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International Journal of
Geo-Information
Article
Simulation and Evaluation of Urban Growth for
Germany Including Climate Change Mitigation and
Adaptation Measures
Jana Hoymann 1, *,† and Roland Goetzke 2 ,†
1
Federal Institute for Research on Building, Urban Affairs and Spatial Development, Deichmanns Aue 31–37,
53179 Bonn, Germany
2
Federal Ministry for Transport and Digital Infrastructure, Robert-Schumann-Platz 1, 53175 Bonn, Germany;
roland.goetzke@bmvi.bund.de
*Correspondence: jana.hoymann@bbr.bund.de; Tel.: +49-228-99401-2133; Fax: +49-228-9910401-2133
† These authors contributed equally to this work.
Academic Editor: Wolfgang Kainz
Received: 3 March 2016; Accepted: 15 June 2016; Published: 23 June 2016
Abstract:
Decision-makers in the fields of urban and regional planning in Germany face new
challenges. High rates of urban sprawl need to be reduced by increased inner-urban development
while settlements have to adapt to climate change and contribute to the reduction of greenhouse
gas emissions at the same time. In this study, we analyze conflicts in the management of urban
areas and develop integrated sustainable land use strategies for Germany. The spatial explicit land
use change model Land Use Scanner is used to simulate alternative scenarios of land use change
for Germany for 2030. A multi-criteria analysis is set up based on these scenarios and based on a
set of indicators. They are used to measure whether the mitigation and adaptation objectives can
be achieved and to uncover conflicts between these aims. The results show that the built-up and
transport area development can be influenced both in terms of magnitude and spatial distribution to
contribute to climate change mitigation and adaptation. Strengthening the inner-urban development
is particularly effective in terms of reducing built-up and transport area development. It is possible
to reduce built-up and transport area development to approximately 30 ha per day in 2030, which
matches the sustainability objective of the German Federal Government for the year 2020. In the
case of adaptation to climate change, the inclusion of extreme flood events in the context of spatial
planning requirements may contribute to a reduction of the damage potential.
Keywords: urban growth; Germany; scenario; mitigation; adaptation; Land Use Scanner
1. Introduction
Since more than half of the world’s population lives in cities the impacts of climate change on
urbanized areas are a key challenge for most countries [1].
Urbanization has progressed in recent decades and is projected to continue. It is therefore not
surprising that 60 to 70% of the world’s CO
2
emissions are related to urban areas and it goes along
with serious changes of the available land resources [
2
]. Aside from these processes, there are also
regions that face a period of urban restructuring due to demographic and industrial decline and where
the settlement structure reaches carrying capacity problems [
1
,
3
]. These regions reveal the chance for
an active restructuring in terms of climate change adaptation needs.
Possible impacts of the anthropogenic climate change are well known. Although the occurrence
probability and intensity of certain extreme weather events is highly uncertain, they appear with serious
damage potential or health risks [
4
]. Beyond others increased surface runoff due to surface sealing
in urban areas leads to massive inundation and damage [
5
]. Urbanized areas have historically often
developed close to rivers and are thus located in flood prone areas and affected by floods. Together with
ISPRS Int. J. Geo-Inf. 2016,5, 101; doi:10.3390/ijgi5070101 www.mdpi.com/journal/ijgi
ISPRS Int. J. Geo-Inf. 2016,5, 101 2 of 22
inefficient spatial planning, protection of these settlements is inadequate [
6
–
8
]. Another implication
induced by climate change is the development of urban heat islands, which influences human health
by heat-related illness or mortality [
9
,
10
]. Furthermore, urbanization reduces the availability of natural
open space due to built-up and transport development, leading to a discussion about the optimal
provision of urban green areas in settlements [
11
]. Against this background the question arises, how
can land and, in particular urban land, be managed in a sustainable way?
The Federal Government in Germany adopted the sustainability strategy and the strategy for
adaptation to climate change as roadmaps for a sustainable land management [
12
,
13
]. In both
documents, advice is given how adaptation to climate change can be achieved. One finding is,
that settlement structures need to be adapted to the possible impacts of climate change. By which
measures this could be achieved is the central research question of this study.
The above-mentioned strategies result in new challenges for regional planning [
14
].
Especially climate change mitigation is becoming increasingly important, because 7.5% of the
greenhouse gas emissions in Germany directly result from land use and land use changes (activities
in forestry, agriculture and built-up and transport areas) [
15
]. Moreover, biomass is a sink for carbon
dioxide and thus mitigates climate change. Simultaneously, built-up and transport areas consume
69 ha (2011 to 2014) of the land resources in Germany every day [
16
]. This land is therefore no
longer available for other land uses and thus also not available for the purpose of climate mitigation.
Bart (2010) found evidence for the necessity to reduce built-up and transport area development to
reduce CO2emissions in Europe [17].
The aim of this study is to examine what measures have an effect on the achievement of climate
change mitigation objectives and what measures are appropriate to adopt the land use and settlement
patterns to climate change adaptation. Such measures may lead to changes in planning practice and
usually affect multiple ecosystem services in different ways [
18
]. The adopted measures can result in
synergies or conflicts on achievements of climate change mitigation and adaptation. They have been
discussed in detail in a national and a regional participation process with decision-makers within the
project in which this study is carried out.
In this article, land use scenarios for 2030 in Germany are presented that implement policy
measures for the development of built-up and transport areas to contribute to climate change mitigation
and adaption. The scenarios are simulated with the GIS-based land use change model Land Use
Scanner. A number of alternative policy scenarios are compared with a reference scenario to evaluate
the impact of different measures on the achievement of climate change mitigation and adaptation
objectives. For evaluation purposes, a set of indicators is developed that enable to visualize the
qualitative and partially quantitative differences to the reference scenario.
2. Methods and Data
2.1. Modelling Land Use Change
A diverse suite of modeling approaches has evolved during the last decade, some of these
deal explicitly with processes leading to urban growth, others consider urban growth as one type
of land-use change amongst others. Recent surveys of operational land-use change models offer
insights into elementary model concepts and characteristics, including aspects of urban growth [
19
–
21
].
Many land-use change models are based on a multi-model approach. This means that global trends for
specific land-use types or the general economic and demographic development of the study area are
analyzed in external models, which are loosely coupled to the land-use change model by transferring
their results as regional demand within administrative units. The land-use change model allocates
these land-use based on the local suitability for a land-use category. Examples for these kinds of models
are the Land Use Scanner [
22
], DynaClue [
23
], EU-ClueScanner [
24
] or Environment Explorer [
25
].
The local suitability for land use is often assessed in a previous analysis, where the effect of different
location characteristics is evaluated statistically, e.g., in a binomial or multiple logistic regression
analysis [6].
ISPRS Int. J. Geo-Inf. 2016,5, 101 3 of 22
Criteria of model selection for our study have been:
‚the ability to simulate urban and non-urban land use types;
‚to integrate regional demands for land use for sub-regions of a study area; and
‚to simulate a large country like Germany with high spatial resolution.
Upon these requirements, we have chosen the Land Use Scanner, which is in its current
development similar to the EU-CLUE scanner and incorporates the knowledge of other models,
such as CLUE or the “Environment Explorer”.
2.2. The Land Use Scanner Model
The Land Use Scanner is an operational, spatially explicit simulation model that uses an
optimization algorithm to allocate the demand for land to appropriate grid cells (see Figure 1) [
22
,
26
].
The basic characteristics of the Land Use Scanner for Germany are [27]:
‚A spatial resolution of 100 m.
‚13 land-use classes (6 urban land-use classes). In general, any number of land-use classes can be
implemented in Land Use Scanner.
‚A discrete modeling algorithm, where a raster cell represents only one land-use class (see [22]).
‚By using the same parameters, the model results are reproducible.
‚Planning regulations are included in the simulation.
‚The model gives results in subsequent time steps (five year steps).
“The discrete allocation model allocates equal units of land (cells) to those types of land use that
have the highest suitability, taking into account regional land use demand. This discrete allocation
problem is solved through a form linear programming, the solution of which is considered optimal
when the sum of all suitability values corresponding to the allocated land use is maximal. The allocation
is subject to the following constraints:
‚the amount of land allocated to a cell cannot be negative;
‚in total, only 1 ha can be allocated to a cell; and
‚
the total amount of land allocated to a specific land-use type in a region should be between the
minimum and maximum claim for that region.
Mathematically the allocation problem can be formulated as:
max
xÿ
cj
Scj Xcj (1)
subject to:
Xcj ě0 for each c and j;
ř
j
Xcj = 1 for each c;
Ljr ďř
c
Xcj ďHjr for each jand rfor which claims are specified;
in which:
Xcj is the amount of land allocated to cell cto be used for land-use type j;
Scj is the suitability of cell cfor land-use type j;
Ljr is the minimum claim for land-use type jin region r; and
Hjr is the maximum claim for land-use type jin region r.” [22]
The demand is determined in external models for regions such as counties. Suitable grid cells
are identified by using suitability maps, where location factors such as current land use, physical
conditions, planning regulations or the accessibility of infrastructures are combined. The individual
types of land use thereby compete with each other. A detailed description of the model, the data sets
applied as well as the development of regional demand numbers for the land use types and suitability
maps considered can be found in Goetzke and Hoymann (in press) [27].
ISPRS Int. J. Geo-Inf. 2016,5, 101 4 of 22
ISPRS Int. J. Geo-Inf. 2016, 5, 101 4 of 22
Figure 1. Overview of the German Land Use Scanner model application.
2.3. Land Use Change Scenarios
First of all, a reference scenario has been simulated. In this scenario, we assumed that the
observed demographic and economic development will continue in the next two decades and that
the effect of these developments on the change of built-up and transport area will be similar, too.
Based on this reference scenario, alternative scenarios are implemented into the model to analyze
their contribution to and effect on climate change mitigation and adaptation as well as environmental
protection and conservation. These measures either control the demand for a certain land use type
(quantity control) or their spatial distribution patterns (locational effect). They have been discussed
in an intensive consultation process with planners at the regional level and with stakeholders at the
national level. Through appropriate modeling, the effect of the different measures can be quantified
and compared to the reference scenario.
The modeled measures are listed in Table 1. It should be noted that measures without an impact
to land use changes have not been considered, even if they make a quite significant contribution to
climate change mitigation and adaptation. Such measures, for example, are the adaptation of the
sewage system to heavy rain events or the use of communal roof areas for solar energy. In this article,
three measures will be presented exemplary in more detail.
The objective of climate change mitigation in this study is to reduce the growth of built-up and
transport area as much as possible in order to reduce land loss, to preserve carbon sinks, and to form
compact and efficient settlement structures. Different measures can contribute to this objective. It is
not the aim of the study to quantify the saving of CO2 emissions but to quantify their effect on land
use change. Climate change adaptation in contrast aims at the reduction of potential climate change
induced risks for built-up and transport area as well as residents. Finally, nature conservation tries
to preserve as much natural areas as possible and promotes the retreat of built-up and transport area
in regions with low or without demand for built-up areas.
Figure 1. Overview of the German Land Use Scanner model application.
2.3. Land Use Change Scenarios
First of all, a reference scenario has been simulated. In this scenario, we assumed that the observed
demographic and economic development will continue in the next two decades and that the effect of
these developments on the change of built-up and transport area will be similar, too. Based on this
reference scenario, alternative scenarios are implemented into the model to analyze their contribution
to and effect on climate change mitigation and adaptation as well as environmental protection and
conservation. These measures either control the demand for a certain land use type (quantity control)
or their spatial distribution patterns (locational effect). They have been discussed in an intensive
consultation process with planners at the regional level and with stakeholders at the national level.
Through appropriate modeling, the effect of the different measures can be quantified and compared to
the reference scenario.
The modeled measures are listed in Table 1. It should be noted that measures without an impact
to land use changes have not been considered, even if they make a quite significant contribution to
climate change mitigation and adaptation. Such measures, for example, are the adaptation of the
sewage system to heavy rain events or the use of communal roof areas for solar energy. In this article,
three measures will be presented exemplary in more detail.
The objective of climate change mitigation in this study is to reduce the growth of built-up and
transport area as much as possible in order to reduce land loss, to preserve carbon sinks, and to form
compact and efficient settlement structures. Different measures can contribute to this objective. It is
not the aim of the study to quantify the saving of CO
2
emissions but to quantify their effect on land
use change. Climate change adaptation in contrast aims at the reduction of potential climate change
induced risks for built-up and transport area as well as residents. Finally, nature conservation tries to
preserve as much natural areas as possible and promotes the retreat of built-up and transport area in
regions with low or without demand for built-up areas.
ISPRS Int. J. Geo-Inf. 2016,5, 101 5 of 22
Table 1.
Measures that control the settlement development, their assignments to a land use strategy, and to indicators that are used to measure their effect (Measures
and indicators presented in this publication are marked in italics).
Land Use Strategies Indicators
Climate
Change
Mitigation
Climate
Change
Adaptation
Nature
Protection
Increasing
Built-Up
and
Transport
Areas
Access to
New
Built-Up
Areas
Locational
Integration
of New
Built-Up
Areas
Changing
Carbon
Storage in
Land Use
and Soils
Increasing
Built-Up and
Transport in
Flood-Prone
Areas
Increasing
Built-Up and
Transport in
Areas with
Thermal
Heat Load
Greening
Settlement
Areas
Soil
Sealing
Increasing
Built-Up and
Transport in
Areas
Designated for
Nature and
Landscape
Protection
Increasing
Built-Up and
Transport in
Unfragmented
Space
Measures
Preserving and
developing urban
green areas
x xx x x xxx xx xx x
Strengthening
inner-urban
development
xx x x xxx x x x x x
Realizing higher
densities in new
built-up areas
xx x x x x x x x
Strengthening
public transport xx x xx xx x x
Reducing land
take by transport
infrastructure
xx x x x x x x
Settlement retreat,
recentralization xx xx xx x x x x x x x x x x
Designating more
priority and
preserved areas
in regional
planning
xx xx xx xx x x x
Enhanced flood
protection xx xx
Restrictive open
space and nature
protection
x xx x xx xx
Energy
production on
non-agricultural
areas unsuitable
for settlement
purposes
xx x
ISPRS Int. J. Geo-Inf. 2016,5, 101 6 of 22
Table 1. Cont.
Land Use Strategies Indicators
Climate
Change
Mitigation
Climate
Change
Adaptation
Nature
Protection
Increasing
Built-Up
and
Transport
Areas
Access to
New
Built-Up
Areas
Locational
Integration
of New
Built-Up
Areas
Changing
Carbon
Storage in
Land Use
and Soils
Increasing
Built-Up and
Transport in
Flood-Prone
Areas
Increasing
Built-Up and
Transport in
Areas with
Thermal
Heat Load
Greening
Settlement
Areas
Soil
Sealing
Increasing
Built-Up and
Transport in
Areas
Designated for
Nature and
Landscape
Protection
Increasing
Built-Up and
Transport in
Unfragmented
Space
Land use
strategies
Climate
protection xxxx
Climate change
adaptation x x x x
Nature protection x x
ISPRS Int. J. Geo-Inf. 2016,5, 101 7 of 22
Some of the above-mentioned measures may affect the regional land claims of built-up and
transport areas and thus the demand for land, which is currently assigned to another (mostly
agricultural) land use. Other measures do not affect the built-up and transport area demand, but the
spatial distribution of built-up and transport area development. Therefore, the definition of suitability
maps must be adjusted. Finally, some measures also include a change in the “conversion cost” of a
land use to another. In the model, the “conversion costs” are implemented as a conversion matrix
which describes the possible land use type-specific changes.
The land-use data used in this model is based on the LBM-DE, a digital landscape model for
Germany that has been created for governmental purposes. The LBM-DE has the geometry and
spatial resolution of ATKIS (“Authorative Topographic-Cartographic Information System”), which
is the official topographic and cartographic information system for Germany. In LBM-DE the spatial
information is combined with the Corine Land Cover classification scheme [
28
]. LBM-DE was created
for the first time in 2009. Thus, there was no time series available for this dataset. Therefore, the
conversion matrix was developed from a change detection analysis of the Corine Land Cover datasets
of the years 1990 and 2006. The analysis revealed the probability of change from one land use type to
another. Due to the different spatial scales of LBM-DE and Corine Land Cover small inaccuracies are
possible, but the general trend of land use change can be analyzed adequately with Corine Land Cover.
In this publication it is described in what way three exemplary scenarios differ from the reference
scenario and how the assumptions are operationalized and implemented in the Land Use Scanner.
Results are also compared for three exemplary indicators (see Section 2.4).
2.3.1. Preserving and Developing Urban Green Areas
Settlement areas are particularly affected by climate change-induced changes in temperature due
to the development of urban heat islands. Especially long-lasting heat waves may mean a burden on
the health of residents, especially older and sick people as well as infants. The thermo-mechanical load
of buildings and infrastructures rises with an increasing number of hot days [
9
,
29
]. Higher building
densities and an expansion of the settlement area amplify this problem. In addition, soil sealing
increases the direct runoff of rainfall because the water retention in soil and vegetation is impeded [
30
].
The measure “Preserving and developing urban green areas” aims at the thermal stress relief
of cities with an expected increase in hot days by strengthening the recreation areas in cities as well
as increasing the natural water retention and groundwater recharge rate. This is primarily done by
improving the supply and accessibility of green and open space within the settlement area and between
settlements. Thus, this measure primarily addresses climate adaptation aspects. In addition, positive
effects for climate change mitigation may result from a higher carbon sequestration potential due to
extensive green volume in urban areas.
The measure is implemented into the land use model by a bundle of individual assumed
developments, which include the following:
‚
securing existing inner-urban green and recreational areas as well as appropriate open space and
green space planning in new built-up areas;
‚developing new urban green areas;
‚preserving regionally important open space functions as well as green and blue structures; and
‚demolishing and concentrating urban structures as well as renaturation.
In the model, existing urban green areas are protected by an increase in the “conversion
costs” (changes in the conversion matrix) of urban green areas into built-up land. In addition,
inner-urban development is reduced, leading to an increase in new built-up land at the urban boundary.
Furthermore, these new built-up areas need to be combined with a systematic development of blue and
green structures. The lowered inner-urban development rate is calculated as the ratio of the housing
development between 2009 and 2012 and the development of built-up areas in the same time span.
For regions with an above-average ratio, their value is decreased to the average, which corresponds
to a compact but not a highly densified architecture. This leads to a slight increase in demand for
built-up areas.
ISPRS Int. J. Geo-Inf. 2016,5, 101 8 of 22
New urban green areas are developed on brownfields and vacant lots. This is implemented in the
model by reducing the “conversion costs” of the conversion matrix for a change from brownfields to
urban green areas. Also, the demand for urban green areas is increased. In a study on behalf of the
German Federal Agency for Nature Conservation (BfN) 55.75 m
2
of urban green per inhabitant are
recommended [
31
]. In the scenario, the amount of urban green space is increased to this value in those
regions that currently have lower amounts. The accessibility of urban green areas is also considered.
The mentioned study by BfN recommends a distance of not more than 500 m for green space near to
residential areas [
31
]. Therefore, the suitability for urban green areas is increased in residential areas
with a distance of more than 500 m to existing urban green areas.
Important regional open space functions and regional green belts, defined in regional spatial
plans, are preserved in the model by defining spatial planning preservation areas as priority areas.
While in preservation areas interests of different land uses have to be weighted by the spatial planning
authorities, in priority areas other land uses than the designated land use is prohibited.
In shrinking regions and cities with a high vacancy rate, part of the existing buildings will be
demolished in order to create more opportunities for urban green space. The 2011 German Census
was used as a database to estimate the vacancy. Taking a fluctuation reserve of 3% of the housing stock
into account, there are more than 660,000 empty homes in Germany. However, this stock cannot be
fully demolished, as the clearing of whole buildings cannot always be carried out consistently. This is
why the measure distinguishes between growing and shrinking regions. In the latter, the potential
for dismantling is greatest. Between 2001 and 2010, about 284,700 homes were demolished in East
Germany [
32
]. According to rough estimates from a study by BBSR, at least 115,000 buildings from the
current building stock could be demolished by 2030 [
33
]. In addition to the dismantling of the currently
existing vacancies, the measure also considers a deconstruction of forecasted vacancies. Results of the
study “Current and future development of housing vacancy rates in the sub-regions of Germany” form
the basis for their calculation [
33
]. In this study, the development of unoccupied dwellings is projected
by 2030 in various scenarios. The results show that another 170,000 buildings could be demolished. For
the implementation into the land use model it is assumed that only as many buildings are demolished
and space is renatured as is necessary to satisfy the demand for urban green areas.
2.3.2. Strengthening Inner-Urban Development
A higher urban density has a positive climate impact, since it entails less greenhouse gas
emissions caused by transport, heating and energy supply compared to lower urban densities [
34
].
However, there is potential for conflict regarding the reinforced need to adapt the inner-urban
development to climate change, because higher building densities may increase the heat island effect.
Accordingly, urban structures and climatic conditions must be considered, so that the inner-urban
development satisfies the need for protecting the climate, reducing land consumption as well as for
adapting to climate change.
The measure aims to reduce land consumption at the edge of urban areas. The revitalization of
brownfields, the closure of gaps between buildings, the use of vacancies as well as the densification
of built-up areas are used as ways to reduce land consumption while satisfying the demand for
building land. They lead to a compact and efficient settlement structure and infrastructure follow-up
costs are reduced. Thus, climate change targets are in the focus of this measure, but also the nature
conservation strategy benefits from an improved inner-urban development. Inner-urban development
potentials (IUD) have not been surveyed nationwide before. Therefore, several data sources are used
to implement this policy into the land use model.
The above-mentioned database on vacancies in Germany (2011 Census) also reveals the potential
reuse. From the vacancy rates and the projected built-up development the reuse potential can be
determined to calculate the land use demand. In addition to the reuse of the currently existing
vacancies, the reuse of forecasted vacancies is also calculated based on current estimates [
33
].
Using similar estimation methods as described in the previous section, there are more than 1.25 million
ISPRS Int. J. Geo-Inf. 2016,5, 101 9 of 22
additional dwellings that could be reused, theoretically. Based on the assumptions of the current and
projected vacancy reuse, a new development of more than 25,000 buildings does not take place in
this scenario compared to the reference. This value takes into account that large amounts of vacant
dwellings cannot be reused in shrinking regions because of the missing demand. In growing regions
the vacancies can be nearly completely reused and the demand for space could be largely satisfied by
the consistent use of vacancies. In counties or cities without permanent vacancy (e.g., Munich), this
measure is without effect.
Brownfield redevelopment and the development of vacant lots are the most important elements
of inner-urban development. To represent this process in the land use model, several approaches are
combined. First, the conversion costs of brownfield sites in residential and commercial areas have
been reduced and brownfields will be completely redeveloped by 2030. Furthermore, the need for
built-up and transport areas is reduced due to the use of brownfields. The magnitude of this potential
has been estimated in the project “Implementation of measures to reduce land consumption—Internal
development potential” [
35
]. According to these results, the IUD potential in Germany is from
15 to 20 m
2
per inhabitant, which corresponds to approximately 120,000 to 165,000 ha nationwide.
The amount of IUD was extrapolated based on the values for counties listed in Table 2. In this way,
the proportion of projected built-up and transport area demand, which can be realized as inner-urban
development, is calculated. However, only 70% of the potentials are classified as “can be activated” [
35
].
Therefore, the available potential is reduced by 30% for each county.
Table 2. Inner-urban development potential per inhabitant classified by population development.
Population Development Population Development 1/1/2009 to 31/12/2011 IUD/Inhabitant (m2)
Strongly growing at least 1.5% p.a. approx. 8 m2
Growing 0.25% up to below 1.5% p.a. approx. 12 m2
Stagnating ´0.25% up to below 0.25% p.a. approx. 13 m2
Shrinking ´1.5% up to below ´0.25% p.a. approx. 17 m2
Strongly shrinking more than ´1.5% p.a. approx. 38 m2
The term densification is often put on the same level as inner-urban development. Here, however,
densification is understood as an intensified use of far less used areas within the existing building
environment. Densification thus happens “on land which has already been built but which has more
open space potentials” [
35
], resulting in a structural compaction. Such potentials for example include
row buildings or buildings in the backyard. A simple procedure for estimating densification potentials
was applied. It was based on the amount of impervious surface for residential and commercial areas.
Therefore, the Copernicus Fast Track Service Soil Sealing Layer delivered by the European Environment
Agency (EEA) was used. The overbuilding degree (between 0 and 1) is compared with the designations
set by the German Land Utilization Ordinance (Baunutzungsverordnung BauNVO). It takes into
account that not all lots can be equally densified according to these limits. Depending on the measured
degree of sealing, it is lifted to the next limit. This takes the claim into account that settlements should
be qualitatively densified and their original village or urban character should be preserved. Since in
reality the possibility of densification is limited—e.g., by a lack of access to open spaces, structural
constraints and so forth—it is assumed that roughly only 10% of the available potential can be used.
2.3.3. Enhanced Flood Protection
The objective of the measure is to strengthen regional planning instruments for flood protection.
It includes plans to precautionary exclude areas from settlement development that show an increased
risk of flooding. In the current planning practice flood plains with a probability of being flooded with
a 100 year return period (HQ100) are designated as priority or preservation areas.
The modeled measure goes beyond these areas and defines all HQ
100
as well as HQ
extreme
areas
as priority areas. HQ
extreme
is an extreme flood event with a low probability. Such events do not
ISPRS Int. J. Geo-Inf. 2016,5, 101 10 of 22
have a defined return period but show the flooded areas for the case that flood protection structures
(e.g., dikes) fail. In this scenario, built-up areas are allocated to other grid cells than in the reference
scenario leading to an alternative settlement pattern. In this way, large areas with increased frequency
of extreme floods remain free from new buildings, which significantly reduce the potential for damage.
The assumptions do not affect the building stock, which cannot simply be withdrawn.
2.4. Impact Assessment of Measures with Indicators
In addition to projecting land use changes in alternative scenarios, the impact analysis of modeled
measures is of importance. In order to evaluate the very different measures objectively, an assessment
system is required, which allows comparing the impact of the measures qualitatively or quantitatively
in relation to the reference scenario. Van den Bergh et al. (1999) analyzed the applicability of the
ecological footprint and discussed a number of criteria an indicator or system of indicators should
fulfill [
36
]. These criteria were principally approved by Giljum et al. (2011) [
37
]. Here, the sustainable
urban development as well as the contribution to climate change mitigation and adaptation is evaluated.
For this reason, indicators are required that can map these climate-related aspects. The impact of
climate change and therefore the need for adaptation measures were analyzed by Lung et al. (2013) at
the European level on the basis of indicators concerning heat stress, flood risk and forest/bush fire
risk [
38
]. Siedentop et al. (2011) concluded in a study on integrated scenarios of spatial development in
Germany that built-up areas with above-average heat load are a field of action for spatial development
policy [
39
]. In that way we are able to analyze different scenarios in a comparative way and to identify
possible trade offs in the achievement of the climate change mitigation and adaptation aims of the
applied measures. Therefore, a multi-criteria evaluation approach was developed, which is based on a
system of indicators and fulfills most of the above-mentioned criteria. They have been implemented in
the Land Use Scanner model. The application of the indicators for the different alternative scenarios is
presented in Table 1.
The applied indicators are calculated as index values. Therefore, individual index values for
every county in Germany based on the national average value at a reference time are calculated [
40
].
The national average of all counties as of 2009 is used as a reference value. In general, the indicators
presented capture the simulated changes in built-up and transport areas between 2009 and 2030.
The reference value, that means the national average in 2009, receives a value of 100.
The individual values of the counties differ positively at values of >100 and negatively for values <100
from the reference value. The range of index values is limited to a scale of 0 to 200 in order to reduce
the effect of outliers. Index values <0 therefore receive the value 0 and index values >200 receive the
value 200. The measurement values are first multiplied by 100 and then divided by the reference
value [40].
Since there is rarely a simple cause-and-effect relationship in the calculated measures, different
perspectives are taken into account when measuring the effect. The indicators used shall describe the
effect of a measure as holistically as possible. This allows decision-makers to capture the effectiveness
of a measure at a glance. Therefore, an indicator consists of multiple sub-indicators that describe
individual elements of a measure as simple and comprehensive as possible. In the overall indicator
calculation, all sub-indicators are equally weighted. A content-related weighting might be appropriate
in individual cases but it cannot be quantified.
To evaluate the 10 measures (Table 1), 10 indicators are used. Four indicators target the evaluation
of the achievement of climate change objectives, another four target aspects of adaptation to climate
change and two nature conservation aspects. The table matrix shows the many to many relationships
between measures and indicators. Three indicators are introduced in this study.
2.4.1. Increasing Built-Up and Transport Areas
The increase in built-up and transport area is measured in two ways. First, the land consumption
per day in hectare for Germany is used. This measuring unit is applied because it corresponds to the
ISPRS Int. J. Geo-Inf. 2016,5, 101 11 of 22
sustainability objective of the German Federal Government to reduce daily land consumption to 30 ha
per day until 2020.
LCr,01 “1
nyears
ˆ1
365 days ˆÿ
rcu
Xcu (2)
The second way of analyzing the increasing built-up and transport area is to aggregate the changes
observed in the land use grid to regions and show the percentage change between 2009 and 2030.
LCr,02 “řcu Xcu
řcXu
ˆ100 r%s(3)
with:
Xcu is the amount of land allocated to cell cto be used for urban land-use type u;
LCr,01 is the daily land consumption within a period of nyears for all regions r; and
LCr,02 is the share of new built-up and transport area of region r.
2.4.2. Increasing Built-Up and Transport Area in Flood Prone Areas
The indicator consists of two sub-indicators. To examine if built-up and transport area
development takes place outside of HQ
extreme
areas, the application of the sub-indicator “Land
consumption by built-up and transport areas in flood-prone areas” (IND_FL
r,02
) is sufficient. In order
to demonstrate at the same time which areas already have a high ratio of built-up and transport areas
in flood-prone areas at the same time, a link to an overall indicator is set by the sub-indicator “Current
built-up and transport areas in flood-prone areas” (IND_FL
r,01
). Thus, the indicator consists of two
subindicators in order to consider the future land consumption as well as the current setting with
built-up and transport area. The sub-indicators and the combined indicator are calculated as follows:
IND_FLr, 01 “
řcu Xcu in_HQextrem e
řcu Xcu
1
nrˆřrřcu Xcu in_HQextrem e
řcu Xcu
ˆ100 (4)
IND_FLr,02 “
řcu newXcuin_HQe xtreme
řcu newXcu
1
nrˆřrřcu newXcu
řcu newXcu
ˆ100 (5)
IND_FLr“0, 5 ˆIND_FLr,01 `05 ˆIND_FLr,02 (6)
with
X
cu
_in_HQextreme is the current amount of land allocated to cell cto be used for urban land-use
type uin areas prone to extreme flood events;
Xcu is the amount of land allocated to cell cto be used for urban land-use type u;
IND_FL
r,01
is the indicator of the share of the current built-up and transport area in areas prone to
extreme flood events in comparison to the built-up and transport area of the region r;
newX
cu
_in_HQextreme is the amount of land allocated due to simulation to cell cto be used for
urban land-use type uin areas prone to extreme flood events;
X
cu
is the amount of land allocated due to simulation to cell cto be used for urban land-use type u;
IND_FL
r,02
is the indicator the share of the new built-up and transport area in areas prone to
extreme flood events in comparison to new built-up and transport area of the region r; and
IND_FLr= the total indicator “Increasing built-up and transport in flood prone areas”
The results are finally categorized for a map representation. The classification rules are presented
in Table 3. The average value is 100.
In contrast to all other indicators, only those urban land use types area included in the calculation
that considerably contribute to soil sealing and thus surface runoff. That means, that recreational
ISPRS Int. J. Geo-Inf. 2016,5, 101 12 of 22
areas are not considered due to the assumption that a flooding of urban green areas do not lead to
substantial economic damage.
Table 3. Classification scheme for index values.
Classification Index Values
Well below average 0–50
Below average 50–100
Above average 100–150
Well above average 150–200
2.4.3. Increasing Built-Up and Transport Area in Areas with Thermal Heat Load
In many areas, there are already more than 7 hot days per year (maximum temperature >30
˝
C).
The most affected areas are the Upper and Middle Rhine, the Rhine-Main area and Brandenburg.
By 2100, the number of hot days will further increase significantly in many regions of Germany.
The applied indicator is called “Increasing built-up and transport area in areas with thermal heat
load”. This indicator consists of two sub-indicators, which are included with equal weighting in the
overall indicator:
1. Proportion of land consumption for built-up areas in areas with thermal heat load.
2.
Share of built-up areas on municipal areas in 2030 (residential development) within a distance of
500 m to green and blue structures (urban green and recreational areas, forests, wetlands, water).
3. Results
The results section presents a selection of results to show the manifold possibilities of analysis.
The section is structured by the different indicators. It contrasts the indicator values for the reference
scenario as well as the three alternative scenarios.
3.1. Indicator Increasing Built-Up and Transport Area
Figure 2shows the past development as well as the expected land consumption per day for the
reference scenario until 2030 (LC
r,01
). The daily consumption of new land for built-up and transport
will decrease from 69 ha in 2014 to about 45 ha per day in 2030. The goal of the Federal Government’s
sustainability strategy to reduce the daily consumption of new land for built-up and transport area
to 30 ha in 2020 will not be achieved although a significantly decreasing trend can be observed.
However, only 19.5 ha of them are claimed by built-up land. For recreation and green areas, daily
land consumption is just under 9 ha and, for traffic areas, it is 15.5 ha. The daily land consumption
for operational areas (without mining) then reaches 1 ha. Without the recreation and green space,
land consumption will be almost under 36 ha per day in 2030. The regional differences in land use
development, which have been observed in the past, continue.
The following Table 4shows the results for the alternative scenarios in the time period 2026
to 2030.
Table 4.
Daily land consumption of built-up and transport area in Germany during the time period
2026 until 2030 in ha.
Built-Up and Transport Area
Buildings and
Open Space
Transport
Areas
Recreational Areas
Incl. Cemeteries
Operational Areas
Excl. Mining
Total Land
Consumption
Reference scenario 19.5 15.5 9 1 45
Preserving and developing
urban green areas 17.7 15.3 13 1 47
Strengthening inner-urban
development 6.6 13.9 8.5 1 30
Enhanced flood protection 19.5 15.5 9 1 45
ISPRS Int. J. Geo-Inf. 2016,5, 101 13 of 22
The daily consumption of land taking place between 2026 and 2030 by implementing the measure
“Preserving and developing urban green areas” is 47 ha/day and only slightly above the reference
scenario (Table 4). This is primarily a result of a higher demand for urban green and recreational areas
(13 ha/day). The reason for the differences between this scenario and the reference scenario is that
housing is relocated to the urban boundary as open space within settlements is converted into more
green areas than according to the reference scenario. In addition, a lower housing density is required
resulting in long access roads.
ISPRS Int. J. Geo-Inf. 2016, 5, x 13 of 22
reference scenario (Table 4). This is primarily a result of a higher demand for urban green and
recreational areas (13 ha/day). The reason for the differences between this scenario and the reference
scenario is that housing is relocated to the urban boundary as open space within settlements is
converted into more green areas than according to the reference scenario. In addition, a lower
housing density is required resulting in long access roads.
Figure 2. Daily land consumption for built-up and transport area between 1992 and 2030 for the
reference scenario.
By using brownfields and vacant lots within settlements, land consumption could be reduced
by 120,000–165,000 hectares according to a recent study [35]. This is more than one third of the
estimated 2030 demand. A substantial part thereof cannot, or only with difficulty, be activated, and
supply and demand do not always meet. The effectiveness of the measure “Strengthening inner-
urban development” is well below the above-mentioned potential. In addition to the priority use of
these areas, the measure also includes the use of building vacancy rates and the use of redensification
potentials. Among those assumptions adopted in the model, this leads to a decrease in daily land
consumption in 2030 from 45 ha/day in the reference scenario to 30 ha/day (Table 4). In 2020, the daily
land consumption by built-up and transport is a little over 32 hectares. By consistently implementing
this measure, the 30-hectare objective of the Federal Government’s sustainability strategy might be
approached.
The daily land consumption of the measure “Enhanced flood protection” equals the results of
the reference scenario. For this scenario, only the suitability map in the land use model was adapted,
not the demand figures.
Figure 3a shows the development of built-up and transport area between 2009 and 2030 for
German municipalities in percent (LCr,02). The map is a result of the simulations with the Land Use
Scanner model. Although the model simulates land use changes on grid-level they are hardly visible
in a map for the whole of Germany. Therefore, the simulation results have been aggregated for
municipalities. In this way regions with considerable changes in built-up and transport area can be
highlighted. These are especially the densely populated cities in West Germany and around Berlin.
Figure 2.
Daily land consumption for built-up and transport area between 1992 and 2030 for the
reference scenario.
By using brownfields and vacant lots within settlements, land consumption could be reduced
by 120,000–165,000 hectares according to a recent study [
35
]. This is more than one third of the
estimated 2030 demand. A substantial part thereof cannot, or only with difficulty, be activated, and
supply and demand do not always meet. The effectiveness of the measure “Strengthening inner-urban
development” is well below the above-mentioned potential. In addition to the priority use of these
areas, the measure also includes the use of building vacancy rates and the use of redensification
potentials. Among those assumptions adopted in the model, this leads to a decrease in daily land
consumption in 2030 from 45 ha/day in the reference scenario to 30 ha/day (Table 4). In 2020, the daily
land consumption by built-up and transport is a little over 32 hectares. By consistently implementing
this measure, the 30-hectare objective of the Federal Government’s sustainability strategy might
be approached.
The daily land consumption of the measure “Enhanced flood protection” equals the results of the
reference scenario. For this scenario, only the suitability map in the land use model was adapted, not
the demand figures.
Figure 3a shows the development of built-up and transport area between 2009 and 2030 for
German municipalities in percent (LC
r,02
). The map is a result of the simulations with the Land
Use Scanner model. Although the model simulates land use changes on grid-level they are hardly
visible in a map for the whole of Germany. Therefore, the simulation results have been aggregated for
municipalities. In this way regions with considerable changes in built-up and transport area can be
highlighted. These are especially the densely populated cities in West Germany and around Berlin.
ISPRS Int. J. Geo-Inf. 2016,5, 101 14 of 22
Figure 3b–d shows the deviation in built-up and transport area development of the particular
alternative scenario to the reference scenario. Thus, the maps represent quantitative changes between
alternative scenarios and the reference scenario. Furthermore, they represent different allocations of
changes due to the implemented measures in the alternative scenarios. Blue and green colors therefore
mean that the increase in built-up and transport area is much lower in the alternative scenario than
in the reference scenario. Yellow color shows an equal development and red colored regions have
a higher increase in built-up and transport area development in the alternative scenario than in the
reference scenario.
ISPRS Int. J. Geo-Inf. 2016, 5, x 14 of 22
Figure 3b–d shows the deviation in built-up and transport area development of the particular
alternative scenario to the reference scenario. Thus, the maps represent quantitative changes between
alternative scenarios and the reference scenario. Furthermore, they represent different allocations of
changes due to the implemented measures in the alternative scenarios. Blue and green colors
therefore mean that the increase in built-up and transport area is much lower in the alternative
scenario than in the reference scenario. Yellow color shows an equal development and red colored
regions have a higher increase in built-up and transport area development in the alternative scenario
than in the reference scenario.
Figure 3. Developing built-up and transport areas for the reference scenario (a); and differences in
measures in municipalities between 2010 and 2030 ((b) preserving and developing urban green areas;
(c) strengthening inner-urban development; and (d) enhanced flood protection).
Figure 3.
Developing built-up and transport areas for the reference scenario (
a
); and differences in
measures in municipalities between 2010 and 2030 ((b) preserving and developing urban green areas;
(c) strengthening inner-urban development; and (d) enhanced flood protection).
ISPRS Int. J. Geo-Inf. 2016,5, 101 15 of 22
Figure 3b shows that built-up and transport area will strongly increase for large parts of western
and southern Germany. In these areas a number of large prosperous and highly densified cities
are located, where a development of urban green within the existing built-up area is difficult.
Therefore, more land is consumed at the urban boundary for lower density housing and urban
green areas. In the eastern part of the country land consumption is considerably lower than in the
reference scenario. This is due to the use of vacancies and the demolition of buildings to satisfy the
demand for recreational areas.
In this spatial context, the measure “Strengthening inner-urban development” has very different
effects (Figure 3c). In some regions more than half of the built-up area demand could be met by
stronger internal development. Examples include Berlin and its surrounding region, parts of the
Ruhr area or several regions in Lower Saxony. In other regions characterized by strong urban sprawl,
however, the inner-urban development can only cover a part of the demand for space. Due to the high
demand, the potential is already largely exhausted (e.g., Munich and surroundings). Another reason
is that in economically prosperous regions with a rural settlement structure only a few vacancies or
brownfields exist (e.g., Emsland, Upper Bavaria, and Swabia). Even regions that have a very high
inner-urban development potential, such as the Altmark, can reduce their built-up and transport
development only to a limited extent. The reason is that the demand for built-up areas has already
come largely to a standstill and that the existing potentials cannot be used. Recreational areas and
national traffic infrastructure projects (e.g., A14 motorway) contribute to further land consumption.
This development will not be affected by the measure.
The fact that the decline is not even higher depends first and foremost on the regional mismatch
of demand for housing and the availability of vacant buildings or brownfields. A breakdown of
the “Increasing built-up and transport areas” indicator into the various land use types (Table 4)
reveals that the decline in land consumption is primarily driven by the buildings and open space
category. The small decline in traffic area development is closely linked to the building and open space
development, as fewer access roads are required due to the lower expansion of built-up areas.
Figure 3d shows solely the different allocation of built-up and transport area demand in
comparison to the reference scenario, because the demand figures are the same in both scenarios. It can
clearly be seen, that single municipalities that are located directly at a river have a lower increase in
built-up and transport area development while municipalities located within some distance to the
river system have an increase. This illustrates the different allocation of land uses due to the changes
in the suitability maps. Therefore, less built-up and transport area development takes place in areas
prone to extreme flood events.
3.2. Indicator Increasing Built-Up and Transport Area in Flood Prone Areas
The result of this calculation for all scenarios is shown in Figure 4. Figure 4a shows the indicator
IND_FL for the reference scenario. Land consumption by built-up and transport is considerable in
areas prone to extreme flood events (HQ
extreme
). In Figure 4b–d, land consumption in flood-prone
areas by 2030 (IND_FL
r,02
) is illustrated. Only in Figure 4d, HQ
extreme
areas are taken into account
as priority areas. This figure illustrates two issues: First, the flood risk for built-up and transport
areas can significantly be reduced by designating further priority areas for flood protection in the vast
majority of the counties. Exceptions are the counties in which the built-up area demand is very high
and in which, at the same time, the amount of available open space outside flood-prone areas is very
small. Secondly, it appears that areas that have been affected by the flood in 2013 on the Elbe and its
tributaries, seem to have a comparatively low flood risk. This is due to the small built-up area demand
and a sufficient number of retention areas compared with the national average.
The indicator also shows a slight improvement for some regions in the scenario that aims
at “Preserving and developing urban green areas” (Figure 4b). Affected areas are for example in
Brandenburg, Saxony-Anhalt or Mecklenburg-Western Pomerania. As mentioned earlier, in these
regions buildings can be demolished and the corresponding areas can be developed as urban green,
thus the demand for built-up and transport area decreases.
ISPRS Int. J. Geo-Inf. 2016,5, 101 16 of 22
ISPRS Int. J. Geo-Inf. 2016, 5, x 16 of 22
Figure 4. Increasing built-up and transport in flood prone areas. Comparing the (a) reference and
alternative scenarios with: (b) “Preserving and developing urban green areas”; (c) “Strengthening
inner-urban development”; and (d) “Enhanced flood protection”.
Figure 4.
Increasing built-up and transport in flood prone areas. Comparing the (
a
) reference and
alternative scenarios with: (
b
) “Preserving and developing urban green areas”; (
c
) “Strengthening
inner-urban development”; and (d) “Enhanced flood protection”.
ISPRS Int. J. Geo-Inf. 2016,5, 101 17 of 22
Figure 4c shows that the measure leads to a considerable relief of areas prone to extreme flood
events in regions that have only small demands for built-up and transport area. Those regions with
high pressure on land due to considerable demand for built-up and transport area like Bavaria, the
Hamburg region or the surrounding of Stuttgart also show an above average development of built-up
and transport in areas prone to extreme flood events.
3.3. Indicator Increasing Built-Up and Transport Area in Areas with Thermal Heat Load
Figure 5a shows that large parts of the areas with thermal heat load are those with a considerable
increase in built-up and transport area anyway. The measure is effective particularly in regions that
have a high proportion of brownfields or vacant lots and with a low demand for built-up and transport
areas (Figure 5b). In these regions, there are more developments of new green areas or demolition
activities. This is particularly the case in parts of Saxony, in southern Brandenburg as well as in parts
of Thuringia and Saxony-Anhalt. However, the total demand for new built-up and transport areas
increases by the consistent application of the measure, because the possibilities of inner development
are limited and a lower housing density is sought. For some regions, the consequence is that the
“Increasing built-up and transport area in areas with thermal heat load” indicator slightly increases
because of higher building and open space development compared to the reference scenario. As a
result, there are only a few differences between the alternative scenario implementing the measure
“Preserving and developing urban green areas” and the reference scenario because in many regions
the positive effects resulting from a better accessibility of urban green and blue structures are offset by
a higher land consumption of built-up and transport areas (including urban green areas) in thermally
polluted areas.
It is striking that the measure “Strengthening inner-urban development” has a more distinctive
effect than the measure “Preserving and developing urban green areas”. Reasons might be the
strong influence of the sub-indicator land consumption. As explained earlier, land consumption is
considerably lower for the measure “Strengthening inner-urban development”. As an improvement
mainly occurs in the regions that have a low availability of free space per capita (south-west of
Germany), but at the same time land consumption can be reduced, a weighting of the sub-indicators is
considered in order to reach plausible results.
ISPRS Int. J. Geo-Inf. 2016, 5, x 17 of 22
Figure 4c shows that the measure leads to a considerable relief of areas prone to extreme flood
events in regions that have only small demands for built-up and transport area. Those regions with
high pressure on land due to considerable demand for built-up and transport area like Bavaria, the
Hamburg region or the surrounding of Stuttgart also show an above average development of built-
up and transport in areas prone to extreme flood events.
3.3. Indicator Increasing Built-Up and Transport Area in Areas with Thermal Heat Load
Figure 5a shows that large parts of the areas with thermal heat load are those with a considerable
increase in built-up and transport area anyway. The measure is effective particularly in regions that
have a high proportion of brownfields or vacant lots and with a low demand for built-up and
transport areas (Figure 5b). In these regions, there are more developments of new green areas or
demolition activities. This is particularly the case in parts of Saxony, in southern Brandenburg as well
as in parts of Thuringia and Saxony-Anhalt. However, the total demand for new built-up and
transport areas increases by the consistent application of the measure, because the possibilities of
inner development are limited and a lower housing density is sought. For some regions, the
consequence is that the “Increasing built-up and transport area in areas with thermal heat load”
indicator slightly increases because of higher building and open space development compared to the
reference scenario. As a result, there are only a few differences between the alternative scenario
implementing the measure “Preserving and developing urban green areas” and the reference
scenario because in many regions the positive effects resulting from a better accessibility of urban
green and blue structures are offset by a higher land consumption of built-up and transport areas
(including urban green areas) in thermally polluted areas.
It is striking that the measure “Strengthening inner-urban development” has a more distinctive
effect than the measure “Preserving and developing urban green areas”. Reasons might be the strong
influence of the sub-indicator land consumption. As explained earlier, land consumption is
considerably lower for the measure “Strengthening inner-urban development”. As an improvement
mainly occurs in the regions that have a low availability of free space per capita (south-west of
Germany), but at the same time land consumption can be reduced, a weighting of the sub-indicators
is considered in order to reach plausible results.
Figure 5. Cont.
Figure 5. Cont.
ISPRS Int. J. Geo-Inf. 2016,5, 101 18 of 22
ISPRS Int. J. Geo-Inf. 2016, 5, x 18 of 22
Figure 5. Land consumption in areas with heat stress. Comparing the (a) reference and alternative
scenarios with: (b) “Preserving and developing urban green areas”; (c) “Strengthening inner-urban
development”; and (d) “Enhanced flood protection”.
4. Discussion
Even if the largest effect to reduce land consumption through inner-urban development can be
seen in core cities and densely populated regions, the implementation of the measure is especially
important in rural areas and in small- and medium-sized cities. The reason is that inner-urban
development counteracts the emptying of city centers and the obliteration of neighborhoods and
helps to maintain lively villages and towns. To develop political measures or incentives is beyond
this study.
The contrasting juxtaposition of the scenarios shows that a reduction of built-up and transport
area development has a considerable impact on the thermal relief than relocating the demand to the
municipal border. A qualitatively development of built-up and transport area with adequate urban
green space is therefore a prerequisite. Future research should analyze the optimal relationship
between densification of settlement development and the appropriate supply of urban green spaces.
Nevertheless, the weighting of sub-indicators needs to be analyzed in a next step.
Strengthening inner-urban development not only provides a contribution to thermal relief, but
it also reduces the damage potential of extreme flood events in some areas. Thus, the measure does
not only contribute to climate change mitigation but also contributes to several areas of climate
change adaptation.
Figure 5.
Land consumption in areas with heat stress. Comparing the (
a
) reference and alternative
scenarios with: (
b
) “Preserving and developing urban green areas”; (
c
) “Strengthening inner-urban
development”; and (d) “Enhanced flood protection”.
4. Discussion
Even if the largest effect to reduce land consumption through inner-urban development can be
seen in core cities and densely populated regions, the implementation of the measure is especially
important in rural areas and in small- and medium-sized cities. The reason is that inner-urban
development counteracts the emptying of city centers and the obliteration of neighborhoods and helps
to maintain lively villages and towns. To develop political measures or incentives is beyond this study.
The contrasting juxtaposition of the scenarios shows that a reduction of built-up and transport
area development has a considerable impact on the thermal relief than relocating the demand to
the municipal border. A qualitatively development of built-up and transport area with adequate
urban green space is therefore a prerequisite. Future research should analyze the optimal relationship
between densification of settlement development and the appropriate supply of urban green spaces.
Nevertheless, the weighting of sub-indicators needs to be analyzed in a next step.
Strengthening inner-urban development not only provides a contribution to thermal relief, but
it also reduces the damage potential of extreme flood events in some areas. Thus, the measure does
not only contribute to climate change mitigation but also contributes to several areas of climate
change adaptation.
ISPRS Int. J. Geo-Inf. 2016,5, 101 19 of 22
5. Conclusions
This article has shown how it is possible to evaluate the effect of policy measures to control the
settlement development through the combination of methods of land use modeling with scenario
techniques and an indicator-based measurement approach. Based on empirical data on current and
past trends of settlement development, a reference scenario of land use has been calculated for 2030.
Various measures that contribute to climate change mitigation, the adaptation to climate change or
natural and environmental protection have been drawn based on the reference scenario. Indicators
were developed that allow an assessment of the individual measures. Most indicators are composed of
several sub-indicators, which are included with equal weighting in the calculation.
Based on the presented results it can be illustrated that different social objectives may cause
different patterns of settlement development. Strengthening the inner-urban development leads to
an economical use of soil resources and supports climate change mitigation. With respect to climate
change and the expected increasing number of hot days, the objective is to ensure the sufficient
accessibility and quality of urban green areas. To achieve this objective may lead to an increase in
land consumption due to the increased demand for urban green areas. However, this demand does
not cause an increase in sealed surfaces. Nevertheless, there is potential for conflict between the
strengthened inner-urban development and the need to adapt to climate change, which is why it
requires an in-depth examination of the various development and conservation objectives in urban
land use management. It is clear that the relevant measures may not be mutually exclusive. Instead, we
need strategies for sustainable urban development that combine aspects for climate change mitigation
and adaptation to climate change [29].
One important aspect has to be considered when interpreting the model results. As documented in
Goetzke and Hoymann (2016), a large amount of data from different sources were used to calculate the
suitability maps [
27
]. The thematic accuracy of the datasets shall not be discussed here but inaccuracies
due to different spatial resolutions, different base years or thematic resolution are probable. All spatial
datasets have been resampled to a 100 m grid. Since some datasets have a lower resolution, the
resampling pretends an accuracy that is not inherent to the data. However, it can be expected, that
this inaccuracy in the input data is smaller than the inaccuracy in results when not using the datasets.
A similar assumption can be made for the different base years. The ambition was to collect the most
up-to-date datasets for the calculation of the suitability maps. Most of the datasets are updated
frequently but for different points in time. Considering the applied land use classification, the ambition
was to apply rather homogenous land use types. That is why the urban land use types have been
differentiated into four types. By this, it was possible to simulate scenarios that focused on certain
land uses and to get an insight into the development of settlement areas without using a specific urban
growth model.
The decision to apply the Land Use Scanner model was helpful since in the scenarios not only
urban growth was simulated but also shrinkage as well as relocation of parts of settlement areas.
The applied model is very flexible to these kinds of developments.
In the next step of the work, the measures are combined with each other to obtain land use
strategies. This leads to three more scenarios (climate change mitigation, adaptation to climate
change and natural and environmental protection) that are evaluated in each case with several of the
indicators presented.
Starting from an empirical database, a model such as the Land Use Scanner allows to develop
scenarios of land use change and to derive indicators with policy-relevant statements. To assess
specific individual projects with this approach is not possible, but it can help support decision-makers
at the national or federal state level in assessing the impact of measures or instruments and initiate
discussion processes.
Acknowledgments:
The research at the Federal Institute for Research on Building, Urban Affairs and Spatial
Development (BBSR) was funded by the German Federal Ministry of Education and Research (FKZ: 01LL0909B).
ISPRS Int. J. Geo-Inf. 2016,5, 101 20 of 22
Author Contributions:
Jana Hoymann and Roland Goetzke developed idea and concept of the paper;
Jana Hoymann built up the database and performed the simulation of the reference scenario; Roland Goetzke
implemented the measures into the land use change model and developed the evaluation approach; and
Jana Hoymann wrote the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
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