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

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

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)

1. TECHNOLOGICAL INNOVATION

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

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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• 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

Principles of environmental policy design in order to encourage 'green' innovation

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• 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.

The Role of Policy Flexibility: The Estimated Effect on Patented Environmental Inventions

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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

The Role of Policy Predictability: The Estimated Effect of Volatility in Public R&D on Inventive Activity

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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)

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.

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)

2. INVESTMENT AND FINANCE

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

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

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

Means of Overcoming Intermittency

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• 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

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

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).

Benefits of Investing in Transmission Capacity: Simulation of Capacity Requirements

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150

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Cap

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2010 2012 2014 2016 2018 2020Product

WCAP_ABS WCAP_denspathWCAP_congpath WPEN_EUR

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

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’

% 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

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Spain

Unite

d Kin

gdom

Unite

d Sta

tes

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

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

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)

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)

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Renew

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200

400

600

800

1000

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)

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)

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)

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)

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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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)

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

MORE INFORMATION AT:

WWW.OECD.ORG/ENVIRONMENT/INNOVATION

&

WWW.OECD.ORG/ENVIRONMENT/FINANCE

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