master of science in maritime science - universiteit gent
TRANSCRIPT
MASTER OF SCIENCE IN MARITIME SCIENCE
MASTER DISSERTATION
Academic year 2015 – 2016
The effect of electric mobility on a social
cost-benefit analysis
Student: Thari Schokkaert
Submitted in partial fulfillment of the
requirements for the degree of:
Master of Science in Maritime Science
Supervisor: Pr. Dr. Frank Witlox
Assessor: Dirk Lauwers
Confidentiality clause
PERMISSON
The signee declares the content of this master dissertation can be consulted/reproduced, if
cited.
Thari Schokkaert
I
Foreword
This master dissertation is written in the partial fulfillment of the requirements for the
Advanced Master degree in Maritime Science at Universiteit Gent and Vrije Universiteit
Brussel.
I wish to thank my promoter, Dr. Pr. Frank Witlox for his guidance throughout the year and
my family Daniël, Ilian, Liam en Rowan Schokkaert, for their support.
II
Table of contents
List of abbreviations ............................................................................................................................. IV
List of tables .......................................................................................................................................... V
List of graphs ........................................................................................................................................ VI
List of figures ........................................................................................................................................ VI
1 Introduction.................................................................................................................................... 8
1.1 Research question .................................................................................................................. 9
1.2 Structure ................................................................................................................................. 9
2 Literature review .......................................................................................................................... 10
2.1 Electric mobility in ports ....................................................................................................... 10
2.1.1 Electric truck types ....................................................................................................... 10
2.1.2 Current practices .......................................................................................................... 13
2.1.3 Belgian implementation................................................................................................ 13
2.2 Belgian social cost-benefit analysis standard for port infrastructure .................................... 15
2.3 External effects from port induced hinterland transport ...................................................... 18
2.3.1 Influence of electric mobility on a SCBA standard ........................................................ 21
2.3.1.1 Environmental external costs ................................................................................... 21
2.3.1.1.1 Climate ............................................................................................................... 25
2.3.1.1.2 Air quality ........................................................................................................... 26
2.3.1.1.3 Key figures for air quality and climate externalities in the standard .................. 27
2.3.1.1.4 With traffic and transport model: electric trucks ............................................... 30
2.3.1.1.5 Without traffic and transport model: electric truck key figures ......................... 31
2.3.1.2 Noise ......................................................................................................................... 35
2.3.1.3 Accidents .................................................................................................................. 36
2.3.1.4 Congestion and infrastructure costs ......................................................................... 38
2.4 Result .................................................................................................................................... 39
3 Case study .................................................................................................................................... 40
3.1 Port of Antwerp .................................................................................................................... 40
3.1.1 Electric mobility implementation .................................................................................. 43
3.2 Saeftinghe Development Area .............................................................................................. 45
3.3 Hinterland transport ............................................................................................................. 47
3.3.1 Step 1: Determining the influence on hinterland transport .......................................... 48
3.3.2 Step 2: Key figures for external costs of the hinterland transport ................................ 51
III
3.3.3 Step 3: Calculating the total external costs of the hinterland transport ....................... 56
3.3.4 Sensitivity analysis ........................................................................................................ 60
4 Conclusion .................................................................................................................................... 62
Bibliography .......................................................................................................................................... XI
IV
List of abbreviations
CH4 = Methane
CO2 = Carbon dioxide
CV = conventional vehicles
GHG = Greenhouse-Gas
ICE = Internal Combustion Engine
N20 = Nitrous oxide
NMHCs = Non-methane hydrocarbons
NOx = Nitrogen oxides
O3 = Ozone
PHEV = Plug-in Hybrid Electric Vehicles
SCBA = Social Cost-Benefit Analysis
V
List of tables
Table 1: Electric vehicle registration in Belgium ................................................................................... 13
Table 2: External cost categories .......................................................................................................... 19
Table 3: Externalities calculation methods ........................................................................................... 20
Table 4: GHGs produced in power plants ............................................................................................. 23
Table 5: Air pollutants produced in power plants ................................................................................. 23
Table 6: External costs of greenhouse gasses ....................................................................................... 25
Table 7: Global warming potential (CO2-equivalent) ............................................................................ 25
Table 8: Damage of air pollutants per volume-unit (euro per kg, price level 2010). ............................. 26
Table 9: Emission factors light trucks (<12 ton) .................................................................................... 27
Table 10: Emission factors heavy trucks (>12 ton) ................................................................................ 28
Table 11: Damage of emissions (euro per 100 vkm, price level 2010). ................................................. 29
Table 12: Key figures marginal external costs of emissions for electricity production, transport and
distribution in 2010 and expected evolutions in 2020 (in euro/g, price level 2010) ............................. 31
Table 13: Vehicle electricity consumption ............................................................................................ 32
Table 14: Marginal external cost (€/100 km) ........................................................................................ 33
Table 15: Marginal external environmental costs ................................................................................. 34
Table 16: Marginal noise costs per vehicle kilometer (euro per 100 vkm, price level 2010)................. 35
Table 17: Damage of victim costs (euro per victim, price level 2010) ................................................... 36
Table 18: Damage for traffic victims (euro per victim, price level 2010) ............................................... 37
Table 19: Marginal external costs for road traffic (euro per 100 vkm) .................................................. 39
Table 20: Distances to production centers from Antwerp (in KM). ....................................................... 44
Table 21: Investment costs for the Saeftinghe Development Area ....................................................... 46
Table 22: External effects from hinterland transport ............................................................................ 47
Table 23: Modal split for hinterland transport...................................................................................... 47
Table 24: Average load per vehicle ....................................................................................................... 48
Table 25: Distance saved in hinterland traffic – international point of view ......................................... 49
Table 26: Average distance increase in hinterland traffic – national point of view ............................... 50
Table 27: Key figures for external costs of hinterland transport (€/100 km vehicle kilometer) ............ 51
Table 28: Evolution of the key figures ................................................................................................... 52
Table 29: Marginal external costs for hybrid trucks > 12 tons .............................................................. 53
Table 30: Evolution of the key figures for valuing external costs of hinterland transport ..................... 54
Table 31: Evolution of the key figures for valuing external costs of hinterland transport- continued ... 55
VI
Table 32: Savings in external costs in hinterland transport – international point of view .................... 56
Table 33: Savings in external costs in hinterland transport (continued) ............................................... 57
Table 34: Increase in external costs in hinterland transport – national point of view .......................... 58
Table 35: Increase in external costs in hinterland transport (continued).............................................. 59
Table 36: Sensitivity analysis – unchanged modal split of hinterland transport. .................................. 60
Table 37: Sensitivity analysis – (continued). ......................................................................................... 61
List of graphs
Graph 1: Belgian energy mix................................................................................................................. 22
Graph 2: Nox reduction 2000 - 2013 .................................................................................................... 42
Graph 3: Particulate matter reduction 2000 - 2013 .............................................................................. 42
Graph 4: CO2 reduction 2000 - 2013 ..................................................................................................... 43
List of figures
Figure 1: Electric truck power train configuration ................................................................................ 11
Figure 2: Truck classification by gross vehicle weight ........................................................................... 12
Figure 3: Eleven steps in the SCBA for Belgian ports ............................................................................ 16
Figure 4: Location of the Port of Antwerp ............................................................................................ 40
Figure 5: Road network of the Port of Antwerp ................................................................................... 41
Figure 6: Map of the Port of Antwerp and the Saeftinghe Development Area (nr. 6)........................... 45
Figure 7: Plan of phase one and the three project alternatives ............................................................ 46
VII
8
1 Introduction
Burns et al found five building blocks that promise incremental improvements over today’s roadway
transportation services, one in particular is the use of advanced propulsion systems: moving trucks
using alternative energy sources and power systems, and typically entails electric propulsion, in
addition to oil and combustion engines (Burns, Jordan, Scarborough, 2013). Electric road
transportation has the advantage of reducing externalities relative to their gasoline counterparts,
which is of the importance for the transport industry as externalities are exceptionally higher and
more diffuse as it covers large distances and happens in open space (Blauwens, De Baere, Van De
Voorde, 2012; Holland, Mansur, Muller, Yates, 2015). Electrifying road transport could locally help
mitigate urban air pollution and globally, address climate change (Delucchi, Yang, Ogden, Kurani,
Kessler, Sperling, 2014, Leurent & Windisch, 2015).
Belgium is highly dependent on foreign trade and due to its central geographical location in Europe
and connection via the North Sea, the big four Belgian ports, Antwerp, Gent, Zeebruge and Ostend
have become a centre of multinational operations and a shipping hub with a significant amount of
transshipment traffic. Global gateway ports like Antwerp and Ghent generate a substantial proportion
of freight movements, which travel to hinterland locations and markets mostly constituting of trucks
as the main mode (Berechman, 2009). The contribution of the ports to the region’s economy is
substantial, for example Antwerp constituting 5,2 % of the Belgian GDP and 9% for Flanders, so
decision makers are often inclined to support expansion plans of the port’s infrastructure, however,
these projects and the traffic pattern it generates has some considerable societal cost such as
increased emissions from trucks traveling to and from ports, which produce significant environmental
costs (Berechman & Tseng, 2012).
The externalities from port induced road transport can potentially be reduced in case these are
substituted by electric alternatives. Ports are closely situated to power generation facilities and at the
same time metropolitan areas. It is an energy hub with energy intense industries, power generation
and distribution systems, and is often located in areas appropriate for renewable energy: wind,
waves, tide differentials and large surfaces for solar panels, which means the electricity generated for
the electric vehicles can potentially stem from clean energy sources and generate low emissions
(Acciaro, Ghiara, Cusano, 2014; Leurent & Windisch, 2015).
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A growing interest and development of electric road transport in ports will lead to new ideas and
methodologies for executing a social cost-benefit analysis for large port infrastructures, e.g. building a
quay wall or a lock. More importantly for the electric mobility building block, replacing an internal
combustion engine with advanced electric propulsion systems can result into different interpretations
of assessing the external effects of transport flows the port infrastructures invoke.
1.1 Research question
Considering the rise in popularity of electric mobility, the image of electric vehicles having lower
externalities, ports as an ideal location for electric mobility in Belgium and the risk of various
interpretations of executing a social cost-benefit analysis for port infrastructures, this dissertation
takes a look at the influence of electric mobility on a social cost-benefit analysis in Belgian ports,
leading to the following research question.
How does electric mobility influence the assessment of externalities
resulting from hinterland transport flows in a social cost-benefit analysis for
Belgian port infrastructures?
1.2 Structure
To address this research question, this master dissertation will extend the externality assessment of
hinterland traffic flows in a social cost-benefit analysis for Belgian ports with electric mobility
influences. Section 2 elaborates on what is meant by electric mobility for ports and discusses a
particular SCBA standard used in Belgian ports. The external effects of hinterland transport discussed
in the standard will be compared to those defined for electric mobility in the literature. Section 3
provides a case study on freight movements from a new port development area in the port of
Antwerp, looking for the main influences of electric mobility on externalities in practice. The
dissertation concludes in section 4 with a discussion of the main findings. The main objective of this
dissertation is to identify and measure externalities associated with additional electric road transport
engendered by port development to help researchers and policy-makers uniformly assess the
influence of electric mobility when setting up a SCBA for new port infrastructures.
10
2 Literature review
2.1 Electric mobility in ports
Electric mobility is defined according to Abdelkafi et al as a system of interacting actors, technologies
and infrastructures that aims to achieve sustainable transportation by means of electricity. It involves
different technologies enabling the energy generation, distribution and storage of electricity and is
characterized by a number of different parties such as fleet operators, manufacturers and the
government (Abdelkafi, Makhotin, Posselt, 2012).
A sea port project such as building a new lock or quay wall will generate hinterland traffic that mostly
consists of trucks handling the cargo, therefore this dissertation will focus on electric truck
alternatives. Port drive cycles consist of fixed routes and schedules taking away range anxiety for the
fleet operators, which is normally electric mobility’s weakest point (Zhao, Noori, Tatari, 2016). The
trucks can work for example in between the city and ports on the same route every day, so systematic
recharging is feasible (Lee, Thomas, Brown, 2013). Also low speed crawling and idling in the port area
most of the time can favor electric mobility alternatives. Idling trucks won’t require the ICE to run,
which saves fuel so not only reducing cost but preventing emissions. Zhao et al. show that
hybridization and electrification of truck drive trains can significantly improve fuel economy and
reduce CO2 emissions of conventional diesel trucks, which is also a priority for fleet operators
composing their vehicle fleet (Zhao, Burke, Zhu, 2013; Zhao, et al. 2016). The electric trucks can make
use of a centralized charging station at the terminal where the trucks are parked when not in use or
make use of a central battery swapping system present in the port area (Zhao, et al. 2016; Mak, Y.,
Rong, Y., Shen, 2013).
2.1.1 Electric truck types
Electric trucks can be subdivided in to pure electric trucks, hybrids and those using hydrogen (Zhao,
et al. 2013). The first category is the pure battery-electric vehicle (BEV). BEVs have an all-electric
drive train powered by an electric motor and a battery (Delucchi, et al. 2014; Graham-Rowe, Gardner,
Abraham, Skippon, Dittmar, Hutchins, et al. 2012). The second is a Plug-in hybrid electric vehicle
(PHEV), also known as hybrids, which differs from the previous category because it has an internal
combustion engine next to the electric motor and battery, which the driver can choose from while
driving (Dijk, Orsato, Kemp, 2013). The vehicle can be propelled by the engine, the electric motor, or
both at the same time.
11
The electric motor and battery are sized to meet the maximum power required in the electric only
mode (Zhao, et al. 2013). Both the BEV and PHEV are referred to as plug-in vehicles. Graham-Rowe et
al adds another category to the configuration dependant on the time of using the electric motor. If
the vehicle first uses the electric battery until it is run out and afterwards shifts to the ICE, than the
author calls it ‘range-extended electric vehicles’ (Graham-Rowe, et al. 2012).
The third category is a hydrogen fuel-cell electric vehicle (HFCV), which has a hydrogen storage
system, a fuel cell, an electric drive train and sometimes a peak-power battery. The vehicle will store
instead of fuel, hydrogen on board, which will be used to generate electricity, however, in case of
strong power requirements, the vehicle can still shift to a battery or capacitor. The hydrogen model
has one particular advantage towards the known hybrid and all-electric trucks it will cover distances
up to 400 and 500 km, which is triple their current distance range (Delucchi, et al. 2014).
Figure 1: Electric truck power train configuration
(Zhao, et al. 2013).
Additional categorization for electric trucks will be according to size. Trucks are usually divided into
light, medium and heavy duty trucks and each subdivided into different classes. Light trucks have
class 1 to 3, average trucks 4 to 6 and heavy trucks have two classes 7 and 8 (NAP, 2000).
12
Figure 2: Truck classification by gross vehicle weight
(NAP, 2000).
These different types of electric trucks will each serve a different market. The light-duty trucks are
used as commercial delivery vehicles and will drive in congested areas during peak hours, requiring
them to frequently accelerate, decelerate and idle during operation (Zhao, et al 2016). Medium-duty
trucks will operate in an urban environment, which means driving at lower speeds and often braking.
In both truck sizes regenerative braking can be utilized. Using regenerative braking, the electric truck
is able to recover some portion of the vehicle’s kinetic energy as it slows down, improving efficiencies
(Davis & Figliozzi, 2013). Hybrid and electric vehicles are thus an ideal candidate for this vehicle fleet.
Medium duty electric delivery trucks currently have an average range of 129 to 160 km (Davis &
Figliozzi, 2013).
Heavy-duty trucks will be used for the delivery of freight between cities and in the vicinity of ocean
ports and warehouses. These applications feature near constant speeds on the highway, low speed
driving in the port and frequent idling for pickup and delivery of the freight (Zhao, et al. 2013). This
type of operation is ideal for hybrid heavy-duty trucks. When the truck is stopped or moving below a
specified speed, it runs in the all-electric mode with the battery until it is depleted. In case the
battery is low or the traction motor runs at maximum power, the engine is turned on. When the
vehicle speed is above the specified all-electric speed, the vehicle runs in hybrid mode. Hybrid mode
means the engine is turned on and it propels the vehicle and charges the battery at the same time if
required. Full depletion never happens in case of hybrid heavy duty vehicles as it is completely
recharged using the engine/ generator or an off-board battery charger, which is not the case for light-
duty hybrids (Zhao, et al. 2013).
13
2.1.2 Current practices
The port of Los Angeles is actively testing, utilizing and evaluating electric trucks in the port. Three
electric truck manufacturers, Transpower, Us Hybrid and Balqon, are testing the use of full electric
heavy-duty trucks. Transpower trucks are picking up containers at the terminals and delivering them
to warehouses in close proximity of the port. The trucks are equipped with an on-board battery
charger that is integrated into a battery management system which eliminates the need for an
external stand-alone battery charger. The drivers range is 160 km to 240 km in normal operating
conditions. US Hybrid and Balgon are also implementing trucks, promising a drivers range of 160 km
in fully loaded conditions (Port of Los Angeles, 2016). In the Port of Shanghai, the vehicle fleet
already started converting to plug-in hybrid electric vehicles: 200 PHEVs will be operational in 2016
and they are expecting an increase in the future because of china air quality concerns. The hybrids
are able to first run on the electric motor for about 160 km and then shift to the internal combustion
engine (Container Management, 2016).
2.1.3 Belgian implementation
The Belgian government published the following evolution in registered electric trucks, vans and
tankers. The following figures do not entail tractors, as there are only three of them which were
already on the market since 2007. The database does not differentiate between hybrid or all-electric
vehicles, so the assumption is made that the largest part of this vehicle fleet will be hybrids. Note
that the test drive plates are not included so the numbers can be higher in reality.
Table 1: Electric vehicle registration in Belgium
Year Trucks, vans and tankers
2007 48
2008 53
2009 59
2010 65
2011 71
2012 248
2013 399
2014 500
2015 565
(FOD Economie, K.M.O., Middenstand en Energie, 2015).
14
There is no information available on where these vehicles are being used. Also the Belgian road
pricing system stated it had no knowledge of the routes of the electric trucks, thus whether or not
they are used in port areas. We can, however, recall practices and developments of electric trucks in
Belgian port areas. For example, at the Port of Antwerp, BASF is testing the use of electric trucks; a
12 ton truck with a pure battery electric drive train and a driver’s range of 110 km will be used at the
production site that covers almost 6 km² with over 100 delivery points on site (Essers, 2016). The port
authorities of the Port of Ghent made an extensive study on hybrid electric truck opportunities in the
port for both public and private possibilities. One of the more controversial ideas would be only
allowing cargo traffic with alternative fuel propulsions in the port. Another idea is to build towards a
central battery swapping service in the port in the long run, which can be either a private or public
initiative, that is plugged into the smart grid so it would serve as an energy storage medium in
batteries in case of a surplus. In the midterm a future truck parking lot at a new container terminal
will have fast charging facilities and a battery swapping station, publicly accessible (ARCADIS, 2012).
This dissertation will not focus on the use of hydrogen trucks, as there is no expectation for the fuel
technology to breakthrough for cargo transport in Belgium without strong policy support (Vankerkom,
De Vlieger, Schrooten, Vliegen, Styns, 2009). Belgian ports are interested in the possibility but are not
in the same development phase as for full or hybrid electric trucks. There exists the ‘interreg-project
hydrogen region’ which developed hydrogen possibilities for busses, forklifts, cars and garbage trucks,
however, not yet for hydrogen trucks. Currently there are trucks being tested, but not yet
commercialized (Waterstofnet, 2016). Hydrogen trucks are not yet categorized in the current road
pricing system in Belgium, implemented April 2016. Neither the 2015 database of the vehicle fleet in
Belgium nor the TREMOVE model registered hydrogen trucks (FOD, 2015; TREMOVE, 2010).
15
2.2 Belgian social cost-benefit analysis standard for port
infrastructure
A social cost benefit analysis calculates the relevant costs and benefits for a project/policy
from the standpoint of the society as a whole and weighs them against each other. The costs and
benefits are defined, quantified and monetized. This instrument supports a decision by
the balance between not only economically but also socially profitable (Eigenraam, Koopmans, Tang,
Verster, 2000). The SCBA goes further than what in practice is already executed; the treatment of
indirect effects and taking into consideration external effects, such as the environment, the analysis
of risks and uncertainties, which is important in the case of electric mobility (RebelGroup, 2013).
Costs are considered to be those caused by the project, both direct and indirect, for example
preparation costs, investment costs, operational or demolition costs. The benefits will be less
technical and will be mostly dependant on the value assigned to new transport projects/policies
(Eigenraam, et al. 2000). Characteristic to the SCBA is monetizing effects for which no market prices
exist. Even though this does not seem attainable in practice, pricing can be executed by for example
travel time gains or measures for reducing CO2, which can be determined by surveys or revealed
preferences. Not every effect can be monetized and taken on in the calculations; however, they will
be extensively discussed in the qualitative analysis. The SCBA has a cross-border nature, considering
local and global costs and benefits, but also taking into account both the government, the public and
businesses. It can also be interpreted as transboundary between countries as the effect does not only
ripple to the whole economy, but also abroad (RebelGroup, 2013; Eigenraam, et al. 2000). The SCBA
will preferably be executed in every stage of the decision making process but because of the time
consuming activity, the researcher will need to weigh in on the one hand making an analysis in an
early phase to make a decision of the further development of project alternatives, an instrument of
design, or on the other hand a comprehensive analysis in the later stage where there is already
sufficient information gathered, an instrument of accountability (RebelGroup, 2013; Eigenraam, et al.
2000). The date of performing a SCBA is dependent on the moment sufficient information is available,
but can also be seen as an iterative process (Eigenraam, et al. 2000). It is important to use a
standardized procedure for the SCBA to ensure a uniform interpretation of the data. Every researcher
can have its own interpretation of external effects, the discount rate etc. Economic effects can be
estimated differently which can lead to discussion about the social return of a project (Eigenraam, et
al. 2000). Through standardizing, the scientific quality of a project-SCBA is assured and the SCBA of
different projects are all executed in a similar and more transparent way (RebelGroup, 2013).
16
As mentioned ports can serve as a platform for boosting electric mobility in Belgium, therefore this
paper will use a SCBA standard developed for Belgian ports. A general standard methodology was
published in 2013: ‘the standard methodology for SCBA for transport infrastructure projects’ by the
RebelGroup, treating all types of transport projects and transport modes. The general guidance is to
be used in conjunction with the key figures book (in which indicators for the valuation of certain costs
and benefits are included) and some specific additions. One of these is the ‘seaport projects’ with
specific methods and guidelines for the SCBAs of seaport projects. Combining the gives us the
‘standard methodology for SCBA for transport infrastructure projects: seaport projects’ that
performs eleven steps presenting methods for determining costs and benefits that are specific to
seaport projects. The seaport project consists of an operation on the maritime access, port
infrastructure or land access of the port (RebelGroup, 2013).
Figure 3: Eleven steps in the SCBA for Belgian ports
STEP 0: Problem analysis and project design
STEP 1: Project description
STEP 2: Identification of the project effect
STEP 3: Determining the relevant exogenous developments
STEP 4: Valuing direct effects
STEP 5: Valuing indirect effect
STEP 6: Valuing external effects
STEP 7: Estimation of the project costs
STEP 8: Adding costs and benefits
STEP 9: Risks and uncertainties
STEP 10: Division of costs and benefits
STEP 11: presentation of the results of the SCBA
(RebelGroup, 2013).
17
Considering the importance of externalities for electric mobility, step six: valuing external effects will
be evaluated. A new port infrastructure will cause new hinterland traffic, for example building a new
quay wall can lead to new trucks arriving on a daily basis. These transport flow will cause externalities
which can be differently assessed in case these conventional vehicles are being replaced with electric
alternatives. Electric trucks are nowhere mentioned in the SCBA standard, limiting the hinterland
transport to conventional light trucks (>12 ton) and heavy trucks (>12 ton). According to the author
the reason for not implementing electric alternatives is that there was at that time no solid uniform
source for establishing key figures from electric vehicles. Furthermore, in most SCBAs, the emissions
from electric vehicles are not that important as there are not many vehicles operational. Though, the
author encourages implementation in case the SCBA specifically promotes the use of electric
vehicles.
This dissertation will discuss every external cost category possibly influenced by electric mobility and
structures these in a similar way by first of all providing a discussion of the methodology for the given
cost category in the standard, secondly updating these for electric mobility for critical parameters
used in the calculations and finally calculating the unit cost values for the electric truck. The analysis
is based on the ‘EU Handbook on External Costs of Transport’ published in 2008 and update in 2014
to ensure a uniform interpretation is given to the externalities and recent publications of the
influence of electric mobility on a SCBA. There has not yet been a study on the influence of electric
mobility in particular on the 2013 Belgian SCBA standard or the OEI leidraad, the SCBA on which this
standard is based. There is, however, a study published in 2012 assessing the societal cost and benefit
of smart grids in the period 2011 - 2050, that uses the Dutch OEI-leidraad developed in 2000. The
external effects are, however, not defined for using the vehicles, but consuming energy and using the
grid; Lower energy use has lesser influence on the environment by reducing CO2 which is an
environmental benefit, however, installing transformer stations and masts can hinder the view (Blom,
Bles, Leguijt, Rooijers, van Gerwen, van Hameren, Verheij, 2012).
18
2.3 External effects from port induced hinterland transport
Externalities (or transaction spillovers) occur when the production or consumption of a good imposes
costs or benefits on a third party, who did not choose to incur that cost or benefit. The externalities
have no price tag: the offender shall neither pay nor get the benefits, it is not transmitted through
prices thus does not influence the decision of the producer/consumer (RebelGroup, 2013; Proost &
Rousseau, 2012). Externalities are in transport exceptionally higher and more diffuse as it covers
large distances and happens in open space; also the majority of the external costs are produced by
the road transport sector (Blauwens, et al. 2012, European Commission, 2014b). An external cost
arises, when the social or economic activities of one group of persons have an impact on another
group and when that impact is not fully accounted for, by the first group (Bickel & Friedrich, 2005).
The need for charging for external costs comes from the fact that everybody is obliged to pay for the
entire cost they cause and only then, the right transport decision is made, so externalities should be
corrected by internalizing the third party costs or benefits (Blauwens, et al. 2012). External costs can
be defined as the difference between social and private costs. Social costs reflect all costs occurring
due to the provision and use of transport infrastructure such as wear and tear, congestion, accident
costs, environmental costs etc. Private costs are those directly borne by the transport user such as
energy cost of vehicle use, time cost, taxes and charges. A further distinction can be made between
private marginal costs and social marginal costs (European Commission, 2014b). The marginal societal
cost equals the marginal private costs and the marginal external costs. The problem is the market
price does not fully reflect the marginal societal cost of the transport activity. The purpose is to
impose a charge that corresponds to the marginal external cost, i.e. the cost that an additional user
imposes on others (Blauwens, et al. 2012). When considering the life cycle assessment of a transport
activity, the external costs can be divided into
1) Vehicle operation: accidents, noise, air pollution, climate change, up and down stream
processes, congestion
2) Vehicle fleet: use of public space
3) Transport infrastructure: damage to nature and biodiversity, visual intrusion
This master dissertation will assess the external costs of the first category in case of electric mobility
vehicle operation since it discusses the transport flow induced by port infrastructure so not the
external costs for electric mobility charging infrastructure itself such as the use of public space or
damage to nature and biodiversity and visual intrusion. The major external costs categories are
according to the EU handbook on external costs of transport represented in the table.
19
Table 2: External cost categories
Cost component Private and social cost External part Road transport
Cost of scarce
infrastructure
Costs for traffic users and
society caused by high
traffic densities, e.g.: time,
reliability, operation,
missed economic activities
Extra costs imposed
on all other users and
society exceeding own
additional costs
External cost is the
difference between
marginal cost and average
costs based on a
congestion cost function.
Accident costs Direct and indirect costs of
an accident, e.g.: material,
medical assistance,
production, grief costs
Costs not considered
in own and collective
risk anticipation and
not covered by (third
party) insurance
Costs of a self-induced
accident part of collective
risk anticipation
Environmental
costs
Damages of environmental
nuisances, e.g.: health
costs, material and
biosphere damages and
long term risks
Social costs that are
not considered, thus
not paid for
Depending on national
legislation, environmental
taxation or liability to
realize avoidance measures
(European Commission, 2014b).
These three categories strongly differ with respect to the parts of society affected: congestion is for
the collective transport user caught in a traffic jam, accident costs for identifiable individuals and
environmental externalities are imposed to the society at large (European Commission, 2014b).
Externalities first need to be measured through environmental technology such as for example the
dose-response functions. Secondly it needs to be monetized, through an economic evaluation
method for non marketed goods. The total economic value can be determined through either use of
non-use value. The economic valuation approach fundamentally differs in three ways. It can start of
from existing markets and market prices, costs or the influence on production. Secondly it can be
based on the actual behavior in related markets or thirdly is based on hypothetical markets and starts
from the valuation that individuals give to environmental goods, when asked for it (Proost &
Rousseau, 2012).
20
Table 3: Externalities calculation methods
Approach Method Utility
Existing markets Based on prices Market prices Utility
Based on costs Avoided costs
Replacement cost
Repair costs
Utility
Based on production Productivity method Utility
Revealed preferences
(Related markets)
Travel cost method
Hedonic pricing
Utility
Stated preferences
(hypothetical markets)
Contigent valuation
Choice experiments
Utility and non-utility
(Proost & Rousseau, 2012).
These external costs will need to be internalized, which can be done through market based
incentives: pricing tools for charging the external costs to those generating them such as fuel taxes,
kilometer charges, congestion or road pricing. Another possibility is command-and-control
instruments, imposing restrictions and legislation in order to reduce external effects (Proost &
Rousseau, 2012).
21
2.3.1 Influence of electric mobility on a SCBA standard
Step six of the Belgian seaports SCBA standard discusses the quantification and valuation of external
effects of a seaport project. The standard states external effects can be caused by the use or
existence of the seaport infrastructure or up and downstream processes related to it. The transport
flows caused by the port infrastructure are separated into ships and hinterland transport. The
external effect categories relevant for hinterland transport flows using the port infrastructure is
narrowed down to ‘Climate’, ‘Air quality’, ‘Noise’, ‘Accidents’ (RebelGroup, 2013).
2.3.1.1 Environmental external costs
The ‘Climate’ and ‘Air quality’ category in the SCBA standard falls under both the use of the transport
infrastructure as the up- and downstream processes and is narrowed down to emission of
greenhouse gasses and air pollutants by vehicles. The emission of greenhouse gasses contributes to
climate change and subsequent costs, location is not of the essence. The emissions of air pollutants
cause health problems and damage to buildings and crops and location does play a role, the damage
will increase for pollutants emitted in dense areas. The estimation of these external costs will depend
on whether a traffic and transport model is being used (RebelGroup, 2013).
The standard makes a difference between indirect and direct emissions for road traffic. Direct
emissions are the tailpipe emissions and indirect emissions stem from the production of fuels
(RebelGroup, 2013). In electric mobility, there is also a difference in indirect and direct emissions, but
the interpretation will be different. Tailpipe emissions are emissions during the actual driving of the
vehicle. The all-electric trucks produce no tailpipe emissions and the hybrids neither in case they are
running in the all-electric mode but it will do so when the internal combustion is used for longer
distances. In case of hybrids it is more difficult to assess whether the vehicle uses its electrical range
or not and exactly how much will be emitted. An argument for low petroleum use is that one does
not make use of a hybrid unless one is conscious about the environment and does extensively use the
electricity charge (Delucchi, et al. 2014). Upstream emissions are calculated by the impact of
additional emissions contributing to air pollution and climate change based on a life cycle analysis
(European Commission, 2014b). Electric truck emissions relating to the complete life cycle result from
vehicle production, end material disposal and electricity (Thiel, et al. 2010). In this dissertation the
electricity production, generation and distribution will be important as we follow the same reasoning
as the author for indirect emissions, indirect emissions ones coming from the production of
(alternative) fuels: electricity.
22
The pure electric trucks will not produce tail-pipe emissions, but emissions from electricity
generation can be substantial (Lee, et al. 2013). This cost will depend on how electricity is generated
for driving the electric vehicle (Delucchi, et al. 2014). Electricity can be generated through low
emission renewable energy sources or nuclear power plants or through the highly emitting coal and
gas power plants. This will have a larger impact for pure EVs, than it would have for hybrids (Delucchi,
et al. 2014). So the benefit of an electric vehicle relative to a gasoline alternative can be positive,
negative or negligible depending on what energy mix a country has. For example, Germany has a
relatively clean electric grid and high damages from gasoline trucks, so electric vehicles will
automatically get an environmental benefit (Holland, et al. 2015). The Belgian energy mix at the
moment consists of 35% gas power plants and 35% nuclear power plants. It only has limited amount
of renewable energy sources and coal power plants still have a share of 6%. Belgium is trying to
reform its energy mix, which will be characterized in the future by the disappearance of nuclear
power plants, decarbonisation and an uplift in renewable energy. Belgium is a net importer of
electricity and has foreseen to remain so in the future, approximately 6% of electricity demand is met
by neighboring countries (Belgian government, 2015).
Graph 1: Belgian energy mix
(Belgian Government, 2015).
Biomass Solar panels
Other
Nuclear
Coal Offshore
wind
Onshore wind
Hydro
Gas
2010
Biomass
Solar panels
Other
Nuclear Coal
Offshore wind
Onshore wind
Hydro
Gas
2030
23
Wind, solar, hydro and nuclear only have indirect emissions, created during the life cycle of the
energy source, coal is the biggest emitter and gas the second (Koch, 2000).
Table 4: GHGs produced in power plants
Fuel CO2 gram equivalent /kWh
Coal 790 – 1182
Gas 389 – 511
Biomass 15 – 101
Solar 13 – 731
Wind 7 – 124
Hydro 2 – 48
Nuclear 2 – 59
(Koch, 2000).
Table 5: Air pollutants produced in power plants
Fuel So2 emissions
mg/kWh
NOx emissions
mg/kWh
NMVOX mg/kWh Particulate matter
mg/kWh
Coal 700-32.321 700-5273 18-29 30-663
Gas 4-15.000 13-1500 72-164 1-10
Biomass 12-140 701-1950 0 217-320
Solar (PV) 24-490 16-340 70 12-190
Wind 21-87 14-50 0 5-35
Hydro 5-60 3-42 0 5
Nuclear 3-50 2-100 0 2
(Koch, 2000).
Recharging an electric vehicle will increase electricity demand that is met by power plants not in the
immediate vicinity of the infrastructure, which also leads to spatially different emission patterns,
even though the vehicle is driven in the same location. The gasoline vehicle emits pollutants where it
is driven, while the power plant is located hundreds of feet above the ground, which leads to
heterogeneity in the damage produced by the two vehicle types (Muller, Tong, Mendelsohn, 2009).
24
The pure electric and hybrid trucks have two types of refueling infrastructures available: battery
swapping and recharge points. Alternatively for charging the battery in the port, which can be time
consuming and expensive, companies are offering swapping stations, where the driver can swap its
battery. After depletion, the battery can be handed over at the station, and exchanged with fully
charged battery (Delucchi, et al. 2014; Mak, et al. 2013). The driver can also have this battery readily
available in his/her own car, however, the battery tends to release its power in time (Delucchi, et al.
2014). The GHG emissions of electric trucks are mainly generated during electricity generation and
transmission phases, but the life cycle GHG emission savings can offset all of the electricity emissions
in case Vehicle-to-Grid auxiliary services are provided. As a storage media, it allows grid operators to
control the precise timing of the valuable electricity flows into or out of the grid. Renewable energy
sources will fluctuate during the day as the availability of the sources cannot be predicted accurately,
which can be eliminated by storing the excess energy in batteries used for battery swapping for
electric trucks (Zhao et al. 2016). Vehicle to Grid systems are a promising substitute traditional gas
turbine generators, which are relatively inefficient and have high emissions impacts (Noori, Zhao,
Onat, Gardner, Tatari, 2016).
25
2.3.1.1.1 Climate
The cost component of climate change as an external effect is the damages of global warming. It is
generally measured through avoidance costs to reach the Kyoto targets per country or to reach long
term reduction targets. It will vary with the consumption of fossil fuels and will be fully external. The
difference between marginal and average costs for climate change requires a complex cost function.
A more simplified approach is that the marginal damage costs is similar to the average costs or in case
of avoidance costs, the marginal costs are higher than the average costs (European Commission,
2014b).
The SCBA standard quantifies the influence of transport on the climate change by measuring the
annual amount of greenhouse gasses emitted (in CO2 equivalents). Greenhouse gasses have a global
impact so if the project merely causes a shift in between ports and not a general rise in the emission
of greenhouse gasses, it has no effect. The costs of greenhouse gasses are calculated by multiplying
the costs of emissions per volume unit by the emission volume per vehicle kilometer.
Table 6: External costs of greenhouse gasses
Year Euro/tonnage Euro/kg
2010 20 0,02
2020 60 0,06
2030 100 0,10
2040 160 0,16
2050 220 0,22
(RebelGroup, 2013).
After 2050, the values remain the same and the key figures do not have to be readjusted according to
the base year of the SCBA. The value in the table is directly applied, regardless of the base year of the
SCBA. The following table shows the CO2-equivalent of the most important greenhouse gasses. For
one kg emissions of CH4 (methane), the amount needs to be multiplied 25 times (RebelGroup, 2013).
Table 7: Global warming potential (CO2-equivalent)
Greenhouse gas CO2 CH4 N20
GWP 1 25 298
(RebelGroup, 2013).
26
2.3.1.1.2 Air quality
The underlying reasoning for air quality is the same as for climate, only quantifying the impact of air
quality is done through measuring the annual amount of air pollutants such as SO2, NOX etc and the
emissions are highly location-specific and depend on factors such as the local traffic conditions. It will
vary depending on the vehicle kilometers, energy consumption and environmental performance and
this externality is considered to be fully external. The marginal and average air pollution costs are
similar (European Commission, 2014b).
The air quality costs are valued in the standard according to key figures available in the past. These
are based on the valuing of damage to health, building and crops: the value of agriculture and
forestry products. The report expects air quality to drop with 80% up to 2010, and remain constant
afterwards (RebelGroup, 2013).
Table 8: Damage of air pollutants per volume-unit (euro per kg, price level 2010).
Pollutant Direct emissions Indirect emissions
NOx 7,00 6,88
SO2 11,59 11,08
VMVOS 7,20 7,14
PM 2,5 Highway 139,70 23,05
Other rural roads 144,28 23,05
Other urban roads 489,95 23,05
PM 10 25,73 5,22
(RebelGroup, 2013).
For future years, the key figures will increase with purchasing power growth and population growth.
The key figures are based on the price level of 2010. In case a SCBA uses another price level, the key
figures need to be adjusted (RebelGroup, 2013).
27
2.3.1.1.3 Key figures for air quality and climate externalities in the standard
The presented key figures of external costs per emitted unit (kg) is multiplied by the emission
volumes (in kg) to extract the total external costs of vehicle emissions (RebelGroup, 2013). A
difference is made between direct and indirect emissions. The indirect emissions are either from the
production of fuels (well-to-tank) or the production of electricity, but the latter only in case of rail
traffic (trains with electric propulsion). In case of rail transport the emissions are more centralized in
industrial locations further removed from living areas and the pollutants are emitted higher in the sky
(RebelGroup, 2013). The SCBA does not makes no distinction between short, long, or day drive
externalities, even though in the literature, differences have been found in fuel consumption and thus
emissions in the application of the hybrids (Zhao, et al. 2013).
Table 9: Emission factors light trucks (<12 ton)
Average all road types
2010 2020 2030
Direct emissions
CO2 351,74 323,55 298,06
CH4 0,0187 0,0043 0,0017
N20 0,0229 0,0232 0,029
NOx 2,6468 1,3527 1,0669
SO2 0,0022 0,0021 0,0019
NMVOS 0,1216 0,347 0,0202
PM (exhaust) 0,0679 0,0250 0,0159
PM (non exhaust) 0,0292 0,0294 0,0290
Indirect emissions
CO2 45,85 42,20 38,88
CH4 0,0193 0,0178 0,0164
N20 0,0192 0,0175 0,0161
NOx 0,2751 0, 2533 0,2336
SO2 0,4872 0,4496 0,4151
NMVOS 0,1393 0,1282 0,1181
PM (exhaust) 0,0381 0,0351 0,0824
(RebelGroup, 2013).
28
Table 10: Emission factors heavy trucks (>12 ton)
Average all road types
2010 2020 2030
Direct emissions
CO2 918,90 833,05 759,49
CH4 0,0611 0,0144 0,0056
N20 0,300 0,300 0,300
NOx 7,8287 4,1374 3,3565
SO2 0,0059 0,0053 0,0048
NMVOS 0,2121 0,0426 0,0148
PM (exhaust) 0,1395 0,0474 0,0333
PM (non exhaust) 0,0380 0,0380 0,0380
Indirect emissions
CO2 119,70 108,52 98,94
CH4 0,0505 0,0458 0,0418
N20 0,0505 0,0458 0,0418
NOx 0,7173 0,6503 0,5929
SO2 1,2668 1,1485 1,0471
NMVOS 0,3638 0,3298 0,3007
PM (exhaust) 0,0992 0,0900 0,0820
(RebelGroup, 2013).
Preferably, the project effects of the emission volumes are extracted from the environmental report
or calculated using an emission model, such as MIMOSA. In case no data is available, the key figures
are used by multiplying them with number of vehicle kilometers, calculating the influence of the
project on emission volumes. The latter will be multiplied with the damage amounts per kg emission
volumes, to retrieve the total external costs caused by the emission of greenhouse gasses and air
pollutants. The result is shown in the following table. It gives an overview of the damage per vehicle
kilometers of emissions by greenhouse gasses and air pollutants in road transport (RebelGroup,
2013).
29
Table 11: Damage of emissions (euro per 100 vkm, price level 2010).
Direct emissions
greenhouse gasses
Direct emissions air
pollutants
Indirect emissions
greenhouse gasses
Indirect emissions air
pollutants
2010 2020 2030 2010 2020 2030 2010 2020 2030 2010 2020 2030
Light trucks (<12 tons)
Highway 0,576 1,584 2,456 2,357 1,368 1,113 0,085 0,231 0,358 0,745 0,842 0,945
Rural 0,874 2,436 3,785 7,528 4,506 4,229 0,127 0,352 0,548 1,115 1,276 1,435
urban 0,709 1,949 2,982 3,155 1,971 1,932 0,102 0,282 0,430 0,901 1,014 1,124
Average
all road
types
0,718 1,982 3,049 3,972 2,426 2,275 0,104 0,287 0,441 0,917 1,037 1,155
Heavy trucks (<12tons)
Highway 1,607 4,339 6,600 6,790 4,110 4,026 0,235 0,634 0,964 0,2060 2,278 2,505
Rural 2,349 6,410 9,749 17,794 10,045 9,540 0,344 0,940 1,430 3,019 3,378 3,714
Urban 1,840 5,014 7,618 7,846 4,777 4,632 0,269 0,734 1,113 2,363 2,636 2,895
Average:
all road
types
1,859 5,055 7,685 9,300 5,509 5,419 0,271 0,740 1,123 2,387 2,657 2,921
(RebelGroup, 2013).
The key figures are expressed in the price level of 2010, so in case a SCBA uses another base year, the
key figures need to be adjusted, but only in case of the key figures for air pollutants. The key figures
for greenhouse gasses are not altered and can be immediately applied despite the base year of aSCBA
(RebelGroup, 2013).
30
2.3.1.1.4 With traffic and transport model: electric trucks
In case the influence of the project is determined through the strategic persons model from the
Flemish traffic center, the emission quantity can be calculated with the MIMOSA. The MIMOSA
model is an environmental-impact module that simulates the emissions of 16 air pollutants from
traffic in Flanders. The model uses the most recent data concerning the vehicle characteristics and
the composition of the vehicle fleet. The air pollutants consist of those regulated by the European
Union: CO, NoX, particulate matters, SO2, CO2, CH4 etc. The emission factors are upgraded based on
on-road measurements executed by Vito or based on the most recent data handed over by the
MEET/Copert-III methodology. Trucks are being subdivided into light and heavy trucks. The MIMOSA
model uses hourly speeds for the different vehicle categories. The scenario manager enables users to
change variables and evaluate the influence on emissions from air pollutants, for example the
definition of the vehicle fleet, the type of fuel used, etc (Vito). The MIMOSA model had an upgrade
in 2009, MIMOSA 4, implementing electric trucks. Only hybrid alternatives were introduced and only
the lower tonnage classes had the hybrid alternative, the hybrid diesel. They are assumed to have the
possibility to drive a certain distance with only the electric engine propelling the vehicle, full hybrid.
The future diesel- and gasoline cars on Belgian roads will have a certain degree of hybridization,
which is estimated at 5% until 2025 and 10% till 2030. In Flanders, distances are rather short and the
study makes a conservative estimation of the trucks covering 60% of the routes using the electricity
grid and 40% of the distance will be propelled by the combustion engine (Vankerkom, De Vlieger,
Schrooten, Vliegen, Styns, 2009).
31
2.3.1.1.5 Without traffic and transport model: electric truck key figures
In case no traffic and transport model is being used, key figures for electric truck externalities can be
calculated, based on the assumptions made in the MIMOSA model. Also considering this model does
not implement all-electric trucks or large hybrid trucks, key figure calculations will be important in
this stage.
The direct emissions for the hybrid trucks will be those used in the standard, the emissions from the
exhaust gasses, but only applicable to 40% of the covered distance. The full electric trucks will have
no direct emissions. The indirect emissions for hybrid trucks will consist on the one hand of those
that are already estimated in the standard, being the indirect emissions from fuel extraction,
however, for 40% of the covered distance. On the other hand, the indirect emissions will consist of
the emissions produced by the energy generation, covering 60% of the distance driven by the vehicle.
The full electric trucks will only produce indirect emissions, which are coming from electricity
generation. To estimate the emissions coming from energy generation, the standard is consulted
before looking into other methodologies that may not be in line with the SCBA. For instance, the
standard does make an assessment of indirect emissions of energy production for railway traffic
(trains with electric propulsion). It refers to the key figures of ‘sectors energy and industry – high
stacks’ from De Nocker et al. This report does not refer to the use of electric trucks, but defines
marginal external costs of emissions for electricity production, transport and distribution (De Nocker,
Michiels, Deutsch, Lefebvre, Buekers, Torfs, 2010).
Table 12: Key figures marginal external costs of emissions for electricity production, transport and
distribution in 2010 and expected evolutions in 2020 (in euro/g, price level 2010)
2010 2020
CO2 0,020 0,020
PM 2,5 0,02305 0,02833
PM coarse 0,00522 0,00641
PM 10 0,001788 0,02173
NOx 0,00688 0,00845
SO2 0,01108 0,01362
NMVOC 0,00714 0,00878
(De Nocker, et al. 2010).
32
The marginal costs of the electric trucks are expressed in €/100 km, however, the vehicle range is not
necessarily 100 km, which means in case of all-electric trucks ,the battery capacity is either too little
to complete this range or the battery is not fully depleted after 100 km so the full amount of
electricity generated by the power plants stored in the battery is not consumed. In case of hybrids,
the assumption was made that 40% percent of the distance will be covered by the ICE and 60% by the
electric motor. This implies that the battery capacity will only be used for the 60 %, so the battery
capacity does not necessarily release all its power.
Table 13: Vehicle electricity consumption
Battery capacity (KWh)
Range (km) Battery usage (100 km)
Conversion factor
Energy consumption
Truck <12 ton: Hybrid
16 KWh 82 km 19,53125 KWh 60% 11,71875 KWh
Truck <12 ton: all-electric
24 KWh 156 km 18,93939 KWh 100% 18,93939 KWh
Truck > 12 ton: hybrid
15 KWh 196km 7,684426 KWh 60% 4,610656 KWh
Truck > 12 ton: all-electric
400 KWh 193 km 208,333 KWh 100% 208,333 KWh
(Noori, et al. 2016; Zhao, et al. 2013).
It may seem contradictive for the large hybrid truck to have such a small battery, however, in case of
large trucks the battery can be recharged on board. Also for small vehicles, the range seems
overestimated, however due to regenerative breaking this is feasible. Also these vehicles are often
driven until the battery is fully depleted as they are not equipped with on board recharging facilities
(Zhao, et al. 2013).
The key figures for the marginal external costs of emission for electricity generation, distribution and
transport is only estimated for 2020, however, we use the 2020 cost estimates also for 2030.
Multiplying table 12 with the energy consumption (KWh) retrieved from table 13, we can define the
marginal environmental costs for driving in the full-electric mode.
33
Table 14: Marginal external cost (€/100 km)
CO2
(Climate)
PM 2,5 PM
coarse
PM 10 NOx SO2 NMVOC Total air
quality
Electric truck <12 ton: Hybrid (11,71875 KWh)
2010 0,234375 0,2406 0,06117 0,02095 0,80625 0,1298 0,08367 0,646383
2020 0,234375 0,33199 0,075117 0,25465 0,099023 0,08367 0,10289 1,023281
Electric truck <12 ton: all-electric (18,93939 KWh)
2010 0,378788 0,436553 0,098864 0,033864 0,130303 0,209848 0,135227 1,044659
2020 0,378788 0,536553 0,121401 0,411553 0,160038 0,257954 0,166288 1,653788
Electric truck > 12 ton: hybrid: (7,684426 KWh)
2010 0,092213 0,177126 0,040113 0,01374 0,052869 0,085143 0,054867 0,423858
2020 0,092213 0,2177 0,049257 0,166983 0,064933 0,104662 0,067469 0,671004
Electric truck > 12 ton: all-electric (208,333 KWh)
2010 4,166666 4,802076 1,087498 0,372499 1,433331 2,30833 1,487498 11,49123
2020 4,166666 5,902074 1,335415 4,527076 1,760414 2,837495 1,829164 18,19164
Own calculations.
Considering the range and utilized battery capacity in both electric alternatives and the 60/40
assumption for hybrids, the marginal environmental costs can be calculated by multiplying the
adjusted energy consumptions (table 13) of the electric truck with the marginal external costs of
emissions for electricity production, distribution and transportation (table 12), resulting into the
following table.
34
Table 15: Marginal external environmental costs
Air quality Climate Total
environmental
costs
Direct emissions Indirect emissions Direct emissions Indirect emissions
Fuel Electricity Fuel Electricity Fuel Electricity Fuel Electricity
Electric trucks <12 tons: Hybrids
2010 1,588 0 0,3668 0,3878 0,2872 0 0,6463 0,2343 3,5105
2020 0,9704 0 0,4148 0,6139
0,7928 0 1,0232 0,2343 4,0496
2030 0,91 0 0,462 0,6139
1,2196 0 1,0232 0,2343 4,4632
Electric trucks < 12 tons: all-electric truck
2010 0 0 0 1,0446 0 0 0 0,3787 1,4234
2020 0 0 0 1,6537 0 0 0 0,3787 2,0325
2030 0 0 0 1,6537 0 0 0 0,3787 2,0325
Electric trucks > 12 tons: hybrids
2010 3,72 0 0,9548 0,4238 0,7436 0 0,4238 0,0922 6,3583
2020 2,2036 0 1,0628 0,6710 2,022 0 0,6710 0,0922 6,7226
2030 2,2167 0 1,1684 0,4238 3,072 0 0,6710 0,0922 7,6442
Electric trucks > 12 tons: all-electric
2010 0 0 0 11,4912 0 0 0 4,1666 15,6578
2020 0 0 0 18,1916 0 0 0 4,1666 22,3583
2030 0 0 0 18,1916 0 0 0 4,1666 22,3583
(own calculations).
35
2.3.1.2 Noise
Noise is an unwanted sound and can range from 0 to 140 dB. The noise exposure is not just a
disutility because of its annoyance but can result into health impairments and lost productivity and
leisure discomfort. The cost component of noise consists of damages such as opportunity costs of
land value and human health. It is measured through the willingness to pay approach for disturbed
persons, medical costs and risk value due to transport noise. The noise effect varies according to the
traffic volume and environmental performance and is considered to be fully external (European
Commission, 2014b).
The standard calculates hinterland transport based on the total mileage covered by the transport
mode, trucks in this case. The difference between the total traffic volume with and without project
alternatives (expressed in vehicle kilometers) is multiplied by the key figures for costs of noise
hindrance per vehicle kilometer. For future years, the key figures are elevated with the purchasing
power growth (RebelGroup, 2013).
Table 16: Marginal noise costs per vehicle kilometer (euro per 100 vkm, price level 2010).
Rural Urban Average
Light truck (<12 ton) 0,0930 12,1120 2,8570
Heavy truck (>12 ton) 0,1720 22,2870 4,1530
(RebelGroup, 2013).
The methodology for measuring noise does not differ between electric and gasoline trucks, both
hedonic prices or €/km can be used to measure the external costs, it is up to the researcher what
type of method he/she prefers (Prud’homme & Koning, 2010). A pure battery electric vehicle or a
hybrid driving in the all-electric mode produces almost no sound apart from the tires, air blowing
along the vehicle and sometimes the engine which can cause more accidents as pedestrians do not
hear the engine of the approaching vehicle. Fender suggest installing a device with a 5-in. speaker,
which would simulate a gasoline engine sound and varies the volume level with vehicle speed
(Fender, 2011). These sound generation devices are called ‘acoustic vehicle alerting systems’ and will
be made mandatory for European electric and hybrid trucks, making them safer for
pedestrians/visually impaired persons, after a transitional period of 5 years (European Commission,
2014a). Up to then the SCBA will be differentiated because the current situation still allows for lower
noise production from electric vehicles. These differences will disappear from the moment the
acoustic vehicle alerting systems are installed, with the assumption they will then make the same
noise as their gasoline alternative. Therefore this paper concludes no difference in noise externalities
between the conventional and electric vehicles.
36
2.3.1.3 Accidents
External accident costs are the social cost of traffic accidents, not covered by risk oriented insurance
premiums. The cost will depend on the level of accident and the insurance system which will
determine the share of internal costs. The cost components of accidents are the additional cost of
medical care, economic production losses, material damages, administrative costs and risk value as a
proxy for suffering, pain and grief. The value of human life is estimated using studies for willingness to
pay to reduce accident risks. These will vary depending on different factors such as the vkm. Accident
costs are only external for the part that is not covered by individual insurance, especially opportunity
cost, suffering and grief. Therefore the accident costs covered by own private insurance is considered
to be an internal cost and pain and grief as an external cost, measured through the value of a
statistical life. The marginal external accident costs are dependent on traffic intensity. The seriousness
of the accident is much higher due to higher speed levels. Congested areas are more likely to have
accidents but with less serious consequences, which results in a positive externality (European
Commission, 2014b).
The standard determines the number of accidents and victims by multiplying the vehicle kilometers
with the marginal number accidents/fatalities/severely injured/slightly injured per vehicle kilometer
and road type. The input values for the first factor will be dependent on the results of the analysis
from project induced traffic flows. The second factor, the marginal accident risk, is not extensively
researched, so the standard uses key figures for marginal accident risks. The number of accidents etc
is then multiplied with the key figures per victim category for which also key figures are published
RebelGroup, 2013).
Table 17: Damage of victim costs (euro per victim, price level 2010)
Fatalities Severely injured Slightly injured
Non-material damage 1.859.000 242.000 18.600
Pure economic costs 186.000 60.000 1.400
Total damage victim
costs
2.045.000 311.000 20.000
(RebelGroup, 2013).
37
Table 18: Damage for traffic victims (euro per victim, price level 2010)
Marginal accident risk
Marginal accident costs (€/100 vkm, price level 2010
Fatalities Severely injured Slightly injured
Truck – highways 9,4 29 167 3,15
Truck – other roads 13,5 46 453 5,10
Truck – all roads 10,9 35 271 3,86
(RebelGroup, 2013).
According to Jochem et al accident costs are not significantly different in electric mobility so the
external costs for electric and conventional trucks can be treated similar and also Also Prud’homme
and Koning leave out accidents costs (Jochem, et al. 2016; Prud’homme & Koning, 2016). A lack of
recognizable sound makes electric vehicles more hazardous to pedestrians, especially at speeds of 10
mph and less, however, this will no longer be the case in the future as the vehicles will need to be
equipped with a higher external sound level (Fender, 2011, European Commission, 2014a). Another
factor could be the speed and weight of the electric truck as the drive train power and weight will
differentiate. In case of light duty vehicles, the average weight for an internal combustion vehicle is
3719 kg, for an extended range electric vehicle (EREV), only 2594 and a battery electric vehicle will
weigh 3185 (Zhao, et al. 2016). Medium-duty vehicles were found to be ‘fairly similar’ in weight by
Davis (2013). Zhao et al (2013) found heavy-duty trucks not to differentiate in weight in hybrid and
all-electric vehicles opposed to conventional vehicles and even if the vehicle weights are adjusted to
reflect the size/weight of the traction motor and batteries, fleet operators will increase payloads up
to the maximum allowable gross vehicle weight limit for tractor trailers. Overall, the speed profile
does not differ for hybrids as the hybrid truck switches from the all-electric mode to the hybrid mode
when the truck speed is above the speed threshold. Usually the truck switches driving higher than 30
km/h (Zhao, et al. 2013). All medium-duty electric trucks had similar speed profiles to the
conventional alternative (Davis & Figliozzi, 2013).
38
2.3.1.4 Congestion and infrastructure costs
According to the standard, the externalities almost overlap with environmental effects. Congestion
costs are acknowledged by the authors to be an externality, however, due to practical reasons it is
categorized as a direct effect. Also infrastructure costs are not mentioned as an externality
(RebelGroup, 2013). The transport and electric mobility literature do define congestion and
infrastructures costs, wear and tear, as an externality (European Commission, 2014b).
Congestion costs
Congestion consists of the external additional time and operating costs. A user of a road network
affects, by his/her decision to use the network for driving from A to B, the utility of all other users
who want to use the same network road. The utility loss, aggregated over all those other users, is the
negative external effect of the respective user’s decision to go from A to B. This utility will be
translated to monetary terms before aggregation, i.e. the willingness to pay for avoiding the utility
loss. Thus, the external effect is measured in terms of a monetary amount per trip. The average costs
are internal to the user; the difference with the marginal cost is the external cost. A transport user
will only take into account their own time losses, not the additional they impose on others, being
higher fuel consumption, greater inconvenience and predominantly time loss (European Commission,
2014b). According Keefe et al. congestion increases for both hybrid and diesel trucks, however, due to
the rebound effect, it is higher for hybrids than it is for Diesel (Keefe, Griffin, Graham, 2008). The
rebound effect is defined as ‘increases in consumption due to environmental efficiency interventions
that can occur through a price reduction’ (European Commission, 2011). The congestion cost applies
only to the rebound miles of travel (Keefe, et al. 2008).
Infrastructure costs
Marginal road infrastructure costs exist because of increased road maintenance and repair
expenditures higher traffic volumes induce, which will differ according to road type and vehicle class.
Heavier vehicles will cause more damage to the road. It is considered to be an external cost, unless
he/she pays for the maintenance of the infrastructure (European Commission, 2014b). The electric
mobility literature contemplates congestion and infrastructure cost is common to both fuel and
electric trucks and would cancel each other in the comparison (Prud’homme & Koning, 2012).
39
2.4 Result
Based on the previously discussed categories, the dissertation extends the marginal external costs for
hinterland transport caused by the port infrastructure, with an e-driven alternative in case of air
quality and climate external costs. Noise, congestion, infrastructure and accident costs are similar.
Table 19: Marginal external costs for road traffic (euro per 100 vkm)
Air quality Climate Total environmental costs
Direct emissions Indirect emissions Direct emissions Indirect emissions
Fuel Electricity Fuel Electricity Fuel Electricity Fuel Electricity
Conventional trucks <12 ton
2010 3,972 0 0,917 0 0,718 0 0,104 0 5,711
2020 2,426 0 1,037 0 1,982 0 0,287 0 5,732
2030 2,275 0 1,155 0 3,049 0 0,441 0 6,92
Conventional trucks > 12 tons
2010 9,3 0 2,387 0 1,859 0 0,271 0 19,326
2020 5,509 0 2,657 0 5,055 0 0,74 0 13,961
2030 5,419 0 2,921 0 7,685 0 1,123 0 17,148
Electric trucks <12 tons: Hybrids
2010 1,588 0 0,3668 0,3878 0,2872 0 0,6463 0,2343 3,5105
2020 0,9704 0 0,4148 0,6139
0,7928 0 1,0232 0,2343 4,0496
2030 0,91 0 0,462 0,6139
1,2196 0 1,0232 0,2343 4,4632
Electric trucks < 12 tons: all-electric truck
2010 0 0 0 1,0446 0 0 0 0,3787 1,4234
2020 0 0 0 1,6537 0 0 0 0,3787 2,0325
2030 0 0 0 1,6537 0 0 0 0,3787 2,0325
Electric trucks > 12 tons: hybrids
2010 3,72 0 0,9548 0,4238 0,7436 0 0,4238 0,0922 6,3583
2020 2,2036 0 1,0628 0,6710 2,022 0 0,6710 0,0922 6,7226
2030 2,2167 0 1,1684 0,4238 3,072 0 0,6710 0,0922 7,6442
Electric trucks > 12 tons: all-electric
2010 0 0 0 11,4912 0 0 0 4,1666 15,6578
2020 0 0 0 18,1916 0 0 0 4,1666 22,3583
2030 0 0 0 18,1916 0 0 0 4,1666 22,3583
(Own calculations).
40
3 Case study
This dissertation evaluates the influence of replacing conventional trucks by electric alternatives on
externalities by looking at an existing SCBA executed in the Port of Antwerp for the development of a
new zone, the Saeftinghe Development Area.
3.1 Port of Antwerp
The Port of Antwerp, located at the Western Scheldt estuary, is the largest Belgian port handling a
freight volume of 208.42 million tonnes, passing the threshold of 9 million containers and is still
growing. It is currently the biggest port in the container business and the second-largest port in
Europe in general. The port of Antwerp is home to over 900 companies, with strongly linked
maritime, logistical and industrial activities (Port of Antwerp, 2015a).
Figure 4: Location of the Port of Antwerp
(Port of Antwerp, 2015a).
Because of the port’s centralized location in the European Union, it provides direct road access to a
dense international network of motorways to France, the Netherlands, Germany and beyond. There
is a high concentration of road transport companies in the port, which also puts a lot of pressure on
the environment. Thousands of trucks are entering and leaving the port area on a daily basis, which
will transport almost half of the maritime cargo and over 4,5 million TEUs (Port of Antwerp, 2015a).
41
Figure 5: Road network of the Port of Antwerp
(Port of Antwerp, 2015a).
The port has opted for a sustainable growth strategy and aims at becoming a green port, considering
people and the planet in their greener value chains. It wants to be a leading sustainable port, taking
on measures to improve environmental quality. The port already took measures on improving air, soil
and water quality by including the rewarding of clear ships, waste management, safeguard protected
species on ecological infrastructure and LNG bunker facilities. More importantly it made investments
in renewable energy, being the home to the largest Belgian windmill park. Their sustainability report
did not yet implement electric mobility as a possible pathway, but it did, however, grant a concession
to ENGIE for the next 30 years to set up an alternative energy hub at two quays in the port, installing
fast chargers for electric vehicles (Port of Antwerp, 2016b).
The port is still a hotspot zone for particulate matters and NO2. The emissions of road traffic and
other modes of transport are influencing the air quality in the port of Antwerp. The port is actively
reducing SO2, NOx and particulate matters in the port area, mostly realized by the energy generation
sector (Port of Antwerp, 2016a). The following graphs show the share of road and rail transport in
Nox, SO2 and particulate matters emissions, which remains almost untouched. These emissions can
be further reduced, implementing electric trucks powered by clean energy sources.
42
Graph 2: Nox reduction 2000 - 2013
(Port of Antwerp, 2015b).
Graph 3: Particulate matter reduction 2000 - 2013
(Port of Antwerp, 2015b).
43
Graph 4: CO2 reduction 2000 - 2013
(Port of Antwerp, 2015b).
3.1.1 Electric mobility implementation
Charging facilities can be located near the companies and can receive available access to the grid or a
central battery swapping system can immediately provide batteries to trucks loading/unloading. The
following table shows the distances from Antwerp to main production centers, establishing the
vicinity of key players and the possibility for electric mobility opportunity despite its limited range.
The pure electric vehicles can be used if R&D developments push the range to over 200 km, the
hybrids can already be used for this road transport.
44
Table 20: Distances to production centers from Antwerp (in KM).
To Antwerp
Germany
Duisburg 179
Cologne 222
Ludwigshafen 424
Frankfurt 414
Munich 780
France
Valenciennes 168
Lille 132
Paris 362
Strasbourg 491
The Netherlands
Venlo 151
Geleen 128
Amsterdam 160
(Port of Antwerp, 2015a).
45
3.2 Saeftinghe Development Area
The port of Antwerp set up a SCBA to support the decision making of an optimum use of available
space for the expansion of the port of Antwerp, the Saeftinghe development area. It is the latest large
port infrastructure development; a new large tidal dock with accompanying terminal capacity
covering an area of more than 1000 hectares, that offers space for logistics, freight handling and
industry, number six on the map. The SCBA consists of 4 project alternatives; the container
transshipment, container x VAL, container + industry and phase 1. Every project alternative is
executed in two phases; the first phase is for every alternative the same as it covers a partial spatial
planning of the Saeftinghe zone to answer the immediate container capacity and industrial needs.
The second phase, which is different for every alternative, will establish the functional interpretation
of the dock, emphasizing either container transshipment, container value added logistics (VAL) or
port industry (Gauderis, 2015).
Figure 6: Map of the Port of Antwerp and the Saeftinghe Development Area (nr. 6).
(Gauderis, 2015).
46
Table 21: Investment costs for the Saeftinghe Development Area
Phase 1
Construction and spatial planning
Dredging works
Share in costs of the western access
Total phase 1
Phase 2
Container transshipment Container + VAL Container + industry
677,3 619,5 609,3
Total (Phase 1 + phase 2
Container transshipment Container + VAL Container + industry
1.411,3 1.353,5 1.343,3
(Gauderis, 2015).
Figure 7: Plan of phase one and the three project alternatives
(Gauderis, 2015).
47
3.3 Hinterland transport
The SCBA distinguishes five external effects of the SDA, one of them being the external costs of the
hinterland transport generated by activities in the area; emissions, noise, congestion and accidents.
From an international point of view, the SDA will attract a cargo flow that is normally assigned to
other ports located in the Hamburg-Le Havre range. Because of the central location of the Port of
Antwerp, the average distances covered from Antwerp will be lower than ports situated in this range.
Due to the deflection of the cargo flow to other ports, this project leads to a reduction of the
distances from hinterland transport and the associated external costs. From a national point of view
the external costs will differ, as the shift in hinterland transport flows becomes a factor to consider.
The total distances for hinterland transport are lower in case the goods are being shipped through
the port of Antwerp, however, a larger share will be transported across Belgium, which will increase
the associated external costs. It is therefore important to assess the effect of electric mobility on
external costs both in an international/national point of view (Gauderis, 2015).
Table 22: External effects from hinterland transport
External effects International National
Decrease of emissions and congestion due
to reduced average distances covered by
hinterland transport
Increase of emissions and
congestion due to the increased
cargo flow
(Gauderis, 2015).
The influence of the project on hinterland transport flows were calculated in two steps. First the total
container transshipment coming from the transshipment volume is being deducted and secondly the
cargo is divided amongst the different inland transport modes (Gauderis, 2015).
Table 23: Modal split for hinterland transport
Transshipment volume 35%
2014 road transport 51%
Railway 7%
Inland navigation 42%
2030 Road transport 43%
Railway 15%
Inland navigation 42%
(Gauderis, 2015).
48
The external costs of hinterland transport were calculated following three steps:
- Step 1: Determining the influence of the project on hinterland transport volumes for the
different transport modes
- Step 2: Determining the average external cost per vehicle kilometer for the different
transport modes
- Step 3: Multiplying the influence on the average vehicle kilometer and the average external
cost per vehicle kilometer.
3.3.1 Step 1: Determining the influence on hinterland transport
Assumptions are made relating to the size and average loading of the trucks, represented in the table.
The standard based these figures on a representative vehicle for every hinterland mode and a load
factor of 75%.
Table 24: Average load per vehicle
Hinterland mode Representative vehicle Capacity Average load
Road transport Tractor-trailer 28 – 40
ton
2 TEU 1,5 TEU
(Gauderis, 2015).
National level
The SDA will lead to increased transport through Belgium, which results into an increase of Belgian
hinterland transport. The standard estimates the covered distance by additional transport on Belgian
on the distance between the port of Antwerp and the Belgian boarders. The additional traffic is only
applicable to the transit traffic and containers with origins or destinations in Belgium will not add to
the hinterland transport. The additional hinterland traffic on Belgian territory from a national
perspective in case of road transport will be an average of 150 km; 70 km Rekkem and 220 km to the
German border Aken.
International level
As mentioned there is a decrease in hinterland transport on an international level because of the
reduced hinterland distances. Calculating the average saved hinterland transport is done by using the
transport benefits as a direct effect. The following table shows the calculation; the transport benefits
per TEU are divided by the average drive costs per TEU-km resulting into the average distance
savings.
49
Electric truck market penetration rates in the Port of Antwerp
The MIMOSA model foresees a hybrid diesel alternative for conventional trucks in the lower tonnage
classes being rigid trucks <12 tonnage and articulated trucks between 14 and 20 tons. The
expectations are a 5% increase till 2025 and 10% increase till 2030 (Vankerkom, et al. 2009). The case
study, however, only estimates hinterland transport for larger trucks, which is not being estimated in
the Flemish reports model and also looks at distance covered, not the amount of trucks present on
the Belgian roads. Therefore, we foresee a ‘hybrid scenario’, in which this mileage normally covered
by large trucks is now covered by large hybrid alternatives.
Table 25: Distance saved in hinterland traffic – international point of view
Year Average distance saving
2015 - 2033 31,5
2016 - 2034 32,6
2017 - 2035 33,6
2018 - 2036 34,9
2019 - 2037 35,5
2020 - 2038 36,1
2039 36,7
2021 - 2040 37,3
2022 14,2
2023 17,2 2041 37,9
2024 20,0 2042 38,4
2025 22,6 2043 38,9
2026 23,5 2044 39,9
2027 25,0 2045 40,4
2028 25,8 2046 40,9
2029 27,2 2047 41,8
2030 28,5 2048 42,2
2049 42,6
2031 29,7 2050 43,5
2032 30,9
(Gauderis, 2015).
50
Table 26: Average distance increase in hinterland traffic – national point of view
Year Average distance increase
2015 - 2033 79,23497
2016 - 2034 84,59484
2017 - 2035 89,64317
2018 - 2036 95,40034
2019 - 2037 95
2020 - 2038 100,3263
2039 106,1453
2021 - 2040 106,3331
2022 32,32999
2023 37,2807 2041 110,9411
2024 47,41379 2042 110,9445
2025 52,9661 2043 116,7401
2026 53,125 2044 121,5827
2027 58,40164 2045 121,9168
2028 63,50806 2046 127,0718
2029 69,37562 2047 132,7014
2030 69,20078 2048 132,714
2049 137,931
2031 78 2050 142,9483
2032 79,14339
(Gauderis, et al. 2015).
51
3.3.2 Step 2: Key figures for external costs of the hinterland transport
The second step is calculating the external costs of the hinterland transport. The key figures are
extracted from the 2013 ‘kengetallenboek van de Vlaamse Standaardmethodiek voor de
maatschappelijke kosten-batenanalyse van infrastructuurprojecten’, the same figures on which the
theoretical part of this dissertation based its calculations. The key figures are calculated by
multiplying the emission factor from the TREMOVE-model with the key figures for the monetary
value of the damage of emissions per kg from a study carried out by VITO within the framework of
drafting the Flanders Environment Report. An additional external cost taken on by the SCBA for the
SDA is the excise revenue, which is a negative cost and considered as a partial compensation for the
external transport cost (Gauderis, et al. 2015).
Table 27: Key figures for external costs of hinterland transport (€/100 km vehicle kilometer)
External cost Road traffic
Emissions greenhousegasses 2010 1.859
2020 5,055
2030 7,685
Emissions air pollutants 2010 9,344
2020 5,522
2030 5,325
Noise 4,169
Accidents 3,870
Congestion 8,449
Excise revenue -14,792
(Gauderis, et al. 2015).
52
Table 28: Evolution of the key figures
External cost Evolution
Emission greenhouse gasses Interpolation of values in previous table, continuous after 2030
Emission air pollutants Interpolation of values in previous table, continuous after 2030
Noise Growth of the GDP per capita: +1,1% in 2013 – 2019, +1% in
2019 – 2030, + 1,4 % after 2030
Accident cost Growth of the GDP per capita: +1,1% in 2013 – 2019, +1% in
2019 – 2030, + 1,4 % after 2030 + additional decrease of
accident risk in road traffic with 30% between 2010 and 2020
and with 40% between 2020 and 2030
Congestion 75% of growth of the GDP per capita
Excise revenue Constant
(Gauderis, 2015).
The 2013 SCBA standard does not explicitly define fuel taxes as an externality, however, the case
study, performed by the same lead author, does include fuel taxes to be an externality. Theoretically,
fuel pricing is an instrument to solve transport externalities, it does not become one (Proost & Van
Regemorter, 2001). The scope of this master dissertation is assessing the difference in externalities of
truck transport in ports and not the effects of internalizing the externalities so no comparisons are
made to the hybrid alternative. We do leave the excise revenues in the calculations to preserve
comparability between the road truck alternatives.
The key figures in the case study are based on the ‘sectors energy and industry – high stacks report’,
which was also the same report used for our estimations in the theoretical part, so no calculations
made for the electric alternatives need to be altered for this specific SCBA. Therefore we can use the
same table we calculated previously. Due to the case study only using larger trucks for hinterland
transport, the small electric truck estimations are not relevant. Also considering the modest
assumption of our ‘hybrid scenario’, only hybrid trucks will be used up to 2030.
53
Table 29: Marginal external costs for hybrid trucks > 12 tons
Air quality Climate
Total
environmental
costs
Direct emissions Indirect emissions Direct emissions Indirect emissions
Fuel Electricity Fuel Electricity Fuel Electricity Fuel Electricity
Electric trucks > 12 tons: hybrids
2010 3,72 0 0,9548 0,4238 0,7436 0 0,4238 0,0922 6,3583
2020 2,2036 0 1,0628 0,6710 2,022 0 0,6710 0,0922 6,7226
2030 2,2167 0 1,1684 0,4238 3,072 0 0,6710 0,0922 7,6442
(own calculations).
54
Table 30: Evolution of the key figures for valuing external costs of hinterland transport
Emission Greenhouse Gas
Emission greenhouse gas: hybrid scenario
Emission Air pollutant
Emission air pollutant hybrid scenario
Noise Accident costs
Congestion
Excise revenue
Total external costs
Total external costs: hybrids
2015 3,46 1,2727 7,43 5,0856 4,34 4,03 8,71 -14,67 13,29 8,7683
2016 3,78 1,2727 7,05 5,0856 4,37 4,06 8,76 -14,67 13,35 8,8783
2017 4,1 1,2727 6,67 5,0856 4,41 4,09 8,81 -14,67 13,4 8,9983
2018 4,42 1,2727 6,29 5,0856 4,46 4,14 8,88 -14,67 13,51 9,1683
2019 4,74 1,2727 5,9 5,0856 4,51 4,18 8,96 -14,67 13,61 9,3383
2020 5,06 2,7852 5,52 3,9374 4,56 4,23 9,03 -14,67 13,72 9,8726
2021 5,32 2,7852 5,5 3,9374 4,61 4,28 9,1 -14,67 14,13 10,0426
2022 5,58 2,7852 5,48 3,9374 4,66 4,32 8,18 -14,67 14,55 9,2126
2023 5,84 2,7852 5,46 3,9374 4,71 4,37 9,25 -14,67 14,97 10,3826
2024 6,11 2,7852 5,44 3,9374 4,76 4,42 9,33 -14,67 15,39 10,5626
2025 6,37 2,7852 5,42 3,9374 4,81 4,47 9,41 -14,67 15,81 10,7426
2026 6,63 2,7852 5,4 3,9374 4,86 4,52 9,49 -14,67 16,23 10,9226
2027 6,9 2,7852 5,38 3,9374 4,92 4,57 9,56 -14,67 16,66 11,1026
2028 7,16 2,7852 5,36 3,9374 4,97 4,62 9,64 -14,67 17,08 11,2826
2029 7,42 2,7852 5,34 3,9374 5,03 4,67 9,72 -14,67 17,51 11,4726
2030 7,69 3,8353 5,33 3,8089 5,08 4,72 9,8 -14,67 17,94 12,5742
(Gauderis, 2015; own calculations).
55
Table 31: Evolution of the key figures for valuing external costs of hinterland transport- continued
EmissionGreenhouse Gas
Emission Greenhouse gas: hybrid scenario
Emission Air pollutant
Emission air pollutant: hybrid scenario
Noise Accident costs
Congestion
Excise revenue
Total external costs
Total external costs: hybrids
2031 7,69 3,8353 5,33 3,8089 5,15 4,78 9,9 -14,67 18,18 12,8042
2032 7,69 3,8353 5,33 3,8089 5,23 4,85 10,01 -14,67 18,42 13,0642
2033 7,69 3,8353 5,33 3,8089 5,3 4,92 10,11 -14,67 18,67 13,3042
2034 7,69 3,8353 5,33 3,8089 5,37 4,99 10,22 -14,67 18,92 13,5542
2035 7,69 3,8353 5,33 3,8089 5,45 5,06 10,33 -14,67 19,17 13,8142
2036 7,69 3,8353 5,33 3,8089 5,52 5,13 10,43 -14,67 19,43 14,0542
2037 7,69 3,8353 5,33 3,8089 5,6 5,2 10,54 -14,67 19,68 14,3142
2038 7,69 3,8353 5,33 3,8089 5,68 5,27 10,65 -14,67 19,95 14,5742
2039 7,69 3,8353 5,33 3,8089 5,76 5,35 10,77 -14,67 20,21 14,8542
2040 7,69 3,8353 5,33 3,8089 5,84 5,42 10,88 -14,67 20,48 15,1142
2041 7,69 3,8353 5,33 3,8089 5,92 5,5 10,99 -14,67 20,75 15,3842
2042 7,69 3,8353 5,33 3,8089 6 5,57 11,11 -14,67 21,03 15,6542
2043 7,69 3,8353 5,33 3,8089 6,09 5,65 11,22 -14,67 21,3 15,9342
2044 7,69 3,8353 5,33 3,8089 6,17 5,73 11,34 -14,67 21,59 16,2142
2045 7,69 3,8353 5,33 3,8089 6,26 5,81 11,46 -14,67 21,87 16,5042
2046 7,69 3,8353 5,33 3,8089 6,35 5,89 11,58 -14,67 22,16 16,7942
2047 7,69 3,8353 5,33 3,8089 6,44 5,98 11,7 -14,67 22,45 17,0942
2048 7,69 3,8353 5,33 3,8089 6,53 6,06 11,83 -14,67 22,75 17,3942
2049 7,69 3,8353 5,33 3,8089 6,62 6,14 11,95 -14,67 23,05 17,6842
2050 7,69 3,8353 5,33 3,8089 6,71 6,23 12,07 -14,67 23,35 17,9842
(Gauderis, 2015 and own calculations).
56
3.3.3 Step 3: Calculating the total external costs of the hinterland transport
The third step calculates the total external cost of the hinterland transport by multiplying the
influence of the project on the amount of vehicle kilometers and the average external cost per
vehicle kilometer. In case of the international point of view, there is a saving in external cost (so total
benefits). In the national point of view, there is an external cost. Note that the table in the national
point of view does not include the key figures for greenhouse gasses as they are considered to have
an global influence, not local (Gauderis, 2015).
Table 32: Savings in external costs in hinterland transport – international point of view
Year Savings
hinterland
transport
(Min vkm)
External
cost per
vkm
(€/100
vkm)
External
cost per
vkm (€/100
vkm) hybrid
scenario
Savings in
external
costs
(million €)
Savings in
external
costs
(million €),
hybrid
scenario
Difference
in savings
between
normal and
hybrid
scenario
(million €)
2015 - 13,29 8,7683 - -
2016 - 13,35 8,8783 - -
2017 - 13,40 8,9983 - -
2018 - 13,51 9,1683 - -
2019 - 13,61 9,3383 - -
2020 - 13,72 9,8726 - -
2021 - 14,13 10,0426 - -
2022 5,7 14,55 9,2126 0,8 0,525118 -0,27488
2023 8,0 14,97 10,3826 1,2 0,830608 -0,36939
2024 12,0 15,39 10,5626 1,8 1,267512 -0,53249
2025 15,1 15,81 10,7426 2,4 1,622133 -0,77787
2026 15,7 16,23 10,9226 2,5 1,714848 -0,78515
2027 18,3 16,66 11,1026 3,1 2,031776 -1,06822
2028 20,6 17,08 11,2826 3,5 2,324216 -1,17578
2029 23,6 17,51 11,4726 4,1 2,707534 -1,39247
2030 24,7 17,94 12,5742 4,4 3,105827 -1,29417
(Gauderis, 2015 and own calculations).
57
Table 33: Savings in external costs in hinterland transport (continued)
(Gauderis, 2015).
Year Savings
hinterland
transport
(Min vkm)
External
cost per
vkm
(€/100
vkm)
External
cost per
vkm (€/100
vkm) hybrid
scenario
Savings in
external
costs
(million €)
Savings in
external
costs
(million €),
hybrid
scenario
Difference in
savings
between
normal and
hybrid scenario
(million €)
2031 27,7 18,18 12,8042 5,0 3,546763 -1,45324
2032 30,9 18,42 13,0642 5,7 4,036838 -1,66316
2033 31,5 18,67 13,3042 5,9 4,190823 -1,70918
2034 34,8 18,92 13,5542 6,6 4,716862 -1,88314
2035 38,1 19,17 13,8142 7,3 5,26321 -2,03679
2036 41,8 19,43 14,0542 8,1 5,874656 -2,22534
2037 42,6 19,68 14,3142 8,4 6,097849 -2,30215
2038 45,8 19,95 14,5742 9,1 6,674984 -2,42502
2039 48,9 20,21 14,8542 9,9 7,263704 -2,6363
2040 49,8 20,48 15,1142 10,2 7,526872 -2,67313
2041 53,0 20,75 15,3842 11,0 8,153626 -2,84637
2042 53,8 21,03 15,6542 11,3 8,42196 -2,87804
2043 57,1 21,30 15,9342 12,2 9,098428 -3,10157
2044 61,2 21,95 16,2142 13,2 9,92309 -3,27691
2045 62,0 21,87 16,5042 13,6 10,2326 -3,3674
2046 65,4 22,16 16,7942 14,5 10,98341 -3,51659
2047 69,7 22,45 17,0942 14,6 11,91466 -2,68534
2048 70,4 22,75 17,3942 16,0 12,24552 -3,75448
2049 73,9 23,05 17,6842 17,0 13,06862 -3,93138
2050 78,2 23,35 17,9842 18,0 14,06364 -3,93636
58
Table 34: Increase in external costs in hinterland transport – national point of view
Year Increase in
hinterland
transport
(Min vkm)
External
cost per
vkm
(€/100
vkm)
Without
GHG
External
cost per
vkm (€/100
vkm) hybrid
scenario
Without
GHG
Increase in
external
costs
(million €)
Increase in
external
costs
(million €),
hybrid
scenario
Difference
in costs
between
normal and
hybrid
scenario
(million €)
2015 - 9,83 7,4956 - -
2016 - 9,57 7,6056 - -
2017 - 9,31 7,7256 - -
2018 - 9,09 7,8956 - -
2019 - 8,88 8,0656 - -
2020 - 8,67 7,0874 - -
2021 - 8,82 7,2574 - -
2022 32,32999 8,97 6,4274 2,9 2,077978 0,822022
2023 37,2807 9,12 7,5974 3,4 2,832364 0,567636
2024 47,41379 9,28 7,7774 4,4 3,68756 0,71244
2025 52,9661 9,44 7,9574 5,0 4,214724 0,785276
2026 53,125 9,60 8,1374 5,1 4,322994 0,777006
2027 58,40164 9,76 8,3174 5,7 4,857498 0,842502
2028 63,50806 9,92 8,4974 6,3 5,396534 0,903466
2029 69,37562 10,09 8,6874 7,0 6,026938 0,973062
2030 69,20078 10,26 8,7389 7,1 6,047387 1,052613
(Gauderis, 2015).
59
Table 35: Increase in external costs in hinterland transport (continued)
Year Increasing
hinterland
transport
(Min vkm)
External
cost per
vkm
(€/100
vkm)
without
GHG
External
cost per
vkm (€/100
vkm) hybrid
scenario
Without
GHG
Increase in
external
costs
(million €)
Increase in
external
costs
(million €),
hybrid
scenario
Difference in
costs
between
normal and
hybrid
scenario
(million €)
2031 78 10,0 8,9689 7,8 6,995742 0,804258
2032 79,14339 10,74 9,2289 8,5 7,304064 1,195936
2033 79,23497 10,98 9,4689 8,7 7,50268 1,19732
2034 84,59484 11,23 9,7189 9,5 8,221687 1,278313
2035 89,64317 11,49 9,9789 10,3 8,945402 1,354598
2036 95,40034 11,74 10,2189 11,2 9,748865 1,451135
2037 95 12,00 10,4789 11,4 9,954955 1,445045
2038 100,3263 12,26 10,7389 12,3 10,77394 1,52606
2039 106,1453 12,53 11,0189 13,3 11,69604 1,60396
2040 106,3331 12,79 11,2789 13,6 11,9932 1,6068
2041 110,9411 13,07 11,5489 14,5 12,81248 1,68752
2042 110,9445 13,34 11,8189 14,8 13,11242 1,68758
2043 116,7401 13,62 12,0989 15,9 14,12427 1,77573
2044 121,5827 13,90 12,3789 16,9 15,05061 1,84939
2045 121,9168 14,19 12,6689 17,3 15,44552 1,85448
2046 127,0718 14,48 12,9589 18,4 16,46711 1,93289
2047 132,7014 14,77 13,2589 19,6 17,59475 2,00525
2048 132,714 15,07 13,5589 20,0 17,99456 2,00544
2049 137,931 15,37 13,8489 21,2 19,10193 2,09807
2050 142,9483 15,67 8,9689 22,4 12,82089 9,57911
(Gauderis, 2015).
60
3.3.4 Sensitivity analysis
The 2013 SCBA standard assesses in the sensitivity analysis ‘step 9: risks and uncertainties’ the
scenario in case there is no change in the modal split of hinterland transport. The basic SCBA assumes
the modal shift of hinterland transport will change to a more environmental approach. The 2030
modal split is more or less an ambition and not a prognosis. The sensitivity analysis assumes the
ambition will not be met and the modal split will remain unchanged relative to the situation in 2014.
The influence on the net present value is moderate both from a national and international point of
view. The explanation is twofold; the cost (decrease) of the hinterland transport is relatively small to
the total benefit and the share of environmental transport modes in the hinterland transport flow is
already relatively high in the current situation.
In case researchers are reluctant to add electric mobility in ‘step six: external effects’, they can add a
sensitivity analysis for electric mobility in step 9 of their analysis, questioning what would happen in
case the modal shift of hinterland transport changes to hybrid trucks. The researcher can also add all-
electric trucks in the analysis, taking it a step further.
Table 36: Sensitivity analysis – unchanged modal split of hinterland transport.
NCW basis NCW sensitivity Absolute
difference
% difference
Internat. National Internat. National Internat. National Internat. National
Laag container scenario
Phase 1 4.495 3.526 4.510 3.500 15 -26 0% -1%
Phase 2
container
transshipment
256 213 257 214 1 1 0% 1%
Phase 2
container +
VAL
277 537 277 538 1 1 0% 0%
Phase 2
container +
industry
283 465 284 467 1 1 0% 0%
(Gauderis, 2015).
61
Table 37: Sensitivity analysis – (continued).
NCW basis NCW sensitivity Absolute
difference
% difference
Internat. National Internat. National Internat. National Internat. National
Middle scenario
Phase 1 4.519 3.823 4.533 3.796 15 -27 0% -1%
Phase 2
container
transshipment
2.156 1.469 2.172 1.457 17 -12 1% -1%
Phase 2
container +
VAL
2.189 2.139 2.205 2.217 17 -12 1% -1%
Phase 2
container +
industry
1.988 1.743 2.003 1.730 16 -12 1% -1%
High container scenario
Phase 1 4.287 3.882 4.301 3.854 14 - 0% -1%
Phase 2
container
transshipment
4.058 2.770 4.087 2.750 30 1% -1%
Phase 2
container +
VAL
3.270 3.087 3.292 3.070 22 1% -1%
Phase 2
container +
industry
2.285 2.123 2.302 2.109 17 1% -1%
(Gauderis, 2015).
62
4 Conclusion
This paper investigated the applicability of electric mobility to the maritime industry by reviewing its
influence on a SCBA for port infrastructure projects and more importantly where electric mobility
makes the difference, externalities. Using a standardized SCBA approach for Belgian seaports, the
dissertation examined four categories of externalities for hinterland transport that could be
influenced by electric and hybrid trucks in all weight categories; air quality, climate, accidents and
noise.
The categories ‘accidents’ and ‘noise pollution’ are influenced by electric mobility but will likely align
with conventional trucks in the future. Electric vehicles produce almost no sound apart from the tires,
air blowing along the vehicle and sometimes the engine, which reduces the air pollution dramatically
but can cause more accidents as pedestrians do not hear the engine of the approaching vehicle
(Fender, 2011). As a new EU directive will demand electric vehicles to be equipped with ‘acoustic
vehicle alerting systems’, this difference will be canceled in the future (European Commission, 2014a).
Also speed profiles remain are similar for both conventional and hybrids or all-electric trucks (Zhao, et
al. 2013). The only difference is the weight resulting from a lighter drive train for the electric
alternative, however, assuming full truck loads, these trucks will generate the same external costs as
they will be loaded to their full capacity (Zhao, et al. 2016)
The categories ‘air quality’ and ‘climate change’ show the biggest methodology differences with
respect to electrified road transport. The all-electric truck does not produce tailpipe emissions, but
will emit pollutants in case upstream emissions are taken into consideration. CO2, NOx, particulate
matters and other air pollutants coming from electricity production for driving the vehicle will cause
damages to the environment. The external environmental costs will be almost zero if energy is being
generated using clean renewable energy. For hybrids, there are still direct emissions, but limited to
the distance covered by the internal combustion engine. The indirect emissions will consist of those
from fuel extraction and electricity generation.
The Port of Antwerp served as a case study as the port authority is adopting a sustainable growth
strategy, trying to convert to a green port. It does, however, not implement the use of electric
mobility in a SCBA for large port infrastructure. This dissertation assessed the influence of a large
hybrid truck alternative in their latest port infrastructure assessment, the Saefthinge Development
Area SCBA. There was a significant drop in external costs caused by hinterland traffic from a national
point of view if an electric alternative was taken on in the analysis.
63
The use of electric trucks for estimating the impact from additional road traffic induced by port
infrastructure is still at an early stage as there are limited electric trucks on the Belgian market and
uncertainties regarding the estimation of external costs. Researchers can therefore instead of
adjusting ‘step 6: external costs’, address the issue in ‘Step 9: risks and uncertainties’, which will test
the robustness of the results against alternative assumptions for key parameters. The standard can
add a sensitivity analysis for a modal shift in road transport to electric trucks, or can make the
assumption of electric trucks already covered in the external cost, but then increase or decrease their
share in step 9. Either way electric trucks will have a larger share in the vehicle fleet and will need to
be taken into account setting up a SCBA for port infrastructure considering new business models such
as leasing and battery swapping, advanced electric drive technologies improving the batteries and
engines, updating recharging systems and higher conventional fuel prices (Dijk et al. 2013).
XI
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