Health Impacts of Bike Sharing Systems in Europe

Full text

Contents lists available at ScienceDirect
Environment International
journal homepage: www.elsevier.com/locate/envint
Health impacts of bike sharing systems in Europe
I. Otero
a,b,c,d,1
, M.J. Nieuwenhuijsen
a,c,d,e,1
, D. Rojas-Rueda
a,c,d,e,⁎,1
a
ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
b
Unidad Docente de Medicina Preventiva y Salud Pública H. Mar- UPF- ASPB, Barcelona, Spain
c
Municipal Institute of Medical Research (IMIM-Hospital del Mar), Barcelona, Spain
d
Universitat Pompeu Fabra (UPF), Barcelona, Spain
e
CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
ARTICLE INFO
Handling editor: Martí Nadal
Keywords:
Bike sharing systems
Health impact assessment
Physical activity
Air pollution
Traffic incidents
ABSTRACT
Background: Bike-sharing systems (BSS) have been implemented in several cities around the world as policies to
mitigate climate change, reduce traffic congestion, and promote physical activity. This study aims to assess the
health impacts (risks and benefits) of major BSS in Europe.
Methods: We performed a health impact assessment study to quantify the health risks and benefits of car trips
substitution by bikes trips (regular-bikes and/or electric-bikes) from European BSS with > 2000 bikes. Four
scenarios were created to estimate the annual expected number of deaths (increasing or reduced) due to physical
activity, road traffic fatalities, and air pollution. A quantitative model was built using data from transport and
health surveys and environmental and traffic safety records. The study population was BSS users between 18 and
64 years old.
Results: Twelve BSS were included in the analysis. In all scenarios and cities, the health benefits of physical
activity outweighed the health risk of traffic fatalities and air pollution. It was estimated that 5.17 (95%CI:
3.11–7.01) annual deaths are avoided in the twelve BSS, with the actual level of car trip substitution, corre-
sponding to an annual saving of 18 million of Euros. If all BSS trips replaced car trips, 73.25 deaths could be
avoided each year (225 million Euros saving) in the twelve cities.
Conclusions: The twelve major Bike-sharing systems in Europe provide health and economic benefits. The pro-
motion of shifting car drivers to use BSS can significantly increase the health benefits. BSS in Europe can be used
as a tool for health promotion and prevention.
1. Introduction
Motorized vehicles help the transportation of people and goods,
stimulating the economy. However, the increasing use of motorized
transport is also negatively influencing people's health and the en-
vironment due to high levels of pollution and traffic incidents (Khreis
et al., 2016). Motorized vehicles are one of the major sources of en-
vironmental pollution and noise in urban areas (Schwela et al., 2008).
About 70% of environmental pollution and 40% of greenhouse gas
emissions in European cities comes from motorized transport (European
Environment Agency, 2010).
Several international organizations have requested the im-
plementation of public policies to increase the use of active transport,
such as walking or cycling, and public transport in order to reduce car
use in urban areas, reducing greenhouse gas emissions, climate change
impacts, encouraging physical activity and traffic safety (Dora and
Phillips, 2000;Kim and Dumitrescu, 2010).
Bike-sharing systems (BSS) have been implemented in several cities
around the world as policies to mitigate climate change, reduce traffic
congestion, and promote physical activity. A bike-sharing system or
bike-share scheme is a service in which bikes are made available for
shared use to individuals on a very short-term basis. BSS allow people
to borrow a bike from one point and return it to a different point. BSS
has become very popular in cities across Europe, Asia and America, and
in 2013 > 500 BSS were implemented around the world (Larsen, 2013).
The first bike share began in Europe in 1965, and the first large-scale
bike-sharing program was launched in 1995, in Copenhagen as By-
cyklen (City Bikes) with 1100 bikes (Shaheen et al., 2010). Currently
the BSS in Paris called “Vélib”, is the biggest in Europe with 23,600
bikes and 1800 stations; other BSS have also reached a considerable
large size as London (12,000 bikes), Barcelona (6000), Lyon (4000) or
Valencia, Seville, Milan or Brussels with > 2000 bikes. In some
https://doi.org/10.1016/j.envint.2018.04.014
Received 11 December 2017; Received in revised form 2 April 2018; Accepted 7 April 2018
⁎
Corresponding author at: Barcelona Institute for Global Health (ISGlobal), Barcelona Biomedical Research Park, Dr. Aiguader, 88, 08003 Barcelona, Spain.
1
Barcelona Institute for Global Health (ISGlobal), C. Doctor Aiguader, 88, 08003 Barcelona, Spain.
E-mail address: david.rojas@isglobal.org (D. Rojas-Rueda).
Environment International 115 (2018) 387–394
Available online 15 April 2018
0160-4120/ © 2018 Elsevier Ltd. All rights reserved.
T

countries like Spain, there has been a rapid increase in the number of
BSS, almost doubling the number of systems implemented from 58 to
97 between 2008 and 2009. Currently, the world's largest systems are in
China, in the cities of Hangzhou and Wuhan, with 90,000 and 70,000
bikes, respectively (Oortwijn, 2015). Recently new BSS's have also in-
troduced electric-bikes in their systems as part of the bicycle fleet.
Previous studies have estimated the health risks and benefits of
replacing the car trips by bike trips from BSS's in Barcelona (Rojas-
Rueda et al., 2011) and London (Woodcock et al., 2014). These two
previous studies have found that health benefits (from physical activity)
can outweigh health risks (from traffic incidents and air pollution in-
halation). Until now a comprehensive analysis of the health implication
of multiple BSS has not been performed. Neither, any of the previous
studies have included electric-bicycles in their assessments. This study
aims to assess the health impacts (in travelers) of major BSS's across
Europe, describing the differences between cities according to their
travel and BSS characteristics, levels of air pollution and traffic safety.
This study also includes, for the first time, the assessment of health risks
and benefits related to the introduction of electric-bikes in BSS's.
2. Methods
2.1. Framework and BSS selection
We used a health impact assessment (HIA) approach to quantify the
health risk and benefits of car trips substitution by bikes trips from
European BSS with > 2000 bikes. The estimated health outcome was
the annual expected number of deaths (increased or avoided) due to
physical activity, road traffic fatalities and air pollution (particulate
matter < 2.5 μm (PM
2.5
)) due to car trips substitution for BSS trips
(Fig. 1). The analysis was focused only on BSS with > 2000 bikes (bikes
and/or e-bikes) in cities of the European Union of 28 countries
(Table 1). This selection criterion was based on the assumption that the
larger BSS would impact larger populations, and will have a greater
(temporal and political) stability to produce a long-term usability and
impacts. London, UK, BSS was excluded from the analysis because a
recent assessment has been performed (Woodcock et al., 2014). Bar-
celona, Spain, BSS was included in the analysis in spite of existing a
previous assessment (Rojas-Rueda et al., 2011) because of its recent
expansion, introducing e-bikes in the system and this expansion has not
been considered in the previous assessment. Another European BSS's
like the case of Strasbourg or Grenoble were not included in the ana-
lysis, although they met the inclusion criteria of > 2000 bikes because
it was not possible to access the data (number of trips, distance, dura-
tion, etc.) required to perform the assessment.
2.2. Scenarios and input data
Four scenarios were created to assess the health impacts of shifting
from car to BSS bikes: the first scenario was focused on the observed
(reported by a travel survey performed by each BSS) car substitution by
BSS bike trips in the 12 cities (see supplemental material); the other
three scenarios were focused on assumptions to assess “what if”the cars
substitution would be larger for the 12 BSS (Table 2). Scenario 1) the
car trip substitution (by BSS trips) used in this scenario was the
minimum percentage reported by each city (for those cities that have
not reported the percentage of car trip substitution, was applied the
minimum reported (4.7%) between the 12 cities); Scenario 2) what if
the car trip substitution (by BSS) was the maximum reported (12%) in
the 12 European cities; Scenario 3) what if 50% of all BSS trips come
from car trips; and 4) what if 100% of all BSS trips would come from car
trips (see supplemental material). These last three scenarios (2,3and 4)
were aimed to show the potential of the BSS if higher levels of car trip
substitution could be achieved.
Fig. 1. Conceptual framework of bike sharing systems and health.
I. Otero et al. Environment International 115 (2018) 387–394
388

Table 1
Description of 12 European bike sharing systems (BSS) included in the analysis.
City City population
(inhabitants)
Bike sharing system
name
Type of
bicycle
Number of regular-
bikes
Number of E-bikes BSS trips per day Bike usability ratio (daily trips/
bike)
Year of implementation Operator
Barcelona 1,604,555 Bicing Bike & 6000 300 38,946 6,2 2007 Clear Channel
E-bike
Brussels 1,187,890 Villo Bike 5000 –4320 0,9 2009 JCDecaux-Cyclocity
Hamburg 1,787,408 StadtRAD Hamburg Bike 2450 –6671 2,7 2009 Deutsche Bahn “Call a
Bike”
Lille 233,897 V'Lille Bike 2200 –7900 3,6 2011 Kéolis
Lyon 506,615 Vélo'v Bike 4000 –30,000 7,5 2005 JCDecaux - Cyclocity
Madrid 3,165,883 BiciMAD E-bike –2028 6935 3,4 2014 Bonopark S.L.
Milan 1,345,851 BikeMi Bike & 3650 1000 17,700 3,8 2008 Clear Channel
E-bike
Paris 2,273,305 Vélib’Bike 23,600 –110,000 4,7 2007 JCDecaux -SOMUPI
Seville 690,566 Sevici Bike 2500 –11,618 4,6 2007 JC Decaux-Cyclocity
Toulouse 466,297 Vélô Bike 2600 –13,000 5,0 2007 JC Decaux-Cyclocity
Valencia 787,266 Valenbisi Bike 2700 –30,560 11,3 2010 JC Decaux-Cyclocity
Warsaw 1,748,900 Veturilo Bike 4925 –21,333 4,3 2012 Nextbike Polska
E-bike: Electric bicycle.
Table 2
Results of the 12 European bike sharing systems (BSS) by scenario, in annual deaths avoided.
Observed substitution What if…
BSS by city Scenario 1 Scenario 2 Scenario 3 Scenario 4
Minimum observed car trips
substitution
Deaths avoided per 1000
bikes
12% of BSS trips come from
car trips
a
50% of BSS trips come from
car trips
100% of BSS trips come from
car trips
Increase 10,000 BSS trips
per day
Increase 1000 cyclists per
day
Deaths/year (95% CI) Deaths/year Deaths/year (95% CI) Deaths/year (95% CI) Deaths/year (95% CI) Deaths/year (95% CI) Deaths/year (95% CI)
Barcelona −0.80 (−1.03, −0.47) −0.13 −1.00 (−1.29, −0.59) −4.18 (−5.38, −2.46) −8.37 (−10.76, −4.92) −2.13 (−2.74, −1.25) −1.27 (−1.65, −0.65)
Brussels −0.07 (−0.10, −0.03) −0.01 −0.12 (−0.17, −0.06) −0.52 (−0.73, −0.25) −1.04 (−1.46, −0.51) −2.41 (−3.8, −1.19) −0.85 (−1.19, −0.42)
Hamburg −0.13 (−0.19, −0.08) −0.05 −0.13 (−0.19, −0.07) −0.55 (−0.80, −0.30) −1.11 (−1.61, −0.59) −1.66 (−2.41, −0.89) −0.58 (−0.85, −0.31)
Lille −0.07 (−0.09, −0.04) −0.03 −0.17 (−0.23, −0.11) −0.71 (−0.99, −0.47) −1.43 (−1.98, −0.94) −1.81 (−2.51, −1.19) −0.72 (−1.00, −0.47)
Lyon −0.48 (−0.68, −0.39) −0.12 −0.81 (−1.17, −0.67) −3.40 (−4.89, −2.79) −6.80 (−9.78, −5.59) −2.26 (−3.26, −1.86) −0.74 (−1.07, −0.61)
Madrid −0.07 (−0.09, −0.01) −0.03 −0.13 (−0.18, −0.01) −0.54 (−0.75, −0.06) −1.09 (−1.50, −0.13) −1.58 (−2.16, −0.19) −0.55 (−0.76, 0.06)
Milan −0.14 (−0.19, −0.07) −0.03 −0.37 (−0.50, −0.17) −1.54 (−2.09, −0.73) −3.09 (−4.18, −1.46) −1.54 (−2.08, −0.72) −1.03 (−1.43, −0.39)
Paris −2.54 (−3.38, −1.48) −0.11 −3.80 (−5.07, −2.21) −15.85 (−21.1, −9.22) −31.70 (−42.28, −18.45) −2.88 (−3.84, −1.67) −0.98 (−1.30, −0.57)
Seville −0.11 (−0.14, −0.06) −0.04 −0.27 (−0.35, −0.15) −1.13 (−1.49, −0.63) −2.26 (−2.98, −1.27) −1.94 (−2.57, −1.09) −0.68 (−0.90, −0.38)
Toulouse −0.13 (−0.17, −0.07) −0.05 −0.32 (−0.45, −0.19) −1.35 (−1.89, −0.79) −2.71 (−3.78, −1.58) −2.08 (−2.91, −1.21) −0.79 (−1.10, −0.46)
Valencia −0.24 (−0.33, −0.16) −0.09 −0.61 (−0.84, −0.40) −2.55 (−3.53, −1.66) −5.10 (−7.06, −3.33) −1.67 (−2.31, −1.09) −0.59 (−0.81, −0.38)
Warsaw −0.40 (−0.58, −0.25) −0.08 −1.02 (−1.48, −0.63) −4.25 (−6.19, −2.66) −8.50 (−12.38, −5.32) −3.98 (−5.8.07, −24.96) −1.40 (−2.05, −0.88)
Total −5.17 (−7.01, −3.11) −0.07** −8.79 (−11.97, −5.30) −36.62 (−49.90, −22.07) −73.25 (−99.80, −44.14) −26.00 (−36.02, −14.90) −10.24 (−14.17, −5.64)
BSS: Bikes sharing system; CI: Confidence intervals;
a
12% is the maximum reported car trip replacement by BSS trips between the 12 BSS included; **Average deaths avoided per 1000 bikes between the 12 BSS.
I. Otero et al. Environment International 115 (2018) 387–394
389

The input data used for the analysis was obtained from official re-
cords on transport (travel surveys and/or travel counts), health (health
surveys, traffic safety, and health statistics) and air quality database
(World Health Organization database of air quality (Ambient Air
Pollution Database, WHO, 2016)). BSS data was obtained from a
combination of data sources provided directly by the BSS management
companies, official city records, and travel surveys (see supplemental
material).
2.3. Quantitative model
A comparative risk assessment approach was followed to estimate
the number of mortality cases related to each health determinant
(physical activity, air pollution, and traffic incidents) (Perez and Kunzli,
2009;World Health Organization, 2008)(Fig. 1). The “TAPAS tool”
developed and used in previous HIAs (Rojas-Rueda et al., 2012, 2016)
was used to estimate the health impacts in this study. The TAPAS tool
methods description has been reported elsewhere (Rojas-Rueda et al.,
2012, 2016). The dose-response functions (DRF) used in the TAPAS
tool, between physical activity, air pollution, and all-cause mortality,
were selected from meta-analyses. The risk estimates from traffic
fatalities by kilometer traveled were collected from health and trans-
port statistics from each city. Exposure levels of each health determi-
nant were estimated for each city and scenario. We estimated a relative
risk of all-cause mortality and each health determinant by scenario, and
the city following the risk assessment approach, and translated this into
a population attributable fraction. Using the mortality rate in each
country and the attributable fraction in each scenario and city, we es-
timated the number of deaths attributable to each scenario, city, and
health determinant (Rojas-Rueda et al., 2011, 2016). The number of
expected deaths was estimated only for individuals between 16 and
64 years (similar to the populations included in the DRF).
2.3.1. Physical activity model
The physical activity exposure was estimated based on the trip
duration, trip frequency, and physical activity intensity, using meta-
bolic equivalent of task (MET). The physical activity was defined as 6.8
METs for bikes, 6.12 METs for e-bikes using “standard assistance”, and
2 METs for car travelers (Ainsworth et al., 2011;Gojanovic et al., 2011;
Louis et al., 2012;Simons et al., 2009) (see supplemental material). A
sensitivity analysis was also performed assuming e-bike “high assis-
tance”mode, defined as 5.4 METs. The relative risk of all-cause mor-
tality was based on the DRF provided by a meta-analysis (Woodcock
et al., 2011), assuming a non-linear DRF. The physical activity assess-
ment takes into account the basal levels of physical activity in each
population (country) to estimate a relative risk for each scenario before
to be translated into a populational attributable fraction and estimate
mortality cases (see supplemental material).
2.3.2. Air pollution model
The air pollution assessment focused only on the exposure to par-
ticulate matter with a diameter < 2.5 μm (PM
2.5
), which has shown
strong association with all-cause mortality (Laden et al., 2000;Pope,
2007;Wichmann et al., 2000). We identified the annual average con-
centration of PM
2.5
in each city, using the World Health Organization
database of air quality (Ambient Air Pollution Database, WHO, 2016)
(see supplemental material). We estimated the concentration of PM
2.5
in each microenvironment (bike and car), using background/car or bike
ratios provided by previous meta-analysis (de Nazelle et al., 2017),
following a similar approach as reported in previous studies (Rojas-
Rueda et al., 2011, 2012, 2016). The inhaled dose was estimated using
the minute ventilation according to the intensity of physical activity (in
METs) in each mode of transport (bike, e-bike, and car), PM
2.5
con-
centration in the mode of transport and trip duration (Rojas-Rueda
et al., 2013, 2011, 2016). The DRF for PM
2.5
and all-cause mortality
from a meta-analysis was used (RR = 1.06 (1.04, 1.08)) for each
increment of 10 μg/m
3
of PM
2.5
)(Hoek et al., 2013). Finally using the
comparative risk assessment approach, we estimated the relative risk,
attributable fraction, and expected deaths for each scenario, and city
(see supplemental material).
2.3.3. Road traffic model
Road traffic fatalities in each scenario and city were estimated using
the traffic fatalities reported in each city and mode of transport (using
traffic fatalities per billion of kilometers traveled) (see supplemental
material). For each scenario and city we estimated the number of
kilometers traveled by car, bike, and e-bike. The expected traffic
fatalities by mode of transport were estimated using the traffic fatalities
per billion of kilometer traveled and the distance traveled in each mode
of transport and city (Hartog et al., 2010;Rojas-Rueda et al., 2011).
Then was calculated a relative risk of mortality in a road traffic crash
for cyclists (regular-bike or e-bike) compared with car drivers. The re-
lative risk was translated to an attributable fraction and to a final
number of fatality cases in each scenario (see supplemental material).
2.3.4. Economic assessment
An economic assessment was included using the value of statistical
life for each country reported by the Organization for Economic Co-
operation and Development (OECD, 2012). The estimated deaths in
each city and scenario were multiplied to the value of statistical life of
their corresponding county and calculated the economic values, fol-
lowing the methods proposed by World Health Organization in the
Health economic assessment tool for cycling (Kahlmeier et al., 2014).
2.3.5. Electric bikes (E-bikes)
The TAPAS tool was developed for regular-bikes, for this reason in
this assessment we updated the TAPAS tool to include e-bikes. The e-
bike update was focused on including specific values for physical ac-
tivity (METs), speed, traffic fatalities rates (for kilometers traveled) and
inhalation rates for e-bikes (see supplemental material).
A specific analysis of e-bikes was also performed distinguishing two
different types of e-bikes, “standard assistance”e-bikes, and “high-as-
sistance”e-bikes (see supplemental material). For each of those type of
e-bikes we selected different physical activity levels (METs) and speed.
The “standard assistance”e-bike was used as a common reference in all
scenarios, and the “high assistance”e-bike was used for sensitivity
analysis. In terms of physical activity, “standard assistance”e-bikes was
defined as an e-bike that requires 90% of the physical activity of a
regular-bike, and “high assistance”e-bike was defined as an e-bike that
requires 75% of the physical activity of a regular-bike (Gojanovic et al.,
2011;Louis et al., 2012;Simons et al., 2009). In terms of speed,
“standard assistance”e-bikes were defined as an e-bike that increases in
average 21% the speed of a regular-bike, and “high assistance”e-bike
was defined as an e-bike that increases on average 33% the speed of a
regular-bike (Gojanovic et al., 2011;Simons et al., 2009). For traffic
fatalities, e-bikes were assumed to have an odds ratio of 1.92
(1.48–2.48) compared with a regular-bike as proposed by Schepers
et al. (Schepers et al., 2014).
3. Results
Twelve BSS were included in the analysis, nine BSS with regular-
bikes (Brussels, Hamburg, Lille, Lyon, Paris, Seville, Toulouse, Valencia,
and Warsaw), two with regular-bikes and e-bikes (Barcelona and Milan)
and one BSS with only e-bikes (Madrid). The number of bikes in the BSS
ranged between 2200 in Lille, and 23,600 in Paris. The BSS trips per
day range from 4320 in Brussels to 11,000 in Paris. The number of trips
per day by bike range from 0,9 daily trips per bike in Brussels to 11,3
daily trips per bike in Valencia. In all the cases (cities and scenarios),
the health benefits of physical activity outweighed the health risk of
traffic fatalities and inhalation of air pollution (Fig. 2).
I. Otero et al. Environment International 115 (2018) 387–394
390

3.1. Scenario 1. Minimum observed car trips substitution
In the scenario 1, we estimated that 5.17 (95%CI: 7.01–3.11) deaths
are avoided each year corresponding to 18.1 million Euros (95%CI:
31.5–12.4) (Table 3) when the twelve systems are added up. The city
with the highest estimated benefits was Paris with 2.53 deaths avoided
per year and 10 million Euros, followed by Barcelona with 0.80 annual
deaths avoided per year and 2.5 million Euros. The BSS with the fewest
deaths avoided were Brussels, Madrid, and Lille with < 0.07 annual
deaths avoided within each city. The estimation of deaths avoided per
1000 bikes ranged from 0,01 deaths avoided per year in Brussels per
every 1000 bikes to 0,13 deaths annual avoided per every 1000 bikes in
Barcelona, with an average of 0,07 deaths avoided per year by 1000
bikes between the twelve BSS's.
3.2. Scenario 2. The 12% of the BSS trips come from car trips
For scenario 2 if 12% (maximum reported car trips substitution) of
the BSS trips come from car trips, we estimated that 8.79 (95%CI:
11.97–5.30) deaths would be avoided each year corresponding to 39.3
million Euros (95%CI: 48.5–21.5) for the twelve systems together and.
The city with the highest estimated benefits was Paris with 3.80 deaths
avoided per year and 15.2 million Euros saved. The BSS with the fewest
deaths avoided were Brussels, Madrid, and Hamburg with < 0.13 an-
nual deaths avoided in each city.
3.3. Scenario 3. The 50% of the BSS trips come from car trips
In the scenario 3 if 50% of BSS trips come from car trips, we esti-
mated that 36.6 (95%CI: 49.90–22.07) deaths would be avoided each
year corresponding to 112.9 million Euros (95%CI: 186.5–89.3) for all
twelve systems together. The city with highest benefits would be Paris
with 15.85 deaths avoided per year and 63.5 million Euros. The BSS's
with fewer deaths avoided would be Brussels, Madrid, and Hamburg
with < 0.60 annual deaths avoided in each city.
Fig. 2. Number of annual deaths prevented per year per 100,000 cyclists, by health determinant, if 100% of BSS trips come from car trips (Scenario 4).
Table 3
Results of the 12 European bike sharing systems (BSS) by scenario, in million Euros saved per year.
Observed substitution What if…
BSS by city Scenario 1
Minimum observed car trips substitution
Million Euros/year (95% CI)
Scenario 2
12% of BSS trips come from car trips
a
Million Euros/year (95% CI)
Scenario 3
50% of BSS trips come from car trips
Million Euros/year (95% CI)
Scenario 4
100% of BSS trips come from car trips
Million Euros/year (95% CI)
Barcelona 2.571 (1.511. 3.311) 3.218 (1.892. 4.138) 13.407 (7.882. 17.241) 26.815 (15.761. 34.483)
Brussels 0.319 (0.157. 0.446) 0.547 (0.271. 0.766) 2.286 (1.125. 3.202) 4.573 (2.256. 6.404)
Hamburg 0.562 (0.330. 0.816) 0.562 (0.330. 0.816) 2.353 (1.269. 3.526) 4.706 (2.535. 6.814)
Lille 0.268 (0.176. 0.372) 0.688 (0.452. 0.957) 2.863 (1.890. 3.981) 5.731 (3.776. 7.962)
Lyon 1.906 (1.570. 2.743) 3.272 (2.687. 4.701) 13.629 (11.202. 19.584) 27.262 (22.400. 39.169)
Madrid 0.224 (0.025. 0.307) 0.422 (0.051. 0.576) 1.758 (0.211. 2.405) 3.513 (0.422. 4.810)
Milan 0.519 (0.245. 0.696) 1.319 (0.625. 1.785) 5.497 (2.609. 7.438) 10.998 (5.219. 14.877)
Paris 10.156 (5.911. 13.537) 15.235 (8.867. 20.321) 63.488 (36.947. 84.676) 126.977 (73.898. 169.352)
Seville 0.339 (0.192. 0.448) 0.871 (0.486. 1.146) 3.625 (2.033. 4.782) 7.251 (4.067. 9.564)
Toulouse 0.512 (0.300. 0.712) 1.305 (0.764. 1.818) 5.438 (3.180. 7.585) 10.877 (6.360. 15.167)
Valencia 0.768 (0.502. 1.063) 1.963 (1.284. 2.716) 8.177 (5.345. 11.319) 16.354 (10.691. 22.635)
Warsaw 2.351 (1.469. 3.421) 5.995 (3.756. 8.740) 24.994 (15.653. 36.409) 49.988 (31.307. 72.825)
Total 18.150 (12.393. 31.511) 39.320 (21.471. 48.486) 112.980 (89.352. 186.573) 225.962 (178.698. 404.066)
BSS: Bikes sharing system; CI: Confidence intervals.
a
12% is the maximum reported car trip replacement by BSS trips between the 12 BSS included.
I. Otero et al. Environment International 115 (2018) 387–394
391

3.4. Scenario 4. The 100% of the BSS trips come from car trips
In the scenario 4 if the 100% of the BSS trips come from car trips, we
estimated that 73.25 (95%CI: 99.81–44.14) deaths would be avoided
each year and 225.9 million Euros saved (95%CI: 404–1178) for the
twelve systems together. The city with the highest benefits would be
Paris with 31.70 deaths avoided per year and 126.9 million Euros
saved. The BSS with the fewest deaths avoided would be Brussels,
Madrid, and Hamburg with < 1.20 annual deaths avoided in each city.
4. Discussion
This is the first study assessing the health impacts of multiple bike-
sharing systems in Europe. This study included the 12 larger BSS in
Europe, in six different countries (Belgium, France, Germany, Italy,
Poland and Spain). This is also the first health impact assessment of e-
bikes. BSS's have increased and become popular around the world in
recent years. This study provided a systematic assessment comparing
different BSS's across Europe.
This study found that the 12 larger European BSS could prevent up
to 73 deaths each year with an economic value of 225 million Euros if
100% of BSS trips were replacing car trips. In the most conservative
scenario (minimum reported car trips substitution), we estimated that
each year 5 deaths could be prevented by the 12 BSS systems in Europe,
with an economic value of > 18 million Euros. In all the cities and
scenarios assessed the health benefits overweighed the health risks with
a benefit/risk ratio of 19:1 (see supplemental material). The benefits
are mainly driven by the increase in physical activity derived from the
use of the bike or e-bikes as a means of daily transportation.
In this study, we found that health impacts vary among BSS's and
cities. Using the most conservative scenario (scenario 1 “minimum re-
ported car trips substitution), was estimated the annual deaths avoided
per 1000 bikes (Table 2), resulting in a range of 0,01 annual deaths
avoided per 1000 bikes in Brussels to 0,13 deaths avoided per 1000
bikes in Barcelona. This variability in the health impacts of the same
amount of bikes can be explained because each BSS have different us-
ability ratio (number of daily trips per bike) (see Table 1), different trip
duration, traffic safety and air quality. If the local authorities work to
improve those factors (bike usability rate, traffic safety, and air quality)
the potential health benefits of current BSS's could be greater. Similar to
this analysis, an estimation of the future increment of BBS trips or users
(new cyclist) was estimated assuming that these new trips and new
cyclists come from car trips (Table 2). If the BSS increase by 1000 trips
per day, the health benefits could be translated into 0,15 annual deaths
avoided in Milan or Madrid to 0,39 in Warsaw. If the BSS increase by
1000 new cyclist, the health benefits could be translated into 0,55
deaths per year in Madrid to 1.40 in Warsaw. In the case of scenario 3
and 4 where most of the BSS trips are assumed to come from car trips,
we acknowledge that these are unrealistic, but provide a sense of the
magnitude of health impacts in the best case scenarios.
Vélib’the BSS in Paris was the system with the largest health ben-
efits compared with the other European cities. This can be explained
because it is the largest system in Europe, with > 23 thousand bikes
and 110,000 trips per day, representing 2.04% of total trips made in the
urban area of Paris. Also, Paris has a high car trip substitution of BSS
trips (8.0%) that is almost the double of the reported in other cities like
Seville (4.7%). Furthermore, Paris trips have longer distances (3.3 km)
compared to the average of the rest of the cities (2.92 km) (see sup-
plemental material). Lille, Madrid, and Brussels were the cities where
the estimated health benefits were lower compared to the rest of cities
included. These three cities were characterized by having a small bike
fleets (between 2028 and 5000 bikes) and the lowest number of daily
trips (between 4320 and 7900).
Madrid was the only BSS composed to 100% by e-bikes, which were
related to lower physical activity, higher speed, and traffic incidents
and produced overall fewer health benefits than a BSS with regular-
bikes. Barcelona and Milan also have a BBS's with a mix of regular-bikes
and e-bikes. The impacts produced only by the e-bikes in the three cities
varied significantly (annual death avoided: Madrid 1, Milan 0.3, and
Barcelona 0.05)(in Scenario 4). This can be explained because the
number of e-bikes in each city is also different (number of e-bikes:
Madrid 2028, Milan 1000, and Barcelona 300). In these three cities, the
e-bikes were analyzed assuming a “standard assistant”mode, which
was defined as 6.5 METs of physical activity compared to a regular-bike
(7 METs)(see supplemental material). We also performed a sensitivity
analysis assumed a “high assistance”mode of e-bikes, assuming 5.4
METs. In this sensitivity analysis, we still found health benefits in the
three BSS's (Barcelona, Madrid, and Milan) in spite of the increased risk
of traffic fatalities and lower levels of physical activity. Although the
health benefits of e-bikes are lower than regular-bikes, the availability
of e-bikes can attract a new group of bike users (i.e., older people) or
the substitution of longer or hillier trips. Unfortunately, these con-
siderations were not taken into account in this analysis due to the lack
of information about the e-bike users and route characteristics in the
cities. Some cities like Lyon (Crouzet, 2017) are planning to introduce
e-bikes in the future to deal with hilliness and attracting more users (as
made by Barcelona, Madrid, and Milan). For that reason, it will be
important to improve BSS data collection (i.e., travel surveys) with
special attention describing user's characteristics and route preferences.
Physical activity provides the largest health impacts in this analysis.
Physical exercise prevents cardiovascular diseases, reduces the risk of
diabetes mellitus, certain cancers, and mortality (Rojas-Rueda et al.,
2013). This study only included as health outcome all-cause mortality
(for physical activity, air pollution, and traffic incidents), because it was
expected to be the health outcome with the largest health and economic
impacts (Rojas-Rueda et al., 2013). This assessment was performed
using the TAPAS tool for cycling; this tool has been used in previous
active transportation assessments (Rojas-Rueda et al., 2012, 2013,
2011, 2016). The TAPAS tool for cycling estimates the physical activity
health benefits using a non-linear DRF, considering the basal level of
physical activity in the population under assessment (Woodcock et al.,
2014). This approach takes into account that those who already were
physically active would gain fewer benefits compared to those that are
more sedentary. This non-linear approach results in fewer health ben-
efits from physical activity than using a linear model (Rojas-Rueda
et al., 2016).
The air pollution assessment in this study only considered the health
risk associated with the inhalation of PM
2.5
during the bike trip (Rojas-
Rueda et al., 2016). Other changes in air pollution exposure, associated
to car-bike substitution at the city level, where not included in this
study. Although, additional co-benefits could be expected on air quality
associated with car trip substitution. This study only focused on PM
2.5
,
although other pollutants, (e.g. NO
2
or black carbon), could also be
used in this type of assessments. These pollutants are highly correlated
and produce similar health outcomes. In order to avoid double counting
in the air pollution model, we decided to include only PM
2.5
. This study
found differences in the air pollution exposure among cities. These
differences can be explained by the PM
2.5
concentrations at the city
level, trip duration and frequency, and the intensity of physical activity
(regular-bikes, e-bikes standard or high assisted). In all the cities and
scenarios, the air pollution was found as a risk factor for cycling.
Compared with the other health determinants included in the analysis,
air pollution was the one with fewer health impacts (Fig. 2). The cities
that had the worst levels of air pollution (PM
2.5
) were Milan (30 μg/m
3
)
and Warsaw (26 μg/m
3
), but none of the cities assessed had levels under
the World Health Organization recommendations (< 10 μg/m
3
)(WHO,
2006). If the cities improve the air pollution levels, the overall health
benefits of the BSS could be bigger. The car-bike substitution could also
produce a reduction in air pollution emissions and concentrations,
bringing health benefits to the general population, but these impacts
were not in the scope of this study.
The traffic incident model estimates the risk of traffic fatalities per
I. Otero et al. Environment International 115 (2018) 387–394
392

kilometer traveled. The risk of kilometer traveled was obtained from
the health and transport records from each city (see supplemental
material). Traffic fatalities can be influenced by multiple causes, tra-
veler behavior, infrastructure, traffic laws, and mode of transport. This
study took into account the risk of the mode of transport (car, bike or e-
bike), but did not assess the different impacts related to age, sex, or
route due to the lack of information on these characteristics in the
traffic safety records of each city. In all the cities (with the exception of
Seville) we found that car-bike substitution increases the risk of traffic
fatalities in the travelers (see Fig. 2). Milan, Brussels, and Warsaw
provided the highest risk of traffic fatality between cities when the car
is substituted by bike. Seville is the only city that reported a lower risk
of traffic fatalities in bike compared to a car. This can be explained
because in the last few years Seville has invested in bike infrastructure,
especially in segregated bike lanes, traffic signaling, and bike promo-
tion and education (Ayuntamiento de Sevilla, 2007; Junta de
Andalucía, 2014).
The results of this study agree with the findings from previous
publications, (Rojas-Rueda et al., 2011) that performed a HIA on the
BSS of Barcelona, including the same health determinants (physical
activity, air pollution, and traffic incidents). Unlike this previous Bar-
celona assessment, our study included an update of the TAPAS model,
introducing a non-linear DRF for physical activity, different car-bike
substitution scenarios, update DRF for air pollution, and the e-bikes
assessment. Our study estimates a range of 0.8 to 8 deaths avoided each
year in Barcelona, compared to the 12 deaths avoided as estimated in
the previous study. This difference can be explained because of the
different scenarios that were used in the studies and the inclusion of a
non-linear DRF for physical activity in this new analysis. Woodcock
et al. (2014) also performed a health impact modeling study of the BSS
in London, finding health benefits associated with the use of the BSS.
This study was focused only on measuring the health impacts of car
trip substitution by BSS trips. This choice was justified because the car
trips substitution was suggested to provide more health benefits
(compared to other transport modes). A previous health impact as-
sessment compared the potential benefits of shifting from different
transport modes (car, public transport, and walking) to bike (Rojas-
Rueda et al., 2016). This assessment found that car-bike substitution
provided the highest health benefits (Rojas-Rueda et al., 2016). Fur-
thermore, car-bike substitution also brings co-benefits at the city level,
improving traffic noise, air quality, traffic safety, emissions of green-
house gases, and the use of public space, among others.
As in all risk assessments, our study was limited by the availability
of data and the necessity to make assumptions to model likely sce-
narios. In terms of the scenarios modeled a conservative scenario
(Scenario 1) was created using data from travel surveys knowing the
current car-bike substitution between the BSS users. In the case of lack
of data on car-bike substitution, we assumed a conservative shift using
the minimum car-bike substitution reported between the 12 cities
(4.7%). The other scenarios (“what if”scenarios) showed the actual
potential of the BSS to improve health. That is especially relevant
considering that the BSS already exist in those cities, and if more ac-
tions could be taken to promote car-bike substitution (through media
campaigns, education, economic incentives, urban infrastructure and
transport planning improvements) higher health and economic benefits
could be achieved by the BSS. One limitation was the definition of e-
bikes, because there exists different type of e-bikes, plus e-bike users
also can choose different level assistance when they use the e-bikes. To
assess the different possibilities of types and use of e-bikes, we defined
two different levels of assistance in our analysis, “standard assistance”
used in the main analysis and “high assistance”used as a sensitivity
analysis. An important part of the work performed in this study was
data collection, for that reason the BSS managers were contacted di-
rectly by the researchers to collect the information from each BSS and
city. For this reason, a survey was performed to collect systematically
the data required in the analysis. When BSS managers reported the
absence of data, the BSS was excluded from the analysis. This happened
for the BSS of Strasbourg or Grenoble. Finally, if the majority of the
data was available, but still data missing, the missing data were esti-
mated using a secondary analysis (crossover analysis) using data from
other BSS or cities. To assess the uncertainty in our estimates con-
fidence intervals were included. The confidence intervals were com-
posed by the variability of the input data, using the ranges (maximum
and minimum) and the confidence intervals from the DRF from physical
activity and air pollution.
Some general recommendations can be derived from the actual
study to different stakeholders and researchers. For the BBS managers
and transport authorities it is recommended to systematically collect
data about the BSS's (number of trips, frequency, and duration, user
characteristics, routes, etc.), also to harmonize data between BBS and
cities, and the publication of the BSS data (in a free and open access
formats). For local authorities, it is recommended to provide and collect
harmonize traffic safety data for different mode of transports (including
BSS) between European cities. In terms of research it is important to
obtain more evidence on e-bikes, characterizing better the type of e-
bikes available in the BSS, levels of assistance in the e-bikes, e-bike trips
description (route, duration, speed, type of user), e-bikes traffic safety
data across Europe, and a better definition of the physical activity re-
lated to the different types of e-bikes. Also, new data sources on phy-
sical activity and transportation are available from crowdsourced da-
tabases (Afzalan and Sanchez, 2017;Oates et al., 2017;Sun and
Mobasheri, 2017;Sun et al., 2017). These new databases can provide
relevant information to understand cyclist behaviors, routes, exposures,
among others, and better inform policymakers.
5. Conclusions
This study found that BSS in Europe can provide health and eco-
nomic benefits. The health benefits are driven by physical activity, with
minor risks due to exposure to air pollution (PM
2.5
) and road traffic
fatalities. The health impacts of the BSS differ across European cities
depending on the car-bike substitution level, traffic safety, and air
quality. This study also included e-bikes, which were found to provide
less health and economic benefits in BSS's than regular-bikes. The
promotion of BSS use among car drivers can significantly increase the
health, and economic benefits of BSS and BSS can be used as a tool for
health promotion and prevention.
Acknowledgments
We acknowledge to Audrey Masquelin (V'Lille), Élodie Vanpoulle
(V'Lille), Emilio Minguito (Sevici), Laurent Defremont (V'Lille), Manuel
Martín (Sevici), Niccolò Panozzo (Villo), Ricardo Marqués (Sevici),
Rheda Zetchi (Bicing), Valentino Sevino (BikeMi), Violette Legrand
(Villo) and Virginio Moreno (Sevici), for contributing and/or providing
information to accomplish this study.
Contributors
DR-R study concept. DR-R and IO designed, collected, analyzed and
interpreted the data for this study, and they wrote the manuscript. IO,
DR-R, and MJN edited and approved the final version for submission.
Funding
This research was founded with internal funding from ISGlobal and
Parc de Salut Mar - Hospital del Mar R3-2017.Role of the funding
sources
The sponsors have had no role in the study design; in the collection,
analysis, and interpretation of data; in the writing of the report; or in
the decision to submit the paper for publication.
I. Otero et al. Environment International 115 (2018) 387–394
393

Competing interests
All authors have completed the ICMJE uniform disclosure form at
www.icmje.org/coi_disclosure.pdf (available on request from the cor-
responding author) and declare: no support from any organization for
the submitted work; no financial relationships with any organizations
that might have an interest in the submitted work in the previous three
years; no other relationships or activities that could appear to have
influenced the submitted work.
Ethical approval
Not required.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://
doi.org/10.1016/j.envint.2018.04.014.
References
Afzalan, N., Sanchez, T., 2017. Testing the use of crowdsourced information: case study of
bike-share infrastructure planning in Cincinnati, Ohio. Urban Planning 2 (3), 33.
Ainsworth, B.E., Haskell, W.L., Herrmann, S.D., Meckes, N., Bassett, D.R., Tudor-Locke,
C., et al., 2011. 2011 compendium of physical activities: a second update of codes and
MET values. Med. Sci. Sports Exerc. 43, 1575–1581.
Ambient Air Pollution Database, WHO. 2016. WHO. Available: http://www.who.int/
phe/health_topics/outdoorair/databases/cities/en/ [accessed 28 June 2017].
de Andalucía, Junta, 2014. Plan de andaluz de la bicicleta. In: PAB, pp. 2014–2020.
Ayuntamiento de Sevilla, 2007. Plan director para el fomento del transporte en bicicleta.
In: Sevilla, pp. 2007–2010.
Crouzet, F., 2017. Lyon's bike-share service Velo'v turns electric. In: This is Lyon,
Available: https://thisislyon.fr/news/lyons-bike-share-service-velov-turns-electric/,
Accessed date: 20 September 2017.
Dora, C., Phillips, M., 2000. Transport, Environment and Health, 89th ed. WHO Eur,
Copenhagen.
European Environment Agency, 2010. The European Environment - State and Outlook.
pp. 2010.
Gojanovic, B., Welker, J., Iglesias, K., Daucourt, C., Gremion, G., 2011. Electric bicycles as
a new active transportation modality to promote health. Med. Sci. Sports Exerc. 43,
2204–2210. http://dx.doi.org/10.1249/MSS.0b013e31821cbdc8.
Hartog, J.J., Boogaard, H., Nijland, H., Hoek, G., 2010. Do the health benefits of cycling
outweigh the risks? Environ. Health Perspect. 118, 1109–1116. http://dx.doi.org/10.
1289/ehp.0901747.
Hoek, G., Krishnan, R.M., Beelen, R., Peters, A., Ostro, B., Brunekreef, B., et al., 2013.
Long-term air pollution exposure and cardio-respiratory mortality: a review. Environ.
Health 12, 43.
Kahlmeier, S., Kelly, P., Foster, C., Götschi, T., Cavill, N., Dinsdale, H., et al., 2014. Health
economic assessment tools (HEAT) for walking and for cycling. In: Methods and User
Guide, 2014 Update. Economic Assessment of Transport Infrastructure and Policies
WHO, Copenhagen.
Khreis, H., Warsow, K., Verlinghieri, E., Guzman, A., Pellecuer, L., Ferreira, A., et al.,
2016. The health impacts of traffic-related exposures in urban areas: understanding
real effects, underlying driving forces and co-producing future directions. J. Transp.
Health 3 (3), 249–267 (Jul).
Kim, P., Dumitrescu, D., 2010. Share the road: investment in walking and cycling road
infrastructure. In: United Nations Environment Programme. UNEP, Nairobi.
Laden, F., Neas, L., Dockery, D., Schwartz, J., 2000. Association of fine particulate matter
from different sources with daily mortality in six US cities. Environ. Health Perspect.
108, 941–947.
Larsen, J., 2013. Bike-Sharing Programs Hit the Streets in Over 500 Cities Worldwide.
Earth Policy Inst, Washington, D.C.
Louis, J., Brisswalter, J., Morio, C., Barla, C., Temprado, J.-J., 2012. The electrically as-
sisted bicycle: an alternative way to promote physical activity. Am J Phys Med
Rehabil 91, 931–940. http://dx.doi.org/10.1097/PHM.0b013e318269d9bb.
de Nazelle, A., Bode, O., Orjuela, J.P., 2017 Feb. Comparison of air pollution exposures in
active vs. passive travel modes in European cities: a quantitative review. Environ. Int.
99, 151–160.
Oates, G.R., Hamby, B.W., Bae, S., Norena, M.C., Hart, H.O., Fouad, M.N., 2017.
Bikeshare use in urban communities: individual and neighborhood factors. Ethn. Dis.
27 (Suppl. 1), 303–312.
OECD, 2012. Mortality Risk Valuation in Environment, Health, and Transport Policies.
OECD, Paris.
Oortwijn, J., 2015. Bike-Sharing Systems to Grow to Multi-billion Business. Available:
http://www.bike-eu.com/sales-trends/nieuws/2015/10/bike-sharing-systems-to-
grow-to-multi-billion-business-10124878, Accessed date: 31 January 2017.
Perez, L., Kunzli, N., 2009. From measures of effects to measures of potential impact. Int.
J. Public Health 54, 45–48.
Pope, C.A., 2007. Mortality effects of longer term exposures to fine particulate air pol-
lution: review of recent epidemiological evidence. Inhal. Toxicol. 19, 33–38.
Rojas-Rueda, D., de Nazelle, A., Tainio, M., Nieuwenhuijsen, M.J., 2011. The health risks
and benefits of cycling in urban environments compared with car use: health impact
assessment study. BMJ 343, d4521. http://dx.doi.org/10.1136/bmj.d4521.
Rojas-Rueda, D., de Nazelle, A., Teixidó, O., Nieuwenhuijsen, M.J., 2012. Replacing car
trips by increasing bike and public transport in the greater Barcelona metropolitan
area: a health impact assessment study. Environ. Int. 49, 100–109. http://dx.doi.org/
10.1016/j.envint.2012.08.009.
Rojas-Rueda, D., de Nazelle, A., Teixidó, O., Nieuwenhuijsen, M.J., 2013. Health impact
assessment of increasing public transport and cycling use in Barcelona: a morbidity
and burden of disease approach. Prev. Med. 57, 573–579. http://dx.doi.org/10.
1016/j.ypmed.2013.07.021.
Rojas-Rueda, D., De Nazelle, A., Andersen, Z.J., Braun-Fahrländer, C., Bruha, J., Bruhova-
Foltynova, H., et al., 2016. Health impacts of active transportation in Europe. PLoS
One 11, e0149990.
Schepers, J.P., Fishman, E., den Hertog, P., Wolt, K.K., Schwab, A.L., 2014. The safety of
electrically assisted bicycles compared to classic bicycles. Accid. Anal. Prev. 73,
174–180. http://dx.doi.org/10.1016/j.aap.2014.09.010.
Schwela, D., Zali, O., Schwela, P., 2008. Motor vehicle air pollution. In: Public Health
Impact and Control Measures. WHO, Geneva.
Shaheen, S.A., Guzman, S., Zhang, H., 2010. Bikesharing in Europe, the Americas, and
Asia past, present, and future. Transp. Res. Rec. 2143, 159–167. http://dx.doi.org/
10.3141/2143-20.
Simons, M., Van Es, E., Hendriksen, I., 2009. Electrically assisted cycling: a new mode for
meeting physical activity guidelines. Med. Sci. Sports Exerc. 41, 2097–2102.
Sun, Y., Mobasheri, A., 2017. Utilizing crowdsourced data for studies of cycling and air
pollution exposure: a case study using Strava data. Int. J. Environ. Res. Public Health
14 (3), 274.
Sun, Y., Mobasheri, A., Hu, X., Wang, W., 2017. Investigating impacts of environmental
factors on the cycling behavior of bicycle-sharing users. Sustain. For. 9 (6), 1060.
WHO, 2006. Health Effects and Risks of Transport Systems: The HEARTS Projects. The
World Health Organization, Europe.
Wichmann, H., Spix, C., Tuch, T., Wölke, G., Peters, A., Heinrich, J., et al., 2000. Daily
mortality and fine and ultrafine particles in Erfut, Germany. Res. Rep. Health Eff. Inst.
98, 5–86.
Woodcock, J., Franco, O.H., Orsini, N., Roberts, I., 2011. Non-vigorous physical activity
and all-cause mortality: systematic review and meta-analysis of cohort studies. Int. J.
Epidemiol. 40, 121–138.
Woodcock, J., Tainio, M., Cheshire, J., O'Brien, O., Goodman, A., 2014. Health effects of
the London bicycle sharing system: health impact modelling study. BMJ 348, 1–14.
http://dx.doi.org/10.1136/bmj.g425.
World Health Organization, 2008. The Global Burden of Disease: 2004 Update. WHO.
I. Otero et al. Environment International 115 (2018) 387–394
394