Renewable and Sustainable Energy Reviews 132 (2020) 110105
Available online 25 July 2020
1364-0321/© 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Modelling the renewable transition: Scenarios and pathways for a
decarbonized future using pymedeas, a new open-source energy
systems model
J. Sol�e a,o,*
, R. Sams�o a
, E. García-Ladona a
, A. García-Olivares a
, J. Ballabrera-Poy a
,
T. Madurell a
, A. Turiel a
, O. Osychenko a
, D. �Alvarez b
, U. Bardi c
, M. Baumann d
, K. Buchmann e
,
�I. Capell�an-P�erez b
, M. �Cerný l
, �O. Carpintero b
, I. De Blas b
, C. De Castro b
, J.-D. De Lathouwer g
,
C. Duce b
, L. Eggler d
, J.M. Enríquez b
, S. Falsini c
, K. Feng j,n
, N. Ferreras b,p
, F. Frechoso b
,
K. Hubacek j,k
, A. Jones e
, R. Kaclíkov�a i
, C. Kerschner l,m
, C. Kimmich l
, L.F. Lobej�on b
, P.
L. Lomas b
, G. Martelloni c
, M. Mediavilla b
, L.J. Miguel b
, D. Natalini e
, J. Nieto b
, A. Nikolaev f
,
G. Parrado b
, S. Papagianni h
, I. Perissi c
, C. Ploiner d
, L. Radulov f
, P. Rodrigo b
, L. Sun j,n
,
M. Theofilidi h
a
Institute of Marine Sciences (ICM-CSIC). Passeig Marítim, 37-49. 08003, Barcelona, Spain
b
Research Group on Energy, Economics and System Dynamics (GEEDS), University of Valladolid, Paseo Del Cauce, 59 47011, Valladolid, Spain
c
Consorzio Interuniversitario Nazionale per La Scienza e La Tecnologia Dei Materiali (INSTM), C/o Dipartimento di Chimica, Universit�a Degli Studi di Firenze, Via Della
Lastruccia 3, 50019, Sesto Fiorentino, Firenze, Italy
d
Austrian Energy Agency (AEA), Mariahilfer Straße 136, 1150, Wien, Austria
e
Global Sustainability Institute, Anglia Ruskin University (ARU), East Road, Cambridge, CB1 1PT, UK
f
Black Sea Energy Research Center (BSERC). 7, Viktor Grigorovich Str., Fl. 2, “Kriva Reka” RA, 1606, Sofia, Bulgaria
g
Blue 4 You (B4Y). Avenue Brugmann 611, 1180, Brussels, Belgium
h
Centre for Renewable Energy Sources and Saving (CRES), 9th Km Marathonos Ave, 19009, Pikermi Attiki, Greece
i
Hnutí DUHA - Friends of the Earth Czech Republic, Údolní 33, 602 00, Brno, Czech Republic
j
International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A2361, Laxenburg, Austria
k
Integrated Research on Energy, Environment and Society (IREES), Research Institute Groningen (ESRIG), University of Groningen, Groningen, 9747 AG, the
Netherlands
l
Department of Environmental Studies, Masaryk University (MU), Jo�stova 10, 60200, Brno, Czech Republic
m
Modul University, Department of Sustainability, Governance, and Methods, Am Kahlenberg 1, 1190, Vienna, Austria
n
Department of Geographical Sciences, University of Maryland, College Park, MD, 20742, USA
o
Department of Mechanical Engineering, Universitat Rovira i Virgili, Campus Sescelades, Av. Països Catalans 26, 43007, Tarragona, Spain
p
CARTIF Foundation, Parque Tecnol�ogico de Boecillo 205, 47151, Boecillo, Valladolid, Spain
A R T I C L E I N F O
Keywords:
Biophysical constraints
Climate damage
Energy efficiency
GHG emissions
Raw materials
Energy costs
A B S T R A C T
This paper reviews different approaches to modelling the energy transition towards a zero carbon economy. It
identifies a number of limitations in current approaches such as a lack of consideration of out-of-equilibrium
situations (like an energy transition) and non-linear feedbacks. To tackle those issues, the new open source integrated
assessment model pymedeas is introduced, which allows the exploration of the design and planning of
appropriate strategies and policies for decarbonizing the energy sector at World and EU level. The main novelty
of the new open-source model is that it addresses the energy transition by considering biophysical limits,
availability of raw materials, and climate change impacts. This paper showcases the model capabilities through
several simulation experiments to explore alternative pathways for the renewable transition. In the selected
scenarios of this work, future shortage of fossil fuels is found to be the most influential factor of the simulations
system evolution. Changes in efficiency and climate change damages are also important determinants influencing
model outcomes.
* Corresponding author. Institute of Marine Sciences (ICM-CSIC), Passeig marítim, 37-49, 08003, Barcelona, Spain.
E-mail addresses: jsole@icm.csic.es, jordi.sole@urv.cat (J. Sol�e).
Contents lists available at ScienceDirect
Renewable and Sustainable Energy Reviews
journal homepage: http://www.elsevier.com/locate/rser
https://doi.org/10.1016/j.rser.2020.110105
Received 26 July 2019; Received in revised form 14 July 2020; Accepted 14 July 2020
Renewable and Sustainable Energy Reviews 132 (2020) 110105
2
1. Introduction
Today’s societal challenges require new tools and models that
consider, in an integrative way, aspects such as climate change impacts
[1], impacts and vulnerabilities due to resource limitations [2], biophysical
resources management [3] and human impacts on ecosystems
[4].
Models can be classified following the fields/areas they analyse or
the features they can represent. For instance, for economic analysis the
most used models are Computational General Equilibrium (CGE) models
and the Dynamic Stochastic General Equilibrium (DSGE) models. In the
field of energy market modelling, bottom-up dynamic partial equilibrium
models like the MARket ALlocation models and TIMES, are widely
used, and combine technical engineering and economic approaches [5].
If the features they represent are analysed, eight aspects could be
considered [6]: (i) complexity, non-linearity, non-ergodicity and deep
uncertainty, (ii) the importance of time, (iii) agents’ heterogeneity and
behavioural elements, (iv) interdisciplinary aspects (v) role of institutions
and social context, (vi) ethical and philosophical aspects, (vii)
finance and (vii) multiple equilibria/disequilibrium. In the case of CGE
or the DSGE for instance, they do not have the possibility to represent
out-of-equilibrium situations (like an economic crisis or energy transition),
non-linear feedbacks or other system characteristics related to
complexity. In this line [6], classified eleven models following these
eight aspects and four general types: econometric, system dynamics,
agent-based and Stock-Flow Consistent (SFC) models. In the econometric
type they distinguish those that are Keynesian and
post-Keynesian [6]. identified eleven models that met such criteria and
in the case of pymedeas, it is a system dynamics, econometric (using an
input-output approach) model framed in a post-Keynesian approach.
Although there are different models combining System Dynamics
and input-output analysis in the literature [7], most of them put the
emphasis on only one side: System Dynamics [8,9] or input-output
analysis [10,11]. More balanced presentations are rare and specific for
applications, such as ecosystem services analysis [12,13] or other more
focused applications. From the point of view of climate impacts and
socio-economy aspects, Integrated Assessment Models (IAM) have been
used to explore future scenarios under Climate Change IPCC projections
[14]. They use different models for each of 5 Shared Socio-economic
Pathways (SSP), some of them are IMAGE [15], MESSAGE [16] or
POLES [17] (a review of IAMs for IPCC scenarios could be found at the
IAMC website https://www.iamconsortium.org, and at [18]). However,
SSPs do not consider resource limitation scenarios as a main hypothesis.
The resources role (minerals, fossil fuels availability/cost) in the projections
is considered, for instance, in the World 6 model [19]. World
models have their origin in the limits to growth work [20], all World
models are global with no geographical disaggregation. Thus, the current
different simulation tools here mentioned (economy, energy, IAMs)
have a constellation of different aspects, scattered through different
models, but they do not consider the possibility to jointly analyse the
resource limitations’ role, the impact of climate and the feedbacks with
the environment, the economy evolution (in terms of efficiency) and
also how these different aspects are affecting lower geographical levels
(region and country).
Thus, this approach of linking the two methodologies for energyeconomic
analyses at different geographical levels represents a step
forward to the current models’ state of the art because pymedeas is a
model that mixes economy and energy-resources analysis using a novel
coupled Input-Output energy-resources methodology. The novelty of
pymedeas within the existing modelling approaches lies also in the new
scenarios defined to frame the model runs. Different scenarios, which
define initial hypotheses on key parameters and system evolution hypotheses,
can be fed to the model as input data. The scenarios act as a
framework that helps understand the links between exogenous variables
and endogenous variables of the model like GHG emissions and the
energy-economy evolution and thus, climate change impacts on the
economy. The key exogenous variables that make up each scenario
include those related to the expected rate of deployment/implementation
of RES, transport electrification, the efficiency improvement, population
growth or economic growth, amongst others.
Here we introduce the detail of pymedeas, a new open-source piece of
software (written in Python 3), for exploring the transition to a low
carbon socio-economy [21,22]. This process relies mainly on the
commitment of the European Union to reach at least 32% renewables by
2030 and reduce its GHG by 40% until 2030 and by 80–90% until 2050,
compared to 1990 [23,24]. This requires intense emission reduction
[25], particularly in the energy sector, changing it from fossil fuel-based
to RES based, in a process called the Renewable Transition.
The pymedeas models have three main objectives. The first is to
identify the key physical variables for the energy transition and their
relationships with economic indicators, socio-economic variables and a
range of different environmental impacts. The second objective is to
quantify the implications of the emerging challenges within the implementation
of a transition to a low-carbon economy. These may include
the impact of technological parameters (e.g. RES capacity factors, Energy
Return On Energy Investment or energy intensities evolution), and
how to overcome possible drawbacks and provide solutions. Finally, the
third objective is to suggest policies and strategies to face such challenges
when drafting the roadmap to a European future socio-economic
transition to a sustainable energy system.
The approach to energy systems modelling builds on four pillars:
open source, transparency, user friendly and community -based. The
model was originally developed with the Vensim® software [26] and
later translated, with the help of the PySD library, into Python code to
have an open source and transparent tool. In this paper we use the Python
version of the model. The open source nature of the Python code is
Abbreviations
Business as Usual (BAU)
Computational General Equilibrium models (CGE)
Dynamic Stochastic General Equilibrium models (DSGE)
Electric vehicles (EV)
Energy Return On Energy Invested (EROI)
Environmental and Climate damage (CLIMA)
Evolution of Energy Efficiencies (EFFI)
Fossil fuels scarcity (SCAR)
Graphical User Interface (GUI)
Greenhouse Gases (GHG)
Green Growth narrative (GG)
Gross Domestic Product (GDP)
Hydrofluorocarbon (HFC)
Input-Output tables (IOT)
Methane (CH4)
Optimal Level Transition (OLT)
Perfluorocarbons (PFC)
Pumping-storage hydroelectricity (PSH)
Representative Concentration Pathways (RCPs)
Renewable Energy Sources (RES)
Root Mean Square Difference (RMSD)
Shared Socioeconomic Pathways (SSP)
Special Report on Emissions Scenarios (SRES)
Sulfur hexafluoride (SF6)
World Input-Output Database (WIOD)
J. Sol�e et al.
Renewable and Sustainable Energy Reviews 132 (2020) 110105
3
guaranteed by the MIT license; all code can be downloaded for free from
the project’s website (https://medeas.eu/model/medeas-model) or
from a public Gitlab repository (https://gitlab.com/MEDEAS/pymede
as_models). The transparency aspect is guaranteed by a detailed and
extensive documentation of the code, also available in the website. The
MIT license, the Git repository (contributions from any third party are
welcome) and a users’ forum in the projects’ website are expected to
encourage the development of a sense of a community around the tool.
Finally, user friendliness is achieved by providing a GUI for plotting
simulation results and allowing the visualization and comparison of
outputs from independent simulations.
The pymedeas model can be framed in the set of energy system
models within the Open Energy Modelling (OpenMod) initiative (https
://www.openmod-initiative.org). Currently, there is a growing interest
in opening up energy models for increasing transparency [27,28] so
initiatives as OpenMod are gaining users and the number of models
there, has been enlarged. In this sense, pymedeas supports the open
modelling initiatives, not only by distributing the software under an
open-source license but also the MEDEAS database management system
(MEDEAS database: https://medeas.cmima.csic.es).
This paper is structured in four sections. In materials and methods,
the structure and features of the pymedeas software, and the scenarios
and hypotheses are described. In addition, the benchmark procedure
used to compare simulation results from different scenarios is presented.
In the Results section the simulation results under the different hypotheses
are interpreted, focusing on characteristic output variables.
Finally, the last section is dedicated to discuss the model and the main
results in the framework of the state of the art of the current modelling
tools for energy systems analysis.
2. Methods
The pymedeas models can simulate at three geographical levels:
World, the European Union and Austria (pymedeas_w, pymedeas_eu and
pymedeas_at respectively). Although in this work we will only use World
and EU geographical levels, the user can also take the region-based
(pymedeas_eu) and country level (pymedeas_at) models and adapt
them to different regions and countries. The model design and structure
accounts for aggregated variables in an hybrid bottom-up top-down
approach, and the default simulation period spans from 1995 to 2050.
This simulation period can be shortened or extended according to the
needs of the analysis. The pymedeas models at the different geographical
levels are nested in a one-way approach (the model in a larger
geographical level, called parent model, includes the one with a smaller
geographical level, also referred to as child model). Therefore, a
coherent simulation of the parent model must be run in order to generate
the boundary conditions to be used by the child model. Both models
have the same structure and characteristics, and they both include the
following aspects: a) resources limitation, b) climate change impacts, c)
energy efficiency and d) dynamic evolution of the EROI [29,30]. The
EROI of a system corresponds to the ratio between the final usable energy
(exergy) and the amount of energy used to obtain such energy. The
model structure, software, scenarios and main hypotheses to compare
the behaviour of the model are introduced bellow.
2.1. The model’s general structure
The model takes into account the effects of biophysical limits,
resource limitations and climate change impacts on the economy [26,31,
32]. Fig. 1 shows the interrelations between the 7 modules included in
the model, represented by boxes, the main characteristics of which are
[26]:
� Economy and population: the economy is modelled [33] assuming
non-clearing markets (i.e. not forcing general equilibrium),
demand-led growth and supply constraints. This assumes that
demand matters in both the long as well as the short run, so that a
competitive market economy has no natural or automatic tendency
towards full employment [34,35]. The economic structure is
captured by the integration of IOT (35 industrial sectors and
households) following the WIOD (www.wiod.org), for each region
(World and EU). MEDEAS builds on the open-access database WIOD
[36], to remain consistent with the open-source aspect. The link
between the monetary and the energy one is performed through the
modelling of sectoral final energy intensities [37].
� Energy: this module includes renewable and non-renewable energy
(extraction reserves and resources) and availability, taking into account
biophysical and temporal constraints. In total, 5 final energy
carriers (electricity, heat, solids, gases and liquids) and a diversity of
energy technologies are modelled. A net energy approach is applied
[38] endogenously and dynamically accounting for the EROI of both
individual technologies and that of the system. The demand of energy
is affected by the variation of the EROI of the system [38].
� Energy infrastructures: represents the infrastructures of power plants
to generate electricity and heat.
� Climate: this module projects the climate change levels (rising temperature)
due to the anthropogenic GHG emissions, which will produce
economic costs [39,40]. This module is an adapted version of
the C-Roads climatic model [41].
� Materials: the module tracks the material requirements for the construction,
operation and maintenance of the energy infrastructures.
The extraction demand is subsequently compared with the levels of
available reserves and resources.
� Land-use: this part of the model accounts for the land requirements
of the RES.
� Social and environmental impacts: this module contextualizes the
implications for human societies in terms of well-being for each
simulation. It translates the “biophysical” results of the simulations
into metrics related with social and environmental impacts.
The model evolution and integration across modules requires, for
each time step, the energy demand and supply to be balanced dynamically.
The energy demand is driven endogenously by the economic activity
simulated by the evolution of IOT structure [42] and the energy
intensities (which translate the monetary flows into final energy by
carrier) and also by the expected GDP evolution introduced exogenously
by the user (in the input file). The IOT provide the basis to account for all
direct and indirect demands from each sector, including energy
demand-based on the sector-level energy intensities. WIOD includes a
detailed, sector-level disaggregation of energy demand in its environmental
accounts [43]. When final energy demand is lower than final
energy availability, the expected demand (external, desired GDP) is
fulfilled and economic growth is possible. On the other hand, if demand
exceeds supply, then final demand adapts to available final energy.
Thus, the demand of the next time-step is estimated taking as reference
the consumption of the last time-step, constrained by an energy scarcity
function to account for fuel supply limitation.
The final energy consumption by fuel is covered by a mix of technologies
(energy infrastructures module), in the consumption of primary
energy. In the current version of the model, material availability does
not directly constrain the deployment of technologies given the uncertainty
in available metrics of reserves and resources.
The extent of the primary energy consumption by fuel translates into
a certain amount of GHG emissions, which affect climate change which,
in turn, feeds back into human societies through a certain level of impacts
on e.g. adaptation costs, increase in energy needs. Climate effects
are translated to the economy by a cost damage function [41], which
will increase the energy consumption depending on the atmospheric
GHG concentrations in each time step.
J. Sol�e et al.
Renewable and Sustainable Energy Reviews 132 (2020) 110105
4
2.2. The pymedeas code
The pymedeas code is the result of translating to Python the Vensim®
versions of the models [26], using the PySD library [45]. The code can
be downloaded either from the project website (https://medeas.eu/mod
el/medeas-model) or from the public git repository (https://medeas.eu/
model/medeas-model). The version of the code used in this work corresponds
to v0.3.1.
When running simulations, either the pymedeas_w.py or the pymedeas_eu.py
(depending on the users’ choice) is loaded as a PySD Model
object [45]. By design, the Model class maintains only a single state of
the system in memory, and therefore the future state of the model is
calculated strictly based upon their current state. The integration is
carried out using the Euler method.
The user interacts with the models through a command line interface
that wraps the main functionalities of the PySD. This way, users can
modify model parameters, the time-step, and the final simulation date
(among others) without any previous knowledge of Python. The outputs
of the simulations are stored in the form of a Pandas DataFrame. The
three model files (pymedeas_w.py, pymedeas_eu.py and pymedeas_at.
py) share elements in common listed in Appendix A. pymedeas comes
with a plot tool (plot_tool.py) which is a GUI aimed at displaying
simulation results obtained with the pymedeas models (for more
description please refer to Appendix A).
The model input data is provided with the downloaded package, but
can also be visualized/downloaded from the MEDEAS database management
system linked in the project website or at www.medeas.cmima.
csic.es.
2.3. Scenarios
The scenarios used here are Business as Usual (BAU), which projects
into the future the current trends of all input variables, and Optimal
Level Transition (OLT), which increases significantly the deployment of
RES. OLT, represents a scenario of economic growth and reduction of
energy use through improvements in energy efficiency. In addition, OLT
increases recycling rates of minerals and boosts the transition to low
carbon energy production [23,46].
The differences between the two scenarios start to manifest from
2020, which is the date in which most policies start to take action in the
OLT scenario. The two scenarios were previously explored from the GHG
emissions perspective [47], where the carbon budget (the cumulative
carbon emissions) [48] for the world and the EU were used to estimate
decarbonization pathways until 2050. Thus, taking the policy of the
European Union to reduce emissions by 80–90% with respect to their
1990 value by 2050 [23,46], the emissions from 2020 to 2050 can be
estimated with a further constraint on cumulative greenhouse gases
released until 2050. The scenarios used here take a decarbonization
hypotheses [37] as starting point, but, the model trajectories are not
constrained by the cumulative GHG emissions till 2050. OLT sets a target
on the annual GHG emissions, rather than cumulative emissions,
following EU policy. In this way, the BAU scenario does not follow Paris
Agreement while OLT does. The interest of BAU is to warn about the
consequences of not complying with the current international agreements
on climate change [49].
The BAU and OLT scenarios take the SSP [51] as a reference. SSPs are
scenario frameworks for facilitating the integrative analysis of future
climate change impacts, vulnerabilities, adaptation and mitigation.
They are based in five narratives, describing socio-economic and
Fig. 1. MEDEAS model with modules and links.
Table 1
Summary of scenarios characteristics.
Scenarios characteristics BAU OLT
GDP per capita and population change SSP2 SSP2
NRE energy resource availability Medium Medium
RES deployment Medium growth Very rapid
Technology development Slow Medium
Environmental protection Reactive Both reactive and proactive
J. Sol�e et al.
Renewable and Sustainable Energy Reviews 132 (2020) 110105
5
political evolution, including aspects such as fossil-fuel development,
regional rivalry or inequality. A summary of the characteristics for the
two scenarios is shown in Table 1. Both BAU and OLT follow SSP2 for
population and GDP per capita projections, while the other variables
and hypotheses differ from SSP2. In BAU, RES deployment growth follows
current trends, while in OLT it assumes significant, very rapid,
deployment, to reduce GHG emissions. OLT RES rapid deployment is
supposed by a medium technological development (compared to slow
development of BAU).
In each scenario the general framework is setup; however, particular
conditions can be considered exogenously in each scenario, such as the
assumptions for the evolution of economic indicators, technological
efficiencies or biophysical constraints. Some of the multiple choices of
the model are explained in the next subsection.
2.4. Hypotheses used
The behaviour of the model is evaluated by comparing a set of
simulation results obtained by turning on one at a time, the following
model hypotheses in both scenarios:
� Evolution of Energy Efficiencies (EFFI): defines the way the energy
intensities of the different industrial sectors evolve over time. Historical
values of the energy intensities are used between 1995 and
2009. The evolution of the energy efficiency using the energy intensities
is already explored in previous literature [34,37]. By
default, in the BAU scenario, energy intensities from 2009 evolve
following historical trends while in OLT the trends from 2009 are
imposed. Modifying the default state of this parameter corresponds
to keeping the energy intensity constant at its value in 2009 until
2050, for BAU and OLT. It is assumed fixed trends in the evolution of
Energy Efficiencies (using the energy intensity) are the simplest
assumption to consider. This behaviour is also consistent with the IO
tables, which in their historical yearly evolution do not show high
variability of sectoral flows.
� EROI feedback on energy demand (EROI): the EROI accounts for net
energy and corresponds to the energy delivered from a process
divided by the energy required to get it over its lifetime [52,53]. The
pymedeas model endogenously calculates the EROI of the whole
system over time [38] Using the default setting (for both BAU and
OLT), the variations of the EROI of the system over time do not affect
the energy demand. Modifying the default state corresponds to
making the energy demand sensitive to the variation of the EROI at
every time step.
� Environmental and Climate damage (CLIMA): The modelling of the
damages caused by environmental/climate changes is performed
through the integration of an energy loss function that reduces the
overall net energy delivered to the society, based on growing atmospheric
CO2 concentrations and land-use change. The severity of
the damage is regulated by selecting among the four RCPs [54],
which provide different scenarios of the temperature change due to
GHG emissions. With the default setting, climate change does reduce
the net energy delivered to society, RCP 8.5 is used for BAU and RCP
2.6 for OLT. This is also consistent with the damage functions used in
the literature [55].
� Fossil fuels scarcity (SCAR): limitations in the availability of fossil
fuels caused by resource constraints e.g. Peak-Oil [56]. The default
setting imposes maximum depletion curves [57], for the different
fuels obtained from recent literature, therefore putting limits to
production. A Hubbert curve is a bell-shaped curve that shows an
approximation for the production rate of a limited resource over
time. Modifying the default setting removes any limitations for the
production of non-renewable fuels, making them infinitely available.
The four hypotheses introduced here help to analyse the role of the
combined impact of climate/environment, economic evolution,
technological change and resources in the future evolution of the socioeconomy.
Such analysis are aligned with recent research focused in the
trade-off between sustainability and over-consumption [58]. Overall
there are 4 hypotheses that can take two different values (default,
non-default) each. This results in a total of 16 simulations per scenario
(see Table B1), hence 32 simulations per geographical level and 64
model simulations considering the two nested models. Consistency is
kept between the scenarios of the parent and the nested models (World
and EU), meaning that, the EU simulations for BAU correspond to the
World BAU, and the same for OLT. Each simulation is given an identifier
following the convention shown in Fig. 2.
2.5. Comparison methodology
The results of each of the simulations are compared to those of the
simulation with default settings (S1111). Although it is theoretically
possible that Europe might opt to undertake the renewable transition
regardless of the situation at global scale, this situation will not be
considered and therefore the simulations World-BAU and EU-OLT will
not be compared. This choice is taken to reduce the number of scenarios’
combinations to study, as the aim of this work is to analyse the model
hypotheses that have more influence on the outputs.
The Root Mean Square Difference (RMSD) is used to quantify the
difference between the simulation with default settings and the other
simulations for twelve output variables only, to facilitate the interpretation
of the results. The twelve output variables considered are shown
in Table 2 (note that temperature change is only endogenously calculated
in the World model):
3. Results
In this section, we show some of the simulation results for the scenarios
and hypotheses presented above. For each scenario (BAU and
OLT) a set of simulations is compared with the simulation with default
settings (S1111) (see Fig. 2 and Table B1 (Appendix B) for more detail on
the keys of the simulation codes). Thus, based on the default simulation
(all hypothesis switches activated) we switch off the four hypotheses one
at time (section 3.1) or in combination (section 3.2). The aim is to
identify the most influential hypothesis, compared to the reference
(section 3.1) and the impacts and induced changes by the combinations
of switches (section 3.2). Fig. 3 shows the values of the default simulation
with the World and EU models (left column: World, right column:
EU) until 2050 and in both scenarios for variables: GDP, share of blue
water, temperature change (World) and CO2 emissions for EU. For each
model, the differences between the two scenarios are particularly
important for the environmental variables water and temperature. The
World model (left column) shows systemic changes when the system
achieves the maximum of fossil fuel production (Peak Oil or ‘Hubbert
peak’) [59] in the BAU scenario (green line). These systemic changes
have important effects on the economy (shown through the decline of
GDP). In comparison, for the OLT scenario in the World model (red line),
Fig. 2. Graphical representation of the simulation identifiers. “S” for simulation,
each of the four digits corresponds to one hypothesis, number 1 corresponds
to the default value, and number 0 implies that the default state has
been altered.
J. Sol�e et al.
Renewable and Sustainable Energy Reviews 132 (2020) 110105
6
GDP keeps growing while temperature change has slowed. One can see
similar effects in the EU simulations, with a higher impact on the
economy due to the lower EU GDP historical growth.
Table C1, C2, C3 and C4 in Appendix C show the RMSD over the
whole simulated period (1995–2050) for simulations performed for BAU
and OLT at the two geographical levels (64 simulations in total)
compared with the corresponding default simulation (S1111).
To analyse the RMSD shown in Appendix C, the analysis is split in:
1. Altering the default state of one hypothesis at a time: those that
compare the results of altering the default scenario of one hypotheses
at a time in each scenario and each geographical level. This corresponds
to simulations S1110, S1101, S1011, and S0111.
2. Altering the default state of multiple hypotheses simultaneously:
these are simulations with different combinations of hypotheses,
including those in which the default state of more than one hypothesis
are altered. This will give the rest of simulations in
Tables C1–C4 and is shown by means of the maximum combination
variable differences in Table 3.
3.1. Altering the default state of one hypothesis at a time
To begin with, the following model-scenario combinations are
considered: World-BAU, World-OLT, EU-BAU and EU-OLT. The combinations
shown below for all four model-scenario duets include the
simulation with the default hypothesis (S1111) vs. S0111, S1011, S1101
and S1110. To interpret the results, we focus only on the variable ‘GDP’
(Table 2).
Table 2
Output variables analysed.
Name of the variable Meaning
eroist_system Evolution of the dynamic EROI of the system
gdp Gross Domestic Product evolution
gdppc GDP per capita
percent_res_vs_tpes Percentage of Renewable Energy Sources
compare with the Total Primary Energy
Sources
real_tfec Real Total Final Energy Consumption
share_blue_water_use_vs_ar Share of Blue Water compared to accessible
runoff water
temperature_change (*) Temperature change due to GHG emissions
estimated by the climate module
tfes_intensity_ej_tdollar Energy intensities of the final energy sources
(Exajoule/Dollar)
total_extraction_nre_ej Total extraction of non-renewable energy
sources in Exajoule
total_co2_emissions_gtco2 Total CO2 emissions in Giga tonnes of CO2
equivalent
total_land_requirements_renew_mha Land occupied for RES implementation in
mega hectares
tpe_from_res_ej_ Total primary energy coming from RES in
Exajoule
Fig. 3. Reference simulations (S1111): BAU (green line) and OLT (red line) scenarios for World (A,B,C)and EU (D,E,F) geographical levels. Variables: GDP (A,D),
Share of blue water (B,E), temperature change World (C) and CO2 emissions for EU (F). (For interpretation of the references to colour in this figure legend, the reader
is referred to the Web version of this article.)
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Renewable and Sustainable Energy Reviews 132 (2020) 110105
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3.1.1. World-BAU
The values of the RMSD calculated for the selected output variables
obtained from each simulation with respect to the values of the same
variables from the simulation with default hypothesis (S1111) are presented
in Table C1, Appendix C. With regards to GDP, it can be noted
that climate impacts are modulated by the effect of resource limitation
(Fig. 4). If there is energy scarcity, then no big differences in the system
behaviour due to climate change impacts are observed. This can be
observed by comparing S1111 and S1101 (simulation neglecting the
impacts of climate change on net energy availability) and small differences
are detected. Among all hypotheses, the fossil fuel scarcity is the
one that gives the largest differences, followed by the energy intensities
and the EROI of the system.
3.1.2. World-OLT
This corresponds to the scenario in which the transition is undertaken
with maximum efforts and at the highest pace under a plausible
future. The results for each of the twelve output variables in terms of the
RMSD calculated with respect to simulation S1111, is shown in Table C2
in Appendix C. For the sake of example, and as it was done in the previous
section (World-BAU), in Fig. 5 the results of the different simulations
are compared using GDP. The energy intensity of 2009 in the
future (S0111) presents worse results in terms of GDP, while activating
the link between EROI and available net energy (S1011) results in a
lower GDP growth due to the to the net energy decrease of RES over
time.
3.1.3. EU-BAU
The results at EU level are coherent with those at global scale.
Indeed, the effects of limiting the availability of fossil fuels (default state
for SCAR hypothesis) at World scale translates into energy scarcity at EU
level. Indeed, only when the default hypothesis is altered (S1110: unrestricted
availability of fossil fuels) can GDP continue to grow after
2020 (Fig. 6d).
3.1.4. EU-OLT
Results at EU level are in accordance with the results at World level,
also for the OLT scenario. Fig. 7 shows GDP stagnation of the World
translates into a decrease of European GDP, although less dramatically,
mainly due to the lower historical GDP growth for the region. From such
decline follows stagnation and a slight increase at the end of the period.
3.2. Altering the default state of multiple hypotheses simultaneously
To evaluate the effects of altering the default state of more than one
of the four hypotheses at a time, the simulation with the maximum
RMSD (with respect to S1111) is highlighted for each variable from the
selected set of outputs (i.e. 12 for the World and 10 for the EU, since
variables temperature_change and total_extraction_nre_ej are not
endogenously calculated in the EU model). As in the previous section,
the results are interpreted individually for each model-scenario couple
(i.e. World-BAU, World-OLT, EU-BAU and EU-OLT).
3.2.1. World-BAU
As in the previous subsection, in this case the most significant differences
between the simulations with default hypothesis and those
altering the default hypotheses are observed when assuming limitless
availability of fossil fuels (the right-most figure in the simulation identifier
set to 0) (Table 3). The only exception is the variable representing
the energy intensity of the whole system (tfes_intensity_ej_tdollar),
which is the ratio between the energy consumption (in EJ) and the GDP
(in T$). This exception is due to the fact that both in S1111 and S0011
resources are limited, but in the later the energy demand is higher
(constant energy intensities of 2009 over time). Therefore, in S0011 the
resource limitation hits before they do in S1111, sending the GDP down,
which results in higher values of tfes_intensity_ej_tdollar. On the contrary,
when removing the non-renewable energy limitation (as in
S0000), even when the increase of energy demand caused by keeping
energy intensities constant is considered, it is compensated with infinite
availability of non-renewable energy sources, and the ratio between
energy consumption and GDP remains relatively constant and smaller
than in S0011, hence more similar to that of S1111. As expected, CO2
emissions and temperature changes give maximum differences with
respect to S1111 in simulation S0000, where all hypotheses have been
Fig. 4. GDP in selected World BAU simulations: a) S1111 vs. S0111, b) S1111 vs. S1011, c) S1111vs. S1101 and d) S1111 vs. S1110. In the name of the simulation 1
means default and 0 non-default. The first number after the S corresponds to Energy efficiency evolution (EFFI), the second corresponds to the effects of EROI on net
energy availability, the third is the climate change impacts (CLIMA) and the fourth is the availability of fossil fuels (SCAR).
J. Sol�e et al.
Renewable and Sustainable Energy Reviews 132 (2020) 110105
8
altered. In such simulation, although there is no limit for fossil fuels
availability the GDP is disconnected from the climate damage produced
by GHG emissions.
3.2.2. World-OLT
Very similar results to those of World-BAU are obtained in this case.
The only notable difference occurs for variables GDP and per capita
GDP, which have the highest RMSD when including the impacts of EROI
on the net energy available. This result can be understood by the fact
that, in OLT, more RES is implemented compared to BAU and thus, the
resulting lower EROI of the system has a bigger impact even in the case
of unlimited supply or non-renewable energy.
Fig. 5. GDP in selected World OLT simulations: a) S1111 vs. S0111, b) S1111 vs. S1011, c) S1111vs. S1101 and d) S1111 vs. S1110. In the name of the simulation 1
means default and 0 non-default. The first number after the S corresponds to Energy efficiency evolution (EFFI), the second corresponds to the effects of EROI on net
energy availability, the third is the climate change impacts (CLIMA) and the fourth is the availability of fossil fuels (SCAR).
Fig. 6. GDP in selected EU BAU simulations: a) S1111 vs. S0111, b) S1111 vs. S1011, c) S1111vs. S1101 and d) S1111 vs. S1110. In the name of the simulation 1
means default and 0 non-default. The first number after the S corresponds to Energy efficiency evolution (EFFI), the second corresponds to the effects of EROI on net
energy availability, the third is the climate change impacts (CLIMA) and the fourth is the availability of fossil fuels (SCAR).
J. Sol�e et al.
Renewable and Sustainable Energy Reviews 132 (2020) 110105
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3.2.3. EU-BAU
At EU scale results are again similar than those of the couple WorldBAU.
In this case, the main differences are observed for variables total
land requirements and GDP and per capita GDP. This is mainly due to
the role played by the energy intensities and the imports in the child EU
model.
3.2.4. EU-OLT
Again, in this regional simulation (Table 3) the differences from the
corresponding parent model (World-OLT) are related to the energy intensities,
which as in the EU-BAU are related to the import function and
the scarcity function. Both will affect the economic activity. As a general
result, it can be seen that the most restrictive hypothesis is the limited
availability of fossil fuels, followed, in some cases, by the choice of the
evolution of energy intensities. The climate change impacts on the
economy are important when they act together with resource limitations.
The role of EROI is important when a massive deployment of RES
takes place and it also contributes significantly in the OLT scenarios for
the EU, due to constraints in imports and the scarcity function on the
evolution of the system.
Overall, results show the importance of non-renewable resources in
the future evolution of the economy. In particular, simulation results
show the influence of fossil fuel supply in the economy and their impact
(through GHG emissions) on global temperature. Following the Business
as Usual path (non-compliant with Paris Agreement) will conduct to not
only environmental but also to potentially catastrophic economic consequences,
which is an scenario that must be avoided by all means [49,
60].
4. Discussion
This paper introduces a new open-source, python programmed,
modelling tool for energy-economy-environment analysis. Following the
two categorizations of [61], pymedeas is a Mathematical (mechanistic)
and Energy Economics model with a flexible bottom-up top-down
approach depending on the sector. The most used (and known)
bottom-up energy models are MARKAL (MARket ALlocation) family of
models [5] and their evolution [5] TIMES, developed by the International
Energy Agency’s Energy Technology Systems Analysis Program
(IEA-ETSAP), and the MESSAGE (Model for Energy Supply Strategy
Alternatives and their General Environmental Impacts) family of models
[62]. The open source energy model systems, OSeMOSYS framework
[63] is a widely used energy modelling system, which is a bottom-up
model system that has a global domain and can be applied to different
geographical levels and with different levels of complexity. pymedeas
complements such kind of bottom-up approaches by providing the
top-down approach, using aggregated variables.
There are a number of novelties [26]:
First, this tool combines system dynamics and input-output analysis
Fig. 7. GDP in selected EU OLT simulations: a) S1111 vs. S0111, b) S1111 vs. S1011, c) S1111vs. S1101 and d) S1111 vs. S1110. In the name of the simulation 1
means default and 0 non-default. The first number after the S corresponds to Energy efficiency evolution (EFFI), the second corresponds to the effects of EROI on net
energy availability, the third is the climate change impacts (CLIMA) and the fourth is the availability of fossil fuels (SCAR).
Table 3
Simulations with the highest RMSD per variable, compared to the reference
simulation S1111 for World and EU under BAU and OLT scenarios. The code
follows the reference key shown in Appendix B, Table B1. In the name of the
simulation 1 means default and 0 non-default. The first number after the S
corresponds to Energy efficiency evolution (EFFI), the second corresponds to the
effects of EROI on net energy availability, the third is the climate change impacts
(CLIMA) and the fourth is the availability of fossil fuels (SCAR).
Variable WORLD
BAU
WORLD
OLT
EU
BAU
EU
OLT
Code Code Code Code
eroist_system S0010 S0000 S0000 S0000
gdp S1100 S1000 S0100 S0000
gdppc S1100 S1000 S0100 S0000
percent_res_vs_tpes S1000 S0000 S0000 S0000
real_tfec S0000 S0000 S0000 S0000
share_blue_water_use_vs_ar S1000 S1000 S0000 S0000
temperature_change S0000 S0000 – –
tfes_intensity_ej_tdollar S0011 S0011 S0011 S0011
total_co2_emissions_gtco2 S0000 S0000 S0000 S0000
total_extraction_nre_ej S0000 S0000 – –
total_land_requirements_renew_mha S1000 S1000 S0000 S0000
tpe_from_res_ej S0000 S0000 S0000 S0000
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Renewable and Sustainable Energy Reviews 132 (2020) 110105
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[7,64] to study the evolution of the energy system at two geographical
levels under environmental and biophysical constraints. The system
dynamics approach allows the analysis of non-linear feedback in the
system whereas the input-output approach provides very detailed representation
of socio-economic variables albeit using fixed coefficients (e.
g. Refs. [42]). The input-output matrix at the core of pymedeas enables
the analyst to assess environmental, economic and social impacts
resulting from each scenario. The pymedeas model takes the data from
the open-access database World Input-Output tables and Data (WIOD,
www.wiod.org), which provides the social and environmental accounts
to conduct such analysis [36].
Secondly, a set of scenarios and hypotheses were developed to enable
projections on the future evolution of the system. The MEDEAS scenarios
used are Business as Usual, based on current trends, and Optimal Level
Transition, supposing economic growth while achieving a reduction of
energy, improvements in energy efficiency, increase in recycling rates of
minerals, and low carbon energy production. Both are based on the
shared socio-economic pathways also used by the IPCC. The set of hypotheses
and the differences show how the fossil fuels availability
constraint is the most important factor modifying the models’ output.
This is particularly significant depending on the scenario chosen. For
BAU, the consequences of fossil resources scarcity [2,43] are considerable
compared to OLT [53,65,66]. GHG emissions act at the end of the
simulation period where the climate change damage on the economy is
playing a visible role. The other hypotheses have minor effects
compared to resources’ limitation. In general, the propagation of 2009
energy intensities is the second most impactful factor, followed by EROI
activation in the case of the OLT scenarios. The massive requirement for
energy during RES deployment reduces the net energy availability for
the functioning of the system. It should be noted here that pymedeas also
gives estimations of raw materials needs for each scenario but the
analysis of the models’ output in this aspect is out of the scope of this
paper.
Thirdly: The modularity of the model in both structure and functions,
gives the user multiple options to make detailed investigations on a
range of subjects. This allows the analyst to include for example job
effects of energy policies at the low carbon transition. Current studies
usually show positive job effects associated with higher renewable energy
deployment compared to BAU among those using input-output
approaches (e.g. Refs. [67–70]). The effects of renewable energy
expansion on employment (see arrow to Social Module in Fig. 1) are
especially critical to foster public acceptance of the transition [71,72].
Thus, future expansion of model capabilities could take advantage of
such modularity, for instance, by considering the implementation of an
agent-based model in the existing pymedeas social module to analyse
the job effects.
Fourthly, pymedeas also adds a new original approach focusing not
only on the demand side. The main energy system model approaches are
oriented to analyse the demand side of the energy sector and thus, the
behaviour of the model is driven by the energy needed to keep the
economic activity. These equilibrium models solve the trade-off between
the demand side (which try to maximize utility) and the supply side
(which maximizes profit) [61] by using energy prices. The pymedeas
model takes a different and original approach, firstly by using energy
(instead of prices) in a dynamical equilibrium between supply and demand.
The pymedeas model considers not only the systems’ demand
driven evolution and a (optionally unlimited) supply, but also situations
where scarcity of a fuel (or the constraints imposed by climate change,
allowing the user the option to keep fossil resources underground) have
an important role in the behaviour of the system. Moreover, the pymedeas
design uses a (one way) nested approach by a system dynamics
import function. This also implies a nested approach for the IOT, linked
at all geographical levels. To illustrate the model’s capabilities, we have
constructed a comparative set of simulations to show how the model
responds to some key features.
These set of novelties aim to give a complementary approach to the
existent literature on the Shared Socioeconomic Pathways (SSP) [51].
These scenarios provide different socio-economic projections up to
2100, based on a set of five narratives: sustainability (SSP1) [22], middle
of the road (SSP2) [73], regional rivalry (SSP3) [74], inequality (SSP4)
[75] and fossil-fuelled development (SSP5) [76]. In this work it is
considered SSP2 scenario as a reference, with additional hypotheses.
The new aspects and hypothesis implemented in pymedeas allow to
analyse the impact of the economy (energy intensities evolution) RES
deployment (EROI impact) climate change and energy scarcity on the
evolution of the system. The implications of using pymedeas, besides
their openness and transparency, are the possibility to analyse how the
different climate and sustainability policies are affected by resources
limitations (energy/materials) and the climate impacts itself as a sink of
energy/resources. On the other side, it allows to analyse the impacts of
RES massive deployment and the role played by the socio-economy
(need of growth). As this study shows, all these factors can be analysed
together or (by switching on/off the functions) separately.
4.1. Limitations
The current versions of the MEDEAS models have some limitations
that are expected to be overcome in future releases [26]. The main
limitation in the Economy module resides in the fact that the IOT are
static (historical values for year 2009); hence even in scenarios of severe
regime changes such as those simulated in the current work, the economic
structure remains unchanged. Another limitation, in this case in
the Energy and infrastructures module, lies in the value of the EROI for
the different energy sources, which is only dynamically calculated for
the RES technologies that generate electricity. The EROIs of the rest of
energy sources remain constant over time. Hence it is not possible to
evaluate the effects of the progressive decline of the EROI of fossil fuels
that has historically occurred and is expected to continue in the future
[79].
The Energy and infrastructures module does not explicitly model
electricity grids, given that these infrastructures are regional/national
by definition. However, an estimate of the additional grids per MW of
variable RES (overgrids and inter-regional grids) to be constructed to
integrate the renewable variable electricity generation is made. Thereafter
the additional material requirements associated to such grid developments
are computed, which affects the EROI of the RES variables
for electricity generation.
Regarding the climate module, only the emissions of CO2 are simulated,
while those of the other GHG (N2O, PFC HFC, SF6 and CH4) are
obtained from projections (RCPs). Similarly, the models do not include
any carbon sequestration technology, although they allow to indirectly
store carbon through an afforestation program covering 345 MHa. The
potential CO2 sequestration obtained by enforcing such program (1.5
GtC/year over 50 years) is discussed in Ref. [78]. The inclusion of carbon
sequestration technologies in the code requires a detailed analysis
on the impacts of these technologies in the EROI of RES [65] and the
other energy sources, which could be a subject for a future work.
Despite it being possible to simulate environmental impacts such as
land and water use and CO2 emissions, others such as land degradation,
biodiversity loss, noise and visual impacts, pollution of water and air
cannot be evaluated.
Finally, it should be noted that the pymedeas code has been tested in
November 2018 in a workshop with stakeholders (MEDEAS lab). Some
of the stakeholders’ suggestions helped to improve the GUI and model
capabilities, other are part of the limitations listed above. Based on the
mentioned above limitations, future research could take several directions.
One possible development, besides the already mentioned
before, could be implementing a social behaviour module (i.e. agentbased
model) and also including an open source model for the electricity
production sector in pymedeas regional (EU) or country models.
As all community-based codes, pymedeas aims to evolve as the users of
the model enlarge. Thus, pymedeas aims to contribute to the energy
J. Sol�e et al.
Renewable and Sustainable Energy Reviews 132 (2020) 110105
11
research area in their urgent need for transparency and open-source
codes and databases [59].
5. Conclusions
The pymedeas models are useful tools that can easily be adapted, not
only by modifying the input scenarios, but also changing the model
equations and relationships based on the particular needs of the user.
This flexibility is attributable to the fact that the models are written in
Python, which is a popular high-level programming language with
English-like syntax and semantics and with a gentle learning curve. The
strategy followed by pymedeas is totally aligned with the open-source
software initiative [60], which include the rights to study, change,
and distribute the software to anyone and for any purpose. Any suggestions
or contributions to the code can be shared through the MEDEAS
User Forum (https://medeas.eu/forum). This allows for greater transparency
and usability, and opens this tool to a wide audience, from
specialists to policy makers and from consultancy to teaching at undergraduate
level.
Software: pymedeas is available on public Gitlab repository (htt
ps://gitlab.com/MEDEAS/pymedeas_models).
CRediT
J. Sol�e: writing-original draft, conceptualization, methodology,
validation, funding acquisition, project administration, supervision. R.
Sams�o: software, methodology, formal analysis, data curation, investigation,
writing-review & editing, visualization. E. García-Ladona:
software. A. García-Olivares: investigation and writing-review & editing.
J. Ballabrera-Poy: writing-review & editing.T. Madurell: project
administration, resources and writing-review & editing. A. Turiel:
writing-review & editing. O. Osychenko: software. UVa team (D.
�Alvarez, �I. Capell�an-P�erez, �O. Carpintero, I. De Blas, C. De Castro, C.
Duce, J.M. Enríquez, N. Ferreras, F. Frechoso, L.F. Lobej�on, P.L. Lomas,
M. Mediavilla, L.J. Miguel, J. Nieto, G. Parrado, P. Rodrigo): investigation
& software. INSTM team (U. Bardi, S. Falsini, G. Martelloni, I.
Perissi): resources. AEA (M. Baumann, L. Eggler, C. Ploiner): software,
validation. ARU (A. Jones, K. Buchmann, D. Natalini): writing-review &
editing and A. Jones English revision. BSERC (L. Radulov, A. Nikolaiev):
writing-review & editing. B4Y (J. D. De Lathouwer): visualization. CRES
(S. Papagianni, M. Theofilidi): data curation. DUHA (R. Kaclíkov�a): resources.
IIASA and IVEM (K. Feng, K. Hubacek, L. Sun) writing-review &
editing. MU and Modul (M. �Cerný, C. Kimmich, C. Kerschner): writingreview
& editing.
Funding
This work was supported by the European Union through the funding
of the MEDEAS project under the Horizon 2020 research and innovation
programme [grant agreement No 69128].
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. C: Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.rser.2020.110105.
Appendix A
Models shared common characteristics (please note that not all PySD functions are described hereafter, but just those that are used by the models
described in this document):
� Model files are self-contained, in the sense that they contain all the required data to be run by the simulation engine included in PySD (no data
needs to be imported).
� Models are loaded as a PySD Model object with the load function of the pysd module, before the simulation is executed using the run method of the
Model class.
� Parameter values can be modified at the time of executing the run method.
� All model equations and parameters are defined as Python functions.
� The _namespace is a Python dictionary containing model variables names as defined in the original Vensim code as keys, and Python-safe names
(replacing spaces by underscores) as values.
� The _subscript_dict is a Python dictionary containing the subscript name as key and the subscripts in a list as values.
� Matrices are represented as objects of the xarray DataArray class.
� Table functions are represented using the lookups function of the functions.py module, which performs linear interpolation on the data, to be able
to access intermediate values between actual data points.
� Initial, Integ and Delay objects are child objects of the Stateful class found in the functions.py module, which all have state (current state of the
object) and ddt attributes that the integrator uses to obtain their value at the next time-step.
� PySD uses caching so that functions being called several times at each time-step run faster after the first call. The cache function wrappers serve to
store the output of the function in memory. The ‘step’ cache stores the function return until the next time-step, while the ‘run’ cache stores it until
the next model initialization.
The plot GUI can be used to plot simultaneously as many simulation results as needed. It can either be launched automatically at the end of a
simulation or in standalone mode, to plot results from previous simulations (in.csv format). A different line style is used to distinguish between
historical (until 2014) and projected data. The search bar, limits the number of variable names displayed on the left panel to those that coincide with
the keywords introduced by the user. The Clear data button, resets the plot GUI to its default state: the list of variables and the displayed plots are
cleared. The Save button, allows the user to save the displayed plot in.png format. The default file name is that of the variable selected by the user. The
rest of the buttons serve to configure the display of the plot (zoom, pan, etc).
Appendix B
J. Sol�e et al.
Renewable and Sustainable Energy Reviews 132 (2020) 110105
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Table B1
pymedeas simulations and code names to run with the World and EU models and for the BAU and OLT scenarios.
Code EFFI EROI CLIMA SCAR
S1111 default default default default
S1110 default default default non-default
S1101 default default non-default default
S1100 default default non-default non-default
S1011 default non-default default default
S1010 default non-default default non-default
S1001 default non-default non-default default
S1001 default non-default non-default non-default
S0111 non-default default default default
S0110 non-default default default non-default
S0101 non-default default non-default default
S0100 non-default default non-default non-default
S0011 non-default non-default default default
S0010 non-default non-default default non-default
S0001 non-default non-default non-default default
S0000 non-default non-default non-default non-default
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