# Greenhouse Gas Emissions Performance of Electric and Fossil-Fueled Passenger Vehicles with Uncertainty Estimates Using a Probabilistic Life-Cycle Assessment

^{*}

## Abstract

**:**

_{2}-e/km), but particularly for electric vehicles (98–287 g CO

_{2}-e/km), which reflects the differences in fuel mix for electricity generation in the different states and territories. Electrification of the Tasmanian on-road fleet has the largest predicted fleet average reduction in LCA greenhouse gas emissions of 243–300 g CO

_{2}-e/km. A sensitivity analysis with alternative input distributions suggests that the outcomes from this study are robust.

## 1. Introduction

_{2}emission rates and fuel economy [25]. The results of this study will inform policy makers about the current and future emission reduction potential of electrification of the transport sector. It is noted that there are many types of environmental impacts that can be assessed with LCA such as toxicity, mineral resource depletion, total life-cycle cost and land use [4,5,28], whereas the scope of this study is restricted to an assessment of GHG emission impacts.

## 2. Materials and Methods

#### 2.1. Probabilistic LCA (pLCA)

#### 2.2. Model Definition

_{2}-e/vehicle km. Carbon dioxide equivalent (CO

_{2}-e) emissions are computed by multiplying emissions of a particular greenhouse gas with its Global Warming Potential (GWP) and taking the sum of these emissions. Five GHG emission life-cycle aspects are considered: (1) production of the vehicle (manufacturing of non-battery components, manufacturing of the BEV battery), (2) production of (fossil) fuels for ICEVs (extraction, transport and fuel refining), (3) production of electricity for BEVs (extraction and transport of fossil fuels, electricity generation, electricity distribution losses and power generation infrastructure), (4) on-road operation or use of the vehicle (ICEV fossil-fuel use, BEV energy use, and BEV battery charging losses) and (5) disposal and recycling of the vehicle at the end of its life.

_{ICEV}and e

_{BEV}are computed with two additive models and sub-models (if applicable). In Equations (1) and (3), e

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

_{2}-e/km) for life-cycle aspect I and vehicle type j.

_{ICEV}= e

_{vehicle,ICEV}+ e

_{infra,ICEV}+ e

_{upstream,ICEV}+ e

_{road,ICEV}+ e

_{disposal,ICEV}

_{vehicle,ICEV}= W

_{ICEV}φ

_{v,ICEV}/M

_{BEV}= e

_{vehicle,BEV}+ e

_{infra,BEV}+ e

_{upstream,BEV}+ e

_{road,BEV}+ e

_{disposal,BEV}

_{vehicle,BEV}= ((W

_{BEV}− W

_{BAT}) φ

_{v,BEV}+ θ

_{b}φ

_{b})/M

_{infra,BEV}= ε σ

_{s}/(η

_{g}η

_{b})

_{upstream,BEV}= ε ϕ

_{s}/η

_{b}

_{road,BEV}= ε ω

_{s}/(η

_{g}η

_{b})

_{ICEV}= ICEV vehicle weight (kg), W

_{BEV}= BEV vehicle weight (kg), W

_{BAT}= BEV battery weight (kg), φ

_{v,ICEV}and φ

_{v,BEV}are the respective carbon intensities of vehicle production (g CO

_{2}-e/kg vehicle), φ

_{b}= carbon intensity battery production (g CO

_{2}-e/kWh battery capacity), θ

_{b}= battery capacity (kWh), M = lifetime vehicle mileage, σ

_{s}= GHG emission intensity electricity infrastructure (g CO

_{2}-e/kWh generated) for scenario s, ϕ

_{s}= GHG emission intensity upstream fuels for electricity generation (g CO

_{2}-e/kWh consumed ‘at the power point’) for scenario s, ω

_{s}= GHG emission intensity electricity generation (g CO

_{2}-e/kWh generated) for scenario s, η

_{g}= grid transmission efficiency (−), η

_{b}= battery recharging efficiency (−) and ε = real-world electricity use BEV (kWh/km).

#### 2.3. Input Distribution Development

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

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

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

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

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

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

**E**: s). The non-standard beta distribution (

**B**: s, s) and the skew t-distribution (

**S**: m, s, a, df) were also included to allow for additional flexibility in the fitting process [34,35]. The Dirac Delta function (

**D**: m) is used to describe a constant value. Appendix A provides further information regarding the range, parameters and PDFs of the distributions. The location-scale t-distribution was also offered as candidate, but was not selected as the best fit throughout the analysis. Truncation is applied to the fitted distributions using the ‘truncdist’ R package by setting a lower limit a and an upper limit b [36]. This explicit definition of a plausible range prevents the use of unrealistic values in the pLCA. The R packages ‘fitdistr’ and ‘fitdistrplus’, ‘extraDistr’, ‘sn’ and ‘truncdist’ were used in the optimized fitting process [34,36,37,38].

_{2}-e/km) for a particular life-cycle aspect and vehicle type. Second, Monte Carlo simulation is used to propagate the uncertainty and variability reflected in the parametric input distributions to the model outputs e

_{ICEV}and e

_{BEV}. The output PDFs express both central tendencies and the variability in the output variables arising from the variation in the input variables. Uncertainty in the outputs is defined as a 95% confidence interval (CI) of the mean value and is stated as a value range (asymmetric confidence interval) or a percentage (symmetric confidence interval).

#### 2.4. Scenario Definitions

## 3. Input Distributions

#### 3.1. Overview of Input Distribution Definitions

#### 3.2. Vehicle Manufacturing

_{vehicle,ICEV}and e

_{vehicle,BEV}) were developed by defining the input distributions for models 2 and 4 (Section 2.2) as follows.

_{2}-e/kg of passenger vehicle, with a typical value of 5 kg CO

_{2}-e/kg vehicle [46], (

**T**: 4.0, 6.5, 5.0). The average weight of an Australian passenger vehicle is 1800 kg, with an estimated uncertainty (95% confidence interval) of 1% [24] (

**U**: 1783, 1817). A study into worldwide BEV characteristics (n = 218) reported an average BEV vehicle mass of 1689 kg with an uncertainty of ±4% [47] (

**U**: 1625, 1753). The fleet average weight for Australian BEVs is comparable with a value of 1600 kg [26]. GHG emissions for battery production need to be estimated separately and added. Battery manufacturing emissions are likely to fall between 41 and 156 kg CO

_{2}-e per kWh of battery capacity, with a current average of approximately 100 kg CO

_{2}-e per kWh [5,48] (

**T**: 41, 156, 100). Average worldwide BEV battery capacity is estimated to be 46 kWh with an uncertainty of 8% [47] (

**U**: 42, 50). BEV weight is corrected for the weight of the battery. A plausible range for battery energy density is assumed to be 0.12–0.16 kWh per kg of battery [7,49,50,51]. Using the plausible range in BEV battery capacity of 42–50 kWh, average battery weight is estimated to be 335 kg, which is 20% of total BEV vehicle weight (

**T**: 265, 413, 335).

_{2}-e/km for ICEVs (plausible range 40–59) and 59 g CO

_{2}-e/km for BEVs (plausible range 39–83). A triangular and non-standard beta distribution provides the best maximum likelihood fit to the sampling distributions for ICEV and BEV manufacturing, respectively (Table 2 and Figure 2). The results suggest that for the Australian market, BEV production produces on average approximately 20% more GHG emissions as compared with conventional fossil-fueled passenger vehicles, adding approximately 10 g CO

_{2}-e per km to total life-cycle emissions for BEVs. Previous studies have used 35 to 46 g CO

_{2}-e/km for ICEVs and 37 to 95 g CO

_{2}-e/km for BEVs [4,15,46,48]. This study estimates a higher carbon footprint for Australian ICEVs, which is explained by the large proportion of large and heavy fossil-fueled passenger vehicles, as compared to, for instance, the EU market. The estimate for BEVs is within the ranges reported in other studies mentioned previously.

#### 3.3. On-Road Driving ICEVs

_{2}-e, which is close to 85.2 million tons of CO

_{2}-e reported by the Australian Greenhouse Emissions Information System (AGEIS) [55], a difference of 0.1%.

_{2}-e/km. The GHG to fuel ratio is 3.192. Analysis of the AFM/COPERT Australia results shows that average GHG emission rates are 247 g CO

_{2}-e/km for petrol vehicles and 318 g CO

_{2}-e/km for diesel vehicles. Diesel passenger vehicles have GHG emissions per kilometer that are 28% higher than their petrol counterparts. A recent study found that the main reason for this is that Australian diesel PVs are, on average approximately 40% heavier than petrol PVs [24]. Other diesel vehicle design parameters also adversely affect CO

_{2}emission rates, including a higher proportion of 4WD vehicles, 15% higher engine capacity and a low portion of CVT transmissions [24]. A (weighted) bootstrap analysis [31] using travel (VKT) by vehicle class as weights, estimates an uncertainty in this fleet average emission factor (ICEV) of approximately ±15%. These bootstrap results are similar to the reported uncertainty in fuel consumption of Australian PVs by the Australian Bureau of Statistics (ABS) of 5% to 11% [56]. A non-standard beta distribution (

**B**: 9.89, 16.86) provides the best fit to the bootstrap sampling distribution of the fleet average emission factors with truncation at 225 and 298 g CO

_{2}-e/km.

_{2}-e/km by multiplication of a factor of 3.192 mentioned earlier.

_{2}-e/km, respectively. The highest fuel consumption rate of 90 g/km (11.3 L/100 km) and corresponding GHG emission factor of 286 g CO

_{2}-e/km is reported for NT due to the high proportion of diesel use in this jurisdiction and resulting higher fuel density (Table 1). The plausible range for truncation varies between ±7% and ±14%, depending on the jurisdiction. The relative uncertainty in the converted ABS figures is assumed to be ± 3 RSE and follow a truncated normal distribution (

**N**: 3.192 × FC, RSE × 3.192 × FC). For Australia as a whole, the uncertainty is reportedly smaller (±4%) and defined with a truncated normal distribution for the GHG emission factor (

**N**: 265, 3). The distributions, typical values and truncation limits are shown in Table 3.

_{2}-e/km) is 3% higher than the value predicted by COPERT Australia/AFM (257 g CO

_{2}-e/km). The SMVU based GHG emission factor and associated uncertainty are used in the probabilistic technology assessment as this method enables prediction of these emission factors for all scenarios or jurisdictions. The results from the COPERT Australia/AFM method will be used to test the sensitivity of the study outcomes. The input distributions for on-road ICEV GHG emission factors are shown in Table 3 and Figure 3.

#### 3.4. Electricity Production and Consumption

_{2}-e/kWh for the 2018–2019 financial year. However, the values vary between jurisdictions with 160 g CO

_{2}-e/kWh for Tasmania to 960 g CO

_{2}-e/kWh for Victoria, as is shown in Figure 1.

_{2}-e/kWh generated, which is 4% lower than the NGA factor (760 g CO

_{2}-e/kWh) likely due, in part, to grid losses that are not yet reflected.

**U**: 1.05, 1,10) through a Monte Carlo simulation to create sampling distributions for the average grid-connected emission intensity for each fuel type in Australia. The sampling distributions were then used to determine the best parametric distribution through maximum likelihood fit. The results are shown in Table 4 and Figure 5.

#### 3.5. On-Road Driving BEVs

**T**: 0.18, 0.19, 0.21).

**T**: 0.73, 0.85, 0.95).

_{2}-e/km (95% confidence interval = 155–198). For the alternative scenarios with a different fuel/energy mix, the normalized values are 207 g CO

_{2}-e/km (95% CI = 183–236) for Scenario 2 and 19 g CO

_{2}-e/km (95% CI = 17–22) for Scenario 3. For the Australian jurisdictions, the emission factors are NSW: 182 g CO

_{2}-e/km (95% CI = 156–210), VIC: 221 g CO

_{2}-e/km (95% CI = 191–256), QLD: 184 g CO

_{2}-e/km (95% CI = 159–213), SA: 69 g CO

_{2}-e/km (95% CI = 59–80), WA: 154 g CO

_{2}-e/km (95% CI = 133–179), TAS: 37 g CO

_{2}-e/km (95% CI = 32–43) and NT: 131 g CO

_{2}-e/km (95% CI = 113–151). There are large differences in greenhouse gas emission rates from BEVs with Victoria being the highest with 221 g CO

_{2}-e/km and Tasmania the lowest with 37 g CO

_{2}-e/km, a factor of six difference. This reflects the different electricity generation systems in use in Australia with Victoria mainly relying on brown coal and Tasmania using mainly using hydro power (Table 1). The sampling distributions were used to determine the best theoretical distribution through maximum likelihood fit. The results are shown in Table 5 and Figure 6.

#### 3.6. Infrastructure for Electricity Generation

**U**: 1.05, 1,10), BEV real-world energy consumption (

**T**: 0.18, 0.21, 0.19) and battery charging efficiency (

**T**: 0.73, 0.95, 0.85), using the fuel type percentages as weights (Table 1). The sampling distributions were used to determine the best parametric distribution through maximum likelihood fit. The results are shown in Table 7 and Figure 7.

#### 3.7. Infrastructure for Fossil Fuels

_{2}-e/kWh electricity generated (Table 6). The energy content of crude oil is taken as 45.3 MJ/kg fuel [58], which equates to 12.6 kWh/kg fuel. Power plant efficiency is expected to be between 38 and 48%. Combining this information produces an estimate for fossil fuel infrastructure of 5 to 18 g CO

_{2}-e per kg of fuel produced. To account for additional uncertainty the plausible range is extended to 2 to 30 g CO

_{2}-e per kg of fuel produced. Using the average on-road fuel consumption of 80 g per km for PVs (refer to Section 3.3) then computes an average GHG emission intensity range for refinery infrastructure of approximately 0.2–2.4 g CO

_{2}-e per km. A uniform distribution was assumed for e

_{infra,ICEV}(

**U**: 0.2, 2.5). This range is uncertain, but the error of omission (assuming zero emissions intensity) is considered to be larger than the error in the estimated range.

#### 3.8. Upstream Emissions for Fossil Fuels

_{2}-e/km, and a typical value of 51.4 g CO

_{2}-e/km. A uniform distribution was assumed for e

_{upstream,ICEV}(

**U**: 35.9, 72.0).

#### 3.9. Upstream Emissions for Electricity Generation

_{2}-e/kWh for the 2018–2019 financial year. This is 10% of the combined Scope 2 and 3 emission intensity.

_{2}-e/kWh (WA) and a maximum of 120 g CO

_{2}-e/kWh (Queensland). A similar range has been reported in other studies. For instance, upstream emissions for different subregion grids in the USA vary between 27 and 140 g CO

_{2}-e/kWh [45]. Given the complexity in quantifying upstream emission factors, the uncertainty in Scope 3 NGA emission factors is expected to be larger than the uncertainty in Scope 2 NGA emission factors for electricity production (5–13%, refer to Section 3.4), which is based on reported fuel use data.

**T**: 0.73, 0.95, 0.85) and BEV real-world energy consumption (

**T**: 0.18, 0.19, 0.21). The sampling distributions were used to determine the best parametric distribution through maximum likelihood fit. The results are shown in Table 8 and Figure 9.

**D**: 0).

**U**: 1.05, 1,10), battery charging efficiency (

**T**: 0.73, 0.95, 0.85) and BEV real-world energy consumption (

**T**: 0.18, 0.21, 0.19), using the fuel type percentages as weights (Table 1). The sampling distributions were used to determine the best parametric distribution through maximum likelihood fit. The results are shown in Table 10 and Figure 10.

_{2}-e/km, whereas the LCA meta-study method generates slightly lower typical values between 1 and 23 g CO

_{2}-e/km. The biggest difference is observed for Western Australia (WA) where the NGA Scope 3 factor has a low value of approximately 2 g CO

_{2}-e/km but it is unclear why this should be so, given the largely fossil-fueled fuel mix used in this state.

#### 3.10. Vehicle Recycling and Disposal

_{2}-e/kWh (Section 3.4) and the upstream GHG emission intensity of 80 g CO

_{2}-e/kWh (Section 3.8), a total of 840 g CO

_{2}-e/kWh, estimates 55,884 ton of CO

_{2}-e emissions each year due to vehicle recycling. Dividing this value by total VKT (560,000 vehicles times lifetime mileage of 200,000 km), results in a GHG emission rates due to disposal of 0.5 g CO

_{2}-e/km. A plausible range of 0.1 to 2.0 g CO

_{2}-e/km has been assumed for vehicle recycling and disposal. The same value is used for ICEVs and BEVs and a uniform distribution was assumed for e

_{disposal,ICEV}and e

_{disposal,BEV}(

**U**: 0.1, 2.0).

## 4. Results and Discussion

#### 4.1. Probabilistic Technology Assessment

_{infra,BEV}, e

_{upstream,BEV}, e

_{road,BEV}, refer to Section 2.2), a lumped emission factor distribution was developed for the three life-cycle aspects combined (Appendix B, Table A2). In the development of the lumped emission factor distributions, the common inputs were first drawn from elicited distributions in each MC simulation and then the life-cycle aspects were computed and then summed, with a new parametric distribution fitted for their sum. These parametric input distributions along with the appropriate parametric distributions fitted in Section 3 were combined in ten separate Monte Carlo simulations with a million simulations for the three Scenarios and seven jurisdictions. The results of the probabilistic technology assessment for the ten simulations are shown in Table 11.

_{2}-e/km), but particularly for BEVs (98–287 g CO

_{2}-e/km), which reflects the differences in fuel mix for electricity generation in the different states and territories (Table 1). As a consequence, the potential reduction in LCA GHG emissions per vehicle kilometer through electrification of the on-road fleet is different, as is shown in Figure 11. Figure 11 presents box plots for each scenario or jurisdiction showing the results of the probabilistic analysis for the absolute and relative differences in fleet average GHG emission rate distributions of BEVs and ICEVs.

_{2}-e/km (243–300 g CO

_{2}-e/km), closely followed by South Australia (SA) with 222 g CO

_{2}-e/km (191–252 g CO

_{2}-e/km). It demonstrates that electrification in two Australian states will already achieve large reductions in GHG emissions from passenger vehicles of 74% (TAS) and 61% (SA), respectively.

_{2}-e/km) and 108 g CO

_{2}-e/km (60–151 g CO

_{2}-e/km), respectively, for the current situation. Electrification in these two Australian states is expected to achieve significant reductions in 2018/2019 in GHG emissions from passenger vehicles of 21% (VIC) and 29% (NSW), respectively. However, these values will improve substantially as the electricity generation system is further decarbonized. This is evident from the positive results that were obtained for Scenario 3 (more renewables), Tasmania and South Australia.

#### 4.2. Sensitivity Analysis

- (a)
- Using COPERT Australia and the Australian Fleet Model, a non-standard beta distribution (
**B**: 9.89, 16.86) with truncation at 225 and 298 g CO_{2}-e/km was developed for on-road ICEVs (Section 3.3). The alternative parametric distribution was used in a repeat of the probabilistic technology assessment for the current (2018/19) Australian electricity mix (Scenario 1). The results are presented in Appendix C. The mean LCA GHG emission factor for ICEVs is reduced from 369 to 356 g CO_{2}-e/km (3.5%), compared to the probabilistic technology assessment in Section 4.1. The overall predicted effect of electrification (Figure 11) is similar. Section 4.1 predicted that BEVs will reduce GHG emission rates by 36% on average and by between 29% and 41%. Using COPERT Australia and the Australian Fleet Model as an alternative input, it is predicted that BEVs will reduce GHG emission rates by 33% on average and by between 26% and 40%, a similar result. The probability that BEVs exceeds the minimum predicted ICEV LCA GHG emission factor is zero for both simulations, which means that none of the million simulations generated a higher emission rate for BEVs as compared with ICEVs. - (b)
- Alternative upstream GHG emission factor distributions for Australian BEVs were developed using Scope 3 data from the National Greenhouse Accounts and overseas publications (Section 3.8). The alternative parametric distributions (Table 8) were used in a repeat of the probabilistic technology assessment, including development of alternative lumped emission factor distributions (Appendix B, Table A3). The results are presented in Appendix D. The mean LCA GHG emission factors for BEVs vary by approximately ±15%, compared to the probabilistic technology assessment in Section 4.1. The largest change is observed for NT, where the mean BEV GHG emission factor is reduced by 14% from 218 to 188 g CO
_{2}-e/km. For the majority of jurisdictions, this difference is typically approximately ±5% or less. The overall predicted effect of electrification (Figure 11) is remarkably stable as is shown in Figure A2, which suggests that the results from the probabilistic analysis are robust.

#### 4.3. Expansion and Refinement

## 5. Conclusions

_{2}-e/km) for passenger vehicles for all Scenarios and for all jurisdictions, but that the extent of the reduction in GHG emissions and associated uncertainty varies. For the current (2018/19) Australian electricity mix (Scenario 1), which is still largely fossil fuels based, the weight of evidence suggests that BEVs will reduce GHG emission rates by 36% on average (95% confidence interval: 29% to 41%). For the worst-case ‘fossil fuels only’ marginal electricity scenario (Scenario 2) electric passenger vehicles are still expected to significantly reduce average GHG emission rates for passenger vehicles between 10% and 32%. Large reductions by between 74% and 80% in fleet average LCA GHG emission rates for passenger vehicles through electrification are predicted for more renewables (Scenario 3).

_{2}-e/km), but particularly for BEVs (98–287 g CO

_{2}-e/km), which reflects the differences in fuel mix for electricity generation in the different states and territories. Electrification of the Tasmania (TAS) on-road fleet has the largest emission reduction with a predicted absolute value of 272 g CO

_{2}-e/km (243–300 g CO

_{2}-e/km), closely followed by South Australia (SA) with 222 g CO

_{2}-e/km (191–252 g CO

_{2}-e/km). This demonstrates that electrification in two Australian states will already achieve large reductions in GHG emissions from passenger vehicles of 74% (TAS) and 61% (SA), respectively.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A. Distribution Definitions

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

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

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

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

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

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

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

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

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

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

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

## Appendix B. Lumped GHG Distributions

**Table A2.**Lumped (infra, upstream, road) GHG emission factor (g CO

_{2}-e/km) distribution definitions for Australian BEVs by scenario or jurisdiction.

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

Scenario 1 (Australia 2018/19) | Non-standard beta, B (8.91, 29.73) | 194.00 | 153.00–257.00 |

Scenario 2 (Marginal Electricity) | Skewed t, S (210.66, 25.20, 1.96, 19870.21) | 229.00 | 177.00–306.00 |

Scenario 3 (More Renewable Electricity) | Non-standard beta, B (5.32, 24.15) | 29.00 | 20.00–46.00 |

NSW | Skewed t, S (182.18, 25.83, 1.91, 19344.81) | 200.00 | 148.00–281.00 |

VIC | Skewed t, S (209.99, 23.74, 2.07, 379.39) | 227.00 | 184.00–300.00 |

QLD | Lognormal, L (5.27, 0.08) | 196.00 | 156.00–263.00 |

WA | Non-standard beta, B (7.26, 16.91) | 177.00 | 136.00–251.00 |

SA | Non-standard beta, B (7.26, 16.91) | 86.00 | 62.00–127.00 |

TAS | Non-standard beta, B (6.53, 11.81) | 38.00 | 31.00–49.00 |

NT | Non-standard beta, B (5.83, 12.95) | 163.00 | 122.00–238.00 |

**Table A3.**Lumped (infra, upstream, road) GHG emission factor (g CO

_{2}-e/km) distribution definitions for Australian BEVs by scenario or jurisdiction used in Section 4.2 based on alternative parametric upstream distributions (Table 8).

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

Scenario 1 (Australia 2018/19) | Non-standard beta, B (5.30, 9.63) | 195.00 | 165.00–242.00 |

Scenario 2 (Marginal Electricity) | - | - | - |

Scenario 3 (More Renewable Electricity) | - | - | - |

NSW | Non-standard beta, B (9.37, 16.99) | 206.00 | 161.00–265.00 |

VIC | Non-standard beta, B (5.32, 9.49) | 245.00 | 205.00–297.00 |

QLD | Non-standard beta, B (5.81, 10.54) | 237.00 | 198.00–288.00 |

WA | Non-standard beta, B (4.63, 8.22) | 164.00 | 137.00–201.00 |

SA | Non-standard beta, B (8.32, 14.88) | 90.00 | 75.00–109.00 |

TAS | Non-standard beta, B (7.26, 12.69) | 45.00 | 37.00–55.00 |

NT | Non-standard beta, B (4.59, 8.11) | 130.00 | 111.00–157.00 |

## Appendix C. Sensitivity Analysis Using Alternative On-Road ICEV Distribution

**Table A4.**LCA GHG emission factors (g CO

_{2}-e/km) for ICEVs and BEVs by scenario or jurisdiction, including associated uncertainty (95% confidence interval), percent change, probability that any BEV simulation exceeds the minimum value for ICEVs and overlap of confidence intervals.

Scenario/ Jurisdiction | LCA GHG ICEV g CO _{2}-e/km(95% CI) | LCA GHG BEV g CO _{2}-e/km(95% CI) | Relative Difference % (95% CI) | Probability BEV > ICEV |
---|---|---|---|---|

Scenario 1 (Australia Current) | 356 (332 to 381) | 237 (221 to 255) | −33 (−40 to −26) | 0.0 * |

**Figure A1.**Box plots of the absolute (left) and relative (right) differences in LCA GHG emission rates between BEVs and ICEVs for Scenario 1 (Section 4.1) and Alternative Scenario 1 (Section 4.2).

## Appendix D. Sensitivity Analysis Using Alternative Upstream BEV GHG Distributions

**Table A5.**LCA GHG emission factors (g CO

_{2}-e/km) for ICEVs and BEVs by scenario or jurisdiction, including associated uncertainty (95% confidence interval), percent change, probability that any BEV simulation exceeds the minimum value for ICEVs and overlap of confidence intervals.

Scenario/ Jurisdiction | LCA GHG ICEV g CO _{2}-e/km(95% CI) | LCA GHG BEV g CO _{2}-e/km(95% CI) | Relative Difference % (95% CI) | Probability BEV > ICEV |
---|---|---|---|---|

Scenario 1 (Australia Current) | 369 (349 to 390) | 250 (231 to 270) | −32 (−25 to −39) | 0.0 * |

Scenario 2 (Marginal Electricity) | - | - | - | - |

Scenario 3 (More Renewable Electricity) | - | - | - | - |

NSW | 368 (344 to 393) | 258 (238 to 280) | −30 (−37 to −22) | 0.0 * |

VIC | 364 (340 to 389) | 298 (276 to 323) | −18 (−26 to −9) | 6.1 × 10^{-5} * |

QLD | 375 (351 to 400) | 290 (268 to 314) | −23 (−30 to −14) | 0.0 * |

WA | 387 (363 to 412) | 220 (202 to 240) | −43 (−49 to −37) | 0.0 * |

SA | 364 (340 to 389) | 147 (135 to 160) | −60 (−64 to −55) | 0.0 * |

TAS | 369 (343 to 395) | 104 (93 to 115) | −72 (−75 to −68) | 0.0 * |

NT | 390 (357 to 423) | 188 (173 to 204) | −52 (−57 to −46) | 0.0 * |

**Figure A2.**Box plots of the absolute (

**top**) and relative (

**bottom**) differences in LCA GHG emission rates between BEVs and ICEVs by scenario (red) or jurisdiction (blue).

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**Figure 1.**Scope 2 GHG emission intensities (g CO

_{2}-e/kWh consumed) for electricity consumption in Australia by jurisdiction (WA = Western Australia, NT = Northern Territory, SA = South Australia, QLD = Queensland, NSW = New South Wales, ACT = Australian Capital Territory, VIC = Victoria, and TAS = Tasmania).

**Figure 2.**Parametric GHG emission factor (g CO

_{2}-e/km) distributions by life-cycle aspect and technology group (bootstrap sampling distribution = vertical lines; fitted parametric distribution = shaded polygon).

**Figure 3.**On-road GHG emission factor (g CO

_{2}-e/km) distributions for Australian ICEVs by scenario or jurisdiction.

**Figure 4.**GHG emission intensity distributions for electricity consumption in Australia by jurisdiction (AUS = Australia, NSW = New South Wales, VIC = Victoria, QLD = Queensland, SA = South Australia, WA = Western Australia, TAS = Tasmania, and NT = Northern Territory).

**Figure 5.**GHG emission intensity distributions for grid loss-corrected electricity generation by fuel type (blue) in Australia and for Scenarios 2 and 3 (green), sampling distribution = vertical lines; fitted parametric distribution = shaded polygon.

**Figure 6.**On-road GHG emission factor (g CO

_{2}-e/km) distributions for Australian BEVs by scenario or jurisdiction. Sampling distribution = vertical lines; fitted parametric distribution = shaded polygon.

**Figure 7.**Infrastructure GHG emission factor (g CO

_{2}-e/km) distributions for Australian BEVs by scenario or jurisdiction. Sampling distribution = vertical lines; fitted parametric distribution = shaded polygon.

**Figure 8.**Upstream GHG emission factor uncertainty distributions based on Australian and USA upstream data, bootstrap sampling distribution = vertical lines; fitted parametric distribution = shaded polygon.

**Figure 9.**Upstream GHG emission factor (g CO

_{2}-e/km) distributions for Australian BEVs by scenario or jurisdiction. Sampling distribution = vertical lines; fitted parametric distribution = shaded polygon.

**Figure 10.**Upstream GHG emission factor (g CO

_{2}-e/km) distributions for Australian BEVs by scenario or jurisdiction. Sampling distribution = vertical lines; fitted parametric distribution = shaded polygon.

**Figure 11.**Box plots of the absolute (

**top**) and relative (

**bottom**) differences in LCA GHG emission rates between BEVs and ICEVs by scenario (red) or jurisdiction (blue).

**Table 1.**Percentage of electricity generated by fuel type for each scenario or jurisdiction [24].

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

Australia Current (SC1) | 58.4% | 20.0% | 1.9% | 0.0% | 6.0% | 6.7% | 1.3% | 5.6% |

Australia Marginal Electricity (SC2) | 73.0% | 24.0% | 3.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% |

Australia More Renewable (SC3) | 5.0% | 5.0% | 0.0% | 0.0% | 30.0% | 25.0% | 5.0% | 30.0% |

New South Wales (NSW) Current | 80.7% | 3.3% | 0.5% | 0.0% | 3.0% | 5.2% | 1.6% | 5.7% |

Victoria (VIC) Current | 70.8% | 6.8% | 0.4% | 0.0% | 5.6% | 10.0% | 1.5% | 4.9% |

Queensland (QLD) Current | 73.8% | 14.1% | 1.4% | 0.0% | 1.5% | 0.6% | 1.9% | 6.8% |

Western Australia (WA) Current | 23.8% | 61.5% | 5.8% | 0.0% | 0.5% | 4.4% | 0.3% | 3.7% |

South Australia (SA) Current | 0.0% | 48.5% | 1.1% | 0.0% | 0.1% | 38.2% | 0.6% | 11.5% |

Tasmania (TAS) Current | 0.0% | 5.3% | 0.2% | 0.0% | 83.2% | 9.7% | 0.2% | 1.4% |

Northern Territory (NT) Current | 0.0% | 78.6% | 17.8% | 0.0% | 0.0% | 0.0% | 0.2% | 3.4% |

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

P | ICEV | e_{vehicle,ICEV} | Triangular, T (40.38, 44.98, 58.61) | 45.00 | 40.00–59.00 |

P | BEV | e_{vehicle,BEV} | Non-standard beta, B (7.30, 8.73) | 59.00 | 39.00–83.00 |

I | ICEV | e_{infra,ICEV} | Uniform, U (0.20, 2.50) | 1.30 | 0.20–2.50 |

I | BEV | e_{infra,BEV} | Non-standard beta, B (5.81, 10.44) ** ^{(a)} | 5.07 | 0.74–10.76 |

U | ICEV | e_{upstream,ICEV} | Uniform, U (35.90, 72.00) | 51.40 | 35.90–72.00 |

U | BEV | e_{upstream,BEV} | Lognormal, L (2.53, 0.53) ** ^{(b)} | 14.18 | 1.00–49.00 |

O | ICEV | e_{road,ICEV} | Normal, N (265, 3) ** ^{(c)} | 265.00 | 259.00–272.00 |

O | BEV | e_{road,BEV} | Non-standard beta, B (5.81, 10.44) ** ^{(d)} | 175.00 | 142.00–215.00 |

D | ICEV | e_{disposal,ICEV} | Uniform, U (0.10, 2.00) | 0.50 | 0.20–2.50 |

D | BEV | e_{disposal,BEV} | Uniform, U (0.10, 2.00) | 0.50 | 0.20–2.50 |

^{(a)}Refer to refer to below table: Infrastructure GHG emission factor (g CO

_{2}-e/km) distribution definitions for Australian BEVs by scenario or jurisdiction.

^{(b)}Refer to below table: Upstream GHG emission factor (g CO

_{2}-e/km) distribution definitions for Australian BEVs by sce-nario or jurisdiction.

^{(c)}Refer to below table: On-road GHG emission factor (g CO

_{2}-e/km) distribution definitions for Australian ICEVs by scenario or jurisdiction.

^{(d)}Refer to below table: On-road GHG emission factor (g CO

_{2}-e/km) distribution definitions for Australian BEVs by scenario or jurisdiction.

**Table 3.**On-road GHG emission factor (g CO

_{2}-e/km) distribution definitions for Australian ICEVs by scenario or jurisdiction.

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

Scenario 1 (Australia 2018/19) | Normal, N (265, 3) | 265 | 256–275 |

Scenario 2 (Marginal Electricity) | Normal, N (265, 3) | 265 | 256–275 |

Scenario 3 (More Renewable Electricity) | Normal, N (265, 3) | 265 | 256–275 |

NSW | Normal, N (264, 7) | 264 | 242–286 |

VIC | Normal, N (260, 7) | 260 | 240–279 |

QLD | Normal, N (271, 7) | 271 | 249–293 |

SA | Normal, N (260, 7) | 260 | 241–280 |

WA | Normal, N (283, 7) | 283 | 262–303 |

TAS | Normal, N (265, 8) | 265 | 240–289 |

NT | Normal, N (286, 13) | 286 | 247–326 |

**Table 4.**GHG emission intensities (g CO

_{2}-e/kWh consumed) distribution definitions for grid-loss corrected electricity generation by fuel type in Australia.

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

Biomass | Lognormal, L (4.27, 0.67) | 89.00 | 27.00–295.00 |

Coal | Non-standard beta, B (7.69, 17.85) | 1023.00 | 913.00–1201.00 |

Gas | Lognormal, L (6.30, 0.04) | 545.00 | 467.00–635.00 |

Hydro | Normal, N (0.23, 0.12) | 0.23 | 0.00–0.80 |

Oil | Triangular, T (638, 1430, 1824) | 1430.00 | 638.00–1824.00 |

Solar | Gamma, G (8.23, 12.42) | 0.66 | 0.14–1.85 |

Wind | Lognormal, L (−0.69, 0.24) | 0.52 | 0.23–1.25 |

Scenario 2 | Skewed t, S (882.01, 27.39, 1.33, 385.26) | 900.00 | 826.00–983.00 |

Scenario 3 | Skewed t, S (78.66, 4.52, 2.87, 27.08) | 82.00 | 74.00–96.00 |

**Table 5.**On-road GHG emission factor (g CO

_{2}-e/km) distribution definitions for Australian BEVs by scenario or jurisdiction.

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

Scenario 1 (Australia 2018/19) | Non-standard beta, B (6.16, 12.20) | 175 | 144–219 |

Scenario 2 (Marginal Electricity) | Non-standard beta, B (5.86, 10.53) | 207 | 170–258 |

Scenario 3 (More Renewable Electricity) | Lognormal, L (2.94, 0.07) | 19 | 14–25 |

NSW | Non-standard beta, B (8.43, 17.03) | 182 | 142–230 |

VIC | Non-standard beta, B (10.88, 24.20) | 221 | 172–284 |

QLD | Non-standard beta, B (8.61, 16.72) | 184 | 144–232 |

SA | Non-standard beta, B (9.70, 20.15) | 69 | 53–90 |

WA | Non-standard beta, B (8.63, 17.20) | 154 | 121–204 |

TAS | Non-standard beta, B (8.80, 19.70) | 37 | 29–48 |

NT | Non-standard beta, B (8.29, 16.14) | 131 | 103–172 |

**Table 6.**GHG emission intensities (g CO

_{2}-e/kWh generated) distribution definitions 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 |

**Table 7.**Infrastructure GHG emission factor (g CO

_{2}-e/km) distribution definitions for Australian BEVs by scenario or jurisdiction.

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

Scenario 1 (Australia 2018/19) | Non-standard beta, B (2.26, 3.00) | 5.07 | 0.74–10.76 |

Scenario 2 (Marginal Electricity) | Normal, N (4.35, 2.38) | 4.35 | 0.21–9.77 |

Scenario 3 (More Renewable Electricity) | Lognormal, L (1.99, 0.41) | 7.93 | 2.00–19.72 |

NSW | Non-standard beta, B (1.97, 2.77) | 6.07 | 0.66–13.60 |

VIC | Non-standard beta, B (2.33, 3.32) | 5.73 | 0.65–12.92 |

QLD | Non-standard beta, B (1.95, 2.73) | 5.66 | 0.61–12.66 |

WA | Non-standard beta, B (3.05, 4.41) | 2.61 | 0.52–5.59 |

SA | Non-standard beta, B (2.70, 5.98) | 4.35 | 1.00–10.47 |

TAS | Non-standard beta, B (2.31, 2.88) | 3.18 | 0.83–6.11 |

NT | Gamma, G (8.44, 7.95) | 1.06 | 0.38–2.39 |

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

_{2}-e/km) distribution definitions for Australian BEVs by jurisdiction based on NGA data.

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

Scenario 1 (Australia 2018/19) | Non-standard beta, B (14.19, 53.81) | 18.42 | 12.80–26.00 |

Scenario 2 (Marginal Electricity) | - | - | - |

Scenario 3 (More Renewable Electricity) | - | - | - |

NSW | Lognormal, L (2.91, 0.09) | 18.39 | 13.00–26.00 |

VIC | Skewed t, S (21.04, 2.86, 1.62, 233.11) | 22.99 | 16.00–32.00 |

QLD | Skewed t, S (25.19, 3.48, 1.67, 102603.40) | 27.57 | 20.00–38.00 |

WA | Lognormal, L (0.83, 0.09) | 2.30 | 1.70–3.30 |

SA | Lognormal, L (2.77, 0.09) | 16.10 | 11.75–22.75 |

TAS | Skewed t, S (4.21, 0.58, 1.64, 2061567.00) | 4.61 | 3.30–6.60 |

NT | Non-standard beta, B (14.35, 40.67) | 11.48 | 7.60–16.00 |

**Table 9.**GHG emission intensities (g CO

_{2}-e/kWh generated) distribution definitions for upstream electricity generation infrastructure by fuel type.

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

Biomass | Exponential, E (0.028) | 35.30 | 1.00–87.00 |

Coal | Lognormal, L (3.81, 0.98) | 66.45 | 7.00–230.00 |

Gas | Normal, N (105.25, 73.29) | 105.25 | 0.56–280.00 |

Hydro | Dirac, D (0.00) | 0.00 | 0.00–0.00 |

Oil | Gamma, G (5.02, 0.20) | 25.60 | 11.00–38.00 |

Solar | Dirac, D (0.00) | 0.00 | 0.00–0.00 |

Wind | Dirac, D (0.00) | 0.00 | 0.00–0.00 |

**Table 10.**Upstream GHG emission factor (g CO

_{2}-e/km) distribution definitions for Australian BEVs by scenario or jurisdiction.

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

Scenario 1 (Australia 2018/19) | Lognormal, L (2.53, 0.53) | 14.18 | 1.00–49.00 |

Scenario 2 (Marginal Electricity) | Lognormal, L (2.73, 0.54) | 17.69 | 1.00–58.00 |

Scenario 3 (More Renewable Electricity) | Weibull, W (2.65, 2.81) | 2.49 | 0.15–6.50 |

NSW | Skewed t, S (2.50, 11.02, 25.32, 4.46) | 13.04 | 1.50–54.50 |

VIC | Skewed t, S (3.00, 10.25, 15.23, 5.20) | 12.53 | 1.40–46.00 |

QLD | Lognormal, L (2.54, 0.57) | 14.94 | 1.70–52.00 |

WA | Non-standard beta, B (2.53, 4.51) | 21.28 | 1.00–58.00 |

SA | Non-standard beta, B (2.03, 3.72) | 13.81 | 0.15–40.00 |

TAS | Weibull, W (1.90, 1.68) | 1.49 | 0.02–4.40 |

NT | Non-standard beta, B (2.06, 3.61) | 23.34 | 0.70–62.50 |

**Table 11.**LCA GHG emission factors (g CO

_{2}-e/km) for ICEVs and BEVs by scenario or jurisdiction, including associated uncertainty (95% confidence interval), percent change, probability that any BEV simulation exceeds the minimum value for ICEVs and overlap of confidence intervals.

Scenario/ Jurisdiction | LCA GHG ICEV g CO _{2}-e/km(95% CI) | LCA GHG BEV g CO _{2}-e/km(95% CI) | Relative Difference % (95% CI) | Probability BEV > ICEV |
---|---|---|---|---|

Scenario 1 (Australia Current) | 369 (349 to 390) | 237 (221 to 255) | −36 (−41 to −29) | 0.0 * |

Scenario 2 (Marginal Electricity) | 369 (349 to 390) | 289 (256 to 328) | −22 (−32 to −10) | 3.6 × 10^{-4} * |

Scenario 3 (More Renewable Electricity) | 369 (349 to 390) | 85 (74 to 96) | −77 (−80 to −74) | 0.0 * |

NSW | 368 (344 to 393) | 261 (227 to 301) | −29 (−39 to −17) | 3.0 × 10^{-6} * |

VIC | 364 (340 to 389) | 287 (257 to 325) | −21 (−31 to −9) | 5.4 × 10^{-4} * |

QLD | 375 (351 to 400) | 256 (226 to 288) | −32 (−41 to −22) | 0.0 * |

WA | 387 (363 to 412) | 231 (209 to 255) | −40 (−47 to −33) | 0.0 * |

SA | 364 (340 to 389) | 143 (126 to 161) | −61 (−66 to −55) | 0.0 * |

TAS | 369 (343 to 395) | 98 (87 to 109) | −74 (−77 to −70) | 0.0 * |

NT | 390 (357 to 423) | 218 (194 to 246) | −44 (−52 to −35) | 0.0 * |

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**MDPI and ACS Style**

Smit, R.; Kennedy, D.W.
Greenhouse Gas Emissions Performance of Electric and Fossil-Fueled Passenger Vehicles with Uncertainty Estimates Using a Probabilistic Life-Cycle Assessment. *Sustainability* **2022**, *14*, 3444.
https://doi.org/10.3390/su14063444

**AMA Style**

Smit R, Kennedy DW.
Greenhouse Gas Emissions Performance of Electric and Fossil-Fueled Passenger Vehicles with Uncertainty Estimates Using a Probabilistic Life-Cycle Assessment. *Sustainability*. 2022; 14(6):3444.
https://doi.org/10.3390/su14063444

**Chicago/Turabian Style**

Smit, Robin, and Daniel William Kennedy.
2022. "Greenhouse Gas Emissions Performance of Electric and Fossil-Fueled Passenger Vehicles with Uncertainty Estimates Using a Probabilistic Life-Cycle Assessment" *Sustainability* 14, no. 6: 3444.
https://doi.org/10.3390/su14063444