Benefits of shift from car to active transport
Graphical abstract
Results for mortality costs and benefits per individual who switches from car to bicycle for commuting to work (2⁎5 km roundtrip, 5⁎46 weeks/yr) in large cities of EU. Similar results for walking. Error bars indicate confidence intervals.
Highlights
► We have evaluated the benefits of switching from car to walking or biking. ► Results are shown as annual monetary values per individual for typical scenarios. ► Most important is the life expectancy gain due to physical activity, about 1300 €/yr. ► Effects of pollution and accidents are much smaller.
Introduction
There is a growing awareness of the need to change our transportation habits by reducing our use of cars and shifting instead to active transport, i.e. walking and bicycling. Such change can bring about significant benefits for our health and environment. To help policy makers, urban planners and local administrators make the appropriate choices, it is necessary to quantify all the significant impacts of such a change. There are countless possible effects, some of which are extremely difficult to evaluate, for instance impacts on the social fabric of a community, on the sense of well-being of the population, even on the crime rate. But health impacts of the physical activity (PA) and of air pollution are especially important, and at least their associated benefit in terms of reduced mortality can be evaluated quite reliably.
Two recent studies have carried out such an assessment for specific cities or regions: Woodcock et al. (2009) evaluated the health impacts that can be expected for London and for New Delhi, and de Hartog et al. (2010) evaluated mortality impacts for the Netherlands. For the benefits of reduced air pollution these studies used detailed site-specific models for atmospheric dispersion and chemistry. Unfortunately it is not clear how such results can be transferred to other sites. Rojas-Rueda et al. (2011) evaluated the health benefit of the bike sharing program in Barcelona; they included the effect of pollution exposure for the bicyclists, but not the public benefit due to reduced vehicle emissions.
In the present paper we carry out a similar assessment of the health impacts, but to calculate the population exposure to air pollution we use results of the most comprehensive assessment of automotive pollution impacts in Europe, namely the transportation study of ExternE (2000) (ExternE, “External Costs of Energy”, is a multidisciplinary and multinational project series of the European Commission DG Research that has been continuing since 1991). This allows us to derive generic estimates that can be applied to a wide range of sites: large cities, small cities and rural areas, even outside the EU. By contrast to the limitations of a site-specific study we offer our analysis in the spirit of “better approximately right than precisely wrong”. In addition to our detailed analysis of PA and air pollution we also look at accident statistics, and we cite external cost estimates for further benefits of active transport: reduced CO2 emissions, noise and congestion. We include a wider range of impacts than Woodcock et al. (2009) and de Hartog et al. (2010), and for the health benefits of active transport we use the most recent reviews by the World Health Organization (WHO, 2008, WHO, 2010).
We calculate results per individual driver who switches to active transport. We consider a trajectory of 5 km for bicycling (and 2.5 km for walking) and provide a detailed evaluation of four effects when people change their transportation mode from driving to bicycling or walking:
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the health benefit of the physical activity,
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the health benefit for the general population due to reduced pollution,
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the change in air pollution impacts for the individuals who make the change,
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and changes in accidents.
There is a wide variety of possible health impacts, but here we focus on mortality, because the dose-response functions and accident data for this end point have the lowest uncertainty. In monetary terms the mortality impacts are especially large, and they also tend to weigh heavily in public perception. But we also indicate how the conclusions might change if other health endpoints are included.
The inclusion of other endpoints and of items such as congestion implies a variety of incommensurate impacts that would complicate any practical application of the results, unless one uses monetary valuation to measure all the impacts on a common scale. For that reason we present our results in monetary terms, while noting that simple division of the mortality costs by the respective unit costs yields the corresponding changes in life expectancy and number of deaths.
Our calculations require only a simple spreadsheet and we document all the equations and parameters, to enable the reader to modify the parameter choices and see the consequences. We also analyze the uncertainties.
We have tried to provide estimates for all the effects that appear to be most important in monetary terms, both for the individuals who switch their transport mode and for the general public. The results can be used for cost-benefit analysis of programs and projects that encourage active transport, if one can estimate the number of individuals who are induced to switch their transport mode. But that number may be very difficult to determine, as we find when we attempt a comparison of costs and benefits of a large and politically important bike sharing program, the Vélib program of Paris.
Section snippets
Concepts, tools and literature
In this section we describe the general concepts and tools, before proceeding to detailed implementation in Section 3. To begin we list abbreviations and acronyms in Table 1.
Summary of key assumptions
We begin by choosing the scenarios, namely a change in the transport mode for commuting to and from work. For the assessment of bicycling we consider an individual who switches from car to bicycle for a trajectory of 5 km one way. The assumptions for trip duration and average speed are typical of bicycling. For cars they are realistic for typical congestion in large cities; for smaller cities or rural sites the speed would be higher and the emission of pollutants per km somewhat lower. For a
Results
The steps of the calculations and the results for an individual who switches from car to bicycle are shown in Table 5. The results are plotted in Fig. 2. The calculations for drivers who switch to walking are similar.
For our walking scenario the benefit of PA is 1192 €/yr. The public benefit is only 16.5 €/yr because the trip is half as long as for bicycling. The change in pollution exposure and intake implies a cost of 15 €/yr for the individual. We have not evaluated a possible change in
Discussion
Despite the uncertainties, and whatever one assumes about the scenarios and the impacts of car emissions, the key conclusions about the health impacts are not affected: by far the most important item is the health benefit due to physical activity. The benefit for the general population due to reduced air pollution is much smaller, and in large cities it is larger than the cost due to changed exposure for a driver who switches from car to bicycle; in small cities or rural zones the public
Conclusion
We have carried out a detailed analysis of the mortality impacts of a shift to active transport, using specific scenarios that are reasonable but can readily be modified by the reader. Despite large uncertainties one can firmly conclude that by far the most important item is the health benefit due to the physical activity. The benefit for the general population due to reduced air pollution is much smaller, but in large cities it is larger than the cost due to changed exposure for a driver who
Acknowledgments
The work is part of the European-wide project Transportation Air pollution and Physical ActivitieS: an integrated health risk assessment progamme of climate change and urban policies (TAPAS), which has partners in Barcelona, Basel, Copenhagen, Paris, Prague and Warsaw. TAPAS is a four year project (partly) funded by the Coca-Cola Foundation, AGAUR, and CREAL. The funders have no role in the planning of study design; in the collection, analysis, and interpretation of data; in the writing of the
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