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1
Can shared micromobility programs reduce greenhouse
gas emissions: Evidence from urban transportation big
data
Shouheng Sun1, Myriam Ertz2*
*Corresponding author
1 Shouheng Sun, School of Economics and Management, University of Science and
Technology Beijing, Beijing, 100083, China.
2 Myriam Ertz , LaboNFC, Université du Québec à Chicoutimi, 555 Boulevard de
l’Université, Chicoutimi (QC), G7H 2B1, Canada, Phone : +1 418-545-5011, Email :
Myriam_Ertz@uqac.ca
Highlights
⚫ The impact of global shared micromobillity programs on GHG emissions is estimated.
⚫ Station-based and free-floating bikes, e-bikes, and e-scooters have been examined.
⚫ Technological progress has not fostered desirable GHG benefits for shared micromobillity.
⚫ Overcommercialized shared micromobillity causes adverse GHG impacts.
⚫ The GHG impact of shared micromobillity shows regional differences.
Abstract: Shared micromobillity has been extensively developed globally in the past few
decades, but its impact on the environment remains unclear. This study quantitatively estimates
the effects of global shared micromobillity programs on greenhouse gas (GHG) emissions using
a life cycle assessment (LCA) perspective. Specifically, it takes major countries and cities
around the world as examples to empirically analyze the impact of station-based bike-sharing
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(SBBS), free-floating bike-sharing (FFBS), free-floating e-bike sharing (FFEBS), and free-
floating e-scooter sharing (FFESS) programs on the GHG emissions of urban transportation.
The results show that, with the exception of SBBS, the other shared micromobillity programs
have not achieved desirable GHG emissions reduction benefits. Contrarily to subjective
expectations, although the rapid progress of technology in recent years has promoted the
vigorous development of shared micromobility, it has brought negative impacts on the GHG
emissions rather than the positive benefits claimed by related promoters and operators. The
overcommercialization and low utilization rate makes shared micromobility more likely to be
an environmentally-unfriendly mode of transportation. In addition, the regional differences in
mode choice, operational efficiency, fleet scale, and market potential of shared micromobility
and the corresponding impacts on GHG emissions vary greatly. Therefore, authorities should
formulate appropriate shared micromobility plans based on the current conditions and goals of
the region. This empirical study helps to better understand the environmental impact of the
global shared micromobility program and offers valuable references for improving urban
sustainability.
Keywords: Sustainability; Big data; Shared micromobility; Sustainable transportation; Life
cycle assessment; Greenhouse gas emissions
1 Introduction
Increasingly severe urban traffic and environmental problems have prompted the
continuous exploration, development, and diversification of green and sustainable
transportation modes worldwide (Eisenack & Roggero, 2022; Ornetzeder & Rohracher, 2013;
Pettifor et al., 2017; Sun & Ertz, 2021b). The COVID-19 outbreak further modified individuals’
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mobility behaviors due to telehealth, teleconferencing, e-learning, or e-shopping, which
decreased (increased) the occurrence of long (shorter) trips (Mouratidis and Papagiannakis,
2021). However, knowledge of those modifications in travel behavior tends to be immature,
especially regarding their impact on the environment (Benita, 2021). As an innovative
transportation strategy, shared micromobility enables users to gain short-term access to
transportation modes on an “as-needed” basis (Reck et al., 2021; Shaheen et al., 2020). Part of
its sustainable aspect resides in the fact that micromobility refers squarely to vehicles that are
smaller than cars, such as bicycles or scooters (Hosseinzadeh et al., 2021; Krauss et al., 2022;
Reck et al., 2021). In addition, the integration of shared micromobilty into the extensive public
transportation network can improve the last mile connectivity and facilitate the promotion of
Mobility as a Service (MaaS), which is widely regarded as a promising way to improve urban
sustainability (Le Pira et al., 2021; Mao et al., 2021; Reck et al., 2021; Shaheen et al., 2020).
Shared micromobility is a specific part of the broader pseudo-sharing economy (Ertz, 2020),
which has been proposed as a pathway to sustainability under certain conditions (Sun & Ertz,
2021c).
Driven by technological innovation and colossal venture capital, some new types of shared
micromobility services have emerged and have experienced explosive growth in just a few
years, such as free-floating bike-sharing (FFBS), free-floating e-bike sharing (FFEBS), and
free-floating e-scooter sharing (FFESS) (Sun and Ertz, 2020; Hosseinzadeh et al., 2021;
NABSA, 2021; Qxcu Industrial Research Institute, 2021; Reck et al., 2021), which are often
operated and managed by privately-owned shared micromobility platforms (also known as
transportation network companies [TNCs]). These profit-driven platforms have been actively
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expanding their market scale and rapidly updated products and services, making the traditional
station-based bike-sharing (SBBS) program incomparable with emerging FFBBS, FFEBS, and
FFESS in terms of market scale and development speed (CSIC, 2020; NABSA, 2021; World
Resource Institute, 2019; Zhang, 2017). So, does the evolution from SBBS to FFBS, and then
to FFEBS and FFESS, follow merely the capital market, or has technological progress spurred
a sustainable tide across urban transportation and in favor of the environment? This is still
debatable.
The potential environmental benefits of shared micromobility are mainly based on the
premise of replacing car trips. Qiu & He (2018) estimated the impact of Beijing FFBS on urban
road traffic emissions, arguing that if 75% of shared bike miles replaced car travel, the carbon
dioxide (CO2), carbon monoxide (CO), nitrogen dioxide (NO2) and particulate matter (PM) can
be reduced by nearly 616,040 tons, 1,587 tons, 59 tons and 21 tons, respectively. Kou et al.
(2020) quantified the GHG emission reduction effects of bike-sharing programs in New York,
Chicago, Boston, Philadelphia, Washington, D.C., Los Angeles, San Francisco, and Seattle in
2016 by using high rates of car trip replacement (i.e., approximately 65%–80% of bike-sharing
trips replaced car trips), and the results show that annual reductions in GHG emissions for these
different cities ranged from 41–5, 417 tons of CO2-eq. Liu et al. (2022) estimated that if 1% of
Nanjing's resident population uses shared e-bikes instead of cars for commuting, the one-way
CO2 emission reduction can reach about 9.55 tons. However, a growing body of evidence
suggests that estimates of the environmental benefits of shared micromobility may be overly
optimistic. Some studies argue that the actual substitute rate of shared micromobility trips for
car travel in many cities (e.g., Paris, Edinburgh, Washington D.C., Shanghai, Melbourne, New
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York, Minnesota, Seattle, and Brisbane) is not high, with only about 10%–30% (D’Almeida et
al., 2021; de Bortoli & Christoforou, 2020; Fan & Harper, 2022; Teixeira et al., 2021; Zhu,
2021). Furthermore, shared micromobility may not be an environmentally-friendly mode of
transport if viewed from a life cycle perspective, taking into account emissions during the
manufacturing and maintenance phases (Saltykova et al., 2022; Teixeira et al., 2021) .Sun &
Ertz (2021b) investigated the environmental impact of mutualized mobility in Beijing and
Toronto from a life cycle perspective and found that emission intensity (CO2-eq per passenger-
kilometer) of SBBS is almost double that of public transit. In this case, if a large portion of
shared mobility replaces an environmentally-friendly mode of transportation, there may be a
negative impact on the environment (Bozzi & Aguilera, 2021). Reck et al. (2022) found that
shared e-scooters and shared e-bikes in Zurich brought more CO2 emissions than the modes of
transport they replaced from a life cycle perspective. Moreau et al. (2020) conducted a life cycle
assessment (LCA) on the environmental impact of the FFESS system in Brussels. Considering
its substitution for other modes of transportation, the use of shared e-scooter generated an
additional 21 g of CO2-eq per passenger-kilometer on average. Similarly, de Bortoli &
Christoforou (2020) used life cycle analysis to quantify the GHG emissions impact of FFESS
in Paris. They found that 82% of skateboarding trips substituted for low-emission modes of
transport (60% for public transit and 22% for walking) and estimated that 1 million FFESS
users could generate an additional 13,000 tonnes of CO2-eq per year.
Although extensive research has been conducted on the environmental impact of shared
micromobility, there are still shortcomings. First of all, most studies lack a comprehensive and
systematic consideration of actual operating characteristics and related life cycle factors of the
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shared micromobilty such as service life of the vehicle, utilization rate, trip distance, the
substitution rates of various transportation modes in the urban transportation system, and the
impact of manufacturing, rebalancing and collecting (Fishman, 2016; Fishman et al., 2014; Li
et al., 2021; Teixeira et al., 2021; Zhang & Mi, 2018), which leads to deviations in research
conclusions. Moreover, existing research only focuses on one or two modes of shared
micrombility, and there is no research to compare and analyze all the shared micrombility
modes simultaneously. In particular, these studies are only case studies in individual regions,
and the related assumptions and conclusions are thus non-representative and not generalizable.
Furthermore, due to differences in social, economic, cultural, demographic, geographical
variables as well as urban road network construction, the operational characteristics and
development status of shared micromobility vary significantly across different regions
(CACTO, 2018; Hosseinzadeh, Algomaiah, et al., 2021; NACTO, 2020). As a result, there may
be significant regional differences in the environmental impact of shared micromobility, which
have not been studied so far. Overall, although shared micromobility is growing rapidly
worldwide, there is a lack of a comprehensive understanding of its environmental impact
globally. However, such an understanding appears crucial for improved governance and the
development of shared micromobility, which is still in a nascent stage.
Therefore, considering these research gaps, this study combines the life cycle assessment
(LCA) framework and actual shared micromobility operation data of major countries and cities
worldwide. The overarching objective is to investigate the impact of shared micromobility
programs on greenhouse gas emissions. In particular, this study compares and analyzes the
impact of several typical shared micromobility programs on greenhouse gas emissions from the
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urban transportation system, including SBBS, FFBS, FFEBS, and FFESS. The research
framework and findings have valuable theoretical and practical significance for improving the
sustainability of shared micromobility markets and urban transportation systems.
2 Material and methods
The analysis process of this study can be divided into two steps. First, the LCA method
calculated the GHG emission factors (EF) of shared micromobility based on the actual
operational characteristics and data of specific shared micromobility schemes. Then, combining
the EF of shared micromobility and other modes of transportation within the urban
transportation system and the substitution rate of shared micromobility for different
transportation modes, the GHG emissions reduction benefits (RB) of shared micromobility can
be further compared and estimated.
Specifically, this study empirically analyzed and compared the GHG emissions reduction
benefits of SBBS and FFBS programs in 39 cities worldwide. In terms of FFEBS and FFESS,
the operational data for the city-level market are lacking due to the short operating history.
Therefore, this paper takes the United States, the largest FFESS market, and China, the world's
largest FFEBS market, as an example to conduct a comparative analysis of the impacts of
FFEBS and FFESS on GHG emissions from transportation systems. In order to avoid analysis
bias, this paper does not select the data of the shared micromobility system in a particular year
for analysis but is based on the overall statistical characteristics of the system's multi-year
operational data. The dataset in terms of shared micromobility type, temporal period, and spatial
context is summarized in Table 1.
Table 1. The datasets involved in this study
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Shared micromobility type
Statistical period (year)
Spatial context
Station-based bike sharing (SBBS)
2011– 2020
Vancouver, Montreal, Toronto, London,
Melbourne, Antwerp, Paris, Milan,
Barcelona, Vienna, Moscow, Shanghai,
Beijing, Guangzhou, Hangzhou,
Nanjing, Ningbo, Seattle, Los Angeles,
Bay Area, Philadelphia, Boston,
Washington D.C., Chicago, New York,
Brisbane, Minneapolis
Free-floating bike sharing (FFBS)
2016 – 2020
Beijing, Shanghai, Hangzhou, Nanjing,
Shenzhen, Guangzhou, Xi'an, Wuhan,
Chengdu, Jinan, Seattle, Washington
D.C.
Free-floating e-bike sharing (FFEBS)
2018 – 2020
China (The entire FFEBS market)
Free-floating e-scooter sharing (FFESS)
2018 – 2020
the United States (The entire FFESS
market)
2.1. Life Cycle Assessments
LCA is a standardized method used to measure the environmental and energy impacts of a
product or service throughout its life cycle (Escobar et al., 2020; ISO, 2006b, 2006a; Kjaer et
al., 2018). The analysis framework and specific processes of LCA have been widely used to
assess the energy and environmental impacts associated with particular transportation methods
and the entire transportation system (D’Almeida et al., 2021; Hollingsworth et al., 2019; Kjaer
et al., 2018; Sun & Ertz, 2021b). This study uses the world's leading LCA software SimaPro
9.0 and the life cycle inventories (LCI) database Ecoinvent 3.6 to conduct the LCA analysis.
The specific analysis process follows the standardized LCA procedure provided by ISO (2006a,
2006b), as shown below.
2.1.1. Goal and scope definition
In the LCA analysis of transportation mode, the vehicle life cycle can be divided into three
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stages: manufacturing, use, and end-of-life (Dave, 2010; de Bortoli & Christoforou, 2020; ISO,
2006a, 2006b; Sun & Ertz, 2020), so the corresponding system boundaries for shared
micromobility in this study are shown in Fig. 1. In the SBBS system, we need to consider the
ancillary facilities (i.e., stations and docks). In order to improve the operating efficiency of
shared micromobility systems, operators need to frequently relocate shared vehicles from
overcrowded sites to shortage sites (Chiariotti et al., 2018; Hollingsworth et al., 2019).
Therefore, these rebalancing activities are also an essential part of the system boundary for
shared micromobility LCA analysis (Hollingsworth et al., 2019; Sun & Ertz, 2021b). As for the
electricity-powered vehicles (i.e., e-bikes and e-scooters), the energy consumption and the
collection and recharging processes (i.e., regularly recharging the battery) need to be included
in the use phase.
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Fig. 1. LCA System boundary for shared micromobility
In order to facilitate comparison and analysis, the functional unit used in this study is
passenger-kilometer (i.e., pkm) (Dave, 2010; Hollingsworth et al., 2019), and the measurement
unit of the GHG emission factor (i.e., EF) of the transportation mode is g CO2-eq/pkm.
(Hollingsworth et al., 2019; Kjaer et al., 2018; Kou et al., 2020; Sun & Ertz, 2021b). Eq. (1)
presents the expression for EF (Dave, 2010; Hollingsworth et al., 2019; Kjaer et al., 2018).
(1)
In Eq. (1), refers to the total life cycle GHG emissions, which is the sum of the
emissions in these three stages of the vehicle life cycle. refers to the cumulative
passenger-kilometers during the vehicle life cycle, which can be calculated by Eq. (2).
(2)
Where refers to average vehicle occupancy per trip, DTR (i.e., daily turnover rate)
refers to the average number of trips per vehicle per day. refers to the average
distance per trip. Finally, refers to the lifespan of the shared vehicle.
2.1.2 Inventory analysis
The shared micromobility LCA inventory data are mainly from the Ecoinvent database,
peer-reviewed published articles, industry statistical reports, and the operational reports
released by shared micromobility platforms.
For the SBBS, the weight of the shared bicycle is about 18 to 23 kg, and the service life
(i.e., lifespan) ranges from 5 to 8 years (Bonilla‐Alicea et al., 2020; Chen et al., 2020a; NABSA,
2021; NACTO, 2019; Sun & Ertz, 2021a; Xu, 2018). In terms of the stations and docks for the
SBBS system, the service life is set to 10 years (Bonilla‐Alicea et al., 2020; Luo et al., 2019),
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and the associated GHG emissions during the lifespan will be evenly distributed to every
kilometer of bike trips (Bonilla‐Alicea et al., 2020; Sun & Ertz, 2021b). In addition, various
commercial vehicles (e.g., small trucks and vans) are used to rebalance shared bicycles within
the city, with an average GHG emission intensity of about 300 g CO2-eq/km during the
rebalance process (Chen et al., 2020a; Hollingsworth et al., 2019). As to the end-of-life stage,
90% of metal materials of stations and docks can be recycled, and the recycling rate of the metal
materials for SBBS bikes ranges from 60% to 90% (CAICT, 2019; Luo et al., 2019; NABSA,
2021). In addition, other parts and materials (e.g., plastic, rubber, glass, and electronic
components) are subjected to a series of disposal processes such as landfill, incineration and
treated as solid waste and electronic waste (Chen et al., 2020b; Mao et al., 2021; Xu, 2018).
The operating characteristics of SBBS are presented in Table 2 and Table 3. In addition, the
primary inventory data and processes for SBBS and the supporting facilities are shown in
Supplementary materials A and B.
Table 2. The operating characteristics of SBBS
DTR
(Triangular distribution)
Docks per bike
(Triangular distribution)
Bikes per station
(Triangular distribution)
Distance per trip
(km)
(Normal
distribution)
Min
Avergae
Max
Min
Avergae
Max
Min
Avergae
Max
Mean
Standard
Deviation
Vancouver
1.13
1.25
1.37
1.75
1.90
2.05
9.00
10.00
11.00
3.00
0.065
Montreal
2.16
2.25
2.33
1.85
2.02
2.20
11.58
11.80
12.00
2.70
0.068
Toronto
1.16
1.32
1.42
1.65
1.75
1.95
10.96
11.56
17.10
3.23
0.075
London
2.30
2.42
2.60
1.85
1.99
2.21
14.00
15.33
16.00
3.50
0.088
Melbourne
0.70
0.78
1.10
1.80
1.98
2.20
11.50
11.76
12.50
4.40
0.081
Antwerp
1.85
1.95
2.15
1.45
1.68
2.18
11.00
11.92
13.00
3.00
0.075
Paris
4.75
4.93
5.20
2.30
2.56
2.80
11.00
12.05
13.00
2.81
0.070
Milan
3.20
3.42
3.60
1.85
2.00
2.20
15.50
16.49
17.50
1.50
0.025
Barcelona
4.40
4.68
4.80
1.95
2.16
2.35
14.00
14.15
15.00
2.91
0.073
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Vienna
1.70
1.87
2.10
1.70
1.95
2.20
12.00
13.50
15.00
2.36
0.059
Moscow
2.50
2.71
2.90
1.25
1.45
1.65
8.50
10.00
11.50
2.80
0.070
Shanghai
2.00
2.20
2.40
1.40
1.55
1.80
23.00
25.00
27.00
3.00
0.075
Beijing
1.25
1.46
1.70
1.40
1.51
1.73
27.00
29.50
32.00
2.20
0.055
Guangzhou
1.15
1.31
1.65
1.30
1.36
1.58
20.0
26
30.00
2.35
0.045
Hangzhou
3.50
3.75
4.00
1.40
1.46
1.60
22.00
24.00
26.00
1.39
0.028
Nanjing
1.10
1.27
1.50
1.40
1.46
1.65
34.00
36.31
38.00
2.40
0.060
Ningbo
1.40
1.57
1.80
1.40
1.48
1.71
22.00
23.92
26.00
3.90
0.098
Seattle
0.50
0.61
0.90
2.10
2.24
2.40
6.50
7.85
8.50
2.03
0.051
Los Angeles
0.55
0.66
1.00
1.65
1.77
1.85
10.00
11.92
14.00
1.97
0.049
Bay Area
1.05
1.26
1.50
2.90
3.22
3.38
4.50
5.69
7.00
2.50
0.063
Philadelphia
1.10
1.34
1.50
2.05
2.23
2.40
7.00
8.60
11.00
2.72
0.068
Boston
1.70
1.89
2.10
2.80
3.19
3.42
4.50
5.50
7.00
2.75
0.069
Washington D.C.
1.50
1.63
1.80
1.40
1.56
1.80
9.00
10.58
12.00
1.63
0.041
Chicago
1.50
1.72
1.90
1.50
1.74
1.90
8.50
9.89
11.50
2.74
0.055
New York
2.50
2.69
2.85
1.75
1.94
2.15
13.50
15.26
17.00
2.69
0.067
Brisbane
0.40
0.50
0.70
1.45
1.62
1.80
12.00
13.33
15.00
3.20
0.080
Minneapolis
0.70
0.90
1.20
1.85
2.06
2.20
8.00
10.00
12.00
3.50
0.088
Note: Source from Beijing Transport Institute (2016); Bike Share Research (2021); Bike Share Toronto (2020); CACTO (2018); Deng et al.
(2017); Fishman et al. (2013, 2014); Kou et al. (2020); Liu et al. (2016); Mei et al. (2019); Morency et al. (2017); SURC & TDRI (2016, 2020);
and Zheng and Zhu (2014).
Table 3. Parameters and distribution of uncertainty analysis for SBBS
Distribution type
Distribution parameters
Service life (year)
Triangular
Mean =6.5
Min=5
Max=8
Weight of shared bike bicycle (kg)
Triangular
Mean =20
Min=18
Max=23
Recycle rate (%)
Triangular
Mean =60%
Min=75%
Max=90%
Rebalance distance (for serving
one-kilometer SBBS trip) (km)
Normal
distribution
Mean=0.04
Standard Deviation (SD) = 0.0042
Note: Source from Beijing Transport Institute (2016); Bike Share Research (2021); Bike Share Toronto (2020); Deng et al. (2017); Fishman et
al. (2013, 2014); Kou et al. (2020); Liu et al. (2016); Mei et al. (2019); Morency et al. (2017); NACTO (2018); SURC & TDRI (2016, 2020);
and Zheng and Zhu (2014).
As to the FFBS, the weight of the shared bicycle is about 18 to 23 kg, and the life span is
about 2 to 3 years. (Bonilla‐Alicea et al., 2020; Chen et al., 2020a, 2020b; CSIC, 2020; Luo et
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al., 2019; NACTO, 2020). Although FFBS does not require stations and docks, FFBS bicycles
are equipped with additional photovoltaic panels, electronic components, and batteries
(Bonilla‐Alicea et al., 2020; Luo et al., 2019; Sun & Ertz, 2021a; Xu, 2018). In addition, the
rapid expansion of the scale of FFBS in cities and the lack of adequate market supervision have
brought about oversupply and resource waste (Gao & Li, 2020; Schellong et al., 2019; Shaheen
& Cohen, 2021; Teixeira et al., 2021), the recycling rate of FFBS systems ranged from 30% to
75% (CAICT, 2019; Chen et al., 2020a, 2020b; CSIC, 2020; Hu, 2019; Mao et al., 2021;
NACTO, 2020; Xu, 2018). The operating characteristics of FFBS are presented in Table 4 and
Table 5. In addition, the primary inventory data and processes for FFBS are summarized in
Supplementary material C.
Table 4. The operating characteristics of FFBS in different regions
DTR
(Triangular distribution)
Distance per trip (km)
Normal distribution
Min
Average
Max
Mean
Standard Deviation
Beijing
0.9
1.42
2
1.650
0.133
Shanghai
1.1
1.35
1.5
1.840
0.046
Hangzhou
0.75
0.89
1.1
1.150
0.058
Nanjing
2.4
2.86
3.1
1.350
0.046
Shenzhen
1.45
1.77
2.2
1.550
0.039
Guangzhou
1.95
2.35
2.5
2.200
0.125
Xi'an
0.7
0.79
1
1.110
0.067
Wuhan
0.6
0.67
0.9
1.690
0.047
Chengdu
1.6
1.80
2.1
1.670
0.056
Jinan
0.9
1.06
1.34
1.680
0.042
Seattle
0.75
0.85
1.1
2.030
0.065
Washington D.C.
0.6
0.68
0.9
1.630
0.035
Note: Source from Chen et al. (2020a); CAICT (2019); CSIC (2020); Luo et al., 2019; Mao et al., 2021; NACTO, 2019; NACTO, 2018; World
Resource Institute, 2019).
Table 5. Operating characteristics and the Parameter distribution of uncertain factors for FFBS
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Distribution type
Distribution parameters
Service life (year)
Triangular
Mean =2.0
Min=3.0
Max=4.0
Weight of shared bike bicycle (kg)
Triangular
Mean =18
Min=20
Max=23
Recycle rate (%)
Triangular
Mean =30%
Min=50%
Max=75%
Rebalance distance (serving one-kilometer
FFBS trip) (km)
Normal
distribution
Mean=0.10
Standard Deviation = 0.0075
Note: Source from Chen et al. (2020b); CAICT (2019); CSIC (2020); Luo et al. (2019); Mao et al. (2021); NACTO (2019); NACTO (2018);
and World Resource Institute (2019).
For the FFEBS project, the weight of the shared e-bike is about 24 to 39 kg, and the life
span is about 2 to 4 years (Aurora Mobile, 2021; CAICT, 2019; iiMedia Research, 2020; Qxcu
Industrial Research Institute, 2021; WTDSRI, 2020). The energy consumption per 100
kilometers ranged from 1.45 kWh to 2.25 kWh (Aurora Mobile, 2021; CAICT, 2019; Hello Inc,
2021; iiMedia Research, 2020; Qxcu Industrial Research Institute, 2021; WTDSRI, 2020). The
GHG emissions per kilowatt-hour of electricity produced and supplied in China are about 972
g CO2-eq (China National Energy Administration, 2020). The rebalance, collection, and
recharging distance for serving a 1 km FFEBS trip was about 0.103 – 0.262 km (Aurora Mobile,
2021; CAICT, 2019; Hello Inc, 2021; iiMedia Research, 2020; Qxcu Industrial Research
Institute, 2021). The shared e-bike recycling rate in the FFEBS system ranged from 30% to 75%
(CAICT, 2019; iiMedia Research, 2020; Qxcu Industrial Research Institute, 2021).
Regarding FFESS, the weight of shared e-scooter ranges from 10 to 19 kg, with an average
of about 1 kg (Barnes, 2019; de Bortoli & Christoforou, 2020; Hollingsworth et al., 2019;
Mobility Foresights, 2021; Moreau et al., 2020). The service life of shared e-scooters, in the
15
early stages of development, was only 1-5 months (Mobility Foresights, 2021; Moreau et al.,
2020). However, with improved manufacturing technology and process, the durability of shared
e-scooters has been improved, and the life span has been increased from 9 to 18 months (de
Bortoli & Christoforou, 2020; Hollingsworth et al., 2019; Moreau et al., 2020). The energy
consumption per 100 kilometers ranged from 1.09 kWh to 2.15 kWh (Barnes, 2019;
Hollingsworth et al., 2019; Mobility Foresights, 2021). The GHG emission intensity of U.S.
electricity throughout its life cycle is approximately 203.5 g CO2-eq/MJ (732.6 g CO2-eq/kWh)
(National Renewable Energy Laboratory, 2020). The rebalance, collection, and recharging
distance for serving a 1 km FFESS trip was about 0.058 – 0.157 km (on average 0.102km) (de
Bortoli & Christoforou, 2020; Hollingsworth et al., 2019; Moreau et al., 2020; Zou et al., 2020).
The recycling rate of the shared e-scooter ranged from 30% to 75%, which is similar to FFBS
(Mobility Foresights, 2021; Moreau et al., 2020; NACTO, 2020; U.S. Department of
Transportation, 2021). The characteristics and operational data of FFEBS and FFESS are
presented in Table 6. In addition, the primary inventory data and processes for FFEBS and
FFESS are shown in Supplementary materials D and E, respectively.
Table 6. The characteristics and operational data of FFEBS and FFESS
FFESS in the United States
FFEBS in China
Distribution type
Distribution parameters
Distribution type
Distribution
parameters
Lifespan
(Months)
Triangular
First stage :
Mean =2.5
Min=1.0
Max=5.0
Second stage :
Mean =12.0
Min=9.0
Max=18.0
Triangular
Mean =36
Min=24
Max=48
Weight of shared
vehicle (kg)
Triangular
Mean =10.0
Min=15.0
Max=19.0
Triangular
Mean =30
Min=24
Max=39
16
Recycle rate
Triangular
Mean =30%
Min=50%
Max=75%
Triangular
Mean =30%
Min=50%
Max=75%
Rebalance,
collection, and
recharging
distance for
serving 1 km
shared mobility
trip
Normal distribution
Mean=0.102
SD = 0.0164
Normal
distribution
Mean=0.167
SD= 0.0266
DTR
Triangular
distribution
Mean =1.78
Min=0.987
Max=5.85
Triangular
distribution
Mean =1.52
Min=1.05
Max=3.45
Distance per trip
(km)
Triangular
distribution
Mean =1.95
Min=0.98
Max=3.10
Triangular
distribution
Mean =2.50
Min=1.85
Max=3.40
Energy use
(kWh/100km)
Normal distribution
Mean=1.55
SD= 0.1817
Normal
distribution
Mean=2.10
SD= 0.1297
Note: Source from Aurora Mobile (2021); Barnes (2019); Bozzi and Aguilera (2021); CAICT (2019); de Bortoli and Christoforou (2020);
Hello Inc (2021); Hollingsworth et al.(2019); iiMedia Research (2020); Mobility Foresights (2021); NABSA (2021); NACTO (2019, 2020);
Qxcu Industrial Research Institute (2021); U.S. Department of Transportation (2021); WTDSRI (2020); and Zou et al., (2020).
2.1.3. Impact assessment
The hierarchist impact assessment method (i.e., ReCiPe 2016) was used in Simapro 9.0 to
calculate the environmental impact indicator score, and the impact category “Global warming”
was selected to quantitatively estimate the EF and GHG emissions reduction benefits of shared
micromobility (Huijbregts et al., 2017; Sun & Ertz, 2021b). In addition, considering the
potential bias resulting from the uncertainty of the input data, the corresponding uncertainty
analysis was based on 10,000 Monte Carlo simulations.
2.2. GHG emissions reduction benefits of Shared Micromobility
17
The GHG emissions reduction benefit of shared micromobility can be obtained by Eq. (3)
(3)
With referring to the GHG emissions reduction benefit of the shared miromobility. A
positive value of RB means that GHG emissions reduction benefit has been obtained, while a
negative value indicates that it has increased GHG emissions.
refers to the EF of shared
micromobility. and refer to the share of shared micromobility trips used to replace
another transportation mode i within the urban transportation system (i.e., car trip, public transit,
privately-owned bike, and walking) and the EF of transportation mode i. refers to the
share of new trips, which refers to the trips that would not be made if the shared micromobility
mode were unavailable.
The functional unit for the GHG emissions reduction benefit of the shared micromobility
is set to passenger kilometer (pkm). Precisely, the decomposition of the substitution of shared
micromobility to other transportation modes (or new trips) was calculated based on the
weighted kilometer-based modal shifts, and the corresponding GHG emissions reduction
benefit of the shared micromobility can be expressed as g CO2-eq/pkm (de Bortoli &
Christoforou, 2020; Sun & Ertz, 2021b). The decomposition of shared micromobility trips was
calculated based on surveys and statistics reports of transportation departments in various
regions. The EF of other transportation modes (non-shared micromobility) is presented in
Supplementary material F. The details about the substitution rates of shared micromobility for
traditional travel modes are shown in Supplementary materials G and H.
3 Results
18
Here, it first analyzed and compared the impact of pedal bike sharing on GHG emissions
(i.e., SBBS and FFBS) and then estimated the effect of electric shared micromobility programs
on GHG emissions (i.e., FFEBS and FFEES). For each type of shared micromobility, EF and
RB were calculated sequentially, and a corresponding parameter uncertainty analysis and a
sensitivity analysis were also performed.
3.1 GHG emissions reduction benefit of SBBS and FFBS
For the SBBS system, the average EF ranged from 30.01 to 187.27g CO2-eq/pkm (see
Fig.2). Due to the significant differences in the actual operating characteristics of SBBS systems
in different cities, the EF values vary significantly across other regions. For example, the SBBS
in Barcelona, Paris, London, Moscow, New York, Shanghai, Ningbo, and Hangzhou had lower
EF (about 30-40 CO2-eq/pkm). In contrast, the EF of the SBBS system in Seattle, Los Angeles,
Brisbane, and the Bay Area (San Francisco) was relatively high (over 100 g CO2-eq/pkm). From
the perspective of the decomposition value, it can be seen that the stations and docks, as well
as the rebalance stage, account for the majority of the emissions.
19
Fig. 2 The GHG emissions factors of SBBS
The FFBS system’s EF ranged from 60.22 to 265.29 CO2-eq/pkm (see Fig.3). The FFBS
in Guangzhou, Chengdu, and Nanjing have lower GHG emission factors (about 60–90 g CO2-
eq/pkm). In contrast, the EF values of FFBS systems in Hangzhou, Xi'an, Seattle, and
Washington D.C. were relatively high (over 200 g CO2-eq/pkm). From the perspective of the
decomposition value, it can be seen that the manufacturing and the rebalance processes account
for the majority of the GHG emission of the FFBS system. It is worth noting that for the
decomposition value of emission factors, the decomposition value of FFBS in the
manufacturing stage was much higher than that of SBBS. This is mainly because the life cycle
0,00 50,00 100,00 150,00 200,00 250,00
Washington D.C.
Vienna
Vancouver
Toronto
Shanghai
Seattle
Philadelphia
Ningbo
New York
Nanjing
Moscow
Montreal
Minneapolis
Milan
Melbourne
Los Angeles
London
Hangzhou
Guangzhou
Paris
Chicago
Brisbane
Boston
Beijing
Bay Area (San Francisco)
Barcelona
Antwerp
Emission Factor (g CO2-eq/pkm)
Manufacturing Station and Dock Maintenance Rebalance End of life
20
vehicle kilometers traveled (VKT) of FFBS was much lower than SBBS. Although there is not
much difference in the amount of GHG emissions between manufacturing an SBBS bike and
manufacturing an FFBS bike (FFBS=161.56 kg CO2-eq vs. SBBS=183.57 kg CO2-eq/pkm),
the average life cycle VKT of the FFBS bike is only about a fifth of the SBBS bike (see
Supplementary material I). Therefore, according to Eq. (1) (for shared micromobility systems,
VKT is equivalent to passenger-kilometers traveled), on a per passenger-kilometer basis, the
GHG emission of FFBS in the manufacturing stage was much larger than that of SBBS.
Fig. 3 The GHG emissions factors of FFBS
It can be found that the EF of FFBS was significantly higher than that of the SBBS system.
This can be intuitively reflected in cities that have two types of shared bicycle systems, such as
Beijing (FFBS=124.61 g CO2-eq/pkm vs. SBBS=60.08 g CO2-eq/pkm), Hangzhou
(FFBS=213.27 g CO2-eq/pkm vs. SBBS=42.40 g CO2-eq/pkm), Nanjing (FFBS=73.12 g CO2-
050 100 150 200 250 300 350 400
Xi'an
Wuhan
Washington D.C.
Shenzhen
Shanghai
Seattle
Nanjing
Jinan
Hangzhou
Guangzhou
Chengdu
Beijing
Emission Factor (g CO2-eq/pkm)
Manufacturing Maintenance Rebalance End of life
21
eq/pkm vs. SBBS=61.45 g CO2-eq/pkm), Seattle (FFBS=265.28 g CO2-eq/pkm vs.
SBBS=187.27 g CO2-eq/pkm), Shanghai (FFBS=101.11 g CO2-eq/pkm vs. SBBS=36.29 g
CO2-eq/pkm) and Washington D.C. (FFBS=216.64 g CO2-eq/pkm vs. SBBS=87.27 g CO2-
eq/pkm) (see Supplementary material J).
Based on the CE value, the GHG emissions reduction benefits (RB) of shared
micromobility can be further estimated and compared. As shown in Fig.4, SBBS has a
significant GHG emission reduction potential compared with FFBS. Except for Seattle, Los
Angeles, and Brisbane, the GHG emissions reduction benefits of SBBS systems in other cities
range from 20.54 g CO2-eq/pkm to 66.70 g CO2-eq/pkm. However, the promotion of FFBS has
not brought desirable GHG emissions reduction benefits. In particular, the FFBS systems in
Seattle, Xi'an, Hangzhou, Washington, and Wuhan have increased GHG emissions by as much
as 155.77 g CO2-eq/pkm, 151.85 g CO2-eq/pkm, 132.63 g CO2-eq/pkm, 104.37 g CO2-eq/pkm
and 101.44 g CO2-eq/pkm, respectively. Uncertainty analysis results (see Supplementary
material K) show that neither SBBS nor FFBS can obtain GHG emissions reduction benefits in
some cities, such as Seattle. Some cities with SBBS and FFBS systems have achieved GHG
emissions reduction benefits, such as Guangzhou and Nanjing. However, the GHG emissions
reduction benefits are not significant.
22
Fig. 4 The GHG emissions reduction benefits of SBBS and FFBS
A sensitivity analysis was also carried out to identify further and analyze the key factors
affecting the GHG emissions reduction benefits of SBBS and FFBS. The variance contribution
(VC) of uncertain variables in the FFBS and SBBS systems are presented in Supplementary
materials L and M, respectively. It shows that the main factors affecting the GHG emission
-300 -250 -200 -150 -100 -50 0 50 100 150
Washington D.C.
Vienna
Vancouver
Toronto
Shanghai
Seattle
Philadelphia
Ningbo
New York
Nanjing
Moscow
Montreal
Minneapolis
Milan
Melbourne
Los Angeles
London
Hangzhou
Guangzhou
Paris
Chicago
Brisbane
Boston
Beijing
Bay Area
Barcelona
Antwerp
Xi'an
Wuhan
Washington D.C.
Shenzhen
Shanghai
Seattle
Nanjing
Jinan
Hangzhou
Guangzhou
Chengdu
Beijing
SBBS FFBS
GHG emissions reduction benefit (g CO2-eq/pkm)
23
factor and GHG emissions reduction benefit of the SSBS system are DTR (VC = −72.1%), the
distance per trip (VC = −11.6%), the rebalance distance (VC = 9.5%), and docks per trip (VC
= 3.1%). The main factors affecting the GHG emission factor and GHG emissions reduction
benefit of the FFBS system are DTR (VC = −73.3%), the rebalance distance (VC = 8.9%),
service life (VC = −8.5%), and distance per trip (VC = −6.2%). It can be seen that the GHG
emissions reduction benefits of these bike-sharing programs are mainly affected by the
utilization of the shared bike, that is, the life cycle VKT, which is determined by DTR, lifespan,
and distance per trip. Increasing DTR, life span, and distance per trip can significantly reduce
the GHG emission factor, increasing the corresponding GHG emissions reduction benefits. The
difference in GHG emissions reduction benefits between SBBS and FFBS is mainly due to the
life cycle VKT. The life cycle VKT of FFBS is much lower than that of SBBS. In addition, the
rebalancing process is also an important factor affecting the GHG emissions reduction benefit
of FFBS and SBBS. Reducing the rebalancing and distribution distance can also increase the
emission reduction potential of SBBS and FFBS.
3.2 GHG emissions reduction benefit of FFEBS
The average EF of FFEBS in China was about 145.19 g CO2-eq/pkm (Standard Deviation=
26.33, Coefficient of Variation= 0.1813, 95% CI= 102.91– 204.96) (see Fig.5). The
manufacturing stage and rebalancing process were the main sources, accounting for about 40.64%
and 32.12% of GHG emissions, respectively. The average GHG emissions reduction benefit of
FFEBS in China was about −19.46 g CO2-eq/pkm (standard deviation= 31.72, Coefficient of
Variation= 1.63, 95% CI= −37.66– 88.31 g CO2-eq/pkm) (see Fig.5), which means that the per
kilometer FFBS trip increased GHG emissions by 19.46 g CO2-eq. Therefore, the probability
24
that FFEBS can obtain GHG emissions reduction benefits is approximately 27.79%. The
sensitivity analysis results (see Supplementary material N) show that the GHG emissions
reduction benefits of FFEBS are mainly affected by the utilization rate of the shared e-bike (i.e.,
life cycle VKT). Increasing DTR, life span, and distance per trip can significantly increase the
GHG emissions reduction benefits of FFEBS. Similarly, reducing the rebalancing and
distribution distance (VC = 15.0%) and the bike weight (VC=5.6%) can also increase the GHG
emissions reduction potential of FFEBS.
Since expanding the utilization (life cycle VKT) of the shared e-bike is the key to
improving the GHG emissions reduction benefit of FFEBS, we estimated the GHG emission
factors and GHG emissions reduction benefit of FFEBS under different life cycles VKT (see
Fig.6).
Fig. 5 EF and RB of FFEBS. The uncertainty analysis results were based on statistics of 10,000 simulations.
0
50
100
150
200
250
Shared E-bike
g CO2-eq/pkm
EF
End of life
Energy use
Rebalance and
Recharging
Maintenance
Manufacturing
0%
20%
40%
60%
80%
100%
0
0,02
0,04
0,06
0,08
-140
-125
-110
-95
-80
-65
-50
-35
-20
-5
10
25
40
55
70
Cumulative probability
Probability
RB (g CO2-eq/pkm)
RB
25
Fig. 6 EF and RB of FFEBS under different life cycle mileage
It can be found that the EF and GHG emissions reduction benefits of FFEBS significantly
depend on its life cycle mileage. The inflection points of the emission factor curve and the GHG
emissions reduction benefit curve are about 4,000 km. When VKT is less than 4,000 kilometers,
as VKT increases, the emission factor and GHG emissions reduction benefit of FFEBS will
drop sharply. When VKT is greater than 4,000 kilometers, its sensitivity to GHG emission
factors and GHG emissions reduction benefits will gradually weaken. Therefore, it is found that
the life cycle VKT of shared e-bikes needs to exceed 7,000 km to obtain GHG emissions
reduction benefits. When the life cycle VKT reaches 8000 kilometers, the average GHG
emission factor and GHG emissions reduction benefit are about 117.86 g CO2-eq/pkm (95% CI:
98.20 –139.51 g CO2-eq/pkm) and 8.22 g CO2-eq/pkm (95% CI: −29.34– 49.10 g CO2-eq/pkm),
respectively. For example, if the life cycle VKT reaches 10,000 kilometers, the average GHG
emission factor and GHG emissions reduction benefit could be about 108.61 g CO2-eq/pkm
0
100
200
300
400
500
600
1000 3000 5000 7000 9000 11000 13000 15000
g CO2-eq/pkm
Life cycle VKT (km)
EF
-500
-400
-300
-200
-100
0
100
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
11000
12000
13000
14000
15000
g CO2-eq/pkm
Life cycle VKT (km)
RB
26
(95% CI: 89.59– 129.47 g CO2-eq/pkm), and 17.37 g CO2-eq/pkm (95% CI: −20.24– 58.19 g
CO2-eq/pkm), respectively. In particular, when the life cycle VKT reaches 15 000 kilometers,
the GHG emission factor can be reduced to 96.37 g CO2-eq/pkm (95% CI: 77.79– 116.81 g
CO2-eq/pkm), and the average GHG emissions reduction benefit could be increased to 29.17 g
CO2-eq/pkm (95% CI: −8.08– 69.74 g CO2-eq/pkm).
3.3 GHG emissions reduction benefit of FFESS
The development of FFESS can be divided into two stages. In the first stage of the
development of FFESS, the service life of the shared e-scooter was only about 1–5 months. The
average EF of FFESS in the US was about 599.75 g CO2-eq/pkm (standard deviation= 321.26,
Coefficient of Variation= 0.5357, 95% CI= 221.98– 1417.85g CO2-eq/pkm) (see Fig.7). The
manufacturing stage was the primary source, accounting for approximately 76.51% of GHG
emissions. In the second phase of the development of FFESS, the service life of the shared e-
scooter has increased to 9–18 months. The average EF of shared e-scooter was about 158.58 g
CO2-eq/pkm (standard deviation= 59.14, Coefficient of Variation= 0.3729, 95% CI= 84.05–
309.91g CO2-eq/pkm) (see Fig.7). From the perspective of the decomposition value, the
manufacturing stage and rebalancing process were the primary sources, accounting for about
57.57% and 19.25 % of the total lifecycle GHG emissions, respectively.
27
Fig. 7 GHG emission factors of FFESS
Fig. 8 GHG emissions reduction benefit of FFESS.
It can be found that the promotion of FFESS has not achieved a desirable GHG emission
reduction effect. As shown in Fig. 8, in the first stage of the development of FFESS, the service
life of the shared e-scooter was only about 1–5 months, and the average GHG emissions
0
300
600
900
1200
1500
Lifespan=1-5 Months
Emission Factor (g CO2-eq/pkm)
End of life
Energy use
Rebalance and
Recharging
Maintenance
Manufacturing
0
50
100
150
200
250
300
350
Lifespan=9-18 Months
Emission Factor (g CO2-eq/pkm)
End of life
Energy use
Rebalance and
Recharging
Maintenance
Manufacturing
0
0,02
0,04
0,06
0,08
0,1
-1400
-1300
-1200
-1100
-1000
-900
-800
-700
-600
-500
-400
-300
-200
-100
0
Probability
RB (g CO2-eq/pkm)
Lifespan=1–5 Months
0
0,2
0,4
0,6
0,8
1
0
0,02
0,04
0,06
0,08
0,1
-300
-270
-240
-210
-180
-150
-120
-90
-60
-30
0
30
60
Cumulative probability
Probability
RB (g CO2-eq/pkm)
Lifespan=9–18 Months
28
reduction benefit (RB) of FFESS was about −482.05 g CO2-eq/pkm (standard deviation=
321.46, Coefficient of Variation= 0.6669, 95% CI= −1303.14 – −105.81g CO2-eq/pkm), which
means that per kilometer FFESS trip increased GHG emissions by 482.05 g CO2-eq. Therefore,
at this stage, FFESS can hardly obtain positive GHG emissions reduction benefits (the
probability was about 0). However, when the lifespan of the shared e-scooter was increased to
9 –18 months, the average RB value of FFESS in the US rose to −40.87g CO2-eq/pkm (standard
deviation= 60.09, Coefficient of Variation= 1.47, 95% CI= −192.84–37.96 g CO2-eq/pkm),
which means that per kilometer FFESS trip increased GHG emissions by 40.87 g CO2-eq.
Moreover, the probability of FFESS achieving a positive GHG emissions reduction benefit was
about 26.52%.
The sensitivity analysis results show that the utilization (life cycle VKT) of the shared e-
scooter was the primary factor affecting the GHG emissions reduction benefit of FFESS (see
Supplementary material O), and the GHG emission factors and GHG emissions reduction
benefit of FFESS under different life cycle VKT are presented in Fig.9.
29
Fig. 9. EF and RB of FFESS under different life cycle VKT
It can be found that the GHG emission and GHG emissions reduction benefits of FFESS
significantly depend on its life cycle mileage. The inflection points of the emission factor curve
and the GHG emissions reduction benefit curve of FFESS are about 1000 km. When VKT is
less than 1000 kilometers, as VKT increases, the emission factor and GHG emissions reduction
benefit of FFESS will drop sharply. When VKT is greater than 1,000 kilometers, its sensitivity
to emission factors and GHG emissions reduction benefits will gradually weaken. Therefore, it
is found that the life cycle mileage of a shared e-scooter needs to exceed 3,000 kilometers to
obtain GHG emissions reduction benefits. When the life cycle VKT reaches 4,000 kilometers,
the average emission factor and GHG emissions reduction benefit are about 96.14 g CO2-
eq/pkm (95% CI: 79.92 –112.79 g CO2-eq/pkm) and 21.65 g CO2-eq/pkm (95% CI: −3.86–
48.07g CO2-eq/pkm), respectively. For example, if the life cycle VKT reaches 5,000 kilometers,
the average GHG emission factor and GHG emissions reduction benefit could be about 85.61
g CO2-eq/pkm (95% CI: 70.85 –100.48 g CO2-eq/pkm), and 32.13 g CO2-eq/pkm (95% CI:
0
200
400
600
800
1000
1200
250
750
1250
1750
2250
2750
3250
3750
4250
4750
5250
5750
g CO2-eq/pkm
Life cycle VKT (km)
EF
-1 000
-800
-600
-400
-200
0
200
250
750
1250
1750
2250
2750
3250
3750
4250
4750
5250
5750
g CO2-eq/pkm
Life cycle VKT (km)
RB
30
−7.03– 57.76 g CO2-eq/pkm), respectively. In particular, when the life cycle mileage of an E-
scooter reaches 6000 kilometers, the GHG emission factor can be reduced to 78.70 g CO2-
eq/pkm (95% CI: 65.05 –92.50 g CO2-eq/pkm), and the average GHG benefit can be increased
to 38.96g CO2-eq/pkm (95% CI: 14.79– 64.41 g CO2-eq/pkm).
4 Discussion
This study estimates the impact of global shared micromobillity programs on GHG
emissions from the LCA perspective. In contrast to previous studies focused on one or two
modes of shared micromobility (Bieliński et al., 2021; Bonilla‐Alicea et al., 2020; J. Chen et
al., 2020a; Hollingsworth et al., 2019; Luo et al., 2019), this study focuses simultaneously on
all the primary modes of shared micromobility. Specifically, it empirically compares and
analyzes the GHG emissions impact of traditional shared micromobility modes (i.e., SBBS)
and emerging shared micromobility modes (i.e., FFBS, FFEBS, and FFESS) on urban areas’
transportation systems. In particular, it presents regional differences in the operating
characteristics and development status of global shared micromobility programs. This might be
the first study to provide a comprehensive perspective of the GHG emissions reduction benefits
of global shared micromobility.
The GHG emissions reduction benefits of shared micromobility were calculated using the
LCA method based on the actual operating characteristics and traffic big data of shared
micromobility (see Fig.10). Compared with previous exploratory studies only based on the
assumption that the usage of shared micromobilty substitute (or reduce) car travel during the
vehicle use stage (Fishman, 2016; Fishman et al., 2014; Li et al., 2021; Teixeira et al., 2021;
Zhang & Mi, 2018), this study adopts a more robust and systematic approach, so that the
31
conclusions drawn are characterized by greater accuracy and credibility. This paper partially
supports the view that shared micromobility has particular GHG emission reduction potential
in previous studies (Fishman, 2016; Fishman et al., 2013, 2014; Luo et al., 2019; Teixeira et al.,
2021), but only for SBBS. SBBS has achieved desirable GHG reduction benefits in several
cities across Europe, North America, and China (over 20 cities in this study). However, the
emerging modes driven by the technology progress and the sharing economy have not brought
the claimed GHG emissions reduction benefits. Neither FFBS and FFEBS, which have been
widely promoted in China, nor FFESS, which has been widely developed in Europe and the US,
have lived up to the GHG emissions reduction expectations.
Fig. 10 Average GHG emission reduction benefits of different shared micromobility modes
The results of this study suggest that the environmental benefits of shared micromobility
may have been overestimated in previous studies. One of the main reasons for this difference
is that the substitute rate of shared micromobility trips for car travel within the cities was
overestimated. For example, Qiu and He (2018) estimated the environmental impact of FFBS
30,25
-48,47
-19,46
-482,05
-40,87
-500 -400 -300 -200 -100 0 100
SBBS
FFBS
FFEBS
FFESS(Lifespan=1-5 Months)
FFESS(Lifespan=9-18 Months)
g CO2-eq/pkm
32
in Beijing using a 75% substitute rate. As another example, Kou et al. (2020) quantified the
GHG emission reduction effects of bike-sharing programs in New York, Chicago, Boston,
Philadelphia, Washington, D.C., Los Angeles, San Francisco, and Seattle in 2016 by using the
assumption that approximately 65%–80% of bike-sharing trips replaced car trips, Yet, this study
uses statistical substitute rates based on actual transportation data that are much lower than
these assumptions.
Furthermore, although some studies have corrected the substitute rate of car trips, they have
ignored the negative impact of shared micromobility in the manufacturing and maintenance
phases (Li et al., 2021; Teixeira et al., 2021; Yi & Yan, 2020). GHG emissions from the
manufacturing and maintenance phases account for most of the total GHG emissions in the
shared mircomobility lifecycle. According to Eq. (1), if the life cycle mileage of a shared
micromobility vehicle is too short, it may become a high-emission mode of transportation.
When shared micromobility replaces other more environmentally-friendly ways of
transportation (e.g., public transit and walking), its GHG emission reduction benefits will be
further diminished. Some studies on China's FFBS market augured that the low service life and
utilization rate of shared vehicles makes the GHG emission factor of FFBS much higher than
that of public transit and privately-owned bicycles, thus making the promotion of FFBS have a
negative environmental impact (Chen et al., 2020; Sun & Ertz, 2020). The latest research on
shared e- scooters in the European and US markets also indicated that a too short lifespan can
cause the use of shared electric scooters to generate more CO2 emissions than the mode of
transportation they replace (de Bortoli & Christoforou, 2020; Hollingsworth et al., 2019;
Moreau et al., 2020; Reck et al., 2022). Similarly, the results of this study also indicate that
33
shared micromobility maybe not be an environmentally-friendly transportation mode. As
shown in Fig. 11, the utilization rate of emerging shared micromobility modes (i.e., FFBS,
FFEBS, and FFESS) is much lower than traditional shared micromobility modes (i.e., SBBS).
The average life cycle VKT of the shared bike in SBBS was about 16245.71 km (95% CI:
5072.60– 32953.22 km), while the average life cycle VKT of the FFBS bike, shared e-bike, and
shared e-scooter were about 2799.04 km (95% CI: 990.05–5333.03 km), 5555.99 km (95% CI:
2900.05– 9614.77 km), and 493.73 (95% CI:153.93– 1143.00 km)– 2274.11 km (95% CI:
810.49–4851.33km), respectively. As a result, the average EF of SBBS, FFBS, FFEBS, and
FFESS were about 50.79 g CO2-eq/pkm, 125.57 g CO2-eq/pkm, 145.19 g CO2-eq/pkm, and
158.58–599.75 g CO2-eq/pkm, respectively (see Supplementary material P). The EF of FFBS,
FFEBS, and FFESS are significantly higher than those of public transport and private bicycles.
The rapid development of shared micromobility driven by technological progress provides
a new potential path for improving the sustainability of urban transportation. However, many
transportation network companies have fierce market competition driven by venture capital and
commercial interests. To seize market share as soon as possible, they continued to put vehicles
into the market and quickly updated products, resulting in an oversupply and severe resource
waste. This over-sharing phenomenon has been detrimental to the sustainability of shared
micromobility. It reduces the overall utilization and the GHG emissions reduction potentials of
the emerging shared micromobility fleet and even worsens the sustainability of the entire
transportation system.
34
Fig. 11. Life cycle VKT of shared micromobility
In order to better achieve the GHG benefits, authorities and operators should take a series
of practical measures to improve the efficiency of the shared micromobility system, such as
building more reasonably-sized fleets, optimizing the distribution and rebalancing process, and
improving the DTR. In addition, operators should strengthen cooperation with manufacturers
and recycling organizations to actively participate in the complete life cycle management of the
products in shared micromobility (from design, manufacturing, to recycle). Such cooperation
following a cradle-to-cradle philosophy might better realize the closed-loop management of a
shared transportation mode, extend its service life, and improve resource utilization, thus
contributing to the circular economy (Ertz et al., 2019a, 2019b). Moreover, the public should
be encouraged to actively use these systems for daily transport to increase the utilization of
shared vehicles and the proportion of green travel in urban traffic.
0
0,05
0,1
0,15
0,2
0 5000 10000 15000 20000 25000
Probability
Km
Life cycle VKT
SBBS FFBS FFEBS FFESS(Lifespan = 1-5 Months) FFESS(Lifespan = 9-18 Months)
35
Driven by technological innovation, better product design and more durable materials can
be applied to shared micromobility to extend the service life of the shared transportation mode.
This would further contribute to promote the improved design strategy for extending product
lifetimes, a strategy that remains very marginal in comparison to others (e.g., distribution,
maintenance, recovery) (Ertz et al., 2019a, 2019b). Moreover, with the application and
promotion of intelligent management based on machine learning, big data, and the Internet of
Things, the operation efficiency of the entire shared micromobility system can be significantly
improved (Ertz et al., 2022). All these can help increase the resource utilization rate of the city’s
shared micromobility system, thereby increasing the future sustainability of the shared
micromobility.
City-dwellers worldwide are shifting lifestyles due to the COVID-19 pandemic, especially
in daily transport (Bert et al., 2020; Tiako & Stokes, 2021; Wang & Noland, 2021). During the
pandemic, shared micromobility has become a resilient and safe ways to move around for
essential needs, as it promotes social distancing and helps cities to not rely exclusively on
private cars to replace public transit trips, especially for short-distance travel within the city
(Awad-Núñez et al., 2021; Dias et al., 2021; Li et al., 2020; Tokey, 2020). In particular, when
public transit is considered dangerous or disrupted, shared micromobility can provide resilience
to the entire transportation system during public health emergencies and disasters (Jiang et al.,
2021; Schwedhelm et al., 2020; Wang & Noland, 2021). Covid-19 has, is, and will continue to
shape urban mobility. Cities worldwide have responded to this shift by formulating various
policies and plans (Awad-Núñez et al., 2021; Combs & Pardo, 2021). Shared micromoblity is
viewed as a more attractive and safe mobility option and contributes to the city’s resilience and
36
sustainability (Jobe & Griffin, 2021; Nikiforiadis et al., 2020; Tokey, 2020). Urban planners
and designers are rethinking urban, and transport infrastructure planning and construction as
cities worldwide reopen to adapt to a post-pandemic world. Shared micromobility systems can
become an option for building urban resilient infrastructure architecture.
5 Conclusions
This study combines the life cycle assessment (LCA) framework and the actual shared
micromobility operation big data of major countries and cities worldwide to investigate the real
impact of shared micromobility programs on urban transportation and the environment.
Furthermore, it compares and analyzes the GHG emissions reduction benefits of multiple types
of shared micromobility such as SBBS, FFBS, FFEBS, and FFESS. The main conclusions of
this study are as follows.
Shared micromobility has particular potentialities for GHG emissions reduction, but it
needs to achieve a specific utilization rate. On average, an SBBS trip can reduce about 32.25g
CO2-eq per kilometer, while an FFBS trip may increase it by approximately 48.47g CO2-eq. As
for electric free-floating shared micromobility modes, an FFEBS trip may increase about 19.46
g CO2-eq per kilometer, and an FFESS trip may increase approximately 40.87g− 482.05 g CO2-
eq per kilometer on average. The emerging shared micromobilty modes (i.e., FFBS, FFEBS,
and FFESS) seem less environmentally-friendly than the traditional shared micromobility mode
(SBBS). This is mainly because the utilization rate of the emerging shared micromobility modes
was much lower than that of the conventional shared micromobility mode. The average life
cycle VKT of these new shared micromobility modes was only about one-third to one-fifth of
37
the average life cycle VKT of SBBS.
Contrary to subjective expectations, although the rapid progress of technology in recent
years has promoted the vigorous development of shared micromobility, it has not yet brought
the GHG emissions reduction benefits claimed by related promoters and operators. In addition
to the substitution rate of shared micromobility trips for cars, trips were generally overestimated.
Another important reason is that the low utilization rate (i.e., short life cycle mileage) makes
shared micromobility more likely to be an environmentally-unfriendly mode of transportation.
Considering that the operating characteristics and development status of the shared
micromobility market vary considerably, authorities should rethink the shared micromobility
program and formulate appropriate plans based on the current conditions and goals of the region
to better improve the utilization rate of the shared micromobility and promote the sustainability
of the urban transportation system.
This empirical study helps to better understand the environmental impact of the global
shared micromobility programs. Furthermore, the analysis framework and findings offer
valuable references for researchers and managers committed to improving urban sustainability.
As what is likely the first study to provide a comprehensive picture of the GHG emissions
reduction benefits of global shared micromobility, this research not only fills the academic gap
that lacks empirical evidence for the environmental impact of shared micromobility, but also
constitutes a new approach for further research on the sustainability of emerging shared
micromobility.
This study also has several limitations that pave the way for future research. First, this
article only studies GHG emissions in terms of environmental analysis. It does not include
38
broader impacts such as air pollution, terrestrial acidification, resource scarcity, water
consumption, land use, and so on. In addition to the environmental impact, the social and
economic effects of shared micromobility deserve further exploration and research. Besides,
this study did not consider the substitution rate among different shared micromobility modes
due to the lack of available data. This may affect the GHG emissions reduction benefits of the
shared micromobility to a certain extent, and it needs to be further improved based on more
sufficient data in the future. Finally, the quantitative analysis in this study is based on the overall
statistical characteristics of the system's multi-year operational data. Although multi-year
aggregation can mitigate some data biases, this operation will eliminate the signals reflecting
time-series changes of shared micromobility industrial development. Therefore, future research
will further analyze the evolution of the industry development of shared micromobility and the
corresponding environmental impacts from a dynamic perspective.
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Zou, Z., Younes, H., Erdoğan, S., & Wu, J. (2020). Exploratory analysis of real-time e-scooter trip data
in Washington, DC. Transportation Research Record, 2674(8), 285–299.
50
Supplementary materials
Supplementary material A. Main inventory data and processes for a shared bike in the SBBS system.
Process
Inputs from the Technosphere/Outputs to the Technosphere
unit
value
Manufacturing
stage
Inputs from the
technosphere
polyurethane, flexible foam
kg
3.50E-02
chromium steel removed by turning,
average, conventional
kg
1.88E-01
heat, district, or industrial, other than
natural gas
MJ
2.28E-01
wire drawing, steel
kg
3.99E-01
powder coat, aluminum sheet
m2
4.13E-01
synthetic rubber
kg
6.64E-01
tap water
kg
8.78E-01
welding, arc, aluminum
m
8.85E-01
steel, chromium steel 18/8, hot rolled
kg
1.88E+00
injection molding
kg
2.31E+00
road vehicle factory
unit
1.12E-09
polyethylene, high density, granulate
kg
2.31E+00
section bar extrusion, aluminum
kg
4.45E+00
steel, low-alloyed, hot rolled
kg
5.78E+00
electricity, medium voltage
kWh
8.13E+00
aluminum, wrought alloy
kg
8.89E+00
Use stage
heat, district or industrial, natural gas
MJ
1.60E+01
Outputs to
technosphere, wastes
municipal solid waste
kg
5.31E+00
used bicycle
unit
1.00E+00
wastewater
m3
1.00E-03
Inputs from the
technosphere
aluminum alloy, AlMg3
kg
4.45E-01
chromium steel removed by turning,
average, conventional
kg
2.69E-01
injection molding
kg
1.16E+00
polyethylene, high density, granulate
kg
1.16E+00
polyurethane, flexible foam
kg
3.50E-02
road
my
5.81E-05
section bar extrusion, aluminum
kg
4.45E-01
steel, low-alloyed, hot rolled
kg
2.69E-01
synthetic rubber
kg
1.99E+00
tap water
kg
8.80E-02
Outputs to the
technosphere, wastes
waste plastic, mixture
kg
1.19E+00
waste rubber, unspecified
kg
9.96E-01
End of life
Outputs to
Transport
km
2.25E+00
51
stage(Recycling)
technosphere
waste plastic, mixture
kg
2.35E+00
waste rubber, unspecified
kg
3.32E-01
End of life
stage (Leaving a
shared bicycle
without disposal
options)
Outputs to
technosphere
aluminum treatment of waste
aluminum, sanitary landfill
kg
8.89E+00
Polyethylene treatment of waste
polyethylene, sanitary landfill
kg
2.31E+00
Polyurethane treatment of waste
polyurethane, sanitary landfill
kg
3.50E-02
Steel treatment of scrap steel, inert
material landfill
kg
8.18E+00
Synthetic rubber treatment of waste
rubber, unspecified, municipal
incineration
kg
6.64E-01
Transport
km
2.15E+00
Note: 1) The data presented in the table is for a 20kg SBBS bicycle. Considering that there is no significant
difference in materials and components of various SBBS bicycles, the inventory data in the manufacturing
stage is scaled with bike mass.
2) Data comes from Bike Share Research (2021); Bike Share Toronto (2020); Bonilla-Alicea et al. (2020);
Deng et al. (2017); Kou et al. (2020); Liu et al. (2016); Mei et al., 2019; Morency et al., (2017); NACTO
(2018); Sun and Ertz (2021a); Wernet et al. (2016); Zheng and Zhu (2014).
Supplementary material B. Main material consumption for making one station and one dock.
Component
Material / Ecoinvent unit process
Unit
Value
Station
Aluminum alloy, AlMg3
kg
3.85E+01
Battery, Li-ion, rechargeable, prismatic
kg
8.15E+01
Electronics, for control units
kg
1.00E+01
Flat glass, uncoated
kg
6.80E+00
Photovoltaic panel, multi-Si wafer
m2
1.50E+00
Steel, chromium steel 18/8
kg
4.54E+01
Dock
Steel, chromium steel 18/8
kg
3.00E+01
Note : Source from Bonilla‐Alicea et al.(2020); Luo et al.(2019); Sun and Ertz (2021a, 2021b)
52
Supplementary material C. Main inventory data and processes for a shared bike in the FFBS system.
Process
Inputs from the Technosphere/Outputs to the Technosphere
unit
value
Manufacturing
stage
Inputs from the
technosphere
section bar extrusion, aluminum
kg
4.40E+00
Printed circuit board
kg
3.00E-01
aluminum, wrought alloy
kg
8.75E+00
Battery
kg
2.00E-01
wire drawing, steel
kg
3.99E-01
chromium steel removed by turning,
average, conventional
kg
1.88E-01
steel, chromium steel 18/8, hot rolled
kg
1.88E+00
Electronic equipment
kg
3.00E-01
polyethylene, high density, granulate
kg
2.31E+00
tap water
kg
8.78E-01
polyurethane, flexible foam
kg
3.50E-02
synthetic rubber
kg
6.64E-01
injection molding
kg
2.31E+00
steel, low-alloyed, hot rolled
kg
5.78E+00
electricity, medium voltage
kWh
8.13E+00
welding, arc, aluminum
m
8.85E-01
Photovoltaic panel
m2
2.00E-02
powder coat, aluminum sheet
m2
4.13E-01
heat, district, or industrial, other than
natural gas
MJ
2.28E-01
heat, district or industrial, natural gas
MJ
1.60E+01
Use stage
road vehicle factory
unit
1.12E-09
Outputs to
technosphere, wastes
municipal solid waste
kg
5.31E+00
wastewater
m3
1.05E-03
used bicycle
unit
1.00E+00
Inputs from the
technosphere
road
my
5.81E-05
polyurethane, flexible foam
kg
3.50E-02
tap water
kg
8.80E-02
steel, low-alloyed, hot rolled
kg
2.69E-01
chromium steel removed by turning,
average, conventional
kg
2.69E-01
aluminum alloy, AlMg3
kg
4.45E-01
section bar extrusion, aluminum
kg
4.45E-01
polyethylene, high density, granulate
kg
1.16E+00
injection molding
kg
1.16E+00
synthetic rubber
kg
1.99E+00
Outputs to the
technosphere, wastes
waste plastic, mixture
kg
1.19E+00
waste rubber, unspecified
kg
9.96E-01
53
End of life
stage(Recycling)
Outputs to
technosphere
Used Li-ion battery
kg
2.50E-01
waste rubber, unspecified
kg
3.32E-01
Waste electric and electronic
equipment
kg
6.00E-01
Transport
km
2.25E+00
waste plastic, mixture
kg
2.35E+00
End of life
stage (Leaving a
shared bicycle
without disposal
options)
Outputs to
technosphere
aluminum treatment of waste
aluminum, sanitary landfill
kg
8.885
Used battery
kg
2.00E-01
Polyurethane treatment of waste
polyurethane, sanitary landfill
kg
3.50E-02
Waste electric and electronic
equipment
kg
6.00E-01
Synthetic rubber treatment of waste
rubber, unspecified, municipal
incineration
kg
6.64E-01
Transport
km
2.00E+00
Polyethylene treatment of waste
polyethylene, sanitary landfill
kg
2.31E+00
Steel treatment of scrap steel, inert
material landfill
kg
8.18E+00
Note: 1) The data presented in the table is for a 20kg FFBS bicycle. Considering that there is no significant
difference in materials and components of various types of FFBS bicycles, the inventory data in the
manufacturing stage is scaled with bike mass.
2) Data comes from Bonilla‐Alicea et al.(2020); Chen et al.(2020a); Chen et al.(2020b); CSIC (2020); Luo
et al.(2019); NACTO (2020) ; Sun and Ertz(2021a); Wernet et al.(2016).
Supplementary material D. Main inventory data and processes for a shared e-bike in the FFEBS
system.
Process
Inputs from the Technosphere/Outputs to the
Technosphere
unit
value
Manufacturing stage
Inputs from the
technosphere
Aluminum, cast alloy
kg
3.01E+00
Aluminum, wrought alloy
kg
6.40E+00
Battery, Li-ion, rechargeable, prismatic
kg
4.77E+00
Chromium steel is removed by turning
kg
1.99E-01
Electric motor,
kg
5.50E+00
Electricity, medium voltage
kWh
8.61E+00
Heat, district or industrial,
MJ
1.72E+01
Injection molding
kg
2.45E+00
Polyethylene, high density, granulate
kg
2.45E+00
Polyurethane, flexible foam
kg
3.75E-02
Powder coat, aluminium sheet
m2
4.37E-01
54
Road vehicle factory
p
1.65E-09
Section bar extrusion, aluminium
kg
4.71E+00
Steel, chromium steel 18/8, hot rolled
kg
1.99E+00
Steel, low-alloyed, hot rolled
kg
6.13E+00
Synthetic rubber
kg
7.03E-01
Tap water
kg
9.30E-01
Welding, arc, aluminium
m
9.37E-01
Wire drawing, steel
kg
4.22E-01
Printed circuit board
kg
3.50E-01
Electronic control unit
kg
3.50E-01
Photovoltaic panel
m2
2.00E-02
Outputs to
technosphere, wastes
used e-bike
unit
1.00E+00
municipal solid waste
kg
5.63E+00
wastewater
m3
1.00E-03
Use stage
Inputs from the
technosphere
Aluminum alloy, AlMg3
kg
3.49E-01
Battery, Li-ion, rechargeable, prismatic
kg
4.77E+00
Chromium steel is removed by turning,
kg
2.11E-01
Injection moulding
kg
9.06E-01
Polyethylene, high density, granulate
kg
9.06E-01
Polyurethane, flexible foam
kg
2.78E-02
Section bar extrusion, aluminium
kg
3.49E-01
Steel, low-alloyed, hot rolled
kg
2.11E-01
Synthetic rubber
kg
1.56E+00
Tap water
kg
6.90E-02
Outputs to the
technosphere, wastes
waste rubber, unspecified
kg
7.81E-01
Used Li-ion battery
kg
4.77E+00
waste plastic, mixture
kg
9.34E-01
End of life
stage(Recycling)
Outputs to
technosphere
waste plastic, mixture
kg
2.48E+00
waste rubber, unspecified
kg
3.15E+00
Waste electric and electronic
equipment
kg
6.00E-01
Used Li-ion battery
kg
4.77E+00
Transport
km
2.00E+00
End of life stage
(Leaving a shared
bicycle
without disposal
options)
Outputs to
technosphere
aluminum treatment of waste
aluminum, sanitary landfill
kg
1.35E+01
Waste electric and electronic
equipment
kg
7.00E-01
Used electric motor, vehicle
kg
5.49E+00
Rubber treatment of waste rubber,
unspecified,
municipal incineration
kg
3.15E+00
Transport
km
2.00E+00
55
Used Li-ion battery
kg
4.77E+00
waste plastic, mixture treatment of
waste plastic, sanitary landfill
kg
2.48E+00
Steel treatment of scrap steel, inert
material landfill
kg
8.45E+00
Note: 1) The data presented in the table is for a 30kg shared E-bicycle. Considering that there is no significant difference in
materials and components of various types of shared E-bicycles, the inventory data in the manufacturing stage is
scaled with the mass of shared E-bicycle.
2) Data comes from Aurora Mobile (2021); CAICT(2019); iiMedia Research (2020); Qxcu Industrial Research
Institute (2021);Wernet et al.(2016); WTDSRI (2020).
Supplementary material E. Main inventory data and processes for a shared e-scooter in FFEES
system.
Process
Inputs from the Technosphere/Outputs to the Technosphere
unit
value
Manufacturing stage
Inputs from the
technosphere
Aluminum alloy, AlMg3
kg
6.74E+00
Aluminium, cast alloy
kg
3.01E-01
Battery, Li-ion, rechargeable, prismatic
kg
3.25E+00
Charger, for electric scooter
kg
4.53E-01
Electric motor, for electric scooter
kg
1.40E+00
Light emitting diode
kg
1.88E-02
Polycarbonate
kg
3.22E-01
Printed wiring board, surface mounted,
unspecified, Pb containing
kg
6.94E-02
Steel, low-alloyed
kg
1.59E+00
Synthetic rubber
kg
1.39E+00
Tap water
kg
8.75E-01
Transistor, wired, small size, through-
hole mounting
kg
7.29E-02
Powder coat, aluminium sheet
m2
4.12E-01
Welding, arc, aluminium
m
8.82E-01
Electronic control unit
kg
3.00E-01
Electricity, medium voltage
kWh
7.63E+00
Heat, district or industrial, natural gas
MJ
1.51E+01
Heat, central or small-scale, other than
natural gas
MJ
2.14E-01
Outputs to
technosphere, wastes
used e-scooter
unit
1.00E+00
municipal solid waste
kg
2.74E+00
wastewater
m3
1.00E-03
Use stage
Inputs from the
technosphere
Aluminum alloy, AlMg3
kg
2.79E-01
Chromium steel removed by turning,
average, conventional
kg
1.69E-01
Injection moulding
kg
7.25E-01
56
Polyethylene, high density, granulate
kg
7.25E-01
Polyurethane, flexible foam
kg
2.20E-02
Section bar extrusion, aluminium
kg
2.79E-01
Steel, low-alloyed, hot rolled
kg
1.69E-01
Synthetic rubber
kg
1.25E+00
Tap water
kg
5.50E-01
Outputs to the
technosphere, wastes
waste rubber, unspecified
kg
6.30E-01
waste plastic, mixture
kg
7.50E-01
End of life
stage(Recycling)
Outputs to
technosphere
waste plastic, mixture
kg
2.74E-01
waste rubber, unspecified
kg
1.19E+00
Waste electric and electronic equipment
kg
3.50E-01
Used Li-ion battery
kg
3.27E+00
Transport
km
2.00E+00
End of life stage (Leaving
a shared bicycle
without disposal options)
Outputs to
technosphere
aluminum treatment of waste aluminum,
sanitary landfill
kg
5.98E+00
Waste electric and electronic equipment
kg
7.45E-01
Used electric motor for electric scooter
kg
1.19E+00
Rubber treatment of waste rubber,
unspecified,
municipal incineration
kg
1.19E+00
Transport
km
2.00E+00
Used Li-ion battery
kg
3.27E+00
waste plastic, mixture treatment of
waste plastic, sanitary landfill
kg
2.74E-01
Steel treatment of scrap steel, inert
material landfill
kg
1.47E+00
Note: 1) The data presented in the table is for 15kg shared E-scooters. Considering that there is no significant
difference in materials and components of various types of shared E-scooters, the inventory data in the
manufacturing stage is scaled with the mass of E-scooters.
2) Data comes from Barnes (2019); de Bortoli and Christoforou (2020); Hollingsworth et al.(2019); Mobility
Foresights(2021); Moreau et al. (2020); National Renewable Energy Laborator (2020); U.S. Department
of Transportation(2021); Wernet et al.(2016); Zou et al.(2020).
Supplementary material F. EF of non-shared micromobility modes in the urban transport system
Transportation mode
GHG Emission factor (g CO2-eq/pkm)
Public transit trip
89.5
Car trip
256.8
Privately-owned bike (POB)
15.4
Walk
0.0
Note: Source from Hollingsworth et al.(2019), Luo et al. (2019), and Wernet et al.(2016).
57
Supplementary material G. Substitution rates of shared micromobility for pedal bike sharing (SBBS
and FFBS)
City
Transportation mode substitution
rate
City
Transportation mode substitution
rate
Antwerp
min
max
mean
Minneapolis
min
max
mean
car
6%
44%
25%
car
13%
57%
26%
Public transit
58%
13%
27%
Public transit
45%
11%
29%
POB
8%
27%
28%
POB
6%
9%
7%
Walk
25%
13%
13%
Walk
32%
14%
32%
new trip
4%
3%
6%
new trip
4%
9%
6%
Barcelona
min
max
mean
Montreal
min
max
mean
car
7%
41%
27%
car
15%
52%
24%
Public transit
56%
15%
28%
Public transit
43%
15%
34%
POB
7%
29%
24%
POB
10%
8%
7%
Walk
27%
13%
15%
Walk
28%
12%
26%
new trip
3%
2%
6%
new trip
4%
13%
9%
Bay Area
(San
Francisco)
min
max
mean
Moscow
min
max
mean
car
14%
56%
26%
car
5%
42%
26%
Public transit
41%
17%
29%
Public transit
52%
18%
31%
POB
8%
9%
7%
POB
10%
23%
19%
Walk
30%
15%
32%
Walk
25%
15%
18%
new trip
7%
3%
6%
new trip
8%
2%
6%
Beijing
min
max
mean
Nanjing
min
max
mean
car
6%
35%
16%
car
5%
34%
17%
Public transit
35%
31%
43%
Public transit
37%
30%
42%
POB
30%
9%
21%
POB
29%
6%
16%
Walk
13%
12%
11%
Walk
15%
15%
14%
new trip
17%
13%
9%
new trip
14%
15%
11%
Boston
min
max
mean
New York
min
max
mean
car
13%
57%
26%
car
11%
56%
24%
Public transit
45%
11%
29%
Public transit
46%
16%
32%
POB
6%
12%
7%
POB
8%
11%
9%
Walk
32%
10%
32%
Walk
31%
10%
29%
new trip
4%
11%
6%
new trip
4%
7%
6%
Brisbane
min
max
mean
Ningbo
min
max
mean
car
21%
42%
29%
car
7%
31%
16%
Public transit
41%
26%
36%
Public transit
32%
37%
38%
POB
9%
21%
13%
POB
22%
8%
21%
Walk
25%
10%
21%
Walk
19%
9%
13%
new trip
4%
1%
1%
new trip
20%
15%
12%
Chengdu
min
max
mean
Philadelphia
min
max
mean
car
5%
35%
17%
car
14%
54%
25%
Public transit
37%
30%
42%
Public transit
43%
16%
29%
58
POB
29%
5%
21%
POB
7%
9%
7%
Walk
15%
15%
11%
Walk
31%
10%
29%
new trip
14%
15%
9%
new trip
5%
11%
9%
Chicago
min
max
mean
Seattle
min
max
mean
car
13%
53%
26%
car
13%
57%
26%
Public transit
38%
18%
31%
Public transit
45%
11%
29%
POB
9%
7%
8%
POB
6%
11%
7%
Walk
33%
11%
29%
Walk
32%
11%
32%
new trip
7%
11%
6%
new trip
4%
11%
6%
Paris
min
max
mean
Shanghai
min
max
mean
car
7%
41%
25%
car
7%
35%
17%
Public transit
57%
19%
27%
Public transit
34%
28%
42%
POB
8%
25%
28%
POB
29%
5%
21%
Walk
24%
12%
13%
Walk
15%
17%
11%
new trip
4%
3%
6%
new trip
15%
15%
9%
Guangzhou
min
max
mean
Shenzhen
min
max
mean
car
8%
33%
16%
car
5%
35%
19%
Public transit
33%
40%
39%
Public transit
37%
30%
40%
POB
24%
5%
20%
POB
27%
5%
21%
Walk
19%
10%
12%
Walk
15%
15%
11%
new trip
16%
12%
13%
new trip
16%
15%
9%
Hangzhou
min
max
mean
Toronto
min
max
mean
car
6%
35%
18%
car
12%
50%
23%
Public transit
39%
35%
42%
Public transit
41%
20%
32%
POB
26%
7%
17%
POB
6%
11%
7%
Walk
12%
14%
11%
Walk
32%
9%
29%
new trip
17%
9%
11%
new trip
9%
10%
9%
Jinan
min
max
mean
Vancouver
min
max
mean
car
5%
35%
17%
car
14%
55%
26%
Public transit
37%
30%
42%
Public transit
42%
13%
30%
POB
29%
5%
21%
POB
10%
13%
12%
Walk
15%
15%
11%
Walk
28%
12%
26%
new trip
14%
15%
9%
new trip
6%
7%
6%
London
min
max
mean
Vienna
min
max
mean
car
6%
44%
25%
car
8%
40%
22%
Public transit
58%
13%
27%
Public transit
54%
17%
33%
POB
8%
27%
28%
POB
10%
29%
23%
Walk
25%
13%
13%
Walk
22%
10%
16%
new trip
4%
3%
6%
new trip
6%
4%
6%
Los
Angeles
min
max
mean
Washington
D.C.
min
max
mean
car
11%
49%
26%
car
13%
56%
29%
Public transit
39%
20%
29%
Public transit
43%
17%
27%
POB
7%
11%
7%
POB
6%
13%
9%
59
Walk
32%
11%
32%
Walk
30%
10%
28%
new trip
11%
9%
6%
new trip
8%
4%
7%
Melbourne
min
max
mean
Wuhan
min
max
mean
car
21%
42%
29%
car
5%
35%
17%
Public transit
41%
26%
36%
Public transit
37%
30%
42%
POB
9%
21%
13%
POB
29%
5%
21%
Walk
25%
10%
21%
Walk
15%
15%
11%
new trip
4%
1%
1%
new trip
14%
15%
9%
Milan
min
max
mean
Xi'an
min
max
mean
car
6%
44%
25%
car
7%
31%
16%
Public transit
58%
13%
27%
Public transit
31%
36%
35%
POB
8%
27%
28%
POB
24%
10%
18%
Walk
25%
13%
13%
Walk
17%
13%
15%
new trip
4%
3%
6%
new trip
21%
10%
16%
Note: Source from CSIC (2018); Fishman (2016); Fishman et al. (2014, 2013); Fitch et al.(2020); Jiang et al., (2021); Kou et al. (2020); Link
et al. (2020); Mao et al. (2021); Morency et al.(2017); NACTO(2019); Qxcu Industrial Research Institute (2021); Shaheen and Cohen (2021);
SURC & TDRI (2020); Teixeira et al. (2021); and U.S. Department of Transportation (2021).
Supplementary material H. Transportation mode substitution rate of FFEBE and FFESS
FFEBS in China
(Triangular Distribution)
FFESS in the United States
(Triangular Distribution)
min
mean
max
Min
mean
max
Car
21%
31%
57%
23%
40%
52%
Public
38%
41%
28%
36%
13%
11%
POB
21%
13%
5%
17%
8%
8%
Walk
11%
9%
6%
19%
37%
30%
New trip
9%
6%
4%
5%
2%
0%
Sum
100%
100%
100%
100%
100%
100%
Note: Source from Barnes (2019); Bozzi and Aguilera (2021); Chicago Department of Transportation (2021); Liu et al. (2019); NABSA (2021);
NACTO (2020); Qxcu Industrial Research Institute (2021); U.S. Department of Transportation (2021); WTDSRI (2020); Yan et al. (2021); and
Zou et al. (2020).
60
Supplementary material I. The life cycle VKT of the SBBS and FFBS
0
5000
10000
15000
20000
25000
30000
35000
Antwerp
Barcelona
Bay Area
Beijing
Boston
Brisbane
Chicago
Paris
Guangzhou
Hangzhou
London
Los Angeles
Melbourne
Milan
Minnesota
Montreal
Moscow
Nanjing
New York
Ningbo
Philadelphia
Seattle
Shanghai
Toronto
Vancouver
Vienna
Washington D.C.
Beijing
Chengdu
Guangzhou
Hangzhou
Jinan
Nanjing
Seattle
Shanghai
Shenzhen
Washington D.C.
Wuhan
SBBS FFBS
Km
Lifecycle VKT
61
Supplementary material J. Comparison of GHG emission factors between SBBS system and FFBS system
050 100 150 200 250 300 350 400
Xi'an_FFBS
Wuhan_FFBS
Washington D.C. _FFBS
Washington D.C.
Vienna
Vancouver
Toronto
Shenzhen_FFBS
Shanghai_FFBS
Shanghai
Seattle_FFBS
Seattle
Philadelphia
Ningbo
New York
Nanjing_FFBS
Nanjing
Moscow
Montreal
Minneapolis
Milan
Melbourne
Los Angeles
London
Jinan_FFBS
Hangzhou_FFBS
Hangzhou
Guangzhou_FFBS
Guangzhou
Grand Paris
Chicago
Chengdu_FFBS
Brisbane
Boston
Beijing_FFBS
Beijing
Bay Area(San Francisco)
Barcelona
Antwerp
Emission Factor (g CO2-eq/pkm)
62
Supplementary material K. Uncertainty analysis of GHG emissions reduction benefits of SBBS and
FFBS. The green bar represents the probability of obtaining GHG emissions reduction benefits, while
the red bar represents increasing GHG emissions.
-100,00% -50,00% 0,00% 50,00% 100,00%
Washington D.C.
Vienna
Vancouver
Toronto
Shanghai
Seattle
Philadelphia
Ningbo
New York
Nanjing
Moscow
Montreal
Minneapolis
Milan
Melbourne
Los Angeles
London
Hangzhou
Guangzhou
Paris
Chicago
Brisbane
Boston
Beijing
Bay Area
Barcelona
Antwerp
Xi'an
Wuhan
Washington D.C.
Shenzhen
Shanghai
Seattle
Nanjing
Jinan
Hangzhou
Guangzhou
Chengdu
Beijing
SBBS FFBS
Reduce GHG emissions Increase GHG emissions
63
Supplementary material L. Variance contribution of uncertain variables in FFBS system
Supplementary material M. Variance contribution of uncertain variables in SBBS system
-0,1%
1,3%
-6,2%
-8,5%
8,9%
-73,3%
-80,0% -70,0% -60,0% -50,0% -40,0% -30,0% -20,0% -10,0% 0,0% 10,0% 20,0%
Recycle rate
Bike weight
Distance per trip
Service life
Rebalance
DTR
FFBS
-0,1%
0,2%
-0,6%
-0,9%
3,1%
9,5%
-11,6%
-72,1%
-80,0% -70,0% -60,0% -50,0% -40,0% -30,0% -20,0% -10,0% 0,0% 10,0% 20,0%
Recycle rate
Bike weight
Service life
Bikes per station
Docks per bike
Rebalance
Distance per trip
DTR
SBBS
64
Supplementary material N. The sensitivity analysis results for FFEBS
Supplementary material O. The sensitivity analysis results for FFESS
-51,5%
-13,4%
-14,0%
5,6%
-0,1% 15,0%
0,5%
-60% -40% -20% 0% 20%
DTR
Distance per trip
Life span
Bike Weight
Recycle rate
Rebalance and Distribution
Energy use
Variance contribution
-58,85% -18,60%
-17,35%
4,05%
-0,10%
0,95%
0,20%
-60% -50% -40% -30% -20% -10% 0% 10%
DTR
Distance per trip
Life span
Weight rate
Recycle rate
Rebalance and distribution
Energy use
Variance contribution
65
Supplementary material P. Average GHG Emission factors of shared micromobility
Reference
Aurora Mobile. (2021). Report on the social value of e-bike sharing.
Barnes, F. (2019). A scoot, skip, and a JUMP away: Learning from shared micromobility systems in San
Francisco.
Beijing Transport Institute. (2016). Beijing Transport Annual Report 2016. </