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).

9

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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