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It has become increasingly clear that a promising way to address waste-related issues resides in waste prevention by resource conversation. Research has therefore started to focus on preventing waste production by improving resource conservation capabilities. This study investigates how the societal transition to the fourth generation of bike-sharing system, known as free-floating bike-sharing (FFBS), presents not only a technological leap but also an environmental one by fostering stronger resource conservation capabilities which can be promoted by marketers. Taking Beijing as an example, a quantitative analysis compares the changes in resources utilization before and after the emergence of an FFBS scheme as compared to the third generation of bike-sharing systems, called station-based bike-sharing (SBBS), and of privately-owned bikes (POB). The results show that FFBS can improve the resources utilization
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Contribution of Bike-Sharing to Urban Resource Conservation: The Case of
Free-Floating Bike-Sharing
Shouheng Sun, Myriam Ertz*
*Corresponding author
Shouheng Sun, LaboNFC, Université du Québec à Chicoutimi, 555 Boulevard de
l’Université, Chicoutimi (QC), G7H 2B1, Canada, Email : shouheng.sun1@uqac.ca
Myriam Ertz , LaboNFC, Université du Québec à Chicoutimi, 555 Boulevard de
l’Université, Chicoutimi (QC), G7H 2B1, Canada, Email : Myriam_Ertz@uqac.ca
Abstract
It has become increasingly clear that a promising way to address waste-related issues
resides in waste prevention by resource conversation. Research has therefore started to
focus on preventing waste production by improving resource conservation capabilities.
This study investigates how the societal transition to the fourth generation of bike-
sharing system, known as free-floating bike-sharing (FFBS), presents not only a
technological leap but also an environmental one by fostering stronger resource
conservation capabilities which can be promoted by marketers. Taking Beijing as an
example, a quantitative analysis compares the changes in resources utilization before
and after the emergence of an FFBS scheme as compared to the third generation of
bike-sharing systems, called station-based bike-sharing (SBBS), and of privately-
owned bikes (POB). The results show that FFBS can improve the resources utilization
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of the urban bicycle system. It can reduce aluminium consumption by 15.3%, steel
consumption by 10.6%, plastic consumption by 13.0% and rubber consumption by
18.4% for each bicycle trip throughout the city. FFBS presents, therefore,
transformative properties for both society and the environment by showing greater
potential in resource conservation especially in comparison to SBBS.
Keywords: Bike sharing, Access-based consumption, Sharing, Mutualization,
Sustainability, Resource conservation.
1Introduction
Bike-sharing is a mutualized mobility mode that has become increasingly popular
due to its convenience, low-cost, and ease of access (Parkes et al., 2013; Shaheen et al.,
2010). Bike-sharing first appeared in the 1960s in Amsterdam with Witte Fietsen (White
Bikes). In that first generation of bike-sharing, bicycles could be used by people for
free and parked anywhere in the city. The next generation, called coin-deposit, emerged
in Denmark in the 1990s. Bikes used to be parked on fixed parking spots, and users
needed to put coins in a parking meter to use it. The third generation of station-based
bike sharing (SBBS) draws on IT-based systems but requires that bikes be placed on
stations or docks, while users provide personal information to register and then use the
smart card to pick up a bike (DeMaio, 2009; El-Assi et al., 2017; Pal and Zhang, 2017).
However, with further technological developments (e.g., GPS, mobile technology,
online payment) and the rise of mutualization-based business schemes known as
“access-based consumption” (e.g., Zipcar, Car2Go) in the 2010s (Bardhi and Eckhardt,
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2012), a large number of innovative business models emerged. In the area of
transportation, they have been coined “Mobility as a Service(MaaS) (Hensher et al.,
2020).
In this context appeared the fourth generation of bike-sharing, known as free-
floating bike sharing (FFBS) or bike-sharing 4.0, in which bikes can be locked to any
solid frame or standalone, eliminating the need for specific docks or stations (Pal and
Zhang, 2017). Initially launched on a small-scale basis in Germany in 2000 with
Deutsche Bahn’s Call a Bike, FFBS were first developed in China by Ofo and Mobike,
in 2014 and 2015 (Zhao et al., 2018). It then developed on a much larger scale in 2016
throughout the whole country, and joined FFBS expansion worldwide (DeMaio, 2009;
Fannin, 2017). Unlike SBBS, which requires parking at a fixed site, bikes operating
within FFBS can be parked freely. Bikes can be unlocked and paid for using a
smartphone and can be picked up and left in any parking area at usersconvenience. In
contrast to existing small-scale, station-based bike sharing systems, FFBS are more
flexible as they do not require tedious registration processes. Users simply use their
smartphone to scan the QR code on the bike to open the lock, and as such, FFBS provide
a practical solution to the ‘first/last mile problemfor urban commuters using green
transportation (Ma et al., 2018). In China, the country’s official state-run press agency
Xinhua called it one of the “four great new inventions in modern times (with e-
commerce, mobile payment and high-speed railways) (Ibold and Nedopil, 2018).
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In view of these benefits, FFBS have been praised for changing urban travel patterns
and creating a shift towards sustainability for the current unsustainable transport sector
(Cohen et al., 2016). In this regard, numerous academic studies demonstrated in
meaningful ways how bike-sharing has desirable environmental, economic and social
benefits. For example, riding a bike instead of driving a car can reduce oil consumption,
carbon dioxide (CO2) emissions, traffic congestion and noise pollution, as well as
bringing physical health benefits to users (Higgins and Higgins, 2005; Rojas-Rueda et
al., 2013; Caulfield et al., 2017; Qiu and He, 2018; Shen et al., 2018; Luo et al., 2019;
Cao and Shen, 2019). Past research assessed very specifically the environmental
benefits of bike-sharing in terms of reduced oil consumption, and ensuing traffic,
congestion, air pollution and greenhouse gas emissions (GHG) (e.g., Shaheen et al.,
2011; Zhang and Mi, 2018).
Meanwhile, FFBS have also sparked a lot of media, business, public and
government attention, especially in China, due to their haphazard development
throughout cities (The Guardian, 2017; Qiu and He, 2018; Zhao et al., 2018; Ma et al.,
2018). Without the need for fixed parking lots or docks, FFBS proliferated during a
“bike-sharing craze (DW, 2018), and have been criticized for over-consuming space
and material resources across cities as well as creating rising amounts of waste in “bike
share graveyards(The Guardian, 2017). Excessive amounts of FFBS bikes introduced
randomly on the streets by some 80 start-ups, coupled with low demand, created
sustainability issues (e.g., public space issues, environmental problems, economic
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struggles between providers), which led to stricter governmental regulation (Ibold and
Nedopil, 2018). Therefore, while FFBS have been considered a pathway for sustainable
transportation, they have also raised a lot of concerns regarding waste production and
sustainability. Academia tried to settle this debate by suggesting governance initiatives
(Hauf and Douma, 2019) or designing resilient or smart mobilities (Christensen et al.,
2019; Degael, 2019). However, from a scholarly research perspective, little is known
about how bike-sharing, in general, and FFBS, in particular, impact raw resources. In
fact, the impact of FFBS in this regard is mixed. On the one hand, FFBS might better
conserve resources than SBBS by avoiding the construction of expensive and resource-
intensive docking stations as well as kiosk machines (Pal and Zhang, 2017). Yet, the
disorganized development of FFBS has spurred critical waste-related issues (The
Guardian, 2017; DW, 2018), which could nullify resource conservation gains.
Additionally, little research has sought to quantitatively assess what a transition to
FFBS might incur in terms of waste prevention through resource conservation. Yet, this
knowledge is crucial since resource conservation, especially of materials, has become
a key element for sustainable waste management (Brunner and Rechberger, 2015). This
knowledge is also important to assess the contribution to sustainability of new business
models and revenue streams, such as FFBS, that are sharply growing in China (Ibold
and Nedopil, 2018), and in other parts of the world (Ji et al., 2014). Such insights may
also benefit, not only the economy, but also the environment and society. Therefore, in
order to better assess the resource benefits of FFBS, this study investigates the impact
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of FFBS on waste prevention through raw material resources conservation, by using
actual urban transportation data and data from the bicycle industry.
Based on findings from extant research as well as the database from Beijing
Transportation Institute, this study provides fresh new insights into FFBS by
quantitatively calculating the impacts of FFBS on raw material resource conservation.
By so doing, the paper provides a valuable reference for the regulation of urban bicycle
system, by informing researchers, managers and policy makers of the environmental
implications of a transition toward FFBS.
2Materials and methods
2.1. Scope definition
While FFBS was already widespread in some parts of the world such as in Germany
since the 2000s (DeMaio, 2009; Fannin, 2017), it has been introduced in China more
recently, in 2016. This new generation of bike-sharing system has spread rapidly in
major cities across China, and more than 23 million bikes have been put on the market
since then (China State Information Center, 2019). The FFBS scale in Beijing has
exceeded 1 million, making it the world’s largest FFBS market (Beijing Transport
Institute, 2019). Therefore, Beijing is a very good case study and a representative
example. This paper quantitatively estimates the impacts of FFBS on resources. More
specifically, we analyzed the potential of FFBS in improving efficiency of resources
utilizing. Resources mainly include aluminum, steel, plastic, rubber, photovoltaic
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panels, electronic equipment, battery, and glass, etc. At present, there are three types of
bicycle subsystems in Beijing:
(1) privately-owned bikes (POB);
(2) station-based bike-sharing (SBBS);
(3) free-floating bike-sharing (FFBS).
This study uses the materials consumed per trip as the unit for comparing and
analyzing resource utilization efficiency. First, we calculate and compare the resource
utilization efficiency of different bicycle subsystems. Then, based on the characteristics
and scale of each type of bicycle, and specific urban transportation development
planning, we conduct a series of scenario analysis to evaluate the contribution of FFBS
to resource conservation. Finally, we compare the impact of two different development
models (focus on FFBS or focus on SSBS) on resource conservation for the entire
bicycle system.
2.2 Calculation method
First, we calculate and compare the materials consumed for providing one trip by
each bicycle system. This analysis enables to determine whether FFBS better
contributes to waste prevention by resource conservation, compared to POB and SBBS.
The material consumption per trip can be calculate by equation (1).

 
(1)
With referring to the type of raw material, referring to the type of bike system. R
refers to the amount of raw material consumed by each bike system per year. refers
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to the number of trips provided by each bicycle system per year. 
 refers to the
average amount of materials consumed for providing one trip by each bicycle system.
As to the number of trips provided by each bicycle system per year, it can be
calculated by Equation (2).
      (2)
In Equation (1), referring to the type of bike system, refers to the total
number of trips per day provided by each type of bike system, and referring to the
scale of each type of bike within the city.  refers to the average number of uses of
a bike per day (DTR, daily turnover rate) for each type of bicycle systems within the
city.
In terms of the amount of raw material consumed for each bike system per year, we
need to estimate the raw material resource consumption for manufacturing and
maintenance. For POB and FFBS, it is sufficient to calculate the raw material
consumption during production and maintenance. For SBBS, it is necessary to consider
the raw material resources required for the construction of the stations and docks. The
raw materials resources required for the construction and maintenance of the station
and docks are evenly divided among the bicycles forming part of SBBS. Since bicycles
and facilities have a certain life span, in order to maintain the normal operation of the
urban bicycle system, new bicycles need to be produced to replace scrapped bicycles
and related facilities also need to be renovated and rebuilt. The raw material resources
consumption per year can be calculated by Equation (3) for POB and FFBS and
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Equation (4) for SBBS, and the raw material resources consumption per year by the
entire bicycle system can be calculated by Equation (5).

 
 (3)

 
 
  
 
  
 (4)
 (5)
With referring to the type of raw material, referring to the type of bike.
refers to the scale of the bike system. R refers to the amount of raw material consumed
for each bike system per year.  is the life span of the bike.  and 
are the life span of the stations and docks, respectively. Besides, refers to the amount
of raw material required to manufacture and maintain one unit of new bicycle, station
and dock.
The resource utilization efficiency of the entire urban bicycle system can be
obtained by Equation (6)-(8).
      (6)
      
 (7)

(8)
While refers to the type of raw material, refers to the amount of raw material
resources consumption per year in order to maintain the normal operation of the entire
bicycle system within the city, which can be calculated by summing up the annual
resource consumption of each type of bicycle system. refers to the number of trips
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provided by the entire bicycle system of the city per year. 
refers to the average
amount of materials consumed for providing one trip by the entire bicycle system of
the city.
2.3. Data and parameters
The data about bicycle ownership and operating characteristics used in this paper
are mainly drawn from the Beijing Transport Annual Report (Beijing Transport Institute,
2019, 2018, 2017), government policy document, industry statistics, and other publicly-
available data (Beijing Municipal Transportation Commission, 2019, 2017; China State
Information Center, 2019, 2018). The data on bicycle production and maintenance
mainly comes from existing publications and secondary data research (Chen and Chen,
2018; Xu, 2018; Heda, 2012; Shaheen et al., 2013; Luo et al., 2019).
During the manufacturing phase, the main components are similar across FFBS,
SBBS and POB, since there are no major differences in the main raw materials and
quantities required for manufacturing each type of bicycle (Ding et al., 2018). The
inventory data of bike productions are scaled with bike mass, based on an LCA report
of the manufacturing of a 17 kg urban-used bicycle (Leuenberger and Büsser, 2010).
Currently, the weight of shared bicycles in Beijing is between 15.5-20 kg, which is
similar to regular privately-owned bicycles (Xu, 2018). The types and average
quantities of the main raw materials required to manufacture mutualized bicycles and
privately-owned bicycles do not differ significantly (Ding et al., 2018; Xu, 2018; Luo
et al., 2019). Therefore, this study assumes that an average bicycle weights 17 kg and
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that the main raw materials used for manufacturing a bike are aluminum, steel, rubber
and plastic. More specifically, it takes an average of 8.37 kg aluminum, 5.94 kg steel,
1.73 kg plastic and 0.96 kg rubber to manufacture an urban-used POB and a mutualized
bicycle (Cherry et al., 2009; Xu, 2018; Zheng et al., 2019). In addition, the free-floating
shared bike additionally requires a photovoltaic panel, a rechargeable battery, and
electronic components (e.g. smart bicycle lock) (Luo et al., 2019; Ma et al., 2018), and
the station-based shared bikes require the construction of stations and docks (Luo et al.,
2019).
According to the standard of building stations and docks for SBBS around the world
(Heda, 2012; Shaheen et al., 2013; Toole Design Group and Pedestrian and Bicycle
Information Center, 2012), and combined with the actual situation in Beijing (Beijing
Transport Institute, 2019), we calculate the materials required for supporting facility
of SBBS system (see Appendix A, B, and C). In this study, according to Luo et al.
(2019), the life span of stations and docks is set at 10 years. Considering that the average
lifespan of a privately-owned bicycle in Beijing is about 4-6 years (Ding et al., 2018)
and the life span of a mutualized bicycle is mandated by the government to be 3 years
(Beijing Municipal Transportation Commission, 2017), the life span of privately-owned
bicycle is set at 5 years and that of shared bicycle is set at 3 years in this study.
It is typically difficult to obtain specific data related to maintenance of bicycles and
thus, we used the estimates of extant research. The material consumption of a bicycle
during the maintenance phase is related to its usage. In Beijing, one private bicycle is
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used about 0.5 times a day on average, while a mutualized bike is used about 1.6 times
a day on average. According to Xu (2018), during the entire life cycle of POB, an
average of 3% of aluminum, 10% of steel, 30% of plastic, and 50% rubber components
and parts (materials) need to be replaced (consumed). Aluminum is mainly used for
frame and support pillar, so that the consumption rate is low. For higher effectiveness,
mutualized bicycles use durable designs and materials (Ding et al., 2018). For example,
while POB use inner-inflated tires, mutualized bicycles use solid tires, which greatly
reduce the consumption of rubber materials during usage (Chen and Chen, 2018; Ding
et al., 2018). However, in contrast to privately owned bicycles, mutualized ones incur
higher DTR, higher damage rates, and are located outside with increased exposure to
climate changes. At the current DTR and during the entire life cycle of SBBS, an
average of 6% of aluminum, 20% of steel, 40% of plastic and 30% of rubber need to be
replaced (consumed) (Zjol, 2012; Xu, 2018). Although FFBS share the same average
DTR as SBBS, their damage rate is double that of SSBS (Zjol. 2012; Yang et al., 2019).
Therefore, the materials consumed in the FFBS maintenance process account for about
12% of aluminum, 40% of iron, 60% of plastic and 40% of rubber incurred during the
production process (Chen and Chen, 2018). In addition, when the DTR doubles, the
average damage rate increases by about 20% (Chen and Chen, 2018; Ding et al., 2018).
2.4. Scenario setting
Scenarios are scientifically-sound methods of conducting research and as such have
been implemented in numerous past studies (e.g., Harmsen et al., 2013; Van der Voet et
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al., 2019; Gustafsson et al., 2020). Scenarios are a useful methodological approach to
anticipate on potential future conditions and events (e.g., the International Energy
Agency’s world energy outlook or the Intergovernmental Panel on Climate Change
(IPCC) emissions scenarios) (Kuipers et al., 2018). They do not predict the future but
show what could occur next under a series of key assumptions (Marcus, 2016). They
are mainly characterized by plausibility in that they provide plausible trajectories for
the future in complex systems where outcomes are typically uncertain (Schweizer and
Kurniawan, 2016).
The scenarios are partially aligned with the development plan of FFBS in Beijing
(Beijing Municipal Transportation Commission, 2019, 2017) and developed with the
following information:
(1) SBBS in Beijing has been developing slowly in recent years. Before the
emergence of FFBS, there were about 81 000 SBBS bicycles in Beijing, with
an average DTR of 1.69 (Beijing Transport Institute, 2017). FFBS has grown
rapidly since 2016. At present, the number of FFBS bicycles is 1.015 million
and the average DTR is 1.58, while the scale of SBBS is 104 000 and its DTR
reaches 1.6 (Beijing Municipal Transportation Commission, 2019). It can be
seen that the emergence of FFBS has a relatively small impact on the DTR of
SBBS. In addition, the scale of SBBS is controlled by the government at around
100,000 units (Beijing Municipal Transportation Commission, 2016). Therefore,
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it can be assumed that the scale (i.e. 104 000) and the DTR (i.e.1.6) of SBBS
will both be stable;
(2) The number of POB in Beijing has topped 8 million for years, and their
dormancy rate remains high (Beijing Transport Institute, 2017). The daily
turnover rate of POB has been maintained at around 0.48 (Beijing Transport
Institute, 2017, 2018). Considering that the cycling demand of people who own
bicycles is often relatively stable, it can be assumed that the DTR of POB will
be stable at 0.48;
(3) Currently, the DTR of FFBS is almost equivalent to the DTR of SBBS, and is
roughly three times that of POB, while sharing the commonality with POB of
being station-less. FFBS can thus significantly improve the utilization
efficiency of the entire bicycle system in the city. In addition, the large-scale
promotion of FFBS can also increase the total number of urban cycling trips to
a certain extent. However, the large number of FFBS has also brought many
problems to urban governance. Therefore, Beijing plans to reduce the scale of
FFBS and strive to improve the operating efficiency of shared bicycles.
According to the development plan of FFBS in Beijing, the scale of FFBS will
be gradually reduced to about 600,000 (Beijing Municipal Transportation
Commission, 2019, 2017).
(4) Due to large scale of FFBS deployed in the city, the good convenience and
availability of FFBS can make some people give up owning their private
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bicycles to a certain degree. According to (Jiang and Zhou, 2017; Peng, 2018;
Beijing Transport Institute, 2019), under the current scale of the FFBS, the
number of POB within the city will be reduced by about 25%. When the scale
of FFBS is reduced by half, this ratio will drop to about 10% due to the decline
in availability (Zhiyan, 2019; Beijing Transport Institute, 2019, 2020).
In addition, when the scale of FFBS decreases, the DTR of FFBS may also change.
Here we set different DTR values according to the scale law. Scale law is widely present
in the growth and decline of organisms, cities, economies and companies (West, 2017).
For example, urban infrastructural features usually exhibit economies of scale (Kühnert,
Helbing and West, 2006; Isalgue, Coch and Serra, 2007; West, 2017). The relationship
between transportation demand and the supply of transportation facilities is usually
super-linear, that is, the growth of transportation demand is always faster than the
growth of transportation facilities supply (Downs, 1962; Isalgue, Coch and Serra, 2007;
Kühnert, Helbing and West, 2006). However, although many things or systems can
achieve exponential growth, due to various constraints, they usually cannot expand
indefinitely (West, 2017). Logistic curve is a famous case, which was proposed by
Pierre François Verhulst when studying its relationship with population growth
(Verhulst, 1845). In the world of ecology, when a species moves into a new ecosystem,
it will grow indefinitely under ideal environmental conditions, as the J-shaped curve
shown in Fig 1. If the species has natural enemies in the ecosystem, and the system has
insufficient resources such as food and space (i.e. non-ideal environment), the growth
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function of this species satisfies the logistic equation (i.e. S-shaped growth curve in
Fig.1A) (Verhulst, 1845; Meyer, Yung and Ausubel, 1999). Its feature is: roughly
exponential growth in the initial stage; as the population size approaches saturation, the
increase slows down; finally, the increase stops when it reaches the maximum
environmental carrying capacity (Verhulst, 1845; Kucharavy and De Guio, 2015).
The logistic curve can be expressed by the differential equation, as shown in
Equation (9).

      (9)
Where y refers to the population size at time t, K refers to the maximum population
size that the system can sustain under the resource and environmental constraints.
refers to the scaling factor, that is, the growth rate of the population 
 is proportional
to the product of the population size y(t) and the degree of population size close to the
saturation level (i.e. Ky).
Fig. 1. Logistic curve and the development trend of FFBS market in China. A). Characteristics and
description of the Logistic curve. B). The overall development trend of FFBS market in China's first -
and second-tier cities from 2016-2017.
Note: source from Verhulst (1845); Meyer, Yung and Ausubel (1999); Kucharavy and De Guio (2015);
Penguin Intelligence (2017); Trustdata (2017).
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Shared bikes can be regarded as urban public transport facilities which do also
follow the scale law. As shown in Fig. 1B, the development trend of FFBS is similar to
the Logistic curve. In the early stage of the development of FFBS, with the increasing
bike scale and availability of FFBS, and the number of FFBS trips showed a super linear
growth. At this time, the DTR increases with the expansion of the scale. However, the
FFBS trip demand of urban residents is limited. When the trip volume approaches the
city’s maximum demand, the relationship between the trip volume and the bike scale
of FFBS becomes a sublinear relationship. That is, as the bike scale of FFBS continues
to increase, the DTR will gradually decrease. In fact, the bike scale of FFBS may
already be in a stage of oversupply.
Therefore, based on the scale law and logistic equation, we try to set different
potential DTR when the scale of FFBS in Beijing dropped from 1 million to 600,000.
We considered a variety of logistic curves with different characteristics that Beijing
FFBS market may conform to, and set three potential scenarios. The details are shown
in Table 1.
Table 1.
Different DTR scenarios when the scale of FFBS drop to 600 000 in Beijing
Potential scenario
Description
When the bike scale of FFBS decreases, the DTR
increases. According to the scale law and the logistic
curve, as the scale of FFBS decreases, the overall
trip volume will also decrease. Currently, FFBS trip
volume is about 1.6 million per day. Therefore, in
this study, we set that the DTR rises to 2.5 when the
scale drops from 1 million to 600,000. FFBS trip
volume will be about 1.5 million per day. It can be
regarded as a best-case scenario.
0
1
2
3
4
0
50
100
150
200
050 100 150
DTR
Number of FFBS trips
Bike scale of FFBS
18
When the bikescale of FFBS drops to 600 000, the
DTR will not change much. Here, we still set it to
1.58. The FFBS trip volume will be about 0.95
million per day.
When the bike Scale of FFBS drops to 600,000, the
DTR also decreases. The DTR value is set with
reference to the actual operating conditions of
Shanghai and Guangzhou, which have similar urban
areas and populations to Beijing. The scale of FFBS
in Shanghai and Guangzhou has been controlled
between 400,000-800,000 in recent years, and the
DTR are all above 1.3 (Shanghai Urban and Rural
Construction and Transportation Development
Research Institute, 2019; Sohu, 2019) Therefore, the
DTR is set to 1.2 in this study. The FFBS trip volume
will be about 0.72 million per day. It can be regarded
as a worst-case scenario.
Note: According to the Beijing Transport Institute (2019), the maximum demand for FFBS trips in
Beijing is set to about 1.7 million per day, which was used to simulated the logistic curve in the table.
Therefore, within the framework of this specific background, we designed five
scenarios to estimate the resource conservation potential in situations. The five bike
sharing market scenarios are based on differentiated narratives:
(1) Baseline scenario refers to the situation before the advent of FFBS;
(2) Scenario 1 refers to the current situation in 2020 with 1.015 million FFBS and
a DTR of 1.58.
0
0,5
1
1,5
2
0
50
100
150
200
050 100 150
DTR
Number of FFBS trips
Bike scale of FFBS
19
(3) Scenario 2 is hypothetical and posits a scale of 600,000 FFBS bikes and a DTR
of 2.5;
(4) Scenario 3 is hypothetical and posits a scale of FFBS at 600 000 and a DTR of
1.58;
(5) Scenario 4 is hypothetical and posits a scale of FFBS at 600 000 and a DTR of
1.2;
Then, we summarized the scale and DTR for the three types of bicycle systems
across the five scenarios, as shown in Table 2.
Table 2
The scale and daily turnover rate (DTR) for each type of bicycle system across the five
scenarios
SBBS
FFBS
POB
Scale
DTR
Scale
DTR
Scale
DTR
Baseline Scenario
81 000
1.69
0
8 000 000
0.48
Scenario 1
104 000
1.60
1 015 000
1.58
6 000 000
0.48
Scenario 2
104 000
1.60
600 000
2.5
7 200 000
0.48
Scenario 3
104 000
1.60
600 000
1.58
7 200 000
0.48
Scenario 4
104 000
1.60
600 000
1.2
0
In order to further compare the difference in resource utilization of the entire bicycle
system between FFBS and SBBS, two groups of calculations are performed. One group
is named “FFBS”, as shown in Table 2. In the other group of calculations, we swap
FFBS and SBBS in Scenario 1-4, and keep all other things being equal. This group
named “SBBS”, and the specific scenario settings are shown in Table 3.
Table 3
20
The setting of scale and daily turnover rate (DTR) for each type of bicycle system across the
five scenarios in group “SBBS”
SBBS
FFBS
POB
Scale
DTR
Scale
DTR
Scale
DTR
Baseline Scenario
81 000
1.69
0
8 000 000
0.48
Scenario 1
1 015 000
1.58
104 000
1.60
6 000 000
0.48
Scenario 2
600 000
2.5
104 000
1.60
7 200 000
0.48
Scenario 3
600 000
1.58
104 000
1.60
7 200 000
0.48
Scenario 4
600 000
1.2
104 000
1.60
7 200 000
0.48
For these two groups, by calculating the resource consumption of the entire bicycle
system within the city from Scenario 1 to Scenario 4, we can further compare the impact
of the development of the two different development models of bike-sharing on
resource conservation for the entire bicycle system.
3. Results
3.1 Comparison of resource utilization efficiency of different bicycle subsystems
Comparison in major material consumption per trip for three bicycle systems are
shown in Fig.2 (aluminum, steel, plastic and rubber for POB, SBBS and FFBS) and
Fig.3 (electronic equipment, battery, glass and photovoltaic panel for SBBS and FFBS).
As can be seen from Fig. 2 and Fig. 3, although the introduction of shared bike can
increase the consumption of battery, electronic equipment and photovoltaic panels, they
all show some potential for resources saving. Compared with POB, FFBS can
significantly reduce the consumption of aluminum, iron, plastic and rubber. With the
current DTR (i.e. about 1.6), each FFBS trip can reduce aluminum consumption by
45%, iron consumption by 36%, plastic consumption by 38%, and rubber consumption
by 53% compared to each POB trip. When the DTR of FFBS rises to 2.5, the
21
consumption of aluminum, steel, plastic and rubber can be reduced by 65%, 58%, 59%
and 69% respectively. Even if the DTR drops to 1.2, each FFBS trip can still save 28%
of aluminum, 16% of steel, 19% of plastic and 38% of rubber.
As to SBBS, although SBBS may also reduce the consumption of plastic and rubber
compared to POB, it greatly increases the consumption of steel. It can also increase the
consumption of aluminum when the DTR is less than 1.6.
Since our objective is to show the different potential in resource conservation
between third and fourth generation bike-sharing systems, we mainly compare the
difference between FFBS and SBBS. With the same DTR, each FFBS trip can reduce
steel consumption by about 78% (10.9-22.8 g) and aluminum consumption by about 39%
(2.2-4.6 g). Each FFBS trip can also reduce about 73% of electronic equipment and 82%
of battery as compared to an SBBS trip. However, it should be noted that, one FFBS
trip consumes about 14% (0.14-0.25g) more plastic, 8% (0.04-0.07g) more rubber and
29% (0.02- 0.04cm2) more photovoltaic panels compared to one SBBS trip, on average.
Yet, albeit a small increase in plastic, rubber and photovoltaic panels, FFBS can
significantly reduce resource consumption compared to SBBS. In addition, the
tremendous reduction in battery and electronic equipment, prevent the proliferation of
electrical and electric equipment waste (EEEW), which is harmful for the environment
(Tsydenova and Bengtsson, 2011). Especiallyeven when the DTR of FFBS is less than
that of SBBS, FFBS still has obvious advantages in resource saving potential (see Fig.
2 and Fig. 3).
22
Fig. 2. Comparision in aluminum, steel, plastic and rubber consumption per trip for POB, SBBS and
FFBS
Fig. 3. Comparision in electronic equipment, battery, photovoltaic panels and glass consumption per
trip for SBBS and FFBS
0
2
4
6
8
10
12
14
1,2 1,6 2,5
Material consumption per trip
(unit: g)
DTR of Shared bike
Aluminum
SBBS FBBS POB
0
5
10
15
20
25
30
35
1,2 1,6 2,5
Material consumption per trip
(unit: g)
DTR of Shared bike
Steel
SBBS FBBS POB
0
0,5
1
1,5
2
2,5
3
1,2 1,6 2,5
Material consumption per trip
(unit: g)
DTR of Shared bike
Plastic
SBBS FBBS POB
0
0,5
1
1,5
2
1,2 1,6 2,5
Material consumption per trip
(Unit: g)
DTR of Shared bike
Ruber
SBBS FBBS POB
0
0,2
0,4
0,6
0,8
1
1,2
SBBS FBBS SBBS FBBS SBBS FBBS
1.2 1.6 2.5
Electronic equipment (g) Photovoltaic Panel (cm2) Battery (g) Glass (g)
23
3.2 Contribution of FFBS to resource utilization efficiency of the entire urban bicycle
system
Then, we calculate the materials consumption per trip for the entire bicycle systems
within Beijing, across the five scenarios, in order to analyze the changes in waste
prevention through resource conservation brought by FFBS. The detailed results are
shown in Fig. 4 and Fig. 5.
As can be seen from Fig. 4, FFBS has the potential to make a great contribution in
reducing material resource consumption. The emergence of FFBS (Scenario 1) can
reduce aluminum consumption by 15.3%, steel consumption by 10.6%, plastic
consumption by 13.0% and rubber consumption by 18.4% for each bicycle trip
throughout the city. As the scale of FFBS drops to 600 000 and the DTR rises to 2.5
(Scenario 2), compared to the Baseline Scenario, the materials consumption of
aluminum, steel, plastic and rubber for each trip can be reduced by nearly 18.8%, 15.7%,
17.2% and 20.3% respectively. When the Scale of FFBS drops to 600 000 and the DTR
remains at current level (i.e. 1.58) (Scenario 3), the materials consumption of aluminum,
steel, plastic and rubber for each trip can be reduced by nearly 9.1%, 6.0%, 7.8% and
11.1% respectively. In particular, even if the scale of FFBS drops to 600 000 and the
DTR drops to 1.2 (Scenario 4), compared to the Baseline Scenario, the materials
consumption of aluminum, steel, plastic and rubber for each trip can still be reduced by
nearly 4.4%, 1.3%, 3.3% and 6.6% respectively.
24
Fig.4. Aluminum, steel, plastic and rubber consumption per trip for the entire bike system in Beijing
Fig.5. Electronic equipment, battery, photovoltaic panels and glass consumption per trip for the
entire bike system in Beijing
In addition, the large-scale promotion of FFBS has also increased the consumption
of materials such as batteries, electronic equipment, and photovoltaic panels. However,
by reducing the scale of FBBS and increasing the utilization rate of bicycle (i.e. DTR),
these negative effects can be significantly reduced (see Fig 5).
3.3 Comparison of FFBS and SBBS in terms of resource saving for entire bicycle system
9,73
7,87
2,51 1,60
8,25 7,03
7,91 6,63
2,08 1,28
8,85 7,40
9,30 7,77
2,43 1,50
0,00
2,00
4,00
6,00
8,00
10,00
12,00
Aluminum Steel Plastic Rubber
Material consumption per trip (unit:g)
Baseline Scenario Scenario 1 Scenario 2 Scenario 3 Scenario 4
24,62
2,87
15,59
1,30
96,60
42,88 46,95
61,87
24,17 31,55
72,93
28,49 37,19
1,53
0
20
40
60
80
100
Electronic
equipment(mg) Photovoltaic Panel(cm2) Battery (mg) Glass(mg)
Baseline Scenario Scenario 1 Scenario 2 Scenario 3 Scenario 4
25
Next, we compare the impact of the development of the two different development
models of bike-sharing on resource conservation for the entire bicycle system. No
comparison can be made to the Baseline Scenario since FFBS did not exist at that
time. The detailed results are shown in Fig. 4, Appendix D and Appendix E.
Table. 4.
Comparation in annual materials consumption for the development of two focal bicycle
systems under various conditions
Resource
Scenario 1
Scenario 2
Scenario 3
Scenario 4
Change
value
Change
rate
Change
value
Change
rate
Change
value
Change
rate
Change
value
Change
rate
Aluminum
(ton)
1832.71
13.04%
986.53
6.65%
997.83
6.73%
1002.85
6.77%
Steel
(ton)
9075.65
75.76%
4914.57
39.47%
4941.30
39.86%
4953.18
40.03%
Plastic
(ton)
-105.07
-2.82%
-64.99
-1.67%
-57.21
-1.48%
-53.75
-1.39%
Rubber
(ton)
-29.15
-1.31%
-18.03
-0.75%
-15.87
-0.67%
-14.91
-0.63%
Electronic
equipment (ton)
297.98
181.07%
162.24
139.68%
162.24
139.68%
162.24
139.68%
Photovoltaic
Panel(m2)
-1361.26
-18.64%
-741.15
-16.33%
-741.15
-16.33%
-741.15
-16.33%
Battery
(ton)
210.47
263.16%
114.59
193.48%
114.59
193.48%
114.59
193.48%
Glass
(ton)
21.36
875.96%
11.63
476.92%
11.63
476.92%
11.63
476.92%
Note: “Change valuerefers to the value of group “SBBSminus the value of group “FFBS”.
As can be seen from Table 4, although both SSBS and FFBS have the potential to
reduce urban resource consumption, FFBS has significant advantages in terms of waste
prevention through resource conservation, as compared to SBBS. In the current
situation (Scenario 1, scale of shared bike is about 1 000 000), if all the FFBS were
26
replaced by SBBS, the bicycle systems within the city will consume roughly an
additional 1 800 tons (+13%) of aluminum, 9 000 tons (+76%) of steel, 300 tons
(+181%) of electronic equipment, and 210 tons of battery (+263%) each year. It should
be stressed that SBBS saves 100 tons of plastic, 30 tons of rubber and 1,360 square
meters of photovoltaic panels each year. When the scale drops to 600 000 (Scenario 2,
Scenario 3 and Scenario 4), it also consumes about 6.7 % (1 000 tons) more aluminum,
40% (4900 tons) more steel, 140% (160 tons) more electronic equipment and 193%
(115 tons) more battery for the entire bicycle system within the city each year as
compare to FFBS. At the same time, the consumption of plastic, rubber and
photovoltaic panels can decrease by 1.5%, 0.7 %, and 16%, respectively.
Due to the limitation of urban space, it is difficult to develop SBBS on a large scale.
Moreover, a large number of supporting facilities may even lead to an excessive waste
of resources. As can be seen from the comparison in Table 4, if the municipal authorities
develop SBBS rather than FFBS on a large scale, this policy will most likely generate
excessive resources consumption. The most prominent is that such policy will consume
an additional 9 000 tons of steel compared to FFBS under the current situation.
Compared to SBBS, FFBS seems to present more desirable properties from a waste
management perspective.
4Discussion
The results show that FFBS has great potential to pursue waste prevention through
resource conservation. Compared to POB, it can significantly reduce the consumption
27
of aluminum, iron, plastic and rubber. In addition, the results also show that FFBS has
significant advantages in resources conservation compared to SBBS. Although FFBS
has almost the same DTR as SBBS currently, the consumption in aluminum and steel
for one FFBS trip is, respectively, 3.5 g (-39%) and 17.1 g (-78%) less than that for one
SBBS trip. In particular, under the current scenario (Scenario 1), the adoption of a large-
scale development of SBBS consumes 76% more steel and 13% more aluminum for the
bicycle system each year within the city than adopting large-scale development of FFBS.
Due to the limitation of urban space and raw material resources, it is difficult to develop
SBBS on a large scale. Moreover, a large number of supporting facilities are more likely
to lead to excessive waste of resources, in exchange of a marginal gain in the number
of supplementary bikes in circulation. FFBS doesn’t require stations and docks. Most
importantly, it is more flexible and convenient than SBBS, and possibly easier to be
implemented by local authorities and more likely to be widely accepted and adopted by
consumers. Therefore, FFBS can be considered as a better choice to improve resource
utilization and reduce resource consumption compared to its close competitor SBBS for
cities. These results hold particularly for cities with dense population and scarce space
resources.
Although the result in this article shows that FFBS consume less materials than
POB, thus can significantly improve the resource utilization of the entire urban bicycle
system, the results obtained are mainly based on the assumption that the weight of an
FFBS bike is 17 kg and that the lifespan of privately-owned bicycle is 5 years. In fact,
28
the weight of bicycles for FFBS on the market is about 15.55-20 kg, and the lifespan of
POB is about 4-6 years. Consider that fluctuations in the values of these two parameters
may affect the final result. We further conduct an uncertainty analysis based on these
two parameters to discuss the robustness of the results of this paper. The detailed results
are shown in Fig.6.
Fig. 6. Comparison of FFBS and POB in resource utilization efficiency under different conditions
Note: The red line represents the material consumption of each trip provided by POB under different
POB lifetimes. The black line, blue line and green line represent the material consumption of each
FFBS trip under the condition of the DTR of FFBS is 1.2, 1.6 and 2.5 respectively.
0
2
4
6
8
10
12
14
15,5 16 16,5 17 17,5 18 18,5 19 19,5 20
Material consumption per trip
(unit :g)
Weight of the FFBS bike (unit : kg)
Aluminum
1.2 1.6 2.5
4 years 5 years 6 years
0
2
4
6
8
10
15,5 16 16,5 17 17,5 18 18,5 19 19,5 20
Material consumption per trip
(unit :g)
Weight of the FFBS bike (unit : kg)
Steel
1.2 1.6 2.5
4 years 5 years 6 years
0
0,5
1
1,5
2
2,5
15,5 16 16,5 17 17,5 18 18,5 19 19,5 20
Material consumption per trip
(unit :g)
Weight of the FFBS bike (unit : kg)
Rubber
1.2 1.6 2.5
4years 5years 6years
0
0,5
1
1,5
2
2,5
3
3,5
15,5 16 16,5 17 17,5 18 18,5 19 19,5 20
Material consumption per trip
(unit :g)
Weight of the FFBS bike (unit : kg)
Plastic
1.2 1.6 2.5
4 years 5 years 6 years
29
As can be seen from Fig.6, when the DTR of FFBS is equal to or greater than 1.6
(Scenario 1, 2, and 3 in Section 3) no matter how the weight of the FFBS bike and life
span of POB change, FFBS always shows the advantage of saving resources on all types
of materials. With the decline of the DTR, the resource saving advantage of FFBS also
gradually decreases. When the DTR is 1.2 (Scenario 4 in this paper), in addition to
rubber, FBBS may be lower than POB in terms of utilization of other types of materials.
In terms of aluminum consumption, when the life of the bicycle is 6 years, the weight
of the FFBS vehicle needs to be less than 19.5 kg in order for FFBS to maintain its
resource saving advantage. As for steel consumption, the weight of FFBS bike needs to
be controlled below 16.5 kg in order to make FFBS always have a higher resource
utilization efficiency than POB. Similarly, in terms of plastic consumption, the weight
of FFBS bike needs to be less than 17 kg. It can be seen that when the DTR of FFBS
drops to 1.2, the resource saving advantage of FFBS for steel and plastics is uncertain.
Since the DTR affects the resource saving potential of FFBS, authorities and related
organizations should take effective measures to improve the DTR of FFBS.
Simultaneously, FFBS operators should continue to optimize bicycle design and
manufacturing technology, and use lighter and more durable materials to reduce the
weight of the vehicle without affecting the user experience. For example, Mobike (a
Chinese FFBS company) has continuously reduced the weight of the bike through
technological innovation and optimized design: from the original classic version of
about 20 kg to the “Liteversion of 17 kg, and then to the “New Liteversion of 15.5
30
kg (Xu, 2018). All these measures and efforts can significantly improve the resource
saving potential of FFBS.
5. Summary and conclusions
FFBS was once thought to be a pathway to reach circularity (Ibold and Nedopil,
2018). However, specific issues of excessive supply of bikes by FFBS have sparked
attention of the authorities and in academia (The Guardian, 2017; Ibold and Nedopil,
2018). This research contends that despite some of its weaknesses, FFBS incurs great
potential in terms of waste prevention through resources conservation and that these
characteristics could make up for more negative externalities.
This study contributes to extant scholarly research by conducting a quantitative
analysis to assess the changes in resources utilization before and after the emergence of
an FFBS scheme as compared to station-based bike-sharing (SBBS) and privately-
owned bike (POB) systems. In addition, although the positive externalities of bike-
sharing have been discussed in many academic studies, the discussion focuses on the
desirable environmental, economic and social benefits of FFBS. These include lower
oil consumption, traffic, congestion, air pollution and greenhouse gas emissions
(Higgins and Higgins, 2005; Rojas-Rueda et al., 2013; Caulfield et al., 2017; Qiu and
He, 2018; Shen et al., 2018; Zhang and Mi, 2018; Luo et al., 2019; Cao and Shen, 2019).
There is very little research on the resources impact of bike-sharing, in general, and
much less for FFBS, in particular. Specifically, for the emerging FFBS, it is only
intuitive to think that FFBS better contribute to resource conservation compared to
31
SBBS, by avoiding construction of expensive and resource-intensive docking stations
(Pal and Zhang, 2017). Yet, no study has sought to quantitatively assess what a
transition to FFBS might incur in terms of environmental potential on resource
conservation.
As what is likely the first study to explore the impact of FFBS on resources
conservation, this paper fills this academic void by providing a grounded analysis based
on actual urban transportation data and bicycle industry data. Moreover, it also provides
a new approach to the contribution of bike-sharing 3.0 to 4.0 transition to the
environment. The nuanced findings about the raw materials consumption, incurred by
different bicycle systems across various scenarios have also wider implications for
mutualization systems (Bardhi and Eckhardt, 2012). The current research suggests that
achieving a pathway towards sustainability requires balance and limitations. This paper
further constitutes a preliminary reference point for the subsequent study of governance
and sustainable development of the collaborative economy, a research trend
underscored by Ertz and Leblanc-Proulx (2018), as well as better realization of a
circular economy.
Due to the lack of more relevant data support, this paper did not compare the
material resources used in the manufacturing of different bicycle systems. Besides, we
did not calculate the consumption of given resources (e.g., electricity) during the
operation and management of specific bike systems, especially third- (i.e., SBBS) and
fourth- (i.e., FFBS) generation ones. All of these limitations require more research in
32
the future. Moreover, future research should assess the impact of using mutualized
transportation on the continuance of ownership. In fact, this condition warrants further
research, and especially in the extent to which individuals are ready to abandon private
ownership in favour of FFBS, if such bike-sharing services are available.
Appendix A.
Condition of SBBS in Beijing
Variable
value
Number of stations
3575
Number of bikes
104000
Number of docks
156000
Bikes/per station
29.1
Docks/per station
43.6
Note: SBBS data are based on (Beijing Transport Institute, 2019) .
Appendix B.
Station Dimensions for a 43-44 docks station
Variable
value
Length(m)
28
width (m)
3
Space (m2)
84
Number of bikes
29.1
Space/ per bike(m2)
2.88
Note: Data of station dimensions are calculated based on (Beijing Transport Institute, 2019) and (Heda,
2012; Shaheen et al., 2013; Toole Design Group and Pedestrian and Bicycle Information Center, 2012).
33
Appendix C.
Material consumption for making one station, and one dock
Component
Value
Unit
Material
Station
1.5
m2
Photovoltaic Panel
45.36
Kg
Steel
38.5
Kg
Aluminum
6.8
Kg
Glass
81.5
Kg
Battery
10
kg
Electronic components
Dock
13.6
kg
Aluminum
2.72
kg
Electronic components
67.8
kg
Steel
Note: Data of station, and one dock are based on (Luo et al., 2019).
Appendix D.
The aluminium, steel, plastic and rubber consumption consumption per trip for the development
of two focal bicycle systems under various conditions
9,73
8,25
9,32
7,91
8,43
8,85
9,44
9,30
9,93
7,87
7,03
12,36
6,63
9,25
7,40
10,35
7,77
10,88
2,51
2,18
2,12
2,08
2,04
2,31
2,28
2,43
2,39
1,60
1,31
1,29
1,28
1,27
1,42
1,41
1,50
1,49
0,00 2,00 4,00 6,00 8,00 10,00 12,00 14,00
FFBS
SBBS
FFBS
SBBS
FFBS
SBBS
FFBS
SBBS
Baseline
Scenario Scenario 1 Scenario 2 Scenario 3 Scenario 4
Rubber Plastic Steel Aluminum
34
Appendix E.
The electronic equipment, battery, photovoltaic panels and glass consumption per trip for the
development of two focal bicycle systems under various conditions
Funding acknowledgment
This study has been funded by a grant from Social Sciences and Humanities Research Council
(SSHRC) of Canada [Grant no. 430-2018-00415].
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... In this way, sustainability literature has explored public bicycle platforms' benefits from several perspectives, including users' viewpoints (e.g., Cerutti et al. 2019). For example, these studies point out benefits for urban mobility, citizens' health, city economics, and environmental preservation, as the bike-sharing system might emit fewer polluting gases into the air and nature (Cohen, Almirall, and Chesbrough 2016;Levrini and Nique 2019;Sun and Ertz 2021a). However, it is necessary to understand how urban administrators, traffic coordinators, consumers, and economy providers formulate urban public policies to develop sustainable consumption through bike-sharing platforms (Buehler and Pucher 2012;Henley 2013;Wang and Moriarty 2018;Cerutti et al. 2019). ...
... Fundamentally, bike-sharing platforms create value by reducing pollutions and traffic jams in big cities fostering sustainable collaborative consumption (SCC) (Cohen and Kietzmann 2014). Those critical benefits of bike-sharing platforms depend on how many consumers, cities, and providers share different roles, times, and participation (Cerutti et al., 2019;Sun and Ertz 2021a). For this, the sharing economy (SE) presents as a part of society's movement associated with the diffusion of new technologies, devices, and CC (Heinrichs 2013;Ertz and Boily 2019). ...
... Empirical evidence has demonstrated the proenvironmental benefits of bike-sharing, indicating that bike-sharing contributes to reducing the number of cars and buses on the road (Cohen and Kietzmann 2014;Lee, Lee, and Yoo 2020), changes positively individuals' behavior in metropolitan areas (Fishman 2016), and that its most advanced form (i.e., free-floating bike-sharing) fosters resource conservation (Sun and Ertz 2021a). Therefore, new consumption and mobility behaviors are emerging in big cities and may spur sustainability through CC, especially among the young (Cerutti et al., 2019;Echegaray and Hansstein 2020). ...
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... It was found that the use of FFBS contributes to a significant reduction of the annual greenhouse gas emissions 2021 S. Sun and M. Ertz [26] Resource utilization efficiency data Investigation how the transition to the fourth generation of BSS, known as FFBS, presents an environmental and technological leap. ...
... The financial issues were raised in many studies [29,59,87]. Many research works also focus on free-floating bike-sharing [26,57,88,89]. In Poland, as of today, there are only traditional SBBS systems in the cities. ...
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... As a consequence of these issues, around 2019, scholars started to raise concerns about the lack of studies related to the sustainability of BSS, which was especially alarming considering that it had been almost a decade since their popularization. Since then, there has been an increasing number of studies analyzing their environmental impact [14,21,22,23,24,25,26,27,28,29]. One of the early assessments on the environmental impacts of bicycle-sharing was carried out by Luo et al. [14] who conducted a Life Cycle Assessment (LCA) of station-based and dockless bicycles. ...
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... Access-based consumption's ability to bolster the well-being of consumers may be particularly crucial under conditions of uncertainty, as it aids them in navigating periods of liminality (i.e., social role transitions; Ozanne and Ozanne, 2020), or as sharing materials helps build collective resilience (Baker and Baker, 2016). Moreover, sharing can bolster well-being by allowing consumers access to a variety of shared goods, such as developmentally appropriate toys for children (Ozanne and Ballantine, 2010), fashion items (Lang, 2018), vehicles (Bardhi and Eckhardt, 2012), bikes (Sun and Ertz, 2021), and furniture (Kapoor and Vij, 2021). More specific to our research, scholars have also proposed that access-based consumption may improve financially-constrained consumers' well-being, whether by highlighting new job opportunities (Dillahunt and Malone, 2015), or by reducing perceived social inequality (Eckhardt and Bardhi, 2016). ...
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... In other words, the mediation and flattening process of artifacts develop connections, inductions, and engagements that heighten collaborative consumption, improving sharing practices among users and service providers (Da Silveira and Hoppen, 2019; Bajde, 2014) -for instance, the use of apps, bike stations, communication devices, interactive maps whose primary function is to reap the benefits of sharing and using bike-sharing systems for the environment and individuals' health (Da Silveira et al., 2021). Consequently, users using bikesharing or ridesharing apps have exemplified ways to execute sustainable practices through DP (Cohen et al., 2016;Sun and Ertz, 2021). Thus, this process contributes to the materiality of sharing practices through a network of associations, alliances, and relations negotiated between individuals, information, physical equipment, and digital technological artifacts (Da Silveira et al., 2021). ...
Chapter
The sharing economy (SE) includes economic, social, and technological arrangements to promote collaborative relations between users and providers willing to share assets through digital platforms (DP). Even evolving fast, there is an opportunity to discuss how DP establishes connections between users and providers and uses a digital agency to mediate and flatten consumption relations in SE. Therefore, the authors propose a framework and future research directions that explore characteristics of the actants (roles, agency, behavioral attitudes) in the process of flattening consumption relations through DP in SE (connections, mediation, induction). To structure this framework, the authors consolidated the various definitions of its main elements and adopted the actor-network theory concept of translation as the theoretical-methodological approach to analyze the associations that determined how flattening consumption relations occur in SE.
... Bicycle sharing is framed as a sustainable transport option [60] due to reductions in air pollution, GHG emissions [61], parking spaces [62], and road congestion [63]. Studies show that, for example, free-floating bicycle sharing systems have great potential to reduce resource use compared to station-based schemes or private ownership scenarios [64]. However, this form of bicycle sharing has also attracted negative attention [65] because these schemes sometimes offer an oversupply of bicycles to overcome the competition, in addition to having poorly designed management systems. ...
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The environmental performance of shared micromobility services compared to private alternatives has never been assessed using an integrated modal Life Cycle Assessment (LCA) relying on field data. Such an LCA is conducted on three shared micromobility services in Paris - bikes, second-generation e-scooters, and e-mopeds - and their private alternatives. Global warming potential, primary energy consumption, and the three endpoint damages are calculated. Sensitivity analyses on vehicle lifespan, shipping, servicing distance, and electricity mix are conducted. Electric micromobility ranks between active modes and personal ICE modes. Its impacts are globally driven by vehicle manufacturing. Ownership does not affect directly the environmental performance: the vehicle lifetime mileage does. Assessing the sole carbon footprint leads to biased environmental decision-making, as it is not correlated to the three damages: multicriteria LCA is mandatory to preserve the planet. Finally, a major change of paradigm is needed to eco-design modern transportation policies.
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In the transition towards fossil-free transports, there is an increasing interest in upgraded biogas, or biomethane, as a vehicle fuel. Liquefied biogas has more than twice as high energy density as compressed biogas, which opens up the opportunity for use in heavy transports and shipping and for more efficient distribution. There are several ways to produce and distribute compressed and liquefied biogas, but very few studies comparing them and providing an overview. This paper investigates the energy balance, environmental impact and economic aspects of different technologies for upgrading, liquefaction and distribution of biogas for use as a vehicle fuel. Furthermore, liquefaction is studied as a method for efficient long-distance distribution. The results show that the differences between existing technologies for upgrading and liquefaction are small in a well-to-tank perspective, especially if the gas is transported over a long distance before use. Regarding distribution, liquefaction can pay back economically after 25–250 km compared to steel container trailers with compressed gas, and reduce the climate change impact after 10–30 km. Distribution in gas grid is better in all aspects, given that it is available and no addition of propane is required. Liquefaction can potentially expand the geographical boundaries of the market for biogas as a vehicle fuel, and cost reductions resulting from technology maturity allow cost-effective liquefaction even at small production capacities.
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In China, dockless bike-sharing programs (DBSPs) have changed people’s travel modes, alleviated urban traffic congestion, and reduced carbon emissions. However, a number of DBSPs have experienced financial crises since 2018. This means that research on DBSPs must be considered not only in terms of the environment and technology, but also in the operation of the program. In this paper, we modeled the DBSP operations in a certain area using a system dynamics simulation approach. The main purpose was to explore the dynamics of the program’s operation process and evaluate possible improvement strategies for maximizing the revenue of the overall DBSP. Specifically, the analysis focused on the economic profits of DBSPs in an environment of competition and government regulation. The research findings revealed that the dockless bike-sharing industry has great economic profits, but in the current environment, the market needs to be regulated by the local government. If a DBSP does not introduce new technologies or find new profit channels, it will be difficult to develop sustainably by only relying on riding profits. In addition, we provide a case study of Mobike’s operations in Beijing to support these findings and validate the developed model. Finally, we discuss Mobike’s possible improvement strategies.
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The growing field of the collaborative economy is expanding geometrically and little retrospective work on this evolution has been made so far. A number of literature reviews have been focusing on specific business models of the collaborative economy deemed sustainable such as car-sharing, sharing, peer-to-peer business models, crowdsourcing, access-based consumption, community, or specific platforms (e.g. Uber, Airbnb), and some others with broader areas of focus. This paper presents a thorough bibliometric and network analysis combining both Scopus and Web of Science databases that provides fresh new insights into the evolution of the collaborative economy research field and its increasing coverage of sustainability-related topics. A first step identifies 729 published studies and uses bibliometrics to provide a description of the research field. A second stage involves networks analysis to identify influential authors, impactful publications, as well as established and emergent research clusters. A more thorough content analysis identifies key research topics, the attention granted to sustainability, interrelations, and collaboration patterns in the field. Data mapping techniques graphically depict the evolution of publications over time and identify areas of current research interests and potential directions for future research, namely in sustainability.
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The widespread adoption of smartphones, ridesharing and carsharing have disrupted the transport sector. In cities around the world, new mobility services are both welcomed and challenged by regulators and incumbent operators. Mobility as a Service (MaaS), an ecosystem designed to deliver collaborative and connected mobility services in a society increasingly embracing a sharing culture, is at the center of this disruption. Understanding Mobility as a Service (MaaS): Past, Present and Future examines such topics as how likely MaaS will be implemented in one digital platform app; whether MaaS will look the same in all countries; the role multi-modal contract brokers play; mobility regulations and pricing models; and MaaS trials, their impacts and consequences. Written by the leading thinkers in the field for researchers, practitioners, and policy makers, Understanding Mobility as a Service (MaaS): Past, Present and Future serves as a single source on all the current and evolving developments, debates, and challenges.
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In recent years, the popularity of sharing economy has been growing worldwide. Therefore, its features must be understood to adapt the economic development for the enterprises. The performance of Mobike in Beijing is used as a case in this study. Mobike is the largest shared bike company in the world and plays a prominent role in the sharing economy. Based on data obtained, online reports, and actual situations, the factors affecting the environment from shared bikes and the usage of bikes have been analyzed qualitatively and quantitatively. From the qualitative analysis, the model of the contribution of carbon dioxide emission reduction to the society has been constructed with the significant variables of registered rate, riding distance, and usage rate for shared bikes. The influence degree of each variable and their interactions are evaluated through response surface method and Minitab. The influencing extent of factors on CO2 emission reduction is in the following order: riding distance > proportion of registered users > usage rate of shared bikes. The results show that shared bike plays comprehensive and positive roles for economy and environment. The study will provide a significant help for policymakers and business supervisors on development of shared bikes.
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Dockless bike share systems present an opportunity to expand access to bike share in cities by lowering costs and geographic barriers, but they also create additional challenges in the areas of maintenance, parking, and right-of-way management. Most dockless providers are private, venture-capital funded entities, representing a significant departure from current public and non-profit approaches. In several cities, municipal authorities have encountered challenges in securing cooperation from these operators in areas such as data transparency. This raises a key question: To what extent can municipal authorities use contracts and governance to exchange use of the public rights of way for operating requirements that advance equity, accessibility, innovation, and other goals? Using case studies from other U.S. cities and drawing insights from the wider “smart mobility” literature, this research presents recommendations for regulating dockless bike share in cities and ties these approaches to the implementation of Nice Ride Minnesota’s dockless pilot. It also examines prominent challenges in coordination and implementation and highlights novel approaches with an eye towards the future of bike share in the Twin Cities region (Minneapolis and St. Paul, MN). It concludes that providers of shared mobility services should expect to be regulated in exchange for use of public rights of way, while city authorities should provide transparent and proactive regulation, maintain an environment of iteration, define their own metrics for success, place planning goals before innovation, and strive for regional collaboration.
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Bike sharing system (BSS) is growing worldwide. Although bike sharing is viewed as a sustainable transportation mode, it still has environmental footprints from its operation (e.g., bike rebalancing using automobiles) and upstream impacts (e.g., bike manufacturing). Thus, evaluating the environmental impacts of BSS from the life cycle perspective is vital to inform decision making for the system design and operation. In this study, we conducted a comparative life cycle assessment (LCA) of station-based and dock-less BSS in the U.S. The results show that dock-less BSS has a greenhouse gas (GHG) emissions factor of 118 g CO2-eq/bike-km in the base scenario, which is 82% higher than the station-based system. Bike rebalancing is the main source of GHG emissions, accounting for 36% and 73% of the station-based and dock-less systems, respectively. However, station-based BSS has 54% higher total normalized environmental impacts (TNEI), compared to dock-less BSS. The dock manufacturing dominants the TNEI (61%) of station-based BSS and the bike manufacturing contributes 52% of TNEI in dock-less BSS. BSS can also bring environmental benefits through substituting different transportation modes. Car trip replacement rate is the most important factor. The results suggest four key approaches to improve BSS environmental performance: 1) optimizing the bike distribution and rebalancing route or repositioning bikes using more sustainable approaches, 2) incentivizing more private car users to switch to using BSSs, 3) prolonging lifespans of docking infrastructure to significantly reduce the TNEI of station-based systems, and 4) increasing the bike utilization efficiency to improve the environmental performance of dock-less systems.
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As an icon of sharing economy and product service systems, bicycle sharing is gaining an increasing global popularity, yet there is little knowledge about the environmental performance of this emerging traveling mode. To seek the answer to the question, the paper employs a survey-based method and a life cycle assessment (LCA) approach. We first conduct a questionnaire-based survey to identify the changes in traveling modes after the introduction of shared bicycles. The survey results show that the use of shared bicycles is more popular among young and low-income populations, and shared bicycles are predominantly used to replace walking and bus-taking. Based on the survey results, we model the environmental impacts of the changed traveling behaviors and the life cycle of shared bicycle with the aid of Gabi software. The LCA results shows that bicycle sharing is currently an environmentally friendly practice, as it brings environmental savings in all the indicators except metal consumption. Further, the results of sensitivity analysis show that aging, rising rental fees, and increasing volume of shared bicycles would impart negative impacts on the environmental performance of bicycle sharing. The findings of this work facilitate the management and development of bicycle sharing.
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Most sharing mobility business models promise green and affordable transport in cities. However, their rapid scale-up processes have often caused significant disruption and stresses to urban governance. Free-floating bike sharing (FFBS) is highly-touted in Shanghai as a means to bring biking habits back to an overly car-congested city. Despite substantially changing the behaviour of Shanghai citizens to adopt shared bikes within a short period of time (2016–2017), the FFBS has hit a threshold of oversupply, under-distribution and user misbehaviour problems, which endanger the environmental and social sustainability of innovative urban mobility schemes. In this paper, we focus on the FFBS case study and examine how commercial, political and social actors interact in addressing the emerging public problems in the FFBS scale-up process from a collaborative governance perspective. We find that the lack of recognition and integration of new social actors, such as user groups, as agents in the scheme are key obstacles to a fully-functioning government-business-society collaborative regime. We argue that this hindrance is a function of the existing socio-economic relations within the city. Our results suggest that the city's government needs to be more agile to accommodate, nurture and integrate emerging social actors as governance partners in the sharing economy, in order to ensure its efficacy, resilience and sustainability. We propose an alternative governance model to improve the effectiveness of the collaborative governance regime towards urban sustainability through engaging the society in better and smarter ways in the sharing economy.