The annual passenger car emissions for each country were calculated in a bottom up manner at a country level using country specific activity data which is the transport demand as vehicle kilometers for the specified year for cars and the associated stock-fleet-average emission factors for the country in that year.
The annual country specific emissions were then spatially distributed across the country grid by using a spatial proxy as further described in 2.2.
2.1.1 Market development of drivetrain technologies
In 2021, the electric vehicle market share in new passenger car registration exceeded 15% in Germany and China, two of the world’s biggest automotive markets, according to Marklines (2022). However, the crucial factor for reducing passenger car emissions is the proportion of these vehicles in the overall vehicle stock. Despite widespread attention to electric vehicles (EVs), their share in the global passenger car fleet remains at only approximately 1.4% (International Energy Agency 2022), with a significant concentration of EVs in China, the USA, and EU countries. To achieve a significant reduction in global emissions, it is crucial to extend the EV transformation beyond a few countries and expand it worldwide. As discussed in Section 2.1.4, transportation demand is expected to increase in some developing countries such as India and Brazil, while it will be nearly constant in European countries. However, the number of electric cars in stock is negligible at the moment in those countries, as can be seen in Fig. 1.
In order to effectively assess the emissions of vehicles in the stock, it is crucial to analyze the powertrain distribution of the vehicles in question. Although many different sources are available for the diffusion of alternative powertrains, it is difficult to find a centralized analysis that covers many different regions individually and is updated frequently to cover new industry development, such as the EU internal combustion engine (ICE) ban (European Comission 2022). Therefore, for this study, we used our function to able to cover the recent developments in the industry.
The literature generally suggests that the market penetration of electric vehicles will follow an S-shaped curve, leading to numerous studies that utilize this method to analyze the market development of electric vehicles (Plötz et al. 2014; Collett et al. 2021; Rietmann et al. 2020). Where the data is available, the S-shaped logistic growth functions are applicable to models of various scales and complexities, ranging from elementary particles to the evolution of starts because of their fractal characteristics (Kucharavy and Guio 2011). As a result, the S-curve logistic growth function has been utilized in previous studies to analyze the growth of alternative powertrain shares across various regions globally (Teske 2019; Rietmann et al. 2020). The function used to estimate the stock share development of alternative powertrains is presented in Eq. 3. \({S}_{c,p,t}\) is the share of powertrain p in country c at time t. \({S0}_{c,p}\) is the share of related powertrain at the time zero (2015 in our model). \({\varDelta }_{c,p}\) is the target share of the powertrain in country c. \({\gamma }_{c}\) is the γ is the growth rate parameter of the country based on the target year and A is a scaling parameter that determines the initial growth rate of the market of the curve. More details about the functions are given in Appendix 1.
$${S}_{c,p,t}\left(unnormed\right)={S0}_{c,p}+ \frac{{\varDelta }_{c,p}}{1+A.{e}^{{\gamma }_{c}.(t-{t}_{0})}}$$
( 3 )
$${S}_{c,p,t}=\frac{{S}_{c,p,t}\left(unnormed\right)}{\sum _{p}{S}_{c,p,t} \left(unnormed\right)}$$
As the development of the vehicle stock depends on many different variables, from prices to infrastructure availability, Eq. 3 will be able to provide limited explanation and flexibility about the electric vehicles’ fleet development. Therefore, later we compared our results with different sources to ensure that our calculations were in parallel with the current literature and to discuss discrepancies. The main input in the current model is the target shares. Those target shares in the model are based on the announced targets from the government of selected countries or analyses from different studies. Table 1 shows the target shares used in the model and the sources those shares are based on.
Table 1
Electrification targets used in the model and the sources the values are based on
Region/ Country | Target stock BEV share | Target Year | Literature considered | Source |
Australia | 70% | 2065 | 65% sales in 2050 | Bureau of Infrastructure, Transport and Regional Economics 2019 |
Total BEV and PHEV sales share is 30% in 2030 | Australian Government Department of Industry, Science, Energy and Resources 2021 |
OECD Europe (Germany) | 100% | 2060 | 100% BEV in 2035 | European Comission 2022 |
North America (the USA) | 100% | 2060 | 100% government vehicles in 2035 | Shepardson and Klayman 2021 |
50% electric vehicle sales share in 2030 | The White House 2021 |
10 US states are in the ZEV Alliance, where 100% of EV sales were aimed between 2035 and 2050. | ZEV Alliance 2021 |
China | 100% | 2060 | Net 0 in 2060 | Bloomberg News 2022 |
India | 75% | 2070 | 30% sales in 2030 and 75% in 2050 | Kamboj et al. 2022 |
15–20% sales in 2030 | Jain et al. 2022 |
EV stock in 2040, 10% in Stated Policies Scenario and 60% in Sustainable Development Scenario | The International Energy Agency 2022 |
Japan | 60% | 2050 | 100% HEV or EV sales in 2030 | Nikkei 2020 |
Africa (South Africa) | 65% | 2070 | 40% fleet will be EV by 2050 | Goosen 2022 |
50% stock share in 2050 | Chege 2022 |
80% new sales are EV in 2045 | Ahjum et al. |
South America (Brazil) | 15% | 2050 | 61% hybrid, 11% BEV fleet in 2050 | Feil and Alex Sandro 2020 |
Eastern Europe (Russia) | 10% | 2055 | 10% sales in 2030 | Russia Briefing 2021 |
2.1.2 CO2 emission factors
CO2 emission factors are computed based on the transport scenario model TRAEM (Transport Energy Model) which gives passenger car energy intensity figures from 2015 to 2050 differentiated per drivetrain technology and world region, assuming that energy efficiency improves over time. The energy intensity data were derived from the German Aerospace Centre (DLR) vehicle databases and the state-of-the-art literature (Teske 2019). According to the considered country clustering, it is assumed that within each world region all countries have the same CO2 emission factors (cf. Appendix 3). For each country, the emission factors per drivetrain technology are then weighted according to the vehicle stock shares of each drivetrain technology (cf. section 3.1) which results in stock-fleet-average CO2 emission factors.
Following Eq. 4 is derived based on Economic Commission for Europe of the United Nations (UN/ECE) (2012) to calculate the specific CO2 emission factors.
$${ef}_{CO2, d,f,t}={ec}_{d,f,t}*\left(1-b\right)*r*\frac{1}{{LHV}_{f}}*\frac{{\rho }_{f}}{{c}_{f}}*\frac{1}{0.273}$$
( 4 )
Where \({ef}_{CO2, d,t}\) is the drivetrain (d), fuel (f) and time (t) -specific CO2 emission factor which is calculated based on fuel consumption. This can be obtained from the drivetrain and fuel -specific energy consumption per year (\({ec}_{d,f,t}\)), from which a biofuel rate \(b\) is subtracted in order to only regard the fossil fuel share; an on-road fuel economy gap factor \(r\) which varies between 1.2 and 1.7 is further applied to consider real-world energy consumption and the term is then divided by the lower heating value of the respective fuel (\({LHV}_{f}\)). Further the density of the fuel \({\rho }_{f}\)and a fuel dependent factor \({c}_{f}\) are required. According to UN/ECE (2012) \({c}_{f}\) is 0.118 for petrol fueled vehicles, 0.116 for diesel vehicles and 0.1336 for CNG vehicles. The applied biofuel rate values in Eq. 4 are also based on TRAEM data for the reference scenario (Teske 2019) and can be found in appendix 2.
The resulting region- and drivetrain-specific CO2 emission factors for the base years 2015, 2030 and 2050 are presented in Table 2.
Table 2
Region- and drivetrain-specific CO2 emission factors in 2015, 2030 and 2050.
Region/ Country | Year | CO2 EF [g/km] |
| | G | G-HEV | D | D-HEV | CNG | G-PHEV | D-PHEV |
Australia | 2015 | 174.7 | 121.9 | 190.0 | 138.2 | 178.9 | 109.3 | 123.9 |
2030 | 127.9 | 96.2 | 136.5 | 109.8 | 132.8 | 86.0 | 98.2 |
2050 | 96.1 | 65.0 | 110.5 | 76.4 | 90.3 | 62.5 | 73.4 |
South America (Brazil) | 2015 | 177.7 | 106.8 | 179.9 | 136.7 | 192.4 | 96.2 | 123.2 |
2030 | 142.0 | 93.6 | 154.7 | 119.9 | 167.9 | 84.6 | 108.3 |
2050 | 120.1 | 81.5 | 134.5 | 104.7 | 135.0 | 75.0 | 96.3 |
China | 2015 | 138.2 | 116.4 | 162.6 | 119.2 | 182.1 | 125.9 | |
2030 | 109.0 | 76.5 | 114.1 | 80.8 | 135.3 | 106.4 | |
2050 | 82.7 | 55.5 | 89.5 | 59.6 | 90.8 | 83.9 | |
OECD Europe (Germany) | 2015 | 147.6 | 94.8 | 141.8 | 96.9 | 152.8 | 116.1 | 118.8 |
2030 | 136.7 | 89.4 | 125.9 | 94.1 | 139.4 | 94.3 | 99.3 |
2050 | 117.8 | 69.0 | 113.5 | 73.1 | 107.1 | 73.3 | 77.6 |
India | 2015 | 223.8 | 113.7 | 203.9 | 143.6 | 184.5 | 147.8 | 186.5 |
2030 | 150.3 | 95.4 | 174.7 | 135.5 | 155.6 | 117.6 | 167.0 |
2050 | 112.8 | 77.6 | 133.0 | 121.9 | 117.0 | 94.9 | 149.1 |
Japan | 2015 | 174.7 | 121.9 | 190.0 | 138.2 | 178.9 | 109.3 | 123.9 |
2030 | 127.9 | 96.2 | 136.5 | 109.8 | 132.8 | 86.0 | 98.2 |
2050 | 96.1 | 65.0 | 110.5 | 76.4 | 90.3 | 62.5 | 73.4 |
Eastern Europe (Russia) | 2015 | 176.1 | 121.0 | 166.4 | 130.2 | 160.3 | 113.0 | 121.7 |
2030 | 149.3 | 99.1 | 142.4 | 109.1 | 136.7 | 99.3 | 109.3 |
2050 | 117.7 | 80.4 | 123.8 | 90.6 | 109.8 | 84.3 | 95.1 |
Africa (South Africa) | 2015 | 200.9 | 133.1 | 196.8 | 141.4 | 196.9 | 144.5 | 138.6 |
2030 | 164.3 | 114.0 | 152.9 | 122.4 | 196.9 | 116.5 | 111.5 |
2050 | 130.8 | 91.7 | 132.1 | 100.6 | 197.0 | 99.0 | 94.4 |
North America (USA) | 2015 | 268.1 | 144.0 | | | 172.0 | 102.7 | |
2030 | 165.9 | 99.5 | | | 128.5 | 79.6 | |
2050 | 94.3 | 52.9 | | | 40.7 | 52.4 | |
2.1.3 Pollutant emission factors
Nine selected countries are analyzed in detail to obtain passenger car drivetrain-specific emission factors for two pollutant species, PM2.5 and NOx. It is assumed that each of the countries represent a respective world region. For each country, the emission factors per drivetrain technology are then weighted according to the vehicle stock shares of each drivetrain technology (cf. section 3.1) which results in stock-fleet-average emission factors. Each of the representative countries’ fleet-average emission factors are allocated to the remaining countries of the associated world region according to the country clusters described in Appendix 3. Different approaches and data sources are considered for estimating the drivetrain-specific emission factors of each analyzed country. Table 3 summarizes the studied countries and the methods and references for the derivation of the pollutant emission factors.
Generally, the pollutant emission factors of gasoline and diesel hybrid electric vehicles (G-HEV and D-HEV) are set equal to their conventional gasoline and diesel fueled vehicles for each country. Plug-in hybrid electric vehicles’ (PHEV) emission factors are estimated according to the Germany-based emission factor ratio between conventional gasoline/diesel vehicles and corresponding gasoline/diesel PHEVs. Therefore, PHEV emission factors are derived dependent on the emission factor development of the conventional vehicles in each country. Compressed natural gas (CNG) fueled vehicles’ pollutant emission factors are based on the data sources for Germany, except India for which country-specific data is considered.
Table 3
Overview of the pollutant emission factor estimation methods for the studied countries.
Region/ Country | Pollutant EF estimation method/model | Source |
Australia | EF weighting according to Euro class fleet shares; Estimation of the composition of the vehicle fleet by Euro classes based on the time lag of the introduction of the Euro-classes-corresponding Australian standards compared to Europe. | (Infras 2019) |
South America (Brazil) | Based on the São Paulo official vehicular emissions inventory (CETESB) and the stock distribution of vehicles by age of use (VEIN model) | (Environmental Agency of São Paulo State 2019) (Ibarra-Espinosa et al. 2018) |
China | Based on literature and Euro-class based data | (Wu et al. 2017); (Infras 2019) |
OECD Europe (Germany) | Based on the HBEFA 4.1 database | (Infras 2019) |
India | Based on literature | (Goel and Guttikunda 2015) |
Japan | Based on literature and Euro-class based data | (Kurokawa and Ohara 2020); (Infras 2019); |
Eastern Europe (Russia) | EF weighting according to Euro class fleet shares; Estimation of the composition of the vehicle fleet by Euro classes based on literature and the time lag of the introduction of the Euro-classes in Russia compared to Europe. | (Infras 2019; Ginzburg et al. 2019) |
Africa (South Africa) | EF weighting according to Euro class fleet shares; Estimation of the composition of the vehicle fleet by Euro classes based on literature and the time lag of the introduction of the Euro-classes in South Africa compared to Europe. | (Infras 2019); (Thambiran and Diab 2011); |
North America (USA) | Based on the EMFAC2017 database | (CARB 2017) |
Germany
Emission factors for German passenger cars are obtained from the Emission factors for Road Transport Handbook (HBEFA). The emission factor values are differentiated according to the emission species (NOx, PM2.5), the calendar years (2015–2050), the drivetrain/fuel types (gasoline, diesel, CNG, G-HEV, D-HEV, G-PHEV, D-PHEV), the Euro classes (Pre-Euro till Euro 6) and the traffic situation/road type (urban, rural, motorway). For the weighting of the emission factors, Euro class stock shares are taken from HBEFA, drivetrain/fuel stock shares come from our own modelling (cf. section 3.1) and traffic situation/road type shares are obtained from the project Transport and the Environment (VEU) (Matthias et al. 2020).
Australia, Russia and South Africa
For the countries Australia, Russia and South Africa, no publicly available country-specific emission factor data sources are available. As in these countries the vehicle pollutant emissions are regulated based on the European standards, the base approach of obtaining stock-fleet average emission factors is the estimation of the composition of the gasoline and diesel vehicle stocks by Euro classes. This is based on information from the literature and / or the time lag of the introduction of the Euro standards in the respective country, considering the Euro-class based passenger car stock composition and emission factors from Germany (Infras 2019) as baseline. Figure 2 presents the resulting gasoline and diesel passenger car stock compositions according to Euro classes from 2015–2050 in Australia, Russia and South Africa.
In Australia, the Euro 5 and Euro 6 stages have been introduced via the Australian Design Rules (ADR) ADR79/03, ADR79/04, and ADR79/05. Full Euro 5 requirements were applied since November 2016, while the introduction of Euro 5 in the EU was in January 2010. Euro 6 came into effect in July 2018 in Australia, which corresponds to a delay of about 3.5 years compared to the original introduction date. Previous Euro stages were also introduced with a delay of approx. 5 years in Australia compared to the EU (DieselNet; Ntziachristos and Samaras 2019). Based on these findings, Australian’s petrol and diesel passenger car fleet compositions are estimated based on Germany’s fleet compositions considering a delay of 5–10 years.
In Russia, the most recent emission standard adopted is Euro 5 which has been implemented between 2014 and 2016. Based on information from literature on the Russian passenger car stock composition in 2015 (Ginzburg et al. 2019) and assuming a delay of 5–10 years between Russia’s and Germanies fleet compositions, the development showed in Fig. 2 is obtained.
In 2008, South Africa’s petrol and diesel passenger car fleets mainly existed of Pre-Euro (or Euro 0 as displayed in Fig. 2) and Euro 1 vehicles (Thambiran and Diab 2011). Euro 2 is the most recent emission standard that have been introduced between 2006 and 2008 (BorgWarner). Considering this information and additionally assuming a delay of approx. 20 years between the development of the passenger car stock of South Africa and Germany, South Africa’s stock composition between 2015 and 2050 is estimated.
China
Mean PM2.5 and NOx emission factors for gasoline passenger cars in China, which constitute approx. 90% of the stock fleet in 2015 (cf. Figure 5), are obtained from literature (Wu et al. 2017). The values are given for calendar years between 2015 and 2030. Values between 2030 and 2050 are estimated based on the corresponding pollutant emission factor development trends in Germany, assuming similar technological trends between the two countries. Emission factors for diesel and CNG vehicles are taken from Germanies’ HBEFA based values. As both drivetrains have neglectable stock fleet shares during the whole timeline, no significant bias is to be expected from this assumption.
Japan
Average PM2.5 and NOx emission factor values for Japanese gasoline and diesel passenger cars in 2015 are taken from Kurokawa and Ohara (2020). The yearly development of the average pollutant emission factors for the Japanese gasoline and diesel passenger car stock fleets is assumed to be similar to the corresponding development in Germany. Therefore, emission factor values for the remaining years are derived by applying Germanies’ gasoline and diesel vehicle-fleet-based pollutant emission factor development trends. Average CNG passenger car emission factors for Germany are applied.
India
Mean PM2.5 and NOx emission factors for gasoline, diesel and CNG passenger cars in the greater Delhi region based on Goel and Guttikunda (2015) are considered as representative data for India. Emission factor values are given for the years 2012 and 2030. By interpolation, values for the years in between are obtained. Then, the emission factor development trend is extrapolated till 2050.
Brazil
Country-specific PM2.5 and NOx emission factors for passenger cars fueled with gasoline, ethanol or various blending ratios of both fuels, with flex-fuel engines, can be found in the VEIN book as described from Ibarra-Espinosa et al. (2018). The Brazilian emission factors origin from the São Paulo official vehicular emissions inventory from the Environmental Agency of São Paulo State (2019) which basically publish the FTP-75 certification test results as an averaged database by type of vehicle and year. For this study, emission factors for vehicles registered in 1982 or before until registration year 2017 are considered. As the emission factors are available per registration year, the age distribution of the vehicle stock fleet is derived based on data of the estimate of the vehicle stock in 2017, which is also obtained from the Environmental Agency of São Paulo State (2019). Assuming that the oldest vehicles in use are 30 years old, fleet-average emission factors per calendar year and fuel/drivetrain type can therefore be calculated. As a result, average emission factors until calendar year 2017 for
-
passenger cars using gasoline blended with 25% of ethanol,
-
passenger cars with engines that use pure ethanol,
-
passenger car with flex-fuel engines using gasoline blended with 25% of ethanol and
-
passenger cars with flex-fuel engines that use pure ethanol
are obtained. In order to yield one average emission factor for passenger cars with gasoline engines, first the flex-fuel emission factors are weighted by assuming a 50–50 share, as no further information on ethanol fuel shares of flex-fuel vehicles are available. Next, ethanol and flex-fuel vehicles are weighted according to their estimated stock fleet shares. Emission factors for two groups of gasoline engine vehicles are obtained, one with ethanol shares of 25% and less and one with higher ethanol shares. According to S&P Global Mobility statistical data on new vehicle registrations in Brazil, between 2016 and 2018, 95% of the new vehicles are flex-fuel or ethanol vehicles, while 5% are (mainly) gasoline fueled passenger cars (S&P Global Mobility 2018). As a simplified approach, these percentages are assumed as weighting factors in order to result in one average gasoline passenger car emission factor. The gasoline vehicle emission factors for the years between 2017 and 2050 are calculated by extrapolating the corresponding emission factor trends of the 2010–2017 timeframe. The mean emission factors for the diesel and CNG based drivetrains are used basing on the data for the German vehicle stock fleet. As gasoline-based vehicles are predominant in Brazilians’ vehicle stock fleet throughout the considered timeframe, no significant bias is expected form this simplified assumption.
USA
Emission factors for gasoline and diesel passenger cars are received from the EMFAC2017 web database (CARB 2017). EMFAC is a model that estimates the official emissions inventories of on-road vehicles in California. Besides total emissions, emission factors (or emission rates, as termed in the database) can be selected as datatype. As region, “statewide” is chosen as the most comprehensive option. 2015, 2020, 2030, 2040 and 2050 are chosen as the base calendar years. Annual values are needed, all fuel types and aggregated values in terms of vehicle model years and speeds. After all selections are completed, the data can be computed and exported via .csv format. EMFAC gives out emission factors for different activities or operating conditions. In this study, running exhaust emissions are considered, expressed in g/mile. The emission factors are then calculated in per km values. For CNG vehicles, which have < 1% stock fleet share (Fig. 5), emission factors from Germany are applied.
Appendix 4 contains the considered PM2.5 and NOx emission factors per studied country and per drivetrain technology for the three base years 2015, 2030 and 2050.