Comparison of macroeconomic developments in ten scenarios of energy system transformation in Germany: National and regional results

Abstract

Background
Different strategies have been proposed for transforming the energy system in Germany. To evaluate their sustainability, it is necessary to analyze their macroeconomic and distributional effects. An approach to do this analysis in an integrated consistent framework is presented here.

Methods
Comparing ten energy transition scenarios with emission reduction targets by 2050 of 80% or 95%, respectively, allows evaluating a broad range of energy system transformation strategies with respect to the future technology and energy carrier mix. For this purpose, an energy system model and a macroeconometric model are combined, thus re-modeling the unified scenarios. An important extension of the model was concerned with the integration of synthetic fuels into the energy-economy model. One focus besides the overall macroeconomic assessment is the regional analysis. For this purpose, own assumptions on the regional distribution of the expansion of renewable energies were developed.

Results
The effects on gross domestic product (GDP) and employment are similar on average from 2030 to 2050 across the scenarios, with most of the more ambitious scenarios showing slightly higher values for the socioeconomic variables. Employment in the construction sector shows the largest effects in most scenarios, while in the energy sector employment is lower in scenarios with high energy imports. At the regional level, the differences between scenarios are larger than at the national level. There is no clear or stable regional pattern of relative loss and profit from the very ambitious transformation, as not only renewable energy expansion varies, and hydrogen strategies enter the scene approaching 2050.

Conclusions
From the relatively small differences between the scenarios, it can be concluded that, from a macroeconomic perspective, it is not decisive for the overall economy which (supply side) strategy is chosen for the transformation of the energy system. More effort needs to be put into improving assumptions and modeling approaches related to strategies for achieving the final 20% CO 2 reduction, for example the increasing use of hydrogen.

Full text

Ulrichetal. Energy, Sustainability and Society (2022) 12:35
https://doi.org/10.1186/s13705-022-00361-5
ORIGINAL ARTICLE
Comparison ofmacroeconomic
developments inten scenarios ofenergy
system transformation inGermany: National
andregional results
Philip Ulrich1* , Tobias Naegler2, Lisa Becker1, Ulrike Lehr1,4, Sonja Simon2, Claudia Sutardhio3 and
Anke Weidlich3
Abstract
Background: Different strategies have been proposed for transforming the energy system in Germany. To evaluate
their sustainability, it is necessary to analyze their macroeconomic and distributional effects. An approach to do this
analysis in an integrated consistent framework is presented here.
Methods: Comparing ten energy transition scenarios with emission reduction targets by 2050 of 80% or 95%,
respectively, allows evaluating a broad range of energy system transformation strategies with respect to the future
technology and energy carrier mix. For this purpose, an energy system model and a macroeconometric model are
combined, thus re-modeling the unified scenarios. An important extension of the model was concerned with the
integration of synthetic fuels into the energy-economy model. One focus besides the overall macroeconomic assess-
ment is the regional analysis. For this purpose, own assumptions on the regional distribution of the expansion of
renewable energies were developed.
Results: The effects on gross domestic product (GDP) and employment are similar on average from 2030 to 2050
across the scenarios, with most of the more ambitious scenarios showing slightly higher values for the socioeconomic
variables. Employment in the construction sector shows the largest effects in most scenarios, while in the energy
sector employment is lower in scenarios with high energy imports. At the regional level, the differences between
scenarios are larger than at the national level. There is no clear or stable regional pattern of relative loss and profit from
the very ambitious transformation, as not only renewable energy expansion varies, and hydrogen strategies enter the
scene approaching 2050.
Conclusions: From the relatively small differences between the scenarios, it can be concluded that, from a macroe-
conomic perspective, it is not decisive for the overall economy which (supply side) strategy is chosen for the trans-
formation of the energy system. More effort needs to be put into improving assumptions and modeling approaches
related to strategies for achieving the final 20% CO2 reduction, for example the increasing use of hydrogen.
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Open Access
Energy, Sustainability
and Society
*Correspondence: ulrich@gws-os.com
1 Institute of Economic Structures Research (GWS), Heinrichstraße 30,
49080 Osnabrück, Germany
Full list of author information is available at the end of the article
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Ulrichetal. Energy, Sustainability and Society (2022) 12:35
Background
Many studies describe development paths for achiev-
ing large reductions in greenhouse gas emissions in
the short to medium term, and climate neutrality in
the long term. At a global level, Gielen etal. [1] give a
recent overview. e findings are that energy transition
scenarios converge in the main strategies but diverge in
some of the details. e role of renewable energy under
the different scenarios and the need for electrification
seem to be mainly agreed upon across international
scenarios, while the role of bioenergy, carbon capture
and storage (CCS) and carbon capture and utilization
(CCU) is debated [2].
For Germany, the picture is similar, with a focus
on different technologies and different implementa-
tion speed, depending on the level of ambition of the
respective scenario [3]. Even scenarios achieving the
targets in 2050 do so under different technology path-
ways, speeds, and energy mixes [4, 5].
While sustainability analysis needs to go beyond
the mere megawatt and cost debate and increasingly
focuses on coupling of models [6, 7], the socio-eco-
nomic dimension is featuring highly, also in the just
transition debate. Next to the macroeconomic indica-
tors such as GDP and employment, structural change
and distribution aspects increasingly enter the pub-
lic debate [see, e.g., 8, 9]. Social aspects need to be
addressed to make the energy transition just and inclu-
sive and increase acceptance in the population.
Along the economic dimension, single scenario anal-
yses comparing more ambitious scenarios to less ambi-
tious business-as-usual (BAU) cases or a counterfactual
scenario often show positive effects for gross domestic
product (GDP) and employment in Germany [10–12].
Similar to the present analysis, Hartwig etal. [13] cou-
ple a bottom-up energy model with a macroeconomic
model to estimate the effects of an ambitious energy
policy on GDP and employment. In contrast, how-
ever, this study does not compare various energy sys-
tems with different energy mixes, but rather a stronger
enforcement of efficiency development with a baseline
scenario. Here, as well, the macroeconomic effects are
positive. At the EU level, transformation scenarios are
also assessed in terms of their socioeconomic conse-
quences by soft linking energy system models with
macroeconomic models [14, 15]. In the study by Frag-
kos etal. [14], there are some small negative effects on
GDP, while employment is slightly higher than in the
reference development. Vrontisi etal. [15] point out
that the direction of economic effects varies depend-
ing on the political conditions: thus, negative economic
effects arise for the EU member states when asymmet-
ric ambitions of climate policies exist. In contrast, eco-
nomic benefits in the EU can be achieved if there are
globally coordinated efforts to achieve the Paris climate
goals. An integrated assessment of EU-wide energy
transition pathways provides a combined evaluation of
environmental and economic aspects. In this way, Nieto
etal. [16] analyze a large number of scenarios conclud-
ing that only a post-growth scenario can achieve emis-
sions reduction targets without negative impacts on
employment.
ese scenario comparisons are undertaken with dif-
ferent models and under different framework assump-
tions, making the comparison difficult. To create a
more comprehensive picture, however, a consistent
comparison of the economic outcomes in a consist-
ent framework is much needed. It will also contribute
to the wider picture of measuring the full sustainable
footprint of different pathways of the energy transition
to enhance public acceptance. Moreover, it answers
the question if the socioeconomic benefits of an ambi-
tious energy transition pathway differ largely, or if the
decision for the energy transition can be made rather
independently of socioeconomic indicators, as long
as ambitious targets are set and reached. If the mod-
eling framework can solve and produce results annu-
ally, socioeconomic results along the pathway, such as
effects of earlier or later investment in certain technol-
ogies can be included in the analysis.
To contribute to this discussion on economic and
distributional effects of energy transition and sustain-
ability assessment, we compare ten different already
published energy transition scenarios, all assuming a
reduction of CO2 emissions by at least 80% relative to
1990 in 2050. e methodological challenge is to har-
monize assumptions across these different works in the
literature, to enable this comparison across scenarios
and provide meaningful differences. e joint assump-
tion is that the energy transition as such has been
decided [17, 18], and the sustainability analysis helps
comparing the different pathways to attain the targets.
erefore, the technology mix in the future energy sys-
tem is the crucial distinguishing feature in each sce-
nario. ere are scenarios with a focus on synthetic
fuels or with a particularly large amount of renewable
Keywords: Energy scenario, Macroeconomic modeling, Energy system modeling, Impact assessment, Regional
impacts, Social indicators, Sustainability, Renewable energy, Hydrogen strategies
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Ulrichetal. Energy, Sustainability and Society (2022) 12:35
electricity and storage. e expansion of renewable
energy (RE) and the importance of synthetic fuels var-
ies, as does the role of energy imports.
Socioeconomic outcomes differ along with different
investment pathways, different price regimes and other
differences in drivers. To compare these outcomes, we
apply the macroeconometric simulation model PANTA
RHEI, which is the environmentally extended version of
the simulation and forecasting model INFORGE [19].
Besides the comprehensive economic core, the energy
sector, and emissions as well as transport and housing are
modeled in detail. is paper thus presents the simula-
tion results of the German economy under ten energy
transition scenarios. e output of this analysis are reali-
zations of a set of economic indicators for the ten scenar-
ios to assess macroeconomic effects on the national and
regional scale. ese results were used in further sustain-
ability assessments of energy transitions [20]. According
to a comprehensive review of the methods, measures and
issues of the sustainability assessment of energy produc-
tion compiled by Turkson etal. [21], economic indicators
feature less often in these assessments. e results pre-
sented in the following try to address the gap. In addition,
Turkson etal. [21] stress the importance to address dif-
ferent levels of geographic scale in the analysis. Besides
drawing a more comprehensive picture, a regional analy-
sis helps to share insights into the regional outcomes of
the energy transitions, and hence increase acceptance on
this level.
Regional actors need to be involved in developing
solutions for transformation, which in turn requires a
sound understanding of issues of regional distribution
and impacts [22, 23]. Social and distributional aspect are
often blurred if the analysis stops at the national level.
Several studies assess regional effects of an energy system
transformation in Germany. Sievers etal. [10] compare
an energy transition scenario with a reference develop-
ment until 2030. ey conclude that the eastern federal
states and generally coastal states would have the high-
est positive deviations in relative terms. Changes in the
electricity market are the main reason for the regional
differences. Ulrich et al. [24] use the scenarios for the
German energy transition developed in Lutz et al. [11]
for a model-based regional analysis until 2040. Here, as
well, many German states in the north and east show
particularly high positive deviations. No systematic
comparison between different ambitious transformation
paths until 2050 has yet been undertaken. In studies on
the impacts of an energy transition, regional distribution
aspects at the subnational level too often are ignored and
not addressed. To bridge this gap, a modeling framework
was developed here, which allows evaluating of long-
term economic effects for 13 regions. We particularly
address which regional effects and regional differences
can be expected for selected transformation strategies.
e results contribute to the debate on just transition
policies and provide valuable quantitative input for deci-
sion makers and regional stakeholders alike.
e paper starts in the methodological section with a
description of the energy system, the macroeconomic
modeling and how the models interact. In addition, the
method of regionalization is outlined. e results are
presented in the subsequent section: after showing the
results of the energy system modeling and the macroeco-
nomic effects, the paper turns to the regional dimension
of impacts of an energy transition. At the end, based on
the obtained findings, the conclusions are drawn.
Methods
To answer the questions outlined above, a combina-
tion of different models and data compilations has been
employed (Fig. 1). e energy system model translates
scenarios from the literature to a comparable data set,
by harmonizing across assumptions of GDP, labor force,
population, the level of energy efficiency and other indi-
cators enabling a ceteris paribus comparison where only
the characteristic elements of each scenario drive results.
ese harmonized energy system scenarios feed the mac-
roeconometric assessment with the model PANTA RHEI.
To perform regional analyses, key variables of the harmo-
nized scenarios need to be regionalized using selected
indicators. ese regionalized scenarios enable the sub-
national analysis of value creation and jobs in a regional
model coupled with the national macroeconomic model.
Techno‑economic energy system modeling
Macro-economic impacts of national transformation
strategies are based on scenario results from the harmo-
nized re-modeling of different published transition sce-
narios for Germany [11, 25–32]. e scenarios describe
the transformation of the entire energy system (electric-
ity, heat, transport) in Germany up to 2050. Details on
the selection process can be found in Naegler etal. [6],
Additional file1: TableS1 lists the underlying original
studies. Scenarios I–V describe moderately ambitious
strategies to reduce direct energy-related CO2 emissions
by 80% until 2050, whereas the highly ambitious scenar-
ios VI to X reach an emission reduction of at least 95%.
Sector-specific defossilization strategies were identified
from the original studies, i.e., market shares of climate-
friendly technologies in the end-use and conversion sec-
tors. Subsequently, these supply side strategies were set
as boundary conditions for a harmonized remodeling of
the scenarios: drivers such as development path for the
economy (exports), population, the development of use-
ful energy demand and transport services, etc., were
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Ulrichetal. Energy, Sustainability and Society (2022) 12:35
held identical in all remodeled scenarios. In this way, the
essence of the technical strategies of the original studies
can be preserved. At the same time, the different studies
are made comparable, as different boundary conditions
(e.g., population and GDP development) are harmonized
and biases between the scenarios due to different bound-
ary conditions are avoided.
e energy system model (accounting framework)
MESAP [33, 34] and the electricity market simulation
model flexABLE [35] were used for the re-modeling.
MESAP is an accounting framework for the German
energy and transport system, mainly used for developing
of backcasting scenarios until 2050 in annual to 5-yearly
time steps. In MESAP, the variables that centrally deter-
mine the development of the energy system (such as
energy intensities and market shares of different energy
and transport technologies) can be specified exogenously.
erefore, MESAP is very well suited for a harmonized
re-modeling of existing scenarios. MESAP includes a
wide variety of technologies, in particular for the genera-
tion of power, heat, and fuels from renewable sources. In
addition to complete energy balances, MESAP also calcu-
lates installed capacities for energy conversion technolo-
gies, as well as investments, system costs, etc. A more
detailed description of MESAP can be found in the Addi-
tional file1 (Sect.2.1 and Additional file1: Fig.S1).
FlexABLE is an is an agent-based electricity mar-
ket simulation model. e model follows a bottom-
up approach and includes main types of generation
assets such as thermal power plants, variable renewable
generators, and storage units. ese assets, represented
by agents, can participate in both an energy exchange
market and a control reserve market. In addition, eligible
power plants capable of heat co-generation, such as coal
and gas power plants, can participate in a regional dis-
trict heating market. e model calculates market results
and the corresponding plant dispatch in a 15min time
resolution.
Here in this approach, MESAP is used to calculate
complete annual energy balances for Germany for the
conversion and end-use sectors. flexABLE then takes
MESAP results and verifies or calculates capacity fac-
tors and installed capacities of flexible power generators,
energy storages units, and electrolyzers. In the next step,
MESAP integrates the flexABLE results and calculates
annual gross new and decommissioned capacities for all
relevant system technologies (including cars and trucks).
Finally, results for economic indicators such as the
annual investments in new technologies, capital, oper-
ating and maintenance costs resulting from new instal-
lations and existing plants, levelized costs of electricity,
import quotas, and total system costs, are determined
and exported to the macroeconomic model (see section
‘PANTA RHEI’). ey define the economic dimension
of the energy system transformation and are exogenous
inputs to PANTA RHEI. Note that for the transport sec-
tor, only fuel costs, but no capital costs are considered.
More details on scenario re-modeling and model cou-
pling between energy system models and the macro-eco-
nomic model can be found in Naegler etal. [6].
Fig. 1 Overview of the workflow and the model interactions at the national and regional levels as well as key indicators
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Ulrichetal. Energy, Sustainability and Society (2022) 12:35
Energy system modeling results were used as bound-
ary conditions for the macroeconomic calculations with
PANTA RHEI. In addition to energy variables, economic
scenario results such as investments in the expansion of
renewable energy technologies are decisive for macro-
economic assessment. ese inputs from MESAP are
exogenously fed into PANTA RHEI, placing the energy
system part in a macroeconomic context.
Regional distribution
e regional distribution of environmental, social and
economic impacts of an energy scenario is relevant for
a sustainability assessment, because larger regional ine-
qualities are usually not preferred, and a good regional
balance of effects is associated with a just transition [23,
36]. Although political questions of equity and balancing
of interests are related to just transition, these assump-
tions are not integrated and discussed in the remod-
eled scenarios. erefore, three different distribution
keys for the expansion of renewable energies were used
to elaborate different aspects and criteria of allocation.
e regional level modelled are the German federal
states. Distribution keys and evaluation were based on
13 regions, as the three city states Berlin, Hamburg and
Bremen were merged with respective territorial states
Brandenburg, Schleswig–Holstein and Lower Saxony.
For new capacities in wind onshore and solar pho-
tovoltaic (PV), which will see the largest expansion in
all considered scenarios, three different regionalization
approaches are considered for both capacity and invest-
ments from 2020 onwards, to allow for a sensitivity
analysis. e different approaches are selected to rep-
resent technical criteria, current planning strategy and
social criteria separately. Investments are solely based on
capacity expansion, which leads to a slight inaccuracy,
neglecting repowering. e three selected distribution
criteria are:
a. Distribution according to the technical potentials for
respective technologies
b. Distribution according to the National Grid Develop-
ment Plan for power
c. Distribution according to social criteria to include
the aspect of promoting structurally weak regions
For renewable power sources that face a small expan-
sion in all scenarios, such as biomass, hydro and geother-
mal energy, the expansion was allocated according to the
current distribution as considered in the model, based
on data from the national renewable energy agency in
Germany [37]. To harmonize the developing paths from
the base year onto 2050, some adjustments and further
assumptions were applied. e capacity of PV and wind
for each year up to 2050 and in each of the federal states
results from the respective existing capacity and the new
installation. us, the model applies the current distri-
bution of capacities based on AEE [37] up to 2019, con-
sistent with the other renewables. New wind offshore
installations are allocated to the three coastal regions
Niedersachsen–Bremen (59%), Schleswig–Holstein–
Hamburg (24%) and Mecklenburg–Vorpommern (16%),
based on a social criteria distribution, described below.
e distribution according to the technical poten-
tial considers the availability of solar and onshore wind
sources. e total technical potentials are taken from a
comprehensive potential study for the German federal
states [38]. We calculate shares of the national potential
of installable capacity for each region (Table1). Federal
states with a high share of the potential record an equally
high share of new installations per year. is target distri-
bution in 2050 gradually evolves from the current distri-
bution of existing capacity.
e distribution of PV and wind onshore according to
the National Grid Development Plan for power (GRDP)
was adapted from the German transmission system oper-
ators [39]. e regional distribution assumptions in the
GRDP were generally prepared in three steps, which are
the mapping of existing plants, analysis of technical and
yield potential, and modelling of the new installations.
e new installations of PV and wind onshore plants
were calculated from the difference between the total
installation in the target years (in the study year 2030
and 2035) and the existing installation (for PV in 2017,
for wind onshore in 2016). According to the study, the
shares of each technology per state hardly change over
the years despite growing capacities. For this reason, it is
Table 1 Distribution of PV and wind potentials in Germany
based on AEE [37]
Capacity potential Wind onshore
(%) PV (%)
Baden–Württemberg 11 10
Bavaria 20 8
Brandenburg and Berlin 7 13
Hesse 7 6
Mecklenburg–Vorpommern 6 13
Lower Saxony and Bremen 14 14
North Rhine–Westphalia 10 15
Rhineland–Palatinate 6 6
Saarland 1 1
Saxony 5 4
Saxony–Anhalt 4 4
Schleswig–Holstein and Hamburg 6 5
Thuringia 4 3
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Ulrichetal. Energy, Sustainability and Society (2022) 12:35
assumed that the shares of new installations per state in
2035 remain the same until 2050.
e distribution according to social criteria is the
result of a combination of six indicators. e number of
unemployed is the primary distribution criterion and it
is multiplied by a factor resulting from the combination
of normalized individual indicators (see Table2): labor
productivity (producing industries), subsidies paid from
the structural funds, employment in lignite coal mining,
the share of the construction sector in the region and
the gross employment from renewable energy expan-
sion. e latter distinguishes existing employment in the
wind and PV sectors, to generate specific distributions.
e social distribution should reflect concentration of
existing energy-related jobs (both conventional and RE)
and favor structurally weak regions. Each indicator has a
weight to reflect the content importance and generate a
realistic but more contrasting regional distribution com-
pared to the other allocation schemes.
Figure2 shows the distribution of new installed capaci-
ties according to the three distribution keys. e shares
of new installations for onshore wind energy differ more
from distribution of the population than those for PV. It
becomes obvious that the GRDP distribution in general
is very similar to that according to natural potentials.
For wind energy, the GRDP sees much more expansion
share in Lower Saxony und less in Schleswig–Holstein.
e distribution according to social criteria is a more
contrasting assumption. Note that fossil-fuel power plant
decommissioning was not performed using region-spe-
cific assumptions. e deconstruction is proportional to
the region-specific inventory in the base year.
PANTA RHEI
In line with the United Nations Agenda 21, the eco-
nomic dimension is an important element for a com-
prehensive sustainability assessment [40]. Indicators
proposed there, such as employment–population ratio,
investment share in GDP, or expenditure on research
and development, emphasize that securing jobs, main-
taining the investment capability of companies and
the government, and the long-term stability of the
economy characterize a sustainable development from
the economic perspective. us, economic indicators
are also part of the sustainability assessment of energy
systems, as included in the analyses of, for example,
Afgan et al. [41] and Rösch et al. [42]. To determine
the macroeconomic effects of the energy transition,
the simulation model needs to reflect the responses
of the economy to changes in the energy system. e
energy system is strongly embedded in almost all areas
of the economy, which means that the energy transition
impacts the economy at many points. In this context,
the different effects cannot be calculated separately, but
the feedback and second round effects as well as inter-
actions must be included in a consistent framework of
the entire economy with all its actors [43].
e analysis is based on results from the macro-econo-
metric input–output model PANTA RHEI, which cov-
ers not only the German energy sector, but the overall
economy with all its linkages. e long-term simulation
model allows analyses to be carried out up to 2050, cal-
culated annually. e latter is utterly relevant to com-
pare different pathways towards the targets of the energy
transition. Annual outcomes influence the acceptance
and cannot be mapped with other modeling approaches,
such as CGEs. PANTA RHEI was most recently used to
estimate the socio-economic effects of the German Cli-
mate Change Act and alternative target paths [12] and
to model rebound effects in the energy consumption of
industries [44]. e model’s philosophy and properties as
well as its applications are summarized in Lehr, Lutz [45].
A short overview of the model can be found in the Addi-
tional file1: (Sect. 3.2 and Additional file1: Fig.S2) as
well es on https:// www. gws- os. com/ en/ energy- and- clima
te/ models/ detail/ panta- rhei.
Table 2 Indicators combined for the distribution by social criteria, specific weights and data sources
Indicator Weight Source
Number of unemployed Primary key Federal Employment Agency
Labor productivity 0.25 National accounts of the federal states
Subsidies paid from the structural funds 0.2 Federal Institute for Research on Building, Urban Affairs and Spatial
development (2018): Indikatoren und Karten zur Raum- und Stad-
tentwicklung (INKAR)
Employees in lignite coal mining 0.15 Federal Ministry for Economic Affairs and Energy (2018): Der Bergbau
in der Bundesrepublik Deutschland—Bergwirtschaft und Statistik
2016
Share of construction sector in region 0.2 National accounts of the federal states
Gross employment from renewable energy expan-
sion 2016 (Wind energy or PV) 0.2 Ulrich and Lehr (2018): Erneuerbar beschäftigt in den Bundesländern
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Ulrichetal. Energy, Sustainability and Society (2022) 12:35
e macroeconomic core of the energy-economy
model PANTA RHEI comprises national accounts and
input–output tables [46], leading to disaggregation to at
least 63 sectors or commodity groups. Econometric esti-
mation of behavioral equations determines the model’s
parameters. e model is solved annually in an itera-
tive process and does not emphasize either the supply
or demand side [19]. e comprehensive economic core
is complemented by energy and environment modules.
e energy module contains energy balances [47], satel-
lite balances for renewable energy [48], and energy prices
[e.g., 49], as well as energy-specific behavioral equations.
New energy sources such as power-to-X technologies are
not yet explicitly contained in the statistics, but highly
relevant for future scenarios. erefore, the data struc-
tures must be extended for modeling future develop-
ments. A proposal for integrating these energy sources
into energy balances can be found in Lehr etal. [50].
e energy module shows a multidimensional linkage
with the economic core through trade, investment, and
prices. To reflect the particular structure of renewable
energy and energy efficiency investment, which is crucial
for any energy transition, technology-specific investment
converters are used. ese cost structures are based on
surveys, and they use the economic sector classification
NACE [51] applied throughout the model [52, 53].
Regional modeling
e macroeconomic effects determined by comparing
scenarios might show only minor deviations for aggre-
gated indicators. At the regional level, however, the
effects can be significantly larger, as the conventional
energy sector, investment goods production and renew-
able energy potentials are concentrated [54, 55]. e
LÄNDER model is used to analyze and project struc-
tural changes at the level of the 16 federal states of Ger-
many [see 56]. It is directly linked to the national model
PANTA RHEI and uses the sectoral results for value
added and employment determined there at the regional
level. e model enables the analysis of different simula-
tion scenarios at the federal state level. In the modeling,
the regional labor markets (number of employed persons
and employees), gross value added and indicators of wage
and salary development are modelled at the level of 37
economic sectors. In addition, the effects of a selection of
intermediate inputs are represented at the regional level.
Fig. 2 Distribution of newly installed capacities up to 2050 along the regions
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Ulrichetal. Energy, Sustainability and Society (2022) 12:35
In the version used to analyze the energy system trans-
formations, further links between the regional economic
and energy systems were established [24].
e comprehensive model thus enables the analy-
sis of regional effects of different transformation paths
and considers the entire economy of the federal states
with their specific structures. However, such an analysis
requires explicit assumptions on the design of the trans-
formation in the regional context. e default configura-
tion for the comparison of developments between the ten
scenarios is the distribution along the natural potentials.
For the scenarios I and VI, distributional effects were
examined using the alternative regional distribution keys,
namely, ‘GRDP’ and ‘social indicators’.
A large share of the value added created from the
investment in the expansion of renewable energies is
generated by the production of the systems in Germany.
Depending on the technology, a substantial share of the
direct and indirect demand effects is also attributable to
on-site installation. us, the demand associated with the
investment is significantly redistributed spatially com-
pared to a proportional allocation. Based on the findings
in Ulrich, Lehr [57, 58] and the model used there, we
analyze the effects of this redistribution in a sensitivity
analysis. e used model—here referred to as hyBRID—
is an input–output model with an integrated spatial
reallocation algorithm [see 59, 60]. e model allocates
direct demand by detailed data on RE expansion and
productions sites as well as indirect effects by a regional
representation of input–output tables. In addition to
techno-economic data sets, detailed information on the
production locations of the RE sector as well as specific
structures of the 16 federal states are stored as essential
bases of the model. In this model framework, scenario
VI was compared with scenario I and deviations were
implemented in the composite model PANTA RHEI–
LÄNDER. e results—the redistribution by additional
regional demand from investment—is integrated in the
synthesis of regional impacts into the section with the
regional results. It reflects the potential additional effect
from the specific structure of the German RE manufac-
turing market in the present.
Results
Outline oftheenergy system transformations
In the following section, we summarize some central
results from the scenario modeling. A detailed docu-
mentation of the results can be found on https:// zenodo.
org/ record/ 59931 77 (Zenodo), and https:// zenodo. org/
record/ 59924 32).
Figure3 (left panel) shows the gross power demand in
2050 in the different scenarios, differentiated by applica-
tion. It can be seen that the gross power demand in the
moderately ambitious scenarios I–V (between 528 and
598 TWh in 2050) is similar to today’s power demand
(577 TWh in 2020), although new applications—in par-
ticular road transport and power-to-heat, but also syn-
thetic fuels and gases (P2X)—contribute increasingly to
the demand. e highly ambitious scenarios SCEN VI–
IX are characterized by a significantly higher gross power
demand (between 924 TWh in SCEN VI and almost
1700 TWh in SCEN X), mainly due to a higher demand
for synthetic fuels and gases (P2X), along with a higher
degree of direct electrification of road transport and
Fig. 3 Left panel: comparison of gross power demand per application sector (including power demand for P2X imports) in 2050 in all scenarios
and comparison with statistical data for 2020; right panel: comparison of the installed capacity for power generation per technology/energy carrier
in 2050 in all scenarios as well as statistical data for 2020. Note that also power generation capacities for net power imports and for imports of
synthetic fuels and gases (P2X) are shown
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Ulrichetal. Energy, Sustainability and Society (2022) 12:35
heat. In SCEN VIII and SCEN X, a significant share of
synthetic fuels and/or gases are assumed to be imported
for Germany, increasing the power demand abroad
significantly.
Figure 3 (right panel) shows the resulting capacities
for power generation, which broadly follow the power
demand. PV, wind onshore and wind offshore provide
the bulk of power generation, but the scenarios differ sig-
nificantly with respect to the share of these technologies
in total installed power generation capacity. Estimates
for the installed capacity of PV range between 64 and
913 GW, for wind onshore between 81 and 267 GW, and
wind offshore between 26 and 114 GW. e import of
electricity and P2X requires significant installed capaci-
ties abroad in some scenarios (particularly pronounced in
SCEN VIII and SCEN X).
It is noticeable that the range of possible solutions for
the energy system is significantly higher in the case of the
very ambitious scenarios SCEN VI—SCEN X than in the
case of SCEN I—SCEN V which achieve only 80% CO2
reductions. e wider spread of results for the 95% sce-
narios illustrates the higher uncertainty as to how the
final 15–20% CO2 emission reductions can be achieved.
An 80% reduction in GHG emissions is largely achievable
with technologies that are very advanced today. However,
the path to (near) net zero emissions requires the use of
new energy carriers (such as H2 or synthetic liquid fuels)
and new technologies (such as H2 for steel production)
in many areas, especially in industrial processes and the
transportation sector. Furthermore, the advanced elec-
trification of heat and transport requires much higher
efforts for the system integration of all technologies. In
addition, the import of synthetic energy carriers plays an
increasingly important role. For all these options, how-
ever, there are still great uncertainties regarding costs,
potentials, acceptance, and other constraints, so that
the ideas about the role these technologies will play in
the future diverge considerably in the very ambitious
scenarios.
Figure4 summarizes the energy system model output
which either has been used as an input for the macroeco-
nomic modeling or for the assessment of macroeconomic
model results below. Panel (a) shows the development of
gross energy imports (fossil fuels, P2X, electricity, …),
which decreases in all scenarios from today’s level of ca.
10,000 PJ/a to values between 1350 PJ and more than
4500PJ. Panel (b) shows the decrease in direct energy
related CO2 emissions by 80% (SCEN I–V) and by 95%
Fig. 4 Comparison of results for all ten scenarios: (a) gross imports of energy carriers (in PJ/a); (b) CO2 emissions relative to 1990; (c) power demand
for P2X generation (including power demand for P2X imports); (d) annual capital costs (annuities); (e) annual O&M costs; (f) total system costs
(= capital costs + O&M costs)
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Ulrichetal. Energy, Sustainability and Society (2022) 12:35
(SCEN VI–X) relative to 1990. In panel (c), we see the
increase in power demand for P2X generation (domestic
and abroad) in all scenarios (compare with Fig.3). Panel
(d) shows the annual capital costs (annuities) resulting
from the investments necessary to achieve the different
transformation paths. Panel (e) in Fig.4 shows the annual
operation and maintenance (O&M) costs, and panel (f)
the total system costs, i.e., the sum of capital and O&M
costs. Here it can be seen that the different transfor-
mation strategies and CO2 emission reduction targets
result not only in different power demand and technol-
ogy portfolios, as can be seen in Figs.3 and 4, but also in
significantly different system costs. e highly ambitious
scenarios SCEN VI–X tend to show significantly higher
system costs over most of the transformation paths than
the moderately ambitious scenarios SCEN I–V, reflecting
higher investment due to a faster and deeper transfor-
mation, but also higher O&M costs than the moderately
ambitious scenarios. A detailed documentation of the
scenario results can be found in the supplementary mate-
rial hosted on ZENODO.
Results fromthenational macroeconomic analysis
GDP, investment, imports, consumer prices and employ-
ment have been selected as indicators for evaluating
macroeconomic results of the model. As we evaluate
economy-wide net-effects, indicators only representing
the energy sector or new technologies were not included.
GDP, despite being criticized as being too broad and
unilateral [61], still serves as the indicator of choice for
economic development and growth. Investment indi-
cates future returns and wealth; it also shows how much
effort is needed to initiate and drive the energy transi-
tion. Investment drives production, be it domestically
or imported. When produced domestically, additional
production drives value added and leads to more value
and employment in the respective sectors. Employment
provides livelihoods and contributes to people’s wellbe-
ing. Additional income feeds back to the economy as
additional consumption, thus increasing the multiplier
effects. In an open economy, additional activities also
spur imports, thus lowering the overall result. Over time,
investment will be recovered by higher costs. In addition,
substitution of domestically produced energy by imports
affects prices. Policies to drive the energy transition, such
as carbon pricing, put additional pressure on prices. e
ten scenarios compared contain different levels and path-
ways for the above, leading to different outcomes of the
economic simulation model runs.
e simulation results for a selection of key indicators
are shown in Fig.5. When comparing the ten scenarios,
the first observation is that the macroeconomic vari-
ables are very close to each other on average from 2030
to 2050. Most of the more ambitious scenarios VI to X
show higher levels of GDP and employment at the end
of the projection period than the scenarios aiming at an
80% GHG reduction compared to 1990 (I to V). Invest-
ment is generally higher in the more ambitious scenarios,
resulting in more positive economic effects. Scenarios
VII and IX, for example, assume a high expansion of off-
shore wind and photovoltaics, respectively, which tend
to be more employment-intensive than onshore wind.
However, the development of GDP also depends on the
development of price levels, operating and maintenance
costs, and imports along the path. For example, the com-
paratively low employment in the very ambitious sce-
nario X is the result of an investment level comparable
to that of an 80% scenario and high energy imports. In
GDPInvestment,
Construcon
Investment,
EquipmentImportsConsumer
Price IndexEmployment
2015 = 1001 000
Scenario
Scen I3752 275545 2729 147.9 43880
Scen II 3749 273542 2724 147.8 43870
Scen III 3756 276549 2730 148.0 43888
Scen IV 3749 275542 2724 147.4 43890
Scen V3757 280556 2740 147.7 43936
Scen VI 3775 285558 2736 147.8 43983
Scen VII3788 292571 2753 148.3 44009
Scen VIII 3762 281553 2735 148.0 43946
Scen IX 3784 295576 2762 147.6 44048
Scen X3744 276546 2734 147.8 43875
price-adjusted, Bn Euro
Average of the years 2030 to 2050
Fig. 5 Simulation results of the macroeconomic model PANTA RHEI for selected economic indicators, averages for the period 2030–2050. See also
Naegler et al. [6] for further indicators
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Ulrichetal. Energy, Sustainability and Society (2022) 12:35
scenario seven, macroeconomic imports and the price
level develop dynamically. However, domestic demand
impulses dominate, and less energy is imported. Over-
all, the small differences between the alternative path-
ways can be considered plausible. All scenarios represent
a transformation, with investments in new technologies
and an energy mix that is fundamentally different from
that of the past. In scenario VII, GDP is around 1 per-
cent higher than in Scenario I. A comparison with sce-
nario without substantial low carbon policies would
show significantly larger differences in the long term. For
example in Lutz etal. [11] deviations rise to 3.5 percent
in Germany up to 2050. An analysis by Lutz etal. [12]
finds deviations of a similar magnitude to those shown in
our analysis, since different low carbon scenarios are also
compared there. It should also be remembered that final
energy consumption is roughly the same in all scenarios,
so that no scenario can set itself apart with import sav-
ings, for example. More ambitious scenarios have rather
high overall imports. Scenario VII reaches the highest
economy-wide price level by 2050, Scenario IV the low-
est. Scenarios with high transformation-related demand
through investment on average arrive at a slightly higher
price level. Constellations in which high prices meet
rather low employment are problematic. erefore, Sce-
nario VIII does not have much more favorable character-
istics than the less ambitious Scenario V.
Of particular importance is how the individual eco-
nomic sectors develop, and which economic sectors
benefit from the transformation, and which may be dis-
advantaged by 2050. is analysis is carried out based
on employment in 2050. We analyze differences between
scenarios to better illustrate these effects. To better high-
light the differences, we selected one of the 80% scenarios
(Scenario I) as a reference and compare the simulation
results of the other nine scenarios to this reference sce-
nario. Relative differences in percentage provide the
scenario’s relative impact when all other factors are held
equal (ceteris paribus assumption). Relative differences
allow us to see whether the impact on a particular sector
of the economy is large or small compared to develop-
ments in Scenario I.
e scenarios have different effects on employment in
the respective sectors. Figure6 shows the relative differ-
ences in employment in seven sectors (groups of ISIC-
rev4 sections). Construction shows the largest positive
effects, especially in four out of five very ambitious sce-
narios. Investment leads to higher demand, which is most
Fig. 6 Employment in selected economic sectors, relative differences of 9 scenarios compared to Scenario I, based on Naegler et al. [6]
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Ulrichetal. Energy, Sustainability and Society (2022) 12:35
evident in construction, but also in business services. In
the energy sector and industry, the effects are different
but also small. As a rule, this sector in terms of employ-
ment cannot sustainably benefit from the additional
investment in the very ambitious scenarios. Higher prices
reduce the demand effect due to stronger labor produc-
tivity increases. Scenario X, as a special case, has lower
employment compared to the reference scenario, as it
relies mainly on hydrogen imports, with little investment
in domestic infrastructure or technology. In particular,
scenarios with high energy imports (see below) lead to
reduced employment in the energy sector.
Note that total employment will decline in all scenar-
ios by 2050 due to demographic change. e number of
people in the labor force will decrease accordingly. In
addition, the ceteris paribus assumption includes the
general structural shift toward services in all scenarios.
In Fig.7, four energy system indicators are correlated
with GDP. Graph (a) clearly shows the cluster of ambi-
tious and very ambitious scenarios regarding the CO2
reduction until 2050. Furthermore, four of the five sce-
narios with more than 90% energy-related CO2 reduc-
tion show a higher GDP than all less ambitious scenarios.
Graph (b) illustrates that most of the ambitious scenar-
ios have significantly higher system costs. Overall, the
realizations of GDP at the end of the projection period
increase with the system costs. Scenario X is the least
advantageous here, with lowest GDP and one of the
Fig. 7 Comparison of realizations for GDP in 2050 with indicators from energy system modeling
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Ulrichetal. Energy, Sustainability and Society (2022) 12:35
highest system costs. Scenario VI attains a high GDP with
rather low system costs. In contrast to total economic
imports (of all goods), gross energy imports in physical
units show a different relationship with economic out-
put. e ambitious scenarios save large amounts of fossil
fuel imports. erefore, low gross imports are accompa-
nied by high GDP realizations [Graph (c) in Fig.7]. How-
ever, scenario VII has very high energy imports and still
achieves the highest GDP, as investment is particularly
high. Technologies for the use of hydrogen and synthetic
fuels are needed to varying degrees. For the Scenarios
VIII, IX and X, the demand for hydrogen and synthetic
fuels reaches amounts of 1300 PJ and more by 2050. In
nearly all very ambitious scenarios, these new energy
carriers play a substantial role, which does not have a
negative impact on the economic outlook. It should be
mentioned, however, that due to a lack of data, it had to
be assumed that the prices of synthetic fuels do not differ
fundamentally in level and development from compara-
ble conventional energy sources. A striking finding is that
a reduction of greenhouse gas emissions by another 15%,
since 1990 compared to the 80% scenarios significantly
changes the macroeconomic developments. e less
ambitious scenarios hardly differ one from another, while
the 95% scenarios show a wider range of developments
until 2050. is corresponds to the higher diversity of the
energy systems among the more ambitious scenarios as
shown in the energy system results above.
Regional results
e regions show different levels of GDP and employ-
ment across the scenarios by 2050. In fact, a deviation
analysis from a reference (here scenario I) shows that
regional GDP can be lower, although a higher GDP is
achieved for Germany as a whole. For example, not all
German states can benefit from a very ambitious sce-
nario in a regional comparison. Attributes were selected
for clustering to summarize the results for ten scenarios
and 13 regions. One attribute is the standard deviation
of the regional share across the scenarios. Furthermore,
the largest and smallest regional shares were identi-
fied for each federal state to assign them to the group of
ambitious and very ambitious scenarios. e shares in
employment in 2045 were evaluated, since the differences
in this year are comparatively high. e differences are
high, because both the national level of investment and
the respective differences between the scenarios are high
in that period. In general, at the end of the projection
period, structural developments as long-term macroeco-
nomic adjustments to the transformation paths are more
likely to occur.
Figure 8 shows how the regional distribution of
employment is affected throughout the transformation
scenarios. For example, the regional share of Lower
Saxony–Bremen within Germany varies across the sce-
narios in 2045 between 9.08 and 9.18 percent. If the aver-
age share across the very ambitious scenarios (VI to X) is
higher than the average of the average across scenario I
to V, they are declared to have relatively high advantage
from a zero-carbon transformation. ese regions for
2045 are located in nearly every part of the country. ey
only do not occur in the southwest. e pattern changes
slightly between 2040 and 2050, as northern federal states
are increasingly unlikely to belong to the group of regions
for which a very ambitious transformation path is par-
ticularly advantageous. e sensitivity to changes in the
technology mix is defined by the standard deviation of
regional shares across scenarios. If the value is above the
highest tercile, the sensitivity is considered high. In 2045,
the uncertainty is highest in Lower Saxony–Bremen and
North Rhine–Westphalia, but also in Hesse, Baden–
Württemberg, Saxony and Brandenburg–Berlin, and the
economic development proves to be very dependent on
the transformation strategy in detail. e results show
that familiar patterns of positive effects dissipate, when
long-term transformation strategies are compared. All
scenarios assume a substantial expansion of RE-facil-
ities, so that other changes in the energy system and in
the whole economy have a higher impact, especially the
evolving hydrogen strategies from 2040 and later. None
of the scenarios leaves individual regions behind in terms
of development. However, there are individual scenarios
that are highly polarizing, such as Scenario VII, Scenario
V and Scenario IX.
e structural characteristics in the major regions,
those of the energy industry but also across all sectors,
are decisive for the regional effects. Regions specialized
in a certain technology due to technical potential are
sensitive to the technology mix. An important factor is
the focus of RE use in the scenarios. Scenarios 6 and 7
focus on the expansion of wind energy. Here, those fed-
eral states achieve the highest shares that have a focus
on wind energy—today and according to the distribution
keys for the future expansion (the north). Scenario IX in
contrast achieves the highest PV expansion and favors
of regions, such as Baden–Württemberg and Saxony.
Higher sensitivity also can result from a higher focus on
fossil power generation and no RE focus in the past. As
for Hesse, this is more clearly a question of ambitiousness
of the transformation. e results for North Rhine–West-
phalia and Saxony, which also fall into the “fossil” group,
show that power generation structures are overlapped by
effects of overall economic structures and dynamics. A
major structural effect is that in the east-German regions,
construction has a high weight on regional employment.
In scenarios with high investment, the construction
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Ulrichetal. Energy, Sustainability and Society (2022) 12:35
sector benefits particularly strongly, which is less notice-
able in regions with a disproportional low weight of this
sector.
Focusing on Scenario I and VI, the analyses were
enhanced. e results are summarized in Fig.9. First, the
effects of the alternative regional distribution keys for the
expansion of renewable energies were examined as sensi-
tivities. e alternative, regional distribution keys—‘Grid
Development Plan’ (GRDP) and ‘Social indicators’—for
the expansion of renewable energies reinforce the already
described gradient from southwest to northeast. e
assumption for the GRDP favors most of the eastern
German states (except Mecklenburg–Vorpommern), but
also Lower Saxony–Bremen. Compared to distribution
by potential, the distribution key with social indicators
places greater emphasis on eastern German regions,
especially Saxony–Anhalt and Brandenburg. ere are
hardly any differences for North Rhine–Westphalia,
although this federal state benefits slightly from a dis-
tribution according to social criteria in comparison. e
entire south and southwest have an advantage in a distri-
bution by RE potentials. e spatial deviation pattern is
almost the same in scenario I and scenario VI.
e second modeling extension was the analysis of
the impact of regional additional demand triggered by
investment. e allocation of additional demand from
RE investment in scenario VI compared to scenario I,
both with resource potential distribution, was estimated
by processing the regional input–output model and by
refeeding results into the macroeconomic projection.
Fig. 8 Clustering of regional effects on employment for 13 major regions in 2045
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As can be seen from Fig.10, the demand is redistributed
to the north and parts of east-Germany. is is due to
the spatial focus of expansion in these regions. In addi-
tion, however, it is also noticeable that the manufactur-
ers of wind turbines and PV modules are located in these
regions. Parts of this pattern can also be found in the
studies on the jobs of renewable energy expansion, for
which the same model was used [57]. From these stud-
ies, it was also deduced that in principle all regions ben-
efit from the expansion, regardless of the location of the
plants. However, the relative importance for the overall
economy is particularly high in the regions marked with
the green buttons in Fig.9.
Discussion
At the national level, the comparison of different, long-
term target scenarios, exhibits patterns which are often
found in macroeconomic analysis of the energy transi-
tion to date. Investments are decisive and dominant,
prices and imports less so. Differences among the more
ambitious scenario towards 95% CO2 reduction are
higher than among the 80%-scenarios. In summary,
the pathways of GDP and employment are not differing
largely. Furthermore, an ambitious approach up to 2050
tends to have a positive effect on the variables, overall,
results are in line with similar studies for Germany.
GRDP-distribuon Social distribuon
Scenario I
Scenario VI
Fig. 9 Results of the spatial sensitivity analysis on employment 2045—the most advantageous distribution in terms of employment for the regions
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e modeling approach takes several relevant aspects
of a sustainability analysis of the outcomes of energy
transitions further; however, some important aspects
of the transformation have had to be sacrificed for the
sake of harmonized scenarios. In particular, the sce-
narios do not differ with regard to energy efficiency or
system costs in transportation. Although different poli-
cies are assumed in some of the scenarios compared,
and they are modeled explicitly, in the above analysis
no explicit modeling of policies to achieve the targets
is included. e analysis focuses on the investments,
saving and the respective prices taken from the vari-
ous studies, independent of the policy behind them,
i.e., if prices are a result of carbon taxes or other market
dynamics in the respective study. Even though mecha-
nisms und structures of an increasing use of hydrogen
and synthetic fuels are implemented in the model, only
weak assumption could be made about prices (relation
development) of these fuels. From an overall national
macroeconomic perspective, there will be no regret
no matter which transformation path is taken, and
due to the harmonization, differences between GDP-
development among the very ambitious scenarios are
underestimated.
When comparing regional developments across the ten
scenarios, the differences are low, but still larger than at
the national level. e north/east-to-southwest divide
often identified in energy transition impact assessment
changes to a more complex pattern when only transfor-
mation scenarios are compared. Regions have different
structures and preconditions that are reflected as region-
ally differentiated effects in the context of transforma-
tion paths. However, it is not only structures but also
industry-specific dynamics and development contexts
that determine the region-specific developments. e
regional impact patterns of effects slightly differ from
previous studies with time horizon 2030, as scenarios
represent all high but different renewable energy expan-
sion strategies. e important issue of regional implica-
tions of expanded production and use of hydrogen could
not be addressed with regional specific assumptions, as
there is only sparse data.
Conclusions
is paper compares socio-economic outcomes under
different energy transition scenarios. e indicators
reported and selected are GDP, investment, imports,
consumer prices and employment, all as net relative
differences. is is one of the main contributions of
this research to the sustainability discussion. Net rela-
tive differences at different points in time and differ-
ent geographical scale enhance the understanding of
socio-economic outcomes of different energy transition
pathways in a coherent and consistent way. For this anal-
ysis, ten existing scenarios were selected, harmonized,
and remodeled in an energy system model. Two basic
targets of CO2 reduction (80% and 95%) group the ten
scenarios, which represent very different strategies with
respect to the future technology and energy carrier mix.
e comparison employs a macroeconometric simu-
lation model framework along with regional modeling
approaches, addressing regional distribution effects and
thus the social sustainability dimension in new detail.
Another aspect is the almost complete substitution of
fossil fuels in the energy system—a regime that has not
yet been extensively analyzed in studies up to 2030 or
2040. e majority of the 95% scenarios assume high
importance of synthetic fuels, and some see increas-
ing energy imports. e process of import substitu-
tion through the expansion of RE is partly phased out
approaching 2050. is is reflected in the trade balances
under different scenarios.
e claim of the deeply disaggregated and empirically
based model is to map the future on current behavior
Fig. 10 Regional share in scenario VI, deviations through
re-allocation of additional demand derived by comparing scenario VI
with scenario I
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Ulrichetal. Energy, Sustainability and Society (2022) 12:35
and trends by econometric analyses. However, disrup-
tive transitions cannot be foreseen based on past behav-
ior. To meet this claim, we combine data driven empirical
modeling and energy system backcasting accompanied
by intensive technical discussions on specific scenarios to
simulate new technological pathways.
Assessing regional impacts needs a debate on the future
regional distribution of new energy production capaci-
ties and thus investment. Regional concepts fitted to
national objectives and scenarios need to be developed,
to improve coordination between regional and national
policies, and to address implications more precisely in
impact analysis.
e combination of the energy system model with the
national and regional economic models leads to a more
comprehensive picture of growth employment and distri-
bution effects. e good news in terms of decision mak-
ing and policy recommendations is that the differences
in the socioeconomic indicators at the national level
between the scenarios are not very large, but all scenarios
performed much better than a less ambitious scenario.
In particular in the light of the current debate on the
price effects of climate change mitigation and renewable
energy, the results of the above analysis showed the bene-
fits of aiming high in terms of CO2 reduction. In terms of
future research, data on regional value added and interre-
gional trade could strengthen the results and make them
even more applicable and useful for a full sustainability
analysis. Particularly, data on the regional distribution of
P2X technology are needed, because of their importance
in the more ambitious scenarios. A sensitivity analysis
varying these localizations could be a valuable contribu-
tion compared to existing impact assessments scenarios.
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s13705- 022- 00361-5.
Additional le1: TableS1. Overview of all scenario studies taken into
account during the scenario selection process. Figure S1. Schematic
overview of the structure and workflow of MESAP. For details see main
text. Figure S2. Overview of the PANTA RHEI model.
Acknowledgements
The authors would like to thank Jens Buchgeister, Wolfgang Hauser, Heidi
Hottenroth, Tobias Junne, Oliver Scheel, Ricarda Schmidt-Scheele, Ingela Tietze
and Tobias Viere for and their repeated contribution to the discussion and
their feedback on our results.
Author contributions
Conception, P.U., L.B., U.L.; acquisition, T.N., U.L., analysis, P.U., L.B., U.L., S.S., C.S.
interpretation of data, P.U., T.N., U.L.; writing original draft, P.U., L.B., S.S., C.S.;
writing—review and editing, U.L., A.W. All authors read and approved the final
manuscript.
Funding
This research was funded by the German Federal Ministry for Economic Affairs
and Energy (BMWi) under the project number 03ET4058 as part of the project
InNOSys—Integrated Sustainability Assessment and Optimization of Energy
Systems.
Availability of data and materials
The data sets generated and/or analyzed during the current study are avail-
able on https:// www. innos yspro jekt. de, accessed on 12 August 2021.
The documentation of the underlying scenarios as well as the data itself can
be found on Zenodo (https:// zenodo. org/ record/ 59924 32 and https:// zenodo.
org/ record/ 59931 77).
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1 Institute of Economic Structures Research (GWS), Heinrichstraße 30,
49080 Osnabrück, Germany. 2 German Aerospace Centre (DLR), Institute
of Networked Energy Systems, Curiestraße 4, 70563 Stuttgart, Germany.
3 Department of Sustainable Systems Engineering (INATECH), University
of Freiburg, Emmy-Noether-Str. 2, 79110 Freiburg, Germany. 4 The World Bank
Group, 1818 H Street, NW, Washington, DC 20433, USA.
Received: 16 August 2021 Accepted: 5 August 2022
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