ASSESSING THE WIDER ECONOMY AND SOCIAL IMPACTS OF HIGH SPEED RAIL IN AUSTRALIA

Transcription

1 ASSESSING THE WIDER ECONOMY AND SOCIAL IMPACTS OF HIGH SPEED RAIL IN AUSTRALIA Report prepared for the Australasian Railway Association 04 June 2012 David A. Hensher Richard Ellison Corinne Mulley Institute of Transport and Logistics Studies The University of Sydney Business School The University of Sydney

2 TABLE OF CONTENTS INTRODUCTION... 3 THE APPROACH TO IDENTIFYING ECONOMIC AND SOCIAL IMPACTS OF PROPOSED HSR... 6 THE MEASUREMENT OF EFFECTIVE EMPLOYMENT & SOCIAL DENSITY AS MEASURES OF ECONOMIC AND SOCIAL IMPACT... 8 MEASURING ECONOMIC AND SOCIAL AGGLOMERATION BENEFITS APPLICATION TO PROPOSED HSR BETWEEN SYDNEY AND MELBOURNE VIA CANBERRA AND REGIONAL CENTRES IN NSW AND VICTORIA SOURCING OF AGGLOMERATION ELASTICITIES CALCULATION OF POST-HSR IMPLEMENTATION EFFECTIVE DENSITIES MAIN FINDINGS CONCLUSIONS Appendi A. Development of an LGA by LGA Multimodal Transport Network The NSW Sub-Model as an Eample of the Methodology Appendi B. The Agglomeration Effect B1 The Measurement of Effective Employment Density B2 The Estimation of Agglomeration Elasticities REFERENCES Cover image: courtesy of Siemens - Velaro Spain 2

3 INTRODUCTION The Institute of Transport and Logistics Studies (ITLS) has been asked by the Australasian Railway Association (ARA) to provide advice on the potential economic and social benefits of a high speed rail (HSR) service between Sydney and Melbourne (via Canberra). The particular focus is on the identification of the wider economy impacts (WEIs) of HSR for regional centres in NSW and Victoria, who might benefit in terms of improved access to jobs (including improved access to particular locations for work-related activities), as well as the reduction in social eclusion consequent on increased potential accessibility to activities (additional to jobs) as a result of greater connectivity offered by HSR associated with increasing nearness of potential opportunities. Of particular interest is the etent to which HSR, when added to the eisting modal mi of available services to and from each Local Government Area (LGA) in NSW and Victoria, will deliver additional wider economic and social benefits, the former epressed as a proportion of real gross domestic product (GDP) and the latter as a proportion of total household income. To be able to obtain a forecast of WEIs and link them to GDP, we need to build a model system that can reveal the potential economic and social impacts of new transport infrastructure. There are two critical inputs into the model. The first is a regional origin-destination (OD) matri of trips and trip characteristics (travel times, travel costs and trip distances) for all eisting modes (car, air, train, bus) before the introduction of HSR and then after the introduction of HSR, together with HSR market shares and trip characteristics. The OD matri will be defined spatially at the LGA level. The second input is two indices respectively measuring effective economic (employment) and effective social density, which in broad terms provide, correspondingly, evidence of the potential gains in work-related output (often referred to as productivity gains) and potential gains in non-work-related outputs. These in turn are associated with gains in individual and household benefit attributable to improved accessibility to services linked with populations and particular locations. Given the magnitude of the task, we focus our efforts on the impact of the NSW and Victoria part of the HSR network (defined by the most likely route and station locations) in terms of regional impacts (essentially a case study), which we believe will be sufficient to demonstrate regional economic and social impacts across the wider spatial impact of the proposed HSR network. The NSW and Victoria evidence would be epected to signal similar implications for regional centres in Queensland and other possible HSR routes and station locations within NSW and Victoria. 3

4 Given the proposed route and station locations for HSR (see Figure 1), ITLS has developed an OD network for all of NSW and Victoria at the LGA level. We are not aware of any such transport network being developed for Australia in the recent past. Details of our approach are summarised in Appendi A. The network and its performance (in terms of trips) is defined by the following service attributes for eisting modes (car, plane, bus, train) and HSR: LGA/Zone of trip origin LGA/Zone of trip destination Number of trips by air each OD pair Number of trips by car each OD pair Number of trips by bus each OD pair Number of trips by train each OD pair Number of trips by HSR each OD pair Main mode travel time Access time for mode (origin) Egress time for mode (destination) Fare or cost this OD route (if serviced) Distance between each LGA centroid. 4

5 Figure 1: Route and station locations of proposed HSR This report is structured as follows. First our approach to identifying economic and social impacts of proposed HSR are detailed, and this is followed by the measurement of effective employment and social density as measures of economic and social impact. This leads to an eposition of how economic and social agglomeration benefits can be measured. This approach is then applied to the proposed HSR between Sydney and Melbourne via Canberra and Regional Centres in NSW and Victoria. The presentation of the results is followed by the main findings of the study. 5

6 THE APPROACH TO IDENTIFYING ECONOMIC AND SOCIAL IMPACTS OF PROPOSED HSR To estimate the potential economic and social benefits from improvements to long-distance travel (referred to as agglomeration benefits associated with access to jobs and activities in general, the latter ecluding jobs respectively), a methodology is developed, based on ideas from Graham and Melo (2011), that assumes constancy of trip decay with respect to equal average travel times, but variability with respect to distance. The intuition for this approach is that improvement in travel times will reduce the etent to which travellers perceive distance as an obstacle to interaction, which is a key contribution of HSR. High-speed rail has the potential to fundamentally change connectivity between cities and other locations rather than within cities. The key task of this project is to consider whether high-speed rail investment gives rise to agglomeration (economic and social) benefits, and if so, how substantial might these benefits be? Levinson suggests that, [t]he magnitude of agglomeration economies is uncertain (and certainly location-specific), but I think presents the best case that can be made in favor of HSR in the US. (Levinson 2012, p.4). We develop and implement a methodology for the assessment of the potential order of magnitude of such agglomeration benefits associated with improvements in long-distance travel between Sydney, Canberra and Melbourne (with stations at Regional centres), with multimodal connections to other Regional Centres (at the Local Government Area level). The empirical work undertaken is intended to indicate general orders of magnitude of agglomeration benefits that may arise from improvements in long-distance connectivity. We do not make any statements about impact on land values or relocation of firms and hence jobs. These impacts would be additional to the impacts we focus on in this report. A review of land use impacts is provided in a separate report (Hensher et al. 2012) Agglomeration economies occur when agents (i.e., firms, workers, individuals, households) benefit from being near to other agents. Nearness can involve physical proimity, but transport (and communications) plays a crucial role because, in most contets, speed and low transportation costs provide a direct substitute for physical proimity. We are specifically concerned with two types of agglomeration economies (i) production agglomeration economies, which derive from proimity between firms and the sources of these agglomeration economies: workers, other firms, and other facilities; and (ii) non-work activity agglomeration, which derives from proimity between households and sources of utility-deriving activities (be they social, personal, business or other non-work related activities). It is therefore important to understand what mechanisms drive production-related and household (non-work-related) activity related agglomeration economies. Production agglomeration economies usually mean that the productivity of individual firms rises with the overall amount of activity in other nearby firms, or with the number of nearby workers or consumers. The literature traditionally emphasises three key sources of agglomeration economies: input-output linkages between intermediate and final goods suppliers, 6

7 labour market interactions, and knowledge spillovers. Input-output linkages occur because savings on transport costs means that firms benefit from locating close to their suppliers and customers. Larger, denser labour markets may for eample, allow for a finer division of labour or provide greater incentives for workers to invest in skills. Finally, knowledge or human capital spillovers arise when spatially concentrated firms or workers are more easily able to learn from one another than if they were spread out over different geographical jurisdictions. Household non-work activity agglomeration economies relate to the increased ease with which household members can access services and people not engaged in economic production per se, and are related to the population of residents in a specific location and the change in the connectivity to activities in other locations. Allowing for specific characteristics of households such as income enables identification of the distributional benefits of improved connectivity, and hence the ability to comment on the etent to which classes of households (e.g., low income households) at particular locations might disproportionately benefit from improved connectivity delivered by HSR. 7

8 THE MEASUREMENT OF EFFECTIVE EMPLOYMENT & SOCIAL DENSITY AS MEASURES OF ECONOMIC AND SOCIAL IMPACT The empirical implementation involving estimation of productivity benefits from agglomeration economies associated with work-related travel, has been described elsewhere (see for eample, Graham 2007a,b, Graham et al. 2009, and Hensher et al. 2011). Briefly, for (economic) production agglomeration, this involves estimating the statistical relation between the economic output of firms and the degree of agglomeration eperienced by the respective firms. This is usually achieved by fitting a production function in which both private inputs (labour, capital, materials, etc.) and public inputs (e.g., local goods, agglomeration) eplain the variation in the level of economic output of firms. Hensher et al. (2011) have implemented this approach in Australia, and Graham and colleagues have done this for the U.K. and New Zealand. Appendi B summarises the Australian approach and evidence on agglomeration impacts on economic output. For household agglomeration, the focus is on the household and not the firm. Whilst in this section we focus primarily on production (or employment-related) agglomeration economies (or what we refer to as economic impacts); the eact same method applies to social impact (linked to non-work activity) but with different definitions of output. The difference for household agglomeration is the use of population instead of employment in effective density, and the estimation of a social agglomeration (or nearness) elasticity based on the generalised cost of travel (as a proy for location utility). For non-work related activities (or purposes) we use utility (or the change in utility) in contrast to production (or change in production) as the key output metric. Transport improvements can increase the strength of agglomeration economies to the etent that they increase connectivity within the spatial economy. It is clear that agglomeration economies depend on the flows of goods, people or information between locations. Therefore, the geographical scope of agglomeration economies will depend on the rate at which these flows decrease with distance. A key issue in understanding the spatial scope of agglomeration economies relates to the construction of the agglomeration term itself. This needs to be a variable that represents the potential opportunities for a firm (and households) to benefit from the agglomeration mechanisms in their locality, given the definition of what is meant by locality. We define agglomeration, A it, as an aggregation of workers or firms in the geographical neighbourhood of each firm i and time period t. In terms of employment, locality is defined in terms of administrative zones (e.g., LGAs), or, more generally, by aggregating employment with higher weights applied to locations close to a firm, and lower weights to locations further a- field. This type of agglomeration inde has the form given in (1). 8

9 A it a( ) (1) ji c ijt z jt The weights a(c ijt ) are decreasing in the costs or time, c ijt, incurred in travelling between location i and locations j in time period t, and z jt is a variable aggregated to create the agglomeration inde. Here A it is effective density (as defined by Graham (2006)) and z jt is LGA-level employment, c ijt is the straight line distance between LGAs (or d ijt ) with an inverse distance weighting system (as a well known gravity-based model) being specified as given by equation (2). (c a ) ijt 1 d and ijt A it ji z (2) jt d ijt In the contet of jobs, we refer to Ait as effective employment (or economic) density (EED); and in the contet of non-work activities we refer to effective social (or population) density (ESD). The effective density measure represents the amount of agglomeration eperienced by a firm (or individual, household) located at a location i, defined in terms of the quantity of employment (or population) in another location j (zjt), and the connectedness of location (i.e., an LGA) i with another location (i.e., an LGA) j (dijt). The parameter α is assumed to be greater than zero, such that employment at place j has less and less potential influence on a firm (or the population of individuals) in LGA i as the distance between i and j increases Graham and Melo 2011). The larger the value of α, the more rapidly the potential effect of employment diminishes with distance dijt. The market potential measure described above is identical in form to accessibility indices commonly used in transport analysis (see, for eample, Hensher et al. 2011). One important difference, however, is that market and population potential indices commonly use straight line distances to capture the relative spatial separation between locations, whereas transport accessibility indices use travel times, or generalised cost (using estimates of the monetary value of travel time savings and fuel costs) along eisting transport networks. Accessibility, market potential, or effective density measures based purely on distance depend only on the amount of surrounding employment (and/or population) and how far away that employment (or population) is. This may, however, not be the best way to evaluate the effects of transport improvements that bring firms and/or workers and/or populations in general closer, as is proposed by the HSR. Distance is simply a proy for the transport cost or travel time separating two locations which, with little or no congestion outside the capital cities, is highly correlated with travel time and cost. To obtain an estimate if a change in effective density associated with a proposed transport improvement, we need to convert the epected reduction in travel times or travel costs in each direction into an equivalent reduction in distance. For eample, if a given transport improvement reduces travel costs to the west of an LGA by 10 percent, then the new effective density at that LGA will change in a way that is equivalent to moving employment (or population) to the west 10 percent closer. 9

10 Thus a better way to incorporate transport costs or times into estimates of local (LGA) economic mass is to base these estimates on eisting transport costs or times rather than geographic distances. To do this local employment (or population) counts are aggregated up using a penalty that increases with travel costs or times rather than simple distance. It is then easy to see how to convert a policy-induced change (i.e., HSR) in travel costs or time into a change in accessibility. However, the eisting transport network and service is in part dependent on transport demand, which is in turn dependent on the level of economic activity (as well as size and composition of the population) in a given location and this is why Graham (2006) in particular, uses straight line distances, rather than network distances, times or costs, in effective density calculations. Our methodology incorporates a risk of inferring that closer connection to employment increases productivity (or population increases utility), when it is in fact productivity (or utility) that has encouraged closer connections through development of the eisting transport network. y A a y A 1 y Ab b (3) y The subscripts b and a identify the period before and after the HSR transport intervention respectively, and A is the productivity elasticity of economic agglomeration. The equivalent elasticity measure for non-work-related travel activities is the generalised cost elasticity of social agglomeration. The parameters used to allow for the spatial decay of productivity (social) benefits are thus based on distance. We need to estimate gravity models to provide evidence on the spatial decay of work and non-work-related trips between and within LGAs in NSW and Victoria. To account for the association between the sources of agglomeration (economic and social) economies and transport movements, we estimate distance decay coefficients for work and non-work-related trips separately. We note however that for Australia, given data limitations, it is only possible to distinguish trip purpose outside of a metropolitan area for the broad categorisation of work-related and non-work related purposes, both etracted from the national tourism monitor. The census only has journey to work data by mode. This distinction is relevant to our methodology given that we believe that the marginal value of distance decay is different between these two trip purposes. We have (see below) estimated a model for each trip purpose that provides the relevant parameter for α (see equation 2). We epect work related travel to be connected with labour market pooling eternalities (reflecting a more productive matching between job and worker skills), while non-work movements are epected to be related to population linkages (ease of access to other people, including friends and relatives and specialised professional services). The gravity model for work-related and non-work related trips is given in equation (4). F M M d ep( ),i, j 1,...,N (4) ij i j ij 10

11 which can be generalised to allow for the presence of origin and destination scale factors that measure the relationship between work- and non-work related flows and the respective sizes of origin i and destination j, given in equation (5). F cm ep( ),i, j,...,n i M d (5) ij j ij Fij is the number of one-way work or non-work related trips between origin i and destination j; c is a constant; Mi is the mass of the origin and consists of the population at each origin. Mj is the size of the destination and consists of the employment (for work-related trips) and population (for non-work related trips) at each destination. The parameter β1 (β2) determines the relationship between work and non-work related travel flows and the size of origins (destinations). The spatial distance between each pair of LGAs is based on road distances, dij. The model to be estimated is shown in equation (6), which takes logarithmic transformations so that an ordinary least squares (OLS) regression model can be estimated. F M M D D log c log log d (6) ij ij ij 1 i 2 j i j A set of control variables for origins (D i ) and destinations (D j ) based on the regional/rural-urban classification (essentially capital city origin and destination dummy variables), are introduced to allow for the possible presence of spatial heterogeneity. εij is the residual term, is assumed independent and identically normally distributed (i.i.d.). The results obtained are reported in Table 1. Over 92 (97) percent of the variation in total work-related (non-work related) trips between each of the 32,204 LGA pairs can be eplained by the population residing at each origin-destination pair or employment at the destination, and the distance between the LGAs, controlling for the capital city vs. other location heterogeneity. The decay gradient (α) is based on a function of distance in which α is itself a function of distance, as identified by a natural logarithm quadratic specification of distance: work: α = /distance squared nonwork: α = /distance squared There will be a different α for each LGA-pair for each trip, purpose in the full calculation of WEIs and WSBs. For eample, for the average distance of 663km for all LGA pairs, α (work) is , and α (non-work) is , calculated from the formulae above. This suggests a reduction in trip flows, varying between 0.019% to 0.079% over the average distance range for one additional kilometre. Thus, for every additional 100 kms, a reduction in average trip flows, varying between 1.9% and 7.9% is implied. 11

12 Table 1: Gravity Models for All Trips (Data based on 2006 Census), t-value in brackets Dependent variable: Natural log of Trips (in 000s) EXPLANATORY VARIABLE WORK-RELATED TRIPS NON-WORK RELATED TRIPS Constant (183.2) (180.1) Natural log of population in origin (169.3) (225.2) Natural log of population in destination (212.4) Natural log of employment in destination (88.7) - Distance (km) (56.7) (117.3) Natural log of Quadratic of distance (km 2 ) (-127.1) (-249.1) Observations 39,204 OD pairs Goodness-of-fit (adjusted R 2 ) Controls: Capital city origin (23.3) (38.2) Capital city destination (95.9) (32.6) 12

13 MEASURING ECONOMIC AND SOCIAL AGGLOMERATION BENEFITS The calculations we present here offer an approach for the measurement of long-distance agglomeration benefits. In particular, our approach addresses the issue of distance versus temporal decay, which is particularly important in the contet of long-distance and HSR investments. We use the evidence presented above to allow constancy of decay with respect to equal average travel times, but variability with respect to distance. In other words, we allow two equal distances to have different decay profiles if travel times vary, but the decay is always the same for equal travel times. This causes the distance decay parameter to change following a transport investment such as HSR. It is assumed that travel times, not distances, are the dominant factor underlying perceptions of the attenuation factor for any LGA zone. Furthermore, we assume that the spatial economy is initially at an equilibrium state, in which speeds and flows are consistent. To represent the relationship between interactions and travel time, we use the following epression (Graham and Melo 2011): log F log F d. v t d t (7) Epression (7) is the semi-log derivative of trip flows (F) with respect to travel time (t), and is obtained as the product of the distance decay gradient (α) from the gravity model estimated above (summarised in Table 1), and the average speed (v) between OD pairs both before and after HSR. It measures the proportionate change in interactions given a unit change in travel times. Assuming that this relationship is constant, an increase in average speed is associated with a reduction in the distance decay gradient, reflecting the fact that with the higher speed, distance (as offered with HSR in the mi) becomes less of an obstacle to interaction. The potential magnitude of agglomeration benefits associated with improvements in travel time due to a new high-speed rail connection is then obtained using the relationships of equations (8a) and (8b) for economic and social agglomeration respectively. WEI HSR EED. EED y EED.GDP (8a) WTI HSR ESD. ESD y ESD.THI (8b) 13

14 EwD (defining w=e=economic or z=s=social) is a measure of effective (w=e=economic or z=s=social) density, defined below in equation (9); y EED is the output (production) elasticity with respect to effective economic density (Graham et al., 2009) for y work-related travel, and ESD is the generalised cost (or accessibility) elasticity with respect to effective social density for non-work related travel; GDP is the Gross Domestic Product (either for the study area or Australia as a whole), and THI is total household income (either for the study area or Australia as a whole). We reasonably argue that the non-work-related wider economy impacts are likely to be accounted for as an etension of the benefits associated with the traditional set of user benefits, and hence might be better called wider transport benefits or impacts (WTI) and is meaningfully related to total household earnings, recognising that generalised cost includes both time cost and money cost. Effective density (EwD) is defined as as per equation 2 above (Graham, 2007; Maré and Graham, 2009; Melo, Graham and Noland, 2009 and Appendi B): N j EwD,i j i1 Z d ; i=1,,n locations (9) ep( ) ij Z j is the measure of the level of opportunities at destination zone j, A j is measured by population for non-work related flows, and employment for work-related flows. The change in the effective density resulting from an increase in the average speed of flows following an HSR transport investment is incorporated through a new (smaller) distance decay gradient (i.e., α 1 <α 0 ): EzD EzD EzD EzD 0 EzD 1 0 N A d j j ep( ) ep( ) i1 1 ij i1 0 N N A j d ep( ) i1 0 ij A d ij (10) The after transport improvement distance decay gradient for long-distance journeys is given in equation (11). log F d.. d t v (11) and the after transport improvement average speed is: v v k.,k (12) k is a positive constant that reflects the increase in average speed. Thus, in our approach, the distance decay parameter is revised following some transport intervention that changes the average speed in the transport network. The new distance decay parameter (α 1 ) is derived from the condition α 1 v 1 =α 0 v 0 given the average speeds calculated for each OD pair before 14

15 and after the introduction of HSR. The intuition here is that improvements in travel times (resulting from faster journeys) will reduce the etent to which travellers perceive distance as an obstacle to interaction. 15

16 APPLICATION TO PROPOSED HSR BETWEEN SYDNEY AND MELBOURNE VIA CANBERRA AND REGIONAL CENTRES IN NSW AND VICTORIA The methodology described above is used to evaluate the potential economic and social impacts in the contet of the case study of HSR implementation between Sydney and Melbourne via Canberra and four Regional Centres (Table 2). The information required, in addition to baseline data on eisting modes and travel activities, is the service level (times and fares) offered by HSR, the predicted market share of trips by HSR, and the consequent implications on offered average speeds after HSR is introduced. These are used to adjust the distance decay parameter and for the calculation of equation (10) for both economic and social impacts. Table 2: The HSR Stations between Sydney and Melbourne ROUTE EXPECTED STATION LOCATIONS STATION TYPE Sydney Central City Station Southern Highlands Moss Vale Parkway station Canberra Airport City Station Riverina Wagga Parkway station Murray Albury Wodonga Regional Station Goulbourn Valley Shepparton Parkway station Melbourne Southern Cross City Station Given that the mean speed of the proposed HSR is currently not finalised, we evaluate three scenarios, each providing different travel times between each station pair on the proposed route. The three commercial speeds are 150km/h, 200km/h, and 250km/h, which, given distances between stations, can be used also to calculate average HSR travel times between stations. To calculate the average speed of the entire transport network between each LGA pair, we use the average speed derived from access time + in vehicle time + egress time) / distance between each OD pair weighted by distance for each mode individually, and then calculate the average of these, in the presence and absence of HSR. 16

17 In addition to the improvement in travel times, we also need to consider the potential effect of high-speed rail on modal market share. To do this, we have three potential sources: 1. Use the Phase 1 HSR study report released by the Federal Government in mid 2011 (AECOM 2011) to derive a table of modal activity before HSR and after HSR but the limited information available only provides a matri of travel shares at a very high spatial resolution. 2. Use the OD revealed preference matri of aggregate travel times and costs as an aggregate modal choice model developed at an LGA level. This requires the splicing of the HSR alternative onto the estimated model (using generic time and cost parameters associated with eisting modes) in order to predict new modal shares in the presence of HSR. This is complicated by the way in which there is no known parameter value for the HSR-specific constant, which is a critical determinant of modal share. This parameter is typically obtained from a stated preference study: whilst we have access to SP data for HSR it is dated (mid 1990s) and relates to an earlier Very Fast Train (VFT) undertaken by ITLS (see Gunn et al. 1992). 3. Estimate the RP-SP model using the VFT data and, apply that, or to use the relative mode-specific constants, to import a constant into the aggregate mode choice model given the mode-specific constants for eisting modes. After etensive investigation of the three options and the running of eploratory models, we decided that the best approach is to use a mi of evidence obtained from the earlier ITLS Speedrail Study (Sydney-Canberra) (Hensher 1997), and the reported 2011 findings from the Phase 1 study for HSR, given that the latter is regarded by government as the best available estimates, albeit preliminary in nature1. This best evidence available on patronage is summarised in Table 3. Table 4 uses information, updated to 2036, from the earlier Speedrail (Sydney-Canberra) study undertaken by ITLS (Hensher 1997), to interpolate what we believe are reasonable estimates of the HSR modal shares for each trip purpose. These estimates were then applied to the Sydney-Melbourne route, taking into account evidence on induced demand from various published and unpublished sources. 1 The Federal government awarded a Phase 2 contract to AECOM in December 2011 to undertake new patronage studies as well as the full costing, design and impact studies. 17

18 Table 3: 2036 Shares of Travel for eisting and HSR modes before and after HSR is introduced ( 000s) HSR trips by each OD pair ORIGIN DESTINATION Melbourne Regional Canberra Regional Sydney Melbourne - 1, ,993 Regional Vic/NSW 1, Canberra ,291 Regional NSW ,378 Sydney 3, ,291 1,378 - Total 6,701 2,393 4,587 1,768 8,901 Total trips by all modes in absence of HSR ORIGIN DESTINATION Melbourne Regional Canberra Regional Sydney Melbourne - 25,001 1,042 6,285 Regional Vic/NSW 25,001-2, Canberra 1,042 2,000-1,379 3,757 Regional NSW - 1,379-13,376 Sydney 6, ,757 13,376 - Total 32,328 27,843 8,178 14, ,104 Total trips by all modes in presence of HSR ORIGIN DESTINATION Melbourne Regional Canberra Regional Sydney Melbourne 30,001 1,250 7,542 Regional Vic/NSW 30,001 - Canberra 1,250 2,400-1,665 4,508 Regional NSW 1,665 16,051 Sydney 7,542 1,010 4,508 16,051 Total 38,794 33,412 9,814 17,730 1,059,725 18

19 Table 4: 2036 Speedrail (Sydney-Canberra) Travel before and after HSR Source: ITS

20 SOURCING OF AGGLOMERATION ELASTICITIES The methods developed to obtain empirical estimates of employment-related agglomeration elasticities are set out in Appendi B, taken from Hensher et al. (2011), together with a case study on how we obtained empirical estimates for the Sydney Metropolitan Area. While these output elasticities with respect to agglomeration are informative (and are indeed the first such evidence in Sydney), they are not applicable to an entire State. Given the much lower baseline densities of employment, and greater distances between employment locations outside of Sydney, the distribution of jobs is different for the LGA zones around Sydney compared to all of NSW because of the urban/rural split even along the proposed HSR corridor. The output elasticity used in this study, consequently, was derived from new model estimation summarised in Table 5. For work-related trips the output (productivity) elasticity is obtained from the model form in equation (13). ln( AWR ) ln( EED ) i 1,...,N (13) i i i i i Where AWR i is the average wage rate (a proy for productivity gain given absence of firm-specific spatial data on other factor inputs) in location i and EED i is the effective employment density inde for location i. The evidence is summarised in Table 5. The output elasticity with respect to effective employment density is This is lower than the elasticity estimate for work related travel in Sydney estimated at 0.021(Hensher et al. 2012), as might be epected, suggesting that the influence of gains in effective employment density will be far less than would be the case in the Sydney metropolitan area. This seems very plausible (and in line with epectations of Levinson (2012)). Table 5: Agglomeration Elasticity for Work Related Trips (Data based on 2006 Census) t-values in brackets EXPLANATORY VARIABLE WORK RELATED TRIPS Constant (206) Natural logarithm of Effective Employment Density (LEED) (2.35) Overall goodness-of-fit (adjusted R 2 ) Number of observations 594 For non-work related trips the measure of output needs to be defined. We can reasonably assume that for personal business, leisure, social, and visiting friends and relatives travel, that a utility maimising representation of the improved benefit of improved access or nearness is an appropriate measure of output. We cannot measure the utility associated with the destination (or LGA) per se as a consequence of improved accessibility, but we can use the generalised cost of travel 20

21 defined by travel times and costs for all current modes, weighted by the use of each mode, as a proy measure. This weighted generalised cost of travel can then be used as the dependent variable in a model in which the main eplanatory variable is effective social density and where the distance between each LGA pair and population at the destination is our representation of the agglomeration or nearness impact of interest. We need to estimate this model (equation 14) in order to obtain an elasticity of destination utility with respect to effective social density, also known as the social agglomeration elasticity. ln(gc ) ln( ESD ) i 1,...,N (14) i i i i i ln(gc i ) is the logarithmic (or percentage) change in the generalised cost between LGA i and and all other LGAs and ln(esd i )) is the logarithmic (or percentage) change in accessibility to non-work related activities. i is an location-specific constant term, and i is the measure of agglomeration elasticity for location i. The evidence is summarised in Table 6 for 594 observations. The output or utility elasticity with respect to effective social density is This is higher than the elasticity estimate for work related travel, as might be epected, suggesting that the greatest potential benefit of any improvement in the distance decay due to improved accessibility throughout the States will be in the contet of non-work related travel. Table 6: Agglomeration Elasticity for Non-work Related Trips (Data based on 2006 Census) t-values in brackets EXPLANATORY VARIABLE NON-WORK RELATED TRIPS Constant (-9.20) Natural logarithm of Effective Social Density (LESD) (30.0) Overall goodness-of-fit (adjusted R 2 ) Number of observations

22 CALCULATION OF POST-HSR IMPLEMENTATION EFFECTIVE DENSITIES The calculation of the new (with HSR) effective densities requires a new value for α to incorporate changes to travel times into the distance decay gradient. This is done by using the ratio of average speed before and after HSR to adjust α, where the relationship is defined as: (15) 0 and 1 are the average speeds with and without HSR. Travel times and distances (and average speeds) for HSR are calculated by taking the eisting network and finding the closest HSR station to the origin LGA and the closest HSR station to the destination LGA. Access and egress times are assumed to be the same as the time required to travel by car between LGA pairs (i.e., origin LGA to departure HSR station LGA and arrival HSR station to destination LGA). The HSR route distances are calculated assuming HSR distance is 1.15 times the straight line distance between HSR stations. This is in turn used to calculated in-vehicle travel times at the three scenarios of different HSR speeds (150km/h, 200km/h and 250km/h). Graham and Melo (2011) used an average speed for the whole network in their calculation but this assumption would be unreasonable in an area as large as NSW. Having a single average speed would assume the effect of HSR on the distance decay gradient is equal for all LGA pairs even where HSR is not a viable mode due to very large access and egress times. Thus using a single average speed would both understate the changes in effective density for LGAs close to HSR and overstate it for LGAs far away from HSR. For this reason, we have made two changes to the approach used by Graham and Melo (2011). First, average speed and the new α is calculated for each pair of LGAs individually. Second, α is changed only if the generalised cost of HSR is less than or equal to the highest generalised cost of the eisting modes. The spatial distribution of changes in travel time and the changes in effective employment density (EED) and effective social density (ESD) for the three HSR speed scenarios is shown below in Figure 2. Figure 3 provides an ordered representation of the same percentages. 22

23 Figure 2(i) Spatial Impact of HSR Percent changes in Travel Time 23

24 Figure 2(ii) Spatial Impact of HSR Percent changes in Effective Employment Density 24

25 Figure 2(iii) Spatial Impact of HSR Percent changes in Effective Social Density 25

26 Figure 3a: Percent changes in Travel Time 26

27 Figure 3b: Percent changes in Effective Density 27

28 MAIN FINDINGS The introduction of HSR between Sydney and Melbourne would reduce the travel times (and by etension access) to the capital cities from regional areas of NSW and VIC. It is reasonable to epect that if the changes to travel times are sufficiently large, certain LGAs will become more attractive as a place to live (and work). As such, the resulting change to the relative competitiveness of LGAs in terms of access to Sydney and to Melbourne is of interest and gives an indication of the likely winners and losers from HSR (relative to other LGAs). Adapting the method by Sánchez-Mateos and Givoni (2009), this is achieved by calculating the average travel time before HSR to Sydney/Melbourne from every other LGA and then ranking them in ascending order, with the LGA with lowest travel time given rank 1. This process is then repeated for HSR running speeds of 150, 200 and 250km/h. For HSR running at an average speed of 150km/h, the largest gains in rank for travel time to Sydney are seen in LGAs near Wagga Wagga, Albury, Shepparton and Melbourne. Perhaps surprisingly, with higher average speeds, the biggest winners are LGAs in Melbourne (and to a lesser etent, Albury). Due to the nature of HSR, the distance between stations is much larger than for conventional rail and this results in an increase in the access and egress times to and from the closest HSR station. This means that for HSR to improve travel times there has to be a decrease in in-vehicle travel times greater than the increase in access and egress times. This is illustrated by the results for Melbourne and surrounding LGAs which reflect that the greater time required to travel to the closest HSR station in Melbourne is offset by the significant decrease in in-vehicle travel times. In contrast, LGAs further away from an HSR station and/or closer to Sydney see a somewhat lower decrease in total travel time. This in turn results in a greater (positive) change in rank. When rank is based on travel time to Melbourne, LGAs near Shepparton, Albury, Wagga Wagga and Moss Vale see the largest gains in ranking at 150km/h. At 250km/h, these same areas see the largest gains in rank in addition to the ACT and Sydney. Similar to the rankings of travel time to Sydney, at higher speeds, it is LGAs with relatively short access times and those further away from Melbourne that see the greatest increase in rank. Table 7 lists the top 10 LGAs in increasing order of the change in rank. Figure 4 shows the change in rank to all LGAs for travel times to Sydney and Melbourne with HSR running at 250km/h. It is important to note that although these LGAs gain the most relative to other LGAs, the average travel time from LGAs to Sydney drops from 7.78 hours to 7.25 hours at 250km/h, while the average travel time from LGAs to Melbourne has a bigger percentage fall going from 8.36 hours to 7.65 hours at 250km/h. Furthermore, at 250km/h there is a decrease in average travel time to Sydney from more than 80 percent of the LGAs, and nearly 90 percent of the LGAs to Melbourne. 28

29 Table 7: LGAs with largest change in travel times in rank order of travel times to Sydney and to Melbourne SYDNEY MELBOURNE 150km/h 200km/h 250km/h 150km/h 200km/h 250km/h Indigo Indigo Melbourne Wakool Wakool Benalla Campaspe Campaspe Yarra Berrigan Jerilderie Wingecarribee Alpine Melbourne Indigo Jerilderie Berrigan Jerilderie Hay Wodonga Wodonga Urana Urana Berrigan Melbourne Maribyrnong Maribyrnong Deniliquin Conargo Urana Wodonga Alpine Port Phillip Forbes Benalla Conargo Moreland Port Phillip Moreland Central Darling Queenscliffe Queenscliffe Coolamon Moreland Albury Conargo Wingecarribee Corowa Shire Gannawarra Moonee Valley Moonee Valley Queenscliffe Deniliquin ACT Maribyrnong Albury Campaspe Wingecarribee Parkes Indigo 29

30 Figure 4: Change in rank to travel time to Sydney and Melbourne 30

31 Table 8 summarises the GDP and Household Income in 2006 that is used as the base to epress relative WEI impacts. Table 9 summarises the values obtained for each of the terms included in equation (8) and the assumptions described above. Table 9 thus provides an order of magnitude for agglomeration benefits according to the three scenarios of travel time improvements attributable to three speed assumptions for HSR. Each of the scenarios is combined with the HSR market share for the matri of origins and destinations. Figure 3 provides a spatial pictorial of the percentage changes in effective density and travel time associated with the introduction of HSR (under the three HSR speeds), and Table 8: GDP and Household Income in 2006 AREA GDP (BILLIONS) TOTAL HOUSEHOLD INCOME (BILLIONS) New South Wales $306.6 $184.1 Victoria $232.8 $132.6 NSW + VIC $539.4 $316.7 Australia $952.8 $537.9 Sources: ABS Household Income and Income Distribution ( ); ABS Australian National Accounts: State Accounts ( reissue) Table 9: Agglomeration and Benefits from Improvements in Long-Distance Travel Times ($ 2006) Note: The GDP and THI values used are for NSW plus Victoria, whereas the evidence based on GDP and THI for Australia as a DATA ITEM whole is denoted with a * WORK RELATED TRAVEL NON-WORK RELATED TRAVEL Mean distance decay (α) before HSR introduced Share of HSR in total trips Output elasticity with respect to effective employment density Accessibility (generalised cost) elasticity with respect to effective social density GDP ($million) NSW + VIC 539,400 - Total Household Income (THI) ($million) NSW + VIC - 316,700 Scenario A: HSR average speed (kph) 150 Mean distance decay (α) after HSR introduced Change in effective density (ΔED/ED beforehsr ) Wider economy impact (WEI) from HSR ($million) 5.80 (10.24*) Wider economy impact (WEI) from HSR (% of GDP)

32 Wider transport impact (WTI) from HSR ($million) 2,131.6 (3,620.5*) Wider transport impact (WTI) from HSR (% of THI) 0.67 Scenario B: HSR average speed (kph) 200 Mean distance decay (α) after HSR introduced Change in effective density (ΔED/ED beforehsr ) Wider economy impact (WEI) from HSR ($million) 9.03 (15.96*) Wider economy impact (WEI) from HSR (% of GDP) Wider transport impact (WTI) from HSR ($million) 3,407.6 (5,787.7*) Wider transport impact (WTI) from HSR (% of THI) 1.08 Scenario C: HSR average speed (kph) 250 Mean distance decay (α) after HSR introduced Change in effective density (ΔED/ED beforehsr ) Wider economy impact (WEI) from HSR ($million) (19.53*) Wider economy impact (WEI) from HSR (% of GDP) Wider transport impact (WTI) from HSR ($million) 4,128.2 (7,011.5*) Wider transport impact (WTI) from HSR (% of THI) 1.30 THI Australian Bureau of Statistics, Household Income and Income Distribution, Australia Under HSR speed scenario A, representing an improvement to travel times of 3.82 percent for all of NSW and VIC, and average HSR speed of 150kph, and assuming mode shares as described above, we estimate an agglomeration benefit of $5.80m (10.24m*) for work-related travel, and $2,131.6m ($3,620.5m*) for non-work related travel, a total of $2,137.4m. In relative terms, these benefits correspond to and 0.67 percent, respectively, of GDP and THI (total household income). Under HSR speed scenario B, representing an improvement to travel times of 5.41 percent for all of NSW and VIC, and average HSR speed of 200kph, and assuming mode shares as described above, we estimate an agglomeration benefit of $9.03m ($15.96m*) for work-related travel, and $3,407.6m ($5,787.7m*) for non-work related travel. In relative terms, these benefits correspond to and 1.08 percent, respectively, of GDP and THI. Under HSR speed scenario C, representing an improvement to travel times of 6.27 percent for all of NSW and VIC, and average HSR speed of 250kph, and assuming mode shares as described above, we estimate an agglomeration benefit of $11.06m ($19.53m*) for work-related travel, and $4,182.2m ($7,011.5m*) for non-work related travel. In relative terms, these benefits correspond to and 1.3 percent, respectively, of GDP and THI. 32

33 CONCLUSIONS The main conclusion of the research presented above is that the potential order of magnitude of the employment-related agglomeration benefit is no more than percent of GDP (or $19.53m) for work-related travel even under a very optimistic scenario for the improvement in long-distance travel times (with an HSR speed of 250kph), and the forecast market share of HSR.. This compares to the range in Graham and Melo (2011) of 0.02 to 0.19 percent for reductions in travel time of 25 and 50 percent in the UK (under a pessimistic, a moderate, and an optimistic scenario for rail s market share from zero to 50%). We suggest that % is a realistic estimate reflecting the greater distances between major population centres in South-Eastern Australia and despite the potential for improving access to larger markets from regional areas. This is illustrated by the travel time from Melbourne to Sydney, which even under the most optimistic scenario is approimately four hours, because of the considerably longer distances involved in Australia, as compared to the UK. For non-work related travel, the potential order of magnitude of the social-related agglomeration benefit is 1.30 percent of THI. This evidence is part of what we refer to as the wider transport impacts (WTIs). It is an accessibility measure of the increased value to the population of individuals of the opportunity to access destinations throughout the study area for nonwork related activities, epressed as a percentage of the total household income of the population. This calculation was not included in the UK studies where the focus was only on work-related travel. This is equivalent to a benefit of $4.2bn per annum which is clearly a sizeable and significant sum. The evidence presented herein (on agglomeration impacts) is consistent with the viewpoint of Levinson (2012, p. 4). He suggests that: The evidence from the [broader literature] research shows that [HSR] lines have two major impacts. There are positive accessibility benefits in metro areas served by stations, but there are negative nuisance effects along the lines themselves. High speed lines are unlikely to have local accessibility benefits separate from connecting local transit lines because there is little advantage for most people or businesses to locate near a line used infrequently (unlike public transit). However they may have more widespread metropolitan level effects. They will retain, and perhaps worse, have much higher nuisance effects than local transit The local land use effects at HSR stations are likely to be small to non-eistent. Agglomeration benefits may eist, but there is little ground for concluding their size. If high-speed rail lines can create larger effective regions, that might affect the distribution of who wins and loses from such infrastructure. The magnitude of agglomeration economies is uncertain (and certainly locationspecific), but I think presents the best case that can be made in favor of HSR in the US. We believe that we have identified an order of magnitude agglomeration benefit for work-related activity. It is however relatively small compared to the significant gains associated with non-work related travel activity, namely $11.06m compared to $4.128bn. The evidence suggests that the greatest benefits associated with HSR, especially for those residents outside of the major metropolitan areas, will be non-work related travel activity. 33

34 Appendi A. Development of an LGA by LGA Multimodal Transport Network The NSW Sub-Model as an Eample of the Methodology Note: The actual parameters reported in this Appendi are for the NSW sub-model and thus differ from the model developed for NSW, ACT and Victoria that is used in this HSR study. The development of a regional modelling capability requires both demand- and supply- side information about transportation movements and networks. Furthermore, this information must be available in the same geographic units as used in the model. Whilst much supply-side information is publicly available (for eample, census data on vehicle ownership, public transport timetables, airline route schedules etc.), demand-side data is more difficult to obtain. The reasons for this are many: trips by personal vehicle are not centrally recorded; commercial operators may consider their patronage figures to be commercially sensitive, and governments may consider figures from nationalised operations to be politically sensitive. In the absence of actual demand-side figures, travel surveys become one of the main methods of obtaining demand-side data. The collection of primary survey data is a costly process however, particularly if the survey is to be conducted at a high level of geospatial disaggregation. In Australia, there is etensive demand-side information available about localised transport movements (such as for journeys to work) and also information about the broader transport behaviour of persons resident in major metropolitan areas (such as from the Sydney Household Travel Survey). Financial constraints eliminated the possibility of collecting primary survey data specifically for this project, and the most recent National Travel Survey conducted in Australia occurred in The requirement for current demand-side data about long distance travel movements therefore necessitated a different approach. The approach developed below has a natural synergy with the literature known as modifiable areal unit problem (MAUP). MAUP is a problem resulting from the imposition of artificial units of spatial reporting on continuous geographical phenomenon resulting in the generation of artificial spatial patterns. Scale and aggregation define the key elements of MAUP. Scale refers to the variation in numerical results that occurs due to the zonal system selected and aggregation refers to the zoning scheme used as a given level of aggregation (in moving from a grouping of smaller into larger units). Wrigley (1995), for eample, suggests that the use of well chosen grouping variables to adjust the area-level results may produce reliable estimates of underlying individual-level relationships, providing at least a partial solution to the MAUP with respect to the ecological fallacy, namely the individual-level inference based on area-wide analyses. In our study, the focus is on the specific dis-aggregation problem of moving from the grouping of large (tourist) areas into smaller (local government) units for only the dependent variable (i.e., origin-destination traffic activity by mode) since we have been able to develop level of service and socioeconomic O-D matrices at the spatial level of interest, namely LGA-LGA. Importantly however the prediction of traffic of interest is obtained from a modelling system in contrast to an adjustment 34

35 using a grouping variable (the latter is appropriate when all variables of interest are at a different spatial resolution), simply because we are only focusing on predicting the dependent variable. The model is estimated at a tourism region O-D level (Table 1) and used to predict the traffic levels at the LGA O-D level, assuming by implication that the parameter estimates from this model are representative of the estimates obtained as if we were directly estimating a model at the LGA O-D level (i.e., we knew the LGA O-D level traffic levels). Spatial error in the predicted traffic levels at the LGA O-D level is captured by the residual error and is assumed independent of the parameter estimates of the eplanatory variables. As such the link with the literature on MAUP is in terms of using one level of spatial aggregation to predict traffic movements at another level of spatial aggregation, where the latter is a sub area of the former. Scale in our contet relates to variation in the predictions of modal activity at an LGA O-D level being dependent on a model calibrated at a tourism region O-D level. The best approach (see below) is to ensure that the summation of LGA O-D movements by mode can be summed to equal the movements between tourism regions that define the LGA set, including in both case intra-zonal movements 2. We are not able to go down to a specific decision unit such as a trip, recognizing that even at the LGA O-D level there is significant spatial aggregation. The primary data source that was available was the National Visitor Survey (NVS) conducted annually by Tourism Research Australia (TRA2008). Each year, 120,000 Australian residents are surveyed using random digit dialling and a computer aided telephone interview (CATI). This survey is the principal source of information on domestic tourism movements in Australia. Information about the movements of international visitors is collected separately. As tourism ' in this contet is defined to include holidaying, visiting friends and relatives and travel for business, education or employment, the survey covers a broad range of passenger movements. The principal limitation of this data source was that it provided data at a very high level of geospatial aggregation 1515 origin-destination pairs for the geographic area concerned when the intention was to model transport movements at a much lower level of aggregation origin-destination pairs. NSW/ACT tourism regions (TRA) and local government areas are shown in Figure A1. The number of tourism region OD-pairs for which data eists was: car, 218; bus, 113; air, 86; and other, 105. These numbers include intra-zonal contets. 2 At the Tourism Region level, we had to allow for intra-zonal travel given its importance, especially for car trips. This is less so for air travel and even rail activity (the latter being a small amount of activity anyway). 35

36 Figure A1: NSW/ACT Tourism Regions and Local Government Areas To overcome this limitation, a regression model was constructed from the aggregate survey data for each of the four long distance transportation modes: car, coach, train and plane (Table A1). Origin and destination populations feature in all models with positive coefficients as epected. The use of double log models permits direct interpretation of the elasticities for the eplanatory variables. Distance ehibits negative coefficients for both the road-based modes, with car trips having a greater sensitivity to distance than bus trips. Distance occurs as linear and quadratic terms in the Plane and Train models, but with opposite signs. The result of this is that air travel is more appealing for medium-range travel (~500km); conversely, train travel is least appealing over these distances. Anecdotal evidence suggests that air travel within NSW over longer distances would involve non-trunk routes and often a transfer to a smaller aircraft, which is likely to be a disincentive. The presence of household size in the car model is eplained by the cost effectiveness to larger families in using a private motor vehicle in comparison with other modes. Finally, the reduced travel by car and increased travel by train for travel wholly within Sydney (as evidenced by the presence of the Sydney dummy), can be eplained by the presence of a comprehensive urban transport network within this region that is not present in the regional and rural tourism regions. 36