1. LEVERAGING PRIVATE FINANCE
AND INDUCING INNOVATION FOR
CLIMATE MITIGATION THROUGH
POLICY DESIGN
Presentation by
Nick Johnstone, Ivan Hascic and Miguel Cardenas
Rodriguez
at Sustainable Prosperity
Ottawa, Nov. 8th, 2012

2. Background (1)
• Technological innovation and allocation of private finance towards on „clean‟
energy (and other environmental fields) share two key attributes
• Public policy context plays a key role in determining the returns on
investment
• Many of the expenditures are irreversible (i.e. non-recuperable R&D
expenditures; long-lived and specific capital)
Small differences in policy conditions can have long-lived implications for
trajectory of the economy
Small changes in policy conditions can have significant implications for the
rate of change of transition of the economy

3. Background (2)
1. Innovation
• role of different policy instruments on high-value renewable energy
patents and alternative fuel vehicle patents
• role of policy stringency, flexibility and predictability on „environmental‟
patents more generally
• the role of technology agreements and the CDM on technology and
knowledge transfers
2. Investment and Finance (preliminary)
• role of different policy measures on allocation of private finance for
projects of different technological maturity
• the extent of crowding out/crowding in which exists between different
public (i.e. grants) and private (i.e. equity) sources of finance;
•the benefits of complementary projects in terms of ease of access to
finance (e.g. investments in transmission infrastructure and grid quality)

4. 1. TECHNOLOGICAL INNOVATION

5. CC Mitigation and Adaptation Technologies
(Number of patent applications - claimed priorities, worldwide)
Source: Haščič, I. et al. (2010), “Climate Policy and Technological Innovation and
Transfer: An Overview of Trends and Recent Empirical Results”, OECD Environment
Working Papers, No. 30 http://dx.doi.org/10.1787/5km33bnggcd0-en

6. Prices matter – and spur innovation
The Effect of an Energy Tax on Patent Counts in Combustion
Efficiency and Renewable Energy
Source: OECD Energy and Climate Policy and Innovation (2012). Based on estimation of
sample of OECD economies over period 1978-2008. Results indication that if oil price is
approximately equal to prices reached in 2008 oil shock => switch from fossil fuel combustion
efficiency innovation to renewable energy innovation. See also ENV WKP 45

7. Pricing as a Necessary but not Sufficient
Condition
• Difficulty of targeting environmental „bad‟ directly and
excessive administrative costs – i.e. environmental policy
and transaction costs
• Secondary „non-environmental‟ market failures – i.e.
information failures, split incentives, network externalities
• „Credibility‟ of policy-induced price signals over the longer
term may not be sufficient for risky investments
• Inertia in the market which can favour incumbent firms and
technologies – “deadweight of past” may correlate with
environment-intensity”
7

8. Principles of environmental policy design in order to
encourage 'green' innovation
• Stringency – how ambitious is the policy objective relative to
“BAU”
• Predictability – how certain and credible is the signal given by the
policy
• Flexibility – how much space is provided to identify new
technologies and methods
• Incidence – how closely does it target the underlying policy
objective
• Depth – does it generate incentives across the full range of
possible outcomes
Source: Johnstone et al. “Environmental Policy Design Characteristics and Technological
Innovation” ENV WKP No. 16.
8

9. The Role of Policy Flexibility: The Estimated
Effect on Patented Environmental Inventions
1.8
1.6
1.4
1.2
1
Stringency Alone
0.8
Stringency & Flexibility
0.6
0.4
0.2
0
Model 1 (No Year FE) Model 2 (Year FE)
Note: Figure shows the estimated importance of different characteristics of environmental policy
framework (policy stringency, policy flexibility) in encouraging inventive activity in environmental
technologies. Measured as the number of patent applications (claimed priorities) deposited during
1975-2006.
Source: OECD (2011) Invention and Transfer of Environmental Technologies
9
www.oecd.org/environment/innovation. See also ENV WKP No. 16.

10. The Role of Policy Predictability: The Estimated
Effect of Volatility in Public R&D on Inventive
Activity
0.50
0.40
0.30
0.20
0.10
Model 1 (No FE)
Model 2 (FE)
0.00
R&D Volatility R&D Level
-0.10
-0.20
-0.30
-0.40
Note: Figure shows the estimated response to a 1% increase in the level and volatility of public
R&D in encouraging inventive activity in environmental technologies, measured as the number
of patent applications (claimed priorities) deposited during 1975-2007 in a cross-section of
OECD countries.
Source: Kalamova, Johnstone and Hascic (2012) in V. Constantini and M. Mazzanti (eds.) The
10
Dynamics of Environmental and Economic Systems (Springer, forthcoming).

11. The Need for a Mix of Policies:
Sequencing and Complementarity in AFV Technologies
10
Fuel prices
9
Standards
8
7
6
5 Fuel prices
4
Public R&D Public R&D Standards
3
2
1
0
Electric Hybrid
Note: For ease of interpretation elasticities have been normalised such that effect of
R&D=1. Unfilled bars indicate no statistical significance at 5% level.
Source: OECD (2011) Invention and Transfer of Environmental Technologies.

12. Policy Impacts and Distance from “Market”
• To induce a 1% increase in electric vehicle innovations, the
alternatives are:
– Increase R&D by 14% (i.e. $26 mln instead of $23 mln per
year per country, on average)
– Increase fuel price by 63% (i.e. $1.30 instead of $0.80, on avg)
• To induce a 1% increase in hybrid vehicle innovations, the
alternatives are:
– Increase R&D by 53% (i.e. $35 mln instead of $23 mln per
year per country, on average)
– Increase fuel price by 5% (i.e. $0.84 instead of $0.80, on avg)

13. 2. INVESTMENT AND FINANCE

14. Directing Change Without “Picking Winners”
• Since “neutral” pricing of externality is not „sufficient‟ = >
necessity to be „prescriptive‟ (at least to some extent) => main
challenge for policy makers
• Some general principles:
• Support a „portfolio‟ of projects and technologies to
diversify risk of getting it “wrong”
• Benefits of chosen portfolio should be robust with respect
to information uncertainty (i.e. ancillary benefits)
• Identify “local general purpose” technologies and
investments which complement a variety of
emission-reducing strategies

15. An Example: Intermittency of (some)
Renwables and Targeting of Incentives

16. Challenge of Increased Penetration of
Renewables
• The most important renewable energy sources
(wind, solar, ocean/tide) are ‘intermittent’
• Generation potential is subject to significant temporal variation
(minutes, hours, days, seasons), which is uncertain and often
correlated, and negatively correlated with peak demand (in
some cases)
• This means that increased capacity of renewable energy
generation is not a perfect substitute for ‘dispatchable’
generation capacity (e.g. fossil fuels)
• Challenge of LOLP becomes greater as share rises – note that
some countries have targets > 40%, where capacity credit starts
to converge to zero

17. Means of Overcoming Intermittency
• Reduce correlation of variation in intermittent sources
and/or allow for ex ante/ex post adjustment. How?
o “Back up” dispatchable sources (include some hydro)
o Spatial dispersion of sources (within type) and diverse
portfolio of sources (across types)
o Improvements in load management and distribution
o Trade in electricity services (states, countries)
o Investment in advanced energy storage
o Malleability of demand (e.g. smart grids)
• Benefits hypothesised to vary at different levels of
„penetration‟ of intermittent renewable power
17

18. Public R&D on Advanced Energy Storage
and Advanced Grid Management
1400
1200
1000
800 Wind
Solar
600 Tide/Ocean
Storage
400 Grid Management
200
0
Note - IEA Totals $2010 Million

19. Summary Results of Empirical Model
(Estimated Effects of „Strategy‟)
Although ECF mostly
depends on ecological
factors (wind
speed), it is also
significantly affected
by other explanatory
variables
Note. Summary results (elasticities) for the European sample (21 countries – 322
obs). Source: D. Benatia et al. „Increasing the Productivity and Penetration of
Intermittent Renewable Energy Power Plants‟ (ENV/WPCID(2012)2).

20. Benefits of Investing in Transmission Capacity:
Simulation of Capacity Requirements
.12
250
.12
.11
200
.1
.1
Capacity in GW
WPEN_EUR
.09
.08
150
.08
.07
.07
.06
100
.06
.06
.05
.05
.04
50
2010 2012 2014 2016 2018 2020
Product
WCAP_ABS WCAP_denspath
WCAP_congpath WPEN_EUR
20

21. Benefits of Investing in Transmission Capacity:
Value of Capital Stock
40
20
$2009 billion
0
-20
-40
2012 2015 2018 2020
mean of cost_denspath mean of cost_densify
mean of cost_congpath mean of cost_congestion
21

22. Asset Finance for Renewable Energy Projects*
($US Million)
* Wind, solar, geothermal, biomass, waste, small hydro. Source: OECD Project on “Leveraging
Private Finance for Clean Energy Through Public Policy Design: Finding Evidence from Micro-Data”

23. % of Renewable Energy Projects* Financed
from Balance Sheet
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
* Wind, solar, geothermal, biomass, waste, small hydro. Source: OECD Project on
“Leveraging Private Finance for Clean Energy Through Public Policy Design: Finding

24. Targeting of Grants for Renewable Energy Projects in
Selected Countries (1990-2011)
United States Canada
GP_CapitalSubsidy GP_CapitalSubsidy
GP_Demonstration GP_Demonstration
GP_ProductDevelopment GP_ProductDevelopment
GP_PureResearch GP_PureResearch
Australia China
GP_CapitalSubsidy GP_CapitalSubsidy
GP_Demonstration GP_Demonstration
GP_ProductDevelopment GP_ProductDevelopment
GP_PureResearch GP_PureResearch

25. Grants to “New Energy” Projects in Canada
(2000-2011)
Source: OECD Project on “Leveraging Private Finance for Clean Energy Through Public
Policy Design: Finding Evidence from Micro-Data” (OECD, forthcoming)

26. Main Equity Providers in Canada for Renewable
Energy Projects (2000-2011 - $US million)
1000
900
800
700
600
500
400
300
200
100
0
Source: OECD Project on “Leveraging Private Finance for Clean Energy Through Public
Policy Design: Finding Evidence from Micro-Data” (OECD, forthcoming)

27. Largest Grant-Giving Agencies
(all “new energy” – 2000-2012)
$US
Recipients > $50M
Million
Asian Development Bank 1257.25 China, Indonesia, Nepal, Sri Lanka, Philippines, Thailand, Vietnam
Inter-American Development Bank 1102.97 Argentina, Barbados, Bolivia, Dominican Republic, Nicaragua, Peru
Norway Ministry of Foreign Affairs 1000 Brazil
Japan Int’l Cooperation Agency 988.4 Egypt, Indonesia, Kenya, Vietnam
World Bank 785.2 India, Uganda, Philippines, Thailand, Vietnam
Agence Francaise de Developpement 421.9 Kenya, Morocco, Vietnam
European Investment Bank 338.6 China, Nicaragua, South Africa
Federal Republic of Germany 257.25 Kenya, South Africa, China
European Commission 196.6 Bulgaria
Nordic Investment Bank 146.6 Lithuania
International Finance Corp 71.7 South Africa
International Bank for R&D 62.2 Argentina
Source: OECD Project on “Leveraging Private Finance for Clean Energy Through Public
Policy Design: Finding Evidence from Micro-Data” (OECD, forthcoming)

28. Main North-South Asset Finance Flows in
Renewable Energy* (2000-2012)
* Wind, solar, geothermal, biomass, waste, small hydro.
Source: OECD Project on “Leveraging Private Finance
for Clean Energy Through Public Policy Design:
Finding Evidence from Micro-Data” (OECD,

29. Main North-South VC Flows in Renewable
Energy* (2000-2012)
* Wind, solar, geothermal, biomass, waste, small hydro. Source: OECD “Leveraging Private Finance for Clean Energy
Through Public Policy Design: Finding Evidence from Micro-Data” (OECD, forthcoming)

30. Exposure to “New Energy” of Asset Finance
Providers
Note: Based on Weighted Mid-Point of BNEF Classes. Source: OECD Project on
“Leveraging Private Finance for Clean Energy Through Public Policy Design: Finding
Evidence from Micro-Data” (OECD, forthcoming)

31. Estimated Effects of Different Renewable Energy
Project Characteristics on Gearing Ratio
(Preliminary)
0.05
0
-0.05
-0.1
-0.15
-0.2
Note: Elasticities for continuous variables and marginal effects for discrete variables. Source: OECD
Project on “Leveraging Private Finance for Clean Energy Through Public Policy Design: Finding Evidence
from Micro-Data” (OECD, forthcoming)

32. Estimated Effects of FITs/RECs on the Value of
Assets (Preliminary)
Note: Figure shows the estimated elasticity in terms of disclosed transaction values of assets per
MW to a 1% increase in the level of the respective policy measures. Unbalanced panel of 31
countries (OECD & BRICs) over 12 years (2000-2012). Unfilled bars indicate not statistically
significant at 5% level. Source: OECD Project on “Leveraging Private Finance for Clean Energy
Through Public Policy Design: Finding Evidence from Micro-Data” (OECD, forthcoming) 32

33. Tax Preferences: Stream vs. One-off (1990-2011)
1 - Wind
5 - Geothermal
3 - Biomass&Waste FP_stream
FP_single
2 - Solar
4 - SmallHydro
0% 20% 40% 60% 80% 100%

34. Estimated Effects of Policy Leveraging on
Private Finance (Preliminary)
1200
1000
800
$US Million
600
400
200
0
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
-200 Capital Demo Project** Prod Dvlpmt Research FP_single*** FP_stream*** FIT_d* REC_d
Subsidy**
Note: Figure shows predicted effect of the presence of different policies on allocation of private
finance towards different clean energy projects. Dotted line represents mean value. *‟s represent
degree of significance. Source: OECD Project on “Leveraging Private Finance for Clean Energy
Through Public Policy Design: Finding Evidence from Micro-Data” (OECD, forthcoming) 34

35. MORE INFORMATION AT:
WWW.OECD.ORG/ENVIRONMENT/INNOVATION
&
WWW.OECD.ORG/ENVIRONMENT/FINANCE
35