On the Exploration of German Mitigation
Scenarios
vorgelegt von
M.Sc. Econometrics and Operations Research
Eva Schmid
aus Augsburg
von der Fakultät VI – Planen Bauen Umwelt
der Technischen Universität Berlin
zur Erlangung des akademischen Grades
Doktor der Wirtschaftswissenschaften
- Dr.rer.oec -
genehmigte Dissertation
Promotionsausschuss:
Vorsitzender: Prof. Dr. Volkmar Hartje
Berichter: Prof. Dr. Ottmar Edenhofer
Berichter: Prof. Dr. Dr. h.c Ortwin Renn
Tag der wissenschaftlichen Aussprache: 22.04.2013
Berlin 2013
D 83
Contents
Summary 7
Zusammenfassung 9
1 Introduction 11
1.1 Why Ambitious Mitigation Efforts? 14
1.2 The German Energy Transition - A Brief History 15
1.3 Methodological Limitations of Existing German Mitigation Scenarios and
Potential Remedies 20
1.4 A Normative Model of the Science-Policy Interface 24
1.5 Objectives and Outline 27
1.6 References 30
2 Social Acceptance in Quantitative Low Carbon Scenarios 35
2.1 Introduction 37
2.2 Barriers to Collaboration 39
2.3 The Collaborative Scenario Definition Process 41
2.4 The ENCI LowCarb Experience 47
2.5 Limitations and Comparison 51
2.6 Conclusion 53
2.7 References 55
3 REMIND-D: A Hybrid Energy-Economy Model of Germany 59
3.1 Introduction 64
3.2 The Model REMIND-D 66
3.2.1 Fundamentals 68
3.3 The Macroeconomic Module 71
3.3.1 Optimization Objective 71
3
4 Contents
3.3.2 Production Function 72
3.3.3 Energy Demand 74
3.3.4 Hard Link 75
3.4 The Energy System Module 76
3.4.1 Primary Energy Carriers 77
3.4.2 Characteristics of Technologies 79
3.4.3 Conversion Technologies 82
3.4.4 Distribution Technologies 90
3.4.5 Transport Technologies 92
3.5 CO2Emissions 96
3.6 Model Validation 96
3.7 References 99
4 Ambitious Mitigation Scenarios for Germany: A Participatory Approach 107
4.1 Introduction 110
4.2 Methodology 113
4.2.1 Participatory Scenario Definition 115
4.2.2 The Hybrid Energy-Economy Model REMIND-D 116
4.2.3 Participatory Scenario Evaluation 118
4.3 Scenario Definition 118
4.4 Scenario Results 124
4.4.1 CO2Emissions by Sector 125
4.4.2 Transport Sector 126
4.4.3 Electricity Sector 129
4.4.4 Mitigation Costs 131
4.5 Scenario Evaluation 133
4.6 Summary and Conclusion 134
4.7 References 137
5 Renewable Electricity Generation in Germany: A Meta-Analysis of Mitiga-
tion Scenarios 145
5.1 Introduction 148
5.2 From Scenarios to Strategies 150
5.2.1 Scenario Projections 150
5.2.2 Barriers to Implementation 158
5.3 From Assumptions to Scenarios 161
Contents 5
5.3.1 Modeling Methodology 162
5.3.2 Techno-Economic Assumptions 165
5.4 Summary and Conclusion 168
5.5 References 170
6 Synthesis and Suggestions for Future Research 177
6.1 Collaborative Scneario Definition and Evaluation Process 179
6.2 Meta-Analysis of German Mitigation Scenarios for the Electricity Sector 184
6.3 Suggestions for Future Research 185
6.4 References 187
Statement of Contribution 189
Tools and Resources 191
Acknowledgments 193
6 Contents
Summary
The decisive mitigation of greenhouse gas emissions in order to avoid dangerous anthropogenic
interference with the global climate system constitutes one of the greatest challenges of the 21st
century. Germany is being observed by the global community on its unprecedented quest for decoupling
a highly industrialized country’s economy from CO2 emissions and has ambitious long-term mitigation
targets. Due to the complex challenge of transforming Germany’s energy system, political actors
frequently demand scientific expertise in the form of long-term, model-based mitigation scenarios.
However, existing mitigation scenarios for Germany suffer from severe methodological shortcomings
and are highly intransparent on their implicit normative assumptions. This is not reconcilable with the
good principles for the science-policy interface. Thus, the guiding theme of this thesis is to explore how
implicit normative considerations in model-based mitigation scenarios can be made explicit.
The first part of this thesis conducts an exploratory research that intends to overcome the current
limitations in model-based mitigation scenario development by applying a collaborative scenario
definition and evaluation process engaging civil society stakeholders. Taking an analytical-deliberative
approach to participation, civil society stakeholders from the transport and electricity sector frame the
definition of boundary conditions for the hybrid energy-economy model REMIND-D and evaluated the
resulting scenarios with regard to plausibility and socio-political implications. The developed mitigation
scenarios for Germany achieve 85% CO2 emission reduction in 2050 relative to 1990. However, the
scenario evaluation unravels that the technological solutions to the mitigation problem proposed by the
model give rise to significant societal and political implications that deem at least as challenging as the
mere engineering aspects of low-carbon technologies. These insights underline the importance of
comprehending mitigation of energy-related CO2 emissions as a socio-technical transition embedded in
a political context. The second part of this thesis explores alternative German mitigation scenarios for
identifying what kinds of energy strategies for transforming the German electricity sector towards high
shares of renewable electricity generation (RES-E) they embody and under which premises they are
viable. It performs a comparative meta-analysis of ten model-based mitigation scenarios from six recent
publications, including those developed in the first part of the thesis. The scenarios group into three
different energy strategies that exploit the basic options of increasing RES-E shares (domestic RES-E
production, energy efficiency improvements and RES-E imports) to a different extent. Substantial
behavioral, institutional and engineering barriers to implementation that apply to all suggested energy
strategies are identified. Upon investigating the reasons why the different scenario projections diverge,
it turns out that they are in many cases based on expert judgments rather than resulting from numerical
modeling. These involve normative judgments and need to be made more explicit in future research.
In sum, this thesis reveals in exploratory research that the realization of a collaborative mitigation
scenario definition and evaluation process, as a means to address normative considerations in model-
based mitigation scenarios explicitly, is possible in small scale and scope. Hence, the primary message
for future research is that such a participatory process should be repeated in the form of a more
comprehensive assessment of German mitigation scenarios, which requires refined participatory
methods so as to keep transaction costs within boundaries. It is commendable to adapt the elaborated
methods developed in the literature on inclusive risk governance, which extensively deals with the
questions of whom to include in a discourse, for what reasons and by means of which methods.
Summary 7
8 Summary
Zusammenfassung
DiemaßgeblicheReduktionvonTreibhausgasemissionenzurVermeidungvongefährlichen
anthropogenenStörungendesKlimasystemsstellteinedergrößtenHerausforderungendes21.
Jahrhundertsdar.DeutschlandwirdbeiseinemBestrebenalshochindustrialisiertesLandsein
volkswirtschaftlichesWachstumvonCO2Emissionenzuentkoppeln vonderWeltgemeinschaft
aufmerksamverfolgt.AufGrundderkomplexenHerausforderungdiedieTransformationdesdeutschen
Energiesystemsdarstellt,bestehtseitensderpolitischenAkteureeineNachfragenachlangfristigen,
modellbasiertenKlimaschutzszenarien.AllerdingsleidenexistierendeKlimaschutzszenarienunter
schwerwiegendenmethodologischenLimitationenundsindzudemsehrintransparentbezüglichihrer
implizitennormativenAnnahmen.DieslässtsichnichtmitdergutenPrinzipienvonwissenschaftlicher
Politikberatungvereinen.DaheristdasLeitthemadieserDissertationzuexplorierenwieimplizite
normativeAbwägungeninmodellbasierenKlimaschutzszenarienexplizitgemachtwerdenkönnen.
DerersteTeildieserDissertationführteineExplorationsforschungdurch,diedieheutigenLimitationen
vonKlimaschutzszenarienzuüberwindenintendiertindemeinkollaborativerProzesszurDefinitionund
EvaluationvonKlimaschutzszenarienangewandtwirdwelcherzivilgesellschaftlicheStakeholder
einbindet.MittelseinesanalytischͲdeliberativenAnsatzeszurPartizipationgestaltenzivilͲ
gesellschaftlicheStakeholderausdemTransportͲ undElektrizitätssektordieDefinitionder
RahmenbedingungfürdashybrideÖkonomieͲEnergieModellREMINDͲDundevaluierendiedaraus
resultierendenSzenarienbezüglichihrerPlausibilitätundsozioͲpolitischerImplikationen.Die
entwickeltenKlimaschutzszenarienerreicheneineMinderungderCO2Emissionenvon85%imJahr2050
gegenüber1990.JedochzeigtsichbeiderEvaluationderSzenarien,dassdietechnologischenLösungen
zurVermeidungvonCO2EmissionenwiesiedasModellvorschlägtsignifikantegesellschaftlicheund
politischeImplikationenzurFolgehat,welchesichalsmindestenssogroßeHerausforderungwiedierein
ingenieurstechnischenAspektevonkohlenstoffarmenTechnologiendarstellen.DieseEinsichten
unterstrichendieWichtigkeitdieVermeidungvonenergiebedingtenCO2EmissionenalssozioͲ
technischeTransitiondieineinenpolitischenKontexteingebettetistzubegreifen.DerzweiteTeildieser
DissertationexploriertalternativeKlimaschutzszenarienumzuidentifizierenwelcheEnergiestrategien
zurTransformationdesdeutschenElektrizitätssektorshinzuhohenAnteilenvonerneuerbarenEnergien
(EE)sieverkörpernundunterwelchenPrämissensierealisierbarsind.EswirdeinekomparativeMetaͲ
AnalysevonzehnmodellbasiertenKlimaschutzszenariendurchgeführt,welchesechsaktuellen
Publikationenentnommensind,einschließlichderSzenarienausdemerstenTeildieserDissertation.Die
SzenarienlassensichindreiGruppeneinteilen,diediegrundsätzlichenOptionenzurErhöhungdes
AnteilsvonEEimStromsektor(ErhöhungderinländischenEEͲProduktion,ReduktionderNachfrage
durchEnergieeffizienzmaßnahmen,ImportvonEEͲStrom)inunterschiedlichenMaßenausschöpfen.Es
werdensubstanzielleHürdenzurImplementierungderEnergiestrategienidentifiziertdiesichauf
Verhaltensweisen,InstitutionenundingenieurstechnischeAspektebeziehen.BeiderErforschungder
GründewarumsichdieProjektionenderSzenarienunterscheidenstelltsichheraus,dassdieseinvielen
FällenaufExpertenurteilenberuhenanstattausnumerischerModellierungzuresultieren.Diesesindmit
normativenWerturteilenverbundenundmüsseninzukünftigerForschungexplizitergemachtwerden.
ZusammenfassendzeigtdieseDissertationineinerExplorationsforschung,dassdieVerwirklichungeines
kollaborativenProzesseszurDefinitionundEvaluationvonKlimaschutzszenarien,alsMittelum
normativeAbwägungeninmodellbasiertenKlimaschutzszenarienexplizitzuadressieren,ingeringer
Zusammenfassung 9
GrößenordnungundmitlimitiertemUmfangmöglichist.DaheristdieprimäreBotschaftfürzukünftige
Forschung,dasssolcheinpartizipativerProzessinFormeinesumfangreicheren„Assessments“ von
deutschenKlimaschutzszenarienwiederholtwerdensollte,welchesverbesserterpartizipativer
MethodenbedarfumdieTransaktionskostenimRahmenzuhalten.Esempfiehltsichdafüraufdie
elaboriertenMethodenzurückzugreifendieinderLiteraturüber„InclusiveRiskGovernance“entwickelt
wordensind,diesichextensivmitdenFragenbeschäftigtwerineinenDiskurseinbezogenwerdensoll,
auswelchenGründenundmittelswelcherMethoden.
10 Zusammenfassung
Chapter 1
Introduction
11
12 Chapter 1 Introduction
The decisive mitigation of greenhouse gas emissions in order to avoid dangerous anthropogenic
interference with the global climate system constitutes one of the greatest challenges of the 21st
century. The question of how much an individual nation should mitigate emissions, particularly energy-
related carbon dioxide, and by which means is at its heart a political question, involving bargaining,
negotiation and compromise. Due to the complex challenge of transforming a nation’s energy system
towards a low-carbon future, political actors frequently demand scientific expertise in the form of long-
term, model-based mitigation scenarios as guiding input to the political discussion.
Model-based mitigation scenarios depict individual transformation pathways from the sheer infinite
space of possible futures. They essentially constitute complex analytical thought experiments converting
a wide range of input assumptions into projections of future developments of key variables in the
energy system. Currently, mitigation scenarios predominantly focus on proposing portfolios of low-
carbon technologies for the different sectors of the energy system. The underlying models root in
engineering approaches to energy system modeling and stand in a tradition of providing factual
knowledge. Yet, by selecting specific means to the policy end of mitigation, mitigation scenarios
inherently rely on normative assertions to justify the choice. Since science does not have the mandate to
determine the desirable course of action for society, it is problematic if mitigation scenarios’ normative
assumptions are (i) not made transparent, (ii) hidden behind seemingly factual statements and (iii)
defined by science alone. To the author’s knowledge these criteria apply to all existing long-term,
model-based mitigation scenarios for Germany. Since the German Government aims at very ambitious
mitigation targets but the German energy policy arena still discusses the appropriate policy means
controversially thereby demanding and relying on scientific advice, there is a clear need for mitigation
scenarios that adhere to the good principles of the science-policy interface.
The guiding theme of this thesis is thus to explore how implicit normative considerations in model-based
mitigation scenarios can be made explicit. More specifically, this thesis deals with the exploration of
German mitigation scenarios in two ways: First, it conducts an exploratory research that intends to
overcome the abovementioned limitations in model-based mitigation scenario development by applying
a collaborative scenario definition and evaluation process engaging civil society stakeholders, taking an
analytical-deliberative approach. Second, it explores alternative German mitigation scenarios for
identifying what kinds of energy strategies for transforming the German electricity sector towards high
shares of renewable electricity generation they embody and under which premises they are viable.
Before the concise objectives and the outline of this thesis are presented in Section 1.5, the following
provides a more comprehensive outline of the problem setting. Section 1.1 recapitulates the global
context, motivating the need for ambitious mitigation efforts. Section 1.2 gives brief background
information on the history and status quo of the energy transition in Germany. Section 1.3 discusses
methodological limitations of existing German mitigation scenarios and proposes potential remedies.
Section 1.4 provides the theoretical foundation of this thesis by drawing on a normative model of the
science-policy interface with regard to mitigation scenario development.
13
1.1. Why Ambitious Mitigation Efforts?
The fundamental problem in climate change is that increases in the atmospheric concentration of long-
lived greenhouse gases (GHGs) alter the energy balance of the climate system (IPCC, 2007b, p.5). The
most important GHGs that induce a global warming effect are carbon dioxide (CO2), methane (CH4) and
nitrous oxide (N2O). Their atmospheric concentrations have risen considerably as a result of human
activities since entering the industrial age (from 1750 onwards), mainly due to fossil energy
consumption and shifts in land-use patterns. The already incurred increase in global mean temperature
(GMT) between 1850–1899 and 2001–2005 amounts to 0.76°; a continuation of recent trends in GHG
emissions may lead to an increase of GMT of up to 6°C until the end of the century, versus 1989-1999
levels (IPCC, 2007b). The risks associated with an increase in GMT are diverse (IPCC, 2007a; Smith et al.,
2009), ranging from risks to unique and threatened systems, e.g. increased damage or irreversible loss
of systems such as coral reefs, tropical glaciers, endangered species, biodiversity hotspots and small
island states to extreme weather events such as heat waves, droughts, floods, wildfires or tropical
cyclones. Also, large-scale components of the earth system may alter their qualitative state under global
warming (Lenton et al., 2008), including arctic summer sea-ice loss, melting of the Greenland ice shield,
dieback of the amazon rainforest and chaotic changes in the Indian Summer Monsoon.
In the year 1992, 154 nation states have signed the United Nations Framework Convention on Climate
Change (UNFCCC) in Rio de Janeiro. Its primary objective as stated in §2 is to “achieve […] stabilization of
greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous
anthropogenic interference with the climate system […] within a time frame sufficient to allow
ecosystems to adapt naturally to climate change, to ensure that food production is not threatened and
to enable economic development to proceed in a sustainable manner.” (United Nations, 1992, p. 4). To
date, 194 nation states and the European Union have ratified the treaty (UNFCCC, 2012). The 15th
Conference of the Parties in 2009 resulted in the Copenhagen Accord, which specifies the UNFCCC’s
objective in that the increase in global mean temperature shall remain below 2°C as compared to pre-
industrial levels and reconfirms “strong political will to urgently combat climate change in accordance
with the principles of common but differentiated responsibilities and respective capabilities” (UNFCCC,
2009, p.5). However, instead of a agreeing on a second commitment period for the Kyoto protocol,
which specified legally binding GHG emission targets for developed countries, individual countries
submitted voluntary mitigation pledges for the year 2020. Figure 1 illustrates how the range of the
Copenhagen pledges is inconsistent with GHG emission levels that are likely to keep to the 2°C target.
More ambitious domestic mitigation pledges as well as their realization are required in order to achieve
the articulated objective of the UNFCCC. This is valid both for the short term and long term development
of GHG emissions. In fact, the window of opportunity for stabilizing GHG concentrations in the
atmosphere at levels that allow for a likely chance to keep the politically defined 2°C target is urging for
timely action. There is scientific consensus that such an emissions trajectory requires global GHG
emissions to peak before 2020 and decrease substantially thereafter (Fisher et al., 2007).
14 Chapter 1 Introduction
Figure 1. Copenhagen pledges to reduce GHG emissions, in comparison to emission trajectories and corresponding projected
increases in global mean temperature over the 21st century. Source: UNEP (2010, p. 10).
CO2 constitutes the most important anthropogenic GHG; its concentration has risen from a pre-
industrial value of around 280 ppm to 379 ppm in 2005 (IPCC, 2007b) and 394 ppm in 2012 (NOAA,
2012). The primary source of global CO2 emissions has been and still is the combustion of fossil
resources, which as of today constitutes the backbone of the global economy. Coal, oil and gas jointly
supplied 81.4% of total global primary energy demand in 2008 (IEA, 2010). For CO2 emissions to peak in
the near term, the global energy system will need to undergo a deep, structural transformation. Energy
systems are characterized by highly planning- and capital-intensive assets with technical lifetimes of up
to several decades, so significant lead times are required for investments into new infrastructure and
energy conversion technologies. In the meantime, existing fossil-fuel based existing infrastructure will
continue to emit. The International Energy Agency has quantified the estimated CO2 emissions from
existing energy system infrastructure and concludes: “The door towards 2°C is closing - will we be locked
in?” (IEA, 2011). Whether or not humankind will ultimately keep a foot in the door depends on future
international efforts to mitigate GHG emissions, especially CO2.
1.2. The German Energy Transition – A Brief History
Germany is being observed by the global community on its unprecedented quest for decoupling a highly
industrialized country’s economy from CO2 emissions. Until 2011 it achieved a 21% decrease in CO2
emissions relative to 1990 (BMWi, 2012). Also, Germany is recognized for its tremendous increase in the
share renewable electricity generation from 5% of domestic electricity production in 1990 to 20% in
2011 (BMU, 2012b). Yet, the plans of the German Government go much further: In 2010, it released a
1.2 The German Energy Transition - A Brief History 15
long-term strategy for Germany’s future energy provision, the “Energy Concept”, which aims at a CO2
emission reduction target of 80-95% until 2050 relative to 1990 (Federal Government, 2010). The
undertaking has been coined as the “Energy transition – future made in Germany” (BMU, 2010) and
intends to stimulate the largest infrastructure project of the coming decades (Bim in Lippert, 2012). An
important element is the transformation of the electricity system towards high shares of renewable
electricity generation, which is manifested in §1(2) of the Renewable Energy Sources Act: It defines
minimum targets of 50%, 65% and 80% in 2030, 2040 and 2050, respectively. However, as the President
of the Association of German Chambers of Industry and Commerce puts it: “regarding the German
energy transition, nothing is resolved but the targets” (Neuerer, 2012, p.1).
In order to grasp the present dynamics in German energy politics and policy, it is worthwhile to briefly
recapitulate their historical context. Since World War 2, Western Germany’s energy policy has
experienced distinct phases that differ with respect to the dominant objectives and technology focus
(Czakainski, 1993; Brauch, 1997), as well as the type of involved and affected actors and the type of
general, underlying consensus on energy supply (Lippert, 2012). During the 1950s, while reconstructing
Germany, the main objective has been economic efficiency and domestic lignite and hard coal
constituted the most important primary energy sources. In the 1960s, increased competition between
coal and cheap oil as well as natural gas led to protective energy policy for domestic hard coal mining.
Energy policy increasingly adopted the objective of import independence and public R&D investments in
nuclear energy increased substantially, leading to the emergence of a nuclear industry. In the 1950s and
60s, a cooperative, “practiced” energy consensus bonded the few involved actors, confined to the
Federal and State Governments, as well as the coal and nuclear industry, underpinned by sectorial
consensus in the form of protective coal policies and nuclear support (Lippert, 2012).
During the 1970s, with the global oil crises and public mass protest against nuclear energy in Germany,
security of supply and social sustainability moved into the focus of energy policy. The public played an
increasingly important role by openly questioning the sustainability of a coal and nuclear based energy
supply. This tendency manifested in the 1980s in the form of a huge public debate on forest dieback
supposedly associated with coal electrification. Environmental protection moved towards a central
objective of energy policy, as further signified by establishing a Federal Ministry for the Environment,
Nature Conservation and Nuclear Safety in 1986, five weeks after the Tschernobyl nuclear disaster. In
fact, Tschernobyl gave the final impetus for the already crumbling energy consensus of earlier decades
to break in its decisive elements (Schmidt-Preuß, 1995). Public protests against the peaceful use of
nuclear energy substantiated and the political process slowly steered towards negotiating a nuclear
phase-out. At the same time, climate protection was placed high on the political agenda, additive to the
historically accumulated energy policy objectives of security of supply, social sustainability and
environmental protection. In 1988, the final report of the Enquête Commission “Preventive Measures to
Protect the Earth’s Atmosphere”, commissioned by the German Parliament, was released – even before
the first assessment report of the IPCC. It indicated that an increase of GMT of 1-2°C was on the one
hand unavoidable, and on the other hand the maximum acceptable increase. There was a high
consensus across political parties that climate protection should be a central topic in energy policy,
however, there was no consensus on how this should be achieved (Watanabe, 2011) .
16 Chapter 1 Introduction
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1.2 The German Energy Transition - A Brief History 17
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feed-in t
a
electricit
y
in total e
the Rene
w
(Jacobsso
inefficien
c
awaited.
Figure 2. E
n
and as a sh
a
The shar
e
technolo
g
2003/30/
markets.
2009, th
e
i
dly introdu
c
l tax reform
i
e
4, may pa
r
e
ls fluctuatin
g
eviously stab
the ecologic
a
to settle th
n
that paved
n
on renewa
b
a
riff scheme
t
y
generation
f
lectricity de
m
w
able Energ
y
n and Laub
e
c
y (Frondel
n
ergy productio
n
a
re in total sect
o
e
of RES in he
a
g
ies in both
EG required
After oversh
o
e
European le
c
ed several
e
i
n 1999. The
r
tly be attrib
u
g
around 900
le level of ar
o
a
l tax reform
e decade-ol
d
the way fo
r
b
le energy so
t
hat guarant
e
f
rom renewa
m
and increas
e
y
Act receive
d
e
r, 2006; W
ü
e
t al., 2010
)
n
from renewab
l
o
rial demand. O
w
a
t and fuel p
r
sectors. E
u
a minimum
o
oting the t
a
gislation wa
s
e
nergy polic
y
continuous
d
u
ted to the
e
PJ until 200
0
o
und 1300 P
J
remains dis
p
d
conflict o
n
r
a nuclear p
u
rces (RES) s
u
e
es primary f
e
ble energy s
o
e
d significan
t
d
much meri
t
ü
stenhagen
a
)
and subjec
t
l
e energy sourc
e
w
n illustration
b
r
ovision also
u
ropean leg
i
share of 5.
7
a
rget, Germa
n
s
strengthen
e
y
changes: F
d
ecrease in c
r
e
co tax – he
a
0
to below 6
0
J
to 800 PJ in
p
uted (Wata
n
the use o
f
hase-out un
t
u
pport by m
e
e
ed-in and fi
x
o
urces. As be
t
ly in respon
s
t
for initial m
a
a
nd Bilharz,
2
t
to ongoin
g
e
s (RES), in the
e
b
ased on data fr
o
increased ov
e
i
slation trig
g
7
5% of biof
u
n
y has kept
t
e
d and direct
i
irst, they p
u
r
ude oil cons
a
ting oil de
m
0
0 PJ in 201
1
2010 (BMWi
,
nabe, 2011).
f
nuclear po
w
t
il 2022. And
e
ans of the R
e
x
ed remuner
a
comes evide
n
s
e, from 5%
arket format
2
006), it is
h
g
debate. A
n
e
lectricity, heat
a
o
m BMU (2012)
e
r the last d
e
g
ered these
u
els or othe
r
t
he constant
i
ve 2009/28/
u
shed throu
g
umption ov
e
m
and for hou
s
1
and petrol
d
, 2012). Yet,
t
Second, the
w
er by imp
o
third, in 20
0
e
newable En
e
a
tions schem
n
t by Figure
5
in 2000 to 2
0
ion for rene
w
h
eavily critic
i
n
other amen
a
nd fuel sector
b
.
e
cade, largel
y
developme
n
r
RES based
minimum s
h
/
EG prescribe
g
h a long-de
e
r the past d
e
s
eholds decr
e
d
emand decr
e
t
he actual st
e
new Gover
n
o
sing a cha
n
0
0, they ref
o
ergy Act in 2
0
es for 20 ye
a
5
, the share
o
0
% in 2011.
w
able techno
i
zed for eco
n
dment in 2
0
b
oth in absolut
e
y
based on bi
o
n
ts. The dir
e
fuels in do
m
h
are since 20
s different bi
bated
e
cade,
eased
e
ased
e
ering
n
ment
n
ge in
o
rmed
0
00, a
a
rs for
o
f RES
While
logies
n
omic
0
12 is
e
terms
o
mass
e
ctive
m
estic
09. In
i
nding
18 Chapter 1 Introduction
targets for each member states’ RES shares in final energy demand for 2020. Upon non-attainment,
sanctions can be imposed. Germany hence committed to advance from its share of 12.2% RES in final
energy demand in 2011 (BMU, 2012a) to 18% in 2020. In order to translate this into domestic policy, a
RES heat subsidy program was implemented in 2009 with the Renewable Heat Act and the amended
Biofuel Quota Act. However, particularly the introduction of biofuels is opposed by the German public
manifested in refusal to use petrol with 10% biofuel additive (E10) which endangers Germany’s
fulfillment of the biofuel quota (MWV, 2011).
Summarizing, the objective of CO2 mitigation has gradually advanced on the priority list of German
energy policy over the past three decades and now enjoys top priority next to the historical objectives of
security of supply, social sustainability and environmental protection other than climate protection.
While some progress is already achieved, the ambitious targets set by the Government are still far out of
reach and RES shares are required to increase significantly in all energy system sectors over the coming
decades. With an increasing deployment of RES technologies, the amount of involved and affected
actors in the energy policy process increases dramatically. This is both due to spatially distributed RES
technologies and concomitant infrastructure requirements constituting a new form of land use that
alters German landscapes and energy service consumers being involved in low-carbon solutions, which
require alternative end-use appliances or shifting modes of energy consumption. Thus, in order to
enable the energy transition as a profound change process, the interests of a wide range of affected
parties need to be accommodated so as to allow for the deployment and implementation of low-carbon
solutions that are not impeded by acts of refusal of individual parties. The call for social acceptance of
renewable energies (Wüstenhagen et al., 2007) has become a keyword in the German energy policy
arena. Often, it is understood as something that can be established ex-post to investment or policy
decisions by providing sufficient information to the public (e.g. Federal Government, 2010). However,
attempts to explain acceptance and opposition increasingly resorts to procedural and institutional
factors like perceived fairness and levels of trust (Devine-Wright, 2008). Rayner (2010) argues that the
process of how a society chooses an energy future itself is as important for a socially, politically,
economically and environmentally sustainable outcome as the availability of low-carbon technologies.
The Ethics Commission for a Safe Energy Supply, installed by Chancellor Merkel in 2011, calls for a basic
consensus on the basis and future of prosperity, the idea of progress, the willingness to take risks and
the safety to be achieved as a basic requirement for changing the energy supply structure (Ethics
Commission for a Safe Energy Supply, 2011). It further emphasizes that “the energy transition will only
succeed through a collective effort spanning all levels of politics, business and society” (p. 5). However,
currently, the German energy policy arena is characterized by a multitude of conflicts on what kinds of
policy measures for incentivizing mitigation are appropriate and which technology solutions are
desirable. Because of the long planning horizon and the long lifetimes of energy system technologies the
course for the state of the energy system in several decades needs to be set now. Due to the complexity
of the challenge, several political actors have demanded scientific expertise in the form of long-term,
model-based mitigation scenarios as guidance in the energy policy debate. These scenarios are
frequently put forward as a scientifically sound discussion basis; however, in fact they suffer from severe
methodological limitations as will be outlined in the following section.
1.2 The German Energy Transition - A Brief History 19
1.3. Methodological Limitations of Existing German
Mitigation Scenarios and Potential Remedies
For evaluating the scientific quality of mitigation scenarios two criteria are of special importance. First,
the analytical quality of the underlying model: Is it based on sound theory? Does it represent the pivotal
features of the modeled system? Second, the input assumptions: Are they plausible? Who determined
the input assumptions? To date, there are three studies presenting mitigation scenarios for the German
energy system which are based on quantitative energy system models and are consistent with the
Government’s mitigation target of 80-95% CO2 emission reduction in 2050 relative to 1990. They were
all commissioned by political actors as indicated by Table 1, which additionally summarizes their applied
models, problem statements and the imposed policy targets.
Table 1. Salient features of existing long-term, model based and ambitious mitigation scenarios for Germany that cover all
sectors of the energy system, i.e. heat, transport and electricity.
Publication and Sponsor Models Problem Statement Targets
“Model Germany”
(WWF, 2009), commissioned by the
World Wildlife Fund Germany
•Bottom-up demand
model(Prognos)
•Dispatch model
(Prognos)
“What can and needs to
happen technologically
and how do associated
policies look like?” (p.1)
95% GHG emission
reduction in 2050
relative to 1990.
“Energy Scenarios for an Energy
Concept of the Federal Government”
(EWI/GWS/Prognos, 2010),
commissioned by the Federal Ministry
of Economics and Technology
•Bottom-up demand
model (Prognos)
•Dispatch model
(DIME)
•Macro-econometric
model (Panta Rhei)
“Which technical
measures that reduce
energy demand and
GHG emissions are
suitable for reaching the
targets?” (p.2)
40% / 85% CO2 emission
reduction in 2020 /2050
relative to 1990,
ш 18% share of RES in
final energy demand in
2020,
ш 50% RES in primary
energy supply in 2050
“Long Term Scenarios and Strategies
for the Expansion of Renewable
Energies in Germany under the
Consideration of Developments in
Europe and Globally”
(DLR/IWES/IFNE, 2010; 2012),
commissioned by the Federal Ministry
for the Environment, Nature
Conservation and Nuclear Safety
•Spreadsheet models
(SZENAR, ARES)
•Simulation model
German electricity
sector (SimEE)
•Optimization model
European electricity
sector (REMix)
Describe a self-
consistent quantity
framework of the
expansion of renewable
energies; derive and
discuss the structural
and economic effects of
this expansion.
80% CO2 emission
reduction in 2050
relative to 1990, RES-E
share ш 50% / 65% / 80%
in 2030 / 2040 / 2050; in
one scenario also 100%
RES-E share in 2050
To start with the analytical quality of the underlying models one needs to note first, that all studies
apply several partial models which are soft-linked in order to jointly cover all sectors of the energy
system and, if applicable, macroeconomic dynamics. An advantage of soft-coupling numerically
intensive partial models is that more technological or sectorial detail can be covered, as opposed to an
integrated model of the energy system and macro economy. Soft-linking, however, eliminates the
potential for feedback effects since the exchange of data between two or more models yields seemingly
20 Chapter 1 Introduction
deterministic model input for the respective other model(s). Two important aspects in modeling energy
system developments are whether investment decisions, reflected in capacity additions of available
technologies, are modeled endogenously or imposed as exogenous assumption and how the larger
economic development in terms of GDP growth is treated.
WWF (2009) applies several models of the Prognos AG: A detailed bottom-up energy demand model,
consisting of a variety of sectorial sub-modules that generate differentiated projections of the energy
demands of the industry and the residential and commercial sectors, and a high-resolution power plant
model (p. 12). The demand model relies on an exogenously imposed GDP projection as a driving input
variable. One of the outputs of the demand model is the annual electricity demand, along with its load
profile. An exogenously determined deployment path of renewable electricity generation (RES-E)
capacities, along with exogenously set full load hours, yields a RES-E feed-in profile, which is subtracted
from the annual electricity demand load profile on an hourly basis (p. 18). The resulting residual load is
an input to the power plant model of the Prognos AG, which determines the technology mix of
conventional electricity generation technologies; their selection is based on maximizing their rate of
return on equity (p.18). Thus, the model setup falls into the category “bottom-up simulation” and by
construction excludes feedback effects of energy sector development on both GDP growth and RES-E
investments. For reaching the ambitious target of 95% GHG emission reduction in 2050, relative to 1990,
a strong focus is put on demand-side policies.
The EWI/GWS/Prognos (2010) study, commissioned by the Federal Ministry for Economics and
Technology (BMWi) takes a very similar approach and soft-couples the bottom up energy demand
model of the Prognos AG, the European electricity market model DIME (Dispatch and Investment Model
for Electricity Markets in Europe) and the macro-econometric model Panta Rhei. The differentiated
sectorial final energy demand is provided by the demand model of Prognos. The resulting electricity
demand for Germany serves as an input to DIME, which is a dynamic optimization model that
determines the cost-minimal coverage of European electricity demand, considering the technical and
economic parameters, by simulating the future power plant dispatch. The deployment path of RES-E
capacities is apparently exogenous to DIME, as “the scenario construction of the electricity generation
sector is further based on a model of renewable energies in Europe that represents several RES-E
technologies in a regionally differentiated manner” EWI/GWS/Prognos (2010, p. 28). More detail on how
precisely the deployment path of RES-E is obtained is not provided. Results of both the Prognos demand
model and DIME serve as an exogenous input to Panta Rhei, which then determines direct and indirect
macroeconomic effects, i.e. GDP projections. However, there is no iterative feedback of information
from the Panta Rhei model to the demand model or DIME. The resulting GDP paths are very similar to
the one imposed to the demand model at the outset. Again, the model setup falls into the category
“bottom-up simulation”.
Finally, the study DLR/IWES/IFNE (2010) and its successor DLR/IWES/IFNE (2012), commissioned by the
Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU), primarily relies
on the simulation tool ARES for determining a ‘quantity framework’ for a ‘realistic’ development path of
renewable energy technologies (p.1). The term ‘realistic’ is defined the sense that existing energy policy
‘action possibilities’ and instruments, structural barriers and friction losses are considered, and
1.3 Methodological Limitations of Existing German Mitigation Scenarios and
Potential Remedies 21
deployment paths of renewable energy technologies are not unrealistically ambitious. The market
development of individual renewable energy technologies is an exogenous input to ARES. It is stated
that “the capacity deployment path of RES-E technologies results from an extrapolation of the historical
dynamics, under the assumption of priority feed-in for RES-E until 2050” (DLR/IWES/IFNE, 2010, p. 46).
The RES-E capacity deployment path is validated for selected scenarios and years in a dynamic
simulation with the soft-coupled models SimEE of IWES and REMix of DLR. This, however, does not
replace an endogenous representation of systemic effects. As before, the core model setup is a
“bottom-up” simulation.
A common denominator of these “bottom-up” modeling approaches is that their analytical quality
leaves substantial room for improvement. The investment dynamics in innovative technologies are
determined exogenously and are based on expert opinion rather than resulting from numerical
optimization. This has deep implications as the resulting scenarios are essentially normative projections,
which is not indicated transparently next to their presentation in the scenario reports. Also, important
systemic effects are not taken into account endogenously. Further, there exists no underlying theory for
“bottom-up” models. Rather, they take on an engineer’s perspective by incorporating detailed
descriptions of technologies while assuming market adoption of the most efficient technologies
(Hourcade and Robinson, 1996). It is further postulated that current market forces do not operate
perfectly and an efficiency gap exists between the current technology penetration and best available
techniques. Suggested mitigation policy implications are to remove barriers to adoption of the best
available technique. In the global energy system models literature, “bottom-up” models have been
found to be highly optimistic regarding the technical mitigation potential, due to picking “low-hanging
fruits” which are in fact not picked today (Grubb et al., 1993; Hourcade and Robinson, 1996). Economists
argue that the postulated malfunctioning of market forces is only apparent and can be explained by
complexity and heterogeneity of consumer preferences as well as hidden costs, e.g. information costs or
perceived risks associated with capital costs (Hourcade and Robinson, 1996). On the contrary, in
calibrated “top-down” models, this complex set of behavioral factors is captured in price and income
elasticities. This alternative approach to modeling energy systems adopts an economic perspective,
often at the cost of technological detail, but allowing for an integrated analysis with feedback
mechanisms between economic and technological development and endogenous representations of
technological change. An important aspect is that such models are backed by economic theory and
hence have an analytical foundation.
In global energy system modeling, hybrid approaches have become increasingly popular, exemplified by
e.g. the models REMIND-R (Leimbach et al., 2010), WITCH (Bosetti et al., 2006) or IMACLIM-R (Crassous
et al., 2006). Such hybrid models contain both representations of the economy and a “bottom-up”
energy supply model representing individual energy carriers and conversion technologies. Hybrid
models reconcile the advantages of both concepts, i.e. the technological detail of “bottom-up” models
and the endogenous representation of behavioral factors by means of price and income elasticities.
Economic theory suggests different approaches to numerical modeling, the most popular being growth
models, derived from macroeconomic theory, and computable general equilibrium (CGE) models,
rooted in microeconomic theory. Both types of models solve an optimization problem, which is,
22 Chapter 1 Introduction
however, formulated in a different manner. CGE models explicitly consider different economic agents
that maximize their respective objective function. The model solves for each time step by determining
the market prices for which markets clear and are in a state of general equilibrium. An advantage of this
approach is the representation of rich economic detail with individual economic agents, distribution
effects and the possibility to include policy instruments explicitly. The major disadvantage of CGE
models is the lack of intertemporal efficiency, as the model is solved recursively for each time step. For
macroeconomic growth models, advantages and disadvantages are vice versa.
The most prominent macroeconomic growth model is the Ramsey-Cass-Koopmans model (Ramsey,
1928; Cass, 1965; Koopmans, 1965). It belongs to a class of economic models rooted in the theory of
welfare economics, which is also referred to as normative economics. Normative economics addresses
questions about what should be done in a particular set of circumstances and is founded in utilitarian
ethics (Perman et al., 2003). Utility is a term introduced by early utilitarian writers (David Hume, Jeremy
Bentham and John Stuart Mill) that refers to the individual’s pleasure or happiness. Welfare is then
some aggregation of individual utilities. Being a consequentialist theory, utilitarians postulate that
actions which increase welfare are right and actions that decrease welfare are wrong, in a normative
sense (Perman et al., 2003). Following from this moral judgment framework, the objective function of
macroeconomic growth models is to maximize an intertemporal social welfare function. In the Ramsey-
Cass-Koopmans model, social welfare is defined as the present value of logarithmic per capita
consumption over the time span of analysis. Consumption is determined via a macroeconomic
production function that considers the production factors capital and labor in the classical model, and
additionally energy when applied to hybrid energy-economy modeling. Given the side constraints to the
optimization problem, maximizing the social welfare function yields an intertemporally optimal solution
that would have been picked by a benevolent social planner. Thus, scenarios produced from growth
models are intertemporally optimal and also referred to as “first-best solution”, “benchmark solution”,
or “best-case reference”. The particular notion of optimality applied here is that the optimization results
are Pareto efficient, meaning that nobody could be better off without somebody else being worse off.
Drawing on the second fundamental theorem of welfare economics, the social planner solution is equal
to the decentralized market solution under conditions of perfect competition. The Ramsey-Cass-
Koopmans model applied in energy scenarios has been particularly criticized on ethical grounds for the
practice of pure time discounting, which assumes that consumption today is valued more than
consumption in the future. This is not reconcilable with the ethical principle of intergenerational equity.
Also, it has been criticized that defining social welfare exclusively in terms of per capita consumption is
an inadequate representation for environmental economic problem settings (Perman et al., 2003).
To summarize, adopting a theory-based economic perspective on modeling energy systems is also not
limitation-free and its analytical quality depends primarily on how well the model is calibrated to the
system under analysis. Here, the major challenge is data scarcity and measurement problems as the
determination of price and income elasticities are problematic. Given that the hybrid approach to
energy system modeling can reveal important insights on optimal and sartorially integrated transition
pathways it appears worthwhile to develop such a model for Germany.
1.3 Methodological Limitations of Existing German Mitigation Scenarios and
Potential Remedies 23
Having sketched the alternatives approaches to energy system modeling, one crucial issue remains: The
necessary input assumptions for projecting future developments. As has been revealed in the discussion
above, the largest part of the scenario projections in the existing studies for Germany constitute
exogenous assumptions. Thus, the scenario projections are in fact input assumptions which are chosen
by the modelers on a normative basis; however, the studies refrain from providing both a transparent
motivation and a detailed reporting of input assumptions. This impedes an evaluation of their
plausibility. Scenario projections are nevertheless reported in a very technical and factual manner,
thereby hiding implicit value judgments. Since science does not have the mandate to determine the
desirable course of action for society this practice is problematic, and especially so if the mitigation
scenarios serve as policy advice. Potential remedies for this limitation include a transparent reporting of
all input assumptions used in mitigation scenario development and an explicit indication of assumptions
that are based on normative considerations. Furthermore, those scenario assumptions involving value
judgments are ideally not determined by science alone but are at best obtained in a public debate as the
following elucidates.
1.4. A Normative Model of the Science-Policy Interface
The conceptual interplay between science and the policymaker has spurred a long-standing debate in
the political and philosophical literature. The basic problem at stake is: Who should define the policy
ends and who should decide on adequate policy means to attain these ends? For the focal issue of this
thesis, domestic mitigation scenarios for Germany, the question translates into: Who should define a
German mitigation target? Who should decide on the policy means that are implemented to attain this
target? The conceptual answers to these questions depend on which normative model of science-policy
interface is adopted and influence the ideal methodology for developing domestic mitigation scenarios.
Habermas (1971) provides a seminal description of three conceptual models for the science-policy
interface that prevail to date. These are the ‘decisionist’, ‘technocratic’ and ‘pragmatic’ model. In
simplified terms, the decisionist model assumes that policy ends are set by legitimized policymakers
alone, as they reflect inherent value judgments about desirable futures. Science provides the most
effective means to attain these ends in a value-free, rational and objective manner. The technocratic
model on the other hand supposes that the policy problem is too complex to be conceived without
expert knowledge and both policy ends and means need to be provided by science. The role of the
policymaker is then restricted to implementing the policy measures determined by scientists in the form
of rational, absolutely objective knowledge. In both models, the public is not involved in the policy
process. Essentially, the existing mitigation scenarios for Germany presented in the preceding Section
are examples of scientific results developed under the decisionist paradigm as they adopt the mitigation
targets determined by the German Government and report policy means in a seemingly factual manner.
As outlined by Edenhofer and Kowarsch (2012), the crucial assumption of both the decisionist and
technocratic model – that science can possibly deliver rational, objective and value-free knowledge – is
flawed. The main argument shattering the fact/value dichotomy on philosophical grounds is that
24 Chapter 1 Introduction
scientific
2002). Su
robustne
s
Thus, sci
e
one need
German
e
plausible
constitut
e
mitigatio
n
Edenhof
e
pragmati
s
amended
conceptu
a
postulate
pragmati
s
practical
as a set
problem
a
problem
develops
combinat
the selec
t
suggeste
d
policy int
e
Figure 3. T
h
Kowarsch (
The type
rather fo
r
judgments
p
ch epistemic
s
s or predict
i
e
ntific knowl
e
s to acknowl
e
e
nergy syste
m
input assu
m
e
value-lade
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r and Kow
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h
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o
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and the ide
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h
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by Edenho
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2012), Figure 4.
of public de
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ms accordin
g
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dge is inevi
t
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a
rsch (2012)
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rsch (2012)
g
ure 7.
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s
o
f science-p
o
m employe
d
Dewey and
e
vertheless
b
h
as establish
D
eweyan inq
u
consists of
f
u
gh analysis
a
i
n-view”. In
possible e
n
c
es. Fifth, aft
d
for the nex
t
e
weyan inqu
d
model (PEM).
S
n
clude every
ituation. Ide
a
owledge (Pu
t
c
ation, plaus
i
e
ought to r
e
tigation sce
n
g
y solutions f
o
rgy system
m
o
s thus nece
s
c
ientific advi
c
s
of philoso
o
licy interfac
d
for derivin
Hilary Putn
a
b
e “objective
”
ed their rep
u
iry (Dewey,
f
ive steps: F
i
a
nd framing
o
a third step
n
ds-in-view/
m
er a implem
e
t
inquiry. Th
e
iry to the sc
i
S
ource: Edenho
f
single citize
n
a
lly, all partie
t
nam,
i
bility,
e
ason.
n
arios,
o
r the
m
odel,
s
sarily
c
e on
phical
e, an
g the
a
m. It
”
in a
eated
1938)
i
rst, a
o
f the
, one
m
eans
e
nting
e
PEM
i
ence-
f
er and
n
, but
s that
1.4 A Normative Model of the Science-Policy Interface 25
are required to identify, analyze and frame the problematic situation, are able to contribute possible
means to solve it, have a stake in its solution and are potentially affected by direct or indirect
consequences of policy means should be part of the public debate in the different stages of the PEM
process. Due to the complexity and significant transaction costs of operationalizing such a dialogue, it is
suggested to primarily engage representatives of the stakeholder groups. Furthermore, essential
participants in the public debate are policymakers and science.
According to Edenhofer and Kowarsch (2012) the role of science in the public debate is that of a
facilitator, stimulator and advisor; taking advantage of its elaborated methods. In the first stage of the
PEM, science can analyze and criticize the problem framing that policymakers and stakeholders discuss
before policymakers decide on ends-in-view. In the context of climate policy, this implies assessing the
problematic impacts of climate change and discussing means to cope with it, such as mitigating
emissions by deploying RES technologies and adopting RES targets. Given that impacts of climate change
are global while mitigation measures and targets are introduced on a national level, global and domestic
considerations are clearly interdependent. Thus, there should be both a global PEM assessment cycle
and a nested national PEM assessment cycle that considers a nation’s mitigation targets and means such
as domestic RES targets in the broader context. It is important to note that the mitigation target of 80-
95% CO2 emission reduction in 2050 relative to 1990 articulated by the German Government constitutes
such an end-in-view. From a PEM perspective it is now important to scrutinize possible means for
achieving this end-in-view and their direct and indirect consequences, which constitutes the second
stage of the PEM process.
Here, the role of science is especially vital for assessing possible ends-in-view/means combinations, i.e.
mitigation scenarios for the case at hand, given a high transparency of scientific assumptions and
methods, value judgments and uncertainties. Also, the interests and preferences of affected parties
need to be taken into account in order not to overlook possible societally relevant means or means-
consequences. For Germany, this pivotal step of the PEM process is yet outstanding, as the discussion
above has shown that the existing mitigation scenarios do not satisfy the transparency criteria. As it is
impossible for science to explore all possible ends-in-view/means combinations in the third stage of the
PEM process, Edenhofer and Kowarsch (2012) suggest scrutinizing a selection in detail for their possible
consequences, in close cooperation with policymakers and stakeholders in a public debate. Here, it is of
particular importance to include all those scientific disciplines which are necessary to provide a
comprehensive picture of the direct and indirect consequences of the analyzed ends-in-view/means
combinations. Thus, the important task of science in this stage is to “make policy options more explicit,
including their main conditions as well as (normative or other) premises under which these policy
options are viable and reasonable” (Edenhofer and Kowarsch, 2012, p. 20). Finally, in the fourth stage,
science can contribute by rigorously monitoring the outcomes of the process and record lessons learned
for improved performance in future assessments of the policy problem.
To date, no such comprehensive PEM-guided assessment has been pursued for the problem of CO2
emission mitigation in a rigorous manner, neither on global nor on national scale. There are at least two
convincing reasons why the PEM as a normative model of the science-policy interface is advantageous:
First, on a conceptual level it supports the ideals of democracy and its application to the mitigation
26 Chapter 1 Introduction
policy process could lead to more democratic, transparent and fair decisions. Second, on a practical level
an application of a PEM-guided assessment is likely to lead to more intelligent policy outcomes that
have a high chance of being realized as their possible consequences have been assessed and negotiated
ex-ante. Particularly for the policy problem of mitigation it is of essential importance to draw on the
knowledge stock held by those who are directly and indirectly affected by conceivable policy means.
Here, the lead times from decision making to deployment of low-carbon solutions are particularly long-
stretched due to energy system assets being highly planning- and capital-intensive. Moreover, the
energy system is highly interdependent and non-realization of one element of the system can easily lead
to underperformance or malfunctioning of the system as a whole. For example, if power grid extensions
that are bottlenecks for ensuring security of electricity supply are impeded due to local protest by
individual communities, this may adversely affect the successful transition of the German electricity
towards high shares of renewables as a whole. Given that the door towards stabilizing GHG emissions is
closing, it is of utmost importance to develop policy ends and means combinations that have a high
chance of being realized without effective resistance of individual actors who suffer from direct or
indirect consequences of policy measures and have been ignored in the negotiating process, i.e. policy
means that enjoy a high level of social acceptance. However, the successful realization of a PEM-guided
assessment for the policy problem of German mitigation presents itself as a formidable challenge due to
the complexity of the task of engaging science, society and policymakers in a cooperative dialogue.
1.5. Objectives and Outline
Having sketched the urge for ambitious mitigation efforts, the demand for mitigation scenarios in the
German energy policy arena, the severe limitations of existing literature on the topic as well as potential
analytical and conceptual remedies, it unfolds that this thesis strives to explore how German mitigation
scenarios that adhere to the good principles of the science-policy interface can be developed in practice.
This objective corresponds to realizing an elaboration of possible ends-in-view/means combinations in a
public debate as suggested by stage two of the PEM (Edenhofer and Kowarsch, 2012). In more specific
terms this objective translates into the engagement of civil society stakeholders in the development and
evaluation of model-based mitigation scenarios for Germany that explore how the end-in-view of 85%
CO2 emission reduction in 2050 relative to 1990 as articulated by the German Government (Federal
Government, 2010) can be achieved, given the normative considerations of civil society stakeholders.
Taking the policy process proposed by the PEM one stage further, a second objective of this thesis is to
explore alternative German mitigation scenarios for identifying what kinds of energy strategies they
embody as well as making the premises under which they are viable more explicit. The scope of the
second objective is confined to the electricity sector, whose transition towards high shares of electricity
generation from renewable energy sources constitutes an important pillar of German mitigation
ambitions. The contributions of this thesis thus comprise both methodological advancements in terms of
how to apply a PEM-guided assessment in practice as well insights on how the German energy transition
can be enabled. Figure 4 visualizes the two objectives of this thesis in relation to the PEM as its
theoretical foundation.
1.5 Objectives and Outline 27
Figure 4. O
b
science-pol
i
This thes
explorat
o
civil soci
e
1999) in
w
civil soci
e
scenario
r
sets of re
s
The seco
n
scenarios
b
jectives of this
i
cy interface. So
i
s is structu
r
o
ry research
t
e
ty stakehold
w
hich deliber
e
ty stakehold
e
r
esults deriv
e
s
earch quest
i
•What kin
d
in mitiga
t
definitio
n
•Which mi
are they
e
n
d part of th
e
for the elect
•What kin
d
high shar
e
scenarios
reasons f
o
t
hesis in relatio
n
urce: Own illust
ed in two p
a
t
hat aims at
ers, taking a
ation frames
e
rs frame th
e
e
d with an en
i
ons arise:
d
of organiza
t
ion scenario
n
o
f
model-b
a
tigation sce
n
e
valuated?
W
e
thesis pursu
ricity sector
a
d
s of energy
e
of renewa
b
for German
y
o
r diverging
s
n
to its theoreti
c
ration and Eden
a
rts along t
h
developing
n analytical-
d
analysis and
e
scenario d
e
ergy system
m
tional proje
c
developme
n
a
sed mitigati
o
n
arios for Ge
r
W
hat are the
k
es a compar
a
a
nd deals wit
h
strategies fo
r
b
le electricit
y
y
? Which bar
s
cenario proj
c
al foundation,
t
hofer and Kow
a
h
e lines of t
h
m
odel-based
d
eliberative
a
analysis info
r
e
finition acco
m
odel for G
e
c
t design is s
u
n
t? How can
s
o
n scenarios
?
r
many emer
g
k
ey findings
f
a
tive meta-a
n
h
the followi
n
r
transformi
n
y
generation
a
riers to impl
e
ections?
t
he pragmatic-e
a
rsch (2012), Fig
h
e objective
s
mitigation
s
a
pproach (St
rms delibera
t
rding to thei
e
rmany in an
u
itable to en
g
s
takeholder
p
?
g
e from the e
f
or enabling
t
n
alysis of sel
e
n
g research
q
n
g the Germ
a
a
re embodie
d
e
mentation
c
nlightened mod
ure 4.
s
. The first p
s
cenarios in
ern and Fin
e
t
ion. The und
r preference
s
iterative pro
c
g
age civil soc
p
references
f
e
xploratory r
e
t
he German
e
e
cted Germa
n
q
uestions:
a
n electricity
d
by selecte
d
c
an be identi
f
el (PEM) of the
art constitu
t
collaboratio
n
e
berg, 1996;
erlying idea i
s
and evalua
t
c
ess. The foll
o
iety stakeho
l
f
rame the
e
search and
h
e
nergy trans
n
mitigation
sector towa
r
d
mitigation
f
ied? What a
t
es an
n
with
Renn,
s that
t
e the
o
wing
l
ders
h
ow
ition?
r
ds a
re
28 Chapter 1 Introduction
The outline of this thesis is as follows: In order to realize a collaborative scenario definition and
evaluation process, necessary prerequisites are first a functional project design and second an energy
system model for Germany. Chapter 2 proposes an innovative project design blueprint that can serve as
a starting point for the methodology of future collaborative mitigation scenario exercises. As regards the
energy system model, it has been outlined above that there exists currently no model for Germany that
is based on sound economic theory. In order to fill this gap, the hybrid energy-economy model REMIND-
D has been developed in the course of this research. In order to provide full transparency, Chapter 3
provides a detailed documentation of the model structure, the input data used to calibrate the model to
the Federal Republic of Germany and the techno-economic parameters of the technologies considered
in the energy system module. With the project design and the energy system model at hand, the
collaborative scenario definition and evaluation process is conducted with a number of German civil
society stakeholders. Chapter 4 presents the outcomes of the stakeholder dialogues and the resulting
German mitigation scenarios obtained with REMIND-D, as well as the civil society stakeholders’
evaluation of the mitigation scenarios. Addressing the second objective of this thesis, Chapter 5 pursues
a comparative meta-analysis of selected German mitigation scenarios for the electricity sector, including
two of the scenarios developed in Chapter 4. It investigates a number of strategic questions relevant for
the long-term transformation of the German electricity sector towards high shares of electricity
generation from renewable energy sources and groups the scenarios according to the energy strategy
they embody. Furthermore, Chapter 5 identifies barriers to implementations that are applicable to all
identified energy strategies and explores the reasons for diverging scenario projections. Finally, Chapter
6 summarizes the findings of the core chapters in this thesis by answering the research questions posed
above and concludes this exploration of German mitigation scenarios with suggestions for future
research.
1.5 Objectives and Outline 29
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34 Chapter 1 Introduction
Chapter 2
Social Acceptance in Quantitative Low Carbon Scenarios
∗
Eva Schmid
Brigitte Knopf
Meike Fink
Stéphane La Branche
∗published as: E. Schmid, Knopf, B., Fink, M., La Branche, S. (2012) Social Acceptance in Quantita-
tive Low Carbon Scenarios. In: Renn, O., Reichel, A. Bauer, J. (eds) Civil Society for Sustainability. A
Guidebook for Connecting Science and Society. Europäischer Hochschulverlag, Bremen.
35
36 Chapter 2 Social Acceptance in Quantitative Low Carbon Scenarios
232
Social Acceptance in Quantitative Low Carbon Scenarios
Eva Schmid, Brigitte Knopf, Stéphane La Branche, Meike Fink
Introduction
Significant reductions of global greenhouse gas emissions play a key role in
addressing the problem of dangerous anthropogenic climate change. In
order to achieve low-stabilization targets, emissions have to peak before
2020 (UNEP 2010). Yet, greenhouse gas mitigation is a challenge for which
no simple and single recipe exists, as exemplified by the abortive
developments of international negotiations and limited mitigation success
since the ratification of the Kyoto protocol. The dominant source of
greenhouse gas emissions, especially CO2, is the anthropogenic use of fossil
resources for energy supply. Consequently, mitigation requires a long-term
transformation towards low carbon energy systems. To date, the majority of
research efforts on how to achieve this have concentrated on identifying
innovative technological solutions and assessing optimal deployment
pathways, as well as suitable policies in the different sectors of the energy
system. This tendency manifests itself in the predominantly model-based
low carbon energy roadmaps published recently, e.g. the “Lead Study” in
Germany (Nitsch et al. 2010) or on the European level the EU Roadmap 2050
(European Commission 2011a). Central topics are aggregate assumptions on
the developments of energy demand, energy efficiency, investment costs of
future technologies, technical potentials, demographic structures, etc., that
serve as an input or are an output of the respective quantitative energy
system model frameworks. However, sociological dimensions, in particular
the social acceptance of implications of the suggested energy futures, are
rarely addressed. This is a serious shortcoming, as the transformation of
national energy systems represents a profound and long-term change
process involving society as a whole. Moreover, a high level of
unacceptability could result in group’s refusal of climate measures or in
avoidance strategies that would then decrease its potential efficiency; the
question of “social acceptability” is also one of relative unacceptability and
its consequences.
In the context of energy system strategies, social acceptance has three
dimensions (Wüstenhagen 2007): (i) socio-political acceptance, referring to
the acceptance of technologies and policies by the public, key stakeholders
and policy makers, (ii) community acceptance of site-specific local projects
and (iii) market acceptance, referring to the process of consumers’ and
investors’ adoption of innovative low-emission products. For addressing
2.1 Introduction 37
233
these dimensions in the context of energy system transformation scenarios,
it is necessary to extend the engineering/economics toolbox of research
methods towards truly interdisciplinary approaches by combining them with
methodology developed in other strands of social science and the
humanities. One implication is to not only rely on quantitative methods, but
also on qualitative methods, that are useful when the specific individual
perspective of the research subject is focused upon, the research subject has
been poorly investigated so far and verbal data is to be interpreted (Bortz &
Döring 1995); all of these issues apply in the present context.
One approach to address social acceptance in low carbon scenarios to is to
include well-managed and repetitive stakeholder consultations as an
integrative part of an energy system model scenario definition process. The
parameters and input variables of the aggregated model are carefully
translated into tangible, real-life, implications for the public and then
evaluated by civil society representatives with respect to their social
acceptance. The considerations emerging from these stakeholder
consultations are translated back into configurations of technical model
parameters, i.e. political framework conditions, and result in different low
carbon energy system scenarios. These integrated scenarios are calculated
by the quantitative models and the results are again translated into tangible
meaning and presented to the civil society stakeholders, emphasizing at
least the first of the three dimensions of social acceptance in energy system
strategies. Such a collaborative scenario definition process has been
undertaken together by non-governmental organizations (NGOs) and
research institutes within the EU-project ENCI LowCarb (Engaging Civil
Society in Low Carbon Scenarios) for France and Germany. Based on the
ENCI LowCarb experience, this paper proposes a pragmatic project design
blueprint, intending to foster repetitive collaboration between civil society
and science for introducing dimensions of social acceptance into model-
based, low carbon energy system scenarios.
Section 2 presents the existing barriers to interdisciplinary research and
collaboration processes between science and civil society in the context of
energy system scenarios. Section 3 introduces a conceptual project design
blueprint intended to overcome these difficulties. It describes four distinct
phases of a collaborative scenario definition process. Section 4 elaborates on
the specific experiences from the ENCI LowCarb project and problems
encountered during the process. Section 5 reflects on limitations and
compares to other scenario projects involving stakeholders. Section 6
concludes.
38 Chapter 2 Social Acceptance in Quantitative Low Carbon Scenarios
234
Barriers to Collaboration
In order to derive a project design that encourages collaboration between
engineering, economics and other strands of social science as well as civil
society, it is worthwhile to step back and analyse why comprehensive
collaborative projects between scientific disciplines and civil society are to
date rare, especially in the field of energy system scenarios. Three general
observations are helpful. First, one has to acknowledge that “science” and
“civil society” are umbrella terms for communities that again consist of a
large variety of distinct sub-communities. Second, these communities and
sub-communities are distinct with respect to their raison d’être, objectives
and culture, i.e. values, norms and language. Third, they have a tendency to
coexist, in the sense that there are few institutional intersections per se;
collaborative projects across communities are often preceded by proactive,
innovative, and open-minded individuals.
Civil Society and Non-Governmental Organizations
Civil society is a rather vague umbrella term, Reverter-Bañón (2006) argues
that her understanding of civil society is three-fold: as associational life
(Putnam, cited in Reverter-Bañón 2006), as good society, and as public
sphere (Habermas, cited in Reverter-Bañón 2006). In more concrete terms,
the World Bank (2004) defines the notion as follows: “The term civil society
refers to the wide array of non-governmental and not-for-profit
organizations that have a presence in public life, expressing the interests and
values of their members or others, based on ethical, cultural, political,
scientific, religious or philanthropic considerations. Civil society
organizations (CSOs) therefore refer to a wide array of organizations:
community groups, non-governmental organizations (NGOs), labor unions,
indigenous groups, charitable organizations, faith-based organizations,
professional associations, and foundations”. CSOs are formed as people with
similar interests organize themselves and represent a certain set of claims,
beliefs, norms and values. Often, the term CSO and NGO are conflated.
Willets (2002) defines the term NGO as an independent voluntary
association of people acting together on a continuous basis and for some
common purpose. In this paper, CSO is used as the umbrella term and NGOs
are considered as a subset of CSOs.
Many CSOs intend to change the status quo of a certain affair;
environmental NGOs lobby for reducing pollution, churches preach
humanitarian values and citizens’ initiatives fight for local projects. Often,
2.2 Barriers to Collaboration 39
235
CSO activists operate at the grass-roots level and are ideal-driven. In terms
of climate change mitigation, environmental NGOs have played a visible role
with projects focused on greenhouse gas emission reduction involving
lobbying, campaigning or protesting against specific local affairs. With the
intention to scientifically back up their lobbying work, NGOs have
increasingly been seeking contact to the scientific communities. Moreover,
many environmental NGOs have shifted from constituting an activist
movement towards more mature organizations employing scientists that did
not want to continue a purely academic career. NGOs have published
comprehensive scientific studies to underpin their claims and objectives with
research results, e.g. WWF (2008; 2009) and Greenpeace (2007). However,
these studies were largely commissioned to research institutions and
prepared in principal-agent relationships more than in structured
collaboration processes. In sum, it appears natural to foster collaboration
between NGOs, rooted in the civil society community, and scientists as a
starting point for incorporating social acceptance into energy system
scenarios. In later steps, CSO representatives are included in the
collaboration process.
Scientific Cultures and Mitigation
In terms of public attention and academic outreach visibility, the mitigation
problem has mainly spurred natural scientists and engineers to develop and
assess low-emission technologies, system scientists to perform integrated
analyses of optimal deployment paths, and economists to analyse energy
market forces and suitable policies. Politically prominent theoretical
research results on long-term mitigation strategies have been obtained by
engineering or economic methods: mathematical modelling, optimization,
game theory, statistics and econometrics, i.e. quantitative methods. Maybe
this is due to the seductive charm of hard numbers and associated “scientific
facts”. Yet, a recent publication of the German Academies of Sciences
strongly encourages collaborations between engineers, natural and cultural
scientists, as they consider this a prerequisite for achieving ambitious
climate policy targets in Germany (Renn 2011). Within the social science
literature, the refusal, acceptance, or avoidance strategies of actors
regarding mitigation measures is less investigated. There are efforts to
understand the public and local acceptance of renewable energies by means
of specific case studies, e.g. Zoellner, Schweizer-Ries and Wemheuer (2008),
Musall and Kuik (2011) and Nadaï (2007), involving qualitative interviews and
questionnaire-based survey analysis. However, there are to date no visible
efforts to combining these findings with purely quantitative energy/
40 Chapter 2 Social Acceptance in Quantitative Low Carbon Scenarios
236
economics models. One possible explanation is the coexistence of the
different scientific sub-communities.
Even within different scientific disciplines, there are many coexisting and
often conflicting strands of research. Many of the conflicts root from
methodological issues. Albeit scholars of both the quantitative and the
qualitative tradition share the overarching goal of producing valid descriptive
and causal inferences (Brady & Collier, cited in Mahoney & Goertz 2006),
there are substantial discrepancies in basic assumptions and practices.
Schrodt (cited in Mahoney & Goertz 2006) observes that the dynamics of the
debate between quantitative and qualitative scholars on the validity of their
methods are best understood by comparing it to one about religion, with
deep cleavages between the two. Mahoney and Goertz (2006) provide an
excellent discussion on how the two research traditions are to be understood
as alternative cultures with proprietary values, beliefs, norms and language
that may lead to severe “cross-cultural” communication problems when
“forced” to work with each other. Thinking of different research traditions in
terms of ethnocentric, coexisting and potentially conflicting cultures helps
for explaining and mastering the challenges of collaborative research
projects. One can draw on the large body of literature on culture in other
academic disciplines, e.g. organizational behaviour and cultural studies.
Clearly, parallels exist between methodological, organizational and
ethnological culture. Considering the effective cultural barriers to
collaboration even within science, it is not surprising that the barriers
towards collaboration between science and NGOs or CSOs are even higher.
The Collaborative Scenario Definition Process
The collaborative scenario definition process proposed in the following61 is a
pragmatic interdisciplinary approach that aims at producing quantitative
engineering/economics model scenarios in collaboration with civil society
stakeholders. It is organized in four distinct phases. Phase 1 is concerned
with establishing a fully functional project team and Phase 2 with
establishing the technological framework conditions for the scenarios. The
political framework conditions are elaborated with civil society stakeholders
during Phase 3, resulting in scenarios that differ with respect to their degree
of social acceptance. Phase 4 synthesizes.
61 It is based on the experience from ENCI LowCarb, but presented on a meta level. It is
applicable to projects involving the definition of scenarios with both technological
and political framework conditions.
2.3 The Collaborative Scenario Definition Process 41
237
Core Project Partners
To accommodate the interdisciplinary requirements of the objective to
include social dimensions in quantitative mitigation scenarios, core project
partners come from both the scientific and the civil society communities.
From the latter, NGOs constitute good candidates, as they form a
continuously working formal entity, which cannot necessarily be generalized
to all CSOs. Additionally, one can expect that NGOs are well embedded
within the CSO landscape and act as facilitators between scientists and other
CSOs. From the scientific communities, it is on the one hand necessary to
have project partners from one or more research institutions that operate an
engineering/economic type of quantitative model (here an energy system
model), termed quantitative modelers, hereafter. On the other hand it is
necessary to have project partners from the social sciences or humanities
that are proficient in both quantitative and qualitative research methods of
their discipline, termed social scientists hereafter62. Due to the distinct
professional cultures of the project partners, it is decisive to stimulate their
awareness for cultural issues in general and cultural differences in particular.
A trivial, but effective means to achieve this is to define a core project team
with each a research institution and NGO from at least two countries that do
not share the same language. There are several practical advantages of
combining the three different professional cultures with two or more
different national cultures. Project partners communicate in a non-mother
language, which fosters the awareness for unfamiliar terms and alleviates
barriers to clarification requests during conversations. Furthermore, as the
problem of climate change mitigation presents itself and is addressed very
differently in individual countries, the transnational perspective helps to
reframe and to challenge the purely domestic point of view.
Phase 1: Intra-Group Development
Albeit the intra-group development of project teams is a fairly standard
procedure, a conscious group-formation process is of particular importance
for collaboration across project partners from different communities. A
suitable organizational structure is proposed in Figure 1. It resembles a
matrix structure and enables vigorous communication flows between all
62 In the following, it is assumed that both modelers and social scientists are from one
research institution and the representative terms research institution, quantitative
modeler, social scientist and NGO will be used in singular for simplicity; more than
one project partner may be included from each community.
42 Chapter 2 Social Acceptance in Quantitative Low Carbon Scenarios
238
project partners; the colour codes visualize the different communities and
countries. Tuckman (1965) observed that groups generally develop by
passing through four distinct stages: forming, storming, norming and
performing. Given project partners from different communities, with their
respective cultural backgrounds, the first three stages need special attention
for being successful in the fourth.
The forming stage of group development is characterized by uncertainty:
project partners from the different communities are “testing the waters” and
get acquainted to each other by exchanging ideas, expectations and world
views; one gathers information and impressions of each other, but avoids
open controversies or conflicts (Tuckman 1965). It is very likely that during
this stage, many of the others’ positions are not immediately obvious and
even beyond clear assessment to the individual project partner. During the
storming phase of group development, which is characterized by intra-group
conflict and requires tolerance and patience, project partners express
opinions and views more openly, including criticism (Tuckman 1965). One
can expect that substantial cultural distance (Triandis 1994) exists between
each the social scientist, the quantitative modeler and the NGO member. To
overcome these, and enable the group to develop interdisciplinary
approaches with regard to the specific research question of the project,
conscious intercultural communication is advantageous. McDaniel, Samovar
and Porter (2009, p. 13) argue that five aspects of culture are especially
relevant to intercultural communication: perception, cognitive thinking
patterns, verbal behaviours, nonverbal behaviours and the influence of
context.
Research
Institution
NGO NGO
Country A Country B
Research
Institution
Research
Institution
NGO NGO
Country A Country B
Research
Institution
Figure 1. Organizational structure during Phase 1 of the collaborative scenario definition process.
A promising format to foster viable cross-cultural communication is to
employ formal “wish-lists”. The quantitative modeler receives model
features that the others would like to see in the model and what kind of
results they expect. The social scientist receives ideas on how social
2.3 The Collaborative Scenario Definition Process 43
239
acceptance is defined and will be explored, interpreted and measured. The
NGO member receives considerations on what kind of stakeholders to
consult. Such a process allows project partners to get a good understanding
on how the others perceive their discipline. In a meeting, they present what
they originally planned to contribute in the project and relates it to the
“wish-list” items. Such an exercise will reveal their cognitive thinking
patterns. After each presentation, sometime is reserved for clarifying terms,
so project partners have a chance to realize potential verbal and nonverbal
barriers to communication. Finally, in thematic sessions, the history and
status quo of the domestic energy system can be presented, so one learns
facts and context of the other country’s challenges. During the “wish-list”
process, the project partners have a chance to develop a common language
and gain realistic expectations of the abilities of the quantitative model, the
concept of social acceptance and the stakeholder landscape. In repetitive
exchange, project partners develop a joint idea of the research methods they
will employ. Finally, they pass the norming stage of group development,
characterized by cohesiveness and in-group feeling, on to the performing
stage, during which group energy is channelled into the task (Tuckman
1965).
Phase 2: Technological Framework Conditions
Phase 2 of the scenario definition process is concerned with model
development and the technological framework conditions of the scenarios
by involving external experts. The task is to refine the national quantitative
models and bring them to a stage, in which they are applicable to
stakeholder consultations, fulfilling as many “wish-list” items as feasible,
driven by the overarching question of “What is technically possible in the
future?”. Thus, the social science issues regarding social acceptability do not
yet enter the stage, they will be integrated in the next phase. Figure 2
proposes an organizational structure during Phase 2; with the core structure
prevailing, but now the national sub-teams, indicated in green, have formed
a tighter entity. This ideally results from the intense communication flows
during Phase 1. The yellow shading of the consulted experts symbolizes the
notion that they will most likely be closer to the researchers in terms of
“professional culture” than to NGO members.
44 Chapter 2 Social Acceptance in Quantitative Low Carbon Scenarios
240
Experts
Experts
Research
Institution
NGO NGO
Country A Country B
Research
Institution
Experts
Experts
Research
Institution
NGO NGO
Country A Country B
Research
Institution
Figure 2. Organizational structure during Phase 2 of the collaborative scenario definition process.
Expert workshops are organized in each country, for the national sub-teams
to engage in focus group discussions with experts for obtaining state-of the
art knowledge on technical details. Thereby, the experts can assess the
validity of the quantitative model and have a control function on the
scientific quality. In the end of Phase 2, a finalized version of the energy
system model exists, along with a detailed documentation that is also
understandable to the non-technical reader. It is necessary to provide such a
document during the stakeholder consultations in order to create
transparency and alleviate the frequent black-box accusation when it comes
to quantitative scenario building. Central to the model description are
detailed translation rules, from “model parameters” to “real-world
implications” and vice versa, that serve as a basis for taking into account
political framework conditions explicitly.
Phase 3: Political Framework Conditions and Corresponding Scenarios
A central issue in Phase 3 of the collaborative scenario definition process is to
elaborate different and potentially controversial political framework
conditions with relevant CSO stakeholders. The political framework
conditions relate to the quantitative model by applying the aforementioned
translation rules from model parameters to “real-world implications”.
Coherent sets of political framework conditions form one scenario, differing
with respect to the articulated level of social (un-)acceptability of mitigation
options. The integrated scenarios are again evaluated by the CSO
stakeholders. Figure 3 proposes an organizational structure for Phase 3. The
blue shading of the CSO Stakeholders indicate that they are culturally close
to the NGO project members, these indeed serve as facilitators in a two-step
interaction in workshop format.
2.3 The Collaborative Scenario Definition Process 45
241
Research
Institution
NGO NGO
Country A Country B
Research
Institution
Stake-
holder:
CSOs
Stake-
holder:
CSOs Research
Institution
NGO NGO
Country A Country B
Research
Institution
Stake-
holder:
CSOs
Stake-
holder:
CSOs
Figure 3. Organizational structure during Phase 3 of the collaborative scenario definition process.
Before inviting CSO stakeholders, the sub-national project teams identify
sectors of the domestic energy system that are of particular interest or
controversy regarding social acceptance. Together with a professional and
neutral moderator, the national sub-teams develop concrete workshop
agendas. The social scientist selects suitable methods for capturing
stakeholder’s assessments during the workshops. A practical format is a
questionnaire with Likert scales (Likert 1932), measuring the level of
agreement or disagreement of the respondent towards specific statements.
The specific statements are the translated “real-world implications” and
postulate particular and tangible developments63. Per item, two Likert scales
are employed: Stakeholders are once asked to indicate whether they find the
proposed development realistic and once whether they would welcome it
from the point of view of their organization. Stakeholders are unlikely to
express a uniform opinion, so several different sectoral “scenario building-
bricks” in terms of political framework conditions will emerge from the
workshops. The national sub-teams combine them into coherent scenarios
for the fully integrated energy system, which serve as an input to the
quantitative model. During the second sectoral stakeholder workshops,
ideally attended by the same CSO representatives, the developed scenarios
are presented, discussed, and evaluated. The feedback loop ensures that the
social acceptance considerations are actually realized and gives the CSO
representatives a chance to indicate their assessment of social (un-
)acceptance of the integrated scenarios. At this stage, one possible outcome
of the scenario definition process may be that the CSO representatives judge
63 An example from the transport sector workshop of the ENCI LowCarb project is
“Cycling and Walking will contribute substantially to the Modal Split. Please indicate
your perception whether this is realistic and, separately, welcome, from the point of
view of your organization on a 7-point scale from Yes to No.”.
46 Chapter 2 Social Acceptance in Quantitative Low Carbon Scenarios
242
one or more integrated scenarios socially unacceptable as a whole, even
though individual building bricks can be in line with their preferences.
Phase 4: Synthesis
The last phase is concerned with the synthesis of results obtained
throughout the collaborative process, formalizing the outcomes of the
stakeholder consultations as well as an evaluation of the final scenarios in
terms of social acceptance. Ideally, a workshop communicates the scenarios
to policy makers, stakeholders, and the wider public. Possibly valuable
extensions for the collaborative process are to elaborate the political
feasibility of the scenarios’ political framework conditions as well as the
reasons for social (un-)acceptance of specific mitigation options in more
detail. Here, one could extend the socio-political point of view adopted
during the collaborative scenario definition process, and analyse market and
community acceptance.
The ENCI LowCarb Experience
The ENCI LowCarb project is financed in the 7th Framework of the EU
Commission and constitutes a rather novel format involving both research
institutes and NGOs. The core project partners are the Potsdam Institute for
Climate Impact Research (PIK), Germanwatch, the Centre International de
Recherche sur l’Environnement et le Développement (CIRED), and Reseau
Action Climat France; the project phases, identified ex post, are summarized
in Table 1.
2.4 The ENCI LowCarb Experience 47
243
Phase 1 Phase 2 Phase 3 Phase 4
Objective
Intra-group
development of
the project team
Model building
“What is
technologically
possible?”
Stakeholder
workshops “What
is socially
desired?”
Synthesis,
communication of
scenarios
Leadership Fragmented
Research
Institution
for sub-team in
each country
NGO
for sub-team in
each country
Joint responsibility
Events
Kick-off meeting,
Planning
workshop
Expert workshops,
Planning
workshop
Repetitive
CSO
stakeholder
workshops
Synthesis
workshop,
Communication
Workshop
Deliverables /
Output
“Wish-Lists”
and
feedback
Workshop
summaries, model
with description
Workshop
summaries,
national
scenarios
Country
reports,
Comparative
report
Time Horizon 6 months 12 months 12 months 6 months
Table 1. Overview of the phases in the collaborative scenario definition process within the ENCI LowCarb
project.
ENCI LowCarb had two main project objectives: developing a reproducible
methodology for engaging civil society, and preparing the German and
French integrated energy system scenarios. The following reports on specific
experiences made during the project on a more abstract level, with the
intention to deliver beneficial input for future projects that involve
collaboration between science and civil society, additive to the blueprint
outline in Section 3. The German and French domestic energy system
scenarios are accessible on the project website64 upon publication.
Attitudes and Politics
In the beginning of the project, the different professional cultures and
different intrinsic objectives of NGO members and scientists became
tangible. NGOs are generally interested in developing scientific (counter-)
expertise that can be used for proper lobbying activities, corresponding to
their fundamental values. Especially, if the NGO is a network composed by
several NGOs (like RAC-France), with individual future energy visions, there
can be a strong internal pressure for obtaining politically relevant outcomes.
Scientists have an interest in producing coherent, technically sound and
objective research and tend to care less about the politics. These potentially
64 http://www.lowcarbon-societies.eu
48 Chapter 2 Social Acceptance in Quantitative Low Carbon Scenarios
244
conflicting attitudes were made explicit early in the project. In later stages,
many conflicts could be avoided due to project partners pointing out that the
argument or problem at hand actually had to do with our different attitudes
and perceptions, resulting in more productive discussions. Raising awareness
for such issues proved to be crucial, especially during the definition of the
integrated scenarios, as these were based on the stakeholders’ assessments
of political framework conditions.
Joint Understanding of Quantitative Models
Large and complex quantitative models are a very powerful tool for pursuing
integrated system analyses; however, the models and their output are often
meaningful only to the expert or insider. Outsiders are not enabled to judge
the quality and validity of model results, and either have to believe the
modelers, or not. During the ENCI LowCarb project, it was very important for
the NGO members to learn more about quantitative models in general, and
the models of the project partners in particular, so that modeling results can
be put into perspective. It was a rather time-intensive process for the
quantitative modelers to explain the models and was perceived as a real
cross-cultural communication effort. During this process, it was very
enlightening for the modelers to learn about the requirements from an NGO
perspective, which sometimes differs substantially from academic peer
group discussions.
For the NGOs, it was important to distinguish between means and measures
in the energy system models: technical solutions, e.g. offshore wind
turbines, and political measures to foster them, e.g. feed-in-tariffs. Whereas
energy system models contain a whole range of technical solutions, it is not
possible to integrate the full impact of political measures. NGOs are
interested in a mixture of both, so it is helpful to differentiate and focus on
what is feasible in the model during the project. The joint effort of clarifying
the capabilities of the energy system models turned out to be a crucial
success factor for the ENCI LowCarb project. The “wish-lists” introduced
earlier were invented during the explanation process and turned out to be an
extremely useful tool. The modeling teams were forced to think about the
“real-world implications” of the aggregate model results and develop
concrete translation rules on how parameters and variables may be
expressed in tangible meaning. During the preparation and post processing
of the stakeholder workshops these translation rules served as a helpful
structuring element for the quantitative modelers.
From the perspective of the quantitative modelers, the expert meetings in
Phase 2 were very helpful and stimulating. The modeling teams learned a lot
2.4 The ENCI LowCarb Experience 49
245
and sometimes revised the models according to the experts’ opinions.
Expert meetings are much more interactive than research conferences,
where models are compared with other models, but not scrutinized in detail.
For the NGOs, it was important to point out the sometimes double faced
nature of experts, who are in fact also stakeholders, e.g. technical subjects
like the necessary length of new transmission lines are a politically critical
subject and even experts are not able to exclude this dimension from their
opinions. For the NGOs, it was destabilizing sometimes that the modelers
continuously improved their models, until the final scenarios were
calculated. From the point of view of the researcher, this was natural to do,
but it resulted in a situation in which the NGOs and became rather impatient
as they wanted to see the model finished and ready to use. This should be
anticipated and accompanied by setting and enforcing deadlines, sounding
trivial, but proved to be a major source of conflict and dissatisfaction within
the project team.
Stakeholder Workshops and Scenario Definition
The stakeholder workshops were the focal point toward which all efforts in
the ENCI LowCarb projects were directed to. However, it was absolutely
necessary to go through the first two phases of intra-group and model
development for reaching a stage in which the project team was enabled to
understand the stakeholders’ requirements and translate them into coherent
quantitative model scenarios. The preparation of the first stakeholder
workshop was very demanding, as the agenda set here would determine the
success of the collaborative procedure. The translation rules, from “the
model” to “the real-world” and vice versa, had to be thematically
summarized to determine those energy sectors (e.g. transport, electricity,
heat) for which a feedback process was technically possible. For developing
the agendas of the first sectoral CSO stakeholder workshops, the project
team had to strike a balance between anticipating the areas in which social
acceptance is problematic, and being prescriptive in the selection of topics.
Furthermore, it was challenging to decide on how the stakeholder
assessments would be collected, formalized, and grouped for constructing
the integrated scenarios.
The stakeholder workshops on different energy sectors were stimulating and
successful events. The instructions on the “scenario building bricks” in terms
of political framework conditions were very valuable to the modelers. The
workshops helped the project partners to understand which political
scenario assumptions are socially more or less accepted, and specifically
why. Due to the sector specific stakeholder workshops, particular attention
50 Chapter 2 Social Acceptance in Quantitative Low Carbon Scenarios
246
had to be paid to assure inter-sectoral coherence without neglecting the
statements of the stakeholders for defining the final scenarios. A basic
problem is that regarding energy system futures, there are many
problematic technologies or developments in terms of social acceptance.
However, it is not possible to define one scenario for each issue. This implies
that the different options have to be combined into “worlds” that are
structurally different, but still coherently reflect the stakeholder’s
assessments. Without the lengthy preparation of the translation rules from
model to reality the project would have failed at this point. The synthesis
phase can under certain circumstances be disappointing for the NGO
partners. The final outcomes can be opposing to the principles of the NGO,
which then hinders their communication on project results or even
challenges the overall NGO strategy.
Limitations and Comparison
Limitations to the presented conceptual approach relate mainly to the
reduction of complexity during the collaborative scenario definition process.
One practical limit of the project’s intention to develop socially acceptable
scenarios is the necessity to find a compromise concerning the
representation of stakeholder opinions. The national sub-teams select and
invite stakeholders, thereby consciously limiting the wide range of opinions
to a manageable number. It is an important task for the social scientist to
ensure the representativeness of stakeholders. Furthermore, stakeholders
that are invited to express their assessment and opinions during the
workshop are situated in an artificial situation with rules established by the
project partners, which may bias the discussion.
The focus of the ENCI LowCarb project was on socio-political acceptance; a
representation of market and community acceptance were beyond the
scope. One could, however, extend this in future projects and include more
case-studies or field research for the social scientists to investigate and
elaborate on these issues. It would also be interesting to include more than
one model of each country, to overcome the risk of model bias. Another
aspiration could be to include also industry and policy makers in the
collaborative procedure, or, in a supplementary phase, one that would try to
take into account the political feasibility of the measures generated by the
previous process. Generally speaking, one should be careful about including
too many core project partners, as this may be detrimental to Phase 1 of the
process.
For putting these limitations into context, it is helpful to consider the
methods and setup of other scenario processes that involved civil society
2.5 Limitations and Comparison 51
247
and/or stakeholder assessments and how they compare to ENCI LowCarb.
However, there is to our knowledge no comparable project that was as
transparent about the civil society stakeholders’ roles. For example, Friends
of the Earth Europe (FOEE) and the Stockholm Environment Institute (SEI)
formally describe themselves as partners in a project aiming at developing
an ambitious European mitigation scenario. The roles between FOEE and
the SEI, however, were close to a traditional client agent relationship. FOEE
fixed in advance technical assumptions on the availability of certain
technologies in line with their internal strategy and SEI delivered the
technical modeling knowledge. Nevertheless, several national FOEE
associations were included in the initiative and a continuous exchange was
established. It is interesting that the project partners decided to publish one
publication each, supporting different communication strategies: FOEE
(2009) and Heaps et al. (2009).
Another example is the European Climate Foundation (ECF) “Roadmap
2050” (ECF 2010), which outlines technically feasible pathways to achieve an
80% emission reduction target in 2050. Representatives of the EU
institutions have been consulted periodically throughout the course of the
project and a wide range of stakeholders (companies, consultancy firms,
research centers and NGOs) have counselled ECF in the preparation of this
report. Their names are mentioned, but not the method of how opinions
were weighted, neither the rhythm of meetings. The hierarchy varied
between project partners (a group of consultancies and research centers),
core working group participants (European utilities, transmission system
operators, clean tech manufacturers and CSOs) and further outreach (40
more companies, NGOs and research institutes). ECF tried to follow the
recommendations in the scenarios, but claims to be solely responsible for
the choices.
Then, the “Roadmap 2050” for a low carbon economy published in March
2011 by the European Commission (EC) (European Commission 2011a)
comes with an impact assessment (European Commission 2011b) of three
DGs, evaluating a set of possible future decarbonisation scenarios. The EC
consulted individuals and stakeholders on their vision and opinion regarding
an EU low carbon economy by 2050 through an online questionnaire
“Roadmap for a low carbon economy by 2050”; 281 responses have been
submitted. In its impact assessment, the EC declares that the wide range of
views on how the EU can decarbonize its economy have been taken into
account. However, the robustness of such an online questionnaire may be
questioned. The core difference between these scenario processes and the
ENCI LowCarb project is that here, domestic mitigation scenarios are one
52 Chapter 2 Social Acceptance in Quantitative Low Carbon Scenarios
248
outcome, embedded in a project foster cooperation between science and
CSOs.
Conclusion
Quantitative low carbon scenarios, developed in response to the problem of
climate change, clearly benefit from an introduction of sociological
dimensions, in particular social acceptance. Addressing the social
acceptance of mitigation options can by definition not be a one-way process
from science to the public. In this paper, we propose a project design
intending to foster collaboration between science and civil society for that
purpose. One distinct feature is a conscious emphasis on intra-group
development, accounting for the issue that collaboration partners come
from significantly different and potentially conflicting professional cultures;
a situation that may give rise to severe communication barriers. NGOs and
researchers can learn substantially of each other and create a mutual
understanding of appropriate methods and perspectives. This enables the
development of interdisciplinary research methodologies, intertwining both
qualitative and quantitative tools of the different scientific disciplines.
In order to structure a collaborative scenario definition process, it is helpful
to differentiate between technological and political framework conditions
that serve as an input to the quantitative models. Experts are invited to
define the technological framework conditions. The configurations of
political framework decisions are guided and evaluated by relevant CSO
stakeholders. A necessary prerequisite is that aggregate model input and
output data are translated into tangible meaning. However, the
methodology and organization of such a process is to date not well studied
and should be developed formally for empowering more collaborative
scenario definition processes in the future. In the end, this process can lead
to the conceptualization of innovative low carbon scenarios that take into
account social dimensions, in particular the social (un-)acceptance of
mitigation options.
Meaningful energy system scenarios and policy roadmaps can only be
developed if such organizational setups become more mainstream. Civil
society has to be involved in solutions of climate change mitigation; purely
academic solutions will not be successful. Climate change is not an isolated
environmental problem like the ozone-hole, where there is one clear cause
and one clear solution, but it is a problem whose solution will affect the
entire economy and therefore the whole global society.
2.6 Conclusion 53
249
Acknowledgements
This research was funded by the project ENCI LowCarb (213106) within the
7th Framework Programme for Research of the European Commission. We
thank the tree anonymous reviewers from the International Conference
“Connecting Civil Society and Science – A Key Challenge for Change towards
Sustainable Development” in Stuttgart, Germany on 20/21 October 2011 for
valuable suggestions.
54 Chapter 2 Social Acceptance in Quantitative Low Carbon Scenarios
250
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2.7 References 57
58 Chapter 2 Social Acceptance in Quantitative Low Carbon Scenarios
Chapter 3
REMIND-D: A Hybrid Energy-Economy Model of
Germany ∗
Eva Schmid
Brigitte Knopf
Nico Bauer
∗published as: E. Schmid, Knopf, B., Bauer, N. (2012). REMIND-D: A Hybrid-Energy Economy Model
of Germany Fondazione Eni Enrico Mattei (FEEM) Working Paper Series No. 9.2012.
59
60 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
NOTA DI
LAVORO
9.2012
REMIND-D: A Hybrid
Energy-Economy
Model of German
y
By Eva Schmid, Brigitte Knopf and
Nico Bauer, Potsdam Institute for
Climate Impact Research
61
The opinions expressed in this paper do not necessarily reflect the position of
Fondazione Eni Enrico Mattei
Corso Magenta, 63, 20123 Milano (I), web site: www.feem.it, e-mail: worki[email protected]
Climate Change and Sustainable Development Series
Editor: Carlo Carraro
REMIND-D: A Hybrid Energy-Economy Model of Germany
By Eva Schmid, Brigitte Knopf and Nico Bauer, Potsdam Institute for
Climate Impact Research
Summary
This paper presents a detailed documentation of the hybrid energy-economy model
REMIND-D. REMIND-D is a Ramsey-type growth model for Germany that integrates a
detailed bottom-up energy system module, coupled by a hard link. The model provides a
quantitative framework for analyzing long-term domestic CO2 emission reduction
scenarios. Due to its hybrid nature, REMIND-D facilitates an integrated analysis of the
interplay between technological mitigation options in the different sectors of the energy
system as well as overall macroeconomic dynamics. REMIND-D is an intertemporal
optimization model, featuring optimal annual mitigation effort and technology deployment
as a model output. In order to provide transparency on model assumptions, this paper
gives an overview of the model structure, the input data used to calibrate REMIND-D to the
Federal Republic of Germany, as well as the techno-economic parameters of the
technologies considered in the energy system module.
Keywords: Hybrid Model, Germany, Energy System, Domestic Mitigation
JEL Classification: O41, O52, Q43
Address for correspondence:
Eva Schmid
Potsdam Institute for Climate Impact Research
P.O. Box 601203
14412 Potsdam
Germany
Phone: +49 331 288 2674
Fax: +49 331 288 2570
E-mail: eva.schmid@pikpotsdam.de
62 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
❘❊▼■◆❉✲❉✿ ❆ ❍②❜,✐❞ ❊♥❡,❣②✲❊❝♦♥♦♠②
▼♦❞❡❧ ♦❢ ●❡,♠❛♥②
❊✈❛ ❙❝❤♠✐❞
∗
✱ ❇+✐❣✐--❡ ❑♥♦♣❢✱ ◆✐❝♦ ❇❛✉❡+
6♦-7❞❛♠ ■♥7-✐-✉-❡ ❢♦+ ❈❧✐♠❛-❡ ■♠♣❛❝- ❘❡7❡❛+❝❤
❏❛♥✉❛+②✱ ✷✵✶✷
❈♦♥#❡♥#%
✶ ■♥#$♦❞✉❝#✐♦♥ ✷
✷ ❚❤❡ ▼♦❞❡❧ ❘❊▼■◆❉✲❉ ✹
✷✳✶ ❋✉♥❞❛♠❡♥*❛❧, ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✻
✸ ❚❤❡ ▼❛❝$♦❡❝♦♥♦♠✐❝ ▼♦❞✉❧❡ ✾
✸✳✶ ❖♣*✐♠✐③❛*✐♦♥ ❖❜❥❡❝*✐✈❡ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✾
✸✳✷ 9:♦❞✉❝*✐♦♥ ❋✉♥❝*✐♦♥ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✶✵
✸✳✸ ❊♥❡:❣② ❉❡♠❛♥❞ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✶✷
✸✳✹ ❍❛:❞ ▲✐♥❦ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✶✸
✹ ❚❤❡ ❊♥❡$❣② ❙②=#❡♠ ▼♦❞✉❧❡ ✶✹
✹✳✶ 9:✐♠❛:② ❊♥❡:❣② ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✶✺
✹✳✷ ❈❤❛:❛❝*❡:✐,*✐❝, ♦❢ ❚❡❝❤♥♦❧♦❣✐❡, ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✶✼
✹✳✸ ❈♦♥✈❡:,✐♦♥ ❚❡❝❤♥♦❧♦❣✐❡, ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✷✵
✹✳✸✳✶ 9:✐♠❛:② *♦ ❙❡❝♦♥❞❛:② ❊♥❡:❣② ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✷✵
✹✳✸✳✷ ❙❡❝♦♥❞❛:② *♦ ❙❡❝♦♥❞❛:② ❊♥❡:❣② ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✷✼
✹✳✹ ❉✐,*:✐❜✉*✐♦♥ ❚❡❝❤♥♦❧♦❣✐❡, ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✷✽
✹✳✺ ❚:❛♥,♣♦:* ❚❡❝❤♥♦❧♦❣✐❡, ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✳ ✸✵
✺
CO2
❊♠✐==✐♦♥= ✸✹
✻ ▼♦❞❡❧ ❱❛❧✐❞❛#✐♦♥ ✸✹
∗
❈♦""❡$♣♦♥❞✐♥❣ ❛✉,❤♦"✿ ❚❡❧✳ ✰✹✾ ✸✸✶ ✷✽✽ ✷✻✼✹✱ ❋❛①✿ ✰✹✾ ✸✸✶ ✷✽✽ ✷✺✼✵✱ ❊♠❛✐❧✿ ❡✈❛✳$❝❤♠✐❞❅♣✐❦✲
♣♦,$❞❛♠✳❞❡✱ G✳❖✳ ❇♦① ✻✵✶✷✵✸✱✶✹✹✶✷ G♦,$❞❛♠
✶
63
✶ ■♥#$♦❞✉❝#✐♦♥
●❧♦❜❛❧ ❝❧✐♠❛(❡ ♠♦❞❡❧+ ✐♥❞✐❝❛(❡ (❤❛( ❛ ♠✐(✐❣❛(✐♦♥ ❡✛♦0( ♦❢
≈
✺✵✪ ❣❧♦❜❛❧ ❣0❡❡♥❤♦✉+❡ ❣❛+
✭●❍●✮ ❡♠✐++✐♦♥+ ✐♥ ✷✵✺✵ 0❡❧❛(✐✈❡ (♦ ✶✾✾✵ ②✐❡❧❞+ ❛ ❧✐❦❡❧② ❝❤❛♥❝❡ ♦❢ ❦❡❡♣✐♥❣ ❣❧♦❜❛❧ ✇❛0♠✐♥❣
❜❡❧♦✇ ✷
◦
❈ ✭▼❡✐♥+❤❛✉+❡♥ ❡( ❛❧✳ ✷✵✵✾✮✳ ●❡0♠❛♥② ❝♦♥(0✐❜✉(❡❞ ♥❡❛0❧② ✺✪ ♦❢ ❣❧♦❜❛❧ ●❍●
❡♠✐++✐♦♥+ ✐♥ ✷✵✵✼ ✭❯◆❋❈❈❈ ✷✵✵✾✮✱ ♦❢ ✇❤✐❝❤ ❝❛0❜♦♥ ❞✐♦①✐❞❡ ✭
CO2
✮ ❝♦♥+(✐(✉(❡❞ (❤❡ ❧❛0❣❡+(
+❤❛0❡ ✇✐(❤ ✽✼✪✳ ❋✐❣✉0❡ ✶ ✐❧❧✉+(0❛(❡+ ❤♦✇ ●❡0♠❛♥ ❞♦♠❡+(✐❝
CO2
❡♠✐++✐♦♥+ ❝❛♥ ❜❡ ❛(✲
(0✐❜✉(❡❞ (♦ (❤❡ +❡❝(♦0+ ❧❛♥❞ ✉+❡✱ ✐♥❞✉+(0✐❛❧ ♣0♦❝❡++❡+
✶
❛♥❞ (❤❡ ❡♥❡0❣② +❡❝(♦0 ✐♥ (❤❡ ②❡❛0
✷✵✵✼✳ ❚❤❡ ❡♥❡0❣② +❡❝(♦0 ❤❛+ ❜❡❡♥ ❝❛✉+✐♥❣ ❛ +(❛❜❧❡ +❤❛0❡ ♦❢
±
✽✵✪ ♦❢ (♦(❛❧ ●❡0♠❛♥
CO2
❡♠✐++✐♦♥+ ❡✈❡0② ②❡❛0 +✐♥❝❡ ✶✾✾✵ ✭❯❇❆ ✷✵✶✵✮✳ ❍❡♥❝❡✱ ❞❡❝❛0❜♦♥✐③✐♥❣ (❤❡ ❡♥❡0❣② +②+(❡♠
✐+ ❝❡♥(0❛❧ (♦ ❛❝❤✐❡✈✐♥❣ ❝✉(+ ✐♥ ●❡0♠❛♥ ●❍● ❡♠✐++✐♦♥+✳ ❆ ❧♦♥❣✲(❡0♠
CO2
❡♠✐++✐♦♥ 0❡✲
❞✉❝(✐♦♥ (❛0❣❡( ♦❢ ✽✵✲✾✺✪ ✐♥ ✷✵✺✵ 0❡❧❛(✐✈❡ (♦ ✶✾✾✵ ❤❛+ ❜❡❡♥ ❛♥♥♦✉♥❝❡❞ ❜② (❤❡ ●❡0♠❛♥
●♦✈❡0♥♠❡♥( ✭❇✉♥❞❡+0❡❣✐❡0✉♥❣ ✷✵✶✵✮✳ ❆❝❤✐❡✈✐♥❣ +✉❝❤ ❛♥ ❛♠❜✐(✐♦✉+ ♠✐(✐❣❛(✐♦♥ (❛0❣❡( ✇✐❧❧
0❡P✉✐0❡ ❛ +(0✉❝(✉0❛❧ (0❛♥+❢♦0♠❛(✐♦♥ ♦❢ (❤❡ ●❡0♠❛♥ ❡♥❡0❣② +②+(❡♠✳
Industrial
processes
10%
Landuse
4%
Energy
86%
❋✐❣✉0❡ ✶✿ ❙❤❛0❡+ ✐♥ ●❡0♠❛♥
CO2
❡♠✐++✐♦♥+ ✐♥ ✷✵✵✼ ❜② +♦✉0❝❡✳ ❖✇♥ ✐❧❧✉+(0❛(✐♦♥ ✇✐(❤ ❞❛(❛
❢0♦♠ ❯❇❆ ✭✷✵✶✵✮✳
❊♥❡0❣② +②+(❡♠ (0❛♥+❢♦0♠❛(✐♦♥+ ❛0❡ ❧❛0❣❡✲+❝❛❧❡ ♣0♦❝❡++❡+ +✉❜❥❡❝( (♦ ✐♥❡0(✐❛✱ ❞✉❡ (♦ ❝❛♣✐(❛❧
✐♥(❡♥+✐✈❡ ✐♥❢0❛+(0✉❝(✉0❡ ❛♥❞ ❝♦♥✈❡0+✐♦♥ (❡❝❤♥♦❧♦❣✐❡+ ❛+ (❤❡+❡ ✉+✉❛❧❧② ❤❛✈❡ (❡❝❤♥✐❝❛❧ ❧✐❢❡✲
(✐♠❡+ ♦❢ +❡✈❡0❛❧ ❞❡❝❛❞❡+✳ ▲♦♥❣✲(❡0♠ ♣❧❛♥♥✐♥❣ ✐+ ♥❡❝❡++❛0② ❢♦0 ❡♥❛❜❧✐♥❣ ❧♦✇ ❝❛0❜♦♥ (❡❝❤✲
♥♦❧♦❣✐❡+ ✐♥ ❢✉(✉0❡ ❡♥❡0❣② +②+(❡♠ ♣♦0(❢♦❧✐♦+✳ ❆♥ ✐♠♣♦0(❛♥( (♦♦❧ ❢♦0 ❡①♣❧♦0✐♥❣ (❤❡ ❢✉(✉0❡
❛♥❞ ❞❡❛❧✐♥❣ ✇✐(❤ ❝♦♠♣❧❡①✐(② ❛♥❞ ✉♥❝❡0(❛✐♥(② ❛0❡ +❝❡♥❛0✐♦+✱ ❡+♣❡❝✐❛❧❧② ✇❤❡♥ ❢♦0♠❛❧✐③❡❞
❜② ♠❡❛♥+ ♦❢ ❛♥ ❡♥❡0❣②✲❡❝♦♥♦♠② ♠♦❞❡❧✳ ■❞❡❛❧❧②✱ +✉❝❤ ❛ ♠♦❞❡❧ ✐♥❝❧✉❞❡❞ ❛❧❧ (❡❝❤♥♦❧♦❣✐❝❛❧
❛♥❞ +♦❝✐♦✲❡❝♦♥♦♠✐❝ ♣0♦❝❡++❡+ ❛♥❞ +②+(❡♠✐❝ ❢❡❡❞❜❛❝❦ ❧♦♦♣+ (❤❛( ❛0❡ ♦❜+❡0✈❡❞ ✐♥ 0❡❛❧✐(②✳
❯♥❢♦0(✉♥❛(❡❧②✱ ❝♦♠♣✉(❛(✐♦♥❛❧ ❝♦+(+✱ ❞❛(❛ +❝❛0❝✐(② ❛♥❞ ❞❛(❛ ✉♥♦❜+❡0✈❛❜✐❧✐(② ❛+ ✇❡❧❧ ❛+ ❛
❧❛❝❦ ♦❢ ❝♦♥❝❡♣(✉❛❧ ❢0❛♠❡✇♦0❦+ ❛♥❞ ❡❝♦♥♦♠✐❝ (❤❡♦0✐❡+ +❡( ❧✐♠✐(✐♥❣ ❜♦✉♥❞❛0✐❡+✳
✶
❚❤❡#❡ ❛%❡ ♠❛✐♥❧② ❡♠✐##✐♦♥# ❢%♦♠ ♠✐♥❡%❛❧ ♣%♦❞✉❝1#✱ ❝❤❡♠✐❝❛❧ ✐♥❞✉#1%② ❛♥❞ ♠❡1❛❧ ♣%♦❞✉❝1✐♦♥✳
✷
64 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
❊①✐#$✐♥❣ ❡♥❡(❣②✲❡❝♦♥♦♠② ♠♦❞❡❧# (❡♣(❡#❡♥$ #❡❧❡❝$❡❞ ❛#♣❡❝$# ♦❢ $❤❡ ❡♥❡(❣②✲❡❝♦♥♦♠② ♥❡①✉#
❛♥❞ $❤❡✐( (❡#✉❧$# ✐♥❤❡(❡♥$❧② (❡✢❡❝$ $❤❡ ❛❞♦♣$❡❞ ♠❡$❤♦❞♦❧♦❣② ♦❢ $❤❡ ♠♦❞❡❧✳ ❈❧❛##✐✜❝❛✲
$✐♦♥ $②♣♦❧♦❣✐❡# ✈❛(② ❣(❡❛$❧② ✐♥ $❤❡ ❧✐$❡(❛$✉(❡✱ ❡✳❣✳ ❛❝❝♦(❞✐♥❣ $♦ ✭♥✉♠❡(✐❝❛❧✮ ♠❡$❤♦❞♦❧♦❣②
✭◆❛❦❛$❛ ✷✵✵✹✮ ♦( ❞❡#❝(✐♣$✐✈❡ ✈❡(#✉# ♥♦(♠❛$✐✈❡ ❛(❣✉♠❡♥$❛$✐♦♥ #$(✉❝$✉(❡# ✭▼❝❉♦✇❛❧❧ ❛♥❞
❊❛♠❡# ✷✵✵✻✮✳ ❆ ✇✐❞❡❧② ❛❣(❡❡❞ ❞✐✛❡(❡♥$✐❛$✐♦♥ ✐# $♦ ❣(♦✉♣ ❡♥❡(❣②✲❡❝♦♥♦♠② ♠♦❞❡❧# ✐♥$♦
✏$♦♣✲❞♦✇♥✑ ✈❡(#✉# ✏❜♦$$♦♠✲✉♣✑ ❛♣♣(♦❛❝❤❡#✳ ❚♦♣✲❞♦✇♥ ♠♦❞❡❧# ❢♦❧❧♦✇ ❛♥ ❡❝♦♥♦♠✐❝ ❛♣✲
♣(♦❛❝❤ ❛♥❞ ❡♥❞♦❣❡♥✐③❡ ❜❡❤❛✈✐♦(❛❧ (❡❧❛$✐♦♥#❤✐♣# ❜② ❝❛❧✐❜(❛$✐♥❣ ♦♥ ♠❛(❦❡$ ❞❛$❛✱ ❛##✉♠✐♥❣
♥♦ ❞✐#❝♦♥$✐♥✉✐$✐❡# ✐♥ ❤✐#$♦(✐❝❛❧ $(❡♥❞#✳ ❇♦$$♦♠✲✉♣ ❛♣♣(♦❛❝❤❡#✱ ♦♥ $❤❡ ♦$❤❡( ❤❛♥❞✱ ❢♦❧❧♦✇
❛♥ ❡♥❣✐♥❡❡(✐♥❣ ❛♣♣(♦❛❝❤ ❛♥❞ ❝♦♥$❛✐♥ ❞❡$❛✐❧❡❞ ❞❡#❝(✐♣$✐♦♥# ♦❢ $❡❝❤♥♦❧♦❣✐❡# ❛♥❞ $❡❝❤♥✐❝❛❧
♣♦$❡♥$✐❛❧#✱ ❛##✉♠✐♥❣ ♠❛(❦❡$ ❛❞♦♣$✐♦♥ ♦❢ $❤❡ ♠♦#$ ❡✣❝✐❡♥$ $❡❝❤♥♦❧♦❣✐❡# ✭❍♦✉(❝❛❞❡ ❛♥❞
❘♦❜✐♥#♦♥ ✶✾✾✻✮✳
■♥ ❡❛(❧② ❣❧♦❜❛❧ ♠✐$✐❣❛$✐♦♥ ❛♥❛❧②#❡#✱ ❜♦$$♦♠✲✉♣ ♠♦❞❡❧# #②#$❡♠❛$✐❝❛❧❧② ✐♥❞✐❝❛$❡❞ ❧❛(❣❡(
●❍● (❡❞✉❝$✐♦♥ ♣♦$❡♥$✐❛❧# $❤❛♥ $♦♣✲❞♦✇♥ ♠♦❞❡❧#✳ ❍❡♥❝❡✱ ●(✉❜❜ ❡$ ❛❧✳ ✭✶✾✾✸✮ ❧❛❜❡❧❡❞
$♦♣✲❞♦✇♥ ♠♦❞❡❧# ❛# ♣❡##✐♠✐#$✐❝ ❛♥❞ ❜♦$$♦♠✲✉♣ ♠♦❞❡❧# ❛# ♦♣$✐♠✐#$✐❝✳ ❚❤❡② ❛$$(✐❜✉$❡❞
$❤❡ ❞✐✛❡(❡♥❝❡ $♦ $❤❡ ❡①✐#$❡♥❝❡ ♦❢ ♥❡❣❛$✐✈❡ ❝♦#$ ♣♦$❡♥$✐❛❧#✱ #♦ ❝❛❧❧❡❞ ✬♥♦ (❡❣(❡$#✬ ♦♣$✐♦♥#✱
✐♥ ❜♦$$♦♠✲✉♣ ❛♣♣(♦❛❝❤❡#✳ ❚❤❡#❡ (❡❢❡( $♦ ❡♠✐##✐♦♥ (❡❞✉❝$✐♦♥# ❝❛✉#❡❞ ❜② $❤❡ ❛❞♦♣$✐♦♥ ♦❢
❜❡#$ ❛✈❛✐❧❛❜❧❡ $❡❝❤♥✐W✉❡# ✇❤♦#❡ ❝♦#$# ❛(❡ ❧♦✇❡( $❤❛♥ $❤❡ $❡❝❤♥♦❧♦❣✐❡# ❝✉((❡♥$❧② ✐♥ ✉#❡✱ ✐✳❡✳
❛♥ ❡✣❝✐❡♥❝② ❣❛♣✳ ❚❤❡ #✐③❡ ❛♥❞ ♠❡❛♥✐♥❣ ♦❢ $❤✐# ❡✣❝✐❡♥❝② ❣❛♣ ✐# #✉❜❥❡❝$ $♦ ❝♦♥$(♦✈❡(#②
✐♥ $❤❡ ❞❡❜❛$❡ ❜❡$✇❡❡♥ ♠♦❞❡❧✐♥❣ ❛♣♣(♦❛❝❤❡#✳ ■$ ❛(✐#❡# ♣❛($✐❝✉❧❛(❧② ❞✉❡ $♦ $❤❡ ❞✐✛❡(❡♥$
❛♣♣(♦❛❝❤❡# ♦❢ ♠♦❞❡❧✐♥❣ $❡❝❤♥♦❧♦❣✐❝❛❧ ❝❤❛♥❣❡✳
❊♥❣✐♥❡❡(✐♥❣✲♦(✐❡♥$❡❞ ❜♦$$♦♠✲✉♣ #$✉❞✐❡# #✉❣❣❡#$ $❤❛$ ♠❛(❦❡$ ❢♦(❝❡# ❞♦ ♥♦$ ♦♣❡(❛$❡ ♣❡(✲
❢❡❝$❧② ❛♥❞ $❤❡ ♣♦❧✐❝② ✐♠♣❧✐❝❛$✐♦♥ ✐# $♦ (❡♠♦✈❡ ❜❛((✐❡(# $♦ ❛❞♦♣$✐♦♥ ♦❢ $❤❡ ❜❡#$ ❛✈❛✐❧❛❜❧❡
$❡❝❤♥✐W✉❡ ✭❍♦✉(❝❛❞❡ ❛♥❞ ❘♦❜✐♥#♦♥ ✶✾✾✻✮✳ ❖♣♣♦#✐♥❣❧②✱ ❡❝♦♥♦♠✐#$# ❛(❣✉❡ $❤❛$ $❤❡#❡ ♣♦#✲
$✉❧❛$❡❞ ♠❛(❦❡$ ❢❛✐❧✉(❡# ❛(❡ ♦♥❧② ❛♣♣❛(❡♥$ ❛♥❞ ❝❛♥ ❜❡ ❡①♣❧❛✐♥❡❞ ✐♥ $❡(♠# ♦❢ $✇♦ ♦$❤❡(
❢❛❝$♦(#✿ ❝♦♠♣❧❡①✐$② ❛♥❞ ❤❡$❡(♦❣❡♥❡✐$② ♦❢ ❝♦♥#✉♠❡( ♣(❡❢❡(❡♥❝❡# ❛♥❞ ❤✐❞❞❡♥ ❝♦#$#✱ ❡✳❣✳ ✐♥✲
❢♦(♠❛$✐♦♥ ❝♦#$# ♦( ♣❡(❝❡✐✈❡❞ (✐#❦# ❛##♦❝✐❛$❡❞ ✇✐$❤ ❝❛♣✐$❛❧ ❝♦#$#✳ ■♥ ❝❛❧✐❜(❛$❡❞ $♦♣✲❞♦✇♥
♠♦❞❡❧#✱ $❤✐# ❝♦♠♣❧❡① #❡$ ♦❢ ❜❡❤❛✈✐♦(❛❧ ❢❛❝$♦(# ✐# ❝❛♣$✉(❡❞ ✐♥ ♣(✐❝❡ ❛♥❞ ✐♥❝♦♠❡ ❡❧❛#$✐❝✐$✐❡#✳
■♥ ❛ ♠♦(❡ (❡❝❡♥$ ❛♥❛❧②#✐#✱ ❱✉✉(❡♥ ❡$ ❛❧✳ ✭✷✵✵✾✮ ✜♥❞ ♥♦ #②#$❡♠❛$✐❝ ❞✐✛❡(❡♥❝❡ ✐♥ $❤❡ (❡❞✉❝✲
$✐♦♥ ♣♦$❡♥$✐❛❧ (❡♣♦($❡❞ ❜② #$❛$❡✲♦❢✲$❤❡✲❛($ $♦♣✲❞♦✇♥ ❛♥❞ ❜♦$$♦♠✲✉♣ ♠♦❞❡❧# ❛$ $❤❡ ❣❧♦❜❛❧
#❝❛❧❡✳ ❍♦✇❡✈❡(✱ $❤❡ (❡#✉❧$# ❛$ $❤❡ #❡❝$♦(✐❛❧ ❧❡✈❡❧ #❤♦✇ ❝♦♥#✐❞❡(❛❜❧❡ ❞✐✛❡(❡♥❝❡# ✐♥ $❡(♠# ♦❢
$❡❝❤♥✐❝❛❧ ✈❡(#✉# ❡❝♦♥♦♠✐❝❛❧ (❡❞✉❝$✐♦♥ ♣♦$❡♥$✐❛❧✳ ■$ ✐# ❝♦♥❝❧✉❞❡❞ $❤❛$ $❤❡ $✇♦ ❛♣♣(♦❛❝❤❡#
❛(❡ ❝♦♠♣❧❡♠❡♥$❛(② ✐♥ $❤❡ #❡♥#❡ $❤❛$ $❤❡② ❛❞❞ ❞✐✛❡(❡♥$ $②♣❡# ♦❢ ✐♥❢♦(♠❛$✐♦♥✳ ❲❤✐❧❡
$❤❡ ❜♦$$♦♠✲✉♣ ❛♣♣(♦❛❝❤ ✐# #$(♦♥❣❡( ✐♥ $❡(♠# ♦❢ $❡❝❤♥♦❧♦❣② (❡#♦❧✉$✐♦♥✱ $♦♣✲$♦✇♥ ♠♦❞❡❧#
❡♥❛❜❧❡ ❛ #❡❝$♦(✐❛❧❧② ✐♥$❡❣(❛$❡❞ ❛♥❛❧②#✐# ❜② ✐♥❝♦(♣♦(❛$✐♥❣ ❡❝♦♥♦♠✐❝ ❢❡❡❞❜❛❝❦ ❧♦♦♣#✳
❋♦( ❛♥❛❧②③✐♥❣ ❞♦♠❡#$✐❝
CO2
(❡❞✉❝$✐♦♥ ♣♦$❡♥$✐❛❧# ✐♥ ●❡(♠❛♥②✱ ❜♦$$♦♠✲✉♣ ♠♦❞❡❧# ❞♦♠✐✲
♥❛$❡ $❤❡ ❧✐$❡(❛$✉(❡✱ ❡✳❣✳ ^❊❘❙❊❯❙ ✭❋✐❝❤$♥❡( ❡$ ❛❧✳ ✷✵✵✶✮✱ ❚■▼❊❙✲❉ ✭❇❧❡#❧ ❡$ ❛❧✳ ✷✵✵✼✮✱
■❑❆❘❯❙ ✭▼❛($✐♥#❡♥ ❡$ ❛❧✳ ✷✵✵✻✮ ❛♥❞ $❤❡ ^(♦❣♥♦# ♠♦❞❡❧ ✭❑✐(❝❤♥❡( ❡$ ❛❧✳ ✷✵✵✾✮✳ ❚❤❡② ❛(❡
❞❡♠❛♥❞ ❞(✐✈❡♥ ❛♥❞ $❡❝❤♥♦❧♦❣② ♦(✐❡♥$❡❞✳ ❚❤❡ ♠♦❞❡❧# #♦❧✈❡ ❛ ♣❛($✐❛❧ ❡W✉✐❧✐❜(✐✉♠ ♣(♦❜❧❡♠
❜② ♠✐♥✐♠✐③✐♥❣ ❛♥ ❡♥❡(❣② #②#$❡♠ ❝♦#$ ♠❡$(✐❝✱ ❝♦♥#✐#$✐♥❣ ♦❢ $♦$❛❧ ❢✉❡❧✱ ♠❛✐♥$❡♥❛♥❝❡ ❛♥❞
✐♥✈❡#$♠❡♥$ ❝♦#$#✳ ❘❡❝❡♥$❧②✱ #♦♠❡ ❡✛♦($ ❤❛# ❜❡❡♥ ♠❛❞❡ $♦ ❡#$❛❜❧✐#❤ #♦❢$ ❧✐♥❦# ❜❡$✇❡❡♥
❞✐✛❡(❡♥$ ♠♦❞❡❧# $♦ ❝♦♥#✐❞❡( ❢❡❡❞❜❛❝❦ ❧♦♦♣#✱ ❡✳❣✳ ❙❝❤❧❡#✐♥❣❡( ❡$ ❛❧✳ ✭✷✵✶✵✮ ❝♦✉♣❧❡ $❤❡
❜♦$$♦♠✲✉♣ ^(♦❣♥♦# ♠♦❞❡❧ ✇✐$❤ $❤❡ $♦♣✲❞♦✇♥ ❡❝♦♥♦♠❡$(✐❝ ^❍❆◆❚❆ ❘❍❊■ ✭▼❡②❡( ❡$ ❛❧✳
✸
3.1 Introduction 65
✷✵✵✼✮ ♠♦❞❡❧ ❛♥❞ ❛ ❞❡+❛✐❧❡❞ ❞✐-♣❛+❝❤ ♠♦❞❡❧ ♦❢ +❤❡ ●❡3♠❛♥ ❡❧❡❝+3✐❝✐+② -❡❝+♦3✳ ❙♦❢+✲❧✐♥❦✐♥❣
❛❧❧♦✇- ❢♦3 -♦♠❡ ❢❡❡❞❜❛❝❦✱ ❜✉+ +❤❡ ❞✐✛❡3❡♥+ ♠♦❞❡❧- ❝♦♥+✐♥✉❡ +♦ ✐♥❞✐✈✐❞✉❛❧❧② ♦♣+✐♠✐③❡ +❤❡✐3
♦❜❥❡❝+✐✈❡ ❢✉♥❝+✐♦♥-✳ ❲❤✐❧❡ +❤❡ ●❡3♠❛♥ ●❍● 3❡❞✉❝+✐♦♥ ♣♦+❡♥+✐❛❧ ❤❛- ❜❡❡♥ ❡①+❡♥-✐✈❡❧②
❛♥❛❧②③❡❞ ✐♥ +❡3♠- ♦❢ +❡❝❤♥✐❝❛❧ ♣♦+❡♥+✐❛❧✱ +❤❡ ❡❝♦♥♦♠✐❝ ♣♦+❡♥+✐❛❧ ❤❛- 3❡❝❡✐✈❡❞ ✈❡3② ❧✐++❧❡
❛++❡♥+✐♦♥✱ ❞✉❡ +♦ ❛ ❧❛❝❦ ♦❢ ♠♦❞❡❧- -✉✐+❛❜❧❡ ❢♦3 +❤✐- +②♣❡ ♦❢ ❛♥❛❧②-✐-✳
■♥ ♦3❞❡3 +♦ ✜❧❧ +❤✐- ❣❛♣✱ ❛ ❤②❜3✐❞ ❡♥❡3❣②✲❡❝♦♥♦♠② ♠♦❞❡❧ ❢♦3 ●❡3♠❛♥② ❤❛- ❜❡❡♥ ❞❡✈❡❧♦♣❡❞
❛+ +❤❡ G♦+-❞❛♠ ■♥-+✐+✉+❡ ❢♦3 ❈❧✐♠❛+❡ ■♠♣❛❝+ ❘❡-❡❛3❝❤✿ ❘❊▼■◆❉✲❉ ✭❘❡✜♥❡❞ ▼♦❞❡❧ ♦❢
■♥✈❡-+♠❡♥+ ❛♥❞ ❚❡❝❤♥♦❧♦❣✐❝❛❧ ❉❡✈❡❧♦♣♠❡♥+ ✲ ❉❡✉+-❝❤❧❛♥❞✮✳ ❍❛3❞✲❧✐♥❦ ❤②❜3✐❞ ♠♦❞❡❧-
✐♥+❡❣3❛+❡ ❛ ❞❡+❛✐❧❡❞ ❜♦++♦♠✲✉♣ ❡♥❡3❣② -❡❝+♦3 ✐♥+♦ ❛ +♦♣✲❞♦✇♥ 3❡♣3❡-❡♥+❛+✐♦♥ ♦❢ +❤❡ ♠❛❝3♦
❡❝♦♥♦♠②✳ ■♥ +❤✐- ♠❛♥♥❡3✱ ❝❛♣✐+❛❧ ❛♥❞ 3❡-♦✉3❝❡- ❢♦3 ❡♥❡3❣② ❣❡♥❡3❛+✐♦♥ ❛3❡ ❛❧❧♦❝❛+❡❞
♦♣+✐♠❛❧❧② ✇✐+❤ 3❡-♣❡❝+ +♦ +❤❡ ✇❤♦❧❡ ❡❝♦♥♦♠② ✭❇❛✉❡3 ❡+ ❛❧✳ ✷✵✵✽✮✳ ❍②❜3✐❞ ♠♦❞❡❧- ❤❛✈❡
❜❡❡♥ ❞❡✈❡❧♦♣❡❞ +♦ ♦✈❡3❝♦♠❡ +❤❡ ❞3❛✇❜❛❝❦- ♦❢ ♣✉3❡ +♦♣✲❞♦✇♥ ♦3 ❜♦++♦♠✲✉♣ ♠♦❞❡❧- ❛♥❞
❛3❡ ✇❡❧❧ ❡-+❛❜❧✐-❤❡❞ ✐♥ ❣❧♦❜❛❧ ✐♥+❡❣3❛+❡❞ ❛--❡--♠❡♥+ ❡①❡3❝✐-❡-✱ ❡✳❣✳ ❲■❚❈❍ ✭❇♦-❡++✐ ❡+
❛❧✳ ✷✵✵✻✮ ❛♥❞ ❘❊▼■◆❉✲❘ ✭▲❡✐♠❜❛❝❤ ❡+ ❛❧✳ ✷✵✶✵✮✳ ❘❊▼■◆❉✲❉ ❜✉✐❧❞- ♦♥ +❤❡ -+3✉❝+✉3❛❧
❡V✉❛+✐♦♥- ♦❢ +❤❡ -+❛+❡✲♦❢✲+❤❡ ❛3+ ❣❧♦❜❛❧ ✐♥+❡❣3❛+❡❞ ❛--❡--♠❡♥+ ♠♦❞❡❧ ❘❊▼■◆❉✲❘✳ ❆❧❧
-+3✉❝+✉3❛❧ ❡V✉❛+✐♦♥- ❛3❡ 3❡♣♦3+❡❞ ✐♥ ❞❡+❛✐❧ ✐♥ ❇❛✉❡3 ❡+ ❛❧✳ ✭✷✵✶✶✮
✷
✳ ❍❡♥❝❡✱ +❤✐- ❞♦❝✉♠❡♥+
3❡❢3❛✐♥- ❢3♦♠ 3❡♣3♦❞✉❝✐♥❣ ❛❧❧ ❡V✉❛+✐♦♥- ✐♥ ❘❊▼■◆❉✲❉✳ ■♥-+❡❛❞✱ ✐+ ✐♥+❡♥❞- +♦ ♣3♦✈✐❞❡ ❛♥
❡①+❡♥-✐✈❡ ❞♦❝✉♠❡♥+❛+✐♦♥ ♦❢ +❤❡ ✐♥♣✉+ ❞❛+❛ ✉-❡❞ +♦ ❝❛❧✐❜3❛+❡ ❘❊▼■◆❉✲❉ +♦ +❤❡ ❋❡❞❡3❛❧
❘❡♣✉❜❧✐❝ ♦❢ ●❡3♠❛♥②✳
✷ ❚❤❡ ▼♦❞❡❧ ❘❊▼■◆❉✲❉
❚❤❡ ❜❛-✐❝ ♣✉3♣♦-❡ ♦❢ ❘❊▼■◆❉✲❉ ✐- +♦ ♣3♦✈✐❞❡ ❛ V✉❛♥+✐+❛+✐✈❡ ❢3❛♠❡✇♦3❦ ❢♦3 ❛♥❛❧②③✐♥❣
❧♦♥❣✲+❡3♠ ❞♦♠❡-+✐❝ ♠✐+✐❣❛+✐♦♥ -❝❡♥❛3✐♦- ❢♦3 ●❡3♠❛♥②✱ ❡♥❛❜❧✐♥❣ ❛ ❢♦❝✉- ♦♥ +❤❡ ❡❝♦♥♦♠✐❝
3❡❞✉❝+✐♦♥ ♣♦+❡♥+✐❛❧✳ ❚❤❡ +❡❝❤♥♦❧♦❣✐❝❛❧ 3❡❞✉❝+✐♦♥ ♣♦+❡♥+✐❛❧ ✐- ❝♦♥-✐❞❡3❡❞ ❡①♣❧✐❝✐+❧② ❜② ❛
❞❡+❛✐❧❡❞ ❜♦++♦♠✲✉♣ ❡♥❡3❣② -②-+❡♠ ♠♦❞✉❧❡✳ ❘❊▼■◆❉✲❉ ❢❛❝✐❧✐+❛+❡- ❛♥ ✐♥+❡❣3❛+❡❞ ❛♥❛❧②-✐-
♦❢ +❤❡ ❧♦♥❣✲+❡3♠ ✐♥+❡3♣❧❛② ❜❡+✇❡❡♥ +❡❝❤♥♦❧♦❣✐❝❛❧ ♠✐+✐❣❛+✐♦♥ ♦♣+✐♦♥- ✐♥ +❤❡ ❞✐✛❡3❡♥+ -❡❝+♦3-
❛- ✇❡❧❧ ❛- ♠❛❝3♦❡❝♦♥♦♠✐❝ ❞②♥❛♠✐❝-✳
❆ -+②❧✐③❡❞ ♦✈❡3✈✐❡✇ ♦❢ ❘❊▼■◆❉✲❉✬- -+3✉❝+✉3❡ ✐- ✐❧❧✉-+3❛+❡❞ ✐♥ ❋✐❣✉3❡ ✷✳ ❚❤❡ +♦♣✲❞♦✇♥
♠❛❝3♦❡❝♦♥♦♠✐❝ ♠♦❞✉❧❡ 3❡-❡♠❜❧❡- ❛ ❘❛♠-❡②✲+②♣❡ ♥❡♦❝❧❛--✐❝❛❧ ♦♣+✐♠❛❧ ❣3♦✇+❤ ♠♦❞❡❧
✭❈❛-- ✶✾✻✺❀ ❑♦♦♣♠❛♥- ✶✾✻✺❀ ❘❛♠-❡② ✶✾✷✽✮✳ ❖✉+♣✉+ ✐- ♣3♦❞✉❝❡❞ ❜② ❛❣❣3❡❣❛+✐♥❣ +❤❡
♣3♦❞✉❝+✐♦♥ ❢❛❝+♦3- ❝❛♣✐+❛❧✱ ❧❛❜♦3 ❛♥❞ ❡♥❡3❣② ✈✐❛ ♥❡-+❡❞ ❈♦♥-+❛♥+ ❊❧❛-+✐❝✐+② ♦❢ ❙✉❜-+✐+✉✲
+✐♦♥ ✭❈❊❙✮ ❢✉♥❝+✐♦♥-✳ ❚❤❡ ♣3♦❞✉❝+✐♦♥ ❢❛❝+♦3 ❡♥❡3❣② ✐- -✉❜❞✐✈✐❞❡❞ -♦ ❛- +♦ ♠❛+❝❤ +❤❡
❛❣❣3❡❣❛+❡❞ ✜♥❛❧ ❡♥❡3❣② ❞❡♠❛♥❞ ♦❢ +❤❡ ✐♥❞✉-+3② ❛♥❞ 3❡-✐❞❡♥+✐❛❧ ✫ ❝♦♠♠❡3❝✐❛❧ -❡❝+♦3 ❛-
✇❡❧❧ ❛- +❤❡ ❡♥❡3❣② -❡3✈✐❝❡ ❞❡♠❛♥❞ ♦❢ +❤❡ +3❛♥-♣♦3+ -❡❝+♦3✳ ❚❤❡-❡ V✉❛♥+✐+✐❡- ❛3❡ ♣3♦✈✐❞❡❞
❜② ❛ ❜♦++♦♠✲✉♣ ❡♥❡3❣② -②-+❡♠ ♠♦❞✉❧❡ +❤❛+ ❝♦♥-✐❞❡3- +❤❡ +❡❝❤♥♦✲❡❝♦♥♦♠✐❝ ❝❤❛3❛❝+❡3✲
✐-+✐❝- ♦❢ ❝♦♥✈❡♥+✐♦♥❛❧ ❛♥❞ ♣3♦-♣❡❝+✐✈❡ ❡♥❡3❣② ❝♦♥✈❡3-✐♦♥ +❡❝❤♥♦❧♦❣✐❡- ❡①♣❧✐❝✐+❧②✳
CO2
❡♠✐--✐♦♥- ❛❝❝♦✉♥+✐♥❣ ✐- ♣✉3-✉❡❞ ✈✐❛ ❡♠✐--✐♦♥ ❢❛❝+♦3- ♦♥ ❢♦--✐❧ ❢✉❡❧ ❝♦♥-✉♠♣+✐♦♥✳ ❋♦3 -♦❧✈✲
✐♥❣ ❘❊▼■◆❉✲❉ ♥✉♠❡3✐❝❛❧❧②✱ ✐+ ✐- ❢♦3♠✉❧❛+❡❞ ❛- ❛♥ ✐♥+❡3+❡♠♣♦3❛❧ -♦❝✐❛❧ ♣❧❛♥♥❡3 ♣3♦❜❧❡♠
✷
❆❝❝❡##✐❜❧❡ ♦♥❧✐♥❡ ✈✐❛
❤!!♣✿✴✴✇✇✇✳♣✐❦✲♣♦!+❞❛♠✳❞❡✴0❡+❡❛0❝❤✴+✉+!❛✐♥❛❜❧❡✲+♦❧✉!✐♦♥+✴♠♦❞❡❧+✴
0❡♠✐♥❞✴0❡♠✐♥❞✲❡6✉❛!✐♦♥+✳♣❞❢
✹
66 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
CO2
3.2 The Model REMIND-D 67
♣♦"✐$✐✈❡ ❝♦✲❜❡♥❡✜$" ♦❢ ♠✐$✐❣❛$✐♦♥ ❛0❡ ♥♦$ ✐♥❝❧✉❞❡❞ ✐♥ $❤❡ "♦❝✐❛❧ ✇❡❧❢❛0❡ ❢✉♥❝$✐♦♥✳
❯♥❞❡0❧②✐♥❣ ❛""✉♠♣$✐♦♥" ♦❢ $❤❡ ♦♣$✐♠✐③❛$✐♦♥ ❛♣♣0♦❛❝❤ ✇✐$❤ ❛ ❘❛♠"❡②✲$②♣❡ ❣0♦✇$❤ ♠♦❞❡❧
❛0❡ ❞✐"❝✉""❡❞ ❡①$❡♥"✐✈❡❧② ✐♥ ❡✳❣✳ ▼❛✉=♥❡0 ❛♥❞ ❑❧✉♠♣✳ ✭✶✾✾✻✮✳ ❚❤❡ ♠♦"$ ✐♠♣♦0$❛♥$ ♦♥❡"
✐♥❝❧✉❞❡ $❤❛$ $❤❡ ❡❝♦♥♦♠② ✐" ❝❧♦"❡❞ ❛♥❞ ♥♦ ❣♦✈❡0♥♠❡♥$ ❡①✐"$" $❤❛$ ❞❡♠❛♥❞" ♦0 "✉♣♣❧✐❡"
❣♦♦❞"✳ ❚❤❡ ❡❝♦♥♦♠② ✐" ❝♦♠♣0✐"❡❞ ♦❢ $✇♦ "❡❝$♦0"✿ ❤♦✉"❡❤♦❧❞" ❛♥❞ ✜0♠"✳ ❋✐0♠" ♣0♦✲
❞✉❝❡ ♦✉$♣✉$ ❜② ✉"✐♥❣ $❤❡ $❤0❡❡ ♣0♦❞✉❝$✐♦♥ ❢❛❝$♦0" ❝❛♣✐$❛❧✱ ❧❛❜♦0 ❛♥❞ ❡♥❡0❣②✳ ❍♦✉"❡❤♦❧❞"
❛0❡ ❡I✉❛❧ ✐♥ ✐♥✐$✐❛❧ ❡♥❞♦✇♠❡♥$" ❛♥❞ ♣0❡❢❡0❡♥❝❡"✱ ✇❤✐❝❤ ❛0❡ ♦0❞✐♥❛❧✳ ❚❤❡ ❛""✉♠♣$✐♦♥ ♦❢
0❡♣0❡"❡♥$❛$✐✈❡ ❤♦✉"❡❤♦❧❞" ❛❧❧♦✇" ❢♦0 ❛♥ ✐♥$0❛❣❡♥❡0❛$✐♦♥❛❧ ❛❣❣0❡❣❛$✐♦♥ ♦❢ ✐♥❞✐✈✐❞✉❛❧ ✉$✐❧✐✲
$✐❡"✳ ❚❤❡ ♦0❞✐♥❛❧ ♣0❡❢❡0❡♥❝❡ ♦0❞❡0✐♥❣" ❥✉"$✐✜❡" $❤❡ ✐♥$❡0$❡♠♣♦0❛❧ ❛❣❣0❡❣❛$✐♦♥ ♦❢ ✉$✐❧✐$✐❡"✱
✇❤✐❝❤ ✐" ❛❝❤✐❡✈❡❞ ❜② "✉♠♠✐♥❣ ❞✐"❝♦✉♥$❡❞ ✉$✐❧✐$✐❡"✳ ❊✈❡♥ $❤♦✉❣❤ $❤❡"❡ ❛""✉♠♣$✐♦♥" ❛0❡
❞✐"♣✉$❛❜❧❡✱ $❤❡② ❛0❡ ♥❡❝❡""❛0② "✐♠♣❧✐✜❝❛$✐♦♥" ❢♦0 $❤❡ ❛♥❛❧②$✐❝❛❧ ❢0❛♠❡✇♦0❦ ❛♥❞ 0❡❧❛①✲
❛$✐♦♥" ✐♥❝✉00❡❞ ♣0♦❤✐❜✐$✐✈❡❧② ❤✐❣❤ ♥✉♠❡0✐❝❛❧ ❝♦"$" ❞✉❡ $♦ $❤❡ ✐♥$❡❣0❛$✐♦♥ ♦❢ $❤❡ ❝♦♠♣❧❡①
❜♦$$♦♠✲✉♣ ❡♥❡0❣② "②"$❡♠ ♠♦❞✉❧❡✳
❆♥ ✐♠♣❧✐❝❛$✐♦♥ ♦❢ $❤❡"❡ ✉♥❞❡0❧②✐♥❣ ❛""✉♠♣$✐♦♥" ✐" $❤❛$ ❛ ❘❛♠"❡②✲$②♣❡ ❣0♦✇$❤ ♠♦❞❡❧ ✐"
♦♥❧② "✉✐$❛❜❧❡ ❢♦0 ❛♥❛❧②③✐♥❣ ❝❡0$❛✐♥ I✉❡"$✐♦♥"✳ ❋♦0 ❡①❛♠♣❧❡✱ ❘❊▼■◆❉✲❉ ✐" ✐❧❧✲"✉✐$❡❞ $♦
❛♥❛❧②③❡ $❤❡ ❞✐"$0✐❜✉$✐♦♥❛❧ ❡✛❡❝$" ♦❢ ❝❧✐♠❛$❡ ♣♦❧✐❝②✳ ❖0✐❣✐♥❛❧❧②✱ ✭❘❛♠"❡② ✶✾✷✽✮ ❛"❦❡❞ $❤❡
I✉❡"$✐♦♥ ♦❢ ✏❍♦✇ ♠✉❝❤ "❤♦❧❞ ❛ ♥❛$✐♦♥ "❛✈❡❄✑ ❛♥❞ ♦♣❡0❛$✐♦♥❛❧✐③❡❞ ✐$ ❜② ❛"❦✐♥❣ ✏❍♦✇ ♠✉❝❤
"❤♦✉❧❞ ❛ ♥❛$✐♦♥ ❝♦♥"✉♠❡❄✑ ✐♥"$❡❛❞✳ ❇② ✐♥$❡❣0❛$✐♥❣ ❡♥❡0❣② ❛" ❛♥ ❛❞❞✐$✐♦♥❛❧ ♣0♦❞✉❝$✐♦♥
❢❛❝$♦0 ❛" ✇❡❧❧ ❛" ❛ ❞❡$❛✐❧❡❞ 0❡♣0❡"❡♥$❛$✐♦♥ ♦❢ ✐$" "✉♣♣❧② ❝❤❛✐♥ ❛♥❞ $❤❡ ❝❛0❜♦♥ ❡①$❡0♥❛❧✐$②
✐♥$♦ $❤❡ ♠♦❞❡❧✐♥❣ ❢0❛♠❡✇♦0❦✱ ❘❊▼■◆❉✲❉ "❤✐❢$" $❤❡ ❢♦❝✉" ♦❢ ❛♥❛❧②"✐"✳ ❚❤❡ "$❛♥❞❛0❞
♠♦❞❡ ♦❢ ❛♥❛❧②"✐" 0❡❛❞" ❛"✿ ✏●✐✈❡♥ $❤❡ ●❡0♠❛♥ ❡♥❡0❣② "②"$❡♠ ✐" "✉❜❥❡❝$ $♦ ❛ "♣❡❝✐✜❝
❝❛0❜♦♥ ❜✉❞❣❡$ ❛♥❞ "❡$ ♦❢ "❝❡♥❛0✐♦ ❞❡✜♥✐$✐♦♥ ❝♦♥"$0❛✐♥$"✱ ✇❤❛$ ✐" $❤❡ ♠♦"$ ✇❡❧❢❛0❡✲♦♣$✐♠❛❧
♠✐$✐❣❛$✐♦♥ "$0❛$❡❣②❄✑✳
❚❤❡ ❢♦❧❧♦✇✐♥❣ "✉♠♠❛0✐③❡" ❢✉♥❞❛♠❡♥$❛❧ ✐♥❢♦0♠❛$✐♦♥ ♦♥ ❘❊▼■◆❉✲❉✳ ❈❛❧✐❜0❛$✐♦♥ ✐♥♣✉$ ❢♦0
$❤❡ ♠❛❝0♦❡❝♦♥♦♠✐❝ ❛♥❞ ❡♥❡0❣② "②"$❡♠ ♠♦❞✉❧❡" ✐" ♣0❡"❡♥$❡❞ ✐♥ ❙❡❝$✐♦♥ ✸ ❛♥❞ ❙❡❝$✐♦♥ ✹✳
❚❤❡ ❝❛❧✐❜0❛$✐♦♥ ❜❛"❡ ②❡❛0 ✐" ✷✵✵✼✳ ❙❡❝$✐♦♥ ✺ 0❡♣♦0$" ♦♥ $❤❡
CO2
❡♠✐""✐♦♥ ❛❝❝♦✉♥$✐♥❣
♣0♦❝❡❞✉0❡✳ ❋✐♥❛❧❧②✱ ❙❡❝$✐♦♥ ✻ ♣0♦✈✐❞❡" ❛ ❜0✐❡❢ ✈❛❧✐❞❛$✐♦♥ ♦❢ ♠♦❞❡❧ 0❡"✉❧$"✳
✷✳✶ ❋✉♥❞❛♠❡♥*❛❧,
!♦❣!❛♠♠✐♥❣ ▲❛♥❣✉❛❣❡ ❛♥❞ ❙♦❧✈❡!
❚❤❡ ♠♦❞❡❧ ✐" ✇0✐$$❡♥ ✐♥ ●❆▼❙ ❛♥❞ ✉"❡" $❤❡ ♥♦♥✲
❧✐♥❡❛0 "♦❧✈❡0 ❈❖◆❖a❚✳
❚✐♠❡
❚❤❡ $✐♠❡ ❤♦0✐③♦♥ ❢♦0 $❤❡ ♦♣$✐♠✐③❛$✐♦♥ ✐" ✷✵✵✺✲✷✶✵✵✱ ✇✐$❤ ❛ ❞✐"❝0❡$❡ $✐♠❡ "$❡♣
0❡"♦❧✉$✐♦♥ ♦❢ ✺ ②❡❛0"✳ ❚❤❡ ✜0"$ $✐♠❡ "$❡♣✱ ✏✷✵✵✺✑✱ ❝♦✈❡0" $❤❡ ♣❡0✐♦❞ ✷✵✵✺✲✷✵✵✾✳
❚❤❡ ❝❛❧✐❜0❛$✐♦♥ ♦❢ $❤❡ ♠♦❞❡❧ ✐" ♣❡0❢♦0♠❡❞ ❢♦0 $❤❡ ②❡❛0 ✷✵✵✼✱ $❤❡ ♠❡❞✐❛♥ ②❡❛0
✐♥ $❤❡ 0❛♥❣❡✳ ❙✉❜❥❡❝$ $♦ ❛♥❛❧②"✐" ❛0❡ $❤❡ ❝♦♥"❡❝✉$✐✈❡ $✐♠❡ "$❡♣" ❢0♦♠ ✷✵✵✺ $♦
✷✵✺✵✳ ❚❤❡ 0❡❛"♦♥ ❢♦0 ❡①❝❧✉❞✐♥❣ $❤❡ ❧❛$❡0 ②❡❛0" ❢0♦♠ $❤❡ ❛♥❛❧②"✐" ✐" $❤❡ ♦❝❝✉00❡♥❝❡
♦❢ ✉♥❞❡"✐0❛❜❧❡ ✏❜✉0♥✲♦✉$✑ ❡✛❡❝$" $♦✇❛0❞" $❤❡ ❡♥❞ ♦❢ $❤❡ "✐♠✉❧❛$✐♦♥ ♣❡0✐♦❞✳ ■$ ✐"
❝♦♠♠♦♥ ♣0❛❝$✐❝❡ ✐♥ ♦♣$✐♠✐③❛$✐♦♥ ♠♦❞❡❧" $♦ ❝✉$ ♦✛ $❤❡ ♣❡0✐♦❞ ♦❢ ❛♥❛❧②"✐" ❛❤❡❛❞ ♦❢
$❤❡ ❡♥❞ ♦❢ $❤❡ $✐♠❡ ❤♦0✐③♦♥✳
✻
68 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
❋❧✉❝$✉❛$✐♥❣ ❘❡♥❡✇❛❜❧❡-
❱❛"✐❛❜❧❡ "❡♥❡✇❛❜❧❡ ❡❧❡❝*"✐❝✐*② ❣❡♥❡"❛*✐♦♥ ✢✉❝*✉❛*❡0 ♦♥ ✈❡"②
0❤♦"* *✐♠❡ 0❝❛❧❡0✳ ❙✐♥❝❡ *❤❡ *✐♠❡ "❡0♦❧✉*✐♦♥ ✐♥ ❘❊▼■◆❉✲❉ ✐0 ✐♥ ✺ ②❡❛" *✐♠❡✲
0*❡♣0✱ *❤❡0❡ ❡✛❡❝*0 ❝❛♥♥♦* ❜❡ ♠♦❞❡❧❡❞ ❡①♣❧✐❝✐*❧②✳ ❍♦✇❡✈❡"✱ ♥❡❣❧❡❝*✐♥❣ *❤❡ 0②0*❡♠
"❡D✉✐"❡♠❡♥*0 *❤❛* ❛"✐0❡ ❢"♦♠ ❤✐❣❤ ♣❡♥❡*"❛*✐♦♥0 ♦❢ ✢✉❝*✉❛*✐♥❣ "❡♥❡✇❛❜❧❡0 0✐❣♥✐✜✲
❝❛♥*❧② ✉♥❞❡"0*❛*❡0 *❤❡ ✐♥*❡❣"❛*✐♦♥ ❝♦0*0 ♦❢ "❡♥❡✇❛❜❧❡0✳ ■♥ ❘❊▼■◆❉✲❉✱ ❛ "❡0✐❞✉❛❧
❧♦❛❞ ❞✉"❛*✐♦♥ ❝✉"✈❡ ❛♣♣"♦❛❝❤ ❝❛♣*✉"❡0 ♠♦0* ♦❢ *❤❡ ❝❤❛❧❧❡♥❣❡0 *❤❛* ❛"✐0❡ ❢"♦♠ ❤✐❣❤
0❤❛"❡0 ♦❢ ✢✉❝*✉❛*✐♥❣ "❡♥❡✇❛❜❧❡0 ✇✐*❤♦✉* ✐♥❝"❡❛0✐♥❣ *❤❡ *❡♠♣♦"❛❧ "❡0♦❧✉*✐♦♥ ♦❢ *❤❡
♠♦❞❡❧✳ ❯❡❝❦❡"❞* ❡* ❛❧✳ ✭✷✵✶✶✮ ❡❧❛❜♦"❛*❡0 ♦❢ *❤❡ ❝♦♥❝❡♣* ❛♥❞ ✈❛❧✐❞❛*❡0 *❤❡ ❛♣♣"♦❛❝❤
✇✐*❤ ❛ ❞❡*❛✐❧❡❞ ❞✐0♣❛*❝❤ ♠♦❞❡❧ ♦❢ ●❡"♠❛♥②✳
●❡♦❣0❛♣❤✐❝❛❧ ❘❡-♦❧✉$✐♦♥
❆0 ❛ 0②0*❡♠ ❜♦✉♥❞❛"② ❢♦" ❘❊▼■◆❉✲❉✱ *❤❡ ❣❡♦❣"❛♣❤✐❝❛❧ ❜♦"✲
❞❡"0 ♦❢ ●❡"♠❛♥② ❣✉✐❞❡ *❤❡ ❝✉*✲♦✛ 0✐♥❝❡ *❤❡ ❢♦❝✉0 ♦❢ *❤❡ ♠♦❞❡❧ ✐0 ♦♥
❞♦♠❡$%✐❝
♠✐*✐❣❛*✐♦♥✳ ■♠♣♦"*❡❞ ❡♥❡"❣② ❝❛""✐❡"0 ❝♦♠❡ ❛* ❡①♦❣❡♥♦✉0 ♣"✐❝❡0 ❛♥❞ ●❡"♠❛♥② ✐0
❛00✉♠❡❞ *♦ ❛❝* ❛0 ❛ ♣"✐❝❡ *❛❦❡"✳ ❲✐*❤✐♥ *❤❡ ♠♦❞❡❧✱ *❤❡ ❣❡♦❣"❛♣❤✐❝❛❧ ❞✐♠❡♥0✐♦♥ ✐0
♣❛"❛♠❡*❡"✐③❡❞ ✐♥ ❛♥ ❛♣♣"♦♣"✐❛*❡ ✇❛② ❢♦" ❝♦✈❡"✐♥❣ ❣❡♦❣"❛♣❤✐❝ ✜"0*✲♦"❞❡" ❡✛❡❝*0✱ ❡✳❣✳
❞✐0*"✐❜✉*✐♦♥ *❡❝❤♥♦❧♦❣✐❡0✳ ❘❊▼■◆❉✲❉ ✐0 ❛ 0✐♥❣❧❡✲"❡❣✐♦♥ ♠♦❞❡❧✳
❉❡♠❛♥❞ ❙❡❝$♦0-
❘❊▼■◆❉✲❉ ❝♦♥0✐❞❡"0 *❤❡ ❛❣❣"❡❣❛*❡❞ ❞❡♠❛♥❞ 0❡❝*♦"0 ✐♥❞✉0*"② ✭■◆❉✮✱
"❡0✐❞❡♥*✐❛❧ ✫ ❝♦♠♠❡"❝✐❛❧ ✭❘❊❙✫❈❖▼✮ ❛♥❞ *"❛♥0♣♦"*✳ ❊❛❝❤ 0❡❝*♦" ❞❡♠❛♥❞0 ❞✐✛❡"✲
❡♥* ✜♥❛❧ ❡♥❡"❣✐❡0✱ ♦" ✐♥ *❤❡ ❝❛0❡ ♦❢ *❤❡ *"❛♥0♣♦"* 0❡❝*♦" ❡♥❡"❣② 0❡"✈✐❝❡0✳ ❊❧❛0*✐❝✐*✐❡0
♦❢ 0✉❜0*✐*✉*✐♦♥ ❞❡*❡"♠✐♥❡ *❤❡ ❡♥❞♦❣❡♥♦✉0 ❞❡✈❡❧♦♣♠❡♥* ♦✈❡" *✐♠❡✳
❊8✉✐❧✐❜0✐✉♠
❚❤❡ ❝♦♥❝❡♣* ♦❢ ❡D✉✐❧✐❜"✐✉♠ ♠❡❛♥0 *❤❛* ❛ 0②0*❡♠ ✐0 ✐♥ ❛ 0*❛*❡ *❤❛* ✇✐❧❧
♥♦* ❝❤❛♥❣❡ ✉♥❧❡00 ❡①*❡"♥❛❧ ✐♥✢✉❡♥❝❡0 ❝❤❛♥❣❡ ♦♥❡ ♦" ♠♦"❡ ✈❛"✐❛❜❧❡0✳ ❆ ♠❛"❦❡*
*❤❛* ✐0 ✐♥ ❡D✉✐❧✐❜"✐✉♠ ✐0 ✐♥ ❛ 0*❛*❡ 0✉❝❤ *❤❛* 0✉♣♣❧② ❛♥❞ ❞❡♠❛♥❞ ♠❛*❝❤ ❛* *❤❡
❡D✉✐❧✐❜"✐✉♠ ♣"✐❝❡✳ ❚❤❡"❡ ❛"❡ ♠❛♥② ✇❛②0 *♦ ✜♥❞ *❤❡ ❡D✉✐❧✐❜"✐✉♠ 0♦❧✉*✐♦♥ ❢♦" ❛
0②0*❡♠✳ ❘❊▼■◆❉✲❉ ❝❤♦♦0❡0 *♦ ❞♦ 0♦ ❜② ♠❛①✐♠✐③✐♥❣ *❤❡ ✐♥*❡"*❡♠♣♦"❛❧ ✇❡❧❢❛"❡✳
❆❝❝♦"❞✐♥❣ *♦ *❤❡ ✷♥❞ *❤❡♦"❡♠ ♦❢ ✇❡❧❢❛"❡ ❡❝♦♥♦♠✐❝0✱ 0✉❝❤ ❛ 0♦❧✉*✐♦♥ ❝♦✐♥❝✐❞❡0 ✇✐*❤
*❤❡ ♠❛"❦❡* 0♦❧✉*✐♦♥ ✉♥❞❡" *❤❡ ❛00✉♠♣*✐♦♥ ♦❢ V❛"❡*♦✲❡✣❝✐❡♥❝②✳ ❘❊▼■◆❉✲❉ ✜♥❞0
❛ 0✐♠✉❧*❛♥❡♦✉0 ❡D✉✐❧✐❜"✐✉♠ ✐♥ ❝❛♣✐*❛❧ ❛♥❞ ❡♥❡"❣② ♠❛"❦❡*0✳
9❡0❢❡❝$ ❋♦0❡-✐❣❤$
❚❤❡ ❛00✉♠♣*✐♦♥ ♦❢ ♣❡"❢❡❝* ❢♦"❡0✐❣❤* ✐0 ❛ *❤❡♦"❡*✐❝❛❧ ❛00✉♠♣*✐♦♥ ♥❡❝✲
❡00❛"② ✐♥ *❤❡ ♠♦❞❡❧ 0❡*✉♣ ❢♦" ✜♥❞✐♥❣ ❛ 0♦❧✉*✐♦♥ *♦ *❤❡ ❡D✉✐❧✐❜"✐✉♠ ♣"♦❜❧❡♠✳ V❡"❢❡❝*
❢♦"❡0✐❣❤* ❡00❡♥*✐❛❧❧② ♠❡❛♥0 *❤❛* *❤❡ ❧♦♥❣✲*❡"♠ ❝♦♥0❡D✉❡♥❝❡0 ♦❢ ❛ ♣❛"*✐❝✉❧❛" ❞❡❝✐0✐♦♥
✐♥ ❛ ♣❛"*✐❝✉❧❛" ②❡❛" ❛"❡ ❡♥*✐"❡❧② ❢♦"❡0❡❡❛❜❧❡ ❢♦" *❤❡ 0♦❧✉*✐♦♥ ♣"♦❝❡00✳ ❚❤❡ 0♦❧✉✲
*✐♦♥ ♣"♦❝❡00 ❢♦" ❘❊▼■◆❉✲❉ ✐0 ✐*❡"❛*✐✈❡✱ ♠❡❛♥✐♥❣ *❤❡ 0♦❧✈❡" ❝❛❧❝✉❧❛*❡0 ❛ ♣❛"*✐❝✉❧❛"
0♦❧✉*✐♦♥ ♣❛*❤✇❛② ♦✈❡" *❤❡ *✐♠❡ ❤♦"✐③♦♥ ❛♥❞ "❡❛❝❤❡0 ❛ ♣❛"*✐❝✉❧❛" ✈❛❧✉❡ ❢♦" *❤❡ ♦♣✲
*✐♠✐③❛*✐♦♥ ♦❜❥❡❝*✐✈❡ ❛♥❞ 0*♦"❡0 ✐*✳ ■♥ *❤❡ ♥❡①* ✐*❡"❛*✐♦♥✱ 0♦♠❡ ❛❧*❡"♥❛*✐✈❡ ❞❡❝✐0✐♦♥
✐0 ♠❛❞❡ ✐♥ *❤❡ 0♦❧✉*✐♦♥ ♣❛*❤✇❛② ❛♥❞ *❤❡ 0♦❧✈❡" ❝♦♠♣❛"❡0 *❤❡ ♥❡✇ ✈❛❧✉❡ ❢♦" *❤❡
♦♣*✐♠✐③❛*✐♦♥ ♦❜❥❡❝*✐✈❡ *♦ *❤❡ ♦♥❡ ♣"❡✈✐♦✉0❧② ♦❜*❛✐♥❡❞✳ ■❢ ✐* ✐0 ❤✐❣❤❡"✱ *❤❡ ♦❧❞❡"
♣❛*❤✇❛② ✐0 ❞"♦♣♣❡❞ ❛♥❞ *❤❡ ♥❡✇ ♣❛*❤✇❛② 0❡"✈❡0 ❛0 ❛ ❜❡♥❝❤♠❛"❦✳ ❆❣❛✐♥✱ 0♦♠❡
❞❡❝✐0✐♦♥ ✐0 ❛❧*❡"❡❞ ❛♥❞ *❤❡ ♦❜❥❡❝*✐✈❡ ✈❛❧✉❡ ❝♦♠♣❛"❡❞✳ ❚❤✐0 ♣"♦❝❡00 "❡♣❡❛*0 ✉♥*✐❧
*❤❡ ❝❤❛♥❣❡ ✐♥ *❤❡ ♦♣*✐♠✐③❛*✐♦♥ ♦❜❥❡❝*✐✈❡ ✐0 ❝♦♥*✐♥✉♦✉0❧② ❜❡❧♦✇ ❛ ❝❡"*❛✐♥ *❤"❡0❤♦❧❞✱
✇❤✐❝❤ ✐0 ❛ ✈❡"② 0♠❛❧❧ ♥✉♠❜❡"✳ ■♥ *❤✐0 ❝❛0❡✱ *❤❡ 0♦❧✉*✐♦♥ ♣"♦❝❡00 ❡♥❞0 ❛♥❞ ❛♥ ♦♣*✐♠❛❧
0♦❧✉*✐♦♥ ✐0 "❡♣♦"*❡❞✳ ❚❤❡ ❝♦♥❝❡♣* ♦❢ ♣❡"❢❡❝* ❢♦"❡0✐❣❤* ✐♥ ❘❊▼■◆❉✲❉ ✐♠♣❧✐❡0 *❤❛*
✼
3.2 The Model REMIND-D 69
❤❡ #❡$✉❧ $ ♦❢ ❤❡ ♠♦❞❡❧ #❡♣#❡$❡♥ ♦♣ ✐♠❛❧ ♣❛ ❤✇❛②$ ❛♥❞ ❛#❡ ♥♦ ❡①♣❡# ❢♦#❡❝❛$ $
♦# $✐♠✉❧❛ ✐♦♥$✳
▼②♦♣✐❝ ❇❡❤❛✈✐♦+
❋✐①✐♥❣ ❝❡# ❛✐♥ ✈❛#✐❛❜❧❡$ ❢♦# ❛ $❡❧❡❝ ❡❞ ♣❡#✐♦❞ ♦❢ ✐♠❡ ♦♥ ❛ ♣❛ ❤✇❛②
❤❛ ❞♦❡$ ♥♦ ❝♦✐♥❝✐❞❡ ✇✐ ❤ ❤❡ ♦♣ ✐♠❛❧ $♦❧✉ ✐♦♥ ✐$ ❛ ♠❡❛♥$ ♦❢ ✐♥ #♦❞✉❝✐♥❣ ♠②♦♣✐❝
❜❡❤❛✈✐♦# ✐♥ ♦ ❤❡ ♠♦❞❡❧✳ ❯♣♦♥ ❝♦♠♣❛#✐♥❣ #❡$✉❧ $ ❢#♦♠ ❛ ❝♦♠♣❧❡ ❡ ♣❡#❢❡❝ ❢♦#❡$✐❣❤
♠♦❞❡❧ #✉♥ ✇✐ ❤ ♦♥❡ ❤❛ ✐♥❝❧✉❞❡$ ♠②♦♣✐❝ ❜❡❤❛✈✐♦# ❛❧❧♦✇$ ❢♦# ❞✐$ ✐❧❧✐♥❣ ✐ $ ❡✛❡❝ $✳
❉✐-❝♦✉♥0✐♥❣
❚❤❡ ♣✉#❡ ✐♠❡ ♣#❡❢❡#❡♥❝❡ #❛ ❡ ✐♥ ❘❊▼■◆❉ ✐$ #❛ ❡ ✐$ $❡ ♦ ✶✪ ✐♥ ❤❡
$ ❛♥❞❛#❞ $❡ ✐♥❣✳ ❊♥❞♦❣❡♥♦✉$❧②✱ ❤❡ ✐♥ ❡#❡$ #❛ ❡ ❛❞❥✉$ $ ♦
±
✸✪✱ ❞❡♣❡♥❞✐♥❣ ♦♥
❤❡ $❝❡♥❛#✐♦ ❛♥❞ ✐♠❡ $ ❡♣✳ ❚❤✉$✱ ❢♦# ❤❡ ❞✐$❝♦✉♥ ✐♥❣ ♦❢ ●❉G ❧♦$$❡$✱ ❛ ❞✐$❝♦✉♥
#❛ ❡ ♦❢ ✸✪ ✐$ ✉$❡❞ ✐♥ ❤❡ $ ❛♥❞❛#❞ $❡ ✐♥❣✳
❊♥❞♦❣❡♥♦✉- ▲❡❛+♥✐♥❣
❘❊▼■◆❉✲❉ ❞#❛✇$ ♦♥ ❤❡ ❝♦♥❝❡♣ ♦❢ ❧❡❛#♥✐♥❣✲❜②✲❞♦✐♥❣ ✭❆##♦✇
✶✾✻✷✮ ❢♦# ♠♦❞❡❧✐♥❣ ❤❡ ❝♦$ ❢✉♥❝ ✐♦♥$ ♦❢ ✐♥♥♦✈❛ ✐✈❡ ❧♦✇ ❝❛#❜♦♥ ❡❝❤♥♦❧♦❣✐❡$ ❡♥✲
❞♦❣❡♥♦✉$❧②✳ ❚❤❡ ❛♣♣❧✐❝❛ ✐♦♥ ♦❢ ❤❡ ❝♦♥❝❡♣ ♦ ❜♦ ♦♠✲✉♣ ❡♥❡#❣② $②$ ❡♠ ♠♦❞❡❧$
✇❛$ ♣✐♦♥❡❡#❡❞ ❜② ▼❡$$♥❡# ✭✶✾✾✼✮ ❛♥❞ ❇❛##❡ ♦ ✭✷✵✵✶✮✳ ❋♦# ❛ ❝#✐ ✐❝❛❧ ❞✐$❝✉$$✐♦♥ $❡❡
❑❛❤♦✉❧✐✲❇#❛❤♠✐ ✭✷✵✵✽✮ ♦# ◆♦#❞❤❛✉$ ✭✷✵✵✽✮✳ ❚❤❡ ✉♥❞❡#❧②✐♥❣ ✐❞❡❛ ✐$ ❤❛ ✱ ❤✐$ ♦#✐✲
❝❛❧❧②✱ ❤❡ $♣❡❝✐✜❝ ✐♥✈❡$ ♠❡♥ ❝♦$ $ ♦❢ ❡❝❤♥♦❧♦❣✐❡$ ❤❛✈❡ ❜❡❡♥ #❡❞✉❝❡❞ $✐❣♥✐✜❝❛♥ ❧②
✇✐ ❤ ✐♥❝#❡❛$❡❞ ✐♥$ ❛❧❧❡❞ ❝❛♣❛❝✐ ②✳ ▲❡❛#♥✐♥❣ #❛ ❡$ ❛#❡ ❛ ♠❡❛♥$ ♦ ❡①♣#❡$$ ❤♦✇ ♠✉❝❤
❤❡ $♣❡❝✐✜❝ ✐♥✈❡$ ♠❡♥ ❝♦$ $ #❡❞✉❝❡ ✉♣♦♥ ❛ ❞♦✉❜❧✐♥❣ ♦❢ ✐♥$ ❛❧❧❡❞ ❝❛♣❛❝✐ ②✳ ❚❤❡
✐♥♥♦✈❛ ✐✈❡ ❧♦✇✲❝❛#❜♦♥ ❡❝❤♥♦❧♦❣✐❡$ ✐♥ ❘❊▼■◆❉✲❉ ❛#❡ $✉❜❥❡❝ ♦ ♥♦♥✲❧✐♥❡❛#✱ ❡♥✲
❞♦❣❡♥♦✉$ ❧❡❛#♥✐♥❣ ❤❛ ✐$ $♣❧✐ ✐♥ ♦ ❞♦♠❡$ ✐❝ ❛♥❞ ❣❧♦❜❛❧ ❝♦♠♣♦♥❡♥ $✱ ✐♠♣❧②✐♥❣ ❤❡
#❡❛$♦♥✐♥❣ ❤❛ ❢♦# $♦♠❡ ❝♦♠♣♦♥❡♥ $ ❣❧♦❜❛❧ ❝❛♣❛❝✐ ✐❡$ ❛#❡ ❤❡ ♠❛✐♥ ❞#✐✈❡#$ ❛♥❞ ❢♦#
♦ ❤❡#$ ♥❛ ✐♦♥❛❧ ❝❛♣❛❝✐ ✐❡$✳
❙❝❡♥❛+✐♦
❚❤❡ ❡#♠ $❝❡♥❛#✐♦ #❡❢❡#$ ♦ ♦♥❡ ♣❛# ✐❝✉❧❛# $❡ ♦❢ ❝♦♥$ #❛✐♥ $ ♦❢ ❤❡ ♦♣ ✐♠✐③❛ ✐♦♥
$♣❛❝❡✱ ✐✳❡✳ ♦♥❡ $❡ ♦❢ ❡①♦❣❡♥♦✉$ ❛$$✉♠♣ ✐♦♥$✳
▼✐0✐❣❛0✐♦♥ ❊♥❢♦+❝❡♠❡♥0
■♥ ❤❡ $ ❛♥❞❛#❞ $❡ ✐♥❣✱ ♠✐ ✐❣❛ ✐♦♥ ✐$ ❡♥❢♦#❝❡❞ ✈✐❛ ❛ ❞♦♠❡$ ✐❝
CO2
❜✉❞❣❡ ♦✈❡# ❤❡ ✐♠❡ ❤♦#✐③♦♥✱ ✐♥$♣✐#❡❞ ❜② ▼❡✐♥$❤❛✉$❡♥ ❡ ❛❧✳ ✭✷✵✵✾✮ ❛♥❞
❲❇●❯ ✭✷✵✵✾✮✳ ❖ ❤❡# ♣♦$$✐❜❧❡ ✐♠♣❧❡♠❡♥ ❛ ✐♦♥$ ✐♥❝❧✉❞❡ ♣#❡$❝#✐❜✐♥❣ ❛
CO2
❛① ♦#
❛ $♣❡❝✐✜❝ ❛♥♥✉❛❧ ❡♠✐$$✐♦♥ #❛❥❡❝ ♦#②✳
❇❛-❡❧✐♥❡ ❙❝❡♥❛+✐♦
■♥ ❤❡ ■♥ ❡❣#❛ ❡❞ ❆$$❡$$♠❡♥ ❝♦♠♠✉♥✐ ②✱ ♦❢ ❡♥ ❛ ❜❛$❡❧✐♥❡ $❝❡♥❛#✐♦ ✐$
♦♥❡ ❤❛ ❤❛$ ✉♥❝♦♥$ #❛✐♥❡❞ ●❍● ❡♠✐$$✐♦♥$✳ ❋♦# ●❡#♠❛♥②✱ $✉❝❤ ❛ ♣✉#❡ ❜❛$❡❧✐♥❡ ✐$
✉♥❧✐❦❡❧② ❛$ ❡♠✐$$✐♦♥ #❡❞✉❝ ✐♦♥ ♣♦❧✐❝✐❡$ ❛#❡ ❛❧#❡❛❞② ✐♥ ♣❧❛❝❡ ❛♥❞ ❝♦♠♠✐ ♠❡♥ $ ❛#❡
❤✐❣❤✳ ❚❤❡ ❞❡✜♥✐ ✐♦♥ ♦❢ ❛ ❜❛$❡❧✐♥❡ $❝❡♥❛#✐♦ ❢♦# ❘❊▼■◆❉✲❉ ❝♦♥$❡[✉❡♥ ❧② ❢♦❧❧♦✇$ ❤❡
✐❞❡❛ ❤❛ ♠✐ ✐❣❛ ✐♦♥ ❝♦♥ ✐♥✉❡$ ❛ ❛ ♠♦❞❡#❛ ❡ ❧❡✈❡❧✱ ✐✳❡✳ #❡❛❝❤❡$ ❛#♦✉♥❞ ✹✵✪
CO2
❞♦♠❡$ ✐❝ ❡♠✐$$✐♦♥ #❡❞✉❝ ✐♦♥ ✐♥ ✷✵✺✵ ✈❡#$✉$ ❤❡ ✶✾✾✵ ❧❡✈❡❧✳
9♦❧✐❝② ❙❝❡♥❛+✐♦
■♥ ❤❡ ❝♦♥ ❡① ♦❢ ❘❊▼■◆❉✲❉ ❛ ♣♦❧✐❝② $❝❡♥❛#✐♦ ✐$ ♦♥❡ ❤❛ ✐$ $✉❜❥❡❝ ♦
❛ $ #✐❝ ❡#
CO2
❡♠✐$$✐♦♥ #❡❞✉❝ ✐♦♥ ❛#❣❡ ❤❛♥ ❤❡ ❜❛$❡❧✐♥❡ $❝❡♥❛#✐♦✳
▼✐0✐❣❛0✐♦♥ ❈♦-0-
❈♦♠♣❛#✐♥❣ ❤❡ #❡$✉❧ $ ♦❢ ❛ ❜❛$❡❧✐♥❡ ❛♥❞ ♣♦❧✐❝② $❝❡♥❛#✐♦ ❤❛ ❞✐✛❡# ♦♥❧②
✇✐ ❤ #❡$♣❡❝ ♦ ❤❡ ❡♠✐$$✐♦♥ ❝♦♥$ #❛✐♥ ❛❧❧♦✇$ ❢♦# ❞❡ ❡#♠✐♥✐♥❣ ❤❡ ❞✐✛❡#❡♥ ✐❛❧ ❡✛❡❝ $
♦❢ ♠✐ ✐❣❛ ✐♦♥ ♣♦❧✐❝②✳ ❚❤✐$ ✐♠♣❧✐❡$ ❛ ❝♦$ ✲❡✛❡❝ ✐✈❡♥❡$$ ♠♦❞❡ ♦❢ ❛♥❛❧②$✐$✳ ❈❧✐♠❛ ❡
❞❛♠❛❣❡$ ❛♥❞ ♣♦$✐ ✐✈❡ ❝♦✲❜❡♥❡✜ $ ♦❢ ♠✐ ✐❣❛ ✐♦♥ ❛#❡ ♥♦ ❝♦♥$✐❞❡#❡❞ ✐♥ ❘❊▼■◆❉✲❉✳
✽
70 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
▼✐"✐❣❛"✐♦♥ ❝♦("( ❛)❡ ✐♥❤❡)❡♥"❧② ♥❡❣❛"✐✈❡ ❛♥❞ ♠❛② ❜❡ ❛♥❛❧②③❡❞ ♦♥ ❛❧❧ ❧❡✈❡❧(✱ ❡✳❣✳
❢)♦♠ ●❉8 ❧♦((❡( "♦ ❞✐✛❡)❡♥❝❡( ✐♥ ❡❧❡❝")✐❝✐"② ♣)✐❝❡(✳
✸ ❚❤❡ ▼❛❝'♦❡❝♦♥♦♠✐❝ ▼♦❞✉❧❡
❚❤❡ ♠❛❝)♦❡❝♦♥♦♠✐❝ ♠♦❞✉❧❡ ♦❢ ❘❊▼■◆❉✲❉ ❝♦♠♣)✐(❡( "❤❡ ♦♣"✐♠✐③❛"✐♦♥ ♦❜❥❡❝"✐✈❡✱ ❛ (♦✲
❝✐❛❧ ✇❡❧❢❛)❡ ❢✉♥❝"✐♦♥✱ ❛♥❞ "❤❡ ♣)♦❞✉❝"✐♦♥ ❢✉♥❝"✐♦♥✳ ❚❤❡② ❛)❡ ❝❛❧✐❜)❛"❡❞ "♦ )❡♣)❡(❡♥" "❤❡
❛❣❣)❡❣❛"❡ ♦❢ ●❡)♠❛♥ ❤♦✉(❡❤♦❧❞( ❛♥❞ ✜)♠(✱ )❡(♣❡❝"✐✈❡❧②✳ ❲❤✐❧❡ ❛ ❤②❜)✐❞ ❡❝♦♥♦♠②✲❡♥❡)❣②
(②("❡♠ ♠♦❞❡❧ ✐( "❤❡♦)❡"✐❝❛❧❧② ✐♥")✐❣✉✐♥❣✱ ✐" ✐( ✈❡)② ❝❤❛❧❧❡♥❣✐♥❣ "♦ ❝❛❧✐❜)❛"❡ ✐" "♦ ❛ ♣❛)"✐❝✲
✉❧❛) ❝♦✉♥")②✳ ❚❤✐( ✐( ❞✉❡ "♦ "❤❡ ❢❛❝" "❤❛" ❡♥❡)❣② ❞❡♠❛♥❞ ✐( )❡♣)❡(❡♥"❡❞ ❡♥❞♦❣❡♥♦✉(❧② ❜②
♥❡("❡❞ ❈❊❙✲❢✉♥❝"✐♦♥(✱ ✇❤✐❝❤ )❡H✉✐)❡ (✉❜("✐"✉"✐♦♥ ❡❧❛("✐❝✐"✐❡(✱ ❢❛❝"♦) ♣)♦❞✉❝"✐✈✐"② ❣)♦✇"❤
)❛"❡( ❛♥❞ ✐♥✐"✐❛❧ )❡❧❛"✐✈❡ ♣)✐❝❡( ❢♦) ❝❛❧✐❜)❛"✐♦♥✳ ❚❤❡ ✉(✉❛❧ ♣)♦❝❡❞✉)❡ ❢♦) ❛ ❘❛♠(❡②✲"②♣❡
❣)♦✇"❤ ♠♦❞❡❧ ✐( "♦ ♦♣❡)❛"❡ ✉♥❞❡) ❛♥ ✐♥♣✉"✲✈❛❧✐❞❛"✐♦♥ ♣❛)❛❞✐❣♠ ❛♥❞ ❡("✐♠❛"❡ "❤❡♠ ❡❝♦♥♦✲
♠❡")✐❝❛❧❧② ❜❛(❡❞ ♦♥ ♣❛(" ❞❛"❛✳ ❍♦✇❡✈❡)✱ ❢♦) "❤❡ ♠♦(" ♦❢ "❤❡ ♣)♦❞✉❝"✐♦♥ ❢❛❝"♦)( ✐♥ "❤❡ ❝❛(❡
❛" ❤❛♥❞✱ "❤❡(❡ ❞❛"❛ ❛)❡ ✉♥♦❜(❡)✈❛❜❧❡✳ ❚❤❡ "✐♠❡ (❡)✐❡( ✇❤✐❝❤ ❛)❡ ♣♦"❡♥"✐❛❧❧② ❛✈❛✐❧❛❜❧❡ ♦♥❧②
❣♦ ❜❛❝❦ "♦ ✶✾✾✶ ❢♦) ✉♥✐✜❡❞ ●❡)♠❛♥②✳ ❙✉❝❤ (❤♦)" "✐♠❡ (❡)✐❡( ②✐❡❧❞ ✐♥(✐❣♥✐✜❝❛♥" ❡❝♦♥♦✲
♠❡")✐❝ )❡(✉❧"(✳ ❆♥ ❛❧"❡)♥❛"✐✈❡ ✐( "♦ ❝❛❧✐❜)❛"❡ "❤❡ ♠♦❞❡❧ ❜❛(❡❞ ♦♥ ♦✉"♣✉"✲✈❛❧✐❞❛"✐♦♥✳
❖♥❡ ♠❡❛♥( ♦❢ ♣)♦✈✐❞✐♥❣ ♦✉"♣✉"✲✈❛❧✐❞❛"✐♦♥ ✐( "♦ )❡❧② ♦♥ ❤❡✉)✐("✐❝( ❛♥❞ ❝❛❧✐❜)❛"❡ "❤❡ ♠♦❞❡❧
❜❡❤❛✈✐♦) (♦ ✐" )❡♣)♦❞✉❝❡( ❢✉"✉)❡ ❞❡✈❡❧♦♣♠❡♥"( "❤❛" ❛)❡ ❥✉❞❣❡❞ ❛( ❤✐❣❤❧② ❧✐❦❡❧② ❜② ❡①♣❡)"
❝♦♥(❡♥(✉(✳ ❚✇♦ ❤❡✉)✐("✐❝( (❡)✈❡ ❢♦) ❝❛❧✐❜)❛"✐♥❣ ❘❊▼■◆❉✲❉ ❢♦) ●❡)♠❛♥②✳ ✭✶✮ ■♥ ❛ ❜❛(❡❧✐♥❡
(❝❡♥❛)✐♦✱ ✇✐"❤ ♦♥❧② ♠♦❞❡)❛"❡ ♠✐"✐❣❛"✐♦♥✱ ❤✐("♦)✐❝❛❧ ")❡♥❞( ✐♥ ♦❜(❡)✈❛❜❧❡ ✈❛)✐❛❜❧❡( ✇✐❧❧
❝♦♥"✐♥✉❡ (♠♦♦"❤❧②✳ ✭✷✮ ■♥ ❛♥ ❛♠❜✐"✐♦✉( ♠✐"✐❣❛"✐♦♥ ♣♦❧✐❝② (❝❡♥❛)✐♦✱ ❡♥❡)❣② ❞❡♠❛♥❞ ✇✐❧❧
❡✈♦❧✈❡ ✐♥ ❧✐♥❡ ✇✐"❤ "❤❡ ♣)❡❞✐❝"✐♦♥( ♦❢ ❞❡"❛✐❧❡❞ ❜♦""♦♠✲✉♣ ❡♥❡)❣② (②("❡♠ ♠♦❞❡❧(✳ ❚❤❡
❝❛❧✐❜)❛"✐♦♥ ♣❛)❛♠❡"❡)( ✐♥ "❤❡ ♠❛❝)♦❡❝♦♥♦♠✐❝ ♠♦❞✉❧❡ ❛)❡ ❛❞❥✉("❡❞ "❤)♦✉❣❤ ")✐❛❧✲❛♥❞✲❡))♦)
(♦ ❛( "♦ ❢✉❧✜❧❧ "❤❡(❡ "✇♦ ❤❡✉)✐("✐❝( ❛( ❣♦♦❞ ❛( ♣♦((✐❜❧❡✳ ❚❤❡ ❝❛❧✐❜)❛"✐♦♥ ✇❛( ❡✈❛❧✉❛"❡❞
❛♥❞ ✐♠♣)♦✈❡❞ ✐♥ ❞❡❞✐❝❛"❡❞ ❡①♣❡)" ✇♦)❦(❤♦♣( ✇✐"❤✐♥ "❤❡ ❊◆❈■ ▲♦✇❈❛)❜ ✭❊♥❣❛❣✐♥❣ ❈✐✈✐❧
❙♦❝✐❡"② ✐♥ ▲♦✇ ❈❛)❜♦♥ ❙❝❡♥❛)✐♦(✮
✸
♣)♦❥❡❝"✳
✸✳✶ ❖♣%✐♠✐③❛%✐♦♥ ❖❜❥❡❝%✐✈❡
❚❤❡ ♦♣"✐♠✐③❛"✐♦♥ ♦❜❥❡❝"✐✈❡ ♦❢ ❘❊▼■◆❉✲❉ ✐( ❛♥ ✐♥"❡)"❡♠♣♦)❛❧ (♦❝✐❛❧ ✇❡❧❢❛)❡ ❢✉♥❝"✐♦♥ "❤❛"
❞❡♣❡♥❞( ♦♥ "❤❡ ✐♥"❡)"❡♠♣♦)❛❧ (✉♠ ♦❢ ❧♦❣❛)✐"❤♠✐❝ ♣❡) ❝❛♣✐"❛ ❝♦♥(✉♠♣"✐♦♥✱ ✐✳❡✳ ✉"✐❧✐"②
U
✳
❋♦) "❤❡ ✉♥❞❡)❧②✐♥❣ ❛((✉♠♣"✐♦♥( ❝♦♥(✉❧" ▼❛✉U♥❡) ❛♥❞ ❑❧✉♠♣✳ ✭✶✾✾✻✮✳
U=
T
X
t=t0∆t·eξ(t−t0)Lt·ln Ct
Lt
✭✶✮
❚❤❡ ✈❛)✐❛❜❧❡(
Lt
❛♥❞
Ct
❛)❡ ♣♦♣✉❧❛"✐♦♥ ❛♥❞ ❝♦♥(✉♠♣"✐♦♥ ❛♥❞ "❤❡ (✉❜(❝)✐♣"
t
✐♥❞✐❝❛"❡(
"✐♠❡✳ ❲❡ ❛((✉♠❡ ❛ ♣✉)❡ )❛"❡ ♦❢ "✐♠❡ ♣)❡❢❡)❡♥❝❡
ξ
♦❢ ✶✪✳ ❚❤❡ ❧♦❣❛)✐"❤♠✐❝ ❢✉♥❝"✐♦♥❛❧
✸
❊◆❈■ ▲♦✇❈❛(❜ ✐+ ✜♥❛♥❝❡❞ ❜② 2❤❡ ✼2❤ ❋(❛♠❡✇♦(❦ 8(♦❣(❛♠♠❡ ❢♦( ❘❡+❡❛(❝❤ ♦❢ 2❤❡ ❊✉(♦♣❡❛♥ ❈♦♠♠✐+✲
+✐♦♥✳ ❋♦( ❢✉(2❤❡( ✐♥❢♦(♠❛2✐♦♥ ♣❧❡❛+❡ ✈✐+✐2 ✇✇✇✳❧♦✇❝❛(❜♦♥✲+♦❝✐❡2✐❡+✳❡✉✳
✾
3.3 The Macroeconomic Module 71
❚❛❜❧❡ ✶✿ ❆((✉♠❡❞ ❞❡✈❡❧♦♣♠❡♥0 ♦❢ 0❤❡ ●❡4♠❛♥ ♣♦♣✉❧❛0✐♦♥ ✐♥ ▼✐❧❧✐♦♥ ✐♥❤❛❜✐0❛♥0( ✭❑✐4❝❤✲
♥❡4 ❡0 ❛❧✳ ✷✵✵✾✮✳
✷✵✵✺ ✷✵✶✵ ✷✵✶✺ ✷✵✷✵ ✷✵✷✺ ✷✵✸✵ ✷✵✸✺ ✷✵✹✵ ✷✵✹✺ ✷✵✺✵
✽✷✳✹✶ ✽✶✳✽✾ ✽✶✳✶✵ ✼✾✳✽✵ ✼✾✳✶✾ ✼✽✳✺✽ ✼✼✳✷✽ ✼✺✳✾✽ ✼✹✳✵✼ ✼✷✳✶✼
4❡❧❛0✐♦♥(❤✐♣ ❜❡0✇❡❡♥ ♣❡4✲❝❛♣✐0❛ ❝♦♥(✉♠♣0✐♦♥ ❛♥❞ ✉0✐❧✐0② 4❡(✉❧0( ❢4♦♠ ❛((✉♠✐♥❣ 0❤❡ ✐♥✲
0❡40❡♠♣♦4❛❧ ❡❧❛(0✐❝✐0② ♦❢ (✉❜(0✐0✉0✐♦♥ 0♦ ❡H✉❛❧ ♦♥❡✳ ❱✐❛ 0❤❡ (0❡❛❞② (0❛0❡ ❝♦♥❞✐0✐♦♥( ❛♥❞
0❤❡ ❑❡②♥❡(✲❘❛♠(❡② 4✉❧❡✱ 0❤❡ ❡♥❞♦❣❡♥♦✉( ✐♥0❡4❡(0 4❛0❡ ❛♠♦✉♥0( 0♦ ❛4♦✉♥❞ ✸✪❀ 0❤❡ ❡①❛❝0
✈❛❧✉❡ ✉❧0✐♠❛0❡❧② ❞❡♣❡♥❞( ♦♥ 0❤❡ ❡♥❞♦❣❡♥♦✉( ❡❝♦♥♦♠✐❝ ❣4♦✇0❤ 4❛0❡ ✐♥ 0❤❡ 4❡(♣❡❝0✐✈❡ 0✐♠❡
(0❡♣✳ ■❢ ❞❡(✐4❡❞✱ 0❤❡ ♣✉4❡ 4❛0❡ ♦❢ 0✐♠❡ ♣4❡❢❡4❡♥❝❡ ✐♥ 0❤❡ ♠♦❞❡❧ ❝❛♥ ❜❡ ❛❧0❡4❡❞✳ ❚❛❜❧❡ ✶ 4❡✲
♣♦40( 0❤❡ ♣♦♣✉❧❛0✐♦♥ ❢♦4❡❝❛(0 0❤❛0 ✐( ❛((✉♠❡❞ ✐♥ ❘❊▼■◆❉✲❉✳ ■0 ✐( ❞❡4✐✈❡❞ ❢4♦♠ ✭❑✐4❝❤♥❡4
❡0 ❛❧✳ ✷✵✵✾✮✱ ✇❤♦ ❜❛(❡ 0❤❡✐4 ❢♦4❡❝❛(0 ♦♥ 0❤❡ ♣4♦❣♥♦(✐( ❢4♦♠ 0❤❡ ♥❛0✐♦♥❛❧ (0❛0✐(0✐❝( ❜✉4❡❛✉
✭❙0❛0✐(0✐(❝❤❡( ❇✉♥❞❡(❛♠0 ✷✵✵✻✮✳
✸✳✷ #$♦❞✉❝)✐♦♥ ❋✉♥❝)✐♦♥
❚❤❡ ❜❛❝❦❜♦♥❡ ♦❢ 0❤❡ ♠❛❝4♦❡❝♦♥♦♠✐❝ ♠♦❞✉❧❡ ✐( 0❤❡ ♣4♦❞✉❝0✐♦♥ ❢✉♥❝0✐♦♥✱ ✇❤✐❝❤ ✉❧0✐♠❛0❡❧②
❞❡0❡4♠✐♥❡( 0❤❡ ♠❛❝4♦❡❝♦♥♦♠✐❝ ♦✉0♣✉0
Y
✱ ✐✳❡✳ 0❤❡ ❣4♦(( ❞♦♠❡(0✐❝ ♣4♦❞✉❝0 ✭●❉W✮✳ ❚❤❡
♣4♦❞✉❝0✐♦♥ ❢✉♥❝0✐♦♥ ❛♣♣❧✐❡❞ ✐♥ ❘❊▼■◆❉✲❉ ✐( ❛ ♥❡(0❡❞ ✏❈♦♥(0❛♥0 ❊❧❛(0✐❝✐0② ♦❢ ❙✉❜(0✐✲
0✉0✐♦♥✑ ✭❈❊❙✮ ♣4♦❞✉❝0✐♦♥ ❢✉♥❝0✐♦♥✳ ❖♥ 0❤❡ ❤✐❣❤❡(0 ❧❡✈❡❧✱ 0❤❡ ♣4♦❞✉❝0✐♦♥ ❢❛❝0♦4 ✐♥♣✉0(
❝♦♥(✐❞❡4❡❞ ❛4❡ ❝❛♣✐0❛❧✱ ❧❛❜♦4 ❛♥❞ ❡♥❡4❣②✱ ✇✐0❤ 0❤❡ ❧❛00❡4 ❜❡✐♥❣ ❞❡0❡4♠✐♥❡❞ ❜② (❡✈❡4❛❧
(✉❜✲♥❡(0❡❞ ❈❊❙✲❢✉♥❝0✐♦♥( 0❤❛0 ❛4❡ ❝♦♥(04✉❝0❡❞ ❛❝❝♦4❞✐♥❣ 0♦ 0❤❡ (✉❜(0✐0✉0❛❜✐❧✐0② ✐♥ 0❡4♠(
♦❢ ♣4♦✈✐❞✐♥❣ (✐♠✐❧❛4 ✉(❡❢✉❧ ❡♥❡4❣② ♦4 ❡♥❡4❣② (❡4✈✐❝❡(✳
❋♦4♠❛❧❧②✱ 0❤❡ ♣4♦❞✉❝0✐♦♥ ❢✉♥❝0✐♦♥ ✐( ❞❡✜♥❡❞ ❛( ❢♦❧❧♦✇( ❢♦4 ❡❛❝❤ ❧❛②❡4 ❞❡(❝4✐❜❡❞ ❜② 0❤❡
♠❛♣♣✐♥❣
MCES
✱ ❛((✐❣♥✐♥❣ 0❤❡ 4❡(♣❡❝0✐✈❡ ♦✉0♣✉0 ❢❛❝0♦4
Vt(υout)
0♦ 0❤❡ ❛✈❛✐❧❛❜❧❡ ✐♥♣✉0
❢❛❝0♦4(
Vt(υin)
✳
Vt(υout) = φ(υout)·
X
MCES
(θt(υin)·Vt(υin))ρ(υout)
1
ρ(υout)
∀t, υout
✭✷✮
MCES = (υin ×υout)∈MCES
❚❤❡ ♣❛4❛♠❡0❡4
φ(υout)
✐( ❛ (❝❛❧✐♥❣ ❢❛❝0♦4 0❤❛0 4❡♣4❡(❡♥0( 0♦0❛❧ ❢❛❝0♦4 ♣4♦❞✉❝0✐✈✐0② ❛♥❞ ✐(
(❡0 ❡H✉❛❧ 0♦ ♦♥❡ ✐♥ ❘❊▼■◆❉✲❉✳ ❚❤❡ ♣❛4❛♠❡0❡4
θt(υin)
4❡♣4❡(❡♥0( ❛♥ ❡✣❝✐❡♥❝② ❢❛❝0♦4 0❤❛0
✐( ❞❡0❡4♠✐♥❡❞ ❡♥❞♦❣❡♥♦✉(❧② ❢♦4 ❡❛❝❤ ♣4♦❞✉❝0✐♦♥ ❢❛❝0♦4 ✐♥ 0❤❡ ✜4(0 0✐♠❡ ♣❡4✐♦❞ ❜❛(❡❞ ♦♥
✐0( ✐♥❝♦♠❡ (❤❛4❡ ❛♥❞ 0❤❡ 4❡❧❛0✐✈❡ ♣4✐❝❡ ♦❢ (✉♣♣❧②✐♥❣ ♦♥❡ ✉♥✐0 ♦❢ 0❤❡ ❞❡♠❛♥❞❡❞ ♣4♦❞✉❝0✐♦♥
❢❛❝0♦4✳ ❚❤❡ 4❡❧❛0✐✈❡ ♣4✐❝❡( ✐♥ 0❤❡ ✜4(0 0✐♠❡ ♣❡4✐♦❞ ❛4❡ ❞❡4✐✈❡❞ ❢4♦♠ 0❤❡ ❝❛❧✐❜4❛0❡❞ ❡♥❡4❣②
(②(0❡♠✳ ❚❤❡ ❣4♦✇0❤ 4❛0❡ ♦❢ 0❤❡ ❡✣❝✐❡♥❝② ❢❛❝0♦4 ✐( ❛♥ ❡①♦❣❡♥♦✉( ✐♥♣✉0✳ ❚❤❡ ♣❛4❛♠❡0❡4
✶✵
72 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
ρ(υout)
σ
σ=1
(1 −ρ)
MCES
σ
θt(υin)
σ
3.3 The Macroeconomic Module 73
❚❛❜❧❡ ✷✿ ❆((✉♠❡❞ ❣-♦✇0❤ -❛0❡( ♦❢ 0❤❡ ❡✣❝✐❡♥❝② ❢❛❝0♦-
θt(υin)
✐♥ ✪✳
✪ ■♥❞✉(0-② ❘❊❙✫❈❖▼ ●0✴●♣✲❦♠ ❋-❡✐❣❤0 H❋❉ H❙❉
◆❛0✉-❛❧ ●❛( ✶✳✹✹ ✵✳✹✵ ❙❤✐♣ ✵✳✺✵
❊❧❡❝0-✐❝✐0② ✶✳✶✽ ✶✳✹✼ ❚-✉❝❦ ✵✳✺✵
❉✐(0-✐❝0 ❍❡❛0 ✶✳✺✷ ✵✳✹✵ ❚-❛✐♥ ✵✳✺✵ ✶✳✺✵ ✶✳✺✵
❍❡❛0✐♥❣ ❖✐❧ ✷✳✼✺ ✲✾✳✵✵ ❈❛- ✶✳✺✵ ✶✳✺✵
❇✐♦♠❛(( ✶✳✶✽ ✵✳✻✵ ▲✐❣❤0 ❘❛✐❧ ✶✳✷✵
▲♦❝❛❧ ❍❡❛0 ✵✳✻✵ ❇✉( ✶✳✺✵ ✶✳✷✵
❈♦❦❡ ✷✳✻✺ ❆✐-♣❧❛♥❡ ✶✳✷✵
❍❛-❞ ❈♦❛❧ ✷✳✻✺
♣-❡0❛0✐♦♥✳ ❋♦- ❡①❛♠♣❧❡✱ ❢-♦♠ ❛♥ ❡♥❣✐♥❡❡-✐♥❣ ♣♦✐♥0 ♦❢ ✈✐❡✇ ✐0 ✐( ❛ (✐♠♣❧❡ 0❛(❦ 0♦ (✉❜(0✐0✉0❡
❛♥ ♦✐❧ ❢✉-♥❛❝❡ ❢♦- ❛ ❣❛( ❢✉-♥❛❝❡ ✐♥ ❤♦✉(❡❤♦❧❞(✳ ❍♦✇❡✈❡-✱ ❡♥❡-❣② ❢♦- ✐♥❞✉(0-② ❛♥❞ ❡♥❡-❣②
❢♦- 0-❛♥(♣♦-0 ❛-❡ ❡❝♦♥♦♠✐❝ ❝♦♠♣❧❡♠❡♥0(✳ ■♥ ❣❡♥❡-❛❧✱ 0❤❡ (✉❜(0✐0✉0❛❜✐❧✐0② ✐♥❝-❡❛(❡( ✇✐0❤
0❤❡ ❧❡✈❡❧ ♦❢ ❞❡0❛✐❧ ✐♥ 0❤❡ ❜-❛♥❝❤❡(✳ ❉❡♣❡♥❞✐♥❣ ♦♥ 0❤❡ (✉❜(0✐0✉0✐♦♥ ❡❧❛(0✐❝✐0② ♦❢ 0❤❡ -❡(♣❡❝✲
0✐✈❡ ❈❊❙✲♥❡(0✱ 0❤❡ ❡✛❡❝0 ♦❢ 0❤❡ ❡✣❝✐❡♥❝② ❣-♦✇0❤ -❛0❡( ✐( (✉❜(0❛♥0✐❛❧❧② ❞✐✛❡-❡♥0✿ ■❢
σ < 1
✱
0❤❡ ♣-♦❞✉❝0✐♦♥ ❢✉♥❝0✐♦♥ ❞❡♠❛♥❞( -❡❧❛0✐✈❡❧② ❧❡(( ❢-♦♠ ❛♥ ✐♥♣✉0 ✇✐0❤ ❤✐❣❤❡-
θt(υin)
✱ ❛♥❞
✈✐❝❡ ✈❡-(❛ ✐❢
σ > 1
✳ ❚❤✐( ✐( ❛❧(♦ ✈❛❧✐❞ ❢♦- ❛❣❣-❡❣❛0❡ ✐♥0❡-♠❡❞✐❛0❡ ❢❛❝0♦-(✳ ❆((✉♠♣0✐♦♥(
❛❜♦✉0 0❤❡ ❣-♦✇0❤ -❛0❡( ♦❢ 0❤❡ ❡✣❝✐❡♥❝② ❢❛❝0♦-(
θt(υin)
❛-❡ ❞✐✣❝✉❧0 0♦ ♦❜0❛✐♥ ❢-♦♠ ❡♠✲
♣✐-✐❝❛❧ ❞❛0❛ ❛( 0❤❡(❡ ❡✣❝✐❡♥❝② ❣-♦✇0❤ -❛0❡( ✉♥✐❢② ❛ ✈❛-✐❡0② ♦❢ ✉♥♦❜(❡-✈❛❜❧❡ ❢❛❝0♦-(✳ ❚❤❡
✉♥❞❡-❧②✐♥❣ ✐❞❡❛ ✐( 0❤❛0 ♦✈❡- 0✐♠❡ ♠♦-❡ ♦✉0♣✉0 ♠❛② ❜❡ ♣-♦❞✉❝❡❞ ❢-♦♠ 0❤❡ (❛♠❡ ❛♠♦✉♥0
♦❢ ✐♥♣✉0 ❜❡❝❛✉(❡ 0❤❡ ✉(❡ ♦❢ 0❤❡ ✜♥❛❧ ❡♥❡-❣② ❜❡❝♦♠❡( ❡✈❡- ♠♦-❡ ❡✣❝✐❡♥0✳ ❊((❡♥0✐❛❧❧②
0❤✐( ❛-❣✉♠❡♥0 -❡(0( ♦♥ 0❤❡ ✐❞❡❛ ♦❢ 0❡❝❤♥♦❧♦❣✐❝❛❧ ♣-♦❣-❡((✳ ❍♦✇❡✈❡-✱ 0❤❡ 0❡❝❤♥♦❧♦❣✐❝❛❧
♣-♦❣-❡(( ✐♥ 0❤❡ ❡♥❡-❣② (✉♣♣❧② ❝❤❛✐♥ ✐( -❡♣-❡(❡♥0❡❞ ❡①♣❧✐❝✐0❧② ✐♥ 0❤❡ ❡♥❡-❣② (②(0❡♠ ♠♦❞✲
✉❧❡✳ ❙❡♣❛-❛❜✐❧✐0② ♦❢ 0❡❝❤♥♦❧♦❣✐❝❛❧ ♣-♦❣-❡(( ❛♥❞ ❞❡♠❛♥❞ -❡❞✉❝0✐♦♥( ❞✉❡ 0♦ (✉✣❝✐❡♥❝② ✐(
♥♦0 ♠❡❛(✉-❛❜❧❡✳ ❍❡♥❝❡✱ 0❤❡ ❡①♦❣❡♥♦✉( ❣-♦✇0❤ -❛0❡( ♦❢ 0❤❡ ❡✣❝✐❡♥❝② ❢❛❝0♦-(
θt(υin)
❛-❡
❝❤♦(❡♥ ❛( 0♦ ❢✉❧✜❧❧ 0❤❡ 0✇♦ ❤❡✉-✐(0✐❝( ✐♥0-♦❞✉❝❡❞ ❛❜♦✈❡✳
■♥ 0❤❡ ❝❛❧✐❜-❛0✐♦♥ ②❡❛- ✷✵✵✼✱ 0❤❡ ●❉H ✐♥ ●❡-♠❛♥② ✇❛( ✷✹✷✽ ❜✐❧❧✐♦♥
e
✭❙0❛0✐(0✐(❝❤❡( ❇✉♥✲
❞❡(❛♠0 ✷✵✶✷✮ ❛♥❞ 0❤❡ ❝❛♣✐0❛❧ (0♦❝❦ ❛♠♦✉♥0❡❞ 0♦ ✶✵✱✷✵✻ ❜✐❧❧✐♦♥
e
✭❙0❛0✐(0✐(❝❤❡( ❇✉♥❞❡✲
(❛♠0 ✷✵✵✾✮✳ ❚❤❡ ♣-♦❞✉❝0✐♦♥ ❢❛❝0♦- ❧❛❜♦- ✐( ❛((✉♠❡❞ 0♦ ❜❡ ♣-✐❝❡✲✐♥❡❧❛(0✐❝ ❛♥❞ ♣♦♣✉❧❛0✐♦♥
✐( ✉(❡❞ ❛( ❛ ♣-♦①②✳ ❆( ❛ ❝♦♥(❡]✉❡♥❝❡ ♦❢ 0❤✐( (✐♠♣❧✐❢②✐♥❣ ♣-❛❝0✐❝❡✱ 0❤❡ ❧❛❜♦- ❢♦-❝❡ ✐( ❛(✲
(✉♠❡❞ 0♦ ❞❡✈❡❧♦♣ ♣-♦♣♦-0✐♦♥❛❧❧② 0♦ 0❤❡ 0♦0❛❧ ♣♦♣✉❧❛0✐♦♥✳ ❋♦- 0❤✐( -❡❛(♦♥✱ ❘❊▼■◆❉✲❉ ✐(
♥♦0 (✉✐0❛❜❧❡ 0♦ ❛♥❛❧②③❡ 0❤❡ ❧❛❜♦- ♠❛-❦❡0 ✐♠♣❧✐❝❛0✐♦♥( ♦❢ ♠✐0✐❣❛0✐♦♥✳
✸✳✸ ❊♥❡%❣② ❉❡♠❛♥❞
❚❤❡ ❡♥❡-❣② ❞❡♠❛♥❞ ✐♥ ❘❊▼■◆❉✲❉ ✐( ♠♦❞❡❧❡❞ ❛( ❛♥ ❛❣❣-❡❣❛0❡ ❢♦- ❡❛❝❤ ♦❢ 0❤❡ 0❤-❡❡ ❡♥❞✲
✉(❡ (❡❝0♦-( ✐♥❞✉(0-②✱ -❡(✐❞❡♥0✐❛❧ ✫ ❝♦♠♠❡-❝✐❛❧ ✭❘❊❙✫❈❖▼✮ ❛♥❞ 0-❛♥(♣♦-0✱ ❛( ❞❡✜♥❡❞
✐♥ 0❤❡ ●❡-♠❛♥ ❡♥❡-❣② ❜❛❧❛♥❝❡( ✭❆●❊♥❡-❣✐❡❜✐❧❛♥③❡♥ ✷✵✶✵✮✳ ■♥ ❘❊▼■◆❉✲❉ 0❤❡ (❡❝0♦-(
✐♥❞✉(0-② ❛♥❞ 0❤❡ ❘❊❙✫❈❖▼ ❞❡♠❛♥❞ ✜♥❛❧ ❡♥❡-❣② ❝❛--✐❡-(❀ 0❤❡ (♣❡❝✐✜❝ ❛♣♣❧✐❛♥❝❡( 0❤❛0
❝♦♥✈❡-0 0❤❡(❡ ❡♥❡-❣② ❝❛--✐❡-( 0♦ ✉(❡❢✉❧ ❡♥❡-❣② ❛-❡ ❜❡②♦♥❞ 0❤❡ (❝♦♣❡ ♦❢ 0❤❡ ♠♦❞❡❧✳ ❚❤✐(
✶✷
74 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
❚❛❜❧❡ ✸✿ ❚❤❡ ❧❡❢) ♣❛♥❡❧ ❞✐.♣❧❛②. )❤❡ ✜♥❛❧ ❡♥❡1❣② ❞❡♠❛♥❞ ✐♥ ●❡1♠❛♥② ❢♦1 ✷✵✵✼ ✐♥ 9❏✱
)❤❡ ❞❛)❛ ❛1❡ ❢1♦♠ ❆● ❊♥❡1❣✐❡❜✐❧❛♥③❡♥ ✭✷✵✶✵✮✳ ❚❤❡ 1✐❣❤) ♣❛♥❡❧ ❞✐.♣❧❛②. )❤❡
❡♥❡1❣② .❡1✈✐❝❡ ❞❡♠❛♥❞ ♦❢ )❤❡ .❡❝)♦1. ❞♦♠❡.)✐❝ ❋1❡✐❣❤) ❛♥❞ 9❛..❡♥❣❡1 ❚1❛♥.♣♦1)
✐♥ ❜✐❧❧✐♦♥ )♦♥✲❦♠ ✭●)✲❦♠✮ ❛♥❞ ❜✐❧❧✐♦♥ ♣❡1.♦♥✲❦♠ ✭●♣✲❦♠✮✱ 1❡.♣❡❝)✐✈❡❧②✳ 9▲❉
.)❛♥❞. ❢♦1 ✬♣❛..❡♥❣❡1 ❧♦♥❣ ❞✐.)❛♥❝❡✬✱ 9❙❉ ❢♦1 ✬♣❛..❡♥❣❡1 .❤♦1) ❞✐.)❛♥❝❡✬✳ ❉❛)❛
❛1❡ ❜❛.❡❞ ♦♥ ❇▼❱❇❙ ✭✷✵✵✽✮❀ ❑✐1❝❤♥❡1 ❡) ❛❧✳ ✭✷✵✵✾✮❀ ❯❇❆ ✭✷✵✵✾✮✳
9❏ ■♥❞✉.)1② ❘❊❙✫❈❖▼ ●)✴●♣✲❦♠ ❋1❡✐❣❤) 9▲❉ 9❙❉
◆❛)✉1❛❧ ●❛. ✾✹✺ ✶✸✶✻ ❙❤✐♣ ✻✺
❊❧❡❝)1✐❝✐)② ✽✺✵ ✾✽✺ ❚1✉❝❦ ✹✼✻
❉✐.)1✐❝) ❍❡❛) ✶✺✶ ✷✾✵ ❚1❛✐♥ ✶✶✹ ✸✺ ✹✺
❍❡❛)✐♥❣ ❖✐❧ ✶✸✻ ✽✻✸ ❈❛1 ✸✸✾ ✺✹✾
❇✐♦♠❛.. ✻✹ ✶✽✾ ▲✐❣❤) ❘❛✐❧ ✶✼
▲♦❝❛❧ ❍❡❛) ✷✶ ❇✉. ✶✼ ✸✼
❈♦❦❡ ✶✻✾ ❆✐1♣❧❛♥❡ ✺✾
❍❛1❞ ❈♦❛❧ ✶✻✼
✐. ❞✐✛❡1❡♥) ❢♦1 )❤❡ )1❛♥.♣♦1) .❡❝)♦1 ✕ ❤❡1❡ ❡♥❡1❣② .❡1✈✐❝❡. ✐♥ )❡1♠. ♦❢ )♦♥✲❦♠ ✭)✲❦♠✮ ♦1
♣❡1.♦♥✲❦♠ ✭♣✲❦♠✮ ❛1❡ ❞❡♠❛♥❞❡❞✱ .✐♥❝❡ )1❛♥.♣♦1) )❡❝❤♥♦❧♦❣✐❡. ❛1❡ ♠♦❞❡❧❡❞ ❡①♣❧✐❝✐)❧② ✐♥
)❤❡ ❡♥❡1❣② .②.)❡♠ ♠♦❞✉❧❡✳ ❚❛❜❧❡ ✸ 1❡♣♦1). )❤❡ ✐♥✐)✐❛❧ ❡♥❡1❣② ❞❡♠❛♥❞. ✐♥ )❤❡ ❝❛❧✐❜1❛)✐♦♥
②❡❛1 ✷✵✵✼✳ ❚❤❡ ■♥❞✉.)1② .❡❝)♦1 ❝♦♥.✐.). ♦❢ )❤❡ ❜1❛♥❝❤❡. ♠✐♥✐♥❣✱ .)♦♥❡ ✫ ❝❧❛② c✉❛11②✐♥❣
❛♥❞ ♠❛♥✉❢❛❝)✉1✐♥❣ ❛♥❞ ✐. ❜❛.❡❞ ♦♥ )❤❡ ❝❧❛..✐✜❝❛)✐♦♥ ❜② )❤❡ ❋❡❞❡1❛❧ ❙)❛)✐.)✐❝❛❧ ❖✣❝❡✳
❚❤❡ ❘❊❙✫❈❖▼ .❡❝)♦1 ✐. 1❛)❤❡1 ❤❡)❡1♦❣❡♥❡♦✉. ❛♥❞ ✐♥❝❧✉❞❡. ♣1✐✈❛)❡ ❤♦✉.❡❤♦❧❞.✱ ♠❛♥✲
✉❢❛❝)✉1✐♥❣ ✜1♠. ✇✐)❤ ❢❡✇❡1 )❤❛♥ ✷✵ ❡♠♣❧♦②❡❡. ♥♦) ✐♥❝❧✉❞❡❞ ✐♥ ♠❛♥✉❢❛❝)✉1✐♥❣ ✐♥❞✉.)1②✱
❝♦♠♠❡1❝✐❛❧ ♣1♦♣❡1)✐❡. ❛♥❞ ❡♥)❡1♣1✐.❡ ♣1❡♠✐.❡.✱ ❛❣1✐❝✉❧)✉1❡✱ ❝♦♠♠❡1❝✐❛❧ ❡♥)❡1♣1✐.❡. ❛♥❞
♣1✐✈❛)❡ ❛♥❞ ♣✉❜❧✐❝ .❡1✈✐❝❡ ❝♦♠♣❛♥✐❡. ❛♥❞ ♦1❣❛♥✐③❛)✐♦♥.✳ ■♥ )❤❡ )1❛♥.♣♦1) .❡❝)♦1✱ ❛ ❣❡♥✲
❡1❛❧ ❞✐✛❡1❡♥)✐❛)✐♦♥ ✐. ♠❛❞❡ ❜❡)✇❡❡♥ ❢1❡✐❣❤) )1❛♥.♣♦1) ❛♥❞ ♣❛..❡♥❣❡1 )1❛♥.♣♦1)✳ 9❛..❡♥❣❡1
)1❛♥.♣♦1) ✐. ❢✉1)❤❡1 .✉❜❞✐✈✐❞❡❞ ✐♥)♦ ♠♦❞❛❧ .♣❧✐) ❛♥❞ ❧♦♥❣ ❛♥❞ .❤♦1) ❞✐.)❛♥❝❡✳
✸✳✹ ❍❛%❞ ▲✐♥❦
❚❤❡ ❝♦.) .✐❞❡ ♦❢ )❤❡ ❤❛1❞ ❧✐♥❦ ❜❡)✇❡❡♥ )❤❡ ❡♥❡1❣② .②.)❡♠ ♠♦❞✉❧❡ ❛♥❞ )❤❡ ♠❛❝1♦❡❝♦♥♦♠✐❝
♠♦❞✉❧❡ ✐. ❡♥.✉1❡❞ ❜② )❤❡ ❜✉❞❣❡) ❡c✉❛)✐♦♥ ✐❧❧✉.)1❛)❡❞ ✐♥ ❊c✉❛)✐♦♥ ✹✱ ♣♦.✐♥❣ )❤❛) ♦✉)♣✉)
Yt
❤❛. )♦ ❝♦✈❡1 )❤❡ ✐♥✈❡.)♠❡♥). ✐♥)♦ )❤❡ ♠❛❝1♦❡❝♦♥♦♠✐❝ ❝❛♣✐)❛❧ .)♦❝❦
It
❛♥❞ ❛❧❧ ❝♦.).
✐♥❝✉11❡❞ ❜② )❤❡ ❡♥❡1❣② .②.)❡♠
Et
✳ ❈♦♥.✉♠♣)✐♦♥
Ct
❡♥)❡1. )❤❡ .♦❝✐❛❧ ✇❡❧❢❛1❡ ❢✉♥❝)✐♦♥✳
❚❤❡ ♣1♦❞✉❝)✐♦♥ ❢❛❝)♦1 ♣❛1) ♦❢ )❤❡ ❤❛1❞ ❧✐♥❦ ♦♣❡1❛)❡. ✈✐❛ ✐♥❞✐✈✐❞✉❛❧❧② ❡c✉❛)✐♥❣ )❤❡ ✜♥❛❧
❡♥❡1❣② ❛♥❞ ❡♥❡1❣② .❡1✈✐❝❡ ❞❡♠❛♥❞. ♦❢ )❤❡ ♠❛❝1♦❡❝♦♥♦♠✐❝ ♠♦❞✉❧❡ ✇✐)❤ )❤♦.❡ ❣❡♥❡1❛)❡❞
❜② )❤❡ ❜♦))♦♠✲✉♣ ❡♥❡1❣② .②.)❡♠ ♠♦❞✉❧❡✳
Yt=Ct+It+Et∀t
✭✹✮
✶✸
3.3 The Macroeconomic Module 75
→ →
76 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
✹✳✶ #$✐♠❛$② ❊♥❡$❣②
❚❤❡ ❊❙▼ ♦❢ ❘❊▼■◆❉✲❉ ❝♦♥/✐❞❡2/ 2❡♥❡✇❛❜❧❡ ❡♥❡2❣② ❝❛22✐❡2/✱ ❜✐♦♠❛// ❛♥❞ ❡①❤❛✉/=✐❜❧❡
❢♦//✐❧ ❡♥❡2❣② ❝❛22✐❡2/✳ ❚❤❡② ❝❤❛2❛❝=❡2✐/=✐❝/ ❞✐✛❡2 ✐♥ =❡2♠/ ♦❢ ❛//♦❝✐❛=❡❞
CO2
❡♠✐//✐♦♥/
❛♥❞ ✇❤❡=❤❡2 ✐♥❝2❡❛/❡❞ ✉/❛❣❡ ❧❡❛❞/ =♦ ❛♥ ✐♥❝2❡❛/❡ ✐♥ ❢✉❡❧ ❝♦/=/✳ ❘❡♥❡✇❛❜❧❡ ❡♥❡2❣② ✐/ ❢2❡❡
♦❢
CO2
❡♠✐//✐♦♥/ ❛♥❞ ❢2❡❡ ♦❢ ❢✉❡❧ ❝♦/=/✳ ❇✐♦♠❛// ✐/ ❢2❡❡ ♦❢
CO2
❡♠✐//✐♦♥/ ❜✉= ✐♥❝2❡❛/❡❞
✉/❛❣❡ ❧❡❛❞/ =♦ ❛♥ ✐♥❝2❡❛/❡ ✐♥ ❢✉❡❧ ❝♦/=/✳ ❍♦✇❡✈❡2✱ =❤❡ ✉/❡ ♦❢ 2❡♥❡✇❛❜❧❡ ❡♥❡2❣✐❡/ ❛/ ✇❡❧❧
❛/ ❜✐♦♠❛// ✐/ ❧✐♠✐=❡❞ =♦ ❛ /♣❡❝✐✜❝ =❡❝❤♥✐❝❛❧ ♣♦=❡♥=✐❛❧✳ ❊①❤❛✉/=✐❜❧❡ ❢♦//✐❧ ❡♥❡2❣② ❝❛22✐❡2/
❛2❡
CO2
✐♥=❡♥/✐✈❡ ❛♥❞ ✐♥❝2❡❛/❡❞ ✉/❛❣❡ ❧❡❛❞/ =♦ ❛♥ ✐♥❝2❡❛/❡ ✐♥ ❢✉❡❧ ❝♦/=/✳
❘❡♥❡✇❛❜❧❡ ❊♥❡(❣② ❙♦✉(❝❡/
❘❡♥❡✇❛❜❧❡ ❞♦♠❡/=✐❝ ♣2✐♠❛2② ❡♥❡2❣② /♦✉2❝❡/ ✐♥❝❧✉❞❡ /♦❧❛2✱
✇✐♥❞ ♦♥/❤♦2❡✱ ✇✐♥❞ ♦✛/❤♦2❡✱ ❞❡❡♣ ❣❡♦=❤❡2♠❛❧✱ ❣❡♦=❤❡2♠❛❧ ♥❡❛2✲/✉2❢❛❝❡ ✭❢♦2 ❤❡❛=✮ ❛♥❞
❤②❞2♦✳ ❚❛❜❧❡ ✹ ❣✐✈❡/ ❛♥ ♦✈❡2✈✐❡✇ ♦❢ =❤❡ =❡❝❤♥✐❝❛❧ ♣♦=❡♥=✐❛❧/ ❡/=✐♠❛=❡❞ ❜② ❞✐✛❡2❡♥= /=✉❞✐❡/
❢♦2 ●❡2♠❛♥②✳ ❙♦♠❡ ❞✐✛❡2 /✉❜/=❛♥=✐❛❧❧② ❛❝2♦// =❤❡ ✈❛2✐♦✉/ /=✉❞✐❡/✳ ❘❡❛/♦♥/ ❢♦2 =❤❡
❞✐✛❡2❡♥❝❡/ ❧✐❡ ✐♥ ❞✐✛❡2✐♥❣ ❛//✉♠♣=✐♦♥/ ♦♥ ✇❤✐❝❤ =❤❡ ❝❛❧❝✉❧❛=✐♦♥ ♦❢ =❤❡ =❡❝❤♥✐❝❛❧ ♣♦=❡♥=✐❛❧
2❡/=/✳ ❚❤❡/❡ ❛2❡ I✉✐=❡ ❝♦♠♣❧❡①✱ ✐♥❝❧✉❞✐♥❣ ❡✳❣✳ =❤❡ /✐③❡ ♦❢ =❤❡ ❣❡♦❣2❛♣❤✐❝❛❧ 2❡❣✐♦♥ ♦♥
✇❤✐❝❤ ❛ ♣2✐♠❛2② ❡♥❡2❣② ❝❛22✐❡2 ♠❛② ❜❡ ❡①♣❧♦✐=❡❞ ❛♥❞ =❤❡ ❞✐/=2✐❜✉=✐♦♥ ♦❢ ✇✐♥❞ /♣❡❡❞ ♦2
/♦❧❛2 ✐22❛❞✐❛=✐♦♥✳ ■♥ ❘❊▼■◆❉✲❉✱ ❡❛❝❤ 2❡♥❡✇❛❜❧❡ ♣♦=❡♥=✐❛❧ ✐/ /✉❜❞✐✈✐❞❡❞ ✐♥=♦ ❞✐✛❡2❡♥=
❣2❛❞❡/✱ 2❡♣2❡/❡♥=✐♥❣ =❤❡ ❞✐✛❡2❡♥= I✉❛❧✐=② ❝❧❛//❡/ ♦❢ ❣❡♦❣2❛♣❤✐❝❛❧ /✐=❡/ ✇✐=❤ 2❡/♣❡❝= =♦
❛✈❡2❛❣❡ ❛♥♥✉❛❧ ❢✉❧❧ ❧♦❛❞ ❤♦✉2/✳ ❘❡♥❡✇❛❜❧❡ ❡♥❡2❣② =❡❝❤♥♦❧♦❣✐❡/ =❤✉/ ❡①❤✐❜✐= ❛ ❣2❛❞✉❛❧
❡①♣❛♥/✐♦♥ ✇✐=❤ =❤❡ ❜❡/= ❣❡♦❣2❛♣❤✐❝❛❧ /✐=❡/ ❡①♣❧♦✐=❡❞ ✜2/=✱ ❢♦❧❧♦✇❡❞ ❜② =❤♦/❡ ②✐❡❧❞✐♥❣ ❧❡//
❡♥❡2❣② ♣❡2 ❛2❡❛ ❛♥❞ ②❡❛2✳
❚❛❜❧❡ ✹✿ ❖✈❡2✈✐❡✇ ♦❢ =❡❝❤♥✐❝❛❧ ♣♦=❡♥=✐❛❧ ❡/=✐♠❛=❡/ ❢♦2 2❡♥❡✇❛❜❧❡ ❡♥❡2❣② /♦✉2❝❡/ ✐♥
❚❲❤✴❛✳ ❚❤❡ ♣♦=❡♥=✐❛❧/ ❛//✉♠❡❞ ✐♥ ❘❊▼■◆❉✲❉ ❛2❡ ❜❛/❡❞ ❢✉2=❤❡2 ♦♥ ❇▼❯
✭✷✵✵✽✮ ❙❝❡♥❛2✐♦ ❊✲✸✱ ◆✐=/❝❤ ❡= ❛❧✳ ✭✷✵✵✹✮ ❛♥❞ T❛/❝❤❡♥ ❡= ❛❧✳ ✭✷✵✵✸✮✳
❚❲❤✴❛ ❇▼❯ ✭✷✵✵✽✮ ❯❇❆ ✭✷✵✶✵✮ ❙❘❯ ✭✷✵✶✵✮ ❘❊▼■◆❉✲❉
❙♦❧❛2✲❡❧✳ ✶✵✺ ✷✹✽ ✶✶✷ ✶✵✺
❙♦❧❛2✲=❤✳ ✸✵✵ ✲ ✲ ✶✵✵
❲✐♥❞✲♦♥✳ ✻✽ ✶✽✵ ✾✵ ✾✵
❲✐♥❞✲♦✛✳ ✶✸✺ ✶✽✵ ✸✶✼ ✶✽✵
●❡♦✲❡❧✳ ✶✺✵ ✺✵ ✷✷✸ ✻✹
●❡♦✲=❤✳ ✸✸✵ ✲ ✲ ✶✵✵
❍②❞2♦ ✷✺ ✷✹ ✷✽ ✷✽
❇✐♦♠❛//
❇✐♦♠❛// ❞✐✛❡2/ ❢2♦♠ ♦=❤❡2 2❡♥❡✇❛❜❧❡ ❡♥❡2❣② ❝❛22✐❡2/ ✐♥ =❤❡ /❡♥/❡ =❤❛= ✐♥✲
❝2❡❛/❡❞ ✉/❛❣❡ ❧❡❛❞/ =♦ ❛♥ ✐♥❝2❡❛/❡ ✐♥ ❢✉❡❧ ❝♦/=/✳ ❚❤✐/ ✐/ 2❡♣2❡/❡♥=❡❞ ❜② ❛ ❜✐♦♠❛// /✉♣♣❧②
❝✉2✈❡ ✇❤✐❝❤ ✐/ ❞❡✜♥❡❞ ♦♥❧② ✉♣ =♦ ❛ ♣♦=❡♥=✐❛❧ ❧✐♠✐=✳ ❆/ ❣2♦✇♥ ❜✐♦♠❛// ✐/ ✐♥ ❝♦♠♣❡=✐=✐♦♥
✇✐=❤ =❤❡ ❢♦♦❞ ✐♥❞✉/=2②✱ =❤❡ ♣♦=❡♥=✐❛❧ ❧✐♠✐= ✐/ ✉♣ =♦ ♣♦❧✐=✐❝❛❧ ❞❡❝✐/✐♦♥/ ♦♥ ❤♦✇ ♠✉❝❤ ❛❣2✐✲
❝✉❧=✉2❛❧ ❧❛♥❞ ♠❛② ❜❡ ✉/❡❞ ❢♦2 ❡♥❡2❣❡=✐❝ ❛♥❞ ❤♦✇ ♠✉❝❤ ♠❛② ❜❡ ✉/❡❞ ❢♦2 ❢♦♦❞ ♣✉2♣♦/❡/✳
✶✺
3.4 The Energy System Module 77
❚❛❜❧❡ ✺ ✐❧❧✉()*❛)❡( )❤❡ ❛((✉♠❡❞ ❞♦♠❡()✐❝ ❤✐❣❤❡*✲❤❡❛)✐♥❣ ✈❛❧✉❡ ♣♦)❡♥)✐❛❧( ❢♦* ●❡*♠❛♥② ✐♥
✷✵✵✺ ❛♥❞ ✷✵✺✵✱ ✇❤✐❝❤ ❛*❡ *❛)❤❡* ❝♦♥(❡*✈❛)✐✈❡✳ ■) ✐( ❛((✉♠❡❞ )❤❛) ♣♦)❡♥)✐❛❧( ❢♦* ❧✐❣♥♦❝❡❧✲
❧✉❧♦(❡✱ (✉❣❛*✴()❛*❝❤ ❛♥❞ ♦✐❧② ❜✐♦♠❛(( ❧✐♥❡❛*❧② ✐♥❝*❡❛(❡ ✉♥)✐❧ ✷✵✺✵ ❛♥❞ )❤❡♥ ()❛② ❝♦♥()❛♥)✳
❲❡ ❛((✉♠❡ )❤❛) ❧✐❣♥♦❝❡❧❧✉❧♦(❡ ✐( ♦♥❧② ❣❛✐♥❡❞ ❢*♦♠ (❝*❛♣ ✇♦♦❞✳ ❚❤❡ ❢❛*♠❧❛♥❞ ✉(❡❞ ❢♦*
)❤❡ ❜✐♦♠❛(( ♣♦)❡♥)✐❛❧ ♠❛② ❛) ♠♦() ❜❡ @✉❛❞*✉♣❧❡❞ ❛( ❝♦♠♣❛*❡❞ )♦ ✷✵✵✺✳ ❚❤❡ ♣♦)❡♥)✐❛❧
❢♦* ♠❛♥✉*❡ ✐( ❛❧*❡❛❞② *❡❛❝❤❡❞✱ ❛( ❛ ♠❛❥♦* ❡①♣❛♥(✐♦♥ ♦❢ )❤❡ ❧✐✈❡()♦❝❦ ✐♥❞✉()*② ✐♥ ●❡*♠❛♥②
✐( ♥♦) ❧✐❦❡❧②✳
❚❛❜❧❡ ✺✿ ❇✐♦♠❛(( ♣♦)❡♥)✐❛❧( ✐♥ ❘❊▼■◆❉✲❉ ❢♦* ✷✵✵✺✴✷✵✺✵✱ ❢*♦♠ ◆✐)(❝❤ ❡) ❛❧✳ ✭✷✵✵✹✮ ❱❛*✐✲
❛♥) ✏◆❛)✉*(❝❤✉)③ Q❧✉(✑ ❙❝❡♥❛*✐♦ ❇✳ ❚❤❡② ❛*❡ ❛((✉♠❡❞ )♦ ✐♥❝*❡❛(❡ ❧✐♥❡❛*❧② ❜❡✲
)✇❡❡♥ ✷✵✵✺ ❛♥❞ ✷✵✺✵✳
❇✐♦▲❈ ❇✐♦❙❙ ❇✐♦❖ ❇✐♦▼
✭▲✐❣♥♦❝❡❧❧✉❧♦(❡✮ ✭❙✉❣❛*✫❙)❛*❝❤✮ ✭❖✐❧✮ ✭▼❛♥✉*❡✮
Q♦)❡♥)✐❛❧❬Q❏✴❛❪ ✹✺✵✴✼✵✵ ✹✵✴✷✺✵ ✻✵✴✷✵✵ ✶✺✵✴✶✺✵
❊①❤❛✉%&✐❜❧❡%
❚❤❡ ❢♦((✐❧ ♣*✐♠❛*② ❡♥❡*❣② ❝❛**✐❡*( ❝*✉❞❡ ♦✐❧✱ ♥❛)✉*❛❧ ❣❛( ❛♥❞ ❤❛*❞ ❝♦❛❧ ❛*❡
✐♠♣♦*)❡❞ ❛) ❡①♦❣❡♥♦✉(❧② (❡) ♣*✐❝❡(✱ ❜❛(❡❞ ♦♥ )❤❡ ❛((✉♠♣)✐♦♥ )❤❛) ●❡*♠❛♥② ❛❝)( ❛( ❛ ♣*✐❝❡
)❛❦❡*✳ ❚❤✐( ❛♣♣❡❛*( *❡❛(♦♥❛❜❧❡ ❛( )❤❡ ❛♠♦✉♥) ♦❢ ❢♦((✐❧ ❡♥❡*❣② ❝❛**✐❡*( ✉(❡❞ ✐♥ ●❡*♠❛♥②
✐( *❡❧❛)✐✈❡❧② (♠❛❧❧ ❝♦♠♣❛*❡❞ )♦ ❣❧♦❜❛❧ ✈♦❧✉♠❡(✳ ❆❧❜❡✐) ❤❛*❞ ❝♦❛❧ ❛♥❞ ♥❛)✉*❛❧ ❣❛( ❛*❡ ❛❧(♦
❡①)*❛❝)❡❞ ❞♦♠❡()✐❝❛❧❧②✱ )❤❡(❡ (♦✉*❝❡( ❛*❡ ♥❡❣❧❡❝)❡❞ ✐♥ ❘❊▼■◆❉✲❉✳ ❚❤❡ *❡❛(♦♥ ✐( )❤❛)
)❤❡ ❛♠♦✉♥) ♦❢ ♥❛)✉*❛❧ ❣❛( ❡①)*❛❝)❡❞ ❞♦♠❡()✐❝❛❧❧② ✐( )♦♦ (♠❛❧❧ )♦ ♠❛❦❡ ❡①♣❧✐❝✐) ♠♦❞❡❧✐♥❣
✇♦*)❤✇❤✐❧❡✳ ❙❤❛❧❡ ❣❛( ✐( ♥♦) ❝♦♥(✐❞❡*❡❞✳ ❍❛*❞ ❝♦❛❧ ♠✐♥✐♥❣ ✐( ❤❡❛✈✐❧② (✉❜(✐❞✐③❡❞✱ ✇❤✐❝❤
✇✐❧❧ ❜❡ ♣❤❛(❡❞✲♦✉) ✉♥)✐❧ ✷✵✶✽✳ ❚❛❜❧❡ ✻ *❡♣♦*)( )❤❡ ✐♠♣♦*) ♣*✐❝❡ ♣❛)❤( ❢♦* )❤❡ ()❛♥❞❛*❞
(❡))✐♥❣ ✐♥ ❘❊▼■◆❉✲❉✳
❚❛❜❧❡ ✻✿ ■♠♣♦*) ♣*✐❝❡( ♦❢ ❢♦((✐❧ ♣*✐♠❛*② ❡♥❡*❣② *❡(♦✉*❝❡( ✐♥
e2005
♣❡* ●❏✳ ❖✐❧✱ ♥❛)✉*❛❧
❣❛( ❛♥❞ ❤❛*❞ ❝♦❛❧ ♣*✐❝❡( ❛*❡ ❢*♦♠ ❇▼❯ ✭✷✵✵✽✮ ❙❝❡♥❛*✐♦ ✏▼❛❡((✐❣✑✱ ✉*❛♥✐✉♠
♣*✐❝❡( ❛*❡ ❢*♦♠ ❉✉ ❛♥❞ Q❛*(♦♥( ✭✷✵✵✾✮✳
✷✵✵✺ ✷✵✶✵ ✷✵✶✺ ✷✵✷✵ ✷✵✷✺ ✷✵✸✵ ✷✵✸✺ ✷✵✹✵ ✷✵✹✺ ✷✵✺✵
❖✐❧ ✼✳✺✶ ✽✳✻✻ ✾✳✺✻ ✶✵✳✺✹ ✶✶✳✺✷ ✶✷✳✹✾ ✶✸✳✷✾ ✶✹✳✵✽ ✶✹✳✻✵ ✶✺✳✶✷
◆❛)✳ ●❛( ✹✳✻✻ ✻✳✾✷ ✼✳✻✺ ✽✳✹✸ ✾✳✷✷ ✾✳✾✾ ✶✵✳✻✸ ✶✶✳✷✻ ✶✶✳✻✽ ✶✷✳✶✵
❍❛*❞ ❈♦❛❧ ✷✳✶✵ ✸✳✹✻ ✸✳✽✷ ✹✳✷✷ ✹✳✻✶ ✺✳✵✵ ✺✳✸✷ ✺✳✻✸ ✺✳✽✹ ✻✳✵✺
❯*❛♥✐✉♠ ✵✳✹✺ ✵✳✺✵ ✵✳✺✾ ✵✳✼✶ ✵✳✽✹ ✶✳✵✵ ✶✳✶✽ ✶✳✹✶ ✶✳✻✼ ✶✳✾✾
▲✐❣♥✐)❡ ✐( ❡①❝❧✉(✐✈❡❧② ♠✐♥❡❞ ❛♥❞ ❝♦♥(✉♠❡❞ ❞♦♠❡()✐❝❛❧❧②✱ (♦ ✇❡ ✉(❡ ❛♥ ❡①)*❛❝)✐♦♥ ❝♦()
❝✉*✈❡ ❛♣♣*♦❛❝❤ ✐♥ ❘❊▼■◆❉✲❉✳ ❚❤❡ ♣*✐❝❡ ♦❢ ❧✐❣♥✐)❡ *✐(❡( ✇✐)❤ )❤❡ ❝✉♠✉❧❛)✐✈❡ ❡①)*❛❝)✐♦♥✱
✇❤✐❝❤ ✐( ❧✐♠✐)❡❞ )♦ ✻✳✶ ●)✳ ❚❤✐( ♥✉♠❜❡* ❝♦**❡(♣♦♥❞( )♦ )❤❡ ❛♠♦✉♥) ♦❢ ❧✐❣♥✐)❡ )❤❛)
♠❛② ()✐❧❧ ❜❡ ❡①)*❛❝)❡❞ ❢*♦♠ ❛❧*❡❛❞② ❛❝)✐✈❡ ♦♣❡♥ ❝❛() ♠✐♥❡( ✭❉❊❇❘■❱ ✷✵✵✾✮✳ ❘❡(❡*✈❡(
❛*❡ ❧❛*❣❡* ✐♥ ●❡*♠❛♥②✱ ❜✉) ♦♣❡♥✐♥❣ ♥❡✇ ♠✐♥❡( ✇✐❧❧ ♠♦() ❧✐❦❡❧② ❜❡ ✐♠♣❡❞❡❞ ❜② ♣✉❜❧✐❝
♣*♦)❡()✳
✶✻
78 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
❚❤❡ ✉$❡ ♦❢ ❡①❤❛✉$)✐❜❧❡ ❢♦$$✐❧ ❡♥❡.❣② ❝❛..✐❡.$ ❧❡❛❞$ )♦
CO2
❡♠✐$$✐♦♥$✱ ✇❤❡.❡❜② )❤❡ ❛♣♣❧✐✲
❝❛)✐♦♥ ♦❢ ❈❛.❜♦♥ ❈❛♣)✉.❡ ❛♥❞ ❙)♦.❛❣❡ ✭❈❈❙✮ )❡❝❤♥♦❧♦❣✐❡$ ♠❛② ❝♦♥).✐❜✉)❡ )♦ $✐❣♥✐✜❝❛♥)
.❡❞✉❝)✐♦♥$✳ ❈♦♥✈❡.$✐♦♥ )❡❝❤♥♦❧♦❣✐❡$ ✉$✐♥❣ ❜✐♦♠❛$$ ♠❛② ❛❧$♦ ❜❡ ✉$❡❞ ✐♥ ❝♦♠❜✐♥❛)✐♦♥ ✇✐)❤
❈❈❙✱ ❤❡.❡ ✐) ✐$ ♣♦$$✐❜❧❡ )♦ ✐♥❝✉. ✏♥❡❣❛)✐✈❡✑
CO2
❡♠✐$$✐♦♥$ ❛$ ❜✐♦♠❛$$ ❝❛♣)✉.❡$
CO2
❢.♦♠
)❤❡ ❛)♠♦$♣❤❡.❡✳
◆✉❝❧❡❛. ❡♥❡.❣② ✐$ ❛ ❤✐❣❤❧② ❝♦♥).♦✈❡.$✐❛❧ ♣♦❧✐)✐❝❛❧ )♦♣✐❝ ✐♥ ●❡.♠❛♥②✳ ❚❤❡ ❛)♦♠✐❝ ❡♥❡.❣②
❧❛✇ ✭❆)●✮ ✐♥ ●❡.♠❛♥② ❤❛$ ✉♥❞❡.❣♦♥❡ )❤.❡❡ ♠❛❥♦. .❡✈✐$✐♦♥$ ✐♥ )❤❡ ♣❛$) )❡♥ ②❡❛.$✳ ■♥
✷✵✵✷✱ )❤❡ ❧❛✇ ✇❛$ ❝❤❛♥❣❡❞ )♦ ❡♥$✉.❡ ❛ ♥✉❝❧❡❛. ♣❤❛$❡✲♦✉) ✉♥)✐❧ ❛.♦✉♥❞ )❤❡ ②❡❛. ✷✵✷✵✳ ■♥
✷✵✶✵✱ )❤❡ ❧❛✇ ✇❛$ .❡✈✐$❡❞ )♦ ♣♦$)♣♦♥❡ )❤❡ ♣❤❛$❡✲♦✉) ✉♥)✐❧ ❛.♦✉♥❞ ✷✵✺✵✳ ❍♦✇❡✈❡.✱ ❛❢)❡.
❋✉❦✉$❤✐♠❛✱ )❤❡ ❣♦✈❡.♥♠❡♥) ❞❡❝✐❞❡❞ ✐♥ ❆✉❣✉$) ✷✵✶✶ )♦ ❝❧♦$❡ ❞♦✇♥ ❡✐❣❤) ♥✉❝❧❡❛. ♣♦✇❡.
♣❧❛♥)$ ✐♠♠❡❞✐❛)❡❧② ❛♥❞ $✉❜$❡M✉❡♥)❧② ❞❡❝♦♠♠✐$$✐♦♥ )❤❡ .❡♠❛✐♥✐♥❣ ♦♥❡$ ✉♥)✐❧ ✷✵✷✷✳ ■♥
❘❊▼■◆❉✲❉ )❤❡ ♥✉❝❧❡❛. ♣❤❛$❡✲♦✉) ❛❝❝♦.❞✐♥❣ )♦ ❆)●✷✵✶✶ ✐$ ✐♠♣❧❡♠❡♥)❡❞✳
✹✳✷ ❈❤❛&❛❝(❡&✐+(✐❝+ ♦❢ ❚❡❝❤♥♦❧♦❣✐❡+
▼❛✐♥ ■♥♣✉'
❊❛❝❤ )❡❝❤♥♦❧♦❣② ✐$ ❛$$✐❣♥❡❞ ❛ ♠❛✐♥ ✐♥♣✉) ❡♥❡.❣② ❝❛..✐❡.✳
❖'❤❡+ ■♥♣✉'
■♥ ❝❛$❡ ❛ )❡❝❤♥♦❧♦❣② ♥❡❡❞$ $♦♠❡ ❛❞❞✐)✐♦♥❛❧ ✐♥♣✉) ❢♦. ✐)$ ♣.♦❝❡$$✱ )❤✐$ ✐♥♣✉)
✐$ .❡♣.❡$❡♥)❡❞ ❜② ♠❡❛♥$ ♦❢ ❛ ✜①❡❞ ✐♥♣✉) ❝♦❡✣❝✐❡♥)✳
▼❛✐♥ ,+♦❞✉❝'
❊❛❝❤ )❡❝❤♥♦❧♦❣② ✐$ ❛$$✐❣♥❡❞ ❛ ♠❛✐♥ ♦✉)♣✉)✳
❈♦✉♣❧❡ ,+♦❞✉❝'
❙♦♠❡ )❡❝❤♥♦❧♦❣✐❡$ ✐♥❤❡.❡♥)❧② ♣.♦❞✉❝❡ ❝♦✉♣❧❡ ♣.♦❞✉❝)$ ✐♥ )❤❡✐. ♣.♦❝❡$$✳
■♥ ❝❛$❡ )❤❡✐. ❡♥❡.❣❡)✐❝ $❤❛.❡ ✐$ ♥♦) ♥❡❣❧✐❣✐❜❧❡✱ )❤❡② ❛.❡ ♠♦❞❡❧❡❞ ❜② ♠❡❛♥$ ♦❢ ✜①❡❞
❝♦✉♣❧❡ ♣.♦❞✉❝) ❝♦❡✣❝✐❡♥)$ )❤❛) .❡❧❛)❡ )❤❡ ❡♥❡.❣❡)✐❝ ❝♦✉♣❧❡ ♣.♦❞✉❝) ♦✉)♣✉) )♦ )❤❡
♠❛✐♥ ♦✉)♣✉)✳
❈♦♥✈❡+3✐♦♥ ❊✣❝✐❡♥❝②
❚❤❡ ❝♦♥✈❡.$✐♦♥ ❡✣❝✐❡♥❝② ♦❢ ❛ )❡❝❤♥♦❧♦❣② ❞❡)❡.♠✐♥❡$ )❤❡ .❛)✐♦
❜❡)✇❡❡♥ ❡♥❡.❣② ✐♥♣✉) ❛♥❞ ♦✉)♣✉)✳ ❚❡❝❤♥♦❧♦❣✐❡$ )❤❛) ❛.❡ ❝♦♥$✐❞❡.❡❞ )♦ ❜❡ )❡❝❤♥✐✲
❝❛❧❧② ♠❛)✉.❡ ❤❛✈❡ ❛ ❝♦♥$)❛♥) ❝♦♥✈❡.$✐♦♥ ❡✣❝✐❡♥❝② ♦✈❡. )✐♠❡✳ ❚❡❝❤♥♦❧♦❣✐❡$ )❤❛) ❛.❡
❡①♣❡❝)❡❞ )♦ ❜❡ .❡✜♥❡❞ ✐♥ )❤❡ ❢✉)✉.❡ ❤❛✈❡ )✐♠❡✲❞❡♣❡♥❞❡♥) ❝♦♥✈❡.$✐♦♥ ❡✣❝✐❡♥❝✐❡$✳
❈❛♣❛❝✐'✐❡3
❍✐$)♦.✐❝❛❧ ❝❛♣❛❝✐)② ❛❞❞✐)✐♦♥$ )❤❛) ❤❛✈❡ )❛❦❡♥ ♣❧❛❝❡ ✐♥ ●❡.♠❛♥② $✐♥❝❡ ✶✾✸✵
❛.❡ ❛♥ ✐♥♣✉) )♦ )❤❡ ♠♦❞❡❧✳ ❊❛❝❤ ✈✐♥)❛❣❡ ❤❛$ ❛ $♣❡❝✐✜❝ ❝♦♥✈❡.$✐♦♥ ❡✣❝✐❡♥❝②✳ ❖✈❡.
)❤❡ ♦♣)✐♠✐③❛)✐♦♥ ♣❡.✐♦❞✱ )❤❡ $)♦❝❦ ♦❢ ✐♥$)❛❧❧❡❞ ❝❛♣❛❝✐)② ✐$ ✐♥❝.❡❛$❡❞ ❜② ✐♥✈❡$)♠❡♥)$
❛♥❞ ❞❡❝.❡❛$❡❞ ✇❤❡♥ ❝❛♣❛❝✐)✐❡$ .❡❛❝❤ )❤❡ ❡♥❞ ♦❢ )❤❡✐. )❡❝❤♥✐❝❛❧ ❧✐❢❡)✐♠❡✳
❚❡❝❤♥✐❝❛❧ ▲✐❢❡'✐♠❡
❊❛❝❤ )❡❝❤♥♦❧♦❣② ✐$ ❛$$✐❣♥❡❞ ❛ $♣❡❝✐✜❝ )❡❝❤♥✐❝❛❧ ❧✐❢❡)✐♠❡ ✭❚▲❚✮✳ ❈❛✲
♣❛❝✐)✐❡$ ❜✉✐❧) ✉♣ ✐♥ ❛ ❝❡.)❛✐♥ )✐♠❡ $)❡♣
t
❡①✐$) ❛♥❞ ♣.♦❞✉❝❡ ♦✉)♣✉) ✉♥)✐❧ )❤❡ )✐♠❡
$)❡♣
t+TLT
✳ ❖♣)✐♦♥❛❧❧②✱ ❧✐❣♥✐)❡ ❛♥❞ ❝♦❛❧ ♣♦✇❡. ♣❧❛♥)$ ❛.❡ ❡①❡♠♣)❡❞✳
❋✉❧❧ ▲♦❛❞ ❍♦✉+3
■♥$)❛❧❧❡❞ ❣❡♥❡.❛)✐♦♥ ❝❛♣❛❝✐)✐❡$ ♣.♦❞✉❝❡ ♦✉)♣✉) ♦♥❧② ✐♥ ❛ ❢.❛❝)✐♦♥ ♦❢ )❤❡
❡♥)✐.❡ ②❡❛. ❞✉❡ )♦ ♠❛✐♥)❡♥❛♥❝❡ ♦. ♣❤②$✐❝❛❧ ❝♦♥$).❛✐♥)$✳ ❍❡♥❝❡✱ ❡❛❝❤ )❡❝❤♥♦❧♦❣②
❤❛$ ❛ ❝❤❛.❛❝)❡.✐$)✐❝ ❢✉❧❧ ❧♦❛❞ ❤♦✉. .❛)✐♦ )❤❛) .❡❧❛)❡$ )❤❡ ♥✉♠❜❡. ♦❢ ♣.♦❞✉❝✐♥❣ ❤♦✉.$
)♦ )❤❡ )♦)❛❧ ❤♦✉.$ ✐♥ ❛ ②❡❛.✳ ❋♦. ❡①✐$)✐♥❣ )❡❝❤♥♦❧♦❣✐❡$✱ )❤✐$ ♥✉♠❜❡. ✐$ ❞❡.✐✈❡❞
❢.♦♠ ❡♠♣✐.✐❝❛❧ ♦❜$❡.✈❛)✐♦♥$✳ ❋♦. .❡♥❡✇❛❜❧❡ ❡♥❡.❣✐❡$ ❛ ❞✐$❝.❡)❡ ❣.❛❞❡ $).✉❝)✉.❡
✶✼
3.4 The Energy System Module 79
❤❛ ❞✐✛❡'❡♥ ✐❛ ❡) ❜❡ ✇❡❡♥ )✐ ❡) ♦❢ ❞✐✛❡'❡♥ .✉❛❧✐ ② ✐) ✐♠♣❧❡♠❡♥ ❡❞✳ ❋♦' '❛♥)♣♦'
❡❝❤♥♦❧♦❣✐❡) ❤✐) ♣❛'❛♠❡ ❡' ✐) ♦ ❜❡ ✐♥ ❡'♣'❡ ❡❞ ❛) ♣❡')♦♥✲❦♠ ♦' ♦♥✲❦♠ ♣❡' ✈❡❤✐❝❧❡
♣❡' ②❡❛'✳ ❋♦' ❡❧❡❝ '✐❝✐ ② ❣❡♥❡'❛ ✐♥❣ ❡❝❤♥♦❧♦❣✐❡)✱ ❤❡ ❢✉❧❧ ❧♦❛❞ ❤♦✉') ❛'❡ ❡♥❞♦❣❡♥♦✉)
♦ ❘❊▼■◆❉✲❉ ❢'♦♠ ✷✵✶✵ ♦♥✇❛'❞)✳ ❉❡ ❛✐❧) ♦♥ ❤✐) ✐))✉❡ ❛'❡ ✐♥ ❯❡❝❦❡'❞ ❡ ❛❧✳
✭✷✵✶✶✮✳
■♥✈❡$%♠❡♥% ❈♦$%$
❇✉✐❧❞✐♥❣ ✉♣ ❝❛♣❛❝✐ ✐❡) ♦❢ ❛ ❡❝❤♥♦❧♦❣② ✐♥❝✉') ✐♥✈❡) ♠❡♥ ❝♦) )✳ ❊❛❝❤
❡❝❤♥♦❧♦❣②
te
✐) ❛))✐❣♥❡❞ ❛ )♣❡❝✐✜❝ ✉'♥❦❡② ✐♥✈❡) ♠❡♥ ❝♦)
int,te
✐♥
e
✴
❦❲
✱ ❞❡'✐✈❡❞
❢'♦♠ ❤❡ ❡❝❤♥✐❝❛❧ ❧✐ ❡'❛ ✉'❡✳ ❊.✉❛ ✐♦♥ ✺ ❞❡✜♥❡) ❤❡ ♦ ❛❧ ✐♥✈❡) ♠❡♥ ❝♦) )
INt
✐♥❝✉''❡❞ ✐♥ ❛ '❡)♣❡❝ ✐✈❡ ✐♠❡ ) ❡♣
t
✱ ❞❡♣❡♥❞✐♥❣ ♦♥ ❤❡ ❝❛♣❛❝✐ ② ❛❞❞✐ ✐♦♥)
∆capt,te
✳
INt=X
te
(int,te ·∆capt,te +γte ·adjt,te)∀t, te
✭✺✮
❋♦' ♠❛ ✉'❡ ❡❝❤♥♦❧♦❣✐❡)✱ ❤❡ )♣❡❝✐✜❝ ✐♥✈❡) ♠❡♥ ❝♦) ) ❛'❡ ❝♦♥) ❛♥ ♦✈❡' ✐♠❡❀ ❢♦'
❧❡❛'♥✐♥❣ ❡❝❤♥♦❧♦❣✐❡) ❤❡② ❝❛♥ ❞❡❝'❡❛)❡ ❞✉❡ ♦ ❧❡❛'♥✐♥❣✲❜②✲❞♦✐♥❣ ❡✛❡❝ )✳ ❚♦ ♣'❡✲
✈❡♥ ❤❡ ♠♦❞❡❧ ❡①❤✐❜✐ ✐♥❣ ❡①❝❡))✐✈❡❧② ❧❛'❣❡ ❡①♣❛♥)✐♦♥ '❛ ❡) ✐♥ ❛ ❝❡' ❛✐♥ ✐♠❡ ) ❡♣✱
✐♥✈❡) ♠❡♥ ❝♦) ) ❛'❡ ♣♦ ❡♥ ✐❛❧❧② ✐♥❝'❡❛)❡❞ ❜② ❡❝❤♥♦❧♦❣②✲)♣❡❝✐✜❝ ❛❞❥✉) ♠❡♥ ❝♦)
adjt,te
✱ )❝❛❧❡❞ ✇✐ ❤ ❛ )❝❛❧✐♥❣ ❝♦❡✣❝✐❡♥
γte
✱ )❡ ♦ ✵✳✹✳ ❆❞❥✉) ♠❡♥ ❝♦) ) ❛'❡ ❛ ♠❡❛♥)
♦ ✐♥❝'❡❛)❡ ♠♦❞❡❧ '❡❛❧✐)♠✳
▲❡❛+♥✐♥❣ ❚❡❝❤♥♦❧♦❣✐❡$
❋♦' )♦♠❡ ❡❝❤♥♦❧♦❣✐❡) )♣❡❝✐✜❝ ✐♥✈❡) ♠❡♥ ❝♦) ) ❛'❡ ❡①♣❡❝ ❡❞ ♦
❞❡❝'❡❛)❡ ✇✐ ❤ ❤❡ ❝✉♠✉❧❛ ✐✈❡ ✐♥) ❛❧❧❡❞ ❝❛♣❛❝✐ ②✱ ❛❝❝♦'❞✐♥❣ ♦ ❤❡ ❝♦♥❝❡♣ ♦❢ ✏▲❡❛'♥✲
✐♥❣ ❜② ❞♦✐♥❣✑✳ ■♥ ❘❊▼■◆❉✲❉✱ ❛ ♠♦❞✐✜❡❞ ♦♥❡✲❢❛❝ ♦' ❧❡❛'♥✐♥❣ ❝✉'✈❡ ❝♦♥❝❡♣ ✐) ✉)❡❞
❤❛ ✐) )✉♠♠❛'✐③❡❞ ✐♥ ❊.✉❛ ✐♦♥ ✻✱ ❞❡ ❡'♠✐♥✐♥❣ ❤❡ )♣❡❝✐✜❝ ✐♥✈❡) ♠❡♥ ❝♦) )✱
int,te
✱
❢♦' ❤❡ )✉❜)❡ ♦❢ ❧❡❛'♥✐♥❣ ❡❝❤♥♦❧♦❣✐❡)
tel ⊂te
✳
int,te =α·capcumβ
t,te +inFte +inGt,te ∀t, tel ⊂te
✭✻✮
α=in2005,te −inFte
inβ
2005,te
β=in2005,te
in2005,te −inFte
·ln (1 −lte)
ln 2
❊)♣❡❝✐❛❧❧② ❢♦' ♦♥)❤♦'❡ ❛♥❞ ♦✛)❤♦'❡ ✇✐♥❞ ❛) ✇❡❧❧ ❛) )♦❧❛' ♣❤♦ ♦✈♦❧ ❛✐❝✱ ❤❡ ❞♦♠❡) ✐❝
❝✉♠✉❧❛ ✐✈❡ ✐♥) ❛❧❧❡❞ ❝❛♣❛❝✐ ②
capcumt
✐) ❡①♣❡❝ ❡❞ ♦ ❤❛✈❡ ♦♥❧② ❛♥ ✐♠♣❛❝ ♦♥ ❧♦❝❛❧
❝♦♠♣♦♥❡♥ ) ♦❢ ❤❡ )♣❡❝✐✜❝ ✐♥✈❡) ♠❡♥ ❝♦) )✱ ❧✐❦❡ ❢✉♥❞❛♠❡♥ )✱ ❣'✐❞ ❝♦♥♥❡❝ ✐♦♥)✱
♦' ❛))❡♠❜❧②✳ ❍❡♥❝❡✱ ❤❡ )♣❡❝✐✜❝ ✐♥✈❡) ♠❡♥ ❝♦) ) ❢♦' ❤❡)❡ ❤'❡❡ ❡❝❤♥♦❧♦❣✐❡) ❛'❡
)♣❧✐ ✐♥ ♦ ❛♥ ✐♥✐ ✐❛❧ ❧♦❝❛❧ ❝♦♠♣♦♥❡♥
in2005,te
✱ ❤❛ ❡①❤✐❜✐ ) ❝♦) ❞❡❝'❡❛)❡) ✇✐ ❤ ❛
❧❡❛'♥✐♥❣ '❛ ❡
lte
✉♣ ♦ ❛ ❝❡' ❛✐♥ ✢♦♦' ❝♦)
inFte
✱ ❛♥❞ ❛ ❣❧♦❜❛❧ ❝♦♠♣♦♥❡♥
inGt,te
❤❛
❡①♣❡'✐❡♥❝❡) ❝♦) ❞❡❝'❡❛)❡) ♦♥ ❛♥ ✐♥ ❡'♥❛ ✐♦♥❛❧ ❧❡✈❡❧ ❛♥❞ '❡♣'❡)❡♥ ) ❤❡ )♦❧❛' ♣❛♥❡❧
♦' ❤❡ ❣❡♥❡'❛ ♦' ❢♦' ✇✐♥❞ ✉'❜✐♥❡)✳ ❋♦' ❧❡❛'♥✐♥❣ ❡❝❤♥♦❧♦❣✐❡) ♦ ❤❡' ❤❛♥ ✇✐♥❞ ❛♥❞
)♦❧❛' ♣❤♦ ♦✈♦❧ ❛✐❝ ✐ ✐) ❛))✉♠❡❞✱ ❤❛ ❞♦♠❡) ✐❝ ❝❛♣❛❝✐ ✐❡) ❛'❡ ❤❡ ❞♦♠✐♥❛♥ ❞'✐✈❡'
❢♦' ✐♥✈❡) ♠❡♥ ❝♦) )✳
✶✽
80 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
❆❞❥✉$%♠❡♥% ❈♦$%$
❚♦ ♣#❡✈❡♥' '❤❡ ♠♦❞❡❧ ❢#♦♠ ❡①❤✐❜✐'✐♥❣ ❡①❝❡22✐✈❡ ❡①♣❛♥2✐♦♥ #❛'❡2 '❤❛'
✇♦✉❧❞ ♥♦' ♦❝❝✉# ✐♥ '❤❡ #❡❛❧ ✇♦#❧❞ ❞✉❡ '♦ ✐♥❡#'✐❛ ❛♥❞ ❣❡♥❡#❛❧ ❜♦''❧❡♥❡❝❦2✱ ❛❞❥✉2'♠❡♥'
❝♦2'2 ❛#❡ ✐♠♣❧❡♠❡♥'❡❞✳ ❚❤❡ ✐❞❡❛ ♦❢ ❛❞❥✉2'♠❡♥' ❝♦2'2 ✐2 '♦ ❢♦#❝❡ '❤❡ ♠♦❞❡❧ ✐♥'♦
♠♦#❡ ❣#❛❞✉❛❧ ❡①♣❛♥2✐♦♥ ♣❛'❤2 ❜② ♣✉♥✐2❤✐♥❣ ❢❛2' ✐♥❝#❡❛2❡2 ❛♥❞ ❞❡❝#❡❛2❡2 ♦❢ #❡❧❛'✐✈❡
❝❛♣❛❝✐'② ❛❞❞✐'✐♦♥2 ✇✐'❤ 2❝❛❧❡❞ ♠♦♥❡'❛#② ❝♦2'2
adjt,te
'❤❛' ❛#❡ 2♣❡❝✐✜❝ ❢♦# ❡❛❝❤
'❡❝❤♥♦❧♦❣② ❛♥❞ ❞❡♣❡♥❞ ♦♥ '❤❡ #❡❧❛'✐✈❡ ❝❛♣❛❝✐'② ❛❞❞✐'✐♦♥2 ❜❡'✇❡❡♥ '✇♦ 2✉❜2❡<✉❡♥'
②❡❛#2✳ ❊<✉❛'✐♦♥ ✼ 2❤♦✇2 '❤❡ ❢✉♥❝'✐♦♥❛❧ #❡❧❛'✐♦♥2❤✐♣✳
adjt,te =(∆capt−1,te −∆capt,te)2
∆capt−1,te +ǫte
∀t, te
✭✼✮
❋♦# ❡❛❝❤ '❡❝❤♥♦❧♦❣②✱ ❛ 2♣❡❝✐✜❝ ❝❛♣❛❝✐'② '❤#❡2❤♦❧❞
ǫ
✐2 ❞❡✜♥❡❞✱ #❡♣#❡2❡♥'✐♥❣ ❛♥ ❡2'✐✲
♠❛'❡ ♦❢ #❡❛❧✐2'✐❝ ❝❛♣❛❝✐'② ❛❞❞✐'✐♦♥2✱ ❜❛2❡❞ ♦♥ ♣❛2' ♦❜2❡#✈❛'✐♦♥2✳ ❋♦# ❛♥② ❝❛♣❛❝✐'②
✐♥❝#❡❛2❡ ❜❡②♦♥❞ '❤❡ '❤#❡2❤♦❧❞✱ ❛❞❥✉2'♠❡♥' ❝♦2'2 ✇♦✉❧❞ ❜❡ ✐♥❝✉##❡❞ ❛♥❞ '❤❡#❡❜②
✐♥❝#❡❛2❡❞ '❤❡ 2♣❡❝✐✜❝ ✐♥✈❡2'♠❡♥' ❝♦2'2 ❢♦# ❛ 2♣❡❝✐✜❝ '❡❝❤♥♦❧♦❣② ✐♥ ❛ 2♣❡❝✐✜❝ ②❡❛#✳
❍♦✇❡✈❡#✱ '❤❡ ♠♦❞❡❧ ♠✐♥✐♠✐③❡2 ❛❞❥✉2'♠❡♥' ❝♦2'2 '♦ ❛ ♥❡❣❧✐❣✐❜❧❡ ❧❡✈❡❧ ❛♥❞ ✐♥2'❡❛❞
2♠♦♦'❤❡♥2 '❤❡ ❡①♣❛♥2✐♦♥ ♣❛'❤2✳ ❙♦ '❤❡ ❝♦♥❝❡♣' ✐2 #❛'❤❡# '❤❡♦#❡'✐❝❛❧ ❛♥❞ ❛ ♠❡❛♥2
'♦ ✐♥❝#❡❛2❡ ♠♦❞❡❧ #❡❛❧✐2♠✳
❖♣❡-❛%✐♦♥ ❛♥❞ ▼❛✐♥%❡♥❛♥❝❡ ❈♦$%$
❇❡2✐❞❡2 ✐♥✈❡2'♠❡♥' ❝♦'2✱ ❡❛❝❤ '❡❝❤♥♦❧♦❣② ✐♥❝✉#2 ✈❛#✐✲
❛❜❧❡ ❛♥❞ ✜①❡❞ ♦♣❡#❛'✐♦♥ ❛♥❞ ♠❛✐♥'❡♥❛♥❝❡ ❝♦2'2 ✭❖✫▼ ❝♦2'2✮ #❡'#✐❡✈❡❞ ❢#♦♠ '❤❡
'❡❝❤♥✐❝❛❧ ❧✐'❡#❛'✉#❡✳ ❋✐①❡❞ ❖✫▼ ❝♦2'2✱
omfte
✱ ❛#❡ ❞❡✜♥❡❞ ✐♥
e
✴
❦❲
❢♦# ❡❛❝❤ '❡❝❤✲
♥♦❧♦❣②❀ ✈❛#✐❛❜❧❡ ❖✫▼ ❝♦2'2✱
omvte
✐♥
e
✴
▼❲❤
✳ ❊<✉❛'✐♦♥ ✽ 2❤♦✇2 ❤♦✇ '♦'❛❧ ❖✫▼
❝♦2'2✱
OMt
✱ ✐♥ ❛ #❡2♣❡❝'✐✈❡ ②❡❛#
t
❛#❡ ❞❡'❡#♠✐♥❡❞ ❜② '❤❡ ✐♥2'❛❧❧❡❞ ❝❛♣❛❝✐'✐❡2
capt,te
❛♥❞ ❛♠♦✉♥' ♦❢ ♠❛✐♥ ♣#♦❞✉❝'
MPt,te
❢♦# ❡❛❝❤ '❡❝❤♥♦❧♦❣②
te
✳
OMt=X
te
(omfte ·capt,te +omvte ·MPt,te)∀t, te
✭✽✮
❋✉❡❧ ❈♦$%$
❋✉❡❧ ❝♦2'2 ❛#❡ ✐♥❝✉##❡❞ ❜② '❤♦2❡ '❡❝❤♥♦❧♦❣✐❡2 '❤❛' ♥❡❡❞ ❝♦2'❧② ♣#✐♠❛#② ❡♥❡#✲
❣✐❡2 ❛2 ❛♥ ✐♥♣✉'✳ ❚❤❡2❡ ❛#❡ ❤❛#❞ ❝♦❛❧✱ ❧✐❣♥✐'❡✱ ♥❛'✉#❛❧ ❣❛2✱ ✉#❛♥✐✉♠ ❛♥❞ ❜✐♦♠❛22❀
♣#✐❝❡ ♣❛'❤2 ❛#❡ ❞✐2❝✉22❡❞ ✐♥ ❙❡❝'✐♦♥ ✹✳✶✳ ❚♦'❛❧ ❢✉❡❧ ❝♦2'2
FUt
✐♥ ❛ #❡2♣❡❝'✐✈❡ '✐♠❡
2'❡♣ ❛#❡ ❞❡'❡#♠✐♥❡❞ ❜② '❤❡ ♣#✐♠❛#② ❡♥❡#❣② ❞❡♠❛♥❞ ♦❢ ❛ '❡❝❤♥♦❧♦❣②
dt,te,P E
♠✉❧'✐✲
♣❧✐❡❞ ✇✐'❤ '❤❡ ♣#✐❝❡ ♦❢ '❤❡ ♣#✐♠❛#② ❡♥❡#❣②
pt,P E
✳
FUt=X
te,P E
(pt,P E ·dt,te,P E )∀t, te
✭✾✮
❊♥❡-❣② ❙②$%❡♠ ❈♦$%$
❚♦'❛❧ ❡♥❡#❣② 2②2'❡♠ ❝♦2'2
Et
✐♥ ❛ #❡2♣❡❝'✐✈❡ '✐♠❡ 2'❡♣
t
❛#❡ ❞❡✲
♣✐❝'❡❞ ✐♥ ❊<✉❛'✐♦♥ ✶✵✳ ❚❤❡② ♥❡❡❞ '♦ ❜❡ ❝♦✈❡#❡❞ ❜② '❤❡ ●❉S ✐♥ ❡❛❝❤ '✐♠❡ 2'❡♣✳
❚❤✐2 ✐2 '❤❡ ♠♦♥❡'❛#② ♣❛#' ♦❢ '❤❡ ❤❛#❞ ❧✐♥❦ ❜❡'✇❡❡♥ '❤❡ ❡♥❡#❣② 2②2'❡♠ ❛♥❞ '❤❡
♠❛❝#♦❡❝♦♥♦♠✐❝ ♠♦❞✉❧❡ ✐♥ ❘❊▼■◆❉✲❉✳
Et=INt+OMt+F Ut∀t
✭✶✵✮
✶✾
3.4 The Energy System Module 81
✹✳✸ ❈♦♥✈❡()✐♦♥ ❚❡❝❤♥♦❧♦❣✐❡)
✹✳✸✳✶ $%✐♠❛%② *♦ ❙❡❝♦♥❞❛%② ❊♥❡%❣②
❆♥ ♦✈❡%✈✐❡✇ ♦❢ )❤❡ +❊
→
❙❊ ❝♦♥✈❡%/✐♦♥ )❡❝❤♥♦❧♦❣✐❡/ ❛♥❞ )❤❡✐% ❛❝%♦♥②♠/ ✐/ ❣✐✈❡♥ ✐♥ ❚❛✲
❜❧❡ ✼✳ ❚❤❡ %❡/♣❡❝)✐✈❡ ❛❜❜%❡✈✐❛)✐♦♥/ ❛%❡ %❡♣♦%)❡❞ ✐♥ ❚❛❜❧❡ ✽✳ ▼✐//✐♥❣ ✐♥ )❤✐/ ♦✈❡%✈✐❡✇
✐/✱ ❞✉❡ )♦ /♣❛❝❡ ❝♦♥/)%❛✐♥)/✱ )❤❡ ❚❤❡%♠❛❧ ◆✉❝❧❡❛% ❘❡❛❝)♦% ✭❚◆❘✮ )❤❛) ❝♦♥✈❡%)/ ✉%❛♥✐✉♠
✐♥)♦ ❡❧❡❝)%✐❝✐)②✱ ❡)❤❛♥♦❧ ♣%♦❞✉❝)✐♦♥ ❢%♦♠ ❇✐♦♠❛// ❙✉❣❛%✫❙)❛%❝❤ ✭❇✐♦❙❙✲❊❚◆✮ ❛♥❞ ❞✐❡/❡❧
♣%♦❞✉❝)✐♦♥ ❢%♦♠ ❇✐♦♠❛// ❖✐❧ ✭❇✐♦❖✲❉■❊✮✳ ■♥ ❝❛/❡ )❡❝❤♥♦❧♦❣✐❡/ ❛♣♣❡❛% ✐♥ /❡✈❡%❛❧ ✜❡❧❞/✱
)❤✐/ ✐♥❞✐❝❛)❡/ )❤❛) )❤❡② ❛%❡ /✉❜❥❡❝) )♦ ❝♦✲♣%♦❞✉❝)✐♦♥✳ ❆ ♣%♦♠✐♥❡♥) ❡①❛♠♣❧❡ ✐/ ❝♦♠❜✐♥❡❞
❤❡❛) ❛♥❞ ♣♦✇❡%✳ ❈♦✲♣%♦❞✉❝)✐♦♥ ♦❝❝✉%/ ❛❧/♦ )♦ ❛ ❧❡//❡% ❡①)❡♥) ✇✐)❤ ♦)❤❡% )❡❝❤♥♦❧♦❣✐❡/✱
②❡) ❢♦% )❤❡ /❛❦❡ ♦❢ %❡❛❞❛❜✐❧✐)② )❤❡② ❛%❡ ♥♦) ❝♦♥/✐❞❡%❡❞ ✐♥ )❤❡ ♦✈❡%✈✐❡✇ )❛❜❧❡✳ ❆/ ❜❡❝♦♠❡/
❡✈✐❞❡♥)✱ ❤❛%❞ ❝♦❛❧✱ ❧✐❣♥✐)❡ ❛♥❞ ❧✐❣♥♦❝❡❧❧✉❧♦/❡ ❛%❡ ✈❡%② ✢❡①✐❜❧❡ ♣%✐♠❛%② ❡♥❡%❣② ❝❛%%✐❡%/ ❛/
)❤❡② ♣❡%♠✐) )❤❡ ♣%♦❞✉❝)✐♦♥ ♦❢ ❛❧♠♦/) ❛❧❧ )②♣❡/ ♦❢ /❡❝♦♥❞❛%② ❡♥❡%❣② ❝❛%%✐❡%/✳ ❘❡♥❡✇❛❜❧❡
❡♥❡%❣② /♦✉%❝❡/ ❛%❡ ❡/♣❡❝✐❛❧❧② ❛♣♣❧✐❝❛❜❧❡ ❢♦% ♣%♦❞✉❝✐♥❣ ❡❧❡❝)%✐❝✐)②✳ ❚❤❡ /❡❝♦♥❞❛%② ❡♥❡%❣②
❝❛%%✐❡%/ ❡❧❡❝)%✐❝✐)②✱ ❤②❞%♦❣❡♥✱ ❣❛/✱ ❞✐/)%✐❝) ❤❡❛)✱ ❝♦❦❡ ❛♥❞ ♣❡)%♦❧ ❛%❡ ❛/ /✉❝❤ ✉/❛❜❧❡ ❢♦%
❛♥ ❡♥❞✲❝♦♥/✉♠❡% ♦♥❝❡ ❞✐/)%✐❜✉)❡❞ )♦ )❤❡ ♣❧❛❝❡ ♦❢ ❝♦♥/✉♠♣)✐♦♥✳ ▼✐❞❞❧❡ ❞✐/)✐❧❧❛)❡ ✐/ ❛♥
✐♥)❡%♠❡❞✐❛)❡ ♣%♦❞✉❝)✳ ❚❤❡ /❡❝♦♥❞❛%② ❡♥❡%❣② ❧♦❝❛❧ ❤❡❛) ✐/ ❛ ♣/❡✉❞♦✲❡♥❡%❣② ❝❛%%✐❡% ❛/ ❧♦❝❛❧
❤❡❛) ✐/ ❣❡♥❡%❛)❡❞ ❛) )❤❡ ♣❧❛❝❡ ♦❢ ❝♦♥/✉♠♣)✐♦♥✳
❚❤❡ /)%✉❝)✉%❡ ♦❢ ❚❛❜❧❡ ✼ ✐/ /✉❣❣❡/)✐✈❡ ♦❢ ❛ /❡) ♦❢ ❜❛❧❛♥❝❡ ❡O✉❛)✐♦♥/ )❤❛) %❡❧❛)❡ )❤❡ ♣%✐♠❛%②
❡♥❡%❣② ❞❡♠❛♥❞ )♦ /❡❝♦♥❞❛%② ❡♥❡%❣② ♣%♦❞✉❝)✐♦♥ ✈✐❛ ❝♦♥✈❡%/✐♦♥ ❡✣❝✐❡♥❝✐❡/ ❛♥❞ ❢✉❧❧ ❧♦❛❞
❤♦✉%/ ♦♥ )❤❡ )❡❝❤♥✐❝❛❧ /✐❞❡✳ ❖♥ )❤❡ ❡❝♦♥♦♠✐❝ /✐❞❡ ❡❛❝❤ )❡❝❤♥♦❧♦❣② ❤❛/ /♣❡❝✐✜❝ ✐♥✈❡/)♠❡♥)✱
✈❛%✐❛❜❧❡ ❛♥❞ ✜①❡❞ ♠❛✐♥)❡♥❛♥❝❡ ❝♦/)/ ❛♥❞ ❛ )❡❝❤♥✐❝❛❧ ❧✐❢❡)✐♠❡✳ ❚❤❡/❡ ♣❛%❛♠❡)❡%/ ❛%❡
♣%❡/❡♥)❡❞ ✐♥ )❤❡ ❢♦❧❧♦✇✐♥❣ ❢♦% ❡❛❝❤ )❡❝❤♥♦❧♦❣②✱ ♦%❣❛♥✐③❡❞ ❜② /❡❝♦♥❞❛%② ❡♥❡%❣✐❡/ )❤❛) ❛%❡
)❤❡ ♠❛✐♥ ♣%♦❞✉❝)✳ ❚❤❡ ❞❛)❛ ✐/ ❜❛/❡❞ ♦♥ )❤❡ %❡❢❡%❡♥❝❡❞ )❡❝❤♥✐❝❛❧ ❧✐)❡%❛)✉%❡ ❛♥❞ %❡♣%❡/❡♥)/
❜❡/) ❛✈❛✐❧❛❜❧❡ )❡❝❤♥✐O✉❡ ✈❛❧✉❡/ ✐♥ ♠♦/) ❝❛/❡/✳
❊❧❡❝*%✐❝✐*② ❛♥❞ ❉✐5*%✐❝* ❍❡❛*
❆❧❧ ♥♦♥✲✢✉❝)✉❛)✐♥❣ ❡❧❡❝)%✐❝✐)② ❣❡♥❡%❛)✐♦♥ )❡❝❤♥♦❧♦❣✐❡/✬
)❡❝❤♥♦✲❡❝♦♥♦♠✐❝ ♣❛%❛♠❡)❡%/ ❛%❡ %❡♣♦%)❡❞ ✐♥ ❚❛❜❧❡ ✾✳ ▲✐❣✲+❈ ❛♥❞ ❈♦❛❧✲+❈ ❛%❡ ❝♦♥✈❡♥✲
)✐♦♥❛❧ ❝♦❛❧ ♣♦✇❡% ♣❧❛♥)/ ✇✐)❤ )❤❡ ❤✐❣❤❡/)
CO2
❡♠✐//✐♦♥ ✐♥)❡♥/✐)② ♦❢ ❛❧❧ ❡❧❡❝)%✐❝✐)② ❣❡♥✲
❡%❛)✐♥❣ )❡❝❤♥♦❧♦❣✐❡/✳ ❆ ♠✐♥♦% ✐♠♣%♦✈❡♠❡♥) ❝♦♥/)✐)✉)❡/ )❤❡ ❝♦♥/)%✉❝)✐♦♥ ♦❢ +❈✰ ♣♦✇❡%
♣❧❛♥)/✱ /✉♣❡%❝%✐)✐❝❛❧ ❝♦❛❧ ♣♦✇❡% ♣❧❛♥)/ )❤❛) ❛❝❤✐❡✈❡ ❛ ❤✐❣❤❡% ❝♦♥✈❡%/✐♦♥ ❡✣❝✐❡♥❝②✳ ❆
❝♦♠❜✐♥❛)✐♦♥ ✇✐)❤ )❤❡ ❈❛%❜♦♥ ❈❛♣)✉%❡ ❛♥❞ ❙❡O✉❡/)%❛)✐♦♥ ✭❈❈❙✮ )❡❝❤♥♦❧♦❣② ❛❧❧♦✇/ ❢♦%
/❡✈❡%❡❧② ✭✽✵✲✾✵✪✮ %❡❞✉❝✐♥❣ )❤❡
CO2
❡♠✐//✐♦♥/ ✐♥)❡♥/✐)② ❜✉) /)✐❧❧ ✉/❡ ❝♦❛❧ ❛/ ❛ ♣%✐♠❛%②
❡♥❡%❣② /♦✉%❝❡✱ ✇❤✐❝❤ ❝♦✉❧❞ ❜❡ ♦❢ ✐♥)❡%❡/) ❢♦% )❤❡ ❞♦♠❡/)✐❝ ❧✐❣♥✐)❡ %❡/♦✉%❝❡/ ❛♥❞ ❝♦♥/✐❞❡%✲
✐♥❣ )❤❡ ❛❜✉♥❞❛♥) ❣❧♦❜❛❧ ❤❛%❞ ❝♦❛❧ %❡/♦✉%❝❡/✳ ❈♦❛❧✲+❈✴❈❈❙ ❛♥❞ ▲✐❣✲+❈✴❈❈❙ %❡♣%❡/❡♥)
)❤❡ ♣♦/)✲❝♦♠❜✉/)✐♦♥ )❡❝❤♥♦❧♦❣② )❤❛) /❡♣❛%❛)❡/ )❤❡
CO2
❢%♦♠ )❤❡ ✢✉❡ ❣❛/ ✐♥ ❛ ❝❤❡♠✐❝❛❧
♣%♦❝❡// ❛❢)❡% ❝♦♥✈❡♥)✐♦♥❛❧❧② ❜✉%♥✐♥❣ )❤❡ ♣✉❧✈❡%✐③❡❞ ❝♦❛❧✳ ❚✇♦ ♠♦%❡ ❈❈❙ )❡❝❤♥♦❧♦❣✐❡/
❛%❡ ❝♦♥/✐❞❡%❡❞✿ ❖①②❢✉❡❧ ✭+❈✴❈❈❙✲❖✮ ❛♥❞ +%❡✲❈♦♠❜✉/)✐♦♥ ✭■●❈❈✴❈❈❙✮✳ ❚❤❡ ❖①②❢✉❡❧
♣%♦❝❡// ✐/ ❞✐✛❡%❡♥) ❛/ )❤❡ ❝♦❛❧ ✐/ ❜✉%♥) ✐♥ ❛♥ ❛)♠♦/♣❤❡%❡ )❤❛) ❝♦♥/✐/)/ ♦❢ %❡✲❝✐%❝✉❧❛)❡❞
✢✉ ❣❛/ ❡♥%✐❝❤❡❞ ✇✐)❤ ♣✉%❡ ♦①②❣❡♥✳ ❚❤%♦✉❣❤ )❤❡ %❡✲❝✐%❝✉❧❛)✐♦♥ ♣%♦❝❡//✱ )❤❡ ✢✉ ❣❛/ ❡✈❡♥✲
)✉❛❧❧② ❝♦♥/✐/)/ )♦ ❛ ✈❡%② ❧❛%❣❡ ❡①)❡♥) ♦❢
CO2
❛♥❞ ❝❛♥ ❝♦♥✈❡♥✐❡♥)❧② ❜❡ ♣%♦❝❡//❡❞ ❢✉%)❤❡%✳
✷✵
82 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
❚❛❜❧❡ ✼✿ ❖✈❡)✈✐❡✇ ♦❢ .❤❡ ♣)✐♠❛)② .♦ 3❡❝♦♥❞❛)② ✭8❊
→
❙❊✮ ❡♥❡)❣② ❝♦♥✈❡)3✐♦♥ .❡❝❤♥♦❧♦❣✐❡3
)❡♣)❡3❡♥.❡❞ ✐♥ ❘❊▼■◆❉✲❉✳
❙❡❝♦♥❞❛'②
❊♥❡'❣②
❈❛''✐❡'-
.'✐♠❛'② ❊♥❡'❣② ❈❛''✐❡'-
❍❛)❞ ❈♦❛❧ ▲✐❣♥✐.❡ ●❛3 ❇✐♦▲❈ ❇✐♦▼ ❘❊❙
❊❧❡❝.)✐❝✐.②
❈♦❛❧✲%❈ ▲✐❣✲%❈ ●❛*✲❚❯❘ ❇✐♦▲❈✲❈❖▼ ❇✐♦▼✲❈❍% ❙♦❧❛3✲%❱
❈♦❛❧✲%❈✰ ▲✐❣✲%❈✰ ●❛*✲❈❈ ❇✐♦▲❈✲❈❈❍% ❲✐♥❞✲❖❋❋
❈♦❛❧✲%❈✴❈❈❙ ▲✐❣✲%❈✴❈❈❙ ●❛*✲❈❈✴❈❈❙ ❇✐♦▲❈✲●❈❍% ❲✐♥❞✲❖◆
❈♦❛❧✲%❈✴❈❈❙✲❖ ▲✐❣✲%❈✴❈❈❙✲❖ ●❛*✲❈❍% ❇✐♦▲❈✲■●❈❈ ●❡♦✲❍❉❘
❈♦❛❧✲■●❈❈✴❈❈❙ ▲✐❣✲■●❈❈✴❈❈❙ ❇✐♦▲❈✲■●❈❈✴❈❈❙ ❍②❞3♦
❈♦❛❧✲❈❍% ▲✐❣✲❈❍%
❍②❞)♦❣❡♥
❈♦❛❧✲❍✷ ▲✐❣✲❍✷ ●❛*✲❙▼❘ ❇✐♦▲❈✲❍✷
❈♦❛❧✲❍✷✴❈❈❙ ▲✐❣✲❍✷✴❈❈❙ ●❛*✲❙▼❘✴❈❈❙ ❇✐♦▲❈✲❍✷✴❈❈❙
●❛3
❈♦❛❧✲●❆❙ ▲✐❣✲●❆❙ ●❛*✲❚❘ ❇✐♦▲❈✲●❆❙ ❇✐♦▼✲●❆❙
❉✐3.)✐❝.
❍❡❛.
❈♦❛❧✲❍% ▲✐❣✲❍% ●❛*✲❍% ❇✐♦▲❈✲❍% ❇✐♦▼✲❈❍%
❈♦❛❧✲❈❍% ▲✐❣✲❈❍% ●❛*✲❈❍% ❇✐♦▲❈✲❈❈❍%
❇✐♦▲❈✲●❈❍%
❈♦❦❡
❈♦❛❧✲❈❖❑
8❡.)♦❧
❇✐♦▲❈✲❊❚◆
▼✐❞❞❧❡✲
❞✐3.✐❧❧❛.❡
❈♦❛❧✲❚▲ ▲✐❣✲❚▲ ❇✐♦▲❈✲❚▲
❈♦❛❧✲❚▲✴❈❈❙ ▲✐❣✲❚▲✴❈❈❙ ❇✐♦▲❈✲❚▲✴❈❈❙
▲♦❝❛❧ ❍❡❛.
❙♦❧❛3✲❚❍
●❡♦✲❍%❯
❚❛❜❧❡ ✽✿ ❆❜❜)❡✈✐❛.✐♦♥3 ✐♥ ❛❧♣❤❛❜❡.✐❝❛❧ ♦)❞❡)✳
❈❈✿ ❈♦♠❜✐♥❡❞ ❈②❝❧❡ ■●❈❈✿ ■♥.❡❣)❛.❡❞ ●❛3✐✜❝❛.✐♦♥ ❈❈
❈❈❍8✿ ❈♦♠❜✉3.✐♦♥ ✇✐.❤ ❈❍8 ❖❋❋✿ ❖✛3❤♦)❡
❈❈❙✿ ❈❛)❜♦♥ ❈❛♣.✉)❡ ❛♥❞ ❙.♦)❛❣❡ ❖◆✿ ❖♥3❤♦)❡
❈❍8✿ ❈♦♠❜✐♥❡❞ ❍❡❛. ❛♥❞ 8♦✇❡) 8❈✿ 8✉❧✈❡)✐③❡❞ ❈♦♠❜✉3.✐♦♥
❈❖❑✿ ❈♦❦✐♥❣ 8❈✰✿ ❙✉♣❡)❝)✐.✐❝❛❧ 8❈
❊❚◆✿ ❊.❤❛♥♦❧ ♣)♦❞✉❝.✐♦♥ 8❱✿ 8❤♦.♦✈♦❧.❛✐❦
●❆❙✿ ●❛3✐✜❝❛.✐♦♥ ❙▼❘✿ ❙.❡❛♠ ▼❡.❤❛♥❡ ❘❡❢♦)♠✐♥❣
●❈❍8✿ ●❛3✐✜❝❛.✐♦♥ ✇✐.❤ ❈❍8 ❚❍✿ ❚❤❡)♠❛❧ ❍♦. ❲❛.❡) ●❡♥❡)❛.✐♦♥
❍✷✿ ❍②❞)♦❣❡♥ 8)♦❞✉❝.✐♦♥ ❚▲✿ ▲✐V✉❡✜❝❛.✐♦♥
❍❉❘✿ ❍♦.✲❉)②✲❘♦❝❦ ❚❘✿ ❚)❛♥3❢♦)♠❛.✐♦♥
❍8❯✿ ❍❡❛. 8✉♠♣ ❚❯❘✿ ❚✉)❜✐♥❡
✷✶
3.4 The Energy System Module 83
♦"#✲❝♦♠❜✉"#✐♦♥ ❛❝❤✐❡✈❡" ❤✐❣❤❡0 0❡♠♦✈❛❧ 0❛#❡"✳ ❚❤❡ 0❡✲❈♦♠❜✉"#✐♦♥ #❡❝❤♥♦❧♦❣② 0❡❧✐❡"
♦♥ #❤❡ ❣❛"✐✜❝❛#✐♦♥ ♦❢ ❝♦❛❧ ✐♥ ❛ ✜0"# "#❡♣ ❛♥❞ #❤❡♥ "❡♣❛0❛#❡" #❤❡
CO2
❜❡❢♦0❡ ❝♦♠❜✉"#✐♥❣
#❤❡ ❤②❞0♦❣❡♥✲0✐❝❤ "②♥#❤❡#✐❝ ❣❛" ✐♥ ❛ ❣❛" #✉0❜✐♥❡✳ ■♥ #❤❡ ♠♦❞❡❧✱ "❡♣❛0❛#❡❞
CO2
❡♥#❡0" ❛
"#②❧✐③❡❞ ❈❈❙✲❈❤❛✐♥ #❤❛# 0❡♣0❡"❡♥#" ❛
CO2
✲♣✐♣❡❧✐♥❡ ✐♥❢0❛"#0✉❝#✉0❡ ❛♥❞ "❡>✉❡"#0❛#✐♦♥ "✐#❡"✳
❚❤❡ ❝♦♠♣0❡""✐♦♥ ♦❢
CO2
❢♦0 "❡>✉❡"#0❛#✐♦♥ 0❡>✉✐0❡" ❡❧❡❝#0✐❝✐#②✱ #❤❡ ❧♦""❡" ✐♥ #❤✐" ♣0♦❝❡""
❛0❡ ❛❝❝♦✉♥#❡❞ ❢♦0 ❜② 0❡❞✉❝✐♥❣ #❤❡ ❝♦♥✈❡0"✐♦♥ ❡✣❝✐❡♥❝② ♦❢ #❤❡ #❡❝❤♥♦❧♦❣✐❡" ❢❛❝✐❧✐#❛#✐♥❣
❈❈❙✳
❆♣❛0# ❢0♦♠ "✉♣❡0❝0✐#✐❝❛❧ ♦0 ❈❈❙ ♣♦✇❡0 ♣❧❛♥#"✱ #❤❡ ❝♦♠❜✐♥❡❞ ❤❡❛# ❛♥❞ ♣♦✇❡0 ✭❈❍ ✮
#❡❝❤♥♦❧♦❣② ❝♦♥"#✐#✉#❡" ❛ ♠✐#✐❣❛#✐♦♥ ♦♣#✐♦♥✳ ■♥ ❛ ❈❍ ♣❧❛♥#✱ #❤❡ ✇❛"#❡✲❤❡❛# ✐" 0❡❝②❝❧❡❞
❜② ✢♦✇✐♥❣ #❤0♦✉❣❤ ❛ ❞✐"#0✐❝# ❤❡❛# ♥❡#✇♦0❦ ❛♥❞ ✐" ✉"❡❞ ❢♦0 ✇❛0♠ ✇❛#❡0 ❛♥❞ ❤❡❛#✐♥❣ ✐♥
❤♦✉"❡❤♦❧❞" ♦0 ✐♥❞✉"#0②✳ ❆ ❈❍ ♣❧❛♥# ❝❛♥ ❡✐#❤❡0 ♣0♦❞✉❝❡ ❤❡❛# ♦0 ❡❧❡❝#0✐❝✐#② ❛" ❛ ♠❛✐♥
♣0♦❞✉❝#✳ ■♥ ●❡0♠❛♥②✱ #❤❡② ❛0❡ ❣❡♥❡0❛❧❧② ♣0♦❞✉❝✐♥❣ ♠♦0❡ ❤❡❛# #❤❛♥ ❡❧❡❝#0✐❝✐#②✳ ■♥ #❤❡
❡①#0❡♠❡ ❝❛"❡ ♦❢ ♣0♦❞✉❝✐♥❣ ♦♥❧② ❞✐"#0✐❝# ❤❡❛#✱ #❤❡② ❛0❡ #❤❡♥ "✐♠♣❧② ❤❡❛# ♣❧❛♥#" ✭❍ ✮✳
❊❧❡❝#0✐❝✐#② ❣❡♥❡0❛#✐♦♥ ❢0♦♠ ♥❛#✉0❛❧ ❣❛" ❤❛" #❤❡ #❡❝❤♥✐❝❛❧ ❛❞✈❛♥#❛❣❡ ♦✈❡0 ❝♦❛❧ #❤❛# ❣❛"
♣♦✇❡0 ♣❧❛♥#" ❛0❡ ❛❜❧❡ #♦ 0❛♠♣ ✉♣ ❛♥❞ ❞♦✇♥ ✇✐#❤✐♥ ✈❡0② "❤♦0# #✐♠❡ "❝❛❧❡" ❛♥❞ ❤❡♥❝❡
❛0❡ ❛ ❣♦♦❞ ❝♦♠♣❧❡♠❡♥# #♦ ✢✉❝#✉❛#✐♥❣ ❘❊❙✱ ❡"♣❡❝✐❛❧❧② ✈❛❧✐❞ ❢♦0 ❣❛" #✉0❜✐♥❡" ✭●❛"✲❚❯❘✮✳
●❛"✲❚❯❘ ❤❛✈❡ #❤❡ ❝❤❛0❛❝#❡0✐"#✐❝ ♦❢ ✈❡0② ❧♦✇ "♣❡❝✐✜❝ ✐♥✈❡"#♠❡♥# ❝♦"#" ❜✉# ❤✐❣❤ ❢✉❡❧ ❝♦"#"
❛" ❝♦♥✈❡0"✐♦♥ ❡✣❝✐❡♥❝✐❡" ❛0❡ ♠♦❞❡0❛#❡ ❛♥❞ ●❛" ✐" ❛ 0❡❧❛#✐✈❡❧② ❡①♣❡♥"✐✈❡ ♣0✐♠❛0② ❡♥❡0❣②
❝❛00✐❡0✳ ❈♦♠❜✐♥❡❞ ❝②❝❧❡ ♣❧❛♥#" ✭●❛"✲❈❈✮ ❤❛✈❡ "✐❣♥✐✜❝❛♥#❧② ❤✐❣❤❡0 ❝♦♥✈❡0"✐♦♥ ❡✣❝✐❡♥❝✐❡"✱
❜✉# ❛0❡ ❧❡"" ✢❡①✐❜❧❡✳ ❚❤❡② ♠❛② ❛❧"♦ ❜❡ ❝♦♥"#0✉❝#❡❞ ✇✐#❤ ♣♦"#✲❝♦♠❜✉"#✐♦♥ ❈❈❙✱ ②❡#
#❤✐" ♦♣#✐♦♥ ✐" ♠♦0❡ ❝♦"#❧② ❛♥❞ ♣♦""❡""❡" ❛♥ ❡✈❡♥ ❧♦✇❡0 ❞❡❣0❡❡ ♦❢ ✢❡①✐❜✐❧✐#②✳ ❊❧❡❝#0✐❝✐#②
♣0♦❞✉❝#✐♦♥ ❢0♦♠ ♥❛#✉0❛❧ ❣❛" ❤❛" ❛♣♣0♦①✐♠❛#❡❧② ❤❛❧❢ #❤❡
CO2
❡♠✐""✐♦♥ ✐♥#❡♥"✐#② #❤❛♥ ❢0♦♠
❧✐❣♥✐#❡ ❛♥❞ ❛" "✉❝❤ ♣0❡"❡♥#" ✐#"❡❧❢ ❛" ❛ ♠✐#✐❣❛#✐♦♥ ♦♣#✐♦♥✳ ❋0♦♠ ❛ ❣❡♦♣♦❧✐#✐❝❛❧ ♣♦✐♥# ♦❢
✈✐❡✇✱ #❤❡ ✐♥❝0❡❛"❡❞ ❞❡♣❡♥❞❡♥❝❡ ♦♥ ♥❛#✉0❛❧ ❣❛" ✇♦✉❧❞ ♠❛❦❡ ●❡0♠❛♥② ♠♦0❡ ❞❡♣❡♥❞❡♥# ♦♥
"✉♣♣❧② ❝♦✉♥#0✐❡"✳ ❆ ♠❛❥♦0 ♣♦""✐❜✐❧✐#② ❢♦0 ❞♦♠❡"#✐❝ ❣❛" "✉♣♣❧② ❝♦✉❧❞ ❜❡ #❤❡ ♠❡#❤❛♥❛#✐♦♥
♦❢ ❤②❞0♦❣❡♥ ♣0♦❞✉❝❡❞ ❞✉0✐♥❣ #❡♠♣♦0❛0② ♦✈❡0♣0♦❞✉❝#✐♦♥ ♦❢ ❡❧❡❝#0✐❝✐#② ❜② ❘❊❙❀ #❤✐" ♦♣#✐♦♥
✐" ♥♦# ②❡# ✐♥❝❧✉❞❡❞ ✐♥#♦ ❘❊▼■◆❉✲❉ ❜✉# ✇♦0❦ ✐" ✐♥ ♣0♦❣0❡""✳
▲✐❣♥♦❝❡❧❧✉❧♦"❡ ✐" ❝✉00❡♥#❧② ❝♦♠❜✉"#❡❞ ❢♦0 ❡✐#❤❡0 ♦♥❧② ♣♦✇❡0 ❣❡♥❡0❛#✐♦♥ ✭❇✐♦▲❈✲❈❖▼✮✱
❜♦#❤ ❤❡❛# ❛♥❞ ♣♦✇❡0 ✭❇✐♦▲❈✲❈❈❍ ✮ ♦0 ♦♥❧② ❤❡❛# ✭❇✐♦▲❈✲❍ ✮✳ ●❛"✐✜❝❛#✐♦♥ ♦❢ ❧✐❣♥♦✲
❝❡❧❧✉❧♦"✐❝ ❜✐♦♠❛"" ✐" ❛ ❢✉#✉0❡ #❡❝❤♥♦❧♦❣② #❤❛# ✐" "#✐❧❧ ✐♥ ❛ ❞❡♠♦♥"#0❛#✐♦♥ ♣❤❛"❡ ❜✉# ♠❛②
❜❡❝♦♠❡ ✈❡0② ❛##0❛❝#✐✈❡ ✐♥ #❤❡ ❢✉#✉0❡✱ ❜♦#❤ ❢♦0 ❝♦✲❣❡♥❡0❛#✐♦♥ ✭❇✐♦▲❈✲●❈❍ ✮ ❛♥❞ "♦❧❡
❡❧❡❝#0✐❝✐#② ♣0♦❞✉❝#✐♦♥ ✭❇✐♦▲❈✲■●❈❈✮✳ ❚❤❡ ❧❛##❡0 ♠❛② ❛❧"♦ ❜❡ ❝♦♠❜✐♥❡❞ ✇✐#❤ ❈❈❙✱ ✐#
✇♦✉❧❞ #❤❡♥ ❜❡ ♣♦""✐❜❧❡ #♦ ♥♦# ♦♥❧② ❜❡
CO2
❡♠✐""✐♦♥✲♥❡✉#0❛❧✱ ❛" ✐" #❤❡ ❝❛"❡ ❢♦0 ❛❧❧ ❇✐♦▲❈
#❡❝❤♥♦❧♦❣✐❡"✱ ❜✉# ❡✈❡♥ ❝0❡❛#❡ ♥❡❣❛#✐✈❡
CO2
❡♠✐""✐♦♥"✳ ❚❤❡ ❇✐♦▼❈❍ #❡❝❤♥♦❧♦❣② 0❡❧✐❡"
♦♥ ♠❛♥✉0❡ #❤❛# ✐" ❜❡✐♥❣ ♠✐①❡❞ ✇✐#❤ "♦♠❡ ♣❛0#" ♦❢ ❙✉❣❛0 ❛♥❞ ❙#❛0❝❤ ❇✐♦♠❛"" ✭❇✐♦❙❙✮
❢♦0 ❛❝❤✐❡✈✐♥❣ ❛♥ ❛♥❛❡0♦❜✐❝ ❣❛"✐✜❝❛#✐♦♥✳ ❆❢#❡0 ❝❧❡❛♥✐♥❣ #❤✐" ❣❛" ✐# ✐" ✉"❡❞ ✇✐#❤ ❛ ♥♦0♠❛❧
❜✉0♥❡0 ❛♥❞ #✉0❜✐♥❡ #♦ ♣0♦❞✉❝❡ ❤❡❛# ❛♥❞ ♣♦✇❡0✳ ❍②❞0♦ 0❡♣0❡"❡♥#" ❛ "#❛♥❞❛0❞ 0✉♥♥✐♥❣
✇❛#❡0 ❤②❞0♦♣♦✇❡0 ♣❧❛♥# ❛♥❞ ●❡♦✲❍❉❘ #❤❡ ♣0♦❞✉❝#✐♦♥ ♦❢ ❡❧❡❝#0✐❝✐#② ❢0♦♠ ❤②❞0♦#❤❡0♠❛❧
0❡"♦✉0❝❡"✳ ❚❤❡ ❢✉❧❧ ❧♦❛❞ ❤♦✉0" 0❡♣♦0#❡❞ ❛0❡ ❛♥ ❛✈❡0❛❣❡✱ ❛" ❛ ❞✐"❝0❡#❡ ❣0❛❞❡ "#0✉❝#✉0❡
❞✐"#0✐❜✉#❡" #❤❡ ♣♦#❡♥#✐❛❧ #♦ "❧✐❣❤#❧② ❞✐✛❡0❡♥# >✉❛❧✐#② "✐#❡" ✇✐#❤ ❞✐✛❡0✐♥❣ ❢✉❧❧ ❧♦❛❞ ❤♦✉0"✳
❉❖❚ 0❡❢❡0" #♦ ❛ ❞✐❡"❡❧ ♦✐❧ #✉0❜✐♥❡✱ ✇❤✐❝❤ ✐" ❛❝#✉❛❧❧② ❛ ❙❊
→
❙❊ #❡❝❤♥♦❧♦❣②✱ ❜✉# ✐" ✐♥❝❧✉❞❡❞
✐♥#♦ #❤✐" ♦✈❡0✈✐❡✇ #❛❜❧❡✳
✷✷
84 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
❚❛❜❧❡ ✾✿ ❚❡❝❤♥♦✲❡❝♦♥♦♠✐❝ ♣❛/❛♠❡0❡/✐③❛0✐♦♥ ♦❢ ✭4❊
→
❙❊✮ ❡♥❡/❣② ❝♦♥✈❡/;✐♦♥ 0❡❝❤♥♦❧♦❣✐❡;
/❡♣/❡;❡♥0❡❞ ✐♥ ❘❊▼■◆❉✲❉✱ 0❤❛0 ♣/♦❞✉❝❡ ❡❧❡❝0/✐❝✐0② ♦/ ❤❡❛0 ❛; ♠❛✐♥ ♣/♦❞✉❝0 ❛♥❞
❛/❡ ♥♦♥✲✢✉❝0✉❛0✐♥❣ 0❡❝❤♥♦❧♦❣✐❡;✳ ❋✉❧❧ ❧♦❛❞ ❤♦✉/; ❛/❡ ❡♠♣✐/✐❝❛❧ ✈❛❧✉❡; ♦❢ ✷✵✵✼
❛♥❞ ❛/❡ ♦♥❧② ✜①❡❞ ✐♥ 0❤❡ ✜/;0 0✐♠❡ ;0❡♣ ♦❢ ❘❊▼■◆❉✲❉✳ ❙♦✉/❝❡;✿ ❍❛❦❡ ❡0 ❛❧✳
✭✷✵✵✾✮✱ ❙❝❤❧❡;✐♥❣❡/ ❡0 ❛❧✳ ✭✷✵✶✵✮✱ ■❊❆ ✭✷✵✶✵✮✱ ❇❛✉❡/ ❡0 ❛❧✳ ✭✷✵✵✾✮✱ ▼■❚ ✭✷✵✵✼✮✱
❊❈ ✭✷✵✵✻✮✱◆✐0;❝❤ ❡0 ❛❧✳ ✭✷✵✵✹✮✱ ❙❝❤✉❧③ ✭✷✵✵✼✮✱ ❑♦♥;0❛♥0✐♥ ✭✷✵✵✾❛✮✱❑♦♥;0❛♥0✐♥
✭✷✵✵✾❜✮✱ ❚❤/U♥ ❡0 ❛❧✳ ✭✷✵✵✾✮✱ ❇▼❯ ✭✷✵✵✽✮✱ ♦✇♥ ❝❛❧❝✉❧❛0✐♦♥;✳
❚▲❚ ■♥✈❡;0♠❡♥0 ❋✐① ❱❛/✐❛❜❧❡ ❈♦♥✈✳ ❋✉❧❧
❈♦;0; ❈♦;0; ❈♦;0; ❊✛✳ ▲♦❛❞
❨❡❛/
e2005
✴❦❲
e2005
✴❦❲
e2005
✴▼❲❤ ✪ ❤✴♣❛
❈♦❛❧✲4❈ ✹✺ ✶✶✺✵ ✷✷ ✻✳✽✺ ✹✹ ✻✽✸✵
❈♦❛❧✲4❈✰ ✹✵ ✶✽✵✵ ✸✻ ✼✳✾✾ ✺✵ ✻✽✸✵
❈♦❛❧✲4❈✴❈❈❙ ✹✺ ✶✽✵✵ ✷✾ ✶✶✳✹✶ ✸✽ ✻✽✸✵
❈♦❛❧✲4❈✴❈❈❙✲❖ ✹✵ ✶✾✵✵ ✸✹ ✶✸✳✼ ✹✶ ✻✽✸✵
❈♦❛❧✲■●❈❈✴❈❈❙ ✹✵ ✷✵✵✵ ✹✹ ✶✸✳✼ ✹✷ ✻✽✸✵
❈♦❛❧✲❈❍4 ✹✵ ✹✸✵ ✾ ✹✳✺✼ ✻✷
th
✴✷✹
el
✺✵✵✵
❈♦❛❧✲❍4 ✹✺ ✸✺✵ ✶✶ ✷✳✼✻ ✾✸
th
✹✷✾✵
▲✐❣✲4❈ ✹✺ ✶✸✵✵ ✷✷ ✾✳✶✸ ✹✸ ✼✵✵✵
▲✐❣✲4❈✰ ✹✵ ✶✻✵✵ ✷✼ ✼✳✾✾ ✹✽ ✼✵✵✵
▲✐❣✲4❈✴❈❈❙ ✹✺ ✷✶✵✵ ✷✾ ✶✹✳✽✹ ✸✺ ✼✵✵✵
▲✐❣✲4❈✴❈❈❙✲❖ ✹✵ ✷✷✵✵ ✸✺ ✶✼✳✶✷ ✸✾ ✼✵✵✵
▲✐❣✲■●❈❈✴❈❈❙ ✹✵ ✷✸✵✵ ✹✻ ✶✼✳✶✷ ✹✵ ✼✵✵✵
▲✐❣✲❈❍4 ✹✵ ✺✸✵ ✶✶ ✺✳✶✹ ✺✼
th
✴✶✽
el
✺✼✵✵
▲✐❣✲❍4 ✺✵ ✹✵✵ ✶✷ ✷✳✼✻ ✾✶
th
✻✼✺✵
●❛;✲❚❯❘ ✸✵ ✸✵✵ ✾ ✶✳✽✹ ✸✷ ✶✼✺✵
●❛;✲❈❈ ✸✺ ✺✵✵ ✸✵ ✵✳✺✸ ✺✺ ✶✼✺✵
●❛;✲❈❈✴❈❈❙ ✸✺ ✽✺✵ ✸✹ ✶✳✽✼ ✺✶ ✶✼✺✵
●❛;✲❈❍4 ✸✺ ✸✽✵ ✷✸ ✵✳✸✹ ✺✵
th
✴✸✵
el
✺✵✵✵
●❛;✲❍4 ✹✺ ✷✹✵ ✼ ✶✳✽✹ ✾✺
th
✼✽✾✵
❇✐♦▲❈✲❈❖▼ ✹✵ ✷✷✵✵ ✼✼ ✻✳✶✾ ✷✼ ✼✵✶✵
❇✐♦▲❈✲❈❈❍4 ✹✵ ✸✼✵✵ ✶✸✵ ✸✳✽✵ ✶✹ ✺✾✻✵
❇✐♦▲❈✲●❈❍4 ✹✵ ✹✵✵✵ ✶✹✵ ✷✳✼✼ ✸✽ ✺✾✻✵
❇✐♦▲❈✲■●❈❈ ✹✵ ✶✺✵✵ ✻✵ ✷✳✽✾ ✹✷ ✼✵✶✵
❇✐♦▲❈✲■●❈❈✴❈❈❙ ✹✵ ✷✵✻✶ ✽✷ ✹✳✻✹ ✸✶ ✼✵✶✵
❇✐♦▲❈✲❍4 ✹✵ ✹✺✵ ✶✷ ✶✳✷✵ ✽✺
th
✹✾✾✵
❇✐♦▼✲❈❍4 ✹✵ ✷✼✵✵ ✶✸✺ ✶✳✼✵ ✸✽ ✼✵✶✵
❍②❞/♦ ✽✵ ✺✵✵✵ ✶✵✵ ✲ ✶✵✵ ✹✽✷✵
●❡♦✲❍❉❘ ✸✺ ✹✹✷✼ ✶✼✼ ✲ ✶✵✵ ✽✵✵✵
❉❖❚ ✹✵ ✸✷✷ ✶✵ ✵✳✾✷ ✸✵ ✽✵✵
✷✸
3.4 The Energy System Module 85
❚❛❜❧❡ ✶✵✿ ❚❡❝❤♥♦✲❡❝♦♥♦♠✐❝ ♣❛0❛♠❡1❡0✐③❛1✐♦♥ ♦❢ 1❤❡ ✢✉❝1✉❛1✐♥❣ ❧❡❛0♥✐♥❣ 1❡❝❤♥♦❧♦❣✐❡7 ❙♦❧✲
9❱✱ ❲✲❖❋❋ ❛♥❞ ❲✲❖◆✳ ❚❤❡ ✜071 ♥✉♠❜❡0 ❣✐✈❡♥ ❢♦0 ✐♥✈❡71♠❡♥1 ❝♦717 0❡❢❡07
1♦ 1❤❡ ❧♦❝❛❧ 7❤❛0❡✱ 1❤❡ 7❡❝♦♥❞ ♥✉♠❜❡0 1♦ 1❤❡ ❣❧♦❜❛❧ 7❤❛0❡✳ ❋❧♦♦0 ❝♦717 ❛♥❞
❧❡❛0♥✐♥❣ 0❛1❡7 ❛♣♣❧② ♦♥❧② 1♦ ❧♦❝❛❧ ❝♦♠♣♦♥❡♥17✳ ❚❤❡ ♠♦❞❡❧ 1❛❦❡7 1❤❡ 7✉♠ ♦❢
❜♦1❤ ♥✉♠❜❡07 ❛7 ✐♥✈❡71♠❡♥1 ❝♦717 ✐♥ ❡❛❝❤ ②❡❛0✳ ❙♦✉0❝❡7✿ ◆❡✐❥ ❡1 ❛❧✳ ✭✷✵✵✸✮✱
◆✐17❝❤ ❡1 ❛❧✳ ✭✷✵✵✹✮✱ ❏✉♥❣✐♥❣❡0 ❡1 ❛❧✳ ✭✷✵✵✹✮✱ ❏✉♥❣✐♥❣❡0 ❡1 ❛❧✳ ✭✷✵✵✽✮✱ ❑♦♥71❛♥1✐♥
✭✷✵✵✾❛✮✱ ❙❝❤✐✛❡0 ✭✷✵✵✽✮✱ ❱0✐❥♠♦❡❞ ❡1 ❛❧✳ ✭✷✵✶✵✮✱ ♦✇♥ ❝❛❧❝✉❧❛1✐♦♥7✳
❚▲❚ ■♥✈❡71♠❡♥1 ❋❧♦♦0 ▲❡❛0♥✐♥❣ ❈✉♠✉❧❛1❡❞ ❋✐① ❖♣❡0❛1✐♥❣
❈♦717 ❈♦717 ❘❛1❡ ■♥71❛❧❧❡❞ ❈♦717
✭✐♥ ✷✵✵✺✮ ❈❛♣❛❝✐1②
✭✐♥ ✷✵✵✼✮
❨❡❛0
e2005
✴❦❲
e2005
✴❦❲ ✪ ▼❲
e2005
✴❦❲
❙♦❧✲9❱ ✷✺ ✶✻✵✵✰✷✹✵✵ ✹✷✵ ✷✵ ✸✽✶✶ ✹✵
❲✲❖◆ ✸✺ ✸✺✵✰✽✸✵ ✷✽✵ ✶✷ ✷✷✷✹✼ ✷✷
❲✲❖❋❋ ✷✺ ✶✺✵✵✰✶✵✵✵ ✺✽✵ ✷✺ ✵✳✵✵✶ ✶✷✺
❚❛❜❧❡ ✶✶✿ ❉❡✈❡❧♦♣♠❡♥1 ♣❛1❤ ♦❢ 1❤❡ ❡①♦❣❡♥♦✉7 ❣❧♦❜❛❧ ❧❡❛0♥✐♥❣ ❝♦♠♣♦♥❡♥1 ✐♥
e2005
✴❦❲✳
❚❤❡ ❞❛1❛ ✐7 0❡10✐❡✈❡❞ ❢0♦♠ ❛ ❘❊▼■◆❉✲❘ ✷
◦
7❝❡♥❛0✐♦✳
✷✵✵✺ ✷✵✶✵ ✷✵✶✺ ✷✵✷✵ ✷✵✷✺ ✷✵✸✵ ✷✵✸✺ ✷✵✹✵ ✷✵✹✺ ✷✵✺✵
❙♦❧✲9❱ ✷✹✵✵ ✶✹✺✾ ✶✵✼✵ ✽✺✻ ✼✷✽ ✻✺✺ ✻✵✷ ✺✻✵ ✺✷✼ ✺✵✵
❲✲❖◆ ✽✷✽ ✼✵✺ ✻✷✼ ✻✵✷ ✺✽✾ ✺✽✸ ✺✼✽ ✺✼✸ ✺✼✵ ✺✻✻
❲✲❖❋❋ ✶✵✵✵ ✾✹✾ ✽✶✽ ✼✺✸ ✼✷✷ ✼✵✼ ✻✾✽ ✻✾✷ ✻✽✽ ✻✽✺
❋❧✉❝1✉❛1✐♥❣ ❘❊❙ ✐♥❝❧✉❞❡ ❙♦❧❛0✲9❱✱ ❲✐♥❞✲❖❋❋ ❛♥❞ ❲✐♥❞✲❖◆❀ 1❤❡✐0 1❡❝❤♥♦✲❡❝♦♥♦♠✐❝
♣❛0❛♠❡1❡07 ❛0❡ 0❡♣♦01❡❞ ✐♥ ❚❛❜❧❡ ✶✵✳ ❚❤❡② ❛0❡ ✐♠♣❧❡♠❡♥1❡❞ ❛7 ❧❡❛0♥✐♥❣ 1❡❝❤♥♦❧♦❣✐❡7 ❜②
♠❡❛♥7 ♦❢ 1❤❡ ❧❡❛0♥✐♥❣✲❜②✲❞♦✐♥❣ ❛♣♣0♦❛❝❤✱ ❛7 ❞❡7❝0✐❜❡❞ ✐♥ ❙❡❝1✐♦♥ ✹✳✷✳ ❚❤❡ ✐❞❡❛ ✐7 1❤❛1 1❤❡
7♣❡❝✐✜❝ ✐♥✈❡71♠❡♥1 ❝♦717 ♦❢ 1❤❡7❡ ❘❊❙ ✇✐❧❧ ❞❡❝0❡❛7❡ ✐♥ 1❤❡ ❢✉1✉0❡ ❞✉❡ 1♦ ❝♦71 ❡✣❝✐❡♥❝②
❞❡✈❡❧♦♣♠❡♥17 ✐♥ ♣0♦❞✉❝1✐♦♥ ❛♥❞ ❞❡♣❧♦②♠❡♥1 ✇✐1❤ ✐♥❝0❡❛7✐♥❣ ✐♥71❛❧❧❡❞ ❝❛♣❛❝✐1✐❡7✳ ❆7
❧❡❛0♥✐♥❣✲❜②✲❞♦✐♥❣ ❡✛❡❝17 ♦♣❡0❛1❡ ♦♥ 1❤❡ ❣❧♦❜❛❧ 7❝❛❧❡ ♦♥❡ ❝❛♥♥♦1 ✉7❡ ❡①❝❧✉7✐✈❡❧② ●❡0♠❛♥
✐♥71❛❧❧❡❞ ❝❛♣❛❝✐1✐❡7 ❢♦0 ❡①10❛♣♦❧❛1✐♥❣ ❢✉1✉0❡ ❝♦71 ❞❡❝0❡❛7❡7✳ ❋♦0 ❛❧❧ 1❤0❡❡ 1❡❝❤♥♦❧♦❣✐❡7✱
7♦♠❡ ♣❛017 ♦❢ 1❤❡ 7♣❡❝✐✜❝ ❝❛♣✐1❛❧ ✐♥✈❡71♠❡♥1 ❝♦717 ❛0❡ 0❡❧❛1❡❞ 1♦ ❧♦❝❛❧ ❝♦♠♣♦♥❡♥17✱ 7✉❝❤
❛7 ❜✉✐❧❞✐♥❣ 1❤❡ ❢✉♥❞❛♠❡♥1 ♦0 1❤❡ ❣0✐❞ ❝♦♥♥❡❝1✐♦♥ ♦❢ ❛ 7♦❧❛0 ♣❛♥❡❧ ♦0 ✇✐♥❞ 1✉0❜✐♥❡✳ ❙✉❝❤
❡①♣❡0✐❡♥❝❡7 ❤❛✈❡ 1♦ ❜❡ ♠❛❞❡ ✇✐1❤✐♥ ♦♥❡ ❝♦✉♥10② ❛♥❞ ❞♦♠❡71✐❝ ✐♥71❛❧❧❡❞ ❝❛♣❛❝✐1② ✐7 ❛ ❣♦♦❞
♣0♦①② ❞0✐✈❡0 ❢♦0 ❧♦❝❛❧ ❝♦♠♣♦♥❡♥17✬ ❝♦71 0❡❞✉❝1✐♦♥7✳ ❍♦✇❡✈❡0✱ 1❤❡ 7♦❧❛0 ♣❛♥❡❧ ♦0 1❤❡ ✇✐♥❞
1✉0❜✐♥❡✬7 ❣❡♥❡0❛1♦0 ♠❛② ❜❡ 10❛❞❡❞ ✐♥1❡0♥❛1✐♦♥❛❧❧② ❛♥❞ ❤❡0❡ ❣❧♦❜❛❧ ✐♥71❛❧❧❡❞ ❝❛♣❛❝✐1✐❡7✬ ❛0❡
❛♥ ❛♣♣0♦♣0✐❛1❡ ❞0✐✈❡0✳ ❚❤❡ 1❡❝❤♥♦✲❡❝♦♥♦♠✐❝ ♣❛0❛♠❡1❡0✐③❛1✐♦♥ ❢♦0 1❤❡ ✢✉❝1✉❛1✐♥❣ ❧❡❛0♥✐♥❣
❝♦♠♣♦♥❡♥17 ✐7 ✐❧❧✉710❛1❡❞ ✐♥ ❚❛❜❧❡ ✶✵✳ ❚❤❡ ❞❡✈❡❧♦♣♠❡♥1 ♣❛1❤ ♦❢ 1❤❡ ❣❧♦❜❛❧ ✐♥✈❡71♠❡♥1
❝♦717 ❝♦♠♣♦♥❡♥17 ❛0❡ 7❤♦✇♥ ✐♥ ❚❛❜❧❡ ✶✶✱ ❞❡0✐✈❡❞ ❢0♦♠ ❛ ❘❊▼■◆❉✲❘ ✷
◦
7❝❡♥❛0✐♦✳
✷✹
86 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
❚❛❜❧❡ ✶✷✿ ❚❡❝❤♥♦✲❡❝♦♥♦♠✐❝ ♣❛0❛♠❡1❡0✐③❛1✐♦♥ ♦❢ 1❤❡ ♣0✐♠❛0② 1♦ 5❡❝♦♥❞❛0② ✭8❊
→
❙❊✮ ❡♥✲
❡0❣② ❝♦♥✈❡05✐♦♥ 1❡❝❤♥♦❧♦❣✐❡5 0❡♣0❡5❡♥1❡❞ ✐♥ ❘❊▼■◆❉✲❉ 1❤❛1 ❤❛✈❡ ❤②❞0♦❣❡♥
✭❍✷✮ ♦0 ❣❛5 ❛5 ❛ ♠❛✐♥ ♣0♦❞✉❝1✳ ❙♦✉0❝❡5✿ ❨❛♠❛5❤✐1❛ ❛♥❞ ❇❛00❡1♦ ✭✷✵✵✺✮✱ ●L❧
❡1 ❛❧✳ ✭✷✵✵✼✮✱ ❍❛♠❡❧✐♥❝❦ ✭✷✵✵✹✮✱ ◆✐15❝❤ ❡1 ❛❧✳ ✭✷✵✵✹✮✱ ♦✇♥ ❝❛❧❝✉❧❛1✐♦5
❚▲❚ ■♥✈❡51♠❡♥1 ❋✐① ❱❛0✐❛❜❧❡ ❈♦♥✈✳ ❋✉❧❧
❈♦515 ❈♦515 ❈♦515 ❊✛✳ ▲♦❛❞
❨❡❛0
e2005
✴❦❲
e2005
✴❦❲
e2005
✴▼❲❤ ✪ ❤✴♣❛
❈♦❛❧✲❍✷ ✺✵ ✶✵✷✵ ✸✶ ✵✳✹✷ ✺✾ ✼✵✵✵
❈♦❛❧✲❍✷✴❈❈❙ ✺✵ ✶✶✺✵ ✸✺ ✵✳✹✾ ✺✼ ✼✵✵✵
▲✐❣✲❍✷ ✺✵ ✶✵✶✺ ✸✶ ✵✳✹✷ ✺✼ ✼✵✵✵
▲✐❣✲❍✷✴❈❈❙ ✺✵ ✶✶✺✵ ✸✺ ✵✳✹✾ ✺✺ ✼✵✵✵
●❛5✲❙▼❘ ✹✺ ✹✵✵ ✶✷ ✶✷✳✼✵ ✼✸ ✼✽✾✵
●❛5✲❙▼❘✴❈❈❙ ✹✺ ✹✹✺ ✶✸ ✶✻✳✾✶ ✼✵ ✼✽✾✵
❇✐♦▲❈✲❍✷ ✹✺ ✶✶✷✼ ✶✶✸ ✵✳✾✼ ✻✶ ✼✽✽✵
❇✐♦▲❈✲❍✷✴❈❈❙ ✹✺ ✶✸✻✽ ✶✸✼ ✵✳✾✼ ✺✺ ✼✽✽✵
❊❧❡❝✳✲❍✷ ✶✼ ✷✹✶ ✶✷✳✵✺ ✵✳✷✺ ✻✷ ✼✽✽✵
❈♦❛❧✲●❆❙ ✺✵ ✼✷✺ ✷✷ ✵✳✸✽ ✻✵ ✹✽✵✵
▲✐❣✲●❆❙ ✺✵ ✼✷✺ ✷✷ ✵✳✸✽ ✺✽ ✼✵✵✵
❇✐♦▲❈✲●❆❙ ✹✵ ✷✽✶✼ ✶✹✶ ✶✳✸✽ ✺✺ ✼✹✺✵
❇✐♦▼✲●❆❙ ✹✵ ✷✹✶✺ ✶✷✶ ✶✳✶✵ ✻✵ ✼✹✺✵
❍②❞#♦❣❡♥ ❛♥❞ ●❛*
❚❤❡ 1❡❝❤♥♦✲❡❝♦♥♦♠✐❝ ♣❛0❛♠❡1❡0✐③❛1✐♦♥ ♦❢ 1❡❝❤♥♦❧♦❣✐❡5 ♣0♦❞✉❝✐♥❣
❣❛5❡♦✉5 5❡❝♦♥❞❛0② ❡♥❡0❣② ❝❛00✐❡05 ❛0❡ ❞✐5♣❧❛②❡❞ ✐♥ ❚❛❜❧❡ ✶✷✳ ❈✉00❡♥1❧②✱ ❤②❞0♦❣❡♥ ✐5
♠❛✐♥❧② ✉5❡❞ ❢♦0 ❝❤❡♠✐❝❛❧ ♣0♦❝❡55❡5 ❜✉1 ♥♦1 ❛5 ❛ 5♦✉0❝❡ ♦❢ ❡♥❡0❣②✳ ❍♦✇❡✈❡0✱ ✐1 ❝♦✉❧❞
♣♦1❡♥1✐❛❧❧② ❜❡ ✉5❡❢✉❧ ✐♥ 1❤❡ ❢✉1✉0❡ ❢♦0 ❞❡❧✐✈❡0✐♥❣ ♣0♦❝❡55 ❤❡❛1 1♦ ✐♥❞✉510② ♦0 ❛5 ❢✉❡❧ ✐♥
♥♦♥51❛1✐♦♥❛0② ❛♣♣❧✐❛♥❝❡5 ❧✐❦❡ ❝❛05 ❛♥❞ ❜✉5❡5✳ ❈♦♥✈❡♥1✐♦♥❛❧ 1❡❝❤♥♦❧♦❣✐❡5 ❢♦0 ♣0♦❞✉❝✐♥❣
❤②❞0♦❣❡♥ ✐5 51❡❛♠ ♠❡1❛♥❡ 0❡❢♦0♠✐♥❣ ✭❙▼❘✮ ❢0♦♠ ♥❛1✉0❛❧ ❣❛5 ❛♥❞ ❡❧❡❝10♦❧②5✐5✱ ✇❤✐❝❤ ✐5 ❛
❙❊
→
❙❊ 1❡❝❤♥♦❧♦❣②✳ ❙▼❘ ❝❛♥ ❛❧5♦ ❜❡ ❝♦✉♣❧❡❞ ✇✐1❤ ❈❈❙✱ 1❤❡♥ 1❤❡ ❤②❞0♦❣❡♥ ♣0♦❞✉❝1✐♦♥
✇♦✉❧❞ ❜❡ ❛❧♠♦51 ❝❛0❜♦♥ ♥❡✉10❛❧✳ ❖1❤❡0 ♣♦55✐❜❧❡ 1❡❝❤♥♦❧♦❣✐❡5 ❢♦0 ♣0♦❞✉❝✐♥❣ ❤②❞0♦❣❡♥
✐♥❝❧✉❞❡ ❝♦♥✈❡01✐♥❣ ❤❛0❞ ❝♦❛❧✱ ❧✐❣♥✐1❡ ♦0 ❧✐❣♥♦❝❡❧❧✉❧♦5✐❝ ❜✐♦♠❛55 ✜051 ✐♥1♦ 5②♥1❤❡1✐❝ ❣❛5
❛♥❞ 1❤❡♥ ✐♥1♦ ❤②❞0♦❣❡♥✱ ❜♦1❤ ✇✐1❤ ❛♥❞ ✇✐1❤♦✉1 ❈❈❙✳
●❛5 ✐5 ❝✉00❡♥1❧② ✐♠♣♦01❡❞ 1♦ ❛ ❧❛0❣❡ ❡①1❡♥1 ✐♥ 1❤❡ ❢♦0♠ ♦❢ ♥❛1✉0❛❧ ❣❛5 ♦❜1❛✐♥❡❞ ❢0♦♠
❞0✐❧❧✐♥❣✳ ❨❡1 1❤✐5 ♣0✐♠❛0② ❡♥❡0❣② ❝❛00✐❡0 ❝♦✉❧❞ ❛❧5♦ ❜❡ ♣0♦❞✉❝❡❞ ❜② 1❤❡ ❣❛5✐✜❝❛1✐♦♥ ♦❢
❤❛0❞ ❝♦❛❧✱ ❧✐❣♥✐1❡ ❛♥❞ ❧✐❣♥♦❝❡❧❧✉❧♦1✐❝ ❜✐♦♠❛55✳ ❯♥❞❡0 1❤❡ ❊❊● 5❝❤❡♠❡✱ 1❤❡ ♣0♦❞✉❝1✐♦♥ ♦❢
❜✐♦❣❛5 ❜② ❢❡0♠❡♥1❛1✐♦♥ ♦❢ ♠❛♥✉0❡ ✇✐1❤ ❣0❛55 ♦0 ♠❛✐③❡ 5✐❧❛❣❡ ❤❛5 ❜❡❡♥ 5✉❜5✐❞✐③❡❞✱ ❤❡♥❝❡✱
0❡❝❡♥1❧② 5❡✈❡0❛❧ ❜✐♦❣❛5 ♣❧❛♥15 51❛01❡❞ ♦♣❡0❛1✐♥❣ ✐♥ ●❡0♠❛♥② ✭❚❤0b♥ ❡1 ❛❧✳ ✷✵✵✾✮✳
▲✐-✉✐❞* ❛♥❞ ❖0❤❡#*
❚❤❡ ✈❛51 ♠❛❥♦0✐1② ♦❢ ❢✉❡❧5 ❢♦0 10❛♥5♣♦01 ✇❛5 ♣0♦❞✉❝❡❞ ❢0♦♠ ❢♦55✐❧
❝0✉❞❡ ♦✐❧ ✐♥ ✷✵✵✼✳ ❘❊▼■◆❉✲❉ ❢❡❛1✉0❡5 ❛ 0❡✜♥❡0② 5❡❝1♦0 1❤❛1 ✐5 ❡①♣❧❛✐♥❡❞ ✐♥ ❞❡1❛✐❧ ✐♥
✷✺
3.4 The Energy System Module 87
❚❛❜❧❡ ✶✸✿ ❚❡❝❤♥♦✲❡❝♦♥♦♠✐❝ ♣❛0❛♠❡1❡0✐③❛1✐♦♥ ♦❢ 1❤❡ ♣0✐♠❛0② 1♦ 5❡❝♦♥❞❛0② ✭8❊
→
❙❊✮ ❡♥✲
❡0❣② ❝♦♥✈❡05✐♦♥ 1❡❝❤♥♦❧♦❣✐❡5 0❡♣0❡5❡♥1❡❞ ✐♥ ❘❊▼■◆❉✲❉✱ 1❤❛1 ❤❛✈❡ 0❛✣♥❛1❡✱
❞✐❡5❡❧✱ ♣❡10♦❧✱ ❝♦❦❡ ♦0 ❧♦❝❛❧ ❤❡❛1 ❛5 ❛ ♠❛✐♥ ♣0♦❞✉❝1✳ ❙♦✉0❝❡5✿ ❑0❡② ✭✷✵✵✻✮✱
❨❛♠❛5❤✐1❛ ❛♥❞ ❇❛00❡1♦ ✭✷✵✵✺✮✱ ●P❧ ❡1 ❛❧✳ ✭✷✵✵✼✮✱ ❍❛♠❡❧✐♥❝❦ ✭✷✵✵✹✮✱ ❘❛❣❡11❧✐
✭✷✵✵✼✮✱ ❚✐❥♠❡♥5❡♥ ❡1 ❛❧✳ ✭✷✵✵✷✮✱ ◆✐15❝❤ ❡1 ❛❧✳ ✭✷✵✵✹✮✱ ♦✇♥ ❝❛❧❝✉❧❛1✐♦5
❚▲❚ ■♥✈❡51♠❡♥1 ❋✐① ❱❛0✐❛❜❧❡ ❈♦♥✈✳ ❋✉❧❧
❈♦515 ❈♦515 ❈♦515 ❊✛✳ ▲♦❛❞
❨❡❛0
e2005
✴❦❲
e2005
✴❦❲
e2005
✴▼❲❤ ✪ ❤✴♣❛
❆❚❉❊❙ ✸✵ ✸✼ ✸✳✼ ✵✳✶✸ ✺✸ ✼✽✽✵
❈♦❛❧✲❚▲ ✺✵ ✽✵✺ ✹✵ ✵✳✸✽ ✹✵ ✼✹✺✵
❈♦❛❧✲❚▲✴❈❈❙ ✺✵ ✽✹✵ ✹✻ ✵✳✸✽ ✹✵ ✼✹✺✵
▲✐❣✲❚▲ ✺✵ ✽✵✺ ✹✵ ✵✳✸✽ ✸✽ ✼✹✺✵
▲✐❣✲❚▲✴❈❈❙ ✺✵ ✽✹✵ ✹✻ ✵✳✸✽ ✸✽ ✼✹✺✵
❇✐♦▲❈✲❚▲ ✹✺ ✷✵✶✷ ✽✵ ✵✳✾✼ ✹✵ ✼✾✼✵
❇✐♦▲❈✲❚▲✴❈❈❙ ✹✺ ✷✹✶✺ ✾✼ ✵✳✾✼ ✹✶ ✼✾✼✵
❇✐♦❖✲❉■❊ ✹✺ ✶✵✹ ✺ ✵✳✹✻ ✾✸ ✼✽✽✵
❇✐♦❙❙✲❊❚◆ ✹✺ ✸✾✹ ✹✺ ✸✳✺✽ ✺✺✳✸ ✼✾✷✵
❇✐♦▲❈✲❊❚◆ ✹✺ ✶✾✶✽ ✶✷✺ ✽✳✾✹ ✸✻✳✸ ✼✾✷✵
❈♦❛❧✲❈❖❑ ✹✵ ✷✹✵ ✶✷ ✵✳✸✽ ✽✵ ✺✷✺✵
❙♦❧❛0✲❚❍ ✷✺ ✶✶✷✼ ✸✹ ✲ ✶✵✵ ✽✻✼
●❡♦✲❍8 ✸✺ ✶✻✶✵ ✹✽ ✲ ✶✵✵ ✹✸✽✵
❙❡❝1✐♦♥ ✹✳✸✳✷ ❛5 ✐1 ❝♦♥❝❡♣1✉❛❧❧② ❜❡❧♦♥❣5 1♦ 1❤❡ ❝❧❛55 ♦❢ 5❡❝♦♥❞❛0② 1♦ 5❡❝♦♥❞❛0② ❡♥❡0❣② ❝♦♥✲
✈❡05✐♦♥ 1❡❝❤♥♦❧♦❣✐❡5✳ ❚❤❡ ✜051 51❡♣ ✐♥ ❛ 0❡✜♥❡0② ✐5 1❤❡ ❛1♠♦5♣❤❡0✐❝ ❞✐51✐❧❧❛1✐♦♥ ✭❆❚❉❊❙✮✱
✐♥ ✇❤✐❝❤ 1❤❡ ❝0✉❞❡ ♦✐❧ ❣♦❡5 1❤0♦✉❣❤ ❛ ❢0❛❝1✐♦♥❛❧ ❞✐51✐❧❧❛1✐♦♥ ❛1 ❛1♠♦♣5♣❤❡0✐❝ ♣0❡55✉0❡✳
❚❤❡ ♠❛✐♥ ♦✉1♣✉1 ♦❢ 1❤❡ ❆❚❉❊❙ ♣0♦❝❡55 ✐5 0❛✣♥❛1❡✱ ❝♦✉♣❧❡ ♣0♦❞✉❝1✐♦♥ ②✐❡❧❞5 ✸✹✳✹✺✪ ♦❢
♠✐❞❞❧❡ ❞✐51✐❧❧❛1❡✱ ✶✵✳✻✵✪♦❢ ♣❡10♦❧ ❛♥❞ ✶✳✻✵✪ ♦❢ ❤❡❛✈② ❢✉❡❧ ♦✐❧✳ ❚❤❡ ❣❛5❡♦✉5 ❢0❛❝1✐♦♥ ✐5
♥❡❣❧❡❝1❡❞ ❛5 ✐1 ✐5 ♦♥❧② ❛ 5♠❛❧❧ ❡♥❡0❣❡1✐❝ ❢0❛❝1✐♦♥ ❛♥❞ ♦❢1❡♥ 1❤❡ 0❡✜♥❡0② ❣❛5✱ ❛1 ✐1 ✐5 ❝❛❧❧❡❞✱
✐5 0❡✲✉5❡❞ ✐♥ 1❤❡ 0❡✜♥❡0② ✐15❡❧❢ ❢♦0 ❤❡❛1✐♥❣ ♣✉0♣♦5❡5 ✐♥ 1❤❡ ❞✐51✐❧❧❛1✐♦♥ ♣0♦❝❡55❡5✳ ▼✐❞❞❧❡
❞✐51✐❧❧❛1❡ ✐5 ❢✉01❤❡0 0❡✜♥❡❞ 1♦ ♣❡10♦❧✱ ❞✐❡5❡❧ ♦0 ❤❡❛1✐♥❣ ♦✐❧ ❛♥❞ ❝❛♥ ❛❧5♦ ❜❡ ♣0♦❞✉❝❡❞ ❢0♦♠
❤❛0❞ ❝♦❛❧✱ ❧✐❣♥✐1❡ ♦0 ❧✐❣♥♦❝❡❧❧✉❧♦5✐❝ ❜✐♦♠❛55✳
❉✉❡ 1♦ 5❡✈❡0❛❧ ✐♥❝❡♥1✐✈❡ 5❝❤❡♠❡5✱ ❜✐♦❢✉❡❧5 ❤❛❞ ❛ ♠✐♥♦0 5❤❛0❡ ♦❢ ✽✪ ❢♦0 ❞✐❡5❡❧ ❝♦♥5✉♠♣1✐♦♥
❛♥❞ ✷✪ ❢♦0 ♣❡10♦❧ ❝♦♥5✉♠♣1✐♦♥ ✐♥ ●❡0♠❛♥② ✐♥ ✷✵✵✼✳ ❇✐♦5②♥1❤❡1✐❝ ❞✐❡5❡❧ ❝❛♥ ❜❡ ❞✐0❡❝1❧②
♣0♦❞✉❝❡❞ ❢0♦♠ ♦✐❧② ❜✐♦♠❛55✱ ♠❛✐♥❧② 0❛♣❡5❡❡❞ ♦✐❧ ✐♥ ●❡0♠❛♥②✱ ❜② ♠❡❛♥5 ♦❢ 10❛♥5❡51❡0✐✜✲
❝❛1✐♦♥ ✇✐1❤ ♠❡1❤❛♥♦❧ ✭❇✐♦❖✲❉■❊✮✳ ❊1❤❛♥♦❧ ✐5 ♣0♦❞✉❝❡❞ ❢0♦♠ 5✉❣❛0 ❛♥❞ 51❛0❝❤ ❜✐♦♠❛55
✭❇✐♦❙❙✲❊❚◆✮ ❛♥❞ ❛❞♠✐①❡❞ 0❡❝❡♥1❧② ✇✐1❤ ✺✪ 1♦ 1❤❡ 51❛♥❞❛0❞ ♣❡10♦❧✳ ▲✐d✉❡❢❛❝1✐♦♥ ♦❢ ❧✐❣✲
♥♦❝❡❧❧✉❧♦5✐❝ ❜✐♦♠❛55 ✐5 ❦♥♦✇♥ ✉♥❞❡0 1❤❡ ❦❡②✇♦0❞ 5❡❝♦♥❞✲❣❡♥❡0❛1✐♦♥ ❜✐♦❢✉❡❧ ♣0♦❞✉❝1✐♦♥
✷✻
88 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
❛♥❞ ♠❛② ❜❡❝♦♠❡ ❛ ✈✐❛❜❧❡ ❧❛,❣❡✲/❝❛❧❡ ♣,♦❞✉❝2✐♦♥ ♦❢ ❜✐♦❢✉❡❧/ 2❤❛2 ✐/ ♥♦2 /✉❜❥❡❝2 2♦ ❡2❤✐❝❛❧
♣,♦❜❧❡♠/ ✐♥ 2❤❡ ❢✉2✉,❡✳ ❖♥ 2❤❡ ❝♦♥2,❛,②✱ ♦✐❧② ❛/ ✇❡❧❧ ❛/ /✉❣❛, ❛♥❞ /2❛,❝❤ ❜✐♦♠❛// ♠❛②
❜❡ ✉/❡❞ ❛/ ❢♦♦❞ ✐♥/2❡❛❞ ♦❢ ❡♥❡,❣❡2✐❝ ✉/❡✱ ✇❤✐❝❤ ❧❡❛❞/ 2♦ /❡✈❡,❡ ♣♦❧✐2✐❝❛❧ ❞✐/❝✉//✐♦♥/ ✐♥
●❡,♠❛♥②✳
❖2❤❡, ;❊
→
❙❊ "❡❝❤♥♦❧♦❣✐❡+ ❛-❡ "❤❡ ❝♦❦✐♥❣ ♣-♦❝❡++ "❤❛" ♣-♦❞✉❝❡+ ❝♦❦❡ ❢-♦♠ ❤❛-❞ ❝♦❛❧
"❤❛" ✐+ ♠❛✐♥❧② ✉+❡❞ ✐♥ +"❡❡❧ ♣-♦❞✉❝"✐♦♥ ❛♥❞ ❤❡❛" ♣✉♠♣+ ❢♦- ❞♦♠❡+"✐❝ ✉+❡✳ ❆+ ❛❧-❡❛❞②
♠❡♥"✐♦♥❡❞✱ ❤❡❛" ♣✉♠♣+ ♣-♦❞✉❝❡ ❧♦❝❛❧ ❤❡❛" ❛" "❤❡ -❡+✐❞❡♥"✐❛❧ ♣❧❛❝❡ ♦❢ ❝♦♥+✉♠♣"✐♦♥✳ ❚❤❡②
✉+❡ ❡❧❡❝"-✐❝✐"② ❛+ ✐♥♣✉"✱ ❜❡+✐❞❡+ "❤❡ +♦❧❛- "❤❡-♠❛❧ ♦- ❧♦✇✲♣-❡++✉-❡ ❣❡♦"❤❡-♠❛❧ ♣♦"❡♥"✐❛❧✳
✹✳✸✳✷ ❙❡❝♦♥❞❛+② -♦ ❙❡❝♦♥❞❛+② ❊♥❡+❣②
❆♣❛-" ❢-♦♠ "❤❡ "❡❝❤♥♦❧♦❣✐❡+ ❡❧❡❝"-♦❧②+✐+ ❛♥❞ ❞✐❡+❡❧ ♦✐❧ "✉-❜✐♥❡✱ "❤❛" ✇❡-❡ ❛❧-❡❛❞② ❞✐+❝✉++❡❞
✐♥ "❤❡ ❧❛+" +❡❝"✐♦♥✱ "❤❡ -❡✜♥❡-② +❡❝"♦- ✐+ ✐♠♣❧❡♠❡♥"❡❞ ❛+ ❛ +❡" ♦❢ ❙❊
→
❙❊✲"❡❝❤♥♦❧♦❣✐❡+ ❛+
✐❧❧✉+"-❛"❡❞ ✐♥ ❋✐❣✉-❡ ✺✳ ■" ✐+ ♠♦❞❡❧❡❞ ✐♥ ❛ +"②❧✐③❡❞ ✇❛② "♦ -❡♣-❡+❡♥" "❤❡ ❝♦♠♣❧❡①✐"② ♦❢
❛ -❡❛❧✲✇♦-❧❞ -❡✜♥❡-② ❛♥❞ ♣❡-♠✐" "❤❡ ♥❡❝❡++❛-② ❞❡❣-❡❡+ ♦❢ ❢-❡❡❞♦♠ -❡❣❛-❞✐♥❣ "❤❡ ♦✉"♣✉"
♠✐①✳ ❚❤❡ ✜-+" +"❡♣ ✐♥ "❤❡ ❝♦♥✈❡♥"✐♦♥❛❧ -❡✜♥❡-② ♣-♦❝❡++ ✐+ "❤❡ ❛"♠♦+♣❤❡-✐❝ ❞✐+"✐❧❧❛"✐♦♥
✭❆❚❉❊❙✮✱ "❤❛" ♣-♦❞✉❝❡+ -❛✣♥❛"❡ ❛+ ❛ ♠❛✐♥ ♣-♦❞✉❝"✱ ✇✐"❤ ✜①❡❞ ❝♦✉♣❧❡ ♣-♦❞✉❝"✐♦♥ ♦❢
♣❡"-♦❧✱ ♠✐❞❞❧❡ ❞✐+"✐❧❧❛"❡ ❛♥❞ ❤❡❛✈② ❢✉❡❧ ♦✐❧ ✭❍❋❖✮✱ ❛+ ❞✐+❝✉++❡❞ ✐♥ "❤❡ ❧❛+" +❡❝"✐♦♥✳ ❘❛❢✲
✜♥❛"❡ ❛♥❞ ♠✐❞❞❧❡ ❞✐+"✐❧❧❛"❡ -❡♣-❡+❡♥" ✐♥"❡-♠❡❞✐❛"❡ ♣-♦❞✉❝"+✱ "❤❛" ❛-❡ ❢✉-"❤❡- ♣-♦❝❡++❡❞
✐♥"♦ ✉+❛❜❧❡ ❢✉❡❧+✳ ❚❤❡ -❡+♣❡❝"✐✈❡ "❡❝❤♥♦❧♦❣✐❡+ ❤❛✈❡ +❤♦-" "❡❝❤♥✐❝❛❧ ❧✐❢❡"✐♠❡+ ♦❢ ✶✵ ②❡❛-+✱
+♦ "❤❡ -❡✜♥❡-② +❡❝"♦- ❞♦❡+ ♥♦" ♣❡- +❡ ❞✐❝"❛"❡ "❤❡ ♠♦❞❡❧ "❤❡ ❢✉❡❧ ♠✐① ✉+❡❞ ✐♥ "❤❡ "-❛♥+♣♦-"
+❡❝"♦-✳ ❘❛✣♥❛"❡ ♠❛② ❜❡ ❝♦♥✈❡-"❡❞ ✐♥ L❡"-♦❧ ♦- ❍❋❖ ✇✐"❤ "❤❡ "❡❝❤♥♦❧♦❣✐❡+ ❘❛❢✲L❊❚
❛♥❞ ❘❛❢✲❍❋❖✱ "❤❡+❡ "❡❝❤♥♦❧♦❣✐❡+ -❡♣-❡+❡♥" "❤❡ ✈❛❝✉✉♠ ❞✐+"✐❧❧❛"✐♦♥ ✐♥ ❛ -❡❛❧✲✇♦-❧❞ -❡✜♥✲
❡-②✳ ▼✐❞❞❧❡ ❉✐+"✐❧❧❛"❡ ♠❛② ❜❡ ❝♦♥✈❡-"❡❞ ✐♥"♦ ❞✐❡+❡❧ ✭▼❉✲❉■❊✮✱ ❍❡❛"✐♥❣ ❖✐❧ ✭▼❉✲❍❖✮
♦- ❑❡-♦+❡♥❡ ✭▼❉✲❑❊❘✮✳ ❚❤❡ "❡❝❤♥♦✲❡❝♦♥♦♠✐❝ ♣❛-❛♠❡"❡-✐③❛"✐♦♥ ♦❢ "❤❡+❡ "❡❝❤♥♦❧♦❣✐❡+ ✐+
❞❡-✐✈❡❞ ❢-♦♠ ❛❣❣-❡❣❛"✐♦♥ ♦❢ "❤❡ ✈❡-② ❞❡"❛✐❧❡❞ -❡✜♥❡-② -❡♣-❡+❡♥"❛"✐♦♥ ✐♥ ❑-❡② ✭✷✵✵✻✮ ❛♥❞
-❡♣♦-"❡❞ ✐♥ ❚❛❜❧❡ ✶✹
❚❛❜❧❡ ✶✹✿ ❚❡❝❤♥♦✲❡❝♦♥♦♠✐❝ ♣❛-❛♠❡"❡-✐③❛"✐♦♥ ♦❢ "❤❡ ✐♥"❡-♠❡❞✐❛"❡ -❡✜♥❡-② ♣-♦❝❡++❡+✳
❙♦✉-❝❡+✿ ❑-❡② ✭✷✵✵✻✮✱ ▼❲❱ ✭✷✵✵✽✮✱ ♦✇♥ ❝❛❧❝✉❧❛"✐♦+
❚▲❚ ■♥✈❡+"♠❡♥" ❋✐① ❱❛-✐❛❜❧❡ ❈♦♥✈✳ ❋✉❧❧
❈♦+"+ ❈♦+"+ ❈♦+"+ ❊✛✳ ▲♦❛❞
❨❡❛-
e2005
✴❦❲
e2005
✴❦❲
e2005
✴▼❲❤ ✪ ❤✴♣❛
❘❛❢✲L❊❚ ✶✵ ✶✺✼ ✼✳✽✺ ✵✳✺✵✹ ✾✵ ✼✽✽✵
❘❛❢✲❍❋❖ ✶✵ ✹✶ ✷✳✵✺ ✵✳✶✵✹ ✾✵ ✼✽✽✵
❘❛❢✲▼❉ ✶✵ ✶✸✹ ✻✳✼✵ ✵✳✹✹✼ ✾✵ ✼✽✽✵
▼❉✲❑❊❘ ✶✵ ✷✹ ✶✳✷✵ ✵✳✾✶✾ ✾✵ ✼✽✽✵
▼❉✲❍❖ ✶✵ ✶✻ ✵✳✽✵ ✵✳✾✶✾ ✾✵ ✼✽✽✵
▼❉✲❉■❊ ✶✵ ✽ ✵✳✹✵ ✵✳✾✶✾ ✾✵ ✼✽✽✵
✷✼
3.4 The Energy System Module 89
90 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
❚❛❜❧❡ ✶✺✿ ❖✈❡*✈✐❡✇ ♦❢ /❤❡ ❞✐2/*✐❜✉/✐♦♥ /❡❝❤♥♦❧♦❣✐❡2 ✐♥ ❘❊▼■◆❉✲❉✳
❙❡❝♦♥❞❛'②
❊♥❡'❣②
❈❛''✐❡'-
■♥❞✉2/*② ❘❊❙✫❈❖▼ ❚*❛♥2♣♦*/
◆❛/✉*❛❧ ●❛2 ❉❴●❛2✲■◆❉ ❉❴●❛2✲❘❊❙✫❈❖▼ ❉❴●❛2✲❚*❛♥2
❊❧❡❝/*✐❝✐/② ❉❴❊❧✲■◆❉ ❉❴❊❧✲❘❊❙✫❈❖▼ ❉❴❊❧✲❚*❛♥2
❉✐2/*✐❝/ ❍❡❛/ ❉❴❉❍❡❛/✲■◆❉ ❉❴❉❍❡❛/✲❘❊❙✫❈❖▼
❍❡❛/✐♥❣ ❖✐❧ ❉❴❍❡❛/❖✐❧✲■◆❉ ❉❴❍❡❛/❖✐❧✲❘❊❙✫❈❖▼
▲♦❝❛❧ ❍❡❛/ ❉❴▲❍❡❛/✲❘❊❙✫❈❖▼
❈♦❦❡ ❉❴❈♦❦❡✲■◆❉
❍❋❖ ❉❴❍❋❖✲■◆❉
❍✷ ❉❴❍✷✲■◆❉ ❉❴❍✷✲❚*❛♥2
K❡/*♦❧ ❉❴K❡/✲❚*❛♥2
❉✐❡2❡❧ ❉❴❉✐❡✲❚*❛♥2
❑❡*♦2❡♥❡ ❉❴❑❡*✲❚*❛♥2
◆❛/✉*❛❧ ❣❛2 ♥❡/✇♦*❦2 ❝♦♥2✐2/ ♦❢ ♠❛❥♦* ❧♦♥❣✲❞✐2/❛♥❝❡ ♣✐♣❡❧✐♥❡2 ❛♥❞ ❧♦❝❛❧ ❞✐2/*✐❜✉/✐♦♥ ✐♥✲
❢*❛2/*✉❝/✉*❡✱ ❡2♣❡❝✐❛❧❧② ❢♦* /❤❡ ❘❊❙✫❈❖▼ 2❡❝/♦*✳ ❋♦* /❤❡ /*❛♥2♣♦*/ 2❡❝/♦* ✐2 ❛22✉♠❡❞
/❤❛/ ♦♥❧② /❤❡ ❢✉❡❧✲✜❧❧✐♥❣ ✐♥❢*❛2/2/*✉❝/✉*❡ ❛♥❞ /❤❡ ❛❝❝❡22 /♦ /❤❡ ♣✐♣❡❧✐♥❡✲2②2/❡♠ ✐2 *❡Q✉✐*❡❞
❛❞❞✐/✐♦♥❛❧❧② ❛♥❞ ❡①✐2/✐♥❣ ❣❛2 2/❛/✐♦♥2 ❝❛♥ ❜❡ *❡/*♦✜//❡❞✳ ❊❧❡❝/*✐❝✐/② ❣*✐❞2 ✐♥ ●❡*♠❛♥②
❡①✐2/ ✐♥ /❤*❡❡ ❞✐✛❡*❡♥/ ❢♦*♠❛/2✿ ♠❛①✐♠✉♠ ✈♦❧/❛❣❡ ✭✷✷✵ ♦* ✸✽✵ ❦❱✮✱ ♠❡❞✐✉♠ ✈♦❧/❛❣❡ ✭✻ /♦
✸✵ ❦❱✮ ❛♥❞ ❧♦✇ ✈♦❧/❛❣❡ ✭✷✹✵ ♦* ✹✵✵ ❱✮ ❛♥❞ ♥❡❡❞ /♦ ❜❡ ❡①/❡♥❞❡❞ ❢♦* ❝♦♣✐♥❣ ✇✐/❤ ❛ ❧❛*❣❡
2❤❛*❡ ♦❢ ❘❊❙ ✐♥ /❤❡ 2②2/❡♠✱ ✇❤✐❝❤ ✐2 ♥❡❝❡22❛*② ✐♥ ❝❧✐♠❛/❡ ♣♦❧✐❝② 2❝❡♥❛*✐♦2✳ ❖❢ ❝♦✉*2❡✱ ❛
♣*♦♣❡* *❡♣*❡2❡♥/❛/✐♦♥ ♦❢ ❣*✐❞2 ♥❡❡❞2 ❛ ✜♥❡ ❣❡♦❣*❛♣❤✐❝❛❧ *❡2♦❧✉/✐♦♥ ✐♥ /❤❡ ❡♥❡*❣② 2②2/❡♠✳
■♥ ❘❊▼■◆❉✲❉ /❤❡ ❡①♣❡♥2❡2 ❢♦* ❡❧❡❝/*✐❝✐/② ❣*✐❞2 ❛*❡ ❛♣♣*♦①✐♠❛/❡❞✳ ❋♦* /❤❡ ❡❧❡❝/*✐✜❝❛✲
/✐♦♥ ♦❢ /❤❡ /*❛♥2♣♦*/ 2❡❝/♦*✱ ❡✈❡♥/✉❛❧❧② ❛ ♥❡/✇♦*❦ ♦❢ ❝❤❛*❣✐♥❣ 2/❛/✐♦♥2 ✐2 ♥❡❝❡22❛*②✳ ❙✐♥❝❡
❝❤❛*❣✐♥❣ *❡Q✉✐*❡2 ✉♣ /♦ 2❡✈❡*❛❧ ❤♦✉*2✱ ✐/ ✐2 ✉♥❧✐❦❡❧② /❤❛/ /❤❡ ❡①✐2/✐♥❣ ♣❡/*♦❧ 2/❛/✐♦♥ ♥❡/✲
✇♦*❦ ♠❛② ❜❡ /❤❡ ❝♦*❡ ♦❢ /❤❡ ❢✉/✉*❡ ❝❤❛*❣✐♥❣ ✐♥❢*❛2/*✉❝/✉*❡✳ ❉✐2/*✐❝/ ❤❡❛/✐♥❣ ♥❡/✇♦*❦2 ❛*❡
♣✐♣❡❧✐♥❡ 2②2/❡♠2 /❤❛/ ❛*❡ ❡✐/❤❡* ✉♥❞❡* ♦* ❛❜♦✈❡ ❣*♦✉♥❞✳ ❍❡❛/✐♥❣ ❖✐❧ ❛♥❞ ❍❋❖ ✐2 ❛22✉♠❡❞
/♦ ❜❡ /*❛♥2♣♦*/❡❞ ✇✐/❤ /*✉❝❦2 ❛♥❞ ❤❛2 ✈❡*② ❧♦✇ ✉♣❢*♦♥/ ✐♥✈❡2/♠❡♥/ ❝♦2/2 /❤❛/ *❡♣*❡2❡♥/
/❤❡ ❝♦2/2 ❢♦* 2♣❡❝✐❛❧ ❢✉❡❧ /*✉❝❦2 ✇✐/❤ 2❤♦*/ /❡❝❤♥✐❝❛❧ ❧✐❢❡/✐♠❡2✳ ❖♥ /❤❡ ❞✐2/*✐❜✉/✐♦♥ ♦❢ ❝♦❦❡
/❤❡*❡ ✐2 ✈❡*② ❧✐//❧❡ ✐♥❢♦*♠❛/✐♦♥ ❛✈❛✐❧❛❜❧❡✱ ✐/ ✐2 ❛22✉♠❡❞ /❤❛/ ❝♦❦❡ ✐2 ♣*♦❞✉❝❡❞ 2♣❛/✐❛❧❧②
❝❧♦2❡ /♦ /❤❡ 2✐/❡ ♦❢ ✐♥❞✉2/*✐❛❧ ❝♦♥2✉♠♣/✐♦♥✱ 2♦ ❞✐2/*✐❜✉/✐♦♥ ❝♦2/2 ❛*❡ ✈❡*② 2♠❛❧❧✳
❚❤❡ ❜✉✐❧/✲✉♣ ♦❢ ❛ ❤②❞*♦❣❡♥ ♥❡/✇♦*❦ ❢♦* ❞❡❧✐✈❡*✐♥❣ ♣*♦❝❡22 ❤❡❛/ ❢♦* /❤❡ ✐♥❞✉2/*② 2❡❝/♦*
*❡Q✉✐*❡❞ ♣✐♣❡❧✐♥❡ ✐♥❢*❛2/*✉❝/✉*❡✳ ❋♦* /❤❡ /*❛♥2♣♦*/ 2❡❝/♦*✱ ♥♦/ ♦♥❧② /❤❡ ♣✐♣❡❧✐♥❡2 ❛*❡
♥❡❡❞❡❞✱ ❜✉/ ❛❧2♦ ❛ *❡/*♦✜/ ♦❢ ❡①✐2/✐♥❣ ♣❡/*♦❧ 2/❛/✐♦♥2 ✇✐/❤ ❍✷✲✜❧❧✐♥❣ ❞❡✈✐❝❡2✳ ❉✉❡ /♦ ❢❛2/
✜❧❧✲✉♣ ♦❢ /❤❡ /❛♥❦✱ /❤❡ ❡①✐2/✐♥❣ ♣❡/*♦❧ 2/❛/✐♦♥2 ♠❛② ❜❡ ♠❛✐♥/❛✐♥❡❞✳ ❋♦* ♣❡/*♦❧✱ ❞✐❡2❡❧
❛♥❞ ❦❡*♦2❡♥❡ /❤❡ *❡❛2♦♥✐♥❣ ✐2 2✐♠✐❧❛* ❛2 ✇✐/❤ ❤❡❛/✐♥❣ ♦✐❧ ✲ ❢✉❡❧2 ❛*❡ /*❛♥2♣♦*/❡❞ ✇✐/❤
❢✉❡❧ /*✉❝❦2 /♦ /❤❡✐* ♣❧❛❝❡ ♦❢ ❝♦♥2✉♠♣/✐♦♥ ❛♥❞ ✉♣❢*♦♥/ ✐♥✈❡2/♠❡♥/ ❝♦2/2 ❛*❡ ❧♦✇✳ ❚❤❡
✐♥❢*❛2/*✉❝/✉*❡ ♦❢ ❣❛2 2/❛/✐♦♥2 ❛❧*❡❛❞② ❡①✐2/2 ❛♥❞ ♦♥❧② ♥❡❡❞2 /♦ ❜❡ ♠❛✐♥/❛✐♥❡❞✳
✷✾
3.4 The Energy System Module 91
❚❛❜❧❡ ✶✻✿ ❚❡❝❤♥♦✲❡❝♦♥♦♠✐❝ ♣❛0❛♠❡1❡0✐③❛1✐♦♥ ♦❢ 1❤❡ ❞✐510✐❜✉1✐♦♥ 1❡❝❤♥♦❧♦❣✐❡5 0❡♣0❡5❡♥1❡❞
✐♥ ❘❊▼■◆❉✲❉✳ ❖✇♥ ❝❛❧❝✉❧❛1✐♦♥5✳
❚▲❚ ■♥✈❡51♠❡♥1 ❋✐① ❈♦♥✈✳ ❋✉❧❧
❈♦515 ❈♦515 ❊✛✳ ▲♦❛❞
❨❡❛0
e2005
✴❦❲
e2005
✴❦❲ ✪ ❤✴❛
❉❴●❛5✲■◆❉ ✺✺ ✶✻✶ ✵✳✵✷ ✾✵ ✼✵✶✵
❉❴❊❧✲■◆❉ ✺✺ ✶✵✵✻ ✵✳✶✵ ✾✼ ✼✵✶✵
❉❴❉❍❡❛1✲■◆❉ ✺✺ ✶✻✶ ✵✳✵✷ ✾✺ ✸✺✵✵
❉❴❍❡❛1❖✐❧✲■◆❉ ✺✺ ✷✵ ✵ ✶✵✵ ✻✺✼✵
❉❴❍❋❖✲■◆❉ ✺✺ ✷✵ ✵ ✶✵✵ ✻✺✼✵
❉❴❈♦❦❡✲■◆❉ ✺✺ ✷✵ ✵✳✵✶ ✶✵✵ ✼✽✽✵
❉❴❍✷✲■◆❉ ✺✺ ✷✹✶ ✵✳✵✷ ✶✵✵ ✼✵✶✵
❉❴●❛5✲❘❊❙✫❈❖▼ ✺✺ ✸✷✷ ✵✳✶✵ ✾✵ ✹✸✽✵
❉❴❊❧✲❘❊❙✫❈❖▼ ✺✺ ✶✺✷✾ ✵✳✼✻ ✾✹ ✹✸✽✵
❉❴❉❍❡❛1✲❘❊❙✫❈❖▼ ✺✺ ✶✻✶ ✵✳✵✷ ✾✺ ✸✺✵✵
❉❴❍❡❛1❖✐❧✲❘❊❙✫❈❖▼ ✺✺ ✹✵ ✵✳✵✷ ✶✵✵ ✹✸✽✵
❉❴▲❍❡❛1✲❘❊❙✫❈❖▼ ✺✺ ✵✳✵✵✵✶ ✵ ✶✵✵ ✽✼✻✵
❉❴●❛5✲❚0❛♥5 ✺✺ ✶✻✶ ✵✳✵✷ ✾✵ ✼✵✶✵
❉❴❊❧✲❚0❛♥5 ✺✺ ✶✺✵✵ ✵✳✵✽ ✶✵✵ ✻✶✸✵
❉❴❍✷✲❚0❛♥5 ✺✺ ✷✹✶ ✵✳✶✷ ✶✵✵ ✺✷✻✵
❉❴Y❡1✲❚0❛♥5 ✺✺ ✽✵ ✵✳✵✽ ✶✵✵ ✻✶✸✵
❉❴❉✐❡✲❚0❛♥5 ✺✺ ✽✵ ✵✳✵✽ ✶✵✵ ✻✶✸✵
❉❴❑❡0✲❚0❛♥5 ✺✺ ✽✵ ✵✳✵✽ ✶✵✵ ✻✶✸✵
✹✳✺ ❚$❛♥'♣♦$* ❚❡❝❤♥♦❧♦❣✐❡'
❚❤❡ 10❛♥5♣♦01 5❡❝1♦0✱ ❝♦♥✈❡01✐♥❣ ❢✉❡❧5 1♦ ❡♥❡0❣② 5❡0✈✐❝❡5 ✐♥ 1❤❡ ❢♦0♠ ♦❢ 5♣❛1✐❛❧ 0❡❧♦❝❛✲
1✐♦♥ ♦❢ ❣♦♦❞5 ❛♥❞ ♣❛55❡♥❣❡05✱ ✐5 ❡①♣❧✐❝✐1❧② ✐♥❝❧✉❞❡❞ ✐♥ ❘❊▼■◆❉✲❉✳ ❚♦ ❢✉❧✜❧❧ ♠♦❜✐❧✐1②
0❡^✉✐0❡♠❡♥15✱ ❝♦♥✈❡♥1✐♦♥❛❧ ❛♥❞ ✐♥♥♦✈❛1✐✈❡ 10❛♥5♣♦01 1❡❝❤♥♦❧♦❣✐❡5 ♦❢ ✈❛0✐♦✉5 ♠♦❞❡5 ❛0❡
❝♦♥5✐❞❡0❡❞✱ 5❡❡ ❚❛❜❧❡ ✶✼✳
▲♦♥❣✲❞✐51❛♥❝❡ ♣❛55❡♥❣❡0 10❛♥5♣♦01 ✐5 ♣0♦✈✐❞❡❞ ❜② ❞♦♠❡51✐❝ ❛✈✐❛1✐♦♥ ✭Y❧❛♥❡✲❑❊❘✮✱ ■♥✲
1❡0❝✐1② ❛♥❞ ■❈❊ 10❛✐♥5 ✭❚0❛✐♥✲❊▲✮ ❛♥❞ ❧♦♥❣✲❞✐51❛♥❝❡ ❜✉5❡5 ✭❈♦❛❝❤✲❉■❊✮✱ ❛5 ✇❡❧❧ ❛5 ❜②
♠♦1♦0✐③❡❞ ♣0✐✈❛1❡ 10❛♥5♣♦01 ✭▼Y❚✮✳ ■♥ ●❡0♠❛♥②✱ ❛ ❧❛0❣❡ 5❤❛0❡ ♦❢ 1❤❡ ❝❛0 ✢❡❡1 ❝♦♥5✐515
♦❢ ❞✐❡5❡❧ ❝❛05✱ ✇❤✐❝❤ ❛0❡ ❝❤❛0❛❝1❡0✐③❡❞ ❜② 5♦♠❡✇❤❛1 ❤✐❣❤❡0 ✉♣❢0♦♥1 ❝♦515✱ ❜✉1 ❞✐❡5❡❧ ✐5
0❡❧❛1✐✈❡❧② ❧❡55 1❛①❡❞ 1❤❛♥ ♣❡10♦❧✳ ❈♦♥5❡^✉❡♥1❧②✱ 1❤♦5❡ ✇❤♦ ♥❡❡❞ 1♦ ❢0❡^✉❡♥1❧② 10❛✈❡❧ ❧♦♥❣
❞✐51❛♥❝❡5 ❝❤♦♦5❡ ❞✐❡5❡❧ ❝❛05✳ ❖❜✈✐♦✉5❧②✱ ♦♥❡ ❝❛♥ ❛❧5♦ 10❛✈❡❧ 5❤♦01 ❞✐51❛♥❝❡5 ✇✐1❤ ❞✐❡5❡❧
❝❛05✱ ❛5 ✇❡❧❧✱ ❛♥❞ ✈✐❝❡ ✈❡05❛ ♦♥❡ ❝❛♥ 10❛✈❡❧ ❧♦♥❣ ❞✐51❛♥❝❡5 ✇✐1❤ ♣❡10♦❧ ❝❛05 1❤❛1 ❛0❡ ♦✇♥❡❞
♠❛✐♥❧② ❢♦0 1❤❡ ♣✉0♣♦5❡ ♦❢ 5❤♦01 ❝♦♠♠✉1✐♥❣✳ ■♥ ❘❊▼■◆❉✲❉✱ 1❤✐5 ❢❛❝1 ✐5 ❛❝❝♦✉♥1❡❞ ❢♦0 ❜②
❞❡✜♥✐♥❣ ❛ ♠❛✐♥ ♣✉0♣♦5❡ ❢♦0 ❛ ❝❧❛55 ♦❢ ❝❛05 ❛♥❞ 1❤❡♥ ❡♥5✉0✐♥❣ ❛ 5❡❝♦♥❞ ♣✉0♣♦5❡ 1❡❝❤♥✐✲
✸✵
92 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
❚❛❜❧❡ ✶✼✿ ❖✈❡*✈✐❡✇ ♦❢ /*❛♥1♣♦*/ /❡❝❤♥♦❧♦❣✐❡1 ✐♥ ❘❊▼■◆❉✲❉✳ ❆❜❜*❡✈✐❛/✐♦♥1 ❛*❡ ❍②❜*✐❞
✭❍②✮✱ E❧✉❣✲✐♥ ❍②❜*✐❞ ✭E❍②✮ ❛♥❞ ❋✉❡❧ ❈❡❧❧ ✭❋❈✮✳
❙❡❝♦♥❞❛'②
❊♥❡'❣②
❈❛''✐❡'-
❊♥❡'❣② ❙❡'✈✐❝❡-
E❛11❡♥❣❡* ▲♦♥❣ ❉✐1/❛♥❝❡ E❛11❡♥❣❡* ❙❤♦*/ ❉✐1/❛♥❝❡ ❋*❡✐❣❤/
✭E▲❉✮ ✭E❙❉✮ ✭❋✮
E❡/*♦❧ ❈❛*✲E❊❚
❈❛*✲E❊❚✴❍②
❈❛*✲E❊❚✴E❍②
❉✐❡1❡❧
❈❛*✲❉■❊ ❈❛*✲❉■❊✴E❍② ❚*✉❝❦✲❉■❊
❈❛*✲❉■❊✴❍② ❚*❛✐♥✲❉■❊ ❚*❛✐♥✲❉■❊
❈♦❛❝❤✲❉■❊ ❇✉1✲❉■❊ ❙❤✐♣✲❉■❊
❇✉1✲❉■❊✴❍②
◆❛/✉*❛❧
●❛1 ❈❛*✲●❆❙
❈❛*✲●❆❙✴❍②
❊❧❡❝/*✐❝✐/② ❚*❛✐♥✲❊▲ ❈❛*✲❊▲ ❚*❛✐♥✲❊▲
❚*❛✐♥✲❊▲
▲✐❣❤/❘❛✐❧✲❊▲
H2
❈❛*✲
H2
✴❍②
❈❛*✲
H2
✴❋❈
❇✉1✲
H2
❑❡*♦1❡♥❡ E❧❛♥❡✲❑❊❘
❝❛❧❧② ❜② ♠❡❛♥1 ♦❢ ✬❝♦✉♣❧❡ ♣*♦❞✉❝/✐♦♥✬ ♦❢ /❤❡ /*❛♥1♣♦*/ /❡❝❤♥♦❧♦❣②✳ ❚❤❡ ❝❧❛11✐✜❝❛/✐♦♥ ♦❢
❚❛❜❧❡ ✶✼ *❡✢❡❝/1 /❤❡ ♠❛✐♥ ♣✉*♣♦1❡1 ♦❢ /❤❡ *❡1♣❡❝/✐✈❡ /*❛♥1♣♦*/ /❡❝❤♥♦❧♦❣✐❡1✳ ❋♦* ▼E❚
/*❛♥1♣♦*/✱ /❤❡*❡ ❛*❡ ❛❞❞✐/✐♦♥❛❧❧② ✈❛*✐♦✉1 ✐♥♥♦✈❛/✐✈❡ ❝❛* /❡❝❤♥♦❧♦❣✐❡1✳ ▲♦❝❛❧ /*❛✐♥1 *❡♣*❡✲
1❡♥/ *❡❣✐♦♥❛❧ ♦* ♠❡❞✐✉♠✲❞✐1/❛♥❝❡ /*❛✐♥1 /❤❛/ ❡✐/❤❡* *✉♥ ♦♥ ❞✐❡1❡❧ ♦* ❡❧❡❝/*✐❝✐/②✳ ■♥♥❡*✲❝✐/②
♣✉❜❧✐❝ /*❛♥1♣♦*/ ✐1 ❝♦✈❡*❡❞ ❜② ❧✐❣❤/ *❛✐❧ /*❛✐♥1 ❛♥❞ ❞✐❡1❡❧✱ ❛1 ✇❡❧❧ ❛1 ✐♥♥♦✈❛/✐✈❡ ❜✉1❡1✳
❚❤❡ ❢*❡✐❣❤/ /*❛♥1♣♦*/ 1❡❝/♦* ❝♦♥1✐1/1 ♦❢ /*✉❝❦1✱ /*❛✐♥1 ❛♥❞ ✐♥❧❛♥❞ ♥❛✈✐❣❛/✐♦♥✳
❚❛❜❧❡ ✶✽ ♣*❡1❡♥/1 /❤❡ /❡❝❤♥♦✲❡❝♦♥♦♠✐❝ ♣❛*❛♠❡/❡*✐③❛/✐♦♥ ❢♦* ❛❧❧ ▼E❚ ❝❛* /❡❝❤♥♦❧♦❣✐❡1
✇✐/❤ ✐♥✐/✐❛❧ ✐♥✈❡1/♠❡♥/ ❝♦1/1 ♣❡* ❝❛*✱ ❢✉❡❧ ❞❡♠❛♥❞✱ ②❡❛*❧② 1❤♦*/✲ ❛♥❞ ❧♦♥❣✲❞✐1/❛♥❝❡ ♣❡*❢♦*✲
♠❛♥❝❡ ❛♥❞ ✈❛*✐❛❜❧❡ ❝♦1/1✳ ❋✐①❡❞ ❝♦1/1 ❛*❡ ♥♦/ ❝♦♥1✐❞❡*❡❞ ❛1 ❞❛/❛ ✐1 ✈❡*② ❝❛1❡✲1♣❡❝✐✜❝ ❛♥❞
❛❧1♦ 1❝❛*❝❡✱ ❡1♣❡❝✐❛❧❧② ❢♦* ♣✉❜❧✐❝ /*❛♥1♣♦*/ ❛♥❞ ❝♦♠♠❡*❝✐❛❧ /*✉❝❦✐♥❣ /❡❝❤♥♦❧♦❣✐❡1✳ ❚❤❡
✐♥✈❡1/♠❡♥/ ❝♦1/1 ♦❢ ✐♥♥♦✈❛/✐✈❡ ❝❛* /❡❝❤♥♦❧♦❣✐❡1 ❝❛♥ ❜❡ *❡❞✉❝❡❞ ♦✈❡* /✐♠❡ ❜② /✇♦ ♠❡❛♥1✿
❚❡❝❤♥♦❧♦❣②✲1♣❡❝✐✜❝ ❧❡❛*♥✐♥❣✲❜②✲❞♦✐♥❣ ❜② ❜✉✐❧❞✐♥❣ ✉♣ ❝❛♣❛❝✐/✐❡1 ♦* ❝❧✉1/❡*✲❧❡❛*♥✐♥❣ ❢♦*
❜❛//❡*✐❡1✳ ❋♦* ❤②❜*✐❞✱ ♣❧✉❣✲✐♥ ❤②❜*✐❞ ❛♥❞ ❡❧❡❝/*✐❝ /❡❝❤♥♦❧♦❣✐❡1✱ ❛♥ ✐♥❝*❡❛1✐♥❣ 1❤❛*❡ ♦❢
/❤❡ 1♣❡❝✐✜❝ ✐♥✈❡1/♠❡♥/ ❝♦1/1 ✐1 ❝❛✉1❡❞ ❜② /❤❡ ❜❛//❡*② ♣❛❝❦ ❛♥❞ *❡❧❛/❡❞ /❡❝❤♥♦❧♦❣②✳ ■♥
/❤❡ ❜❛//❡*② 1❡❝/♦*✱ 1✉❜1/❛♥/✐❛❧ ❝♦1/ *❡❞✉❝/✐♦♥1 ❝❛♥ ❜❡ ❡①♣❡❝/❡❞✳ ❆1 ❧❡❛*♥✐♥❣✲❜② ❞♦✐♥❣
❡✛❡❝/1 ❛*❡ ♦❝❝✉**✐♥❣ ❛/ ❛ ❜❛//❡*②✲❧❡✈❡❧✱ /❤❡ ❝❛♣❛❝✐/② ❛❞❞✐/✐♦♥1 ♦❢ ❛❧❧ /❡❝❤♥♦❧♦❣✐❡1 /❤❛/ ✉1❡
❜❛//❡*✐❡1 ❛*❡ ❝♦♥/*✐❜✉/✐♥❣ /♦ /❤❡ ❧❡❛*♥✐♥❣✳ ❚❤❡ ✐♥✈❡1/♠❡♥/ ❝♦1/1 ❢♦* ❜❛//❡*✐❡1 ❛*❡ ❛❣❛✐♥
✸✶
3.4 The Energy System Module 93
❚❛❜❧❡ ✶✽✿ ❚❡❝❤♥♦✲❡❝♦♥♦♠✐❝ ♣❛0❛♠❡1❡0✐③❛1✐♦♥ ♦❢ ▼5❚ 1❡❝❤♥♦❧♦❣✐❡7 ✐♥ ❘❊▼■◆❉✲❉✳
❙❉✴▲❉ ✐♥❞✐❝❛1❡7 1❤❡ ②❡❛0❧② 7❤♦01✴❧♦♥❣✲❞✐71❛♥❝❡ ❞0✐✈✐♥❣✳ ■♥✈❡71♠❡♥1 ❝♦717 ❛0❡
7♣❧✐1 ✐♥1♦ ❝❤❛77✐7✴❞0✐✈❡10❛✐♥ ✰ ❜❛11❡0②✲0❡❧❛1❡❞ ❝♦717✱ ✇✐1❤ 1❤❡ ❧❛11❡0 ❡①❤✐❜✐1✲
✐♥❣ ❝❧✉71❡0 ❧❡❛0♥✐♥❣ ❛❝0♦77 ❛❧❧ 1❡❝❤♥♦❧♦❣✐❡7✳ ❈❛0✲❍✷✴❍② ❛♥❞ ❈❛0✲❍✷✴❋❈ ❛❞✲
❞✐1✐♦♥❛❧❧② ❤❛✈❡ ❧❡❛0♥✐♥❣ ✐♥ 1❤❡ ❝❤❛77✐7✴❞0✐✈❡10❛✐♥ ✐♥✈❡71♠❡♥1 ❝♦717 ❜② ✻✳✼ ❛♥❞
✶✸✳✽ ❚7❞✳
e
✱ 0❡7♣❡❝1✐✈❡❧②✱ ✇✐1❤ ❛ ❧❡❛0♥✐♥❣ 0❛1❡ ♦❢ ✺✪✳ ❙♦✉0❝❡7✿ ❲✐❡17❝❤❡❧ ❡1 ❛❧✳
✭✷✵✶✵✮✱ ❊❞✇❛0❞7 ❡1 ❛❧✳ ✭✷✵✵✽❜✮✱ ❊❞✇❛0❞7 ❡1 ❛❧✳ ✭✷✵✵✽❛✮✱ ●W❧ ✭✷✵✵✽✮✱ ❑✐0❝❤♥❡0
❡1 ❛❧✳ ✭✷✵✵✾✮✱ ❑0❡② ✭✷✵✵✻✮✱ ♦✇♥ ❝❛❧❝✉❧❛1✐♦♥7✳
❚▲❚ ■♥✈❡71♠❡♥1 ❋✉❡❧ ▲❉ ❙❉ ❱❛0✐❛❜❧❡
❈♦717 ❉❡♠❛♥❞ ❈♦717
❚7❞✳
e2005
❦❲❤ ❚7❞✳❦♠ ❚7❞✳❦♠
e2005
❨❡❛0 ✴❝❛0 ✴✶✵✵ ❦♠ ✴❛ ✴❛ ✴❦♠
❈❛0✲❊❚◆ ✶✷ ✶✾✳✺ ✻✽✳✵✵ ✷✳✹ ✾✳✻ ✵✳✵✷✼
❈❛0✲❊❚◆✴❍② ✶✷ ✶✾✳✺✰✻✳✹ ✹✶✳✻✺ ✷✳✹ ✾✳✻ ✵✳✵✸✸
❈❛0✲❊❚◆✴5❍② ✶✷ ✶✾✳✺✰✽✳✶ ✹✹✳✾✵ ✷✳✹ ✾✳✻ ✵✳✵✼✸
❈❛0✲❉■❊ ✶✵ ✷✶✳✹ ✻✼✳✸✷ ✶✺✳✹ ✻✳✻ ✵✳✵✷✺
❈❛0✲❉■❊✴❍② ✶✵ ✷✶✳✹✰✻✳✹ ✸✽✳✻✶ ✶✺✳✹ ✻✳✻ ✵✳✵✸✵
❈❛0✲❉■❊✴5❍② ✶✶ ✷✶✳✹✰✽✳✶ ✸✾✳✵✵ ✷✳✹ ✾✳✻ ✵✳✵✼✸
❈❛0✲●❆❙ ✶✷ ✷✶✳✻ ✺✷✳✵✵ ✶✼✳✻ ✹✳✹ ✵✳✵✷✼
❈❛0✲●❆❙✴❍② ✶✷ ✷✶✳✻✰✻✳✹✸ ✸✽✳✼✵ ✶✼✳✻ ✹✳✹ ✵✳✵✸✵
❈❛0✲❍✷✴❍② ✶✷ ✷✻✳✽✰✻✳✹ ✸✾✳✸✵ ✸✳✵ ✶✷✳✵ ✵✳✵✸✵
❈❛0✲❍✷✴❋❈ ✶✷ ✸✸✳✸✰✶✳✻ ✷✸✳✸✵ ✸✳✵ ✶✷✳✵ ✵✳✵✼✺
❈❛0✲❊▲ ✶✵ ✶✾✳✻✰✶✼✳✼ ✶✺✳✵✵ ✵ ✶✺✳✵ ✵✳✵✾✾
7♣❧✐1 ✐♥1♦ ❛ ❧♦❝❛❧ ❛♥❞ ❣❧♦❜❛❧ ❝♦♠♣♦♥❡♥1✳ ■♥ 1❤❡ ❢✉1✉0❡✱ 1❤❡ ❢✉❡❧ ❞❡♠❛♥❞ ♦❢ ❝♦♥✈❡♥1✐♦♥❛❧
❝❛0 1❡❝❤♥♦❧♦❣✐❡7 ✐7 ❡①♣❡❝1❡❞ 1♦ ❢♦❧❧♦✇ 1❤❡ ❞❡❝❧✐♥✐♥❣ 10❡♥❞ ♦♥ ❛ ♣❡0 ✶✵✵❦♠ ❜❛7✐7✳ ❚❛❜❧❡
✶✾ ✐❧❧✉710❛1❡7 1❤❡ 1❡❝❤♥♦✲❡❝♦♥♦♠✐❝ ♣❛0❛♠❡1❡0✐③❛1✐♦♥ ❢♦0 1❤❡ ♣✉❜❧✐❝ 10❛♥7♣♦01 1❡❝❤♥♦❧♦❣✐❡7
❛♥❞ ❚❛❜❧❡ ✷✵ ❢♦0 1❤❡ ❢0❡✐❣❤1 10❛♥7♣♦01 1❡❝❤♥♦❧♦❣✐❡7
❚❤❡ ❞②♥❛♠✐❝7 ♦❢ 1❤❡ 10❛♥7♣♦01❛1✐♦♥ 7❡❝1♦0 ❛0❡ ✈❡0② ❞✐✣❝✉❧1 1♦ ❜❡ 0❡♣0❡7❡♥1❡❞ ✐♥ ❛♥
❡♥❡0❣② 7②71❡♠ ♠♦❞❡❧ 1❤❛1 ❢♦❧❧♦✇7 1❤❡ ❧♦❣✐❝ ♦❢ ✐♠♣❧✐❝✐1❧② ♠✐♥✐♠✐③✐♥❣ ❝♦717✳ ❋♦0 ♣❛77❡♥❣❡0
10❛♥7♣♦01✱ ♥♦♥✲`✉❛♥1✐✜❛❜❧❡ ❢❛❝1♦07 7✉❝❤ ❛7 ♠✐♥✐♠✐③✐♥❣ 10❛✈❡❧ 1✐♠❡ ♦0 ♠❛①✐♠✐③✐♥❣ 10❛✈❡❧
❝♦♠❢♦01 ❛0❡ ❢0❡`✉❡♥1❧② ♠♦0❡ ✐♥✢✉❡♥1✐❛❧ ❢♦0 ❝❤♦♦7✐♥❣ ❛ ♣❛01✐❝✉❧❛0 ❦✐♥❞ ♦❢ 10❛♥7♣♦01❛1✐♦♥
♠♦❞❡ 1❤❛♥ ♣✉0❡ ❝♦71 ❝❛❧❝✉❧❛1✐♦♥7✳ ❯0❜❛♥✐③❛1✐♦♥ 1❡♥❞❡♥❝✐❡7 ❛♥❞ ❣❡♥❡0❛❧ ❞❡♠♦❣0❛♣❤✐❝
❞❡✈❡❧♦♣♠❡♥17 ❞♦ ❤❛✈❡ ❛♥ ✐♥✢✉❡♥❝❡✱ 1♦♦✳ ■♥ 1❤❡ ❝❛7❡ ♦❢ ♠♦1♦0✐③❡❞ ♣0✐✈❛1❡ 10❛♥7♣♦01
✭▼5❚✮ ❝❛0 ♦✇♥❡07 ♦❢1❡♥ ❞♦ ♥♦1 ❜❛7❡ 1❤❡✐0 ✐♥✈❡71♠❡♥1 ❝❤♦✐❝❡7 ♦♥ ❝❧❡❛♥ ❝♦71 ❝❛❧❝✉❧❛1✐♦♥7✱
❜✉1 ❝♦♥7✐❞❡0 1❤❡✐0 ❝❛0 ❛7 ❢✉❧✜❧❧✐♥❣ ♦1❤❡0 ♣✉0♣♦7❡7 1❤❛♥ ❥✉71 1❤❡ 1❡❝❤♥✐❝❛❧ 10❛♥7♣♦01❛1✐♦♥✱
❡✳❣✳ 71❛1✉7 7②♠❜♦❧✱ 7❡❧❢✲❡①♣0❡77✐♦♥✳ ❆7 0❡❣❛0❞7 ❢0❡✐❣❤1 10❛♥7♣♦01✱ 1❤❡ ❣0♦✇1❤ 0❛1❡ ♦❢
10❛♥7♣♦01❡❞ 1♦♥✲❦♠ ❤❛7 ❤✐71♦0✐❝❛❧❧② ❜❡❡♥ ✈❡0② ❝❧♦7❡❧② ❝♦00❡❧❛1❡❞ 1♦ 1❤❡ ❣0♦✇1❤ 0❛1❡ ♦❢
●❉5 ✭❋❡✐❣❡ ✷✵✵✼✮✳ ❆7 1❤❡ ✉♥❞❡0❧②✐♥❣ ❞0✐✈❡07 ♦❢ 1❤✐7 ❧✐♥❦ ❛0❡ 0❛1❤❡0 ❝♦♠♣❧❡①✱ 1❤❡0❡ ✐7
♥♦ ❞✐0❡❝1 ❧✐♥❦ ❜❡1✇❡❡♥ ●❉5 ❛♥❞ ❢0❡✐❣❤1 10❛♥7♣♦01 ✈♦❧✉♠❡ ✐♥ ❘❊▼■◆❉✲❉✳ ■♥ ♣0✐♥❝✐♣❧❡✱
1❤❡② ❝♦✉❧❞ ❜❡❝♦♠❡ ❞❡❝♦✉♣❧❡❞ ✐♥ 1❤❡ ❢✉1✉0❡✱ ✐❢ 1❤❡ ❡❝♦♥♦♠② ❜❡❝❛♠❡ ♠♦0❡ ❡✣❝✐❡♥1 ✐♥
✸✷
94 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
❚❛❜❧❡ ✶✾✿ ❚❡❝❤♥♦✲❡❝♦♥♦♠✐❝ ♣❛0❛♠❡1❡0✐③❛1✐♦♥ ♦❢ ♣✉❜❧✐❝ 10❛♥5♣♦01 1❡❝❤♥♦❧♦❣✐❡5 ✐♥
❘❊▼■◆❉✲❉✳ ❚❤❡ 1♦♣ ♣❛♥❡❧ ❞✐5♣❧❛②5 1❡❝❤♥♦❧♦❣✐❡5 1❤❛1 5❡0✈❡ 5❤♦01 ❞✐51❛♥❝❡
❞0✐✈✐♥❣✱ 1❤❡ ❜♦11♦♠ ♦♥❡ ❧♦♥❣ ❞✐51❛♥❝❡ ❞0✐✈✐♥❣✳ ❋♦0 ❇✉5✲❍✷✱ 1❤❡ ✼✵❚5❞✳
e
❛0❡
5✉❜❥❡❝1 1♦ ❧❡❛0♥✐♥❣ ✇✐1❤ ❛ 0❛1❡ ♦❢ ✺✪✳ ❙♦✉0❝❡5✿ ❑0❡② ✭✷✵✵✻✮✱ ❲✐❡15❝❤❡❧ ❡1 ❛❧✳
✭✷✵✶✵✮✱ ♦✇♥ ❝❛❧❝✉❧❛1✐♦♥5✳
❚▲❚ ■♥✈❡51♠❡♥1 ❋✉❡❧ ◆✉♠❜❡0 ❨❡❛0❧② ❋✐① ❱❛0✐❛❜❧❡
❈♦515 ❉❡♠❛♥❞ ♦❢ ❘❛♥❣❡ ❈♦515 ❈♦515
❚5❞✳
e2005
❦❲❤ X❛55❡♥✲ ❚5❞✳ ❦♠
e2005
✴✈❡❤✐❝❧❡ ✴✶✵✵ ❦♠ ❣❡05 ✴❛ ✪ ✴❦♠
❇✉5✲❉■❊ ✶✸ ✷✽✵ ✹✶✻ ✷✵ ✻✶✷ ✲ ✵✳✹✶✷
❇✉5✲❉■❊✴❍② ✶✸ ✸✷✽ ✷✾✶ ✷✵ ✻✶✷ ✲ ✵✳✹✶✷
❇✉5✲❍✷ ✶✸ ✷✽✵✰✼✵ ✹✵✵ ✷✵ ✻✶✷ ✲ ✵✳✹✵✺
❚0❛✐♥✲❉■❊ ✷✻ ✷✷✼✵ ✶✺✸✵ ✽✵ ✷✾✻✵ ✵✳✵✷ ✶✳✾
❚0❛✐♥✲❊▲ ✷✻ ✷✵✾✵ ✾✶✹ ✽✵ ✺✻✵✵ ✵✳✵✷ ✶✳✽
▲✐❣❤1❘❛✐❧✲❊▲ ✷✻ ✷✵✸✵ ✽✶✶ ✺✺ ✹✶✷✺ ✵✳✵✷ ✶✳✽
❈♦❛❝❤✲❉■❊ ✶✸ ✷✽✵ ✷✹✵ ✷✺ ✽✼✺ ✲ ✵✳✹✶✷
❚0❛✐♥✲❊▲ ✷✻ ✶✻✼✶✵ ✷✶✵✵ ✷✷✸ ✻✻✾✵✵ ✶✳✺ ✷✳✺
X❧❛♥❡✲❑❊❘ ✶✼ ✷✷✻✵✵ ✽✵✵✵ ✶✶✺ ✷✽✼✺✵ ✵✳✵✶✸ ✸✳✼✷
❚❛❜❧❡ ✷✵✿ ❚❡❝❤♥♦✲❡❝♦♥♦♠✐❝ ♣❛0❛♠❡1❡0✐③❛1✐♦♥ ♦❢ ❢0❡✐❣❤1 10❛♥5♣♦01 1❡❝❤♥♦❧♦❣✐❡5 ✐♥
❘❊▼■◆❉✲❉✳ ❙♦✉0❝❡✿ ❑0❡② ✭✷✵✵✻✮✱ ♦✇♥ ❝❛❧❝✉❧❛1✐♦♥5✳
❚▲❚ ■♥✈❡51♠❡♥1 ❋✉❡❧ ▲♦❛❞ ❨❡❛0❧② ❋✐① ❱❛0✐❛❜❧❡
❈♦515 ❉❡♠❛♥❞ ❈❛♣❛✲ ❘❛♥❣❡ ❈♦515 ❈♦515
❚5❞✳
e2005
❦❲❤ ❝✐1② ❚5❞✳ ❦♠
e2005
✴✈❡❤✐❝❧❡ ✴✶✵✵ ❦♠ 1 ✴❛ ✪ ✴❦♠
❚0✉❝❦✲❉■❊ ✶✵ ✸✸✳✻ ✷✷✺ ✺ ✶✷✺ ✲ ✵✳✵✼✷✹
❚0❛✐♥✲❉■❊ ✷✼ ✸✺✵✵ ✷✼✽✵ ✹✸✹ ✸✵✸✽✵ ✵✳✵✼✻ ✸✳✵✶
❚0❛✐♥✲❊▲ ✷✼ ✸✼✵✵ ✶✷✺✵ ✹✸✹ ✸✵✸✽✵ ✵✳✵✺ ✸✳✵✷
❙❤✐♣✲❉■❊ ✹✼ ✷✸✹✵ ✶✶✵✵✵ ✾✶✽ ✷✹✷✸✺ ✵✳✵✼ ✶✳✾✹
1❡0♠5 ♦❢ 10❛♥5♣♦01✲❦♠ ♣❡0 ●❉X✳ ❚♦ ❛❝❝♦✉♥1 ❢♦0 1❤❡5❡ ❢❛❝1♦05✱ 1❤❡ ②❡❛0❧② 1♦1❛❧ ❛♠♦✉♥15 ♦❢
❞❡♠❛♥❞❡❞ 1♦♥✲❦♠ ❛♥❞ ♣❛55❡♥❣❡0✲❦♠ ❢♦0 ❧♦♥❣✲ ❛♥❞ 5❤♦01✲❞✐51❛♥❝❡ 10❛✈❡❧❧✐♥❣ ❛0❡ ♣❛01 ♦❢ 1❤❡
5❝❡♥❛0✐♦ ❞❡✜♥✐1✐♦♥ ✐♥ ❘❊▼■◆❉✲❉ ❛♥❞ ❛0❡ ❡①♦❣♦♥♦✉5✱ ✐❢ ♥♦1 ❡①♣❧✐❝✐1❧② 51❛1❡❞ ♦1❤❡0✇✐5❡✳
❲✐1❤♦✉1 1❤❡5❡ ❝♦♥510❛✐♥15✱ 1❤❡ ♠♦❞❡❧ ❤❛5 ❛ 1❡♥❞❡♥❝② 1♦ 5❡✈❡0❡❧② ❞❡❝0❡❛5❡ ❢0❡✐❣❤1 ❛♥❞
5❤♦01✲❞✐51❛♥❝❡ ♣❛55❡♥❣❡0 10❛♥5♣♦01 ❛♥❞ ✐♥❝0❡❛5❡ ❧♦♥❣✲❞✐51❛♥❝❡ ♣❛55❡♥❣❡0 10❛♥5♣♦01 ✐♥ 1❤❡
♣0❡5❡♥❝❡ ♦❢ ❛ 510✐❝1❡0
CO2
❡♠✐55✐♦♥5 ❜✉❞❣❡1✳ ❚❤✐5 ❝❛♥ ❜❡ ❡❛5✐❧② ✉♥❞❡051♦♦❞ ❢0♦♠ ❛♥
❡♥❡0❣②✲❡✣❝✐❡♥❝② ♣♦✐♥1 ♦❢ ✈✐❡✇✱ ❤♦✇❡✈❡0✱ ✐1 ❞♦❡5 ♥♦1 0❡✢❡❝1 0❡❛❧✐1② ❞✉❡ 1♦ 1❤❡ ♠✐55✐♥❣ ♥♦♥✲
b✉❛♥1✐✜❛❜❧❡ ❞0✐✈❡05 ✐♥ 1❤❡ ♠♦❞❡❧✳ ❚❛❜❧❡ ✷✶ ♣0❡5❡♥15 1❤❡ ❛55✉♠❡❞ ❢✉1✉0❡ ❞❡✈❡❧♦♣♠❡♥15 ✐♥
❛ 51❛♥❞❛0❞ 5❡11✐♥❣✳
✸✸
3.4 The Energy System Module 95
❚❛❜❧❡ ✷✶✿ ❆))✉♠❡❞ ❞❡✈❡❧♦♣♠❡♥1 ♣❛1❤) ♦❢ ❢4❡✐❣❤1 ❛♥❞ ♣❛))❡♥❣❡4 ❡♥❡4❣② )❡4✈✐❝❡) ❞❡♠❛♥❞✳
❙♦✉4❝❡✿ ▲❡♥③ ❡1 ❛❧✳ ✭✷✵✶✵✮✳
✷✵✵✺ ✷✵✶✵ ✷✵✶✺ ✷✵✷✵ ✷✵✷✺ ✷✵✸✵ ✷✵✸✺ ✷✵✹✵ ✷✵✹✺ ✷✵✺✵
❋4❡✐❣❤1 ✼✳✺✶ ✽✳✻✻ ✾✳✺✻ ✶✵✳✺✹ ✶✶✳✺✷ ✶✷✳✹✾ ✶✸✳✷✾ ✶✹✳✵✽ ✶✹✳✻✵ ✶✺✳✶✷
✺
CO2
❊♠✐$$✐♦♥$
❘❊▼■◆❉✲❉ ❝♦♥)✐❞❡4) ♦♥❧②
CO2
❡♠✐))✐♦♥) ❢4♦♠ 1❤❡ ❡♥❡4❣② )❡❝1♦4 1❤❛1 )1❡♠ ❢4♦♠ 1❤❡
❝♦♠❜✉)1✐♦♥ ♦❢ ❢♦))✐❧ ❢✉❡❧)✳ ❚❤❡ )1❛♥❞❛4❞ ♦♣❡4❛1✐♥❣ ♠♦❞❡ ♦❢ ❘❊▼■◆❉✲❉ ✐) ✈✐❛ ❛
CO2
❡♠✐))✐♦♥ ❜✉❞❣❡1 ♦✈❡4 1❤❡ ❡♥1✐4❡ ♦♣1✐♠✐③✐♥❣ 1✐♠❡ ❤♦4✐③♦♥✳ ❚❤✐) ♠❡1❤♦❞ ②✐❡❧❞) 1❤❡ ♠❛①✲
✐♠✉♠ ❢4❡❡❞♦♠ ❢♦4 1❤❡ ♠♦❞❡❧ 1♦ ❛❧❧♦❝❛1❡ 1❤❡ ❡♠✐))✐♦♥) ♦✈❡4 1✐♠❡✳ ❘❊▼■◆❉✲❉ ❝❛♥ ❛❧)♦
❜❡ ♦♣❡4❛1❡❞ ❜② ✐♠♣❧❡♠❡♥1✐♥❣ ❛ )♣❡❝✐✜❝
CO2
❡♠✐))✐♦♥ ♣❛1❤ ♦4 ❛
CO2
1❛① ♣❛1❤✳ ❚❤❡
CO2
❡♠✐))✐♦♥ ❛❝❝♦✉♥1✐♥❣ ✐♥ ❘❊▼■◆❉✲❉ ✐) ✐♠♣❧❡♠❡♥1❡❞ ✈✐❛ 1❤❡ ♣4✐♠❛4② ❡♥❡4❣② ❞❡♠❛♥❞
♦❢
CO2
✲✐♥1❡♥)✐✈❡ ❡♥❡4❣② ❝❛44✐❡4) ❛♥❞ 1❤❡✐4 ❡♠✐))✐♦♥ ❢❛❝1♦4)✳ ❚❤❡)❡ ❛4❡ ✺✻
tCO2/T J
❢♦4
●❛)✱ ✼✷
tCO2/T J
❢♦4 ❍❛4❞ ❈♦❛❧✱ ✶✶✸
tCO2/T J
❢♦4 ▲✐❣♥✐1❡ ❛♥❞ ✼✷
tCO2/T J
❢♦4 ❈4✉❞❡
❖✐❧ ✭❙14♦❣✐❡) ❛♥❞ ●♥✐✛❦❡ ✷✵✵✾✮✳ ❚❤❡)❡ ❛4❡ 1❤❡ ❡♠✐))✐♦♥ ❢❛❝1♦4) ✉)❡❞ ✐♥ 1❤❡ ❝❛❧❝✉❧❛1✐♦♥
♦❢ 1❤❡ ❑②♦1♦ ♣4♦1♦❝♦❧ 4❡♣♦41✐♥❣✳ ❆❧❧ ♦1❤❡4 ♣4✐♠❛4② ❡♥❡4❣② ❝❛44✐❡4) ❝♦♠❡ ✇✐1❤♦✉1
CO2
❡♠✐))✐♦♥)✳ ■♥ ♣4✐♥❝✐♣❧❡✱ 1❤❡ ✉)❡ ♦❢ ❢♦))✐❧ ❛♥❞ ❜✐♦♠❛)) ❡♥❡4❣② ❝❛44✐❡4) ❧❡❛❞) 1♦
CH4
✱
SOx
✱
NOx
❡♠✐))✐♦♥) ❡1❝✳✱ ✇❤✐❝❤ ❛4❡✱ ❤♦✇❡✈❡4✱ ♥♦1 ❝♦♥)✐❞❡4❡❞ ✐♥ ❘❊▼■◆❉✲❉ ❛1 1❤❡ ♠♦♠❡♥1✳
✻ ▼♦❞❡❧ ❱❛❧✐❞❛.✐♦♥
❱❛❧✐❞❛1✐♥❣ ❝❛✉)❛❧✲❞❡)❝4✐♣1✐✈❡ ♠♦❞❡❧) 1❤❛1 ❣❡♥❡4❛1❡ ♣4♦❥❡❝1✐♦♥) ✇❡❧❧ ✐♥1♦ 1❤❡ ❢✉1✉4❡ ✐) ❛♥
✐♥❤❡4❡♥1❧② ❝❤❛❧❧❡♥❣✐♥❣ 1❛)❦✳ ❚❤❡ ❝♦♥❝❡♣1 ♦❢ ✈❛❧✐❞✐1② ❛) )✉❝❤ ❤❛) ❜❡❡♥ )✉❜❥❡❝1 1♦ ❛ ❧❡♥❣1❤②
❛❝❛❞❡♠✐❝ ❞❡❜❛1❡✱ )14♦♥❣❧② 1✐❡❞ 1♦ ♣❤✐❧♦)♦♣❤② ♦❢ )❝✐❡♥❝❡ ✐))✉❡)✳ ❇❛4❧❛) ✭✶✾✾✻✮ )✉❣❣❡)1)
1❤❛1 ❛ ♠♦❞❡❧ ✐) ✈❛❧✐❞ ✐❢ ✐1 ❞❡♠♦♥)14❛1❡) ✬1❤❡ 4✐❣❤1 ❜❡❤❛✈✐♦✉4 ❢♦4 1❤❡ 4✐❣❤1 4❡❛)♦♥✬✳ ❍❡♥❝❡✱
❛ ✈❛❧✐❞ ♠♦❞❡❧ ♣4♦❞✉❝❡) 4❡)✉❧1) 1❤❛1 ❛4❡ ❛1 ♦♥❝❡ 14✉)1✇♦41❤②✱ ❥✉)1✐✜❛❜❧❡ ❛♥❞ ♠❡❛♥✐♥❣❢✉❧
❢♦4 1❤❡ ♣4♦❜❧❡♠ ✉♥❞❡4 ❛♥❛❧②)✐)✳ ■♥ ❢❛❝1✱ 1❤❡ ✈❛❧✐❞❛1✐♦♥ ♦❢ ❛ ♠♦❞❡❧ ♠✉)1 ❜❡ ✉♥❞❡4)1♦♦❞ ❛)
❛ ♣4♦❝❡))✱ ✇❤✐❝❤ ✐) ♥♦1 )❡♣❛4❛❜❧❡ ❢4♦♠ 1❤❡ ♠♦❞❡❧✐♥❣ ♣4♦❝❡)) ✐1)❡❧❢ ✭▲❛♥❞4② ❡1 ❛❧✳ ✶✾✽✸✮✳
❆) ❛ ❢✉❧❧✲✢❡❞❣❡❞ ✈❛❧✐❞❛1✐♦♥ ❡①❡4❝✐)❡ ✐) ❜❡②♦♥❞ 1❤❡ )❝♦♣❡ ♦❢ 1❤✐) ❞♦❝✉♠❡♥1✱ 1❤✐) ❙❡❝1✐♦♥
✐♥1❡♥❞) 1♦ ❣✐✈❡ ❛ ❜4✐❡❢ ✐♥❞✐❝❛1✐♦♥ ♦❢ ❤♦✇ ♠♦❞❡❧ 4❡)✉❧1) ♦❜1❛✐♥❡❞ ✇✐1❤ ❘❊▼■◆❉✲❉ 4❡❧❛1❡
1♦ ❡♠♣✐4✐❝❛❧ ❞❛1❛✳
❋✐❣✉4❡) ✻✱ ✼ ❛♥❞ ✽ ❞✐)♣❧❛②
CO2
❡♠✐))✐♦♥) ❢4♦♠ ❡♥❡4❣② ✉)❡✱ ●❉_ ❛♥❞ ✜♥❛❧ ❡♥❡4❣② ❞❡♠❛♥❞
❢♦4 ●❡4♠❛♥②✳ ❍✐)1♦4✐❝❛❧ ❞❛1❛ ✐) ♣❧♦11❡❞ 1♦❣❡1❤❡4 ✇✐1❤ ♠♦❞❡❧ 4❡)✉❧1) ❢4♦♠ 1✇♦ )❝❡♥❛4✐♦
4✉♥)✱ ❢♦4 ✇❤✐❝❤ 1❤❡ ❝♦♥✜❣✉4❛1✐♦♥ ♦❢ ❘❊▼■◆❉✲❉ ❞✐✛❡4) ♦♥❧② ✇✐1❤ 4❡)♣❡❝1 1♦ 1❤❡ ❡♠✐))✐♦♥
❜✉❞❣❡1✳ ❉✐)♣❧❛②❡❞ ♠♦❞❡❧ ❞❛1❛ ❛4❡ ❢4♦♠ 1✇♦ 4✉♥) ♦❢ 1❤❡ ✬❝♦♥1✐♥✉❛1✐♦♥✬ )❝❡♥❛4✐♦✱ ❡❧❛❜♦4❛1❡❞
✐♥ ❙❝❤♠✐❞ ❛♥❞ ❑♥♦♣❢ ✭✷✵✶✷✮✳ ❚❤❡ ✬▼♦❞❡❧ ❇❛)❡❧✐♥❡✬ 4✉♥ ❛❝❤✐❡✈❡) ♠♦❞❡4❛1❡ ✹✵✪
CO2
❡♠✐))✐♦♥ 4❡❞✉❝1✐♦♥ ✐♥ ✷✵✺✵ 4❡❧❛1✐✈❡ 1♦ ✶✾✾✵✱ 1❤❡ ✬▼♦❞❡❧ _♦❧✐❝②✬ 4✉♥ ❛♠❜✐1✐♦✉) ✽✽✪✳
✸✹
96 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
❋✐❣✉$❡ ✻✿ ●❡$♠❛♥
CO2
❡♠✐,,✐♦♥, ❢$♦♠ ❡♥❡$❣② ✉,❡✳ ❉❛2❛ ❢$♦♠ ✶✾✾✵✲✷✵✵✾ ❛$❡ ❡♠♣✐$✐❝❛❧
✭❯❇❆ ✷✵✶✵✮✳ ▼♦❞❡❧ $❡,✉❧2, ❛$❡ ♦❜2❛✐♥❡❞ ✇✐2❤ ❘❊▼■◆❉✲❉ ❢♦$ 2❤❡ ②❡❛$, ✷✵✵✼✲
✷✵✺✵✳
0
200
400
600
800
1000
1200
1990 2000 2010 2020 2030 2040 2050
CO2emissionsfromEnergy[Mt]
ModelBaseline
ModelPolicy
HistoricalData
❋✐❣✉$❡ ✼✿ ●❡$♠❛♥ ●$♦,, ❉♦♠❡,2✐❝ K$♦❞✉❝2 ✭●❉K✮ ✐♥ ❇♥
e
✳ ❉❛2❛ ❢$♦♠ ✶✾✾✵✲✷✵✵✾ ❛$❡
❡♠♣✐$✐❝❛❧ ✭❙2❛2✐,2✐,❝❤❡, ❇✉♥❞❡,❛♠2 ✷✵✶✷✮✳ ▼♦❞❡❧ $❡,✉❧2, ❛$❡ ♦❜2❛✐♥❡❞ ✇✐2❤
❘❊▼■◆❉✲❉ ❢♦$ 2❤❡ ②❡❛$, ✷✵✵✼✲✷✵✺✵✳
0
500
1000
1500
2000
2500
3000
3500
1990 2000 2010 2020 2030 2040 2050
GrossDomesticProduct[Bn€ 2005]
ModelBaseline
ModelPolicy
HistoricalData
✸✺
3.6 Model Validation 97
❋✐❣✉$❡ ✽✿ ●❡$♠❛♥ ✜♥❛❧ ❡♥❡$❣② ❞❡♠❛♥❞ ✐♥ 0❏✳ ❉❛4❛ ❢$♦♠ ✶✾✾✵✲✷✵✵✾ ❛$❡ ❡♠♣✐$✐❝❛❧✭❆●
❊♥❡$❣✐❡❜✐❧❛♥③❡♥ ✷✵✶✵✮✳ ▼♦❞❡❧ $❡E✉❧4E ❛$❡ ♦❜4❛✐♥❡❞ ✇✐4❤ ❘❊▼■◆❉✲❉ ❢♦$ 4❤❡
②❡❛$E ✷✵✵✼✲✷✵✺✵✳
0
2000
4000
6000
8000
10000
1990 2000 2010 2020 2030 2040 2050
FinalEnergyDemand[PJ]
ModelBaseline
ModelPolicy
HistoricalData
❚❤❡
CO2
❡♠✐EE✐♦♥E ❢$♦♠ 4❤❡ ❡♥❡$❣② E❡❝4♦$ ✐♥ 4❤❡ ❝❛❧✐❜$❛4✐♦♥ ②❡❛$ ✷✵✵✼ ❛$❡ $❡♣$♦❞✉❝❡❞
✇❡❧❧ ❜② 4❤❡ ♠♦❞❡❧ $❡E✉❧4E ♦❢ ❘❊▼■◆❉✲❉✳ ❙✐♥❝❡ 4❤❡② ❛$❡ ❛♥ ♦✉4❝♦♠❡ ♦❢ 4❤❡ ❝❛❧✐❜$❛4✐♦♥
♣$♦❝❡❞✉$❡✱ 4❤❡ ❣♦♦❞ ✜4 ✐E ❛♥ ✐♥❞✐❝❛4✐♦♥ ❢♦$ 4❤❡ ✈❛❧✐❞✐4② ♦❢ ❘❊▼■◆❉✲❉✬E E4$✉❝4✉$❡✳ ■♥4❡$✲
❡E4✐♥❣❧②✱ 4❤❡ ❡♠♣✐$✐❝❛❧
CO2
❡♠✐EE✐♦♥ ✐♥ ✷✵✵✾ ❧✐❡ ♦♥ 4❤❡ 4$❛❥❡❝4♦$② ♦❢ 4❤❡ ✬▼♦❞❡❧ 0♦❧✐❝②✬
E❝❡♥❛$✐♦✱ ✇❤✐❝❤ ❧❡❛❞E 4♦ ❛♥ ❛♠❜✐4✐♦✉E ♠✐4✐❣❛4✐♦♥ ♠✐4✐❣❛4✐♦♥ 4❛$❣❡4 ♦❢ ✽✽✪
CO2
❡♠✐EE✐♦♥
$❡❞✉❝4✐♦♥ ✐♥ ✷✵✺✵ $❡❧❛4✐✈❡ 4♦ ✶✾✾✵✳ ❍♦✇❡✈❡$✱
CO2
❡♠✐EE✐♦♥E ✇❡$❡ ♣❛$4✐❝✉❧❛$❧② ❧♦✇ ✐♥ ✷✵✵✾
❞✉❡ 4♦ 4❤❡ ✜♥❛♥❝✐❛❧ ❝$✐E✐E ❛♥❞ ✐4 ✐E ✉♥❝❧❡❛$ ✇❤❡4❤❡$ 4❤✐E 4$❡♥❞ ❝♦♥4✐♥✉❡E✳ ❚❤❡ ✬▼♦❞❡❧
❇❛E❡❧✐♥❡✬ 4$❛❥❡❝4♦$② ♣❡$❢♦$♠E ✇❡❧❧ ✐♥ ❡①4$❛♣♦❧❛4✐♥❣ 4❤❡ ❤✐E4♦$✐❝❛❧ 4$❡♥❞ ✐♥ ❡♠✐EE✐♦♥ $❡✲
❞✉❝4✐♦♥✳ ●❉0 ❛♥❞ ✜♥❛❧ ❡♥❡$❣② ❞❡♠❛♥❞ ❛$❡ $❡♣$♦❞✉❝❡❞ ❜② ❘❊▼■◆❉✲❉ ❡①❛❝4❧② ✐♥ ✷✵✵✼
❛E 4❤❡② ❛$❡ ❛ ❝❛❧✐❜$❛4✐♦♥ ✐♥♣✉4✳ ●❉0 ❣$♦✇4❤ ✐E E❧✐❣❤4❧② E❧♦✇❡$ ✐♥ 4❤❡ ♠♦❞❡❧ $❡E✉❧4E 4❤❛♥
♦❜E❡$✈❡❞ ❤✐E4♦$✐❝❛❧❧②✳ ❚❤❡ $❡❛E♦♥ ✇❤② ●❉0 4$❛❥❡❝4♦$✐❡E ❛$❡ ❞✐✈❡$❣✐♥❣ ❜❡4✇❡❡♥ 4❤❡ 4✇♦
♠♦❞❡❧ $✉♥E ✐E 4❤❡ ❛❞❞✐4✐♦♥❛❧ ❛♥❞ ❜✐♥❞✐♥❣
CO2
❜✉❞❣❡4 ❝♦♥E4$❛✐♥4 ✐♥ 4❤❡ ✬▼♦❞❡❧ 0♦❧✐❝②✬
$✉♥✳ ❚❤❡ ❤✐E4♦$✐❝❛❧ 4$❡♥❞ ✐♥ ✜♥❛❧ ❡♥❡$❣② ❞❡♠❛♥❞ ✐E $❡♣$♦❞✉❝❡❞ ✇❡❧❧ ❜② 4❤❡ ✬▼♦❞❡❧ ❇❛E❡✲
❧✐♥❡✬ 4$❛❥❡❝4♦$②✳ ❆❣❛✐♥✱ ❛E ✐E 4❤❡ ❝❛E❡ ❢♦$ 4♦4❛❧
CO2
❡♠✐EE✐♦♥E✱ 4❤❡ ♦✈❡$❧❛♣♣✐♥❣ ②❡❛$E
✷✵✵✼✲✷✵✵✾ ❝♦✐♥❝✐❞❡ ✇✐4❤ 4❤❡ ✬▼♦❞❡❧ 0♦❧✐❝②✬ ❞❛4❛✳ ❆ ♠♦$❡ ❡①4❡♥E✐✈❡ ♠♦❞❡❧ ✈❛❧✐❞❛4✐♦♥✱ ✐♥✲
❝❧✉❞✐♥❣ 4❤❡ E4$✉❝4✉$❡❞ ❝♦♠♣❛$✐E♦♥ ❜❡4✇❡❡♥ 4❤❡ $❡E✉❧4E ♦❢ ❘❊▼■◆❉✲❉ ❛♥❞ 4❤♦E❡ ♦❢ ♦4❤❡$
♠♦❞❡❧E ♦❢ ●❡$♠❛♥②✱ ✇✐❧❧ ❜❡ ❛❞❞$❡EE❡❞ ✐♥ ❢✉4✉$❡ ✇♦$❦✳
✸✻
98 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
❆❝❦♥♦✇❧❡❣❡♠❡♥*+
❚❤✐# $❡#❡❛$❝❤ ✇❛# ♣❛$*❧② ❢✉♥❞❡❞ ❜② *❤❡ ♣$♦❥❡❝* ❊◆❈■✲▲♦✇❈❛$❜ ✭✷✶✸✶✵✻✮ ✇✐*❤✐♥ *❤❡ ✼*❤
❋$❛♠❡✇♦$❦ E$♦❣$❛♠♠❡ ❢♦$ ❘❡#❡❛$❝❤ ♦❢ *❤❡ ❊✉$♦♣❡❛♥ ❈♦♠♠✐##✐♦♥✳
❘❡❢❡.❡♥❝❡+
❆● ❊♥❡$❣✐❡❜✐❧❛♥③❡♥ ✭✷✵✶✵✮✳
❊♥❡#❣✐❡❜✐❧❛♥③ ❞❡# ❇✉♥❞❡-#❡♣✉❜❧✐❦ ❉❡✉1-❝❤❧❛♥❞ ✷✵✵✼✳ ❙1❛♥❞
❆✉❣✉-1 ✷✵✶✵✳
✉!❧
✿
❤!!♣✿✴✴✇✇✇✳❛❣✲❡♥❡,❣✐❡❜✐❧❛♥③❡♥✳❞❡✴✈✐❡✇♣❛❣❡✳♣❤♣❄✐❞♣❛❣❡❂✻✸
✳
❆●❊♥❡$❣✐❡❜✐❧❛♥③❡♥ ✭✷✵✶✵✮✳
;#❡❢❛❝❡ 1♦ 1❤❡ ❊♥❡#❣② ❇❛❧❛♥❝❡- ❢♦# 1❤❡ ❋❡❞❡#❛❧ ❘❡♣✉❜❧✐❝
♦❢ ●❡#♠❛♥②
✳ ❚❡❝❤✳ $❡♣✳ ❆● ❊♥❡$❣✐❡❜✐❧❛♥③❡♥ ❡✳❱✳ ✭❆●❊❇✮✳
✉!❧
✿
❤!!♣✿✴✴✇✇✇✳❛❣✲
❡♥❡,❣✐❡❜✐❧❛♥③❡♥✳❞❡✴✈✐❡✇♣❛❣❡✳♣❤♣❄✐❞♣❛❣❡❂✷✷✾
✳
❆$$♦✇✱ ❑❡♥♥❡*❤ ❏✳ ✭✶✾✻✷✮✳ ✏❚❤❡ ❊❝♦♥♦♠✐❝ ■♠♣❧✐❝❛*✐♦♥# ♦❢ ▲❡❛$♥✐♥❣ ❜② ❉♦✐♥❣✑✳ ■♥✿
❚❤❡
❘❡✈✐❡✇ ♦❢ ❊❝♦♥♦♠✐❝ ❙1✉❞✐❡-
✷✾✳✸✱ ♣♣✳ ✶✺✺✕✶✼✸✳
❇▼❯ ✭✷✵✵✽✮✳
▲❡✐1-1✉❞✐❡ ✷✵✵✽ ✲ ❲❡✐1❡#❡♥1✇✐❝❦❧✉♥❣ ❞❡# ❆✉-❜❛✉-1#❛1❡❣✐❡ ❊#♥❡✉❡#❜❛#❡ ❊♥✲
❡#❣✐❡♥
✳ ❯♥*❡$#✉❝❤✉♥❣ ✐♠ ❆✉❢*$❛❣ ❞❡# ❇▼❯✳ ❇✉♥❞❡#♠✐♥✐#*❡$✐✉♠ ❢[$ ❯♠✇❡❧*✱ ◆❛*✉$#❝❤✉*③
✉♥❞ ❘❡❛❦*♦$#✐❝❤❡$❤❡✐*✳
❇▼❱❇❙ ✭✷✵✵✽✮✳
❱❡#❦❡❤# ✐♥ ❩❛❤❧❡♥ ✷✵✵✽✴✷✵✵✾
✳ ❊❞✳ ❜② ❇❛✉ ✉♥❞ ❙*❛❞*❡♥*✇✐❝❦❧✉♥❣ ❇✉♥✲
❞❡#♠✐♥✐#*❡$✐✉♠ ❢[$ ❱❡$❦❡❤$✳ ❉❱❱ ▼❡❞✐❛ ●$♦✉♣✱ ♣✳ ✸✺✵✳
❇❛$❧❛#✱ ❨❛♠❛♥ ✭✶✾✾✻✮✳ ✏❋♦$♠❛❧ ❛#♣❡❝*# ♦❢ ♠♦❞❡❧ ✈❛❧✐❞✐*② ❛♥❞ ✈❛❧✐❞❛*✐♦♥ ✐♥ #②#*❡♠ ❞②✲
♥❛♠✐❝#✑✳ ■♥✿
❙②-1❡♠ ❉②♥❛♠✐❝- ❘❡✈✐❡✇
✶✷✳✸✱ ♣♣✳ ✶✽✸✕✷✶✵✳
✐$$♥
✿ ✶✵✾✾✲✶✼✷✼✳
❞♦✐
✿
✶✵✳✶✵✵✷✴
✭❙■❈■✮✶✵✾✾✲✶✼✷✼✭✶✾✾✻✷✸✮✶✷✿✸❁✶✽✸✿✿❆■❉✲❙❉❘✶✵✸❃✸✳✵✳❈❖❀✷✲✹
✳
✉!❧
✿
❤!!♣✿✴✴❞①✳
❞♦✐✳♦,❣✴✶✵✳✶✵✵✷✴✭❙■❈■✮✶✵✾✾✲✶✼✷✼✭✶✾✾✻✷✸✮✶✷✿✸❁✶✽✸✿✿❆■❉✲❙❉❘✶✵✸❃✸✳✵✳❈❖❀✷✲✹
✳
❇❛$$❡*♦✱ ▲✳ ✭✷✵✵✶✮✳ ✏❚❡❝❤♥♦❧♦❣✐❝❛❧ ▲❡❛$♥✐♥❣ ✐♥ ❊♥❡$❣② ❖♣*✐♠✐#❛*✐♦♥ ▼♦❞❡❧# ❛♥❞ ❉❡♣❧♦②✲
♠❡♥* ♦❢ ❊♠❡$❣✐♥❣ ❚❡❝❤♥♦❧♦❣✐❡#✑✳ E❤❉ *❤❡#✐#✳ ❙✇✐## ❋❡❞❡$❛❧ ■♥#*✐*✉❡ ♦❢ ❚❡❝❤♥♦❧♦❣②✳
❇❛✉❡$✱ ❈❤$✐#*✐❛♥ ❡* ❛❧✳ ✭✷✵✵✾✮✳
❋✐♥❛❧ #❡♣♦#1 ♦♥ 1❡❝❤♥✐❝❛❧ ❞❛1❛✱ ❝♦-1-✱ ❛♥❞ ❧✐❢❡ ❝②❝❧❡ ✐♥✈❡♥✲
1♦#✐❡- ♦❢ ❛❞✈❛♥❝❡❞ ❢♦--✐❧ ♣♦✇❡# ❣❡♥❡#❛1✐♦♥ -②-1❡♠-✳ ◆❊❊❉❙ ❉❡❧✐✈❡#❛❜❧❡ ♥
◦
✼✳✷ ✲ ❘❙ ✶❛
✳
❚❡❝❤✳ $❡♣✳ ❇$✉##❡❧#✱ ❇❡❧❣✐✉♠✿ ◆❊❊❉❙ ✐♥*❡❣$❛*❡❞ ♣$♦❥❡❝*✱ ❊✉$♦♣❡❛♥ ❈♦♠♠✐##✐♦♥✳
❇❛✉❡$✱ ◆✐❝♦ ❡* ❛❧✳ ✭✷✵✵✽✮✳ ✏▲✐♥❦✐♥❣ ❡♥❡$❣② #②#*❡♠ ❛♥❞ ♠❛❝$♦❡❝♦♥♦♠✐❝ ❣$♦✇*❤ ♠♦❞❡❧#✑✳
■♥✿
❈♦♠♣✉1❛1✐♦♥❛❧ ▼❛♥❛❣❡♠❡♥1 ❙❝✐❡♥❝❡
✺ ✭✶✮✱ ♣♣✳ ✾✺✕✶✶✼✳
✐$$♥
✿ ✶✻✶✾✲✻✾✼❳✳
✉!❧
✿
❤!!♣✿
✴✴❞①✳❞♦✐✳♦,❣✴✶✵✳✶✵✵✼✴L✶✵✷✽✼✲✵✵✼✲✵✵✹✷✲✸
✳
❇❛✉❡$✱ ◆✐❝♦ ❡* ❛❧✳ ✭✷✵✶✶✮✳
❘❊▼■◆❉✿ ❚❤❡ ❡T✉❛1✐♦♥-
✳ ❚❡❝❤✳ $❡♣✳ E♦*#❞❛♠ ■♥#*✐*✉*❡ ❢♦$
❈❧✐♠❛*❡ ■♠♣❛❝* ❘❡#❡❛$❝❤✳
✉!❧
✿
❤!!♣✿✴✴✇✇✇✳♣✐❦✲♣♦!L❞❛♠✳❞❡✴,❡L❡❛,❝❤✴L✉L!❛✐♥❛❜❧❡✲
L♦❧✉!✐♦♥L✴♠♦❞❡❧L✴,❡♠✐♥❞✴,❡♠✐♥❞✲❡Q✉❛!✐♦♥L✳♣❞❢
✳
❇❧❡#❧✱ ▼❛$❦✉# ❡* ❛❧✳ ✭❋❡❜✳ ✷✵✵✼✮✳ ✏❘♦❧❡ ♦❢ ❡♥❡$❣② ❡✣❝✐❡♥❝② #*❛♥❞❛$❞# ✐♥ $❡❞✉❝✐♥❣ ❈❖✷
❡♠✐##✐♦♥# ✐♥ ●❡$♠❛♥②✿ ❆♥ ❛##❡##♠❡♥* ✇✐*❤ ❚■▼❊❙✑✳ ■♥✿
❊♥❡#❣② ;♦❧✐❝②
✸✺✳✷✱ ♣♣✳ ✼✼✷✕
✼✽✺✳
✐$$♥
✿ ✵✸✵✶✲✹✷✶✺✳
✉!❧
✿
❤!!♣✿✴✴✇✇✇✳L❝✐❡♥❝❡❞✐,❡❝!✳❝♦♠✴L❝✐❡♥❝❡✴❛,!✐❝❧❡✴♣✐✐✴
❙✵✸✵✶✹✷✶✺✵✻✵✵✷✸✹✺
✳
✸✼
3.7 References 99
❇♦"❡$$✐✱ ❱✳ ❡$ ❛❧✳ ✭✷✵✵✻✮✳ ✏❲■❚❈❍✿ ❆ ❲♦8❧❞ ■♥❞✉❝❡❞ ❚❡❝❤♥✐❝❛❧ ❈❤❛♥❣❡ ❍②❜8✐❞ ▼♦❞❡❧✑✳
■♥✿
❚❤❡ ❊♥❡%❣② ❏♦✉%♥❛❧
❙♣❡❝✐❛❧ ■""✉❡✳ ❍②❜8✐❞ ▼♦❞❡❧✐♥❣ ♦❢ ❊♥❡8❣②✲❊♥✈✐8♦♥♠❡♥$ J♦❧✐❝✐❡"✿
❘❡❝♦♥❝✐❧✐♥❣ ❇♦$$♦♠✲✉♣ ❛♥❞ ❚♦♣✲❞♦✇♥✱ ♣♣✳ ✶✸✕✸✽✳
❇✉♥❞❡"8❡❣✐❡8✉♥❣ ✭✷✵✶✵✮✳
❊♥❡%❣✐❡❦♦♥③❡♣1 ❢3% ❡✐♥❡ ✉♠✇❡❧16❝❤♦♥❡♥❞❡✱ ③✉✈❡%❧;66✐❣❡ ✉♥❞
❜❡③❛❤❧❜❛%❡ ❊♥❡%❣✐❡✈❡%6♦%❣✉♥❣
✳ ❚❡❝❤✳ 8❡♣✳
✉!❧
✿
❤!!♣✿✴✴✇✇✇✳❜✉♥❞❡,-❡❣✐❡-✉♥❣✳❞❡✴
❈♦♥!❡♥!✴❉❊✴❙!❛!✐,❝❤❡❙❡✐!❡♥✴❇-❡❣✴❊♥❡-❣✐❡❦♦♥③❡♣!✴❡♥❡-❣✐❡❦♦♥③❡♣!✲❢✐♥❛❧✳♣❞❢
✳
❈❛""✱ ❉❛✈✐❞ ✭✶✾✻✺✮✳ ✏❖♣$✐♠✉♠ ●8♦✇$❤ ✐♥ ❛♥ ❆❣❣8❡❣❛$✐✈❡ ▼♦❞❡❧ ♦❢ ❈❛♣✐$❛❧ ❆❝❝✉♠✉❧❛✲
$✐♦♥✑✳ ■♥✿
❚❤❡ ❘❡✈✐❡✇ ♦❢ ❊❝♦♥♦♠✐❝ ❙1✉❞✐❡6
✸✱ ♣♣✳ ✷✸✸✕✷✹✵✳
❉❊❇❘■❱ ✭✷✵✵✾✮✳
❇%❛✉♥❦♦❤❧❡ ✐♥ ❉❡✉16❝❤❧❛♥❞ ✷✵✵✾ ✲ E%♦✜❧ ❡✐♥❡6 ■♥❞✉61%✐❡③✇❡✐❣❡6
✳ ❚❡❝❤✳
8❡♣✳ ❇✉♥❞❡"✈❡8❜❛♥❞ ❇8❛✉♥❦♦❤❧❡✳
❉✉✱ ❨❛♥❣❜♦ ❛♥❞ ❏♦❤♥ ❊✳ J❛8"♦♥" ✭✷✵✵✾✮✳
❯♣❞❛1❡ ♦♥ 1❤❡ ❈♦61 ♦❢ ◆✉❝❧❡❛% E♦✇❡%
✳ ❲♦8❦✐♥❣
J❛♣❡8 ❲J✲✷✵✵✾✲✵✵✹✳ ▼❛""❛❝❤✉"❡$$" ■♥"$✐$✉$❡ ♦❢ ❚❡❝❤♥♦❧♦❣②✳
✉!❧
✿
❤!!♣✿✴✴✇❡❜✳♠✐!✳❡❞✉✴
❝❡❡♣-✴✇✇✇✴♣✉❜❧✐❝❛!✐♦♥,✴✇♦-❦✐♥❣♣❛♣❡-,✴✷✵✵✾✲✵✵✹✳♣❞❢
✳
❊❈ ✭✷✵✵✻✮✳
❘❡❢❡%❡♥❝❡ ❉♦❝✉♠❡♥1 ♦♥ ❇❡61 ❆✈❛✐❧❛❜❧❡ ❚❡❝❤♥✐L✉❡6 ❢♦% ▲❛%❣❡ ❈♦♠❜✉61✐♦♥
E❧❛♥16
✳ ❚❡❝❤✳ 8❡♣✳ ❊✉8♦♣❡❛♥ ❈♦♠♠✐""✐♦♥✳
❊❞✇❛8❞"✱ ❘♦❜❡8$ ❡$ ❛❧✳ ✭✷✵✵✽❛✮✳
❲❡❧❧✲1♦✲❲❤❡❡❧6 ❆♥❛❧②6✐6 ♦❢ ❋✉1✉%❡ ❆✉1♦♠♦1✐✈❡ ❋✉❡❧6 ❛♥❞
E♦✇❡%1%❛✐♥6 ✐♥ 1❤❡ ❊✉%♦♣❡❛♥ ❈♦♥1❡①1 ✲ ❚❆◆❑✲❚❖✲❲❍❊❊▲❙ ❘❡♣♦%1 ❱❡%6✐♦♥ ✸✱ ❆♣♣❡♥❞✐①
✶ ❱❡❤✐❝❧❡ %❡1❛✐❧ ♣%✐❝❡ ❡61✐♠❛1✐♦♥
✳ ❚❡❝❤✳ 8❡♣✳ ■♥"$✐$✉$❡ ❢♦8 ❊♥✈✐8♦♥♠❡♥$ ❛♥❞ ❙✉"$❛✐♥❛❜✐❧✐$②
♦❢ $❤❡ ❊❯ ❈♦♠♠✐""✐♦♥" ❏♦✐♥$ ❘❡"❡❛8❝❤ ❈❡♥$8❡ ✭❏❘❈✴■❊❙✮ ✫ ❈❖◆❈❆❲❊ ✫ ❊✉8♦♣❡❛♥
❈♦✉♥❝✐❧ ❢♦8 ❆✉$♦♠♦$✐✈❡ ❘✫❉ ✭❊❯❈❆❘✮✳
✖ ✭✷✵✵✽❜✮✳
❲❡❧❧✲1♦✲❲❤❡❡❧6 ❆♥❛❧②6✐6 ♦❢ ❋✉1✉%❡ ❆✉1♦♠♦1✐✈❡ ❋✉❡❧6 ❛♥❞ E♦✇❡%1%❛✐♥6 ✐♥ 1❤❡
❊✉%♦♣❡❛♥ ❈♦♥1❡①1 ✲ ❚❆◆❑✲1♦✲❲❍❊❊▲❙ ❘❡♣♦%1 ❱❡%6✐♦♥ ✸
✳ ❚❡❝❤✳ 8❡♣✳ ■♥"$✐$✉$❡ ❢♦8 ❊♥✲
✈✐8♦♥♠❡♥$ ❛♥❞ ❙✉"$❛✐♥❛❜✐❧✐$② ♦❢ $❤❡ ❊❯ ❈♦♠♠✐""✐♦♥" ❏♦✐♥$ ❘❡"❡❛8❝❤ ❈❡♥$8❡ ✭❏❘❈✴■❊❙✮
✫ ❈❖◆❈❆❲❊ ✫ ❊✉8♦♣❡❛♥ ❈♦✉♥❝✐❧ ❢♦8 ❆✉$♦♠♦$✐✈❡ ❘✫❉ ✭❊❯❈❆❘✮✳
❋❡✐❣❡✱ ■8❡♥❡ ✭✷✵✵✼✮✳
❚%❛♥6♣♦%1✱ ❚%❛❞❡ ❛♥❞ ❊❝♦♥♦♠✐❝ ●%♦✇1❤ ✲ ❈♦✉♣❧❡❞ ♦% ❉❡❝♦✉♣❧❡❞❄
❙♣8✐♥❣❡8✳
❋✐❝❤$♥❡8✱ ❲✳ ❡$ ❛❧✳ ✭❆✉❣✳ ✷✵✵✶✮✳ ✏❚❤❡ ❡✣❝✐❡♥❝② ♦❢ ✐♥$❡8♥❛$✐♦♥❛❧ ❝♦♦♣❡8❛$✐♦♥ ✐♥ ♠✐$✐✲
❣❛$✐♥❣ ❝❧✐♠❛$❡ ❝❤❛♥❣❡✿ ❛♥❛❧②"✐" ♦❢ ❥♦✐♥$ ✐♠♣❧❡♠❡♥$❛$✐♦♥✱ $❤❡ ❝❧❡❛♥ ❞❡✈❡❧♦♣♠❡♥$ ♠❡❝❤❛✲
♥✐"♠ ❛♥❞ ❡♠✐""✐♦♥ $8❛❞✐♥❣ ❢♦8 $❤❡ ❋❡❞❡8❛❧ ❘❡♣✉❜❧✐❝ ♦❢ ●❡8♠❛♥②✱ $❤❡ ❘✉""✐❛♥ ❋❡❞❡8❛$✐♦♥
❛♥❞ ■♥❞♦♥❡"✐❛✑✳ ■♥✿
❊♥❡%❣② E♦❧✐❝②
✷✾✳✶✵✱ ♣♣✳ ✽✶✼✕✽✸✵✳
✐$$♥
✿ ✵✸✵✶✲✹✷✶✺✳
✉!❧
✿
❤!!♣✿
✴✴✇✇✇✳,❝✐❡♥❝❡❞✐-❡❝!✳❝♦♠✴,❝✐❡♥❝❡✴❛-!✐❝❧❡✴♣✐✐✴❙✵✸✵✶✹✷✶✺✵✵✵✵✶✸✾✼
✳
●c❧✱ ❚✐♠✉8 ✭✷✵✵✽✮✳ ✏❆♥ ❊♥❡8❣②✲❊❝♦♥♦♠✐❝ ❙❝❡♥❛8✐♦ ❆♥❛❧②"✐" ♦❢ ❆❧$❡8♥❛$✐✈❡ ❋✉❡❧" ❢♦8
❚8❛♥"♣♦8$✑✳ J❤❉ $❤❡"✐"✳ ❊❚❍ ❩c8✐❝❤✳
●c❧✱ ❚✐♠✉8 ❡$ ❛❧✳ ✭✷✵✵✼✮✳ ✏❍②❞8♦❣❡♥ ❛♥❞ ❇✐♦❢✉❡❧" ❆ ▼♦❞❡❧❧✐♥❣ ❆♥❛❧②"✐" ♦❢ ❈♦♠♣❡$✐♥❣
❊♥❡8❣② ❈❛88✐❡8" ❢♦8 ❲❡"$❡8♥ ❊✉8♦♣❡✳✑ ■♥✿
E%♦❝❡❡❞✐♥❣6 ♦❢ 1❤❡ ❲♦%❧❞ ❊♥❡%❣② ❈♦♥❣%❡66
❊♥❡%❣② ❋✉1✉%❡ ✐♥ ❛♥ ■♥1❡%❞❡♣❡♥❞❡♥1 ❲♦%❧❞✳ ✶✶✶✺ ◆♦✈❡♠❜❡% ✷✵✵✼✱ ❘♦♠❡✱ ■1❛❧②✳
●8✉❜❜✱ ▼✐❝❤❛❡❧ ❡$ ❛❧✳ ✭◆♦✈✳ ✶✾✾✸✮✳ ✏❚❤❡ ❈♦"$" ♦❢ ▲✐♠✐$✐♥❣ ❋♦""✐❧✲❋✉❡❧ ❈❖✷ ❊♠✐""✐♦♥"✿
❆ ❙✉8✈❡② ❛♥❞ ❆♥❛❧②"✐"✑✳ ■♥✿
❆♥♥✉✳ ❘❡✈✳ ❊♥❡%❣②✳ ❊♥✈✐%♦♥✳
✶✽✳✶✱ ♣♣✳ ✸✾✼✕✹✼✽✳
✐$$♥
✿
✶✵✺✻✲✸✹✻✻✳
✉!❧
✿
❤!!♣✿✴✴❞①✳❞♦✐✳♦-❣✴✶✵✳✶✶✹✻✴❛♥♥✉-❡✈✳❡❣✳✶✽✳✶✶✵✶✾✸✳✵✵✷✶✹✺
✳
✸✽
100 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
❍❛❦❡✱ ❏✳✲❋✳ ❡) ❛❧✳ ✭✷✵✵✾✮✳
!♦❥❡❦&✐♦♥)!❡❝❤♥✉♥❣❡♥ ❜✐) ✷✵✺✵ ❢3! ❞❛) ❊♥❡!❣✐❡)②)&❡♠ ✈♦♥
❉❡✉&)❝❤❧❛♥❞
✳ ❚❡❝❤✳ 3❡♣✳ ❙)✉❞✐❡ ✐♠ ❆✉❢)3❛❣ ❞❡= ❱❡3❡✐♥= ❉❡✉)=❝❤❡3 ■♥❣❡♥✐❡✉3❡ ✭❱❉■✮✱ B3♦✲
❥❡❦) ❋✉)✉3❡ ❈❧✐♠❛)❡ ❊♥❣✐♥❡❡3✐♥❣ ❙♦❧✉)✐♦♥=✳ ❆✉❢❣)3❛❣♥❡❤♠❡3✿ ❋♦3=❝❤✉♥❣=③❡♥)3✉♠ ❏I❧✐❝❤✳
❍❛♠❡❧✐♥❝❦✱ ❈❛3❧♦ ✭✷✵✵✹✮✳ ✏❖✉)❧♦♦❦ ❢♦3 ❆❞✈❛♥❝❡❞ ❇✐♦❢✉❡❧=✑✳ B❤❉ )❤❡=✐=✳ ❯♥✐✈❡3=✐)❡✐)
❯)3❡❝❤)✳
❍♦✉3❝❛❞❡✱ ❏❡❛♥✲❈❤❛3❧❡= ❛♥❞ ❏♦❤♥ ❘♦❜✐♥=♦♥ ✭❖❝)✳ ✶✾✾✻✮✳ ✏▼✐)✐❣❛)✐♥❣ ❢❛❝)♦3=✿ ❆==❡==✐♥❣
)❤❡ ❝♦=)= ♦❢ 3❡❞✉❝✐♥❣ ●❍● ❡♠✐==✐♦♥=✑✳ ■♥✿
❊♥❡!❣② ♦❧✐❝②
✷✹✳✶✵✲✶✶✱ ♣♣✳ ✽✻✸✕✽✼✸✳
✐!!♥
✿
✵✸✵✶✲✹✷✶✺✳
✉$❧
✿
❤!!♣✿✴✴✇✇✇✳'❝✐❡♥❝❡❞✐-❡❝!✳❝♦♠✴'❝✐❡♥❝❡✴❛-!✐❝❧❡✴♣✐✐✴❙✵✸✵✶✹✷✶✺✾
✻✵✵✵✼✶✼
✳
■❊❆ ✭✷✵✶✵✮✳
❑❡② ❲♦!❧❞ ❊♥❡!❣② ❙&❛&✐)&✐❝)
✳ ❚❡❝❤✳ 3❡♣✳ ■♥)❡3♥❛)✐♦♥❛❧ ❊♥❡3❣② ❆❣❡♥❝②✳
❏✉♥❣✐♥❣❡3✱ ▼❛3)✐♥ ❡) ❛❧✳ ✭✷✵✵✹✮✳ ✏❈♦=) ❘❡❞✉❝)✐♦♥ B3♦=♣❡❝)= ❢♦3 ❖✛=❤♦3❡ ❲✐♥❞ ❋❛3♠=✑✳
■♥✿
❲✐♥❞ ❊♥❣✐♥❡❡!✐♥❣
✷✽✱ ♣♣✳ ✾✼✕✶✶✽✳
❞♦✐
✿
✶✵✳✶✷✻✵✴✵✸✵✾✺✷✹✵✹✶✷✶✵✽✹✼
✳
❏✉♥❣✐♥❣❡3✱ ▼❛3)✐♥ ❡) ❛❧✳ ✭✷✵✵✽✮✳
❚❡❝❤♥♦❧♦❣✐❝❛❧ ❧❡❛!♥✐♥❣ ✐♥ &❤❡ ❡♥❡!❣② )❡❝&♦!
✳ ❙❝✐❡♥)✐✜❝
❆==❡==♠❡♥) ❛♥❞ B♦❧✐❝② ❆♥❛❧②=✐= ❢♦3 ❈❧✐♠❛)❡ ❈❤❛♥❣❡✳ ❯♥✐✈❡3=✐)② ♦❢ ❯)3❡❝❤)✱ ❊❈◆✳
✉$❧
✿
❤!!♣✿✴✴✇✇✇✳-✐✈♠✳♥❧✴❜✐❜❧✐♦!❤❡❡❦✴-❛♣♣♦-!❡♥✴✺✵✵✶✵✷✵✶✼✳♣❞❢
✳
❑❛❤♦✉❧✐✲❇3❛❤♠✐✱ ❙♦♥❞❡= ✭❏❛♥✳ ✷✵✵✽✮✳ ✏❚❡❝❤♥♦❧♦❣✐❝❛❧ ❧❡❛3♥✐♥❣ ✐♥ ❡♥❡3❣②✲❡♥✈✐3♦♥♠❡♥)✲
❡❝♦♥♦♠② ♠♦❞❡❧❧✐♥❣✿ ❆ =✉3✈❡②✑✳ ■♥✿
❊♥❡!❣② ♦❧✐❝②
✸✻✳✶✱ ♣♣✳ ✶✸✽✕✶✻✷✳
✐!!♥
✿ ✵✸✵✶✲✹✷✶✺✳
✉$❧
✿
❤!!♣✿✴✴✇✇✇✳'❝✐❡♥❝❡❞✐-❡❝!✳❝♦♠✴'❝✐❡♥❝❡✴❛-!✐❝❧❡✴♣✐✐✴❙✵✸✵✶✹✷✶✺✵✼✵✵✸✽✶✸
✳
❑✐3❝❤♥❡3✱ ❆❧♠✉) ❡) ❛❧✳ ✭✷✵✵✾✮✳
▼♦❞❡❧❧ ❉❡✉&)❝❤❧❛♥❞ ❑❧✐♠❛)❝❤✉&③ ❜✐) ✷✵✺✵✿ ❱♦♠ ❩✐❡❧ ❤❡!
❞❡♥❦❡♥
✳ ❚❡❝❤✳ 3❡♣✳ ❙)✉❞✐❡ ✐♠ ❆✉❢)3❛❣ ❞❡= ❲❲❋ ❉❡✉)=❝❤❧❛♥❞✳ ❆✉❢)3❛❣♥❡❤♠❡3✿ B3♦❣♥♦=
❆● ✫ c❦♦✲■♥=)✐)✉)✳
❑♦♥=)❛♥)✐♥✱ B✳ ✭✷✵✵✾❛✮✳ ✏B3❛①✐=❜✉❝❤ ❊♥❡3❣✐❡✇✐3)=❝❤❛❢)✿ ❊♥❡3❣✐❡✉♠✇❛♥❞❧✉♥❣✱✲)3❛♥=♣♦3)
✉♥❞✲❜❡=❝❤❛✛✉♥❣ ✐♠ ❧✐❜❡3❛❧✐=✐❡3)❡♥ ▼❛3❦)✑✳ ■♥✿ ❱❉■✲❇✉❝❤✳ ❙♣3✐♥❣❡3✳ ❈❤❛♣✳ ✼✳ ❑3❛❢)✇❡3❦❡✱
❚❡❝❤♥✐❦ ✉♥❞ ❑♦=)❡♥✱ ♣♣✳ ✷✼✶✕✸✹✻✳
✐!❜♥
✿ ✾✼✽✸✺✹✵✼✽✺✾✶✵✳
✉$❧
✿
❤!!♣✿✴✴❜♦♦❦'✳❣♦♦❣❧❡✳
❞❡✴❜♦♦❦'❄✐❞❂▲♦❦❙'✷❜E❢✵'❈
✳
✖ ✭✷✵✵✾❜✮✳ ✏B3❛①✐=❜✉❝❤ ❊♥❡3❣✐❡✇✐3)=❝❤❛❢)✿ ❊♥❡3❣✐❡✉♠✇❛♥❞❧✉♥❣✱✲)3❛♥=♣♦3) ✉♥❞✲
❜❡=❝❤❛✛✉♥❣ ✐♠ ❧✐❜❡3❛❧✐=✐❡3)❡♥ ▼❛3❦)✑✳ ■♥✿ ❱❉■✲❇✉❝❤✳ ❙♣3✐♥❣❡3✳ ❈❤❛♣✳ ✽✳ ❑3❛❢)✲❲g3♠❡✲
❑♦♣♣❧✉♥❣✱ ❚❡❝❤♥✐❦✱ ❑♦=)❡♥❛✉❢)❡✐❧✉♥❣✱ ♣♣✳ ✷✽✸✕✸✷✽✳
✐!❜♥
✿ ✾✼✽✸✺✹✵✼✽✺✾✶✵✳
✉$❧
✿
❤!!♣✿
✴✴❜♦♦❦'✳❣♦♦❣❧❡✳❞❡✴❜♦♦❦'❄✐❞❂▲♦❦❙'✷❜E❢✵'❈
✳
❑♦♦♣♠❛♥=✱ ❚✳❈✳ ✭✶✾✻✺✮✳ ✏❖♥ )❤❡ ❈♦♥❝❡♣) ♦❢ ❖♣)✐♠❛❧ ❊❝♦♥♦♠✐❝ ●3♦✇)❤✑✳ ■♥✿
♦♥&✐✜❝✐❛❡
❆❝❛❞❡♠✐❛❡ ❙❝✐❡♥&✐❛!✉♠ ❙❝!✐♣&❛ ❱❛!✐❛
✷✽✳✶✱ ♣♣✳ ✷✷✺✕✸✵✵✳
❑3❡②✱ ❱♦❧❦❡3 ✭✷✵✵✻✮✳ ✏❱❡3❣❧❡✐❝❤ ❦✉3③✲ ✉♥❞ ❧❛♥❣❢3✐=)✐❣ ❛✉=❣❡3✐❝❤)❡)❡3 ❖♣)✐♠✐❡3✉♥❣=❛♥=g)③❡
♠✐) ❡✐♥❡♠ ♠✉❧)✐✲3❡❣✐♦♥❛❧❡♥ ❊♥❡3❣✐❡=②=)❡♠♠♦❞❡❧❧ ✉♥)❡3 ❇❡3I❝❦=✐❝❤)✐❣✉♥❣ =)♦❝❤❛=)✐=❝❤❡3
B❛3❛♠❡)❡3✑✳ B❤❉ )❤❡=✐=✳ ❋❛❦✉❧)g) ❢I3 ▼❛=❝❤✐♥❡♥❜❛✉ ❛♥ ❞❡3 ❘✉❤3✲❯♥✐✈❡3=✐)g) ❇♦❝❤✉♠✳
▲❛♥❞3②✱ ▼❛✉3✐❝❡ ❡) ❛❧✳ ✭◆♦✈✳ ✶✾✽✸✮✳ ✏▼♦❞❡❧ ✈❛❧✐❞❛)✐♦♥ ✐♥ ♦♣❡3❛)✐♦♥= 3❡=❡❛3❝❤✑✳ ■♥✿
❊✉✲
!♦♣❡❛♥ ❏♦✉!♥❛❧ ♦❢ ❖♣❡!❛&✐♦♥❛❧ ❘❡)❡❛!❝❤
✶✹✳✸✱ ♣♣✳ ✷✵✼✕✷✷✵✳
✐!!♥
✿ ✵✸✼✼✲✷✷✶✼✳
✉$❧
✿
❤!!♣✿
✴✴✇✇✇✳'❝✐❡♥❝❡❞✐-❡❝!✳❝♦♠✴'❝✐❡♥❝❡✴❛-!✐❝❧❡✴♣✐✐✴✵✸✼✼✷✷✶✼✽✸✾✵✷✺✼✻
✳
▲❡✐♠❜❛❝❤✱ ▼❛3✐❛♥ ❡) ❛❧✳ ✭✷✵✶✵✮✳ ✏▼✐)✐❣❛)✐♦♥ ❈♦=)= ✐♥ ❛ ●❧♦❜❛❧✐③❡❞ ❲♦3❧❞✿ ❈❧✐♠❛)❡ B♦❧✐❝②
❆♥❛❧②=✐= ✇✐)❤ ❘❊▼■◆❉✲❘✑✳ ■♥✿
❊♥✈✐!♦♥♠❡♥&❛❧ ▼♦❞❡❧✐♥❣ ❛♥❞ ❆))❡))♠❡♥&
✶✺ ✭✸✮✱ ♣♣✳ ✶✺✺✕
✶✼✸✳
✐!!♥
✿ ✶✹✷✵✲✷✵✷✻✳
✉$❧
✿
❤!!♣✿✴✴❞①✳❞♦✐✳♦-❣✴✶✵✳✶✵✵✼✴'✶✵✻✻✻✲✵✵✾✲✾✷✵✹✲✽
✳
✸✾
3.7 References 101
▲❡♥③✱ ❇❛'❜❛'❛ ❡) ❛❧✳ ✭✷✵✶✵✮✳
❙❤❡❧❧ ▲❦✇✲❙(✉❞✐❡ ✲ ❋❛❦(❡♥✱ ❚1❡♥❞2 ✉♥❞ 3❡12♣❡❦(✐✈❡♥ ✐♠
❙(1❛7❡♥❣9(❡1✈❡1❦❡❤1 ❜✐2 ✷✵✸✵
✳ ❚❡❝❤✳ '❡♣✳ ❉❡✉)7❝❤❡7 ❩❡♥)'✉♠ ❢;' ▲✉❢)✲ ✉♥❞ ❘❛✉♠❢❛❤')
❡✳❱✳ ✭❉▲❘✮✱ ❙❤❡❧❧ ❉❡✉)7❝❤❧❛♥❞✱ ❛♥❞ ❍❛♠❜✉'❣✐7❝❤❡7 ❲❡❧)❲✐')7❝❤❛❢)7■♥)7)✐)✉) ✭❍❲❲■✮✳
✉!❧
✿
❤!!♣✿✴✴✇✇✇✲'!❛!✐❝✳'❤❡❧❧✳❝♦♠✴'!❛!✐❝✴❞❡✉✴❞♦✇♥❧♦❛❞'✴❛❜♦✉!'❤❡❧❧✴♦✉4❴
'!4❛!❡❣②✴!4✉❝❦❴'!✉❞②✴'❤❡❧❧❴!4✉❝❦❴'!✉❞②❴✷✵✸✵✳♣❞❢
✳
▼■❚ ✭✷✵✵✼✮✳
❚❤❡ ❋✉(✉1❡ ♦❢ ❈♦❛❧ ✲ ❆♥ ■♥(❡1❞✐2❝✐♣❧✐♥❛1② ▼■❚ ❙(✉❞②
✳ ❚❡❝❤✳ '❡♣✳ ▼❛7✲
7❛❝❤✉77❡)7 ■♥7)✐)✉)❡ ♦❢ ❚❡❝❤♥♦❧♦❣②✳
✉!❧
✿
❤!!♣✿✴✴✇❡❜✳♠✐!✳❡❞✉✴❝♦❛❧✴
✳
▼❲❱ ✭✷✵✵✽✮✳
▼✐♥❡1❛❧F❧✇✐1(2❝❤❛❢(2✈❡1❜❛♥❞ ❡✳ ❱✳ ❋♦❧✐❡♥2❛(③ ✷✵✵✽ ❚❡✐❧ ❉✿ ❘❛✣♥❡1✲
✐❡♥✴❘❛✣♥❡1✐❡♣1♦③❡22❡
✳
▼❛')✐♥7❡♥✱ ❉❛❣ ❡) ❛❧✳ ✭✷✵✵✻✮✳ ✏❆ ❚✐♠❡ ❙)❡♣ ❊♥❡'❣② P'♦❝❡77 ▼♦❞❡❧ ❢♦' ●❡'♠❛♥② ✲ ▼♦❞❡❧
❙)'✉❝)✉'❡ ❛♥❞ ❘❡7✉❧)7✑✳ ■♥✿
❊♥❡1❣② ❙(✉❞✐❡2 ❘❡✈✐❡✇
✶✹✳✶✱ ♣♣✳ ✸✺✕✺✼✳
▼❛✉W♥❡'✱ ❆✳ ❛♥❞ ▼✳ ❑❧✉♠♣✳ ✭✶✾✾✻✮✳
❲❛❝❤2(✉♠2(❤❡♦1✐❡
✳ ❇❡'❧✐♥✱ ●❡'♠❛♥②✿ ❙♣'✐♥❣❡'✳
▼❝❉♦✇❛❧❧✱ ❲✐❧❧✐❛♠ ❛♥❞ ▼❛❧❝♦❧♠ ❊❛♠❡7 ✭❏✉❧② ✷✵✵✻✮✳ ✏❋♦'❡❝❛7)7✱ 7❝❡♥❛'✐♦7✱ ✈✐7✐♦♥7✱ ❜❛❝❦✲
❝❛7)7 ❛♥❞ '♦❛❞♠❛♣7 )♦ )❤❡ ❤②❞'♦❣❡♥ ❡❝♦♥♦♠②✿ ❆ '❡✈✐❡✇ ♦❢ )❤❡ ❤②❞'♦❣❡♥ ❢✉)✉'❡7 ❧✐)❡'✲
❛)✉'❡✑✳ ■♥✿
❊♥❡1❣② 3♦❧✐❝②
✸✹✳✶✶✱ ♣♣✳ ✶✷✸✻✕✶✷✺✵✳
✐$$♥
✿ ✵✸✵✶✲✹✷✶✺✳
✉!❧
✿
❤!!♣✿✴✴✇✇✇✳
'❝✐❡♥❝❡❞✐4❡❝!✳❝♦♠✴'❝✐❡♥❝❡✴❛4!✐❝❧❡✴♣✐✐✴❙✵✸✵✶✹✷✶✺✵✺✵✵✸✹✻✵
✳
▼❡✐♥7❤❛✉7❡♥✱ ▼❛❧)❡ ❡) ❛❧✳ ✭❆♣'✳ ✷✵✵✾✮✳ ✏●'❡❡♥❤♦✉7❡✲❣❛7 ❡♠✐77✐♦♥ )❛'❣❡)7 ❢♦' ❧✐♠✐)✐♥❣
❣❧♦❜❛❧ ✇❛'♠✐♥❣ )♦ ✷
◦
❈✑✳ ■♥✿
◆❛(✉1❡
✹✺✽✳✼✷✹✷✱ ♣♣✳ ✶✶✺✽✕✶✶✻✷✳
✐$$♥
✿ ✵✵✷✽✲✵✽✸✻✳
✉!❧
✿
❤!!♣✿✴✴❞①✳❞♦✐✳♦4❣✴✶✵✳✶✵✸✽✴♥❛!✉4❡✵✽✵✶✼
✳
▼❡77♥❡'✱ ❙❛❜✐♥❡ ✭✶✾✾✼✮✳ ✏❊♥❞♦❣❡♥✐③❡❞ )❡❝❤♥♦❧♦❣✐❝❛❧ ❧❡❛'♥✐♥❣ ✐♥ ❛♥ ❡♥❡'❣② 7②7)❡♠7
♠♦❞❡❧✑✳ ■♥✿
❏♦✉1♥❛❧ ♦❢ ❊✈♦❧✉(✐♦♥❛1② ❊❝♦♥♦♠✐❝2
✼ ✭✸✮✳ ✶✵✳✶✵✵✼✴7✵✵✶✾✶✵✵✺✵✵✹✺✱ ♣♣✳ ✷✾✶✕
✸✶✸✳
✐$$♥
✿ ✵✾✸✻✲✾✾✸✼✳
✉!❧
✿
❤!!♣✿✴✴❞①✳❞♦✐✳♦4❣✴✶✵✳✶✵✵✼✴'✵✵✶✾✶✵✵✺✵✵✹✺
✳
▼❡②❡'✱ ❇❡'♥❞ ❡) ❛❧✳ ✭❏✉♥❡ ✷✵✵✼✮✳ ✏▼❛)❡'✐❛❧ ❡✣❝✐❡♥❝② ❛♥❞ ❡❝♦♥♦♠✐❝✲❡♥✈✐'♦♥♠❡♥)❛❧ 7✉7✲
)❛✐♥❛❜✐❧✐)②✳ ❘❡7✉❧)7 ♦❢ 7✐♠✉❧❛)✐♦♥7 ❢♦' ●❡'♠❛♥② ✇✐)❤ )❤❡ ♠♦❞❡❧ P❆◆❚❆ ❘❍❊■✑✳ ■♥✿
❊❝♦✲
❧♦❣✐❝❛❧ ❊❝♦♥♦♠✐❝2
✻✸✳✶✱ ♣♣✳ ✶✾✷✕✷✵✵✳
✐$$♥
✿ ✵✾✷✶✲✽✵✵✾✳
✉!❧
✿
❤!!♣✿✴✴✇✇✇✳'❝✐❡♥❝❡❞✐4❡❝!✳
❝♦♠✴'❝✐❡♥❝❡✴❛4!✐❝❧❡✴♣✐✐✴❙✵✾✷✶✽✵✵✾✵✻✵✵✺✺✶✾
✳
◆❛❦❛)❛✱ ❚♦7❤✐❤✐❦♦ ✭✷✵✵✹✮✳ ✏❊♥❡'❣②✲❡❝♦♥♦♠✐❝ ♠♦❞❡❧7 ❛♥❞ )❤❡ ❡♥✈✐'♦♥♠❡♥)✑✳ ■♥✿
31♦❣1❡22
✐♥ ❊♥❡1❣② ❛♥❞ ❈♦♠❜✉2(✐♦♥ ❙❝✐❡♥❝❡
✸✵✳✹✱ ♣♣✳ ✹✶✼✕✹✼✺✳
✐$$♥
✿ ✵✸✻✵✲✶✷✽✺✳
✉!❧
✿
❤!!♣✿
✴✴✇✇✇✳'❝✐❡♥❝❡❞✐4❡❝!✳❝♦♠✴'❝✐❡♥❝❡✴❛4!✐❝❧❡✴♣✐✐✴❙✵✸✻✵✶✷✽✺✵✹✵✵✵✶✹✵
✳
◆❡✐❥✱ ▲❡♥❛ ❡) ❛❧✳ ✭✷✵✵✸✮✳
❊①♣❡1✐❡♥❝❡ ❈✉1✈❡2✿ ❆ ❚♦♦❧ ❢♦1 ❊♥❡1❣② 3♦❧✐❝② ❆22❡22♠❡♥(
✳ ❚❡❝❤✳
'❡♣✳ ▲✉♥❞ ❯♥✐✈❡'7✐)②✳
◆✐)7❝❤✱ ❏♦❛❝❤✐♠ ❡) ❛❧✳ ✭✷✵✵✹✮✳
U❦♦❧♦❣✐2❝❤ ♦♣(✐♠✐❡1(❡1 ❆✉2❜❛✉ ❞❡1 ◆✉(③✉♥❣ ❡1♥❡✉❡1❜❛1❡1
❊♥❡1❣✐❡♥ ✐♥ ❉❡✉(2❝❤❧❛♥❞
✳ ❚❡❝❤✳ '❡♣✳ ❋♦'7❝❤✉♥❣7✈♦'❤❛❜❡♥ ✐♠ ❆✉❢)'❛❣ ❞❡7 ❇✉♥❞❡7♠✐♥✲
✐7)❡'✐✉♠7 ❢;' ❯♠✇❡❧)✱ ◆❛)✉'7❝❤✉)③ ✉♥❞ ❘❡❛❦)♦'7✐❝❤❡'❤❡✐) ✭❯❇❆✮✳ ❆✉❢❣)'❛❣♥❡❤♠❡'✿
❉❡✉)7❝❤❡7 ❩❡♥)'✉♠ ❢;' ▲✉❢)✲ ✉♥❞ ❘❛✉♠❢❛❤') ✭❉▲❘✮ ■♥7)✐)✉) ❢;' ❚❡❝❤♥✐7❝❤❡ ❚❤❡'♠♦❞②✲
♥❛♠✐❦❀ ■♥7)✐)✉) ❢;' ❊♥❡'❣✐❡✲ ✉♥❞ ❯♠✇❡❧)❢♦'7❝❤✉♥❣ ✭✐❢❡✉✮❀ ❲✉♣♣❡')❛❧ ■♥7)✐)✉) ❢;' ❑❧✐♠❛✱
❯♠✇❡❧) ✉♥❞ ❊♥❡'❣✐❡✳
◆♦'❞❤❛✉7✱ ❲✐❧❧✐❛♠ ✭✷✵✵✽✮✳ ✏❚❤❡ P❡'✐❧7 ♦❢ )❤❡ ▲❡❛'♥✐♥❣ ▼♦❞❡❧ ❋♦' ▼♦❞❡❧✐♥❣ ❊♥❞♦❣❡♥♦✉7
❚❡❝❤♥♦❧♦❣✐❝❛❧ ❈❤❛♥❣❡✑✳ ❨❛❧❡ ❯♥✐✈❡'7✐)②✳
✉!❧
✿
❤!!♣✿✴✴♥♦4❞❤❛✉'✳❡❝♦♥✳②❛❧❡✳❡❞✉✴
❞♦❝✉♠❡♥!'✴▲❡❛4♥✐♥❣G❡4✐❧'❴✈✽✳♣❞❢
✳
✹✵
102 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
❛"❝❤❡♥✱ ❍❡)❜❡)+ ❡+ ❛❧✳ ✭✷✵✵✸✮✳
▼!❣❧✐❝❤❦❡✐)❡♥ ❣❡♦)❤❡,♠✐.❝❤❡, ❙),♦♠❡,③❡✉❣✉♥❣ ✐♥ ❉❡✉).❝❤✲
❧❛♥❞ ✲ ❙❛❝❤.)❛♥❞.❜❡,✐❝❤)
✳ ❚❡❝❤✳ )❡♣✳ ❇6)♦ ❢6) ❚❡❝❤♥✐❦❢♦❧❣❡♥✲❆❜"❝❤>+③✉♥❣ ❜❡✐♠ ❉❡✉+"❝❤❡♥
❇✉♥❞❡"+❛❣ ✭❚❆❇✮✳
❘❛❣❡++❧✐✱ ▼❛)+✐♥ ✭✷✵✵✼✮✳ ✏❈♦"+ ♦✉+❧♦♦❦ ❢♦) +❤❡ ♣)♦❞✉❝+✐♦♥ ♦❢ ❜✐♦❢✉❡❧"✑✳ ▼❆ +❤❡"✐"✳ ❊❚❍
❩6)✐❝❤✳
❘❛♠"❡②✱ ❋✳ ✳ ✭❉❡❝✳ ✶✾✷✽✮✳ ✏❆ ▼❛+❤❡♠❛+✐❝❛❧ ❚❤❡♦)② ♦❢ ❙❛✈✐♥❣✑✳ ■♥✿
❚❤❡ ❊❝♦♥♦♠✐❝
❏♦✉,♥❛❧
✸✽✳✶✺✷✱ ♣♣✳ ✺✹✸✕✺✺✾✳
✐!!♥
✿ ✵✵✶✸✵✶✸✸✳
✉$❧
✿
❤!!♣✿✴✴✇✇✇✳❥(!♦*✳♦*❣✴(!❛❜❧❡✴
✷✷✷✹✵✾✽
✳
❙❘❯ ✭✷✵✶✵✮✳
▼!❣❧✐❝❤❦❡✐)❡♥ ✉♥❞ ●,❡♥③❡♥ ❞❡, ■♥)❡❣,❛)✐♦♥ ✈❡,.❝❤✐❡❞❡♥❡, ,❡❣❡♥❡,❛)✐✈❡, ❊♥✲
❡,❣✐❡=✉❡❧❧❡♥ ③✉ ❡✐♥❡, ✶✵✵❇✉♥❞❡.,❡♣✉❜❧✐❦ ❉❡✉).❝❤❧❛♥❞ ❜✐. ③✉♠ ❏❛❤, ✷✵✺✵
✳ ❚❡❝❤✳ )❡♣✳ ❙+✉❞✐❡
✐♠ ❆✉❢+)❛❣ ❞❡" ❙❛❝❤✈❡)"+>♥❞✐❣❡♥)❛+ ❢6) ❯♠✇❡❧+❢)❛❣❡♥✳ ❆✉❢+)❛❣♥❡❤♠❡)✿ ❉❡✉+"❝❤❡" ❩❡♥✲
+)✉♠ ❢6) ▲✉❢+✲ ✉♥❞ ❘❛✉♠❢❛❤)+ ✭❉▲❘✮✳
❙❝❤✐✛❡)✱ ❍❛♥"✲❲✐❧❤❡❧♠ ✭✷✵✵✽✮✳
❊♥❡,❣✐❡♠❛,❦) ❉❡✉).❝❤❧❛♥❞
✳ ✶✵✳ ❆✉✢❛❣❡✳ ❑_❧♥✿ ❚`❱
▼❡❞✐❛ ●♠❜❍ ❚`❱ ❘❤❡✐♥❧❛♥❞ ●)♦✉♣✱ ♣✳ ✺✹✹✳
❙❝❤❧❡"✐♥❣❡)✱ ▼✐❝❤❛❡❧ ❡+ ❛❧✳ ✭✷✵✶✵✮✳
❊♥❡,❣✐❡.③❡♥❛,✐❡♥ ❢E, ❡✐♥ ❊♥❡,❣✐❡❦♦♥③❡♣) ❞❡, ❇✉♥❞❡.,❡✲
♣✉❜❧✐❦ ❉❡✉).❝❤❧❛♥❞✳ G,♦❥❡❦) ◆,✳ ✶✵✴✶✷ ❞❡. ❇✉♥❞❡.♠✐♥✐.)❡,✐✉♠. ❢E, ❲✐,).❝❤❛❢) ✉♥❞ ❚❡❝❤✲
♥✐❦ ✭❇▼❲✐✮
✳ ❚❡❝❤✳ )❡♣✳ ❆✉❢+)❛❣♥❡❤♠❡)✿ )♦❣♥♦" ❆●✱ ❊♥❡)❣✐❡✇✐)+"❝❤❛❢+❧✐❝❤❡" ■♥"+✐+✉+
❛♥ ❞❡) ❯♥✐✈❡)"✐+>+ ③✉ ❑_❧♥ ✭❡✇✐✮ ✉♥❞ ●❡"❡❧❧"❝❤❛❢+ ❢6) ✇✐)+"❝❤❛❢+❧✐❝❤❡ ❙+)✉❦+✉)❢♦)"❝❤✉♥❣
✭❣✇"✮✳
✉$❧
✿
❤!!♣✿✴✴✇✇✇✳❜♠✇✐✳❞❡✴❇▼❲✐✴◆❛✈✐❣❛!✐♦♥✴❙❡*✈✐❝❡✴♣✉❜❧✐❦❛!✐♦♥❡♥✱❞✐❞❂
✸✺✻✷✾✹✳❤!♠❧✳
✳
❙❝❤♠✐❞✱ ❊✈❛ ❛♥❞ ❇)✐❣✐++❡ ❑♥♦♣❢ ✭✷✵✶✷✮✳ ✏❆♠❜✐+✐♦✉" ▼✐+✐❣❛+✐♦♥ ❙❝❡♥❛)✐♦" ❢♦) ●❡)♠❛♥②✿
❆ ❛)+✐❝✐♣❛+♦)② ❆♣♣)♦❛❝❤✑✳ ■♥✿
❙✉❜♠✐))❡❞ )♦ ❊♥❡,❣② G♦❧✐❝②
✳
❙❝❤✉❧③✱ ❚❤♦)"+❡♥ ❋)❛♥❦ ✭✷✵✵✼✮✳ ✏■♥+❡)♠❡❞✐❛+❡ ❙+❡♣" +♦✇❛)❞" +❤❡ ✷✵✵✵✲❲❛++ ❙♦❝✐❡+② ✐♥
❙✇✐+③❡)❧❛♥❞✿ ❆♥ ❊♥❡)❣②✲❊❝♦♥♦♠✐❝ ❙❝❡♥❛)✐♦ ❆♥❛❧②"✐"✑✳ ❤❉ +❤❡"✐"✳ ❊❚❍ ❩6)✐❝❤✳
❞♦✐
✿
✶✵✳✸✾✷✾✴❡!❤③✲❛✲✵✵✺✹✼✽✸✼✸
✳
✉$❧
✿
❤!!♣✿✴✴❡✲❝♦❧❧❡❝!✐♦♥✳❡!❤❜✐❜✳❡!❤③✳❝❤✴✈✐❡✇✳♣❤♣❄
♣✐❞❂❡!❤✿✷✾✽✾✾✫❧❛♥❣❂❡♥
✳
❙+❛+✐"+✐"❝❤❡" ❇✉♥❞❡"❛♠+ ✭✷✵✵✻✮✳
❇❡✈!❧❦❡,✉♥❣ ❉❡✉).❝❤❧❛♥❞. ❜✐. ✷✵✺✵ ✲ ✶✶✳ ❦♦♦,✲
❞✐♥✐❡,)❡ ❇❡✈!❧❦❡,✉♥❣.✈♦,❛✉.❜❡,❡❝❤♥✉♥❣
✳ ❚❡❝❤✳ )❡♣✳
✉$❧
✿
❤!!♣✿✴✴✇✇✇✳❞❡(!❛!✐(✳
❞❡ ✴ ❥❡!(♣❡❡❞ ✴ ♣♦*!❛❧ ✴ ❝♠( ✴ ❙✐!❡( ✴ ❞❡(!❛!✐( ✴ ■♥!❡*♥❡! ✴ ❉❊ ✴ P*❡((❡ ✴ ♣❦ ✴ ✷✵✵✻ ✴
❇❡✈♦❡❧❦❡*✉♥❣(❡♥!✇✐❝❦❧✉♥❣✴❜❡✈♦❡❧❦❡*✉♥❣(♣*♦❥❡❦!✐♦♥✷✵✺✵✱♣*♦♣❡*!②❂❢✐❧❡✳♣❞❢
✳
✖ ✭✷✵✵✾✮✳
❆♥❧❛❣❡✈❡,♠!❣❡♥ ♥❛❝❤ ❙❡❦)♦,❡♥
✳
✖ ✭✷✵✶✷✮✳
❉❡✉).❝❤❡ ❲✐,).❝❤❛❢) ✷✵✶✶
✳ ❚❡❝❤✳ )❡♣✳
✉$❧
✿
❤!!♣✿✴✴✇✇✇✳❞❡(!❛!✐(✳❞❡✴
❥❡!(♣❡❡❞✴♣♦*!❛❧✴❝♠(✴❙✐!❡(✴❞❡(!❛!✐(✴■♥!❡*♥❡!✴❉❊✴❈♦♥!❡♥!✴P✉❜❧✐❦❛!✐♦♥❡♥✴
❋❛❝❤✈❡*♦❡❢❢❡♥!❧✐❝❤✉♥❣❡♥ ✴ ❱♦❧❦(✇✐*!(❝❤❛❢!❧✐❝❤❡●❡(❛♠!*❡❝❤♥✉♥❣❡♥ ✴
❉❡✉!(❝❤❡❲✐*!(❝❤❛❢!◗✉❛*!❛❧✱♣*♦♣❡*!②❂❢✐❧❡✳♣❞❢
✳
❙+)♦❣✐❡"✱ ▼✐❝❤❛❡❧ ❛♥❞ ❛+)✐❝❦ ●♥✐✛❦❡ ✭✷✵✵✾✮✳
❇❡,✐❝❤)❡,.)❛))✉♥❣ ✉♥)❡, ❞❡, ❑❧✐♠❛,❛❤✲
♠❡♥❦♦♥✈❡♥)✐♦♥ ❞❡, ❱❡,❡✐♥)❡♥ ◆❛)✐♦♥❡♥ ✷✵✵✾ ✲ ◆❛)✐♦♥❛❧❡, ■♥✈❡♥)❛,❜❡,✐❝❤) ③✉♠ ❞❡✉).❝❤❡♥
❚,❡✐❜❤❛✉.❣❛.✐♥✈❡♥)❛, ✶✾✾✵ ✲ ✷✵✵✼
✳ ❚❡❝❤✳ )❡♣✳ ❯♠✇❡❧+❜✉♥❞❡"❛♠+ ✭❯❇❆✮✱ ❇❡)❧✐♥✳
✹✶
3.7 References 103
❚❤"#♥✱ ❉❛♥✐❡❧❛ ❡+ ❛❧✳ ✭✷✵✵✾✮✳
▼♦♥✐$♦%✐♥❣ ③✉% ❲✐%❦✉♥❣ ❞❡- ❊%♥❡✉❡%❜❛%❡✲❊♥❡%❣✐❡♥✲●❡-❡$③❡-
✭❊❊●✮ ❛✉❢ ❞✐❡ ❊♥$✇✐❝❦❧✉♥❣ ❞❡% ❙$%♦♠❡%③❡✉❣✉♥❣ ❛✉- ❇✐♦♠❛--❡
✳ ❩✇✐4❝❤❡♥❜❡"✐❝❤+ ❊♥+✇✐❝❦✲
❧✉♥❣ ❞❡" ❙+"♦♠❡"③❡✉❣✉♥❣ ❛✉4 ❇✐♦♠❛44❡ ✷✵✵✽✳ ❙+✉❞✐❡ ✐♠ ❆✉❢+"❛❣ ❞❡4 ❇✉♥❞❡4♠✐♥✐4+❡"✐✉♠
❢E" ❯♠✇❡❧+✱ ◆❛+✉"4❝❤✉+③ ✉♥❞ ❘❡❛❦+♦"4✐❝❤❡"❤❡✐+✳ ❆✉❢+"❛❣❤❡❤♠❡"✿ ❉❡✉+4❝❤❡4 ❇✐♦♠❛44❡✲
❋♦"4❝❤✉♥❣4❩❡♥+"✉♠ ✭❉❇❋❩✮ ✉♥❞ ❚❤E"✐♥❣❡" ▲❛♥❞❡4❛♥4+❛❧+ ❢E" ▲❛♥❞✇✐"+4❝❤❛❢+ ✭❚▲▲✮✳
❚✐❥♠❡♥4❡♥✱ ▼✐❝❤✐❡❧ ❏✳ ❆✳ ❡+ ❛❧✳ ✭❆✉❣✳ ✷✵✵✷✮✳ ✏❊①♣❧♦"❛+✐♦♥ ♦❢ +❤❡ ♣♦44✐❜✐❧✐+✐❡4 ❢♦" ♣"♦✲
❞✉❝+✐♦♥ ♦❢ ❋✐4❝❤❡" ❚"♦♣4❝❤ ❧✐R✉✐❞4 ❛♥❞ ♣♦✇❡" ✈✐❛ ❜✐♦♠❛44 ❣❛4✐✜❝❛+✐♦♥✑✳ ■♥✿
❇✐♦♠❛-- ❛♥❞
❇✐♦❡♥❡%❣②
✷✸✳✷✱ ♣♣✳ ✶✷✾✕✶✺✷✳
✐!!♥
✿ ✵✾✻✶✲✾✺✸✹✳
✉$❧
✿
❤!!♣✿✴✴✇✇✇✳'❝✐❡♥❝❡❞✐-❡❝!✳❝♦♠✴
'❝✐❡♥❝❡✴❛-!✐❝❧❡✴♣✐✐✴❙✵✾✻✶✾✺✸✹✵✷✵✵✵✸✼✺
✳
❯❇❆ ✭✷✵✵✾✮✳
❉❛$❡♥ ③✉♠ ❱❡%❦❡❤%
✳ ❚❡❝❤✳ "❡♣✳ ❯♠✇❡❧+❜✉♥❞❡4❛♠+ ✭❯❇❆✮✳
❯❇❆ ✭✷✵✶✵✮✳
❊♥❡%❣✐❡③✐❡❧ ✷✵✺✵✿ ✶✵✵✪ ❙$%♦♠ ❛✉- ❡%♥❡✉❡%❜❛%❡♥ ◗✉❡❧❧❡♥
✳ ❚❡❝❤✳ "❡♣✳ ❚❤♦♠❛4
❑❧❛✉4 ❛♥❞ ❈❛"❧❛ ❱♦❧❧♠❡" ❛♥❞ ❑❛+❤"✐♥ ❲❡"♥❡" ❛♥❞ ❍❛""② ▲❡❤♠❛♥♥ ❛♥❞ ❑❧❛✉4 ▼E4❝❤❡♥✳
❇❡"❧✐♥✿ ❯♠✇❡❧+❜✉♥❞❡4❛♠+✳
❯❇❆ ✭✷✵✶✵✮✳
◆❛$✐♦♥❛❧❡ ❚%❡♥❞$❛❜❡❧❧❡♥ ❢I% ❞✐❡ ❞❡✉$-❝❤❡ ❇❡%✐❝❤$❡%-$❛$$✉♥❣ ❛$♠♦-♣❤K%✐-❝❤❡%
❊♠✐--✐♦♥❡♥
✳
❯◆❋❈❈❈ ✭✷✵✵✾✮✳
◆❛$✐♦♥❛❧ ❣%❡❡♥❤♦✉-❡ ❣❛- ✐♥✈❡♥$♦%② ❞❛$❛ ❢♦% $❤❡ ♣❡%✐♦❞ ✶✾✾✵ ✷✵✵✼✳
❚❡❝❤✳
"❡♣✳ ❯♥✐+❡❞ ◆❛+✐♦♥ ❋"❛♠❡✇♦"❦ ❈♦♥✈❡♥+✐♦♥ ♦♥ ❈❧✐♠❛+❡ ❈❤❛♥❣❡✳
❯❡❝❦❡"❞+✱ ❋❛❧❦♦ ❡+ ❛❧✳ ✭✷✵✶✶✮✳ ✏❱❛"✐❛❜❧❡ ❘❡♥❡✇❛❜❧❡ ❊♥❡"❣② ✐♥ ♠♦❞❡❧✐♥❣ ❝❧✐♠❛+❡ ❝❤❛♥❣❡
♠✐+✐❣❛+✐♦♥ 4❝❡♥❛"✐♦4✑✳ c"❡4❡♥+❡❞ ❛+ +❤❡ ✷✵✶✶ ■♥+❡"♥❛+✐♦♥❛❧ ❊♥❡"❣② ❲♦"❦4❤♦♣ ✐♥ ❙+❛♥❞❢♦"❞✱
❯❙✳
✉$❧
✿
❤!!♣✿✴✴❡♠❢✳'!❛♥❢♦-❞✳❡❞✉✴❢✐❧❡'✴❞♦❝'✴✸✷✷✴❯❡❝❦❡-❞!✲A❛♣❡-✳♣❞❢
✳
❱"✐❥♠♦❡❞✱ ❙✉③❛♥♥❡ ❡+ ❛❧✳ ✭✷✵✶✵✮✳
▲❡❛%♥✐♥❣ %❛$❡- ♦❢ ❧♦✇ ❝❛%❜♦♥ $❡❝❤♥♦❧♦❣✐❡-
✳ ❚❡❝❤✳ "❡♣✳
❙+✉❞✐❡ ✐♠ ❆✉❢+"❛❣ ✈♦♥ ❏♦✐♥+ ❘❡4❡❛"❝❤ ❈❡♥+"❡ ✭❏❘❈✮✿ ❆✉❢+"❛❣♥❡❤♠❡"✿ ❊❝♦❢②4 ■♥+❡"♥❛✲
+✐♦♥❛❧ ❇❱✳
❱✉✉"❡♥✱ ❉❡+❧❡❢ c✳ ✈❛♥ ❡+ ❛❧✳ ✭❉❡❝✳ ✷✵✵✾✮✳ ✏❈♦♠♣❛"✐4♦♥ ♦❢ +♦♣✲❞♦✇♥ ❛♥❞ ❜♦++♦♠✲✉♣
❡4+✐♠❛+❡4 ♦❢ 4❡❝+♦"❛❧ ❛♥❞ "❡❣✐♦♥❛❧ ❣"❡❡♥❤♦✉4❡ ❣❛4 ❡♠✐44✐♦♥ "❡❞✉❝+✐♦♥ ♣♦+❡♥+✐❛❧4✑✳ ■♥✿
❊♥❡%❣② Q♦❧✐❝②
✸✼✳✶✷✱ ♣♣✳ ✺✶✷✺✕✺✶✸✾✳
✐!!♥
✿ ✵✸✵✶✲✹✷✶✺✳
✉$❧
✿
❤!!♣✿✴✴✇✇✇✳'❝✐❡♥❝❡❞✐-❡❝!✳
❝♦♠✴'❝✐❡♥❝❡✴❛-!✐❝❧❡✴♣✐✐✴❙✵✸✵✶✹✷✶✺✵✾✵✵✺✸✾✹
✳
❲❇●❯ ✭✷✵✵✾✮✳
❑❛--❡♥-$✉%③ ❢I% ❞❡♥ ❲❡❧$❦❧✐♠❛✈❡%$%❛❣ ❉❡% ❇✉❞❣❡$❛♥-❛$③
✳ ❙♦♥✲
❞❡"❣✉+❛❝❤+❡♥ ✷✵✵✾ ■❙❇◆ ✾✼✽✲✸✲✾✸✻✶✾✶✲✷✻✲✹✳ ❲✐44❡♥4❝❤❛❢+❧✐❝❤❡" ❇❡✐"❛+ ❞❡" ❇✉♥✲
❞❡4"❡❣✐❡"✉♥❣ ●❧♦❜❛❧❡ ❯♠✇❡❧+✈❡"#♥❞❡"✉♥❣❡♥✳
❲✐❡+4❝❤❡❧✱ ▼❛"+✐♥ ❡+ ❛❧✳ ✭✷✵✶✵✮✳
❱❡%❣❧❡✐❝❤ ✈♦♥ ❙$%♦♠ ✉♥❞ ❲❛--❡%-$♦✛ ❛❧- ❈❖✷✲❢%❡✐❡ ❊♥✲
❞❡♥❡%❣✐❡$%K❣❡%
✳ ❚❡❝❤✳ "❡♣✳ ❙+✉❞✐❡ ✐♠ ❆✉❢+"❛❣ ❞❡" ❘❲❊ ❆●✳ ❆✉❢+"❛❣♥❡❤♠❡"✿ ❋"❛✉♥❤♦❢❡"✲
■♥4+✐+✉+ ❢E" ❙②4+❡♠✲ ✉♥❞ ■♥♥♦✈❛+✐♦♥4❢♦"4❝❤✉♥❣✳
❨❛♠❛4❤✐+❛✱ ❑❡✐ ❛♥❞ ▲❡♦♥❛"❞♦ ❇❛""❡+♦ ✭❖❝+✳ ✷✵✵✺✮✳ ✏❊♥❡"❣②♣❧❡①❡4 ❢♦" +❤❡ ✷✶4+ ❝❡♥+✉"②✿
❈♦❛❧ ❣❛4✐✜❝❛+✐♦♥ ❢♦" ❝♦✲♣"♦❞✉❝✐♥❣ ❤②❞"♦❣❡♥✱ ❡❧❡❝+"✐❝✐+② ❛♥❞ ❧✐R✉✐❞ ❢✉❡❧4✑✳ ■♥✿
❊♥❡%❣②
✸✵✳✶✸✱ ♣♣✳ ✷✹✺✸✕✷✹✼✸✳
✐!!♥
✿ ✵✸✻✵✲✺✹✹✷✳
✉$❧
✿
❤!!♣✿✴✴✇✇✇✳'❝✐❡♥❝❡❞✐-❡❝!✳❝♦♠✴'❝✐❡♥❝❡✴
❛-!✐❝❧❡✴♣✐✐✴❙✵✸✻✵✺✹✹✷✵✹✵✵✹✾✽✵
✳
✹✷
104 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
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NOTE DI LAVORO PUBLISHED IN 2012
CCSD 1.2012 Valentina Bosetti, Michela Catenacci, Giulia Fiorese and Elena Verdolini: The Future Prospect of PV and CSP
Solar Technologies: An Expert Elicitation Survey
CCSD 2.2012 Francesco Bosello, Fabio Eboli and Roberta Pierfederici: Assessing the Economic Impacts of Climate
Change. An Updated CGE Point of View
CCSD 3.2012 Simone Borghesi, Giulio Cainelli and Massimiliano Mozzanti: Brown Sunsets and Green Dawns in the
Industrial Sector: Environmental Innovations, Firm Behavior and the European Emission Trading
CCSD 4.2012 Stergios Athanassoglou and Valentina Bosetti: Modeling Ambiguity in Expert Elicitation Surveys: Theory and
Application to Solar Technology R&D
CCSD 5.2012 William Brock, Gustav Engstrom and Anastasios Xepapadeas: Energy Balance Climate Models and the
Spatial Structure of Optimal Mitigation Policies
CCSD 6.2012 Gabriel Chan, Robert Stavins, Robert Stowe and Richard Sweeney: The SO2 Allowance Trading System and
the Clean Air Act Amendments of 1990: Reflections on Twenty Years of Policy Innovation
ERM 7.2012 Claudio Morana: Oil Price Dynamics, Macro-Finance Interactions and the Role of Financial Speculation
ES 8.2012 Gérard Mondello: The Equivalence of Strict Liability and Negligence Rule: A « Trompe l'œil » Perspective
CCSD 9.2012 Eva Schmid, Brigitte Knopf and Nico Bauer: REMIND-D: A Hybrid Energy-Economy Model of Germany
3.7 References 105
106 Chapter 3 REMIND-D: A Hybrid Energy-Economy Model of Germany
Chapter 4
Ambitious Mitigation Scenarios for Germany: A Participatory
Approach ∗
Eva Schmid
Brigitte Knopf
∗under revision in Energy Policy
107
108
Chapter 4 Ambitious Mitigation Scenarios for Germany: A Participatory
Approach
Ambitious Mitigation Scenarios for Germany:
A Participatory Approach
Eva Schmida,∗
, Brigitte Knopfa
aPotsdam Institute for Climate Impact Research,
P.O. Box 601203, 14412 Potsdam, Germany
Abstract
This paper addresses the challenge of engaging civil society stakeholders in
the development process of ambitious mitigation scenarios that are based on
formal energy system modeling, which allows for the explicit attachment of
normative considerations to technology-focused mitigation options. It presents
the definition and model results for a set of mitigation scenarios for Germany
that achieve 85% CO2emission reduction in 2050 relative to 1990. During
consecutive dialogues, civil society stakeholders from the transport and electric-
ity sector framed the definition of boundary conditions for the energy-economy
model REMIND-D and evaluated the scenarios with regard to plausibility and
social acceptance implications. Even though the limited scope of this research
impedes inferential conclusions on the German energy transition as a whole, it
demonstrates that the technological solutions to the mitigation problem pro-
posed by the model give rise to significant societal and political implications
that deem at least as challenging as the mere engineering aspects of innovative
technologies. These insights underline the importance of comprehending miti-
gation of energy-related CO2emissions as a socio-technical transition embedded
in a political context.
Keywords: Social Acceptance, Stakeholder Dialogue, Energy System
Modelling
∗Corresponding author, Tel.: +49 331 288 2674; Fax. +49 331 288 2570
Preprint submitted to Elsevier June 11, 2012
109
1. Introduction
Ambitious domestic mitigation efforts by Annex I countries are necessary for
maintaining a likely chance to keep global warming below 2◦C (UNEP, 2010).
The European Union has committed itself to reduce CO2emissions by 20% in
2020 relative to those in 1990 (European Parliament and the European Coun-
cil, 2009). Member states share the mitigation effort according to individual
capabilities. This decision led Germany to target a 21% cut in domestic CO2
emissions by 2020. In the long-term, the German Government endorses an am-
bitious target of 80-95% energy system related CO2emission reduction by 2050
relative to 1990 (Federal Government, 2010). Model-based mitigation scenar-
ios that indicate how this transformation can be accomplished are a frequently
demanded form of scientific policy advice.
As energy system modeling has traditionally been the domain of experts,
particularly engineers, existing mitigation scenarios frame mitigation largely
as a technology problem that can be solved by switching to innovative low-
carbon technologies. For Germany, several model-based scenario studies have
demonstrated that achieving the Government’s long-term mitigation target will
be technically feasible if best available technologies penetrate the market in
large scale (e.g. Schlesinger et al., 2010; Nitsch and Wenzl, 2009; Nitsch et al.,
2010; Kirchner et al., 2009). To achieve this, the studies suggest rigorous energy
policy measures with far-reaching implications for the German society.
However, it was not subject of the analysis in these scenario studies whether
their projected developments align with societal preferences. In case they do not
align, social refusal to adopt or allow for the adoption of low carbon technologies
may challenge ambitious mitigation targets. Indications that this is a real chal-
lenge in Germany are already observed. Local protest against the exploration
of carbon sequestration sites contribute to the paralysis in the policy process
for passing European legislation on carbon sequestration. Widespread refusal
to use petrol with 10% biofuel additive (E10) endangers Germany’s fulfillment
of the European biofuel quota (MWV, 2011). Local opposition against the con-
2
110
Chapter 4 Ambitious Mitigation Scenarios for Germany: A Participatory
Approach
struction of new power plants is considered as the most important market entry
barrier for utilities (Deloitte, 2011). Further, local opposition against onshore
wind farms, due to e.g. negative landscape externalities (Meyerhoff et al., 2010),
have resulted in 40 negative out of 61 community referendums between 2009 and
2012 (L¨ohle, 2012).
Since public or local oppositions and other acts of societal refusal can severely
delay the rapid and large-scale deployment of best available technologies, the
notion of ’social acceptance’ has become a keyword in the energy policy arena.
Often, social acceptance is understood as something that can be established
ex-post to investment or policy decisions by providing sufficient information
to the public (e.g. Federal Government, 2010). However, attempts to explain
acceptance and opposition in literature increasingly resort to procedural and
institutional factors like beliefs, concern, place attachment, perceived fairness
and levels of trust (Devine-Wright, 2008) which cannot be mediated by mere
information campaigns. Rayner (2010) argues that the process of how a society
chooses an energy future itself is as important for a socially, politically, econom-
ically and environmentally sustainable outcome as the availability of low-carbon
technology options.
The Ethics Commission for a Safe Energy Supply, appointed by the Fed-
eral Government, corroborates that in order to ensure a high level of societal
acceptance for the energy supply, transparency in the decisions made by both
parliament and government as well as participation by societal groups in the
decision-making process is a prerequisite (Ethics Commission for a Safe Energy
Supply, 2011). Due to the decisive role that model-based mitigation scenar-
ios can play as a form of scientific policy advice, the call for transparency and
participation in their design and development process is valid accordingly. A
further convincing argument for engaging societal groups that have a stake in
energy system developments is that the choice of low-carbon technologies re-
quires a wide range of normative considerations and value judgments for which
science alone does not have a mandate.
For taking into account societal preferences, the German Academies of Sci-
3
4.1 Introduction 111
ences advocate the application of ’analytical-deliberative’ approaches (Renn
et al., 2011) which originate in the field of risk management (e.g. Stern and
Fineberg, 1996; Renn, 1999). Their notable trait is to provide a recursive link-
age between the two discrete processes of analysis, the use of replicable methods
developed by experts, and deliberation, the thoughtful weighting of options.
A careful deliberation of mitigation options requires that direct and indirect
implications of mitigation options are considered, discussed and reflected by
the spectrum of affected stakeholders, collectively. In order to develop model-
based mitigation scenarios that explicitly take into account stakeholders’ judg-
ments and preferences, they need to be elicited and translated to configurations
of model input parameters. Model results then carry contextual, normative
meaning and enable substantive discussions on the socio-political implications
of technology-focused mitigation options. This can only be achieved in a par-
ticipatory approach in which deliberation frames analysis and analysis informs
deliberation.
Examples of participatory approaches to model-based mitigation scenarios
are scarce in literature. The scenarios of the ’Roadmap 2050 for a low carbon
economy’ by the European Commission (2011) have been assessed on their im-
pact through an online questionnaire which is a unilateral method only. The Eu-
ropean Climate Foundation (ECF) periodically consulted a wide range of stake-
holders throughout the preparation of mitigation scenarios for their ’Roadmap
2050’ (ECF, 2010) but the concrete procedure is not described. To the authors’
knowledge, there are no contemporary applications of participatory approaches
to developing ambitious mitigation scenarios for Germany.
This paper aims to contribute in filling the gap by exploring a methodology
for developing a set of model-based, long-term mitigation scenarios for Germany
that are defined and evaluated in a participatory process with civil society or-
ganization (CSO) stakeholders from the transport and electricity sector. It
addresses the domestic mitigation challenges not only from a techno-economic
point of view but also from a socio-political perspective by combining both
analytical and deliberative elements in a participatory methodology. The ex-
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Chapter 4 Ambitious Mitigation Scenarios for Germany: A Participatory
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ploratory research was conducted as a part of the EU project ENCI LowCarb
(Engaging Civil Society in Low Carbon Scenarios). Due to the pilot project
character, the scenario results are to be interpreted as indicative of trends rather
than being representative for the German civil society as a whole.
In dedicated stakeholder dialogues, CSO representatives discussed available
mitigation options for the transport and electricity sector. Their judgments
and preferences framed the scenario definition and corresponding parameter
configurations for the hybrid energy-economy model REMIND-D (Schmid et al.,
2012a). REMIND-D is based on the structural equations of the state-of-the-
art global Integrated Assessment Model (IAM) REMIND-R (Leimbach et al.,
2010). Since REMIND-D is a hybrid model, integrating a detailed bottom-up
energy system module into a top-down representation of the macro economy, the
scenarios can be analyzed both with respect to their technological and economic
feasibility. In a second round of dialogues, stakeholders evaluated the plausibility
of the scenarios and identified potential socio-political implications of the model-
based mitigation scenarios.
The outline is as follows: Section 2 presents the methodology. Section 3
discusses the outcomes of the participatory scenario definition process. Sec-
tion 4 guides through the scenario results obtained with REMIND-D, focusing
on structural trends in the development of CO2emissions by sector, modal splits
in the freight and passenger transport sector and the electricity generation mix.
Mitigation costs, along with a sensitivity analysis on how they depend on the
stringency of the mitigation ambition, are presented in Section 4.4. Section 5
reports the CSO stakeholders’ evaluations of the mitigation scenarios. Section 6
summarizes and concludes.
2. Methodology
The objective of this research is to develop ambitious mitigation scenarios
for Germany that integrate both techno-economic and socio-political dimen-
sions of the domestic mitigation challenge. In order to build a bridge between
5
4.2 Methodology 113
SCENARIOEVALUATION
IdentifysocioͲpolitical
implications of technologyͲ
focused mitigation strategies
Discuss mitigation options inthe
transport and electricity sector
with civil society representatives,
focus onlikely/desirable futures
SCENARIODEFINITION
Translate judgments and
preferences intocoherentsetsof
parsimonious narratives
RunenergyͲeconomic
model in3configurations
corresponding to sets of
parsimonious narratives
SCENARIORESULTS
Analyze scenario results,
identify sectorial trends
and mitigation costs
DELIBERATION
DELIBERATION ANALYSIS
Discuss plausibility of scenarios,
identify where projected
developments could raise
concerns about social acceptance
Figure 1: Stylized overview of the applied methodology
the two, the specific requirements on the research team go beyond pure ex-
pertise on energy-economy modeling and call for project partners that are well
embedded in the civil society sphere. Thus, the core research team consisted
of both non-governmental organization (NGO) partners and researchers that
collaborated closely throughout the project. The participatory scenario defini-
tion and evaluation process illustrated in this paper was preceded by an intense
preparatory phase in which the interdisciplinary research team developed a joint
understanding of how stylized model parameters and results may be translated
into real-world implications and vice versa. Details on this preparatory phase
and its organizational setup are presented in Schmid et al. (2012b).
The focus of the research was on the one hand on the electricity sector
- a sector for which technology options are readily available and where the
discussion about mitigation has a longer lasting tradition in Germany. On the
other hand, the transport sector was chosen as it is acknowledged that there
are major difficulties in decarbonizing the transport sector (e.g. Luderer et al.,
2012). Due to the limited scope of the project, a deliberation of technological
mitigation options in the industrial and residential heat sector was not included
in the participative process. However, the methodology outlined in Figure 1 and
explained in the following can be transferred to more comprehensive scenario
exercises in future research.
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Chapter 4 Ambitious Mitigation Scenarios for Germany: A Participatory
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2.1. Participatory Scenario Definition
Scenarios are a linking tool that integrates qualitative narratives and quan-
titative formulations based on formal modeling (Nakicenovic et al., 2000). In
order to define scenarios, i.e. formalize the link between the two elements,
”parsimonious narratives” have been established in the IAM community. They
consist of contextual information on anticipated key future developments and
corresponding quantitative projections for boundary conditions (Kriegler et al.,
2010) and intend to convey substantive meaning to a particular set of boundary
conditions for IAMs.
Several parsimonious narratives for key future developments in the transport
and electricity sector were developed in collaboration with CSO stakeholders
during two dedicated stakeholder dialogues. One dialogue was conducted for
each sector to allow for an in-depth discussion. The interdisciplinary research
team pre-selected focal topics for each sector by striking a balance between
technological mitigation options that are crucial from the point of view of the
energy-economy model and developments that are likely to be subject to con-
troversies regarding their social acceptance. The NGO partners conducted the
selection of participants so as to cover the range of interest groups as good as
possible given the limited scope of the project. The 11 and 13 participants in the
transport and electricity sector stakeholder dialogues included representatives
from environmental NGOs, industry and consumer associations, topic-related
interest groups, urban planning, trade unions and industry. A detailed list
of the represented organizations can be found in the Appendix. During the
stakeholder dialogues, pre-selected mitigation options and associated key future
developments were discussed with respect to direct and indirect implications
and their perceived desirability. After each discussion, stimulated by an intro-
ductory question, a questionnaire elicited CSO stakeholders’ positions for formal
analysis.
The seven-point Likert-scale questionnaire (Likert, 1932) elicited judgments
and preferences on possible future developments of key variables in the trans-
port and electricity sector. For a number of possible developments, it asked to
7
4.2 Methodology 115
indicate whether its realization is perceived as likely or not as well as desirable
or not. Due to the small sample size, the data is not suited for econometric
analysis. Instead, descriptive statistic measures of central tendency are em-
ployed. Mean, standard deviation and mode give an indication of whether the
perceptions of likely and desirable developments diverge and whether there is a
degree of agreement across stakeholders.
Along with the qualitative information obtained during the discussions as
well as expert judgments from literature, the elicited data serves as a basis
for generating sets of parsimonious narratives. Parsimonious narratives were
developed for those mitigation options where stakeholders had an opinion and
judgments on likely versus desirable developments diverged significantly or the
desirability was particularly subject to dissent amongst stakeholders. This re-
sulted in three scenarios. In order to keep the scenario definition tractable, a
selection had to be made by the interdisciplinary research team and not all is-
sues discussed during the stakeholder dialogues are actually differentiated in the
scenarios. For those mitigation options which are not explicitly addressed by
the scenario definition, the deployment decisions are endogenous to the model
REMIND-D and boundary conditions are set equally for the scenarios according
to expert judgments from literature. They can be consulted in the model docu-
mentation (Schmid et al., 2012a). It needs to be acknowledged that a mitigation
scenario definition according to the criteria of likeliness, desirability with con-
sent and desirability with dissent is not unique and influenced by the modeler’s
choice. Finally, the modeling team translates the parsimonious narratives into
corresponding input parameter configurations for the model REMIND-D.
2.2. The Hybrid Energy-Economy Model REMIND-D
REMIND-D is a Ramsey-type growth model that integrates a detailed bottom-
up energy system module coupled by a hard link (Bauer et al., 2008). It fa-
cilitates an integrated analysis of the long-term interplay between technological
mitigation options in the different sectors of the German energy system as well
as general macroeconomic dynamics. A detailed description of REMIND-D is
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provided in Schmid et al. (2012a). REMIND-D builds on the structural equa-
tions of the state-of-the-art IAM REMIND-R (Leimbach et al., 2010) which are
reported in Bauer et al. (2011). The objective of REMIND-D is to maximize
welfare, i.e. the intertemporal sum of discounted logarithmic per capita con-
sumption. Mitigation is enforced by means of a strict emission budget of 16
Gt CO2over the time horizon of the analysis, 2005-2050, resulting in roughly
85% emission reduction in 2050 relative to 1990. The budget approach is in-
spired by Meinshausen et al. (2009). When budgeting emissions, the model can
choose annual emissions endogenously allowing for flexibility in the selection of
mitigation options.
In REMIND-D, future scarcities of energy carriers and CO2emissions are an-
ticipated through shadow prices, implying perfect foresight. Hence, REMIND-D
features optimal annual mitigation effort and technology deployment as a model
output. Available mitigation options fall into four categories: (i) Deploying al-
ternative low-emission technologies, (ii) substituting final energy and energy
service demands, (iii) improving energy efficiency and (iv) reducing demand.
The latter is generally avoided by the model as demand reductions have a neg-
ative impact on GDP. Limitations of REMIND-D are mainly that it abstracts
from secondary and final energy imports and possesses coarse technology res-
olution in the residential and commercial heat sector. Further, infrastructure
investments are only represented for energy distribution technologies but not
for transport system infrastructure like railroad tracks due to a lack of data.
The energy system module of REMIND-D is endowed with a variety of al-
ternative technologies that it may deploy endogenously. Endogenous capacity
deployment is subject to potential and resource constraints for renewable pri-
mary energies and fuel costs for fossil primary energies. The fossil primary
energy carriers hard coal, natural gas and crude oil are imported at exoge-
nous prices (Nitsch and Wenzl, 2009, price path B). Domestic lignite resources
are represented by an extraction cost curve approach. Approximately 70 en-
ergy conversion technologies are considered explicitly, as are 20 distribution and
40 transport technologies. Conversion technologies produce the secondary en-
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4.2 Methodology 117
ergy carriers electricity, district heat, local heat, hydrogen, gas, petrol, diesel,
kerosene and heating oil. Distribution technologies convert secondary energies
into final energies as the industry and residential & commercial sector demands.
Transport technologies provide energy services for passenger and freight reloca-
tion. Upon choice, the Carbon Capture and Sequestration (CCS) technology is
available for the electrification and liquefaction of coal, lignite, gas and biomass
from 2020 onwards. According to the decisions of the German Government,
nuclear capacities are phased out until 2022. Domestic renewable energy po-
tentials include lignocellulose, oily and sugar & starch biomass, manure, deep
and near-surface geothermal, hydro, wind onshore, wind offshore and solar ir-
radiation. Despite the time resolution in five-year steps, the model accounts for
fluctuation of renewable electricity generation on short time scales explicitly via
a residual load duration curve approach (Ueckerdt et al., 2011).
2.3. Participatory Scenario Evaluation
In the second round of stakeholder dialogues, the same CSO stakeholders
as in the first round of dialogues evaluated the mitigation scenarios obtained
with REMIND-D by discussing their plausibility and identifying where projected
developments could raise concerns about social acceptance. The objective was
to characterize critical socio-political implications of technological mitigation
options. A better understanding of how goals of climate protection and energy
security may conflict with those of an affordable energy supply for everybody
and how these trade-offs can be tackled is essential for transforming Germany
towards a low-carbon energy future.
3. Scenario Definition
As outlined in Section 2.1, the development of parsimonious narratives, con-
sisting of contextual information on anticipated key future developments and
corresponding quantitative projections for boundary conditions, is central to
this scenario definition process. Three scenarios were defined according to the
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criteria of likeliness, desirability with consent and desirability with dissent. The
’continuation’ scenario enforces a set of parsimonious narratives in the trans-
port and electricity sector that are deemed likely by CSO stakeholders. The
’paradigm shift’ scenario reproduces a set of parsimonious narratives perceived
as desirable by the majority of CSO stakeholders. A variant of the latter, the
’paradigm shift+’ scenario, additionally allows for the deployment of several
technological mitigation options which the stakeholders judged as undesirable
or discussed controversially. Yet these technologies, e.g. CCS, are favored e.g.
by the coal industry. Along the lines of the discussion questions raised during
the stakeholder dialogues, the different parsimonious narratives are elaborated
in the following.
Table 1: Selected results of the Likert-Scale questionnaire of the CSO stakeholder dialogue
on the transport sector with 11 participants. All statements relate to the time horizon until
2050. 1 indicates disagreement, 4 neutrality and 7 agreement. STD = Standard Deviation,
MS = Modal Split, MIT = Motorized Individual Transport, PT = Public Transport
Likely Desirable
Future Development Mean STD Mode Mean STD Mode
Annual t-km truck increases 6.55 0.69 7 3.09 2.25 1
Shift t-km from road to rail 3.73 1.74 3 6.09 1.38 7
Decouple freight&GDP growth 4.09 1.3 3/4 5.90 1.87 7
MS MIT decreases to ≤50% 3.91 1.64 3/5 4.73 2.28 7
MS PT increases significantly 3.64 1.75 5 5.64 1.63 7
MS cycling&walking increases 4.55 2.07 2/7 5.64 1.97 7
Bioethanol ≥50% share 3.33 1.55 2 3.33 2.33 1
Biodiesel ≥50% share 3.33 1.79 3/5 3.33 2.33 1
Hydrogen dominant fuel 3.55 1.92 3 3.64 1.45 3
Is an increase of total annual freight mileage unavoidable? Historically,
freight transportation and GDP growth rates correlated strongly, however, their
11
4.3 Scenario Definition 119
causal relationship is not straightforward (Feige, 2007). It is intertwined through
the indirect influence of transport technologies on production and distribution
structures as well as other aspects of industrial organization and fundamental
economic variables, e.g. the degree of specialization, economies of scale, compar-
ative advantage and diffusion of technological progress. As indicated in Table 1,
decoupling freight and GDP growth rates by reducing annual truck mileage and
shifting freight from road to rail is perceived as a desirable mitigation option
by CSO stakeholders. Yet they anticipate annual ton-km (t-km) mileage with
fossil-fuel-based trucks to increase continuously until 2050. This scenario is cor-
roborated by expert judgments. Lenz et al. (2010), e.g., predict a dramatic
increase in diesel truck mileage from 466 Bn t-km in 2005 to 787 Bn t-km in
2030, constituting a severe carbon lock-in. In the ’continuation’ scenario, this
trend is enforced by an exogenous linear increase of annual freight transport
with trucks up to 787 Bn t-km in 2050 as a conservative estimate. However, the
CSO stakeholders strongly advocated policy efforts directed at reducing total
transport mileage and achieve a shift from road to rail. They claim that viable
solutions exist but lack of political will impedes their implementation. Holzhey
(2010) finds that a doubling of freight transport with rail in Germany until
2030 is technically possible even though concerted investments are required.
Consequently, in the two ’paradigm shift’ scenarios, it is assumed that freight
transport and GDP growth can be decoupled in the future.
Is multi-modality a viable option for decarbonizing the passenger transport
sector? The modal split in the passenger transport sector is heavily biased to-
wards motorized individual transport (MIT) with cars accounting for roughly
80% of travelled person-km (p-km) annually (BMVBS, 2008). CSO stakehold-
ers expect MIT to remain the dominant mode of transportation in the future.
Hence, the ’continuation’ scenario is bound to a share of 80% MIT in modal
split annually. However, CSO stakeholders perceive a structural change in the
modal split as a desirable future development, seeing some potential for pub-
lic transport (PT) and also non-motorized short distance transport to increase,
e.g. by means of a fast bicycle lane network. CSO stakeholders particularly
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stress the importance of increasing infrastructure investments for PT to enable
multi-modality transport patterns, supporting the proposals of the European
Commission’s white paper on transport (EC, 2011). By prescribing an increase
in the share of PT in the modal split for both short and long distance passen-
ger transport, these developments are reproduced in the two ’paradigm shift’
scenarios.
Which alternative low-carbon fuels ought to be dominant in the future? In-
stead of a shift in the mode of transportation, less carbon-intensive fuels for
conventional vehicles are another technological mitigation option. Biodiesel can
be produced from bio-oils and bioethanol from sugar and starch biomass; in
the future, second generation biofuels from lignocellulose will possibly become
available (e.g. Schulz et al., 2007). Other low carbon technologies for fuel pro-
duction include the liquefaction of hard coal or lignite in combination with CCS
and a shift towards hydrogen. CSO stakeholders are controversial about the de-
sirability of first-generation biofuels and doubt that second-generation biofuel
technologies will be available in large scale. Likewise, they doubted the techno-
logical feasibility of a hydrogen future (e.g. Fischedick et al., 2005), exploiting
overproduction of REG capacities via electrolysis. Since the desirability of these
technological options was contested, they are available to the model only in the
’paradigm shift+’ scenario.
Are landscape externalities of renewable electricity generation (REG) ca-
pacities and transmission lines problematic and what are potential remedies? A
concomitant effect of large-scale deployment of REG and transmission line (TL)
capacities is that they technologize the landscape. This landscape externality
was in fact considered problematic with regard to social acceptance. Especially
biogas electrification, accompanied by large corn monocultures, were judged as
unacceptable, see Table 2. CSO stakeholders expect that substantial TL ex-
tensions, necessary to distribute and balance fluctuating REG, are potentially
impeded due to local resistance. However, they find it desirable that such local
oppositions are resolved and encourage that REG technologies, with the excep-
tion of biogas electrification, constitute a very large share of the electricity mix
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4.3 Scenario Definition 121
in the future. Possible remedies for fostering social acceptance towards REG and
TL capacities include procedural justice and increased participation and own-
ership by the local population (Musall and Kuik, 2011; Zoellner et al., 2008).
To represent the effect of a certain degree of social refusal towards large-scale
REG and transmission line deployment in REMIND-D, the REG potentials in
the ’continuation’ scenario are lower than in both ’paradigm shift’ scenarios.
Table 2: Selected results of the Likert-Scale questionnaire of the CSO stakeholder dialogue
on the electricity sector with 13 participants. All statements relate to the time horizon until
2050. 1 indicates disagreement, 4 neutrality and 7 agreement. STD = Standard Deviation,
TL = Transmission Lines, IND = Industry, HHS = Households, PP = Power Plant, CCS =
Carbon Capture and Sequestration
Likely Desirable
Future Development Mean STD Mode Mean STD Mode
Local resistance impedes TL 3.57 1.40 2/3/5 1.46 0.66 1
Deploy heavily wind offshore 5.64 1.34 5 4.92 1.89 7
Deploy heavily biogas plants 4.21 1.25 5 3 1.63 2
Elec. demand IND decreases 4.71 1.86 6 4.77 1.94 4/6/7
Elec. demand HHS decreases 4.07 1.90 3 5.07 2.10 7
Rebound effect compensates 5.14 1.35 5 2.92 1.55 1/3/4
Increase Gas PP next decade 5.43 1.16 5 5.54 2.03 6
Decommission existing Coal PP 4.36 1.55 5 5.23 2.24 7
Large scale availability CCS 3.54 1.94 1/4 3.58 2.35 1
Which energy efficiency growth rate is feasible and what is the role of the
rebound effect? It is widely agreed that energy efficiency improvements are an
important mitigation option in Germany especially for the electricity sector. Yet
CSO stakeholders expect electricity demand to remain stable or increase in the
future, despite judging high efficiency growth rates as a desirable development.
Institutional barriers to exploiting technical energy efficiency potentials are sub-
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stantial, e.g. lack and asymmetry of information, principal-agent problems, split
incentives, hidden costs or bounded rationality (Gillingham et al., 2004). Also,
the rebound effect is likely to prove itself as a real obstacle. It postulates that
energy efficiency increases make individual energy services cheaper, leading to
an increase in their consumption or the consumption of other carbon-intensive
energy services (e.g. Sorrell et al., 2009). In order to translate these judgments,
efficiency growth rates of the final energy demand perpetuate historical trends
in the ’continuation scenario’ averaging 0.5 % annually. The two ’paradigm
shift’ scenarios assume significant improvements and the exogenous efficiency
growth rates of final energy demand amount to an average of 2.3 % annually.
Which thermal electricity generation capacities are acceptable in the next
decades? Due to the phase-out of nuclear until 2022, these generation capacities
need to be replaced within the next decade. CSO stakeholders oppose the built-
up of new CO2emission-intensive coal power plants. Instead, they consider it
both likely and desirable to deploy gas power plants which are not only less
CO2-intensive but are also better capable of balancing fluctuating REG (dena,
2010). 33% of all energy-related German CO2emissions in 2009 were incurred
by lignite and hard coal power plants. The option of decommissioning them
before the end of their techno-economic lifetime and replacing them with REG
capacities, albeit hardly discussed, constitutes an effective mitigation option.
Even though CSO stakeholders judged this option as desirable, they consider
it as moderately realistic. To simulate a carbon lock-in from persistent coal
electrification, existing hard coal and lignite power plants are subject to a must-
run constraint in the ’continuation’ scenario. This must-run constraint implies
that the coal power plants may not be put out of service before the end of their
technical lifetime. A large-scale deployment of the CCS technology was judged
as neither particularly likely nor desirable and is hence available to the model
only in the ’paradigm shift+’ scenario from 2025 onwards.
Table 3 summarizes the model constraints defining the three scenarios. As
already mentioned, the deployment of all mitigation options not mentioned in
Table 3 is left endogenous to the model REMIND-D. Given that all scenarios
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4.3 Scenario Definition 123
Table 3: Summary overview of the model constraints that define the three scenarios, resulting
from the participatory process. FT = Freight Transport, PT = Public Transport, MS =
Modal Split, REG = Renewable Electricity Generation, PP = Power Plant, CCS = Carbon
Capture and Sequestration
Model Constraint Continuation Paradigm Shift Paradigm Shift+
Decoupling FT&GDP no yes yes
PT share in MS constant increase increase
REG potential medium high high
Energy efficiency medium high high
Decommission Coal PP no yes yes
CCS by 2025 no no yes
Biofuel potential low low high
are required to achieve ambitious mitigation, the scenario definition indicates
that the ’continuation’ scenario represents the most restrictive setup, especially
because the freight transport and electricity sector are bound to certain CO2
emissions by definition. Thus, the scenario constitutes a counterfactual exercise
illustrating what would need to happen in the other sectors for achieving ambi-
tious mitigation if these likely trends persisted and energy efficiency and REG
potentials are not fully exploitable due to institutional barriers and societal re-
sistance. On the contrary, the two ’paradigm shift’ scenarios correspond to a
world in which fundamental policy changes are successfully implemented. Here,
tremendous progress is achieved in energy efficiency and REG deployment and
carbon lock-in in terms of committed CO2emissions is avoided.
4. Scenario Results
The model REMIND-D finds an optimal solution for each of the scenario
configurations, despite the strict emission budget of 16 Gt CO2. Before going
through the results, it needs to be highlighted once more that they are derived
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under the assumption of perfect foresight and constitute deterministic first-best
solutions rather than forecasts. This is especially relevant to the counterfactual
’continuation’ scenario which is forced to achieve ambitious mitigation despite
restrictive boundary conditions. Notwithstanding these abstractions, the sce-
nario results yield valuable insights into stylized trends and interrelations across
sectors under different scenario configurations. The following presents for each
scenario the CO2emissions, trends in the transport and electricity sector as
well as mitigation costs.
4.1. CO2Emissions by Sector
Mitigation shares of the three sectors transport, electricity and heat struc-
turally differ across scenarios as illustrated in Figure 2. CO2emission reductions
between 2005 and 2015 are similar in all scenarios – a fast decrease of emissions
of 29-32% in the electricity sector, 29-32% in the industrial, residential and
commercial heat sectors and 4-9% reduction in the transport sector. From 2015
onwards, there are structural differences between the developments in the ’con-
tinuation’ and both ’paradigm shift’ scenarios. The speed of emission reduction
in the electricity sector stagnates in the ’continuation’ scenario due to the must-
run constraint for the existing lignite and hard coal power plants. Additional
committed emissions in the ’continuation’ scenario originate in the prescribed
increase in freight transport with trucks. The total carbon lock-in over the time
horizon of analysis, 2005-2050, amounts to 6.15 Gt CO2from coal electrification
and 2.67 Gt CO2from freight transport. In sum, these 8.8 Gt CO2deplete 55%
of the total emission budget. Consequently, the heat sector needs to deliver a
substantially higher mitigation effort in the ’continuation’ scenario than in the
two ’paradigm shift’ scenarios in order to meet the total CO2emission budget.
In the two ’paradigm shift’ scenarios, the electricity sector decreases CO2
emissions much faster, delivering a reduction of 80% between 2005 and 2020.
Therefore, more CO2emissions can be incurred in the heat sector providing
process heat for industry and residential heating. This structural effect is even
more pronounced in the ’paradigm shift+’ scenario; here, the availability of new
17
4.4 Scenario Results 125
0
50
100
150
200
250
300
350
2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
[MtCO
2
]
ElectricitySector
Continuation
ParadigmShift
ParadigmShift+
0
50
100
150
200
250
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350
2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
[MtCO
2
]
TransportSector
Continuation
ParadigmShift
ParadigmShift+
0
100
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400
500
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2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
[MtCO
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]
AllSectors
Continuation
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ParadigmShift+
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250
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2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
[MtCO
2
]
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Continuation
ParadigmShift
ParadigmShift+
Figure 2: Annual CO2emissions from energy in Germany for 2005-2050 in Mt per year, by
scenario and sector. These model results are obtained with REMIND-D
low-carbon technologies leads to an almost complete decarbonization of the
freight and electricity sectors by 2035. These findings illustrate the advantage
of an integrated approach to mitigation modeling allowing for an analysis of the
interplay between different sectors.
4.2. Transport Sector
Until 2050, total CO2emissions within the transport sector decrease by 47%
in the ’continuation’, 73% in the ’paradigm shift’ and 93% in the ’paradigm
shift+’ scenario versus 2005. The majority of annual reductions are achieved
during the next two decades, yet the drivers differ across the three scenarios.
Clear structural breaks emerge in both modal splits in the two ’paradigm shift’
scenarios.
Aggregate trends in the freight sector for each scenario are illustrated in
Figure 3. The y-axis measures annual freight transport mileage in Bn t-km per
year, whereas the x-axis displays the three sectors for each scenario. Time is
indicated by color coding. First, Figure 3 visualizes the structure of the sectoral
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relationships in one scenario, highlighted by the connecting lines in the years
2005, 2020 and 2050. Second, the sectoral trends over time can be compared
across scenarios. And third, it emphasizes the speed of transformation: The
larger the white areas are within a bar, the faster is the CO2emission reduction
between two time steps.
In all scenarios, freight transport by inland water navigation remains con-
stant due to its limited potential. In the ’continuation’ scenario, freight train ca-
pacities also remain at today’s levels, however, freight transport with trucks in-
creases continuously due to the scenario assumption of coupled GDP and freight
transport growth rates. In consequence, the freight sector’s annual emissions
remain constant at 60-70 Mt CO2as the availability of alternative low-emission
fuels is limited in this scenario. These committed emissions are avoided in both
’paradigm shift’ scenarios. Here, the decoupling indicator (t-km/GDP) does
not increase by 20% from 2005 to 2050 but decreases by 20% and 10%, respec-
tively. Apart from keeping freight transport mileage constant at today’s level,
through a restructuring the economic system towards less transport-intensive
value chains, mitigation is enabled by massive rail infrastructure expansions al-
lowing for train mileage to tripe until 2030. In the ’paradigm shift+’ scenario,
the truck mileage remains at higher levels than in the ’paradigm shift’ scenario
due to the availability of alternative low emission fuel technologies, e.g. sec-
ond generation biofuels and liquefaction of lignite in combination with the CCS
technology.
As regards the passenger sector, annual per capita mileage decreases from
13,000 km in 2005 to 11,000 km in the year 2050 in both ’paradigm shift’
scenarios; the parsimonious narrative foresees that one part of the difference
will be substituted by non-motorized traffic, i.e. cycling and walking. In the
’continuation’ scenario, however, the per capita p-km are forced to decrease
down to 9000 p-km in 2050 due to mitigation pressure induced by the carbon
lock-in in the freight and electricity sector.
The total annual p-km by transport mode for each scenario are illustrated
in Figure 4. Here, the structural change in both ’paradigm shift’ scenarios
19
4.4 Scenario Results 127
0
200
400
600
800
Ship Train Truck Ship Train Truck Ship Train Truck
Continuation ParadigmShift ParadigmShift+
FreightTransprt[Bn.tͲkm/year]
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
Figure 3: Annual freight transport mileage for 2005-2050 in Bn ton-km (t-km) per year, by
scenario and mode. These model results are obtained with REMIND-D
0
100
200
300
400
500
600
MIT PT MIT PT MIT PT MIT PT MIT PT MIT PT
Short Long Short Long Short Long
Continuation ParadigmShift ParadigmShift+
PassengerTransport[Bn.pͲkm/year]
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
Figure 4: Annual passenger transport mileage for 2005-2050 in Bn passenger-km (p-km) per
year, by scenario and mode. These model results are obtained with REMIND-D. MIT =
Motorized Individual Transport, PT = Public Transport
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Chapter 4 Ambitious Mitigation Scenarios for Germany: A Participatory
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becomes evident: MIT decreases at a decreasing rate until 2050 and PT steadily
increases until 2020, remaining constant thereafter. Hybrid buses, electrified
light rail and regional trains deliver additional short distance PT. Together,
they account for roughly 50% of the modal split of short distance transport in
2050. Incremental long distance PT will be delivered with electric trains. In
all scenarios, anticipated carbon budget restrictions and implicit carbon pricing
make conventionally fuelled cars too expensive to operate so they are phased out
entirely until 2030. Diesel cars, predominantly suitable for long distance driving,
are first substituted by diesel hybrids and then by hybrid gas cars in all scenarios.
Petrol cars are replaced with hybrid-plug in gasoline cars which are electric
cars with a petrol-fuelled rage extender. In the ’paradigm shift+’ scenario,
they are partly replaced with hydrogen hybrid cars as hydrogen is produced
from lignocelluloses with CCS here, with the ability to extract CO2from the
atmosphere and producing de-facto ”negative” CO2emissions. In all scenarios,
there is a trend to gradually electrify the transport sector with the total demand
of electricity for transport increasing by several orders of magnitude until 2050,
yet never exceeding 15% of the total electricity production.
4.3. Electricity Sector
The aggregated technology mix of the electricity sector for the three scenarios
is illustrated in Figure 5. In the two ’paradigm shift’ scenarios, where the
model is given the option to decommission existing hard coal and lignite power
plants from 2015 onwards, these capacities are shut down by 2020. They are
temporarily replaced by gas turbines, about 25 GW capacity are built between
2015 and 2020. Once enough REG capacity is installed, the gas turbines go out
of service again in both ’paradigm shift’ scenarios by 2030. In the ’continuation’
scenario, there is no such temporary increase in gas capacities as existing coal
and lignite power plants continue to produce electricity. In all scenarios, REG
is rapidly expanded and doubling over the next five years.
From 2020 onwards, the installed REG capacities stagnate in the ’continua-
tion’ scenario. This is due to the moderate potential in the scenario definition,
21
4.4 Scenario Results 129
0
100
200
300
400
500
Nuclear
Coal
Gas
RES
Nuclear
Coal
Gas
RES
Nuclear
Coal
Gas
RES
Continuation ParadigmShift ParadigmShift+
ElectricityGeneration[TWh/year]
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
Figure 5: Annual electricity generation for 2005-2050 in MWh per year, by scenario and
aggregated technologies. These model results are obtained with REMIND-D
motivated by a restrictive public attitude that constrains the incremental de-
ployment of RE capacities and transmission lines. Total electricity production
is forced to decrease from 620 TWh in 2005 to 375 TWh in 2050. Because of the
carbon lock-in from freight transport and coal electrification, the model cannot
afford to allocate more CO2from the emission budget to the electricity sector
for covering gas turbines. These could provide more balancing capacities so so-
lar potentials could be fully exploited which is not the case in the ’continuation’
scenario. Instead, REMIND-D opts for the least attractive mitigation option
of imposing electricity demand reductions in all sectors, including industry. A
consequence of this is a reduction in GDP growth.
In both ’paradigm shift’ scenarios, REG capacities continuously expand, es-
pecially offshore wind, and total electricity production stabilizes between 530
and 560 MWh. The slightly reduced demand is due to high efficiency growth
rates. In 2050, onshore wind capacities reach a maximum of 100 GW in both
’paradigm shift’ scenarios. Offshore capacities reach 150 GW in the ’paradigm
shift’ scenario and 180 GW in the ’paradigm shift+’ scenario. Geothermal elec-
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Chapter 4 Ambitious Mitigation Scenarios for Germany: A Participatory
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tricity production also plays a vital role in all scenarios with 20-35 GW installed
capacity. REMIND-D installs 110 GW of solar photovoltaic in the ’continuation’
scenario by 2050. In the ’paradigm shift’ scenarios, other less expensive tech-
nologies, e.g. wind onshore and offshore, provide sufficient electricity generation
potential and solar photovoltaic plays only a minor role. Biomass electrification
plays a subordinate role in all scenarios as REMIND-D prefers to use all avail-
able biomass for fuel production. In the ’paradigm shift+’ scenario, 14 GW of
lignite power plants with the oxyfuel CCS technology are installed as well as
25 GW of natural gas combined cycle plants with CCS. When compared to the
’paradigm shift’ scenarios, these capacities somewhat reduce the need for REG.
4.4. Mitigation Costs
Comparing the results of two scenarios that differ with respect to the emis-
sion constraint only allows for determining the differential effects of mitigation
enforcement. One measure of economic mitigation costs is the cumulative differ-
ence in discounted GDP losses (referred to as cumulative GDP losses hereafter)
between two scenario runs that have the same restrictions, except for the size
of the CO2emission budget.
Figure 6 illustrates how cumulative GDP losses between scenarios diverge
with increasingly strict carbon budgets. For ease of interpretation, the x-axis
displays the respective % of CO2emission reduction achieved in 2050 relative
to 1990. Macroeconomic mitigation costs in terms of cumulative GDP losses for
the ’continuation’, ’paradigm shift’ and ’paradigm shift+’ scenario amount to
3.5%, 1.4% and 0.8% between 2005 and 2050. The respective reference case with
a larger carbon budget leads to moderate 40-45% CO2emission reduction in
2050 relative to 1990. For moderate mitigation targets up to 65% CO2emission
reduction in 2050, GDP losses remain below 0.5% in all scenarios. Mitigation
costs in this order of magnitude are also found by global IAM analyses (e.g.
Edenhofer et al., 2010; Luderer et al., 2012). However, for more ambitious
targets, the mitigation costs in the ’continuation’ scenario increase relatively
faster than in the two ’paradigm shift’ scenarios. This divergence is induced
23
4.4 Scenario Results 131
Continuation 3.5%
ParadigmShift
1.4%
ParadigmShift+
0.8%
0
1
2
3
40% 50% 60% 70% 80% 90%
GDPlossesversusreferencecase[%]
CO2emissionreductionin2050relativeto1990
Figure 6: Mitigation cost curve for the three scenarios, in terms of cumulative discounted
GDP losses compared to a respective reference scenario with 40-45% CO2emission reduction
in 2050. These model results are obtained with REMIND-D.
through the differences in scenario assumptions.
The main drivers for increasing GDP losses in the ’continuation’ scenario are
moderate efficiency growth rates and endogenously enforced demand reductions
because of the aforementioned carbon lock-in in the freight and electricity sector.
GDP losses remain significantly lower for all mitigation targets in the ’paradigm
shift’ scenario. Higher efficiency growth rates in all sectors of the economy, larger
REG potential and the option to avoid the carbon lock-in are responsible for
this. In terms of the underlying parsimonious narratives, the results indicate
that ambitious mitigation in Germany can be achieved at relatively lower costs
if structural changes in modal splits of the freight and passenger transportation
sector and a fast decarbonization of the electricity sector are pursued.
Mitigation costs in the ’paradigm shift+’ scenario remain even lower for all
levels of mitigation ambition. This is due to additionally available technological
mitigation options in the form of CCS and larger biofuel potentials and in line
with findings in other scenario exercises (e.g. Edenhofer et al., 2010; Luderer
et al., 2012). Yet the incremental effect is not as decisive as moving from the
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Chapter 4 Ambitious Mitigation Scenarios for Germany: A Participatory
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’continuation’ to the ’paradigm shift’ scenario.
5. Scenario Evaluation
CSO stakeholders perceive three projected developments in the ’continu-
ation’ scenario as implausible mainly due to socio-political implications that
conflict with objectives in other policy arenas. First, the model results indicate
a strong decrease of motorized individual transport that is not compensated
for by more public transport mileage. Massive state intervention would be nec-
essary to induce behavioral changes of such magnitude, e.g. through carbon
pricing policies entailing prohibitively high transport costs. In such a world,
individual mobility would become a luxury good. The CSO stakeholders assess
that such policies will lack social acceptance and strongly emphasize the value
of individual mobility in modern societies. Second, the required electricity and
heat demand reductions are considered as politically not enforceable in reality.
To induce such a development, again, rigorous carbon pricing policies would be
required which would increase the price of electricity and heating substantially.
Several stakeholders pointed out the dangers of energy poverty if any such mit-
igation policy is not accompanied by effective redistribution schemes. Third,
the CSO stakeholders doubt that the projected CO2emission reductions and
efficiency improvements in the heat sector can be realized, seeing institutional
barriers as for example the well-known landlord-tenant conflict of responsibility.
In sum, these critical socio-political implications motivated the CSO stake-
holders to assess the ’continuation’ scenario as highly undesirable, despite the
fact that it reaches the required mitigation target. Yet they reconfirmed the
likeliness of its projected developments in the freight transport and electricity
sector, leading to a lock-in into current behavior and carbon-intensive infras-
tructure. In consequence, they conclude that, if the carbon lock-in becomes
reality, ambitious mitigation targets will likely be out of reach.
The ’paradigm shift’ scenarios see the carbon lock-in resolved. CSO stake-
holders largely corroborate the desirability of its proposed developments, espe-
25
4.5 Scenario Evaluation 133
cially the fast increase in renewable electricity generation. However, they point
out that several model projections appear unrealistic such as the near-term de-
commissioning of coal power plants, the rapid shift from road to rail in freight
transport or the widespread electrification of private transport until 2030 and
the simultaneous shift to public transport. They doubt that it is possible to
establish the necessary collective political will for enforcing policies that lead to
such technology deployment.
Several concerns were articulated for policies that aim at inducing the struc-
tural breaks from historical trends inherent to the ’paradigm shift’ scenario:
The quality of public transport services needs to increase significantly, both in
urban environments and in rural areas. Inter alia, this would require a redi-
rection of infrastructure investments from road to rail, an issue considered long
overdue by the CSO stakeholders. Furthermore, the projected rapid decom-
missioning of existing coal power plants may entail increasing regional unem-
ployment rates in Germany’s structurally weak lignite mining areas. Finally, a
fast deployment of renewable electricity generation and transmission line capac-
ities requires high procedural justice throughout the planning and installation
process, including institutionalized possibilities for local communities to partic-
ipate, also financially. CSO stakeholders preferred the ’paradigm shift’ scenario
over the ’paradigm shift+’ scenario as they predict substantial public protest
against the large-scale deployment of CCS infrastructure and biofuel produc-
tion. They argue that the incremental effect on decreasing mitigation costs may
not outweigh the direct and indirect costs of public protest.
6. Summary and Conclusion
This paper presents three model-based mitigation scenarios for Germany
that achieve 85% CO2emission reduction in 2050 relative to 1990. These sce-
narios were defined and evaluated in a participatory process with CSO stake-
holders. During separate dialogues, their preferences on future developments
related to mitigation in the transport and electricity sector were discussed and
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Chapter 4 Ambitious Mitigation Scenarios for Germany: A Participatory
Approach
elicited. Along with findings from literature, the input from the CSO stakehold-
ers built the basis to generate parsimonious narratives on future developments
of key variables in the transport and electricity sector according to the criteria
of likeliness, desirability with consent and desirability with dissent.
The ’continuation’ scenario is characterized by enforcing a set of develop-
ments that are deemed highly likely by all participants. These include the domi-
nance of motorized individual transport, unabated coal electrification, moderate
energy efficiency growth rates, local resistance against windmills and transmis-
sion lines as well as the continuation of coupled freight transport and GDP
growth rates. Coal electrification and fossil-fuel-based freight transport mileage
induce 8.8 Gt CO2of committed emissions. This carbon lock-in accounts for
55% of the total CO2emission budget over the time horizon of analysis from
2005 to 2050. As a consequence, non-technical mitigation options slowing down
economic growth are exploited by REMIND-D for meeting the CO2budget con-
straint. These include significant energy service demand reductions in passenger
transportation as well as final energy demand reductions for electricity and the
provision of heat. Additionally bound to moderate energy efficiency improve-
ments, the ’continuation’ scenario exhibits mitigation costs of 3.5 % cumulative
GDP losses over the period 2005-2050 as compared to a reference case that
achieves 40% CO2emission reduction in 2050 relative to 1990. Stakeholders
judged the results of this counterfactual scenario as highly problematic from a
socio-political point of view and conclude that under carbon lock-in, ambitious
mitigation will likely be out of reach.
The two ’paradigm shift’ scenarios reproduce future developments judged as
desirable by participating stakeholders. These include a decrease in total freight
transport mileage, a shift in the modal split of freight transport sector from road
to rail, a substantial increase of public and non-motorized transport in the modal
split of passenger transportation, a widespread electrification of private trans-
port by 2030, a phase-out of conventional coal electrification until 2020, a rapid
and large-scale deployment of renewable electricity generation and transmission
line capacities as well as a fourfold increase in energy efficiency growth rates.
27
4.6 Summary and Conclusion 135
REMIND-D immediately exploits these mitigation options whereby mitigation
costs decrease by more than half when compared to the ’continuation’ scenario,
with 1.4% of cumulative GDP losses. Yet the necessary fundamental policy
changes for such a scenario are put into question by stakeholders as they doubt
that sufficient collective political will can be established. The ’paradigm shift+’
scenario which additionally allows for the controverial use of CCS and large-
scale biofuel production achieves even lower mitigation costs of 0.8%. However,
CSO stakeholders remain skeptical whether these technologies are feasible in
large scale, particularly due to social refusal.
Overall, the deliberative elements in this participatory mitigation scenario
exercise have demonstrated that the transformation towards a low-carbon en-
ergy system constitute as much a societal effort as an engineer’s project. Socio-
political implications of technological mitigation options are abundant and would
indeed have an impact on the society as a whole. It is questionable, however, if
the institutional aspects to the use of energy services can be adapted as rapidly
as suggested by the optimal scenarios derived under the assumption of perfect
foresight. This corroborates the thoughts of Unruh (2000) who suggests that
energy model results are biased due to abstracting from technological evolution
and institutions. He argues that sectors of the energy systems cannot be com-
prehended as discrete technological artifacts but rather as complex systems of
technologies embedded in a powerful conditioning social context of public and
private institutions.
However, the direct implementation of social context and institutions into
numerical energy system models appears impossible due to a lack of theoretical
concepts and unobservability of data. In order to attach contextual meaning
to parameters in available energy system models, the use of narratives, as ex-
plored in this paper, proves to be a promising avenue. Pursuing a participatory
approach to developing mitigation scenarios results in a much stronger focus
on the process of scenario definition and evaluation and allows for the explicit
attachment of normative consideration to modeling results. As a form of scien-
tific policy advice, such scenarios deal with value judgments openly and do not
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Chapter 4 Ambitious Mitigation Scenarios for Germany: A Participatory
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attempt to hide them behind seemingly factual or technical statements.
Even though the limited scope of this research impedes inferential conclu-
sions on the German energy transition as a whole, it has demonstrated that the
technological solutions to the mitigation problem proposed by the model results
give rise to significant societal and political implications that deem at least as
challenging as the mere engineering aspects of innovative technologies. These in-
sights underline the importance of comprehending mitigation of energy-related
CO2emissions as a socio-technical transition embedded in a political context.
Thus, in future mitigation scenario exercises the questions of how to govern the
transition and which kinds of policy instruments are suitable for enabling the
transition should be treated more explicitly. If this participatory research could
be repeated under these considerations and at larger scope and scale, emerging
mitigation scenarios potentially enjoyed a higher level of ownership and accep-
tance amongst societal and political actors and ideally contributed to shared
vision-building.
Acknowledgements
This research was partly funded by the project ENCI-LowCarb (213106)
within the 7th Framework Programme for Research of the European Commis-
sion. The authors would like to thank the ENCI-LowCarb partners, especially
Jan Burck (Germanwatch) for the inspiring teamwork as well as Michael Pahle
and two anonymous reviewers for insightful comments.
Appendix
List of organizations participating in the stakeholder dialogues on the trans-
port sector: World Wide Fund For Nature (WWF), Germanwatch e.V., FUSS
e.V. - Fachverband Fußverkehr Deutschland, Verkehrsclub Deutschland e.V,
Allgemeiner Deutscher Automobil-Club e.V. (ADAC), Allgemeiner Deutscher
Fahrrad-Club e.V. (ADFC), Verband Deutscher Verkehrsunternehmen e.V. (VDV),
29
4.7 References 137
Allianz pro Schiene e.V., Region Hannover Verkehrsentwicklung und Verkehrs-
management, Daimler AG, Verband der deutschen Biokrafstoffindustrie e.V.
List of organizations participating in the stakeholder dialogues on the elec-
tricity sector: Naturschutzbund Deutschland e.V. (NABU), klima-allianz deutsch-
land, e5 - European Business Council for Sustainable Energy, World Wide Fund
For Nature (WWF), Germanwatch e.V., Brot f¨ur die Welt (Diakonisches Werk
der Evangelischen Kirche in Deutschland e.V.), Bundesverband Erneuerbare En-
ergie e.V. (BEE), Bundesverband Verbraucherzentralen, TenneT TSO GmbH,
50Hz Transmission GmbH, LichtBlick AG, RWE AG, Industriegewerkschaft
Bergbau, Chemie, Energie (IG BCE).
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36
144
Chapter 4 Ambitious Mitigation Scenarios for Germany: A Participatory
Approach
Chapter 5
Renewable Electricity Generation in Germany: A
Meta-Analysis of Mitigation Scenarios∗
Eva Schmid
Michael Pahle
Brigitte Knopf
∗submitted to Energy Policy
145
146
Chapter 5 Renewable Electricity Generation in Germany: A Meta-Analysis of
Mitigation Scenarios
1
Renewable Electricity Generation in Germany:
A Meta-Analysis of Mitigation Scenarios
ͳǡǡ
aPotsdam Institute for Climate Impact Research (PIK),
P.O. Box 601203, 14412 Potsdam, Germany
Abstract
This paper investigates a number of strategic questions relevant for the long-term transformation of
the German electricity sector towards high shares of electricity generation from renewable energy
sources (RES-E), by means of a meta-analysis of ten model-based mitigation scenarios from six
recent publications. The scenarios group into three different energy strategies that exploit the basic
options of domestic RES-E production, energy efficiency improvements and RES-E imports to a
different extent. We identify several behavioral, institutional and engineering barriers to
implementation that apply to all suggested energy strategies. Furthermore, we analyze RES-E
technology choice and RES-E capacity investment requirements. The scenario projections indicate
that wind offshore and onshore are the most important technologies. Yet, the studies rarely address
the systemic effects of high shares of fluctuating RES-E explicitly, resulting in doubtfully optimistic
techno-economic assumptions. Upon investigating the reasons why scenario projections diverge, it
turns out that they are in many cases based on expert judgments rather than resulting from
numerical modeling. These involve normative judgments and need to be made more explicit in
future research. Also, scenario assumptions need to be motivated and elaborated on more explicitly
since they have a decisive impact on the analytical quality of scenario projections.
Keywords: Energy strategy, Institutional barriers to implementation, Energy System Modeling
1 Corresponding author, Tel.: +49 331 288 2674, Fax: +49 331 288 2570
Email address: eva.schmid@pik-potsdam.de (Eva Schmid)
147
1.
Having
a
aims at
emissio
n
underg
o
pathwa
y
transiti
o
consist
e
objectiv
particul
a
identify
findings
The sco
p
energy
generat
i
electrici
t
deploy
m
installe
d
Sources
2011 §
1
provisio
respecti
within t
h
Figure 1.
S
RES-E = el
Introdu
c
a
ccomplishe
d
an ambitio
u
n
reduction
s
o
a substanti
y
s, model-b
a
o
n towards
e
nt with the
e functions,
a
r interest
t
energy stra
t
? What are
r
p
e of this m
e
related CO
2
i
on from re
n
t
y provision
m
ent over t
h
d
with the G
Act from t
h
1
(2) was cha
n” after 20
2
vely. Hence
,
h
e next dec
a
S
chematic ove
r
ectricity gener
a
c
tion
d
20% CO
2
e
u
s long-ter
m
s
, the pred
al transfor
m
a
sed mitiga
t
a low-carb
o
Governmen
t
quantitativ
e
t
o understa
t
egies that
a
r
easons and
e
ta-analysis
2
emissions
n
ewable ene
(BMU, 201
2
h
e past two
rid Feed-In
L
h
e year 200
0
nged from
a
2
0 to defini
n
,
the Germa
n
a
des (Federa
r
view of strate
g
a
tion from ren
e
e
mission red
u
m
target of
ominantly
f
m
ation. By pr
o
t
ion scenari
o
o
n Germany
t
’s mitigatio
e
models a
n
nd how av
a
a
re commo
n
drivers for s
is confined
t
in German
y
rgy sources
2
b), resultin
g
decades. Th
L
aw
in 1991
0
. In the la
s
a
iming at a
“
n
g minimum
n
Governm
e
l Governme
n
g
ic options for
e
wable energy
2
u
ction in 20
1
8
0-95% in
2
f
ossil fuel
b
o
viding con
s
o
s are an i
m
can be rea
n targets ex
i
n
d choices o
a
ilable miti
g
n
across sce
n
imilarities a
n
t
o the electr
i
y
in 2011 (
B
(RES-E) alre
a
g
from a glo
b
is develop
m
and accele
r
s
t amendm
e
“
continuous
targets of
5
e
nt strives t
o
n
t, 2010).
the long-term
sources
1
1 relative t
o
2
050 (2010)
.
b
ased Germ
a
s
istent proje
c
m
portant so
u
lized. Sever
i
st in the lit
e
f uncertain
g
ation scen
a
n
arios? Are
t
n
d differenc
e
i
city sector
w
B
MWi, 201
2
a
dy contrib
u
b
ally unprec
e
m
ent was tri
g
r
ated by its
s
e
nt of the R
e
increase in
5
0%, 65% a
n
o
transition
t
transformatio
o
1990, the
G
.
In order t
o
a
n energy
s
c
tions of te
c
u
rce of info
r
al mitigatio
n
e
rature, eac
h
input assu
m
a
rios compa
t
here robus
t
e
s across mi
t
w
hich was r
e
2
). In the s
a
u
ted as muc
h
e
dented gro
w
g
gered by a
s
uccessor t
h
e
newable E
n
the share
o
n
d 80% in 2
0
t
owards the
n of the Germ
a
G
erman Go
v
o
achieve s
u
s
ystem will
c
hnology de
p
r
mation on
n
scenarios
h
relying on
m
ptions. Thu
s
re: Is it po
t
– or contr
o
t
igation sce
n
e
sponsible f
o
a
me year,
e
h
as 20% to
d
w
th in RES-E
feed-in tari
f
h
e Renewabl
n
ergy Sourc
e
o
f RES-E in
e
0
30, 2040 a
“age of ren
e
a
n electricity s
e
v
ernment
u
ch deep
have to
p
loyment
how this
that are
different
s
, it is of
ssible to
o
versial –
n
arios?
o
r 45% of
e
lectricity
d
omestic
capacity
f
f system
e Energy
e
s Act in
e
lectricity
nd 2050,
e
wables”
e
ctor.
148
Chapter 5 Renewable Electricity Generation in Germany: A Meta-Analysis of
Mitigation Scenarios
3
Against this background the German policy maker is confronted with a number of strategic questions
relevant for the long-term transformation of the German electricity sector towards a high share of
RES-E. A first set of questions relates to the basic strategic options available for the transformation,
compare Figure 1: Which level of domestic RES-E production is required? What is the role of energy
efficiency and RES-E imports in this context? A highly relevant question is which RES-E technologies
are of major importance, e.g. for the further development of the German feed-in tariff system. And
finally, how much needs to be invested in RES-E capacities? In Section 2.1, this paper investigates
answers to these energy strategy relevant questions by means of a meta-analysis of scenario
projections of ten mitigation scenarios drawn from six recent publications, see Table 1.
Table 1. Overview of mitigation scenarios considered in this analysis. Selection criteria for the scenarios included that
they are (i) based on quantitative modeling (ii) consistent with the Government’s long-term mitigation target and (iii)
analyzing the time horizon until 2050
Publication Scenario(s)
(WWF, 2009) Innovation scenario [Inn] and its variant allowing for the Carbon Capture and
Sequestration (CCS) technology [Inn_CCS]
(EWI/GWS/Prognos, 2010) [A1], the scenario with the lowest extension of the nuclear-phase out of 4
years (until 2026)
(DLR/IWES/IFNE, 2010) Scenarios [A] and [B-100%-S/H2], which assumes a higher share (33% vs. 66%)
of electro mobility in 2050’s motorized individual transport mileage
(DLR/IWES/IFNE, 2012) Updated version of scenario [A], assumes 50% share of electro mobility
(Schmid and Knopf, 2012) Paradigm shift scenario [PS] and its variant [PS+], which additionally allows for
CCS and large-scale biofuel production
(SRU, 2011) Scenarios [2.1.a] and [2.1.b], which differ in assumptions on electricity
demand in 2050 (500 vs. 700 TWh)
It turns out that the scenarios can be grouped into three distinct energy strategies which exploit the
strategic options of increasing domestic RES-E production, decreasing electricity demand and
increasing RES-E imports to different extents. Section 2.2 identifies several behavioral, institutional
and engineering barriers to implementation that apply to all identified energy strategies. For
developing a robust energy strategy for the German electricity sector it is of particular interest to
understand why the scenario projections are so different. In order to elucidate reasons and drivers
for diverging scenario projections Section 3 investigates the individual modeling methodologies and
compares underlying techno-economic assumptions. Section 4 summarizes and concludes.
5.1 Introduction 149
4
2. From Scenarios to Strategies
Mitigation scenarios derived with quantitative models can be seen as complex thought experiments
of the semantic form “if ї then”. The “if” is resembled by the set of input assumptions; the arrow
represents the quantitative model which yields projections of energy system developments
representing the “then”. This part of the meta-analysis considers only the “then” dimension for
investigating the aforementioned strategy-relevant questions on the long-term transformation of
the German electricity sector towards high shares of RES-E. Doing so implies a strict model
democracy in the sense of valuing results equally regardless of the quality of input assumption and
analytical merits (cp. Knutti, 2010).
2.1 Scenario Projections
2.1.1 Strategic Options for Transforming the Electricity Sector
The three strategic options that are available for transforming the German electricity sector towards
high shares of RES-E include (i) increasing domestic RES-E production, (ii) decreasing electricity
demand by means of progress in energy efficiency, and (iii) increasing RES-E imports. The following
investigates to which extent the mitigation scenarios exploit each of these strategic options.
To start with the central question of which level of RES-E production is required, Figure 2 reveals at a
first glimpse that no unambiguous answer can be given by the scenario projections. The only robust
conclusion that can be drawn is that domestic RES-E production will need to continue the upward
trend of earlier decades and at least double until 2050 as compared to the de facto production of
120 TWh in 2011. However, significant discrepancies arise between the individual scenario
projections. While the scenarios already suggest RES-E levels of 200-350 TWh in 2020, the spread in
long-term projections increases up to as much as 250-700 TWh in 2050. Interestingly, the scenario
projections are scattered rather evenly within the range spanned by the lowest and highest RES-E
expansion pathway. Whether RES-E production ought to increase by factor two or five over the
coming four decades does make a considerable difference for the design and volume of required
RES-E support schemes and, correspondingly, on the future size of the domestic RES-E market, as
well as for the magnitude of the concomitant burden from infrastructure and RES-E capacity
deployment. Pursuing a high RES-E expansion strategy would require a doubling of the domestic
150
Chapter 5 Renewable Electricity Generation in Germany: A Meta-Analysis of
Mitigation Scenarios
RES-E
m
RES-E e
x
Figu
sele
c
Figure 3.
N
selection
o
The thr
e
electrici
t
project
m
arket alrea
d
x
pansion str
a
re 2. Electricit
y
c
tion of model
-
N
ormalized El
e
o
f model-base
d
e
e by far lo
w
t
y demand,
reductions
o
d
y in the co
m
a
tegy allow
e
y
production f
r
-
based, long-te
r
e
ctricity Dema
n
d
, long-term m
i
w
est RES-E e
x
i.e. substan
o
f 25%-35%
m
ing decade
e
d three mo
r
r
om domestic
r
m mitigation
s
n
d;
includes his
i
tigation scena
r
x
pansion sce
tial improv
e
in 2050 co
m
5
and thus ca
r
e decades f
o
RES-E;
include
s
s
cenarios.
torical data (B
M
r
ios with [base
narios are a
t
e
ments in e
n
m
pared to
2
lled for tim
e
o
r the same
d
s
historical dat
M
Wi, 2012) wit
year = 1].
t
the same ti
n
ergy efficie
2
010. Henc
e
e
ly action. O
n
d
evelopme
n
a (BMU, 2012
b
h [2010=1] an
d
me those th
ncy. As Figu
e
, according
n
the contra
n
t.
b
) and projecti
o
d
projections fr
o
at project t
h
re 3 illustra
t
to the scen
a
ry, a low
o
ns from a
o
m a
h
e lowest
t
es, they
arios, an
5.2 From Scenarios to Strategies 151
energy
decisive
20% ov
e
must b
e
2010).
S
aims at
term e
n
improv
e
electrici
t
GDP is
p
deman
d
in othe
DLR/IW
E
the em
e
ambitio
n
Figu
dat
a
long
-
for
m
This im
p
electrici
t
scenari
o
strategy th
a
turnaroun
d
e
r the past
t
e
attributed
S
uch a delib
e
a 10% redu
c
n
ergy conce
p
e
ments in e
t
y demand i
p
rojected to
d
is tantamo
u
r words in
c
E
S/IFNE (20
1
e
rging elect
r
n
s appear t
o
re 4. Share of
d
a
provided); in
c
-term mitigati
o
m
ulated in §1(2
)
p
ression is
c
t
y consump
t
o
s achieve a
a
t relies on
d
in energy
e
t
wo decade
s
to the glob
a
e
rate energ
y
c
tion in elec
p
t (Federal
G
nergy effici
e
n Figure 3,
b
grow conti
n
u
nt to a dec
o
c
reasing en
e
1
2) scenario
s
r
ic transpor
t
o
be inversel
y
d
omestic RES-
E
c
ludes historica
o
n scenarios. C
r
)
of the Renew
a
c
orroborate
d
t
ion/produc
t
domestic R
E
low domes
t
e
fficiency tr
e
s
, albeit sta
g
a
l financial
c
y
saving str
a
tricity dema
Governmen
t
e
ncy are n
o
b
ut also to a
n
uously in a
o
upling pro
c
e
rgy efficie
n
s
between
2
t
sector. Ov
y
related.
E
production in
l data (BMU, 2
0
r
osses indicate
a
ble Energy So
u
d
by Figure
4
t
ion (depen
d
E
S-E share a
6
t
ic RES-E p
r
e
nds: Germa
g
nating in t
h
c
risis rather
t
a
tegy is also
nd in 2025
a
t
, 2010). It
o
t only ass
u
certain ext
e
ll scenarios,
c
ess of elect
r
n
cy. The u
p
2
040 and 20
5
erall, dome
s
German elect
r
0
12b; BMWi, 2
0
official minim
u
u
rces Act – not
e
4
which dep
d
ing on the
d
s high as 7
0
r
oduction m
n electricity
h
e recent y
e
t
han to ded
i
favored by
a
nd 25% in
2
needs to b
e
u
med for sc
e
nt for thos
e
so a stable
r
icity consu
m
p
ward tren
d
5
0 is due to
s
tic RES-E p
r
icity consump
t
0
12) and proje
c
u
m targets shar
e
that they ad
d
icts the sha
d
ata reporte
d
0
% by 2050.
ust at the
s
demand ha
e
ars. Howev
e
i
cated effici
e
the Germa
n
2
050 relativ
e
e
acknowled
enarios dis
p
e
which rem
or slightly
d
m
ption from
d
in electri
c
additional
e
roduction a
t
ion/producti
o
c
tions from a s
e
es of RES-E in
e
d
itionally allow
re of dome
s
d
). Even the
A stunning
f
s
ame time
s been incr
e
e
r, this dev
e
e
ncy policie
s
n
Governme
e
to 2008 in
ged that su
p
laying a d
e
ain stable o
v
d
ecreasing
e
economic g
c
ity deman
d
e
lectricity d
e
nd energy
e
o
n (depending
o
e
lection of mo
d
e
lectricity provi
for RES-E imp
o
s
tic RES-E in
low RES-E e
x
f
eature of Fi
induce a
e
asing by
e
lopment
s
(BMWi,
nt which
its long-
bstantial
e
creasing
v
er time.
e
lectricity
rowth or
d
of the
e
mand of
e
fficiency
o
n the
d
el-based,
sion as
o
rts
German
x
pansion
gure 4 is
152
Chapter 5 Renewable Electricity Generation in Germany: A Meta-Analysis of
Mitigation Scenarios
that ha
l
targets
rate: 15
option
o
four sc
e
share o
f
2050 as
all scen
a
deploy
m
As has
a
would i
m
with 50
-
from R
E
the Mi
d
electrici
t
by putti
extensi
v
achieve
d
acceler
a
a politi
c
domain
,
Figure 5.
E
based, lo
n
l
f of the sc
e
until 2040,
percentage
o
f RES-E imp
e
narios that
d
f
RES-E over
early as 20
2
a
rios projec
t
m
ent of syst
e
a
lready bee
n
m
ply that G
e
-
200 TWh in
E
S-E in Euro
p
d
dle East No
t
y imports;
h
ng forward
v
e imports
p
d
and the
a
ted, implyi
n
c
al level thi
s
,
are raised t
E
lectricity imp
o
n
g-term mitiga
t
e
narios follo
c
orrespondi
points per
d
orts these s
c
d
o not rely
o
the next tw
2
5 to 2035.
A
t
RES-E sha
r
e
m integrati
o
n
mentioned
,
e
rmany turn
e
2050 (Figur
p
ean countr
i
rth Africa (
M
h
owever, th
e
the exploit
a
p
resuppose
t
physical i
n
n
g the expa
n
s
requires t
h
o a transnat
o
rt balance; in
c
t
ion scenarios.
w almost t
h
ng to a line
d
ecade. It n
e
c
enarios are
o
n electricit
y
o decades a
A
total of fo
u
r
es betwee
n
o
n options is
,
several sce
e
d from a n
e
e 5). The sc
e
i
es; DLR/IW
E
M
ENA). EWI
/
e
ir share is
n
a
tion of mor
t
hat a surpl
u
n
tegration
o
n
sion of tran
h
at energy s
ional level i
n
c
ludes historic
a
Imports are as
s
7
h
e same lin
e
ar increase
e
eds to be
m
still consist
e
y
imports di
s
nd reach th
e
u
r scenarios
a
n
40-70% as
presuppos
e
narios expl
o
e
t exporter
o
e
narios assu
m
E
S/IFNE (20
1
/
GWS/Prog
n
n
ot spelled
o
e profitable
u
s of RES-E
p
o
f the Eur
o
smission inf
ecurity con
s
n
a cooperat
a
l data (BMWi,
2
s
umed to be pr
e
ar trajecto
r
extrapolati
n
m
entioned th
e
nt with the
s
play a signi
f
e
Governme
a
chieve a R
E
early as 20
e
d for their f
e
o
it the strat
e
o
f electricity
m
e that this
1
0; 2012) ad
n
os (2010) i
n
o
ut. The pu
b
potentials
o
p
roduction i
o
pean and/
o
rastructure
a
s
iderations,
ive manner.
2
012) and proj
e
oduced with R
E
r
y just abo
v
n
g the recen
at due to e
x
Governme
n
f
icantly fast
e
nt’s 80% mi
n
E
S-E share o
f
20, a delib
e
e
asibility (se
e
gic option
o
towards a n
imported e
ditionally c
o
n
principle
a
b
lications m
o
o
utside of G
n exporting
o
r MENA
e
a
nd interco
n
which are
c
e
ctions from a
E
S-E technolog
i
v
e the Gove
r
tly observe
d
x
ploiting the
n
t target in 2
e
r accelerati
o
n
imal target
f
100% in 20
5
e
rate progre
s
e Section 2.
2
o
f RES-E imp
o
et importin
g
lectricity is
p
o
nsider imp
o
a
lso conside
r
o
tivate RES-
E
ermany. Te
c
countries i
s
e
lectricity
m
n
nectors. Fu
c
urrently a
s
selection of m
o
i
es.
r
nment’s
d
growth
strategic
050. The
o
n in the
share of
5
0. Since
s
s in the
2
.2).
o
rts. This
g
country
p
roduced
o
rts from
r
nuclear
E
imports
c
hnically,
s
actually
m
arket is
rther, on
s
overeign
o
del-
5.2 From Scenarios to Strategies 153
Table 2. S
transfor
m
To sum
m
increasi
n
energy
e
in Tabl
e
emphas
projecti
o
respecti
[100%-
S
three g
r
relies o
n
relativel
imports
efficien
c
compen
high sh
a
– accor
d
tylized compa
r
m
ing the Germ
a
m
arize, the
n
g domesti
c
e
fficiency an
e
2, which
ize each st
r
o
ns for eac
h
ve category
S
H2] scenari
o
r
oups, whic
h
n
exploiting
y low dome
s
and balanc
e
c
y improve
m
sates relati
v
a
res of RES-
E
d
ing to the s
c
r
ative account
a
n electricity s
e
investigatio
n
c
RES-E pro
d
d (iii) increa
s
gives a styl
r
ategic opti
o
h
option in
rating. Not
e
o
was not co
h
differ with
energy effi
c
s
tic RES-E p
r
e
s moderate
m
ents. The
t
v
ely higher
e
E
in the Ger
m
c
enario proj
e
of the extent t
e
ctor towards
h
n
has reveal
e
d
uction, (ii)
s
ing RES-E i
m
ized comp
a
o
n. In orde
r
2050 is di
v
e
that for ele
nsidered fo
r
respect to t
c
iency pote
n
r
oduction an
to high do
m
t
hird group
e
lectricity d
e
m
an electric
i
e
ctions.
8
o which the sc
e
h
igh shares of
R
e
d that the
decreasing
e
m
ports to a
v
rative acco
u
r
to conden
s
v
ided by th
r
ctricity dem
a
r
calculating
t
he energy s
t
n
tials to red
u
d moderate
m
estic RES-E
puts a simil
e
mand with
i
ty sector ca
n
e
narios exploi
t
R
ES-E.
scenarios e
x
e
lectricity d
e
v
ery differen
u
nt on the
s
e informat
i
r
ee and the
n
a
nd, the out
t
he spread.
T
t
rategy they
u
ce electrici
t
RES-E impo
r
production
ar focus on
RES-E impo
n
be achiev
e
t
each of the t
h
x
ploit the st
e
mand by
m
t extent. Thi
extent to
w
i
on, the sp
r
n
scenarios
lier of the D
T
he scenari
o
embody. T
h
t
y demand,
r
ts. A secon
d
against high
domestic
R
rts. Thus, t
h
e
d with disti
n
h
ree strategic
o
rategic opti
o
m
eans of pr
o
s finding is
v
w
hich the
s
r
ead of the
are attribu
t
LR/IWES/IF
N
o
s can be clu
h
e first grou
p
which is sa
t
d
group refr
a
to modera
t
R
ES-E produ
c
h
e target of
n
ct energy s
o
ptions for
o
ns of (i)
o
gress in
v
isualized
s
cenarios
scenario
t
ed their
N
E (2012)
stered in
p
heavily
t
isfied by
a
ins from
t
e energy
c
tion but
reaching
trategies
154
Chapter 5 Renewable Electricity Generation in Germany: A Meta-Analysis of
Mitigation Scenarios
2.1.3
R
Since a
n
questio
n
order t
o
scenari
o
product
potenti
a
generat
i
Togeth
e
betwee
n
domina
n
biomas
s
importa
potenti
a
most e
x
does os
t
domest
i
those s
c
wind of
f
a lesser
Figure 6.
R
ES-E T
e
n
increase
i
n
is which R
E
o
abstract fr
o
s Figure 6 d
ion over th
e
a
l is alread
y
i
on from wi
e
r, wind ons
h
n
2010 and
n
t role, win
s
provides
1
nt role in
b
a
l of biomas
s
x
pensive tec
t
ensibly not
i
c RES-E pro
d
c
enarios tha
t
f
shore and
o
extent solar
Shares of indi
v
e
chnolog
i
i
n domestic
E
S-E techno
l
om the div
e
isplays the
p
e
period 20
1
y
exploited
nd is the
m
h
ore and of
f
2050. With
d onshore
a
1
0-30%. Bio
m
b
alancing flu
s
is limited
m
hnology in
t
play a majo
d
uction. Fin
a
t
refrain fro
m
o
nshore are
t
PV as well
a
v
idual RES-E te
c
i
es
RES-E pro
d
l
ogies are o
f
e
rging absol
u
p
ercentage
s
1
0-2050. Hy
d
in German
y
m
ost import
a
f
shore cont
r
the excepti
a
nd offshor
e
m
ass is in
f
ctuations f
r
m
ainly due
t
t
erms of lev
r role in eit
h
a
lly, geother
m
RES-E imp
t
he most im
p
a
s geotherm
a
c
hnologies in c
9
d
uction is e
f
major imp
o
u
te levels o
f
s
hare of eac
h
d
ropower is
y
. At a firs
t
a
nt pillar of
r
ibute 55-7
0
on of the S
R
e
contribute
f
act a disp
a
om the var
t
o ecological
elized cost
o
h
er of the s
c
mal electric
i
orts. Hence,
p
ortant tech
a
l.
umulative do
m
xploited in
o
rtance for
t
f
domestic
R
h
RES-E tec
h
excluded fr
o
t
glimpse,
F
the future
0
% of cumul
a
R
U (2011) s
c
in equal p
a
a
tchable RE
S
iable techn
o
concerns (
N
o
f electricit
y
c
enarios wit
h
i
ty generati
o
in relative
nologies, cl
o
m
estic RES-E pr
o
all scenari
o
t
he German
R
ES-E produ
c
h
nology in t
o
o
m the ana
l
F
igure 6 re
v
technology
a
tive dome
s
c
enarios wh
a
rts. Electri
c
S
-E technol
o
o
logies win
d
N
itsch et al.,
y
today (Fis
c
h
as little as
o
n plays a su
terms the s
c
o
sely follow
e
o
duction over
t
o
s, a highly
energy tran
c
tion in the
o
tal cumulat
i
l
ysis as the
d
v
eals that
e
mix in all s
c
s
tic RES-E pr
ere offshor
e
c
ity product
i
o
gy, which
d
and solar.
2004). Sola
c
hedick et a
l
3-16% of cu
bstantial rol
c
enarios sug
g
e
d by bioma
s
t
he period 20
1
relevant
sition. In
di
f
ferent
i
ve RES-E
d
omestic
e
lectricity
c
enarios.
oduction
e
plays a
i
on from
plays an
Yet, the
r PV, the
l
., 2011),
mulative
e only in
g
est that
s
s, and to
1
0 to 2050.
5.2 From Scenarios to Strategies 155
Given t
h
does n
o
diverse
offshor
e
projecti
o
Act was
0.076 t
o
could s
o
accrued
which i
s
While a
momen
t
RES-E in
solar P
V
Figure 7.
E
from a sel
GW capa
c
average n
How m
u
RES-E c
a
well as
investm
specific
interpol
multipl
y
h
e large spr
e
o
t come at a
pathways o
v
e
with scena
r
o
ns – while
t
installed in
o
25 GW in
2
o
far not be
to as little
a
s
far below t
n analysis o
f
t
it suffices
t
n
the electri
c
V
market in s
i
E
lectricity Pro
d
l
ection of mod
e
c
ity in 2030 (Fe
d
umber for 202
0
2.1.3 RE
S
u
ch needs t
o
a
pacity inve
installed ca
p
ent costs c
o
investmen
t
ation, und
e
y
ing specific
e
ad in absol
surprise th
a
v
er time. W
h
r
io projectio
t
he opposit
e
2000, capac
2
011 (BMU,
triggered b
y
a
s 280 MW
he tentative
f
why the p
t
o realize th
a
c
ity sector t
h
i
ze within th
d
uction from S
o
e
l-based, long-
t
d
eral Governm
0
across scena
r
S
-E Capa
c
o
be investe
d
stments, ho
p
acities in
G
o
mparable,
t
costs and
e
r the assu
m
investment
ute domest
i
a
t the proje
c
h
en compari
n
ns, it is eye-
c
e
is true for
t
ity deploym
e
2012b). On
y
the Rene
w
(BMU, 2012
Governme
n
rojections a
r
a
t, accordin
g
h
e wind offs
h
e coming d
e
o
lar PV and Wi
n
t
erm mitigatio
n
ent, 2010), wh
i
r
ios)
c
ity Inve
d
in RES-E c
a
wever, the
G
W for indi
v
we perfor
m
capacity a
d
m
ption tha
t
costs with
t
10
i
c RES-E pro
c
ted develo
p
n
g de facto
e
c
atching tha
t
he latter (Fi
g
e
nt of solar
P
the contrar
y
w
able Energ
y
b). Currentl
y
n
t target of
2
r
e diverging
g
to the sce
n
h
ore market
e
cade.
n
d Offshore; i
n
n
scenarios. Th
e
i
ch is converte
d
stments
a
pacities?
M
majority re
p
v
idual years
m
the follow
d
ditions fo
r
t
capacities
t
he capacit
y
duction acr
o
p
ments of t
h
e
lectricity pr
t for the for
m
g
ure 7). Sin
c
P
V in Germ
a
y
, a substan
t
y
Sources A
c
y
, planned
o
2
5 GW by 2
0
so much is
n
ario project
needs to d
e
n
cludes historic
e
cross indicate
d
to productio
n
M
ost publica
t
p
orts specifi
between 2
0
ing calculat
i
r
every fift
h
are fully
d
y
additions,
t
o
ss scenario
s
h
e individua
l
oduction fr
o
m
er, reality
h
c
e the Rene
w
a
ny increase
d
t
ial deploym
c
t and insta
l
o
ffshore proj
0
30 (Federal
postponed
ions, for ac
h
e
velop urge
n
al data (BMU,
2
s the tentative
n
with a capaci
t
t
ions refrain
c investme
n
0
10 and 20
5
i
on for all s
h
year in t
h
d
epreciated
t
otal invest
m
s
discussed
l
technologi
e
o
m solar PV
a
h
as overtak
e
w
able Energ
y
d
tremendo
u
ent of wind
l
led capacit
y
ects accrue
Governme
n
to Section
3
h
ieving high
s
n
tly and ove
r
2
012b) and pr
o
government t
a
t
y factor of 0.4
3
from provi
d
n
t costs in
€
5
0. In order
cenarios:
W
h
e period
b
after 20
y
m
ent costs
f
above, it
e
s follow
a
nd wind
e
n model
y
Sources
u
sly from
offshore
y
in 2011
to 9 GW
n
t, 2010).
3
, for the
s
hares of
r
take the
o
jections
a
rget
of
25
3
(the
d
ing total
€
/kWh as
to make
W
e derive
b
y linear
y
ears. By
f
or every
156
Chapter 5 Renewable Electricity Generation in Germany: A Meta-Analysis of
Mitigation Scenarios
fifth ye
a
distribu
t
with a d
(2010-2
0
The pre
s
range o
f
costs d
o
Figure
2
investm
scenari
o
counter
spread
i
value o
f
as com
p
Figure 8.
P
discount
r
the years
Across
s
coming
present
scenari
o
RES-E r
e
especial
scenari
o
require
a
r can be i
t
ed uniform
iscount rate
0
50), togeth
s
ent value
o
f
80-120 Bn
€
o
es not coin
c
2
. Given t
h
ent costs a
n
o
s’ present
intuitive. Fo
r
i
n present v
f
total RES-E
p
ared to the
P
resent value
o
r
ate of 3%; incl
2010 and 201
1
s
cenarios a
d
decade, giv
e
values for 2
o
s suggest t
h
e
quires a d
e
ly the case
o
s which ref
r
higher inve
s
dentified.
W
ly. The resu
of 3%. Figu
r
er with the
a
o
f total inves
€
. Interestin
g
c
ide with th
e
h
e similariti
e
n
d average
a
values o
f
r
the period
alues increa
investment
s
other scena
r
o
f RES-E capaci
uded are only
s
1
are from BM
U
d
isproportio
n
e
n that the
010-2050. A
h
at the tran
e
cisive and
t
in those
s
r
ain from RE
S
s
tments int
o
W
e further
a
lting annual
r
e 8 reports
t
a
lready incu
r
tment costs
g
ly, upon a
c
e
order in te
r
e
s in techn
a
nnual full l
o
f
RES-E in
v
2010-2050,
ses up to 1
8
s
attains onl
y
r
ios. Section
ty investment
s
s
cenarios for
w
U
(BMU, 2011;
B
n
ately high f
r
present val
u
n equal dist
r
sformation
o
t
imely inves
t
s
cenarios t
h
S
-E imports
a
o
RES-E cap
a
11
a
ssume tha
t
investmen
t
t
he results f
o
r
red invest
m
into RES-E
c
c
loser look,
t
r
ms of RES-E
ology shar
e
o
ad hours a
v
estments
this observ
a
8
0-350 Bn
€
y
a moderat
e
3 investigat
s
over the peri
o
w
hich specific in
B
MU, 2012a);
h
r
action of in
v
u
es for 201
0
r
ibution acr
o
o
f the Ger
m
t
ment effor
t
h
at heavily
e
a
nd exploit
e
a
cities also
t
investme
n
t
flows are
c
o
r the short
-
m
ents of 201
0
c
apacities o
v
t
he order of
production
e
s, differen
c
ppear to be
than total
a
tion holds
e
€
. Yet, the s
c
e
level of d
o
es this issue
o
d 2010-2020 (
vestment cost
s
h
istorical data
o
v
estments i
n
0
-2020 acco
u
o
ss decades
i
m
an electrici
t
t
than may
s
e
xploit ene
r
e
nergy effici
e
in the later
n
ts within f
i
c
onverted t
o
-
term (2010
-
0
and 2011.
v
er the next
scenarios in
for the yea
r
c
es in assu
m
more imp
o
RES-E pr
o
e
ven more p
c
enario with
o
mestic RES-
in further d
e
left) and 2010
-
s
are reported.
o
n wind offsho
r
n
to RES-E ca
p
u
nt for 30-5
4
i
mplied a va
t
y sector to
w
s
ubsequentl
r
gy efficien
c
e
ncy potent
i
decades. R
e
i
ve-year pe
r
o
their pres
e
-
2020) and l
o
decade is
w
terms of in
v
r
2020 as dis
p
m
ptions on
o
rtant driver
o
duction,
w
ronouncedl
y
the highes
t
E productio
n
e
tail.
-
2050 (right) a
t
Undiscounted
d
r
e is lacking
p
acities occ
u
4
% of the r
e
lue of 25%.
T
w
ards high
s
y be relaxe
d
c
y potential
i
als to a less
e
e
lating ann
u
r
iods are
e
nt value
o
ng-term
w
ithin the
v
estment
p
layed in
specific
s for the
w
hich is
y
and the
t
present
n
in 2050
t
a
data for
u
rs in the
e
spective
T
hus, the
s
hares of
d
. This is
s. Those
e
r extent
u
al RES-E
5.2 From Scenarios to Strategies 157
12
investments to GDP in the respective year corroborates this finding: While the share amounts to
0.4-0.6% of GDP across scenarios in the next decade, it decreases to 0.1-0.5% by 2050.
In the years 2010 and 2011 investment volumes of 24.5 Bn € and 21 Bn € into RES-E capacities were
attracted by the Renewable Energy Sources Act (BMU, 2011; BMU, 2012a), corresponding to 0.98%
and 0.81% of GDP (Statistisches Bundesamt, 2012). Thus, the required short-term investments
appear feasible if investment dynamics continue the trend. In the scenarios investments into solar
PV heavily dominate over the period 2010-2020, with 37-66% of total RES-E capital investments. This
is also the case for the de facto investments of 2010 and 2011 in which solar PV had a share of 77%.
The disproportionate share of solar PV in historical investments is due to the fact that the specific
investment costs of solar panels have decreased continuously by more than 60% since 2006 (BSW
Solar, 2012), but feed-in tariffs have been reduced only sporadically. This resulted in double-digit
profit margins and solar-PV attracted as much as 35 Bn € of capital investments in 2010 and 2011
alone. However, according to the scenarios, capital investments need to diversify towards the other
RES-E technologies in the near-term future, especially towards wind onshore and offshore.
2.2 Barriers to Implementation
Despite the numerous discrepancies between scenario projections, the meta-analysis has revealed
several robust findings across scenarios. These include that in order to transform the German
electricity sector towards high shares of RES-E, energy efficiency potentials need to be exploited
more than has been the case to date, system integration measures for uncertain, fluctuating and
spatially dispersed RES-E technologies need to be in place in due time, and wind offshore
deployment has to accelerate significantly. Yet there is evidence of a number of behavioral,
institutional and engineering obstacles that need to be overcome in order to realize the energy
strategies suggested by the scenarios.
2.2.1 Energy Efficiency: The Behavioral and Market Failure
Challenge
Energy efficiency constitutes a controversial policy domain for which it is highly unclear whether
ambitious targets can be realized. A long-standing debate in the literature has observed numerous
policy puzzles, the most prominent being the rebound effect. It postulates that an increase in energy
efficiency of a specific energy service may lead to a direct increase in the demand of that service or
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indirectly increase the demand of other energy-intensive services and thus to an increase in net
energy consumption (Sorrell et al., 2009). Attempts to measure the rebound effect is subject to
numerous methodological and data measurement problems, however, a recent literature survey
suggests that the direct rebound effect may reduce energy efficiency projections of engineering
models by as much as 30% (Sorrell, 2007). In any case, rebound effects should be taken into account
when designing energy efficiency policy and may be mitigated through carbon taxes or mitigation
caps – that is through higher prices on carbon-intensive energy (Sorrell, 2007). A further puzzle is
that many proposed energy efficiency policies are in fact energy saving policies targeted at forcing
the incumbent, regulated utilities to implement energy efficiency programs which after all reduce
the demand for their product (Brennan, 2011). Alternative business models seem necessary to
exploit energy efficiency opportunities, e.g. energy contracting. In Germany, one company started to
offer energy contracting for consumers, but could not establish themselves on the market and
withdrew their activities in the end-customer segment, now focusing only on organizational and
institutional customers (Kofler Energies Power AG, 2011).
A collection of market and behavioral failures to explain the difficulties in promoting energy
efficiency have been suggested in literature (e.g. Sorrell et al., 2004; Gillingham et al., 2009;
Thollander et al., 2010). Many of the identified market failures (e.g. environmental externalities,
average-cost electricity pricing, liquidity constraints, R&D and learning-by-doing spillovers) are not
unique to energy efficiency, thus they call for a broader policy response including carbon pricing,
innovation policies and electricity market reforms. Information and behavioral failures, such as lack
and asymmetry of information, principal-agent problems, split incentives, hidden costs or bounded
rationality on the other hand call for more specific energy efficiency policies (Gillingham et al., 2009).
In sum, relying on pivotal increases in energy efficiency for mitigation appears to be a risky strategy –
given the evidence.
2.2.2 System Integration: The Institutional and Engineering
Challenge
For accommodating increasingly high shares of wind and solar PV which are uncertain, fluctuating
and spatially dispersed RES-E technologies system integration measures need to be in place in due
time. In order to guarantee system stability electricity demand and supply need to be matched at
any time and at any place, which becomes increasingly challenging with growing shares of RES-E.
Technical solutions include an extension of (i) power grid infrastructure for transporting electricity to
demand centers and large-area pooling of fluctuations, (ii) dispatchable generation capacities to
5.2 From Scenarios to Strategies 159
14
provide short-term balancing as well as back-up capacities in low RES-E feed-in periods, (iv) demand
side measures to reduce peak load, (iii) storage capacities to cope with fluctuations on both short
and long time-scales, thereby avoiding the need for curtailment in high RES-E periods, and (v)
improved operational and planning methods (Sims et al., 2011). Currently, none of the options is
deployed at a sufficient level to integrate rising shares of RES-E as projected by the mitigation
scenarios into the German power grid (dena, 2010) – the stability of German power grids was
already critical in the winter 2011/2012 (Federal Network Agency, 2012).
In order to guarantee stability and security of electricity supply in the future the deployment of an
optimal portfolio of system integration measures needs to be incentivized. Doing so requires
adaptations of the institutional framework conditions such as speeding up approval procedures for
power grid extensions (dena, 2010), reforming the market design of control power markets (Federal
Network Agency, 2012) and introducing capacity markets to ensure the profitability of dispatchable
generation units (Cramton and Ockenfels, 2012). Also, the pricing system for end customers will
need to be re-designed in a more flexible fashion, e.g. real-time prizing, to incentivize demand side
measures (Sims et al., 2011). As regards electricity storage most available technologies are immature
and still require considerable research and development effort to enable their large-scale
deployment (ETG Task Force Energiespeicher, 2008). The only short- and long-term storage
technology that constitutes a profitable investment today is pumped hydro storage; however, the
German potential is too small to do the job alone (ETG Task Force Energiespeicher, 2008). A
controversially discussed possibility is to rely on the huge reservoir capacities in Norway for long-
term storage in a European integrated grid, which is e.g. assumed by the SRU (2011) scenarios. In
sum, providing sufficient system integration measures for high shares of RES-E requires decisive
reforms of institutional framework conditions and technological progress especially regarding
electricity storage.
2.2.3 Wind Offshore: The Challenge of Raising Capital
As Section 2.1.3 has revealed, the German feed-in-tariff scheme has incentivized a disproportionate
share of solar PV in total RES-E investments in the past; however, according to the scenarios a
diversification into other RES-E technologies, particularly wind offshore, is necessary in the future.
Since solar PV constitutes a modular, low-risk and low-maintenance technology that is frequently
installed in small scales, it is perfectly accessible to the small investor: Private persons and farmers
owned as much as 51% of total RES-E capacities in 2010 (trend:research, 2011). On the contrary,
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wind offshore constitutes a centralized, high-risk and high-maintenance technology that is only
worthwhile installing at large scale. A decisive growth in wind offshore capacities thus requires
tapping the capital resources of industry and institutional investors, or pooling private investments.
Either source of capital will only be accessible if the return on investment is deemed sufficiently
certain, i.e. projects are assessed as profitable. Because of currently small and uncertain returns on
investment, wind offshore projects in the German waters are of interest only to strategic investors
but not to financial investors (Richter, 2009). Due to the Wadden Sea National Park only far-shore
projects are allowed for in the North Sea, which has the larger wind offshore potential as compared
to the Baltic Sea. Increasing water depths and distance to shore are the main influencing factors for
investment costs (Prässler and Schaechtele, 2012), rendering projects in less challenging sites
outside of the German seas more interesting for profit-seeking investors. In fact, German far-shore
sites are expected to deliver an acceptable return on investment only upon deployment of
particularly high yield wind turbines with 5 MW capacity (Zeelenberg and van der Kloet, 2007). Only
37 of these turbines have been installed worldwide to date, of which 29 are in Germany (IWES,
2012). The fact that 5 MW turbines are still an immature technology discourages project developers,
banks, insurances and financial investors (Richter, 2009). In RES-E markets, proven reliability of a
technology is found to be a necessary condition for investing in it (Masini and Menichetti, 2012).
Gaining experience with 5 MW turbines is thus a prerequisite for making the German offshore
market accessible to financial investors and will need to be pursued by strategic investors such as
large utilities and sufficiently incentivized by tailored policy measures.
3. From Assumptions to Scenarios
For developing a robust energy strategy for the German electricity sector it is of particular interest to
understand why the scenarios exploit the strategic options of increasing domestic RES-E, decreasing
electricity demand and increasing RES-E imports to such different extents. Does it depend on input
assumptions, inter alia particular targets, or is it due to different modeling approaches? In order to
elucidate reasons and drivers for the different scenario projections Section 3.1 investigates the
methodologies by which the scenarios were modeled and Section 3.2 compares underlying techno-
economic assumptions. Furthermore, the following comes back to two questions that were raised in
the meta-analysis: Why are solar PV and wind offshore deployment so diverse across scenarios? Are
differences in techno-economic assumptions decisive for the scenarios’ projections of total RES-E
capacity investment requirements?
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16
3.1 Modeling Methodology
While all publications analyzed in this paper rely on quantitative modeling for deriving the mitigation
scenarios, they differ with respect to how electricity demand, deployment pathways of RES-E
capacities and imports are determined. The main question in this context is whether they are
defined as model input and determined exogenously, or whether they are a model result, or both.
The latter is common practice when soft-coupling partial models, which is advantageous when a
strong focus is put on technological detail, but comes at the cost of impeding systemic feedback. As
can be seen immediately from Table 3, none of the publications yield demand, capacities and
imports as model results simultaneously. In fact, to our knowledge there exists to date no model for
Germany that is capable of providing the necessary sectorial detail and regional and temporal scope,
likely due to numerical constraints. Each publication opts for a different approach, thereby
abstracting from reality in distinct respects.
Table 3. Overview of how electricity demand, RES-E capacities and RES-E imports are determined in the publications.
Publication Electricity Demand RES-E capacities RES-E Imports
(WWF, 2009)
Model result
(from bottom-
up simulation)
Model input
(to dispatch
model)
Model input
(to dispatch model) Residual value
(EWI/GWS/Prognos,
2010)
Model result
(from bottom-
up simulation)
Model input
(to dispatch
model)
Model input
(to dispatch model)
Model result
(from dispatch model)
(DLR/IWES/IFNE,
2010; 2012)
Model result
(from “quantity framework”)
Model input
(to “quantity
framework”)
Model result for
selected years
(from EU electricity
sector model)
(Schmid and Knopf,
2012)
Model result
(from integrated welfare maximization) Not considered
(SRU, 2011)
Model input
(to EU electricity
sector model)
Model result for 2050
(from EU electricity
sector model),
Interpolation before
Model result for 2050
(from EU electricity
sector model)
One of the most important variables in energy strategies is the absolute magnitude of future
electricity demand. Here, the approaches range from a detailed, bottom-up representation over an
economic top-down production function to treating demand as exogenous by relying on projections
of other publications. The first extreme is pursued in both WWF (2009) and EWI/GWS/Prognos
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(2010) which apply the same detailed bottom-up simulation model of the Prognos AG, consisting of
a variety of sectorial sub-modules that generate differentiated projections of the energy demands of
the industry and the residential and commercial sectors. One of the model outputs is the annual
electricity demand along with its load profile. Bottom-up models take on an engineer’s perspective
by incorporating detailed descriptions of technologies while assuming market adoption of the most
efficient technologies (Hourcade and Robinson, 1996). These models postulate that market forces do
not operate perfectly and frequently identify an efficiency gap between the current technology
penetration and best available techniques. Suggested mitigation policy implications are mainly to
remove barriers to adoption of the best available technique. In the global energy system models
literature, bottom-up models have been found to be highly optimistic regarding the technical
mitigation potential, due to picking “low-hanging fruits”, which are in fact not picked today (Grubb
et al., 1993; Hourcade and Robinson, 1996).
The highly optimistically decreasing electricity demand projections postulated in both the WWF
(2009) and EWI/GWS/Prognos (2010) scenarios are thus most likely an implication of using a
bottom-up demand model. The “quantity framework” applied in DLR/IWES/IFNE (2010; 2012) is a
spreadsheet simulation tool that is similarly assumption-driven, but not as detailed as the Prognos
demand model. Schmid and Knopf (2012) apply the hybrid energy economy model REMIND-D
(Schmid et al., 2012) which determines energy demand endogenously in a top-down approach by
means of a calibrated and parameterized production function. Here, electricity demand can respond
to changes in the energy system, e.g. in terms of rebound effects. Finally, SRU (2011) choose
electricity demand pathways as entirely exogenous input for their European electricity sector model
REMix (Scholz, 2010). They motivate their choice of trajectories as the upper and lower boundary of
the range projected by existing scenarios.
For determining the technology mix in the electricity sector the publications either rely on detailed
dispatch modeling, spreadsheet calculation, or optimization methods. Both WWF (2009) and
EWI/GWS/Prognos (2010) apply a dispatch model albeit adopting a different regional scope with
Germany only and Europe, respectively. Dispatch models determine the cost-minimal dispatch of
power plants for covering residual load, i.e. the demand time series minus priority feed-in of
fluctuating RES-E technologies. Hence by construction both RES-E capacity deployment and the
associated feed-in need to be determined exogenously before running a dispatch model. RES-E
capacities are also exogenous to the “quantity framework” of DLR/IWES/IFNE (2010; 2012). None of
the publications provide in-depth information on the rationale behind the selected RES-E
deployment pathways and feed-in projections. WWF (2009) relies on the RES-E projections of DLR
5.3 From Assumptions to Scenarios 163
18
(2008), an earlier version of DLR/IWES/IFNE (2010; 2012). EWI/GWS/Prognos (2010) state: “The
scenario construction of the electricity generation sector is further based on a model of renewable
energies in Europe that represents several RES-E technologies in a regionally differentiated manner.”
(p. 28). Yet, it is unclear how particular technologies and their deployment levels were chosen.
DLR/IWES/IFNE (2010) state: “The capacity deployment path of RES-E technologies results from an
extrapolation of the historical dynamics, under the assumption of priority feed-in for RES-E
technologies until 2050.” (p. 46). Here, for selected years the technical viability of the chosen
technology mix is validated with the simulation tool SimEE; however, this does not replace an
endogenous economic optimization approach. In sum, none of the three publications that consider
deployment and feed-in of RES-E technologies as exogenous input motivate their choices explicitly.
An important implication of the exogenous and sparsely motivated determination of RES-E
technology deployment pathways and associated RES-E feed-in is that these scenario projections are
beyond analytical traceability. Thus, the resulting scenarios are entirely normative scenarios that aim
at delivering particular targets. Underlying input parameters are determined via expert judgments so
as to achieve the targets in an optimal way. This is particularly problematic if the assumptions are
not made explicit and/or are at odds with systemic effects that arise upon endogenous
consideration of RES-E deployment and feed-in, an issue that is elucidated below.
In Schmid and Knopf (2012) and SRU (2011), RES-E capacities are determined endogenously. They
are an output of the optimization models REMIND-D and REMix, respectively. REMIND-D adopts a
social planner perspective with perfect foresight and determines the welfare-optimal RES-E
deployment pathway for Germany in five-year time steps. The variability of wind and solar PV is
addressed by means of a residual load duration curve (RLDC) approach (Ueckerdt et al., 2011). The
REMix model covers all of Europe and Northern Africa and determines the cost-optimal dispatch of
RES-E technologies, based on an exogenous target share, and considering their temporal and
geographical availability as well as electricity grid and storage options explicitly (Scholz, 2010). For
the SRU (2011) scenarios only the year 2050 was analyzed with REMix, imposing a target share of
RES-E of 100%. The deployment path between 2010 and 2050 was derived by interpolation. Hence,
nothing can be said on the optimality of the transitional RES-E deployment here. Yet, the results of
Schmid and Knopf (2012) indicate that a much faster increase in RES-E production over the next
decades would be optimal from an integrated welfare maximization point of view (see Figure 1).
Also, the share of solar PV is much lower in their scenarios – the only ones that consider system
integration effects and intertemporally optimal capacity deployment over time endogenously.
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Finally, the role of RES-E imports primarily depends on the regional scope of the applied model(s).
As the dispatch model in WWF (2009) only represents Germany, imports are a residual figure after
considering demand, domestic RES-E production and cost-minimal thermal electricity production. In
EWI/GWS/Prognos (2010) the dispatch model has 12 regions and calculates cost-optimal imports
and exports. In DLR/IWES/IFNE (2010; 2012), it is not entirely clear how imports are determined.
Nonetheless, they are validated with the European electricity sector model REMix which establishes
cost-minimal generation and associated import and export flows for each country. Schmid and Knopf
(2012) abstract from imports as REMIND-D is a closed economy model. In the selected scenarios of
SRU (2011), net electricity imports are imposed to be zero.
In sum, this Section has elucidated that the choice of modeling methodology and particularly
exogenous assumptions and predefined normative targets are decisive drivers for the strategy
relevant variables domestic RES-E production, electricity demand and RES-E imports.
3.2 Techno-Economic Assumptions
Two kinds of assumptions are of special importance for the profitability of RES-E technologies: The
development of specific investment costs and the yield of installed capacities in terms of average
annual full load hours (FLH). A common denominator in the publications is that they assume
technological learning processes to occur, in particular learning-by-doing (cp. Junginger et al., 2010).
Technological learning entails a decrease in specific investment costs of innovative technologies as
they reach maturity. Learning effects have been detected empirically by means of identifying a
statistical relationship between investment costs and cumulative capacity, a so-called experience
curve, that is quantified by a parameter termed the learning rate, and for which a coefficient of
determination (R2) characterizes the explanatory power (cp. Neij, 2008; Junginger et al., 2010). The
learning rate expresses the rate by which specific investment costs decrease upon a doubling of
cumulative capacity. For modeling purposes regression estimates of learning rates are interpolated
into the future and either serve as an input to the model, if learning is modeled endogenously, or as
an orientation to estimate cost reductions exogenously. Thus, the learning rate constitutes a central
driver for projected RES-E capacity investment requirements.
Table 4 presents the learning rates that were assumed in the mitigation scenario publications under
analysis. A stunning feature is that only two publications give a full account of the learning rates, and
two publications do not report them at all. This lack of transparency constitutes a major obstacle to
tracing the differences in RES-E deployment across scenarios. Nevertheless, two observations yield
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20
insights. The learning rate of solar PV is rather similar in those scenarios which report learning rates
and thus cannot be held responsible for the differences in deployment patterns. The learning rate
for wind offshore in SRU (2011) is set at the highest value of all scenarios with 18.6% and most likely
explains the strong expansion path of offshore deployment in their scenarios. Generally, the learning
rates in the SRU (2011) scenario are much higher than in the other scenarios.
Table 4. Learning rate assumptions of the different RES-E technologies as reported in the publications.
Publication Solar PV Wind
Onshore
Wind
Offshore
Geo-
thermal Biomass
(WWF, 2009) n.a. n.a. n.a. n.a. n.a.
(EWI/GWS/Prognos, 2010) n.a. n.a. n.a. n.a. n.a.
(DLR/IWES/IFNE, 2010) 20% n.a. 10% n.a. n.a.
(DLR/IWES/IFNE, 2012) 20% n.a. 10% n.a. n.a.
(Schmid and Knopf, 2012) 20% 6% 12% 0% 0%
(SRU, 2011) 25.9% 11.5% 18.6% n.a. 2.2%
Table 5. Average annual full load hours (FLHs) in 2010/2050 as reported in the publications. In Schmid and Knopf (2012)
these numbers are not reported; they are provided by the authors here
Publication[Scenario] Solar PV Wind
Onshore
Wind
Offshore
Geo-
thermal Biomass
(WWF, 2009) n.a. n.a. n.a. n.a. n.a.
(EWI/GWS/Prognos, 2010) 900-1000/
1000-1500
1200-2800/
1400-3900
2000-4200/
3000-5500 n.a. n.a.
(DLR/IWES/IFNE, 2010) 909/946 2050/2550 3200/3900 6100/6645 7030/6725
(DLR/IWES/IFNE, 2012) n.a. n.a. n.a. n.a. n.a.
(Schmid and Knopf, 2012)[PS] 970/850 2000/1500 3500/3000 7700/3700 6200/1500
(Schmid and Knopf, 2012)[PS+] 970/740 2000/1600 3500/3400 7700/5200 7500/7200
(SRU, 2011) 800/1000 1500/2500 3000/4500 7500 7000
However, literature provides ample evidence that the application of learning rates for projecting
RES-E investment costs suffers from serious shortcomings. The estimation of learning rates is highly
sensitive to the timing of the underlying data both in terms of when the forecast was made and the
duration of the data set (Nemet, 2009). Furthermore, while the R2 as an indicator for the explanatory
power of the estimation is rather high in learning rate estimations for the modular technology solar
PV (Junginger et al., 2008), it is very low in estimates for wind offshore, where little data exists (Neij,
2008; van der Zwaan et al., 2011). For wind offshore it has been shown that investment costs
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actually increased in the recent years and cost-reductions in the future are assessed as uncertain
(Heptonstall et al., 2012). These insights reveal that the practice of using both deterministic and
highly optimistic learning rates in scenario development may result in underestimating total RES-E
investment requirements for a given projected deployment pathway.
Regarding the scenario assumptions on average annual full load hours (FLHs), Table 5 displays the
values reported in the publication for the years 2010/2050. A highly interesting pattern emerges. In
all publications that treat RES-E levels as an exogenous variable the FLHs increase significantly over
the next four decades. This development is justified by technological progress and maintaining
priority feed-in of RES-E production. However, in the Schmid and Knopf (2012) scenarios, which
model both RES-E capacity additions and average annual dispatch endogenously with REMIND-D, the
FLHs show the opposite tendency and decrease, particularly so in the [PS+] scenario. There are three
reasons for this development. First, REMIND-D represents the domestic RES-E potential for each
technology by means of a grade structure that postulates that the highest-quality sites are exploited
first and then, subsequently, the less efficient sites. With rising capacity deployment this leads to a
decrease in FLHs. Second, REMIND-D considers that FLHs of dispatchable capacities decrease
significantly with high shares of RES-E due to their role of providing peak load during few periods of
the year. In the [PS+] scenario, where CCS plants are available they provide the service. In the [PS]
scenario CCS is not available and peak load has to be provided mainly by biomass and geothermal,
strongly reducing their FLHs. Third, REMIND-D takes into account the effect that solar and wind
capacities need to be curtailed during high production periods if the correlation with demand is
disadvantageous. This effect becomes increasingly pronounced with very high shares of RES-E. Since
the [PS] scenario attains a 100% RES-E share in 2050, curtailment significantly reduces FLHs
particularly for wind offshore. In sum, a considerably higher absolute level of RES-E capacities needs
to be in place in this scenario for producing the same level of RES-E production as compared to other
scenarios, thereby incurring higher investment requirements. Given that the reasons for decreasing
FLHs with increasing shares of RES-E originate in systemic effects that render a sustained priority
feed-in questionable, the assumption of increasing FLHs appears problematic.
To summarize the answers on the question of why solar PV and wind offshore deployment is so
diverse across scenarios, the analysis of the modeling methodology has shown that for the majority
of scenarios this development is determined exogenously and hence beyond analytical traceability.
Also, assumptions on learning rates and FLHs do not seem to have a systematic impact. Thus, the
question remains largely unanswered. Regarding the question of whether differences in techno-
economic assumptions are decisive for the scenarios’ projections on required RES-E capacity
5.3 From Assumptions to Scenarios 167
22
investments, the reported numbers for learning rates and FLH convey a clear message: Yes. The
assumption of high learning rates results in decreasing specific investment costs. The assumption of
increasing FLHs results in relatively lower capacity requirements for equal levels of electricity
production. As the discussion above has shown that both assumptions appear overly optimistic, the
present values of RES-E capacity investments in Section 2.1.2 are to be consumed with care and are
likely to be underestimating actual investments required for realizing the projected levels of RES-E
production.
Overall, this section has elucidated that both the choice of modeling methodology and assumptions
on learning rates and FLHs have a significant impact on the scenario projections which served as a
basis for identifying possible energy strategies for transforming the German electricity sector
towards high shares of RES-E in Section 2.1. Thus, a sound evaluation of any energy strategy based
on the results of quantitative energy system modeling equally implies an evaluation of the
underlying methodological and techno-economic assumptions. In other words, for evaluating
assertions of the semantic form “if ї then”, the analytical quality of the “if” and the “ї” is pivotal.
4 Summary and Conclusions
In the first part, this paper investigated a number of strategic questions relevant for the long-term
transformation of the German electricity sector towards high shares of RES-E: Which level of
domestic RES-E production is required? What is the role of energy efficiency and RES-E imports in
this context? Which RES-E technologies are of major importance? How much needs to be invested in
RES-E capacities? The analysis was based on a meta-analysis of scenario projections of ten mitigation
scenarios drawn from six recent publications. It has been shown that the scenarios exploit the basic
strategic options of domestic RES-E production, energy efficiency improvements and RES-E imports
to a different extent and can be clustered in three groups.
The first group heavily relies on exploiting energy efficiency potentials to reduce electricity demand,
which is accompanied by comparatively low levels of domestic RES-E production and moderate RES-
E imports. However, the exploitation of efficiency potentials cannot be steered directly and
additionally energy efficiency policies may lead to unintended effects like a direct or indirect
rebound. A second group of scenarios refrains from imports and balances moderate to high
domestic RES-E production against high to moderate efficiency improvements. The third group puts
a similar focus on domestic RES-E production but compensates relatively higher electricity demand
with substantial RES-E imports. It needs to be acknowledged that such a strategy requires an
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accelerated integration of European and/or MENA electricity markets as well surplus RES-E
production for export in these countries. Thus, according to the scenarios the transformation of the
electricity sector towards high shares of RES-E can be attained with low, moderate or high levels of
RES-E production, depending on the development of electricity demand and RES-E import potential.
However, the behavioral and institutional requirements for achieving a decrease in electricity
demand are not explicitly considered in most scenarios and may pose significant barriers to
implementation of such a strategy.
Regarding technology choice, the scenarios suggest that wind offshore and onshore are the most
important technologies, providing 55-70% of cumulative domestic RES-E production over the period
2010-2050. Electricity generation from biomass contributes 10-30% and solar PV 3-16%. Geothermal
electricity generation only plays a role in scenarios that refrain from RES-E imports. With increasingly
high shares of variable electricity provision from wind and solar, both technical and institutional
system integration solutions and measures need to be in place in due time. The majority of scenarios
does not consider these challenges explicitly and instead maintain the assumption of sustained
priority feed-in for RES-E production, which is highly doubtful from a systems perspective.
A calculation of present values of total investment costs into RES-E capacities indicates a range of 80-
120 Bn € for the decade 2010-2020 and 180-350 Bn € for the long-term period 2010-2050. On an
annual basis, this corresponds to 0.4-0.6% of GDP over the decade 2010-2020 and decreases to 0.1-
0.5% of GDP by 2050. Given that RES-E capacity investments accrued to 0.98% and 0.81% of GDP in
2010 and 2011 (BMU, 2011; BMU, 2012a; Statistisches Bundesamt, 2012), these investment volumes
appear feasible if the trend continues. However, the scenarios suggest that the dominant role of
solar PV in historical investments will need to be reduced and investments will have to diversify
towards the other RES-E technologies. Particularly, the wind offshore market needs to accelerate
significantly and overcome the current difficulties in attracting the required financial capital.
Yet, these numbers have to be interpreted with care as overly optimistic assumptions on specific
investment costs and average annual full load hours appear to be more decisive drivers for RES-E
capacity investment requirements than total RES-E production, which is counterintuitive. The second
part of the paper has revealed that more often than not exogenous assumptions and predefined
targets – instead of numerical optimization – in the modeling methodology are pivotal drivers for
scenario projections, particularly for the deployment pathways and feed-in of RES-E technologies.
Thus, the resulting scenarios are beyond analytical traceability and constitute entirely normative
scenarios that aim at delivering particular targets. Underlying input parameters are determined via
5.4 Summary and Conclusion 169
24
expert judgments so as to achieve the targets in an optimal way. This is particularly problematic if
the assumptions are not made explicit and/or are at odds with systemic effects, e.g. the assumption
of sustained priority feed-in of RES-E. These insights corroborate the importance of taking into
account the entire argumentation structure of model-based mitigation scenarios, which can be
conceived as complex thought experiments of the semantic form “if ї then”. From this perspective,
an explication of the “if” and the “ї” is required to attach meaning to the “then” assertion. Further,
in order to develop robust energy strategies for transforming the German electricity sector towards
high shares of RES-E it is necessary to vary the “if” dimension in future research, i.e. vary
assumptions from optimistic to worst case. In this manner, the resulting scenario projections span a
robust range of possible energy system futures.
While current mitigation scenarios largely demonstrate that under optimistic assumptions a
transformation towards high shares of RES-E is theoretically feasible, future research should also
increasingly tackle questions such as: How realistic are these assumptions? What kind of policy
measures are required that these assumptions come true? To what extent are energy strategy
relevant findings dependent on possibly overly optimistic assumptions? In order to investigate these
questions the modeling tools should be improved towards optimizing under high temporal and
spatial resolution and considering all sectors of the energy system and their technology options as
well as system integration measures and infrastructure requirements in an integrated manner. To
enable a scientific discourse on model methodologies, underlying techno-economic assumptions and
institutional requirements, future mitigation scenario publications should be elaborating on these
issues more transparently and explicitly.
Acknowledgements
The authors would like to thank Ottmar Edenhofer and Falko Ueckerdt for helpful comments.
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Chapter 6
Synthesis and Suggestions for Future Research
177
178 Chapter 6 Synthesis and Suggestions for Future Research
The guiding theme of this thesis has been to explore how implicit normative considerations in model-
based mitigation scenarios can be made explicit in order to adhere to the good principles of scientific
policy advice. It strived to realize the second and third stage of the pragmatic-enlightened model (PEM)
of the science-policy interface (Edenhofer and Kowarsch, 2012) for the German mitigation policy
process, which imply an elaboration of possible policy ends-in-view/means combinations (second stage)
and scrutinizing a selection in detail for their possible consequences (third stage) – both in a public
debate. This thesis elaborated on mitigation scenarios for Germany that attain the end-in-view of 85%
CO2 emission reduction in 2050 relative to 1990, in line with the target corridor articulated by the
German Government (Federal Government, 2010). The scenarios were developed in collaboration
between science and civil society stakeholders, taking an analytical-deliberative approach. Further, it
scrutinized a selection of mitigation scenarios in detail with respect to the type of energy strategy they
embody for transforming the German electricity sector towards high shares of renewable electricity
generation and which possible consequences they imply.
The contributions of this thesis to the mitigation scenario literature are fourfold: First, it provides a
methodology for developing mitigation scenarios in collaboration between science and civil society that
can serve as a starting point for future scenario exercises. Second, it presents a hybrid energy-economy
model for Germany that is based on economic theory and for which all input assumptions are made
transparent. Third, it contributes three mitigation scenarios for Germany that deal with normative
considerations more openly and explicitly than existing scenarios. And fourth, it supplies a comparative
meta-analysis of mitigation scenario projections for the German electricity sector.
The following Synthesis summarizes the findings presented in the core chapters of this thesis by
answering the research questions posed in the Introduction and concludes the exploration of German
mitigation scenarios with suggestions for future research.
6.1 Collaborative Scenario Definition and Evaluation Process
In the first part of this thesis, an exploratory research addressed the challenge of engaging civil society
stakeholders in the development process of ambitious mitigation scenarios based on formal energy
system modeling. This approach allows for the explicit attachment of normative considerations to
technology-focused mitigation options. During consecutive dialogues, civil society representatives
framed the definition of boundary conditions for the energy-economy model REMIND-D and evaluated
the emerging scenarios with regard to plausibility and social acceptance considerations, corresponding
to an analytical-deliberative approach (Stern and Fineberg, 1996; Renn, 1999) in which deliberation
frames analysis and analysis informs deliberation. The first set of questions relates to the process itself:
•What kind of organizational project design is suitable to engage civil society stakeholders
in mitigation scenario development? How can stakeholder preferences frame the
definition of model-based mitigation scenarios?
6.1 Collaborative Scneario Definition and Evaluation Process 179
A necessary condition for developing mitigation scenarios based on formal energy system modeling
jointly with civil society stakeholders is that functional communication flows are be enabled. Given that
quantitative modeling of the energy system relies on strong simplifications for determining numerical
solutions, the parameters and variables are as such not meaningful to anyone who is not familiar with
the model. In order to allow for communication between civil society stakeholders and scientists and
ultimately with the energy system model, several translation steps are necessary. These are potentially
impeded by communication gaps that arise from cultural distance between the distinct communities of
science and civil society. They differ with respect to their raison d’être, objectives and culture, i.e.
values, norms and language. Thus, successful collaboration between civil society and science requires a
deliberate preparation phase preceding the interaction phase.
Chapter 2 proposes an organizational project design that takes into account these considerations by
means of an interdisciplinary approach that involves both scientists and members of environmental non-
governmental organizations (NGOs) as core project partners. It appears natural to foster collaboration
between NGOs, rooted in the civil society, and scientists as a starting point, and involve civil society
stakeholders in later steps of the process. Due to the distinct professional cultures of the project
partners, it is decisive to stimulate their awareness for cultural issues in general and cultural differences
in particular. In order to achieve this, it is suggested to organize a core project team that is composed of
each a research institution and NGO from at least two countries that do not share the same language.
Practical advantages of cross-combining professional cultures with national cultures are that project
partners are required to communicate in a non-mother tongue, which fosters awareness for
communication gaps like unfamiliar terms and alleviates barriers to clarification requests. Further, as the
challenge of domestic mitigation presents itself and is addressed very differently in individual countries,
the trans-national perspective helps to reframe and to challenge the purely domestic perspective.
The organizational project design proposed in Chapter 2 consists of four distinct phases that differ with
respect to their objectives. The first phase is entirely dedicated to intra-group development of the
project team to reduce the cultural distance between project partners and intends to establish a fully
functional project team with a common language. This is achieved by consciously passing though the
initial stages of group development involving forming, storming and norming that precede the
performing stage (Tuckman, 1965). It is suggested to employ formal “wish-lists” that make mutual
expectations of project partners explicit and serve as a discussion basis for developing a joint
understanding of the collaborative scenario definition and evaluation process to follow.
The second phase of the collaborative scenario definition and evaluation process is concerned with the
development of the energy system model. External experts are involved in dedicated workshops for
obtaining state-of-the art knowledge on technical details for low-carbon technologies. Thereby, external
experts can assess the validity of the quantitative model and have a control function on their scientific
quality. Further, in this stage of the scenario definition process the core project partners jointly develop
translation rules from “model parameters” to “real-world implications” and vice versa, serving to
identify possible direct and indirect consequences of the technology-focused mitigation options that are
considered in the energy system model. In this translation process, the technological framework
conditions for scenario development are disentangled from the political framework conditions. Here, it
180 Chapter 6 Synthesis and Suggestions for Future Research
is important to identify which normative considerations are implicit in the energy system model
parameter configurations. The translation rules serve as a basis for the stakeholder engagement in the
following, third phase of the collaborative process. An important output of the second phase is a
detailed model description with a transparent reporting of all input assumptions used to calibrate the
energy system model as well as all techno-economic assumptions that determine the characteristics of
the technological mitigation options at the model’s disposal.
The third phase of the collaborative scenario definition and evaluation process is concerned with the
repetitive engagement of civil society stakeholders. It combines deliberative elements (stakeholder
dialogues) with analytical elements (formal energy system modeling) and thus takes on an analytical
deliberative approach in which deliberation frames analysis and analysis informs deliberation (Stern and
Fineberg, 1996; Renn, 1999). In a first set of stakeholder dialogues, civil society representatives are
invited to discuss possible direct and indirect consequences of the technology-focused mitigation
options at the energy system model’s disposal and associated key future developments of energy-
system related variables, i.e. political framework conditions for the mitigation scenarios. For different
sectors of the energy system a representative sample of civil society organizations is invited; the NGO
project partners are responsible for the choice as they are assumed to have a good overview of the civil
society landscape. A selection of discussion-stimulating introductory questions is pre-determined by the
core project team based on their understanding of which topics particularly subject to normative
considerations or subject to societal controversies. After each discussion a questionnaire employing
social science methods elicits the stakeholders’ judgments and preferences on which developments in
the energy system they perceive as likely versus unlikely and desirable versus undesirable from the point
of view of their organization. Along with the qualitative information obtained during the discussions and
expert judgments from literature, the elicited data serves as a basis for generating sectorial “scenario-
building-bricks”, referred to as parsimonious narratives in Chapter 4. Parsimonious narratives are
developed for those mitigation options for which stakeholders have an opinion and judgments on likely
versus desirable developments diverge significantly or the desirability is particularly subject to dissent
amongst stakeholders. It is suggested to combine the sectorial parsimonious narratives into integrated
mitigation scenarios according to the criteria of likeliness, desirability with consent and desirability with
dissent. In order to keep the scenario definition traceable, a selection has to be made by the core
project team and not all issues that are addressed in the stakeholder dialogues are reflected in the final
mitigation scenarios. Finally, the translation rules developed earlier are employed to define the
parameters of the energy system model in such a way as to reflect the combinations parsimonious
narratives. The resulting scenarios then carry contextual meaning in terms of normative considerations.
In a second set of stakeholder dialogues with the same civil society representatives the integrated
scenario results of the energy system model are presented and evaluated in terms of their plausibility
and where projected developments could raise concerns about social acceptance. This serves to identify
socio-political implications of technology-focused mitigation scenarios.
Finally, the fourth phase of the collaborative scenario definition and evaluation process is concerned
with the synthesis of the results and the development of comprehensive scenario reports.
6.1 Collaborative Scneario Definition and Evaluation Process 181
The collaborative scenario definition and evaluation process as sketched above has been carried out in
the EU FP7 Project “Engaging Civil Society in Low Carbon Scenarios” (ENCI LowCarb). Doing so required
the development of an energy system model for Germany. The author of this thesis developed a hybrid
energy-economy model of Germany, REMIND-D. In order to provide transparency, Chapter 3 provided a
detailed description of the model setup and the underlying techno-economic assumptions. REMIND-D
constitutes a Ramsey-type growth model that determines the intertemporal welfare-optimal
development of the German energy system given a set of boundary conditions, i.e. input parameter
configurations. An important feature of REMIND-D is that is an integrated energy system model that
considers all sectors of the energy system and produces scenario projections as a model output.
The second set of research questions developed in the Introduction is concerned with the results of the
exploratory research:
•Which mitigation scenarios for Germany emerge from the exploratory research and how
are they evaluated? What are the key findings for enabling the German energy transition?
Chapter 4 presents three model-based mitigation scenarios for Germany that were defined and
evaluated in a participatory process with 11 and 13 civil society stakeholders from the German transport
and electricity sector, respectively. Their background ranged from environmental NGOs over industry
and consumer associations, topic-related interest groups, urban planning and trade unions to industry.
During separate dialogues, their preferences on future developments related to mitigation in the
transport and electricity sector were discussed and elicited by the method presented above. Chapter 4
gives a detailed account of the parsimonious narratives for likely and desirable future developments as
perceived by the consulted civil society stakeholders. They were developed along the lines of the
following six introductory discussion questions: Is an increase of total freight transport unavoidable? Is
multi-modality a viable option for decarbonizing the passenger transport sector? Which alternative low-
carbon fuels ought to be dominant in the future? Are landscape externalities of renewable electricity
generation capacities and transmission lines problematic and what are potential remedies? Which
energy efficiency growth rate is feasible and what is the role of the rebound effect? Which thermal
electricity generation capacities (i.e. conventional power plants) are acceptable in the next decades?
The parsimonious narratives were translated to input parameter configurations for the hybrid energy-
economy model REMIND-D. Three scenarios were defined according to the criteria of likeliness,
desirability with consent and desirability with dissent. In line with the German Government’s mitigation
targets, all scenarios are subject to a carbon budget constraint that leads to a CO2 emission reduction of
85% in 2050 relative to 1990. The 'continuation' scenario enforces a set of parsimonious narratives in
the transport and electricity sector that are deemed likely by civil society stakeholders. The 'paradigm
shift' scenario reproduces a set of parsimonious narratives perceived as desirable by the majority of civil
society stakeholders. A variant of the latter, the 'paradigm shift+' scenario, additionally allows for the
deployment of several technological mitigation options which the stakeholders judged as undesirable or
discussed controversially.
The 'continuation' scenario foresees a dominance of motorized individual transport, unabated coal
electrification, moderate energy efficiency growth rates, local resistance against windmills and
182 Chapter 6 Synthesis and Suggestions for Future Research
transmission lines that translate into moderate potentials for renewable electricity generation as well as
a continuation of coupled freight transport and GDP growth rates. Coal electrification and fossil-fuel-
based freight transport mileage induce 8.8 Gt CO2 of committed emissions. This carbon lock-in accounts
for 55% of the total CO2 emission budget over the time horizon of analysis from 2005 to 2050. Facing a
strict carbon budget that enforces ambitious mitigation, the ‘continuation’ scenario constitutes a
counterfactual exercise illustrating what would need to happen in the other sectors for achieving
ambitious mitigation if these likely trends persisted and energy efficiency and renewable electricity
generation potentials are not fully exploitable due to societal resistance. As a consequence, non-
technical mitigation options slowing down economic growth are exploited by REMIND-D for meeting the
CO2 budget constraint. These include significant energy service demand reductions in passenger
transportation as well as final energy demand reductions for electricity and the provision of heat.
Massive state intervention would be necessary to induce behavioral changes of such magnitude, e.g.
through carbon pricing policies entailing prohibitively high transport costs. In such a world, individual
mobility would become a luxury good. The stakeholders assess that such policies will lack social
acceptance and strongly emphasize the value of individual mobility in modern societies. Also, the
required electricity and heat demand reductions are considered as politically not enforceable in reality.
Several stakeholders pointed out the dangers of energy poverty if any such mitigation policy is not
accompanied by effective redistribution schemes. Bound to moderate energy efficiency improvements,
the 'continuation' scenario exhibits mitigation costs of 3.5 % cumulative GDP losses over the period
2005-2050 as compared to a reference case that achieves 40% CO2 emission reduction in 2050 relative
to 1990. In sum, stakeholders judged he results of this counterfactual scenario as highly problematic
from a socio-political point of view. Yet they reconfirmed the likeliness of its projected developments in
the freight transport and electricity sector, leading to a lock-in into current behavior and carbon-
intensive infrastructure. In consequence, they conclude that, if the carbon lock-in becomes reality,
ambitious mitigation targets will likely be out of reach.
The two 'paradigm shift' scenarios reproduce future developments judged as desirable by participating
stakeholders. These include a decrease in total freight transport mileage, a shift in the modal split of
freight transport sector from road to rail, a substantial increase of public and non-motorized transport in
the modal split of passenger transportation, a widespread electrification of private transport by 2030, a
phase-out of conventional coal electrification until 2020, a rapid and large-scale deployment of
renewable electricity generation and transmission line capacities as well as a fourfold increase in energy
efficiency growth rates. REMIND-D immediately exploits these mitigation options whereby mitigation
costs decrease by more than half when compared to the 'continuation' scenario, with 1.4% of
cumulative GDP losses. Yet the necessary fundamental policy changes for such a scenario are put into
question by stakeholders as they doubt that sufficient collective political will can be established. Several
concerns were articulated for policies that aim at inducing the structural breaks from historical trends
inherent to the 'paradigm shift' scenario: The quality of public transport services needs to increase
significantly, both in urban environments and in rural areas. Inter alia, this would require a redirection of
infrastructure investments from road to rail, an issue considered long overdue by the CSO stakeholders.
Furthermore, the projected rapid decommissioning of existing coal power plants may entail increasing
regional unemployment rates in Germany's structurally weak lignite mining areas. Finally, a fast
6.1 Collaborative Scneario Definition and Evaluation Process 183
deployment of renewable electricity generation and transmission line capacities requires high
procedural justice throughout the planning and installation process, including institutionalized
possibilities for local communities to participate, also financially. The 'paradigm shift+' scenario which
additionally allows for the controversial use of the Carbon Capture and Sequestration (CCS) technology
and large-scale biofuel production achieves even lower mitigation costs of 0.8%. However, civil society
stakeholders remain skeptical whether these technologies are feasible in large scale, particularly due to
social refusal. They argue that the incremental effect on decreasing mitigation costs may not outweigh
the direct and indirect costs of public protest.
Even though the small sample size of civil society stakeholders engaged in this research impedes
inferential conclusions on the perceptions of civil society on the German energy transition as a whole, it
has demonstrated that the technological solutions to the mitigation problem proposed by the model
results give rise to significant societal and political implications that deem at least as challenging as the
mere engineering aspects of innovative technologies. These insights underline the importance of
comprehending mitigation of energy-related CO2 emissions as a socio-technical transition embedded in
a political context (Unruh, 2000). Thus, the questions of how to govern the transition and which kinds of
policy instruments are suitable are equally important as the engineering-focused question of which low-
carbon technologies to deploy.
6.2 Meta-Analysis of German Mitigation Scenarios for the
Electricity Sector
The second part of the thesis pursues a meta-analysis of scenario projections for the electricity sector of
ten mitigation scenarios drawn from six recent publications, including the ‘paradigm shift’ and ‘paradigm
shift +’ scenarios developed in Chapter 4. It explores the research questions of:
•What kinds of energy strategies for transforming the German electricity sector towards a
high share of renewable electricity generation are embodied by selected mitigation
scenarios for Germany? Which barriers to implementation can be identified? What are
reasons for diverging scenario projections?
Chapter 5 shows that the scenarios exploit the basic strategic options for transforming the German
energy system towards a high share of renewable electricity generation (RES-E), namely increasing (i)
domestic RES-E production, (ii) energy efficiency improvements and (iii) RES-E imports to a different
extent and can be clustered in three groups: The first group heavily relies on exploiting energy efficiency
potentials to reduce electricity demand, which is accompanied by comparatively low levels of domestic
RES-E production and moderate RES-E imports. A significant barrier to implementation of such a
strategy is that the exploitation of efficiency potentials cannot be steered directly and additionally
energy efficiency policies may lead to unintended effects like a direct or indirect rebound. A second
group of scenarios refrains from imports and balances moderate to high domestic RES-E production
against high to moderate efficiency improvements. The third group puts a similar focus on domestic
184 Chapter 6 Synthesis and Suggestions for Future Research
RES-E production but compensates relatively higher electricity demand with substantial RES-E imports. It
needs to be acknowledged that such a strategy requires an accelerated integration of European and/or
Middle Eastern and Northern African electricity markets as well surplus RES-E production for export in
these countries. Thus, according to the scenarios the transformation of the electricity sector towards
high shares of RES-E can be attained with low, moderate or high levels of RES-E production, depending
on the development of electricity demand and RES-E import potential. However, the behavioral and
institutional requirements for achieving a decrease in electricity demand are not explicitly considered in
most scenarios and may pose significant barriers to implementation of such a strategy. With increasingly
high shares of variable electricity provision from wind and solar, both technical and institutional system
integration solutions and measures need to be in place in due time. The majority of scenarios does not
consider these challenges explicitly and instead maintain the assumption of sustained priority feed-in for
RES-E production, which is highly doubtful from a system’s perspective.
Chapter 5 further reveals that more often than not exogenous assumptions and predefined targets –
instead of numerical optimization – in the modeling methodology are pivotal drivers for diverging
scenario projections, particularly for the deployment pathways and feed-in of RES-E technologies. Thus,
the resulting scenarios are beyond analytical traceability and constitute entirely normative scenarios
that aim at delivering particular targets. Underlying input parameters are determined via expert
judgments so as to achieve the targets in an optimal way. This is particularly problematic if the
assumptions are not made explicit and/or are at odds with systemic effects, e.g. the assumption of
sustained priority feed-in of RES-E. These insights corroborate on the one hand the necessity of
improved energy system modeling tools and on the other hand the importance of taking into account
the entire argumentation structure of model-based mitigation scenarios, which can be conceived as
complex thought experiments of the semantic form “if ї then”. From this perspective, an explication of
the “if” (the input assumptions) and the “ї” (the energy system model) is required to attach meaning
to the “then” assertion (the scenario projections). The explication of the “if” dimension refers to the
techno-economic parameters as much as the implicit normative considerations.
6.3 Suggestions for Future Research
Attempting to explore how implicit normative considerations in model-based mitigation scenarios can
be made explicit, this thesis revealed in an exploratory research setting that the realization of a
collaborative mitigation scenario definition and evaluation process engaging German civil society
stakeholders is possible in small scale and scope. Hence, the primary message for future research is that
such a participatory process should be repeated in the form of a more comprehensive PEM-guided
assessment of German mitigation scenarios both in terms of the scale and scope, which requires refined
participatory methods so as to keep transaction costs within acceptable boundaries. Based on the
findings of this thesis, the following suggests avenues for future research concentrating on three aspects
that should be addressed for such an assessment: (i) refining the participatory method for realizing an
assessment of mitigation scenarios in public debate that addresses normative considerations explicitly,
6.3 Suggestions for Future Research 185
(ii) the scope of explored policy ends/means combinations and their direct and indirect consequences
and (iii) improving the energy system modeling tools and their mode of application.
In order to refine the participatory method for realizing a more comprehensive assessment of mitigation
scenarios in public debate that addresses normative considerations explicitly, one needs to address the
three basic questions of “who, why and how?”, i.e. whom to include in the discourse, for what reasons
and by which methods and instruments, more conscientiously. For doing so, it is highly commendable to
draw on the body of literature developed in the field of governance, and particularly in the field of
inclusive risk governance. In general, governance analyzes the structures and processes for collective
decision making involving both governmental and non-governmental actors (Nye and Donahue, 2000).
Competing concepts of governance give different answers on the basic “who, why and how” questions
and are essentially motivated by distinct normative models of democracy (Immergut, 2011). Risk
governance is an established scientific discipline that deals with collective decision making processes for
assessing and handling risks to human health and the environment. Inclusive approaches to risk
governance spell out elaborated concepts on whom to include in the assessment, for what rationale and
with what instruments, thereby explicitly considering the normative dimension inherent to choosing a
particular governance approach as well as the value judgments inherent to making a decision in the
process (Renn, 2008; Renn and Walker, 2008; Renn and Schweizer, 2009). The aim of this research
avenue would be to adapt the elaborated methods developed in inclusive risk governance to the
assessment process of mitigation scenarios for Germany. Also, it would be of pivotal importance that
the German policymaker legitimizes the process and gives a credible commitment to implementing the
commendable policy options that emerge from the assessment.
Regarding the number of policy ends/means combinations and the variety of their direct and indirect
consequences explored in future assessments of mitigation scenarios it is commendable to enlarge the
scope in both respects. This research considered a single mitigation target of 85% CO2 emission
reduction in 2050 relative to 1990. Future research should also critically reconsider the feasibility of this
target in view of the consequences arising from policy means that are required for its attainment and
explore alternative policy targets in a comparative manner. The exploration of direct and indirect
consequences should be pursued in greater depth and breadth and explicitly include institutional
consequences arising from conceivable governance structures, policy instruments and market designs
and investment incentives required to implement the technical mitigation options considered in energy
system models. For accommodating such a diverse set of mechanisms within the formal energy system
modeling approach, it deems necessary to improve the process and method for developing rigorous
translation rules from “model parameters” to “real-world implications”.
Finally, for future assessments of mitigation scenarios, the energy system modeling tools and their mode
of application should be improved. The spatial, temporal and sectorial resolution of energy system
models needs to increase for considering system integration measures for variable renewables,
infrastructure requirements, and the trade-offs between technological mitigation options in different
sectors of the energy system in an integrated manner. Instead of employing only one energy system
model, it is commendable to perform a model comparison exercise embedded in the assessment
process to improve the robustness of model results. The idea is to have several energy system models
186 Chapter 6 Synthesis and Suggestions for Future Research
calculating scenarios with the same set of input assumptions, as for example in the ADAM (Edenhofer et
al., 2010) or RECIPE project (Luderer et al., 2011). This allows for determining the bias introduced by the
specific choice of modeling methods on scenario projections in terms of which scenario results are
independent of the employed model and its specific assumption. Furthermore, the assumptions should
be varied from worst-case to best-case to determine a robust corridor of possible mitigation pathways.
6.4 References 187
References
Edenhofer, O., B. Knopf, T. Barker, L. Baumstark, E. Bellevrat, B. Chateau, P. Criqui, M. Isaac, A. Kitous, S.
Kypreos, M. Leimbach, K. Lessmann, B. Magné, S. Scrieciu, H. Turton and D. van Vuuren (2010).
"The Economics of Low Stabilization: Model Comparison of Mitigation Strategies and Costs." The
Energy Journal(Special Issue 1).
Edenhofer, O. and M. Kowarsch (2012). A Pragmatist Approach to the Science-Policy Interface. Working
Paper available from http://www.mcc-
berlin.net/fileadmin/data/pdf/Edenhofer_Kowarsch_PEM_Manuscript_2012.pdf
Federal Government (2010). Energiekonzept für eine umweltschonende, zuverlässige und bezahlbare
Energieversorgung.
http://www.bundesregierung.de/Content/DE/StatischeSeiten/Breg/Energiekonzept/energiekon
zept-final.pdf
Immergut, E. M. (2011). "Democratic Theory and Policy Analysis: Four Models of “Policy, Politics and
Choice”." dms – der moderne staat – Zeitschrift für Public Policy, Recht und Management 1: 69-
86.
Luderer, G., V. Bosetti, M. Jakob, M. Leimbach, J. Steckel, H. Waisman and O. Edenhofer (2011). "The
economics of decarbonizing the energy system—results and insights from the RECIPE model
intercomparison." Climatic Change Online First: 1-29.
Nye, J. S. and J. D. Donahue (2000). Governance in a Globalizing World. Washington, D.C., Brookings
Institutions Press.
Renn, O. (1999). "A Model for an Analytic-Deliberative Process in Risk Management." Environmental
Science & Technology 33(18): 3049-3055.
Renn, O. (2008). Risk Governance: Combining Facts and Values in Risk Management. Risks in Modern
Society.Topics in Safety, Risk, Reliability and Quality. H.-J. Bischoff, Springer Netherlands. 13: 61-
125.
Renn, O. and P.-J. Schweizer (2009). "Inclusive risk governance: concepts and application to
environmental policy making." Environmental Policy and Governance 19(3): 174-185.
Renn, O. and K. D. Walker (2008). Global Risk Governance. Concept and Practice Using the IRGC
Framework. Berlin and Heidelberg, Springer.
Stern, P. C. and V. Fineberg (1996). Understanding Risk: Informing Decisions in a Democratic Society.
Washinglton, DC, National Academy Press.
Tuckman, B. (1965). "Developmental sequence in small groups." Psychological Bulletin 63(6): 384-399.
Unruh, G. C. (2000). "Understanding carbon lock-in." Energy Policy 28(12): 817-830.
188 Chapter 6 Synthesis and Suggestions for Future Research
Statement of Contribution
The chapters of this thesis are written by the author of this thesis in collaboration with her advisers Prof.
Dr. Ottmar Edenhofer and Dr. Brigitte Knopf. The model development of REMIND-D was supervised by
Dr. Nico Bauer. The author of the thesis has made significant contributions to all chapters from
conceptual design, to technical development, numerical implementation and writing. This section details
the contribution of the author to the four core chapters of this thesis and acknowledges major
contributions of others.
Chapter 2
The author was responsible for the conceptual design, handling and writing of the article. Brigitte Knopf
made important contributions to the structure of the article and edited the manuscript in several
iterations. Meike Fink contributed to the initial outline and the comparison section. Stéphane
LaBranche provided advice and proofread the article.
Chapter 3
The author was responsible for the conceptual design, handling and writing of the article. Also, the
author collected all data and literature and was responsible for the numerical implementation of the
model REMIND-D. The original source code of REMIND-G was provided by the Potsdam Institute for
Climate Impact Research; however, the author adapted the code and calibrated the model so as to
represent the Federal Republic of Germany. Nico Bauer gave continuous support in all stages of this
process and supervised the development of the model REMIND-D. Brigitte Knopf contributed in
extensive discussions on the model results.
Chapter 4
The author was responsible for the conceptual design, handling and writing of the article. Also, the
author was responsible for the technical implementation of the scenarios and the development of the
model results. The article was developed in close cooperation with Brigitte Knopf who contributed
through extensive discussion in all stages of the research, including the conceptual design, and edited
the article several times.
Chapter 5
The author was responsible for handling and writing of the article. The research question, conceptual
design and interpretations were developed in close collaboration with Michael Pahle and Brigitte Knopf.
The author was responsible for retrieving the data and generating the graphs, figures and tables.
Statement of Contribution 189
190 Statement of Contribution
Tools and Resources
This thesis relies on numerical modeling. Naturally, a number of software tools were used to create and
run the models, and to process, analyze and visualize the results. This section lists these tools.
Modeling: All model experiments performed by the author made use of the General Algebraic
Modeling System (GAMS), version 22.7.2 (Brooke et al., 1988) and the CONOPT3 solver, version 3.14S,
for non-linear programs (Drud, 1994).
Data Processing: Model output was analyzed using The MathWorks’ MATLAB, version 6.5 release 13
(MATLAB, 1998), Microsoft Excel, version 2003 and 2010, and Microsoft PowerPoint, version 2003 and
2010.
Typesetting: This document was prepared using LATEX2ɸ (Lamport, 1994), particularly the pdfpages
package (Matthias, 2006), to include Chapters 2 to 5 in their given layouts. Chapter 1, 5 and 6 wre
written with Microsoft Word 2010. Chapter 2 was written with Microsoft Word 2003. Chapters 3 and 4
were written with LATEX2ɸ (Lamport, 1994).
Brooke, A., D. Kendrick, A. Meeraus, R. Raman, and R. E. Rosenthal (2005). GAMS. A Users Guide. GAMS
Development Corporation.
Drud, A. (1994). CONOPT – a large-scale GRG code. ORSA Journal on Computing 6(2), 207–216.
Lamport, L. (1994). LaTeX: A Document Preparation System User’s Guide and Reference Manual.
Amsterdam. Addison-Wesley Longman.
Matthias, A. (2006). The pdfpages package. CTAN, CTAN://macros/latex/contrib/pdfpages.
Tools and Resources 191
192 Tools and Resources
Acknowledgments
MysincerethanksgotoOttmarEdenhoferforprovidinginspiringguidanceoverthecourseof
maneuveringintheseasofpossiblethesisfociaswellastoBrigitteKnopfforheroutstandingly
dedicatedmentorshipthroughoutthisexpedition.
Iamgratefultoallmycolleaguesoftheformerenergysystemmodeling(ESM)group,particularlyNico
BauerforbearingwithallmyquestionsduringtheeternalphaseofcalibratingREMINDͲD,Gunnar
Ludererforhisopendooracrossthefloor,MichaelLükenforinitiatingmetotheworldofREMIND,
MarkusHallerforelucidatingMatlabscripts,RobertPietzckerforsharingknowledgeontheartof
coding,SylvieLudigforcountlessexchange,LaviniaBaumstarkformanagingthefamousKirscheͲPlots,
FalkoUeckerdtforintroducingintermittency,JessicaStreflerforverygoodtimesinouroffice,Alexander
Körnerforhishumor,DavidKleinforhiscalmingaura,andJanaSchwanitzforthoughtfulreflections.I
wouldfurtherliketothankAnjaBrummeandOliverTietjenfortheircontributionsasinterns.
Mygratitudeextendstoallformerandpresentcolleaguesofthepolicyinstruments(PI)group,notonly
forhavingmeatveryworthwhileliteratureseminarsandfieldtrips,butalsoforthethoughtͲprovoking
discussions.IwouldliketoespeciallythankMichaelPahleforunremittinglychallengingmetoreconsider
myarguments,JanSteckelforunfoldinggrowthmodelmetaphors,ChristianFlachslandforstraightͲtoͲ
theͲpointcommentsandSteffenBrunnerforelaboratingonthedeepermattersofsociety.
InadditionIwouldliketoacknowledgeallunnamedcolleaguesatPIKwithwhomIexchangedideasand
whomadethethreeyearson“thehill”atrulyfruitfulexperience,theITstaffwhoprovidesthehighest
qualityinfrastructureofhardͲandsoftwareaswellasthealwayscommittedcoordinationteam.
AsregardstheoutsideworldIwouldliketothanktheprojectteamofENCILowCarbforthegreat
collaboration.IamindebtedtoMartinKowarschforenlighteningmeonphilosophicalinquiries,Lion
HirthforteachingmealessononpassioninresearchandRomanMeierforsemanticcontemplation.
LastbutnotleastIamgratefulforthetremendousmoralsupportofmyfamily:Bärbel,Jörg,Leo,Felix
andMeckie–itisawesometoknowthatIcancountonyouingoodandbadtimesalike.And,
ultimately,specialthanksgoouttoallmydearfriendswhoheardmerespondingonthetelephone
“Sorry,wecannotmeet,Iamwritingmythesis”somanytimesbutneverstoppedcalling.
Acknowledgments 193
