The climate change mitigation effects of daily active travel in cities

Active travel (walking or cycling for transport) is considered the most sustainable form of personal transport. Yet its net effects on mobility-related CO 2 emissions are complex and under-researched. Here we collected travel activity data in seven European cities and derived life cycle CO 2 emissions across modes and purposes. Daily mobility-related life cycle CO 2 emissions were 3.2 kgCO 2 per person, with car travel contributing 70% and cycling 1%. Cyclists had 84% lower life cycle CO 2 emissions than non-cyclists. Life cycle CO 2 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 CO 2 emissions by 3.2 kgCO 2 /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. 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 CO 2 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 identies the main determinants and models the effects of mode choice and usage on life cycle carbon emissions. Further analysis identies 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 benets of active travel in reducing total life cycle carbon emissions in cities. lower 2 primary data collected in a large European multicentre study to derive total purpose-specic life cycle CO 2 emissions from 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 benets of active travel using a variety of metrics that could be used in other European cities and beyond.


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 (CO 2 ) 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;Saelensminde, 2004;Scheepers et al., 2014;Tainio et al., 2017;Woodcock et al., 2018). This could reduce CO 2 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 reported.
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 CO 2 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 CO 2 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, CO 2 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-CO 2 greenhouse gases methane (CH 4 ) and nitrous oxide (N 2 O), adding approximately 1% to total greenhouse gas emissions over and above CO 2 .

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 cityspeci 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 data.

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; ebiking; 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 the primary exposure analysis may be used to estimate life cycle CO 2 emissions directly from trip mode share data. Due to low counts of ebiking 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 CO 2 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.

Outcome variables: carbon dioxide emissions
The primary outcome of interest was daily life cycle CO 2 emissions (mass of carbon dioxide in gram or kilogram per day) attributable to passenger travel. Life cycle CO 2 emissions categories considered were operational emissions, 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 CO 2 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) gCO 2 per car trip, 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 ( Table 1 for each city, disaggregated by emissions category and transport mode. 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 CO 2 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.

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 CO 2 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 weight(kg)/height(m) 2 (Dons et al., 2018).

Statistical analysis
In a rst step, bivariate analyses were performed to assess the association between transport-related CO 2 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 CO 2 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 CO 2 emissions were heavily skewed towards the right (see also Figure 1), we applied the transformation 'ln([x/mean(x)]+0.01)' (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 e β 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 CO 2 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 CO 2 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.

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.

Levels and sources of life cycle CO 2 emissions
Life cycle CO 2 emissions from all travel activity were 3.18 (SD 7.68) kilograms of CO 2 (kgCO 2 ) per person per day, with the majority from car travel at 2.23 (SD 7.25) kgCO 2 /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) kgCO 2 /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 CO 2 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 CO 2 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 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). 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 CO 2 emissions (mean 4.56 kgCO 2 /day and 3.93 kgCO 2 /day, respectively) due to the higher car mode shares, while those in Barcelona and Vienna produced lower emissions (mean 2.47 kgCO 2 /day and 2.65 kgCO 2 /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 CO 2 emissions overall (mean 3.49 kgCO 2 /day). These gures are generally in line with regional per capita CO 2 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).

Transport mode usage
We found statistically signi cant associations between life cycle CO 2 emissions and transport mode usage across all modes of travel (Table   3a): those who cycled or walked more had lower daily mobility-related CO 2 emissions, while those who drove more or used public transport more had higher daily total CO 2 emissions. In the fully-adjusted model, log-transformed life cycle carbon emissions decreased 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 CO 2 emissions from transport decreased by 62% (95%CI 61% to 63%) while for each additional bike trip life cycle CO 2 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 CO 2 emissions from transport by 67% (95%CI 66% to 68%). # 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.
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/m 2 ) 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 CO 2 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 CO 2 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.

Main mode of travel (by daily distance travelled)
We also found statistically signi cant associations between life cycle CO 2 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 CO 2 while walking decreased CO 2 . In the fully adjusted model (model 2) CO 2 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 kgCO 2 /day higher life cycle CO 2 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 kgCO 2 /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) loglinear 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 kgCO 2 /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 CO 2 emissions. The associations for log-transformed CO 2 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.

Cycling frequency and cycling vs not cycling
Associations between life cycle CO 2 emissions and cycling frequency were all highly signi cant. Table 3c shows that in the fully adjusted model (model 2) life cycle CO 2 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 CO 2 . 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 CO 2 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 CO 2 emissions for work or education by 78 (95%CI 75 to 80) percent, for social or 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 CO 2 emissions than 'non-cyclists' (Table   2d and Supplementary Table S12); this translated into 0.97 (95%CI 0.54 to 1.74) kgCO 2 /day lower life cycle CO 2 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).

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 CO 2 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, CO 2 effects for car travel were highest in Barcelona (and Vienna to some extent) and lowest in London and Rome.

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 -overallby 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 CO 2 emissions. We showed that cyclists had signi cantly lower total CO 2 emissions than non-cyclists. More cycling or walking decreased mobility-related life cycle CO 2 emissionssuggesting 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.

Comparison with previous studies
Mean total CO 2 emissions of 3.18 kgCO 2 /day were much higher than the median (0.81 kgCO 2 /day) and near the upper end of the derived interquartile range (0.07-3.27 kgCO 2 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 CO 2 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) kgCO 2 /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) kgCO 2 /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 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 area.
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 CO 2 emissions by about 7.1 kgCO 2 /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 CO 2 emissions provides further evidence of mode substitution away from motorized travel.
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 CO 2 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 . 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).

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 CO 2 emissions are also in uenced by other factors such as other built environment factors or lifestyle and socio-cultural factors 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 multicity 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.

Conclusion
This paper started on the premise that we still do not know very much about how much carbon from passenger transport is saved -overallby travelling actively. It investigated to what extent active travel is associated with lower mobility-related life cycle CO 2 emissions by using primary data collected in a large European multicentre study to derive total and purpose-speci c life cycle CO 2 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, CO 2 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