Question:
You are given the full text of a scientific research paper.

    Your task is to generate a structured **policy brief** that includes the following components:

    - A concise, descriptive **title** that captures the central theme or policy issue addressed in the paper (do not label it with “Title:”).
    - **Policy Problem** - What societal or strategic challenge is the paper addressing?
    - **Scientific Research Findings** - What are the key results relevant to this issue?
    - **Scientific Research Study Methods** - What methodology or data supports these findings?
    - **Policy Implications** - How might the findings guide or influence policy?

    Write each section in a clear, accessible style suitable for policymakers. Avoid overly technical language, but use essential terms when necessary. Structure the output as a labeled five-part policy brief document.

    Scientific Research Paper:
    Articles
https://doi.org/10.1038/s41560-018-0277-y
Energy Politics Group, Department of Humanities, Social and Political Sciences, ETH Zurich, Zürich, Switzerland. *e-mail: florian.egli@gess.ethz.ch;
bjarne.steffen@gess.ethz.ch; tobiasschmidt@ethz.ch
K
eeping climate change within safe limits and achieving the
goals of the Paris Agreement require fast and ample redirec-
tion of financial flows towards low-carbon technologies1–3. As
approximately two-thirds of global greenhouse gas emissions stem
from the energy sector4, the rapid deployment of low-carbon energy
technologies, such as renewable energy technologies (RETs), is cru-
cial for emissions reductions5. Solar photovoltaics (PVs) and wind
will probably play central roles in this transition6. Importantly, as
RETs are more capital intensive than fossil fuel technologies, large
portions of their life-cycle cost are incurred upfront and need to
be financed7,8. Extant literature has established the adverse effect of
high costs of capital on the levelized costs of electricity (LCOEs) for
RETs9, CO2 abatement cost7,10 and RET deployment in integrated
assessment models11. Consequently, high costs of capital are consid-
ered major obstacles to RET deployment12,13. By the same logic, low
costs of capital can contribute to the observed cost reductions for
solar PV and wind energy14–16. Financial markets thus have a con-
straining or enabling role in the low-carbon energy transition17–20.
While individual investors know their cost of capital (CoC), typi-
cally this information remains unavailable to researchers21,22, espe-
cially concerning developments over time. This paper addresses this
gap, by analysing the German solar PV and onshore wind power
financing market, which has a particularly long investment his-
tory23. We exploit the fact that utility-scale renewable energy invest-
ments in Germany are almost exclusively realized in project finance
structures24 (see Supplementary Note 1 for a description), in which
the costs of capital reveal unbiased information about the under-
lying investment projects and technologies25. We proceed in four
steps. First, using newly compiled project data, we depict the CoC
and its components and analyse the changes over 18 years. Second,
we use qualitative insights from in-depth interviews with 41 invest-
ment professionals to identify the drivers of the observed changes
in financing conditions. Third, we quantify an experience effect
within the renewable energy finance industry, leading to lower costs
of capital. Fourth, we quantify the effect of the observed changes in
costs of capital on LCOEs. The methods are structured along the
same four steps. We find that the CoC declined by 69% for solar
PV and by 58% for wind onshore projects between the early period
of the RET finance industry (2000–2005) and 2017. For both tech-
nologies, the cost of debt decreased more than the cost of equity.
Focusing on the cost of debt, we identify and estimate a financing
experience curve. For each doubling of cumulative investment, the
debt margins (see Supplementary Table 1 for definitions of financial
terms) decreased by 11% for both technologies. During the same
time, we observe a decline in the general interest rate resulting in
lower costs of capital that had a substantial effect on the economic
attractiveness of RETs. Finally, we estimate that 41% of total solar
PV LCOE reductions and 40% of wind onshore LCOE reductions
between 2000–2005 and 2017 were due to lower financing costs.
These result from three effects: lower capital expenditures (CAPEX)
to be financed (strongest effect for solar PV), lower general interest
rate (strongest effect for wind onshore), and financing experience.
We conclude with implications for researchers and policymakers.
Changes in financing conditions
In the first step, we analyse the temporal dynamics of the CoC and
its components (see Methods). We compile data on the financing
conditions of 133 representative utility-scale renewable energy proj-
ects, undertaken between 2000 and 2017, to establish the temporal
dynamics of costs of capital for solar PV and wind onshore. The
project data are provided by leading renewable energy investors,
covering lead arrangers responsible for 85% of the solar PV and 80%
of the wind onshore investment sums between 2000 and 2017 (see
Supplementary Fig. 1). Figure 1 displays the cost of debt, the cost
of equity and the CoC for all projects in our data set. Both solar
PV and wind onshore projects experienced substantial decreases
in costs of capital. While some variance in CoC is normal due to
slightly different project conditions, the data show a clear decrease
in the lower bound for cost of debt and cost of equity over time. The
lower bound of cost of debt dropped from around 5% to less than
0.5% for both technologies. Lower bound equity returns fell from
around 10% to below 4%.
Figure 2 draws on the same data as Fig. 1 to calculate the average
across projects and compares the early period of the RET finance
industry (2000–2005) to 2017. It first shows that the cost of debt
decreased more than the cost of equity in relative terms and that
A dynamic analysis of financing conditions for
renewable energy technologies
Florian Egli   *, Bjarne Steffen   * and Tobias S. Schmidt   *
Renewable energy technologies often face high upfront costs, making financing conditions highly relevant. Thus far, the dynam-
ics of financing conditions are poorly understood. Here, we provide empirical data covering 133 representative utility-scale
photovoltaic and onshore wind projects in Germany over the last 18 years. These data reveal that financing conditions have
strongly improved. As drivers, we identify macroeconomic conditions (general interest rate) and experience effects within the
renewable energy finance industry. For the latter, we estimate experience rates. These two effects contribute 5% (photovoltaic)
and 24% (wind) to the observed reductions in levelized costs of electricity (LCOEs). Our results imply that extant studies may
overestimate technological learning and that increases in the general interest rate may increase renewable energies’ LCOEs,
casting doubt on the efficacy of plans to phase out policy support.
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decreases in both components were more pronounced for solar PV
than for wind onshore. However, the project CoC also depends on
the leverage and the corporate tax rate. Leverage denotes the share
of debt of the total investment sum (see Methods). As equity bears
the first project losses, a higher leverage is an indication for lower
project risk. For both technologies, the leverage increased, reaching
over 80% debt financing in 2017 (see Supplementary Fig. 2). During
this period, the German corporate tax rate decreased from 41% to
30%, resulting in relatively higher costs of debt as interest rate pay-
ments are deductible from taxable revenues.
Figure 2c,d summarizes the resulting after-tax CoC. The CoC in
2017 was in the range of 1.6% (solar PV) to 1.9% (wind onshore),
corresponding to a low-risk corporate bond of a financial service
firm (BB+ to BBB)26. Stated differently, CoC declined by over two-
thirds (3.5 percentage points) for solar PV projects and more than
half (2.6 percentage points) for wind onshore projects. While the
CoC for solar PV projects in 2000–2005 was higher than for wind
onshore projects, the former had a lower CoC than the latter in
2017. Similar trends were observed for additional financial indica-
tors, such as loan tenors and debt service coverage ratios (DSCRs;
see Supplementary Fig. 2). Over our study period, the duration of
the feed-in tariff stayed constant at 20 years. Banks offering longer
loan tenors is therefore an indication of higher confidence in the
project. The DSCR is a measure of project cash flows available to
0
5
10
15
2005
2000
2015
2010
2020
Cost of capital (%)
0
5
10
15
2010
2005
2000
2015
2020
Solar PV
Wind onshore
Cost of equity
Cost of debt
Cost of capital
a
Year
b
Year
Cost of equity
Cost of debt
Cost of capital
Fig. 1 | CoC over time. a,b, Cost of debt, cost of equity and CoC in Germany for 43 solar PV (a) and 78 wind onshore (b) projects between 2000 and 2017
(N = 121). We show CoC numbers only for projects where cost of debt and cost of equity, as well as capital structure (leverage), are known (29 solar PV
and 26 wind onshore projects).
9.3
4.8
5.5
1.5
0
2
4
6
8
10
–49%
–72%
Cost of equity
Cost of debt
2.3
2.8
2.2
2.5
1.2
0.6
6
0
2
4
0.9
5.1
1.6
–69%
Avg 2000–2005
Avg 2000–2005
2017
2017
change
change
9.5
5.4
4.8
1.6
0
2
4
6
8
10
–43%
–67%
2.1
2.4
1.4
2.3
1.1
1.0
4
0
6
2
1.9
0.9
4.5
–58%
Unleveraged,
no tax
considered
Leveraged,
after-tax
Solar PV
Wind onshore
a
b
c
d
Lev. = 0.7
Tax = 41%
Lev. = 0.87
Tax = 30%
Lev. = 0.75
Tax = 41%
Lev. = 0.82
Tax = 30%
Tax
Tax
%
%
%
%
Fig. 2 | Components and dynamics of CoC. a,b, Changes in unleveraged cost of debt and cost of equity for solar PV (a) and wind onshore (b) projects.
c,d, Changes in leveraged (after-tax) CoC for solar PV (c) and wind onshore (d) projects. The tax effect was due to a decrease in the corporate tax rate
that led to a smaller cost reduction from tax-deductible debt interest payments.
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pay debt obligations, namely the principal repayment and interest
rate payments. Lower DSCRs can thus be interpreted as an addi-
tional indication for lower project risk.
Drivers of change
In the second step, we use qualitative interviews with investment
professionals (N = 41, see Supplementary Table 3) to inductively
reveal and understand the underlying drivers of the observed
changes in financing conditions27. Averaging more than ten years of
renewable energy investment experience, the interviewees demon-
strate an in-depth understanding of the market dynamics over time.
From these interviews, we distil drivers of CoC reductions on three
nested levels: the macroeconomic environment (economy), the
renewable energy sector and the renewable energy finance indus-
try (see Methods). The latter two are related to experience gained
through deployment and financing of RET. Figure 3 illustrates the
main drivers on the three identified levels.
On the economy level, expansive monetary policies in the after-
math of the 2008–2009 financial crisis resulted in low refinancing
costs for banks, which decreased the CoC of the economy28. The
large supply of capital increased the pressure on bank fees and even-
tually lowered them too. At the same time, extensive bank lending
tended to lead to overconfident credit issuance, thereby increasing
default rates29,30. The extensive lending made the evaluation of com-
panies’ credit eligibility more difficult and thereby increased the
investment attractiveness of projects with predictable cash flows,
such as RET assets in project finance structures.
On the renewable energy sector level, technology deployment
had a favourable impact on financing conditions. As more renewable
energy projects were undertaken, technologies became more
mature (that is, more reliable). In parallel, the availability of data
on technology performance made an assessment of this increasing
reliability possible, while financial data showed low default rates.
Together with a higher confidence in partners for construction
and operation of RET assets, as these companies had increasingly
established track records, these developments provided impetus
for investment professionals to convince their boards to invest in
renewable energy assets. According to our qualitative results, the
deployment effect was more pronounced for solar PV than for
wind onshore projects because of wind turbines’ larger operational
risk due to their design complexity31 and moving parts. Moreover,
wind resource availability is more difficult to predict than solar
irradiation. Partly as a result of these factors, the CoC decreased
faster for solar PV projects than for wind onshore projects. Finally,
stable and reliable RET support policies were a prerequisite for
RET investment in Germany in the past23. However, gradually,
some RET projects are being partly exposed to market prices32,33,
which is reflected by few shorter loan tenors and higher DSCRs (see
Supplementary Fig. 2).
On the level of the renewable energy finance industry, investors
benefited from growing RET markets and subsequent learning-by-
doing (for example, better risk assessment)34. Larger markets allowed
banks to form in-house project finance teams specialized in RETs.
The knowledge and data that these teams accumulated allowed for
a more accurate technology assessment. Consequently, project risks
declined. For example, as the market had accumulated experience
on historical wind speeds, investors shifted from calculating project
returns on wind resource estimations with 90% certainty (90th per-
centile of the distribution, p90) to trusting the median (p50). While
the observed increases in loan tenors and decreases in DSCRs (see
Supplementary Fig. 2) confirm lower project risk, we see two diver-
gent trends in project leverage. On the one hand, investors advanced
to higher leverages to increase returns on equity; on the other hand,
some investors started to accept lower leverages to place their equity
in a market environment with few renewable energy investment
opportunities on offer.
Moreover, the investor ecosystem matured and competition
increased. In a maturing investment market, institutional investors
(for example, insurers and pension funds) started to perceive renew-
able energy project finance as an attractive asset class. Institutional
investors usually demand lower returns and larger project sizes than
smaller early-stage investors35. The capital inflow from the new
group of institutional investors hence created an incentive to build
larger projects and increased competition for projects, which gener-
ally compressed debt margins further. While lower margins lead to
lower LCOEs and are thus potentially conducive to RET deploy-
ment, some investors fear that the increasing capital inflow could
create an asset bubble with financing conditions that would no lon-
ger reflect project risks. Last, the use of standardized deal structures
facilitated the investment process and contributed to more efficient
financing markets with lower margins.
Financing experience rates
The third step of our analysis focuses on the effects that are related
to experience with deployment and financing of RETs (see Fig. 3).
The innovation literature has identified a roughly constant percent-
age unit cost decline—the experience or learning rate—with each
doubling of cumulative production (Wright’s law, see Methods)36.
This experience effect is a well-known characteristic of RET invest-
ment cost14–16,37,38. Our results from the second step demonstrate that
experience matters for the renewable energy finance industry. We
hence propose a financial experience effect analogous to Wright’s
law39 and estimate a corresponding experience rate.
To identify the experience rate, we focus on debt, because this
is where most cost reductions have occurred (compare Fig. 2). We
Economy
Renewable
energy
sector
Renewable
energy
finance
industry
Drivers of changes in financing conditions
Capital markets: low-cost liquidity, few
investment alternatives, low return
expectations
Banks: low-cost refinancing, low bank
fees, preference for project finance
Availability of performance data:
accumulated operation experience of
RET assets
Technology reliability: proven track
record of technology, low default rates of
projects
Support policies: regulatory
environment; for example, introduction of
exposure to market risks
Learning by doing: in-house RET
knowledge, better risk assessment and
due diligence processes
Investment ecosystem: standardized
investment structures, frame contracts,
partner networks
Market entry of investors: new investor
types (for example, large banks, insurers,
pension funds), increasing investor competition
Level
Drivers related to experience with deployment and financing of RET
Drivers related to general economic development
1
2
1
2
Fig. 3 | Drivers of changes in financing conditions in a nested hierarchy. The
general economic environment led to more favourable financing conditions
(for all sectors) over the period of our study. All drivers in the renewable
energy sector and the renewable energy finance industry contributed to
more favourable financing conditions (in the renewable energy sector) over
the period of our study, with the exception of changes to support policies,
which potentially introduce new uncertainties into the RET sector.
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analyse three debt indicators that reflect investment safety mar-
gins, namely, the debt margin, DSCR and loan tenors. For riskier
projects, investors demand higher debt margins as compensation,
a higher DSCR to create a buffer in case of cash flow complica-
tions, and a lower loan tenor to reduce the risk exposure to a shorter
period. More experienced investors should be able to judge invest-
ment projects more accurately, thereby reducing the required safety
margins and generating an empirically observable experience effect.
Figure 4 shows the experience rates for the three variables. We find
an experience rate of 11% on the debt margins of both technologies.
We also detect experience rates of 13% for the DSCR of solar PV
projects and of 17% for the DSCR of wind onshore projects (see
Methods). Regarding the loan tenors, we find an experience rate
of −3%, that is, increasing loan tenors with increasing experience.
However, this finding is insignificant for wind onshore projects.
In sum, the third step of our analysis establishes the statistical sig-
nificance of the experience effect in renewable energy financing, as
found qualitatively in the second step. Increased RET deployment
contributes to better financing conditions.
In the following, we compare the experience effect with the exog-
enous effect (economy level) from changes in general interest rates.
The cost of debt of a RET project can be decomposed into two ele-
ments covering the baseline country risk and project specific risk40.
Figure 5 shows the yields of a 10-year German government bond
(the best proxy for baseline country risk) and the estimated debt
margins (the best proxy for project specific risk). While the bond
yields are driven by monetary policy and exogenous to renewable
energy deployment, the debt margins reflect dynamics related to
experience with deployment and financing of RET.
Three observations can be made in Fig. 5. First, the change in debt
margins seems small compared with government bond yields but is
economically substantial. While government bond yields decreased
by 5 percentage points, debt margins have declined by 1.5 percentage
points for solar PV projects and 1 percentage point for wind onshore
projects between 2000 and 2017. For comparison, this experience-
driven decrease corresponds to a change in the corporate ratings of a
financial service firm from B+ to AAA for solar PV or from BBB to
AAA for wind onshore26. Second, Fig. 5 reveals different dynamics
between the two technologies. Due to larger increases in cumulative
investment for solar PV, its debt margin decreased more than was the
case for wind onshore projects. As a relatively novel technology, solar
PV projects were perceived riskier and thus charged with a higher
debt margin in 2000. In 2017, investors no longer make a difference
and charge almost identical margins. This catch-up of solar PV con-
firms the pattern shown in Fig. 2 and the qualitative findings from
the previous section. Third, debt margins are higher than the base-
line country risk rate in 2017, largely as a result of exceptionally low
government bond yields due to the expansive monetary policy after
the financial crisis. Considering the observed trend towards higher
leverages and the concurrently increasing importance of the cost of
debt, this finding points out that changes in the general interest rate
level potentially have a large impact on the CoC for RETs.
Impact on LCOE
In the fourth and final step, we calculate the LCOE in the early
period of the RET finance industry (2000–2005) and in 2017 (see
Methods).
2000
2005
2010
2015
2020
5
4
0
1
2
6
3
Year
0.3%
2.1%
Bond yield or margin (%)
2.5%
5.3%
1.0%
1.1%
Solar PV debt margin
Wind onshore debt margin
General interest rate level
(10-year German government bond)
Fig. 5 | Changes in the general interest rate level versus debt margins.
Debt margins are predicted values using the estimated experience rate from
Fig. 4 and global investment data from 2000 to 2017 (see Methods). A data
validity check regarding the decomposition of the cost of debt into debt
margin and government bond yield is provided in Supplementary Fig. 4.
0.1
1.0
10.0
1
100
10,000
0.01
0.1
1.0
1
100
10,000
1
10
100
1
100
10,000
0.01
0.1
1.0
1
100
10,000
1
10
100
1
100
10,000
0.1
1.0
10.0
1
100
10,000
a
b
c
d
e
f
ER = 11 ± 8%
ER = 11 ± 7%
ER = 13 ± 10%
ER = 17 ± 8%
ER = –3 ± 2%
ER = –3 ± 3%
Wind onshore
Solar PV
Debt margin (%)
DSCR
Loan tenor (years)
Cumulative world investment
(US$ billion)
Cumulative world investment
(US$ billion)
Fig. 4 | Experience rates for risk metrics including the 95% confidence
interval. a,b, Debt margin experience rate (ER) for solar PV projects
(N = 27) (a) and for wind onshore projects (N = 22) (b). c,d, DSCR
experience rate for solar PV projects (N = 35) (c) and for wind onshore
projects (N = 36) (d). e,f, Loan tenor experience rate for solar PV projects
(N = 36) (e) and for wind onshore projects (N = 34) (f). All axes are
logarithmic, and fits are linear. All linear fits are significant at the 5%
level or below, except for the wind onshore loan tenors (f). The data
are from German projects between 2000 and 2017. Between 2000 and
2017, global cumulated solar PV investment doubled eight times, and
wind onshore investment just short of six times. The results are robust to
including investor fixed effects (for example, controlling for different sizes
of investors), choosing Europe as the relevant scope for experience (that
is using European instead of global investment data) and using alternative
data to measure investment (see Supplementary Tables 5–7).
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Figure 6 shows that the LCOE declined for both technolo-
gies, bringing both technologies into the generation cost ranges
for fossil fuel-fired power plants, estimated to be between US$50
and US$170 for G20 countries in 201741. Around 60% of this
decline is due to lower technology cost (CAPEX) with the remain-
ing 40% due to lower financing cost. Three effects contribute to
the change in financing costs. First, the initial investment to be
financed (CAPEX) decreased, which lowers the financing cost.
Second, the general interest rate decreased. Third, an experience
effect led to the compression of financing margins. The three
effects differ in importance between the two studied technologies.
The large reduction in solar PV CAPEX during the period of our
study (see Supplementary Table 4) led to lower financing costs,
which contributed roughly one-third (36%) of LCOE reductions.
Conversely, onshore wind CAPEX stayed relatively constant (see
Supplementary Table 4), increasing the relative importance of the
general interest rate effect, which contributed one-fifth (20%) of
LCOE reductions. Thus, the channels through which financing
costs contribute to lowering LCOEs vary according to the relative
reductions in CAPEX. As solar PV and wind onshore are becom-
ing mature technologies and future CAPEX reductions become less
likely, the relative importance of the general interest rate and expe-
rience effects will increase.
Discussion
This paper compiles a project-level data set for financing conditions
and makes three contributions. First, it identifies the drivers of the
changes in financing conditions. Second, it estimates an experience
effect for financing conditions and compares it with the changes
in the general interest rate. Third, it demonstrates the effect of the
changes in financing conditions on the LCOE.
For researchers, our results suggest that the dynamics of
financing conditions should receive more attention in models
that include investments in low-carbon technologies. In failing to
account for these dynamics, researchers could overestimate the
technology learning effect by attributing the full LCOE change
to reductions in capital and operating expenditures. Accounting
for different channels of LCOE reductions via financing costs may
be particularly important—especially as the increasing use of auc-
tions makes data on generation costs readily available, increas-
ing the use of LCOE learning curves41. To include sensitivity
analyses regarding the dynamics of financing conditions in models,
further research should help improve the understanding of
the processes that affect renewable energy financing condi-
tions. While we have separated three effects contributing to
financing costs’ LCOE effect, their dynamics are yet to be fully
understood, opening up avenues for future research. For example,
it is not evident whether deployment and the associated reduc-
tions in investment costs would have been as large as observed
without reductions in the CoC. The accumulation of experi-
ence in the finance industry, the excess availability of capital and
the reductions in investment costs all depend on each other
and together constitute the impact of financing costs on
the LCOE. Future research also should investigate to what extent
this paper’s conclusions are applicable to other regions and
other technologies.
For policymakers, our findings stress the importance of
policies that are conducive to favourable financing conditions
for RETs. First, our results suggest an important co-benefit of
deployment policies: the acceleration of technological change
by allowing the finance industry to experiment and learn. RET
investments are long-term, and the finance industry typically
struggles to assess long-term risks of new technologies without
a track record34,42. For instance, green state investment banks
can be an instrument to accelerate learning in the finance indus-
try, helping investors assess projects and build confidence in
new technologies43. Second, our results indicate that a large RET
financing market and a high degree of competition between inves-
tors were crucial in creating more favourable financing conditions
for RETs. Therefore, policies should try to crowd-in a broad spec-
trum of investors. Third, our findings point out that policymakers
should be vigilant in responding to changes in monetary poli-
cies that have an impact on RET costs. As RET generation costs
approach grid parity, policymakers in some countries consider
phasing out fixed remuneration schemes for RETs. While some
have argued that achieving high RET shares requires de-risking
policies in any case44, our results stress the particular impor-
tance of policy intervention (for example, RET support or carbon
pricing) given the likelihood of an imminent increase in interest
rates. Ending policies might be premature and put climate change
targets at risk. Policymakers also could evaluate new approaches,
such as green monetary policies, to ensure attractive financ-
ing conditions for RETs and other low-carbon technologies in
the future45.
313
187
8
0
100
200
300
400
500
1%
59%
36%
LCOE (US$ MWh–1)
4%
59
2017
500
67
Wind onshore
Solar PV
a
41%
change in
financing cost
85
28
54
7
0
20
40
60
80
100
120
LCOE (US$ MWh–1)
4%
60%
20%
17%
2017
62
113
40%
change in
financing cost
General interest rate effect
Experience effect
Lower capital expenditures
Change in financing cost from:
Capital and operating expenditures
Financing expenditures
LCOE components
b
Avg 2000–2005
Avg 2000–2005
Fig. 6 | Historical impact of changes in financing costs on LCOE. a,b, LCOE for solar PV (a) and wind onshore (b). The percentages indicate the
contributions of the respective parts to the change in LCOE. We parameterize the LCOE model using data for Germany (see Supplementary Table 4).
Sensitivities are provided in Supplementary Fig. 5. The numbers do not always add up due to rounding.
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Methods
Case selection. The case selection includes three dimensions: technology, country
and project type. First, we focus on solar PV and wind onshore technologies, the
most deployed non-hydro RETs. In 2016, solar PV and wind onshore technologies
accounted for a global capacity of 291 GW and 452 GW, respectively (for example,
compared with 14 GW for wind offshore generation)46. Second, we focus on
Germany, one of the earliest markets to adopt these technologies. Germany
added the most solar PV capacity in 13 of the 17 years analysed, and the most
wind onshore capacity in 8 of the 17 years analysed46. Our sample period begins
in 2000, when Germany enacted its landmark legislation on renewable energy
sources (EEG), with a feed-in tariff that triggered large-scale renewable energy
investments23. The feed-in tariff was never changed retroactively. The German
electricity market has been liberalized since 199823, and the vast majority of
investment in RET was private47. Third, we restrict the analysis to project finance
structures, exploiting the fact that 96% of large solar PV projects and 88% of large
wind onshore projects in Germany between 2000 and 2015 were undertaken using
project finance24.
Data collection. We contacted leading investors directly to assemble two sets
of data: quantitative data on the financing conditions of reference projects, and
qualitative data on the drivers of changes in financing conditions. The former is
used for steps one, three and four of the paper, and the latter is used for step two.
All investor interviews were conducted between September 2017 and January
2018, following the Chatham House Rule, which states that ‘participants are free
to use the information received, but neither the identity nor the affiliation of
the speaker(s) […] may be revealed’48. The interviews were conducted in person
or over the phone by one to three researchers who took individual notes. All
interviews were recorded and transcribed verbatim.
We use theoretical sampling to include the most revelatory interviewees and
balance our sample to represent various perspectives from the finance industry27,49.
The sampling took place in three stages. First, we searched for publicly available
addresses of senior investment professionals working at large debt and equity
investment firms, using the Bloomberg New Energy Finance database50. Second,
we used the contact network of a private renewable energy finance industry
partner in the INNOPATHS research consortium, Allianz Climate Solutions, to
reach out to relevant market actors. Third, we employed snowball sampling by
asking key contacts from our network to refer us to relevant actors and teams, and
then continued to ask for references following each contact with an investment
professional. The resulting sample is well balanced among different kinds of
financial actor and includes 17 debt providers (13 commercial banks and 4
investment banks), 16 equity providers, 7 public actors (4 public utilities and 3
public investment banks) and 1 former researcher (see Supplementary Table 3 for
the full interviewee sample). The sampled financial actors were lead arrangers in
81% of solar PV capacity additions and 85% of the solar PV investment sum, and in
49% of onshore wind capacity additions and 80% of the onshore wind investment
sum, between 2000 and 2017 (see Supplementary Fig. 1). Thus, our sample covers
the relevant actors in a balanced manner and is relevant in size to elicit financing
conditions that are representative of the German investment market. Reflecting the
international nature of the renewable energy finance industry, the investors in our
sample are based in Germany, Switzerland, the UK, the Netherlands, France, Italy,
Luxembourg and Norway (see Supplementary Table 3).
Quantitative data. To ensure comparable data on project financing conditions, we
defined a reference project with an investment sum of €20 million, using standard
technology from established manufacturers (polycrystalline modules without a
tracker for solar PV projects and 1.5–2 MW turbines on a standard foundation
for wind onshore projects). While the relatively small standard deviation of the
data (see Supplementary Table 2) indicates a good comparability of projects
across investors, we control for investor differences (for example, investor size) by
including investor fixed effects in the estimations of experience rates (see below).
We asked the investment professionals to provide information on the all-in
CoC, cost of debt, cost of equity, debt margin, DSCR, leverage (that is, project
capital structure) and loan duration (tenor) for any such reference project that they
had financed (which is the case for 37 interviewees) or had advised on (which is
the case for 4 interviewees; see Supplementary Table 3) between 2000 and 2017 (see
Fig. 1 and Supplementary Fig. 2). For CoC components (cost of debt, cost of equity
and leverage), the interviewees were free to indicate ranges instead of absolute
values, in which case, we take the average by project. Wherever possible, the debt
providers indicate not only the all-in cost of debt, but also the debt margins. For
projects with available information on debt margins, we calculate the all-in cost
of debt as the sum of the baseline rate (10-year government bond51) and the debt
margin. This approach yields all-in cost of debt data comparable to where debt
providers revealed all-in costs of debt (see Supplementary Fig. 4). We also screen
publicly available onshore wind park investment prospectuses—mainly from civic-
owned assets (German Bürgerwindparks)—between 2000 and 2017 for the data on
the cost of debt. We do not consider this source for the cost of equity data because
investment prospectuses often offer overly optimistic equity returns ex ante. On the
other hand, the cost of debt figures reflect the rates offered by banks. The resulting
data set consists of 48 solar PV and 85 wind onshore projects. The number of
observations, means, standard deviations, and minimums and maximums for all
variables are described in Supplementary Table 2.
To estimate experience rates, we use investment data from the United Nations
Environment Programme52, which is available from 2004 onward. For the years
before 2004, we take global investment costs per megawatt for solar PV and
wind onshore projects50 and multiply these figures with global capacity from the
International Renewable Energy Agency (IRENA)46. For 2000 and 2001, we used
the solar PV investment costs from 2002 because ref. 46 provides no data. For 2017,
we extrapolate the changes from the previous year.
Qualitative data. To develop the drivers of changes in financing conditions, we
apply an interview case study design with two stages of data collection53. First,
open exploratory interviews (N = 8) were conducted to gain early insights on
the dynamics and drivers of changes in financing conditions and to define the
structure for the second phase of the interviews. Second, we conducted 33 semi-
structured interviews with employees from debt and equity investment firms
who had significant experience in the renewable energy finance industry (23 of
these interviewees are the same individuals who provided the quantitative data
mentioned above). Note that we contacted three investment professionals from the
exploratory interviews again for the semi-structured interviews and the collection
of project financing conditions data.
If more than one researcher conducted an interview (N = 15), one of them
summarized it using the recording, transcript and notes. If only one researcher
conducted the interview (N = 26), the resulting summary was cross-checked by
another researcher. This procedure ensures accurate and consistent recording,
expands the scope of insights and enhances confidence in the findings53. Following
Eisenhardt’s approach53, we continued holding interviews until no additional
insights were observed.
Changes in financing conditions. In the first step of the paper, we show the data as
elicited without tax considerations and we calculate the after-tax CoC because the
German corporate tax rate was cut 4 times, from an initial 52% in 2000 to 30% in
2008, and remained at that level until 201754 (see Supplementary Fig. 3). Equations
(1) and (2) define the CoC without considering tax and the after-tax CoC.
=
+
K E
V
K D
V
CoCnotaxconsidered
(1)
E
D
-
=
+
−
K E
V
K D
V
T
After tax CoC
(1
)
(2)
E
D
In these equations, E and D denote equity and debt investment, respectively; V
signifies the total investment sum; KE and KD refer to cost of equity and cost of
debt, respectively; and T represents the corporate tax rate. The leverage L is equal
to D/V. To analyse the changes over time, we use the average during the 2000–2005
period as the starting point due to limited data availability in the early years. As
costs of capital decreased already between 2000 and 2005, this approach yields a
conservative estimate for the changes over time.
Taking the derivatives of equation (2) yields equations (3)–(6), below.
Equations (3) and (4) show that the changes in the cost of equity and debt affect
the CoC, depending on leverage and corporate tax rate. Equation (5) shows that
the effect of increasing leverage depends on the difference between KD and KE.
More precisely, if (1 − T)KD < KE holds, the CoC decreases with increasing project
leverage. Typically, this condition holds in reality (see characteristics of project
finance above). Equation (6) illustrates that a change in the tax rate affects costs of
capital in the opposite direction; that is, a decrease in the tax rate increases costs of
capital.
∂
∂
=
−
K
L
CoC
(1
)
(3)
E
∂
∂
=
−
K
T L
CoC
(1
)
(4)
D
∂
∂
=
−
−
L
T K
K
CoC
(1
)
(5)
D
E
∂
∂
= −
T
LK
CoC
(6)
D
Figure 2c,d represents this fact with a grey upward bar for tax changes, indicating
the higher CoC due to the lower corporate tax rate.
Drivers of change. In the second step, we use qualitative data to establish the
drivers behind the changes in financing conditions. The interviews were semi-
structured in the sense that the interviewees were free to name and explain the
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main drivers that led to the changes in financing conditions, but the conversations
followed a pre-determined set of topics. At the end of each interview, we asked
the interviewee whether crucial points were missing. This feedback was included
iteratively in the first few interviews. Key statements were summarized by two
researchers after each interview. Once the interview summaries were completed
(see data collection above), we loosely followed the ‘grounded theory’ approach55
by comparing incident (that is, statement) to incident to iteratively create common
patterns and drivers. We constantly compared new incidents with emerging drivers
(recursive cycling among different investor interviewees)27. Two researchers
conducted this ‘constant comparison’, verifying drivers and ensuring their
accuracy56. As a result, we identified eight drivers, which we categorized in a nested
hierarchy of three levels: economy, renewable energy sector and renewable energy
finance industry.
Financing experience rates. In the third step, we apply a one-factor experience
curve, following Wright’s law39, and adapt it to financial indicators. Applying a
one-factor experience curve may lead to estimates that are biased upwards due to
an omitted variable bias57. The most commonly cited omitted factor is research
and development (R&D) spending38,58. However, service industries, such as the
finance industry, typically do not use R&D departments, or even the term R&D.
Instead, innovation activities are organized in project-based teams59. Perhaps as
a consequence, some empirical evidence even points to a negative effect of R&D
spending on service innovation60. Finally, the evidence of our interviews points to
factors such as track records, improved processes or market competition as drivers
of the experience effect. Quantifying these factors individually is impossible, which
is why we choose to use a one-factor experience curve and discuss the components
qualitatively in step two. For each of the financial indicators (that is, debt margin,
DSCR and loan tenor), we define experience curves as follows:






=
−
I
I
I
I
DebtMargin( )
DebtMargin( )
(7)
t
t
b
0
0
1






=
−
I
I
I
I
DSCR( )
DSCR( )
(8)
t
t
b
0
0
2






=
−
I
I
I
I
Tenor( )
Tenor( )
(9)
t
t
b
0
0
3
In equation (7), DebtMargin denotes the debt margin in percentage points. In
equation (8), DSCR signifies the transformed DSCR. We transform the elicited
DSCR values by subtracting 1 because the DSCR has a natural lower bound of 1.
As we are taking the log in the next stage, we transform one value in our sample
from 0 to 0.01. In equation (9), Tenor represents loan tenor duration in years. In
all three equations, I refers to the cumulative world investment volume in billions
of US dollars, and b1–3 signifies the experience parameter for each variable. In each
of equations (7)–(9), I0 denotes the first investment, and It represents cumulative
investment at time t.
We define an individual experience rate, ER = 1 − 2−b, for each variable
of interest and quantify it by estimating equations (7)–(9) separately for both
technologies i, using an ordinary least squares regression according to equation (10):
β
β
ε
=
+
+
V
I
ln(D
)
ln( )
(10)
it
i
i
it
it
0
1
in which t denotes the year, DV denotes the dependent variable (see equations (7)–(9)),
and, again, I signifies cumulative world investment in billions of US dollars.
As mentioned previously, a potential caveat concerning the data is the
heterogeneity of investors. Thus, we apply investor fixed effects in a robustness
check, which does not change the results (see Supplementary Tables 5 and 6). The
choice of the independent variable in the specification of the experience rate also
is subject to some debate in extant literature. Depending on the technology and
application, the relevant geographical scope to accumulate experience changes61,
which affects the empirical identification of experience rates58. While the evidence
indicates global experience effects for RETs because innovation benefits cannot
be kept locally34, this argument should hold even more for the finance industry—
especially as large investors usually are active internationally. Our choice of
cumulative global investment is driven by exploratory investor interviews, which
point out that the financing of large project finance deals is international and
increasingly global, so global investment figures appear to be the most relevant.
However, because our investor sample is Europe-based (see Supplementary Table
3), we test for a European specification of the experience effect by using cumulative
European investment. We do so by using capacity data for Europe from IRENA46
and investment cost data for Germany from the Bloomberg New Energy Finance
database50 (three-year moving average). The results remain very similar for solar
PV, but estimates for the wind onshore experience rate become larger (see columns
3 and 4 in Supplementary Tables 5 and 6). Finally, we conduct a robustness check
with alternative investment data sources, using global data on investment
cost per megawatt50 (three-year moving average) and IRENA data on global
capacity additions46. The results do not change (see columns 5 and 6 in
Supplementary Tables 5 and 6). We always use robust standard errors to allow
for heteroscedastic residuals (for example, decreasing variance of the error
term with decreasing debt margins because the market is becoming more
competitive). Along most specifications, the results remain very similar. In cases
when they change, we report a conservative experience effect by using global
cumulative investment (that is, typically equal or close to the lowest value across
specifications). We report the range of the estimated experience rates across all
specifications in Supplementary Table 7.
Impact on LCOE. In the fourth step, we calibrate an LCOE model according to
equation (11) to quantify the effect of the observed changes in financing costs on
lifetime RET generation costs. We calculate the LCOE for both technologies i (solar
PV and wind onshore) and the two points in time, t (t = 1 in 2000–2005; t = 2 in
2017), as displayed in Fig. 6.
=
+ ∑
∑
τ
τ
τ
τ
=
=
+
=
=
+
τ
τ
τ
τ
C
LCOE
(11)
it
it
C
1
20
(1
CoC )
1
20
FLH
(1
CoC )
it
it
it
it
Cit denotes the initial investment cost (CAPEX) per megawatt at τ = 0, Citτ
represents the operation and maintenance costs (OPEX) per megawatt per year
from τ = 1 to τ = 20 (constant) and FLHitτ signifies the full load hours of the asset
per year from τ = 1 to τ = 20 (constant). Our discount rate CoCit is the technology-
and time-specific CoC.
On the OPEX, we assume 2% annual inflation. We parametrize the LCOE
model by using real data for full load hours, investment cost (US$ MW−1) and
operation and maintenance cost (US$ MW−1 yr−1) in Germany, and the CoC from
our project database (see Supplementary Table 4).
For both points in time t, we estimate a baseline with 0% CoC. We separate this
baseline into CAPEX, represented by the first term of equation (12) and an OPEX
component represented by the second term.
=
∑
+
∑
∑
τ
τ
τ
τ
τ
τ
τ
τ
τ
=
=
=
=
=
=
=
C
C
LCOE
FLH
FLH
(12)
it
it
it
it
it
,CoC 0
1
20
1
20
1
20
We then estimate the same model with the observed CoC from our data rit and
define the change to the baseline as the financing expenditures δit of the LCOE (see
equation (13)). Note that rit depends on the project leverage and tax rate according
to equation (2).
δ =
−
=
=
LCOE
LCOE
(13)
it
it
r
it
,CoC
,CoC 0
i
i
As a result, we obtain three LCOE components (CAPEX, OPEX and financing
expenditures) for both technologies at both points in time, which allows us to
display the changes in each component over time. We define the change in the
financing expenditures, δit, as ∆i following equation (14). Note that in Fig. 6, ∆i is
denoted ‘change in financing cost’.
Δ
δ
δ
=
−
=
=
(14)
i
i t
i t
,
1
,
2
We disentangle three effects that contribute to the change in financing cost, namely
experience effect Δi
EXP, general interest rate effect Δi
INT and the effect resulting
from lower CAPEX to be financed Δi
CAPEX. The sum of the three effects equals the
total change in financing cost by definition as shown in equation (15).
Δ
Δ
Δ
Δ
=
+
+
(15)
i
i
i
i
EXP
INT
CAPEX
We start with the last term and define the effect resulting from lower CAPEX as
the hypothetical LCOE change with constant CoC (part 1 of equation 16) minus
the ‘pure’ CAPEX and OPEX changes (identical to the LCOE at CoC = 0). In doing
so, we define a counterfactual scenario of identical technological change (that
is, lower capital expenditure), absent changes in financing conditions. Given the
mutually reinforcing mechanism of financing conditions and technological change
(for example, it is not clear that the capital expenditure would have decreased,
absent improvements in financing conditions), this approach might overestimate
the part of change attributed to Δi
CAPEX. As a consequence, equation (15) provides
conservative estimates of the other two effects:
Δ
=
−
−
−
=
=
=
=
=
=
=
=
LCOE
LCOE
(LCOE
LCOE
)
(16)
i
i t
t
i t
t
i t
i t
CAPEX
,
1,CoC[
1]
,
2,CoC[
1]
,
1,CoC 0
,
2,CoC 0
To separate the remaining part of the change in financing cost into experience
effect and general interest rate effect, we use the share of the debt margin of the
total change in cost of debt (φ). In equation (17), d denotes the difference between
the value in 2017 and the value in 2000–2005, and GenIntRate represents the
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general interest rate. Note that this is computing the share of the changes displayed
in Fig. 5.
φ
=
+
d(DebtMargin )
d(DebtMargin
GenIntRate)
(17)
i
i
i
DEBT
We assume that a similar relation holds for the equity side and stipulate
φ
φ
φ
=
=
i
i
i
DEBT
EQUITY
. Combining equations (15), (16) and (17), we now can
identify the experience effect Δi
EXP and the general interest rate effect Δi
INT, which
are shown in equations (18) and (19).
Δ
φ Δ
Δ
=
−
(
)
(18)
i
i
i
i
EXP
CAPEX
Δ
φ
Δ
Δ
=
−
−
(1
) (
)
(19)
i
i
i
i
INT
CAPEX
Ethics statement. No ethics approval needed. The methodology used in this paper
does not require institutional ethical approval according to the guidelines set out
by ETH Zurich. Informed consent was obtained from all the interviewees.
No signature requirement for informed consent. In advance of participating in the
interview, respondents were provided with an information sheet describing the
type of questions they would be asked. The information sheet also emphasized
the anonymity of data and their right to withdraw from the study. Choosing to
participate in the interview beyond that point was interpreted as informed consent.
Reporting Summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.
Data availability
The data displayed in Figs. 1, 2 and 4 and used for calculations in Figs. 5 and 6 are
available upon reasonable request to T.S.S.
Received: 4 April 2018; Accepted: 1 October 2018;
Published online: 5 November 2018
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expressed and arguments employed herein do not necessarily reflect the official views
of the Swiss Government. This work was conducted as part of the European Union’s
Horizon 2020 research and innovation programme project INNOPATHS under grant
agreement no. 730403.
Author contributions
T.S.S., B.S. and F.E. developed the research idea. F.E., B.S. and T.S.S. conducted the
investor interviews, collected, analysed and interpreted the data, and wrote the
manuscript. T.S.S. secured project funding.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/
s41560-018-0277-y.
Reprints and permissions information is available at www.nature.com/reprints.
Correspondence and requests for materials should be addressed to F.E. or B.S.
or T.S.S.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
© The Author(s), under exclusive licence to Springer Nature Limited 2018
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Acknowledgements
The authors thank M. Jäger, M. Pahle, F. Polzin, L. Reile and O. Tietjen from the
INNOPATHS project, participants of the 2017 oikos Finance Academy at the University
of Zurich, participants of the 41st IAEE International Conference in Groningen (2018)
and members of ETH Zurich’s Energy Politics Group for helpful comments on earlier
drafts of the paper. This work was supported by the Swiss State Secretariat for Education,
Research and Innovation (SERI) under contract number 16.0222. The opinions
Nature Energy | VOL 3 | DECEMBER 2018 | 1084–1092 | www.nature.com/natureenergy
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Mixed-method study, consisting of (semi-)structured interviews, descriptive statistics and regression analysis
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The sample consists of 41 renewable energy investment professionals with experience in the German market. The resulting sample is
well balanced  among  different  kinds  of  financial  actors  and includes  17  debt  providers  (13 commercial  banks  and  4  investment
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actors were lead arrangers in 81% of solar PV capacity additions and 85% of the solar PV
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    Policy Brief:

Answer:
**Learning in the financial sector is essential for reducing renewable energy costs**

**Policy Problem**Achieving the goals of the Paris Agreement requires significant continued reduction in the cost of renewable energy. Ambitious policies have already led to large reductions in renewable energy cost. These reductions are often attributed to a reduction in hardware cost but the role of financing conditions and trends has remained largely unexamined. As renewable energy costs approach grid parity, it is increasingly important to understand how and why financing conditions changed over time. While individual investors know their cost of capital, typically this information remains unavailable to policymakers. Such information can inform decisions about when and how to phase out remuneration schemes such as feed-in tariffs or auctions.

**Scientific Research Findings**We find that financing conditions improved greatly for solar photovoltaics (PV) and onshore wind energy in Germany between the introduction of the feed-in tariff in 2000 and 2017. During this period, the cost of capital decreased from 5.1% to 1.6% for solar PV and from 4.5% to 1.9% for onshore wind. These reductions stem from two effects. First, a strong reduction in the general interest rate level, following the financial crisis. Second, a reduction of debt margins, due to increased investment experience with renewable energy technologies.

**Scientific Research Study Methods**We compiled a novel dataset on the financing conditions of 133 representative German utility-scale renewable energy projects undertaken between 2000 and 2017. We combined this with qualitative interviews with 41 investment professionals to understand the underlying drivers of change in financing conditions. This allowed us to disentangle the cost-reducing effect of lower interest rates from the cost-reducing effect of experience in the renewable energy financing industry.

**Policy Implications**• The learning process in the renewable energy finance industry helps speed up the energy transition.
• Renewable energy investment policies should aim at attracting a broad spectrum of investors in order to create competition that drives down financing cost.
• State investment banks with a ‘green’ mandate prove useful to build investor confidence in new technologies.
• Renewable energy deployment should be safeguarded against potential interest rate hikes, as these can disrupt financial learning and reverse cost reduction trends.