Assessing the Strength and Effectiveness of Renewable Electricity Feed-in Tariffs

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" 

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

3. RES-E Policy Levers

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

5. Years of RES-E policy enactment in Europe: Feed-in tariff Quota

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

7. The Traditional Approach Linear OLS pooled cross-section regression: Capacity Added = β1(Policy Dummy) + β2(Some Controls) Inevitably, β1 is positive and highly significant. So the policy is effective! Except for... Two Problems Omitted Variables Bias “What you don’t see can hurt you.” 1 Policy Heterogeneity“Not all FIT’s are created equal.” 2

8. Problem 1: Omitted Variables Bias

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

10. Our Model Added Capacity FIT Strength Incremental Share Additional RES-E nameplate generation capacity added each year Our new measure of the generation incentive provided by a FIT Measure of quota stringency developed by Yin and Powers (2009) ln(Added Capacityist) = β0 + β1SFITist + β2INCRQMTSHAREst + βxZist + βyWist + μs + uist Policy Controls Socio-Economic Controls Country Fixed Effects Suite of binary policy control variables for other RES-E policies Suite of socioeconomic controls Controls for country characteristics that do not change over time for energy technology i, in country s, in year t.

11. Problem 2: Policy Heterogeneity

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

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

15. Results of Cross-Sectional Regressions Dependent Variable: Added RES-E Capacity (ln) Feed-in tariffs appear to drive RES-E development. Policy Variables Cannot be interpreted as causal because of OVB Socio-Economic Controls How do the results change when we control for fixed country characteristics? Fuel Mix Variables *** <1% significance, ** <5% significance, * <10% significance

16. Results of Fixed-Effects Regressions Dependent Variable: Added RES-E Capacity (ln) Coefficients on FIT variables are universally lower Even when innate country traits are controlled for, FIT policies have driven RES-E development since 1998 No statistically significant relationship between FIT enactment and solar PV development once country characteristics are controlled for Unobserved country characteristics positively bias the pooled cross-section results Highly significant when SFIT is used instead of binary • 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 Binary variables obscure the true relationship between FIT policies and solar PV development *** <1% significance, ** <5% significance, * <10% significance

17. If you take one thing away from this paper, let it be... Model 1: Cross-sectional Approach Model 2: Fixed Effects Approach Model 3: Nuanced Approach FIT Variable SFIT Binary Binary Fixed Effects? No Yes Yes Do FITs work? Too Well Yes Varies Understates effectiveness Overstates effectiveness Just right Nuanced indicators and smart controls are key for accuracy and consistency in energy policy analysis

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!

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

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