, 32) | 142 | 83–209 |

O 2050 | HC-FCEV | e_{road,FCEV,HCV} | Non-standard beta, B (1.53, 1.81) | 193 | 113–821 |

O 2050 | AT-FCEV | e_{road,FCEV,AT} | Non-standard beta, B (1.67, 1.78) | 560 | 331–814 |

**Table 13.**Infrastructure (I) GHG emission factor (g CO

_{2}-e/km) distributions for Australian ICEVs.

Life-Cycle Aspect | Vehicle Technology | LCA Model Input Variable | Distribution | Typical Value | Plausible Min–Max Value |
---|---|---|---|---|---|

I 2019 | MC-ICEV | e_{infra,ICEV,MCV} | Uniform, U (0.4, 6.2) | 3 | 0–6 |

I 2019 | HC-ICEV | e_{infra,ICEV,HCV} | Uniform, U (0.6, 8.3) | 4 | 1–8 |

I 2019 | AT-ICEV | e_{infra,ICEV,AT} | Uniform, U (0.9, 13.5) | 7 | 1–14 |

I 2050 | MC-ICEV | e_{infra,ICEV,MCV} | Uniform, U (0.4, 5.7) | 3 | 0–6 |

I 2050 | HC-ICEV | e_{infra,ICEV,HCV} | Uniform, U (0.5, 7.6) | 4 | 1–8 |

I 2050 | AT-ICEV | e_{infra,ICEV,AT} | Uniform, U (0.8, 12.3) | 6 | 1–12 |

**Table 14.**GHG emission intensities (g CO

_{2}-e/kWh generated) distributions for commissioning and decommissioning electricity generation infrastructure by fuel type.

Fuel Type | Distribution | Typical Value | Plausible Min–Max Value |
---|---|---|---|

Biomass | Uniform, U (0.04, 2.00) | 0.45 | 0.04–2.00 |

Coal | Uniform, U (0.8, 46.0) | 8.00 | 0.80–46.00 |

Gas | Triangular, T (0.60, 1.85, 3.10) | 1.85 | 0.60–3.10 |

Hydro | Uniform, U (3.10, 20.00) | 7.40 | 3.10–20.00 |

Oil | Triangular, T (1.00, 2.20, 3.00) | 2.20 | 1.00–3.00 |

Solar | Exponential, E (0.015) | 67.94 | 20.00–190.00 |

Wind | Uniform, U (3.00, 41.00) | 18.93 | 3.00–41.00 |

Life-Cycle Aspect | Vehicle Technology | LCA Model Input Variable | Distribution | Typical Value | Plausible Min–Max Value |
---|---|---|---|---|---|

I 2019 | MC-BEV | e_{infra,BEV,MCV} | Non-standard beta, B (2.25, 2.82) | 19 | 4–37 |

I 2019 | HC-BEV | e_{infra,BEV,HCV} | Non-standard beta, B (2.18, 2.61) | 26 | 5–51 |

I 2019 | AT-BEV | e_{infra,BEV,AT} | Non-standard beta, B (2.19, 2.59) | 71 | 15–137 |

I 2050 | MC-BEV | e_{infra,BEV,MCV} | Gamma, G (6.38, 0.29) | 22 | 7–49 |

I 2050 | HC-BEV | e_{infra,BEV,HCV} | Lognormal, L (3.31, 0.40) | 30 | 10–67 |

I 2050 | AT-BEV | e_{infra,BEV,AT} | Non-standard beta, B (2.11, 4.82) | 81 | 28–183 |

Life-Cycle Aspect | Vehicle Technology | LCA Model Input Variable | Distribution | Typical Value | Plausible Min–Max Value |
---|---|---|---|---|---|

F 2019 | MC-ICEV | e_{fuel,ICEV,MCV} | Location-scale t, O (2,180,536, 43, 8) | 43 | 28–59 |

F 2019 | HC-ICEV | e_{fuel,ICEV,HCV} | Uniform, U (37, 79) | 57 | 37–79 |

F 2019 | AT-ICEV | e_{fuel,ICEV,AT} | Uniform, U (63, 127) | 94 | 63–127 |

F 2050 | MC-ICEV | e_{fuel,ICEV,MCV} | Location-scale t, O (1,556,144, 36, 7) | 36 | 23–51 |

F 2050 | HC-ICEV | e_{fuel,ICEV,HCV} | Normal, N (48, 10) | 49 | 31–68 |

F 2050 | AT-ICEV | e_{fuel,ICEV,AT} | Non-standard beta, B (1.54, 1.79) | 80 | 51–112 |

Life-Cycle Aspect | Vehicle Technology | LCA Model Input Variable | Distribution | Typical Value | Plausible Min–Max Value |
---|---|---|---|---|---|

F 2019 | MC-BEV | e_{fuel,BEV,MCV} | Lognormal, L (4.26, 0.07) | 71 | 58–88 |

F 2019 | HC-BEV | e_{fuel,BEV,HCV} | Lognormal, L (4.56, 0.07) | 96 | 78–121 |

F 2019 | AT-BEV | e_{fuel,BEV,AT} | Lognormal, L (5.57, 0.07) | 263 | 214–328 |

F 2050 | MC-BEV | e_{fuel,BEV,MCV} | Weibull, W (2.67, 7.85) | 7 | 1–16 |

F 2050 | HC-BEV | e_{fuel,BEV,HCV} | Non-standard beta, B (4.39, 11.13) | 10 | 1–22 |

F 2050 | AT-BEV | e_{fuel,BEV,AT} | Non-standard beta, B (4.38, 11.32) | 26 | 4–61 |

Life-Cycle Aspect | Vehicle Technology | LCA Model Input Variable | Distribution | Typical Value | Plausible Min–Max Value |
---|---|---|---|---|---|

U 2019 | MC-FCEV | e_{upstream,FCEV,MCV} | Normal, N (64, 13) | 64 | 40–91 |

U 2019 | HC-FCEV | e_{upstream,FCEV,HCV} | Location-scale t, O (2,191,700, 86, 17) | 86 | 54–122 |

U 2019 | AT-FCEV | e_{upstream,FCEV,AT} | Non-standard beta, B (2.01, 2.16) | 249 | 157–355 |

U 2050 | MC-FCEV | e_{upstream,FCEV,MCV} | Triangular, T (3.8, 10.3, 5.8) | 7 | 4–10 |

U 2050 | HC-FCEV | e_{upstream,FCEV,HCV} | Triangular, T (5.4, 13.9, 7.9) | 9 | 6–14 |

U 2050 | AT-FCEV | e_{upstream,FCEV,AT} | Non-standard beta, B (1.91, 2.59) | 27 | 16–40 |

Life-Cycle Aspect | Vehicle Technology | LCA Model Input Variable | Distribution | Typical Value | Plausible Min–Max Value |
---|---|---|---|---|---|

D | MC-ICEV | e_{disposal,ICEV,MCV} | Uniform, U (0.1, 1.4) | 1 | 0–1 |

D | HC-ICEV | e_{disposal,ICEV,HCV} | Uniform, U (0.2, 4.1) | 2 | 0–4 |

D | AT-ICEV | e_{disposal,ICEV,AT} | Uniform, U (0.1, 2.7) | 1 | 0–3 |

D | MC-EV | e_{disposal,EV,MCV} | Uniform, U (0.1, 1.7) | 1 | 0–2 |

D | HC-EV | e_{disposal,EV,HCV} | Uniform, U (0.3, 5.6) | 3 | 0–6 |

D | AT-EV | e_{disposal,EV,AT} | Uniform, U (0.2, 3.3) | 2 | 0–3 |

Vehicle Class | Powertrain Technology | Year of Assessment | Mean | Median | Lower 99.7% Confidence Limit (mean) | Upper 99.7% Confidence Limit (mean) |
---|---|---|---|---|---|---|

MCV | ICEV | 2019 | 714 | 714 | 658 | 773 |

MCV | BEV | 2019 | 909 | 907 | 792 | 1059 |

MCV | FCEV | 2019 | 799 | 790 | 603 | 1139 |

HCV | ICEV | 2019 | 981 | 981 | 899 | 1067 |

HCV | BEV | 2019 | 1171 | 1167 | 1011 | 1380 |

HCV | FCEV | 2019 | 1041 | 1030 | 784 | 1483 |

AT | ICEV | 2019 | 1563 | 1563 | 1491 | 1636 |

AT | BEV | 2019 | 3070 | 3062 | 2750 | 3471 |

AT | FCEV | 2019 | 2627 | 2623 | 2166 | 3239 |

MCV | ICEV | 2050 | 598 | 597 | 531 | 670 |

MCV | BEV | 2050 | 104 | 102 | 79 | 140 |

MCV | FCEV | 2050 | 198 | 198 | 123 | 288 |

HCV | ICEV | 2050 | 806 | 805 | 711 | 908 |

HCV | BEV | 2050 | 141 | 140 | 104 | 192 |

HCV | FCEV | 2050 | 258 | 258 | 160 | 375 |

AT | ICEV | 2050 | 1310 | 1310 | 1195 | 1430 |

AT | BEV | 2050 | 337 | 331 | 257 | 458 |

AT | FCEV | 2050 | 697 | 697 | 432 | 1001 |

**Table 21.**Mean and plausible range (99.7% CI, within brackets) of the percentage share of the life-cycle GHG emission factor (g CO

_{2}-e/km) for each life-cycle aspect by vehicle class, powertrain technology and year.

Vehicle Class | Powertrain Technology | Year of Assessment | Vehicle Manufacturing | Upstream Infrastructure | Upstream Fuel and Energy | Operational (on-Road + Maintenance) | Disposal and Recycling |
---|---|---|---|---|---|---|---|

MCV | ICEV | 2019 | 2.5 (1.7–3.4) | 0.5 (0.1–0.9) | 6.0 (4.1–7.9) | 91.0 (88.6–93.5) | 0.1 (0.0–0.2) |

MCV | BEV | 2019 | 7.4 (3.1–15.6) | 2.3 (0.5–4.4) | 8.5 (6.9–10.4) | 81.6 (74.0–86.8) | 0.1 (0.0–0.2) |

MCV | FCEV | 2019 | 20.5 (6.1–44.3) | 2.7 (0.5–5.6) | 8.1 (4.3–13.0) | 68.6 (47.7–83.3) | 0.1 (0.0–0.3) |

HCV | ICEV | 2019 | 5.3 (3.6–7.3) | 0.4 (0.1–0.8) | 5.8 (3.9–7.7) | 88.2 (85.2–91.2) | 0.2 (0.0–0.4) |

HCV | BEV | 2019 | 11.4 (5.7–21.2) | 2.2 (0.4–4.2) | 8.2 (6.5–10.1) | 78.0 (69.1–84.0) | 0.3 (0.0–0.5) |

HCV | FCEV | 2019 | 24.3 (9.4–47.1) | 2.5 (0.5–5.3) | 7.8 (4.1–12.4) | 65.2 (45.1–80.0) | 0.3 (0.0–0.7) |

AT | ICEV | 2019 | 2.2 (1.6–2.8) | 0.5 (0.1–0.9) | 6.0 (4.1–7.9) | 91.2 (88.9–93.7) | 0.1 (0.0–0.2) |

AT | BEV | 2019 | 4.7 (1.8–9.5) | 2.4 (0.5–4.5) | 8.8 (7.3–10.7) | 84.0 (79.1–88.0) | 0.1 (0.0–0.1) |

AT | FCEV | 2019 | 8.6 (2.8–21.9) | 2.8 (0.6–5.7) | 9.5 (5.6–14.2) | 79.1 (66.4–87.7) | 0.1 (0.0–0.1) |

MCV | ICEV | 2050 | 0.9 (0.2–2.0) | 0.5 (0.1–0.9) | 6.1 (4.1–8.0) | 92.5 (89.9–95.2) | 0.0 (0.0–0.1) |

MCV | BEV | 2050 | 13.1 (5.1–23.7) | 21.3 (8.4–39.3) | 6.9 (1.1–15.6) | 58.4 (43.6–73.3) | 0.3 (0.0–0.9) |

MCV | FCEV | 2050 | 10.8 (3.0–30.1) | 11.6 (3.6–27.6) | 3.5 (1.7–6.6) | 74.0 (52.5–87.6) | 0.1 (0.0–0.5) |

HCV | ICEV | 2050 | 2.0 (0.4–4.4) | 0.5 (0.1–0.9) | 6.0 (4.1–8.0) | 91.4 (87.9–94.8) | 0.1 (0.0–0.2) |

HCV | BEV | 2050 | 20.8 (8.1–35.4) | 19.6 (7.5–37.6) | 6.4 (1.0–14.5) | 52.6 (38.5–68.7) | 0.6 (0.0–2.1) |

HCV | FCEV | 2050 | 14.8 (4.3–34.9) | 11.1 (3.5–26.5) | 3.4 (1.7–6.3) | 70.4 (48.9–85.7) | 0.4 (0.0–1.3) |

AT | ICEV | 2050 | 0.8 (0.2–1.7) | 0.5 (0.1–0.9) | 6.1 (4.2–8.0) | 92.6 (90.0–95.2) | 0.0 (0.0–0.1) |

AT | BEV | 2050 | 8.3 (2.4–17.7) | 23.5 (9.5–42.4) | 7.7 (1.2–17.1) | 60.4 (44.4–76.1) | 0.2 (0.0–0.5) |

AT | FCEV | 2050 | 4.6 (1.2–13.1) | 11.9 (3.7–28.5) | 3.9 (1.9–7.4) | 79.6 (61–90.5) | 0.1 (0.0–0.3) |

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## Share and Cite

**MDPI and ACS Style**

Smit, R.; Helmers, E.; Schwingshackl, M.; Opetnik, M.; Kennedy, D.
Greenhouse Gas Emissions Performance of Electric, Hydrogen and Fossil-Fuelled Freight Trucks with Uncertainty Estimates Using a Probabilistic Life-Cycle Assessment (pLCA). *Sustainability* **2024**, *16*, 762.
https://doi.org/10.3390/su16020762

**AMA Style**

Smit R, Helmers E, Schwingshackl M, Opetnik M, Kennedy D.
Greenhouse Gas Emissions Performance of Electric, Hydrogen and Fossil-Fuelled Freight Trucks with Uncertainty Estimates Using a Probabilistic Life-Cycle Assessment (pLCA). *Sustainability*. 2024; 16(2):762.
https://doi.org/10.3390/su16020762

**Chicago/Turabian Style**

Smit, Robin, Eckard Helmers, Michael Schwingshackl, Martin Opetnik, and Daniel Kennedy.
2024. "Greenhouse Gas Emissions Performance of Electric, Hydrogen and Fossil-Fuelled Freight Trucks with Uncertainty Estimates Using a Probabilistic Life-Cycle Assessment (pLCA)" *Sustainability* 16, no. 2: 762.
https://doi.org/10.3390/su16020762