1. Assessing the Strength and Effectiveness of Renewable Electricity Feed-in Tariffs Joe Indvik, ICF International Steffen Jenner, Harvard University Felix Groba, DIW Berlin USAEE/IAEE 2011 North American Conference: "Redefining the Energy Economy: Changing Roles of Industry, Government and Research" 1

2. Background Renewable electricity (RES-E) is rapidly expanding in magnitude and geographic scope Literature generally claims that government incentives are justified by... Climate and pollution externalities Barriers to entry Energy security concerns 2

3. RES-E Policy Levers PriceQuantity Investment Investment subsidies Tax credits Low interest/ soft loans Tendering systems for investment grants Generation Feed-in tariffs Renewable portfolio standards (RPS) Tendering systems for long term contracts 3

4. Price-based RES-E production incentive Funded by state budget and/or electricity price increase Helps renewables achieve grid parity Everything you need to know about FIT’s in 60 seconds RES-E Generator Grid Electricity Price State budget Tariff Contract End User kWh € 4

5. Years of RES-E policy enactment in Europe: Feed-in tariff Quota BE CZBG HUEEIE ITDKGR FR LTNLMTROBG DEITLUESATPTGBSESISKCY 19901992199319941998200120022003200420052006 5

6. FIT Policies and RES-E Capacity FIT policies enacted Annual RES-E capacity added* * Solar PV and onshore wind Correlation = 0.87 Do feed-in tariffs significantly increase onshore wind power and solar PV development? Causation? Policies Megawatts 6

7. The Traditional Approach Capacity Added = β 1 (Policy Dummy) + β 2 (Some Controls) Inevitably, β 1 is positive and highly significant. So the policy is effective! Except for... Two Problems 1 Policy Heterogeneity “Not all FIT’s are created equal.” Omitted Variables Bias “What you don’t see can hurt you.” 2 Linear OLS pooled cross-section regression: 7

8. Problem 1: Omitted Variables Bias 8

9. Establishing Causality Policy Capacity Growth Political Environment Natural Resources Socio- Economics Electricity Prices Other Policies Region Transmission Unobserved State Traits Broader Trends Bias 9

10. Our Model ln(Added Capacity ist ) = β 0 + β 1 SFIT ist + β 2 INCRQMTSHARE st + β x Z ist + β y W ist + μ s + u ist Incremental Share Measure of quota stringency developed by Yin and Powers (2009) Policy Controls Suite of binary policy control variables for other RES-E policies Socio-Economic Controls Suite of socioeconomic controls Country Fixed Effects Controls for country characteristics that do not change over time Added Capacity Additional RES-E nameplate generation capacity added each year for energy technology i, in country s, in year t. FIT Strength Our new measure of the generation incentive provided by a FIT 10

11. Problem 2: Policy Heterogeneity 11

12. 1/0 Binary Variable: The king of renewable energy policy analysis thus far. Duration Magnitude Electricity price Risk and uncertainty Binary variables do not accurately represent the true production incentive created by a policy Buy what does it neglect? Production cost 12

13. SFIT: A more nuanced approach Contract Duration Tariff Amount FIT contract length (years) Size of FIT contract established in year t (Eurocents/kWh) Electricity Price Wholesale market price of electricity (Eurocents/kWh) Capacity Lifetime Lifetime of PV or wind capacity installed in year t (years) Generation Cost Average lifetime cost of electricity production (Eurocents/kWh) 13 for energy technology i, in country s, in year t.

14. SFIT: A more nuanced approach Expected profit over the lifetime of capacity installed under a FIT contract Expected generation cost over the lifetime of capacity Return on Investment 14 for energy technology i, in country s, in year t.

15. Results of Cross-Sectional Regressions Dependent Variable: Added RES-E Capacity (ln) Solar Photovoltaic Onshore Wind (1)(2)(3)(4) Binary FIT0.654*** (0.184) 1.011*** (0.215) SFIT1.025*** (0.128) 0.412*** (0.151) Binary Tax or Grant-0.109 (0.186) 0.179 (0.167) 0.179 (0.325) -0.305 (0.337) Binary Tendering Scheme-0.567** (0.239) 0.131 (0.210) 0.235 (0.399) 0.138 (0.409) INCRQMTSHARE, ln-8.402** (3.978) -1.079 (3.051) 5.154 (4.745) -3.121 (4.329) GDP per capita, ln0.990** (0.450) -0.165 (0.341) 3.672*** (0.376) 3.847*** (0.377) Area, ln0.509*** (0.101) 0.387*** (0.071) 1.086*** (0.094) 1.129*** (0.088) Net import ratio, ln-0.314* (0.186) 0.018 (0.167) 0.005 (0.245) 0.002 (0.262) Energy cons. per capita, ln0.076 (0.429) 0.305 (0.373) -2.011*** (0.510) -1.780*** (0.509) Nuclear share, ln-0.322 (0.524) -0.008 (0.444) -0.728 (0.795) -1.224 (0.759) Oil share, ln-20.501 (15.250) -19.261* (10.868) -22.747* (11.842) -12.115 (11.626) Natural gas share, ln1.160 (1.111) 1.259 (0.878) 1.760* (1.067) 1.020 (1.024) Coal share, ln0.755 (0.672) 0.671 (0.459) 2.614*** (0.592) 2.957*** (0.599) EU 2001 binary-0.121 (0.226) 0.114 (0.175) -0.177 (0.302) -0.144 (0.307) N253 264 R2R2 0.3280.5750.6650.654 Policy Variables Socio- Economic Controls Fuel Mix Variables Feed-in tariffs appear to drive RES-E development. Cannot be interpreted as causal because of OVB *** <1% significance, ** <5% significance, * <10% significance How do the results change when we control for fixed country characteristics?

16. Results of Fixed-Effects Regressions Dependent Variable: Added RES-E Capacity (ln) Solar Photovoltaic Onshore Wind (1)(2)(3)(4) Binary FIT 0.068 (0.197) 0.758*** (0.280) SFIT 0.743*** (0.106) 0.262* (0.156) Binary Tax or Grant -0.327 (0.380) -0.411 (0.342) 0.052 (0.531) 0.037 (0.541) Binary Tendering Scheme 0.052 (0.286) -0.047 (0.258) -0.946** (0.406) -1.090*** (0.407) INCRQMTSHARE, ln 4.600 (5.584) 1.544 (5.062) -3.500 (7.864) -5.754 (7.928) GDP per capita, ln 0.689 (0.699) -0.073 (0.630) 3.187*** (0.912) 2.626** (1.130) Area, ln (dropped) Net import ratio, ln -0.145 (0.252) -0.019 (0.229) -0.117 (0.350) -0.152 (0.353) Energy cons. per capita, ln -1.038 (1.590) -1.550 (1.427) -0.809 (2.137) 0.937 (2.142) Nuclear share, ln -1.929 (1.534) -2.517* (1.386) -0.281 (2.147) 0.355 (2.163) Oil share, ln 98.175*** (32.774) 76.960*** (29.643) 11.882 (46.330) 13.754 (46.867) Natural gas share, ln 4.235*** (1.142) 2.391** (1.060) 2.162 (1.621) 1.257 (1.614) Coal share, ln -10.249*** (2.477) -6.480*** (2.288) 3.427 (3.386) 3.518 (3.511) EU 2001 binary -0.064 (0.192) 0.080 (0.174) -0.212 (0.267) -0.220 (0.270) N Yes R2R2 253 264 *** <1% significance, ** <5% significance, * <10% significance Coefficients on FIT variables are universally lower No statistically significant relationship between FIT enactment and solar PV development once country characteristics are controlled for Highly significant when SFIT is used instead of binary Binary variables obscure the true relationship between FIT policies and solar PV development Unobserved country characteristics positively bias the pooled cross-section results For a 10 percentage point increase in ROI provided by a FIT, countries will install 7.4% more solar PV capacity per year 2.6% more onshore wind capacity per year Even when innate country traits are controlled for, FIT policies have driven RES-E development since 1998 16

17. If you take one thing away from this paper, let it be... FIT Variable Fixed Effects? Model 1: Cross-sectional Approach Model 2: Fixed Effects Approach Model 3: Nuanced Approach Do FITs work? Binary SFIT Yes Varies Too Well NoYes Overstates effectiveness Understates effectiveness Just right Nuanced indicators and smart controls are key for accuracy and consistency in energy policy analysis 17

18. Conclusion Feed-in tariffs have driven solar PV and onshore wind power development in Europe since 1998. Controlling for policy design elements and country characteristics is crucial. Policy design matters more than the enactment of a policy alone! 18

19. Thank you! Questions? Joe Indvik, ICF International joe.indvik@gmail.com 515-230-4665 Steffen Jenner, Harvard University steffen.jenner@googlemail.com 857-756-0361 Felix Groba, DIW Berlin fgroba@diw.de +49-30-89789-681 19

20. Data Sources Capacity: Eurostat and the UN Energy Statistics Database Policy: GreenX (University of Vienna) and supplemental sources Cost: GreenX (University of Vienna) 2006 – 2009 actual 2010 – 2020 projected 1998 – 2005 linearly extrapolated