RETRACTED: Introducing a transport carbon dioxide emissions vulnerability index for the Greater Dublin Area

Journal of Transport Geography 19 (2011) 1059–1071

Contents lists available at ScienceDirect

Journal of Transport Geography journal homepage: www.elsevier.com/locate/jtrangeo

John Carty a,⇑, Aoife Ahern b b

Urban Institute Ireland, University College Dublin, Richview, Dublin 14, Ireland School of Architecture, Landscape and Civil Engineering, University College Dublin, Dublin 4, Ireland

a r t i c l e

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Energy consumption and carbon dioxide emissions from the transport sector have continued to rise, adding to growing concerns about the environmental impacts caused by transport systems and related landuse patterns. The transport sector in Ireland is a significant fuel consumer, accounting for 36% (5771 kTOE3) of Ireland’s primary energy demand in 2007. The sector was responsible for 36% (17,014 kt5 CO2) of Ireland’s energy-related CO2 emissions, higher than any other sector. Energy use in the transport sector grew by 181% (6.3% per annum on average) between 1990 and 2007. A key characteristic that distinguishes energy use in transport is the almost total dependence on imported oil as a fuel – over 99%, EPA (2009). Given the levels and growth of energy demand in transport, there is a clear imperative for policymakers to develop and implement measures and programmes that maximise energy efficiency and renewableenergy penetration. In this paper we develop a transport carbon dioxide emissions vulnerability index, using the Census of Population of Ireland 2006 Place of Work – Census of Anonymised Records (POWCAR) Dataset. The transport carbon dioxide emissions vulnerability index will be developed for the Greater Dublin Area to represent spatially in terms of transport carbon emissions the regional differentiations in commuting distances and modal shares. The results of this research can then be used to assess the transport carbon dioxide emissions of future development plans and therefore allow greater transport sustainability to be achieved through improved design of the location and form of major new development. Ó 2011 Elsevier Ltd. All rights reserved.

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Keywords: Transport carbon dioxide emissions Transport energy consumption Urban form

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Introducing a transport carbon dioxide emissions vulnerability index for the Greater Dublin Area

1. Introduction

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Since the middle of the last century, most developed countries have experienced a continuing increase in urban populations through population growth and immigration. This has also been demonstrated by the Irish Central Statistics Office (ICSO, 1986, 1991, 1996, 2002, 2006), where urban in-migration began in the 1960s, increasingly affluent and mobile urban populations have come to rely on private cars, prompting greater awareness of environmental impacts of transport, and especially the role of emissions to air in regard to climate change. Issues related to the spatial distribution of homes and workplaces and the travel patterns that they generate, have raised interest and debate about the desirability of integrating land use policies to promote higher urban densities and transport policies aimed at reducing energy consumption and emissions. Understanding the connection between land-use and transport can be promoted by analysing the urban spatial structure. Spatial struc⇑ Corresponding author. Tel.: +353 1 7162699. E-mail address: [email protected] (J. Carty). 0966-6923/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.jtrangeo.2011.03.006

ture can be defined as a combination of land use formation, its densities and the spatial design of infrastructure such as transportation and communication (Anderson et al., 1996). It can alternatively be characterised by three elements: the urban form, the human interaction in the city and the organising principles that define the relationship between the two (Bourne, 1982). Either way, the urban spatial structure of a city seems to have a significant influence on the transportation flows within its area; yet, it does not determine them entirely. Relationships between the built environment and travel behaviour have typically been studied on a neighbourhood or regional (metropolitan) scale (Donoso et al., 2006; Ewing et al., 2007; Hunt, 2003; Rodier et al., 2002). Groups of cities (and their GHG emissions) have been studied using cross-sectional data (Bento et al., 2004) or for the purpose of predicting metropolitan level travel activity (Cameron et al., 2003). Previous work has employed the ASIF (emissions are the product of activity [A], modal share [S], modal energy intensity [I], and fuel mix [F]) and IPAT (environmental impact [I] is the product of population [P], affluence [A], and technology [T]) frameworks to model travel behaviour (GrimesCasey et al., 2009; Schipper et al., 2000; Zegras, 2007) and

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To assess how sustainable different urban forms are with regard to transport carbon emissions the study adopts a case study approach using the Greater Dublin Area (GDA) as the test case. The GDA was selected as the case study area because over one third of both the population and employment in the country are located there and in recent years it has witnessed extremely interesting and unique changes in terms of development patterns, population changes and employment growth. The GDA has witnessed a major increase in car dependence in recent years, especially in outlying areas. This dependency on the car has been exacerbated by recent trends in residential development that has seen significant developments of low density and one-off housing in the outer parts of the GDA. Many of these areas are insufficiently served by public transport and with their new populations living further from urbanised areas, employment and services they are highly dependent on the use of the car for essential trips. Furthermore, due to the lack of sufficient critical mass in these areas, the provisions of acceptable alternative modes to the use of the car are difficult in such areas. In areas where alternative modes do exist, public transport in particular struggles to replicate the comfort, convenience and flexibility of travel by car. For these reasons, policies to reduce car dependence will only be successful and meet with public approval where the multiple roles of transport and the interdependence on land use are fully understood and well thought out alternatives to car travel are offered to transport users. In terms of a research gap, our study expands on previous work in a couple of ways. Firstly the majority of the research into this area has been conducted in North America and to a lesser extent in Europe. This research will aid the knowledge base for this type of research, especially in Ireland. Secondly by incorporating the use of Geographical Information Systems (GIS) to assign public transport modes to individuals based on their geographical location in relation to public transport modes. This paper attempts to clarify the debate concerning the relationship between transport related CO2 emissions levels and commuting behaviour. The results can constitute a basis for further research, which aims to determine the robustness of spatial structures in a climate of incipient fuel scarcity. A better understanding of this matter will uncover social and spatial evolutions, and leads to a policy that facilitates a more sustainable development.

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concluded that activity (e.g., VKT per capita) is an important factor in transportation emissions. Recent modelling of the US Midwest suggests that compact growth could achieve long-term emission reductions equivalent to the hybridization of the light duty vehicle fleet (Stone et al., 2009). Numerous studies have tested hypotheses regarding the relationship between urban structure, especially density, and transport emissions and energy. Under these hypotheses, raising urban densities is expected to lead to a decrease in energy consumption and consequently transport emissions (e.g. Newman and Kenworthy, 1989). In most developed countries, policy makers and urban planners accept this insight with great enthusiasm as a solution for air quality and pollution problems too. Many European countries promote the concept of the ‘Compact City’ on the basis of environmental arguments. This policy is as a result of adopting the conventional wisdom that there is a negative correlation between urban density and energy consumption in the transportation system. The concept underlying the planning for compact cities originates from the perception that reversing urban structure from low density to an urban form that dominated 60 years ago is feasible. This, it is often argued, would re-embrace the usage of public transportation, walking and cycling (Fulford, 1996). Planning for high density has two main goals in the context of transport emissions: firstly by reducing trip length and total mobility by concentrating residential, employment and services areas and secondly by changing the modal split to reduce the share of the private vehicle use in relation to public transportation, walking and cycling. However, not all research shows this to be the case. Buchanan et al. (2006) state that it may be the case that in large cities with large populations a compact city is desirable but very little research has been carried out to examine the relationships between transport and urban form in medium size cities like Dublin. Urban density levels are not likely to reach the same levels in medium sized cities as in much bigger cities. Urban density is not the only factor playing a role in travel patterns and behaviour, so even if compact cities are developed, travel patterns may not become sustainable. Researchers acknowledge the existence of an intricate relationship between urban form and socio-economic and attitudinal factors affecting travel behaviour (Cervero and Duncan, 2002; Krizek and Waddell, 2002). Some authors, controversially, put forward a different viewpoint and contend that continued dispersal will lead to a natural ‘co-location’ of activities and reduced travel (Gordon and Richardson, 1997). Such views tend to be based on the US suburban context. There are also issues raised around the acceptability of various policy stances, particularly the public acceptability of compaction (Breheny, 1992, 2001). (Gordon and Richardson, 1997) Contends that suburbanisation of the labour force has actually led to reduced trip lengths, with both residences and jobs being located in the suburbs. Schimek (1996), concluded that all else being equal households in higher density areas travelled less in private cars. However, the effect of density was found to be so small that even a relatively large-scale shift to urban densities would have a negligible impact on total vehicle travel. It is argued here that suburbanisation has been stimulated by lifestyle choice and that attempts towards urban compaction are running against the aspirations of the majority of the public. Breheny warns of the need to test compaction policies for veracity, feasibility and acceptability. An element of self-selection operates in high-density public transport-orientated environments, whereby individuals choose to reside in such areas precisely because their values predispose them to using public transport and walking (Cervero and Duncan, 2002). In addition, high density areas are often much closer to cities and therefore, car use is not as essential as in areas further out of the city. The complexity inherent in the rationale for travel appears to make analysis of urban form and travel problematic.

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2. Greater Dublin Area (GDA) case study The GDA consists of Counties Dublin, Meath, Kildare, Louth and Wicklow and has a land area of approximately 7800 km2 with a population of over 1.6 million. County Dublin is also made up of four local authorities; Dublin City, Fingal, Dun Laoghaire–Rathdown and South Dublin. The GDA represents 38% of the total population of the country. The well-documented economic growth experienced in Ireland during the 1990s directly contributed to a significant rise in living standards, placing Ireland among the top positions on this ranking at international level (Walsh, 2004). Between 1996 and 2006 Ireland has seen unprecedented economic growth with the doubling of Gross Domestic product (GDP), a 40% increase in employment accompanied and facilitated the high growth rates experienced in Ireland during the past decade, in 2006 there were 2.1 million people in employment in Ireland. House prices in the Greater Dublin Area have risen at high rates on a sustained basis also during the study period. The Dublin and Mid-East region in particular has experienced unprecedented levels of growth and change in the past decade. Concern has grown among urban planners and the public at the increasing financial difficulties of first-time buyers of homes in the region to afford homes at relatively close distances to their workplaces. This has contributed to the rapid development of

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new housing estates far from the main regional employment centres, with many of the smaller towns between 50 and 80 km from central Dublin becoming local focal points for new housing developments and extending further the commuter belt. Car ownership rates in Ireland have increased rapidly along with rising incomes and employment rates. In 1996, Ireland had 382 cars per 1000 adults. By 2006, this had risen to 528 cars per 1000 population (ICSO, 2006). Although the increase was one of the highest recorded for the EU15 member-states, the car ownership rate remains significantly below the European average of 558. On the supply side, there has been little increase in the total length of key arterial roads nationally (namely, motorway, national primary and national secondary roads) in the past decade, despite the growth and regeneration particularly in inner-city districts. The combination of all of these factors has had major implications for commuting and travel patterns in the Dublin region. The number travelling to work by all modes of transport has increased by over 16% in the most recent census period 1996– 2002, further accentuating trends documented by Horner (1999) for the period 1981–1996. These related to the increasing proportions of long-distance commuters, focused around the major urban centres, and the increased use of the car for shorter journeys. In 2006, the study area had a modal split of 60.6% private motorised vehicles, 12% Bus, 6.7% Train and 15.4% Walking/cycling (DTO, 2006). The public transport network in the study area consists of radial bus and train networks where all services travel to or from the city centre. The train network consists of both a heavy rail system and a light rail system. The heavy rail category has two systems the main diesel commuter trains which service the intercity routes and the DART system (Dublin Area Rapid Transit is an electrified suburban service running at a relatively high frequency along the east coast of Dublin and Wicklow from Howth in north Dublin to Greystones in Wicklow). The light rail system was introduced to the south side of Dublin City in 2004; this consists of two unconnected lines called the ‘Green line’ and the ‘Red Line’. The Green line serves Sandyford to St. Stephen’s Green in the city centre and the Red Line serves Tallaght in the south west of the city and Connelly Station in the city centre. The city centre terminals are located 1 km apart (see Fig. 1).

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Fig. 1. Study area: The Greater Dublin Area.

traffic. The last two arguments indicate that the study of the home-to-work commute remains very important. It is important to understand that the rigidity of the commute, both with respect to distance covered and with respect to modal choice, it is not only a spatial issue. The attitude of the commuter towards the destination and itinerary, and in particular its habits, determine this rigidity to a large extent. Consequently, we should consider the travel pattern as a result of the interaction between space, motivation and habit (Gardner, 2009).

3. Focus on studying journey to work trips

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Accurate data on journey to work trips is more often available than data on other trips. This is probably the main reason why many studies, such as those of Dodson and Sipe (2008), focus on journey to work trips to quantify the sustainability of travel patterns. However, in studies focusing on a small enclosed area, it is easier to incorporate different kinds of trips, as was done by Saunders et al. (2008). This paper too is based on journey to work trips data. This trip is not representative of all trips, but does affect significantly nonwork related trips. From the theory of the constant travel time budget (Schafer, 2000), it can be said that commuters who spend a lot of time travelling to work will spend less time on other trips. This means that they will make more efficient chained trips and that they will look for destinations closer to home, but also that they will choose more frequently for fast means of transport (i.e. the car). Moreover, the home-to-work commute is more inert than other trips are which can be illustrated on the basis of price elasticities (Mayeres, 2000). Given this rigidity, changes in preconditions, such as fluctuations in fuel prices, will be more problematic for commuting patterns than for non-work trips. Furthermore, commuting trips cover more often large distances (Zwerts and Nuyts, 2004), and thus contribute significantly to the negative effects of

4. The research framework This paper seeks the urban, transportation and social conditions that contributes to the contrasting levels of transport carbon dioxide emissions in the Greater Dublin Area. The dependent variable in the analysis is transport carbon dioxide emissions in the journey to work. Commuting trips were obtained from the Census 2006 Place of Work – Census of Anonymised Records (POWCAR) Dataset. The POWCAR dataset which was derived from the 2006 census includes only persons who at the time of the census ere enumerated in a private household, were 15 years old or over, were enumerated at home and indicated that their present principal status was working for payment or profit. Electoral Divisions (EDs) are the smallest legally defined administrative areas in the State for which Small Area Population Statistics (SAPS) are published from the Census. Therefore the average personal daily transport carbon dioxide emissions in the morning commute to work are aggregated to the ED level. It is our aim to develop a spatial sustainability indicator that enables the mapping of regional and urban differences in transport carbon emissions. There are two important arguments that can be put forward to develop such an index. First, there is the growing importance of the energy factor in the public debate. The sharp fluctuations in oil prices, the debate about peak oil and

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5. Commute emissions vulnerability index To enable the representation of the relationship between transport carbon dioxide emissions and the surrounding spatial structure a transport carbon dioxide emissions vulnerability index is developed. The index is calculated by dividing the total amount of transport carbon dioxide emissions for journey to work trips per electoral division by the working population that lives in the electoral division. More formally:

CEV i ¼

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Public transport vehicle

Average occupancy

Bus (Dublin bus only) Intercity bus CIE DART Suburban rail LUAS (green line) LUAS (red line)

55 30 507 494 227 177

national fleet which was the Diesel Euro 3 < 2.0 L engine. Combining the emission levels associated with the corresponding distance and time data from POWCAR the CO2 emission factors were estimated in relation to the average speed of the journey (see Fig. 3). Emission levels for Dublin Bus were obtained from the EPA (http://cmt.epa.ie./Global/CMT/emission_factor_sources.pdf) and are 0.077 kg CO2/passenger km. In terms of intercity buses, the emission factor from Walsh et al. (2008) was used, their calculation of 0.84 kg CO2/km which equates to 0.028 kg CO2/passenger km using the average occupancy rate of 30 passengers. The emission factors for LUAS (the two light rail systems operating in Dublin city) were calculated using their traction effort curves provide by the rail procurement agency. (Fig. 4) provides the amount of energy consumed by the LUAS red line tram at various speeds, and average speed was estimated for a typical tram journey on the basis of data provided in the light rail transit system website in http://www.lrta.org/luasindex.html. Given a line length of 15 km and journey time of 38 min, this results in an average speed of 24 km/h. From Fig. 4, a tram travelling at 24 km/h requires approximately 800 amps at 750 V. Fig. 5 demonstrates the electric current returns during braking. Braking frequency is dependent on many variables such as other traffic and weather conditions. To account for this a breaking speed of 10 km/ h is estimated and it is assumed that a tram brakes during 10% of journey time. The amount of power required (in Watts) is calculated by multiplying the voltage (750 V) by the current in amperes. If it is assumed that the average speed is maintained for an hour this results in an estimate of 600 kW h. If the same method is applied to braking then approximately 13.5 kW h are saved during braking (Walsh et al., 2008). This results in an overall power demand of 586 kW h/h, during which the tram travels 24 km. This equates to 24.4 kW h/km. Based on the 2004 Irish electricity generation fuel mix, 1.0 kW h results in emissions of 0.6 kg CO2. This suggests that a kilometre travelled by tram indirectly emits approximately 14.64 kg CO2. The peak time LUAS red line occupancy rate was estimated at 177 passengers based on data provided by the Dublin Transport Office. This results in emissions of 0.0827 kg of CO2 per passenger kilometre.

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CEVi (Commute emissions vulnerability index) is the average carbon dioxide emissions per member of the work force for their journey to work from their electoral division i. Di is the distance travelled in the journey to work from the electoral division i. Cms is the carbon dioxide emission level per km of the particular mode m related to the average journey speed of that mode (the speed of the journey is only taken into account for private car trips). Ni is the number of members of the work force for that particular electoral division i. In order to take into account the varying carbon emissions between different modes, individual emission rates for each public transport mode and each private motorised means of travel have been set, all non-motorised trips emissions are equal to zero. Passenger kilometre emissions are calculated using average vehicle occupancy rates supplied by the Dublin Transportation Office for the morning peak hour (8 am–9 am) for 2006 (see Fig. 2 and Table 1). Figures were not available for intercity buses so the occupancy rate was set to approximately half of the maximum occupancy of 57. The emission factor for private cars was estimated using data on engine size distribution within the national fleet and carbon emissions related to the engine sizes. The most representative engine in the national fleet was found to be the Euro 3 Petrol (1.4–2.0 L range based on DOEHLG 2005). Combining this information with time and distance data from POWCAR the emissions are calculated in relation to the average speed of the journey. The same methodology is used when calculating the emission factors associated with car passengers except for dividing by the vehicles occupancy rate. The emission factor for motor bikes was obtained from the EPA (http://cmt.epa.ie./Global/CMT/emission_factor_sources.pdf) and is 0.0939 kg per passenger kilometre. The emission factor for vans was estimated using the most representative engine range in the

Table 1 Average occupancy levels for public transport during peak hour 8 am–9 am 2006. Source: Dublin Transportation Office.

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the efforts made to reduce emissions of greenhouse gases play a role in this discussion (Witze, 2007). Second, the relationship between spatial structure and travel is a vexed issue. Travel patterns are highly heterogeneous, and vary with the morphology and the use of the space.

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CO2 Emissions EURO 3 Petrol

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CO2 Emissions EURO 3 Diesel

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Vehicle Speed [km/h] Fig. 2. CO2 Emissions for Euro 3 Petrol (1.4–2.0 L). Source: Transport Research Laboratory, UK.

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Vehicle Speed [km/h] Fig. 3. CO2 Emissions for Euro 3 Diesel <2.0 L. Source: Transport Research Laboratory UK.

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individuals needed to be assigned to their most probable mode. Using Geographical Information Systems (GIS), individuals were assigned to their nearest mode based on their location. So for example individuals who selected the train and were located in EDs along a Luas line were assigned to the luas as their mode of transport, likewise with individuals living along the DART line. 6. Results 6.1. Spatial distribution of the commute emissions vulnerability (CEV) index: a first glance

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Fig. 4. Traction curve of LUAS travelling on the ‘‘red line’’.

The CEV index for commute trips was calculated based on the commute trip origin. In the next stage the relationship between the CEV index and the spatial characteristics will be introduced. In order to analyse the CEV index, the relevant spatial characteristics of the case study area are mapped as well, based on data from the census 2006 POWCAR dataset. The main road and rail transport infrastructure is added to each map. Fig. 7 shows the average commute trip distance per member of the active work force. For this, data on commute trip distances are aggregated for each electoral division and divided by the working population. Figs. 8–11 present the frequency with which the modes, that are known to be energyefficient, are used as main transport mode for commuting trips. The purpose of the maps is thus to give a global overview of the variation of these parameters over the Greater Dublin Area. According to the mapped CEV index, emissions for commuting travel increases with increasing distance from Dublin’s city centre, with the exception of the larger towns in the surrounding hinterland. In particular in outlying hinterland Electoral Division’s (EDs) transport carbon dioxide emissions are as much as 15 times higher than city centre Electoral Divisions. This would be expected considering the higher accessibility to public transport in the city centre and the more favourable modal split, the central core area has a car mode share of only 36.5% compared to a mode share of 58% for the overall GDA. It is clear that the increased accessibility by the presence of a motorway has contributed to enlarge commuting distances and the increased importance of the car as a transport mode. The area, in which the transport carbon emissions are predominantly low, is within the boundary of the M50 motorway in Dublin. This result concurs with what might be expected, as the Dublin city area represents the largest job market of the country, and also has the highest population densities. It is therefore consistent with the idea that the match in the labour market supply and demand is achieved within short distances. Moreover, the metropolitan spatial structure is responsible for the relatively large influence of other parameters on the transport emissions levels, such as modal split and vehicle ownership. This will be discussed below. In all regional urban areas, lower transport related carbon dioxide emissions than the average was found. But also outside the metropolitan and regional urban areas, there are a number of regions that come out on the right side by their significantly lower emissions. These areas are generally characterised by a high concentration of employment mixed with residential structure. The importance of location-bound industries, in particular in the agricultural sector, probably plays a part in this. So, the distance between home and workplace remains relatively confined.

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Fig. 5. Traction curve for LUAS tram braking on the ‘‘red line’’.

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A similar method of calculation was used to estimate the emissions of the LUAS green line trams, in this case the tram’s average speed was calculated to be 24.54 km/h with a peak hour occupancy of 227 passengers. This results in an emissions factor of 0.254 kg CO2/passenger km. In relation to Dublin Area Rapid Transit (DART) trains (an electric commuter system operating in the Greater Dublin Area) it is estimated that at national scale in 2003 electric rail accounts for 2 kt of oil equivalent (kTOE) in electricity consumption (O’Leary et al., 2006). Based on the 2003 electricity generation fuel mix, 1.0 TOE (tonne of oil equivalent) results in emission of 7.57 tonnes of CO2 (SEI, 2007). By applying the conversion factor of 7.57 tonnes CO2/TOE, the emissions for DART were estimated to be 15,140 tonnes of CO2/yr. This is divided by 1,970,000 train kilometres for 2000 (provided by Dodgson et al. (2001)), which results in an emission factor of 7.7 kg CO2/km. Using the peak hour occupancy rate of 507, this results in an emission factor of 0.0152 kg CO2/ passenger km. In terms of average emissions per passenger kilometre for diesel intercity and commuter trains in Ireland in 2006. This factor has been calculated based on the total diesel consumed by passenger rail and the total number of passenger kilometre in 2006 (EPA http://cmt.epa.ie./Global/CMT/emission_factor_sources.pdf). This results in an emission factor of 0.0443 kg/passenger km. Another problem which had to be surmounted was the fact that the question in the census form regarding an individual’s choice of mode only had two options concerning public transport either train or bus. Considering that there are a number of various forms of each mode with significantly different carbon dioxide emissions

6.2. Spatial patterns and relation to commuting distance The CEV index map (Fig. 6) shows a remarkable resemblance with the map that visualises the mean commuting distances (Fig. 7). The Pearson’s correlation coefficient between the two sets of indicators is 0.991. It can therefore be concluded, that the carbon

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Fig. 6. The CEV index map. Average personal daily transport carbon dioxide emissions per electoral division in the morning commute to work.

emissions for commuting trips are first and foremost determined by the distance between home and workplace. Contrary to what is generally assumed, it appears that the used transport mode plays only a very limited role. This can partly be explained by the fact that the average distance covered by train commuters (on average 36.3 km in 2006) is much larger than the average journey that is made by car (on average 15.8 km). Secondly, the bicycle is only an alternative for short trips, which makes this mode only marginally represented in the total number of kilometres. 6.3. Relation to the use of the bicycle Ireland is not particularly noted for its bicycle use, between 1996 and 2006 the modal share of the bicycle dropped by 25.5%

in the Greater Dublin Area. In the 2006 census for the Greater Dublin Area the bicycle modal share was 3.2%. In recent years there has been a marked improvement in bicycle use in Dublin thanks to new bicycle lanes and government incentives for purchasing bicycles for commuting (see Graph 1). When looking at the cycling map (Fig. 9) and the distance map (Fig. 7) at one glance, the largest share of cyclists occurs at first in those regions where the commuting distances are the shortest. According to POWCAR 2006, the average length of a cycle trip in travelling to work amounts to 5.3 km. Clearly, the bicycle plays a role in areas where the commuting distances are of this small magnitude. The influence of the bicycle on the total transport carbon dioxide emissions for commuting trips is very limited. Even in areas with a relatively high share, the bike

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Fig. 7. The average commute trip distance per member of the active work force.

use share in commuting is less than 12% of the total number of trips. As trips with other modes cover much larger distances per trip (see Graph 2), the gain in terms of carbon dioxide emissions made by cyclists is of little significance. The low emissions levels in areas with a high proportion of cyclists are largely on the account of the small absolute distances, regardless of the transport mode used. But the positive impact of the short distances in these areas is reinforced by the larger share of cyclists that substitutes car use on short distances, in comparison with other regions. 6.4. Relation to the use of the train Figs. 10 and 11 visualise the level of use of public transport. Fig. 10 deals with the share of train, LUAS and DART travellers,

while Fig. 11 demonstrates the share of Bus travellers. High concentrations of train commuters can be found along the railway axes, particularly along the north–south rail line along the east coast, along the Luas and DART networks and in close proximity to the stations. In the outlying hinterland of the Greater Dublin Area, and particularly the west and south west areas, commuters hardly ever use the train, as commuting distance increases the modal share of the train decreases. The average commuter rail passenger covers larger distances than the average car driver (36.3 km compared with 15.8 km). When the average CO2 emissions per kilometre is multiplied (0.0443 kg CO2/passenger km, respectively 0.169 kg CO2/passenger km) by the average number of kilometres per mode, it appears that in home-to-work travel the energy consumption of a train trip

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Fig. 8. Car driver modal split.

per person is 40% less than the CO2 emissions of a trip by car. This difference is less impressive than the expectations raised by the sustainable image of the train. Finally, the majority of train journeys entail travel to and from the station, which often means an additional trip by car. On the other hand, the train substitutes long car trips, at least where rail transport supply is present. Furthermore, the train is doing this – calculated per kilometre – in a more energy-efficient way. Nevertheless, the share of train commuters, even in the concentrated areas, is limited to a maximum of approximately 15%. In general it may be said that there occurs only a positive impact on transport related emissions by use of the train in those regions where the average home-to-work distances are already large. But the effect is still too small to be visible on the commute emissions vulnerability map (Fig. 6).

6.5. Relation to the use of urban and regional bus transportation Fig. 11 shows the modal split in relation to bus transport in commuting trips. The greatest use of this mode is found in the city. In particular the north side of the city has rates of between 20% and 30% where as the south side on average would have a modal share of approximately 10–15%. This is mainly due to the influence of the two LUAS lines which have converted many bus users to rail since its introduction in 2004. To the northwest along the main arterial roads the influence of the bus network reaches into the hinterland, this influence is more noticeable in the north-west than any other area most probably due to the fact that there is no competing railway along these routes. The central parts of Dublin city that exhibit lower transport related CO2 emissions per capita have also a high to very high

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Fig. 9. Bicycle mode share.

proportion of public transport users. It is clear that the high density of inhabitants and jobs in this area combines the positive effects of the proximity of functions with an energy-efficient and well exploited urban transport system. The relative good energy efficiency and the high patronage of the LUAS, DART and rail network supplemented with the Bus network, account for this. In the rest of the Greater Dublin Area’s regional transport, based on diesel buses and trains, hardly plays a part in the energy performance of the commuting public. The supply, which is limited in comparison with the metropolitan area and the non-competitive speed of this kind of public transport, contributes to the limited success of the regional transport in the market segment of home-

to-work travel. Although this will not be discussed more profoundly, the relatively low-ridership outside the Dublin metropolitan area also works to the detriment of the energy performance of public transport. 6.6. Relation to car mode share With regard to car mode share, the percentage of people travelling to work as car drivers strongly contrasts with all other modes, particularly with those walking, cycling or travelling by bus. In this case the smallest percentages travelling by car are found in the city and some other minor outlying urban areas. Among other things, spatial factors are at the basis of this. The high density, the

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Fig. 10. Train mode share.

significant mix of functions and good supply of public transportation makes it relatively easy to live without a car in the city centre. Also, a number of social elements play a part, since it is precisely in these areas that the family sizes are small. However, it is difficult to isolate environmental and social factors. The environment and the rent and house prices in the city are often not adapted to the lifestyle of families with children. In addition, those families need more often combined trips, for which the car is usually the appropriate means of transport. Meanwhile the largest proportions travelling by car can be found almost everywhere else outside the city. It is clear that the car remains the most dominant mode of

transport in the GDA, where even the smallest percentage covers up to 30% of users.

7. Future research The CEV index is also useful in interpreting the relationship with a range of spatial–morphological characteristics and socioeconomic characteristic. In future research, the relationship between Transport CO2 emissions and notable spatial characteristics such as: residential density, proximity to the main road and

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Fig. 11. Bus mode share.

railway network, jobs housing balance, proximity to urban and rural areas will be outlined. It will also examine socio-economic characteristics such as; age, sex, socio-economic group, education level, car ownership and family size.

8. Conclusions The negative impacts of transportation systems on air quality, noise, and energy consumption are the basic motivation for studies aiming at innovative and effective remedies. The current study is part of that stream. Specifically, it is part of the course of study seeking to reduce transport related CO2 emissions through landuse policy, but it does not assume that land-use policy is a mirac-

ulous solution to cure the pollution and energy ills. The goal of this study is to sharpen our knowledge/beliefs in the possibilities of influencing CO2 by transportation through means of land-use policy. It has been argued that the CO2 emissions performance of the transport system is an important approximate indicator for the sustainability of a spatial structure. This is certainly true when advocating a so-called low carbon economy is put increasingly higher on the political agenda. Obviously the link with the spatial or urban (re)development of cities should be made as well. Having a better understanding of the mechanisms that cause the major observed regional variations in transport related emissions will lead to better land-use planning in practice. The issue of proximity in planning remains very important. In commuting trips, the distance between home and workplace is to a very large

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extent determinant for the energy performance of the commuting system. Contrary to the conventional belief, the mode used is of much less importance. Hence, the opportunity for someone to find a suitable job nearby his or her living environment, or the ease with which someone can move in the vicinity of his or her work will increasingly determine the robustness of a spatial-economic system in a climate of rising oil prices. It appears that travel behaviour remains largely determined by the rigidity of the housing stock, which makes short term policy intervention not easy. In this respect the development and implementation of a commute emissions vulnerability index seems a useful indicator to assess both transport and spatial planning policies with respect to inducing sustainable development. Whatever may happen in the future the reality is that the suburban development that has already been built will simply not disappear along with the intrinsic car dependency it creates. The results from the research would suggest that reducing low density sprawl type development would not effect the transport related CO2 emissions of the existing vast stock of car dependent development but it could decrease future transport energy consumption by encouraging more high density mixed use developments specifically closer to strong urban centres and good public transport links. The problem with Ireland’s transportation system is that it is tied to a low density, dispersed land use pattern that is simply not amenable to alternative modes of transportation. The more this type of development is constructed the more extreme the transport CO2 emissions will become. While new vehicle technology

may reduce the demand for petrol and diesel in the future, this may not happen before the dwindling supplies of easily recoverable oil begin to significantly increase in price, causing potentially serious economic dislocations between the opposite ends of the commute emissions vulnerability index. Acknowledgements This work was completed as part of the Urban Environment Project and supported by the Environmental Protection Agency. We would like to acknowledge the use of the Central Statistics Office – Census Place of Work Anonymised Microdata FileÓ Government of Ireland. References Anderson, P.W., Kanaroglou, P.S., Miller, E.J., 1996. Urban forms, energy and the environment: review of issues, evidence and policy. Urban Studies 33 (1), 7–35. Bento, A.M., Cropper, M.L., Mobarak, A.M., Vinha, K., 2004. The impact of urban spatial structure on travel demand in the United States. University of Colorado. Institute of Behavioral Science, Research Program on Environment and Behavior. Working paper EB2004-0004. Bourne, L.S., 1982. Urban spatial structure: an introductory essay on concepts and criteria. In: Bourne, L.S. (Ed.), Internal Structure of the City, 2nd ed. Oxford University Press, New York. Breheny, M., 1992. The contradictions of the compact city: a review. In: Breheny, M. (Ed.), Sustainable Development and Urban Form. Pion, London. Breheny, M., 2001. Densities and sustainable cities: the UK experience. In: Echenique, M., Saint, A. (Eds.), Cities for the New Millennium. Spon.

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