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Active travel (walking or cycling for transport) is considered the most sustainable form of personal transport. Yet its net effects on mobility-related CO2 emissions are complex and under-researched. Here we collected travel activity data in seven European cities and derived life cycle CO2 emissions across modes and purposes. Daily mobility-related life cycle CO2 emissions were 3.2 kgCO2 per person, with car travel contributing 70% and cycling 1%. Cyclists had 84% lower life cycle CO2 emissions than non-cyclists. Life cycle CO2 emissions decreased by -14% per additional cycling trip and decreased by -62% for each avoided car trip. An average person who ‘shifted travel modes’ from car to bike decreased life cycle CO2 emissions by 3.2 kgCO2/day. Promoting active travel should be a cornerstone of strategies to meet net zero carbon targets, particularly in urban areas, while also improving public health and quality of urban life.
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The climate change mitigation effects of daily active travel in cities
Christian Brand ( )
University of Oxford
Evi Dons
Centre for Environmental Sciences, Hasselt University, Diepenbeek
Esther Anaya-Boig
Centre for Environmental Policy, Imperial College London, London
Ione Avila-Palencia
Urban Health Collaborative, Dornsife School of Public Health, Drexel University, Philadelphia, PA
Anna Clark
Trivector Trac, Stockholm
Audrey de Nazelle
Centre for Environmental Policy, Imperial College London, London
Mireia Gascon
ISGlobal, Barcelona
Mailin Gaupp-Berghausen
Austrian Institute for Regional Studies, Vienna
Regine Gerike
Dresden University of Technology, Chair of Integrated Transport Planning and Trac Engineering, Dresden
Thomas Gotschi
University of Oregon, School of Planning, Public Policy and Management, Eugene, Oregon
Francesco Iacorossi
Agenzia Roma Servizi per la Mobilita' Srl, Rome
Sonja Kahlmeier
Fernfachhochschule Schweiz, Brig
Michelle Laeremans
Flemish Institute for Technological Research (VITO), Mol
Mark Nieuwenhuijsen
ISGlobal, Barcelona
Juan Orjuela Mendoza
Transport Studies Unit, University of Oxford, Oxford
Francesca Racioppi
World Health Organization Regional Oce for Europe, European Centre for Environment and Health, Bonn
Elisabeth Raser
University of Natural Resources and Life Sciences Vienna, Institute for Transport Studies, Vienna
David Rojas Rueda
Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado
Arnout Standaert
Flemish Institute for Technological Research (VITO), Mol
Erik Stigell
Trivector Trac, Stockholm
Simona Sulikova
Transport Studies Unit, University of Oxford, Oxford
Sandra Wegener
University of Natural Resources and Life Sciences Vienna, Institute for Transport Studies, Vienna
Luc Int Panis
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Transportation Research Institute (IMOB), Hasselt University, Diepenbeek
Research Article
Keywords: CO2 emissions, active mobility, walking; cycling, climate change mitigation, sustainable urban transport
License: This work is licensed under a Creative Commons Attribution 4.0 International License.  Read Full License
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Active travel (walking or cycling for transport) is considered the most sustainable form of personal transport. Yet its net effects on mobility-
related CO2 emissions are complex and under-researched. Here we collected travel activity data in seven European cities and derived life cycle
CO2 emissions across modes and purposes. Daily mobility-related life cycle CO2 emissions were 3.2 kgCO2 per person, with car travel
contributing 70% and cycling 1%. Cyclists had 84% lower life cycle CO2 emissions than non-cyclists. Life cycle CO2 emissions decreased by
-14% per
cycling trip and decreased by -62% for each
car trip. An average person who ‘shifted travel modes’ from car to bike
decreased life cycle CO2 emissions by 3.2 kgCO2/day. Promoting active travel should be a cornerstone of strategies to meet net zero carbon
targets, particularly in urban areas, while also improving public health and quality of urban life.
1. Introduction
Transport has been one of the most challenging sectors for reducing its signicant impacts of fossil energy use and associated greenhouse
gas (GHG) emissions since the 1990s (Sims et al., 2014). In Europe, GHG emissions decreased in the majority of sectors between 1990 and
2017, with the exception of transport (EEA, 2019). Modal shifts away from carbon-intensive to low-carbon modes of travel hold considerable
potential to mitigate carbon emissions (Cuenot et al., 2012). Given the urgency of moving to a ‘net zero’ carbon emissions economy, there is
growing consensus that technological substitution via electrication will not be sucient or fast enough to transform the transport system
(Creutzig et al., 2018; IPCC, 2018). Beyond a net reduction in travel demand, one of the more promising ways to reduce transport carbon
dioxide (CO2) emissions[1] is to promote and invest in active modes of transport (e.g. walking, cycling, e-biking) while ‘demoting’ motorized
modes that rely on fossil energy sources (Bearman and Singleton, 2014; Castro et al., 2019; de Nazelle et al., 2010; ECF, 2011; Frank et al., 2010;
Goodman et al., 2012; Keall et al., 2018; Neves and Brand, 2019; Quarmby et al., 2019; Sælensminde, 2004; Scheepers et al., 2014; Tainio et al.,
2017; Woodcock et al., 2018). This could reduce CO2 emissions from road transport more quickly than technological measures alone,
particularly in urban areas (Beckx et al., 2013; Creutzig et al., 2018; Graham-Rowe et al., 2011; Neves and Brand, 2019). This may become even
more relevant considering the vast economic effects of the COVID-19 pandemic, which may result in reduced capacities of individuals and
organizations to renew the rolling stock of vehicles in the short and medium period, and of governments to provide incentives to eet renewal.
So how much carbon can be saved – overall – by travelling actively? The complex relationships between carbon emissions and transport have
been investigated for many years. Previous research has shown that travel carbon emissions are determined by transport mode choice and
usage, which in turn are inuenced by journey purpose (e.g. commuting, visiting friends and family, shopping), individual and household
characteristics (e.g. location, socio-economic status, car ownership, type of car, bike access, perceptions related to the safety, convenience and
social status associated with active travel), land use and built environment factors (which impact journey lengths and trip rates), accessibility
to public transport, jobs and services, and metereological conditions (Adams, 2010; Alvanides, 2014; Anable and Brand, 2019; Bearman and
Singleton, 2014; Brand and Boardman, 2008; Brand and Preston, 2010; Cameron et al., 2003; Carlsson-Kanyama and Linden, 1999; Ko et al.,
2011; Nicolas and David, 2009; Stead, 1999; Timmermans et al., 2003). Yet active travel studies are often based on analyses of the potential
for emissions mitigation (Yang et al., 2018), the generation of scenarios (Goodman et al., 2019; Lovelace et al., 2011; Tainio et al., 2017;
Woodcock et al., 2018) or smaller scale studies focusing on a single city, region or country (Brand et al., 2014; Neves and Brand, 2019). To
better understand the carbon-reduction impacts of active travel, it is important to assess the key determinants of travel carbon emissions
across a wide range of contexts and include a detailed, comparative analysis of the distribution and composition of emissions by transport
mode (e.g. bike, car, van, public transport, e-bike) and emissions source (e.g. vehicle use, energy supply, vehicle manufacturing). To answer the
above question it is also important to understand why, where, when and how far people travel – many studies do not dig that deep and across
different contexts. While cycling cannot be considered a ‘zero-carbon emissions’ mode of transport, life cycle emissions from cycling can be
more than ten times lower per passenger-km travelled than those from passenger cars (ECF, 2011). For most journey purposes active travel
covers short to medium trips – typically 2km for walking, 5km for cycling and 10km for e-biking (Castro et al., 2019). Typically, the majority of
trips in this range is made by car (Beckx et al., 2013; JRC, 2013; Keall et al., 2018; Neves and Brand, 2019; U.S. Department of Transportation,
2017), with short trips contributing disproportionately to emissions because of ‘cold starts’, especially in colder climates (Beckx et al., 2010; de
Nazelle et al., 2010). On the other hand, these short trips, which represent the majority of trips undertaken by car within cities, would be
amenable to at least a partial modal shift towards active travel (Beckx et al., 2013; Carse et al., 2013; de Nazelle et al., 2010; Goodman et al.,
2014; Keall et al., 2018; Neves and Brand, 2019; Vagane, 2007). To investigate these issues, we included seven European cities with different
travel activity patterns, transport mode shares, infrastructure provisions, climates, mobility cultures and socio-economic makeups. To the best
of our knowledge no international multicenter study on the associations of daily active and motorized travel and carbon emissions has been
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This study also addresses a number of practical needs. First, there is a lack of standardized denitions and measurements (self-reported or
measured) to identify groups within a population who use a ‘main’ mode of transport (e.g. based on distance, duration or frequency over a
given time period), or who may be classied as ‘frequent cyclists’, ‘occasional walkers’ – or simply ‘cycling’ (yes/no). These should be split as
much as possible as there may be different effects on overall CO2 emissions. Second, given the dominance of travel by car and public
transport, active modes must be included to the extent possible by oversampling people using these modes. And nally, instead of focusing on
the commute journey only, as with many studies that rely on Census data, trips for a wider range of journey purposes should be considered.
This paper aims to investigate to what extent active travel is associated with lower carbon emissions from daily travel activity. Using primary
data collected in a large European multicenter study of transport, environment and health, the paper rst describes how total life cycle CO2
emissions from daily travel activity were derived at the individual and population levels, considering urban transport modes, trip stages, trip
purposes and emissions categories. The core analysis then identies the main determinants and models the effects of mode choice and usage
on life cycle carbon emissions. Further analysis identies and compares differences in life cycle carbon emissions between ‘groups of
transport users’, including by ‘main’ mode of transport and different categories of cycling frequency. By doing so, the paper provides a detailed
and nuanced assessment of the benets of active travel in reducing total life cycle carbon emissions in cities.
[1] For transport, CO2 is by far the most important greenhouse gas, comprising approximately 99% of direct greenhouse gas emissions.
Surface transport is still dominated by vehicles with internal combustion engines running on petrol (gasoline) and diesel fuels. These
propulsion systems emit relatively small amounts of the non-CO2 greenhouse gases methane (CH4) and nitrous oxide (N2O), adding
approximately 1% to total greenhouse gas emissions over and above CO2.
2. Materials And Methods
2.1 Study design and population
This study used longitudinal data from the ‘Physical Activity through Sustainable Transport Approaches’ (PASTA) project (Dons et al., 2015;
Gerike et al., 2016). The analytical framework of PASTA distinguished hierarchical levels for various factors (i.e. city, individual, and trips), and
four main domains that inuence mobility behavior, namely factors relating to transport mode choice and use, socio-demographic factors,
socio-geographical factors, and socio-psychological factors (Dons et al., 2015; Götschi et al., 2017). Seven European cities (Antwerp,
Barcelona, London, Orebro, Rome, Vienna, and Zurich) were selected to provide a good representativeness of urban environments in terms of
size, built environment, transport provision, modal split and ambition to increase levels of active travel (Raser et al., 2018). To ensure
suciently large sample sizes for different transport modes, users of less common transport modes such as cycling were oversampled (Raser
et al., 2018). Participants were recruited opportunistically on a rolling basis following a standardized guidance for all cities and also some city-
specic approaches. A comprehensive user engagement strategy was applied to minimize attrition over the two-year timeframe. Further details
on the recruitment strategy are given elsewhere (Gaupp-Berghausen et al., 2019).
A total of 10,722 participants entered the study on a rolling basis between November 2014 and November 2016 by completing a baseline
questionnaire (BLQ). Participants provided detailed information on general travel behavior, daily travel activity, geolocations (home, work,
education), vehicle ownership (private motorized, bicycle, etc.), public transport accessibility and socio-demographic characteristics. Follow-up
questionnaires were distributed every two weeks: every third of these follow-up questionnaires also included a one-day travel diary, henceforth
labelled a ‘long follow-up’ (long FUQ) (Dons et al., 2015). All valid travel diaries were included in the analyses, implying that some participants
provided multiple diary data at different time points. Using longitudinal data aimed to improve measurement of ‘typical’ travel behavior
(Branion-Calles et al., 2019). Participants had to be 18 years of age (16 years in Zurich) or older, and had to give informed consent at
registration. Data handling and ethical considerations regarding condentiality and privacy of the information collected were reported in the
study protocol (Dons et al., 2015). Table S2 in the Supplementary Information provides an excerpt of the PASTA BLQ, including travel diary
2.2 Exposure: transport mode choice and use
The primary exposure variables were daily trip frequencies obtained from the travel diaries, for each of the main modes: walking; cycling; e-
biking; motorcycle or moped; public transport; and car or van. The most common metric used by local and national administrations across the
world is mode share (or split) by trip frequency, not by distance (EPOMM, 2020; U.S. Department of Transportation, 2017); hence the results of
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the primary exposure analysis may be used to estimate life cycle CO2 emissions directly from trip mode share data. Due to low counts of e-
biking and motorcycle trips, e-biking was merged with cycling, with indirect emissions derived from observed bike/e-bike shares (see also
footnote of Table 1). Also, motorcycle was merged with car as reported CO2 emission rates for motorcycles are comparable to cars
on a per
passenger-km basis
(BEIS, 2019). Participants provided information on each trip made on the previous day, including start time, location of
origin, transport mode, trip purpose, location of destination, end time and duration (Supplementary Table S2). The diary was based on the
established KONTIV-Design (Brög et al., 2009; Socialdata, 2009), with some adaptations for online use. 5623 participants provided a valid
travel diary in either the BLQ or the long FUQ; out of those 3836 participants completed valid baseline surveys and travel diaries. In the travel
diary, trip purpose, duration and location were self-reported. Total trip duration was also derived as the difference between start and end time,
while trip distance was obtained retrospectively feeding origin and destination coordinates to the Google Maps Application Programming
Interfaces (API), which returned the fastest route per mode between origin and destination.
Three secondary exposure variables were developed to explore differences between groups of individuals. First, participants were categorized
as using a ‘main mode’ of travel based on furthest daily distance (levels: walking, cycling, car, public transport). Further categorizations based
on cycling frequency included a dichotomous variable of ‘cycling’ on the diary day (yes/no) as well as a trichotomous variable characterizing
participants as ‘frequent cyclist’ (three or more times a day), ‘occasional cyclist’ (once or twice a day), or ‘non-cyclist’ (none). Table 2 shows
sample sizes and mean (SD) values of the primary outcome variable for each group.
2.3 Outcome variables: carbon dioxide emissions
The primary outcome of interest was daily life cycle CO2 emissions (mass of carbon dioxide in gram or kilogram per day) attributable to
passenger travel. Life cycle CO2 emissions categories considered were
energy supply
emissions and
vehicle production
emissions. First, operational emissions were derived
for each trip
based on trip distance (computed from travel diary data), ‘hot’ carbon
emissions factors, emissions from ‘cold starts’ (for cars only) and vehicle occupancy rates (passengers/vehicle) that varied by trip purpose.
The method for cars and vans considered mean trip speeds (derived from the travel diaries), location-specic vehicle eet compositions (taking
into account the types of vehicle operating in the vehicle eets during the study period) and the effect of ‘real world driving’ (adding 22% to
carbon emissions derived from ‘real world’ test data based on BEIS (2019) and ICCT (ICCT, 2017)) to calculate the so called ‘hot’ emission of
CO2 emitted per car-km. For motorcycle, bus and rail, fuel type shares and occupancy rates were based on BEIS (2019). Buses were mainly
powered by diesel powertrains; motorcycles were 100% gasoline; and urban rail was assumed to be all electric. For cars, ‘cold start’ excess
emissions were added to ‘hot’ emissions based on the vehcile eet composition, ambient temperatures (see Supplementary Table S13) and trip
distances observed in each city: across the seven cities, cold start emissions averaged 126 (SD 42) gCO2 per car
, with the trip share of a
car operating with a ‘cold’ engine averaging 13 (SD 8) percent. Cold start emissions were higher-than-average in Orebro and Zurich, and lower
in Barcelona. Second, carbon emissions from energy supply considered upstream emissions from the extraction, production, generation and
distribution of energy supply, with values taken from international databases for fossil fuel emissions (2016; JEC, 2014; Odeh et al., 2013) and
emissions from electricity generation and supply (Ecometrica, 2011). Third, vehicle life cycle emissions considered emissions from the
manufacture of vehicles, with aggregate carbon values per vehicle type (cars, motorcycles, bikes and public transport vehicles) derived
assuming typical lifetime mileages, mass body weights, material composition and material-specic emissions and energy use factors. The
main functional relationships and data are provided in the Supplementary Information. The derived emissions rates are shown in Table 1 for
each city, disaggregated by emissions category and transport mode.
Table 1: Mean CO2 emissions per passenger-km by city, emissions category and transport mode (showing 2014-2016 averages to match study
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Operational Energy/fuel supply Vehicle supply
Car, van or
motorcycle *
Car, van,
Bicycle, e-bike
Car, van,
Bicycle, e-
bike #
gCO2/pkm gCO2/pkm gCO2/pkm gCO2/pkm gCO2/pkm gCO2/pkm gCO2/pkm gCO2/pkm
mean SD mean SD mean SD mean SD mean SD mean SD mean SD mean SD
Antwerp 141.5 38.6 14.5 0.1 27.2 5.6 20.9 0.5 0.111 0.003 12.9 1.9 2.9 0.0 5.1 0.0
Barcelona 142.2 45.8 17.2 0.1 27.5 7.2 30.5 0.6 0.171 0.004 10.6 1.6 3.0 0.0 5.1 0.0
London 165.6 46.7 52.0 0.3 30.4 6.8 43.5 0.6 0.236 0.004 12.9 1.9 3.6 0.0 5.1 0.0
Oerebro 153.3 46.8 68.8 0.3 27.5 6.1 16.7 0.1 0.011 0.000 13.2 2.1 4.2 0.0 5.1 0.0
Rome 144.5 37.5 36.4 0.2 27.7 6.0 33.9 0.6 0.200 0.004 11.5 1.8 3.1 0.0 5.1 0.0
Vienna 156.1 45.2 24.7 0.1 29.4 6.6 16.6 0.2 0.087 0.002 13.3 2.0 2.8 0.0 5.1 0.0
Zurich 143.6 45.7 43.4 0.2 26.8 6.2 9.8 0.1 0.002 0.000 12.5 1.8 3.4 0.0 5.1 0.0
* This incorporates different journey speeds, vehicle occupancy rates by trip purpose, national fuel shares of the vehicle eet, and cold start
emissions. A 22% uplift was applied to account for ‘real world’ driving conditions. For example in Antwerp in 2016, the car eet was
assumed to comprise the national eet mix of 38% gasoline, 61% diesel and 1% electric; buses were 100% diesel; motorcycles 100%
gasoline. Car occupancy rate was between 1.16 (commuting) and 2.02 (education), average 1.54 for all trip purposes. Cold/hot ratio of 1.3
and cold trip distance of 3.45 km.
Operational emissions are for bus using average occupancy rates. Energy/fuel supply assume a bus/rail share based on EPOMM Modal
Split Tool. ( For example, Antwerp bus/rail share was 37.5% in 2016.
# The observed e-bike share was 4.5%; therefore, average emissions include 4.5% e-bike, 95.5% normal bike.
Sources: hot and cold emissions factor coecients (EEA, 2012; EMEP/EEA, 2016); vehicle eets (ACEA/ANFAC, 2014; DEFRA/DECC, 2016;
DfT, 2015; SMMT, 2016).
Total daily emissions were calculated as the sum of emissions for each trip, mode and purpose (e.g. the sum of 4 trips on a given day = trip 1:
home to work by car, trip 2: work to shop by bike, trip 3: shop to work by bike; and trip 4: work to home by car). Secondary outcomes of interest
were total life cycle CO2 emissions for four aggregated journey purposes: (1) work or education/school trips; (2) business trips; (3) social or
recreational trips; and (4) shopping, personal business, escort or ‘other’ trips.
2.4 Covariates
A number of covariates were hypothesized to confound the association between carbon emissions and transport mode choice and use (e.g.
Brand et al., 2013; Büchs and Schnepf, 2013; Goodman et al., 2019). Demographic and socio-economic covariates considered in the analyses
were age, sex, employment status, household income, educational level, and household composition (e.g. single occupancy, or having children
or not). Vehicle ownership covariates considered were car accessibility, having a valid driving license, and bicycle accessibility. Health
covariates considered were self-rated health status and Body Mass Index (BMI), which have been shown to inuence motorized travel and
transport CO2 emissions (Goodman et al., 2012). The perceived walking times to the nearest bus stop, tram stop or railway station were
included as public transport accessibility measures. All of the covariates were self-reported. BMI was derived from self-reported weight and
height as
(Dons et al., 2018).
2.5 Statistical analysis
In a rst step, bivariate analyses were performed to assess the association between transport-related CO2 emissions, the exposure variables,
and the potential covariates. Only covariates with p-value<0.1 were included in the linear mixed-effects models. In a second step, differences in
CO2 emissions between the different transport mode users were identied by using mixed-effects linear regression models with city as a
random effect (to take account of correlation among responses from the same city). The analysis used multiple data points for each
individual, obtained on different weekdays; therefore, respondents and weekdays were also included as random effects. Because CO2
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emissions were heavily skewed towards the right (see also Figure 1), we applied the transformation
(adding 0.01 to
avoid turning zeros into missing values) in the comparative analysis. This improved our regression diagnostics, with residuals closer to a
normal distribution and their variance less heteroscedastic. Note a log transformation changes the focus from absolute to relative or
percentage change; therefore, any regression coecient β is a mean difference of the
log outcome
comparing adjacent units of a predictor.
This is practically useless, so we exponentiate the parameter eβ and interpret this value as a geometric mean difference (Vittinghoff et al.,
2012). Three regression models were tted: (0) unadjusted (exposure only); (1) adjusted by socio-demographic covariates: sex, age, education
level, employment status, household income, household composition; and (2) adjusted by all covariates from model 1 and additionally other
covariates of interest (those found to be statistically signicant in previous literature described earlier): holding a valid driving license, access
to a car or van, bicycle ownership, self-rated health, BMI, walking-time accessibility to the nearest bus stop, and walking-time accessibility to
the nearest train station. Age was included as a continuous variable as a proxy for time. The same set of models were tted for each of the
four journey purposes.
Potential interaction by sex, employment status, income, car access, BMI and city were investigated with Type II Wald chisquare tests in the
fully-adjusted models. We observed signicant interactions for some transport modes (e.g. use of all modes and car access; public transport
use and gender; car use and income); therefore, all models’ sensitivity to different levels of the above factors were tested. We also tested the
models’ sensitivity to a number of other factors: age (‘<35 years’), working status (‘working’), car access (‘not having access to a car’), body
weight (‘being overweight’), household income (‘high income’) and city (Table 2). Participants were also ranked according to their CO2
emissions (all travel and by trip purpose) then split into ten emissions deciles. Chi-square tests were performed on selected covariates to prole
the ‘bottom’ and ‘top’ deciles. Possible mediation of the effect of transport mode use on CO2 emissions was assessed for three potential
mediators: total daily distance travelled, BMI and self-rated health (VanderWeele, 2016; Wanner et al., 2012). Only observations without
missing data were included. R statistical software v3.6.1 was used for all analyses.
3. Results
3.1 Sample description and summary statistics
The nal sample included 3836 participants who had completed 9858 one-day travel diaries reporting 34203 trips (Table 2). The sample was
well balanced between male and female, and between the seven cities. Participants were highly educated with 79% of the participants having
at least a secondary or higher education degree. Aged between 16 and 91, the majority of participants were employed full-time (66%), with 72%
on middle to high household incomes (i.e. >€25k) and 34% reported to have children living at home. The share of participants without access
to a car was 21%. While cycling and public transport were the most frequent transport modes among our participants, people travelled furthest
by public transport and car. Transport mode usage was similar between sexes, with a slightly higher prevalence of male cyclists and drivers vs.
female walkers and public transport users. Participants reported an average of 3.47 (SD 1.83) trips per day ranging from 3.10 (SD 1.63) trips
per day in Rome to 3.75 (SD 2.0) trips per day in Antwerp (Table 2). The observed cycling trip share for our sample was between 17% in
Barcelona and 54% in Antwerp (Supplementary Table S1), i.e. somewhat higher than cycling shares reported for the cities (EPOMM, 2020) and
a direct result of purposively oversampling cyclists. Reported trip durations and distances were highly variable between subjects and cities,
with respondents travelling on average 36.1 (SD 63.5) km a day and for 87.8 (SD 70.4) min a day. Average trip lengths across the cities were
1.1 (SD 1.6) km for walking, 5.0 (SD 5.3) km for cycling, 20.5 (SD 45.9) km for driving and 16.7 (SD 33.6) km for public transport. Further
results for each city are given in Supplementary Table S3.
3.2 Levels and sources of life cycle CO2 emissions
Life cycle CO2 emissions from all travel activity were 3.18 (SD 7.68) kilograms of CO2 (kgCO2) per person per day, with the majority from car
travel at 2.23 (SD 7.25) kgCO2/day – i.e. 70% of the daily total (see Table 2). In contrast, life cycle emissions from cycling (which included a
4.5% share of e-biking across the sample) amounted for only 0.03 (SD 0.05) kgCO2/day. Direct (operational) emissions from all travel activity
made up the majority (70%) of total life cycle emissions. While travel to work or place of education produced the largest share of CO2
emissions (37%), there were also considerable contributions from social and recreational trips (34%), business trips (11%) and travel for
shopping or personal business (17%). Figure 1 shows a highly unequal distribution of emissions. It also shows that the top decile of emitters
were responsible for 59% (all purposes), 47% (work or education), 78% (business), 67% (social or recreational) and 58% (shopping, personal
business, escort or other) of the respective life cycle CO2 emissions. Those in the top decile were more likely to be male, have higher household
incomes, holding a driving license and always have access to a car, be in full-time employment, have higher BMI, have poor bus or train
accessibility and live in Orebro, Antwerp or Rome. In contrast, those in the bottom decile of emitters were more likely to be female, economically
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inactive or a student, living in a household without kids, be on lower household incomes, not to hold a driving license, without access to a car,
own a bike, have lower BMI, live nearer to train stations, and live in Barcelona or London. To explain this it is worth highlighting that while
Antwerp and Orebro had signicantly[2] higher cycling trip shares amongst the case study cities, they also had higher car shares (together with
Rome) and low walking shares (also together with Rome). On the contrary, Barcelona and London had lower car trip shares (like Vienna and
Zurich) and higher walking shares (Supplementary Table S3).
Table 2: Summary statistics of outcomes, exposures and other covariates
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Total study sample (n=9858) and mean (SD) values
CO2 emissions All modes, life cycle 3.18 (7.68)
(kg per day) Car, life cycle 2.23 (7.25)
Public transport, life cycle 0.93 (2.90)
Bike, life cycle 0.03 (0.05)
Walk, life cycle 0 (--)
Al modes, direct only
2.22 (5.62)
All modes, indirect only
0.96 (2.20)
Transport mode usage Car 0.69 (1.29)
(trips per day) Public transport 0.90 (1.24)
Bike 1.05 (1.58)
Walk 0.82 (1.36)
All modes 3.47 (1.83)
Average distance travelled Car 14.61 (50.32)
(km per day) Public transport 15.51 (43.62)
Bike 5.06 (9.71)
Walk 0.88 (2.08)
All modes 36.06 (63.51)
Average travel time (min/day) All modes 87.84 (70.45)
Age (years) All 39.19 (11.16)
BMI (kg/m2)All 23.66 (3.83)
Sub samples/groups and mean(SD) values of main outcome measure
Life cycle CO2 (mean (SD)), in kg/day n (%)
Main mode Car 9.139 (12.532) 2307 (23%)
(based on distance) Public transport 2.746 (5.292) 3546 (36%)
Bike 0.169 (0.468) 3012 (31%)
Walk 0.031 (0.159) 993 (10%)
Cycling category Non-cyclist (none) 4.438 (8.892) 6031 (61%)
(based on trips per day) Occasional cyclist (once or twice) 1.517 (5.552) 2329 (24%)
Frequent cyclist (thrice or more) 0.708 (2.343) 1498 (15%)
Cycling (yes/no) Not cycling on the day 4.438 (8.892) 6031 (61%)
Cycling on the day 1.201 (4.589) 3827 (39%)
City Antwerp 3.487 (7.763) 1713 (17%)
Barcelona 2.468 (5.792) 1806 (18%)
London 3.209 (7.788) 1027 (10%)
Oerebro 4.559 (9.451) 607 (6%)
Rome 3.929 (10.012) 1061 (11%)
Vienna 2.651 (6.153) 1752 (18%)
Zurich 3.199 (8.16) 1892 (19%)
Page 10/23
Sex Male 3.305 (8.043) 5061 (51%)
Female 3.051 (7.282) 4797 (49%)
Age (for sensitivity analysis) Age <35 years 2.903 (6.398) 4199 (43%)
Age >=35 years 3.387 (8.507) 5659 (57%)
Age >55 years 3.807 (9.551) 981 (10%)
Self-rated health Excellent 3.197 (7.857) 1036 (10%)
Very good 3.074 (7.854) 4221 (43%)
Good 3.331 (7.575) 3839 (39%)
Fair or poor 3.001 (6.998) 762 (8%)
BMI (for sensitivity analysis) Healthy BMI (18.5<=BMI<25) 3.019 (7.307) 6927 (70.3%)
Overweight (BMI>=25) 3.599 (8.649) 2599 (26.4%)
Household income Low income (Less than €25k) 2.884 (7.436) 2713 (28%)
Middle income (€25k to €75k) 3.176 (7.449) 5535 (56%)
High income (€75k or more) 3.699 (8.503) 1610 (16%)
Employment status Working (full-time or part-time) 3.241 (7.761) 8404 (85%)
Not working (retired/student/etc.) 2.838 (7.208) 1454 (15%)
Education level Higher education or degree 3.124 (7.261) 7814 (79%)
No higher education or degree 3.401 (9.118) 2044 (21%)
Household composition HH two or more adults, no kids 3.156 (7.462) 4788 (49%)
Single HH, no kids 2.778 (6.133) 1750 (18%)
HH with kids 3.431 (8.662) 3320 (34%)
Car accessibility Always or sometimes 3.561 (8.093) 7755 (79%)
Never 1.781 (5.719) 2103 (21%)
^ Direct: tailpipe emissions. ^ Indirect: well-to-tank (fuel/energy production) plus vehicle manufacture. BMI: body mass index.
In our sample, respondents in Orebro and Rome produced signicantly higher-than-average CO2 emissions (mean 4.56 kgCO2/day and 3.93
kgCO2/day, respectively) due to the higher car mode shares, while those in Barcelona and Vienna produced lower emissions (mean 2.47
kgCO2/day and 2.65 kgCO2/day, respectively) due to higher share of walking (Barcelona) and a combination of lower car and higher public
transport shares (Vienna) (Table 2 and Supplementary Table S3). Those in Antwerp had the highest active travel share, but also higher (than
sample average) car and lower public transport shares, resulting in higher than average CO2 emissions overall (mean 3.49 kgCO2/day). These
gures are generally in line with regional per capita CO2 emissions estimates. Differences between cities can partially be explained by
differences in sample demographics, socio-economics and observed mode shares (Supplementary Table S1 and Table S3).
3.3 Transport mode usage
We found statistically signicant associations between life cycle CO2 emissions and transport mode usage across all modes of travel (Table
3a): those who cycled or walked more had lower daily mobility-related CO2 emissions, while those who drove more or used public transport
more had higher daily total CO2 emissions. In the fully-adjusted model, log-transformed life cycle carbon emissions
by a factor of
0.15 (95%CI 0.13 to 0.17)
for each additional cycling trip
. They also decreased by a factor of 0.96 (95%CI 0.94 to 0.98) for one less car trip. Or
in other words, for each avoided car trip daily life cycle CO2 emissions from transport decreased by 62% (95%CI 61% to 63%) while for each
additional bike trip life cycle CO2 emission decreased by 14% (95%CI 12% to 16%).[3] Those who made one less car trip and one more bike trip
a day (a proxy for mode shift from car to bike) decreased life cycle CO2 emissions from transport by 67% (95%CI 66% to 68%).
Page 11/23
Table 3: Results from the linear xed-effects and mixed-effects models for the four exposures (n=9858). Full models are presented in the
Supplementary Information.
Model 0: unadjusted
(xed effects)
Model 1: partly adjusted
(mixed effects)
Model 2: fully adjusted
(mixed effects) #
Coecient (95% CI) p-value Coecient (95% CI) p-value Coecient (95% CI) p-value
(a) Association between log-transformed life cycle CO2 emissions and transport mode usage (trips/day) (full model in Table S4)
Bike -0.154 (-0.172 to -0.137) < 0.0001 -0.16 (-0.179 to -0.142) < 0.0001 -0.151 (-0.17 to -0.132) < 0.0001
Car 0.997 (0.976 to 1.017) < 0.0001 0.974 (0.953 to 0.996) < 0.0001 0.962 (0.94 to 0.983) < 0.0001
Public transport 0.741 (0.719 to 0.763) < 0.0001 0.737 (0.714 to 0.76) < 0.0001 0.748 (0.724 to 0.771) < 0.0001
Walk -0.287 (-0.305 to -0.269) < 0.0001 -0.278 (-0.297 to -0.259) < 0.0001 -0.273 (-0.292 to -0.254) < 0.0001
(b) Association between log-transformed life cycle CO2 emissions and main transport mode categories (full model in Table S6)
Bike 0 -- 0 -- 0 --
Car 3.89 (3.84 to 3.939) < 0.0001 3.881 (3.829 to 3.932) < 0.0001 3.866 (3.813 to 3.919) < 0.0001
Public transport 2.599 (2.554 to 2.643) < 0.0001 2.624 (2.575 to 2.673) < 0.0001 2.635 (2.586 to 2.684) < 0.0001
Walk -1.071 (-1.137 to -1.005) < 0.0001 -0.956 (-1.023 to -0.888) < 0.0001 -0.931 (-0.999 to -0.862) < 0.0001
(c) Association between log-transformed life cycle CO2 emissions and cycling frequency categories (full model in Table S7)
None 0 -- 0 -- 0 --
Once or twice a day -1.697 (-1.781 to -1.612) < 0.0001 -1.768 (-1.855 to -1.681) < 0.0001 -1.747 (-1.835 to -1.659) < 0.0001
Three+ times a day -2.016 (-2.116 to -1.916) < 0.0001 -2.071 (-2.177 to -1.966) < 0.0001 -2.038 (-2.145 to -1.932) < 0.0001
(d) Association between log-transformed life cycle CO2 emissions and cycling (yes/no) (full model in Table S8)
Not cycling 0 -- 0 -- 0 --
Cycling -1.822 (-1.893 to -1.75) < 0.0001 -1.875 (-1.952 to -1.797) < 0.0001 -1.848 (-1.927 to -1.769) < 0.0001
Model 1 adjusted for sex, age, education level, employment status, household income, household composition; city and person as random
#Model 2 adjusted for sex, age, education level, employment status, household income, household composition, driver license, car access,
bike access, self-rated health, BMI, bus accessibility, rail accessibility; city, person and day of the week as random effects.
Adjusting for demographic, socio-economic and other individual variables only slightly changed the estimates in the partly and the fully
adjusted models (model 1 and model 2) compared to the unadjusted model (model 0). The addition of car availability and bus station
accessibility in the fully adjusted model (model 2) slightly lowered the estimate for car but increased the estimate for public transport use
compared to the unadjusted (0) and partly adjusted models (1).
The effects of transport mode use on transformed carbon emissions was partially mediated via total distance travelled (see Figure 2): the
indirect effects of total distance travelled were +0.13 for car use (13% mediated), -0.02 for cycling (14% mediated), +0.10 for public transport
use (13% mediated), and -0.05 for walking (18% mediated). Neither BMI nor health status mediated this association.
Page 12/23
A series of sensitivity analyses largely conrmed our results (Figure 2a): excluding participants older than 35 or on lower incomes did not
change our conclusions; and differences between those ‘working’ and ‘not working’ and those being ‘overweight’ (above 25 kg/m2) and ‘healthy
weight’ were small. For people who did not have access to a car the effects were larger for motorized travel and smaller for active travel,
suggesting that active travel for non-car owning households may substitute for public transport and other active travel.
The associations between log-transformed life cycle CO2 emissions for the four trip purposes (secondary outcomes) and transport mode
usage were also largely signicant (Table 4a and Supplementary Figure S3a). Cycling frequency had larger effects on emissions from
commuting to work or place of education than on emissions from all purposes (primary outcome models). Motorized transport mode use
showed larger effects on life cycle CO2 emissions from social, shopping and recreational travel than for work/business travel. The
‘economically inactive’ (home duties, retired, unemployed, etc.) showed signicantly higher emissions for social, recreational, shopping and
personal business purposes, with lower emissions from work or educational trips, as expected. Those with children living at home had
signicantly lower business, social and recreational emissions, while emissions from shopping, personal business and escort trips were higher.
Poor bus accessibility and better car access meant higher emissions from work or educational trips.
3.4 Main mode of travel (by daily distance travelled)
We also found statistically signicant associations between life cycle CO2 emissions and the main modes of travel according to daily distance
travelled (Table 3b): when compared to using a bike as the main mode, using the car or public transport increased CO2 while walking
decreased CO2. In the fully adjusted model (model 2) CO2 emissions were 98 (95%CI 98 to 98) percent higher for using a car or van as the main
mode than for using a bike. An average person using a car or van as the main mode had 7.1 kgCO2/day higher life cycle CO2 emissions than
someone using a bike as their main mode of transport. A comparison with the results of the non-transformed model suggested that using a car
or van increased emissions by 8.9 kgCO2/day when compared to cycling as the main mode (Supplementary Table S5 and Figure S2) –
suggesting the linear model slightly overestimated differences in emissions by main mode when compared to the (statistically superior) log-
linear model. Those using public transport as the main mode had 71 (95%CI 71 to 71) percent lower emissions than those mainly using a car,
van or motorcycle; for an average person this difference equated to 5.1 kgCO2/day.
Again, the sensitivity analysis (Figure 2b) largely conrmed our results. Total distance travelled partially (12%) mediated the effects of main
mode (by daily distance) on transformed life cycle CO2 emissions. The associations for log-transformed CO2 emissions by journey purpose
were also all signicant (Supplementary Table S10 and Figure S3), with the strongest effects for mainly using public transport for work or
education and car for social and shopping trips. Women, those with a degree or no access to a car had signicantly lower work or education
emissions. As expected, the economically inactive had signicantly higher social, recreational and shopping/personal business emissions, yet
lower work/education emissions.
3.5 Cycling frequency and cycling vs not cycling
Associations between life cycle CO2 emissions and cycling frequency were all highly signicant. Table 3c shows that in the fully adjusted
model (model 2) life cycle CO2 emissions were 83 (95%CI 81 to 84) percent lower for ‘occasional cyclists’ (i.e. those cycling ‘once or twice a
day’) than for those who did not cycle; and they were even lower for ‘frequent cyclists’ (those cycling ‘three or more times a day’) with 87
(95%CI 86 to 88) percent lower daily life cycle CO2. The sensitivity analysis (Figure 2c) generally conrmed our results, with slightly higher
effects for high earners and lower effects if you were younger or without access to a car. Regular cycling was also associated with reduced life
cycle CO2 emissions for all the four trip purposes, with the strongest effect observed for commuting and social trips (Supplementary Table
S11): cycling three or more times a day decreased life cycle CO2 emissions for work or education by 78 (95%CI 75 to 80) percent, for social or
Page 13/23
recreational trips by 53 (95%CI 46 to 59) percent, for shopping and personal business by 29 (95%CI 19 to 38) percent, and for business travel
by 20 (95%CI 10 to 28) percent.
As expected, the binary cyclist/non-cyclist analysis showed similar effect sizes and correlations to the analysis of cycling frequency for both
primary and secondary outcome measures. ‘Cyclists’ had 84 (95%CI 83 to 85) percent lower life cycle CO2 emissions than ‘non-cyclists’ (Table
2d and Supplementary Table S12); this translated into 0.97 (95%CI 0.54 to 1.74) kgCO2/day lower life cycle CO2 emissions for an average
person who cycled. The sensitivity analysis showed that the effects were lower for the younger respondents and those without access to a car,
and higher for those on higher incomes (Figure 2d).
Table 4: Results from the fully-adjusted mixed-effects models for the four exposures, by trip purpose
Page 14/23
n=9858 Work or education #Business #Social or recreational #Shopping, personal business,
escort, or ‘other’ #
Coecient (95%
CI) p-
value Coecient (95%
CI) p-
value Coecient (95%
CI) p-
value Coecient (95% CI) p-value
(a) Association between log-transformed life cycle CO2 emissions and transport mode usage (trips/day) (full model in Table S9)
Bike -0.24 (-0.27 to
-0.209) <
0.001 0.019 (-0.008 to
0.046) 0.174 0.062 (0.031 to
0.094) <
0.001 0.158 (0.126 to 0.189) < 0.001
Car 0.191 (0.157 to
0.226) <
0.001 0.172 (0.141 to
0.203) <
0.001 0.725 (0.689 to
0.761) <
0.001 0.826 (0.79 to 0.861) < 0.001
PT 0.412 (0.375 to
0.449) <
0.001 0.168 (0.134 to
0.201) <
0.001 0.475 (0.436 to
0.514) <
0.001 0.393 (0.354 to 0.431) < 0.001
Walk -0.272 (-0.302 to
-0.242) <
0.001 -0.064 (-0.091 to
-0.037) <
0.001 -0.092 (-0.123 to
-0.061) <
0.001 -0.025 (-0.056 to 0.006) 0.112
(b) Association between log-transformed life cycle CO2 emissions and main transport mode categories (full model in Table S10)
Bike 0 -- 0 -- 0 --  --
Car 1.532 (1.424 to
1.641) <
0.001 0.762 (0.662 to
0.863) <
0.001 2.281 (2.164 to
2.397) <
0.001 1.987 (1.867 to 2.108) < 0.001
PT 1.873 (1.774 to
1.973) <
0.001 0.469 (0.378 to
0.561) <
0.001 1.002 (0.895 to
1.108) <
0.001 0.677 (0.566 to 0.787) < 0.001
Walk -0.648 (-0.787 to
-0.509) <
0.001 -0.141 (-0.27 to
-0.011) 0.033 -0.784 (-0.934 to
-0.634) <
0.001 -0.462 (-0.617 to -0.306) < 0.001
(c) Association between log-transformed life cycle CO2 emissions and cycling frequency categories (full model in Table S11)
None 0 -- 0 -- 0 --  --
times -1.086 (-1.188 to
-0.983) <
0.001 -0.433 (-0.522 to
-0.344) <
0.001 -0.865 (-0.977 to
-0.754) <
0.001 -0.77 (-0.882 to -0.658) < 0.001
times -1.498 (-1.622 to
-1.374) <
0.001 -0.218 (-0.324 to
-0.111) <
0.001 -0.756 (-0.89 to
-0.622) <
0.001 -0.344 (-0.479 to -0.21) < 0.001
(d) Association between log-transformed life cycle CO2 emissions and cycling (yes/no) (full model in Table S12)
cycling 0 -- 0 -- 0 --  --
Cycling -1.229 (-1.321 to
-1.136) <
0.001 -0.356 (-0.435 to
-0.277) <
0.001 -0.826 (-0.925 to
-0.727) <
0.001 -0.617 (-0.717 to -0.517) < 0.001
# Models are mixed effects models fully adjusted for sex, age, education level, employment status, household income, household
composition, driver license, car access, bike access, self-rated health, BMI, bus accessibility, rail accessibility; city, person and day of the
week as random effects.
PT=public transport.
3.6 Sensitivity: city effects
The random intercepts of city explained 2.2% (a: transport mode usage), 5.4% (b: main mode of transport), 2.6% (c: cycling frequency) and
2.5% (d: cycling yes/no) of the residual variance in the fully adjusted models. Mean CO2 emissions were signicantly lower in Barcelona and
Vienna and higher in Orebro and Rome (Table 2). Further sensitivity analyses of the fully adjusted models stratied by city showed that the
effect estimates for cycling were generally the lowest in Barcelona and highest in Orebro and Rome (Figure 3). By comparison, CO2 effects for
car travel were highest in Barcelona (and Vienna to some extent) and lowest in London and Rome.
Page 15/23
[2] Comparing deciles with chi-square tests of independence.
[3] To derive percentage changes of the untransformed outcome, we exponentiated the regression coecient, subtracted 1 and multiplied by
100 (Vittinghoff et al, 2012).
4. Discussion
4.1 Summary of results
This paper started on the premise that we still do not know very much about how much carbon from passenger transport is saved –
by travelling actively. In this multi-city study with thousands of participants providing nearly 10,000 valid person-days of travel activity, we
found highly signicant associations between transport mode choice and total life cycle CO2 emissions. We showed that cyclists had
signicantly lower total CO2 emissions than non-cyclists. More cycling or walking decreased mobility-related life cycle CO2 emissions –
suggesting that active travel indeed substitutes for motorized travel (i.e. this was not just additional travel over and above motorized travel).
This means that even if not all car trips could be substituted by bicycle trips the potential for decreasing emissions is very high. A number of
sensitivity analyses conrmed our main results and provided new insights into differences of emission levels and exposures by city and
journey purpose. The differences in mean emissions and effect sizes in the seven cities may be explained by contextual factors such as
differences in modal shares, mode trip lengths, and the provision (or not) of good public transport services and active travel infrastructure – it
may also be due to differences in sampling (Raser et al., 2018). The analysis of emissions for each trip purpose highlighted the relative
importance of emissions from non-work/business trips, particularly trips for social and shopping purposes.
4.2 Comparison with previous studies
Mean total CO2 emissions of 3.18 kgCO2/day were much higher than the median (0.81 kgCO2/day) and near the upper end of the derived
interquartile range (0.07-3.27 kgCO2 per day), conrming a positively skewed distribution of emissions. In other words, a relatively small share
of individuals was responsible for the vast majority of carbon emissions, a nding that is very much in line with the evidence on unequal
carbon emissions distributions (Brand and Boardman, 2008; Büchs and Schnepf, 2013; Ko et al., 2011; Preston et al., 2013; Susilo and Stead,
2009). Our ndings that the likelihood of being in top or bottom emissions decile depended on demographic, socio-economic, car availability,
health, public transport accessibility and contextual factors further support the growing evidence on travel emissions inequalities (Banister,
2018; Bel and Rosell, 2017; Brand, 2008; Ko et al., 2011).
The analysis of transport mode use as the main exposure showed that each additional cycling trip reduced life cycle CO2 emissions from all
travel activity by about 14% when compared to baseline emissions. On average, those who did one less trip by car and one more by bike or
public transport decreased emissions by 67% and 19% respectively. To further aid interpretation of the factorial results we need to apply the
percentage changes to baseline (or mean) levels of our measured outcomes. For example, an average person ‘shifting modes’ from car (from 3
to 2 trips a day) to bike (from 0 to 1 trip a day) decreased emissions by 3.2 (95%CI 2.0 to 5.2) kgCO2/day. Similarly, a person ‘shifting modes’
from car (from 3 to 2 trips a day) to public transport (from 0 to 1 trip a day) decreased emissions by 0.9 (95%CI 0.6 to 1.5) kgCO2/day. If 10%
of the population were changing travel behavior this way, emissions would be expected to decrease by about 10% (caràbike) and 3%
(caràpublic transport). The size and direction of emissions changes are in line with some of the few empirical (Brand et al., 2013; Goodman et
al., 2012) and scenario/modelling (Goodman et al., 2019; Rabl and de Nazelle, 2012; Tainio et al., 2017; Woodcock et al., 2018) studies in this
The differences in emissions between people using different modes for the majority (dened by max. distance travelled) of their daily travel
were also highly signicant, although the effects were partially (12%) mediated by total daily distance travelled. Our nding that, on average,
using a bike as the main mode decreased life cycle CO2 emissions by about 7.1 kgCO2/day when compared to using a car or van suggests
that making more sustainable choices on to how we get from A to B has signicant carbon benets. Similarly, our nding that doing at least
one trip a day by bike signicantly decreased mobility-related life cycle CO2 emissions provides further evidence of mode substitution away
from motorized travel.
Page 16/23
Much of the research in this area has focused on travel activity and associated carbon emissions from work and business travel (Bearman and
Singleton, 2014; Clark et al., 2016). In our study, commuting, education and business travel emissions represented ‘only’ about half (49%) of
total emissions, ranging from 39% in Antwerp to 59% in London and Rome. The ndings that life cycle CO2 emissions from social, shopping,
personal business and recreational journeys were more strongly associated to car and, to some extent, public transport use suggest for
research and policy to go beyond commuting and business travel and consider the whole range of journey purposes when investigating mode
shift away from motorized to active travel (Brand et al., 2013). This seems to be particularly important with the growing shares of the elderly in
the population. Shopping and personal business trips were found to be signicantly shorter, therefore increasing the potential for mode shift to
active travel.
The mediation analysis by distance travelled indicated that lower carbon emissions for cyclists was unlikely to be entirely caused by increased
bike usage. The remaining emissions difference might be explained by distance-related factors that inuence mode choice such as urban form
and location of housing, services and jobs (Banister et al., 1997; Beenackers et al., 2012; Curtis, 1996; Welch, 2013).
While focusing on cycling above we also found that using public transport was more benecial than private motorized transport across all
exposure measures, thus conrming ndings from the large body of literature that already exists in this area (see e.g. Banister, 2008; Graham-
Rowe et al., 2011; Nieuwenhuijsen, 2020; Woodcock et al., 2009).
4.3 Limitations of the study
In interpreting these ndings we need to bear in mind the study’s limitations. First, the recruitment and sampling strategy means that our
sample cannot be assumed to be representative of the general population, especially for education level and age. Orebro was the lone city that
made a concerted effort for random sampling, whereas in other cities an opportunistic recruitment strategy was followed (Dons et al., 2015).
However, by oversampling some of the less frequent transport modes, we had a suciently large sample of cyclists in all cities to nd
statistically signicant associations. Second, recall bias and participant burden of a substantive survey instrument may have impacted the
travel diary reporting, which may have reduced the number of reported trips. However, the observed trip frequencies (e.g. 3.47 trips per person
per day on average) and mode shares (e.g. signicantly higher cycling shares in Antwerp, lower cycling shares in Barcelona, higher public
transport shares in London, Vienna and Zurich) were in line with gures reported for the cities (Raser et al., 2018). While trip distances were
derived from Google API data, trip durations were self-reported. Trip durations from self-completion travel diaries are known to be over-reported
(Kelly et al., 2013), so mean speeds may have been lower than actual speeds leading to increased emissions rates in urban areas. However,
further investigation of mean speeds by mode of transport showed that the derived mean speeds of 4.8 kph for walking, 15.6 kph for
cycling/e-biking, 39.9 kph for driving a car or van, and 17.9 kph for urban public transport were in line with gures reported elsewhere (Raser et
al., 2018). Note these are daily averages not just peak-time speeds (as often reported). Third, outcome and exposure variables were reported at
different time points and days of the week – this was taken into account in the mixed effect models by including ‘day of the week’ and person
ID as random (intercept) variables. Other periodic effects cannot be excluded and we tried to cover for that as much as possible by including
relevant time-varying covariates (such as participant age) and factors inuencing outcomes such as ambient temperature (for ‘cold start’
emissions). Fourth, our analysis is cross-sectional, meaning that the direction of causality (if any) behind many of the observed associations is
unclear. A longitudinal analysis of change in emissions by change in exposures is underway and will be reported in due course. Fifth, while we
accounted for several inuencing factors that were often not available in previous studies, such as trip data by mode and purpose,
accessibility and health status, our regression models did not account for more than 78% of the variation in the population (see Supplementary
results). This suggests that travel choices and associated CO2 emissions are also inuenced by other factors such as other built environment
factors or lifestyle and socio-cultural factors (Brand et al., 2019; Panter et al., 2013; Weber and Perrels, 2000). We initially explored and added
more ‘objective’, GIS based data at both home and work locations to the analysis, including street density, building density, richness of
facilities, home-work distance, and public transport availability (timetables, frequency) (Gascon et al., 2019). However, none of these factors
improved the models signicantly, and the main ndings were unchanged. Sixth, we excluded carbon emissions from dietary intake as the
evidence is not strong on whether day-to-day active travel (as opposed to performance/sport activity) signicantly increases overall dietary
intake when compared to motorized travel (Tainio et al., 2017). Finally, the interdisciplinary breadth of the PASTA study meant that we
measured daily travel behavior, individual and spatial-environmental characteristics using briefer survey tools than might have been feasible in
a single-discipline study. This may have introduced some measurement error that could have attenuated our effect sizes. However, the multi-
city approach in different countries with different travel patterns, built environments, public transport accessibility levels and active mobility
use adds value to the analysis, which clearly showed additional insights compared to smaller, single-location studies.
Page 17/23
5. Conclusion
This paper started on the premise that we still do not know very much about how much carbon from passenger transport is saved –
by travelling actively. It investigated to what extent active travel is associated with lower mobility-related life cycle CO2 emissions by using
primary data collected in a large European multicentre study to derive total and purpose-specic life cycle CO2 emissions from travel activity at
the individual and population levels. The analysis of a sample of thousands of participants and nearly 10,000 person-days of daily travel
across the seven sites provided quantitative estimates of the life cycle carbon benets of active travel using a variety of metrics that could be
used in other European cities and beyond.
Active travel has attributes of social distancing that are likely to be desirable for some time (Kissler et al., 2020). It could help to cut back
transportation energy use, CO2 emissions and air pollution while improving population health (Nieuwenhuijsen, 2020; Shaw et al., 2014) as
connement is eased. Therefore, locking in, investing in and promoting active travel should be a cornerstone of sustainability strategies,
policies and planning (Andor et al., 2020; Creutzig et al., 2016; Creutzig et al., 2020) to meet our very challenging sustainable development
goals that are unlikely to be met without signicant mode shift to sustainable transport (Creutzig et al., 2018).
European Commission, FP7 project PASTA Grant Agreement No. 602624. UK Research and Innovation, Centre for Research on Energy
Demand Solutions - Grant Agreement No. EP/R035288/1.
This work was supported by the European project Physical Activity through Sustainable Transportation Approaches (PASTA). PASTA
( was a four-year project funded by the European Union’s Seventh Framework Program (EU FP7) under European
Commission Grant Agreement No. 602624. CB is also supported by UK Research and Innovation (UKRI) under the Centre for Research on
Energy Demand Solutions (CREDS, Grant agreement number EP/R035288/1). ED is also supported by a postdoctoral scholarship from FWO –
Research Foundation Flanders. ML held a joint PASTA/VITO PhD scholarship. SS is supported by the Martin Filko Scholarship from the
Ministry of Education in Slovakia.
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Supplementary Files
This is a list of supplementary les associated with this preprint. Click to download.
Full-text available
Climate mitigation solutions are often evaluated in terms of their costs and potentials. This accounting, however, shortcuts a comprehensive evaluation of how climate solutions affect human well-being, which, at best, may only be crudely related to cost considerations. Here, we systematically list key sectoral mitigation options on the demand side, and categorize them into avoid, shift and improve categories. We show that these options, bridging socio-behavioral, infrastructural and technological domains, can reduce counterfactual sectoral emissions by 50-80% in end use sectors. Based on expert judgement and literature survey, we then evaluate 324 combinations of wellbeing outcomes and demand side options. We find that these are largely beneficial in improving wellbeing across all measures combined (76% have positive, 22% neutral, and 2.4% have negative effects), even though confidence level is low in the social dimensions of wellbeing. Implementing demand-side solution requires i) an understanding of malleable not fixed preferences, ii) consistently measuring and evaluating constituents of wellbeing, and iii) addressing concerns of incumbents in supply-side industries. Our results shift the emphasis in the climate mitigation solution space from supply-side technologies to demand-side service provision.
Full-text available
By displacing gasoline and diesel fuels, electric cars and fleets reduce emissions from the transportation sector, thus offering important public health benefits. However, public confidence in the reliability of charging infrastructure remains a fundamental barrier to adoption. Using large-scale social data and machine-learning based on 12,720 electric vehicle (EV) charging stations, we provide national evidence on how well the existing charging infrastructure is serving the needs of the rapidly expanding population of EV drivers in 651 core-based statistical areas in the United States. We deploy supervised machine-learning algorithms to automatically classify unstructured text reviews generated by EV users. Extracting behavioural insights at a population scale has been challenging given that streaming data can be costly to hand classify. Using computational approaches, we reduce processing times for research evaluation from weeks of human processing to just minutes of computation. Contrary to theoretical predictions, we find that stations at private charging locations do not outperform public charging locations provided by the government. Overall, nearly half of drivers who use mobility applications have faced negative experiences at EV charging stations in the early growth years of public charging infrastructure, a problem that needs to be fixed as the market for electrified and sustainable transportation expands.
Full-text available
Government policies during the COVID-19 pandemic have drastically altered patterns of energy demand around the world. Many international borders were closed and populations were confined to their homes, which reduced transport and changed consumption patterns. Here we compile government policies and activity data to estimate the decrease in CO2 emissions during forced confinements. Daily global CO2 emissions decreased by –17% (–11 to –25% for ±1σ) by early April 2020 compared with the mean 2019 levels, just under half from changes in surface transport. At their peak, emissions in individual countries decreased by –26% on average. The impact on 2020 annual emissions depends on the duration of the confinement, with a low estimate of –4% (–2 to –7%) if prepandemic conditions return by mid-June, and a high estimate of –7% (–3 to –13%) if some restrictions remain worldwide until the end of 2020. Government actions and economic incentives postcrisis will likely influence the global CO2 emissions path for decades. COVID-19 pandemic lockdowns have altered global energy demands. Using government confinement policies and activity data, daily CO2 emissions have decreased by ~17% to early April 2020 against 2019 levels; annual emissions could be down by 7% (4%) if normality returns by year end (mid-June).
Full-text available
Car owners underestimate total vehicle costs. Giving consumers this information could encourage the switch to cleaner transport and reduce emissions. Car owners underestimate total vehicle costs. Giving consumers this information could encourage the switch to cleaner transport and reduce emissions.
Full-text available
Introduction Half the world population lives in cities and this is likely to increase to 70% over the next 20 years. Suboptimal urban and transport planning has led to e.g. high levels of air pollution and noise, heat island effects and lack of green space and physical activity and thereby an increase in morbidity and premature mortality. How can better urban and transport planning improve public health? Methods A narrative meta-review around a number of cutting edge and visionary studies and practices on how to improve public health through better urban and transport planning reported in the literature and from meetings over the past few years. Results We describe the latest quantitative evidence of how cities can become healthier through better urban and transport planning. It focuses and provides evidence for important interventions, policies and actions that can improve public health, including the need for land use changes, reduce car dependency and move towards public and active transportation, greening of cities, visioning, citizen involvement, collaboration, leadership and investment and systemic approaches. Health impact assessment studies have recently provided new powerful quantitative evidence on how to make cities healthier and will be used as examples. At the same time these measures make also our cities more sustainable (i.e. carbon neutral) and liveable creating multiple benefits. Conclusion Better urban and transport planning can lead to carbon neutral, more liveable and healthier cities, particularly through land use changes, a move from private motorised transportation to public and active transportation and greening of cities.
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What happens next? Four months into the severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) outbreak, we still do not know enough about postrecovery immune protection and environmental and seasonal influences on transmission to predict transmission dynamics accurately. However, we do know that humans are seasonally afflicted by other, less severe coronaviruses. Kissler et al. used existing data to build a deterministic model of multiyear interactions between existing coronaviruses, with a focus on the United States, and used this to project the potential epidemic dynamics and pressures on critical care capacity over the next 5 years. The long-term dynamics of SARS-CoV-2 strongly depends on immune responses and immune cross-reactions between the coronaviruses, as well as the timing of introduction of the new virus into a population. One scenario is that a resurgence in SARS-CoV-2 could occur as far into the future as 2025. Science , this issue p. 860
Full-text available China has emerged as a leading electric vehicle (EV) market, accounting for approximately half of the global EV sales volume. We employed an atmospheric chemistry model to evaluate the air quality impacts from multiple scenarios by considering various EV penetration levels in China and assessed the avoided premature mortality attributed to fine particulate matter and ozone pollution. We find higher fleet electrification ratios can synergistically deliver greater air quality, climate and health benefits. For example, electrifying 27% of private vehicles and a larger proportion of certain commercial fleets can readily reduce the annual concentrations of fine particulate matter, nitrogen dioxide and summer concentrations of ozone by 2030. This scenario can reduce the number of annual premature deaths nationwide by 17,456 (95% confidence interval: 10,656–22,160), with the Beijing–Tianjin–Hebei, Yangtze River Delta and Pearl River Delta regions accounting for ~37% of the total number. The high concentration of health benefits in populous megacities implies that their municipal governments should promote more supportive local incentives. This study further reveals that fleet electrification in China could have more health benefits than net climate benefits in the next decade, which should be realized by policymakers to develop cost-effective strategies for EV development.
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Background: Although walking for travel can help in reaching the daily recommended levels of physical activity, we know relatively little about the correlates of walking for travel in the European context. Objective: Within the framework of the European Physical Activity through Sustainable Transport Approaches (PASTA) project, we aimed to explore the correlates of walking for travel in European cities. Methods: The same protocol was applied in seven European cities. Using a web-based questionnaire, we collected information on total minutes of walking per week, individual characteristics, mobility behavior, and attitude ( N = 7,875 ). Characteristics of the built environment (the home and the work/study addresses) were determined with geographic information system (GIS)-based techniques. We conducted negative binomial regression analyses, including city as a random effect. Factor and principal component analyses were also conducted to define profiles of the different variables of interest. Results: Living in high-density residential areas with richness of facilities and density of public transport stations was associated with increased walking for travel, whereas the same characteristics at the work/study area were less strongly associated with the outcome when the residential and work/study environments were entered in the model jointly. A walk-friendly social environment was associated with walking for travel. All three factors describing different opinions about walking (ranging from good to bad) were associated with increased minutes of walking per week, although the importance given to certain criteria to choose a mode of transport provided different results according to the criteria. Discussion: The present study supports findings from previous research regarding the role of the built environment in the promotion of walking for travel and provides new findings to help in achieving sustainable, healthy, livable, and walkable cities.
Synergistically addressing local and global environmental damages rather than optimizing a specific aspect of the policy conundrum helps to effectively foster climate action in road transport while maintaining public acceptance and socially fair outcomes.
Mobility is a service that demands energy. Energy, in turn, is fraught with complexity in terms of its forms, availability, infrastructure, ownership, extraction, conversion and combustion, and the sociopolitical implications of all of these factors. It is also complex in that the fundamental laws of thermodynamics render it simultaneously extremely productive and profligately wasteful. This has led to a science and policy of energy conservation which is a testing mix of calls to use energy resources more sparingly while applying an engineering focus on maximising work and minimising waste.
Measuring bicycling behaviour is critical to bicycling research. A common study design question is whether to measure bicycling behaviour once (cross-sectional) or multiple times (longitudinal). The Physical Activity through Sustainable Transport Approaches (PASTA) project is a longitudinal cohort study of over 10,000 participants from seven European cities over two years. We used PASTA data as a case study to investigate how measuring once or multiple times impacted three factors: a) sample size b) participation bias and c) accuracy of bicycling behaviour estimates. We compared two scenarios: i) as if only the baseline data were collected (cross-sectional approach) and ii) as if the baseline plus repeat follow-ups were collected (longitudinal approach). We compared each approach in terms of differences in sample size, distribution of sociodemographic characteristics, and bicycling behaviour. In the cross-sectional approach, we measured participants long-term bicycling behaviour by asking for recall of typical weekly habits, while in the longitudinal approach we measured by taking the average of bicycling reported for each 7-day period. Relative to longitudinal, the cross-sectional approach provided a larger sample size and slightly better representation of certain sociodemographic groups, with worse estimates of long-term bicycling behaviour. The longitudinal approach suffered from participation bias, especially the drop-out of more frequent bicyclists. The cross-sectional approach under-estimated the proportion of the population that bicycled, as it captured ‘typical’ behaviour rather than 7-day recall. The magnitude and directionality of the difference between typical weekly (cross-sectional approach) and the average 7-day recall (longitudinal approach) varied depending on how much bicycling was initially reported. In our case study we found that measuring bicycling once, resulted in a larger sample with better representation of sociodemographic groups, but different estimates of long-term bicycling behaviour. Passive detection of bicycling through mobile apps could be a solution to the identified issues.