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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"
1
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
RES-E Policy Levers
Price Quantity
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
 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
€
4
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
5
FIT Policies and RES-E Capacity
0
2000
4000
6000
8000
10000
12000
14000
0
5
10
15
20
25
1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
FIT policies
enacted
Annual RES-E
capacity added*
* Solar PV and onshore wind
Correlation = 0.87 Causation?
Policies
Megawatts
6
7
Have feed-in tariffs significantly
increased onshore wind power and
solar PV deployment in Europe?
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
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:
8
Problem 1: Omitted Variables Bias
9
Establishing Causality
Policy
Capacity
Growth
Political
Environment
Natural
Resources
Socio-
Economics
Electricity
Prices
Other
Policies
Region Transmission
Unobserved
State Traits
Broader
Trends
Bias
10
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
11
Problem 2: Policy Heterogeneity
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
13
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)
14
for energy technology i, in country s, in year t.
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
15
for energy technology i, in country s, in year t.
Results of Cross-Sectional Regressions
Dependent 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 253 253 264 264
R2 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?
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) (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 Yes
R2 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
17
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) (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 Yes
R2 253 253 264 264
*** <1% significance, ** <5% significance, * <10% significance
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
18
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) (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 Yes
R2 253 253 264 264
*** <1% significance, ** <5% significance, * <10% significance
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
19
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
YesVaries
Too
Well
No Yes
Overstates
effectiveness
Understates
effectiveness
Just right
Nuanced indicators and smart controls are key for
accuracy and consistency in energy policy analysis 20
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!
21
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
22
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

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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" 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
  • 3. RES-E Policy Levers Price Quantity 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 € 4
  • 5. 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 5
  • 6. FIT Policies and RES-E Capacity 0 2000 4000 6000 8000 10000 12000 14000 0 5 10 15 20 25 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 FIT policies enacted Annual RES-E capacity added* * Solar PV and onshore wind Correlation = 0.87 Causation? Policies Megawatts 6
  • 7. 7 Have feed-in tariffs significantly increased onshore wind power and solar PV deployment in Europe?
  • 8. 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 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: 8
  • 9. Problem 1: Omitted Variables Bias 9
  • 11. 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 11
  • 12. Problem 2: Policy Heterogeneity 12
  • 13. 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 13
  • 14. 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) 14 for energy technology i, in country s, in year t.
  • 15. 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 15 for energy technology i, in country s, in year t.
  • 16. Results of Cross-Sectional Regressions Dependent 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 253 253 264 264 R2 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?
  • 17. 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) (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 Yes R2 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 17
  • 18. 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) (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 Yes R2 253 253 264 264 *** <1% significance, ** <5% significance, * <10% significance 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 18
  • 19. 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) (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 Yes R2 253 253 264 264 *** <1% significance, ** <5% significance, * <10% significance 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 19
  • 20. 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 YesVaries Too Well No Yes Overstates effectiveness Understates effectiveness Just right Nuanced indicators and smart controls are key for accuracy and consistency in energy policy analysis 20
  • 21. 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! 21
  • 22. 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 22
  • 23. 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

Notes de l'éditeur

  1. IntroPublic/Private/Academic collaborationTime zone synergies
  2. - Quick
  3. - Quick
  4. - Won’t discuss data in presentation but we are happy to discuss after
  5. - Will not discuss the other variables but have some interesting things to say
  6. “Goldilocks” diagram Professor Carley and Professor Shrimali
  7. - We live in a world