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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