GRACE D3 – Marginal cost case studies for road and rail transport
SIXTH FRAMEWORK PROGRAMME
PRIORITY [Sustainable surface transport]
Call identified: FP6-2003-TREN-2
GRACE
Generalisation of Research on Accounts and Cost Estimation
D3 – Marginal cost case studies for road and rail transport
Version 1
November 2006
Authors:
Gunnar Lindberg (VTI) with contribution from partners
Contract: FP6-006222
Project Co-ordinator: ITS, University of Leeds
Funded by the European Commission
Sixth Framework Programme
GRACE Partner Organisations
University of Leeds; VTI; University of Antwerp; DIW; ISIS; Katholieke
University of Leuven; adpC; Aristotle University of Thessalonika; BUTE;
Christian-Albrechts University; Ecoplan; IER University of Stuttgart; TNO
Inro, EIT University of Las Palmas; University of Gdansk
1
GRACE D3 – Marginal cost case studies for road and rail transport
GRACE
FP6-006222
Generalisation of Research on Accounts and Cost Estimation
Marginal cost case studies for road and rail transport
This document should be referenced as:
Lindberg, G (2006), Marginal cost case studies for road and rail transport Deliverable D 3,
GRACE. Funded by Sixth Framework Programme. ITS, University of Leeds, Leeds,
November 2006
24 November 2006
Version No: 1
Authors:
as above.
PROJECT INFORMATION
Contract no: FP6-006222:
Generalisation of Research on Accounts and Cost Estimation
Website:
www.grace-eu.org
Commissioned by: Sixth Framework Programme Priority [Sustainable surface transport] Call
identifier: FP6-2003-TREN-2
Lead Partner: Institute for Transport Studies, University of Leeds (UK)
Partners: UNIVLEEDS, VTI, University of Antwerp (UA), DIW, ISIS, KUL, adpC, AUTH,
BUTE, CAU, Ecoplan, IER, TNO Inro, EIT, Gdansk
BEI (Büro für Evaluation + Innovation) is a subcontractor to DIW.
DOCUMENT CONTROL INFORMATION
Status:
Final submitted
Distribution: European Commission and Consortium Partners
Availability: Public (on acceptance by European Commission)
Filename:
D3 version 1
Quality assurance:
Co-ordinator’s review: Chris Nash
Signed:
Date:
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GRACE D3 – Marginal cost case studies for road and rail transport
Table of contents
0
Summary ............................................................................................................................ 5
0.1
Infrastructure cost ....................................................................................................... 5
0.2
Road congestion and rail scarcity............................................................................... 6
0.3
Accidents .................................................................................................................... 7
0.4
Air pollution and Greenhouse gases........................................................................... 7
0.5
Noise........................................................................................................................... 8
0.6
Sensitive areas ............................................................................................................ 8
1
Introduction ...................................................................................................................... 10
2
Infrastructure cost ............................................................................................................. 12
2.1
Methodology and definition ..................................................................................... 12
2.1.1
Econometric approach ...................................................................................... 13
2.1.2
Engineering, lifetime or duration approach...................................................... 14
2.2
Overview of Case studies on infrastructure cost ...................................................... 15
2.2.1
Road ................................................................................................................. 15
2.2.2
Rail ................................................................................................................... 17
2.3
Results ...................................................................................................................... 20
2.3.1
Elasticity ........................................................................................................... 20
2.3.2
Average and marginal cost. .............................................................................. 27
2.3.3
Discussion ........................................................................................................ 30
2.4
Conclusions .............................................................................................................. 36
3
Congestion and scarcity ................................................................................................... 38
3.1
Road ......................................................................................................................... 38
3.1.1
Overview .......................................................................................................... 42
3.1.2
Results .............................................................................................................. 43
3.2
Rail ........................................................................................................................... 45
3.2.1
Introduction ...................................................................................................... 45
3.2.2
The case study .................................................................................................. 45
3.2.3
Results .............................................................................................................. 46
3.3
Conclusions .............................................................................................................. 48
4
Accidents .......................................................................................................................... 50
4.1
Methodology and definitions ................................................................................... 50
4.1.1
Definition of external cost of accidents............................................................ 50
4.1.2
Methodology .................................................................................................... 52
4.2
Valuation of accidents .............................................................................................. 53
4.3
Risk perception ......................................................................................................... 55
4.4
The risk elasticity ..................................................................................................... 56
4.5
Insurance cost ........................................................................................................... 57
4.6
Conclusions .............................................................................................................. 58
5
Air pollution and Greenhouse gases................................................................................. 60
5.1
Road Transport and Air pollution ............................................................................ 60
5.1.1
Description of Case Studies ............................................................................. 60
5.1.2
Emissions from road vehicles .......................................................................... 62
5.2
Greenhouse gases ..................................................................................................... 63
5.3
Rail Transport ........................................................................................................... 63
5.4
Results ...................................................................................................................... 64
6
Noise................................................................................................................................. 66
6.1
Noise impacts ........................................................................................................... 66
6.2
Valuation of Annoyance........................................................................................... 68
7
Sensitive areas .................................................................................................................. 70
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GRACE D3 – Marginal cost case studies for road and rail transport
7.1
Definition and indicators .......................................................................................... 70
7.2
Cost categories ......................................................................................................... 70
7.2.1
Air pollution ..................................................................................................... 71
7.2.2
Noise................................................................................................................. 71
7.2.3
Visual intrusion ................................................................................................ 72
7.2.4
Accidents .......................................................................................................... 72
7.2.5
Infrastructure costs ........................................................................................... 72
7.3
Conclusion ................................................................................................................ 73
8
References ........................................................................................................................ 74
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GRACE D3 – Marginal cost case studies for road and rail transport
0 Summary
This report presents the Case studies made on road and rail in the GRACE project. Each Case
study contains a huge amount of specific information. This report tries to summarise the main
results from the Case studies and presents an overview of each study. The original studies are
presented in Annex. Below we summarise the main conclusions for each cost category
considered.
0.1 Infrastructure cost
•
•
•
•
The cost-elasticity with respect to the traffic-output describes the relationship between
average cost and marginal cost such that Marginal Cost = Elasticity *Average Cost.
o The elasticity for road infrastructure cost decreases as the measure changes
from renewal to maintenance and to operation. The average elasticity for
renewal cost is between 0.58 and 0.87, for an aggregate of renewal and
maintenance cost the elasticity is between 0.48 and 0.58 while the elasticity for
only maintenance and operation are from 0.12 to zero.
o The elasticity for rail infrastructure cost is lower than the elasticity for road
and doesn’t show the same difference between different measures. The average
elasticity is between 0.26 and 0.30 for an aggregate of renewal and
maintenance, for maintenance it is between 0.20 and 0.24 and for operation or
short term maintenance it is 029 to 0.32.
o The majority of the studies suggest that the elasticity decreases with increased
traffic. Thus highly used infrastructure has a lower elasticity than low volume
infrastructure. All elasticities reported above are from the average traffic in the
studies.
The operation or short term maintenance is related to total trainkm or total vehiclekm
while the renewal and maintenance usually are related to gross tonnekm or HGVkm.
o Few of the studies have been able to test which type of traffic influences the
infrastructure cost. In general, this has been decided a priori based on other
information.
Most of the studies use an econometric approach with paneldata. However, a minority
of the studies did use paneldata models but use pooled ordinary least square to
estimate the cost function.
o In two studies a duration model is used where a function of the lifetime of a
road pavement or railtrack is estimated. The result can be used to derive a
marginal renewal cost. The rail study gave results in line with the econometric
study and supported the conclusion drawn from the econometric studies that
there indeed exists a marginal cost related to renewal on railways. The result
was similar between the two approaches. However, the road study suggested a
very low effect of traffic on the observed lifetime of a pavement. A possible
explanation with some support is that the authority predicts the higher traffic
volume when deciding on the pavement thickness. The marginal cost is thus
not found in observed lifetime but in increased cost of the measures taken.
The average cost is less homogenous than would be expected.
o For road studies the average renewal cost is 0.036 €/HGVkm in the Swedish
all roads study and 1.59 €/HGVkm for the German motorway study. The
Polish study allocated the renewal cost to all vehicles and suggests a cost of
5
GRACE D3 – Marginal cost case studies for road and rail transport
•
0.21 €/vehkm. For the aggregate of renewal and maintenance measures the
average cost is 0.059 €/HGVkm in the Swedish study. Operation is in the
Swedish study allocated to all vehicles and has an average cost of 0.024
€/vkm.
o In the rail infrastructure cost study maintenace and renewal has an average cost
of 0.0028 €/Gtkm in Sweden and 0.0036 €/Gtkm in Switzerland. The
maintenance only average cost is 0.0021€/Gtkm in Sweden and 0.0022 €/Gtkm
in Switzerland. The UK study shows an average maintenance cost of 0.0052
€/Gtkm. Operation has an average cost of 0.153 €/trainkm in Sweden. The
Hungarian study suggests a cost for ‘train movement’ 3.5 €/trainkm and
0.0041 €/Gtkm.
The marginal cost follows from the elasticities and the average costs.
o The marginal cost on roads has a huge variability depending on the huge
variability in average cost. The cost on German motorways is 1.39 €/HGVkm
for renewal only. Corresponding cost for all Swedish paved roads are 0.032
and 0.12 in Poland. The Swedish results for gravel roads is 0.236 €/HGVkm.
Aggregating renewal and maintenance generates a marginal cost of 0.040
€/HGVkm in Sweden and 0.13 €/HGVkm in Poland. Operation is not
associated with traffic volume according to the Swedish case study.
o The marginal cost in the rail sector is 0.00070 €/Gtkm inSweden for renewal
and maintenance and 0.00097 €/Gtkm in Switzerland. Maintenace only has a
cost of 0.00031 €/Gtkm in Sweden and 0.00045 €/Gtkm in Switzerland. The
marginal cost in UK is estimated to 0.002 €/Gtkm. Operation has a marginal
cost of 0.054 €/trainkm in Sweden. The Hungerian study concludes on a
marginal cost of 0.22 €/trainkm for train movements.
0.2 Road congestion and rail scarcity
•
•
The main focus of the road congestion case study was to identify reasons why
previous case studies show such a huge variability in road congestion costs. It was
found that these differences can be variously attributed to:
o differences in the definition of “optimal” tolls – the term is often quite loosely
applied. For example; the term sometimes relates only to congestion tolls
(rather than covering other externalities), sometimes allows for the cost of
implementation of the tolls (and sometimes not), and sometimes relates only to
simple tolls - such as cordons (rather than tolls which vary in space and time).
o differences in the way that optimal tolls (however defined) are calculated. For
example, do they fully reflect the behaviour of travellers at the margin or are
they derived from a theoretical representation of the marginal impacts?
o differences in the nature of the cities being studied. Factors which are
particularly likely to influences the result include the degree of congestion, the
availability and attractiveness of alternative modes, the drivers’ tolerance of
congestion, and the capacity of the network to absorb additional demand.
o differences in the valuation of different externalities – perhaps reflecting
different values of time and resource costs.
o differences in the models used to estimate system performance.
For rail transport we find that a substantial peak scarcity charge per slot is justified;
o the off-peak charge would only be 10% of this level. The results seem to
confirm the view that existing variable charges for the use of infrastructure on
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GRACE D3 – Marginal cost case studies for road and rail transport
key main lines where capacity is scarce are too low as a result of the neglect of
scarcity in the charges set.
o The private slot value is different from the social slot value which indicates
problems with a simple market based solution. This result is an effect of high
congestion cost on the road network in the CS and that this is not internalized
in a road pricing regime.
o The institutional arrangements behind franchising suggest that where the
dominant operator is a franchisee, data may be available for the rail regulator
to perform a scarcity estimate along the lines of the GRACE case study. This is
certainly true in Britain.
0.3 Accidents
•
This Case study only consists of an overview and state-of-the-art survey. The result is
thus not based on any new research made within the GRACE project. The following
conclusions can be drawn;
o A growing consensus on the method to estimate the value of statistical life
(VSL) seems to emerge. The HEATCO project suggests specific values for
each Member State.
o Nevertheless, the research on VSL continues with the aim to explore the
numerous biases that have been found to potentially affect the estimates.
o On the question of the proportion of internal and external cost and especially
the perception of road users risk no new conclusions can be drawn. This is still
an area of large uncertainty.
o However, assuming something on the perceived cost, actual databases can be
used to estimate the proportion of internal cost.
o There is still no consensus on the risk elasticity. Surprisingly, many studies
find decreasing risk with increasing traffic volume. This could be a problem of
the studies or behaviour effects. If we do not control for infrastructure quality,
we may find that roads with higher expected traffic volume are designed with a
higher traffic safety standard. In addition, road users may react to a perceived
increased risk by driving more carefully and slower. This is an unobserved cost
component that would increase the cost.
0.4 Air pollution and Greenhouse gases
•
Four case studies for road transport within densely built areas have been conducted.
They are expected to complete the picture on air pollution from existing studies and to
analyse the variations of environmental costs and the driving parameters. Assessing
data availability and due to the fact that a broad range of European countries and local
meteorological conditions should be considered, the cities selected for this purpose
were Berlin, Prague, Copenhagen and Athens.
o The results show that for all vehicle types the higher marginal costs due to
airborne emissions correspond to the city of Athens, followed by Berlin,
Copenhagen and Prague in that order.
o The factors that seem to be more relevant for these results are the wind speed
and the population density. The high share of low wind speeds for the
Athenian area together with a population density close to 20 000 hab/km2 in
some zones, leads to a pollutant exposure of the population which is about a
factor of two higher compared to the other cities.
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GRACE D3 – Marginal cost case studies for road and rail transport
o Petrol cars cause lower cost per vehicle kilometre compared to diesel cars as
they emit much less fine particles, leading to lower health impacts.
•
A European abatement cost of €20 per tonne of CO2 represents a central estimate of
the range of values for meeting the Kyoto targets in 2010 in the EU based on estimates
by Capros and Mantzos (2000).
o They report a value of €5 per tonne of CO2 avoided for reaching the Kyoto
targets for the EU, assuming a full trade flexibility scheme involving all
regions of the world. For the case that no trading of CO2 emissions with
countries outside the EU is permitted, they calculate a value of €38 per tonne
of CO2 avoided. It is assumed that measures for a reduction in CO2 emissions
are taken in a cost effective way. This implies that reduction targets are not set
per sector, but that the cheapest measures are implemented, no matter in which
sector.
o Recent work has confirmed the assumption that emissions in future years will
have greater total impacts than emissions today.
o For application in GRACE we recommend using a range of €14 to €51 (with a
central value of €22 per tonne of CO2- equivalent emission in the period 2000
to 2009). These shadow prices were derived from Watkiss et al. (2005b),
converting from ₤2000/t C to €2002 (factor prices).
0.5 Noise
•
This case study is in this report only based on a state-of-the-art review. Subsequent
workpackages will present new estimates.
o Existing estimates show considerable non-linearities of marginal noise cost
with background noise levels. Based on case studies in Berlin, Stuttgart and
Helsinki the following conclusions can be drawn.
ƒ In Berlin the average number of persons per road kilometre affected by
noise is slightly higher than in Stuttgart. However, the costs are more
than a factor of three lower due to the much higher number of vehicles
and higher speeds on Frankfurter Allee leading to a higher background
noise level. In Helsinki the population density along the route
considered is lower than in Berlin and Stuttgart, furthermore the
average distance from buildings is higher – leading to lower noise
costs.
0.6 Sensitive areas
•
The impact pathway has been used to estimate a factor that relates the cost in Alpine
regions to the cost in ‘flat’ regions.
o For air pollution only local effects are relevant as regional effects would be the
same in both regions. The biggest effect is found from the topographical and
meteorological conditions.
o An alpine region would have a cost 5 times higher than a flat area for local air
pollution for road transport with a slightly higher factor for cars than for HGV.
If this factor is to be applied on all pollutions (local and regional) the factor
would be around 2.5.
o The corresponding factor for rail is around 3.5.
8
GRACE D3 – Marginal cost case studies for road and rail transport
o The noise cost is also estimated to be about 5 times higher in road transport
and 4 times for rail transport.
o The number of accidents is higher per kilometre in Alpine regions suggesting a
factor of 1.2 for road transport.
o The infrastructure maintenance cost is for the road sector about 4.5 times
higher and for rail transport 1.4 times.
o In addition, a factor for visual intrusion is suggested to be around 10 due to the
specific alpine conditions. This has however, no corresponding marginal cost.
9
GRACE D3 – Marginal cost case studies for road and rail transport
1 Introduction
The GRACE project aims to support policy makers in developing sustainable transport
systems by facilitating the implementation of such pricing and taxation schemes that reflect
the costs of infrastructure use. In order to carry out this mandate, five major areas of research
are covered:
• New case study research to address gaps in the existing level of knowledge of
marginal social costs for road, rail, air and waterborne transport.
• Development and refinement of methods to enable the use of transport accounts as
monitoring instrument for the implementation of transport pricing reform in an
enlarged Europe.
• Innovative research on the appropriate degree of complexity in transport charges.
• Guidance on the marginal social cost of the different modes of transport in specific
circumstances and on simple and transparent methods for determining charges.
• Modelling the broad socio-economic impacts of pricing reform.
This deliverable is dedicated to the first area of GRACE. It addresses the question of marginal
costs of infrastructure use in the road and rail sector. The deliverable is the outcome of a
number of individual case studies conducted within a general framework. The individual Case
studies are presented in an Annex. The Case studies covers following cost elements (Table 1).
Table: 1 Cost elements covered in this Deliverable
Chapter
2
3
4
5
6
7
Cost element
Infrastructure cost
Congestion and scarcity cost
Accidents
Air pollution and greenhouse gases
Noise
Costs in sensitive areas.
The infrastructure cost case studies (Chapter 2) explore the marginal cost of motorways in
Germany and a broader set of roads in Poland and Sweden. Lifetime model is developed to
analyse the renewal costs on Swedish roads. The same approach is used on Swedish railways
together with an econometric approach. Pioneering work in the same area is made in
Switzerland, UK and Hungary. In the area of congestion and scarcity cost (Chapter 3) the aim
is to clarify the variability in current estimates on congestion cost and suggest a more unified
approach. Novel research is conducted in the area of rail scarcity where a modelling approach
is used to derive estimates. The area of Accident cost (Chapter 4) focus on a state-of-the-art
survey and the insurance market is included in the approach. Numerous studies on air
pollution and green house gases exist which this deliverable expands upon, with the addition
of new case studies (Chapter 5), while attempting to create a clearer picture of transferable
results. The marginal cost of noise is analysed with a review (Chapter 6). Finally, the
environmental cost of transport in sensitive areas has been discussed in policy documents and
the concept is here further developed (Chapter 7).
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GRACE D3 – Marginal cost case studies for road and rail transport
Table: 2 Case studies
1.2A
1.2B I
1.2B II
1.2C
1.2D I
1.2D II
1.2E
1.2F
1.2G
1.3A
1.3B
1.4
1.5A
1.6A
1.6B
1.7
Marginal motorway infrastructure costs for Germany
Road infrastructure cost in Sweden - renewal
Road infrastructure cost in Sweden - econometric
Road infrastructure cost in Poland
Rail infrastructure cost in Sweden - econometric
Rail infrastructure cost in Sweden – renewal
Rail infrastructure cost in Switzerland
Rail infrastructure cost in Hungary
Rail infrastructure cost in UK
Estimating Rail Scarcity Costs – modelling
Road congestion
Accidents – State-of-the-art survey
Urban case studies for road and rail
Noise - Urban case studies for road and rail
Noise - Extra-urban case studies for road and rail
Environmental costs in sensitive areas
DIW
VTI
VTI
UG and DIW
VTI
VTI
ECOPLAN
BUTE
ITS
ITS/VTI
ITS/AUTH
VTI
IER
IER
IER
ECOPLAN/IER
The single Case studies are presented as appendices to this main report.
11
GRACE D3 – Marginal cost case studies for road and rail transport
2 Infrastructure cost
The short-run marginal infrastructure cost related to an additional vehicle or train consists of
four components; first, the increased wear of the infrastructure leading to additional routine
maintenance, secondly, the damage to the infrastructure leading to earlier future periodic
maintenance. A third component is the increased cost inflicted on other vehicles/trains.
Fourthly, congestion or scarcity cost, and corresponding peak load pricing, is in many sectors
a necessary part of understanding and developing cost allocation or pricing principles in the
transport sector.
This chapter focuses on the two first categories; routine maintenance (including operation)
and renewal while congestion and scarcity is covered in Chapter 3.
The remaining part of this section is organised as follows; section 2.1 discusses the
methodology and definitions used, section 2.2 describes shortly each Case Study while the
results are compiled in the following section 2.3. Next section, 2.4, summarise the existing
literature and benchmark the GRACE results against other studies. In the last section 2.5 the
conclusions are presented.
2.1 Methodology and definition
The focus is exclusively on infrastructure costs and no costs for running the traffic are
included in the analysis. It is thus oriented towards a transport policy which separates
infrastructure management from the traffic decisions.
We use here three different categories of infrastructure costs. Infrastructure operation, for
example snow removal, is defined to have a very short time horizon and is undertaken to keep
the infrastructure open and functioning for traffic. Maintenance activities have a longer time
horizon and are preventive measures to avoid degradation. Finally, renewal activities have a
longer time horizon and are undertaken to bring the infrastructure back to its original
condition1. New construction and improvements are not included in the marginal cost
analyses but have of course a link to the congestion and scarcity cost discussed in chapter 3.
Table: 3 Definitions
Measure
Operation
Maintenance
Renewal
Purpose
To keep the infrastructure
open for traffic
Preventive measures
against deterioration of the
infrastructure or corrective
measures to repair minor
damages
Bringing the infrastructure
back to its original
condition
1
Other name
Routine maintenance,
Preventive maintenance,
annual maintenance
Periodic maintenance,
Structural repair, structural
maintenance
Example
Snow removal
signals
Crack sealing, patching,
shoulder maintenance etc
Tamping, ballast cleaning
etc
Repair, reinforcement and
resurfacing
Track renewal
In European standards (EN 13306:2001) ‘maintenance’ is defined as ‘combination of all technical,
administrative and managerial actins during the life cycle of an item intended to retain it in, or restore it to, a
state in which it can perform the required function’. Both the measure maintenance and renewal in the table is
thus a part of the general maintenance term.
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GRACE D3 – Marginal cost case studies for road and rail transport
Two distinct approaches have been used; an econometric approach and a lifetime approach.
• In the econometric approach a cost function is estimated to describe the variability of
costs as a function of infrastructure characteristics, geographical, climate information
and finally traffic volumes. The observed correlation between traffic and cost is then
the base to estimate the marginal cost. In this approach information on expenditure is
collected over a number of years. The observation unit is in some cases a single road
or rail segment while other studies use information over a larger network, usually a
Maintenance Delivery Unit (MDU) where the maintenance work has been contracted
out. The expenditure is expressed separately for the different measures related to
operation, maintenance or renewal/repair. In some cases the expenditure information
is constructed from physical information on measures taken.
• The duration model, or engineering approach, is based on the lifetime of a piece of
infrastructure and is used to calculate renewal cost. This approach does not require
expenditure information but lifetime information. A lifetime or duration function is
estimated as a function of infrastructure characteristics, geographical and climate
information and traffic volumes as in the econometric approach. The change in the
lifetime as the traffic changes will affect the present value of the future renewal costs
and is thus the basis for the marginal cost calculation.
2.1.1 Econometric approach
The dominant approach has been the econometric approach. To fix the idea we use a double
log functional form:
ln(Ci ) = α + β1 ln(QAi ) + β11 ln(QAi ) 2 ... + β 2 ln(QBi ) + β 21 ln(QBi ) 2 + γ ln( I i ) + δ ln( Pi )
(1)
Where
• C i is the cost per annum for section or zone i;
• Q i is outputs for section or zone i ; here in terms of traffic with vehicles of different
types (A and B). Above is also a squared term included;
• I i is a vector of fixed input levels for section or zone i – these include the
infrastructure variables i.e. track length, track quality or pavement type etc;
• Pi is a vector of input prices.
Given that we succeed in the estimation of the function in (1) the marginal cost can be derived
as the product of the average cost (AC) and the cost elasticity ε. In the example above we
included the square of the traffic variable QA which means that the elasticity with respect to
vehicle type A is non-constant if β11 is non-zero.
εA =
dC Q A
d ln C
=
= β 1 + 2 β 11 ln( Q A )
C dQ A d ln Q A
(2)
The average cost is simply the cost C divided by the relevant output variable Q. However, the
average cost will depend on the traffic volume Q. Usually this is expressed as the mean in the
sample. But it should be clear that the marginal cost will usually depend on the traffic volume.
MC = εAC = ε
C
C
= [β1 + 2β11 ln(QA )]
QA
QA
(3)
13
GRACE D3 – Marginal cost case studies for road and rail transport
Two additional observations should be highlighted.
• First, while the theoretical specification above includes different outputs in terms of
different vehicles the reality is more problematic. In general, the correlation between
different outputs is so strong that the econometric model can not distinguish between
the effect from, for example, different vehicle types. This means that we a priori need
to decide on only one output variable to use in a study.
• Secondly, input prices are often assumed to be constant between sections or areas and
thus not included in the studies.
2.1.2 Engineering, lifetime or duration approach
This approach starts with the observation that long time series on expenditure information is
difficult to find. The basic assumption is that the length of an interval between two renewal
measures depends on the aggregate of traffic that has used a certain section. Existing literature
(Newbery 1988b, Small et al. 1989) focuses on road and assumes that the number of standard
axles that can use a road before the pavement has to be renewed is a design parameter of road
construction. Lindberg (2002) however makes use of the fact that the number of standard
axles which the road can accommodate after all is a function of the actual, not the predicted
traffic volume. Adding or subtracting vehicles to the original prediction will therefore affect
the timing of a reinvestment and there is, consequently, a marginal cost associated with
variations in traffic volume. The lifetime of a pavement – the number of years between
resealing – (T) is a function of the constant annual number of vehicles that pass the
infrastructire (call it QA) and the strength of the infrastructure where Θ denotes the number of
vehicles the infrastructure can accommodate and m indicates the climate dependent
deterioration:
⎡ Θ(Q) ⎤ − mT
T=⎢
⎥e
⎣ QA ⎦
(4)
Each renewal of the infrastructure has a cost of C. The first renewal takes place at year 0. We
can then calculate the present value of an infinite number of renewals as (5) if considering the
cost from the perspective of the initial overlay (PVC0); r is the relevant discount rate. To
study the effect of annual traffic on the cost the annualised present value of an average piece
of infrastructure (ANCave) can be expressed as (6).
PVCo = C (1+ e-rT + e-r2T …+ e-r n T )
PVC t = e- r(T - t)
lim PVCo =
n→∞
C
(1 - e- rT )
C
(1- e-rT )
(5)
(6)
The marginal cost caused by shortening the renewal intervals due to higher traffic loads can
be obtained by differentiating the annualised present value of the infrastucture with the annual
traffic volume. By using the deterioration elasticity ε – the change of lifetime due to higher
traffic loads (equation (7)) – and the definition of average costs AC=C/T=C/QT the marginal
costs for an average road MCAverage can be expressed as (8).
14
GRACE D3 – Marginal cost case studies for road and rail transport
ε=
dT Q
dQ T
.
(7)
MC Average = - ε AC
(8)
2.2 Overview of Case studies on infrastructure cost
In GRACE nine different case studies have been carried out. The table below summarise the
main characteristics of the studies. The majority of the studies use an econometric approach
but two studies try with an engineering or duration approach. The focus is on maintenance
and renewal cost even if operation is included in some studies. The majority of studies use
data on road or rail section while two studies have data on a more aggregate level
(Maintenance Delivery Units - MDU). The time span is between 55 years and 1 year.
Although most studies collect data as a panel database only one study succeeds in panel
modelling (Case study 1.2D). The definition of the cost function is for most studies such that
it allows for variable elasticities.
All
Road
1950-2005
(142331)
All
Road
2002-2004
264
National
Rail
1999-2002
185
All
Rail
1999;2006
1400
All
Rail
2003-2005
371
All
Rail
2001-2005
All
Rail
2005-2006
1
723
53
All
Note: The Hungarian CS uses a different approach based on the national network (1 observation) and 723 cost
categories (activities).
2.2.1 Road
For road infrastructure cost four case studies (CS) have been carried out. The CS in Germany
and Poland have a similar approach and are based on paneldata over expenditure by road
section. In Sweden two CS have been carried out with two different approaches; one based on
paneldata on expenditure in maintenance delivery units (MDU), i.e. organizational units
which take care of the road maintenance in a limited geographical area, and the other on
observed lifetime of pavements which results in deterioration elasticities.
15
Variable
elasticity
145
Constant
Elasticity
1998-2002
Elasticity
Duration model
Road
Paneldata
Motorway
POLS
221
Infrastructure
type
1980-1999
Observations
per year
Road
Time
Model
MDU
1.2A
German
1.2B
Sweden I
1.2B
Sweden II
1.2C
Poland
1.2 D
Sweden I
1.2D
Sweden II
1.2E
Switzerland
1.2F
Hungary
1.2G
UK
Section
Data
Renewal
Mode
Maintenace
Measure
Operation
Case study
Engineering
Method
Econometric
Table: 4 Infrastructure cost Case Studies
GRACE D3 – Marginal cost case studies for road and rail transport
1.2A - Germany
The German CS includes one production cost oriented study (Model I) and one study focused
on the influence of traffic (Model II). The studies are based on detailed physical information
of renewal measures on (West) German motorway sections during 20 years. The database
consisted originally of 1830 sections but only sections where renewal has taken place during
the period were included in the database (221 sections)2. Based on unit costs for each type of
construction3 a database on renewal expenditure was constructed. The annual data was
summed up over the period which resulted in a cross-sectional database of 221 observations
of total renewal expenditure over the period 1980 to 1999.
The explanatory variables include factor input prices (regional), type of material used,
regional dummies (i.e. information on in which region the road is located), annual daily traffic
volume (AADT) of passenger cars and goods vehicles and climate data. Additional
information on the age of the motorway and previous renewal expenditures was collected but
excluded from the final model due to partly wrong sign and lack of significance in the
statistical analysis.
1.2B - Sweden – econometric models
The cost data is based on the Swedish National Road Administrations accounting system
(VERA). The observation unit are 145 small areas, so called Maintenance delivery units
(MDU), which were established by the Road Administration when maintenance contract were
procured on the market. This means that we can find information on actual maintenance and
operation expenditure by MDU directly in the accounting system. For some cost categories
detailed analyses have been necessary to identify the right MDU for the expenditure. The
GRACE study is focused on maintenance for paved and gravel roads, winter operation as well
as ordinary operation of paved and gravel roads. The database covers the period 1998 to 2002.
The traffic information for passenger cars and heavy goods vehicles for paved and gravel
roads are also collected from the Road Administration and aggregated over the MDU’s. In
addition data on road length of paved and gravel roads as well as roads of different categories
for each MDU are collected.
1.2B – Sweden – duration analysis
This CS takes an alternative approach to estimate the renewal costs. The basic idea is to
analyse the interval between pavements renewal measures for road sections and find the
influence of traffic on these intervals. This influence is expressed as deterioration elasticity,
which accounts for the percentage change in lifetime years due to a 1% change in traffic
volume. Together with information on pavement cost the deterioration elasticity is used to
derive the marginal renewal cost. The basic database of this CS is extensive: it includes
observations on every completed renewal interval in the Swedish national road network from
1928 to 2005. The dataset also contains information on the traffic of passenger cars and
HGVs. In addition, information on speed limits, road width, road type and in which region the
road is located are available. The CS excludes gravel roads and limits the period to roads
which have been repaved after 1951. The subset consists of 142331 complete observations4.
The CS develops the theory to take into account random elements in the lifetime function5. A
2
The omission of non-renewed sections could bias the results?
Bituminous concrete, bituminous mastic asphalt, bitumen binder, mastic asphalt with crushed materials, cement
concrete, thin layer, others.
4
of which 46464 are censored in the sense that the last interval has not yet ended.
5
The lifetime is here assumed to follow a Weibull distribution.
3
16
GRACE D3 – Marginal cost case studies for road and rail transport
priori we expect the lifetime to decrease with more traffic, higher speed, narrow lanes,
rougher climate, higher road category which has a lower threshold value and design quality.
For the latter a proxy based on intervals of traffic volume (traffic class) has been used.
1.2C - Poland
The Polish CS is based on a database with all sections of national roads where renewal works
have been conducted between 2002 and 2004. Renewals include rebuilding, strengthening,
refurbishing or modernization. The final database consists of 264 sections with an average
length of 6 km. For these sections also maintenance cost has been added6. The explanatory
variables are AADT for motorbikes, passenger cars, LGVs, HGVs with and without trailer
and buses. The study also includes regional dummies, information on location of the road
(urban/non-urban) and whether the road has one or two lanes.
The Polish database covers a short time period (3 years). As a first approach the data were
treated as in the German CS, with annual expenditures summarised over the three years and
the database then collapsed to a cross-section database with 264 observations. The analyses
based on this dataset were not successful due to interdependencies and low significance of the
estimates. Instead a dataset based on the assumption that the sections were independent
between years was constructed. This dataset has 264*3=792 observations. To solve the
problem that renewals of a section occurs about every ten year and the database only covers
three years the observed information on renewal expenditure was divided by the average road
lifetime in the region generating some kind of average renewal cost per year. Finally, the cost
is expressed per kilometre. The result of this second approach was judged as reasonable
although the first approach would have been favoured had it been possible.
2.2.2 Rail
For rail infrastructure costs five case studies were carried out. The studies in UK, Switzerland
and one of the Swedish studies have similar approaches based on econometrics. The second
Swedish study is an attempt to use the duration approach in railways while the Hungarian
study has a much different statistical approach.
1.2D - Sweden
The collected information includes operation, maintenance and renewal costs on 185 track
sections over the period 1999 to 2002 and originates from the Rail Administration accounting
system. Infrastructure operation is dominated by snow removal (80 %). The traffic variables
have been the most difficult information to collect and include information on gross tonnes
and number of trains per section by passenger and freight trains over the four years.
Combined with information on track length, this leads to traffic volumes expressed as gross
ton kilometres and train kilometres. Dividing tonnage by the number of trains yields an
average train weight per track section, for total traffic, and per freight and passenger train.
Some track sections have no passenger services so average weight is not computable for these
observations. We have also computed two freight traffic ratios, total freight train/gross tonne
kilometres divided by total train/gross tonne kilometres.
A wide range of technical features of the track are collected including for example rail weight,
curvature, joint, switches etc. In addition regional dummies (i.e. where in which region the
track is located) are available. It should however be noted that the majority of the variables
6
The omission of sections where renewal has not taken place may bias the result.
17
GRACE D3 – Marginal cost case studies for road and rail transport
show very little variation over time at a track section level and are highly correlated with each
other. This will most likely give rise to multicollinearity problems in our model estimations.
The dataset consists thus of a panel database. In contrast to the other CS and to previous
Swedish CS on the same dataset (Andersson 2006) this CS uses and succeeds with a panel
data model. The panel data allows variables to vary in both the i (individual) and t (time)
dimensions. A standard pooled ordinary least square regression (POLS) assumes only a
common constant term. The most basic forms of panel data models can be grouped into fixed
effects (FE) and random effects (RE) models. The FE model uses an individual specific
constant and possibly also a time specific constant. A feature of the FE estimator is that it
requires a large number of parameters to be estimated, which consumes degrees of freedom
when there are a large number of individual effects to account for. The RE model does not
come with this feature as it (like POLS) only estimates a common constant but assumes
individual- and time-specific random elements.
1.2D - Sweden – duration model
Following the idea developed for the road sector and presented in the Swedish road duration
model (1.2B) this case study endeavours to apply this same approach to the railway sector.
Two data samples from the track information system have been matched, the first from 1999
and the second from the end of 2005. Changes between these years can be identified through
changes in the infrastructure information. From the information of the year when the track is
laid, we can derive an age variable for each observation. A change during the study period
results in two observations, one for the initial track that is replaced (this observation is
uncensored, i.e. it contains a full lifecycle) and one for the new track that, if no change is
observed, is registered as a censored observation (i.e. not a full lifecycle) at the end of 2005.
Since no comprehensive traffic database exists, we need to create a time series of data based
on known information. Andersson (2006) has created a database for the period 1999 – 2002.
This database is extended back to 1993 based on track segment information from the main
freight and passenger operating companies during this period. From 1993 and back, we
extrapolate the most recent existing information back to the year the track is laid, adjusted for
annual traffic growth from Swedish official statistics (SIKA Officiell Statistik). From 2002
and forward we extrapolate using traffic growth coefficients from Banverket. This method
gives an estimate of annual track segment traffic for the time window of our observations.
The data sample consists of 1,631 observations but missing age and traffic data reduces the
number of observations to 1,493 out of which 1,333 observations are censored.
1.2E - Switzerland
The data used is based on the whole railway network of Switzerland including all main lines
divided into almost 500 sections. Some defined track sections are maintained by other
countries, other railway companies (not SBB), some are marshalling yards or have been
redefined. This results in 371 observations (track sections) per year that can be analysed with
complete information for the years 2003, 2004 and 2005. A section is not strictly
homogeneous, that is, between its endpoints it can vary in terms of rail and sleeper types,
ballast, curvature, slope etc.
The cost data contains information on: operation maintenance (e.g. cleaning, snow and ice
removal), track maintenance, forestry, engineering, signal tower maintenance, wire
18
GRACE D3 – Marginal cost case studies for road and rail transport
maintenance and electronic installation. Moreover, within these different cost categories SBB
separates between short-run maintenance costs (“Contracting A”) that arise yearly and longrun costs which arise periodically and have the characteristics of renewal costs (“Contracting
B”). Due to the fact that the data base is only available since 2003, the estimation of renewal
costs is based on a relatively short time period of three years. Therefore, we do not estimate
renewal costs by themselves but in combination with maintenance costs.
Traffic data includes average daily data on number of trains, axle load and gross-tons per
track, as well as yearly data on train kilometres, axle load kilometres and gross-ton-kilometres
per track for the main lines. Infrastructure variables includes track length, switches, bridges
and tunnels, level crossings, radius and slope, noise and fire protection, rail age and sleepers
age as well as maximum speed.
1.2F - Hungary
The cost data in the Hungarian study is based on detailed cost accounting information. The
information is recorded on 723 cost items relevant for infrastructure management. The cost
information is thus very detailed and includes the measures operation, maintenance and
renewal. Some of the items are recorded on the level of section. The account was collected for
five years, 2001 – 2005.
The output measure contains data for train km, station usage and traffic performance divided
into information for passenger and freight trains. Information on weights is also included.
Unfortunately, the traffic information is collected on an aggregate level (nation level) and not
on sections. For each output measure only five observations are thus available.
The approach taken in this CS is different from the other econometric CS. For each of the cost
items a model is estimated which tries to explain the cost with the different performance
indicators as explanatory variables. Based on the result from the regression of the model a
performance indicator is dedicated to each specific cost item. For the final analyses the cost
items are aggregated into six activity groups based on the type of activity and the performance
indicator (cost drivers) established in the detailed analyses.The activity groups are: train
movement, path allocation, interim passenger train services, beginning/end of line passenger
services, marshalling/shunting for freight wagons and consignment of freight wagons. These
aggregated cost items/activities are modelled with information on the performance indicator.
The marginal cost is then the derivation of the estimated function w.r.t each included output
measure.
1.2G - UK
In the UK study cross section data from Network Rail for 53 Maintenance Delivery Units
(MDUs) for 2005/06 is used. 67% of total maintenance expenditure is available at the MDU
level. The remaining expenditure (33% of the total maintenance budget) includes maintenance
of electrification and plant equipment and other expenditure and can not be allocated to
individual MDUs. Instead it is allocated to one of 18 Maintenance Areas or more aggregate
levels. The cost categories allocated to MDU consist of signalling and telecoms (15%),
Permanent way (34%) and General MDU expenditures.
Traffic data is available at three levels of disaggregation; from total traffic at the highest level
to intercity passenger traffic, other passenger and freight traffic at the most granular level.
Efforts have been made to investigate whether, after accounting for the average weight of
trains, there exist detectable differences in the wear and tear impacts of different types of
19
GRACE D3 – Marginal cost case studies for road and rail transport
trains. Information on the infrastructure includes data on length by track type, maximum
speed and load, signalling equipment, rail age and length of electrification. Of these variables
length of track, proportion of track length with maximum axle load greater than 25 tonnes,
with maximum line speed greater than 100 mph, with continuous welded rail (CWR) or
proportion with rail age above 30 years and a labour price index, were included in the final
model.
2.3 Results
In GRACE we have tried to look at infrastructure costs in a way that ensures consistency
across modes (road and rail). A general conclusion is that the approach is successful and
fruitful comparisons between the results can be made. In the following, the results are
presented first for the cost elasticity (2.3.1), followed by a discussion on the available average
and marginal cost estimates (2.3.2).
2.3.1 Elasticity
The figure below summarises the estimated (average) elasticities in our econometric case
studies for road and rail infrastructure. The elasticities are divided into renewal (R),
maintenance (M) and operation cost (O). The following general results should be mentioned;
• The (average) elasticity with respect to traffic is below one in all our studies
• The elasticity in the rail sector is smaller than in the road sector
• The elasticity decreases as we move from renewal measures to maintenance and
operation in the road sector. That pattern is not clear in the rail sector.
Figure: 1 Cost elasticity with respect to traffic
1
0.9
0.8
0.7
0.6
Elast Rail
0.5
Elast Road
0.4
0.3
0.2
0.1
RG
er
m
a
RR- n y
Sw P
o
l
e
R- de and
Sw n p
ed av
en ed
RM g ra
RM - S vel
w
-S
e
w den
itz
RM erla
-S nd
w
RM ede
-P n
o
M la n
-S
d
M
we
-S
d
M witz en
-U
er
K
l
(m and
od
M
-S
e
wi l V
)
tz
er
la
n
M
-P d
o
O lan
d
-S
w
ed
O
e
O
-S -Sw n
w
ed ed
O
en
-S en
w
w
in
ed
en te r
pa
ve
d
0
20
GRACE D3 – Marginal cost case studies for road and rail transport
Additionally we observe;
• In the majority of the studies the elasticity is constant or decreases with increased
traffic (β11<0). Only one study, on German Motorways, suggests an increasing
elasticity.
• The Hungarian rail study uses a different classification but points at elasticities
between 0.07 and 0.16.
• Renewal production shows considerable economies of scale.
Road - elasticity
The average elasticity plotted against the average traffic volume (expressed in HGVs per day)
used in the studies depicts the main conclusions from these case studies. The average
elasticity is always below 1. The pattern whereby the elasticity decreases as we move towards
short-term measures is here clear, i.e. the elasticity related to renewal (R elast) is highest
followed by the elasticity related to an aggregate measure of renewal and maintenance (R+M
elast). The elasticity related to operation is almost zero.
Figure: 2 Average elasticity from GRACE road case studies
1
0.9
0.8
Elasticity
0.7
0.6
R elast
R+M elast
0.5
M/O elast
0.4
0.3
0.2
0.1
0
0
1000
2000
3000
4000
5000
6000
HGV per day
Note: The HGV measure is used to compare the different studies. As can be seen from the table below each
study has found a specific form of traffic measure that best fit their model.
The table below summarises the estimated elasticities as well as the estimated parameters in
the studies. The German study suggests that the elasticity increases with more traffic (β11>0)
while the Swedish studies suggests the opposite (β11<0). However, as can be seen from the
figure above the Swedish and the German studies have data from road sections with very
different traffic levels. The Polish studies are based on fixed elasticity models (β11=0). The
German study includes an interaction term between HGV and passenger cars. To simplify the
interpretation of the results in the table below we once again present the general expression
for the elasticity (equation 2) derived previously.
εA =
dC Q A
d ln C
=
= β 1 + 2 β 11 ln( Q A )
C dQ A d ln Q A
21
(2)
GRACE D3 – Marginal cost case studies for road and rail transport
None of the studies was able to clearly verify which output variable predominantly drives the
cost. Contrary to the rail sector these road studies need to rely on rather rough measures of
traffic. The output variable distinguishes only between vehicle classes, for example passenger
cars and heavy goods vehicle (HGV), and does not include any more sophisticated weight
information. The correlations between these aggregate output variables are strong and usually
an a priori decision has to be taken on which of them to include. However, thanks to the
correlation the elasticity (but not the average cost) may be similar between different output
variables.
Table: 5 Road elasticities
β1
Germany R
0.15
Poland R
0.57
4.95
Sweden R
paved
Sweden R
gravel
Sweden
duration model
β11*
lnQ
0.38
β2*
lnX
-0.26
-0.38
0.68
Mean
Q
Renewal
Elasticit
y
5002
Output
(Q)
Interaction
term X
0.87
HGV
Passenger
carsC)
No
8592 [1403]A)
0.57
AADT
B)
87594 [158]
0.72
HGVkm in region
No
718 [5]B)
0.68
HGVkm in region
No
0.039DE
HGV
No
88313 [125] B)
0.58
HGVkm in region
No
A)
0.48
AADT
No
Renewal and Maintenace
Sweden R+M
Poland R+M
3.3
-0.24
0.48
8592 [1403]
Maintenace/Operation
Poland M
Sweden O
8592 [1403]A)
0.12
0.147
-0.007
Sweden O
0.21 -0.0152
winter
Sweden O
0.495 -0.034
paved
Sweden O
1.11
-0.136
gravel
Note: DE=Deterioration elasticity,
A)
Average HGV traffic
B)
Output measure expressed per km road.
C)
Mean volume 26632
(In parenthesis)= non significant estimates
0.12
AADT
No
B)
(0.05)
vkm in region
No
B)
869962 [1232]
(0.007)
vkm in region
No
859463 [1554]B)
(0.03)
vkm in region
No
10498 [69]B)
(-0.09)
vkm in region
No
869962 [1232]
The first part of the German study (Model I) suggests that renewal production shows
considerable economies of scale where a 1% increase in the tendered lot only increases the
cost by 0.66%. The second part of the study, where renewal costs can be related to traffic
volume, meets with problems related with the significance of material input and the sign of
the effect of passenger cars. As a squared term of goods vehicle as well as an interaction term
between goods vehicles and passenger cars is included the cost elasticity will depend on the
goods traffic volume as well as the passenger car traffic volume. The cost elasticity for goods
vehicles (with an average number of standard axles) was estimated between 0.005 and 1.17 as
the AADT of goods vehicles increases evaluated at the mean of the passenger car volume (see
figure below). The mean elasticity is 0.87.
22
GRACE D3 – Marginal cost case studies for road and rail transport
Figure: 3 Cost elasticity (ratio between marginal and average costs) of trucks at German motorways
1.2
1
MC / AC
0.8
0.6
0.4
0.2
0
5000
25000
45000
65000
85000
AADT trucks
Source: Case study 1.2A.
As in many other studies the Polish study shows strong correlation between different
measures of traffic and the final model considers total traffic (i.e. all vehicles). The model
does not include squared terms or interaction terms. This means that the estimated elasticity is
constant over traffic volume. The elasticity of renewal and maintenance cost with respect to
total traffic volume is 0.48. The model with only renewal cost shows the elasticity 0.57 and
with only maintenance cost the elasticity is 0.12.
In the Swedish CS it has a priori been assumed that maintenance cost is caused by heavy
goods vehicles and operation by all vehicles. The model includes a squared term on traffic
volume and both fixed and random effects models are tested but the fixed effects model
finally chosen. The elasticity at mean traffic volume for operation is 0.05 but this is not
statistically significant. That is true also for the disaggregating into winter operation (0.0073)
and operation on paved (0.028) and gravel (-0.0955) roads. The main finding from these
estimates is that we cannot conclude that operation expenditure is related to traffic volume.
The marginal cost is thus zero.
For maintenance cost a dynamic approach could have been appropriate to take
interdependencies over time into account. However, these models where not successful and
instead a simpler approach similar to the one utilised in the German and Polish CS has been
used where the expenditures are expressed as an average over the time period considered. The
cost elasticity for all maintenance expenditures with respect to heavy goods vehicles is 0.58 at
the mean traffic volume and the elasticity is decreasing with traffic volume. For paved roads
only the elasticity is 0.72 and for gravel roads 0.57.
23
GRACE D3 – Marginal cost case studies for road and rail transport
Figure: 4 Elasticity for maintenance cost, Sweden (within 95% confidence interval)
Source: Case study 1.2.Bi
Duration modelling
The estimated models have a good overall fit. In the complete model with all variables
included almost all variables are significant and their sign is consistent with the a priori
assumptions. However, the flow of passenger cars has no significant influence on pavement
lifetime in the complete model. This is probably due to its close correlation to traffic class
dummies. As a matter of fact, the passenger car coefficient becomes significant if the traffic
class dummies are dropped as is done in one of the models. The flow of HGVs on the other
hand has an impact that is clearly significant, in the complete model as well as in the reduced
models. In the complete model the deterioration elasticity (HGV) is -0.039. Thus an
additional percent of HGVs means that the pavement lifetime decreases by 0.039 percent,
quite a small number. The results also tell us that the pavement on wider roads lasts longer
and that a higher speed limit causes the pavement lifetime to decrease. A comparison between
24
GRACE D3 – Marginal cost case studies for road and rail transport
the complete model and the reduced model shows estimates to be stable for different model
specifications.
Rail - elasticity
All rail infrastructure CS show a constant or decreasing (β11<0) elasticity with traffic volume.
The elasticities are in the same range for all measures and in the range of 0.2 and 0.3 for the
econometric models. In addition, we find significant deterioration elasticity in the duration
model approach which suggests that renewals are affected by traffic volume and thus
connected with a marginal cost. This is consistent with the econometric studies which suggest
a higher elasticity when renewals are added to the maintenance measure.
Table: 6 Rail elasticity
β1
β11*ln
Q
β2*lnX
Mean Q
Renewal
Sweden (duration)
Maintenance and Renewal
7445989
Sweden
Switzerland (A+B)
1.567
0.265
-0.0844
Sweden
Switzerland (A)
UK (model V)
Switzerland (part of A)
1.47
0.200
5.834
0.285
-0.0844
Maintenace
7445989
-0.1818
4809570
Sweden
3.314
-0.79
Operation
0.0495
15499
Hungary
train movement
path allocation
interim passenger train
service
beg/end of line pass
train servic
marshall/shunt for
freight wagons
consigment of freight
wagons
DE=Deterioration elasticity; GT=Grosse Tonne
Elasticity
lnQ
Interaction
term (X)
0.109DE
0.146DE
GT Freight
GT Passenger
0.302
0.265
Grosse Tonnes
Grosse Tonnes
0.204
0.200
0.239
0.285
Gross Tonne
Gross Tonne
Gross Tonne
Gross Tonne
0.324
Trains
0.063
0.085
0.081
Train km
0.108
No of pass train
0.161
No of wagons
0.090
No of consigned wagons
lnQ*lnQ
No pass.train stops
The Swedish econometric study continues the work in Andersson (2005) and succeeds in
estimating a paneldata model. The results are very similar to previous analyses with POLS
and can thus be seen as stable. The new analysis reinforces the conclusion that renewal has a
significant marginal cost associated to it as well as operation and maintenance. All of the
estimated elasticities decrease with the output volume. The elasticity is 0.302 for maintenance
and renewal, 0.204 for only maintenance and 0.324 for operation. The figure below depicts
the function for the elasticity of maintenance and renewal cost.
25
GRACE D3 – Marginal cost case studies for road and rail transport
Figure: 5 Maintenance and renewal elasticity, Sweden
Source: Case study 1.2DI
Following the findings of Gaudry and Quinet (2003), who found a difference between the
impact of additional gross tonnes resulting from heavier trains, as opposed to that resulting
from more trains of the same weight, the UK Case study adopts both a measure of the density
of train miles per track-km and the average weight of those trains. While the CS finds
significant elasticities of both density and average weight it cannot be concluded that they
differ. Consequently, the CS cannot support the idea that there is a difference between more
trains of the same weight and heavier trains in the same number. The models are therefore
estimated with Gross tonnekm as output variable. The CS also tries to distinguish between
freight and passenger trains with some interesting but not totally robust results. The final
model includes a square term on traffic and the result is a mean elasticity of 0.239 with a
negative b11 coefficient.
Similarly to the experience of the CS for Sweden the Swiss CS does not face any serious
problems adding renewal costs to maintenance costs. The inclusion of regional dummies
(districts) has a relatively small effect on the explanatory power of the estimated models.
Most coefficients (not the regional dummies) are significant at the 1 percent level and mostly
have expected signs. Unexpected signs were noted for tunnel distance and speed which both
reduced the cost. The elasticities are constant within a rather small range from 0.2 for
maintenance, 0.265 for maintenance and renewal and 0.285 for a limited part of the
maintenance cost.
The Hungarian CS has a totally different approach but can nevertheless derive elasticities for
the six activity groups. The result of this model is very low elasticities compared to the other
econometric studies7, ranging from 0.063 for the cost item train movement related to train
kilometre up to 0.16 for the cost of marshalling related to wagon.
7
The CS does not report t-value for the estimated parameters.
26
GRACE D3 – Marginal cost case studies for road and rail transport
Duration modelling
The Swedish case study on lifetime of railways generated results that are consistent with the
econometric studies. There is a significant and price relevant cost related to rail renewal in
line with what has been found in econometric studies of renewal cost data. The CS estimates
marginal costs for freight and passenger trains separately in the range of €0.00012 – 0.00028
per gross tonne kilometre. A weighted marginal cost based on gross tonnes per observation
gives an estimate for freight traffic at € 0.00012 per gross tonne kilometre. The equivalent for
passenger traffic is € 0.00028. Somewhat surprisingly, the marginal cost for passenger trains
is higher than for freight trains, but a possible explanation to this is higher quality demands
for passenger trains.
There is also a significant ageing effect after controlling for traffic loads. This indicates that
there are climate and weather effects that reduce the life of Swedish railway tracks.
It was suggested above that one possible explanation for the low deterioration elasticity in the
road sector was the possibility to adapt the measure taken, for example the thickness of the
pavement, as the expected traffic increases. It could be the case that the technology of rail
infrastructure is less flexible and similar measures, for example type of track, are taken
irrespectively of the expected traffic volume.
2.3.2 Average and marginal cost.
The estimated elasticities resulted in a reasonably narrow range of estimates. These elasticities
can then be multiplied by the average cost to give the marginal cost. Our estimates of the
average cost vary substantially between the case studies.
Road
The marginal cost can be calculated as the product of the average cost and the elasticity.
Although the elasticity is constant the average cost is not and it falls with increasing traffic
volume. Consequently, the marginal cost decreases with increasing traffic.
Applying the elasticity on the average cost per goods vehicle kilometre in the German study
(1.59 €/vkm) results in a marginal cost of 0.08 €/HGV-km at very low traffic volume
increasing to 1.87 €/HGV-km on roads with the highest traffic volume. Evaluated at the
average traffic volume the cost is 1.39 €/HGV-km.
In Poland, evaluated at the average traffic volume, the renewal and maintenance average cost
is 0.27 €/vkm (for all vehicles) and the marginal cost 0.13 €/vkm. The corresponding number
for renewal only is 0.21 €/vkm and the MC is 0.12 €/vkm.
The Swedish CS reports a significant difference between the marginal cost of paved and
gravel roads. The former have a marginal cost of 0.032 €/HGV-km and the latter almost ten
times higher marginal cost, 0.24 €/HGV-km. An aggregate of renewal and maintenance
suggests an average cost of 0.040 €/HGV-km. The model for operation cost does not show a
significant marginal cost and it can then be assumed to be zero.
The resulting average cost per vkm differs largely between the regions in Sweden with the
highest cost in north of Sweden for both maintenance (0.182 €/HGV vkm) and operation
(0.011 €/vkm) with the lowest cost in the south (0.013 and 0.003).
27
GRACE D3 – Marginal cost case studies for road and rail transport
The table below summarise the average and marginal cost for the road case studies. Observe
that the mean traffic volume is very different between the studies with the highest traffic
volume in the German study and the lowest in the Swedish study (see table 5).
Table: 7 Average and marginal cost in the road sector
AC
MC
€/Xkm
€/Xkm
Renewal
Germany R
1.590
1.390
Poland R
0.210
0.120
Sweden R paved
0.036
0.032
Sweden R gravel
0.415
0.236
Sweden duration model
0.0013
Reneval and Maintenace
Sweden R+M
0.059
0.040
Poland R+M
0.270
0.130
Maintenace/Operation
Poland M
Na
na
Sweden O
0.024
(0.002)
Sweden O winter
0.015
(0.001)
Sweden O paved
0.003
(0.001)
Sweden O gravel
0.066
(0.010)
Outputvariable
Q
HGV
All veh
HGV
HGV
HGV
HGV
All veh
All veh
All veh
All veh
All veh
All veh
Duration model
Based on a unit cost of 7.05 €/m2 pavement an average cost over a pavement interval can be
estimated to 0.028 €/vkm. Applying the elasticities and a correction factor following the
choice of a Weibull distribution on these average costs suggests a marginal cost of 0.0013
€/vkm with an interval from almost zero to 0.004. The reason for this low marginal cost in
this approach is of course the low elasticity. Previous research has assumed that this is due to
a weather/climate effect but this CS can reject any such influences.
One possible explanation not further analysed in the CS is that the responses from the Road
authority are such that the unit cost per sqm differs depending on traffic volume. If a higher
volume is expected a more expensive pavement measure is taken. This is supported by the
Road Administrations own data (Zarghamp 2002) from which the following simple function
on maintenance cost per square meter and measure (SEK/ m2)8 has been estimated9.
Ln(SEK/m2) = 2.466 (0.271) + 0.212 (0.0345) * ln(Q)
The traffic volume Q increases the cost per square meter with the elasticity 0.2. If we believe
this information two things happen as the traffic volume increase; first the lifetime of the
pavement is reduced and secondly, the road authority responds to this expected reduction in
lifetime with a more expensive maintenance measure.
8
1 SEK = 0.1085 €
Data on SEK/sqm over 40 years for ’built roads’ divided by number of pavement measures in different traffic
classes in region Mitt, Mälardalen and counties F and M. R2 = 0.67.
9
28
GRACE D3 – Marginal cost case studies for road and rail transport
Rail
The average and marginal cost in the Swedish and Swiss CS are rather similar, regarding both
average cost and marginal cost. The marginal cost related to maintenance is in Sweden 0.31
€/1000GTkm and in Switzerland 0.45 €/1000GTkm. Adding renewals to the maintenance
increases the marginal cost to 0.70 €/1000GTkm in Sweden and 0.97 €/1000GTkm in
Switzerland. In addition, the Swedish study finds a marginal cost for operation which is 0.054
per trainkm.
The CS from UK reports both higher average costs and marginal costs compared to the other
studies. The maintenance marginal cost is estimated to 2.0 €/1000GTkm. The Hungarian CS
results in much higher average and marginal costs as presented in the table below. However,
that study is based on a different approach.
Table: 8 Average and marginal cost in the rail sector
AC
€/Xkm
MC
€/Xkm
Outputvariable
X
0.00028
0.00012
Gross Tonne (Passenger)
Grosse Tonne (Freight)
0.00285
0.00364
0.00070
0.00097
Grosse Tonnes
Grosse Tonnes
0.00209
0.0022
0.00517
0.00133
0.00031
0.00045
0.001978
0.00038
Gross Tonne
Gross Tonne
Gross Tonne
Gross Tonne
0.153
0.054
Trains
3.5
29.8
13.4
17.1
5.03
8.22
0.22
2.52
1.09
1.85
0.81
0.74
Train
No of train
No pass.train stops
No of pass train
No of wagons
No of consigned wagons
Renewal
Sweden – duration model
Maintenance and Renewal
Sweden
Switzerland (A+B)
Maintenace
Sweden
Switzerland (A)
UK (model V)
Switzerland (part of A)
Operation
Sweden
Hungary
train movement
path allocation
interim passenger train service
beg/end of line pass train servic
marshall/shunt for freight wagons
consigment of freight wagons
Duration models
The Swedish case study on lifetime of railways generated results that are consistent with the
econometric studies. There is a significant and price relevant cost related to rail renewal in
line with what has been found in econometric studies of renewal cost data. We estimate
marginal costs for freight and passenger trains separately in the range of € 0.00012 – 0.00028
per gross tonne kilometre. Somewhat surprisingly, the marginal cost for passenger trains is
higher than freight trains, but a possible explanation to this is higher quality demands for
passenger trains.
29
GRACE D3 – Marginal cost case studies for road and rail transport
2.3.3 Discussion
The aims of this section is to presents a survey of the literature and try to understand in a
wider context the result of the GRACE case studies.
Road
The table below summarise studies from US, Canada and Australia on the share of
infrastructure cost that can be attributed to traffic load (standard axles). It can be compared to
the elasticities reported from the GRACE case studies. The table includes both rehabilitation
(renewal) and routine maintenance. We can conclude that the load shares come in a number of
different forms but that they always are below 1 (or equal to). The load share seems to be
higher for rehabilitation than for routine maintenance which reinforces the conclusion from
the GRACE case studies. In addition, we note that flexible pavement has a lower impact of
load related factors than rigid pavements in these studies from US.
Table: 9 Load shares in US-studies
Study
Year
Flexible
JCP
CRC
Composite
Rehabilitation 10
Li et.al. (2001)
1995-1997
0.28
0.78
0.38
Indiana HCAS
1984
0.42
0.78
0.38
ARRB Study (Australia)
0.88
0.88
0.88
Federal HCAS
1997
0.84-0.89
0.78-0.86
0.84-0.89
Routine maintenance11
Li et.al. (2002)
1995-1997
0.257
0.357
0.632
0.28
Indiana HSC Approach
1984
0.21
0.54
1.00
0.29
Ontario study
1990
0.25-0.33
Note: HCSA = Highway Cost Allocation Study; JCP=joint concrete pavement, CRC=continuously reinforced
concrete and Composite
Hajek et.al. (1993) estimates the marginal pavement cost of truck damage in Ontario defined
as the cost of providing pavement structure for one additional standard axle. In general the
approach is to define a minimum thickness of the layer and then allocate the extra cost for
additional thickness to the extra load. The method is similar to the federal method used in US
but this study focus on the marginal effect while the general approach is to analyse the
average effect. The study states that ‘a small increment in thickness permits a significant
increase in traffic loads’ (p 52). The economies of scale in accommodating additional load
results in a decreasing average cost function for additional standard axles. The marginal cost
derived with this method is depicted in the figure below.
10
11
Source: Li et.al (xxxx)
Source: Li et.al. (2002)
30
GRACE D3 – Marginal cost case studies for road and rail transport
Figure: 6 Marginal cost for rehabilitation in Ontario on new and in-service asphaltic
concrete pavements
1.00
MC New pavement
0.90
MC In-service pavement
0.80
0.70
€/km
0.60
0.50
0.40
0.30
0.20
0.10
0.00
0
10 000
20 000
30 000
40 000
50 000
60 000
70 000
ESAL per year per lane
Source: own figure based on Hajek et.al. (Exchange rate 0.7013 €/Canadian$)
The marginal cost is decreasing as in numerous of the GRACE studies. However, this study
has a totally different approach. The driving force behind this decreasing cost function is the
economies of scale in layer thickness. If the authority predicts a higher traffic load in deciding
on the measure we will, due to an adaptation of technology, observe a decreasing average and
marginal cost ex post if the prediction is fulfilled.
In a situation where the technology is fixed we may expect a totally different form of cost
function. Such a fixed technology can be detected with more detailed information of the
infrastructure characteristic. Lindberg (2002) analysed the marginal cost on Swedish roads
with an engineering approach. The data originated from the Swedish Long Term Pavement
Performance program and contained detailed data on infrastructure characteristics. The
resulting marginal cost is shown in the table below. For a given road strength (SCI) the
marginal cost is increasing with increasing traffic load. However, if the probability of
observing a road with higher road strength is higher when the traffic volume increases we
may observe a function with a shape more as the line.
31
GRACE D3 – Marginal cost case studies for road and rail transport
Figure: 7 Marginal cost per standard axle on Swedish roads depending on road strength
(surface curvature index =SCI).
0.035
0.03
SCI 50
0.025
SCI 75
SCI 100
0.02
€/km
SCI 125
SCI 150
0.015
SCI 175
SCI 200
0.01
SCI 250
0.005
0
0
100
200
300
400
500
600
700
800
900
1000
Standrad axles per day
1 € = 8.92 SEK
Source: Lindberg (2002)
An increasing cost function is, as has been seen, also the result of the German GRACE study.
This study is a two-model study where the technology is modelled separately (with economies
of scale as a result). The remaining marginal cost takes a form more similar to the result in the
table above. We may suspect that the studies on renewal cost doesn't control for the
technology in a similar way. This may create an uncontrolled variation between different
studies.
The US Federal Highway Cost Allocation Study (1997 and addendum 2000) allocates the cost
for the federal Highway program to different user categories. For each category an average
cost is the calculated. The table below summarise the result also in € per km.
Table: 10 US Federal Highway Cost Allocation Study 2000
cent/mile
0.8
0.76
3.2
Car
Pickup/Van
Buses
Single Unit Truck (weight in pounds)
<25000
25001-50000
>50000
All Single Units
Combination Trucks (weight in pounds)
<50000
50001-70000
70001-75000
75001-80000
80001-100000
>100001
All Combinations
All Trucks
2.2
5.46
18.12
4.38
3.43
5.21
7.62
8.65
15.32
20.28
8.43
6.74
32
€/km
0.0039
0.0037
0.0157
0.0108
0.0268
0.0888
0.0215
0.0168
0.0255
0.0374
0.0424
0.0751
0.0994
0.0413
0.0330
GRACE D3 – Marginal cost case studies for road and rail transport
The ‘All trucks’ result from the US cost allocation study is similar to the result from the
Swedish econometric study on renewal and maintenance (0.033 compared to 0.040). This is
only a coincidence but suggests that the US figures above are on the low end of the result
from the GRACE studies as the Swedish values are low compared to the other GRACE
studies.
The table below presents the result from a series of research studies and is an extension of the
table in Case study 1.2A.
Table: 11 Marginal infrastructure cost around the world
Source
Scope of the study
Type of cost considered
Hajek et.al.
(1993)
Ontario
Urban Freeway
Major Arterial
Minor Arterial
Collector
Local
Austria
Rehabilitation
Herry and
Sedlacek 2002
Lindberg (2002) Total road network
Sweden
Link (2002)
Germany
Maintenance and
renewals
Cost of pavement
resurfacing
Renewal
Newbery
Tunisian roads
(1988a)
Newbery (1990) UK road network
Cost of pavement
resurfacing
Cost of pavement
resurfacing
Ozbay et al.
Highways Northern Cost of pavement
(2000)
New Yersey
resurfacing
Schreyer et al
Sweden
Maintenance, renewals
2002
and upgrades
Small and
US highways
Cost of pavement
Winston (1988)
resurfacing
Small et al.
US rural and urban
Cost of pavement
(1989)
freeways
resurfacing
Note: € per $; 0.7888; km per mile; 1.609.
Marginal cost (MC)
estimate
Cost elasticity
MC/AC
0.002 $/HGVkm
0.007
0.012
0.031
0.461
2.17 €Cents/vkm
(per HGV)
0.77 ... 1.86 €
0.1…0.8
Cents/vkm
0.05 – 2.70 €Cents/vkm
(per HGV)
0.13 … 2.58 US$/ESAL 0.19…1.07
km
0.035 pence/ ESAL km n.a.
0.062 US$/ vehicle mile n.a.
3.62 – 5.17 €Cents/vkm
(per HGV)
0.022 … 0.023
US$/ESAL mile
0.0148 … 0.0432 US$/
ESAL mile
n.a.
n.a.
In the figure below we have converted all the estimates to a common unit, €/HGVkm or
€/vkm. We have assumed 1.5 ESAL per HGV and current exchange rates12. The comparison
is rough. In the figure we have not presented the full value of the GRACE German Case study
(should be 1.39 €/HGVkm). For some studies we present a lower and a higher value.
12
€ per $ = 0.7888; € per £= 1.48; km per mile=0.62, ESAL per HGV = 1.5.
33
GRACE D3 – Marginal cost case studies for road and rail transport
Figure: 8 Rough comparison between the GRACE case studies and other estimates
US HWCA all combinations
All veh
US HWCA all truck
HGV
GRACE 1.2C (Poland Renewal and Maintenace)
GRACE 1.2Bi (Sweden Renewal and Maintenace)
GRACE 1.2Bi (Sweden Renewal paved)
GRACE 1.2C (Poland Renewal)
GRACE 1.2A (Germany Renewal)
Small et al. (1989)
Small et al. (1989)
Small and Winston (1988)
Schreyer et al 2002
Schreyer et al 2002
Ozbay et al. (2000)
Newbery (1990)
Link (2002)
Link (2002)
Lindberg (2002)
Lindberg (2002)
Herry and Sedlacek 2002
Ontario Local
Ontario Collector
Ontario Minor Arterial
Ontario Major Arterial
Ontario Urban freeway
0.000
0.025
0.050
0.075
0.100
0.125
0.150
0.175
0.200
€/Xkm
Note: 1.5 ESAL per HGV has been used
Grace 1.2A ; 1.39 €/HGVkm; Ontario Local; 0.323 €/HGVkm.
Railway
In the railway sector the difference between different studies are not as big as for the road
sector. One explanation could be that the technology is more homogenous and easier to
control which makes studies less vulnerable to the problem with unobserved variables.
Another, less positive, explanation could be that studies are less common in the railway sector
and still starts from a similar approach. The table below summarise a number of current
studies.
34
GRACE D3 – Marginal cost case studies for road and rail transport
Table: 12 Results from other studies compared against the estimated models
Study (maintenance
costs only) / Model
estimated
Country
Johansson and Nilsson
(2004)
Johansson and Nilsson
(2004)
Tervonen and Idstrom
(2004)
Munduch et al (2002)
Gaudry and Quinet
(2003)
Andersson (2005)
Booz Allen &
Hamilton (2005)
Elasticity of cost with respect to
tonne-km
Sweden
Marginal Cost
Estimates (Average)
Euro per Thousand
Gross Tonne-km
0.127
Finland
0.239
0.167 (average)
Finland
0.18
0.133-0.175
Austria
France
0.55
Not reported
0.27
0.37 (average)
Sweden
0.293 (pooled OLS
model) 0.272 (random
effects model)
Approx 1.5
0.1944 (average pooled OLS
model) 0.1837 (average Random
effects model)
Proportion of maintenance cost
variable with traffic: 0.18; 0.24
for track maintenance
UK
0.169 (average)
Source: UK CS
The figure below summarise the GRACE case studies where results are presented per Gtkm
and the results from the survey presented in the table above.
Figure: 9 Rough comparisons between the GRACE case studies and other estimates
€/Gtkm
GRACE 1.2E (Switzerland, Operation/Maintenace)
GRACE 1.2G (UK, Maintenace)
GRACE 1.2E (Switzerland, Maintenace)
GRACE 1.2Di (Sweden Maintenace)
GRACE 1.2E (Switzerland Maintenace and Renwal (A+B))
GRACE 1.2Di (Sweden Maintenace and Renwal)
GRACE 1.2Dii (Sweden, duration model freight)
€/Gtkm
GRACE 1.2Dii (Sweden, duration model passenger)
Booz Allen & Hamilton (2005)
Andersson (2005)
Munduch et al (2002)
Tervonen and Idstrom (2004)
Johansson and Nilsson (2004)
Johansson and Nilsson (2004)
0
0.5
1
1.5
€/Xkm
35
2
2.5
GRACE D3 – Marginal cost case studies for road and rail transport
It can be observed that the highest cost can be found for two studies in UK, the GRACE study
1.2G and Booz Allen & Hamilton (2005). Renewal has a significant impact on the cost as can
be seen when comparing the Grace studies in Sweden and Switzerland.
2.4 Conclusions
This GRACE report summarises 9 Case studies on the marginal infrastructure cost. The
individual CS are presented in appendices. Each CS contains interesting information and the
summary in this report cannot cover all topics presented in these studies.
However, we have tried hereafter to present the general picture and the common results.
These can be discussed in five different areas, i) methodology, ii) elasticity, iii)
differentiation, iv) average cost and v) marginal cost.
i.
ii.
iii.
iv.
Most of the studies use an econometric approach and collect paneldata. However, a
minority of the studies use paneldata models. In two studies a duration model is used
where a function of the lifetime of a road pavement or railtrack is estimated. The result
can be used to derive a marginal renewal cost. The rail study gave results in line with
the econometric study and supported the conclusion drawn from the econometric
studies that there indeed exists a marginal cost related to renewal on railways. The
result was similar between the two approaches. However, the road study suggested a
very low effect of traffic on the observed lifetime of a pavement. A possible
explanation with some support is that the authority predicts the higher traffic volume
when deciding on the pavement thickness. The marginal cost is thus not found in
observed lifetime but in the increased cost of the measures taken.
The cost-elasticity with respect to the traffic-output describes the relationship between
average cost and marginal cost such that Marginal Cost = Elasticity *Average Cost.
The elasticity for road infrastructure cost decreases as the measure changes from
renewal to maintenance and to operation. The elasticity for rail infrastructure cost is
lower than the elasticity for road and doesn’t show the same difference between
different measures. In addition, the majority of the studies suggest that the elasticity
decreases with increased traffic. Thus highly used infrastructure has a lower elasticity
than low volume infrastructure. All elasticities reported above are for the average
traffic in the studies.
The operation or short term maintenance is related to total trainkm or total vehiclekm
while the renewal and maintenance are usually related to gross tonnekm or HGVkm.
Few of the studies have been able to test which type of traffic predominantly
influences the infrastructure cost. In general, this has been decided a priori based on
other information. However, one study conducted a test on the difference between
additional trains of the same weight or additional weight of the same number of trains
but could not find any significant difference.
The average cost is less homogenous than what could be expected. For road studies
the average renewal cost is 0.036 €/HGVkm in the Swedish all roads study and 1.59
€/HGVkm for the German motorway study. The Polish study allocates the renewal
cost to all vehicles and suggests a cost of 0.21 €/vehkm. For the aggregate of renewal
and maintenance measures the average cost is 0.059 €/HGVkm in the Swedish study.
Operation is in the Swedish study allocated to all vehicles and has an average cost of
0.024 €/vkm. In the rail infrastructure cost study maintenance and renewal has an
average cost of 0.00285 €/Gtkm in Sweden and 0.00364 €/Gtkm in Switzerland. The
maintenance only average cost is 0.00209€/Gtkm in Sweden and 0.0022 €/Gtkm in
36
GRACE D3 – Marginal cost case studies for road and rail transport
v.
Switzerland. However, the UK study shows an average maintenance cost of 0.00828
€/Gtkm. Operation has an average cost of 0.153 €/trainkm in Sweden. The Hungarian
study suggests a cost for ‘train movement’ 3.5 €/trainkm and 0.0041 €/Gtkm.
The marginal cost follows from the elasticities and the average costs. The marginal
cost on roads has a huge variability depending on the huge variability in average cost.
The cost on German motorways is 1.39 €/HGVkm for renewal only. The
corresponding cost for all Swedish paved roads is 0.032 and 0.12 in Poland. The
Swedish result for gravel roads is 0.236 €/HGVkm. Aggregating renewal and
maintenance generates a marginal cost of 0.040 €/HGVkm in Sweden and 0.13
€/HGVkm in Poland. Infrastructure operation costs do not vary with traffic volume
according to the Swedish case study. The marginal cost in the rail sector is 0.00070
€/Gtkm inSweden for renewal and maintenance and 0.00097 €/Gtkm in Switzerland.
Maintenance only has a cost of 0.00031 €/Gtkm in Sweden and 0.00045 €/Gtkm in
Switzerland. The marginal cost in UK is estimated in 0.002 €/Gtkm. Operation has a
marginal cost of 0.054 €/trainkm in Sweden. The Hungarian study concludes on a
marginal cost of 0.22 €/trainkm for train movements.
37
GRACE D3 – Marginal cost case studies for road and rail transport
3 Congestion and scarcity
Infrastructure has a long lifetime and is difficult to adjust for fluctuations in demand. The
capacity will therefore be limited and a problem of allocation will occur when traffic
increases. On the road the result is congestion – increased traveltime for all users – and in the
rail sector the main result is that other operators will not be able to get the slot they want –
scarcity.
This section summarises the CS on road congestion (3.1) and rail congestion (3.2). The
former focuses on the question – can we explain the difference in estimates that has been
reported in the past? The latter develops an approach to evaluate the cost of scarcity.
3.1 Road
In contrast to other cost categories and modes, there have been a host of studies involving the
estimation of marginal road congestion cost. The difficulty is that the available estimates
vary considerably between different studies, and not always in the ways one might anticipate.
The table below provides a range of results from reviewed studies and illustrates this
variation.
The original values estimated by the studies reviewed are presented, along with the same
values updated to 2003 prices. It can be difficult to compare values between cordon schemes
and distance related schemes. In order to facilitate a comparison of the values it is assumed
that the average car trip length is around 10.5 kms, as suggested by the UK DfT (2003) for
trips in medium urban areas. It can be seen that there are wide ranging differences between
the values presented by the six studies for what are essentially medium sized cities. The
highest values are put forward by Newberry and Santos (2003) whose present two sets of
values, one calculated from an area wide speed-flow curve and the other using SATURN.
The area wide speed flow values are consistently higher than the SATURN values (ranging
from 10% to nearly 60% higher) with a highest value of close to 526 pence for Northampton
and a lowest value of 12.74 pence for Bedford. At the other end of the scale are the values
calculated by Milne (2002) which for similar size cities produce values of less than 1 pence
per car unit km but these appear to be averages over very wide areas, whereas the cordon
charge studies will only charge radial trips to the centre.
38
GRACE D3 – Marginal cost case studies for road and rail transport
Table: 13 Comparison of Values from previous studies
Study
Sansom et al. (2001)
Values Measured
Value Measured – Short run MEC: values without brackets are the low
estimates & figures in brackets are high estimates.
Inner London:
Central London:
Motorways: 20.10
Motorways: 53.75
Trunk & Principal: 54.13
Trunk & Principal: 71.09
Other: 94.48
Other: 187.79
Values in 2003 Prices Pence
Value Measured – Short run MEC: values without brackets are the low
estimates & figures in brackets are high estimates.
Inner London:
Central London:
Motorways: 21.34
Motorways: 57.08
Trunk & Principal: 57.48
Trunk & Principal: 75.49
Other: 100.33
Other: 199.41
Outer London:
Motorways: 31.09
Trunk & Principal: 28.03
Other: 39.66
Inner Conurbation::
Motorways: 53.90
Trunk & Principal: 33.97
Other: 60.25
Outer London:
Motorways: 33.01
Trunk & Principal: 29.77
Other: 42.11
Inner Conurbation::
Motorways: 57.24
Trunk & Principal: 36.07
Other: 63.98
Outer Conurbation:
Motorways: 35.23
Trunk & Principal: 12.28
Other: 0.00
Urban>25 km2
Trunk & Principal:10.13
Other: 0.72
Outer Conurbation:
Motorways: 37.41
Trunk & Principal: 13.04
Other: 0.00
Urban>25 km2
Trunk & Principal: 10.76
Other: 0.76
Urban 15-25 km2
Trunk & Principal: 7.01
Other: 0.00
Urban 10-15 km2
Trunk & Principal: 0.00
Other: 0.00
Urban 15-25 km2
Trunk & Principal: 7.44
Other: 0.00
Urban 10-15 km2
Trunk & Principal: 0.00
Other: 0.00
Urban 5-10 km2
Trunk & Principal:2.94
Other: 0.00
Urban 0.01-5 km2
Trunk & Principal: 1.37
Other: 0.00
Urban 5-10 km2
Trunk & Principal:3.12
Other: 0.00
Urban 0.01-5 km2
Trunk & Principal: 1.45
Other: 0.00
Rural:
Motorway: 4.26
Trunk & Principal: 9.00
Other: 1.36
Unit – Per Car Unit Km
Rural:
Motorway: 4.01
Trunk & Principal: 8.84
Other: 1.28
Unit – Per Car Unit Km (1998 prices & values – pence)
39
GRACE D3 – Marginal cost case studies for road and rail transport
Comparison of Studies – Values – Continued………
Study
Newberry & Santos
(2003)
Values Measured
Values Measured – MEC. 1st figures calculated from Area Wide Speedflow Curves; Figures in brackets calculated using Saturn.
Values in 2003 Prices and Values – Pence
Values Measured – MEC. 1st figures calculated from Area Wide Speedflow Curves; Figures in brackets calculated using Saturn.
Milne (2002)
Northampton: 495 (315)
Kingston Upon Hull: 209 (166)
Cambridge: 80 (71)
Norwich: 16 (14)
Lincoln: 78 (67)
York: 60 (44)
Bedford: 12 (11)
Hereford: 72 (57)
Unit-Per Car Unit Km (1998 prices & values-pence)
Values Measured – MEC
Northampton: 525.64 (334.50)
Kingston Upon Hull: 221.94 (176.28)
Cambridge: 84.95 (75.39)
Norwich: 16.99 (14.87)
Lincoln: 82.83 (71.15)
York: 63.71 (46.72)
Bedford: 12.74 (11.68)
Hereford: 76.46 (60.53)
Unit-Per Car Unit Km
Values Measured – MEC
May et al. (2002)
Helsinki: 0.26
Edinburgh: 0.65
Salzburg: 0.92
Unit-Per Car Unit Km (1998 Prices & values-pence)
Values Measured – MEC. 1st best pricing based on Saturn.
Helsinki: 0.28
Edinburgh: 0.69
Salzburg: 0.98
Unit-Per Car Unit Km
Values Measured – MEC. 1st best pricing based on Saturn.
Top 10 links with uniform charges: 0.80
Top 10 links with two levels of charges: 0.50 & 2.00
Unit-Per Car Unit/ Trip (2000 Prices- £s)
Top 10 links with uniform charges: 83 (7.9)
Top 10 links with two levels of charges: 52 & 208 (5.0 &19.8)
Unit-Per Car Unit/ Trip
Values Measured - Judgemental Cordons.
Inner 1 – 0.50
Inner 2 – 0.75
Outer 1 – 2.25
Outer 2 – 0.75
Unit-Per Car Unit/ Trip (2000 prices & values- £s)
Values Measured - Judgemental Cordons.
Inner 1 – 52 (5.0)
Inner 2 – 78 (7.4)
Outer 1 – 234 (22.3)
Outer 2 – 78 (7.4)
Unit-Per Car Unit/ Trip (Unit-Per Car Km)
40
GRACE D3 – Marginal cost case studies for road and rail transport
Comparison of Studies – Values – Continued………
Study
Santos (2004)
Santos (2000)
Values Measured
Value Measured – MEC based on an optimal toll that maximises social
surplus: defined as total utilities of all trips minus sum of total costs of
all trips.
Values in 2003 Prices Pence
Value Measured – MEC based on an optimal toll that maximises social
surplus: defined as total utilities of all trips minus sum of total costs of
all trips.
Northampton: 3.47
Kingston upon Hull: 3.73
Cambridge: 1.60
Lincoln: 1.07
Norwich: 0.80
York: 1.60
Bedford: 1.60
Hereford:1.60
Unit – Optimal Toll Per Car Unit/Trip (2002 prices & values- £) for a
single cordon scheme.
Northampton: 352 (33.5)
Kingston upon Hull: 378 (36.0)
Cambridge: 162 (15.4)
Lincoln: 108 (10.3)
Norwich:81 (7.7)
York: 162 (15.4)
Bedford: 162 (15.4)
Hereford: 162 (15.4)
Unit – Optimal Toll Per Car Unit/Trip (Per Car Unit Km)
Northampton: 2.40 & 2.40
Kingston upon Hull: 3.20 & 0.53
Cambridge: 0.80 & 2.67
Lincoln: 0.80 & 1.07
Norwich: 0.80 & 0.80
York: 1.07 & 1.33
Bedford: 2.7 & 2.40
Hereford:1.07 & 1.07
Unit – Per Car Unit/Trip (2002 prices & values- £) for a double optimal
toll
Values Measured – MEC
Northampton: 243 & 243 (23.1 & 23.1)
Kingston upon Hull: 324 & 54 (30.9 &5.1)
Cambridge: 81 & 271 (7.7 & 25.8)
Lincoln: 81 & 108 (7.7 & 10.3)
Norwich: 81 & 81 (7.7 & 7.7)
York: 108 & 135 (10.3 & 12.9)
Bedford: 274 & 243 (26 & 23.1)
Hereford: 108 & 108 (10.3 & 10.3)
Unit – Per Car Unit/Trip (Per Car Unit Km)
Value Measured – MEC
Cambridge-Morning Peak: 61.4
Cambridge-Evening Peak: 51.0
York-Morning Peak: 48.9
York-Evening Peak: 49.9
York-Off Peak: 42.7
Unit-Per Car Unit Km (1996 prices & values-pence)
Cambridge-Morning Peak: 65.20
Cambridge-Evening Peak: 54.16
York-Morning Peak: 51.93
York-Evening Peak: 52.99
York-Off Peak: 45.34
Unit-Per Car Unit Km
41
GRACE D3 – Marginal cost case studies for road and rail transport
We can find a number of reasons for the difference in results from previous studies. The
figure below suggests a number of different measures on the cost that could be attributed to
congestion. In the figure we have a Free flow cost that is independent of the traffic volume.
As the traffic increases the private marginal cost (PMC) increases as the delay becomes more
severe. The key concept in pricing is that the social marginal cost (SMC) increases faster. The
external cost is the difference between these to cost curves. In addition, we have indicated the
existence of other externalities. The demand (D) is decreasing as cost increases and the
private optimal is in the figure around 80 ‘traffic units’ while the social optimal is
approximately 70 units.
Figure: 10 The concept
250
Index Cost per km
200
FreeFlow
150
PMC
SMC
OtExt
100
D
50
0
0
20
40
60
Q*
80
100
120
Index Traffic
The aim of this GRACE report is to show estimates of the external marginal cost. Focusing on
congestion the appropriate measure is the difference between the social marginal cost and the
private marginal cost (the arrow). If the purpose is to find the optimal congestion price to be
paid we need to look at the external marginal cost at the optimal traffic level, i.e. at Q*.
However, in addition to the congestion externality also other externalities exists, such as
accidents, air pollution, climate change and noise which should be included in the price. The
optimal price to pay is thus p*+e* (e= OtExt).
As simpler concept is the measure in relation to the free flow speed. This is the delay cost.
However, it should be clear that delay cost also exists at the optimal level of traffic. Free flow
is never, or seldom, the optimal condition for infrastructure use.
3.1.1 Overview
The investigation is designed to throw particular light on the reasons for some unexpected
differences in the results reported for different cities in previous studies. In order to retain
experimental control the work is based on a carefully specified series of “city scenarios”
rather than on a set of real cities which differ from each other in a myriad of ways.
42
GRACE D3 – Marginal cost case studies for road and rail transport
Results are presented for four city scenarios – a reference case and three variant scenarios
each of which differs from the reference case in only one respect. The reference city has a
built up area of approximately 10 km2 and has a population of about 800 000 producing
somewhat over 100 000 car movements in the morning peak – creating significant peak
period congestion. The modelled area is about 30 km across (thus extending well beyond the
main built-up area) and the modelled road network comprises three types of link “principal”,
“other”, and “special” – the latter, which includes links close to schools and hospitals, was
introduced as a class of links which are particularly susceptible to externalities caused by road
traffic)- which add up to nearly 500 kilometres.
The first variant city has 20% greater population, the second has a more restricted network
(with about 25% fewer road kilometres – though leaving the principal road network largely
intact) and the third has an enhanced network (with an additional orbital route, comprising
links of type “other” which adds about 20% extra road kilometres to the network).
Table: 14 City scenarios
•
•
•
•
Base – city of 10 km2 with 800,000 inh
Higher – 20% greater population
Fewer – 25% fewer road kilometre
Extra – 20% more road kilometre
The results for each city scenario relate to a typical morning peak hour and have been
produced using a SATURN model. Results are produced for each city for a “without tolls”
run and a “with tolls” run. The “without tolls” results are based on user equilibrium
assignment. The “with tolls” results show the equilibrated situation after application of
optimal tolls reflecting the externalities caused by the traffic. The model allows the imposition
of these tolls to affect route choice and the decision on whether to travel by car during the
peak (the net effect of decisions on trip frequency, trip timing and mode).
3.1.2 Results
The Case study in Annex gives detailed results from the different scenarios. We have here
focused on, and calculated, cost per kilometre based on the definitions in the figure above.
The non optimal case is marked as the 0-case while the optimal case is marked with an
asterisk (*).
The first and basic observation is related to the type of city. The table below uses the optimal
base case (Base *) as reference case. We can expect a difference of up to 40% only because
the type of city differs.
Table: 15 Cost between different cities (relation to optimal base case)
Cost element
p
p+e
e
Base 0
0.89
Base *
1.00
1.00
1.00
High 0
High *
1.37
1.29
0.91
0.90
Fewer 0
0.90
Fewer *
0.98
0.97
0.91
Extra 0
0.94
Extra *
1.02
1.02
0.99
The marginal external congestion cost is 37% higher than the base case in the high demand
city. The congestion price per trip is lowest in the city with extra links. However, per
43
GRACE D3 – Marginal cost case studies for road and rail transport
kilometre the costs become slightly higher. It should be noted that the distance differs
between the cities. Adding other externalities to the congestion cost does not change the
relation between different cities dramatically.
In addition to the cost of congestion and other externalities presented above, we have
calculated the ‘delay’ cost per kilometre based on the Case study. The figure below depicts
the kilometre cost for the different cases.
Figure: 11 Cost per kilometre (€/vkm) – the optimal congestion toll (p), the cost of other
externalities (e) and the delay cost (d) at optimum (*) and non-optimum (0).
0.40
0.35
Cost per km (€/vkm)
0.30
0.25
p
0.20
d
e
0.15
0.10
0.05
0.00
Base 0
Base *
High 0
High *
Fewer 0
Fewer *
Extra 0
Extra *
Besides the observation that city structure strongly affects the result we make three
observations:
• The first concerns the importance of the definition. If the delay cost is included in a
study the external marginal congestion cost is overestimated by a factor of 4. If other
externalities are included the overestimation is around 3.7.
• The second observation is on the importance of other externalities. If these are not
included the external marginal cost is underestimated by around 20%.
• The third observation is on the importance of measuring at the optimal situation. The
table below summarises the measure of other externalities and the delay cost.
Table: 16 The importance of the definition
(d+p)/p*
(d+p+e)/(p+e)*
p/(p+e)*
e/e*
d/d*
p/p*
Base*
High *
Fewer *
Inclusion of delay cost
4.01
4.16
4.30
3.50
3.78
3.77
Inclusion of other externalities
0.83
0.88
0.84
Measure at non-optimum
0.89
0.99
0.99
1.27
1.26
1.22
na
na
na
44
Extra *
4.08
3.57
0.83
0.96
1.26
na
GRACE D3 – Marginal cost case studies for road and rail transport
3.2 Rail
Congestion is only the appropriate capacity cost where the train in question represents an
additional train with respect to what would otherwise have been run; where the train in
question runs instead of some other train the appropriate capacity cost is the opportunity cost
of trains forced off the system by lack of capacity.
3.2.1 Introduction
Charging for scarce capacity would require estimation of the opportunity cost of a slot. The
most attractive solution to this problem in theory is to 'auction' scarce slots. There are many
practical difficulties however, including the complicated ways in which slots can be put
together to produce a variety of types of service, and the fact that the value of a particular slot
for a particular use depends on how other slots are being used (in terms of the operation of
complementary or competing trains). Nilsson (2002) provides a more detailed consideration
of auctioning and argues that it is a feasible solution. He argues that train operating
companies could be asked to bid on the basis of what they are willing to pay for their most
desired slot, indicating also the discount they would require per minute earlier or later than the
ideal their slot is. An optimisation algorithm would then produce the best feasible solution,
and train operating companies would be given a chance to revise their bids. Nilsson accepts,
however, that this might not converge where train operators are competing in the same
market, as their bids will be heavily dependant on what slots other operators get. Actually
charging operators on the basis of the second highest bid would both give an incentive to
correct revelation of willingness to pay, and ensure that charges actually reflected opportunity
cost.
A different approach, recommended by NERA (1998), is to identify sections of infrastructure
where capacity is constrained and to charge the long run average incremental cost of
expanding capacity. However, this is a very difficult concept to measure (the cost of
expanding capacity varies enormously according to the exact proposal considered, and it is
not easy to relate this to the number of paths created, since they depend on the precise number
and order of trains run).
An alternative considered in the GRACE CS is for the track charging authority to attempt to
calculate directly the costs involved in depriving another operator of the slot. For instance, if a
train has to be run at a different time from that desired, it is possible to use studies of the value
people place on departure time shifts to estimate the value to its customers of the cost
involved. Similarly, the costs of slower speeds may be estimated from passengers' values of
time.
3.2.2 The case study
This case study concerns the stretch of the East Coast Main Line from London to Doncaster.
It is heavily used, particularly between London and Doncaster, which is where the main lines
to Leeds, Hull and an important route to Scunthorpe and Grimsby branch off.
There is one main operator of long distance passenger services on this route. A few years ago
a new open access operator was granted access rights to operate through trains between
London and Hull. The line from London to Doncaster also carries freight traffic.
45
GRACE D3 – Marginal cost case studies for road and rail transport
The basis of the approach taken here is that operators should be charged for the capacity they
use in accordance with the social opportunity cost of that capacity. In order to implement this
approach it is necessary first to measure the amount of capacity used by each train run, and
then to estimate its opportunity cost.
Thus the approach investigated in the CS is the construction of a tariff based on the
opportunity cost of the slot to the existing operator. If the new operator requires capacity that
would deprive the existing of more than one slot then they would be charged for the
appropriate number of slots. Since the existing operator is known, and is required to make
data available to the regulator, this approach to charging should be feasible. Of course, if there
are several other operators competing for the slot, and they all have higher values than the
franchisee, then this will understate the true opportunity cost of the slot. However, basing
charges on the identity of unknown possible new entrants appears difficult, at least until they
start operating and data becomes available.
The opportunity cost of a slot for this type of service can be estimated as the sum of:
• the additional amount of traffic attracted to rail by the presence of this train
multiplied by the price it pays
• the consumers’ surplus to rail users as a result of the additional quality and
capacity provided by the train
• the savings of external costs to road users and the public at large from the train
attracting passengers from road.
• Less the train operating, infrastructure and external cost savings from failing to
run this train.
3.2.3 Results
Unfortunately, the results can not be expressed in absolute cost terms due to the use of secret
information. Instead, the results are related to the ‘Total benefit of the existing operator at
peak’.
The value of the slot for the ‘existing peak operator’ is thus 100%. The value of the slot for
the existing operator off-peak is in total 6% of the value at peak. The main positive value is
related to effects on other modes (mainly congestion) as can be seen in the figure below. The
negative value for the rail modes depends on a negative profit and a negative tax revenue
effect.
A new operator at peak generates a net value of 10% above the loss when removing the
existing operator from the slot. The main positive (net) benefit is once again the reduced
congestion on other modes while lost tax revenues give a negative net effect on the rail mode.
A new operator off-peak has a negative net-value of 14% depending on a substantial loss to
the rail operator.
46
GRACE D3 – Marginal cost case studies for road and rail transport
Table: 17 Summary of slot value (Full value for existing operator at peak=100)
Env+Safety
Infrastructure costs
Tax revenues
Consumer surplus
Congestion
Mohring
Operators profit
Full value
Existing operator
at peak
Rail
Other
-0.9
13.4
0.0
1.0
-12.4
-18.2
18.8
0.0
0.0
52.7
0.0
-1.7
51.2
-4.1
100.0
Existing operator
off peak
Rail
Other
-0.9
2.9
0.0
0.2
-3.1
-3.9
2.6
0.0
0.0
11.3
0.0
-0.4
-1.9
-0.9
6.0
New operator
at peak
Rail
Other
-0.9
4.8
0.0
0.4
-4.8
-6.5
0.1
0.0
0.0
18.8
0.0
-0.6
2.0
-1.4
11.9
New operator
off peak
Rail Other
-0.9
2.0
0.0
0.1
-1.1
-2.7
0.7
0.0
0.0
7.7
0.0
-0.2
-19.3
-0.6
-14.1
The results suggest a substantial scarcity charge for peak slots, the charge for off peak slots
would only be some 10% of this value.
In terms of net social benefits, the existing operator’s use of the peak path gives the highest
values for passenger use. The off-peak slot and the use of the peak slot by the new operator
have much smaller, positive social values. This is driven by the small overall changes in total
passenger demand arising from these two scenarios, and the small changes in operators’
profits. In the case of the new operator, the increase in the new operator’s profits is at the
expense of the existing operator, and in the case of the off-peak slot actually reduced overall.
47
GRACE D3 – Marginal cost case studies for road and rail transport
Figure: 12 Private, modal and Societies benefits
New Off-Peak
New Peak
Other modes
Other effects rail mode
Profit operator of slot
Net profit all operators
Existing Off-Peak
Existing Peak
-30
-20
-10
0
10
20
30
40
50
60
70
80
Percentage of benefit Existing operator at peak
The figure above presents the elements of the slot value and in addition the profit to the
operator of the slot. The profit to the operator that got the slot is always positive and higher
than the net profit of all affected operators. The highest value is to the existing operator (67%
of the benefit at peak for existing operator). The new operator will have a profit of 41% at
peak and 8% off-peak. The lowest profit is for the existing operator off-peak (1%).
Comparing the private value for the slot operator (the profit) and the social effect (the sum of
the effect on other modes, the other effects on the rail modes and the net profit for operators)
suggests that very large differences exist. This in turn suggests that a market solution (for
example auctioning) without taking into account the effect especially on other modes would
not necessarily give the social optimum result. However, appropriate pricing of other modes
would considerably change this supposition.
3.3 Conclusions
Our overall conclusion regarding road congestion, based on the theoretical investigations and
modelling work, is that it is not surprising that the reported performance of “optimal” road
user tolls differs in different studies. We conclude that these differences can be variously
attributed to:
• differences is the definition of “optimal” tolls – the term is often quite loosely
applied. For example; the term sometimes relates only to congestion tolls
(rather than covering other externalities), sometimes allows for the cost of
implementation of the tolls (and sometimes not), and sometimes relates only to
simple tolls - such as cordons (rather than tolls which vary in space and time).
• differences in the way that optimal tolls (however defined) are calculated. For
example, do they fully reflect the behaviour of travellers at the margin or are
they derived from a theoretical representation of the marginal impacts?
• differences in the nature of the cities being studied. Factors which are
particularly likely to influences the result include the degree of congestion, the
availability and attractiveness of alternative modes, the drivers’ tolerance of
48
GRACE D3 – Marginal cost case studies for road and rail transport
•
•
congestion, and the capacity of the network to absorb additional demand. Even
for a single city These
differences in the valuation of different externalities – perhaps reflecting
different values of time and resource costs.
differences in the models used to estimate system performance. Key issues
include:
ƒ the representation of traveller response (which responses are
represented?, what degree of equilibrium is assumed?)
ƒ the representation of the time dimension (where tolls vary over time,
how accurately does the model reflect behaviour at the margin of
different toll levels?)
ƒ the degree of detail with which the network is represented and the
number of differently behaving groups included in the model (a greater
degree of disaggregation will lead to a less volatile aggregate result).
For rail transport we find that a substantial peak scarcity charge per slot is justified; the offpeak charge would only be 10% of this level. The results seem to confirm the view that
existing variable charges for the use of infrastructure on key main lines where capacity is
scarce are too low as a result of neglecting scarcity in the charges set.
The private slot value is in the CS far away from the social slot value which indicates
problems with a simple market based solution. This result is an effect of high congestion cost
on the road network in the CS that is not internalized in a road pricing regime.
The institutional arrangements behind franchising mean that we have data for the franchisee
from which we can calculate the opportunity cost of the use of each slot and thus the scarcity
value. Our approach is that if any operators wish to use capacity not required for the specified
minimum level of service they should pay the opportunity cost of the use of the path by the
franchisee. Clearly in trying to evaluate what would be the outcome of the imposition of a
scarcity charge we have had to use information on potential entrants. The CS has used typical
industry data applied with a detailed rail passenger simulation model which produces
estimates of revenue, costs, consumer surplus and diversion to/from other modes. Estimates
of changes in external costs are then made to derive results for the overall social benefits of
alternative allocations of capacity.
The CS suggests that the imposition of scarcity charges based on the value of slots to the
franchisee is both feasible and likely to be socially beneficial. However more work is needed
on exactly what the tariff should look like and what its overall effects would be.
49
GRACE D3 – Marginal cost case studies for road and rail transport
4 Accidents
The external marginal accident cost is an important component in the pricing of transport.
This chapter first presents a definition and discusses different methods to estimate the external
marginal accident cost (4.1). Based on this the remaining part of the chapter discusses the
current knowledge around valuation of accidents (section 4.2), internal and external cost (4.3)
and risk elasticity (4.4). Section 4.5 discusses the insurance externality approach and 4.6
concludes with a summary.
4.1 Methodology and definitions
4.1.1 Definition of external cost of accidents
The total annual cost of accidents (TC), where vehicle type j has been involved, can be
written as equation (1) where A is the number of accidents and (a+b+c) the cost components
discussed below. By “involved” we mean that the vehicle has been one of the parts in the
accident, irrespectively of who was hurt or who was at fault. The risk (r) of category j to be
involved in an accident (2) may be affected by an increase in the volume of traffic of category
j (Q). This effect is expressed as a risk-elasticity (E), equation (3). The marginal cost with
respect to the traffic volume for a vehicle of category j can be written as equation (4). We
derive the external marginal cost as equation (5), where PMC is the private marginal cost
already internalised. If we introduce θ as the share of the accident cost per collision that falls
on category j (6) the external marginal accident cost can conveniently be expressed as (7).
TC j = A( a + b + c ) = rQ ( a + b + c )
r=
(1)
A
Q
(2)
∂r Q
∂Q r
∂A
MC j =
(a + b + c) = r ( E + 1)(a + b + c)
dQ
E=
(3)
(4)
MC ej = MC − PMC j
θ=
(5)
PMC j
(6)
r (a + b )
MC ej = r (a + b )[1 − θ + E ] + rc (1 + E )
(7)
Liability
The theory presented above does not explicitly discuss liability. Assume that one group (A) is injured
and the second (B) is the other party in the accident. Without any liability, the injured user (A) will
bear all costs and the other part (B) will not bear any cost. The external marginal cost for each group is
then:
MC eA = rA ( a + b + c)[1 + E A ] − rA (a + b) = rA (a + b + c)[ E A ] + rAc
e = r (a + b + c)[1 + E ]
MC B
B
B
50
(8)
(9)
GRACE D3 – Marginal cost case studies for road and rail transport
The final expression of the general theory (equation 7) is a weighted sum of these two expressions,
where θ expresses the probability of being the injured user. Under a negligence rule, users in group B
will not bear any cost as long as they behave legally. If they break the law, they will be responsible for
some of the costs as compensation (d) to user A or as a fine (M). These costs will be included in their
private marginal cost and the external cost will decrease. At the same time, the compensation (d) will
reduce the expected cost of an injured user in group A; consequently the external marginal cost of
group A will increase. While this conclusion at first looks disturbing, it should be noted that the
criminal user B will have a higher generalised cost, than the legal user B. The criminal user has to pay
fine, compensation and external marginal cost.
Legal user B => MC Be = rB (a + b + c)[1 + E B ]
Criminal user B => MC Be = rB (a + b + c)[1 + E B ] − rB (d + M )
(10)
Not compensated user A => MC eA = rA (a + b + c)[ E A ] + rAc
Compensated user A => MC eA = rA (a + b + c)[ E A ] + rA (c + d )
(12)
(11)
(13)
With strict liability for user B, he or she will always pay the cost in the form of compensation (d) or a
fine (M) in the case of an accident – in principle he or she is always ‘criminal’ as in equation 11
above. Assume that both A and B are car users and that we cannot ex ante identify the criminal user;
we have to assume that the probability of being in either group is 50/50. Consequently, while the
marginal cost of group B is reduced, it is increased for group A through the compensation (d); the
effect on the joint marginal cost for all car users disappears and the external marginal cost can be
written once again as in the general theory (7). However, a fine (M) will affect the result. This theory
assumes that users perceive the compensation and fine as a part of their cost ex ante.
An interesting case is if the victim (user A) is guilty of the accident, for example a pedestrian that
crosses the street illegally and is hit by a car. Depending on the legal situation the car driver should ex
ante be charged as equation (11) or (12) and the pedestrian as (13). Consequently, the innocent car
driver shall pay a charge ex ante.
Risk-avoiding behaviour
Most of the empirical work suggests that the risk decreases with traffic volume (E<0) – see section
below. This highlights one of the problems of the presented approach – risk-avoiding behaviour.
The user may react in a number of different ways when he perceives that the risk level has changed.
Peltzman (1975) developed the hypothesis of risk compensation and presented evidence showing that
the user when given a safer environment compensates this with a higher degree of risk taking. In the
same way a more unsafe environment may be compensated by the user with reduced exposure to risk.
This reaction generates a cost to the user and reduces the observed change in risk. The cost of this riskavoiding behaviour has to be included in the external marginal cost. Peirson et al (1994) introduces a
form of risk avoiding behaviour where the users, when they selfishly adapt their behaviour, reduce the
risk for all other users. The risk avoiding behaviour includes an element of positive externality.
Johansson (1996) shows how this can be internalised through the accident externality charge in a
second-best situation where the behaviour is not subsided per se.
The marginal cost above is estimated based on the change in risk. This change in risk can be
influenced by traffic safety behaviour, which the increased number of vehicles has forced the user to
take. The cost of this behaviour is an externality.
In the following we divide the users into two groups, the first group (A) is injured and the second (B)
is the other part in the accident. We assume that the level of safety (s) on a given trip is associated with
a cost (g), which increases as the level of safety increases. The total annual cost (TC) for accidents and
traffic safety can then be written as equation (14).
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GRACE D3 – Marginal cost case studies for road and rail transport
(14)
TC = A(a + b + c) + Q A g ( s A ) + QB g ( s B )
We allow only for risk-avoiding behaviour that increases the ‘internal’ safety, i.e. the user’s own
safety13. Users that expect to be victims (user A; e.g. unprotected road users) will adjust behaviour (sA)
to protect themselves from an accident. To the marginal accident cost, the cost of all victims’ riskavoiding behaviour has to be added. The total external cost for category B, the unharmed user (θ=0)
will be:
[
]
e = r (a + b + c) 1 + E + Q dg ds A
MC B
B
B
A ds dQ
A
B
(15)
The last term in equation (15) is the cost of user group A’s risk-avoiding behaviour triggered by an
increased number of trips of category B. A part of the risk avoiding behaviour, lower speed, can be
traced to the congestion cost and handled as such. Another part can be found in infrastructure cost,
where a higher number of, for example flights, increases the necessary number of safety staff and the
dimension of the rescue capacity at the airport. However, we have not identified all of these effects.
4.1.2 Methodology
Based on own research (for example in UNITE) and surveying the literature we have identified three
approaches to estimate the external marginal accident cost in principle consistent with the definitions
discussed above: i) UNITE, ii) Insurance externality, and iii) Computable General Equilibrium models
(CGE).
In the UNITE approach equation 7 is used and each element is estimated separately and added
together. The critical elements are the value of statistical life (VSL), the proportion of internal costs,
the risk and the risk elasticity.
In studies of the ‘Insurance externality’ the relationship between the traffic flow and the insurance
premium are estimated based on aggregate data. The underlying precondition is that the insurance
covers all cost. The average driver then pays the average accident cost either in the form of an
insurance premium or by bearing accident risk. An additional distance driven by a driver will increase
the insurance premium by a small amount. However, as all users are affected the externality will be
substantial. The method is most suitable for non-fatal accidents where VSL does not play such a
dominant role.
Finally, a more general framework such as CGE would be able to cover also the effect of risk avoiding
behaviour and could include secondary income effects through the loss to the economy of accidents.
However, this approach is dependent on the same detailed information on elasticities etc as the first
approach. If behaviour adjustments are included also this could be covered but the underlying
knowledge on behaviour adaptation has to come from other sources.
The table below summarises these three methods
13
In the literature (Johansson (1996) there also exists a discussion on a possible positive externality in relation to
this risk avoiding behaviour if it is not only internal safety that is affected (see PETS (1998) for a summary).
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GRACE D3 – Marginal cost case studies for road and rail transport
Table: 18 Different methods to estimate the marginal external accident cost
Name
i) UNITE
ii) Insurance
iii) CGE
Definition
Uses eq. 7 and estimates
each component
Estimates the effect on
insurance premiums of
increased traffic density
In a CGE framework
feedback effects into the
economy at large as well
ass more behaviour
adaptations can be
included.
Includes
VSL
Yes
Includes the
risk elasticity
Yes
Risk avoiding
behaviour
No
No
Yes
No
Yes
Yes
Possible
Note: CGE=computable general equilibrium
4.2 Valuation of accidents
The valuation of an accident can be divided into direct economic costs, indirect economic
costs and a value of safety per se. The direct cost is observable as expenditure today or in the
future. This includes medical and rehabilitation cost, legal cost, emergency services and
property damage cost. The indirect cost is the lost production capacity to the economy that
results from premature death or reduced working capability due to the accident. However,
these two components do not reflect the well-being of people. People are willing to pay large
amounts to reduce the probability of premature death irrespectively of their production
capacity. The willingness-to-pay estimates the amount of money people are willing to forgo to
obtain a reduction in the risk of death.
Two biases in recent CVM studies have to be highlighted, the first is the hypothetical bias and
the second is what we call here the scale bias. The underlying problem in relation to risk
reductions appears to be that people have not formed their preferences yet (Kahneman and
Tversky (2000)).
When people are confronted with a hypothetical question the answer does not always reflect
their actual behaviour and the hypothetical WTP often exceeds the actual WTP. Respondents
can be uncertain about their true valuation; a yes-response can be a yes-maybe and a yesdefinitely. Several studies have shown that the preference uncertainty can be a key to reduce
the hypothetical bias (Li, Löfgren and Hanemann (1996)). Recent studies that explore
preference uncertainty suggest that the ‘certain’ WTP could be 50% to 60% of the WTP
expressed by all respondents (Blumenschein et.al. (2005), Hultkrantz et.al. (2005)). The
‘certain’ WTP is in some of these studies comparable with the revealed actual WTP.
The scale bias refers to the tendency of the respondents to report the same WTP irrespectively
of the size of the risk reduction. These effects can be owing to what is known as the “warm
glow effect”, that is, the responses “reflect the willingness to pay for a moral satisfaction of
contributing to public goods, not the economic value of these goods” (p.57 Kahnman and
Knetch (1992)). Another possible explanation is that it can be difficult for the respondents to
53
GRACE D3 – Marginal cost case studies for road and rail transport
understand small changes in small probabilities; this can be called scale bias. The result is that
respondents report the same WTP for a larger safety improvement as for a smaller
improvement. According to standard economic theory the increase in the WTP should be
approximately proportional to the size of the risk reduction (Hammit and Graham (1999)). If
the responses are only weakly dependent of the magnitude of the risk reduction almost any
VSL can be derived from the studies.
Attempts have been made to overcome the scale bias through improved visual aid (Corso,
Hammit and Graham (2001)) or by presenting the effect as the number of reduced victims
instead of reduced risk - frequencies of occurrence rather than probabilities (Beattie (1998),
Lindberg (2003)). Nevertheless, the problem seems to remain between samples. Results from
Hammit and Graham (1999) indicate that preference uncertainty could explain both the
hypothetical bias and the scale bias. Newer studies (Hultkrantz et.al (2005)) do not support
this conclusion. Alberini et.al. (2004) added question on the respondent’s certainty in their
response in addition to training the respondents to safety questions. They found indication on
a WTP almost proportional to the level of the risk reduction when using only the most certain
responses. However, the risk level was expressed over ten years (annually 1/10000 and
5/10000) far above what is common in studies on road accidents. Nevertheless, this recent
research indicates that well executed CV studies may overcome the problems discussed
above.
Throughout the world empirical estimates of VOSL diametrically differ between different
studies, ranging from a value of less than 200 000 to 30 million US dollars (Blaeij (2003)).
Making meta-analysis of this material is difficult and it is important to focus on the reliable
studies. Carthy et. al. (1999) use the contingent valuation method to estimate a WTP for less
severe outcomes and a risk/risk analysis to link this WTP to fatality. This is the approach used
in the UNITE project and in the recent HEATCO project.
The HEATCO project has made a survey of the current European practice. The result is
depicted in the figure below. The variability in accident cost is huge between different
member states.
Figure: 13 The current practice in use of VOSL (HEATCLO)
1.8
North/
West
Million EUR per fatality
1.6
1.4
1.2
1
East
UK
FI
SE NL
FR
DE
FR-road
HU
0.8
South
LV
LT
0.6
CH
DK
CZ
0.4
IT
PT
SK
0.2
ES
0
35
45
55
65
75
85
95
105
GDP/Capita 2002 EU-25 PPS
Source: Heatco
54
115
125
135
GRACE D3 – Marginal cost case studies for road and rail transport
Irrespectively of this, or depending on this, the HEATCO makes recommendations on
methods to adopt when estimating VSL. In addition, based on the work in UNITE the project
recommends default values per member states in situations where no available up to date
value exists.
Table: 19 Recommended values from HEATCO (€2002, factor prices)
Country
Fatality
Severe injury
Slight Injury
Austria
1,683,000
231,300
18,300
Belgium
1,606,000
244,000
15,700
Cyprus
1,012,000
129,900
9,600
Czech Republic
935,000
118,100
8,800
Denmark
1,672,000
210,300
16,500
Estonia
627,000
79,500
5,900
Finland
1,551,000
208,600
15,600
France
1,551,000
217,800
16,400
Germany
1,496,000
209,400
17,100
Greece
1,067,000
136,500
10,500
Hungary
803,000
103,000
7,600
Ireland
1,837,000
235,100
18,000
Italy
1,496,000
190,700
14,700
Latvia
539,000
67,700
5,100
Lithuania
572,000
73,000
5,400
Luxembourg
2,915,000
432,700
27,200
Malta
1,133,000
142,800
10,700
Netherlands
1,672,000
223,600
18,000
Norway
2,057,000
307,000
21,500
Poland
627,000
79,500
5,900
Portugal
1,056,000
137,400
9,700
Slovakia
704,000
89,100
6,600
Slovenia
1,023,000
130,000
9,700
Spain
1,298,000
160,900
12,100
Sweden
1,573,000
239,300
17,000
Switzerland
1,804,000
262,800
20,100
United Kingdom
1,617,000
211,100
16,800
Notes: Material damages not included. Value of safety per se based on UNITE (see Nellthorp et al., 2001):
fatality €1.50 million (market price 1998 – 1.25 million factor costs 2002); severe/slight injury 0.13/0.01 of
fatality; Direct and indirect economic costs: fatality 0.10 of value of safety per se; severe and slight injury based
on European Commission (1994).
For a general approach these values can be used as a in equation 7. Direct and indirect
economic costs have to be estimated separately and have to be split into internal and system
external (c ). In addition, it should be noted that the values above are expressed at factor price.
4.3 Risk perception
The question of internal and external accident cost can be broken into two parts; i) do users
consider their own risks and ii) do they consider the risk of others? The straightforward
assumption is yes to the first question and no to the second. When VSL is estimated users are
asked about their trade-off between accident risk and money. The reply is used to derive
VOSL. We thus believe they can value changes in hypothetical risk. If, in a real situation,
they also understand and value risk changes, the VSL will be internal. If they do not
understand the risk related to their decision we have an information failure. It has often been
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GRACE D3 – Marginal cost case studies for road and rail transport
shown that individuals overestimate small risk and underestimate large risks – “In general,
rare causes of death [are] overestimated and common causes of death [are] underestimated”14.
This is also the result of a recent study on road users’ risk where the perceived risk is
compared with the objective risk of the same road user group (Andersson and Lundborg
(2006)). If this result is used to discuss how one person perceives different risk levels the
suggestion is that they underestimate risk changes15. Other results suggest that with a more
detailed definition of the risk, the timing of the risk16 or using risk defined for relevant age
groups17, the difference between actual and perceived risk diminish or disappear. But, exactly
how individuals assess the marginal risk related to a change in driven distance or a new trip is
unclear. Much of the analysis is based on the assumption that users understand risk changes.
This is not a trivial assumption18.
4.4 The risk elasticity
It is well known that when the traffic volume increases on a road the speed goes down and the
average travel time increases. But what about the accident risk? As the number of vehicles
increases the number of accidents will most probably increase; we have not seen any evidence
on the opposite effect. However, exactly how the number of accidents increases is important;
will the number of accidents increase in proportion to the increase in traffic volume, or will
the increase be progressive or degressive? If the number of accidents increases in proportion
to the traffic volume the risk, i.e. the number of accidents per vehicle or vehicle kilometer,
will be constant; the risk elasticity (E) will take the value nil. If the increase is degressive the
accident risk will decline and the elasticity will be negative. This means that an additional
user reduces the risk for an accident for all other users. Finally, if the number of accidents
increases progressively the risk will increase. An additional vehicle will impose an increased
threat to all other vehicles and the external effect will be larger, the elasticity will be positive.
As the number of vehicles increases the number of possible interactions increases with the
square. This suggests that the risk should increase with traffic volume. Dickerson, Peirson and
Vickerman (2000) find that the accident elasticity varies significantly with the traffic flow.
They argue that the accident externality is close to zero for low to moderate traffic flows,
while it increases substantially at high traffic flows. This is also found by Fridstrøm et al
(1995). Winslott Hiselius (2005) concludes also from other literature that the accident risk
involving only motor vehicles on urban-road links is independent of the traffic flow. At
intersections the evidence is increasing accident risk.
However, she also concludes that the estimates on rural roads show a great variation.
Vitaliano and Held (1991) show in their estimation that the relationship between accidents
and flows is nearly proportional and thus the risk elasticity is close to zero. In an overview of
six international studies, Chambron (2000) finds a less than proportional increase in injury
and fatal accidents. This has also been found by Hauer and Bamfo (1997) and a majority of
the results review in Ardekani et al. (1997). Edlin (2003) studied the effect of traffic density
on insurance premiums as well as on fatalities accident only. He found that fatalities decrease
14
Slovic, Fischhoff, and Lichtenstein 1982, p 467
See Viscusi (1998) for a short discussion on this topic.
16
Viscusi et.al. (1997).
17
Benjamin et al (2001) but this was not the result of Andersson and Lundborg (2006).
18
Our assumption is close to the risk homeostasis theory (Wilde 1981, 1982) where the user acts as a utility
maximizer and tries to keep his risk in equilibrium with his target risk. The zero risk model (Näätänen and
Summala 1974, 1976) suggests that in most circumstances the traffic risk is perceived to be equal to zero.
15
56
GRACE D3 – Marginal cost case studies for road and rail transport
with traffic density in low density states but increases in high density state. He found the same
pattern for insurance premiums. Ozbay et.al estimated the full marginal costs of highway
transportation in New Jersey. From these estimates the elasticites in the figure below can be
derived. Property damage and injury accidents increase with traffic volume in urban areas
while fatality accidents decline. On freeway and expressway also property damage and injury
accidents decline with traffic volume while they increase on interstate roads with increased
traffic volume.
Figure: 14 Riskelasticity New Jersey
2
Arterial-Local-Collector
Freeway and Expressway
Interstate
Riskelasticity
1.5
1
0.5
0
Prop.Damage
Fatality
Injury
-0.5
Winslott Hiselius (2005) estimates the relationship between accident and traffic flow on 83
Swedish road sections with information on hourly traffic flow. When the traffic is treated as
homogenous (i.e. cars and lorries added together) the result is a decreasing accident risk, i.e. a
negative elasticity. However, when car are studied separately the result suggests that the
accident rate is constant or increases. However, the result with respect to lorries is reversed,
indicating a decreasing number of accidents as the number of lorries increases. This is also the
result from the study in UNITE (Lindberg (2003)).
Unfortunately, the survey of the literature does not give one single recommendation on the
magnitude and the sign on the risk elasticity. The most surprising result is that so many
studies find negative elasticities. This is true also for studies that seem to be well executed and
control for infrastructure quality etc.
4.5 Insurance cost
Edlin (2003a and 2003b) estimates the effect on average insurance premiums of increased
traffic density on state level in US. He also has information on insurance cost. This approach
includes an aggregate measure of accidents, i.e. insurance cost or premiums, with covers both
accident frequencies and accident severity. The underlying precondition is that the insurance
system covers all cost, i.e. no underinsurance exists. However, as Edlin notes, fatalities is a
specific problem where we can expect underinsurance.
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GRACE D3 – Marginal cost case studies for road and rail transport
The results in Edlin (2003b) suggest that the insurance premium increases strongly in high
density states with increased traffic. In California it is estimated that the extra insurance
premium could be between $1271 (+- 490) and $2432 (+-670) per year per driver depending
on model specification. In low density states the effect is much smaller and sometimes
negative. In South Dakota the yearly cost changes with between negative $60 (+-28) to
positive $94 (+-36) per driver per year depending on specification.
Figure: 15 Insurance externality, US states (lowest, middle and highest) for different
model specifications.
4000
3500
US dollars/driver
3000
2500
Quad.Prem
2000
Lin.Prem
Quad.Cost
1500
Lin.Cost
1000
500
0
-500
North
Dakota
South
Dakota
Montana
Kentucky
South
Carolina
California
New
Jersey
Hawaii
4.6 Conclusions
This Case study only consists of an overview and state-of-the-art survey. The result is thus not
based on any new research made within the GRACE project. We start with a review of the
principle of external marginal accident cost as developed for example in UNITE. The
principle lends itself easily to an approach where each element in turn is estimated and the
marginal cost constructed as a product of these estimated parameters. On the other hand, the
insurance externality approach tries to estimate a kind of cost function where the dependent
variable is the average insurance premium or insurance cost. This approach presupposes that
no underinsurance exists. The third approach mentioned in the introduction is to model the
external accident cost in a CGE framework which will cover more of the behavioural and
feedback effects discussed in the first approach. However, this approach depends on separate
estimates on each component.
On each component we may conclude;
•
A growing consensus on the method to estimate the value of statistical life (VSL)
seems to emerge. The HEATCO project suggests specific values for each Member
State.
•
Nevertheless, the research on VSL continues with the aim to explore the numerous
biases that are found in the currently available estimates.
•
On the question on the proportion of internal and external costs and especially the
perception of road users risk no new conclusions can be drawn. This is still an area of
large uncertainty.
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GRACE D3 – Marginal cost case studies for road and rail transport
•
•
However, making assumptions on the perceived cost, the available databases can be
used to estimate the proportion of internal costs.
There is still no consensus on the risk elasticity. Surprisingly many studies find
decreasing risk with increasing traffic volume. This could be a problem of the studies
or due to behaviour effects. If we do not control for infrastructure quality, we may find
that roads with higher expected traffic volume are designed with a higher traffic safety
standard. In addition, road users may react to a perceived increased risk by driving
more carefully and slower. This is an unobserved cost component that would increase
the cost.
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GRACE D3 – Marginal cost case studies for road and rail transport
5 Air pollution and Greenhouse gases
This chapter is divided into four sections. Section 5.1 deals with air pollution from road
transport, Greenhouse gases are discussed in section 5.2 and air pollution from railways in
sections 5.3. The results are presented in section 5.4.
5.1 Road Transport and Air pollution
Several case studies were undertaken in order to estimate the marginal costs due to airborne
pollution and greenhouse gases originated by road and rail transport. In order to improve the
knowledge regarding the influence of the local conditions and site specific parameters on the
calculation of external costs, this document presents the results of the cases studies conducted
in four European cities, namely Berlin, Copenhagen, Prague and Athens, which cover a broad
range of European countries and local meteorological conditions.
The methodology used follows the Impact Pathway Approach, bottom-up methodology
developed in the ExternE project series. The starting point for the bottom-up approach for
quantification of marginal cost is the micro level, i.e. the traffic flow on a particular route
segment. Then, the marginal external costs of one additional vehicle are calculated for a
single trip on this route segment.
5.1.1 Description of Case Studies
Four case studies for road transport within densely built areas have been conducted. They are
expected to complete the picture on air pollution from existing studies and to analyse the
variations of environmental costs and the driving parameters. Assessing data availability and
due to the fact that a broad range of European countries and local meteorological conditions
should be considered, the cities selected for this purpose were Berlin, Prague, Copenhagen
and Athens.
Berlin
The population of Germany’s capital has stabilised at 3.39 million since 2000, following a
slight dip in previous years. The number of commuters between Berlin and Brandenburg has
risen slowly in recent years. In comparison with other major cities in western Germany,
however, total commuter numbers are low. Of the approximately 1.27 million motor vehicles
in Berlin, about 81% are passenger cars and the level of motorisation - i.e. the number of
motor vehicles per head of the population - has risen steadily since 1970 but at 322 cars per
1 000 inhabitants is still well below the average of 480 vehicles per 1 000 inhabitants in the
old federal states (Department or Urban Development, 2004). When applied to the area of the
city, this means 1 400 motor vehicles per square kilometre.
For dispersion modelling on the local scale data sets based on 10 year’s averages of 3-hourly
measured data by the German meteorological service were used. Detailed population data was
also used to model the exposure from atmospheric dispersion of the pollutants on a local
scale.
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GRACE D3 – Marginal cost case studies for road and rail transport
Copenhagen
Copenhagen is the capital city of Denmark and with its population of more than a million
inhabitants it is also the largest of this country. Copenhagen is located on the eastern shore of
the island of Zealand (Sjælland) and partly on the island of Amager. It faces to the east the
Øresund, the strait of water that separates Denmark from Sweden, and that connects the North
Sea with the Baltic Sea. On the Swedish side of the Øresund, directly across from
Copenhagen, lie the towns of Malmö and Landskrona. Copenhagen is also a part of the
Øresund region, which consists of the eastern part of Zealand in Denmark and the western
part of Skåne in Sweden.
For dispersion modelling on the local scale data sets of meteorological data from the Danish
THOR system from the National Environmental Research Institute (NERI) were used. Geocoded population data from NERI was also used for Copenhagen.
Athens
Athens is Greece’s capital and largest city and its administrative, economical and cultural
centre. It is located in a basin of approximately 450 km2. and is surrounded on three sides by
fairly high mountains (Mt. Parnis, Mt. Pendeli, Mt. Hymettus and Mt. Aegaleon), while to the
SW it is open to the sea. These mountains are physical barriers with small gaps between them,
being the opening of the basin to the sea toward the Saronic Gulf. The City of Athens lies at
the heart of the conurbation, with around a quarter of its population (745,514 inhabitants =
23.39%; National Statistical Service of Greece, 2001 Census). Almost the entire basin could
be considered as an urban area, characterized by a high concentration of industry (about 50%
of the Greek industrial activities) and high motorization (about 50% of the registered Greek
cars).
For dispersion modeling on the local scale, data sets based on values of averaged 10-Minute
interval measured data for the year 2000 were used. Due to the lack of information regarding
not only wind speed and direction but also mixing height and stability classification, data
modeled with the NCAR / Penn State Mesoscale Model (MM5) calculated by Vautard (2006)
was used for this case. The information was also compared with data provided by the
automatic meteorological station of the National Technical University of Athens (NTUA).
Population data from the National Statistical Service of Greece (2001) and the 2001 census
tables for Eurostat were used to generate the required Geo-coded data.
Prague
Prague is the capital and largest city of the Czech Republic. Situated on the Vltava River in
central Bohemia, it is home to approximately 1.2 million people. Reflecting the trend in the
new Member States, the number of motorized vehicles has been growing over the last years
with an increasing traffic density, phenomenon which health issues affects the population and
the urban environment substantially. The automobile traffic in Prague is not as heavy as in
some other European cities; the number of cars for 100 inhabitants is lower and the public
transport network is well developed. The total number of motor vehicles registered on the
Prague territory has been continuously growing, the major portion of the motor vehicle
number increment goes to passenger cars. In 2005 the number of registered vehicles increased
by about 14 500, yielding the total number of registered vehicles of over 750 000 at the years
end so there was one car per 1.6 inhabitants in Prague (ÚDI 2006).
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GRACE D3 – Marginal cost case studies for road and rail transport
For dispersion modeling on the local scale, data sets based on values of averaged 10-Minute
interval measured data for the year 2000 were used. Due to the lack of information regarding
not only wind speed and direction but also mixing height and stability classification, data
modeled with the NCAR / Penn State Mesoscale Model (MM5) calculated by Vautard was
used for this case. The information was also compared with data provided by the Czech
Hydro meteorological Institute in Prague. Population data from the Czech Statistical Office
(2001), Population and Housing Census (2001) and Večerková et. al (2006) were used to
generate the required Geo-coded data.
5.1.2 Emissions from road vehicles
Road vehicle types covered comprise passenger cars, light and heavy duty vehicles (LDV, and
HDV respectively) with both petrol and diesel fuelled engine, except for HDV (diesel only).
Vehicle emissions were modelled taking into account driving patterns and traffic situations
common in city centres.
The emission factors are mainly provided by COPERT III (Computer Programme to calculate
Emissions from Road Transport) and HBEFA (Handbook of emission factors for road
transport).
In addition to exhaust emissions, non-exhaust emissions due to tyre and brake wear and road
dust suspension should be considered for an accurate calculation of fine particulate matter
emissions. However, knowledge on specific non-exhaust emissions per different road classes,
vehicle categories and driving conditions is scarce and still in the process of scientific
discussion. Available information on tyre and brake wear emission factors was recently
published in (EEA 2003). Meanwhile first measurements and derived emission factors have
been published for different European driving conditions that give a more accurate assessment
of non-exhaust emissions (e.g. Düring & Lohmeyer 2004, Gehrig et al. 2003). Besides the
emissions from the vehicle operation, the emissions due to fuel provision were also
considered. It is assumed that the petrol and diesel are produced in refineries under
representative European conditions and with average production technology.
Table: 20 Emissions caused by fuel production processes in g/kg fuel
Type of fuel CO2 NOx NMVOC SO2 PM10
Petrol
560 0.105 1.80
1.90 1.10
Diesel
400 0.047
0.62
1.40 0.96
Source: IFEU (1999) Friedrich and Bickel (2001)
Furthermore, emissions associated with fuel production are valued with average damage
factors for emissions in the corresponding country. These damage factors were calculated
based on the assumption that the emission source is not located within densely populated
areas.
Table: 21 EU25 Average damage factors for emissions from refineries
Pollutant
NOx
EU25
2750
Source: Own calculations.
€ per tonne emitted
NMVOC
SO2
830
3100
62
PM10
15600
GRACE D3 – Marginal cost case studies for road and rail transport
5.2 Greenhouse gases
The method of calculating costs due to the emission of greenhouse gases (usually expressed
as CO2 equivalents) basically consists of multiplying the amount of CO2 equivalents emitted
by a cost factor. Due to the global scale of the damage caused, there is no difference how and
where in Europe the emissions of greenhouse gases take place. For this reason we recommend
to apply the same values in all countries.
The CO2 equivalent of a greenhouse gas is derived by multiplying the amount of the gas by
the associated Global Warming Potential (GWP). The GWP for methane is 23, for nitrous
oxide 296, and for CO2 it is 1.
A European abatement cost of €20 per tonne of CO2 represents a central estimate of the range
of values for meeting the Kyoto targets in 2010 in the EU based on estimates by Capros and
Mantzos (2000). They report a value of €5 per tonne of CO2 avoided for reaching the Kyoto
targets for the EU, assuming a full trade flexibility scheme involving all regions of the world.
For the case that no trading of CO2 emissions with countries outside the EU is permitted, they
calculate a value of €38 per tonne of CO2 avoided. It is assumed that measures for a reduction
in CO2 emissions are taken in a cost effective way. This implies that reduction targets are not
set per sector, but that the cheapest measures are implemented, no matter in which sector.
However, there is a need to strive for more stringent reduction targets than Kyoto. The EU
target of limiting global warming to an increase of 2°C of the earth’s average temperature
above pre-industrial levels may lead to marginal abatement costs as high as about €95/t CO2.
However it is an open question whether such an ambitious goal with such high costs will be
accepted by the general population.
Recent work has confirmed the assumption that emissions in future years will have greater
total impacts than emissions today (see e.g. Watkiss et al.; 2005a). In a recent report for the
Social Cost of Carbon Review on behalf of UK’s Defra, Watkiss et al. (2005b) derive shadow
price values, taking into account the expected future development of damage costs and
abatement costs. This study is the most current and comprehensive exercise providing
consistent values for CO2 emissions. Whereas the damage cost estimates do not rely on
specific assumptions for the UK, the abatement cost estimates are based on the UK’s
government long-term goal of meeting a 60% CO2 reduction in 2050 (which is broadly
consistent with the EU’s 2°C target). On the one hand the costs for reaching a domestic
reduction of 60% are higher than implementing a more flexible reduction scheme. On the
other hand, the abatement costs only influence the cost curve for later years (starting around
2030) when uncertainties are higher. In addition, the damage cost estimates do not include
some important risks.
For application in GRACE we recommend using a range of €14 to €51 (with a central value
of €22 per tonne of CO2- equivalent emission in the period 2000 to 2009). These shadow
prices were derived from Watkiss et al. (2005b), converting from ₤2000/t C to €2002 (factor
prices).
5.3 Rail Transport
The rail transport options for passengers in the four urban locations already presented were
also analyzed. The relevant options considered are tram, metro (underground train) and light
train, all with electrical traction.
Two assumptions were analysed for the provision of electricity for an additional train:
63
GRACE D3 – Marginal cost case studies for road and rail transport
a) The electricity is produced in a coal-fired power plant, which is common for supplying
additional energy demand,
b) The electricity is bought on the European electricity market, assuming the UCTE
power production mix.
Emission factors for the coal power plant were based on German emission factors for the
electricity production in the year 2000 (UBA 2005). Emission factors for power plants in the
Czech Republic, Denmark and Greece were modified based on data from the European
Pollutant Emission Register (EPER) for the reporting year 2001. Emission factors for fuel
production were taken from European Commission (1999b)
The combination of the emission factors with country-specific damage factors per tonne of
emission for the pollutants NOx, SO2, NMVOC and PM10 resulted in the cost factors
presented in Table 3.7. The differences result from different emission factors (mainly for NOx
and CO2) and variations in the cost per tonne of emission between countries. Global warming
values were calculated using the range of values recommended per tonne of CO2-equivalent.
Furthermore, emissions originated by the wear of overhead wires, rail and tyres were also
considered.
5.4 Results
The table below presents the results for selected vehicles for each city. The marginal costs
include vehicle use, up- and downstream processes and greenhouse gases. A detailed
description of the data and results can be found in the appendices.
Prague
Diesel HDV Euro IV
Petrol LDV EURO V
Diesel, LDV EURO II
Petrol, car EURO V
Diesel car EURO IV
Diesel, car EURO II
Petrol, car EURO II
Copenhagen
Berlin
Athens
0.00
5.00
10.00
15.00
20.00
25.00
EURO
Figure: 16 Marginal costs due to airborne emissions in EUR /100 vkm
The results show that for all vehicle types the higher marginal costs due to airborne emissions
correspond to the city of Athens, followed by Berlin, Copenhagen and Prague in that order.
The factors that seem to be more relevant for these results are the wind speed and the
64
GRACE D3 – Marginal cost case studies for road and rail transport
population density. The high share of low wind speeds for the Athenian area together with a
population density close to 20 000 hab/km2 in some zones, leads to a pollutant exposure of the
population which is about a factor of two higher compared to the other cities. Petrol cars
cause lower cost per vehicle kilometre compared to diesel cars as they emit much less fine
particles, leading to lower health impacts.
Table: 22 Marginal cost for urban passenger rail operation, assuming the electricity is
produced in a coal-fired power plant and UCTE Mix
City
Athens
Berlin
Copenhagen
Prague
Train
type
Energy use
Cost factor Electricity
Total cost
PM10 EUR/km
per vehicle kWh/km production (EUR / kWh)
EUR/ 100 vkm
With
UCTE
Mix
Tram
3.81
0.0404
1.05
15.4
4.5
Metro
3.22
0.0404
1.05
13.0
3.8
21.9
6.4
Light
Train
Tram
5.42
0.0404
1.05
3.81
0.0293
0.34
11.2
4.5
Metro
3.22
0.0293
0.34
9.4
3.8
5.42
0.0293
0.34
15.9
6.4
3.81
0.0286
0.30
10.9
4.5
3.22
0.0286
0.30
9.2
3.8
15.5
6.4
Light
Train
Tram
Metro
Light
Train
Tram
5.42
0.0286
0.30
2.92
0.0321
0.27
9.4
3.4
Metro
2.89
0.0321
0.27
9.3
3.4
Light
Train
5.47
0.0321
0.27
17.6
6.5
It can be noticed that the environmental costs associated with electric trains depends on the
sources used to produce the electricity. The UCTE mix considered is shown in the figure
below, being the high participation of nuclear energy in the electricity production evident.
This fact leads to costs which are a factor of almost three lower than the costs if the electricity
is produced in a coal-fired power plant.
Industry gas
Other
Lignite
Hydro
Coal
Oil
Nuclear
Natural Gas
Figure: 17 UCTE electricity production mix in the year 2000
65
GRACE D3 – Marginal cost case studies for road and rail transport
6 Noise
The perception of sound follows a logarithmic scale, which results in considerable nonlinearities of the impacts and associated costs due to a change in noise levels (in the following
we refer to the equivalent noise level LAeq). The background noise level plays an important
role: whereas in a quiet neighbourhood (40 dB (A)) an additional 40 dB(A), i.e. a doubling of
the noise, results in a total level of 43 dB(A), the same noise increment of 40 dB(A) only
leads to a total noise level of 60.04 dB(A) in a noisy environment with a background noise
level of 60 dB(A). Besides this peculiarity of energetic addition of noise levels the perception,
in particular the disturbance caused by changes in the noise level have to be considered. This,
together with the very local character of noise makes impact assessment a challenging task;
and the models used to quantify noise exposure must be able to map the environment
(receptors, buildings), the vehicle technology (PC, HGV etc.) and the traffic situation (e.g.
speed and traffic volume) adequately.
First approaches to quantify costs due to noise were using general values per dB, which
mostly were derived from hedonic pricing studies. Such studies established a relationship
between rents or house prices and properties of the flat or house, one of which was the noise
exposure. The next step towards a more differentiated assessment was the inclusion of health
effects caused by noise in the analysis. However, annoyance effects were usually still valued
based on hedonic pricing studies. This was the case as well in UNITE (see Bickel et al.,
2003). In the meantime the available knowledge has improved and allows going a step further
towards following the principles of the impact pathway approach.
6.1 Noise impacts
Two major impacts are usually considered when assessing noise impacts:
-
Annoyance, reflecting the disturbance which individuals experience when exposed to
(traffic) noise.
Health impacts, related to the long term exposure to noise, mainly stress related health
effects like hypertension and myocardial infarction.
It can be assumed that these two effects are independent, i.e. the potential long term health
risk is not taken into account in people's perceived noise annoyance.
A large amount of scientific literature on health and psychosocial effects considering a variety
of potential effects of transport noise is available. For instance, De Kluizenaar et al. (2001)
reviewed the state of the art, reporting risks due to noise exposure in the living environment.
They identified quantitative functions for relative and absolute risks for the effect categories
presented in the table below.
66
GRACE D3 – Marginal cost case studies for road and rail transport
Table: 23 Categorisation of effects and related impact categories (source: De Kluizenaar
et al., 2001).
Category
Stress related health effects
Psychosocial effects
Sleep disturbance
RR = relative risk; AR = absolute risk
Measure given
RR
AR
AR
Impacts
Hypertension and ischemic heart disease
Annoyance
Awakenings and subjective sleep quality
A more recent study undertaken in Switzerland (Bundesamt für Raumentwicklung, 2004)
reviewed additional empirical studies and concluded that for impacts from road and rail noise
only few evidence has emerged in addition to De Kluizenaar et al. (2001), which was the
basis for calculations in the UNITE project (see Bickel et al. 2003). Figure 6.1 shows the
exposure-response functions predicting annoyance reactions on the population level as
recommended by European Commission (2002).
Air
Road
100
80
Rail
100
100
80
80
60
Annoyed
40
20
Little annoyed
Percentage of adults
Percentage of adults
Percentage of adults
Little annoyed
60
40
Annoyed
20
50 55
60 65
70 75
Annoyed
Highly annoyed
0
45
Little annoyed
40
20
Highly annoyed
0
60
Highly annoyed
0
45
50 55
LDEN
60
LDEN
65 70
75
45
50 55
60
65 70
75
LDEN
Figure: 18 Percentage of adult population feeling little annoyed, annoyed and highly
annoyed as a function of noise levels (source: European Commission 2002).
The general procedure for taking into account the site and technology specific characteristics
when calculating marginal noise costs is the following: Two scenarios are calculated: a
reference scenario reflecting the present situation with traffic volume, speed distribution,
vehicle technologies etc., and the case scenario which is based on the reference scenario, but
includes the changes due to the project alternative considered. The difference in damage costs
between both scenarios represents the noise costs due to the project assessed. It is important to
quantify total exposure levels and not only exposure increments, because for certain impacts
67
GRACE D3 – Marginal cost case studies for road and rail transport
thresholds have to be considered. For instance, some exposure-response functions for health
impacts are applicable only above a threshold of 70 dB(A) (see De Kluizenaar et al., 2001).
Depending on the exposure-response relationships available different noise indicators are
required for the quantification of impacts. Examples of indicators that are commonly used are
equivalent noise levels for different times of day, e.g. LAeq(7.00-19.00), LAeq(19.00-23.00),
LAeq(23.00-7.00) and the compound day-evening-night noise indicator LDEN (see European
Commission, 2002 for details on noise indicators). Usually noise levels are calculated as
incident sound at the façade of the buildings
6.2 Valuation of Annoyance
Given its high importance for the results and the challenges in its measurement, the value of
annoyance caused by noise requires particular consideration. The main cost component of
annoyance is disutility experienced, for which no market exists. Stated preference (SP) and
revealed preference (RP) methods have been employed to estimate the economic value of
changes in noise levels. The noise valuation literature is dominated by Hedonic Price (HP)
studies (most of them old) on road traffic and aircraft noise of varying quality. HP studies
analyse the housing market to explore the extent to which differences in property prices
reflect individuals´ willingness-to-pay (WTP) for lower noise levels. Resulting values seem to
be problematic to transfer, however, both theoretically and in practice (Day 2001).
The number of SP studies on road traffic noise is increasing, but only a few present WTP in
terms of “euro per annoyed person per year” for different annoyance levels (little annoyed,
annoyed and highly annoyed), which correspond to the endpoints of exposure-response
functions. Due to the low number of studies that can be used for this approach, a “secondbest” alternative was to evaluate the SP studies available with regards to quality (e.g. avoid
using studies with scenarios based on changes in exposure rather than annoyance and health
impacts), choose the best ones, and calculate a value in terms of “euro per dB per person per
year”. This was done by Navrud (2002) to establish an EU-value.
To enable the application of the exposure-response functions predicting annoyance reactions
on the population level as recommended by European Commission (2002), the project
HEATCO’s carried out stated preference surveys in five European countries (see Navrud et
al. 2006). Based on surveys in Germany, Hungary, Norway, Spain, Sweden and the UK,
values for application in Europe were derived for the annoyance levels highly annoyed,
annoyed and little annoyed.
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GRACE D3 – Marginal cost case studies for road and rail transport
Table: 24 Annual willingness-to-pay by annoyance level for reducing annoyance (€2002
factor costs per person).
€2002 factor costs per person
Road, aircraft – little annoyed
Road, aircraft – annoyed
Road, aircraft – highly annoyed
Rail – little annoyed
Rail – annoyed
Rail – highly annoyed
30
68
68
30
48
48
Existing estimates show considerable non-linearities of marginal noise cost with background
noise levels. The figure below presents exemplary results of the UNITE project for noise costs
from passenger cars in urban areas. Costs are increasing from day to night, reflecting the
higher disturbance effect of noise during night time. In Berlin the average number of persons
per road kilometre affected by noise is slightly higher than in Stuttgart. However, the costs are
more than a factor of three lower due to the much higher number of vehicles and higher
speeds on Frankfurter Allee leading to a higher background noise level. In Helsinki the
population density along the route considered is lower than in Berlin and Stuttgart,
furthermore the average distance from buildings is higher – leading to lower noise costs.
EUR / 100 vkm
Berlin
Helsinki
0
1
2
3
4
5
day
night
day
Stuttgart
night
day
night
Figure: 19 Marginal noise costs due to a passenger car in Helsinki, Berlin and Stuttgart
(source: Bickel et al. (2003)).
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GRACE D3 – Marginal cost case studies for road and rail transport
7 Sensitive areas
The main purpose of this CS is to explain and to assess the differences in the transport costs
per unit of transport performance (vehicle- or train-kilometre) between a sensitive and an
“insensitive” area. These cost differential factors – and not new cost rates – are the main
output of the case study. The factors can be applied to existing estimates of average and
marginal environmental costs to assess the differences between the cost rates in a sensitive
and in an “insensitive” area in absolute terms.
7.1 Definition and indicators
The notion of sensitive areas is often used in connection with environmental policy questions.
The underlying idea is that there are some areas that require stronger protection than others.
The Eurovignette Directive (1999/62/EC and 2006/38/EC) allows for the possibility to apply
mark-ups to tolls in the case of roads in sensitive areas, in particular in mountain regions
(Alps, Pyrenees, etc.) for cross-financing the investment costs of other transport
infrastructures of a high EU interest in the same corridor and transport zone.
However, despite its frequent use and its intuitive appeal, there is no commonly agreed
definition of what constitutes a sensitive area. Although there are many attempts to define
sensitive areas, there exists no clear EU-wide definition as yet. At least, most definitions give
a concrete idea of what is meant by sensitivity: Thus we define sensitive areas as areas
• where damages are higher;
− because of higher environmental pressures
− and / or because of more damaging effects of the same pressure level
•
and possibly where unique natural resources or cultural heritages are in danger.
None of the analysed definitions in the literature mentions the traffic volume. Hence, a high
traffic volume alone cannot make an area sensitive, but contributes to higher environmental
pressures. Most the definitions are not operationalised so as to allow differentiating between
sensitive and “insensitive” areas. A common rescue is to cite examples of areas which are
sensitive such as protected areas (national parks, landscape protection reserves, nature
conservation areas, natural monuments, biosphere reserves, and forest reserves), UNESCO
World Heritage Sites, mountain areas (area covered by the Alpine Convention), densely
populated areas, wetlands or coastal zones, certain marine areas, and urban areas.19 However,
this does not mean that all other areas are “insensitive”.
Hence, what is meant by sensitivity seems pretty clear, but an exact definition which allows
drawing a borderline between sensitive and insensitive areas is still missing.
7.2 Cost categories
In this chapter we estimate the cost differentials between an Alpine area and a flat, “insensitive” area for road and rail transport and the reasons behind them. As the main result we
derive factors between the costs in Alpine and flat areas – differentiated for passenger and
goods transport.
19
See for example European Commission (2003), Sensitive areas and transport, p. 2 and T&E
(2005), Sustainable Freight Transport in Sensitive Areas, p. 33-34.
70
GRACE D3 – Marginal cost case studies for road and rail transport
The method is based on the impact pathway approach. For each step in the pathway a
comparison is made between a Alpine area and a flat area is made. The factors for each step
are added together to suggest a total cost difference between the Alpine and the flat area. The
impact pathway steps considered is Emissions, Concentration and Impacts.
7.2.1 Air pollution
Pollutants which are formed at considerable distances from the emission source (e.g. nitrate
aerosols from NOx) or are transported over large distances are important to consider when
analysing the full costs of air pollution, but not when we analyse the costs of traffic through
mountain areas. The only pollutant with local effects is PM10. The effects of primary PM10
(with local effects) and secondary PM10 (with regional effects) has, however, to be
disentangled. It follows that the costs per vkm or trainkm for crop losses and forest damages
are equal in flat and Alpine areas, since these effects are caused by regional pollutants. The
factors we derive apply to the health costs20 and damages to buildings which are caused
locally.
Table: 25 Results for local air pollution
Impact pathway
Emissions
Cost driver
Gradients
Road factor
1.06
(1.02 – 2.28)
1.35
(1.10-1.60)
4.22
(2.50 – 6.25)
0.87
5.25
2.55 – 19.8)
Altitude
Concentration
Impacts
Total
Topographical and
meteorological conditions
Population density
Rail factor
1
1
4.22
(2.50 – 6.25)
0.83
3.5
(2.08 – 5.19)
The overall factor is 5.25 for road transport. For cars the factor is slightly higher (5.35) than
for HGVs (5.15). The difference between cars and HGV is explained by the large emissions
of cars on steep gradients, while for the HGVs the emissions increase less when the gradient
rises. However, the interval is large, reaching from 2.4 to 19.8. This shows the large
uncertainties involved in the calculations. For rail transport the overall factor is smaller: 3.5.
The reason is that the higher emissions due to the gradients are not emitted along the rail
track, but at the location of electricity production. Thus only the factor for population density
and higher concentration for the same emissions (due to abrasion and whirling up which seem
to be identical in flat and Alpine areas) apply.
7.2.2 Noise
For road noise we find higher motor noise emissions due to gradients. Furthermore, noise
propagation conditions are better in mountain valleys than in flat areas due to temperature
inversion and amphitheatre effects and reflections. Due to these effects a much larger distance
from the road is necessary to reduce noise to a certain level along mountainsides than in a flat
area.
20
For residents
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GRACE D3 – Marginal cost case studies for road and rail transport
Table: 26 Results for noise
Impact pathway
Emissions
Cost driver
Gradients
Concentration
Topographical and
meteorological conditions
Population density
Impacts
Total
Road factor
1.15
(1.06 – 1.82)
5
(2.5 – 12.5)
0.87
5.0
(2.3 – 19.8)
Rail factor
1
5
(2.5 – 12.5)
0.83
4.15
(2.1 – 10.4)
Due to the lower population density in Alpine areas and the higher emissions, the final result
for road noise is also a factor of 5 (2.3 – 19.8). For rail the results are similar; noise
propagation conditions are identical, the population density along the Gotthard rail line is
slightly lower than along the Gotthard motorway, but emissions seem not to be higher in
Alpine areas: The main noise source of rail traffic is not the motor of the engine but the noise
from moving rolling stock (wheels on the rail track). In the literature we could only find some
hints on higher noise emissions on gradients, but no quantifications. Thus the factor for rail is
4.2.
7.2.3 Visual intrusion
Visual intrusion is more severe in Alpine areas where the traffic routes can be seen from
much farther away (from the mountain flanks) than in a flat area. However, visual intrusion is
rather irrelevant for the GRACE-perspective (marginal costs), but a relevant alpine-specific
cost factor (average costs). The CS makes a pioneering attempt to quantify the extra cost due
to visual intrusion.
7.2.4
Accidents
It is well known that accidents in tunnels and on bridges can have more serious consequences
than accidents on a “normal” traffic route. Moreover, on descending slopes the braking
distance is larger. However, to our knowledge no evaluations on accident rates for Alpine and
flat areas exist. Therefore the CS evaluated detailed accident data from the Swiss motorways
to fill this gap. In a comparison between the Gotthard motorway and the main motorway in
the flat area of Switzerland the causality rate (casualties per vkm) on motorways was 1.22
times higher in the Alpine area. In contrast, we have to assume that rail accidents are identical
in Alpine and flat areas, because the external accident costs of rail freight transport are almost
negligible. Hence, we could not find any evidence that the costs are higher in an Alpine
environment.
7.2.5
Infrastructure costs
It seems clear that infrastructure costs are higher in Alpine areas than in flat areas: on the one
hand more tunnels and bridges are necessary and on the other hand the road or rail track must
adjust to the Alpine topography which means more curvy roads or rail tracks and thus longer
traffic routes. This is especially clear when planning new infrastructures in the Alpine area
since the investment costs tend to be higher. The exact amount of costs, however, is very
project specific and need not be determined here as investment costs are always taken into
account when planning new roads or rail tracks.
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GRACE D3 – Marginal cost case studies for road and rail transport
Instead, we concentrate on maintenance costs. Road maintenance costs are higher in Alpine
areas due to bridges, tunnels, and rutting of slow HGV traffic. Therefore we evaluated data on
motorway maintenance costs per canton, the most comprehensive data we could find. A rough
estimation shows that, although the traffic volume in the Alpine area is about 3 times lower,
the maintenance costs per kilometer motorway are about 1.5 times higher. Hence, the factor
for road maintenance costs per vkm between Alpine and flat areas is about 4.5. For rail
maintenance costs the Grace CS 1.2E has been used and the result is that the costs in Alpine
regions are 1.4 times higher.
7.3 Conclusion
The figure below summarizes all the results for the factors between Alpine and flat areas
(where we use the reduced factors for total instead of local air pollution). For road transport
the highest factor of more than 10 is observed for visual intrusion. For noise and infrastructure
costs a factor of 5 is estimated. Effects of local air pollution are also in that magnitude. But
due to the regional air pollutants the factor is about halved to 2.1. The factor for accidents of
1.2 is again about half of this.
Figure: 20 Factors Alpine / flat for the different effects for road (car and HGV) and rail
transport (passenger and freight transport)
11
10
9
factor Alpine / flat
8
7
6
5
4
3
2
1
0
air pollution
car
noise
HGV
visual
intrusion
accidents
passenger transport
73
infrastructure
freight transport
total
GRACE D3 – Marginal cost case studies for road and rail transport
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