Directed Technological Change in the Electricity Sector
Preliminary and Incomplete
Please Do Not Cite without Permission
Abstract
Technological innovations in energy, facilitate the use of a higher share of renewable
resources in electricity generation. Furthermore, many governments have introduced
incentives to shift energy production from fossil fuels toward renewable sources. In this
paper, we investigate the determinants of technological innovations in the electricity
sector, and assess environmental policies for promoting renewable energy like wind,
solar or biomass. We first derive testable hypotheses from a directed technological
change model applied to the electricity sector. Then, we construct a global, firm-level
energy patent database from 1978 to 2011 to empirically test our hypotheses. Our
preliminary results suggest that higher natural gas prices, a firm’s stock of knowledge
and knowledge spillovers increase patenting in clean technologies. These results have
strong policy implications.
Keywords: electricity sector, directed technological change, climate change
1
Introduction
Scientists and policymakers seek options to reconcile concerns about climate change with
global economic growth. One such option is to increase the share of renewable sources in
electricity generation. To this end, many governments have introduced renewable energy
incentives to shift energy production from fossil fuels toward renewables.
While these
incentives have resulted in a range of technological innovations in renewable energy, their
use in global electricity generation remains modest. The main challenge is to increase the
share of renewables in the electricity mix. In this paper, we investigate the determinants of
technological innovations in the electricity sector, and assess environmental policies aimed
at increasing the share of renewable energy in electricity generation.
We focus on the electricity sector for two reasons. First, electricity generation is the
largest single emitter of carbon dioxide in the U.S. For example, in 2011, burning fossil fuels
yielded 38% of total carbon dioxide emissions and 32% of total greenhouse gas emissions
(EPA 2011). Second, global electricity subsidies are large. For example, estimates suggest
that over the period 2002-2008 subsidies in the U.S. to support R&D in fossil fuels were $72
billion while renewable energy received $29 billion (Adeyeye, 2009).
The main goal in this study is to investigate the determinants of technological innovations
in the electricity sector. Specifically, we want to answer the following three questions: First,
what are the drivers of innovation in renewable energy sources? Second, what factors drive
the shift from fossil fuels to renewables? And, third, have R&D subsidies been successful at
directing innovation toward renewable energy? To answer these questions, we conduct an
empirical analysis in two parts.
First, to identify firm-level incentives for innovation in renewable energy, we construct a
global, firm-level database of energy patents. We combine patents from the OECD Triadic
Patent Database, firm-level information from the OECD HAN database and energy prices
2
from the IEA database. The database we built is unique because it contains information
about firm-level patents (clean and dirty) from 73 countries from 1978 to 2011. In addition,
we focus on the most valuable patents which we identify as patents registered with the three
main patent offices globally.1
Second, we study how to direct R&D efforts toward clean electricity generation, and
thereby reduce the gap between fossil fuels and renewables. We do so by evaluating the
impact of the most significant R&D subsidies from 1978 to 2011.
The model we estimate is based on the theoretical framework of Acemoglu et al. (2012a).
We apply the model to the electricity sector where dirty and clean firms create new innovations
that make the existing technologies obsolete. From this theoretical model, we construct
testable hypotheses that describe firm-level incentives to innovate. In particular, we look at
how incentives to innovate in clean and dirty sectors depend on coal and natural gas prices,
and the knowledge stock of clean and dirty innovations. Our empirical strategy is closely
related to that of Aghion et al. (2012). We estimate a patent count model using a poison
estimator and a negative binomial estimator.
Our preliminary results suggest that an increase in the price of natural gas is associated
with more patenting in the clean sector. Furthermore, we find that firms’ existing knowledge
stock is an important determinant of clean patenting. Interestingly, we find that industry
spillovers related to clean technologies have a positive impact on dirty patenting. This
suggests that clean knowledge spillovers may induce energy efficiency in fossil fuel technologies.
This paper relates to three branches of the literature: a theoretical literature studying
endogenous economic growth and climate change, an empirical literature with applications
to endogenous growth, and, an empirical literature that studies energy prices and induced
innovation in environmental technology. We contribute to this literature with an empirical
1
OECD Triadic Patent Database contains patents taken at the European Patent Office (EPO), the
Japanese Patent Office (JPO), and the U.S. Patent and Trademark Office (USPTO) to protect the same
idea.
3
study of directed technological change in the electricity sector.
The literature on how to reconcile economic growth and climate change is large. Particularly,
the theoretical literature is extensive. See for example, Bovenberg and Smulders (1995,
1996), Acemoglu et al. (2012a), Goulder and Schneider (1999), Goulder and Mathai (2000)
and Zwaan et al. (2002). Our paper is closer related to Acemoglu et al. (2012a), since our
work is an application of their theoretical model to the electricity sector.
The most relevant empirical literature is the one that applies directed technological
change models to study climate change (see e.g. Noailly and Smeets, 2012; Aghion et al.,
2012; Acemoglu et al., 2012b, 2013). Noailly and Smeets (2012) study the determinants
of directed technical change in electricity generation technologies. They employ European
patent data, while we construct a global patent database. Aghion et al. (2012) study directed
technical change in the auto industry. While we follow their econometric strategy, we study
a different industry. Finally, Acemoglu et al. (2012b) and Acemoglu et al. (2013) study how
policies can affect the transition from dirty to clean technologies. Acemoglu et al. (2012b)
use micro data on R&D expenditures, patents, sales, employment, and firm entry and exit
to calibrate their model, while Acemoglu et al. (2013) investigate policies (subsidies) that
may either encourage investment in R&D or discourage innovation and growth by reducing
reallocation. Our paper differs from this work since we focus on the effect of historical
subsidies instead of running simulations.
The remainder of the paper unfolds as follows. In Section 2, we present our database.
In Section 3, we describe our empirical strategy while in Section 4 we discuss our results.
Finally, Section 5 concludes.
4
2
Data
In this section, we describe the construction of our global, firm-level, energy patent database.
We combine the OECD’s Triadic Patent Families Database, OECD’s HAN database and
energy prices from the IEA.
2.1
Patents and patent families
In our study, we utilize patent data to measure technological change. The main advantages
of using patent data for our purposes are twofold. First, patents are available at the firm
and technology level. In contrast to more aggregate measures such as R&D expenditures,
which are generally only available at the industry level and for limited technology types, each
individual patent contains detailed information about the inventor(s), applicant(s), and the
specific type of technology (Popp, 2005). Information about the applicant is the most useful
for our purposes, as it allows us to identify specific firms, while the International Patent
Classification (IPC) codes assigned to each patent makes it possible to identify technologies
related to electricity generation. Thus, the detailed nature of patent data proves especially
useful when examining firm-specific incentives to innovate in select technologies.
Second, patents provide a measure of innovation output of firms’ research activities that
is close to the actual time of invention. Since patent applications are normally submitted
early in the research process, as indicated by the “priority date,” they also provide a good
measure of overall innovative activity of a given firm (Popp, 2005). While this is the case,
there are drawbacks of patent data that must be addressed.
First, patents are not granted for every invention and the propensity to patent may
differ considerably across countries and industries (Van Pottelsberghe et al., 2001). Second,
a submitted patent application does not guarantee that the technology has actually been
adopted (Popp, 2005). Third, individual patents differ considerably in their worth, with
5
many patents having low values (Aghion et al., 2012).
These limitations motivate the extraction of our patents from the OECD’s Triadic Patent
Database which contains patent family data from 1978 to 2011. Triadic patent families
are useful because they are collections of patents that protect the same idea in different
countries. For example, a particular patent application must have an equivalent application
at the European Patent Office (EPO), Japanese Patent Office (JPO) and the United States
Patent Office (USPTO) in order to qualify as a patent family member. Because triadic
patents are applied for in three separate offices, they include only the highest valued patents
and allow for a common worldwide measure of innovation that avoids the heterogeneity
of individual patent office administrations (Aghion et al., 2012). Furthermore, the OECD
utilizes “extended families,” which are designed to identify any possible links between patent
documents (Martinez, 2010). This is advantageous, as it provides the most comprehensive
method of consolidating patents into distinct families, allowing us to to include an extensive
number of patented ideas.
One disadvantage of triadic patent families includes the lag times associated with the
USPTO. Legal delays for publishing applications is 18 months after the priority date and
up to 5 years between the priority date and publication date (Dernis and Khan, 2004). As a
consequence, U.S. patent grants may delay the completion of data on triadic patent families.
In order to mitigate this limitation, the OECD utilizes forecasts called “nowcasting” in order
to improve the timeliness of triadic patents (Dernis and Khan, 2004). Despite this difficulty,
triadic patents still provide the most inclusive measure of high-value, firm-level, innovative
performance.
2.2
Electricity generation technologies
Electricity generation is chosen as a specific case study for several reasons.
Although
renewable energy can provide a clean source of electricity production, the majority of the
6
world’s electricity generation, 67.8% in 2011, still comes from burning fossil fuel (IEA, 2013).
Recent predictions indicate that the demand for energy is expected to grow by 84% from 2007
to 2035 in the developing world alone, likely leading to serious consequences for greenhouse
gas emissions as these countries continue to gain greater access to electricity (Wolfram et al.,
2012). Given the signifiance of these statistics in terms of climate change and environmental
quality, investigating the incentives to innovate in renewable sources of electricity can shed
light on policies that are designed to direct innovation from fossil fuel to renewable generation
sources.
In order to accomplish this objective, we select patents that are specific to electricity
generation based on IPC codes. We then categorize them into two groups: renewable energy
and fossil fuel based technologies. Renewable energy technologies are identified from the
World Intellectual Property Office’s (WIPO) IPC Green Inventory.2 Specifically, we select
patents whose technology classes are related to alternative energy production. This includes
integrated gasification combined cycle (ICGG), fuel cells, pyrolysis, harnessing energy from
manmade waste, hydro energy, wind, solar, geothermal energy, other production or use of
heat, using waste heat, and devices for producing mechanical power from muscle energy.
Specific descriptions of the IPC codes used in this paper are presented in Tables 1 - 4. Fossil
fuel technologies are selected from the general fossil fuel technology IPC codes reported in
Lanzi et al. (2011).
The selection of technology classes through IPC codes requires careful consideration. For
example, recent work by Noailly and Smeets (2012) classifies firm-level patents into renewable
and fossil fuel technologies to study the main drivers of innovation and the direction of
technological change. They select patents related to renewable technologies from IPC codes
reported in Johnstone et al. (2010). When comparing patent families selected by these IPC
2
The IPC codes listed in the IPC Green Inventory have been compiled by the IPC Committee of Experts
in concordance with the United Nations Framework Convention on Climate Change (UNFCCC). For more
information see http://www.wipo.int/classifications/ipc/en/est/.
7
Table 1: Definitions of IPC patent classes for clean patents
Description
Fuel cells
Electrodes
Inert electrodes with catalytic activity
Non-active parts
Within hybrid cells
IPC code
H01M 4/86-4/98, 8/00-8/24, 12/00-12/08
H01M 4/86-4/98
H01M 4/86-4/98
H01M 2/00-2/04 , 8/00-8/24
H01M 12/00-12/08
Pyrolysis or gasification of biomass
C10B 53/00
C10J
Harnessing energy from manmade waste
Agricultural waste
Fuel from animal waste and crop residues
Incinerators for field, garden or wood waste
Gasification
C10L 5/00
C10L 5/42, 5/44
F23G 7/00, 7/10
C10J 3/02, 3/46
F23B 90/00
F23G 5/027
B09B 3/00
F23G 7/00
C10L 5/48
F23G 5/00, 7/00
C21B 5/06
Chemical waste
Industrial waste
Using top gas in blast furnaces to power pigiron production
Pulp liquors
Anaerobic digestion of industrial waste
D21C 11/00
A62D 3/02
C02F 11/04, 11/14
F23G 7/00, 7/10
B09B 3/00
F23G 5/00
B09B
B01D 53/02, 53/04, 53/047, 53/14, 53/22, 53/24
C10L 5/46
F23G 5/00
Industrial wood waste
Hospital waste
Landfill gas
Separation of components
Municipal waste
Hydro energy
Water-power plants
Tide or wave power plants
Machines or engines for liquids
E02B 9/00-9/06
E02B 9/08
F03B
F03C
F03B 13/12-13/26
F03B 15/00-15/22
Using wave or tide energy
Regulating, controlling or safety means of
machines or engines
Propulsion of marine vessels using energy
derived from water movement
B63H 19/02, 19/04
F03G 7/05
Ocean thermal energy conversion (OTEC)
8
Table 2: Definitions of IPC patent classes for clean patents
Description
Wind energy
Structural association of electric generator
with mechanical driving motor
Structural aspects of wind turbines
IPC code
F03D
H02K 7/18
B63B 35/00
E04H 12/00
F03D 11/04
B60K 16/00
B60L 8/00
Propulsion of vehicles using wind power
Electric propulsion of vehicles using wind
Power
Propulsion of marine vessels by windpowered motors
Solar energy
Photovoltaics (PV)
Devices adapted for the conversion of
radiation energy into electrical energy
B63H 13/00
H01L 27/142, 31/00-31/078
H01G 9/20
H02N 6/00
H01L 27/30, 51/42-51/48
H01L 25/00, 25/03, 25/16, 25/18, 31/042
C01B 33/02
C23C 14/14, 16/24
C30B 29/06
G05F 1/67
Using organic materials as the active part
Assemblies of a plurality of solar cells
Silicon; single-crystal growth
Regulating to the maximum power available
from solar cells
Electric lighting devices with, or
rechargeable with, solar cells
Charging batteries
Dye-sensitised solar cells (DSSC)
F21L 4/00
F21S 9/03
H02J 7/35
H01G 9/20
H01M 14/00
F24J 2/00-2/54
F24D 17/00
F24D 3/00, 5/00, 11/00, 19/00
F24J 2/42
F03D 1/04, 9/00, 11/04
F03G 6/00
C02F 1/14
F02C 1/05
Use of solar heat
For domestic hot water systems
For space heating
For swimming pools
Solar updraft towers
For treatment of water, waste water or sludge
Gas turbine power plants using solar heat
source
Hybrid solar thermal-PV systems
Propulsion of vehicles using solar power
Electric propulsion of vehicles using solar
power
Producing mechanical power from solar
energy
Roof covering aspects of energy collecting
devices
Steam generation using solar heat
H01L 31/058
B60K 16/00
B60L 8/00
F03G 6/00-6/06
E04D 13/00, 13/18
F22B 1/00
F24J 1/00
F25B 27/00
Refrigeration or heat pump systems using
solar energy
Use of solar energy for drying materials or
objects
Solar concentrators
F26B 3/00, 3/28
F24J 2/06
G02B 7/183
Solar ponds
F24J 2/04
9
Table 3: Definitions of IPC patent classes for clean patents
Description
Geothermal energy
Use of geothermal heat
Production of mechanical power from
geothermal energy
Other production or use of heat, not derived
from combustion, e.g. natural heat
Heat pumps in central heating systems using
heat accumulated in storage masses
Heat pumps in other domestic- or spaceheating systems
Heat pumps in domestic hot-water supply
systems
Air or water heaters using heat pumps
Heat pumps
Using waste heat
To produce mechanical energy
Of combustion engines
Of steam engine plants
Of gas-turbine plants
As source of energy for refrigeration plants
For treatment of water, waste water or
sewage
Recovery of waste heat in paper production
For steam generation by exploitation of the
heat content of hot heat carriers
Recuperation of heat energy from waste
incineration
Energy recovery in air conditioning
Arrangements for using waste heat from
furnaces, kilns, ovens or retorts
Regenerative heat-exchange apparatus
Of gasification plants
Devices for producing mechanical power from
muscle energy
IPC code
F01K
F24F 5/00
F24J 3/08
H02N 10/00
F25B 30/06
F03G 4/00-4/06, 7/04
F24J 1/00, 3/00, 3/06
F24D 11/02
F24D 15/04
F24D 17/02
F24H 4/00
F25B 30/00
F01K 27/00
F01K 23/06-23/10
F01N 5/00
F02G 5/00-5/04
F25B 27/02
F01K 17/00, 23/04
F02C 6/18
F25B 27/02
C02F 1/16
D21F 5/20
F22B 1/02
F23G 5/46
F24F 12/00
F27D 17/00
F28D 17/00-20/00
C10J 3/86
F03G 5/00-5/08
10
Table 4: Definitions of IPC patent classes for dirty patents
Description
Production of fuel gases by carburetting air or other
gases without pyrolysis
Steam engine plants; steam accumulators; engine plants
not otherwise provided for; engines using special
working fluids or cycles
Gas-turbine plants; air intakes for jet-propulsion plants;
controlling fuel supply in air-breathing jet-propulsion
plants
Hot-gas or combustion-product positive-displacement
engine; use of waste heat of combustion engines,not
otherwise provided for
Steam generation
Combustion apparatus; combustion processes
Furnaces; kilns; ovens; retorts
IPC code
C10J
F01K
F02C
F02G
F22
F23
F27
codes to the WIPO IPC classifications, significantly more patent families can be extracted
using the latter. Table 5 provides a comparison of the number of patent families that are
selected using codes solely from Johnstone et al. (2010) and those included in both Johnstone
et al. (2010) and the WIPO IPC Green Inventory. In total, 38,370 patent families are gained
by using the more inclusive codes, with the majority of additional families stemming from
biomass and waste. The IPC codes in Johnstone et al. (2010) also omit certain categories,
such as hydro energy, that are important sources of renewable electricity generation.
Table 5: Comparison of patent family counts
Technology class
All Renewables
Wind
Solar
Geothermal
Ocean
Biomass and Waste
2.3
(Johnstone et al., 2010)
8,245
662
2,996
170
189
4,306
(Johnstone et al., 2010) + WIPO
46,615
1,118
9,730
1,443
192
35,577
Families gained
38,370
456
6,734
1,273
3
31,271
Firms
We aggregate individual patent counts at the firm-level. By utilizing the OECD Harmonized
Applicants Names (HAN) Database, a register that contains clean applicant names which are
matched against company names from business register data, we are able to link patents to
11
firms and individuals. Unfortunately, the HAN database does not contain firm information
for every patent application in our sample. Names that cannot be matched using the HAN are
synchronized using applicant information contained in the Triadic Patent Families Database.
Although this allows us to match every patent to an applicant, it poses two difficulties. First,
applicant names in the Triadic Patent Database contain a number of spelling, character, and
name variations. For example, “3M INNOVATIVE PROPERTIES” and “3M INNOVATIVE
PROPERTIES CO” would be incorrectly treated as separate firms in the absence of name
harmonization. Second, the Triadic Patent Families Database does not directly link patent
applications to applicant names. Instead, applicant names are linked to family identifiers.
Thus, if a given family contains more than one firm name, it is impossible to tell which firm
to associate with each patent. In order to minimize complications that may result from these
challenges, we restrict our sample to those patents applications that can i.) be matched fully
from the HAN register or ii.) have a single applicant and are the sole member of patent
family. We conduct further harmonization using algorithms, although some name variation
still remains.
In contrast to Noailly and Smeets (2012), who select only on European firms, we consider
15,081 total firms that claim residence in 73 countries. Table 6 illustrates the countries that
contain the highest concentrations of innovating firms. Since the majority of innovative
activities originate in the United States, Japan, and Germany, it is not surprising that these
countries contain the highest numbers of firms in the sample.
2.4
Descriptive statistics
In total, we identify 99,739 unique triadic patent applications across all firms during the
sample time period of 1978 to 2011. Of this total, 61,709 are designated as clean technologies,
while 38,014 are classified as dirty. In contrast to prior empirical works,3 our the number
3
See Aghion et al. (2012) and Noailly and Smeets (2012)
12
Table 6: Distribution of firms across countries
United States
Japan
Germany
France
United Kingdom
Switzerland
Sweden
Canada
Number of firms
5,210
2,359
2,011
1,131
669
418
384
367
of clean patents is greater than the number of dirty patents. This is likely due to our
usage of more comprehensive IPC codes for clean technologies. It may also be the case that
clean technologies, though newer, have more general applications that are more likely to be
patented Dechezleprêtre et al. (2013). Figure 1 illustrates the time series of clean and dirty
patenting over the sample time period. While clean and dirty patent counts tend to be very
close in the late 1970s and early 1980s, clean patenting surpasses dirty patenting by a wide
margin by 2000. It may be the case that dirty technologies, which have existed for a longer
time period and are built upon a larger knowledge stock, are harder to innovate upon. Thus,
dirty innovations may only be incremental and harder to patent. Both series exhibit a sharp
decline around 2000.4
It may also be of interest to study the main patent holders of each innovation category
over the time period considered. Table 7 illustrates that the main clean patent holders,
with the exception of Air Products Chemicals, are located outside of the United States.
Specifically, Japanese firms tend to dominate this list. In contrast, Table 8 illustrates that
the top two dirty patenting firms, General Electric and United Tech Corp, are both U.S.
firms. Furthermore, these two firms have a higher total number of patents than the other
firms which are ranked within the tables. This indicates that although the total number
of clean patents exceeds the total number of dirty patents in the entire sample, a higher
4
Though often noted, no concrete explanation for this has been cited in the literature.
13
0
Number of patents
1000
2000
3000
4000
Figure 1: Patenting over time
1980
1990
2000
2010
Year
Clean patent counts
Dirty patent counts
concentration of dirty patenting occurs for the top patent holding firms. Although most of
the firms in these rankings tend to specialize in either dirty or clean patenting, Germany’s
Siemens AG and Japan’s Matsushita Electric has a substantial share of both types.
2.5
Energy prices
Country level energy price data, quoted in U.S. dollars per unit, comes from the International
Energy Agency (IEA). We consider the following fuel types based on availability: steam coal,
coking coal, auto diesel fuel, high sulpher fuel oil, light fuel oil, and natural gas. There are
two limitations of this specific dataset that we now address. First, price data exists for
only 33 countries. This is significantly less than our firm coverage of 73 countries. Second,
there are many missing values within the dataset. Although energy prices do differ across
countries due to taxes and regulations, they tend to move together over time. Figure 2
14
Table 7: Main clean patent holders
Firm name
CANON KK
MATSUSHITA ELECT IND
NISSAN MOTOR
AIR PRODUCTS CHEMICALS
TOYOTA JIDOSHA KK
NGK INSULATORS
SHIN ETSU HANDOTAI
PANASONIC CORP
SIEMENS AG
WOBBEN ALOYS
firm country
JP
JP
JP
US
JP
JP
JP
JP
DE
DE
clean patents
1353
1078
665
566
477
467
457
442
428
427
dirty patents
1
857
27
129
74
92
5
17
998
0
total patents
1354
1935
692
695
551
559
462
459
1426
427
Table 8: Main dirty patent holders
Firm name
GENERAL ELECTRIC
UNITED TECH CORP
SIEMENS AG
MATSUSHITA ELECT IND
SNECMA
HITACHI
WESTINGHOUSE ELECT CORP
ASEA BROWN BOVERI AG
PRATT WHITNEY CANADA CORP
NGK SPARK PLUG
firm country
US
US
DE
JP
FR
JP
US
CH
CA
JP
15
dirty patents
3752
1923
998
857
755
369
362
348
317
217
clean patents
476
175
428
1078
36
172
172
38
15
29
total patents
4228
2098
1426
1935
791
541
534
386
332
246
illustrates prices for coal, one of the leading sources of electric power generation, for the top
innovating countries in our sample (?). The series are similar in their movement, illustrating
greater volatility in the 1980s and rising prices in the last decade.
50
Price ($ per tonne)
100
150
200
Figure 2: Coking coal prices for top innovating countries
1980
1990
2000
2010
Year
United States
Japan
France
Sweden
Germany
Switzerland
United Kingdom
In order to mitigate the aforementioned problems we use U.S. prices when faced with zero
or missing values. Figure 3 illustrates the selection of energy prices for the United States
over the time period considered. Prices for oil, including light fuel oil and high sulpher fuel
oil, are considerably higher and more volatile than the other energy prices, which remain
relatively stable from 1978 to 2011.
16
0
Price ($ per unit)
400
200
600
800
Figure 3: U.S. fuel prices over time
1980
1990
2000
2010
Year
Auto Diesel Fuel
Steam Coall
Light Fuel Oil
3
Coking Coal
Natural Gas
High Sulphur Fuel Oil
Econometrics and identification strategy
In this section we consider the following Poisson specification for the determinants of clean
and dirty patent counts related to electricity generation.
P ATz,it = exp(βz,p lnF Pjt−1 + Az,it−1 + Tz,t )ηzi + µz,it
(1)
where z indicates separate equations for clean (c) and dirty (d) technologies, i indicates
firms, j represents countries, and t illustrates time. P ATz,it is the number of triadic patents
applied for, either clean or dirty, by firm i in year t. F Pjt represents the country-level fuel
prices described in the data section.5 Az,it indicates the firm’s existing stock of knowledge
which includes its cumulative history of clean and dirty patenting activity as well as knowledge
5
We take the natural log of fuel prices, however, we do not do so for knowledge stocks as these can contain
zero values.
17
spillovers, proxied by aggregate counts of clean and dirty technologies of all firms within the
same country. More specifically, a firm’s total knowledge stock is given by
Az,it = βz,1 Kc,it + βz,2 Kd,it + βz,3 SP ILLc,it + βz,4 SP ILLd,it
(2)
where Kz,it is firm i’s patent stock of the designated technology type and SP ILLz,it is
the spillover for firm i, calculated as the sum of all other firm’s patent stocks that are located
P
domestically. This is defined as
Kz,ijt .
i
We lag these and knowledge stocks and prices by one period, as in Aghion et al. (2012),
in order to adequately reflect the delayed response of firms. We also include a full set of time
dummies, represented by Tz,t , and firm fixed effects indicated by ηzi .
4
Estimation results
Our baseline regression results are shown in Tables 9 and 10. All estimates include firm fixed
effects and year dummies. Table 9 illustrates six separate regressions, one for each fuel type
considered, where the number of clean patents in a firm is the dependent variable.
There are several observations based on these results that we now discuss. First, only
four of the fuel price coefficients are significant in the clean patent regressions: coking coal,
auto diesel fuel, high sulpher fuel oil, and natural gas. These coefficients are negative,
with the exception of natural gas, indicating that an increase in fuel prices is associated
with a decline in clean patenting. Second, firms’ existing knowledge stocks are important
determinants of clean patenting. Firms who have innovated in clean technology in the past
and those who are exposed to larger clean knowledge stocks from other geographically local
firms are significantly less likely to produce clean patents. While this is largely inconsistent
18
Table 9: Regression of clean patents with various fuel types
(1)
Steam coal price
(2)
Clean patent counts
(3)
(4)
(5)
(6)
-0.128
(0.116)
Coking coal price
-0.447**
(0.210)
Auto diesel fuel price
-0.632***
(0.244)
High sulphur fuel oil price
-0.559***
(0.143)
Light fuel oil price
0.0669
(0.179)
Natural gas price
0.418*
(0.245)
-0.000671** -0.000627** -0.000671**
(0.000294)
(0.000295)
(0.000285)
0.000967*** 0.000960*** 0.000979***
(0.000259)
(0.000263)
(0.000266)
-6.67e-05** -9.07e-05*** -0.000147***
(2.97e-05)
(3.06e-05)
(4.45e-05)
1.72e-05
2.60e-05
6.13e-05**
(2.01e-05)
(2.11e-05)
(2.65e-05)
Own clean stock
Own dirty stock
Clean spillover
Dirty spillover
-0.000632**
(0.000294)
0.000941***
(0.000269)
-8.67e-05***
(3.00e-05)
2.85e-05
(1.96e-05)
Observations
11,184
Number of firms
1,874
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
-0.000622**
(0.000303)
0.000933***
(0.000257)
-8.12e-05***
(2.89e-05)
2.20e-05
(2.02e-05)
-0.000627*
(0.000334)
0.000850**
(0.000359)
-1.28e-05
(3.51e-05)
-2.45e-06
(2.13e-05)
11,184
1,874
9,578
1,693
19
11,184
1,874
11,184
1,874
11,184
1,874
with the idea of path dependency, a firm’s own history of dirty patenting is associated with
more clean innovation. This result is consistent with Aghion et al. (2012). Exposer to larger
stocks of dirty patents from other firms does not have a significant effect on clean patenting,
with the exception of regression (6) which includes natural gas prices. The magnitudes of
these coefficients are similar regardless of the fuel price included in the regression.
Table 10: Regression of dirty patents with various fuel types
(7)
Steam coal price
(8)
Dirty patent counts
(9)
(10)
(11)
-0.214*
(0.125)
Coking coal price
-0.375*
(0.226)
Auto diesel fuel price
-1.121**
(0.474)
High sulphur fuel oil price
-0.653***
(0.249)
Light fuel oil price
-0.397
(0.339)
Natural gas price
Own stock clean
Own stock dirty
Clean spillover
Dirty spillover
(12)
-0.00107
(0.00114)
0.000961***
(0.000207)
8.61e-05
(5.33e-05)
-5.26e-05*
(2.95e-05)
Observations
9,039
Number of firms
1,365
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
-0.000900
(0.00116)
0.000938***
(0.000207)
9.32e-05*
(5.41e-05)
-6.38e-05**
(3.14e-05)
3.86e-05
(0.00116)
0.000678***
(0.000237)
0.000135*
(7.69e-05)
-0.000100***
(3.84e-05)
9,039
1,365
7,533
1,200
-0.000868
-0.000767
(0.00114)
(0.00113)
0.000953*** 0.000956***
(0.000214)
(0.000215)
0.000113**
9.46e-05*
(5.56e-05)
(5.45e-05)
-7.00e-05** -6.74e-05**
(3.27e-05)
(3.40e-05)
9,039
1,365
9,039
1,365
0.161
(0.294)
-0.000838
(0.00116)
0.000952***
(0.000213)
5.98e-05
(6.78e-05)
-4.56e-05
(4.67e-05)
9,039
1,365
Table 10 illustrates the same set of regressions with dirty patents as the dependent
variable. Coefficients for steam coal, coking coal, auto diesel fuel prices, and high sulpher
fuel oil are significant and also negative. A firm’s own history of clean innovation does not
appear to have any significant impact on dirty patenting, while its history of dirty patenting
20
has a positive and significant impact on future dirty innovations. The magnitudes of the
“own stock dirty” coefficients are very similar to those in Table 9. Interestingly, spillovers
related to clean technologies have a positive impact on dirty patenting in regressions (8) (11), indicating the possibility that clean knowledge spillovers may induce energy efficiency
or energy saving fossil fuel based technologies. Interestingly, dirty knowledge spillovers have
a significant negative impact on dirty patenting in regressions (7) - (11).
5
Conclusions
To be written
21
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