Dynamic EROI of the global energy system in future scenarios of transition to
Research Group on Energy, Economy and System Dynamics
University of Valladolid, Spain
Carlos de Castro
Applied Physics Department
Escuela de Arquitectura, Av Salamanca, 18
University of Valladolid, 47014, Valladolid, Spain
Luis Javier Miguel González
Systems Engineering and Automatic Control
Escuela de Ingenierías Industriales, Paseo del Cauce s/n,
University of Valladolid, 47011 Valladolid, Spain
The transition from fossil fuels to Renewable Energy Sources (RES) is an indispensable
condition to achieve sustainable socio-economic systems. Despite their indisputable
environmental benefits, their technical performance can be, in some cases, worse than those
of fossil fuels. This is the case of the Energy Return on Energy Invested (EROI). Much work
has been carried out to estimate the EROI of individual RES technologies; fierce debates
about methodological issues are still not closed. In this work, we approach this issue by
dynamically estimating the EROI of the whole energy system in future scenarios of transition
to renewables. For this, we apply the global MEDEAS-World simulation model, which
computes the dynamic EROI (standard, EROIst) of individual renewable technologies as a
function of the associated energy requirements to build the infrastructure (construction phases
and materials). The EROI point of use (EROIpou) of the whole energy system is obtained
taking into account the additional energy investments to cope with RES intermittency (i.e.
storage, overcapacities and overgrids) as well as the related distribution energy losses. Two
scenarios up to 2050 are simulated: (1) Business-as-usual (BAU, continuation of current
trends) and (2) “Green Growth” (GG, higher economic growth, faster transition to RES,
higher efficiency improvements, etc.). The contribution of RES in the energy mix increases
from ~15% to over 30% in BAU and almost 50% in GG by 2050. This penetration of RES
technologies in the energy mix translates into a decrease of the EROIpou of the whole energy
system from current 6:1 to 5:1 (BAU) and below 3:1 (GG) by 2050. These results put into
question the viability of the Green Growth paradigm as it is being currently presented.
Energy Return on Energy Investment; high penetration of renewables; energy trap; Green
Growth; integrated assessment modelling
* Corresponding author
The transition from fossil fuels to Renewable Energy Sources (RES) is an indispensable
condition to achieve sustainable socio-economic systems. Despite their indisputable
environmental and social benefits (e.g. lower pollution  and the possibility to be managed
at local, participative level ), the technical performance of RES technologies can be, in
some cases, worse than those of fossil fuels. In fact, fossil fuels are characterized by favorable
physical-chemical properties (e.g. high power density, storable, inert at standard ambient
conditions, etc.) that allow manageable, high-quality energy flows to easily supply human
societies. In contrast, RES technologies generally require more land surface (i.e. lower power
density, [3–5]), their use competes with other processes of the biosphere REF, while those
with a higher potential (i.e. wind, solar) are critically affected by their intermittence and
variability [4,6,7] and have been generally found to have lower Energy Return on Energy
Invested (EROI), the energy delivered from a process divided by the energy required to get it
over its lifetime, than fossil fuels [8,9]:
Considering the EROI allows to take a “net energy” approach in energy systems analysis,
which represents a number of advantages in relation to the conventional “gross energy”
approach: the relevant dimension is the energy available to the society (not the energy
produced by power plants) [10–12], internalization of factors that affect the whole energy
system that are not captured by the monetary costs of individual power plants (such as the
additional costs for the system related with distribution, intermittency of RES, etc.) [13–18];
and detection of potential harmful situations of increasing gross energy output while
decreasing the net energy delivered to the society, i.e. the so-called “energy trap” [19,20].
Much work has been carried out to estimate the EROI of individual RES technologies [9,21–
24]; however important differences exist depending on the technology, system design and
location, and the field is plagued with methodological discrepancies related with the
functional units (e.g., a megajoule of heat energy versus a megajoule of grid electricity) or the
boundaries of the analysis (i.e. mine-mouth vs end use or energy technology vs energy
system) [11,25–29]. From a societal/metabolic point of view, the relevant dimension is the
energy available to the society (not the energy produced by power plants). In fact, a
favourable EROI over the long-term has been identified as an historical driver of evolution
and increasing complexity [10–12]. Societies with high EROIst values are generally more
prosperous, given that more energy is available for discretionary purposes relative to that
which must be reinvested in the energy sector and basic maintenance .  and 
calculated that discretionary economic production drops rapidly when EROIst falls below 5:1.
Therefore, for a society to be prosperous, the EROIst of its energy sources should be much
greater than 5:1.  estimated that an EROIst of 10–15:1 is the minimum EROIst needed for
modern industrial consumer societies to support such things as modern healthcare, education,
and arts (discretionary spending) in addition to basic needs (e.g., food, shelter, and clothing),
a result similar to the one obtained by .
Thus, it is of key importance to understand the socioeconomic consequences of the large-scale
replacement of fossil fuels with RES. The energy transition to renewable resources and new
energy conversion and storage devices will affect the fraction of energy reinvestment
available for discretionary economic production [14,16,17,34], even having the potential to
create scenarios known as of “energy trap”, which may imply a reduction of the net energy
available to society if the construction of new infrastructure grows too rapidly [20,34].
The literature review reveals that recent work has been directed to estimate both (1) the
historic evolution EROI of national energy systems, and (2) the EROI associated to high RES
penetration scenarios. A diversity of methodologies is being applied, including proxy methods
based on economic data [33,35], input-ouput tables , optimization of electricity mix ;
some including storage in the framework such as  and .
The aforementioned studies apply the EROI as a static concept, i.e. assuming that the energy
invested is proportional to the energy obtained along the lifespan of the functioning power
plant. However, power plants require, in fact, energy investment upfront to construct,
providing energy returns only over the lifespan of the facility. This representation worsens the
negative implications of potential energy trap scenarios. In this sense, different works have
focused on the dynamic integration of EROI to obtain more realistic results [19,34,38,39].
Here we present the developed methodology to implement the net energy approach in the
MEDEAS simulation model, a global energy-economy-environment system dynamics model
focused on the biophysical dimensions and interactions of the transition towards RES .
This model, which computes the dynamic EROI (standard, EROIst) of individual renewable
technologies as a function of the associated energy requirements to build the infrastructure
(construction phases and materials). The EROI point of use (EROIpou) of the whole energy
system is obtained taking into account the additional energy investments to cope with RES
intermittency (i.e. storage, overcapacities and overgrids) as well as the related distribution
A variation in the EROI of the energy system has implications for the rest of the energy-
economy-environment system. However, this has been very rarely taken into account in the
literature. In this sense, having the energy system embedded in the whole biophysical and
socio-economic system as considered in MEDEAS allows to account for the net energy
actually available for the society, and its implications for the rest of the system.
As it will be shown in the paper, this novel dynamic, energy-systems approach, allows to
reconcile some of the extant methodological discrepancies currently existing in the field.
The representation of the net energy approach in the MEDEAS model includes 5 key
novelties which significantly improve the current state-of-the-art of the field:
1. Endogenous calculation of the EROIst of individual technologies taking as a starting
point the materials required in the construction, operation and maintenance phases as
well as their recycling rates ,
2. Dynamic and endogenous representation of the EROIst of individual technologies
accounting for the up-front costs per technology as well the configuration of the
energy mix (i.e. requirement of overcapacities to deal with intermittency in high RES
3. Allocation of technologies based on their relative EROIst (higher EROI technologies
tend to cover a larger share of the energy capacity demand).
4. Computation of the EROI of the whole energy system (including overcapacities,
storage and overgrids),
5. Incorporation of the implications of the variations in the EROI of the system for the
total final energy demand.
An extensive literature review has been performed to identify the materials required to
construct, operate and maintain the so-called “scalable” RES technologies for electricity
generation, i.e. (solar CSP, solar PV, wind onshore and wind offshore), i.e. those renewable
sources characterized by a higher techno-sustainable potential [42,43]. Two more
technologies are considered in this bottom-up assessment of material requirements which are
also considered key for the large-scale deployment of RES: electric batteries and overgrids.
This way, requirements for a total of 58 materials have been reviewed (of which 19 minerals).
This approach allows to endogenize the EROIst of each technology depending on the
recycling rate of the minerals (the energy consumption per unit of material consumption is
very different depending on the fact if the material is virgin or recycled). The applied
methodology is fully documented in .
In relation to the estimation of EROI of the system, it is not appropriate to approach the
question by using estimates of “buffered” EROIs for each renewable technology (as done for
example by  considering pumped hydro storage for wind and solar PV) given that these
values are of little or no use given that energy systems are designed so that different
technologies can partially complement and substitute for each other . In this work a step
further is performed in relation to previous works by jointly considering the implications of
complementarity and intermittence of different RES sources for the EROI of the system. This
way, the required overcapacities, storage and overgrids are not assigned to a particular
technology but to the whole energy system.
Two scenarios are simulated in MEDEAS global model to 2050 in order to illustrate the
importance of considering all the aforementioned factors in the planning of the transition
towards a low carbon economy: (1) Business-as-usual (BAU, continuation of current trends)
and (2) “Green Growth” (GG, higher economic growth, faster transition to RES, higher
efficiency improvements, etc.). We select the GG paradigm as alternative scenario to current
trends given that key global international organizations have embraced these concepts
including the World Bank, the UNEP, the OECD, the European Commission and it is the
center of debate in international forums [46–51]. In a word, it is the alternative paradigm
assumed by the establishment to avoid the adverse impacts on human societies of the global
2.1. EROIpou of the system
Ideally, the concept of EROIext should be used when assessing systemic implications of the
variation of EROI over time. However, the practical estimation of EROIext is very complex
and subject to many uncertainties. To date, few studies have attempted to evaluate it
estimating the economic costs associated with the construction of the energy system, and
using average energy intensities to transform to energy inputs (e.g. [26,28]). This
methodology is questioned by other authors, which prefer to assign a “zero” energy cost to
Here we take a conservative approach estimating the EROI of the system from both a
standard ( ) and point-of-use ( ) approach.
Different energy flows and conversions are required in the social metabolism in order to make
available final energy to the society:
(1) Useful energy used by society
(2) direct (i.e. on site) and indirect (i.e. offsite energy needed to make the products used on
site) energy requirements to build, operate, maintain and disposal the plant of energy
(3) Additional energy requirements so the system correctly handles RES intermittency
(4) Distribution losses
(5) Energy requirements to build the machines and infrastructure required to construct the
capital which allows to make the energy investments (2), (3) and (4)
Attending to the definition of standard EROI, the EROI of the system is defined as the ratio
between the final energy delivered to society and the energy required for the production of
energy vectors ( ):
If including more factors such as distribution losses and the additional energy requirements so
the system correctly handles RES intermittency, i.e. extending the boundaries, the EROI of
the system from a “point of use” approach ( ) can be defined as follows:
The following assumptions are taken to compute the :
1. For the sake of simplicity, the EROIst of non-renewable energy sources (oil, gas, coal
and uranium) is assumed to be constant over time. This simplification can be
considered as conservative, given that in the long term the EROI of these fuels will
tend to decrease. Indeed, recent analyses have found that the trend is already
decreasing for fuels such as oil and gas [9,52].
2. The EROIst is dynamically estimated for renewable technologies for the generation of
electricity. The EROIst of other renewables such as liquid biofuels or technologies for
heat generation is considered to be constant over time.
3. Overgrids and overcapacities related to the increasing penetration of variable
renewable technologies in the system are endogenously obtained in the model.
Overcapacities reduce the effective CF of each technology decreasing its EROI.
Overgrids are modelled as an additional component of the material intensity (kg/MW)
each technology as described in .
4. Additional storage losses are modelled following . The reduction of EROIst at grid
scale depends on the ratio of electrical energy stored over the lifetime of a storage
device to the amount of embodied electrical energy required to build the device (i.e. an
analog to EROI for storage technologies, the Energy Stored on Energy Invested
(ESOI)); a certain level of curtailement (φ) and the efficiency of the electric storage
A step further, at least conceptually, would be to accounting for the energy requirements to
build, operate, maintain and dispose the machines and infrastructure (5) required to make the
energy investments (2), (3) and (4). This way we would arrive to an “extended” definition of
the EROI of the system:
2.2. Modelling framework of MEDEAS
MEDEAS-World (MEDEAS-W) is a global, one-region energy-economy-environment model
(or integrated assessment model). It is a simulation model which has been designed applying
System Dynamics,1 which facilitates the integration of knowledge from different perspectives
and disciplines as well as the feedbacks from different subsystems. The model typically runs
from 1995 to 2050 (although the simulation horizon may be extended to 2100 if necessary,
e.g. when focusing on climate change issues). MEDEAS-W is structured into seven main
submodules: Economy, Energy, Infrastructures, Materials, Land Use, Social and
Environmental Impacts Indicators and Climate Change (see Figure 2). The main variables that
connect the different modules are represented by arrows.
(NR & RES
CC damage function
Requi red mate rials
for energy systems
Mater ial consumption
Energy for material
Figure 1: MEDEAS-World model schematic overview. Source: .
The main characteristics of each module are:
• Economy and population: the global economy in MEDEAS is modelled following a
post-Keynesian approach assuming non-clearing markets (i.e. not equilibrium) and
demand-led growth, combined with supply-side constraints such as energy
availability. The economic structure is captured by the dynamic integration of global
WIOD input-output tables which include 35 industrial sectors and households .
Final energy intensities by sector are obtained combining information from WIOD
environmental accounts  and the IEA Balances (2018). Population evolves
exogenously as defined by the user. See  for more details on this submodule.
• Energy: this module includes the renewable and non-renewable energy resources
potentials and availability taking into account biophysical and temporal constraints. In
particular, the availability of non-renewable energy resources depends on both stock
and flow constraints [57–59]. In total, 34 primary energy sources and 5 final fuels are
1 Developed in Vensim DSS software for Windows Version 6.4E (x32). Also available in Python open-source
code. Both codes are available in http://www.medeas.eu/.
considered (electricity, heat, solids, gases and liquids), with large technological
disaggregation. The intermittency of RES is considered in the framework, computing
endogenous levels of overcapacities, storage and overgrids depending on the
penetration of variables RES technologies. A net energy approach accounting for the
EROI of both individual technologies and the EROI of the system is applied. This
submodule is mainly based on the previous model WoLiM . Transportation is
modelled in high detail, differentiating between different types of vehicles for
households, as well as freight and passenger inland transport (see  for details).
• Energy infrastructures represent power plants to generate electricity and heat, allowing
to consider planning and construction delays.
• Climate: this module projects the climate change levels due to the GHG emissions
generated by human societies (non-CO2 emissions are exogenously set taking as
reference RCPs scenarios ). The carbon and climate cycle is adapted from C-
ROADS [62,63]. This module includes a damage function which impacts sectors’
economic output depending on the level of global temperature change .
• Materials: materials are required by the economy with emphasis on those required for
the construction and O&M of alternative energy technologies . Option of
• Land-use: this module currently mainly accounts for the land requirements of the RES
• Social and environmental impacts: this module translates the “biophysical” results of
the simulations into metrics related with social and environmental impacts. The
objective of this module is to contextualize the implications for human societies in
terms of well-being for each simulation.
The model dynamically operates as follows. For each period: firstly, a sectoral economic
demand is estimated from an exogenous and dynamic GDPpc objective. Using energy-
economy hybrid Input-Output Analysis, and combining monetary output and energy
intensities by final energy sources, the final energy demand required to meet economic
demand is obtained. Secondly, the energy submodel computes the net available final energy
supply, which may satisfy (or not) the required demand: the economy adapts to eventual fuel
scarcity. Thirdly, materials required to build, operate, maintain, dismantle, etc. are estimated.
This allows to estimate the EROI of the system as well as to assess eventual material
bottlenecks (although material availability does not constrain economic output in current
model version). Fourthly, the climate submodel computes the GHG emissions, whose
accumulation derives into a certain level of climate change, which in turns feed-back the
economic output. Land and water additional requirements are accounted for. Finally, social
and environmental impacts are translated from the biophysical results. This way, MEDEAS
incorporates two limits to growth that are rather rarely considered (even separately) in the
literature: consistent climate change impacts and energy availability (which interact with the
variation of EROI level of the system).
For a detailed documentation of the MEDEAS-World model, see .
Figure 3 shows the dynamic evolution up to 2050 of the EROIpou of the system obtained in
the simulation of the BAU and GG scenarios with MEDEAS-W model. The obtained results
reveal that, under the applied assumptions, the current EROIpou of the system is ~6:1 values,
and that it has decreased from ~7:1 since 1995.
In BAU scenario, this trend continues reaching a value of 5:1 by 2050, due to the slight
penetration of RES in the system (which almost reaches 30%, doubling its current
contribution to the total primary energy supply –TPES-). In GG scenario, the fastest pace of
penetration of RES technologies (which almost reach 50% of TPES by 2050, drive the
EROIpou of the system to values below 3:1.
Figure 2: Dynamic evolution of the EROIpou of the energy system for scenarios BAU and
The reduction in the EROI of the system has implications for the rest of the system: in order
to satisfy the same level of final net energy consumption, the system needs to process more
energy and materials to make it available for the society. This phenomenon is modelled in
MEDEAS-W through a function of overdemand. In BAU scenario, the overdemand does not
represent significant levels and remains below +2% in almost all the simulated period.
However, in scenario GG, overdemand skyrockets over the period almost reaching +25% by
2050. This means that, in order to satisfy the same final net energy demand, the system needs
to process 25% more of energy.
The additional increase of final energy demand related with the deployment of RES in GG
scenario has also important implications for the efficiency of the system. In terms of final
energy intensities, this effect has the potential to counteract the effect of higher exogenous
efficiency improvements which are assumed in this scenario. It is noteworthy that when
computing the total final energy intensity without the feedback of the EROI of the system, the
total final energy intensity steadily decreases over the simulated period, while including the
feedback produces a rebound in this metric in the 2040 decade which points towards a
rematerialization of the economic system caused by RES penetration in the mix.
The obtained results show that net energy analysis is key to correctly model the transition
towards energy systems based on RES. In this sense, findings from previous works are
confirmed [13,15,18,34]. Renewables at low market penetration represent relatively low
integration costs for the full energy system. However, as the penetration increases and
displaces conventional dispatchable fuel sources, the energetic costs associated with the
required overcapacities, overgrids and storage substantially reduce the EROI of the whole
system due to energy requirements for both construction and operation of the modified energy
system. In particular, the obtained values below 3:1 for the EROIpou of the system in the
Green Growth scenario are below the thresholds identified in the literature to sustain high
levels of development (<10:1 Hall et al., [17,28], <5:1 ). This result puts into question the
viability of the Green Growth paradigm as it is being currently presented. In fact, one the key
assumptions of this narrative, i.e. the absolute decoupling of economic growth in relation to
energy use, is showed not to be consistent with the levels of material and energy required to
perform the energy transition towards RES.
From a methodological point of view, this works presents a number of novel contributions in
relation to the state-of-the art of energy systems analysis and EROI, allowing to reconcile
some of the extant discrepancies in the literature [11,25–29]: (1) the dynamic approach allows
to overcome the limitations of the common static approaches; and (2) the required
overcapacities and storage in high RES penetration scenarios are not assigned to any specific
technology, but rather to the whole energy system.
The computation of both the EROI of the system and the EROI-based allocation of RES
technologies in the energy mix represents a key novelty in relation to the current modelling
state of the art. Virtually all models used for policy-advice are based on gross energy output
and rely on price-based allocations methods (e.g. IEA, IPCC, national governments, etc.). To
our knowledge, very few models take a net energy approach (GEMBA ; NETSET ,
and even less are the studies considering the allocation of technologies depending on their
relative EROI (e.g. ). However, it should be keep in mind that the EROI does not capture
all the benefits and disadvantages of a given technology. For example, in the case of rooftop
PV, despite its lower efficiency in relation to ground-based plants, it does not require
As any modelling study, this work presents a number of limitations. These may be addressed
in further work. For example, the implications of the drop of the EROI of the system to very
low levels are not fully captured in the current framework. In reality, if the system does not
include « inteligent/correcting controls » a sharp drop in the EROI of the system to such low
levels should endogenously induce a collapse of the system (as for example in , where the
model allows to endogenously estimate the relevant EROI threshold). An option would be to
consider the link between the energetic investments in the energy module and the related
monetary investments in the Economy module (as performed by [34,65]).
Further work may deepen the study of the allocation of energy technologies depending on
their relative EROI. This would allow to improve the criteria for successfully planning the
transition to RES. From the point of view of material availability, given that the model tracks
the material consumption of alternative technologies, further work could be directed to the
analysis of the implications for potential material bottlenecks in the context of transition to
RES (e.g. [66–69]). Further work may also be directed to explore alternative ways to analyse
the implications of the evolution of the EROI of the energy system to the whole socio-
economic system. In this sense, IO seems a promising approach .
Finally, a holistic analysis of the full energy-economy-environment system in the context of
the transition towards RES is needed, taking into account the interaction between declining
EROI levels with other key factors such as climate change impacts, non-renewable energy
resources availability or demand-management policies which go beyond the usual
This work has been partially developed under the MEDEAS project, funded by the European
Union’s Horizon 2020 research and innovation programme under grant agreement No
691287. Iñigo Capellán-Pérez also acknowledges financial support from the Juan de la Cierva
Research Fellowship of the Ministry of Economy and Competitiveness of Spain (no. FJCI-
 IPCC. Climate Change 2014: Mitigation of Climate Change. Fifth Assess Rep Intergov
Panel Clim Change 2014.
 Becker S, Kunze C. Transcending community energy: collective and politically
motivated projects in renewable energy (CPE) across Europe. People Place Policy
 Capellán-Pérez I, de Castro C, Arto I. Assessing vulnerabilities and limits in the
transition to renewable energies: Land requirements under 100% solar energy scenarios.
Renew Sustain Energy Rev 2017;77:760–82. doi:10.1016/j.rser.2017.03.137.
 MacKay DJC. Solar energy in the context of energy use, energy transportation and
energy storage. Philos Trans R Soc Lond Math Phys Eng Sci 2013;371:20110431.
 Scheidel A, Sorman AH. Energy transitions and the global land rush: Ultimate drivers
and persistent consequences. Glob Environ Change 2012;22:588–95.
 Trainer T. A critique of Jacobson and Delucchi’s proposals for a world renewable
energy supply. Energy Policy 2012;44:476–81. doi:10.1016/j.enpol.2011.09.037.
 Wagner F. Considerations for an EU-wide use of renewable energies for electricity
generation. Eur Phys J Plus 2014;129:1–14. doi:10.1140/epjp/i2014-14219-7.
 Hall CAS. Will EROI be the Primary Determinant of Our Economic Future? The View
of the Natural Scientist versus the Economist. Joule 2017;1:635–8.
 Hall CAS, Lambert JG, Balogh SB. EROI of different fuels and the implications for
society. Energy Policy 2014;64:141–52. doi:10.1016/j.enpol.2013.05.049.
 Hall CAS. Energy Return on Investment as Master Driver of Evolution 2017:59–72.
 Hall CAS, Klitgaard KA. Energy and the Wealth of Nations: Understanding the
Biophysical Economy. New York, NY: Springer New York; 2012.
 King CW. Information Theory to Assess Relations Between Energy and Structure of the
U.S. Economy Over Time. Biophys Econ Resour Qual 2016;1:10. doi:10.1007/s41247-
 Barnhart CJ, Dale M, Brandt AR, Benson SM. The energetic implications of curtailing
versus storing solar- and wind-generated electricity. Energy Environ Sci 2013;6:2804–
 Carbajales-Dale M, Barnhart CJ, Brandt AR, Benson SM. A better currency for
investing in a sustainable future. Nat Clim Change 2014;4:524–7.
 Carbajales-Dale M, Barnhart CJ, Benson SM. Can we afford storage? A dynamic net
energy analysis of renewable electricity generation supported by energy storage. Energy
Environ Sci 2014;7:1538. doi:10.1039/c3ee42125b.
 Dale M, Krumdieck S, Bodger P. Global energy modelling — A biophysical approach
(GEMBA) part 1: An overview of biophysical economics. Ecol Econ 2012;73:152–7.
 Hall CAS, Balogh S, Murphy DJR. What is the Minimum EROI that a Sustainable
Society Must Have? Energies 2009;2:25–47. doi:10.3390/en20100025.
 Palmer G. A Framework for Incorporating EROI into Electrical Storage. Biophys Econ
Resour Qual 2017;2:6. doi:10.1007/s41247-017-0022-3.
 Kessides IN, Wade DC. Deriving an Improved Dynamic EROI to Provide Better
Information for Energy Planners. Sustainability 2011;3:2339–57.
 Zenzey E. Energy as a Master Resource. State World 2013 Sustain. Still Possible,
Worldwatch Institute, Washington: Island Press; 2013, p. 73–83.
 Bhandari KP, Collier JM, Ellingson RJ, Apul DS. Energy payback time (EPBT) and
energy return on energy invested (EROI) of solar photovoltaic systems: A systematic
review and meta-analysis. Renew Sustain Energy Rev 2015;47:133–41.
 de Castro C, Carpintero Ó, Frechoso F, Mediavilla M, de Miguel LJ. A top-down
approach to assess physical and ecological limits of biofuels. Energy 2014;64:506–12.
 Kubiszewski I, Cleveland CJ, Endres PK. Meta-analysis of net energy return for wind
power systems. Renew Energy 2010;35:218–25. doi:10.1016/j.renene.2009.01.012.
 Price L, Kendall A. Wind Power as a Case Study. J Ind Ecol 2012;16:S22–7.
 De Castro C, Capellán-Pérez I. Concentrated Solar Power: actual performance and
foreseeable future in high penetration scenarios of renewable energies 2018.
 Ferroni F, Hopkirk RJ. Energy Return on Energy Invested (ERoEI) for photovoltaic
solar systems in regions of moderate insolation. Energy Policy 2016;94:336–44.
 Murphy DJ, Carbajales-Dale M, Moeller D. Comparing Apples to Apples: Why the Net
Energy Analysis Community Needs to Adopt the Life-Cycle Analysis Framework.
Energies 2016;9:917. doi:10.3390/en9110917.
 Prieto PA, Hall CAS. Spain’s Photovoltaic Revolution: The Energy Return on
Investment. 2013th ed. Springer; 2013.
 Raugei M, Sgouridis S, Murphy D, Fthenakis V, Frischknecht R, Breyer C, et al. Energy
Return on Energy Invested (ERoEI) for photovoltaic solar systems in regions of
moderate insolation: A comprehensive response. Energy Policy 2017;102:377–84.
 Day JW, D’Elia CF, Wiegman ARH, Rutherford JS, Hall CAS, Lane RR, et al. The
Energy Pillars of Society: Perverse Interactions of Human Resource Use, the Economy,
and Environmental Degradation. Biophys Econ Resour Qual 2018;3:2.
 Murphy DJ. The implications of the declining energy return on investment of oil
production. Philos Trans R Soc Math Phys Eng Sci 2014;372:20130126.
 Brandt AR. How Does Energy Resource Depletion Affect Prosperity? Mathematics of a
Minimum Energy Return on Investment (EROI). Biophys Econ Resour Qual 2017;2:2.
 Lambert JG, Hall CAS, Balogh S, Gupta A, Arnold M. Energy, EROI and quality of life.
Energy Policy 2014;64:153–67. doi:10.1016/j.enpol.2013.07.001.
 Sers MR, Victor PA. The Energy-missions Trap. Ecol Econ 2018;151:10–21.
 Atlason RS. EROI and the Icelandic society. Energy Policy 2018;120:52–7.
 Brand-Correa LI, Brockway PE, Copeland CL, Foxon TJ, Owen A, Taylor PG.
Developing an Input-Output Based Method to Estimate a National-Level Energy Return
on Investment (EROI). Energies 2017;10:534. doi:10.3390/en10040534.
 Limpens G, Jeanmart H. Electricity storage needs for the energy transition: An EROI
based analysis illustrated by the case of Belgium. Energy 2018;152:960–73.
 Neumeyer C, Goldston R. Dynamic EROI Assessment of the IPCC 21st Century
Electricity Production Scenario. Sustainability 2016;8:421. doi:10.3390/su8050421.
 Sgouridis S, Csala D, Bardi U. The sower’s way: quantifying the narrowing net-energy
pathways to a global energy transition. Environ Res Lett 2016;11:094009.
 Capellán-Pérez I, de Blas I, Nieto J, De Castro C, Miguel LJ, Mediavilla M, et al.
MEDEAS Model and IOA implementation at global geographical level. GEEDS,
University of Valladolid; 2017.
 De Castro C, Capellán-Pérez I, Miguel LJ. Revised standard EROI of alternative
technologies for the energy transition 2018.
 IPCC. Special Report on Renewable Energy Sources and Climate Change Mitigation.
United Kingdom and New York (USA): Cambridge University Press; 2011.
 Smil V. Energy Transitions: History, Requirements, Prospects. Santa Barbara,
California, USA: Praeger; 2010.
 Weißbach D, Ruprecht G, Huke A, Czerski K, Gottlieb S, Hussein A. Energy intensities,
EROIs (energy returned on invested), and energy payback times of electricity generating
power plants. Energy 2013;52:210–21. doi:10.1016/j.energy.2013.01.029.
 Trainer T. Estimating the EROI of whole systems for 100% renewable electricity supply
capable of dealing with intermittency. Energy Policy 2018;119:648–53.
 European Commission. A Roadmap for moving to a competitive low carbon economy in
2050. Brussels: COMMUNICATION FROM THE COMMISSION TO THE
EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND
SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS; 2011.
 Jacobs M. Green growth: economic theory and political discourse. Cent Clim Change
Econ Policy Work Pap No 108 Grantham Res Inst Clim Change Environ Work Pap No
 OECD. OECD work on green growth.
Http://Www.Oecd.Org/Greengrowth/Oecdworkongreengrowth.Htm (Retrieved 12-3-
2018): OECD; 2018.
 OECD. Towards green growth. Paris: Organisation for Economic Co-operation and
 UNEP. Towards a Green Economy: Pathways to sustainable development and poverty
eradication. United Nations Environment Programme; 2011.
 World Bank. Inclusive green growth: the pathway to sustainable development. World
Bank Publications; 2012.
 Gagnon N, Hall CAS, Brinker L. A Preliminary Investigation of Energy Return on
Energy Investment for Global Oil and Gas Production. Energies 2009;2:490–503.
 Dietzenbacher E, Los B, Stehrer R, Timmer M, Vries G de. The Construction of World
Input–Output Tables in the Wiod Project. Econ Syst Res 2013;25:71–98.
 Genty A. Final database of environmental satellite accounts: technical report on their
compilation. WIOD Deliverable 4.6, Documentation.; 2012.
 IEA. IEA World Energy Statistics and Balances. Paris (France): IEA/OECD; 2018.
 Nieto J, Carpintero Ó, Miguel LJ, de Blas I. Is it worth more growth? Macro-economic
modelling under energy constraints 2018.
 Campbell CJ, Laherrère J. The end of cheap oil. Sci Am 1998;278:60–5.
 Kerschner C, Capellán-Pérez I. Peak-Oil and Ecological Economics. In: Spash CL,
editor. Routdlege Handb. Ecol. Econ. Nat. Soc. Routledge, Abingdon: 2017, p. 425–35.
 Mohr SH, Wang J, Ellem G, Ward J, Giurco D. Projection of world fossil fuels by
country. Fuel 2015;141:120–35. doi:10.1016/j.fuel.2014.10.030.
 Capellán-Pérez I, Mediavilla M, de Castro C, Carpintero Ó, Miguel LJ. Fossil fuel
depletion and socio-economic scenarios: An integrated approach. Energy 2014;77:641–
 van Vuuren DP, Edmonds JA, Kainuma M, Riahi K, Weyant J. A special issue on the
RCPs. Clim Change 2011;109:1–4. doi:10.1007/s10584-011-0157-y.
 Fiddaman T, Siegel LS, Sawin E, Jones AP, Sterman J. C-ROADS simulator reference
 Sterman J, Fiddaman T, Franck T, Jones A, McCauley S, Rice P, et al. Climate
interactive: the C-ROADS climate policy model. Syst Dyn Rev 2012;28:295–305.
 Capellán-Pérez I, de Castro C. Integration of global environmental change threat to
human societies in energy-economy-environment models, Budapest (Hungary): 2017.
 Dale M, Krumdieck S, Bodger P. Global energy modelling — A biophysical approach
(GEMBA) Part 2: Methodology. Ecol Econ 2012;73:158–67.
 EC. Critical raw materials for the UE. Report of the Ad-hoc Working Group on defining
critical raw materials. European Commission; 2010.
 Elshkaki A, Graedel TE. Dynamic analysis of the global metals flows and stocks in
electricity generation technologies. J Clean Prod 2013;59:260–73.
 García-Olivares A, Ballabrera-Poy J, García-Ladona E, Turiel A. A global renewable
mix with proven technologies and common materials. Energy Policy 2012;41:561–74.
 Valero A, Valero A, Calvo G, Ortego A. Material bottlenecks in the future development
of green technologies. Renew Sustain Energy Rev 2018;93:178–200.