LEVERAGING PRIVATE FINANCE AND INDUCING INNOVATION FOR CLIMATE MITIGATION THROUGH POLICY DESIGN Presentation byNick Johnstone, Ivan Hascic and Miguel Cardenas Rodriguez at Sustainable Prosperity Ottawa, Nov. 8th, 2012
Background (1)• Technological innovation and allocation of private finance towards „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 fortrajectory of the economy Small changes in policy conditions can have significant implications for therate 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 transfers2. 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)
CC Mitigation and Adaptation Technologies (Number of patent applications - claimed priorities, worldwide)Source: Haščič, I. et al. (2010), “Climate Policy and Technological Innovation andTransfer: An Overview of Trends and Recent Empirical Results”, OECD EnvironmentWorking Papers, No. 30 http://dx.doi.org/10.1787/5km33bnggcd0-en
Prices matter – and spur innovation The Effect of an Energy Tax on High-Value Patent Counts in Combustion Efficiency and Renewable EnergySource: OECD Energy and Climate Policy and Innovation (2012). Based on estimation ofsample of OECD economies over period 1978-2008. Results indicate that if oil price isapproximately equal to prices reached in 2008 oil shock => switch from fossil fuel combustionefficiency innovation to renewable energy innovation. See also ENV WKP 45
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=> Therefore – need to „unbundle‟ the attributes of policies which lead to innovation (beyond overly simplisctic MBI > CC)
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 outcomesSource: Johnstone et al. “Environmental Policy Design Characteristics and TechnologicalInnovation” ENV WKP No. 16. 8
The Role of Policy Flexibility: The Estimated Effect on Patented (High-Value) 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 policyframework (policy stringency, policy flexibility) in encouraging inventive activity in environmentaltechnologies. Measured as the number of patent applications (claimed priorities) deposited during1975-2006. Zero-inflated negative binomial models.Source: OECD (2011) Invention and Transfer of Environmental Technologies 9www.oecd.org/environment/innovation. See also ENV WKP No. 16.
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 -0.40Note: Figure shows the estimated response to a 1% increase in the level and volatility of publicR&D in encouraging inventive activity in environmental technologies, measured as the numberof patent applications (claimed priorities) deposited during 1975-2007 in a cross-section ofOECD countries. Zero-inflated negative binomial models.Source: Kalamova, Johnstone and Hascic (2012) in V. Constantini and M. Mazzanti (eds.) The 10Dynamics of Environmental and Economic Systems (Springer, forthcoming).
The Need for a Mix of Policies: Sequencing and Complementarity in AFV Technologies10 Fuel prices 9 Standards 8 7 6 5 Fuel prices 4 Public R&D Public R&D Standards 3 2 1 0 Electric HybridNote: Zero-inflated negbin model.s For ease of interpretation elasticities have beennormalised such that effect of R&D=1. Unfilled bars indicate no statistical significance at5% 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 targeted public R&D by 14% (i.e. $26 mln instead of $23 mln per year per country, on average) – Increase post-tax 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 targeted public R&D by 53% (i.e. $35 mln instead of $23 mln per year per country, on average) – Increase post-tax fuel price by 5% (i.e. $0.84 instead of $0.80, on avg)
Directing Change Without “Picking Winners”• Since “neutral” pricing of externality is not always sufficientand/or feasible = > necessity to be „prescriptive‟ (at least to someextent) => main challenge for policy makers• Some general principles: • Support a „portfolio‟ of projects and technologies to diversify downside 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 related to renewable energy
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• 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 Disperse (space) and diverse (type) of sources 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
Summary Results of Empirical Model (Estimated Effects of „Strategy‟ Variables) Although ECF mostly depends on ecological factors (wind speed), it is also significantly affected by other explanatory variablesNote. Summary results (elasticities) for the European sample (21 countries – 322obs). Source: D. Benatia et al. „Increasing the Productivity and Penetration ofIntermittent Renewable Energy Power Plants‟ (ENV/WPCID(2012)2).
Benefits of Investing in Transmission Capacity: Value of Capital Stock 40 20$2009 billion 0 -20 -40 2012 2015 2018 2020 mean of cost_denspath mean of cost_densify mean of cost_congpath mean of cost_congestion 21
Asset Finance for „New Build‟ Renewable Energy Projects* ($US Million)* Wind, solar, geothermal, biomass, waste, small hydro. Source: OECD Project on “LeveragingPrivate Finance for Clean Energy Through Public Policy Design: Finding Evidence from Micro-Data”(OECD, forthcoming). Note – only „new build‟
Exposure to “New Energy” of Asset Finance ProvidersNote: Based on Weighted Mid-Point of BNEF Classes. Source: OECD Project on“Leveraging Private Finance for Clean Energy Through Public Policy Design: FindingEvidence from Micro-Data” (OECD, forthcoming)
% 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: FindingEvidence from Micro-Data” (OECD, forthcoming)
Targeting of Grants for Renewable Energy Projects inSelected 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
Grants to “New Energy” Projects in Canada (2000-2011)Source: OECD Project on “Leveraging Private Finance for Clean Energy Through PublicPolicy Design: Finding Evidence from Micro-Data” (OECD, forthcoming)
Main Equity Providers in Canada for Renewable Energy Projects (2000-2011 - $US million) 1000 900 800 700 600 500 400 300 200 100 0Source: OECD Project on “Leveraging Private Finance for Clean Energy Through PublicPolicy Design: Finding Evidence from Micro-Data” (OECD, forthcoming)
Largest Grant-Giving Agencies (all “new energy” – 2000-2012) $US Recipients > $50M MillionAsian Development Bank 1257.25 China, Indonesia, Nepal, Sri Lanka, Philippines, Thailand, VietnamInter-American Development Bank 1102.97 Argentina, Barbados, Bolivia, Dominican Republic, Nicaragua, PeruNorway Ministry of Foreign Affairs 1000 BrazilJapan Int’l Cooperation Agency 988.4 Egypt, Indonesia, Kenya, VietnamWorld Bank 785.2 India, Uganda, Philippines, Thailand, VietnamAgence Francaise de Developpement 421.9 Kenya, Morocco, VietnamEuropean Investment Bank 338.6 China, Nicaragua, South AfricaFederal Republic of Germany 257.25 Kenya, South Africa, ChinaEuropean Commission 196.6 BulgariaNordic Investment Bank 146.6 LithuaniaInternational Finance Corp 71.7 South AfricaInternational 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 inRenewable 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,
Main North-South VC Flows in Renewable Energy* (2000-2012)* Wind, solar, geothermal, biomass, waste, small hydro. Source: OECD “Leveraging Private Finance for Clean EnergyThrough Public Policy Design: Finding Evidence from Micro-Data” (OECD, forthcoming)
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 perMW to a 1% increase in the level of the respective policy measures. Unbalanced panel of 31countries (OECD & BRICs) over 12 years (2000-2012). Unfilled bars indicate not statisticallysignificant at 5% level. Source: OECD Project on “Leveraging Private Finance for Clean EnergyThrough 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 -0.2Note: Elasticities for continuous variables and marginal effects for discrete variables. Source: OECDProject on “Leveraging Private Finance for Clean Energy Through Public Policy Design: Finding Evidencefrom Micro-Data” (OECD, forthcoming)
Estimated Effects of Policy Leveraging on Private Finance Flows (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: SUR – dependent variables are private and public finance. Fgure shows predicted effect ofthe presence of different policies on allocation of private finance towards different clean energyprojects. Dotted line represents mean value. *‟s represent degree of significance. Source: OECDProject on “Leveraging Private Finance for Clean Energy Through Public Policy Design: Finding 33Evidence from Micro-Data” (OECD, forthcoming)