sustainability Systematic Review
Multiple Criteria Decision Making for the Achievement of the
UN Sustainable Development Goals: A Systematic Literature
Review and a Research Agenda
Manuel Sousa *, Maria Fatima Almeida * and Rodrigo Calili *





Citation:Sousa, M.; Almeida, M.F.;
Calili, R. Multiple Criteria Decision
Making for the Achievement of the
UN Sustainable Development Goals:
A Systematic Literature Review and a
Research Agenda.Sustainability2021,
13, 4129.
su13084129
Academic Editor: Blanca
P²rez Gladish
Received: 25 February 2021
Accepted: 26 March 2021
Published: 7 April 2021
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Technical Scientic Center, Pontical Catholic University of Rio de Janeiro, Rio de Janeiro 22453-900, Brazil
*
Correspondence: [email protected] (M.S.); [email protected] (M.F.A.); [email protected] (R.C.)
Abstract:Multiple-criteria decision making (MCDM) methods have been widely employed in various
elds and disciplines, including decision problems regarding Sustainable Development (SD) issues.
The main objective of this paper is to present a systematic literature review (SLR) on MCDM methods
supporting decisions focusing on the achievement of UN Sustainable Development Goals (SDGs) and
the implementation of the 2030 Agenda for Sustainable Development in regional, national, or local
contexts. In this regard, 143 published scientic articles from 2016 to 2020 were retrieved from the
Scopus database, selected and reviewed. They were categorized according to the decision problem
associated with SDGs issues, the MCDM methodological approach, including the use (or not) of fuzzy
set theory, sensitivity analysis, and multistakeholder approaches, the context of MCDM applications,
and the MCDM classication (if utility-based, compromise, multi-objective, outranking, or other
MCDM methods). The widespread adoption of MCDM methods in complex contexts conrms that
they can help decision-makers solve multidimensional problems associated with key issues within
the 2030 Agenda framework. Besides, the state-of-art review provides an improved understanding
of this research eld and directions for building a research agenda for those interested in advancing
the research on MCDM applications in issues associated with the 2030 Agenda framework.
Keywords:
multicriteria decision-making methods; MCDM; UN sustainable development goals;
2030 agenda; systematic literature review
1. Introduction
The 2030 Agenda comprises 17 Sustainable Development Goals (SDGs) and169 global
targets, all oriented to a systemic vision for a better and sustainable world. In September
2015, the SDGs were established by Heads of State and Government and High Representa-
tives of 193 countries on a participatory basis [1].
The SDGs are a set of 17 interlinked goals designed to be a “blueprint to achieve a better
and more sustainable future for all” [2]. They are: (i) “No Poverty”, (ii) “Zero Hunger”,
(iii) “Good Health and Well-being”, (iv) “Quality Education”, (v) “Gender Equality”, (vi)
“Clean Water and Sanitation”, (vii) “Affordable and Clean Energy”, (viii) “Decent Work
and Economic Growth”, (ix) “Industry, Innovation and Infrastructure”, (x) “Reduced
Inequalities”, (xi)”Sustainable Cities and Communities”, (xii) “Responsible Consumption
and Production”, (xiii) “Climate Action”, (xiv)”Life Below Water”, (xv) “Life On Land”,
(xvi) “Peace, Justice, and Strong Institutions”, and (xvii) “Partnerships for the Goals”.
Following the stages of a generic policy-planning towards global targets achievement
by 2030, the implementation of the 2030 Agenda framework started in 2016. From that
time, an increasing number of guidelines, frameworks, methodological assessments, and
academic studies on this subject have been published [3–21]. Nevertheless, all SDGs'
achievements require decision-making processes, usually in complex contexts, considering
multiple criteria, synergies, and trade-offs between objectives. Accordingly, the request for
Sustainability2021,13, 4129.

Sustainability2021,13, 4129 2 of 37
methods to assess future risks and support decision-making for sustainability has increased
time after time.
In the last decade, multiple criteria decision-making (MCDM) methods have been
widely considered by researchers, scientists, and practitioners. MCDM is a branch of opera-
tional research dealing with nding optimal results in complex scenarios, including various
indicators, conicting objectives, and criteria. Due to the exibility for decision-makers
to take decisions while considering all the criteria and objectives simultaneously, MCDM
methods have signicant applications in several research elds, including management,
engineering, science, and business.
As a result of growing interest by academicians and practitioners in this subject, an
increasing number of scientic and technical documents have been published from 2010
to 2020. By 2009, 4606 scientic documents on MCDM methods had been published and
indexed in the Scopus database, while in the last two decades, the number of articles grew
to 19,671 documents.
Covering the time frame 2010 to 2020 and focusing more specically on previous stud-
ies that employed the systematic literature review (SLR) approach to give a comprehensive
overview of what has been done in the MCDM research eld, a literature search was
conducted accessing documents from the Scopus database. This search yielded70 reviews,
but only a few are concerned with MCDM applications for sustainable development
issues [22–28].
Kumar et al. (2017) [22] have reviewed MCDM techniques for renewable energy
development. They developed an insight into various MCDM techniques, discussed
progress made by considering renewable energy applications over MCDM methods and
prospects in this area.
To provide a systematic literature review on the application and use of decision-
making approaches regarding energy management problems, Mardani et al. (2016) [23]
selected and reviewed 196 published papers from 1995 to 2015, chosen from the “Web of
Science” database. They concluded that hybrid MCDM and fuzzy MCDM approaches
(27.92%) had been used more than other approaches. Besides, AHP (Analytic Hierarchy
Process) and fuzzy AHP approaches (24.87%) had the second rank. ELECTRE (Elimination
et Choix Traduisant la Realit²), fuzzy ELECTRE, and multicriteria analysis approaches
with 25 papers had the third and fourth rank (12.69%). Moreover, TOPSIS (Technique for
Order Preference by Similarity to Ideal Solution), fuzzy TOPSIS, PROMETHEE (Preference
Ranking Organization Method for Enrichment of Evaluations), and fuzzy PROMETHEE
held fth and sixth rank with ten papers (5.08%).
Based on a review of 163 articles, Rigo et al. [24] identied the most common MCDM
methods adopted in the renewable energy area and the decision problems they helped to
solve. The authors identied ve decision problems in this area: source selection, location,
sustainability, project performance, and technological performance. They also associated
the MCDM methods in each article with ve evaluation steps of the MCDM process,
i.e., alternative selection, criteria selection, criteria weighting, alternative evaluation, and
post-assessment analysis.
Bhardwaj et al. [25] investigated how MCDM approaches have been employed in
energy policy decisions for considering multiple social and environmental objectives. They
review 167 articles and concluded that MCDM methods could help the implementation
challenges of the SDGs and the Paris Agreement, which create incentives for energy
decision-makers to consider development and climate issues simultaneously.
Malek and Desai (2020) [26] investigated how sustainable manufacturing research has
grown in the last few years by conducting a comprehensive descriptive study through a
systematic literature review of 541 selected articles (from January 2001 to March 2019). Out
of these articles, only 122 (22.55%) studies are reported with the application of MCDM
methods which shows the limited interest of researchers in ranking and prioritizing the
signicant factors of sustainable manufacturing. The content analysis identied that
AHP/fuzzy AHP is the most utilized MCDM method with 30 manuscripts, followed by

Sustainability2021,13, 4129 3 of 37
TOPSIS/fuzzy TOPSIS with 19 and DEMATEL (Decision Making Trial and Evaluation
Laboratory) with 16 publications.
Santos et al. (2019) [27] conducted a systematic literature review on the AHP method
supporting decision-making for sustainable development. In this regard, they analyzed and
reviewed 173 manuscripts published between 2014 and 2018, which were indexed by the
Web of Science, Scopus, and Science Direct databases. Their ndings objectively mapped
the advancements in the state-of-the-art of the AHP method's contributions for sustainable
development issues. Implications for research and practice, as well as promising challenges
for further research, were presented.
Kandakoglu et al. (2019) [28] presented a systematic review of the literature on MCDM
methods, covering 343 articles dealing with decision-making in sustainable development
contexts, published in the period from 2010 to 2017. The selected articles were reviewed and
categorized by MCDM approach, as follows: preference modeling, uncertainty approaches,
sensitivity analysis, long-term assessment, and stakeholder involvement. The results
showed that AHP/ANP (AHP/Analytic Network Process) were the most used among
the MCDM methods, followed by TOPSIS-VIKOR (TOPSIS-VIekriterijumsko KOmpro-
misno Rangiranje), ELECTRE, PROMETHEE, and MAUT (Multi-Attribute Utility Theory).
For future studies, the authors suggested that decision-making processes should closely
investigate social well-being and encourage the participation of stakeholders.
Although MCDM contributions to SD issues have been highlighted in these previous
reviews, to the best of our knowledge, no systematic review has been performed that
focused on the achievement of SDGs within the 2030 Agenda framework. So, a systematic
literature review on the MCDM applications addressed to SDGs' achievements in regional,
national, or local contexts emerged as a research gap to be investigated and expanding
previous reviews' scope. So, the research questions addressed in this paper are:

Considering the context of the decision-making process within the Agenda 2030
framework, what types of results from the MCDM applications are expected to help
decision-makers solve multidimensional problems associated with SDGs?

What are the main MCDM methodological approaches adopted in studies focusing
on decision issues within the 2030 Agenda framework?

For which SDGs, is there a higher incidence of MCDM applications? In which contexts
have these applications been developed? Local, national, or regional?

From the state-of-art review, which research directions can be identied to build a
research agenda on this topic?
Therefore, this paper aims to present a systematic literature review (SLR) on the
MCDM applications to decision-problems regarding SDGs' achievements within the 2030
Agenda framework in different contexts. In this regard, 143 published scientic articles
from 2016 to 2020 were retrieved from the Scopus database, selected, and reviewed.
All the selected papers were categorized according to the decision problem associated
with SDGs issues, the MCDM methodological approach, the use (or not) of fuzzy set theory,
sensitivity analysis, and multistakeholder approaches, the context of MCDM applications,
and the MCDM classication (if utility-based, compromise, multi-objective, outranking, or
other MCDM methods).
The article is structured in ve sections. Following the introduction, the second
section describes the methodology adopted for searching relevant articles, rening the
search, and then making a nal selection of the most relevant articles. Section
a descriptive analysis of the reviewed articles to characterize the scientic production
prole in this research topic. Section
the in-depth analysis of the literature on MCDM applications for SDGs achievement. The
reviewed articles were classied into ve categories: (i) the 2030 Agenda for Sustainable
Development; (ii) multiple SDGs; (iii) economy (SDG 8, SDG 9, SDG 10, and SDG 12); (iv)
society (SDG 2, SDG 3, SDG 4, SDG7, and SDG 11), and (v) biosphere (SDG 6, SDG 13,
SDG 14, and SDG 15). Lastly, Section

Sustainability2021,13, 4129 4 of 37
research agenda for those interested in advancing the research on MCDM applications in
decision issues associated with the 2030 Agenda framework.
2. Methodology
A systematic literature review is a research methodology designed to identify, evaluate,
and interpret all available researches relevant to a particular research question, or topic
area, or phenomenon of interest [29]. In order to ensure that the ndings were obtained
in a reliable and valid manner, this review followed a three-stage approach as proposed
by Traneld et al. [30] and Denyer and Traneld [31], namely: (i) planning the review, (ii)
conducting the review by analyzing scientic articles, and (iii) reporting emerging themes
and recommendations for future studies. Figure
results per stage.
Figure 1.The review process according to [30,31].
2.1. Planning Stage
The planning stage comprised three steps: (i) exploring the literature on applications of
MCDM in decision problems concerning Sustainable Development (SD) issues, particularly

Sustainability2021,13, 4129 5 of 37
within the 2030 Agenda framework; (ii) selecting panel members for the review process;
and (iii) identifying research gaps and dening review scope and objectives.
In this stage, an exploratory search on MDCM literature in Scopus covering the
time frame 2010 to 2020 was conducted to identify previous studies on the research topic
that employed the SLR approach. This search yielded 70 reviews, but only a few were
concerned with MCDM applications in decision problems concerning SD issues. None
gave a comprehensive view of the MCDM approaches focusing on the achievement of UN
Sustainable Development Goals (SDGs) within the 2030 Agenda framework. So, a research
gap to be investigated was identied in the planning stage.
To integrate the review panel, the authors selected four senior experts recognized
locally and internationally in both elds–MCDM approaches and Sustainable Development–
MCDM methods and Sustainable Development (SD)–to identify and rene the study's
objectives and develop review protocols. The selection was impartial and based on an
objective search in the National Directory of Research Groups organized by the National
Research Council (authors' country). Ethical and representativeness issues were considered
for this selection, avoiding conicts of interest and biases in judgments. None of them are
co-authors of this paper or related to them.
2.2. Conducting Stage
The conducting stage involved the systematic search on the Scopus database covering
the period from 2016 to 2020. The choice of this time-frame and keywords was aligned with
the establishment of the “2030 Agenda for Sustainable Development” on 25 September
2015 by Heads of State and Government at a special UN summit. Search history in the
Scopus database is presented in Appendix, Table.
As shown in Figure, the selection criteria were: (i) scientic articles published
in journals; (ii) articles whose scopes refer to MCDM applications in decision problems
associated with at least one of the 17 SDGs, and (iii) articles written in English. Articles
written in languages other than English were excluded a priori because the further analysis
of full manuscripts in other languages could be very complicated.
Table
from the pre-processing conducted with the support of Bibliometrix, an open-source R-
package environment for bibliometric analysis developed by Aria and Cuccurullo [32].
Table 1.Bibliographic data collection.Description Results
Documents 863
Sources (journals, books, among others) 294
Author's keywords (DE) 2590
Period 2016–2020
Average citations per document 13.37
Authors 2481
Author appearances 3254
Authors of single-authored documents 32
Authors of multi-authored documents 2449
Single-authored documents 35
Documents per author 0.35
Authors per document 2.87
Co-authors per document 3.77

Sustainability2021,13, 4129 6 of 37
The 863 articles retrieved from the Scopus database were organized on a worksheet
Microsoft®Ofce Excel (Microsoft Corporation, Washington, WA, USA) in a way that the
panel members could analyze and assign relevance scores in an independent mode. So,
they analyzed the keywords, titles, and abstracts of the 863 downloaded articles to select
the articles for this systematic literature review based on the exclusion criteria previously
dened, namely: (i) articles did not directly deal with MCDM applications to decision-
problems regarding SDGs' achievement within the 2030 Agenda framework, and (ii) articles
did not describe the MCDM approach they have adopted.
The panel members scored all papers based on their potential relevance to the research
topic in a binary manner (yes = 1/no = 0), resulting in scores from 0 (min) to 4 (max). As
a result of this process, 143 articles gained the highest score of 4 (cluster 1), 342 articles
obtained a score of 3 (cluster 2), 55 got scores of 2 and 1 (cluster 3), and 355 score 0 (cluster 4).
Given the large number of documents grouped in clusters 1 and 2 (485 articles), the panel
members decided to include only cluster 1 for further analysis. Backward citation search
was not considered in this case since the articles regarding the 2030 Agenda framework
began to be published only in 2016, according to the Scopus database's search results.
Throughout the conducing stage, a systematic approach for protocol development and
searches on Scopus Database was followed to eliminate the risks of bias related to the SLR
methodology's inappropriate use, as proposed by [30,31]. Besides, the participation of four
senior panel members and the proper denition of inclusion/exclusion criteria mitigated
the risk of bias during the selection process. They could complete their judgments on the
relevance of the downloaded articles, and the whole process's reliability was achieved.
Panel member's agreement met with an acceptable Krippendorff's alpha of 0.681.
2.3. Reporting Stage
From the nal stage of the review process, descriptive statistics and qualitative content
analysis of the selected articles were reported and discussed, as well as recommendations
for future research.
3. Descriptive Analysis
The prole of the scientic production in this research eld covers: (i) the annual
evolution of scientic production (2016–2020), (ii) MCDM applications concerning the 2030
Agenda framework (i.e., the 2030 Agenda as a whole, multiple SDGs, and single SGDs
classied into three broader categories), (iii) MCDM methods applied to the 2030 Agenda
Framework, (iv) MCDM methodological approaches, (v) contexts of MCDM applications,
and (vi) sources with the most signicant number of publications.
3.1. Annual Evolution of Scientic Production: 2016–2020
Figure
addressed to SDGs' achievement from 2016 to 2020, based on the 143 reviewed papers.
Figure 2.Annual scientic production from 2016 to 2020.

Sustainability2021,13, 4129 7 of 37
As can be observed in Figure, right after the launch of the 2030 Agenda in 2016,
the growth rate of publications in 2016–2017 was 56%. However, in the last two years
(2019–2020), the rate achieves 122%, which indicates the spread of MCDM applications in
decision objectives concerning SDGs issues in various contexts at different levels.
3.2. MCDM Applications concerning the 2030 Agenda Framework
Table
Table 2.The 2030 Agenda framework issues and MCDM applications.
MCDM Applications SDG Number of Articles
The 2030 Agenda for Sustainable Development All SDGs 5
Multiple SDGs Nexus approaches 13
Economy: SDG 8, SDG 9, SDG 10, and SDG 12
SDG8 6
SDG9 15
SDG10 2
SDG12 10
Society: SDG 2, SDG 3, SDG 4, SDG 7, and SDG 11
SDG2 11
SDG3 7
SDG4 5
SDG7 24
SDG11 9
Biosphere: SDG 6, SDG 13, SDG 14, and SDG 15
SDG6 7
SDG13 14
SDG14 6
SDG15 9
As depicted in Table, the articles reviewed in this study were classied into ve cate-
gories: the rst is concerned with MCMD applications to the 2030 Agenda for Sustainable
Development, the second refers to applications addressed to multiple SDGs issues, and the
remaining categories follows a taxonomy proposed by Rockström and Sukhdev [33]. They
are: (i) “Economy” (SDG 8, SDG 9, SDG 10, and SDG 12), (ii) “Society” (SDG 2,SDG 3,
SDG 4, SDG7, and SDG 11), and (iii) “Biosphere” (SDG 6, SDG 13, SDG 14, and SDG 15).
Amongst the 143 selected articles, ve refer to the 2030 Agenda framework as a whole,
while 13 are addressed to problem-solving concerning more than one SDG, including two
studies focusing on the water–energy–food nexus. In turn, 33 articles are classied into the
“Economy” category, 56 into the “Society” category, and 36 into the “Biosphere” category.
Individually, works related to SDG 7 (“Affordable and Clean Energy”) appear in the rst
position of the selected documents (17% of the articles), followed by SDG 9 (“Industry,
Innovation, and Infrastructure”), and SDG 13 (“Climate Action”), both answer to 10%.
It is important to highlight that only four of all 17 SDGs had no work identied in the
review process about MCDM applications within the 2030 Agenda framework (See search
history in Appendix). They are SDG 1 (“No poverty”), SDG 5 (“Gender Equality”), SDG
16 (“Peace, Justice, and Strong Institutions”), and SDG 17 (“Partnerships for the Goals”).
3.3. MCDM Methods Applied to the 2030 Agenda Framework
Table
Accordingly, the most popular MCDM methods are the AHP method (73 articles), TOPSIS
(31 articles), DEMATEL (13 articles), PROMETHEE (12 articles), and VIKOR (11 articles).
Following the taxonomy proposed by Danesh et al. [34], these methods could be classied
into: (i) utility-based (12 methods), (iii) compromise (10 methods), (ii) multi-objective
decision-making (6 methods), (iii) outranking (3 methods), and (v) other MCMD methods
(7 methods).

Sustainability2021,13, 4129 8 of 37
Table 3.MCDM methods applied to the 2030 Agenda framework.
MCDM Method Ref.
Number of Articles
Classication [34]
n %
AHP [35] 73 51.05 Utility-based method
TOPSIS [36] 31 21.68 Compromise method
DEMATEL [37] 13 9.09 Other MCDM method
PROMETHEE [38] 12 8.39 Outranking method
VIKOR [39] 11 7.69 Compromise method
ANP [40] 10 6.99 Utility-based method
ELECTRE [41] 10 6.99 Outranking method
COPRAS [42] 5 3.50 Compromise method
DEA [43] 5 3.50
Multi-objective
decision-making method
EDAS [44] 5 3.50 Compromise method
GP [45] 5 3.50
Multi-objective
decision-making method
SAW [46] 5 3.50 Compromise method
Remaining
methods *
Various <5 ** 23.08 See notes below.
Notes:
The abbreviations signify the following: AHP—Analytic Hierarchy Process; TOPSIS—Technique for
Order Preference by Similarity to Ideal Solution; DEMATEL—Decision Making Trial and Evaluation Labo-
ratory; PROMETHEE—Preference Ranking Organization Method for Enrichment of Evaluations; VIKOR—
VIekriterijumsko KOmpromisno Rangiranje; ANP—Analytic Network Process; ELECTRE—Elimination et Choix
Traduisant la Realit²; COPRAS—Complex Proportional Assessment; DEA—Data Envelopment Analysis; EDAS—
Evaluation Based on Distance from Average Solution; GP—Goal Programming; SAW—Simple Additive Weighting.
(*) The remaining 25 methods are: (i) Utility-based methods (MAUT, ARAS, CPP, DEX, IFPPSI, MOOSRA, SWARA,
TODIM, and VF); (ii) Compromise methods (BMW, CODAS, ISWM, MABAC, and WSM); (iii) Multi-objective
decision making methods (HSMAA, MOLP, NMOP, and BOD-MCDM); (iv) Outranking methods (SMAA); (v)
Other MCDM methods (WASPAS, LBWA, MA, MAV, SMART, UTAUT). (**) Since there are 25 remaining methods,
the number of articles < 5 means that fewer than ve items adopted these methods.
Utility-based methods, also known as multi-attribute techniques, compensatory meth-
ods, or performance aggregation-based methods, aims to allocate a utility amount to
every alternative, considering uncertainty and providing options for the alternatives to
communicate with each other, like AHP, and ANP methods [34,35,40].
The compromise methods are an interactive MCDM approach that drivers by aggre-
gating features that provide bonding to the ideal solution and a foundation for discussions
concerning decision-making based on the factors' weight, like TOPSIS, VIKOR, COPRAS,
EDAS, and SAW [34,36,39,42,44,46].
The multi-objective decision-making methods (e.g., DEA and GP) are also known
as continuous methods or mathematical programming methods. They deal with various
targets simultaneously without having a clear direction as to which refer to performances
and which to issues by applying a mathematical optimization solver, expecting to optimize
more than one objective function simultaneously [34,43,45].
The outranking methods, also known as partially compensatory or preference
aggregation-based methods, evaluate a set of preferences to determine whether one option
is at least as effective as another, like PROMETHEE and ELECTRE [34,38,41].
Finally, the last category, “Other MCDM methods” comprises discrete methods that
cannot be categorized as utility-based, outranking, or compromise methods due to their
complexities (for example, DEMATEL, WASPAS, LBWA, MA, MAV, SMART, UTAUT
techniques) [34,37].
Figure
proposed by Danesh et al. [34].

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Figure 3.
Distribution of the reviewed articles according to the taxonomy proposed by
Danesh et al. [34].
As can be observed in Figure, utility-based methods lead the MCDM applications
addressing the 2030 Agenda issues (32%), followed by compromise methods (26%) and
other MCDM methods (18%).
3.4. MCDM Methodological Approaches
Figure
MCDM methodological approaches, i.e., application of single MCDM methods, integration
of MCDM methods, combination of MCDM and non-MCDM methods, use of fuzzy set
theory (FST) with MCDM methods, use of sensitivity analysis in MCDM results, and
adoption of multi-stakeholder approaches and participatory mechanisms.
Figure 4.
MCDM methodological approaches adopted in the reviewed articles. Note: More than
one methodological MCDM approach can be adopted in one single article so that the sum of the
methodological categories exceeds 143 articles.
The results shown in Figure
studies on MCDM applications for SD issues, besides the application of single MCDM
methods [47–49].
Concerning the integration of MCDM methods, 53 articles used this approach. The
most common is the hybrid AHP-TOPSIS approach. Another important hybrid MCDM
approach refers to DEMATEL-ANP method. DEMATEL is used in more than 70% of the
cases in which the ANP method is also employed.
With regard to the combination of MCDM and non-MCDM methods, this approach
was adopted in 52 articles. Some examples include MCDM methods with SWOT analysis,

Sustainability2021,13, 4129 10 of 37
Delphi technique, Geographical Information Systems (GIS), and Articial Intelligence (AI)
algorithms. The most popular MCDM and non-MCDM combinations are those related to
the AHP method with GIS.
Using sensitivity analysis in MCDM results can add further value to a given study
because it allows decision-makers to judge whether the results are accurate and robust
enough to decide [50]. Moreover, it provides a means for judging the stability of results
when the parameter values are changed. Nevertheless, only 42 of the reviewed articles
employed sensitivity analysis to improve the results' robustness.
It is important to highlight that MCDM methods cannot consider the ambiguity
and vagueness of selecting, scoring, and weighting unless fuzzy set theory is combined
with them to accommodate human judgments' subjectivity [51]. Analogously to the
low use of sensitivity analysis, only 33 articles have incorporated fuzzy logic in their
methodological approaches.
Finally, multi-stakeholder approaches and participatory mechanisms are intrinsically
linked to decisions concerning the 2030 Agenda and the SDGs implementation [52,53].
Based on this assumption, 38 articles adopted this approach associated with MCDM
applications addressed to decision problems within the 2030 Agenda framework.
3.5. Contexts of MCDM Applications
Figure
MCDM applications.
Figure 5.
Contexts of the MCDM applications within the 2030 Agenda framework. Note: All
26 articlesclassied in the `unidentied context' category refer to generic MCDM frameworks that
can be applied in different contexts.
One can observe that national contexts lead the reviewed articles with 40% of the
total, followed by local contexts answering 31% of the selected articles. Local contexts
refer to districts, municipalities, states, provinces, villages, or any other subdivision within
a country.
The regional contexts account for 11% of the articles, focusing on SDGs issues in
regional blocs (for example, European Community, for example) or any other regional
groupings. Finally, it is relevant to mention that although it was not possible to identify the
MCDM application context (if in a region, country, or municipality) in 26 articles, they refer
to generic decision frameworks focusing on SDGs that can be applied in different contexts.

Sustainability2021,13, 4129 11 of 37
Focusing on the reviewed articles that applied MCDM methods in national contexts,
Figure
were conducted.
Figure 6.MCDM applications in national contexts within the 2030 Agenda framework.
The largest number of articles concerned with SDGs issues in national contexts are in
Iran (7 studies), followed by Spain, India, and China (5 each), Taiwan (4 each), and Brazil
and Turkey (3 each).
Local contexts of MCDM applications stand for any country subset, e.g., districts,
municipalities, states, provinces, and villages. These studies are even more granulated
than those concerning national contexts. The most signicant number of local studies refer
to India (8 articles), followed by Iran (6), China (4), Spain and Turkey (3 each), and then
Saudi Arabia (2).
3.6. Sources of the Reviewed Articles
Table
articles published in these journals. Sustainability (MDPI Publisher) stands out among the
main sources, followed by the Journal of Cleaner Production (Elsevier Publisher). They are
followed by the Water Resources Management (Springer Publisher), the Sustainable Cities
and Society, and Sustainable Cities and Society (Elsevier Publisher).
Table 4.Main sources of the reviewed studies.
Source Publisher Number of Articles Total Citations
Sustainability (Switzerland) MDPI 24 121
Journal of Cleaner Production Elsevier 7 181
Water Resources Management Springer 4 16
Sustainable Cities and Society Elsevier 3 39
Renewable and Sustainable Energy
Reviews
Elsevier 3 21
Notwithstanding the fact that the top 3 journals are responsible for 24% of the to-
tal (143 articles), they are accountable for more than 37% of the total citations, demon-
strating their relevance in the research eld of MCDM applications concerning the 2030
Agenda framework.

Sustainability2021,13, 4129 12 of 37
4. In-Depth Analyses of the Literature: Results and Discussion
Even though many MCDM methods are available, decision-makers have been facing
difculties in selecting the best MCDM methodological approach to elaborate relevant
answers addressed to the posed decision questions [54]. In this review, an attempt was
made to summarize the main MCDM methodological approaches and discuss how MCDM
methods helped decision-makers solve multidimensional problems in achieving the SDGs
in different application contexts.
Based on Roy (1996) [54], decision-makers may formulate the MCDM problems in
four different ways, as follows:
Choice: MCDM is used to select the best option from a set of alternatives;

Sorting: MCDM is employed to assign a set of alternatives to the categories that have
been designed a priori;
Ranking: MCDM is applied to order the alternatives wholly or partially;

Description: MCDM is used to define the alternatives, build a set of criteria and determine
the performance of all or some alternatives taking into account additional information.
The papers reviewed in this study are classied into ve categories: (i) The 2030
Agenda for Sustainable Development; (ii) Multiple SDGs; (iii) Economy (SDG 8, SDG 9,
SDG 10, and SDG 12); (iv) Society (SDG 2, SDG 3, SDG 4, SDG7, and SDG 11), and (v)
Biosphere (SDG 6, SDG 13, SDG 14, and SDG 15). As mentioned in the introductory section,
only four SDGs had no work identied in the review process. They are: SDG 1 (“No
Poverty”), SDG5 (“Gender Equality”), SDG 16 (“Peace, Justice, and Strong Institutions”),
and SDG 17 (“Partnerships for the Goals”).
In some cases, the articles showed the potential to fall into several categories. Never-
theless, an attempt was made to select the best category according to each article's main
issues and decision objectives. Due to the large number of articles, all the papers identied
in each category are presented in separate tables in chronological order.
4.1. The 2030 Agenda for Sustainable Development
A fundamental issue in implementing the UN 2030 Agenda in different contexts
is the systemic analysis of global targets' interactions, considering the context-specic
understanding of these interactions within a long-term vision. Another critical issue
is how to apply and combine MCDM approaches to provide a consistent analysis for
evidence-based decision-making on the SDGs and respective global targets.
Table
related to this category [55–59].
Kara¸san and Kahraman [55] developed and applied an Interval-Valued Neutrosophic
EDAS method to choose the SDGs that Turkey should start to invest in the context of the
national 2030 Agenda. An expert group determined the criteria and alternatives, assigned
weights to the criteria, and prioritize the SDGs that should integrate the country's 2030
Agenda. Oliveira et al. [56] proposed a systemic and contextual framework to prioritize
SDG targets for the Brazilian 2030 Agenda by integrating two fuzzy MCDM methods
(fuzzy AHP and fuzzy TOPSIS), prospective structural analysis (PSA), and network theory
tools. The applicability of the proposed framework could be demonstrated through a
socio-technical experiment carried out by local stakeholders during 2018 to dene the
Brazilian 2030 Agenda. Resce and Schiltz [57] developed a MCDM approach to evaluate
European countries' performance on SDGs' achievement by employing the Hierarchical
Stochastic Multicriteria Acceptability Analysis (HSMAA) and using data from Eurostat
database. The results showed that Denmark outperforms other European countries, while
lower performance levels were observed in Romania and Bulgaria.

Sustainability2021,13, 4129 13 of 37
Table 5.Articles reviewed in “The 2030 Agenda for Sustainable Development” category.
Author(s) Method(s) Methodological Approach Context of Application
Kara¸san and Kahraman (2018)
[55]
IVN EDAS Single MCDM. National (Turkey)
Oliveira et al. (2019) [56]
Fuzzy AHP + Fuzzy TOPSIS +
PSA + network analysis.
Integration of MCDM methods.
Combination of MCDM and
non-MCDM methods.
Use of fuzzy logic.
National (Brazil)
Resce and Schiltz (2020) [57] HSMAA Single MCDM. Regional (EU)
Breu et al. (2020) [58] AHP + PSA + network analysis.
Combination of MCDM and
non-MCDM methods.
National (Switzerland)
Ben½tez and Liern (2020) [59] Unweighted TOPSIS
Single MCDM.
Case studies.
National (country unidentied)
Note:
The abbreviations signify the following: IVN EDAS—Interval-Valued Neutrosophic Evaluation Based on Distance from Average So-
lution; AHP—Analytic Hierarchy Process; TOPSIS—Technique for Order Preference by Similarity to Ideal Solution; HSMAA—Hierarchical
Stochastic Multicriteria Acceptability Analysis; PSA—Prospective Structural Analysis.
Focusing on the case of Switzerland, Breu et al. [58] tested and enhanced existing
approaches for assessing interactions among the 2030 Agenda's targets and for analyzing
the systemic relevance of priority targets. With participation of local stakeholders, they
established a cross-impact matrix for Switzerland's priority targets and applied a network
analysis, including a prospective structural analysis (PSA) for understanding the systemic
impact of targets, establishing an order of priority for policy actions on the SDGs in
Switzerland. The contribution by Ben½tez and Liern [59] refers to the usefulness of a new
TOPSIS version, called unweighted TOPSIS (uwTOPSIS), for eliminating the problem of
weighting sustainability criteria for ranking alternatives concerning SDGs.
By reviewing the articles classied in this rst category, it became apparent that the
combination of MCDM methods with non-MCDM methods could improve the exibility
and accuracy of decisions concerning the prioritization of SDGs' targets to be included in
a country's 2030 Agenda [56,58]. Besides, fuzzy logic, combined with MCDM methods,
helped decision-makers weighting criteria and prioritize targets under uncertainty [56].
Finally, three other MCDM methods (IVN EDAS, HSMAA, and uwTOPSIS were employed
to dene criteria and prioritize SDGs targets to be integrated into 2030 Agendas in national
or regional contexts [55,57,59].
4.2. Multiple Sustainable Development Goals
Table
ological approaches and contexts of application of MCDM methods [60–72].
The multiple interlinkages between SDGs and global targets within the 2030 Agenda
framework point out that integrated and synergistic implementation would benet dif-
ferent contexts (regional, national, or local). From this perspective, several selected arti-
cles addressed applications of MCDM methods by adopting the nexus approach for the
SDGs implementation.
Focusing on three SDGs' issues (SDG 7, SDG 9, and SDG 11), Jayaraman et al. [60] used
Goal Programming (GP) to prioritize resource mobilization for energy efciency, diversied
electricity generation, and new modes of transportation for the United Arab Emirates
(UAE). Regarding SDG11 and SDG 13, Mukherjee et al. [61] employed fuzzy TOPSIS to
rank and select sustainable transportation systems in Delhi (India), with linguistic ratings to
the potential alternatives against the selected criteria. Concerning six key areas associated
with SDGs 4, 5, and 17, Monson½s-Pay¡et al. [62] applied the AHP method to provide
tools for policy- and decision-makers in Europe regarding these areas (governance, public
engagement, gender equality, science education, and open science and ethics).

Sustainability2021,13, 4129 14 of 37
Table 6.Articles reviewed in the ”Multiple Sustainable Development Goals” category.
Author(s) Method(s) Methodological Approach Context of Application
Jayaraman et al. (2016) [60] GP Single MCDM. National (UAE)
Mukherjee et al. (2017) [61] Fuzzy TOPSIS
Single MCDM.
Use of fuzzy logic.
Local (Delhi, India)
Monson½s-Pay¡et al. (2017) [62] AHP Single MCDM. Regional (Europe)
Karabulut et al. (2019) [63]
TOPSIS + correlation analysis + scenario
planning
Combination of MCDM and
non-MCDM methods.
Regional (Mediterranean region)
Mostafaeipour and Sadeghi
(2019) [64]
Fuzzy AHP + TODIM + SAW + TOPSIS +
VIKOR + sensitivity analysis
Integration of MCDM methods.
Use of fuzzy logic.
Use of sensitivity analysis.
National (Iran)
De and Majumder (2019) [65] AHP + BFOA + FA
Integration of MCDM methods
Combination of MCDM and
non-MCDM methods.
Use of articial intelligence.
National (India)
Llorente-Marrân et al. (2020) [66] TOPSIS + DID
Combination of MCDM and
non-MCDM methods.
National (Haiti)
Pamucar et al. (2020) [67] LBWA + WASPAS + sensitivity analysis
Integration of MCDM methods.
Use of sensitivity analysis.
National (Iran)
Munasinghe-Arachchige et al.
(2020) [68]
PROMETHEE + sensitivity analysis
Single MCDM.
Use of sensitivity analysis.
Unidentied
Zamani et al. (2020) [69] Fuzzy TOPSIS + Fuzzy PROMETHEE II
Integration of MCDM methods.
Use of fuzzy logic.
Local (Jarreh, Iran)
Kumar et al. (2020) [70] Fuzzy TOPSIS
Single MCDM.
Use of fuzzy logic.
Unidentied
Radmehr et al. (2020) [71] TOPSIS + NMOP Integration of MCDM methods. National (Iran)
Das et al.(2020) [72] MOLP Single MCDM. Local (Eastern India)
Note:
The abbreviations signify the following: GP—Goal Programming; fuzzy TOPSIS—fuzzy Technique for Order Preference by
Similarity to Ideal Solution; AHP—Analytic Hierarchy Process; TODIM—Tomada de Decis¢o Interativa Multicrit²rio; SAW—Simple
Additive Weighting; VIKO—VIekriterijumsko KOmpromisno Rangiranje; BFOA—Bacterial Foraging Optimization Algorithm; FA—Firey
Algorithm; DID—Differences in Differences; LBWA—Level Based Weight Assessment; WASPAS—Weighted Aggregated Sum Product
Assessment; PROMETHEE—Preference Ranking Organization Method for Enrichment of Evaluations; NMOP–Nonlinear Multi-Objective
Optimization; MOLP—Multi-Objective Linear Programming.
Three studies investigated the coherence among sectoral policies and selected mea-
sures for managing the water-energy-food nexus in different contexts, namely: local
(Eastern India) [72], national (Iran) [71], and regional (The Mediterranean region in Eu-
rope) [63]. Two of them combined MCDM and non-MCDM methods [63,71] while [72]
used MOLP to evaluate nexus-sustainability and conventional approaches for optimal
water-energy-land-crop planning in an irrigated canal command in Eastern India.
In this category, most of the studies adopted compromise methods (TOPSIS and
VIKOR, for example) [61,63,64,66,69–71], whereas three employed a utility-based method
(AHP) [62,64,65], two applied an outranking method (PROMETHEE) [68,69], and the re-
maining studies [60,67,71,72] used multi-objective methods (e.g., GP and MOLP). Finally,
it should be emphasized that four studies [61,64,69,70] combined fuzzy logic with MCDM
methods, and one of them [65] employed articial intelligence algorithms (i.e., Bacterial For-
aging Optimization Algorithm and the Firey Algorithm) to optimize objective functions
for the intelligent allocation of energy to different contributors to surface water treatment
plants in India.
The next sections refer to 13 single SDGs, classied according to Rockström and
Sukhdev's taxonomy [33]. The ndings concerning the three categories below are presented
and discussed:
Economy: SDG 8, SDG 9, SDG 10, and SDG 12;
Society: SDG 2, SDG 3, SDG 4, SDG 7, and SDG 11;
Biosphere: SDG 6, SDG 13, SDG 14, and SDG 15.
4.3. Economy: SDG 8, SDG 9, SDG 10, and SDG 12
The contributions of MCDM methods for the achievement of the SDGs in this cat-
egory are pointed out in 33 studies, as follows: SDG 8–`Decent Work and Economic

Sustainability2021,13, 4129 15 of 37
Growth' [73–78], SDG 9–`Industry, Innovation and Infrastructure' [79–93], SDG 10–`Reduced
Inequalities' [94,95], and SDG 12–`Responsible Consumption and Production' [96–105].
4.3.1. SDG 8: Decent Work and Economic Growth
Table 69–74], highlighting the
methods, methodological approaches, and contexts of the MCDM applications.
Table 7.Articles reviewed concerning ”SDG 8: Decent Work and Economic Growth”.
Author(s) Method(s) Methodological Approach Context of Application
Michailidou et al. (2016) [73]
ELECTRE III + sensitivity analysis
Single MCDM.
Use of sensitivity analysis.
National (Greece)
Jafari-Moghadam et al. (2017) [74] DEMATEL + ANP Integration of MCDM methods. National (Iran)
Suganthi (2018) [75] Fuzzy AHP + VIKOR + DEA
Integration of MCDM methods.
Use of fuzzy logic.
National (unidentied country)
Sitaridis and Kitsios (2020) [76]
PROMETHEE II + TOPSIS +
Non-weighted method
Integration of MCDM methods. National (Greece)
Norese et al. (2020) [77] ELECTRE II Single MCDM. National (South Africa)
Prevolšek et al. (2020) [78] DEX Single MCDM.
National (Bosnia and
Herzegovina)
Das et al.(2020) [72] MOLP Single MCDM. Local (Eastern India)
Note:
The abbreviations signify the following: ELECTRE III—Elimination et Choix Traduisant la Realit²III; DEMATEL—Decision
Making Trial and Evaluation Laboratory; ANP—Analytic Network Process; fuzzy AHP—fuzzy Analytic Hierarchy Process; VIKOR—
VIekriterijumsko KOmpromisno Rangiranje; DEA—Data Envelopment Analysis; PROMETHEE II—Preference Ranking Organization
Method for Enrichment of Evaluations; TOPSIS—Technique for Order Preference by Similarity to Ideal Solution; DEX—Decision Expert.
Most articles focused on decision problems concerning tourism activities [73,74,78] or
multisectoral areas [75,76]. Michailidou et al. (2016) [73] used ELECTRE III and sensitivity
analysis to rank options for mitigating emissions and adapting tourism businesses and
destinations to Greece's changing climate conditions. Jafari-Moghadam et al. (2017) [74]
employed a hybrid DEMATEL- ANP method to weigh and prioritize entrepreneurship
policy dimensions by tourism entrepreneurs, tourism policymakers, and experts. Prevolšek
et al. (2020) [78] used a multi attribute DEX approach for assessing ethno-villages in Bosnia
and Herzegovina based on sustainability criteria and subcriteria.
Sitaridis and Kitsios [76] use and compare the applicability of an outranking method
(PROMETHEE II), together with TOPSIS and a non-weighted MCDM, to evaluate en-
trepreneurial ecosystems (EEs) in Greece according to the Global Entrepreneurship Monitor
(GEM) framework. Only one study integrated fuzzy set theory to a MCDM approach [75].
In this study, Suganthi [75] proposed a thorough model to assess the effectiveness of sectoral
investments made by a nation for achieving sustainable development. The author inte-
grated fuzzy AHP to determine the weights of a set of sustainability criteria, fuzzy VIKOR
to rate the various sectors based on the importance of sustainability criteria. In contrast,
DEA was used to determine whether the sectoral investments are appropriately budgeted
and how they can be improved for sustainable development and economic growth.
According to the MCDM taxonomy used in this review [34], three utility-based meth-
ods (ANP, AHP, and DEX) were applied in [74,75,78], respectively. Two compromise
methods (VIKOR and TOPSIS) were used in [75,76]. Outranking MCDM methods (ELEC-
TRE II and III, and PROMETHEE II) were employed in [73,76,77], and a multi-objective
method (DEA) was used in [75] integrated with fuzzy AHP and VIKOR methods.
4.3.2. SDG 9: Industry, Innovation, and Infrastructure
A summary of the reviewed papers related to SDG 9 issues [79–93] is presented in
Table.

Sustainability2021,13, 4129 16 of 37
Table 8.Articles reviewed concerning ”SDG 9: Industry, Innovation, and Infrastructure”.
Author(s) Method(s) Methodological Approach Context of Application
Stosic et al. (2016) [79] AHP Single MCDM. Unidentied
Wang et al. (2018) [80] GPCA Single MCDM. National (China)
Lee et al. (2018) [81] ANP + DEMATEL + ZOGP Integration of MCDM methods. National (Taiwan)
Yang et al. (2018) [82] DEMATEL + ANP + VIKOR Integration of MCDM methods. National (China)
Hung et al. (2019) [83]
DEA based on the slacks-based
measure (SBM) + sensitivity analysis
Single MCDM.
Use of sensitivity analysis.
National (Taiwan)
Sansabas-Villalpando et al. (2019) [84]
Fuzzy CODAS + fuzzy AHP +
sensitivity analysis
Integration of MCDM methods.
Use of fuzzy logic.
Use of sensitivity analysis.
Unidentied
Lee et al. (2020) [85] DEA + VIKOR
Combination of MCDM and
non-MCDM methods.
National (Taiwan)
Ovezikoglou et al. 2020) [86] ELECTRE III + sensitivity analysis
Single MCDM.
Use of sensitivity analysis.
Unidentied
Gupta et al. (2020) [87] BWM Single MCDM. National (India)
Asees Awan and Ali (2019) [88]
Fuzzy VIKOR + GRA + sensitivity
analysis
Combination of MCDM and
non-MCDM methods.
Use of fuzzy logic.
Use of articial intelligence.
Use of sensitivity analysis.
Regional (China-Pakistan Economic
Corridor)
Yang and Wang (2020) [89]
Fuzzy AHP + fuzzy TOPSIS +
sensitivity analysis
Integration of MCDM methods.
Use of sensitivity analysis.
National (China)
Turskis et al. (2020) [90] AHP + fuzzy WASPAS + WSM
Integration of MCDM methods.
Use of fuzzy logic.
Regional (Europe)
Stoilova et al. (2020) [91]
ANP + Hierarchical Cluster Analysis +
K-Means Cluster Analysis +
sensitivity analysis
Combination of MCDM and
non-MCDM methods.
Use of sensitivity analysis.
Regional (Europe)
Soares et al. (2020) [92] Fuzzy SAW
Single MCDM.
Use of fuzzy logic.
National (Brazil)
Lai et al. (2020) [93]
Fuzzy Z- DNMA + Z-TOPSIS +
Z-VIKOR + sensitivity analysis
Integration of MCDM methods.
Use of sensitivity analysis.
Unidentied
Note:
The abbreviations signify the following: AHP—Analytic Hierarchy Process; GPCA—Global Principal Component Analysis;
ANP—Analytic Network Process; DEMATEL—Decision Making Trial and Evaluation Laboratory; ZOGP—Zero-One Goal Programming;
VIKOR—VIekriterijumsko KOmpromisno Rangiranje; SBM-DEA—Slacks-Based Measure-Data Envelopment Analysis; fuzzy CODAS—
fuzzy Combinative Distance-based Assessment; ELECTRE III—Elimination et Choix Traduisant la Realit²III; BWM—Best-Worst Method;
GRA—Grey Relational Analysis; TOPSIS–Technique for Order Preference by Similarity to Ideal Solution; fuzzy WASPAS—fuzzy Weighted
Aggregated Sum Product Assessment; Fuzzy SAW—fuzzy Simple Additive Weighting; Fuzzy Z-DNMA—fuzzy Z-number-based Double
Normalization-based Multiple Aggregation; Z-TOPSIS—Z-number-based Technique for Order Preference by Similarity to Ideal Solution;
Z-VIKOR—Z-number-based VIekriterijumsko KOmpromisno Rangiranje.
In most articles concerning SDG 9, MCDM approaches were applied to evaluate the
industry's sustainability performance in national contexts to overcome barriers to achieve
this SDG [80,81,84,85,87,89]. By way of illustration, Wang et al. [80] used Grey Principal
Component Analysis (GPCA) to assess the industrial sector under the pressure of climate
change adaptation and mitigation in China's Capital Economic Circle. Lee et al. [81]
integrated three MCDM methods (ANP, DEMATEL, and ZOGP) to choose strategic options
addressed to green aviation eet management in Taiwan. Their evaluation showed that
the proposed mixed strategy portfolio for green aviation eet management could be
determined using limited resources. Some authors concentrated their efforts on selecting
or ranking strategies to make the infrastructure sector more sustainable [81,87,89–94].
Regarding the integration of MCDM methods, this approach was adopted in six stud-
ies [79,82,84,85,90,95]. For example, based on a four-dimensional service innovation model,
Yang et al. [82] integrated DEMATEL, ANP, and VIKOR to select technological innovations
for service-orientated enterprises in China. In relation to hybrid approaches combining
MCDM and non-MCDM methods, Stoilova et al. [91] combined an MCDM method (ANP)
with non-MCDM methods (Hierarchical Cluster Analysis and K-Means Cluster Analysis) to
investigate infrastructural development strategy for the regions connected through the axis
of TEN-T railway corridor Genoa–Rotterdam, taking into account the following criteria:
economic development, spatial development, rail operation, environment, and logistics.

Sustainability2021,13, 4129 17 of 37
Aligned with the MCDM taxonomy chosen for this review, eight studies employed compro-
mise methods, namely VIKOR, CODAS, TOPSIS, BWM, and SAW studies [82,84,85,87–89,92,93].
Two utility-based methods were applied in seven studies, as follows: AHP [79,84,89,90], and
ANP [81,82,91]. Multi-objective methods were used in three studies, namely ZOGP [81] and
DEA [83,85]. An outranking method (i.e., ELECTRE III) was applied by Ovezikoglou et al. [86]
for selecting environmental indicators to be applied to industrial investment evaluation. Thus,
18 scenarios were built and ranked as alternatives, based on relevant data from the literature and
taking into account the principles of prevention, planning, and design. Finally, it is important
to mention that six studies [84,88–90,92,93] combined fuzzy logic with MCDM methods, and
one [88] used artificial intelligence (i.e., GRA) to select and rank the best sustainable reverse
logistics recovery options, focusing on the case of China Pakistan Economic Corridor (CPEC).
4.3.3. SDG 10: Reduced Inequalities
Table 94,95].
Table 9.Articles reviewed concerning ”SDG 10: Reduced Inequalities”.
Author(s) Method(s) Methodological Approach Context of Aplication
Labella et al. (2020) [94] AHPSort II Single MCDM. Regional (Europe)
Sant'Anna et al. (2020) [95] CPP + sensitivity analysis
Single MCDM.
Use of sensitivity analysis.
Regional (Some countries)
Note:The abbreviations signify the following: AHPSort II—Analytic Hierarchy Process Sort; CPP—Composition of Probabilistic Preferences.
Labella et al. [94] developed a novel approach to evaluate the inequalities in the
European Union (EU) countries based on a compromise method (AHPSort II). This method-
ological approach allowed the authors to obtain a classication of the EU countries accord-
ing to their achievements in reducing inequalities to subsequently carry out an in-depth
performance analysis to conclude as to the evolution of inequality in them over the years.
In turn, Sant'Anna et al. [95] used a utility-based method (Composition of Probabilistic
Preferences or CPP) to generate human development indices with a variable number of
components. They developed and calculating indices combining eight components, four of
which address different dimensions of inequality. As posed by the authors, the main advan-
tage of this approach is its systematic nature, which facilitated the addition of new dimen-
sions, more components in each direction, and more types of component measurements.
4.3.4. SDG 12: Responsible Consumption and Production
Table
MCDM methods for achieving SDG 12 [96–105].
Four studies employed MCDM methods aiming to prioritize viable alternatives con-
cerning responsible consumption and production [97,102,104,105]. For illustrative pur-
poses, Mangla et al. [97] applied fuzzy AHP and sensitivity analysis to identify barriers
to SDG 12 achievement and prioritize alternatives concerning sustainable consumption
and production trends in a supply-chain context. Other studies prioritized waste disposal
options [105], materials to be recycled [104], and sanitary landll sites to be mapped [102].
In two articles, their authors ranked the risks to be mitigated in chemical plants [96]
and cotton manufacturing [103]. In [99], an aggregate indicator of a regional green economy
was proposed and applied using the TOPSIS method to assess the level of the green
economy in Polish regions and its changes in the period 2004–2016.

Sustainability2021,13, 4129 18 of 37
Table 10.Articles reviewed concerning ”SDG 12: Responsible Consumption and Production”.
Author(s) Method(s) Methodological Approach Context of Application
Khakzad and Reniers (2016) [96] AHP + Bayesian Network (BN)
Combination of MCDM and
non-MCDM methods.
Use of articial intelligence.
Unidentied
Mangla et al. (2018) [97] Fuzzy AHP + sensitivity analysis
Single MCDM.
Use of sensitivity analysis.
Use of fuzzy logic.
Unidentied
Eikelboom et al. (2018) [98] AHP + Delphi technique
Combination of MCDM and
non-MCDM methods.
Unidentied
Godlewska et al. (2019) [99] TOPSIS Single MCDM. National (Polish)
Bhatia et al.(2019) [100]
Fuzzy TOPSIS + fuzzy GRA +
fuzzy VIKOR + sensitivity
analysis
Integration of MCDM methods.
Use of articial intelligence.
Use of fuzzy logic.
Use of sensitivity analysis.
National (India)
Schlickmann et al. (2020) [101] AHP Single MCDM. Unidentied
Cobos Mora and Solano Pel¡ez
(2020) [102]
AHP + GIS
Combination of MCDM and
non-MCDM methods.
Local (Azuay, Ecuador)
Bhalaji et al. (2020) [103]
Fuzzy DEMATEL + ANP +
PROMETHEE
Integration of MCDM methods.
Use of fuzzy logic.
National (India)
Bose et al. (2020) [104]
ARAS + MABAC + COPRAS +
MOOSRA
Integration of MCDM methods. Unidentied
Chauhan et al. (2020) [105] DEMATEL Single MCDM.
Local (Dehradun, Saharanpur,
and Moradabad, India)
Note:
The abbreviations signify the following: AHP—Analytic Hierarchy Process; BN—Bayesian Network; TOPSIS—Technique for
Order Preference by Similarity to Ideal Solution; Fuzzy GRA—Grey Relational Analysis; fuzzy VIKOR—fuzzy VIekriterijumsko KOm-
promisno Rangiranje; GIS—Geographical Information System; fuzzy DEMATEL—Decision Making Trial and Evaluation Laboratory;
ANP—Analytic Network Process; PROMETHEE—Preference Ranking Organization Method for Enrichment of Evaluations; ARAS—
Additive Ratio Assessment; MABAC—Multi-Attributive Border Approximation area Comparison; COPRAS—Complex Proportional
Assessment; MOOSRA—Multi-Objective Optimization by Simple Ratio Analysis.
In respect to MCDM methods' integration, three studies adopted this methodological
approach [100,103,104]. For instance, Bhatia et al. [100] integrated fuzzy TOPSIS, fuzzy
GRA, and fuzzy VIKOR methods to decide the appropriate location to establish the reman-
ufacturing facilities in the reverse supply chain. Several conicting criteria were considered
before establishing a remanufacturing facility. MCDM and non-MCDM methods were
combined in three studies to support decisions concerning responsible consumption and
production issues [96,98,102].
Concerning the MCDM taxonomy adopted in this review [34], most of the MCDM
methods employed are utility-based methods, namely AHP [96–98,101,102] and ANP [103].
For example, Khakzad and Reniers [96] combined the AHP method with Bayesian Network
(BN) to nd an optimal layout for chemical plants by taking safety measures and land
using planning regulations.
The remaining studies employed MOOSRA (a multi-objective method) [104],
PROMETHEE (an outranking method) [103], and DEMATEL [103,105]. Considered an
effective method for identifying cause-effect relationships of a complex system, DEMATEL
deals with evaluating interdependent relationships among factors and nding the critical
ones through a visual structural model. Amongst the MCDM methods applied in the
reviewed 143 studies, DEMATEL appeared in the third position (13 articles) (See Table).
Finally, four studies can be highlighted regarding the use of fuzzy logic and articial
intelligence combined with MCDM methods. Three used fuzzy logic with AHP, TOPSIS,
and VIKOR: fuzzy AHP [97], fuzzy TOPSIS, and fuzzy VIKOR [100]. Two works [96,100]
employed articial intelligence (i.e., Bayesian Network and GRA).

Sustainability2021,13, 4129 19 of 37
4.4. Society: SDG 2, SDG 3, SDG 4, SDG 7, and SDG 11
The contributions of MCDM methods for the achievement of SDGs belonging to this
category are highlighted in 56 studies, as follows: SDG 2–“Zero Hunger and Sustainable
Agriculture” [106–116], SDG 3–“Good Health and Well-being” [117–123], SDG 4–“Inclusive
and Quality Education” [124–128], SDG 7–“Affordable and Clean Energy” [129–152], and
SDG 11–“Sustainable Cities and Communities” [153–161].
4.4.1. SDG2: Zero Hunger and Sustainable Agriculture
Table 106–116].
Table 11.Articles reviewed concerning ”SDG 2: Zero Hunger and Sustainable Agriculture”.
Author(s) Method(s) Methodological Approach Context of Application
Fagioli et al. (2017) [106] ELECTRE III Single MCDM.
Regional (European Community
countries)
Emami et al. (2018) [107] AHP + TOPSIS + SWOT analysis
Integration of MCDM methods.
Combination of MCDM and
non-MCDM methods.
National (Iran)
Jamil et al. (2018) [108] Fuzzy AHP + GIS
Combination of MCDM and
non-MCDM methods.
Use of fuzzy logic.
Local (Bijnor, India)
Aldababseh et al. (2018) [109] AHP +GIS + sensitivity analysis
Combination of MCDM and
non-MCDM methods.
Use of sensitivity analysis.
Local (Emirate Abu Dabi, UAE)
Ujoh et al. (2019) [110] AHP +GIS
Combination of MCDM and
non-MCDM methods.
Local (Benue, Nigeria)
Deepa et al. (2019) [111]
MIW + AHP + CRITIC + COPRAS +
SAW
Integration of MCDM methods.
Combination of MCDM and
non-MCDM methods.
Local (Taiwan)
Movarej et al. (2019) [112] ANP Single MCDM. National (Iran)
Banaeian and Pourhejazy (2020) [113]
Delphi technique + AHP + fuzzy
TOPSIS
Integration of MCDM methods.
Combination of MCDM and
non-MCDM methods.
Use of fuzzy logic.
Local (Guilan, Iran)
Sari et al. (2020) [114] AHP + PROMETHEE Integration of MCDM methods. Local (Konya, Turkey)
Puertas et al. (2020) [115] TOPSIS + ELECTRE + CE Integration of MCDM methods. Regional (Europe)
Zandi et al. (2020) [116]
Fuzzy AHP + fuzzy TOPSIS + FMEA +
sensitivity analysis
Integration of MCDM methods.
Combination of MCDM and
non-MCDM methods.
Use of fuzzy logic.
Use of sensitivity analysis.
Unidentied
Note:
The abbreviations signify the following: ELECTRE III—Elimination et Choix Traduisant la Realit²III; SWOT—Strengths, Weaknesses,
Opportunities, and Threats; AHP—Analytic Hierarchy Process; TOPSIS—Technique for Order Preference by Similarity to Ideal Solution;
GIS—Geographical Information System; MIW—Modied Integrated Weighting; CRITIC–Criteria Importance Through Intercriteria Correla-
tion; COPRAS—Complex Proportional Assessment; SAW—Simple Additive Weighting; PROMETHEE–Preference Ranking Organization
Method for Enrichment of Evaluations; CE—Cross-Efciency; FMEA—Failure Mode and Effects Analysis.
In three studies [108–110], their authors combined a utility-based method (AHP)
with Geographical Information Systems (GIS) in different localities, namely Bijnor (India),
Emirate Abu Dhabi (UAE), and Benue (Nigeria), to access cropland suitability for choosing
the best area for a specic crop and the areas where effective management is required.
Considering the prioritization of harvesting or mechanization technologies [107,113],
the authors used AHP and TOPSIS, combined with other methods (e.g., SWOT analysis
and Delphi technique). In all these cases, they aimed to achieve food security with their
strategies. Furthermore, Zandi et al. [116] used a set of MCDM methods to rank risks in
agriculture activities, indicating the most critical, namely water supply, energy supply,
climate uctuations, and pests.
Two studies [106,112] used single MCDM methods (ANP and ELECTRE III). In the
rst study, Fagioli et al. [106] applied an outranking method (ELECTRE III) in a regional
context (European Community countries) to assess the level of multi-functionality along the
entire food value chain in Europe. Initially, a set of indicators was dened to measure the
level of multi-functionality of agrifood systems. Then, ELECTRE III was used to implement

Sustainability2021,13, 4129 20 of 37
an evaluation process by assigning specic importance to each indicator. The second study
was developed in a national context (Iran). Movarej et al. [112] used the Analytic Network
Process (ANP) to analyze interventions affecting the development of nutrition-sensitive
agriculture production in this country.
In an attempt to integrate sustainability criteria into the machinery selection decisions
in the agriculture sector in Guilan (Iran), Banaeian and Pourhejazy [113] combined Delphi
technique with two MCDM methods and fuzzy logic (AHP and fuzzy TOPSIS). Besides,
two more studies also applied fuzzy logic [108,113,116] combined with MCDM and non-
MCDM methods (FMEA, for example). Emami et al. (2018) [107] used a SWOT analysis
technique to identify key internal and external factors that affect the development of
agricultural mechanization in Iran. These factors were then weighted using a utility-based
method (AHP), and the mechanization strategies were prioritized employing a compromise
method (TOPSIS).
In summary, most of the studies reviewed in this category employed utility-based
methods (AHP and ANP) [107–114,116], followed by compromise methods (TOPSIS, CO-
PRAS, SAW) [105,111,113,115,116], and outranking methods (ELECTRE III, PROMETHEE,
and ELECTRE) [106,114,115].
4.4.2. SDG3: Good Health and Well-being
A summary of the MCDM methods applied in this category is presented in
Table117–123].
To evaluate alternative mobile health care and determine the best option for satisfying
the aspirations of consumers, two studies [119,120] employed hybrid MCDM approaches
in different contexts of application (mobile health care in China and Spain). In both studies,
DEMATEL was chosen for its effectiveness in identifying cause-effect chain components
of complex systems. It helps to evaluate interdependent relationships among factors and
nding the critical ones through a visual structural model. Moreover, Kolvir et al. [121]
combine MCDM methods (TOPSIS and SAW, for example) with articial intelligence tools
(HANN and ANFIS) for improving the predictive accuracy of their assessments in Central
Iran. In a different context (Russia), Trubnikov et al. [122] also combined an MCDM
method (AHP) with an articial intelligence algorithm (Random Forest algorithm). In turn,
Halder et al. [123] used the AHP method with GIS to assess hospital sites' suitability in
Rajpur–Sonarpur (India) by spatial information technologies.
To sum up, most of the studies reviewed in this category integrated MCDM meth-
ods [117,119,121,122], and three combined MCDM with non-MCDM
methods [119,121,123]. According to the MCDM taxonomy used in this review, two
utility-based methods (AHP and ANP) were applied in [117,119,122,123], and compromise
methods were used in [117,119,120].
Since data analyzed in the contexts of good health, safety, and well-being should have
maximum accuracy, articial intelligence algorithms (e.g., Random Forest, HANN, and AN-
FIS) and fuzzy logic combined with MCDM methods were used in three
studies [117,121,122]. Hu and Tzeng [117] integrated fuzzy DEMATEL, fuzzy ANP, and
fuzzy VIKOR to create strategies addressed to continuous improvement and sustainable
development. Their analysis was based on the performance of the dimensions and crite-
ria associated with better life development in an imprecise information environment. In
turn, Kolviret al. [121] combined MCDM methods (TOPSIS and SAW, for example) with
articial intelligence tools (HANN and ANFIS) for improving the predictive accuracy of
their assessments in Central Iran. In a different context (Russia), Trubnikov et al. [122]
also combined an MCDM method (AHP) with an articial intelligence algorithm (Random
Forest algorithm).

Sustainability2021,13, 4129 21 of 37
Table 12.Articles reviewed concerning ”SDG 3: Good Health and Well-being”.
Author(s) Method(s) Methodological Approach Context of Application
Hu and Tzeng (2017) [117]
Fuzzy DEMATEL + fuzzy ANP +
fuzzy VIKOR
Integration of MCDM methods.
Use of fuzzy logic.
Regional (OECD)
Peirâ-Palomino and Picazo-Tadeo
(2018) [118]
DEA + Benet-of-the-Doubt (BoD)
principle + MOLP
Combination of MCDM and
non-MCDM methods.
Regional (OECD Countries + Brazil,
Russia and South Africa)
Liu et al. (2019) [119] DEMATEL+ ANP + VIKOR Integration of MCDM methods. National (China)
Yazdani et al. (2020) [120] DEMATEL + BWM + EDAS Integration of MCDM methods. National (Spain)
Kolviret al.(2020) [121] HANN + ANFIS + TOPSIS + SAW
Integration of MCDM methods.
Combination of MCDM and
non-MCDM methods.
Use of articial intelligence.
Local (Central Iran)
Trubnikov et al. (2020) [122] AHP + Random Forest (RF) algorithm
Integration of MCDM methods.
Use of articial intelligence.
National (Russia)
Halder et al. (2020) [123] AHP + GIS
Combination of MCDM and
non-MCDM methods.
Local (Rajpur–Sonarpur, India)
Note:
The abbreviations signify the following: DEMATEL—Decision Making Trial and Evaluation Laboratory; ANP—Analytic Network
Process; VIKOR—VIekriterijumsko KOmpromisno Rangiranje; DEA—Data Envelopment Analysis; MOLP—Multiple Objective Linear
Programming; BWM—Best-Worst Method; EDAS—Evaluation based on Distance from Average Solution; HANN–Hybrid Articial Neural
Network; ANFIS—Adaptive Neuro-Fuzzy Inference System; TOPSIS—Technique for Order Preference by Similarity to Ideal Solution;
SAW—Simple Additive Weighting; AHP—Analytic Hierarchy Process; GIS—Geographical Information System.
4.4.3. SDG 4: Inclusive and Quality Education
Table SDG 4
achievement [124–128].
Table 13.Articles reviewed concerning ”SDG 4: Quality Education”.
Author(s) Method(s) Methodological Approach Context of Application
Kurilovas (2018) [124] ETAS-M + UTAUT
Combination of MCDM and
non-MCDM methods.
Unidentied
Weng et al. (2019) [125] DEMATEL + ANP + IPA
Integration of MCDM methods.
Combination of MCDM and
non-MCDM methods.
National (China)
Aldowah et al. (2019) [126] DEMATEL Single MCDM. Unidentied
Zia et al. (2019) [127] AHP Single MCDM. National (Malaysia)
Coco et al. (2020) [128] DEA + SMAA
Combination of MCDM and
non-MCDM methods.
Regional (OECD Countries)
Note:
The abbreviations signify the following: ETAS-M—Educational Technology Acceptance & Satisfaction Model; UTAUT—Unied
Theory of Acceptance and Use of Technology; DEMATEL—Decision Making Trial and Evaluation Laboratory; ANP—Analytic Network
Process; IP—Importance Performance Analysis; AHP—Analytic Hierarchy Process; DEA—Data Envelopment Analysis; SMAA—Stochastic
Multicriteria Acceptability Analysis.
By reviewing the articles focusing on quality education issues, most authors com-
bined MCDM methods with non-MCDM methods [124,125,127] for more reliable results
concerning performance assessment problems within the educational area. In contrast, two
studies [126,127] applied single MCDM methods, namely AHP and DEMATEL.
According to the MCDM taxonomy adopted in this review [34], two utility-based
methods (AHP and ANP) were used to help decision-makers involved in educational
questions in China and Malaysia [125,127]. A multi-objective method (DEA) combined
with SMAA was employed to produce an overall (probability) ranking of schools, aiming
to evaluate the inequality within and across OECD countries and then identify educational
inequality trends in a given time frame [128].
Finally, Kurilovas [124] evaluated the suitability, acceptance, and use of Augmented
Reality (AR) applications in real-life pedagogical situations in educational institutions by
combining the Educational Technology Acceptance & Satisfaction Model (ETAS-M) with
the Unied Theory of Acceptance and Use of Technology (UTAUT). Both methods are

Sustainability2021,13, 4129 22 of 37
specic for this category and can help teachers and students select the most suitable AR
applications for their needs and improve learning quality and effectiveness.
4.4.4. SDG 7: Affordable and Clean Energy
Table129–152].
For ”Affordable and Clean Energy” (SDG 7), the MCDM methods are primarily used
to plan and manage clean energy projects, being the AHP method the most applied among
the MCDM methods (17 amongst 24 studies). Besides, most of the reviewed studies referred
to SDG 7 were conducted as part of environmental impact assessments. Authors who used
MCDM methods to choose energy policy scenarios considered the promotion of the use
of energy from Renewable Energy Sources (RES) [140,141,145,149] or RES compared with
energy efciency policies [135,152]. It is worth mentioning that all of these authors used
the AHP method or an MCDM approach that uses multi-attribute techniques since the
central idea prioritized policy scenarios.
Seven studies employed single MCDM methods, namely AHP [135,136,139,140],
MAUT [137], and PROMETHEE [148,152]. By way of illustration, Mirjat et al. [135] applied
the AHP method and sensitivity analysis to build electricity generation scenarios for Pak-
istan's sustainable energy planning. Combined with fuzzy logic, the AHP method was also
used by Acar et al. [136] to analyze hydrogen production systems' sustainability to guide
researchers, policy-makers, different industries, and energy market customers.
Regarding the integration of MCDM methods, this approach was adopted in
13 studies [131,133,134,138,141,142,144–146,149–152]. For example, Debbarma et al. [131]
integrated AHP, PROMETHEE II, and VIKOR to determine the optimal performance-
emission trade-off vantage in a hydrogen-biohol dual fuel endeavor. To evaluate and select
the most appropriate renewable energy alternatives in Turkey, Büyüközkan et al. [133]
developed a fuzzy AHP/COPRAS to help policy-makers better structuring local energy
policies concerning global efforts in this country. In turn, Ren and Toniolo [134] integrated
three MCDM methods (DEMATEL, EDAS, and ISWM) and used sensitivity analysis to
rank hydrogen production pathways under uncertainty.
In relation to hybrid approaches combining MCDM and non-MCDM methods, ve
studies employed different combinations, as follows: Delphi technique, SWOT analysis,
and AHP [125], fuzzy AHP and GRA [132], fuzzy AHP and fuzzy AD [143], DEMATEL
and GRA [147], fuzzy AHP, fuzzy WASPAS, and Delphi technique [149].
Aligned with the MCDM taxonomy adopted in this review, eight studies employed
compromise methods, namely VIKOR [131,142], COPRAS [133,144], EDAS [134,144],
ISWM [134], TOPSIS [138,141,142,145]. Four utility-based methods were applied in most of
the studies, as follows: AHP [129–133,135,136,138–145,149,151,152], MAUT [137,146], MA
and MAV [150]. Four studies used outranking methods, namely: PROMETHEE II [131,152],
PROMETHEE [148,151] and ELECTRE [151].
Finally, it is important to mention that eight studies [130,132,133,136,138,142,143,149]
combined fuzzy logic with MCDM methods, and two [132,147] used articial intelligence
approaches. For instance, Ocon et al. [132] used Grey Relational Analysis (GRA) combined
with a fuzzy-AHP approach to select hybrid energy systems for off-grid electrication in
Marinduque (Philippines).

Sustainability2021,13, 4129 23 of 37
Table 14.Articles concerning “SDG 7: Affordable and Clean Energy”.
Author(s) Method(s) Methodological Approach Context of Application
Guerrero-Liquet et al.
(2016) [129]
Delphi technique + SWOT analysis +
AHP
Combination of MCDM and
non-MCDM methods.
National (Dominican Republic)
Wang et al. (2016) [130] Fuzzy AHP
Single MCDM.
Use of fuzzy logic.
Local (Jiangsum, China)
Debbarma et al. (2017) [131] AHP + PROMETHEE II + VIKOR Integration of MCDM methods. Unidentied
Ocon et al. (2018) [132] Fuzzy AHP + GRA
Combination of MCDM and
non-MCDM methods.
Use of fuzzy logic.
Use of articial intelligence.
Local (Marinduque, Philippines)
Büyüközkan et al. (2018) [133] Fuzzy AHP + fuzzy COPRAS
Integration of MCDM methods.
Use of fuzzy logic.
National (Turkey)
Ren and Toniolo (2018) [134]
DEMATEL + EDAS + ISWM +
sensitivity analysis
Integration of MCDM methods.
Use of sensitivity analysis.
Unidentied
Mirjat et al. (2018) [135] AHP + sensitivity analysis
Single MCDM.
Use of sensitivity analysis.
National (Pakistan)
Acar et al. (2018) [136] Fuzzy AHP + sensitivity analysis
Single MCDM.
Use of fuzzy logic.
Use of sensitivity analysis.
Unidentied
Simsek et al. (2018) [137] MAUT Single MCDM method. Unidentied
Acar et al. (2019) [138]
Fuzzy AHP + fuzzy TOPSIS +
sensitivity analysis
Integration of MCDM methods.
Use of fuzzy logic.
Use of sensitivity analysis.
Unidentied
Kumar et al. (2019) [139] AHP Single MCDM. Local (Hilly, Nepal)
Ingole et al. (2019) [140] AHP Single MCDM. National (India)
Aryanpur et al. (2019) [141]
AHP + TOPSIS + Summed Rank
Analysis
Integration of MCDM methods. National (Iran)
Taylan et al. (2020) [142]
Extended fuzzy AHP + fuzzy VIKOR
+ fuzzy TOPSIS + sensitivity analysis
Integration of MCDM methods. National (Saudi Arabia)
Feng (2020) [143]
Fuzzy AHP + fuzzy AD + sensitivity
analyses
Combination of MCDM and
non-MCDM methods.
Use of fuzzy logic.
Use of sensitivity analysis.
National (China)
Abdel-Basset et al. (2020) [144] AHP + COPRAS + EDAS Integration of MCDM methods. Unidentied
Jadoon et al. (2020) [145] AHP + TOPSIS + sensitivity analysis
Integration of MCDM methods.
Use of sensitivity analysis.
National (Pakistan)
Rasheed et al. (2020) [146]
SMART + MAUT + sensitivity
analysis
Integration of MCDM methods.
Use of sensitivity analysis.
Regional (South Asian)
Li et al. (2020) [147] DEMATEL + GRA
Combination of MCDM and
non-MCDM methods.
Use of articial intelligence.
Unidentied
Phillis et al. (2020) [148] PROMETHEE + sensitivity analysis
Single MCDM.
Use of sensitivity analysis.
Regional (Europe)
Solangi et al. (2020) [149]
Fuzzy AHP + fuzzy WASPAS +
Delphi technique
Integration of MCDM methods.
Combination of MCDM and
non-MCDM methods.
Use of fuzzy logic.
National (Turkey)
Kurttila et al. (2020) [150] MA + MAV Integration of MCDM methods. National (Finland)
Singh et al. (2020) [151]
AHP + PROMETHEE + ELECTRE +
sensitivity analysis
Integration of MCDM methods.
Use of sensitivity analysis.
National (Nepal)
Neofytou et al. (2020) [152] PROMETHEE II + AHP Integration of MCDM methods.
National (14 countries of different
continents, proles, and progress
concerning sustainable energy
transition.
Note:
The abbreviations signify the following: SWOT—Strengths, Weaknesses, Opportunities, and Threats; AHP—Analytic Hierar-
chy Process; PROMETHEE II—Preference Ranking Organization Method for Enrichment of Evaluations; VIKOR—VIekriterijumsko
KOmpromisno Rangiranje; GRA—Grey Relational Analysis; fuzzy AD—fuzzy Axiomatic Design; COPRAS—Complex Proportional
Assessment; DEMATEL—Decision Making Trial and Evaluation Laboratory; EDAS—Evaluation based on Distance from Average Solution;
ISWM—Interval Sum Weighting Method; GIS—Geographical Information System; fuzzy AD—fuzzy Axiomatic Design; SMART—Simple
Multi-Attribute Rating Technique; MAUT—Multi-Attribute Utility Theory; TOPSIS—Technique for Order Preference by Similarity to Ideal
Solution; WASPAS—Weighted Aggregated Sum Product Assessment; MA—Multicriteria Approval; MAV–Multicriteria Approval Voting;
ELECTRE—Elimination et Choix Traduisant la Realit².

Sustainability2021,13, 4129 24 of 37
4.4.5. SDG 11: Sustainable Cities and Communities
A summary of the MCDM research concerning SDG 11 [153–161] is presented in
Table.
Table 15.Articles reviewed concerning ”SDG 11: Sustainable Cities and Communities”.
Author(s) Method(s) Methodological Approach Context of Application
Said et al. (2017) [153] COPRAS Single MCDM. Local (Sarawak, Malaysia)
Zinatizadeh et al. (2017) [154] ELECTRE + TOPSIS + SAW + IFPPSI Integration of MCDM methods. Local (Kermanshah, Iran)
Lehner et al. (2018) [155] AHP + GIS + sensitivity analysis
Combination of MCDM and
non-MCDM methods.
Use of sensitivity analysis.
Local (generic)
Gökçeku¸s et al. (2019) [156] Fuzzy PROMETHEE
Single MCDM.
Use of fuzzy logic.
Unidentied
Ahmed et al. (2019) [157]
AHP + TOPSIS + OSM + sensitivity
analysis
Integration of MCDM methods.
Combination of MCDM and
non-MCDM methods.
Use of sensitivity analysis.
Unidentied
Phonphoton and Pharino (2019) [158] AHP Single MCDM. Local (Bangkok, Thailand)
Nesticáet al. (2020) [159]
ANP + ZOGP + fuzzy Delphi
technique
Integration of MCDM methods.
Combination of MCDM and
non-MCDM methods.
Use of fuzzy logic.
Local (Campania, Italy)
Mansour et al. (2020) [160] AHP + PLS-SEM + sensitivity analysis
Combination of MCDM and
non-MCDM methods.
Use of sensitivity analysis.
Unidentied
Chen and Zhang (2020) [161] IOWA Single MCDM. Local (Liaoning, China)
Note:
The abbreviations signify the following: COPRAS—Complex Proportional Assessment; ELECTRE—Elimination et Choix Traduisant
la Realit²; TOPSIS—Technique for Order Preference by Similarity to Ideal Solution; SAW—Simple Additive Weighting; IFPPSI—
Improved Full Permutation Polygon Synthetic Indicator; AHP—Analytic Hierarchy Process; GIS—Geographical Information System;
fuzzy PROMETHEE—fuzzy Preference Ranking Organization Method for Enrichment of Evaluations; OSM—Optimal Scoring Method;
ANP—Analytic Network Process; ZOGP—Zero-One Goal Programming; PLS-SEM—Partial Least Squares–Structural Equation Modeling;
IOWA—Induced Ordered Weighted Averaging.
Concerning sustainable urban construction problems, two articles integrated the AHP
method with other methods and used sensitivity analysis [157,160]. Ahmed et al. [157] in-
tegrated AHP, TOPSIS, and OSM methods to prioritize sustainable concrete supplementary
materials. In turn, Mansour et al. [160] also used a hybrid MCDM approach to prioritize
investment in the construction industry to achieve SDG 11 targets in Saudi Arabia.
Four articles are centered on investigating other problems in urban areas.
Said et al. [153] ranked alternatives for dealing with housing affordability. Zinatizadeh
et al. [154] focused on assessing and predict urban sustainability in different areas. Nesticá
et al. [159] used the ANP method integrated with ZOGP and fuzzy Delphi technique to
dene urban land use policy in Campania (Italy). Chen and Zhang [161] used the IOWA
method to evaluate the sustainability performance of 14 cities in China.
Four studies used utility-based methods, namely the AHP method, single or combined
with other MCDM methods for different purposes [155,157,159,160]. By way of illustration,
Lehner et al. [155] used the AHP method combined with GIS and sensitivity analysis to
identify the most relevant urban sustainability indicators for monitoring cities' services and
quality of life (QoL) employing remote sensing techniques. Phonphoton and Pharino [159]
employed only the AHP method to choose appropriate alternatives to mitigate the impact
of municipal solid waste management services during oods in cities.
As depicted in Table, four studies employed compromise methods, as follows (i)
COPRAS [153], (ii) TOPSIS [154,157], (iii) SAW and IFPPSI [154], and (iv) IOWA [161]. The
remaining studies applied outranking methods (PROMETHEE and ELECTRE [154,161]
and the multi-objective ZOGP [159].
4.5. Biosphere: SDG 6, SDG 13, SDG 14, and SDG 15
For the achievement of SDGs belonging to this category, several applications of MCDM
methods reported in 36 studies are presented and discussed here, as follows: SDG 6–“Clean

Sustainability2021,13, 4129 25 of 37
Water and Sanitation” [162–168], SDG 13–“Climate Action” [169–182], SDG 14–“Life below
Water” [183–188], and SDG 15–“Life on Land” [189–197].
4.5.1. SDG 6: Clean Water and Sanitation
Table
MCDM methods for the achievement of SDG 6 [158–164].
Table 16.Articles reviewed concerning ”SDG 6: Clean Water and Sanitation”.
Author(s) Method(s) Methodological Approach Context of Application
Kumar et al. (2016) [162] Fuzzy ELECTRE-III-H
Single MCDM.
Use of fuzzy logic.
Local (Tarragona, Spain)
Woltersdorf et al. (2018) [163] AHP Single MCDM. Local (Outapi, Namibia)
Salisbury et al. (2018) [164] MAUT Single MCDM. Local (eThekwini, South Africa)
Ezbakhe et al. (2018) [165] MAUT and ELECTRE III
Two single MCDM methods are used
separately.
National (Kenya)
Nie et al. (2018) [166]
BWM + DEMATEL + fuzzy TOPSIS +
sensitivity analysis
Integration of MCDM methods.
Use of fuzzy logic.
Use of sensitivity analysis.
Local (industrial regions in China)
Vidal et al. (2019) [167]
ELECTRE III + scenario analysis +
sensitivity analysis
Combination of MCDM and
non-MCDM methods.
Use of sensitivity analysis.
Unidentied
Oliveira Campos et al. (2020) [168] TOPSIS Single MCDM. Local (Itaperuna, Brazil)
Note:
The abbreviations signify the following: ELECTRE III-H—Elimination et Choix Traduisant la Realit²III–Hi²rarchie; AHP—Analytic
Hierarchy Process; MAUT—Multi-Attribute Utility Theory; BWM—Best-Worst Method; DEMATEL—Decision Making Trial and Evaluation
Laboratory; TOPSIS—Technique for Order Preference by Similarity to Ideal Solution.
Most of the articles considered water security as a strategy for achieving SDG 6 targets
and prioritizing options by using several MCDM approaches [162,163,165,166,168]. Based
on information synthesized in Table, it is evident that most studies used single MCDM
methods, particularly ELECTRE III-H [162], AHP [163], MAUT [164], and TOPSIS [168].
For example, Kumar et al. [162] developed scenarios for future imbalances in water supply
and demand for one water-stressed Mediterranean area of Northern Spain (Tarragona)
and tested the applicability of fuzzy ELECTRE-III-H method for evaluating sectoral water
allocation policies.
The integration of MCDM methods could be observed in two studies [165,166].
Ezbakhe et al. [165] integrated MAUT with ELECTRE III for considering data uncertainty in
water sanitation and hygiene planning in Kenya. To evaluate water security sustainability
in industrial regions in China, Nie et al. [166] developed a multistage decision support
framework, combining BWM, DEMATEL, fuzzy TOPSIS, and sensitivity analysis. Regard-
ing hybrid approaches combining MCDM and non-MCDM methods, only one study [167]
employed this approach. Vidal et al. [167] used ELECTRE III combined with scenario
analysis and sensitivity analysis to assess the sustainability of on-site sanitation systems.
Following the MCDM taxonomy adopted in this review, eight studies three studies
employed utility-based methods, namely AHP [163] and MAUT [164,165]. Concerning
outranking methods, they were used in three studies: ELECTRE-III-H [158], ELECTRE
III [165,167]. Since various degrees of ambiguity in deciding are observed, it is recom-
mended to combine MCDM methods with fuzzy logic, which was observed in [162,166].
4.5.2. SDG 13: Climate Action
Table
MCDM methods related to the achievement of SDG 13 [169–182].

Sustainability2021,13, 4129 26 of 37
Table 17.Articles reviewed concerning ”SDG 13: Climate Action”.
Author(s) Method(s) Methodological Approach Context of Application
Song et al. (2016) [169]
TOPSIS + RUS + Delphi technique +
sensitivity analysis
Combination of MCDM and
non-MCDM methods.
Use of sensitivity analysis.
National (South Korea)
Panhalkar and Jarag (2017) [170] AHP + GIS
Combination of MCDM and
non-MCDM methods.
Local (Maharashtra, India)
Maanan et al. (2017) [171] GIS + AHP
Combination of MCDM and
non-MCDM methods.
National (Morocco)
Brudermann and Sangkakool (2017)
[172]
AHP + SWOT analysis
Combination of MCDM and
non-MCDM methods.
Regional (Europe)
Zahmatkesh and Karamouz (2017)
[173]
AHP + Monte Carlo (MC) simulation
Combination of MCDM and
non-MCDM methods.
Local (New York, USA)
Seenirajan et al. (2018) [174] AHP+ GIS
Combination of MCDM and
non-MCDM methods.
Local (Ambasamudram, India)
Mallick et al. (2018) [175]
Fuzzy AHP + WLC + GIS + sensitivity
analysis
Combination of MCDM and
non-MCDM methods. Use of fuzzy
logic.
Use of sensitivity analysis.
Local (Asee, Saudi Arabia)
Mistage and Bilotta (2018) [176] AHP + sensitivity analysis
Single MCDM.
Use of sensitivity analysis.
Unidentied
Alhumaid et al. (2018) [177]
AHP + PROMETHEE II + Sensitivity
analysis
Integration of MCDM methods.
Use of sensitivity analysis.
Local (Buraydah, Saudi Arabia)
Yazdani et al. (2019) [178]
SWARA + FMEA + EDAS +
Sensitivity analysis
Combination of MCDM and
non-MCDM methods.
Use of sensitivity analysis.
Local (Alboraya, Spain)
Florindo et al. (2020) [179] Fuzzy TOPSIS + SWOT analysis
Combination of MCDM and
non-MCDM methods.
Use of fuzzy logic.
National (Brazil)
Stricevi´c et al. (2020) [180] AHP + TOPSIS Integration of MCDM methods. National (Serbia)
Dutta et al. (2020) [181] AHP Single MCDM. Local (West Bengal, India)
Gandini et al. (2020) [182] AHP + VF + GIS
Integration of MCDM methods.
Combination of MCDM and
non-MCDM methods.
Local (Northern Spain)
Note:
The abbreviations signify the following: TOPSIS—Technique for Order Preference by Similarity to Ideal Solution; RUS—Robustness
Uncertainty-Sensitivity; AHP—Analytic Hierarchy Process; GIS—Geographical Information System; WLC—Weighted Linear Combination;
PROMETHEE II—Preference Ranking Organization Method for Enrichment of Evaluations II; SWARA—Step-wise Weight Assessment Ratio
Analysis; FMEA—Failure Mode and Effects Analysis; EDAS—Evaluation based on Distance from Average Solution; SWOT—Strengths,
Weaknesses, Opportunities, and Threats; VF—Value Function.
Most of the decision problems concerning SDG 13 were linked to map and describe
areas with ood risks associated with climate change impacts [170,171,173,174,177,182].
From the results shown in Table, one can observe that the AHP method (integrated
or not with other methods) was the most used in these articles. By way of illustration,
Panhalkar and Jarag [170] assessed ood risk assessment of Panchganga River in Maha-
rashtra (India) using the AHP method combined with GIS. In turn, Maanan et al. [171] also
used this methodological approach to assess coastal vulnerability, resulting from human
activity, population density, erosion, and climate change-induced sea-level rise in Morocco.
Seenirajan et al. [174] applied an AHP/GIS approach to rank and displayed the potentially
risky areas in the watersheds area of Ambasamuthiram Town (India).
Three studies used the TOPSIS method, combined with other methods [169,179,180].
For example, Song et al. [170] employed TOPSIS and RUS, combined with the Delphi
technique and sensitivity analysis, to evaluate and rank the spatial ood vulnerability to
climate change in South Korea. Florindo et al. [179] applied TOPSIS and SWOT analysis to
rank possible Carbon Footprint reduction actions in the Brazilian beef production chain.
Finally, to rank different agricultural projects planned to mitigate the ood risks and
their impacts on the sustainability of an agriculture supply chain in Alboraya (Spain),
Yazdani et al. [178] combined MCDM with non-MCDM methods, namely SWARA, EDAS,
FMEA, and sensitivity analysis.

Sustainability2021,13, 4129 27 of 37
4.5.3. SDG 14: Life below Water
Regarding MCDM applications to achieve this SDG, a summary of the reviewed
papers related to SDG 14 [183–188] is presented in Table.
Table 18.Articles reviewed concerning ”SDG 14: Life below Water”.
Author(s) Method(s) Methodological Approach Context of Application
Wijenayake et al. (2016) [183] AHP + GIS
Combination of MCDM and
non-MCDM methods.
National (Sri Lanka)
Nayak et al. (2018) [184] AHP + GIS
Combination of MCDM and
non-MCDM methods.
Local (Central Himalayas, India)
Henr½quez-Antipa and C¡rcamo
(2019) [185]
SWOT analysis + AHP
Combination of MCDM and
non-MCDM methods.
National (Chile)
Chen et al. (2019) [186] Delphi technique + AHP
Combination of MCDM and
non-MCDM methods.
National (Taiwan)
Luna et al. (2019) [187] AHP + GA
Integration of MCDM Methods.
Use of articial intelligence.
National (Spain)
Dorfan et al. (2020) [188] Fuzzy AHP + GPM
Integration of MCDM methods.
Use of fuzzy logic.
Local (Dayyer Port, Iran)
Note:
The abbreviations signify the following: AHP—Analytic Hierarchy Process; GIS—Geographical Information System; SWOT—
Strengths, Weaknesses, Opportunities, and Threats; GA—Genetic Algorithm; GPM—Goal Programming Model.
Most of the reviewed articles concerning SDG 14 focused on culture-based shery
development in several contexts [183,184,186,188]. For instance, Wijenayake et al. [183]
combined a utility-based method (AHP) with GIS to select non-perennial reservoirs for
culture-based shery development in Sri Lanka, whereas Nayak et al. [184] employed the
same methods to assess the soil, water, and infrastructure facilities for enhancing shery
resource development in Central Himalayas (India). Chen et al. [186] used the Delphi
technique and the AHP method to establish an evaluation structure for high-use shery
harbors in Taiwan, while Dorfan et al. [188] used the Goal Programming Model integrated
into the fuzzy AHP approach. Besides, fuzzy logic combined with MCDM methods was
employed in [188], aiming to support decision-making processes concerning shrimp shery
in Iran.
To interpret stakeholders' multidimensional perceptions on policy implementation
gaps regarding the current status of Chilean small-scale seaweed aquaculture, Henr½quez-
Antipa and C¡rcamo [185] applied the AHP method combined with SWOT analysis. In
turn, Luna et al. [187] employed AHP integrated with Genetic Algorithm (GA) to determine
the best feeding strategies in aquaculture farms in Spain.
As shown in Table, the combination of MCDM methods with non-MCDM methods
(Delphi technique, SWOT analysis, Genetic Algorithm, and Geographical Information
Systems) was adopted by most of the reviewed articles [183–186].
4.5.4. SDG 15: Life on Land
Table
SDG 15 [189–197].
Five studies combined MCDM methods (utility-based, compromise, or outranking)
with Geographical Information Systems [189,191,193,195,196]. By way of illustration, Ah-
madi Sani et al. [189] adopted an AHP-GIS approach to rank alternative land uses in
Zagros (Iran) to improve the management of vulnerable ecosystems and prevent further
degradation and increasing sustainability of land use in that region.

Sustainability2021,13, 4129 28 of 37
Table 19.Articles reviewed concerning ”SDG 15: Life on Land”.
Author(s) Method(s) Methodological Approach Context of Application
Ahmadi Sani et al. (2016) [189] GIS + AHP
Combination of MCDM and
non-MCDM methods.
Local (Zagros, Iran)
Diaz-Balteiro et al. (2016) [190] GP Single MCDM. Local (Northwestern Spain)
Çali¸skan (2017) [191] GIS + S-TOPSIS
Combination of MCDM and
non-MCDM methods.
Local (Trabzon, Turkey)
Tecle and Verdin (2018) [192] AHP + sensitivity analysis
Single MCDM. Use of sensitivity
analysis.
Local (Durango, Mexico)
Gigovi´c et al. (2018) [193] GIS + AHP
Combination of MCDM and
non-MCDM methods.
Local (Nevesinje, Bâsnnia)
Korkmaz and Gurer (2018) [194] TOPSIS Single MCDM. Local (Bucak and Sutculer, Turkey)
Jeong (2018) [195]
PROMETHEE + PGIS + sensitivity
analysis
Combination of MCDM and
non-MCDM methods. Use of
sensitivity analysis.
National (Spain)
Kacem et al. (2019) [196] GIS + fuzzy AHP + sensitivity analysis
Combination of MCDM and
non-MCDM methods. Use of fuzzy
logic.
Use of sensitivity analysis.
National (Morocco)
Wu et al. (2020) [197] AHP Single MCDM. Local (Guandong and Tibet, China)
Note:
The abbreviations signify the following: GIS—Geographical Information System; AHP—Analytic Hierarchy Process; S-TOPSIS—
Spatial Integrated Technique for Order Preference by Similarity to Ideal Solution; PROMETHEE—Preference Ranking Organization Method
for Enrichment of Evaluations; PGIS—Participatory Geographical Information System.
The remaining studies [190,192,194,197] used single MCDM methods (GP, AHP, and
TOSPSIS) for different purposes. Diaz-Balteiro et al. [190] used a multi-objective method
(Goal Programming) to rank industrial forest plantations in Northwestern Spain, from
the perspective of sustainability, while Tecle and Verdin [192] employed a utility-based
MCDM approach and sensitivity analysis to determine the most efcient way of allocating
a budget for multi-purpose forest management in Durango (Mexico).
In summary, ve studies used a utility-based method (AHP), combined or not with
non-MCDM methods [189,192,193,196,197], while two applied compromise methods
(S-TOPSIS and TOPSIS) [191,194] and the remaining employed a multi-objective method
(Goal Programming) and an outranking method (PROMETHEE) [190,195] respectively.
5. Conclusions
In this paper, an attempt was made to conduct a systematic literature review on the
MCDM applications in various contexts concerning SDGs achievements. In this regard,
143 published scientic articles from 2016 to 2020 were retrieved from the Scopus database,
selected, and reviewed. From the 17 SDGs dened in the 2030 Agenda framework, almost
all were considered in this review. Only four SDGs had no work identied in the review
process (i.e., SDG1, SDG 5, SDG 16, and SDG 17 (see Appendix).
The objectives of this study were achieved, and the ndings summarized in
Sections
focusing on the 2030 Agenda framework. In fact, the results shed light on the main MCDM
applications to support decisions concerning the 2030 Agenda as a whole, multiple SDGs
issues, and single SDGs classied into three categories: economy, society, and biosphere.
The main conclusions associated with the research questions dened in the introduc-
tory section can be stated as follows.
The results shown in Figure
MCDM literature, i.e., the integration of MCDM methods, the combination of MCDM with
non-MCDM methods. Concerning the integration of MCDM methods, the most common
is the hybrid AHP-TOPSIS method. The integration of ANP and DEMATEL methods can
also be highlighted since DEMATEL is used in more than 70% of the studies in which ANP
is employed. In turn, focusing on the 52 articles that combine MCDM and non-MCDM
methods, some studies include MCDM methods with SWOT analysis, Delphi technique,
and Geographical Information Systems (GIS). The most popular MCDM and non-MCDM

Sustainability2021,13, 4129 29 of 37
combinations are those related to the AHP method with GIS. This combination appears in
83% of articles when GIS is used.
In terms of the higher incidence of MCDM applications within the 2030 Agenda frame-
work, the category with more MCDM applications is “Society”, encompassing56 studies,
being 24 studies focused on decision-problems concerning SDG 7 (“Affordable and Clean
Energy”). In the second and the third positions, “Biosphere” comprises 36 studies, and
“Economy” 33 studies. Finally, 18 studies are associated with the rst two categories–”The
2030 Agenda” and “Multiple SDGs”.
From the perspective of building a research agenda in this eld, out of 143 reviewed
articles, more than 50% suggested future directions to expand the MCDM knowledge
base applied to decision-making processes concerning issues within the 2030 Agenda
framework. Accordingly, further research suggestions can be summarized as follows:

Broader utilization of MCDM methods (single or hybrid) to expand the MCDM
knowledge-base to be widely applied within the 2030 Agenda framework for SDGs
achievement in the most diverse contexts (regional, national, or local contexts);

Replication of reviewed conceptual MCDM models amongst the various categories
above mentioned, and also in studies focusing MCDM applications in SDGs not
covered in the literature (i.e., SDG 1, SDG5, SDG 15, and SDG16);

Combination of MCDM and non-MCDM methods to explore the potential of articial
intelligence and advanced management and statistical tools to enhance the analytical
accuracy of studies;

Utilization of different versions of fuzzy set theory (e.g., hesitant fuzzy sets and
intuitionistic fuzzy) combined with MCDM methods;

Prospective analysis and foresight tools (e.g., prospective structural analysis) to com-
plement MCDM approaches, considering the time-frame of the 2030 Agenda;

MCDM processes applied to issues within the 2030 Agenda framework should en-
courage the engagement of stakeholders representing multiple sectors and levels.
The ndings presented in this paper can help policy-makers, researchers, and prac-
titioners by providing directions about MCDM applications in various contexts concern-
ing SDGs achievements within the 2030 Agenda framework. The previously mentioned
ndings and research agenda here presented can support new research projects and teach-
ing activities related to MCDM methods from the perspective of their potential use in
those contexts.
As discussed in this paper, policy-makers can better explore MCDM applications to
prioritize projects and programs for SDGs achievement and dene public policies addressed
to the 2030 Agenda implementation in different contexts. Besides, practitioners within
public and private organizations from diverse sectors can replicate and improve existing
MCDM models to enhance their strategic decision-making processes regarding resource
allocation to corporate strategies associated with one or more SDGs.
Author Contributions:
Conceptualization, M.S. and M.F.A.; methodology, M.F.A.; formal analysis,
M.S., R.C., and M.F.A.; investigation, M.S., R.C., M.F.A.; data curation, M.S., R.C.; writing—original
draft preparation, M.F.A. and M.S.; writing—review and editing, M.F.A., R.C.; visualization, M.S.;
supervision, M.F.A. and R.C.; project administration, M.F.A. and R.C. All authors have read and
agreed to the published version of the manuscript.
Funding:
This research was nanced in part by the Coordenaç¢o de Aperfeiçoamento de Pessoal de
N½vel Superior—Brazil (Capes)—Finance Code 001.
Institutional Review Board Statement:Not applicable.
Informed Consent Statement:Informed consent was obtained from all subjects involved in the study.
Data Availability Statement:Data available in a publicly accessible repository.

Sustainability2021,13, 4129 30 of 37
Acknowledgments:
The authors wish to thank the four panel members for their crucial contributions
during the conducting stage of the literature review. Special thanks go to the anonymous reviewers
for their careful reading of the manuscript.
Conicts of Interest:The authors declare no conict of interest.
Appendix A. Search History in the Scopus Database
Table 1.Search strategy in Scopus database.
Ref. Keyword Search Documents
#1
(TITLE-ABS-KEY (“*criteria decision mak*”) OR TITLE-ABS-KEY (“*criteria decision-mak*”) OR TITLE-ABS-KEY (MCDM) OR
TITLE-ABS-KEY (“*criteria decision analy*”) OR TITLE-ABS-KEY (“*criteria decision-analy*”) OR TITLE-ABS-KEY (MCDA))
24,502
#2
(TITLE-ABS-KEY (SDG) OR TITLE-ABS-KEY (“sustainable development goal*”) OR TITLE-ABS-KEY (“2030 Agenda”) OR
TITLE-ABS-KEY (“sustainable development”))
228,771
#3 #1 AND #2 1756
#4
#3 AND (LIMIT-TO (PUBYEAR, 2020) OR LIMIT-TO (PUBYEAR, 2019) OR LIMIT-TO (PUBYEAR, 2018) OR LIMIT-TO
(PUBYEAR, 2017) OR LIMIT-TO (PUBYEAR, 2016)
1169
#5 #4 AND (LIMIT-TO (DOCTYPE, “article”) 867
#6 #5 AND (LIMIT-TO (LANGUAGE, “English”) 863
#7 #6 AND TITLE-ABS-KEY (“poverty eradication”) OR TITLE-ABS-KEY (“no poverty”) TITLE-ABS-KEY (SDG 1) 0
#8
#6 AND (TITLE-ABS-KEY (“zero hunger”) OR TITLE-ABS-KEY (“food security”) OR TITLE-ABS-KEY (“improved nutrition”)
OR TITLE-ABS-KEY (“agriculture”) OR TITLE-ABS-KEY (SDG 2))
18
#9
#6 AND (TITLE-ABS-KEY (“healthy lives”) OR TITLE-ABS-KEY (“health system*”) OR TITLE-ABS-KEY (“ well-being”) OR
TITLE-ABS-KEY (SDG 3))
6
#10
#6 AND (TITLE-ABS-KEY (“equitable education”) OR TITLE-ABS-KEY (“education”) OR TITLE-ABS-KEY (“life-long
learning”) OR TITLE-ABS-KEY (SDG 4))
4
#11 #6 AND (TITLE-ABS-KEY (gender AND equality) OR TITLE-ABS-KEY (SDG 5)) 0
#12
#6 AND (TITLE-ABS-KEY (“clean water”) OR TITLE-ABS-KEY (“sanitation”) OR TITLE-ABS-KEY (“water supply”) OR
TITLE-ABS-KEY (“water conservation” OR TITLE-ABS-KEY (SDG 6))
38
#13
#6 AND (TITLE-ABS-KEY (“energy efciency”) OR TITLE-ABS-KEY (“energy policy”) OR TITLE-ABS-KEY (“alternative
energy”) OR TITLE-ABS-KEY (“renewable energy”) OR TITLE-ABS-KEY (“energy utilization”) OR TITLE-ABS-KEY
(“renewable energies”) OR TITLE-ABS-KEY (“renewable energy resources”) OR TITLE-ABS-KEY (“electricity generation”) OR
TITLE-ABS-KEY (“energy conservation”) OR TITLE-ABS-KEY (“energy planning”) OR TITLE-ABS-KEY (“wind power”) OR
TITLE-ABS-KEY (“electric power generation”) OR TITLE-ABS-KEY (“solar energy”) OR TITLE-ABS-KEY (SDG 7))
162
#14
#6 AND (TITLE-ABS-KEY (“decent work”) OR TITLE-ABS-KEY (“sustainable economic growth”) OR TITLE-ABS-KEY
(“economic and social effects”) OR TITLE-ABS-KEY (“economic development”) OR TITLE-ABS-KEY (SDG 8))
86
#15
#6 AND (TITLE-ABS-KEY (“resilient infrastructure”) OR TITLE-ABS-KEY (“sustainable industrialization”) OR
TITLE-ABS-KEY (innovation) OR TITLE-ABS-KEY (manufacturing) OR TITLE-ABS-KEY (“environmental technology”) OR
TITLE-ABS-KEY (“sustainable supply chain*”) OR TITLE-ABS-KEY (“sustainability performance”) OR TITLE-ABS-KEY
(“supplier selection”) OR TITLE-ABS-KEY (SDG 9))
127
#16 #6 AND (TITLE-ABS-KEY (reduced AND inequalities) OR TITLE-ABS-KEY (SDG 10)) 2
#17
#6 AND (TITLE-ABS-KEY (“sustainable cities”) OR TITLE-ABS-KEY (“Urban Planning”) OR TITLE-ABS-KEY (“urban area”)
OR TITLE-ABS-KEY (“municipal solid waste”) OR TITLE-ABS-KEY (SDG 11))
45
#18
#6 AND (TITLE-ABS-KEY (“sustainable consumption”) OR TITLE-ABS-KEY (“sustainable production”) OR TITLE-ABS-KEY
(“life cycle analysis”) OR TITLE-ABS-KEY (“life cycle assessment”) OR TITLE-ABS-KEY (“waste management”) OR
TITLE-ABS-KEY (SDG 12))
134
#19
#6 AND (TITLE-ABS-KEY (“climate change”) OR TITLE-ABS-KEY (“greenhouse gases”) OR TITLE-ABS-KEY (“emission
control”) OR TITLE-ABS-KEY (“carbon footprint”) OR TITLE-ABS-KEY (“carbon dioxide”) OR TITLE-ABS-KEY (“global
warming”) OR TITLE-ABS-KEY (SDG 13))
113
#20
#6 AND (TITLE-ABS-KEY (sustainably AND use AND of AND oceans) OR TITLE-ABS-KEY (sustainably AND use AND of
AND seas) OR TITLE-ABS-KEY (sustainable AND use AND of AND marine AND resources) OR TITLE-ABS-KEY (SDG 14))
7
#21
#6 AND (TITLE-ABS-KEY (“life on land”) OR TITLE-ABS-KEY (“sustainable use of terrestrial ecosystems”) OR
TITLE-ABS-KEY (“sustainable management of forest*”) OR TITLE-ABS-KEY (SDG 15))
65
#22 #6 AND (TITLE-ABS-KEY (peace AND justice AND strong AND institutions) OR TITLE-ABS-KEY (SDG 16)) 0

Sustainability2021,13, 4129 31 of 37
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