The climate change mitigation impacts of active travel: Evidence from a longitudinal panel study in seven European cities
Introduction
The transport sector remains at the center of any debates around energy conservation, exaggerated by the stubborn and overwhelming reliance on fossil fuels by its motorized forms, whether passenger and freight, road, rail, sea and air. The very slow transition to alternative fuel sources and propulsion systems to date has resulted in this sector being increasingly and convincingly held responsible for the likely failure of individual countries to meet their obligations under consecutive international climate change agreements (Sims et al., 2014). In Europe, greenhouse gas (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). There is growing consensus that technological substitution via electrification will not be sufficient or fast enough to transform the transport system (Creutzig et al., 2018, IPCC, 2018). Investing in and promoting ‘active travel’ (i.e. walking, cycling, e-biking) is one of the more promising ways to reduce transport carbon dioxide (CO2) emissions1 (Amelung et al., 2019, Bearman and Singleton, 2014, Castro et al., 2019, de Nazelle et al., 2010, ECF, 2011, Elliot et al., 2018, 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). As the temporary shift in travel behaviors due to the COVID-19 pandemic has shown, mode shift 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 road vehicles in the short and medium term, and of governments to provide incentives to fleet renewal.
The net effects of changes in active travel on changes in mobility-related CO2 emissions are complex and under-researched. Previous research has shown that travel carbon emissions are determined by transport mode choice and usage, which in turn are influenced by journey purpose (e.g. commuting, visiting friends and family, shopping), cost (time cost, money cost), 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), infrastructure factors (density, diversity, design, transport system quantity and quality, which impact on trip 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). For instance, individuals drive for fewer trips if they live close to public transport, at higher population densities, and in areas with greater mix of residences and workplaces, and employed individuals with driver’s license living in households with easy car access make a higher share of trips by car (Buehler, 2011). A recent review (Javaid et al., 2020) found that individuals are most motivated to shift modes, if they are well informed, if personal norms match low-carbon mode use, and, most importantly, if they perceive to have personal control over decisions. However, the review also found that the overall margin of shift as induced by individual and social settings remains limited. Instead, the infrastructure factors (such as the transport system and built environment) explains considerable differences in mode choice. Especially, accessibility metrics, such as distance to jobs, and street connectivity, an important measure of pedestrian access, as well as dedicated bike infrastructures play a crucial role in enabling modal shift.
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, Mason et al., 2015, 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). Many of the latter are cross-sectional, so the direction of causality remains unclear. Longitudinal studies are needed to investigate change in CO2 emissions as a result of changes in active travel activity; however, longitudinal panel studies (with or without controls) are scarce. A small number of intervention studies have been reported, for instance by Keall et al (2018) who in a case study in New Zealand found modest associations between new cycling and walking infrastructure and reduced transport CO2 emissions.
To better understand the carbon-reduction impacts of active travel, it is important to assess (and adjust for) 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). While cycling cannot be considered a ‘zero-carbon emissions’ mode of transport, lifecycle 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 2 km for walking, 5 km for cycling and 10 km 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, Mason et al., 2015, Neves and Brand, 2019, Vagane, 2007).
A key consideration is thus to accurately assess the net mode substitution (or shift) away from one mode to another, as opposed to using alternative, more convenient routes (route substitution) or newly induced travel through intervention or policy. Route substitution tends to have little effect on carbon emissions. Induced demand for active travel (that is, demand that is in addition to previous demand) does not substitute for trips previously done by motorized modes of transport. Here, we use travel surveys to measure daily travel activity and mode choice at different time points and explore the changes in CO2 emissions as a result of changes in travel activity. As cycling has some lifecycle CO2 impact, any induced demand for cycling would increase emissions. Conversely, any increase in cycling that is substituting (or shifting away from) motorised modes would result in lower emissions. Our main hypothesis in this study is therefore: do increased levels of active modes decrease daily CO2 emissions, independent from other changes in motorised travel?
To address these needs, this paper aimed to investigate to what extent changes in active travel are associated with changes in mobility-related carbon emissions from daily travel activity across a wide range of urban contexts. To achieve this aim, we included seven European cities with different travel activity patterns, transport mode shares, infrastructure provisions, climates, mobility cultures and socio-economic makeups. We also addressed a number of practical needs. First, as 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), we based the main analysis on changes in trip frequencies by mode and purpose. Second, there is a lack of standardized definitions and measurements (self-reported or measured) to identify groups within a population who changed their ‘main mode’ of transport (e.g. based on distance, duration or frequency over a given time period), or who changed from being a ‘frequent cyclist’ to ‘occasional cyclists’, or simply from ‘not cycling’ to ‘cycling’. These should be split as much as possible as there may be different effects on net CO2 emissions. Third, 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 were considered in this study, including travel for business, shopping, social and recreational purposes.
Using primary data collected in a large European multicenter study of transport, environment and health, the paper first describes how lifecycle CO2 emissions from daily travel activity were derived at the individual and population levels across time and space, considering urban transport modes, trip stages, trip purposes and emissions categories. The core analysis then identifies the main contributing factors and models the effects of changes in mode choice and usage over time on changes in mobility-related lifecycle carbon emissions. Further analysis models changes in lifecycle carbon emissions from switching 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 climate change mitigation effects of changes in active travel in cities.
Section snippets
Study design and population
This study used longitudinal panel data from the ‘Physical Activity through Sustainable Transport Approaches’ (PASTA) project (Dons et al., 2015, Gerike et al., 2016). The study design, protocol and evaluation framework have been published previously (Dons et al., 2015, Götschi et al., 2017). Briefly, the analytical framework distinguished hierarchical levels for various factors (i.e. city, individual, and trips), and four main domains that influence mobility behavior, namely factors relating
Summary statistics and sample description
The final longitudinal sample included 1,849 participants completing 3,698 travel diaries reporting 12,793 trips in total. As shown in Fig. 1, the sample was well balanced between male and female, and between the seven cities. Participants were highly educated with 78% of the participants having at least a secondary or higher education degree. Aged between 16 and 79 at baseline, the majority of participants were employed full-time (63%), with 72% on middle to high household incomes (i.e.
Summary of results and comparison with previous studies
In our panel of 1,849 participants from seven European cities of different sizes, built environments, socio-demographic make-ups and mobility cultures, we found highly significant associations between changes in daily transport mode use and changes in mobility-related lifecycle CO2 emissions. The finding that an increase in cycling or walking at follow-up (including those who already cycled at baseline) decreased mobility-related lifecycle CO2 emissions suggests that active travel substitutes
Key findings
There can be little doubt that active travel has many benefits, including net benefits on physical and mental health (in most settings), as well as being low cost and reliable (Mindell, 2015). This paper started by asking a question that keeps coming up, namely whether more cycling or walking actually reduces mobility-related carbon emissions – as opposed to representing added or induced demand that does not substitute for motorised travel. Using longitudinal panel data from seven European
CRediT authorship contribution statement
Christian Brand: Conceptualization, Methodology, Data curation, Validation, Formal analysis, Writing - original draft, Writing - review & editing, Funding acquisition, Investigation, Visualization, Supervision, Project administration. Thomas Götschi: Conceptualization, Data curation, Writing - review & editing, Funding acquisition, Investigation. Evi Dons: Conceptualization, Data curation, Formal analysis, Writing - review & editing. Regine Gerike: Methodology, Writing - review & editing,
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors alone are responsible for the views expressed in this article and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated.
Acknowledgements
This work was supported by the European project Physical Activity through Sustainable Transportation Approaches (PASTA). PASTA (http://www.pastaproject.eu/) was a four-year project funded by the European Union’s Seventh Framework Program (European Commission Grant Agreement No. 602624). CB also gratefully acknowledges support from UK Research and Innovation through the UK Energy Research Centre (grant reference number EP/S029575/1). ED is also supported by a postdoctoral scholarship from FWO –
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