The Pennsylvania State University
The Graduate School
College of Earth and Mineral Science
THREE ESSAYS OF ECONOMICS AND POLICY ON
RENEWABLE ENERGY AND ENERGY EFFICIENCY
A Dissertation in
Energy and Mineral Engineering
by
Yuxi Meng
© Yuxi Meng
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Doctor of Philosophy
August 2014
The dissertation of Yuxi Meng was reviewed and approved* by the following:
Zhen Lei
Assistant Professor of Energy and Environmental Economics
Dissertation Co-Adviser
Co-Chair of Committee
Seth Blumsack
Associate Professor of Energy Policy and Economics
Dissertation Co-Adviser
Co-Chair of Committee
Karen Fisher-Vanden
Associate Professor of Environmental and Resource Economics
Jeffrey R. S. Brownson
Associate Professor of Energy and Mineral Engineering
Luis F. Ayala H.
Graduate Program Chair of Energy and Mineral Engineering
*Signatures are on file in the Graduate School
ii
Abstract
In face of the crisis in energy security, environmental contamination, and climate change, energy
saving and carbon emission reduction have become the top concerns of the whole human world. To
address those concerns, renewable energy and energy efficiency are the two fields that many countries are
paying attention to, which are also my research focus. The dissertation consists of three papers, including
the innovation behavior of renewable energy producers, the impact of renewable energy policy on
renewable innovation, and the market feedback to energy efficient building benchmarking ordinance.
Here are the main conclusions I have reached in this dissertation. First, through the study on
foreign patenting intention with the case study of Chinese solar PV industry, I looked at the patenting
behaviors of 15 non-Chinese solar PV producers in solar PV technologies in China, and pointed out that
foreign firms may file patents in the home country or production base of their competitors in order to earn
the competitive edge in the global market. The second study is about the “Innovation by Generating”
process. I specifically focused on Renewable Portfolio Standard (RPS) in the United States and the
innovation performance within each state, and found out that wind power generation in RPS states has
developed rapidly after the adoption of RPS, while the “Innovating by Generating” effect is more
significant in solar PV technologies. In general, the innovations of the two technology groups are not
prominently encouraged by RPS. My last study is about the benchmarking law and market response in the
scenario of Philadelphia Benchmarking Law. By comparing the rental rate of LEED/EnergyStar buildings
and ordinary buildings in the city of Philadelphia before and after the adoption of the building energy
efficiency benchmarking law, I believe that the passage of Philadelphia Benchmarking Law may be
helpful in improving the public awareness and understanding of energy efficiency information of
buildings.
iii
Table of Contents
List of Tables .......................................................................................................................................................... v
List of Figures ........................................................................................................................................................ vi
Chapter 1
Introduction .................................................................................................................................... 1
1.1 Background ...........................................................................................................................................1
1.2 Outline ..................................................................................................................................................1
1.3 Main Contribution .................................................................................................................................2
Chapter 2
Patenting at Competitors’ Home Bases: Evidence from Foreign Patenting in Solar PV in China ...... 4
2.1 Introduction ..........................................................................................................................................4
2.2 Background ...........................................................................................................................................8
2.3 Hypothesis ..........................................................................................................................................12
2.4 Empirical Practice ...............................................................................................................................13
2.5 Empirical Results .................................................................................................................................18
2.6 Conclusion and Discussion ..................................................................................................................26
Appendix 1 Robustness Check Results ......................................................................................................42
Chapter 3
Renewable Portfolio Standards and Innovating by Generating ..................................................... 47
3.1 Introduction ........................................................................................................................................47
3.2 Renewable Portfolio Standard and Innovating by Generating: Conceptual Issue ..............................52
3.3 Empirical Strategy and Data ...............................................................................................................55
3.4 RPS and Renewable Energy Generation..............................................................................................59
3.5 “Innovating by generating” in Renewable Energy ..............................................................................62
3.6 Local innovation in Wind Energy and Solar PV after RPS ....................................................................66
3.7 Conclusion and Discussion ..................................................................................................................67
Appendix 2 Robustness Check Results ......................................................................................................79
Chapter 4
Building Benchmarking Law and Increased Awareness about Building Energy Efficiency .............. 89
4.1 Introduction ........................................................................................................................................89
4.2 Building Benchmarking Law in Philadelphia .......................................................................................94
4.3 Hypothesis, Empirical strategy and Data ............................................................................................97
4.4 Results ...............................................................................................................................................102
4.5 Conclusion .........................................................................................................................................107
Appendix 3 Robustness Check Results ....................................................................................................119
References ......................................................................................................................................................... 122
iv
List of Tables
Table 2-1 Top 10 solar module manufacturers in the world (2011) ............................................................................ 35
Table 2-2 Groups of the technologies and patent application statues .......................................................................... 36
Table 2-3 Top 15 non-Chinese solar PV producers in China and FDI status .............................................................. 37
Table 2-4 Basic regression results ............................................................................................................................... 38
Table 2-5 Results of non-PCT and PCT patents .......................................................................................................... 39
Table 2-6 Results of process and product foreign patents ........................................................................................... 40
Table 2-7 Results of foreign patenting in China vs. Australia ..................................................................................... 41
Table 3-1 Enactment and Implementation of RPS across States ................................................................................. 72
Table 3-2 Regression results of wind generation and RPS .......................................................................................... 73
Table 3-3 Regression results of solar generation and RPS .......................................................................................... 74
Table 3-4 Regression results of wind innovation and wind generation ....................................................................... 75
Table 3-5 Regression results of solar PV innovation and solar generation ................................................................. 76
Table 3-6 Regression results of wind innovation and RPS .......................................................................................... 77
Table 3-7 Regression results of solar PV innovation and RPS .................................................................................... 78
Table 4-1 Benchmarking Law in the United States ................................................................................................... 112
Table 4-2 Results with LEED and EnergyStar Buildings .......................................................................................... 113
Table 4-3 Results with Ordinary Buildings ............................................................................................................... 114
Table 4-4 Results of D-D-D Model for all Buildings ................................................................................................ 115
Table 4-5 Results with LEED and EnergyStar Buildings in Pseudo Tests ................................................................ 116
Table 4-6 Results with Ordinary Buildings in Pseudo Tests ..................................................................................... 117
Table 4-7 Results of D-D-D Model in Pseudo Tests ................................................................................................. 118
v
List of Figures
Figure 2-1 Solar PV Installation in China and Rest of the World and Chinese Solar PV Production ......................... 29
Figure 2-2 Non-Chinese Solar PV Patent Application and Chinese Solar PV Production and Market ....................... 30
Figure 2-3 Non-PCT and PCT Patent Applications and Chinese Solar PV Production and Market ........................... 31
Figure 2-4 Process and Product Patent Application and Chinese Solar PV Production and Market ........................... 32
Figure 2-5 Solar PV market and production in China (CN) and Australia (AU) ......................................................... 33
Figure 2-6 Patent applications of the 15 firms in Australia and Australian solar PV market and production ............. 34
Figure 3-1 Renewable power generation in RPS states (1990-2008) .......................................................................... 69
Figure 3-2 Wind Power Generation and Patent Applications in RPS states (1990-2008) .......................................... 70
Figure 3-3 Wind Power Generation and Patent Applications in RPS states (1990-2008) ........................................... 71
Figure 4-1 Timeline of Philadelphia Benchmarking Law ......................................................................................... 109
Figure 4-2 Average Rental Rate of Certified Buildings versus Ordinary Buildings .................................................. 110
vi
Chapter 1
Introduction
1.1 Background
In face of the crisis in energy security, environmental contamination, and climate
change, energy saving and carbon emission reduction have become the top concerns of
the whole human world. To address those concerns, renewable energy and energy
efficiency are the two fields that many countries are paying attention to. Currently, large
numbers of energy and environmental policies are targeting at technology innovation
improvement and market cultivation as they are both critical in the development of
renewable energy and energy efficiency. Therefore, understanding the economics and
policy complications in renewable energy and energy efficiency fields is crucial in
tackling the challenging energy problems.
In my dissertation, three independent studies are covered in the effort of
economics and policy analysis on renewable energy and energy efficiency topics. They
include renewable energy production and innovation investigation and energy efficiency
exploration in building sector, and range from China to the U.S.
1.2 Outline
The whole dissertation is organized as following. The first chapter is a brief
introduction of the background and three studies. Chapter 2 is the study of foreign
patenting intention in solar PV industry in China. In Chapter 3, the “Innovating by
Generating” process in renewable energy technology is addresses with the setting of
Renewable Portfolio Standard in the United States. The last chapter is a study of
1
benchmarking law and market response with the case study of Philadelphia
Benchmarking Law in the United States.
1.3 Main Contribution
Through the analysis of the three studies, several conclusions are reached as
following.
In the study of foreign patenting intention in Chinese solar PV industry, after
compared the patenting behavior of the non-Chinese solar PV patent producers in China
and the U.S., I find that the evidences are supportive to the foreign patenting intention in
competing with their competitors at their home and production base (in China). This
finding has provided a new perspective in analyzing foreign patenting intention besides
market covering, technology licensing, and foreign direct investment intentions.
In the second study, the “innovating by generating” process is empirically studied
through the analysis in the technology innovation in wind power and solar PV with the
setting of Renewable Portfolio Standard (RPS) policy in the United States. Because the
two technologies are in different stages of development, I see a more significant
“innovating by generating” effect in solar PV but a faster generation growth in wind
power. However, in general, Renewable Portfolio Standard has not significantly
contributed to the technologies innovation in both fields. In addition, the analysis in this
study on RPS features might also be helpful in improving the design of Renewable
Portfolio Standard policy in the future.
At last, the market response to benchmarking law is investigated with a case study
of Philadelphia Benchmarking Law. I compare the demand of energy efficient buildings
(LEED and EnergyStar certified buildings) with the demand of ordinary buildings before
2
and after the Philadelphia Benchmarking Law, and show some preliminary evidences on
an increase in the awareness of energy efficient buildings after the implementation of
benchmarking law. It is the first empirical study on the market response to the building
benchmarking law, which would provide some insights to the policy makers, building
owners, and energy efficient technology developers in understanding the energy efficient
building market development and consumer response.
3
Chapter 2
Patenting at Competitors’ Home Bases: Evidence from
Foreign Patenting in Solar PV in China
2.1 Introduction
Economists have long held that foreign patenting, patenting by an applicant in a
country other than her home country, is associated with exporting, licensing, or foreign
direct investment (FDI) by the applicant (Grundmann 1976; Eto and Lee 1993; Eaton and
Kortum 1996; Branstetter 2006; Yang and Kuo 2008). When a firm exports its products
to or has FDIs to produce in a foreign country, it will have incentives to file patents in
that country to prevent the imitation or infringement on its inventions from domestic
firms. Similarly, having patent protection for a technology in a foreign country would
facilitate licensing from the patentee to the firms there that would otherwise have been
able to use it free.
Moreover, studies have shown that exporting, technology licensing, and FDIs are
all important channels of international technology transfer, in particular from developed
countries to less developed ones.1 (Schiffel and Kitti 1978; Keller 2004; Branstetter 2006)
Since foreign patenting is often thought to go hand-in-hand with these activities, data on
foreign patenting has been widely used as a metric for foreign technology transfer, both
in academic research and in policy discussion.
This paper presents a novel perspective on the intention of foreign patenting. I
study foreign patenting in China by major non-Chinese solar photovoltaic (PV) firms that
produce solar PV cells and modules. Solar PV in China features, on one hand, a small
1
With exporting, licensing, or FDI, foreign firms bring products or technologies and skills to a recipient
country, promoting technology transfer and diffusion to that country.
4
and slow-developing domestic market in terms of solar PV installation2, but on the other
hand, a fast-growing, very competitive and strong Chinese solar PV manufacturing
industry that supplies about 60 percent of solar PV cells and modules in the world market
in 2012 3 and includes a number of top-tiered and dominant Chinese producers in the
world (Table 2-1). Therefore, none of the three activities mentioned above (exporting,
licensing, and FDIs by these non-Chinese firms) seems to offer a convincing explanation
for why their patenting in China has been growing fast since early 2000s. Given the pale
Chinese domestic solar PV market (especially compared to major markets in Europe such
as Germany and Spain4), the significant foreign patenting in solar PV in China is unlikely
to be driven by the intent of exporting to Chinese market. Second, since these nonChinese solar PV firms compete fiercely with Chinese firms in the global market, as
evidenced by the recent anti-dumping and anti-subsidy cases in the United States and
Europe brought by non-Chinese solar PV firms against Chinese5, it is doubtful that the
2
The Chinese solar PV market in early 2000s is very small. The annual solar PV installation is no more
than 20 megawatts per year until 2008. Since 2009, the solar PV market in China started to take off. The
installation surged to over 150MW in 2009, and tripled in the following year, attributed to the Golden sun
program implemented in 2009. As both the program and the market growth are not expected before and
beyond the time window of this study (2002-2007), they are not considered in this paper. Detailed Chinese
solar PV market data could be found in the dataset “Annual Installed Solar Photovoltaics Capacity in
Selected Countries and the World, 2000-2012” at the website of Earth Policy Institute. <URL:
http://www.earth-policy.org/data_center/C23 >
3
For details about Chinese solar PV production, please refer to the dataset “Annual Solar Photovoltaics
Production by Country, 1995-2012” published by Earth Policy Institute on their website. <URL:
http://www.earth-policy.org/data_center/C23>
4
For a long time, Europe is the main solar PV market in the world. Germany, Italy and Spain are all major
solar PV market in Europe as well as the world. For details, refer to the dataset “Cumulative and NewlyInstalled Solar Photovoltaics Capacity in Ten Leading Countries and the World, 2012” at the website of
Earth Policy Institute. <URL: http://www.earth-policy.org/data_center/C23>
5
In the United States, the preliminary anti-dumping duty is imposed on Chinese solar cells and panels in
May, 2012. <URL: http://www.solarworld-usa.com/news-and-resources/news/anti-dumping-duties.aspx>
U.S. Department of Commerce has issued the final ruling in Octobor, 2012, and tariffs ranging from 24 to
nearly 36 percentage will be impose on most Chinese solar PV product, according to NYTimes. <URL:
http://www.nytimes.com/2012/10/11/business/global/us-sets-tariffs-on-chinese-solar-panels.html>
European Union initiated investigates on Chinese solar PV product since September, 2012. <URL:
http://www.nytimes.com/2012/09/07/business/global/eu-investigates-chinese-solar-
5
large and growing number of patent applications filed in China by these non-Chinese
firms is driven by their intention to license their technologies to their Chinese competitors.
Lastly, there is little evidence suggesting that non-Chinese firms increase their foreign
patenting in China mostly because of their FDIs in China which has been very small 6
(De La Tour, Glachant et al. 2011).
In this paper, I postulate instead that foreign patenting in China in solar PV
technology is driven by non-Chinese firms’ strategy to compete with their Chinese rivals
in their home and manufacturing base, which provides the most cost-effective way for
them to prevent Chinese firms from copying and exporting to many (sometimes
unexpected) markets in the world. In other words, I hypothesize that foreign patenting in
China’s solar PV market has occurred to prevent Chinese rivals from using foreigndeveloped technologies to compete in markets outside China, including markets that had
not yet developed at the time of technology development.
This hypothesis is supported by the empirical finding that the propensity for filing
patents in China by major non-Chinese solar PV firms is significantly and positively
related to the manufacturing output of Chinese solar PV firms, but not to Chinese
installed solar PV capacity (as I would expect if the market-covering theory of foreign
patenting were true in this case). Further analysis on differentiating PCT from non-PCT
patents, product from process patents, and comparing foreign patenting in China versus in
panels.html?pagewanted=all> EU-China solar panel dispute is reported to be settled after ten months with
an “amicable solution” in July 2013, according to Solar Daily.
<URL: http://www.solardaily.com/reports/EU_China_settle_solar_panel_dispute_999.html>
6
In general, FDIs in China increased after August, 2010 and dropped significantly in 2012. <URL:
http://www.scmp.com/business/economy/article/1284534/china-fdi-june-surges-us144-billion> In June
2013, it surged to $14.4 billion, according to NYTimes. This fluctuation is beyond our study period. <URL:
http://www.nytimes.com/2013/07/18/business/global/strong-growth-in-foreign-investment-inchina.html?_r=0> In addition, I checked the FDIs in solar PV industry from the target firms in this study,
very few of them have FDIs in China, details could be found in the latter parts.
6
Australia by the same group of major non-Chinese solar PV firms, have lend further
supports to this hypothesis.
This study, for the first time, highlights and provides empirical evidence in
support of a strategic motivation for foreign patenting: the desire of firms to protect their
inventions in the manufacturing base of foreign competitors. Although this aspect of
foreign patenting has received little attention in economic literature, it has been suggested
by practitioners. The Head of GE’s Foreign Patenting Operation in 1980s, suggested long
before that “by covering the competitor’s home or major manufacturing country, the
applicant has a better chance of preventing the competitor from entering into markets
regardless of where such markets might develop” (Helfgott 1986). However, up to now,
few rigorous empirical evidences have been provided.
This study yields some novel insights into the relationship between foreign
patenting and international technology transfer, and on using foreign patenting data as a
proxy for foreign technology diffusion. If firms patent in a foreign country not to cover
the market (through exporting, licensing and FDI), but to exclude their competitors in
that country, more foreign patenting could imply a taller “patent wall” against local firms
and a larger barrier to technology diffusion in the short term.
The paper also contributes to a growing literature on strategic patenting by firms
to compete with competitors, gain bargaining power, or increase firm reputation, to name
a few (Eaton and Kortum 1996; Hall and Ziedonis 2001; Blind, Edler et al. 2004; Thumm
2004; Blind, Edler et al. 2006; Giuri, Mariani et al. 2007; Blind, Cremers et al. 2009). In
particular, scholars and policymakers have been concerned about the practice of patenting
to block competitors, where firms seek patents for inventions that they do not
7
commercialize, to prevent rivals from entering into markets 7 . This study provides an
interesting variant of strategic patenting in competing with competitors, not concerning
their domestic market, but a larger market worldwide.
The remainder of the paper is organized as follows. Section 2 describes literature
in foreign patenting and some background in Chinese solar PV market and manufacturing
industry. Section 3 discusses the hypothesis on the motivation of foreign patenting to
compete with their competitors at their home or manufacturing bases. Section 4 is about
the empirical strategy and data that are applied in this study. Empirical results are
summarized in Section 5. The final Section concludes.
2.2 Background
2.2.1 Motivations of Foreign Patenting
A seminal study by Graham (1957) pointed out two primary reasons for U.S.
organizations to obtain patents outside the U.S.: protecting their operations from
infringement in foreign markets, and licensing or selling patents to foreign entities.
Subsequent studies on both developed and developing countries have mostly followed
this line and focused on three intentions of foreign patenting: foreign market protection,
technology licensing, and FDIs (Penrose 1973; Grundmann 1976; Schiffel and Kitti 1978;
Basberg 1987; Eto and Lee 1993). Eaton and Kortum (1996) focused on the marketcovering justification of foreign patenting, in their modeling growth and technology
7
In 2011, World Intellectual Property Organization (WIPO) highlighted “blocking competitors” in its
World Intellectual Property Report, indicating the strategic patenting behaviors that listed above has
become an increasing patenting practice that policymakers should be concerned with. For details, please
refer to WIPO publication: “World Intellectual Property Report: The changing Face of Innovation”, 2011,
93-94.
8
diffusion of OECD countries. Keller (2004) pointed out that trade (import and export)
and FDI explained the purpose of foreign patenting. Yang and Kuo (2008), based on
cross-patenting from 30 member countries in the World Intellectual Property
Organization (WIPO) between 1995 and 1998, confirmed that outbound international
patenting is strongly and positively associated with trade related factors including exports
and foreign direct investments.
Extant studies on foreign patenting in China have largely attributed it to the large
Chinese market (Sun 2003). In addition, some researches extend the intention of foreign
patenting to the competition in the Chinese market, either between foreign firms and
Chinese firms or between foreign firms (Hu 2010; Liang and Xue 2010). Liang and Xue
(2010) mentioned the multinationals may use patenting strategically to create an obstacle
for Chinese firms to catch-up, even though they didn’t provide an empirical evidence to
justify their proposition. Hu (2010) tried to explain the foreign patenting surge in China,
and found they are not driven by the increased returns but to compete with both Chinese
and foreign firms in Chinese market.
2.2.2 Chinese Solar PV Manufacturing Industry
There were few solar PV producers in China before 2000 (Marigo 2007), however,
Chinese solar PV manufacturing industry has been growing rapidly since early 2000s, for
the following reasons. First, the development of Chinese solar PV manufacturing industry
has been encouraged and supported by the Chinese government. 8 Second, the Chinese
8
There are several national planning and policies that highlighted the development of renewable energy,
for example, the “New Energy and Renewable Energy Industry Development Planning (2000-2015)”, were
released in 2000. Renewable Energy Law in China has also been implemented in 2005, which emphasize
the renewable energy development that included solar PV technology.
9
solar PV industry is benefited from the rapid growth in the global market, particularly in
Europe and the United States.
The Chinese solar PV industry has grown into one of the major players in the
world market in a short period of time. The Chinese share of world solar PV production
has increased rapidly, from only 7% in 2005 to 45% in 2010, and become the top solar
PV producing country in the world9. Of the top 10 solar PV panel and module producers,
five are Chinese firms10, as shown in Table 2-1.
2.2.3 Chinese Solar PV Market
Chinese solar PV market, however, did not develop as fast as its solar PV
manufacturing industry. First, the cost of solar PV power generation per Megawatt is
higher than wind power, so solar PV is less competitive in comparison with wind. On the
other hand, unlike many of the European countries and the United States, for a long time
Chinese domestic solar PV market is not guided or stimulated by Chinese government
through preferential policies. The major solar PV markets in the world are Germany,
Spain, Italy, U.S. and Japan11. In contrast, the share of Chinese solar PV market in the
world is very minor. By 2009, China solar PV market consists only 1.33 percent of the
world solar PV capacity (Brown 2011). Given the small domestic market, most of solar
PV cells and modules produced by Chinese solar PV firms have been exported to the
global market, where they fiercely compete with, and often outperform, non-Chinese
9
For details about Chinese solar PV production, please refer to the dataset “Annual Solar Photovoltaics
Production by Country, 1995-2012” published by Earth Policy Institute on their website. <URL:
http://www.earth-policy.org/data_center/C23>.
10
Chinese firms here include only the Chinese mainland solar PV producers. If I count Taiwan (China)
producers in, this number should be six.
11
Germany has been the top one in the global market since 2004, with its accumulative solar PV
installation taking 43% of the world capacity in 2010. Data source: Earth Policy Institute (2012).
10
firms. See the comparison between Chinese solar PV production and solar PV market in
Figure 2-2.
In fact, Chinese solar PV suppliers have become the primary rivals of nonChinese solar PV producers in the global market. This is vividly evident in the recent
anti-dumping and anti-subsidy cases in the United States and Europe, which were
brought by non-Chinese solar PV firms against Chinese firms. In October 2011, the antidumping and anti-subsidy case was filed to U.S. Department of Commerce by
SolarWorld and six other U.S. based solar producers. China was blamed in global solar
PV dominance in illegal government subsidy to Chinese solar producers 12 . Similar
investigation was brought by European Union in September 2012. Until now, Chinese
solar PV case is still under heated discussion.
2.2.4 Foreign Patenting in Solar PV in China
The Chinese Patent Law was enforced in 1985 and amended in 1992, 2000, and
2008, respectively. With patent protection strengthened in China, both foreign and
domestic patent applications at the State Intellectual Property Office (SIPO) of China
grew rapidly (Sun 2003). With regard to foreign patenting in solar PV technology at
SIPO, the number of foreign filings by non-Chinese firms has been growing fast after
2002.
12
A complete report of China’s solar industry and U.S. anti-dumping and anti-subsidy trade case could be
reached here:
http://www.chinaglobaltrade.com/sites/default/files/china-global-trade-solarmanufacturing_may2012_0.pdf
11
Figure 2-2 shows the annual amount of foreign solar PV filings at SIPO13, the
annual installed solar PV capacity (i.e. the annual size of solar PV market) in China, and
the annual production by Chinese solar PV firms over the period of 2002 to 2007 (which
is the time window of this study). I found that foreign patent applications in solar PV
technology at SIPO grew fast since 2002, and expedited after 2005. This trend went well
with the increase of Chinese solar PV production, but far exceeded the growth of Chinese
solar PV market.
2.3 Hypothesis
Given the small domestic solar PV market but a very strong and competitive solar
PV manufacturing industry in China, I do not think the conventional motivations of
foreign patenting, including those associated with activities of exporting, licensing, and
FDIs, would provide convincing explanation for why foreign patenting in China has been
growing fast since early 2000s until 2008. Export of solar PV cells and modules to China
by foreign firms has been little given the small market but very competitive domestic
producers. Meanwhile, both FDIs and technology licensing are unlikely motivations of
foreign patenting in solar PV technology in China. First, De La Tour et al. (2011) showed
that FDIs and joint venture flows in China in solar PV have been small. I also checked
the FDIs of 15 main non-Chinese producers in China, and only 2 of them have direct
investments (Details are shown in Table 2-3). Also, as discussed earlier and indicated by
the anti-dumping cases in the United States and Europe, non-Chinese solar PV firms take
13
The filing here are invention patent filings, which exclude the utility model patents and design patents. In
China, there are three types of patents, invention patents, utility model patents, and design patents. Among
them, only invention patents are considered as real innovation. Utility model and design patents are usually
less innovative, which are taken as a registration in the patent system. The patent filings in this study are all
invention filings, if not specifically pointed out.
12
Chinese as their major competitors in the global market and it is highly implausible for
them to be willing to license their technologies to their Chinese rivals.
Instead, I offer another explanation: non-Chinese solar PV firms file so many
patent applications in China to prevent Chinese firm using their technologies at home
base and exporting to the global market. In reality, to obtain competitive advantage over
Chinese, non-Chinese firms could file patents in each of their main markets and constrain
the Chinese exports into these countries. However, protecting their inventions in China,
the home and manufacturing base of the Chinese rivals, provides the most cost-effective
way for non-Chinese firms to prevent Chinese from using the patented technology in
production and then selling in the markets outside China. The motivation to patent in
competitors’ home or manufacturing base is particularly strong when manufacture
requires a large amount of capital investment and it is not easy for manufacturers to move
around, which is the case for solar PV production14 (Helfgott 1986).
2.4 Empirical Practice
In this study, I empirically tested whether foreign patenting in solar PV in China
is motivated by competing with their Chinese rivals at home and manufacturing base.
Given that technology licensing and FDIs are clearly not the motivations of these foreign
filings, I primarily test whether the propensity to file in China is more responsive to the
growth of Chinese solar PV firms or the development of Chinese solar PV market
(related to solar cell and module exporting by non-Chinese solar PV firms). In other
14
After the suggestion that foreign patenting should “cover the competitor’s home or major manufacturing
country”, Helfgott (1986) went on to suggest that “Where only a limited investment is needed to
manufacture the product, greater focus should be given to covering the major market countries rather than
the manufacturing countries, since it would be easy for competitors to shift manufacture in order to avoid a
patent.
13
words, is foreign patenting firm competing with Chinese firms or market covering in
China?
Several further tests were taken in this study. First, I compare foreign patenting in
China through Patent Cooperation Treaty (PCT foreign filings) versus directly patenting
at SIPO (non-PCT foreign filings). Second, product and process patent filings of foreign
firms in China are further compared. At last, a comparison of foreign patenting in China
versus in Australia by the same non-Chinese solar PV firms is made15. All these tests
support my hypothesis that foreign patenting in solar PV in China is motivated by
competing with their Chinese competitors at home and manufacturing base.
2.4.1 Empirical Strategy
The econometric specification is as follows:
(Ⅰ)
On the left hand side of the equation, the dependent variable,
, is the
number of patent applications in solar PV technology in China (at SIPO) by a foreign
solar PV firm i in year t. To capture the development of Chinese solar PV firm
throughout the years, the solar cell and module production of Chinese firms in each year
are employed, which is
(in 100 Megawatt). I measure Chinese solar PV
15
Similarly, none of the non-Chinese solar PV firms in this study is based in Australia, so the foreign
patenting of them in Australia is comparable with those in China.
14
market with the annual solar PV installation in China in each year t, which is
(in Megawatt).
is the number of patent applications in
solar PV technology in the United States by the same firm i in the same year t.
are firm fixed effects and year fixed effects.
In this specification,
and
and
is the residue of the regression.
are the coefficients that are of key interest to me,
which could be interpreted as the responsiveness of the propensity to patent in China to
the development of Chinese solar PV firms and Chinese solar PV market, respectively.
After partial differential,
CN _ Filingi ,t
US _ Filingi ,t
represents the propensity of foreign solar PV
firms to patent in China as they file patents in the US. Therefore,
shows how the
propensity to patent in China would respond to Chinese solar PV production (firmtargeting). Similarly,
identifies the response of propensity to file patents in China to
Chinese solar PV market (market-driven).
(
)
(
)
The trend in Chinese solar PV market expectation and Chinese solar PV
technology innovation are both controlled in this study. The market expectation
variable,
, captures the potential market expectation variation in solar
PV in China from 2002 to 2007. Chinese Renewable Energy Law, which encourages the
exploration of renewable energy in China, was passed in 2005 and enforced in 2006.
Even though the market growth in 2005 to 2007 is not prominent according to the records,
the enforcement of this law may impact the market expectation somehow since 2005.
Therefore I use the market expectation variable to capture the possible expectation
15
fluctuation coming with the Renewable Energy Law.
is a dummy
variable which equals to 0 in the years prior to Chinese Renewable Energy Law (2004
and before) and 1 afterwards (2005 and after). In addition, to rule out the possibility that
the foreign patenting floated because of the growth of technology innovation in China
solar PV industry, I use the innovation performance of the top 5 Chinese solar PV firms
to control the innovation development in China16.In this paper,
is the
patent filing of the five Chinese solar PV firms in year t.
2.4.2 Data
In this study, China Patent Database (2011) is the main source of patent filing
records, and to shield the effect of China officially joining WTO at the end of 2001 and
the impact of financial crisis after 2007, I select the patent applications filed from 2002 to
2007 in this study17. The data of solar PV patent applications in China are extracted from
China Patent Database according to the IPC green inventory published by the World
Intellectual Property Organization (WIPO)18. According to WIPO, all solar PV related
technologies could be divided into seven categories, and having discussed with the
experts in solar PV technologies, I further assorted the sever categories into three groups
based on the phases the patents been applied in solar PV production process. The three
groups are silicon production, solar cell production and photovoltaic system, and final
16
The patent filings of the top 5 Chinese solar PV firms from 2002 to 2007 are taken as the proxy of
Chinese solar PV technology innovation development. I use the top 5 firms instead of the total Chinese
patent filing in solar PV because some of Chinese solar PV patents are from small firms, which may not
properly reflect the general innovation trend in this industry in China. The five firms are: Suntech, Yingli,
China Electric Equipment Group, Canadian Solar, and Linyang.
17
Actually, the hypothesis is also tried with data from 2002 to 2009, considering the solar PV production of
Chinese firms keep going up after 2007, but I report the results from 2002 to 2007 to shield the potential
impacts of WTO and financial crisis.
18
The “IPC Green Inventory” was developed by the IPC Committee of Experts in order to facilitate
searches for patent information relating to so-called Environmentally Sound Technologies. More
information could be found at http://www.wipo.int/classifications/ipc/en/est/.
16
products (Table 2-2). In this table, the number of patent applications related to solar cell
and system (group2, under categories 2, 3, 4, and 5) take a large percentage of the
applications throughout the whole value chain.
As Chinese solar PV producers, who are direct competitors with foreign firms, are
mostly focus on solar cell and system production, this paper will emphasize on the
analysis of foreign patenting intentions in the solar cell and system patent group. For
robustness checks, I also run regressions for all foreign patent filings at SIPO throughout
the technology categories related to solar PV technologies. The results, as shown in the
Appendix, are similar.
Besides the patent data in China, the patent applications of the foreign firms in the
US and Australia are also collected and applied in this study. They are all extracted from
the Thomson Innovation Patent Database 19 with the same IPC codes from WIPO.
Chinese solar PV production and installation data were compiled from the data released
by Earth Policy Institute20 and in China Solar PV Industry Development Report 21(20022007).
The data structure in this study is a panel data. I selected 15 non-Chinese solar PV
producers as representatives of foreign patent applicants in this industry, as listed in
Table 2-3. The 15 firms are all main producers in the world solar PV industry from 2002
19
Thomson Innovation Patent Database is a platform where the patent information of most developed
countries and some developing countries could be reached. The website of this database is:
http://info.thomsoninnovation.com/.
20
The data could be found at the website of Earth Policy Institute.
<URL: http://www.earth-policy.org/data_center/C23>
21 The reports are compiled a
s products of China Renewable Energy Development Project. The project is funded by China National
Development and Reform Commission, Global Environment Fund, and World Bank.
17
to 2007, in terms of solar PV shipment in 2004 and 2005.22 According to the Chinese
patent database (2011), most of them are also active solar PV patent applicants in China
throughout this period. For example, Sharp, which topped in global solar PV production
during that time, owns 63 solar PV technology patents in China.
2.5 Empirical Results
2.5.1 Main Results
The regression results of solar cell and system are listed in Table 2-4, and I find
that the propensity for foreign applicants to filing solar PV patents in China is positively
related to Chinese solar PV production rather than the Chinese solar PV market. In Table
2-4, column (1) to column (4) are the regression results of solar cell and system patent
applications on actual size of Chinese solar PV market, production, and innovation. In
column (2) and column (3), I add Chinese solar PV market expectation and Chinese solar
PV innovation 23 to the regression individually. The two controls are both included in
column (4). Throughout the results in the four columns, the coefficients of the interaction
term of Chinese solar PV production are positive and significantly related to the foreign
solar PV filings, while none of the coefficients of Chinese solar PV market interaction
term is significant. After I have controlled the variations in the market expectation and
innovation drift in China, this trend indicates that as Chinese solar PV production surged,
22 Prometheus Institute has indicated in their monthly report published in August 2005 that during that
time, Sharp, BP solar, Kyocera, Mitsubishi, Sanyo, Shell solar and Isofoton are all major solar PV
producers in the world market. Details can be found online.
<URL:http://www.prometheusinstitute.org/admin/researchnotes/uploads/PV2-0401.01_Global_PV_Module_manufacturers-.pdf>
23
The Chinese solar PV innovation here is the patent filings of the top 5 Chinese solar PV firms from 2002
to 2007.
18
the propensity of foreign firms to file patents in China has prominently increased, which
is supportive to the hypothesis.
As a robust check, I use the annual increment of Chinese solar PV market,
production, and innovation instead in column (5) to (8), and the results are consistent.
The coefficients of the interaction term between US filing and Chinese production are all
significant and positive, and no significant result is found in the Chinese solar PV market
interaction. Another group robust check is made with the 15 firms’ patent applications in
the whole value chain, and the trend is similar. I also tried the regressions with the
applications of the 15 firms from 2002 to 2009 and from 2002 to 2008, and the
interaction term with Chinese solar PV production is still significant. Detailed results are
compiled in Appendix A.
2.5.2 PCT versus Non-PCT Foreign Patenting
Patent Cooperation Treaty (PCT) is an international patent law treaty that was
drafted in 1970s at Washington D.C, and formally enforced in 1978. Lapenne detailed
how PCT works and the difference this system brought about compared with the
conventional ways of international patent filing. He pointed out that PCT provides the
applicants through this system advantages in filing patents in contracting states (both in
relative simpler international patent application process and additional time to decide
which country they should get their patents filed) (Lapenne 2010).
In this section, I separate PCT foreign filings from non-PCTs, in order to
eliminate the uncertainty brought about by PCT patents. As PCT provide the patent
applicants through this system a privilege in additional time and simpler filing process,
PCT applicant filing patents in China may simply be an “add-on” rather than specifically
19
targeting at Chinese solar PV firms or market. Therefore, PCT applications may not be
good patent candidates when examining the intentional foreign patenting behavior. In
contrast, non-PCT patent filings that foreign applicants applied directly in China would
provide more implications to this study. Because unlike in PCT patents, the non-PCT
applicants must make decisions on which country they should go and protect their
technologies quickly and consciously. So the intention of foreign patent filings in China
would be reflected by the non-PCT patents clearly. Therefore, in this section, I regress
with PCT and non-PCT patent applications of the 15 firms separately, in order to isolate
the potential noises that may brought about by PCT patents.
By doing this, it is expected that having excluded the PCT filings, the non-PCT
filings, which precisely reflect the foreign filing intentions, would show a positive and
significant relationship with Chinese solar PV production rather than Chinese solar PV
market. The trend of PCT and non-PCT patent application as well as Chinese solar PV
production and installation are all depicted in Figure 2-3. In this figure, the non-PCT
filings increased fast after 2003 and accelerated after 2005, which is faster than PCT
filings. I also find out that the trend of non-PCT applications is closer to the development
of Chinese solar PV production.
PCT and non-PCT patent applications of the 15 non-Chinese firms are separated
and used independently as dependent variables in this part, and the results are highly
supportive to my hypothesis. Similarly, I include the market and production terms as well
as the market expectation and innovation control variables in all regressions. All the
results are compiled in Table 2-5. In column (1) and (2), the actual size of Chinese solar
PV production, installation, and innovation are used, and in column (3) and (4), they are
20
replaced by the increments. Column (1) and column (3) are the results of non-PCT
patents, and it is clear that Chinese solar PV production has made significant contribution
to the non-PCT solar PV foreign filing growth in China. In column (2) and column (4)
are the PCT patents. In contrast to the non-PCTs, the PCT patent increase are responsive
to both Chinese production and market (although the market is trivial at that time), which
also implies that PCT applicants are not as targeting and focused as non-PCT patent filers
are. Comparison between non-PCT applications (column (1) and (3)) and PCT
applications (column (2) and (4)) show that the coefficients of production interaction
term in non-PCT regressions are greater, indicating that non-PCT filings are more
targeting at Chinese solar PV firms in comparison with PCT patents, which is supportive
to my hypothesis.
All these results support the hypothesis that foreign patents solar PV filers are
intentionally targeting at Chinese solar PV firms rather than the Chinese solar PV market
when they are filing patents in China. The regressions with the applications in the whole
value chain are also made as a robustness check. The results are compiled in Appendix B,
and the trend is the quite similar.
2.5.3 Process versus Product Foreign Patenting
Through further observations on the foreign filings in solar PV technology in
China, I find that most of the patents include either one or both types of the following
information, product information and method of production. The product information
generally includes the detailed structure, design, or characteristics of a certain product.
And the method of production describes a unique manufacturing process or method that
would be applied in the production of a certain product. Therefore, I could generally
21
identify two type of patents based on the information of a patent application (including
title, abstract, or claims of a patent filing).
In this section, all the foreign patents are sorted into two patent groups, product
patents or product patents, and a comparison is made in between throughout the analysis
of this section24. In this paper, product patent and process patent are defined as following.
A product patent is mainly an introduction or explanation of one specific device or
product, while a process patent describes how a product is made or a method that could
be used in a specific production process.
In line with my hypothesis, as the foreign applicants are competing with Chinese
firms in the international market, I believe they have more incentives to file product
patents in China. For example, when a US firm is competing with China firms on solar
cell A in the global market, the firm should be prone to apply a product patent of A to
protect their technology and market, no matter what process or method has been used in
the production of A. In practice, there are two ways in front of the US firm, either
applying a patent of solar cell A in its target markets individually, such as Germany,
France, or Italy, or directly filing a solar cell A patent in China to limit the production of
solar cell A in China, and beat Chinese firms in any market wherever the market is. In
this case, the US firm may have a higher probability to take the later choice, because it is
the most cost-effective way to beat Chinese solar PV producers, especially when Chinese
firms are their main competitors.
Therefore, I would expect a significant and positive relationship between the
Chinese solar PV production and foreign product patent filings, rather than the process
24
As we mentioned before, there might be some patent overlap of these two patent groups, which means a
patent is both product patent and process patent, because such patents may include both the product
information and method of the product manufacturing.
22
patents. I show how the product and process patent applications of the 15 firms have been
changed together with Chinese solar PV market and production throughout the years
from 2002 to 2007 in Figure 2-4. In this figure, the product patents grew way much faster
than process patents since 2003. Also, the trend of product patents is closer to the
Chinese solar PV production than the process patents. The regression results of process
and product patents are shown in Table 2-6.
The results in Table 2-6 are supportive to the proposition that the increase of
Chinese solar PV production has significant impacts on foreign product patent
applications rather than process patents. In this part, their filings in the US were also
categorized into two groups and used instead of all US filings in all regressions. Results
in column (1) and (2) use the actual size of market, production, and innovation in the
interaction terms, while column (3) and (4) use the increments instead. The results of
product patents in column (2) and (4) indicate that product patent filings are positively
influenced by Chinese solar PV production, which means that as Chinese solar PV
production increased foreign applicants are prone to file more product patents in China.
In contrast, process patent analysis in column (1) and (3) don’t show any significant
results in Chinese market or production. A robustness check is taken with the applications
of the whole value chain. The trend is similar. See the details in the Appendix C.
2.5.4 Foreign Patenting in China versus Australia
In this part, a comparison is made between foreign filings of the 15 firms in China
and Australia, because they share many characteristics in domestic solar PV market but
are quite different in solar PV manufacturing industry development. On one hand, both
countries have a huge potential solar PV market, but none of them is well developed in
23
2002 to 2007. Both Australia and China have a large territory and abundant solar energy
resource. Even though solar energy explorations are generally encouraged by both
governments in state policies and planning, none of them established a well-developed
solar PV market. On the other hand, solar PV industry developments in the two countries
are quite different. During 2002 to 2007, Chinese firms are among the top solar PV firms
of the world, and the solar PV production of Chinese went up exponentially. Nevertheless,
the solar PV production in Australia remains trivial throughout this period.
With similar domestic market and diverse industrial development in China and
Australia, I expect that, in contrast to the relationship between foreign patenting and
domestic production discovered in China, the applications of the 15 solar PV firms in
Australia may not be related to Australian solar PV production. A comparison on market
and production between China and Australia are shown in Figure 2-5. In this figure,
Australian solar PV market is minor, similar to Chinese solar PV market. However,
dislike Chinese solar PV production explosion during this period, the solar PV production
in Australia remains little. The count of patent applications of the 15 firms in Australia as
well as the solar PV market and production in Australia are portrayed in Figure 2-6. In
this figure, no relationship between applications and market and production could be
observed.
The regressions here are based on specification ( Ⅰ ), but the independent
variables are slightly different. First, the production and installation terms in Australian
regressions are the numbers of Australian solar PV industry. In addition, the market
expectation variable for Australia is correspondent to “the Australian photovoltaic
industry roadmap”. Supported by Australian government, Australian Business Council
24
for Renewable Energy has published “the Australian photovoltaic industry roadmap” in
2004. The roadmap highlighted the potential and benefits of Australia solar PV industry
in the next twenty five years, and initiated the “joint-up” actions between the government
and industry to achieve this potential.25After the amendment of the federal Renewable
Energy (Electricity) Act in 2000, this roadmap was an important signal of the government
support on the Australian solar PV industry. So the market expectation variable of
Australia is a dummy variable which equals to 0 in 2002 and 2003, and equals to 1 in
2004 and after. At last, the innovation term is skipped in this part because in Australia the
local innovation in solar PV is relatively small.
The patent filings of the 15 firms in China and Australia are used as dependent
variables individually, and no relationship between Australian foreign applications and
Australian production or market is found, which is supportive to my hypothesis. All the
results are compiled in Table 2-7. Column (2) and (4) are the results of Australia, and
column (1) and (3) are the results of foreign patenting in China, as a comparison. The
regressions in column (1) and (2) are based on actual size of production and installation
in the two countries. The production coefficient for Australian patents in column (2) is
not significant as the one of China in column (1). Column (3) and (4) are results with
production and installation increment. The coefficients of production and market terms of
Australian patent filings in column (4) are still not significant. In the robustness checks
which regressed on the applications of the whole value chain, the trend is basically the
same. They are compiled in Appendix D.
25
The report is published by Australian Business Council for Renewable Energy, and it is available online
at: http://efa.solsticetrial.com/admin/Library/David/Published%20Reports/2004/PVRoadmap.pdf
25
2.6 Conclusion and Discussion
This study provides an interesting case where firms patent in a foreign country to
compete with competitors in their home or manufacturing bases and beat competitors in
other markets. Such motivation is distinct from the conventional motivations of foreign
patenting including for purposes of market covering, licensing or FDIs. The patent filings
in solar PV in China by major foreign solar PV firms are studied. The results suggest that
competing with increasingly powerful Chinese solar PV firms rather than exporting to
Chinese solar PV market is the main consideration for foreign solar PV firms to file
patents in China.
In this paper, the first empirical evidence on this interesting motivation of foreign
patenting is presented, despite its possible wide use by firms in practices but neglected in
the literature. It provides an interesting implication for the often assumed link between
foreign patenting and international technology transfer and diffusion. Foreign patenting is
believed to be associated with exporting, licensing or FDIs that brings products and
technologies to the local economy. But through this paper, I believe foreign patenting to
compete with their rivals at home base may impede the international technology transfer
and diffusion in the short run. In a certain period of time, because the key technologies
are controlled by the foreign firms, the patent “wall” is high, and the R&D activities of
local firms might be prohibited, which would limit the development of the local firms and
economy.
However, it might be arbitrary to conclude that foreign patent filings under this
motivation are helpless to local innovation and economy. Even though the foreign firms
have no product exported, no technology licensed, and no investment to the country,
26
which seems made little contribution to the local economy, the patents filed are indeed
added to the knowledge base of such industries in the target country. In the long run, with
strong motivation from the local firms and proper incentives of local government, those
foreign patent filings may in turn promote the innovation jump and spur the economy
development within the country.
I do not have the counterfactual that what Chinese solar PV industry would have
been, had foreign firms not filed so many patents in China, but I did follow the track of
the main Chinese solar PV producers in recent years. Some of Chinese solar PV firms are
struggling, with several major Chinese solar PV firms going bankrupt26. Meanwhile, the
patent strategy of foreign firms also gave a lesson to Chinese firms and a few of them
have started to catch up in technology innovation. A piece of evidence is from Yingli
Solar Inc. The annual patent filing of Yingli skyrocketed since 2008 and has grown to
over 50 filings in 2010, in comparison with less than 10 filings in 200727.
In addition, this study also sheds light on the effectiveness of the Chinese patent
system. It has been often argued that despite that the patent law in China, though
appearing comparable to those in developed countries, the system does not function well
in practice because of the weak enforcement. In particular, it has been long complained
that patent protection in China has discriminated against foreign patentees. The finding
that non-Chinese solar PV firms decided to file patents in China to compete with their
Chinese rivals at home, would suggest that the Chinese patent system at least to some
26
Suntech, which used to be a leader of Chinese solar PV industry, is facing bankruptcy in China. More
details could be found in the report of Wall Street Journal named “Suntech Is Pushed Into Chinese
Bankruptcy Court”.
<URL: http://online.wsj.com/news/articles/SB10001424127887324557804578372082733827860>
27
The data are collected from Thomson Innovation patent database in 2013.
<URL: http://info.thomsoninnovation.com/ >
27
extent works for them. Otherwise, they would have chosen to file patents in markets
where they compete with Chinese such as Europe and the U.S.
28
Figure 2-1 Solar PV Installation in China and Rest of the World and Chinese Solar PV
Production
Solar PV Production and Installation
(MWs)
30000
25000
20000
15000
10000
5000
0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Year
China solar PV installation
China solar PV production
Rest of the world PV installation
Notes: All the data are from Earth Policy Institute28
28
Data are available at the website of Earth Policy Institute: http://www.earth-policy.org/
29
600
1400
500
1200
1000
400
800
300
600
200
400
100
200
0
0
2002
2003
2004
2005
Year
Non-Chinese patent applications
2006
Chinese Solar PV Production and Market
(MWs)
# of Foreign Patent FIlings
Figure 2-2 Non-Chinese Solar PV Patent Application and Chinese Solar PV Production and
Market
2007
Chinese Solar PV market
Chinese solar PV production
Note: Patents data are invention patent applications compiled by the authors from Chinese Patent Database (2011); the
Chinese production and market data are from Earth Policy Institute29
29
Data are available at the website of Earth Policy Institute: http://www.earth-policy.org/
30
450
1400
400
1200
# of Patent Filings
350
1000
300
250
800
200
600
150
400
100
200
50
0
0
2002
2003
2004
2005
2006
2007
Chinese Solar PV Production and Market (MWs)
Figure 2-3 Non-PCT and PCT Patent Applications and Chinese Solar PV Production and
Market
Year
PCT
Chinese solar PV market
nonPCT
Chinese solar PV production
Note: Patents data are invention patent applications compiled by the authors from Chinese Patent Database (2011); the
Chinese production and market data are from Earth Policy Institute30
30
Data are available at the website of Earth Policy Institute: http://www.earth-policy.org/
31
80
1,000
70
900
800
60
700
50
600
40
500
30
400
300
20
200
10
100
0
Chinese solar PV Production and Market
(MWs)
# of Forieng Patent Filings
Figure 2-4 Process and Product Patent Application and Chinese Solar PV Production and
Market
0
2002
2003
2004
2005
2006
2007
Year
Process patents
Chinese solar PV production
Product patents
Chinese solar PV market
Note: Patents data are invention patent applications compiled by the authors from Chinese Patent Database (2011); the
Chinese production and market data are from Earth Policy Institute31
31
Data are available at the website of Earth Policy Institute: http://www.earth-policy.org/
32
Solar PV Market and production(MWs)
Figure 2-5 Solar PV market and production in China (CN) and Australia (AU)
1200
1000
800
600
400
200
0
2002
2003
2004
2005
2006
2007
Year
AU market
AU production
CN market
CN production
Notes: Chinese solar PV production and market data are from Earth Policy Institute 32; Australian solar PV production
and market data are compiled by the authors from National Survey Report of PV Power Applications in Australia by
International Energy Agency (2004-2010).
32
Data are available at the website of Earth Policy Institute: http://www.earth-policy.org/
33
Figure 2-6 Patent applications of the 15 firms in Australia and Australian solar PV market
and production
50
45
# of Patent Filings
25
40
35
20
30
15
25
20
10
15
10
5
5
0
Solar PV Market and Production (MWs)
30
0
2002
2003
2004
2005
2006
2007
Year
AU application
AU production
AU market
Notes: Patent application data are compiled by the authors from Thomson Innovation Database (2013); Australian
production and market data are all from National Survey Report of PV Power Applications in Australia by International
Energy Agency (2004-2010)
34
Table 2-1 Top 10 solar module manufacturers in the world (2011)
Manufacturers
First Solar
Suntech Power
Yingli Green Energy
Trina Solar
Canadian Solar
Sharp
Hanwha Solar One
Jinko Solar
LDK Solar
SolarWorld
Total
Country
United States
China
China
China
China
Japan
Korea
Taiwan(China)
China
Germany
Share of global module
production (%)
7
6.5
5.5
4.9
4.8
4.1
2.9
2.8
2.7
2.7
43.9
Module production in
2011 (MW)
2001
1866
1554
1395
1363
1155
825
782
774
767
12482
Notes: All the data are from Cleantechnica website33
33
Data are compiled based on the news released on the website of Cleantechnica: Top 10 solar panel
companies in 2011. <URL:http://cleantechnica.com/2012/03/15/top-10-solar-panel-companies-in-2011/>
35
Table 2-2 Groups of the technologies and patent application statues
Groups
Silicon production
Solar cell production
and photovoltaic
system
Final Products
Category
Chinese
Foreign
Sub-total
1. Silicon; single-crystal growth
43
104
147
2. Devices adapted for the conversion
of radiation energy into electric
energy
182
630
812
3. Assemblies of a plurality of solar
cells
78
680
758
4. Regulating to the maximum power
available from solar cells
1
5
6
5. Dye-sensitized solar cells
1
51
52
6. Electric lighting devices with, or
rechargeable with, solar cells
36
29
65
7. Charging batteries
24
18
42
365
1517
1882
Total
Notes: Data are compiled from Chinese Patent Database (2011) by the authors. The data are the invention patent
filings of Chinese and foreign firms in China in 2002-2007.
36
Table 2-3 Top 15 non-Chinese solar PV producers in China and FDI status*
List
Firm
Country/Region
FDIs (Y/N)
1
Sharp
Japan
N
2
Kyocera
Japan
Y (2003, Tianjin)
3
BP Solar
United Kingdom
N
4
Mitsubishi
Japan
N
5
Shell Solar
Netherland
N
6
Sanyo
Japan
N
7
Isofoton
Spain
N
8
Evergreen
United States
N
9
First Solar
United States
N
10
Motech
Taiwan
Y (2006, Suzhou)
11
Q-cells
Germany
N
12
Schott
Germany
N
13
SolarWorld
Germany
N
14
Sunpower
United States
N
15
United Solar Ovonic
United States
N
Notes: Top 7 firms are in accordance with the ranking of their shipments report (August, 2005) which is prepared by
the Prometheus Institute. The rest firms are in alphabetic order. The FDI statues information is collected from the
official websites of each of the 15 firms.
37
Table 2-4 Basic regression results
(1)
(2)
(3)
(4)
With actual production, installation, and innovation
VARIABLES
AppUS*CNproduction
AppUS*CNinstallation
(5)
(6)
(7)
(8)
With production, installation, and innovation increment
Solar
Solar
Solar
Solar
Solar
Solar
Solar
Solar
0.0931***
0.0924***
0.0818***
0.0915***
0.150***
0.145***
0.149***
0.158***
(0.0179)
(0.0223)
(0.0175)
(0.0217)
(0.0308)
(0.0269)
(0.0286)
(0.0274)
-0.00374
-0.00352
0.000968
-0.00109
-0.000412
-6.80e-05
0.00166
0.00132
(0.00559)
(0.00988)
(0.00750)
(0.0100)
(0.00352)
(0.00450)
(0.00355)
(0.00423)
AppUS*CNexpectation
0.00554
(0.189)
AppUS*CNinnovation
-0.171
0.0274
(0.290)
(0.141)
0.00831
0.0167
-0.0412
(0.168)
0.00830***
0.00901*
(0.00207)
(0.00427)
(0.00592)
(0.0111)
0.0256
0.0212
-0.0695
-0.0292
-0.0385
-0.0445
-0.0686
-0.0621
(0.138)
(0.217)
(0.177)
(0.225)
(0.0815)
(0.0919)
(0.0861)
(0.0973)
1.476**
1.474**
1.445**
1.464**
1.443**
1.445**
1.415**
1.410**
(0.556)
(0.554)
(0.573)
(0.553)
(0.595)
(0.596)
(0.609)
(0.617)
Firm fixed effect
Y
Y
Y
Y
Y
Y
Y
Y
Year fixed effect
Y
Y
Y
Y
Y
Y
Y
Y
Observations
90
90
90
90
90
90
90
90
0.678
0.678
0.685
0.693
0.670
0.670
0.694
0.696
15
15
15
15
15
15
15
15
AppUS
Constant
R-squared
Number of firm
Notes: Standard errors clustered by foreign firms. *, **, *** significant at 10%, 5%, and 1%, respectively. The
regressions are specified in specification (Ⅰ). The dataset is a panel data, and the sample consists of invention patent
filings of top 15 non-Chinese solar PV firm in solar PV technology in China between 2002 and 2007. AppUS indicate
the patent filing of each non-Chinese firm in the US. CNproduction is annual Chinese solar PV production from 2002
to 2007. CNinstallation is annual Chinese solar PV installation from 2002 to 2007. CNexpectation equals to 0 in 2002
to 2004, and 1 afterwards. CNinnovation is the patent filings of Chinese firms in each year. In column (1) to (4),
China’s annual solar PV production, installation, and innovation in the regressions are used, and in column (5) to (8),
the annual increments are used instead.
38
Table 2-5 Results of non-PCT and PCT patents
(1)
(2)
With actual production, installation, and innovation
VARIABLES
AppUS*CNproduction
AppUS*CNinstallation
(3)
(4)
With production, installation, and innovation increment
nonPCT
PCT
nonPCT
PCT
0.0644***
0.0271***
0.0994***
0.0583***
(0.0205)
(0.00402)
(0.0272)
(0.0108)
-0.00710
0.00601**
-0.00169
0.00301***
(0.00809)
(0.00230)
(0.00416)
(0.000588)
AppUS*CNexpectation
-0.163
-0.00802
-0.0377
-0.00353
(0.272)
(0.0869)
(0.173)
(0.0535)
AppUS*CNinnovation
0.00904
0.00765
0.00451**
0.00450
(0.00709)
(0.00705)
(0.00203)
(0.00303)
AppUS
Constant
0.175
-0.205***
0.0643
-0.126***
(0.181)
(0.0487)
(0.0928)
(0.00981)
0.451
1.013***
0.393
1.017***
(0.417)
(0.229)
(0.469)
(0.230)
Firm fixed effect
Y
Y
Y
Y
Year fixed effect
Y
Y
Y
Y
Observations
90
90
90
90
0.584
0.657
0.570
0.662
15
15
15
15
R-squared
Number of firm
Notes: Standard errors clustered by foreign firms. *, **, *** significant at 10%, 5%, and 1%, respectively. The
regressions are specified in specification (Ⅰ). The dataset is a panel data, and the sample consists of invention patent
filings of top 15 non-Chinese solar PV firm in solar PV technology in China between 2002 and 2007. All the patent
filings are grouped into two categories, PCT patent and non-PCT patent. AppUS indicate the patent filing of each nonChinese firm in the US. CNproduction is annual Chinese solar PV production from 2002 to 2007. CNinstallation is
annual Chinese solar PV installation from 2002 to 2007. CNexpectation equals to 0 in 2002 to 2004, and 1 afterwards.
CNinnovation is the patent filings of Chinese firms in each year. In column (1) and (2), China’s annual solar PV
production, installation, and innovation in the regressions are used, and in column (3) and (4), the annual increment is
used instead.
39
Table 2-6 Results of process and product foreign patents
(1)
(2)
With actual production, market and innovation
VARIABLES
AppUS_process*CNproduction
AppUS_process*CNmarket
AppUS_process*CNexpectation
AppUS_process*CNinnovation
AppUS_process
Process
Product
(3)
Process
0.0521
0.0797
(0.0316)
(0.0555)
-0.00337
0.000125
(0.00591)
(0.00262)
0.0188
6.09e-05
(0.352)
(0.218)
-0.00375
-0.000386
(0.0149)
(0.00632)
0.00651
-0.0541*
(0.0736)
AppUS_product*CNmarket
AppUS_product*CNexpectation
AppUS_product*CNinnovation
AppUS_product
0.0796***
0.141***
(0.0193)
(0.0281)
0.000740
0.00103
(0.00403)
(0.00134)
-0.166
-0.0911
(0.205)
(0.122)
0.0130
0.00731*
(0.00964)
(0.00375)
-0.0196
-0.0145
(0.105)
Year fixed effects
Product
(0.0254)
AppUS_product*CNproduction
Constant
(4)
With production, market, and innovation increment
(0.0465)
0.751**
0.991
0.710*
0.956
(0.290)
(0.573)
(0.334)
(0.607)
Y
Y
Y
Y
Firm fixed effects
Y
Y
Y
Y
Observations
90
90
90
90
0.428
0.709
0.427
0.714
15
15
15
15
R-squared
Number of firm
Notes: Standard errors clustered by foreign firms. *, **, *** significant at 10%, 5%, and 1%, respectively. The
regressions are specified in specification (Ⅰ). The dataset is a panel data, and the sample consists of invention patent
filings of top 15 non-Chinese solar PV firm in solar PV technology in China between 2002 and 2007. All the patent
filings are identified into two patent groups, product patents and process patents. AppUS_product indicate the product
patent filing of each non-Chinese firm in the US. AppUS_process indicate the process patent filing of each nonChinese firm in the US. CNproduction is annual Chinese solar PV production from 2002 to 2007. CNinstallation is
annual Chinese solar PV installation from 2002 to 2007. CNexpectation equals to 0 in 2002 to 2004, and 1 afterwards.
CNinnovation is the patent filings of Chinese firms in each year. In column (1) and (2), China’s annual solar PV
production, installation, and innovation are used in the regressions, and in column (3) and (4), the annual increment is
used instead.
40
Table 2-7 Results of foreign patenting in China vs. Australia
(1)
(2)
With actual market and production
VARIABLES
(4)
AU
CN
0.0924***
1.165
0.145***
-1.418
(0.0223)
(5.069)
(0.0269)
(1.534)
-0.00352
-0.00436
-6.80e-05
-0.0244
(0.00988)
(0.0130)
(0.00450)
(0.0228)
0.00554
-0.134
0.0274
-0.0481
(0.189)
(0.437)
(0.141)
(0.101)
0.0212
-0.408
-0.0445
0.0221
(0.217)
(1.693)
(0.0919)
(0.0265)
1.474**
0.934***
1.445**
0.917***
(0.554)
(0.271)
(0.596)
(0.305)
Year fixed effects
Y
Y
Y
Y
Firm fixed effects
Y
Y
Y
Y
Observations
90
90
90
90
0.678
0.146
0.670
0.176
15
15
15
15
AppUS*Production
AppUS*Installation
AppUS*Expect
AppUS
Constant
R-squared
Number of firm
CN
(3)
With market and production increment
AU
Notes: Standard errors clustered by foreign firms. *, **, *** significant at 10%, 5%, and 1%, respectively. The
regressions are specified in specification (Ⅰ). The dataset is a panel data, and the sample consists of invention patent
filings of top 15 non-Chinese solar PV firm in solar PV technology in China and Australia between 2002 and 2007.
AppUS indicates the patent filing of each non-Chinese firm in the US. Production is annual solar PV production from
2002 to 2007 in China or Australia, depending on which country the regression is about. Installation is annual solar PV
installation from 2002 to 2007 in China or Australia, depending on which country the regression is about. Expectation
equals to 0 in 2002 to 2004, and 1 afterwards for Chinese regressions, and equals to 0 in 2002 to 2003, and 1 afterward
for Australian regressions. In column (1) and (2), annual solar PV production, installation, and innovation of China and
Australia are used in the regressions, and in column (3) and (4), the annual increment is used instead.
41
Appendix 1 Robustness Check Results
Appendix A
Table A1 Basic regression results of the whole value chain
(1)
(2)
(3)
(4)
With actual production, installation, and innovation
VARIABLES
AppUS*CNproduction
AppUS*CNinstallation
(5)
(6)
(7)
(8)
With production, installation, and innovation increment
Whole
Whole
Whole
Whole
Whole
Whole
Whole
Whole
0.0913***
0.0895***
0.0786***
0.0843***
0.148***
0.141***
0.144***
0.148***
(0.0167)
(0.0192)
(0.0177)
(0.0184)
(0.0287)
(0.0243)
(0.0272)
(0.0259)
-0.00361
-0.00299
0.000965
-0.000896
-0.000205
0.000202
0.00165
0.00148
(0.00589)
(0.00809)
(0.00731)
(0.00844)
(0.00302)
(0.00372)
(0.00314)
(0.00345)
AppUS*CNexpectation
0.0151
-0.117
0.0319
(0.173)
(0.274)
(0.145)
AppUS*CNinnovation
0.00838
0.0137
-0.0181
(0.175)
0.00756**
0.00783
(0.00281)
(0.00501)
(0.00488)
(0.0118)
0.0280
0.0156
-0.0642
-0.0265
-0.0364
-0.0438
-0.0630
-0.0597
(0.145)
(0.178)
(0.172)
(0.190)
(0.0757)
(0.0774)
(0.0791)
(0.0813)
1.513**
1.510**
1.483**
1.494**
1.478**
1.481**
1.451**
1.448**
(0.558)
(0.567)
(0.567)
(0.559)
(0.609)
(0.605)
(0.611)
(0.612)
Firm fixed effect
Y
Y
Y
Y
Y
Y
Y
Y
Year fixed effect
Y
Y
Y
Y
Y
Y
Y
Y
AppUS
Constant
Observations
R-squared
Number of firm
90
90
90
90
90
90
90
90
0.685
0.685
0.693
0.697
0.678
0.679
0.700
0.700
15
15
15
15
15
15
15
15
Notes: Standard errors clustered by foreign firms. *, **, *** significant at 10%, 5%, and 1%, respectively. The
regressions are specified in specification (Ⅰ). The dataset is a panel data, and the sample consists of invention patent
filings of top 15 non-Chinese solar PV firm in solar PV technology in China between 2002 and 2007. AppUS indicate
the patent filing of each non-Chinese firm in the US. CNproduction is annual Chinese solar PV production from 2002
to 2007. CNinstallation is annual Chinese solar PV installation from 2002 to 2007. CNexpectation equals to 0 in 2002
to 2004, and 1 afterwards. CNinnovation is the patent filings of Chinese firms in each year. In column (1) to (4),
China’s annual solar PV production, installation, and innovation are used in the regressions, and in column (5) to (8),
the annual increment is used instead.
42
Table A2 Basic regression results of solar cell and system patents
(1)
(2)
With real production, market and innovation
VARIABLES
Application 02-08
0.0650**
0.130*
0.192***
(0.0251)
(0.0619)
(0.0534)
-0.00337
-0.0201
-0.0102
0.00212
(0.00215)
(0.0158)
(0.00812)
(0.0123)
0.0913
0.0975
0.235***
-0.0354
(0.139)
(0.134)
(0.0692)
(0.0710)
0.00292
-0.00889
-0.00854
0.00715
(0.00454)
(0.0141)
(0.00548)
(0.00749)
-0.0756**
0.205
-0.109***
-0.0452**
(0.0307)
(0.220)
(0.0345)
(0.0178)
2.600**
2.294**
2.469***
1.882**
(0.964)
(0.875)
(0.810)
(0.811)
Firm fixed effects
Y
Y
Y
Y
Year fixed effects
Y
Y
Y
Y
120
105
120
105
0.555
0.556
0.555
0.685
15
15
15
15
AppUS*CNinstallation
AppUS*CNexpectation
AppUS*CNinnovation
AppUS
Constant
Observations
R-squared
Number of firm
Application 02-08
0.0297**
(0.0114)
(4)
Application 02-09
AppUS*CNproduction
Application 02-09
(3)
With production, market and innovation
Notes: Standard errors clustered by foreign firms. *, **, *** significant at 10%, 5%, and 1%, respectively. The
regressions are specified in specification (Ⅰ). The dataset is a panel data, and the sample consists of invention patent
filings of top 15 non-Chinese solar PV firm in solar PV technology in China between 2002 and 2007. AppUS indicate
the patent filing of each non-Chinese firm in the US. CNproduction is annual Chinese solar PV production from 2002
to 2007. CNinstallation is annual Chinese solar PV installation from 2002 to 2007. CNexpectation equals to 0 in 2002
to 2004, and 1 afterwards. CNinnovation is the patent filings of Chinese firms in each year. In column (1) and (2),
China’s annual solar PV production, installation, and innovation are used in the regressions, and in column (3) and (4),
the annual increment is used instead.
43
Appendix B
Table B1 NonPCT and PCT regression results of the whole value chain
(1)
(2)
With actual production, installation, and innovation
VARIABLES
AppUS*CNproduction
AppUS*CNinstallation
AppUS*CNexpectation
AppUS*CNinnovation
AppUS
Constant
Firm fixed effect
(3)
(4)
With production, installation, and innovation increment
nonpct
pct
nonpct
pct
0.0593***
0.0250***
0.0904***
0.0572***
(0.0182)
(0.00493)
(0.0258)
(0.0129)
-0.00773
0.00683***
-0.00190
0.00338***
(0.00755)
(0.00190)
(0.00396)
(0.000964)
-0.110
-0.00771
-0.0110
-0.00716
(0.263)
(0.0974)
(0.177)
(0.0602)
0.00561
0.00807
0.00313
0.00469
(0.00719)
(0.00729)
(0.00240)
(0.00319)
0.196
-0.223***
0.0740
-0.134***
(0.171)
(0.0391)
(0.0894)
(0.0204)
0.412
1.083***
0.359
1.089***
(0.433)
(0.219)
(0.481)
(0.221)
Y
Y
Y
Y
Year fixed effect
Y
Y
Y
Y
Observations
90
90
90
90
0.581
0.675
0.566
0.679
15
15
15
15
R-squared
Number of firm
Notes: Standard errors clustered by foreign firms. *, **, *** significant at 10%, 5%, and 1%, respectively. The
regressions are specified in specification (Ⅰ). The dataset is a panel data, and the sample consists of invention patent
filings of top 15 non-Chinese solar PV firm in solar PV technology in China between 2002 and 2007. All the patent
filings are grouped into two categories, PCT patent and non-PCT patent. AppUS indicate the patent filing of each nonChinese firm in the US. CNproduction is annual Chinese solar PV production from 2002 to 2007. CNinstallation is
annual Chinese solar PV installation from 2002 to 2007. CNexpectation equals to 0 in 2002 to 2004, and 1 afterwards.
CNinnovation is the patent filings of Chinese firms in each year. In column (1) and (2), China’s annual solar PV
production, installation, and innovation are used in the regressions, and in column (3) and (4), the annual increment is
used instead.
44
Appendix C
Table C1 Process and product regression results of the whole value chain
(1)
(2)
With actual production, market and innovation
VARIABLES
AppUS_process*CNproduction
AppUS_process*CNmarket
AppUS_process*CNexpectation
AppUS_process*CNinnovation
AppUS_process
Process
Product
(3)
Process
0.0480*
0.0763
(0.0270)
(0.0500)
-0.000444
0.00151
(0.00529)
(0.00201)
0.0203
-0.00594
(0.315)
(0.203)
-0.00242
0.000358
(0.0134)
(0.00598)
-0.0569
-0.0812***
(0.0651)
AppUS_product*CNmarket
AppUS_product*CNexpectation
AppUS_product*CNinnovation
AppUS_product
0.0720***
0.130***
(0.0190)
(0.0282)
0.00134
0.00131
(0.00394)
(0.00112)
-0.125
-0.0743
(0.206)
(0.131)
0.0108
0.00647
(0.0105)
(0.00442)
-0.0326
-0.0204
(0.109)
Year fixed effects
Product
(0.0262)
AppUS_product*CNproduction
Constant
(4)
With production, market, and innovation increment
(0.0480)
0.825**
1.062
0.793**
1.034
(0.303)
(0.629)
(0.341)
(0.657)
Y
Y
Y
Y
Firm fixed effects
Y
Y
Y
Y
Observations
90
90
90
90
0.420
0.692
0.425
0.698
15
15
15
15
R-squared
Number of firm
Notes: Standard errors clustered by foreign firms. *, **, *** significant at 10%, 5%, and 1%, respectively. The
regressions are specified in specification (Ⅰ). The dataset is a panel data, and the sample consists of invention patent
filings of top 15 non-Chinese solar PV firm in solar PV technology in China between 2002 and 2007. All the patent
filings are identified into two patent groups, product patents and process patents. AppUS_product indicate the product
patent filing of each non-Chinese firm in the US. AppUS_process indicate the process patent filing of each nonChinese firm in the US. CNproduction is annual Chinese solar PV production from 2002 to 2007. CNinstallation is
annual Chinese solar PV installation from 2002 to 2007. CNexpectation equals to 0 in 2002 to 2004, and 1 afterwards.
CNinnovation is the patent filings of Chinese firms in each year. In column (1) and (2), China’s annual solar PV
production, installation, and innovation are used in the regressions, and in column (3) and (4), the annual increment is
used instead.
45
Appendix D
Table D1 CN and AU regression results of the whole value chain
(1)
(2)
With actual market and production
VARIABLES
AppUS*CNproduction
AppUS*CNinstallation
AppUS*CNexpectation
AppUS
CN
AU
(3)
(4)
With market and production increment
CN
AU
0.0895***
6.145
0.141***
-1.098
(0.0192)
(5.051)
(0.0243)
(1.413)
-0.00299
-0.0132
0.000202
-0.0210
(0.00809)
(0.0115)
(0.00372)
(0.0287)
0.0151
-0.530
0.0319
-0.104
(0.173)
(0.473)
(0.145)
(0.127)
0.0156
-2.090
-0.0438
0.0343
(0.178)
(1.685)
(0.0774)
(0.0325)
1.510**
0.870**
1.481**
0.809*
(0.567)
(0.303)
(0.605)
(0.425)
Year fixed effects
Y
Y
Y
Y
Firm fixed effects
Y
Y
Y
Y
Observations
90
90
90
90
0.685
0.235
0.679
0.193
15
15
15
15
Constant
R-squared
Number of firm
Notes: Standard errors clustered by foreign firms. *, **, *** significant at 10%, 5%, and 1%, respectively. The regressions are
specified in specification (Ⅰ). The dataset is a panel data, and the sample consists of invention patent filings of top 15 nonChinese solar PV firm in solar PV technology in China and Australia between 2002 and 2007. AppUS indicates the patent filing
of each non-Chinese firm in the US. Production is annual solar PV production from 2002 to 2007 in China or Australia,
depending on which country the regression is about. Installation is annual solar PV installation from 2002 to 2007 in China or
Australia, depending on which country the regression is about. Expectation equals to 0 in 2002 to 2004, and 1 afterwards for
Chinese regressions, and equals to 0 in 2002 to 2003, and 1 afterward for Australian regressions. In column (1) and (2), annual
solar PV production, installation, and innovation of China and Australia are used in the regressions, and in column (3) and (4),
the annual increment is used instead.
46
Chapter 3
Renewable Portfolio Standards and Innovating by Generating
3.1 Introduction
Renewable energy - wind, solar, geothermal, biomass and ocean energy – plays important
roles in tackling energy and environment problems and ensuring sustainable economic growth.
Currently, however, market penetration of renewable energy, although growing has been limited,
largely because renewable energy on its own is less cost competitive than fossil fuels in the
current market.34
Various government policies, including feed-in-tariffs, production quota and tax
incentives, have been introduced in an effort to accelerate market development of renewable
energy. It is hoped that government support for penetration of renewable energy improve its
economic competitiveness through: (1) incentivizing, through “market pull”, firm investment in
technological innovation in renewable energy, and (2) driving (quality-adjusted) costs of
renewable energy down the “learning curve”, through “learning by doing” in manufacturing of
renewable energy equipment such as wind turbines and solar panels.
This paper highlights another possible process, namely “innovating by generating” or
“innovating by deploying”, which could improve renewable energy’s efficiency and cost
competitiveness during the market development of renewable energy. Such an aspect of
“innovating by generating” has been largely neglected in the literature and policy discussions on
government policies for promoting renewable energy. When a renewable energy project, such as
a wind farm or a roof solar PV project, is constructed, the developer often not only needs to
34
The market currently does not have appropriate price signals that take into account cost of environmental
pollution and carbon emission. Given such market failures, government interventions are justified. Government
supports for firm R&D in renewable energy is also based on the notion that firm research and innovation create
knowledge spillover and positive externality on the economy.
47
adapt the technology to the local environment but also to address technical issues that arise
during the construction. Furthermore, after the project is built and put into operation, the operator
may encounter technical problems during the operation, which have to be solved. In all these
cases, “innovating by generating” might occur, resulting in innovations that improve
technological efficiency and economic competitiveness of renewable energy.
One interesting feature of “innovating by generating”, that occur during development and
operation of a renewable energy project, is that it is decentralized and very likely involves local
innovation (made by local inventors that are technical personnel affiliated with the project or in
local firms to which the developer and operator of the project turn to for help in solving technical
issues). In contrast, innovation in renewable energy that is often discussed is referred to those
made by renewable technology companies that innovate either through R&D or through
“learning by doing” in manufacturing.
This paper empirically investigates “innovating by generating” of renewable energy, in
the context of the Renewable Portfolio Standard policy (RPS) in the United States. RPS is a state
level policy that requires electric utilities to provide a certain amount (in megawatts or in
percentage) of electricity from renewable energy sources. The policy has been adopted in 37
states and District of Columbia by the end of 2013. 35 There has been significant increase in
renewable energy deployment following RPS mandates in the RPS-adopted states, providing a
good empirical setting for testing “innovating by generating” in renewable energy.
The number of U.S. patents in renewable energy at the state level is applied as an
indicator of local innovation, and whether there exist a positive relationship between renewable
energy development in some prior periods of time and local innovation in renewable energy at
35
Among them, 30 states have mandatory RPS and the rest 8 states are voluntary RPS, they are Indiana, North
Dakota, Iowa, South Dakota, Oklahoma, Utah, Virginia, and Vermont.
48
the state level is empirically tested. The empirical strategy of this study in testing “innovating by
generating” is analogous to the methodology in empirical studies on “learning by doing” in
production that often relates productivity to prior experience in production (often measured by
accumulative production in some prior periods).36
Specifically “innovating by generating” in two major renewable technologies, wind
power and solar PV, are tested separately. The two technologies are in different stages of
technological development: wind power is a relatively mature technology, with costs close to
grid parity37; whereas solar PV is on its own still less cost competitive than fossil fuel based
electricity (Reichelstein and Yorston 2013). I am interested to see if there are any differences in
“innovating by generating” between these two renewable technologies.
There are several interesting findings in the paper. First, I find that development of wind
power, but not of solar PV, has accelerated at the state level, following the enactment of RPS.
This result makes economic senses, given that wind power is more cost competitive than solar
PV and that RPS in most states lumps together various renewable energy resources, without
setting separate targets for each.
Second, it is found that, at the state level, renewable electricity generation in the prior
years has significantly positive impacts on local innovation for both wind power and solar PV;
however, the impacts for the former technology are much smaller than for the latter. This
suggests that not only is “innovating by generating” indeed at play, but also it appears to be more
important for solar PV, likely because solar PV is a less mature technology and thus has more
room for technological improvement and innovation than wind power.
36
See Thompson (2010) for an overview of theories and empirical studies on “learning by doing” in production.
“Grid parity” means that the cost of electricity from renewable energies such as wind power or solar PV is on par
with the cost of electricity from fossil fuels.
37
49
Third, despite the presence of “innovating by generating” during market development of
wind power and solar PV, RPS in the U.S. overall has not stimulated significant amount of
innovation in the two main renewable technologies, and for different reasons. For wind power,
the potential of “innovating by generating” is relatively small, despite the rapid development of
wind power following RPS in the U.S. For solar PV, although the effects of “innovating by
generating” are more salient, market penetration of solar PV has been small after RPS.
The findings of this paper have interesting policy implications for RPS designs. One
important motivation for a state to adopt RPS is to stimulate renewable energy development that
would lead to employment and economic growth within the state. This study suggests that RPS,
through “innovating by generating” has the potential in promoting local innovation and
technological capability, in particular in those less mature and early-stage renewable
technologies, in the state that would further benefit the state economy. However the current RPS
designs have failed these states in realizing this potential gain. Given that renewable technologies
differ significantly in technological maturity and closeness to market, RPS that does not
distinguish various renewable technologies would to large extent promote market penetration of
more mature technologies such as wind power, which paradoxically needs less of government
supports and for which there exists less room for “innovating by generating”. RPS that sets a
target for wind power and solar PV separately would better encourage market development of
solar PV that is less mature and has more potential in “innovating by generating”.
This paper is related to a large literature on “learning by doing” that has developed since
the seminal work by Arrow (1962)38 and includes studies on “learning by doing” in renewable
38
Arrow (1962) refers “learning by doing” to knowledge acquisition during production. Subsequent studies on this
topic have focused on either productivity improvement or cost reduction due to accumulation of experience in
production Lucas, R. E. (1988). "On the mechanics of economic development." Journal of monetary economics
22(1): 3-42, Jovanovic, B. and Y. Nyarko (1996). "Learning by Doing and the Choice of Technology."
50
technologies.39 The literature has primarily focused on “learning by doing” by manufacturers,
measured by increased productivity and/or reduced production cost. This study highlights
another type of “learning by doing” during the deployment of new technologies, by downstream
users rather than producers. As such, the paper is also related to an interesting strand of literature
on “user innovation” that highlights users as an important source of technological innovation, as
users often immerse themselves in the context within which the technology is used and thus are
in a better position to identify unmet needs and innovate (Urban and Von Hippel 1988; Kline and
Pinch 1996; Oudshoorn and Pinch 2003). In this paper users include developers and operators of
renewable energy projects and associated local firms who are engaged in adopting technologies
to local conditions or solving technical problems during project construction and operation.
The remainder of this paper is organized as follows. In Section 2, the conceptual issue
and background of RPS and Innovating by Generation are reviewed. Section 3 discusses the
hypothesis, empirical strategy and data. The relation between RPS and generation, generation
and innovation, and RPS and innovation are specifically discussed in Section 4 to Section 6. The
paper is concluded in Section 7, and some policy implications are given.
Econometrica 64(6): 1299-1310. Empirical studies have been conducted in a variety of industries such as chemical
processing, semiconductor, oil drilling, to name a few Lieberman, M. B. (1984). "The learning curve and pricing in
the chemical processing industries." The RAND Journal of Economics 15(2): 213-228, Irwin, D. A. and P. J.
Klenow (1994). "Learning-by-doing spillovers in the semiconductor industry." Journal of Political economy: 12001227, Kellogg, R. (2011). "Learning by drilling: Interfirm learning and relationship persistence in the Texas
oilpatch." The Quarterly Journal of Economics 126(4): 1961-2004.. Also see Thompson (2010) for an overview.
39
Christiansson (1995) estimated the learning rate of wind and solar PV technologies, pointing out that niche market
increases the learning. Subsequent studies on the learning curves in solar PV and wind technology include Harmon
(2000), Harmon, C. (2000). "Experience curves of photovoltaic technology." Laxenburg, IIASA 17, Junginger, M.,
A. Faaij, et al. (2005). "Global experience curves for wind farms." Energy Policy 33(2): 133-150., and Turkenburg.
McDoanld and Schrattenholzer (2001) finds that the learning rates of solar PV panels/modules and wind power are
in general higher than those of nuclear, hydropower, and gas turbine.
51
3.2 Renewable Portfolio Standard and Innovating by Generating: Conceptual
Issue
Many states in the U.S have adopted Renewable Portfolio Standard (RPSs) as a policy
tool for promoting renewable electricity generation40-41. An RPS requires that a minimum
amount of renewable energy is included in the portfolio of electric-generating resources serving
a state, and the required amount increases over time. The stated intent of RPSs is usually some
combination of increasing the diversity, reliability, public health and environmental benefits of
the energy mix, and promoting employment (creation of “green” jobs) and economic growth. By
the end of 2013, 37 states and the District of Columbia had implemented RPS policies, 30 being
mandatory and 8 voluntary.
The institutional details of RPS policies vary significantly across those RPS-adopted
states, which could impact market development of renewable energy in those states following the
adoption of RPS.42 For example, states differ in tradability of Renewable Energy Credits (RECs),
a certificate of proof that one kWh of electricity has been generated by a renewable energy
source.43 Some states allow a tradable RECs system, where utilities (or generators) can purchase
40
The U.S. is the first country that implemented RPS. The first RPS in the U.S. dates back to 1983, when Iowa
passed the Alternate Energy Production Law (revised in 1991) requiring its two investor-owned utilities -- MidAmerican and Interstate Power and Light -- to contract for a combined total of 105 megawatts (MW) of generation
from renewable energy resources. The policy became increasingly popular in the 1990s. In 1995, California enacted
its RPS in 1995; and two years later Massachusetts, Minnesota and Nevada established their RPSs. See Wiser and
Barbose (2008) and Wiser et al. (2007).
41
In addition to the U.S., a number of countries including United Kingdom, Japan, and Australia also implemented
RPS. See Fischer, C. (2006). "How can renewable portfolio standards lower electricity prices." Resources for the
Future Discussion Paper, Resources for the Future, Washington, DC, Wiser, R., C. Namovicz, et al. (2008).
"Renewables portfolio standards: A factual introduction to experience from the united states." Many European
countries, however, have implemented Feed-in-tariff (FIT) policy to promote renewable energy development, about
ten years earlier than the adoption of RPS in the U.S. (Buckman, 2011).
42
I focus on three major variations in RPS designs across states, which I think to be most relevant for our study. See
Wiser et al. (2007) and Yin and Powers (2010) for more details on differences in RPS across states.
43
RPS requires electric utilities (or electricity generators, depending on policy design) to demonstrate, through
ownership of RECs, that they have supported an amount of renewable energy generation equivalent to some
percentage of their total annual electricity sales. Utilities (or generators) are typically allowed to decide for
themselves whether to invest in renewable energy projects and generate their own RECs, or simply to purchase
RECs.
52
RECs from outside states; however, other states have imposed restrictions, called in-state
requirements, on the system by either allowing no REC trading (requiring that a certain
percentage of RECs must be purchased from within the state) or giving extra credit to in-state
renewable generation.44 Second, certain energy efficient technologies based on fossil fuels (such
as combined heating and power, CHP) are qualified for RPS in some states, but not in others.
Since some of these technologies are more technologically developed than renewable
technologies, whether they are counted in RPS might impact the development of renewable
energy in a state following implementation of RPS in the state. Third, penalties for failure to
comply with RPS targets also differ across states, which could impact the effectiveness of RPS
and market penetration of renewable energy (Blair, Short et al. 2006; Cory and Swezey 2007).
Studies have in general found increase in the overall penetration and development of
renewable energy following RPS at the state level (Carley, 2009; Yin and Powers, 2010).
Regarding individual renewable technologies, the correlation between deployment of wind
energy and RPS is well established (Langniss and Wiser 2003; Bird, Bolinger et al. 2005; Menz
and Vachon 2006); however, studies have indicated that the development of early stage
technologies like solar PV has not picked up after RPS (Foxon and Pearson 2007; Lipp 2007;
Johnstone, Haščič et al. 2010), suggesting the need for RPS that are currently undifferentiated
toward different technologies to be modified with designs, such as banding or carve-outs for
44
These restrictions aim to localize the economic and environmental benefits from RPS programs but may risk
losing the opportunity of utilizing cheaper renewable resources outside of the state. Some researches argue that the
in-state requirements of RPS can be challenged under the dormant commerce clause of the U.S. Constitution Ferrey,
S. (2006). "Renewable orphans: Adopting legal renewable standards at the state level." The Electricity Journal 19(2):
52-61, Cory, K. S. and B. G. Swezey (2007). Renewable portfolio standards in the states: Balancing goals and
implementation strategies, National Renewable Energy Laboratory, ibid..
53
specific technologies, 45 to promote deployment of less mature and higher-cost types of
renewable technologies (Cory and Swezey 2007; Wiser and Barbose 2008; Buckman 2011).
One difficulty in identifying the causal effect between RPS, which the existing studies
have not adequately addressed, is the endogenous nature of a state enacting a RPS. A number of
factors, including renewable energy resources, air quality, government partisanship, and
neighboring states’ behavior, that studies find to be important in driving a state government to
adopt RPS (Chandler, 2009; Lyon and Yin, 2010), can impact the renewable energy development
in the state directly. For example, extant studies that employ a Difference-In-Differences strategy
use non-RPS-adopted states as the control group to estimate the impacts of RPS on renewable
energy generation, despite that the trend in renewable energy development between RPS-adopted
and non-RPS-adopted states is likely to differ in the absence of RPS due to the difference in their
renewable energy resources.
In addition to economic benefits such as creation of green jobs that RPS would bring
about (Cory and Swezey 2007; Wei, Patadia et al. 2010; Carley, Lawrence et al. 2011), a RPSadopted state might as well hope that RPS could advance the competitiveness of the renewable
technology industry within the state. One possible mechanism is that as the overall size and
growth of the renewable market is specified, generators may be incentivized to
become
competitive by reducing production costs and improving renewable technology, likely through
innovation (Berry and Jaccard 2001; Espey 2001) (Loiter and Norberg-Bohm 1999; Carley,
Lawrence et al. 2011).
“Innovating by generating”, the focus of this paper, is another channel through which
RPS could potentially advance the competitiveness and technological capacity and
45
Banding refers to the policy that governments mandate higher multiples of RECs for high-cost types of renewable
technologies, and carve-outs mean that governments prescribe parts of a RPS target that can be met only by a
particular type, or types, of technologies.
54
competitiveness of local renewable energy industry. This potential benefit of RPS to a RPSadopted state has been significantly recognized by neither the literature nor policy discussions on
RPS.
3.3 Empirical Strategy and Data
3.3.1 Empirical Strategy
The empirical strategy used to investigate “innovating by generating” in market
development of renewable energy involves a two-step analysis at the state level: (1) first, I test
whether renewable energy generation increases following the implementation of RPS; and (2)
second, I investigate whether an increase in renewable energy generation leads to an increase in
innovation in renewable energy technology.
The second step is the focus of this study, and I establish the causal effect between local
renewable energy generation and local technological innovation, by regressing renewable energy
innovation over renewable energy generation in the previous years at the state level. By contrast,
in the first step, I only need to detect a correlation between RPS and renewable energy
generation (i.e. whether renewable energy generation indeed increases after RPS), since the
purpose of the step is to show the context where “innovating by generating” might occur. As
mentioned earlier, the causality between RPS and renewable energy development is difficult to
identify given that the decision by a state to adopt RPS is endogenous.
This paper focuses on the potential “innovating by generating” in two major renewable
energy technologies, wind power and solar PV, which are eligible for RPS in all the RPSadopted states in the U.S. 46 Moreover, wind power and solar PV represent two types of
46
Other renewable energies such as tidal energy and geo-thermal energy are eligible for RPS in some RPS-adopted
states.
55
renewable technologies that are in different stages of technology development (Jacobsson and
Johnson, 2000). Wind power is a relatively mature technology that has levelized cost of energy
close to grid parity and is competitive to fossil based electricity. 47 Solar PV, on the other hand, is
still hardly cost competitive on its own (without government subsidies), despite that many utilityscale solar PV plants have already been established (Reichelstein and Yorston 2013).
Technological challenges remain in improving conversion efficiency and reducing cost of solar
PV. Thus it is interesting to see if the potential of “innovating by generating” differ between
these two technologies.
3.3.2 Patents as a proxy for innovation
I use patents granted by the U.S. Patent and Trademark Office (USPTO) to inventors in a
given state as a metric for innovation at the state level.48 Patents, sorted by their application date,
provide a good indicator of innovative activity (Griliches 1998). Moreover, patent data are
readily available in highly disaggregated forms, and patent classifications can be used to identify
innovation in renewable energy. Note that there are also limitations when working with patent
data (Griliches 1998). Not all innovation is patented, 49 and the propensity to patent new
inventions could vary across industries and over time (Levin, Klevorick et al. 1987). The quality
of individual patents also varies.
47
Levelized cost of wind power could be found in the report published by U.S. Energy Information Administration
(USEIA), “Levelized Cost of New Generation Resources in the Annual Energy Outlook 2013”. It is available on the
website of USEIA: http://www.eia.gov/forecasts/aeo/electricity_generation.cfm. “Grid parity” refers to the case
where the cost of renewable energy electricity to be on par with the cost of electricity from fossil fuels such as coal
and natural gas.
48
R&D expenditure, measuring inputs of innovative activities, is another widely used indicator for innovation. Since
this study focuses on “innovating by generating”, I consider patenting to be a better measure of innovation because
such innovations may not involve significant R&D.
49
In return for patent protection, an inventor is required to publicly disclose the information about the invention.
The inventor may prefer to keep the invention secret (trade secrecy), rather than patenting and disclosing it.
56
Based on the International Patent Classification (IPC) codes for wind technology and solar
PV, which are identified by the IPC Green Inventory compiled by the World Intellectual
Property Office, 50 I collect the information of U.S. patents (granted by the USPTO) in wind
power and solar PV from Thompson Innovation Patent Database.51 I focus on U.S. patents that
are filed at USPTO between 1990 and 2008. Most of the RPS states announced their RPS
requirements after mid-1990s, so I set the starting year at 1990 to capture the trend of renewable
innovations before the implementation of RPS and compare it with the innovation development
afterwards. I decide the study period to end in 2008, taking into account the significant lag
between patent filing and patent grant at the USPTO 52 and also the potential impacts of financial
crisis on the renewable energy development in the U.S.
3.3.3 Data on RPS and renewable energy generation
The detailed information on the state level RPS policies is collected from both DSIRE and
the state government websites.53 Table 3-1 shows the timing of enactment and implementation of
RPS. As shown there, 30 states have adopted the mandatory RPS policy, and another 8 have
applied voluntary RPSs. I also collect information about RPS targets and timelines, tradability of
RECs cross states, penalty on the compliance of RPS, inclusion in RPS of energy efficient
technologies based on nonrenewable energy sources.
50
The IPC codes for wind technology is F03D; and for solar PV technology include H01L 27/142, 31/00-31/078,
H01G 9/20, H02N 6/00, H01L 25/00, 25/03, 25/16, 25/18, H01L 31/042, G05F 1/67. See
http://www.wipo.int/classifications/ipc/en/est/index.html.
51
A closer examination of retrieved U.S. patents based on the WIPO-identified IPC codes find that patent data in
wind technology is clean, but some retrieved patents in solar PV are actually related to semiconductor technology,
not directly to solar PV, though these two technologies are to some extent intermingled. I further clean the patents in
solar PV by a keyword search of “solar” in patent abstracts.
52
It would take some time for a patent to get published after its initial filing. Normally, the patent will be
automatically published after eighteen months, but there are some exceptions which may postpone the publication.
See the details: http://www.uspto.gov/web/offices/pac/mpep/s1120.html. According to our experiences dealing with
the patent data, I believe by the time I collect the patent data from the database in 2012, patents filed by 2008 would
mostly get published and available for our research.
53
DSIRE, http://www.dsireusa.org/, is a website, developed by U.S. Department of Energy, which contains energy
related policies for individual states.
57
State level annual electricity generation from wind power and solar PV, respectively, is
collected from the website of U.S. Energy Information Administration (EIA). They are used as
the measurement of market penetration and deployment of wind power and solar PV. Since this
study focuses on innovative activities during the deployment of renewable energy that could
arise from both construction and operation of renewable energy projects, I believe renewable
power generation to be more relevant measure for market deployment and penetration than
renewable power capacity.
I also collect other data that is used as controls in my analyses from EIA, DSIRE or state
government websites. These data includes: (1) electricity price and total power generation at the
state level is collected and used to control the overall demand and supply of electricity markets;
(2) the restructuring status of state electricity market, which might influence the link between
RPS and renewable energy generation and innovation in renewable technologies;
54
(3)
information on two other renewable policies, Green Power Option (GPO) and Public Benefit
Fund (PBF), that have been implemented in many states to promote renewable energy adoption
and innovation. A state GPO program requires utilities to provide customers optional green
power program and could be positively related to the deployment of renewable energy in the
state (Menz and Vachon 2006). A state PBF program provides funds to support R&D on
renewable energy technologies in the state, which would stimulate local innovation in renewable
technologies.
54
RPS was initially proposed as a mechanism to promote the development of renewable energy development in
restructured states; thus the status of market restructuring in a state may impact the development of renewable
energy (Cory et al., 2007). Refer: Cory, K. S. and B. G. Swezey (2007). "Renewable portfolio standards in the states:
Balancing goals and rules." The Electricity Journal 20(4): 21-32, Carley, S. (2009). "State renewable energy
electricity policies: An empirical evaluation of effectiveness." Energy Policy 37(8): 3071-3081. Market competition
could also impact firm incentive in R&D and innovation (Cohen and Levin, 1989; Aghion et al., 2001). Refer:
Cohen, W. M. and R. C. Levin (1989). "Empirical studies of innovation and market structure." Handbook of
industrial organization 2: 1059-1107. and Aghion, P., C. Harris, et al. (2001). "Competition, imitation and growth
with step-by-step innovation." The review of economic studies 68(3): 467-492.
58
3.4 RPS and Renewable Energy Generation
I first investigate the correlation between RPS and renewable power generation. For the
purpose of this study, it suffices to examine whether there is significant increase in renewable
electricity generation following the implementation of RPS at the state level, rather than a causal
effect between these two, as it is only used as the context for the analyses on “innovating by
generating” in the next step. As mentioned earlier, it is difficult to identify the casual effects of
RPS on renewable energy development because the decision by a state to adopt RPS is
endogenous and influenced by its resource endowment, social preference, and even neighboring
states’ behavior.
The main specification involves the 27 states that have implemented mandatory RPS
prior to 2008. 55 In this specification, I take advantage of variations in the timing of RPS
enactment to more carefully estimate the increase in renewable energy generation following RPS
than the existing studies that include non-RPS-adopted states as the control that are quite
different from RPS-adopted states in many aspects such as renewable energy resources. However,
there are still potential omitted variable problems (i.e. some other policies that a state implements
to promote renewable energy that coincide with the enactment of RPS) that could prevent me
from identifying a causal effect between RPS and renewable energy generation.
3.4.1 Graphic evidence
In Figure 3-1, the generation of wind power and solar PV are shown respectively, before
versus after RPS, for an average RPS-adopted state. Following the implementation of RPS, wind
power generation increased significantly, from less than 500,000 MWh before RPS to nearly
55
I exclude nine states that implemented voluntary RPS in the analyses, as voluntary RPS might be quite different
from mandatory RPS. These states are Indiana, North Dakota, South Dakota, Oklahoma, Utah, Virginia, West
Virginia, Vermont, Iowa.
59
1,000,000 MWh in the fourth year after RPS. However, generation of solar PV electricity in an
average RPS-adopted state shows a very mild increase after RPS.
3.4.2 Econometric specification
As mentioned above, the main specification focuses on RPS-adopted states in the U.S.
The econometric model is as follows:
(Ⅰ)
The dependent variable,
, is the amount of generation (in 1000
MWh), from either wind power or solar PV, in state i in year t.
is a binary indicator for
whether state i implemented RPS in year t. It equals to 0 before the year when state i enacted
RPS, and 1 afterwards. 56
is a set of binary indicators for various institutional
designs of RPS, including no out-state tradability of RECs, inclusion of energy efficiency
technology for fossil fuels, and penalty for RPS noncompliance. The coefficients for the
interaction variables,
state fixed effect and
* RPS_Featurei, indicate the impacts of these RPS designs.
is year dummy.
is
is the error term.
I also include a group of control variables,
, in the regressions, as discussed in the
Data section. These control variables include the restructuring status of the electricity market, the
electricity price and total power generation, and the existence of a Green Power Option (GPO) or
Public Benefit Fund (PBF) program, for state i in year t.
56
In the Appendix, the results with the RPS variable defined as the RPS ambition (equal to the targeted RPS per
year; i.e. the RPS target divided by the number of years for achieving the target) are presented. The results remain
similar.
60
In the other specification, which used as a robustness check, I include non-RPS-adopted
states in the analyses and use two sets of year dummies for RPS-adopted states and non-RPSadopted states separately to account for the potential difference in renewable energy
development in the two groups of states in the absence of RPS.
3.4.3 Results
The results of the relation between RPS and wind power generation are shown in Table
3-2, and it could be found that wind power generation increased significantly in states with nontradable RPS policies. The three columns on the left are the results in mandatory RPS states only.
Column (4) to (6) are the results of regressions that include all the 42 states (mandatory RPS and
non-RPS states, no voluntary RPS states), and they are used as a robustness check here. In all
regressions, a significant and positive correlation between non-tradable RPS and wind generation
growth is found. It indicates that, in the states where renewable energy credits could not be trade
with others, the wind power generation experienced a prominent increase. The magnitude of the
non-tradability coefficients in column (1), (2) and (3) are relatively smaller than all states results
in column (4) to (6) because the difference between RPS states and non-RPS states in general is
excluded. The coefficients related to the variable if energy efficient technologies are eligible is
negative and consistent. The negative effect represents that wind power generation development
was restrained in the states where energy efficiency technologies are counted in meeting RPS
target.
Compared with wind power generations, the increase of solar power generation is not
significantly related to RPS in both RPS states and all state regressions (Table 3-3). Because the
data from EIA did not identify the sources of solar generation, if they are from solar PV or solar
thermal power generation, the solar power generation that used here is the sum of solar power
61
generation from two sources. But I believe it should not be a problem in this study because the
number of solar thermal power plant is small in the United State, and according to the fact sheet
of C2ES, even in 2012, the share of solar thermal capacity in all solar capacity is less than three
percent57. Therefore, even though the total solar generation includes solar thermal generation, the
share from solar thermal should be minor. In column (3) and (6), the effect of market structure
and RPS on solar generation is negative significant, which may imply that in the states with
restructured electricity market, the utilities are less likely to invest and develop solar generations
even with RPS requirements.
I also did regressions which replace the RPS dummy variables with the RPS targets for
further robustness check58. The results are compiled in Appendix A. It is found that the trend in
Appendix A is similar to the findings above59.
3.5 “Innovating by generating” in Renewable Energy
Having examined the relation between renewable generation and RPS, I turn to test
whether electricity generation from wind power and solar PV in prior years has impact on the
local innovation in the two technology groups, wind power and solar PV, respectively. Unlike
the tests I did in the previous part, here I am focusing on the causality between power generation
and innovation in solar PV and wind power technologies, in other words, the “innovating by
generating” that I am interested in is empirically tested in this part. Because wind power
technologies are more mature than solar PV technologies, I would expect that the increased
57
The factsheet could be reached on the website of C2ES: http://www.c2es.org/technology/factsheet/solar.
The target I used across this paper is the average target of the RPS policy. It is calculated from the final target
over the time span of the RPS policy, which represents the intensity of the RPS policy. For example, if the state
starts its RPS policy in year 2005, and set a final target as 20% in 2025, then the average RPS target of this state is 1%
per year (20 percentage/20 years).
59
In the results with RPS target, even though the magnitude and sign of the coefficients in wind power is consistent
with the results in Table 2, but the coefficients are significant.
58
62
generation practice from solar PV would lead to significant innovation increase in solar PV
technologies, but this effect on wind power generation and innovation might not be prominent.
3.5.1 Graphic Evidence
Graphically, different relations between sum of past two year power generation 60 and
innovation in two technologies are observed. Figure 3-2 shows the trend of average wind power
generation and innovation in mandatory RPS states at a certain age of RPS policy. It could be
found that before the application of RPS, the trend of generation and innovation in RPS stats are
quite similar, but they diverge as the implementation of RPS policy. After the RPS policies were
adopted, the average wind power generation in RPS states increased prominently, but the
innovation in these states went up at a much slower pace. Figure 3-3 shows the relation of
generation and innovation in solar PV. The solar PV innovation went up almost the same pace as
the increment of solar PV generation. According to the graphs, I would expect a significant
positive relationship between solar PV power generation and innovation in the RPS states.
3.5.2 Econometric specification
The econometric model used in this part is slightly different from the previous one, but
the data structure is the same, a panel data with 27 mandatory states. The econometric
specification is as follows:
(Ⅱ)
60
I use the generation of past two year generation here to be consistent with the regression results in this part. The
reason that I use the past two years will be explained later.
63
When considering the renewable power generation term in this stage, the sum of
renewable generations in the past two years is used as a proxy. In earlier studies of “learning by
doing”, many of them measure production experience with total production prior to the current
year. However, Kellogg pointed out that using the past two year production instead of total
previous production is more practical and reasonable because “forgotten” mechanism may
weaken the impact of experience long ago on the productivity today (Kellogg 2011). Therefore,
in this study the past two year renewable power generation is applied as the indicator of
“generation”. Renewable innovation variable
is the dependent variable in
specification (Ⅱ), which stands for the innovation on renewable technology in state i in year t. In
this specification, the coefficients of previous renewable power generation (
) is of particular
interest to me.
Besides the renewable generations, RPS dummy variable, RPS features variable, as long
as other control variables are all included in the regressions of this part. It is because I suspect
the state level RPS policies may have some direct impacts on the renewable innovations besides
the “innovating by generating” path. Including those variables into the regressions would be
helpful to controlling the direct impact of RPS on innovation and strengthen the examination on
“innovating by generating”.
Some robustness checks are also made in this part too. First, I replace the dummy RPS
with the target of each RPS states in percentage. Also, the sum of lag one year and lag two year
renewable power generation is taken place by other renewable generation combinations to verify
the “innovating by generating” effect in both technology groups.
64
3.5.3 Results
The results of wind and solar PV are summarized in Table 3-4 and Table 3-5 respectively,
and I find that the “innovating by generating” effect exists in both technology groups, but this
effect on wind is much smaller than solar PV. In both groups of regressions, the coefficients of
wind power generation are positive and significant, while the magnitude is quite small. The
coefficients of non-tradable RPS variable are also significant in Table 3-4. It means that even
though RPS encouraged in-state wind power generation, the non-tradable RPS may prohibit the
wind power innovation within the state. Because the wind power targets are in-state, the
competition from outside the state is largely excluded, and the developers and users of wind
power may have less incentive to innovate. Some robustness checks are made with the sum of
current year and lagged one year wind power generation, and with RPS target instead of RPS
dummy variable. The results are mostly consistent with the results here (For details, refer to
Appendix B).
In contrast to wind power technology, significant and positive impact of solar generation
on solar PV innovation is shown in all regressions. The “innovating by generating” effect in solar
PV, which reflected by the coefficient of solar generation, is stronger than wind power, both in
magnitude and significance. In all the six columns, the coefficients are consistently positive and
significant, and it is supportive to the hypothesis of “innovating by generating” effect in less
developed technologies, which is solar PV. The regressions with the sum of current year and
lagged one year solar generation as well as with RPS target indicators are also tried out as
robustness checks, and all the results are consistent with the results in Table 3-4 and Table 3-5
(Details of these regressions were compiled in Appendix C).
65
3.6 Local innovation in Wind Energy and Solar PV after RPS
Having tested the relation between innovation and generation as well as the relation
between generation and RPS, at last, I am wondering if renewables innovation is directly related
to RPS policies. According to the previous results, wind power generation went up significantly
after RPS, but the “innovating by generating” effect is not very significant. On the other hand,
even though the solar PV generation didn’t go up as significantly as wind power did under RPS
policies, the innovation improvement in solar PV is substantial. In this way, I doubt the
innovation of both technology groups be directly responsive to RPS.
3.6.1 Econometric specification
The specification used in this section is as follows.
(Ⅲ)
The definition of
,
and control variables
are the same as those in
previous specifications. The only difference in this part is that I look at the relation between
innovation in wind power and solar PV and RPS policies directly. In this specification,
is the
coefficients I am interested in, even though I am not expecting a significant effect in this section.
Besides, some robustness exercises are taken to provide more evidences to the hypothesis and
conclusion. In this part, I replace the dummy RPS term into the targets of RPS in percentage and
see if there is any difference is shown.
66
3.6.2 Results
Table 3-6 has shown the results of wind power, and in general, the relationship between
wind power innovation and RPS is insignificant. As the previous results about wind power have
shown, the power generation expansion of wind power increased significant under non-tradable
RPS, but the innovation stimulated by this generation explosion is minor. In Table 3-6, the
coefficients of RPS variable are all insignificant, which indicates the overall impact of RPS on
wind power innovation is not prominent.
Similar to the results of wind power, no significant results are found in solar PV
technologies analysis (Table 3-7). In previous results, even though the solar PV generation was
not greatly increased after RPS, with significant “innovating by generating” effect, the modest
solar PV generation increases still lead to significant innovation in solar PV technologies. When
the effects of the two stages are stacked up in this part, no remarkable influence of RPS on solar
PV technology innovation is found.
I also checked the robustness of the results by using the RPS target variable. It is found
out that the trend and significance of these results are similar. Those results are compiled and
listed in Appendix D.
3.7 Conclusion and Discussion
In this study, I investigate the possibility of “innovating by generating” during
deployment and development of wind energy and solar PV. In the process of building and
operating renewable energy projects, technology adaption to local conditions is required and
technical problems would be encountered, leading to novel solutions and innovations by local
inventors. I confirm that such “innovating by generating” indeed exists for both wind power and
67
solar PV: prior electricity generation from wind and solar PV significantly results in patented
innovation in these technologies at the state level. Moreover, the magnitude of “innovating by
generating” is greater in solar PV than wind power, consistent with the notion that the former
technology is less mature and thus perhaps has more room for technological improvement than
the latter.
Unfortunately, the current RPS in the U.S., largely undifferentiated towards wind power
and solar PV, has failed to realize the benefit of “innovating by generating”. Because wind
power is more cost competitive than solar PV, I find that following RPS that only allows in-state
trading there is a significant increase in wind electricity generation, the potential of “innovating
by generating”, however, is relatively small. For solar PV, which is less mature technology, more
expensive, there has no overall significant increase in generation of solar PV electricity after
states implemented RPS, even though there is much greater potential in “innovating by
generating” for solar PV.
These findings have interesting policy implications. Government supports for renewable
technologies, or any early-stage technology for which there exists some market failures, are
meant to promote the competitiveness of the technology and the industry that would in turn lead
to economic growth. Technological innovations during the process of deployment could be one
important mechanism that helps achieve these goals. However, policies need to be carefully
designed to fully exploit such potential of “innovating by deploying”. In the case of RPS, it
seems that some carve-out for solar PV is necessary to stimulate deployment and development of
the technology and lead to realization of the significant potential of “innovating by generating”61.
61
Up to now, there are some states that have applied independent solar PV requirements (such as Maryland, New
Mexico, and Delaware), but since the history of those requirements is relative short compared to the time span of
this study, I have not incorporated those requirements into the analysis of this paper. I believe it might be an
important issue for future analysis in RPS studies
68
1200
100
1000
80
800
60
600
40
400
20
200
0
Solar PV Generation (GWs)
Wind Power Generation (GWs)
Figure 3-1 Renewable power generation in RPS states (1990-2008)
0
-4
-3
-2
-1
0
1
2
3
Age of RPS policy
Wind generation per RPS state
Solar generation per RPS state (Right)
4
Notes: A. Wind and solar generation are averaged over the number of RPS states at a certain age. B. The negative
age of RPS policy indicates the years before RPS policies are applied.
69
Figure 3-2 Wind Power Generation and Patent Applications in RPS states (1990-2008)
15
1850
12
1450
9
1050
6
650
3
250
# of Wind Patent Filing
Wind Power Generation
(1000MWhs)
2250
0
-4
-3
-2
-1
0
1
Age of RPS Policy
2
3
4
Sum of past 2 year wind generation per RPS state
Wind power patent filing per RPS state (Right)
Notes: A. Wind power generation and patent filings are averaged over the number of RPS states at a certain age. B.
The negative age of RPS policy indicates the years before RPS policies are applied.
70
Figure 3-3 Wind Power Generation and Patent Applications in RPS states (1990-2008)
5
100
4
80
3
60
2
40
1
20
# of Solar PV Patent Filing
Solar Power Generation
(1000 MWhs)
120
0
-4
-3
-2
-1
0
1
Age of RPS policy
2
3
4
Sum of past 2 year solar generation per RPS state
Solar PV patent filing per RPS state (Right)
Notes: 1. Solar generation and solar PV patent filings are averaged over the number of RPS states at a certain age. 2.
The negative age of RPS policy indicates the years before RPS policies are applied.
71
Table 3-1 Enactment and Implementation of RPS across States
No.
1
2
3
State
Arizona
California
Colorado
Mandatory RPS (Y/N)
Y
Y
Y
Start year
2007
2003
2004
Passed year
2006
2002
2004
4
5
6
7
8
9
Connecticut
Delaware
Hawaii
Illinois
Indiana
Iowa
Y
Y
Y
Y
N
N
1998
2005
2004
2007
2011
2011
1998
2005
2004
2007
2011
2011
10
11
12
13
14
15
Kansas
Maine
Maryland
Massachusetts
Michigan
Minnesota
Y
Y
Y
Y
Y
Y
2009
2000
2004
2002
2008
2007
2009
1999
2004
1997
2008
2007
16
17
18
19
20
21
Missouri
Montana
Nevada
New Hampshire
New Jersey
New Mexico
Y
Y
Y
Y
Y
Y
2008
2005
1997
2007
1999
2007
2008
2005
1997
2007
1999
2007
22
23
24
25
26
27
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Y
Y
N
Y
N
Y
2004
2008
2007
2009
2010
2007
2004
2007
2007
2008
2010
2007
28
29
30
31
Pennsylvania
Rhode Island
South Dakoda
Texas
Y
Y
N
Y
2005
2004
2008
1999
2004
2004
2008
1999
32
33
34
35
36
37
Utah
Vermont
Virginia
Washington
Washington DC
West Virginia
N
N
N
Y
Y
Y
2008
2005
2007
2006
2005
2009
2008
2005
2007
2006
2005
2009
38
Wisconsin
Y
2001
1999
Note: The data in this table is collected from the website of each state as well as the website DSIRE, which is
funded by U.S. Department of Energy.
72
Table 3-2 Regression results of wind generation and RPS
(1)
(2)
WindG
WindG
(3)
(4)
WindG
WindG
RPS states
VARIABLES
RPS
RPS*notrade
RPS*tradition
PBF
WindG
WindG
-184.8
-59.59
-4.658
-180.2
-57.31
(445.5)
(446.6)
(306.5)
(460.6)
(466.3)
1,034***
1,045**
1,099***
1,412**
1,420**
1,475**
(371.6)
(384.0)
(371.6)
(592.2)
(587.1)
(566.4)
-533.9*
-527.1*
-518.1*
-697.4*
-687.9*
-678.2**
(302.9)
(292.4)
(274.5)
(367.3)
(347.8)
(333.1)
48.85
69.15
54.93
76.43
(60.88)
(62.00)
(68.71)
(70.63)
RPS*restructure
GPO
All states
-28.97
restructure
TotalGeneration
(6)
(325.0)
RPS*penalty
ElectricityPrice
(5)
111.3
85.84
(194.3)
(159.8)
-320.8
-318.2
(278.6)
(291.2)
6.595
6.244
4.063
-52.04
-51.86
-54.40
(75.62)
(75.04)
(79.51)
(59.79)
(59.80)
(62.51)
31.89**
31.52**
31.51**
19.89*
19.61*
19.64*
(14.08)
(13.60)
(13.72)
(11.70)
(11.29)
(11.37)
858.2**
897.8***
899.2***
754.0**
799.7**
792.5**
(327.5)
(311.7)
(320.4)
(330.2)
(306.4)
(307.0)
-135.7
-172.4
-198.3
-207.7
-248.1*
-269.4*
(131.7)
(122.1)
(129.3)
(137.4)
(136.8)
(150.5)
-1,994
-1,966
-1,950
-843.5
-826.0
-810.5
(1,302)
(1,274)
(1,313)
(1,033)
(1,016)
(1,040)
Year fixed effects (RPS states)
Y
Y
Y
Y
Y
Y
Year fixed effects (nonRPS states)
N
N
N
Y
Y
Y
State fixed effects
Y
Y
Y
Y
Y
Y
Constant
Observations
R-squared
Number of statecode
513
513
513
798
798
798
0.384
0.387
0.391
0.346
0.350
0.353
27
27
27
42
42
42
Notes: A. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. B. Column (1) - (3) are the results
of RPS state exclusively, and column (4) - (6) are the results that have include non-RPS states. C. The control
variables, electricity price, total power generation in state, Public Benefit Fund program (PBF), and Green Power
Option program (GPO) are added to all the regressions. D. In column (1) - (3), year fixed effects are controlled by
one set of year dummies, but in column (4) – (6), two sets of year dummies are added to control the possible
different trends in RPS and non-RPS states.
73
Table 3-3 Regression results of solar generation and RPS
(1)
(2)
(3)
(4)
RPS states
VARIABLES
RPS
RPS*notrade
RPS*tradition
SolarG
SolarG
(5)
SolarG
SolarG
SolarG
0.161
-1.372
3.494
0.244
-1.357
3.904
(2.870)
(3.909)
(4.360)
(2.813)
(4.085)
-9.349
-9.238
-5.164
-8.103
-8.025
-4.123
(7.821)
(7.795)
(5.160)
(6.592)
(6.651)
(4.367)
0.163
0.230
0.589
-0.390
-0.303
0.123
(5.067)
(5.279)
(5.123)
(5.313)
(5.476)
(5.226)
0.481
1.965
0.501
1.991
(0.890)
(1.475)
(0.911)
(1.512)
restructure
RPS*restructure
-5.786*
-4.600*
(3.163)
(2.651)
-13.93*
-14.83*
(7.868)
TotalGeneration
GPO
PBF
Constant
SolarG
(4.318)
RPS*penalty
ElectricityPrice
(6)
All states
(8.342)
0.341
0.337
-0.0502
0.155
0.157
-0.164
(0.751)
(0.735)
(0.710)
(0.564)
(0.560)
(0.580)
0.118
0.115
0.104
0.0778
0.0753
0.0692
(0.130)
(0.126)
(0.102)
(0.0835)
(0.0809)
(0.0664)
-5.311
-4.921
-8.798
-5.660
-5.244
-8.760
(5.220)
(4.671)
(6.435)
(5.462)
(4.870)
(6.441)
10.38
10.01
10.66
10.13
9.763
10.21
(10.83)
(10.29)
(9.788)
(10.61)
(10.06)
(9.537)
3.211
3.486
6.821
2.553
2.712
5.205
(17.77)
(17.33)
(14.06)
(11.35)
(11.07)
(9.010)
Year fixed effects (RPS states)
Y
Y
Y
Y
Y
Y
Year fixed effects (nonRPS states)
N
N
N
Y
Y
Y
State fixed effects
Y
Y
Y
Y
Y
Y
Observations
R-squared
Number of statecode
513
513
513
798
798
798
0.104
0.106
0.170
0.103
0.104
0.167
27
27
27
42
42
42
Notes: A. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. B. Column (1) - (3) are the results
of RPS state exclusively, and column (4) - (6) are the results that have include non-RPS states. C. The control
variables, electricity price, total power generation in state, Public Benefit Fund program (PBF), and Green Power
Option program (GPO) are added to all the regressions. D. In column (1) - (3), year fixed effects are controlled by
one set of year dummies, but in column (4) – (6), two sets of year dummies are added to control the possible
different trends in RPS and non-RPS states.
74
Table 3-4 Regression results of wind innovation and wind generation
(1)
(2)
(3)
(4)
RPS states
VARIABLES
WindG_lag1and lag2
RPS
RPS*notrade
RPS*tradition
wind
wind
wind
PBF
Constant
wind
wind
0.000350*
0.000322**
0.000349*
0.000324*
0.000296**
(0.000202)
(0.000154)
(0.000203)
(0.000187)
(0.000141)
0.504
-0.182
0.161
0.498
-0.186
0.217
(0.808)
(0.474)
(0.697)
(0.799)
(0.465)
(0.720)
-1.671**
-1.562**
-1.229***
-1.801**
-1.698**
-1.362***
(0.748)
(0.624)
(0.423)
(0.772)
(0.656)
(0.413)
0.250
0.246
0.248
0.291
0.290
0.292
(0.542)
(0.503)
(0.546)
(0.531)
(0.491)
(0.534)
0.217
0.326
0.216
0.327
(0.145)
(0.224)
(0.144)
(0.225)
RPS*restructure
GPO
wind
0.000375
restructure
TotalGeneration
(6)
(0.000220)
RPS*penalty
ElectricityPrice
(5)
All states
-0.526
-0.332
(0.528)
(0.444)
-0.990
-1.131
(0.953)
(1.019)
0.0991*
0.0961*
0.0666
0.130**
0.128**
0.103*
(0.0525)
(0.0475)
(0.0546)
(0.0600)
(0.0558)
(0.0551)
0.0200**
0.0190**
0.0197**
0.0260***
0.0252***
0.0258***
(0.00803)
(0.00840)
(0.00934)
(0.00853)
(0.00893)
(0.00911)
-0.448
-0.247
-0.524
-0.390
-0.188
-0.413
(0.547)
(0.459)
(0.688)
(0.520)
(0.430)
(0.649)
0.361
0.186
0.255
0.392
0.219
0.249
(0.309)
(0.327)
(0.344)
(0.305)
(0.318)
(0.337)
-1.756*
-1.421
-0.918
-2.436**
-2.212**
-1.904*
(0.930)
(0.835)
(1.154)
(0.992)
(0.938)
(0.984)
Year fixed effects (RPS states)
Y
Y
Y
Y
Y
Y
Year fixed effects (nonRPS states)
N
N
N
Y
Y
Y
State fixed effects
Y
Y
Y
Y
Y
Y
Observations
R-squared
Number of statecode
459
459
459
714
714
714
0.214
0.236
0.270
0.202
0.219
0.245
27
27
27
42
42
42
Notes: A. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. B. Column (1) - (3) are the results
of RPS state exclusively, and column (4) - (6) are the results that have include non-RPS states. C. The control
variables, electricity price, total power generation in state, Public Benefit Fund program (PBF), and Green Power
Option program (GPO) are added to all the regressions. D. In column (1) - (3), year fixed effects are controlled by
one set of year dummies, but in column (4) – (6), two sets of year dummies are added to control the possible
different trends in RPS and non-RPS states.
75
Table 3-5 Regression results of solar PV innovation and solar generation
(1)
(2)
(3)
(4)
RPS states
VARIABLES
SolarG_lag1andlag2
RPS
RPS*notrade
RPS*tradition
PV
PV
PV
PV
PV
0.0266***
0.0265***
0.0257***
0.0262***
0.0262***
0.0257***
(0.00353)
(0.00368)
(0.00363)
(0.00344)
(0.00359)
(0.00345)
0.116
0.0677
-0.0241
0.111
0.0733
0.0159
(0.621)
(0.492)
(0.498)
(0.615)
(0.488)
(0.488)
-0.0305
-0.0254
-0.0354
-0.280
-0.277
-0.285
(0.600)
(0.577)
(0.607)
(0.532)
(0.514)
(0.530)
-0.303
-0.303
-0.314
-0.218
-0.217
-0.224
(0.369)
(0.367)
(0.369)
(0.349)
(0.346)
(0.347)
0.0153
0.0142
0.0120
0.0104
(0.0701)
(0.0697)
(0.0684)
(0.0665)
restructure
RPS*restructure
TotalGeneration
GPO
(6)
PV
RPS*penalty
ElectricityPrice
(5)
All states
-0.328
-0.188
(0.288)
(0.246)
0.228
0.143
(0.501)
(0.463)
-0.173
-0.173
-0.178
-0.122
-0.122
-0.124
(0.140)
(0.141)
(0.143)
(0.119)
(0.119)
(0.120)
-0.0166*
-0.0168*
-0.0168*
-0.00911
-0.00918
-0.00925
(0.00939)
(0.00952)
(0.00932)
(0.00774)
(0.00779)
(0.00768)
-0.152
-0.140
-0.234
-0.0943
-0.0842
-0.137
(0.460)
(0.436)
(0.485)
(0.456)
(0.433)
(0.464)
0.449
0.438
0.515
0.506
0.497
0.541
(0.529)
(0.525)
(0.545)
(0.523)
(0.520)
(0.530)
3.567
3.594
3.843
2.235
2.247
2.351
(2.282)
(2.275)
(2.350)
(1.683)
(1.672)
(1.710)
Year fixed effects (RPS states)
Y
Y
Y
Y
Y
Y
Year fixed effects (nonRPS states)
N
N
N
Y
Y
Y
State fixed effects
Y
Y
Y
Y
Y
Y
PBF
Constant
Observations
R-squared
Number of statecode
459
459
459
714
714
714
0.132
0.132
0.134
0.126
0.126
0.126
27
27
27
42
42
42
Notes: A. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. B. Column (1) - (3) are the results
of RPS state exclusively, and column (4) - (6) are the results that have include non-RPS states. C. The control
variables, electricity price, total power generation in state, Public Benefit Fund program (PBF), and Green Power
Option program (GPO) are added to all the regressions. D. In column (1) - (3), year fixed effects are controlled by
one set of year dummies, but in column (4) – (6), two sets of year dummies are added to control the possible
different trends in RPS and non-RPS states.
76
Table 3-6 Regression results of wind innovation and RPS
(1)
(2)
(3)
(4)
RPS states
VARIABLES
RPS
RPS*notrade
RPS*tradition
wind
wind
PBF
Constant
wind
wind
wind
-0.277
0.0956
0.481
-0.277
0.142
(0.451)
(0.694)
(0.802)
(0.447)
(0.718)
-1.221**
-1.166**
-0.872*
-1.251**
-1.214**
-0.934**
(0.546)
(0.484)
(0.455)
(0.563)
(0.494)
(0.422)
0.0839
0.117
0.144
0.0871
0.128
0.162
(0.664)
(0.590)
(0.583)
(0.657)
(0.574)
(0.561)
0.238
0.345
0.237
0.345
(0.153)
(0.240)
(0.152)
(0.240)
RPS*restructure
GPO
wind
0.481
restructure
TotalGeneration
(6)
(0.807)
RPS*penalty
ElectricityPrice
(5)
All states
-0.354
-0.202
(0.536)
(0.444)
-1.053
-1.158
(1.005)
(1.069)
0.0930*
0.0913**
0.0643
0.103*
0.104**
0.0826
(0.0491)
(0.0424)
(0.0585)
(0.0533)
(0.0492)
(0.0578)
0.0277***
0.0259***
0.0252***
0.0280***
0.0268***
0.0265***
(0.00501)
(0.00583)
(0.00756)
(0.00602)
(0.00651)
(0.00721)
-0.236
-0.0433
-0.305
-0.232
-0.0343
-0.251
(0.449)
(0.345)
(0.648)
(0.446)
(0.338)
(0.616)
0.238
0.0590
0.0939
0.239
0.0643
0.0727
(0.277)
(0.323)
(0.350)
(0.276)
(0.320)
(0.347)
-2.034***
-1.898***
-1.667**
-2.150***
-2.074***
-1.912***
(0.685)
(0.515)
(0.441)
(0.638)
(0.616)
(0.705)
Year fixed effects (RPS states)
Y
Y
Y
Y
Y
Y
Year fixed effects (nonRPS states)
N
N
N
Y
Y
Y
State fixed effects
Y
Y
Y
Y
Y
Y
Observations
R-squared
Number of statecode
513
513
513
798
798
798
0.175
0.202
0.230
0.172
0.194
0.215
27
27
27
42
42
42
Notes: A. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. B. Column (1) - (3) are the results
of RPS state exclusively, and column (4) - (6) are the results that have include non-RPS states. C. The control
variables, electricity price, total power generation in state, Public Benefit Fund program (PBF), and Green Power
Option program (GPO) are added to all the regressions. D. In column (1) - (3), year fixed effects are controlled by
one set of year dummies, but in column (4) – (6), two sets of year dummies are added to control the possible
different trends in RPS and non-RPS states.
77
Table 3-7 Regression results of solar PV innovation and RPS
(1)
(2)
(3)
(4)
RPS states
VARIABLES
RPS
RPS*notrade
RPS*tradition
PV
PV
PV
PBF
Constant
PV
PV
0.250
0.364
0.350
0.250
0.415
(0.393)
(0.360)
(0.468)
(0.392)
(0.356)
-0.554
-0.546
-0.390
-0.625*
-0.620*
-0.473
(0.377)
(0.361)
(0.383)
(0.337)
(0.326)
(0.336)
-0.381
-0.376
-0.368
-0.369
-0.363
-0.350
(0.304)
(0.302)
(0.299)
(0.298)
(0.294)
(0.288)
0.0317
0.0878
0.0314
0.0873
(0.0650)
(0.0547)
(0.0641)
(0.0549)
RPS*restructure
GPO
PV
0.352
restructure
TotalGeneration
(6)
(0.471)
RPS*penalty
ElectricityPrice
(5)
All states
-0.447
-0.277
(0.327)
(0.284)
-0.371
-0.485
(0.434)
(0.428)
-0.101
-0.101
-0.119
-0.0793
-0.0792
-0.0927
(0.101)
(0.104)
(0.105)
(0.0869)
(0.0887)
(0.0893)
1.12e-05
-0.000232
-0.000779
0.00114
0.000979
0.000672
(0.0103)
(0.0105)
(0.00963)
(0.00698)
(0.00709)
(0.00661)
-0.335
-0.309
-0.521
-0.323
-0.297
-0.459
(0.539)
(0.514)
(0.589)
(0.537)
(0.512)
(0.570)
1.135
1.112
1.178
1.141
1.117
1.153
(0.871)
(0.869)
(0.864)
(0.854)
(0.851)
(0.844)
1.216
1.234
1.395
0.829
0.839
0.946
(1.053)
(1.079)
(0.980)
(0.691)
(0.708)
(0.658)
Year fixed effects (RPS states)
Y
Y
Y
Y
Y
Y
Year fixed effects (nonRPS states)
N
N
N
Y
Y
Y
State fixed effects
Y
Y
Y
Y
Y
Y
Observations
R-squared
Number of statecode
513
513
513
798
798
798
0.094
0.094
0.102
0.092
0.092
0.098
27
27
27
42
42
42
Notes: A. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. B. Column (1) - (3) are the results
of RPS state exclusively, and column (4) - (6) are the results that have include non-RPS states. C. The control
variables, electricity price, total power generation in state, Public Benefit Fund program (PBF), and Green Power
Option program (GPO) are added to all the regressions. D. In column (1) - (3), year fixed effects are controlled by
one set of year dummies, but in column (4) – (6), two sets of year dummies are added to control the possible
different trends in RPS and non-RPS states.
78
Appendix 2 Robustness Check Results
Appendix A
Table A1 Results of wind generation and RPS (with RPS target)
(1)
(2)
(3)
(4)
RPS states
VARIABLES
Target
Target*notrade
Target*tradition
WindG
WindG
(5)
WindG
WindG
WindG
-10.50
-51.70
91.38
-37.44
-65.70
91.28
(360.6)
(350.2)
(266.1)
(376.8)
(364.4)
533.4
518.3
644.0
702.9
693.0
822.3
(446.5)
(423.4)
(417.3)
(559.7)
(530.1)
(534.1)
-524.1
-527.0
-509.6
-668.3
-670.6
-645.5*
(329.6)
(327.5)
(302.3)
(397.8)
(400.4)
(379.9)
10.36
27.79
7.080
26.55
(35.59)
(38.94)
(36.93)
(40.21)
restructure
224.9
Target*restructure
223.1
(205.2)
(202.9)
-386.1*
-422.9*
(217.1)
TotalGeneration
PBF
GPO
Constant
WindG
(253.4)
Target*penalty
ElectricityPrice
(6)
All states
(215.9)
45.27
48.71
36.91
-5.688
-3.539
-14.82
(71.97)
(78.62)
(82.02)
(60.53)
(67.42)
(70.31)
36.17**
36.19**
35.39**
23.94
23.94
23.47
(14.95)
(14.94)
(14.62)
(14.36)
(14.34)
(14.04)
-87.66
-93.30
-132.5
-135.6
-139.5
-175.9
(130.6)
(126.1)
(126.0)
(121.9)
(122.7)
(137.8)
841.3**
854.2**
860.7**
711.0**
719.6**
721.5**
(336.6)
(340.9)
(358.4)
(336.0)
(338.2)
(346.6)
-2,541*
-2,566*
-2,431*
-1,407
-1,421
-1,316
(1,298)
(1,357)
(1,346)
(1,211)
(1,266)
(1,252)
Year fixed effects (RPS states)
Y
Y
Y
Y
Y
Y
Year fixed effects (nonRPS states)
N
N
N
Y
Y
Y
State fixed effects
Y
Y
Y
Y
Y
Y
Observations
R-squared
Number of statecode
513
513
513
798
798
798
0.372
0.372
0.380
0.320
0.320
0.330
27
27
27
42
42
42
Notes: A. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. B. Column (1) - (3) are the results of RPS state
exclusively, and column (4) - (6) are the results that have include non-RPS states. C. The control variables, electricity price, total
power generation in state, Public Benefit Fund program (PBF), and Green Power Option program (GPO) are added to all the
regressions. D. In column (1) - (3), year fixed effects are controlled by one set of year dummies, but in column (4) – (6), two sets
of year dummies are added to control the possible different trends in RPS and non-RPS states.
79
Table A2 Results of solar generation and RPS (with RPS target)
(1)
(2)
(3)
(4)
RPS states
VARIABLES
Target
Target*notrade
Target*tradition
SolarG
SolarG
(5)
SolarG
SolarG
SolarG
4.301
1.689
6.873
4.254
1.644
7.095
(4.299)
(5.078)
(6.264)
(4.246)
(5.202)
-2.452
-3.408
-0.0550
-2.124
-3.042
0.197
(3.248)
(3.883)
(2.775)
(2.810)
(3.441)
(2.598)
-1.988
-2.172
-0.906
-2.272
-2.479
-0.936
(5.328)
(5.660)
(4.552)
(5.510)
(5.824)
(4.538)
0.657
1.446
0.654
1.468
(0.658)
(1.044)
(0.651)
(1.052)
restructure
Target*restructure
-4.588**
-3.639**
(2.064)
(1.725)
-14.31*
-14.88*
(7.875)
TotalGeneration
PBF
GPO
Constant
SolarG
(6.337)
Target*penalty
ElectricityPrice
(6)
All states
(8.091)
-0.193
0.0251
-0.473
-0.240
-0.0416
-0.489
(0.489)
(0.600)
(0.763)
(0.393)
(0.487)
(0.681)
0.0653
0.0668
0.0510
0.0465
0.0467
0.0358
(0.108)
(0.111)
(0.0752)
(0.0724)
(0.0732)
(0.0505)
9.804
9.446
10.84
9.721
9.363
10.58
(10.19)
(9.854)
(9.445)
(10.08)
(9.746)
(9.308)
-6.393
-5.575
-10.60
-6.577
-5.780
-10.48
(6.264)
(5.358)
(7.058)
(6.397)
(5.494)
(7.014)
10.36
8.761
13.20
7.148
5.841
9.446
(13.68)
(14.75)
(10.84)
(8.807)
(9.667)
(7.335)
Year fixed effects (RPS states)
Y
Y
Y
Y
Y
Y
Year fixed effects (nonRPS states)
N
N
N
Y
Y
Y
State fixed effects
Y
Y
Y
Y
Y
Y
Observations
R-squared
Number of statecode
513
513
513
798
798
798
0.107
0.113
0.194
0.106
0.112
0.192
27
27
27
42
42
42
Notes: A. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. B. Column (1) - (3) are the results of RPS state
exclusively, and column (4) - (6) are the results that have include non-RPS states. C. The control variables, electricity price, total
power generation in state, Public Benefit Fund program (PBF), and Green Power Option program (GPO) are added to all the
regressions. D. In column (1) - (3), year fixed effects are controlled by one set of year dummies, but in column (4) – (6), two sets
of year dummies are added to control the possible different trends in RPS and non-RPS states.
80
Appendix B
Table B1 Regression results of wind generation and wind innovation (sum of lag one year
and lag two year wind power generation with RPS target)
(1)
(2)
(3)
(4)
RPS states
VARIABLES
WindG_lag1and lag2
Target
Target*notrade
Target*tradition
wind
wind
wind
wind
wind
0.000434
0.000516
0.000299
0.000371
0.000449
(0.000285)
(0.000314)
(0.000250)
(0.000252)
(0.000279)
-0.561
2.455
1.634
-0.534
2.474
1.700
(1.723)
(1.751)
(1.901)
(1.706)
(1.744)
(1.926)
0.118
1.196
0.418
-0.0119
1.018
0.326
(1.302)
(1.903)
(1.458)
(1.243)
(1.820)
(1.394)
0.413
0.739
0.691
0.517
0.869
0.797
(0.732)
(1.376)
(1.406)
(0.749)
(1.366)
(1.365)
-0.774
-0.882
-0.769
-0.872
(0.515)
(0.561)
(0.514)
(0.562)
Target*restructure
ElectricityPrice
0.259
(0.291)
TotalGeneration
0.00416
(0.0162)
(0.0180)
Constant
wind
0.000340
restructure
PBF
(6)
(0.000269)
Target*penalty
GPO
(5)
All states
-0.00510
-1.025
-0.775
(0.794)
(0.670)
2.269
2.132
(2.525)
(2.539)
0.0628
0.286
0.0512
0.113
(0.328)
(0.239)
(0.277)
(0.307)
(0.228)
-0.00158
-0.000718
0.0140
0.0120
0.0119
(0.0168)
(0.0139)
(0.0142)
(0.0136)
-1.244
-2.316
-2.304
-1.149
-2.169
-2.090
(1.025)
(1.670)
(1.877)
(0.985)
(1.626)
(1.817)
1.570
2.082
2.258
1.611
2.130
2.249*
(1.494)
(1.355)
(1.354)
(1.491)
(1.356)
(1.329)
-1.632
0.677
0.101
-2.407
-0.675
-1.103
(2.179)
(2.954)
(2.192)
(1.899)
(2.410)
(1.861)
Year fixed effects (RPS states)
Y
Y
Y
Y
Y
Y
Year fixed effects (nonRPS states)
N
N
N
Y
Y
Y
State fixed effects
Y
Y
Y
Y
Y
Y
Observations
R-squared
Number of statecode
459
459
459
714
714
714
0.151
0.342
0.365
0.150
0.328
0.349
27
27
27
42
42
42
Notes: A. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. B. Column (1) - (3) are the results of RPS state
exclusively, and column (4) - (6) are the results that have include non-RPS states. C. The control variables, electricity price, total
power generation in state, Public Benefit Fund program (PBF), and Green Power Option program (GPO) are added to all the
regressions. D. In column (1) - (3), year fixed effects are controlled by one set of year dummies, but in column (4) – (6), two sets
of year dummies are added to control the possible different trends in RPS and non-RPS states.
81
Table B2 Regression results of wind generation and wind innovation (sum of current year
and lag one year wind power generation)
(1)
(2)
(3)
(4)
RPS states
VARIABLES
WindG_currentandlag1
RPS
RPS*notrade
RPS*tradition
wind
wind
wind
wind
wind
0.000231**
0.000216**
0.000204**
0.000213**
0.000197**
0.000184***
(0.000107)
(9.70e-05)
(7.85e-05)
(9.57e-05)
(8.64e-05)
(6.82e-05)
0.490
-0.206
0.109
0.481
-0.212
0.163
(0.794)
(0.457)
(0.672)
(0.782)
(0.445)
(0.692)
-1.528**
-1.437***
-1.139**
-1.677**
-1.593***
-1.296***
(0.605)
(0.500)
(0.421)
(0.636)
(0.537)
(0.388)
0.231
0.239
0.250
0.281
0.293
0.305
(0.586)
(0.539)
(0.566)
(0.565)
(0.518)
(0.546)
0.219
0.319
0.218
0.319
(0.146)
(0.226)
(0.145)
(0.227)
restructure
RPS*restructure
TotalGeneration
GPO
PBF
Constant
(6)
wind
RPS*penalty
ElectricityPrice
(5)
All states
-0.457
-0.263
(0.537)
(0.450)
-0.913
-1.051
(0.924)
(0.990)
0.0850*
0.0830*
0.0554
0.120**
0.119**
0.0959
(0.0485)
(0.0438)
(0.0626)
0.0144
0.0134
0.0134
(0.0565)
(0.0522)
(0.0595)
0.0208**
0.0201**
(0.00978)
(0.0105)
(0.0119)
(0.00913)
0.0202*
(0.00965)
(0.0101)
-0.487
-0.287
-0.546
-0.418
-0.216
-0.420
(0.540)
(0.451)
(0.692)
(0.505)
(0.416)
(0.640)
0.236
0.0604
0.119
0.267
0.0941
0.114
(0.304)
(0.366)
(0.383)
(0.293)
(0.354)
(0.376)
-0.938
-0.854
-0.653
-1.598**
-1.535*
-1.362
(0.898)
(0.903)
(1.242)
(0.786)
(0.781)
(0.983)
Year fixed effects (RPS states)
Y
Y
Y
Y
Y
Y
Year fixed effects (nonRPS states)
N
N
N
Y
Y
Y
State fixed effects
Y
Y
Y
Y
Y
Y
Observations
R-squared
Number of statecode
486
486
486
756
756
756
0.196
0.218
0.246
0.187
0.204
0.225
27
27
27
42
42
42
Notes: A. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. B. Column (1) - (3) are the results of RPS state
exclusively, and column (4) - (6) are the results that have include non-RPS states. C. The control variables, electricity price, total
power generation in state, Public Benefit Fund program (PBF), and Green Power Option program (GPO) are added to all the
regressions. D. In column (1) - (3), year fixed effects are controlled by one set of year dummies, but in column (4) – (6), two sets
of year dummies are added to control the possible different trends in RPS and non-RPS states.
82
Table B3 Regression results of wind generation and wind innovation (sum of current year
and lagged 1 year wind power generation and RPS target)
(1)
(2)
(3)
(4)
RPS states
VARIABLES
WindG_currentandlag1
Target
Target*notrade
Target*tradition
wind
wind
PBF
Constant
wind
wind
wind
0.000276*
0.000327*
0.000200
0.000232*
0.000281*
(0.000149)
(0.000169)
(0.000131)
(0.000127)
(0.000146)
-0.705
2.385
1.518
-0.677
2.405
1.575
(1.753)
(1.724)
(1.897)
(1.735)
(1.716)
(1.915)
0.141
1.232
0.483
-0.00133
1.045
0.370
(1.312)
(1.921)
(1.485)
(1.251)
(1.843)
(1.428)
0.528
0.785
0.695
0.643
0.925
0.812
(0.770)
(1.411)
(1.411)
(0.783)
(1.396)
(1.373)
-0.777
-0.892
-0.772
-0.884
(0.513)
(0.557)
(0.513)
(0.557)
Target*restructure
GPO
wind
0.000232
restructure
TotalGeneration
(6)
(0.000146)
Target*penalty
ElectricityPrice
(5)
All states
-0.770
-0.574
(0.720)
(0.601)
2.365
2.262
(2.518)
(2.531)
0.249
-0.0111
0.0588
0.279
0.0463
0.109
(0.286)
(0.320)
(0.232)
(0.274)
(0.300)
(0.220)
-0.00126
-0.00564
-0.00449
0.00899
0.00762
0.00783
(0.0168)
(0.0169)
(0.0151)
(0.0136)
(0.0131)
(0.0123)
-1.397
-2.418
-2.276
-1.276
-2.248
-2.054
(1.049)
(1.671)
(1.861)
(0.994)
(1.621)
(1.795)
1.656
2.076
2.183
1.691
2.113
2.179
(1.629)
(1.407)
(1.349)
(1.629)
(1.410)
(1.331)
-1.200
0.948
0.361
-2.063
-0.409
-0.850
(2.101)
(2.824)
(2.075)
(1.860)
(2.321)
(1.775)
Year fixed effects (RPS states)
Y
Y
Y
Y
Y
Y
Year fixed effects (nonRPS states)
N
N
N
Y
Y
Y
State fixed effects
Y
Y
Y
Y
Y
Y
Observations
R-squared
Number of statecode
486
486
486
756
756
756
0.159
0.346
0.371
0.157
0.333
0.355
27
27
27
42
42
42
Notes: A. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. B. Column (1) - (3) are the results of RPS state
exclusively, and column (4) - (6) are the results that have include non-RPS states. C. The control variables, electricity price, total
power generation in state, Public Benefit Fund program (PBF), and Green Power Option program (GPO) are added to all the
regressions. D. In column (1) - (3), year fixed effects are controlled by one set of year dummies, but in column (4) – (6), two sets
of year dummies are added to control the possible different trends in RPS and non-RPS states.
83
Appendix C
Table C1 Regression results of solar PV generation and solar PV innovation (sum of lag
one year and lag two year wind power generation with RPS target)
(1)
(2)
(3)
(4)
RPS states
VARIABLES
SolarG_lag1andlag2
Target
Target*notrade
Target*tradition
(5)
(6)
All states
PV
PV
PV
PV
PV
PV
0.0279***
0.0276***
0.0258***
0.0276***
0.0273***
0.0258***
(0.00361)
(0.00369)
(0.00396)
(0.00354)
(0.00361)
(0.00386)
-0.206
-0.303
-0.231
-0.190
-0.300
-0.219
(0.422)
(0.432)
(0.505)
(0.419)
(0.432)
(0.497)
0.372
0.333
0.344
0.313
0.270
0.297
(0.420)
(0.446)
(0.430)
(0.391)
(0.415)
(0.398)
-0.174
-0.184
-0.177
-0.130
-0.143
-0.133
(0.242)
(0.251)
(0.245)
(0.220)
(0.231)
(0.224)
Target*penalty
0.0256
0.0362
0.0291
0.0405
(0.0557)
(0.0553)
(0.0544)
(0.0535)
restructure
Target*restructure
-0.0883
(0.222)
-0.151
-0.184
(0.378)
(0.353)
ElectricityPrice
-0.199
(0.137)
(0.140)
(0.133)
(0.118)
(0.121)
(0.115)
TotalGeneration
-0.0182**
-0.0181**
-0.0178**
-0.0111
-0.0110
-0.0110
(0.00840)
(0.00865)
(0.00817)
(0.00809)
(0.00827)
(0.00804)
-0.0878
-0.0570
-0.171
-0.0165
0.0178
-0.0661
(0.433)
(0.418)
(0.425)
(0.428)
(0.415)
(0.412)
GPO
PBF
-0.190
-0.177
(0.255)
-0.195
-0.156
-0.147
-0.152
0.467
0.455
0.530
0.504
0.490
0.540
(0.581)
(0.584)
(0.584)
(0.577)
(0.579)
(0.574)
2.855**
2.793**
2.867**
1.972*
1.915*
1.976*
(1.301)
(1.328)
(1.236)
(1.079)
(1.104)
(1.051)
Year fixed effects (RPS states)
Y
Y
Y
Y
Y
Y
Year fixed effects (nonRPS states)
N
N
N
Y
Y
Y
State fixed effects
Y
Y
Y
Y
Y
Y
Constant
Observations
R-squared
Number of statecode
459
459
459
714
714
714
0.133
0.133
0.135
0.126
0.126
0.127
27
27
27
42
42
42
Notes: A. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. B. Column (1) - (3) are the results of RPS state
exclusively, and column (4) - (6) are the results that have include non-RPS states. C. The control variables, electricity price, total
power generation in state, Public Benefit Fund program (PBF), and Green Power Option program (GPO) are added to all the
regressions. D. In column (1) - (3), year fixed effects are controlled by one set of year dummies, but in column (4) – (6), two sets
of year dummies are added to control the possible different trends in RPS and non-RPS states.
84
Table C2 Regression results of solar PV generation and solar PV innovation (sum of
current year and lag one year wind power generation)
(1)
(2)
(3)
(4)
RPS states
VARIABLES
SolarG_currentandlag1
RPS
RPS*notrade
RPS*tradition
(5)
(6)
All states
PV
PV
PV
PV
PV
PV
0.0127***
0.0126***
0.0110***
0.0125***
0.0125***
0.0112***
(0.00336)
(0.00339)
(0.00318)
(0.00326)
(0.00329)
(0.00310)
0.300
0.232
0.242
0.295
0.232
0.287
(0.511)
(0.405)
(0.365)
(0.506)
(0.402)
(0.356)
-0.312
-0.306
-0.250
-0.475
-0.472
-0.416
(0.411)
(0.392)
(0.446)
(0.379)
(0.364)
(0.397)
-0.346
-0.344
-0.347
-0.294
-0.292
-0.292
(0.320)
(0.315)
(0.313)
(0.309)
(0.303)
(0.301)
0.0216
0.0501
0.0197
0.0474
(0.0653)
(0.0526)
(0.0640)
(0.0501)
RPS*penalty
restructure
RPS*restructure
-0.229
(0.305)
(0.266)
-0.0717
-0.172
(0.409)
(0.380)
ElectricityPrice
-0.129
(0.121)
(0.123)
(0.124)
(0.103)
(0.104)
(0.104)
TotalGeneration
-0.00731
-0.00749
-0.00752
-0.00301
-0.00312
-0.00316
(0.00928)
(0.00948)
(0.00917)
(0.00697)
(0.00708)
(0.00692)
-0.177
-0.159
-0.323
-0.141
-0.125
-0.238
(0.482)
(0.462)
(0.520)
(0.477)
(0.458)
(0.497)
GPO
PBF
Constant
-0.129
-0.394
-0.140
-0.0932
-0.0931
-0.101
0.808
0.792
0.891
0.838
0.824
0.884
(0.650)
(0.656)
(0.691)
(0.639)
(0.645)
(0.672)
2.091*
2.105*
2.239*
1.465
1.473
1.565
(1.105)
(1.130)
(1.122)
(0.926)
(0.939)
(0.939)
Year fixed effects (RPS states)
Y
Y
Y
Y
Y
Y
Year fixed effects (nonRPS states)
N
N
N
Y
Y
Y
State fixed effects
Y
Y
Y
Y
Y
Y
Observations
R-squared
Number of statecode
486
486
486
756
756
756
0.104
0.104
0.108
0.101
0.101
0.103
27
27
27
42
42
42
Notes: A. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. B. Column (1) - (3) are the results of RPS state
exclusively, and column (4) - (6) are the results that have include non-RPS states. C. The control variables, electricity price, total
power generation in state, Public Benefit Fund program (PBF), and Green Power Option program (GPO) are added to all the
regressions. D. In column (1) - (3), year fixed effects are controlled by one set of year dummies, but in column (4) – (6), two sets
of year dummies are added to control the possible different trends in RPS and non-RPS states.
85
Table C3 Regression results of solar PV generation and solar PV innovation (sum of
current year and lagged 1 year wind power generation and RPS target)
(1)
(2)
(3)
(4)
RPS states
VARIABLES
SolarG_currentandlag1
Target
Target*notrade
Target*tradition
PV
PV
PV
PV
PV
0.00678***
0.00671***
0.00607***
0.00673***
0.00665***
0.00605***
(0.000569)
(0.000570)
(0.000584)
(0.000554)
(0.000558)
(0.000575)
0.0919
-0.0510
0.138
0.0960
-0.0577
0.155
(0.210)
(0.275)
(0.268)
(0.209)
(0.276)
(0.257)
0.173
0.120
0.224
0.166
0.110
0.230
(0.277)
(0.280)
(0.328)
(0.268)
(0.271)
(0.314)
-0.276
-0.286
-0.242
-0.272
-0.285
-0.233
(0.200)
(0.209)
(0.180)
(0.190)
(0.198)
(0.169)
0.0362
0.0658**
0.0388
0.0703***
(0.0435)
(0.0261)
(0.0430)
(0.0253)
restructure
Target*restructure
TotalGeneration
GPO
PBF
Constant
(6)
PV
Target*penalty
ElectricityPrice
(5)
All states
-0.216
-0.105
(0.270)
(0.237)
-0.507*
-0.564**
(0.265)
(0.254)
-0.147
-0.135
-0.151
-0.121
-0.110
-0.125
(0.113)
(0.114)
(0.108)
(0.0977)
(0.0985)
(0.0946)
-0.00600
-0.00589
-0.00609
-0.00325
-0.00322
-0.00345
(0.00734)
(0.00762)
(0.00675)
(0.00583)
(0.00602)
(0.00556)
-0.266
-0.222
-0.429
-0.230
-0.184
-0.356
(0.462)
(0.446)
(0.489)
(0.456)
(0.439)
(0.469)
0.864
0.847
0.931
0.873
0.854
0.915
(0.687)
(0.685)
(0.704)
(0.680)
(0.677)
(0.693)
2.198**
2.105**
2.248**
1.548*
1.468*
1.596**
(1.002)
(1.012)
(0.918)
(0.780)
(0.790)
(0.741)
Year fixed effects (RPS states)
Y
Y
Y
Y
Y
Y
Year fixed effects (nonRPS states)
N
N
N
Y
Y
Y
State fixed effects
Y
Y
Y
Y
Y
Y
Observations
R-squared
Number of statecode
486
486
486
756
756
756
0.117
0.119
0.125
0.114
0.115
0.121
27
27
27
42
42
42
Notes: A. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. B. Column (1) - (3) are the results of RPS state
exclusively, and column (4) - (6) are the results that have include non-RPS states. C. The control variables, electricity price, total
power generation in state, Public Benefit Fund program (PBF), and Green Power Option program (GPO) are added to all the
regressions. D. In column (1) - (3), year fixed effects are controlled by one set of year dummies, but in column (4) – (6), two sets
of year dummies are added to control the possible different trends in RPS and non-RPS states.
86
Appendix D
Table D1 Regression results of wind innovation and RPS (with RPS target)
(1)
(2)
(3)
(4)
RPS states
VARIABLES
Target
Target*notrade
Target*tradition
wind
wind
PBF
Constant
wind
wind
wind
2.363
1.586
-0.681
2.381
1.634
(1.733)
(1.954)
(1.764)
(1.726)
(1.974)
0.342
1.462
0.834
0.230
1.307
0.756
(1.325)
(1.965)
(1.533)
(1.245)
(1.862)
(1.455)
0.320
0.535
0.412
0.414
0.658
0.504
(0.855)
(1.498)
(1.491)
(0.866)
(1.482)
(1.445)
-0.770
-0.871
-0.767
-0.867
(0.517)
(0.569)
(0.516)
(0.568)
Target*restructure
GPO
wind
-0.699
restructure
TotalGeneration
(6)
(1.782)
Target*penalty
ElectricityPrice
(5)
All states
-0.655
-0.474
(0.719)
(0.594)
2.109
2.023
(2.603)
(2.613)
0.267
0.0110
0.0781
0.277
0.0440
0.101
(0.296)
(0.331)
(0.247)
(0.280)
(0.307)
(0.231)
0.0128
0.0111
0.0149
0.0170
0.0168
0.0188**
(0.0141)
(0.0133)
(0.0110)
(0.0118)
(0.0103)
(0.00915)
-1.109
-2.068
-1.869
-1.073
-2.007
-1.770
(1.022)
(1.684)
(1.867)
(0.995)
(1.656)
(1.826)
1.662
2.081
2.168
1.680
2.100
2.145
(1.619)
(1.400)
(1.355)
(1.626)
(1.406)
(1.336)
-2.340
-0.461
-1.175
-2.628
-1.095
-1.593
(2.004)
(2.694)
(1.923)
(1.760)
(2.224)
(1.637)
Year fixed effects (RPS states)
Y
Y
Y
Y
Y
Y
Year fixed effects (nonRPS states)
N
N
N
Y
Y
Y
State fixed effects
Y
Y
Y
Y
Y
Y
Observations
R-squared
Number of statecode
513
513
513
798
798
798
0.150
0.335
0.354
0.150
0.324
0.342
27
27
27
42
42
42
Notes: A. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. B. Column (1) - (3) are the results of RPS state
exclusively, and column (4) - (6) are the results that have include non-RPS states. C. The control variables, electricity price, total
power generation in state, Public Benefit Fund program (PBF), and Green Power Option program (GPO) are added to all the
regressions. D. In column (1) - (3), year fixed effects are controlled by one set of year dummies, but in column (4) – (6), two sets
of year dummies are added to control the possible different trends in RPS and non-RPS states.
87
Table D2 Regression results of solar PV innovation and RPS (with RPS target)
(1)
(2)
(3)
(4)
RPS states
VARIABLES
Target
Target*notrade
Target*tradition
PV
PV
(5)
PV
PV
PV
0.197
0.0133
0.276
0.198
0.00445
0.294
(0.253)
(0.226)
(0.193)
(0.254)
(0.219)
0.101
0.0340
0.201
0.108
0.0404
0.219
(0.247)
(0.245)
(0.312)
(0.247)
(0.245)
(0.306)
-0.311
-0.324
-0.259
-0.320
-0.335
-0.256
(0.222)
(0.236)
(0.183)
(0.219)
(0.232)
(0.173)
0.0461
0.0864***
0.0485
0.0911***
(0.0446)
(0.0244)
(0.0442)
(0.0247)
restructure
Target*restructure
-0.261
-0.139
(0.278)
(0.244)
-0.725**
-0.790**
(0.305)
TotalGeneration
GPO
PBF
Constant
PV
(0.194)
Target*penalty
ElectricityPrice
(6)
All states
(0.306)
-0.144
-0.128
-0.154
-0.121
-0.106
-0.129
(0.109)
(0.109)
(0.102)
(0.0943)
(0.0944)
(0.0905)
-0.00315
-0.00304
-0.00381
-0.00128
-0.00127
-0.00188
(0.00953)
(0.00985)
(0.00816)
(0.00717)
(0.00737)
(0.00642)
-0.361
-0.304
-0.571
-0.333
-0.274
-0.501
(0.520)
(0.495)
(0.554)
(0.517)
(0.492)
(0.536)
1.136
1.110
1.187
1.138
1.112
1.165
(0.898)
(0.890)
(0.879)
(0.887)
(0.878)
(0.865)
1.717
1.604
1.828**
1.254*
1.157
1.349**
(1.009)
(1.030)
(0.826)
(0.740)
(0.756)
(0.648)
Year fixed effects (RPS states)
Y
Y
Y
Y
Y
Y
Year fixed effects (nonRPS states)
N
N
N
Y
Y
Y
State fixed effects
Y
Y
Y
Y
Y
Y
Observations
R-squared
Number of statecode
513
513
513
798
798
798
0.092
0.093
0.107
0.089
0.091
0.103
27
27
27
42
42
42
Notes: A. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1. B. Column (1) - (3) are the results of RPS state
exclusively, and column (4) - (6) are the results that have include non-RPS states. C. The control variables, electricity price, total
power generation in state, Public Benefit Fund program (PBF), and Green Power Option program (GPO) are added to all the
regressions. D. In column (1) - (3), year fixed effects are controlled by one set of year dummies, but in column (4) – (6), two sets
of year dummies are added to control the possible different trends in RPS and non-RPS states.
88
Chapter 4
Building Benchmarking Law and Increased Awareness about
Building Energy Efficiency
4.1 Introduction
Buildings, especially commercial buildings, are significant sources of energy
consumption. In 2010, about 41% of the total primary energy consumption in the United States
was due to the building sector,62 and 18.9% was due to commercial buildings that had growth
rate of 58% in floor space and 69% in primary energy use between 1980 and 2009. 63 Thus,
improving energy efficiency use in buildings, has been an important area in tackling energy and
environmental problems associated with growing energy consumption.
However, there exist a number of barriers for building owners to improve building energy
efficiency, through adoption of energy efficient technologies or better energy management
related to buildings. One important impediment is related to information in the market. Energy
management in a building is a complex task and the building owner may not know whether the
building is being operated in an energy efficient way, relative to similar buildings. Constructing
more energy efficient buildings are expensive and retrofitting incumbent buildings involve
significant upfront cost, in both cases if the cost could be compensated by future benefits is
uncertain to building owners. In particular, in cases that a more energy efficient commercial
building is being rented to tenants who pay energy bills or the tenants are unaware of or
62
The transportation sector accounts for 29 % of primary energy consumption and the industrial sector 30 % in the
U.S. Refer to U.S. Department of Energy, Building Energy Data Book,
http://buildingsdatabook.eren.doe.gov/ChapterIntro1.aspx
63
Commercial buildings consumed a total of 17.9 quads of primary energy in 2009. The top three sources of energy
use are space heating, lighting, and space cooling, which together count for half of energy use by commercial
buildings. The Energy Information Administration (EIA) projects energy consumption by commercial buildings to
to grow at slower rates between 2009 and 2035, 28% and 22%, respectively. See Department of Energy, Building
Energy Data Book of Commercial Sector, http://buildingsdatabook.eren.doe.gov/ChapterIntro3.aspx.
89
convinced that they are paying less energy bills (due to some information asymmetry), the owner
may not be able to appropriate a rent premium for the lower energy expenditure for the building,
and thus have no incentive to invest in building energy efficiency improvement.
A number of initiatives or policies have been in place, in an effort to address these
information problems in the market. Building energy auditing helps building owners and
operators gather detailed information about energy use in the buildings and find ways to reduce
energy consumption. LEED (Leadership in Energy & Environmental Design) and EnergyStar
programs are voluntary building-rating systems that issue certificates to buildings based on their
energy and environmental performance. 64 The LEED and EnergyStar programs enable buildings
with better performance in energy efficiency and sustainability to signal their excellence to the
market and get compensation for their efforts in energy efficiency improvement. Quigley (2010)
find that LEED buildings indeed appropriate higher rents and sale prices than non-LEED
buildings.
Recently, several cities in the U.S. enacted the building energy benchmarking policy in
efforts to enhance building energy efficiency. The policy often has two components: (1) building
energy benchmarking as “a public yardstick of energy-use performance in buildings” (Chung
2011), by collecting data on building energy consumption (based on types of buildings, climate
zones, etc.) creating standardized metric to measure building energy performance, and
comparing energy efficiency among similar buildings65 (Pérez-Lombard, Ortiz et al. 2009); and
64
The EnergyStar program was developed by U.S. Environmental Protection Agency and Department of Energy in
1992, to reduce energy consumption in buildings. For details, visit http://www.energystar.gov/index.cfm. The
LEED program was initiated by The U.S. Green Building Council (USGBC) in 2000. For details, visit
http://www.usgbc.org/home.
65
Benchmarking is an effective way to increase the awareness and improve the information transparency of
buildings. See “Driving Transformation to Energy Efficient Buildings: Policies and Actions”, 2nd Edition, the
Institute for Building Efficiency,
http://www.institutebe.com/InstituteBE/media/Library/Resources/Energy%20and%20Climate%20Policy/DrivingTransformation-to-EE-Buildings.pdf>
90
(2) disclosure of building energy benchmarking information to the public and market. By
requiring building owners/operators to collect information on energy use and learn their
performance relative to similar buildings and by enabling participants in the market (such as
buyers and tenants) to get access to these information so that they can distinguish buildings with
different energy use performance, the building benchmarking policy aims to incentivizing
building owners to improve building energy efficiency and in particular to invest in building
renovation and adopting energy efficient building technologies.
This study is a preliminary research on another information-related aspect of the building
benchmarking policy, namely, increasing market participants’ awareness and attentiveness to
energy efficiency. The enactment and implementation of the building benchmarking law by the
city government involves significant public discourses, media exposure and campaign that can
increase public awareness about the issue of building energy efficiency. If with the passage of
the benchmarking law market participants, such as buyers and tenants, become more attentive to
existing information about building energy efficiency and more likely to distinguish buildings
based on such information, there might be changes in the sale prices or rental rates in the
building markets that reflects the change in market participants’ attentiveness to information on
building energy efficiency, shortly following the enactment of the policy and before additional
information about building energy use that is mandated by the policy become accessible to the
market.
To test the hypothesis that the passage of Benchmarking law increases market
participants’ awareness about building energy efficiency which in turn leads to market responses
to already-existing information on building energy efficiency, I investigate the market response
to LEED or EnergyStar buildings in the City of Philadelphia, in the aftermath of the passage of
91
the Benchmarking Law by the city even before building energy consumption and efficiency
information is reported and well prior to the disclosure of such information to the market.
Although these buildings had obtain LEED or EnergyStar certificates before the Benchmarking
Law and thus the information about their superior performance in energy efficiency and
sustainability had been available in the market, I find that, following the passage of the Law, the
rental rate significantly increased for commercial LEED or EnergyStar buildings that are in the
rent market and the tenants pay energy bills, but not for LEED or EnergyStar buildings whose
owners pay energy bills. The results suggest that the passage of the Law rendered tenants to be
more attentive to the energy efficiency performance of the building they lease, and in the case of
tenants paying energy bill, tenants are willing to pay a higher rent.
This paper makes germane contribution to the literature on market failure due to
information asymmetry in general and to a strand of studies on mandatory information disclosure
in particular. Although there are theoretical debates on whether mandatory information
disclosure is necessary, 66 empirical studies suggest that market participants are responsive to
disclosed information, leading to improved social welfare.67 Unlike these existing studies that
66
A strand of theoretical literature, initiated with the seminal work by Akerlof (1970), argues for
mandatory information disclosure. Farrell (1985) points out that in used car market, sellers won’t disclose
information voluntarily, although consumers welcome more information disclosure. However, other
studies, represented by Grossman (1981) and Jovanovic (1982) argue that mandatory disclosure is
unnecessary. Fishman and Hagerty (2013) generalize the standard model including the consumers
understanding to sellers’ disclosure and pointed out that, in a market that a low percentage of consumers
could understand the information, a voluntary disclosure may not happen and a mandatory disclosure
would be beneficial to the informed consumers and harmful for sellers.
67
Zarkin et al. (1993) studies the change in consumer behavior in response to disclosed information due
to The Nutrition Labeling and Education Act in 1990. Mathios (2000) find that the regulation on
disclosure of nutritional profile has significant impact on the consumer food choices. Mathios, A. D.
(2000). "The Impact of Mandatory Disclosure Laws on Product Choices: An Analysis of the Salad
Dressing Market*." The Journal of Law and Economics 43(2): 651-678.Mathios, A. D. (2000). "The
Impact of Mandatory Disclosure Laws on Product Choices: An Analysis of the Salad Dressing Market*."
The Journal of Law and Economics 43(2): 651-678.Mathios, A. D. (2000). "The Impact of Mandatory
Disclosure Laws on Product Choices: An Analysis of the Salad Dressing Market*." The Journal of Law
and Economics 43(2): 651-678.. Focusing on restaurant hygiene grading and displaying in Los Angeles,
92
investigate the impacts of information disclosure after the information is disclosed to the public,
this study shows that the passage of mandatory information disclosure policy could increase
people’s attentiveness to information that is already available, well before some additional
information is disclosed.
This paper is the first empirical study on the impacts of the building benchmarking law
which has gained popularity among cities in the U.S. and worldwide. Existing studies on the
building benchmarking policy primarily focus on methodology and practical issues for buildings
to conduct benchmarking (Chung, Hui et al. 2006; Chung 2011). Having reviewed regulatory
and voluntary building energy efficiency policies, Lee and Yik pointed out that including factors
that may affect building energy use and updating the current database are challenges (Lee and
Yik 2004).
The rest of this paper is organized as follows. Section 2 introduces the background on the
Philadelphia Benchmarking Law, and Section 3 presents the hypothesis, empirical strategy and
data. Results are discussed in Section 4, followed by Section 5 where I conclude.
Jin and Leslie (2003) show that consumers are sensitive to restaurants’ hygiene conditions, and the
hygiene conditions of restaurants were improved because of the mandatory grade card display.
Dumanovsky, Huang et al. (2010) shows that posting calorie information on the menu in New York
increases consumers awareness and more people are using these information everyday. Studies also
suggest that prior cognation is necessary for significant change in consumer behavior, and in the case of
nutrition labeling, it is believed that consumer has little insights on the raw information disclosed, and
they need to be educated to digest these information (Days 1976; Jacoby et ah, 1977). Refer: Day, G. S.
(1976). "Assessing the effects of information disclosure requirements." The Journal of Marketing: 42-52,
Jacoby, J., R. W. Chestnut, et al. (1977). "Consumer use and comprehension of nutrition information."
Journal of Consumer Research: 119-128, Grunert, K. G. and J. M. Wills (2007). "A review of European
research on consumer response to nutrition information on food labels." Journal of Public Health 15(5):
385-399..
93
4.2 Building Benchmarking Law in Philadelphia
Many cities or states around world have implemented building benchmarking policies
since 1990s. 68 The first building benchmarking legislation was enacted in Denmark in 1997,
which requires certification from homes and buildings. It was then followed by cities in other
countries including Turkey, Norway, and Brazil. In the U.S., the first benchmarking law was
introduced in California in 2007. Since then the state of Washington and seven major cities,
including City of Philadelphia, have implemented the law. See Table 1 for detailed information
about the enactment of the benchmarking law in the U.S.
The building energy benchmarking and disclosure law in Philadelphia (hereafter, the
Philadelphia Benchmarking Law) was implemented as the energy consumption of commercial
buildings in Philadelphia increased in the past few years. As the population growth and building
construction and renovation pick-up in Philadelphia after 2007, the overall building energy usage
of Philadelphia in 2012 is significantly higher than the 2006 level even after accounting for
population and building area growth69. The residential energy use is reported to remain stable
during this period, so the commercial buildings become the main target in solving this problem
and a new building code dealing with the existing commercial buildings energy use is urgent.
Therefore, Philadelphia Benchmarking Law targeting at large commercial buildings is passed
unanimously in 2012.
In addition, Philadelphia Benchmarking Law is a critical part of the city’s sustainable
plan Greenworks Philadelphia. The plan was initiated by the Mayor of Philadelphia, Michael A.
Nutter, back in 2009. It has set 15 targets to be met by 2015 with the objective of making
68
See Pérez-Lombard et al. (2009) for a review of building benchmarking..
According to the Greenworks Philadelphia 2013 progress report, the baseline of building energy use in 2006 is
122.06 Trillion BTUs, and the number of 2012 is 129.36 Trillion BTU. The report is available at:
http://www.phila.gov/green/pdfs/GW2012Report.pdf.
69
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Philadelphia “the Greenest city in America”. Some targets are directly related to the energy
consumption and efficiency, including lowering citywide building energy consumption by 10%
from 2006 levels and reducing city government energy consumption 30% from 2008 levels. With
the plan, City of Philadelphia has made some progress in municipal energy use reduction since
2009. However, the building energy consumption has consistently increased, making the
Philadelphia Benchmarking Law a necessity to the city and the plan.
The Building Benchmarking Law in the City of Philadelphia was passed in June, 2012. It
requires the nonresidential spaces larger than 50,000 square feet to report the energy and water
use every year, and these information will be disclosed to the public afterwards70. In the settings
of the city of Philadelphia, the demand of LEED and EnergyStar buildings and ordinary
buildings are used as a proxy of the public awareness in EEBs, so the changes on rental rates of
those buildings would reflect the variation of consumer response after the Benchmarking Law.
On June 21, 2012, the Bill No. 120428 (widely known as Philadelphia Benchmarking
Law), is unanimously passed by Philadelphia City Council, to amend the Philadelphia Code of
“Energy Conservation” in Chapter 9-3400. This ordinance applies to any commercial building
containing 50,000 or more square feet and commercial portion of mixed-used buildings with
50,000 or more square feet. According to the ordinance, all covered buildings should report their
building energy use (including electric, natural gas, stream, and oil), water usage along with
building characteristics71 to the city through “Portfolio Manager” system72. The implementation
of the ordinance is managed by the City’s Office of Sustainability (COS, hereafter). Starting in
70
Since October, 2013, the building owner will need to submit their report to the Mayor’s Office annually. The
information of the benchmarking law in the City of Philadelphia could be found on the website of
Phillybuildingbenchmarking. <URL: http://www.phillybuildingbenchmarking.com/index.php/home>
71
The building characteristics include building’s age, uses(s), operating hours, etc.
72
Portfolio Manager is a web based system initiated by U.S. Environmental Protection Agency. Details could be
found at the website: https://www.energystar.gov/buildings/facility-owners-and-managers/existing-buildings/useportfolio-manager.
95
November of 2013, owners will be required to annually submit above information to COS.
Failure to comply would face a fine of $300 in first 30 days following the compliance date, and
$100 per day following the initial 30 days. Starting in 2014, COS would make results of the
reporting available to the public73.
Figure 4-1 shows the timing of important events during the passage and implementation
of the Philadelphia Benchmarking Law. The bill is proposed in May, 2012 by COS partnered
with Councilor Blondell Reynolds Brown. The Public Hearing on this bill is held on June 5 th,
201274. Five Philadelphia Councilors along with a Program Manager from U.S. Environmental
Protection Agency and the Director of Building Energy Performance Policy from Institute for
Market Transformation presented during the Hearing. Many parties across the city including
property management firms and building owners/developers have shown their supports to the bill,
and issues related to benchmarking tools, information disclosure, as well as the benchmarking
timeline are intensely discusses throughout the Hearing. After the bill is passed in June 21, 2012,
this bill is widely exposed in news reports75. In addition, a specialized website with a focus on
providing Philadelphia Benchmarking Law information as well as reporting and compliance
guidance is available to the whole society online. The Mayor’s Office of Sustainability, along
with Energy Efficient Building HUB76, a U.S. Department of Energy innovation cluster based in
73
For more details about the implementation of the benchmarking law in the city of Philadelphia, please refer to the
website: http://www.phillybuildingbenchmarking.com/.
74
The transcript of the Hearing is available on the website of Philadelphia government, please refer to:
http://legislation.phila.gov/transcripts/Public%20Hearings/environment/2012/en060512.pdf.
75
The local website of Philadelphia, Philly.com, has posted an article titled: Philadelphia Council to require energy
reports. It could be reached at its website: http://articles.philly.com/2012-06-22/business/32353149_1_energyreports-energy-star-efficiency. The benchmarking law is also covered by the Institute for Market Transformation.
The link is here: http://www.imt.org/news/the-current/Philly-Passes-Benchmarking-Bill.
76
The Energy Efficient Building HUB is one of the Department of Energy’s (DOE) Innovation Hubs, led by Penn
State University. It was established on February 1, 2011, and brings scientists cross the country to collaborate more
efficiently on the energy efficient building projects. In April, 2014, it was renamed as Consortium for Building
Energy Innovation (CBEI). Website: http://www.cbei.psu.edu/index.html.
96
Philadelphia, has organized a number of workshops periodically77. Those workshops conveyed
the information of the benchmarking law to the players in the commercial building market, and
help different parties in the society to get involved in the implementation process of
benchmarking. The first energy and water use report of each covered commercial building is due
in November of 2013. The report from the city on the energy use results would be available to
the society in 2014, and the compliance of the buildings is due in June every year ever since.
4.3 Hypothesis, Empirical strategy and Data
4.3.1 Hypothesis and testing
This paper investigates whether the passage and implementation of a building
benchmarking policy increase market participants’ awareness of building energy efficiency.
With increased attentiveness to building energy efficiency, building users might pay more
attention to their utility bills and be more inclined to choose energy efficient buildings. Thus,
even before the information about building energy use is actually disclosed as mandated by the
Law, the demand for building that have been marked as “energy efficient buildings” would
therefore go up.
To test this hypothesis, I empirically explore the change in the rental rate of LEED and
EnergyStar certified buildings with the passage and implementation of the Philadelphia
Benchmarking Law. The LEED and EnergyStar programs issue “Green” certificate to buildings
with superior performance in energy performance and sustainability. I focus on those LEED and
EnergyStar buildings that had been issued certificates before the passage of the benchmarking
law, so that the information about these buildings’ energy performance had already been
77
The information of upcoming workshops held by the City Office is available online:
http://www.phillybuildingbenchmarking.com/events/.
97
available to the market, though market participants may not pay attention to such information. If
the passage of the Law increases the awareness of the public about building energy efficiency,
the demand for these LEED and EnergyStar buildings should go up following the passage of the
Law and before additional information about building energy efficiency is disclosed to the
market.
More specifically, I look at the rental rates of those LEED and EnergyStar buildings in
Philadelphia. I choose the rental market for three-fold reasons. First, the consumer response to
EEBs in the rental building market is distinct and critical to the effect of Benchmarking Law. In
owner occupied buildings, the owners are the users who pay the utility bill. Building owner who
paid for the energy efficient investment would easily get compensated from the energy saving.
As long as energy efficient buildings construction/retrofit is profitable, the owner occupied
building would easily get involved in the market. The story in the rental market is different. In
many lease types, because the tenant will pay utility bills themselves, the owners of the buildings
would not directly earn benefit from energy efficiency investment78. Even though some argued
that the building value could be improved by its energy efficiency characteristics, this
improvement could only be accredited through transactions with users who value building
energy performance (Brounen and Kok 2011). Second, the rental market is vibrant and would
timely reflect the changes in the market and consumer preference in a short time. This feature is
critical for this study as the benchmarking law in Philadelphia is relatively new and the quick
78
For commercial buildings, there are several lease types. For example, in “Net Lease”, the tenant needs to pay the
expenses of the property lease, such as taxes, maintenance, and utilities. More detailed information could be found at
the website: http://www.tokcommercial.com/MarketInformation/LearningCenter/LeaseTypesDefined.aspx
98
reaction of the rental market to the benchmarking law would enable me to observe those changes
timely. Third, rental rate is available to the public.79
With the passage of the benchmarking law, the demand of energy efficient buildings
would increase significantly, and the rental rate of LEED and EnergyStar buildings would go up
accordingly. In a short time after the Benchmarking Law was introduced, the supply of the EEB
market remains constant because new and retrofit project on energy efficient buildings would
take a certain time to be available to the market. On the other hand, as the public awareness of
energy efficient buildings got improved because of the Benchmarking Law, the current tenants
may incline to choose energy efficient buildings instead of ordinary buildings. Therefore, the
rental rate of the LEED and EnergyStar buildings in this study would increase significantly after
the benchmarking law.
4.3.2 Empirical strategy
To test the hypothesis that the rental rate for LEED and EnergyStar building would
increase significantly after the implementation of benchmarking law, both the Difference-inDifference (Diff-Diff) model and Triple Difference (D-D-D) model is applied in this study.
With the Diff-Diff model, the change in the rental rate before and after the Benchmarking
Law for the LEED and EnergyStar certified buildings where tenants pay energy bills is compared
with the rented LEED and EnergyStar certified buildings where the owners pay the bills. The
idea behind using LEED and EnergyStar buildings where utility is covered by the landlord as a
control is that if the bill is paid by tenants, then the energy efficiency characteristics might be
79
Besides the rental rate data, the transaction data of buildings might also be a good candidate essentially.
However, when taking the time span of transactions and other factors besides the building characteristics
that would affect the transaction price into account (for example, future building market, land use
planning, etc.), I believe the rental rate of rental market is a better choice for this study.
99
more attractive to tenants because it would save money in their pockets. But if the utility is
covered in the rent, then the tenants may be less interested, because no matter how energy
efficient the buildings are, there is no direct impact on their own benefit. This feature is believed
to play an important role in the rental market.
The specification of the model is as following.
(1)
On the left,
is the rental rate of property i in year t. Benchmarking law is
represented by
, which is 0 before the benchmarking law is passed in June
201280, and changed to 1 afterwards. I also include the utility payment status into consideration,
and
equals to 1 if the utility bill is paid by tenants and 0 if the landlord pays. Building
fixed effect variable
and time fixed effect
are both included in this specification.
is the
error term.
Coefficient
is of particular interest to me. For the LEED and EnergyStar certified
buildings, I would expect a positive and significant effect. As the consumer awareness in EEB
got improved, I believe the consumers would more likely choose those certified buildings, so the
rental rate after the Benchmarking Law would increase accordingly.
As a robustness check, I also run the same D-D regression for the group of rental
buildings in Philadelphia that are neither LEED nor EnergyStar buildings. I would not expect
any significant increase after the passage of the Law for these non-LEED and non-EnergyStar
buildings whose energy bills are paid by tenants, in comparison with those whose utility is paid
by the building owners.
80
The time when the benchmarking law is actually implemented is taken as the action date in this study. Because
our data is organized quarterly, the treatment effect is taken since the third quarter of 2012.
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Finally, I put all the building data together and run a D-D-D regression, comparing the
rental rate before and after the benchmarking law between LEED and EnergyStar certified
buildings and ordinary buildings. The econometric specification is as following.
(2)
Compared with specification (1), some elements are added to this specification. As two
groups, LEED and EnergyStar buildings and ordinary building, are put together, a dummy
variable indicates when and whether the building is certified is added as
. For LEED
and EnergyStar buildings, it is 0 before the building is certified, and changed to 1 afterwards. For
ordinary buildings,
interesting
to
me.
equals to 0 at all times. The three interaction term is particular
Three
two
term
interactions
,
are included in the regressions as
(
,
) and individual benchmarking variable
.
Coefficient β in specification (2) is the coefficient I paid attention. After all the control
variables are included, β gives how the rent of LEED and EnergyStar certified buildings in which
utility is paid by tenant has changed after the implementation of benchmarking law, in
comparison with the ordinary buildings with same utility payment status. If the hypothesis of this
paper is true, I would expect a significant and positive β in the regressions.
4.3.3 Data
The data structure is panel data, which consists of all the rentable commercial buildings
in the city of Philadelphia which is larger than 5,000 square feet. The rental rate data are
quarterly, so the whole dataset is organized on a quarterly basis. The time span of this study is
101
from the first quarter in 2008 (2008 Q1) to the second quarter in 2013 (2013 Q2) 81, which covers
the time the benchmarking law in the city of Philadelphia is implemented, June 2012. The
historical rental rates are extracted from the CoStar building database 82 . Besides, the CoStar
database also comprises some general information of the rental lease, such as what type of lease
the landlord is offering, from which I could know who would pay for the utilities of the buildings.
4.4 Results
In this section, all the regression results in this study are listed. To examine the
hypothesis, I have applied the econometric model that illustrated in previous section along with
different restrictions. The results are supportive to the hypothesis that the passage of the
benchmarking law has increased the market participants’ awareness in energy efficient buildings,
and the rental rate of LEED and EnergyStar certified buildings has risen.
4.4.1 Graphic Evidence
The trend of the rental rate of LEED or EnergyStar certificated buildings in comparison
with ordinary buildings are shown in Figure 4-2. In this figure, before the second quarter of 2012,
the trend of ordinary building rental rate is up or close to that of LEED and EnergyStar buildings.
Since the third quarter of 2012, certified buildings move up and over the trend of ordinary
buildings. It indicates that the benchmarking law may actually stimulate the demand of energy
efficient buildings such as LEED and EnergyStar buildings, which reflected the increased
81
I choose 2008 to eliminate the possible impact of financial crisis on the building rental market. Constrained by the
data availability issue, the time window is shut down at the second quarter of 2013. Because the benchmarking law
is passed in June 2012, this time span has left me one year to observe the trend in the building rental market
although it’s not ideal.
82
CoStar real estate build database is developed by the CoStar Group (NASDAQ: CSGP). It includes a larger part of
the real estate information of the commercial buildings across the US and UK, including the location, size,
certification, rental and transaction records, etc. The information I used in this paper is extracted online at:
http://www.costar.com/.
102
awareness of market participants to the benchmarking law, well before the disclosure of building
energy use information.
In LEED and EnergyStar Buildings, I observe a significant different trend in rental rate of
buildings that utilities are paid by tenants and paid by owners. In Figure 4-3, the average rental
rate of buildings that utilities are paid by tenants is shown in comparison with the average rental
rate of buildings whose utilities are covered by owners. Before the passage of Philadelphia
Benchmarking Law in 2012 Q2, the trend of rental rate of buildings whose utilities are covered
by the owners is mostly above that of the buildings that utilities are tenant paid. After 2012 Q2,
the rental rate of tenant paid buildings went up constantly while the price of buildings that
utilities paid by owners dropped. The increased rental rate of tenant paid utility buildings is
supportive to the hypothesis that, as the passage of Philadelphia Benchmarking Law, the market
participants are more aware of the energy efficiency of buildings and they would incline to
choose LEED and EnergyStar buildings and benefit from lower utility bills.
4.4.2 Regression Results
To examine the hypothesis of this study, both Diff-Diff and D-D-D model are used
together with different settings in this part. First, I use two time windows, 2008 to 2013 and 2011
to 2013. Second, two constrains are further applied, which are if there is any missing rental rate
data after the second quarter of 2012 and if the LEED and EnergyStar certificate is issued after
2012 Q2. The no missing data constrain is helpful in the observation of treatment effect, and the
certified buildings issued after 2012 Q2 (when the Benchmarking Law is enacted) are excluded
in order to eliminate the noises brought about. Besides, I do two pseudo tests at 2010 Q2 and
2010Q4, to eliminate the possible random trend caused by other factors beyond my control in
this study.
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Basic model
Based on the basic models, I analyzed the hypothesis that the rental rate of LEED and
EnergyStar certified buildings trend up after the passage of benchmarking law, and the results
are supportive to the hypothesis. I first used the Diff-Diff model and Table 4-2 shows the results
of LEED and EnergyStar certified buildings. On the left are the regression results using dataset
from the 2008 Q1 to 2013 Q2, and in the right three columns, the dataset from 2011 Q1 to 2013
Q2 is used. In column (1), all the buildings in the dataset are included, and the coefficient of the
interaction term is positive and significant. It indicates that after the benchmarking law, the rental
rate of LEED and EnergyStar buildings that utilities are paid by tenants increased significantly.
In the second column, the no missing data constrain, if there is any missing value after 2012 Q2,
is applied to the dataset. In column (3), I further exclude the buildings that certified after 2012
from the dataset. The coefficient I am interested in is more significant and bigger in magnitude in
column (2) and (3) compared with the coefficient in column (1). I do similar regression in
column (4) to (6) with 2011 Q1 to 2013 Q2 dataset as a robustness check. The results in all six
columns are consistent and the coefficients of the interaction term are all positive and significant,
providing supportive evidences to the hypothesis. Similarly, I use Diff- Diff model in the
analysis of ordinary buildings as another robustness check. The results of the ordinary buildings
are summarized in Table 4-3. Unlike the significant results I found in the LEED and EnergyStar
buildings, the coefficients of ordinary buildings are not significant. In comparison, it could be
found that, after the implementation of Philadelphia Benchmarking Law, the rental rate of LEED
and Energy certified buildings in Philadelphia increased significantly, but no significant trend is
observed in ordinary buildings.
104
Besides the Diff-Diff model, I put all the data together and use the D-D-D model to test
my hypothesis. The results of D-D-D model are listed in Table 4-4. The left two columns are the
results with 2008 Q1 to 2013 Q2 dataset, and the right two columns show the results with 2011
Q1 to 2013 Q2 dataset. As robustness checks, I show the results with no missing data constrain
(in column (1) and (3)), and with both the missing data constrain and no certified building after
2012 Q2 constrain (in column (2) and (4)). In the D-D-D model, the coefficient of the three
interaction term is the coefficient I am interested inAcross the four columns, the coefficients are
consistently positive and significant, which supports the hypothesis that compared with the
ordinary buildings, the rental rate of LEED and EnergyStar certified buildings that energy bills
are paid by tenants increased significantly after the passage of Benchmarking Law.
To further check the robustness of the results, I do another group of robustness exercises
by excluding the data of 2012 Q2. Philadelphia Benchmarking Law is passed in June, 2012,
which sits in 2012 Q2, and in previous regressions, I use the data of 2012 Q2 as non-treated. I am
a little worried that the rental rate of 2012 Q2 may have already reflected some (although trivial)
impact of the benchmarking law, which may bias my analysis. So in this group of robustness
check, the data of 2012 Q2 are excluded from the dataset in order to eliminate this ambiguity.
The results with the new dataset are summarized in the Appendix, and the increase of LEED and
EnergyStar buildings whose energy bills are paid by tenants after Philadelphia Benchmarking
Law is still significant and positive, which is supportive to my hypothesis.
Pseudo tests
In addition to the basic model, I introduced two pseudo tests to further test my hypothesis.
I use the pseudo tests to address two concerns relevant to this study. The first one is if there is
any other potential variation in year 2012 which would impact the rental rate of LEED and
105
EnergyStar Buildings. The other one is if the rental rate fluctuation has any correlation with
seasons, which may impede the evidences in the previous part. Therefore, I choose another two
time spots 2010 Q2 (different year same season) and 2010 Q4 (different year different season) to
test my hypothesis from the opposite side and see if there is any rental rate change in LEED and
EnergyStar buildings after those two fake treatment83. To eliminate the noises of benchmarking
law after 2012 Q3, the time window of the analysis in this part is 2008 Q1 to 2012 Q2.
Similarly, Diff-Diff and D-D-D regressions are applied, as specified in Equation (1) and
(2), in the two pseudo tests. In those tests, the benchmarking variable is taken placed by the
pseudo treatment variable, which equals to 0 before the time of pseudo treatment and switches to
1 afterwards.
The pseudo test results of LEED and EnergyStar certified building group with Diff-Diff
model are summarized in Table 4-5. Results of the first pseudo test at 2010 Q2 time spot are on
the left and the right three columns show the results of the second pseudo test at 2010 Q4.
Similar to the basic model, the results in column (1) and (4) are done with no data constrain. In
column (2) and (5), the buildings with missing rental rate data in one year after the fake
treatment effect is excluded, and the buildings that got LEED and EnergyStar certificate after
2010 are further excluded in column (3) and (6). In comparison with the results in Table 4-5,
even though the settings in the basic model and pseudo tests are almost the same except that the
time when the treatment is made, the results are totally different. In the basic model analysis,
significant and positive effects after the implementation of Benchmarking Law in 2012 Q2 are
observed, but this trend is not repeated in any of the pseudo tests. Similarly, I do the same
83
One of the two time spot is set at the same month in which the benchmarking law is passed, to track the trend of
rental rate during that specific period of the year. The other time spot is put at the end of the year. I do this
intentionally because I am curious how the rental rate would change at the end of the year and see if there is any
trend in the rental rate fluctuation throughout the year.
106
pseudo test to the ordinary building group (Table 4-6), and as expected no significant results are
found.
Also, pseudo tests are also made with the D-D-D model, and the results are shown in
Table 4-7. To testify the hypothesis, I would expect no significant result which means none of
the group show significant change in rental rate at the two fake time spots. From another angel,
this trend would support the hypothesis in that it is the Benchmarking Law that made the
difference. In Table 4-7, no significant result on the coefficient of the three interaction term is
found, which is favorable to my hypothesis.
4.5 Conclusion
This paper provides some preliminary evidences that the passage of a mandatory
information disclosure law increases the awareness of market participants who become more
attentive to the information that already exists and is available. I study the Building
Benchmarking Law passed in May 2012 in Philadelphia, requiring commercial building owners
to report information on building energy use to the city which is then disclosed to the public. I
investigate the rental rate for existing LEED and EnergyStar buildings in Philadelphia following
the enactment of the Law and well before new information disclosed to the market due to the
Law. The main finding is that after the implementation of benchmarking law, the rental rate of
the LEED and EnergyStar buildings where tenants pay energy bills has increased significantly,
relative to that of LEED and EnergyStar buildings where owners pay bills, suggesting that the
passage of the Law increased tenants’ awareness about building energy efficiency and, in the
case of tenants paying energy bills, tenants are more willing to pay a higher rent for LEED and
EnergyStar buildings that are more energy efficient.
107
Previous studies on mandatory information disclosure policies often focus on the impacts
of the availability of new information. This study, however, suggests that a mandatory
information disclosure policy would impact the market even before new information becomes
available, by increasing market participants’ attentiveness to already-existing information in the
market. As such, it has interesting implication for governments in terms of how to maximize
such effects during the process of passage and implementation of a policy.
This study would also contribute to the understanding of benchmarking law and its
impacts on energy efficient building market, which is essential for the government, building
owners, and EEB technology developers. Dislike many other industries, building industry is a
unique industry in the complicated interaction between its inner systems, which make the energy
use information of buildings inexplicit to both building owner and users. This study on
Philadelphia Benchmarking Law takes a peek in this industry and tells how the energy use
information disclosure would impact the consumer behavior in this market, which is important
for government in policy making, design, and improvement in the future. Besides, the consumer
response in EEB market shown in this study would in turn encourage the building owners in
adopting EEB technologies and systems in their buildings. It would serve as a signal for all
parties, regulators, building owners, and technology providers across the building industry.
In the future, as more building energy efficiency and market performance information
become available to the public, more studies would be expected, either on similar consumer
response with better data availability and longer time span, or on other benchmarking related
topics, such as the feedback of energy consumptions in buildings after benchmarking law and the
energy efficiency information become public to the society.
108
Figure 4-1 Timeline of Philadelphia Benchmarking Law
Bill
introduced
initially
City
Council
Hearing on
the bill
Bill first
reading in
full council
City
Council
passed
legislation
First
compliance
deadline
Annual
compliance
deadline
Year
May 17th,
2012
June 5th,
2012
June 7th,
2012
June 21st,
2012
109
November
2013
June
2014
20
29.5
19.5
29
19
28.5
18.5
28
18
27.5
17.5
Y13Q2
Y13Q1
Y12Q4
Y12Q3
Y12Q2
Y12Q1
Y11Q4
Y11Q3
Y11Q2
Y11Q1
Y10Q4
Y10Q3
Y10Q2
Y10Q1
Y09Q4
Y09Q3
Y09Q2
Y09Q1
Y08Q4
Y08Q3
17
Y08Q2
27
Rent of Ordinary Buildings ($/sf/year)
30
Y08Q1
Rent of Certified Buildings ($/sf/year)
Figure 4-2 Average Rental Rate of Certified Buildings versus Ordinary Buildings
Time
Certified Building
Ordinary Building
Notes: The data of rental rate and building information are extracted from the CoStar commercial building database,
and cleaned and calculated by the author.
110
Y13Q2
Y13Q1
Y12Q4
Y12Q3
Y12Q2
Y12Q1
Y11Q4
Y11Q3
22.5
Y11Q2
23
23.25
Y11Q1
23.5
23.75
Y10Q4
24
24.25
Y10Q3
24.5
24.75
Y10Q2
25.25
Y10Q1
25
Y09Q4
25.5
25.75
Y09Q3
26
26.25
Y09Q2
26.5
26.75
Y09Q1
27.25
Y08Q4
27
Y08Q3
27.5
27.75
Y08Q2
28.25
Y08Q1
Rental rate of Buildings ($/sf/year)
Figure 4-3 Average Rental Rate of Certified Buildings with Two Types of Utility Payment
Time
Buildings Tenant Pay Utilities
Buidlings Landlord Pay Utilities
Notes: The data of rental rate and building information are extracted from the CoStar commercial building database,
and cleaned and calculated by the author.
111
Table 4-1 Benchmarking Law in the United States
Jurisdiction
Cities
Buildings to be Benchmarked
Disclosure
Commercial
Multi-Family
Extent of
Disclosure
Period of
Reporting
Philadelphia
50K+
Not required
Public
Annual
Austin
10K+
Not required
Local government
and potential
owners
Annual
District of Columbia
50K+
50K+
Public
Annual
Minneapolis
50K+
Not required
Public
Annual
New York City
50K+
50K+
Public
Annual
San Francisco
10K+
Not required
Public
Annual
Annual
Seattle
10K+
5+ Units
Local government,
tenants and
transactional
counterparties
Washington
10K+
Not required
Transactional
counterparties
At time of sale,
lease, or financing
California
1K+
Not required
Local government
and transactional
counterparties
At time of sale,
lease or financing
States
*All units are in square feet unless otherwise specified.
Source: Philybuildingbenchmarking.com
112
Table 4-2 Results with LEED and EnergyStar Buildings
With data from 2008 Q1 to 2013 Q2
VARIABLES
With data from 2011Q1 to 2013 Q2
(1)
(2)
(3)
(4)
(5)
(6)
Benchmarking*Tenant
1.002**
1.122**
1.177**
0.861***
0.933***
0.979***
(0.412)
(0.415)
(0.442)
(0.290)
(0.285)
(0.305)
Constant
25.75***
25.86***
26.35***
25.47***
25.57***
26.11***
(0.427)
(0.451)
(0.474)
(0.138)
(0.140)
(0.154)
Year fixed effect
Y
Y
Y
Y
Y
Y
Building fixed effect
Y
Y
Y
Y
Y
Y
Observations
R-squared
472
447
414
216
210
190
0.074
0.090
0.103
0.190
0.202
0.213
Number of building
24
21
19
24
21
19
Notes: A. Robust standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. B. In column (1), all the data from 2008 Q1 to 2013 Q2 are
included, and in column (4), only data from 2011 Q1 to 2013 Q2 are included. C. In column (2) and (5), with the same model, a constrain is
added to the data used in (1) and (4), which is only the buildings with no missing data after 2012 Q3 would be considered. D. In column (3) and
(6), still the same model, one more constrain is added to the data used in (2) and (5), which is only the buildings that are certified before 2012 Q3
would be considered.
113
Table 4-3 Results with Ordinary Buildings
With data from 2008 Q1 to 2013 Q2
VARIABLES
Benchmarking* Tenant
(1)
(2)
With data from 2011Q1 to 2013 Q2
(3)
(4)
-0.216
-0.272
0.0440
0.0397
(0.389)
(0.392)
(0.286)
(0.289)
18.34***
18.33***
18.07***
18.20***
(0.255)
(0.224)
(0.145)
(0.151)
Year fixed effect
Y
Y
Y
Y
Building fixed effect
Y
Y
Y
Y
Observations
3,560
3,275
1,906
1,766
R-squared
0.013
0.017
0.010
0.012
Constant
Number of building
253
197
248
197
Notes: A. Robust standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. B. In column (1), all the data from 2008 Q1 to 2013 Q2 are
included, and in column (3), only data from 2011 Q1 to 2013 Q2 are included. C. In column (2) and (4), with the same model, a constrain is
added to the data used in (1) and (3, which is only the buildings with no missing data after 2012 Q3 would be considered.
114
Table 4-4 Results of D-D-D Model for all Buildings
With data from 2008 Q1 to 2013 Q2
VARIABLES
Benchmarking*Certified*Tenant
Benchmarking*Tenant
Benchmarking*Certified
Certified*Tenant
Certified
With data from 2011Q1 to 2013 Q2
(1)
(2)
(3)
(4)
1.220**
1.282**
0.910**
0.910**
(0.595)
(0.603)
(0.429)
(0.430)
-0.188
-0.191
0.102
0.102
(0.371)
(0.372)
(0.280)
(0.281)
-0.680
-0.682
-0.647**
-0.647**
(0.463)
(0.464)
(0.314)
(0.314)
0.251
0.317
-0.470*
(0.374)
(0.385)
(0.248)
-0.00385
0.00325
(0.232)
(0.234)
19.23***
19.23***
19.00***
18.96***
(0.200)
(0.201)
(0.135)
(0.136)
Quarter fixed effect
Y
Y
Y
Y
Building fixed effect
Y
Y
Y
Y
Observations
3,722
3,689
1,976
1,956
R-squared
0.016
0.016
0.014
0.014
218
216
218
216
Constant
Number of building
Notes: A. Robust standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. B. In column (1) and (2), the time window is from 2008 Q1 to
2013 Q2, and in column (3) and (4), the time window is from 2011 Q1 to 2013 Q2. C. In column (1) and (3), the buildings with no missing data
after 2012 Q2 are included. D. In column (2) and (4), one more constrain that the buildings that are certified before 2012 are added to the data
used in column (1) and (3).
115
Table 4-5 Results with LEED and EnergyStar Buildings in Pseudo Tests
Pseudo Test 1 with data from 2008 Q1 to 2012 Q2
VARIABLES
Tenant*Pseudo1
(1)
(2)
(3)
0.327
0.583
0.740
(0.329)
(0.361)
(0.454)
Tenant*Pseudo2
Constant
Observations
R-squared
Pseudo Test 2 with data from 2008 Q1 to 2012 Q2
(4)
(5)
(6)
0.465
0.753*
0.914*
(0.332)
(0.378)
(0.472)
25.55***
25.90***
27.15***
25.47***
25.83***
27.12***
(0.248)
(0.272)
(0.384)
(0.242)
(0.267)
(0.383)
472
386
254
472
374
242
0.027
0.053
0.156
0.039
0.075
0.179
Number of building
24
18
12
24
17
11
Notes: A. Robust standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. B. Across column (1) to (6), the time window of data is from
2008 Q1 to 2012 Q2. C. Column (1) to (3) are the results of pseudo test 1, in which the fake time spot is set at the end of 2010 Q2, the season
when Benchmarking Law in Philadelphia is enacted. D. Column (4) to (6) are the results of pseudo test 2, in which the fake time spot is set at the
end of 2010 Q4, the opposite season when Benchmarking Law in Philadelphia is enacted. E. In column (2) and (5), a constrain is added to the
data used in (1) and (4), which is only the buildings with no missing data in the year after the fake time spot would be considered. D. In column
(3) and (6), one more constrain is added to the data used in (2) and (5), which is only the buildings that are certified before 2010 would be
considered.
116
Table 4-6 Results with Ordinary Buildings in Pseudo Tests
Pseudo Test 1 with data from 2008 Q1 to 2012 Q2
VARIABLES
Tenant*Pseudo1
(1)
(2)
-0.288
-0.0939
(0.303)
(0.268)
Tenant*Pseudo2
Pseudo Test 2 with data from 2008 Q1 to 2012 Q2
(3)
(4)
-0.266
-0.143
(0.278)
(0.277)
18.55***
18.06***
18.54***
18.15***
(0.219)
(0.208)
(0.202)
(0.231)
Observations
3,560
1,963
3,560
1,915
R-squared
0.012
0.024
0.012
0.025
Constant
Number of building
253
100
253
101
Notes: A. Robust standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. B. Across column (1) to (4), the time window of data is from
2008 Q1 to 2012 Q2. C. Column (1) to (2) are the results of pseudo test 1, in which the fake time spot is set at the end of 2010 Q2, the season
when Benchmarking Law in Philadelphia is enacted. D. Column (3) to (4) are the results of pseudo test 2, in which the fake time spot is set at the
end of 2010 Q4, the opposite season when Benchmarking Law in Philadelphia is enacted. E. In column (2) and (4), a constrain is added to the
data used in (1) and (3), which is only the buildings with no missing data in the year after the fake time spot would be considered.
117
Table 4-7 Results of D-D-D Model in Pseudo Tests
Pseudo Test 1 with data from 2008 Q1 to 2012 Q2
VARIABLES
Certified*Tenant*Pseudo1
Tenant*Pseudo1
Certified*Pseudo1
(1)
(2)
0.119
0.179
(0.528)
(0.544)
-0.0973
-0.107
(0.270)
(0.286)
0.459
0.471
(0.371)
(0.381)
(3)
Certified*Tenant*Pseudo2
Tenant*Pseudo2
Certified*Pseudo2
Certified*Tenant
Pseudo Test 2 with data from 2008 Q1 to 2012 Q2
(4)
0.526
0.608
(0.520)
(0.523)
-0.286
-0.294
(0.349)
(0.351)
0.419
0.502*
(0.309)
(0.283)
0.527
0.788*
0.438
0.658**
(0.508)
(0.410)
(0.436)
(0.271)
Certified
-0.199
-0.0190
-0.251
0.0353
(0.338)
(0.269)
(0.261)
(0.179)
Constant
19.35***
19.06***
19.43***
19.14***
(0.206)
(0.216)
(0.211)
(0.222)
Quarter fixed effect
Y
Y
Y
Y
Building fixed effect
Y
Y
Y
Y
Observations
1,888
1,780
1,828
1,720
R-squared
0.028
0.030
0.033
0.036
118
112
118
112
Number of building
Notes: A. Robust standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. B. Across column (1) and (4), the time window is from 2008
Q1 to 2012 Q2. C. Column (1) to (2) are the results of pseudo test 1, in which the fake time spot is set at the end of 2010 Q2, the season when
Benchmarking Law in Philadelphia is enacted. D. Column (3) to (4) are the results of pseudo test 2, in which the fake time spot is set at the end of
2010 Q4, the opposite season when Benchmarking Law in Philadelphia is enacted. E. In column (1), the buildings with no missing data one year
after 2010 Q2 are included. In column (3), the buildings with no missing data one year after 2010 Q4 are included. F. In column (2) and (4), one
more constrain that the buildings that are certified before 2010 are added to the data used in column (1) and (3).
118
Appendix 3 Robustness Check Results
Table A1 Results with LEED and EnergyStar Buildings (2012 Q2 excluded)
With data from 2008 Q1 to 2013 Q2
VARIABLES
With data from 2011Q1 to 2013 Q2
(1)
(2)
(3)
(4)
(5)
(6)
1.040**
1.162**
1.216**
0.944***
1.025***
1.071***
(0.426)
(0.431)
(0.458)
(0.313)
(0.308)
(0.328)
25.75***
25.86***
26.35***
25.46***
25.57***
26.11***
(0.429)
(0.452)
(0.475)
(0.134)
(0.135)
(0.148)
Year fixed effect
Y
Y
Y
Y
Y
Y
Building fixed effect
Y
Y
Y
Y
Y
Y
Benchmarking*Tenant
Constant
Observations
R-squared
Number of building
451
426
395
195
189
171
0.079
0.096
0.110
0.213
0.228
0.239
24
21
19
24
21
19
Notes: A. Robust standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. B. In column (1), all the data from 2008 Q1 to 2013 Q2 are
included, and in column (4), only data from 2011 Q1 to 2013 Q2 are included. C. In column (2) and (5), with the same model, a constrain is
added to the data used in (1) and (4), which is only the buildings with no missing data after 2012 Q3 would be considered. D. In column (3) and
(6), still the same model, one more constrain is added to the data used in (2) and (5), which is only the buildings that are certified before 2012 Q3
would be considered.
119
Table A2 Results with Ordinary Buildings (2012 Q2 excluded)
With data from 2008 Q1 to 2013 Q2
VARIABLES
Benchmarking* Tenant
(1)
(2)
With data from 2011Q1 to 2013 Q2
(3)
(4)
-0.282
-0.341
0.00174
-0.00346
(0.430)
(0.433)
(0.324)
(0.327)
18.35***
18.34***
18.05***
18.19***
(0.253)
(0.221)
(0.143)
(0.148)
Year fixed effect
Y
Y
Y
Y
Building fixed effect
Y
Y
Y
Y
Observations
3,373
3,092
1,719
1,583
R-squared
0.012
0.016
0.010
0.012
253
197
248
197
Constant
Number of building
Notes: A. Robust standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. B. In column (1), all the data from 2008 Q1 to 2013 Q2 are
included, and in column (3), only data from 2011 Q1 to 2013 Q2 are included. C. In column (2) and (4), with the same model, a constrain is
added to the data used in (1) and (4), which is only the buildings with no missing data after 2012 Q3 would be considered.
120
Table A3 Results of D-D-D Model for all Buildings (2012 Q2 excluded)
With data from 2008 Q1 to 2013 Q2
VARIABLES
Benchmarking*Certified*Tenant
Benchmarking*Tenant
Benchmarking*Certified
Certified*Tenant
Certified
With data from 2011Q1 to 2013 Q2
(1)
(2)
(3)
(4)
1.366**
1.425**
1.068**
1.068**
(0.648)
(0.656)
(0.474)
(0.475)
-0.245
-0.249
0.0668
0.0663
(0.407)
(0.408)
(0.313)
(0.314)
-0.766
-0.769
-0.755**
-0.755**
(0.518)
(0.519)
(0.364)
(0.364)
0.145
0.211
-0.490*
(0.369)
(0.379)
(0.262)
0.0512
0.0581
(0.224)
(0.226)
19.26***
19.25***
19.00***
18.96***
(0.198)
(0.199)
(0.133)
(0.133)
Quarter fixed effect
Y
Y
Y
Y
Building fixed effect
Y
Y
Y
Y
Observations
3,518
3,487
1,772
1,754
R-squared
0.016
0.016
0.014
0.014
218
216
218
216
Constant
Number of building
Notes: A. Robust standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1. B. In column (1) and (2), the time window is from 2008 Q1 to
2013 Q2, and in column (3) and (4), the time window is from 2011 Q1 to 2013 Q2. C. In column (1) and (3), the buildings with no missing data
after 2012 Q2 are included. D. In column (2) and (4), one more constrain that the buildings that are certified before 2012 are added to the data
used in column (1) and (3).
121
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Vita
Yuxi Meng

Academic Background
Ph.D. Energy Management and Policy, Pennsylvania State University (2010-2014)
Research focus: Energy Economics
M.S. Environmental Management, Peking University (China) (2007-2010)
B.S. Environment Engineering, China University of Mining and Technology (China) (2003-2007)

Research Projects
Benchmarking Law and Energy Efficient Building Market in New York City and the City of
Philadelphia (2013-2014)
Regional Building Energy Efficient Retrofit Market Modeling (2013-2014)
Renewable Portfolio Standards and Local Renewables Innovation in the United States (2012-2014)
The Development of Energy Efficient Building Innovation Cluster in Greater Philadelphia Area
(2011-2013)
The Intention of Foreign Patenting in China Solar PV Industry (2011-2013)
The Innovation Capability of the solar PV Manufactures in China (2011-2012)
Local Content Requirement and Domestic Wind Power Innovation in China (2010-2011)

Awards
Dow Sustainability Innovation Student Challenge Award (SISCA) Grand Prize Winner (Penn State
University 2013)
Energy and Environmental Economics and Policy Initiative (EEEPI) Grant Award (Interdepartmental)
(Penn State University 2013)
USAEE student scholarship (International) (United State Association of Energy Economics 2013)
Top 25 finalists of “Go Green in the City” Case Challenge (International) (Paris, 2012)
Energy and Environmental Economics and Policy Initiative (EEEPI) Grant Award (Penn State
University 2012)