NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Clean Energy Innovation: 

Sources of Technical and 

Commercial Breakthroughs 

Thomas D. Perry IV and Mackay Miller

National Renewable Energy Laboratory

Lee Fleming

Harvard Business School

Kenneth Younge

University of Colorado

James Newcomb

National Renewable Energy Laboratory

Current Affiliation: Rocky Mountain Institute

Technical Report

NREL/TP-6A20-50624

March 2011
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

National Renewable Energy Laboratory

1617 Cole Boulevard

Golden, Colorado 80401

303-275-3000 • www.nrel.gov

Contract No. DE-AC36-08GO28308

Clean Energy Innovation: 

Sources of Technical and 

Commercial Breakthroughs 

Thomas D. Perry IV and Mackay Miller

National Renewable Energy Laboratory

Lee Fleming

Harvard Business School

Kenneth Younge

University of Colorado

James Newcomb

National Renewable Energy Laboratory

Current Affiliation: Rocky Mountain Institute

Prepared under Task No. SA10.102C

Technical Report

NREL/TP-6A20-50624

March 2011
NOTICE

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iii

Acknowledgments

We appreciate and acknowledge the support of the Harvard Business School Department 

of Research, the National Renewable Energy Laboratory, and NSF 0830287 (Creating a 

Patent Collaboration Network Database to Examine the Social Production of 

Knowledge). The authors would like to thank R. Lai and A. D’Amour of Harvard 

Business School and C. Worley of Colorado School of Mines for their assistance with 

data and analysis and V. Cardona of Heslin Rothenberg Farley & Mesiti P.C., Albany, 

New York, for sharing their patent search methodology. D. Bilello, T. James, D. Kline, 

and R. Newmark of NREL and L. Bony of the Rocky Mountain Institute provided helpful 

comments on the paper. All errors remain ours.
iv

Executive Summary

Low-carbon energy innovation and implementation is essential to combat climate change, 

promote economic competitiveness, and achieve energy security. Our study of clean 

energy patenting in the United States is undertaken to elicit fundamental trends and 

implications that can inform public and private innovation investment, resulting in greater 

efficiency of research and development programs. Using U.S. patent data and additional 

patent-relevant data collected from the Internet, we map the landscape of low-carbon 

energy innovation in the United States since 1975. We isolate 10,603 renewable and 

10,442 traditional energy patents and develop a database that characterizes proxy 

measures for technical and commercial impact, as measured by patent citations and Web 

presence, respectively. Regression models and multivariate simulations are used to 

compare the social, institutional, and geographic drivers of breakthrough clean energy 

innovation. Results indicate statistically significant effects of social, institutional, and 

geographic variables on technical and commercial impacts of patents and unique 

innovation trends between different energy technologies. We observe important 

differences between patent citations and Web presence of licensed and unlicensed patents 

indicating the potential utility of using screened Web hits as a measure of commercial 

importance. We offer hypotheses for these revealed differences and suggest a research 

agenda with which to test these hypotheses. These preliminary findings indicate that 

leveraging empirical insights to better target research expenditures could augment the 

speed and scale of innovation and deployment of clean energy technologies. 
v

Table of Contents

List of Figures.............................................................................................................................................vi

List of Tables...............................................................................................................................................vi

1 Introduction ...........................................................................................................................................1

2 Analytical Approach .............................................................................................................................4

2.1 Patent Search ........................................................................................................................4

2.2 Patent Interrogation ..............................................................................................................5

2.3 Patent Value Definition........................................................................................................5

2.4 Regressions ..........................................................................................................................8

3 Analytical Insights ..............................................................................................................................10

3.1 Historical Macro Sources of Clean Energy Inventive Activity .........................................10

3.2 Sources of Clean Energy Technical and Commercial Breakthroughs ...............................18

3.3 Breakthrough Sources ........................................................................................................22

3.4 Predicting Breakthroughs...................................................................................................28

4 Suggestions for Further Research....................................................................................................30

5 Conclusions.........................................................................................................................................32

References .................................................................................................................................................33
vi

List of Figures

Figure 1. Average patent citations ( ) γ per patent by year ................................................. 7

Figure 2. Stylized schematic of regression comparison of two related populations (a and 

b) of patents or Web hits to identify the significance and simulated difference (∆b-a)....... 8

Figure 3. Annual granted U.S. energy patents (1975–2008) by technology area ............. 10

Figure 4. A comparison of renewable energy patents generated by U.S. and non-U.S. 

inventors ............................................................................................................................ 13

Figure 5. Example collaboration networks for a selected biofuel inventor (dashed circle).

........................................................................................................................................... 16

Figure 6. Average collaboration and inventing team size is increasing in energy .......... 16

Figure 7. Summary network visualizations for each technology area .............................. 17

Figure 8. Web hits versus future prior art citations from patents in 1995 (A) and 2005 (B) 

solar energy patents........................................................................................................... 20

Figure 9. Signal (A, C) and aggregator (B, D) Web hits versus future prior art citations 

from patents for licensed and unlicensed National Renewable Energy Laboratory patents

........................................................................................................................................... 21

Figure 10. Predictive commercial value of patents using proposed Web hits relationship

........................................................................................................................................... 29

Figure 11. An idealized schematic of how future databases might be built and linked in 

order to understand how science investment influences economic outcomes .................. 31

List of Tables

Table 1. Domestic Sources by State for Granted U.S. Energy Patents (1975–2008) ...... 12

Table 2. Market Sources of Patents .................................................................................. 14

Table 3. Regressed and Simulated Determination of Sources for Solar Technical and 

Commercial Breakthrough Innovation ............................................................................. 24

Table 4. Regressed and Simulated Determination of Sources for Wind Technical and 

Commercial Breakthrough Innovation ............................................................................. 26

Table 5. Regressed and Simulated Determination of Sources for Biofuel Technical and 

Commercial Breakthrough Innovation ............................................................................. 27
1

1 Introduction

Development and implementation of low-carbon electricity generation technologies are 

important for several reasons. To name a few, moving away from fossil-based electricity 

generation can help increase energy security [1], promote the diversification of energy 

sources [2], stimulate economic growth [3] and national competitiveness [4], reduce 

pollution [5], and reduce local water demand [6]. Also, large-scale movement away from 

practices that create greenhouse gases will help combat climate change.

The scientific consensus on the implications of climate change is serious [7, 8]. 

Anthropogenic greenhouse gas emissions from energy production and industry sources 

are causing an “unequivocal ... warming of the climate system” [9]. This warming trend 

will affect important earth systems that affect global health and prosperity [9, 10]. While 

the case has been made that we “already possess the fundamental scientific, technical, 

and industrial know-how to solve the carbon and climate problem for the next half 

century” [5], the diffusion of low-carbon technologies continues to face significant 

economic, technical, and political obstacles. Low-carbon technologies can assist in the 

avoidance and reduction of greenhouse gases and other emissions that have been linked 

to climate change. Sustained energy technology innovation and market development 

policies are essential to overcoming these implementation obstacles and are increasingly 

recognized as a national policy priority for developed and emerging economies alike [11, 

12].1

Innovation relies upon a complex ecosystem of fundamental capabilities, an “industrial

commons” [13], that serves as a vital source of breakthrough, evolutionary, and 

incremental innovation [14] and is particularly important for the maturation of new 

alternative energy products [15]. However, for the past several decades, observers have 

noted persistent underinvestment in the U.S. energy infrastructure [16], manufacturing 

[17], and research and development innovation bases [18–20]. While there are 

indications that some recent research investments are accelerating the development of 

new technologies [21], several studies indicate insufficient investment [18, 20] to achieve 

the necessary performance for market adoption [22], to support new technology 

development [23], and to support the U.S. leadership position in the global economy [24]. 

For all these reasons, current economic leaders recently called for a massive increase in 

public renewable energy research and development spending [23]. 

 In this context, the need for rigorous investigation of innovation dynamics of lowcarbon technologies is essential to crafting effective innovation and technology transfer 

policy. 

In recent years, corporate research, development, and deployment dollars have also been 

important for advancement of the renewable energy economy [25]. However, the 

recession over the last two years has further hampered domestic corporate research

investments [26]. Simultaneously, massive investment in industrial innovative capacity 

is occurring in other countries, with a specific focus on renewable energy innovation [16, 

27]. Because of global competition for market share in alternative energy, innovation in 

 

1 Useful resources to determine national and regional policies are 

http://www.iea.org/textbase/pm/?mode=re and http://www.dsireusa.org/. 
2

energy will be a major contributor to national economic competitiveness in the coming 

decades. For the United States to establish and maintain a leadership position in the 

energy economy, new investments are needed to effectively and efficiently stimulate, 

commercialize, and deploy new energy innovations within our borders and abroad. If 

this does not occur, the United States runs a risk of falling short of its ability to 

effectively compete and lead in the global marketplace.

The potential financial opportunities and the opportunity to lead in the global economy 

have elicited a surge in patenting and investment in clean energy technologies. A leading 

barometer of inventive activity, the Clean Energy Patent Growth Index [28], shows 

continuing increases in clean energy patenting activity, yet we currently lack an

empirically grounded understanding of how these technologies are invented and enter the 

market. Policy prescriptions that are a mixture of technology and market stimulation 

activities may accelerate invention in one industry yet might be ineffectual or even 

counterproductive in another. For example, if breakthroughs have historically arisen 

from understanding basic science in one field, and more of these types of innovations are 

required, then the optimal allocation of funds would be to support early-stage research at

universities, national laboratories, or some other similar organization that has previously 

performed this type of work. On the other hand, if the technology is mature and 

development of breakthroughs needs to be focused on commercialization and the market, 

then applied research in government laboratories (most national research facilities 

increasingly perform a mixture of basic and applied research) or market development 

activities targeted at private firms might be preferred. Alternatively, fundamental science 

and understanding may play a far less important role in the development of new 

technologies. This could manifest if independent inventors were the sources of technical 

breakthroughs [29] because commercially important innovations could be made without 

sophisticated experimental facilities (e.g., solar thermal versus photovoltaics). In this 

case, facilitating commercial deployment of technologies may only require helping 

functional breakthroughs get to market by providing small business and entrepreneurship 

loans, incentives for private investment, or other prescriptions to facilitate robust markets 

for technology [30, 31]. 

Managing a portfolio of research, development, and deployment investments in the 

constantly and rapidly evolving energy landscape is a great challenge to public and 

private research managers. The ultimate aim of this paper is to suggest a line of inquiry 

to inform research investment decisions across all clean energy technologies and the 

whole market, ultimately allowing more efficient and effective technology and market 

investments. This report and its recommendations are preliminary and dependent on the 

new techniques developed as a part of this work; we expect the scope and specificity of 

recommendations to continue to evolve as our methodology improves. 

This analysis began by asking the question, where do breakthrough innovations in energy 

originate? While certainly omitting some other potential measures of the advancement of 

knowledge, such as publications, this investigation uses the patent record as a proxy for 

innovation [32] and analyzes the sources of breakthrough patents using cutting-edge 

analytical techniques. This analysis begins by determining patenting trends in biofuel, 

geothermal, hydro, solar, and wind technologies and continues by mapping the 
3

geographical distribution of patenting activity and illustrating the social networks of each 

field from co-authorship relationships. While observing gross innovation trends in clean 

technologies, we also focus our analysis on the source of commercial and technical 

breakthrough innovations. In particular, it is important to focus on these valuable 

innovations as unique from bulk patenting trends because most patents are useless or of 

moderate value [33], as opposed to breakthroughs that significantly change the landscape 

of technological or business practice. The impact of inventions is then measured in each 

technology field through two proxy measurements of each patent, technical impact (as 

measured by future citations) [34], and societal and commercial influence (as quantified 

by a novel measure of Web presence introduced in this manuscript). These dependent 

variables enable estimations of the impact of social, institutional, and geographical 

influences upon the processes of invention for each technology. In the final section of 

this paper, we provide hypotheses to explain these differences along with a research 

agenda that we hope can inform policy suggestions. 
4

2 Analytical Approach

2.1 Patent Search

Lai et al. [35] developed a disambiguated database of granted U.S. patents (1975–2008). 

Using a software text search tool (available at the Harvard Dataverse Network Patent 

Project [35]) designed to interrogate the U.S. Patent and Trademark (USPTO) database 

(http://patft.uspto.gov/), patent abstracts were searched using energy keyword search 

terms [18, 20, 36]. We focused our search on technology areas for which search 

ontologies were already developed and therefore omit other possible areas of important 

carbon-reduction innovations, such as carbon capture and sequestration (CCS). We 

experimented with searching only patent titles and the entire patent body for our 

keywords and found that searching abstracts provided the best mixture of inclusion and 

accuracy. The resultant lists were cleaned using text analysis functions and by hand to 

ensure their accuracy. Patents were organized and analyzed by application date to best 

position the patent within the appropriate inventive context [37] because there is a 

variable and increasing time lag between patent application and issuance [38]. 

Our keyword-based search of energy patent titles and abstracts, while effective at 

identifying patents related to energy technologies, favors patents focused on later-stage 

innovations that include mention of the production of energy or fuels. For example, 

when comparing patents that the U.S. Department of Energy (DOE) funded (which 

presumably are parts of the energy knowledge space) with our keyword-search-driven 

energy patent list, we observe an overlap of less than 3%. Presumably, this is because the 

DOE funds very early-stage research (such as algal strains or materials chemistry) that 

does not lend itself solely to energy generation or fuel applications and may not 

uniformly include applications to energy in the language of their patent applications. 

However, while limited to later-stage technologies, our energy list is accurate and 

includes only energy patents. The accuracy of the keyword search methodology was 

robust (average of 3.0% initial patent results were determined to not be energy patents 

after manual inspection) except for nuclear (20.7%) and wind (34.6%) technology areas. 

The errors in nuclear and wind technology areas were by-in-large a result of patents that 

included homographic keywords (e.g., “nuclear” in reference to genetic techniques and 

“turbine” in reference to aeronautic applications, respectively). However, we

acknowledge that we are likely omitting many earlier-stage energy technologies because 

of the keyword selection. We intend to develop a more sophisticated ontology that will 

combine a more inclusive keyword set while assuring accuracy by using patent 

classification codes in combination. 

Because energy technologies draw heavily upon proceeding innovations (e.g., solar 

draws upon semiconductor manufacturing), this improved ontology will allow interesting 

investigations of knowledge flow [39] and the organic development of new technology 

areas [37]. Although this investigation would be interesting, it is beyond the scope of this 

manuscript. All analyses include only energy patents and not other patents that may have 

been invented by the included authors. This artificial boundary around the energy patents 

creates rigid and deterministic barriers for our investigation and limits the determination 

of the role of the entire inventive career and collaborations therein but is necessary for 
5

this first exploratory work. Follow-on work will provide more comprehensive 

investigation of the historic social variables involved in energy innovation. 

2.2 Patent Interrogation

Three independent variables were developed to provide insight into the structural origins 

of inventive activity: “Government Interest,” “Assignee Organization Type,” and 

“References to Science.” Government interest patents are those that the government 

retains a right to, usually because they funded the work. Patents with government 

interests were identified using a USPTO search tool [35] to search the government 

interest field for identifying terms such as “defense” or “energy.” The search terms could 

not be as rigid as “Department of Defense” because often the patents cited the “Dept. of 

Defense,” so search terms were developed to be inclusionary. Assignee organization type 

defines what kind of organization patented the invention. Text search algorithms were 

developed to search the patent assignee field for terms (e.g., “university,” “corporation,” 

and “LLC”) that identified the organizational type of the inventing institution. Assignees 

were classified into five types: Private, University, Government, Independent (i.e., no 

assignee), and Other. The results of the automated text classification system were then 

manually reviewed for accuracy. References to science were identified if at least one 

other non-patent reference in the “Other References” field was coded as referencing 

science (these references are mostly to scientific publications [40]). While the variable 

for references to science was included as a covariate control in our model, its influence 

was not simulated for this investigation. 

Three independent variables were developed to provide insight into the social origins of 

inventive activity: “Team Size,” “Mentorship,” and “Dispersion.” Team size counted the 

number of inventors listed on the patent application. Patents with two or more inventors 

were classified as team patents. This variable was included in our simulation results. 

Mentorship compared the time-in-field of inventors on a patent. If a difference of 10 

years or greater existed between any two inventors, the patent was classified as arising 

from a mentorship relationship. Time-in-field was derived by subtracting the application 

year of each inventor’s first patent from the application year of the patent in question. 

Dispersion measured the maximum distance between any two inventors on a single 

patent, with a distance greater than 100 miles classified as dispersed. Distance between 

inventors was calculated using the zip code values corresponding to the Inventor City 

field and so returned values only for patents with two or more U.S. inventors. Mentorship 

and dispersion variables were included as covariate controls but were not simulated for 

this manuscript. We also performed qualitative analysis of U.S. inventor patent coauthorship with collaborators in other countries for work unrelated to this manuscript and 

demonstrated the ability to map collaboration between the United States and other 

countries. Further research should include work to understand the impact of non-U.S. 

innovations on domestic innovation, both from the perspective of contribution to the 

collective knowledge as well as more direct individual collaboration effects. 

2.3 Patent Value Definition

2.3.1 Technical Importance: Citation Analytics

Citations have a long history in the bibliometric literature and the first application to 

patents identified was by Trajtenberg [34]. Heretofore, patent citations are the dominant 
6

means by which differences in patent quality were accessed, especially in large datasets 

where in-depth qualitative evaluation of individual patents is not possible. Citations to 

prior patents are submitted as part of a new patent application in order to establish “prior 

art.” The applicant is required to disclose the prior art to the examiner and must then 

establish how their invention goes beyond that prior art. The examiner often adds 

citations they are aware of, such that half of the citations come, on average, from the 

examiner [41]. While it is not incumbent upon the applicant or the examiner to be 

exhaustive in their citation inclusion, important patents do on average receive more 

citations [34]. Similar to publications [42], inventors (authors) often cite their own work 

in part to show the evolution of knowledge but also, perhaps, to influence their citation 

relevance [43, 44] or Eigenfactor [45], which are used as the basis of tenure and other 

promotion decisions [46]. Therefore, for all analysis herein, patent technology value is 

defined by citations from future patents less inventor self-citations, henceforth denoted 

simply as citations (γ). Patents that are more highly cited are thought to provide the basis 

for future technical advance and often change the bases of competition between firms and 

industries. Hence, we might consider highly cited patents as technical breakthroughs. 

Besides measuring the impact of individual patents, citations have also been used to 

measure status and deference [47], knowledge flow [48], and to disambiguate inventor 

careers [35]. They have also been shown to correlate weakly with financial value as 

reported by a survey of patent holders [49]. 

While very popular, we recognize that patent citations are an imprecise and flawed 

measure of technological importance and breakthrough [50]. Selecting a like patent set 

for comparison and evaluation of anomalies is essential because it has been observed that 

different industries have different citation patterns and norms [42] (e.g., the 

pharmaceutical industry is a highly collaborative and citing industry). Also, since 

citations accrue to a patent over a period of several years after it issues, citation counts 

for recent patents are disadvantaged compared to older patents, an effect known 

as “truncation” [42], and this is true with energy patents as well (Figure 1). In this figure, 

traditional energy (10,442 patents) and renewable energy (10,603 patents) patent sets 

were identified by keyword search; the random baseline includes 10,000 randomly 

selected patents from the entire U.S. patent dataset.
7

Figure 1. Average patent citations ( ) γ per patent by year 

Citations also do not strictly measure non-technical or commercial impact, and 

correlations with these measures likely reflect some underlying but omitted variable. In 

short, citations measure the impact of invention on future technological trajectories but 

neglect the commercial and non-technical impact of the technology. However, as a proxy 

for technological “breakthroughness,” patent citations previously have been used 

effectively [51] and are currently the best measure available for very large datasets [32]. 

Given these inherent limitations of citation analysis, this work aims to enrich the measure 

of patent impact via novel proxies.

2.3.2 Commercial Importance: Web Presence Analytics 

We developed and tested a new quantitative measure of Web presence on several patent 

sets in an attempt to develop a novel proxy measure for quantifying the commercial 

impact of patents. A count of Web hits (ω) was collected by searching for each patent 

using the search terms “patent <patent number>,” aggregating the resultant URLs,

2

 and 

applying an evaluative algorithm to screen the root domains. The evaluative screening 

algorithm compares URLs to a manually created list of "aggregator" website URLs that 

essentially replicate information from the USPTO. Those URLs were defined as 

aggregator URLs (ωagg), while the remaining were defined as potential signal URLs 

(ωsig

 

2 In computing, a Uniform Resource Locator (URL) specifies where an identified resource is available and 

the mechanism for retrieving it. The best known example of the use of URLs is for the addresses of Web 

pages on the World Wide Web. Accessed December 31, 2010.

). Taken together, these new quantifiable variables for a single or a population of 

patents can be effectively used to estimate commercial importance. 
8

2.4 Regressions 

To determine the effect of factors of interest (§2.2) on the commercial and technical 

impact of patents, we estimated long-tailed [33] Poisson models of citation counts and 

Web hits to clean energy patents. In essence, we determine the statistical likelihood (p

value) and difference (∆b-a) between two populations (a and b) of patents (Figure 2). We 

use comprehensive pair-wise comparison of like patents to estimate the differences 

between any two unique factors of interest (e.g., biofuels patents from Colorado versus 

California or biofuels patents from corporate versus university assignees). An observed 

and statistically relevant simulated difference indicates that there is an important 

divergence (positive or negative) between the populations of patents and that the factor of 

interest is causing some modification in the distribution of the patents’ values and may 

cause or hamper the production of breakthroughs. 

Figure 2. Stylized schematic of regression comparison of two related populations (a and 

b) of patents or Web hits to identify the significance and simulated difference (∆b-a) 

This model uses robust [52] and quasi-maximum-likelihood [53] estimation. Because 

interactions in non-linear models depend upon the value of other variables in the model 

[54] as well as the covariance matrix of coefficient estimates in the model [55, 56], we do 

not interpret marginal effects alone. Instead, we simulate the count at specific levels of 

the independent variable of interest, holding all other covariates at a reasonable value, 

such as the mean.3

We evaluated the data in several ways in order to ensure the robustness of results. First, 

only the patents within each field were considered. Second, we ran a full model with all 

fields that included field-specific interactions. Third, we checked to see if the results held 

up for a more recent sample of the data. Finally, we ran models with and without 

controls for yearly fixed effects, such as citation truncation (Figure 1) to remove yearly 

variation in citation patterns, across all fields. In all models, we coded a focal 

 We assess whether the interaction effects are statistically significantly 

different by determining the probability that the magnitude of each simulated difference 

is different from zero [57, 58]. 

 

3 All discussion of “simulated results” in the paper refers to this alternative method of estimating effect 

size.
9

comparison of interest [59] (e.g., between California and Colorado) so that we could 

examine whether the predicted difference between two different levels was statistically 

significant. If we saw similar results across multiple models of the data, we flagged this 

as an opportunity for investigation and performed a qualitative inspection of the patent 

populations to determine what may be the root causes of the differences. 
10

3 Analytical Insights

This report examines the sources of drivers for renewable energy innovations. In 

particular, it compares data across clean technology fields to determine the observed 

historical differences between the fields and the opportunities for tailored innovation 

policies and strategies that are specific to the energy technology of interest. It describes 

the revealed sources of energy innovations from a variety of perspectives and then 

identifies the morphologies of innovation in each technology area that have historically 

led to breakthrough technical and commercial innovations. 

3.1 Historical Macro Sources of Clean Energy Inventive Activity

Starting with the U.S. patent database of over 3.7 million patents, energy patents were 

identified by keyword search of the USPTO database as belonging to one of nine 

technology areas: biofuels, coal, geothermal, hydro, natural gas, nuclear, oil, solar 

(photovoltaics and solar thermal), and wind (Figure 3). Given the low amount of 

identified patenting in geothermal, hydro, and nuclear technologies, there was little signal 

in the count models, and we have focused our analysis of renewable energy patents in the 

wind, biofuels, and solar renewable technology areas. 

In response to the 1970s oil crisis, the United States rallied the largest investment in 

energy in the last several decades [11] that resulted in a peak in energy patenting in the 

late 1970s (Figure 1 and Figure 3). Since then, funding steadily declined and reached a 

low point in the mid-1990s. A second surge has recently occurred likely due to the 

perceived growing economic opportunity and resultant corporate investment, particularly 

in wind and solar technologies (the very recent fall-off in all technologies is likely an 

artifact of application and granting delay at the USPTO [37]). 

Figure 3. Annual granted U.S. energy patents (1975–2008) by technology area
11

3.1.1 Geographic Sources

In the United States, we observe increasing inventive activity in the clean energy sector 

over the last several decades along with increasing geographic concentration (Table 1). 

California is a dominant geography for energy inventive activity across all technology 

areas, especially solar, which is likely based on the concentration of the semiconductor 

and venture capital industries there. High patenting activity is also observed in Texas, 

Massachusetts, Colorado, and New York. Pennsylvania shows active patenting across all 

traditional energy technologies, perhaps due to geographic choices in the early oil 

industry. And not surprisingly, Texas shows strong oil-related patenting activity due to 

the strength of the industry there. Because of the dominance of California, 

Massachusetts, New York, and Texas and the high levels of activity in Colorado, we 

focus our analysis on breakthroughs in those states. In the future, we can extend this 

analysis for all states—and similarly, any feature of interest that we identified—and run 

comparisons between them. 
12

Table 1. Domestic Sources by State for Granted U.S. Energy Patents (1975–2008) 
13

We observe that bulk energy patenting activity is increasingly coming from non-U.S. 

sources; the U.S. share of issued clean energy patents has been declining by about 1% per 

year since 1975 (Figure 4). The recent observed increase in percentage of patents granted 

to U.S. institutions is likely a mathematical artifact created by comparing the largely 

declining granted patent trends (Figure 1, Figure 3, and Figure 4). Different countries are 

responsible for the majority of international patenting, but in general we observe 

significant activity from Germany, Japan, Canada, and France. Interestingly, three major 

sources of clean energy activity—India, China, and Brazil [60, 61]—are not even in the 

top 10 most active patenting countries, although this may be an artifact of their recent 

entry into the field and USPTO granting delays. In keeping with general features of 

foreign patents, foreign energy patents have a lower citation count than their U.S. 

counterparts, presumably because foreign firms build upon their domestic knowledge 

base and do not spend as much time and effort tracking the U.S. intellectual property 

landscape [62]. This makes evaluating their impact using patent citations challenging. 

Future work should include evaluation of a disambiguated global intellectual property 

database as well as publications to truly measure the innovative capacity of the global 

economy.

Figure 4. A comparison of renewable energy patents generated by U.S. and non-U.S. 

inventors
14

3.1.2 Institutional Patenting Trends

To illustrate the institutional bases of this patenting effort over time, we categorize 

institutional sources of invention as private firms, government agencies, universities, and 

independent inventors (the latter are patents without an assignee and are typically owned 

by the listed inventors) and characterize whence the innovations came (Table 2).

Table 2. Market Sources of Patents

As an example of why this analysis is useful, one might think that wind power is the 

province of mechanically inclined tinkerers in their garage, while biofuels innovations 

come from large corporations because of the technical complexity and experimental 

requirements for discovery. This theory appears to hold true with independent inventors 

responsible for 54.5% of patents in wind and only 22.1% in biofuels. Hence, independent 

inventors appear to be a substantial source of technology in wind power and firms in 

biofuels. A modest proportion of solar patents (33%) also appear to come from 

independent inventors, which may be counter-intuitive considering the advanced 

materials chemistry required with most photovoltaic technologies. However, the time 

series of bulk patenting activity also shows high levels of independent invention during 

periods when entrepreneurial semiconductor activity was on the rise (1985–1995), and 

our definition of solar also includes solar thermal technologies that do not require 

advanced laboratories to fabricate. The technology with the lowest proportion of 

technologies coming from independent inventors is nuclear (11.9%), which may be due 

to (1) the inherent dangers of the technology, (2) the lack of market for new innovations 

because of diminished new construction since the 1980s [63], or (3) the dependence on a 

limited pool of large corporations for essential equipment manufacturing [64]. 

Before the early 1980s in solar, independent inventors and corporations were equally

dominant patent producers. Since then, however, corporations have produced the 

majority of patents, with independent inventors contributing only about 25% of patents. 

From 1995–2008, corporations have extended their dominance, responsible for up to 

three quarters of the granted solar patents. This transition may be due to the transition of 

technology dominance from solar thermal to photovoltaics, which are much more 

experimentally complicated technologies and more the provenance of corporations than 

of independent inventors. 

Wind energy demonstrates a somewhat similar pattern. Independent inventors 

contributed the most patents in the late 1970s and early 1980s, after which large firms 

supplanted them. Since then, corporations and independent inventors have contributed 

about equally to the patent pool. In terms of gross number of patents, universities and 

national laboratories never figure prominently in prolifically producing wind energy 
15

patents, although we will demonstrate later that universities may, on average, produce 

more patents with technical breakthroughs. 

Patenting in biofuels has been steadily increasing over the time period, and this increase 

appears to have come mainly from large firms, which maintain a dominant role in 

patenting activity. This raises the question of whether independent inventors are 

inventing poor quality technology or whether other issues, such as commercialization 

challenges or a lack of social networks that diffuse knowledge, are the cause. 

3.1.3 Funding Sources

We do not observe broad variability over time in the role of government funding in 

producing inventive activity. Broadly speaking, in solar, both DOE and the National 

Aeronautics and Space Administration (NASA) historically account for about one-third 

of the patents with government rights associated with them. A similar trend is observed 

in wind and biofuels. The Department of Defense (DOD) and National Science 

Foundation (NSF) both play a small role in funding patents (< %5 each). It would be 

interesting to attempt to correlate government technology funding (e.g., Anadon et al. 

[11]) with patent output. With sufficient data resolution, bibliometric scholars could 

make some significant evaluations of the effectiveness of funding agencies on stimulating 

innovation and the efficiency of research and development organizations in converting 

public funding into public good [65, 66]. 

3.1.4 Social Features

We also performed preliminary analysis of social networks of patenting. Unfortunately, 

our keyword-driven search approach creates artificial boundaries around social networks 

of invention, and for this reason, we do not calculate a unique social influence variable 

for each inventor since their true social influence should be derived from their entire 

inventive career, which is typically much broader than this dataset would indicate (Figure 

5). In this figure, an NREL innovator is identified (circled), and their energy patents 

identified in our database are mapped (A). However, that analytical method may 

underestimate the actual network of collaborators that the inventor has previously worked 

with (B) and the total network of their inventive sphere (C). The inventor’s inventive 

career likely has spanned other disciplines besides energy, and identifying formative 

collaborations that lead to energy innovations is essential. Each node is an inventor 

connected by lines to their collaborators (distance is a network plotting artifact and does 

not indicate closeness), colors indicate different patent assignees, and square nodes and 

red lines indicate energy patents versus black lines an circles for non-energy patents. 
16

Figure 5. Example collaboration networks for a selected biofuel inventor (dashed circle)

However, we are able to make some preliminary observations about the social features of 

clean energy patenting. For example, consistent with Wuchty et al. [67], we observe a 

changing collaborative structure of energy innovation over the time period of study with

the typical number of inventors per patent increasing (Figure 6). While the linear trends 

are not significantly correlated, in the aggregate, we observe team size growing at 2%–

3% per year since 1975 for all renewable energy and wind patents. 

Figure 6. Average collaboration and inventing team size is increasing in energy
17

Figure 7 shows network dynamics and representations of collaborative networks for all of 

the energy technologies. For each technology area, we show the complete inventive 

network (excluding non-collaborating inventors), the largest component of each network, 

and the network of the most highly cited inventor (node and line coloring is described in 

Figure 5). As noted above, accurate quantitative descriptions of inventive social 

networks demand a level of technology-specific boundary robustness that is beyond the 

scope of this preliminary examination. Additionally, inventor-level social influence can 

only be accurately characterized by analyzing the social network signature across the full 

USPTO dataset. But even though our datasets exhibit artificial social network 

boundaries, preliminary results suggest significant collaborative network differences 

between the technology fields at both the macro and micro levels. At the macro level, 

biofuels patenting activity is more islanded with few inter-organizational linkages 

connecting network components. Wind patenting activity sees moderate levels of interorganizational linkage, and solar sees by far the highest level of the three technology 

areas. 

Figure 7. Summary network visualizations for each technology area 

As a preliminary test of whether these macro levels of isolation are a genuine network 

feature or an artifact of the bounded dataset, we isolated the immediate inventive 
18

networks of the highest cited inventors in each technology-bounded dataset and 

compared these networks to the same inventors’ "second-degree" networks extracted 

from the entire USPTO dataset (Figure 7). In this way, we can gain a first approximation 

of the full extent of social influence of top inventors (Figure 5). Comparing the networks 

of the largest components and most highly cited inventors across technology areas 

suggest differences in career connectivity of inventors in the different technology areas 

(Figure 7), hinting at fruitful avenues for future research. Furthermore, we did a 

preliminary comparison of the energy inventive space with that of software and 

pharmaceuticals and observe massive differences in the collaboration networks. Here we

focus on energy patents so this thread of inquiry is not continued, but there are lessons to 

be learned from these highly collaborative and innovative industries, which will be the 

focus of future analysis. 

Certainly, the historical sources of innovation, the industrial roots, and the resource 

intensity of these technology areas affect these collaboration norms. Validating these 

disparate network connectivity characteristics and comparing them to other industries 

will require more extensive patent datasets, but we suspect that there are indeed 

substantive differences between the technology fields and important lessons to be 

learned. 

3.2 Sources of Clean Energy Technical and Commercial Breakthroughs 

The count-based methods used in this first analytical effort do not account for patent 

quality, which misses most of the impact of patents, as the distribution of commercial 

value is extremely skewed [68]. It is very difficult to predictively quantify the impact of 

these patents, and this quandary is almost universal for bibliometric scholars of research 

and innovation. While recent databases of patents and papers [42, 69] have greatly 

facilitated large-scale analyses, they stop short of providing a comprehensive picture of 

the later stages of application and commercialization. 

Breakthrough innovations are fundamentally new technologies or services that change the 

field of practice. This dramatic shift is relative in comparison to incremental innovations 

that improve existing technologies and relatively useless innovations that do not 

significantly advance the technical or commercial practice. Breakthrough energy 

innovations have been deemed necessary to meaningfully compete with incumbent 

technologies [70], overcome the significant market and political barriers to system

transformation [71], and play a meaningful role in mitigating climate disruption [72]. At 

the same time, these innovations are likely to be uniquely difficult to achieve from a 

technical perspective [73]. Given the disproportionate impact of breakthroughs on 

technical advance and societal change [33, 34, 42], we now focus on these most 

technically and commercially important patents as measured by citations and Web 

presence, respectively. As previously mentioned, citations have a long history in the 

bibliometric literature, and we will use them to identify and quantify technical 

breakthroughs. However, patent citations are a limited measure of technical importance 

and do not necessarily infer commercial relevance or likelihood of deployment. 

Supplementing the use of patent citations, we propose to assess commercial value

through automated searches of the Web and content analysis of the resulting URLs. We 
19

demonstrate the proof-of-concept herein using energy patents and propose it as a 

candidate tool for general use for the entire patent record. This variable will add an 

entirely new measure that is systematically calculable across all patents to the toolkits of 

innovation researchers. This work brings innovation researchers—in clean energy as 

well as other industries—closer to a measure of deployment and commercial impact. 

3.2.1 Web Hit Analysis

We propose that websites and their content can contain valuable data on the value of 

patents that is supplemental to citation data. The presence of a description of a patent on 

a non-aggregating website requires qualitative human intervention (i.e., someone needs to 

create original content that elevates the patent's unique Web presence). This investment 

of time and author’s perceived value of the patent [74] is captured and codified in the 

Web page. We can then count those Web pages and quantify a perception of unique 

value. 

For example, U.S. Patent 6,852,920, “Nano-architected/assembled solar electricity cell”

elicits 209 Web hits. While most of these URLs are patent aggregator results, others 

provide an indication of commercial interest, such as the following4

The company uses copper indium gallium diselenide—which achieves up 

to 19.9% efficiency in laboratory samples

 portion of a 

Wikipedia entry on the firm that owns the patent (italics added):

[13]—to build their thin film solar 

cells. The company's technology gained early industry recognition with 

the presentation of a Small Times Magazine award at a leading nanotech 

business event in 2005.[14] Nanosolar's solar cells have been verified by 

NREL to be as efficient as 14.6% in 2006[15] and 15.3% in 2009.[6]

Technical details of Nanosolar's new manufacturing techniques have been 

disclosed in patent applications.

[17] Some information about their process 

has become available in a Scientific American article (in German).

Citation number 17 (other citations are Wikipedia references and omitted for brevity) 

cites U.S. Patent 6,852,920 and thus provides the link to evidence that the patent is 

commercially valuable and being actively developed. This Web page has been “peer 

reviewed” by the open-source community [75]: the page was edited by 147 unique users 

286 times from 2006 to 2010 with the most active contributor providing 12 edits to the 

page. 

[15]

Conversely, aggregator Web sites generally only replicate the USPTO database 

information, e.g. patent title, abstract, inventors, and application and grant dates, and 

provide no new signal on the value of a patent. Figure 8 illustrates systematic 

relationships between the number of future prior art citations and the number of Web hits 

for solar patents in 1995 (A) and in 2005 (B). As can be seen, the relationship between 

citations and Web hits is positive for both aggregator and signal URLs. In the 1995 

patents (8A) there exists a significant correlation between cites and aggregator URLs 

 

4 Wikipedia. “Nanosolar.” http://en.wikipedia.org/wiki/Nanosolar. Accessed December 15, 2010.
20

(ω agg = 4.3±0.1(γ) + 41.1±2.5, p < 0.1), which results from the prior art fields in the 

aggregator websites and provides little new information on the value of the patent even 

though the signal is amplified. 

Figure 8. Web hits versus future prior art citations from patents in 1995 (A) and 2005 (B) 

solar energy patents

However, signal websites (ω sig =ω −ω agg ) can provide unique information that diverges 

from the predominantly linear correlation observed in aggregator Web hits because their 

Web presence is the result of human intervention and is a measure of market importance 

or commercial interest. Indeed, the signal Web hits contain significantly more variability 

and do not correlate (ω sig= 1.1±0.2(γ) + 17.4±2.8, p > 0.5). Interesting outliers can be 

identified when the patents have a significantly higher ratio of signal Web hits to 

citations, such as those patents that are circled by the dotted lines in Figure 8. These data 

points highlight Web information, which may be unique compared to prior art 

information. Qualitative inspection of the highlighted patents shows that the Web hits 

result from an active description similar to the aforementioned Wikipedia example. Only 

one of seven highlighted patents has a high signal count that comes from a true error 

wherein the search terms occur in websites; these websites that use the same terms as 

designations for an unrelated company. These data indicate that while the measure is 

nuanced and requires some manual validation, the signal contained in these Web hits is 

different from the citation data and worth further investigation. 

To extend this initial investigation, we also compared two populations of patents, those 

that were licensed (n = 81) and those that were not (n = 304). An assumption is that 

licensed patents are of more commercial or market importance than the unlicensed 

patents [76]. We expect that they may have a different signature of Web hits as a 

measure of that commercial impact. Each patent received a mixture of aggregator (B)

and signal (A) Web hits (Figure 9), which appeared to have different signatures for 

licensed versus unlicensed patents. 
21

Figure 9. Signal (A, C) and aggregator (B, D) Web hits versus future prior art citations from 

patents for licensed and unlicensed National Renewable Energy Laboratory patents

To determine if there is a significant difference between the citation and Web hit 

signatures of the licensed versus unlicensed patents, the patents were analyzed using a 

simple regression that incorporated a dummy variable to indicate if each patent was 

licensed and determined the effect of being licensed on the correlation between citations 

(γ) and Web hits (ω): 

ω agg,sig = k1(γ ) + k2 (λ) + k3(γλ) + b
22

where λ defines if the patent is licensed (licensed = 1, unlicensed = 0), b is the y-axis 

intercept, and the k’s are constants. We observe significant differences in the signals of 

these patent populations—calculated results and stylized representations of the data for 

signal and aggregator Web hits results are presented (Figure 9C and 9D, respectively). 

While both scale linearly with patent citations, licensed patents receive more signal Web 

hits per citation than unlicensed patents. Licensed patents also appear to accumulate 

aggregator Web hits to a greater degree per citation than unlicensed patents. This 

acceleration of accumulating Web hits in licensed patents may allow this measure to 

appear more quickly than citations because of the delay in patent issuance [42], thus 

representing a harbinger of technical importance. 

Scholars use patent citation analysis to retrospectively assess the patent record to 

understand modes of collaboration, analogous to the work done in this study. However, 

this Web presence indicator may allow innovators, scholars, and research managers to 

obtain more dynamic and real-time intelligence regarding the commercial value of 

patents. For example, two unique patents—one commercially used and the other not—

are indiscernible in terms of vintage and number of citations but can exhibit significantly

different Web presence signatures. Since the mechanics of Web presence accumulation 

are materially different from those underlying citations, this novel measure provides new 

information on the value of a patent. Populations of licensed and unlicensed patents are 

known to be different given the ex post information about whether they have generated

commercial interest, and we demonstrate that the populations exhibit different signatures 

in the aggregator and signal Web hits that are statistically different. The differences in 

the signal Web hits are the expected differences in the commercial impact of the patents 

while the aggregator Web hits are likely an amplified measure of the technical impact of 

the patents, typically measured by citations. With further refinement, aggregator Web 

hits may be able to mirror analysis that currently uses citations to predict technical 

impact, while adding signal Web hits as a measure of commercial importance. 

Combination of these metrics may allow for easier and faster assessment of patent 

impact. While this measure is noisy and requires refinement, these preliminary data 

indicate that Web presence analysis may be able to help determine or predict commercial 

value of patents as distinguished from technical merit. 

In an attempt to extend these observations that signal Web hits are a measure of 

commercial importance, we performed the same Poisson regressions using signal hits that 

we performed on the patent citations to test differences between the factors producing

commercially relevant, rather than technically relevant, innovations. 

3.3 Breakthrough Sources

Technology importance (citation based) and commercial importance (Web presence

based) findings are summarized by technology in the following sections. Effect sizes are 

expressed in terms of absolute citation or Web hit count differences, and we report both 

observed and simulated variance of like patents. Observed mean values for the patent 

group of interest (b) are compared against all available control groups (a) for the contrast 

variable of interest. We report all significant findings (regressed or simulated p < 0.1); 

greater coefficient size and agreement between the two statistical methods indicates 

increased certainty. For each reported effect, there is an unreported significant finding in 
23

the opposite direction (a - b). For example, a simulated difference of +1.5 for Texas 

biofuels patents over New York patents (Table 5, line 2) denotes that we expect that 

patents from Texas receive 1.5 more citations than patents from New York (i.e., patents 

from Texas are on average 30% more highly cited). Inversely, New York biofuels 

patents are 24% less highly cited and therefore less technologically important than 

patents from Texas. Data herein is preliminary and represents a limited set of identified 

energy patents. However, it does provide interesting evidence of differences between 

technology areas and paths for future exploration in clean energy patenting and 

innovation. In the following sections, we provide descriptions of the sources of technical 

and commercial breakthrough patents in the solar, wind, and biofuels technology areas. 

3.3.1 Solar 

For technical importance, unassigned solar patents from independent inventors are 

significantly less cited than patents owned by universities, national labs, and corporations 

[29] (Table 3 for all solar data). Drawing on the previous discussion (§3.1.2.), while 

most solar patents came from corporations compared to independent inventors, it appears 

that those patents from corporations were more technically important. There likely may 

be some data artifacts here due to the technological differences between photovoltaics 

and solar thermal innovations, but the differences between sources of total patents 

compared to breakthrough patents is still relevant. Solar patents owned by corporations 

and universities are the most highly cited patents, with an insignificant citation increase

to university patents. We do not observe any strong relationship to funding sources. 

Increasing team size correlates with increasing technical breakthroughs. In terms of 

geographic effects, patents from California and Massachusetts are both highly cited. The 

effect in California is likely due to the predominance of the semiconductor industry and 

resultant solar-applicable technologies that are heavily concentrated there, [77] while the 

effect in Massachusetts is more likely driven by universities and a few highly innovative 

solar firms (e.g., Konarka and Evergreen Solar). Colorado patents are less highly cited 

relative to patents from other states. This observation may be due to the higher 

percentage of solar patents in Colorado originating from independent inventors than in 

the whole solar dataset (46% versus 33%, respectively). Singh and Fleming [29] have 

previously demonstrated that independent inventors often face challenges in bringing 

their technologies to fruition. However, the patents from these independent inventors 

enjoy an average citation count higher than the patents from the national laboratories (6.7 

versus 6.1, respectively), indicating that perhaps these patents are more technically

relevant. 
24

Table 3. Regressed and Simulated Determination of Sources for Solar Technical and 

Commercial Breakthrough Innovation

1 State designations: According to convention except Misc, which is all states excluding CA, MA, NY, TX, 

and CO. Funding designations: DOE, Department of Energy; NASA, National Aeronautic and Space 

Administration; DOD, Department of Defense; O-Govt, Other government agencies, (e.g., National 

Science Foundation); Other, All patents that did not have government rights identified in the patent. 

Assignee designations: Univ, Academia; Prvt, Corporations; Govt, Government and National Laboratories; 

None, Independent. Inventor designation: One Inv, a single named inventor; Two Inv, Two named 

inventors; Three+, Three or more named inventors. 

In solar, we generally observe weak signal significance for all findings of commercial 

importance. Colorado creates more commercially relevant solar patents than all other 

states. We observe a higher ratio of Web hits to citations from government patents than 

these independent patents (8.9 versus 6.0, respectively), which may be the result of truly 

higher commercial importance. However, the National Renewable Energy Laboratory 

advertises its available intellectual property on a website, the Energy Innovation Portal,5

 

5 DOE. “Energy Information Portal.” 

which may skew the results. Determining the sources of these differences in signal 

patterns warrants further investigation and again is essential for validating this 

methodology. 

http://techportal.eere.energy.gov/. Accessed February 16, 2011.
25

Texas appears to produce more commercially relevant solar patents than most other 

states. NASA appears to fund commercially important research, while no other 

government agencies do in this dataset. Universities dominantly produce more 

commercially impactful solar innovations. Again, increasing team size appears to 

produce more commercially impactful patents.

These preliminary data hint at a range of potential prescriptive actions that could be taken 

to accelerate the development of breakthrough innovations in solar. First, public and 

private research could focus on funding teams of researchers rather than individuals. 

Previous research tells us that independent inventors typically produce more failed 

inventions but also can create more disruptive breakthroughs because independent 

innovators do not have collaborators inducing “groupthink” [78, 79]. Despite the 

promise of radical breakthroughs, independent inventors on average produce lower value 

innovations and may hinder efforts to optimize research dollars toward increasing the

likelihood of major impact. Alternately, perhaps, independent innovators should 

continue to be allowed to innovate independently but then be provided with better 

mechanisms to support technology commercialization through collaboration with large 

organizations; e.g., the NREL Commercialization Assistance Program (NCAP).

6

3.3.2 Wind

 

Research increasingly shows that breakthrough innovations can be accelerated in 

environmental contexts that maximize cross-pollination and the free flow of ideas [79]. 

Our results provide quantitative insight on the effectiveness of particular geographic 

clusters and indicate which of these may be exploited. The data suggest that California 

may excel at producing important but incremental innovations, while Colorado may 

produce more commercially valuable innovations. While the institutional and social 

sources of these differences merit further investigation, innovation clusters are 

increasingly considered to be path-dependent: accumulated human, physical, and 

financial capital in a particular technology sector predisposes the cluster to future 

innovation in that sector [80, 81]. Building on this observation, universities with 

historically important solar innovations should be looked to as breeding grounds for new 

commercially impactful breakthroughs, while corporations may be better at creating 

technical breakthroughs that incrementally enrich the technological landscape of the 

whole industry. Finally, on a more qualitative bent requiring further research, it is 

apparent that NASA has effectively sponsored high impact research from both a technical 

and commercial perspective. Qualitative assessment of their funding priorities, process, 

and management may provide additional insights on how to improve other public 

departments’ efforts. 

DOE- and NASA-funded wind patents are cited more strongly than other governmentfunded wind patents (Table 4 for all wind data). Larger teams are associated with more 

highly cited patents. Texas wind patents are less highly cited than those from other states. 

It appears that this is due to a high percentage of independent inventors (55% versus 49% 

average) that produce uncharacteristically low value patents (average independent Texas 

citations = 4.3 versus average independent citations for all wind patents = 5.9). Wind 

 

6 NREL. “Commercialization Assistance Program,” http://www.nrel.gov/technologytransfer/ncap.html. 

Accessed March 7, 2011.
26

patents from corporations appear to be the most highly cited. As previously noted, the 

number of collaborators per patent has been steadily increasing in wind and renewable 

energy [29, 67] (Figure 6), and this trend also appears to positively affect the likelihood 

of breakthroughs in wind. While we cannot currently extrapolate how multidisciplinary 

the teams are [37], this finding indicates that work to find breakthrough technical 

innovations should be purposefully collaborative. 

For commercial breakthroughs, Colorado produces significantly more commercially 

relevant wind patents. This effect may be due in part to the presence of the National 

Renewable Energy Laboratory, which has produced 18% of the wind patents in Colorado. 

However, the more commercially impactful wind patents come from independent 

inventors: independent patents receive more than twice as many signal Web hits than the 

average for all wind patents. The wind sector also appears to be unique in that 

independent inventors excel in both technical and commercial impact. 

Table 4. Regressed and Simulated Determination of Sources for Wind Technical and 

Commercial Breakthrough Innovation

1 Contrast Variable definitions: see Table 3. 

3.3.3 Biofuels 

We found that government funding had a significant impact on citations as a measure of 

highly important technical innovations (Table 5 for all biofuels data). DOE-funded 

research that resulted in biofuels patents was less cited relative to “other” government 

sources. Similar to DOE, NASA biofuels patents suffered a penalty compared to other 

government funding sources. Inventor teams did not benefit from greater size in biofuels, 

which runs counter to recent research [29, 67] and our findings in wind; this anomaly 

bears investigation. This analysis did not observe any strong geographic effect on where 

breakthroughs are coming from, except to note that patents from New York appear to 

have slightly lower citation rates. More than half of the 40 biofuels patents in the group 
27

from New York were invented by two people from Texaco Inc. and were related to fuel 

additives or mixtures. We cannot rationalize the observed citation penalty from these 

data but suggest that these particular inventions play a role in weighing down the citation 

count in this geography. Finally, we observe a weakly significant signal for university 

patents being more highly cited than inventions from independent inventors; no other 

significant effects from inventing institutions are observed. 

Table 5. Regressed and Simulated Determination of Sources for Biofuel Technical and 

Commercial Breakthrough Innovation

1

 

Contrast Variable definitions: see Table 3. 

For commercial breakthroughs, the only geographic effect that we observe is that biofuels 

patents from Massachusetts are highly discounted in their commercial impact. The single 

largest assignee of biofuels patents from Massachusetts is the Massachusetts Institute of 

Technology (MIT) (n = 4) with a single inventor responsible for all these patents. MIT is 

highly lauded as an innovation and entrepreneurship center, and we observe a strong Web 

hits signal for these patents—in fact, one of these patents is also the most highly cited—

so we expect the observed penalty is not from these patents. The remaining patents in the 

dataset are for a diverse group of corporations and independent inventors. 

There are some important differences in the intellectual property practices of different 

market players that need to be properly accounted for to improve this measure of Web 

presence. The observation of a weak Web hit signal from corporations may be the result 

of two related but distinct factors: (1) Universities must “advertise” their inventions to 

get them licensed and into commercial practice, which could increase their Web hits 

measure, (2) Corporations may purposefully not advertise their innovations and, if they 

are a small company without much outside interest in the blogosphere, may avoid 

detection by the Web community, decreasing their Web hits signal. Determining which 

of these factors are at play requires further investigation and is essential to the correct use 

of the Web hits measure but is beyond the scope of this paper. There is a strong but not 

very significant observation that biofuels patents from universities and national 

laboratories are more commercially relevant. We do not observe any effect of funding or 

team size on the commercial importance of biofuels patents. 
28

These empirically derived findings contribute to the dialogue on where and how 

innovations, and energy innovations in particular, originate and how we can create more 

of them. In particular, it has been widely accepted that collocated communities of 

practice—universities, corporations, and venture capital—are helpful and perhaps 

essential for creating vibrant innovation ecosystems [82, 83]. Indeed, in many industries, 

such as those related to energy including biotechnology (biofuels) and semiconductors 

(solar), the areas of Silicon Valley [84, 85] and Greater Boston [82, 86] are indicated as 

innovation and company hotbeds. However, these data demonstrate that indentified 

biofuels patents from California are mildly discounted when compared to all other 

patents. Similarly, the patents from Massachusetts, while perhaps technically important, 

have limited commercial importance. We realize the limitations of these interpretations 

and hesitate to extend these observations much further without significantly more 

investigation. However, unique technology development and deployment dynamics exist 

within the energy sector due to the nature of the technologies and markets. These unique 

differences require us to reevaluate how public and private research, development, and 

deployment capital is used.

Biofuels is our smallest dataset, with only 938 patents. Each of the geographical, social, 

and institutional patent sub-populations is proportionally limited, so finding statistically 

significant correlations between the patent populations is more difficult than in the solar 

and wind sets. Furthermore, the work of prescribing reliable funding strategies becomes 

more tenuous as the sample size decreases, so we will focus our preliminary prescriptive 

efforts on the richest dataset available—solar patents. However, we expect to be able to 

add prescriptive detail as patent sample sizes increase through improved search methods. 

3.4 Predicting Breakthroughs

Through a retrospective analysis of energy patents, we have demonstrated the ability to 

ascertain key drivers of breakthrough technical and commercial impact. The introduction 

of the new measure of screened Web hits as a predictor of commercial value shows 

significant promise as a tool for bibliometric researchers. The logical next step is to 

determine if this type of analysis can be used predicatively to anticipate breakthrough or 

commercially impactful innovations. While we are admittedly a long way from being 

able to do this with any assurance of accuracy, we define the following equation in an 

attempt to begin a thread of inquiry wherein scholars and practitioners alike can 

anticipate the value of a patent. For the licensed and unlicensed NREL patents, we 

calculate the following ratio for each one: 

( ) ( ) sig sig agg −+−= ωωωωκ agg

where κ is the calculated potential impact of the patent, and the average signal or 

aggregator Web hit value is subtracted from the unique patent Web hit value. If κ for a 

patent is positive, that patent is more likely to be impactful or commercially relevant. 

Indeed, for our two populations of patents, 62% of the licensed patents are correctly 

tagged positive while only 30% of the unlicensed patents are. Furthermore, comparing 

the average commercial potential for the licensed κ ( )lic versus unlicensed κ ( ) unlic patents 

by year shows that the relationship predominantly correctly predicts commercial value 
29

for the licensed patents within a given year (Figure 10) while accounting for truncation in 

both citations and Web hits. For each year, the calculated variable generally correctly 

approximates commercial value of the licensed patents higher than the unlicensed 

patents, i.e. κ lic -κ unlic > 0. Similar to citations (Figure 1), Web presence appears to suffer 

a degree of signal truncation, although less severe than citations: patents from the last 

decade average at least 15–20 signal Web hits. Hence, albeit with significant future 

refinements, we believe that it is possible to construct a predictive relationship using the 

Web hits metric. Furthermore, this measure may be a complement to citation analysis 

and may appear earlier than those records due to the delay in citation evolution. 

Figure 10. Predictive commercial value of patents using proposed Web hits relationship
30

4 Suggestions for Further Research

The data herein rely heavily upon a keyword identification of clean energy patents that 

focus on late-stage innovations. Future work needs to update and make available 

identification and categorization using a more general and flexible ontology comprised of 

a mixture of keyword, patent classification code, and assignee-based identification that is 

simultaneously inclusive of earlier-stage technologies but still accurate.

Significant work will need to be undertaken to further validate the Web-presence 

methodology. It would be extremely difficult to do this for the complete patent dataset 

due to the labor intensity of determining whether each patent has demonstrated 

commercial value. However, future work could include other institutions’ licensed 

versus unlicensed patents as a method of calibrating this method. Qualitative surveys 

with active inventors will enrich this line of analysis. Lines of inquiry may include the 

inventor’s qualitative assessment of the patent’s value, the method of commercialization 

(start-up, license to large firm, license to non-profit, not deployed and why), and the level 

of commercial success. Additional focus on refining data quality around regional, 

organizational, collaborative, and institutional factors, as well as the funding sources for 

the research (particularly if the research was supported by a government grant) would be 

helpful. Also, further effort should be expended in the automated analysis of the content 

of each type of website. A variety of schemes could be investigated [87], including 

simple search term counts, dictionary look-up of appropriate terms, and semi-supervised 

machine learning algorithms (at least for the patents with enough hits apply such methods 

[88]).

We hope that this work will contribute to an open platform for future research by the 

community of science and policy scholars. The focus in this proposal on clean energy is 

just one slice of this effort. We hope to build, through the collaborative work of many 

scholars, a series of integrated databases that would enable the tracing of scientific 

investment from grants all the way to commercialization. Figure 11 illustrates an 

idealized schematic of this goal. This effort would help accelerate the development of a 

system where most grants could be traced to papers, papers to papers through citations, 

papers to patents through people and citations, and patents to commercial outcomes 

through people (for example, on boards of directors), licensing agreements, and the 

proposed Web-based measure of commercial impact and deployment. This would be a 

powerful tool for both DOE and the research community to better assess the value and 

success of research and development investments. This capability will be essential as the 

market quickly evolves, research investments increasingly come from the private sector, 

and there is a call for increased transparency on public research expenditures. Further 

development of this database and the addition of new datasets outside our control through 

an open-research model will enable this tool to be broadly used to investigate innovation, 

commercialization, and deployment for DOE and the public sector in general.
31

Figure 11. An idealized schematic of how future databases might be built and linked in 

order to understand how science investment influences economic outcomes

GRANTS PAPERS PATENTS ECON/SOCIAL

Compustat

USPTO

NIH

Pubmed

VC dbases

EPO

NSF

Private

IEEE

DOE

Web 

Impact

measures
32

5 Conclusions

This work has expanded existing methods of patent analysis, introduced novel methods 

of Web presence analysis, and applied both to the empirical investigation of energy 

innovation. Preliminary results confirm and provide quantification of the notion that 

different energy technology areas exhibit unique patterns of invention and innovation. 

Generally, all energy sectors exhibit relatively isolated collaboration networks compared 

to the rich collaboration networks observed in other sectors (e.g., software and 

pharmaceuticals). The macro factors shaping these more collaborative industries help us 

understand the industrial, regulatory, and technical features that limit energy innovation 

and result in policy suggestions to drive richer collaboration in energy. 

For energy as a whole, our results confirm the general notion that policymakers should 

encourage multidisciplinary collaboration, university and national laboratory 

commercialization efforts, and tailored support for regional innovation clusters. These 

insights may be extremely helpful in the development and implementation of two new 

initiatives, the Energy Frontier Research Centers and the Energy Innovation Hubs. These 

initiatives are focused on discrete technology questions with specific innovation targets. 

The analytical tools now at our disposal could help evaluate what are important social, 

sectoral, market, and geographic considerations. For example, the Hubs are envisioned 

as “multidisciplinary, multi-investigator, multi-institutional integrated research centers ... 

each comprise[d of] a large set of investigators spanning science, engineering, and policy 

disciplines focused on a single critical national need identified by the Department [of 

Energy] [89].” With enough data resolution, the analytical tools described in this work 

can help evaluate the proposed institutional arrangements and identify structures that can 

improve the likelihood of success. 

Importantly, our results indicate that the dynamics of energy innovation are 

heterogeneous across sectors, and the relative benefits of a particular strategy are unlikely 

to translate smoothly from one technology area to another. Policies to foster innovation 

and the deployment of those innovations need to be tailored to each specific technology 

and its revealed pattern of innovation. This capability is important as the DOE embarks 

on a series of experiments to drive innovation across multiple sectors. 

With further work, we expect to increase the size and interconnectedness of the energy 

patents database, achieve higher resolution on social, geographic, and institutional 

variables, and refine the statistical methods of investigating linkages between 

morphologies of invention and the production of breakthroughs. Together, these 

objectives will constitute a novel analytic and technical foundation for informing 

innovation policymaking. 
33

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Clean Energy Innovation: Sources of Technical and Commercial 

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Thomas D. Perry IV and Mackay Miller (NREL)

Lee Fleming (Harvard Business School) 

Kenneth Younge (University of Colorado) 

James Newcomb (NREL, Current Affiliation: Rocky Mountain 

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14. ABSTRACT (Maximum 200 Words)

Low-carbon energy innovation is essential to combat climate change, promote economic competitiveness, and 

achieve energy security. Using U.S. patent data and additional patent-relevant data collected from the Internet, we 

map the landscape of low-carbon energy innovation in the United States since 1975. We isolate 10,603 renewable 

and 10,442 traditional energy patents and develop a database that characterizes proxy measures for technical and 

commercial impact, as measured by patent citations and Web presence, respectively. Regression models and 

multivariate simulations are used to compare the social, institutional, and geographic drivers of breakthrough clean 

energy innovation. Results indicate statistically significant effects of social, institutional, and geographic variables on 

technical and commercial impacts of patents and unique innovation trends between different energy technologies. 

We observe important differences between patent citations and Web presence of licensed and unlicensed patents, 

indicating the potential utility of using screened Web hits as a measure of commercial importance. We offer 

hypotheses for these revealed differences and suggest a research agenda with which to test these hypotheses. 

These preliminary findings indicate that leveraging empirical insights to better target research expenditures would 

augment the speed and scale of innovation and deployment of clean energy technologies.

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patents; clean energy; Web hits; innovation; breakthroughs; low carbon

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