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Effects of Government Incentives on Wind Innovation in the United States Nathaniel Horner*

*Department of Engineering and Public Policy, Carnegie Mellon University

Advisors: Inês Azevedo,* David Hounshell†*

†Department of Social and Decision Science, Carnegie Mellon Univerisity

Center for Climate and Energy Decision Making

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Photos (top to bottom): (1)  Wind dynamo installed by inventor Charles F. Brush in his Cleveland

backyard, operational from 1888-1908. 12 kW, 56’ diameter rotor. [http://wiki.windpower.org/index.php/Charles_F._Brush]

(2)  Smith-Putnam wind turbine at Grandpa’s Knob, VT. Private venture produced first megawatt-class turbine in 1941. [http://www.wind-works.org/photos/Smith-PutnamPhotos.html]

(3)  Jacobs wind motors exemplified the small wind plants installed throughout the American plains from the 1930s through the 1950s. [http://www.wincharger.com/jacobs/jacobsphoto.jpg]

(4)  One of three DOE/NASA MOD-2 2.5 MW Boeing turbines erected at Goodnoe Hills, WA in 1980. [http://en.wikipedia.org/wiki/File:Mod-2_Wind_Turbine_Cluster3.jpg]

(5)  Wind farm at Altamont Pass, CA, using “low technology,” rugged Danish design. [http://www.theepochtimes.com/n2/science/wine-turbines-bats-4075.html]

(6)  Modern Vestas V90 3 MW wind turbine [http://www.rechargenews.com/business_area/finance/article197073.ece]

Research Focus

History has shown that government policy has a profound effect on wind generation capacity (Righter 1996). In addition to policy and market drivers, the viability of the wind industry also depends on the state of the technology. This project examines the interplay between policy and innovation in wind technology in the United States. I use a regression model relating various policy variables to patent counts, which is a proxy measure of innovation.

Innovation Policy Mechanisms There are several ways to categorize innovation policy dynamics in the literature: •  Demand Pull vs. Technology Push, where innovation is either driven

by the market (pull) or directed advances in science and technology (push). Demand-pull policies increase private payoffs, while technology-push policies reduce private costs. [1]

•  Market incentive vs. Command-and-control (“carrots” vs. “sticks”), where policies incentivize desired behavior either by adjusting the market (e.g., through tax policy) or by mandating compliance (e.g., through renewable portfolio standards).

•  Downstream vs. Upstream vs. Interface, where policies invest in technology sources, “market creation,” or improving the interface between the two [2].

I broadly categorize policies as aimed at either technology development or market development and include the following policies in the study:

Model & Results

Using the patent and policy data shown in Fig. A in addition control variables (the overall level of patenting activity, electricity price, electricity consumption, and GDP), I ran several regressions of the following general negative binomial form: Results for four different formulations are shown in the table. Model 1 includes individual RPS targets, with a 2-year horizon, for four of the largest wind-producing states. Model 2 instead uses the aggregate RPS target for all 50 states. Models 3 and 4 are identical to Models 1 and 2, respectively, except that they include only patents originating in the U.S.

Wind Technology Innovation

I measure innovative activity using patent counts. While there are some important caveats, the availability, richness, and correlation with R&D activity make patent counts a common proxy for innovation [3] [4]. A hybrid search on the U.S. Patent and Trademark Office (USPTO) database using wind-related patent classes and as well as keywords identified approximately 3500 wind technology patents filed in the U.S. between 1974 and 2009. Patenting activity has been increasing rapidly over the past fifteen years (Fig. A). This analysis investigates which policy variables are most highly correlated with this rise in patenting activity. Two distinct phases of U.S. policy can be observed: a decade of high R&D and investment tax credits from the late ’70s to the mid ’80s, and a period of lower R&D coupled with the PTC and rising state-level RPSs beginning in the mid-’90s. The study differs from other work in this area by focusing exclusively on the United States and by creating continuous measures for policy variables in an effort to capture policy attributes, such as the volatility in the PTC. While it is tangential to the model, we can observe that foreign firms have accounted for an increasing portion of wind patents in the U.S. (Fig. B).

Conclusions

While there are some important limitations to this study’s approach, the analysis indicates the following general conclusions:

•  Renewable Portfolio Standards have more effectively spawned innovation in the U.S. wind industry than have tax credits and R&D.

•  The PTC has not been particularly effective at promoting innovation.

•  Federal R&D has not been as effective as might be expected. While some have found that R&D has been effective [5], this paper supports the conclusion, found elsewhere, that federal wind energy R&D has been—at the very least—misallocated [6] [7] [1]

•  The anticipatory timing of the positive effect on R&D due to a state’s RPS varies by state. For instance, CA’s RPS had a greater and more significant effect 2-3 years in advance of its implementation, while TX’s target is most effective at only 0-1 years out.

•  Foreign firms have responded to the growing U.S. wind market more markedly than have domestic firms.

Finally, it is important to recognize that these results speak to the policy relationships innovation only, whereas the primary goal of such policies is to increase deployment.

References [1] Nemet G F 2009 Demand-pull,

technology-push, and government-led incentives for non-incremental technical change Research Policy 38 700–9

[2] Taylor M R 2008 Beyond technology-push and demand-pull: Lessons from California’s solar policy. Energy Economics 30 2829–54

[3] Griliches Z 1998 Patent statistics as economic indicators: a survey R&D and Productivity: The Econometric Evidence (Chicago: University of Chicago Press) 287–343

[4] Johnstone N, Kalamova M and Ha!"i" I 2010 Environmental policy characteristics and technological innovation Economia politica 27 275–299

[5] Ruegg R and Thomas P 2009 Linkages from DOE’s Wind Energy Program R&D to Commercial Renewable Power Generation (Washington, DC: US Department of Energy)

[6] Righter R W 1996 Wind Energy in America: A History (Norman, OK: University of Oklahoma Press)

[7] Musgrove P 2010 Wind Power (Cambridge: Cambridge University Press)

Market Development! Technology Development •  Investment Tax Credits

(Federal and CA, 1978-1985) •  Production Tax Credit (PTC)

(1992-present, with lapses in ’99, ’02, ’04).

•  State-level Renewable Portfolio Standards (RPSs)

•  DOE Wind Program R&D

(The timeline of these policies, juxtaposed with patenting

activity, can be seen in Fig. A.)

Frequency

1975 1985 1995 2005

50

100

150

200

250

300

350

No.

Win

d Pa

tent

s

Proportion

1975 1985 1995 2005

0.0

0.2

0.4

0.6

0.8

1.0

US

DE DK

JP

Other

Prop

ortio

n W

ind

Pate

nts

Wind Patents by Country Over Time

0

100

200

300

400

Wind Patenting Activity and Policy Timeline

Year

Win

d Pa

tent

s

1975 1980 1985 1990 1995 2000 2005 2010

Investment Tax Credit

Production Tax Credit(Lapses in '99, '02, '04)

0

50

100

150

200

250

DO

E W

ind

R&D

(201

1 $m

illion

s)

Wind R&D

Solar Energy Research Act (DOE Wind Program)

Wind Energy Systems Act

RE & EE Tech. Competitiveness Act

American Recovery and Reinvestment Act

0

5

10

15

20

No.

Sta

tes

w/ R

PS P

olic

ies

RPS PoliciesWind Patents

A

B

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(1) (2) (3) (4) State Aggregate US-State US-Aggregate

DOE R&D 0.00325 *** 0.00315 *** 0.00347 ** 0.00351 ** ($millions) (0.00097) (0.000921) (0.00125) (0.00119)

PTC Horizon 0.000107 0.000212 -0.000361 -0.000386 (months) (0.00241) (0.00219) (0.00317) (0.00286)

ITC-Fed 0.0179 0.0177 0.0268 0.0252 (%-pts) (0.012) (0.0116) (0.0155) (0.0149)

ITC-CA 0.00989 0.00957 0.0138 0.0134 (%-pts) (0.00652) (0.00633) (0.00846) (0.00817)

RPS-CA2 0.0283 *** 0.0105 (annual TWh) (0.00804) (0.0117)

RPS-TX2 0.109 ** 0.152 * (annual TWh) (0.0421) (0.0615)

RPS-IA2 0.117 0.148 (annual TWh) (0.76) (1.023)

RPS-MN2 0.121 * 0.107 (annual TWh) (0.0557) (0.0799)

RPS-Tot2 0.0295 *** 0.0253 *** (annual TWh) (0.00201) (0.00267

All Patents 0.0119 *** 0.013 *** 0.00619 0.00577 (thousands) (0.00273) (0.00239) (0.00374 (0.00324

Elect. Price -0.0358 -0.0256 -0.19 -0.177 (cents/kWh) (0.0864) (0.08) (0.113 (0.103

Elec. Cons. -0.000937 *** -0.00103 *** -0.000797 * -0.000773 ** (annual TWh) (0.000236) (0.000221) (0.000314) (0.000296)

N 36 36 36 36 Log-likelihood -139.3 -138.9 -127.9 -127.5 !2 115.1 115.8 80.85 81.69 p > !2 0.00 0.00 0.00 0.00 Standard errors in parentheses * (p<0.05) ** (p<0.01) *** (p<0.001) 1

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