The optimum density for the sustainable city: the case of Athens

Transcription

1 Sustainable Development and Planning II, Vol The optimum density for the sustainable city: the case of Athens D. Milakis, N. Barbopoulos & Th. Vlastos School of Surveying Engineering, Department of Geography and Regional Planning, National Technical University of Athens, Greece Abstract Over the last decade there has been a great deal of research on the definition of the sustainable city. There is also an ongoing debate on city shape and on the optimum distribution of activities. The dominant theory, often to be found in political documents, favours the compact city. Its main principles are the mix of land uses and the increase of density. It also promotes improvement of public transport and of environmentally friendly means of transport, such as cycling and walking. In this paper, we offer an estimate for a critical threshold of density for the case of Athens. This threshold reflects the density required in order for significant changes in travel behaviour to appear. It results from a statistical analysis of data concerning urban and transport characteristics. This threshold is much higher than those identified in other cities around the world. We then discuss the policy implications of such a threshold. Keywords: residential density, socio-economic characteristics, travel behaviour. 1 The discussion on optimum density Over the last two decades there has been a growing concern over implementing principles of sustainability in cities. The major question to be addressed is how to define the appropriate guidelines, needed to obtain to obtain urban structures that are energy efficient and do not pollute the environment excessively. There is a growing number of policy documents referring to urban planning. These documents set the following targets: the reduction of car use, the reduction of distance traveled by car, and

2 26 Sustainable Development and Planning II, Vol. 1 the promotion of public transport and non-motorized means, namely walking and cycling. Such documents suggest developing compact urban structures, with high residential density and mixed land uses. Two typical reports along these lines have been launched by the European Commission (CEC [1, 2]). There are also reports produced by expert advisors on the urban environment (EGUE [3]), which clearly state that land use planning must and can constitute a tool for achieving these aims. The key notion in these new planning guidelines is density. An increase in density is considered as environmentally beneficial, causing a decrease in energy consumed in transportation. This is quite reasonable, since high density means that land uses are concentrated in less space and the ability to travel shorter distances by sustainable mean of transport is increased. This hypothesis was tested by Newman and Kennworthy [4], who elaborated data from 32 cities in four continents and found a strong negative statistical correlation (R 2 =0.8594) between residential density and energy consumption per capita by car. This relationship was exponential for densities beneath 30 persons/ha and linear for higher levels. Other studies have also come to the conclusion that residential density affects travel behavior, but have identified different critical thresholds for it. For example, Frank and Pivo [5] found for Washington DC that population density should exceed approximately 13 persons/acre (32 persons/ha), before a significant modal shift occurs from Single-Occupant Vehicles (SOV) to transit use and walking for shopping trips. In contrast, Stead [6], in his research on Britain, identified a critical range for density between 40 and 50 persons/hectare, which is associated with low travel distances. In our paper, the influence of density on travel behavior will be investigated in the case of Athens. A comparison between density and socio-economic characteristics regarding their significance in explaining the variability of travel characteristics will be applied, too. Moreover, a density threshold will be estimated: this threshold defines the area where the increase in density will have significant impact on travel behaviour. Finally, the policy implications of the results will be addressed, given that Athens already has a very high density in comparison with other European and American cities. 2 Estimation of the range of density values corresponding to low car use and energy savings: the case of Athens 2.1 Methodology The aim of this research is to explore the relationship between density and travel behavior and, if possible, to define a density threshold beneath which its increase will have significant effects on travel behaviour. However, in a large number of studies it is argued that socio-economic characteristics should be taken into account when exploring the relationship between urban form characteristics and travel behavior (Gomez-Ibanez [7] Stead and Marshall [8]). This is crucial, since

3 Sustainable Development and Planning II, Vol it is possible that some of the travel characteristics may be more influenced by socio-economic characteristics, than by urban form parameters. At the second level of the analysis we investigated the significance of density in explaining travel behavior in relation to three socio-economic characteristics. To this end, five multiple regressions were applied, with density and the three socioeconomic characteristics serving as independent variables and five travel characteristics as dependent. The socio-economic characteristics (independent variables) were the following: 1. Mean household income 2. Level of car ownership 3. Household size As dependent variables, we chose the following travel data: 1. Number of journeys/person/day by public transport 2. Number of journeys/person/day by car 3. Number of journeys/person/day on foot 4. Mean length of car journeys 5. Energy consumption per capita by car The descriptive statistics of data are presented in table 1. Table 1: Descriptive statistics of data. Parameter Unit Mean SD Min. Max. Independent Variables persons/hectare Household income euros Car ownership car/1000 inhabitants Household size persons/household ,13 Journeys per person by public transport Journeys per person by car Journeys per person on foot Mean journey length by car Energy consumption per capita by car Dependent Variables number of journeys number of journeys number of journeys meters MJ Data Our study case is the Athens metropolitan area. It comprises 82 municipalities with a total population of persons. The municipality was chosen as

4 28 Sustainable Development and Planning II, Vol. 1 spatial unit of analysis, due to lack of empirical data on any lower scale. 95% of the population of the study area is concentrated in a basin of approximately 1270 km 2. This basin has physical boundaries, the sea in the south, and mountains in the north. The majority of the municipalities are located inside this basin. Their mean area is 15 km 2. The data used is taken from the following inventories, which were compiled in 1996, by Athens Metro Development study: Land uses and socio-economic characteristics (AM-DPGS [9]) Travel characteristics (AM-DPGS [10]), A vast inventory of land uses was created, covering hectares and blocks. Travel characteristics were estimated through the analysis of data acquired through household interviews. 2.3 Travel characteristics We define the threshold beneath which density affects significantly travel behaviour in relation to the following travel characteristics: Modal split Mean travel length by car, Energy consumption per capita due to car use. To describe modal split, we employed the mean number of journeys per person per day by public transport, car and on foot. Walking journeys were defined as being longer than 500 m. The mean journey length by car (Mean Journey Length MJL) was calculated according to the origin/destination records in the municipality level: t i ij d ij MJL i = t (1) t ij : number of car journeys with origin municipality i and destination municipality j. d ij : Euclidian distance between municipalities i,j. Finally, to estimate energy consumption per capita by car, we defined mean energy consumption per kilometer. For this reason we used the equations regarding fuel consumption employed in the research program CORINAIR - COPERT III (Ntziachristos and Samaras [11]), which vary according to car technology (by year of manufacturing), cubic capacity and mean travel speed in urban areas. We also drew on data concerning the composition of car fleet in the study area, provided by the National Statistical Service. The mean fuel consumption is liters/kilometer. This is converted to energy, given that the energy equivalent of one petrol liter being 44.7 MJ. The energy consumption per capita due to car use therefore was calculated as the product of average car journey length per capita and energy consumption per kilometer. i ij

5 Sustainable Development and Planning II, Vol Results was found to be the most significant parameter in accounting for the variability of the four (out of the five) travel characteristics. The only exception is the parameter journeys by car, which is explained more significantly by car ownership than by residential density (t-value: 6.572) (tables 2-6). The same results are found in the comparison of the level of influence of the explanatory variables on travel characteristics. On the basis of standardized coefficients, net residential density exercises the strongest influence on four out of the five travel characteristics. Only in the case of journeys by car is car ownership more influential than density (0.812) (see tables 2-6). Density was found to be positively correlated to journeys by public transport and on foot, and negatively correlated to journeys by car, mean journey length and energy consumption per capita by car. Interestingly, the relationship between residential density and all the travel characteristics is better described by a logarithmic curve. In figure 1, graphical representations of these relationships are given. A threshold for density can be easily identified in these five diagrams. In the case of Athens, it is found to be around 200 persons/ha. Policies regarding increase in density up to this value might involve the following aspects: 1. Increase of public transport use and walking 2. Reduction of car use, mean journey length and energy consumption by car. It is also apparent that if density increases over this threshold, public transport passengers and walkers will also increase in absolute numbers. However, the alteration of modal split will occur to a much lower degree. For example, an increase in residential density in municipalities with 10 persons/ha to 30 persons/hectare would cause an 18.6% increase in public transport use. On the contrary, an increase in residential density in municipalities with 210 persons/ha to 230 persons/ha would cause only a 1.6% increase in public transport use. The impacts of density change between these values on travel characteristics are presented in table 7. Table 2: Results of multiple regressions for journeys by public transport. (Logarithmic transformation) Coefficient Standardized coefficient t-value (sig.) (0.000) Income E (0.752) Car ownership E (0.391) Household size (0.239) R F-value (sig.) (0.000)

6 30 Sustainable Development and Planning II, Vol. 1 Table 3: Results of multiple regressions for journeys by car. (Logarithmic transformation) Coefficient Standardized coefficient t-value (sig.) (0.012) Income E (0.581) Car ownership (0.000) Household size (0.066) R F-value (sig.) (0.000) Table 4: Results of multiple regressions for journeys on foot. (Logarithmic transformation) Coefficient Standardized coefficient t-value (sig.) (0.003) Income E (0.168) Car ownership 7.311E (0.791) Household size (0.507) R F-value (sig.) (0.000) Table 5: Results of multiple regression for mean journey length by car. (Logarithmic transformation) Coefficient Standardized coefficient t-value (sig.) (0.000) Income (0.185) Car ownership (0.010) Household size (0.068) R F-value (sig.) (0.000)

7 Sustainable Development and Planning II, Vol Table 6: Results of multiple regressions for energy consumption by car. (Logarithmic transformation) Coefficient Standardized coefficient t-value (sig.) (0.000) Income (0.499) Car ownership (0.008) Household size (0.751) R F-value (sig.) (0.000) Table 7: The effects on travel characteristics from the density increases. Journeys/person by car Journeys/person by public transport Journeys/person on foot Mean journey length by car Energy consumption by car Residential density increase Residential density increase from 10 to 30 persons/hectare from 200 to 230 persons/hectare % + 1.6% % - 0,6% % + 1.9% % - 2.5% % - 3.1% 3 Discussion: policy implications of the optimum density in the case of Athens The central municipality of Athens accommodates about 1/3 of the total metropolitan population and occupies the area defined by the city limits as they were in the 50s. It has a very high density (750 persons/ha) in comparison to the other municipalities of the metropolitan area and is usually considered as a bad paradigm for urban development. A strong anti-density outlook exists in almost all of the municipalities. It is argued that it is environmentally correct to lower residential densities in order to increase open and green spaces. However, the implications of such a planning principle regarding transport are not taken into account. On the other hand it is apparent that any increase in densities in order to alter travel behaviour would be ineffectual, if no additional measures promoting public transport and discouraging private car use were applied.

8 32 Sustainable Development and Planning II, Vol. 1 R 2 =0,565 R 2 =0,356 Journeys/person by Public Transport R 2 =0,195 R 2 =0,668 Journeys/person on foot Mean journey length by car R 2 =0,659 Energy consumption by car Journeys/person by car Figure 1: Graphical representation of the relationships between density and travel characteristics.

9 Sustainable Development and Planning II, Vol Increase in density reduces total traveling distance, which already quite beneficial. However, such increases should be accompanied by policies aimed at the reconstruction of the urban and road environment, thereby rendering making it friendlier for the pedestrian or cyclist and policies promoting public transport. Moreover, higher densities increase the number of the potential passengers of public transport. It is a fact that the inhabitants of Athens metropolitan area immigrate to the suburbs, where they can fulfill their desire for a detached house with a garden and a less congested road network. It needs great effort in order to reverse this trend. The social acceptance of the compact city concept depends on the magnitude of the investments for appraising urban environment and public transport infrastructure. This investment will be made quickly, if it is shown that the city will consume less energy, be more attractive to the visitors and more functional. Acknowledgements This research is co-funded by the European Social Fund (75%) and National Resources (25%). References [1] Commission of the European Communities (C.E.C.), Green Paper on the Urban Environment [COM(90)218], Office for Official Publications of the European Communities, Luxembourg. [2] Commission of the European Communities (C.E.C.), Towards a thematic strategy on the urban environment. Communication from the European Commission [COM(2004)60], Office for Official Publications of the European Communities, Luxembourg. [3] Expert Group on the Urban Environment (E.G.U.E.), Towards more Sustainable Urban Land Use. Advice to the European Commission for Policy and Action. Report of the Expert Group on the Urban Environment. European Commission, DGXI, Environment, Nuclear Safety and Civil Protection, Brussels. [4] Newman P. & Kenworthy J., Gasoline Consumption and Cities. A comparison of U.S. Cities with a Global Survey, Journal of the American Planning Association 55 (1): [5] Frank L.D. & Pivo G., Impacts of Mixed Use and Density on Utilization of Three Modes of Travel: Single-Occupant Vehicle, Transit, and Walking. Transportation Research Record 1466: [6] Stead D., Relationships between land use, socioeconomic factors, and travel patterns in Britain. Environment and Planning B: Planning and Design 28 (4): [7] Gomez-Ibanez J., A Global View of Automobile Dependence, Journal of the American Planning Association 57 (3):

10 34 Sustainable Development and Planning II, Vol. 1 [8] Stead D. & Marshall S., The Relationships between Urban Form and Travel Patterns. An International Review and Evaluation. European Journal of Transport and Infrastructure Research 1 (2): [9] Attiko Metro S.A., Department of Planning and General Studies, Metro Development Study. Land Use and Socioeconomic Characteristics. AM-DPGS, Athens [10] Attiko Metro S.A., Department of Planning and General Studies, Metro Development Study. Travel Characteristics. Household surveys in Typical Period. Technical Report. AM-DPGS, Athens. [11] Ntziachristos, L., Samaras, Z., COPERT III. Computer programme to calculate emissions from road transport. Methodology and emission factors [Technical Report No 49]. European Environmental Agency, Copenhagen.