economic analysis of scientific publications and …...supplementary figure6 – probability of...
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SUPPLEMENTARY INFORMATIONARTICLE NUMBER: 16020 | DOI: 10.1038/NENERGY.2016.20
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Supplementary Information
Advice for Energy R&D Policy from an Economic Analysis of Scientific Publications
February 2016
David PoppProfessorDepartment of Public Administration and International AffairsCenter for Policy ResearchThe Maxwell SchoolSyracuse University426 Eggers HallSyracuse, NY 13244-1020
and
Research AssociateNational Bureau of Economic Research
ph: 315-443-2482fax: 315-443-1075e-mail: [email protected] page: http://faculty.maxwell.syr.edu/dcpopp/index.html
Economic analysis of scientific publications and implications for energy research and development
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This document provides describes the data used, elaborates on methodology, and
provides additional results and robustness checks. The sections are as follows:
Supplementary Figures p. 1 Supplementary Tables p. 10Supplementary Notes:1. Web of Science Keyword Searches p. 302. Data Description p. 303. Identifying the Lag Length p. 334. Discussion of Instrument Validity p. 365. Detailed Results and Robustness Checks p. 386. Detailed Results and Methodology for Patent Citations p. 447. Additional Results on Diminishing Returns to R&D p. 46
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SUPPLEMENTARY FIGURES
Supplementary Figure 1 – Trends in Energy R&D and Publications by Country: Biofuels
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Supplementary Figure 2 – Trends in Energy R&D and Publications by Country: Energy Efficiency
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Supplementary Figure 3 – Trends in Energy R&D and Publications by Country: Solar Energy
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Supplementary Figure 4 – Trends in Energy R&D and Publications by Country: Wind Energy
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Supplementary Figure 5 – Cumulative effect of energy R&D on publications
Figure shows the cumulative effect of energy R&D on publications through year t+x, where xrepresents years since funding occurred, shown on the x-axis. Calculated using the first differenced model with instrumental variables for contemporary energy R&D
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Supplementary Figure 6 – Probability of patent citation over time
A. Annual probability of patent citation
B. Cumulative probability of patent citation
Panel A shows the annual probability, and panel B the cumulative probability, of an article receiving a citation t years after publication.
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Supplementary Figure 7 – Marginal effect of R&D per year: Models including R&D squared
Figure shows the marginal effect of energy R&D on publications in year t+x, where x represents years since funding occurred, shown on the x-axis. Calculated at different percentiles of energy R&D spending, using the first differenced model including R&D2 with instrumental variables for contemporary energy R&D.
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Supplementary Figure 8 – Cumulative effect of R&D: Models including R&D squared
Figure shows the cumulative marginal effect of energy R&D on publications through year t+x,where x represents years since funding occurred, shown on the x-axis. Calculated at different percentiles of energy R&D spending, using the first differenced model including R&D2 with instrumental variables for contemporary energy R&D.
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Supplementary Figure 9—Number of countries doubling energy R&D per year
Figure shows the number of countries whose energy R&D budget for a given technology is at least double that of the previous year.
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SUPPLEMENTARY TABLES
Supplementary Table 1 – Keyword SearchesBiofuels#11 TS = ("biomass" NEAR/5 "electricit*" OR "biomass fuel*" OR
"biomass heat*" OR "biomass energy" OR "Bio feedstock*" OR "biofeedstock*" OR "Hydrotreated vegetable oil*" or "lignocellulosic biomass*" OR "cellulosic ethanol*" or "biomass to liquid*" OR "bio synthetic gas*" OR "algae-based fuel*" OR "landfill gas*" or "Biohydrogen production*" or "Biological hydrogen production*" or "bio energy" or "bioenergy" or "biofuel*" or "bio fuel*" or "biodiesel*" or "bio diesel*" or "biogas*" or "bio gas*" OR "Bio syngas*" or "bio oil" or "bio ethanol*" or "bioethanol*" OR "fuel ethanol*" OR "Biomethanol*" OR "bio methanol*") NOT TS = ("co-combust*" or "cocombust*" or "co-fir*" or "cofir*" or "multi-combust*" or "multicombust*" or "multi-fir*" or "multifir*" or "fuel cell*" or"biofuel cell*") Refined by: Web of Science Categories=( ENERGY FUELS OR SPECTROSCOPY OR BIOTECHNOLOGY APPLIED MICROBIOLOGY OR ENTOMOLOGY OR ENGINEERING CHEMICAL OR ENVIRONMENTAL SCIENCES OR POLYMER SCIENCE OR AGRICULTURAL ENGINEERING OR ENGINEERING ENVIRONMENTAL OR GEOSCIENCES MULTIDISCIPLINARY OR CHEMISTRY MULTIDISCIPLINARY OR TRANSPORTATION SCIENCE TECHNOLOGY OR FOOD SCIENCE TECHNOLOGY OR CHEMISTRY PHYSICAL OR CHEMISTRY APPLIED OR GENETICS HEREDITY OR BIOCHEMISTRY MOLECULAR BIOLOGY OR BIOLOGY OR WATER RESOURCES OR THERMODYNAMICS OR CHEMISTRY ORGANIC OR AGRONOMY OR PHYSICS ATOMIC MOLECULAR CHEMICAL OR GEOCHEMISTRY GEOPHYSICS OR PLANT SCIENCES OR ENGINEERING MECHANICAL OR CHEMISTRY ANALYTICAL OR MULTIDISCIPLINARY SCIENCES OR METEOROLOGY ATMOSPHERIC SCIENCES OR MATERIALS SCIENCE BIOMATERIALS OR AGRICULTURE MULTIDISCIPLINARY OR DEVELOPMENTAL BIOLOGY OR MICROBIOLOGY OR ECOLOGY OR MECHANICS OR ENGINEERING INDUSTRIAL OR FORESTRY OR HORTICULTURE OR BIOCHEMICAL RESEARCH METHODS OR NANOSCIENCE NANOTECHNOLOGY OR ENGINEERING MULTIDISCIPLINARY OR SOIL SCIENCE OR MATERIALS SCIENCE PAPER WOOD OR METALLURGY METALLURGICAL ENGINEERING OR MATERIALS SCIENCE TEXTILES OR ELECTROCHEMISTRY OR ENGINEERING CIVIL OR MATERIALS SCIENCE MULTIDISCIPLINARY ) Timespan=1991-2010. Databases=SCI-EXPANDED. Lemmatization=Off
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Energy Efficiency
Note: The energy efficiency search strategy uses the three separate searches below, and excludes publications included in other energy categories,
#1 TS = ("waste heat" NEAR/3 (recover* OR convert* OR utilize* OR use)) OR TS = ("district heat*" OR "district cool*") OR TS = (("LED" OR "light emitting diode") NEAR/1 (lighting OR lightbulb* OR "light bulb*" OR lamp* OR “solid state light*” OR “solid state lamp*”)) OR TS = (("CFL" OR "compact fluorescent") NEAR/1 (lighting OR lightbulb* OR "light bulb*" OR lamp*)) OR TS = "solid state light*"
#2 (TS = ((electric OR hybrid OR “electric drive” OR “hybrid drive”) NEAR/0 (vehicle* OR automobile* OR "auto" OR "autos" OR"car" OR "cars")) NOT TS = (“fuel cell*” OR “fuel-cell*” OR “Hybrid Car-Parrinello”)) Refined by: [excluding] Web of Science Categories=( ASTRONOMY ASTROPHYSICS OR DERMATOLOGY OR INFECTIOUS DISEASES OR LITERATURE BRITISH ISLES OR MEDICINE GENERAL INTERNAL OR PHARMACOLOGY PHARMACY OR HISTORY PHILOSOPHY OF SCIENCE OR SPECTROSCOPY OR VETERINARY SCIENCES OR CLINICAL NEUROLOGY ) Timespan=1991-2010. Databases=SCI-EXPANDED. Lemmatization=Off
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#3 (TS=((energy OR fuel OR gas* OR electric* OR petrol*) NEAR/1(consum* OR use OR using OR usage OR burn*) NEAR/1 (reduc* OR less OR lower OR decreas*)) OR TS=((energy OR fuel OR gas* OR petrol) NEAR/1 (efficien* OR economy OR mileage OR productivity) NEAR/1 (improv* OR increas* OR better OR greater OR more)) OR TS=((energy OR fuel OR gas* OR electric* OR petrol*) NEAR/1 (saving* OR save OR saves OR saved))) NOT TS = ("fuel cell*" OR “fuel-cell*”)Refined by: [excluding] Web of Science Categories=( ENVIRONMENTAL STUDIES OR FOOD SCIENCE TECHNOLOGY OR ZOOLOGY OR NUTRITION DIETETICS OR PHYSIOLOGY OR BIOCHEMISTRY MOLECULAR BIOLOGY OR BIOLOGY OR METEOROLOGY ATMOSPHERIC SCIENCES OR CARDIAC CARDIOVASCULAR SYSTEMS OR ASTRONOMY ASTROPHYSICS OR MATHEMATICS APPLIED OR ROBOTICS ) AND Web of Science Categories=( ENERGY FUELS OR ENGINEERING CHEMICAL OR ENGINEERING ELECTRICAL ELECTRONIC OR ENGINEERING MECHANICAL OR THERMODYNAMICS OR ENVIRONMENTAL SCIENCES OR CONSTRUCTION BUILDING TECHNOLOGY OR MATERIALS SCIENCE MULTIDISCIPLINARY OR ENGINEERING ENVIRONMENTAL OR METALLURGY METALLURGICAL ENGINEERING OR TELECOMMUNICATIONS OR COMPUTER SCIENCE INFORMATION SYSTEMS OR COMPUTER SCIENCE HARDWARE ARCHITECTURE OR COMPUTER SCIENCE THEORY METHODS OR TRANSPORTATION SCIENCE TECHNOLOGY OR ENGINEERING MULTIDISCIPLINARY OR MATERIALS SCIENCE PAPER WOOD OR AGRICULTURAL ENGINEERING OR AUTOMATION CONTROL SYSTEMS OR MATERIALS SCIENCE CERAMICS OR COMPUTER SCIENCE SOFTWARE ENGINEERING OR MINING MINERAL PROCESSING OR ENGINEERING INDUSTRIAL OR COMPUTER SCIENCE ARTIFICIAL INTELLIGENCE OR AGRONOMY OR MATERIALS SCIENCE COMPOSITES OR MATERIALS SCIENCE TEXTILES OR OPERATIONS RESEARCH MANAGEMENT SCIENCE OR ENGINEERING MARINE OR ENGINEERING OCEAN OR URBAN STUDIES )Databases=SCI-EXPANDED Timespan=1991-2010Lemmatization=Off
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Solar Energy
The solar energy search combines searches for specific types of solar energy (e.g. solar PV) and a more general search strategy:
Solar Thermal Power#1 (TS = (solar NEAR/2 thermoelectr*) OR TS = (solar NEAR/2 “power
plant”) OR TS = (“concentrat* solar” NEAR/2 power) OR TS= (“solar thermal” NEAR/2 (power OR electric*)) OR TS=(parabolic* NEAR/2 trough*) OR TS=((parabolic NEAR/2 dish*) AND solar) OR TS = (stirling NEAR/2 dish*) OR TS=((Fresnel NEAR/2 (reflector* OR lens*)) AND solar)) NOT (TS = (cell* OR photovoltaic* OR PV) OR TS = (hydrogen NEAR/1 (generat* or product*)) OR TS = (battery OR batteries) OR TS = (storage OR store OR storing)) Refined by: [excluding] Web of Science Categories=( ENGINEERING AEROSPACE OR ASTRONOMY ASTROPHYSICS ) Timespan=1991-2010. Databases=SCI-EXPANDED. Lemmatization=Off
#2 TS=(solar NEAR/2 tower) NOT (TS = (cell* OR photovoltaic* OR PV) OR TS = (hydrogen NEAR/1 (generat* or product*)) OR TS = (battery OR batteries) OR TS = (storage OR store OR storing)) Refined by: [excluding] Web of Science Categories=( ASTRONOMY ASTROPHYSICS OR NUCLEAR SCIENCE TECHNOLOGY OR METEOROLOGY ATMOSPHERIC SCIENCES ) Timespan=1991-2010. Databases=SCI-EXPANDED. Lemmatization=Off
Solar Photovoltaic#3 TS = ("photovoltaic energ*" OR "solar cell*" OR "photovoltaic power
*" OR "photovoltaic cell*" OR "photovoltaic solar energy*") NOT (TS = (hydrogen NEAR/1 (generat* or product*)) OR TS = (battery OR batteries) OR TS = (storage OR store OR storing))
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Solar General#4 TS = (“solar panel*” OR “solar array*” OR “solar resource*” OR
“solar potential” OR “solar energy” OR “solar collector*”) NOT (#5 OR #8 OR #9 OR TS = (hydrogen NEAR/1 (generat* or product*)) OR TS = (battery OR batteries) OR TS = (storage OR store OR storing)) Refined by: Web of Science Categories=( AUTOMATION CONTROL SYSTEMS OR CHEMISTRY ANALYTICAL OR CHEMISTRY INORGANIC NUCLEAR OR CHEMISTRY MULTIDISCIPLINARY OR CHEMISTRY ORGANIC OR CHEMISTRY PHYSICAL OR CONSTRUCTION BUILDING TECHNOLOGY OR ELECTROCHEMISTRY OR ENERGY FUELS OR ENGINEERING CIVIL OR ENGINEERING ELECTRICAL ELECTRONIC OR ENGINEERING MULTIDISCIPLINARY OR ENVIRONMENTAL SCIENCES OR IMAGING SCIENCE PHOTOGRAPHIC TECHNOLOGY OR MATERIALS SCIENCE CERAMICS OR MATERIALS SCIENCE COATINGS FILMS OR MATERIALS SCIENCE MULTIDISCIPLINARY OR MECHANICS OR METALLURGY METALLURGICAL ENGINEERING OR MINING MINERAL PROCESSING OR NANOSCIENCE NANOTECHNOLOGY OR OPTICS OR PHYSICS APPLIED OR PHYSICS CONDENSED MATTER OR PHYSICS NUCLEAR OR POLYMER SCIENCE OR THERMODYNAMICS OR WATER RESOURCES ) AND [excluding] Web of Science Categories=( METEOROLOGY ATMOSPHERIC SCIENCES OR ENGINEERING AEROSPACE OR ASTRONOMY ASTROPHYSICS)Timespan=1991-2010. Databases=SCI-EXPANDED. Lemmatization=Off
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15
Wind Energy
#1 TS = ("wind power" OR "wind energy" OR "wind turbine*" OR "wind farm*" OR "wind park*" OR "wind plant*") NOT TS = (battery OR batteries OR storage OR store OR storing OR "hydrogen production*" OR "wind" NEAR "hydrogen" OR "grid integration*" OR "load management" OR "offshore" NEAR/5 ("connect*" OR "link*" OR "electric*" OR "grid*")) Refined by: Web of Science Categories=( ENERGY FUELS OR MATERIALS SCIENCE COMPOSITES OR ENGINEERING ELECTRICAL ELECTRONIC OR ORNITHOLOGY OR ENGINEERING MECHANICAL OR ENVIRONMENTAL SCIENCES OR COMPUTER SCIENCE ARTIFICIAL INTELLIGENCE OR MECHANICS OR MATERIALS SCIENCE CHARACTERIZATION TESTING OR ENGINEERING CIVIL OR PHYSICS MULTIDISCIPLINARY OR THERMODYNAMICS OR STATISTICS PROBABILITY OR MATHEMATICS INTERDISCIPLINARY APPLICATIONS OR METEOROLOGY ATMOSPHERIC SCIENCES OR ENGINEERING MARINE OR ENGINEERING MULTIDISCIPLINARY OR ECOLOGY OR METALLURGY METALLURGICAL ENGINEERING OR AUTOMATION CONTROL SYSTEMS OR INSTRUMENTS INSTRUMENTATION OR MATERIALS SCIENCE MULTIDISCIPLINARY OR MULTIDISCIPLINARY SCIENCES OR BIOLOGY OR PHYSICS APPLIED OR COMPUTER SCIENCE THEORY METHODS OR ENGINEERING AEROSPACE OR CONSTRUCTION BUILDING TECHNOLOGY OR REMOTE SENSING OR ENGINEERING OCEAN OR OPERATIONS RESEARCH MANAGEMENT SCIENCE OR ACOUSTICS OR COMPUTER SCIENCE INTERDISCIPLINARY APPLICATIONS OR MARINE FRESHWATER BIOLOGY OR ENGINEERING INDUSTRIAL OR ZOOLOGY OR PHYSICS MATHEMATICAL OR MATHEMATICS APPLIED ) AND [excluding] Web of Science Categories=( ASTRONOMY ASTROPHYSICS OR GEOSCIENCES MULTIDISCIPLINARY ) Databases=SCI-EXPANDED Timespan=1991-2010Lemmatization=Off
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Supplementary Table 2 – Countries included for each technology
BiofuelsEnergy
EfficiencySolar
Energy WindAustria X X X XCanada X X X XDenmark X X XFrance X X X XGermany X X X XItaly X X X XJapan X X X XNetherlands X X X XNew Zealand XNorway X X X XPortugal X X XSpain X X X XSweden X X XSwitzerland X X X XUnited Kingdom X X XUnited States X X X XTOTAL 15 14 14 14
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Supplementary Table 3 – Top 10 publication sources, by technology
Biofuels Energy EfficiencyUnited States 4428.62 United States 3753.56Peoples R China 1817.09 Peoples R China 1932.32India 1279.08 Japan 1308.66Brazil 949.75 South Korea 839.05Turkey 793.54 United Kingdom 834.05Japan 761.61 Germany 719.78United Kingdom 749.06 Canada 634.77Canada 735.17 Italy 613.12Germany 735.05 Taiwan 545.87Spain 714.40 France 477.72
Solar Energy WindUnited States 6323.95 United States 916.47Peoples R China 5044.19 United Kingdom 571.13Japan 4314.37 Denmark 337.15Germany 3525.28 Germany 290.77South Korea 2415.65 Spain 268.43India 2123.81 Peoples R China 255.60Taiwan 1506.67 Canada 251.87United Kingdom 1409.72 Japan 222.81France 1219.91 Greece 200.63Spain 1187.10 Turkey 197.62
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Supplementary Table 4– Descriptive Statistics for R&D and Publicationstechnology variable N mean sd min maxbiofuels R&D 315 25.48 86.02 0.00 1186.90
weighted publications 315 35.06 96.38 0.00 1138.25energy efficiency R&D 294 102.31 216.09 0.01 2149.17
weighted publications 294 29.94 55.95 0.00 493.99solar energy R&D 294 33.49 51.17 0.00 401.70
weighted publications 294 74.66 128.47 0.00 1208.84wind R&D 294 10.69 17.54 0.00 187.19
weighted publications 294 11.83 20.03 0.00 188.20Total R&D 1197 42.69 123.79 0.00 2149.17
weighted publications 1197 37.82 88.68 0.00 1208.84NOTES: R&D in millions of 2010 US $
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Supplementary Table 5 – Other Variables and data sourcesV
aria
ble
Sour
ceB
iofu
els
Ene
rgy
Eff
icie
ncy
Sola
rW
ind
Polic
y Va
riab
les
Gas
olin
e ta
xes (
2010
US
$ pe
r lite
r)IE
AX
% re
new
able
s req
uire
d by
RPS
OEC
D-E
PAU
XX
Feed
-in ta
riff,
sola
r PV
(201
0 U
S $
per k
Wh)
OEC
D-E
PAU
XFe
ed-in
tarif
f, w
ind
(201
0 U
S $
per k
Wh)
OEC
D-E
PAU
XC
ontr
ol V
aria
bles
ln(p
er c
apita
GD
P) (2
005
US$
)O
ECD
XX
XX
Gas
olin
e pr
ices
, w/o
ut ta
xes (
2010
US
$ pe
r lite
r)IE
AX
Gas
olin
e pr
ices
, inc
. tax
es (2
010
US
$ pe
r lite
r)IE
AX
Hou
seho
ld e
lect
ricity
pric
e (2
010
US$
per
MW
h)IE
AX
Cru
de o
il pr
oved
rese
rves
per
cap
ita (m
illio
n ba
rrel
s)EI
A/O
ECD
aX
XN
atur
al g
as p
rove
d re
serv
es p
er c
apita
(bill
ion
cu. f
t.)EI
A/O
ECD
aX
Coa
l pro
duct
ion
per c
apita
(sho
rt to
ns)
EIA
/OEC
Da
X%
ele
ctric
ity fr
om h
ydro
pow
erEI
AX
X%
ele
ctric
ity fr
om n
ucle
ar
EIA
XX
grow
th ra
te o
f ele
ctric
ity c
onsu
mpt
ion
EIA
XX
Inst
rum
ents
Dum
my:
is e
xecu
tive
bran
ch p
arty
orie
ntat
ion
right
-win
gD
PIX
XX
XD
umm
y: is
exe
cutiv
e br
anch
par
ty o
rient
atio
n ce
nter
DPI
XX
XX
Tax
reve
nue
(exc
ludi
ng so
cial
secu
rity)
, % o
f GD
PO
ECD
XX
XX
Gen
eral
gov
ernm
ent c
onsu
mpt
ion
expe
nditu
re, %
of G
DP
OEC
DX
XX
XG
over
nmen
t ene
rgy
R&
D: b
iofu
els
IEA
XX
Gov
ernm
ent e
nerg
y R
&D
: ene
rgy
effic
ienc
yIE
AX
XG
over
nmen
t ene
rgy
R&
D: e
nerg
y st
orag
eIE
AX
XG
over
nmen
t ene
rgy
R&
D: n
ucle
ar fu
sion
IEA
XG
over
nmen
t ene
rgy
R&
D: s
olar
ene
rgy
IEA
Gov
ernm
ent e
nerg
y R
&D
: win
dIE
AX
XD
PI: D
atab
ase
of P
oliti
cal I
nstit
utio
ns 2
012;
1EI
A: U
S En
ergy
Info
rmat
ion
Adm
inis
tratio
n; IE
A: I
nter
natio
nal E
nerg
y A
genc
y En
ergy
Pr
ices
and
Tax
es D
atab
ase;
OEC
D:O
ECD
Stat
; OEC
D-E
PAU
: OEC
D R
enew
able
Ene
rgy
Polic
y D
atab
ase,
2 an
upda
te a
nd e
xten
sion
of
the
data
set o
rigin
ally
use
d in
John
ston
e et
al.3
a : Res
erve
dat
a fr
om E
nerg
y In
form
atio
n A
dmin
istra
tion;
pop
ulat
ion
data
to c
alcu
late
per
cap
ita v
alue
s fro
m O
ECD
Stat
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20
Supplementary Table 6 – AIC statistic for alternative number of years of lagged R&DBi
ofue
lsEn
ergy
Effi
cien
cyFD
IVP
DL
IVFD
IVP
DL
IVal
l con
trols
sing
le y
ear
all c
ontro
lssi
ngle
yea
ral
l con
trols
sing
le y
ear
all c
ontro
lssi
ngle
yea
rA
IC la
g 1:
2811
.928
06.3
AIC
lag
1:21
8221
71.6
AIC
lag
2:24
06.5
2400
.5A
IC la
g 2:
2059
.720
45A
IC la
g 3:
2408
2402
.4A
IC la
g 3:
2070
.920
46.8
AIC
lag
4:24
01.2
2401
.924
01.2
2435
.8A
IC la
g 4:
2079
.420
47.3
2076
.420
47.3
AIC
lag
5:23
97.2
2403
.824
25.6
2434
.8A
IC la
g 5:
2083
.420
44.3
2071
.520
53.6
AIC
lag
6:24
01.6
2399
2425
.624
28.4
AIC
lag
6:20
9120
46.3
2086
.320
53.9
AIC
lag
7:23
94.1
2400
.724
21.9
2428
.5A
IC la
g 7:
2097
.320
48.5
2096
.520
62.1
AIC
lag
8:23
78.3
2400
.224
13.7
2427
.5A
IC la
g 8:
2105
.920
48.7
2093
.320
69.4
AIC
lag
9:23
5623
72.6
2388
.824
17.5
AIC
lag
9:21
1420
47.8
2114
.120
67.5
AIC
lag
10:
2351
.323
56.9
2389
.823
97.2
AIC
lag
10:
2110
2038
.121
30.6
2062
.1A
IC la
g 11
:23
51.5
2352
.324
08.2
2420
.1A
IC la
g 11
:21
14.1
2038
.821
33.5
2063
.1
Sola
r Ene
rgy
Win
d En
ergy
FDIV
PD
L IV
FDIV
PD
L IV
all c
ontro
lssi
ngle
yea
ral
l con
trols
sing
le y
ear
all c
ontro
lssi
ngle
yea
ral
l con
trols
sing
le y
ear
AIC
lag
1:27
42.8
2732
.5A
IC la
g 1:
1839
.518
44.9
AIC
lag
2:27
04.1
2688
.2A
IC la
g 2:
1738
.717
42.5
AIC
lag
3:27
14.2
2689
.9A
IC la
g 3:
1748
.117
44A
IC la
g 4:
2672
.926
41.2
2652
.626
32.9
AIC
lag
4:17
50.8
1745
1764
.317
45.0
AIC
lag
5:26
74.6
2643
.226
57.3
2636
.6A
IC la
g 5:
1756
.517
46.9
1771
.817
58.3
AIC
lag
6:26
76.4
2640
.426
54.2
2625
.5A
IC la
g 6:
1763
.317
47.8
1773
.217
65.3
AIC
lag
7:26
76.8
2641
2644
.726
24.6
AIC
lag
7:17
66.5
1737
.317
70.6
1760
.5A
IC la
g 8:
2682
.426
40.3
2638
.926
23.7
AIC
lag
8:17
77.1
1739
.317
88.2
1767
.7A
IC la
g 9:
2689
.126
36.9
2631
.826
26.0
AIC
lag
9:17
47.7
1740
.217
58.6
1768
.7A
IC la
g 10
:27
60.2
2639
.426
23.8
2626
.0A
IC la
g 10
:17
46.3
1737
.717
41.3
1768
.7
a: 2
nd d
egre
e po
lyno
mia
l; b:
3rd
deg
ree
poly
nom
ial;
c: 4
th d
egre
e po
lyno
mia
l
NATURE ENERGY | www.nature.com/natureenergy
SUPPLEMENTARY INFORMATIONDOI: 10.1038/NENERGY.2016.20
21
Supplementary Table 7 – First-stage energy R&D results (Instruments and lagged own energy R&D)
Biofuels Eng. Eff. Solar WindR&D: Biofuels 0.170*** 0.0670**
(0.0287) (0.0276)R&D: Energy Efficiency 0.481*** 0.0546***
(0.0892) (0.0184)R&D: Energy Storage 0.578 2.351***
(0.463) (0.652)R&D: Nuclear Fusion -0.00596
(0.120)R&D: Wind 0.410 4.561***
(0.408) (0.937)Political Leadership: Right -0.526 -12.51 -2.991 0.648
(3.849) (11.14) (3.056) (1.391)Political Leadership: Center -7.513 -4.975 -4.863 -4.331
(14.87) (16.87) (6.982) (6.353)Govt. Consumption (% GDP) -5.201 -7.246 -0.250 0.794
(3.776) (6.549) (2.565) (0.956)Tax Revenue (% GDP) -1.281 0.476 -1.610 -0.317
(1.975) (3.788) (1.240) (0.663)RD(t-1) 0.0220 0.213*** -0.00981 -0.286***
(0.0974) (0.0706) (0.0831) (0.0622)RD(t-2) -0.0586 -0.195*** 0.247** -0.0789
(0.0901) (0.0480) (0.125) (0.105)RD(t-3) 0.0644 -0.131 -0.214 -0.209*
(0.259) (0.146) (0.134) (0.121)RD(t-4) -0.195 0.101 -0.0371 0.0729
(0.352) (0.186) (0.125) (0.113)RD(t-5) -0.162 -0.114 -0.239 -0.163
(0.274) (0.135) (0.232) (0.122)RD(t-6) 0.0273 0.199 0.146 -0.0997
(0.436) (0.205) (0.199) (0.0974)RD(t-7) 0.0663 0.291* -0.133
(0.231) (0.148) (0.105)RD(t-8) 0.556 -0.372*
(0.456) (0.217)RD(t-9) 0.792* -0.181
(0.417) (0.146)RD(t-10) -0.191 -0.0391
(0.550) (0.221)N 300 280 280 280F-stat all instruments 32.51 17.50 11.40 38.49Standard errors in parenthesesAll models use robust standard errors with correction for autocorrelation.* p<0.1, ** p<0.05, *** p<0.01
NATURE ENERGY | www.nature.com/natureenergy
SUPPLEMENTARY INFORMATION DOI: 10.1038/NENERGY.2016.20
22
Supplementary Table 8 -- First-stage energy R&D results (controls)
Biofuels Eng. Eff. Solar WindR&D(t-1) through (t-10) 0.922 -0.229 -0.107 -0.897**
(1.050) (0.379) (0.405) (0.372)gas tax -49.517
(56.72)FIT dummies
FIT wind -5.672(72.61)
FIT pv -38.819(34.18)
FIT geo
REC dummy
REC levels 3.13 0.292(4.041) (2.384)
% hydro -2.818** -0.351(1.356) (1.444)
% nuclear 0.046 -1.358(1.936) (1.241)
lnGDP -94.652 375.008 -155.17 25.817(180.2) (566.3) (123.0) (61.52)
grow_elec -2.685 -2.315(3.593) (1.955)
gas price 98.727(131.3)
gas price no taxes 49.743(153.7)
oil per capita -17565.6 -108127(24391.6) (89925.4)
gas per capita 19784.34(14992.1)
coal per capita 44.093(47.62)
electric price -0.136(0.749)
N 300 280 280 280Standard errors in parenthesesAll models use robust standard errors with correction for autocorrelation.* p<0.1, ** p<0.05, *** p<0.01
NATURE ENERGY | www.nature.com/natureenergy
SUPPLEMENTARY INFORMATIONDOI: 10.1038/NENERGY.2016.20
23
Supplementary Table 9 – First-differenced panel regression results: Government R&DIV
exog
IVex
ogIV
exog
IVex
ogR
D0.
152*
**0.
152*
**
0.
0184
***
0.01
13**
-0
.026
70.
372*
**0.
0983
**0.
124*
**(0
.015
3)(0
.015
1)
(0.0
0648
)
(0.
0052
4)
(0.2
76)
(0.0
839)
(0.0
440)
(0.0
246)
RD
(t-1)
0.37
6***
0.37
6***
0.04
57**
*
0.
0443
***
0.60
1***
0.66
3***
0.19
7***
0.21
2***
(0.0
259)
(0.0
245)
(0
.006
75)
(
0.00
705)
(0
.167
)(0
.150
)(0
.052
0)(0
.046
2)R
D(t-
2)
0.
429*
**0.
429*
**
0.
0718
***
0.06
97**
*0.
483*
*0.
331*
0.33
5***
0.34
8***
(0.0
196)
(0.0
196)
(0
.008
78)
(
0.00
845)
(0
.246
)(0
.200
)(0
.054
7)(0
.054
9)R
D(t-
3)
0.
0071
90.
0072
1
-0.
0136
-0.
0185
0.
154
0.31
2**
0.03
130.
0525
(0.0
719)
(0.0
720)
(0
.029
6)
(0
.029
4)
(0.1
44)
(0.1
27)
(0.0
580)
(0.0
587)
RD
(t-4)
0.17
6**
0.17
6**
0.01
48
0.01
90
0.44
7**
0.60
1***
0.02
630.
0287
(0.0
891)
(0.0
891)
(0
.025
3)
(0
.025
3)
(0.1
91)
(0.2
25)
(0.0
585)
(0.0
575)
RD
(t-5)
-0.1
01-0
.100
-
0.03
06
-
0.03
24
0.03
500.
116
0.01
300.
0216
(0.1
09)
(0.1
09)
(0
.025
5)
(0
.025
3)
(0.2
43)
(0.2
25)
(0.0
532)
(0.0
523)
RD
(t-6)
0.09
400.
0942
0.01
55
0
.008
91
0.38
5**
0.34
7*0.
0618
0.06
39(0
.104
)(0
.104
)
(0.0
174)
(0.0
165)
(0
.194
)(0
.186
)(0
.051
9)(0
.051
5)R
D(t-
7)
0.
0585
0.05
88
0.0
0067
3
0
.003
61
0.12
1***
0.12
7***
(0.0
965)
(0.0
956)
(0
.022
9)
(0
.022
2)
(0.0
451)
(0.0
436)
RD
(t-8)
0.08
840.
0882
-
0.03
29
-
0.03
31
(0.1
18)
(0.1
16)
(0
.025
4)
(0
.024
9)
RD
(t-9)
0.55
8***
0.55
8***
0.03
85
0.04
08
(0.1
60)
(0.1
62)
(0
.026
7)
(0
.026
6)
RD
(t-10
)
0.31
8**
0.31
9**
0.06
01**
*
0.
0616
***
(0.1
52)
(0.1
51)
(0
.021
0)
(0
.021
2)
Cum
ulat
ive e
ffect
s:
R
&D
2.
155*
**2.
158*
**
0.1
88**
*
0.17
5***
2.07
8***
2.74
2***
0.88
3***
0.97
7***
(0.3
48)
(0.3
53)
(0
.052
1)
(0.0
511)
(0.5
99)
(0.5
73)
(0.2
25)
(0.2
02)
N30
030
0
280
280
280
280
280
280
AIC
23
51.3
2351
.3
21
10.0
21
08.3
2676
.426
48.1
1766
.517
65.3
BIC
27
03.2
2703
.2
25
35.3
25
33.5
2999
.929
71.5
2115
.521
14.3
EU
x y
ear c
hi-s
quar
ed23
.11
23.0
4
2
0.00
19
.94
26.3
227
.84
34.6
634
.54
EU
x y
ear p
-val
ue0.
284
0.28
7
0
.458
0.
462
0.15
50.
113
0.02
200.
0227
F R
D 1
st s
tage
32.5
1
1
7.50
11
.40
38.4
9H
anse
n J
p-va
lue
0.
293
0.1
51
0.49
10.
502
End
ogen
eity
test
p-v
alue
0.97
9
0
.687
0.
0459
0.25
8S
tand
ard
erro
rs in
par
enth
eses
. A
ll m
odel
s us
e ro
bust
sta
ndar
d er
rors
with
cor
rect
ion
for a
utoc
orre
latio
n.*:
sig
nific
ant a
t 10%
leve
l. **
: sig
nific
ant a
t 5%
leve
l. *
**: S
igni
fican
t at 1
% le
vel.
Bio
fuel
sE
nerg
y E
ffici
ency
Sol
ar E
nerg
yW
ind
Ene
rgy
NATURE ENERGY | www.nature.com/natureenergy
SUPPLEMENTARY INFORMATION DOI: 10.1038/NENERGY.2016.20
24
Supplementary Table 10 – First differenced panel regression results: ControlsIV
exog
IVex
ogIV
exog
IVex
ogC
umul
ative
effe
cts:
R&
D
2.15
5***
2.15
8***
0
.188
***
0.
175*
**2.
078*
**2.
742*
**0.
883*
**0.
977*
**(0
.348
)(0
.353
)
(0.0
521)
(0
.051
1)(0
.599
)(0
.573
)(0
.225
)(0
.202
) ln
GD
P
-6
.353
-6.5
49
54
.831
60
.706
-290
.964
*-2
17.0
63-4
7.68
5-4
8.34
7(7
4.85
)(7
4.97
)
(73
.35)
(7
2.62
)(1
74.4
)(1
62.4
)(3
3.00
)(3
3.09
) g
as p
rice
no ta
xes
2
40.4
49**
*24
0.23
***
(81.
66)
(81.
56)
gas
tax
-3
.763
-3.7
52(2
5.22
)(2
5.21
) g
as p
rice
-16
.975
-1
7.86
5
(19
.51)
(1
9.66
) o
il pe
r cap
ita
7211
.473
7235
.793
-8
090.
286
-9
278.
706
(101
75.8
)(1
0120
.7)
(1
7340
.3)
(1
7305
.9)
gas
per
cap
ita
-
1388
.5
-124
5.72
2
(219
3.2)
(2
181.
3) c
oal p
er c
apita
-11.
051*
-1
2.21
*
(6.
395)
(6
.566
) e
lect
ric p
rice
0.
100
0.10
7
(0.
121)
(0
.122
) g
row
_ele
c
-5.2
58-3
.77
-0.8
72-0
.802
(5.6
88)
(5.0
39)
(0.9
20)
(0.9
29)
% h
ydro
0.62
91.
627
1.04
30.
98(2
.065
)(1
.788
)(0
.718
)(0
.737
) %
nuc
lear
-4
.819
*-4
.592
*-0
.499
-0.4
21(2
.493
)(2
.454
)(0
.678
)(0
.681
) F
IT w
ind
9.
827
12.0
61(2
5.86
)(2
6.07
) F
IT p
v
18
.874
39.5
23(6
0.28
)(5
3.99
) R
EC
leve
ls
15.0
92**
13.5
24**
2.13
32.
102
(7.2
34)
(6.3
52)
(1.3
76)
(1.3
52)
Bio
fuel
sE
nerg
y E
ffici
ency
Sol
ar E
nerg
yW
ind
Ene
rgy
NATURE ENERGY | www.nature.com/natureenergy
SUPPLEMENTARY INFORMATIONDOI: 10.1038/NENERGY.2016.20
25
Supplementary Table 11 – Effect of EU-specific year effects: Government R&DE
U x
yea
rno
EU
EU
x y
ear
no E
UE
U x
yea
rno
EU
EU
x y
ear
no E
UR
D0.
152*
**
0
.159
***
0.01
84**
*
0.
0187
***
-0.0
267
0
.007
68
0.09
83**
0.09
56**
(0
.015
3)
(0.0
178)
(0.0
0648
)
(0.
0068
8)
(0.2
76)
(
0.31
3)
(0.0
440)
(0
.046
9)
RD
(t-1)
0.37
6***
0.3
61**
*
0.
0457
***
0.04
40**
*0.
601*
**
0
.609
***
0.19
7***
0.1
96**
*(0
.025
9)
(0.0
296)
(0.0
0675
)
(0.
0076
5)
(0.1
67)
(
0.21
6)
(0.0
520)
(0
.057
1)
RD
(t-2)
0.42
9***
0.3
97**
*
0.
0718
***
0.07
29**
*0.
483*
*
0
.566
*
0.33
5***
0.3
38**
*(0
.019
6)
(0.0
229)
(0.0
0878
)
(0.
0095
4)
(0.2
46)
(
0.30
1)
(0.0
547)
(0
.063
5)
RD
(t-3)
0.00
719
0.07
26
-
0.01
36
-
0.01
60
0.15
4
0
.141
0.
0313
0.04
12
(0.0
719)
(0
.078
9)
(0
.029
6)
(0
.032
3)
(0.1
44)
(
0.17
2)
(0.0
580)
(0
.061
3)
RD
(t-4)
0.17
6**
0.2
94**
0.
0148
0.
0111
0.
447*
*
0
.390
*
0.02
63
0.
0222
(0
.089
1)
(0.
121)
(0.0
253)
(0.0
272)
(0
.191
)
(0.
220)
(0
.058
5)
(0.0
662)
R
D(t-
5)
-0
.101
-
0.02
70
-
0.03
06
-
0.01
34
0.03
50
0
.136
0.
0130
0
.006
34
(0.1
09)
(
0.11
8)
(0
.025
5)
(0
.028
5)
(0.2
43)
(
0.30
3)
(0.0
532)
(0
.059
4)
RD
(t-6)
0.09
40
0
.139
0.
0155
0.
0140
0.
385*
*
0
.374
*
0.06
18
0.
0942
(0
.104
)
(0.
127)
(0.0
174)
(0.0
180)
(0
.194
)
(0.
219)
(0
.051
9)
(0.0
596)
R
D(t-
7)
0.
0585
0.05
87
0
.000
673
0.
0142
0.
121*
**
0
.145
***
(0.0
965)
(
0.12
1)
(0
.022
9)
(0
.024
7)
(0.0
451)
(0
.051
0)
RD
(t-8)
0.08
84
0.0
0444
-0.
0329
-0.
0356
(0
.118
)
(0.
147)
(0.0
254)
(0.0
279)
R
D(t-
9)
0.
558*
**
0
.559
***
0.03
85
0.04
28
(0.1
60)
(
0.20
9)
(0
.026
7)
(0
.031
7)
RD
(t-10
)
0.31
8**
0.3
65*
0.
0601
***
0.06
49**
*(0
.152
)
(0.
214)
(0.0
210)
(0.0
191)
C
umul
ative
effe
cts:
R&
D
2.15
5***
2
.383
***
0.1
88**
*
0
.218
***
2.
078*
**
2.2
23**
*
0.88
3***
0
.939
***
(0
.348
)
(0.
444)
(0.0
521)
(0.0
534)
(0
.599
)
(0.
727)
(0
.225
)
(0.
246)
N
300
30
0
280
28
028
0
280
280
28
0A
IC
2351
.3
23
67.0
21
10.0
21
02.8
26
76.4
2682
.1
1766
.5
17
60.1
B
IC
2703
.2
26
44.8
25
35.3
24
29.9
29
99.9
2932
.9
2115
.5
20
36.3
E
U x
yea
r p-v
alue
0.28
4
0
.458
0.
155
0.02
20F
RD
1st
sta
ge
32
.51
29.
82
17.
50
19.
95
11.4
0
1
3.44
38
.49
32.
07
Han
sen
J p-
valu
e
0.29
3
0
.411
0
.151
0
.109
0.
491
0.6
32
0.50
2
0
.540
E
ndog
enei
ty te
st p
-val
ue
0.
979
0.7
37
0.6
87
0.5
95
0.04
59
0
.124
0.
258
0.1
92
Sta
ndar
d er
rors
in p
aren
thes
es.
All
mod
els
use
robu
st s
tand
ard
erro
rs w
ith c
orre
ctio
n fo
r aut
ocor
rela
tion.
*: s
igni
fican
t at 1
0% le
vel.
**: s
igni
fican
t at 5
% le
vel.
***
: Sig
nific
ant a
t 1%
leve
l.
Bio
fuel
sE
nerg
y E
ffici
ency
Sol
ar E
nerg
yW
ind
Ene
rgy
NATURE ENERGY | www.nature.com/natureenergy
SUPPLEMENTARY INFORMATION DOI: 10.1038/NENERGY.2016.20
26
Supplementary Table 12 – Effect of EU-specific year effects: ControlsE
U x
yea
rno
EU
EU
x y
ear
no E
UE
U x
yea
rno
EU
EU
x y
ear
no E
UC
umul
ative
effe
cts:
R&
D
2.15
5***
2
.383
***
0.1
88**
*
0
.218
***
2.
078*
**
2.2
23**
*
0.88
3***
0
.939
***
(0
.348
)
(0.
444)
(0.0
521)
(0.0
534)
(0
.599
)
(0.
727)
(0
.225
)
(0.
246)
ln
GD
P
-6
.353
-41.
66
54.8
31
65.0
24
-290
.964
*
-231
.318
-4
7.68
5
-40
.879
(7
4.85
)
(87
.24)
(73
.35)
(71
.26)
(1
74.4
)
(17
5.7)
(3
3.00
)
(34
.85)
g
as p
rice
no ta
xes
2
40.4
49**
* 2
93.5
45**
*
(81.
66)
(
94.1
9)
gas
tax
-3
.763
-8.8
2
(25.
22)
(
28.1
4)
gas
pric
e
-
16.9
75
-
10.4
72
(
19.5
1)
(
21.3
3)
oil
per c
apita
72
11.4
73
583
3.28
9
-8
090.
286
987
9.78
6
(101
75.8
)
(847
3.9)
(173
40.3
)
(13
051.
2)
gas
per
cap
ita
-
1388
.5
-1
346.
34
(2
193.
2)
(2
100.
3)
coa
l per
cap
ita
-1
1.05
1*
-7.5
4
(
6.39
5)
(
7.20
1)
ele
ctric
pric
e
0.10
0
0
.108
(0.
121)
(0.
108)
g
row
_ele
c
-5.2
58
2
.215
-0
.872
-0.0
33
(5.6
88)
(
4.95
9)
(0.9
20)
(
0.88
8)
% h
ydro
0.62
9
1
.102
1.
043
1.2
36
(2.0
65)
(
2.33
5)
(0.7
18)
(
0.79
4)
% n
ucle
ar
-4.8
19*
-1.8
43
-0.4
99
-0
.163
(2
.493
)
(2.
551)
(0
.678
)
(0.
677)
F
IT w
ind
9.
827
16.4
95
(25.
86)
(
27.5
7)
FIT
pv
18.8
74
-70
.981
(6
0.28
)
(66
.22)
R
EC
leve
ls
15.0
92**
7.0
73
2.13
3
1
.753
(7
.234
)
(7.
325)
(1
.376
)
(1.
440)
Bio
fuel
sE
nerg
y E
ffici
ency
Sol
ar E
nerg
yW
ind
Ene
rgy
NATURE ENERGY | www.nature.com/natureenergy
SUPPLEMENTARY INFORMATIONDOI: 10.1038/NENERGY.2016.20
27
Supplementary Table 13 – Polynomial distributed lag regression results: Government R&D
IVex
ogIV
exog
IVex
ogIV
exog
RD
0.14
35**
*0.
1449
***
0.02
45**
*0.
0100
0.26
78**
0.39
09**
*0.
1085
*0.
1276
***
(0.0
123)
(0.0
143)
(0.0
064)
(0.0
073)
(0.1
228)
(0.0
852)
(0.0
568)
(0.0
323)
RD
(t-1)
0.31
21**
*0.
3127
***
0.03
41**
*0.
0410
***
0.38
72**
*0.
4460
***
0.25
07**
*0.
2647
***
(0.0
278)
(0.0
283)
(0.0
083)
(0.0
053)
(0.1
041)
(0.1
013)
(0.0
451)
(0.0
411)
RD
(t-2)
0.38
22**
*0.
3826
***
0.04
07**
*0.
0506
***
0.45
32**
*0.
4692
***
0.26
58**
*0.
2764
***
(0.0
181)
(0.0
181)
(0.0
147)
(0.0
084)
(0.1
431)
(0.1
405)
(0.0
514)
(0.0
551)
RD
(t-3)
0.37
68**
*0.
3774
***
0.04
44**
0.04
47**
*0.
4655
***
0.46
04**
*0.
2021
***
0.21
04**
*(0
.036
6)(0
.036
5)(0
.019
2)(0
.010
0)(0
.162
0)(0
.154
9)(0
.055
5)(0
.060
4)R
D(t-
4)
0.
3210
***
0.32
19**
*0.
0451
**0.
0291
***
0.42
43**
*0.
4198
***
0.10
78*
0.11
47*
(0.0
614)
(0.0
615)
(0.0
212)
(0.0
104)
(0.1
518)
(0.1
461)
(0.0
552)
(0.0
594)
RD
(t-5)
0.24
15**
*0.
2426
***
0.04
29**
0.00
950.
3295
**0.
3472
**0.
0312
0.03
70(0
.072
9)(0
.073
6)(0
.020
8)(0
.010
4)(0
.135
5)(0
.141
9)(0
.049
7)(0
.052
3)R
D(t-
6)
0.
1669
**0.
1681
**0.
0378
**-0
.008
30.
1811
0.24
280.
0204
0.02
52(0
.072
0)(0
.073
7)(0
.018
0)(0
.010
3)(0
.179
7)(0
.196
6)(0
.039
7)(0
.039
7)R
D(t-
7)
0.
1274
*0.
1287
*0.
0297
**-0
.018
3*0.
1237
**0.
1271
**(0
.071
7)(0
.074
4)(0
.013
7)(0
.010
1)(0
.051
7)(0
.049
7)R
D(t-
8)
0.
1553
*0.
1564
*0.
0186
*-0
.014
9(0
.085
5)(0
.088
4)(0
.010
9)(0
.010
2)R
D(t-
9)
0.
2843
**0.
2854
**0.
0046
0.00
78(0
.117
5)(0
.119
5)(0
.016
2)(0
.013
4)R
D(t-
10)
0.
5502
***
0.55
15**
*-0
.012
30.
0556
**(0
.197
1)(0
.197
8)(0
.028
4)(0
.023
0)C
umul
ative
effe
cts:
R&
D
3.06
12**
*3.
0723
***
0.31
02**
*0.
2067
***
2.50
85**
*2.
7764
***
1.11
01**
*1.
1830
***
(0.4
413)
(0.4
611)
(0.1
001)
(0.0
633)
(0.6
621)
(0.7
313)
(0.2
765)
(0.2
792)
N30
030
028
028
028
028
028
028
0A
IC
2389
.823
89.8
2085
.220
84.1
2641
.126
37.1
1770
.617
70.0
BIC
26
30.5
2630
.523
32.3
2331
.328
62.8
2858
.820
17.8
2017
.1S
tand
ard
erro
rs in
par
enth
eses
. A
ll m
odel
s us
e ro
bust
sta
ndar
d er
rors
with
cor
rect
ion
for a
utoc
orre
latio
n.*:
sig
nific
ant a
t 10%
leve
l. **
: sig
nific
ant a
t 5%
leve
l. *
**: S
igni
fican
t at 1
% le
vel.
Bio
fuel
sE
nerg
y E
ffici
ency
Sol
ar E
nerg
yW
ind
Ene
rgy
NATURE ENERGY | www.nature.com/natureenergy
SUPPLEMENTARY INFORMATION DOI: 10.1038/NENERGY.2016.20
28
Supplementary Table 14 – Polynomial distributed lag regression results: ControlsIV
exog
IVex
ogIV
exog
IVex
ogC
umul
ative
effe
cts:
R&
D
3.06
12**
*3.
0723
***
0.31
02**
*0.
2067
***
2.50
85**
*2.
7764
***
1.11
01**
*1.
1830
***
(0.4
413)
(0.4
611)
(0.1
001)
(0.0
633)
(0.6
621)
(0.7
313)
(0.2
765)
(0.2
792)
lnG
DP
-2.1
057
-2.8
775
71.1
088
51.4
496
-1.9
e+02
-1.7
e+02
-41.
2015
-41.
6738
(82.
2929
)(8
1.76
42)
(87.
4775
)(6
9.23
78)
(169
.493
9)(1
68.1
542)
(33.
6773
)(3
3.87
59)
gas
pric
e no
taxe
s
221.
8810
***
221.
2094
***
(84.
4800
)(8
3.84
67)
gas
tax
-3
.194
1-3
.139
3(2
7.23
91)
(27.
1973
) g
as p
rice
-4
.349
6-6
.853
9(2
4.26
36)
(21.
2315
) o
il pe
r cap
ita
7.1e
+03
7.1e
+03
-1.1
e+04
-1.2
e+04
( 1.1
e+04
)( 1
.1e+
04)
( 8.2
e+03
)( 8
.4e+
03)
gas
per
cap
ita
-4.0
e+03
-1.5
e+03
( 2.5
e+03
)( 2
.2e+
03)
coa
l per
cap
ita
-16.
5876
**-1
2.09
52**
(6.5
131)
(5.2
584)
ele
ctric
pric
e
0.06
940.
0774
(0.1
283)
(0.1
238)
gro
w_e
lec
-3
.287
8-2
.704
7-0
.771
7-0
.716
7(5
.142
4)(4
.954
5)(1
.016
9)(1
.002
6) %
hyd
ro
-0
.074
30.
0766
0.44
730.
4144
(1.6
134)
(1.5
613)
(0.8
803)
(0.8
923)
% n
ucle
ar
-3.6
867
-3.6
852
-0.0
177
0.03
23(2
.575
5)(2
.509
7)(0
.755
3)(0
.768
5) F
IT w
ind
25
.060
025
.836
9(2
6.42
34)
(26.
5270
) F
IT p
v
55
.477
955
.773
9(6
0.09
10)
(59.
4518
) R
EC
leve
ls
14.4
206*
*13
.540
7*2.
6813
*2.
6451
*(7
.239
4)(6
.929
3)(1
.556
0)(1
.526
8)S
tand
ard
erro
rs in
par
enth
eses
. A
ll m
odel
s us
e ro
bust
sta
ndar
d er
rors
with
cor
rect
ion
for a
utoc
orre
latio
n.*:
sig
nific
ant a
t 10%
leve
l. **
: sig
nific
ant a
t 5%
leve
l. *
**: S
igni
fican
t at 1
% le
vel.
Bio
fuel
sE
nerg
y E
ffici
ency
Sol
ar E
nerg
yW
ind
Ene
rgy
NATURE ENERGY | www.nature.com/natureenergy
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29
Supplementary Table 15 – Time to first patent citation hazard regression
full sample articles from 2000 or laterBiofuels Solar Wind Biofuels Solar Wind
citation lag 0.706*** 0.333*** 0.531*** 1.359*** 0.691*** 0.973**(0.142) (0.0500) (0.157) (0.326) (0.109) (0.480)
(citation lag)^2 -0.0249*** -0.0167*** -0.0191*** -0.0682*** -0.0412*** -0.0562(0.00754) (0.00331) (0.00731) (0.0231) (0.00866) (0.0377)
Multiple country dummy -0.0301 -0.0294 -1.234** -0.0478 -0.0821 -1.775*(0.282) (0.123) (0.626) (0.322) (0.151) (1.058)
Constant -8.987*** -7.500*** -7.353*** -11.22*** -8.621*** -8.283***(0.948) (0.597) (1.096) (1.324) (0.659) (1.655)
N 55790 164957 21895 35568 95725 13670AIC 1407.3 4306.9 831.2 881.6 2444.9 487.3BIC 3827.1 6930.5 2709.8 2170.4 3780.1 1525.5log likelihood -432.6 -1891.5 -180.6 -288.8 -1081.5 -105.6* p<0.10, ** p<0.05, *** p<0.01Standard errors in parentheses. All standard errors clustered by article. All regressions include publication year x country fixed effects, with publications from 2001 in the US as the base category.
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30
SUPPLEMENTARY NOTES
Supplementary Note 1. Web of Science Keyword Searches
Our publication data were provided as a custom database from Thomson Reuters, created
using keyword searches in Web of Science developed by the researchers in consultation with
experts at Thomson Reuters. As noted in the text, we devised searches that would be narrow, so
as to avoid irrelevant articles, as such articles would respond differently to alternative energy
R&D trends and thus bias our results downward. Thus, for each category, we devised keyword
searches that are refined by focusing on specific subject categories in the Web of Science
database. The searches used for each technology are listed in Supplementary Table 1.
Supplementary Note 2. Data Description
Our data set combines the publication data provided by Thomson Reuters with publicly
available energy R&D data from the International Energy Agency. Additional publicly available
data are used as controls. As the R&D data series are incomplete for some countries, the set of
countries included for each technology depends on both the country actively publishing in that
area and energy R&D data being available. R&D data go back as far as 1974 for some countries.
Using a starting date of 1992 for the publication data, we select countries with at least 10 years
of lagged R&D data for a given technology. Supplementary Table 2 lists the countries included
in the regressions for each technology. Supplementary Table 3 lists the top 10 publishing
countries for each technology. While the top 10 includes a few emerging economies, such as
China, our final data set includes most of these top publishing countries.
Supplementary Table 4 provides descriptive statistics for both R&D and the weighted
publications. R&D data are shown in millions of 2010 dollars. Evaluated at the means, for most
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technologies we see a bit less than one million dollars of R&D spending per publication. The
exception is energy efficiency, for which a little over $3 million of R&D is spent per publication.
While this may indicate that energy efficiency R&D is less productive than other R&D spending,
it may also indicate that our keyword searches identify a lower percentage of relevant energy
efficiency articles than for other technologies.
Figure 1 in the main text shows the aggregate trends in both energy R&D and
publications for all countries in each regression. Here, Supplementary Figures 1-4 illustrate
these trends by technology for each country. Note that there is variation both across times and
across countries, which is important for separately identifying the effect of energy R&D, as
opposed to simply finding that publications increase due to increased global attention to climate
change. For example, prior to the 2009 stimulus, the U.S. experiences two peaks in wind energy
R&D – one after the 1970s energy crises, and another in the mid-1990s. In contrast, Denmark
and the Netherlands have relatively flat R&D budgets, with the exception of the Netherlands in
1985. Note also that countries also choose to emphasize different technologies. For example, in
2010 the United States spent over ten times as much on wind energy as Japan in 2007, but nearly
twice as much over twice as much on biofuels research as on solar energy, whereas Japan spent
more on solar energy R&D than on biofuels. These figures also show the importance of lagged
R&D effects, as within individual countries, R&D spending generally peaks several years before
publication counts. Examples include wind energy in Germany, Italy, and Japan. As suggested
by our empirical results, the importance of lags is particularly evident in the case of energy
efficiency.
Supplementary Table 5 lists the policy variables, controls, and instruments used to
estimate equations (1) – (3) for each technology. The columns indicate which variables are
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included for each technology. As noted in the methods section, our instruments include two
categories of variables. First, we include instruments for R&D spending on related technologies,
with the specific technologies chosen for each technology indicated in the table.
Overidentification tests were used to verify the validity of all technologies included and to rule
out invalid instruments. Note that any related technologies are not used as instruments. For
example, nuclear R&D is only used as an instrument for biofuels, a transportation-based
technology, but not for any technologies pertaining to electricity. Since the share of nuclear
energy is a control for solar and renewable energy, decisions on nuclear R&D cannot be
considered exogenous for these technologies. Similarly, policies that promote renewable
electricity sources often target both solar and wind energy. Thus, these technologies cannot
serve as instruments for each other.
Second, since all energy R&D decisions are a result of a political process, we also
include instruments that model the political process determining R&D funding.
Overidentification tests assume that at least one instrument is valid. Since all energy R&D
decisions are made through similar political processes, additional instruments that control for the
political pressures that shape R&D decisions help ensure the validity of the overidentification
tests. The policy instruments control for a country’s general proclivity for government spending
and for the political leanings of the government. For example, Baccini and Urpelainen show that
political shifts affect the volatility of public energy R&D.4
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Supplementary Note 3. Identifying the Lag Length
As noted in the text, we employ several strategies to identify the appropriate lag length.
First, as is common in the literature, the primary criterion is finding the minimum AIC statistic
across a range of models.2 Alternatively, we also calculated the BIC statistic for each model,
which includes a greater penalty for including irrelevant variables. As a result, collinearity
among the lagged R&D values often leads the BIC to recommend fewer lags than the AIC.
However, in the case of collinear lagged R&D values, we can still estimate long-run effects that
are jointly significant, even when individual year coefficients are estimated imprecisely.
Moreover, leaving out relevant lags would lead to omitted variable bias. Thus, I focus on the
findings of the AIC statistic in the discussion that follows. One complication is that our model
includes not only lagged values of the R&D variables, but also of several control variables.
These controls are often individually insignificant. Thus, we also run models including only a
single year of the control and policy variables, to see if this changes the recommended number of
lags. We initially examine models including up to ten lags of R&D. Adding additional lags is
problematic when the model includes renewable energy policy variables as only two countries in
our sample adopted renewable energy certificates before 2003, so that estimates of the lagged
value of the REC variable beyond eight years are unreliable. The first US states to adopt REC
limits do so in 1998, and Italy adopts an REC limit in 2002. Most states first limits appear in the
data in 2003. However, adding up to 11 years of lags is possible for biofuels and energy
efficiency.
In addition, even collinearity among the R&D variables themselves may cause the AIC
statistic to favor smaller lags. Thus, we also run both the full and single control models using a
polynomial distributed lag model (PDL). The PDL models provide similar results to the main
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first differenced models, but by requiring fewer parameters to estimate multiple lags, they offer
the potential for lower standard errors on the R&D coefficients. Supplementary Table 6 presents
the AIC statistics.
In some cases, these strategies recommend different lag lengths. Thus, we also consider
the cumulative long-run effect of R&D when evaluating the lag length. These long-run effects,
illustrated in Supplementary Figure 5, tend to be similar across the various estimation
techniques, so we focus on the results using first differenced instrumental variables. When the
AIC statistic conflicts across models, we use the pattern of cumulative effects as an additional
guide. In particular, we consider the lag length at which the cumulative effect of government
energy R&D spending levels out, as this is evidence that all relevant lags have been included.
Turning first to biofuels, we see relatively consistent recommendations across models.
Using the AIC criterion we find an optimal lag length of ten years using the full model and
eleven years using a single year of controls. The PDL results suggest an optimal lag length of
nine years in the full model and ten when using a single year of controls. As the AIC is lowest
for the full FD model, which has an optimal lag of ten years, I use a lag length of ten years for
analyzing the biofuels results.
The results for wind demonstrate the potential advantages of the PDL model and for
looking at the results of models with a single year of controls. Using the standard first
differenced model, the AIC suggests an optimal lag length of just two years. Using just a single
year of controls, the AIC suggests using seven years of lagged data. In contrast, the PDL model
suggests ten year lags in the full model and four lags when using a single year of controls. This
difference, however is not caused by adding lags of energy R&D, but rather lags of the tradable
permit policy variable, which becomes large and significant beginning with the ninth lagged
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variable. However, beyond nine years of lags, this coefficient is identified off of just one
country, as only the United States had renewable credit trading before 2002. Thus, we turn to the
cumulative effect of energy R&D to help sort through the options. In all four models (FD, FD
single control, PDL, PDL single control), the cumulative effect grows significantly between
years 6 and 7 before leveling off, suggesting a lag of seven years (as suggested by the FD model
with a single year of controls) is appropriate. Indeed, the eighth and ninth lags of energy R&D
are insignificant and close to zero. Thus, I use a lag length of seven years for analyzing the wind
energy results.
For solar energy, shorter lags appear to suffice, although the choice using the AIC
statistic varies across models. In the FD model, the AIC statistic is minimized with just four lags
in the full model and with nine lags when using just a single year of controls. In contrast, the
AIC suggests ten years of lags in the PDL model with full controls, and eight when using just a
single year of controls.
Thus, again we turn to the estimated cumulative effects for more information. Here, we
see a large increase in the cumulative effect between years three and four, and again between
years five and six. While the individual coefficient for year t-6 is only significant at the 10
percent level, both the large size of the coefficient, as well as the continued growth in the
cumulative effect through year six, suggests that six years should be included in the solar energy
models. There is little value to adding additional lags of energy R&D in the PDL models.
Indeed, results in year 10 seem to be largely driven by changes in the REC variable, as the sign
of the effect of R&D becomes negative in the model with full controls. In contrast, the
cumulative effect of energy R&D remains level beyond year 6 in the model using a single year
of control variables.
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The appropriate lag length is more difficult to identify in the case of energy efficiency, as
the effect of energy R&D itself is smaller. In the FD model, the AIC statistic suggest a lag of
two years using the full model but ten years when using a single year of controls. In the PDL
model, the AIC is minimized using a lag of five years using a full set of controls, and four lagged
years with a single year of controls. Given these divergent results, we turn to the cumulative
effect of energy R&D. The cumulative effect levels off after year six before rising again
beginning in year 9. Among models with 9 to 11 lags, the AIC is minimized in the main FD
model when using 10 lags. Thus, I use ten years of lagged R&D when evaluating energy
efficiency.
Supplementary Note 4. Discussion of Instrument Validity
We include two categories of instrumental variables in our model. The first category
includes public R&D spending on other energy technologies. The key assumption is that these
spending levels are related to energy R&D funding for the technology included in each
regression, but do not lead to additional publications for the endogenous technology. For
example, governments may increase funding for biofuels and wind energy at the same time, but
new biofuels R&D funding should not lead to additional wind energy publications. This
assumes that public R&D decisions are part of a common political process. Of course, if that is
the case, then researchers may also change their research strategies after political shifts, with the
expectation that other factors besides R&D funding may make renewable energy research more
(or less desirable). Thus, we also include a set of instruments related to the political process,
such as changes in the political party in power and the share of GDP devoted to government
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spending. By including these variables, the R&D instruments can be interpreted as the effect of
increases in other R&D spending conditional on changes in the political climate.
The success of this instrumental variable strategy depends on their being common factors
that influence energy R&D funding across multiple sources. Thus, an understanding of the
energy R&D funding process is important. Funding decisions are typically made by national
governments. Although there are some examples of long-term funding decisions, such as
Japan’s New Sunshine program instituted in 1993 to develop solar PV technologies, most
funding decisions are made on an annual basis.6 As a result, political changes may result in
energy R&D funding changes. Examples include reductions in energy funding after President
Reagan took office in the United States in 1981 and after a party change in the leadership of
Denmark in 2002.6 Baccini and Urpelainen provide statistical evidence that changes in party
leadership affect energy R&D volatility.4 Importantly, while common trends such as changes in
global oil prices that our year effects pick up affect R&D funding decisions,7 both the above
evidence and our descriptive data presented in Supplementary section 2 suggest that there is
variation in funding decisions within countries at different times, enabling our instruments to
capture variation in the funding process.
To further assess the ability of our instruments to predict energy R&D funding,
Supplementary Tables 7 and 8 present the first stage regression results for our main models.
Supplementary Table 7 includes the instruments and individual years of lagged energy R&D that
remain in the model, as only current energy R&D is treated as endogenous. Supplementary
Table 8 includes the cumulative effect of the various control variables on current energy R&D.
As noted elsewhere, the F-stat for the joint significance of all instruments is above 10 for all
models, suggesting that the instruments do a good job explaining energy R&D funding. In
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particular, the level of other types of energy R&D spending do a particularly good job, as most
are significant. These appear to capture most of the variation in energy R&D funding, as none of
the political variables are individually significant. Similarly, with a few exceptions, the current
funding levels of related technologies are even more strongly correlated with energy R&D
funding than lagged R&D levels for the same technology, as shown both by the lack of
significance and generally lower magnitudes for these lagged variables. As in the main models,
the various control variables have little effect on energy R&D spending.
Supplementary Note 5. Detailed Results and Robustness Checks
In this section, we provide tables with detailed regression results for both the main model
specification and several robustness checks. We also discuss tests confirming the validity of our
instrumental variables.
5.1 Main results
Having identified the appropriate lag length, Supplementary Tables 9-10 present results
for the first differenced models for each technology. Supplementary Table 9 shows the results
for government R&D for both individual years and the cumulative effect. Supplementary Table
10 shows the cumulative effects of the various controls included in each model. In each table,
the first column for each technology includes results using instruments for current R&D and the
second includes results assuming all variables are exogenous. All results include robust standard
errors that have been corrected for both heteroskedasticity and autocorrelation. Because of the
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similarities across models, I focus on the first-differenced panel estimates, which avoid imposing
any structure on the lag process. Results using the PDL models are included in section 5.3
Before turning to technology-specific results, we first discuss general trends across all
models. Public sponsored energy R&D generally leads to increases in publications, although the
magnitude of the effect varies across technologies. Other demand characteristics appear less
important, as most controls are insignificant, both individually and in the cumulative effects over
time. This contrasts with most research focusing on private sector research efforts, where policy
plays an important role.3,8-10 Given the long term nature of basic R&D, it is not necessarily
surprising that demand factors such as policy shocks have less influence on research supported
by the public sector. However, these results also suggest that public R&D funding is not simply
replicating support that would otherwise be provided by the private sector. We discuss
exceptions to these findings in the technology-by-technology results below.
Turning to the quality of our instruments, the Hansen J test reveals that the instruments
are valid in all cases (Supplementary Table 9). We also present the p-value of the endogeneity
test, where the null hypothesis is that the current value of energy R&D is exogenous. We fail to
reject the null hypothesis that energy R&D is exogenous for all technologies except solar energy.
However, as the results are generally unchanged across endogenous and exogenous
specifications, we choose to be conservative and focus on the IV results when presenting the
various robustness checks that follow.
Energy R&D has the largest cumulative effect in biofuels. One million dollars of
additional government R&D support results in slightly more than two new publications over ten
years. One reason for the stronger biofuels effect is the long lag length. While the effect of
public R&D levels out for other technologies after six or seven years, for biofuels we find a
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strong effect even in years nine and ten. There is a strong contemporary effect, with 0.376
publications being induced in year t-1. The FD results suggest a cyclical pattern, with strong
effects also found in years two and four in the first differenced model before picking up again in
year t-9. One possibility that the long range effects suggest is that public R&D leads to increases
in the supply of scientific personnel and infrastructure devoted to biofuels research. While
verifying such an explanation is beyond the scope of the current data set, such questions about
the long run impact of public R&D are a fruitful avenue for further study. While most individual
controls are insignificant, gasoline prices net of taxes have a large positive impact on
publications. Higher prices make substitutes such as biofuels look more attractive and
researchers seem sensitive to the market potential of biofuels when deciding on research projects.
Also, while the cumulative effect of per capita oil reserves is insignificant, individual year effects
have a significant positive effect in years t-3, t-7, and t-8.
Similar to biofuels, the lagged effect for energy efficiency R&D is long. While the largest
single year effect occurs in year t-2, the magnitudes in year t-10 are similar. Compared to the
other technologies, the magnitude of both the individual year effects and cumulative effects for
energy efficiency are much smaller, with one million of energy efficiency R&D generating just
under 0.2 new publications in the long run. Compared to the other technologies in this study,
energy efficiency covers a wider range of potential applications, including specific equipment for
production, vehicles, and even high technology applications such as computers and server farms.
Thus, the smaller magnitude may be an artifact of our data, which relies on keyword searches to
identify relevant articles and may miss some of these broader applications. However, it is also
possible that, because of the broader nature of energy efficiency research, the costs of generating
new energy efficiency results is larger, as the ability to stand on the shoulders of past researchers
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may be lower in a more diverse research field. Also, while the cumulative effect of per capita oil
reserves is insignificant, individual year effects have a significant effect in years t-3 through t-7,
with a positive effect in all years except t-5.
Solar energy is the one case in which instrumental variables lead to changes in the results.
We reject the null hypothesis that current energy R&D expenditures are exogenous, as the p-
value of the endogeneity test is just 0.049. The cumulative effect of energy R&D falls from
2.742 to 2.078 when using instruments for current R&D. The differences are largely driven by
changes in the contemporary effect of R&D, which is much larger when exogenous. Recall from
the theory section that current variables not only pick up the supply of research projects, but also
the demand of editors to publish research on a given topic. As this unobserved demand may be
correlated with energy R&D, and that we would expect some time to pass before R&D led to a
new publication, it is likely using instrumental variables helps correcting for such demand
effects. Policy also plays a role in the case of solar, with larger renewable energy targets
inducing more publications.
Finally, for wind energy, the results are consistent across all specifications. In the long-
run, a million dollars of energy R&D leads to approximately one new publication. Unlike
biofuels, the effect is quick, with most new publications occurring within the first three years.
After a brief leveling off, the cumulative effect continues to rise in years six and seven, after
which it levels off again, as shown earlier in Supplementary Figure 5. While none of the
controls have a significant cumulative effect, the immediate lag is significant for both the
percentage of electricity from hydro and nuclear, although unexpectedly positive for both.
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5.2 Results without European Union fixed effects
To account for unreported energy R&D support from the European Union to its member
countries, the results in the text include separate year fixed effects for EU countries.
Supplementary Table 11 compares the results from the first-differenced instrumental variables
model with and without the EU-specific fixed effects for the R&D coefficients, and
Supplementary Table 12 compares the results for the controls.
Dechezlepretre and Popp report that the EU’s share of renewable energy R&D funding is
small,11 so that unaccounted for EU spending is unlikely to be a major factor in scientific
productivity. For example, EU RD&D investments dedicated to the six technologies covered by
the EU Strategic Energy Technology Plan (wind, solar (photovoltaics and concentrated solar
power), electricity grids, bioenergy, carbon capture and storage, fuel cells and hydrogen and
nuclear fission) accounted for just 11 percent of public R&D spending on these technologies in
2010. Supplementary Tables 11 and 12 confirm that the effect of any omitted EU-wide R&D is
minimal, as the results are virtually the same when excluding the EU-specific year effects. The
cumulative effects of energy R&D range from 6 to 15 percent higher without the EU-specific
year trends, which is consistent with the expected bias, assuming that EU funding follows similar
trends as country-level funding. F-tests for the joint significance of all EU-specific year fixed
effects fail to reject the null hypothesis for all technologies except wind. Nonetheless, to avoid
any bias from unreported EU-wide energy R&D spending, the results in the main text include
EU-specific year fixed effects.
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5.3 Polynomial Distributed Lag Results
The following tables present results using the polynomial distributed lag (PDL) model.
To accommodate instrumental variables for current energy R&D, I used the following steps:
1. Estimate a first-stage model for R&D using the same number of lagged controls
as in the final PDL regression to be estimated.
2. Obtain predicted energy R&D from this regression.
3. Use the predicted current R&D, along with actual values of lagged R&D, to
construct first differenced data.
4. Use first differenced data to construct the polynomials for estimation.
Polynomials from degree 2-4 were used, as were lags from 4-10 years.
As noted in the text, there are few differences between the PDL results and those simply
including all lags in the model. Moreover, there is little gain in efficiency from using the PDL
model, but it does hele to identify the appropriate lag length for each technology. Thus, except
for the discussion of lag length, I focus on the unrestricted model in the discussion in the text.
For reference, complete PDL results are presented here. Supplementary Table 13 shows the
results for government R&D for both individual years and the cumulative effect, and
Supplementary Table 14 shows the cumulative effects of the various controls included in each
model.
The main differences between the unrestricted results and the PDL results are as follows.
First, for biofuels, the FD results suggest a cyclical pattern, with strong effects also found in
years two and four in the first differenced model before picking up again in year t-9. In contrast,
the PDL model suggests a more gradual effect of energy R&D, with the effect initially peaking
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in year t-2, and gradually fading until recurring in years nine and ten. However, the long-run
cumulative effects are similar in both models.
Supplementary Note 6. Detailed Results and Methodology for Patent Citations
Linking publications to eventual patents citing these publications is important, as creating
a new usable technology is the ultimate goal of any research funding. Here we provide detailed
results from the patent citation hazard regression, as well as a complete description of the
methodology used to simulate the effect of an additional one million dollars of public R&D
spending in Figure 3 of the main text.
Supplementary Table 15 presents the results of the patent hazard regression (equation 5
in the methods section), showing that the coefficients on citation lags are significant at the one
percent level, except for the squared term on wind energy. To interpret the time to citation, panel
A of Supplementary Figure 6 illustrates how the annual probability of citation changes over time.
For the post-2000 sample (shown using solid lines), the annual probability peaks between 8-10
years after publication. The peak shifts out to 11-14 years when considering the full sample
(dashed lines). As both the average and median lag between initial application and grant for
patents in our sample is 5 years, this means that patents citing these articles are filed 3-5 after
publication of the article in the post-2000 sample. Panel B shows the cumulative probability of
citation, which begins to grow rapidly 4-6 years after publication, and levels out about 12-14
years afterwards in the post-2000 sample.
The increased probability of an NPL citation resulting from an additional one million
dollars of new energy R&D funding depends on both the number of articles induced by this
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R&D each year and on the probability of an article from any given year being cited in the future.
Thus, Figure 3 in the main text use the results of both our R&D estimation and the NPL citation
regression (IV results in Supplementary Table 9 and Supplementary Table 15):
(1) 𝑄𝑄𝑄𝑄𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡 = ∑ 𝛽𝛽𝛽𝛽𝑡𝑡𝑡𝑡−𝑠𝑠𝑠𝑠𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡−𝑠𝑠𝑠𝑠𝑇𝑇𝑇𝑇𝑠𝑠𝑠𝑠=0 + ∑ 𝛄𝛄𝛄𝛄𝐭𝐭𝐭𝐭−𝐬𝐬𝐬𝐬𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡−𝑠𝑠𝑠𝑠𝑇𝑇𝑇𝑇
𝑠𝑠𝑠𝑠=0 + ∑ 𝛅𝛅𝛅𝛅𝐭𝐭𝐭𝐭−𝐬𝐬𝐬𝐬𝐗𝐗𝐗𝐗𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡−𝑠𝑠𝑠𝑠𝑇𝑇𝑇𝑇𝑠𝑠𝑠𝑠=0 + 𝛼𝛼𝛼𝛼𝑖𝑖𝑖𝑖 + 𝜂𝜂𝜂𝜂𝑡𝑡𝑡𝑡 + 𝜖𝜖𝜖𝜖𝑖𝑖𝑖𝑖,𝑡𝑡𝑡𝑡
(2) ℎ(𝑡𝑡𝑡𝑡) = exp(𝛼𝛼𝛼𝛼0 + 𝛼𝛼𝛼𝛼1𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑡𝑡𝑡𝑡𝑐𝑐𝑐𝑐𝑡𝑡𝑡𝑡𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 + 𝛼𝛼𝛼𝛼2𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑡𝑡𝑡𝑡𝑐𝑐𝑐𝑐𝑡𝑡𝑡𝑡𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐2 +𝛼𝛼𝛼𝛼3𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑐𝑐𝑐𝑐𝑡𝑡𝑡𝑡𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑚𝑚𝑚𝑚𝑐𝑐𝑐𝑐𝑡𝑡𝑡𝑡𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 + 𝛄𝛄𝛄𝛄𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐏𝐢𝐢𝐢𝐢,𝐭𝐭𝐭𝐭)
Let 𝛃𝛃𝛃𝛃 = [𝛽𝛽𝛽𝛽𝑡𝑡𝑡𝑡 𝛽𝛽𝛽𝛽𝑡𝑡𝑡𝑡−1 ⋯ 𝛽𝛽𝛽𝛽𝑡𝑡𝑡𝑡−𝑇𝑇𝑇𝑇] represent a row vector of coefficients on contemporary and
lagged R&D from Supplementary Equation (1). Using the results of Supplementary Equation
(2), the annual probability that a publication from year t receives a citation in year t + s can be
written as:
𝑤𝑤𝑤𝑤𝑠𝑠𝑠𝑠 = exp {𝛼𝛼𝛼𝛼0 + 𝛼𝛼𝛼𝛼1𝑠𝑠𝑠𝑠 + 𝛼𝛼𝛼𝛼2𝑠𝑠𝑠𝑠2}
Next, define a matrix WAnnual representing the annual probability that an article published
in year t, represented by the rows of the matrix, will be cited in year t + s, represented by the
columns of the matrix:
𝐖𝐖𝐖𝐖𝐀𝐀𝐀𝐀𝐀𝐀𝐀𝐀𝐀𝐀𝐀𝐀𝐀𝐀𝐀𝐀𝐚𝐚𝐚𝐚𝐚𝐚𝐚𝐚 =
⎣⎢⎢⎢⎡𝑤𝑤𝑤𝑤0 𝑤𝑤𝑤𝑤1 𝑤𝑤𝑤𝑤2
𝑤𝑤𝑤𝑤0 𝑤𝑤𝑤𝑤1 𝑤𝑤𝑤𝑤0
⋯ 𝑤𝑤𝑤𝑤𝑀𝑀𝑀𝑀 𝑤𝑤𝑤𝑤𝑀𝑀𝑀𝑀−1
⋱ 𝑤𝑤𝑤𝑤0 ⎦
⎥⎥⎥⎤
Similarly, the cumulative probability of a publication in year t receiving a citation by year t + s is
given by the following matrix:
𝐖𝐖𝐖𝐖𝐏𝐏𝐏𝐏𝐀𝐀𝐀𝐀𝐂𝐂𝐂𝐂𝐀𝐀𝐀𝐀𝐚𝐚𝐚𝐚𝐚𝐚𝐚𝐚𝐭𝐭𝐭𝐭𝐢𝐢𝐢𝐢𝐂𝐂𝐂𝐂𝐂𝐂𝐂𝐂 =
⎣⎢⎢⎢⎡𝑤𝑤𝑤𝑤0 𝑤𝑤𝑤𝑤0 + 𝑤𝑤𝑤𝑤1 𝑤𝑤𝑤𝑤2 + 𝑤𝑤𝑤𝑤1 + 𝑤𝑤𝑤𝑤0
𝑤𝑤𝑤𝑤0 𝑤𝑤𝑤𝑤1 + 𝑤𝑤𝑤𝑤0 𝑤𝑤𝑤𝑤0
⋯ 𝑤𝑤𝑤𝑤𝑀𝑀𝑀𝑀 + 𝑤𝑤𝑤𝑤𝑀𝑀𝑀𝑀−1 + … + 𝑤𝑤𝑤𝑤0 𝑤𝑤𝑤𝑤𝑀𝑀𝑀𝑀−1 + 𝑤𝑤𝑤𝑤𝑀𝑀𝑀𝑀−2 + … + 𝑤𝑤𝑤𝑤0
⋱ 𝑤𝑤𝑤𝑤0 ⎦
⎥⎥⎥⎤
Using these matrices, the product βWAnnual yields a row matrix with the increase in the
annual probability of an NPL citation each year after an additional one million dollars of energy
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R&D, and the product βWCumulative yields a row matrix with the increase in the cumulative
probability resulting from an additional one million dollars energy R&D.
Supplementary Note 7. Additional Results on Diminishing Returns to R&D
Table 2 in the main paper provides results of regressions designed to test for the
possibility of diminishing returns to government R&D spending. To provide further
interpretation of the results including squared R&D, here we show how the effect of R&D varies
as the level of R&D spending varies. When adding a quadratic term, the marginal effect of R&D
will be a function of the R&D spending in a given year. Supplementary Figure 7 evaluates the
marginal effect of energy R&D for a given year for various lags, and Supplementary Figure 8
shows the cumulative effect. In each, these effects are evaluated for average levels of energy
R&D spending, as well as for the 25th, 75th, and 90th percentile. If diminishing returns are a
concern, we should expect to see lower marginal effects in the upper percentiles.
Our results provide almost no support for diminishing returns. In Supplementary Figure
7, we see slightly higher annual marginal effects for the 90th quantile of solar energy R&D for
most years, and for biofuels and energy efficiency in the later years. Only for wind do we see
the annual marginal effects fall for the highest quantiles, and only in the middle years.
Supplementary Figure 8 provides more insight, showing how the cumulative effect of
energy R&D varies over time for various levels of R&D funding. For biofuels and energy
efficiency, the cumulative effect is nearly identical across quantiles until the later years. This
would be consistent with the notion that large increases in R&D result in positive spillovers
providing dividends in future years, such as by attracting more researchers into the field. For
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energy efficiency R&D, the cumulative effect for the 90th percentile is below those of other
percentiles through year three before catching up, raising the possibility of a period of
adjustment for large increases in energy R&D.
Solar energy provides evidence of positive spillovers from large energy increases, with a
significant positive quadratic terms. As shown in Supplementary Figure 8, the cumulative effect
of R&D is lowest for the 25th percentile, increasing significantly for each higher percentile.
Finally, the only technology showing any evidence of long run diminishing returns is wind.
Here, we see that the cumulative effect of wind energy is slightly larger in the 90th percentile in
early years, but soon becomes lower than the cumulative effect of other percentiles.
Finally, our second test of diminishing returns asks whether R&D spending is less
effective after large increases, represented by at least a doubling of energy R&D budgets for a
given technology. Supplementary Figure 9 shows that the number of countries choosing to
double energy R&D in a given year is generally equally dispersed across time, with one or two
countries per year experiencing such increases. The one exception is the large number of
countries choosing to double energy R&D as part of stimulus packages in 2009 and 2010.
Overall, between 6 and 12 percent of all country/year observations from 1981-2011 include a
doubling of energy R&D.
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Supplementary References
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