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Environmental Impact Assessment Review xxx (2011) xxx–xxx

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Environmental Impact Assessment Review

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Towards sustainable settlement growth: A new multi-criteria assessment forimplementing environmental targets into strategic urban planning

Sophie Schetke a,⁎, Dagmar Haase b, Theo Kötter a,1

a Institute of Geodesy and Geoinformation, Dept. of Urban Planning and Real Estate Management, University of Bonn, Nußallee 1, 53115 Bonn, Germanyb Humboldt University of Berlin, Department of Geography, Rudower Chaussee 16, 10099 Berlin, Germany, Helmholtz Centre for Environmental Research – UFZ,Department of Computational Landscape Ecology, Permoserstr. 15, 04318 Leipzig, Germany

⁎ Corresponding author. Tel.: +49 228 732613; fax:E-mail addresses: [email protected] (S. Schetke)

(D. Haase), [email protected] (T. Kötter).1 Tel.: +49 228 732612; fax: +49 228 733708.

0195-9255/$ – see front matter © 2011 Elsevier Inc. Alldoi:10.1016/j.eiar.2011.08.008

Please cite this article as: Schetke S, et al, Tenvironmental targets into strategic urba

a b s t r a c t
a r t i c l e i n f o
Article history:Received 2 March 2010Received in revised form 29 August 2011Accepted 30 August 2011Available online xxxx

Keywords:Urban land useLand consumptionEcological targetsMulti-criteria assessmentDecision support system

For nearly one decade, the German political and research-agenda has been to a large extent determined bythe ongoing question of how to limit the expansion of settlement areas around cities in order to preserve nat-ural resources, make settlement growth more sustainable and to strengthen the re-use of existing inner-urban areas (see a.o. Kötter et al. 2009a, 2010; Schetke et al. 2009, 2010b). What is already under discussionwithin the international literature are the recommendations of the German Council for Sustainability toquantitatively reduce the daily greenfield consumption from the current rate of over 100 ha per day to arate of 30 ha per day in 2020 and to bring urban infill development up to a ratio of 3:1 with greenfield devel-opment (German Council for Sustainability, 2004).). This paper addresses the added value beyond those ab-stract political targets and presents an innovative, multi-criteria assessment (MCA) of greenfield and infillsites to evaluate their sustainability and resource efficiency. MCA development and its incorporation into aDecision Support System (DSS) were accomplished by utilising a stakeholder-driven approach. The resultingtool can be applied in preparing and revising land-use plans. The paper presents the concept and the devel-opment process of the MCA-DSS. Test runs with planners prove that the evaluation of potential housing sitesusing individually weighted environmental indicators helps to identify those strategies of housing develop-ment that accord most closely with sustainability goals. The tests further show that the development ofgreenfield sites generally exhibits less sustainability than that of infill sites.

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owards sustainable settlement growth: An planning, Environ Impact Asses Rev (201

© 2011 Elsevier Inc. All rights reserved.

1. Introduction

For nearly one decade, the German political and research-agendahas been to a large extent determined by the ongoing question ofhow to limit the expansion of settlement areas around cities inorder to preserve natural resources, make settlement growth moresustainable and to strengthen the re-use of existing inner-urbanareas (see a.o. Kötter et al., 2009a, 2010; Schetke et al., 2009,2010b). What is already under discussion within the international lit-erature are the recommendations of the German Council for Sustain-ability to quantitatively reduce the daily greenfield consumption fromthe current rate of over 100 ha per day to a rate of 30 ha per day in2020 and to bring urban infill development up to a ratio of 3:1 withgreenfield development (German Council for Sustainability, 2004).But where is the added value? What is still lacking are applicabletools to fill those abstract goals with life, to make them usable for

planners and concrete empirical findings of current research onhow to achieve this.

The need to incorporate such targets into strategic decision-makinghas increased over the last few years and is frequently discussed in aninternational context. In the context of urban development, there is aspecific need to analyse and assess the environmental effects of settle-ment growth at the stage of preparatory land-use planning (Blancoet al., 2009; Hemmati, 2002). The development of a decision-supportsystem (DSS) has been suggested as a tool to assist in this process. Inparticular, DSS must assess the environmental effects of land-useplans as a primary instrument of urban planning (Diez and McIntosh,2009; Poch et al., 2004).

However, various problems with the development and applicationof DSS have been identified, both internationally (McIntosh et al.,2006) and in Germany (Wende et al., 2009). The assessment of the en-vironmental effects of land-use plans has been addressed by various au-thors (Carsjens and Ligtenberg, 2007; Geneletti, 2008; Mendoza andMartins, 2006). Existing bottlenecks in the implementation of DecisionSupport Systems, a computer-based information system that assistsorganisational decision-making activities, into strategic planning havebeen discussed in an international context by Klosterman and Pettit

new multi-criteria assessment for implementing1), doi:10.1016/j.eiar.2011.08.008

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(2005), McIntosh et al. (2006) and Vonk et al. (2005), and in Heilandet al. (2003) in the German context. All agree that the major challengein this area is to better link decision-support tools to the ways inwhich stakeholders use those tools. The authors conclude that it is es-sential to create a better understanding of user needs and to undertakeresearch to establish empirically the facts regarding the gap betweentool design and use (McIntosh et al., 2006).

Such research requires the development of an approach grounded ina better understanding of how scientific and statistical data, informationand methods, which packaged and delivered in the form of decisionsupport system (DSS) technology, influence and impact policy andmanagement processes (Courtney, 2003; McIntosh et al., 2006). TheMulticriteria Assessment Decision Support System (MCA-DSS) pre-sented in this paper addresses the problem of analysing and assessingthe environmental effects of settlement growth on the strategic levelof preparatory land-use planning. The link between multi-criteria as-sessment methods (MCA) and DSS is essential in order “to compare al-ternative courses of action on the basis of multiple factors, and toidentify the best performing solution” (Geneletti and van Duren, 2008referring to Massam, 1988) using Geographical Information Systems(GIS). The focus of this DSS is on the assessment of those environmentalconditions that determine the degree of sustainability of future settle-ment development.

A MCA-DSS for strategic land-use planning must specifically addressissues of greenfield vs. infill development in order to preserve or enhanceecological processes. Environmental resources and potentials can be pre-served by an optimal allocation of newhousing siteswithin a city. As landdevelopment for housing and infrastructure purposes remains themajordriver of land consumption and urban sprawl in most European cities(EEA, European Environmental Agency, 2006; Federal EnvironmentalAgency [Umweltbundesamt], 2009; Siedentop, 2001 Nov 8–9), strategicland-use planning requires improved knowledge and new tools that en-hance planners' capacities to address this ongoing spatial problem. Thethreats of urban sprawl and greenfield development are best understoodin relation to their significant and numerous ecological impacts, such asthe loss of soil functions, the loss of connected habitats, the negative ef-fects from increasing traffic and disturbance of the natural environment.Descriptions of the negative environmental and social effects of urbanland consumption and infill densification have been synthesised in anumber of publications (Alberti, 2005, 2009; Gainsborough, 2002;Haase, 2009; Haase and Nuissl, 2007; Krampulz, 2005; Marzluff et al.,2008; Nuissl et al., 2009; Siedentop, 2005).

In spatial planning, peri-urban greenfield development has been cri-ticised for another reason. For at least two decades, such developmenthas becoming increasingly decoupled from population growth or de-cline in Germany and elsewhere (Deng et al., 2009; Johnson, 2001;Nuissl et al., 2009; Rieniets, 2009; Schultz and Dosch, 2005; Siedentopand Fina, 2008 Sep; Torrens and Alberti, 2000). Urban land recycling,such as brownfield redevelopment (Thomas, 2002), high-quality densi-fication of housing estates and compact city development are consid-ered promising ways of counteracting sprawl (Jenks and Burgess,2000; Jenks et al., 1996). Therefore, measures to reduce peri-urbanland consumption and foster urban infill development are consideredeffectivemethods for steering settlement growth towardmore sustain-ability (Banzhaf and Höfer, 2008; Kötter et al., 2009a; Pauleit et al.,2005; Schetke et al., 2009). The preservation of open spaces and recre-ation areas in cities, along with the renovation of existing housingstocks, brownfield recycling and a reduction of commuting, are majorissues to be addressed to achieve the political aspirations for sustainablecities. Therefore, both the quantitative reduction of peri-urban landconsumption and the valuation of inner-urban land potentials mustbe supported by a MCA-DSS (Nuissl et al., 2009).

As outlined in the comprehensive work of Geertman and Stillwell(2009), the development of such a MCA-DSS must meet several cri-teria: (i) It must consider a wide range of environmental resources,and (ii) it must meet the demands of applicability and adaptability

Please cite this article as: Schetke S, et al, Towards sustainable settlemenvironmental targets into strategic urban planning, Environ Impac

within planning processes because such processes essentially deter-mine its use.

Tools such as integrated or MCA models (e.g., Parker et al., 2002),DSS (e.g., Giupponi, 2007) and Geographical Information Systems(GIS) (e.g., Malczewski, 2006) are suitable to support complex deci-sion processes (Van Daalen et al., 2002). However, there are recog-nised gaps between the claims made regarding the usefulness ofsuch tools in science and, for example, their demonstrated utility inthe implementation of ecological targets in land-use planning, andmore generally in environmental land management (McIntosh et al.,2006). To be utilised in daily planning practices, a MCA-DSS toolshould be demand-driven in orientation (Reeve and Petch, 1999)and utilise participatory methods during the development processto better reflect stakeholders' needs for information in the design ofthe DSS tools.

Therefore, the interface with end-users, if not the entire tool de-velopment process itself, should conform to the preferred communi-cation systems of targeted end-users (Reeve and Petch, 1999). Theadded value of a stakeholder-consultative approach derives fromthe fact that particular components, such as those that make availablemunicipal data (which is often unknown to urban planners) andidentify case study-specific interests and indicators (McIntosh et al.,2006), can help bridge the gap between science and practice and in-crease the acceptance of the DSS.

Set against this background, the central questions discussed in thispaper are as follows:

1. How can the assessment of the ecological impacts of land develop-ment be systematically implemented into a comprehensive MCA-DSS applicable to land-use planning?

2. Is the proposed MCA-DSS capable of assessing infill planning strate-gies vs. greenfield development to reduce further land consumption,while simultaneously avoiding the deterioration of the environmen-tal quality of already built areas?

3. How can a MCA-DSS be designed to bridge the gap between scienceand practice?

In the remainder of this section, the methodological discussionwill focus on the elaboration process of the MCA-DSS. The results por-tion will address the results of the collaborative process of MCA-DSSdevelopment while also highlighting the functionality of the indicatorset and the concrete outcomes of the multi-criteria assessment. ThisMCA-DSS has been tested in the city of Essen, Germany.

2. The case study

The subject of this case study is the city of Essen, located in theGerman Ruhr-Area (Fig. 1). Essen has roughly 580,000 inhabitantsand is characterised by urban growth that is spatially under pressuredue to surrounding cities and towns, all of which tend to merge.Housing development is characterised first by suburban greenfielddevelopment, second by infill development processes at large-scalebrownfields originating from the former steel industry and third byurban re-densification. Due to its industrial past, Essen is denselybuilt-up, and inner-urban green space is very limited. Such spatialpatterns are particular vulnerable to new housing sites. Essen's popu-lation is continuously decreasing, and by 2020 an average vacancyrate of some 10% of the housing market is projected (Schauerte,2007). Yet, the current demand for one-family homes and for high-quality housing is very high; new housing areas are required and,therefore, are planned.

In this paper, we analyse 31 sites of different sizes that are spreadover the municipal area of Essen, all of which are the object of newhousing development. Their formal planning status and time horizonfor development differ due to their current status, which is either thatof preliminary or of binding land use planning (Kötter et al., 2010;Schetke, 2010). Of these sites, 19 out of the 31 are infill developments;

ent growth: A new multi-criteria assessment for implementingt Asses Rev (2011), doi:10.1016/j.eiar.2011.08.008


Fig. 1. Examples of assessed future housing sites.(Data-sources: picture top-left, pictures top right and below, selection of housing potentials (City of Essen, derived from draft of the regional land use plan of (status 2008); Cadas-tral land use data (ALK) Office of Geoinformation, Surveying and Cadastre City of Essen (2007)).

3S. Schetke et al. / Environmental Impact Assessment Review xxx (2011) xxx–xxx

the other 12 are greenfield sites. These potential housing sites are de-rived from a preliminary version (as of 20082) of the future regionalland-use plan for the city of Essen (see Fig. 1 showing the sample sitesanalysed in this study).

Local planners from different departments of the City of Essen andscientists at the University of Bonn were involved in the MCA-DSS de-velopment process we analyse here. Four aspects of the process werethe subject of regular workshops : i) the indicator set, ii) the decision-making relevance of each indicator and the assignment of individual in-dicator weights, iii) the design and usability of theMCA-DSS and iv) theMCA-DSS outputs in terms of their suitability for use in decision-makingprocesses. These four aspects were used to define both the conceptualand the technical requirements of the MCA-DSS.

3. Methodology

3.1. Basic parameters of MCA-DSS development

An all-encompassing assessment of both greenfield and infill sitesrequires an approach that includes different dimensions of the urban

2 The potential housing sites that were displayed in the draft of the regional land useplan dating from 2008 and analysed within this research do not entirely match withthe final version of the regional land-use plan from 03/05/2010. No legal claims canbe asserted.


environment. A MCA-DSS should be necessarily applicable in differentroutines and stages within a planning process and understandable fordifferent stakeholders such as e.g. planners, policy makers, natureconservationists.

During the set-up of the MCA-DSS, the authors of this paper paidcareful attention to the essentials of MCA-DSS conceptualisation as pre-sented in the literature. During discussions with various stakeholders(see Section 3.2), it became clear that different users make differentdemands upon a MCA-DSS. The findings of a study by Coenen (1999)suggest that user-specific demands can refer to the target-relation,receiver-specific aggregation of information, political steering abili-ty, comprehensibility for politicians and the public, a society-relatedminimum consensus on indicators and the representation of processesand interrelations. These manifold demands not only highlight claimsrelevant to the conceptualisation of a MCA-DSS, but also imply bot-tlenecks which we tried to eliminate during implementation (see es-pecially the studies of Klosterman and Pettit, 2005; Vonk et al.,2005).

To ensure the applicability of the presented MCA-DSS within theplanning processes of the City of Essen, we considered the technical,software and usability-related requirements that determine boththe use and the function of the DSS. A brief overview of these require-ments is provided in Table 1. This figure subsumes selected results ofinternational research on the application of MCA-DSS that were con-sidered for our case study. According to the findings listed in Section 1of this paper, the authors followed the conceptual demands of a DSS


image of Fig.�1


Table 1Constraints and demands of the MCA-DSS (Source: Schetke, 2010; Kötter et al., 2009b referring to studies of Brückner, 2001; Heiland et al., 2003; Korczak, 2002; Nijkamp andOuwersloot, 1997; Wolter, 2001; Wrbka, 2003).

Conceptual demands Technical demands Indicator functions and task of MCA-DSS

Pay respect to meaning and content of single indicators and areto be developed in accordance to the specific, national targetsof sustainable development (translated and modifiedaccording to BfS/BUWAL, 1999:11)

Pay respect to data-availability and demands of official statistics(translated and modified according to BfS/BUWAL, 1999:11)

Describe the individual function of eachindicator and the MCA within theplanning process

Link to practice:Relevance for planning decisions

Data sets Analysis/communication: Applicableoperationalisation of complexinterrelations

Traceability:ClearnessModular conception

Data quality Steering, warning and decisionsupport: Steering of planning processesby analysing and assessing settlementgrowth

Significance/Adoption of local “Leitbilder” Data compatibility:To enhance transferability of the MCA between different case-studies

Monitoring: Awareness-raising ofstakeholders

Compatibility of content:“Learning from others”/What is already there?


while taking both scientific and user-oriented requirements into ac-count. This approach basically determines the design of the MCA-DSS. Another essential demand to be addressed covers anything relat-ed to data availability and its applicability within the assessment pro-cess. Finally, the task a MCA-DSS must fulfil within the planningprocess must be taken into account because the task determines thedesign, concept and degree of accuracy of the assessment.

In addition to these general demands, we also identified practical de-terminants during the collaborative development process of the MCA-DSS concept. The development processwas part of a facilitated group ex-ercise that collects knowledge from among the participating planners(Vennix, 1999) and brings that knowledge to bear on the issue at hand— in our case, the sustainability assessment of land development. Inmak-ing use of that knowledge, one gains detailed insights into the require-ments of planners; insights that might sometimes conflict with theaspirations of scientists. Table 2 shows practical and science-related de-mands and restrictions on the use of a MCA-DSS.

3.2. Integration of MCA-DSS into land-use planning

For the City of Essen, the central task of the MCA-DSS is the assess-ment of future (potential) housing sites in terms of sustainability(Kötter et al., 2010; Schetke et al., 2009). In order to inject strategic

Table 2Requirements for the MCA-DSS from the viewpoints of planners and scientists (authors dra

Planners

- Development strategies in line with the legal requirements of environmental assessmeBuilding Code (BauGB), Environmental Impact Assessment Act (UVPG), Federal Naturtion Act (BNatschG)

- Target-oriented protection (e.g. landscape or nature conservation areas)

- No additional work steps or manpower necessary to apply MCA-DSS and Avoidansoftware

- Demand for innovative, integrative analyses

- Concise and critical assessment that can be adjusted towards political obligationsConsequences➔ Need of commona wording➔ Limited thematic content➔ Reduced set of indicators➔ Flexibility and handling of the tool

a For scientists and practitioners.


integration into the planning process, the site assessment refers tosites displayed in current preparatory land-use plans (Flächennutzungs-plan) or land-use plans under elaboration (Planaufstellungsverfahren).The MCA-DSS attempts to analyse and assist in decision-making andmonitoring with regard to land use in the process of enabling sustain-able spatial development and is applied in preparatory land-use plan-ning at the municipal level.

Within German land-use planning, a land-use plan is an instru-ment of preparatory land-use planning that displays the differentuses of an area, such as housing, industry or mixed use; areas for so-cial and technical infrastructure; regional and local highways/streets;green and open spaces; and areas for agriculture and forestry (Section5 German Federal Building Code, BauGB). The land-use plan, there-fore, represents the major development vision of a municipalitywith a planning horizon of ten to fifteen years. This plan also includesdesired municipal development trends. The embedding of a MCA-DSSinto this strategic level of land-use planning provides planners withthe opportunity to adjust or orientate policy and planning targetsaccording to the goals of sustainability. In preparatory plans, housingdevelopment is not yet legally binding, and the designated sites re-main a type of “potential area”.

For preparatory land-use planning, the elaboration process of aMCA-DSS is determined by three steps: (1) the data input, (2) a so-called“participatory” filter (Kötter et al., 2010) and (3) the final output (Fig. 2).

ft).

Scientists

nt: Federale Conserva-

- Concise operationalisation of sustainability and translation ofplanners' awareness into actions

- Additional environmental and ecological assessment beyond legallybinding protection targets or areas or specific habitats

- Sustainable and functional oriented protection (landscape func-tions) instead of solely target-directed protection

- Neutral analyses bound to existing administrative infrastructureand existing data base

ce of new - Need for understanding the planners and of the changes of land usedevelopment

- Independence from political obligations



Input OutputFilter

• Expert-workshops (1-2 per year)

• Stakeholder-workshops (3 per year)

• Indicator-frameworkincorporating and aggregating qualitative & quantitative indicators

• Additional standards

• Integration of known and new indicators

• Multicriteria DecisionSupport System (MCA-DSS)

• Communal Data Sets

• Environmental legislation

• Informal ecological indicators already implemented withinplanning processes

• Normative or legally basedplanning standards

Fig. 2. Structure of the process for developing indicators.Modified according to Schetke et al., 2010b).


(1) Input: To foster a better understanding of the MCA-DSS andplanners' independent application of it as a stand-alone tool,we paid particular attention to the underlying data and wheth-er or not they are understood by the users. This activity repre-sents the step of indicator compilation contained within theDSS. The MCA-DSS should be exclusively based on existingand publicly available municipal data and should not requireany further data gathering, transformation, processing or anal-ysis (see listing in Table 2). Due to the requirement for a timelyand administrative neutral assessment, the indicators usedwere associated with legal environmental targets that aremandatory for planners. Some of the indicators already setbenchmarks for possible settlement growth.3

A second focus was on the environmental aspects of both infilland greenfield development. Table 3 highlights three essentialcategories of environmental assessment, underlying data and re-spective criteria that helped to develop suitable indicators. Amore specific indicator application in the MCA-DSS is providedin Section 4.

(2) Participatory filter: The selection of the most suitable indicatorsfrom a large number of potential indicators was achievedthrough the participatory process. Initially, local planners andscientists from various disciplines were consulted to providetheir expert knowledge regarding the relevance of indicatorsof sustainable land development within planning processes.The main requirement for a “good” indicator was its relevancefor planning practitioners. Three workshops each year over thethree years of the project's lifewere heldwith local stakeholdersfrom the Department of Urban Renewal and Land Management(Amt für Stadterneuerung und Bodenmanagement) and the De-partment of Urban Planning and Building Regulations (Amt fürStadtplanung und Bauordnung) to cluster the number of indica-tors, to select the most decision-relevant and applicable indica-tors and to test the prototype of the MCA-DSS with regard tostability, design of the graphical user interface (GUI) and han-dling.Therefore, all indicators actually implemented in the MCA-DSSwere chosen by scientists and local planners. Indicators thustake into account legally binding standards, expert knowledge,planners' advice and experience and data availability. The num-ber of workshops was framed first by time restrictions of thelocal planners involved, second by the time required to acquirethe underlying data and third by the composition of the tool it-self. One or two smaller, expert workshops per year were also

3 Such as the required integration of legally protected areas (Federal Nature Conser-vation Act, Bundesnaturschutzgesetz ) and the prohibition on the development ofareas prone to flooding (Flood protection Law; Hochwasserschutzgesetz).


held to ensure the scientific approval of the selected indicatorsfor the MCA-DSS and the establishment of planning-standardsand threshold values.

(3) Output: The output of the MCA-DSS is in the form of an indicatorset that is consistent with local planning regulations (Schetke,2010; Schetke et al., 2009). This output integrates both qualita-tive and quantitative indicators to assess the manifold aspectsof the environmental consequences of land consumptionthat are sometimes difficult to measure or assess numerically.To reduce objections to implementing the MCA-DSS and to pro-mote better application, the graphical user interface (GUI) wasconfigured using the Visual Basic programming language withinthe MS Office software package that is commonly used in plan-ning agencies in Germany. Thus, the MCA-DSS requires neitheradditional expensive software nor separate installation.

3.3. Environmental focus of the MCA

The aim of the MCA-DSS is to protect natural resources in accor-dance with requirements contained in the national legal framework4

and to preserve landscape functions and ecosystem services (Costanzaet al., 1997; Kötter et al., 2009a; Millennium Ecosystem Assessment,2005; Schetke et al., 2009, 2010a; Zebisch et al., 2004). It also considersthe natural risk potential (Termorshuizen and Opdam, 2009) as man-dated for planners during the planning process.We define environmen-tal indicators as parameters that are determined by all the abioticfactors, such as air, water, soil, etc.; these must be considered duringthe impact assessment of settlement development (Nuissl et al., 2009).

Given these requirements, the MCA procedure is built in two con-secutive steps. First is an indicator-based assessment of changes oflandscape functions (as performed by Costanza et al., 1997; Daily,1997; de Groot et al., 2002; Gill et al., 2008; Millennium EcosystemAssessment, 2005; Vrščaj et al., 2008). Second is an indicator-basedassessment of changes caused by urban development on distinctivelandscape features (such as flood areas or valuable soils) to measurethe impact of the development on the people using and inhabitingthat landscape.

The indicators used are provided in Table 4 and are as follows: regu-lative function, biotope quality, seepage, isolation, sealing rate, protectedareas, soil quality and flood risk. Table 4 highlights their origin as eitherscientific, informal, or legally defined planning standards.

Fig. 3 depicts the assessment methodology implemented in theMCA-DSS and briefly describes the procedure from single indicator

4 See German Federal Building Code (Baugesetzbuch, BauGB), Environmental ImpactAssessment Act (Gesetz zur Umweltverträglichkeitsprüfung, UVPG), Federal NatureConservation Act (Bundesnaturschutzgesetz, BNatSchG)


image of Fig.�2


Table 3Development criteria of environmental indicators (authors draft).

Category Baseline data Criterion Focus of the evaluation Method of collectingexpert opinion

Ecosystem functions Cadastral land use data,habitat inventory, cadastreof protected areas

Quality of urban greenspaces/degree of humanmodificationEcological productivityQuantity of urbangreen spaces

Modification of urban ecosystemsand their functions due to division,isolation and degradation of naturalhabitats and biodiversity; identificationof affected processes, structures,microclimate and of natural resources

Literature reviewSurvey of public institutionsIterative Delphi Surveywith scientific expertsand practitionersinterviews

Resource protection Land use maps, inventoriesof specific natural resources,protected areas andagro-ecosystems

Conservation Preservation of single specificresources and structures duringsettlement-development such as soiland its functions or e.g. protected areas

Literature reviewSurvey of public institutionsIterative Delphi Surveywith scientific expertsand practitionersInterviews

Risk potential Data on site-specific naturalrisk-factors

Hydrology, floodprotection

Vulnerability of settlement-structuresdue to external environmental impactsand consideration of economic andsocial effects due to housing-developmentat affected sites

Literature reviewSurvey of public institutionsIterative Delphi Surveywith scientific expertsand practitionersInterviews

Table 4Environmental indicators of the MCA-DSS (source: Schetke, 2010).

Indicator Dataset Source Character/scale Units Increment Values/CLasses Background

ClimateregulatingFunction

Cadastral Data/Automatisier-teLiegen-schaftskarte (2007)

Provided by: Office ofGeoinformation, Measurementand Cadastre, City ofEssen/Amt für Geoinformation,Vermessung und Kataster derStadt Essen

Quantitative/ordinal None 1=(site suitable): 0–2=low performance

Local planning directives(climate analysis city ofEssen/Klimanalyse StadtEssen 2002)

2=(site partially suitable):0–3=medium performance3=(unsuitable): 3–4=highperformanceOwn classification accordingto original values of Singer,19950=site has no performance1=site has low performance2=site has mediumperformance3=site has high performance4=site has very highperformance

Biotope Quality Cadastral Data/Automatisier-teLiegen-schaftskarte (2007)

Provided by: Office ofGeoinformation, Measurementand Cadastre, City ofEssen/Amt fürGeoninformation, Vermessungund Kataster der Stadt Essen

Quantitative/ordinal None 1=(site suitable): 0–2=low performance

Scientific

2=(site partially suitable):0–3=medium performance3=(unsuitable): 3–4=highperformanceOwn classification accordingto Singer, 1995 (see aboveaccordingly)

Seepage Soil Map of North Rhine-Westphalia/Bodenkarte NRW1:50,000 (2006)

Geological Survey NRW (©Geowissenschaftliche Daten:Geologischer Dienst NRW,Krefeld, 136/2006)

Quantitative/ordinal cm/d 1=(sitesuitable):N100 cm/d=highperformance

Scientific, local planningdirectives (e.g. city ofEuskirchen)

2=(site partiallysuitable):40–100 cm/d=mediumperformance3=(unsuitable):b40 cm/d=lowperformanceOwn classification referring toDIN 18130

Isolation/use ofbiotopestructures

Habitat cadastreLinfos/FachinformationssystemLinfos (2007)

Provided by: State Office of theEnvironment, Landscape andConsumer Protection of NRW(LANUV)

Qualitative None 1=(site suitable): site notlocated within biotopestructures

Scientific

2=(site partially suitable):site partially located withinbiotope structures3=(unsuitable): site locatedwithin biotope structuresOwn classification


Please cite this article as: Schetke S, et al, Towards sustainable settlement growth: A new multi-criteria assessment for implementingenvironmental targets into strategic urban planning, Environ Impact Asses Rev (2011), doi:10.1016/j.eiar.2011.08.008


Table 4 (continued)

Indicator Dataset Source Character/scale Units Increment Values/CLasses Background

Sealing Rate Cadastral Data/Automatisier-teLiegen-schaftskarte (2007)

Provided by: Office ofGeoinformation, Measurementand Cadastre, City ofEssen/Amt für Geoinformation,Vermessung und Kataster derStadt Essen

Quantitative/ordinal % 1=(site suitable): sealingrate b80%

Local planning directives(climate analysis city ofEssen/Klimanalyse StadtEssen 2002)

2=(site partially suitable):sealing rate partially above 80%3=(unsuitable): sealingrate N80%Own classification referringCity of Essen2002

Protected Areas Habitat cadastreLinfos/FachinformationssystemLinfos (2007)

State Office of theEnvironment, Landscape andConsumer Protection ofNRW/Landesamt für Natur,Umwelt undVerbraucherschutz Nordrhein-Westfalen (LANUV)

Qualitative None 1=(site suitable): site notlocated within protectionareas or buffers

Legal (Federal NatureConservation Act,BNatschG)

2=(site partially suitable):site partially located withinprotection areas or 250 m-buffer or entirely within500 m-buffer3=(unsuitable): site locatedwithin protection area or250 m-bufferOwn classification referring tothe approach of Geneletti et al.,2007

Soil Quality/Yield stability

Soil Map of North Rhine-Westphalia/Bodenkarte NRW1:50,000 (2006)

Geological Survey (©Geowissenschaftliche Daten:Geologischer Dienst NRW,Krefeld, 136/2006)

Quantitative “Soilquality”(0–100)

1=(site suitable): soilquality b55

Scientific, (Press release ofthe German Farmers’Association from 16.05.09www.bauernverband.de/?redid=214093

2=(site partially suitable):soil quality 55–753=(unsuitable): soil qualityN75 Own classification referringto “BodenkundlicheKartieranleitung” (AG Boden,BGR, 1994)

Flood risk Cadastre of flooding areas(“HochwassergefährdeteBereiche in NRW” ) (LANUV2003)

State Office of theEnvironment, Landscape andConsumer Protection ofNRW/Landesamt für Natur,Umwelt undVerbraucherschutz Nordrhein-Westfalen (LANUV)

Qualitative None 1=(site suitable): locationof site outside flood areas

Legal (Flood ProtectionLaw)

2=(site partially suitable):location of site partiallywithin flood areas3=(unsuitable): location ofsite within flood areasOwn classification


performance to aggregated assessment. The initial part (step 1) is aGIS-based data analysis for each potential housing site. It is followed by theapplication of indicators and respective threshold values (step 2) to

Fig. 3. Methodology of environmental assessment showing different types of spatial overl


assess the performance of each indicator at each site. Step 3 encompassesan aggregation of all indicators, including their transformation into astandardised ranking. In step 4, the tool facilitates the multiplication of

ap of building site and protected area and resulting classified values (draft: authors).


http://www.bauernverband.de/?redid=214093

http://www.bauernverband.de/?redid=214093

image of Fig.�3



individual indicator weights in accordance with the respective end-user.In total, the assessment procedure, including the aggregation, follows thescheme of a utility analysis (step 5).

4. Results

The key results from the collaborative process, including the indicatorcompilation and the development of the tool in the FIN.30 project, arethreefold: first, the creation of a comprehensible and compact indicatorset for a land-use change impact assessment with regard to settlementgrowth; second, the integration of municipal data (of which municipalplanners were hitherto unaware) into the DSS utilising this assessment;and third, a MCA-DSS that is implemented to support decision-makingprocesses of preliminary land-use planning in cities (see expanded find-ings in Kötter et al., 2010; Schetke, 2010).

The following discussion will present the results of the assessmentof infill and greenfield sites and the elaboration process of the MCA-DSS as described in Section 3. The focus of the discussion will be on i)indicator typology and performance, ii) weighting and iii) the aggrega-tion/utility function and the indicator set (in Section 4.1). In Section 4.2,close attention will be paid to the functionality of the MCA-DSS and itsconcrete application in decision-making.

4.1. Functionality of the indicator set

Depending on the quality of the data, either quantitative indicatorvalues for each site (e.g. soil quality) or qualitative indicators that re-late the location of the sites to structural elements of the landscape(cf. isolation of habitats) were derived. Both figures also depict the ty-pology of indicator classification and standardisation according tothree class values (see also Table 4). The three classes were definedaccording to specific indicator thresholds. For qualitative indicators,the spatial localisation (entire, partially, none) and overlap of a hous-ing site within the respective area of investigation (e.g., connectedhabitats, Fig. 4) were used to determine the three classes.

For quantitative indicators, the numerical expression of the indicatorwas used to define the three standardised classes. This expression ac-counts, for example, for the valuation index of the indicator soil qual-ity/yield stability (Fig. 5), which was separated into three classes: N75(very valuable farmland=unsuitable as building land), 75–55 (farmlandof medium quality=site partially suitable as building land) and b55(farmland of low quality=suitable as building land).

Fig. 4. Examples of GIS-based application of indicators at future housing sites—the exampleData: Linfos–data base/Fachinformationssystem Linfos (State Office of the Environment, Lanof Geoinformation, Surveying and Cadastre City of Essen, (2007).


4.1.1. Indicator-typologyThe indicators are presented in two groups: Group 1 comprises dy-

namic indicators and is associated with ecosystem functions that areto be preserved during settlement development. Group 2 comprises in-dicators that represent static natural resources, such as valuable soils,that are to be preserved during settlement growth (Schetke, 2010).

Table 5 presents mean indicator values of infill and greenfield sites.For this overview, these values remain evenly weighted and only pro-vide a first impression of a preliminary evaluation of site performancesof infill and greenfield sites. This evaluation can be oriented towards theclasses defined above, i.e., 1 (suitable), 2 (partially suitable) and 3 (unsui-table). The utility function that flows from the integration of single stan-dardised indicator values and expert weights will be presented below.The last column of this table provides short explanatory comments onthe effects of strategic planning and understanding of the indicator andits application.

Group 1: According to Schetke (2010) and Kötter et al. (2010), thosefuture housing sites are to be favoured from an environmental point ofview in which no high-quality ecosystem functions are threatened byprocesses of settlement development and where a high degree ofembeddedness into “already humanely modified urban patterns andlandscape elements” already exists (Schetke, 2010: 45).

Group 2: According to Schetke (2010) (also Kötter et al., 2010),this indicator group focuses onmerely static indicators. This group as-sesses the preservation of valuable biotope structures and resourcesthat should not be used and acts as warning lights. This group indi-cates those areas in which settlement development should not takeplace due to the policy of preserving natural resources.

Table 5 provides us with initial tendencies regarding the status ofinfill and greenfield sites. But we still cannot derive an integrative state-ment on the QoP of infill and greenfield sites with these preliminary re-sults. We need robust information on the relevance of each indicatorwithin a process of land-use planning and on the degree to whicheach indicator influences the overall statement on a site's QoP. The fol-lowing discussion highlights a second result from the experts' workshopthat is essential to deriving such information: expert-based weighting.

4.1.2. Expert-based weightingOne essential result derived from the workshops attended by plan-

ners and decision-makers was the selection, as presented in Table 4,of environmental indicators according to plausibility and applicability.The relevance of each indicatorwithin processes of settlement develop-ment could be indicated by an evaluation of individual weights. These

Legend:Connected habitats

1=(site suitable): site not locatedwithin biotope structures2=(site partially suitable): sitepartially located within biotopestructures3=(unsuitable): site located withinbiotope structures

“Isolation/use of biotope structures”.dscape and Consumer Protection of NRW (2007)), Cadastral land use data (ALK) (Office


image of Fig.�4


Fig. 5. Examples of GIS-based application of indicators at future housing sites-the example “soil quality/yield stability”.Data: Soil Map 1:50,000 of NRW, Geological Survey (© Geowissenschaftliche Daten: Geologischer Dienst NRW, Krefeld, 136/2006) [http://www.gd.nrw.de]; Cadastral land use data(ALK) (Office of Geoinformation, Surveying and Cadastre City of Essen, (2007).

Table 5Even-weighted mean indicator-values of infill and greenfield sites (source: Schetke, 2010).

Group Indicator Mean valuein fill site

Mean valuegreen-field

Evaluating comments/planning recommendations

1 Climate regulativefunction

1.16 2.17 - Shows a higher ecological suitability of infill-sites

- Human modification of the regulative function is already high at infill sites- A favoured development of those sites would mean a higher probability of protection of those areas within acity where the regulative function is still high and the degree of human modification is low.

Biotope quality 1.0 1.0 - For sites of infill-development similar reasons as presented for the indicator before apply- But according to the definition of Singer (1995) also the land use class “farmland”, which applies for greenfieldsites is assigned with the value 1 as they might provide limited prerequisites as acting as a highly diversehabitat.

Seepage 2.16 2.42 - Implication of a seeping rate higher at infill-sites compared to greenfield sites and enabled high degree ofdecentralised rain water infiltration- But note: background data derive from a soil map of the scale 1:50,000 and considerable amount ofuncertainty –especially in built-up areas- needs to be taken into account

Sealing rate 1.0 1.0 - Explanation of equal values: none of the assessed sites of infill- and greenfield-development exceed the intable 3 indicated upper threshold value of 80% (value derived from a climate analysis conducted by the City ofEssen, 2002).

2 Isolation, loss ofbiotope structures

1.26 1.17 - This controversial result can be explained by the fact that selected infill-sites partially overlap with the biotopestructuresStill, taken into account that the housing sites are derived from a scale of 1:50,000 also here explains a certaindegree of uncertainty

Protected areas 1.58 2.50 - Partial overlap of infill sites and protected areas or their buffered closer surroundings (250–500 m, see tab.3)- Protection of these legally defined areas (Federal Nature Conservation Act) limits possibilities for greenfielddevelopment in Essen- Still: overlap of greenfield sites with buffered closer surroundings of protected areas strong- Disturbance of protected areas due to noise pollution in their close surroundings (eg noise of constructionmeasures) possible

Soil quality, yieldstability

2.11 2.00 - As this would controversially mean that the most valuable soils are being affected by infill urban developmentneeds to question the quality of the respective background data- The soil-data are provided in a scale of 1:50,000 and imply a certain degree of uncertainty in inner-urban areas

Flood risk 1.0 1.0 - None of the assessed sights are directly affected by flood events- The reduced severity of this topic in Essen is also highlighted by an expert-weight of 5%- But still, considering a transfer of this DSS to other cities, this indicator gains importance especially whenfacing climate change and increasingly extreme flood events.


Please cite this article as: Schetke S, et al, Towards sustainable settlement growth: A new multi-criteria assessment for implementingenvironmental targets into strategic urban planning, Environ Impact Asses Rev (2011), doi:10.1016/j.eiar.2011.08.008

image of Fig.�5

http://www.gd.nrw.de



weights represent the common understanding of the relevance of eachindicator on the part of participating stakeholders (see Fig. 6).

The indicators “climate regulation”, “seepage”, “isolation” and “pro-tected areas”were deemed relevant and together explain 75% of all dis-tributable indicator weights. The indicators “flood risk”, “sealing rate”,“biotope quality” and “soil quality” were of minor importance for thestakeholders (Schetke, 2010).

4.1.3. Aggregation and utility-functionThe integration of single indicator values and individual weights are

essential in deriving a detailed assessment and utility function for the en-vironmental prerequisites and site conditions of infill and greenfieldsites. This so-called “Quality of Place” (QoP, Schetke, 2010) determinesthe degree of sustainability and resource preservation associated withthe settlement development on those sites. To complete this integrationand to assess the final aggregated result of all indicators for one individ-ual housing site, the utility function of QoP (Figs. 3 and 7) is underpinnedby three threshold values that determine the suitability of a future hous-ing site when evaluated in light of all indicator values and their weights.Fig. 7 shows the procedure for linking single standardised indicatorvalues (classes 1, 2, 3) and associated weights.

As presented in Section 3, the highest QoP of a housing site, andtherefore the highest degree of resource preservation, is assigned athreshold value of less than 1.49. A limited QoP is located withinthe range of 1.49 and 2.49. A low QoP is indicated when its value ex-ceeds the upper threshold of 2.49 (Kötter et al., 2010; Schetke, 2010).

Regulation;1.16/ 2.17

20%

Isolation;

Indicator nameInfill/greenfield

weight (%)

1.26/ 1.1715%

Sealing Rate;1/ 110%

Prot1.

Flood Risk; &Biotope Quality

1/1each 5%

1,00

2,00

3,00

1,00

mea

n v

alu

e o

f in

dic

ato

r p

erfo

rman

ce (

gre

enfi

eld

sit

es)

mean value of indicato

Legend:

Suitability

Fig. 6. Presentation of indicator-weightSource: Schetke (2010: 90).


A contextualisation of these benchmarks with the assessed infill andgreenfield sites is provided in the next section.

4.2. The MCA-DSS

Fig. 8 displays the graphical user interface (GUI) of the MCA-DSS.The GUI enables an individual assessment of a single site. A simulta-neous comparison of several sites is not foreseen. The tool providesthree essential functions. First is the input of indicator values (column“performance”). Here, the user can select from indicator-specificthreshold classes (see Table 4) that lead to standardised indicatorvalues signalling good, medium or low ecological performance. Withsuch input, qualitative and quantitative indicators can be aggregatedand integratively assessed.

Second, an indicator weighting (column “weight [%]”) was inte-grated into the tool in order to enable the user to apply individualweights to each indicator according to its relevance to the user. Inthis application, the planners' individual preferences for specific indi-cators (in terms of what they think best represents the impact of thedevelopment, as well as possible alternative land management strat-egies) can be supplied.

Third, the MCA-DSS provides an aggregated qualitative and quan-titative output for the utility function (in this case the QoP). Here, thecolumn “assessment” delivers a verbal assessment of the utility func-tion in accordance with the three threshold values of QoP described inSection 3. The last column provides the utility function.

Seepage;2.16/ 2.42

15%

ected Areas;58/ 2.5025%

Soil Quality2.11/ 2.00

5%

3,002,00

r performance (infillsites)

Limitedsuitability

Nosuitability

s derived from expert-workshops.


image of Fig.�6


Fig. 7. Methodology of indicator-transformation and aggregation to one final result of environmental assessment.Source: Schetke (2010:59).


This derivation of a numeric value for the utility function for a sin-gle site enables planners to generate a numeric ranking as a last, op-tional, step. This step is not part of the GUI, but is described in Fig. 10below. This step supports decision-making and helps detect the envi-ronmentally best housing sites in a municipality regardless of wheth-er these are greenfield or infill sites. This step, therefore, enables a

Fig. 8. GUI of the MCA-DSS for environmental assessment displaying indicators, standardi(draft : authors).


direct site comparison. The ranking can be presented graphically(Fig. 9) or as part of a diagram (Fig. 10).

We see that relatively clear distinctions regarding the environmen-tal suitability of infill and greenfield sites can be derived. The majorityof infill sites had scores under the threshold value of 1.49, indicating un-limited suitability and a very good QoP. The majority of greenfield sites

sed performance-classes, fields for indicators-weights and one aggregated final result


image of Fig.�7

image of Fig.�8


Fig. 9. Map of ranked housing sites according to aggregated assessment.Modified according to Kötter et al. (2010: 65).


indicated only limited environmental suitability and had scores thatexceeded the threshold value of 1.49. But no environmental suitabilityexceeding the upper threshold value of 2.49 was indicated at thosesites. Here, these limiting results can be improved by a simple spatialadjustment of single sites and a limited overlap with high-quality natu-ral resources (Schetke, 2010).

Some selected sites designated as infill development exceeded thethreshold value of 1.49, which indicates unlimited suitability (of QoP).This outcomewas due to the fact that theywere classified as infill devel-opment, therefore humanely modified, but located in a predominatelyrural environment. These singular contradictory results can also beexplained by spatial insecurities due to a spatial scale of 1:50,000 forthe selected housing sites in Essen.

5. Discussion

The collaborative process of MCA-DSS development uncoveredvarious ways of implementing such a tool within the structure of


municipal land-use planning: the tool can be applied at differentstages of preliminary land-use planning. First, it can be utilised duringthe preparation phase of a land-use plan (see our example of the Cityof Essen); second, it can be utilised during an ex-ante analysis or dur-ing ex-post revisions. Third, it can be utilised to carry out a ranking offuture housing sites (Fig. 10) forecast in an existing land-use plan inorder to select sites to be developed first in accordance with thelocal sustainability agenda (Kötter et al., 2010).

Another major outcome of the collaborative process for developingthe MCA-DSS is the resulting database underpinning all environmentalindicators. During the process of defining indicators and the joint dis-cussions with local stakeholders, it became clear that decision makersare not always aware of already existing digital data located in both fed-eral and local agencies. A concise overview of existing datasets and anetwork of local and regional administrative institutions led to amuch better integration of previously disregarded environmental com-ponents into the DSS. By incorporating available data and knowledge,the MCA-DSS became a tool that reduces the demand for primary field


image of Fig.�9


Fig. 10. Diagram of housing sites according to aggregated assessment.Modified according to Schetke (2010: 88).


studies. Such studies are often unrealistic to conduct given the limitedmunicipal budgets available (e.g., theNorth Rhine-Westphalia Local Au-thorities Confederation 2009 referred to critical economic conditions inmany communes in North Rhine-Westphalia).

The majority of the input data, such as inundation data, maps ofprotected areas and soil quality maps, can be incorporated into thetool in the form of GIS vector or raster data. For other input data,such as cadastral land-use maps, further data manipulation in theform of calculating specific attributes is necessary to derive the indi-cator values for regulative functions, biotope quality and the sealingrate from cadastral land-use classes. As part of this task, much new(environmental) information could be extracted from existing dataand maps. The additional GIS calculations can also be executed bylocal stakeholders using the MCA-DSS, which is based on comprehen-sive documentation.

The MCA-DSS presented in this paper represents the result of a suc-cessful collaboration between scientists and planners who were broughttogether to assess the environmental suitability and sustainability of infilland greenfield sites for housing development. During the stakeholder-driven process (Section 3), a core set of indicators was identified as a re-sult of agreement between scientists and practitioners. This tool can as-sist in making development decisions that achieve the political targetsof both reducing land consumption and improving inner urban areasthrough ongoing, enhanced and resource-preserving settlement growth.

These are needs that must be addressed within the processes ofurban land-use planning. In the course of addressing those needs,we have developed an applicable DSS for the assessment of housingsites within a planning process. This project attempts to integrate aDSS into strategic planning. The DSS presented here is specifically tar-geted for this problem and to a specific land use-planning framework(in our case, the German framework).

Applied at the preliminary stage of land-use planning, the resultsobtained from the MCA-DSS highlight individual environmental condi-tions on each site, a result which helps planners assess the suitability ofthe specific housing sites in the context of the two overall targets of sus-tainability and reduced land/resource consumption. Moreover, this re-sult fosters the consideration of inner urban areas for new housing


consistent with the idea of a sustainable settlement growth. Thus, it en-ables planners to adjust and steer present and future land developmentof their municipality towards more sustainability.

The collaborative process that resulted in the development of theMCA-DSS was characterised by technical, conceptual and “social” re-strictions. These restrictions result from administrative and personalconstraints, as well as the prevailing mode of interaction between sci-ence and planning practice in the field of land-use planning. Never-theless, the MCA-DSS presented meets two essential requirementsof such tools as elaborated by various other authors:

i) It expands already existing DSS and planning support systems(PSS) by integrating specific aspects of sustainable environmental plan-ning, such as the avoidance of high-compensation in-advancemeasures,by considering the environmental quality of each site, its degree ofhuman modification, and habitat networks to sustain as much as possi-ble the preservation of unconnected habitats within the city (see Hahnet al., 2009; Hoeven v.d. et al., 2009 and Sudhira and Ramachandra,2009 on themanagement of urban sprawl; Pelizaro et al., 2009 for an ex-ample from Australia on green space planning, Besio and Quadrelli,2009, Italy, on landscape planning andMiller et al., 2009, UK, on resourcemanagement). (ii) In addition to context-driven factors and political tar-gets to reduce land consumption, elements such as the applicability andadaptability of the MCA-DSS within planning processes are decisive forits use. These factors can help raise awareness of the various environ-mental impacts (whether positive or negative) of expansive and infillland development. This awareness allows planners to better adjust cur-rent settlement growth to a reasonable level of land resource consump-tion (Haines-Young and Potschin, 2008; Vrščaj et al., 2008). The majorrequirements for planning-oriented indicator systems required to pro-mote sustainable settlement growth in Germany have been widely dis-cussed (see a.o. Coenen, 1999; Döring et al., 2004; Flacke, 2003;Gehrlein, 2003; Heiland et al., 2003; Korczak, 2002; Wrbka, 2003). Suchdiscussions provide a good starting point for MCA-DSS development. Inan inter-European context, Ravetz (2000) promotes “… simplifyingwhat…[can]… be simplified and quantified, and to provide a frameworkfor deliberation of more complex and qualitative arguments” bridgingthe gap between “technical debates and policy/public debates.”


image of Fig.�10



With reference to the research questions listed in Section 1, wecan answer question 1 – How can the assessment of the ecological im-pacts of land development be systematically implemented into acomprehensive MCA-DSS applicable to land-use planning? – as fol-lows: In terms of the design of the indicator set, scientists were forcedto make several concessions. Instead of a complex modelling of envi-ronmental patterns, functions and impacts of settlement growth, apragmatic and feasible indicator set was developed due to the restric-tions posed by data availability and applicability. These results are inagreement with those made in several other case studies conductedacross the world (cf. a statement by Carsjens and Ligtenberg, 2007 citingBatey and Breheny, 1978 and Wong, 1998; Geneletti, 2008; Jakemanet al., 2006) and appear to be indispensiblewhen setting up and enteringinto science-policy collaboration processes. Data availability is under-stood to be amajor boundary condition for avoiding additional investiga-tion and facilitating a stand-alone application. Planners' preferences andunderstandings of suitable indicators were the second major constraint.Such characteristics as comprehensibility and relevance for future plan-ning processes are of particular importance to planners and must to betaken into account when bridging the gap between science and planningpractice in the development of land-use change assessment tools. Testingdifferent indicator sets and choosing among different preferences pro-vide planners with the opportunity to learn about different alternatives(cf. Walker, 2002). In addition to these considerations, the complexityof indicators also had to be reduced and standardisation operationswere applied (Kötter et al., 2009b).

Based on these findings, we posed question 2: Is the proposedMCA-DSS capable of assessing infill planning strategies vs. greenfielddevelopment to reduce further land consumption, while simulta-neously avoiding the deterioration of the environmental quality of al-ready built areas? As reported by Reeve and Petch (1999), we foundthat the tool developers must move from a ‘technology push’ to a ‘de-mand pull’ orientation. In addition to purely technical restrictions (setby the presence of existing equipment in administrative planning),the thematic and issue-related requirements of the planners involvedin the collaborative process determined both the design and the func-tionality of the MCA-DSS. This situation ultimately led to a dissonancebetween the required functionality and the technical realisation.While a Visual Basic-basedMCA-DSS could fulfil the planners' demandfor an easy-to-understand and easy-to-apply programme, such a toolcannot automatically read-out GIS-based input data and calculationsthat lead to quantitative assessment results, or the visualisation ofthose results (Table 2). Another bottleneck for the current applicationwas the overall availability of GIS data (cf. Jakeman et al., 2006).

For the planners, the collaborative development process had clearbenefits. Previously, informal indicators were integrated into theMCA-DSS. These indicators were then formalised by supportingthem with public data. Moreover, rules were developed regardingthe aggregation and association of attributes with environmental, so-cial and economic data. In the City of Essen, a network of data pro-viders was devised, along with a rising awareness of the need tointegrate environmental considerations that extended beyond thosecontained in existing legal frameworks.

Additionally, a common language between planners and scientistswas established and initial reservations concerning a scientific ap-proach to land-use change assessment were partly overcome. How-ever, a more comprehensive and long-term implementation of thenew tool could not be executed during the runtime of FIN.30.

Finally, we addressed the third question. How can a MCA-DSS bedesigned to bridge the gap between science and practice? And, in addi-tion, how can this design be accomplished among practitioners? Wefind our results to be in agreement with Diez and McIntosh (2009),who stated that “a gap does exist but that the gap is partly a gap of per-ception — a gap between how we as tool developers think our toolsought to be used by others, and the ways in which they are used anddo have an impact” (page 44). Referring to the answer to question 1,


scientists had to accept a reduction in the complexity of the MCAscheme and the easy-to-use-design of the associated DSS. Both aspectsmight not represent the latest technical and scientific state-of-the-art interms of modelling and assessing settlement growth, but they are nec-essarily adopted in the planning practice. The pragmatic indicator setthat was developed entails a very limited additional workload withinthe daily business of planning institutions. Thus, the probability that itwill be used is high (BBR, 2001; Coenen, 1999).

6. Conclusions

The process of developing a MCA-DSS discussed in this paperbrought important new insights. Scientists learned that the transparencyof such a tool can be considerably enhanced by usingmunicipal data forthe set-up (see also Li et al., 2009). In doing so, the MCA-DSS benefitsfrom the provision of high-grain land-use data that are used fordecision-making in Germanmunicipalities. Municipal data have anotherclear advantage: they are “owned” by the stakeholders and, thus, in-crease the credibility of the tool and enhance the acceptance of itsresults.

The planners also achieved several insights. One insight was how toderive new indicators using existing data. Further, networks betweendifferent departments of the administration were strengthened. Theneed for qualified land monitoring to control sustainable settlementdevelopment became very clear. In accordance with the findings ofthe international EU-project Pastille (2002), (“Promoting Action forSustainability through Indicators at the Local Level in Europe”), theaspect of awareness-raising among decision-makers is achievable.In our study, indicators focusing on the quality, and not exclusivelyon the pure availability, of such natural resources as “soil or biotopequality” imply the existence a more strategic and long-term view re-garding the provision of natural resources. This development clearlysupports further enhancements of strategic planning and the mea-suring and monitoring of sustainability.

Acknowledgements

We would like to thank our colleagues from the FIN.30 (Grant ID0330733) and PLUREL (no. 036921) projects. Further, we thank NadjaKabisch and Annette Bauer for their useful comments on an earlier ver-sion of themanuscript. TheMCA-DSS presented in this studywas devel-oped as part of research project FIN.30 in the Department of UrbanPlanning and Real EstateManagement at the University of Bonn. ProjectFIN.30 was funded by the German Ministry of Education (BMBF) underits larger research initiative REFINA (“Research for reduced land con-sumption”) and was conducted between 2006 and 2009. Thanks alsoto an anonymous reviewer for valuable and helpful comments on anearlier version of the manuscript.

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