sustainability
Article
Environmental Impact of Subsidy Concepts for
Stimulating Car Sales in Germany
Malte Scharf , Ludger Heide * , Alexander Grahle , Anne Magdalene Syré
and Dietmar Göhlich
Department of Methods for Product Development and Mechatronics, Technical University of Berlin,
*Correspondence: ludger[email protected]; Tel.: +49-(0)30-314-73-858
Received: 6 November 2020; Accepted: 28 November 2020; Published: 1 December 2020
Abstract:
In 2020, vehicle sales decreased dramatically due to the COVID-19 pandemic. Therefore,
several voices have demanded a vehicle subsidy similar to the “environmental subsidy” in Germany
in 2009. The ecological efficiency of vehicle subsidies is controversially discussed. This paper
establishes a prognosis of the long-term environmental impacts of various car subsidy concepts.
The CO
2
emissions of the German car fleet impacted by the purchase subsidies are determined.
A balance model of the CO2emissions of the whole car life cycle is developed. The implementation
of different subsidy scenarios directly affects the forecasted composition of the vehicle population
and, therefore, the resulting life-cycle assessment. All scenarios compensate the additional emissions
required by the production pull-in within the considered period and, hence, reduce the accumulated
CO
2
emissions until 2030. In the time period 2019–2030 and for a total number of 0.72 million
subsidized vehicles—compensating the decrease due to the COVID-19 pandemic—savings of
between 1.31 and 7.56 million t CO
2
eq. are generated compared to the scenario without a subsidy.
The exclusive funding of battery electric vehicles (BEVs) is most effective, with an ecological
break-even in 2025.
Keywords:
subsidy; automotive industry; prognosis; COVID-19; environmental impact;
life-cycle analysis
1. Introduction
As a result of the containment measures against the COVID-19 pandemic, international vehicle
sales collapsed dramatically in the first half of 2020. The German Association of the Automotive
Industry (Verband der Automobilindustrie, VDA) predicts a decline of
−
23% of new passenger car
registrations in Germany compared to the prior year [
1
]. With more than 800,000 employees and an
annual turnover of
435 billion
EUR in 2019, the German automotive industry is essential for prosperity
and employment in Germany [
2
]. According to the VDA president Hildegard Müller, the massively
reduced production will lead to a decrease in employment [1].
From an economic point of view, the COVID-19 pandemic shows some similarities to the 2008
financial crisis: At that time, the passenger car registrations in Germany decreased to the lowest level
since the German reunification. For the following year, the forecast without any car sales stimulation
predicted 2.8 million new registrations, almost 0.3 million less than the already historically low number
of 3.09 million new registrations in 2008.
In reaction, the German government decided to introduce a subsidy program in 2009, in which
a purchase bonus for new cars could be earned if the old ones were scrapped. One target of the
“environmental bonus” was to replace old cars with high specific emissions with new and more
efficient ones. The government’s goal was to reduce pollution and stimulate car sales at the same time.
Sustainability 2020,12, 10037; doi:10.3390/su122310037 www.mdpi.com/journal/sustainability
Sustainability 2020,12, 10037 2 of 27
Thus, the number of new registrations in 2009 rose by 23.3% to 3.81 million vehicles and provided,
according to Höpfner et al. [
3
], reduced pollution due to the rejuvenation of the German passenger
car fleet.
Due to the renewed economic challenges as a result of the COVID-19 pandemic, a considerable
number of voices from the automotive industry [
4
], automotive lobby [
5
], and parts of German
politics [
6
] demand a subsidy concept similar to the “environmental bonus” introduced in 2009.
The scope of this potential subsidy has not yet been worked out. Unlike in 2009, electromobility has
found its way into the automotive market, and therefore, specific funding of battery electric vehicles
(BEVs) and plug-in hybrid electric vehicles (PHEVs) is conceivable to further reduce German car fleet
emissions. Contrary to the concept proposed in this paper, existing subsidy concepts for electric and
hybrid cars [
7
] do not require a replacement of conventional cars. An additional promising concept
includes rewarding purchases of smaller vehicles.
The goal of this paper is a quantified evaluation of various subsidy concepts with regards to the
ecological aspects, focusing on the CO
2
emissions. To provide a holistic view on the environmental
impact of the various subsidy concepts, the cradle-to-grave life cycle, including production, operation,
and end-of-life (EoL) emissions, is considered.
2. Literature Review
As presented in Section 1, the financial crisis of 2008 and the introduced car subsidy show some
similarities to the current situation due to the COVID-19 pandemic.
The “environmental bonus” introduced in Germany has been analyzed extensively. Shortly after
the “environmental bonus” was introduced, Höpfner et al. [
3
] determined a positive ecological
effect of the subsidy. In addition, they demonstrated that the CO
2
emissions of the pulled-forward
manufacturing were compensated after 6000 km driving distance due to the reduced use-phase
emissions of the new cars.
Klößner et al. [
8
] examined the impact of European car scrappage programs on new vehicle
registrations and respective CO2emissions. Using a multivariate synthetic control method with time
series of economic predictors, they found that the German subsidy had a positive effect on stabilizing
the car market. However, the economic benefit caused 2.4 million tons of additional CO
2
emissions
according to their work.
Various economic examinations of the “environmental bonus” have been made. The micro- and
macroeconomic effects of the “environmental bonus” were investigated by
Läufer et al. [9].
They concluded
that the “environmental bonus” was not very effective, but created macroeconomic stability.
Müller et al. [
10
] analyzed the impact of the subsidy on the overall car sales by using a dataset
provided by the Organisation for Economic Co-operation and Development (OECD) for 23 countries
and found a positive effect of car scrappage programs on overall car sales as long as the subsidy was
in place.
The pull-forward effect of the German car scrappage scheme was examined by Böckers et al. [
11
],
who created a monthly dataset of new car registrations owned by private consumers. According to
them, the small and upper small car segments benefited specifically from the scrappage program,
as they made up 84% of the newly registered cars during the program.
In response to the financial crisis, other countries besides Germany introduced car subsidy
programs. The “Summary of the Consumer Assistance to Recycle and Save Act of 2009” (CARS)
was launched in 2009 by the US government [
12
] and has been broadly reviewed. Lenski et al. [
13
]
analyzed the net effect of CARS on greenhouse gas emissions from a full vehicle-life-cycle perspective.
They found that CARS had a one-time effect of preventing 4.4 million metric tons of CO
2
eq. emissions,
about 0.4% of US annual light-duty vehicle emissions.
By comparing the predicted fuel economy without the existence of the program and the actual
data, Sivak et al. [
14
] determined an improved average fuel economy of the US passenger car fleet in
July and August 2009.
Sustainability 2020,12, 10037 3 of 27
Li et al. [
15
] investigated the effects of the CARS program on new vehicle sales and the
environment. By using Canada as the control group in a difference-in-differences framework,
they determined that CARS increased new vehicle sales only by about 0.37 million during July
and August of 2009, implying that approximately 45% of the spending went to consumers who
would have purchased a new vehicle anyway. They calculated a reduction of the CO
2
emissions by
9–28.2 million tons.
In summary, the literature shows disagreement about the environmental impact of the car subsidy
in 2009, and does not answer whether a new subsidy would have a positive impact on the environment.
To the best of our knowledge, there is no study that predicts the environmental impact of a car
subsidy that addresses the decrease of vehicles sales due to the COVID-19 pandemic. We perform a
life-cycle analysis of the German passenger fleet with a level of detail that no previous study has shown.
3. Methodology
The model developed in this paper calculates the annual CO
2
emissions of the German passenger
car fleet iteratively starting in 2019. New registrations and decommissions of the present year lead to
the fleet data of the subsequent year. For the baseline of the CO
2
emissions, the impact of the COVID-19
pandemic on the car sales profile is considered. Three different subsidy concepts are applied to the
baseline scenario, and variations of the CO2emissions are determined.
3.1. Scope
To analyze the environmental impacts of various subsidy concepts, the full life cycles of passenger
cars are taken into account. Therefore, temporal and geographical system boundaries must be defined.
To predict the long-term impacts, the time period covers the years from 2019 to 2030. Due to the rapid
technological progress, the reliability of the predicted data after 2030 decreases significantly.
As effects that extend beyond the national borders are subject to large uncertainties, the system
boundary is drawn around Germany. After deregistration from the German transport system, only the
EoL (decommissioning) emissions and no further driving performances will be accounted for in the
life-cycle analysis, as shown in Figure 1.
German passenger car transport system
-Time period: 2019–2030
new registrations deregistrations
Production
emissions
Driving
emissions
EoL
emissions
Figure 1. System boundaries.
The car population is categorized according to two main attributes: segment and drive train
technology. The segment categorization is based, amongst other parameters, on the weight and size of
the vehicle, according to the Federal Motor Transport Authority (Kraftfahrtbundesamt, KBA) clustering
method [
16
]. In this work, we use an English translation of the segment names as used by the KBA
(Table 1).
Sustainability 2020,12, 10037 4 of 27
Table 1. Segment translations.
Segment Name According to the KBA [16] English Segment Name
Minis mini class
Kleinwagen small class
Kompaktklasse compact class
Mittelklasse middle class
Obere Mittelklasse upper middle class
Oberklasse upper class
Sport Utility Vehicles (SUVs) sport utility vehicles (SUVs)
Mini-Vans mini vans
Großraum-Vans large vans
Sportwagen sports cars
Geländewagen off-roaders
Wohnmobile caravans
In this analysis, there is no consideration of the segments of sports cars and caravans. This is
because of their exclusive use for recreational or leisure purposes, which makes the expenditure
of public funds for these vehicles unjustifiable. Furthermore, commercial vehicles like trucks are
excluded, since the subsidy presented here is focused solely on private customers. Finally, off-roaders
are excluded, as they are used either commercially (e.g., forestry) or for leisure purposes. This limits
the scope to 84% of the whole German passenger car population.
Furthermore, we focus only on the following drive train technologies: gasoline, diesel, electric,
and plug-in hybrid. In this context, a plug-in hybrid vehicle contains a gasoline engine and an electric
drive train.
3.2. Current Vehicle Distribution
At first, a model was defined that describes the German passenger car population for the next ten
years. Therefore, 2019 is considered as a baseline with real data. From 2020–2030, we take recourse to
prognosis data, as shown in Section 3.3.
To obtain a sufficient dataset for the 2019 baseline, the vehicle data from the 2017 “Mobility in
Germany” (Mobilität in Deutschland, MID) study by the German Federal Ministry of Transport and
Digital Infrastructure [
17
] were categorized into segments and drive train technologies according to
Section 3.1. Given the negligible change of 0.2 years in the average vehicle age between 2017 and
2019 [
18
], we assumed that the overall age distribution of the German vehicle stock did not change
significantly. Therefore, a linear transformation of the 2017 data was made to obtain the age distribution
of the segments in 2019. Figure 2presents the resulting age distribution of the German passenger
car fleet.
The same data are also available for the vehicle drive train technology and are linked with the
year of manufacture and the segment category.
Combined with the number of the total vehicle stock [19], the baseline for 2019 was built.
Sustainability 2020,12, 10037 5 of 27
Year of
manufacture
before 2003
2003–2007
2008–2009
2010–2011
2012–2013
2014–2015
2016–2017
2018–2019
Segment
mini class
small class
compact class
middle class
upper middle class
upper class
SUVs
mini vans
large vans
1%
2%
3%
4%
Share of German car fleet
Figure 2.
Age distribution of the German vehicle fleet in 2019 (vehicles in scope: 82% of overall
vehicle stock).
3.3. German Car Population Development until 2030
In order to predict the car population until 2030, estimations for new registrations and
deregistrations must be made.
3.3.1. New Passenger Car Registrations
To determine new passenger car registrations, the total number and their distribution in the
categories defined in Section 3.1 are needed. The annual number of new registrations and their
classification regarding the drive train technology were obtained from the “proKlima” scenario of
Agora 2018 [20] (Figure 3).
Additionally, an estimation concerning the segments was created based on the trend of the
new registration numbers from previous years. For the large van, mini van, upper class, and upper
middle class segments, we assumed that the share of new registrations remains constant. In addition,
we assumed that the new registration share of segments not considered in this analysis will stay
constant, which results in a non-varying share of the overall scope of 82%. A noticeable growth of the
sport utility vehicle (SUV) segment has been observed over the last years [
21
]. The vehicle segment
development is visualized in Figure 4.
This trend is expected to continue and is anticipated to reach an annual new registration share
of 40% in 2030. Since this value is based on an assumption, a sensitivity analysis is performed in
Section 4.4.2.
The growth in the SUV segment was subtracted proportionally from the other segments depending
on their share in the reference year 2019. The shares of the large van and mini class segments were not
modified, as customers are unlikely to change to an SUV due to the completely different usage profiles.
Sustainability 2020,12, 10037 6 of 27
2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
0%
20%
40%
60%
80%
100%
Share of newly registered passenger cars
2%
29%
68%
2%
29%
66%
3%
29%
64%
4%
29%
62%
4%
29%
61%
5%
29%
59%
6%
29%
57%
8%
27%
54%
11%
25%
51%
13%
23%
48%
15%
21%
45%
18%
19%
42%
3% 4% 5% 6% 7% 8% 11% 14% 16% 19% 22%
gasoline diesel BEV PHEV
Figure 3. Linear interpolation of the “proKlima” drive drain technology scenario.
2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
0%
10%
20%
30%
40%
50%
60%
70%
80%
Share of newly registered passenger cars
6%
13%
20%
10%
4%
1%
21%
2%
3%
6%
13%
19%
10%
3%
1%
24%
2%
3%
6%
12%
18%
9%
3%
1%
26%
2%
3%
6%
12%
18%
9%
3%
1%
28%
2%
3%
6%
11%
17%
8%
3%
1%
30%
2%
3%
6%
11%
16%
8%
3%
1%
32%
2%
3%
6%
10%
15%
8%
3%
1%
34%
2%
3%
6%
10%
15%
7%
3%
1%
36%
2%
3%
6%
9%
14%
7%
3%
1%
37%
2%
3%
6%
9%
14%
7%
2%
1%
38%
2%
3%
6%
9%
13%
7%
2%
1%
39%
2%
3%
6%
8%
13%
6%
2%
0%
40%
1%
3%
large vans
mini vans
SUVs
upper class
upper middle class
middle class
compact class
small class
mini class
Figure 4. Segment scenario.
Sustainability 2020,12, 10037 7 of 27
3.3.2. Vehicle Disposal
In this analysis, we assumed that every vehicle that is deregistered from the German transport
system, as defined in Section 3.1, will immediately be disposed of, meaning scrapped or recycled.
As usage profiles of exported vehicles are unknown, the vehicles are disposed of simultaneously with
their deregistration in this model.
For the last four years, a consistent age-dependent deregistration trend can be seen in Figure 5.
Therefore, the future deregistration rates are assumed to be identical to those in 2019. The peak after
three years can be explained by the end of many leasing contracts, which can contribute to exportation
of vehicles. Additionally, the first legally required technical inspection (“Hauptuntersuchung”, HU)
of the vehicles and the associated phasing out of early defective vehicles take place in this period.
This might add to the observed peak. The peaks that occur every two years, especially for older
vehicles, are also due to the interval of the mandatory technical inspections in Germany. As technical
defects are detected and repairs may become necessary, it is often cheaper to scrap or export the
vehicles than to continue their operation.
As explained in Section 3.4, vehicle production is modeled separately from deregistrations,
and only for new vehicles. The negligible number of new registrations (negative deregistration) for
very old vehicles (probably as vintage vehicles) is ignored.
0 5 10 15 20 25 30 35
Vehicle age in years
0%
5%
10%
15%
20%
Deregistration rate
2016
2017
2018
2019
Figure 5. Age-dependent deregistration data for 2016–2019 (based on [22]).
3.4. Modeling Car Population Development
The development of the car population for different scenarios is calculated using an iterative
deregistration and production process. In the first step, the vehicle population (starting with the 2019
population from [
22
]) for each year of manufacture is reduced according to the deregistration rate for
the respective age. Subsequently, the age groups are shifted back one year, and the vehicle production
of the considered year is set to maintain a constant total vehicle population. The vehicle deregistration
Sustainability 2020,12, 10037 8 of 27
rate is scaled from the real data shown in Figure 5in order to match the prognosis of newly registered
cars between 2020 and 2030 and of the “proKlima” [20] scenario.
In the next step, the modified scenarios (shown in Figure 6) are derived from the baseline by
adapting the deregistrations only. First, the deregistration rate is decreased to reflect the reduced
registrations due to owners’ financial uncertainty during the COVID-19 pandemic. The total vehicle
registrations in 2020 are decreased by 23% compared to 2019 [
1
]. To simulate a subsidy in 2021,
the deregistration of vehicles older than 10 years is increased to match the total number of subsidized
vehicles. For the baseline scenario, this number is set to the predicted new registration decrease due
to the COVID-19 pandemic (0.72 million). In Section 4.4, we discuss the impact of the number of
subsidized cars; therefore, additional analyses of twice and four times the baseline amount (1.43 and
2.87 million) were made.
This approach considers the reduced vehicle production in subsequent years after the subsidy is
introduced (pull-out effect) and the increased total vehicle production due to early deregisterations
compared to a scenario without a subsidy.
0 5 10 15 20 25 30 35
Vehicle age (years)
0.0
0.1
0.2
0.3
0.4
Deregistration rate [%]
no Covid-19, no subsidy
Covid-19, no subsidy
Covid-19, subsidy (0.72 M vehicles)
Covid-19, subsidy (1.43 M vehicles)
Covid-19, subsidy (2.87 M vehicles)
Figure 6. Deregistration rate distributions of different scenarios.
Figure 7shows the annual and the accumulated vehicle registrations for different scenarios.
The economic uncertainty due to the COVID-19 pandemic reduces the total number of vehicle
registrations by forcing owners to keep their vehicles longer. On the contrary, the subsidy leads
to earlier replacements and, therefore, increases the new registrations. As already explained for
Figure 5, there is a first peak of vehicle decommissioning after three years.
This can also be seen in the new registrations, as the additional vehicles produced due to the
subsidy also lead to an increased number of vehicles being taken out of service three years after the
subsidy. In turn, this leads to an increase in new purchases that can be seen four years after the subsidy.
Sustainability 2020,12, 10037 9 of 27
This time delay is due to the fact that we model the transition from one year to the next, and thus,
the ages of the vehicles in the previous year are taken into account for the scrapping and new purchases
in the year under consideration.
2020 2022 2024 2026 2028 2030
Year
3
4
5
6
Annual registrations (Millions)
31.4 31.6 31.8 32.0 32.2 32.4 32.6 32.8
Vehicles (Millions)
Accumulated
registrations
no Covid-19, no subsidy
with Covid-19, no subsidy
Covid-19, subsidy (0.72 M vehicles)
Covid-19, subsidy (1.43 M vehicles)
Covid-19, subsidy (2.87 M vehicles)
Figure 7. Annual and accumulated vehicle registrations.
3.5. Life-Cycle Emissions
To perform a cradle-to-grave life-cycle analysis, the production, use, and EoL phases are
considered. The focus is primarily on the calculation of greenhouse gas emissions (CO
2
equivalent
emissions, 20-year global warming potential (GWP 20)). In this analysis, the direct CO
2
emissions of
the combustion engines in the use phase are set identically to their CO2equivalent emissions.
The amounts of production and EoL emissions are assumed to be constant for the upcoming
years. They are only dependent on the segment and power train type of the vehicle and do not change
with the production and disposal date.
In order to analyze the life-cycle emissions of the drive train technologies per segment,
specific input data (weight, direct CO
2
emissions, fuel, and electric energy consumption) are needed.
Complete data for gasoline and diesel vehicles are available. For PHEVs and BEVs, the required input
is derived from defined reference vehicles. Currently, in some segments, there are no vehicles available.
In these cases, input data are interpolated from adjacent segments.
3.5.1. Production
All production analyses were performed with the Ecoinvent 3.5 database [
23
]. The cutoff allocation
was used and all location settings were set to global.
Sustainability 2020,12, 10037 10 of 27
Petrol and Diesel
To perform a life-cycle analysis for gasoline and diesel vehicles, the curb weight was used as the
input value (Table 2).
Table 2. Weight distribution of vehicles with combustion engines [24].
Segment Curb Weight [kg]
mini class 1038
small class 1191
compact class 1389
middle class 1617
upper middle class 1831
upper class 2035
SUVs 1506
mini class—vans 1514
large vans 1754
It was assumed that there is no curb weight difference between gasoline and diesel vehicles.
The emissions were calculated with the processes passenger car production, gasoline and passenger
car production, diesel in Ecoinvent. To consider production only, the Ecoinvent data (flow) manual
dismantling of a used passenger car with internal combustion engine were excluded.
Battery Electric Vehicles (BEVs)
In order to perform the production analysis for the BEVs, the curb weight is divided into the
battery weight and the remaining vehicle weight. The battery weight is calculated based on the battery
capacity. Therefore, a constant energy density for all BEVs is defined.
A representative energy density at a battery packaging level is defined by averaging the values
of the VW E-golf (113 Wh/kg) [
25
] and the 2017 Tesla Model S P100D (160 Wh/kg) [
26
]. This results
in an energy density of 136 Wh/kg, which is used to calculate the battery weight of all BEVs based
on the battery capacity. For each segment, a reference BEV is defined based on the number of newly
registered vehicles in 2019 [
27
] (Table 3). In the SUV segment, the Hyundai Kona Electro and the Audi
E-Tron both had nearly the same registration numbers in 2019. Since the share of compact SUVs is
expected to continue growing [27], the Hyundai Kona Electro is chosen as the reference model.
Table 3. Weight distribution of battery electric vehicles (BEVs).
Segment
Curb
Weight
(kg)
Battery
Capacity
(kWh)
Battery
Weight
(kg)
Remaining
Curb
Weight (kg)
Reference
mini class 1095 17.6 129 966 smart EQ fortwo. [28]
small class 1345 42 308 1037 BMW i3 [29]
compact class 1545 40 293 1252 Nissan Leaf [30]
middle class 1611 55 404 1207 Tesla Model 3 [31]
upper middle class - - - - -
upper class 2290 100 734 1556 Tesla Model S [31]
SUVs 1593 39 288 1305 Hyundai Kona Elektro [32]
mini vans 1610 39 288 1322 Kia e-soul [33]
large vans - - - - -
The emissions emitted from the BEV battery pack production were determined with the process
battery production, Li-ion, rechargeable, prismatic. This process also considers the transportation of the
single cells from Peking to Amsterdam by ship and a 1000 km transportation route within Europe
by truck.
Sustainability 2020,12, 10037 11 of 27
For the remaining electric vehicles, the process passenger car production, electric, without battery [
34
]
was used with the remaining curb weight from Table 3.
Plug-in Hybrid Electric Vehicles (PHEVs)
In this analysis, the PHEV is a combination of a conventional and an electric vehicle. Therefore,
the PHEV consists of a battery, electric power train components, and the remaining vehicle,
which contains all components of the combustion power train. This analysis is based on manufacturers’
data from reference vehicles (Table 4).
Table 4. Manufacturers’ data for plug-in hybrid electric vehicles (PHEVs).
Segment
Curb
Weight
(kg)
Battery
Capacity
(kWh)
Maximum
Power of
Electric Drive
Train (kW)
Reference
mini class - - - -
small class 1660 7.6 65 Mini Cooper SE Countryman [35]
compact class 1750 8.8 65 BMW 225xe Active Tourer [36]
middle class 1780 9.8 50 Kia Optima Plug-In Hybrid [37]
upper middle class 1910 11.2 62 BMW 530 e [38]
upper class 2170 14.1 100 Porsche Panamera 4 E-Hybrid [39]
SUVs 1971 13.8 70 Mitsubishi Outlander [40]
mini vans 1725 15.6 75 Mercedes B 250 e [41]
large vans - - - -
Similarly to the BEV batteries, the PHEV battery weight is calculated based on the battery capacity
with a constant energy density for all types of plug-in vehicles. We consider all batteries at the
packaging level. Since the PHEV batteries are significantly smaller than the BEV ones, an assumption
of the same energy density is not suitable.
Therefore, the mean value of the energy densities of the Kia Optima (75 Wh/kg) [
37
] and the
Mercedes B 250 e (104 Wh/kg) [42] of 89.5 Wh/kg are assumed for all PHEVs.
In addition, the electric power train components, such as an electric motor, a charger, and cables,
are considered. Due to lacking data on the actual weight of these components, the weight is scaled with
the maximum power of the electric power train. In this analysis, the baseline is made according to the
default value in Ecoinvent of a 100 kW power train with a weight of 70 kg. The resulting parameters
(Table 5) are used in Ecoinvent for the production analysis.
Table 5. Calculated weight distribution of PHEVs.
Segment
Remaining
Curb
Weight
(kg)
Battery
Weight
(kg)
Electric
Power
Train
Weight (kg)
Reference
mini class - - - -
small class 1525 85 50 Mini Cooper SE Countryman [35]
compact class 1601 99 50 BMW 225xe Active Tourer [36]
middle class 1632 110 39 Kia Optima Plug-In Hybrid [37]
upper middle class 1724 125 50 BMW 530 e [38]
upper class 1935 158 77 Porsche Panamera 4 E-Hybrid [39]
SUVs 1763 154 54 Mitsubishi Outlander [40]
mini vans 1393 174 58 Mercedes B 250 e [41]
large vans - - - -
Sustainability 2020,12, 10037 12 of 27
For the battery pack production of the PHEVs, the same method as that used for the BEVs was
applied. The process electric motor production, vehicle was used to determine the emitted emissions to
produce the components of the electric power train.
The remaining part of the vehicle is assumed to be a gasoline engine car. Therefore, the process
passenger car production, petrol was applied to analyze the production.
3.5.2. Use Phase
The emissions emitted during the use phase are composed of the emissions based on fuel and
electric energy consumption, the well-to-tank emissions, and the emissions due to maintenance of the
vehicle and the road infrastructure.
In 2019, the average annual mileage of passenger cars was 13,602 km [
43
]. In this model, it is
assumed that, in the future, every car will have the 2019 average annual mileage.
Combustion Engine Vehicles
The use phase emissions of the gasoline- and diesel-powered vehicles are dependent on the
vehicle’s segment and age. Based on these criteria, the KBA [
44
] provides a database of the average
direct CO
2
emissions and fuel consumption, which are displayed in the supplementary materials in
Table S1. In this work, they are assumed to be identical to the CO2equivalent emissions.
Battery Electric Vehicles (BEVs)
In this analysis, the BEVs cause indirect CO
2
emissions because of their consumed electric energy
during the use phase.
For each segment, a reference BEV is defined based on the number of newly registered vehicles
in 2019 [
27
]. If a model in this segment would have a dominant share of the newly registered cars,
it is considered as the reference BEV. Otherwise, the two models with the highest number of newly
registered vehicles in the segment are selected, and a weighted average of the energy consumption
(weight factor) according to their new registration numbers is built (Table 6).
Table 6. Energy consumption of BEVs.
Segment Reference Model
Energy
Consumption
(kWh/100 km)
Weight
Factor
Weighted
Average Energy
Consumption
(kWh/100 km)
mini class smart EQ fortwo. [28] 14.0 1 14.0
small class BMW i3 [29] 13.1 0.66 14.5
Renault Zoe [45] 17.2 0.34
compact class VW E Golf [46] 12.9 0.64 14.4
Nissan Leaf [30] 17.1 0.36
middle class Tesla Model 3 [31] 16.0 1 16.0
upper middle class - - - -
upper class Tesla Model S [31] 19.0 1 19.0
SUVs Hyundai Kona Electro [32] 15.0 0.51 18.6
Audi E-Tron [47] 22.4 0.49
mini vans Kia e-soul [33] 15.6 1 15.6
large vans - - - -
In 2019, there were not any BEVs newly registered in the upper middle class or large van
segments. Since the market for electric vehicles will grow [
20
], we assume that future electric vehicles
will also be available in these segments. Based on the curb weight of the combustion engine vehicles,
we estimate that upper middle class and large vans have the same energy consumption. The electric
energy consumption of upper middle class is assumed as an average of those of the middle class and
upper class.
Sustainability 2020,12, 10037 13 of 27
Plug-In Hybrid Electric Vehicles (PHEVs)
To determine comparable CO
2
emissions from plug-in hybrids, a combination of the direct exhaust
emissions and the energy consumption of the electric drive train is made (Table 7).
The manufacturers’ data for the reference vehicles either rely completely on the New European
Driving Cycle (NEDC) or were measured with the Worldwide Harmonized Light Vehicles Test
Procedure (WLTP) and were transferred back to the NEDC. The NEDC considers half of the driving
cycle using the combustion engine and the other half using the electric power train. In these data,
the electric energy consumption is considered emission-free. In order to correct this effect, we add the
appropriate indirect emissions for electric power consumption.
Table 7. CO2emissions and energy consumption of PHEVs.
Segment
CO2
Emissions
(Combined)
(g/km)
Fuel
Consumption
(L/100 km)
Energy
Consumption
(kWh/100 km)
Reference Model
mini class - - - -
small class 45 2,0 14.0 Mini Cooper SE Countryman
compact class 42 1.9 13.5 BMW 225xe Active Tourer
middle class 37 1.6 12.2 Kia Optima Plug-In Hybrid
upper middle class 42 1.8 14.8 BMW 530 e
upper class 62 2.7 16.1 Porsche Panamera 4 E-Hybrid
SUVs 40 1.8 14.8 Mitsubishi Outlander
mini vans 32 1.4 14.7 Mercedes B 250 e
large vans - - - -
Indirect Emissions
In this model, the consumed electric energy causes indirect emissions based on the specific carbon
dioxide emissions of the German electricity mix. In 2020, the generation of one kWh electric energy is
predicted to cause 432 g CO2eq. emissions [48].
Pehnt et al. [
48
] calculated 318 g CO
2
eq. emissions for the electricity mix in 2030; the value
here decreases linearly for the years 2020 to 2030. The well-to-tank factors are used to determine
the upstream emissions of consuming fuel. Here, Schallaboeck et al. [
49
] stated that 685 g or 408 g
CO
2
equivalent emissions produce one liter of gasoline or diesel, respectively. The data for the fuel
consumption are displayed in the supplementary materials in Table S2. All PHEVs are assumed to
have a gasoline engine.
Maintenance
During the use phase, the replacement of spare vehicle parts and the maintenance of the road
network are considered. To calculate the vehicle maintenance, the flow passenger car maintenance
was used in Ecoinvent and was adapted according to the vehicle’s weight and power train type [
50
].
We followed the same approach as in the Agora (2019b) [
51
] “basic scenario”, assumed for each vehicle
a lifespan of 150,000 km, and did not consider any battery exchange for BEVs and PHEVs.
In order to determine the maintenance of the road network, the flow road maintenance is used.
This introduces a weight-dependent contribution during the use phase.
3.5.3. End-of-Life
In this model, it is assumed that every deregistered vehicle is eventually disposed of.
The equivalent CO
2
emissions due to this process were determined in Ecoinvent using the flows manual
dismantling of used passenger car with internal combustion engine,treatment of used glider, passenger car,
shredding,treatment of used internal combustion engine, shredding,treatment of used powertrain for electric
passenger car, dismantling, and market for used Li-ion battery according to [34].
Sustainability 2020,12, 10037 14 of 27
3.6. Subsidy Concepts
Three subsidy concepts are introduced in this chapter. They reflect aspects of the current
discussions about the possible subsidy scope. Höpfner et al. [
3
] showed that 84% of the subsidy
from the “environmental bonus” in 2009 was used to purchase small cars (mini class, small class,
and compact class). Therefore, 84% of all new registrations due to the subsidy will happen in the mini
class, small class, and compact class categories. For this distribution, the predicted new car registration
shares for 2021 are used. All segment shares are evenly scaled in order to reach an 84% share in the
small cars category. The impact of this effect is discussed in Section 4.4.1.
The distribution shown in Figure 8is used as a baseline for all subsidy concepts. In Section 4.4.1,
the influence of this distribution is discussed.
Similarly to the “environmental bonus” in 2009, a mandatory requirement to earn the subsidy is a
minimum vehicle age (in this case, 10 years).
0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
Share of subsidized vehicles
mini class
small class
compact class
middle class
upper middle class
upper class
SUVs
mini vans
large vans
Figure 8. Subsidized vehicle distribution of the “broad funding” concept.
3.6.1. “Broad Funding” Concept
In this concept, the subsidy is not restricted to a certain vehicle type. Except for the higher
demand for the mini, small, and compact class segments, the same composition of newly registered
cars as predicted without the subsidy will occur. The subsidy only increases the total number of newly
registered and deregistered cars. For every newly registered car due to the subsidy, a car in the same
segment, manufactured in 2010 or earlier, is disposed of. Vehicle purchases in higher segments are
not subsidized.
Sustainability 2020,12, 10037 15 of 27
3.6.2. “Innovation” Concept
With this concept, only BEVs are subsidized. For the baseline model, the same distribution of
subsidized cars as in Section 3.6 is considered, except only BEVs will be newly registered due to
the subsidy.
3.6.3. “Downsizing” Concept
Lastly, a concept is shown that only allows a subsidy if the new car is at least one segment smaller
(defined by the segment’s CO
2
emissions) than the one traded in. The respective segment shifts in the
“downsizing” concept are displayed in Table 8.
Table 8. Segment shifts in “downsizing” concept.
Segment of Vehicle Traded in Segment of New Vehicle
mini class -
small class mini class
compact class small class
middle class compact class
upper middle class middle class
upper class upper middle class
SUVs middle class
mini vans middle class
large vans mini vans
As per the CO
2
emission-based downsizing definition, the SUVs and mini vans are shifted to the
middle class segment.
4. Results
The goal of this work is to analyze the impact of a subsidy on the CO
2
eq. emissions of the
German passenger car fleet. This is achieved by considering categorized vehicle emissions and fleet
composition data.
4.1. Life-Cycle Analysis
The life cycle of a vehicle consists of production, maintenance, use phase, and EoL processes.
For each element, the respective emissions are calculated per segment and per drive train technology.
4.1.1. Production
Using the process described in Section 3.5.1, an analysis of the production emissions for each
vehicle is made. The results for each vehicle class and drive train technology are shown in Figure 9.
The production of BEVs and PHEVs results in higher CO
2
equivalent emissions than the
production of conventional vehicles. This phenomenon emerges in every segment and can be attributed,
in addition to the higher vehicle weight of BEVs and PHEVs, to the greater effort of producing batteries
and electrical parts. The BEV production effort increases substantially for larger segments as more
battery capacity is implemented. This effect is less pronounced for PHEVs.
4.1.2. Maintenance
The emissions from the vehicle and road maintenance per driven kilometer (Figure 10) scale
mainly with the vehicle weight.
Sustainability 2020,12, 10037 16 of 27
0.0
2.5
5.0
7.5
10.0
12.5
15.0
17.5
20.0
CO2eq. emissions [t]
gasoline
PHEV
diesel
BEV
mini class
small class
compact class
middle class
upper middle class
upper class
SUVs
mini vans
large vans
Figure 9. Production emissions per vehicle, segmented by class and drive train technology.
0
5
10
15
20
25
CO2eq. emissions [g/km]
gasoline
PHEV
diesel
BEV
mini class
small class
compact class
middle class
upper middle class
upper class
SUVs
mini vans
large vans
Figure 10. Maintenance emissions.
Sustainability 2020,12, 10037 17 of 27
4.1.3. Use Phase
The CO
2
equivalent emissions depend on the vehicle specifications. In addition to the direct
exhaust emissions, vehicles with combustion engines cause indirect emissions via the procurement of
the fuel. Figure 11 shows the combined specific CO2eq. emissions per kilometer in 2019.
0
50
100
150
200
250
300
CO2eq. emissions [g/km]
gasoline
PHEV
direct exhaust emissions
electric energy emissions
diesel
BEV
well-to-tank emissions
mini class
small class
compact class
middle class
upper middle class
upper class
SUVs
mini vans
large vans
Figure 11. Composition of CO2eq. emissions per driven kilometer.
Vehicles with a gasoline engine have the highest specific emissions across every segment, followed
by diesel engine vehicles. The BEVs have the lowest specific emissions of the considered drive train
technologies in all segments.
4.1.4. End of Life
Due to the high recycling effort for the batteries, we see increased EoL emissions for the BEVs and
PHEVs compared to the conventionally powered vehicles (Figure 12). The higher battery capacity in
larger segments leads to higher EoL emissions for the BEVs compared to the PHEVs. Again, the vehicle
weight is an important driver of EoL emissions.
Sustainability 2020,12, 10037 18 of 27
0
250
500
750
1000
1250
1500
1750
2000
CO2eq. emissions [kg]
gasoline
PHEV
diesel
BEV
mini class
small class
compact class
middle class
upper middle class
upper class
SUVs
mini vans
large vans
Figure 12. End-of-life emissions per vehicle, segmented by class and drive train technology.
4.2. Subsidy’s Impact on Fleet Emissions for 2019–2030
Using a combination of the results from Section 4.1 and the German car population scenario
from Section 3.3, a prediction of the CO
2
equivalent emissions emitted from the German passenger
car system until 2030 is made. The emissions decrease in 2020 due to reduced vehicle production
(Figure 13). As shown in Figure 7, the vehicle production increases in the subsequent years due to the
subsidy. Therefore, higher emissions are expected before the vehicle fleet benefits from the increased
efficiency that leads to reduced annual emissions.
The cumulative CO
2
eq. emissions of the baseline scenario without a subsidy (Figure 13) are
compared to the cumulative emissions of the various subsidy concepts. Each concept is calculated
with 0.72 million subsidized vehicles. The developed model enables the analysis of any number of
subsidized vehicles.
Figure 14 shows the comparison of the different subsidy concepts relative to the baseline
scenario. As a result, the accumulated differences of the CO
2
equivalent emissions are shown for the
respective scenarios.
The cumulative difference shows significant additional emissions in 2021 for all subsidy concepts.
The effect displays the higher number of new vehicles and the related production emissions for this
year. The emission savings are dependent on the subsidy concept.
The “innovation” subsidy clearly lowers the CO
2
equivalent emissions of the German passenger
car transport system until 2030 the most out of all considered concepts. Even with the highest CO
2
equivalent emissions in 2021, the “innovation” concept reaches the break-even earliest in the year 2025.
The additional CO
2
equivalent emissions of the “innovation” concept in 2021 are 8.6% higher than the
“broad funding” and 20.8% higher than the “downsizing” concepts. This effect can be attributed to the
higher production effort for electric vehicles, as shown in Figure 9.
Sustainability 2020,12, 10037 19 of 27
2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
1.20
1.25
1.30
1.35
1.40
1.45
Annual CO2eq. emissions [t]
×108
2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
0.0
0.5
1.0
1.5
Cumulative CO2eq. emissions [t]
×109
Figure 13. Life-cycle CO2eq. emissions of the baseline scenario without a subsidy.
2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
−0.8
−0.6
−0.4
−0.2
0.0
0.2
0.4
0.6
0.8
Cumulative difference of CO2eq. emissions [t]
×107
“broad funding” “downsizing” “innovation”
Figure 14. Cumulative difference of CO2eq. emissions compared to the scenario without a subsidy.
Sustainability 2020,12, 10037 20 of 27
Despite having the highest CO
2
equivalent emissions in 2021, the accumulated CO
2
equivalent
emission savings until 2030 of the “innovation” concept are 285.7% higher than the “downsizing” and
576.7% higher than the “broad funding” concepts. Compared with the scenario without a subsidy,
the “innovation” concept reaches a cumulative difference of
−
7.56
×
10
6
t CO
2
equivalent emissions
until 2030.
In the “downsizing” concept, compared to the “broad funding” concept, smaller vehicles are
produced. Therefore, in 2021, the CO
2
equivalent emissions of the “downsizing” concept are lower
than the emissions in the “broad funding” concept. In addition, the “downsizing” concept reaches the
break-even point in 2027, one year earlier than the “broad funding” subsidy concept.
4.3. Sensitivities
Due to the complexity of the model, dependencies on key parameters are reviewed and their
respective impacts on the subsidy concepts are determined.
4.4. Number of Subsidized Cars
As shown in Section 3.4, we assume that the number of subsidized cars matches the predicted
registration decrease in 2020 due to the COVID-19 pandemic. To determine the impact of the number
of subsidized cars (
∼
0.7 million), this value is doubled (
∼
1.4 million) and quadrupled (
∼
2.8 million).
As expected, the cumulative difference of CO
2
eq. emissions in 2030 is proportional to the number
of subsidized cars, which can be seen in Figure 15.
717,078 1,434,156 2,868,312
Number of subsidized cars
−3.0
−2.5
−2.0
−1.5
−1.0
−0.5
0.0
Cumulative difference of CO2eq. emissions [t] in 2030
×107
“broad funding” “downsizing” “innovation”
Figure 15. Number of subsidized cars.
Sustainability 2020,12, 10037 21 of 27
4.4.1. Vehicle Distribution
As discussed in Section 3.6, the “environmental bonus” in 2009 was primarily used to purchase
smaller vehicles. This can be attributed to the fairly high share of the subsidy compared to the
purchase price.
It would be conceivable to design the subsidy in a way that the amount is determined by a fixed
percentage of the purchase price. It is assumed that this leads to a vehicle distribution of the subsidized
cars proportional to the predicted new passenger car registrations in 2021.
Figure 16 shows the difference between a scenario proportional to 2021 and a scenario shifted to
smaller cars. The modified distribution of subsidized vehicles has an impact on the results.
As shown in Table 9, all subsidy concepts are less efficient when using the distribution proportional
to 2021. In this context, the “broad funding” concept even increases the CO
2
eq. emissions compared
to the no-subsidy baseline. As a result, subsidizing smaller cars significantly increases the efficiency of
the subsidy.
Table 9.
Difference of cumulative CO
2
eq. emissions compared to the scenario without a subsidy
in 2030.
Subsidy Concept Difference to “Without Subsidy” Baseline
Shifted Distribution to Smaller Cars Proportional to 2021 Distribution
“broad funding” −0.080% 0.037%
“downsizing” −0.161% −0.017%
“innovation” −0.462% −0.401%
0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
Share of subsidized vehicles
mini class
small class
compact class
middle class
upper middle class
upper class
SUVs
mini vans
large vans
proportional to 2021 distribution shifted distribution to smaller cars
Figure 16. Subsidized car distribution for two scenarios under investigation.
Sustainability 2020,12, 10037 22 of 27
4.4.2. New Vehicle Registrations
Drive Train Technologies
To predict the German car population until 2030, we rely on Section 3.3 and on the “ProKlima”
scenario stated in Agora (2018) [
20
] to describe the trend of the drive train technologies. To estimate
the impact of the subsidy for a baseline scenario with a higher share of newly registered PHEVs and
BEVs, an analysis with the “ProKlima Plus” scenario is made.
In Table 10, the values for the drive train technology development of the different scenarios can
be seen. To determine the share of newly registered cars for each year, a linear interpolation between
the displayed values is made.
Table 10. Annual share of newly registered cars.
Drive Train Technology “ProKlima” “ProKlima Plus”
2025 2030 2025 2030
gasoline 57% 42% 27% 28%
diesel 29% 19% 28% 10%
PHEV 8% 22% 20% 29%
BEV 6% 18% 25% 47%
Figure 17 shows the cumulative CO
2
eq. emission difference of the “innovation” concept with
the two different assumptions for the annual newly registered cars until 2030. It is shown that the
subsidy loses efficiency if the baseline share of PHEVs and BEVs rises. The cumulative CO
2
eq.
emission difference will be smaller due to the overall more efficient German passenger car fleet with
the “proKlima Plus” scenario.
2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
−0.8
−0.6
−0.4
−0.2
0.0
0.2
0.4
0.6
0.8
Cumulative difference of CO2eq. emissions [t],
compared with no subsidy scenario
×107
proKlima proKlima plus
Figure 17.
Cumulative CO
2
eq. emission difference between the “no subsidy” baseline and the
“innovation” concept using the “proKlima” and “proKlima Plus” registration predictions.
Segments
In Section 3.3, an assumption of the development of the segment composition of newly registered
cars is introduced. Here, we assume that the SUV segment will reach a 40% share of the annual newly
Sustainability 2020,12, 10037 23 of 27
registered cars in 2030. Since this parameter is uncertain, analysis with either a 30% or 50% proportion
of SUVs of the newly registered cars in 2030 is made. For the “innovation” concept, the 30% SUV share
scenario generates 2.40% less CO
2
eq. emissions savings compared to the 40% SUV share baseline due
to the overall more efficient car fleet. On the contrary, the cumulative CO
2
eq. emissions savings of the
“innovation” concept in the 50% SUV share scenario are 0.84% higher compared to the baseline.
This shows that the results of the study are robust against this parameter because the trend and
the statement of the results are not changed by a variation of the SUV share.
5. Discussion
The absolute CO
2
eq. emissions savings are proportional to the number of subsidized cars.
For the results shown, we set the subsidized cars in the model to about 0.72 million cars. This is more
conservative than the actual replacements of 1.95 million [
3
] during the 2009 “environmental bonus”.
Our model is able to deal with a variable number of cars, as shown in the sensitivity analysis
in Section 4.4. The actual number of subsidized cars would depend on micro- and macroeconomic
factors, as well as technology-specific concerns, with different scenarios needing different funding to
reach the same total number of replaced vehicles. Additionally, an investigation of the basic conditions,
e.g., charging facilities, is needed to match the implementation of the number of subsidized vehicles.
However, since we are not conducting an economic analysis, quantifying these factors reliably is out of
the scope of this work. Our model can be easily updated to calculate the effects of arbitrary numbers of
subsidized vehicles. In case the 2020 program would have the same impact on car sales, the absolute
savings grow accordingly.
In the absence of detailed prognosis data for the technological improvements, we assume no
efficiency gains regarding the CO
2
eq. emissions. The trend of the combustion engines indicates a
saturation for the CO
2
efficiency [
44
]. Assuming that the electric drive train technologies will still gain
efficiency [
52
] and more electricity will be generated from renewable sources, the total savings for the
“innovation” concept would be higher.
Since only the direct CO
2
emissions for the driving during the use phase of combustion engines
are available, the actual CO
2
eq. emissions are slightly higher. As this effect is not relevant to BEVs,
since CO
2
eq. emissions are considered for the electricity production, the “innovation” concept would
generate marginally higher savings when considering all emissions from the combustion engines.
This paper shows that all considered subsidy concepts eventually have a positive effect on
reducing the CO
2
equivalent emissions of the German passenger car fleet. This corresponds with both
Höpfner et al. [
3
] and Lenski et al. [
13
], who stated that the 2009 car subsidy had a positive effect on
the environmental performance of the various car fleets.
Contrary to Klößner et al. [
8
], the long-term effect on the CO
2
emissions is positive, as windfall
gains (subsidized purchases that would happen without the subsidy) are not included and BEVs
and PHEVs are considered. We only expect additional CO
2
emissions to occur immediately after the
subsidy is introduced due to the increased production, which is overcompensated by savings during
the use phase.
To calculate the LCA emissions, we clustered the vehicle fleet into segments to reflect the actual
distribution. Agora (2019) [
53
] used only one reference vehicle for each drive train technology.
For comparable segments, the production emissions of the BEVs show similar results. In this work,
the gasoline and diesel vehicles result in higher production emissions compared to those in Agora
(2019) [53]. This can be attributed to a deviation in parameter settings.
6. Conclusions
In order to calculate the cumulative CO
2
eq. emissions of the German passenger car fleet from
2019–2030, a granular model was developed. Three different subsidy concepts were introduced and
the impact on the CO2eq. emissions was determined.
Sustainability 2020,12, 10037 24 of 27
Subsidizing the German passenger car system due to the COVID-19 pandemic shows long-term
CO
2
emissions savings for nearly all investigated scenarios. Only in the sensitivity analysis, the “broad
funding” concept leads to slightly increased emissions when using a vehicle distribution proportional
to that of 2021. Taking the sensitivity analyses into account, the “innovation” concept shows the most
significant emissions savings in the German passenger car system.
Considering the time period 2019–2030 and a total number of 0.72 million subsidized vehicles,
the “innovation” concept generates about 7.56 million t less CO
2
eq. emissions compared to the
scenario without a subsidy. This equates to 0.46% of the total CO
2
eq. emissions of the addressed
segments in this period. The “downsizing” and “broad funding” concepts create savings of 0.16% and
0.08%, respectively.
In 2021, the increased vehicle production leads to higher CO
2
emissions for all subsidy scenarios
compared to the scenario without a subsidy. The ecological break-even is reached in 2025 for
the “innovation” concept, in 2027 for the “downsizing” concept, and in 2028 for the “broad
funding” concept.
If, from an economic and political point of view, a subsidy program for passenger cars in Germany
is considered to be desirable, we clearly recommend the exclusive funding of BEVs because the
“innovation” concept achieves the highest positive climate impact at the earliest time.
7. Outlook
To further investigate the environmental impact of passenger car subsides in Germany,
additional greenhouse gases in the use phase and air pollutants must be considered. It seems reasonable
to add greenhouse gases with a high GWP. Due to the current discussion on driving bans in German
city centers, the subsidy’s impact on nitrogen oxides would enhance the model’s result.
It is conceivable to examine further subsidy concepts, such as focusing on PHEVs or
particular segments.
In the current model, the technological status of the vehicles is assumed to remain at the level
of 2019. The indirect emissions of only BEVs and PHEVs will decrease until 2030 due to the slight
reduction of the specific carbon dioxide emissions of the German electricity mix. This means that the
vehicle weight and direct exhaust emissions of each newly registered vehicle will remain constant in
the current model.
In our model, we define a reference vehicle to describe the BEVs and PHEVs due to a lack of data
provided by the KBA. To determine the detailed PHEV and BEV model parameters more precisely,
the data of all vehicles available need to be consolidated.
As Höpfner et al. [
3
] notes, the vehicle subsidy in 2009 led to reduced used car sales. This was
not implemented in the current model and might affect the disposal rate and the number of newly
registered cars.
This paper did not investigate the economic impacts of subsidy scenarios. A detailed market
analysis is needed to estimate consumers’ buying behavior. To improve the model, it is necessary to
further examine the subsidy’s impact on the newly registered cars and to address economic effects,
like on-top sales and windfall gains.
Supplementary Materials:
The following are available online at http://www.mdpi.com/2071-1050/12/23/
10037/s1: Table S1: Direct CO2emissions per kilometer, Table S2: Fuel consumption per 100 km.
Author Contributions:
Conceptualization, L.H. and A.G.; methodology, M.S. and L.H.; software, A.M.S., L.H.,
and M.S.; validation, A.M.S., A.G., M.S., L.H., and D.G.; investigation, M.S., A.G., A.M.S., and L.H.; resources,
A.M.S. and D.G.; data curation, M.S.; writing—original draft preparation, M.S.; writing—review and editing,
A.M.S., A.G., L.H., and M.S.; visualization, L.H. and M.S.; supervision, D.G.; funding acquisition, D.G. All authors
have read and agreed to the published version of the manuscript.
Funding:
This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research
Foundation), grant number 398051144, project title: “Analysis of strategies to fully de-carbonize urban transport”.
Acknowledgments:
We acknowledge the support from the German Research Foundation and the Open Access
Publication Fund of TU Berlin.
Sustainability 2020,12, 10037 25 of 27
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design of the
study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the results.
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