Economic Analysis of a Multi-Emissions Strategy
Prepared for: Senators James M. Jeffords and Joseph I. Lieberman
U.S. Environmental Protection Agency Office of Air and Radiation
Office of Atmospheric Programs
October 31, 2001
Executive Summary
In response to a May 17, 2001 request from Senators James M.
Jeffords (VT) and Joseph I. Lieberman (CT), this report describes
the results of a modeling study done to evaluate the potential
impacts of reducing nitrogen oxides (NOx), sulfur dioxide (SO2),
mercury (Hg), and carbon dioxide (CO2) emissions from the US
electric power sector. In their request, Senators Jeffords and
Lieberman asked the Environmental Protection Agency to undertake an
economic assessment of four technology-based scenarios designed to
achieve the following emissions caps in the US electric power
sector by the year 2007:
•
Reduce nitrogen oxides (NOx) emissions to 75 percent
below 1997 levels;
•
Reduce sulfur dioxide (SO2) emissions to 75 percent below
full implementation of the Phase II requirements under title
IV;
•
Reduce mercury (Hg) emissions to 90 percent below 1999
levels; and
•
Reduce carbon dioxide (CO2) emissions to 1990
levels.
The request also specified that EPA should evaluate the cost of
achieving these reductions using four alternative technology
scenarios:
•
The Energy Information Agency's Standard Technology
Scenario.
•
The Energy Information Agency's High Technology Scenario,
including technology assumptions with earlier introduction, lower
costs, higher maximum market potential, or higher efficiencies than
the Standard Scenario.
•
Two scenarios from Scenarios for a Clean Energy Future
published by Oak Ridge National Laboratory, National Renewable
Energy Laboratory, and Lawrence Berkeley National Laboratory, which
include assumptions about changes in consumer behavior, additional
research and development, and voluntary and information
programs.
Under each scenario, the costs of meeting the emission
constraints are included in the price of electricity. Such costs
include the purchase and installation of emissions control
equipment and the purchase of emissions permits. Factors that
mitigate projected cost increases include the availability of more
cost-effective, energy efficient technologies for both consumers
and electricity suppliers. EPA's analysis indicates that, under the
conditions described above:
•
Electricity prices in 2015 would increase by about 32% to
50%, depending on the technology scenario.
•
Coal-fired electric generation would decline by 25% to
35% by the year 2015.
•
Overall costs, measured by the decline in household
consumption of goods and services, would be between $13 and $30
billion annually or 0.1% to 0.3% of total consumption. Under all
four of the policy scenarios evaluated in this assessment, gross
domestic product (GDP) would remain relatively unchanged as
sacrificed consumption permits higher investment and government
spending to reduce emissions.
•
Oil and gas-fired generation would be expected increase
by about 8% under more restrictive technology assumptions, but
decrease by as much as 20% under scenarios that
embody more optimistic assumptions about energy-efficiency
demand and supply technologies.
The combination of increased prices and the availability of more
energy-efficient equipment and appliances are projected to reduce
electricity demand by about 10%. With the combination of higher
prices and improved efficiency, total expenditures for electricity
consumption in 2015 are projected to increase by about 17% to 39%,
depending on the scenario.
The increase in electricity prices and cost of the program, as
well as the impact on the fuel mix, varies considerably based the
technology future that is assumed. For example, the 30% electricity
price increase, the $13 billion reduction in personal consumption,
and the 25% decline in coal use are all associated with the Clean
Energy Future Advanced Scenario, which includes the most optimistic
technology assumptions. Likewise, the 50% electricity price
increase, the $30 billion reduction in personal consumption, and
the 35% decline in coal usage are all associated with EIA's
Standard Technology Scenario.
EPA was not asked to evaluate the merits of the alternative
technology scenarios. We note, however, that they are the subject
of considerable controversy. The Clean Energy Future scenarios have
been criticized on several grounds: assumed changes in consumer
behavior that are not consistent with historic behavior patterns,
results from research and development funding increases that have
not occurred, and voluntary and information programs for which
there is no analytic basis for evaluating the impacts. On the other
hand, supporters of those scenarios point to economic analyses
showing that the assumed investments can pay for themselves over
time. The range of estimates associated with the different
technology scenarios highlights the importance of the technology
assumptions.
In conducting the modeling requested by Senators Jeffords and
Lieberman, EPA has assumed that the reductions would be achieved
through a nationwide "cap-and-trade" system similar to the Acid
Rain program established under the 1990 Amendments to the Clean Air
Act, together with increasing penetration and performance of energy
technologies. In accordance with the Senators' request, the
analysis also assumes the use of banked allowances made possible by
early emissions reductions achieved in the years 2002 through 2006.
(In practice, significant reductions beginning in 2002 would be
difficult to achieve.) Because of the contribution of those banked
allowances to overall emissions reductions, the analysis shows
emissions in 2007 above the caps. Regardless, 2007 emissions are
substantially reduced from current levels. At the end of 2015 a
small pool of banked allowances continues to be available for use
in later years. The analysis contained in the report covers the
years 2002 through 2015.
The results provided in this analysis should not be construed as
forecasts of actual scenario outcomes. Rather, they are assessments
of how the future might unfold compared to a previously defined
reference case - given the mix of technology and policy assumptions
embodied in each of the scenarios. The results also imply a
national commitment that is successful in achieving the level of
emission reductions described within the report.
The economic impacts of the emissions reduction scenarios are
evaluated using Argonne National Laboratory's AMIGA model, a
200-sector computer general equilibrium model of the U.S. economy.
The modular design and economy-wide coverage of the AMIGA model
makes it a logical choice to analyze alternative technology
scenarios. Although it does employ the same plant-level coverage of
the electricity sector as the IPM and NEMS models used in other
analyses, the pollution control technology assumptions are not
included at the same level of detail as the IPM model. This may be
particularly relevant for mercury controls, where the effectiveness
varies by coal type, and may be difficult to model correctly
without additional detail. In addition, we note that the AMIGA
model is relatively new and has not been subject to the same degree
of peer-review and scrutiny as the older IPM and NEMS models. It
would be desirable in future work to establish the comparability of
results across these models.
1. Introduction
1.1. Background
Responding to an earlier Congressional request, the Energy
Information Administration (EIA) released a detailed study
reviewing the effects of a so-called "three pollutant" strategy in
December 2000 (Energy Information Administration, 2000). The three
emissions in the EIA assessment included nitrogen oxides (NOx),
sulfur dioxide (SO2), and carbon dioxide (CO2). Although a
coordinated climate and air quality policy appeared to lower costs
compared to a series of separate policy initiatives, the EIA
assessment indicated significant costs associated with capping
emissions.
At about the same time, five of the nation's national energy
laboratories released an extensive review of some 50 different
policy options that might achieve cost-effective reductions of both
air pollutants and carbon dioxide (CO2) emissions. The study,
Scenarios for a Clean Energy Future (Interlaboratory Working Group,
2000), indicated that domestic investments in energyefficient and
clean energy supply technologies could achieve substantial
reductions in both sets of emissions at a small but net positive
benefit for the economy.
On May 17, 2001, Senators James M. Jeffords (VT) and Joseph I.
Lieberman (CT) sent a letter to EIA and EPA seeking further clarity
in the scenarios examined by the December EIA analysis, stating
that "the analysis appears to unnecessarily limit the market and
technology opportunities that might significantly affect the costs
and benefits of emission reductions. In particular, the potential
contributions of demand-side efficiency, gas-fired cogeneration and
of renewable energy sources appear to be inadequately
represented."
In responding to this request, EPA modeled the combined impacts
of both the emissions caps and the advanced technology scenarios
specified by the Senators. We are aware that EIA has modeled the
combined impacts but has also modeled the effects of the emission
caps and the advanced technology scenarios separately. This
approach provides perhaps a better technique for isolating the
actual costs of the emissions caps. We have reviewed the EIA
analysis of these separate effects and we believe that they offer
interesting and important insights and that if we had performed the
same kind of analysis we would have seen similar results.
This report responds to the Senators' request. The results
provided in this analysis should not be construed as forecasts of
actual scenario outcomes. Rather they are assessments of how the
future might unfold compared to a previously defined reference case
- given a national commitment to achieve the emission reductions,
and given the mix of technology and policy assumptions embodied in
each of the scenarios.
1.2. Technology Scenarios
In the letter to Administrator Whitman, Senators Jeffords and
Lieberman asked for an analysis of four different scenarios,
requesting that EPA "analyze the cost and benefits, including all
sectors of the economy and impacts on both the supply and demand
side of the equation, of the following multi-pollutant emission
control scenarios for the nation's electricity generators. Where
feasible, this should include power plants both within the
conventionally defined electric utility sector as well as
electricity generated by industrial cogenerators and other
independent power producers."
The four scenarios are identified as follows:
•
Scenario A: Standard Technology Scenario. Assume standard
technology characteristics as defined in AEO2001. Further assume a
start date of 2002. By 2007 reduce NOx emissions 75 percent below
1997 levels, reduce SO2 emissions to 75 percent below full
implementation of the Phase II requirements under title IV, reduce
mercury emissions 90 percent below 1999 levels, and reduce CO2
emissions to 1990 levels.
•
Scenario B: High Technology Scenario. Continue the 2002
start date, but assume the advanced technology assumptions of both
the supply and demand-side perspectives that are referenced in
AEO2001. By 2007 reduce NOx emissions 75 percent below 1997 levels,
reduce SO2 emissions to 75 percent below full implementation of the
Phase II requirements under title IV, reduce mercury emissions 90
percent below 1999 levels, and reduce CO2 emissions to 1990
levels.
•
Scenario C: Moderate Clean Energy Future Scenario.
Continue the 2002 start date, but assume the moderate supply and
demand-side policy scenario of the Clean Energy Future (CEF) study.
By 2007 reduce NOx emissions 75 percent below 1997 levels, reduce
SO2 emissions to 75 percent below full implementation of the Phase
II requirements under title IV, reduce mercury emissions 90 percent
below 1999 levels, and reduce CO2 emissions to 1990
levels.
•
Scenario D: Advanced Clean Energy Future Scenario.
Continue the 2002 start date, but assume the advanced supply and
demand-side policy scenario of the Clean Energy Future study. By
2007 reduce NOx emissions 75 percent below 1997 levels, reduce SO2
emissions to 75 percent below full implementation of the Phase II
requirements under title IV, reduce mercury emissions 90 percent
below 1999 levels, and reduce CO2 emissions to 1990
levels.
In requesting an analysis of these four scenarios, the Senate
request asked for "…results through 2020, in periods of five years
or less, using the Annual Energy Outlook 2001 (AEO2001) as the
baseline."
1.3. Multi-Emission Targets
Table 1 identifies the 2007 emission caps used for each of the
four scenarios. The emission cap is defined by a benchmark emission
level that is modified by the desired level (percentage) of
reduction. For example, the benchmark for the SO2 emissions cap is
the Phase II requirements of the Clean Air Act Amendments. That
total, 8.95 million short tons, is reduced by a specific percentage
(75 percent) to reach the emissions cap of 2.24 million tons.
Following a similar pattern, the remaining emission caps are set as
1.51 million tons for NOx emissions, 4.8 tons for mercury
emissions, and 475 million metric tons (MtC) of carbon
emissions.
Table 1. Benchmark Emission Levels and Assumed Emission Caps
1.4. Other Analytical Assumptions
As previously noted, the letter from Senators Lieberman and
Jeffords requested that EPA use four different sets of technology
and policy assumptions to meet the specified emission caps shown in
Table 1. The full set of technology and policy assumptions are
described more fully in section two of this report. All scenarios
are implemented in 2002. At the same time, there are other key
assumptions that EPA adopted to facilitate the evaluation of the
four scenarios.
In addition to the different technology scenarios, EPA was asked
to include the assumption that utilities would begin to make
cost-effective emission reductions in the five years that precede
the 2007 compliance date. These early reductions would be "banked"
for use in the post-2007 period of analysis. For purposes of this
simulation, the amount of allowances banked from 2002 through 2006
was calculated as the simple difference between the reference case
projections and the actual emission trajectory of each scenario.
The decision to earn and hold early allowances is based on the
assumption that allowances are viewed as an asset that must earn at
least an 8% real return.1
Following the assumption used in the CEF study, all four of the
policy scenarios assume nationwide restructuring of the electric
utility industry. This implies that prices are based on the
marginal rather than the regulated, cost-of-service pricing now
used throughout much of the country.
EPA employed the Argonne National Laboratory's AMIGA modeling
system to evaluate the impact of capping emissions under the four
different technology scenarios. AMIGA is a 200 plus sector model of
the U.S. economy that captures a wide variety of technology
characteristics and their resulting impact on key indicators such
as emissions, employment and income.2 EPA
1
In practice, it is more likely that significant reductions that
contribute to any kind of allowance bank would be difficult to
achieve before 2004. Assuming a delay in implementation to 2004
would raise the economic impact of any of the scenarios.
2
AMIGA is especially suited to the t ask identifying and
evaluating a different mix of technologies in the production of
goods and services within the United States. It is not only a 200
plus sector model of the U.S. economy, but it also includes the
Argonne Unit Planning and Compliance model and database that
captures a wide variety of technology characteristics within the
electric generating sector, including industrial combined heat and
power systems and the typically available emission control
technologies. When the electricity module is integrated with
asked Argonne to benchmark AMIGA to the reference case
projections of AEO2001. AMIGA was then modified to approximate the
assumptions behind each of the four scenarios.
An economic analysis of a policy compares the world with the
policy (the policy scenario) to the world absent the policy (the
reference case or baseline scenario). The impacts of policies or
regulations are measured by the resulting differences between these
two scenarios. In effect, any meaningful analysis should compare
the full set of benefits and costs to the extent possible.
For purposes of this exercise, there are at least seven
categories of costs and four benefits that might be reviewed. The
costs include: (1) direct investment costs, (2) operating and
maintenance costs, (3) research and development and other
government program costs, (4) transaction, search, and compliance
costs, (5) adjustment costs associated with large changes in
specific capital stocks, (6) lost economic flexibility created by
additional emission requirements, and (7) potential interactions
with the existing tax system. At the same time, there are at least
four categories of benefits. These include: (1) direct savings from
lower compliance costs, (2) process efficiency and other
productivity gains, (3) environmental and health benefits not
captured within normal market transactions, and (4) spillovers
and/or learning induced by either the technology investment, or the
R&D efforts.
The costs associated with the emission limits in each scenario
are computed as the increased expenditures on pollution control,
investment in more efficient equipment and appliances, research and
development, tax incentives, and additional government programs -
all relative to the reference case. The increased costs are coupled
with credits for reductions in fuel use and productivity gains from
technology. The economic impact of each scenario is reported in two
ways. The first is as a change in household personal consumption,
measuring the goods and services available for consumers to enjoy
after subtracting these net expenditures. The second is as a change
in economic output measured as Gross Domestic Product (GDP).
The AMIGA model reasonably captures those costs and benefits
noted above that arise in market transactions. Some, such as loss
of flexibility and adjustment costs on the cost side, and health
benefits and spillovers on the benefit side, remain beyond the
scope of this analysis.
2. Multi-Emissions Analysis
This section provides additional details about the technology
assumptions that underpin the four emission scenarios. It also
describes the results of the scenario analysis, both in terms of
the various marginal costs associated with emission control
strategies and the economy-wide impact of each scenario. Although
EPA made every effort to calibrate AMIGA to the AEO2001 reference
case, AMIGA is a different modeling system than EIA's National
Energy Modeling System (NEMS). Hence, it was not possible to
reproduce the exact AEO2001 reference case
the larger macroeconomic system, the model can then generate key
outputs including projected electricity sales and net generation,
resulting emissions for each of the four pollutants under
consideration, and the set of energy and permit prices associated
with the resulting production levels. Finally, AMIGA can provide an
estimate of the consequent impact on the economy including key
indicators as consumption, investment, government spending, GDP,
and employment (Hanson, 1999). For more background on the AMIGA
model, see Appendix 5.1.
projections. Moreover, Argonne researchers recently upgraded
AMIGA to incorporate SO2, NOx, and mercury emissions. For this and
other reasons, AMIGA currently reports results only through the
year 2015. Nonetheless, the differences in the resulting baseline
projections are minor for the purposes of this analysis.
2.1. Modeling Technology Assumptions
Scenarios A and B are based on the AEO2001 standard and advanced
technology characteristics, respectively. The standard technology
assumptions of scenario A were used by EIA in the development of
the AEO2001 "reference case" projections. The advanced technology
assumptions of scenario B were used as a sensitivity analysis in
the AEO2001. They demonstrated the effects of earlier availability,
lower costs, and/or higher efficiencies for more advanced equipment
than the reference case.3
Scenarios C and D are based on the recently published
DOE-sponsored report, Scenarios for a Clean Energy Future
(Interlaboratory Working Group, 2000; see also, Brown, et al,
2001). Both of the CEF scenarios assumed nationwide restructuring
of the electric utility industry. From an analytical perspective,
this means that prices are based on the marginal costs of
generation, transmission and distribution of electricity rather
than the regulated, cost-of-service pricing now used throughout
much of the country. Moreover, both scenarios reflected increased
spending for research and development and other programs designed
to accelerate the development and deployment of low-carbon, energy
efficient technologies. Each of the scenario assumptions are
described more fully in the sections that follow.
2.1.1. Reference Case Scenario
The scenario A reference case assumes a "business-as-usual"
characterization of technology development and deployment. As
projected in the AEO2001 assessment, the nation's economy is
projected to grow at 2.9% per year in the period 2000 through 2020.
Given anticipated energy prices and the availability of standard
technologies, the nation's primary energy use is expected to grow
1.3% annually while electricity consumption is projected to
increase by 1.8% annually. Further details are provided in Appendix
5.2.1.
2.1.2. Advanced Technology Scenario
Under the AEO 2001 advanced technology characterization,
scenario B assumes that a large number of technologies have earlier
availability, lower costs, and/or higher efficiencies. For example,
the high efficiency air conditioners in the commercial sector are
assumed to cost less than in scenario A. This encourages a greater
rate of market penetration as electricity prices rise in response
to the emissions caps. Building shell efficiencies in scenario B
are assumed to improve by about 50 percent faster than in scenario
A.
The AEO2001 was published in December 2000 (Energy Information
Administration, 2000).
On the utility's side of the meter, the heat rates for new
combined cycle power plants are assumed to be less compared to the
standard case assumptions. This means that more kilowatthours of
electricity are generated for every unit of energy consumed by the
power plants. Moreover, wood supply increases by about 10% and the
capacity factor of wind energy systems increases by about 15-20%
compared to the reference case assumptions. In the AEO2001 report,
the combination of higher efficiencies and earlier availability of
the technologies lowers the growth in electricity use from 1.8% in
the reference case to 1.6%.
2.1.3. CEF Moderate Case Scenario
The authors of the Clean Energy Future (CEF) report describe
their analysis as an attempt to "assess how energy-efficient and
clean energy technologies can address key energy and environmental
challenges facing the US" (Brown, et al, 2001). In that regard,
they evaluated a set of about 50 policies to improve the technology
performance and characterization of the residential, commercial,
industrial, transportation, and electricity generation sectors. The
policies include increased research and development funding,
equipment standards, financial incentives, voluntary programs, and
other regulatory initiatives. These policies were assumed to change
business and consumer behavior, result in new technological
improvements, and expand the success of voluntary and information
programs.
The selection of policies in the CEF study began with a
sector-by-sector assessment of market failures and institutional
barriers to the market penetration of clean energy technologies in
the US. For buildings, the policies and programs include additional
appliance efficiency standards; expansion of technical assistance
and technology deployment programs; and an increased number of
building codes and efficiency standards for equipment and
appliances. They also include tax incentives to accelerate the
market penetration of new technologies and the strengthening of
market transformation programs such as Rebuild America and Energy
Star labeling. They further include so-called public benefits
programs enhanced by electricity line charges.
For industry, the policies include voluntary agreements with
industry groups to achieve defined energy efficiency and emissions
goals, combined with a variety of government programs that strongly
support such agreements. These programs include expansion and
strengthening of existing information programs, financial
incentives, and energy efficiency standards on motors systems.
Policies in the CEF analysis were assumed to encourage the
diffusion and improve the implementation of combined heat and power
(CHP) in the industrial sector. For electricity, the policies
include extending the production tax credit of 1.5 cents/kWh over
more years and extending it to additional renewable
technologies.
Broadly speaking, the CEF Moderate scenario can be thought of as
a 50% increase in funding for programs that promote a variety of
both demand-side and supply-side technologies. For example, the
moderate scenario assumes a 50% or $1.4 billion increase in
cost-shared research, development, and demonstration of efficient
and clean-energy technologies (in 1999 dollars with half as federal
appropriations and half as private-sector cost share). It further
assumes a careful targeting of funds to critical research areas and
a gradual, 5-year ramp-up of funds to allow for careful planning,
assembly of research teams, and expansion of existing teams and
facilities. In addition, the CEF moderate scenario anticipates
increased program spending of $3.0 and $6.6 billion for the years
2010 and 2020, respectively. These expenditures include production
incentives and investment tax credits for renewable energy, energy
efficiency and transportation technologies. They further include
increased spending for programs such as DOE's Industrial Assessment
Centers and EPA's Energy Star programs.
The combined effect of the R&D and program expenditures,
together with other policies described in the CEF report, implies a
steady reduction in total energy requirements over the period 2000
through 2020. By the year 2020, for example, primary energy
consumption and electricity sales were projected to decrease by 8%
and 10%, respectively, compared to the CEF reference case.
2.1.4. CEF Advanced Technology Scenario
Building on the policies of the moderate scenario, the CEF
advanced scenario assumes a doubling of cost-shared R&D
investments, resulting in an increased spending of $2.9 billion per
year (again, in 1999 dollars with half as federal appropriations
and half as private-sector cost share). In addition, the advanced
scenario anticipates increased program spending of $9.0 and $13.2
billion for the years 2010 and 2020, respectively. The added
spending covers all sectors including buildings, industry,
transportation, and electric generation.
The combined effect of the program and R&D expenditures,
together with other policies described in the CEF report (including
a $50 carbon charge applied in the CEF Advanced Scenario), drove a
steady reduction in the need for energy compared to the CEF
reference case. By 2020 total energy use fell by 19% compared to
the reference case. At the same time, electricity sales in 2020
were projected to decrease by 24% compared to the CEF reference
case.
2.1.5. Implementation of the Technology Assumptions
The assumptions embedded in each of these scenarios have the
effect of progressively increasing market penetration of higher
performance energy efficiency and energy supply technologies. As
shown in Table 2, the net effect of these assumptions is to lower
the expected level of electricity consumption while continuing to
meet the same level of service demanded by utility customers. The
technology assumptions also have the effect of increasing the
availability of cleaner energy supply technologies that reduce the
level of emissions per kilowatt-hour of generation. The critical
assumption used in the EPA analysis is that program spending
affects both supply and demand technologies in a way that interacts
with the emission caps that are to be imposed in 2007.
Benchmarked to the year 2010, Table 2 shows the percentage
change of key indicators for each scenario with respect to its
respective reference case. These changes provide EPA with
approximate targets so that each of the scenarios can be mapped
into the AMIGA model. As such, the figures in Table 2 should be
seen as inputs into the AMIGA model, not outputs of the model.
Table 2. Influence of Technology Assumptions on Key Scenario
Indicators - 2010
By definition, scenario A assumes the standard technology
assumptions of the AEO2001 reference case. Hence, there are no
additional programs or policies that generate changes in the
reference case technologies when the emission caps are imposed by
the year 2007. The level of technology responsiveness grows for
scenarios B, C, and D as a result of greater program spending.
The CEF advanced scenario, for example, assumes a significant
increase in program funds to promote a variety of both demand-side
and supply-side technologies. As a result of this greater level of
program activity, there is an accelerated penetration of
energy-efficient technologies that drives electricity sales down by
6.8 percent in 2010 (compared to the CEF reference case for that
same year). At the same time, the combination of a lower demand for
electricity and an increased investment in cleaner energy supply
technologies reduces both carbon and NOx emissions by 10.7 and 8.1
percent, respectively (again, compared to the CEF 2010 reference
case). As EPA modeled this scenario, the bundle of policies in the
CEF advanced scenario became, in effect, a complement to the
emission caps imposed by 2007.
To avoid overestimating the impact of the policy scenarios in
this analysis, EPA made a number of adjustments before implementing
the CEF assumptions in the four scenarios reported here. First, the
CEF analysis was benchmarked to a 1999 reference case. In the
AEO2001 reference case, however, the demand for electricity in 2020
is about 10% higher compared to the CEF reference case. Second, the
Senate request asked EPA to assume a 2002 start date in running the
technology and policy scenarios. In effect, there are fewer years
in which programs can achieve the desired level of technology
improvement compared to the CEF scenarios. In addition, the CEF
analysis includes a significant review of transportation
technologies and policies. EPA chose to exclude all assumptions
related to transportation, focusing only on the supply and
demand-side technologies associated with electricity and natural
gas consumption.
With the adjustments described above now reflected in the
current analytical framework, and using the program cost
information documented in the CEF study, Table 3 summarizes the
incremental program costs that were assumed as necessary to drive
the kind of changes in electricity consumption and emissions
described in Table 2. Since transportation programs drove a
significant part of the CEF expenditures, and since there are fewer
years to implement policies, the estimated program expenditures are
also smaller compared to the CEF assumptions.
Table 3. Incremental Policy Costs of the Technology Scenarios
(billion 1999 dollars)
Because scenario A characterizes existing program and technology
performance, no additional funds are required to drive that
scenario. Scenario B, on the other hand, anticipates some changes
in the technology characterization that will affect the electricity
sector as shown in Table
2. While the AEO2001 analysis anticipated no program spending to
drive these changes, EPA assumed that additional spending would be
required for scenario B. Calibrating to the CEF policy scenarios,
EPA estimated that program and policy spending would increase by
$0.8 billion in 2002, rising steadily to $2.9 billion by 2015. For
scenario C, program spending increased by $1.2 billion starting in
2002, rising to $4.8 billion by 2015. Finally, program spending in
scenario D started at $2.1 billion in 2002 and increased to $5.5
billion by the last year of this analysis.4
The net effect of mapping increased program spending together
with adjustments needed to update the assumptions of the CEF policy
scenarios can be highlighted by reviewing the change in electricity
generation for scenario D. In the CEF Advanced Scenario (based on a
1999 reference case), for example, the level of electricity
generation in 2010 was lowered by 10% from the reference case
requirements of 3,920 billion kilowatt-hours (kWh). As the CEF
technology assumptions were applied in scenario D within this
analysis (updated to the AEO2001 reference case), electricity
generation was reduced by 9% from 4,253 billion (kWh). The trend
was more pronounced in 2015. Rather than a roughly 16% reduction
from a generation level of 4,200 billion kWh in the 1999 CEF
Advanced Case, the scenario D equivalent in this analysis achieved
only a 12% reduction from a generation of 4,580 billion kWh.
2.1.6. Reasonableness of the Scenario Assumptions
The results of the technology-driven scenarios should not be
interpreted as an EPA endorsement of any of the policies or
technology assumptions behind each of scenarios described in this
report. On the one hand, EPA has not conducted any significant
review of the EIA assumptions that underpin the AEO2001
projections. On the other hand, some analysts do not necessarily
agree with the assumptions and projected level of impacts in the
CEF assessment despite the fact that it was peer-reviewed and its
findings published this fall in an academic journal. The EIA
(2001), for example, notes that the CEF policies assume changes in
consumer behavior that are not consistent with historically
observed behavior patterns. Moreover, the EIA suggests that there
is little documentation to support the assumed technological
improvements generated by the research and development (R&D)
initiatives described in the report. Finally, EIA notes that
The program spending assumptions developed in this analysis are
used only to approximate the impact of the CEF scenario s. They do
not reflect EPA endorsement of these spending levels.
the effectiveness of voluntary or information programs may be
less than assumed in the CEF scenarios. At the same time, the lead
CEF analysts have responded to the EIA assertions by citing
relevant economic literature and noting that the CEF study is one
of "the most carefully documented and complete analysis of U.S.
energy futures that has ever been funded by the U.S. government"
(Koomey, et al, 2001).
Notwithstanding these concerns, EPA attempted to respond to the
Senators' request by mapping in the critical assumptions of the CEF
as a range of policies that provide a set of alternative
assumptions about the future. In this regard, the scenarios are
more like descriptions of alternative future outcomes rather than
predictions or recommendations about how the future should
unfold.
To provide a more complete context for understanding the
magnitude of the changes in electricity generation that are
suggested by the different scenarios, the figure below illustrates
both the historical and projected trends in the nation's
electricity generation. The information is shown as the number of
kWh per dollar of GDP (measured in constant 1999 dollars). The
historical data covers the period 1970 through 2000 while the
projected trends are through the year 2015. The historical period
shows a moderate level of volatility. The reference case
projections suggest an annual rate of declining intensity of 1.6%
per year through 2015 with a final value 0.33 kWh/$.
Historical and Projected US Electricity Trends (kWh per 1999 $
GDP)
1970
1980
1990
2000
2010
In comparison to the reference case, Scenario D (adapting the
CEF Advanced Case assumptions) reflects a national commitment to
improve both electricity supply and the efficiency of demandside
technologies. The presumption is that such a commitment would be
supported by a significant increase in R&D and program spending
as described above. Under these assumptions, the nation's
electricity intensity is projected to decline at an annual rate of
2.5%,
dropping to a final intensity of 0.28 kWh/$. This level of
decline is greater than previously seen in the recent past. In the
period 1980 through 1986, for example, and again 1993 through 2000,
the annual rate of decline was only 1.7 percent. Hence, it appears
that the assumptions driving the advanced scenario are aggressive.
At the same time, however, the research undertaken by the CEF
analysts indicates that the technology is available to achieve such
a reduction should a national commitment be successful in driving
similar policies.
2.2. Results of the Scenario Analysis
With the model benchmarked to AEO2001, and given the different
mix of scenario assumptions previously described, AMIGA reports the
results in the figures and tables that follow. More complete data,
including reference case assumptions, are available in Appendix
5.2.
2.2.1. Emission Projections
All program and policy assumptions have a start date of 2002.
Moreover, the analysis anticipates the use of banked allowances
made possible by early emissions reductions achieved in the years
2002 through 2006 (as requested in the Senate letter). Figures 1
through 4 on the following page illustrate both the emissions
projections and the impact of banking the early reductions on all
four emissions caps implemented in 2007.
Although all four categories of emissions are down
substantially, they only achieve 50-75% of the proposed cap by 2007
(shown as the dotted horizontal line in each of the above figures).
This is because of the availability of the banked allowances that
can be used by sources to meet emissions caps in 2007 and beyond.
Note that costs would be noticeably higher if power plants were
required to actually hit the target in 2007. In 2015, carbon and
mercury emissions continue to be 15% or more above the target.
The reductions that generate the banked allowances are shown as
the area to the left of each vertical dotted line as the
differences between the reference case and scenario emission
trajectories. The emissions above the cap are shown to the right of
each vertical dotted line and between the scenario emissions and
the dotted horizontal line. Subtracting these two areas on each
graph reveals the level of the bank in 2015. Using Scenario D as an
example, the remaining allowances in 2015 are 100 million metric
tons for carbon, 1.3 million tons for SO2, 0.2 million tons for NOx
and 25 tons for mercury. In the case of carbon, the bank would last
another two years at the rate of drawdown in 2015, or longer if the
drawdown declined.
Figure 1. Carbon Emissions (million metric tons) Figure 2. SO2
Emissions (million tons)
Figure 3. NOx Emissions (million tons) Figure 4. Mercury
Emissions (tons)
2.2.2. Changes in Electric Generation Expenditures
Given the assumptions and economic drivers in each of the
scenarios, the AMIGA model calculates the capital investment,
operation and maintenance, and fuel costs necessary to meet
consumer demand for electricity. The incremental expenditures
required to generate electricity under each of the four scenarios
as compared to the reference case are summarized in Figure 5 (in
billions of 1999 dollars). In effect, the incremental expenditures
reflect the range of decisions made by the electricity sector to
comply with each of the four scenario constraints-but do not
reflect efforts made outside the electricity sector. Because these
expenditures ignore spending on energy efficiency, research and
development outside the electricity sector-spending that can be
substantial-they are not measures of program costs. Note that
incremental expenditures are incurred as early as 2002 in all four
scenarios to generate early reductions that can be banked for use
in 2007 and beyond.
The generation expenditures vary in each of the scenarios change
for at least three reasons: (1) the size of the allowance bank made
possible by early reductions driven, in part, by program spending
prior to the introduction of the caps; (2) the varying levels of
demand for electricity over time, resulting in changes in the
overall mix of generation resources; and, (3) the gradual reduction
in the banked allowances available for withdrawal necessitating
additional actions to reduce emissions.
As expected, scenario A has the largest increase with
expenditures rising by nearly $17 billion in 2015 compared to the
reference case. The higher level of expenditures is driven by a 21%
increase in unit generation costs caused primarily by the emissions
caps and offset only slightly by a small decrease in electricity
demand. With less energy efficiency technology penetrating the
market, a greater level of control equipment must be installed and
operated which, in turn, drives up the cost of generation. Scenario
B follows a similar pattern with expenditure increases being offset
by further reductions in electricity demand as more efficient
technology penetrates the market. The expenditures for scenario C
decline even further as reduced demand continues to lower both the
level generation and the unit cost of that generation compared to
scenario A. Scenario D, on the other hand, actually shows a decline
in total expenditures by 2015. The combination of a 12.5% reduction
on generation load together with only an 11.9% increase in the unit
cost of generation (both with respect to the reference case)
results in a $3.11 billion reduction in total electric generation
expenditures.
Figure 5. Incremental Expenditures on Electric Generation
(Billions of 1999$)
2.2.3. Marginal Costs
The marginal costs of emission reductions over the period 2005
through 2015 are shown in Figures 6 through 9 for all four
scenarios.
Figure 6. Projected Marginal Cost of Carbon Reductions ($/Metric
Ton)
Figure 7. Projected Marginal Cost of SO2 Reductions ($/Ton)
Figure 8. Projected Marginal Cost of NOx Reductions ($/Ton)
Figure 9. Projected Marginal Cost of Hg Reductions
($Million/Ton)
The marginal cost of carbon reductions range from $46 to
$138/metric ton through 2015 with each scenario showing
successively smaller costs as technology characteristics improve
and more energy-efficient and/or low carbon technologies penetrate
the market. The marginal cost of SO2 and NOx reductions through
2015 are less than $450/ and $2,300/ton, respectively, in all four
multi-emissions reduction scenarios. The marginal cost of mercury
reductions by 2015 ranges from $350 million/ton to $432
million/ton, again depending on the scenario.
It is important to note that marginal cost reflects the
additional cost of one more ton of reductions, and not the total
cost associated with each pollutant. One can make a very rough
estimation of this overall cost for each pollutant, on top of the
costs associated with the other three, by multiplying half the
marginal cost (to approximate average cost) by the volume of
reductions. By 2015, as an example, scenario A returns cost
estimates of $15.2 billion for carbon, $1.1 billion for SO2, $2.7
billion for NOx, and $6.4 billion for mercury. In Scenario D, the
cost estimates are $8.6 billion for carbon, $1.6 billion for SO2,
$3.3 billion for NOx, and $7.8 billion for mercury. Note that these
figures cannot be added together for an overall estimate because
they (a) double count the benefits of controlling multiple
pollutants simultaneously, and
(b) ignore the consequences of the underlying technology policy.
We discuss overall costs below.
Surprisingly, the marginal cost of SO2, NOx, and Hg reductions
increases as the marginal cost of carbon decreases. The reason
appears to be that as efficiency technology penetrates the market
and reduces carbon prices, more of a price signal is required to
generate further reductions in the three conventional pollutants.
In the advanced scenarios, for example, both demand reductions and
the increased use of gas tends to reduce carbon emissions. But gas
prices begin to rise which allows coal to make a modest comeback
with respect to scenario A. This is especially true as cleaner and
more efficient coal technologies begin to penetrate the market as
assumed in scenarios B through D. In order to offset the tendency
for coal-generated emissions to increase, permit prices need to
adjust upward.
2.2.4. Fuel Use Impacts
Figure 10 shows both total electricity consumption and the
fossil fuel consumption used in the generation of electricity for
the year 2010. The results are in quadrillion Btu in both the
reference case and each of the four policy scenarios. As each
successive scenario generates a greater reduction in electricity
demand, coal use is reduced significantly (by about 30 percent).
Gas consumption increases slightly in scenarios A and B, and
decreases by a small amount in scenarios C and D as lower
electricity consumption reduces the need for new capacity.
Figure 10. Total Electricity Consumption and Fossil Fuel
Generation in 2010 (Quadrillion Btu)
2.2.5. Energy Price Impacts
The model suggests that under the conditions described above,
electricity prices are expected to increase by about 30% (under
scenario D) to 50% (under scenario A) by the year 2015. This is the
logical result of increased control costs and permit prices. The
combination of increased prices and the availability of more
energy-efficient equipment and appliances reduce electricity demand
by about 10%. Total electricity expenditures increase by about 15%
to 30% depending on the year and the scenario (see Table 3, below,
and the tables in Appendix 5.2 for more detail on the changing
pattern of expenditures).
2.2.6. Economy-wide Impacts
Table 3 provides a summary of key macroeconomic data for the
year 2010 to compare the impact of emissions reductions on both
personal consumption and other components of gross domestic product
(GDP). The effects on personal consumption show a decline of
between $13 billion and $31, or 0.1% to 0.3%, depending on the
scenario. This reflects the cost of the program in terms of the
decreased well being of households who must forego a fraction of
their consumption of goods and services in order to pay for both
research and development programs, energy efficiency improvements,
and more expensive electricity production. Table 3 shows little
change in GDP under any of the policy scenarios, reflecting the
fact that this foregone consumption turns up as expenditures in
other categories of GDP, namely, investment and government
spending.5
Table 3. Summary of Economic Impacts by Scenario - 2010
The AMIGA modeling system reports the costs and benefits of each
scenario with several major exceptions. The first omitted benefit
is spillover and productivity gains beyond energy bill savings. A
number of studies suggest that energy efficiency technology
investments also tend to increase overall productivity of the
economy, especially in the industrial sector. (Sullivan, et al.,
1997; Finman and Laitner, 2001; and Laitner, et al, 2001). To date,
however, no systematic effort has been undertaken to incorporate
such benefits into the current generation of policy models. Hence,
this potential benefit is not reported at this time. The second
missing benefit includes gains in environmental quality, especially
improved health benefits.
On the cost side, the model ignores costs associated with rapid
changes in capital stocks, as well as potential loss of flexibility
and interactions with the existing tax system. For example, the
model forecasts significant changes in the level and composition of
electricity generation in 2002, ignoring the difficulty of rapidly
changing the capital stock by then end of 2001. Losses in
flexibility occur when pollution control activities potentially
interfere with efficiency and other operational programs at a
regulated facility. Finally, there are interactions with the tax
system when, in response to a rise in the relative cost of
purchased goods, people decide to enjoy more
A more complete assessment of each policy scenario can be made
by reviewing the more detailed data contained in the Appendix.
leisure (which is now relatively less expensive), work less, and
lower taxable income (Parry and Oates, 2000).
2.3. The Results in Context
Recent studies suggest significant economic consequences as a
result of substantial emission reduction strategies (EPRI, 2000;
and EIA, 2000). On the other hand, the presumption of a trade-off
between environmental and economic benefits may not provide an
entirely appropriate framework for analysis of such policies
(DeCanio, 1997). Indeed, there are a number of studies that show
net economic benefits may be possible when a full accounting of
both benefits and costs are included within an appropriate analysis
(Krause, et al, 2001; and Bailie, et al, 2001).
At the same time, understanding the proper characterization and
role of technology improvements (Edmonds, et al, 2000), and then
capturing that characterization within an appropriate model
structure (Peters, et al, 2001), is a critical aspect of all such
economic assessments.
Finally, it is important to recognize that the mere existence of
technologies and the potential for positive net benefits does not
assure that these technologies will be commercialized and adopted,
nor that the net benefits will be realized (Jaffe, et al, 2001). An
unanswered question is whether and how policies might encourage
these activities.
This current study, while drawing on credible data sources and
applying a state-of-the-art modeling system, cannot adequately
capture all such nuances associated with emission reduction
scenarios. The results of this analysis should be viewed within
this larger context.
3. Conclusions
The analysis suggests that under the conditions described above,
emissions through 2015 will be significantly reduced although they
won't meet the 2007 target. This is largely because of assumptions
about the banking of allowances earned prior to 2007. At the same
time, coal-fired electric generation is expected to decline by 25%
to 35% by the year 2015. On the other hand, oil and gas-fired
generation is projected to increase by about 8% under more
restrictive technology assumptions, but decrease by as much as 20%
under scenarios that embody more optimistic assumptions about
energy-efficiency demand and supply technologies. Electricity
prices are expected to increase by 32% to 50% in 2015, depending on
the scenario.
The combination of increased prices and the availability of more
energy-efficient equipment and appliances are projected to reduce
electricity demand by about 10% compared to the reference case.
With the combination of higher prices and improved efficiency,
total expenditures for electricity consumption in 2015 are
projected to increase by about 17% to 39% depending on the
scenario. Interacting with other changes in consumer and business
spending that is driven by each of the scenario assumptions, the
personal consumption reduced by about 0.1% to 0.3%. This again
depends on the year and the scenario.
The results provided in this analysis should not be construed as
forecasts of actual scenario outcomes. Rather they are assessments
of how the future might unfold compared to a previously defined
reference case - given the mix of technology and policy assumptions
embodied in each of the scenarios. The results from these scenarios
imply a strong national commitment, one that is successful in
developing the programs and policies necessary to achieve the level
of emission reductions described within the report.
4. References
Alison, Bailie, Stephen Bernow, William Dougherty, Michael
Lazarus, and Sivan Kartha, 2001. The American Way to the Kyoto
Protocol: An Economic Analysis to Reduce Carbon Pollution, Tellus
Institute and Stockholm Environment Institute, Boston, MA, July,
2001.
Brown, Marilyn A., Mark D. Levine, Walter Short, and Jonathan G.
Koomey, 2001. "Scenarios for a clean energy future," Energy Policy
Vol. 29 (November): 1179-1196, 2001.
DeCanio, Stephen J., 1997. "Economic Modeling and the False
Tradeoff Between Environmental Protection and Economic Growth,"
Contemporary Economic Policy, Vol. 15 (October): 10-27, 1997.
Edmonds, Jae, Joseph M. Roop, and Michael J. Scott, 2000.
Technology and the economics of climate change policy, Pew Center
on Global Climate Change, Washington, DC, September 2000.
E-GRID, 2000. Emissions & Generation Resource Integrated
Database, US Environmental Protection Agency, Washington, DC,
http://www.epa.gov/airmarkets/egrid/factsheet.html.
Electric Power Research Institute, 2000. Energy-Environment
Policy Integration and Coordination Study, TR-1000097, Palo Alto,
CA, 2000.
Energy Information Administration, 1998. Impacts of the Kyoto
Protocol on U.S. Energy Markets and Economic Activity,
SR/OIAF/98-03, Washington, DC, October 1998.
Energy Information Administration, 2000. Analysis of Strategies
for Reducing Multiple Emissions from Power Plants: Sulfur Dioxide,
Nitrogen Oxides, and Carbon Dioxide, SR/OIAF/2000-05 (Washington,
DC, December 2000).
Energy Information Administration, 2001. Analysis of Strategies
for Reducing Multiple Emissions from Electric Power Plants with
Advanced Technology Scenarios, SR/OIAF/2001-05 (Washington, DC,
October 2001).
Finman, Hodayah, and John A. "Skip" Laitner, 2001. "Industry,
Energy Efficiency and Productivity Improvements," Proceedings of
the ACEEE Industrial Summer Study, American Council for an
Energy-Efficient Economy, Washington, DC, August 2001.
Hanson, Donald A, 1999. A Framework for Economic Impact Analysis
and Industry Growth Assessment: Description of the AMIGA System,
Decision and Information Sciences Division, Argonne National
Laboratory, Argonne, IL, April, 1999.
Interlaboratory Working Group, 2000. Scenarios for a Clean
Energy Future, ORNL/CON-476 and LBNL-44029 Oak Ridge, TN: Oak Ridge
National Laboratory; Berkeley, CA: Lawrence Berkeley National
Laboratory, November 2000.
Jaffe, AB, RN Newell, and RN Stavins, 2001. "Energy-efficient
technologies and climate change policies: Issues and evidence." In
Climate Change Economics and Policy: An RFF Anthology, edited by MA
Toman. Washington: Resources for the Future.
Jeffords, James, and Joseph Lieberman, 2001. "Letter to EPA
Administrator Christine Todd Whitman," May 17, 2001.
Koomey, Jonathan, Alan Sanstad, Marilyn Brown, Ernst Worrell,
and Lynn Price, 2001. "Assessment of EIA's statements in their
multi-pollutant analysis about the Clean Energy Futures Report's
scenario assumptions," Memo to EPA's Skip Laitner, Lawrence
Berkeley National Laboratory, Berkeley, CA, October 18, 2001.
Krause, Florentin , Paul Baer, and Stephen DeCanio, 2001.
Cutting Carbon Emissions at a Profit: Opportunities for the U.S.,
International Project For Sustainable Energy Paths, El Cerrito, CA,
May 2001.
Laitner, John A. "Skip", Ernst Worrell, and Michael Ruth, 2001.
"Incorporating the Productivity Benefits into the Assessment of
Cost-effective Energy Savings Potential Using Conservation Supply
Curves," Proceedings of the ACEEE Industrial Summer Study, American
Council for an Energy-Efficient Economy, Washington, DC, August,
2001.
Parry, I.W.H. and W.E. Oates. "Policy Analysis in the Presence
of Distorting Taxes" Journal of Policy Analysis and Management
19(4), pp 603-613.
Peters, Irene, Stephen Bernow, Rachel Cleetus, John A. ("Skip")
Laitner, Aleksandr Rudkevich, and Michael Ruth, 2001. "A Pragmatic
CGE Model for Assessing the Influence of Model Structure and
Assumptions in Climate Change Policy Analysis ," Presented at the
2nd Annual Global Conference on Environmental Taxation Issues,
Tellus Institute, Boston, MA, June 2001.
Sullivan, Gregory P., Joseph M. Roop, and Robert W. Schultz,
1997. "Quantifying the Benefits: Energy, Cost, and Employment
Impacts of Advanced Industrial Technologies," 1997 ACEEE Summer
Study Proceedings on Energy Efficiency in Industry, American
Council for an Energy-Efficient Economy, Washington, DC, 1997.
US Environmental Protection Agency, 2000b. Guidelines for
Preparing Economic Analysis, EPA-240-R-00-003, Office of the
Administrator, Washington, DC, September 2000.
5. Appendices
5.1. Description of the AMIGA Model
The All Modular Industry Growth Assessment (AMIGA) model is a
general equilibrium modeling system of the U.S. economy that covers
the period from 1992 through 2030.6 It integrates features from the
following five types of economic models:
1). Multisector - AMIGA starts by benchmarking to the 1992
Bureau of Economic Analysis (BEA) interindustry data, which a
preprocessor aggregates to approximately 300 sectors;
2). Explicit technology representation - AMIGA reads in files
with detailed lists of technologies (currently with a focus on
energy-efficient and low-carbon energy supply technologies,
including electric generating units) containing performance
characteristics, availability status, costs, anticipated learning
effects, and emission rates where appropriate;
3). Computable General Equilibrium - AMIGA computes a
full-employment solution for demands, prices, costs, and outputs of
interrelated products, including induced activities such as
transportation and wholesale/retail trade;
4). Macroeconomic - AMIGA calculates national income, Gross
Domestic Product (GDP), employment, a comprehensive list of
consumption goods and services, the trade balance, and net foreign
assets and examines inflationary pressures;
5). Economic Growth - AMIGA projects economic growth paths and
long-term, dynamic effects of alternative investments including
accumulation of residential, vehicle, and producer capital
stocks.
In addition, the AMIGA system includes the Argonne Unit Planning
and Compliance model that captures a wide variety of technology
characteristics within the electric generating sector. This
includes a system dispatch routine that allows the retirement and
the dispatch of units on the basis of traditional cost criteria as
well as the impact of various permit prices on operating costs. It
also includes non-utility generation sources such as industrial
combined heat and power applications and renewable energy
systems.
Climate change mitigation policy has been the main application
of the AMIGA system to date. But the AMIGA modeling system recently
has been enhanced to include policies involving the reduction of
sulfur dioxide, nitrogen oxide, and mercury emissions. Moreover, a
new intertemporal optimization module has been added to AMIGA that
allows an evaluation of early reductions and the banking of
allowances to be incorporated into policy scenarios. Hence, the
system is well suited to evaluate a variety of multi-emission
strategies that are driven by price incentives as well as R&D
programs, voluntary initiatives, and cap and trade policies.
Because of recent upgrades and enhancements made in the model,
the current reporting period is extended only through the year
2015. We expect the full reporting period to extend back to the
year 2030 in the very near future.
The model includes a complete database of all electric utility
generating units within the United States. The cost and performance
characteristics of the electricity supply technologies generally
follow those modeled within the Energy Information Administration's
National Energy Modeling System. The characteristics associated
with the various emission control technologies generally follow
those modeled within the Integrated Planning Model used by the
Environmental Protection Agency.
The AMIGA modeling system is a highly organized, flexible
structure that is programmed in the C language. It includes modules
for household demand, production of goods, motor vehicles,
electricity supply, and residential and commercial buildings and
appliances.
The production modules contain representations of labor,
capital, and energy substitutions using a hierarchy of production
functions. The adoption rates for cost-effective technologies
depend on energy prices as well as policies and programs that lower
the implicit discount rates (sometimes referred to as hurdle rates)
that are used by households and businesses to evaluate
energy-efficiency and energy supply measures.7
For a more complete documentation of the AMIGA model, see
Hanson, Donald A, 1999. A Framework for Economic Impact Analysis
and Industry Growth Assessment: Description of the AMIGA System,
Decision and Information Sciences Division, Argonne National
Laboratory, Argonne, IL, April, 1999. For an example of other
policy excursions using the AMIGA model, see, Hanson, Donald A. and
John A. "Skip" Laitner, 2000, "An Economic Growth Model with
Investment, Energy Savings, and CO2 Reductions," Proceedings of the
Air & Waste Management Association, Salt Lake City, June 18-22,
2000. Also see, Laitner, John A. "Skip", Kathleen Hogan, and Donald
Hanson, "Technology and Greenhouse Gas Emissions: An Integrated
Analysis of Policies that Increase Investments in Cost Effective
Energy-Efficient Technologies," Proceedings of the Electric
Utilities Environment Conference, Tucson, AZ, January 1999.
5.2. Summary Tables for Study Scenarios
5.2.1.
Reference Case Projections
5.2.2.
Scenario A: Emission Constraints Using Reference Case
Technologies
5.2.3.
Scenario B: Emission Constraints Using Advanced Case
Technologies
5.2.4.
Scenario C: Emission Constraints Using the Moderate CEF
Scenario Assumptions
5.2.5.
Scenario D: Emission Constraints Using the Advanced CEF
Scenario Assumptions
