University of Groningen

Capacity Building for Sustainable Transport. Optimising the energy use of traffic andinfrastructureLensink, Sander

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6 Policy perspectives on infrastructure improvements

6.1 Introduction The optimising CONCRETE model for a network infrastructure has been established in chapter five. Chapter six starts with the results of the CONCRETE model. In section 6.2, it transforms the abstract results into implications for general transport policy aimed at minimising energy use, or at transitions towards a (more) sustainable transport. From current policy initiatives, a theoretical case is deduced. The case results are again interpreted, but into implications for more specific transport policy choices. The significance of the implications mentioned in this chapter is assessed under future conditions of transport and the environment. Finally, the policy comments of the model are discussed83 through application of the model framework in a socio-economic context in section 6.3. It aims to show that the theory not only applies to energy minimisation, but also to the optimisation of societal benefits of infrastructure. A comparison of results of both applications ends the chapter.

6.2 Policy issues

6.2.1 Introduction

The theory developed can assist both in formulating the “grand policy schemes” of transport (e.g. [MinV&W, 2005]) and in supplying additional criteria for choosing road construction alternatives. Figure 6.1 shows a categorisation of various construction strategies, in which the suggestion is raised that environmental objectives and transport objectives contain independent optimisation targets.

environment

transport

"use congestion as pusher of environmentally-friendly alternatives of

road transport"

"build new roads only at congested intersections or at missing link sites"

"enable a better use of current infrastructure, e.g. by creating additional

lanes on existing asphalt roadway surface"

"eliminate all congestion as transport is key driver for economic growth and

welfare"

Figure 6.1 Author’s paraphrases of road construction strategies, characterising different worldviews on transport and the environment. The thesis does not directly compare the construction strategies, thus the location of the strategies in the diagram by no means implies that one strategy is more effective on environmental targets or transport targets than any other strategy. For a general remark on presentations in a 2x2 matrix, see [Schenk et al., 2005].

Section 6.2.2 considers the potential instability in the transition path of the road network, caused by energy minimising policy-targets and travel-time minimising traveller-targets. Section 6.2.3 applies the CONCRETE model to theoretical case, representing city ring-road networks. The section interprets the case results and

83 It is noted that the discussions of the first chapters served to comment on the chapters’ conclusions in order to explain the foundations of the thesis. The discussions in chapter six comment on the results, whereafter the conclusions can draw more finalising concluding remarks. Therefore, the discussions in chapter six precede the conclusions.

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subsequently compares them with actual policy-making. Section 6.2.4 discusses the view on transition paths by considering time-dependencies in the input variables regarding the time discounting and the transport demand.

6.2.2 Construction strategies

6.2.2.1 Default scenario: only capacity improvement, invariable traffic flows

The potential instability of the transition path is examined with the network configuration that is used in chapter five and repeated in figure 6.2. The network validation in the latter chapter shows results regarding the final capacities of the network after an ‘improvement-only’ strategy. This chapter offers a different view on the CONCRETE model outcome by showing the robustness of that outcome, with reference to several system-dynamical phenomena. The CONCRETE model in this section is made out of the road network of figure 6.2, and of the road users that minimise their travel times. The road construction strategy to control the system aims to minimise the systems energy use under the condition of unaltered traffic. Several construction strategies are applied to the system that are explained and commented in the next sections. The strategy leads to equal i/c-ratios on all roads, as far as construction energies permit. Figure 6.3 shows that the capacities of the roads match the respective intensities (equal i/c-ratios), apart from the roads that cease to function at high network capacities. Strategy 1 is the base case presented in chapter five.

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Network capacity improvement x (vkm/h)

i/c-r

atio

4→62→41→3→62→5→61→4

Figure 6.3 Tendency of the system to move towards equal i/c-ratios on all non-abandoned roads. From 100 000 vkm/h of network improvement onwards, footnote 82 applies.

Figure 6.2 Repeat of figure 5.8 of the network configuration modelled. The traffic flows from origins A1, A2 to destination B. From A1, 4000 veh/h choose between routes 1→3→6 and 1→4→6. From A2, 6000 veh/h choose between route 2→4→6 and 2→5→6.

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6.2.2.2 Variable traffic flows leading to transition inefficiencies The enabling of variable traffic flows implies that the improvement of road capacities can lead to road users changing their routes. In essence, two systems with different aims interact leading to transition inefficiencies. Figure 6.4 shows the traffic shifting towards the rural route 1→3→6 temporarily, while ultimately the route is not used. It clearly deviates from the optimal control path, as inefficiency appears in the transition.

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Tra

ffic

inte

nsity

(veh

/h)

4→62→41→41→3→62→5→6

optimal network state

Figure 6.4 Transition inefficiencies. The rural route 1→3→6 is temporarily improved, although finally hardly used.

6.2.2.3 Reduction of capacity leading to lock-in effect The construction costs are estimated at 0.01 TJ·h/vkm in section 3.5.5, and the destruction costs at 0.001 TJ·h/vkm.84 It is assumed that the destruction costs equal the construction costs minus the embodied energy in the materials. The choice between construction and destruction is made on the basis of the difference between the marginal energy benefits γ(x) and the construction/destruction costs α. The difference is shown in figure 6.5.

84 The energy intensity of capacity building is estimated in section 3.3.2.2 at αconstruction=0.01 TJ·h/vkm. Important in the estimation is the ratio of energy required for motorway construction and maintenance of: production (18%), asphalt maintenance (24%), asphalt construction (28%) and sand subbase (18%); see page 135 in [Bos, 1998]. One can approximate an upper bound of the energy intensity of capacity reduction by only taking the production energy of capacity building into account.

( )

h/vkmTJ 004.0 is boundupper The24.01

28.0 Thus

⋅≤

⋅−

≤⇒≤

ndestructio

onconstructindestructioenergyproductiononconstructindestructio

α

αααα

Now, as not all construction elements have to be removed, and as some construction elements will be recycled, it is an upper bound (see the accounting of recycling energy section 2.4.3.1). For arguments sake, the final estimation is αdestruction=0.001 TJ·h/vkm.

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600000 620000 640000 660000 680000 700000 720000 740000

Network capacity x (vkm/h)

Net

ene

rgy

bene

fit

γ(x)

- α (M

J·h/

vkm

)

initial capacity

Figure 6.5 Progression of the net energy benefit, including the option of reduction of network capacity. The temporarily rising net energy benefits while reducing network capacity are visible between the 715 thousand and 705 thousand vkm/h. The rising in time of the net energy benefits implies that lock-in situations can occur. The spikes in the graph result from computer failures to create a smooth utility function explained in section 3.5.1.

As shown in figure 6.5, the network capacity is improved until a level of almost 720 000 vkm/h, after which a reduction becomes more beneficial. Figure 6.6 shows the developments of the individual road capacities in time.

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et e

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y be

nefit

s (M

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A

Figure 6.6 Development of road capacities in time, as the network evolves towards the energy optimum. The horizontal axis denotes the time, but as long as the construction speed is unknown, the axis scale remains arbitrary. Essential in the figure is the rise in marginal net energy benefits as the road capacity of route 1→4 declines rapidly.

At a late moment in the transition towards optimal capacity, i.e. when the network capacity approaches its optimum, it becomes energetically beneficial to reduce the capacity of the road 1→4. Reduction of road capacity temporarily increases the

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marginal energy benefits, as is shown by the (in time) rising line ‘energy effects’ in figure 6.6. The latter behaviour implies that a lock-in might occur. The lock-in occurs if the marginal energy benefit just before capacity reduction (corresponding to the encircled point A) is lower than the marginal energy costs due to the construction works. In other words, as soon as the graph “energy effects” drops below the value zero, no further construction activities are undertaken, even though persistent capacity reduction could still prove beneficial.

6.2.2.4 Reduction of capacity leading to bifurcation Figure 6.7 shows clearly that the option to reduce road capacity leads to a bifurcation of end results. Either all the traffic uses route 1→4 or (almost) none of the traffic uses the route. No attention is warranted for the small amount of traffic residing on the route 1→4 in the end of the model results: first, the theory does not anticipate in total removal of capacity; second and more importantly, the optimisation algorithm uses construction increment steps of 500 vkm/h. The step size leads to a significant uncertainty if the capacities are already small. In other words, the computational steps are no longer sufficiently small to regard the computation results as continuous.

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ffic

inte

nsity

y (v

eh/h

)

1→4 (excl)1→3→6 (incl)1→3→6 (excl)1→4 (incl)

start of bifurcation

Figure 6.7 Traffic intensities in the concurring routes 1→4 and 1→3→6 in two network development schemes: (incl) indicates the scheme in which capacity reduction is optional; (excl) indicates the scheme in which the capacities can only be increased. The bifurcation starts halfway the graph.

Interesting in the figure 6.7 is the bifurcation in the middle of the graph. In figure 6.8, the developments in road capacities are shown. Clearly shown in the difference of both solid lines is that temporary delay of reduction of capacity on the route 1→4, leads to permanent delay, as the traffic flows keep shifting. The conclusion is that the transition path of the network contains a potential breakpoint in the development, when the net energy benefits of reduction of one road segment and net energy benefits of improvement of another road segment are almost equal. The choice between reduction and improvement at that moment determines the final configuration of the network.

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7000

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Cap

acity

x (v

eh/h

)

1→4 (excl)1→3→6 (incl)1→3→6 (excl)1→4 (incl)

Figure 6.8 Traffic intensities in the concurring routes 1→4 and 1→3→6 in two network development schemes (cf. figure 6.7).

6.2.3 A case of urban bypasses

It is compelling to apply the CONCRETE model to a specific, future infrastructural project. It is clear, however, that the scale of a single project challenges the boundary condition of the CONCRETE model that all changes in infrastructure capacity are to be regarded as continuous changes. The validity of the CONCRETE model outcome in the latter application is therefore limited. Under the condition of careful discussion, it is still justified to acquire insight into the system-dynamics by the application. A quick scan of published or intended environmental assessment statements of road construction projects85 shows several corridor studies (e.a. A2 Amsterdam-Utrecht and A6/A1 Almere-Amsterdam), some regional road studies, and a couple of by-pass studies. Chapters three to five comment on capacity improvements of road corridors. Regional roads often contain irregularities as they can be partly located within city centres. The by-passes are chosen for further assessment in this section, as their case provide choices regarding the upgrading of existing road segments or the constructing of parallel road segments. This section shows the application of the CONCRETE model on a case that is based on the southern part of the Groningen ring road. Furthermore, the road network in the latter case is too streamlined to draw any conclusion for the Groningen road network development from this model run only. The problem in the present-day road network focuses on the congestion on the south part of the Groningen ring road. Several of the options considered include the construction of a bypass parallel to the southern segment of the ring road. Figure 6.9 shows the road network schematically. The origin-destination matrix is shown in table 6.1.

85 See e.g. the website of the Netherlands’ Commission for Environmental Impact Statement for an overview on www.eia.nl.

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Ring road(West)

Highway 7(Drachten)

Highway 28(Assen)

Ring road(East)

Highway 7(Hoogezand)

Segment A7.WSegmentRing road(South 1)

SegmentRing road(South 2)

SegmentRing road

(Southeast)

City centre(west)

City SouthCity South-west

Segment“Euvelgunne”Segment A7.E

Southernbypass

City centre(east)

Segment A28

Figure 6.9 Schematic view of the CONCRETE model, based on the Groningen southern ring network. The circles denote the origins and destinations of the traffic; the rectangles are the road segments that are included within the CONCRETE model. The thick lines are high capacity roads that are part of the ring road or part of the national highway system. The dotted lines are to two road segments that are proposed to be constructed: the ‘Euvelgunne’ trajectory, and the southern bypass [MinV&W, 1998b].

Table 6.1 Origin-destination matrix for the Groningen city by-pass case. The data is presented in vehicles per hour per origin-destination pair (as reproduced in [Buurtenoverleg Zuidelijke Ringweg, 1999]).

from

Drachten Ring road City SW Assen Centre W City S Centre E Ring

road Hoogezand to

(A7) (West) (A28) (East) (A7)

A7 West 0 316 344 83 69 110 275 96 83 Ring W 316 0 588 283 43 128 71 57 71 City SW 344 453 0 0 25 63 50 88 88 A28 83 283 0 0 292 234 263 160 146 Centre W 69 43 25 292 0 0 25 80 33 City S 110 128 63 234 0 0 263 183 65 Centre E 275 71 50 263 25 263 0 229 73 Ring E 96 57 88 160 80 183 229 0 252 A7 East 83 71 88 146 33 65 73 252 0 The CONCRETE model is run in four scenarios. The scenarios A1 and A2 suppose isotropic conditions in so far the energy intensities for all road segments are equal. The scenarios B1 and B2 suppose that the existing southern segment of the ring road can only be improved with energy-intensive constructions. Scenario A1: The energy intensity for construction is for all segments α=10 000 MJ·h/vkm. The road network is modified in three steps. The first step optimises the road capacities of the present-day network (i.e. balancing the i/c-ratios).

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The second step uses the results of the first step, and optimises the road capacities after construction of the “Euvelgunne” trajectory. The third step starts with the capacities as computed by step 2 and optimises the road capacities after construction of the southern bypass. Scenario A2: The energy costs are again for all segments α=10 000 MJ·h/vkm. The network consists of all present and planned road segments, and is optimised all at once. Scenario B1: The construction costs are for segments “Ring road south 1” and “Ring road south 2” set at α=100 000 MJ·h/vkm, for all other segments α=10 000 MJ·h/vkm. It implies that the capacity on the southern ring road can only be improved by constructing energy-intensive elements like tunnels or elevated roads. The road network is balanced in three steps, as in scenario A1. Scenario B2: The construction costs are the same as in scenario B1, while the balancing is performed simultaneously, as in scenario A2. A final run, scenario C2, in which the construction costs of the bypass are α=100 000 MJ·h/vkm reveal that a bypass requiring large energy quantities should not be built, regardless of the construction costs of the southern ring road. Interestingly though, the bypass will not likely be constructed at ground level, but in a ‘open tunnel’. The deeper placement has less impact on the surroundings, especially regarding noise and aesthetics [MinV&W, 1998b]. Noise is prevented and aesthetics are preserved on the count of energy use. The results of the optimisation runs in the four scenarios are shown in table 6.2, and presented as optimal road capacities measured in veh/h (thus segment capacities divided by segment length).

Table 6.2 Optimal road capacities (veh/h) in the five scenarios. C2 results are shown by approximation due to model limitations reported in section 5.4.

Scenario A1 A2 B1 B2 C2

Energy intensity α South 1,2 0.01 0.10 0.01 (TJ·h/vkm) Bypass 0.01 0.01 0.10

Traffic counts (2002) Capacity Road segment (veh/h) (veh/h)

A7.W 2542 2243

A28 2471 1900

South 1 2892 2243 2386 1529 1100 2957

South 2 3263 2177 2331 1485 1023 2792 Southeast 1554 1050 950 1050 950 1300

A7.E 1433 200 200 200 200 800

Euvelgunne - 544 544 544 544 322

Bypass - 759 759 759 759 0

Five qualitative conclusions can be drawn: 1) Traffic uses in every scenario the southern ring road more than the bypass. 2) In the case of equal improvement costs, the capacity of the southern ring road together with the capacity on the bypass is fairly constant: capacity improvement on the bypass is paralleled by capacity reduction on the ring road.

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3) High energy requirements for improvement roughly half the optimal capacity. 4) In the case of equal expansion costs, it does not matter significantly in what order the roads are expanded. 5) In the case of high destruction costs, it does matters significantly in what order the road capacities are modified. The validity of the CONCRETE model is limited: i. The bottlenecks on this scale are caused by traffic lights and weaving sections, and not by i/c-ratios86; ii. Most traffic does not have an alternative route for the journey; large i/c-ratios will therefore create far worse energy inefficiencies than the CONCRETE model predicts; iii. The case puts a meso-scale project in a macro-scale model, by which continuity assumptions are violated. Although the CONCRETE model cannot prove the energetically best construction strategy for the transport problems of the city of Groningen, it does seem to suggest: If the southern ring road or bypass is constructed as elevated or deepened roads, it is likely that the energy requirements of the road construction will outweigh the energy benefits of congestion reduction; energetically, it seems better to build the bypass as a single carriage motorway, since its traffic will be less than a third of traffic on the supplying motorways A28 and A7. However, both remarks can only be proven in a more detailed and case-specific energy analysis.

6.2.4 Discussion

6.2.4.1 Future changes in transport The CONCRETE model is, through the CEMENT modules, founded on the methodology presented in chapter three. The stationary rest point where the marginal energy benefits equal the marginal energy costs, typically presented in the thesis in the notation γ(x)=α, exists by virtue of a time-independent transport demand Y(t)≡Y. Furthermore, this section does not consider the transport demand equal to the traffic: y<Y. Figure 3.5 illustrates that the traffic on the Netherlands’ highways is still rising. As the rise of traffic, shown in figure 3.5, is considerably larger than the improvement of available capacity, shown in figure 3.3, it is assumed that the latent transport being effectuated87 by means of road construction is not accountable for the entire growth in traffic. As the transport demand does depend explicitly on time, the systems

86 Ad i. Setting the delay time for the intersection of highway 7 with highway 28 (“Julianaplein”) arbitrarily at 5 minutes (i.e. every car encounters a delay of 5 minutes at the intersection) does not alter the conclusions of this section. 87 Other indications for an autonomous growth in transport demand can be deduced from the trends in population size, trends in vehicle ownership and trends in individual travel patterns; see figure below that uses CBS figures.

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1990 1995 2000

Year

Inde

x (1

990=

100)

Population (p)

Vehicle ownership(veh/p)Vehicle mileage (km/veh)

Personal mobility (km/p)

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behaviour is not described by equations (3.22) to (3.25), and thus not by figure 3.16 and figure 3.17. However, theorem 2) of appendix 3.8.1 is still valid for problems involving a time-dependent transport demand. In case of a time-dependent transport demand, it is thus still possible to draw a vector-chart indicating the behaviour of the optimal-control system, see figure 6.10. A qualitative description of the behaviour of the system as transport demand changes reveals a reasonable but – in a strict sense – not proven optimal construction strategy. As the transport demand increases, the curve γ(x) moves towards the right. The system that originally rested in the point of γ(x)=α, will now find itself on the line Ψ=α, but slightly left of the new intersection of γ(x)=α. The latter condition forced the system to move downwards. The downward motion can be compensated by moving the system far enough to the right. In essence, as long as the maximum construction effort a is sufficiently large, it is possible to remain in the rest point γ(x(t))=α, while the transport demand increases. Therefore, the capacity added should be just enough and just in time to keep up with the rise in transport demand. Another case arises when the transport demand temporarily rises faster than the construction efforts can compensate for. Assume that the system starts in the rest point. After a certain time, the transport demand starts to rise rapidly. Finally, the transport demand stabilises at a constant level. Two construction strategies are possible, given the assumption that the capacity cannot be improved as fast as is needed to minimise the energy use: 1. Reactive policy: lag behind the rise in transport demand. The capacity improvement does not fully compensate the rise in transport demand. The system that started in Ψ=α will, cf. figure 6.10, move downwards as the rest point is out of reach. The downward motion of the system cannot be compensated. It is inevitable that condition 3) of theorem 2) will be violated, i.e. the shadow price will become negative Ψ<0, which is not admissible. Lagging behind is therefore not optimal. 2. Proactive policy: anticipate future rises in transport demand. The capacity is now improved although the current traffic of the system does not yet warrant it. The shadow prices will now start to rise slightly, as the system finds itself on the right of the rest point. Later on, the fast moving changes in the curve γ(x) will overtake the system, after which the system will move as fast as possible towards the final rest point. Such a control path is admissible, but not proven to be optimal. Such construction strategy implies that one would need to reason backwards in time. Starting from the end condition, one determines the earliest moment from which on the rises in transport volume can be compensated by the construction effort. From that point backwards, the maximum construction effort should be applied until the rest point is found again. Anticipating future transport demand changes is therefore probably better than lagging behind.

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ow p

rice

s Ψ (M

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a/δ

g(x)

optimal path

optimal path

W -- W -+

V++

V+-

V+-

V -+

Figure 6.10 The so-called Hamiltonian system that was presented and explained before in figure 3.17. While figure 3.17 was used as a time independent behaviour of the system, in this section the figure shows only the behaviour of the system in a specific moment in time.

The discount function presented in chapter three was ∆(t)=e-ρt. It enables the analytical reformulation of time-dependent theorem 1) into a time-independent theorem 2) of appendix 3.8.1. It can be argued that the global transition towards sustainable development suggest the use of a more complex discount function. Figure 6.11 shows the appearance and consequences of this transitional discount function that is formulated on ground of allowed greenhouse gas emissions. It is furthermore a factor that is associated with uncertainty in sustainable energy use.

6.2.4.2 Future changes in environmental conditions Theoretically, assuming that the transport sector will be fully dependent on fossil fuels throughout the 21st century, and considering that the emissions of greenhouse gases have to be reduced significantly in the same period, one can formulate a discount factor, which is based on the highest emission rate allowed. That would be of a form 1* )1( −−+=∆ tte , with t* the time at which half of the emissions should be reduced. It would be a rising discount rate. A proposed discount factor of te ρ−=∆ is based on the speed of transition to clean energy in the transport sector. The discount rate ρ would then typically be defined as the inverse of the time at which half of the transport energy is clean energy. The combination of both factors:

)1( * ttt ere −− +⋅=∆ ρ , r being the reduction rate to be achieved at time t*, would include both a reduction path for greenhouse gas emissions and a transition path to clean energy. The function still meets the requirement of:

0,10,0 allfor 0)1(limlim ** ><<>=+⋅=∆ −−

∞→∞→trere ttt

ttρρ

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0%

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Figure 6.11 Assembly of alternative discount function.

More elaborately, the assembly of the discount function in figure 6.11 starts with the curve representing the fraction of bio fuel mixed in conventional fuel. It is set at 2% in 2002 and 20% in 2020 as the European Union aims for [European Commission, 2001]. Similarly, the emission ceiling is based on the Netherlands’ Kyoto target (-6% CO2 equivalent greenhouse gas emissions in 2010) and a post-Kyoto aim (-30% in 2020). The curve of the zero-emission vehicles is determined by two parameters: the transition speed is equal to the transition speed in the curve of the Kyoto-emission ceilings, and the transition time is set at 15 years behind the Kyoto-ceilings, 15 years being the average age of the vehicle fleet. With the maximum amount of bio fuel assumed at 40% and the default, infrastructure related, discount factor added, the total discount factor has thus been established. Figure 6.11 shows a fairly constant discount factor between the 75% and 100% in the early stages of the transition process (up to 2035). The discount factor beyond 2035 is approximately represented by the curve ∆(t)=0.800·e-0.03424(t-2026.5). The curve of the simplified discount factor is shown in figure 6.12.

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Figure 6.12 Simplified discount factor. It is defined as:

⎩⎨⎧

≥<

=∆ −− 5.2026e5.20261

)( )5.2026(3424.0 tt

t t

The original optimal control problem, presented in section 3.4, is now modified. The objective function (3.21) was of the form:

( ) [ ] min),()()()(),(0

→+⋅⋅∆= ∫∞

dttxFtuttutxJ α , and with T=2026.5 it becomes (6.1):

( ) [ ] [ ] min),()(),()()(),(

0

→+⋅++⋅= ∫∫∞

−

T

tT

dttxFtuedttxFtututxJ αα ρ (6.1)

The optimal control problem for t>T is identical to the one described in chapter three,

in which the optimal capacity xopt is determined by αδρ

=∂∂

+− ),(1 tx

xF

opt . The

optimal capacity for the period t≤T is similarly determined by αδ

=∂∂

− ),(1 txxF

opt .

It should be noted that the latter determination of the optimal capacity does not define the systems behaviour in the period before 2026, but it does enable some qualitative remarking. The default values for time discounting are killing rate ρ of 80 yr-1 and road wear rate δ=6.67 yr-1, leading to a combined inverse rate of 1/(ρ+δ) or 6.15 yr. Figure 6.12 shows a combined inverse rate for the time beyond 2026 of 5.43 yr. It implies that the optimal capacity for t>2026 is smaller than the one computed according to the methods in the previous chapters. During the transition, however, the combined inverse rate is only 1/δ=6.67 yr, implying a higher optimal capacity. The latter remark can be understood by realising that a lower discount rate means that future benefits weigh more heavily against the instantaneous costs of capacity change.

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6.2.5 Conclusion

For almost all views on transport policy illustrated in figure 6.1, one should try to predict the future traffic flows as accurate as possible. The single focus on current conditions of transport and energy use leads to a risk of inefficient construction strategy, a wrong construction strategy or no construction, even though efficient, at all. One should not anticipate future transport demand more than warranted by the time required for legal procedures and construction activities themselves. Only in the case of severe congestion, energy-intensive solutions like tunnels or a deepened road can lower the overall energy use. More likely in the latter solutions, though, environmental protection harbours a potential trade-off to energy use. Finally, the introduction of low- or zero-emission vehicles does not seem to lead to a different assessment of optimal road capacities for several decades to come.

6.3 Discussion

6.3.1 Introduction to a socio-economic analysis

The theory and optimisation models are not limited to environmental analyses. With minor additions, the CEMENT module and CONCRETE model can encompass the phenomena that are typically included in a socio-economic cost-benefit analysis. The optimisation variable has in socio-economic optimisations monetary dimensions (e.g. euro) instead of MJ or ton CO2. Table 6.3 shows the phenomena included and their monetary value. The values referring to [Immers et al., 2001] are those applied in the case of the Netherlands’ highway 11. Table 6.3 Monetary values for the input parameters.

Item Value/Formula Dimension

CO2 emission trading [Berk et al., 2004] 7.7 €/ton CO2

CO2 emission factors [Kok et al., 2001] 69.3 g CO2/MJ

Road production costs [Immers et al., 2001] 1248 €·h/vkm

Energy production costs α·0.53·10-3 €·h/vkm

PRODUCTION 1.25·103 €·h/vkm

Fuel consumption costs (CO2) g(v)·0.53·10-3 €/vkm Other environmental costs [Immers et al., 2001] 0.013 €/vkm

Delay costs88 [Immers et al., 2001] (L/v)·8.17 €/vkm

VARIABLE { g(v)·0.53·10-3+0.013+(L/v)·8.17 } · y €/h

Furthermore, the time discount rate has been set at 1/15 yr-1, roughly comparable with 7% financial interest89.

6.3.2 Socio-economic analysis without induced traffic

Figure 6.13 shows the difference between the energy analysis and the socio-economic analysis. The optimal capacity in the socio-economic analysis is 35% higher than the optimal capacity in the energy analysis (3500 vkm/h versus 2700 vkm/h). The 88 [de Borger et al., 2002] report travel time costs of €7,67/h in Belgium. 89 The equation (3.7) that includes the road wear rate δ is expressed in units at time t (current value). The time discount rate ρ of equation (3.8) translates the time t values into time-zero values (constant value), see e.g. page 71 in [Sethi et al., 1981]. In the thesis, the choice whether interest is represented by ρ or δ is arbitrary, see equation (3.23). As the equation (3.7) describes the capacity of a road rather than the monetary value of the capacity of the road, the thesis replaces ρ by the interest rate.

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difference is considered significant (see sensitivity analysis of chapter five), as the socio-economic results lies at about +2σ from the energy results. If however, the construction costs would rise, due to complicated technical solutions as elevated highways or tunnels, the difference between the socio-economic optimum and the energy optimum diminishes.

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Energy analysis Socio-economic analysis

marginal energy costs α E marginal socio-economic costs α €

Figure 6.13 Marginal costs and benefits of capacity improvement. The optimal application of energy resources occurs at a capacity of 2700 vkm/h, while the optimal application of socio-economic resources corresponds to a capacity of 3500 vkm/h.

6.3.3 Socio-economic analysis with induced traffic

The socio-economic analysis is repeated including induced traffic. The percentage of transport that is liable to inducement is defined as the highest fraction of latent transport demand possible in the total transport demand. Ten runs are performed, ranging from 0% to 90% of the total transport demand that is liable to the induced traffic phenomenon. In each run, the output data is fitted to the curve

3

2

1)( cxc

ecx +⋅=γ , in which c1 c2 and c3 are the parameter values fitted using the least-squares method [Gauss, 1809], and x is the capacity. The inset of figure 6.14 illustrates the general shape of the fit. Using the fitted curves, it is possible to compute the rest point γ(x)=α analytically:

)/Ln()(

1

23 c

ccxx optopt ααγ +−=⇒=

Figure 6.15 shows the results of the socio-economic analysis of the optimal road capacity for various levels of inducement and compares it to the results of the energy analysis as presented in figure 4.34.

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no inducementinduce 0.1induce 0.2induce 0.3induce 0.4induce 0.5induce 0.6induce 0.7induce 0.8induce 0.9no inducementinduce 0.1induce 0.2induce 0.3induce 0.4induce 0.5induce 0.6induce 0.7induce 0.8induce 0.9

Figure 6.14 General shapes of the fits of the socio-economic analysis for various liabilities to inducement.

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x opt = -3353.7p + 3866.1

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xop

t (ve

h·km

/h)

Energy optimum Socio-economic optimum

194806272.000807.1 −

+=pxopt

Figure 6.15 Comparison of the optimal capacities as function of the inducement p for the energy analysis (parabolic curve) and socio-economic analysis (straight line).

The significance of figure 6.15 lies in the existence of an optimal capacity. The socio-economic optimum lies higher than the energy optimum. Even if a new road generates a lot of new traffic, it will always have an optimal socio-economic optimum. In the energy analysis, however, high levels of inducements will lead to the disappearance of an optimal capacity. It implies that road improvement never leads to reduction of energy use if the new road (capacity) generates too much traffic.

6.3.4 Uncertainty in the socio-economic analysis

The robustness of the outcome of the socio-economic analysis is checked by modifying two parameters: the discount rate (default 15 yr-1) and the CO2-emission trading price (default 7.7 €/ton CO2). Figure 6.16 shows the energy benefits curves for different parameter values.

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fit (€

·h/v

eh·k

m)

€7.7/ton€77/ton€770/ton80yr€7.7/ton€77/ton€770/ton80yr

Figure 6.16 Marginal energy benefit curves for changes in the parameter values for the discount rate and the monetary CO2 price.

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Clearly visible is the small difference between the curves relating to the expected emission trading price of € 7.7/ton CO2 and to the case of a ten times as large price for emitting CO2. The costs for emitting CO2, and thus using energy, are of limited significance in the default socio-economic analyses of infrastructure projects. The importance of travel time savings lead to larger optimal capacities than in the energy analyses, although the higher discount rate lowers it in turn. The essential difference between the environmental analysis and the socio-economic analysis is the disappearance of marginal energy benefits in the case of too large capacities, while marginal socio-economic benefits will always occur even in case of extreme capacity improvement (see figure 6.13) due to the value attributed to travel time savings. Remarkable therefore, from a theoretical point of view, is the limited influence the environmental issues have in the cost-benefit analyses. The use of the parameter values of the cost-benefit analyses in the CEMENT module show that those environmental parameters have no significant impact on the optimal capacity results. The conclusion of the latter remark seems to be at odds with the concern of society through policy-making on the environmental effects of transport. At the same time, one is also at liberty to conclude that the analysis shows that from a strict socio-economic point of view it is seldom beneficial to reduce the CO2 emissions by infrastructure construction policy.

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6.4 Conclusion The CONCRETE model positioned within a network model shows the caveats of two contradictory systems, as is demonstrated in the section 6.2.2. Policy-making that targets different objectives than those of individual travellers, might lead to several types of unwanted systems behaviour: transition inefficiencies, lock-in situations and bifurcations. The optimisation run on a fictitious city ring road (inspired by the current Groningen road congestion problems) leads to confirmation of theoretical conclusions: elevated or deepened highways, as well as tunnels, are unlikely to provide an energy benefit. These construction solutions might provide several other societal and other environmental benefits; the latter benefits will probably be at the expense of energy use, as is shown in section 6.2.3 ‘A case of urban bypasses’. Preferably, from an energy perspective, the road capacity should change just-in-time to accommodate variations in the transport demand. If transport demand changes too rapidly for the road capacity to follow, one should anticipate the rise or decline of transport demand instead of lagging behind the transport demand fluctuations. The optimal network capacity is larger at the early stages of the transition towards a more sustainable transport system than in the later stages. However, the latter conclusion is highly dependent on the speed at which the global greenhouse gas emissions are curbed and the speed at which zero-emission vehicles, and low-carbon fuels are introduced. The CEMENT module is run in a socio-economic setting also. The optimal capacities from a socio-economic perspective lie clearly above the optimal capacities from an energy perspective. Interestingly is the theoretical conclusion that the environmental parameter values as included in socio-economic cost-benefit analyses have no significant impact on the optimal capacities when used in the CEMENT module. The socio-economic cost-benefit analyses do not seem to value the environmental effects of transport as profoundly as one might conclude from the decision-making agendas on transport, as is shown in the section 6.3 ‘Discussion’. In other words, a intriguing discrepancy appears between the depth of most environmental impact statements and the value attributing to environmental damage in socio-economic cost-benefit analyses. Figure 6.17 reflects on the conclusions of this chapter by referring to several road construction strategies.

environment

Whether or not to exclude road expansion depends on the level of expected generated traffic and alternative modes are limited.

Temporary road expansion during rush-hour is win-win for environment and transport,

provided the traffic regulation is not energy intensive. transport

Only new ground-level highways lower the energy use due to congested intersections;

building missing links increases energy use.

Rush-hour traffic should be congested. For tunnels, the difference in optimal congestion

on energy and economy levels out.

Figure 6.17 Author’s paraphrased comments to the supposed views on road construction strategies that were illustrated in figure 6.1, based on the conclusion of this chapter.