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Citation: Giordano, A.; Christidis, P.
Green Last-Mile Delivery: Adapting
Beverage Distribution to Low
Emission Urban Areas. Future Transp.
2025,5, 65. https://doi.org/10.3390/
futuretransp5020065
Copyright: © 2025 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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licenses/by/4.0/).
Article
Green Last-Mile Delivery: Adapting Beverage Distribution to
Low Emission Urban Areas
Alessandro Giordano and Panayotis Christidis *
European Commission, Joint Research Centre, 41092 Seville, Spain
*Correspondence: panayotis.christidis@ec.europa.eu
Abstract: Electrifying urban last-mile logistics is an important step towards reducing carbon
emissions which requires replacing conventional vehicles with low-carbon alternatives that
offer comparable operational and cost characteristics. This study presents a methodology
for evaluating the feasibility of electrifying an urban delivery fleet, using data from a major
beverage company in Seville as a case study. Applying a fleet and route optimization
algorithm for various vehicle combinations, we demonstrate that emerging electric vehicle
options, combined with a redesigned fleet mix and an optimized routing, can already
enable cost-efficient electrification of distribution activities in the city centre. Furthermore,
our analysis suggests that full electrification of the company’s local distribution network
may be possible by 2030, depending on the availability of larger electric trucks. Our results
show that currently available electric vehicles can fully substitute conventional options
in the case study context, with higher capital costs offset by lower energy costs in most
cases. The electrification of urban logistics can yield significant environmental benefits,
particularly if powered by a clean energy mix.
Keywords: low-carbon vehicles; green cities; last-mile delivery; sustainable city logistics;
urban freight transport; commercial fleets
1. Introduction
The city logistics sector plays a vital role in supporting urban economic activities,
but also poses significant environmental and social challenges, including congestion, air
pollution, noise, and road accidents. In Europe, city logistics is responsible for 25% of urban
transport-related CO
2
emissions and 30–50% of other transport-related pollutants [
1
]. The
growing urban population, combined with trends such as the rise in e-commerce and home
delivery, is expected to increase demand for goods and services, leading to a surge in urban
freight demand.
In response to these challenges, the European Commission has adopted several initia-
tives aimed at reducing emissions from the transport sector. The Sustainable and Smart
Mobility Strategy [
1
] and the Action Plan for sustainable mobility outline a comprehensive
approach to achieving a “green and digital transformation” of the EU transport sector, in
line with the goals of the European Green Deal. The main goal is to provide a strategy to
accomplish the “green and digital transformations” of the EU transport sector and meet
the goals as set out by the European Green Deal.
The New Urban Mobility Framework [
2
] further emphasizes the importance of “zero-
emission city freight logistics and last-mile deliveries”, highlighting the need for low-carbon
vehicles, novel distribution models, and sustainable urban logistic plans. This new proposal
includes actions ranging from the inclusion of low-carbon vehicles to the adoption of novel
Future Transp. 2025,5, 65 https://doi.org/10.3390/futuretransp5020065
Future Transp. 2025,5, 65 2 of 16
distribution models, the promotion of sustainable urban logistics plans (SULPs) and the
cooperation between public and private stakeholders, including voluntary data sharing [
3
].
Improving urban freight transport planning is a complex task, particularly for cities
seeking to reduce their environmental footprint. Captive fleets, such as those used by
beverage and food delivery companies, offer a promising opportunity for early adoption of
alternative fuel or vehicle types, reducing emissions, noise, and congestion externalities.
This case study explores the potential for a captive fleet to contribute to achieving
climate neutrality in urban delivery systems. Using realistic data from delivery operations
by a major beverage producer in Seville, we simulate possible scenarios for reducing
emissions and improving operational efficiency. The findings of this study provide valuable
insights into the opportunities and challenges associated with adapting urban delivery
systems to meet the climate goals of both cities and companies.
Several studies evaluated the potential introduction of low-carbon vehicles in urban
freight transport, covering a wide range of issues from operations (routing problems and
technical feasibility) to impacts (economic and environmental aspects, policy options) [
4
].
Perboli and Rosano [
5
] simulated emissions and costs of implementing specific scenarios of
parcel deliveries, using combinations of low-carbon vehicles and diesel vans in Turin, Italy.
Their model assumes the presence of a local/secondary (urban) depot for commercial cargo
bicycles, however only CO
2
emission externalities are considered. The authors use synthetic
distributions of delivery demand and routing, informed by specific parameters from the
city case study and data from the URBeLOG project [
6
,
7
]. As regards the behaviour of urban
freight operators, Stockhammer et al. [
8
] provide an overview of factors that influence their
choice when selecting vehicle modes.
Settey et al. [
9
] and Giordano et al. [
10
] explored the effects of topography, driving
cycles, and load weight/volume on the operational feasibility of electric vehicles and
bicycles in urban environments, respectively. However, their findings are focused on
specific vehicle technologies, rather than on the operational scenarios that companies could
implement to include low-carbon vehicles into their existing fleets. Melo and Baptista [
11
]
developed a traffic simulation model to estimate operational and environmental benefits of
electric cargo bi/tricycles, comparing their energy savings to diesel vans in Porto, Portugal.
Other studies focus on battery electric vehicle (BEV) energy consumption using GPS data
from delivery operations to serve as inputs in their route optimization or vehicle assessment
models [12].
While the existing literature on urban freight transport and last-mile delivery has
highlighted the importance of reducing emissions and environmental impacts, there are
still several gaps and limitations in the research. Many studies have focused on the
technical feasibility of alternative fuel vehicles, such as electric or hybrid vehicles, without
adequately considering the operational and economic implications of their adoption [
13
–
19
].
For instance, the study by Galati et al. [
13
] found that electric vehicles can be a cost-effective
option for short food supply chains, but their analysis was limited to a specific context and
did not account for the variability in demand and delivery routes.
Furthermore, the literature has often relied on simplified assumptions about the
behaviour of freight operators and the characteristics of urban freight demand. For example,
the study by Perboli and Rosano [
5
] simulated the emissions and costs of parcel deliveries
using a combination of low-carbon vehicles and diesel vans, but their model assumed a
fixed distribution of delivery demand and did not account for the potential impacts of
traffic congestion or road network characteristics.
In addition, the existing research tends to focus on specific aspects of urban freight
transport, such as vehicle technology or routing optimization, without considering the
broader systemic implications of different logistics strategies [
20
,
21
]. This limited the
Future Transp. 2025,5, 65 3 of 16
scope of the analysis to a specific situation and did not consider the potential scalability or
transferability of their findings to other contexts.
Several studies have assessed road transport external costs, such as congestion, noise,
road accidents, and road damage. Most focus on conventional passenger cars at country-
level, and their cost estimates are not directly applicable to cities and commercial vehicles.
Reference [
22
] examined the environmental impact of urban road freight in Paris, using
models to estimate traffic flows and assess pollutant emissions, finding significant environ-
mental costs attributed to freight traffic in the region. However, external costs are limited to
airborne emissions, and data availability limitations for freight demand modelling reduce
the accuracy and the applicability of the findings. Differentiations of external costs per road
vehicle types are also present in [
23
–
25
]. However, the level of geographic aggregation
and the limited coverage of commercial vehicles limit the application of their estimates to
specific cities.
In spite of the great variety of scientific literature dealing with last-mile deliveries, few
studies directly address externalities and costs faced by delivery fleet operators, and even
fewer applied their methodologies to model realistic low-carbon vehicle fleet scenarios
in urban environments. To address these limitations, our study aims to provide a more
comprehensive and integrated analysis of the potential for electrifying urban delivery
fleets, taking into account the complex interactions between vehicle technology, logistics
operations, and urban transport systems. By combining a critical review of the existing
literature with empirical analysis and scenario-based modelling, we seek to contribute
to a deeper understanding of the challenges and opportunities associated with reducing
emissions and environmental impacts in urban freight transport.
This case study aims to contribute to the field by applying a scalable approach, based
on data from real operations. It does so by comparing the external and private costs caused
by the implementation of different scenarios of fleet composition (i.e., including BEV and
diesel vans, electric cargo tricycle, or cargo bicycles) in the case of a major beverage delivery
company in the city of Seville, Spain.
2. Materials and Methods
Last-mile distribution is important in terms of urban transport activity and its impacts,
but an optimal balance between economic efficiency and environmental performance is
often difficult to achieve. Given the ambitious strategies of the EU [
2
], a growing number
of cities are exploring strategies to reduce GHG emissions from urban logistics [
26
]. In
the private sector, several operators are aiming at carbon neutrality, from production to
distribution, as well as at testing innovative pilot applications that facilitate the introduction
of electric micro-vehicles. In the work presented here, we analyze a case of adapting last-
mile distribution to the requirements of a new Low Emissions Zone (LEZ) in the city of
Seville. The case study was carried out in collaboration with a major beverage company
operating in the city, through the exchange of data and expertise concerning the operational
requirements by the company. The analysis compares alternative options for the adaptation
of the company’s urban distribution system to the new urban policy requirements. The
approach uses suitable economic and environmental indicators that address both the
operator’s and the city’s priorities.
The data provided by the company cover the distribution demand and routes in Seville
and its suburbs, covering the beverage distribution operations during a typical day (see
Table 1). The data include 141 delivery points, and (as multiple delivery orders are in the
same place) 106 delivery stops. Eight routes were included, with one of them having its
delivery points in the future Seville Low Emissions Zone (LEZ). Figure 1a illustrates the
Future Transp. 2025,5, 65 4 of 16
points of delivery and the current warehouse/distribution centre and Figure 1b zooms into
the LEZ.
Table 1. Data provided by the beverage company operating in Seville for a typical business day.
Points of Delivery (POD) Address Routes (n = 8)
Starting point (SP) address Route number
Time slot of delivery (start/end) Starting point (warehouse address/coordinates)
Estimated delivery duration Start/end time
Delivered quantity (‘equivalent packages’ or kg) Total distance driven
Geo coordinates (POD, SP) Total quantity delivered (‘equivalent packages’ or kg)
Route number Vehicle type
Sequence number in route Maximum vehicle capacity (‘equivalent packages’ or kg)
Based on data availability and interactions with distributors, we applied tailored fleet
management strategies and routing models to test options for the gradual decarbonisation
of the distribution routes, and to assess specific scenarios in terms of their economic and
environmental impacts. The application was developed using an implementation of the
OpenRouteService [
27
] in R and was applied for the combined optimization of fleet mix
and route choice. We tested the scenarios outlined in Table 2, including several variables
such as time horizons (2025 or 2030), combinations of vehicle technologies (diesel/electric
trucks and vans, electric tri/bicycles), geographical coverage limitations (full distribution,
or electric inside LEZ/City Centre and conventional outside), and existence of distribution
centres (a currently used distribution centre, or an additional warehouse located in the
LEZ perimeter).
(a)
Figure 1. Cont.
Future Transp. 2025,5, 65 5 of 16
(b)
Figure 1. (a) Points of delivery (red stars) and distribution centre (red ring). Adjacent PODs are
grouped in the same red star. (b) Planned Low Emissions Zone in Seville (in blue), points of delivery
(POD—red stars) and alternative warehouse for city centre deliveries (green ring). Adjacent PODs
are grouped in the same red star.
Table 2. Scenario descriptions.
Scenario Scenario Group Scenario Description
C Baseline Full conventional fleet
CEZ Vehicle Fleet Conventional routes outside Low Emission Zone
(LEZ), BEVs in LEZ
CTEV Vehicle Fleet Conventional trucks, BEV vans
E Vehicle Fleet All BEVs [vans and trucks]
DEZ1 City Warehouse + Partial Vehicle Fleet Conventional trucks, BEV vans and cargo tricycles in
LEZ/City Centre
DEZ2 City Warehouse + Partial Vehicle Fleet Conventional trucks, BEV vans and cargo tricycles in
LEZ/City Centre, bicycles in LEZ
DEV1 City Warehouse + Partial Vehicle Fleet Conventional trucks, BEV vans, plus BEV vans and
cargo tricycles in LEZ/City Centre
DEV2 City Warehouse + Partial Vehicle Fleet
Conventional trucks, BEV vans, plus BEV vans and
cargo tricycles in LEZ/City Centre, bicycles
inside LEZ
DET1 City Warehouse + Vehicle Fleet ALL BEVs and cargo tricycles in LEZ/City Centre
DET2 City Warehouse + Vehicle Fleet ALL BEVs and cargo tricycles in LEZ/City Centre,
bicycles in LEZ
In each scenario, after accounting for the vehicles’ size, their carrying capacity and
speed, we estimated the required number of vehicles by powertrain and size that can cover
demand at optimal efficiency. In assessing technological options, we made forecasts based
Future Transp. 2025,5, 65 6 of 16
on desk research and existing literature to identify potential options for electric trucks,
vans, and cargo bicycles by 2030. This process included the analysis of factors such as the
vehicles’ purchase and operation costs, energy consumption, carrying capacity, speed, and
range. Additionally, the analysis incorporated cargo capacity and energy consumption data
for both conventional and emerging vehicle technology options available in the market or
close to their commercialization.
The analysis and comparison of the specific scenarios were conducted using indicators
that enabled the assessment of operational, economic, and environmental impacts. For
each scenario, the following indicators were estimated:
•Total driving distance (vehicle/km) per technology (conventional/electric)
•Number of vehicles required (by vehicle technology and size)
•Fuel/electricity energy consumption (conventional/electric)
•
Total cost of ownership (by vehicle technology and size) not including the cost of the
new warehouse
•Total CO2emissions (by vehicle technology and size)
•Total external costs (by vehicle technology and size)
•Total manpower required (by vehicle technology and size)
The costs used for the calculations were based on assumptions concerning vehicle
purchase, maintenance and depreciation costs, and on personnel costs. Electric vans capital
costs were assumed to be higher than diesel vans (40% higher in 2025 and 20% higher in
2030 due to battery price evolution). This is a more conservative approach than in a recent
study by [
28
], where the purchase price parity between diesel and electric vans is expected
earlier. Price differences between electric and diesel trucks are based on [
29
]. Electric truck
purchase costs were 200% higher in 2025 and 70% higher in 2030 compared to diesel trucks.
The intermediate distribution point (city warehouse) is a potential investment made by the
city of Seville and would not have a direct repercussion on the costs of the operators.
The required energy consumption was estimated based on the vehicle technology,
size, and age options, assuming flat topography and using COPERT v.5.5 software for
diesel vehicles, and results from the Handbook on External Costs of Transport [
30
] for
low-carbon vehicle options. GHG emissions per kilowatt/hour were estimated based on
the electricity generation mix at country level from the European Environment Agency [
31
].
In addition to the estimates on fuel consumption and emissions, the approach includes the
assessment of fixed and variable private costs, including the potential impact on labour
demand. Annual mileage projections for the fuel/energy costs are based on the typical day
of operations provided by the beverage company and 300 business days per year, while for
capital costs we assume a vehicle life of twelve years. Finally, the external costs of transport
are calculated for the different scenarios. Besides GHG emissions, these include noise, air
pollution, road damage, congestion and road accidents, based on [25].
3. Results
The first question concerning the potential shift to alternative technologies addresses
the changes in the fleet mix necessary in each scenario. The main factors that affect the
number of vehicles required are the capacity and range for each alternative. Table 3presents
a summary of the distances by each vehicle type, and scenario, and it reports the maximum
cargo weight capacity of each vehicle. Most of delivery points in the City Centre included
in Figure 1b are well within the delivery range of the tricycle, including those outside the
Low Emissions Zone. When analyzing the delivery range of the e-bicycle, we took into
account delivery points located within the Low Emissions Zone next to the new warehouse.
Future Transp. 2025,5, 65 7 of 16
Table 3. Distance per day (km) and type of vehicles employed in each scenario. Vehicles description
includes payload.
Vehicle Type and
Payload C CEZ CTEV E DEZ1 DEZ2 DEV1 DEV2 DET1 DET2
Conventional
truck 6.5 t 82 82 82 - 82 82 82 82 - -
Conventional van
1.4 t 260 152 - - 152 152 - - - -
Electric truck 6.5 t
- - - 82 - - - - 82 82
Electric van 1.4 t - 108 260 260 71 71 222 222 222 222
Electric cargo
tricycle 0.5 t - - - - 21 19 21 19 21 19
Electric cargo
bicycle 0.1 t - - - - - 12 - 12 - 12
Total 342 342 342 342 325 335 325 335 325 335
Table 4indicates the number of vehicles needed (by technology) in each scenario. In the
specific case study assessed in this paper, which included data from a single “typical” day of
operations of the beverage company in Seville, the deliveries performed by the tri/bicycle
do not reduce the number of vans required. It is expected that a more comprehensive
analysis of routes over a more extended period will help to better characterize the delivery
operation, which could result in a reduction in number of vans in the fleet in favour of
tri/bicycles. The required Full Time Equivalent (FTE) personnel remain the same in all
scenarios, since just one full-time van driver is converted into half-time, and only an
additional half-time driver for tri/bicycle is required.
Table 4. Number of vehicles per scenario.
Vehicle Type and
Payload C CEZ CTEV E DEZ1 DEZ2 DEV1 DEV2 DET1 DET2
Conventional
truck 6.5 t 2 2 2 - 2 2 2 2 - -
Conventional van
1.4 t 6 5 - - 5 5 - - - -
Electric truck 6.5 t
- - - 2 - - - - 2 2
Electric van 1.4 t - 1 6 6 1 1 6 6 6 6
Electric cargo
tricycle 0.5 t - - - - 1 1 1 1 1 1
Electric cargo
bicycle 0.1 t - - - - - 1 - 1 - 1
A summary of annual costs is presented in Table 5for 2025 and 2030. Fuel, electricity,
and personnel costs are kept constant between 2025 and 2030 to capture the impact of
vehicle capital costs. While we find that more than 80% of the costs are related to per-
sonnel (truck and van drivers’ wages and insurance costs are assumed to be the same
of tri/bicycles riders). Moreover, the higher capital costs of electric vehicles, compared
to their diesel counterparts, are offset by the reduced electricity expenses in comparison
to fuel costs. These findings are in line with [
13
,
16
,
17
] and are justified by the projected
high annual mileage of the vehicles in the fleet to meet the beverage distribution de-
mand. Similarly to these studies, our results also reveal that fleets with battery electric
vehicles are cost-competitive with their diesel fleet counterparts. Differently from the
above-mentioned literature, our results are applied to fleets scenarios, simulated over real
delivery routes in urban settings. The findings aim to contribute to the discussion on how
Future Transp. 2025,5, 65 8 of 16
to include low-carbon emission vehicles in green fleets and on their expected economic and
environmental impacts.
Table 5. Fleet annualized costs in 2025 and 2030.
Cost (‘000s
EUR/Year) C CEZ CTEV E DEZ1 DEZ2 DEV1 DEV2 DET1 DET2
Vehicles fixed
costs 2025 21.9 22.9 27.8 42.8 23.5 23.9 28.4 28.7 43.4 43.7
Vehicles fixed
costs 2030 21.9 22.4 24.6 29.9 22.8 23.2 25.1 25.4 30.3 30.7
Fuel/energy costs
2025, 2030 32.3 25.9 16.8 12.1 25.1 25.1 16.1 16.1 11.3 11.3
Personnel cost
2025, 2030 223.4 223.4 223.4 223.4 223.4 223.4 223.4 223.4 223.4 223.4
Table 6presents the annualized emissions and costs for 2025 and 2030. The most
significant CO
2
emissions reductions compared to Baseline scenario ‘C’ appear when the
complete van fleet is electric, with reductions greater than 60% and reaching 90% when
the truck fleet is converted to electric. On the other hand, the partial electrification of vans
(i.e., only those crossing the LEZ/City Centre) shows a limited CO
2
emission decrease of
25%. Costs of the different scenarios compared to Baseline scenario ‘C’ are in the range of
[−4%, 0%]
for 2025 and [
−
5%,
−
2%] for 2030. In the latter, lower capital costs are assumed
for electric vehicles compared to 2025.
Table 6. Scenarios’ CO2emissions, cost per day, and comparison to Scenario “C” in 2025 and 2030.
Year 2025 C CEZ CTEV E DEZ1 DEZ2 DEV1 DEV2 DET1 DET2
Emissions
(tCO2/year) 63 47 24 7 46 46 24 24 7 7
Cost (‘000s
EUR/year) 278 272 268 278 272 272 268 268 278 279
Emissions change - −25% −61% −89% −26% −26% −62% −62% −89% −89%
Cost changes - −2% −3% 0% −2% −2% −4% −3% 0% 0%
Year 2030
Emissions
(tCO2/year) 63 46 23 5 46 46 23 23 5 5
Cost (‘000s
EUR/year) 278 272 265 265 271 272 265 265 265 265
Emissions change - −26% −63% −92% −27% −27% −63% −63% −93% −93%
Cost changes - −2% −5% −4% −2% −2% −5% −5% −5% −4%
In order to test the robustness of the results, we carried out a sensitivity analysis
using modified distributions of the delivery points across the urban area. We followed
the approach described in [
32
] and geo-localized 60 bars and restaurants within the LEZ
that could potentially be a client of the beverage distribution system. We performed
500 simulations,
each using a random subset of 20 delivery points. Similarly to the approach
in [
32
], the selection of 20 delivery points in the study is considered as a realistic pattern
for beverage distribution, an assumption confirmed by interviews with operators. In each
simulation, the stochastic selection of delivery points resulted in varying average distances
to the main distribution centre and to the intermediate distribution centre (city warehouse).
As a consequence, the optimal combined fleet mix and routing solution identified by the
Future Transp. 2025,5, 65 9 of 16
OpenRouteService algorithm produced different estimates for total costs and emissions in
each scenario.
Figure 2is an illustration of the variance of the estimates, using the example of scenario
DET2. In 369 of the 500 simulations (73.8%), the total costs for scenario DET2 in 2030 are
estimated to be lower than those for the baseline scenario C, i.e., a shift to a combination
of BEV’s, cargo tricycles and bicycles in the LEZ with an intermediate distribution centre
would result in an economic benefit for the operator. The median corresponds to an annual
benefit of EUR 17,600, with a mean of EUR 13,800, maximum of EUR 48,100 and minimum
of EUR
−
32,500 (loss). The x-axis in Figure 2represents the ratio of the total route distance
within the zone served through the intermediate distribution centre, compared to the total
route distance if all deliveries are made directly from the main distribution centre. A
higher ratio corresponds to the intermediate distribution centre being closer to the main
distribution centre and –as a consequence- at a longer average distance from the delivery
points. The y-axis represents the ratio between the time required to cover each route by
van and that by electric bi/tri-cycle. This ratio captures the difference in speed between the
two modalities and can be a proxy for the difference in driver productivity. The size of the
bubbles in the graph represents the total benefit or loss for the operator in scenario DET2,
compared to the baseline C. It is obvious from the graph that there is a sweet spot where
scenario DET2 is consistently more beneficial than the baseline scenario. Most simulations
that estimate a benefit are concentrated towards cases where the delivery destinations are
located at shorter distances from the city warehouse and can be accessed through the city’s
cycling network. In such cases, cargo bi/tri-cycles may be more efficient than delivery
vans, despite their lower speed and capacity. The cases where the combination of a city
warehouse with bi/tri-cycles may operate at a loss tend to concentrate in two regions of
Figure 2: towards the right side of the figure, in cases where the delivery points are spread
widely across the city, direct distribution from the central distribution point may be more
efficient than using the city warehouse as an intermediate distribution centre; and towards
the bottom left of the figure, in cases where the delivery points are located in less dense
areas, the operational efficiency of bi/tri-cycles is lower than that of delivery vans.
Figure 3summarizes the external cost estimates in the 2025 scenario. The estimates are
a combination of fleet composition described in Tables 3and 4with vehicle externalities’
factors on a per vehicle/kilometre basis. The baseline diesel fleet, corresponding to scenario
‘C’, is the baseline where all deliveries are performed by vehicles with Euro II to Euro V
emission standards (i.e., we take the mean emission estimates between these two standards).
In this scenario, we estimate that the annual external costs of operating two trucks and six
conventional diesel vans, to meet the beverage demand of business in the metropolitan
area of Seville, could vary between EUR 75,000 and 115,000. The main factors contributing
to these costs are congestion, air pollution, noise and GHG emissions. The analysis finds
that the main external cost reductions, derived from introducing BEV and tri/bicycles and
compared to the baseline scenario, are in terms of GHG emissions, air pollution and noise.
These environmental benefits are more pronounced in scenarios “E”, “DET1” and “DET2”,
where internal combustion engine vehicles are entirely excluded. These are followed by
scenarios “CTEV”, “DEV1” and “DEV2”, which permit the incorporation of conventional
trucks in the fleet mix only for operating delivery routes in the city outskirts.
Future Transp. 2025,5, 65 10 of 16
Figure 2. Sensitivity analysis of economic impact of scenario DET2 compared to scenario C, depending
on distribution of delivery points and distribution centre (bubble size proportional to benefit/ loss,
maximum value = EUR 48,100).
Figure 3. Annual external costs of transport by scenario in 2025. Baseline scenario ‘C’ has three diesel
fleet scenarios: using either only new (Euro VI), mean (Euro II-V), or old (Euro 0) vehicles. Diesel
vehicles in the other scenarios are Euro II-V diesel trucks/vans.
Future Transp. 2025,5, 65 11 of 16
Since electric and conventional vans and trucks have practically the same size, we
observe a benefit in terms of the costs of congestion only when the fleet includes lighter
vehicles. However, these positive effects are constrained by the limited mileage tri/bicycles
could cover in the scenarios, given the combination of size and weight of the beverage
goods and the limited load capacity of these vehicles. At an aggregate level, the case
study estimates that benefits, in terms of reduced externalities, can amount to up to EUR
40,000–70,000 annually. The outcomes can also be categorized into three tiers: (i) a low
reduction in external costs (from
−
10% to
−
15%) when only vans entering the LEZ/City
Centre are electrified; (ii) a medium reduction (from
−
25% to
−
30%) when all vans are
converted to electric; and (iii) a high reduction (from
−
37% to 42%) when the entire fleet is
transformed to either electric vehicles or a combination of BEVs and tri/bicycles.
4. Discussion
The results of this case study contribute to the discussion on the potential of emerging
technologies for green distribution and their expected impacts in economic and environ-
mental terms. Our findings on the economic viability of electrifying urban delivery fleets
are in line with those of Gil Ribeiro and Silveira [
16
], who also conclude that fleets with
battery electric vehicles are cost-competitive with their diesel fleet counterparts. However,
our results are applied to various fleet combination scenarios simulated over real delivery
routes in urban settings, extending the scope of the analysis to a combined fleet mix and
logistics optimization problem. In contrast to the study by Galati et al. [
13
], which found
that electric vehicles were not cost-effective for short food supply chains, our results suggest
that the higher capital costs of electric alternatives can already be compensated by the lower
energy costs in most cases.
The environmental benefits of electrifying urban delivery fleets are also consistent with
the findings of other studies [
33
]. For example, the study by Iwan [
34
] found that electric
mobility can reduce emissions in urban freight and logistics, while the study by Napoli
et al. [
35
] found that freight distribution with electric vehicles can reduce emissions and
operating costs. Our results also corroborate the findings of Ehrler et al. [
36
], who found
that electric vehicles can be a viable option for last-mile logistics of grocery e-commerce.
However, our study goes further by considering the potential impacts of different logistics
strategies, such as the introduction of intermediate distribution centres, on the economic
and environmental performance of urban delivery fleets.
The boundary conditions for the use and efficiency of an intermediate distribution
centre in this case study are consistent with the theoretical expectations outlined in Perboli
and Rosano [
5
]. Our findings on the potential of intermediate distribution centres to
facilitate the use of smaller and cleaner vehicles for last-mile distribution are also supported
by case studies on urban logistics, such as the study by Bruni et al. [
37
], Savall-Maño
and Ribas [
38
] and Katsela et al. [
39
]. However, our results highlight the importance of
considering the average weight and volume of individual shipments, as well as urban
density and average distribution distance, when evaluating the suitability of cargo bi/tri-
cycles. This is in line with the findings of Schliwa et al. [
40
], who found that cargo cycles can
be an efficient option for last-mile delivery in urban areas but require careful consideration
of the operational and logistical constraints.
The end-use of electric mobility is a critical component of the overall electromobility
ecosystem. As the demand for electric vehicles grows, the development of charging
infrastructure and the production of electric vehicles will need to keep pace. However, the
end-use of electric mobility is also where the benefits of electrification are most directly
realized, in terms of reduced emissions and improved air quality. By focusing on the end-
use of electric mobility, our study is able to provide insights into the ways in which electric
Future Transp. 2025,5, 65 12 of 16
vehicles can be used to improve the efficiency and reduce the environmental impacts of
urban freight transport. We find that electrifying urban delivery fleets can have significant
economic and environmental benefits, including reduced fuel costs, lower emissions, and
improved air quality. These benefits are consistent with the findings of other studies, which
have highlighted the potential for electric vehicles to reduce emissions and improve air
quality in urban areas [16,34,35].
Overall, our study contributes to the existing literature by providing a more compre-
hensive and integrated analysis of the potential for electrifying urban delivery fleets, taking
into account the complex interactions between vehicle technology, logistics operations,
and urban transport systems. By comparing our results with those obtained by other
scientists, we can see that our findings are consistent with the broader trends and patterns
observed in the field but also provide new insights and perspectives on the challenges and
opportunities associated with reducing emissions and environmental impacts in urban
freight transport. Furthermore, our study’s findings are supported by [
41
], which applied a
quantitative scenario-based model for assessing the impacts of cargo bike transhipment
points in urban districts. In addition, the study’s findings are in line with the results of [
42
],
which investigated the vehicle routing problem with delivery options and found that cargo
bikes can be a viable option for reducing emissions in urban areas.
A particular aspect that needs to be taken into account for the interpretation of the
results is the fact that the intermediate distribution centre—a main element for the design of
the green last-mile delivery scenarios—is assumed to be provided by the city of Seville free
of cost to the operator. The measure was adopted by the city of Seville as part of its strategy
to achieve carbon neutrality in urban logistics [
43
]. This is obviously a site-specific situation
which cannot be generalized for any similar application elsewhere. In the case where the
establishment of an intermediate centre would incur additional costs for the operator, these
can still be compared to the overall net economic benefit from a shift to such a distribution
scheme. In the case study analyzed here, all six scenarios involving a city warehouse appear
to produce economic benefits in the range of EUR 6 to 13 thousand annually, a quantity that
would be sufficient to cover at least a large share of the additional costs. Moreover, as the
sensitivity analysis suggests, there are several possible configurations of the distribution
system that can lead to even larger economic benefits, e.g., up to EUR 48,100 per year in
the case of the maximum for scenario DET2. Nevertheless, there may be an additional
strong point in favour of the involvement of local authorities through the provision of no-
or low-cost transhipment areas. The potential savings in terms of external costs can be
considerable and may exceed the amount of EUR 20,000 annually (Figure 2, scenario DET2
compared to scenario CEZ). Given the potential of the external cost reduction, the choice
for local authorities to subsidize at least part of the cost of an intermediate distribution
centre can be a cost-effective measure, especially if the installations can be shared among
various operators.
There are obviously several additional caveats that should be highlighted. The case
study focuses on a specific business application and context. While the beverage sector has
similar characteristics in terms of logistic patterns with several other sectors that require
deliveries at urban level, the specific delivery sizes, distances and frequencies in each sector
may allow different levels of flexibility in terms of vehicle types and fleet mixes. There
is considerable uncertainty as regards the assumptions on current and future costs, fuel
consumption and emissions, while modelling results cannot be always assumed as accurate.
We explored the potential impacts of several of the factors that lead to uncertainty in the
sensitivity analysis, but there are still issues that could benefit from a dynamic traffic flow
modelling analysis, especially as regards the impact on congestion. The methodology
Future Transp. 2025,5, 65 13 of 16
applied is, nevertheless, transparent in terms of the main assumptions and tools used and
allows a replication in future research using more adequate assumptions.
5. Conclusions
The case study described here explores the potential for the introduction of green
alternatives for last-mile distribution for a specific sector (beverages) in a certain urban
area (Seville). The findings for this application can contribute to the overall discussion on
the potential of emerging technologies for green distribution and the expected impacts in
economic and environmental terms. At the same time, the methodology and the software
tool used in this case study can be easily replicated in other sectoral and geographic settings,
since they allow a realistic simulation of the potential impact of different approaches to
decarbonize the specific distribution system. The results can also be used for the calculation
of efficiency indicators in order to benchmark specific sectors or compare across cities.
The analysis presented in this study focuses on the last link in the chain of electric
efficiency in cities, specifically the end-use of electric mobility in urban freight transport.
As noted in the New Urban Mobility Framework within the EU [
2
], the complex costs of
creating the whole electromobility ecosystem are deliberately not included in the frame-
work. Instead, the framework emphasizes the importance of improving the efficiency and
reducing the environmental impacts of urban transport, with a particular focus on the last
mile of the delivery. The creation of a comprehensive electromobility ecosystem involves
a range of complex costs and challenges, including the development of charging infras-
tructure, the production of electric vehicles, and the integration of electric mobility into
existing transport systems. By focusing on the end-use of electric mobility, our study is able
to provide a detailed analysis of the potential benefits and challenges of electrifying urban
delivery fleets, without being enmeshed by the complexities of the broader electromobility
ecosystem. This approach allows us to identify the key factors that influence the adoption
of electric vehicles in urban freight transport, including the cost of vehicles, the availability
of charging infrastructure, and the operational characteristics of delivery routes.
Our study provides a detailed analysis of the potential benefits and challenges of
electrifying urban delivery fleets, with a focus on the end-use of electric mobility. By
recognizing the complexities of the broader electromobility ecosystem but focusing on
the last link in the chain of electric efficiency in cities, we are able to provide insights into
the ways in which electric vehicles can be used to improve the efficiency and reduce the
environmental impacts of urban freight transport. Our findings have implications for
policymakers, businesses, and individuals seeking to reduce the environmental impacts of
urban transport, and highlight the need for continued research and development into the
potential of electric mobility to transform the urban transport sector.
The methodology followed allows the comparison between different alternative tech-
nologies using a highly parameterized approach. This is particularly useful in situations of
high uncertainty, as is the case of the future evolution of costs and operational characteris-
tics of emerging technologies. The analysis was carried out using current knowledge and
estimates, with the possibility of exploring the sensitivity of the results to each particular
assumption. A number of robust conclusions can therefore be derived:
•
The range and operational characteristics of currently existing electric options are
sufficient to fully substitute the conventional options currently used in the case study
context. In an urban distribution context, the average total distance covered by
distribution vans is lower than 100 km per day, well within the range allowed by
electric vans currently available in the market.
Future Transp. 2025,5, 65 14 of 16
•
The higher capital costs of electric alternatives can already be compensated by the
lower energy costs in most cases. It is expected that the introduction of larger electric
freight vehicles (with a payload of 6.5 t or more) would also make that segment
competitive with conventional options by 2030.
•
The electrification of the vehicles used for distribution would result in clear envi-
ronmental benefits, especially if the electricity consumed comes from a clean power
generation mix. Apart from the direct reductions of CO
2
and pollutant emissions, a
distribution system that uses a suitable combination of emerging distribution tech-
nologies can also reduce other external costs, such as congestion or noise.
•
The findings confirm the potential of electrification in reducing the externalities from
urban logistics. In addition, this work contributes to the literature by applying a
combined fleet and routing optimization model to consider alternative vehicle types
and sizes.
•
The average weight and volume of individual shipments may affect the suitability of
each alternative. In the specific case study presented here, electric bikes and tricycles
can be indeed efficient. Future work can explore applications in different market
segments using varying shipment sizes.
•
Urban density and the average distribution distance can affect the efficiency of a
last-mile delivery system based on electric vehicles. Our results suggest that such an
approach is suitable in the case of distributing beverages in Seville. Future work can
explore the feasibility of such an approach in areas with a lower density of demand.
•
Additional logistic solutions, such as the introduction of intermediate distribution
centres, can facilitate the use of smaller and cleaner vehicles for last-mile distribution.
In this particular case study, the investment required for the intermediate distribution
centre is provided by the city authorities and can be an example of a policy measure
that encourages green logistics.
Author Contributions: Conceptualization, A.G. and P.C.; methodology, A.G. and P.C.; formal analysis,
A.G. and P.C.; writing—original draft preparation, A.G. and P.C.; writing—review and editing, A.G.
and P.C.; visualization, A.G. and P.C. All authors have read and agreed to the published version of
the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Data available on request from the authors.
Conflicts of Interest: The views expressed in this paper are the sole responsibility of the authors
and do not necessarily reflect those of the European Commission. The authors declare no conflict
of interest.
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