A review and catalogue to the use of models in enabling the achievement of.pdf

Journal of Cleaner Production 340 (2022) 130803
Available online 3 February 2022
0959-6526/© 2022 Elsevier Ltd. All rights reserved.
A review and catalogue to the use of models in enabling the achievement of
sustainable development goals (SDG)
Ebrahim Aly a,*
, Sondoss Elsawah a,b
, Michael J. Ryan a,b
a
School of Engineering and Information Technology, University of New South Wales, Canberra, Australia
b
Capability Systems Centre, University of New South Wales, Canberra, Australia
A R T I C L E I N F O
Handling editor: Mingzhou Jin
Keywords:
Sustainable development goals
Modelling and simulation
Sustainability science
2030 Agenda
Uncertainty
Decision support
A B S T R A C T
Sustainable development goals (SDG) represent one of the pillars of the UN’s 2030 agenda aiming to secure a
more sustainable future for all nations. The 17 goals comprising the SDG framework form a transformative action
plan that is designed to help nations achieve a more sustainable future focusing on their environmental, social,
and economic systems. Achieving the goals within the designated timeframe, however, entails overcoming
multiple challenges related to policy design, resources management, and harnessing the synergies and trade-offs
among the goals themselves. Recognizing the vital role modeling and simulation can play in facilitating the
achievement of the SDG, this paper provides a review of the academic literature published between 2015 and
2021 relating to the models developed to tackle SDG related issues. The main purpose of this review is to explore
the modeling practices related to SDG and to determine how aligned those modeling practices are with the
priorities of sustainability science. We developed a multi-dimension classification scheme to categorize the
models retrieved from the literature based on their main characteristics focusing on characteristics such as the
modeling approach used, model uncertainty treatment, model temporal and spatial scales, and which science
priority the model focuses on. The results of this research show that most of the models focused on studying
policy changes needed to achieve the SDG and monitoring the progress of the SDG. The results also highlight
missing important characteristics in SDG models such as cross-scale treatment and advanced uncertainty treat
ment. While this research can be valuable in informing modelers about the gaps need to be filled in SDG
modeling and facilitates the communication between modelers and policy makers, it can be further extended by
including an evaluation for the modeling approaches used in SDG contexts as well as adding more characteristics
to the model categorization scheme.
1. Introduction
The sustainable development goals (SDGs) framework is one of the
pillars of the United Nations’ 2030 agenda which aims to achieve a
better and more prosperous future for Earth and its inhabitants. The set
of 17 overarching goals, with their subsequent 169 targets,1
represents
an ambitious transformative action plan addressing critical grand chal
lenges pertinent to the environmental, social, and economic aspects of
modern societies UN (2015). The United Nation’s member states are
expected to adopt the SDGs framework as a roadmap to guide their
development and investment plans, to keep track of their progress to
ward achieving the goals during the designated time frame of 15 years
(OECD and Paris, 2017; Allen et al., 2018). National endeavours toward
accomplishing SDGs entail overcoming multiple hurdles such as priori
tizing goals and targets, tailoring national strategies and policies to
conform with the SDGs, managing trade-offs and capitalizing on the
synergies among the goals, and securing sufficient resources to facilitate
the transformation toward more sustainable development agendas (Plag
and Jules-Plag, 2019; Messerli et al., 2019; Saito et al., 2017). These
challenges originate from the innate characteristics of the SDGs as an
integrated and indivis ible framework that need to be approached in a
holistic manner, as well as being intertwined and interconnected with
existing conflicts among the goals with environmental focus and those
related to economic and social development (Scherer et al., 2018;
Nilsson et al., 2018; Swain, 2018). Additionally, the SDGs are designed
to be a universal set of goals, that are applicable in all national contexts,
* Corresponding author.
E-mail address: ebrahim.a.aly@gmail.com (E. Aly).
1
For a full list of Sustainable Development Goals, Targets, and indicators, see: https://sdgs.un.org/goals.
Contents lists available at ScienceDirect
Journal of Cleaner Production
journal homepage: www.elsevier.com/locate/jclepro
https://doi.org/10.1016/j.jclepro.2022.130803
Received 14 August 2021; Received in revised form 9 December 2021; Accepted 30 January 2022

2
i.e. apt for all nations regardless their level of development, however,
such universality means that they do not consider the particular con
ditions of the nation applying them. Consequently, their potential to
transform the governance practices of those nations into a more sus
tainable ones can be questionable (Easterly, 2015; Stevens and Kanie,
2016; Cuaresma et al., 2018). Moreover, the ingrained uncertainty
regarding the goals’ trade-offs, feedback impacts, or application at
different scales and contexts means that attaining the SDGs is a “super
wicked” problem that requires unconventional interventions (Allen
et al., 2018, 2021; Georgeson and Maslin, 2018; Cuaresma et al., 2018).
In order to manage the different complexities inherent in planning for
achieving the SDGs, policymakers will need to rely on a suite of decision
support techniques and methodologies that enhances their analytical
capabilities as well as increases their confidence in the decisions they
make. Acknowledging the inadequacy of traditional techniques such as
cost-benefit analysis or macroeconomic modeling in tackling complex
sustainability issues in general and SDGs challenges in particular
(DeCanio, 2003; Scrieciu, 2007; Haldane and Turrell, 2018; Stiglitz,
2018; Meyer and Ahlert, 2019), various research activities have sought
to develop and apply sophisticated techniques that are more capable of
managing the complexities associated with SDGs (Howells et al., 2013;
Joshi et al., 2015; Kumar et al., 2018; Almannaei et al., 2020). The
majority of the techniques deemed effective in guiding policymakers’
endeavours to achieve SDGs fall under the umbrella of modeling and
simulation and form what is known as “model-based decision support
systems” (Allen et al., 2016; Abson et al., 2017; Arnold et al., 2020).
These contemporary techniques or the models built using them, how
ever, need to have a certain set of attributes in order to be well-suited to
the task of attaining the SDGs and to overcome the challenges associated
with them in harmony with long-term sustainability. One of the
fundamental attributes that is essential in the approaches and tech
niques used to inform decision making in the context of SDGs is that they
need to utilize systems thinking in order to realize the multiple in
teractions and contradictions among the economic, ecological, and so
cial components of the SDGs (Liu et al., 2008; van Delden et al., 2011;
Miller et al., 2014; Le Blanc, 2015; Spaiser et al., 2017; Salling et al.,
2018; Scherer et al., 2018; Allen et al., 2018). The Integrated and
multi-disciplinary scope of the used models has also been recognized as
a key attribute to understand the interactions, trade-offs, and synergies
among the SDGs (Allen et al., 2018; Moon, 2017). Additional highly
desirable attributes are capacity of the model’s to capture the un
certainties accompanying the analysis, planning for, and implementa
tion of the SDGs (Allen et al., 2019), in addition to the models’
applicability to multiple temporal and spatial scales, and their ability to
measure the impacts on the socio-environmental-economic systems
(Elsawah et al., 2020; Moon, 2017). Comprehending the position of the
models currently used in SDGs research of those attributes and delin
eating their dominant characteristics represent an important step to
ward enhancing a suite of techniques critical for achieving SDGs and
overcoming different issues related to them. Previous reviews mainly
tried to describe how scientific literature is concerned with the SDGs by
examining a large number of publications that are related to the goals, e.
g. Asatani et al. (2020); Meschede (2020); Armitage et al. (2020). Apart
from those universal reviews, others attempted to include modeling in
their review, however, they were either focused on a single type of
models, e.g. Pedercini et al. (2018), or discussed models and sustain
ability in general such as Boulanger and Bréchet (2005). To address
those issues, this research reviews the literature on SDGs modeling to
answer the following questions:
1. What are the prevalent modeling techniques currently used in
modeling SDGs?
2. Where do the models developed to address SDG issues fit in the
priorities of sustainability science?
3. What are the main characteristics of the models currently used in
modeling SDGs?
The analysis in this work was conducted on literature that discuss
models including single or multiple SDGs. In doing so, we aim to provide
a more detailed view of the characteristics of the models pertaining to
each SDG and to understand how the scientific community interested in
that SDG undertakes modeling efforts related to it. The structure of the
remainder of this paper is as follows. First, we report on a review of the
previous research focused on modeling sustainability and SDGs. Second,
an overview is provided of the methodology used in this research. Next,
we present the analysis and findings of the research. Finally, the im
plications of the findings are discussed along with highlights of research
gaps and potential future research avenues.
2. Methodology
The scope of the review presented in this paper is directed toward the
SDG framework and the different types of models directly and explicitly
related to it. Publications having general sustainability themes or dis
cussing models in sustainability contexts are not within the scope. Prior
to applying the research methodology illustrated in Fig. 1, a couple of
assumptions have been made to guide the steps of the methodology.
First, as our aim is to investigate the models or the modeling research
efforts carried out with the SDGs in mind, we only consider the articles
published after the declaration of the SDG framework, i.e. the articles
published from 2015 onward. This approach has been followed by
multiple reviews related to SDGs, such as Bennich et al. (2020), del Río
Castro et al. (2020), Armitage et al. (2020), Asatani et al. (2020), and
Bordignon (2021). The second assumption is related to the SDG focused
keywords used in the search query. Research efforts attempting to locate
scientific output discussing SDGs usually follow one of two approaches:
the first is to use search terms formed by combining keywords and
phrases directly excerpted from the UN’s SDG documents and textual
data such as NETWORK (2020), Olawumi et al. (2017), Asatani et al.
(2020), and Jayabalasingham et al. (2019). The second approach seeks
to acquire contextual data regarding the SDGs rather than just the
documents referring directly to the goals, e.g. retrieving all the docu
ments discussing the contexts of SDG1 by using terms like “poor
households” or “social protection”. Researchers who use subject area
terms instead of exact terms claim that this approach returns more
relevant results that have a wider scope (Bordignon, 2021; Armitage
et al., 2020). However, using the first approach yields scientific output
that is deliberate in including the SDGs in its research agenda, as well as
reflecting an awareness of the SDGs and their subsequent indicators
(Bennich et al., 2020; Allen et al., 2021; Meschede, 2020). Within the
context of this research we adopt the method of excerpting directly from
the SDGs texts as this method serves the purpose of this review by
limiting the search results to the ones deliberately centered around the
SDG framework.
The methodology outlined in Fig. 1 comprises six main steps. S1
focuses on determining the classification basis for the literature that will
be collected in later steps. In order to answer the research questions
raised in the introduction, three classification dimensions, each of which
is related to a certain research question, will be applied to the retrieved
articles. S2 includes setting the proper keywords that will be used to run
the search query in order to retrieve the most relevant papers. S3 is to
choose the database to be searched and retrieve the search results. S4
involves screening the retrieved results and selecting the relevant
literature. S5 includes applying the classification scheme determined in
S1 on the relevant set of articles resulting from S4. S6 consists of the
analysis of the classified articles, both those discussing modeling of
multiple SDGs and those whose focal point is modeling a single SDG.
Each of the steps is discussed in detail in the following sections.
2.1. Papers classification scheme (S1)
This step includes three sub-steps described in the following sections,
each of which determines a classification dimension related to one of the
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aforementioned research questions. It is worth noting that the order of
the sub-steps is not of particular significance.
2.1.1. Model typology classification (S1.1)
S1.1 aims to determine a typology for the models used in modeling
SDGs based on the modeling approach applied. Establishing a models’
typology and categorizing the models based on that typology allows
comparison of the models and exploring the utility of their subsequent
methodologies and techniques for representing SDGs. Such comparison
also facilitates exploration of the models’ strengths and weaknesses, as
well as indicating which models are most appropriate for the different
types of issues associated with each of the SDGs (Nicholson, 2007).
Despite the increasing use of models in the field of sustainability science,
and the realization of the importance of model classification, a stan
dardized model categorization scheme is still lacking Allen et al. (2016).
However, a few efforts have been made to provide a model typology; for
instance, Boulanger and Bréchet (2005) categorized the models used in
sustainability sciences based on the five most pressing issues related to
that science, and hence recommended six candidate models for sus
tainable development: macro-econometric models, computable general
equilibrium (CGE) models, optimization models, system dynamics
models, multi-agent models, and Bayesian networks. Moffatt (2006)
introduced a different classification which included additional types
such as database models, GIS models, and entropy maximizing models.
A more holistic classification that focused on model families instead of
single techniques was suggested by Allen et al. (2016) and Van Beeck
et al. (2000) who bundled together models with similar characteristics
and presented types such as bottom-up simulation models, bottom-up
optimization models, top-down system dynamics models, and hybrid
models. This latter classification, however, neglects other types of
models that can be very beneficial for sustainability sciences, such as
network models, Bayesian networks, and knowledge based models
(Kelly et al., 2013). Based on these classifications and the new trends in
modeling issues related to sustainability, we selected the eight
sub-dimensions presented in Table 1 to be used for the literature
classification.
2.1.2. Sustainability science priority classification (S1.2)
The S1.2 classification dimension is related to the role played by
science in advancing and accomplishing the SDGs. Since their adoption
in 2015, the SDGs have motivated a growing research agenda dedicated
to solving global sustainability problems and progressing the goals
(Saito et al., 2017). Nevertheless, the slow progress toward achieving
the SDGs, partly caused by the limited human ability to design
evidence-based transformative sustainable strategies, has prompted
multiple researchers to focus on exploring how sustainability-related
scientific output can be more effective in supporting the achievement
of the goals (Messerli et al., 2019; Schneider et al., 2019). Based on a
review of this line of research, Allen et al. (2021) describes these pri
orities as:
• Pr.1 Monitoring and evaluating the progress toward achieving the
SDGs.
• Pr.2 Understanding and managing the synergies and trade-offs
among the SDGs, and the design of interconnections-aware policies.
• Pr.3 Managing the transformations required to achieve the SDGs,
which also entails studying policy changes and exploring different
scenarios that can help in expediting the accomplishment of SDGs.
• Pr.4 Ensuring that achieving SDGs is consistent with preserving the
planetary boundaries and guaranteeing a safe space for humanity.
The retrieved SDGs modeling literature is classified based on which
Fig. 1. Overview of the research methodology.
E. Aly et al.

4
of the four most relevant priorities in order to evaluate how balanced the
modeling efforts are in terms of their relevance to the science priorities.
This classification also reflects the potential of models to bridge the gap
between academic efforts and policy making for each of the four
priorities.
2.1.3. Model attribute classification (S1.3)
The third and final classification dimension comprises three sub-
dimensions associated with the model’s spatial scale, uncertainty
treatment, and time treatment. The importance of the first sub-
dimension originates from the fact that integration of actions across
local, national, regional, and global levels is necessary for the achieve
ment of the SDGs (Stevens, 2018; Downing et al., 2021). Acknowledging
the importance of such consistent cross-scale integration, the 2030
sustainable development agenda requires UN members to set their own
national targets to conform with the global targets (UN, 2015). Incor
porating this multi-scale view in models, however, is a challenging task
that requires having different levels of knowledge and data about those
sub-systems at the considered scales (Elsawah et al., 2020). In order to
survey how SDG models handle the issue of multiple scales across the
sub-systems, a Cross Scale (CS) category was added to the Model spatial
scale.
The second model attribute to be monitored indicates how uncer
tainty is treated. Since uncertainty is consistently stated as one of the
major aspects to be accounted for when studying and modeling SDGs
(Allen et al., 2019), we are interested in the explicit consideration of
uncertainty and not just the statistical error reporting that naturally
comes with some economic and econometric models as this error and
confidence reporting is more suited for indicating statistical significance
without real value in informing decisions under uncertainty (McShane
et al., 2019; Wasserstein et al., 2019; Gelman and Carlin, 2017). Un
certainty, herein, is seen as the lack of clarity about the present and
future behavior of the social, environmental, and economic systems
targeted by the SDGs (Sharif and Irani, 2017). This view can be extended
to include the effects of the design options made during the model
building process, and how those design options affect the model per
formance and its final outcome (Elsawah et al., 2020). Furthermore, an
extensive consideration of uncertainty considers the combined effect of
the different sources of uncertainty, or what is termed as deep uncertainty
(Maier et al., 2016). Based on this view of the different types of uncer
tainty, three sub-dimensions were defined for model uncertainty treat
ment, a) the model does not account for uncertainty in anyway, b) the
Table 1
Sub-dimensions used for model typology classification.
Model type Type
code
Type description
System dynamics SD This modeling technique represents the
modelled system as a network of cause-and-
effect relations between the system variables.
The state of the system variables is represented
by “stocks”, while the rates at which these
variables change are represented by “flows”.
SD is mainly suited to simulating the dynamic
behavior of a certain system and the feedback
reactions between the variables included in the
model. This method is particularly useful for
understanding and exploring different systems
(Elsawah et al., 2017).
Agent based models ABM The system is expressed in terms of agents,
objects, and environments. Agents are
autonomous entities that behave according to a
pre-defined set of rules. This type of models
considered as a bottom-up approach and is
powerful in representing multidisciplinary
systems. As this method allows for stochastic
representation of agents and objects, it is
suitable for managing uncertainty (Epstein and
Axtell, 1996; Boulanger and Bréchet, 2005).
Bayesian networks BN A Bayesian network is an acyclic directed
graph, where variables are represented as
nodes connected with arrows representing the
causal effects between them (Aguilera et al.,
2011). Due to their probabilistic nature,
Bayesian networks are mainly used when
uncertainty is a key consideration in the model
(Kelly et al., 2013). Another strength of BNs is
their ability to handle different types of data, as
well as their ability to perform in cases in
which variables have incomplete data or are
latent. Although they don’t have the ability to
represent feedback loops, nor explicitly
represent spatial and temporal dimensions,
variations of the method, such as dynamic
Bayesian networks and object-oriented
Bayesian networks, can be used to overcome
such limitations (Benjamin-Fink and Reilly,
2017).
Economic models ECON This family includes the models that utilize
macro-economic analysis and are considered as
top-down models. This family of models
include CGE models, input-output models, and
growth models (Van Beeck et al., 2000).
Econometric models ECT This family of models relies on a suite of
statistical methods and techniques such as time
series analysis, structural equation analysis,
and are best suitable for predicting short to
medium term future. These models are also
useful in handling uncertainty (Allen et al.,
2016; Van Beeck et al., 2000).
Integrated models IM This approach combines multiple components
to leverage the strengths of different modeling
techniques. They can be used for a wide range
of applications due to their flexibility and
customizability. Their ability to combine
bottom-up and top-down techniques in one
model means that they are able to overcome
complex systems modeling challenges (
Hamilton et al., 2015). Although integrated
models can be developed using a single
modeling approach or a combination of
modeling approaches, their value comes from
their focus on the integration of various loci in
the modeling process, i.e. integration of
multidisciplinary knowledge, integration of
process, integration of temporal and spatial
scales, or integration with stakeholders (Kelly
et al., 2013). In the context of this analysis, we
categorize a model as an “Integrated model” if
Table 1 (continued)
Model type Type
code
Type description
the researcher states that integration is one of
the model’s intentions.
Knowledge based
modeling
KB Knowledge-based models uses the knowledge
elicited from users and experts to make
inference. They have the ability to incorporate
both qualitative and quantitative data, as well
as to handle uncertainty through the use of
fuzzy logic (Kelly et al., 2013).
Mathematical
quantitative models
MQM This type includes the models that rely on
mathematical and analytical methods such as
optimization models or linear programming
models. One of their main drawbacks is that
they require a high level of mathematical
knowledge and are hard to communicate to
stakeholders (Van Beeck et al., 2000).
Network models NM In network models, variables are represented
as nodes connected with weighted edges,
where the weights represent the levels of
association among the nodes. They are mainly
used to measure interlinkages, synergies, and
trade-offs among system components (
Newman, 2006).
E. Aly et al.

5
model includes basic error reporting techniques such as confidence in
tervals or root square error, and c) the model considers an extended view
of uncertainty. Various methodologies have been developed for dealing
with uncertainty (see for example Refsgaard et al. (2007) and Elsawah
et al. (2020)), however the methods utilized by the models covered in
this research are presented in more detail in the analysis and discussion
section.
The final sub-dimension indicates whether the model is able to
perform analysis over a certain time frame, or whether it is only inten
ded to evaluate a certain point in time, i.e. Dynamic or Static. Table 2
shows the possible values, description, and codes of each of the three
sub-dimensions of the S1.3 dimension.
2.2. Search queries design (S2)
This second step covers the design of the keywords to be used as
search queries to retrieve the documents that discuss modeling of SDGs.
The selection of search queries is an important step in the analysis as it
has a significant effect on the relevance of the retrieved documents and
their ability to answer the research questions. Such a step becomes more
critical when tackling topics such as the SDGs that have caused much
discussion. This is mainly because terms related to the goals are used
“loosely” within research papers, so that the existence of terminology
related to SDGs in an article does not in any way mean that the article
provide a direct concrete contribution to the topic (Armitage et al.,
2020). The same concept applies to the topic of “modeling and simu
lation”, as its terminology is used in several contexts beyond the scope of
this research. An effective search query should not be too narrow and
complex, nor so broad that it becomes prone to polysemy and false
positive results (Bordignon, 2021). As stated in the assumptions at the
beginning of this section, we limited our search using terminology
matching that used in description of each goal writing with the intention
to, first, retrieve only documents whose authors explicitly link their
work to the SDGs, and second, to retain specificity to the goals. Fig. 2,
illustrates the fields used to run the search. The fields can be divided into
three main levels: keywords related to modeling and simulation; SDGs
keywords; the conditions define the search temporal scope, document
types, and language. The exact keywords used for the search at each
level are included in the Appendix Table (A2).
2.3. Selection of database and literature search (S3)
In this third step, the search keywords combinations established in
step S2 were used to search the title, abstract, and keywords of publi
cations in two different multidisciplinary scientific databases, Scopus
and Web of Science (WoS). Previous research analyzing the content of
the two databases discovered considerable discrepancies in the coverage
of the two databases in terms of the number of publications as well as the
covered subject areas (Mongeon and Paul-Hus, 2016; Gavel and Iselid,
2008). Therefore, including search results from the two databases in the
analysis enabled us to draw more accurate conclusions. As shown in
Fig. 2, we limited our search to the documents published between 2015
and 2021 as the SDGs were initiated in 2015. We also designated three
types of documents to be retrieved: Paper articles, Conference pro
ceedings, and Book chapters. Finally, only English documents were
considered. The Scopus search returned 552 results, and the WoS search
returned only 203 documents.
2.4. Search results screening and selection relevant papers (S4)
Prior to filtering the retrieved articles and extracting the most rele
vant ones, the two sets of documents from the two databases are
combined and the duplicate results removed, which resulted in a set of
659 unique documents. Then, the unique search results are reviewed
and manually filtered according to the following relevance criteria:
1. Only papers that have direct contribution to SDGs were retained. For
example, a large number of papers include statements such as “This
research will help in achieving the SDGs” or “The 2030 agenda recom
mends that … " without the article topic being directly related to the
SDGs, nor contributing to any of the science priorities.
2. Only papers that present a model or discuss a modeling technique are
retained. The retained papers include those proposing qualitative
and quantitative models as long as they utilized one of the ap
proaches mentioned in Table 1. Some papers described the use of
certain computer software as a model, while others used the terms
“model” and “modeling” in irrelevant contexts such as building an
information model” or “topic modeling".
Based on these two filtration criteria, 101 papers were selected. In
addition to these 101 papers that discuss modeling of multiple SDGs, the
papers that focus on individual SDG were collected in a separate set. This
resulted in 17 additional sets each of which related to one of the SDGs.
The number of papers included in each of the 17 sets are shown in
Table A1 modeling.
2.4.1. Article model classification (S5)
After determining the most relevant modeling papers that discusses
multiple SDGs and single SDGs, each of the papers was manually clas
sified according to the dimensions presented in step S1. Table 3 sum
marizes the number of papers at each of the sub-dimensions and the
details of the paper classification are in Table SM2 Paper classification in
the supplementary materials.
2.5. Articles analysis (S6)
This step concludes the methodology by performing analysis and
drawing conclusions based on the retrieved documents and their clas
sification. The following section presents the details of the analysis.
3. Results
Application of steps S1 to S5 outlined in Fig. 1 enabled the collection
and classification of the literature ad-dressing the utilization of
modeling and simulation in SDG contexts. This section provides the
results of analyzing the selected papers which discuss modeling the
SDGs. While the main focus of this section ison the literature including
multiple SDGs in the models (the set of selected 101 papers), it also
presents a brief analysis for the literature discussing single SDGs.
3.1. Overview of the retrieved scientific literature
An overview of the collected documents indicates that modeling
applications for SDGs have grown rapidly from 3 publications in 2015, i.
e. the initiation of the agenda, to 28 publications in 2020. In terms of the
distribution of the models tackling the different science priorities, as
shown in Fig. 3, most of the modeling efforts have focused on Priority 3
Table 2
Sub-dimensions of the S1.3 model attributes dimensions.
Sub-dimension Code Description
Model spatial
scale
G R N
S
CS
Global Regional National Sub-national
Cross scale
Uncertainty
treatment
N
BER
EXU
The model does not account for uncertainty even if
the authors recognize uncertainty exists.
The model uses basic error reporting techniques to
represent uncertainty.
The model acknowledges higher levels of
uncertainty and uses special methods and techniques
to account for it.
Model time
treatment
S
D
Static
Dynamic
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i.e. Managing the transformation required to achieve the SDGs. This
trend rises monotonically unlike the trends of the other priorities which
fluctuate.
Priority 2, i.e. understanding and managing the synergies and trade-
offs among the SDGs was the second which modeling activities focused
on. This interest is aligned with the rapidly evolving field of studying the
SDG interactions and the increasing amount of literature produced dis
cussing this topic. Science Priority 1, i.e. monitoring and evaluating the
progress toward achieving the SDGs, did not gain traction until recently
which can be attributed to the fact that the modeling community needed
first to decipher the new framework and understand its dynamics. Sci
ence Priority 4, i.e. ensuring that achieving the SDGs is consistent with
preserving the planetary boundaries, was the focus of the least number
of models despite the concerns raised in recent years that achievement of
the SDGs does not necessarily guarantee the achievement of long-term
sustainability.
A review of the network of sources of the collected articles (Fig. 4)2
shows that they mostly came from multidisciplinary journals or sus
tainability oriented journals. “The Journal of Cleaner Production” and
“Nature Energy” were the most common, followed by “Sustainability
(Switzerland), “Ecological Economics”, and “Sustainability Science”.
The absence of simulation-oriented publications is an indication that the
simulation and modeling community still has significant role to play in
advancing sustainability sciences. In addition to the sources of the
publications, Fig. 4 also shows additional information about each: the
year of the publication represented by the color of the nodes in the
network and the number of citations represented by the size of the node.
We then looked on the most prevalent modeling techniques applied
in the context of SDGs. As Fig. 5 illustrates, 41% of the models included
in the considered publications were developed using an integrated
modeling approach. The ability of this technique to combine multiple
modeling approaches and balance the strengths and weaknesses of such
approaches, means that it is an efficient choice to model the complex
issues associated with SDGs. Additionally, when used in multidisci
plinary research, it allows participating parties from different back
grounds to combine the techniques that are most suitable to each of
them, which increases the model’s usability. Econometric techniques are
also commonly used as well as network modeling and system dynamics
as they represented 17%, 10%, and 9% respectively of the developed
models. The common use of these techniques, particularly econometrics
and network analysis, conforms with the sustainability science priorities
in Fig. 3 as they are mostly used for evaluation and modeling of different
policies in order to test their impacts on achievement of the SDGs as well
as analyzing the interdependencies among the goals. Fig. 5 shows that
techniques such as Bayesian networks and Agent based modelingmod
eling are under-utilized as they, combined, represent less than 5% of the
developed models.
The performance of the other two model attributes, i.e. spatial
treatment and temporal treatment, is presented in Fig. 6, which shows
that there was little variation in the temporal treatment in the models
with the number of static models being 55 and the dynamic models are
46. In terms of spatial scales, global and national models represented the
highest ratio. The dominance of modeling applications on national and
global scales reflects how SDGs are lacking in the level of detail required
for them to be applicable at more aggregate levels, and that aiding in
dicators are needed for the models to be beneficial for sustainability at
finer spatial scales Fioramonti et al. (2019); Bahadur et al. (2015).
Moreover, only 5 models attempted to address the cross scale issue when
modeling SDGs. Within the collected documents, only four publications
Fig. 2. The level of search fields using the set of keywords chosen in step S2.
Table 3
Number of papers retrieved for each of the sub-dimensions used to classify the
101 retrieved papers discussing modeling of multiple SDGs.
Dimension Sub-dimension (code) Number of
papers
Model type System dynamics (SD) 9
Agent Based Models (ABM) 2
Bayesian networks (BN) 3
Economic models (ECON) 7
Econometric models (ECT) 17
Integrated models (IM) 42
Knowledge based modeling (KB) 9
Mathematical quantitative models
(MQM)
2
Network models (NM) 10
Total 101
Sustainability science
priority
Priority 1 (Pr1) 14
Priority 2 (Pr2) 17
Priority 3 (Pr3) 56
Priority 4 (Pr4) 14
Total 101
Model spatial scale Global (G) 31
Regional (R) 16
National (N) 31
Sub-national (S) 18
Cross-scale (CS) 5
Total 101
Uncertainty treatment No uncertainty treatment (N) 42
Basic error reporting (BER) 25
Express uncertainty (EXU) 34
Total 101
Model time treatment Static (S) 55
Dynamic (D) 46
Total 101
2
The publications network analysis was performed using the free software
tool VOS viewer (Van Eck and Waltman, 2010).
E. Aly et al.

7
Fig. 3. Distribution of the number of models representing sustainability science priorities over the years.
Fig. 4. Layout of the journals where the collected articles are published.
Fig. 5. The most used modeling techniques in the collected papers. [IM: integrated models; ECT: econometric models; NM: network models; SD: system dynamics;
KB: knowledge based models; ECON: economic models; BN: Bayesian networks; MQM: mathematical quantitative models; ABN: agent based models].
E. Aly et al.

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presented models that addressed multiple scales. Aguiar et al. (2020)
proposed a novel approach to develop SDG achieving scenarios that
integrate global perspectives with national planning efforts. Their
method incorporates a participatory process where stakeholders repre
senting different scales provide insights about alternative pathways for
achieving SDGS. Shaaban et al. (2021) attempted to build an integrated
model to measure the impact of different agents’ behavior on achieving
the SDGs in agricultural contexts. They handled the cross-scale problem
by integrating different modeling techniques, each of which is known to
be effective in managing multiple scales in the model. Another model
that aimed to use global sustainability conditions to guide local land use
patterns was developed by Heck et al. (2018) who applied a top-down
approach to model the synergies and trade-offs among different SDGs
at different scales. Finally, Lucas et al. (2020) utilized a down scaling
approach to model the impacts of changes in the critical Earth systems
on national plans to achieve the SDGs.
In terms of uncertainty, 41.5% of the models did not include any
uncertainty treatment as shown in Fig. 7. For the models that had a view
of uncertainty beyond the basic error reporting, we found that seven
methods have been mostly used for uncertainty treatment. Scenario
modeling was by far the most used technique to account for uncertainty
in models as they represented 37% of the models included uncertainty
treatment. This method is recognized as one of the most effective
methods to handle uncertainty particularly for models serving decision
making proposes (Liu et al., 2008). Using this method, the modelers
investigate a suite of plausible future scenarios to evaluate potential
risks and opportunities as well as the implications of different decisions
(Allen et al., 2016). The other most used methods included Bayesian
analysis, multi-model analysis, and sensitivity analysis. Bayesian infer
ence is used to represent model parameters in a probabilistic manner
and can also reflect the uncertainty about model structure (Renard et al.,
2010), multi-model simulation on the other hand focuses on the un
certainty about model structure as assessment is performed using
alternative models for the same systems (Butts et al., 2004). The three
least-used methods were model experiments, expert elicitation, and
exploratory analysis.
In the final part of this section we draw a holistic picture of the
relationship among three of the main attributes measured for the
collected models: the science priority targeted by the model, the
modeling technique utilized by the model, and the spatial scale incor
porated in the model. The relationships among these three attributes are
represented by the diagram in Fig. 8, which shows how the choice of the
modeling technique is affected by the science priority targeted by the
model, as well as showing how the chosen modeling technique is related
to the treatment of space. For science priorities 1, 3 and 4, i.e. moni
toring and evaluating the progress toward achieving the SDGs, man
aging the policy transformations required to achieve the SDGs, and
ensuring that achieving the SDGs in consistent with preserving the
planetary boundaries respectively, integrated modeling was the domi
nant modeling technique, since targeting those three priorities is closely
related to dealing with the underlying social, environmental, and eco
nomic systems, therefore the used modeling technique needs to be able
to incorporate knowledge across different disciplines as well as having
the ability to make use of existing models to form a bigger more infor
mative model. In terms of spatial treatment, integrated modeling ap
proaches also proved to be suitable for applications most of the scales,
and particularly for cross-scale dimension. We can also see that at na
tional levels, econometric models are extensively used. This can mainly
be due to the availability of data used by the models on national levels.
In general, the relationships among the three variables indicate the
value of integrated modeling in advancing the science priorities and how
more efforts need to be directed toward advancing science priorities 1
and 4.
3.2. How modeling addresses the four science priorities
This section focuses on the details of the alignment between the
models and the priorities of sustainability science. In order to do so, we
map each of the science priorities to the models used to address it, then
inspect the characteristics of these models. Fig. 9 gives an overview
about the size of representation of each priority as well as the modeling
Fig. 6. Proportion of models belongs to the categories of spatial and temporal treatment among the collected documents. Space treatment abbreviations: [N: Na
tional; G: Global; S: Sub-national; R: Regional] Time treatment abbreviations: [D: dynamic; S: static].
Fig. 7. Distribution of uncertainty treatment categories and the methods used
to handle model uncertainty. [N: no treatment; BER: basic error reporting; EXU:
expressed uncertainty; SM: scenario modeling; SA: sensitivity analysis].
E. Aly et al.

9
techniques used in relation to that priority. At the very outer level, the
figure shows the proportion of the models that considered uncertainty as
a core part of the model development process. Although most of the
models were not developed around the sustainability science priorities,
they help to fulfil the priorities even if they do so unintentionally. The
modeling efforts related to each of the four science priorities are dis
cussed in more details in sub sections 3.2.1, 3.2.2, 3.2.3, and 3.2.4.
3.2.1. Modeling efforts related to science priority 1
The percentage of models in the collected literature that can support
science Priority 1: Monitoring and evaluating the progress toward
achieving the SDGs was found to be only 14%, however, integrated
modeling was used in 43% of these models. Reid et al. (2019), for
example, integrated three sub models related to monitoring the envi
ronmental and social systems in order to measure the progress in
goals#2, #6, #13, #14, and #15. Similarly, Long et al. (2020), estab
lished the key steps for achieving sustainability in an island community,
and measured how pursuing these steps contributes to progressing the
SDGs. Their model included social, physical, and economic sub-models.
In general, researchers who applied integrated modeling under the
umbrella of science priority one, benefited from the ability of the tech
nique to deal with multiple systems concurrently, and seamlessly inte
grate different data collection methods, especially with advances of
observation systems such as GIS. Additionally, being able to build a
model based on existing sub-models, facilitated the validation of the
final product and increased the level of confidence in the results (Kebede
Fig. 8. Links between science priorities, modeling techniques, and the spatial scale categories.
Fig. 9. The mapping between different model types and the priorities of sustainability science.
E. Aly et al.

10
et al., 2020; Marsh et al., 2016; Kostoska and Kocarev, 2019). The sec
ond most common type of models that was used for the same priority, is
econometric models which were used mainly to model applications
related to business and their contribution to achieve the SDGs. Those
business oriented applications mainly utilized the abilities of econo
metric models in dealing with vast amount of data that comes from
different sources and includes multiple inconsistencies (Heravi et al.,
2015; Rasoolimanesh et al., 2019; Alipour et al., 2019). A small portion
of the models in this group utilized the technique of system dynamics.
They mainly relied on this technique due to the dynamic nature of the
system they intended to model, as well as being interested in measuring
the feedback effects within their mode (Spaiser et al., 2019; Pedercini
et al., 2018).
ModelingmodelingThe number of papers related to science Priority
2: Understanding and managing the synergies and trade-offs
among the SDGs is almost equal to those related to the first priority,
with priority 1 representing 17% of the models and priority 1 repre
senting 14%. The most used modeling approach for priority 2 is network
modeling (41%), which is in fact one of the most favored techniques for
studying the interlinkages among SDGs (Bennich et al., 2020). Re
searchers apply network modeling to study the trade-offs and synergies
among the SDGs, capitalizing on the technique’s ability to produce
data-driven models that efficiently handle big-data available at multiple
sources (Sebestyén et al., 2019; Zelinka and Amadei, 2019). Another
important feature of network models appropriate to the second science
priority is how it can map different types of real life complex systems, e.
g. social and environmental systems (Weitz et al., 2018; Lim et al.,
2018). Moreover, unlike other techniques such systems dynamics, the
structure of the networks enable its user to investigate causalities in
modelled systems, therefore it can be used to unravel complex in
teractions among a large number of variables (Dörgo et al., 2018). In
tegrated models represented 18% of the models addressed priority 2
because of their ability to model complex systems that include different
components that have inconsistent natures was one of the major factors
influencing researchers’ selection. In addition, researchers also appre
ciated their ability to comprise trusted and reliable sub-models which
reduced the overall uncertainty of the integrated model Forouli et al.
(2020); Moyer and Hedden (2020). Integrated models were also used
because of their ability to capture the changes in different systems with
great accuracy which, when coupled with components to measure the
changes in the SDGs, revealed the underlying interconnections among
the SDGs (Banerjee et al., 2019; Heck et al., 2018; Mainali et al., 2018).
Science Priority 3: Managing the policy transformations required
to achieve the SDGs originates from the view of SDGs as a trans
formative agenda because in their essence they require serious changes
to current practices and strategies in order for them to progress at a
satisfactory rate (Randers et al., 2018). As the scientific community
began to understand how drastic and deep those changes need to be, it
gave tremendous focus to framing those changes, as a result, our analysis
shows that the third science priority is the most researched among the
four priorities. Research also put special emphasis on the inadequacy of
conventional modeling techniques to handle and manage the policy
transformations needed to make meaningful progress toward a more
sustainable future (Pedercini et al., 2020). This view is echoed in the
modeling efforts related to this third science priority. As it appears in
Figs. 9 and 45% of the modelingmodels related to priority 3 are devel
oped using integrated modeling. The papers related to this context
frequently utilize a combination of existing models that are academi
cally recognized, such as IMAGE (Van Vuuren et al., 2017), AIM (Fuji
mori et al., 2017), or WITCH (Emmerling et al., 2016). In relation to
SDGs, these models either use a single or a group of SDGs as an end
target then apply the model to backcast the policy changes needed to
achieve this goal (Fujimori et al., 2019; Vishwanathan and Garg, 2020;
Grubler et al., 2018; McCollum et al., 2018), or develop different sce
narios based national contexts and test them regarding their impacts on
progressing SDGs (Pedercini et al., 2018; Mulligan et al., 2020; Grubler
et al., 2018; Llorca et al., 2020; Rao et al., 2016; Allen et al., 2019).
Econometric models represented the second most commonly used
models for science priority three as 20% of the models was developed
using this technique. Although they are not as effective as advanced
modeling techniques such as integrated modeling, they are common in
social science and business applications (Zaini and Akhtar, 2019;
Tiyarattanachai and Chhang, 2019; Ul Hassan and Naz, 2020; Zhou
et al., 2020). System dynamics models were also for 9% of the models as
they allow policy makers to explore the impacts of their decisions on the
long term and how different variables in the system can respond to those
decisions at the future.
Science Priority 4: Ensuring that achieving the SDGs is consistent
with preserving the planetary boundaries and guaranteeing a safe
space for humanity is related to the existing contradictions in the SDGs
that have been argued by different researchers whose concern was that
the frame-work favors economic aspects of development over those that
have an environmental focus (del Río Castro et al., 2020; Hickel, 2019).
Evidence from different countries showed that progress toward social
and economic goals, such as SDGs 1 & 10, had adverse impacts on the
environmentally focused goals (Barbier and Burgess, 2017; Scherer
et al., 2018). These contradictions mean that the achievement of SDGs
does not entail following a sustainable development path, on the con
trary, it can indicate a deviation from that path due to the unwise use of
resources particularly for developing countries (Dasgupta et al., 2021).
To avoid such a predicament, some researchers proposed using com
plementary indicators to ensure that by achieving the sustainable
development goals, nations do not compromise long-term sustainability
(Saito et al., 2017; Dasgupta et al., 2021; Pothen and Welsch, 2019).
Moreover, another concern related to how expediting the achievement
of the SDGs may have adverse effects on a nation’s capital assets, is that
eroding the resources base of a country increases their vulnerability to
disasters and hazards, and consequently, decreases their resilience
(Yonehara et al., 2017; Bamberger et al., 2016).
Balancing the achievement of the SDGs and maintenance of the
Earth’s stock of resources to ensure a sustainable and safe future for all is
the focal point of the fourth science priority. It is suggested that this can
be achieved through the integration of the SDGs and other frameworks
that put more focus on resources preservation and maintenance of the
planetary boundaries (Plag and Jules-Plag, 2019; Dasgupta et al., 2021;
Stafford-Smith et al., 2017). This task, however, is not a trivial one
(Häyhä et al., 2016) and modeling can play an effective role in realizing
it (Allen et al., 2021). So far, modelingthe modeling efforts that have
been made toward this end are scarce. Among the notable efforts is the
work of Heck et al. (2018) in which integrated modeling was used to
assess land-use options against their impacts on planetary boundaries.
They did also consider the land uses that contribute to achieving certain
SDGs. Lucas et al. (2020) attempted to operationalize the concept of
planetary boundaries and apply it on a national scale utilizing scenario
modeling to advise SDGs target setting.
3.2.5. Modeling single SDGs
Fewer models have been developed to tackle issues related to a single
SDGs than the ones developed to handle multiple SDGs, as only 83
models have been located in literature covering the 17 SDGs (see
Table A1 for the number of papers related to each SDG). This is mainly
because of the nature of the SDGs as an indivisible and universal
framework. However, for the existing models in which a single SDG is
investigated, SDGs 2 and 6 were the most common with 11 papers for
each (as shown in Fig. 10). SDG 2 pertains to ending hunger, achieving
food security, and promoting sustainable agricultural. The models
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11
related to SDG 2 only covered science priorities 1 and 3, with more
models covering priority 3. The models focusing on policy issues, i.e.
priority 3, attempt to handle issues such the role of investment in
developing countries for advances of SDG2, or how farming policies can
affect food security (Mason-D’Croz et al., 2019; Scott et al., 2020; Gyasi
et al., 2021; Ritchie et al., 2018). The models related to monitoring the
progress of SDG 2, hence contributing to science priority 1, used mainly
econometric models with one model only relying on system dynamics
(see Kopainsky et al. (2018)).
SDG 6, related to ensuring the availability and sustainability of water
resources, gained a lot of focus as well with applications covering sci
ence priorities 2 and 3, however, one of the papers discussing it applied
data driven approach to develop a Bayesian network to its interactions
with other component of the 2030 UN agenda (Requejo-Castro et al.,
2020). Most of the papers in this group, i.e. the ones related to a single
SDG, fall under science priorities 1 and 3, however, SDG 4, 7, 11, and 12
contained papers relating to priority number 4. Under SDG 7, for
example, Tiba and Belaid (2021) used a suite of econometric models to
estimate how renewable energy can contribute to achieving SDG 7 as
well as having positive impacts on long term sustainability including its
social, environmental, and economic aspects. The full list of papers and
their classifications can be found in Table SM2 in the supplementary
materials.
4. Discussion
The results of this review provide useful insights for the modeling
efforts carried out to help expedite the achievement of the SDGs. First,
despite the important role that models play in tackling issues related to
SDGs and to sustainability in general (Pedercini et al., 2020; Allen et al.,
2016), research shows that they have a limited impact on decision
making and that very few models are directly used to influence legis
lation (Will et al., 2021). Among the main causes of this limited use of
models are the lack of a common language between modelers and policy
makers and the mismanaged expectations of policy makers concerning
the models’ capabilities and aspects (Will et al., 2021; Gilbert et al.,
2018). When policy makers cannot understand how the models are
developed and what assumptions affect the model output, they, in such
cases, see models as black boxes that can only be handled by modelers
which reduces their trust and acceptability of the models (Gilbert et al.,
2018). Communicating models’ aspects to decision makers can be a
challenging task for modelers as the former do not have much knowl
edge about the modeling techniques and do not have much interest in
knowing the technical details of the model (Harris and Howes, 2005).
The multi dimension classification of the models used in SDGs’ contexts
helps in simplifying the communication between decision makers and
modelers as the classification makes it easier to discuss the model
characteristics and link them to the model purpose. Having that type of
model typology can also help in the cases where multiple models are
developed for the same purpose as it will facilitate the conversation
between policy makers, stakeholders, and the model developers. Addi
tionally, when the model users understand the design decisions that
were taken by modelers, they can put the model into better use which
will, consequently, increases the model’s utility (Mayer et al., 2017).
That effective communication between modelers and policy makers is
particularly important at the early stages of the collaboration between
them, i.e. at the agenda setting, problem framing, and conceptual
modeling stages (Jann and Wegrich, 2017; McIntosh et al., 2007). The
decisions taken at these stages, either the ones related to policy or the
ones related to the model design, are influenced to a great extent by the
Fig. 10. Mapping single SDGs models to the science priorities and the modeling techniques related to each.
E. Aly et al.

12
mental frameworks of the collaborating parties (Moallemi et al., 2020).
These mental frameworks, or mental models, reflect the parties’
perception of reality and their understanding of the problem and how to
approach it (Blacketer et al., 2021; Gray et al., 2012). Having an
inconsistent shared mental model between policy makers and modelers
can cause conflicts as well as leading to undesirable model performance
(Lai et al., 2020). On the other hand, having a consistent shared mental
model increases the policy makers’ inclination toward adopting the
model as well as enhancing its performance (Hall et al., 2014). There
fore, it is important that policy makers are able to understand, to some
extent, the modeling choices made by the modelers and be aware of the
model design aspects (Voinov et al., 2018; Smajgl, 2015). By revealing
and delineating some of the SDG models’ design aspects, this research
implicitly participates in facilitating the communication between policy
makers and modelers.
Additionally, the results of this research provide another insight
related to policy formulation. It is noticed that at the times of crises, such
as the ongoing COVID 19 pandemic, economic growth is usually prior
itized as it is framed as a necessity for social and environmental devel
opment. Consequently, policies developed at such times tend to lack
environmental perspective as well as failing to have a balanced
consideration of the three sustainability pillars (Spangenberg, 2010).
While the SDG framework aims to help policy makers to keep social and
environmental systems at the center of their policies and to foster in
ternational cooperation (Ferranti, 2019), the framework is also criti
cized for having an imbalanced representation of the social,
environmental, and economic systems (Costanza et al., 2016; Fior
amonti et al., 2019). Such imbalance between the three sustainability
pillars does not guarantee that the policies designed to achieve the SDG
will contribute to long-term sustainability (Giannetti et al., 2020).
Another adverse effect disasters can have on achieving the SDGs is that
they weaken global engagement and motivate nations to retreat from
planetary thinking (DeWit et al., 2020). This goes against one of the
main principles SDGs aim at maintaining which is to leave no one behind
(Willis, 2016). These challenges highlight the importance of utilizing the
science priorities to inform policy making as well as modeling efforts. In
fact, they show that it is becoming more important to jointly consider
multiple priorities when devising policies related to the SDGs. Priority 3,
for example, needs to be an overarching target that is considered along
with any of the other priorities. Considering multiple priorities in SDGs
models entails the need for building more advance integrated models
and increases the complexity of the challenges modelers need to over
come in order to efficiently accomplish such task (Elsawah et al., 2020).
Another critical issue that is highlighted in the results of this review
is the representation and communication of uncertainty in SDG related
models. The models’ uncertainty treatment is among the main factors
that affect their acceptance and adoption by policy makers (Will et al.,
2021; Newcomb et al., 2021; Kolkman et al., 2016). The results of this
research show that uncertainty treatment was mostly either lacking or
done using simplified methods that are not suitable for the models tar
geting SDG issues. In order to robustly support decision making for
interacting social-environmental-economic systems, modelers are
encouraged to utilize more advance uncertainty treatment approaches
such as applying mixed uncertainty treatment frameworks as well as
being elaborate in documenting and reporting the uncertainty treatment
process and its underlying assumptions (Moallemi et al., 2020; New
comb et al., 2021).
Finally, the outcomes of this research can be useful for modelers
through their attempts to develop SDG related models by presenting the
common modeling practices found in literature. In that sense, these
common practices encapsulate the knowledge applied by other modelers
in this particular context, i.e. the context of SDGs related models
(Boissier, 2017). Although we do not provide an evaluation for the
modeling approaches used for the multi-dimensional classification, their
usage trends in literature can be informative for future modeling efforts.
5. Conclusion
This paper highlights the role played by modeling in advancing the
UN’s SDGs. The potential of models to advance the SDGs and sustain
ability is recognized and emphasized by multiple researchers from
different backgrounds. However, the models pertaining to SDGs need to
part of an organized research agenda that set the priorities for these
models and increases their effectiveness. From this start point, we
addressed three research questions regarding the main types of models
used in addressing SDGs; how aligned the developed models are with the
priorities of sustainability science as decided by the scientific commu
nity; and finally the main characteristics of the models developed in
SDGs contexts. In order to answer these questions, we performed a
literature review and analysis for 101 publications that address the ap
plications of modelingmodeling multiple SDGs as well as 82 publications
that address modeling single SDGs between 2015 and 2021. Prior to
analyzing the collected literature, we presented a multi-dimensional
model classification scheme that enables us to understand how models
are contributing to achieving the SDGs, as well as recognizing the pre
vailing characteristics of the models used in this application. The top
level of the scheme benefited from the advances of sustainability science
and the efforts made to delineate the gaps that need to be filled with
SDGs’ models. The classification and analysis of the retrieved literature
enabled us to answer the three research questions underpinning this
work.
The first of the three research questions motivating this work is
related to discovering the main modeling methods currently utilized in
building SDG models. Based on the reviewed articles, we found nine
modeling techniques are consistently used to directly address SDGs
related issues, namely, System dynamics, agent based modeling,
Bayesian networks, Econometric models, Economic models, Integrated
models, Knowledge based models, Network models, and Mathematical
quantitative models. Among these types, integrated modeling was the
most adopted modeling approach followed by econometric methods,
while the least applied approaches are Bayesian networks, agent based
modeling, and mathematical quantitative methods. The suitability of
integrated models to represent SDGs stems from their capacity to
leverage the strengths of different other modeling approaches and pro
vide a more flexible model that overcomes the limitations of other
methods. Additionally, mapping the modeling techniques employed to
the spatial scales of the developed models revealed that integrated
modeling is used for all the possible scales, especially for cross-scale
models which proved to be challenging to develop.
The second research question is underpinned by defining the current
priorities of sustainability research so we can assess if those priorities are
sufficiently addressed by SDG models. Locating the gap between the
topics SDG models focus on and the priorities of sustainability science
will help the modeling community to play a more effective role in
achieving the SDGs. According to recent reviews, there are four areas
that if addressed properly will greatly boost the implementation of the
SDGs. Those areas are: 1) monitoring the progress of the SDGs, 2) un
derstanding and managing the interconnections between the SDGs, 3)
studying the policy transformations needed to achieve the SDGs, and 4)
evaluating the consistency between achieving the SDGs and preserving
the planetary boundaries. According to the analysis of the current SDGs
models, 72% of the models focused on studying the policy
E. Aly et al.

13
transformations needed to achieve the SDGs, as well as understanding
the interconnections among the SDGs. There is therefore a need to direct
more modeling efforts toward studying the coherence between
achieving the SDGs and maintaining the long-term sustainability and
resilience of communities.
Finally, to determine the main characteristics of SDG-focused
models, we first defined three main characteristics with which to cate
gorize the models: the model spatial scale, how the model deals with
uncertainty, and whether the model is static or dynamic in time. Around
62% of the reviewed models were applied either on a global or national
scales, and only 5% of the models applied a cross-scale approach. This
can be attributed to the complexity of addressing multiple scales in the
same models which highlights the need for developing a more accessible
suite of scaling methods that can be applied by researchers from
different backgrounds. In terms of the way the models treat uncertainty,
we found that, surprisingly, a considerable portion of the studied models
(41.5%) do not include any treatment for uncertainty. Those which did
account for uncertainty, however, relied mainly on seven techniques,
the most common of which is scenario modeling followed by using
Bayesian methods. Sensitivity analysis and multi-model analysis were
also used. It was also found that almost half (46%) of the developed
models were dynamic models relying on a large amount of historical
data as well as trying to provide an analysis of future scenarios for
achieving the SDGs.
There are a number of directions for future research efforts. First, and
most important, is the development of models that address the gaps
outlined by the sustainability science priorities. Additionally, there is
more need to utilize probabilistic methods in building SDG models to be
able to address the uncertainty inherent to the social, economic, and
environmental systems underlying them. Also, more attention should be
given to building a more standardized approach for model categoriza
tion as a step toward creating a catalogue of models that allows policy
makers to choose optimal models for each application.
Moreover, while this research focuses on the models related to SDGs
extracted from literature, it is expected that these models reflect only the
point of view of researchers and modelers with academic background
and missing the direct input of policy makers. One way to increase the
applicability and efficiency of models in SDG contexts is to investigate
the models that have been already used by policy makers or applied in
real life projects. Following this investigation, interviews with parties
participating in these projects, i.e. model developers, government offi
cials, and stakeholders from community, can be conducted to extract key
lessons that can guide future modeling efforts. In addition, SDG
Voluntary National Reviews (VNR) can be investigated to check if they
contain any reporting on models’ usage.
Finally, while the focus of this research is on the technical aspects of
models, it is important to consider the political ramifications of those
technical aspects or design choices. Considering the political impacts
associated with policy guided by the model can be very helpful in un
derstanding the values guiding the SDG decision process from a policy
makers’ perspective and compare it to the values that influence the
scientists through their efforts to develop SDG models’ (or complex
systems models in general). While delineating the types and impacts of
values and biases that influence the decisions modelers make while
building environmental management models have been sought by some
research efforts (e.g. Mayer et al. (2017); Moallemi et al. (2020)),
comparing the modelers’ values to their policy makers’ equivalent can
positively affect the usability and efficiency of SDG models.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Appendix
Table A 1
The numbers of papers focusing on modeling
single SDGS
SDG No. of relevant papers
SDG1 6
SDG2 11
SDG3 10
SDG4 6
SDG5 4
SDG6 11
SDG7 7
SDG8 3
SDG9 3
SDG10 0
SDG11 8
SDG12 2
SDG13 3
SDG14 2
SDG15 1
SDG16 2
SDG17 3
Total 82
E. Aly et al.

14
Table A 2
The keywords used to run the search at different levels
Search Level Keywords
Modeling Keywords simulation OR modeling OR modeling OR (“integrated Pre/3 model*") OR “system*dynamics” OR “Bayesian network” OR BN OR “agent*based” OR
abm OR “multi agent” OR “scenario model*"
All SDGs Keywords sustainable development goals OR “SDGs” OR “2030 agenda"
Single SDG
keywords
SDG1 “sdg-1′′
or “sdg1′′
“sustainable development goal” near/3 (1 or one) “sustainable development goal” near/15 (poverty or poor)
SDG2 “sdg-2′′
or “sdg2′′
“sustainable development goal” near/3 (2 or two)
“sustainable development goal” near/15 (hunger or “food security” or “sustainable agriculture")
SDG3 “sdg-3′′
or “sdg3′′
“sustainable development goal” near/3 (3 or three)
“sustainable development goal” near/15 (“healthy lives” or “well-being")
SDG4 “sdg-4′′
or “sdg4′′
“sustainable development goal” near/3 (4 or four)
“sustainable development goal” near/15 (“education” or “learning")
SDG5 “sdg-5′′
or “sdg5′′
“sustainable development goal” near/3 (5 or five)
“sustainable development goal” near/15 (“gender equality” or “gender inequality” or (empower* near/3 women))
SDG6 “sdg-6′′
or “sdg6′′
“sustainable development goal” near/3 (6 or six)
“sustainable development goal” near/15 (“sanitation” or (water near/3 availability) or (water near/3 management))
SDG7 “sdg-7′′
or “sdg7′′
“sustainable development goal” near/3 (“7′′
or “seven")
“sustainable development goal” near/15 (“modern energy” or (sustainable near/3 energy) or (affordable near/3 energy))
SDG8 “sdg-8′′
or “sdg8′′
or “eight")
“sustainable development goal” near/15 (“economic growth” or (productive near/3 employment) or (sustainable near/3 growth) or (sustainable
near/3 economy) or (inclusive near/3 growth))
SDG9 “sdg-9′′
or “sdg9′′
or “nine")
“sustainable development goal” near/15 (“resilient infrastructure” or (sustainable near/3 industr*) or (inclusive near/3 industr*))
SDG10 “sdg-10′′
or “sdg10′′
or “ten")
“sustainable development goal” near/15 (inequality near/3 countries)
or “sdg11′′
or “eleven")
“sustainable development goal” near/15 ((resilient near/3 (cities or settlements)) or (inclusive near/3 (cities or settlements)) or (urban near/3
resilien*))
or “sdg12′′
or “twelve")
“sustainable development goal” near/15 ((sustainable near/3 (consumption or production)) or (clean near/3 (consumption or production)))
or “sdg13′′
or “thirteen")
“sustainable development goal” near/15 (“climate change” or “greenhouse gas” or (climate near/3 adapt*) or (climate near/5 resilien*) or (resilien*
near/5 “natural hazards”) or (resilien* near/5 “natural disaster*"))
or “sdg14′′
or “forteen")
“sustainable development goal” near/15 ((sustainbl* near/5 (ocean* or sea* or marine)) or “life below water")
or “sdg15′′
or “fifteen")
“sustainable development goal” near/15 (forest* or terrestrial or desertification or “land degra- dation” or “biodiversity loss")
or “sdg16′′
or “sixteen")
“sustainable development goal” near/15 (societ* near/3 (inclusi* or peac*) or (justice or effec* or accountable or inclusive) near/3 institutions)
or “sdg17′′
or “seventeen")
“sustainable development goal” near/15 (“global partenership” or “sustainble development finance” or “foreign aid” or “international trade” or
“international support")
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclepro.2022.130803.
E. Aly et al.

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