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

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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"

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 externalitiesBarriers to entryEnergy security concerns

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RES-E Policy Levers

Price Quantity

InvestmentInvestment subsidies

Tax credits

Low interest/ soft loans

Tendering systems for investment grants

Generation Feed-in tariffsRenewable portfolio standards (RPS)

Tendering systems for long term contracts

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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

€

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Years of RES-E policy enactment in Europe:

Feed-in tariff

Quota

BE

CZ BG

HU EE IE

IT DK GR FR LT NL MT RO BG

DE IT LU ES AT PT GB SE SI SK CY

1990 1992 1993 1994 1998 2001 2002 2003 2004 2005 2006

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FIT Policies and RES-E Capacity

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 20080

5

10

15

20

25

0

2000

4000

6000

8000

10000

12000

14000

FIT policies enacted

Annual RES-E capacity added*

* Solar PV and onshore wind

Correlation = 0.87 Causation?

Polic

ies

Megaw

atts

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Have feed-in tariffs significantly increased onshore wind power and

solar PV deployment in Europe?

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The Traditional Approach

Capacity Added = β1(Policy Dummy) + β2(Some Controls)

Inevitably, β1 is positive and highly significant.

So the policy is effective!

Except for...

Two Problems

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Policy Heterogeneity“Not all FIT’s are created equal.”

Omitted Variables Bias“What you don’t see can hurt you.”

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Linear OLS pooled cross-section regression:

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Problem 1: Omitted Variables Bias

Establishing Causality

PolicyCapacity Growth

Political Environment

Natural Resources

Socio-Economics

Electricity Prices

Other Policies

Region Transmission

UnobservedState Traits

Broader Trends

Bias

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Our Model

ln(Added Capacityist) = β0 + β1SFITist + β2INCRQMTSHAREst + βxZist + βyWist + μs + uist

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

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Problem 2: Policy Heterogeneity

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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

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SFIT: A more nuanced approach

Contract DurationTariff 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)


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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

= ROI


Results of Cross-Sectional RegressionsDependent Variable: Added RES-E Capacity (ln)

Solar Photovoltaic Onshore Wind(1) (2) (3) (4)Binary FIT 0.654***(0.184) 1.011***(0.215)SFIT 1.025***(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, ln 0.990**(0.450) -0.165(0.341) 3.672***(0.376) 3.847***(0.377)Area, ln 0.509***(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, ln 0.076(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, ln 1.160(1.111) 1.259(0.878) 1.760*(1.067) 1.020(1.024)Coal share, ln 0.755(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)N 253 253 264 264R2 0.328 0.575 0.665 0.654

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?

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Results of Fixed-Effects RegressionsDependent 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) (dropped) (dropped) (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 Yes Yes YesR2 253 253 264 264*** <1% significance, ** <5% significance, * <10% significance

Coefficients on FIT variables are universally lower

Unobserved country characteristics positively bias the

pooled cross-section results

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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

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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

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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 Binary SFIT

Yes

YesVariesToo Well

No Yes

Overstates effectiveness

Understates effectiveness

Just right

Nuanced indicators and smart controls are key for accuracy and consistency in energy policy analysis

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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!

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Thank you! Questions?

Joe Indvik, ICF [email protected]

515-230-4665

Steffen Jenner, Harvard [email protected]

857-756-0361

Felix Groba, DIW [email protected]

+49-30-89789-681

mailto:[email protected]



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

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

Technology

Transcript of Assessing the Strength and Effectiveness of Renewable Electricity Feed-In Tariffs