Climate Agreement and Technology Diffusion:
Impact of the Kyoto Protocol on International
Patent Applications for Renewable Energy
Technologies
Mai Miyamoto
Kenji Takeuchi

May 2018
Discussion Paper No.1820

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANClimate Agreement and Technology Diffusion:
Impact of the Kyoto Protocol on International Patent Applications for
Renewable Energy Technologies

Mai Miyamoto* and Kenji Takeuchi†
May 10, 2018

Abstract
This paper examines the Kyoto Protocol’s impact on the international diffusion of renewableenergy technologies. Using patent application data from 133 countries from 1990 to 2013, wefind that the Kyoto Protocol increased international patent applications from the countries withemission targets. When we focus on countries with more stringent targets, the effect of the
Kyoto Protocol is even stronger. We find a similar effect in international patent applications tofour developing countries that are large emitters of greenhouse gases (GHGs): China, India,
Brazil, and Mexico. These results suggest that the Kyoto Protocol stimulated international patenting activities from countries that are committed to stringent targets for climate mitigation.

Keywords: Renewable energy; Kyoto Protocol; International patent applications
JEL Classification Numbers: O33, Q42, Q55*

Graduate School of Economics, Kobe University. 2-1, Rokkodaicho, Kobe, Hyogo, 657-8501 Japan.
E-mail: mai.miyamoto.y@gmail.com†
Graduate School of Economics, Kobe University. 2-1, Rokkodaicho, Kobe, Hyogo, 657-8501 Japan.
E-mail: takeuchi@econ.kobe-u.ac.jp

11. Introduction

The Kyoto Protocol commits State Parties to reduce greenhouse gas (GHG) emissions to an av-

erage of 5% against 1990 levels. It sets binding emission reduction targets for countries under“common but differentiated responsibility and respective capabilities.” Since then, many re-searchers have investigated the impact of the Kyoto Protocol (“Protocol”) on GHG emissions.

The Protocol’s effect on the reduction of GHG emissions is supported by recent empirical liter-

ature. Several studies find that countries with binding emission targets under the Protocol havelower GHG emissions than they would have had in the absence of these targets (Aichele andFelbermayr, 2012; Almer and Winkler, 2017; and Grunewald and Martinez-Zarzoso, 2016).

The impact of the Protocol has also been investigated from the viewpoint of techno-

logical development. For instance, Johnstone and Haščič (2010) examine if the Protocol has an

influence on patent applications for renewable energy technologies and find that a country’s

patent applications for wind and solar energy technologies increased after signing to the Proto-col, suggesting that future expectations by firms on stricter regulations are important.

Dechezleprêtre et al. (2008) focus on the transfer of GHG mitigation technologies induced by

the Clean Development Mechanism (CDM), which is one of the flexible mechanisms under the2

Protocol. Based on information collected from the project design documents, they found thatinternational technology transfer takes place in less than half of CDM projects.

This study investigates whether the Protocol influenced an increase in international

patent applications with respect to renewable energy technologies. Such patent applications areof interest for two reasons. First, they can be interpreted as a measure of the cross-border diffu-

sion of climate-friendly technologies (Dechezleprêtre et al., 2013; Haščič and Johnstone, 2011).

The cross-border proliferation of knowledge regarding low-carbon technologies is a key factor

in climate mitigation that requires the cooperation of countries across the globe. By focusing on

international patent applications, we can investigate if the Protocol affects the internationaltransfer of climate-friendly technologies. Second, international patent applications reflect theeconomic value of technology by capturing changes in inventor behavior1. When inventors

recognize a higher expected return from a patent, they will want to secure patent protection invarious countries and regions (Nagaoka et al., 2010). In other words, the higher number of in-

ternational patent applications represents higher economic profit from new technology.

1

An international patent application has a strong relation to the “family size” of the patent. Haščič et al. (2010) explain international patent applications from the viewpoint of family size.

3

This paper makes three contributions to the existing literature on the economics ofrenewable energy technology. First, we investigate the effect of the Protocol on international

patenting activity. There are several studies that explore technology transfer through the Proto-col’s Clean Development Mechanism (Dechezleprêtre et al., 2008, 2009; Haščič and Johnstone,2011). In contrast to these project-level studies, we investigate whether the Protocol affects thetotal amount of international patent activity in each country. Second, we compare the pattern ofinternational diffusion by using several treatment groups and treatment periods. By looking at

the impact of the Protocol in countries with different target levels and in different periods, we

can understand the breadth and strength of the Protocol’s influence on technology diffusion.

Third, we investigate international patent applications to four developing countries that are

fast-growing emerging economies with large GHG emissions: China, India, Brazil, and Mexico.

Focusing on these four countries allows us to explore the “North-to-South” technology diffusion,

which has significant implications for future global GHG emissions.

The structure of this paper is organized as follows. Section 2 reviews previous litera-ture related to the international diffusion of climate mitigation technologies. Section 3 explains

the model and data used in the analysis. Section 4 presents the results of our empirical analysis.

4Section 5 analyzes the impact of international patent applications on four developing countries

emitting large amounts of GHGs. Finally, Section 6 concludes the study.

5

2. Literature Review

Several studies have examined technology transfer by using patent data (Lanjouw and Mody,

1996; Popp, 2006; Haščič and Johnstone, 2011; Dechezleprêtre et al., 2013). Lanjouw and Mo-

dy (1996) investigate the invention and diffusion of environmentally friendly technologies anddescribe in detail the process of international patents. They use the number of foreign patentapplications as a measure of technology transfer. They found that many of the patents filed indeveloped countries are domestic in origin, whereas most of the patents filed in developing

countries are foreign in origin. Haščič and Johnstone (2011) examine the role of the CDM under

the Protocol in the transfer of wind power generation technologies. They analyze patent applica-tion data between Annex I countries and non-Annex I countries. Using patent data from over

100 countries during the period from 1988 to 2008, their estimation results suggest that the

CDM has had a positive influence on the extent of transfer of wind power technologies fromAnnex I to non-Annex I countries. However, the estimated effects of CDM were relatively

small compared with those of other factors and negative when considering the cumulative effectof CDM projects. Dechezleprêtre et al. (2013) investigate the international diffusion of patented

inventions in climate-friendly technologies between 1995 and 2007. They emphasize the im-6portance of intellectual property regimes in the international diffusion of patented knowledge.

Their results suggest that the technological capacity of recipient countries reduces the transfer offoreign technologies.

This study investigates the effect of the Kyoto Protocol on international patent ap-

plications. An international patent application is defined as one in which the nationality of thefirst inventor does not correspond to the country in which the application is filed.2 Some re-searchers regard patent citations as a measure of technology diffusion (Jaffe et al., 1993; Peri,

2005; Popp, 2006). In contrast to patent citations that reflect knowledge spillover, the number ofinternational patent applications can be regarded as a proxy for market-driven knowledge flows(Dechezleprêtre et al., 2013). The number of cross-border patent applications does not reflect

the actual transfer of technologies; instead, it provides a proxy for technology diffusion. In ad-

dition, patent systems are designed to protect new technological inventions from potential imi-tators (Haščič et al., 2010). Patenting might prevent a technology from being transmitted to oth-er nations; nevertheless, technology transfer often involves patent filings in the recipient coun-

2

An innovation is legally protected only in a country in which it is patented. Thus, some part of the technology
crossing borders will show up as foreign patents and knowing the nationality of inventors allows us to identify theseflows (Lanjouw and Mody, 1996).

7

try of new technologies. Therefore, international patenting can be regarded as a channel fortechnology diffusion (Eaton and Kortum, 1996).

8

3. Model and Data

3.1 Model

To estimate the effect of the Protocol on international patent applications, we employ a differ-ence-in-difference (DID) estimator combined with a mix of fixed effects by running the Least

Squares Dummy Variable (LSDV) model. Using the DID method, we compare the change in

the number of international patent applications in countries committed to reducing GHG emis-

sions with that in countries without such commitment. The fixed effects estimation allows us tocontrol for time-invariant country characteristics that may be correlated with the decision tocommit to emission reduction through the Protocol. The general form of the model adopted inthis analysis can be written as follows:

Patent !" = 𝛽! + 𝛽! 𝑇𝑟𝑒𝑎𝑡𝑚𝑒𝑛𝑡×𝑃𝑒𝑟𝑖𝑜𝑑!" + 𝛽! 𝑋!" + 𝛿! + 𝛾! + 𝜀!" ,(1)

where i = 1, …,133 indexes countries as a cross-section unit, t = 1990, …, 2013 indexes theyear as the time unit, X is a set of time-varying country characteristics, δ is a vector of country

dummy, γ is the year dummy, and ε is the error term. The dependent variable (Patent) is the9number of international patent applications for renewable energy technologies. As mentioned in

Section 2, an international patent application is defined as a patent in which the nationality of

the first inventor does not correspond to the country of filing. Treatment is the treatment in-

dicator that takes the value one for countries that have emission targets, so-called Annex B

countries, and zero otherwise. We use two treatment groups: 34 Annex B countries that havequantitative limitations under the Protocol (Treatment 1)3 and 21 countries that have stronger
targets among Annex B countries (Treatment 2)4. Therefore, the control group is the other 99

countries for Treatment 1 and the other 112 countries for Treatment 2. Period represents thetreatment period that takes the value of one for the year during which the Protocol is effective

and zero otherwise. To compare the timing of the treatment, we adopt three treatment periods:

1997–2012 (Period A), 2005–2012 (Period B), and 2008–2012 (Period C). The first treatment

period considers the fact that the Protocol was adopted in 1997. The second treatment period is3

The 34 countries are Austria, Australia, Belgium, Bulgaria, Canada, Switzerland, Czech Republic, Germany, Denmark, Estonia, Spain, Finland, France, the United Kingdom, Greece, Croatia, Hungary, Ireland, Iceland, Italy, Japan,
Lithuania, Latvia, Netherlands, Norway, New Zealand, Poland, Portugal, Romania, Russian Federation, Sweden,
Slovenia, Slovakia, and Ukraine.
4
In this study, we define stronger targets as that is lower than emissions in 1990. The 21 countries are Austria, Belgium, Bulgaria, Canada, Switzerland, Czech Republic, Germany, Denmark, Estonia, United Kingdom, Croatia, Hungary, Italy, Japan, Lithuania, Latvia, The Netherlands, Poland, Portugal, Romania, Slovenia, and Slovakia. Other
countries, such as Portugal, Spain, Russia, and Ukraine, have quantitative targets but they do not have to attain lower
emission levels than 1990.

10

based on the fact that the Protocol became effective in 2005. The third treatment period is de-fined so as to represent the first commitment period of the Protocol.

The number of patent applications is count data and a non-negative integer-valued

random variable. Count data models have been developed to deal with the number of event oc-

currences (Maddala, 1983; Cameron and Trivedi, 1998). Thus, we use a negative binomial

model and maximum likelihood method to estimate Eq. (1). The panel of 133 countries and 24

years (1990–2013) is available, but missing observations for some countries reduce the sample

size in most models estimated.

3.2 Data

Dependent variables: International application

The number of international patent applications is calculated using the Worldwide Patent Statis-

tical Database, PATSTAT (version 2016, autumn). Following Johnstone et al. (2010), we clas-

sify patent data into each renewable energy source according to the IPC codes (see Table A1).

Figure 1 shows the trends in international patent applications for wind and solar energy tech-11

nologies among the 133 countries5 from 1990 to 2013. There is a clear increase in the numberof international patent applications for both types of renewable energy during the 2000s. After

2010, patent applications decrease and are back to the level of the late 2000s. The trends in do-

mestic patent counts follow a similar pattern. Figures 2 and 3 compare the international applica-

tions for wind and solar energy technologies in Annex B countries with those in other countries.

The trends of international applications in these countries are similar prior to the adoption of theProtocol in 1997, but clearly depart afterwards. Figures 4 and 5 illustrate the trends of interna-

tional applications by Annex B countries that have stringent targets and those of other countries.

The figures also suggest that the trend of international applications is similar before 1997, butdiffer after that.

5

The 133 countries are the United Arab Emirates, Antigua and Barbados, Angola, Argentina, Austria, Australia,
Azerbaijan, Barbados, Bangladesh, Belgium, Burkina Faso, Bulgaria, Bahrain, Burundi, Benin, Plurinational State of
Bolivia, Brazil, Bahamas, Botswana, Belarus, Belize, Canada, Democratic Republic of the Congo, Central African
Republic, Congo, Switzerland, Ivory Coast, Chile, Cameroon, China, Columbia, Costa Rica, Cape Verde, Cyprus,
Czech Republic, Germany, Denmark, Dominican Republic, Algeria, Ecuador, Estonia, Egypt, Eritrea, Spain, Finland,Fiji, France, Gabon, the United Kingdom, Ghana, Guinea, Equatorial Guinea, Greece, Guatemala, Honduras, Croatia,
Haiti, Hungary, Indonesia, Ireland, Israel, India, Iran, Iceland, Italy, Jamaica, Jordan, Japan, Kenya, Kiribati, Republic of Korea, Kuwait, Kazakhstan, Lao People's Democratic Republic, Lebanon, Sri Lanka, Liberia, Lesotho, Lithuania, Latvia, Morocco, Moldova, Madagascar, Mali, Mauritania, Malta, Mauritius, Malawi, Mexico, Malaysia,
Mozambique, Namibia, Niger, Nigeria, the Netherlands, Norway, New Zealand, Panama, Peru, Papua New Guinea,
Philippines, Pakistan, Poland, Portugal, Palau, Paraguay, Romania, Russian Federation, Saudi Arabia, Seychelles,
Sweden, Singapore, Slovenia, Slovakia, Sierra Leone, Senegal, El Salvador, Syrian Arab Republic, Swaziland, Chad,
Togo, Thailand, Tunisia, Tonga, Turkey, Trinidad and Tobago, Ukraine, Uganda, Uruguay, Bolivarian Republic ofVenezuela, Yemen, South Africa, and Zimbabwe. The United State of America is dropped from our dataset because
the country withdrew from the Kyoto Protocol in 2001.

12

Control variables

The estimation model includes four types of control variables: GDP per capita, FDI outflows,international application in all technological fields, and domestic patent stock. We use GDP per

capita evaluated at constant US dollars in 2010 from the World Development Indicators provided6 by the World Bank. These data capture the size of the economy in each country in eachyear. FDI refers to direct investment equity flows in an economy and is included in the model tocontrol for investment in foreign countries. Data are in current US dollars. We also include thevariable related to international applications in all technology fields to capture the extent of ac-

tivity abroad. The data are calculated from PATSTAT. To capture the supply of inventions ineach country, we include the total number of patent applications in domestic countries in the last
five years.7 A one-year lag is applied to this variable to reflect the time required to invent andapply for a patent abroad. We take logarithmic form for all continuous variables except for do-

mestic patent stock. Tables 1–4 report descriptive statistics of the dependent and independent

variables used in each estimation. We perform the t-test to examine if the distribution of covari-

ates is the same between the treatment and control groups. There are statistically significant dif6

http://databank.worldbank.org/data/home.aspx
We also check the estimation results by using patent application flows and domestic application variables without
lag and confirm that these estimation results do not differ with the main results.

7

13

ferences between the mean value of the treatment and control groups. Thus, we employ a fixed

effects DID model using the LSDV approach. Furthermore, the estimation models include othervariables to control these differences, particularly between developed and developing countries.

144. Empirical Results

4.1 Effect of the Kyoto Protocol on international patent applications

Table 5 reports the estimation results of models using Treatment 1 that define Annex B coun-tries as the treatment group. As for the treatment period dummy, specifications (1)–(3) use Pe-

riods A–C, respectively. All models include the country and year dummies. The coefficients of

the interaction term between Period A and Treatment 1 are positive and statistically significant

at the 10% level for wind energy and at the 1% level for solar energy in specification (1). The

results suggest that the Protocol increased international patent applications in Annex B countries

after 1997. In other words, the countries that commit to emission targets of GHGs have higher

numbers of international patent applications than other countries. Johnstone et al. (2010) findthat the Protocol encouraged the domestic patent filings of renewable energy technologies. The

results of our study suggest that the Protocol has also promoted technology diffusion throughinternational patent applications.

The coefficient of the interaction term between Period B and Treatment 1 is not sta-tistically significant for both energy types in specification (2). The results suggest that the in-fluence of the Protocol is not observed when we define the treatment period of the Protocol as

15

between 2005 and 2012. The coefficient of the interaction term between Period C and Treat-ment 1 is not statistically significant in specification (3). To summarize, the Protocol increased

the number of international patent applications for renewable energy technologies in the earlier

period after its adoption, but the influence is not significant in the first commitment period from2008 to 2012.

Table 6 reports the estimation results of models using Treatment 2 that define thoseamong Annex B countries that have a strong target as the treatment group. In specification (4),the coefficients of the interaction term between Period A and Treatment 2 are positive and sta-tistically significant at the 1% level for wind energy and at the 5% level for solar energy. The

results suggest that the Protocol increased international patent applications during 1997 and2012. The coefficient of the interaction term between Period B and Treatment 2 is positive andstatistically significant at the 1 % level for wind energy in specification (5). The coefficients of

the interaction terms between Period C and Treatment 2 are positive and statistically significant

at the 1% level for wind energy and at the 5% level for solar energy in specification (6). These

results suggest that the Protocol increased international patent applications in the countries withstringent targets during the first commitment period.

16

4.2 Robustness check

Tables 7 and 8 present the results of the robustness check using the dataset of high-income
countries.8 As shown in Tables 1 and 2, there are statistically significant differences between

the treatment and control groups. To consider the differences, we estimate models using the da-taset only with high-income countries.

Table 7 reports the estimation results of models using the dataset limited tohigh-income countries and Treatment 1. The coefficient of the interaction term between Period

A and Treatment 1 is statistically significant at the 5% level for solar energy. We confirm thatthe effect of the Protocol on international patent applications for solar energy technologies ispositive. On the other hand, we do not observe a statistically significant impact in specifications(8) and (9). The results suggest that the effect of the Protocol is insignificant in the latter period.

Table 8 reports the estimation results of models using Treatment 2. The coefficientsof the interaction term between Period A and Treatment 2 are positive and statistically signifi-

cant at the 1% level for wind energy and at the 5% level for solar energy. The coefficient of theinteraction term between Period B and Treatment 2 is positive and statistically significant at the8

The definition of “high-income countries” is from the World Bank: https://datahelpdesk.worldbank.org/

17

5% level for wind energy in specification (11). Similarly, the coefficient of the interaction term

between Period C and Treatment 2 is positive and statistically significant at the 1% level forwind energy. The results suggest that the Protocol increased international patent applications ofwind energy technologies from countries that have stronger targets under the Protocol.

18

5. International patent applications to developing countries

In this subsection, we explore international patent applications to four developing countries:

China, India, Brazil, and Mexico. These countries are chosen as developing countries with highGHG emissions. The rapid economic growth in these countries not only increases their current

carbon emissions, but is also predicted to result in higher global GHG emissions in the future.

For example, China is currently the largest emitter of energy-related CO2 emissions in the world

and is projected to remain in that position through 2040 (Energy Information Administration,2017).

Table 9 reports the estimation results. The coefficients of the interaction term be-

tween Period A and Treatment 1 are positive and statistically significant at the 5% level for bothof types of renewable energy technology. The results indicate that the Protocol affects interna-

tional patent applications to these four developing countries. From specifications (14) and (15),

we find that the coefficients of the interaction terms between Period B and Treatment 1 andbetween Period C and Treatment 1 are not statistically significant. Thus, the influence of theProtocol on international patent applications to these four developing countries is not confirmedin these periods.

19

Table 10 reports the estimation results using Treatment 2. The coefficient of the in-teraction term between Period A and Treatment 2 is statistically significant at the 5% level forwind energy in specification (16). The coefficients of the interaction term between Period B and

Treatment 2 are not statistically significant in both energies in specification (17). On the other

hand, the coefficients of the interaction term between Period C and Treatment 2 are statisticallysignificant at the 5% level for solar energy and at the 10% level for wind energy in specification(18). These results suggest an increase in applications to the four developing countries even in

the first commitment period.

20

6. Conclusion

This study investigated the effect of the Kyoto Protocol on international patent applications ofrenewable energy technologies. Using a DID approach, we find that the Protocol increased pa-

tent applications in developed and developing countries. These findings highlight three im-portant aspects related to the globalization of renewable energy technologies. First, inventors

increased the number of international applications with respect to renewable energy after theProtocol was adopted. This effect is observed in the earlier period, particularly in the treatment

period starting from 1997. Second, our results indicate that the impact of the Protocol on tech-

nology diffusion is more evident for Annex B countries with stronger GHG reduction targets.

For these countries, the impact of the Protocol on the number of international patent applica-

tions remains strong even in the first commitment period. Third, the Protocol also increased in-ternational patent applications to four developing countries. Although these countries are notobliged to reduce GHG emissions under the Protocol, the estimated results suggest that firms

are expecting higher demand for emission reductions in these countries.

There are two policy implications from this study. First, our results indicate the im-portance of international agreement on climate change for technology diffusion. The Kyoto

21

Protocol indirectly affects climate chage by increasing international patent applications regard-

ing renewable energy technologies and by reducing future emissions. This effect might result infurther emission reductions because international patent applications can be the first step intechnology transfer (Dechezleprêtre et al., 2013) or a channel for technology diffusion (Eaton

and Kortum, 1996). The second policy implication of our study is that the climate agreement

might affect the expected return from renewable energy technologies. By encouraging R&D ofclimate-friendly technologies, international agreement can promote innovation activities thatlead to patent applications. The expectation for expansion of the market for renewable energy

leads to an increase in the number of international applications. The influence of introducing aninternational framework can be strong enough to increase international patent applications todeveloping countries even though the framework does not require their commitment at the cur-rent stage.

The Paris Agreement, adopted in 2015, mentioned the importance of technologicaldevelopment and diffusion, particularly from industrialized countries to developing countries(UN, 2016). Although the impact of this agreement on technology diffusion is yet to be con-firmed, different levels of commitment by countries under the Agreement might have different

22impacts on technology diffusion. Further study on the effect of the climate agreement on tech-

nology diffusion is important to elaborate an international framework for mitigating climatechange.

Acknowledgement

This work was supported by JSPS KAKENHI Grant Number JP16H03006, JP26241033. Help-

ful comments from Taiji Hagiwara, Masanobu Ishikawa, and Kenta Nakamura are gratefully

acknowledged.

23

References

Aichele, R., and Felbermayr, G. (2012). Kyoto and the carbon footprint of nations. Journal ofEnvironmental Economics and Management, 63(3), 336–354.

Almer, C., and Winkler, R. (2017). Analyzing the effectiveness of international environmental

policies: The case of the Kyoto Protocol. Journal of Environmental Economics and Manage-

ment, 82, 125–151.

Cameron, A. C., and Trivedi, P. K. (1998). Regression analysis of count data. Cambridge Uni-

versity Press, Cambridge, NY

Coe, D., and Helpman, E. (1995). International R&D spillovers. European Economic Review.

39(5), 859–997.

Dechezleprêtre, A., Glachant, M., and Ménière, Y. (2008). The Clean Development Mechanism

and the international diffusion of technologies: An empirical study. Energy Policy, 36, 1273–

1283.

Dechezleprêtre, A., Glachant, M., and Ménière, Y. (2009). Technology transfer by CDM pro-jects: A comparison of Brazil, China, India and Mexico. Energy Policy, 37, 703–711.

24

Dechezleprêtre, A., Glachant, M., and Ménière, Y. (2013). What drives the international transfer

of climate change mitigation technologies? Empirical evidence from patent data. Environ-mental Resource Economics, 54, 161–178.

Eaton, J., and Kortum, S. (1996). Trade in ideas patenting and productivity in the OECD. Jour-nal of International Economics, 40, 257–278.

Energy Information Administration. (2017). International Energy Outlook: 2017. U.S. Depart-

ment of Energy, Washington, DC.

EPO. (2016). World patent statistical database (PATSTAT). European Patent Office, Munich.

Grunewald, N., and Martinez-Zarzoso, I. (2016). Did the Kyoto Protocol fail? An evaluation of

the effect of the Kyoto Protocol on CO2 emissions. Environment and Development Eco-nomics, 21(1), 1–22.

Haščič, I., and Johnstone, N. (2011). CDM and international technology transfer: Empirical ev-

idence on wind power. Climate Policy, 11(6), 1303–1314.

Haščič, I., Johnstone, N., Watson, F., and Kaminker, C. (2010). Climate policy and technologi-

cal innovation and transfer: An overview of trends and recent empirical results. OECD En-

vironment Working Papers, No. 30, OECD Publishing.

25

Jaffe, A., Tratjenberg, M., and Henderson, R. (1993). Geographic localization of knowledge

spillovers as evidenced by patent citations. Quarterly Journal of Economics, 108(3), 577–

598.

Johnstone, N., Haščič, I., and Popp, D. (2010). Renewable energy policies and technologicalinnovation: Evidence based on patent counts. Environmental and Resource Economics, 45,

133–155.

Lanjouw, J. O., and Mody, A. (1996). Innovation and the international diffusion of environ-mentally responsive technology. Research Policy, 25, 549–571.

Maddala, G. S. (1983). Limited-dependent and qualitative variables in econometrics. Cam-bridge University Press, Cambridge.

Nagaoka, S., Motohashi, K., and Goto, A. (2010). Patent Statistics as An Innovation Indicator.

Handbook of the Economics of Innovation, 2, 1083–1127.

Peri, G. (2005). Determinants of Knowledge Flows and their effect on Innovation. Review of

Economics and Statistics, 87(2), 308–322.

26

Popp, D. (2006). International innovation and diffusion of air pollution control technologies:

The effects of NOX and SO2 regulation in the US, Japan, and Germany. Journal of Environ-mental Economics and Management, 51(1), 46–71.

Popp, D., Newell, R. G., and Jaffe, A. B. (2010). Energy, the environment, and technologicalchange. Handbook of the Economics of Innovation, Volume 2, 873–937.

Potterie, B.V.P. D.L., and Lichtenberg, F. (2001). Does foreign direct investment transfer tech-

nology across borders? Review of Economics and Statistics, 83(3), 490–497.

United Nations. (2016). Paris Agreement. United Nations, Paris.

World Bank. (2017). World Development Indicators. The World Bank, Washington, DC.

< http://databank.worldbank.org/data/home.aspx >27

Figure 1. International patent applications

28

Figure 2. Annex B and non-Annex B countries: wind energy

29

Figure 3. Annex B and non-Annex B countries: solar energy

30Figure 4. Strong targets in Annex B and others: wind energy

31

Figure 5. Strong targets in Annex B and others: solar energy

32

Table 1. Descriptive Statistics: Treatment 1

Obs

Mean

Treatment1
Std. Dev.

Min

Max

Obs

Mean

Control
Std. Dev.

Min

Max

T value(1) and (2)

Wind
816
Solar
816
Dependent: to developing countries
Wind
816
Solar
816
Interaction term
Period A×Treatment 1
816
Period B×Treatment 1
816
Period C×Treatment 1
816
Control788
GDP it (ln)703
FDIoutflow it (ln)816Interappln it (ln)816
Domestic stock of wind it-1
816
Domestic stock of solar it-1

25.9559
46.001291.5621172.039

0
0

908
1989

2,376
2,376

0.88889
3.15783

6.24553
32.2143

0
0

114
683

-13.2591***-11.5661***4.424026.3063717.8643
31.2249

0
0

187
471

2,376
2,376

0.146460.40025

1.12858
5.64219

0
0

21
146

-11.6079***-8.8129***

0.666670.33333
0.20833

0.47169
0.47169
0.40637

0
0
0

1
1
1

2,376
2,376
2,376

0
0
0

0
0
0

0
0
0

0
0
0

10.040114.8243
7.152840.0858
73.3480.83975
2.95605
2.34649
123.632
268.3857.430672.30259
1.609440011.4251
20.2066
12.0442
1499
3200

2,331
1,740
1,827
2,376
2,376

7.90359
10.44842.870127.30387
5.87711.313853.59873
2.27904
91.797876.3108

4.75181-4.605200011.1215
18.1056
10.82942563
1626-42.7872***-28.5787***-44.2223***-8.0079***-11.0274***

Dependent

Table 2. Descriptive Statistics: Treatment 2

Obs

Mean

Treatment2
Std. Dev.

Min

Max

Obs

Mean

Control
Std. Dev.

Min

Max

T value(1) and (2)

Wind
504
Solar
504
Dependent: to developing countries
Wind
504
Solar
504
Interaction term
Period A×Treatment 2
504
Period B×Treatment 2
504
Period C×Treatment 2
504
Control
476
GDP it (ln)436
FDIoutflow it (ln)504Interappln it (ln)504
Domestic stock of wind it-1
504
Domestic stock of solar it-1

35.0615
63.3452

113.982214.182

0
0

908
1989

2,688
2,688

2.09115
4.87872

10.752734.3805

0
0

180
683

-14.6626***-13.278***

6.13691
8.78373

22.2345
39.2128

0
0

187
471

2,688
2,688

0.32180.62128

2.042595.77788

0
0

40
146

-13.2729***-10.2229***

0.666670.33333
0.20833

0.47187
0.47187
0.40652

0
0
0

1
1
1

2,688
2,688
2,688

0
0
0

0
0
0

0
0
0

0
0
0

9.9523114.62227.06961
47.694496.9147

0.81191
3.26974
2.6704150.18328.7998.18392
2.30259
1.609440011.230920.2066
12.0442
1499
3200

2,643
2,007
2,139
2,688
2,688

8.17162
11.07443.51449.682299.289811.465373.80774
2.69905
88.752981.07624.75181-4.605200011.4251
18.972110.8294
2563
1626

Dependent

33-25.8061***-18.0618***-26.6565***-7.7572***-12.0124***

Table 3. Descriptive Statistics: Treatment 1 and High-income Countries

Obs

Mean

Treatment1
Std. Dev.

Min

Max

Obs

Mean

Control
Std. Dev.

Min

Max

T value(1) and (2)

Wind
696
Solar
696
Dependent: to developing countries
Wind
696
Solar
696
Interaction term
Period A×Treatment 1
696
Period B×Treatment 1
696
Period C×Treatment 1
696
Control
673
GDP it (ln)612
FDIoutflow it (ln)696Interappln it (ln)696
Domestic stock of wind it-1
696
Domestic stock of solar it-1

30.0172
53.52398.5511185.251

0
0

908
1989

408
408

2.25735
14.375

9.12485
74.6255

0
0

80
683

-5.6744***-4.0784***

5.15237.37069

19.2485
33.6984

0
0

187
471

408
408

0.44608
2.13971

2.11047
13.4722

0
0

20
146

-4.9204***-2.9977**

0.666670.33333
0.20833

0.47174
0.47174
0.40641

0
0
0

1
1
1

408
408
408

0
0
0

0
0
0

0
0
0

0
0
0

10.2769
15.2415
7.4612940.0474
81.3879

0.622032.67292
2.32595
130.209289.235

8.543444.828311.609440011.4251
20.2066
12.0442
1499
3200

402
333
369
408
408

9.787911.9791
3.86898
23.05150.703430.55583.963462.65498
191.025
2.604628.69077-4.605200011.121517.7685
10.82942563
18-12.9685***-15.0303***-22.8171***-1.7533*-5.6335***

Dependent

Table 4. Descriptive Statistics: Treatment 2 and High-income Countries

Obs

Mean

Treatment2
Std. Dev.

Min

Max

Obs

Mean

Control
Std. Dev.

Min

Max

T value(1) and (2)

Wind
432
Solar
432
Dependent: to developing countries
Wind
432
Solar
432
Interaction term
Period A×Treatment 2
432
Period B×Treatment 2
432
Period C×Treatment 2
432
Control409
GDP it (ln)383
FDIoutflow it (ln)432Interappln it (ln)432
Domestic stock of wind it-1
432
Domestic stock of solar it-1

40.8565
73.7662

122.174
229.727

0
0

908
1989

672
672

6.19494
16.741118.631965.8633

0
0

180
683

-7.2267***-6.0604***

7.15509
10.2338

23.86842.1868

0
0

187
471

672
672

1.007442.354173.7708111.3705

0
0

40
146

-6.5525***-4.5905***

0.666670.33333
0.20833

0.47195
0.47195
0.40659

0
0
0

1
1
1

672
672
672

0
0
0

0
0
0

0
0
0

0
0
0

10.1372
15.1575
7.5221353.5857
110.8430.70208
2.964972.60036
161.402353.241

8.543444.828311.609440011.230920.2066
12.0442
1499
3200

666
562
633
672
672

10.0675
13.3657
5.32567
21.0253
13.4658

0.602893.72553
2.89937150.52172.69168.69077-4.605200011.4251
18.972110.82942563
1072

Dependent

34-1.729*-7.8661***-12.6512***-3.4093***-6.9233***

Table 5. Results of Treatment 1: Annex B Countries

Period A×Treatment 1

KyotoA(1997-2012)
(1)
wind
solar
0.472*
0.527**(0.263)(0.207)

KyotoB(2005-2012)
(2)
wind
solar

Period B×Treatment 1
Period C×Treatment 1
GDP(ln)FDIoutflow(ln)Interappln(ln)Domestic stock
_cons
N
AIC
BIC
log_likelihood

0.318(0.197)

KyotoC(2008-2012)
(3)
wind
solar

0.114
(0.164)

0.191
0.134
(0.199)(0.159)-0.6960.385-0.6210.429-0.6390.444(0.585)(0.374)(0.581)(0.381)(0.587)(0.385)-0.100**-0.079*-0.093**-0.074-0.091**-0.073(0.045)(0.046)(0.045)(0.048)(0.045)(0.048)0.912*** 1.114*** 0.916*** 1.073*** 0.884*** 1.066***(0.155)(0.113)(0.157)(0.121)(0.152)(0.117)0.000-0.0000.000-0.0000.000-0.000(0.000)(0.000)(0.000)(0.000)(0.000)(0.000)3.331-8.909***2.456-8.793**3.041-8.892**(4.957)(3.366)(5.018)(3.484)(5.087)(3.552)
2035
2035
2035
2035
2035
2035
5410.044 6246.511 5410.896 6250.141 5412.077 6250.084
6275.255 7111.721 6276.107 7115.351 6277.288 7115.295-2551.022 -2969.255 -2551.448 -2971.070 -2552.038 -2971.042

Note: Standard errors are between parentheses. *, **, and *** indicate statistical significance at the p < 0.1, p < 0.05,
and p < 0.01 levels, respectively.

35

Table 6. Results of Treatment 2: Annex B Countries with Stronger Targets
KyotoA(1997-2012)
(4)
wind
solar
Period A×Treatment 2 0.653***
0.345**(0.230)(0.165)Period B×Treatment 2
Period C×Treatment 2GDP(ln)FDIoutflow(ln)Interappln(ln)Domestic stock
_cons
N
AIC
BIC
log_likelihood

KyotoB(2005-2012)
(5)
wind
solar

0.444***(0.163)

KyotoC(2008-2012)
(6)
wind
solar

0.118
(0.128)0.484***
0.308**(0.170)(0.137)-0.6170.444-0.5550.436-0.5790.461(0.582)(0.380)(0.586)(0.385)(0.582)(0.385)-0.103**-0.078-0.098**-0.075-0.093**-0.074(0.046)(0.047)(0.045)(0.048)(0.045)(0.048)0.901*** 1.074*** 0.900*** 1.064*** 0.889*** 1.071***(0.153)(0.114)(0.151)(0.117)(0.151)(0.116)0.000-0.0000.000-0.0000.000-0.000(0.000)(0.000)(0.000)(0.000)(0.000)(0.000)2.507-9.048***1.962-8.743**2.283-9.108***(4.973)(3.446)(5.056)(3.511)(4.995)(3.516)
2035
2035
2035
2035
2035
2035
5407.335 6248.587 5408.985 6250.108 5409.012 6248.858
6272.546 7113.797 6274.195 7115.318 6274.223 7114.069-2549.668 -2970.293 -2550.492 -2971.054 -2550.506 -2970.429

Note: Standard errors are between parentheses. *, **, and *** indicate statistical significance at the p < 0.1, p < 0.05,
and p < 0.01 levels, respectively.

36

Table 7. Robustness Checks: High-income Countries and Treatment 1

Period A×Treatmen t1

KyotoA(1997-2012)
(7)
wind
solar
0.322
0.579**(0.333)(0.272)

KyotoB(2005-2012)
(8)
wind
solar

Period B×Treatment 1
Period C×Treatment 1
GDP(ln)FDIoutflow(ln)Interappln(ln)Domestic stock
_cons
N
AIC
BIC
log_likelihood

0.055(0.253)

KyotoC(2008-2012)
(9)
wind
solar

-0.239(0.219)-0.164-0.523**(0.253)(0.224)
0.803
0.715
0.786
0.557
0.794
0.568
(0.797)(0.505)(0.796)(0.522)(0.805)(0.534)-0.207*** -0.129*** -0.200*** -0.109*** -0.201*** -0.114***(0.061)(0.040)(0.061)(0.040)(0.062)(0.041)0.643*** 1.317*** 0.606*** 1.142*** 0.573*** 1.121***(0.188)(0.155)(0.193)(0.159)(0.189)(0.151)0.000-0.0000.000-0.0000.000-0.000(0.000)(0.000)(0.000)(0.000)(0.000)(0.000)-7.603 -13.664*** -7.059-10.074*-6.690-9.836*(7.974)(5.209)(8.028)(5.350)(8.080)(5.413)
892
892
892
892
892
892
3965.981 4704.846 3966.458 4706.266 3966.307 4704.915
4311.110 5049.975 4311.588 5051.395 4311.436 5050.045-1910.990 -2280.423 -1911.229 -2281.133 -1911.153 -2280.458

Note: Standard errors are between parentheses. *, **, and *** indicate statistical significance at the p < 0.1, p < 0.05,
and p < 0.01 levels, respectively.

37

Table 8. Robustness Checks: High-income Countries and Treatment 2
KyotoA(1997-2012)
(10)
wind
solar
Period A×Treatment 2 0.696***
0.343**(0.251)(0.170)Period B×Treatment 2
Period C×Treatment 2GDP(ln)FDIoutflow(ln)Interappln(ln)Domestic stock
_cons
N
AIC
BIC
log_likelihood

KyotoB(2005-2012)
(11)
wind
solar

0.435**(0.172)

KyotoC(2008-2012)
(12)
wind
solar

-0.032(0.135)0.478***
0.012(0.182)(0.143)
1.019
0.746
0.846
0.600
0.762
0.603
(0.798)(0.511)(0.799)(0.519)(0.790)(0.519)-0.215*** -0.122*** -0.212*** -0.110*** -0.205*** -0.112***(0.061)(0.041)(0.061)(0.041)(0.061)(0.041)
0.698*** 1.258*** 0.674*** 1.190*** 0.648*** 1.198***(0.185)(0.145)(0.187)(0.150)(0.186)(0.148)0.000-0.0000.000-0.0000.000-0.000(0.000)(0.000)(0.000)(0.000)(0.000)(0.000)-10.632-13.424**-8.393-11.098**-7.283-11.213**(8.055)(5.273)(8.090)(5.298)(7.936)(5.289)
892
892
892
892
892
892
3962.037 4705.431 3964.001 4706.651 3964.047 4706.664
4307.166 5050.561 4309.131 5051.781 4309.176 5051.793-1909.018 -2280.716 -1910.001 -2281.326 -1910.023 -2281.332

Note: Standard errors are between parentheses. *, **, and *** indicate statistical significance at the p < 0.1, p < 0.05,
and p < 0.01 levels, respectively.

38

Table 9. International Applications in Developing Countries: Treatment 1

Period A×Treatment 1

KyotoA(1997-2012)
(13)
wind
solar
0.771**
0.845**(0.351)(0.357)

KyotoB(2005-2012)
(14)
wind
solar

Period B×Treatment 1
Period C×Treatment 1
GDP(ln)FDIoutflow(ln)Interappln(ln)Domestic stock
_cons
N
AIC
BIC
log_likelihood

0.428(0.306)

KyotoC(2008-2012)
(15)
wind
solar

0.433
(0.293)

0.169
0.391
(0.316)(0.266)-2.050-0.017-1.9020.439-1.9540.600(1.626)(0.941)(1.647)(0.880)(1.765)(0.881)0.070-0.111*0.089-0.109*0.091-0.103*(0.107)(0.063)(0.107)(0.061)(0.107)(0.062)
1.008*** 0.920*** 1.054*** 0.850*** 1.009*** 0.796***(0.369)(0.280)(0.358)(0.272)(0.372)(0.266)
0.000
0.000
0.000
0.000
0.000
0.000
(0.000)(0.000)(0.000)(0.000)(0.000)(0.000)12.096-3.5339.764-7.49310.901-8.637(12.849)(7.940)(13.543)(7.631)(14.665)(7.790)
2035
2035
2035
2035
2035
2035
2476.794 2795.828 2478.091 2798.090 2479.269 2798.374
3342.005 3661.038 3343.302 3663.301 3344.479 3663.584-1084.397 -1243.914 -1085.046 -1245.045 -1085.634 -1245.187

Note: Standard errors are between parentheses. *, **, and *** indicate statistical significance at the p < 0.1, p < 0.05,
and p < 0.01 levels, respectively.

39

Table 10. International Applications in Developing Countries: Treatment 2

Period A×Treatmen t2

KyotoA(1997-2012)
(16)
wind
solar
0.653**
0.277(0.330)(0.266)

KyotoB(2005-2012)
(17)
wind
solar

Period B×Treatment 2
Period C×Treatment 2GDP(ln)FDIoutflow(ln)Interappln(ln)Domestic stock
_cons
N
AIC
BIC
log_likelihood

0.352(0.249)

KyotoC(2008-2012)
(18)
wind
solar

0.271
(0.200)0.430*
0.479**(0.228)(0.193)-2.0050.398-1.8460.482-1.8040.570(1.646)(0.888)(1.692)(0.882)(1.687)(0.883)0.087-0.111*0.084-0.115*0.091-0.112*(0.109)(0.062)(0.108)(0.061)(0.107)(0.062)
0.992*** 0.753*** 1.015*** 0.776*** 1.008*** 0.793***(0.374)(0.259)(0.368)(0.258)(0.370)(0.260)
0.000
0.000
0.000
0.000
0.000
0.000
(0.000)(0.000)(0.000)(0.000)(0.000)(0.000)11.396-5.9239.704-6.915
9.213-8.117(13.036)(7.696)(13.798)(7.739)(13.810)(7.770)
2035
2035
2035
2035
2035
2035
2477.214 2798.905 2478.299 2798.713 2477.873 2797.365
3342.425 3664.116 3343.509 3663.924 3343.083 3662.576-1084.607 -1245.452 -1085.149 -1245.357 -1084.936 -1244.683

Note: Standard errors are between parentheses. *, **, and *** indicate statistical significance at the p < 0.1, p < 0.05,
and p < 0.01 levels, respectively.

40

Appendix

Table A1. IPC codes
IPC code

Explanation

Wind
F03D 1Wind motors with rotation axis substantially in wind direction

F03D 3

Wind motors with rotation axis substantially at right angle to wind direction

F03D 5

Other wind motors

F03D 7

Controlling wind motors

F03D 9Adaptations of wind motors for special use
Details, component parts, or accessories not provided for in, or of interest apart from,the other groups of this subclass

F03D 11Solar
F03G 6

Devices for producing mechanical power from solar energy

F24J 2

Use of solar heat, e.g. solar heat collectors
Devices consisting of a plurality of semiconductor components sensitive to infra-red
radiation, light—specially adapted for the conversion of the energy of such radiation
into electrical energy
Semiconductor devices sensitive to infra-red radiation, light—adapted as conversion
devices

H01L 27/142
H01L 31/04-078
H02N 6

Generators in which light radiation is directly converted into electrical energy

E04D 13/18

Aspects of roofing for energy collecting devices—e.g. incl. solar panels

Source: Johnstone et al. (2010)41