Leveraging Private Finance and Inducing Innovation for Climate mitigation Through Policy Design
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Transcript of Leveraging Private Finance and Inducing Innovation for Climate mitigation Through Policy Design
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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
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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)
ÞSmall differences in policy conditions can have long-lived implications for trajectory of the economy
Þ Small changes in policy conditions can have significant implications for the rate of change of transition of the economy
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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)
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1. TECHNOLOGICAL INNOVATION
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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
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Prices matter – and spur innovationThe 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
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7
• 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”
Pricing as a Necessary but not Sufficient Condition
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Principles of environmental policy design in order to encourage 'green' innovation
8
• 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.
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The Role of Policy Flexibility: The Estimated Effect on Patented Environmental Inventions
9
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 www.oecd.org/environment/innovation. See also ENV WKP No. 16.
Model 1 (No Year FE) Model 2 (Year FE)0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Stringency AloneStringency & Flexibility
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The Role of Policy Predictability: The Estimated Effect of Volatility in Public R&D on Inventive Activity
10
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 Dynamics of Environmental and Economic Systems (Springer, forthcoming).
R&D Volatility R&D Level
-0.40
-0.30
-0.20
-0.10
0.00
0.10
0.20
0.30
0.40
0.50
Model 1 (No FE)Model 2 (FE)
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The Need for a Mix of Policies: Sequencing and Complementarity in AFV Technologies
0
1
2
3
4
5
6
7
8
9
10
Electric Hybrid
Standards
Fuel prices
Fuel prices
StandardsPublic R&D Public R&D
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.
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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)
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2. INVESTMENT AND FINANCE
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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
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An Example: Intermittency of (some) Renwables and Targeting of Incentives
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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
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Means of Overcoming Intermittency
17
• 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
distributiono 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
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Public R&D on Advanced Energy Storage and Advanced Grid Management
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
0
200
400
600
800
1000
1200
1400
WindSolarTide/OceanStorageGrid Management
Note - IEA Totals $2010 Million
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Although ECF
mostly depends on
ecological factors
(wind speed), it is
also significantly
affected by other
explanatory
variables
Summary Results of Empirical Model(Estimated Effects of ‘Strategy’)
Note. Summary results (elasticities) for the European sample (21 countries – 322 obs). Source: D. Benatia et al. ‘Increasing the Productivity and Penetration of Intermittent Renewable Energy Power Plants’ (ENV/WPCID(2012)2).
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Benefits of Investing in Transmission Capacity: Simulation of Capacity Requirements
20
.05.05
.06
.06
.07
.07
.08
.09
.1
.11
.12
.04
.06
.08
.1.1
2W
PE
N_
EU
R
50
100
150
200
250
Cap
acity
in G
W
2010 2012 2014 2016 2018 2020Product
WCAP_ABS WCAP_denspathWCAP_congpath WPEN_EUR
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Benefits of Investing in Transmission Capacity:Value of Capital Stock
21
-40
-20
02
04
0$
2009
bill
ion
2012 2015 2018 2020
mean of cost_denspath mean of cost_densifymean of cost_congpath mean of cost_congestion
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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” (OECD, forthcoming). Note – only ‘new build’
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% of Renewable Energy Projects* Financed from Balance Sheet
* 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, forthcoming)
Austral
ia
Belgi
umBra
zil
Canad
aChi
le
China
Franc
e
Germ
any
Indi
aIta
ly
Korea
(Rep
ublic
)
Mex
ico
Polan
d
Spain
Unite
d Kin
gdom
Unite
d Sta
tes
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
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Targeting of Grants for Renewable Energy Projects in Selected Countries (1990-2011)
United States
GP_CapitalSubsidy
GP_Demonstration
GP_ProductDevelopment
GP_PureResearch
Canada
GP_CapitalSubsidy
GP_Demonstration
GP_ProductDevelopment
GP_PureResearch
Australia
GP_CapitalSubsidy
GP_Demonstration
GP_ProductDevelopment
GP_PureResearch
China
GP_CapitalSubsidy
GP_Demonstration
GP_ProductDevelopment
GP_PureResearch
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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)
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Main Equity Providers in Canada for Renewable Energy Projects (2000-2011 - $US million)
Source: OECD Project on “Leveraging Private Finance for Clean Energy Through Public Policy Design: Finding Evidence from Micro-Data” (OECD, forthcoming)
Brook
field
Renew
able
Pow
er In
c
Canad
ian
Hyd
ro D
evel
oper
s Inc
Capita
l Pow
er C
orp
Inne
rgex
Ren
ewab
le E
nerg
y Inc
Nova
Scotia
Pow
er In
c
Kruge
r Ene
rgy In
c
Boral
ex In
c
Trans
Alta C
orp
Shear
Win
d In
c
RES Can
ada
Pty L
td
Accio
na S
A
Cartie
r Win
d Ene
rgy I
nc
Sprot
t Pow
er C
orp;
Fire
light
Infras
truc
ture
Par
tner
s LP
Natur
al F
orce
Tec
hnol
ogie
s0
200
400
600
800
1000
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Largest Grant-Giving Agencies (all “new energy” – 2000-2012)
$US Million Recipients > $50M
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)
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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, forthcoming)
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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)
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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)
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Estimated Effects of Different Renewable Energy Project Characteristics on Gearing Ratio (Preliminary)
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)
Capac
ity (M
W)
Value
perM
W
Conce
ntra
tion
in N
E
New
Bui
ld (b
inar
y)
Publ
ic F
inan
ce (b
inar
y)
-0.2
-0.15
-0.1
-0.05
0
0.05
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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)
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Tax Preferences: Stream vs. One-off (1990-2011)
4 - SmallHydro
2 - Solar
3 - Biomass&Waste
5 - Geothermal
1 - Wind
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
FP_streamFP_single
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Estimated Effects of Policy Leveraging on Private Finance (Preliminary)
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)
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
Capital Subsidy**
Demo Project**
Prod Dvlpmt Research FP_single*** FP_stream***
FIT_d* REC_d-200
0
200
400
600
800
1000
1200
1400
$U
S M
illi
on
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MORE INFORMATION AT:
WWW.OECD.ORG/ENVIRONMENT/INNOVATION
&
WWW.OECD.ORG/ENVIRONMENT/FINANCE
35