The global warming policy debate is increasing the calls for
reduction of carbon-dioxide and other greenhouse-gas emissions. In
the wake of the recent hike of oil prices, Congress is scrambling
to develop an energy policy that addresses emissions while avoiding
yet higher energy costs. Although emissions reductions and
stable energy prices are not necessarily mutually exclusive,
the proposal to allow the Environmental Protection Agency (EPA) to
broadly regulate emissions under the Clean Air Act will impose
higher costs on U.S. industries, thus leading to slower economic
growth and lower employment.
This paper estimates that owners of shares in the sampled
industries over the forecast horizon experience:
- Lower average return on equity (e.g., the chemical
industry loses an average of two to four percentage points per
year on equity returns; the steel industry loses an average of 19
percentage points per year);
- Greater volatility in the rate of those returns (e.g., in the
metal industry the standard deviation of the returns in the
baseline case is plus or minus 0.7 point, while in the regulated
cases the standard deviation is plus or minus 6 points).
At a time when other emerging economies are rapidly expanding
and putting competitive pressure on the United States' niche
of lucrative investment opportunities, command-and-control
policies will further erode these investments. Everyone, investors
and non-investors alike, is affected. Lower investments in U.S.
companies will lead to even less economic growth and fewer jobs,
creating a vicious spiral and making it that much more
difficult to invest in energy-efficient technology for the
The Economic Chain Reaction
The EPA's ability to regulate greenhouse gas emissions from
non-stationary and stationary sources of emissions introduces two
constraints into the economy. The first constraint is a higher
level of uncertainty. The second is higher administrative and
other non-productive costs. These constraints change business
calculations, leading to a downward economic spiral.
Uncertainty concerning possible EPA rulings when companies are
deciding whether or not to invest in new technology discounts the
return on an investment more heavily. This makes it more
difficult for a project to meet the required rate of return.
This means less investment will be made, which decreases industry
productivity and growth. Lower productivity weakens an industry's
competitiveness and gives opportunities for global
competitors to make their products at a lower cost. Erosion in
market share by global competitors leads to an even lower return on
equity, thereby diminishing sources of financing for future
investments and raising the cost of capital. The weakened
competitive position puts strain on employment. As demand for a
U.S. industry's product decreases, the need for employees to make
the products decreases as well, resulting in layoffs and job losses
to overseas competitors.
It is not the emissions reductions per se that are the
cause of harm to stockholder equity. Energy efficiency is an
outstanding goal. In fact, firms are finding ways to adjust their
energy use in ways that make good business sense. The contemplated
regulations, though, are dampening the current demand for
investments because firms are unsure about how regulations will
affect that investment. The increased risk that a capital purchase
will not meet future regulations lowers the expected rate of
return. This makes it difficult for the project to exceed the
threshold required rate of return on the investment and
delays investments that could be making incremental steps toward
overall goals of energy efficiency.
The problem with mandates is that they are unresponsive to
technological realities and unforeseen future conditions.
Markets balance expected benefits with expected costs. Since
expected costs do not exceed expected benefits, markets are a
particularly efficient (not wasteful) way of allocating
resources. Because mandated "efficiencies" are
not based on expected costs versus benefits, they often do more
economic harm than good.
Analysts at The Heritage Foundation's Center for Data Analysis
used simulated forecasts of production indexes in a
representative sample of industries to create estimated rates of
return for those industries. These returns on equity were
calculated under the baseline forecast and CO2 policy forecast
contained in the recently rejected Lieberman-Warner
legislation. The EPA's proposed regulations
include a broad range of options, one of which is a carbon-credit
trading program similar to the one Lieberman-Warner would have
The baseline assumptions for legislation such as the Energy
Independence and Security Act (EISA) and state and local
renewable mandates, Corporate Average Fuel Economy (CAFE)
standards, and appliance efficiency standards are also all the same
as those in Lieberman-Warner. However, the EPA's ability to
regulate greenhouse gas emissions under the Clean Air Act gives it
sweeping powers to regulate both non-stationary and stationary
sources of emissions. Therefore, the estimates in this paper can be
seen as a lower bound, or minimum, on the enormous economic costs
of the EPA's expanded authority.
The Lieberman-Warner study was conducted by William Beach, Ben
Lieberman, David Kreutzer, and Nicolas Loris at The Heritage
Foundation. Using the Global Insight long-term macroeconomic
model, they studied the effects of a 70 percent mandated
reduction in atmospheric carbon content on the U.S. economy.
The Global Insight model produces forecasts for more than one
hundred industries classified under the North American
Industry Classification System (NAICS). This paper studies the
implication of those industry-simulation results.
The simulation takes into account the current technology and its
likely trajectory based on discussions with energy industry
experts. For instance, carbon sequestration techniques are one
possible way to meet the requirements, but this technology is not
The returns also show that industries are affected unevenly.
Some industries, such as textiles and food, have relatively
inelastic demand and relatively less-regulated emissions and do not
experience as high a level of diminished operations due to
rising costs. Other industries, such as machinery
and paper, which are more sensitive to price pressure and require
more emissions to operate, will be greatly affected. Thus, the
policy results in tilting the playing field and by doing so
inadvertently picks industry winners and losers. (See Table 1.)
Graphs of the eight representative industries are shown in
Charts 1a and 1b.
Effect on Retirement Savings, Workers,
and Household Wealth
Many of the equity investors in these industries are mutual
funds and pension funds that provide retirement savings for
individuals. Managers of these funds seek targeted rates of return
in the portfolios they manage for individuals. These managers will
not only experience a difficult time hitting these targeted
returns, but will also be challenged to find ways to diversify the
increased risk driven by the uncertainty of these returns.
Individuals who rely on a certain amount of income in retirement
are especially sensitive. Table 2 shows what these lower
returns and greater variance mean for a $1,000,000
investment in an industry at the beginning of 2009. The
columns show how much this investment would be worth in 2025 if the
EPA regulations go into effect as compared to the investment's
value under the baseline. The present value of these dollar losses
are in the right column. The decreased wealth in
individuals' retirement funds, as seen in the losses on a
$1,000,000 investment, means a lower standard of living in
retirement years, longer working years, or both.
Employees are directly and indirectly affected as well.
Decisions regarding investments in new capital and technology are
based on the cost of capital. The increased uncertainty of returns
due to higher volatility increases the industry risk premium.
This raises the cost of capital and decreases the number of
investments. These investments would have allowed companies to
grow, creating more jobs and higher wages. The higher cost of
capital also puts upward pressure on the borrowing costs of all
individuals (those seeking mortgages, loans to start a small
Lower industry returns also make equity investment less
attractive relative to bonds. This may skew financing toward debt
financing, creating more leveraged industries. Leverage can be
a powerful tool but, as seen recently in the financial industry,
pushing past prudent debt-to-equity ratios can severely
constrain business operations during a credit crunch. Furthermore,
using debt instead of equity financing concentrates industry
profits in the hands of fewer owners, which can lead to greater
income disparities in the economy. The equity financing of
capitalist systems allows a broad group of ordinary individuals to
gain ownership in and reap the profit rewards corporations
generate. Diminishing the incentive to invest in equity decreases
the number of average citizens taking part in corporate profits and
limits a powerful method for households to increase their
The new regulations proposed by the EPA will cause major
disruptions in the productivity of U.S. industries. These will
translate into lower returns on equity and create more volatility
in the growth rates of those returns. This can be seen in the
greater fluctuations in the returns under the EPA regulation
scenario versus the baseline scenario in the graphs above. This is
not surprising considering that regulations add increased
uncertainty to the production environment. For owners and CEOs
of corporations, increased uncertainty on the return on equity
makes it difficult to plan for future investment projects. As
explained above, uncertain returns can lead to caution and
conservative investments in order to preserve capital. Lower
investments further weaken growth and the competitiveness of U.S.
industries. This has far-reaching consequences not only for
industry employees and owners, but also for millions of small
investors through mutual funds, pension funds, and other savings
Instead of imposing mandates, government should recognize the
new technologies that are evolving and avoid restrictive regulation
rhetoric that increases uncertainty in the economy and delays
investments. Instead of focusing on reduction "targets," the
focus needs to be on technology and U.S. productivity.
Profit-seeking firms are lowering the energy use/CO2 emissions
per dollar of gross domestic product (GDP)-not because there is a
mandate, but because of market competition. Between 2005 and 2006,
CO2 emissions decreased by 1.3 percent, while the U.S. economy grew
at 3.3 percent. Only 0.9 percent of the decrease was due to a
decrease in overall energy use during this time, which indicates
that the U.S. economy is becoming less carbon-intensive even
without more regulation. Undermining industry
returns reduces the ability of U.S. businesses to switch to more
efficient technologies, practices, and products. Indeed, more and
more firms are searching for ways to bring new products and
energy-efficient products to U.S. households. This commitment by
businesses is already demonstrated by the large marketing expenses
that companies are incurring to promote their new ideas to aid in
the solutions for the United States' energy and environmental
needs. Consumers are already voting for the ideas they think will
work with their everyday economic choices.
The United States can be a productivity powerhouse and
reduce its carbon footprint at the same time. These are not
mutually exclusive goals. Increasing regulatory burdens and
command-and-control approaches, however, are not the way to achieve
Karen A. Campbell, Ph.D.,
is a Policy Analyst in Macroeconomics in the Center for Data
Analysis at The Heritage Foundation.
Appendix Data and
Center for Data Analysis (CDA) analysts obtained historical
seasonally unadjusted return on equity data for 16 industries from
Haver Analytics. The analysts obtained the
historical seasonally unadjusted industry production indices from
the Federal Reserve Bank of St. Louis. The data run from the
fourth quarter of 1980 to the first quarter of 2008.
Return on equity is a financial ratio of a firm's net income to
its total equity (or sometimes average equity). Net income is a
firm's revenue minus its costs. An industry's return on equity can
be affected by changes in an industry's profits or changes in an
industry's total equity position, such as assets minus liabilities.
The profits of an industry should, in theory, be linked to the
production of goods or services the industry provides. Current
production indices for an industry would, therefore, help to
explain an industry's return on equity. Moreover, changes in past
production indices carry information regarding the
profitability of producing the goods and services, for example,
changes in the market price of the goods or changes in production
costs. Likewise, past changes in return on equity indices carry
information about changes in an industry's equity position.
There should be both a short-run and long-run link between an
industry's fundamental operations and its financial
performance. An econometric model using the autoregressive
distributed lags as explanatory variables is used to estimate this
linkage. When these models are written in their error-correction
model form, they capture the dynamic links between two or more time
series data sets. These models have two parts: the short-run
movements due to changes in the variables, and the long-run part
that measures the underlying long-run relationship between the
levels of the two variables.
Because industry codes changed from the SIC classification to
the NAICS classification around 2001, industries were chosen based
on two criteria. The first was whether or not they were part of the
Global Insight model (and, therefore, part of the simulation), and
the second was how little the industry composition changed between
the two classification systems. The Federal Reserve Bank data had
the combined SIC and NAIC industry production index for each
industry data series. The return on equity series were in their SIC
classifications from the fourth quarter of 1980 to the third
quarter of 2001, and the NAICS classification from the fourth
quarter of 2000 through the first quarter of 2008. Heritage
analysts combined each industry return on equity series using the
geometric spline technique. This method uses the
information in the overlap years to adjust the two series to one
Once this is completed, the data series are tested for
stationarity. All returns on equity series are stationary. The
production index series is largely trend stationary and first
difference (I(1)) stationary.
Because changes in industry composition and classification over
the historical period may have severed a meaningful relationship
between the return on equity index and the production index, a
Granger-causality test was performed on the two series for each
industry. This is a test of whether one time series has information
that is useful for predicting another times series. In other words, whether one series
can help predict another series in either a uni-directional way or
bi-directional feedback, or whether there is no predictive
relationship between the two series. In brief, the test uses the
F-statistic, posits a vector autoregressive structure, and tests
whether eliminating the explanatory variables can be accomplished
leaving only an ARIMA-type model. The two models are compared to
see if the one including the explanatory model is significantly
better estimating the dependent variable.
Based on this test, six industries were eliminated from the
sample due to the production index including no predicative
ability. Two other industries, the
vehicle parts and transportation industries, had structural
breaks in the trend and the level in their return on equity
series. Although there are econometric methods for handling
historical structural breaks, finding a consistent estimated
relationship with which to forecast a future return on equity
series from a future production series is not robust enough to
change assumptions regarding those breaks. For this reason, these
two series were dropped from the sample.
The relationship between the historical time-series for the
remaining eight representative industries' return on equity and
production index series was estimated accordingly using the
Provided that there is only one cointegrating relationship,
the ordinary least squares (OLS) estimate outperforms the Johanssen
method. The adjustment parameter of
the cointegrating relationship is not expressly modeled. Instead,
it is combined in the parameter estimates of the levels of the
variables. Peter Kennedy explains, "mixing levels and
differences in the regression is acceptable because the
cointegrated variables automatically combine during estimation to
resolve the dilemma of mixed order of integration."
The purpose of the estimation is to capture the dynamic
relationship of the two series and use this relationship to predict
the effects of the proposed EPA regulations on industry
performance. The historical relationship was estimated using the
above econometric model with STATA/SE 10.1 for Windows. The
estimates were then applied to the baseline production indexes and
the EPA-regulated production indexes that had been simulated using
the Global Insight long-term macroeconomic model of the U.S.
economy. The specific number of lags of the dependent variable
was selected as the minimum needed to remove serial autocorrelation
in the residuals. This was determined by a Breusch-Godfrey test of
the residuals from an OLS regression.
The model fit is determined in two ways: econometrically
and theoretically. First, econometrically, the model is chosen to
minimize the sum of squared residuals. Second, a check is performed
on the predicted average ratio of return on equity to its
production index. If there is a long-term relationship between
the two series, then the ratio of average return on equity to its
average production index should be fairly stable. Thus, the
historical average return on equity to historical average
production index is compared to the model's predicted average ratio
in the baseline scenario. These results are shown in the
Appendix Table 1.
The coefficient estimates and the forecasted series for the
industries are available upon request.
 The current decline in the petroleum
prices reflects the declines in demand caused by slowing economic
activity. Oil prices likely will rise again when economic
growth picks up.
 Businesses often evaluate investment
projects based on an internal desired rate of return. This is
called the required rate of return. If an investment's expected
return on equity does not exceed this required rate the project is
 Market efficiency equates marginal
benefits with marginal costs. If a policy, not the market,
determines the level of energy efficiency, the policy is most
likely pushing a target for which the additional costs exceed the
additional benefits; otherwise the target would not be needed.
This is why these policies are usually accompanied by subsidy
"carrots." Also note: These are expected costs and benefits based
on currently available information. If, after new information
becomes available, it is found that costs do outweigh
benefits, the markets must make a correction. This does not negate
the initial efficiency of the decision.
 Even if mandates are made based on
"market research" by the regulator, the time from market research
to a new targeted mandate cannot respond rapidly enough to
quickly changing market conditions.
 This refers to S. 2191, commonly known as
Lieberman-Warner after the two key sponsors of the legislation,
Senators Joseph Lieberman (I-CT) and John Warner (R-VA). S. 2191
would have placed strict upper limits on the emission of six
greenhouse gases (GHGs) focused primarily on carbon dioxide (CO2).
Emitters would be required to purchase federally created
permits (allowances) for each ton emitted, effectively capping
emissions at a government targeted level.
 EISA was signed into law on December 19,
2007, and takes effect on January 1, 2009. Among other targets,
EISA requires that the Renewable Fuel Standards, which calls for
minimum production levels of renewable fuels, increase nine-fold by
2022 and automobile fuel economy standards increase significantly
by 2020. More information is available at http://www.thompsonhine.com/publications/pdf/
2008/01/energyupdateenergy1326.pdf (November 3, 2008).
 The CAFE standard, which was established
in response to the energy crisis of the 1970s, sets the fuel
economy standards mainly for the auto industry. See also Robert
Bamberger, "Automobile and Light Truck Fuel Economy: The CAFE
Standards," Congressional Research Service, September 25, 2002, at
(October 17, 2008).
 The SIC and NAICS classification system
is defined by the Census Bureau as follows: "The North American
Industry Classification System (NAICS, pronounced Nakes) was
developed as the standard for use by Federal statistical agencies
in classifying business establishments for the collection,
analysis, and publication of statistical data related to the
business economy of the U.S. NAICS was developed under the auspices
of the Office of Management and Budget (OMB), and adopted in 1997
to replace the old Standard Industrial Classification (SIC) system.
It was also developed in cooperation with the statistical agencies
of Canada and Mexico to establish a 3-country standard that allows
for a high level of comparability in business statistics among the
three countries. NAICS is the first economic classification system
to be constructed based on a single economic concept." Further
details and explanations are available at http://www.census.gov/epcd/www/drnaics.htm#q1
(October 31, 2008).
 This is the industry as a whole.
Individual companies within these industries often face much higher
elasticities of demand. The mandates will cause the competitive
landscape to change. Instead of firms being competitive on cost and
quality, firms will need to compete on emissions. This will likely
cause some previously economically viable companies to go out of
business and result in higher consumer prices for these staple
 Present value is the value today of a
future cash stream or future lump-sum payment. If the cash stream
were available today, the money could be invested and earn
interest. Therefore, not having it today represents an opportunity
cost in the amount of the interest that could have been earned. A
present value calculation discounts the stream of cash or lump-sum
future payment by the interest rate that could have been
 Margo Thorning, "The Impact of
America's Climate Security Act of 2007 (S. 2191) on the U.S.
Economy and on Global Greenhouse Gas Emissions," testimony before
the Committee on Environmental and Public Works, U.S. Senate,
November 8, 2007, at /static/reportimages/A3A9B9B4A404E1DDDE6F79DA27BAFB46.pdf
(October 31, 2008).
 Haver Analytics is a database and
software company that maintains over 150 economic and financial
databases. Data available upon request and Haver's approval of
release of its proprietary data.
 Robert J. Hill and Kevin J. Fox,
"Splicing Index Numbers," Journal of Business & Economic
Statistics, Vol. 15, No. 3 (July 1997), pp. 387-389. These
authors show that this technique is more consistent than using the
arithmetic mean. This paper also contains the algorithm and an
example of the technique.
 A true co-integration analysis is based
on the non-stationarity of two or more series. Since both series
here are already stationary, the test for co-integration (that
relies on finding a stationary relationship) is moot. The
investigation here uses the error-correction representation shared
with co-integration analysis to represent the a priori
long-run relationship (due to the accounting identity) rather than
using co-integration analysis to investigate whether there is a
 Note: This test does not necessarily
imply true causality in the sense that one is an independent
variable that can be manipulated to change the dependent variable.
For example, there could be a third factor that is driving both
 One industry, fabricated metal, was
found to have bi-directional feedback between the two series. That
is, industry return on equity helps predict the industry production
index as much as the industry production index helps predict
industry return on equity. This merits further investigation
because, theoretically, at the industry level, changes in return on
equity could indicate that there are changes in industry conditions
that would cause future production levels to change. However,
because this exercise already estimated the future path of
production indexes through simulation, this industry was eliminated
from this study.
 This is the simplest form taken. Some
industries included more lags. The amount of lags selected was the
minimum amount necessary such that the residuals of the regression
were white noise. For some industries, lags were skipped and others
included based on the robust t-statistic for the coefficient.
 Peter Kennedy, A Guide to
Econometrics (Cambridge, Mass.: MIT Press, 2003), pp. 337.
 The model was also estimated using the
Prais-Winston approach that takes into account autocorrelation. The
results were not significantly different.
 In most cases, the model's prediction
is within 1 to 2 percent of its historical ratio. In the
textile and steel industry there is a greater than 5 percent
difference. This is because there were large and volatile swings in
the historical data that dampened the ratio while the Global
Insight baseline production index predicts relatively smooth