Access to this full-text is provided by MDPI.
Content available from Sustainability
This content is subject to copyright.
Academic Editor: Keshuang Tang
Received: 14 October 2024
Revised: 22 November 2024
Accepted: 3 December 2024
Published: 22 January 2025
Citation: Veliˇckovi´c, M.; Stojanovi´c,
Ð.; Ilin, V.; Mirˇceti´c, D. A Combined
Multi-Criteria Decision-Making and
Social Cost–Benefit Analysis
Approach for Evaluating Sustainable
City Logistics Initiatives. Sustainability
2025,17, 884. https://doi.org/
10.3390/su17030884
Copyright: © 2025 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/).
Article
A Combined Multi-Criteria Decision-Making and Social
Cost–Benefit Analysis Approach for Evaluating Sustainable City
Logistics Initiatives
Marko Veliˇckovi´c 1,* , Ður ¯
dica Stojanovi´c 1, Vladimir Ilin 1and Dejan Mirˇceti´c 1,2
1Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovi´ca 6, 21000 Novi Sad, Serbia;
djurdja@uns.ac.rs (Ð.S.); v.ilin@uns.ac.rs (V.I.)
2Institute for Artificial Intelligence Research and Development of Serbia, Fruškogorska 1,
21000 Novi Sad, Serbia
*Correspondence: marvel@uns.ac.rs
Abstract: Decision making in city logistics (CL) is complex due to the numerous concepts
and alternatives, as well as the intricate relationships between measures and effects. This
study introduces a novel approach to evaluating urban freight transport (UFT) by combin-
ing multi-criteria decision making (MCDM) and social cost–benefit analysis (SCBA). This
combination aims to improve decision making for sustainable CL concepts, particularly in
reducing externalities in last-mile delivery. The model assesses various CL initiatives and
urban consolidation center (UCC) concepts for their impact on UFT externalities. It uses
the MCDM for ex ante scenarios assessment and prioritization. Input data were collected
through a survey of experts from various sectors, and the Analytic Hierarchy Process
(AHP) was applied in the case study of Novi Sad, Serbia. The prioritization highlighted the
significance of implementing restrictive regulatory measures, alternative transport modes,
and operational optimization within UCC concepts. By estimating capital, operational,
and external costs, SCBA was applied to the prioritized UCC concepts, which were then
further evaluated using the SCBA outputs. Sensitivity analysis was employed to assess the
robustness of the proposed model. This paper offers valuable insights into the potential
use of existing tools within a hybrid model to enhance decision making in CL.
Keywords: sustainable city logistics; urban freight transport; urban consolidation centers;
initiatives; external costs; multi-criteria decision making; social cost–benefit analysis
1. Introduction
With the growing urban population and increased demand for goods, the negative
impacts of freight transportation have become increasingly prominent. These challenges
are particularly critical in urban areas, where they directly affect quality of life. As a re-
sult, a rising number of both academic and empirical investigations have focused on the
environmental and other adverse effects of urban freight transport (UFT). Consequently,
sustainable goals in UFT development have been introduced at global (UNCTAD Sustain-
able Freight Transport Framework [
1
]) and European levels (Green Deal [
2
]). Sustainability
aims to balance between economic growth, environmental goals and society wealth [
3
].
However, conflicts often arise between these objectives and among stakeholders involved
in sustainable city logistics (CL) development [4].
In recent years, numerous sustainability initiatives in freight transportation have been
implemented in Europe [
5
], with urban consolidation centers (UCCs) gaining particular
Sustainability 2025,17, 884 https://doi.org/10.3390/su17030884
Sustainability 2025,17, 884 2 of 18
attention [
6
]. UCCs enable goods to be delivered in an environmentally friendly manner
to specific urban or suburban areas, or particular places (shopping centers, construction
sites, etc.). Despite their potential to enhance sustainability, UCCs often face substantial
challenges, leading to operational failures [
7
]. Key issues include financial inefficiencies,
insufficient freight volume, limited stakeholder involvement in planning and decision
making, inadequate fleet selection, and suboptimal UCC locations [
8
–
10
]. Moreover, imple-
menting UCCs independently does not necessarily result in a sustainable or competitive
CL system [
11
]. However, UCCs facilitate the cohesive implementation of other CL initia-
tives, commonly referred to as UCC concepts, maximizing synergistic effects and mutual
benefits [6,12].
The process of selecting appropriate UCC concepts is further complicated by the
diverse range of additional CL initiatives and scenarios, each with unique implications.
Research shows that a one-size-fits-all solution for mitigating UFT’s negative externali-
ties and creating a sustainable CL system is unlikely [
13
]. This complexity has led to a
variety of approaches and solutions in the literature, emphasizing the need for tailored
strategies that consider local contexts and specific stakeholder needs [
14
–
16
]. Multi-criteria
decision-making (MCDM) methods, for instance, offer robust frameworks for addressing
complex relationships among decision criteria. Social cost–benefit analysis (SCBA), on
the other hand, evaluates the overall societal impacts of CL initiatives from a financial
perspective [
17
], considering direct, indirect, and external costs (ECs). ECs, as an essen-
tial component of SCBA, provide decision makers with comprehensive insights into the
sustainability implications of UCC concepts.
While MCDM approaches are widely used for prioritizing CL initiatives, they rely on
subjective assessments, leaving the quantitative evaluation of costs inadequately addressed.
Conversely, SCBA studies focus on monetizing effects but often fail to account for multiple
CL initiatives and scenarios in a systematic manner. This fragmentation highlights the need
for a hybrid approach that integrates MCDM and SCBA to bridge the gap between quali-
tative prioritization and quantitative economic evaluation. Such an approach enables the
derivation of multiple benefit–cost ratios, supporting sustainable decisions and addressing
the complexities of CL initiatives.
Surprisingly, the hybrid use of these techniques in evaluating CL initiatives remains
underutilized. Some previous studies have demonstrated the value of combining these
methods in transport project appraisals to address economic, strategic, and sustainable
impacts systematically and transparently. For instance, Barfod et al. [
18
] presented a com-
posite model that integrates MCDM and SCBA, showcasing its effectiveness in supporting
complex transport project decisions by ensuring systematic and transparent assessments.
Similarly, Guhnemann et al. [
19
] highlighted how incorporating SCBA results into an
MCDM framework facilitates consistent project prioritization, links appraisals to policy
goals, and enhances stakeholder confidence through robust sensitivity analyses. Further-
more, Barfod and Salling [
20
] developed a decision support framework combining MCDM
and SCBA for transport infrastructure projects, demonstrating its ability to simultaneously
address economic, strategic, and sustainability considerations. These studies underscore
the potential of hybrid models to enhance decision-making processes.
Building on this foundation, this paper aims to propose a combined MCDM–SCBA
model, providing comprehensive support for decision makers in selecting UCC concepts
for sustainable CL planning. The main contribution of this research lies in enhancing
the application of SCBA to enable a thorough evaluation of the financial viability and
sustainability of diverse UCC concepts and scenarios. Furthermore, the proposed model
contributes to the existing literature by addressing the intersection of environmental and
Sustainability 2025,17, 884 3 of 18
economic domains, highlighting the necessity for stronger integration between these areas
in both sustainable UFT research and practical applications.
The structure of this paper is as follows. Section 2presents the research background
and literature review, focusing on the ECs of UFT, MCDM models for CL decision making,
and SCBA analysis of UCC concepts. Section 3outlines the proposed model and research
methodology. Section 4describes the application of the model in a case study, along with
the presentation and discussion of the main results. In Section 5, the robustness of the
model is tested through sensitivity analysis. Finally, Section 6provides concluding remarks,
discusses limitations, and offers recommendations for future research.
2. Research Background
The most significant negative effects of transportation include traffic congestion, air
pollution, climate change, noise, and traffic accidents [
21
]. In addition to these primary
effects, there are other negative impacts, such as water and soil pollution, damage to
landscapes and nature, the effects of fuel production and consumption, and visual dis-
turbances [
22
,
23
]. Many costs associated with the negative effects of transportation, like
healthcare expenses resulting from air pollution-related illnesses, are not solely borne by
parties involved in transportation. Third parties also incur costs due to the consequences
of these externalities, which are referred to as ECs. These costs are defined as those that
arise when the social or economic activities of one group impact another group without full
compensation or accountability from the originating group [
24
,
25
]. This definition indicates
that ECs of transport are the costs borne by “others”, which can be neighbors, fellow
citizens, the rest of the state, continent, the entire world, or even future generations [26].
The ECs generated by urban freight transportation are typically influenced by variety
of factors, including the characteristics of the urban distribution network (e.g., location,
distance, and number of nodes), network activities (goods and passenger movements),
vehicle type and technology, urban traffic regulations, service levels, and basic costs (e.g.,
land, fuel, and electricity prices) [
26
]. Additionally, ECs of freight transport depend on
vehicle technology (such as the emission class of the propulsion system), the frequency and
severity of accidents, congestion caused by freight vehicles, and other related factors.
UCCs are recognized as a key initiative in CL, with a critical role in mitigating the ECs
of UFT [
27
–
29
]. The purpose of UCCs is to collect shipments from various senders (i.e.,
carriers or logistics operators), sort, consolidate, and deliver them using environmentally
friendly vehicles in a systematic and organized manner. Allen et al. [
30
] define UCCs as: “A
logistics facility situated in relatively close proximity to the geographic area that it serves
(be that a city centre, an entire town or a specific site such as a shopping centre), to which
many logistics companies deliver goods destined for the area, from which consolidated
deliveries are carried out within that area, in which a range of other value-added logistics
and retail services can be provided”. This definition underscores the potential of UCCs in
reshaping urban freight operations and reduce ECs.
The broader objective of UCCs is not only to consolidate freight flows and reduce the
number of vehicles entering the city but also to control the type and structure of the local
vehicle fleet and organize local deliveries. This approach enables more effective manage-
ment of UFT externalities, leading to improved environmental and societal outcomes [
31
].
Anticipated benefits include reduced traffic congestion, improved air quality, minimized
conflicts at loading sites, enhanced safety, heightened service standards for urban recipi-
ents, expanded service offerings, increased sales areas for retailers, reduced operational
costs, optimized inventory management, improved staff motivation, and mitigation of theft
incidents [28,32–34].
Sustainability 2025,17, 884 4 of 18
Despite their potential benefits, the impacts of UCCs on ECs in UFT remain underex-
plored in the literature [
35
]. This gap is notable given their relevance in reducing negative
externalities. Dupas et al. [
36
] emphasized that one of the key limitations in academic
studies on UCC implementation is the inadequate assessment of environmental and social
economic costs, i.e., ECs. Although some scientific papers address the estimation of ECs
within the context of CL, few encompass multiple categories comprehensively. Most studies
focus on emission-related ECs, while other categories, such as noise, traffic congestion, and
accidents, are often overlooked.
For example, Alessandrini et al. [
37
] conducted an assessment of the ECs of emissions
(CO
2
, HC, NOx, PM, and SO
2
) and fuel consumption (energy) for three intermodal UCC
scenarios. Their findings demonstrated significant savings in ECs, particularly when hybrid
vehicles were utilized (82%), highlighting the potential of advanced vehicle technology in
reducing ECs. Similarly, Estrada and Roca-Riu [
38
] assessed the ECs for different distribu-
tion strategies (individual distribution and distribution via UCC) and scenarios (electric
vehicles, electric cargo bikes, and electric cargo bikes + road freight vehicle restriction zone),
concluding that UCCs could achieve daily EC reductions, especially when paired with
electric cargo bikes and vehicle restrictions. Katsela et al. [
39
] provided further evidence
of the EC reduction potential of UCCs in scenarios involving micro terminal consolida-
tion, emphasizing a 75% EC reduction for the UCC + micro terminal case. These findings
reinforce the importance of systematically including ECs in decision-making processes.
Incorporating ECs as a parameter in decision making allows for the comprehensive
expression of all external impacts in monetary terms. The systematic calculation and inte-
gration of ECs into decision-making frameworks are pivotal, as they offer an opportunity
to holistically evaluate and transparently communicate social and environmental impacts.
This approach ultimately fosters greater stakeholder acceptance of investments [
40
]. In-
tegrating freight flow simulation with urban freight planning is essential for accurately
evaluating these impacts. Studies have shown that simulation models can effectively assess
the effects of urban consolidation centers and off-hour deliveries on freight dynamics,
enabling planners to develop more effective policies to enhance sustainability and reduce
congestion [
41
–
43
]. To accurately capture vehicle-kilometer data, it is essential to integrate
the EC model with a UFT model. Several studies, such as Filippi et al. [
44
], emphasize this
integration as crucial for evaluating the consequences of policy changes. By incorporating
UFT models and simulation tools, it becomes possible to predict how different logistics
strategies impact transportation activity, thereby enabling a more precise estimation of ECs
and supporting informed decision making in urban logistics.
Given the multitude of potential UCC concepts, it is imperative to select initiatives
with UCCs within the urban setting under observation. CL literature frequently em-
ploys MCDM methods to evaluate UCC concepts due to their capacity to systematically
assess and prioritize diverse criteria. Many MCDM models have been developed to ad-
dress the numerous alternatives and intricate relationships involved in the sustainable CL
decision-making process. These include AHP, ANP, MAMCA, DEMATEL, VIKOR, TOPSIS,
PROMETHEE, DELPHI, REMBRANDT, WASPAS, CODAS, SWARA, FDMM, FMAGDM,
and others. Fuzzy logic is often integrated into these models to address ambiguity in
stakeholder preferences. For example, Gallo and Maheut [
45
] identified AHP hybrid and
MAMCA as the most commonly utilized methods in MCDM applications. In addition,
Jardas et al. [
46
] recently used MAMCA to evaluate implementation of UCCs and envi-
ronmentally friendly vehicles in the city of Rijeka, Croatia. However, a key limitation lies
in the lack of correlation between the objectives of these models, resulting in substantial
differences in their intended purposes.
Sustainability 2025,17, 884 5 of 18
MCDM models primarily rely on qualitative criteria and do not directly address
ECs. Tadi´c et al. [47] proposed an MCDM model that addresses factors related to external
effects such as natural resources, habitats, and air pollution; however, it does not explicitly
account for all external effects or the ECs associated with transportation. Additionally,
the criteria outlined in Simi´c et al. [
48
], such as air emissions, noise pollution, resource
consumption, public health, and public space usage represent the externalities discussed in
their paper. These studies do not incorporate a direct estimation of the ECs associated with
these externalities. The authors Lasota et al. [
49
] developed a model for urban delivery
planning that integrates the Traveling Salesman Problem (TSP) with MCDM, focusing
exclusively on emissions of harmful compounds among all externalities. The most relevant
paper on MCDM for this research is presented by Perera and Thompson [
50
]. In their study,
the authors considered ECs as environmental costs (emission and noise costs) and social
costs (crash, congestion, and infrastructure costs) among other criteria in the MAMCA
evaluation method. However, this study lacks a clear and systematic linkage between ECs
and sustainable CL initiatives.
SCBA is used to assess the attractiveness of a given project by obtaining net present
value, considering both financial and environmental aspects [
51
]. Decision makers often
employ social cost–benefit analysis but frequently in a non-decisive manner [
52
]. Recent
research has highlighted the importance of integrating stakeholder perspectives and sus-
tainability considerations into SCBA frameworks to enhance decision-making processes
in urban freight planning [
14
,
53
]. Studies have also suggested that effective SCBA should
incorporate comprehensive assessments of environmental externalities and operational
efficiencies associated with UCCs to better inform policy and investment decisions [
15
,
54
].
This holistic approach can aid in addressing the complexities inherent in urban freight
systems and contribute to more sustainable outcomes [55,56].
When applied to the consolidation concept, SCBA should account for both private
costs and ECs to provide a balanced evaluation [
57
]. The literature on the SCBA of UCCs is
relatively scarce [
7
,
58
]. Kin et al. [
7
] evaluated ECs related to air pollution, climate change,
noise, accidents, and congestion. The findings indicated that UCCs yield positive societal
effects when only external effects are considered. The overall cost–benefit trade-off becomes
negative when private costs are included. This study primarily assessed the operation of
UCCs but did not explore decision making related to other CL initiatives and alternatives.
The challenge of creating a business model that effectively aligns environmental, economic,
and operational needs for UCC implementation while addressing the diverse interests of
stakeholders remains a significant [59].
Despite numerous studies and several EU projects, such as BESTUFS, C-LIEGE,
GRASS, NOVELOG, and STRAIGHTSOL, that have addressed decision making on CL
initiatives and their externalities [
60
,
61
], few have integrated MCDM and SCBA to evalu-
ate various CL concepts comprehensively. The STRAIGHTSOL project [
62
], for instance,
combined three methodologies: SCBA, business model analysis, and MAMCA. While
this approach merges MCDM and SCBA, it does not focus primarily on externalities and
ECs, only partially addressing these issues without a systematic approach. In contrast, the
NOVELOG project utilized a more holistic evaluation framework that incorporated multi-
ple methodologies, including impact assessment, SCBA, transferability and adaptability
analysis, and risk analysis. This comprehensive approach integrated behavioral modeling,
enhancing the evaluation of diverse urban logistics concepts. The SCBA in the NOVELOG
project includes ECs related to congestion, air pollution, climate change, accidents, and
noise, as well as impacts on employment and development. However, a notable limitation
is that capital and operational costs are not reflected in the SCBA output, highlighting areas
for further refinement. Furthermore, recent literature emphasizes the need for integrating
Sustainability 2025,17, 884 6 of 18
stakeholder engagement within these frameworks to ensure that diverse perspectives are
considered in the evaluation of urban logistics initiatives [14,63].
3. Methodology and the Model
The structure of the proposed MCDM–SCBA model is illustrated in Figure 1. The first
step involves prioritizing additional CL initiatives within UCC concepts. This is achieved by
assessing their effectiveness in reducing UFT externalities in the specific urban area under
consideration. This component of the model is not intended to identify the optimal UCC
concept. Instead, its purpose is to rank UCC concepts based on their potential to reduce
externalities of UFT. Second, decision makers set the parameters for the most significant
UCC concepts. By utilizing the urban transport model and the recommended methodology,
the ECs of UFT are estimated. Finally, an SCBA is performed for each evaluated alternative,
aiding and streamlining the final decision making.
The widely used AHP technique [
64
] provides a straightforward MCDM approach for
ranking UCC concepts based on their effectiveness in mitigating UFT’s negative impacts.
Applying AHP requires systematic selection of UCC concepts suitable for a specific urban
context, as the growing number of CL initiatives increases data preparation time, making
extensive analysis challenging. To address this, a thorough literature review was conducted
using academic databases, municipal websites, and other online resources, focusing on
initiatives integrated with UCCs. Keywords such as urban logistics initiatives, urban
consolidation centers, city logistics, urban freight transport, and related variations were
used. Table 1summarizes the classification, highlighting five key UCC concepts essential
for reducing UFT’s adverse effects.
Sustainability 2024, 16, x FOR PEER REVIEW 6 of 18
Figure 1. The structure of the proposed model.
The widely used AHP technique [64] provides a straightforward MCDM approach
for ranking UCC concepts based on their effectiveness in mitigating UFT’s negative im-
pacts. Applying AHP requires systematic selection of UCC concepts suitable for a specific
urban context, as the growing number of CL initiatives increases data preparation time,
making extensive analysis challenging. To address this, a thorough literature review was
conducted using academic databases, municipal websites, and other online resources, fo-
cusing on initiatives integrated with UCCs. Keywords such as urban logistics initiatives,
urban consolidation centers, city logistics, urban freight transport, and related variations
were used. Table 1 summarizes the classification, highlighting five key UCC concepts es-
sential for reducing UFT’s adverse effects.
Table 1. Key UCC concepts for mitigating the externalities of UFT.
UCC Concept CL Initiative Source
UCC
+
Alternative UFT vehicles
Electric delivery vehicles [10,33,59,65–68]
Cargo bikes and tricycles [67,69–71]
Transition to smaller trucks [10,72]
Drones [71]
Cargo trams [21]
Other alternative vehicles [21,73]
UCC
+
Time windows
Off-peak deliveries [73–77]
Night deliveries [28,72]
UCC
+
Restrictive and regulatory
measures
Restrictions of vehicle movements (in a certain part of the city
area, for certain types of vehicles, in certain time periods, for ve-
hicles below the defined threshold of the vehicle’s load capacity)
[8,15,21,59,72,73,76,7
8–80]
Restriction on stopping of trucks for loading/unloading [21]
Charge for entering a certain zone [80]
Designated areas for off-street (un)loading [21]
UCC
+
Incentives
Subsidies for the work of UCC [7,35,76]
Subsidies for carriers [59,80]
Subsidies for users of UCC services [76]
UCC
+
Other CL initiatives
Collection of returns [34]
Satellite terminals [33,81]
Optimization at the operational level [82]
Value-added services [34,35]
Application of modern IT solutions [21]
Figure 1. The structure of the proposed model.
Table 1. Key UCC concepts for mitigating the externalities of UFT.
UCC Concept CL Initiative Source
UCC
+
Alternative UFT vehicles
Electric delivery vehicles [10,33,59,65–68]
Cargo bikes and tricycles [67,69–71]
Transition to smaller trucks [10,72]
Drones [71]
Cargo trams [21]
Other alternative vehicles [21,73]
UCC
+
Time windows
Off-peak deliveries [73–77]
Night deliveries [28,72]
Sustainability 2025,17, 884 7 of 18
Table 1. Cont.
UCC Concept CL Initiative Source
UCC
+
Restrictive and regulatory
measures
Restrictions of vehicle movements (in a certain part of the
city area, for certain types of vehicles, in certain time
periods, for vehicles below the defined threshold of the
vehicle’s load capacity)
[8,15,21,59,72,73,76,78–80]
Restriction on stopping of trucks for loading/unloading [21]
Charge for entering a certain zone [80]
Designated areas for off-street (un)loading [21]
UCC
+
Incentives
Subsidies for the work of UCC [7,35,76]
Subsidies for carriers [59,80]
Subsidies for users of UCC services [76]
UCC
+
Other CL initiatives
Collection of returns [34]
Satellite terminals [33,81]
Optimization at the operational level [82]
Value-added services [34,35]
Application of modern IT solutions [21]
The input data required for applying the AHP technique were gathered by convening
an expert panel comprising professionals from various relevant fields, including urban
traffic planning, traffic flow management, urban freight transportation, and logistics, as
well as scientific researchers. Expert opinions were collected through a questionnaire survey.
The questionnaire was organized into three sections to align with the requirements of the
AHP technique:
•Section 1—Collects basic information about the respondent;
•Section 2—Evaluates the relative significance of the adverse impacts of UFT;
•
Section 3—Assesses the relative importance of various alternatives, such as different
UCC concepts.
Ultimately, it is essential to set the parameters for the UCC concept, calculate the ECs
of UFT, and conduct the SCBA analysis. For the ECs calculation, the Handbook on the exter-
nal costs of transport [
83
] was consulted. To ensure relevance and accuracy, a transparent
value transfer mechanism was employed, using GDP per capita as the adjustment factor to
align unit external cost values with the Serbian context. Given that Serbia’s GDP per capita
is approximately one-third of the EU-27 average, unit costs were proportionally adjusted to
the study area. In the SCBA, the costs and benefits categories were defined. UCC concepts
involve significant capital expenditures (CAPEX) and operational expenditures (OPEX),
which can vary depending on the strategies and technologies employed [
7
]. CAPEX typ-
ically includes investments in infrastructure development (such as UCCs construction
or rent costs), technology investments (like IoT, AI, and automation systems for efficient
logistics operations), and fleet acquisition (such as purchasing or leasing vehicles for urban
deliveries). OPEX primarily covers fuel and energy costs (including fuel for conventional
vehicles and electricity for electric delivery vehicles and forklifts), maintenance and opera-
tions (such as repairs, utilities, and staffing), and labor costs (wages for drivers, warehouse
staff, and logistics coordinators).
Additional initiatives that complement UCC implementation may incur capital ex-
penditures. For example, these could include constructing new loading and unloading
zones or installing solar panels to reduce long-term operational expenses. Moreover, op-
erational costs may arise from the maintenance of facilities and infrastructure, as well
Sustainability 2025,17, 884 8 of 18
as IT support and system upgrades. These additional CL initiatives were accounted for
in both the CAPEX and OPEX parts of the SCBA. Beyond these, it is crucial to consider
external effects in the SCBA analysis, as the primary goal of CL initiatives is to reduce
externalities. Including these factors provides decision makers with an essential metric for
making informed decisions and effectively communicating the benefits of CL initiatives to
stakeholders and other relevant parties.
The main benefits of the UCC concept include UCC revenue (such as fees charged to
retailers or logistics companies for using the consolidation services) and UCC subsidies
(funds provided by the government or local authorities to incentivize the success of the CL
initiative). The structure of the SCBA categories is as follows:
•
CAPITAL (infrastructure development, fleet acquisition, and additional CL initia-
tives CAPEX),
•
OPERATIONAL (fuel and energy, labor, UCC revenue, UCC subsidies, additional CL
initiatives OPEX), and
•
EXTERNALITIES (noise, congestion, road safety, air pollution, climate change, up-
and downstream processes, and habitat damage).
The steps of defining the parameters, calculating the ECs, and performing the SCBA
must be iterated until all relevant alternatives are considered and all necessary results for
decision making are achieved.
4. Results and Discussion
The AHP hierarchy (goal–criteria–alternatives) for ranking additional CL initiatives to
form UCC concepts is presented in Figure 2. The criteria for evaluating initiatives are based
on the negative effects of UFT, while the considered alternatives are detailed in Table 1.
These alternatives represent key UCC concepts identified through an extensive literature
review focused on mitigating the externalities of UFT. The literature review provided
a foundation for selecting the most relevant initiatives for evaluation and inclusion in
the model.
Sustainability 2024, 16, x FOR PEER REVIEW 8 of 18
4. Results and Discussion
The AHP hierarchy (goal–criteria–alternatives) for ranking additional CL initiatives
to form UCC concepts is presented in Figure 2. The criteria for evaluating initiatives are
based on the negative effects of UFT, while the considered alternatives are detailed in Ta-
ble 1. These alternatives represent key UCC concepts identified through an extensive lit-
erature review focused on mitigating the externalities of UFT. The literature review pro-
vided a foundation for selecting the most relevant initiatives for evaluation and inclusion
in the model.
Figure 2. The hierarchy for ranking CL initiatives based on their impact on UFT’s external effects.
Novi Sad, Serbia, was chosen as the urban area for the case study. Experts familiar
with UFT operations in Novi Sad were grouped into four groups:
• Municipal administration and traffic planners;
• Retail chains, carriers, and courier services;
• Traffic designers;
• Academia.
A total of 22 experts participated in the survey, with an average professional experi-
ence exceeding 15 years. The breakdown of respondents by sector is as follows: 36% from
academia, 23% from municipal administration, 18% from retail, carrier, and courier ser-
vices, and 23% from the traffic design sector. The experts conducted pairwise comparisons
in the presence of a member of the research team, whose role was to clarify any uncertain-
ties the experts might have had. This approach helps minimize the possibility of misinter-
pretation regarding specific criteria or alternatives being compared.
For the AHP analysis, we used the Superdecisions tool (version 3.2.0). The overall incon-
sistency ratio of the model is 0.02333, indicating that the degree of consistency within the AHP
is acceptable. It is worth noting that some outliers needed to be corrected to improve the con-
sistency ratio, but the required interventions were minimal and had a negligible impact on
final results. The AHP results, outlined in Table 2, shows that experts prioritize certain UCC
concepts for mitigating the adverse impacts of UFT in Novi Sad. These primarily include re-
strictive and regulatory measures, alternative transport modes, and operational optimization.
According to the presented results, a potential UCC concept for this case should in-
clude restrictive and regulatory measures, as well as distribution using alternative UFT
vehicles, such as electric delivery vehicles. Additionally, opportunities for optimizing
freight flows at the operational level, introducing time windows, and implementing mod-
ern IT solutions should be considered.
Figure 2. The hierarchy for ranking CL initiatives based on their impact on UFT’s external effects.
Novi Sad, Serbia, was chosen as the urban area for the case study. Experts familiar
with UFT operations in Novi Sad were grouped into four groups:
•Municipal administration and traffic planners;
•Retail chains, carriers, and courier services;
•Traffic designers;
•Academia.
A total of 22 experts participated in the survey, with an average professional experience
exceeding 15 years. The breakdown of respondents by sector is as follows: 36% from
academia, 23% from municipal administration, 18% from retail, carrier, and courier services,
Sustainability 2025,17, 884 9 of 18
and 23% from the traffic design sector. The experts conducted pairwise comparisons in the
presence of a member of the research team, whose role was to clarify any uncertainties the
experts might have had. This approach helps minimize the possibility of misinterpretation
regarding specific criteria or alternatives being compared.
For the AHP analysis, we used the Superdecisions tool (version 3.2.0). The overall
inconsistency ratio of the model is 0.02333, indicating that the degree of consistency within
the AHP is acceptable. It is worth noting that some outliers needed to be corrected to
improve the consistency ratio, but the required interventions were minimal and had a
negligible impact on final results. The AHP results, outlined in Table 2, shows that experts
prioritize certain UCC concepts for mitigating the adverse impacts of UFT in Novi Sad.
These primarily include restrictive and regulatory measures, alternative transport modes,
and operational optimization.
Table 2. Results of ranking additional initiatives for UCC concepts by their relative importance in
reducing the negative effects of UFT.
Alternatives Normalized Value Rank
Restrictive and regulatory measures 0.201410 1
Alternative UFT vehicles 0.194143 2
Operational optimization 0.134063 3
Time windows 0.124678 4
Contemporary IT solutions 0.107698 5
Returns collection 0.101050 6
Satellite cross-dock terminals 0.096741 7
Added value services 0.040217 8
According to the presented results, a potential UCC concept for this case should
include restrictive and regulatory measures, as well as distribution using alternative UFT
vehicles, such as electric delivery vehicles. Additionally, opportunities for optimizing
freight flows at the operational level, introducing time windows, and implementing modern
IT solutions should be considered.
ECs of UFT are calculated through simulations across various scenarios. These sce-
narios include different numbers of UCCs, various strategies for attracting goods to UCCs,
differing characteristics of restriction zones, varying proportions of EVs for last-mile deliv-
eries, and different levels of night deliveries. The input data for EC calculation, such as OD
matrices of vehicle and goods movements, are sourced from the Novi Sad Transport Model
(NOSTRAM). The data include 3498 freight vehicles entering the inner city daily, consisting
of 69% light commercial vehicles and 31% heavy commercial vehicles. Collectively, these
vehicles transport 10,285.12 tons of goods each day, with 38% of deliveries taking place
during peak hours. The impact of the UCC concept on key UFT performance indicators,
like vehicle-kilometers traveled by vehicle size, type, and fuel, is simulated by an Excel
2019 macro-based tool programmed in VBA. This tool is designed to be user-friendly for
decision makers (the user interface is shown in Figure 3). The utilization of the tool is not
constrained by computer resources, allowing for smooth operation across various systems.
However, the data collection process is a more resource-intensive task, requiring careful
planning and allocation of efforts.
Table 3summarizes the effects of various UCC concepts on UFT ECs, highlighting
their potential to decrease or increase external costs.
An SCBA of all considered alternatives is conducted to guide the final decision on
sustainable CL projects by providing benefit–cost ratios (B–C ratios). Input data needed
for the SCBA analysis are local market prices, such as fuel per liter, electricity per kWh,
UCC investment per m
2
, delivery vehicle acquisition per vehicle, and unloading space
Sustainability 2025,17, 884 10 of 18
price per m
2
and percentage of subsidies for the UCC. In this paper, we assumed that
subsidies would fully cover UCC operational costs during the year of implementation.
This is expected to decrease in the future. Table 4presents SCBA results for one sample
UCC scenario.
Sustainability 2024, 16, x FOR PEER REVIEW 9 of 18
Table 2. Results of ranking additional initiatives for UCC concepts by their relative importance in
reducing the negative effects of UFT.
Alternatives Normalized Value Rank
Restrictive and regulatory measures 0.201410 1
Alternative UFT vehicles 0.194143 2
Operational optimization 0.134063 3
Time windows 0.124678 4
Contemporary IT solutions 0.107698 5
Returns collection 0.101050 6
Satellite cross-dock terminals 0.096741 7
Added value services 0.040217 8
ECs of UFT are calculated through simulations across various scenarios. These sce-
narios include different numbers of UCCs, various strategies for aracting goods to UCCs,
differing characteristics of restriction zones, varying proportions of EVs for last-mile de-
liveries, and different levels of night deliveries. The input data for EC calculation, such as
OD matrices of vehicle and goods movements, are sourced from the Novi Sad Transport
Model (NOSTRAM). The data include 3498 freight vehicles entering the inner city daily,
consisting of 69% light commercial vehicles and 31% heavy commercial vehicles. Collec-
tively, these vehicles transport 10,285.12 tons of goods each day, with 38% of deliveries
taking place during peak hours. The impact of the UCC concept on key UFT performance
indicators, like vehicle-kilometers traveled by vehicle size, type, and fuel, is simulated by
an Excel 2019 macro-based tool programmed in VBA. This tool is designed to be user-
friendly for decision makers (the user interface is shown in Figure 3). The utilization of
the tool is not constrained by computer resources, allowing for smooth operation across
various systems. However, the data collection process is a more resource-intensive task,
requiring careful planning and allocation of efforts.
Figure 3. Example of EC estimation for a selected UCC concept (dashboard).
Figure 3. Example of EC estimation for a selected UCC concept (dashboard).
Table 3. Effects of UCC concepts on UFT ECs.
UCC Concept Major Decrease Median Major Increase
UCC + HGV restriction zone −10.0% +53.3% +129.0%
UCC + Dedicated (un)loading spaces −77.4% −47.2% +8.4%
UCC + Electric delivery vehicles −28.4% +16.7% +107.4%
UCC + Night deliveries −91.9% −52.1% +38.9%
Table 4. Sample SCBA results for selected UCC scenario.
CAPITAL Type Amount [1000 €]
Infrastructure development Cost 2018.48
Fleet acquisition Cost 68.40
Additional CL initiatives CAPEX Cost 0.00
OPERATIONAL
Fuel and energy Cost 2063.34
Labor Cost 24.54
UCC revenue Benefit −102.23
UCC subsidies Benefit −2087.88
Additional CL initiatives OPEX Cost 0.00
EXTERNALITIES
Noise Cost 17.47
Congestion Cost 50.33
Road safety Benefit −3.42
Air pollution Benefit −89.14
Climate change Benefit −100.53
Up- and downstream processes Cost 29.84
Habitat damage Benefit −15.89
B–C ratio 0.56
B–C ratio (without ECs) 0.52
Sustainability 2025,17, 884 11 of 18
The proposed model is designed for what-if analyses of sustainable CL concepts
relative to the current state. It is important to note that some categories in the SCBA may
shift from a benefit to a cost depending on the scenario. For example, in a UCC + Night
Delivery concept, noise costs may increase, turning it into a project cost. Conversely, in
a UCC + electric delivery vehicles concept, low-noise vehicles would turn noise into a
project benefit.
For this paper, we calculated the B–C ratio for 243 scenarios by varying the number
of UCCs (1, 2, or 3), percentage of electric delivery vehicles (0%, 50%, 100%), size of the
restriction zones (center, ring 1, ring 2), coverage of dedicated unloading spaces (without,
center, ring 1), and percentage of night deliveries (0%, 20%, 40%). Table 5presents the
SCBA results for 10 example scenarios, highlighting the B–C ratios with and without the
inclusion of ECs. This comparison highlights the importance of thoroughly accounting
for externalities in SCBA analysis. The case study demonstrates that incorporating ECs
can increase the B–C ratio by up to 0.16 in certain scenarios, such as Scenario 10. However,
as illustrated in Scenario 1, including ECs may sometimes result in a lower B–C ratio,
suggesting that externalities do not always improve project viability.
To contextualize the findings, the results of this study are compared with those from
existing evaluations of UCC concepts in the literature. The calculated average B–C ratio for
the UCC concepts in the case study is 0.58, indicating that societal returns amount to €0.58 for
every €1 invested. This aligns with findings from Kin et al. [
7
], where a B–C ratio of 0.42 was
reported, highlighting that while UCCs demonstrate positive social and environmental
impacts, they often struggle to achieve financial self-sufficiency. This consistency across
studies underscores the common challenge of balancing the societal benefits of UCCs with
their financial viability, reinforcing the importance of targeted policy interventions and
innovative funding mechanisms to support their implementation.
Table 5. Results of the SCBA for selected UCC concepts.
Scenario B–C Ratio
No.
Number of
UCCs EVs Percent
HGV Restriction
zone
Dedicated
Unloading Spaces
Night
Deliveries With ECs Without ECs
1 1 0% C No 0% 0.58 0.59
2 1 100% C No 0% 0.51 0.49
3 1 0% C + R1 + R2 No 0% 0.60 0.64
4 1 0% C C+R1 0% 0.60 0.58
5 2 50% C + R1 C 20% 0.58 0.55
6 2 100% C No 0% 0.51 0.48
7 3 0% C C 40% 0.68 0.56
8 3 0% C + R1 + R2 No 0% 0.63 0.67
9 3 100% C + R1 + R2 No 0% 0.50 0.51
10 3 100% C C 40% 0.62 0.46
Symbols: C—Center; R1—Ring 1; R2—Ring 2.
Overall, low B–C ratio for the year of implementation is mainly due to high initial
expenditures, such as infrastructure development and the acquisition of electric vehicles.
However, the B–C ratio is expected to improve over time as infrastructure costs decrease
and operational expenses decline with economies of scale. Furthermore, the external
benefits are likely to grow, enhancing the overall value of the project. While this is outside
the scope of this paper, it should be considered when conducting a feasibility study.
5. Sensitivity Analysis
To evaluate the robustness of the proposed model, a comprehensive sensitivity analysis
was performed, focusing on key input variables for the SCBA. The analysis examined
Sustainability 2025,17, 884 12 of 18
variations in critical cost factors, including UCC construction costs and the level of subsidies
(Table 6), fuel and electricity prices (Table 7), as well as the procurement costs of EVs and
land allocation for unloading spaces (Table 8). This approach helps to identify the most
sensitive parameters, providing valuable insights into their impact on the overall cost–
benefit outcomes of UCC concepts.
Table 6. Sensitivity of the B–C ratio from changes in level of subsidies and UCC construction price
(brighter shades indicate a greater change in the variables).
Change in UCC Construction Price
−50% −40% −30% −20% −10% 0% 10% 20% 30% 40% 50%
Change
in level
of sub-
sidies
0% 0.77 0.72 0.68 0.64 0.60 0.57 0.54 0.52 0.49 0.47 0.45
−10% 0.71 0.67 0.63 0.59 0.56 0.53 0.50 0.48 0.46 0.44 0.42
−20% 0.66 0.61 0.57 0.54 0.51 0.48 0.46 0.44 0.42 0.40 0.38
−30% 0.60 0.56 0.52 0.49 0.47 0.44 0.42 0.40 0.38 0.36 0.35
−40% 0.54 0.50 0.47 0.44 0.42 0.40 0.38 0.36 0.34 0.33 0.32
−50% 0.48 0.45 0.42 0.40 0.37 0.35 0.34 0.32 0.31 0.29 0.28
−60% 0.42 0.39 0.37 0.35 0.33 0.31 0.30 0.28 0.27 0.26 0.25
−70% 0.36 0.34 0.32 0.30 0.28 0.27 0.26 0.24 0.23 0.22 0.21
−80% 0.30 0.28 0.27 0.25 0.24 0.23 0.21 0.20 0.19 0.19 0.18
−90% 0.25 0.23 0.22 0.20 0.19 0.18 0.17 0.16 0.16 0.15 0.14
−
100%
0.19 0.18 0.16 0.16 0.15 0.14 0.13 0.13 0.12 0.12 0.11
Table 7. Sensitivity of the B–C ratio from changes in fuel and electricity price (brighter shades indicate
a greater change in the variables).
Change in Fuel Price
−50% −40% −30% −20% −10% 0% 10% 20% 30% 40% 50%
Change
in elec-
tricity
price
−50% 0.46 0.48 0.51 0.53 0.55 0.57 0.58 0.60 0.61 0.63 0.64
−40% 0.46 0.48 0.51 0.53 0.55 0.57 0.58 0.60 0.61 0.63 0.64
−30% 0.46 0.48 0.51 0.53 0.55 0.57 0.58 0.60 0.61 0.63 0.64
−20% 0.46 0.49 0.51 0.53 0.55 0.57 0.59 0.60 0.62 0.63 0.64
−10% 0.46 0.49 0.51 0.53 0.55 0.57 0.59 0.60 0.62 0.63 0.64
0% 0.46 0.49 0.51 0.53 0.55 0.57 0.59 0.60 0.62 0.63 0.64
10% 0.46 0.49 0.51 0.53 0.55 0.57 0.59 0.60 0.62 0.63 0.64
20% 0.47 0.49 0.51 0.54 0.55 0.57 0.59 0.60 0.62 0.63 0.64
30% 0.47 0.49 0.52 0.54 0.56 0.57 0.59 0.60 0.62 0.63 0.64
40% 0.47 0.49 0.52 0.54 0.56 0.57 0.59 0.61 0.62 0.63 0.64
50% 0.47 0.49 0.52 0.54 0.56 0.57 0.59 0.61 0.62 0.63 0.65
Table 8. Sensitivity of the B–C ratio from changes in EVs procurement and unloading space price
(brighter shades indicate a greater change in the variables).
Change in EVs price
0% −10% −20% −30% −40% 50%
Change in
unloading
space price
−50% 0.571 0.573 0.575 0.577 0.578 0.580
−40% 0.571 0.573 0.575 0.576 0.578 0.580
−30% 0.571 0.573 0.574 0.576 0.578 0.580
−20% 0.571 0.573 0.574 0.576 0.578 0.579
−10% 0.571 0.572 0.574 0.576 0.577 0.579
0% 0.570 0.572 0.574 0.576 0.577 0.579
10% 0.570 0.572 0.574 0.575 0.577 0.579
20% 0.570 0.572 0.573 0.575 0.577 0.579
30% 0.570 0.571 0.573 0.575 0.577 0.578
40% 0.570 0.571 0.573 0.575 0.576 0.578
50% 0.569 0.571 0.573 0.574 0.576 0.578
To illustrate the sensitivity of the B–C ratio to various cost categories, we created a
specific type of bar chart known as a “tornado” chart, shown in Figure 4. This type of chart
Sustainability 2025,17, 884 13 of 18
is particularly effective for sensitivity analysis because it visually highlights the impact of
each variable on the outcome, making it easier to identify the most influential factors.
Sustainability 2024, 16, x FOR PEER REVIEW 13 of 18
50% 0.569 0.571 0.573 0.574 0.576 0.578
To illustrate the sensitivity of the B–C ratio to various cost categories, we created a
specific type of bar chart known as a “tornado” chart, shown in Figure 4. This type of chart
is particularly effective for sensitivity analysis because it visually highlights the impact of
each variable on the outcome, making it easier to identify the most influential factors.
The SCBA results indicate that variations in subsidy levels and UCC construction
costs significantly affect the B–C ratio. Specifically, as subsidies increase or construction
costs decrease, the B–C ratio improves, suggesting a more favorable cost–benefit outcome
for UCC concepts. Note that subsidies emerge as the most critical factor for UCC success;
without these incentives, the B–C ratio can drop to as low as 0.11. Conversely, fluctuations
in fuel and electricity prices have a comparatively moderate effect on the B–C ratio. The
tornado chart reveals that fuel prices are the primary cause of this sensitivity. This finding
supports the decision to procure electric delivery vehicles due to their long-term benefits.
Figure 4. Sensitivity of the model results to changes in load factor.
6. Conclusions
In this paper, we presented a novel approach to enhance decision making in sustain-
able CL planning and development by integrating MCDM, simulation and SCBA. The
proposed MCDM–SCBA model addresses the complexities of financially evaluating
UCCs concepts and their associated ECs by prioritizing CL initiatives based on their po-
tential to mitigate negative externalities.
To systematically assess and prioritize various UCC concepts, we employed the AHP
technique due to its simplicity. This enabled effective ranking of possible UCC concepts. The
findings revealed that restrictive and regulatory measures, alternative transport options,
and operational optimizations are preferred solutions in the specific case study. However, a
well-known limitation of the AHP is its reliance on subjective impact assessments. This re-
liance makes it difficult to account for the inherent uncertainty in human decision making.
To address this, our approach incorporates analytical calculations of ECs for selected UCC
concepts, based on simulations of urban freight flows. Calculating ECs alongside capital and
operational expenditures within the SCBA component of the model provides quantitative
metrics for decision makers. This approach enhances the decision-making process by offer-
ing a comprehensive analysis of both private and external costs.
In the case of the city of Novi Sad, the experimental results indicated that initial B–C
ratios for the proposed UCC concepts may be below one, signifying current financial
Figure 4. Sensitivity of the model results to changes in load factor.
The SCBA results indicate that variations in subsidy levels and UCC construction
costs significantly affect the B–C ratio. Specifically, as subsidies increase or construction
costs decrease, the B–C ratio improves, suggesting a more favorable cost–benefit outcome
for UCC concepts. Note that subsidies emerge as the most critical factor for UCC success;
without these incentives, the B–C ratio can drop to as low as 0.11. Conversely, fluctuations
in fuel and electricity prices have a comparatively moderate effect on the B–C ratio. The
tornado chart reveals that fuel prices are the primary cause of this sensitivity. This finding
supports the decision to procure electric delivery vehicles due to their long-term benefits.
6. Conclusions
In this paper, we presented a novel approach to enhance decision making in sustainable
CL planning and development by integrating MCDM, simulation and SCBA. The proposed
MCDM–SCBA model addresses the complexities of financially evaluating UCCs concepts
and their associated ECs by prioritizing CL initiatives based on their potential to mitigate
negative externalities.
To systematically assess and prioritize various UCC concepts, we employed the AHP
technique due to its simplicity. This enabled effective ranking of possible UCC concepts.
The findings revealed that restrictive and regulatory measures, alternative transport options,
and operational optimizations are preferred solutions in the specific case study. However,
a well-known limitation of the AHP is its reliance on subjective impact assessments. This
reliance makes it difficult to account for the inherent uncertainty in human decision making.
To address this, our approach incorporates analytical calculations of ECs for selected
UCC concepts, based on simulations of urban freight flows. Calculating ECs alongside
capital and operational expenditures within the SCBA component of the model provides
quantitative metrics for decision makers. This approach enhances the decision-making
process by offering a comprehensive analysis of both private and external costs.
In the case of the city of Novi Sad, the experimental results indicated that initial
B–C ratios for the proposed UCC concepts may be below one, signifying current financial
inefficiency. However, the long-term benefits associated with reduced ECs and improved
operational efficiencies hold promise for enhancing project viability over time. The findings
also emphasize the necessity of systematically including ECs in cost–benefit analyses, as
they significantly contribute to total net benefits.
Sustainability 2025,17, 884 14 of 18
The sensitivity analysis conducted in this research reveals the critical parameters
influencing the B–C ratio, highlighting the significance of subsidies for UCC operations.
Overall, the proposed MCDM–SCBA model serves as a powerful decision-making
tool by providing a quantitative, scenario-based approach to evaluating the effects of
CL initiatives. It helps in shaping policies regarding the application of various UCC
concepts and managing trade-offs between costs and benefits. Decision makers can use
model outputs to strategically implement measures that reduce congestion, emissions, and
operational costs while improving overall efficiency and sustainability in CL.
This research has several noteworthy limitations which imply promising paths for
future research. Regarding the application of SCBA, the analysis was conducted solely for
the year in which city logistics initiatives are potentially implemented. Future work should
extend the SCBA over the next five to ten years to account for evolving trends. Such trends
may involve a shift toward “servitization” models, where businesses pay for equipment
usage rather than ownership, potentially reducing long-term capital expenditures (CAPEX).
Further, the adoption of electric vehicles, despite their higher initial costs, could also lead
to lower operational expenses over time. Additionally, the current SCBA categories do not
encompass all potential costs and benefits but focus on the most significant ones.
While the current approach considers major external costs like noise, congestion, road
safety, and air pollution, other potential external effects (such as land use and visual impact)
are worth considering. Future studies should include benefits such as cost savings for retail-
ers and improved delivery reliability. This would enhance the analysis and provide a more
comprehensive understanding of the economic and environmental implications of UFT.
Finally, developing an adaptive decision-making framework that incorporates incremental
learning techniques to dynamically refine UCC evaluations as new data become available
could significantly enhance the practical applicability of such models. An improvement in
uncertainty analysis methods in future research is also desirable to enhance result reliability.
Author Contributions: Conceptualization, M.V. and Ð.S.; methodology, M.V., Ð.S. and V.I.; soft-
ware, M.V. and V.I.; validation, M.V. and D.M.; formal analysis, M.V. and V.I.; investigation, M.V.;
writing—original draft preparation, M.V. and Ð.S.; writing—review and editing, M.V., Ð.S. and D.M.;
visualization, M.V.; supervision, Ð.S.; project administration, M.V.; funding acquisition, M.V., Ð.S.,
V.I. and D.M. All authors have read and agreed to the published version of the manuscript.
Funding: This research has been supported by the Ministry of Science, Technological Development
and Innovation (Contract No. 451-03-65/2024-03/200156) and the Faculty of Technical Sciences,
University of Novi Sad through project “Scientific and Artistic Research Work of Researchers in
Teaching and Associate Positions at the Faculty of Technical Sciences, University of Novi Sad”
(No. 01-3394/1).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The raw data supporting the conclusions of this article will be made
available by the authors on request.
Conflicts of Interest: The authors declare no conflicts of interest.
References
1.
UNCTAD. SFT Framework. Available online: https://sft-framework.unctad.org/sustainable-freight-transport-and- linkages-to-
relevant-global-processes-on-sustainable-development (accessed on 18 November 2024).
2.
European Commission. The European Green Deal: Striving to Be the First Climate-Neutral Continent. Available online: https:
//commission.europa.eu/strategy-and-policy/priorities-2019-2024/european-green-deal_en (accessed on 18 November 2024).
3.
Tracey, S.; Anne, B. OECD Insights Sustainable Development Linking Economy, Society, Environment: Linking Economy, Society,
Environment; OECD Publishing: Paris, France, 2008.
Sustainability 2025,17, 884 15 of 18
4.
Russo, F.; Comi, A. Investigating the effects of city logistics measures on the economy of the city. Sustainability 2020,12, 1439.
[CrossRef]
5.
Galambos, K.J.; Palomino-Hernández, A.B.; Hemmelmayr, V.C.; Turan, B. Sustainability initiatives in urban freight transportation
in Europe. Transp. Res. Interdiscip. Perspect. 2024,23, 101013. [CrossRef]
6.
Dreischerf, A.J.; Buijs, P. How Urban Consolidation Centres affect distribution networks: An empirical investigation from the
perspective of suppliers. Case Stud. Transp. Policy 2022,10, 518–528. [CrossRef]
7.
Kin, B.; Verlinde, S.; Van Lier, T.; Macharis, C. Is There Life After Subsidy for an Urban Consolidation Centre? An Investigation of
the Total Costs and Benefits of a Privately-Initiated Concept. Transp. Res. Procedia 2016,12, 357–369. [CrossRef]
8.
Allen, J.; Browne, M.; Woodburn, A.; Leonardi, J. The Role of Urban Consolidation Centres in Sustainable Freight Transport.
Transp. Rev. 2012,32, 473–490. [CrossRef]
9. Vahrenkamp, R. 25 Years City Logistic: Why Failed the Urban Consolidation Centres? Eur. Transp. 2016,60, 6.
10.
Van Duin, J.H.R.; Quak, H.; Muñuzuri, J. New Challenges for Urban Consolidation Centres: A Case Study in The Hague. Procedia
Soc. Behav. Sci. 2010,2, 6177–6188. [CrossRef]
11.
Gonzalez-Feliu, J. Sustainable Urban Logistics: Planning and Evaluation. In Systems and Industrial Engineering; Wiley-ISTE: New
York, NY, USA; London, UK, 2018; ISBN 978-1-78630-179-6.
12.
Patella, S.M.; Grazieschi, G.; Gatta, V.; Marcucci, E.; Carrese, S. The adoption of green vehicles in last mile logistics: A systematic
review. Sustainability 2020,13, 6. [CrossRef]
13.
Wang, Y.; Li, Y.; Lu, C. Evaluating the Effects of Logistics Center Location: An Analytical Framework for Sustainable Urban
Logistics. Sustainability 2023,15, 3091. [CrossRef]
14.
Björklund, M.; Johansson, H. Urban consolidation centre—A literature review, categorisation, and a future research agenda. Int. J.
Phys. Distrib. Logist. Manag. 2018,48, 745–764. [CrossRef]
15.
Allen, J.; Browne, M.; Woodburn, A.; Leonardi, J. A Review of Urban Consolidation Centres in the Supply Chain Based on a Case
Study Approach. Supply Chain Forum Int. J. 2014,15, 100–112. [CrossRef]
16.
Muñuzuri, J.; Larrañeta, J.; Onieva, L.; Cortés, P. Solutions applicable by local administrations for urban logistics improvement.
Cities 2005,22, 15–28. [CrossRef]
17.
Taniguchi, E.; Thompson, R.G.; Qureshi, A.G. Modelling city logistics using recent innovative technologies. Transp. Res. Procedia
2020,46, 3–12. [CrossRef]
18.
Barfod, M.B.; Salling, K.B.; Leleur, S. Composite decision support by combining cost-benefit and multi-criteria decision analysis.
Decis. Support Syst. 2011,51, 167–175. [CrossRef]
19.
Guhnemann, A.; Laird, J.L.; Pearman, A.D. Combining cost-benefit and multi-criteria analysis to prioritise a national road
infrastructure programme. Transp. Policy 2012,23, 15–24. [CrossRef]
20.
Barfod, M.B.; Salling, K.B. A new composite decision support framework for strategic and sustainable transport appraisals.
Transp. Res. Part A Policy Pract. 2015,72, 1–15. [CrossRef]
21.
MDS Transmodal. DG MOVE European Commission: Study on Urban Freight Transport. European Commission: Brussels,
Belgium, 2012. Available online: https://ec.europa.eu/transport/sites/transport/files/themes/urban/studies/doc/2012-04-
urban-freight-transport.pdf (accessed on 1 October 2024).
22.
Ambrosini, C.; Gonzalez-Feliu, J.; Toilier, F. A Design Methodology for Scenario-Analysis in Urban Freight Modelling. Eur. Transp.
2013,54, 1–21.
23.
van Essen, H.; Schroten, A.; Otten, M.; Sutter, D.; Schreyer, C.; Zandonella, R.; Maibach, M.; Doll, C. External Costs of Transport in
Europe: Update Study for 2008; CE Delft: Delft, The Netherlands, 2011. Available online: https://www.cedelft.eu/en/publications/
download/1301 (accessed on 5 October 2024).
24.
Bickel, P.; Friedrich, R. ExternE—Externalities of Energy: Methodology 2005 Update; Publications Office: Luxembourg, 2005; ISBN
978-92-79-00423-0. Available online: https://op.europa.eu/de/publication-detail/-/publication/b2b86b52-4f18- 4b4e-a134-b1
c81ad8a1b2 (accessed on 5 October 2024).
25.
European Commission: Directorate-General for Research and Innovation. External Costs: Research Results on Socio-Environmental
Damages Due to Electricity and Transport; Publications Office: Luxembourg, 2003; ISBN 978-92-894-3353-2. Available online:
https://op.europa.eu/en/publication-detail/-/publication/d0ec8569-9c8b-4051-905c- a3937e0d6cf4/language-en (accessed on 5
October 2024).
26.
Mayeres, I.; Ochelen, S.; Proost, S. The Marginal External Costs of Urban Transport. Transp. Res. Part D Transp. Environ. 1996,1,
111–130. [CrossRef]
27.
Janjevic, M.; Ndiaye, A. Investigating the Theoretical Cost-Relationships of Urban Consolidation Centres for Their Users. Transp.
Res. Part A Policy Pract. 2017,102, 98–118. [CrossRef]
28.
Johansson, H. Urban Consolidation Centres: On Relationships Between Customer Needs and Services in City Logistics; Linköping
University Electronic Press: Linköping, Sweden, 2018; ISBN 978-91-7685-380-1.
Sustainability 2025,17, 884 16 of 18
29.
Gonzalez-Feliu, J. Logistics and Transport Modeling in Urban Goods Movement. IGI Global: Hershey, PA, USA, 2019; ISBN
978-1-5225-8292-2.
30.
Allen, J.; Thorne, G.; Browne, M. BESTUFS Good Practice Guide on Urban Freight Transport. 2007. Available online: http:
//www.bestufs.net/download/BESTUFS_II/good_practice/English_BESTUFS_Guide.pdf (accessed on 10 October 2024).
31.
Tadi´c, S.; Zeˇcevi´c, S.; Krsti´c, M. Inicijative City Logistike u Cilju Poboljšanja Održivosti Promenom Konteksta Urbane Sredine.
Tehnika 2014,61, 834–843. [CrossRef]
32.
Browne, M.; Sweet, M.; Woodburn, A.; Allen, J. Urban Freight Consolidation Centres: Final Report; Transport Studies Group,
University of Westminster: London, UK, 2005. Available online: https://www.researchgate.net/publication/228761468_Urban_
Freight_Consolidation_Centres_Final_Report (accessed on 11 October 2024).
33.
Correia, V.D.A.; Oliveira, L.K.D.; Guerra, A.L. Economical and Environmental Analysis of an Urban Consolidation Center for
Belo Horizonte City (Brazil). Procedia Soc. Behav. Sci. 2012,39, 770–782. [CrossRef]
34.
Dablanc, L.; Patier, D.; Gonzalez-Feliu, J.; Augereau, V.; Leonardi, J.; Simmeone, T.; Cerdà, L. SUGAR. Sustainable Urban Goods
Logistics Achieved by Regional and Local Policies. In City Logistics Best Practices: A Handbook for Authorities; Regione Emilia
Romagna: Bologna, Italy, 2011.
35.
Giampoldaki, E.; Madas, M.; Zeimpekis, V.; Vlachopoulou, M. A State-of-Practice Review of Urban Consolidation Centres:
Practical Insights and Future Challenges. Int. J. Logist. Res. Appl. 2023,26, 732–763. [CrossRef]
36.
Dupas, R.; Deschamps, J.-C.; Taniguchi, E.; Qureshi, A.G.; Hsu, T. Optimizing the Location Selection of Urban Consolidation
Centers with Sustainability Considerations in the City of Bordeaux. Res. Transp. Bus. Manag. 2023,47, 100943. [CrossRef]
37. Alessandrini, A.; Salucci, M.V. Using Rail to Make Urban Freight Distribution More Sustainable. Eur. Transp. 2012,50, 1–17.
38.
Estrada, M.; Roca-Riu, M. Stakeholder’s Profitability of Carrier-Led Consolidation Strategies in Urban Goods Distribution. Transp.
Res. Part E Logist. Transp. Rev. 2017,104, 165–188. [CrossRef]
39.
Katsela, K.; Pålsson, H.; Ivernå, J. Environmental Impact and Costs of Externalities of Using Urban Consolidation Centres: A
24-Hour Observation Study with Modelling in Four Scenarios. Int. J. Logist. Res. Appl. 2022,25, 1542–1563. [CrossRef]
40.
Kotowska, I. Assessing the External Costs of Urban Transport Investments: A Socioeconomic Analysis. Zesz. Nauk. Akad. Morsk.
Szczec. 2019,60, 41–46. [CrossRef]
41.
Ghaemi, S.A.; Hosseinlou, M.H. Modeling the Urban Freight-Transportation System Using the System Dynamics Approach.
Systems 2023,11, 409. [CrossRef]
42.
Gillström, H.; Björklund, M. Assessing the benefits of urban consolidation centres: An overview based on a systematic literature
review. Transp. Rev. 2024,44, 972–991. [CrossRef]
43.
Browne, M.; Piotrowska, M.; Woodburn, A.G.; Allen, J. Literature Review WM9: Part I-urban freight transport. In Work Module 1,
Green Logistics Project; University of Westminster: London, UK, 2007.
44.
Filippi, F.; Nuzzolo, A.; Comi, A.; Delle Site, P. Ex-ante assessment of urban freight transport policies. Procedia Soc. Behav. Sci.
2010,2, 6332–6342. [CrossRef]
45.
Gallo, S.A.; Maheut, J. Multi-Criteria Analysis for the Evaluation of Urban Freight Logistics Solutions: A Systematic Literature
Review. Math 2023,11, 4089. [CrossRef]
46.
Jardas, M.; Peri´c Hadži´c, A.; Ogrizovi´c, D. Application of the MAMCA Method in the Evaluation of Delivery Flows within City
Centers: A Case Study of Rijeka. Urban Sci. 2024,8, 149. [CrossRef]
47.
Tadi ´c, S.; Zeˇcevi´c, S.; Krsti´c, M. A Novel Hybrid MCDM Model Based on Fuzzy DEMATEL, Fuzzy ANP and Fuzzy VIKOR for
City Logistics Concept Selection. Expert Syst. Appl. 2014,41, 8112–8128. [CrossRef]
48.
Simi´c, V.; Lazarevi´c, D.; Dobrodolac, M. Picture Fuzzy WASPAS Method for Selecting Last-Mile Delivery Mode: A Case Study of
Belgrade. Eur. Transp. Res. Rev. 2021,13, 43. [CrossRef]
49.
Lasota, M.; Zabielska, A.; Jacyna, M.; Goł˛ebiowski, P.; ˙
Zochowska, R.; Wasiak, M. Method for Delivery Planning in Urban Areas
with Environmental Aspects. Sustainability 2024,16, 1571. [CrossRef]
50.
Perera, L.; Thompson, R.G. Multi-Stakeholder Acceptance of Optimum Toll Schemes. Res. Transp. Bus. Manag. 2021,41, 100654.
[CrossRef]
51.
Padilla-Rivera, A.; Hannouf, M.; Assefa, G.; Gates, I. A Systematic Literature Review on Current Application of Life Cycle
Sustainability Assessment: A Focus on Economic Dimension and Emerging Technologies. Environ. Impact Assess. Rev. 2023,103,
107268. [CrossRef]
52.
Annema, J.A.; Mouter, N.; Razaei, J. Cost-Benefit Analysis (CBA), or Multi-Criteria Decision-Making (MCDM) or Both: Politicians’
Perspective in Transport Policy Appraisal. Transp. Res. Procedia 2015,10, 788–797. [CrossRef]
53.
Flämig, H.; Wolff, J. Impacts of Planning and Policy Strategies on Freight Flows in Urban Areas. Transp. Res. Procedia 2016,12,
584–597. [CrossRef]
54.
Heitz, A.; Dablanc, L. Freight in Urban Planning and Local Policies: Results from a New Survey in Twenty French Cities. In
Proceedings of the City Logistics 2019, 11th International Conference on City Logistics, Dubrovnik, Croatia, 12–14 June 2019;
p. 10.
Sustainability 2025,17, 884 17 of 18
55.
Kervall, M.; Pålsson, H. Barriers to change in urban freight systems: A systematic literature review. Eur. Transp. Res. Rev. 2022,14,
29. [CrossRef]
56.
Lindholm, M.; Behrends, S. The Challenges of Urban Freight Regulation: A Review. J. Transp. Geogr. 2012,22, 129–136. [CrossRef]
57.
Veliˇckovi´c, M.; Stojanovi´c, Ð.; Nikoliˇci´c, S.; Maslari´c, M. Different Urban Consolidation Centre Scenarios: Impact on External
Costs of Last-Mile Deliveries. Transport 2018,33, 948–958. [CrossRef]
58. Department for Transport (DfT). Freight Consolidation Centre Study: Main Report; TTR & TRL: London, UK, 2010.
59.
Björklund, M.; Abrahamsson, M.; Johansson, H. Critical Factors for Viable Business Models for Urban Consolidation Centres. Res.
Transp. Econ. 2017,64, 36–47. [CrossRef]
60.
Russo, F.; Comi, A. Urban freight transport planning towards green goals: Synthetic environmental evidence from tested results.
Sustainability 2016,8, 381. [CrossRef]
61.
Ranieri, L.; Digiesi, S.; Silvestri, B.; Roccotelli, M. A Review of Last Mile Logistics Innovations in an Externalities Cost Reduction
Vision. Sustainability 2018,10, 782. [CrossRef]
62.
Balm, S.; Macharis, C.; Milan, L.; Quak, H. A City Distribution Impact Assessment Framework. In Towards Innovative Freight and
Logistics; Blanquart, C., Clausen, U., Jacob, B., Eds.; Wiley: New York, NY, USA, 2016; pp. 353–367; ISBN 978-1-78630-027-0.
63.
Zenezini, G. A New Evaluation Approach to City Logistics Projects: A Business-Oriented Agent-Based Model. Ph.D. Thesis,
Politecnico di Torino, Turin, Italy, 2018.
64. Saaty, R.W. The Analytic Hierarchy Process—What It Is and How It Is Used. Math. Model. 1987,9, 161–176. [CrossRef]
65.
Browne, M.; Allen, J.; Leonardi, J. Evaluating the Use of an Urban Consolidation Centre and Electric Vehicles in Central London.
IATSS Res. 2011,35, 1–6. [CrossRef]
66.
Faure, L.; Burlat, P.; Marquès, G. Evaluate the Viability of Urban Consolidation Centre with Regards to Urban Morphology. Transp.
Res. Procedia 2016,12, 348–356. [CrossRef]
67.
Aljohani, K.; Thompson, R.G. A Stakeholder-Based Evaluation of the Most Suitable and Sustainable Delivery Fleet for Freight
Consolidation Policies in the Inner-City Area. Sustainability 2018,11, 124. [CrossRef]
68.
Castillo, O.; Álvarez, R. Electrification of Last-Mile Delivery: A Fleet Management Approach with a Sustainability Perspective.
Sustainability 2023,15, 16909. [CrossRef]
69.
Leonardi, J.; Browne, M.; Allen, J. Before-After Assessment of a Logistics Trial with Clean Urban Freight Vehicles: A Case Study
in London. Procedia Soc. Behav. Sci. 2012,39, 146–157. [CrossRef]
70.
Ormond, P.; Telhada, J. Using Urban Consolidation Centre with Cyclocargos Support to Increase the Performance and Sustainability of
Urban Logistics: A Literature Review and a Case Study in the City of São Paulo; University of Porto: Porto, Portugal, 2017. Available
online: https://hdl.handle.net/1822/53011 (accessed on 26 September 2024).
71.
Athanasopoulos, K.; Chatziioannou, I.; Boutsi, A.-M.; Tsingenopoulos, G.; Soile, S.; Chliverou, R.; Petrakou, Z.; Papanikolaou, E.;
Karolemeas, C.; Kourmpa, E.; et al. Integrating Cargo Bikes and Drones into Last-Mile Deliveries: Insights from Pilot Deliveries
in Five Greek Cities. Sustainability 2024,16, 1060. [CrossRef]
72.
Janjevic, M.; Lebeau, P.; Ndiaye, A.B.; Macharis, C.; Van Mierlo, J.; Nsamzinshuti, A. Strategic Scenarios for Sustainable Urban
Distribution in the Brussels-Capital Region Using Urban Consolidation Centres. Transp. Res. Procedia 2016,12, 598–612. [CrossRef]
73.
Taniguchi, E. City Logistics for Sustainable and Liveable Cities. In Green Logistics and Transportation; Fahimnia, B., Bell, M.G.H.,
Hensher, D.A., Sarkis, J., Eds.; Greening of Industry Networks Studies; Springer International Publishing: Cham, Switzerland,
2015; Volume 4, pp. 49–60; ISBN 978-3-319-17180-7.
74.
Johansson, H.; Björklund, M. Urban Consolidation Centres: Retail Stores’ Demands for UCC Services. Int. J. Phys. Distrib. Logist.
Manag. 2017,47, 646–662. [CrossRef]
75.
Marcucci, E.; Gatta, V. Investigating the Potential for Off-Hour Deliveries in the City of Rome: Retailers’ Perceptions and Stated
Reactions. Transp. Res. Part A Policy Pract. 2017,102, 142–156. [CrossRef]
76.
Van Heeswijk, W.; Larsen, R.; Larsen, A. An Urban Consolidation Center in the City of Copenhagen: A Simulation Study. Int. J.
Sustain. Transp. 2019,13, 675–691. [CrossRef]
77.
Holguín-Veras, J.; Wang, C.; Browne, M.; Hodge, S.D.; Wojtowicz, J. The New York City Off-Hour Delivery Project: Lessons for
City Logistics. Procedia Soc. Behav. Sci. 2014,125, 36–48. [CrossRef]
78.
Elbert, R.; Friedrich, C. Simulation-Based Evaluation of Urban Consolidation Centers Considering Urban Access Regulations. In
Proceedings of the 2018 Winter Simulation Conference (WSC), Gothenburg, Sweden, 9–12 December 2018; pp. 2827–2838.
79.
Lin, J.; Chen, Q.; Kawamura, K. Sustainability SI: Logistics Cost and Environmental Impact Analyses of Urban Delivery
Consolidation Strategies. Netw. Spat. Econ. 2016,16, 227–253. [CrossRef]
80.
van Heeswijk, W.J.A.; Mes, M.R.K.; Schutten, J.M.J.; Zijm, W.H.M. A Simulation Framework to Evaluate Urban Logistics Schemes.
2018. Available online: https://ir.cwi.nl/pub/27842/Framework.pdf (accessed on 28 September 2024).
81.
Verlinde, S.; Macharis, C.; Witlox, F. How to Consolidate Urban Flows of Goods Without Setting up an Urban Consolidation
Centre? Procedia Soc. Behav. Sci. 2012,39, 687–701. [CrossRef]
Sustainability 2025,17, 884 18 of 18
82.
Simoni, M.D.; Bujanovic, P.; Boyles, S.D.; Kutanoglu, E. Urban Consolidation Solutions for Parcel Delivery Considering Location,
Fleet and Route Choice. Case Stud. Transp. Policy. 2018,6, 112–124. [CrossRef]
83.
van Essen, H.; van Wijngaarden, L.; Schroten, A.; Sutter, D.; Bieler, C.; Maffii, S.; Brambilla, M.; Fiorello, D.; Fermi, F.; Parolin, R.;
et al. Handbook on the External Costs of Transport: Version 2019—1.1.; Publications Office: Luxembourg, 2019. Available online:
https://data.europa.eu/doi/10.2832/51388 (accessed on 4 October 2024).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.
Available via license: CC BY 4.0
Content may be subject to copyright.
