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Environ. Res. Commun. 6(2024)125008 https://doi.org/10.1088/2515-7620/ad998b
PAPER
Rebound effects flatten differences in carbon footprints between car-
free households, minimal drivers, and green car owners
Juudit Ottelin
1
, Sarah Olson
2
, Vedant Ballal
1
, Áróra Árnadóttir
2
and Jukka Heinonen
2
1
Norwegian University of Science and Technology, Department of Energy and Process Engineering, Trondheim, Norway
2
University of Iceland, Faculty of Civil and Environmental Engineering, Reykjavik, Iceland
E-mail: heinonen@hi.is
Keywords: carbon footprint, comparative LCA, car-free, electric vehicle, biofuel, rebound effect
Supplementary material for this article is available online
Abstract
While the greenhouse gas emissions of most sectors are declining in the EU, transport emissions are
increasing. Passenger cars compose a large share of the transport sector emissions, and a lot of effort
has been made to reduce them. Despite the significantly improved environmental performance of
passenger cars, there is a prevailing belief that they are the most environmentally harmful mode of
ground transport. In the study at hand, we illustrate how rebound effects of consumption may change
this view. Passenger car is a relatively expensive transport mode. Expenditure on car-ownership
reduces the remaining household budget and the related carbon footprint. Here, we compare the total
consumer carbon footprints per capita between fossil-fuel car owners, green car owners, and car-free
households in the Nordic countries, using survey data including 7 400 respondents. When income and
household type are controlled with regression analysis, respondents without a car for climate reasons
and ‘minimal drivers’, meaning the least driving 10% of fossil-fuel car owners, have the lowest carbon
footprints. Other car-free households have 6% higher footprints, electric- and biofuel car owners
18%–24% higher footprints, and the increasingly driving fossil-fuel car owners 30%–189% higher
carbon footprints than the first two groups. However, the working middle-income green car owners,
minimal drivers, and car-free households have very similar sized carbon footprints. The results show
some trade-off between car ownership and flying despite that the data was collected between 2021 and
2022, when COVID-19 was still partly affecting air travel.
Introduction
While the total greenhouse gas (GHG)emissions of the EU have been declining since 1990, the emissions of road
transport and aviation are still increasing. The total exhaust emissions of the transport sector in the EU were 1.1
Gt, corresponding to 26% of the EU’s total emissions in 2019 (EEA 2023). Passenger car transport accounted for
44%, and civil aviation, including international travel but excluding non-CO
2
impacts of aviation, 13% of
transport sector GHG emissions in the EU.
The CO
2
emission performance standards of new vehicles, spreading of green vehicle technologies, and
energy efficiency improvements are hoped to bring the emissions of passenger cars to a declining trend in the EU
in the near future (EEA 2023). At the same time, there seems to be a prevailing belief that passenger cars are the
most environmentally harmful mode of ground transport (Miner et al 2024). Life cycle emissions per passenger
kilometer are usually highest for private cars and lower for other transport modes, such as public transport and
cycling.
In the study at hand, we illustrate how rebound effects of consumption may change this view. Adding the
aspect of price differences between various transport modes raises the question on how transport choices affect
the rest of household budget and the following carbon footprints. Such indirect impacts on carbon footprints
caused by changes in consumption are called rebound effects (Lenzen and Dey 2002, Druckman et al 2011).
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ACCEPTED FOR PUBLICATION
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PUBLISHED
11 December 2024
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Consumer carbon footprint studies covering the whole household budget have revealed that the money that car-
free households save from not owning and maintaining a car, is often spent on other goods and services causing
emissions, and partially on leisure travel (Heinonen et al 2013, Ottelin et al 2017). Many transport studies
covering both long-distance travel and daily travel have shown that there can be a trade-off between these two
(see review by Czepkiewicz et al 2018), although the phenomenon is not always present (Czepkiewicz et al 2019,
Mattioli et al 2021). City living, which facilitates the use of sustainable daily transport modes, such as public
transport and cycling, is simultaneously connected to cosmopolitan lifestyles and increasing leisure travel
(Czepkiewicz et al 2018).
Similar, although smaller, rebound effects can be related to price differences between various passenger
vehicle technologies. Both the capital and operational costs can differ significantly depending on the choice of a
vehicle. Although rebound effects for increasing energy efficiency of passenger cars have been found to be small
in previous literature (Whitehead et al 2015, Craglia and Cullen 2020), these studies have focused on the effects
of fuel prices on vehicle-kilometers travelled. There is much less understanding on rebound effects from
industrial ecology’s perspective (Hertwich 2005), which covers all behavioral and systems responses.
Here, we take a micro-economic industrial ecology point of view to rebound effects. By micro-economic we
mean that we focus on immediate household-level rebound effects and exclude macro-economic impacts that
often occur later in time. By industrial ecology perspective we mean in this context that we cover the whole
household budget, including all consumption-categories. This means that we capture both direct rebound
effects, such as changes in fuel consumption, and indirect rebound effects, meaning changes in other
consumption categories (Chitnis et al 2013). Both are driven by changes in the price of the used transport option.
For the purpose of the study, we reviewed comparative life cycle assessment (LCA)studies on passenger cars,
which compared internal combustion engine vehicles (ICEVs)to greener alternatives, such as electric- and
biofuel vehicles. The aim was to collect the most recent LCA knowledge to improve our carbon footprint model
regarding passenger vehicles. However, the review of these studies also revealed that LCA studies in general do
not consider nor discuss rebound effects, which fall outside their scope. This highlights the importance of
considering rebound effect literature in decision making, since decisions based on LCA literature alone can lead
to unintended outcomes.
Here, we provide a new analysis of carbon footprints of various car type owners compared to car-free
households in the Nordic countries under some modest travel constraints related to the late COVID-19 period.
Our analysis is based on a ‘1.5-degree lifestyles’carbon footprint survey, conducted between autumn 2021 and
spring 2022 (Heinonen et al 2022). In August 2021, the number of commercial flights in the EU was still only
69% of pre-COVID level but in August 2022 it had increased to 92% (Eurostat 2024).
The main research questions of the study are as follows:
1)How high are the carbon footprints of fossil-fuel- and green car owners compared to respondents who have
chosen not to have a car for climate or other reasons?
2)How do the studied vehicle options affect the rest of consumption and the following carbon footprints?
The next section presents the findings of previous comparative LCA studies on passenger cars. After that, we
present the materials and methods used in the study including details of the 1.5-degree lifestyles -survey and our
hybrid-LCA carbon footprint model. Then we present the results of the study including illustrations and the
results of the regression analysis. The paper ends with discussion including comparison to previous literature,
limitations of the study, and policy implications.
Comparative LCA studies on passenger cars
The climate impacts of different vehicle types are usually studied with a comparative LCA. To provide an
overview of current knowledge on the topic, and to collect data for our own carbon footprint model, we
reviewed 24 LCA studies comparing ICEVs to greener alternatives, including electric-, biofuel-, and fuel cell
vehicles. The studies were found by using the Web of Science -search engine. We used various search strings,
such as: ‘compar
*
’AND (‘LCA’OR ‘life cycle’)AND ‘passenger’AND (‘car’OR ‘vehicle’). We selected
comparative LCA studies on passenger cars that provided their findings in a useful format (gCO
2
-eq/vkm)and
were published no more than 10 years ago. We stopped collecting papers, when the results started to saturate, i.e.
the results started to settle somewhere within the already found range of results. The reviewed papers are shown
in table A1 in the appendix.
According to the reviewed studies, the life cycle emissions of fossil-fuel ICEVs vary between 180–300 g
CO
2
-eq/vkm in Europe (table A1 in the appendix). Biofuel vehicles (Messagie et al 2014, Rosenfeld et al 2019,
Ternel et al 2021, Haase et al 2022)and battery electric vehicles (BEVs)using renewable energy (Helmers et al
2017, Held and Schücking 2019, Rosenfeld et al 2019, Buberger et al 2022)are quite consistently estimated to
2
Environ. Res. Commun. 6(2024)125008 J Ottelin et al
have the lowest life cycle emissions, varying between 10–100 g CO
2
-eq/vkm in Europe. In most cases BEVs using
the local electricity mix also have lower emissions than ICEVs, but in countries with fossil-fuel based electricity
production the emissions of BEVs can rise above ICEVs (figure 1).
Regarding biofuels, there is a wide variation in the estimated climate impacts caused by indirect land-use
change (ILUC)(Whitaker et al 2010, Ahlgren & Di Lucia 2014, Jeswani et al 2020). Some recent studies suggest
that the ILUC emissions of biofuels are likely to be smaller than previously believed (Ahlgren and Di Lucia 2014,
Cavalett and Cherubini 2022). Thus, we calculated here the carbon footprint of biofuels both with and
without ILUC.
Most of the reviewed studies do not include the embodied emissions of road infrastructure. The studies that
do, suggest that the emissions are around 10 g CO
2
-eq/vkm (Bauer et al 2015, Cox et al 2020), which is low
compared to the life cycle emissions of fossil-fuel ICEVs but not negligible when it comes to greener vehicles, so
we have included the embodied emissions of road infrastructure in this study. The manufacturing of
infrastructure needed for charging BEVs is also excluded in most studies, but Karaaslan et al (2018)show that it’s
negligible compared to the total life cycle emissions of BEVs.
Figure 1. Greenhouse gas emission reduction potential of BEVs when compared to same size fossil-fuel ICEVs (% of vehicle life cycle
emissions). All the reviewed studies use the local electricity mix in their baseline scenarios. The results of scenarios using 100%
renewable energy are marked with an asterisk.
3
Environ. Res. Commun. 6(2024)125008 J Ottelin et al
None of the reviewed studies discuss potential rebound effects. Yet, economic models show that people
shifting to energy efficient vehicles start driving more due to fuel- and monetary savings (Linn 2016, Craglia and
Cullen 2020). On the other hand, BEVs often have a higher purchase price, which can affect to the opposite
direction and bring additional emission reductions (Font Vivanco et al 2014). While such rebound effects are
outside the scope of traditional LCA studies, they warrant more research and should be considered in decision-
making.
Materials and methods
Survey on climate attitudes and carbon footprints in the Nordic countries
The study is based on survey data collected in the 1.5-degree lifestyles project between autumn 2021 and spring
2022 (University of Iceland 2024). The survey was spread mainly through social media using professional
marketing services, and it was targeted to adult residents of the Nordic countries. The final sample sizes of each
country are as follows: Iceland 1 554; Norway 1 295; Sweden 1 982; Finland 2 084; Denmark 515; in total 7 430
respondents. This gives good coverage of various consumption habits, but it should be noted that the survey is
not fully representative of population, as this was not the aim of the survey. Rather, the aim was to collect high-
quality data on people who are interested in knowing their personal carbon footprints and represent a wide
coverage of different backgrounds and lifestyles in the Nordic countries. The overarching aim of the project was
to find out whether it is possible to achieve 1.5-degree climate target -compatible lifestyles in the Nordic
countries. For the answer, and more details on the data collection, see Heinonen et al (2022). An informed
consent to utilize the responses in scientific research was obtained from each survey participant.
The survey was designed from the perspective of calculating consumer carbon footprints accurately but
efficiently without making the survey too long. Traditionally, household carbon footprints are studied by
combining data from household budget surveys with environmentally extended input-output (EE IO)models.
This approach has its limitations since household budget surveys and national accounts used in IO-models are
designed for collecting data for economic purposes. From the perspective of environmental analyses, they lack
some important information regarding the quality of purchases, and their assumption of linearity between
prices and emissions reduces the applicability of them in studies over different types of lifestyles. Price is not
always a good indicator of the environmental impacts (Girod and De Haan 2010).
In the survey used in the study, one aim was to collect better data on environmental aspects of consumption.
The emphasis of the survey questions was on consumption categories that cause the majority of the emissions:
transport, housing energy, and food. The survey includes several detailed questions on these consumption
categories and utilizes physical quantities instead of monetary purchases to assess the respective carbon
footprints. The carbon footprint of the rest of consumption was estimated more roughly. Also, the survey
differentiated between shared and non-shared consumption domains so that personal consumption was asked
in non-shared domains, such as purchases of services and clothes, but the household perspective was taken in
shared domains, such as housing and car possession. Thus, the unit of analysis in the study is individual carbon
footprint including personal consumption and the respondent’s share of the shared consumption. The survey
questions are available in the appendix of our previous publication (Olson et al 2024)and along with the whole
dataset at Zenodo (University of Iceland 2024).
Carbon footprint model of the study
The carbon footprints were calculated with a hybrid-LCA approach combining environmentally extended
input-output (EE IO)analysis and process-LCA. The calculation relies more heavily on process-LCA based
emissions, which can provide a more accurate estimate on the environmental impacts of specific types of
products and services, but it suffers from truncation errors (Suh et al 2004), meaning that not all processes can be
taken into account, unlike in the EE IO analysis, which is based on infinite mathematical series. Not relying as
heavily on monetary spending leads to lower income elasticity of carbon footprint (Leferink et al 2023), and in
turn is less likely to exaggerate the emissions caused by affluent consumers (Girod and De Haan 2010).
In the study, the carbon footprint of each consumption domain was estimated separately. Several literature
sources and national statistics were used to build up the model. The information sources are listed in more detail
below, under the specific consumption domains. We made some additions and modifications compared to our
previous studies using the same survey data. Since our focus here is car use, this part of the modeling was done in
more detail than the other domains. Overall, the domains were divided as follows:
1. Motor fuels
*
2. Motor vehicles
*
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Environ. Res. Commun. 6(2024)125008 J Ottelin et al
3. Road infrastructure (incl. maintenance)
*
4. Local public transit
5. Leisure travel (excluding car-use)
6. Housing energy
7. Housing construction
*
8. Diet
9. Goods and services
*
*
Additions or updates compared to the original model described in Heinonen et al (2022).
Motor fuels
In the survey, the respondents were asked to report the number of vehicles owned by the households, the type of
fuel of each vehicle, the fuel efficiency of each vehicle, and the number of kilometers driven by each vehicle over
the past year. The carbon footprints of fuels were calculated by multiplying the total fuel consumption of each
fuel type with a respective life cycle emission factor (Cherubini et al 2009, Bieker 2021, see table 1). The outcome
was divided by the household size. A limitation of the data is that it was possible to report a maximum of three
motor vehicles.
Since there are a lot of uncertainties related to the emissions caused by indirect land-use change (ILUC), two
different emission factors were used for biofuels: with and without an estimate of ILUC emissions. There is a
wide variation in the ILUC emission estimates between different studies (Whitaker et al 2010, Ahlgren and Di
Lucia 2014, Jeswani et al 2020). Some recent studies suggest that the ILUC emissions are likely to be smaller than
previously believed (Ahlgren and Di Lucia 2014, Cavalett and Cherubini 2022). The estimates used in the study
(table 1)are relatively high, and thus the results without the ILUC emissions are also provided. The total life cycle
emissions of biofuels have in general a declining trend due to increasing use of second-generation biofuels from
residuals, for example (Jeswani et al 2020, Bieker 2021). In Europe, regulation also plays an important role in
phasing out biofuels with high risk of high ILUC emissions, such as palm oil (Bieker 2021).
For EVs, we used national emission coefficients of electricity (table 2). The coefficients were calculated based
on each country’s electricity mix and the European average life cycle GHG emissions of each energy source
provided by Cherubini et al (2009), which includes the total life cycle (i.e. fuel delivery materials used for
construction, waste treatment, and transport). It should be noted that the GHG impacts of land-use change
caused by the energy sector are not included. Emissions were calculated based on the traveled distance.
Motor vehicles
The embodied emissions of motor vehicles, meaning the emissions caused by vehicle production, were
estimated based on a literature review on comparative LCAs comparing electric- and combustion engine
vehicles (table A1 in appendix). The estimate for the vehicle glider and ICEV powertrain is 30 g CO
2
-eq/vkm
(Helmers et al 2017, Helmers et al 2020, Bouter et al 2020, Ternel et al 2021, Desreveaux et al 2023). Based on the
same sources the embodied emissions of BEV batteries and powertrain were estimated as an additional 30 g
CO
2
-eq/vkm. Estimates based on vehicle kilometers driven are justified in a sense that heavy driving shortens
the lifespan of the vehicle and the battery. Furthermore, new cars are driven more than old cars (Caserini et al
2013), which puts emphasis of the manufacturing emissions on the early years of vehicle use.
Table 1. Life-cycle emissions of motor fuels used in the study.
Fuel production Fuel combustion Total Without ILUC Reference
CO
2
-eq kg/lCO
2
-eq kg/lCO
2
-eq kg/lCO
2
-eq kg/l
Gasoline 0.68 2.24 2.92 Bieker 2021
Diesel 0.98 2.44 3.42 Bieker 2021
Biodiesel 3.42 1.84 Bieker 2021 (derived)
Ethanol 2.29 1.79 Bieker 2021 (derived)
Biofuel from residuals 0.29 0.29 Bieker 2021 (derived)
CO
2
-eq kg/kg CO
2
-eq kg/kg CO
2
-eq kg/kg
Natural gas 0.76 2.71 3.47 Bieker 2021
Biogas 1.28 1.28 Cherubini et al 2009 (derived)
5
Environ. Res. Commun. 6(2024)125008 J Ottelin et al
The survey includes several questions on the vehicle type. Here, we classified vehicles into small, medium, and
large cars and assumed that small cars and motorbikes have 30% smaller glider carbon footprint and large cars 30%
larger glider carbon footprint than medium cars. For example, typical SUVs weigh around 2.0–2.3 t (Karaaslan et al
2018)and compact cars, such as Volkswagen Golf or Nissan Leaf without a battery, around 1.3 t (Petrauskienėet al
2021). These are considered large and small cars here,respectively, meaning that medium cars are assumed to weigh
around 1.8 t without a battery. The embodied emissions of BEV batteries depend heavilyonthebatterycapacity.A
large capacity, providing a large driving range, can as much as double the embodied emissions of the battery. Thus, we
assumed that large BEVs and BEVs with over 25 000 annual vehicle kilometers driven have 60 g CO
2
-eq/vkm
embodied emissions from batteries based on the estimate provided by Bouter and co-authors (2020). Finally, the total
embodied emissions of a household’s vehicles were divided by the number of people in the household.
Road infrastructure
The embodied emissions of road infrastructure were estimated based on a recent study by Rousseau et al (2022)
and Eurostat’s statistics on road traffic. We divided the total embodied emissions of road infrastructure in each
country by the total vehicle kilometers travelled, including both freight traffic and passenger traffic. In addition,
we estimated the emissions caused by road maintenance very roughly based on national average expenditure on
road maintenance in recent years and Exiobase intensity (CO
2
-eq kg/€)for maintenance services in general. The
maintenance emissions are much lower than the embodied emissions of roads. The estimates for the combined
emissions of road infrastructure construction and maintenance used in the study are: 24, 23, and 30 g
CO
2
-eq/vkm for Finland, Sweden, and Norway respectively. Denmark and Iceland did not have all necessary
data available, and thus the values of Sweden and Norway were used, in the respective order. Iceland has a
similarly challenging climate and terrain as Norway, which increases their infrastructure requirements
compared to the other Nordic countries. The estimates based on traveled vehicle kilometers were divided by the
household size.
Note that the above emissions per vehicle kilometer can be used for both passenger and freight transport.
This means that including the road infra used by households directly only covers around 80% of the national
embodied emissions of road infrastructure. We allocated the remaining 20% very roughly to goods and services
based on how much each household spends on these.
Local public transit
The survey respondents were asked to estimate the average weekly distance (km)that they travel using public
transport. To calculate the emissions from public transport, an average intensity (0.12 kgCO
2
e)was used based
Table 2. Life cycle emissions of electricity production used in the study.
Electricity source g(CO
2
e/kWh)Reference
hydropower 18 Average values for electricity and cogen-
eration from Cherubini et al 2009
wind 20
solar PV 99
biomass 81
geothermal 22
coal 1079
oil 899
nuclear 63
natural gas 540
Country Electricity Mix g(CO
2
e/kWh)Reference
Finland nuclear (35%), hydropower (18%), biomass (18%), wind
(9%), coal (8%), natural gas (6%), peat (4%), waste (1%),
oils (0.3%), solar (0.3%)
209 Finnish Energy 2019, Cherubini et al 2009
Iceland hydropower (71%), geothermal (29%)19 Orkustofnun/National Energy Authority
of Iceland 2015, Cherubini et al 2009
Denmark wind (56%), biomass (21%), coal (12%), natural gas (7%),
solar (3%), oil (1%)
199 Danish Energy Agency 2019, Cherubini
et al 2009
Sweden nuclear (39.5%), hydropower (38.7%), wind (11.8%), bio-
mass (5%), waste (3%), coal (1.2%), natural gas (0.4%)
67 International Energy Agency - IEA 2019,
Cherubini et al 2009
Norway hydropower (92%), geothermal (2%), wind (6%)18 Statistics Norway 2020, Cherubini et al
2009
Renewable biomass (25%), wind (25%), hydropower (25%),
solar (25%)
54 assumed electricity mix, Cherubini et al
2009
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Environ. Res. Commun. 6(2024)125008 J Ottelin et al
on indirect emissions from Chester and Horvath (2009)and direct emissions from VTT Technical Research
Centre of Finland Ltd (2021)for all the countries and within-country locations regardless of the available modes.
The public transit use is based on individual travel, not household.
Leisure travel
Respondents were asked about their leisure travel away from their home region over the past year. They reported
the number of short (0–1 000 km), medium (1 000–3 000 km)and long distance (3 000 km+)leisure trips that
they had taken along with the mode of transport that they used (ferry, airplane, train, bus, or car). Both indirect
and direct emissions were included and the emissions factors can be seen in table 3. They were calculated based
on Chester and Horvath (2009)and Aamaas et al (2013)with an assumption of typical occupancy and a medium
trip length in each range. The leisure travel estimate is based on individual travel, not household. Trips with cars
are included in the above-described domains 1–3.
Housing energy
To calculate the emissions caused by housing energy consumption, the survey included questions on the type of
housing (apartment, detached, rowhouse or other), decade of construction, size of home, and heating and
electricity sources. The overall emissions from housing energy were divided by the number of people livingin
the household. For home heating, the energy consumption per square meter was estimated based on the type of
housing and the decade of construction with values from Vimpari (2021). The GHG emission coefficients for
different heating sources were taken from Cherubini et al (2009)and the district heating fuel mixes were defined
by each country’sofficial statistics. The GHG emission coefficients used to estimate the emissions from district
heating are presented in table 4. If the respondent reported a heat pump as the main heating source, the
electricity need was reduced using a coefficient of performance of three for the heat pump.
The coefficients to calculate emissions from home electricity use are based on each country’s electricity mix
and the average emissions from different electricity sources, which are from the values in Cherubini et al (2009)
as shown in table 2. The coefficients in table 2include efficiency, power, capacity factor, lifetime, direct air
pollutants, GHG emissions, solid wastes, and liquid pollutants, but not direct or indirect land-use changes. A
standard value for electricity use along with the size of the home were used to estimate electricity demand of
home appliance electricity.
Housing construction
The embodied emissions of housing were estimated based on the living space and degree of urbanisation. The
average values per m
2
of living space were calculated using the Finnish carbon footprint model from Ottelin et al
(2021)and are shown in table 5below. In the Nordic countries, wood is a traditional and common construction
material in rural areas and detached houses, but less common in urban areas. The model considers the lower life
cycle emissions caused by wood compared to concrete and stone, but not the carbon storage as it is a more
contested topic.
Table 3. Emission factors used for estimating
emissions from leisure travel.
Leisure travel mode Kg CO
2
/km/person
Bus 0.15
Ferry 0.36
Flight (less than 800km)0.34
Flight (greater than 800km)0.28
Train 0.08
Table 4. Life cycle emissions of
district heating used in the study.
Country kg CO
2
-eq/MWh
Denmark 168
Finland 229
Iceland 11
Norway 111
Sweden 79
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Environ. Res. Commun. 6(2024)125008 J Ottelin et al
Diet
The survey respondents were asked to describe their diet as vegan, vegetarian, pescatarian, or omnivore as well as
to quantify their daily or weekly meat intake. The values to calculate the GHG emissions caused by each diet were
taken from Saarinen et al (2019)which range from 1132 kg CO
2
-eq/year for a vegan or vegetarian diet to 3213 kg
CO
2
-eq/year for an omnivore diet with the highest meat intake option (300g meat/day).
Goods and services
The survey respondents were asked to report their monthly purchases over the last twelve months in the
categories of alcohol and cigarettes, clothing and footwear, interior design and housekeeping, health, recreation
and culture, restaurants, hotels, electronics, and other goods and services. These consumption categories were
matched with corresponding categories in the Exiobase IO-model (Stadler et al 2021)to assess the carbon
footprint of goods and services. The used intensities are presented in table 6below. In addition, part of the
embodied emissions and maintenance of road infrastructure were allocated to goods and services based on
freight traffic.
In our previous studies on the 1.5-degree survey, the carbon footprints of pets and second homes have been
separated. Here, we included them in the goods and services. The carbon footprints caused by dog (630 kg
CO
2
-eq)and cat (315 kg CO
2
-eq)ownership were taken from Yavor et al (2020)and Herrera-Camacho et al
(2017)respectively. Other pets were not included in the calculation. The emissions of second homes were
estimated based on a previous Finnish carbon footprint model and estimated as 884 kg CO
2
-eq (Ottelin et al
2015). The emissions caused by pets and second homes were divided by the household size.
Studied groups
For the purpose of the study, we created eight respondent groups (table 7). First, we separated the owners of
fossil-fuel vehicles, and divided these into four different groups based on their amount of driving. We separated
the highest- and lowest driving 10% and then divided the remaining 80% into two groups. Second, we separated
biofuel- and electric vehicle (EV)owners based on the vehicle type of the first reported vehicle. However,
households who own even one fossil-fuel vehicle were classified in the fossil-fuel car owner groups. Finally, we
created two car-free groups. We separated the respondents who reported that their decision not to have a car was
based on an attempt to reduce their personal carbon footprint (somewhat to completely, 3–5 on a likert scale).
Here, we studied all the Nordic countries together in order to get a representative sample of the specific vehicle
owner groups that we were interested in. For country comparisons, see previous publications on the same survey
(Heinonen et al 2022, Árnadóttir et al 2024, Olson et al 2024).
Table 5. Life cycle emissions of housing construction used in
the study.
Annual 50-y. lifetime
CO
2
-eq t m
−2
CO
2
-eq t m
−2
Cities 0.021 1.029
Towns and suburbs 0.018 0.915
Rural areas 0.018 0.880
Table 6. Life cycle emissions of goods and services (kg CO
2
-eq/€)used in the study (Applied from
Exiobase, Stadler et al 2021).
Denmark Finland Sweden Norway Iceland
Alcohol and cigarettes 0.26 0.19 0.14 0.12 0.11
Clothing and footwear 0.07 0.10 0.07 0.14 0.14
Interior design and housekeeping 0.25 0.41 0.23 0.19 0.23
Health 0.18 0.17 0.09 0.10 0.10
Recreation, sports, and culture 0.16 0.15 0.08 0.12 0.13
Restaurants 0.09 0.19 0.10 0.13 0.12
Hotels 0.09 0.19 0.10 0.13 0.12
Electronics 0.28 0.80 0.33 0.32 0.44
Other goods and services 0.20 0.24 0.13 0.18 0.18
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Environ. Res. Commun. 6(2024)125008 J Ottelin et al
Table 7. Sample sizes and descriptive statistics of the studied groups.
Sample sizes Descriptive statistics (averages)
Singles and couples Families Shareof population
a
House-hold size Household income per capita (€)Renewable electricity
City
b
residents
Car-free for climate 874 204 18% 1.9 29 000 63% 72%
Car-free, other 491 58 9% 1.6 26 000 51% 61%
Electric 253 218 7% 2.8 37 000 88% 42%
Biofuel 125 55 3% 2.3 32 000 84% 47%
Fossil 10% (<2250 vkm/c)265 247 7% 2.8 24 000 59% 45%
Fossil 40% (2300–7500 vkm/c)1 010 1045 24% 2.8 28 000 65% 38%
Fossil 40% (7500–20000 vkm/c)1 439 609 26% 2.2 35 000 63% 25%
Fossil 10% (>20000 vkm/c)422 73 6% 1.8 40 000 56% 18%
a
Weighted by countries’population, but not fully representative (see survey description above).
b
According to Eurostat’s classification on degree of urbanisation: Cities, Towns and suburbs, Rural areas.
9
Environ. Res. Commun. 6(2024)125008 J Ottelin et al
Regression analysis
In order to study the impact of vehicle ownership, we used multivariable regression analysis to control other
relevant and known drivers of carbon footprint. The linear regression model used in the study is as follows:
()( )ln Carbon footprint per capita ln Household income per capita
Household type Vehiclegroup
Country u
E
hhi
i
jj
0
bb
bb
b
=+
++
++
where betas are regression coefficients and uis the error term. Household type groups are: young people
(<25 y.), adult singles (25–64 y.), adult couples, young families (child <7y.), other families with children,
senior singles (>64 y.), and senior couples. The vehicle groups are shown in table 6.
In the descriptive result figures (figures 2and 3), we weighed the average carbon footprints by country
population to give an overview of the Nordic countries. However, we did not use any weights in the regression
analysis. Instead, we included country as a control variable.
Results and discussion
Comparison of carbon footprints
The total consumer carbon footprints are relatively similar in size between the studied groups, aside from the
respondents having a fossil-fuel car and driving more than the median, 7 500 vkm per capita (figure 2). This
finding is highlighted when only the working middle-income respondents are studied: the carbon footprints of
the first six groups are all very close to 6.0 tons of CO
2
-eq/year per capita (figure 3). However, there are
interesting differences in the composition. As in our previous study from Finland (Ottelin et al 2017),wefind
that car-free respondents have a relatively high carbon footprint of leisure travel, including flying and other
long-distance travel for leisure (except car trips). This was despite the survey being conducted during the end of
the COVID-19 period, which reduced air travel 10–30% (see introduction).
We separated between respondents, who have chosen not to have a car because of climate concern and those
who do not have a car for other reasons. The climate concerned group seems to have a slightly lower carbon
footprint, despite having a higher income level (table 7). One reason is that the climate concerned respondents
are more often vegans or vegetarians, which leads to a smaller carbon footprint of food (figure 2). However, the
amount of leisure travel is very similar between the two groups. Compared to green car owners, meaning
biofuel- and electric car owners, the car-free households also have a higher carbon footprint from housing
energy. This is partly explained by their smaller household size (table 7), since they do not get energy savings
from sharing spaces and appliances with other household members, which has been found to be an important
Figure 2. Carbon footprints by vehicle group and household size in the Nordic countries (weighted by countries’population). The
fossil-fuel car owners are divided into the lowest- and highest driving 10%, and two moderate driver groups (40% each). vkm/
c=vehicle kilometers divided by household size.
10
Environ. Res. Commun. 6(2024)125008 J Ottelin et al
factor (Ala-Mantila et al 2016). In addition, green car owners are more likely to have renewable electricity than
any other group (table 7), which reduces their carbon footprint from housing energy.
Electric vehicle (EV)owners have higher carbon footprints than biofuel vehicle owners (figure 2), but this is
not because of the emissions related to the vehicle but because of higher income level (table 7)leading to higher
consumption in the domains of leisure travel, housing construction, and goods and services. When income is
controlled, the differences between these two groups even out (figure 3). Respondents, who own a fossil-fuel car
but drive very little, have similar sized carbon footprints as green car owners and car-free respondents. Among
singles and couples, they even have the lowest carbon footprints of all groups (figure 2), supporting our previous
finding from Finland (Ottelin et al 2017). When income is controlled, these respondents have a higher carbon
footprint of leisure travel and housing energy than EV- and biofuel vehicle owners (figure 3), suggesting that the
investment in a green car instead of a small fossil-fuel car may limit consumption in these categories. Without
the climate impact of ILUC, the carbon footprint of biofuels drops from 1 055 kg (figure 2)to 715 kg CO
2
-eq on
average.
The highest driving 10% of fossil-fuel car owners have almost three times as high carbon footprints as the
other groups. Despite their relatively high income level (table 7)it is clear that this is unsustainable and
incompatible with the remaining carbon budgets (Ala-Mantila et al 2023). Also the group that drives the second
most, meaning the high-mid 40% of fossil-fuel car drivers, have around 1.5 times as high carbon footprint as the
other groups (figure 3).
Regression analysis
The regression analysis largely confirms the descriptive findings above. Respondents without a car because of
climate concern and fossil-fuel car owners who drive very little have the lowest carbon footprints, and there is no
statistically significant difference between these two groups (table 8, whole sample). Car-free respondents,
because of practical or other reasons, have a 6% higher footprint than the aforementioned groups. EV owners
have 19% and biofuel car owners 24% (18% without ILUC)higher carbon footprints. Lastly, the more and more
driving fossil-fuel car owners have 30%, 73%, and 189% higher carbon footprints than the two groups with the
lowest footprints, when income, household type, and country are controlled. It should be noted that the
regression analysis suffers from a collinearity problem: income and carbon footprint correlate, which may lead
to the model overestimating the carbon footprints of groups with high income level and underestimate the ones
of groups with low income level. According to VIF analysis, the collinearity issue is not severe here as all VIF
values are below 5 (tables A2–A3 in the appendix). Nonetheless, we also run a model including only the working
middle-income respondents (table 8). This model shows smaller differences between the studied groups,
similarly to figure 3.
Comparison to previous literature
Similarly to previous studies covering the whole household budget (Heinonen et al 2013, Ottelin et al 2017)
instead of just transport emissions, we find that the emission benefits of not owning a car are partly, but not
Figure 3. Carbon footprints of working middle-income respondents by vehicle group in the Nordic countries (weighted by countries’
population). vkm/c=vehicle kilometers divided by household size.
11
Environ. Res. Commun. 6(2024)125008 J Ottelin et al
entirely, offset by higher consumption in other domains. Leisure travel causes the largest share of the emission
rebound. Our analysis shows that this finding holds true in the Nordic countries, even when using a carbon
footprint model mainly based on physical units instead of expenditure, and even during a time period that air
travel was restricted to some extent.
In addition, we found interesting results regarding the potential rebound effects of investing in a green car,
which is a little studied phenomenon. When we limited our analysis to working middle-income respondents, the
results show that the carbon footprints are very similar in size between car-free households, green car owners,
and fossil-fuel car owners who drive very little. Our findings suggest that the investment in a green car limits the
rest of consumption and thus can bring emission cuts in other domains in addition to car use. Thus, the
emission reductions related to green cars are likely to be higher than found in previous comparative LCA studies
(figure 1). The phenomenon has been previously theorized by Font Vivanco et al (2014). This highlights that the
price differences of various transport options should be taken into account in studies that aim to compare the
environmental impacts of different transport modes.
Our analysis included the carbon footprints of individuals who were car-free for climate reasons, however,
the role of climate concern in green car drivers was not assessed in this analysis, so it is unknown what influence
climate concern had on those drivers’overall carbon footprints and consumption. There are few studies that
consider these aspects. One recent study of Finnish drivers found that EV drivers who were more eco-conscious
and economically oriented had significantly lower overall carbon footprints than EV drivers who were more
performance and brand oriented even when both groups had similar income levels (Sandman et al 2024),so
there is a potential for positive spillover effect, but more research is needed to see the relationship between
vehicle choice, climate concern and overall carbon footprints.
In our study, the top 10% of fossil-fuel car drivers had carbon footprints that were substantially higher than
the other groups. Similarly, Wadud et al. (2022)found that the top 5% of drivers in England emit 4.8 times more
CO
2
than the national average and that targeting the top 20% of drivers who exhibit ‘excess’car travel with
mileage rationing could lead to a 26% overall reduction in emissions. Both our findings and the study by Wadud
et al (2022)show that the ‘excess’travelers have high income-level, primarily large fossil-fuel cars, and in
Table 8. Regression analysis: impact of vehicle owner group on carbon footprint per capita (cf).
Whole sample
Working middle-income
respondents
R-squared =0.4626 R-squared =0.4351
Dependent variable: ln(cf)Coeff. Std.err. P>|t|Coeff. Std. err. P>|t|
ln (Income)0.14 0.01 0.000 0.17 0.04 0.000
Household type
Adult singles (ref.)
Young −0.05 0.03 0.032 0.01 0.07 0.918
Adult couples −0.07 0.01 0.000 −0.05 0.03 0.056
Young families −0.14 0.02 0.000 −0.13 0.03 0.000
Other families −0.07 0.02 0.000 −0.03 0.03 0.258
Senior singles 0.05 0.02 0.010 0.04 0.11 0.674
Senior couples −0.06 0.02 0.001 −0.11 0.07 0.137
Vehicle owner group
Car-free for climate (ref.)
Car-free, other 0.06 0.02 0.001 0.05 0.05 0.283
Electric 0.18 0.02 0.000 0.06 0.04 0.119
Biofuel 0.21 0.03 0.000 0.16 0.06 0.005
Fossil 10% (<2250 vkm pc)0.00 0.02 0.973 −0.07 0.04 0.090
Fossil 40% (2300–7500 vkm pc)0.26 0.01 0.000 0.21 0.03 0.000
Fossil 40% (7500–20000 vkm pc)0.55 0.01 0.000 0.48 0.03 0.000
Fossil 10% (>20000 vkm pc)1.06 0.02 0.000 0.97 0.04 0.000
Country
Iceland (ref.)
Norway −0.06 0.01 0.000 −0.08 0.03 0.002
Sweden −0.15 0.01 0.000 −0.16 0.02 0.000
Finland 0.13 0.01 0.000 0.11 0.03 0.000
Denmark 0.14 0.02 0.000 0.10 0.04 0.005
Constant 7.11 0.09 0.000 6.86 0.45 0.000
Biofuel without ILUC
a
0.17 0.03 0.000 0.12 0.06 0.037
a
Separate regression models with carbon footprint excluding the ILUC of biofuels.
12
Environ. Res. Commun. 6(2024)125008 J Ottelin et al
addition to high levels of driving, they also fly significantly more than average. To reach climate targets, it is
essential to address the emission caused by this relatively small share of the population.
Limitations of the study
As described in the method section, the 1.5-degree lifestyles -survey has several advantages compared to
traditional household budget surveys, since it was designed from the perspective of calculating carbon footprints
as accurately as possible. However, there are some disadvantages as well. First, the sample is not fully
representative of the whole population. The comparison of the sample to national statistics can be seen in
Leferink et al (2023). It is possible that climate concerned people were more likely to participate in the survey,
although our findings show that the sample includes respondents with very high carbon footprints as well and
respondents reporting little to no climate concern. The average national carbon footprints of the sample are
close to the national averages shown by previous studies (e.g. Ottelin et al 2019, Ivanova and Wood 2020),
considering the methodological differences. Second, the focus of the survey and thus the modeling was on
transport, housing, and diets. Other parts, such as the carbon footprint of goods and services are estimated much
more roughly than in our previous models based on household budget surveys, which collect expenditure
information systematically. Thus, it’s likely that the model of the study does not fully capture rebound effects
induced by shifts of consumption. On the other hand, models based mainly on expenditure and not on physical
units, are likely to overestimate rebound effects (Olson et al 2024). A third important uncertainty is that the
survey was conducted when travel restrictions caused by the COVID-19 pandemic were still partially in effect. It
would be important to repeat a similar study now that air travel has returned to normal and growing levels.
According to the European Union Aviation Safety Agency (EASA),flights at EU27+EFTA airports, including
Norway and Iceland, are expected to rise from 5.1 million in 2021 (a drop from 9.3 million in 2019 due to
COVID-19)to 12.2 million by 2050 in the most likely baseline scenario, potentially reaching 15.0 million in a
high-traffic scenario (EASA 2022).
Policy implications
In our analysis, the different low-carbon options: not having a car, minimal driving, EVs, and bio-fuel vehicles
were all associated with low carbon footprint levels, but still not low enough to be compatible with the 1.5-degree
climate target (Ala-Mantila et al 2023). This aligns with studies arguing that no single mitigation option will
result in the necessary emissions reductions in the transport domain (Aamaas et al 2013, Dugan et al 2022)and
that integrated and balanced policy mixes are needed (Axsen et al 2020, Dugan et al 2022). Previous scenario
studies of transport emissions have found that the key mitigation measures are reducing travel demand, and
increasing public transport, active transport, and electrification of vehicles (Dillman et al 2021, Montoya-Torres
et al 2023). It is also important to consider the interaction of different policies to prevent negative interactions,
side effects, and rebound effects (Axsen et al 2020), and tailor policies to fit the needs of different groups (Thaller
et al 2021, Steininger et al 2024). For instance, many EV policies have just affected affluent groups, leaving out
low-income groups from adopting EVs (Chen et al 2020, Caulfield et al 2022). Some examples of policies which
address this issue include interest-free loans, loans with longer repayment periods, or actions to increase EVs on
the secondhand market, however these policies alone will not adequately address equity concerns or problems
caused by car dependance (Caulfield et al 2022).
The carbon footprints of the respondents who were car-free for climate concern reasons were some of the
lowest in our analysis. Policy makers can continue to promote awareness about climate change to encourage
individual action (Chan and Tam 2021). However, other factors can have more of an influence on people’s
transport choices than climate concern, such as convenience or comfort (Næss et al 2018, Jakučionytė-Skodienė
and Liobikienė2021), safety (Hsu et al 2019)or other personal attitudes or beliefs (Hunecke et al 2010).
As mentioned earlier, ‘excess’travelers, driving and flying significantly more than others, should be targeted
with stronger policy measures. Since this group has a high income level, they generally have the resources to shift
to more expensive low-carbon vehicles. This should be encouraged by policies, such as subsidies for low-carbon
vehicles and gradual phase-out of fossil-fuel vehicles, starting from the ones with highest fuel consumption.
Other options to address the top 10% of drivers include taxing motor fuels, but this may negatively impact other,
more vulnerable, groups (Büchs et al 2021, Mattioli et al 2023). Addressing workplace policies regarding
business travel and remote working could be another strategy to impact this group (Wadud et al 2022, Leroutier
and Quirion 2023).
In addition, our findings highlight the importance of including air travel more strongly in climate strategies.
The EU’s policy focus on passenger cars has been justified so far, because of their high share of total transport
emission. However, this seems to be changing, and it is not long until passenger air travel emissions will exceed
passenger car emissions in the EU and the Nordic countries. According to our survey data, Norway, Iceland, and
13
Environ. Res. Commun. 6(2024)125008 J Ottelin et al
Denmark are already close to such a turning point, while in Finland the motor fuel emissions still dominate the
carbon footprint caused by traveling.
Although several initiatives and policies aim to mitigate air travel emissions, including the EU Emissions
Trading System (EU-ETS), ReFuelEU Aviation at the European level, and the Carbon Offsetting and Reduction
Scheme for International Aviation (CORSIA)internationally, their effectiveness remains to be seen. These
initiatives provide roadmaps to significant emission cuts by 2030 and 2050. However, they rely heavily on the
increasing use of Sustainable Aviation Fuels (SAF), meaning jet fuels produced from sustainable sources,
primarily biofuels and to a smaller extent e-fuels (Ballal et al 2023). Thus, the sustainability of aviation in the
near- and mid-term future is largely dependent on whether biofuel production can be sustainably up-scaled
(Ansell 2023, Detsios et al 2023). This seems a risky policy approach, considering the uncertainties related to the
climate impacts of ILUC caused by biofuel production (Ansell 2023). In addition, considering the climate- and
circular economy goals of the EU in general, there is an increasing demand for land to produce not only biofuels
but bio-based renewable materials as well, not to mention the aims to restore natural areas and increase natural
carbon sinks.
Bearing the uncertainties of SAFs in mind, we recommend a stronger emphasis on carbon pricing in the
aviation sector, for example higher carbon taxes on flights. Ideally, the greenhouse gas intensity (kg CO
2
-eq/€)
of all consumption-categories should be the same, so that consumers would not need to consider environmental
aspects when making consumption decisions. For example, the overall emission intensity of green car
ownership is likely already close to the emission intensity of average consumption, because of the high capital
costs of vehicles and the vehicle related taxes, insurances, and maintenance costs. At the same time, the emission
intensities of gasoline, diesel, and flights remain high. Flights are particularly problematic from rebound effect
perspective, since they are often part of marginal consumption, meaning that when people get extra income,
they are more likely to spent it on flights than many other consumption categories (Chitnis et al 2013, Ottelin
et al 2017). Thus, the taxation of flights must be tightened.
Acknowledgments
The authors thank the Icelandic Centre for Research (Rannís), grant number 207195-053, for funding the study.
Data availability statement
The data that support the findings of this study are openly available at the following URL/DOI: https://zenodo.
org/records/10656970.
Appendix
14
Environ. Res. Commun. 6(2024)125008 J Ottelin et al
Table A1. Comparative LCA studies on various powertrains of passenger cars. BEV =Battery electric vehicle, PHVE =Plug-in hybrid electric vehicle, HEV =Hybrid electric vehicle, FCEV =Fuel cell electric vehicle, ICEV =Internal
combustion engine vehicle, GHG =Greenhouse gas emissions, Green electricity =Electricity from renewable sources.
Results: life cycle emissions, g CO
2
-eq/vkm Green
electricity
Authors Year Country or region
BEV
local
mix
BEV
renew
PHEV
gasoline
HEV
gasoline
HEV
biofuel
FCEV
(H2)
ICEV
gas
ICEV
gasoline
ICEV
diesel
ICEV
biofuel
BEV versus
ICEV GHG
reduction
BEV versus
ICEV GHG
reduction
Bauer et al 2015 Europe
(Switzerland)
210 —— 250 —290 240 300 250 —−30% —
Bouter et al 2020 France 100 —80–230 170 ———210 180 —−52% —
Buberger et al 2022 Germany 80 25 100 ——130 120 220 170 —−64% −89%
Burchart-Korol
et al
2018 Poland 280 170
a
—————280 —— 0% —
Czech Rep. 210 150
a
—————280 —— −25% —
Cox et al 2020 Europe 200 100 240 220 —380 240 300 250 —−33% −67%
Desreveaux et al 2023 Europe 150 —— — ———220 200 —−32% —
García et al 2020 US 160 —— —110 —— 200 —130 −20% —
Brazil 120 —— —100 —— 200 —110 −40% —
Held &
Schücking
2019 France and
Germany
130 55 —————250 180 —−48% −78%
120 65 —————180 160 —−33% −64%
Helmers et al 2017 Germany 150 65 —————240 —— −38% −73%
Helmers et al 2020 Germany 250 150
a
————240 310 270 —−19% —
Haase et al 2022 Germany 82 57 ———80 —224 —60 −63% −75%
Joshi et al 2022 Nepal 182 76
a
———922; 152
a
—507 —— −64% —
Karaaslan et al 2018 US 245 —313 ——357 —366 361 —−33% —
Messagie et al 2014 Europe 110 —— 210 —180 200 290 200 30 −62% —
Moro & Helmers 2017 Europe 78 —— ————178 145 —−56% —
Moro & Lonza 2018 EU28 75 —— — ———178 145 —−58% —
Sweden (lowest)10 —— — ———178 145 —−94% —
Latvia (highest)200 —— — ———178 145 —12% —
Noshadravan
et al
2015 US 190 —200 250 ——— 350 330 —−46% —
Onat et al 2014 US 190 150 155 175 ——— 260 —— −27% —
Petrauskiene et al 2021 Lithuania 210 55
a
—150 ———160 130 —31% —
Rosenfeld et al 2019 Europe 121 42 80 166 35 147 146 225 —53 −46% −81%
Teimouri et al 2022 New York, US 133 —— — —85 349 350 —— −62% —
Ternel et al 2021 France, Europe 70 —— 165 ——155 195 175 70 −64% —
15
Environ. Res. Commun. 6(2024)125008 J Ottelin et al
Table A1. (Continued.)
Results: life cycle emissions, g CO
2
-eq/vkm Green
electricity
Authors Year Country or region
BEV
local
mix
BEV
renew
PHEV
gasoline
HEV
gasoline
HEV
biofuel
FCEV
(H2)
ICEV
gas
ICEV
gasoline
ICEV
diesel
ICEV
biofuel
BEV versus
ICEV GHG
reduction
BEV versus
ICEV GHG
reduction
Wu et al 2018 China 220 —— — ———233 —— −6% —
Zeng et al 2021 China 180 —190 ————220 —— −18% —
Authors Year Vehicle data LCA scope LCA data Journal
Bauer et al 2015 Modelled Cradle-to-grave ecoinvent v2.2, SimaPro Applied Energy
Bouter et al 2020 Manufacturers’data, travel modelled Cradle-to-grave ecoinvent v3.1, SimaPro Int J Life Cycle Assess
Buberger et al 2022 Realcase vehicles, travel modelled Cradle-to-grave Literature? Renew. and Sust. En. Reviews
Burchart-Korol
et al
2018 Modelled (similar to Nissan Leaf)Cradle-to-grave ecoinvent v3 J. Clean. Pro.
Cox et al 2020 Modelled (Lower mid-size passenger vehicle)Cradle-to-grave ecoinvent 3.4 Applied energy
Desreveaux et al 2023 Real case vehicles, simulated travel based on measured driving cycles Cradle-to-grave Literature, e.g. Bauer et al 2015 Energy
García et al 2020 Real case vehicle (Ford Fiesta), real-life driving cycles Cradle-to-grave, including land-use
change
Renewable Energy
Held &
Schücking
2019 Real case vehicles (minivan: e-Wolf Delta 2), real travel data (commuting)Cradle-to-grave GaBi 2018 Transportation Research Part D
Real case vehicles (compact car: Nissan Leaf), real travel data (commuting)
Helmers et al 2017 Real case vehicles (mini class car: Smart), real travel data Cradle-to-grave ecoinvent v2.2 Int J Life Cycle Assess
Helmers et al 2020 Real case vehicles (VW Caddy), energy consumption measured on the road Cradle-to-grave ecoinvent v2.2 Sustainability
Haase et al 2022 Existing car types (ADAC), not specified Cradle-to-use ecoinvent v3.3 Clean Technologies and Env. Policy
Joshi et al 2022 Hyundai Tucson, Hyundai Kona Electric, Hyundai Nexo Blue (2021)Cradle-to-grave GREET J. Clean. Pro.
Karaaslan et al 2018 Real case vehicles (SUVs)Cradle-to-grave, EIO-LCA EIO-LCA (Carnegie Mellon University),
GREET
Int J Life Cycle Assess
Messagie et al 2014 Volkswagen Golf A4, Nissan Leaf, Honda FCX Clarity (FCEV)Cradle-to-grave ecoscore database (vehicles, Belgium)Energies
Moro & Helmers 2017 Modelled Well-to-wheel+BEV battery Literature, ecoinvent 2.2 Int J Life Cycle Assess
Moro & Lonza 2018 Modelled Well-to-wheel Statistics, ecoinvent Transportation Research Part D
Noshadravan
et al
2015 Modelled compact and midsize cars Cradle-to-use EPA database, literature Int J Life Cycle Assess
Onat et al 2014 Modelled (similar to Toyota Corolla)Cradle-to-grave, EIO-LCA EIO-LCA (Carnegie Mellon University),
GREET
Sustainability
Petrauskiene et al 2021 2018 Nissan Leaf Acenta, 2019 Volkswagen Golf, Toyota Prius Cradle-to-grave ecoinvent v3.5, OpenLCA 1.10 Sustainability
16
Environ. Res. Commun. 6(2024)125008 J Ottelin et al
Table A1. (Continued.)
Results: life cycle emissions, g CO
2
-eq/vkm Green
electricity
Authors Year Country or region
BEV
local
mix
BEV
renew
PHEV
gasoline
HEV
gasoline
HEV
biofuel
FCEV
(H2)
ICEV
gas
ICEV
gasoline
ICEV
diesel
ICEV
biofuel
BEV versus
ICEV GHG
reduction
BEV versus
ICEV GHG
reduction
Rosenfeld et al 2019 Modelled SUVs and compact vehicles (similar to Audi Q5, Volvo XC60,
Hyundai IONIQ, VW Golf)
Cradle-to-use GaBi ts 8 (thinkstep), ecoinvent v 3.3,
GREET
J. Clean. Pro.
Teimouri et al 2022 Real driving data, simulated emissions, Nissan Leaf, Honda Civic DX Cradle-to-grave GREET International journal of hydrogen
energy
Ternel et al 2021 Modelled mid-range passenger cars Cradle-to-grave Sima-Pro, ecoinvent v3.5 Transportation Research Part D
Wu et al 2018 Modelled Cradle-to-grave China Automotive Life Cycle Database
(CALCD)versus GREET (US)
J. Clean. Pro.
Zeng et al 2021 Modelled, BYD Qin Pro series (chinese BEV and PHEV manufacturer)Cradle-to-grave GREET, ecoinvent 3.4 Resources, Cons. & Recyc.
a
Future scenarios.
17
Environ. Res. Commun. 6(2024)125008 J Ottelin et al
Table A2. VIF analysis for regression model: Whole sample
(table 7).
Variable VIF 1/VIF
———
ln_income_hhc 1.42 0.703911
hh_type7
young 1.19 0.843459
adult couples 1.81 0.553782
young families 1.9 0.526978
other families 2.12 0.471764
senior singles 1.33 0.750075
senior couples 1.56 0.643051
veh_class
Car-free, other 1.42 0.705054
Electric 1.46 0.684444
Biofuel 1.18 0.843904
Fossil 10% (<2 250 vkm/c)1.44 0.692042
Fossil 40% (2 300–7 500 vkm/c)2.41 0.414703
Fossil 40% (7 500–20 000 vkm/c)2.25 0.444286
Fossil 10% (>20 000 vkm/c)1.4 0.712637
country
Norway 1.57 0.636032
Sweden 1.89 0.528141
Finland 1.94 0.515108
Denmark 1.28 0.783971
———
Mean VIF 1.64
Table A3. VIF analysis for regression model: Working
middle-income respondents (table 7).
Variable VIF 1/VIF
———
ln_income_hhc 1.07 0.934217
hh_type7
young 1.08 0.92474
adult couples 1.85 0.541464
young families 2.02 0.494671
other families 2.34 0.42747
senior singles 1.04 0.962081
senior couples 1.09 0.917176
veh_class
Car-free, other 1.32 0.760054
Electric 1.7 0.588273
Biofuel 1.23 0.810797
Fossil 10% (<2 250 vkm/c)1.46 0.682879
Fossil 40% (2 300–7 500 vkm/c)2.91 0.344213
Fossil 40% (7 500–20 000 vkm/c)2.59 0.385633
Fossil 10% (>20 000 vkm/c)1.58 0.63472
country
Norway 1.46 0.685774
Sweden 1.7 0.587423
Finland 1.71 0.583196
Denmark 1.22 0.818108
———
Mean VIF 1.63
18
Environ. Res. Commun. 6(2024)125008 J Ottelin et al
ORCID iDs
Juudit Ottelin https://orcid.org/0000-0003-0878-5108
Sarah Olson https://orcid.org/0009-0003-3262-1303
Áróra Árnadóttir https://orcid.org/0000-0002-2345-5919
Jukka Heinonen https://orcid.org/0000-0002-7298-4999
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