Looking at sustainable urban mobility through a cross-assessment model within the framework of land-use and transport integration

Kenji Doi; Masanobu Kii

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Looking at sustainable urban mobility through a cross-assessment model within the framework of land-use and transport integration

Kenji Doi * Masanobu Kii

Faculty of Engineering, Kagawa University, 2217-20 Hayashi-cho, Takamatsu, Kagawa, Japan

article info

abstract

Article history:

Received 17 October 2011

Received in revised form 7 February 2012

Accepted 8 February 2012

Keywords: Urban mobility Land use and transport Cross assessment CO2 emissions

In order to realise sustainable urban transport, it is necessary to combine different kinds of decision-making, including vision-led, plan-led and consensus-led approaches. In this paper, a cross-assessment model that supports both vision-led and consensus-led approaches is proposed as an analytical tool for developing sustainable urban transport and land use strategies for a low-carbon society. It is applied to an impact analysis of public transport and land use strategies in 2030 for all of Japan's 269 urban areas, with outcomes - including the financial balance of public transport operation, user benefits, and CO2 emissions reduction - compared by strategy and urban area.

The analytical results show that three value factors related to efficiency, equity and the environment do not necessarily conflict with each other. In particular, it is clarified that CO2 emissions reduction targets can contribute to the improvement of financial balance and user benefits at the national level. In addition, the results of comparative analysis among the LUTI (land use and transport integration) scenarios demonstrate that a combination of urban transport strategies with land use control in the form of 'corridors and multi-centres' provides greater emissions reduction and increased user benefits.

1. Introduction

One of the central issues in the development and management of urban mobility systems is to identify the most sustainable solutions within the framework of land use and transport integration (LUTI) while involving a large number of stakeholders with multiple, often conflicting, objectives. Such objectives range from the provision of cost-effective transport services to the provision of fair and equitable accessibility opportunities to the realisation of safe and environmentally friendly mobility systems. Achieving these objectives requires integrated strategies including a) infrastructure provision and management, b) attitudinal measures that influence individual travel behaviours and lifestyles, c) land use measures that shape transit-supportive urban structures, and d) pricing.

Although the LUTI framework has been incorporated in urban transport policies in some advanced cities, it has rarely resulted in successful outcomes because of the implementation gap caused by consensus and institutional barriers. Numerous papers have pointed out that the primary barriers to delivery of sustainable transport are institutional ones that reduce the potential for delivery or make it impossible to achieve [1-3].

* Corresponding author. Tel.: +81 878642165. E-mail addresses: doi@eng.kagaw-u.ac.jp (K. Doi), kii@eng.kagawa-u.ac.jp (M. Kii).

To better integrate strategies and reduce implementation barriers, a wide range of stakeholders with different values should be encouraged to participate fully in strategy formulation. This enables the development of a common understanding of objectives and a shared vision for sustainable urban transport. Furthermore, it is necessary for us to find an appropriate combination of vision-led and consensus-led approaches, one that both reconciles conflicting objectives among stakeholders by clarifying the pros and cons of respective strategies and meets the requirements of a low-carbon, ageing society. Fig. 1 shows a vision-led and consensus-led process for sustainable transport. Pursuing the goal of sustainable transport means overcoming a series of challenges that form an ascending spiral: dependence on automobiles, the transition to a low-carbon society, and adapting to an ageing society.

Solving the challenges in each stage and stepping up to the next requires both management of and innovation in transport systems. A consensus among stakeholders is critical in managing systems because success is achieved through utilisation of a portfolio of existing technologies and policies. On the other hand, system innovation requires understanding the future direction of society. It may require new conceptualisations of technology or policy and requires a vision for society's future.

As shown in 'A Decision Makers' Guidebook' for developing sustainable urban land use and transport strategies, the most common approach is a mix of plan-led and consensus-led decision-making [4]. However, plan-led approaches that seek an optimal solution or best alternative can work well only if stakeholders/individuals share

Consensus -lad

—__________Innovation

"rlirnt'ii -oriented

Fig. 1. Necessity of combining vision-led and consensus-led approaches.

a common value system. If value systems differ among stakeholders, however, it has been demonstrated that democracy offers no real cure-all to finding the best solution [5]. Consensus-led approaches primarily emphasise convincing stakeholders; the negotiation process can lead to compromised policies that are materially distorted in terms of efficiency. Vision-led approaches usually involve an individual (typically the mayor or committee leader) with a clear view of the kind of city they want for the future and the policy instruments needed to achieve that vision. The focus then is on implementing them as effectively as possible [4].

Therefore, with a special focus on vision-led and consensus-led decision-making, this paper proposes the innovative framework of a cross-assessment model1 that provides a multi-dimensional and multilateral evaluation of alternative strategies within the LUTI framework. This model is expected to support decision makers in exploring possible directions for sustainable urban transport to meet the requirements of a low-carbon, ageing society. It is designed to make cross-assessments of alternative strategies whose impact on welfare, economy, and the environment are compared. This analysis clarifies the interrelationship of outcomes and suggests alternative strategies to manage and innovate the system using the elucidated interrelationships.

This model is applied to the analysis of transport and land-use strategies for all of Japan's 269 urban areas. Urban density is defined using grid population data with a grid size of 1 kmx 1 km. We establish two urban scenarios for the year 2030: 'trend' and 'compact'. Three outcome indices are selected based on the following value elements: financial balance of public transport operation, user benefits, and transport sector CO2 emissions. We also set three public transport policy alternatives: maximising public transport sector profit, maximising social net benefit, and minimising CO2 emissions. We estimate the impact of policy alternatives on outcome indices. As a result, this study provides a perspective on the impact of urban structures and transport strategies in a society with an ageing and declining population in Japan as well as differences in impact across regions, which has not been fully discussed in past studies.

Based on these results, this chapter underlines the importance of the elaborated LUTI (land-use and transport integration) approach for the long-term management of urban mobility systems.

2. Analytical requirements for sustainable urban transport

Most analytical tools for plan-led approaches are likely to work well if objectives are specified, problems are identified, and measures that satisfy the objectives or solve the problems are easily determined. In such cases, they often focus on problems of limited scope or are based on ad-hoc value systems, regardless of the diverse values found among people. A successful combination of the vision-led and

consensus-led approaches requires an innovative analytical tool capable of cross-assessing the outcomes of alternative strategies from multiple perspectives and values.

In this section, we review previous studies on the relationship between urban structure, transport energy consumption, and public transport policy—essential topics for discussing sustainable urban transport strategies in a society with an ageing and declining population.

2.1. Urban structure and transport energy

Many past studies have tried to clarify the relationship between urban structures and transport energy consumption in order to extract information. Newman and Kenworthy [6] summarise urban transport data from around the world, and offer the famous diagram showing a negative correlation between population density and fuel consumption per capita. A range of studies have been conducted on the relationship between urban density, automobile dependence, and energy consumption [7-11].

Some studies suggest that densification of urban population worsens congestion and does not necessarily contribute to energy savings [12]. Hayashi et al. [13] noted that increased population density decreases transport energy consumption per capita but increases consumption per urban area. This result suggests that urban compaction may worsen local air quality. The Ministry of Land, Infrastructure, Transport and Tourism [14] simulated the effect of urban density on energy consumption in travel and indicated that high density saves energy on the roads but increases energy use inside building due to elevator use.

The impact of urban compaction on transport energy savings would vary by urban structure including the location of activities and infrastructure. Therefore the macro relationship between population density and vehicle energy consumption is not enough to lead to either consensus or a vision for a sustainable transport strategy. We need more detailed information about activity location and transport movement, as well as about the situation of public transport service provision inside the urban area.

2.2. Level of service of public transport

Studies on the relationship between urban structures and transport energy consumption are mostly based on private car travel, but the level of service (LOS) of public transport is also a considerable factor. Urban compactness will increase travel demand density, allowing a higher LOS and modal share of public transport. Modal shift from private to public transport is expected as a mitigation measure for the problem of global warming, but depends on travel density and the efficiency of public transport. Except in very large cities, private cars are the dominant transport mode in most developed cities. This reflects the lower profitability and LOS of public transport that comes with lower travel density. If an administration forces increased public transport service in a region with low travel demand density, it could increase CO2 emissions due to the higher energy intensity of public transport at lower occupancy ratios [15,16].

Ishida et al. [17] quantifies the public transport domain [18,19] by considering the demand and profitability of the transport sector. It evaluates the capable domain of public transport service over urban areas and traffic density at urban centres, but the urban structure is too simplified to analyse the effect of urban compaction.

2.3. Requirements for analysis of sustainable urban transport strategies

Past studies take various approaches to measure the impact of urban compaction and transport policies on CO2 emissions reduction, but these studies do not take into account changes to public transport LOS caused by urban compaction. In addition, it is important to identify regional conditions under which transport policy will be effective

in reducing emissions, but this requires comparable analysis across cities in the target region. There are land-use and transport models (for example Wegener, [20]) that describe choice behaviour for transport modes, routes, and locations in detail. Because these models need a huge amount of data, they are usually applied to one or a few selected cities.

In this study, we provide an urban transport model in which the LOS of public transport is identified endogenously with simplified user behaviour in transport and applied to all of Japan's 269 urban areas. A cross-assessment of urban compaction and public transport policies demonstrates the outcomes and spatial distribution of each transport strategy. The results are used to identify the conditions under which urban compaction is effective in reducing CO2 emissions.

3. Strategic cross-assessment model

3.1. Cross-assessment

The cross-assessment in this study combines the essential elements of multi-criteria analysis and conflict analysis [21-25]. It aims to explore synergistic solutions combining different value systems by assessing the impact on all outcome factors of measures pursuing each value factor as shown in Fig. 2. An iterative feedback in the figure indicates that an appropriate weighting and combination of three value factors should be examined based on the results of cross-assessment. We assume every transport strategy is achievable by government policy measures but may not represent the value system of the government. Decisions are usually made based on consensus among stakeholders whose value systems are different from each other. Each of the three strategies in this study is based on a particular value factor, and the impacts on all of the outcomes are evaluated [26].

3.2. Definition of stakeholders and the conceptual framework of the analysis

In this study, the stakeholders are defined as public transport operators, government, and transport users. Their behaviours are assumed as follows:

Public transport operators

Operators decide the LOS of public transport (bus and train) to maximise their profits under the given spatial distribution of demand, fare, and subsidy. The government determines the latter two factors. Transport users

Users choose a travel mode (private car, bus, train, and walk/bicycle) to minimise the generalised cost for their trip under the given fare level and LOS of public transport.

Soci( -ec

Technology Geography

Profits of transport operators

Users benefits

Synergy or Trade-off

Government

Government devises transport strategies and subsidies to public

transport operators to make the strategies effective. It also leads

the spatial pattern of residence and work place.

We also set triple bottom lines of sustainability as economy, society and environment, assuming the following three strategic targets in transport policy. (Hereafter, the abbreviation in parentheses indicates the target.)

1. Profit maximisation of public transport operator (PM)

2. Net benefit maximisation (NBM)

3. CO2 emissions minimisation in transport sector (CO2)

The first target, PM, is equal to minimisation of subsidy by the government. In the second, net benefits are defined as the sum of user benefits and operator profits. Based on these targets, we set three outcome indices; operator profits, user benefits, and CO2 emissions.

Fig. 3 shows the conceptualised mechanism of mobility style formation through user and operator behaviour under the transport strategy and urban structure (land-use structure) controlled by the government. In the strategic targets described above, profit maximisation mainly attaches importance to operator profitability, while net benefit maximisation attaches importance mainly to users. CO2 minimisation in the transport sector is currently only a government commitment and does not make any direct benefit for users and operators. Every target affects all outcome indices, so pursuing one value element will affect the achievement of other elements as well. We define the cross-assessment as an impact analysis of policy targets on the outcome indices; the cross-assessment model is an attempt to apply this evaluation to real transport strategy.

3.3. Formulation of the cross-assessment model

For the strategic analysis of public transport policies, we need an analytical model representing the transport LOS and activity location as spatial information. In this study, urban space is represented by a grid-based system, and the behaviour of transport operators and users are formulated. In addition, three indices can be estimated:

Fig. 2. Concept of cross-assessment in this study.

Fig. 3. Inter-relationship of stakeholder actions.

the financial balance of transport operations, generalised user cost for travel, and CO2 emissions from the transport sector2. In the formulation, we make the following assumptions:

1. Urban structure of residential and workplace location, transport infrastructure, and public transport fare level are given exogenously.

2. Public and private transport travel speed varies spatially among grids, but does not change depending on traffic volume.

3. A single operator provides both train and bus services in each city.

4. Transport service revenue is proportionate to passenger-km but operation costs depend on vehicle-km.

5. The CO2 emission factor per vehicle-km is fixed for each transport mode.

Regarding the second assumption, the fixed travel speed in a grid may be too tight a condition for assessing real urban areas. It should of course be relaxed in cities with growing population and car ownership because of their impact on LOS. In our target country Japan, both population and car ownership are almost saturated and the population is expected to decline. In the case of Japan, this assumption will bring a negative bias on future road transport LOS because congestion might be alleviated with decreasing population. On the other hand, an increase in elderly drivers may affect traffic by decreasing its speed. The effect of social change on travel speed must be studied with precision. In this study, however, we assume speed to be fixed at the current level.

3.3.1. Profit of public transport

The profit of a public transport operator n at grid m mode k is expressed as follows:

We assume a logit model whose representative term is given by Eq. (4); the expected minimum travel cost on i-j can be written as follows:

Cj = iln( E exp(e-C

ijk\ nijk

Here, 9 is a parameter. If we assume that travel demand on i-j is fixed as Qj, and denote the generalised costs with and without policy measures by Cy"th and Cy"thout respectively, then total user benefit in

the city is EijQi

Cwii Cij

3.3.3. CO2 emissions

CO2 emissions for transport mode k at grid m are formulated as follows:

C°2mk = akLmk(nmk) •

Here, ak is an emission factor of mode k, and Lmk is travel length at grid m. Travel length for public transport and private cars are expressed as follows (suffix t denotes public transport and c denotes private cars):

Lmt (nmt) = Htvmtnmt Lmc qmclmc

lmc is the one-way drive length to pass through grid m. The number of passengers q using transport mode k at grid m, which appears in Eqs. (1) and (8), is defined as follows under the logit model:

nmk = qmk^mkXk_Cmk(nmk)

Here, qmk is the number of passengers at grid m, lm is route length (km), xk is fare rate (yen/km), Cmk is operation cost, and nmk is the number of vehicles in operation. Operation cost is assumed to be proportionate with operated vehicle-km Lmk and can be described as follows:

qmk = E ( QiJPijk) àijmk ij y

exp( e - J %) +e,(

Ek exp(e -Cijlk(nijlk)+ ek

Cmk (nmk ) = a0k + a1kLmk (nmk ) Lmk (nmk) = Hkvmknmk •

Eq. (3) represents vehicle kilometrage as a product of operation hour H and vehicle speed vmk. In this formulation, operator profits are controlled by the number of vehicles in operation or service frequency under the given grid conditions of route length, number of passengers, and fare rate. Thus, the total financial balance in a city is given as E m, k^mk(nmk).

3.3.2. User benefit

We focus on user benefit arising from reduction in travel time and cost. The generalised travel cost C between origin i and destination j by mode k can be defined as follows:

Cijk = cijk + r ' I j

+ E tm

''ijmk

where cijk is the public transport fare for travel between i and j, which is equal to xvlj-; lij is the travel length; r is the value of time or the opportunity cost of time given by the wage rate; tmk and j are travel time and waiting time at grid m on route ij; and Sijmk is a binary value that takes one if m is on the route and takes zero if not. The travel route is fixed for an OD (origin and destination) trip. Additionally, waiting time is defined as j = maxm jlmk/(vmknmk)|rneMjj| J, where Mij = (m|6,jmk = 1}. n,jk is defined as {nmk | m^My], which is the vector of the number of vehicles in operation for the grid on route ij.

Here, 9k is a dummy parameter for mode k. As shown in the next section, travel demand is estimated separately for elderly and non-elderly people. Therefore, the qmk in Eq. (1) is the sum of travel demand for the elderly and for the non-elderly estimated by Eq. (9).

3.3.4. Strategic targets

Fig. 4 shows the links among formulated behaviour and outcome indices. In this model, the number of OD trips only depends on population distribution, but the modal share depends on the generalised travel cost of all modes as formulated in Eq. (10). The generalised cost is determined by the number of in-operation public transport

Papulation (Age, density) D.

Eq.(14H18)

Urban Aransport Strategies [Eq.(ll) -(13)]

OD-LOS Eq.(4),(S) Grid-LOS <„bLl/(vmknml)

Trip generation and distribution [Eq.(al)-(a4)] Modal choice [Eq.(lO)]

¿JljVfri '-i

Traffic (OD) 2A Eq.(9) Traffic (Grid) <lmk

Eq (2) v

Vehicle speed: vmk Route length: tmk

Publictransport operation (n)

Eq.(1)

C User's CÛ2

benefit emission ^^

Fig. 4. Stakeholder's behaviour and outcome indices.

vehicles using Eq. (4). The number of vehicles is calculated endoge-nously, with consideration of the modal share change, to achieve the strategic targets formulated below. When the generalised cost is determined, user benefit is calculated using Eq. (5). In addition, CO2 emissions are also determined using modal share information and Eqs. (6) and (9).

The three strategic targets - profit maximisation, net benefit maximisation, and CO2 emissions minimisation - can be formulated as optimisation problems over the vector of public transport vehicles n as follows:

max E Hmk(n)

max < E nmk(n)- E QijCij(n)

m J< i j

min E COn (n)

n , 2mk( ' n m, k

Here, the profit maximisation strategy eventually leads to the abolition of unprofitable public transport routes. For the other two strategies, public transport services can be subsidised in order to achieve respective targets. In the latter case, the financial results of public transport operators will be negative, with deficits being covered by government subsidies in this paper.

3.3.5. Population scenarios

We set two spatial patterns of population distribution - 'trend' and 'compact' - for each of 269 cities in the year 2030. These are represented as grid-based population datasets. Future municipality populations are as estimated by the National Institute of Population and Social Security Research Japan, and grid population is computed here to be consistent with this data.

We denote the population of grid i in 2000 as Di00, city population as D00, and that in 2030 as D30. Grid population in 2000 is given by the Statistics Bureau of the Japan Ministry of Internal Affairs and Communication. The grid population in 2030 for the 'trend' scenario, denoted by Di30, is calculated as follows:

D30 = D00-D30/D00.

This equation assumes that population distribution scales down/ up with the ratio of urban population of 2030 over 2000.

For the 'compact' scenario, the grid population is set using Eq. (14) if a city's population increases. In case of decrease, it is set as follows:

Dj whereie|M 0 whereie|M

where IM is the grid set of which the sum of the population is equal to D30, where D°0 <D?0 for Vi e IM, and Vj m IM. 7M is the complement of

When the elderly population is denoted by D3°, its population at grid i (denoted by D^,0) and that of the non-elderly population (D3°) are calculated using the following equation:

D30 = (M

00 D00 + Dai

•d3°/d0°

Drf = Dn0(1-/3)-D3°/D00

D3°-EDg0-D30/DQ' EDn?-D3°/D00

j3 is an adjustment factor to make the grid population consistent with city population. Fig. 5 shows some examples of population distribution produced by this procedure.

4. Cross-assessment of transport strategies and their impact on urban structure

4.1. Impact at the national level

In this section, three outcome indices - financial balance of public transport operation, user benefit, and CO2 emissions - are compared under the three public transport strategies and two urban structural scenarios.

Fig. 6 shows the CO2 emissions reduction from 2000 to 2030 in the six scenarios and BAU (business as usual), in which the LOS of public transport for each grid is fixed at 2000 levels. Here, NBM, PM, and CO2 indicate, respectively, the strategic targets of net benefit maximisation, profit maximisation and CO2 minimisation. Net benefits are defined as the sum of public transport operator profits and user benefits. In this figure, even in the case of 'trend' urban structure and BAU public transport LOS, CO2 emissions are reduced by about 5 million tons due to population decrease and ageing. For the 'compact' urban structure, emissions are reduced even more: around 1 million tons of CO2 emissions less than for the 'trend' urban structure for every strategy. Among the four transport strategies, CO2 minimisation naturally shows the largest reduction but profit maximisation also results in a larger reduction than BAU. On the other hand, reduction of NBM is almost the same as with BAU. This means improved public transport LOS does not necessarily contribute to CO2 reduction at the national level.

Fig. 7 shows the financial balance of public transport. Here, the current value is the estimation for 2000. BAU indicates a heavy deficit reflecting decreased transport demand. Financial balance is highly improved under PM. CO2 minimisation also reduces the deficit substantially because services are reduced in unprofitable regions.

Fig. 8 shows user benefit in each case, defined as the difference of generalised cost between the year 2000 and the target scenario3, where the generalised cost is given by Eq. (5). For both urban structure scenarios, NBM gives high positive value and PM gives negative value. The CO2 minimisation strategy gives higher benefits than BAU. This means that the LOS pattern to minimise CO2 emissions gives higher benefits than the current pattern, even as the former emits less CO2 than

Fig. 5. Spatial distribution scenario for population in 2030.

BAU NBM PM

o (0 a. E o O o

BAU NBM PM CO2

5.9 5.9 ] 6.2

0 2 4 6 8 10 CO2 emissions reduction (MT-CO2/yr)

Fig. 6. CO2 emissions reduction from 2000.

the latter. It is also shown that the 'compact' scenario brings lower user benefits than the 'trend' scenario except for the NBM strategy at the national level.

The results above can be summarised as follows:

1. The profit maximisation strategy will reduce CO2 emissions but decrease user benefits,

2. The CO2 minimisation strategy can improve the financial balance of public transport operations and slightly improve user benefits,

3. Urban compaction will be effective for CO2 emissions reduction but may reduce user benefits.

The first and second results indicate that the profit maximisation and CO2 minimisation strategies will have a positive relationship toward their objectives. It can be interpreted that a complex strategy of profit maximisation and CO2 minimisation may be an effective solution for CO2 reduction when creating a common understanding among stakeholders that 'the investment in environment improvement will promote economic development' in the transport sector. However, it should be noted that the CO2 minimisation strategy is expected to increase user benefits while the PM strategy will decrease them.

The third result is not seen in past studies and is caused by the compiling method used in this study; the national total is defined as the sum of the results of all cities estimated separately. Therefore, the result summarised above may not be applicable to individual cities. In the next section, the results are compared among cities to discuss regional conditions of CO2 reduction and benefit improvement as well as the difference of urban compaction impact.

Current

-a BAU

2 K NBM

o rn PM

<N CO2

o m BAU

o CJ CO2

-924 C ■ 10241

I I ~T

-944 [

-961 d -907H

-174[ 275 [

-1200 -1000 -800 -600 -400 -200 0 Financial balance of public transport (billion yen/yr)

-277IZ

-309IZ

□ 15;

Û15Î

-3000 -2000 -1000 0 1000 2000

User benefits(billion yen/yr)

Fig. 8. User benefits.

4.2. Regional difference in outcomes

In this section, we examine CO2 reduction and user benefits in respective urban areas under the CO2 minimisation strategy and discuss conditions under which city compaction is effective with regard to these indices. The examined urban areas, which are set based on "urban employment areas" [27], are shown in Fig. 9. An urban employment area is composed of a central city and its associated outlying municipalities that contribute at least 10% of commuters to the central city. There are a total of 269 urban employment areas in Japan.

Figs. 10 and 11, respectively, show the regional pattern of CO2 emissions reduction and user benefits. Fig. 12 shows the difference between the 'trend' and 'compact' scenarios. Fig. 10 indicates that CO2 emissions are significantly reduced in metropolitan regions for both 'trend' and 'compact' scenarios. However the impact of urban compaction somewhat differs among the three metropolises. Specifically, urban compaction has a positive impact on CO2 reduction in Osaka and Nagoya but a negative one in Tokyo (Fig. 12, left). This difference is caused by the fact that population density in the Tokyo metropolitan region is more than sufficient even under the 'trend' scenario so urban compaction would bring more traffic and CO2 emissions. This result implies that if a city is at a certain density then increasing density further makes CO2 emissions worse.

The 'compact' scenario provides a higher CO2 reduction than the 'trend' scenario in most cities. This means that urban compaction will be effective for CO2 reduction in many cities, except Tokyo and

Q Urban Employment Areas O Non-urban areas

Fig. 7. Financial balance of public transport.

Fig. 9. Urban employment areas.

Trend scenario

User benefits (billion yen/yr) □ 10

Fig. 11. Spatial pattern of user benefits in respective scenarios.

Difference in CO2 reduction

Fig. 12. Difference between 'compact' and 'trend' scenarios.

K. Doi, M. Kii / ¡ATSS Research 35 (2012) 62-70 69

trend corridors corridors & multi-centres

^ -T T

Fig. 13. Targeted LUTI scenarios in a selected region.

some regional cities. Comparing the outcomes in the three metropolitan regions, Osaka shows the highest potential for CO2 reduction due to improved coordination between land use and transport.

User benefits, shown in Fig. 11, are positive for both scenarios in the three largest metropolitan regions: Tokyo, Osaka, and Nagoya. Considering Figs. 10 and 11 together, both emissions reduction and user benefits will be achievable in these areas. However, many regional cities will lose user benefits. This reflects the possibility of lower emissions factor per passenger-km for private cars than for public transport due to a decline in travel demand concurrent with population decrease. The 'compact' scenario has fewer cities whose user benefits are negative, alleviating the negative range of benefits from the 'trend' scenario.

Taking a closer look at the difference in urban scenarios in Fig. 12, there are 123 urban areas (45.7%) where urban compaction has a positive effect on both emissions reduction and benefits and 74 areas (27.5%) where it has a positive effect on CO2 emissions reduction but a negative effect on benefits.

Among the three metropolitan regions, Tokyo and Osaka have lower benefits under the 'compact' scenario than under the 'trend' scenario, but Nagoya has higher benefits. In the former two areas, the LOS of public transport is high enough that the elasticity of benefits with respect to LOS would be low. In addition, compaction would increase the volume of private car use at congested grids such that the average travel time would increase. As a result, user benefits in 'compact' scenario are estimated lower than those in the 'trend' scenario. On the other hand, in Nagoya, improvement in public transport LOS is estimated to exceed the cost increases due to congestion.

Regarding other regional cities, the total benefits of the 'compact' scenario are higher than the 'trend' scenario. This means that the lower nationwide benefits of the 'compact' scenario under the CO2 minimisation strategy shown in Fig. 8 reflect the congestion cost in large metropolises like Tokyo and Osaka.

Altogether, the impact of urban compaction seems to differ depending on the urban situation. The impact on both CO2 emissions reduction and user benefits in the Tokyo area is negative and, conversely, positive in Nagoya. In Osaka, the impact on CO2 emissions reduction is positive and that on user benefits is negative. In most regional cities, the CO2 minimisation strategy is shown to bring a

decline in user benefits, although urban compaction alleviates this negative impact. Therefore, if regionally effective strategies were applied to each area, the nationwide total for CO2 emissions and user benefits could be expected to be higher than those shown above.

It should be noted that the grid LOS for private cars is fixed at the 2000 level. Under this assumption, change in grid congestion due to compaction and population change is not considered. This simplification may have both positive and negative bias on CO2 emissions and user benefits in the evaluation of urban compaction impacts. If road congestion increases, emissions from private cars will increase. On the other hand, demand may shift to railways, which would reduce emissions. Concentrating residential and business locations along public transport routes may increase citywide LOS on average, and user benefits regarding travel can be increased. However, such compaction would enhance land scarcity and possibly reduce benefits from housing. For a more comprehensive assessment of CO2 emissions and user benefits, integration with analyses of endogenous road congestion and land-use economy may be effective.

In addition, if we consider improvement to private car LOS through road construction or the introduction of advanced ITS, urban compaction may have a chance to improve user benefits even in large metropolises like Tokyo and Osaka.

4.3. Impact of LUTI scenarios

Additionally, we have investigated the impacts of alternative LUTI scenarios in a selected region that plans to reshape land use by developing corridors and multi-centres. Fig. 13 shows three land use scenarios: 'trend', 'corridor', and 'corridor and multi-centres', the latter two of which are LUTI scenarios that would be achieved through transit-oriented redevelopment along transit corridors. The 'corridor' scenario is assumed to remove around 10% of the population from non-corridor areas to corridor areas, while the 'corridor and multi-centres' scenario is expected to attract more population to the designated urban-cores along the corridors.

Figs. 14 and 15 show the impact of the two LUTI scenarios. A combination of urban transport strategies and land-use control in the form of 'corridor and multi-centres' contributes to a larger reduction

■8 =

° £ o = «8

100 120

KT-CO2/yr

■S =

BAU NBM PM CO2

20 40 60

bil. yen/yr

Fig. 14. CO2 emissions reduction by transport strategy and land-use scenarios.

Fig. 15. User benefits by transport strategy and land-use scenarios.

of emissions. In the 'corridor and multi-centres' scenario, CO2 emissions would be reduced as much as 47% under the CO2 minimisation strategy. This scenario also shows the largest benefits among the CO2 minimisation strategies as shown in Fig. 15.

5. Conclusions

The cross-assessment of transport strategies clarified that the three value factors of efficiency, equity and environment do not conflict with each other. In particular, it was shown that the CO2 emissions reduction target would contribute to improved financial balance of public transport and user benefits. A strategic combination of the CO2 minimisation and the profit maximisation is expected to bring synergetic effects.

The spatial analysis in all 269 urban areas derives the following possible findings: 1) the CO2 minimisation strategy is effective for emissions reduction and improving benefits in large cities, but the relationship of these two outcomes are a trade-off in small cities,

2) urban compaction in small cities may alleviate the trade-off relationship between emissions reduction and user benefit improvement,

3) too dense compactness in large cities may increase congestion, which consequently increases CO2 emissions and reduces benefits.

In addition, the results of comparative analysis among the three LUTI scenarios demonstrate that the integration of urban transport strategies and land-use control in the form of 'corridors and multi-centres' would provide an even greater reduction in emissions and increase in user benefits.

The results reported above are for Japanese cities with an ageing and declining population. It is clear that they cannot directly be transferred to the context of foreign cities. In addition to population dynamics, differences in the land ownership and public transport management systems may require different settings of the assessment framework. However, they do share some similarities in urban development strategy. For example, polycentric spatial structure has recently become an important development strategy in some Asian megacities. This new urban strategy aims to break up the former single-centre pattern and establish a new polycentric urban system. Our cross-assessment approach is expected to contribute to disentangling the issue of an integrated land use and transport framework and to supporting the building of a LUTI strategy and consensus among the stakeholders.

Urban structure is expected to co-evolve with transport systems including public transport and personal mobility systems. Looking at sustainable urban mobility in the next generation, our study group is now undertaking a "commobility" project that promotes intermodal integration between public transport and shared electric vehicles to enhance both quality of mobility and community cohesion. Our future challenge is to incorporate likely scenarios of intermodal integration and market penetration for electric vehicles, plug-in hybrid vehicles, and related technologies into the cross-assessment framework.

1 This paper focuses on urban passenger transport and does not touch upon inter-city and freight transport issues. In addition, transport and traffic conditions in our modelling are simplified to be analytically tractable and practically operational across all urban areas.

2 This paper focuses specifically on CO2 emissions reductions because it aims to contribute to low-carbon transport, and because long term climate change is largely controlled by CO2 due to its persistence in the atmosphere.

3 In the BAU scenario, the grid pattern of public transport LOS is the same as that in 2000, but the location of activities is different. Therefore, total generalised cost in 2030 is different from that in 2000 even in the BAU case.

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Looking at sustainable urban mobility through a cross-assessment model within the framework of land-use and transport integration Academic research paper on "Social and economic geography"

Abstract of research paper on Social and economic geography, author of scientific article — Kenji Doi, Masanobu Kii

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Academic research paper on topic "Looking at sustainable urban mobility through a cross-assessment model within the framework of land-use and transport integration"