Green spaces and nature-based solutions (NBS) are increasingly considered by land-use planning policies to respond to the multiple challenges related to sustainable development. The multiple benefits brought by NBS make the use of multicriteria decision analysis (MCDA) essential to optimally balance their use. MCDA offers a catalog of methods allowing to structure problems with multiple objectives and to help adopt the optimal solution. However, NBS planning is a recent discipline and research is still ongoing to make this practice more common. We carried out a critical literature review on MCDA-NBS tools and practices, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method on the Web of Science database. We selected 124 papers on the subject between 2000 and 2022. We present a state-of-the-art MCDA approach for NBS and green space planning by looking at where these practices are applied, why and how this process is conducted, and who is involved in it. We found that studies are usually conducted in the global North on a single case study with the help of experts involved in the criteria weighting phase and the help of GIS MCDA tools often integrating a direct ranking method or the AHP method.

  • MCDA methods help to consider all NBS benefits and evaluate planning alternatives.

  • Environmental and social criteria are more represented than economic and technical ones.

  • Stakeholders are rarely involved throughout the entire MCDA process.

  • MCDA tools for NBS planning are rarely accessible and adaptable to various contexts.

  • MCDA processes are mainly conducted in countries of the Global North.

Background

Green spaces play an important role in urban climate adaptation. Nature-based solutions (NBS) are explicitly designed to optimise climate adaptation potential and are increasingly considered as an innovative and more sustainable alternative to current urban stormwater management by gray infrastructures (Hamouz et al. 2020; Steis et al. 2020). They are engineered green systems such as rain gardens, green roofs and walls, ponds, swales, constructed wetlands, and urban forests which allow stormwater control at source by enhancing functions of infiltration, evapotranspiration, retention, conveyance, and water quality enhancement (Kuller et al. 2017). Some of the primary benefits include surface water quality protection, flood reduction, and resource recovery (e.g., water reuse).

Green spaces bring many co-benefits (Dagenais et al. 2017; Skrydstrup et al. 2020) such as improving esthetics, reducing the urban heat islands effect, and increasing biodiversity. The multifunctional potential of NBS highlights the need for careful spatial planning, considering the three pillars of sustainable development: environmental, social, and economic sustainability (Dorst et al. 2019; Monteiro et al. 2020; Brasil et al. 2021; Goodspeed et al. 2022). Most studies focus on environmental aspects (e.g., biodiversity, soil recovery) and stormwater management (Meerow 2020; Monteiro et al. 2020), and only consider a single benefit such as water quantity control (Meerow & Newell 2017; Meerow 2019). Moreover, opportunistic NBS planning leads to unintended results that do not maximize the potential of the multiple benefits of NBS (Kuller et al. 2018; Li et al. 2020; Meerow 2020). Multicriteria decision analysis (MCDA) is well suited to counter this issue by evaluating multiple objectives simultaneously, involving multiple stakeholders and preferences, as well as technical information.

The United Nations conference on Sustainable Development in Rio de Janeiro (Brazil) on 20–22 June 2013 sparked global interest in NBS and led to numerous studies about strategic urban planning, attempting to frame this new practice (Meerow 2020; Hanna & Comín 2021). NBS in urban climate adaptation plans are also referred to as green infrastructure (GI) or blue-green infrastructure (BGI) planning, low impact development (LID), best management practices (BMP), sustainable urban drainage systems (SUDS), water sensitive urban drainage (WSUD), or Sponge City, depending on the study location (Fletcher et al. 2015). The term ecosystem services (ES) is also widely used in this field and refers more broadly to environmental and socio-economic benefits that any type of green space (e.g., natural forests, wetlands, grassland, or engineered systems like the ones mentioned above) can provide to the urban environment (Dagenais et al. 2017; Billaud et al. 2020). In this paper, the term NBS will be used, as it is the term used by the United Nations since the Convention on Biological Diversity COP15 in Montreal in 2022. However, we will conduct our research by considering both purposefully designed (e.g., NBS, GI, BGI) and other (covered by the concept of ES) green urban spaces to address the broad palette of these spaces.

MCDA methods and tools

MCDA is a systematic approach to incorporate multiple objectives and combine subjective preferences with objective information in order to reach a rational decision. MCDA can help decision-makers analyze a complex decision problem that involves different stakeholders. It offers a rich collection of methodologies for structuring planning problems with conflicting objectives, allowing the design, evaluation, and prioritization of decision alternatives from a multicriteria model representing stakeholders' preferences (Ferretti & Montibeller 2016; Marttunen et al. 2017). Obtaining subjective preferences on a problematic situation, including objective weightings, is one of the main parts of MCDA (Aubert et al. 2020). A participatory (Schein 2017) and constructivist (Landry 1995) approach involving stakeholders is recommended by the scientific community (Belton & Pictet 1997), because it is expected to lead to the implementation of 80% of the decisions (Nutt 1999). By ‘participatory’, we refer to a collaborative process in which relevant stakeholders are involved in all steps of decision-making from objective definition to alternative development and preference elicitation. By ‘constructivist’, we refer to a process that consists of several steps that build towards a result.

Belton & Stewart (2002) classified MCDA methods into three categories based on the type of model used (Table 1). Some methods are at the intersection of these models (e.g., the MACBETH method) (Lavoie et al. 2016).

Table 1

MCDA categories by the type of model (value measurement, aspiration, outranking) based on Belton & Stewart (2002) 

Type of modelCharacteristicsMethod examples
Value measurement models Numerical preference scores are synthesized to perform aggregation into preference models. Simple multi-attribute rating technique (SMART), swing, technique for order preferences by similarity to ideal solutions (TOPSIS), ordered weighted averaging (OWA) 
Aspiration models Criterion weights are obtained from pairwise comparisons between criteria, using an eigenvector technique. Weights are aggregated to obtain the global relative weights of the alternatives describing their global preference compared to the other alternatives. Analytic hierarchy process (AHP) 
Outranking models Preferences are obtained by asking whether the advantages of one alternative over another are sufficient to overcome its disadvantages. The degree of dominance is calculated between the alternatives, describing whether an alternative is at least as good as another. Preference Ranking Organization Method for Enrichment and Evaluation (PROMETHEE), Potentially All Pairwise Rankings of all possible alternatives (PAPRIKA), Elimination And Choice Translating Reality (ELECTRE) 
Type of modelCharacteristicsMethod examples
Value measurement models Numerical preference scores are synthesized to perform aggregation into preference models. Simple multi-attribute rating technique (SMART), swing, technique for order preferences by similarity to ideal solutions (TOPSIS), ordered weighted averaging (OWA) 
Aspiration models Criterion weights are obtained from pairwise comparisons between criteria, using an eigenvector technique. Weights are aggregated to obtain the global relative weights of the alternatives describing their global preference compared to the other alternatives. Analytic hierarchy process (AHP) 
Outranking models Preferences are obtained by asking whether the advantages of one alternative over another are sufficient to overcome its disadvantages. The degree of dominance is calculated between the alternatives, describing whether an alternative is at least as good as another. Preference Ranking Organization Method for Enrichment and Evaluation (PROMETHEE), Potentially All Pairwise Rankings of all possible alternatives (PAPRIKA), Elimination And Choice Translating Reality (ELECTRE) 

Existing literature reviews on MCDA for green space planning

The content of this section is based on 28 literature review papers we found during our research related to MCDA for NBS and green space planning (see section 2.1). We summarize the main results of this analysis here.

MCDA has been a relevant tool applied in a wide range of fields in the past years, proving its value, particularly in environmental projects where multiple stakeholders and tradeoffs are at play between the economic, environmental, and social spheres (Kiker et al. 2005).

Since 2000, five reviews focused on the application of MCDA for forest management planning (FMP) approaches, either on the integration of ES (Uhde et al. 2015; Blattert et al. 2017), of biodiversity objectives (Ezquerro et al. 2016), of multiple uses (Baskent 2018), or on forest economics of silviculture (Campos et al. 2017). Facing complex challenges, agricultural systems have also become a topic of interest for MCDA, either in agriculture models classification (Therond et al. 2017), in model-based scenarios for biodiversity changes (Chopin et al. 2019) or in sustainability assessment methods (Soulé et al. 2021). Previous reviews also focused on MCDA for ES, either on current research performed in cities (Haase et al. 2014a, 2014b), on emerging areas of interest and related key themes (Torres et al. 2021), or on a specific service like decision support tools for urban heat island mitigation (Qureshi & Rachid 2021) or flood risk management (Membele et al. 2022; Perosa et al. 2022). Tradeoffs in ES also received attention in a review by Deng et al. (2016) where analysis tools and approaches across spatial and temporal scales were studied, and in a review by Smyth & Drake (2021) where tradeoffs within freshwater and marine ecosystems were classified. Chatzinikolaou et al. (2018) proposed a review of valuation methods and tools to assess the diversity of ES values in rural landscape management through the lens of MCDA. Natural resources management has been addressed in recent reviews, for example by Cook et al. (2019) for geothermal power projects or by Allain et al. (2017) for landscape management methods covering land-use planning, ecosystem conservation, water management, and forest management. Another predominant field of application of MCDA approaches is spatial modeling in land-use planning. Yang et al. (2007) reviewed GIS-MCDA-based evaluation models for land-use evaluation. Legesse Gebre et al. (2021) studied MCDA methods for land allocation problems covering papers from agricultural, forest, ecotourism, conservation, and protected area management. Gomes et al. (2021) reviewed land-use changes and their impact on ES provisioning. Some reviews have a broader scope, for example, Galychyn et al. (2020) reviewed the scientific literature on urban metabolism considering flows of materials, energy, resources, food, and people in cities, whereas some other studies focused on a specific context review, e.g., Escobar-Camacho et al. (2021) who studied the threats of the marine and terrestrial ecosystems of the Galapagos.

On stormwater management infrastructures specifically, Islam et al. (2021) focused on the review of LID approaches and their optimization, performance, and resilience to climate change. Kuller et al. (2017) reviewed existing planning support systems (PSS) for WSUD using GIS and MCDA, providing a comprehensive view of the purposes of those tools and their relevance. More recently, Wu et al. (2020) reviewed sustainable stormwater management (SSWM) concepts in the Global North comparing eight existing decision support tools. Jelokhani-Niaraki (2021) worked on reviewing and categorizing spatial multicriteria evaluation (SME), also called GIS-based multicriteria evaluation (GME) tools and approaches, operating in a collaborative context, according to either a parallel or sequential method, including all fields not necessarily for green spaces or NBS.

Although MCDA in NBS planning is increasingly recognized, no study was found that aimed to comprehensively review MCDA for NBS planning in terms of (i) method, (ii) involvement of stakeholders, (iii) criteria, and (iv) tools used in the studies. NBS and green space planning is a spatial problem and the use of geographic informatic systems (GIS) such as ArcGIS (Esri) or QGIS can assist the decision process. By coupling MCDA and GIS, we can transform and combine geographical data and value judgements expressed by the different criteria. GIS-MCDA applications are increasingly used in NBS and green space planning studies and are thus given special attention to support decision-makers and planners in their use.

Aims and objectives

NBS and green space planning remain underemphasized in planning policies (Langemeyer et al. 2016; Hanna & Comín 2021). Decision-making processes around policies and governance for NBS and green space planning leave room for improvement (Langemeyer et al. 2016). More specifically, decision-makers have expressed a need for knowledge, methods, and tools for planning and design of NBS (Ferreira & Santos 2021; Mubeen et al. 2021; Voskamp et al. 2021). They lack appropriate guidelines (Voskamp et al. 2021), as well as training and expertise in strategic urban NBS planning, resulting in the adoption of sub-optimal approaches (Albert et al. 2021; Voskamp et al. 2021). This systematic literature review thus aims to provide a comprehensive picture of MCDA practices for NBS and green space planning. The objectives are to analyze:

  • 1.

    Where MCDA is applied, by looking at the case study location and the number of case studies conducted.

  • 2.

    Why this process is conducted, by looking at the problem definition, the criteria selected, and the results obtained.

  • 3.

    Who is involved in the process, by looking at the stakeholder type and engagement.

  • 4.

    How this process is conducted, by looking at the MCDA methods and tools.

This work aims to provide knowledge on MCDA practices for NBS and green space planning and to give decision-makers tools and recommendations for their applications. The review will also highlight gaps and limitations in MCDA practices and will provide leads for future research.

First, the research approach is presented, followed by a presentation of the results, a discussion regarding the study's objectives and a section with recommendations for future work. In this paper, an NBS-MCDA ‘tool’ refers to any software, model, module, application, or method providing assistance with MCDA-based planning of NBS or green spaces.

Literature selection

We conducted this literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method developed by Moher et al. (2009). This method consists of four main steps (Figure 1): (i) defining and specifying the search key words and the parameters of the analysis, (ii) reading the abstract to select articles to be considered for the analysis and using inclusion and exclusion criteria, (iii) reading articles to refine the selection and extracting relevant information using predefined parameters, and (iv) synthesizing results for analysis. Using Web of Science, we conducted our literature search on 1 September 2022 and searched for papers published between 2000 and 2022.
Figure 1

Literature review method flow diagram, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis; Moher et al. (2009)).

Figure 1

Literature review method flow diagram, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis; Moher et al. (2009)).

Close modal

We considered any type of urban green space and infrastructure, whether intentionally created to provide ES (e.g., LID, WSUD, NBS) or not (e.g., parks, forests, natural wetlands), using MCDA as the strategic planning tool. In order to cover this broad palette of green urban spaces, we included terminologies related to constructed green spaces with the purpose of climate adaptation (i.e., NBS, GI, WSUD, SUDS, LID, BGI) and other green spaces (covered by the search term ES). As supplementary material, we provided the research iterations, the final research formula, the 474 returned papers, the exclusion criteria for abstract screening and the exclusion rules for full-text screening. We analyzed literature reviews (28) separately (see section 1.3). The final number of articles included in this review is 124.

We performed an analysis of the 124 papers using a spreadsheet, following the framework on ecosystem service assessments and land-use planning developed by Langemeyer et al. (2016). We adopted this framework because it was specifically developed for MCDA approaches in the green space planning process instead of the framework of Belton & Stewart (2002) which focuses on the general MCDA process. The adopted framework specifies six key elements (problem definition, stakeholder analysis and engagement, alternative definition, criteria definition, criteria weighting, and alternative prioritization), each explained below, in section 2.2. We also collected statistics on the year of publication, and the geographical location of the authors and the case study.

Analysis

The framework by Langemeyer et al. (2016) helped to select the relevant data for the analysis and to classify them according to the six key elements (problem definition, stakeholder analysis and engagement, alternatives definition, criteria definition, criteria weighting, and alternative prioritization). We have slightly modified this guide by further developing the ‘stakeholder analysis and engagement’ element, combining the ‘criteria weighting’ and ‘alternative prioritization’ elements, and adding a ‘results’ analysis element. For a more in-depth analysis, we used the work of Sarabi et al. (2019) and Skrydstrup et al. (2020), which present an analysis of relevant stakeholders to consider for green space planning. Skrydstrup et al. (2020) also present an analysis of relevant criteria to consider for NBS and green space planning.

The first element, the problem definition, describes the scope (assessment, investment, selection, prioritization, etc.) and the scale. We classified scale into national (e.g., country), region, basin, local (e.g., city, municipality, metropolitan area) and site (e.g., lot).

The second element, stakeholder analysis and engagement, refers to the type of participation that stakeholders make during the process (workshop, interview, survey, etc.). In this element, we also specified how criteria were selected: (a) defined by the research team, (b) elicited by expert(s) or (c) elicited by stakeholders. We organized the processes of stakeholder engagement based on the moments of involvement: problem definition, alternative definition, criteria definition, criteria weighting, and alternative prioritization, based on the elements provided in Langemeyer et al. (2016).

The third element, alternatives definition, specifies whether a paper describes (a) an evaluation of alternative policies, infrastructures, or management practices or, (b) a selection of geographical sites (i.e., GIS application).

The fourth element, criteria definition, provides an analysis of the selected criteria in the studies. We used the framework presented by Skrydstrup et al. (2020; Figure 5), which classifies criteria following the United Nations' sustainability aspects (environmental, economic, social, and technical) similar to most papers evaluated in our literature review. While criteria classification is often based on the type of ES they provide (regulating, provisioning, cultural services), we opted to go for the above-mentioned framework for its understandability for lay people and the application to the reviewed literature. Besides the class of criteria, we also assessed the number of criteria considered in the studies.

The fifth element, criteria weighting, refers to the MCDA process, and includes both the aggregation rules used to calculate the performance of the alternatives to reach the objectives and the MCDA methods applied for preference elicitation, i.e., regarding the relative importance of the objectives. Those aggregation rules either come directly from the MCDA method (e.g., rank and prioritize one alternative with a pairwise comparison using the AHP method) or using aggregation methods, especially in the case of GIS-MCDA. Langemeyer et al. (2016) identified two different types of aggregation that are the most used in studies: linear or non-linear aggregation (i.e., the sum of all normalized values) and the ideal point approaches (i.e., the sum of normalized differences between the actual and an ideal performance on the criterion) (Langemeyer et al. 2016). Regarding MCDA methods, we used the three categories and the accompanying methods by Belton & Stewart (2002), provided in Section 1.2. We furthermore look at the method used to create value functions, used to compare criteria on a common scale.

We have added a sixth element that analyses the type of results obtained from the studies (e.g., scores, maps).

We recorded the year of publication and compared the geographical location of the authors and the geographical location of the case study. We did not only consider the geographical location of the first author but all geographical locations represented by the authors, as there was a notable diversity in their location. We counted a location only once when an article was authored by several researchers from that location. We considered decision-aid tools, selecting MCDA tools specifically developed to assist the application of MCDA methods and other tools which integrate MCDA to generate alternatives (e.g., GIS tool with MCDA plug-in).

Date

While our search window spanned from 2000 to 2022, we found no papers dating before 2010. The number of publications increased recently, with 80% of papers published between 2016 and 2022. It shows that MCDA for NBS planning is a recent topic of interest to the scientific community (Figure 2).
Figure 2

Number of papers by year of publication.

Figure 2

Number of papers by year of publication.

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Location

We evaluated the location of the authors and case studies separately, as we found no clear relation between them. For example, almost half (48%) of the studies are conducted by authors in Europe but only 30% of the case studies are in Europe (Figure 3). Moreover, most papers (84%) are based on a single case study, with only 16 papers considering multiple case studies.
Figure 3

Statistics regarding all authors' locations and case study location in the reviewed papers (% of papers).

Figure 3

Statistics regarding all authors' locations and case study location in the reviewed papers (% of papers).

Close modal

The research is mainly conducted in Europe, followed by North America, Oceania, Asia, South America, and Africa. Most case studies were conducted in Europe, followed by North America, South America, Asia, Africa, and Oceania. Regarding countries, the USA itself counts 49 case studies representing 25%, followed by Italy (9%), Spain (8%), and China (6%).

Process

Statistics on the reviewed papers with respect to the six key elements of the Langemeyer et al. (2016) framework (section 2.2) are summarized in Tables 2 and 3.

Table 2

Statistics (number and % of reviewed papers) for the first, second, and third key elements of the Langemeyer et al. (2016) framework (problem definition, stakeholder analysis and engagement, alternative definition)

(i) Problem definition
ScopeNumber%
ES 64 52 
GI 19 15 
LID 14 11 
NBS 
SUDS 
WSUD 
Other 10 
ScaleNumber%
Global 
Region 22 18 
Basin 22 18 
Local 50 40 
Site 17 14 
(ii) Stakeholder analysis and engagement
Type
Research team 99 80 
Expert(s) 12 10 
Stakeholders (group) 22 18 
No information 
Involvement phaseNumber%
Problem definition 15 12 
Alternative definition 23 19 
Criteria definition 25 20 
Criteria weighting 85 69 
No involvement 36 29 
Involvement typeNumber%
Survey 26 21 
Interview 19 15 
Workshop 40 32 
Individual exercise 16 13 
None/no information 23 19 
(iii) Alternative definition
Alternative typeNumber%
Selection of suitable geographical sites 63 51 
Evaluation of alternative policies, plans or management practices 69 49 
(i) Problem definition
ScopeNumber%
ES 64 52 
GI 19 15 
LID 14 11 
NBS 
SUDS 
WSUD 
Other 10 
ScaleNumber%
Global 
Region 22 18 
Basin 22 18 
Local 50 40 
Site 17 14 
(ii) Stakeholder analysis and engagement
Type
Research team 99 80 
Expert(s) 12 10 
Stakeholders (group) 22 18 
No information 
Involvement phaseNumber%
Problem definition 15 12 
Alternative definition 23 19 
Criteria definition 25 20 
Criteria weighting 85 69 
No involvement 36 29 
Involvement typeNumber%
Survey 26 21 
Interview 19 15 
Workshop 40 32 
Individual exercise 16 13 
None/no information 23 19 
(iii) Alternative definition
Alternative typeNumber%
Selection of suitable geographical sites 63 51 
Evaluation of alternative policies, plans or management practices 69 49 

Note: ES, ecosystem services; GI: green infrastructures; LID, low impact development; NBS, nature-based solutions; SUDS, sustainable urban drainage systems; WSUD, water sensitive urban design.

Table 3

Statistics (number and % of reviewed papers) for the fourth, fifth, and sixth key elements of the Langemeyer et al. (2016) framework (criteria definition, criteria weighting, results)

(iv) Criteria definition
Number of criteria
Number%
x ≤ 10 74 60 
10 < x ≤ 20 29 29 
20 < x ≤ 30 10 
Above 30 
No information 
(iv) Criteria definition
Number of criteria
Number%
x ≤ 10 74 60 
10 < x ≤ 20 29 29 
20 < x ≤ 30 10 
Above 30 
No information 
Criteria typeNumber%
SOC. Esthetics 35 28 
Recreation 42 33 
Mobility 13 10 
Health 60 48 
Safety and security 58 46 
Connectedness 22 18 
Education 22 18 
Occupation 32 26 
ENV. Water quality 49 39 
Resources 62 26 
Nature 80 64 
ECO. Business development 31 25 
Low cost 42 34 
TEC. Integration with existing infrastructures 22 18 
Flexibility 11 
Simple & transparent 16 13 
Supply safety 34 27 
Criteria typeNumber%
SOC. Esthetics 35 28 
Recreation 42 33 
Mobility 13 10 
Health 60 48 
Safety and security 58 46 
Connectedness 22 18 
Education 22 18 
Occupation 32 26 
ENV. Water quality 49 39 
Resources 62 26 
Nature 80 64 
ECO. Business development 31 25 
Low cost 42 34 
TEC. Integration with existing infrastructures 22 18 
Flexibility 11 
Simple & transparent 16 13 
Supply safety 34 27 
(v) Criteria weighting
Aggregation method
Number%
Linear aggregation 64 52 
AHP 44 35 
PWC 51 41 
Ideal point 
(v) Criteria weighting
Aggregation method
Number%
Linear aggregation 64 52 
AHP 44 35 
PWC 51 41 
Ideal point 
MCDA method
Number%
First Direct ranking 57 46 
TOPSIS 11 
MAVT 
SMART 
SWING 
Second AHP 44 35 
Third PROMETHEE 
ELECTRE 
Other NAIADE 
DELPHI 
VIKOR 
MACBETH 
MCDA method
Number%
First Direct ranking 57 46 
TOPSIS 11 
MAVT 
SMART 
SWING 
Second AHP 44 35 
Third PROMETHEE 
ELECTRE 
Other NAIADE 
DELPHI 
VIKOR 
MACBETH 
Value function scale
Number%
0 < x ≤ 1 41 33 
1 < x ≤ 9 11 
1 < x ≤ 5 10 
1 < x ≤ 100 
0 < x ≤ 5 
0 < x ≤ 10 
0 < x ≤ 1,000 
No information 45 36 
Value function scale
Number%
0 < x ≤ 1 41 33 
1 < x ≤ 9 11 
1 < x ≤ 5 10 
1 < x ≤ 100 
0 < x ≤ 5 
0 < x ≤ 10 
0 < x ≤ 1,000 
No information 45 36 
(vi) Results
Output
Number%
Numerical score 124 100 
Maps 73 59 
Graphs & figures 66 53 
(vi) Results
Output
Number%
Numerical score 124 100 
Maps 73 59 
Graphs & figures 66 53 
Result
Number%
Ranking of alternative 81 65 
Master plan 29 23 
Equitable alternatives 16 13 
Result
Number%
Ranking of alternative 81 65 
Master plan 29 23 
Equitable alternatives 16 13 

Note: AHP, analytic hierarchy process; PWC, pairwise comparison; TOPSIS, Technique for Order Preferences by Similarity to Ideal Solutions; MAVT, Multi-Attribute Value Theory; PROMETHEE, Preference Ranking Organization Method for Enrichment and Evaluation; ELECTRE, Elimination And Choice Translating Reality; NAIADE, Novel Approach to Imprecise Assessment and Decision Environments; MACBETH, Measuring Attractiveness through a Categorical-Based Evaluation TecHnique.

We found that 52% of the papers use the term ES, ecosystem services. The terms GI, LID, NBS, SUDS, and SUDS are less present and articles do not usually specify the technologies considered (e.g., green roof, rain garden). There are an equal number of papers evaluating alternative policies, plans, or management practices for NBS and green space implementation (49%) on the one hand and selecting geographical sites suitable for NBS and green space implementation on the other (51%).

The MCDA process is often performed by the research team (80%) and rarely involves stakeholders (18%) who are mainly solicited during the weighting phase. Moreover, when a group of stakeholders takes part in the MCDA process, their expertise is rarely specified. The research team involved was often mentioned as expert stakeholders, but other potential stakeholders (Skrydstrup et al. 2020, Figure 4) are usually not mentioned or described in sufficient detail.

Regarding the criteria elicitation process, 60% of the studies include a maximum of 10 criteria and rarely more than 20 criteria (83%). The criteria considered most often cover social aspects (90%) and environmental aspects (84%). During the weighting phase of the MCDA process, linear aggregation rules such as the simple additive weighting (SAW) method are used in half of the studies. We also found that 35% of the studies follow the AHP method. The first MCDA method category by Belton & Stewart (2002) is predominant and concerns 65% of the studies, with almost half of the studies not relying on a specific method and using a direct ranking process. Notably, some case studies used more than one method. When applying MCDA methods, an important decision concerns the value function, i.e., the conversion of the criteria's attribute data scales into a common and numerical scale. We found various types of value functions being used, and most frequently a scale between 0 and 1, which appears in 33% of the reviewed papers. Finally, for the prioritization of alternatives, linear aggregation is used in 56% of the case studies. However, this information is not often given.

Tools

GIS tools are used in 56% of the case studies, but references on the tools are usually lacking or the tools are not available in open source (13% not available). Tools are generally developed for specific cases, using a specific MCDA method and the model based on the selected MCDA method.

MCDA tools developed to facilitate MCDA method application are only mentioned in 13% of the studies. 26% of the studies do not use any tool or provide no mention of a tool (Table 4).

Table 4

Statistics on tools used for MCDA application

Tools
Number%
MCDA Logical/Super decision 
PROMETHEE II 
NAIADE 
HUGIN 
D-sigh 
PEST 
Vector MCDA 
Optamos 
DPSIR 
Spatial GIS-based 53 43 
LIAM/LISAM/SUSAM (GIS plug-in) 
GIPS (GIS plug-in) 
ILWIS (GIS plug-in) 
IDRISI (GIS plug-in) 
GISM (GIS plug-in) 
SSANTO (GIS plug-in) 
ARIES (GIS plug-in) 
UrbanBEATS 
Other InVest 
No tool/No information 32 26 
Tools
Number%
MCDA Logical/Super decision 
PROMETHEE II 
NAIADE 
HUGIN 
D-sigh 
PEST 
Vector MCDA 
Optamos 
DPSIR 
Spatial GIS-based 53 43 
LIAM/LISAM/SUSAM (GIS plug-in) 
GIPS (GIS plug-in) 
ILWIS (GIS plug-in) 
IDRISI (GIS plug-in) 
GISM (GIS plug-in) 
SSANTO (GIS plug-in) 
ARIES (GIS plug-in) 
UrbanBEATS 
Other InVest 
No tool/No information 32 26 

Case study objectives

MCDA methods are often used for landscape management integrating environmental, economic, and social issues (Allain et al. 2017). The MCDA process for NBS and green space planning is applied to rank alternative policies, plans, or management practices for NBS and green space implementation or to select geographical sites suitable for NBS and green space implementation. It aims to combine objectives that are measured using different types of information, both qualitative as well as quantitative data. This facilitates the use of social criteria (90% of papers) which are often expressed qualitatively (e.g., esthetics). Indeed, the literature review of Haase et al. (2014a, 2014b) on ES assessment found that studies often focused on biophysical aspects and undervalued social aspects because they are subjective and difficult to quantify. This trend is also reflected in tools for NBS and green space planning which often integrate biophysical factors only (Kuller et al. 2017). However, technical and economic data are less present in the studies, possibly reflecting a lack of knowledge of the design and the cost of NBS. Indeed, research on NBS is recent (no paper found before 2010) but other studies may have been carried out under a different name, without appearing in our research.

Most studies (60%) are limited to 10 criteria which is consistent with the recommendations of the field of multicriteria decision science (Liquete et al. 2016) as too many criteria would reduce their individual impact on the multicriteria model and lead to less obvious results.

Case study and location

Almost all papers included in this literature review (120) are built around case studies. However, the adaptation of the MCDA methods to a different context (cultural, geographical, climate, politics) has not been really explored yet as most of the papers (104) only evaluate them in single case studies which means that the research remains context-specific and not global.

The studies are mainly conducted in the Global North with 57% of them made in Europe and North America. The other continents are underrepresented, which may reflect a lack of resources and capacity available for research and implementation in Africa and South America. Indeed, Kuller et al. (2022) found that studies for NBS implementation in the Global South are hampered by the lack of relevant institutional capacity and stakeholder involvement in planning processes, available data, and government policies. These results can be biased by the fact that NBS and associated terms are European and North American and the research focused on articles in English only. Furthermore, there could be a general lack of knowledge and research about NBS and the potential of MCDA to support their strategic planning. Nevertheless, NBS and green spaces in general are sustainable alternatives to traditional planning that have the potential to mitigate the impacts of climate change and environmental degradation due to urbanization and are worth to be studied globally.

Moreover, the author and study case locations are not always linked, which leads to situations where studies from the Global North are sometimes conducted in the Global South (Africa, South America, and parts of Asia), providing a possibly incomplete perspective. Indeed, local actors have a better knowledge of the issues, policies, and culture of their territory, which probably leads to more appropriate results. Working with local partners when a research group is based on a case study abroad could lead to a better acceptance and application of the results. There may also be more interest in using NBS and MCDA than can be reported from the peer-reviewed literature consulted in this review. Indeed, much of the work may remain hidden in design reports and technical documentation.

Stakeholders

MCDA is intended to be a participative process. However, 29% of the reviewed studies did not integrate stakeholders at all. This is still more common than in the literature review of Chatzinikolaou et al. (2018) which concluded that 60% of the studies for ES assessment do not involve stakeholders. Moreover, stakeholders are often only solicited during the criteria weighting phase (69% of papers) and very little during the other stages which reflects a lack of knowledge and experience in conducting an MCDA process. Indeed, Jelokhani-Niaraki (2021) found that the participatory steps are often limited to the determination of weights in 46% of the case studies. It would be relevant to integrate an expert in MCDA in order to be able to lead the MCDA process and guide the stakeholders through each step.

The value of the MCDA participatory process is that it brings together stakeholders with different fields of expertise. In the papers, this aspect is never developed, and the stakeholders presented usually have an academic background, posing fundamental problems regarding representativeness. Most studies involve one to five experts who carry out the criteria weighting exercises and sometimes help in the choice of criteria. Allain et al. (2017) also found in their literature review that the process of stakeholder selection is not often formally addressed. Furthermore, 80% of the studies use the expertise of the research team to carry out the MCDA process partially or fully. It is important to bring together stakeholders with different expertise in order to bring knowledge on all aspects of sustainable development through the implementation of NBS and obtain a relevant multicriteria model. This is also reflected in the way stakeholder opinion is collected with only 24% of the studies organizing workshop sessions, 44% of the studies not even describing the process and the remaining studies using surveys, interviews, or individual exercises. This contradicts the intention for MCDA to be deliberate processes that lead to collective as opposed to individual decision-making. Allain et al. (2017) showed that workshops are the best way to interact with stakeholders when doing a participative and collaborative study. However, the literature review is essentially based on scientific publications, certainly from the academic field, and remains limited to that.

MCDA methods and tools

A direct ranking method, following an SAW aggregation rule, is used in 46% of the papers which is confirmed by the research of Allain et al. (2017). The analytic hierarchy process (AHP) combined with pairwise comparison is the second most used MCDA method (35% of the papers) even though it is highly criticized by the MCDA community for its important bias risks (Belton & Pictet 1997). Unlike the MACBETH method or the PROMETHEE method, the AHP method does not come with a tool to facilitate its application and the consistency of judgements. In addition, this method offers little transparency in the justification of the final results, which may confuse stakeholders. Other advanced methods (third category of Belton & Stewart 2002) are rarely used, which may reflect a lack of knowledge and expertise in the application and selection of MCDA methods. As mentioned in section 4.3, it would be relevant to include an expert in MCDA in the research team for the choice of the method and its application.

MCDA tools are not (yet) commonly used for NBS and green space planning (13% of the papers reviewed) and GIS-MCDA tools are common (43% of the papers). This is consistent with Ezquerro et al. (2016) who found that 50% of the studies explicitly include GIS data or software. The main issue is that there is not one particular GIS-MCDA tool that gets preference in the field which leads to the development of tools that are built for a particular study context and are not developed in view of its transfer to another context. Moreover, we found that 56% of papers rely on linear aggregation which is the most often used decision rule for GIS-MCDA tools (Jelokhani-Niaraki 2021) for its simplicity in collaborative spatial decision-making. The MCDA for NBS and green space planning research is still in its infancy with a predominance of water management and spatial tools which is in line with the literature review of Lerer et al. (2015) and does not represent all the social, and environmental, technical, and economic aspects of a situation.

Recommendations and future work

It would be interesting to study in more detail the impact of the case study context on the MCDA process for the implementation of NBS or green spaces by applying it to several case studies, in different geographical locations and exposed to different issues. There is a need to develop more studies outside of Europe and North-America to gain insight into the context of the Global South and the good practices to adopt in that context.

The technical knowledge of NBS and the return on investment of these new infrastructures seems to be missing in the literature. More research on the subject could help in the development of new indicators or understand why they are underrepresented in studies.

In order to better represent the different visions of the spatial planning issue for NBS and green spaces, it would be relevant to conduct case studies involving several types of stakeholders in the MCDA process through participatory workshops (Belton & Pictet 1997; Nutt 1999; Skrydstrup et al. 2020). Indeed, none of the studies mention the presence of municipal or citizen representatives nor do they include private sector professionals. Furthermore, for a good application of the MCDA process, it is recommended to involve stakeholders at each step, leading to better ownership of the results and transferability of the method in the future (Nutt 1999).

Advanced methods (i.e., third category and others as MACBETH) have been rarely explored in the studies, although they have shown good results in other spatial planning studies (Lavoie et al. 2016). The impact of the MCDA method itself (SMART, AHP, MACBETH, and PROMETHEE) on the results of the same case study for NBS and green space implementation has not been studied in current literature. This could provide new knowledge on the strengths and limitations of each method and allow a more informed choice of the MCDA method for practitioners. This research could help determine the best method to use in NBS or green space planning.

Finally, various GIS-MCDA tools have been developed for a specific context but their adaptation to other contexts has rarely been explored. Rather than creating new tools, resources may be better spent on adapting and improving existing and available tools. Moreover, no tools for NBS or green space implementation exist that integrate different MCDA methods and could help evaluate the impact of the chosen method on the results obtained. This research could also help determine the best method to use in NBS or green space planning and would simplify the development and improvement of existing tools.

Another point researchers and practitioners need to be aware of concerns the use of the term ecosystem services (ES). It is ambivalent as it designates services (i.e., benefits, criteria) and a green space (which can also be a technology, designated as NBS).

This literature review includes 124 papers published between 2000 and 2022 related to the use of the MCDA process for NBS and green space planning. Those studies are usually conducted in Europe and North America on a single case study and a specific context. Stakeholders are not systematically integrated into the MCDA process and when they are, it is usually a few experts from academia who are called upon for the criteria weighting phase and are not involved in the whole process, as recommended. The criteria considered for the evaluation of alternatives are environmental or social, but only a few are technical or economic. One of the most used MCDA methods in the studies is the AHP method despite its high risk of bias. Generally, studies apply a direct ranking method following SAW rules. Mapping results are produced using GIS tools that integrate the algorithms of the relevant MCDA method.

Research opportunities arise for testing NBS and green space planning approaches and advanced MCDA methods to various contexts, integrating a group of stakeholders with profiles covering all the relevant fields for NBS and green space planning, and developing existing tools for better flexibility and adaptation to a wide variety of contexts.

Morgane Bousquet obtained an ESSOR scholarship from the Marthe and Robert Ménard fund, and the multi-university project obtained a starting grant from the FRQNT CentrEau research cluster and a project grant from Université Laval's EDS Institute.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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