Interaction of GHG policy for transport with congestion ...eutransportghg2050.eu/cms/assets/Uploads/Meeting... · Restricted-Commercial Ref. AEA/ED56293/Task 11 Paper 1 Draft –

EU Transport GHG: Routes to 2050 II Interaction of GHG policy for transport with Contract 070307/2010/579469/SER/C2 congestion and accessibility policies

Restricted-Commercial Ref. AEA/ED56293/Task 11 Paper 1 Draft – Issue No. 1 i

Lower transport speeds have an impact on transport demand. For passenger transport slower transport results in shorter travel distances, particularly in the long run. [The rule of constant travel times even suggests that on average all reduced travel time is compensated by additional travel elsewhere in the transport system (potentially by other modes, times, locations or even users).

Interaction of GHG policy for transport with congestion and accessibility policies

Huib van Essen (CE Delft) Anouk van Grinsven (CE Delft)

14 November 2011 Draft

Interaction of GHG policy for transport with EU Transport GHG: Routes to 2050 II congestion and accessibility policies Contract 070307/2010/579469/SER/C2

ii Ref. AEA/ED56293/Task 3 Paper Draft Final – Issue No. 2 Restricted-Commercial

Huib van Essen (CE Delft) Anouk van Grinsven (CE Delft)

Interaction of GHG policy for transport with congestion and accessibility policies

14 November 2011 Draft

Suggested citation: Huib van Essen and Anouk van Grinsven (2011) Interaction of GHG policy for transport with congestion and accessibility policies. Task 11 Ad-hoc paper 1 produced as part of a contract between European Commission Directorate-General Climate Action and AEA Technology plc; see website www.eutransportghg2050.eu


Restricted-Commercial Ref. AEA/ED56293/Task 11 Paper 1 Draft – Issue No. 1 iii

Executive Summary

The main objective of this paper is to explore the potential interaction between road congestion and climate policies and ways how the two could be combined effectively. Understanding the GHG impacts of congestion Congestion slows down the traffic in case the number of vehicles increases towards the maximum capacity. Due to this slowing down process, congestion results in lower average speeds and often also higher speed dynamics (e.g. stop-and-go-traffic). This way, congestion influences both the total amount of vehicle kilometres as well as the GHG emissions per kilometre. The sum of both effects of congestion reduction depends on local circumstances. On motorways, although the emissions per kilometre during congestion on motorways can in some cases be higher than without congestion, this is never the case for the emissions per minute travelled. Therefore, when the rule of constant travel time budget applies, the reduction of congestion on motorways generally leads to an increase in transport emissions. The effect of the increase in demand is apparently much stronger than the potentially saved emissions from the fuel efficiency improvements at vehicle level. This means that congestion reduction can only lead to lower emissions when there is a shift to travel types with lower emissions per minute travelled, e.g. a shift to non-motorized transport or rail transport. In the case of freight transport, the relationship between transport speed and transport demand may be different than for passenger transport. However, also for freight transport an increase in travel speed will lead to a decrease in transport costs and so in an increase in transport demand. Impacts of policy instruments In this paper the effects on congestion as well as the effects on GHG reduction are assessed for the main policy instruments that are aimed at reducing GHG emissions and/or road congestion. The assessment makes clear that some policies clearly contribute to both objectives, while a broad range of others have a positive impact on GHG emissions but a zero or unclear effect on congestion. A few policies result in less congestion but a clear increase in GHG emissions. There were no policies found with a strong GHG reduction potential but a clear adverse effect on road congestion In general it can be concluded that GHG reduction policy instruments have some co-benefits in terms of congestion reduction or have a more or less neutral effect on congestion. Particularly economic instruments (e.g. road pricing, fuel taxes, congestion charges, etc.) contribute generally to the reduction of both GHG emissions and road congestion. Pricing instruments can be used as an alternative for other policy instruments, but can also be complementary to increase the effectiveness of other policy instruments. Vehicle regulation is very effective in reducing in reducing GHG emissions, but the congestion effects are expected to be small or are unknown. Investments in non-road infrastructure can result in some reduction of congestion, the GHG effect depends strongly on the specific case. When a true shift is achieved, GHG emission reduction can be significant, but when most of the traffic on the new infrastructure comes from other (non-road or -aviation) modes, the GHG impact can be neutral or sometimes even negative.


iv Ref. AEA/ED56293/Task 3 Paper Draft Final – Issue No. 2 Restricted-Commercial

On the contrary to all these instruments that contribute more or less to both policy objectives, many instruments focused on the reduction of congestion can increase GHG emission significantly, especially in the case of expanding infrastructure capacity. Although expanding capacity can result in GHG reduction on the short term, this instrument will induce traffic on the long term when no additional policies, like road pricing, are used. Policy recommendations Overall, it can be concluded that the risk of adverse effects should be taken into account in the decision making process. Because, congestion reduction policy instruments might significantly increase GHG emissions, their GHG impacts should be considered carefully. Particularly in case infrastructure expansion is considered as an option, it should be investigated to what extent the same congestion reduction can be reached without an increase in GHG emissions. As stated earlier, pricing instruments are a better alternative from this perspective and therefore should be tried first.


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Table of Contents

Executive Summary ................................................................................................ iii

1 Introduction ...................................................................................................... 9

1.1 Topic of this paper .............................................................................................. 9

1.2 The contribution of transport to GHG emissions ................................................. 9

1.3 Background to the project and its objectives .....................................................12

1.4 Background and purpose of the paper ..............................................................14

2 The link between congestion and GHG emissions ..................................... 16

2.1 Introduction .......................................................................................................16

2.2 Defining congestion ...........................................................................................16

2.3 The link between congestion and GHG emissions ............................................16

3 Options for reducing congestion or greenhouse gas emission ................ 23

3.1 Introduction .......................................................................................................23

3.2 Options for congestion reduction and their GHG impacts ..................................23

3.3 Impacts on congestion from reducing GHG emissions ......................................24

4 Analysis of policy instruments ..................................................................... 27

4.1 Introduction .......................................................................................................27

4.2 Regulation .........................................................................................................27

4.3 Economic instruments .......................................................................................28

4.4 Infrastructure & spatial planning ........................................................................31

4.5 Speed and traffic management .........................................................................34

4.6 Information and communication ........................................................................35

4.7 Other instruments to stimulate innovation and development .............................35

4.8 Conclusions ......................................................................................................35

5 Conclusions and policy recommendations ................................................. 38

5.1 Conclusions on the relation between link between congestion and GHG

reduction .....................................................................................................................38

5.2 Conclusions on impacts of policy instruments ...................................................39

6 References ...................................................................................................... 41


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List of Tables

Table 4.1: Outcome of experiment on rewarding congestion avoidance .........................31 Table 4.2: Overview of policy instruments* .....................................................................36


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List of Figures

Figures in the main body of the report

Figure 1.1: EU27 greenhouse gas emissions by sector and mode of transport, 2007 ......10 Figure 1.2: Business as usual projected growth in transport’s GHG emissions by mode ..11 Figure 1.3: EU overall emissions trajectories against transport emissions (indexed) ........12 Figure 1.4: Graphic overview of the relation between road congestion policy and

decarbonisation policy ...................................................................................14 Figure 2.1: Correlation average speed (km/h) and CO2 emission (gram/km) including

vehicle dynamics ...........................................................................................18 Figure 2.2: Relative CO2 emissions for different traffic profiles for passenger cars in

relation to a normal drive on a motorway (category H) ...................................19 Figure 2.3: CO2 emissions of road traffic and rail transport per minute travel time ...........21 Figure 3.1: Schematic overview of two approaches to reduce GHG emissions ................26


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Glossary1

CO2

Carbon dioxide, the principal GHG emitted by transport.

GHG Greenhouse gas. Pollutant emissions from transport and other sources, which contribute to the greenhouse gas effect and climate change. GHG emissions from transport are largely CO2.

MtCO2e

Million tonnes of CO2e.

Policy instrument

These may be implemented to promote the application of the options for reducing transport’s GHG emissions.

1 Terms highlighted in bold have a separate entry.


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1 Introduction

1.1 Topic of this paper

This paper is one of a series of reports drafted under the EU Transport GHG: Routes to 2050 II project. This paper focuses on the interaction between the policy fields of congestion and GHG emissions and how those two policy fields could be combined effectively.

1.2 The contribution of transport to GHG emissions

Transport is responsible for around a quarter of EU greenhouse gas emissions making it the second biggest greenhouse gas emitting sector after energy (see figure below). Road transport accounts for more than two-thirds of EU transport-related greenhouse gas emissions and over one-fifth of the EU's total emissions of carbon dioxide (CO2), the main greenhouse gas. However, there are also significant emissions from the aviation and maritime sectors and these sectors are experiencing the fastest growth in emissions, meaning that policies to reduce greenhouse gas emissions are required for a range of transport modes2. While greenhouse gas emissions from other sectors are generally falling, decreasing 15% between 1990 and 2007, those from transport have increased by 36% in the same period. This increase has happened despite improved vehicle efficiency because the amount of personal and freight transport has increased. In the run-up to the Conference of the Parties of the UN Framework Convention on Climate Change in December 2009, the leaders of the EU’s Member States called for significant reductions in global greenhouse gas (GHG) emissions:

“The European Council calls upon all Parties … to agree to global emission reductions of at least 50%, and aggregate developed country emission reductions of at least 80-95%... It supports an EU objective, in the context of necessary reductions according to the IPCC by developed countries as a group, to reduce emissions by 80-95% by 2050 compared to 1990 levels.”3

The key role that transport has to play in this long-term economy-wide aspiration was underlined by European Commission President Barroso in his Political Guidelines for the next Commission4 where he emphasised the need to maintain the momentum towards a low carbon economy and towards decarbonising the transport sector in particular. In March 2010, the Commission, as part of its Europe 2020 strategy5, announced that it would make proposals to decarbonise transport, and in doing so linked the need to decarbonise transport with the wider sustainable growth agenda.

2 EC DG Climate Action (2010): http://ec.europa.eu/clima/policies/transport/index_en.htm

3 Presidency Conclusions, Brussels European Council, 29/30 October 2009; see

http://register.consilium.europa.eu/pdf/en/09/st15/st15265.en09.pdf 4 Barroso, J (2009) Political Guidelines for the next Commission, September 2009, Brussels

5 European Commission (2010) Europe 2020: A strategy for smart, sustainable and inclusive growth COM(2010)2020, Brussels 3.3.2020.

http://ec.europa.eu/clima/policies/transport/index_en.htm



Figure 1.1: EU27 greenhouse gas emissions by sector and mode of transport, 2007

12.0%

30.0%

8.0%

8.0%

3.1%

10.0%

4.7%

17.2%

0.4%

3.3%

0.4%

2.6%

0.2%

0.2%

Transport, 24.2%

Manufacturing and Construction Energy Industrial Processes

Residential Commercial Agricultrural

Other Road transport Domestic navigation

Int'l maritime Domestic aviation Int'l aviation

Rail transport Other transport

Source: EC DG Energy (2010)6

Notes: International aviation and maritime shipping only include emissions from bunker fuels

These high level political statements set the framework within which the original EU Transport GHG: Routes to 2050 project was undertaken. One of the main aims of this project was to provide information and analysis to assist the Commission with its early thinking on a co-ordinated approach to reducing the GHG emissions of all modes of transport. The increasing political importance that is being attached to decarbonising transport reflects the fact that, of all the economy’s sectors, transport has proved to be one of the most problematic in terms of reducing its GHG emissions. As mentioned earlier, since 1990, GHG emissions from transport, of which 98% are carbon dioxide (CO2), had the highest increase in percentage terms of all energy related sectors7. Furthermore, transport’s GHG emissions are predicted to continue to increase, without additional instruments, to over 2,000 MtCO2e by 2050. This increase is shown in the next figure, with a split by mode of transport. The figure is an output from an Excel-based illustrative scenarios tool (IST) called SULTAN (SUstainabLe TrANsport), which was developed under the previous project in order to identify the GHG reductions that transport could potentially deliver by 2050. An increase of the order projected in the next figure would leave transport’s GHG emissions 74% higher in 2050 than they were in 1990 (when the sector’s emissions were nearly 1,200 MtCO2e) and around 25% above 2010 levels. Significant emissions increases between 2010 and 2050 are projected for road freight (for which an increase of more than 45% is projected), aviation (more than 50%) and maritime (more than 65%) without additional policy instruments. Whilst GHG emissions from cars are still projected to contribute the most to the sector’s GHG emissions in absolute terms in 2050, their emissions are projected to have declined slightly from 2010 levels, as anticipated improvements in the energy efficiency of vehicles negate projected increases in demand.

6 Based on historic data from DG Energy (2010) EU energy and transport in figures Statistical Pocketbook 2010 Luxembourg, Publications Office

of the European Union, 2010. Publication and data available for download at: http://ec.europa.eu/energy/publications/statistics/statistics_en.htm 7 DG TREN (2000) Energy and transport in figures 2008-2009

http://ec.europa.eu/energy/publications/statistics/statistics_en.htm



Figure 1.2: Business as usual projected growth in transport’s GHG emissions by mode

0

500

1,000

1,500

2,000

2,500

2010 2015 2020 2025 2030 2035 2040 2045 2050

Co

mb

ine

d (life

cycle

) e

mis

sio

ns, M

tCO

2e

Total Combined (life cycle) GHG emissions, BAU-a

FreightRail

MaritimeShipping

InlandShipping

HeavyTruck

MedTruck

Van

WalkCycle

Motorcycle

PassengerRail

IntlAviation

EUAviation

Bus

Car

BAU-a total

Source: SULTAN Illustrative Scenarios Tool, developed for the EU Transport GHG: Routes to 2050 project

Notes: International aviation and maritime shipping include estimates for the full emissions resulting from journeys to EU countries, rather than current international reporting which only include emissions from bunker fuels supplied at a country level (which are lower).

The figure above shows the baseline, as projected by SULTAN. This is consistent with the range of results from other models and tools, although many of these only project to 20308. Clearly, the predicted continued growth in the EU-27’s GHG emissions from transport has the potential to prevent the EU meeting the long-term GHG emission reduction targets that the European Council supports, if no action is taken to reduce these emissions. Figure 1.3 demonstrates that on current trends, transport emissions could be around 30% of economy-wide 1990 GHG emissions by 20509. Whilst simplistic, in that it assumes linear reductions, the figure demonstrates that there is clearly a need for additional policy instruments to stimulate the take up of technical and non-technical options that could potentially reduce transport’s GHG emissions. The EEA believes that all available policy instruments need to be used to achieve the ambitious GHG reduction targets10.

8 See Appendix 19 SULTAN: Development of an Illustrative Scenarios Tool for Assessing Potential Impacts of Instruments on EU transport GHG

for details of the assumptions used and approach taken in the SULTAN Illustrative Scenarios Tool to projecting business as usual GHG emissions; also see http://www.eutransportghg2050.eu 9 The emissions included in this figure – for both the economy-wide emissions and those of the transport sector – include emissions from

international aviation and maritime transport, in addition to emissions from “domestic” EU transport. 10

EEA (2009) Towards a resource-efficient transport system – TERM 2009: indicators tracking transport and environment in the European Union, EEA Report No2/2010, Copenhagen.



Figure 1.3: EU overall emissions trajectories against transport emissions (indexed)

0%

20%

40%

60%

80%

100%

120%

1990 2000 2010 2020 2030 2040 2050

EU

-27

CO

2 e

mis

sio

ns

(1

99

0 =

10

0%

)

Source: EC DG Energy (2010) and SULTAN Illustrative Scenarios Tool

11

1.3 Background to the project and its objectives

EU Transport GHG: Routes to 2050 II is a 15-month project funded by the European Commission's DG Climate Action and started in January 2011. The context of the project is still the Commission's long-term objective for tackling climate change. The scope of the first project was very ambitious, and the outputs from the study were very detailed and have already proved to be of great value to the European Commission and to industry, governmental and NGO stakeholders. However, there were a number of topic areas where it was not possible within the time and resources available for the study team to carry out completely comprehensive research and analysis. In particular, as the project evolved, both the study team and the Commission Services became aware that there were a number of themes and topic areas that would benefit from further, more detailed research. This new project is a direct follow-on piece of research to the previous EU Transport GHG: Routes to 2050? study, building on the research and analysis carried out for that study and complementing other work carried out for the latest Transport White Paper. In particular, the outputs from this new study will help the Commission in prioritising and developing the key future policy instruments that will be critical in ensuring that GHG emissions from the transport sector can be reduced significantly in future years. Therefore, the key objectives of the EU Transport GHG: Routes to 2050 II are defined as to build on the work carried out in the previous project to:

- Develop an enhanced understanding of the wider potential impacts of transport GHG reduction policies, as well as their possible significance in a critical path to GHG reductions to 2050.

11

Projections based on data from the SULTAN Illustrative Scenarios Tool (BAU-a scenario) and historic data from DG Energy (2010) EU energy and transport in figures Statistical Pocketbook 2010 Luxembourg, Publications Office of the European Union, 2010.

EU-27 all sectors

EU-27 transport

EU-27 transport BAU

projections - SULTAN 60% to 80%

80% to 95%



- Further develop the SULTAN illustrative scenarios tool to enhance its usefulness as a policy scoping tool and carry out further scenario analysis in support of the new project;

- Use the new information in the evaluation of a series of alternative pathways to transport GHG reduction for 2050, in the context of the 50-70% reduction target for transport from the European Commission's Roadmap for moving to a competitive low carbon economy in 205012;

As before, given the timescales being considered, the project will take a quantitative approach to the analysis where possible, and a qualitative approach where this is not feasible. The project has been structured against a number of tasks, which are as follows:

Task 1: Development of a better understanding of the scale of co-benefits associated with transport sector GHG reduction policies;

Task 2: The role of GHG emissions from infrastructure construction, vehicle manufacturing, and ELVs in overall transport sector emissions;

Task 3: Exploration of the knock-on consequences of relevant potential policies;

Task 4: Exploration of the potential for less transport-intensive paths to societal goals;

Task 5: Identification of the major risks/uncertainties associated with the achievability of the policies and instruments considered in the illustrative scenarios;

Task 6: Further development of the SULTAN tool and illustrative scenarios;

Task 7: Exploration of the interaction between the policies that can be put in place prior to 2020 and those achievable later in the time period;

Task 8: Development of a better understanding of the cost effectiveness of different policies and policy packages;

Task 9: Stakeholder engagement: organisation of technical level meetings for experts and stakeholders;

Task 10: Hosting the existing project website and its content;

Task 11: Ad-hoc work requests to cover work beyond that covered in the rest of the work plan.

As in the previous project, stakeholder engagement is an important element of the project. The following meetings are being scheduled:

A large stakeholder meeting currently planned for June 2011 at which the new project will be introduced to stakeholders and interim results presented.

A series of four Technical Focus Group meetings TBC. These are currently scheduled to be held at the start of May 2011 and in November 2011.

A second large stakeholder meeting at which the draft final findings of the project will be presented and discussed, anticipated to be held in February 2012.

As part of the project a number of papers will be produced, all of which will be made available on the project’s website in draft and then final form, as will all of the presentations from the project’s meetings.

12

Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions, A Roadmap for moving to a competitive low carbon economy in 2050, COM(2011) 112 final. Available from DG Climate Actions website at: http://ec.europa.eu/clima/policies/roadmap/index_en.htm

http://ec.europa.eu/clima/policies/roadmap/index_en.htm



1.4 Background and purpose of the paper

In this ad hoc paper we focus on the potential interaction between GHG policy on the one hand and reducing congestion and improving accessibility on the other hand. Decarbonisation and reducing road congestion are two of the key objectives of transport policy. Some policies can contribute to both objectives (see Figure 1.4).

Figure 1.4: Graphic overview of the relation between road congestion policy and decarbonisation policy

However, a whole range of other policies contributes to one of the objectives but have a negative impact on the other. Particularly in the field of infrastructure provisioning, policies that reduce congestion can have adverse affects on climate policy objectives. A good example is expanding the infrastructure capacity by building new roads. On the other hand improved fuel efficiency of cars contributes to climate objectives, but is likely to increase traffic flows (because of the lower marginal user cost) and therefore also congestion. CO2 standards for cars are a good example. In this ad-hoc paper, we explore the potential interaction between road congestion and climate policies and ways how the two could be combined effectively. Congestion can also occur in other transport modes. In the case rail transport, shipping and aviation, infrastructure capacity (certain slots) is allocated to single users by a central operator. In these modes, the impacts of limited infrastructure capacity are usually called scarcity. However, this is not discussed in this paper which is limited to road congestion. Structure of the paper This paper has been structured in the following way:

In Section 2 the link between GHG emissions and congestion is explained in order gain insight in the way those concepts are related.

In Section 3 attention is paid to the mechanisms used to reduce either GHG emissions or congestion in the second part.

Decarbonisation of transport sector

Reducing road congestion

Transport policy

Overlap policy fields



In Section 4 different policy instruments are discussed according to the approach defined in the second part. From this discussion it will become clear which policy instruments can be defined as win-win policies or win-loose policies.

Based on the findings of Section 4 policy recommendations are provided in Section 5.



2 The link between congestion and GHG emissions

2.1 Introduction

This section will deal with the conceptual aspects of congestion and GHG emissions and especially how those two aspects are interrelated. First of all, the definition of congestion will be provided, followed by the link between congestion and GHG emissions. In order to make this link clear we will have a look at the relation between the average speed and CO2 emissions and the relation between the amount of vehicle kilometres and the GHG emissions.

2.2 Defining congestion

To understand the link between congestion and GHG emissions, the concepts should be clear. Congestion can be defined in several ways. At a conference of the European Conference of Ministers of Transport (ECMT, 1999a) the following definition of congestion has been adopted:

‘Congestion is the impedance vehicles impose on each other, due to the speed-flow relationship, in conditions where the use of a transport system approaches its capacity’. (Schneider et al., 2002)

In this definition the focus is on the maximum capacity of a transport system. Congestion slows down the traffic in case the number of vehicles increases towards the maximum capacity. Due to this slowing down process, congestion results in lower speeds and stop-and-go-traffic. (Schneider et al., 2002) Congestion is often described in comparison to free flow conditions. However, this may be unrealistic, because some roads seldom have free flow conditions, while we cannot speak of congestion all the time either. It is important to understand that there is no single state of congestion, but we can speak of different levels. Next to different levels, there are also different causes for congestion: on the one hand congestion can be caused by incidental factors, like car accidents and weather circumstances. However this type of congestion does not fit within the definition given above. On the other hand, we can speak of structural congestion in case a road (section), e.g. a motorway or urban access road, is regularly heavily congested during certain time periods (e.g. peak hours at working days) because of high traffic volumes. This is often caused by commuters. However, it should be noted that also leisure traffic can cause serious congestion, e.g. close to the location of large events such as stadiums. Because speed reduction as a result of congestion limits the amount of kilometres which can be travelled, congestion is often expressed in delay times.

2.3 The link between congestion and GHG emissions

Congestion influences the total amount of vehicle kilometres as well as the GHG emissions per kilometre, which both will be discussed in this section. This can be concluded from the following equation for the total amount of GHG emissions in the transport sector:



Total amount GHG emissions in the transport sector = total amount of vehicle kilometres x GHG emissions per kilometre Both effects are related to the impact congestion has on the traffic speed, in particular on:

The average speed of the traffic.

The speed dynamics of the traffic (reflecting the changes in speed because of braking and accelerating. In the most extreme case this is referred to as ‘stop and go’ traffic).

2.3.1 Impact of traffic speed on the CO2 emissions per kilometre driven

The CO2 emissions per vehicle kilometre driven are linearly related to the fuel consumption. When driving at a constant speed, the fuel consumption increases with speed as the friction from air and road surface increases. However, at low speeds, the efficiency of the engine is relatively low. The combination of the two makes that the most fuel efficient constant speed is a moderate speed in the range of 70 to 90 km/h, depending on vehicle type and characteristics. Congestion does not only affect the average speed, but also the speed dynamics. The higher the dynamics, the more energy is lost in braking and accelerating. This effect is important on congested roads. Especially continuous braking and acceleration (stop and go traffic) result in high CO2 emissions. Figure 2.1 shows the non-linear relationship between the average speed of a car on a motorway and the level of CO2 emissions, taking both effects into account,. The optimum speed in the light of CO2 emissions is around 80 km/h. Between 40 and 110 km/h CO2 emissions are in the range of 100 and 150 gram/km. They are higher outside the boundaries of this speed range. For a good understanding of the graph it must be noticed the speeds reflect average speeds. For example, the average speed of 20 km/h does not imply a constant speed of 20 km/h, but reflects high fluctuations in speeds, which are related to stop-and-go traffic during congestion. The optimum speed of about 80km/h for CO2 emissions is slightly lower than the optimal speed for an optimal flow through, which is about 90 km/h (Hoogendoorn, 2006) in (CE Delft, 2008a). At that speed there are hardly any differences in traffic speeds, resulting in an optimal use of the road capacity13. This overview shows that congestion on motorways can lead to higher fuel consumption per vehicle kilometre driven. However, in the case of modest congestion levels, that decreases the average speed from above 100 km/h to somewhere in the range of 80 km/h to 100 km/h, the emission factors drop because of the congestion.

13

The speed differences that go along with higher speed limits lead to distortions of the smooth and constant traffic flow, de factor lowering the road capacity.



Figure 2.1: Correlation average speed (km/h) and CO2 emission (gram/km) including vehicle dynamics

Source: CE Delft, 2010 (adapted TNO data)

Another way of presenting the difference in CO2 emissions for different type of speed profiles on a motorway is by using a standard traffic profile as a benchmark. In Figure 2.2 category H represents the standard speed profile with an average speed of more than 75 km/h on a motorway with a speed limit of 120 km/h and an intensity of less than 1000 vehicles per lane per hour. Category G is the same but with a speed limit of 100 km/h. Category E and F are the same as G and H but with more traffic on the road: more than 1000 vehicles per lane per hour, but no severe congestion. Category A, B and C reflect various levels of congestion. Category D is a 80 km/h speed limit resulting in a 70 km/h average driving speed. Category I reflects a driving speed of more than 120 km/h and J is choosing an alternative route. This figure makes clear that the relative emissions are particularly high at severe congestion with stop and go traffic with an average speed below 25 km/h. The impact of modest congestion level B and C are very small or zero and in he case of level C even result in lower emission factors than under free flow conditions (H). The graph also makes clear that the lowest emissions are achieved with a speed limit of 80 km/h14.

14

A speed limit of 90 km/h, resulting in an average speed of 80 km/h is likely to be have even lower emission factors, but this scenario was not included in this graph.



Figure 2.2: Relative CO2 emissions for different traffic profiles for passenger cars in relation to a normal drive on a motorway (category H)

Source: CE Delft (2004); CE Delft (2008b) and TNO (2001) in CE Delft (2008a)

From this analysis we can draw the conclusion that the effect of solving severe congestion (stop and go) generally results in a lower average emission factors per kilometre driven, However congestion levels resulting in average traffic speeds in the range of 25 to 70 km/h do not result in higher emission factors, they can sometimes even be lower than in the reference case. It must be noted that both graphs on the relationship between speed and GHG emissions discussed above, only count for motorways and not for urban roads or secondary roads. For traffic on urban roads the share of stop is much higher and so will be the impact on the average emission factor. Therefore on urban roads, a better flow through will generally result in less stop and go and therefore lower emission factors. On secondary roads with typical speed limits of 90 km/h or less, the optimal speed in terms of relative emissions per vehicle-kilometre is about the maximum speed. The reasons is that on such roads the driving speed without any congestion is already at such a level that even lowering a constant driving speed would result in higher emission levels for an average car (because of the lower engine efficiency at low speeds).This means that on such roads reducing congestion leads to lower average emission factors.

2.3.2 Impact of traffic speed on the amount of vehicle kilometres

In addition to the impacts on emission factors, congestion also has an impact on transport volumes and modal split. The reason for this is that congestion affects the speed, user costs and perceived quality of road transport in the following ways:

Congestion reduces the average travel speed, resulting on longer travel times for the same trip than in the case without congestion.

Congestion increases transport costs when fuel consumption of vehicles increases (which is often but not always will be the case, see the previous section). In addition



congestion increases the maintenance costs of vehicles (wear and tear of brakes, engine, etc.). The net effects on the user costs will often be an increase.

Congestion reduces the perceived quality of road transport because it results in less predictable travel times and less comfortable travelling.

The net impact of the three impacts is generally that congestion makes road transport less attractive than it would be in the case without congestion. Therefore congestion leads to a decrease in road transport volume. In cases where road competes with alternative transport modes, congestion leads to some shift. To what extent this is the case, depends strongly on local circumstances. The relation between travel speed and transport demand has been subject of various studies. These revealed that for passenger transport there is a strong correlation between the amount of kilometres travelled and the average travel speed. This is closely related to the empirical fact that the average time spent on travelling per person per day is relatively constant over time: 60-70 minutes per day. This is phenomenon is known as the law of constant travel time budget and implies that the amount of vehicle kilometres travelled is determined by the average speed. An increase of the average speed of road transport as well as a shift to relatively fast modes as private cars and aviation, results in higher vehicle kilometres, because the distance which can be covered within the 60-70 minutes increases. Congestion results in longer travel times for road transport. This will also stimulate users to shift to alternative modes, with usually longer travel times. On the long term it also affects people’s choices of where to live where to work, etc. Therefore, according to the law of constant travel time budget, road congestion reduces the amount of vehicle kilometres travelled and potentially some modal shift to other (usually from a door-to-door perspective slower) transport modes.

2.3.3 Combination of the two effects

A rather transparent way of depicting the relation between traffic speed and transport volume and the resulting emissions, is by expressing the emissions per minute travelled. A comparison of the relative emissions for various levels of congestion was made by CE Delft (2007). In addition these levels were compared to the emissions for rail transport. The result of this is shown in Figure 2.3. The categories of flow through and congestion levels (category A to I) are the same as in Figure 2.2. The categories T1 and T2 represent regional trains and intercity trains, respectively. For both types of rail transport also some travel from the origin and to the final destination was included (in terms of both travel time and emissions). For road transport, the differences in emissions per kilometre driven for the various traffic conditions from Figure 2.2 are included as well.



Figure 2.3: CO2 emissions of road traffic and rail transport per minute travel time

The graph makes clear that although the emissions per kilometre during congestion on motorways can in some cases be higher than without congestion (as we saw in Figure 2.2), this is never the case for the emissions per minute travelled. In addition it becomes clear that the emissions per minute for rail transport are considerably lower than for road transport, except for heavily congested road traffic. The consequence of this is that when the rule of constant travel time budget applies, the reduction of congestion on motorways generally leads to an increase in transport emissions. The effect of the increase in demand is apparently much stronger than the potentially saved emissions from the fuel efficiency improvements at vehicle level. Congestion reduction can only lead to lower emissions when there is a shift to travel types with lower emissions per minute travelled. Examples are:

A shift from road transport to non-motorized transport modes.

A shift from moderate congestion (category C) to rail transport. It should be noted that the reasoning made above has certain limitations. First of all, it only applies to passenger transport, as the rule of constant travel time does not apply as such to freight transport. At the same time it should be noted that the travel speed is a strong driver for freight transport costs. Every hour of delay results in a loss of productivity and wasted depreciation of vehicles, load and salary and all other costs related to of the driver. Therefore, also for freight transport an increase in travel speed will lead to a decrease in transport costs and so in an increase in transport demand15, although this relationship may be different than for passenger transport. A second remark to be made is that the rule of constant travel time budget is an empirical law for the entire transport system. Therefore, local changes may on a local level and at short time horizons result in changes in travel times. However, at the long term and at a aggregated level, there is strong empirical evidence that travel time budgets are rather constant.

15

Significance and CE Delft (2010) showed that, based on meta-analysis of the literature on price elasticities, 10% higher freight transport costs (in € per tkm) results, ceteris paribus, on average in 10% lower transport demand (in tonne-kilometres).



3 Options for reducing congestion or greenhouse gas emission

3.1 Introduction

In the previous study EU Transport GHG Routes to 2050, a clear distinction was made between greenhouse gas reduction options and policy instruments. In this chapter we focus in section 3.3 on the various greenhouse gas reduction options and how their effects on congestion. In a similar way we discus first in section 3.2 the various options for congestion reduction and their impacts on greenhouse gas emissions.

3.2 Options for congestion reduction and their GHG impacts

As mentioned before, in case of congestion the traffic volume on a certain road approaches the maximum capacity of the road. Based on this assumption, there are two main ways of reducing congestion:

1. Increasing the usable maximum capacity at the bottleneck. 2. Reducing the amount of traffic at the bottleneck (specific location and times);

For both options, many different approaches can be followed. In the subsequent sections the impacts on greenhouse gas emissions of each of these options and the mechanisms behind it are discussed.

Reduction potential

The relation between congestion and GHG emissions is now clear, but a good understanding of the reduction potential is also needed. What exactly is the GHG reduction potential, which can be achieved by reducing congestion? In the Netherlands, 9% of total travel time on motorways is spent on delays from congestion. However, looking at the distance shows that only 2% of total vehicle kilometres (500 million vehicle kilometres on an annual basis) can be ascribed to congestion, assuming that the average speed in congestion is on average 25 km/h. The total GHG reduction potential (for the Netherlands) of congestion instruments is therefore relatively limited.

Source: CE Delft, 2008a

3.2.1 Enlarging the maximum capacity at the bottleneck

Enlarging the maximum capacity of the bottleneck can either be done by making better use of the existing infrastructure or by expanding the existing infrastructure. In both cases, the effect of higher capacity is that the average traffic speed will go up. On the long run, this will attract additional traffic, in various ways:

More traffic at the bottleneck of users that used other links or time windows.

More traffic elsewhere because the travel time saved will be spent on other and/or longer trips (on the long run).

Modal shift from slower modes to road transport (on the long run). All these three effects result in higher average travel speeds and lower costs. Therefore, the net traffic volume will at the long run always increase.



In addition, the emission factors of the vehicles at the bottleneck will change. In case there was severe congestion, this will result in lower emission factors at the bottleneck. In other cases, the differences are small or emission factors may even increase (see section 2.3.1). From section 2.3.3 we learned that on motorways, the volume effect is generally more important than the fuel efficiency effect at vehicle level. Therefore, the sum of both effects will be that at the long run greenhouse gas emissions go up. Also at other roads, the extra capacity will generally generate more traffic as the main three effects mentioned earlier in this paragraph will occur. The fuel efficiency effect will also take place there. However, also at other roads than motorways it is likely that the volume effect will be at least as important as the fuel efficiency improvement as vehicle level.

3.2.2 Reducing the traffic volume at the bottleneck

The other option for congestion reduction is reducing the amount of traffic volume at the bottleneck. This can be the result of:

Overall reduction of road traffic in the entire network (because of avoided trips and/or a shift to other transport modes).

Shift of traffic to other links.

Shift of traffic to other times of the day. The net impact of this on the total transport volume depends strongly on the magnitude of these various effects. In some cases it may decrease, while in other cases, the net impact on total transport demand may be negligible. A significant increase in the total transport demand might even occur, e.g. in the case the road traffic reduction is achieved by offering a shift to a very fast alternative. In addition, the emission factors of the vehicles at the bottleneck will change. In case there was severe congestion, this will result in lower emission factors at the bottleneck. In other cases, the differences are small or emission factors may even increase (see section 2.3.1). Also emission factors at other parts of the network may change. Where in the case of an increase of capacity, the direction of the greenhouse impact is clear (an increase), in the case of a decrease in traffic at the bottleneck, the direction of the net greenhouse gas effect may be either positive or negative, depending on the exact effects. This depends on the way the decrease in traffic at the bottleneck has been achieved. In the next chapter this is further elaborated, when various policy instruments are discussed.

3.3 Impacts on congestion from reducing GHG emissions

There are several approaches to reduce GHG emissions from car use. The main reduction options were elaborated in the previous EU transport route to 2050 project. In Figure 3.1, the link between the main reduction options and congestion are summarized. A shift to low carbon energy carrier is not included here, as the impact of congestion depends very much on various aspects, such as impacts on vehicle purchase and vehicle usage costs. In general a distinction can be made between GHG emission savings from fuel efficiency gains and GHG emissions savings from a reduction of vehicle kilometres:

Fuel efficient driving: policy instruments can stimulate behavioural changes, which contribute to more fuel efficient driving. By driving at more favourable and more constant speeds fuel use is reduced. This reduction leads to a decrease of marginal



user costs. Because car use becomes cheaper in this way it becomes more attractive and therefore will result in an increase of vehicle kilometres and thus an increased level of congestion can be expected. At the same time, fuel efficient driving results in more constant traffic as drivers better anticipate and avoid abrupt braking or fast acceleration. This has a decreasing effect on congestion. The net effect depends on the ration between the two.

More fuel efficient vehicles: More efficient fuel driving cannot only be reached by behavioural changes, but also by more fuel efficient vehicles. On the one hand, car use becomes cheaper, because marginal user costs will be reduced as a consequence of the fuel savings. On the other hand car ownership becomes more expensive, because higher vehicle purchase costs may increase due to the application of newer and more advanced technologies. Higher vehicle purchase costs can have a negative effect on the level of car-ownership. Where reduced marginal user costs contribute to congestion by an increase in vehicle kilometres, reduced car-ownership reduces the level of congestion as a consequence of a reduced amount of vehicle kilometres by car. The balance between both effects was discussed in Task 3 of this project on knock on consequences.

Reduced car ownership: as mentioned above higher vehicle purchase costs influence car-ownership, but there are other factors which influence car-ownership. In case of those other factors, the amount of vehicle kilometres by car decreases and thus the level of congestion will also be reduced.

Modal shift to other transport modes: a modal shift from the car to other modes, like the train or bus, will result in a reduction of vehicle kilometres by car and thus a reduced level of congestion. .

Higher vehicle utilisation: by a higher vehicle utilisation, less vehicles are needed or less distance needs to be covered for the same actions. This makes that the amount of vehicle kilometres will be reduced and thus the level of congestion.

Limiting overall transport growth: by making it unnecessary to travel the amount of vehicle kilometres by car and other transport modes will be reduced. Based on the law of constant travel time budget this would imply that the travel times that are no longer spent on car transport, are spent on other transport modes.



Figure 3.1: Schematic overview of two approaches to reduce GHG emissions

GHG emission

reduction

More fuel efficient

vehicles

Fuel efficient

driving

Reduced car-ownership

Higher vehicle utilisation

Modal shift to low-carbon

modes

Limiting overall transport

growth

Reduced level

of congestion

Increased level

of congestion

Increase of

vehicle

kilometres

Reduction of

marginal user

costs

Reduced amount

of vehicle

kilometres by car

Higher vehicle

purchase costs

Smoother traffic

GHG emission

reduction

More fuel efficient

vehicles

Fuel efficient

driving

Reduced car-ownership

Higher vehicle utilisation

Modal shift to low-carbon

modes

Limiting overall transport

growth

Reduced level

of congestion

Increased level

of congestion

Increase of

vehicle

kilometres

Reduction of

marginal user

costs

Reduced amount

of vehicle

kilometres by car

Higher vehicle

purchase costs

Smoother traffic



4 Analysis of policy instruments

4.1 Introduction

In the previous chapter the climate impacts of options for reducing congestion and the congestion impacts of options for reducing greenhouse gas emissions were discussed. In this chapter, policy instruments aimed at reducing greenhouse gas emissions and/or congestion are discussed. In the previous EU Transport GHG: Routes to 2050 study, the following types of policy instruments were distinguished (Skinner et al., 2010):

Regulation

Economic instruments

Infrastructure & spatial planning

Speed and traffic management

Information to raise awareness

Other instruments to stimulate innovation and development In this chapter, relevant policy instruments in each of these categories will be assessed on their effects on congestion and GHG emissions. This assessment will show which instruments can contribute to both policy objectives (win-win policies) and which policies contributes to just one of the two with either a neutral or even a negative impact ion the other objective (win-loose policies). On top of the instruments for reducing greenhouse gas emissions also policy instruments for reducing congestion that were not discussed in the previous project, are included in the analysis.

4.2 Regulation

4.2.1 CO2 regulation of road vehicles

Regulation has proven to be an effective way of reducing air pollution form vehicles by setting standards. Since a couple of years this has also been applied to road vehicles for CO2 emissions. A sales-average target is set for new vehicles in 2015. Next to CO2 regulation of vehicles, the CO2 emissions of fuels can also be regulated (like by setting sustainability criteria dealing with WTT emissions). The total fleet of cars will on average become more fuel efficient by setting standards. Because of the mandatory aspect of standards and the scale of applying those standards (all new cars are subjected to CO2 regulations) effects on GHG emissions will be positive and substantial. Effects on congestion can be positive as well as negative. Due to lower marginal user costs, cars complying with stringent emission standards provide an incentive to use the car more often and therefore will increase mileage impacts. This is also known as the rebound or takeback effect (Alexander, 1997; Greene 1998, UKERC 2007, Litman, 2005). Litman (2005) states this effect can be estimated at 20-40% or in other words a 10% fuel economy gain results in an increase of 2-4% in vehicle mileage. The net fuel savings are therefore only 6-8%. This effect is directly linked to energy prices.



For alternative fuels, vehicle mileage impacts vary. Some of those fuels can reduce vehicle operating costs. Litman (2005) assumes that an alternative fuel that reduces GHG emissions by 10% will increase average annual vehicle travel by 3%. However, alternative fuels can also increase per mile vehicle operating costs, but also can increase vehicle purchase costs, because the alternative technologies used to reach the GHG reduction may be more expensive in comparison to conventional technologies. Regulations for the use of alternative fuels may increase fuel prices and may therefore contribute to the reduction of congestion. In addition to the direct effects on the fleet, CO2 standards for cars can also have indirect effects on car ownership and use. In the Task 3 report of this project, it was concluded that:

The 130 g/km target for 2015 is likely to generate negative knock-on consequences or rebound effects, reducing its intended effect and very likely its cost-effectiveness

Most likely the 95 g/km target will lead to small but net positive knock-on consequences enhancing the effect and effectiveness of the legislation.

This means that if the 130 g/km legislation would have any congestion effects, these are likely to be negative (increase). On the longer term (e.g. the 95 g/km target), the net effect will be a reduction of congestion levels. Overall, we conclude that at the long term it is likely that CO2 standards for road vehicles are likely to be a win-win policy, so having positive co-benefits on congestion.

4.2.2 CO2 regulation of non-road vehicles

Vehicle standards for other modes of transport can have clear GHG effects. The effects on road congestion depend again on the balance between:

Increase in vehicle purchase costs

Decrease in vehicle usage costs It currently lacks information to draw any conclusion on the balance between the two.

4.3 Economic instruments

4.3.1 Flat kilometre charges

Charging a specific amount per kilometre has both a positive effect on GHG emissions as well as on the level of congestion, because vehicle kilometres are discouraged. Van Essen (2010) refers to the conclusions of IMPACT (CE Delft, 2008c) in which it is stated that the introduction of a flat kilometre charge for all road vehicles at the level of marginal external kilometre-related costs could reduce fuel consumption, and thus GHG emissions, of road transport by around 5%. Examples of marginal external kilometre-related costs are infrastructure, air pollution, noise, accidents and congestion costs. Such a scheme can also have positive effects on congestion as the reduction in CO2 emissions is the result of a decrease in the amount of road traffic. The effects of flat kilometre charges on the level of congestion are lower than in the case of more differentiated schemes, however.



Overall, we conclude that flat kilometre charges are likely to be a win-win policy, so reducing both congestion and GHG emissions.

4.3.2 Differentiated kilometre charges

By differentiated kilometre charges a specific policy goal can be steered at. Kilometre charges can be differentiated for example for fuel efficiency, road congestion and noise. Especially differentiating for fuel efficiency and road congestion are relevant in this case and will both have different effects on GHG emissions and road congestion. Therefore both will be discussed here. A differentiated kilometre charging scheme can be applied for congestion by charging for congestion rates during peak hours at busy roads. Van Essen (2010) states that a differentiated kilometre charging scheme for congestion may reduce road congestion by 35-58% according to estimates for the Netherlands. Estimates for the United Kingdom show a similar congestion reduction potential. (Van Essen, 2010) Differentiating based on fuel efficiency of vehicles results in a higher GHG emissions reduction potential compared to non-differentiated kilometre charging, because next to a reduction of vehicle kilometres a shift to fuel efficient vehicles will occur. Overall, we conclude that differentiated kilometre charges are likely to be a strong win-win policy, so reducing both GHG emissions and also strongly reducing congestion.

4.3.3 Congestion charging

In the case of congestion charging the effects on GHG emissions are limited to a certain part of the network, because car driving is only discouraged during peak hours and at bottlenecks. However, the GHG impact in the region which is subject to the charge can be significant. The number of cities that have implemented congestion charging schemes are limited. Because of this limited number and the local circumstances, which determine the possibilities for modal shift, it is difficult to predict the overall GHG reduction potential. Because congestion charging is directly targeted at congestion reduction this policy instrument is likely to have a high impact on the level of congestion. Due to the charge the amount of traffic in the bottleneck will be reduced. A good example of congestion charging is the London congestion charge. Since the start of 2003, car drivers pay a fixed amount per day (of first £5 and now £8) when they enter the congestion zone on week days between 7.00 and 18.30. Residents of the area receive a 90% discount on the charge. The income generated by the charge is invested in improving the traffic system, like in buses, road safety and walking and bicycle infrastructure. The amount of vehicles in the area has been reduced by 25%, where congestion itself is reduced by 30%. Air polluting emissions and CO2 emission also have been reduced. (Geurs and Van den Brink, 2005) (Beevers and Carslaw, 2004). Another example is the introduction of congestion charging in Stockholm. Since 2006 infrared cameras register the vehicles going in and out in the city centre when passing so-called cordons. Toll is paid electronically via DCR-tags in the cars. Photographs help to charge cars without DCR-tags and non-payers. Already after the first week peak hour traffic was reduced by more than 25%. During morning peak queuing was reduced by 30-50%.



Emissions were reduced by 14%. Through a referendum, authorities found out that this new congestion charging system was accepted by the public. (IET, 2010) Overall, we conclude that congestion charges are likely to be a win-win policy, by strongly reducing congestion and also reducing GHG emissions.

4.3.4 Fuel tax / Emission Trading System

Where kilometre charging does not provide any incentive for fuel efficient driving, fuel taxes do. By making car use more expensive a car driver is discouraged to drive. A decrease in vehicle kilometres as a result of those fuel taxes contributes to the reduction of congestion. Litman (2005) assumes that a fuel tax increase required to reduce energy consumption by 10% would reduce vehicle travel by 3.5%. Therefore also some congestion reduction can be expected. However, fuel taxes are less effective in reducing congestion compared to congestion charging and differentiated kilometre charges, because fuel taxes are not able to differentiate for time and place. A cap & trade emission trading scheme will have similar effects on congestion and GHG emissions. Overall, we conclude that fuel taxes and ETS are likely to be a win-win policy, by strongly reducing GHG emissions and also having some benefits on congestion reduction.

4.3.5 Energy and CO2-labelling

The purchase of less CO2 intensive vehicles can be stimulated by providing information on the CO2 aspects of a vehicle by labelling or other forms of information. In this way consumers are stimulated to take into account the CO2 aspects in their choice for a new vehicle. The publication of information on the CO2 emissions of new passenger cars and fuel economy is enforced by the European Union with Council Directive 1999/94/EC and the update of this directive. Although providing information is obligatory, the consumer is still free in their choice for a vehicle. It can be questioned to what extent CO2-labelling contributes to the composition of the total fleet. Therefore the effects on GHG emissions are limited compared to regulating CO2 emissions by setting standards. Because the effects on GHG emissions are limited, the effect on the level of congestion will also be limited.

4.3.6 Rewarding congestion avoidance

Instead of charging car drivers for their drives during peak hours, car drivers can also be rewarded with a financial incentive for avoiding peak hours at a certain trajectory of a road. The effect on GHG emissions depends on the way peak hours are avoided. The following ways of avoiding peak hours can be identified: • avoiding peak hours by driving outside the peak hours • taking an alternative route • choosing a different modality, like public transport or bicycle • working at home In case of avoiding peak hours by driving outside the peak hours GHG emissions are saved by the higher average speed assuming the distance covered stays constant. By taking an alternative route GHG emissions can also be saved by a higher average speed, but it is more likely the distance of the alternative route will differ from the original route. The length of the alternative route is an important factor for the amount of GHG emissions that will be emitted.



In both cases we can speak of a rebound effect in case the time savings as a result of a higher average speed are invested in more vehicle kilometres. When another modality is chosen the GHG emission savings are determined by the modality chosen. Choosing the bicycle instead of the car will results in higher emission savings compared to choosing public transport as an alternative. Emissions are reduced to zero in case peak hours are avoided by staying at home. However, it is very likely that the kilometres of the avoided journey will be compensated by kilometres at another moment according to the law of constant travel time budget. In the last couple of years different pilot projects have been performed in the Netherlands in order to gain insight in the effects of rewarding car drivers for avoiding peak hours on congestion. In Table 4.1 an overview is provided of the outcome of an experiment on rewarding congestion avoidance. As can be seen the preferred alternative is to drive outside the peak hours.

Table 4.1: Outcome of experiment on rewarding congestion avoidance

Reward Total reduction of trips Alternative chosen

€3 46% 35% outside the peak hours

10% modal shift (9% public transport, 1% cycling)

1% worked from home

€7 61% 44% outside the peak hours

14% modal shift (13% public transport, 1% cycling)

3% worked from home

Source: Bliemer et al. 2009 In the light of congestion reduction, rewarding congestion avoidance works effectively, because it is directly aimed at reducing congestion. However, CE Delft (2009) estimated that the net GHG impacts of the schemes in the Netherlands such a schemes are negative, with an estimated increase in net CO2 emissions of about 10% (for the traffic that participates in the scheme). Overall, we conclude that congestion rewarding as a win-loose policy that reduces congestion but leads to an increase in GHG emissions.

4.4 Infrastructure & spatial planning

4.4.1 Investment in infrastructure for non-road transport modes

Investments in walking and cycling infrastructure can reduce short car drives within urban areas. In case infrastructure for cycling and walking is taken from the infrastructure for cars, car driving will become less attractive at the same time. Factors which influence the competitiveness of public transport compared to the car are the frequency of the services and the travel time compared to other modes. Investments in public transport infrastructure can contribute to this. Road congestion also contributes to the competitiveness of those transport modes. Investing in non-road infrastructure, like public transport, but also walking and cycling infrastructure, will only reduce GHG emissions in case no new car users will use the road space, which has become available by the modal shift of car users to non-road infrastructure.



When travelling with other transport modes is faster compared to the car, it may be that those time savings will result in an increase in vehicle kilometres. However, other transport modes are mostly slower than the car. In practice it turns out to be hard to get car users to shift from the car to other modes only by investing in non-road infrastructure. Public transport investments should therefore be combined with other instruments which make the car less attractive, like road pricing, parking policies, traffic management, etc. For this reason, the effects on GHG emissions of those investments are considered to be moderate. This also counts for the effects on the level of congestion. In some cases investments in non-road infrastructure capacity can result in an increase in GHG emissions. This is the case when the shift away from road (or aviation) is small and the shift from other modes (cycling, walking, other public transport) is dominant. So, in some cases this can be expected to be a win-win policy, reducing both GHG emissions and congestion, while in other cases some congestion reduction is achieved, but an GHG effects are zero or even negative.

4.4.2 New road infrastructure

Sometimes it is claimed that extending motorways by adding lanes will reduce GHG emissions. An argument for this assumption is the lower fuel use by cars, because there is less stop-and-go traffic. Williams-Derry (2007) states this can indeed be the case in the 5 to 10 years after building extra capacity. On the long term emissions will increase. According to Williams-Derry one mile of new motorway lane will increase CO2 emissions by more than 100,000 tons over 50 years. This number of CO2 emissions can be split up in the following factors: • CO2 related to construction and maintenance of the road. The emissions related to

manufacturing of materials and the emissions related to fuel consumption during construction work are taken into account here.

• CO2 reduction due to a better flow through: by adding an extra lane to an existing motorway emissions are saved especially in the first decade after opening the new lane.

• Besides the CO2 reduction due to congestion relief an extra lane will also induce extra trips. Williams-Derry refers to a recent California study, which has estimated that more than roughly 90 percent of new lane capacity in congested urban areas is filled within five years after a project has been completed. Similar effects of induced traffic are found by other studies.

• A new lane added to a motorway not only induces traffic on the motorway itself, but also off the motorway. Emissions caused by driving from and to the motorway must also be taken into account. Adding a lane can also contribute to low-density sprawling development. This kind of land use stimulates the use of a car. (Williams-Derry, 2007)

This is fully in line with the conclusions from section 2.3.3 and 3.2.1. Overall, it can be concluded that additional road infrastructure will result on the long run result in a substantial increase of GHG emissions. Therefore it can be labelled a win-loose policy, which reduces congestion but leads to an increase in GHG emissions. An exception to this can be the investments in bus and coach infrastructure (terminals, dedicated bus/coach lanes, etc.). In those cases, GHG reductions could be achieved, depending on the amount of true modal shift achieved.



4.4.3 Urban planning – spatial planning

The GHG emission savings as result of infrastructure investments and spatial planning should come from shorter distances between different functions (a reduction in vehicle kilometres) and a modal shift to low-carbon (and possibly slower) modes. However, urban and spatial planning are mostly focused on other policy areas, like accessibility and social aspects, rather than reducing GHG emissions. It was estimated that about 5% GHG emissions can be saved by making cities more compact, although no evidence is provided. (Kampman et al. 2009) In the EU there is a strong relation between urban sprawl and transport-related GHG emissions. However, it is not very clear to what extent urban planning policy can reach its objectives of reducing the vehicle kilometres and/or making cities more compact. Examples of policy are the regulation of building densities and the stimulation of multi-level buildings. Overall, the impacts on reducing GHG emissions and on congestion occur only on the long term. Both are expected to be positive (on the long term). (Kampman et al., 2009)

4.4.4 Parking policy

The attractiveness of the car depends besides travel time also on marginal usage costs. Parking policy can make the car less attractive by high parking fees and by a limited amount of parking spaces in an (urban) area. Long waiting times for parking licences can influence the level of car ownership in a city. Park and ride facilities may be created at the borders of cities to keep the traffic out of the city centre. Those P+R facilities will result in lower GHG emissions, because the distance to the centre will probably be covered by low carbon modes. Parking policy within a city can also have effect on traffic outside the city, because parking circumstances in a city can be an important factor to take into account in choosing a mode. Due to parking problems people might choose public transport instead of the car. Although these factors seem to reduce GHG emissions, it must also be taken into account those parking policies can result in traffic searching for an available/free parking space. This searching will result in an increase in GHG emissions. A counterproductive effect can also be reached in case people and companies are moving out of the city due to a lack of parking places. The effects on congestion are similar to the effects on GHG emissions: congestion is reduced in case trips and vehicle kilometres are reduced, but congestion can increase by ‘searching’ traffic. A targeted parking policy, in combination with other urban policies can have positive effects on both GHG and congestion reduction. In most cases this is expected to be a win-win policy.

4.4.5 Policies for advanced distribution concepts

By the way the logistical chain of freight transport is organized nowadays a high number of trips is needed to deliver goods in stores. Urban consolidation centres can be used to reduce GHG emissions. Those locations are located at the border of a city. The last part of distribution can be done by low carbon vehicles (for example on CNG or electricity). In this way GHG emissions can be saved, but also because the amount of trips in a city centre will be reduced. The reduction of trips will also decrease the level of congestion in a city centre. Therefore this is a win-win policy.



4.5 Speed and traffic management

4.5.1 Favouring less GHG intensive transport modes in traffic management

The use of less GHG intensive transport modes can be stimulated by favouring those transport modes in traffic management. Lanes, especially reserved for buses are a good example. By this kind of instruments travelling by less GHG intensive transport modes becomes more attractive and will result in a higher share of GHG intensive transport modes in the total fleet. Because there will be a shift to more fuel efficient vehicles GHG emissions will be saved. In addition, a shift to non-car transport can result in congestion reduction. Therefore, it can be regarded as a win-win policy, but only effective in combination with other urban policies.

4.5.2 Advanced traffic management systems

Traffic management systems will enable a better use of existing infrastructure. Congestion reduction and better route planning as result of traffic management systems result in GHG reduction on the one hand, because the number of vehicle kilometres is reduced and speeds are more homogenous and optimised. According to (Klunder et al, 2009) in (Van Essen et al. 2009) the GHG reduction potential is 15-25% for road transport. On the other hand, by this better utilization of existing infrastructure roads can become more attractive and may increase the amount of vehicle kilometres. (Kampman et al., 2009) Overall, we conclude that advanced traffic management systems have the potential of contributing to congestion reduction with also GHG emissions reduction potential, particularly from more fuel efficient vehicle use.

4.5.3 Overall lowering of speed limits

As already concluded from Figure 2.1 the optimal speed of a car in the light of CO2 emissions is a constant speed of about 80 km/h. Lowering motorway speed limits to levels in the range of 80 to 100 km/h will result in GHG emission savings due to more fuel efficient driving. In case this is a reduction of the maximum speed limit, there is in addition a volume effect resulting in additional GHG reduction. The congestion effect of speed limits on motorways will be positive when the limit results in a more constant speed closer to the optimum level for road capacity (about 90 km/h). In other cases, the impacts on congestion can be either positive or negative depending on the local traffic situation. Overall, we conclude that lower speed limits on motorways in the range of 90 to 100 km/h can be a win-win policy for reducing both congestion and GHG emissions (as long as they are also strictly enforced). Other types of lower speed limits generally have positive impacts on GHG emissions, but the congestion effects depend very much on local circumstances.



4.6 Information and communication

4.6.1 Eco-driving programs for individuals and organisations

The aim of eco-driving programmes is to teach car drivers a driving style, which is environmental friendly, safe, without stress and fuel efficient. According to calculation of Vermeulen (2006) for EU-15, eco-driving could reduce GHG emissions by 1.8 Mtonne/y in 2012, increasing to 5.5 Mtonne/y in 2020 if eco-driving is included in the lessons for new drivers. (Brannigan et al., 2009) Because eco-driving will result in more fuel efficient driving, marginal user costs will drop and will result in more vehicle kilometres. At the same time, fuel efficient driving results in more constant traffic as drivers better anticipate and avoid abrupt braking or fast acceleration. This has a decreasing effect on congestion. The net effect depends on the ration between the two and is yet unclear. Effects on GHG emissions as well as on the level of congestion depend on the effectiveness of the training and the duration of the effects. Overall, eco-driving programs have positive GHG effects but unclear net congestion effects.

4.7 Other instruments to stimulate innovation and development

4.7.1 Green public procurement

According to Brannigan et al. (2009) green public procurement (GPP) refers to the procurement process additional environmental criteria are used by contracting authorities when deciding whom to purchase goods or services from. By green public procurement new technologies can be stimulated. Due to an increase in demand unit costs may drop. Due to the limited part of the fleet which is involved in procurement processes the effects on GHG emissions will be limited, but positive. The effects on congestion will be negligible.

4.7.2 Fleet tests and demonstration programmes

Fleet tests and demonstration programmes play a significant role in the Research and Development cycle. It is important to learn from experiences with new technologies under real-world conditions. Fleet test and demonstration programmes can be stimulated by policy instruments in order to accelerate the uptake of new technologies. Due to the limited scale of fleet tests and demonstration programmes the effects on GHG emissions will be limited, but positive. The effects on congestion will be negligible.

4.8 Conclusions

In Table 4.1 an overview is given of the different policy instruments which have been discussed in the previous sections. The table includes scores for the effects on GHG emissions as well as on the effects on congestion.



Table 4.2: Overview of policy instruments*

Policy instruments Effect on GHG emissions

Effect on congestion

Win-win and win-neutral policies

Fuel tax/ETS +++ +

CO2 regulation for road vehicles +++ +/-

CO2 regulation for non-road vehicles +++ ?

Differentiated kilometre charging ++ +++

Flat kilometre charges ++ ++

(lower) speed limits (strictly enforced) ++ +

Congestion charges + +++

Favouring less GHG intensive transport modes in

traffic management

+ +

Policies for advanced distribution concepts + +

Urban planning/spatial planning + +

Parking policy + +

Advanced traffic management systems + +

Energy and CO2-labelling of road vehicles + 0

Green public procurement + 0

Fleet tests and demonstration programmes + 0

Eco-driving for individuals and organisations + ?

New non-road infrastructure ++/- +

Loose-win policies

New road infrastructure --- ++++

Rewarding congestion avoidance - ++

*The amount of + and – indicate the magnitude of the effect, 0 indicates there is no effect, ? mans that the effect is unclear.

Looking at the different policy instruments and their effects on GHG emissions as well as on the level of congestion the following conclusions can be drawn. Some policies clearly contribute to both objectives, while a broad range of others have a positive impact on GHG emissions but a zero or unclear effect on congestion. A few policies result in less congestion but a clear increase in GHG emissions. There were no policies found with a strong GHG reduction potential but a clear adverse effect on road congestion. Win-win policies From the overview of policy instruments it becomes clear that most economic instruments (road pricing, fuel taxes, congestion charges, etc.) can be labelled as win-win policies. These instruments both contribute to the reduction of GHG emissions and the reduction of the level of congestion. Vehicle regulation is very effective in reducing in reducing GHG emissions, but the congestion effects are expected to be small or are unknown.



The differences in the reduction potential of various instruments can mostly be explained by the main target of the policy instrument. In case of congestion, the highest reduction can be reached by policy instruments targeted at the reduction of vehicle kilometres at a specific time and specific place. Policy instruments, which are focused on reducing fuel use, will have more effect on GHG emissions than on congestion. Investments in non-road infrastructure can result in some reduction of congestion, the GHG effect depends strongly on the specific case. When a true shift is achieved, GHG emission reduction can be significant, but when most of the traffic on the new infrastructure comes from other (non-road or -aviation) modes, the GHG impact can be neutral or sometimes even negative. Win-loose policies The policy instruments which are aimed at expanding road capacity or at expanding the total capacity of the transport system can be identified as loose-win policies. Although expanding capacity can result in GHG reduction on the short term, those instrument will induce traffic on the long term when no additional policies, like road pricing, are used. How to integrate this in policy decision making processes? In policy making processes, various objectives have to be taken into account. GHG reduction and congestion reduction are only two of them, among others like improving accessibility, stimulating economic activity, reducing noise, air pollution or accidents, etc. To take account of the various objectives, Verdaas, a politician in the Netherlands proposed a certain order in the various policy instruments to solve traffic related problems. This approach, known as the Staircase of Verdaas, is often referred to and applied in traffic policy making in The Netherlands, particularly at local and regional level.

Staircase of Verdaas

Co Verdaas, a Dutch politician, has designed a systematic way of assessing possible solutions for traffic related problems. This system is often called the staircase of Verdaas, because it mentions which kind of policy instruments should be considered first before choosing for the provision of new infrastructure. The seven steps of the staircase are:

Step 1. Spatial planning Step 2. Pricing policies Step 3. Mobility management Step 4. Public transport Step 5. Utilization / making better use of existing infrastructure Step 6. Adjusting existing infrastructure Step 7. New infrastructure

Following this staircase, is an useful approach fro solving traffic related problems (such as congestion), in alternative ways than increasing road capacity. Such an check-list may be useful for transport policy in the light of decarbonisation targets.



5 Conclusions and policy recommendations

5.1 Conclusions on the relation between link between congestion and GHG reduction

Congestion influences GHG emissions of transport in two ways: changes in the total amount of vehicle kilometres as well as changes in the GHG emissions per kilometre driven. Both effects are related to the impact congestion has on the traffic speed, in particular on the average speed of the traffic and the speed dynamics (changes in speed because of braking and accelerating). The effect of solving severe congestion (stop and go traffic) often results in a lower average emission factors per kilometre driven. This is generally true for urban roads and secondary roads. However congestion levels on motorways resulting in average traffic speeds in the range of 25 to 70 km/h do not result in higher emission factors compared to a free flow situation (and speed limit of 120 km/h or more), they can sometimes even be lower than in the case of free flow traffic. The second effect is related to transport demand impacts of congestion. Congestion results in longer travel times for road transport. This stimulates users to shift to alternative modes, with usually longer travel times. On the long term it also affects people’s choices of where to live where to work, etc. Therefore, according to the law of constant travel time budget, road congestion reduces the amount of vehicle kilometres travelled and potentially some modal shift to other (usually from a door-to-door perspective slower) transport modes. The sum of both effects of congestion reduction depends on local circumstances. On motorways, although the emissions per kilometre during congestion on motorways can in some cases be higher than without congestion, this is never the case for the emissions per minute travelled. Therefore, when the rule of constant travel time budget applies, the reduction of congestion on motorways generally leads to an increase in transport emissions. The effect of the increase in demand is apparently much stronger than the potentially saved emissions from the fuel efficiency improvements at vehicle level. This means that congestion reduction can only lead to lower emissions when there is a shift to travel types with lower emissions per minute travelled. Examples are:

A shift from road transport to non-motorized transport modes.

A shift from moderate congestion (category C) to rail transport. There may be some exceptions to these conclusions. First of all, in the case of freight transport, the relationship between transport speed and transport demand may be different than for passenger transport. However, also for freight transport an increase in travel speed will lead to a decrease in transport costs and so in an increase in transport demand. A second remark to be made is that the rule of constant travel time budget is an empirical law for the entire transport system. Therefore, local changes may on a local level and at short time horizons result in changes in travel times. However, at the long term and at a aggregated level, there is strong empirical evidence that travel time budgets are rather constant.



5.2 Conclusions on impacts of policy instruments

From the different policy instruments and their impacts as described in this paper it becomes clear that policy instruments aimed at reducing GHG emissions and/or congestion can have significant impacts on the other policy objective. Some policies were identified that clearly contribute to both objectives, while a broad range of others have a positive impact on GHG emissions but a zero or unclear effect on congestion. A few policies result in less congestion but a clear increase in GHG emissions. There were no policies found with a strong GHG reduction potential but a clear adverse effect on road congestion. Win-neutral policies Many policy instruments primarily aimed at reducing GHG emissions by pushing low-carbon technology do not or only slightly affect road congestion and thus can be labelled as win-neutral policies. Policy instruments like CO2 standards for vehicles may result in an increase of vehicle kilometres due to the reduction of marginal user costs by a lower fuel use, but because some of those instruments also result in higher purchase costs and in more constant speeds, the effects on congestion are limited. Another reason why the impact of GHG emission reduction instruments on road congestion is limited is because most of those instruments are not bound to a specific time and place. Win-loose policies On the other hand, policy instruments aimed at reducing congestion often have the potential to increase GHG emissions. Those instruments are mostly related to capacity expansion of infrastructure by building new roads or by making better use of existing infrastructure. Short-term as well as long-term effects should be taken into account here. Looking at the short-term, effects may show an emission reduction. However, where taxes and charges discourage car use, expansion of infrastructure will make car use more attractive on the long term due to the higher average speed as a result of reduced congestion. If no additional instruments are taken to discourage car use, those kind of instruments will result in an increase of GHG emissions. Therefore most policy instruments primarily aimed at reducing congestion can be labelled as win-loose policies. Win-win policies The win-win policies, which have a significant effect on both reduction of GHG emissions as well as on the reduction of congestion are mainly economic instruments. Taxes and charges will make car driving less attractive due to an increase in marginal user costs and thus will reduce car use. In case of differentiated charges the effects on GHG emissions and congestions depend on the chosen differentiation. By making car use less attractive economic instruments can help to avoid the rebound effect of policy instruments which in first instance make car use more attractive. Economic instruments can also be used as instruments additional to GHG reduction policy instruments which are aimed at a modal shift to slower transport modes and at less car use. Without making car use less attractive the GHG emission reduction will be limited. Besides the economic win-win policies there are no examples of policy instruments aimed at congestion reduction, which result in a high reduction of GHG emissions. Where policy instruments on GHG emission reduction are not bound to a specific place and time (like CO2-regulation), policy instruments aimed at reducing congestion are often targeted at a specific bottleneck and thus to a specific time and place. Due to this, only a small part of the vehicle fleet will be affected by these policy measures.



Policy implications Overall, it can be concluded that the risk of adverse effects should be taken into account in the decision making process. Because, congestion reduction policy instruments might significantly increase GHG emissions, the focus should therefore be on policy instruments related to the reduction of congestion. In case infrastructure expansion is considered as an option, it should be investigated to what extent the same congestion reduction can be reached without an increase in GHG emissions. As stated earlier, pricing instruments are a better alternative from this perspective and therefore should be tried first. This conclusion is also in line with the Staircase of Verdaas, where price policies are the second best option and new infrastructure is seen as the last option to consider.



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