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Renewable and Sustainable Energy Reviews 199 (2024) 114462
Available online 11 May 2024
1364-0321/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
A review of macroeconomic modelling tools for analysing
industrial transformation
Ahmed M. Elberry
a
,
b
,
*
, Rafael Garaffa
c
, Andr´
e Faaij
b
,
d
, Bob van der Zwaan
a
,
b
,
e
a
Faculty of Science (HIMS), University of Amsterdam, PO Box 94157, 1090 GD, Amsterdam, the Netherlands
b
TNO Energy and Materials Transition, P.O. Box 37154, 1030 AD, Amsterdam, the Netherlands
c
Joint Research Centre, European Commission, 41092, Seville, Spain
d
Copernicus Institute of Sustainable Development, Utrecht University, 3584 CB, Utrecht, the Netherlands
e
School of Advanced International Studies (SAIS), Johns Hopkins University, 40126, Bologna, Italy
ARTICLE INFO
Keywords:
Macroeconomic models
Energy system models
CGE models
Circular economy
Industrial transformation
Technological change
ABSTRACT
This research presents a thorough evaluation of macroeconomic modelling tools in the context of analysing
industrial transformation. It emphasizes the need to link macroeconomic models with energy system models to
accurately depict industrial transformation. The study begins with a broad survey of macroeconomic modelling
tools. A detailed database of 61 tools is then compiled, providing a critical analysis of the tools’ structures and
features. From this broad spectrum, the focus is narrowed to Computable General Equilibrium (CGE) models. The
study develops a multi-criteria analysis framework, applied specically to four CGE modelling tools, which
encompasses 19 criteria categorized under four main pillars: Industrial/Sectoral representation, Technological
change, Employment, and Environment. This framework critically evaluates these tools’ suitability in analysing
industrial transformation, highlighting the diversity of their capabilities and limitations. Although the GEM-E3
model demonstrates a high level of alignment with the framework’s criteria, none of the four tools achieves a
full score in any category, indicating potential areas for improvement. The broader analysis of the database’s
tools reveals issues such as limited accessibility, inadequate representation of social aspects, and insufcient
geographical coverage. Additionally, the study notes a general lack of transparent information concerning the
full features of macroeconomic modelling tools in public literature. Concluding with recommendations for
further research, the study underscores the complexities in macroeconomic modelling and the need for
comprehensive tools that effectively address the multifaceted aspects of industrial transformation. Such ad-
vancements will assist in making informed decisions towards a transformation that is both environmentally and
economically sustainable.
1. Introduction
The year 2050 is approaching in which the Paris Agreement’s carbon
neutrality objective should be achieved [1], so accelerating the energy
transition is imperative. One of the most challenging sectors to decar-
bonize in the energy system is industry, which was responsible for a
quarter of global CO
2
emissions in 2021 [2]. Technological advance-
ments have long been a driving force behind the evolution of industry.
From economies-of-scale, automation, and articial intelligence to the
rise of the internet of things, technology has fundamentally altered the
way in which we not only work but also produce goods and services.
However, industrial transformation is not solely driven by technological
advancements; it involves a complex interplay of economic, social, and
environmental factors. The switch from fossil fuels to renewable fuels, is
a denitive example of industrial transformation. This shift triggers
growth in renewable energy technologies, such as wind turbines while
diminishing other conventional industries such as, coal mining.
Concurrently, the labour market adjusts to the expansion of job oppor-
tunities in renewable energy-related sectors and their contraction in
fossil fuel industries. The impact on global trade can be signicant,
posing challenges for fossil fuel exporters while offering new export
opportunities for countries that are excelling in renewable technologies.
These ripple effects also extend to industries that supply materials and
components for these technologies. Furthermore, governmental policies
(e.g. renewable subsidies), which often guide such transformations, may
change economic incentives and investment strategies across various
industries. Industrial transformation can therefore have far-reaching
* Corresponding author. Faculty of Science (HIMS), University of Amsterdam, PO Box 94157, 1090 GD, Amsterdam, the Netherlands.
E-mail address: a.m.a.i.elberry@uva.nl (A.M. Elberry).
Contents lists available at ScienceDirect
Renewable and Sustainable Energy Reviews
journal homepage: www.elsevier.com/locate/rser
https://doi.org/10.1016/j.rser.2024.114462
Received 1 May 2023; Received in revised form 25 December 2023; Accepted 22 April 2024
Renewable and Sustainable Energy Reviews 199 (2024) 114462
2
effects that extend from changes in investment patterns, productivity,
and international trade, to impacts on employment dynamics and
environment.
Macroeconomic modelling can be employed to inform on the po-
tential impacts of industrial transformation and assist in determining the
optimal technological choices to be made therein, while taking into
account its social, economic, and environmental dimensions. Due to the
intricacy involved in analysing economic systems, macroeconomic
models tend to use simplifying assumptions, which can result in certain
limitations. One such limitation is the lack of detailed technical data
about the energy system, which can lead to challenges in accurately
gauging the inuence of the energy transition on the economy. In con-
trary, bottom-up Energy System Models (ESMs) can provide highly
disaggregated technological details [3]. However, as partial equilibrium
models, they often disregard the interactions of the energy system with
the rest of the economy, and thereby fail to inform on key
socio-economic aspects, such as economic growth, employment, and
households’ consumption. Linking macroeconomic models with ESMs is
therefore a way to partly overcome the drawbacks of each of these
model types [4,5]. This linkage is particularly relevant for analysing
industrial transformation since production factors and energy re-
quirements may vary substantially according to the industrial activity –
e.g. the iron and steel industry is an energy-intensive industry, whereas
vehicle manufacturing is typically capital-intensive. In addition, the
integrated approach proffered by macroeconomic model–ESM coupling
can help in identifying potential synergies and trade-offs between
different policy objectives, such as economic growth and emissions
reduction. In turn, this can provide valuable insights for designing pol-
icies that foster an industrial transformation, which is both economically
sound and environmentally sustainable.
This study distinguishes between ‘modeling tools’ and ’models’. The
term ‘modeling tools’ refers to specic implementations of macroeco-
nomic models, such as the Modular Applied GeNeral Equilibrium Tool
(MAGNET), which are used for examining specic scenarios. These tools
are practical applications of broader model types. In contrast, ’models’
refer to the theoretical frameworks or types, such as CGE (Computable
General Equilibrium) or macroeconometric models, which provide the
foundational methodologies and structures for these tools. Our analysis
predominantly focuses on the practical applications and effectiveness of
modelling tools in the context of industrial transformation. The litera-
ture on macroeconomic modelling can be broadly classied into three
categories: general methodological comparisons, tool specic analyses,
and regional or country specic studies. The rst category comprises
studies that compare the methodologies used in macroeconomic
modelling in general (CGE versus macroeconometric), without a specic
focus on a particular tool. Examples include studies [6–9] that evaluated
different models for their effectiveness in analysing specic policies (e.g.
energy taxes). Other studies have also contributed to the understanding
of macroeconomic models of specic type and its application in
assessing climate change. Bergman, for instance, discussed
environmental policy in relation to CGE models [10]. An et al. and
Babatunde et al. have systemically reviewed the applications of CGE
modelling in evaluating the impacts of low-carbon policies [11,12].
Second category includes research that delve into specic macro-
economic modeling tools, examining their approaches in addressing
particular topics. For example, Faehn et al. reviewed the key approaches
used by seventeen tools in representing the emissions abatement tech-
nologies [13]. In a similar manner, Hafner et al. compared eleven tools
of different types with relevance to the transition in the power sector
[14]. The incorporation of R&D and innovation policies in three tools of
different spatial scales were examined by Akcigit et al. [15]. The nal
category features research that examines similar topics, but with a
specic focus on a particular country or region. For instance, a number
of studies examine the ability of different tools in proving insights spe-
cic to Ireland [18], Australia [19], Europe [20], and the United States
[21] with regard to the macroeconomic impacts of environmental
policies.
This research aligns with the second category but introduces a novel
focus by specically comparing macroeconomic modeling tools within
the context of industrial transformation assessment. Initially, the study
identies and reviews 61 macroeconomic modelling tools in use by
different stakeholders and organizations and documents all results in a
comprehensive online database. From this comprehensive analysis, we
create a shortlist of four CGE modeling tools which are then evaluated
using a comparison framework. The framework is designed to provide
an in-depth comparison of these tools based on their ability to address
the multifaceted aspects of industry while also considering their
adaptability for linking with ESMs.
The remainder of this paper is divided into three main sections.
Section 2 describes the methodologies that we apply in our analysis.
Section 3 reports and discusses the results of our review. Section 4
summarizes the study’s conclusions and provides recommendations for
further research. In each of these sections, we illustrate key macroeco-
nomic terms, and expand on the types of macroeconomic models, as well
as the limitations of macroeconomic modelling in general.
2. Methodology
We here describe the methodology used in creating the database for
macroeconomic modelling tools (section 2.1). We also explain the pro-
cess of creating the comparison framework that includes multiple
criteria aimed at evaluating the suitability of macroeconomic modelling
tools for examining industrial transformation. This framework serves as
a foundation for conducting a multi-criteria analysis (MCA) on a subset
of tools that we refer to as the ‘shortlist’ in the rest of this paper. Fig. 1
outlines the key stages of the research methodology.
2.1. Database of macroeconomic modelling tools
The creation of the database is aimed at comprehensively gathering
Abbreviations
AEEI Autonomous Energy Efciency Improvement
AHP Analytic Hierarchy Process
CGE Computable General Equilibrium
CI Consistency Index
CR Consistency Ratio
DSGE Dynamic Stochastic General Equilibrium
EPPA Economic Projection and Policy Analysis
ESM Energy System Model
GEM-E3 General Equilibrium Model for Economy-Energy-
Environment
GTAP Global Trade Analysis Project
I–O Input-output
IEA International Energy Agency
IGSM MIT Integrated Global System Modeling
ILO International Labour Organization
LULUC Land-Use and Land-Use Change
MAGNET Modular Applied General Equilibrium Tool
MCA Multi-Criteria Analysis
MESM MIT Earth System Model
OECD Organisation for Economic Co-operation and Development
RI Random Index
SDGs The United Nations Sustainable Development Goals
A.M. Elberry et al.
Renewable and Sustainable Energy Reviews 199 (2024) 114462
3
data about available macroeconomic modelling tools. We started the
research by conducting a literature survey utilizing a broad set of por-
tals: Google scholar, ResearchGate, ScienceDirect, SpringerLink, Se-
mantic Scholar, Google books, along with websites of organizations such
as the World Bank and the Organisation for Economic Co-operation and
Development (OECD). After identifying the tools, we collected data
about each tool with respect to the attributes displayed in Table 1. These
attributes can be divided into two categories: generic and specic.
Generic attributes focus on practical overall information such as type of
the model, which can be CGE, Macroeconometric, Input-output (I–O),
and Dynamic Stochastic General Equilibrium (DSGE). Specic attri-
butes, on the other hand, address more detailed information that is
particularly relevant to the scope of this paper, such as the number of
Sectors/Activities represented in the modelling tool (which indicates
how many sectors are responsible for producing goods and services in
the economy).
We compiled the data that we gathered into an online database by
utilizing a Google spreadsheet. To gain a deeper understanding of the
commonalities and disparities among the tools, we processed the data
and generated systematic displays such as visualizations and tabula-
tions, utilizing the Python programming language.
2.2. Framework development and MCA for the shortlisted tools
After a thorough review of the database, we decided that only
Computable General Equilibrium (CGE) modelling tools would be
considered for analysis, despite the presence of other model types. The
reason for this lies in the specic requirements of our research, partic-
ularly the need for a detailed representation of the industrial sector, and
the exibility to model both supply and demand over extended periods
of time. CGE models excel in these areas, which make them well-suited
for analysing industrial transformation [16–18]. While other types of
models may have their own advantages in analysing specic aspects of
the economy, comparing pros and cons of different types of models is
beyond the scope of this paper. In selecting the shortlisted CGE model-
ling tools, we based our decision on a variety of factors, including
literature availability, accessibility by third parties, and the presence of
active software support and updates. We narrowed down the focus to
tools that had the most prominent features with regard to our scope of
analysis, i.e. industrial transformation.
We created a comparison framework for the shortlisted tools, which
served as a foundation for identifying the appropriate criteria needed for
the MCA. This framework places considerable emphasis on the exibility
of the tools to be linked with ESMs, which play a crucial role in un-
derstanding the technical aspects of the industrial transformation as
elaborated in section 1. The framework includes 19 criteria, which are
classied into four categories, namely ‘Industrial/Sectoral
Fig. 1. A pyramid chart of the key stages of the research methodology.
Table 1
Attributes of the database.
Attribute Description
Type of Analysis Two major categories of analysis are static and
dynamic. Static models analyse the system state at one
point in time. Dynamic models examine how the
economy evolves over time by modelling how
variables change from one period to the next. There
are also other more specic types such as comparative
static, recursive dynamic and Intertemporal.
Type of model Examples of model types are CGE, Macroeconometric
and Input-output. Each type has a different modelling
approach. For example, macroeconometric models use
statistical techniques to provide economic forecasts,
while CGE models are mathematical tools that
simultaneously solve a set of equations.
Developer The developer of the tool, which can be an individual,
an institution, or a consortium.
Number of Sectors/
Activities
The number of sectors or activities that produce
commodities and services in the economy.
Accessibility The accessibility to the software and/or data sources
utilized in the tool. In some cases, one or more licenses
are required to access the tool and sometimes it cannot
be accessed at all.
Supporting software The supporting software is the platform in which the
model’s equations and variables are dened, and
where the model runs (e.g. MATLAB, GAMS).
Spatial scale The spatial scale of the tool, which can be Global,
National or Subnational.
Geographical coverage The countries and regions that the tool covers. For
example, a tool specic to the USA may include
disaggregation into its individual states.
Temporal scale The time duration during which the model runs (e.g.,
1990–2050).
Technological change Parameters and method (e.g. exogenous, endogenous)
used to represent technological change in the
economy.
Inclusion of modules The macroeconomic model can have internal modules
that represent some systems in the economy with extra
details (e.g. water-use or emission trading).
Representation of labour/
employment
Parameters and insights generated by the tool in-line
with the labour/employment representation in the
economy. For example, some tools can show the
unemployment rate as a result of a new policy or
technology. Other models can examine the effect of
changes in the minimum wage or social welfare
programs on the different groups of workers, such as
those with different levels of education or experience.
Data Source Public resources, national accounts, or established
databases (e.g. European statistics) used as input data
for the tool.
A.M. Elberry et al.
Renewable and Sustainable Energy Reviews 199 (2024) 114462
4
representation’, ‘Technological change’, ‘Employment’, and ‘Environ-
ment’. The criteria focus on the various components of the industrial
sector and endeavours to capture the pertinent social and environmental
interactions Table 2 presents these criteria and their denitions. In
identifying the criteria per category, we attempted to draw attention to
particular aspects, as discussed in the following paragraphs.
Industrial/Sectoral representation: the focus here is to analyse the
degree to which the industrial sector is represented in a given tool, with
particular emphasis on the number of sectors included and the degree of
exibility in technology choices within each sector. These features are
crucial for ensuring accurate industrial representation, as they can
reduce the aggregation of industrial sectors. For instance, when
considering electricity production, it is more reasonable to have inde-
pendent wind, solar, and coal sectors, rather than aggregating them into
a single sector, such as the power sector. This disaggregation enables
higher sectoral resolution, providing better scrutiny of the details of
each sector. Moreover, when linking macroeconomic models with ESMs,
a signicant challenge faced by modellers is the discrepancy in the
number of sectors and technologies between the two models. ESMs often
represent these aspects with detailed granularity, as exemplied by the
IESA-Opt ESM, which comprises over 700 technologies distributed
among multiple sectors [19]. Another example is the OPERA ESM,
which represents industrial sector in about 104 subsectors [20]. To
establish a link between the two models, the sectors and technologies in
the ESM must be aggregated until they align with those of the macro-
economic model. However, this process of aggregation would lead to a
considerable reduction in the level of detail in the industrial represen-
tation, which may impede the ability to obtain a comprehensive un-
derstanding of the energy system.
Technological change: when examining industrial transformation,
it’s essential to consider the critical role of technological change. At its
core, technological change refers to the potential increase in output
resulting from improvements in the production process [21,22]. Tech-
nological change can be categorized into three groups: exogenous,
endogenous, and semi-endogenous. Exogenous variables are inputs
provided by the user, while endogenous variables are calculated inter-
nally with a model’s equations. Semi-endogenous variables, on the other
hand, are a combination of exogenous and endogenous variables [23,
24]. A key aspect of our comparative framework is maintaining con-
sistency in assumptions across CGE models and ESMs. This is crucial for
effective linkage between these models. Using exogenous parameters,
such as Autonomous Energy Efciency Improvement (AEEI), ensures
that both models operate under the same set of assumptions about
technological progress. While endogenous and semi-endogenous
changes have their merits in modelling dynamic economic in-
teractions, the choice of exogenous technological change in our analysis
is a methodological decision tailored to the practical needs of models
linking.
Employment: in examining the labour force, there are two funda-
mental factors to consider; unemployment types (e.g., cyclical, invol-
untary/voluntary), and categories of skills [25]. Despite the importance
of such elements, most computable general equilibrium (CGE) models
merely take into account unemployment rates and the division of labour
into skilled and unskilled categories, with few incorporating involuntary
unemployment. Despite these limitations, we endeavoured to identify
criteria that would assess the broader interplay within the social loop of
the macroeconomy, such as labour mobility between countries or re-
gions, which is fundamental for enabling industrial transformation [26].
Incorporating these elements when examining changes in industry, can
provide a more comprehensive and nuanced understanding of the labour
force and its impact on the economy.
Environment: this category encompasses ve criteria designed to
assess the tools’ capability in covering today’s most critical environ-
mental issues. In identifying these criteria, we take into account the
limitations of CGE models when it comes to examining complex envi-
ronmental issues. For instance, studying certain issues may require more
Table 2
The framework’s criteria and their denition.
Index Criteria Denition/Aim
1 Industrial/Sectoral
representation
1.1 Number of sectors/activities Identies the number of sectors or
activities in the modelling tool, if the
number of sectors and activities are
unequal, the more comprehensive one is
considered.
1.2 More than one technology per
one commodity
Assesses whether the modelling tool
permits the utilization of more than one
technology for producing the same
commodity, such as the presence of a
green and a conventional technology for
steel production. Note that this criterion
does not consider the electricity
generation sector.
1.3 More than one commodity per
sector
Evaluates the modelling tool’s ability to
accommodate the production of more
than one commodity per sector (e.g. by-
products).
1.4 Fuel substitution per
technology
Veries if the modelling tool allows for
the substitution of one fuel by another for
the same technology.
1.5 Flexibility of aggregation and
disaggregation of sectors
Evaluates if the sectors can be aggregated
into fewer sectors or disaggregated to
include more sectors, which can allow
researchers to adjust the level of sectoral
detail according to the research question
at hand.
2 Technological change
2.1 AEEI/Technical progress Assesses if the modelling tool uses AEEI/
Technical progress and their equivalent
parameters to represent technological
change.
2.2 Learning-by-doing Assesses if the modelling tool uses the
learning-by-doing approach to represent
technological change.
2.3 Learning-by-searching Assesses if the modelling tool uses the
learning-by-searching approach to
represent technological change.
2.4 Exogenous Determines whether at least one of the
technological change parameters in the
modelling tool is exogenous.
3 Employment
3.1 Skilled and unskilled labour Checks if the modelling tool considers at
least two types of labour.
3.2 Labour mobility Determines if the modelling tool
evaluates the labour mobility across
regions or countries or sub-regions.
3.3 Involuntary unemployment
(Imperfect market)
Determines if the modelling tool accounts
for involuntary unemployment or
imperfect market.
3.4 Sectoral employment Checks if the modelling tool assesses the
employment per sector even if the tool
does so for only one sector (e.g.
agriculture).
3.5 Unemployment rate Checks if the modelling tool assesses the
unemployment rate for the whole
economy.
4 Environment
4.1 Water-use Veries if the modelling tool analyses the
water-use throughout the different sectors
as well as the household.
4.2 Land-use Checks if the modelling tool includes land
demand per sector and/or accounts for
land-use change (e.g. building on
cropland).
4.3 Natural resources Checks if the modelling tool evaluates and
accounts for depletion of the natural
resources (e.g. fossil fuel reserves).
4.4 Air pollution and health Evaluates the analyses of GHGs emissions
and/or air pollutants and/or the
population’s health in the modelling tool.
4.5 Material ow/demand/
recycling
Assesses if the modelling tool accounts for
material demand per sector and/or
includes sectors dedicated for material
recycling.
A.M. Elberry et al.
Renewable and Sustainable Energy Reviews 199 (2024) 114462
5
than simply including a detailed environmental module; it may neces-
sitate linking with Earth or atmospheric models. In this category, we try
to shed light on materials and natural resources management, which are
important global issues for several reasons. First, the Earth’s natural
resources are nite and their extraction and use can have negative
environmental impacts, including air and water pollution, and defor-
estation [27]. Second, the increasing global demand for materials and
natural resources, particularly in rapidly developing economies, has led
to resource depletion and price volatility. This has important implica-
tions for global political, and economic stability, and social equity [28].
Finally, the management of natural resources requires international
cooperation and coordination, as has been highlighted by the United
Nations Sustainable Development Goals (SDGs) [29]. The sustainable
management of materials, particularly through recycling, can reduce the
need for extraction and processing of new resources, minimize pollution
and waste, and contribute to the principles of circular economy [30].
However, despite these benets, sustainable materials management
practices tend to be underrepresented in both macroeconomic models
and ESMs [31,32], which is a signicant issue that we aim to highlight in
this paper.
The steps of applying the framework are as follows:
(1) Identifying articles and reports that mention or use the shortlisted
tools.
(2) Analysing and assessing each tool against the criteria listed in
Table 2 for each article or report.
(3) Marking all the criteria that were met by each specic tool with
an ‘x’, where x equals 1 if the criterion was met and 0 if it was not.
For criterion 1.1, we multiplied x by the corresponding number of
sectors. To ensure consistency, we normalized the values of cri-
terion 1.1 to be in the range of [0,1] by applying the linear
normalization method using Eq. (1). Criterion 1.1 is a benecial
attribute, meaning that the higher the number of sectors, the
more favourable the tool. Thus, the tool with the highest number
of sectors would receive the value of x =1 while the remaining
tools would be assigned a fraction of x.
(4) Repeating steps 2 and 3 for several articles and reports until it is
improbable that any more criteria can be met.
Yij =Yij
YMax
j
Eq. 1
To ensure a comprehensive evaluation of the shortlisted tools, we
conducted a total of ve MCAs: four individual MCAs, one for each
category, referred to as local MCAs, and one MCA to evaluate the four
main categories as a whole with relevance to one another, referred to as
the global MCA. This allowed us to assess the performance of each tool in
each category with greater precision. Moreover, such approach averted
any confusion or entanglements that could have arisen from the differ-
ences in nature of the criteria within each of the four categories. For
instance, criteria 1.1 to 1.5 (criteria relevant to industrial/sectoral
representation) were evaluated relative to one another and not with
reference to criterion 3.2 (labour mobility).
We utilized the Analytic Hierarchy Process (AHP) [33,34] to conduct
the MCA. The AHP has been extensively utilized for weighting in MCAs
and has been demonstrated to be competitive when compared to other
methods [35–37]. To construct the pairwise comparison matrices, we
mapped the criteria against Saaty’s scale of relative importance [34].
The next step involved normalizing the pairwise comparison matrices by
averaging each respective row to calculate the weights of the criteria.
The consistency of the weights was subsequently checked and validated
by determining the consistency ratio with relevance to the random index
(RI) scale [38]. The steps for determining the consistency ratio were as
follows:
(1) We calculated the weighted sum by multiplying the weights
assigned to each criterion by their respective relative intensities
of importance;
(2) To determine the lambda values, we used Eq. (2), and then
calculated the lambdaMax(λMax )by averaging the lambdas;
(3) We calculated the consistency index (CI) and consistency ratio
(CR) using Equations (3) and (4), respectively. In these equations,
“c" denotes the number of criteria, and “RI” denotes the random
index, which was determined based on the matrix size through a
table that maps the matrix size to random indexes [39].
Lambda(λ) = Weighted sumj
Weightj
Eq. 2
Consistency index(CI) = λMax −c
c−1Eq. 3
Consistency ratio (CR) = Consistency index (CI)
Random consistency index (RI)Eq. 4
It is important to note that for the weights to be considered consis-
tent, the CR should be less than 0.1 [40]. In case this condition was not
met, the pairwise matrices were re-evaluated accordingly. After veri-
fying the consistency of the weights, the MCA results were used to
compare the tools and rank them based on a preference score.
3. Results and discussion
3.1. The database
Table 3 presents the identied macroeconomic modelling tools as
well as references used to gather the respective information. The com-
plete database is accessible online at the following URL: [https://sites.
google.com/view/macromodelingtools/home]. In subsection 3.1.1, we
provide a summary of the features and characteristics of the tools with
respect to some selected attributes (as shown in Table 1), and also offer a
critical analysis of the relevant ndings. However, it is important to
acknowledge potential source of errors in our database. Given the vast
array of tools examined, there is a possibility that some information
might have been misinterpreted or not fully understood in its original
context. Additionally, these tools are continually evolving, and updates
or modications made after our data collection could lead to discrep-
ancies between our analysis and the current capabilities of the tools.
3.1.1. Insights on key attributes of the database
The database consists of 61 tools, 41 of which are CGE models while
the rest are of different types. We present a summary of our analysis for
the main attributes as follows:
3.1.1.1. Number of sectors/activities. The number of sectors in tools is
depicted by Fig. 2a and b. Analysis of the distribution curve reveals that
the majority of the tools has an average number of around 40 sectors.
Conversely, the likelihood of tools having less than 5 or more than 100
sectors is comparatively low. It is important to note that a large number
of sectors (>100) can increase the complexity of solving the model.
Alternatively, having only 5 sectors may not sufce in addressing certain
research questions. Nevertheless, the choice ultimately rests with the
modelers and the tools at their disposal.
3.1.1.2. Type of analysis. Fig. 2c indicates that recursive dynamic is the
most prevalent type of analysis followed by the dynamic type. Each of
the other types of analysis is associated with about ve modelling tools,
with the static type has the fewest number of tools associated with it.
This observation underscores the importance of dynamic modelling in
providing time-independent insights, which is especially critical for
policy makers.
A.M. Elberry et al.
Renewable and Sustainable Energy Reviews 199 (2024) 114462
6
3.1.1.3. Supporting software and data sources. The analysis of the data-
base shows that GAMS is the mostly used supporting software for all the
tools. One possible reason behind this is that GAMS enables users to
formulate their models in concise mathematical statements. In addition,
GAMS provides discounted licenses to academics, and offers a diverse
range of solvers that endorse the time efciency aspect.
Fig. 2d illustrates the most commonly utilized data source by various
tools. The Global Trade Analysis Project (GTAP) emerged as the most
frequently used data source, surpassing the use of personal databases,
the IEA, and national statistics. GTAP’s comprehensive coverage of
bilateral and multilateral cooperation among more than 140 countries/
regions, combined with its high accessibility, frequent updates, and
rigorous quality assurance of data, are likely factors contributing to its
prominent position [175].
It is also possible that the widespread use of GAMS and GTAP within
the CGE community and the fact that most of the tools in the database
are CGE models, contribute to their prevalence.
3.1.1.4. Spatial scale and accessibility. Fig. 3 presents an overview of the
spatial scale and accessibility of the 61 tools in the database. Among
them, 39 tools have a global coverage, 16 tools focus on the national
level, and the rest consider the subnational level. The popularity of
global models in macroeconomic analysis can be attributed to the
growing signicance of the global economy in today’s world. By simu-
lating the global economy, macroeconomic modellers can capture and
analyse the performance and prospects of the world’s economies,
especially with regards to the economic interactions between countries,
and the potential consequences of national economic decisions and
policies on other economies. However, the accessibility of the tools
varies depending on the model at hand, whether it is global, national or
subnational. Most global models can be freely accessed, while 16 models
allow free access to the code and require licences for either the sup-
porting software (e.g. GEMPACK) or the database (e.g. GTAP) and
sometimes both. Some tools in the three spatial categories are not
accessible at all and only consultancy services could be provided on a
fee-basis per project. A further type of accessibility that is specic to
global models is “Exclusive” accessibility, which means that the tool is
solely available to the host institute and its close partners for their own
projects without consultancy services being available. In some cases,
this lack of accessibility can be due to contractual arrangements related
to a specic project, or concerns related to data privacy.
3.1.1.5. Geographical coverage. Fig. 4 shows the number of tools that
represent countries individually, rather than as part of a region. The
world’s largest economies (e.g. the USA) and countries that have bilat-
eral trade with them, are individually represented by many tools, as
depicted by the map. Most modelling tools, however, do not include
many African, Middle Eastern, and Central Asian countries individually.
This is mainly due to their limited impact on the global economy. It is
important to note that some countries in these regions, particularly
major oil and gas exporters such as Saudi Arabia and the United Arab
Emirates, can have a substantial impact on the global economy.
Nevertheless, many modelling tools group these countries together as
part of the OPEC region because of their collective impact on global oil
production and prices. As production relocation becomes more preva-
lent, there is a growing need for modelling tools to expand their
geographical coverage and represent specic countries in greater detail.
For example, with the potential for hydrogen production in North Af-
rican countries [176,177], it becomes essential to include them
Table 3
The tools contained in the database.
Index Tool References
1 ANARRES [41]
2 CEEEA2.0 [42]
3 CETA [43,44]
4 DELFI [45]
5 DEMETRA [46]
6 DICE [47–49]
7 Dynamic Applied Regional Trade Model
(DART)
[50,51], Personal
communication
8 E3ME [52]
9 ENVISAGE [53,54], Personal
communication
10 ENV-Linkages [55–57]
11 EPPA [58,59]
12 EXIOMOD [60,61]
13 FTAP Model [62–67]
14 FUND [68,69]
15 G-Cubed [70–73], Personal
communication
16 GEM-E3 [74,75], Personal
communication
17 GEMST Personal communication
18 GEMINI-E3 [76–79], Personal
communication
19 GINFORS-E [80]. Personal
communication
20 GLOBE [18,81,82]
21 GRACE [83–85], Personal
communication
22 GTAP [86–90]
23 GTEM [91,92]
24 HERMES model [93–97]
25 HMRC’s CGE mode [98,99]
26 I3E [100,101]
27 ICES (Intertemporal Computable Equilibrium
System)
[102,103], Personal
communication
28 IEG-CGE [104–106]
29 INTERLINK [107,108]
30 LINKAGE [109,110]
31 MAGNET [111]
32 MAMS [112]
33 MANAGE-Mitigation, Adaptation, and New
Technologies Applied General Equilibrium
model
[113,114]
34 MEDEAS-World
MEDEAS-EU
MEDEAS-Country level (Austria and Bulgaria)
[115,116]
35 MONASH [117]
36 MIRAGRODEP-AEZ (MIRAGE (Modelling
International Relationships in Applied
General Equilibrium))
[118,119]
37 MSG3 model [120,121]
38 MULTIMOD [122–124]
39 MyGTAP [125–127]
40 NEMESIS [15,128–131], Personal
communication
41 NiGEM [132]
42 ORANI-G [133–135]
43 PEP-1-t [136]
44 PEP-w-t [137]
45 Phoenix [138]
46 POLES [139–141]
47 PINGO [142]
48 QUEST [143–145]
49 RHOMOLO [146–148]
50 RICE [149–151]
51 Second Generation Model (SGM) [152,153]
52 SNoW-NO [154–156]
53 STAGE [157,158], Personal
communication
54 The ECLAC - CIAM model [159,160]
55 TEA [161]
56 Term [162–164]
57 ThreeME [165–167]
58 US Macro Model [168,169]
Table 3 (continued )
Index Tool References
59 WEGDYN [170,171]
60 WITCH [172]
61 WorldScan [173,174]
A.M. Elberry et al.
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individually in the modelling tools as has been done with some tools
such as GTAP [89] and TIAM-ECN [178].
3.1.1.6. Temporal scale. The analysis reveals that the majority of tools
are designed to run up to the year 2050 or 2100. The underlying
explanation for this can be attributed to the fact that most international
climate strategies aim at 2050 as the year by which important emissions
reductions and climate goals should be achieved. For the year 2100, it is
Fig. 2. Charts for the macroeconomic modelling tools in the database.
Fig. 3. Spatial scale and accessibility of the tools.
A.M. Elberry et al.
Renewable and Sustainable Energy Reviews 199 (2024) 114462
8
because climate changes can have long-term and far-reaching effects,
with a 100-year timescale being a common benchmark. Thus, designing
tools that are capable of projecting outcomes up to these years is a
logical and strategic approach for analysing the impacts of climate
change.
3.1.1.7. Technological change. Fig. 5 displays some of the most
frequently used technological change parameters. An initial evaluation
of this chart suggests that each parameter is uniquely different from the
other, which is not entirely accurate. For example, apart from their
nomenclature, AEEI, technological progress, technological change, and
rate of technological change essentially measure the same concept that
is the technology change through the rate of efciency improvement. In
the same way, technical progress, technical efciency, and technical
change differ only in terms of nomenclature, and it is not clear how
developers of these tools determine the terminology for such similar
parameters. In macroeconomic models, technical change and techno-
logical change play crucial roles in explaining economic growth and
development. While these terms are often used interchangeably, they
have different implications for macroeconomic models. Technical
change is essentially concerned with the organizational changes in the
production function per se such as the enhanced efciency of the labour
Fig. 4. Geographical disaggregation of the tools.
Fig. 5. The most frequently used technological change parameters.
A.M. Elberry et al.
Renewable and Sustainable Energy Reviews 199 (2024) 114462
9
force [179,180]. Thus, a more efcient labour force will result in a
greater production output. Meanwhile, technological change manifests
itself in various forms that are technologically relevant such as adopting
state-of-the-art equipment with high efciency or enhanced materials,
which in turn will also improve the production efciency [181].
In regard to other parameters, such as learning-by-searching,
learning-by-doing and technology spillover, they mostly come as com-
plementary to technological change, and there are slight yet distinctive
differences between them. The reduction in costs of technology over
time as a result of experience and learning is usually described with the
learning curve concept, that correlates the historical increase of manu-
factured units, or installed capacity, to a fall in cost. Both parameters;
learning-by-searching and learning-by-doing are based on the learning
curve concept, however, the driver of the cost reduction in both ap-
proaches is different. Learning-by-doing is relevant to the reduction of
costs due to the repetition of the manufacturing process that leads to a
gain of experience and ultimately a more efcient production process.
Conversely, the main drivers for cost reduction in the learning-by-
searching approach are innovation and knowledge acquisition, which
contribute to the improvement of manufacturing processes [182,183].
There is a conceptual similarity between learning-by-searching, R&D,
and technology spillover parameters, though the latter specically refers
to knowledge acquired by one rm as a result of R&D conducted by
other rms without sharing the costs [184]. For instance, consider a
scenario in which Company ‘A’ conducted research aimed at improving
the production efciency of a specic commodity, and published its
results. Company ‘B’, which had not provided funding for that research,
was able to use the same results to enhance its production process. This
type of knowledge transfer is known as a technology spillover.
Considering the types of technology change used by the tools,
Table 4a demonstrates that exogenous technological change constitutes
the majority of the tools at 60%, while endogenous and semi-
endogenous technological change account for 22% and 3% of the
tools, respectively. On another note, the technological change can be
represented by one or more parameters, such as AEEI and learning-by-
doing or AEEI only. Table 4b illustrates how many parameters are
used to represent technological change in the tools. It compares the
number of tools that uses one parameter versus those that use two or
three parameters. The number of tools that use only one parameter is 37
while only 4 tools base their technological change on three parameters.
This can be due to the positive correlation between the number of pa-
rameters and the complexity of the model, where adding more param-
eters makes the model more complex, and further affect the degree of
freedom. Nine tools lacked any information on the technological change
dimension, which can be seen on both tables.
3.2. Shortlisted CGE modelling tools
In section 2.1, a set of criteria was established to guide the selection
of shortlisted tools. Despite meeting the criteria, some tools could not be
shortlisted due to either inadequate documentation or the existence of
multiple versions of the same model without proper documentation for
the core version. To overcome this information decit, we attempted to
contact these tools’ developers; however, this was not always a suc-
cessful method of obtaining clear information. Additionally, we
observed that despite the presence of some tools that have very inter-
esting features, and developed by large organizations (e.g., the World
Bank), the webpages of these tools were deserted, and did not contain
any updated contact information. This in turn resulted in a very limited
pool of tools that we can choose from. The tools that ideally met the
criteria and were most suitable for the shortlist are as follows: Economic
Projection and Policy Analysis (EPPA), General Equilibrium Model for
Economy-Energy-Environment (GEM-E3), GTAP standard model, and
Modular Applied General Equilibrium Tool (MAGNET). In following
sections, we provide background information on each of these tools, and
present the MCA results.
3.2.1. Background of the shortlisted tools
3.2.1.1. EPPA. EPPA is a CGE model developed by the Massachusetts
Institute of Technology (MIT) Joint Program on the Science and Policy
of Global Change. It is a multisectoral recursive dynamic model that can
be used to assess the effects of different energy and environmental
policies and regulations related to energy production and consumption,
land-use, natural resource depletion, and technologies deployment
[185]. EPPA also calculates the future GHGs emission and air pollutants,
which can be fed to the MIT Earth System Model (MESM) creating the
substrates of MIT Integrated Global System Modeling (IGSM). This can
in turn can be utilized in carrying out advanced climate scenarios ana-
lyses. One of the merits of EPPA, is that fossil fuels can be substituted by
clean fuels, such as hydrogen, which allows for studying the various
pressing issues concerning the new green fuels [186]. GTAP is the pri-
mary database for EPPA, however, with its geographical resolution
aggregated into 10 countries and 8 regions (e.g., Africa). While the in-
dustrial sectors are merely aggregated in EPPA, the power production
sector is quite detailed, providing a robust foundation for representing
the application of advanced technologies in this sector [187]. This is one
of the reasons why EPPA has been extensively used in assessing tech-
nological advancements besides evaluating energy and climate policies.
EPPA has also been used widely in land-use studies thanks to its
distinctive features in that regard where it categorizes land into ve
types (e.g. cropland), and allows farmers to convert their land to a more
competitive type given that they can afford the corresponding conver-
sion costs [188–190].
3.2.1.2. GEM-E3. GEM-E3 is a global recursive dynamic CGE model
that can run up to the year 2100, it is developed by a consortium of
institutions with the National Technical University of Athens as the
leading institution. One of the prominent features of GEM-E3 is its
ability to provide a thorough display of the interlinkages between
economy, environment, and energy system [191]. GEM-E3 typically
covers 38 regions that include the world’s major economies, and it
represents 31 sectors with 50 activities, which allows for a profound
representation of technologies. In addition to its superiority in techno-
logical representation, another interesting attribute of GEM-E3 is its
semi-endogenous technological progress that incorporates
learning-by-doing and learning-by-searching concepts [192]. This in
turn allows for capturing important trends like the effects of R&D in-
vestments on technological advancements. Like many other macroeco-
nomic modelling tools, GEM-E3 uses the GTAP database, however, it
also uses multiple data sources besides the GTAP, most notably, IEA
energy statistics and the International Labour Organization (ILO) data-
base [191,193]. The GEM-E3 model distinguishes between skilled and
unskilled labour, and estimates the corresponding unemployment rates
for each category. Furthermore, it considers involuntary unemployment,
which manifests the market’s imperfection [194]. The model has a
unique environmental module that covers about six of the major
greenhouse gases, and tracks their emission from each sector along with
Table 4
The technological change types and the number of parameters for the tools in the
Database.
(a) The types of technological change
for the tools
(b) The number of technological change
parameters for the tools
Types of
technological change
Number of
tools
Number of technological
change parameters
Number of
tools
Exogenous 37 1 parameter 38
Endogenous 13 2 parameters 10
Semi-endogenous 2 3 parameters 4
Lacking information 9 Lacking information 9
A.M. Elberry et al.
Renewable and Sustainable Energy Reviews 199 (2024) 114462
10
an integrated structure for an emissions trading market [195]. This
provides an opportunity to explore a variety of emission reduction and
trading policies. Materials are considered as one of the core inputs of the
production functions in GEM-E3, which further allows for tracking
material ow and consumption [196]. Numerous studies have used
GEM-E3 to explore various scenarios concerning the nexus of economy,
energy, and environment, such as energy taxes implications, labour
force role in economic development, and the effects of emissions
reduction policies [197–199].
3.2.1.3. GTAP standard model. While GTAP is well-recognized as a
database, there is also a macroeconomic model that holds the exact same
name. It is a multi-sector global CGE model that runs in a comparative
static mode [200]. The model covers 141 regions, and it comprises 65
sectors (based on GTAP database 10) that can be aggregated or dis-
aggregated in line with the research in question [56]. Although GTAP
does not fully meet the criteria to be shortlisted, it has been widely used
as a core model for dozens of other CGE models (e.g. MAGNET. FTAP).
Furthermore, the GTAP standard model has some unique features that
make it stands out, such as the ne industrial/sectoral representation
where the model allows for the production of more than one commodity
from one sector (e.g. by-products) [86,201]. This kind of exibility
provides the users with means for controlling the resolution of their
economic analysis, hence a wider pool of research questions to investi-
gate. There are a number of other GTAP models that are extended from
its standard model (e.g. GTAP-AEZ) [59]. These extended models are
tailored to look into and establish interconnections among different
sectors and systems that are closely linked to the most important issues
of our modern world, namely, land-use, agriculture, labour migration,
and power sectors [86,202].
3.2.1.4. MAGNET. MAGNET is a global CGE model developed by the
MAGNET consortium, which is led by the Wageningen Economic
Research in the Netherlands. It is a recursive dynamic model that is
calibrated to the GTAP database and runs up to the year 2100, dividing
the world into 141 regions. MAGNET is fundamentally based on the
GTAP CGE model but with various new features and upgrades that are
mostly closely related to environment and land-use [203,204]. The
sectoral representation in MAGNET is comprehensive where it covers
114 sectors that can be aggregated and disaggregated at the user’s
convenience and the same exibility in aggregation can also be applied
for the geographical regions [204,205]. In the same context, all the
model’s extra features can be switched off/on, which allows users to
tailor the model’s parameters and features in accordance with their
research questions. One of the attractive aspects of MAGNET is the
representation of some specic sectors that are uncommon in most CGE
models, such as sectors for waste collection as well as recycling of
different types of materials [111]. In a similar vein, MAGNET also
simulates emissions permits trading, and accounts for land-use and
land-use change (LULUC) emissions [111,206]. Therefore, MAGNET can
be regarded as one of the leading CGE models currently available for
analysing land-use related environmental issues.
3.2.2. Framework and MCA results
Table 5 presents the corresponding results obtained by applying the
framework for each tool in the shortlist, with an ‘x’ mark denoting each
fullled criterion except for the number of sectors criterion, which was
determined using Eq. (1) (as discussed in section 2.2). The table also
shows the corresponding weights for each of the criteria, as well as for
the four categories. The weights were calculated according to the steps
outlined in Section 2.2. The last two columns of Table 5 show the results
of the consistency ratios for the criteria’s weights, and as can be seen, all
ratios are below 0.1, indicating that the respective weights are
consistent.
The results of the local MCA are depicted in the radar chart shown in
Fig. 6. It can be seen that GTAP has the precedence for the industrial/
sectoral representation category, succeeded by MAGNET, GEM-E3 and
EPPA, in that order. Although the latter three cover only two out of the
ve criteria for that category, the different weights and values assigned
to each criterion resulted in the scoring discrepancies shown. Among all
the tools, GEM-E3 has the highest in the technological change category,
as it exclusively covers the learning-by-doing and learning-by-searching
criteria that are well-weighted (0,54 and 0,29, resp.) while the other
three tools had a balanced score of 0.2 in this category.
In the environment category, MAGNET and GEM-E3 received
slightly higher scores above 0.8, followed by EPPA and GTAP with
scores of 0.6 and 0.1, respectively. In terms of employment represen-
tation, GEM-E3 received the highest score due to its coverage of four out
of the ve criteria in this category, while MAGNET and GTAP had
balanced scores. EPPA did not cover any of the criteria and hence
Table 5
The framework’s results and the corresponding AHP weights.
Index Criteria EPPA GEM-E3 MAGNET GTAP Weight Consistency Ratio
Criteria The four-categories
1 Industrial/Sectoral representation 0,466 0,011
1.1 Number of sectors/activities 0,35×0,44×x 0,57×0,55 0,094
1.2 More than one technology per one commodity x 0,20
1.3 More than one commodity per sector x 0,14
1.4 Flexibility of aggregation and disaggregation of sectors x x 0,04
1.5 Fuel substitution per technology x x 0,071
2 Technological change 0,277
2.1 AEEI/Technological change x x x 0,12 0,077
2.2 Learning-by-searching x 0,29
2.3 Learning-by-doing x 0,54
2.4 Exogenous x x x x 0,06
3 Employment 0,096
3.1 skilled and unskilled labour x x x 0,48 0,061
3.2 Labour mobility 0,23
3.3 Involuntary unemployment (Imperfect market) x 0,16
3.4 Sectoral employment x 0,04
3.5 unemployment rate x 0,09
4 Environment 0,161
4.1 Water-use 0,14 0,075
4.2 Land-use x x x x 0,03
4.3 Natural resources x x x x 0,08
4.4 Air pollution and health x x x 0,49
4.5 Material ow/demand/recycling x x 0,26
A.M. Elberry et al.
Renewable and Sustainable Energy Reviews 199 (2024) 114462
11
received a score of zero for this category. Notably, none of the four tools
achieved a full score in any of the four categories, indicating areas for
improvement. Additionally, two important criteria, labour mobility and
water use, were not accounted for by any of the four tools, despite their
substantial signicance. This is particularly noteworthy because both
water scarcity and labour migration are recognized as signicant chal-
lenges worldwide [207,208].
The results of the global MCA are presented in Table 6, where the
normalized decision matrix with the weighted criteria is shown. GEM-E3
ranked rst with a score of 0.602 points, followed by MAGNET and
GTAP in second and third places, respectively. EPPA obtained the lowest
score of 0.27, placing it in the fourth and last position.
Based on the analysis of the local and global MCA results, it can be
inferred that GEM-E3 exhibits a strong potential as a modelling tool for
analysing industrial transformation. While GEM-E3 did not perform the
best in the industrial/sectoral representation category, it excelled in
other categories, such as employment representation and environment,
which are equally important when analysing industrial transformation
(as discussed in section 2.2). Furthermore, upgrading a macroeconomic
model to meet the criteria of the industrial/sectoral category may be
relatively straightforward. However, incorporating environmental ex-
ternalities and social aspects into macroeconomic models can be com-
plex and challenging, which has also been emphasized by several studies
(e.g. Refs. [156,209,210]). Therefore, we positively value macroeco-
nomic modelling tools that account for multiple environmental and
social factors.
4. Conclusion
This paper examines 61 macroeconomic tools on the basis of 13 di-
mensions. All the ndings are publicly available in an online database.
We provide a comprehensive presentation of our ndings and a critical
analysis in order to convey a deeper understanding of the multitude of
macroeconomic modelling tools available in the literature.
In analysing the database, the results reveal that:
•Many tools are not accessible by third parties. We here stress that
accessibility to macroeconomic modelling tools is crucial for re-
searchers in the eld of economics and related disciplines, as it en-
ables them to conduct robust and comprehensive analyses of various
economic phenomena. In addition, the availability of these models
can help to promote transparency in research, as it allows other re-
searchers to replicate and build upon existing studies.
•Inadequate or absent representation of social aspects is present in a
number of tools. It is essential to consider social aspects while uti-
lizing macroeconomic models in general, and particularly when
analysing industrial transformation. For instance, structural unem-
ployment is a social aspect that can signicantly impact industrial
transformation. This type of unemployment is often caused by
structural changes in the economy, such as technological advance-
ments or shifts in industry demand, and can persist even in periods of
economic growth. Incorporating such aspect into macroeconomic
models can help policymakers to create a more adaptive and exible
labour market that drives industrial transformation in a way that
benets all members of society.
•Most of the tools have a relatively small number of sectors, averaging
around 40, which we believe is inadequate to provide an accurate
depiction of the industrial sector. Because the energy system is an
integral part of the economy, a comprehensive analysis requires
linking ESMs to macroeconomic models. If the ESM has more sectors
than the macroeconomic model, then the impacts of policies on the
energy system may be overestimated, while the impacts on the
economy may be underestimated. Therefore, it is generally consid-
ered important to match the number of sectors between the two
models as closely as possible, while also ensuring that the sectors are
dened in a consistent manner between the two models. However, it
is important to recognize that there may be practical limitations in
matching the number of sectors exactly, and that some level of ag-
gregation or disaggregation may be necessary depending on the
specic research question being addressed.
•There is a paucity of individual geographical coverage for MENA and
Central Asian countries. These countries are expected to play a key
role in global hydrogen production given their vast renewable energy
potential, which can be used to produce green hydrogen. Several
projects are currently underway in MENA countries to develop
hydrogen infrastructure and production facilities, including pilot
projects for green hydrogen production in Morocco and Tunisia, as
well as plans for blue hydrogen production in Saudi Arabia, the
United Arab Emirates, and Egypt. These initiatives are expected to
contribute signicantly to the development of the hydrogen econ-
omy in the region and beyond. Therefore, we anticipate that most
tools will expand their geographical disaggregation to include some
Fig. 6. Local MCA results; illustrating comparative performance across the four pillars.
Table 6
Ranking and preference Scores of the shortlisted tools based on the global MCA
results.
Tool Preference Score Rank
EPPA 0,268 4th
GEM-E3 0,602 1st
GTAP 0,436 3rd
MAGNET 0,508 2nd
A.M. Elberry et al.
Renewable and Sustainable Energy Reviews 199 (2024) 114462
12
of these countries individually as it is becoming increasingly
important to consider their role in the global energy transition.
For a more comprehensive analysis, we shortlist four modelling tools
(EPPA, GEM-E3, MAGNET, and GTAP) and conduct an MCA using a
comparison framework that has been specically tailored for this study.
Our results indicate that GEM-E3 yields the highest overall score. This
does not rule out the other tools. Rather, it portrays the research op-
portunities manifested in developing and extending their capabilities. It
is also important to acknowledge that our analysis was conducted from a
particular angle (i.e. industrial transformation), which may have
obscured other strengths that these tools might have in other facets. On
the other hand, the MCA demonstrates that none of the shortlisted tools
had a full score in any of the four categories: industrial/sectoral repre-
sentation, technological change, employment, and environment.
Furthermore, none of the tools met the criteria for labour mobility and
water use. As we argue that linking with other models (e.g. hydrological
model) can be a solution for the latter issue, the presence of some basic
elements in macroeconomic models is crucial for such linking to take
place.
Our framework is not impeccable, and it can indeed be improved. For
instance, certain criteria are rather general in nature, as exemplied by
the labour mobility. Here, we observed that when two tools consider
labour mobility as a factor, the depth and manner in which labour dy-
namics are modelled can signicantly vary, which may lead to different
implications in the analysis. The different perspectives of economists,
engineers, and policymakers can also be compiled as part of further
research to identify more criteria. In addition, the framework does not
delve into specic economic concepts (e.g. economic theories) as part of
the criteria, given its focus on global CGE models where signicant
overlap exists among the various tools in this regard.
The ambiguity and lack of basic information in the literature about
many of the tools constitute a major impediment to this study in terms of
forming an integrated analysis. In this regard, we attempted to shed light
on the importance of prospective users having a clear understanding of
the characteristics, features, and limitations of the tools. Otherwise, they
will lack the adequate basis to make an informed decision about which
tool to use. We also observe that despite the growing global focus on
sustainable and circular practices, macroeconomic modelling tools are
failing to fully account for the positive impact these elements can have
on economic growth and productivity. This lack of proper representa-
tion of circular economy principles could result in policymakers missing
opportunities to make well-founded decisions that support sustainable
economic development and preserve natural resources for future gen-
erations. Future research in this area should aim to improve and expand
upon existing macroeconomic models to ensure they are better equipped
to address these pressing issues. Developers of tools should also strive to
include further features to their tools aiming at lling the criteria
identied in this study. Thereby, the tools can be used in carrying out
more holistic and inclusive analyses for industrial transformation.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper
Data availability
I have provided a website for the data in the article
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