Public Investments in Sustainable Technology:
An Evaluation of North Carolina’s Green Business Fund
Michael J. Hall
Department of Economics
University of North Carolina at Greensboro
Greensboro, NC 27402
mjhall@uncg.edu
Public Investments in Sustainable Technology:
An Evaluation of North Carolina’s Green Business Fund
With careful management, new and emerging technologies offer enormous opportunities
for raising productivity and living standards, for improving health, and for conserving the
natural resource base.
— United Nations (1987, p. 182)
Introduction:
The passage above, written over twenty-five years ago, expresses hope that technological
advances can improve the human condition while enhancing environmental sustainability. In the
interim, concerns have spurred the implementation national and international policies to address
environmental sustainability issues, many of which are tied to technology policies. Some of these
policies directly support investments in the research and development (R&D) of technologies
that mitigate the negative impacts of human activity on the environment. Here, these
technologies are called sustainability technologies.1
Economic arguments support the nexus of environmental and technology policies; not only is
there a market failure with regard to environmental externalities, but also there is a market failure
with regard to an underinvestment in sustainability technologies. Inefficiencies in the market for
sustainable technologies can lead to uncertainty in the market for innovative activity directed
towards environmental technologies. Meanwhile, the inefficiencies in the market for innovative
activity reduce the incentives for firms to invest in environmental technologies that could
mitigate inefficiencies in the market for sustainability. As Jaffe and Newell (2005, p.166)
summarize: “[This dual-market] problem compounds, because independent of the externality
associated with pollution, innovation and diffusion are both characterized by externalities as well
as other market failures.”
The public sector’s role in addressing this dual market problem is to remove barriers that
bring about market inefficiencies. When facing the dual set of inefficiencies associated with
1
The benefits from implementing sustainability technologies can take many forms such as reductions in the costs of
pollution abatement, mitigation of future negative impacts of environmental externalities, increases in energy
efficiency, the development of new and non-polluting energy sources, and increased efficiency in the use of nonrenewable resources. Sustainability technologies can take the form of improvements in or the development of
entirely new products or processes.
1
sustainability and innovation, the government can choose from among several approaches to
influence the markets. The government can address each market separately, address the dual
market problem jointly, or use a combination of policies that address each market individually as
well as the dual market.
Addressing only one half of the dual market leaves the potential for inefficiencies to
influence investments in R&D towards environmental technologies. If government choses to
address only the market for innovative activity, it could still be that a lack of market demand
could prevent the diffusion and adoption of environmental technologies. Addressing only the
market for sustainability neglects the spillovers and uncertainty inherent in the market for
innovations. Alternatively, the government could address the dual market by incentivizing
investments in environmental technologies directly.
Targeting the dual market ensures that neither dimension of the market failure is wanting for
policy remedies, although the effect may be limited without policies that address other
inefficiencies. The government could also choose to use a mix of policies, thereby addressing
both markets independently while simultaneously addressing the dual market. For example,
Popp et al. (2010) concluded from their review of the policy literature on the relative efficiency
of addressing the two sides of the dual market individually or jointly that the most effective
means of addressing barriers to environmental innovation is a portfolio of approaches that
includes policies for each market individually as well as policies that span both sides of the dual
market.
Sustainability technology programs are a subset of the policies that overlap the
environmental sustainability and technology markets. These programs directly support
investments in innovative activities whose purpose is to develop new sustainability technologies.
This paper examines one such sustainability technology program, North Carolina’s Green
Business Fund (hereafter, the Fund), from the perspective of quantifying the net social benefits
attributable to that program. Insight gained from evaluating the Fund may prove to be useful to
policy makers and perhaps lead to a more efficient allocation of public monies.
The remainder of the paper is outlined as follows. In Section II, public policies toward the
dual market problem are summarized at both the national level and the state level in an effort to
place the Fund within the spectrum of public policies toward the development of sustainable
technologies. The specifics of the Fund are summarized in Section III. Section IV describes the
2
data used in this paper to evaluate outcomes attributable to the Fund, and the implemented
evaluation model developed is described and implemented in Section V. The paper concludes in
Section VI with summary remarks.
II. Public Policies toward the Dual Market Problem
The United States has a notable record of promulgating policies that overlap the
environmental sustainability and technology markets. Most of these policies focus primarily on
sustainability concerns, but several include measures to address dual market issues. See Table 1
for a summary.
Table 1 about here
States have also implemented sustainability technology programs. Eleven states have
implemented a total of 21 such programs as summarized in Table 2.2 Most of these programs
were established within the last 20 years. New York was the first state to implement a
sustainability technology program, establishing the New York State Energy Research and
Development Administration (NYSERDA) in 1975. Since then, as shown in Figure 1, the
cumulative number of states with sustainability programs has increased exponentially.
Table 2 about here
Figure 1 about here
III. North Carolina’s Green Business Fund
The Fund was established by North Carolina General Statute §143B-437.4(a):
The NC Green Business Fund is established as a special revenue fund in the Department
of Commerce. … The Department of Commerce shall make grants from the Fund to
private businesses with less than 100 employees, nonprofit organizations, local
governments, and State agencies to encourage the expansion of small to medium size
2
The author estimated the probability of a state having implemented a sustainability technology program as a
function of state-level characteristics. The estimation results indicate a quadratic relationship, with the linear term
being positive, between the proportion of state domestic product spent on R&D and it having implemented a
sustainability program. These results are available from the author on request.
3
businesses with less than 100 employees to help grow a green economy in the State.
Moneys in the NC Green Business Fund shall be used for projects that will focus on the
following three priority areas … [t]o encourage the development of the biofuels industry
… [t]o encourage the development of the green building industry … [and to] attract and
leverage private-sector investments and entrepreneurial growth in environmentally
conscious clean technology and renewable energy products and businesses.
The North Carolina Board of Science and Technology (hereafter, the Board), within the
North Carolina Department of Commerce, operated the Fund during fiscal years 2008 through
2011. Their statement of the purpose of the Fund was more focused than that in the General
Statute: (http://www.ncscitech.com/grant-programs/green-business-fund):
[To provide] competitive grants to help N.C. small businesses develop commercial
innovations and applications in the biofuels industry and the green building industry, as
well as attract and leverage private sector investments and entrepreneurial growth in
environmentally conscious technologies and renewable energy products and businesses.
Projects targeted by the Fund fall into one of three categories: projects that focus on biofuel
development and production, projects that focus on green building technologies, and projects that
promote private investments in green industry in North Carolina.
North Carolina provided grant funding for the program for fiscal years FY2008 and FY2009,
while the American Recovery and Reinvestment Act (ARRA) provided funding for FY2010 and
FY2011. This paper considers only state investments in FY2008 and FY2009.3 This choice of
years was based on the pragmatic consideration that detailed data about the program were
available for only those two years—the Board conducted a survey in CY2012 of organizations
that received grants in FY2008 and FY2009, and those data form the empirical basis for this
paper.
The Board awarded $950,000 in both FY2008 and FY2009. Of the 85 applications received
in response to the FY2008 solicitation, 63 were in compliance with the application requirements
that were set forth by the Board. Of those 63, 13 were selected to receive grants. FY2008 award
3
This delimitation should not be interpreted to mean that ARRA funding of green businesses in North Carolina or
elsewhere is of lesser importance. On the contrary, efforts toward government accountability at all levels are
important and such an evaluation effort should be undertaken in the future.
4
amounts ranged from $18,000 to $100,000. For the FY2009 solicitation, 14 additional
organizations received grants ranging from $40,000 to $99,486. Awardee profiles are in Table
3.
Table 3 about here
IV. Board of Science and Technology Survey of Grant Recipients
The General Assembly in North Carolina periodically and systematically reviews agencies,
divisions, and programs financed by State government. This process is known as the
Continuation Review Program. The Continuation Review Program is intended to assist the
General Assembly in determining whether to continue, reduce, or eliminate funding for a
particular agency/division/program. The State’s Office of Science and Technology (hereafter,
the Office), which houses the Board, underwent a continuation review in CY2012. As part of
that review, the Office systematically collected survey information from the organizations
funded in FY2008 and FY2009 through the Fund.
Two sources of data provide background information on grant recipients, the Board’s survey
and time-of-award characteristics.4 Of the 27 organizations that received grants over the two
solicitations, 24 completed or partially completed the survey. This represents an 89 percent
response rate. Of the 24 recipient organizations, 21 are private-sector companies and 3 are
public-sector organizations. In addition to data from the survey, time-of-award data are available
for all grant recipients from the Board.
Summary statistics for the 27 recipient organizations on selected variables from the time-ofaward data are in Table 4. These data are presented to describe the representativeness of the 24
organizations that responded to the survey. Included in Table 4 are: the size of the grant (Grant),
age of the recipient organization as of the 2012 survey (Age), employees at the time of the award
(Employ), percent of organizations that are C-Corporations (C-Corp), and percent of respondents
that are public-sector organizations (Public). A visual inspection of the data in Table 4 suggests
that response bias is not an issue of concern. Results from t-tests of differences in means
indicate population means for respondents and non-respondents are not statistically different.
4
These time-of-award data were supplied by applicants as part of the application process.
5
This descriptive comparison suggests that the sample of 24 respondent grant recipients is
representative of the grant-recipient population.
V. An Evaluation of the Green Business Fund
The following models aim to examine the social benefits of publicly funded technology
programs. To do so, the total surplus of a program, conditional on the elasticity of demand for
the innovations, is compared to cost of operating the program. The models yield a calculated
elasticity of demand below which the benefit-to-cost (B/C) ratio of the program is greater than
one. That is to say, if the actual elasticity of demand is lower than the calculated value then the
program’s social benefits will be greater than its costs. Therefore, calculated elasticities of
demand that are relatively larger provide stronger support that the benefits of a program
outweigh the costs.
Allen et al. (2012) developed a benefit-to-cost (B/C) model under several assumptions.
Assume that each award recipient (hereafter, firm) develops, from the research funded by the
grant, a unique innovation so that it has or will have, at least temporarily, a monopoly position in
the market. Assume as well that the firm faces a linear demand function given as 𝑝 =
𝑎 – 𝑏𝑥, where p represents price and x represents output quantity; and assume that marginal and
average cost are constant.
Following Allen et al. (2012), profit, π, or producer surplus (PS) can be calculated as the total
receipts from the sale of the innovation, 𝑝𝑥, less the total cost of production, 𝑐𝑥, as:
(1)
𝑃𝑆 = (𝑝 − 𝑐)𝑥 =
(𝑝−𝑐)𝑝𝑥
𝑝
The profit maximizing monopolist (*) will price its output where the profit margin equals the
inverse of the elasticity of demand (𝑒):
(2)
(𝑝∗−𝑐)
𝑝∗
1
= 𝑒 . Thus, equation (1) simplifies to:
1
𝑃𝑆 = 𝑒 𝑝∗ 𝑥 ∗
And, under the assumption of a linear demand, consumer surplus (CS) is:
6
1
𝐶𝑆 = 2 (𝑎 − 𝑝)𝑥
(3)
For profit maximization, marginal revenue (MR) equals marginal cost (c):
𝑀𝑅 = 𝑎 − 2𝑏𝑥 = 𝑐
(4)
Solving equation (4) for the profit maximizing price and output quantity yields 𝑥 ∗ =
𝑝∗ =
𝑎+𝑐
2
𝑎−𝑐
2𝑏
and
1
. Substituting these results into equation (3) yields 𝐶𝑆 = 2 𝑃𝑆. Given equation (2), it
follows that:
(5)
1
𝐶𝑆 = 2𝑒 𝑝∗ 𝑥 ∗ .
Equations (1) and (5) were used by Allen et al. (2012) to calculate the value of total surplus (TS):
(6)
𝑇𝑆 =
1
2𝑒
1
𝑝∗ 𝑥 ∗ + 𝑒 𝑝∗ 𝑥 ∗
One may solve for the value of e at which B/C = 1 by setting equation (6) equal to fixed costs
(C). 5 This elasticity value will be denoted as e* and it represents the threshold elasticity, below
which B/C > 1. If the actual elasticity of demand is below e* then the social benefits resulting
from the technology development will be greater than the costs.6
The Allen et al. (2012) model can be extended in two ways: the assumption that the firm
faces a linear demand can be altered, and the assumption that there are no substitutes for the
firm’s innovation can be relaxed.7 Changing these assumptions allows for a test of the
robustness of the Allen et al. (2012) model.
The extended model relies on several assumptions. First, the model assumes that the
innovator is able to exert monopoly power. This assumption is carried over from the Allen, et al.
(2012) model and is supported by the fact that patent protection allows innovators a time-limited
5
Costs (C) here are fixed costs of developing the technologies. These fixed costs include Fund grants and,
potentially, additional investments by award recipients.
6
This is conditional on the validity of the model assumptions.
7
A step-by-step derivation of the extended model is presented in Appendix A.
7
monopoly on their innovations. Second, the initial specification of the extended model assumes
that no close substitutes exist. This assumption will be relaxed in later model specifications.
Close substitutes are considered to be goods that provide outputs that are similar to the
innovation but are not necessarily technologically inferior versions of the innovation.8 And
third, the model assumes an isoelastic demand, which is expressed as:
𝑥 = 𝑎𝑝−𝜀
(7)
The terms in equation (7) are defined as follows: as above, 𝑥 is the quantity demanded and 𝑝 is
the price of the good; 𝜀 is the own-price elasticity of demand, which is assumed to be greater
than one; and 𝑎 is a constant.
Total surplus (TS) is the sum of producer surplus (PS) and consumer surplus (CS). By
applying the inverse elasticity rule, one can express producer surplus as the value of revenues
divided by the elasticity of demand. This condition is expressed as:
1
𝑃𝑆 = (𝜀 ) (𝑝∗ 𝑥 ∗ )
(8)
Again, 𝑝∗ and 𝑥 ∗ are the monopolist’s profit-maximizing price and quantity, respectively.
However, these values are likely to differ than those derived under the assumption of linear
demand.
Consumer surplus is defined to be the area under the demand curve above the price. This
area is expressed as the following integral:
(9)
∞
𝐶𝑆 = ∫𝑝∗ 𝑎𝑝−𝜀 𝑑𝑝
This integral can be simplified and yields the solution:
(10)
𝐶𝑆 = [1/(𝜀 − 1)](𝑝∗ 𝑥 ∗ )
8
Automobiles and horse-drawn carriages are an example of reasonable substitutes as both provide transportation but
do so with different technologies.
8
Total surplus, the sum of consumer and producer surplus, is:
(11)
1
1
𝑇𝑆 = [𝜀−1] (𝑝∗ 𝑥 ∗ ) + (𝜀 ) (𝑝∗ 𝑥 ∗ )
As stated, total surplus is taken to be the benefits to society. One is able to calculate 𝜀 that
yields a benefit-to-cost ratio equal to one by setting equation (11) equal to the fixed costs of the
program and solving for 𝜀. The value of 𝜀 for which B/C = 1 is denoted as 𝜀 ∗ . Again, if the
calculated value of 𝜀 is greater than 𝜀 ∗ then the social benefits of the program are greater than
costs (i.e., B/C > 1).
Figure 2 illustrates the extended model assuming no reasonable substitutes exist. The solid line
is the demand in absence of a substitute. Note that equation (7) gives the functional form of
demand. Total revenue is (0𝑝∗ B𝑥 ∗ ); the darker shaded portion of total revenue is producer
surplus (Ap*BC), an area proportional to total revenue, as expressed in equation (8), by the
amount (1/ε). The lighter shaded region, between the vertical axis and the demand curve and
above the line segment (𝑝∗ 𝐵), depicts consumer surplus. The value of total benefits of the
innovation is the combined areas of the two shaded regions.
Substitutes can be included in the model. This inclusion is done by assuming that some
reasonable substitute exists and is sold at a price 𝑝𝑠 , where 𝑝𝑠 > 𝑝∗ . This relationship between
own and substitute price can be maintained if innovators are assumed to enter the market only if
they’re able to exert monopoly power. Given the existence of a substitute, the demand curve is
now considered to be horizontal at 𝑝𝑠 , and follows its original form at all prices below 𝑝𝑠 . For
convenience, the ratio of 𝑝𝑠 /𝑝∗ will be expressed as 𝑚, which is always greater than one.
Additionally, the relationship 𝑝𝑠 = 𝑚𝑝∗ is used to simplify notation below.
The addition of a substitute does not change the value of producer surplus, as the producer
maintains monopoly power. That is to say, equation (3) still expresses the value of producer
surplus. However, the value of consumer surplus is now zero at all prices above 𝑝𝑠 . The integral
that expresses the value of consumer surplus given the existence of a substitute is:
(12)
𝑚𝑝∗
𝐶𝑆 = ∫𝑝∗
𝑎𝑝−𝜀 𝑑𝑝
9
This integral simplifies to the expression:
(13)
𝐶𝑆 = [1/(𝜀 − 1)](1 − 𝑚1−𝜀 )(𝑝∗ 𝑥 ∗ )
Thus, the value of total surplus under the assumption that a reasonable substitute exists can be
expressed as:
(14)
𝑇𝑆 = (1/𝜀)(𝑝∗ 𝑥 ∗ ) + [1/(𝜀 − 1)](1 − 𝑚1−𝜀 )(𝑝∗ 𝑥 ∗ )
With an assumed value for 𝑚, one is able to calculate the new value of 𝜀 ∗ by setting equation
(14) equal to the fixed costs of the program and solving for 𝜀. Given the complex nature of
equation (14), an analytical solution for 𝜀 ∗ is not easily obtained. However, one may employ an
iterative process to determine the value of 𝜀 ∗ for a given value of 𝑚.
Figure 3 shows how substitutes affect the extended model. The horizontal dotted lines at
𝑚 = 1.5 depicts the inclusion of an existing technology that is 1.5 times as expensive as the new
technology. Demand is now zero for all values of p above 𝑝𝑠 , which is indicated via the dotted
line labeled as m=1.5. The altered demand curve is horizontal at 𝑝𝑠 , and follows its original path
for all values of 𝑝 < 𝑝𝑠 . Thus, m serves to attenuate the quantity of CS that is included in the
calculation of TS. For a given value of the elasticity of demand, 𝜀, the value of CS increases as m
increases.
With reference to the data in hand from the Board’s survey, the calculation of total social
benefits using equation (14) required data on revenues and costs of funded projects. The revenue
and cost specifications used in the analyses are defined as six cases, where each case includes a
different combination of revenues and costs. These cases draw on following values from the
Total column of table 5: To-Date Sales Subtotal, Total Sales, Total Sales + IP Value, Grant
Funding, and Total Investment. The cases are defined as follows: case 1 includes To-Date Sales
Subtotal and Grant Funding, case 2 includes Total Sales and Grant Funding, case 3 Includes
Total Sales + IP Value and Grant Funding, case 4 includes To-Date Sales Subtotal and Total
10
Investment, case 5 includes Total Sales and Total Investment, and case 6 includes Total Sales +
IP Value and Total Investment. 9
Equation (14) can be solved using an iterative process for alternative values of ε and m. See
Table 6. Column (1) in Table 6 identifies the case considered for each row. Column (2) reports
the calculated values of ε*, assuming that there are no substitute technologies. Thus, assuming
no reasonable substitutes and case 1, if the elasticity of demand for the technologies that resulted
from the Fund grant is 19.45, then B/C = 1. It may not be unreasonable to conclude from this
calculated value of ε* of 19.45 that B/C exceeds 1, implying that the projects supported by the
Fund are socially valuable. To the best of the author’s knowledge there are no comparable
elasticizes reported in the literature.
The other values of ε* in column (2) show that as the categories of sales included in the
benefit calculations increase, the values of 𝜀 ∗ also increase. Similarly, as the categories of costs
included in the cost calculations increase, the values of 𝜀 ∗ decrease. Perhaps the most inclusive
representation of benefits and costs is that for which benefits include all current and expected
future sales and for which costs include grant funding and additional funding; this is case 6. In
this case, the values of 𝜀 ∗ is 14.75.
Columns (3) through (8) show that values of 𝜀 ∗ decreases as the relative price of a substitute
innovation decreases. The values of m for these calculations were arbitrarily chosen, for
illustration purposes, to represent a substitute whose prices were 3, 2, 1.5, 1.1, 1.05, and 1.01
times higher than that of the developed technology. These values, or other similar values,
demonstrate the responsiveness of the values of ε* to changes in the relative price. The values of
ε* for m = 3, for example, differ from those where no reasonable substitute exists in only cases 4
and 5, the most conservative in terms of included costs and benefits.
Figure 4 illustrates, for selected cases, the relationship between the values of ε* and 𝑚. The
value of ε* are measured on the vertical axis. The values of 𝑚 are measured on the horizontal
axis. The curves indicate how the value of ε* changes as the value of m changes for cases 4
(solid line), 5 (dash-dotted line) and 6 (dotted line). As observed from figure 3, the value of ε*,
for a given case, increases as 𝑚 increases, but at a diminishing rate.
9
Case 1 makes comparable assumptions about the benefits and costs that are included as the analyses in Allen et al.
(2012).
11
For a given case, as m increases the value of ε* asymptotically approaches the value of ε*
calculated assuming no reasonable substitute. This means that the rate at which these values
converge is faster in cases with relatively high values of ε* under the assumption of no
reasonable substitute. For example, in case 6 the ε values are extremely close at 𝑚 > 1.4.
Comparatively, in case 4 the values of ε converge much more slowly and are still distinguishably
apart at 𝑚 = 3. This means that cases in which the value ε* with no reasonable substitute is
smaller the assumed value of 𝑚 has a larger effect. As such, additional considerations about the
existence and relative price of substitutes should be given to cases in which the calculated value
of ε for which B/C = 1 is small.
VI. Concluding Remarks
The results from this evaluation suggest that the net social benefits associated with the Fund
are positive. Using the most comprehensive set of benefits and costs, the elasticity that resulted
in B/C = 1 was 14.75. If demand for the technologies developed as part of the Fund is less
elastic (i.e., the actual value of ε is less than 14.75) then the benefit-to-cost ratio of the program
will be greater than one. That is to say, the value of the social benefits of the program is greater
than the value of the costs. This conclusion should be interpreted in the light of the assumptions
and limitations placed upon the model.
First, as stated above, the purpose of changing the assumptions of the Allen et al. (2012)
model was to test its robustness. Specifically, this paper introduced an alternative specification of
market demand and assumptions about the existence and relative value of substitutes. As shown
in Table 7, under the assumption of no reasonable substitutes, for each case considered the Allen,
et al. model yielded a value for 𝑒 that was lower than the value of 𝜀 calculated using the extended
model developed herein. This suggests that the Allen, et al. (2012) model provides a more
conservative (i.e., lower) estimate of the elasticity of demand for which B/C = 1, and is therefore
robust to changes in the functional form of market demand. When it is assumed that a reasonable
substitute exists, for all cases except case 3, the value of e* calculated from the Allen, et al.
model was close to the value of 𝜀 calculated when 𝑚 was assumed to be between 1.5 and 1.01. In
case 3, the value of e* calculated via the Allen, et al. (2012) model was lower than the calculated
values of 𝜀 ∗ under all tested values of 𝑚. Within the context of the expanded model in this paper,
12
it is clear that changing assumptions related to the market for sustainability technologies do alter
the resulting calculations and conclusions as to the overall net social performance of the Fund.
Second, only benefits derived from current and expected future sales of the technologies and
includes no estimate of environmental or health benefits. Inclusion of non-pecuniary benefits
would lead to even higher B/C values. As the technologies developed using Fund monies were
sustainability technologies, it is reasonable to assume that some positive environmental benefits
should be included in the analyses. Inclusion of these benefits would likely increase the
calculated elasticity of demand for which B/C = 1.
Third, the findings presented herein are for one small state program; generalizations to other
state programs should only be made with caution. The combined budget of the Fund for the two
years under consideration was two million dollars. Larger programs that cover a broader range
of firms and projects may yield different results than the Fund. This caution is of particular
importance to future policy makers, who must consider the particulars of their situation and how
it differs from that of the Fund.
The research presented here has outlined only a portion of the landscape of evaluating
publicly funded sustainability technology programs. Avenues for future research include
extending the model to include additional categories of benefits such as pollution reduction and
non-renewable resource saving, relaxing the assumptions about the nature of the market for
newly developed technologies, and applying such models to other programs to expand the set of
results that researchers and policy makers might use for comparisons.
13
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NYSERDA. (2008). Development of New Biofuels, Bioproducts, and Feedstocks in New York
State (Program Opportunity Notice: 1195). Retrieved October 01, 2012 from
www.nyserda.ny.gov/Programs/Research-andDevelopment/~/media/Files/FO/Closed%20Opportunities/2008/1195pon.ashx
NYSERDA. (2012). Innovation in the Manufacturing of Clean Energy Technologies Retrieved
October 01, 2012 from http://www.nyserda.ny.gov/Funding-Opportunities/Current-FundingOpportunities/PON-2414-Innovation-in-the-Manufacturing-of-Clean-EnergyTechnologies.aspx
NYSERDA. (2012) Advanced Clean Power Technologies. Retrieved October 01, 2012 from
http://www.nyserda.ny.gov/Funding-Opportunities/Current-Funding-Opportunities/PON2569-Advanced-Clean-Power-Technologies.aspx
NYSERDA (2012) Advanced Transportation Technologies Retrieved October 01, 2012 from
http://www.nyserda.ny.gov/Funding-Opportunities/Current-Funding-Opportunities/PON2584-Advanced-Transportation-Technologies.aspx
North Carolina Board of Science and Technology. (2008) North Carolina Green Business Fund
2008-01 Solicitation.
North Carolina Board of Science and Technology. (2008). North Carolina Green Business Fund
to the Join Legislative Commission on Governmental Operations.
North Carolina Board of Science and Technology. (2009). North Carolina Green Business Fund
Fiscal Year 2009 Report.
North Carolina Board of Science and Technology. (2009). The North Carolina Green Business
Fund FY 2008-2009 Solicitation.
North Dakota Industrial Commission. (2012). Home page for the Renewable Energy Program.
Retrieved October 01, 2012 from http://www.nd.gov/ndic/renew-infopage.htm
15
Popp, D., Newell, R. G., & Jaffe, A. B. (2010) “Energy, The Environment, and Technological
Change.” In Hall, B. H, & Rosenberg, N. (Eds.) Handbook of the Economics of Innovation
New York: Elsevier.
Rhode Island Economic Development Corporation. (2012). Renewable Energy Fund. Retrieved
October 01, 2012 from http://www.riedc.com/business-services/renewable-energy
Technology Advancement Program. (2012). San Pedro Bay Ports Clean Air Action Plan
Retrieved October 01, 2012 from http://www.cleanairactionplan.org/programs/tap/default.asp
Texas Commission on Environmental Quality. (2012). New Technology Research and
Development Program. Retrieved October 01, 2012 from
http://www.tceq.texas.gov/airquality/terp/ntrd.html
2007 Appropriations Act, North Carolina House Bill 1473 §13.2.(a) (2007).
United Nations (1987). Report of the World Commission on Environment and Development: Our
Common Future. Retrieved October 01, 2012 from http://www.un-documents.net/ourcommon-future.pdf
U.S. Bureau of Economic Analysis, “Table 1.1.9. Implicit Price Deflators for Gross Domestic
Product”
http://www.bea.gov/iTable/iTableHtml.cfm?reqid=9&step=3&isuri=1&904=2012&903=13
&906=a&905=2007&910=x&911=0 (Accessed January 10, 2014)
Xcel Energy. (2012). Renewable Development Fund. Retrieved October 01, 2012 from
http://www.xcelenergy.com/Environment/Renewable_Energy/Renewable_Energy_Grants/Re
newable_Development_Fund
16
Table 1
U.S. Sustainability and Technology Policies
Policy
Energy Policy and
Conservation Act
(P.L. 94-163)
Year
1975
Primary Focus
Sustainability R&D Support
Reduce dependence of foreign oil Established CAFE standards.
production, increase vehicle fuel Leading to R&D targeting
efficiency
energy-efficiency of motor
vehicles.
Energy Security Act
(P.L. 96-294)
1980
The ESA’s primary focus was
clean alternatives to the use of
foreign oil as a fuel.
Provided funds for R&D
towards environmentally
friendly power sources.
Energy Policy Act of
1992 (P.L. 102-486)
1992
The Energy Policy Act of 1992
covered a broad range of energy
topics including: federal vehicle
fleet standards, federal building
energy efficiency, renewable
energy programs, toxic waste
disposal and many other topics.
Provided funds for: electric and
hybrid vehicle demonstration
program, clean coal R&D, and
for renewable energy R&D.
Biomass R&D Act
(P.L. 106-224)
2000
Provided support for R&D
Established grant programs to
targeting the production of
fund R&D on biofuels and
Biofuels including the production related industries
of biomass, the development of
products derived from biomass
and the increasing the efficiency
of biofuel creation.
Energy Policy Act of
2005 (P.L. 109-58)
2005
The Energy Policy Act of 2005
covered a broad range of energy
topics. Provisions covered oil
and gas production, energy
efficiency, renewable energy,
and many other energy-related
topics.
R&D support for many energyefficient and renewable energy
technologies such as hydrogen
power, high performance
buildings, and more.
Energy
Independence and
Security Act of 2007
(P.L. 110-140)
2007
Overarching goal of EISA was to
transition the United States to
greater energy independence and
security through reduced
dependence on foreign oil via
energy efficiency and renewable
energy.
Established R&D programs to
incentivize biofuel, solar,
geothermal and hydrokinetic
production. Additionally EISA
provided funds for R&D on
carbon capture and
sequestration.
Energy
Improvement and
Extension Act of
2008 (P.L. 110-343)
2008
The EIEA’s primary focus was to Support for innovation in the
continue and enhance support for field biofuels was provided via
sustainable energy initiatives.
tax credits.
17
American Recovery
and Reinvestment
Act of 2009
(P.L. 111-5)
2009
Large piece of legislation
introduced to counteract the
effect of the Great Recession.
Funding for R&D and adoption
of alternative energy and
energy-efficiency technologies.
America
COMPETES Act
(P.L. 110-69)
2007
The America COMPETES Act’s
focus was STEM education,
increasing scientific research at
the National Science Foundation,
and other support for American
research investment.
Established the Office of
Innovation and
Entrepreneurship in the
Department of Commerce.
America
COMPETES
Reauthorization Act
(P.L. 111-358)
2010
The America COMPETES
Reauthorization Act expanded
upon the America COMPETES
Act. It provided higher levels of
funding support over an
expanded range of areas.
The 2010 reauthorization of the
bill includes funding for many
agencies conducting science
and engineering research,
including ARPA-E.
18
Table 2
State-Level Sustainability Technology Programs
State
CA
Program Name
Used Oil Research and Demonstration Program Grants
Innovative Clean Air Technologies Program (ICAT)
Air Quality Improvement Projects – AB 118 Advanced Technology
Demonstration Projects
Energy Innovations Small Grant Program
Technology Advancement Program
Years
1994-present
1993-2008
2006-Present
CO
Clean Technology Discovery Evaluation Fund
2009-present
CT
Connecticut Clean Energy Fund
Operational Demonstration Program
New Energy Technology Programs
2000-present
2001-present
2005-present
DE
Green Energy Fund
1999-present
MI
Xcel Energy’s Renewable Development Fund
2001-present
NC
North Carolina Green Business Fund
2007-2011
ND
North Dakota’s Renewable Energy Program
2007-present
NJ
Edison Innovation Green Growth Fund
2011-present
NY
New York State Energy Research and Development Authority
(NYSERDA)
NYSERDA – Development of Biofules, Bioproducts and Feed Stocks
in New York State
NYSERDA – Innovation in Manufacturing of Clean Energy
Technologies
NYSERDA – Advanced Clean Power Technologies
NYSERDA – Advanced Transportation Technologies
1975-present
RI
Renewable Energy Fund Grants
1996-present
TX
New Technology Research and Development
2004-present
1998-present
2007-present
*
*
*
*
* Operated under NYSERDA for varying time periods.
Sources:
CA – AQIP http://www.arb.ca.gov/msprog/aqip/aqip.htm
CA – ICAT http://www.arb.ca.gov/research/icat/icat.htm
CA – EISGP http://www.energy.ca.gov/research/innovations/
CA – TAP http://www.cleanairactionplan.org/programs/tap/default.asp
CA – UORDPG http://www.arb.ca.gov/ba/omb/farg/ombwfarg.htm#used
CO – CTDEF
http://www.leg.state.co.us/clics/clics2009a/csl.nsf/fsbillcont/97E2CDDCEF6F7B7787257537001A2EE6?Open&file
=031_enr.pdf
CT – CCEF http://energy.gov/savings/connecticut-clean-energy-fund-ccef
19
CT – ODP
http://www.ctcleanenergy.com/YourBusinessorInstitution/FormerCommercialBusinessPrograms/TechnologyInnova
tionPrograms/OperationalDemoProgram/tabid/601/Default.aspx
CT – NETP http://energy.gov/savings/new-energy-technology-program
DE – GEF http://www.dsireusa.org/incentives/incentive.cfm?Incentive_Code=DE01R
MI – XERDF
http://www.xcelenergy.com/Environment/Renewable_Energy/Renewable_Energy_Grants/Renewable_Development
_Fund
NC – NCGBF http://www.nccommerce.com/scitech/grant-programs/green-business-fund
ND – REP http://www.nd.gov/ndic/renew-infopage.htm
NJ – EIGGF
http://www.njeda.com/web/Aspx_pg/Templates/Npic_Text.aspx?Doc_Id=1454&menuid=1509&topid=718&levelid
=6&midid=1175
NY – NYSERDA http://www.nyserda.ny.gov/en/About.aspx
NY – NYSERDA: DOBBFSNY www.nyserda.ny.gov/-/media/Files/FO/ClosedOpportunities/2008/PON1195summary.pdf
NY – NYSERDA: IMCET www.nyserda.ny.gov/-/media/Files/FO/ClosedOpportunities/2012/PON2414summary.pdf
NY – NYSERDA: ACPT www.nyserda.ny.gov/-/media/Files/FO/ClosedOpportunities/2013/PON2569summary.pdf
NY – NYSERDA: ATT www.nyserda.ny.gov/-/media/Files/FO/Closed-Opportunities/2013/PON2781summary.pdf
RI – REF http://www.riedc.com/business-services/renewable-energy
TX – NTRD http://www.tceq.texas.gov/airquality/terp/ntrd.html
20
Table 3
FY2008 and FY2009 Awardees
Organizations
Funding Amount and Project Description
FY 2008 Awardees
Blue Ridge Biofuels of
Asheville
$77,737.00 to develop and commercialize the conversion of low quality fatty
acids into biofuel through an innovative purification method.
Organofuels of Asheville
$81,944.00 to manufacture algae based fuel for gasoline engines. The project
offers the promise of making algae oil products completive with gasoline.
Ecocurrent of Raleigh
$100,000.00 for a novel technological process that will divert hog manure
from lagoons and convert it to electric power in an economically viable
manner and valuable byproducts such as fertilizer and building materials.
Evans Environmental of
Wilson
$75,000.00 to remove residual water in the final stage of biodiesel
production. The innovative process will facilitate production of commercial
grade biodiesel by 300%.
Alganomics of Southport
$60,000.00 to produce reliable, environmentally responsible, natural and
renewable bioproducts from algal sources, and promote the use of renewable
energy alternatives. The primary bioproduct is extracted oil/fatty acids for
use as a biodiesel fuel feedstock.
Kyma Technologies of
Raleigh
$60,000.00 will work with researchers at North Carolina State University to
develop a normallyoff power switch using novel process enabled by high
quality substrates developed by Kyma.
3F, LLC of Raleigh
$100,000.00 will develop a new natural fiber reinforced concrete
formulation. The resulting lighter weight and yet stronger and tougher
concrete will directly enhance the merits of precast concrete. Less weight for
the same structural efficiency will reduce material use and dead load, and
save transportation cost.
Piedmont Biofuels of
Pittsboro
$75,000.00 to implement a cavitational reactor to produce biodiesel fuel. The
process uses less energy, has a much smaller physical footprint, and causes a
more complete reaction with higher fuel yields.
$57,319.00 to manufacture a novel thermoelectric power generator capable
of converting waste heat into usable electrical power.
Nextreme Thermal of
Durham
Rain Water Solutions of
Raleigh
$18,000.00 to develop the foundation for a new rain barrel manufacturing
process that allows mass production capabilities to 1) meet increasing
demand in a timely manner and 2) provide an inexpensive, appealing option
to consumers desiring to collect rainwater.
Nanotech Labs of
Yadkinville
$70,000.00 to develop and commercialize an ultra-capacitor as an energy
storage device that has extremely high volumetric capacitance but small
overall dimensions.
21
Phasetek of Greensboro
$75,000.00 to develop a new class of thermal transfer and storage building
material for wallboards in order to facilitate thermal efficiency in buildings.
Sencera of Charlotte
$100,000.00 to implement a Photovoltaic Solar Cell production facility in
North Carolina based on a new thin-film manufacturing technology.
FY 2009 Awardees
Aerofab Manufacturing
Corporation
$45,435.00 to increase the efficiency of mist eliminators in metalworking
facilities while decreasing the associated waste stream. Energy and water
consumption are decreased.
Caldwell Community
College and Technical
Institute
$81,000.00 to build a mobile vehicle for green project demonstrations to
educate residents and students on the green economy and how it can impact
their business.
Centralina Council of
Governments
$85,000.00 to integrate existing Charlotte-Mecklenburg Utilities (CMU)
facilities and services with new biodiesel and create a market for brown
grease (waste oil from food preparation found in the wastewater stream) as
an input to biofuel production.
Clean Marine Solutions
$84,602.00 to fund a wastewater treatment system prototype that cleans
water used in high-pressure boat cleaning that is currently polluting the
water at marinas all over the country.
CPS Biofuels
$50,000.00 to develop a fuel additive made from glycerol (a waste product
of biodiesel production). The additive improves fuel economy in gasoline
and diesel engines by increasing octane.
EnSolve Biosystems
$50,000.00 to develop an oil water separator technology for small boats that
uses bacteria to reduce/remove oil contamination from effluent that flows
back into the waterways.
FLS Energy Finance
$60,000.00 to develop a solar hot water installation and financing system,
which reduces the upfront cost of solar power and allows more consumers
access to this green technology.
Innova Homes
$51,160.00 to develop a hybrid green modular product that merges the
energy and material efficiencies of structural polyurethane-insulated floor,
wall and roof panels with the factory construction cost and quality
efficiencies of modular home construction.
InnovaTech
$53,317.00 to develop a novel method to harvest algae for use in biofuel
production. This project will increase the efficiency of algae-to-biofuels
conversion.
Microcell Corporation
$80,000.00 to produce environmentally-friendly fuel cells for emergency
generator substations as an alternative to existing expensive and hazardous
acid cell batteries with a shorter life span.
N.C. State University
$95,000.00. Funds will be used towards becoming an accreditation agency
22
Solar Center
for solar thermal manufacturers. Currently Florida is the only U.S. state
providing certifications, resulting in a 2-year backlog limiting companies
from expanding their business and creating jobs.
PlotWatt (formerly
VisibleEnergy)
$40,000.00 to implement a home energy monitoring system that monitors
specific appliances and behaviors that directly impact energy consumption.
The technology calculates exactly how much energy can be saved.
Semprius
$99,486.00 to develop a Concentrated Photovoltaic system to concentrate
solar energy through a lens, reducing the amount of expensive silicone cell
needed and improving the overall efficiency of the system while reducing
costs.
Vesture Corporation
$75,000.00 to ramp up production of a new home insulation product that
uses phase change materials, reducing consumers’ energy costs.
Sources:
Green Business Fund 2007-2008 Report (North Carolina Board of Science and Technology, 2008).
Fiscal Year 2009 Report (North Carolina Board of Science and Technology, 2009).
23
Table 4
Descriptive Statistics: Mean Values (standard deviations) [t-test standard errors*]
Population of
Grant Recipients
70.37
(20.80)
Survey
Respondents
68.75
(21.13)
NonRespondents
83.33
(14.43)
Difference in
Means
14.58
[9.38]
Age
12.57
(11.02)
13.29
(11.49)
6.758
(1.592)
-6.535
[2.52]
Employ
34.07
(121.5)
37.25
(128.8)
8.667
(4.041)
-28.58
[26.38]
C-Corp
0.519
(0.509)
0.500
(0.511)
0.667
(0.577)
0.167
[0.35]
Public
0.111
(0.320)
0.125
(0.338)
0
(0)
-0.125
[0.07]
27
24
3
-
Grant ($1000)
N
*t-test standard errors were calculated assuming unequal variances.
24
Table 5
Descriptive Statistics on Benefit and Cost Category Variables (n=24, $1000s and rounded)
Category
To-Date Own Sales
To-Date Licensing Revenues
To-Date Sales Subtotal
Mean
Std. Dev.
Minimum
Own and Licensee Sales
796.3
3463.2
0
12.5
61.2
0
808.8
3524.3
0
Maximum
Total
17000
300
17300
19111.5
300
19411.5
Expected Own Sales
Expected Licensing Revenues
Expected Sales Subtotal
649
45.2
694.2
2090.2
162.1
2143.8
0
0
0
10000
750
10300
15575
1085
16660
Total Sales
1503
5633.4
0
27600
36071.5
Patent Value
Patent Pending Value
Copyright Value
Copyright Pending Value
Intellectual Property Values
0
0
3333.3
11196.5
21.0
102.0
0
0
0
0
0
0
0
50000
500
0
0
80000
505
0
Total IP Value
3354.4
0
50000
80505
50300
116576.5
109.5
6500
7205.5
2080.6
16621.9
18702.5
Total Sales + IP Value
Grant Funding**
Additional Investment
Total Investment
11200.2
Total Sales and Intellectual Property Values
4857.4
12697.2
0
Project Costs*
77.1
22.8
632.5
1622.5
692.7
1685.6
*
19.71
0
54.8
Costs were adjusted to $2012 by a chained Gross Domestic Product deflator. (U.S. Bureau of Economic Analysis,
2014).
**
Grant values include investment in non-respondent firms.
25
Table 6
Calculated Elasticities that Yield B/C = 1
(1)
Case
1
2
3
4
5
6
(2)
No
reasonable
substitute
19.45
35.70
114.22
2.97
4.96
14.75
(3)
m=3
(4)
m=2
(5)
m=1.5
(6)
m=1.1
(7)
m=1.05
(8)
m=1.01
19.45
35.70
114.22
2.77
4.92
14.75
19.45
35.70
114.22
2.45
4.78
14.75
19.44
35.70
114.22
1.96
4.32
14.72
17.41
35.00
114.22
1.33
2.73
12.20
14.34
31.70
113.99
1.25
2.45
9.90
10.40
20.92
90.84
1.19
2.25
7.64
Notes:
The cases include alternate specifications of revenues (denoted B) and costs (denoted C). These values are drawn
from the Total column of Table 5.
Case 1: B = To-Date Sales Subtotal; C = Grant Funding
Case 2: B = Total Sales; C = Grant Funding
Case 3: B = Total Sales + IP Value; C = Grant Funding
Case 4: B = To-Date Sales Subtotal; C = Total Investment
Case 5: B = Total Sales; C = Total Investment
Case 6: B = Total Sales + IP Value; C = Total Investment
26
Table 7
Comparison of Results from Allen et al. (2012) with the Extended Model
(1)
Case
1
2
3
4
5
6
(2)
No
reasonable
substitute
19.45
35.70
114.22
2.97
4.96
14.75
(3)
m=1.5
(4)
m=1.1
(5)
m=1.05
(6)
m=1.01
(7)
Allen et al.
19.44
35.70
114.22
1.96
4.32
14.72
17.41
35.00
114.22
1.33
2.73
12.20
14.34
31.70
113.99
1.25
2.45
9.90
10.40
20.92
90.84
1.19
2.25
7.64
14.20
26.39
85.29
1.77
3.30
10.67
Notes:
The cases include alternate specifications of revenues (denoted B) and costs (denoted C). These values are drawn
from the Total column of Table 5.
Case 1: B = To-Date Sales Subtotal; C = Grant Funding
Case 2: B = Total Sales; C = Grant Funding
Case 3: B = Total Sales + IP Value; C = Grant Funding
Case 4: B = To-Date Sales Subtotal; C = Total Investment
Case 5: B = Total Sales; C = Total Investment
Case 6: B = Total Sales + IP Value; C = Total Investment
27
States with Sustainability Technology Programs (Cumulative)
Figure 1
Cumulative Number of States with Sustainability Technology Programs
12
11
10
9
8
7
6
5
4
3
2
1
0
1975
1980
1985
1990
1995
Years
28
2000
2005
2012
Figure 2
Graphical Illustration of the Extended Model
29
Figure 3
Graphical Illustration of the Extended Model with a Substitute Technology
p
Price
m=1.5
p*
A
O
B
C
x*
x
Quantity
30
Figure 4
Changes in ε for which B/C = 1 relative to changes in m
16
14
Elasticity Required for B/C = 1
12
Case 4
Case 5
Case 6
10
8
6
4
2
0
1
1.5
2
Relative Price of Substitute, m
31
2.5
3
Appendix A:
Step-by-Step Derivation of Total Surplus in the Extended Model
𝑥 = 𝑎𝑝−𝜀
𝑥 ∗ = 𝑎𝑝∗ −𝜀
1
𝑃𝑆 = ( ) (𝑝∗ 𝑥 ∗ )
𝜀
No Substitute Case
∞
𝐶𝑆 = ∫ 𝑎𝑝−𝜀 𝑑𝑝
𝑝∗
𝑏
= lim ∫ 𝑎𝑝−𝜀 𝑑𝑝
𝑏→∞ 𝑝∗
= lim [1/(1 − 𝜀)](𝑎𝑝1−𝜀 )]𝑏𝑝∗
𝑏→∞
= lim [
𝑏→∞
=0−[
1
1
] (𝑎𝑏1−𝜀 ) − [
] (𝑎𝑝∗1−𝜀 )
1−𝜀
1−𝜀
1
] (𝑎𝑝∗1−𝜀 )
1−𝜀
=[
1
] (𝑎𝑝∗ −𝜀 )𝑝∗
𝜀−1
=[
1
] (𝑥 ∗ 𝑝∗ )
𝜀−1
1
1
TS = (𝜀 ) (𝑝∗ 𝑥 ∗ ) + [𝜀−1] (𝑥 ∗ 𝑝∗ )
Substitutes Case
𝑚𝑝∗
𝐶𝑆 = ∫
𝑎𝑝−𝜀 𝑑𝑝
𝑝∗
=[
1
∗
] (𝑎𝑝1−𝜀 )]𝑚𝑝
∗
𝑝
1−𝜀
32
=[
1
1
] (𝑎(𝑚𝑝∗ )1−𝜀 ) − [
] (𝑎𝑝∗1−𝜀 )
1−𝜀
1−𝜀
=[
1
] (𝑎𝑝∗1−𝜀 )(𝑚1−𝜀 − 1)
1−𝜀
=[
1
] (𝑎𝑝∗ −𝜀 )𝑝∗ (1 − 𝑚1−𝜀 )
𝜀−1
= [
1
] 𝑝∗ 𝑥 ∗ (1 − 𝑚1−𝜀 )
𝜀−1
TS = {(1/𝜀) + [1/(𝜀 − 1)](1 − 𝑚1−𝜀 )}(𝑝∗ 𝑥 ∗ )
33