Phased implementation of transport pricing for Greater Oslo

Transport Policy 13 (2006) 140–148 www.elsevier.com/locate/tranpol

Phased implementation of transport pricing for Greater Oslo* Arild Vold Institute of Transport Economics, P.O. Box 6110, Etterstad, N-0602 Oslo, Norway Available online 20 January 2006

Abstract We apply a modelling framework, RETRO, based on the land use and transport models for determination of implementation paths for marginal social cost pricing of passenger transport in Greater Oslo. Marginal social cost pricing strategies are assessed through welfare maximisation with respect to the transport instruments that are available. We have constructed implementation paths unconstrained and constrained with regard to acceptable levels of transport instruments, financial situation and equity. The implementation paths are evaluated relative to a base-case situation with continuation of today’s pricing policy. The evaluation includes a discussion of the effects on land use and transport indicators. q 2006 Elsevier Ltd. All rights reserved. Keywords: Optimisation; Marginal cost pricing; Land use; Transport model; RETRO JEL classification: R41; R48; R52

1. Introduction Since the completion of an extensive road investment package in Oslo in the 1990s (the Oslo package 1) road investments have been modest. The toll ring around Oslo city centre was implemented in February 1990 mainly for the purpose of financing the Oslo package 1, and the ring is scheduled to cease operation by 2007. However, increasing road traffic and a decreasing share of trips by public transport (PT) have stimulated political discussion about the possibility of keeping the toll ring beyond 2007. It has been suggested that a greater share of the toll ring revenues be used to finance ongoing PT investments (Oslo package 2) and to maintain and operate transport infrastructure and rolling stock. The debate reflects that efficient pricing of passenger transport generally, and marginal social cost pricing (MSCP) specifically, have become major issues in the Greater Oslo region. Greater Oslo includes the city of Oslo (capital of Norway) and the 22 surrounding municipalities of Akershus * Financial support from the European Commission (EU Fifth Framework research project MC-ICAM (DG TREN, Contract No. GRD1/2000/25475-SI2. 316057)) is gratefully acknowledged. I am also grateful to Robin Lindsey for extensive comments and to Andre´ de Palma and Stef Proost for supplementary comments. E-mail address: [email protected]

0967-070X/$ - see front matter q 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.tranpol.2005.11.009

County in the south-eastern part of Norway. Oslo encompasses an area of 454 km2 that includes 26 city districts and the city forest which covers 307 km2. Akerhus covers 4916 km2. The southern and western municipalities of Akershus County and the Oslo city are located by the Oslo fjord seaside. About 23% of the Norwegian population resides in Greater Oslo. In 1998, half a million people lived in Oslo, and 0.45 million in Akershus. It is expected that by year 2015 the population will grow by 70,000 people in Oslo and there will be a need for 40,000 new residences (Municipality Plan for Oslo, 2000). Population in Akershus is expected to increase by about 100,000 people (County Plan for Akershus, 2000). From 2002 to 2015, car ownership is forecast to increase from 400 to 477 cars/1000 inhabitants. Population growth is the main reason for the expected increases in the number of trips from 2002 to 2015. An increase in the share of car trips is expected due to growing car availability. The aim of this paper is to apply a modelling framework based on the land use and transport model RETRO to determine and evaluate implementation paths for marginal social cost pricing in the Greater Oslo area. An earlier application of the framework by Vold (2005) includes a complete description of both the cost-benefit assessments and the RETRO model. Earlier studies of transport pricing in Oslo include Ramjerdi (1995) and Larsen (1997). Oslo was also a case study in the AFFORD project (Milne et al., 2000) where the outcome of road pricing strategies for Edinburgh, Helsinki

A. Vold / Transport Policy 13 (2006) 140–148

and Oslo were compared (Fridstrøm et al., 2001; Vold et al., 2001). The RETRO model is briefly described in Section 2.1 Implementation paths with strategies for introduction of MSCP in terms of available pricing instruments are described in Section 3. MSCP strategies along the implementation paths are presented in Section 4 along with evaluation of the implementation paths in terms of changes in residential and workplace location and travel behaviour, accessibility, equity, environmental effects and financial flows. Conclusions are given in Section 5. 2. Modelling framework The modelling framework utilises land use and transport indicators from the RETRO model for determining and evaluating MSCP strategies relative to the base-case situation. 2.1. RETRO—land use and transport interaction model RETRO is an equilibrium land use and transport model that encompasses car ownership, land-use and a four-stage transport model. The car ownership submodel is run as the first step, and then the other submodels are run sequentially and iteratively in a loop with exchange of data between them. Equilibrium is found in each run of each submodel, and a method of Powell and Sheffi (1982) is used to guarantee convergence to equilibrium between the submodels. The car ownership model, developed as part of the national model system for private travel (Ramjerdi and Rand, 1992), is applied to determine how fuel costs and growth in real incomes influence car availability for persons in different household types in the zones. The four-stage transport model assesses route-, mode- and destination choice and trip frequency per person in the zones, for periods with peak and off-peak traffic loads. Available mode choices are auto, PT (bus, train, subway, tram and boat) and a slow mode (bicycle/walking). Trip timing is not modelled, but trip frequency for peak and off-peak trips are determined in terms of a geometric distribution (Bhattacharyya and Johnson, 1977; Ben-Akiva and Lerman, 1985, p. 125). Mode and destination choices are described with a nested logit model (Ben-Akiva and Lerman, 1985) for travel within and between the 27 city districts of Oslo and the 22 municipalities of Akershus. A dispersion matrix is used to allocate travel demand between the 49 greater origin- and destination zones in Greater Oslo to corresponding trips between 438 smaller origin- and destination zones. The allocated trips are used in the fixed route assignment algorithm of the EMME/2 software (Emme/2, 1996) for calculation of route choice and transport costs for road and PT between the 438 zones. The transport costs are then aggregated to determine travel costs between the 49 zones. 1

An earlier application of the framework by Vold (2005) includes a more elaborate description of the RETRO model and the cost-benefit assessment approach.

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These costs are then used within the submodels for land use and travel demand. The submodel for household location is based on hedonic housing rent price function representing household’s willingness to pay for locating in the different zones. The hedonic price function determines housing rent per zone as a function of residential density, a measure of attraction- and access utility from the four-stage transport model and a zone specific constant. Improved accessibility increases the housing rent, whereas increasing density reduces the housing rent. The zone specific constants can be interpreted as existing distortions in the market due to housing market regulations. The distortions are assumed to persist since the constants do not change during the MSCP implementation paths. Equilibrium in the housing market is ensured in the sense that housing providers accept the households’ willingness-topay for rent as expressed in terms of the hedonic price functions, and the household do no better than accepting the rent. Equilibrium is also achieved in the sense that the housing provider’s behaviour will be to keep the shares of residences in different zones such that aggregate willingness-to-pay is maximised. Thus, the housing providers are price takers, meaning that they are not behaving collusively but rather according to perfect competition. Household location is based on the assumption that the households always move to the residences provided by the housing providers. The Municipality Plan for Oslo (2000) contains alternative strategies with high and low potentials for land use development of areas already regulated for residential purpose. Based on these potentials the upper and lower limits for the change in the number of residences per zone were set at G 15%. A constrained optimisation algorithm DONLP2 (7/99), developed by P. Spellucci, Technical University at Darmstadt, is applied to maximise housing providers profit by distributing the share of residences in the zones within the upper and lower limits. A sufficiently high shadow cost is imposed if any upper or lower limit is reached (i.e. the shadow cost is the housing providers lost or earned income from housing rent by staying within the limits). It is assumed that these costs are subsequently transferred to the households as an extra housing rent, however. A conventional multinomial logit model originally developed for the IMREL model was applied for employment location. See Anderstig and Mattsson (1991) for further details of IMREL and Vold (2005) for further details of RETRO. 2.2. Base-case description The implementation paths span the period from 2002 to 2030, what is based on determination and evaluation of MSCP strategies for 2002, 2015 and 2030. Given the lack of information about future infrastructure investments, and the lack of detailed forecasts beyond 2015, the 2015 infrastructure and demographic data were also used for 2030. Data for the base-case are based on available statistics and official forecasts, used in several projects led by the City authorities (Prosam, 2000) for demographic development,

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A. Vold / Transport Policy 13 (2006) 140–148

household and employment location for 2015, land-use regulation and infrastructure development towards 2015. It is assumed that prices are real and that base-case levels of policy instruments resemble today’s situation during the whole period, with the only exception that off-peak toll charges are reduced to 10% of today’s level2 by 2015. Travel demand in RETRO is estimated and calibrated with respect to survey data that were divided into morning and evening peak trips, and offpeak trips, with the peak period totalling 4.34 h (Table 1). The RETRO model was calibrated to obtain a perfect fit between model output and data for residential and employment locations in the base-case situation in years 2002 and 2015 (see Vold, 2005 for details). 2.3. Determination and evaluation of MSCP strategies As Rouwendal and Verhoef (2006) explain earlier in this special issue, MSCP can be determined by optimising the set of available pricing instruments such that welfare W is maximised. The modeling framework for the Greater Oslo area evaluates overall change in welfare DWi Z DEEFi KDPCi K DACi relative to a base-case situation in year i. In this expression, DEEFiZDBCDPVF is the economic efficiency gain, where B is the net present value of benefits and PVF3 is the net present value of finance. The second and third components are changes in pollution costs, DPCi (CO, VOC, NOX, PM, Noise), and changes in accidents cost, DACi. The welfare components are assessed by a cost-benefit analysis according to principles of Proost et al. (2003) and Vold (2005), where RETRO assesses land use and transport indicators utilised in the cost-benefit analysis. Since RETRO is a partial equilibrium model, there is no endogenous mechanism that determines the economic effect of redistributing more or less public revenues from transport pricing. To account for this, the public revenue is given a weight. For the implementation paths evaluation presented in this paper the weight is set at 1 assuming zero marginal cost of public funds (MCPF). Analytical expressions for first-best MSCP link charges are easily derived (Yang and Huang, 1998) and can be incorporated directly into the volume-delay functions of the EMME/2 network part of RETRO. By contrast, an analytical approach is not practical for second-best pricing, and values are instead derived by unconstrained or constrained maximisation of the welfare function with respect to the pricing instruments. For effective solution of the optimisation problems along the implementation paths for the 2

Today’s toll ring charges are fixed one way at approximately V1.5 (depending on the exchange rate with the Norwegian kroner) and whether it is an ordinary type of ticket or a type of punch card. The base-case fixed off-peak charge was reduced relative to today’s situation, since this was found optimal in a related study (Vold et al., 2001). 3 PVF is the undiscounted sum of operator’s surplus, government revenue from annual car taxes and revenue from fuel tax. Loans, subsidies and grants from the government can be needed to cover the gap between capital and operational expenditures and any operational surpluses.

Table 1 Base-case equilibrium indicators in peak periods (4.34 h) and off-peak periods in Greater Oslo in 2002 and 2015 2002

Total number of trips Car trips PT trips Walk/bicycle trips Average distance by car (km/trip) Average PT distance (km/trip) Average trip time by car (min) Average PT time along road (min) Average car speed (km/h) Average PT speed along road (km/h) Share of car trips that pass the toll ring (%) Average speed as a fraction of free-flow (road) (%) Total number of cars

2015

Peak

Off-peak

Peak

Off-peak

851,280 487,339 258,498 105,442 19.6

1,027,630 796,302 140,388 90,940 18.5

954,865 619,255 238,724 96,886 17.6

1,364,186 1,079,115 178,258 106,813 17.6

17.4

16.3

17.8

17.4

24

20.4

24.7

22.3

51.1

58

51.4

59.1

49 20.4

54.7 16.8

42.7 20.8

47.4 17.3

20.3

28.62

18.2

27.4

68

81

60

75

417,331

525,115

Greater Oslo area, a method developed by Vold (2005) was utilised. The method represents W and constraints by polynomials that are subsequently used with a general optimisation algorithm. Constraints are easily altered and new optimisations can quickly be run. For impacts on equity among the zones in year k, we use Kolm’s (1976) measure: ! n X expðaðxk Kxi;k ÞÞ ; Kk;a ðxÞ Z ð1=aÞ log ð1=nÞ$ iZ1

where n is the number of zones, aO0 is a transfer sensitive parameter which we have set at 0.0001 (VK1), and xi,kZ di,kCVia,k (V) is the utility changes per household in zone i in year k. Disutility, di,k, and accessibility, Via,k, are obtained from RETRO. Larger values of Ka are associated with reduced equity. The Kolm measure has the property that it is invariant to equal absolute changes in utility for all population groups. The case study does not derive MSCP strategies that internalise CO2 climate costs CCi, because the costs are borne almost wholly outside the local region. However, the global climate costs of the MSCP policy are assessed. This is done by applying the MEET (1999) functions for the relationship between speed and emissions/vehicle kilometre. Vold (2005) describes how the costs of emissions were assessed, as well as the costs of noise and accidents. 3. Policy instruments and constraints along the implementation paths Policy instruments that are currently legal for transport pricing include peak and off-peak toll charges, PT fares and

A. Vold / Transport Policy 13 (2006) 140–148

parking charges.4 For combinatorial reasons only peak toll ring charges and PT fares were used for MSCP. Parking charges are fixed at current levels as in 2002.5 Setting of the instruments is assumed to be either unconstrained, or constrained according to the author’s judgement of publicly and politically acceptable levels of prices, impacts on equity and PVF. Adjustments are also made to endogenously determined non-price instruments such as PT frequency— which is assumed to increase with ridership in order to avoid crowding. The constrained paths preclude peak toll charges outside the range (K100%,C200%) and PT fares outside the range (K75%,C100%) relative to the base-case situation, and prevent equity or PVF from deteriorating relative to the base-case situation. Thus, the MSCP strategies at stages along the constrained implementation paths are found by maximising welfare Wi within these constraints with respect to peak toll charges and overall PT fares. Although it is already technologically feasible to implement advanced schemes for link-based road pricing, political agreement to implement advanced road pricing has yet to be reached. The legality of specific road-pricing technology has not yet been established, and money needs to be raised to install and operate the new technology. Moreover, link charging is based on detailed and possibly sensitive information about peoples’ travel patterns, and political and legal processes are required to ensure that privacy is not compromised. For these reasons it is assumed that road link congestion charging does not become available until 2015 (see Table 2). Residential and workplace locations are assumed fixed for 2002. Pricing in 2002 reflects the actual situation in 2002. Prices include actual levels of toll ring charges with the purpose of financing road and PT development (through the city budget). The MSCP strategy in 2015 comprises welfare maximising levels of peak toll charges and PT fares. It is assumed that the instruments as well as transport behaviour, land use and welfare adjust gradually as represented by linear interpolation for intervening years between 2002 and 2015. Land use as determined for 2015 is assumed fixed in late 2015 (2015C), when first-best road link charging becomes technologically and legally feasible. MSCP for 2015C is determined by applying first-best road link charging and simultaneously maximising welfare with respect to the available second-best instruments, i.e. peak toll charges and overall PT fares that unlike road link charges cannot be differentiated finely by location. MSCP for 2015C gives the short-term road link charges and shortterm toll ring charges and PT fares after a long period of 4

Since fuel taxes are set nationally in Norway, there are legal/institutional barriers that preclude adjustment of the fuel tax to a locally optimal level. There is also the practical/acceptability barrier that travellers may buy fuel in nearby regions with possibly lower fuel tax. 5 Among available road pricing instruments second-best parking charges were found to be closest to base-case levels in an earlier MSCP study by Vold et al. (2001).

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exclusive use of optimal levels on the available second-best instruments.6 The MSCP strategy for 2030 accounts for the long-term effects on residential and employment location by letting RETRO endogenously assess land-use towards 2030 while determining road link charges, peak toll ring charges, and PT fares. Linear interpolation is again used to compute values of instruments and welfare effects between 2015C and 2030. Both the unconstrained and constrained implementation paths are derived in situations with today’s fuel tax (about V0.70/l, which comprises about 70% of the total fuel price) and with an extra fuel tax starting at 0 in 2002 and reaching V0.22/l by 2015. According to Minken et al. (2001) this corresponds to the charge required to reduce national CO2 emissions to an acceptable level (e.g. according to the Kyoto agreement). 4. Results The MSCP strategies were determined for the unconstrained and constrained implementation paths for situations with today’s fuel tax and with an extra fuel tax. This section characterises the implementation paths in terms of the MSCP levels of the available pricing measures, active constraints, the effects on overall welfare and its constituents, and land use and transport indicators. 4.1. MSCP level of pricing instruments and active constraints All MSCP strategies feature reduced PT fares relative to the base-case situation (Table 3). Reductions in PT fares induce shifts of travellers from car to PT and thus relieve road congestion. But the cost of required PT capacity expansion increases (i.e. extra capital costs, operational cost and maintenance of rolling stock and labour), and fewer toll ring crossings and less parking in paid parking space in both peak and off-peak periods cause reductions in PVF. Moreover, low PT fares reduce PT operator’s income, and although tolls are set at the highest feasible level for the constrained path with today’s level of fuel tax before introduction of first-best road link congestion charges in 2015, the constraint on PVF becomes active. The toll charge and PT fares become somewhat lower for the corresponding unconstrained situation in 2015, where PVF declines relative to the base-case. Introduction of road link charges in 2015C causes revenues and PVF to rise, and the constraint is relieved (Table 3). The lower PT fares along all the implementation paths lead to an overall accessibility increase. The increased accessibility improves utility relatively more in zones distant from the city district, which is the main reason for improved equity in 2015. After the introduction of link charges in 2015C, equity is generally less beneficial whereas PVF improves. 6 This way of applying RETRO is based on the assumption that people behave myopically in the sense that they do not account for, or they are not aware of, the introduction of first-best road link congestion charging in late 2015 (2015C).

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Table 2 Description of pricing instruments introduced along the implementation paths for the Greater Oslo area From year

Coverage or scope of the pricing system

Composition and level of prices

Degree of differentiation of prices

2002

2015C

As 2002 plus road link charges

2030

As 2015C

All available pricing instruments are set at approximately current values Unconstrained or constrained second-best MSCP levels of peak toll ring charges and PT fares Road link charges applied for first-best congestion pricing. Additional second-best measures as in 2015 As 2015C

None

2015

Peak and off-peak toll ring charges, parking charges, fuel taxes and PT fares As 2002

Per mode As 2015 As 2015

Source: Authors’ judgment.

Road link charges effectively internalise external congestion costs and make the second-best measures superfluous. The toll charges are actually non-positive in combination with road link charges along the unconstrained implementation paths. The reason is that the distance-dependent car taxes (which include the fuel tax) are approximately V0.09/vehicle kilometre, whereas the local externalities not already internalised as part of the road link charges (i.e. accidents, pollution, noise and dust) amount to only V0.03/vehicle kilometre. The optimisation algorithm tries to find MSCP strategies for 2015C and 2030 that compensate for this distortion by imposing a negative toll charge. It is not considered feasible in practice nor in the model to have negative toll charges, however. Thus, it was necessary to include a lower constraint on toll charges on the initially unconstrained path, whereat welfare was reoptimised (Table 3).7 The results are similar in the situations with today’s and extra fuel taxes, although optimal toll charges are lower with extra fuel taxes since the extra fuel tax internalises some of the external costs. The exception is the 2015C and 2030 stages along the constrained path in the situation with extra fuel tax, where the equity constraint binds. The extra fuel tax yields more revenue, and this boosts PVF in all stages compared with the situation with today’s fuel tax. The increase in revenues is explained by the relatively low elasticity of car use with respect to fuel prices and the corresponding small percentage reduction in total car kilometres relative to the extra revenue per car kilometre. Moreover, both the unconstrained and constrained situations with an extra fuel charge are less equitable than the corresponding situations with today’s fuel charge (Table 3). This is explained by the reduced accessibility for zones that are distant from the city centre where PT is less attractive and the share of car trips is greater.

7 By including the CO2 costs according to the abatement cost of Minken et al. (2001), the externalities not internalised by road link charges amount to V0.055 per vehicle kilometre, which is still below the current distance-dependent car taxes. In an efficient economy it is reasonable to assume that the distancedependent tax should cover road investments and maintenance, etc. Consideration of the latter was beyond the scope of this paper, however.

4.2. Overall welfare Using a discount rate of 7% the present-value welfare gains along the unconstrained path in the situation with today’s fuel tax is V1427 million, of which V222 million is due to climate costs reductions. For the situation with an extra fuel tax, the corresponding benefit is V1402 million, where V277 million is due to climate costs reductions. The implementation path with today’s fuel tax is more efficient in terms of overall welfare. This is not surprising, since today’s fuel tax alone exceeds the sum of local external costs not covered by link charges and the cost of CO2 emissions. An extra fuel tax increases this distortion. Note, however, that welfare is greater with an extra fuel tax in 2015 before link based charges are introduced. The reason is that toll charges alone are not efficient in internalising the congestion costs, and the extra fuel charge helps in this respect, albeit imprecisely. However, greater welfare gains accrue toward the final phases of the implementation paths without an extra fuel tax. The welfare difference between constrained and unconstrained paths shrinks after link charging is introduced in 2015C which adds strongly to revenues and relieves the PVF constraint. The part of the welfare that accrues to land use changes can be determined by subtracting the welfare obtained with the same MSCP strategy but land use fixed as in the base-case situation. For the unconstrained implementation path with today’s fuel tax, we find that the land use effect from 2002 to 2015 comprises C25% of the net welfare increase. The main land use effect in 2015 is that people relocate outside the toll cordon in order to reduce the number of trips they make across the cordon. The relocation causes them to travel longer distances and consequently to pay more in fuel tax revenues. It was assumed that housing rents do not change from 2015 to 2015C. These housing rents are relatively high because the hedonic price function increases with increasing accessibility, which is due to very low PT fares. The relocation that occurs between 2015C and 2030 causes changes to the MSCP strategy and reduces welfare by 3.7%. By looking at Table 5, however, we see that although there is a small loss in overall welfare, the household/travellers are much better off in 2030—both with regard to housing rent and travel costs. With road link charges and higher PT fares, accessibility falls and thus housing rents decrease. With land use in 2030 as

287 290 73 0 292

4.3. Land use and transport indicators

Climate cost reductions are also in tonne 10 CO2/year (in parentheses). a Along constrained paths there are upper and lower limits for toll charges (3.0, 0.0) and PT fares (2.0, 0.75). b According to the MEET functions the CO2 emissions from private gasoline cars driving at 50 km/h amount to 0.17 kg CO2/km.

302 92 0 307 320 62 0 312 324 77 0

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in base-case, the improved accessibility due to the centralisation effect is lost, and the housing rent decreases, and traveller’s monetary and time costs (for all modes of transport combined) are 4.2% higher, which reflects household’s ability to relocate to adapt to MSCP. Overall, the land use effect in 2030 comprises C5.5% of the net welfare gain. The land use effects from 2002 to 2015 along the unconstrained path with extra fuel charge comprise 15% of the net welfare effect. From 2015 to 2030 net welfare is reduced by 3.3%, but households/travellers are better off in 2030. Travellers’ monetary and time costs in 2030 are 0.9% lower with base-case land use. The land use effect in 2030 comprises C5.7% of the net welfare gain.

3

64 (427) 15 (100) 0 (0) 9 (60) 0 (0)

53 (354)

56 (374)

0 (0)

8 (53)

53 (354)

55 (367)

0 (0)

18 (120)

59 (394)

63 (420)

62 (414)

0.42 0.75 Yes 1.0 1.42 223

2030 2015C

0.92 0.75 Yes 1.0 1.44 228

2015

2.32 0.75 No 1.03 1.14 58

2002

1.0 1.0 No 1.0 1.0 0

2030

0.0 0.64 Yes 1.03 1.39 229 0.0 0.85 Yes 0.95 1.47 243

2015C 2015

2.0 0.46 No 1.13 0.99 74 1.0 1.0 No 1.0 1.0 0

2002 2030

0.0 0.75 Yes 1.02 1.36 252 0.0 0.75 Yes 1.01 1.38 267

2015C 2015

3.0 0.8 No 1.08 1.0 50 1.0 1.0 No 1.0 1.0 0

2002 2030

0.0 0.59 Yes 1.07 1.31 256 0.0 0.66 Yes 1.04 1.36 271

2015C 2015

2.4 0.34 No 1.22 0.86 68

2002

1.0 1.0 No 1.0 1.0 0

Peak toll chargea PT fares Road link charges Equity Finance (PVF) Local welfare changes Climate cost reductionsb Sum

Constrained with extra fuel tax Unconstrained with extra fuel tax Constrained with today’s fuel tax Unconstrained with today’s fuel tax

Table 3 Levels of transport instruments, equity, finance and net welfare and climate cost reductions in V106/year relative to the base-case situation along unconstrained/constrained implementation paths with today’s fuel tax and with an extra fuel tax for the Greater Oslo area

A. Vold / Transport Policy 13 (2006) 140–148

Transport indicators in the base-case situation were shown in Table 1, while Table 4 shows the induced changes in transport indicators along the unconstrained implementation path in the situation with today’s fuel tax. Before introduction of road link charges in 2015, the combined effect of higher toll ring charges and lower PT fares induces a modal shift from both car and walk/bicycle toward PT in the peak period but mainly from walk/bicycle in the offpeak period where off-peak toll charges were fixed at base-case levels. The average speed for peak and off-peak PT trips increases because of reduced waiting time due to the built-in modelling assumption that PT-frequency increases with increasing demand for PT trips. The net effect of more buses and fewer cars is to reduce passenger-car equivalent traffic volumes and contributes to higher PT speed as well. Moreover, there is a tendency for residential relocation away from the city centre, a greater share of PT trips by train, and a lower share by (slower) subways within the city. With fewer cars on the roads, travel speeds increase for peak trips by car as well, and this is sufficient to reduce travel time even though average trip distance rises. The increasing travel distance by car is explained by residential relocation and drivers trying to avoid road tolls by driving on longer and less congested routes. Distances decrease for walk/bicycle in the peak and off-peak periods, mainly because long trips are transferred to PT. The introduction of road link congestion charging in 2015C has only a minor short-term effect on the share of trips by car and PT in peak periods. However, higher PT fares increase the walk/bicycle share. Increasing PT fares in combination with road link charges reduces both the car and PT shares of offpeak trips, whereas walk/bicycle trips increases. Moreover, speed increases considerably and the average trip distance is reduced for car trips. Average speed by road as a fraction of free-flow speeds increases to 82 and 87% in peak and off-peak periods. This results in a considerable reduction in travel time by car. Increasing PT fares has the effect that people tend to change from PT to the walk/bicycle mode for the short PT trips. Again, this explains why the average distance by walk/bicycle increases. Land use affects the share of trips to and from the zones. Hence, land use has an indirect effect on transport flows,

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A. Vold / Transport Policy 13 (2006) 140–148

Table 4 Percentage changes in transport indicators for trips by car, PT and walk/bicycle in peak (4.34 h) and off-peak periods along the unconstrained path with today’s fuel tax for the Greater Oslo area Peak

Number of trips Car PT Walk/bicycle Distance Car PT Walk/bicycle Time Car PT Walk/bicycle Speed Car PT Walk/bicycle

Off-peak

2002

2015

2015C

2030

2002

2015

2015C

2030

0 0 0

K8.3 31.0 K23.6

K10.0 29.0 K8.4

K11.0 32.0 K8.0

0 0 0

K0.6 20.4 K26.7

K2.5 11.2 K14.7

K2.5 13.4 K16.2

0 0 0

1.7 13.3 K12.6

0.8 13.6 K1.3

0.2 12.0 K7.4

0 0 0

2.0 14.0 K8.0

K3.1 10.4 K0.8

K3.5 10.0 K4.8

0 0 0

K6.3 5.2 K12.6

K27.7 4.7 K1.3

K29.0 3.4 K7.4

0 0 0

2.0 6.0 K8.0

K18.0 4.3 K0.8

K18.4 3.5 K4.8

0 0 0

8.6 7.7 0.0

39.4 8.5 0.0

42.0 8.4 0.0

0 0 0

0.0 7.5 0.0

18.5 5.8 0.0

18.0 6.0 0.0

transport infrastructure capacity utilisation, and transport costs. Land use also affects attractions and household density in the zones. An unfortunate effect before the introduction of link charges in 2015 is that areas close to the toll ring become less popular. The area within the ring is apparently too small to permit everyone to live and work inside it. People tend to move to less central and less densely populated districts along the southern and north-eastern corridors, and some people move to the western part which is relatively well endowed with residences and workplaces. In doing so they are able to avoid the toll ring. We see the same trend for the work places except for the city centre zone, which is easily accessible by train along the above-mentioned corridors. The pattern of land use change reverses after road link charges are introduced, the toll charge is reduced and people and workplaces move back to the city. The eastern areas are attractive in being little affected by the link charges, and some of the decentralisation in the earlier stages to these large and sparsely populated areas persists. On the other hand, people tend to move from the smaller western part of the study area which is more populated and more congested. Changes are generally similar along the constrained paths and in situations with an extra fuel tax. The number of car trips across the toll ring in the peak period declines by 10% relative to the base-case situation before introduction of link charges in 2015, by 7% when link charges are introduced, and by 5% in 2030. These reductions are due to fewer car trips, as well as changes in workplace and destinations for other activities that allow people to avoid crossing the cordon ring.

V286/capita. It is apparent that car drivers suffer a monetary loss on account of toll charges. But they gain more than 100% back in time savings due to peak-period toll charges and low PT fares which shift travellers from car to PT and thus relieve road congestion. PT users benefit from both money and waiting time savings, where waiting time savings accrue from the endogenous increase in PT frequency to avoid crowding. But as a consequence of lower PT costs, the overall accessibility increase also increases the housing rents as represented by hedonic price functions in RETRO, and non-zero shadow prices come into effect as the number of resident movements reaches upper and lower limits for the share of residents in one or more zones. With the introduction of road link charges in 2015C, the monetary impact of tolling multiplies, and the net annual positive traveller’s utility turns into a whopping loss of V445 million. And because housing rents are assumed not to change from 2015 to 2015C, there is no relief in terms of lower rents. Relocation between 2015C and 2030 eases the loss to V372 million, and housing rents are also substantially reduced. But travellers persistently lose from pricing reform—at least before accounting for any use of revenues. Travellers in aggregate are better off along the unconstrained path than along the constrained path with today’s fuel tax, but sometimes worse off and sometimes better off with an extra fuel tax.

4.4. Travellers/households

The modal shift from auto to transit results in fewer car trips and usage of paid parking space, and thus reduces parking operator’s income in all stages along the unconstrained path with today’s fuel charges. Despite the shift, PT operators also lose on account of lower fares, and the need to invest in and maintain extra rolling stock and labour in order to increase

The sum of traveller’s money and time savings in 2015, before introduction of link charges along the unconstrained path with today’s fuel charges, gives the net annual positive travel utility of V314 million (see Table 5) or approximately

4.5. Operators, government, housing providers and externalities

A. Vold / Transport Policy 13 (2006) 140–148

147

Table 5 Undiscounted costs and benefits (106V/year) at stages along the unconstrained path in the situation with today’s taxes (CO2 cost savings are omitted) for the Greater Oslo area 2002

Travellers utility Money savings, road Money savings, PT Time savings, road Time savings, PT Time savings, walk/bicycle Residential utility Attraction utility Residential disutility Housing renta Housing lower shadow costb Housing upper shadow costb PT operators Invest./maintenance/labour PT fare income Tolling operator Parking charges Road pricing revenue Government and housing providers Revenue from fuel tax Housing renta Housing lower shadow costb Housing upper shadow costb External costs Accident costs Pollution cost savingsc Noise cost savings Dust cost savings Overall welfare

2015

2015C

2030

Peak

Offp

Peak

Offp

Peak

Offp

0 0 0 0 0

K55.70 158.29 57.83 26.25 0.00

K0.64 109.32 K2.68 22.10 0.00

K408.54 80.51 194.87 24.47 0.00

K569.00 53.24 167.91 11.86 0.00

K376.56 97.81 198.77 26.86 0.00

K565.79 64.79 167.65 14.05 0.00

0 0 0 0 0

0.59 6.43 K323.09 5.27 K100.20

0.33

0.83 6.43 K323.09 5.27 K100.20

0.33

K4.16 1.10 K59.56 24.76 K46.64

0 0

K99.49 K184.84

K78.18 K51.26

K89.40 K78.52

0 0

K7.71 43.85

K11.24 893.84

K10.11 860.06

0 0 0 0

K13.25 320.70 K4.80 104.28

K70.05 320.70 K4.80 104.28

K78.84 61.02 K22.05 46.64

0 0 0 0 0

1.75 0.55 2.66 0.38 68.21

0.77 5.60 14.07 2.01 270.65

1.04 5.92 15.84 2.26 256.00

K0.94

a

Based on the hedonic price function. The shadow rent added or subtracted from the housing rent to keep the number of residents within the upper and lower limits on the number of residents in the zones. c Benefits of CO, VOC, NOX emission reductions. b

service frequency. According to the ongoing political discussion mentioned in Section 1, it is likely that the transit deficit could be met by transferring some of the revenues from road pricing and extra fuel charges. By contrast, operators of the ring road and road link tolling systems gain at each stage. On the other hand, the reduction in car trips results in a steady reduction of fuel tax revenues along the implementation paths. Due to the gradual reduction in the number of car trips and average distance per trip, environmental cost savings increase as the implementation path unfolds. However, accident cost reductions are greater before introduction of road link charges. This is explained by the overall mechanism that road pricing alone lead to more bicyclists and pedestrians that are more vulnerable. This is compensated by lower PT fares that reduce car traffic and induce bicyclists and pedestrians to switch to PT, but to a lesser extent after introduction of road link charges. 5. Conclusions The modelling framework presented in this paper was used to determine and evaluate MSCP implementation paths with

and without constraints on available pricing measures and with and without an extra fuel tax. The overall welfare gains were significant at all stages along all paths. Greatest overall welfare gain in 2015, before introduction of link based charges, was found for the unconstrained path with an extra fuel charge (V84 per capita). When road link charges are introduced in 2015 they effectively internalise external congestion costs and make the second-best measures superfluous. But fuel charges and toll charges still play a role in internalising external costs that are not accounted for in the road link charges. Not surprisingly, overall welfare gains are enhanced by the use of more refined pricing instruments, and were found to be greatest along the unconstrained path with today’s fuel charges (V295 per capita/year after introduction of link based charges in 2015 and V284 per capita/year in 2030). There is a positive overall welfare impact of land use changes in 2015, and although there is a negative impact on overall welfare from 2015 to 2030, travellers are better off along all paths. Although MSCP land-use effects contribute a relatively small percentage to the overall welfare, the analyses demonstrate the importance of accounting for the long-term MSCP induced land use changes.

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A breakdown of the welfare component for the unconstrained implementation path in the situation with today’s fuel tax shows that car drivers and PT travellers combined have a positive utility of MSCP based PT fares and toll charges in 2015, which is mainly due to substantial reduction in the PT fares. After the introduction of link based charges in 2015, the monetary expenses for car drivers increases considerably and the net annual positive traveller’s utility turns into a loss. PT operators loose at all stages along the path even though the number of PT travellers increases. The reason is the reduced PT fares and the cost of required capacity increase to accommodate the shift from car to PT. Reduced number of car trips reduces the revenue from today’s fuel tax, but revenue from MSCP is steadily increasing and external costs declines. The constrained paths were constructed to comply with the author’s judgement of acceptable use of transport pricing instruments for MSCP, and the anticipated legal and technological development. Along the unconstrained paths, only the low PT fares violated these constraints. However, before accounting for any use of revenues, car drivers tend to lose along both the unconstrained and the constrained paths. The constraints may not have been stringent enough to assure that the policies would be acceptable in actual practice. A possible remedy would be to add constraints on travellers cost and benefits and/or consider compensation in terms of revenue redistribution or revenue earmarking for investments in transport infrastructure that is to the benefits of the travellers. Conclusively, the fact that costs and benefits of MSCP can be unevenly distributed highlights the importance of conducting a detailed analysis of the incidence of cost and benefits of new transport pricing strategies and to consider schemes for redistribution revenue for compensation of losers. References Anderstig, C., Mattsson, L.G., 1991. An integrated model of residential and employment location in a metropolitan region. Papers in Regional Science 70, 167–184.

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