The effect of differentiating costs of capital by country and technology on the European energy transition

Abstract

Cost of capital is an important driver of investment decisions, including the large investments needed to execute the low-carbon energy transition. Most models, however, abstract from country or technology differences in cost of capital and use uniform assumptions. These might lead to biased results regarding the transition of certain countries towards renewables in the power mix and potentially to a sub-optimal use of public resources. In this paper, we differentiate the cost of capital per country and technology for European Union (EU) countries to more accurately reflect real-world market conditions. Using empirical data from the EU, we find significant differences in the cost of capital across countries and energy technologies. Implementing these differentiated costs of capital in an energy model, we show large implications for the technology mix, deployment, carbon emissions and electricity system costs. Cost-reducing effects stemming from financing experience are observed in all EU countries and their impact is larger in the presence of high carbon prices. In sum, we contribute to the development of energy system models with a method to differentiate the cost of capital for incumbent fossil fuel technologies as well as novel renewable technologies. The increasingly accurate projections of such models can help policymakers engineer a more effective and efficient energy transition.

Full text

Climatic Change (2021) 167: 26
The effect of differentiating costs of capital by country
and technology on the European energy transition
Friedemann Polzin
1
&Mark Sanders
1,2
&Bjarne Steffen
3
&Florian Egli
4
&
Tobias S. Schmidt
4
&Panagiotis Karkatsoulis
5
&Panagiotis Fragkos
5
&
Leonidas Paroussos
5
Received: 11 August 2020 /Accepted: 1 July 2021/
#The Author(s) 2021
Abstract
Cost of capital is an important driver of investment decisions, including the large
investments needed to execute the low-carbon energy transition. Most models, however,
abstract from country or technology differences in cost of capital and use uniform
assumptions. These might lead to biased results regarding the transition of certain
countries towards renewables in the power mix and potentially to a sub-optimal use of
public resources. In this paper, we differentiate the cost of capital per country and
technology for European Union (EU) countries to more accurately reflect real-world
market conditions. Using empirical data from the EU, we find significant differences in
the cost of capital across countries and energy technologies. Implementing these differ-
entiated costs of capital in an energy model, we show large implications for the technol-
ogy mix, deployment, carbon emissions and electricity system costs. Cost-reducing
effects stemming from financing experience are observed in all EU countries and their
impact is larger in the presence of high carbon prices. In sum, we contribute to the
development of energy system models with a method to differentiate the cost of capital
for incumbent fossil fuel technologies as well as novel renewable technologies. The
increasingly accurate projections of such models can help policymakers engineer a more
effective and efficient energy transition.
Keywords Clean energy investments .Cost of capital .Weighted average cost of capital .
Electricity system
https://doi.org/10.1007/s10584-021-03163-4
*Friedemann Polzin
f.h.j.polzin@uu.nl
Extended author information available on the last page of the article
Published online: 31 July 2021
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Climatic Change (2021) 167: 26
1 Introduction
In making the transition to a carbon neutral economy by 2050, the European Union (EU) is
facing a very large challenge. What we know with certainty is that the transition requires large
amounts of private and public investment. Estimates range between 1 and 2% of EU GDP (e.g.
McCollum et al. 2013,2018; OECD/IEA and IRENA 2017; European Commission 2018).
This is well below total annual savings and investment, so the challenge is not raising the
money but rather “Shifting the Trillions”from fossil fuels to renewables (Hansen et al. 2017).
Indeed, policymakers are starting to do so. The European Commission, for example, in its
recent Green Deal programme (European Commission 2020a) has announced it will shift over
1 trillion euros into the transition, as evidenced by the recently approved 7-year budget.
In allocating these funds, policymakers in Europe and elsewhere rely heavily on models
and model projections (Luderer et al. 2012;Caprosetal.2016). Recent model simulations
have shown that a transition towards a 100% renewable energy-based power system by 2050 is
feasible (Jacobson et al. 2018;Bogdanovetal.2019b), although large uncertainties remain
around the most efficient technological pathways and the overall costs for the transition
(Paroussos et al. 2019a). But as illuminating as well-built and carefully calibrated model
simulations are, they inevitably rely on a large number of simplifying assumptions. In addition,
the complexity of the models does not always reveal how sensitive the outcomes to such
assumptions.
In this paper, we zoom in on assumptions concerning the weighted average cost of capital
(WACC).
1
The WACC is a fundamental driver in investment decisions in the private sector.
The typical assumption in models routinely used to simulate transition pathways is that all
technologies face similar or the same WACCs in all countries and over time (e.g. Capros et al.
2016; Bogdanov et al. 2019b;IEA2020). Prior research shows, however, that cost of capital
differs significantly between technologies and countries (Ondraczek et al. 2015;Steffen2020)
(Egli 2020) and also changes over time as technologies mature (a phenomenon referred to as
“financing experience”) (Egli et al. 2018). It has also been demonstrated that outcomes are
very sensitive to changes in the WACC in stylized power generation investment models (Iyer
et al. 2015; Hirth and Steckel 2016). In this paper, we show that taking such differences into
account yields very different modelling outcomes, potentially leading to misguided policy
conclusions. One would, for example, at uniform WACCs over- and underestimate the
adoption of renewables in risky and safer countries, respectively. In a time when energy
transition is high on the policy agenda and the trillions are actually being shifted, such errors
can be costly, and building methodologies and models that more accurately describe the
transition is an urgent challenge for academics.
To address this challenge, we formulate two related research questions. First, we investigate
(RQ A): What is the effect of the introduction of differentiated WACCs for reference scenarios
(based on current legislation) for the electricity system? Second, and even more important
(RQ B): How do differences in cost of capital affect the optimal transition pathway for more
ambitious climate policies in Europe?
To answer our research questions, we make two empirical and one conceptual contribution
to an emerging literature that calls for differentiated data on WACCs for modelling the energy
transition in a more realistic way (Gupta et al. 2014;Eglietal.2019; Bachner et al. 2019).
First, we develop a comprehensive dataset on real-world WACCs for all electricity production
1
We use WACC and cost of capital interchangeably throughout this paper.
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Climatic Change (2021) 167: 26
technologies and all EU countries using new data from a variety of sources and a novel
empirical approach. Second, using this dataset, we compare model projections for a reference
and an ambitious climate policy scenario for European countries up to 2050 to show how
differentiated WACCs change the transition pathways, emissions and costs. Our simulations
are carried out on the GEM-E3-Power model (which is the electricity module of the macro-
economic GEM-E3 model). By considering differences in WACCs for renewable and fossil
fuel-based technologies, we address the shortcoming that there is a strong focus on the WACC
of renewables because these technologies seem to carry more technical and market risks, while
WACC assumptions for fossil fuels facing rising transition and policy risks are set to low
historic or “standard”values from the literature. Third, our approach conceptually opens up the
possibility to model WACCs dynamically over time. As a case in point, we incorporate
financing experience curves (Egli et al. 2018) in the GEM-E3-Power model to project
declining WACCs for renewables as their deployment increases.
Our results show that differentiating WACCs by country for the Europe-28 have significant
impact on our model outcomes. Renewables become more competitive in low-risk countries,
while they are less so in high-risk countries. As country risk and the potential for renewables
are not aligned, this implies that without policy the transition is unlikely to evolve in a cost-
effective manner.
The remainder of this paper is structured as follows: Section 2 provides a brief summary of
the literature on the cost of capital in the energy sector, covering both empirical and model-
based literature on the cost of capital. Our methodology for deriving country- and technology-
specific WACCs is explained in Section 3, where we also present our estimated WACCs.
Section 4 presents the results when these WACCs are implemented in the GEM-E3-Power
model to explore the effects on technology mix, electricity costs, CO2emissions and diffusion
of low-carbon technologies. Section 5 concludes.
2 Conceptual background: financing conditions for energy technologies
The scientific literature on financing investment to mitigate climate change was limited and
fragmented for a long period (Gupta et al. 2014) but has rapidly expanded in recent years
(Steckel et al. 2017;Polzinetal.2017; Mazzucato and Semieniuk 2018). The deployment of
renewable power generation technologies to mitigate carbon emissions typically requires high
upfront investment (Schmidt 2014; Tietjen et al. 2016; Bachner et al. 2019). In the absence of
fuel costs, costs of capital account for a much higher share of total costs compared to
incumbent fossil fuel technologies like gas and coal (Schmidt 2014; Fragkos et al. 2017;
Krey et al. 2019), requiring substantial upfront financing. This puts the cost of capital centre
stage in computing and projecting future levelized costs of electricity (LCOE) and electricity
prices that in turn drive the diffusion of said technologies in energy markets. The empirical
evidence shows that these LCOE can differ widely across technologies and countries (Steffen
2020; Duffy et al. 2020) and change significantly over time, also because of changing costs of
capital (Egli et al. 2018;Egli2020).
Ondraczek et al. (2015), for example, stress the importance of the cost of capital as a
determinant in the cost of solar power and argue this has been overlooked in global assess-
ments of the cost of solar photovoltaic (PV). The authors then used differentiated WACCs to
determine the discount rate in the LCOE for every country in their sample. These WACCs
were shown to differ by as much as a factor 8 between countries, with the lowest values in
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Climatic Change (2021) 167: 26
developed countries such as Japan, the UK and the Netherlands and the highest values in
emerging and developing countries such as Brazil and Madagascar. The authors found that the
variation in costs of capital is more important for investment decisions in solar PV than the
variation in the quality of solar resources and that differences are driven by interest rates, debt
shares and systemic risk.
A similar argument can be made against assuming stable WACCs over time. In addition to
time-varying interest rate levels, risk (or risk perception) is an important driver in the cost of
capital. As low-carbon technologies require more upfront investment than high-carbon tech-
nologies, de-risking will benefit the former more and can be a strong driver for accelerating the
deployment of clean energy technologies (Schmidt 2014;SchinkoandKomendantova2016;
Komendantova et al. 2019). Differentiated WACCs per technology, country and over time can
also emerge because of heterogeneity on the finance supply side. Best (2017)examined
whether a country’s stock of financial capital (credit to the private sector by banks and
outstanding private debt securities) affects its ability to support the energy transition. The
author then argues that on the one hand, competition between capital providers helps to lower
the cost of capital when there is a large supply. On the other hand, if there is a shortage of
financial capital, then certain energy projects may not be economically viable due to high cost
of capital. The author finds that across countries, the availability of financial capital contributes
to investments in more capital-intensive energy technologies (i.e. technologies with a higher
share of capital costs in total lifecycle costs). For lower-income (developing) countries,
financial capital contributes to the transition from (traditional) biomass towards fossil fuels.
For high-income countries, financial capital supports transitions towards more capital-
intensive energy technologies such as wind energy.
Taken together, we conclude from the scientific evidence that WACCs for energy
investments in renewables or fossil fuel–based technologies are important drivers of
investment decisions. Moreover, it is a fact that WACCs differ across countries, between
technologies and over time. This fact, however, has not yet changed the way most
energy-economy system models treat financial markets. Very few models take a consis-
tent perspective and use differentiated costs of capital at the level of technologies,
countries and/or regions (Hirth and Steckel 2016;Paroussosetal.2019b; Bachner
et al. 2019). In line with traditional economic modelling, financial markets are typically
assumed to be highly integrated, such that capital is assumed to be globally available at a
uniform market rate of return. Consequently, there is no comprehensive and transparent
treatment of investment and technology risks in the available economy-energy models
(e.g. Bogdanov et al. 2019b;Kreyetal.2019).
Inarecentpaper,Eglietal.(2019) stressed the need to include differentiated WACC
assumptions by country and technology to improve the understanding of energy transition
pathways (see also Bachner et al. 2019). Egli et al. (2019) argue that using a uniform cost of
capital across countries in energy transition modelling can lead to distorted policy recommen-
dations. They show that the cost of capital is substantially lower in industrialized countries and
thus the LCOE of solar PV is substantially lower in those countries compared to low-income
countries (despite the higher solar radiation, technology efficiency and hence potential in these
countries). The authors conclude that energy system models that compare countries—and
particularly countries across different income and investment risk classes—should use
country-specific (and technology-specific) costs of capital. In their reply, Bogdanov et al.
(2019a) agree that the representation of costs of capital in energy system models should be
improved.
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Climatic Change (2021) 167: 26
There is a small and emerging body of literature presenting model-based evidence on the
role of WACCs in the transition towards a future electricity mix. Hirth and Steckel (2016), for
example, use the power market model EMMA to examine the role of both carbon taxes and
cost of capital in the development of the electricity mix. Their paper focuses on emerging
economies, which are characterized by high cost of capital, and they project that only a
combination of carbon pricing and low cost of capital leads to a significant share of
renewables in energy supply. Bachner et al. (2019) investigated determinants of the WACCs
in Europe’s electricity sector, building on a computable general equilibrium (CGE) model
coupled with electricity modelling (WEGDYN). They concluded that empirically more
realistic differentiated cost of capital for different renewable electricity technologies show
positive effects on the macroeconomic level and that uniform WACC assumptions in energy
models introduce significant biases in the results. Paroussos et al. (2019a)usedtheCGEmodel
GEM-E3 with differentiated assumptions on interest rates and concluded that the Italian
economy can benefit from the low-carbon transition in cases where Italian firms and house-
holds have access to low-cost financial resources. These papers show the relevance of
WACCs, but fall short of introducing WACCs that differ over time, space and technology
in a systematic and comprehensive way.
3 Methods and data
In this section, we describe the methodology we used to assemble the country-specific
WACCs for European countries for a range of fossil fuel (oil, gas, nuclear) and renewable
power technologies (hydro, solar PV, wind onshore and offshore, biomass).
2
3.1 Estimating WACC for energy technologies
To derive reasonable assumptions for the cost of capital of different power generation
technologies, an important differentiation must be made between the financing structures
used for different technologies. In industrialized countries, most fossil fuel-based power
plants (which are dispatchable) are being operated on a merchant basis, selling their
electricity to the wholesale market, and as such have been realized on the balance sheet
of utilities (IEA 2016; Diaz-Rainey and Premachandra 2017; Helms et al. 2020;Steffen
2020). Hence, the cost of capital for thermal and hydro power plants is determined by the
WACC of utilities (Cambini and Rondi 2010; Helms et al. 2020). Historically, the same
is true for hydro power plants. The majority of non-hydro renewable energy plants
(which are not dispatchable) are operated on the basis of a contractually or legally fixed
price per unit of electricity produced (e.g. in the form of a feed-in tariff, feed-in premium
or a long-term power purchase agreement allocated in renewable energy auctions) and
are realized on the balance sheet of non-recourse special purpose vehicles, financed
through project finance (Henderson 2016;OECD2016;Steffen2018). Hence, the cost of
capital for renewable energy plants is determined on the level of individual projects
(Steffen 2020). While in reality not all thermal and hydro plants exclusively rely on
corporate finance and not all renewable energy plants exclusively rely on project finance,
we believe that the differentiation is a reasonable approach to estimate WACCs for
2
Data and code can be made available upon reasonable request.
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Climatic Change (2021) 167: 26
different technologies based on available data. A more nuanced approach with respect to
financing structures by technology and country is an area for further research.
3.1.1 WACCs for utility power plants
To calculate a country-specific and technology-specific average utility WACC, we carried out
the following steps: First, we collected financial data for a representative sample of European
utility companies from Thomson Reuters Eikon (STOXX Europe 600 Utilities index), which
includes these firms’financial data on balance sheets, income statements and cash flow
statements. Second, we followed Kling et al. (2021) in calculating the overall WACC per
utility ifor the years t={2009,…,2018}:
WACCit ¼Lit *rDit þ1−Lit
ðÞrEit;ð1Þ
where Lit denotes the leverage ratio (total debt over total capital) for utility iin year t.We
measure the cost of debt, rD, by using interest expense of utility iin year tdivided by total debt
reported for that period:
rDit≡Interest expenseit
Total debtit
:ð2Þ
To obtain firm-level proxies for the cost of equity, rEit,werelyondividendpaymentsrelative
to the value of equity for utility iin year t:
rEit≡Total cash dividends paidit
Total equityit
ð3Þ
Third, we calculated the country-utility WACC by weighing each overall utility WACC by
their respective country exposure using the country share of utilities’revenue:
WACCct ¼∑N
i¼0sc
itWACCit;ð4Þ
where sc
it is the share of sales in country cfor utility iin year t. If the utility’sWACCwas
missing for a given year, it was replaced with the WACC from the previous year. This resulted
in 110 country-year observations based on 29 utilities active in 13 European countries. These
WACCs include the risk-free rate, rf, a premium for country and policy risk, pc, and for market
and technology risk, pT, as well as a residual of company-specific risks that we may assume is
random.
WACCct ¼rft þpTþpcþεct ð5Þ
where we consider the technology risk premium, pT, a weighted average of technology-
specific risk premia multiplied by the share of that technology in a country’s (utility)
generation mix. To estimate the overall utility WACC as well as technology-specific
utility WACCs for the remaining EU countries, we used the following procedure: From
the country-specific utility WACCs, we first subtracted the long-term government bond
rate to eliminate the risk-free rate and country risk component, rft +pc. We then ran a
simple ordinary least square (OLS) regression (without a constant because the shares
add up to 1) with WACC cleaned of country risk as the dependent variable and the
share of generation per source per country as our independent variables (Eurostat
2020a):
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Climatic Change (2021) 167: 26
WACCct−rfct −pc¼β0Shareofcoalct þβ1Shareofgasct þ
β2Shareofnuclearct þβ3Shareofhydroct þβ4Shareofotherct þϵct
ð6Þ
where share of coal, gas, nuclear, hydro and other renewables represent the share of these
technologies in country c’s power generation mix (expressed in GWh) at time t(taken
from the Eurostat database). The estimated coefficients (included in the online appendix)
now represent the average % point additional financing cost for utilities per 1% point
increase of the share of that source in the country’s electricity mix. An estimated
coefficient of 0.05 for example would imply that the WACC of a utility increases by
0.05% or 5 basis points for every 1% point increase in the share of that technology in its
portfolio (in country c). The obtained values for coal (0,051***), nuclear (0,049***) and
hydro (0,035***) are in the range of other earlier empirical calculations (Ecofys 2014)
and model assumptions (Bachner et al. 2019). Gas plants (0,010 but insignificantly
different from 0) are a lot cheaper than in other studies
3
. Our predicted utility WACCs
per technology and country (see Table 1) then represent the WACC for a hypothetical
case in which a utility invests in a technology in a country that produces all power with
that technology.
3.1.2 Project-level WACCs for non-hydro renewable energy technologies
For project-financed assets, it is not possible to derive the cost of capital directly from
financial market data. Financing is typically provided via bank loans and equity invest-
ment, for which the conditions are typically not disclosed (Krupa and Harvey 2017;
Steffen 2020). Steffen (2020) surveys a number of methods that have been proposed to
estimate cost of capital values for the purpose of model calibration, drawing on deal data,
expert surveys, the replication of auction results and financial market data as a proxy for
untraded assets. Particularly for wind onshore, the coverage is very good (data for 26 out
of 28 countries), whereas less data is available for offshore wind (only 5 out of 23
countries that border the sea) and for solar PV (data for Germany and Greece). Hence,
we start with data available from Steffen (2020) and impute missing values following an
approach proposed in that paper, using the average technology markup between solar
PV, wind onshore and wind offshore from OECD countries. This seems reasonable, as
technology differences in cost of capital appear quite stable across countries (Steffen
2020). Finally, we had to derive values for those countries for which not a single
technology value was available, Malta and Luxembourg, which have very small renew-
able energy markets in the EU context but whose values are required for completeness of
the WACC database. Using geographic and economic proximity as a heuristic, we
assume that the cost of capital for Luxembourg is the average of the values from
Belgium, Germany and France and that the cost of capital for Malta is the average of
values from Italy and Cyprus. All resulting values are given in Table 1. The obtained
values are slightly higher than earlier empirical calculations (Ecofys 2014)andmodel
assumptions (Bachner et al. 2019) potentially better reflecting market and policy risks in
the different countries.
3
We see that gas in particular has low average risk premia, which can be explained by the fact that costs for gas
plants are largely OPEX driven, and gas is considered a “bridge”option towards low-carbon transition and does
not involve high risks for stranded assets (like coal or nuclear).
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Climatic Change (2021) 167: 26
Table 1 Country- and technology-specific WACCs for fossil fuel and renewable power technologies
Country code Country Year Solar PV Wind onshore Wind offshore Hydro Biomass Coal-fired plant Gas plant Nuclear plant
AUT Austria 2015 4.20% 6.10% Not applicable 4.27% 5.73% 5.73% 1.77% 5.85%
BEL Belgium 2015 2.70% 3.50% 5.70% 4.36% 5.82% 5.82% 1.86% 5.94%
BGR Bulgaria 2015 7.70% 9.60% 10.60% 6.01% 7.47% 7.47% 3.51% 7.59%
CYP Cyprus 2015 7.50% 9.40% 10.40% 8.06% 9.52% 9.52% 5.56% 9.64%
CRO Croatia 2015 9.70% 11.60% 12.60% 7.07% 8.53% 8.53% 4.57% 8.65%
CZE Czech Rep 2015 5.70% 7.60% Not applicable 4.10% 5.56% 5.56% 1.60% 5.68%
DEU Germany 2015 2.90% 3.10% 6.30% 4.02% 5.48% 5.48% 1.52% 5.60%
DNK Denmark 2015 3.30% 5.20% 7.90% 4.21% 5.67% 5.67% 1.71% 5.79%
ESP Spain 2015 7.70% 9.60% 10.60% 5.25% 6.71% 6.71% 2.75% 6.83%
EST Estonia 2015 7.40% 9.30% 10.30% 4.96% 6.42% 6.42% 2.46% 6.54%
FIN Finland 2015 4.20% 6.10% 7.10% 4.24% 5.70% 5.70% 1.74% 5.82%
FRA France 2015 3.40% 5.30% 6.30% 4.36% 5.82% 5.82% 1.86% 5.94%
GBR UK 2015 3.60% 5.50% 11.90% 5.31% 6.77% 6.77% 2.81% 6.89%
GRC Greece 2015/2018 12.00% 22.90% 23.90% 7.71% 9.17% 9.17% 5.21% 9.29%
HUN Hungary 2015 9.00% 10.90% Not applicable 6.95% 8.41% 8.41% 4.45% 8.53%
IRL Ireland 2015 6.70% 8.60% 9.60% 4.70% 6.16% 6.16% 2.20% 6.28%
ITA Italy 2015 5.70% 7.60% 8.60% 5.23% 6.69% 6.69% 2.73% 6.81%
LTU Lithuania 2015 6.20% 8.10% 9.10% 4.90% 6.36% 6.36% 2.40% 6.48%
LUX Luxembourg 2015 3.15% 4.20% Not applicable 3.89% 5.35% 5.35% 1.39% 5.47%
LVA Latvia 2015 7.00% 8.90% 9.90% 4.48% 5.94% 5.94% 1.98% 6.06%
MLT Malta 2015 6.60% 8.50% 9.50% 5.01% 6.47% 6.47% 2.51% 6.59%
NLD Netherlands 2015 4.00% 5.90% 9.90% 4.21% 5.67% 5.67% 1.71% 5.79%
POL Poland 2015 7.00% 8.90% 9.90% 6.22% 7.68% 7.68% 3.72% 7.80%
PRT Portugal 2015 5.70% 7.60% 8.60% 5.94% 7.40% 7.40% 3.44% 7.52%
SVK Slovakia 2015 5.80% 7.70% Not applicable 4.41% 5.87% 5.87% 1.91% 5.99%
SVN Slovenia 2015 8.70% 10.60% 11.60% 5.23% 6.69% 6.69% 2.73% 6.81%
SWE Sweden 2015 5.90% 7.80% 8.80% 4.24% 5.70% 5.70% 1.74% 5.82%
ROU Romania 2015 8.80% 10.70% 11.70% 6.99% 8.45% 8.45% 4.49% 8.57%
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Climatic Change (2021) 167: 26
3.2 Financing experience
Research has shown that financing conditions for renewable energy technologies are dynamic
(Egli et al. 2018,2019;Egli2020; Steffen 2020). Specifically, Egli et al. (2018) have identified
an experience effect among renewable energy debt providers: They measured improving
financing conditions as debt providers (e.g. banks) learned and became acquainted with novel
technologies. Additionally, changing policies and the penetration of renewable technologies
may also change the (perceived) risk inherent in traditional power production technologies
over time (e.g. stranded assets and clean-up costs).
Here, we operationalize the concept of “financing experience”by calculating a hypothetical
experience rate on the full cost of capital and introduce dynamics in the WACCs for onshore
wind, offshore wind and solar PV by implementing this experience rate into the GEM-E3-
Power model. We assume that experience only happens on the debt margin, as shown in Egli
et al. (2018). This is a conservative approach for two reasons. First, on the equity side, margins
may decrease with increasing technology data, better assessment models and increasing
investor competition. Given the lack of data, however, we exclude such effects in our
approach. Second, as financing markets for these technologies become more mature, projects
are typically able to attract higher debt shares, resulting in higher leverage. As the cost of debt
is commonly lower than the cost of equity (because the risk to equity holders is greater than the
risk to debt lenders), assuming a constant leverage over time is again a conservative choice.
4
We decompose the after-tax WACC according to the standard formula:
WACCTt ¼DT;avg þmTt

1−τavg

LT;avg þET;avg 1−LT;avg

;ð7Þ
where DT,avg indicates the average cost of debt for technology, T;mTt is the debt margin; τavg is
the tax rate; LT,avg is the leverage ratio; and ET,avg is the cost of equity. As the starting year of
the data is 2015, we use 3-year averages (2014–2016) for the time-invariant components (D, τ,
L, E) and estimated debt margins for each year from 2000 to 2015, as in Egli et al. (2018). The
resulting WACC by technology Tand year tis a number to identify the effect of debt margin
decline on the overall WACC. We use a classic experience curve, where the cost of capital
decreases by a constant percentage, bT, for each doubling of cumulative technology deploy-
ment (Rubin et al. 2015):
WACC YTt
ðÞ
¼WACC YT0
ðÞ
YTt
YT0

bT
:ð8Þ
Solving our equation for bT,weget
bT¼
ln WACC Y Tt
ðÞ
WACC Y T0
ðÞ

ln YTt
YT0
 :ð9Þ
4
However, one has to note that we use the leverage from German projects (2014–2016), which is rather high.
Hence, the cost of debt is comparatively more important in determining the cost of capital than the cost of equity,
and changes in the debt margin are therefore more important.
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Climatic Change (2021) 167: 26
We populate Eq. (9) with historic solar PV and onshore wind deployment and the costs of
capital from 2000 to 2015 to calculate the experience parameter bT. Finally, the financing
experience rate (ER) on the overall cost of capital is given by Eq. (10):
ERT¼1−2bT
:ð10Þ
We then use the parameters bTto model future cost of capital developments for each country
dependent on domestic deployment YTt. Contrary to the estimated experience rate in Egli et al.
(2018), this approach operationalizes financing experience with respect to cumulative tech-
nology deployment (i.e. MW) instead of investment (i.e. USD). For onshore wind and solar
PV, increases in cumulative installed capacity are moderate, whereas increases may be larger
for offshore wind in some countries. In line with this, technology cost reductions (costs per
MW) may be more pronounced for offshore wind and our operationalization may slightly
overestimate cost of capital decreases for offshore wind. Furthermore, this approach
operationalizes experience on a country level (with respect to domestic deployment) instead
of globally as in Egli et al. (2018).
5
For onshore wind, the estimated learning rates range from
3.7% (global) to 5.7% (domestic), and for solar PV, they range from 4.6% (global) to 4.4%
(domestic). For implementation in our model and to illustrate the principle, we chose 5% as the
“financing experience rate”for onshore wind, offshore wind and solar PV technologies, which
is close to the median across different deployment specifications. Choosing a uniform “fi-
nancing experience rate”is a fair approximation in the context of Europe as the European RE
financing market is highly internationalized and cross-border financing in very common (see
e.g. data description in Egli et al. 2018).
3.3 Electricity system modelling
3.3.1 Model description
To illustrate the impact of using more realistic differentiated WACCs per country and
technology over time, we use the GEM-E3-Power model (Capros et al. 2013). This
model is a bottom-up, technologically rich electricity module describing the development
of the power generation mix under alternative policy assumptions in the 2015–2050
period. The electricity module is hard-linked with the core GEM-E3 model, a multi-
sectoral Computable General Equilibrium (CGE) that describes the complex interactions
between the economy, the labour market, the energy system and the environment and has
been extensively used in European Commission energy and climate policy impact
assessments, including the Energy Roadmap 2050 (European Environment Agency
2011), Climate Package for 2030 (European Commission 2019) and the recent Clean
Planet for All strategy (European Commission 2018). The hard link between the power
model and GEM-E3 improves the representation of the electricity sector in conventional
CGE models through constant elasticity of substitution (CES) functions, by integrating
explicit bottom-up modelling of power generation technologies. Here, we use the GEM-
E3-Power as a stand-alone modelling tool without linking it to the CGE model, covering
the 28 EU member states separately (as well as all G20 economies).
5
Note that Egli et al. (2018) show that the estimated experience rates on debt margins are robust to using
European instead of global investment figures.
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Climatic Change (2021) 167: 26
The GEM-E3-Power model simulates a competitive wholesale electricity market subject to
various constraints (i.e. on technology limitations, fuel potentials, storage, grids and systems) and
calculates investment in new power plants, which are influenced by sectoral electricity demand, load
duration curves, decommissioning of old and inefficient power plants (normal and accelerated) and
the already decided investment and policy measures. Electricity demand is set exogenously and is
derived from the official Reference scenario used by the European Commission (Capros et al. 2016).
The model decides on the optimal investment and operation of the electricity system in order to
minimize intertemporal total costs to produce electricity, including capital expenditures (CAPEX),
operation and maintenance (O&M) expenditures, carbon costs and costs to purchase fuels (used as
inputs to power plants), while meeting system constraints in each time segment (e.g. electricity
demand, technology potentials, system reliability and flexibility, power trade, resource availability,
storage and policy constraints). Thirteen power generation options are included in the model (coal,
oil, gas- and biomass-fired, nuclear, hydro, PV, wind onshore, wind offshore, geothermal, carbon
capture and storage (CCS) coal, CCS gas and CCS biomass) and compete based on LCOE to meet
the electricity requirements in each year and time segment. The WACC assumptions influence the
decision to invest in different power supply technologies, as, for example, high WACC values
negatively impact the competitiveness of capital-intensive technologies like PV and wind. The
model can represent various policy instruments that influence the development of the power system
in each country, such as emission trading scheme (ETS) carbon prices, phase-out policies (for coal
and nuclear), renewable subsidies or feed-in-tariffs and technology standards.
The modelling includes non-linear cost-supply curves for fossil fuels, nuclear plants
and renewable technologies. These are numerical functions with increasing slopes serv-
ing to capture exhaustion of renewable energy potential (e.g. for solar PV, wind and
hydro plants), take-or-pay contracts for fuels, the possible promotion of domestically
produced fuels, fuel supply response (increasing prices) to increased fuel demand by the
power sector, difficulties in developing CO2storage areas, acceptability and policies
regarding nuclear site development. The non-linear cost-supply curves are fully included
in the optimization of capacity expansion and operation of the electricity system in the
GEM-E3-Power model.
In line with empirical findings and state-of-the-art in energy-economy modelling, GEM-E3-
Power incorporates endogenous technological progress (especially for low-carbon technolo-
gies like wind and PV), through learning-by-doing curves that define the reduction in
technology costs gained through cumulative capacity installations reflecting learning from
experience and economies of scale in production (Krey et al. 2019;IEA2020). The learning
rates derive from extensive literature review and are presented in Paroussos et al. (2019b). The
integration of endogenous technological progress, grounded in empirical analysis, enables the
improved representation of cost reductions for low-carbon technologies by 2050.
All the above elements included in GEM-E3-Power (e.g. cost-supply curves, reliability,
technology and flexibility constraints, endogenous technological progress, electricity storage)
provide an improved representation of the capacity expansion and operation of the electricity
system, capturing its specificities, constraints and technology dynamics. The model is also
enhanced with the innovative feature of a “financing experience”curve in order to endoge-
nously capture the complex interlinkages between technology deployment, system investment
and financial learning (as presented in Section 3.2). In future extensions, similar dynamics in
utility WACCs might be explored for fossil fuel-based technologies facing increasing risks in
the transition. To avoid confounding these different effects, we abstract from such dynamics
here.
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Climatic Change (2021) 167: 26
3.3.2 Scenario descriptions
A series of specifications are considered to assess the impact of alternative WACC assump-
tions on electricity system development, technology uptake and electricity costs in EU
countries. These different WACC specifications are analysed for two stylized scenarios that
reflect different levels of climate policy ambition. In the “Reference”scenario, ETS and other
climate policies (i.e. Energy Efficiency Directive, measures to support renewable energy
sources [RES] expansion, nuclear-related limitations) continue to 2050 in line with current
legislation (Capros et al. 2016)(Ref).Inthe“Ambitious Decarbonization”scenario, climate
policies’stringency increases to meet the EU long-term mitigation goals with emissions
reductions of 85–95% by 2050 from 1990 levels (European Commission 2018) (Amb). In
both scenarios, ETS prices are the primary policy lever towards electricity sector
decarbonization, as they incentivize the uptake of low and zero-carbon technologies. With
ambitious climate policies, ETS prices increase gradually to 333€/tnCO2in 2050 (in line with
European Commission, 2018), while in the Reference scenario they rise to about 89€/tnCO2in
2050 (see Figure 1). The Ambitious Decarbonization scenario requires a rapid increase in ETS
price in the period by 2035 to incentivize the massive upscaling of low-carbon technologies; in
the longer term, technology learning leads to increased competitiveness of RES technologies
without the need for stronger ETS price signals.
Concerning alternative WACC assumptions, three specifications are considered. The first is
based on the current/default model setting without differentiation in WACCs, using a uniform
WACC value of 8.5% for all countries and technologies, as in leading energy-economy
models (IEA 2020) (UNI). In the second specification, the differentiated WACC values as
calculated in Section 3.1 are used in the GEM-E3-Power model for specific countries and
technologies
6
and are allowed to influence investment decisions (DIFF). The last specification
aims to explore the role of financing experience in technology uptake and costs by explicitly
15
89
15
333
0
50
100
150
200
250
300
350
2020 2025 2030 2035 2040 2045 2050
STEecirp[€]
Ambious decarbonizaon
Referen ce
Fig. 1 Evolution of ETS carbon price in Reference and Ambitious Decarbonization scenarios (€/tnCO2)
6
In this scenario, WACC values are kept frozen until 2050 at their 2015 levels. The only exception is Greece,
where some adjustments are implemented, to reflect the recent reduction in risk premia and WACCs in the last 4
years following the recovery of the Greek economy.
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Climatic Change (2021) 167: 26
integrating financing experience curves for PV, wind onshore and offshore in GEM-E3-Power
(DIFF+FE). In this specification, country WACCs for these technologies are assumed to
decline over time induced by higher cumulative deployment as described in Section 3.2.
4Results
4.1 Impacts on technology uptake and investment decisions
Our scenario results show that differentiated WACC assumptions have large impacts on
investment decisions and technology uptake both in the medium and in the long term. The
implications differ by country and depend highly on the WACC values for power generation
technologies and mostly for solar PV and wind, which account for most of the EU’s electricity
investment. As expected, the impacts are more pronounced in countries that have very
divergent WACC values relative to the uniform benchmark. Below, we discuss the impacts
of using differentiated WACCs in the Reference scenario for the EU overall and for two
specific countries, Germany and Greece, where country risk premia deviate the most and
WACC estimates are based on a broad empirical basis (see Section 3.3.2).
At the EU28 level, we observe that the switch from uniform to differentiated WACCs leads
to lower future shares of fossil fuel-based plants and higher shares of renewables in both the
Reference and the Ambitious Decarbonization scenarios. In the Reference scenario, the uptake
of RES is higher in the differentiated WACC case, with the RES share increasing to 45% in
2030 and 66% in 2050 (relative to 40% and 61%, respectively, in the uniform WACC case).
This effect is mainly driven by a substitution of gas capacity by more solar PV and wind power
plants. WACC values for PV and wind onshore are lower on average (and in most European
countries) than the default WACC value of 8.5%. While gas-fired power plants have even
lower WACCs in most EU countries, the impact of a lower WACC is larger for the more
capital-intensive clean energy technologies. An exception is 2050 in the Ambitious
Decarbonization scenario, which has almost zero fossil fuel capacity (without CCS) across
all specifications. There the differentiated WACCs lead to a substitution of offshore wind with
solar PV. Introducing financing experience affects all RES WACCs similarly and therefore
only has a modest effect on the technology mix.
Concerning Germany, its electricity mix is set for a rapid transformation away from coal
and nuclear combined with a massive uptake of wind and PV (coupled with gas in the
Reference simulations). As the country has very low WACCs for most power generation
technologies, reflecting its credible policy and financial environment with limited risks (Egli
et al. 2018), the deployment of capital-intensive options accelerates when WACCs are
differentiated (Figure 2). This means a faster uptake of solar PV and wind onshore, combined
with a reduced contribution of gas, which is characterized by lower CAPEX and high
operation and fuel costs. The effect is more pronounced in the Reference scenario compared
to the Ambitious Decarbonization scenario.
Concerning Greece, the electricity sector is in the middle of a transition towards renewable
energy but is hindered by the high (perceived) risks and WACCs facing new investment. The
recent Greek policy plans to phase out lignite-based electricity production is reflected in the
Reference case, which shows a rapid reduction of carbon-intensive generation (lignite and oil)
and increased contribution of natural gas, solar PV and wind. The very high WACC values
(see Angelopoulos et al. (2017) for a detailed analysis of the causes) negatively impact the
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Climatic Change (2021) 167: 26
competitiveness of capital-intensive low-carbon options (like wind and PV) and thus in the
Reference scenario the share of renewable energy in 2050 is lower with differentiated WACCs
(72% compared to 85%). On the other hand, gas deployment is higher to meet electricity
requirements, implying a large role for gas as the “bridging”fuel.
Generally, the impacts of using differentiated WACCs are more pronounced in the Refer-
ence case, as the high ETS carbon prices imposed in the decarbonization context have a huge
impact on relative technology competitiveness and constrain the portfolio of competitive
technologies. As observed in Figure 3, this is a robust finding for all EU countries. In countries
with low WACC values (i.e. Germany, Luxembourg, Belgium, Austria, Denmark, the UK and
Italy), the shares of renewable energy are higher in both the Reference scenario and the
Ambitious Decarbonization scenario. In contrast, RES competitiveness and uptake are lower in
countries with high WACC values, including Greece, Ireland, Bulgaria, Croatia and Romania.
GEM-E3-Power shows that other capital-intensive technologies may also benefit in the
differentiated WACC context in cases in which they have lower WACCs than RES (i.e.
nuclear in Finland and Hungary or gas-fired CCS in the Netherlands). The inter-substitution
among RES is mostly driven by the relatively lower WACCs of PV relative to wind onshore
and offshore in most EU countries.
4.2 Impacts on emissions and electricity costs
As shown, WACC assumptions change the technology portfolios. Consequently, they will also
have an impact on both CO2emissions and electricity costs. Countries with low WACC values
for low-carbon technologies show more rapid reduction of emissions and more moderate
electricity system costs as the uptake of RES accelerates.
Starng
point
Reference
Scenario
Ambious Decarbonizaon
Scenario
2020 2030 2050 2030 2050
EU28
Germany
Greece
UNI DIFF DIFF+FE DIFF+FEDIFFUNI UNI DIFF DIFF+FE UNI DIFF DIFF+FE
UNI DIFF DIFF+FE UNI DIFF DIFF+FE UNI DIFF DIFF+FE UNI DIFF DIFF+FE
UNI DIFF DIFF+FE DIFF+FEUNI DIFF UNI DIFF DIFF+FE UNI DIFF DIFF+FE
0%
20%
40%
60%
80%
100%
UNI
0%
20%
40%
60%
80%
100%
UNI
0%
20%
40%
60%
80%
100%
UNI
Coal
PVGas
CCSNuclear Biomass
Hydro
Wind onshore
Wind offshore Other
Fig. 2 Electricity generation shares by technology for all EU-28 countries, Germany and Greece
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Climatic Change (2021) 167: 26
First, with respect to emissions, Figure 4shows that the impact of using differentiated
WACCs (including financing experience) is more pronounced in the Reference case, with 15%
lower EU electricity-related CO2emissions in 2030 (24% in 2050) relative to the uniform
WACC scenario (i.e. around 65–110 Mt lower). Most of this reduction comes from large-
emitting economies with low WACC values for PV and wind (i.e. Germany, the UK, Italy, the
Netherlands, Portugal, Belgium and Austria); in contrast, emissions are higher in countries
where WACCs for low-carbon technologies are high (i.e. Ireland, Greece and Portugal). While
the overall emission impact of differentiated WACCs is very low in the Ambitious
Decarbonization scenario, there are nonetheless important spatial/country effects with lower
emissions in lower-risk countries such as France, Germany or the UK and substantially higher
emissions in higher-risk countries such as Greece and Spain.
Second, with respect to electricity system costs (which include the costs for storage),
Figure 4shows the same inverse-U pattern across time for all specifications and scenarios.
The introduction of high ETS prices would drive a restructuring of the electricity sector,
induced by increased uptake of low and zero-carbon technologies (mostly solar PV and wind).
In the Ambitious Decarbonization scenario, investment requirements and system CAPEX
would increase (despite RES technology progress), but O&M and fuel costs are lower
compared to Reference levels due to the rapid phase-out of fossil fuel–fired power plants.
Overall, the cost impacts of decarbonization are relatively modest in most EU countries, with
Fig. 3 Difference in renewable energy share between DIFF+FE and UNI by country in 2030 shown for the
Reference scenario and the Ambitious Decarbonization scenario. Reading example: +15% means that the RE
share is 15% higher in a given country in 2030 when using differentiated costs of capital including financing
learning compared to uniform costs of capital
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Climatic Change (2021) 167: 26
only limited exceptions in countries that have low potential for key mitigation options like
wind and PV.
The cost impacts of using differentiated WACCs (including financing experience) are
similar across both scenarios (both relative and absolute). As in most countries, average
WACCs are lower relative to the uniform value of 8.5%, and electricity system costs tend to
be lower both in the Reference and in the Ambitious Decarbonization context. Electricity
system costs are on average 0.011 €/KWh and 0.012 €/KWh lower, respectively. In 2018, the
EU net electricity generation totalled 2806 TWh (Eurostat 2020b). Assuming the more realistic
differentiated costs of capital would thus mean a cost reduction at the EU level of €30.87
billion and €33.67 billion per annum until 2050, respectively. Moreover, spatial patterns are
also similar across both scenarios: With the exception of Greece, all countries either encounter
substantially lower or roughly similar costs when accounting for WACC differences. The
reduction is particularly large in lower-risk countries (e.g. Germany, Belgium, the
Netherlands).
The integration of financing experience curves in the model leads to a further reduction in
electricity system costs, especially in countries that show a large reduction in WACCs due to
high-capacity growth over the 2020–2050 period. At the EU level (Figure 4), financing
experience leads to an additional reduction of average power generation costs by 3.5% in
2050 in the Ambitious Decarbonization scenario with differentiated WACCs. This cancels out
about 15% of the cost increases induced by ambitious decarbonization policies, bringing the
average electricity generation costs closer to the Reference scenario levels.
Fig. 4 Electricity-related CO2emissions and costs (excluding T&D) for EU-28. Panels on the left show the
evolution over time. Maps on the right display percentage differences by country in 2050 from DIFF+FE to UNI
for the Reference scenario (middle) and the Ambitious Decarbonization scenario (right). Reading example for the
maps: −20% means that the CO2emissions (or costs) are 20% lower for a given country in 2050 when using
differentiated costs of capital including financing learning compared to uniform costs of capital
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Climatic Change (2021) 167: 26
5 Discussion and conclusions
To date, energy and electricity system models as well as more extensive general equilibrium
and integrated assessment models are used intensively to guide Europe’s Green Deal and
energy transitions in Europe and beyond. These models show that a near-zero emission energy
system is feasible in Europe, and although massive investment by public and private sectors is
required to achieve the ambitious policy targets, the transition also seems economically
feasible (Fragkos and Kouvaritakis 2018; Zappa et al. 2019; European Commission 2020b).
These projections, however, rest on simplifying assumptions on financial markets (Egli et al.
2019). Many models assume that the costs of capital are uniform across countries and
technologies and over time (Bogdanov et al. 2019b;IEA2020). In this paper, we have shown
that this assumption finds little support in the data, and more importantly, relaxing it changes
model-based projections significantly.
When we allow for country-level differences in the cost of capital, the cross-country
variation, even in such an integrated market as the European Union, can drive the allocation
of capital away from a cost-effective energy transition pathway. Capital-intensive renewables
in particular tend to cluster in low-risk countries (Bachner et al. 2019). For wind power, this
may coincide well with the physical availability of wind resources, but for solar PV, this
clearly is not the case, as some southern EU countries with favourable PV potentials tend to
face high country risks and consequently high WACCs (e.g. Greece) (Steffen 2020).
Our paper has important policy implications. First and foremost, it should serve as a caveat
to policymakers taking model simulations as accurate and precise projections of the impact of
their policies; the projections in a model simulation are only as relevant and accurate as the
assumptions underlying the model. When model outcomes prove very sensitive to alternative
(and more realistic) assumptions, as we have shown in this paper they do, then caution is
warranted. Assuming a uniform WACC for all countries or technologies will lead to under-
and/or overestimation of both the speed and costs of transition (as shown in Figure 4)aswell
as to sub-optimal investment decisions for energy technologies and policies. Thus, the
integration of WACCs differentiated by country and technology in energy-economy system
models will improve their representation of investment decisions and ensure consistency with
real-world data and observations.
The simulations in this paper also bring into focus new policy levers. Our simulations show
that differentiated WACCs are likely to lower the overall costs of the low-carbon transition in
the EU. Hence, countries with lower WACCs can opt for a more rapid and ambitious transition
than expected without incurring higher costs, reducing the overall emissions faster. Moreover,
financial experience contributes to lowering transition costs in all countries. However, the
costs of capital for different technologies in different countries of the EU remain very sensitive
to fiscal and financial regulations set directly and indirectly by policymakers at the national
and EU level.
Implementing policies that cause WACCs for renewable energy production to converge to
the lowest levels in the EU, especially for projects in member states with ample underdevel-
oped renewable energy resources, can give the energy transition a boost at relatively low costs.
As long as country risk determines (to a large extent) where renewable energy is penetrating
fastest, a policy that would make such premia converge is an effective tool to promote a more
efficient transition. As part of its Green Deal, Europe might therefore consider setting up a
system to equalize WACCs for RES across the European Union by pooling the risks (Agora
Energiewende 2018). Similarly, the financing experience effects can be considered positive
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Climatic Change (2021) 167: 26
externalities (as e.g. technology learning) that the entire market benefits from but that private
investors have no incentive to finance. This could justify policies that foster data exchange
among investors or provide an important co-benefit to policies that increase deployment (e.g.
public (co-)investments; see Deleidi et al. 2020) as investment experience reduces WACCs
through lower risk premia.
Given the capital intensity of renewable energy production, we claim that such policies can
be efficient and complement energy and climate policies relying on carbon pricing, carbon
taxes and outright subsidies for renewable production per MWh that aim to make renewable
energy competitive on LCOE terms. The cost structure of most renewables implies that their
competitiveness is strongly affected by the cost of capital. Policymakers can directly affect this
key driver through institutions like the European Investment Bank, and thereby affect private
investment decisions in the energy sector.
The analysis in this paper also opens up avenues for future research. First, one could
implement WACCs in more complex and sophisticated models, capturing the interlinkages
between the energy transition and the macro-economy (including the links between the
financial sector and the real economy). In particular, the differentiation of WACCs in leading
energy-economy and integrated assessment models will significantly improve their real-world
relevance and the accuracy of their projections on cost-optimal allocation of low-carbon
investment to different technologies, sectors and regions (such as energy efficiency investment
in the built environment and decarbonization of transport). Second, the WACC estimations
introduced here could be further improved and extended, for example by applying the method
to non-EU countries, which will potentially show larger influence (e.g. in low-income
countries that have very high WACCs). There is, of course, no substitute for collecting more
and better data on WACCs, and we also believe our estimation methods merit further scrutiny
and should not be taken at face value. Third, this paper focuses on power sector technologies
only. In future work, scholars should also estimate costs of capital and endogenize financial
experience curves for other low-carbon technologies that are important for the transition (i.e.
electric vehicles, batteries, heat pumps, biofuels and green hydrogen).
Supplementary Information The online version contains supplementary material available at https://doi.org/
10.1007/s10584-021-03163-4.
Acknowledgements This research was conducted as part of the EU’s Horizon 2020 research and innovation
programme, project INNOPATHS (grant agreement No. 730403), and project GREENFIN (European Research
Council, grant agreement No 948220). As part of the INNOPATHS project, it was partly supported by the Swiss
State Secretariat for Education, Research and Innovation (SERI) under contract number 16.0222. The opinions
expressed and arguments employed here in do not necessarily reflect the official views of the Swiss Government.
Mette Huijgens and Nielja Knecht provided excellent research assistance.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and
indicate if changes were made. The images or other third party material in this article are included in the article's
Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included
in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or
exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy
of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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