Sustainable Development and Planning II, Vol. 1
25
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
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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 (R2=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
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Sustainable Development and Planning II, Vol. 1
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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:
Parameter
Descriptive statistics of data.
Unit
Mean
SD
Min.
Max.
Independent Variables
Net residential density
persons/hectare
218
197
6
903
euros
889
390
402
2638
Car ownership
car/1000 inhabitants
279
76
138
471
Household size
persons/household
3.13
0.32
2.50
4,13
Household income
Dependent Variables
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
number of journeys
0.19
0.09
0.26
0.45
number of journeys
0.817
0.339
0.365
2.62
number of journeys
0.140
0.093
0.000
0.97
meters
7108
2506
3706
13781
MJ
21.7
14.64
8.6
77.9
2.2 Data
Our study case is the Athens metropolitan area. It comprises 82 municipalities
with a total population of 3833400 persons. The municipality was chosen as
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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
km2. 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 km2.
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 74500 hectares and
66600 blocks. Travel characteristics were estimated through the analysis of data
acquired through 29358 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:
MJL i =
∑t d
∑t
i ij
ij
i ij
(1)
tij : number of car journeys with origin municipality i and destination
municipality j.
dij: 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 0.0798 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.
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2.4 Results
Net residential density 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.
Coefficient
Net residential density
(Logarithmic transformation)
Standardized
coefficient
t-value (sig.)
0.054
0.626
6.271 (0.000)
Income
-3.853E-05
-0.047
-0.317 (0.752)
Car ownership
-2.051E-04
-0.143
-0.863 (0.391)
-0.036
-0.107
-1.187 (0.239)
Household size
R
2
F-value (sig.)
0.594
28.153 (0.000)
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Table 3:
Results of multiple regressions for journeys by car.
Coefficient
Net residential density
(Logarithmic transformation)
Standardized
coefficient
t-value (sig.)
-0.052
-0.191
-2.576 (0.012)
-1.563E-04
-0.061
-0.554 (0.581)
Car ownership
0.004
0.812
6.572 (0.000)
Household size
0.131
0.125
1.867 (0.066)
Income
R2
0.775
F-value (sig.)
Table 4:
66.265 (0.000)
Results of multiple regressions for journeys on foot.
Coefficient
Net residential density
(Logarithmic transformation)
Standardized
coefficient
t-value (sig.)
0.031
0.416
3.065 (0.003)
Income
-1.956E-04
-0.280
-1.392 (0.168)
Car ownership
7.311E-05
0.060
0.266 (0.791)
0.023
0.082
0.667 (0.507)
Household size
R
2
0.251
F-value (sig.)
Table 5:
6.435 (0.000)
Results of multiple regression for mean journey length by car.
Coefficient
Net residential density
(Logarithmic transformation)
Income
Car ownership
Household size
R
2
F-value (sig.)
Standardized
coefficient
t-value (sig.)
-1987.1
-0.994
-11.744 (0.000)
3.179
0.168
1.338 (0.185)
-12.252
-0.371
-2.638 (0.010)
-1093.265
-0.142
1.850 (0.068)
0.708
46.673 (0.000)
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Table 6:
Results of multiple regressions for energy consumption by car.
Coefficient
Standardized
coefficient
t-value (sig.)
Net residential density
(Logarithmic transformation)
-7.341
-0.629
-8.577 (0.000)
Income
0.008
0.074
0.680 (0.499)
Car ownership
0.064
0.330
2.705 (0.008)
Household size
0.951
0.021
0.318 (0.751)
R2
0.781
F-value (sig.)
Table 7:
68.641 (0.000)
The effects on travel characteristics from the density increases.
Residential
density
increase
from 10 to 30 persons/hectare
Journeys/person
by car
Journeys/person
by public transport
Journeys/person
on foot
Mean journey length
by car
Energy consumption
by car
3
31
Residential
density
increase
from 200 to 230 persons/hectare
+ 18.6%
+ 1.6%
- 6.9 %
- 0,6%
+ 24.1%
+ 1.9%
- 30.7%
- 2.5%
- 37.2%
- 3.1%
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.
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R2=0,356
Journeys/person by car
Journeys/person by Public Transport
R2=0,565
Net residential density
Net residential density
R2=0,668
Journeys/person on foot
Mean journey length by car
R2=0,195
Net residential density
Net residential density
Energy consumption by car
R2=0,659
Figure 1: Graphical representation of the relationships between density and
travel characteristics.
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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]
[2]
[3]
[4]
[5]
[6]
[7]
Commission of the European Communities (C.E.C.), 1990. Green Paper
on the Urban Environment [COM(90)218], Office for Official
Publications of the European Communities, Luxembourg.
Commission of the European Communities (C.E.C.), 2004. 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.
Expert Group on the Urban Environment (E.G.U.E.), 2001. 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.
Newman P. & Kenworthy J., 1989. Gasoline Consumption and Cities. A
comparison of U.S. Cities with a Global Survey, Journal of the American
Planning Association 55 (1): 24-37.
Frank L.D. & Pivo G., 1994. Impacts of Mixed Use and Density on
Utilization of Three Modes of Travel: Single-Occupant Vehicle, Transit,
and Walking. Transportation Research Record 1466: 44-52.
Stead D., 2001. Relationships between land use, socioeconomic factors,
and travel patterns in Britain. Environment and Planning B: Planning and
Design 28 (4): 499-528.
Gomez-Ibanez J., 1991. A Global View of Automobile Dependence,
Journal of the American Planning Association 57 (3): 376-379.
WIT Transactions on Ecology and the Environment, Vol 84, © 2005 WIT Press
www.witpress.com, ISSN 1743-3541 (on-line)
34 Sustainable Development and Planning II, Vol. 1
[8]
[9]
[10]
[11]
Stead D. & Marshall S., 2001. The Relationships between Urban Form
and Travel Patterns. An International Review and Evaluation. European
Journal of Transport and Infrastructure Research 1 (2): 113-141.
Attiko Metro S.A., Department of Planning and General Studies, 1997.
Metro Development Study. Land Use and Socioeconomic Characteristics.
AM-DPGS, Athens
Attiko Metro S.A., Department of Planning and General Studies, 1998.
Metro Development Study. Travel Characteristics. Household surveys in
Typical Period. Technical Report. AM-DPGS, Athens.
Ntziachristos, L., Samaras, Z., 2000. COPERT III. Computer programme
to calculate emissions from road transport. Methodology and emission
factors [Technical Report No 49]. European Environmental Agency,
Copenhagen.
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