The Price of
Renewable Energy Policy Risk
Empirical Insights
from Choice Experiments
with European Solar Energy Project Developers
Rolf Wüstenhagen
(joint work with Sonja Lüthi)
U Minnesota, April 29, 2010
Good Energies Chair for
Management of Renewable Energies
at the University of St. Gallen (IWÖ-HSG)
• Established in 2009 with support from Good Energies
• Part of one of Europe‘s leading business schools
• Dedicated team (9 people)
• 30+ Bachelor/Master Theses, ≈ 3-4 PhD dissertations p.a.
• Research and teaching on...
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Investment Decisions and Venture Capital
Consumer Decisions and Marketing RE
Business Models for Renewable Energy
Energy Policy
Forthcoming Energy Policy Special Issue:
„Strategic Choices in Renewable Energy Investment“
Handbook of Research on Energy Entrepreneurship
Vision of our Chair –
The Road from 20:80 to 80:20
100%
Non-Renewable Energies
80%
60%
40%
20%
Renewable Energies
0%
2010
2020
2030
2040
2050
Berlin, 24.10.2009 (...) The new German government‘s objective is „a consistent energy policy,
which leads into a new age of renewable energies“, said Chancellor Angela Merkel when she
presented the coalition agreement. „Renewable energy shall account for the major part of German
energy supply“, and conventional energy sources shall continuously be replaced. (Source: dpa)
The paper in a nutshell
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Recent growth of renewable energies in Europe, particular solar
photovoltaics (PV), largely driven by public policy
Seemingly similar policies (e.g. feed-in tariff of similar level) lead
to different results in different countries
Vivid debate about what constitutes efficient and effective
renewable energy policies, recently highlighting importance of
both sides of the risk-return equation
Consensus that policy risk matters. BUT: Which risk factors are
relevant, and how should policy makers prioritize them?
Two approaches to understanding investor behavior:
• Revealed preferences (installed capacities). Limitation: Few cases,
retrospective.
• Stated preferences (choice experiments).
•
Result: Empirically measuring the price of policy risk, leading to
specific policy recommendations.
Solar Energy:
Small market share today, large future potential
2100
64%
EJ/a
Geothermal
1600
Other renewables
Solar Thermal (Heat)
1400
1200
Solar electricity
(PV and Solar Thermal)
1000
2007
0.1%
800
Wind
600
Biomass (modern)
Biomass (traditional)
Hydroelectricity
Nuclear
Gas
Coal
Oil
400
200
0
2000
2010
2020
2030
2040
2050
2100
Global primary energy scenario: Renewables 80% of primary energy by 2100
Source: German Advisory Council on Global Change WBGU
Berlin 2003 www.wbgu.de
Share of newly installed capacity
in 2009 [MW]
Change we can believe in:
New power plant investments in Europe
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
Guess
Who
Solar
Coal is Who:
Oil
Nuclear Hydro
Oil, Gas, Coal, (PV)
Nuclear, Hydro, Wind, Solar (PV)
Wind
Gas
Source: Platts, EWEA
Solar Photovoltaics: Towards „Grid Parity“
R&D
Pilot
Political Market
Self-Sustaining Market
2010 Grid parity European countries
Average household electricity price [€/kWh]
0,40
Grid
Parity
0,35
0,30
Denmark
Italy
0,25
Netherlands
Germany
0,20
Portugal
France
Sweden
0,15
Spain
Hungary
0,10
Greece
0,05
0,00
700
800
900
1000
1100
1200
1300
1400
1500
Solar Irradiation [kWh/m2*a]
Source: Centrotherm May 2008
1600
1700
1800
1900
2000
Greening Goliaths vs. Emerging Davids
Environmental &
Social
Performance
Sustainability
Niche
Emerging Davids
high
Sustainability
Start-up
Sustainability
Transformation
of an Industry
Greening
Goliaths
low
Market
Incumbent
low
Source: Hockerts and Wüstenhagen (2010)
high
Market
Share
9
Solar industry growth so far driven by Technology &
Policy, increasing importance of Business Models
η
§
€
Source: Klein et al. (2006)
Increasing number of countries
with feed-in tariffs for renewable energy
Influence of policy design on the deployment of
renewable energy technologies
- Sufficient and secure support is essential for increasing solar
capacity (Mendonça, 2007; Jacobsson & Lauber, 2006; Butler &
Neuhoff, 2005; Lauber & Toke, 2005; Rowlands, 2005; Wiser et al.
1997):
– Provide financial security: A guaranteed level of tariffs for a sufficiently
long duration (e.g. 20 years) ensures planning security and makes the
investment in solar electricity systems attractive
– Level of tariff higher than the marginal costs of generation (in order to
ensure a sufficient return on investments)
– Solar policy stability: A stable and foreseeable solar policy allows
investors to plan their activities.
- For efficient policy design, the project development and financing
processes need to be understood (Wiser & Pickle, 1998,
Langniss 1999)
Empirical Puzzle: Why do similar policies (PV feed-in
tariffs) lead to different outcomes across countries?
Empirical Puzzle: Level of return fails to explain
level of installed PV capacity
Germany
Spain
Greece
Return
+
(++) ?
++
- Level of tariff
+
(+) ?
++
- Duration of support
+
++
+
- Solar radiation
O
++
++
Risk
++
(+) – –
(– –) –
- Funding stability
++
(+) – –
O
- Promotion cap
++
––
++
- Administrative process
++
+
(– –) –
Level of diffusion
High
Medium
Low
++ = very favorable; + = favorable; O = medium; – = unfavorable; – – = very unfavorable;
( ) = situation in 2006
Source: Lüthi (2010)
1. Objectives
– Empirically measure the price of energy policy risk in the case
of support policies for photovoltaics, in order to…
– provide recommendations for design of effective PV policies.
2. Research Questions
– How important are various attributes of solar energy policies in
influencing the decision of a PV project developer to invest in a
given country?
– What is investors' willingness-to-accept specific policy risks?
?
Empirical Research Design,
Data and Sample
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Stated preference survey among international solar energy
project developers from Germany, Italy, Spain, Greece and
Switzerland, conducted in October-November 2008.
Participation solicited by phone, e-mail, at a solar industry
trade fair, through the University of St. Gallen website and
a leaflet in a solar industry journal.
135 respondents logged on to the survey website, 63
questionnaires were completed.
Each respondent completed 25 choice tasks, resulting in a
total dataset of N=1575 decisions.
Methodology: Adaptive Conjoint Analysis (ACA)
 Simulation of a “real” investment choice situation
 Utility function and decision rule:
U jk  U jk v jk ,  jk   max!
Ujk = utility of policy k for investor j
vjk = vector of deterministic relevant decision attributes which
subsumes feasible attributes of policy k for investor j (zjk)
jk = stochastic random variable which comprises unobservable
policy attributes zjk*, unobservable personal attributes sj* and
measurement errors jk.
Choice Analysis:
Pjk  Pr obU jk  U jn ; k  n; k , n  X t 
Pjk = probability that investor j chooses policy k
 Multinominal logit model (MNL)
 Maximum likelihood estimation
Choice Experiments in Marketing Research:
Sample Choice Task from Sammer/Wüstenhagen 2006
If you would buy a washing machine today, which product would you choose?
(assuming 5 kg wash load capacity)
If you would buy a washing machine today, which product would you choose?
Miele
V-Zug
V-Zug
(assuming 5 kg wash
load capacity)
If you would buy a washing machine today, which product would you choose?
Equipment
Version:
Equipment
Version:
Miele
V-Zug
V-Zug
(assuming 5 kg
wash
load
capacity)
Equipment
Version:
Simple*
Middle*
Middle*
The 5-6 most
important
attributes of the
buying decision
– preferably
independent of
each other
Miele
V-Zug
V-Zug
Equipment
Version:
Equipment
Version:
Consumption
Water Consumption
Water Consumption
EquipmentWater
Version:
Simple*
Middle*
Middle*
39 l/Wash Cycle
39 l/Wash Cycle
58 l/Wash Cycle
Equipment Version:
Equipment Version:
Consumption
Water Consumption
Water Consumption
EquipmentWater
Version:
Simple*
Electricity Consumption Middle*
Electricity ConsumptionMiddle*
Electricity Consumption
39 l/Wash Cycle
39 l/Wash Cycle
58 l/Wash Cycle
0.85 kWh/Wash Cycle
1.3 kWh/Wash Cycle
1.3 kWh/Wash Cycle
Water Consumption
Water Consumption
Water Consumption
Electricity Consumption
Electricity Consumption
Electricity Consumption
’A’ Class Energy
’B’ Class Energy
39 l/Wash Cycle
39 l/Wash Cycle
58 l/Wash Cycle
Class Energy
0.85’C’
kWh/Wash
CycleEfficiency
1.3 kWh/Wash Cycle
1.3 kWh/Wash Cycle
Efficiency
Efficiency
Electricity Consumption
Electricity Consumption
Electricity Consumption
’A’ Class Energy
’B’ Class Energy
Class Energy
0.85’C’
kWh/Wash
CycleEfficiency
1.3 kWh/Wash Cycle
1.3 kWh/Wash Cycle
1890 CHF
3780 CHF
2650 CHF
Efficiency
Efficiency
’A’ Class Energy
3780 CHF
Efficiency
’C’ Class Energy Efficiency
1890 CHF
1890 CHF
3-5 levels per
attribute
’B’ Class Energy
2650 CHF
Efficiency
3780 CHF
2650 CHF
Which of these three models would you buy?
Please mark with a cross!
Which of these three models would you buy?
Please mark with a cross!
1
Respondent
chooses
preferred product
2
3
Which of these three models would you buy?
Please mark with a cross!
1
1
2
2
3
3
e.g. 20 Choice Tasks
with varying attribute levels
Design of the Choice Experiments
Based on qualitative pre-study, the following attributes
were selected for the ACA:
Attributes
Attribute levels
Level of the feed-in-tariff
31; 35; 38; 41; 45 ct/kWh
Duration of support
15, 20, 25 years
Expected time until support cap
will be reached
No cap; reached in 4 years; reached in 1 y.
Duration of the administrative
process
1-2; 3-6; 6-12; 13-18; 19-24 months
Solar policy stability
0, 1, 3 abrupt policy changes
Predefined framework conditions:
solar radiation: 1500 kWh/m2
size of the installation: 500 kW
Sample Choice Task from ACA questionnaire
(paired comparison)
Sample Choice Task from ACA questionnaire
(calibration task)
Relative Importance of Attributes
How important are various attributes of solar energy policies in
influencing project developers' decision to invest in a given country?
30%
25.56%
25%
24.37%
18.72%
20%
17.74%
13.61%
15%
10%
5%
0%
Duration
admin.
process
Feed-in
Tariff level
Cap
Solar policy
changes
Feed-in
Tariff
duration
Normalized Part Worth Utilities of Attribute Levels
30
Number of Duration of
Policy Changes Support
Level of
Feed-in Tariff
20
19.15
18.19
17.74
15
13.61
Cap
25.56
24.37
25
Part Worth Utilities
Duration of
Admin. Process
18.72
13.08
12.47
10.59
10.57
10
7.59
7.05
6.66
5
0
0.00
0.00
0.00
0.00
0.00
9
6
3
0
0
6
12
18
24
Duration of Admin Process [in
months]
15
12
9
6
3
0
No Cap Loose Cap Tight Cap
(unltd.)
(4 y.)
(1 y.)
Implicit Willingness to
Accept [ct/kWh]
12
Implicit Willingness to
Accept [ct/kWh]
15
Implicit Willingness to
Accept [ct/kWh]
Implicit Willingness to
Accept [ct/kWh]
Implications for Policy Makers:
The "Price Tag" of Poor Policies
15
12
9
6
3
0
0
1
2
Number of Policy Changes
3
15
12
9
6
3
0
25
20
15
Duration of Support [in years]
Interpretation of Results
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Solar energy investors are particularly sensitive to duration
of administrative procedures, followed by other policy risks
(policy changes, existence of cap). Duration of support is
relatively less important.
For every half-year increase in the duration of the
administrative process, a government has to pay investors
a premium of about 4 ct/kWh (all else being equal).
Removing a loose (tight) cap will allow governments to
attract the same level of investment at a feed-in tariff that is
about 5 (10) ct/kWh lower.
Limitations and further research
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Sample size > should be increased in future studies
(without compromising on quality of respondents)
Stated preferences > validation of implicit willingness-toaccept by cross-checking with revealed preferences
(especially as longer time series become available)
Relevance of unobserved factors (e.g. language, country
size) and social interaction (e.g. "hype")
Transfer to private investors (e.g. residential homeowners
buying solar panels)
Transfer to corporate finance decisions and explore role of
policy aversion bias
Conclusions
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Ultimately, the achievement of energy policy objectives
hinges on whether policy instruments effectively influence
investor behavior.
Applying a sophisticated research method from another
field (marketing), the research presented here is one of the
first empirical contributions that investigates the influence
of renewable energy policies on investor decisions.
We confirm prior research that points to the importance of
"non-economic" barriers to deployment of renewables,
such as policy instability.
Based on a solid empirical basis, we develop specific
recommendations that enable policy makers to assess the
cost and benefit of reducing various elements of policy risk.
Thank you!
Prof. Dr. Rolf Wüstenhagen
Director
Institute for Economy and the Environment
University of St. Gallen
Tigerbergstrasse 2
CH-9000 St. Gallen / Switzerland
Telephone: +41-71-224 25 87
Mobile: +41-76-306 43 13
E-mail: rolf.wuestenhagen@unisg.ch
http://goodenergies.iwoe.unisg.ch
Further Reading
Lüthi, S. and Wüstenhagen, R. (2010): The Price of Policy Risk – Empirical Insights from
Choice Experiments with European Photovoltaic Project Developers (under review).
Hockerts, K. and Wüstenhagen, R. (2010): Greening Goliaths versus Emerging Davids –
Theorizing about the Role of Incumbents and New Entrants in Sustainable
Entrepreneurship. Journal of Business Venturing, forthcoming.
Känzig, J. and Wüstenhagen, R. (2010): The effect of life-cycle cost information on consumer
investment decisions for eco-innovation. Journal of Industrial Ecology, 1 (14), 121-136.
Bürer, M.J. and Wüstenhagen, R. (2009): Which renewable energy policy is a venture
capitalist's best friend? Empirical evidence from a survey of international cleantech
investors. Energy Policy, 37 (12), 4997-5006.
Wüstenhagen, R., Wolsink, M., Bürer, M.J. (2007): Social acceptance of renewable energy
innovation: An introduction to the concept. Energy Policy 35 (5): 2683-2691.
Wüstenhagen, R. and Teppo, T. (2006): Do venture capitalists really invest in good industries?
Risk-return perceptions and path dependence in the emerging European energy VC
market. Int. J. Technology Management, 34 (1/2), 63-87.
Sammer, K. and Wüstenhagen, R. (2006): The Influence of Eco-Labelling on Consumer
Behaviour – Results of a Discrete Choice Analysis for Washing Machines. Business
Strategy and the Environment, 15, 185-199.
Wüstenhagen, R. and Bilharz, M. (2006): Green Energy Market Development in Germany:
Effective Public Policy and Emerging Customer Demand. Energy Policy, 34, 1681-1696.
Moore, B. and Wüstenhagen, R. (2004): Innovative and Sustainable Energy Technologies: The
Role of Venture Capital, Business Strategy and the Environment, 13, 235-245.