Greenhouse Gas Emissions Performance of Electric, Hydrogen and Fossil-Fuelled Freight Trucks with Uncertainty Estimates Using a Probabilistic Life-Cycle Assessment (pLCA)
Abstract
:1. Introduction
1.1. Life-Cycle Analysis
1.2. Background and Purpose of This Study
2. Materials and Methods
2.1. Probabilistic LCA (pLCA)
2.2. Model Definition
2.3. Developing Input Distributions
eICEV = evehicle,ICEV + einfra,ICEV + efuel,ICEV + eroad,ICEV + edisposal,ICEV | (1) |
evehicle,ICEV = wICEV φv,ICEV/MICEV | |
eBEV = evehicle,BEV + einfra,BEV + efuel,BEV + eroad,BEV + edisposal,BEV | (2) |
evehicle,BEV = ((MBEV − MBAT) φv,BEV + (ΓBAT φBAT θBAT))/MBEV | |
einfra,BEV = ε σelec/(ηg ηb) | |
efuel,BEV = ε ϕelec/ηb | |
eroad,BEV = ε ωelec/(ηg ηb) | |
eFCEV = evehicle,FCEV + einfra, FCEV + efuel,FCEV + eroad,FCEV + edisposal, FCEV | (3) |
evehicle,FCEV = ((MFCEV − MBAT − MFCL) φv,FCEV + (ΓBAT φBAT θBAT) + (ΓFCL φFCL ρFCL))/MFCEV | |
einfra,FCEV = H σH2,p/ηh | |
efuel,FCEV = H ϕH2,p/ηh | |
eroad,FCEV = H ωH2,p/ηr |
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
- 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.
- 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
3.3. Electricity Production, Distribution and Recharging Losses
3.4. Hydrogen Production, Distribution and Refuelling Losses
3.5. Future Improvements in LCA Input Variables
3.6. Truck Manufacturing
3.7. Operational Electricity Use, Fuel Consumption and Emissions (On-Road Driving)
- 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).
- 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.
3.8. Truck Maintenance
3.9. Energy Infrastructure
3.10. Upstream Emissions (Fuel/Energy)
3.11. Vehicle Disposal and Recycling
4. Results and Discussion
4.1. Average Life-Cycle GHG Emission Factors for Trucks
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
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 |
eICEV, eBEV, and eFCEV | 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 | : Minimum, : Maximum, | |
Triangular—T(x:a,b,c) | a ≤ x ≤ b | : Minimum, : Maximum, : Mode, | |
Normal—N(x:m,s) | −∞ ≤ x ≤ +∞ | : Mean, : Standard deviation, | |
Lognormal—L(x:m,s) | 0 ≤ x ≤ +∞ | : Log-mean, : Scale, | |
Weibull—W(x:s,k) | 0 ≤ x ≤ +∞ | : Scale, : Shape, | |
Gamma—G(x:s,k) | 0 ≤ x ≤ +∞ | : Scale, : Rate, | |
Exponential—E(x:s) | 0 ≤ x ≤ +∞ | : Scale, | |
Non-Standard Beta—B(x:s,k,a,b) | a ≤ x ≤ b | : Scale, : Shape, : Minimum, : Maximum, | |
Skew t—S(x:m,s,a,d) | -∞ ≤ x ≤ +∞ | : Mean, : Scale, : Skew, : Degrees of freedom, | where and is the cumulative distribution function. |
Dirac Delta—D(x:m) | -∞ ≤ x ≤ +∞ Practically x = m | : Location, |
Appendix B
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Notation | Description and Units | Time Variable |
---|---|---|
Mx | lifetime mileage for technology x (x = ICEV, BEV, FCEV) (km) | No (S.3.1) |
ΓBAT | battery replacement factor (−) | Yes (S.3.1) |
ΓFCL | fuel-cell replacement factor (−) | Yes (S.3.1) |
Mx | vehicle tare mass for technology x (x = ICEV, BEV, FCEV) (kg) | No (S.3.2) |
MBAT | battery mass for BEV or FCEV (kg) | No (S.3.2) |
MFCL | fuel-cell mass (kg) | No (S.3.2) |
θBAT | battery capacity (kWh) | Yes (S.3.2) |
ρFCL | fuel-cell rated power (kW) | Yes (S.3.2) |
ωelec (1) | GHG emission-intensity electricity generation (g CO2-e/kWh generated) | Yes (S.3.3) |
ηb | battery recharging efficiency (−) | Yes (S.3.3) |
ηg | grid transmission efficiency (−) | Yes (S.3.3) |
ωH2,P | GHG emission-intensity hydrogen production (g CO2-e/g fuel) for production pathway P | Yes (S.3.4) |
σelec | GHG emission-intensity electricity infrastructure (g CO2-e/kWh generated) | Yes (S.3.5) |
ηh | hydrogen distribution efficiency (−) | Yes (S.3.4) |
ηr | hydrogen refuelling efficiency (−) | Yes (S.3.4) |
σH2,P | GHG emission-intensity H2 production infrastructure (g CO2-e/g fuel) production pathway P | Yes (S.3.5) |
ϕelec (2) | GHG emission-intensity upstream fuels for electricity generation (g CO2-e/kWh consumed) | Yes (S.3.5) |
ϕH2,p (2) | GHG emission-intensity upstream H2 production (g CO2-e/g fuel) for production pathway P | Yes (S.3.5) |
φv,ICEV | GHG emission-intensity ICEV production (kg CO2-e/kg vehicle) | Yes (S.3.6) |
φv,BEV | GHG emission-intensity BEV production without battery (kg CO2-e/kg vehicle) | Yes (S.3.6) |
φv,FCEV | GHG emission-intensity FCEV production without battery/fuel cell (kg CO2-e/kg vehicle) | Yes (S.3.6) |
φBAT | GHG emission-intensity battery production (kg CO2-e/kWh battery capacity) | Yes (S.3.6) |
φFCL | GHG emission-intensity fuel-cell production (kg CO2-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) |
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 | MBAT,BEV,MCV | Weibull, W (6.22, 1161.68) | 1080 | 868–1283 |
2019 | HC-BEV | MBAT,BEV,HCV | Weibull, W (6.07, 1891.85) | 1755 | 1384–2101 |
2019 | AT-BEV | MBAT,BEV,AT | Non-standard beta, B (2.91, 3.21) | 3454 | 2352–4541 |
2050 | MC-BEV | MBAT,BEV,MCV | Normal, N (719, 136) | 719 | 578–854 |
2050 | HC-BEV | MBAT,BEV,HCV | Weibull, W (5.69, 1258.83) | 1165 | 915–1395 |
2050 | AT-BEV | MBAT,BEV,AT | Weibull, W (3.54, 2547.64) | 2292 | 1533–3027 |
2019 | MC-FCEV | MBAT,FCEV,MCV | Non-standard beta, B (3.51, 6.28) | 394 | 315–477 |
2019 | HC-FCEV | MBAT,FCEV,HCV | Non-standard beta, B (3.17, 5.98) | 551 | 422–690 |
2019 | AT-FCEV | MFCL,FCEV,AT | Non-standard beta, B (3.68, 4.72) | 888 | 748–1027 |
2050 | MC-FCEV | MBAT,FCEV,MCV | Non-standard beta, B (3.31, 8.19) | 261 | 206–317 |
2050 | HC-FCEV | MBAT,FCEV,HCV | Non-standard beta, B (3.16, 7.83) | 367 | 278–460 |
2050 | AT-FCEV | MBAT,FCEV,HCV | Non-standard beta, B (5.32, 9.75) | 589 | 492–684 |
2019 + 2050 | MC-FCEV | MFCL,FCEV,MCV | Non-standard beta, B (4.82, 10.14) | 223 | 181–265 |
2019 + 2050 | HC-FCEV | MFCL,FCEV,HCV | Non-standard beta, B (4.79, 7.61) | 305 | 255–356 |
2019 + 2050 | AT-FCEV | MFCL,FCEV,AT | Non-standard beta, B (4.76, 9.04) | 608 | 459–756 |
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 |
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 | evehicle,ICEV,MCV | Non-standard beta, B (8.82, 10.47) | 18 | 12–25 |
M 2019 | HC-ICEV | evehicle,ICEV,HCV | Non-standard beta, B (9.66, 11.63) | 52 | 34–72 |
M 2019 | AT-ICEV | evehicle,ICEV,AT | Non-standard beta, B (5.15, 4.170) | 35 | 24–44 |
M 2050 | MC-ICEV | evehicle,ICEV,MCV | Non-standard beta, B (2.66, 3.56) | 6 | 1–12 |
M 2050 | HC-ICEV | evehicle,ICEV,HCV | Weibull, W (2.25, 18.54) | 16 | 4–35 |
M 2050 | AT-ICEV | evehicle,ICEV,AT | Weibull, W (2.28, 12.20) | 11 | 3–22 |
M 2019 | MC-BEV | evehicle,BEV,MCV | Non-standard beta, B (3.15, 39.66) | 68 | 28–150 |
M 2019 | HC-BEV | evehicle,BEV,HCV | Lognormal, L (4.86, 0.29) | 134 | 63–277 |
M 2019 | AT-BEV | evehicle,BEV,AT | Gamma, G (10.37, 0.07) | 145 | 57–300 |
M 2050 | MC-BEV | evehicle,BEV,MCV | Non-standard beta, B (4.94, 10.40) | 14 | 5–25 |
M 2050 | HC-BEV | evehicle,BEV,HCV | Non-standard beta, B (4.02, 7.08) | 30 | 11–56 |
M 2050 | AT-BEV | evehicle,BEV,AT | Non-standard beta, B (5.56, 22.64) | 28 | 8–62 |
M 2019 | MC-FCEV | evehicle,FCEV,MCV | Non-standard beta, B (2.42, 13.12) | 169 | 43–474 |
M 2019 | HC-FCEV | evehicle,FCEV,HCV | Non-standard beta, B (2.76, 11.81) | 260 | 84–658 |
M 2019 | AT-FCEV | evehicle,FCEV,AT | Lognormal, L (5.34, 0.44) | 228 | 73–657 |
M 2050 | MC-FCEV | evehicle,FCEV,MCV | Lognormal, L (2.97, 0.41) | 21 | 6–64 |
M 2050 | HC-FCEV | evehicle,FCEV,HCV | Gamma, G (7.50, 0.20) | 38 | 11–96 |
M 2050 | AT-FCEV | evehicle,FCEV,AT | Lognormal, L (3.37, 0.38) | 31 | 9–84 |
Life-Cycle Aspect | Vehicle Technology | LCA model Input Variable | Distribution | Typical Value | Plausible Min–Max Value |
---|---|---|---|---|---|
O 2019 | MC-ICEV | eroad,ICEV,MCV | Normal, N (644, 13) | 644 | 605–682 |
O 2019 | HC-ICEV | eroad,ICEV,HCV | Normal, N (860, 17) | 860 | 808–911 |
O 2019 | AT-ICEV | eroad,ICEV,AT | Normal, N (1420, 14) | 1420 | 1377–1462 |
O 2050 | MC-ICEV | eroad,ICEV,MCV | Non-standard beta, B (3.57, 3.91) | 547 | 496–603 |
O 2050 | HC-ICEV | eroad,ICEV,HCV | Non-standard beta, B (3.52, 3.77) | 731 | 663–804 |
O 2050 | AT-ICEV | eroad,ICEV,AT | Triangular, T (1111, 1309, 1198) | 1207 | 1111–1309 |
Life-Cycle Aspect | Vehicle Technology | LCA Model Input Variable | Distribution | Typical Value | Plausible Min–Max Value |
---|---|---|---|---|---|
O 2019 | MC-BEV | εMCV | Non-standard beta, B (5.77, 11.49) | 908 | 821–1021 |
O 2019 | HC-BEV | εHCV | Non-standard beta, B (5.53, 11.05) | 1229 | 1113–1384 |
O 2019 | AT-BEV | εAT | Non-standard beta, B (3.81, 7.65) | 3343 | 3080–3695 |
O 2050 | MC-BEV | εMCV | Non-standard beta, B (4.14, 4.58) | 646 | 574–720 |
O 2050 | HC-BEV | εHCV | Non-standard beta, B (4.17, 4.68) | 875 | 778–979 |
O 2050 | AT-BEV | εAT | Non-standard beta, B (2.92, 3.17) | 2380 | 2163–2622 |
Life-Cycle Aspect | Vehicle Technology | LCA Model Input Variable | Distribution | Typical Value | Plausible Min–Max Value |
---|---|---|---|---|---|
O 2019 | MC-FCEV | HMCV | Non-standard beta, B (11.42, 11.82) | 45 | 43–48 |
O 2019 | HC-FCEV | HHCV | Non-standard beta, B (71.83, 62.17) | 61 | 58–64 |
O 2019 | AT-FCEV | HAT | Gamma, G (8168.15, 45.95) | 178 | 173–183 |
O 2050 | MC-FCEV | HMCV | Gamma, G (564.18, 15.63) | 36 | 33–40 |
O 2050 | HC-FCEV | HHCV | Non-standard beta, B (3.07, 3.35) | 49 | 45–54 |
O 2050 | AT-FCEV | HAT | Triangular, T (130, 155, 140) | 142 | 131–154 |
Life-Cycle Aspect | Vehicle Technology | LCA Model Input Variable | Distribution | Typical Value | Plausible Min–Max Value |
---|---|---|---|---|---|
O 2019 | MC-BEV | eroad,BEV,MCV | Non-standard beta, B (6.47, 12.41) | 690 | 618–780 |
O 2019 | HC-BEV | eroad,BEV,HCV | Non-standard beta, B (6.64, 13.03) | 934 | 837–1059 |
O 2019 | AT-BEV | eroad,BEV,AT | Non-standard beta, B (5.01, 9.58) | 2541 | 2305–2837 |
O 2050 | MC-BEV | eroad,BEV,MCV | Non-standard beta, B (9.71, 23.05) | 53 | 46–64 |
O 2050 | HC-BEV | eroad,BEV,HCV | Lognormal, L (4.27, 0.06) | 72 | 62–86 |
O 2050 | AT-BEV | eroad,BEV,AT | Non-standard beta, B (8.24, 19.56) | 195 | 170–232 |
Life-Cycle Aspect | Vehicle Technology | LCA Model Input Variable | Distribution | Typical Value | Plausible Min–Max Value |
---|---|---|---|---|---|
O 2019 | MC-FCEV | eroad,FCEV,MCV | Non-standard beta, B (2.51, 2.60) | 529 | 446–623 |
O 2019 | HC-FCEV | eroad,FCEV,HCV | Gamma, G (157.57, 0.22) | 718 | 611–834 |
O 2019 | AT-FCEV | eroad,FCEV,AT | Non-standard beta, B (1.57, 1.71) | 2083 | 1795–2398 |
O 2050 | MC-FCEV | eroad,FCEV,MCV | Location-scale t, O (2,289,788, 142, 32) | 142 | 83–209 |
O 2050 | HC-FCEV | eroad,FCEV,HCV | Non-standard beta, B (1.53, 1.81) | 193 | 113–821 |
O 2050 | AT-FCEV | eroad,FCEV,AT | Non-standard beta, B (1.67, 1.78) | 560 | 331–814 |
Life-Cycle Aspect | Vehicle Technology | LCA Model Input Variable | Distribution | Typical Value | Plausible Min–Max Value |
---|---|---|---|---|---|
I 2019 | MC-ICEV | einfra,ICEV,MCV | Uniform, U (0.4, 6.2) | 3 | 0–6 |
I 2019 | HC-ICEV | einfra,ICEV,HCV | Uniform, U (0.6, 8.3) | 4 | 1–8 |
I 2019 | AT-ICEV | einfra,ICEV,AT | Uniform, U (0.9, 13.5) | 7 | 1–14 |
I 2050 | MC-ICEV | einfra,ICEV,MCV | Uniform, U (0.4, 5.7) | 3 | 0–6 |
I 2050 | HC-ICEV | einfra,ICEV,HCV | Uniform, U (0.5, 7.6) | 4 | 1–8 |
I 2050 | AT-ICEV | einfra,ICEV,AT | Uniform, U (0.8, 12.3) | 6 | 1–12 |
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 | einfra,BEV,MCV | Non-standard beta, B (2.25, 2.82) | 19 | 4–37 |
I 2019 | HC-BEV | einfra,BEV,HCV | Non-standard beta, B (2.18, 2.61) | 26 | 5–51 |
I 2019 | AT-BEV | einfra,BEV,AT | Non-standard beta, B (2.19, 2.59) | 71 | 15–137 |
I 2050 | MC-BEV | einfra,BEV,MCV | Gamma, G (6.38, 0.29) | 22 | 7–49 |
I 2050 | HC-BEV | einfra,BEV,HCV | Lognormal, L (3.31, 0.40) | 30 | 10–67 |
I 2050 | AT-BEV | einfra,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 | efuel,ICEV,MCV | Location-scale t, O (2,180,536, 43, 8) | 43 | 28–59 |
F 2019 | HC-ICEV | efuel,ICEV,HCV | Uniform, U (37, 79) | 57 | 37–79 |
F 2019 | AT-ICEV | efuel,ICEV,AT | Uniform, U (63, 127) | 94 | 63–127 |
F 2050 | MC-ICEV | efuel,ICEV,MCV | Location-scale t, O (1,556,144, 36, 7) | 36 | 23–51 |
F 2050 | HC-ICEV | efuel,ICEV,HCV | Normal, N (48, 10) | 49 | 31–68 |
F 2050 | AT-ICEV | efuel,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 | efuel,BEV,MCV | Lognormal, L (4.26, 0.07) | 71 | 58–88 |
F 2019 | HC-BEV | efuel,BEV,HCV | Lognormal, L (4.56, 0.07) | 96 | 78–121 |
F 2019 | AT-BEV | efuel,BEV,AT | Lognormal, L (5.57, 0.07) | 263 | 214–328 |
F 2050 | MC-BEV | efuel,BEV,MCV | Weibull, W (2.67, 7.85) | 7 | 1–16 |
F 2050 | HC-BEV | efuel,BEV,HCV | Non-standard beta, B (4.39, 11.13) | 10 | 1–22 |
F 2050 | AT-BEV | efuel,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 | eupstream,FCEV,MCV | Normal, N (64, 13) | 64 | 40–91 |
U 2019 | HC-FCEV | eupstream,FCEV,HCV | Location-scale t, O (2,191,700, 86, 17) | 86 | 54–122 |
U 2019 | AT-FCEV | eupstream,FCEV,AT | Non-standard beta, B (2.01, 2.16) | 249 | 157–355 |
U 2050 | MC-FCEV | eupstream,FCEV,MCV | Triangular, T (3.8, 10.3, 5.8) | 7 | 4–10 |
U 2050 | HC-FCEV | eupstream,FCEV,HCV | Triangular, T (5.4, 13.9, 7.9) | 9 | 6–14 |
U 2050 | AT-FCEV | eupstream,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 | edisposal,ICEV,MCV | Uniform, U (0.1, 1.4) | 1 | 0–1 |
D | HC-ICEV | edisposal,ICEV,HCV | Uniform, U (0.2, 4.1) | 2 | 0–4 |
D | AT-ICEV | edisposal,ICEV,AT | Uniform, U (0.1, 2.7) | 1 | 0–3 |
D | MC-EV | edisposal,EV,MCV | Uniform, U (0.1, 1.7) | 1 | 0–2 |
D | HC-EV | edisposal,EV,HCV | Uniform, U (0.3, 5.6) | 3 | 0–6 |
D | AT-EV | edisposal,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 |
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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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
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 StyleSmit, 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
APA StyleSmit, R., Helmers, E., Schwingshackl, M., Opetnik, M., & Kennedy, D. (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(2), 762. https://doi.org/10.3390/su16020762