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Citation: Ustaoglu, E.; Williams, B.
The Inter-Relationships of Territorial
Quality of Life with Residential
Expansion and Densification: A Case
Study of Regions in EU Member
Countries. Urban Sci. 2024,8, 22.
https://doi.org/10.3390/
urbansci8010022
Academic Editor: Thomas W. Sanchez
Received: 15 February 2024
Revised: 11 March 2024
Accepted: 13 March 2024
Published: 19 March 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
The Inter-Relationships of Territorial Quality of Life with
Residential Expansion and Densification: A Case Study of
Regions in EU Member Countries
Eda Ustaoglu 1, * and Brendan Williams 2,*
1Department of Economics, Abdullah Gul University, 38080 Kayseri, Türkiye
2School of Architecture, Planning and Environmental Policy, University College Dublin,
D04 V1W8 Dublin, Ireland
*Correspondence: eda.ustaoglu@agu.edu.tr (E.U.); brendan.williams@ucd.ie (B.W.)
Abstract: High-density urban development is promoted by both global and local policies in response
to socio-economic and environmental challenges since it increases mobility of different land uses,
decreases the need for traveling, encourages the use of more energy-efficient buildings and modes
of transportation, and permits the sharing of scarce urban amenities. It is therefore argued that
increased density and mixed-use development are expected to deliver positive outcomes in terms
of contributing to three pillars (social, economic, and environmental domains) of sustainability in
the subject themes. Territorial quality of life (TQL)—initially proposed by the ESPON Programme—
is a composite indicator of the socio-economic and environmental well-being and life satisfaction
of individuals living in an area. Understanding the role of urban density in TQL can provide
an important input for urban planning debates addressing whether compact development can be
promoted by referring to potential efficiencies in high-density, mixed land use and sustainable
transport provisions. Alternatively, low-density suburban development is preferable due to its
benefits of high per capita land use consumption (larger houses) for individual households given
lower land prices. There is little empirical evidence on how TQL is shaped by high-density versus low-
density urban forms. This paper investigates this topic through providing an approach to spatially
map and examine the relationship between TQL, residential expansion, and densification processes
in the so-called NUTS2 (nomenclature of terrestrial units for statistics) regions of European Union
(EU) member countries. The relative importance of each TQL indicator was determined through the
entropy weight method, where these indicators were aggregated through using the subject weights
to obtain the overall TQL indicator. The spatial dynamics of TQL were examined and its relationship
with residential expansion and densification processes was analysed to uncover whether the former
or the latter process is positively associated with the TQL indicator within our study area. From
our regression models, the residential expansion index is negatively related to the TQL indicator,
implying that high levels of residential expansion can result in a reduction in overall quality of life in
the regions if they are not supported by associated infrastructure and facility investments.
Keywords: high- and low-density development; territorial quality of life (TQL); entropy weight
method; urban sustainability; EU member countries
1. Introduction
Europe is one of the most urbanised regions in the world with more than 70% of the
population living in cities, and it is projected that this figure will reach 83% by 2050 [
1
,
2
].
The expansion of built-up areas has continued in multiple regions of Europe even in regions
in which the population has declined or stagnated [
3
,
4
]. Artificial areas such as built-up
areas and roads showed an increase of over 6% during the 2000–2018 period [
5
]. This rapid
development has brought many urban problems such as environmental pollution, traffic
congestion, high energy consumption, the degradation of natural resources, shrinking
Urban Sci. 2024,8, 22. https://doi.org/10.3390/urbansci8010022 https://www.mdpi.com/journal/urbansci
Urban Sci. 2024,8, 22 2 of 33
public services, and social segregation [
6
,
7
]. As cities and urban regions continue to grow,
not only do artificial uses accumulate but natural resources also indicate a corresponding
decline [
8
]. This accumulation in artificial assets is related to economic and social devel-
opment [
9
]. Artificial surfaces and their potential adverse impacts have led to sustainable
development being identified as a priority that is aligned with three fundamental pillars as
follows: social, economic, and environmental.
Research gaps were identified by Wolff and Haase [
10
] in finding the optimal compro-
mise between high and low densities and liveability and sustainability [
11
]. As indicated in
Figure 1, on the left side of the turning point (Figure 1), there are low-density developments
associated with higher liveability, and on the right side, there are high densities associated
with higher sustainability A combination of the difficulties in addressing housing needs
and meeting sustainability goals have led to an increased role for increased residential
densities and apartment developments as a preferred policy approach. This is backed
by several factors that are related to sustainable urban form, such as the preservation of
rural landscapes; reductions in fuel emissions from car travel, the encouragement of using
public transportation, walking, and bicycling; the improvement of utility and infrastructure
provisions; and the rejuvenation and regeneration of inner-city areas [6,12,13].
Urban Sci. 2024, 8, x FOR PEER REVIEW 2 of 35
traffic congestion, high energy consumption, the degradation of natural resources, shrink-
ing public services, and social segregation [6,7]. As cities and urban regions continue to
grow, not only do artificial uses accumulate but natural resources also indicate a corre-
sponding decline [8]. This accumulation in artificial assets is related to economic and so-
cial development [9]. Artificial surfaces and their potential adverse impacts have led to
sustainable development being identified as a priority that is aligned with three funda-
mental pillars as follows: social, economic, and environmental.
Research gaps were identified by Wolff and Haase [10] in finding the optimal com-
promise between high and low densities and liveability and sustainability [11]. As indi-
cated in Figure 1, on the left side of the turning point (Figure 1), there are low-density
developments associated with higher liveability, and on the right side, there are high den-
sities associated with higher sustainability A combination of the difficulties in addressing
housing needs and meeting sustainability goals have led to an increased role for increased
residential densities and apartment developments as a preferred policy approach. This is
backed by several factors that are related to sustainable urban form, such as the preserva-
tion of rural landscapes; reductions in fuel emissions from car travel, the encouragement
of using public transportation, walking, and bicycling; the improvement of utility and in-
frastructure provisions; and the rejuvenation and regeneration of inner-city areas
[6,12,13].
Figure 1. Balance of sustainability and liveability based on residential densities. Source: published
in Wolff and Haase [10].
In this context, the compact city approach has been widely investigated and em-
ployed in practice as it can theoretically lead to increased sustainability [14]. This theoret-
ical sustainability of compact cities and the impact of such policies as seen in practice re-
mains a contested debate that requires empirical evidence to validate [15]. High-density
urban development may not offer the desired urban quality of life and may even work
against a sustainable future. Despite the considerable interest in the topic, there is no con-
sensus in the literature on the impact of high- and low-density development on quality of
life (QoL). This stems from the existence of various methods, definitions, and variables
used for explaining the existence of spatial factors which have an influence on QoL [16–
18]. A further reason is that the analysis has been conducted for different case study areas
characterised by different socio-economic and spatial development paerns, and it is
questionable whether a spatial paern of an urban system in one urban area has a similar
effect in determining QoL in a different urban area [19–21].
Thomas and Cousins [22] contend that people living in low-density areas experience
higher urban QoL because of the higher availability of amenities like urban green spaces.
However, QoL derived from green spaces is directly related to its quality and accessibility.
Figure 1. Balance of sustainability and liveability based on residential densities. Source: published in
Wolff and Haase [10].
In this context, the compact city approach has been widely investigated and employed
in practice as it can theoretically lead to increased sustainability [
14
]. This theoretical
sustainability of compact cities and the impact of such policies as seen in practice remains
a contested debate that requires empirical evidence to validate [
15
]. High-density urban
development may not offer the desired urban quality of life and may even work against a
sustainable future. Despite the considerable interest in the topic, there is no consensus in the
literature on the impact of high- and low-density development on quality of life (QoL). This
stems from the existence of various methods, definitions, and variables used for explaining
the existence of spatial factors which have an influence on QoL [
16
–
18
]. A further reason
is that the analysis has been conducted for different case study areas characterised by
different socio-economic and spatial development patterns, and it is questionable whether
a spatial pattern of an urban system in one urban area has a similar effect in determining
QoL in a different urban area [19–21].
Thomas and Cousins [
22
] contend that people living in low-density areas experience
higher urban QoL because of the higher availability of amenities like urban green spaces.
However, QoL derived from green spaces is directly related to its quality and accessibility.
According to Haaland and van den Bosch [
23
], the process of urban densification such
as consolidation and infill development can endanger urban green spaces, and this may
result in a loss of private urban green spaces which is hardly offset by introducing more
public green spaces in an urban area. Breheny [
24
] and Williams et al. [
25
] represent ex-
Urban Sci. 2024,8, 22 3 of 33
amples of authors who are not certain of the link between higher densities and reduced
car trips. They found that short trips in local areas may decrease; however, travelling for
specialised employment, specific shopping, and leisure can be independent of urban den-
sity.
Shim et al. [26]
found an inverse relationship between population and transportation
energy consumption given that an increase in the degree of a city’s concentration decreases
the energy efficiency regarding Korean cities. Fang et al. [
27
] represent another example of
authors who showed a negative correlation between urban continuity and CO
2
emissions,
and the authors asserted that increased irregularity with respect to urban form may increase
CO
2
emissions. A study by Yigitcanlar and Kamruzzaman [
28
] examined the changes in
CO
2
emissions in UK cities and found that the impact of city smartness on CO
2
emissions
does not change over time.
This study develops an innovative approach for understanding the relationship be-
tween quality of life and urban expansion and densification in the pan-European area. The
examples mainly focus on small study areas and cities in Europe [
20
,
29
,
30
]. The study
has the objective of creating a scientific approach to examining quality of life issues in the
context of ongoing urban development trends. The research provided original research
on a high number of indicators that were classified under economic, socio-cultural, and
ecological capitals, applied indicator reduction analysis to construct composite indicators
of QoL in the pan-European area, and constructed the relationship between QoL and
residential expansion and densification to understand whether QoL improves by being
on the left side of the turning point or whether it is the right side which leads to a higher
QoL (Figure 1). Here, we presume that the decline in residential density is associated with
liveability and that an increase in density is related to sustainability. The QoL indicator
comprises sub-indicators that are both related to liveability (green infrastructure in a region,
land, climate, air quality) and sustainability (circular economy, living environment, gov-
ernance, etc.). Most studies that constructed the urban liveability index did not consider
searching relationships between quality of life and urban densities [
31
–
34
]. A small number
of studies published in recent periods examine the relationship between compact urban
forms and QoL, for example, in the city of Kolkota, India [
17
]; in Oslo, Norway [
29
]; in
Jakarta, Indonesia [
35
]; and in US cities [
36
]. However, in these applications, the regions or
the neighbourhoods at the local scale are assumed to be spatially independent despite the
existence of spatial dependence and spillover effects.
This paper’s original contribution is first in constructing the territorial quality of life
(TQL) index in the EU member countries using the NUTS2 regional level indicators and
then computing indicators for residential land expansion and densification. This is fol-
lowed by research on the relationship between the quality of life index and land expansion
and densification indicators. A TQL index is constructed based on the sub-indicators
which were classified under socio-cultural, economic, and ecological capitals as defined by
Zoeteman et al. [37].
Principal component analysis (PCA) was used to construct an uncor-
related set of sub-indicators. For the weighting of sub-indicators, entropy weight method
was applied. Similar approaches were undertaken for the development of indicators in-
cluding by Arifwidodo [
38
], Zoeteman et al. [
37
], Xiao et al. [
39
], Bovkır et al. [
34
], and
Ustaoglu et al. [21].
Residential expansion and densification indicators were constructed
based on residential land use, economic output, and population data (for the review of
urban expansion and densification indicators, refer to Ma and Xu [
40
], Xu and Min [
41
],
and Chen et al. [
42
]). Statistical models can reveal the relationship between quality of life
and urban expansion and densification and provide an improved approach for quanti-
fying the principal factors in comparison to qualitative analysis. This research indicates
geographical clusters based on QoL and urban densities in European regions which will
provide evidence for future research and policy decisions.
2. Literature Review
Understanding the relationship between the European policy context, regional plan-
ning approaches to land use, and resulting spatial and environmental trends observed is
Urban Sci. 2024,8, 22 4 of 33
essential for this research. The European Green Deal sets a package of policy initiatives
that aim to set the EU on the path to a green transition, with the ultimate goal of reaching
climate neutrality by 2050. Target 15.3 of the Sustainable Development Goals for 2030
(SDGs) of the United Nations (UN) focuses on indicators of land degradation at the global
level. Land use and socio-economic data support many other targets and we therefore
aimed to use land use and socio-economic data in the study. Examples include SDG target
15.1 on the conservation, restoration, and sustainable use of ecosystems; SDG target 8 on
sustainable economic growth; and SDG target 11 on sustainable cities and communities. To
address the priorities for socio-economic and environmental sustainability set forth in the
European Green Deal and other EU policies, land cover/use and land use change data must
be integrated into data and policy analysis aiming to provide the sustainable development
of the cities and urban regions.
As land is a limited natural resource, the continued conversion of natural and agri-
cultural land into artificial surfaces primarily to provide for urbanisation, infrastructure,
and property development processes, called “land take”, is often a permanent process.
While facilitating further economic development in the short term, this often has significant
environmental and economic consequences over both the longer and shorter terms [
43
].
The concept of land take involves agricultural, forest, and other semi-natural land being
taken and used instead for urban and other artificial land development [44].
The European Commission [
45
] called for “No Net Land Take” by 2050, and the Euro-
pean Commission’s Roadmap to a Resource Efficient Europe aims to preserve land as a
resource by reducing the pressures of urban development on the natural and managed land-
scapes. The concept of “No Net Land Take” combines reductions in land take with policies
which will encourage land return to non-artificial land categories through re-cultivation
or rewilding to provide the ecosystem services of unsealed and natural environments and
soils once again. The EU Soil Strategy for 2030, published in November 2021, requires
member states to set land take targets with the aim of reaching land take neutrality by 2050.
The continued political difficulties in the adoption and implementation of such mea-
sures were seen with the adoption of the Nature Restoration Law in 2023 following a
contentious debate in the EU Parliament. This law is intended to be a key step in avoiding
ecosystem collapse and preventing the worst impacts of climate change and biodiversity
loss. A majority of MEPs (Members of European Parliament) supported the Commission’s
proposal to put restoration measures in place by 2030 covering at least 20% of all land
and sea areas in the EU. To address the sustainable development of cities and urban re-
gions, the European Commission promotes the compact city growth model which supports
high-density mixed-use growth [
46
]. Density is considered as a crucial factor in defining
sustainable and high-quality urban structures [
47
]. This is supported by a substantial body
of literature suggesting that “significant and ecologically relevant services require scale
and density” (Newman, [
48
]: 278). Compact and mixed land use patterns of development
are associated with less car dependence, shorter journeys to work, more use of public
transport, encouraged social interaction, and less consumption of greenfield land compared
to low-density suburban developments [
49
]. Neumann [
50
] illustrates the diverging views
where high levels of compactness are preferred by policy, yet excessive density can have
negative effects on quality of life, health, and urban well-being.
For this research, an understanding of the planning policy approach in individual
regions and their potential role in development outcomes is necessary. Drawing on recent
ESPON Compass research [
51
], the planning systems of European regions can be identified
as evolving from the differing approaches in European planning traditions. The categories
were based on the scope of planning, the extent of planning at the regional and national
levels, the locus of power, the relative roles of public and private sectors, the legal system,
constitutional provision and administrative traditions, the maturity or completeness of
the system, and the distance between objectives and outcomes [
52
]. The four planning
traditions were as follows: regional economic planning, comprehensive integrated planning,
urbanism, and land use management. This results in broad grouping into discretionary
Urban Sci. 2024,8, 22 5 of 33
and regulatory systems, confirming high diversity as well as the existence of hybrid
categories [53].
The modern housing development typologies proposed with increased scale and
density promoted by key figures such as the architect Le Corbusier are regarded as seminal
influences on later forms of urban development [
54
]. The approach to increased densities
as it subsequently evolved has been criticised as being one-dimensional in that many of
the supporting ideas of transit provision and ancillary facility provision are omitted with a
focus only on increased density and higher buildings. Curtis ([55]: 293) critiqued that “To
reduce the matter to high density when no due attention was given to communal facilities
was to court disaster; to create open space without greenery was to devalue the idea of
the community living in nature. The imitations of the Unitéusually involved such drastic
omissions. Does this mean that the prototype should be blamed for the later disastrous
variations?” Sm˛etkowski et al. [
56
] in their paper discuss development in a market-led
system as sometimes being an organic or evolutionary processes with planning and their
governance consistently trying to play catch-up. Developments are created with a site-
specific mindset as private developers aim to achieve the functional transformation of
lands into newer, marketable uses in line with the needs and transformations of the broader
metropolitan economy.
This ties in with international debates on how liberal economic philosophies have
strongly influenced policy discourses over recent decades globally. Harvey [
57
] has consis-
tently argued that economic liberalisation, including the increasing role of financialisation
and globalisation in housing markets, has transformed both the means and the purpose
of local governance and policy approaches towards the facilitation of capital interests.
Thus, numerous initiatives aimed at developing compact cities have focused on increased
residential density, as well as addressing development difficulties, such as market needs
and social concerns [58,59].
The compact city is therefore becoming one of the most promoted concepts in planning,
with many planners and politicians advancing the compact city with high densities as the
preferred urban form for sustainable development [
60
]. Sustainability of compact cities
is linked to urban form, as illustrated in the case of Oslo [
58
]. However, the extent to
which compact city policies can realise pre-defined policy objectives when implemented
is also of concern, particularly in countries where housing development is industry- or
market-driven [61–63].
3. Methods
3.1. Data
The growing density of people, activities, and amenities is related to the overuse of
infrastructure, increasing land prices, privatisation of urban spaces, traffic congestion, and
environmental pollution. On the one hand, artificial land and its expansion into open land
and brownfields put urban green areas and open spaces under pressure [
64
]. On the other
hand, low densities provide other different benefits such as clean air, closeness to nature,
and habitats for plant and animal species. The three sustainability pillars—the ecological,
sociocultural, and economic domains—as well as the subsystems that make up each of
them are used in the sustainability balancing measure. A development process known
as “sustainable development” strives to promote balanced growth in the robustness and
quality of the environment (also known as “ecological capital”), in human physical and
spiritual wellness (also known as “socio-cultural capital”), and in sound economic growth
(also known as “economic capital”). For monitoring the evolution of each capital and its
relative positions, they are divided down into subsystems called “stocks” utilising soft
system modelling [
65
]. These stocks are vital for the state and growth of each capital as
well as the overall system. The system of the subdivision of indicators under economic,
socio-cultural, and ecological capitals is well defined by Zoeteman et al. [
37
]. The indicators
in the current study were chosen to encompass the sustainability components that were
just described of (1) ecological capital, (2) socio-cultural capital, and (3) economic capital
Urban Sci. 2024,8, 22 6 of 33
(see Zoeteman et al. [
37
] where they are used in order to track the advancement of urban
and regional sustainability in the EU countries). The indicators included in the study and
their data sources are given in Table 1. It can be noted that the databases that we obtained
from the European Environment Agency (EEA), ESPON, and Eurostat serve as our primary
data sources. Additional information on the data sources used in the study is also provided
in Table 1. The information is offered at the national (NUTS0), regional (NUTS1 or NUTS2),
and local (NUTS3) levels. The data are cross-section data covering the post-2015 period. To
create the composite indicators for our analysis, we thus aggregated or disaggregated all
the data at the NUTS2 level, which is the statistical unit of our study. The disaggregation
of data into smaller units means that it is assumed that the values that were calculated at
a coarser scale also hold true for a smaller scale. Because there are no data calculated at
the NUTS2 scale regarding some of the indicators, we had to disaggregate the NUTS0 or
NUTS1 level to the NUTS2 level. Based on the availability of data for the subject indicators,
we can re-conduct composite indicator analysis in the future and compare the findings from
that research with the current findings. A further issue with the data is that of the absence
of indicator data on a time series basis. We could not compute the composite indicators on
a yearly basis and compare the indicators based on their yearly value, rather, we had to use
the cross-section data and compute the composite indicators at a point in time. Depending
on the existence of time series data in the future regarding our selected indicators from
PCA, we can analyse the changes in quality of life in a time series context.
To compute residential densification and expansion indicators, we used high-resolution
remote sensing data that represent the spatial information for various land uses, making
them useful for studying the spatial pattern and analysing changes in land use. In this
context, the Corine land cover/use (CLC) data that have a resolution of 100 m were ob-
tained from the European Environment Agency [
66
]. To compute land use change, we
used the residential land use data for the period between 2000 and 2018. There are 44 land
cover classes listed in the CLC data. Among these, continuous and discontinuous urban
fabric were lumped into a single land use to analyse the dynamics of residential land use
within the 2000–2018 period. Population and economic output data were obtained from
Eurostat’s regional databases for the subject period [
67
]. The use of residential land use and
population data for the construction of residential expansion and densification indicators is
elaborated in the next section.
Table 1. Capitals, stocks, and indicators to assess quality of life in the regions.
Capitals and Stocks Indicators
Economic capital (ECON_C)
Labour Total employment, unemployment, share of full-time employment, involuntary
part-time/temporary employment, non-employed persons (Eurostat)
Economic structure GDP per capita, disposable income of households, job opportunities (Eurostat)
Circular economy
Total employment in material providers, total turnover generated by material providers’
activities, total employment in technology providers’ sectors, total turnover generated by
technology providers’ sectors, total employment in Circular Business Model (CBM) sectors, total
turnover generated by CBM sectors (ESPON CIRCTER Project (2017–2019): Circular Economy
and Territorial Consequences (https://database.espon.eu/project-archives/#/archives) Accessed:
15 September 2023)
Infrastructure and mobility
Internet at home; broadband at home; online interaction with public authorities; internet access
(Eurostat); potential accessibility to rail, air, and multimodal transport (ESPON TIA-
(https://database.espon.eu/project-archives/#/archives) Accessed: 15 September 2023); green
infrastructure initiatives; share of areas in a region that have poor access to the following:
(a) primary schools, (b) secondary schools, (c) hospitals, (d) closest doctors, I pharmacies, (f) bank
office, (g) train station, (h) urban morphological zone, (i) cinemas, (j) shops, (k) regional centres
(ESPON PROFECY Project (https://database.espon.eu/project-archives/#/archives) Accessed:
15 September 2023)
Urban Sci. 2024,8, 22 7 of 33
Table 1. Cont.
Capitals and Stocks Indicators
Knowledge Higher education attainment rate, lifelong learning, employment in high-tech sectors,
employment in science and technology (Eurostat regional education statistics)
Research and Development EU patent applications, EU trade mark applications, EU community design
applications (Eurostat)
Social-cultural capital (SC_C)
Education Lower secondary education completion rate, early school leavers, employment and training,
young people not in education and training (Eurostat regional education statistics)
Health
Life expectancy, unmet medical needs, insufficient food, cancer diseases death rate, hearth
diseases death rate, suicide death rate, infant mortality rate, premature mortality rate, road
accident fatalities (Eurostat regional health statistics)
Safety Crime, safety at night, money stolen in the household, assaulted/mugged (Gallup World
Poll Statistics)
Living environment
Burdensome cost of housing, housing quality, overcrowded housing, lack of adequate heating in
the dwelling, lack of toilet in the dwelling (EU statistics on income and living conditions)
Governance
Control of corruption, government effectiveness, political stability and absence of
violence/terrorism, regularity quality, rule of law, voice and accountability, public service quality,
impartiality (all treated equally, with some receiving special advantages in education, health care,
law), corruption in public service provision, trust in the national government, trust in the legal
system, trust in the police (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1682130
Accessed: 24 November 2023)
Ecological capital (ECOL_C)
Air quality
Average NO2 concentration, average Ozone3 concentration, average PM2.5 concentration,
average PM10 concentration, emissions of CO2per capita, electricity produced by renewable
energy resources (European Environment Agency (EEA), ESPON TIA, Baranzelli et al. [68]
Climate
Change in annual mean number of days with heavy rainfall, change in annual mean number of
days with snow cover, change in annual mean number of summer days, potential vulnerability to
climate change, combined adaptive capacity to climate change, relative change in annual mean
evaporation, relative change in annual mean precipitation in summer months, relative change in
annual mean precipitation in winter months (ESPON CLIMATE Project
(https://database.espon.eu/project-archives/#/archives) Accessed: 15 September 2023)
Waste
Total waste production, municipal solid waste recycling rate, uncollected sewage, sewage
treatment (EEA; ESPON CIRCTER Project
(https://database.espon.eu/project-archives/#/archives) Accessed: 15 September 2023)
Hazards Number of hazards, number of vulnerability causes (EEA)
Water
Water Retention Index, satisfaction with water quality, freshwater consumption per capita
(ESPON GRETA Project (https://database.espon.eu/project-archives/#/archives) Accessed:
15 September 2023)
Soil Capacity of ecosystems to avoid soil erosion, soil retention (ESPON TIA Project)
Green infrastructure Coverage percentage of green infrastructure (GI), forest area within a region, area of
NATURA2000 sites relative to regional area (EEA)
Land
Soil sealing within a region, share of agricultural area in protected areas, share of urban area in
protected areas, percentage share of urban use areas within a region, percentage share of
agricultural use areas within a region, development of urban use per capita, amount of raw
material extracted from natural environment (ESPON SUPER Project
(https://database.espon.eu/project-archives/#/archives) Accessed: 15 September 2023)
3.2. Territorial Quality of Life Index
We computed four different composite indicators to represent the sustainability aspects
aligned with economic capital (ECON_C), socio-cultural capital (SC_C), ecological capital
Urban Sci. 2024,8, 22 8 of 33
(ECOL_C), and the sum of them (TOTAL_SC). The stages of the composite indicator
development methodology that is applied in the research are summarised below.
3.2.1. Normalisation of Indicators
Due to the wide and narrow range in the variation between the variables, normali-
sation of the variables is required in multivariate statistical analysis. Data transformation
inside a particular range (0–1) is facilitated by parameter normalisation. In this study, four
separate indices (SC_C, ECON_C, ECOL_C, and TOTAL_SC) were developed using the
statistical data presented in Table 1. As a result, consistency must be achieved between
the four indices throughout all European regions in order to compare them. All of the
indicators were standardised to have values ranging from 0 to 1. We utilised Equation (1)
to denote a positive indicator and Equation (2) to represent a negative indicator.
Iij =Nij −minNij
maxNij−min(Nij)(1)
Iij =1−Nij −minNij
maxNij−minNij (2)
where I
ij
is the normalised value of the parameters, N
ij
denotes the actual value of the
parameters of j of location i (i.e., NUTS2 region) for the study’s time period (i.e., post-2015),
and min and max represent the minimum and maximum value of the parameters in the
given dataset.
3.2.2. Principal Component Analysis Used for the Selection of Indicators
It is crucial to highlight that the individual indicators, which are typically connected
with one another, may produce information that is excessively repetitive even though the
indicator system can provide comprehensive information on the territorial quality of life
at the regional or municipal size. Rather than utilising every indicator available, from the
perspective of decision makers and urban administrators, using a set of comprehensive
indicators is more manageable and practical. Therefore, the goal should be to build an
extensive set of indicators that indicate the quality of urban life in cities and regions in order
to reduce the overlap of data among different indicators. For this goal, principal component
analysis (PCA), which is an unsupervised technique, is frequently used for ranking and
building composite indicators [
69
,
70
]. Reduced dimensionality and weighting of all data
based on composite indicators are the goals of unsupervised techniques. In quantitative
terms, it establishes an initial set of correlated indicators before generating uncorrelated
components, each of which is a weighted linear composite of the original indicators [
70
].
The linear combination of the original indicators that makes up the examined uncorrelated
components are called PCs. They are computed using the eigenvectors of the indicators’
correlation or covariance matrices. This technique enables us to identify a number of
comprehensive indicators that can better explain disparities in territorial quality of life.
Factor analysis (FA) is an alternative technique used to describe variability among correlated
indicators aiming to obtain a lower number of unobserved indicators called factors. This
technique can also be used for indicator selection, as applied in Bovkır et al. [
34
], who found
that the same results were obtained from the FA and PCA methods. Because two methods
follow similar approaches in the determination of components and the same results were
obtained as claimed by Bovkır et al. [
34
], we used the PCA approach that is commonly
used in the literature for indicator selection within the current study.
PCA was used for the 109 indicators, which comprise the total number of regional sub-
indicators available to EU member states (Table 1). The principal components (PCs) have
been extracted using the eigenvalue criterion, with eigenvalues higher than 1.0 and in other
cases 0.5 being explanatory. The proportion of variance attributed to the relevant PC among
the original indicators was determined next. The PC matrix, commonly referred to as PC
Urban Sci. 2024,8, 22 9 of 33
loadings, is then obtained, which shows the weight of each PC in respect to each original
indicator. The resultant matrix was rotated using the Varimax criterion to obtain loadings
for computing the final outcomes for each PC. Concerning the PC loadings, the PC score
coefficients were analysed, and only a single indicator from each PC was chosen for having
the highest PC score to represent the other correlated indicators pertaining to the same PC
loading. As a result, we decreased the number of variables to 29 and developed a set of
comprehensive indicators to analyse territorial quality of life in European regions. Different
studies in the literature use PCA or alternative methods such as correlation analysis to
eliminate highly correlated indicators and to construct a set of comprehensive indicators.
Examples include Floridi et al. [
71
], Mascarenhas et al. [
72
], Annoni and Bolsi [
73
], and
Bovkır et al. [34].
3.2.3. Weighting the Indicators
Weights used in composite indicator construction can have a big impact on the final
composite indicator and its outcomes. A great deal of composite indicators uses equal
weighting, which assigns the same weight to each component. In other instances, such as
factor analysis, principal component analysis, the entropy method, or data envelopment
analysis (DEA), the weights are derived directly from the data [
21
,
34
]. Other methods
use the AHP (Analytical Hierarchy Process) [
74
,
75
], the best worst method [
76
], SMART
(Simple Multi-Attribute Rating Technique) [
77
], and the delphi method [
78
] to estimate
the weights external to the data. These methods are examples for the expert weight
determination methods and include similarity-based methods such as TOPSIS [
79
], index-
based methods [
80
], and cluster-based methods [
81
]. A full review of the expert-based
methods can be found in Chen et al. [
82
]. There are also examples using hybrid approaches
such as when Zhang et al. [
83
] integrated Grey Relational Analysis (GRA) with TOPSIS
to find the final rank of each alternative for the selection of the optimal green material for
sustainability. Rosic et al. [
84
] used composite indicators developed from DEA and TOPSIS
and proposed PROMETHEE-RS to select a road safety composite index. The combination
of data-driven and participatory weighting approaches is provided by Xu et al. [
85
] as well
as by Lee and Chou [
86
]. Other examples can be found in El Gibari et al. [
87
] and Correa
Machado et al. [
88
]. Although data-based weighting methods such as the entropy method
have their problems, i.e., the method ignores the sub-indicators’ relative importance in
a multidimensional evaluation framework, we selected the data-based method in the
study and excluded the expert-based approaches for two reasons as follows: The first
is the high cost of organising stakeholders at the pan-European level. The second is the
effectiveness of data-based methods at coarser scales as the expert-based methods reflect
the local conditions and these are more powerful at local scales. A further reason for
selecting the entropy method is that according to the findings of Ustaoglu et al. [
21
], it is
the least sensitive method to a change in weights, and it can be considered as a robust and
flexible method in the construction of composite indicators.
Entropy is a scientific notion that is approximately connected with a system’s state
of disorder, variability, or uncertainties; therefore, a minimum production of entropy
might be that of the sustainability standard. Given the intrinsic difficulty of evaluating
sustainability, which necessitates integrating all dimensions (ecological, economic, and
socio-cultural capitals) and their associated indicators in a tangible form, the unbiased
nature of the entropy weight method (EWM) allows it to be suitable for sustainability
analysis by producing unbiased results. The EWM is an objective weighting method built
on Shannon’s [
89
] entropy coefficient. It is defined in several statistical steps to determine
the weight of indicators based on the data they provide, avoiding the unfavourable effects
of subjective factors and producing reliable results. “H”, which satisfies some features
for each p
i
inside an estimated joint probability distribution P, was first used to measure
entropy. It was demonstrated that the only function that meets these requirements is
H=−∑n
ipilog(pi)
[
90
]. If there is significant variation between the options, the criterion
provide a wealth of information and is thus regarded as an essential factor. Assuming
Urban Sci. 2024,8, 22 10 of 33
there are m objects to evaluate, each with n evaluation criteria, the decision matrix can be
constructed as follows: X = {z
ij
,i=1,
. . .
, m; j = 1,
. . .
, n}. For each criterion C
j
, the decision
matrix “X” is normalised, with Pij standing in for the normalised values.
Pij =zij
∑m
i=1zij
(3)
The entropy Ejof each criterion Cjis calculated as follows:
Ej=−g∑m
i=1pij lnpij(4)
where g is a constant, i.e., g = (ln(m))
−1
. The entropy weight w
j
of each criterion C
j
is
calculated as follows:
wj=Dj
∑n
j=1Dj
where Dj=1−Ej(5)
A drawback of the entropy weighting approach is that it assigns weights that may be
incompatible with the conceptual importance of the sub-indicators [
91
]. Additionally, the
discriminating power and the composite indicator’s ability to encapsulate the concept of
the multidimensional phenomena are adversely affected by the weighting of the entropy
index sub-indicator [
92
]. Despite these drawbacks, the entropy weighting approach is one
of the most widely used methods in the literature.
3.2.4. Development of TQL Indicators
The TQL indicator is composed of three different indices that are related to ecological
capital, socio-cultural capital, and economic capital. These indices are ECOL_C, SC_C,
and ECON_C. Then, the following equation was used to compute the quality of life index
across European regions:
TQL =ECOL_C +SC_C +ECON_C where
ECOL_C =∑m
i=1wiXi; SC_C =∑n
j=1wjXj; ECON_C =∑l
k=1wkXk(6)
Here we used an additive aggregation method that summed up the normalised values
of sub-indicators to develop the TQL indicator. Given the identified relative measurement
error of a group of indicators, the additive aggregation method’s continuity characteristic
suggests that the bound for the sustainability index can be determined with precision [
93
].
This characteristic can be applied to uncertainty quantification and sensitivity analysis.
There are two issues to consider: First, all the indicators’ contributions can be totalled
together to produce a total value, suggesting that there is no conflict or synergy between
them—an assumption that appears unlikely in many circumstances [94]. Second, because
the basic character of additive procedures necessitates a compensating logic, the weights
used in these approaches are substitute rates rather than importance coefficients. Therefore,
when there are significant interactions between indicators, additive approaches should not
be used [
93
]. When considering the viewpoint of strong sustainability, the use of compen-
satory methods to aggregate indicators is frequently controversial because these methods
suggest that compensation among the sustainability sub-components is appropriate [
95
].
Non-compensatory aggregation approaches become significant when substituting one
sub-component for another is regarded as inappropriate.
3.3. Residential Expansion and Densification Indicators
There are two indices that were used to measure residential land expansion from 2000
to 2018 as follows: the first one is the residential land expansion rate (RE) that measures the
average land expansion rate in the subject time period and the second one is the residential
land expansion intensity rate (RI) that quantifies the average expansion intensity in the
subject period (Table 2) [
41
,
96
]. Both indices are essential for comparison purposes of the
Urban Sci. 2024,8, 22 11 of 33
residential land use change in different time periods. The normalisation factor accounts
for the difference between the two indices as follows: RI uses the total built area of the last
year as opposed to RE, which uses the total built area of the initial year.
Table 2. The indices and their equation for the population, residential land expansion, and densifica-
tion (adopted from Chen et al. [42]).
Index Equation
Average annual residential land expansion
rate (RE) (%) RE =R2−R1
R1×1
T×100
where R1is the total residential built area at the
initial time period, R2is the total residential built
area at the final time period, and T is the
time period.
Average annual residential land expansion
intensity rate (RI) (%) RI =R2−R1
R2×1
T×100
where the expressions are the same as those for the
residential land expansion rate.
Average annual population growth rate
(PR) (%) PR =P2−P1
P1×1
T×100
where P1is the total population at the initial time
period, P2is the total population at the final time
period, and T is the time period.
Population growth-to-residential land
expansion ratio (PRL) (%) PRL =PR
RE
where PR and RE are as defined previously.
Urban population density (PD) (persons
per km2)PD =Pi
Ri
where Piis the total population and Riis the
residential built-up area in year i.
Decoupling indicator (DI) DI =%∆R
%∆GDP
where R is the percent change in the residential
built-up area and percent change in total GDP.
The population growth rate (PR), a measure of the typical growth rate during a
specified time period, was utilised in this study (Table 2). The study employed two
indicators to look at the connection involved between residential land expansion and
population increase as follows: (1) the ratio of population growth to urban expansion (PRL)
is that of PR to RE and (2) by dividing the total population of cities by the total built-up
area in a year, urban population density (PD) is determined. Higher-density constructions
and the use of PRL as a measure of urban compactness have been proposed in earlier
research as strategies to achieve sustainable urban expansion [
97
,
98
]. Lastly, to measure
the impact of a built-up area on the economic output, the DI is defined as the ratio of the
percentage change in a residential built-up area to the percentage change in the total GDP
in the specified region.
3.4. The Relationship between TQL Indicators and Residential Land Expansion and Densification
3.4.1. Regression Analysis
Landscape metrics are used to calculate and evaluate changes in landscape patterns,
which are a result of changes in land use. As examples of landscape metrics, indices of
residential land expansion and densification were introduced in Table 2. It is of significance
to understand how the direction and magnitude of land use changes relate to quality of life
at the regional scale and to the aim of planning for sustainable land management.
The relationship between land use change and TQL was examined for this purpose
using spatial econometric models. In contrast to the OLS model, the spatial economet-
ric models can more accurately depict the spatial effects of various factors on the TQL
variable by taking into consideration spatial correlation and spillover influences. The
Urban Sci. 2024,8, 22 12 of 33
usual econometric models may not work if the geographical effects are not considered [
99
].
Spatial lag models (SLMs) and Spatial Error Models (SEMs) are the two common spatial
economic models. By enabling outcomes from one area to be influenced by (a) outcomes of
surrounding areas, (b) covariates from adjacent regions, and (c) errors from adjacent areas,
spatial econometric models improve linear regression. In the case of the SLM given by the
following formula, spatial correlation is mirrored in the dependent variable as follows:
Y=αWY +Xβ+e; e ∼N0, δ2In(7)
where Y is the dependent variable, W is the normalised spatial weight matrix that describes
how spaces are related to one another,
α
indicates the geographic impact of the nearby area
observation value WY on the local observation value and is known as the spatial regression
coefficient, X is the independent variables,
β
is the regression coefficient, and e is the error
term. The Spatial Error Model (SEM) looks at the spatial dependency in the error term,
which suggests that there are spatial spillover effects in the error term. The SEM is provided
in the equation below.
Y=Xβ+ρWµ+e; e ∼N0, δ2In(8)
where
ρ
is the space error coefficient of the error term,
µ
is the normally distributed random
error term, and the other parameters are as defined previously. The issues with spatial
autocorrelations in the TQL index and the failure to explain variances that may potentially
be caused by spatial relationships could be solved simultaneously by applying these spatial
regression models while taking into account the spatial lagged responses of the dependent
variable and other unexplained spatial errors.
3.4.2. Global and Local Spatial Correlation Analysis
Spatial autocorrelation is measured using both global and local rating systems. By
representing the mean difference between a variety of geographical units and their neigh-
bouring units, spatial statistical methods are used to show the spatial properties of a
dataset [
100
]. The spatial weight matrix W is employed to represent the spatial–geospatial
relationships among various data points. Explanatory spatial data analysis (ESDA), one
of the methods for spatial statistical assessment, analyses spatial autocorrelation by de-
termining spatial dependency and heterogeneity. One of the most commonly used ESDA
indicators is that of global Moran’s I. Moran’s I technique seeks to quantify how dependent
the data are on their geographic context and evaluates if their spatial pattern is clustered,
random, or dispersed [
100
]. When using a spatial weight matrix, the degree of variance
between two sets of data are determined by how similar and how far apart they are from
one other. In this study, the I index as given in Equation (9) was used to calculate the spatial
correlation of TQL and residential indicators across the NUTS2 regions.
I=n
∑i(xi−x)2
∑i∑j=iwij(xi−x)xj−x
∑i∑i=jwij
(9)
where n represents the number of regions in the study area, x
i
and x
j
are the values of
tested variables,
x=(∑ixi)/n
is the average value of the tested variable, w
ij
is the value
in the spatial weight matrix. The range of values accepted by the Moran’s I statistic is
that in [
1
,
1
]. The data are close to one another but do not share any properties, so if the
estimated I statistical value is close to
−
1, there is negative spatial autocorrelation. If it is
near to +1, then there is positive spatial autocorrelation, meaning that the data are clustered
together and have similar features in specific regions. It is inferred that there are no clusters
or dispersion and that the data are distributed randomly if the estimated I statistical value
equals 0.
As a local correlation analysis, bivariate spatial correlation method was utilised in the
study. The statistical correlations between several variables recorded at the same location
are typically the subject of traditional statistical analysis methods. It is common that the
Urban Sci. 2024,8, 22 13 of 33
QoL measures can extend over a wider area. This problem can be solved by doing a spatial
bivariate analysis to determine the spatial associations between QoL indices and residential
expansion and densification data. This co-location analysis technique was first introduced
by Anselin et al. [
101
] that designed BiLISA to examine the spatial correlation patterns
between two geospatial variables. BiLISA can be used to evaluate the link between a
variable in one region and a second variable in neighbouring regions. Bivariate analysis is
based on the relationship between the dependent variable and explanatory variables that
are isolated at a given time point. Bivariate local Moran’s I (I
local
) was used for the spatial
analysis of the regional data that are provided at the NUTS 2 level.
Ilocal
i=Xa
i∑n
j=1,j=iwijXb
j(10)
where I
ilocal
is the bivariate local Moran’s I at location i, X
ia
and X
jb
are the values of
variables a and b at locations i and j, respectively, and w
ij
is the weight matrix representing
the weighting between locations i and j. Variable a at location i is clearly associated with
variable b in the nearby region when I
ilocal
is strongly positive or negative; otherwise, there
is not a clearly apparent relationship between them. Four types of spatial relationships can
be obtained from this statistic for our study, i.e., low-low (LL—spatial concentration of low
values of the TQL index and low values of the independent variable from neighbouring
regions), high-high (HH—spatial concentration of high values of the TQL index and high
values of the independent variable from neighbouring regions), low-high (LH—spatial
concentration of low values of the TQL index and high values of the independent variable
from neighbouring regions), and high-low (HL—spatial concentration of high values of the
TQL index and low values of the independent variable from neighbouring regions) types
of spatial clustering of different values. The summary of the methodological framework is
presented in Figure 2.
Urban Sci. 2024, 8, x FOR PEER REVIEW 14 of 35
Figure 2. Methodological framework of the study.
4. Results
There are two key findings of the study as follows: First, residential expansion and
densification affected TQL which is composed of economic, socio-cultural, and ecological
capitals. Second, there is spatial variation explicated by spatial correlation analysis be-
tween TQL and residential indicators. From the spatial regression models, we found that
residential land expansion rate and residential densification are correlated with socio-cul-
tural capital. Socio-cultural capital has sub-components including education, health,
safety, and living environment. Regarding the health sub-component, our finding sup-
ports Jackson [102] who argued that the healthiest architecture places occupants in close
Figure 2. Methodological framework of the study.
Urban Sci. 2024,8, 22 14 of 33
4. Results
There are two key findings of the study as follows: First, residential expansion and
densification affected TQL which is composed of economic, socio-cultural, and ecological
capitals. Second, there is spatial variation explicated by spatial correlation analysis between
TQL and residential indicators. From the spatial regression models, we found that resi-
dential land expansion rate and residential densification are correlated with socio-cultural
capital. Socio-cultural capital has sub-components including education, health, safety,
and living environment. Regarding the health sub-component, our finding supports Jack-
son [
102
] who argued that the healthiest architecture places occupants in close proximity
to outside green spaces, vistas of the outdoors, ventilation, and sunlight, which are the
common characteristics of low-density developed urban areas where there are an abun-
dance of green spaces and low-rise buildings. From a different perspective, Berman [
103
]
and Cervero [
104
] claimed that mixed land use and high densities increase pedestrian
and bicycle activity. This supports the health benefits of the residents while protecting
open spaces by consolidating development. In contrast, the proximity of shopping centres,
dwellings, and transportation hubs causes noise pollution that may have a negative impact
on local health outcomes [105].
Residential land expansion was found to be negatively related to economic capital and
ecological capital while the relationship was positive for residential densification. Economic
capital has sub-components that are related to labour, economic structure, circular economy,
infrastructure and mobility, knowledge, and research and development. It was shown that
these sub-components are positively related to densification, which explains the importance
of agglomeration economies, accessibility to different land uses, the sharing of a common
labour pool, and knowledge spillovers. This is also verified by Angel and Blei [
106
] who
showed that densification, relocation, and improved accessibility increased the productivity
of US metropolitan labour markets. Another piece of research in the US and Europe implies
higher productivity in locations with a higher economic density [60].
Ecological capital has sub-components including air quality, climate, waste, hazards,
water, soil, green infrastructure, and land. Some of these sub-components were verified
to have a positive relationship with residential densification. Transport-related air quality
was found to be more improved for the densification scenario than achieving progress in
addressing urban sprawl in Quito, Ecuador [
107
]. A report by the OECD [
108
] highlights
the importance of densification as it saves farmland and natural areas. Another report by
the European Commission in 2016, namely “FUTURE BRIEF: No net land take by 2050?”,
mentions land take as one of the key trends for climate change, biodiversity, landscape
fragmentation, flood risk, and urban heat island effects. This literature points to the benefits
of urban densification as it improves benefits specified under ecological capital.
4.1. Selection of Indicators from PCA and Their Weighting
Table 3presents the findings from PCA concerning the indicators selected from each
PC based on their high score coefficients. The total number of selected indicators from
each PC adds up to twenty-nine because this is the number of PCs that were retained.
The details on the selected indicators from each PC and the variance explained by the
subject PC are provided in Supplementary Materials (see Table S1). The potential impacts
of the indicators on the TQL index are shown in Table 3with plus and minus signs. For
instance, employment in science and technology positively contributes to quality of life
within regions, whereas the share of regions that have poor access to primary schools has a
negative impact. The weights calculated from the entropy weight method are shown in the
last column of Table 3, with the cancer diseases death rate having the highest weight and
freshwater consumption per capita having the lowest weight.
Urban Sci. 2024,8, 22 15 of 33
Table 3. Selected indicators from PCA and their weights from the entropy method.
Capitals and Stocks Indicators (Impact) Weights
Economic capital (ECON_C)
1. Employment in science and technology (+) 0.0355
2. Total employment in Circular Business Model (CBM) sectors (+) 0.0093
3. Higher education attainment rate (+) 0.0437
4. Lifelong learning (+) 0.0437
5. Share of areas in a region that have poor access to primary schools (−) 0.0364
6. Share of areas in a region that have poor access to closest doctors (−) 0.0365
7. Share of areas in a region that have poor access to urban morphological centre (
−
)
0.0285
8. Share of areas in a region that have poor access to cinemas (−) 0.0181
Social-cultural capital
(SC_C)
9. Control of corruption (+) 0.0345
10. Crime (−) 0.0361
11. Assaulted/mugged (−) 0.0364
12. Housing quality (+) 0.0439
13. Early school leavers (−) 0.0424
14. Cancer diseases death rate (−) 0.0552
15. Life expectancy (+) 0.0428
16. Unmet medical meets (−) 0.0151
17. Insufficient food (−) 0.0228
Ecological capital
(ECOL_C)
18. Average Ozone3 concentration (−) 0.0492
19. Emissions of CO2per capita (−) 0.0101
20. Electricity produced by renewable energy resources (+) 0.0479
21. Municipal solid waste recycling rate (+) 0.0456
22. Water Retention Index (+) 0.0472
23. Freshwater consumption per capita (−) 0.0022
24. Soil retention (+) 0.0170
25. Forest area within a region (+) 0.0409
26. Area of NATURA2000 sites relative to regional area (+) 0.0504
27. Share of urban area in protected areas (−) 0.0052
28. Development of urban use per capita (−) 0.0593
29. Change in annual mean number of days with snow cover (−) 0.0329
Bartlett’s test
Chi-square: 2876.339
Degrees of freedom: 406
p-value: 0.000
Kaiser–Meyer–Olkin measure of sampling adequacy
KMO = 0.609
Note: impacts on territorial quality of life are in parentheses, and weights are given in the last column that were
obtained from the entropy method.
4.2. Spatial Variation in TQL Index in Europe
The findings showed that there was a significant variation in TQL indices between
the regions in Europe (Figure 3). Socio-cultural capital was the highest in Ireland, the
Netherlands, Austria, Belgium, Scandinavian countries, and some other regions in Central
Europe.
Analysing the different regional approaches to social protection and investment in this
sector can assist in understanding such trends. The European Commission reported that
the ratio of social protection expenditure to GDP is at least 20% of GDP for the countries
including Finland, France, Denmark, and Austria, and it is less than 10% in Ireland and
the Netherlands [
109
]. With shares less than 10% of GDP in 2018, Denmark, Austria,
and France recorded the highest ratios to GDP regarding health expenditures [
109
]. For
education, the highest shares were registered in Sweden (6.9%), Denmark (6.4%), and
Belgium (6.2%) [
109
]. These expenditures recorded under socio-cultural capital to verify
our findings on the distribution of socio-cultural values in Europe.
Urban Sci. 2024,8, 22 16 of 33
Urban Sci. 2024, 8, x FOR PEER REVIEW 17 of 35
Figure 3. Territorial quality of life indicators measuring total sustainability, as well as the ecological,
socio-cultural, and economic sustainability capitals at the NUTS2 level in Europe.
Analysing the different regional approaches to social protection and investment in
this sector can assist in understanding such trends. The European Commission reported
that the ratio of social protection expenditure to GDP is at least 20% of GDP for the coun-
tries including Finland, France, Denmark, and Austria, and it is less than 10% in Ireland
and the Netherlands [109]. With shares less than 10% of GDP in 2018, Denmark, Austria,
and France recorded the highest ratios to GDP regarding health expenditures [109]. For
education, the highest shares were registered in Sweden (6.9%), Denmark (6.4%), and Bel-
gium (6.2%) [109]. These expenditures recorded under socio-cultural capital to verify our
findings on the distribution of socio-cultural values in Europe.
The details of the top five regions in each category from very high to very low scores
are given in Table A1 in the Appendix A. For instance, the top five regions that have the
highest scores regarding socio-cultural capital are Helsinki; West Finland; Border, Mid-
land, and Western Ireland; Aland (Finland); and Upper Noorland (Sweden). Regarding
Figure 3. Territorial quality of life indicators measuring total sustainability, as well as the ecological,
socio-cultural, and economic sustainability capitals at the NUTS2 level in Europe.
The details of the top five regions in each category from very high to very low scores
are given in Table A1 in the Appendix A. For instance, the top five regions that have the
highest scores regarding socio-cultural capital are Helsinki; West Finland; Border, Midland,
and Western Ireland; Aland (Finland); and Upper Noorland (Sweden). Regarding economic
capital, the highest values were computed for Belgium, Netherlands, Scandinavian coun-
tries, some regions in northern and western France, as well as central Spain and Portugal.
Among these, France, central Spain, and the Scandinavian countries have recorded the
highest GDPs in 2021 [
110
]. And gross domestic expenditure on research and development
(R&D) is highest in France, Belgium, the Netherlands, and Scandinavian countries [
110
]. In
particular, Belgium, France, the Netherlands, and Portugal have been identified as having
two circular economy networks [
111
]. In the case of ecological capital, the highest values
were observed for Northern and Central Europe, Scandinavian countries, and some regions
in Eastern Europe. Except for the Scandinavian countries, these regions have been recorded
Urban Sci. 2024,8, 22 17 of 33
as having the highest livestock and cereal production [
110
]. Scandinavian countries and
some Eastern European countries have the highest number of heating degree days and the
lowest number of cooling degree days [
110
]. These characteristics of the subject countries
verify their being selected as having the highest ecological capital values. From the overall
analysis, when the total score was computed, it was found that the total score was the
highest for the Netherlands, Belgium, Scandinavian countries, and some regions in Central
and Eastern Europe. The total score was lowest in Greece, Bulgaria, Romania, Slovakia,
regions in southern Spain and Portugal. There are also some low values recorded for
Central and Eastern European regions as well as for Southern Italy. The findings from
socio-cultural capital, economic capital, and ecological capital, as well as the overall score
computed at the country level are provided in Figure A1 in the Appendix A.
4.3. Spatial Distribution of RINs
The residential land expansion rate (RE) and residential land expansion intensity rate
(RI) indices are not homogeneously distributed in Europe. It was identified that the highest
values were in Spain, Poland, Latvia, Greece, southern and western France, regions in
Central Italy, Ireland, Portugal, and Central Europe (Figure 4). These are the regions with
already higher residential densities and economic growth (e.g., Netherlands, Belgium,
southern and western Germany, Central and Northern Italy, western France) or regions
that experienced rapid economic development (e.g., Ireland and central Spain and Por-
tugal) [
112
]. The lowest values were observed for Eastern Europe, Central Europe, and
northern regions in Scandinavian countries. These regions are mainly characterised by
their declining population, with there being a negative change in population in many of
these regions during the study period. The population growth rate (PR index) is highest in
the regions where economic activity is concentrated, and these regions achieve population
growth through natural processes and migration from less developed regions. The lowest
values for the population growth rate (PR) and population growth-to-residential land ex-
pansion ratio (PRL index) can be found in Eastern Europe, Greece, Southern Italy, northern
Spain, and Baltic countries. Among these regions, the Baltic states, Croatia, Romania,
Bulgaria, and eastern Germany lost more than 7% of their population in the 2000s [
113
].
The distribution of the PD_00 index is more clustered than that of others, and the highest
value clusters are in Spain, Italy, Greece, and Northern Portugal. Differently, the DI is
distributed in such a way that the highest values can be found in any region in Europe. The
lowest values are distributed with clusters that are observed for Romania, Finland, and
Northern Ireland.
4.4. Results from Regression Analysis
In relation to the aims of the study, it is presumed that residential expansion and
densification have an impact on the territorial quality of life. To understand their impacts
on quality of life, we regressed each of the TOTAL_SC, SC_C, ECON_C, and ECOL_C on
residential indicators (RINs) to represent the intensity of the effect as well as the negative
and positive signs, thereby indicating whether residential indicators are negatively related
to TQL or there is a positive relationship between these indicators. The results from the
SLM and the SEM estimates are presented in Tables 4and 5, respectively. It is important
to note that there is no heterogeneity issue of the variances in these latter models and all
the models were verified for the existence of spatial dependence of the dependent variable
(Table 4) and the error terms (Table 5).
Urban Sci. 2024,8, 22 18 of 33
Urban Sci. 2024, 8, x FOR PEER REVIEW 19 of 35
Figure 4. The sub-indices for residential indicators (RINs) including RE, RI, PR, PRL, PD_00, and DI
(source: the analysis is based on the indices given in Table 2).
Figure 4. The sub-indices for residential indicators (RINs) including RE, RI, PR, PRL, PD_00, and DI
(source: the analysis is based on the indices given in Table 2).
Urban Sci. 2024,8, 22 19 of 33
Table 4. The results from the SLM.
Dependent
Variable
MODEL 1
TOTAL_SC
MODEL 2 SC_C
MODEL 3
ECON_C
MODEL 4
ECOL_C
Independent
variables 1
W_dependent
variable 0.256 ** (0.02) 20.216 **4(0.03) 0.169 ** (0.04) 0.461 ** (0.03)
RE −0.004 * (0.01) 0.008 ** (0.01) −0.007 * (0.01) −0.009 ** (0.01)
PR 0.047 ** (0.01) 0.085 ** (0.01) 0.072 ** (0.01) 0.007 (0.001)
PRL −0.001 (0.01) −0.001 (0.01) 0.001 (0.01) 0.001 (0.01)
PD_00 −2.581 * (1.56) −6.708 ** (2.18) 1.882 * (0.78) 1.471 * (0.691)
DI −0.001 (0.01) −0.001 (0.01) −0.001 (0.01) −0.001 (0.01)
Constant 0.382 ** (0.02) 0.508 ** (0.02) 0.423 ** (0.02) 0.226 ** (0.02)
Number of
observations 226 226 226 226
R-square 0.46 0.49 0.28 0.54
Adjusted
R-square 0.45 0.48 0.27 0.53
Breusch–Pagan
test 2.273 [0.811] 38.399 [0.135] 6.694 [0.244] 1.262 [0.938]
LR test for
spatial lag
dependence
82.853 [0.000] 56.409 [0.000] 17.111 [0.000] 135.76 [0.000]
Root MSE 0.046 0.065 0.083 0.051
Note: (1) RI and PD-18 were omitted from regressions due to multicollinearity issues. (2) Standard errors are in
parentheses. (3) Significance levels are in brackets. (4) * Significant at 0.10 level ** Significant at 0.05 level.
Table 5. Results from the SEM.
Dependent
Variable
MODEL 1
TOTAL_SC
MODEL 2 SC_C
MODEL 3
ECON_C
MODEL 4
ECOL_C
Independent
variables 1
Lamda 0.832 ** (0.03) 20.801 **4(0.04) 0.499 ** (0.07) 0.691 ** (0.02)
RE −0.007 ** (0.01) 0.004 ** (0.01) −0.012 ** (0.01) −0.011 ** (0.01)
PR 0.034 ** (0.01) 0.049 ** (0.01) 0.064 ** (0.01) 0.007 (0.01)
PRL 0.001 (0.01) 0.001 (0.01) 0.001 (0.01) 0.001 (0.01)
PD_00 1.470 (1.37) −3.087 * (1.824) 6.376 ** (2.88) 0.758 * (0.69)
DI −0.001 (0.01) −0.001 (0.01) −0.001 (0.01) −0.001 (0.01)
Constant 0.451 ** (0.01) 0.618 ** (0.02) 0.494 ** (0.01) 0.291 ** (0.01)
Number of
observations 226 226 226 226
R-square 0.69 0.74 0.42 0.65
Adjusted
R-square 0.68 0.73 0.41 0.64
Breusch–Pagan
test 0.632 [0.986] 31.560 [0.541] 4.594 [0.467] 3.317 [0.651]
LR test for
spatial lag
dependence
159.54 [0.000] 160.32 [0.000] 44.755 [0.000] 144.986 [0.00]
Root MSE 0.035 0.046 0.076 0.043
Note: (1) RI and PD-18 were omitted from regressions due to multicollinearity issues. (2) Standard errors are in
parentheses. (3) Standard errors are in brackets. (4) * Significant at 0.10 level ** Significant at 0.05 level.
From Model 1 estimations, it can be followed that RE has a significant coefficient and
is negatively related to the TQL indicator, thereby implying that residential expansion
results in a reduction in overall QoL in the regions. Population growth (PR) is positively
related to TQL in both SLM and SEM regressions, whereas the population growth-to-
residential expansion ratio is insignificant with varying signs. Though significant in the
Urban Sci. 2024,8, 22 20 of 33
SLM, population density, i.e., PD_00, is insignificant in the SEM and its coefficient is
negative in the former regression model while it is positive in the latter model, thereby
implying that the subject variable may not be robust in Model 1 estimations. Regarding
Model 2, RE has a significant positive sign in both SLM and SEM estimations, and PD_00 has
a significant negative sign. This indicates that the residential expansion that is associated
with lower density development and the consumption of more green spaces can result in
a higher socio-cultural capital value. In contrast, high-density residential development
correlated with higher congestion costs, less green space per capita, and pollution impacts
lead to a lower value of socio-cultural index. The population growth rate (PR) is significant
and positively correlated with the QoL indicators in all models except for Model 4. The
coefficient of PR is positive in Model 4, but it is insignificant in both SLM and SEM
estimations. In Model 3 and Model 4, the coefficients of RE and PD_00 are both significant,
and the former has a negative sign while the latter has a positive sign. Because urban
services and activities are easily accessible in high-density neighbourhoods which are
proxied by the PD_00 variable, it makes sense for people to live in close proximity to one
another, which lowers land costs per person and improves quality of life (i.e., ECON_C
and ECOL_C).
Given that the coefficient of RE is negative, this indicates that QoL has a lower associa-
tion with an increase in residential expansion which has negative sustainability implications.
Because this form of development is related to car-based mobility and there are few non-
motorised activities, lower densities offer minimal possibility for interaction. This type
of development is unsustainable on the grounds that it consumes high-value green land,
which has various adverse impacts including negative impacts on energy use, health, the
cost of public infrastructure provision, and the environment. In all the regressions, the
population growth-to-residential expansion ratio (PRL) and the decoupling indicator (DI)
were found to be insignificant, although these two variables had consistent signs through-
out different model estimates in Tables 4and 5. In general, we found that the residential
density variable had the highest effect on QoL indicators in both SLM and SEM estimations,
which is followed by PR and RE, and the smallest effect was estimated for the PRL index
and DI, both of which were found to be insignificant in all the model estimations.
4.5. Results from Spatial Correlation Analysis
The inverse distance method was used to compute global Moran’s I. The results
showed that many of the TQL indicators and residential indicators (RINs) point to signifi-
cant spatial agglomeration (Table 6). Except for the DI, which does not show any spatial
autocorrelation, all the other indicators are assigned with a Moran’s Index that is higher
than zero. Therefore, it can be inferred that TQL or residential indicators exhibit positive
agglomeration, which means high-index-value areas are surrounded by regions with high
index values, with low-index areas being surrounded by regions with low index values.
Because the OLS model does not account for spatial autocorrelation, we confirmed that the
results from this model could not be reliable. Therefore, to measure the factors influencing
TQL in Europe, spatial regression models were used and discussed in the previous section.
Table 6. Global Moran’s I estimates for the parameters.
Variable
TOTAL_SC
RE RI PR PU PD_00 PD_18 DI
Moran’s
Index 0.3396 0.313 0.1938 0.1703 0.1699 0.1154 0.0379 −0.0062
Variance 0.0003 0.0003 0.0003 0.0003 0.0001 0.0004 0.0004 0.0002
Z-Score 18.121 15.944 10.461 9.221 16.1816 6.3757 2.251 −0.1091
p-value 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.9131
The findings of the local bivariate correlation analysis are presented in Figure 5which
shows the five levels (high-high, low-low, low-high, high-low, and insignificant) for the
spatial correlations between TOTAL_SC and residential indicators (RINs) across the NUTS
Urban Sci. 2024,8, 22 21 of 33
2 regions in Europe. The high-high and low-low clusters of TOTAL_SC and RE (or RI) are
mainly located in Eastern Europe and only a few regions in Western Europe. This indicates
a positive relationship between TOTAL_SC and RE (or RI), implying that residential
expansion is positively associated with QoL in these regions. Regarding these regions, it
was shown that liveability is more important than sustainability. Among these countries,
Estonia reported that in Talinn between 2000 and 2008, more than 45% of new residential
developments were established on agricultural land [
114
]. Other examples of cities that
have experienced high land uptake include Bratislava, Prague, Warsaw, and countries such
as Romania, which reported that urban expansion in eight cities was about 280% and that
around 85% of 250 cities has reported concerns of urban sprawl [
115
]. The high-low and
low-high clusters are in Eastern, Southern, and Northern Europe, thereby indicating a
negative relationship between TOTAL_SC and RE (or RI). Sustainable development of these
regions is more important than liveability given that residential expansion is negatively
related to QoL in the subject regions. This implies that urban densification is positively
associated with QoL in these regions among which large parts of Scandinavian countries
had very low values of urban sprawl and very high values were observed for large parts of
Western and Central Europe as computed by the European Environment Agency [116].
Urban Sci. 2024, 8, x FOR PEER REVIEW 22 of 35
Table 6. Global Moran’s I estimates for the parameters.
Variable
TOTAL_SC
RE
RI
PR
PU
PD_00
PD_18
DI
Moran’s Index
0.3396
0.313
0.1938
0.1703
0.1699
0.1154
0.0379
−0.0062
Variance
0.0003
0.0003
0.0003
0.0003
0.0001
0.0004
0.0004
0.0002
Z-Score
18.121
15.944
10.461
9.221
16.1816
6.3757
2.251
−0.1091
p-value
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.9131
The findings of the local bivariate correlation analysis are presented in Figure 5 which
shows the five levels (high-high, low-low, low-high, high-low, and insignificant) for the
spatial correlations between TOTAL_SC and residential indicators (RINs) across the
NUTS 2 regions in Europe. The high-high and low-low clusters of TOTAL_SC and RE (or
RI) are mainly located in Eastern Europe and only a few regions in Western Europe. This
indicates a positive relationship between TOTAL_SC and RE (or RI), implying that resi-
dential expansion is positively associated with QoL in these regions. Regarding these re-
gions, it was shown that liveability is more important than sustainability. Among these
countries, Estonia reported that in Talinn between 2000 and 2008, more than 45% of new
residential developments were established on agricultural land [114]. Other examples of
cities that have experienced high land uptake include Bratislava, Prague, Warsaw, and
countries such as Romania, which reported that urban expansion in eight cities was about
280% and that around 85% of 250 cities has reported concerns of urban sprawl [115]. The
high-low and low-high clusters are in Eastern, Southern, and Northern Europe, thereby
indicating a negative relationship between TOTAL_SC and RE (or RI). Sustainable devel-
opment of these regions is more important than liveability given that residential expan-
sion is negatively related to QoL in the subject regions. This implies that urban densifica-
tion is positively associated with QoL in these regions among which large parts of Scan-
dinavian countries had very low values of urban sprawl and very high values were ob-
served for large parts of Western and Central Europe as computed by the European Envi-
ronment Agency [116].
Figure 5. Bivariate local correlations between RE, RI, PR, PU, PD_00, PD_18, and TOTAL_SC indices
across NUTS2 regions in Europe.
The high-high and low-low clusters of TOTAL_SC and PR are distributed in Eastern,
Western, Northern, and some regions in Central Europe. For these regions, there is a posi-
tive association between QoL and population growth, whereas the relationship is negative
for the high-low and low-high regions that are in Eastern, Central, and Southwestern Eu-
rope. The significant relationships between QoL and the population growth-to-residential
expansion ratio generally cover high-high and low-low clusters which can be found in
Eastern, Southern, and Western Europe and indicate a positive relationship between QoL
and the PRL indicator. These are the regions which have experienced high numbers of
population growth according to [
110
]. There are few high-low clusters that are in the Baltic
states, southern France, and northern Spain. These high-low clusters point to a negative
Urban Sci. 2024,8, 22 22 of 33
association between TQL and the PRL index; therefore, QoL declines when there is an
increase in the population-to-residential expansion index. The population growth between
2021 and 2022 in these regions is slightly low, explaining the negative relationship between
TQL and the PRL index [
110
]. Regarding QoL and density indicators, it can be observed
that high-high and low-low clusters are in Western and Eastern Europe, which indicate a
positive relationship between TQL and residential density in these regions. These high-high
and low-low clusters are the regions where there are more compact urban developments,
and these regions value compactness more than the others considering the covered benefits
of a compact form of urban development. Low-high and high-low clusters can be seen
in Southern and Northern Europe with some scattered regions in Central Europe, which
highlight a negative relationship between TQL and residential density. Among these coun-
tries, the two largest clusters of urban sprawl values are located in northeastern France,
Belgium, and the Netherlands, as well as part of western Germany and in the UK between
London and the Midlands [
116
]. Finally, the TOTAL_SC and DI relationship was observed
(Figure 5) with high-high and low-low clusters located in Southern Europe and low-high
and high-low clusters in Central and Eastern Europe. In the first case, TOTAL_SC and the
DI are positively associated, whereas the relationship is negative in the second case.
5. Discussion
It is frequently stated that urban form design can be a valuable tool to develop a
sustainable urban environment, with major impacts on citizens’ quality of life and wellness.
The results of this paper suggest that urban expansion and densification contribute to TQL
in several ways. From SEM findings, TOTAL_SC, ECON_C, and ECOL_C are negatively
associated with residential expansion, whereas the relationship is positive with residential
densification. Higher levels of QoL in high-density areas result from the high concentration
of people allowing for easy access to different land uses and more socialising amenities,
which allow for greater social networks and more active social lives. In European cities
with a large aggregation of urban people, persistent pressure on infrastructure, and a
limited supply of land use resources, it is obvious that creating a dense and compact urban
form to accomplish service agglomerations becomes more significant. People’s improved
facility access allows them to engage in more socioeconomic activities by enhancing the
spatial qualities of their neighbourhoods. This is often achieved by raising building heights,
allowing for increasing housing and developing connecting street networks. Additionally,
increased amenities and service alternatives in high-density neighbourhoods may help
boost residents’ sense of comfort and safety in these regions [
102
]. However, the extent and
quantity of these amenities are often limited by financial and other practical constraints,
which can result in an inadequate supply of communal facilities in less densely populated
parts of the newly developed urban land.
The compact city approach’s shortcomings in fulfilling the many characteristics of
sustainability are evident through actual application and analysis of compact cities. After
a certain level of environmental efficiency produced by a compact urban shape, certain
negative effects may become more obvious [
117
]. This verifies the concept of the turning
point mentioned by Wolff and Haase [
10
] who show the optimal compromise between high
and low densities. For instance, some empirical data imply that the potential environmental
advantages of compact cities, most notably decreased energy usage for transportation and
associated emissions of greenhouse gases from less travel, are exaggerated and that the
outcomes vary depending on the specific circumstances [
118
,
119
]. Furthermore, density
alone may result in less desirable neighbourhoods and inferior public amenities, endan-
gering social quality of life [
120
]. This is reflected in our estimated models where SC_C
is positively related to residential expansion, and it is negatively related to densification
both in the SLM and SEM. This implies that density negatively affects QoL, particularly
the socio-cultural component, and residential expansion at lower densities, in contrast to
high-density development, improves QoL through the provision of wider green spaces
and a healthy and peaceful environment. While crowding can cause negative, anxious
Urban Sci. 2024,8, 22 23 of 33
feelings, nature has significant perceived restorative qualities that can promote the pleasant
emotions of calmness and relaxation [121].
Our finding on the negative relationship between SC_C and densification is also veri-
fied by Neuman [
50
], who indicated that high density is detrimental to liveability, and by
Morrison [
122
], who suggested that high densities are related to unhappiness. Centred on
the subject debate, critics are increasingly asking whether different styles of urban structure
are more or less sustainable, particularly when linked to concepts of compactness. The
study of the connection between urban form and social sustainability is therefore contro-
versial [
123
,
124
]. The creation of a sustainable city and neighbourhood must consider not
only the technical requirements but also the development’s actual social-cultural, economic,
and environmental implications [
125
]. To what extent the urban compact and dispersed
developed cities have socio-economic and environmental implications is currently unclear
from the literature and the supporting empirical case study evidence. Therefore, this study
provides an empirical framework by focusing on European regions and searching the rela-
tionship between residential expansion/densification and three capitals (i.e., socio-cultural,
economic, and ecological) of territorial quality of life.
In today’s urban context, especially since the global experience of the COVID-19
pandemic, the concept of safety encompasses more than just crime or violence prevention
and includes accessibility, public space, and socialising. The value of the built environment
needed to be optimised by making the most efficient use of space to accommodate the
increased “distance” needed during the COVID-19 pandemic. Kwon et al. [
126
] asserted
that the scope of social distancing measures is limited in compact cities emphasising the
compact city model’s susceptibility to shocks by correlating the spread of diseases with
high-density and mixed land use. To control disease outbreaks efficiently and sustainably
in cities, a multifaceted urban planning strategy that considers polycentric and dispersed
urban form is advised by the authors. This finding by Kwon et al. [
126
] explains the positive
association between socio-cultural capital and residential expansion as well as the negative
relationship between socio-cultural capital and densification in our model. The benefits
of polycentric development include an improved economic performance through the
realisation of economies of scale while minimising expenses related to congestion, increased
economic inclusion, and more regionally balanced growth [
127
]. What is unexplained is the
issue of dealing with violence and crime, which requires a mix of land uses that encourage
continuous utilisation of space at various times of the day and an improvement in nighttime
visibility using appropriate lighting, etc., which can be easily provided by a compact city
model. This follows the generally believed but mostly untested argument that high-density
neighbourhoods feel safer than low-density ones because they are more monitored with
more people in the area [
128
]. A further implication of our findings supporting the positive
association between socio-cultural capital and residential expansion is that of the education
component. Higher quality school provision does not tend to be available in the lower
quality inner-city areas which have both mixed-use and high-density developments [129].
Governance is the other component of socio-cultural capital that we found to be
positively related to residential expansion. In a study by Ustaoglu and Williams [
130
], it
was found that decentralisation and political fragmentation were positively related to urban
land expansion and that the quality of regional government and governance was negatively
associated. In their study, state-led and market-led spatial planning systems, as well as
systems classified as being in-between, were linked to significant levels of agricultural land
consumption. This was also confirmed by Dombi [
131
], who showed that the types of
spatial governance and planning systems can impact urban form.
Land use change can be considered as a complex but self-organising series of synergetic
market actions linked to individuals, businesses, and other agencies changing demand
requirements, often within a regulatory context. Modern land use and development have
evolved as a primarily economic market process in which major development interests
along with policymakers and planners have a decisive influence. Development interests
seek to standardise and provide various sector space requirements as a commodity and
Urban Sci. 2024,8, 22 24 of 33
provide it at a profitable rental or sale price. The planning system in many regions operates
as a legally based process with conflicting views contested at application and appeal stages
by interested parties and regulators. Policy interests and regional planners, in turn, can
seek to facilitate or direct the built environment process to achieve social, environmental,
and economic aims. The development of the appropriate tools and evidence, such as urban
modelling and analytics, which may assist in achieving the best utilisation of scarce public
investment resources is an option which can benefit these processes.
This research and policy analysis can assist and balance effective urban and environ-
mental management with investment strategies for regions and demonstrate them through
evidence-based research. The research can inform choices regarding the optimal locations
for residential development and investment in terms of broad infrastructure provision,
employment capacities, demographic trends, and sustainable development patterns. This
can be assisted by geospatial modelling and evidence analysis based on the data layers
collated from regional and state agencies at the individual area/case study level. Further
research on advanced environmental, urban, and regional modelling as well as analysis
techniques can be used as part of research and policy analysis internationally. The context
for the use of evidence-based indicators within regional planning decision making is the
need for the evaluation of alternatives in decision making. Analysing policies within indi-
vidual jurisdictions and development models would require further research to develop
an understanding of the planning approach in each area. This would give context to the
modelling and analysis which are developed in this project. For such further research, an
analysis of the critical policy and planning practice would allow for unique individual
regional circumstances to be considered.
In recent decades, researchers, policy, and industry interests have made considerable
progress in improving the capabilities and usefulness of G.I.S., Spatial Decision Support
Systems, and geospatial models for the evaluation of urban and regional environmental and
development patterns as well as planning policy. Their rapid development is based on the
need for evidence-based support for decision making in this area. Such tools are particularly
valuable for agencies with environmental and ecological responsibilities, regional and city
managers, planners, and policymakers, as well as for a wide range of economic and
development institutions. Such evidence can be viewed as part of an integrated ecological
approach to decision making with a strong scientific and quantitative basis. A concern with
such approaches is that along with such specialised datasets and models, decision-making
processes are dependent on the values and attitudes of the various socio-economic groups
of city region residents and key decision makers. Earlier research on decision support
tools for managing the urban environment in Ireland [
132
,
133
] provides an overview of
research on the urban environment and integrates its use in the planning policy of data on
air quality, urban transport, biodiversity, climate change, and urban sprawl. Examples of
the key findings of this earlier related research using data analytics and the EU MOLAND
model include how well-designed mixed-use developments can reduce transport-related
emissions by reducing travel to employment and services and could enable an increased
modal shift to public transport. For example, dispersed development in hinterland areas
shows up to 15 times higher transport-related energy consumption. When compared with a
business-as-usual scenario, a compact city scenario represents a saving of 18% in transport-
related energy consumption. In addition, a compact urban form could provide a 16%
decrease in energy demand for space heating under the evaluated climate change scenario.
A further important finding of the current study is that of the negative association
between economic capital and residential expansion, and the relationship is just the reverse
between economic capital and residential densification. This is supported by the study
of Valenzuela et al. [
134
], which is an innovative study of residential densification that
views residential density as an opportunity to implement circular economy cycles at the
local level. Economic output is found to be higher in high-density built areas as shown by
Li [
135
] who indicated that the urban structures that have evolved from low polycentricity
and low dispersion to high polycentricity and low dispersion have undergone the highest
Urban Sci. 2024,8, 22 25 of 33
per capita GDP growth. A further implication is that of the scaling effect represented by
economies of scale and increasing returns, which was found to be stronger for regions with
spatially compact GDP distribution, which has been confirmed by Meijers and Burger [
136
]
and Kuno Pradipto [
137
]. In terms of infrastructure development, it can be asserted that
high-density urban development is more effective and economical than lower density
development when it comes to the provision of underground utilities (i.e., energy and
transport networks, sanitation, etc.), which may reduce pollution [
47
]. It also encourages
the development of a more effective public transportation system and an urban design that
lessens the need for private automobile transportation. QoL in the built environment refers
to the type and degree of accessibility to services and amenities in a particular location.
Although accessibility is a broad word in and of itself, in this context, it can be defined as the
quantity and variety of services and facilities, employment and educational opportunities,
and adequate housing that are available in the neighbourhood. Accessibility also refers
to how one can get to such services and facilities, including through local and distant
networks of public transportation, walking paths, and bicycle lanes. Our study confirms
the findings of research that indicated that accessibility to urban services is improved
by high-density and compact development in contrast to the reduced accessibility of the
dispersed urban form which leads to increased dependency on private modes [
120
,
138
,
139
].
A final component of economic capital is that of research and development which has a
positive association with high-density development, confirming the study of Hamidi and
Zandiatashbar [
140
] which found that urban compaction positively and significantly affects
the number of innovative firms through providing spatial proximity to firms in related
business sectors (see also Bereitschaft [141]).
This research found ecological capital to be negatively related to residential ex-
pansion and positively related to residential densification, which confirm the study of
Kang et al. [142],
thereby indicating that the degree of the land use mix, clustering, and
concentration of development results in better air quality in comparison to dispersed urban
development, which is associated with poorer air quality. This is in line with the findings
of Fan et al. [
143
] who showed that fragmented and complex urban forms generally result
in higher emissions of CO, NO
x
, and PM
2.5
where the compact form of northern China
reduced emissions more effectively than southern China, which had a more fragmented
urban form. Energy consumption was also influenced by the urban form where more
compact and less peripheral urban developments are associated with reduced residential
electricity consumption as confirmed by Wilson [
144
] and Chen et al. [
145
]. The relation
is just the reverse concerning the provision of urban green spaces and ecosystem services.
Woldesemayat and Genovese [
146
] showed that high-density mixed residence use provided
less urban green spaces compared to low-density mixed residential development. Urban
green areas compete with other uses of urban space, such as housing or commerce, and are
frequently seen as a land reserve for housing and other urban development projects [147].
Thus, urban densification may be associated with a loss of green space or a decrease in per
capita green land provision. This is a counter argument and does not support our findings
of the positive relationship between residential densification and ecological capital.
6. Conclusions
In conclusion, our results showed that residential expansion is favourable for the
residents concerning their socio-cultural capital values and that residential densification
provides the residents with higher values of economic and ecological capital. This finding
verifies the “compact city paradox” that refers to the conflict between increasing envi-
ronmental quality and lowering social disadvantages (such as a lack of green spaces)
through densification on the one hand and decreasing negative effects brought on by
urban expansion (such as longer commute times, loss of fertile soils, reduction or loss of
ecosystem services) on the other hand [
148
]. Studies that link urban spatial structure to
socio-economic and environmental values typically concentrate on just one factor, such as
energy consumption, urban green space, transport system, air pollution, health, education,
Urban Sci. 2024,8, 22 26 of 33
or poverty [
102
,
103
,
129
,
136
,
138
,
142
,
146
]. Some incorporate various indicators or combine
these indicators to construct a QoL index [20].
This research dealt with several facets of the quality of life both separately concern-
ing the three different capitals of QoL including socio-cultural, economic, and ecological
capitals, as well as collectively. We also specify some limitations to these findings. In
our analysis, we included residential expansion and densification indicators to explicate
the relationship between these indicators and TQL indices. We note that the process of
urban expansion, which is fuelled by factors such as population growth, motorization rates,
and increased income capacity, emerges as an inevitable aspect of economic development.
Urban growth processes can take on various spatial forms, such as clustering, fragmen-
tation, and sprawl, and it is important to highlight that urban expansion is not equal to
sprawl. Future research is needed to identify the land use patterns of individual regions to
understand whether there is urban sprawl, spatial clustering, or fragmentation.
Urban expansion is a physical process that results from repeated changes in the urban
structure as a fundamental part of urban development. Sprawl, on the other hand, corre-
sponds to a certain expansion pattern that is mostly described as scattered development. In
future work, more detailed urban structure indicators measuring compact and sprawled
developments can be developed and their association with TQL can be quantified. By
including a comprehensive set of indicators, we aimed to consider all the aspects of TQL,
and the inclusion of these indicators are limited based on the availability of data. The data
on our sub-indicators are available for the post-2015 period and time series data regarding
these indicators are not available at present. Based on the future availability of data, new
indicators measuring different aspects of TQL can be considered and time series compar-
isons of QoL indicators can be conducted. A further issue is that of the spatial resolution of
the images that were used for the calculation of residential expansion and densification
indicators. We used a high-resolution (100 m) image from the Corine land cover programme
for the years 2000 and 2018. With the given resolution and spatial details, there is not any
other consistent spatial dataset covering the whole European region. Depending on the
future availability of a spatial dataset with a higher resolution, our analysis can be repeated
to see the impact of image resolution on the urban expansion and densification indicators
and their relationship with the TQL indices.
This research highlights that the promotion of higher densities and compact cities in
isolation may fail to recognise the complex impacts these changes can have on residents
quality of life and the long-term sustainability of urban growth patterns. For planners
and policy makers, the findings suggest that a more nuanced approach is needed for
individual regions. The more localised regional approaches should include discussions on
the benefits of more compact and dense residential developments but should be directly
linked to existing area capacities, resources, and levels of supporting investment involved.
The use of evidence-based models and the analysis of indicators are an integral part of
obtaining a deeper insight into the changes in land use in an urban and regional context
and how their interactions change over time. Such indicators enable analysis and synthesis
of large datasets into clear information on important land use trends. That information can
then be used to facilitate the interpretation of these datasets and support policy makers,
planners, and decision makers to use such evidence, thereby allowing for a shift to evidence-
based environmental policy and urban management. Major policy or paradigm shifts are
necessary to move societies to more sustainable development paths, but this clearly presents
political, economic, and social challenges. A more balanced type of development may be
that of polycentric urban regions which point to regions characterised by the existence of
multiple proximate centres where there is balanced development among these centres [
149
].
For the planning of such regions, the integration of land use planning, urban and regional
planning, and the planning of transport and other infrastructure have been highlighted
to be important and should be considered in the planning and policy-making actions
of authorities. The EU’s 7EAP highlights the significance of resource efficiency, the fact
that land is a limited resource, and the need to take the urban environment into account.
Urban Sci. 2024,8, 22 27 of 33
Planning tools and practices are crucial in influencing changes in land use, especially that
of urban sprawl.
Regional planners and policy makers should already possess a solid understanding of
the elements that cause urban sprawl in their own areas as well as the policies and tools that
would reduce any negative impacts. To help public agencies better manage urban sprawl,
regional planning capacities and resources will need to be strengthened, especially in the
regions with higher rates of residential expansion. These regions were identified in Figure 4
which shows the residential indicators pointing to residential expansion and densification.
The planning tools may need to also be strengthened in regions which have existing high
densities. These regions may struggle with air pollution, congestion in infrastructure
use, high land prices, and other negative impacts of high-density development. These
adverse impacts of high-density development are identified in the literature [
122
,
150
] along
with contrasting studies suggesting that compact development may not be detrimental
to well-being [
151
]. Therefore, more local case studies are essential for the relationship
between QoL and the varying degrees of densification, which can be set as a future research
goal. This in turn will inform more region-specific planning and policy responses to issues
arising rather than a uniform policy response.
Supplementary Materials: The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/urbansci8010022/s1, Table S1. Variance explained by principal
components and selected indicators.
Author Contributions: Conceptualization, E.U. and B.W.; methodology, E.U. and B.W.; software,
E.U.; validation, E.U. and B.W.; formal analysis, E.U.; investigation, E.U. and B.W.; resources, E.U.
and B.W.; data curation, E.U.; writing—original draft preparation, E.U. and B.W.; writing—review
and editing, E.U. and B.W.; visualization, E.U.; supervision, B.W.; project administration, E.U. and
B.W. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: Data will be available upon request.
Acknowledgments: We would like to thank to three anonymous reviewers who contributed to the
earlier versions of this manuscript.
Conflicts of Interest: The authors declare no conflicts of interest.
Appendix A
Urban Sci. 2024, 8, x FOR PEER REVIEW 30 of 35
Figure A1. Territorial quality of life (TQL) indicators measuring total sustainability, and the ecolog-
ical, socio-cultural and economic sustainability capitals, country average scores.
References
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dispersed? A comparative analysis of 15 European urban areas. Landsc. Urban Plan. 2006, 77, 111–130.
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of Kolkata. Cities 2015, 48, 55–65.
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perspective: Evidence from 336 Chinese cities. Land Use Policy 2020, 95, 104576.
19. Faria, P.A.M.; Ferreira, F.A.F.; Jalali, M.S.; Bento, P.; Antonio, N.J.S. Combining cognitive mapping and MCDA for improving
quality of life in urban areas. Cities 2018, 78, 116–127.
Figure A1. Territorial quality of life (TQL) indicators measuring total sustainability, and the ecological,
socio-cultural and economic sustainability capitals, country average scores.
Urban Sci. 2024,8, 22 28 of 33
Table A1. Classification of regions based on TQL indices.
Top Five Scored Regions
Category Value Ranges SC_C Value Ranges ECON_C Value Ranges ECOL_C Value Ranges TOTAL_SC
Very high >0.71
Helsinki-Uusimaa (FI1B);
West Finland (FI19);
Border, Midland, and
Western Ireland (IE01);
Aland (FI20); Upper
Norrland (SE33)
>0.61
Ile de France (FR10),
Helsinki-Uusimaa(FI1B),
Utrecht(NL31),
Madrid(ES30), South
Finland (FI1C)
>0.44
Brandenburg (DE40),
Berlin (DE30),
Arnsberg (DEA5),
Estonia (EE00), North
and East Finland
(FI1D)
>0.55
North and East
Finland (FI1D),
Helsinki-Uusimaa
(FI1B), West Finland
(FI19), Upper
Norrland (SE33),
Utrecht (NL31)
High 0.67–0.71
Trentino-Alto (ITH2),
Detmold (DEA4),
Oberfranken (DE24),
FR61, Karlsruhe (DE12)
0.54–0.61
Southern Denmark
(DK03), Catalonia (ES51),
Brandenburg (DE40),
Berlin (DE30), Aquitaine
(FR61)
0.41–0.44
East Middle Sweden
(SE12), Groningen
(NL11), Oberpfalz
(DE23), Gelderland
(NL22), Oberfranken
(DE24)
0.52–0.55
Oberpfalz (DE23),
Malopolskie (PL21),
Bretagne (FR52),
Tübingen (DE14),
Pays de la Loire
(FR51)
Average 0.63–0.67
Rheinhessen-
Pfalz(DEB3), Münster
(DEA3), Opolskie (PL52),
Aragon(ES24),
Limburg(BE22)
0.49–0.54
Saarland (DEC0),
Auvergne (FR72), Latvia
(LV00), Namur (BE35),
Bourgogne (FR26)
0.37–0.41
Niederbayern (DE22),
BG32,
Kujawsko-Pomorskie
(PL61), Veneto (ITH3),
Nordjylland (DK05)
0.49–0.52
Trentino-Alto (ITH1),
Southern Denmark
(DK03), Freiburg
(DE13), East Flanders
(BE23), Limburg
(BE22)
Low 0.58–0.63
Castile-Leon (ES41),
Lombardia (ITC4), Nord
Pas-de-Calais (FR30),
Eastern Slovakia (SK04),
Norte (PT11)
0.44–0.49
Münster (DEA3), Kriti
(GR43), Veneto (ITH3),
Friuli-Venezia Giulia
(ITH4), Hainaut (BE32)
0.32–0.37
Crotia (HR04), AT31,
Marche (ITI3), Bremen
(DE50), West Sweden
(SE23)
0.44–0.49
Cantabria (ES13),
Podkarpackie (PL32),
Auvergne (FR72),
Lorraine (FR41),
Lisbon (PT17)
Very Low <0.58
Kentriki Makedonia
(GR12), Puglia (ITF4),
Namur (BE35), Estonia
(EE00), Leipzig (DED5)
<0.44
Sterea Ellada (GR24),
Moravian Silesian (CZ08),
Molise (ITF2),
Kujawsko-Pomorskie
(PL61), Puglia (ITF4)
<0.32
Navarre (ES22), FR42,
Bucuresti-lifov (RO32),
Yuzhen tsentralen
(BG42), Western
Transdanubia (HU22)
<0.44
Aragon (ES24), Latvia
(LV00), Region of
Murcia (ES62),
Budapest (HU10),
Severozapad (CZ04)
Note: In parenthesis are the NUTS2 codes of the region.
Urban Sci. 2024,8, 22 29 of 33
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