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energies
Review
Evaluating the Environmental Impact of Using Cargo Bikes in
Cities: A Comprehensive Review of Existing Approaches
Hanna Vasiutina 1, * , Andrzej Szarata 1and Stanisław Rybicki 2
Citation: Vasiutina, H.; Szarata, A.;
Rybicki, S. Evaluating the
Environmental Impact of Using
Cargo Bikes in Cities: A
Comprehensive Review of Existing
Approaches. Energies 2021,14, 6462.
https://doi.org/10.3390/en14206462
Academic Editor: Bjørn H. Hjertager
Received: 22 September 2021
Accepted: 8 October 2021
Published: 9 October 2021
Publisher’s Note: MDPI stays neutral
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1Faculty of Civil Engineering, Cracow University of Technology, 31-155 Kraków, Poland; aszarata@pk.edu.pl
2Faculty of Environmental Engineering, Cracow University of Technology, 31-155 Kraków, Poland;
srybicki@pk.edu.pl
*Correspondence: hanna.vasiutina@doktorant.pk.edu.pl
Abstract:
The impact of the use of cargo bicycles for delivery processes on the environment is
undeniably positive: it leads to the reduction of pollutants, noise, and vibrations caused by traditional
vehicles; decreases traffic jams; causes more effective use of public space; and others. But how should
such an effect be measured? What tools should be used to justify the necessity for change to more
sustainable means of transport? How can we improve the state of the environment considering
the interests of logistics service providers? There is a large amount of scientific literature dedicated
to this problem: by using different modeling approaches, authors attempt to address the issue of
sustainable transport. This paper conducts a literature review in the field of green cargo deliveries,
investigates the benefits and drawbacks of integrating cargo bikes in urban logistics schemes, and
examines methodologies and techniques for evaluating the impact of using cargo bicycles on the
environment. By providing an opportunity to get acquainted with the situation in the sphere of green
deliveries, the authors aim to encourage a breakthrough in the field of sustainable transport that may
be achieved by using cargo bikes in modern cities. We review the existing approaches and tools for
modeling transport emissions and state the significant positive environmental consequences.
Keywords: environmental impact; cargo bikes; sustainable transport; transport modelling
1. Introduction
The constant, alarming, worsening state of the environment is an issue of major con-
cern to the European Union policy-making authorities for more than 50 years. The United
Nations Framework Convention on Climate Change developed in 1992 is considered as the
starting point of the international environmental-protection policymaking. This treaty was
a base for the following and more famous Kyoto Protocol and the Paris Convention, which
introduced the state parties’ regulations for limiting and reducing greenhouse gas (GHG)
emissions. Furthermore, the EU continues providing air-quality politics and ratifies with its
member states legislative acts (starting with the Council Directive 96/62/EC) that establish
the mechanism for acquiring and controlling ambient air quality. As well, all the European
Union member states (that adopt the directives) are obliged for its implementation to
conduct annual reports on the state of the air. Such reports are collected and inspected by
the European Environment Agency (EEA), the leading organization for managing the coop-
eration with the EU members states, which gathers, verifies, and presents the information
on Europe’s environment.
While the polluting sectors (such as agriculture, industry, and others), which undergo
the monitoring of EEA, show a steady downward trend in pollution emissions, the road
transport segment indicates the growth of global emissions since 1990 [
1
]. Moreover, the
EEA states that overall greenhouse gas emissions from transport reach up to a quarter
of all GHG emissions in the EU. Road transport is denoted as the main producer of such
emissions in 2018 (about 70%) [
1
]. Besides being responsible to a large extent for global
warming, transport is accountable for significant social and environmental harm: it causes
Energies 2021,14, 6462. https://doi.org/10.3390/en14206462 https://www.mdpi.com/journal/energies
Energies 2021,14, 6462 2 of 19
health issues, noise pollution, congestion, loss of public space, and negatively affects the
overall livability of the city. On the other hand, transport plays the role of an engine in
economic progress and recovery: it contributes to investment growth, brings employment,
and widens markets. Therefore, the area of sustainable transportations is among the main
targets of EU policymaking. Furthermore, a positive result could only be achieved through
the collective interaction of global and local initiatives by mutual influence of bottom-up
and top-down approaches [2].
Along with the rapid increase of digital technologies and demand for city logistics,
the requirement for improvement of urban life quality also grows. As follows, the bottom-
up approach is gaining momentum due to the growing awareness of the population
about the threat of global warming and specifically about the impact of transport, one
of the main contributors to climate change [
3
,
4
]. Whereas more citizens are concerned
with the living condition of the neighborhood and appeal to city authorities for adequate
reaction, the question of introducing the new, environmentally friendly business models
and strategies arises. As a reaction to that prompt, many cities around the world introduced
urban transport policies that aim to support the use of city space for pedestrians and non-
motorized vehicles, limit speed, and restrict access of heavy diesel motor vehicles to the city
centers (e.g., Cracow [
5
], Paris [
6
], London [
7
], Sao Paolo [
8
], Seoul [
9
], Rio de Janeiro [
10
],
New York [
11
], Barcelona [
12
]). Moreover, officials in turn motivate residents to shift to
more sustainable transport. For instance, purchasing of electric cars and cargo bicycles (CB)
could be induced by funding and tax reduction; traveling by bicycles could be prompted
by infrastructure improvement and encouragement through social initiatives; and use of
public transport by increasing its efficiency and accessibility, and that is the representation
of the top-down approach.
Consequently, there is increased attention for planning, adapting, and shifting to the
new sustainable distribution solutions in the cities that could satisfy all parties interested
in the delivery process. Not only does such a solution design eliminate negative environ-
mental and social impacts from road transport on city inhabitants, but it also minimizes
losses in efficiency and costs for urban logistic operators (ex. operating and investment
costs). Inevitably, cycle logistics is gaining more attention in recent EU research and devel-
opment projects, such as CityChangerCargoBike [
13
], Handshake [
14
], Park4SUMP [
15
],
GreenCharge [16], etc.
Currently, the main concept for estimation of transport impact is to determine the
level of pollution using the various transport emission models, which receive as an input
the parameters’ set of characteristics achieved from the traffic situation. Sometimes, such
characteristics are oversimplified; in other cases, they are obtained from models of a
transportation system (infrastructure of the study region, vehicle fleet characteristics).
Furthermore, atmospheric dispersion models are applied for the evaluation of the behavior
of pollutants in the atmosphere.
The great amount of scientific literature that analyzes alternative/ sustainable distri-
bution scenarios for the last- (or first-) mile deliveries explores CB (and its variations) as an
environmentally friendly means of transport and the main substitute for traditional vans
(in last-mile deliveries). Primarily, the directions of such studies, which are the subject of
interest of this article, could be outlined as:
(1) Analysis of business strategies. Commonly, in such an approach, the potential and the
challenges of using CB as a green transport mode are investigated in the context of
the development of strategies for logistics service providers (LSP) [
12
,
17
–
19
]. While
developing the strategies, the economic impact area for CB use is assessed, its cost-
effectiveness is evaluated, and the competitiveness of LSP is estimated.
(2)
Transportation system modeling (TSM) is used to optimize the transport system
parameters. For this approach, the routing models are usually implemented that
integrate less-polluting vehicles (such as electric vehicles, CB, etc.) into a delivery
system. These models could be used as a strategic and decision-making tool by city
planners and authorities [
20
–
25
]. Additionally, the operations impact area and the
Energies 2021,14, 6462 3 of 19
location of transshipment points are defined by the means of TSM [
26
–
28
]. Another
promising direction of using TSM is the justification of the multimodal schemes with
the incorporation of consolidation centers (for loading and unloading operations) on
the edge of the city center with transshipment to the small, nonmotorized delivery
vehicles, possibly CB [8,29,30].
(3)
Estimation of transport impact. Transport emission and dispersion models are de-
veloped to evaluate amounts of different types of pollutants (CO
2
, PM, SO
2
, NOx,
VOC, and CO) and their distribution in the atmosphere as well as to estimate the
noise level [7,8,31,32].
(4)
Combination and mixture of the approaches listed above [11,33–35].
Despite the great interest in the last years for the topic of ecological modes of trans-
port, there is a lack of research with a special focus on the environmental impact of non-
motorized solutions for goods’ deliveries [
6
,
36
]. The authors of the paper [
37
] underline
that the perspective in the exploration of new techniques and recommendations about
using cargo bikes is vast.
This paper aims to identify the set of basic requirements that should be addressed by
the contemporary methodology for evaluating the environmental impact of cargo bicycles.
For these purposes, we explore the state of art in a topic of sustainable freight deliveries
when CB are used as a mean of transport.
The paper is organized as follows: the next section is dedicated to the exploration
of pluses and minuses of CB as a means of transport; the third part presents the review
of existing studies on delivery schemes used by logistics service providers in real-world
solutions; the fourth section contains a short analysis of the literature on using the transport
systems modeling approach; methods for evaluation of transport impact on the environ-
ment are presented in the fifth section; and the last part provides conclusions and plans for
the future research.
2. Cargo Bike as a Means of Transport: Advantages and Disadvantages
CB is a bicycle that serves for transportation of various freights (goods, passengers, etc.)
and was specifically invented for such purposes nearly a century ago [
2
,
38
]. Depending on
the design, the number of wheels, or their purpose, CBs are called freight, transport or box
bikes, carrier cycles, tricycles (CT) and quadricycles, cycle-trucks, long-john, and others.
Modern CBs are usually electrically assisted, and their models vastly vary from simple
two-wheelers bikes equipped with boxes on the front or back wheel to more progressive
longtails and long johns that can carry weights around 50–100 kg. The most advanced multi-
wheelers or light electric vehicles (LEV) can transport cargo up to 500–700 kg [
2
,
17
,
39
,
40
].
The term “cycle logistics” commonly refers to any type of pedal bicycle [41].
Recent studies confirm the enormous potential of cargo cycles to serve as sustainable
substitution of traditional delivery vehicles. At the same time, the readiness to transition
to more ecological means of transport (powered by electricity) was detected in slightly
more than 60% of studies dedicated to last-mile problems, whereas nearly 50% of scientific
sources advocate for freight bicycle adoption [
42
]. Such great possibility of CB to replace
conventional vans is conditioned, to a large extent, by the small or the medium size and
light weight of the most cargoes and relatively short delivery distances in central areas of
the cities. According to results of the CycleLogistics project, nearly half of all urban goods
transportation in the EU could be carried by CB; furthermore, the average trip distances
in the city centers do not exceed 7 km [
43
]. A similar figure was obtained by the authors
of the paper [
44
]: they observed that the distance of most commercial delivery routes is
less than 10 km. TNT-FEDEX data that was used in the research [
8
] show such average
freight parameters, volume, and weight: 0.025 m
3
and 20 kg (maximum 30 kg), and those
are medium packages of electronics and textiles. Moreover, the authors of the study [
20
]
noted that 80% of all parcel flow are cargoes weighing less than 6 kg.
The following types of freights can be distinguished as the most frequently transported
by CBs:
Energies 2021,14, 6462 4 of 19
•small packages and boxes [17,45]:
#food [6,7,11,17,39,40,46],
#correspondences, documents [6,17,37],
#pharmaceuticals [6];
•medium size: mail, retail [46], and
•service trips [39,46], home deliveries [37].
Especially, advantages of CB could be observed in the narrow streets of histori-
cal city centers, with one-way traffic management and a large number of pedestrian
areas [12,27,41,45,47]
(see Table 1). Such districts frequently have spatial and timing restric-
tions on cars entering and are highly congested due to the population density and high demand
for goods delivery [
17
,
48
]. Moreover, most authors emphasize the effectiveness and suitability
of CB for the last- (or first-) mile urban distribution [6,8,10–12,19,21–23,25,36,40,41,47–50].
Beneficial results of using CBs were indicated in:
•cost savings [6,8,17,20,21,36,48,50];
•decreasing congestions, CO2emissions, and local pollutants [6,8,9,11,19,23,27,51,52];
•improving safety on roads due to reduction of car-related accidents [6,38];
•
presentation of attractive (green) image of a company amongst its
customers [17,20,48]; and
•improving livability of the city [8].
Economic effectiveness could be achieved due to low charging, purchase, and mainte-
nance costs, which are notably less than for traditional vans [
50
]. Furthermore, the increase
in performance could be attained due to the reliability of CB and their agility in urban
centers (possibility to ride on one-way streets, park on the sideways, etc.) [
6
]. By occupying
less than 35% of the space that takes up the van, CBs contribute to eliminating the con-
gestion delays [
11
]. The collaborative scheme of supplies, which includes CT and mobile
depots (MD), indicates a significant reduction in GHG emissions [
10
]. The availability
of ready-to-use bicycles and equipped transshipment hubs can ensure the elimination of
delays in deliveries [53].
Nonetheless, along with the huge advantages of CB, their drawbacks and limitations
must be considered. The most common disadvantages could be found in Table 1. As for
ECB also, potential issues could arise in the areas of battery and engine malfunctions and,
accordingly, the lowering of travel range and time. Additionally, researchers indicate the
lack of regulations (e.g., parking policies) and cycling infrastructure (charging stations and
transshipment points) [17,47,52].
Table 1. Advantages and disadvantages of using bikes for cargo deliveries.
Feature Description Source
Advantages
Compact
•needs little parking space
•can use bike lanes
•suitable for dense city center
[8,11,27,36,47,50]
Clean
•does not emit GHG
•reduction of pollution and
emission
•noiseless
[6,8,11,12,20,27,36,40,47,51,54]
Cheap •purchase cost
•maintenance cost [46,47]
Effectiveness
•congestion reduction
•lesser driving distances
•reduction of delivery time
•energy savings
[6–8,11,12,17,20,21,27,40,46,50,51]
Cost savings •fuel economy
•operational/delivery cost
[8,36,49]
[20,51]
Energies 2021,14, 6462 5 of 19
Table 1. Cont.
Feature Description Source
Safe •safer for pedestrians
•lowering road accidents
[7,11]
[6]
Attractiveness •approval by society
•improve life quality [8,12,17,40]
Disadvantages and limitations
Capacity •payload and volume capacity [9,17,30,36,39,47]
Speed •lower velocities [9,29,36,39,47]
Range •delivery distance limitations [9,25,36,39]
Costs
•driver costs
•loss of efficiency
•high prices on bikes
[47]
[20]
[8]
Lack of infrastructure,
topography features
•bike lanes
•parking
•transshipment points
[36,49]
Emissions •CO2associated with
electricity generation [7]
Working conditions,
risk factors (safety)
•riding behavior
•traffic accidents
•driver physique
•winter conditions
[7]
[49]
[47]
[29]
Special attention should be paid to the parameters, features of CB, and general recom-
mendations when choosing an alternative vehicle. Table 1summarizes the advantages and
disadvantages of cycle logistics and provides a short description. Some disadvantages can
be identified through a comparison of CB with conventional delivery vehicles.
In the literature on cycle logistics, much attention is paid to the problem of the
implementation, concept testing, and determining the location of the urban consolidation
centers (UCC), which are also called micro, mobile, delivery, transshipment center, or
loading hubs, terminals and points, satellites, micro depots, micro-distribution platforms,
and others. Usually, such centers are used for managing freight flows and represent
temporal or permanent storage facilities to which cargo is delivered by one means of
transport (conventional vans) and then is transported to the client by other means (cargo
bikes) [
5
]. Authors of the paper [
19
] characterize a mobile depot as a trailer that is connected
to a loading dock and serves as an office and a warehouse. The main consequence of the
introduction of the UCC is the lowering of transport emissions due to the introduction of
the more sustainable scheme of transport, where the route from the UCC to a customer
was performed by CT [
8
]. In the presented study, the transshipment center (TC), besides
just storing the freights, also serves as a garage for bikes. To use UCC in the form of
containers and truck-trailers was offered as an option in one study [
27
]; the proposed
solution led to a notable decrease in travel distances and annual emissions of local CO
2
.
Additionally, the authors of the manuscript [
30
] underlined the considerable role of UCC
in pollution reduction; however, they pointed out that the large cost is the biggest obstacle
for implementing such centers.
Besides focusing only on UCC, some studies are devoted to an exploration of the
cycling infrastructure. For instance, a field experiment that was carried out using two CB
was a subject of the research [
40
]. The authors observed bike deliveries performed on the
pre-planned routes with aim of examining of quality and affordability of the bike lanes,
road traffic threats, etc. As a result, attention was drawn to the poor bicycle infrastructure,
the necessity of the thorough choice of the fleet, and the huge role of the policymakers
in the sustainable shift. Similarly, by exploring a free CB-sharing system, the authors of
the research [
2
] highlight the importance of extensive cycle infrastructure for the wider
adoption of CBs. Likewise, the focus on bicycle paths as a basic need for the green mode
shift was highlighted in the paper [
37
]. As a result of the literature review and surveys
with representatives of the logistics industry, attention was drawn to the key role of interest
Energies 2021,14, 6462 6 of 19
groups and private businesses as well as the influence of municipalities on the sustainable
development of the cities.
There are not many ways researchers can obtain data for the studies in the trans-
portation area. The most common method is to conduct interviews and surveys; another
more difficult procedure is gaining information from transport organizations (oftentimes,
such data is protected by the confidentiality agreement and cannot be openly displayed).
Furthermore, some perform field tests or observations or use GPS tracking data; others use
knowledge from the literature or previously developed projects. The pie chart in Figure 1
shows the proportion of the data sources that have been used in the scientific papers cited
in this article.
Energies 2021, 14, 6462 6 of 20
was a subject of the research [40]. The authors observed bike deliveries performed on the
pre-planned routes with aim of examining of quality and affordability of the bike lanes,
road traffic threats, etc. As a result, attention was drawn to the poor bicycle infrastructure,
the necessity of the thorough choice of the fleet, and the huge role of the policymakers in
the sustainable shift. Similarly, by exploring a free CB-sharing system, the authors of the
research [2] highlight the importance of extensive cycle infrastructure for the wider adop-
tion of CBs. Likewise, the focus on bicycle paths as a basic need for the green mode shift
was highlighted in the paper [37]. As a result of the literature review and surveys with
representatives of the logistics industry, attention was drawn to the key role of interest
groups and private businesses as well as the influence of municipalities on the sustainable
development of the cities.
There are not many ways researchers can obtain data for the studies in the transpor-
tation area. The most common method is to conduct interviews and surveys; another more
difficult procedure is gaining information from transport organizations (oftentimes, such
data is protected by the confidentiality agreement and cannot be openly displayed). Fur-
thermore, some perform field tests or observations or use GPS tracking data; others use
knowledge from the literature or previously developed projects. The pie chart in Figure 1
shows the proportion of the data sources that have been used in the scientific papers cited
in this article.
Figure 1. Rating of the most frequently used data sources.
The data represented on the graph illustrates that most researchers collect infor-
mation from surveys, interviews, and workshops (nearly 30%). The second most popular
position is shared by parcel delivery companies and scientific literature and projects (each
of these sections gained approximately 20%). Slightly fewer papers refer to such sources
as observations (a little more than 15%). However, GPS, GIS, and maps were used only by
a small percentage of authors (about 7%), and the smallest segment is represented by stud-
ies for the implementation of which data from pilot projects were used (3.6%).
Figure 1. Rating of the most frequently used data sources.
The data represented on the graph illustrates that most researchers collect information
from surveys, interviews, and workshops (nearly 30%). The second most popular position
is shared by parcel delivery companies and scientific literature and projects (each of
these sections gained approximately 20%). Slightly fewer papers refer to such sources as
observations (a little more than 15%). However, GPS, GIS, and maps were used only by a
small percentage of authors (about 7%), and the smallest segment is represented by studies
for the implementation of which data from pilot projects were used (3.6%).
The described results demonstrate that data are mostly collected through question-
naires and surveys; thus, such information is often biased. Moreover, there is clearly a
lack of works based on practical experiments and cooperation between the private sector
and scientific institutions. On the other hand, we can observe the wide engagement of re-
search and development projects that aim the popularization and expansion of sustainable
mobility solutions in the urban areas.
Such projects demonstrate the potential, possibilities, and prospects of environmen-
tally friendly transport to be adapted and implemented by the private sector, researched
by scientists, and promoted among the public. Among recently implemented or started
projects, the following ones should be listed:
Energies 2021,14, 6462 7 of 19
•Ich ersetze ein Auto (I’m replacing a car) [55],
•SMILE [12],
•STRAIGHTSOL [12,19],
•Pro-E-bike [17],
•CycleLogistics [42],
•Ich entlaste Städte (Taking the load off cities) [44],
•Freie Lastenräder [2],
•LCL (Low carbon logistics) [40], and
•LEVV-LOGIC [39].
For the last years, the market of cycle logistics has grown rapidly. Large logistics
companies adopt sustainable business models, take part in various pilot projects to test
possibilities of green means of transport, cooperate with municipal authorities and non-
governmental organizations, and provide data for the research. Some of these operators
are TNT [
8
,
17
], EBC, GKC [
17
], DHL [
17
,
50
,
51
], GLS [
17
], UPS [
18
,
50
], FedEx [
50
], Hermes,
Gnewt Cargo [41], CEP [8], and PonyZero [23].
Deliveries of freights by bicycles are performed in many urban areas around the world,
although a study of the scientific literature of recent years has shown that most research
on CB activities was conducted in the USA in such cities as Seattle, Austin, New York,
Portland, and others. Germany comes second with six heavily researched cities. Slightly
fewer articles were detected in Poland. The further ranking is shown in Figure 2.
Energies 2021, 14, 6462 7 of 20
The described results demonstrate that data are mostly collected through question-
naires and surveys; thus, such information is often biased. Moreover, there is clearly a lack
of works based on practical experiments and cooperation between the private sector and
scientific institutions. On the other hand, we can observe the wide engagement of research
and development projects that aim the popularization and expansion of sustainable mo-
bility solutions in the urban areas.
Such projects demonstrate the potential, possibilities, and prospects of environmen-
tally friendly transport to be adapted and implemented by the private sector, researched
by scientists, and promoted among the public. Among recently implemented or started
projects, the following ones should be listed:
• Ich ersetze ein Auto (I’m replacing a car) [55],
• SMILE [12],
• STRAIGHTSOL [12,19],
• Pro-E-bike [17],
• CycleLogistics [42],
• Ich entlaste Städte (Taking the load off cities) [44],
• Freie Lastenräder [2],
• LCL (Low carbon logistics) [40], and
• LEVV-LOGIC [39].
For the last years, the market of cycle logistics has grown rapidly. Large logistics
companies adopt sustainable business models, take part in various pilot projects to test
possibilities of green means of transport, cooperate with municipal authorities and non-
governmental organizations, and provide data for the research. Some of these operators
are TNT [8,17], EBC, GKC [17], DHL [17,50,51], GLS [17], UPS [18,50], FedEx [50], Hermes,
Gnewt Cargo [41], CEP [8], and PonyZero [23].
Deliveries of freights by bicycles are performed in many urban areas around the
world, although a study of the scientific literature of recent years has shown that most
research on CB activities was conducted in the USA in such cities as Seattle, Austin, New
York, Portland, and others. Germany comes second with six heavily researched cities.
Slightly fewer articles were detected in Poland. The further ranking is shown in Figure 2.
Figure 2. Geographical distribution of studies dedicated to CB issues.
In summary, most scientific papers attribute such main advantages of cargo bicycles
as their environmental friendliness, compactness, accessibility, safety, and efficiency in
urban conditions. However, it is necessary to pay attention to such features as their limited
size, capacity, and speed, and, most importantly, to the working conditions and associated
risks for the person operating such a vehicle.
Figure 2. Geographical distribution of studies dedicated to CB issues.
In summary, most scientific papers attribute such main advantages of cargo bicycles
as their environmental friendliness, compactness, accessibility, safety, and efficiency in
urban conditions. However, it is necessary to pay attention to such features as their limited
size, capacity, and speed, and, most importantly, to the working conditions and associated
risks for the person operating such a vehicle.
3. Approaches to the Improvement of Business Strategies of Logistics
Service Providers
Despite the obvious benefits of using zero-emission alternatives in city logistics, regard-
less of the eventual readiness of local authorities to put into action sustainable solutions, the
decision-making process largely remains to the transportation service providers and their
customers. Moreover, in many cases, such organizations show hesitance and a negative
attitude to the introduction of green innovations [
41
,
46
,
56
]. Whereas the implementation
of new strategies entails additional costs for the private sector, it is essential to consider
such indicators as investment, operating costs, and other costs incurred by the company,
Energies 2021,14, 6462 8 of 19
although some researchers indicate such investment as beneficial, with return for the
business [
49
]. However, authors of publications [
6
,
7
,
17
,
37
,
41
,
57
] emphasize the crucial
role of the city authorities in influencing the implementation of effective green schemes.
Such influence could be realized through policies, regulations (e.g., limitations of delivery
hours, initiation of pedestrian zones, increase of penalties, etc.), and long-term planning.
Additionally, the significance of pilot projects in the encouragement of the modal shift to
environmentally friendly transport was pointed out in works [17,37].
For instance, city municipalities, transport operators, and eco-logistic and car-parking
companies became the participants in the pilot project, which was examined by the authors
of the work [
12
]. In the presented study, two electrically assisted tricycles along with
transshipment points were used in two cities for performing parcel transportations for
last-mile deliveries. Authors evaluate criteria concerning various spheres: economic,
transport operations and energy, environmental, and social area. The outcome underlines
the importance of contemplating the parameters of the study region and the crucial role of
the cooperation between all the stakeholders of the transport system. Another pilot test was
the subject of the study [
17
]. Through extensive research on big companies’ (participants
of the pilot project) operational practices, the major misconceptions about CB efficiency,
load capacity, and reliability were identified. In addition, the environmental and economic
effects of using CB were introduced through the evaluated savings of CO
2
emissions and
energy costs.
Even though most research results indicate a significant positive economic, environ-
mental, and social consequence from the implementation of cycle logistics, the authors
of the paper [
18
] did not achieve any noteworthy outcome in reducing costs and emis-
sions. The presented work describes the analyzes of the pilot test, in which one of the
delivery variants included ECT. However, the general authors’ remarks regarding the use
of alternatives were positive.
Another research that was based on a cargo cycle trial project presents a model that
could be practically used by decision makers [
44
]. The purpose of the experiment was a
comparison of the travel times between vans and CB that perform distribution of goods in a
public space in a mixed-fleet scheme. The estimated results for the option with substitution
of 50% of traditional vehicles by bikes pointed out that expected delays in deliveries by
CB would not exceed 10 min. Moreover, the replacement of 90% of motor vehicles would
not result in delays of more than 20 min. Such promising outcomes could contribute to
companies’ decision to move to sustainable transport.
One more work, which was based on a real-life project, provides information on
the competitiveness of clean transport mode in urban logistics [
55
]. A thorough socio-
demographic overview was carried out on one of the main representatives of the decision-
making group, individual messengers. The research provides a glimpse on indicators that
could prompt the adoption of new technologies, such as the raise of awareness through
arranging advertisement campaigns, field tests, and other forms of social informing.
Searching for an effective solution, numerous studies analyze the benefits and im-
plications of different cooperation strategies that could be adopted for sustainable urban
transportations, such as the estimation of a green delivery concept of using MD in combina-
tion with ET [
19
]. The authors applied the methodology multi-actor multi-criteria analysis
(MAMCA) for the decision-making evaluation of six scenarios. Besides, the environmental,
social, transport, and economic impacts were examined. As a result, the most significant
effect was obtained in decreasing of emission level. However, the delivery time slightly
dropped, and operational costs turned out to be twice higher than during the traditional
transportation process. However, the confirmation of the greater cost-effectiveness of
EACB over trucks was achieved in the cost-function comparison model for four transporta-
tion scenarios, developed by the authors [
50
]. Beneficial results showed the scenario in
which the deliveries were performed by electrically assisted CB (ECB) over short distances
from the distribution center. The research on the integration of green solutions into the
traditional transportation business model is presented in [
20
]. Authors indicate the lack
Energies 2021,14, 6462 9 of 19
of studies dedicated to the adaption of zero-emission schemes by the main participants
of the delivery system, international courier delivery service providers. The GUEST (Go,
Uniform, Evaluate, Solve, Test) business methodology was used for the analysis of the
supply system from the business and operational angles. Further, the decision-support
system (DSS) was developed for the evaluation of different supply scenarios. As a result,
savings in CO
2
emissions were achieved in the green operational scheme. However, the loss
of efficiency by traditional service providers emphasizes the need for a holistic approach
while implementing sustainable concepts.
In addition, the effectiveness of transportations is largely influenced by the productiv-
ity of selected means of transport. For instance, to choose the most suitable electric bike
on the market, the authors applied the multi-criteria decision-making model COMET [
58
].
Evaluating such criteria as battery and engine parameters, speed, driving range, gear
characteristics, price, etc., the comparison of ten EB models was conducted considering the
condition of incomplete knowledge. The proposed model could be freely used in practice.
Furthermore, the efficiency of cycle deliveries varies seasonally. The dependency of the
CB productivity on the cold season was analyzed by [
29
]. Results indicate a decrease in
the bike’s average speed by approximately 30%. Additionally, the authors highlight that
overall effectiveness could be increased by the improvement of working conditions of
bicyclists and winter-specific bicycle maintenance. No less important is the arrangement
of the consolidation centers, which could provide the opportunity to warm up and get
technical support.
The extensive research through the interviews and consultations with the business
owners was conducted by the authors of the paper [
46
] for defining the most influential
conditions for the suitability of LEV for innovative urban logistics. Among other things,
the necessity of using transshipment points as well as thorough planning of a mixed
delivery fleet was strongly emphasized in that study. Similar research on the ability of CT
to compete with diesel vans was presented in the paper [
47
]. The results of the evaluation
of logistics and cost minimization models clearly indicate the effectiveness and suitability
and applicability of CB services in the sustainable goods movement.
4. Transport System Modelling Approach
Using TSM as the first phase of the analysis of transport impact on the environment is
a common practice. In such an approach, at first, the transportation network is inspected:
the characteristics of the research area are explored (infrastructure, terrain properties); fleet
composition, traffic volume, and speed are examined. Afterward, the output parameters
from the TSM can be used as input parameters for the transport emission modeling.
In many cases, advanced TSM use subsystems that describe the rational behavior of the
transport system entities. Such subsystems are often based on the Vehicle Routing Problem
(VRP) algorithms. For instance, in the paper [
21
], the Two-Echelon Capacitated Electric
Vehicle Routing Problem with Time Windows and Partial Recharging (2E-EVRPTW-PR) was
proposed for the last-mile logistics for exploring the possibilities of clean transport modes
(ECB and e-vans). The authors emphasize the effectiveness of such a two-echelon scheme,
where ECBs perform the deliveries inside the restricted city area after the cargos were
delivered to the micro depot by the e-vans. Similarly, obtaining synchronization between
CB and traditional vans with a transshipment point near the city center in the two-echelon
supply scheme was a purpose of the research [
22
]. Through the analysis of different delivery
scenarios, the authors presented the algorithm, based on the GRASP metaheuristics (Greedy
Randomized Adaptive Search Procedure), which aims to assist in a decision-making
process while determining the most optimal solution for the distribution process. Another
decision-supporting system that combines the business management tool Odoo with the
route optimization technique and that is based on the Vehicle Routing Problem with Time
Window (VRPTW) was introduced in the study [
23
]. Analysis of the collaboration of zero-
emission vehicles transporting cargo less than 5 kg with conventional means of transport
shows reduction of CO
2
emissions up to 14 tons per year. The system that assists food
Energies 2021,14, 6462 10 of 19
deliveries by CB was designed by the authors of the research [
24
] for connecting producers,
distributors, consumers, and carriers. As the result, the beneficial results of delegating
fresh food transportations in urban zones to the CB with IoT were noted.
Determining the optimal composition of the transport fleet by analyzing various
alternatives of distribution schemes is exactly the problem that could be resolved using
simulation modeling. Thus, authors of the study [
52
] pursue the exploration of different
delivery scenarios that include small-sized electric vehicles (SEV) that replace vans in
varying proportions. The simulation procedure was implemented in the AIMSUNG
program. The optimal attainable rate that leads to costs, energy, and emission savings
would be the 10% scenario, due to which it is possible to achieve a 3.6% reduction of CO
2
.
However, the positive effects of using SEV are shaded by the unwanted private sector
risks and additional costs. Another simulation system for comparison of two operational
schemes (where deliveries are performed only by trucks or by CB with mobile hubs)
is presented in the paper [
59
]. Considering the varying characteristics of demand, the
evaluation of some key transport efficiency indicators was performed. As the outcome of
the CB delivery variant, the notable reduction (by nearly 150 deliveries per km
2
) in vehicle
distances and times traveled was achieved.
Different route and supply characteristics were examined in the research [
50
] to
ascertain the conditions for the most efficient performance of CB compared to conventional
trucks. The scheme, located near where the UCC clients were served by ECB, turned out
to be more cost-effective than the one with deliveries by trucks. Likewise, the supplies by
the ECB over short distances (about 2 km) were evaluated by the authors of the study [
36
].
The calculation of the optimal number of emission-free vehicles, which could substitute
traditional vans in the city districts without reducing the efficiency of the initial system,
was conducted using the micro-simulation software AIMSUN and the fuel consumption
Well-to-Wheel approach. The results reveal that replacing 10% of vans brings beneficial
results for all the stakeholders of the innovative distribution solution.
The research [
9
] suggests the model for reduction of GHG emissions along with
operational costs by combining delivery trucks with zero-emission vehicles, ECB. Applying
Heterogeneous Fleet Vehicle Routing Problem (HFVRP) in conjunction with the Tier-
1 method for emission evaluation, the authors seek to achieve the optimal proportion
between bikes and trucks in the mixed delivery system. The obtained most favorable fleet
scheme with 29 trucks and 9 ECB showed the costs lowering by nearly 14% and carbon
emissions by 10% compared with the scheme with a fleet of only trucks.
Integrating CB into urban operational scenarios was the objective of the study [
45
].
A developed GIS-based
simulation tool aims to support the planning process while de-
signing alternative distribution patterns that include CB and TP. In addition to the route-
simulation feature, implemented with help of AnyLogic software and Capacitated Vehicle
Routing Problem with Multiple Depots (MDCVRP), the presented system has function-
ality for calculating the economic and environmental impact of the examined network.
Similarly, the research on different variants of delivery scenarios using the simulation
approach presents the evaluation of operational and external costs of different distribution
concepts [
56
]. The outcome shows that a scheme that includes 10 to 25% of self-pickups
plus CB deliveries (performed from DP) is beneficial and can notably reduce external costs
of LSP (nearly by 30%).
Almost no modern, efficient urban logistics solution can function without UCC. Such
concept of providing facilities close to the core of the city is especially useful in case of
using small or light modes of transport for last-mile distribution. Therefore, to incorporate
consolidation centers into the operational scheme, the whole set of methods must be
implemented, such as localization and capacity planning, synchronization, scheduling, and
others. Thus, the authors of the publications [
5
,
26
] used the computer simulation approach
for determining the location of loading hubs near the city area with traffic restrictions. The
proposed freight delivery model, which is based on the simple Facility Location Problem
(FLP), was implemented in Python programming language, and considers the stochasticity
Energies 2021,14, 6462 11 of 19
of the demand for transport services. Likewise, the authors of the paper [
28
] were engaged
with the problem of establishing positions for micro depots and developed DSS aiming to
study different transportation scenarios to reduce total operating costs as well as negative
impact on the environment. The question of optimization of the delivery schemes in a
city center by effectively situating parcel depots and route planning was examined in
the paper [
27
]. As the main instrument for the implementation of Pickup and Delivery
Problem with Time Windows (PDPTW) and location-allocation tasks, the ArcGIS software
was chosen.
The problem of minimization of overall transporting costs while considering the
stochasticity of the process was a goal of the work [54]. The authors explored a 2-Echelon
Vehicle Routing Problem (2EVRP) with synchronization, where deliveries on the first
echelon were performed by vans, and the CB (as a second echelon vehicle) served customers
starting from the depot points. Moreover, an interesting concept of transshipment points
was proposed: using satellites for synchronization between vans and bikes for loading and
unloading operations.
The transport network mathematical model that considers the stochasticity of the
demand for transport services was suggested by the research [
25
]. The open-source library
for performing simulations of the delivery system is designed with help of the Python
programming language and could be used for analysis and optimization of alternative
distribution variants and particularly for implementing CB delivery scenarios. A closely
related problem of modeling a new delivery scheme that integrates CB with micro depots
(MD) into an urban transport network was presented by the work [51]. The simulation of
two supply variants (by traditional vehicles and by CB) was performed in the open-source
framework MATSim. Results indicate the significant reduction in total transport costs as
well as emissions due to the use of CB.
The complex problem was examined by the authors of the paper [
30
]: first, they
search for the UCC localization, and further, the optimal routes and fleet composition are
determined with help of the Multi Depot Vehicle Routing Problem with Heterogeneous
Fleet (MDHFVRP) and Genetic Algorithm (GA). The research proposes using a UCC
sharing system for minimizing operating costs and achieving a positive environmental
impact through the adaptation of sustainable vehicles, CB, and electric vans.
Exploring a case study in which postal delivery vehicles were substituted by CB,
authors attempt to determine the economic effect of such modal shift [
48
]. Analyzing three
different areas of the city and several variants of zero-emission vehicles and considering
consolidation centers, the model of transport network was implemented using a Capaci-
tated Vehicle Routing Problem with Time Window (CVRPTW) and simulation programs
MATSim and jsprit. The proposed model points up the competitiveness of cargo cycles in
terms of cost-saving perspective for the LSP and could be used as a city planning tool.
Table 2highlights algorithms and software that are often used for modeling delivery
systems with CB as a means of transport.
Table 2. Approaches to modeling systems of delivery by means of CB.
Methodology Used Tools Features Source
Algorithms
HFVRP Java, Simulated Annealing determining mixed-fleet size [9]
2E-EVRPTW-PR CPLEX 12.10 DSS: synchronization task [21]
VRPTW Monte Carlo simulations
DSS: module for geo-referencing the data
[20]
Java
Odoo DSS: trips creation module
DSS: business management framework [23]
2EVRP GRASP, C/C++ synchronization between CB and vans [22,54]
PDPTW ArcGIS finding optimal routes from MD to client [27]
MDHFVRP GA UCC localization, route, and
fleet planning [30]
MDCVRP AnyLogic multimodal delivery model using CB [45]
CVRPTW jsprit delivery network modeling [48]
Energies 2021,14, 6462 12 of 19
Table 2. Cont.
Methodology Used Tools Features Source
FLP Python defining the loading hub location [26]
Clarke–Wright
Savings algorithm
Python determining delivery routes [25]
MATLAB [45]
- [56]
Software
Android application C#, MySQL system for food delivery [24]
Maps
Google Maps API travel time estimation [44]
georeferencing the routes [20]
Google Maps navigation API DSS: clients management module [23]
Google Maps Distance
Matrix API trip distances [7]
OpenStreetMaps
infrastructure data, client’s localizations [51]
visualization, distance-matrices [45]
simulation graph [33]
road network data [27]
trip duration [56]
TSM software
MATSim exploring different delivery scenarios [48]
simulation of transport system [51]
MAINSIM simulation of different traffic
configurations, CO2emissions [33]
AIMSUN simulation of different fleet variants [36,52]
GIS-based ArcGIS + Visual Basic traffic performance measures calculation [11]
CyberGIS coupling of MAINSIM and
SCIPUFF models [33]
The wide range of tools and software presented in Table 2is evidence of the big
number of problems to be solved when optimizing the delivery systems that use CB as
a means of transport. On the other hand, here appear the key questions that should be
answered to choose the proper model for estimations of CB impact:
•
Which optimization problems should be solved while simulating the process of deliv-
ering goods by bikes so that the behavior of a transport operator would be adequately
considered in the simulation model?
•
How much would the consideration of the transport operator’s behavior in the model
affect the results of estimations of the CB impact on the environment?
The answers to the listed questions should be obtained at the stage of developing the
structure of a simulation model to be used for assessing the impact of CB.
5. Methods for Evaluation of Sustainable Transport Impact on the Environment
The analysis of the scientific literature clearly indicates the lack of studies on the
environmental impact of sustainable delivery systems. There are, however, many works
that implement various transport emission or dispersion models for evaluation of pol-
lution caused by activities of traditional means of transport [
33
,
35
,
60
,
61
]. Thereafter, by
modeling alternative scenarios and comparing the results, the question of how different
transportation concepts would affect the environment could be answered [8,11].
Another equally important indicator of sustainable city development that is affected
by transport is noise. Road-traffic noise models aim for the evaluation, management,
prediction, and, eventually, the reduction of the sound power and involve such parameters
as area information and traffic characteristics (acceleration, speed, and volume). Among
the different approaches to noise modeling can be found the regression analyzes [
32
] and
such noise emission models as FHWA, NMPB, ASJ-RNT, Imagine [62], and others.
Energies 2021,14, 6462 13 of 19
Vehicle-related pollutants are divided depending on their source: exhaust emissions
are produced and discharged by the internal combustion engine and fuel evaporation,
and non-exhaust emissions are related to vehicle clutch and breaks, tire, and road wear
abrasion. The composition of the exhaust emissions mainly includes carbon dioxide (CO
2
)
and monoxide (CO), nitrogen oxides (NOx, NO, NO
2
), volatile organic compounds (VOC),
particulate matter (PM), nitrous oxide (N
2
O), ammonia (NH
3
), persistent organic pollutants
(POP), and metals. Non-exhaust emissions mostly consist of PM.
Moreover, another pollutant associated with vehicles is mineral dust, which spreads
from the roads and streets surfaces by traffic or wind. Mineral dust, which consists mainly
of silica (SiO
2
) and corundum (Al
2
O
3
), sucked in with the intake air by the motorized
vehicle engine, causes accelerated wear of engine parts. However, the engine could be
protected by using high-efficient air filters [
63
,
64
] that contain elements made of pleated
filter material (cellulose, cellulose with polyester, or with a nanofiber layer). Such filters are
characterized by high filtration efficiency, which is over 99.5% [65,66].
The evaluation of the road transport impact on the environment can be performed
using many different tools and approaches. Transport emission models vastly vary from
the most popular in the EU countries, COPERT methodology, applied for composing of
yearly emission inventories, to EMISENS, IVE, MOVES, HBEFA, VERSIT+, PHEM, MO-
BILE, and others. Such models allow estimation of the level of discharged emissions near
the source. Instead, transport and dispersion models (T&D), also called air quality or
atmospheric dispersion models, determine the spread of pollutants in the atmosphere and
their concentrations at different locations. For the most part, these models are based on La-
grangian, Eulerian, or Gaussian plum models, such as OSPM, SCIPUFF, CALINE, STREET
5, FLEXPART, EPISODE, and others. The extensive literature review on a sequential usage
of models for determining the air and water pollution caused by road transport can be
found in the publication [67].
The main concept for estimation of transport emission is the multiplication of emis-
sion factors with respective activity data for different types of vehicles. Emission factors
are laboratory-obtained measurements (via comprehensive vehicle tests) that depend on
the driving behavior (acceleration, speeding, and braking), vehicle categories, Euro class,
road, and traffic characteristics. In some cases, the approximate approach for the evalu-
ation of CO
2
levels could be applied [
27
]. According to such a method (passed by U.S.
Environmental Protection Agency), a liter of burned diesel fuel is equal to 2.66 kg of CO2.
Emission models differ depending on the input parameters:
•vehicle fuel consumption,
•traffic volume [34] and composition [61],
•meteorological data [34],
•vehicle driving characteristics (speed, acceleration) [35],
•
fleet composition (types of vehicles: size, fuel, evaporation, and exhaust control
systems) [35],
•infrastructure (road map, speed limits) [61], and
•emission factors [61].
The spread of pollution in the atmosphere is mostly affected by the intensity of the
source and meteorological conditions, such as wind strength, temperature, and humidity.
Data for dispersion modelling may contain:
•source parameters [33,61],
•composition of transport network [33],
•terrain characteristics [33], and
•meteorological conditions [33,61].
There is the recent tendency to develop fully integrated models by joining TSM for sim-
ulating vehicle activities with EM or T&D for determining or predicting the environmental
effect caused by traffic [34,35,67].
Energies 2021,14, 6462 14 of 19
For instance, the integrated simulation model that combines mesoscopic TSM, cre-
ated with the use of FlexSim software and the regression model for evaluation of NO
2
concentration was proposed by the authors of research [
34
]. Such a system predicts the
amount of pollution in the atmosphere depending on seasonality and the fleet composition.
This approach could be used for the examination of different sustainable traffic variants to
improve the ecological condition of the city district. Another integrated traffic-emission
computation system was presented in the research [
35
]. After connecting TSM, imple-
mented in Paramix system, with relational MS Access database, authors then added the
emission calculation module based on the IVE model. The presented approach was further
tested and is recommended for the estimation of traffic-related pollution. Furthermore,
the combination of several methodologies, such as road transport emission EMISENS
and evaluation of pollutants dispersion, has been proposed in the paper [
61
]. Assessing
different pollution-reduction scenarios for black carbon (BC) and NOx, the researchers
indicate that eliminating 10% of the biggest emitters will lead to a significant reduction of
the pollutants (nearly 35% of NOx and 16% of BC daily). The analyses of the impact of
different compositions of the conventional transport fleet on the pollution dispersion in the
atmosphere by combining several models were carried out by the researchers in [
33
]. Micro-
scopic TSM, developed in the MAINSIM program, enables to determine the level of emitted
by traffic emissions due to its built-in module. Further, a gas-dispersion module was im-
plemented using the SCIPUFF model. The combination of both modules was performed
using the CyberGIS framework. Authors present how to analyze and make predictions on
the atmospheric pollution behavior depending on the traffic and meteorologic factors.
Investigation of the possibility of changing the transport system and the following
possible environmental consequences is of interest to many authors. For example, the
comparison of several operational scenarios to determine the difference in the resulting
level of pollution was performed in the work [
68
]. The variant with transportation by ECT
showed five times fewer emissions of CO2compared to the option with diesel vans.
The suggestion to reduce the movement of heavy and light foods vehicles through the
adoption of new delivery strategies that implement UCC outside the city together with
the substitution of main emitters with electric vehicles is presented in the research [
31
].
The transport network model for the study was developed in AIMSUN microsimulation
software and further transport emissions were evaluated. The quantification results point
to the main pollutants, heavy vans, responsible for 13.8% CO
2
, 43.7% of NOx, and 9.2%
of PM from the total amount of emissions. For the study [
7
], the delivery data of a
large platform provider were used to determine operational characteristics and amount of
GHG emissions from on-demand deliveries performed by car, moped, and bike. Such an
approach could be taken into consideration for comparison of different fleet compositions
and justification for the introduction of clean modes of transport into the meal-delivery
industry. The substitution of conventional vans by the ECT was examined by the authors
of the paper [
8
] using real-world data. Results indicate the crucial savings in CO
2
e: more
than 95% and nearly a third in operating costs savings, which confirms the competitiveness
of clean freight-distribution strategies. Research [
11
] evaluates traffic parameters and
their environmental impact for the comparative analysis of different good movement
modes: CB and motor vehicles. Using ArcGIS and Visual Basic, the traffic performance
characteristics were calculated. Furthermore, the GHG and vehicle emissions evaluation
was implemented with help of the MOVES model. Consequently, the high competitiveness
of CB in transporting light freights over short distances was denoted by the outcome.
Instead of focusing only on determining the levels of CO
2
emitted by transport, the
authors of the manuscript [
60
] emphasized the necessity of evaluating the concentrations
of local pollutants (CO, NO
2
, hydrocarbons, and PM). Using the approach that is based
on the Gaussian plum model and the data achieved from the radar detectors, researchers
determined amounts of NOx and CO distributed near the road segment. The highest level
of pollutants was observed at a distance of 20 m from the road.
Energies 2021,14, 6462 15 of 19
The methodologies for determining the level of noise caused by transport have a
similar approach: they mainly depend on studied area characteristics as well on traffic
data. The significant impact on a noise level has the congestion, vehicle acceleration,
average speed over time, and the number of stopped vehicles [
62
]. Besides standard
input parameters (traffic speed, volume, and width of the road surface), authors of the
research [
69
] included honking into their noise model, which was based on the graph-
theoretic approach. The study [
32
] focuses on the prediction of the noise level in the
city area: using the regression modeling technique, the authors analyzed the influence of
such urban parameters as street geometry and location and traffic characteristics on the
noise level.
Table 3summarizes the approaches to the estimation of the sustainable transport
impact used by the authors of recently published papers.
Table 3. Approaches to the estimation of the environmental impact of transport and corresponding tools.
Source Main Concept Description Tools
[7]Estimation of emissions based on
analytical models comparing emissions of CB, mopeds, and
conventional cars in meal deliveries analytical model
[11] Traffic performance characteristics + emissions comparing CB and motor vehicle traffic
effectiveness
ArcGIS +
Visual Basic +
MOVES
[33]
Integrated model: TSM + emissions + dispersion
assessing impact of different traffic patterns
on air pollution MAINSIM + SCIPUF +
CyberGIS
[34]
Integrated model: TSM + simplified air pollution
predict NO2concentrations FlexSim +
regression model
[35]Integrated model: TSM +
vehicle emission computation computation of hourly vehicle emissions Paramix +
MS Access + IVE
[61]Integrated model: traffic
emission + dispersion tests of several schemes of the emission
reduction EMISENS
As can be noted from the descriptions in Table 3, to estimate the emissions reduction
as an effect of using CB, the commonly applied are combinations of different tools. That is
explained by the lack of conventions on what to measure and how to estimate the results.
The ambiguity of measures of the sustainable transport impact may be also observed in
Table 4, which summarizes the results of the studies dedicated to the estimation of road
transport’s negative influence.
For various technological solutions listed in Table 4, the results are measured in terms
of costs and emission savings. However, the lack of uniformity in the type of used indicators
makes the comparison of achieved results practically impossible. As the indicator for costs
reductions, operational costs are the most frequently used parameter; however, energy
costs, external costs, or delivery costs are used by researchers. This ambiguity also refers
to the emission savings indicator: although the carbon dioxide equivalent is the standard
parameter to estimate the reduction of emissions, other indicators are also used additionally
to characterize the impact.
Table 4.
Parameters achieved by estimation of the various delivery concepts aiming the reduction of the environmental and
operational impact.
Source Technological Solution Effects
Costs Reduction, Nearly Emission Savings, Up to
[6] Using CB instead of trucks €0.76 M per year CO2: 1.7 tons/day
[8] Replacing diesel vans by ECT operating costs: 31% CO2e: 97% per year
[9] Mixed fleet: CB + vans average 14% CO2: 10%
[11] Comparing CB replacing vans -
CO
2
: 11 metric tons; PM2.5: 0.5 kg
Energies 2021,14, 6462 16 of 19
Table 4. Cont.
Source Technological Solution Effects
Costs Reduction, Nearly Emission Savings, Up to
[17]Comparing alternatives: van, e-van,
and LEV energy cost:
€0.9–11.0 per day CO2: 1.7–21 kg/day
[19] Impact of scheme ECB + MD punctuality drop on 7% CO2: 24%; PM2.5: 99%
[20]Comparing 5 alternative
operational scenarios operational and environmental costs: 30% 14 tons per year
[23] CB transporting cargos < 5 kg - CO2: 14 tons per year
[27] Logistics system: CB + ECB + MD - local CO2: 20 tons per year
[36] Replacing 10% of traditional fleet external costs: 25% CO2: 73%
[51] Deliveries for commercial clients by CB delivery costs: 28% 22%
[52] Replacing 10% of vans by LEV operational costs: 90% 3–4% of energy and CO2
[56] Delivery concept: self-pick-up + CB + DP operational costs: 30% -
[61] Eliminating 10% of emitters - BC: 39%; NOx: 16%
[68]Comparing deliveries: by ECT vs by
diesel vehicles - CO2e: 51–72%
6. Conclusions
Cargo bikes are an environmentally friendly means of transport. However, their use
in commercial delivery schemes, besides obvious advantages, has some restrictions related
to relatively small capacity and low delivery speed. The conducted literature review shows
that the most preferable use of CB is last-mile deliveries in the cities, especially in districts
with traffic restrictions and a high density of the population.
Logistics service providers extensively use CB in real-world solutions. However, the
implementation of this technology needs complex actions of the authorities; besides, they
are often initiated by active citizens and non-governmental organizations. To implement
the delivery scheme that uses CB, the methodology for transportation planning should be
chosen and the appropriate zero-emission fleet should be selected, but also the effect of
these changes should be substantiated in terms of operational costs (from the point of view
of LSP) and emissions reduction (for city authorities and residents).
To estimate the effect of using CB for last-mile deliveries, simulations of the trans-
portation process are usually performed. The completed review of algorithms and software
used for the optimization and simulations of cargo deliveries by bikes shows that there
is a variety of tools available that allow solving the specific problem (routing, optimizing
hub location, scheduling, etc.). However, there is no dedicated instrument that estimates
the resulting technological parameters of the process of goods deliveries by CB (e.g., the
covered distance and operation time). Such a tool would be indispensable for transport
planners and city authorities when substantiating the emissions reduction due to the use
of CB and assessing the corresponding operational costs.
Existing approaches to the evaluation of transport impact on the environment allow
assessing in detail numerous indicators. A tool that is dedicated to the assessment of
emissions reduction for technological schemes with CB as a means of transport must
provide the possibility to check the results for CB comparing to other alternative servicing
technologies. For these, the aggregated indicator (e.g., CO
2
e) reflecting the environmental
impact of transport should be used to avoid ambiguous conclusions.
The above-mentioned features (i.e., the consideration of interests of all parties, the
assessments made based on the set of technological indicators, and the use of the aggregated
emissions indicator) should be addressed by the contemporary methodology for evaluating
the environmental impact of cargo bicycles.
As the direction of future research, the development of the simulation model for
the substantiation of positive environmental effects of CB must be mentioned. We plan
to test this model within the CityChangerCargoBike project while estimating the im-
pact of the CB use in the districts with restricted traffic of cities that are the project
partners—Krakow (Poland), San Sebastian and Vitoria-Gasteiz (Spain), Lisboa (Portu-
gal), and Dubrovnik (Croatia).
Energies 2021,14, 6462 17 of 19
Author Contributions:
Conceptualization, H.V., A.S. and S.R.; methodology, H.V., A.S. and S.R.;
investigation, H.V.; writing—original draft preparation, H.V.; writing—review and editing, A.S. and
S.R.; visualization, H.V.; supervision, A.S. and S.R. All authors have read and agreed to the published
version of the manuscript.
Funding:
The research was carried out as part of the project “ROAD TO EXCELLENCE—a compre-
hensive university support programme,” implemented under the Operational Programme Knowl-
edge Education Development 2014–2020, co-financed by the European Social Fund; agreement no.
POWR.03.05.00-00-Z214/18.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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